From 1f409f9872a0c7c5da333eb3a5a8b545e778fecc Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 6 Apr 2022 05:06:45 -0600 Subject: [PATCH 01/64] Add Tutorial Notebook for VALMOD --- docs/Tutorial_VALMOD.ipynb | 647 +++++++++++++++++++++++++++++++++++++ 1 file changed, 647 insertions(+) create mode 100644 docs/Tutorial_VALMOD.ipynb diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb new file mode 100644 index 000000000..0757670fa --- /dev/null +++ b/docs/Tutorial_VALMOD.ipynb @@ -0,0 +1,647 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "482a2e9b", + "metadata": {}, + "source": [ + "In this tutorial, we would like to implement [VALMOD](https://arxiv.org/pdf/2008.13447.pdf) paper and reproduce its results as closely as possible.\n", + "\n", + "The **VAriable Length MOtif Discovery (VALMOD)** algorithm takes time series `T` and a range of subsequence length `[min_m, max_m]`, and find motifs and discords." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "40bf9a66", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import stumpy\n", + "from stumpy import core, config\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" + ] + }, + { + "cell_type": "markdown", + "id": "1bc907ef", + "metadata": {}, + "source": [ + "# 1- Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "aa1d847c", + "metadata": {}, + "source": [ + "Some important notations that we may use later:\n", + "* subsequence $T_{i,m}$ --> a subsequence of `T` that starts at index `i` and has length `m` \n", + "* Motif set $S^{m}_{r}$ (for a given motif pair $\\{T_{idx,m},T_{nn\\_idx,n}\\}$) --> is a set of subsequences of length `m` that has `distance < r` to either $T_{idx,m}$ or $T_{nn\\_idx,n}$. And, the cardinality of set is called the frequency of the motif set." + ] + }, + { + "cell_type": "markdown", + "id": "8c36e21f", + "metadata": {}, + "source": [ + "### Motif discovery" + ] + }, + { + "cell_type": "markdown", + "id": "fd1568ab", + "metadata": {}, + "source": [ + "We would like to find set $S^{*} = \\bigcup\\limits_{m=min_m}^{max_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", + "\n", + "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index in two different motif sets. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty. \n", + "\n", + "**The authors provided a solution to get top-k motifs from set $S^{*}$. So, this is what can be understood from the statement:**
\n", + "Let us assume we only want to find top-k motifs from all subsequnce with either length `m` or length `m+1`. We try to find motif set for each length...then we should sort the distances (maybe after normalizing them) and then get top-k.\n", + " \n", + "---\n", + "\n", + "**NOTE (from NOTEBOOK producer)**:
\n", + " (1) It is not clear whether the value of `r` can be calculated based on `r`or it should be provided by the user again.
\n", + " (2) It is also not clear whether one should consider trivial matches in $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. Since `m` is changing from one set to another, it may not be easy to understand if two sequences with different length are trivial neighbors of each other or not." + ] + }, + { + "cell_type": "markdown", + "id": "c0455171", + "metadata": {}, + "source": [ + "### Discord discovery" + ] + }, + { + "cell_type": "markdown", + "id": "71cfdcf0", + "metadata": {}, + "source": [ + "First, we need to provide a few definitions..." + ] + }, + { + "cell_type": "markdown", + "id": "3826e0a5", + "metadata": {}, + "source": [ + "**$n^{th}$ best match**: Given a subsequence $T_{i,m}$, the $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", + "\n", + "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?!) best match.
\n", + "\n", + "**Top-k $n^{th}$ discord**: This is k-th value of $P^{n_{th}}$, sorted in ascending order. $P^{n_{th}}$ is the matrix profile that is constructed based on $n^{th}$ best match rather than 1NN.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5167292f", + "metadata": {}, + "source": [ + "**NOTE**:
\n", + "Why should I care about $n^{th}$ discord (n>1)? We provide a simple example below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d9db678", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "T = np.random.uniform(-100,100,size=3000)\n", + "m = 200\n", + "i, j = 100, 1500\n", + "\n", + "T[i:i+m] = 0\n", + "T[j:j+m] = 0\n", + "\n", + "plt.plot(T)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a8e87bc0", + "metadata": {}, + "source": [ + "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0. Therefore, we may need to investigate other neighbors. \n", + "\n", + "For further details, see Fig. 2 of the paper (Notice that `Top-1 2nd discord` has a close 1NN...but it is far from its 2nd closest neighbor.)" + ] + }, + { + "cell_type": "markdown", + "id": "1be2fecb", + "metadata": {}, + "source": [ + "**Variable-length Top-k $n^{th}$ Discord Discovery:**
\n", + "Given a time series `T`, a subsequence length-range `[min_m, max_m]`, and `K` and `N`, we want to find top-k discords $n^{th}$ discord for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." + ] + }, + { + "cell_type": "markdown", + "id": "27b8effd", + "metadata": {}, + "source": [ + "# 2- Lower-Bound Distance Profile (for z-normalize case)" + ] + }, + { + "cell_type": "markdown", + "id": "5f999789", + "metadata": {}, + "source": [ + "The idea goes as follows: \"given the distance profile of $T_{j,m}$, how can I find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` but is longer by `k` elements ?" + ] + }, + { + "cell_type": "markdown", + "id": "03836054", + "metadata": {}, + "source": [ + "In other words, can I find lower bound for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$?" + ] + }, + { + "cell_type": "markdown", + "id": "be9e2963", + "metadata": {}, + "source": [ + "(Note: It is more common to consider `i` as the main index and `j` as the neighbor. Here, however, we choose `j` as the start index of subsequene of interest so to be consistent with the paper.)" + ] + }, + { + "cell_type": "markdown", + "id": "4fc93b47", + "metadata": {}, + "source": [ + "$d^{(m+k)}_{j,i} \\ge \\min{(d)}$, where $d$ is:\n", + "\n", + "$d = \\sqrt{\n", + "\\sum\\limits_{t=1}^{m}{\n", + "(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}})^{2}\n", + "}\n", + "}$, where ($\\mu_{i,m+k}$, $\\sigma_{i,m+k}$), and ($\\mu_{j,m+k}$, $\\sigma_{j,m+k}$) are (mean, standard deviation) of subsequences $T_{i,m+k}$ and $T_{j,m+k}$, respectively." + ] + }, + { + "cell_type": "markdown", + "id": "ff38394a", + "metadata": {}, + "source": [ + "**Note:** The values $\\mu_{j,m+k}$ and $\\sigma_{j,m+k}$ are known. The goal is to find its lower-bound distane to its neighbor `i` (i.e. $T_{i,m+k}$) without using its last `k` elements! The value $d$ shown above is the z-normalized distance between $T_{j,m+k}$ and $T_{i,m+k}$ considering only the `m` first elements. We know that it is already less than $d^{(m+k)}_{j,i}$. So, by minimizing the Right Hand Side of inequation, we can get the Lower Bound (LB)." + ] + }, + { + "cell_type": "markdown", + "id": "49b2a8fc", + "metadata": {}, + "source": [ + "Factoring out $\\frac{1}{\\sigma_{j,m+k}}$ --> Therefore: $d = \\frac{1}{\\sigma_{j,m+k}}\\sqrt{\n", + "\\sum\\limits_{t=1}^{m}{\n", + "(\\frac{T[i+t-1] - \\mu_{i,m+l}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{1})^{2}\n", + "}\n", + "}$ " + ] + }, + { + "cell_type": "markdown", + "id": "4fa6a3a9", + "metadata": {}, + "source": [ + "mulitply by $\\frac{\\sigma_{j,m}}{\\sigma_{j,m}}$ --> Therefore: $\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\n", + "\\sum\\limits_{t=1}^{m}{\n", + "(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}})^{2}\n", + "}\n", + "}$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1634ef47", + "metadata": {}, + "source": [ + "Now, we replace $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$, so we have:" + ] + }, + { + "cell_type": "markdown", + "id": "a86bc201", + "metadata": {}, + "source": [ + "$d = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\n", + "\\sum\\limits_{t=1}^{m}{\n", + "(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}})^{2}\n", + "}\n", + "}$\n", + "\n" + ] + }, + { + "attachments": { + "image.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "5b0f6217", + "metadata": {}, + "source": [ + "**Important Note:**
\n", + "Note the typo in eq(1) of the paper...\n", + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "id": "9682721a", + "metadata": {}, + "source": [ + "Somehow, the authors change $\\mu_{j,m+k}$ to $\\mu_{j,k}$...However, these two values can be different. It seems the LB provided in eq(2) of paper is derived considering such typo. (In fact, the author of this notebook took the derivatives as explained in the paper and achieved one of the term in eq(2). " + ] + }, + { + "cell_type": "markdown", + "id": "4e1ac4d6", + "metadata": {}, + "source": [ + "**>>> In this notebook:
\n", + "we try to calculate LB after correcting such typo. The problem becomes...**" + ] + }, + { + "cell_type": "markdown", + "id": "51f452b1", + "metadata": {}, + "source": [ + "**To find the minimum value of d, we need to minimize the following function:**" + ] + }, + { + "cell_type": "markdown", + "id": "4c09667d", + "metadata": {}, + "source": [ + "$f(\\mu^{'}, \\sigma^{'}) = \\sum\\limits_{t=1}^{m}{\n", + "(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}})^{2}\n", + "}$
\n", + "\n", + "**Let's take its partial derivatives and put them equal to 0...**
\n", + "$\\frac{\\partial f}{\\partial \\mu^{'}} = 0$
\n", + "$\\frac{\\partial f}{\\partial \\sigma^{'}} = 0$" + ] + }, + { + "cell_type": "markdown", + "id": "37330b9c", + "metadata": {}, + "source": [ + "**First, let us first provide some guidelines:**
\n", + "\n", + "(1) We use $T_{i}$ to represent $T[i+t-1]$, and $T_{j}$ to represent $T[j+t-1]$. Since we use them inside $\\sum$, the notation should suffice.
\n", + "(2) We use $\\sum$ without limits. It is alway from $t=1$ to $m$.
\n", + "(3) We define: $X = \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}$
\n", + "(4) Similar to paper, We define: $q = \\frac{\\sum{T_{i}T_{j}} - m\\mu_{i,m}\\mu_{j,m}}{m\\sigma_{i,m}\\sigma_{j,m}}$ (note: $q=1$ for $i=j$)
\n", + "(5) Note that: $\\sum{T_{i}} = m\\mu_{i,m}$, and $T_{j} = m\\mu_{j,m}$.\n", + "(6) We use $\\mu_{j}$ and $\\sigma_{j}$ to represent $\\mu_{j,m}$ and $\\sigma_{j,m}$, respectively. If we want to show $\\mu$ for length `m+k`, we use $\\mu_{j,m+k}$" + ] + }, + { + "cell_type": "markdown", + "id": "0df0e59b", + "metadata": {}, + "source": [ + "**Let us solve it...**" + ] + }, + { + "cell_type": "markdown", + "id": "e330f0c1", + "metadata": {}, + "source": [ + "(1) $\\frac{\\partial f}{\\partial \\mu^{'}} = 0$
\n", + "\n", + "Therefore: $\\sum{\\frac{-2}{\\sigma^{'}}X} = 0$
\n", + "Therefore: $\\sum{X} = 0$ (eq: I)" + ] + }, + { + "cell_type": "markdown", + "id": "30f65c15", + "metadata": {}, + "source": [ + "(2) $\\frac{\\partial f}{\\partial \\sigma^{'}} = 0$
\n", + " \n", + "Therefore: $\\sum{\\frac{-2}{\\sigma^{'2}}(T_{i} - \\mu^{'})X} = 0$
\n", + "Therefore: $\\sum{(T_{i} - \\mu^{'})X} = 0$
\n", + "Therefore (using eq I): $\\sum{T_{i}X} = 0$ (eq II)
" + ] + }, + { + "cell_type": "markdown", + "id": "abe3ee87", + "metadata": {}, + "source": [ + "Also, let us find out the value we are trying to minimize:" + ] + }, + { + "cell_type": "markdown", + "id": "91096e9d", + "metadata": {}, + "source": [ + "$f(\\mu^{'}, \\sigma^{i}) = \\sum{\n", + "(\\frac{T_{i} - \\mu^{'}}{\\sigma^{'}} - \\frac{T_{j} - \\mu_{j,m+k}}{\\sigma_{j}})^{2}\n", + "} \n", + "= \n", + "\\sum{\n", + "[(\\frac{T_{i} - \\mu^{'}}{\\sigma^{'}} - \\frac{T_{j} - \\mu_{j,m+k}}{\\sigma_{j}})X]\n", + "} \n", + "= \n", + "{\n", + "\\frac{\\sum{T_{i}X} - \\sum{\\mu^{'}X}}{\\sigma^{'}} - \\frac{\\sum{T_{j}X} - \\sum{\\mu_{j,m+k}X}}{\\sigma_{j}}\n", + "} $" + ] + }, + { + "cell_type": "markdown", + "id": "7d6213b9", + "metadata": {}, + "source": [ + "And, with help of eq I and II, we can see:
\n", + "$f_{optim} = - \\frac{\\sum{(T_{j}X)}}{\\sigma^{'}} $" + ] + }, + { + "cell_type": "markdown", + "id": "c63a0492", + "metadata": {}, + "source": [ + "Therefore:
\n", + "$f_{optim} = - \\frac{1}{\\sigma^{'}}F$, where:
\n", + "\n", + "$F = \\sum{T_{j}X} = \\frac{\\sum{T_{i}T_{j}} - \\sum\\mu^{'}T_{j}}{\\sigma^{'}} - \\frac{\\sum{T_{j}T_{j}} - \\sum{\\mu_{j,m+k}T_{j}}}{\\sigma_{j}}$" + ] + }, + { + "cell_type": "markdown", + "id": "0fe24576", + "metadata": {}, + "source": [ + "**We need to find $\\mu^{'}$ and $\\sigma^{'}$:**" + ] + }, + { + "cell_type": "markdown", + "id": "b028fd7f", + "metadata": {}, + "source": [ + "eq I: $\\sum{X} = 0$,
\n", + "\n", + "Therefore: $\\sum{\\frac{T_{i} - \\mu^{'}}{\\sigma^{'}} - \\frac{T_{j} - \\mu_{j,m+k}}{\\sigma_{j}}} = 0$
" + ] + }, + { + "cell_type": "markdown", + "id": "e6e9ce06", + "metadata": {}, + "source": [ + "Therefore: ${\\frac{\\sum{T_{i}} - \\sum{\\mu^{'}}}{\\sigma^{'}} - \\frac{\\sum{T_{j}} - \\sum{\\mu_{j,m+k}}}{\\sigma_{j}}} = 0$
" + ] + }, + { + "cell_type": "markdown", + "id": "89ac2637", + "metadata": {}, + "source": [ + "Therefore: ${\\frac{m\\mu_{i} - m{\\mu^{'}}}{\\sigma^{'}} - \\frac{m{\\mu_{j}} - {\\mu_{j,m+k}}}{\\sigma_{j}}} = 0$" + ] + }, + { + "cell_type": "markdown", + "id": "dd128a9f", + "metadata": {}, + "source": [ + "Therefore (given $m \\neq 0$): $\\sigma_{j}(\\mu_{i}-\\mu^{'}) - \\sigma^{'}(\\mu_{j}-\\mu_{j,m+k}) = 0$ (eq III)" + ] + }, + { + "cell_type": "markdown", + "id": "6784d89e", + "metadata": {}, + "source": [ + "---\n", + "\n", + "And, with eq II:
\n", + "$\\sum{T_{i}X} = 0$," + ] + }, + { + "cell_type": "markdown", + "id": "cc05e3e1", + "metadata": {}, + "source": [ + "Therefore: $\\frac{\\sum{T_{i}T_{i}} - \\sum\\mu^{'}T_{i}}{\\sigma^{'}} - \\frac{\\sum{T_{i}T_{j}} - \\sum{\\mu_{j,m+k}T_{i}}}{\\sigma_{j}} = 0$" + ] + }, + { + "cell_type": "markdown", + "id": "b6f83405", + "metadata": {}, + "source": [ + "Therefore: $\\sigma_{j}(m\\mu_{i}^{2} + m\\sigma_{i}^{2} - m\\mu_{i}\\mu^{'}) - \\sigma^{'}(m\\mu_{i}\\mu_{j} + mq\\sigma_{i}\\sigma_{j} - m\\mu_{i}\\mu_{j,m+k}) = 0$ (eq IV)" + ] + }, + { + "cell_type": "markdown", + "id": "71dbe617", + "metadata": {}, + "source": [ + "**solving eq (III) and eq (IV) give us $\\mu^{'}$ and $\\sigma^{'}$ as follows:**" + ] + }, + { + "cell_type": "markdown", + "id": "32537233", + "metadata": {}, + "source": [ + "$\\sigma^{'} = \\frac{\\sigma_{i}}{q}$ (thus, q must be positive.)
\n", + "$\\mu^{'} = \\mu_{i} - \\frac{\\sigma^{'}}{\\sigma_{j}}(\\mu_{j}-\\mu_{j,m+k})$\n", + "\n", + "**If q becomes negative, then:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c0d1430", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b0b16d69", + "metadata": {}, + "source": [ + "To make sure our answers are correct, we plugged them back in eq I and II. To check this, we define functions (just for internal use):" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a90ed8e8", + "metadata": {}, + "outputs": [], + "source": [ + "def _check_derivatives(T,idx,m,k):\n", + " \"\"\"\n", + " This function checks the first (eq I) and second derivatives (eq II) using the optimal values \n", + " provided above.\n", + " \n", + " T: numpy.ndarray\n", + " A time series of interest\n", + " \n", + " idx: int\n", + " start index of subsequence of interest\n", + " \n", + " m: int\n", + " the original window size\n", + " \n", + " k: int\n", + " the additional length (in other words, new window size is m+k.)\n", + " \"\"\"\n", + " M = m + k #larger length (compared to original length m)\n", + " excl_zone = int(np.ceil(M / config.STUMPY_EXCL_ZONE_DENOM)) #unncessary for now! we just need to check \n", + " # that our values are correct...\n", + " \n", + " \n", + " M_T_m, Σ_T_m = core.compute_mean_std(T, m)\n", + " mu_idx, std_idx = M_T_m[idx], Σ_T_m[idx]\n", + " \n", + " M_T_M, Σ_T_M = core.compute_mean_std(T, M)\n", + " mu_IDX, std_IDX = M_T_M[idx], Σ_T_M[idx]\n", + " \n", + " neighbors = np.full(T.shape[0] - M + 1, 1, dtype=bool)\n", + " core.apply_exclusion_zone(neighbors, idx, excl_zone, val = False)\n", + " for i in np.flatnonzero(neighbors):\n", + " mu_i = M_T_m[i]\n", + " std_i = Σ_T_m[i] \n", + " \n", + " q = (1/(m*std_idx*std_i)) * (np.dot(T[i:i+m],T[idx:idx+m]) - m * mu_i * mu_idx)\n", + " \n", + " #finding optimal values to find LB\n", + " std = std_i / q\n", + " mu = mu_i - (std/std_idx) * (mu_idx - mu_IDX)\n", + " \n", + " #calculate first derivative using optimal mu and std\n", + " X = (T[i:i+m] - mu)/std - (T[idx:idx+m] - mu_IDX)/std_idx\n", + " deriv_I = sum(X) #eq I\n", + " deriv_II = sum(X*T[i:i+m]) #eq II\n", + " \n", + " np.testing.assert_almost_equal(deriv_I, 0)\n", + " np.testing.assert_almost_equal(deriv_II, 0)\n", + " \n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "90fc9b10", + "metadata": {}, + "outputs": [], + "source": [ + "T = np.random.uniform(-100, 100, size=1000)\n", + "m = 50\n", + "k = 10\n", + "\n", + "idx = 500\n", + "_check_derivatives(T,idx,m,k)" + ] + }, + { + "cell_type": "markdown", + "id": "bd4a023d", + "metadata": {}, + "source": [ + "Now, we plugged back in the values to find LB:\n", + "\n", + "$LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{f_{optim}}$, where:
\n", + "\n", + "$f_{optim} = - \\frac{1}{\\sigma^{'}}F$, where:
\n", + "\n", + "$F = \\sum{T_{j}X} = \\frac{\\sum{T_{i}T_{j}} - \\sum\\mu^{'}T_{j}}{\\sigma^{'}} - \\frac{\\sum{T_{j}T_{j}} - \\sum{\\mu_{j,m+k}T_{j}}}{\\sigma_{j}}$ in which we should use the optimal value for $\\mu^{'}$ and $\\sigma^{'}$." + ] + }, + { + "cell_type": "markdown", + "id": "bec46a29", + "metadata": {}, + "source": [ + "* If $q \\gt 0$: $LB = \\frac{\\sigma_{j}\\sqrt{\\sigma_{j}}}{\\sigma_{j,m+k}\\sqrt{\\sigma_{i}}} \\sqrt{mq(1-q^{2})}$\n", + "* If $q \\le 0$: $LB = ?$" + ] + }, + { + "cell_type": "markdown", + "id": "3a99be48", + "metadata": {}, + "source": [ + "Note that our formula for LB of distance profile is different than what provided in the paper." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b4e19c1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 53774a58525ae5b621c24a0d166b9386400571df Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 6 Apr 2022 17:49:25 -0600 Subject: [PATCH 02/64] Fix calculation and implement Lower-Bound distance profile --- docs/Tutorial_VALMOD.ipynb | 249 +++++++++++++++++++++---------------- 1 file changed, 145 insertions(+), 104 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 0757670fa..e3e266d0e 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -5,15 +5,15 @@ "id": "482a2e9b", "metadata": {}, "source": [ - "In this tutorial, we would like to implement [VALMOD](https://arxiv.org/pdf/2008.13447.pdf) paper and reproduce its results as closely as possible.\n", + "In this tutorial, we would like to implement VALMOD algorithm proposed in paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf), and reproduce its results as closely as possible.\n", "\n", "The **VAriable Length MOtif Discovery (VALMOD)** algorithm takes time series `T` and a range of subsequence length `[min_m, max_m]`, and find motifs and discords." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "40bf9a66", + "execution_count": 96, + "id": "6534d116", "metadata": {}, "outputs": [], "source": [ @@ -59,18 +59,9 @@ "id": "fd1568ab", "metadata": {}, "source": [ - "We would like to find set $S^{*} = \\bigcup\\limits_{m=min_m}^{max_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", + "We would like to find set $S^{*} = \\bigcup\\limits_{m=min\\_m}^{max\\_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", "\n", - "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index in two different motif sets. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty. \n", - "\n", - "**The authors provided a solution to get top-k motifs from set $S^{*}$. So, this is what can be understood from the statement:**
\n", - "Let us assume we only want to find top-k motifs from all subsequnce with either length `m` or length `m+1`. We try to find motif set for each length...then we should sort the distances (maybe after normalizing them) and then get top-k.\n", - " \n", - "---\n", - "\n", - "**NOTE (from NOTEBOOK producer)**:
\n", - " (1) It is not clear whether the value of `r` can be calculated based on `r`or it should be provided by the user again.
\n", - " (2) It is also not clear whether one should consider trivial matches in $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. Since `m` is changing from one set to another, it may not be easy to understand if two sequences with different length are trivial neighbors of each other or not." + "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index in two different motif sets. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty. " ] }, { @@ -96,7 +87,7 @@ "source": [ "**$n^{th}$ best match**: Given a subsequence $T_{i,m}$, the $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", "\n", - "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?!) best match.
\n", + "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?) best match.
\n", "\n", "**Top-k $n^{th}$ discord**: This is k-th value of $P^{n_{th}}$, sorted in ascending order. $P^{n_{th}}$ is the matrix profile that is constructed based on $n^{th}$ best match rather than 1NN.\n" ] @@ -112,13 +103,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 97, "id": "3d9db678", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -146,9 +137,9 @@ "id": "a8e87bc0", "metadata": {}, "source": [ - "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0. Therefore, we may need to investigate other neighbors. \n", + "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors (maybe the second, third, ...or n-th neighbor as well.) \n", "\n", - "For further details, see Fig. 2 of the paper (Notice that `Top-1 2nd discord` has a close 1NN...but it is far from its 2nd closest neighbor.)" + "For further details, see Fig. 2 of the paper (Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" ] }, { @@ -157,7 +148,7 @@ "metadata": {}, "source": [ "**Variable-length Top-k $n^{th}$ Discord Discovery:**
\n", - "Given a time series `T`, a subsequence length-range `[min_m, max_m]`, and `K` and `N`, we want to find top-k discords $n^{th}$ discord for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." + "Given a time series `T`, a subsequence length-range `[min_m, max_m]`, and `K` and `N`, we want to find **top-k $n^{th}$ discord** for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." ] }, { @@ -280,7 +271,7 @@ "id": "9682721a", "metadata": {}, "source": [ - "Somehow, the authors change $\\mu_{j,m+k}$ to $\\mu_{j,k}$...However, these two values can be different. It seems the LB provided in eq(2) of paper is derived considering such typo. (In fact, the author of this notebook took the derivatives as explained in the paper and achieved one of the term in eq(2). " + "Somehow, the authors change $\\mu_{j,m+k}$ to $\\mu_{j,k}$...However, these two values can be different." ] }, { @@ -323,7 +314,7 @@ "\n", "(1) We use $T_{i}$ to represent $T[i+t-1]$, and $T_{j}$ to represent $T[j+t-1]$. Since we use them inside $\\sum$, the notation should suffice.
\n", "(2) We use $\\sum$ without limits. It is alway from $t=1$ to $m$.
\n", - "(3) We define: $X = \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}$
\n", + "(3) We define: $X_{t} = \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}$
\n", "(4) Similar to paper, We define: $q = \\frac{\\sum{T_{i}T_{j}} - m\\mu_{i,m}\\mu_{j,m}}{m\\sigma_{i,m}\\sigma_{j,m}}$ (note: $q=1$ for $i=j$)
\n", "(5) Note that: $\\sum{T_{i}} = m\\mu_{i,m}$, and $T_{j} = m\\mu_{j,m}$.\n", "(6) We use $\\mu_{j}$ and $\\sigma_{j}$ to represent $\\mu_{j,m}$ and $\\sigma_{j,m}$, respectively. If we want to show $\\mu$ for length `m+k`, we use $\\mu_{j,m+k}$" @@ -344,8 +335,8 @@ "source": [ "(1) $\\frac{\\partial f}{\\partial \\mu^{'}} = 0$
\n", "\n", - "Therefore: $\\sum{\\frac{-2}{\\sigma^{'}}X} = 0$
\n", - "Therefore: $\\sum{X} = 0$ (eq: I)" + "Therefore: $\\sum{\\frac{-2}{\\sigma^{'}}X_{t}} = 0$
\n", + "Therefore: $\\sum{X_{t}} = 0$ (eq: I)" ] }, { @@ -355,9 +346,9 @@ "source": [ "(2) $\\frac{\\partial f}{\\partial \\sigma^{'}} = 0$
\n", " \n", - "Therefore: $\\sum{\\frac{-2}{\\sigma^{'2}}(T_{i} - \\mu^{'})X} = 0$
\n", - "Therefore: $\\sum{(T_{i} - \\mu^{'})X} = 0$
\n", - "Therefore (using eq I): $\\sum{T_{i}X} = 0$ (eq II)
" + "Therefore: $\\sum{\\frac{-2}{\\sigma^{'2}}(T_{i} - \\mu^{'})X_{t}} = 0$
\n", + "Therefore: $\\sum{(T_{i} - \\mu^{'})X_{t}} = 0$
\n", + "Therefore (using eq I): $\\sum{T_{i}X_{t}} = 0$ (eq II)
" ] }, { @@ -378,11 +369,11 @@ "} \n", "= \n", "\\sum{\n", - "[(\\frac{T_{i} - \\mu^{'}}{\\sigma^{'}} - \\frac{T_{j} - \\mu_{j,m+k}}{\\sigma_{j}})X]\n", + "[(\\frac{T_{i} - \\mu^{'}}{\\sigma^{'}} - \\frac{T_{j} - \\mu_{j,m+k}}{\\sigma_{j}})X_{t}]\n", "} \n", "= \n", "{\n", - "\\frac{\\sum{T_{i}X} - \\sum{\\mu^{'}X}}{\\sigma^{'}} - \\frac{\\sum{T_{j}X} - \\sum{\\mu_{j,m+k}X}}{\\sigma_{j}}\n", + "\\frac{\\sum{T_{i}X_{t}} - \\sum{\\mu^{'}X_{t}}}{\\sigma^{'}} - \\frac{\\sum{T_{j}X_{t}} - \\sum{\\mu_{j,m+k}X_{t}}}{\\sigma_{j}}\n", "} $" ] }, @@ -392,7 +383,7 @@ "metadata": {}, "source": [ "And, with help of eq I and II, we can see:
\n", - "$f_{optim} = - \\frac{\\sum{(T_{j}X)}}{\\sigma^{'}} $" + "$f_{optim} = - \\frac{\\sum{(T_{j}X)}}{\\sigma_{j}} $" ] }, { @@ -401,7 +392,7 @@ "metadata": {}, "source": [ "Therefore:
\n", - "$f_{optim} = - \\frac{1}{\\sigma^{'}}F$, where:
\n", + "$f_{optim} = - \\frac{1}{\\sigma_{j}}F$, where:
\n", "\n", "$F = \\sum{T_{j}X} = \\frac{\\sum{T_{i}T_{j}} - \\sum\\mu^{'}T_{j}}{\\sigma^{'}} - \\frac{\\sum{T_{j}T_{j}} - \\sum{\\mu_{j,m+k}T_{j}}}{\\sigma_{j}}$" ] @@ -445,7 +436,7 @@ "id": "dd128a9f", "metadata": {}, "source": [ - "Therefore (given $m \\neq 0$): $\\sigma_{j}(\\mu_{i}-\\mu^{'}) - \\sigma^{'}(\\mu_{j}-\\mu_{j,m+k}) = 0$ (eq III)" + "Therefore: $\\sigma_{j}(\\mu_{i}-\\mu^{'}) - \\sigma^{'}(\\mu_{j}-\\mu_{j,m+k}) = 0$ (eq III)" ] }, { @@ -480,7 +471,7 @@ "id": "71dbe617", "metadata": {}, "source": [ - "**solving eq (III) and eq (IV) give us $\\mu^{'}$ and $\\sigma^{'}$ as follows:**" + "**solving eq (III) and eq (IV) give us optimal values for $\\mu^{'}$ and $\\sigma^{'}$ as follows:**" ] }, { @@ -489,135 +480,185 @@ "metadata": {}, "source": [ "$\\sigma^{'} = \\frac{\\sigma_{i}}{q}$ (thus, q must be positive.)
\n", - "$\\mu^{'} = \\mu_{i} - \\frac{\\sigma^{'}}{\\sigma_{j}}(\\mu_{j}-\\mu_{j,m+k})$\n", + "$\\mu^{'} = \\mu_{i} - \\frac{\\sigma^{'}}{\\sigma_{j}}(\\mu_{j}-\\mu_{j,m+k})$" + ] + }, + { + "cell_type": "markdown", + "id": "bd4a023d", + "metadata": {}, + "source": [ + "Now, we plugged back in the values to find LB:\n", + "\n", + "$LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{f_{optim}}$, where:
\n", "\n", - "**If q becomes negative, then:**" + "$f_{optim} = - \\frac{1}{\\sigma_{j}}F$, where:
\n", + "\n", + "$F = \\sum{T_{j}X} = \\frac{\\sum{T_{i}T_{j}} - \\sum\\mu^{'}T_{j}}{\\sigma^{'}} - \\frac{\\sum{T_{j}T_{j}} - \\sum{\\mu_{j,m+k}T_{j}}}{\\sigma_{j}}$ in which we should use the optimal value for $\\mu^{'}$ and $\\sigma^{'}$." ] }, { - "cell_type": "code", - "execution_count": null, - "id": "2c0d1430", + "cell_type": "markdown", + "id": "bec46a29", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "* If $q \\gt 0$: $LB = \\frac{\\sigma_{j}}{\\sigma_{j,m+k}} \\sqrt{m(1-q^{2})}$\n", + "* If $q \\le 0$: $LB = \\frac{\\sigma_{j}}{\\sigma_{j,m+k}} \\sqrt{m}$ (proof not provided in this notebook)" + ] }, { "cell_type": "markdown", - "id": "b0b16d69", + "id": "3bcf8519", "metadata": {}, "source": [ - "To make sure our answers are correct, we plugged them back in eq I and II. To check this, we define functions (just for internal use):" + "### LB dist profile function" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "a90ed8e8", + "execution_count": 87, + "id": "928fc18c", "metadata": {}, "outputs": [], "source": [ - "def _check_derivatives(T,idx,m,k):\n", + "def _calc_LB_dist_profile(T, D, m, σ, m_target, σ_target):\n", " \"\"\"\n", - " This function checks the first (eq I) and second derivatives (eq II) using the optimal values \n", - " provided above.\n", + " This function finds the lower-bound of a distance profile for a subsequence with window size `m_target` based\n", + " on the distance profile of a subsequence with window size `m` starting from the same index `idx`.\n", + " (note: this is for z-normalize case)\n", " \n", + " Parameters\n", + " ----------\n", " T: numpy.ndarray\n", - " A time series of interest\n", - " \n", - " idx: int\n", - " start index of subsequence of interest\n", + " a time series of interest\n", + " \n", + " D: numpy.ndarray\n", + " Distance profile for a subsequence with length `m` located at an index `idx` in time series `T`\n", " \n", " m: int\n", - " the original window size\n", + " length of subsequence for which the the distance profile D is provided. \n", + " \n", + " σ: float\n", + " standard deviation of subsequence `T[idx : idx + m]`\n", + " \n", + " m_target: int\n", + " new length of subsequence whose lower-bound distance profile will be returned.\n", " \n", - " k: int\n", - " the additional length (in other words, new window size is m+k.)\n", + " σ_target: float\n", + " standard deviation of subsequence `T[idx : idx + m_target]`\n", + " \n", + " Return\n", + " --------\n", + " LB : numpy.ndarray\n", + " Lower_Bound of distance profile for subsequence with length `m_target`, starting at index `idx`.\n", + " \n", " \"\"\"\n", - " M = m + k #larger length (compared to original length m)\n", - " excl_zone = int(np.ceil(M / config.STUMPY_EXCL_ZONE_DENOM)) #unncessary for now! we just need to check \n", - " # that our values are correct...\n", + " if m_target <= m:\n", + " raise ValueError(f\"m_target, {m_target} should be larget than m, {m}\")\n", " \n", + " if len(D) != T.shape[0] - m + 1:\n", + " raise ValueError(f\"length of distance profile D, {len(D)}, should be T.shape[0]-m+1, {T.shape[0]-m+1}\")\n", " \n", - " M_T_m, Σ_T_m = core.compute_mean_std(T, m)\n", - " mu_idx, std_idx = M_T_m[idx], Σ_T_m[idx]\n", + " excl_zone = int(np.ceil(m_target / config.STUMPY_EXCL_ZONE_DENOM))\n", " \n", - " M_T_M, Σ_T_M = core.compute_mean_std(T, M)\n", - " mu_IDX, std_IDX = M_T_M[idx], Σ_T_M[idx]\n", + " k = T.shape[0] - m_target + 1\n", + " T_is_finite = core.rolling_isfinite(T, m_target)\n", " \n", - " neighbors = np.full(T.shape[0] - M + 1, 1, dtype=bool)\n", - " core.apply_exclusion_zone(neighbors, idx, excl_zone, val = False)\n", - " for i in np.flatnonzero(neighbors):\n", - " mu_i = M_T_m[i]\n", - " std_i = Σ_T_m[i] \n", - " \n", - " q = (1/(m*std_idx*std_i)) * (np.dot(T[i:i+m],T[idx:idx+m]) - m * mu_i * mu_idx)\n", - " \n", - " #finding optimal values to find LB\n", - " std = std_i / q\n", - " mu = mu_i - (std/std_idx) * (mu_idx - mu_IDX)\n", - " \n", - " #calculate first derivative using optimal mu and std\n", - " X = (T[i:i+m] - mu)/std - (T[idx:idx+m] - mu_IDX)/std_idx\n", - " deriv_I = sum(X) #eq I\n", - " deriv_II = sum(X*T[i:i+m]) #eq II\n", - " \n", - " np.testing.assert_almost_equal(deriv_I, 0)\n", - " np.testing.assert_almost_equal(deriv_II, 0)\n", + " R = 1 - np.square(D[:k])/(2 * m)\n", " \n", - " return " + " LB = (σ/σ_target) * np.sqrt(m) * np.sqrt(1 - np.square(np.maximum(R,0)))\n", + " core.apply_exclusion_zone(LB, idx, excl_zone, np.inf)\n", + " LB[~T_is_finite] = np.inf\n", + " \n", + " return LB" + ] + }, + { + "cell_type": "markdown", + "id": "52a327c7", + "metadata": {}, + "source": [ + "**Example:**" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "90fc9b10", + "execution_count": 92, + "id": "df09cc41", "metadata": {}, "outputs": [], "source": [ - "T = np.random.uniform(-100, 100, size=1000)\n", - "m = 50\n", - "k = 10\n", - "\n", - "idx = 500\n", - "_check_derivatives(T,idx,m,k)" + "T = np.random.uniform(-100,100, size=1000)\n", + "idx = 500 #start index of subsequence" ] }, { - "cell_type": "markdown", - "id": "bd4a023d", + "cell_type": "code", + "execution_count": 93, + "id": "2f0a21d3", "metadata": {}, + "outputs": [], "source": [ - "Now, we plugged back in the values to find LB:\n", + "m = 10\n", + "_, Σ_T = core.compute_mean_std(T, m)\n", + "Q = T[idx:idx+m]\n", + "excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", "\n", - "$LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{f_{optim}}$, where:
\n", + "#################################################\n", "\n", - "$f_{optim} = - \\frac{1}{\\sigma^{'}}F$, where:
\n", - "\n", - "$F = \\sum{T_{j}X} = \\frac{\\sum{T_{i}T_{j}} - \\sum\\mu^{'}T_{j}}{\\sigma^{'}} - \\frac{\\sum{T_{j}T_{j}} - \\sum{\\mu_{j,m+k}T_{j}}}{\\sigma_{j}}$ in which we should use the optimal value for $\\mu^{'}$ and $\\sigma^{'}$." + "m_target = 11\n", + "_, Σ_T_target = core.compute_mean_std(T, m_target)\n", + "Q_target = T[idx:idx+m_target]\n", + "excl_zone_target = int(np.ceil(m_target / config.STUMPY_EXCL_ZONE_DENOM))" ] }, { - "cell_type": "markdown", - "id": "bec46a29", + "cell_type": "code", + "execution_count": 94, + "id": "35649122", "metadata": {}, + "outputs": [], "source": [ - "* If $q \\gt 0$: $LB = \\frac{\\sigma_{j}\\sqrt{\\sigma_{j}}}{\\sigma_{j,m+k}\\sqrt{\\sigma_{i}}} \\sqrt{mq(1-q^{2})}$\n", - "* If $q \\le 0$: $LB = ?$" + "D = core.mass(Q, T)\n", + "core.apply_exclusion_zone(D, idx, excl_zone, np.inf)\n", + "\n", + "D_target = core.mass(Q_target, T) #true distance profile for length m_target\n", + "core.apply_exclusion_zone(D_target, idx, excl_zone, np.inf) " ] }, { - "cell_type": "markdown", - "id": "3a99be48", + "cell_type": "code", + "execution_count": 95, + "id": "09669a7a", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABHcAAAF1CAYAAABiXwa3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOx9d5gcxbX96c0rrbIAA8IkkYMBgwMGDLZ/2OZhMGCb94BHso39jJ/Bj2CbbAw8Ex45GEcyxiYYY5GjyEEoECSBAIFyWq1W2rwz/ftj9rbu3LlVXd3Ts0Gq8336VjPTXV1VXeHWqXNvBWEYwsPDw8PDw8PDw8PDw8PDw8NjaKJqoDPg4eHh4eHh4eHh4eHh4eHh4ZEentzx8PDw8PDw8PDw8PDw8PDwGMLw5I6Hh4eHh4eHh4eHh4eHh4fHEIYndzw8PDw8PDw8PDw8PDw8PDyGMDy54+Hh4eHh4eHh4eHh4eHh4TGE4ckdDw8PDw8PDw8PDw8PDw8PjyEMT+54eHh4eKxzCILgliAILur7/75BEMwe6DwNFgRB8F9BECwJgmBNEATj+v5u1fdbVG8ZP3OjIAgmB0GwOgiC/8sw3Yrkd30HbxOG3+cGQfA1x7SOD4Lghexy5w7fPjw8PDw81id4csfDw8PDY51GGIbPh2G4Xdx1QRBcEATBHf2Rp4FCEAS1AK4EcGAYhk1hGK7o+/thhR99EoDlAEaGYXhahZ81aBAEwf5BEMyPueaCIAh6+giVNZJYCYJgiyAIngmCoD0IglmSVAmC4KggCD4OgqAtCIJ/BEEwttx88zYxVAiSLEikIAh+HwTB7CAI8kEQHC9+2zkIgseCIFgeBEFYVmY9PDw8PDwqAE/ueHh4eHh4rCMIgqAm5pKNADQAeKcfssOxOYB3wzD0i2Id9/QRKk0K2XY3gKkAxgE4G8C9QRBsAABBEOwE4GYA/4nCu20HcGP/Zn2dwnQAPwHwpvJbD4C/Afh+v+bIw8PDw8PDEZ7c8fDw8PAY8giCYPcgCN7sc/u5BwUCg34rUk8EQfCLIAgW9F07OwiCrwZB8A0AZwE4sk85Mb3v2hOCIJjZd+2HQRD8SKYbBMFpQRAsDYJgURAEJ7DfG4Mg+L8+VcWqIAheCIKgse+3LwRB8FIQBC1BEEwPgmB/S9nmBkHwqyAI3g2CYGUQBH8JgqBB5OEXQRAsBvCXIAjqgyC4OgiChX3/ru77blsA5J7WEgTB031phEEQTDQ8++AgCKb15fOlIAh2teRz7yAIXu8r6+tBEOzd9/0tAI4DcGZf3Za48wRBcFBf+Vb3vZvT+74vUWMo+R0fBMETffc+FwTB5n3XBUEQXNX3blYFQTAjCIKd+36rD4LgiiAIPgkKLmq/o3fT9/sZfe9zYRAEJ8bUkdpGgiAYDuARAJsEaxU5m5jqz5D2tgD2AHB+GIYdYRjeB+AtAEf0XXI0gIfCMJwchuEaAOcCODwIghGGfD7EPs8JguBv7PO8IAh243UcBMFJfc+gd/cQS3K3vjpdFQTBPdQmHcq0fd/7ag4K/e977LdbgiC4IQiCSX31+WoQBFuz3w/su2dVEAQ39r3vHwRBsAOA3wH4Yl8+W9gjx5jSkwjD8IYwDJ8C0Kn8NjsMwz+h/4lRDw8PDw8PJ3hyx8PDw8NjSCMIgjoA/wBwO4CxAP6OtYtfee12AH4KYK8wDEcA+DqAuWEYPgrgEqxVUHym75alAA4GMBLACQCuCoJgD5bkpwCMArApCjv6NwRBMKbvtysAfBbA3n35OhNAPgiCTQFMAnBR3/enA7gv6FNjGHB0X163BrAtgHNEHsaioI45CQV1xxcA7AbgMwA+B+CcMAzfA7BT3z2jwzD8iuV56CvnnwH8CAXVyM0A/hkEQb1y7di+Ml3bd+2VACYFQTAuDMPjAdwJ4LK+un1SedyfAPyo753sDOBpW94EjgbwGwDjAUzrexYAHAhgPxTqazSAIwGs6Pvt0r7vdwMwEYX3d15fWb6Bwjv5fwC2ARAXW0ZtI2EYtgH4JoCFTJGz0JDGt/rIjneCIPgv9v1OAD4Mw3A1+2461r7Hnfo+AwDCMPwAQHdf2SSeA7BvEARVQRBsDKAWwJf6yrwVgCYAM/gNYRj+HsXv7lvs5+8B+AaALQHsCuB4Q9ki9BFeTwC4C8CGAP4DwI1BQYFE+A8AvwYwBsAcABf33TsewL0AfoVCG5uNQt9CGIYzAfwYwMt9+Rwdl56Hh4eHh8e6Bk/ueHh4eHgMdXwBhYXq1WEY9oRheC+A1w3X5gDUA9gxCILaMAzn9i2IVYRhOCkMww/CAp4D8DiAfdklPQAu7HvuwwDWANguCIIqACcCOCUMwwVhGObCMHwpDMMuAMcAeDgMw4fDMMyHYfgEgDcAHGQp4/VhGM4Lw7AZhcXpf7Df8igoO7rCMOxAgey4MAzDpWEYLkNhYfuflrRN+CGAm8MwfLUv/7cC6EKhviX+DcD7YRjeHoZhbxiGdwOYBeBbyrUaelB4JyPDMFwZhqHmFmPCpD7lShcKxNYXgyDYrC/NEQC2BxCEYTgzDMNFQRAEfWX7eRiGzX3EySUA/r0vve8B+EsYhm/3ETQX2B7u0Ebi8DcAOwDYoC9f5wVBQO+3CcAqcf2qvnK5/M7z+SGA1SgQWl8G8BiABUEQbN/3+fkwDPMJ8n1tGIYL+9rkQ33pxuFgFMjUv/S1kzcB3AfgO+ya+8MwfC0Mw14UiCVK9yAA74RheH/fb9cCWOzwTFN6Hh4eHh4e6xQ8uePh4eHhMdSxCYAFIp7Lx9qFYRjOAXAqCgv2pUEQ/NXmKhMEwTeDIHilT1XRgsICczy7ZEXfopHQjsKCezwKrmEacbQ5gO8GBVenlr509wGwsaWM80TZeJ6XhWHI3Ug2QXH55fWu2BzAaSKfmxnSks+k527q+KwjUKjbj/tcbb6YIJ9R3fS5JjUD2CQMw6cBXA/gBgBLgkKw3JEokCjDAExh5Xq073sqi6xvIxzaiBVhGL7bR5LkwjB8CcA1WEt2rEFBEcQxEgWSxuV3iecA7I+Couk5AM+iQOx8ue9zEnBihdp9HDYH8HnRpo5GQX0Wl27Re+nr79Zg1WXk08PDw8PDY8jBkzseHh4eHkMdiwBs2qfIIHzadHEYhneFYbgPCgvNEAUXHfT9P0Kf+9F9KLhXbdTn6vEwAP4cE5ajELdDi+8xD8DtYRiOZv+Gh2H4W0t6m7H/fxoAd++RQYoXolA20/WumAfgYpHPYX2qHAn5THruApcHhWH4ehiGh6LgqvMPFNQsANCGAhEDAAiC4FOld6+tmyAImlBwUVvYl+61YRh+FgX3pW0BnIHCu+kAsBMr16gwDGnRvwil9a3CoY2kCSAdsvvfAbCViKHzGayN+/JO32fKz1YoKNPeM6RN5M6+ff9/DvHkTpZBsOcBeE60qaYwDP8r9s7Ce5lAH/r6+wT2uw/W7eHh4eGxXsOTOx4eHh4eQx0vA+gF8LMgCGqCIDgchTgzJQiCYLsgCL7StyjvRGGRn+v7eQmALfpcqgCgDoWF8jIAvUEQfBOFOC6x6HNv+TOAK4Mg2CQIguogCL7Y99w7UIix8vW+7xuCQmDkCZYkTw6CYEJfbJuzANxjufZuAOcEQbBBX5yS8/qemRR/APDjIAg+HxQwPAiCfwuUYL0oEBrbBoVjuWuCIDgSwI4A/hX3kCAI6oIgODoIglFhGPYAaMXadzIdwE5BEOwWFAL2XqAkcVAQBPv0xV76DYBXwzCcFwTBXn15r0WBJOoEkOt7N39AITbOhn152DQIgq/3pfc3AMcHQbBjEATDAJxvyX5cG1kCYFwQBKMs5T80CIIxfXX8OQA/A/AgAISFOEnTAJzf104OQyG+zX19t9+JQlvaty+ezYUouCHZlDsHAGgMw3A+gOdRiJszDoUTuTQsAbCV4bek+BcK7eQ/gyCo7fu3V1AIiByHSQB2CYLg20HhVLiTUaz4WQJgQl87SIW+ttiAArlW21fnVX2/BX2/1fV9bgiU+FMeHh4eHh4DBU/ueHh4eHgMaYRh2A3gcBQCuq5EIXDu/YbL6wH8FgX1xmIUlCJn9f32976/K4IgeLNvgfwzFBb7KwEcBeCfCbJ2OgonG72OgqvQpQCqwjCcB+DQvucuQ0HNcAbsc/JdKMRy+bDv30WWay9CIYbPjL7nvxlzvYowDN9AIQbM9SiUfw4MQXPDMFyBQjyV01AIWnwmgIPDMFzu+Lj/BDA3CIJWFALjHtOX7nsoEBZPAngfwAvKvXehQMA0oxDA+ui+70eiQOKsRMG1agUKChsA+EVfeV7pe+aTALbre+YjAK5GIajzHFiCO8e1kTAMZ6FAtn3Y54akubT9e99zVgO4DcClffGN+O979qX/WwDf6YulhDAM3+mrrztRCOw8AoWjvE35fQ8FV67n+z63otCeXgzDMGe47U8oxENqCYLgH6a0XdBXXwf2lWkhCn3wUhT6Zdy9ywF8F8BlKLzLHVFo5119lzyNgpJpcRAEru1O4nEUCN+9Afy+7//79f22ed9nUk11YO3pcx4eHh4eHgOOoDhEgYeHh4eHh8dgQhAEcwH8INRPmfLoBwRBEALYpi9mk8cgQJ+iZj6Ao8MwfGag8+Ph4eHh4THQ8ModDw8PDw8PDw+PQY8+V8bRfe5QZ6HgPvXKAGfLw8PDw8NjUMCTOx4eHh4eHh4eHkMBX0ThBLrlAL4F4NthGHYMbJY8PDw8PDwGB7xbloeHh4eHh4eHh4eHh4eHh8cQhlfueHh4eHh4eHh4eHh4eHh4eAxheHLHw8PDw8PDw8PDw8PDw8PDYwijphKJjh8/Ptxiiy0qkbSHh4eHh4eHh4eHh4eHh4fHeokpU6YsD8NwA/l9RcidLbbYAm+88UYlkvbw8PDw8PDw8PDw8PDw8PBYLxEEwcfa994ty8PDw8PDw8PDw8PDw8PDw2MIw5M7Hh4eHh4eHh4eHh4eHh4eHkMYntzx8PDw8PDw8PDw8PDw8PDwGMKoSMwdDw8PDw8PDw8PDw8PDw+PgUVPTw/mz5+Pzs7Ogc6KR0I0NDRgwoQJqK2tdbrekzseHh4eHh4eHh4eHh4eHusg5s+fjxEjRmCLLbZAEAQDnR0PR4RhiBUrVmD+/PnYcsstne7xblkeHh4eHh4eHh4eHh4eHusgOjs7MW7cOE/sDDEEQYBx48YlUlx5csfDw8PDw8PDw8PDw8PDYx2FJ3aGJpK+N++W5eHh4eHh4eHh4eHh4eHhkTlWrFiBr371qwCAxYsXo7q6GhtssAEA4LXXXkNdXV3Zz9h///2xaNEi1NfXo7u7G1/72tdw0UUXYfTo0WWnPZTglTseHh4eHh4eHh4eHh4eHh6ZY9y4cZg2bRqmTZuGH//4x/j5z38efa6rq0Nvb28mz7nzzjsxY8YMzJgxA/X19Tj00EMzSXcowSt3PDw8PDw8PDw8PDw8PDw8+gXHH388xo4di6lTp2KPPfbAiBEj0NTUhNNPPx0AsPPOO+Nf//oXtthiC9xxxx249tpr0d3djc9//vO48cYbUV1dbUy7rq4Ol112GSZOnIjp06fjM5/5TH8Va8DhyR0PDw8PDw8PDw8PDw8Pj3Ucp556KqZNm5ZpmrvtthuuvvrqxPe99957ePLJJ1FdXY0LLrhAvWbmzJm455578OKLL6K2thY/+clPcOedd+LYY4+1pl1dXY3PfOYzmDVrlid3PDwGEh0dHVi8eLHzkW8eHh4eHh4eHh4eHh4eQwff/e53rQocAHjqqacwZcoU7LXXXgAK68QNN9zQKf0wDMvO41CDJ3c8Bh1uvvlmnH/++WhpafGR3T08PDw8PDw8PDw8PDJAGoVNpTB8+PDo/zU1Ncjn89FnOv47DEMcd9xx+N///d9EaedyObz11lvYYYcdssnsEIEPqOwx6LBy5Uq0traul2yrh4eHh4eHh4eHh4fH+oQtttgCb775JgDgzTffxEcffQQA+OpXv4p7770XS5cuBQA0Nzfj448/tqbV09ODX/3qV9hss82w6667Vjbjgwye3PEYdCDW1pM7Hh4eHh4eHh4eHh4e6zaOOOIINDc3Y7fddsNNN92EbbfdFgCw44474qKLLsKBBx6IXXfdFf/v//0/LFq0SE3j6KOPxq677oqdd94ZbW1tePDBB/uzCIMC3i3LY9CByJ18Ph/rh+nh4eHh4eHh4eHh4eEx+GEKnNzY2IjHH39c/e3II4/EkUceaU332WefLTNn6wa8csdj0IEUO1654+Hh4eHh4eHh4eHh4eERD0/ueAw6eLcsDw8PDw8PDw8PDw8PDw93OJE7QRCMDoLg3iAIZgVBMDMIgi9WOmMe6y+8csfDw8PDw8PDw8PDw8PDwx2uMXeuAfBoGIbfCYKgDsCwCubJYz2HV+54eHh4eHh4eHh4eHh4eLgjVrkTBMFIAPsB+BMAhGHYHYZhS4Xz5eGIDz/8cJ0jQXhA5UrjhRdewL333lvx53h4eHh4eHh4eHh4eHh4VAoubllbAVgG4C9BEEwNguCPQRAMr3C+PBwwf/58TJw4EU8++eRAZyVT9Kdb1nXXXYfzzjuv4s/x8PDw8PDw8PDw8PDw8KgUXMidGgB7ALgpDMPdAbQB+KW8KAiCk4IgeCMIgjeWLVuWcTY9NLS0tCAMQzQ3Nye6p7W1tYK5Kh/96ZaVy+X6RSHk4eHh4eHh4eHh4eGxPqKpqWlAn3/88cdjyy23xG677Ybtt98ev/71ryv+zAsuuABXXHGF+v2mm26K3XbbDdtssw0OP/xwvPvuu5k804XcmQ9gfhiGr/Z9vhcFsqcIYRj+PgzDPcMw3HODDTbIJHMedqRxXzrmmGPw4x//uFJZygT9qdwJw9CTOx4eHh4eHh4eHh4eHusIent7S767/PLLMW3aNEybNg233norPvroowHIWQE///nPMW3aNLz//vs48sgj8ZWvfAVZCGRiyZ0wDBcDmBcEwXZ9X30VQDbUkkdZSEPuLFu2rKjhPP3007jtttsyz1s56E/lTj6fX+diFnl4eHh4rF9oa2vD9ttvj5deemmgs+Lh4cHwhS98AX/+858HOhseHoMS06ZNwxe+8AXsuuuuOOyww7By5UosXboUn/3sZwEA06dPRxAE+OSTTwAAW2+9Ndrb27Fs2TIcccQR2GuvvbDXXnvhxRdfBFBQxJx00kk48MADceyxxxqf29nZCQAYPrwQaeapp57C7rvvjl122QUnnngiurq6AABbbLEFli9fDgB44403sP/++0fPOfHEE7H//vtjq622wrXXXhulffHFF2O77bbD1772NcyePdupHo488kgceOCBuOuuu1yrzgjX07L+G8CdfSdlfQjghLKf7FE20pA7YRgil8tFn3//+9/jzTfftHaA/kZ/BlT25I6Hh4eHx1DHsmXLMHv2bMycORN77733QGfHw8OjD9OmTcPMmTMHOhvrBZYuXYoNNtgAQRAMdFYGN049FZg2Lds0d9sNuPrqxLcde+yxuO666/DlL38Z5513Hn7961/j6quvRmdnJ1pbW/H8889jzz33xPPPP4999tkHG264IYYNG4Yf/OAH+PnPf4599tkHn3zyCb7+9a9H/WzKlCl44YUX0NjYWPK8M844AxdddBHmzJmDn/3sZ9hwww3R2dmJ448/Hk899RS23XZbHHvssbjppptw6qmnWvM+a9YsPPPMM1i9ejW22247/Nd//RdmzJiBv/71r5g6dSp6e3uxxx57RERVHPbYYw/MmjUrcR1KOJE7YRhOA7Bn2U/zyBRpSJB8Pl9E7gxGcqO/3bIGW/k9PDw8PDySgOYx72bs4TG44GM79g+WLVuGCRMm4J///Ce+8Y1vDHR2ysLy5cuxaNEi7LLLLgOdlYpi1apVaGlpwZe//GUAwHHHHYfvfve7AIC9994bL774IiZPnoyzzjoLjz76KMIwxL777gsAePLJJ4ti1LS2tmL16tUAgEMOOUQldoCCW9Z3vvMdrFmzBl/96lfx0ksvYfjw4dhyyy2x7bbbRvm44YYbYsmdf/u3f0N9fT3q6+ux4YYbYsmSJXj++edx2GGHYdiwYVFeXJHVetRVueMxCJHGmJMxZiTZMxjg3bI8PDw8PDzc4ckdD4/BCW9n9g9aWlrQ09ODpUuXDnRWysYVV1yB22+/HQsWLKjMA1IobPob++67L55//nl8/PHHOPTQQ3HppZciCAIcfPDBAAr96uWXX1ZJHHK1euCBB6KgyX/84x+LrmlqasL++++PF154AQceeKAxHzU1NdG8Sq5chPr6+uj/1dXVUYyftMqxqVOnYs89y9fSuARU9hikyMItK5/PDzpj0Ct3PDw8PDw8kmOwzeceHus7BqOdPVTw/PPP48QTT3Sy02ltsy7Y9GvWrEFbW9tAZ6PiGDVqFMaMGYPnn38eAHD77bdHKp799tsPd9xxB7bZZhtUVVVh7NixePjhh/GlL30JAHDggQfi+uuvj9KapriZHXbYYVHwZEma9Pb24tVXX8XWW2+N7bffHnPnzsWcOXNK8rHFFltgypQpAID77rsvtkz77bcfHnjgAXR0dGD16tV46KGHnOrivvvuw+OPP47/+I//cLreBk/uDGGkdcuSyp3BNul45Q5w//33Y+eddx50qioPDw8Pj8GH/twU8fDwcEPW/fK5555DEARRgNd1HU899RT+8pe/ONnC/Rmvs9IYjGuzLNDe3o4JEyZE/6688krceuutOOOMM7Drrrti2rRpOO+88wAUSBWgQJYAwD777IPRo0djzJgxAIBrr70Wb7zxBnbddVfsuOOO+N3vfueUhzPOOAO77bYbdt11V+yyyy44/PDD0dDQgL/85S/47ne/i1122QVVVVXRydLnn38+TjnlFOy7776orq6OTX+PPfbAkUceid122w1HHHFE5Eam4aqrroqOQr/jjjvw9NNPI4sTx71b1hBGGhJkKCh3+nOAHqzKnZkzZ+Kdd95BT0+P02Di4eHh4bH+wrtleXgMPmRtz1566aUAgNdeew0HHXRQJmkOZtC4lsvlUFNjX7L258ZwpZHL5daJckiY+sErr7yifk8nZAHAWWedhbPOOiv6PH78eNxzzz0l91xwwQXG599yyy3G37761a9i6tSpJd/vu+++eO+992Kf8/bbb0f/P/vss3H22Wcbn0X32/JaDrxyZwgjC7csGYNnMKA/dyAHq3LHG+oeHh4eHq7wc4aHx+BD1oQDpbe+nAZF5U2i3BmMNn1SDNa1icfQgCd3hjCyCqg82IxB75blJfYeHh4eHu7w5M7gxGBVB3v0D4iUyKpfUluqqlo/lm9cuROHdY3c8WO5R1qsH6PDOoqsjkJPM4D09PQkvscVPqDyuuU77OHhsX5h7ty56O7uHuhsrFfw5M7gxLbbboubb755oLPhMUDI2pYbzMqd6dOnZz7+eOWOh0dyeHJnCCGXy+Gf//xniRHX38qdDz74AMOGDcO7776b6D5XeOWON9Q9PPobP/nJT7DhhhsOdDaGPLq7u7Hzzjtbfds9soefMwYn5s6di7lz5w50NjwGCFnbs4NVufPxxx9jt912w2OPPZZpummUO+vCGFiptclgXO94xCPpextco4OHFU8//TQOPfRQzJgxA8DAHYU+f/589Pb2Yt68eYnuc4UPqOzdsjzsePXVVxEEQXR8pEf5uOmmm7Bs2bKBzsaQR3d3N9ra2rBy5cqBzsp6BT9nDE4MxriGHv2H9UW509raCgBYtWpVpunSeNbb2xt77bqk3MnlcpmPGw0NDVixYsU6UT/rE8IwxIoVK9DQ0OB8jz8tawihs7Oz6O9AHYWeRCaZBv0dUHkwGl7r0g6ER/Z4/PHHAQCPPfaY9ZhFD4/+Rpqxa9q0aVi6dCkOPPDASmVrnYdX7gxODFYbw6N/sL4od7Iq53vvvYcFCxbggAMOKErXu2WVjwkTJmD+/Pl+E2sIoqGhARMmTHC+3pM7QwjSaM4ioHKaXSUaZF2Y9DTozwF6sCt3vFHooYHaxWAz8Dw80oxdl112Gd58803MmjWrUtla5+HnjMEJr9xZv5F1QOXBqtzJqpyXXXYZnn76aXz44YcA1u+AylmXo7a2FltuuWWmaXoMTviVwRCCHOTSDGRZuGXR9ZUidwbzUegdHR245JJLKhpQGvASew87qA9WV1cPcE48PIqRhmTo6emp2HyyvsCTO4MP/p14DHblzg9/+EOceOKJZaeTldq8u7u7KBh/GuWOlodnn30WCxYsKCtv/Qmv+PMoB57cGUIwBVLu74DKlXbLGswBlSdPnoyzzz4bU6ZMqWCuvFuWhx1eueMxWJFmQZvL5So2n6wv8ETC4IN/Jx6DLebO7NmzsWTJkujz+++/j/fffz+zfJVbTrkBnZVy54gjjsC1115bVt76E7lczm/ueqSGXxkMIWRB7mRxFHp/uWUNxoDKVOZKL0S8Uehhgyd3PAYr0ozfntwpH37OGHzw78RjsCl3DjvsMFxwwQXR56zcf7Jyy9LWKDz9uHsBva67urrQ1dVVVt76E+uSi5lH/8OvDIYQpKGQNuZOVm5ZQzGgcmdnZ9EAn3Ri668B17tlDU7MmTMHu+yyy4AHpKO+58kdj8GGNGOXJ3fKhycSBh+8AtejUsqdtHN/a2sr1qxZU5ReFnnLqpwyP1kpd4aam5MndzzKgV8ZJER7ezvOPPNMdHR09PuzTYodH1DZHd/73vfwk5/8JPqcVLnTXwOuNwqzw/HHH4/bbrstk7TeeustvP3225g7d24m6aWFV+54DFakIRmGmuE9mOEXA4MH69MmzSOPPOLjZinIOqAytaW0blnyiO2slDuVcstKEuPTZp8PtcDmntzxKAd+ZZAQr7zyCi6//HK89tpr/f5sU0Dlct2yku6YDmXlzsKFC7Fw4cLo82BX7gylyWiw4uGHH8aLL76YSVoUSHugVQae3PEYrKi0W1YYhnjjjTdS5W1dxvo0Z4RhiDlz5gx0NmLh+k6mTJmCZ555pj+yVBHMnj0bBx10EB599NGBzsqgQ6Xcssohd+QaIEu3rHLTkmuSNModrb9J0miww2/wepQDvzJIiKxZ+CSoVEBlnrYLhrJyR5N8pim7d8saOshyx4bInYGecD254zFYUemAys8++yz22msvzJ49O1X+1lWsT+TOyy+/jG222WbQtwFXG+2SSy7B//zP//RHliqC9vZ2ABgQRftgx2ALqNzb21v2oSq2fA3WmDuDUR36zjvvGEnq/lpreKyb8CuDhBhINjUrckcbOJOqf4DKn5ZViTqWA7xX7qz7yHJS98qdAjo7O3H22Wd7Y96jBJV2y1q1ahUAYPXq1ckztw5jfZozmpubAQArV64c4JzY4fpOenp6orllKKLSNuFQRqWUO2nTG+xuWZWKuTMY3bJ+9KMf4fTTT1d/825ZHuXAkzsJMZjInTSDfJbkTqWUO5VUrUilTlpyp9Lv30sys0MlyJ2Bfi/0/Orq6gF5/quvvopLLrkEL7/88oA832PwIq0a1HVh6MdGHUOR3GlubsaKFSsS3zdUyATXdzIYF55J4PukGZVS7qS1j3t7eyvillXpmDvrIrnT3t4eqd4kfJ/yKAee3EmIgexwJsVOOTukaeMj8L9ZYzC7ZXnlztBDJdyy+mNRMWfOHFx66aXqbwOt3PGGh4cJlXbLWhd3NJ988kncfvvtZaUxFOeMH/7whzj++OMT3zdUyB3XcVK6ogxGTJ48GTvuuKOq1hzIcAWDHVm711RCuZPFe6vUUehZxdwZjG5Z+XzeuEm+Ls5zHv0HT+4kxEB2uCwCKktW3NUg7O7uxj/+8Y+ia4eicqdct6z1PeZOS0sLfvazn6Gzs3Ogs4Kenh6cf/75se4ZQ1W5c++99+KXv/wl2traSn4bLOTOYGufHgMPT+4kx80334xLLrmkrDQG65xhw/Lly1Mpd4YKkTUYlDtZKTPeeecdzJw5U3WF82S/GYNRuTMU3LJk31kXlTue3PGoFDy5kxCDyS0rLbmTRrnzyCOP4LDDDsN77723XgdU7q8Bd7AaSy+88AKuu+46TJs2baCzghkzZuDCCy+MPWWkEuROf+yy2p5F3w00uTPY2qfHwCMLRWnW6Q92ZLHAGiqEB4d0EXHFUFHuuL4TF+XOa6+9hr333jvxxsrEiRPx+9//PtE9GmxlqbSaeygj6/FqXY+5I8tHf8s9Cj2LvGWNXC5nLJdXw3mUA0/uJMRQJ3ekEeGaBvmFdnZ2VtywqmQdazF3ktYfpVNJZGmoT5o0CTNnziw7HaCg4AIGhxFHk2J/7opmodx54403MGPGDOdnaZP/YFPuhGGISy+9FEuXLh2Q/HgMHnjlTjxeeeUVTJ8+PfqcBQE9FMmdJO+dY6iU1dWWcZmjpk6dipdffhnLli1LlIe5c+fi448/TnSPBludD1XC9frrr8fNN98MAFi6dGkm9SSR9XhVTnqa7Z61W1YWR6Hz9LJQ7iRx7epP5PN5YyD1dWGe8xg4eHInIQZyEpPPTmPgSDbctTx8oB0sblmmQGQ2ZKXcqfT7z1Jif/DBB2PHHXcsOx1g8JwWBSSLZzCYlDunnHIKzjrrrLKelZbcOe6443DBBRckukeDHD/mzZuHX/7yl3jooYfKTttjaCMtueO6g7wuGL2nnHIKzj333Ojz+krueOUOot/jypJmJ5/smyz6iotyZyi1PQC46667cM899wAAzjzzTHzve9/L/BmDyS1L2xAbbMod2bezOC1rsJKPNuXOujDPeQwcPLmTEAM5iWXllgUkj9tDAxCP2TOQAZXnzp2LUaNGYerUqYnTLmdi66+YO4N1MhpMyh3XvlgJcqec9Do7O6N6tIH6XJbkzuuvv564z2iQfdTL8j0IaYzSJPPqUF1IcnR1dRXt2GahLhyK5A4pd9577z1MmTLF+b6hUtYslTtpCC2tL7a1taGrq8s5DZ5Hnqb2HFsZqqqq8N///d8l369YsSKxGqlcnH/++fjTn/5UZM+uXLlSjSfkiscff9zqQj0YAiprY2dW9lHWblkmGyNNHgbreJHPx8fcseU5n8/jX//6lyeAPErgyZ2EGMhFd1YBldOkMdiUO0uWLEFvby8WLFiQKO1yyZ2h6JaVJbJW7rz66qt45JFHUt3rusgbbKdl9fb2OrWfSih30u6Um57vyR0PiTRjV5K5bF3Y0azEiTWDdc6wgcajc889Fz/84Q+d70tCdNx///346KOPUuexHPB3cvPNN+OVV15Rr3NR7qSx97S+8o1vfAO//OUvndMg2Owyl/cRhiGuv/76ku/Hjx+PDTfcMHF+ysHf//53PPzww0X13tvbm9qmnTlzJr7+9a/jiSeeKPltMCp3KnEUehrSvbOzs4RolG0pSV831c1gHRtdYu7Y3s3kyZPxrW99K5MNO491C57cSYiBNCxNyp1y3IpcBz0+0CZdyL3wwgv44IMPEufRVq60E6aUKA/WgMqD9eSTrJU7l112GX7xi1+kuneoKnfkws4EF+VOUpRjwGrP1wyx5uZmT/Ksx0jrlsX/2uDJHR2Ddc6wgZQ7HR0d6hHbJiRZ8B1xxBHYY489UuexHPB3cu655+KWW24xXldJ5Q5Pe+HChVi4cKFzGjyPMi3CUFPTUX1ze7anp8cY/yQOdKKlFiog6/GqEsqdgXLLamxsxCabbKKmsz64Zbkod2zvhsdC9fDg8OROQiQdJJ599tnMdo0Gg1tWGuXOcccdh9/+9rfOeXSVI8ZdY7rPK3fSI2vlTjkGVVrJ+5QpU/DWW2+leqZW/qRkiTyKNMmzCGnbYdbkjmynHR0d2HLLLXHXXXeV/QyPoYk0Y3MacmewjY1J4JU7BZByJ2lg5aRlbWlpSZO9ssHbqq2MLsqdNOpIbfc/bRBrW7+L65ODjXAkuy8r5Y5tPh5Myp3B6JbV3Nxc9Nm0RnFpsya1y2AdG13InUqsgwYCixcv9iRUP8KTOwmRtDMdc8wxuPzyyyvy7HICKicliPigmXQHqauryynGiMyji3In6QQnJ7Kkyp3+2qEarIN21sodF6PWhLTKnT333BO77rprqmdK5c6SJUvQ1NRklNtryOVyTm2OJn3baVlJ20ellTtr1qxBa2srlixZUvYzBgqDbSEy1DCU3LKee+45PP7442WnkxTSPTJL19HBNmfYwImdNIqUuHsGui/zvmCb65Iod9L0qyzInaTKndbW1sQbgf0Feh+S3Em70WSzRbK25cpR7gylgMqy7SY5Cl3mYbCSO+UGVO6vGKBZYOONN8Yhhxwy0NnAypUrUx3GM9TgyZ2ESDqAdXZ2ZtaQslDuSKMojXKHT4au+c5aHZN2sB5qyp3BNmhn4ZbEkdbQpHvj8pL1pC7VNEuWLEFXVxc++eQT5zRMyp2f/exnGDFihPFZHOUod7Ig5mT7pPxQnstptw8++CCOOuqoMnOYHoOtzw01pBm7Bsot66KLLsL5559fVhr5fB7XXHNN5JrhAk25U255BusCxgZO7CQhAJK6kw8UJLljW8i5xtwpN6ByJcgdmbeuri5svvnmkYIzTQDnSsLkllWucsdWN4PJLauSMXfKTcu0RsnCLWugxwMJF+VOJTa5BwpaTKr+xg477ICf//znA52NisOTOwmRlFDJ5XKJVCs2VNIty1USzMkR14kwqewziXInqSE71GLuDDZDXSp3Vq1ahR122AHTp09Pld5QJXdMu0suMJE71113HdasWVPyLBu5M9iUO1mQf88//zzuu+++MnOYHkPFUBqs6K+YO1n06a6urrL7w6RJk3DqqafizDPPdL6nv9yyuru7M6mn5uZm3HnnnWWnI0HjUVLS2dVuGWjFCG+rtrnORbmTRjWcpVtWEuVOZ2cnWlpasGjRIgDIzAbOCtTfwnDtaVmVUu5krfYux6YxKXeyyFtW47Jco2QRc2ew2tMuAZVtec5aFVYpuNpUra2tWL58eUXzks/nUV1dXdFnDAZ4cichki7mcrlc6glDIm28HC2NpARROQGVk04elWSsNeVO0vvTPDcpspa4ZgVJOCxYsACzZs3CO++8kyq9csgdlzrKevIzkTtJDW6X610CKg+WmDuS3Cmnf5TTJrKAJ3fKQ9o+wf+6pJ/Fe+rq6iq7rVGbp4WsC/qL3Jk4cSL+8Ic/lJUuABx99NE45phj8P7775edFke5bllDTblTTsydrJQ7Ls/S4KLcMakukih37r77bpx88smJ85cEWQdUtqlWsrYZy0lPIwySbnDG5SsrmzVL5U6W5E4ul0sds1FifVHuuNb75ptvjg022KCiecnlcolPmR2KWPdLmDGSsvC9vb2DWrnjOuiVE1CZdkhcUclFu4ncSULWadd3dnZi6623zkx2mJVbVtY7l1K5U+6EXmnlTqXIHfnspModl+ttyp20u4GS3Jk/f36qBZtpDNLIncMOOwx//vOfE6U9kDtRg91QGuwYSjF3uru7yyYA6uvrASQ7sUSSO1nE3JH1HoYh5s2bh3nz5pWVLlAg8QH9NKByUG5A5e7ubtx4441GG2uglTuViLmTpJ74PHH//ffj3XffrahblmlhnsQGfuyxx/C3v/0tcf6SgN4HfydZBFR2qZtykUXMnUq6ZWVF7pg2kFzuNbllZfEOHnzwQey2225YvHhx2WlJcueaa67BT3/60+g3/lfDUIm549qv+iPwfS6X88odj1IkHSS4W9Y+++yD7373u5k9OwsjOukOGDdCXA2EpIZrJd2y5MIx6URpmjxWrVqFDz/8ELNnz06UHxOy2mnIeufSRG6Uo74Zym5ZaSbXgVbu8PQ222wzbLvttonS0J5vc8t69tln8cYbbzinncvlMttNTIOBJJbWBVTaLStLN4csyZ0k6oRKKndM8bDKARnDWc8naWPuUJleeeUVnHzyyZg8ebIxfY6TTjoJN998c/oMJwS3U2ykiss8mKbd83H6iCOOwE477VRRtyz5l+6hvlFXVxf7nPb29uj6JUuW4LHHHkuc1zho5E5PT0+Rm1YS2N7NYFfuZLWZkhWBkjZ0hC0PWY6Fzc3NyOfzaG1tLTstOe49++yzePLJJwEkU+70t80yY8YMfOYzn3Gug4Em2Tk8ueOhohxy58UXX8S9996b+tkm5U6SQT6ta5fmllWpmDtJBrWkE5xcNCZNx1RfWQ+yWaXHJ8Ssdrp5uuWSO+uKcidJ+nFHodN7omcNxtOy5ALS5pblevQ7IanbZ1q89957mDlzJoDi/IZhiCeffBLHH398RZ+/riJN2xzKbllkKCYhd2SfKGeB1dLSgr333htz5syJ0gKyJcGojGnTuvHGG/H666+XfE9kc9KYO/TuSS1lcqeRaT788MN4+umnnZ9TLgZauaP1lXLJHZvrkantUd+ora2NfQ4nd37/+9/j4IMPTpzXOGhuWbbTKeNgG5MqZRtmSe4MZreswRZzx2aXJYVU7vAYcJVcB5WLt956CzNmzMD8+fOdrvfkTv/DkzsJkWQAo12AweyW5ZqG5pblaiAknTxc1DRpB2uTcsc1nSwmj9bWVqxevdp6TSXcsrJoh1krd8ohd1zabqXInXIIVlKmxD0j69OyqB9WIqAy/dXIHVelEr+e/60UTj311Ci2gxwTnnzySdx6662DXu48GJFm7ErST7M0aLu7u8vuD3T/QCl3/vGPf+Dll1/GhRdeGKXF/2bRjyhGQdq0zjrrLNx2220l33PlThrSQm42SNC7ofx3dXU5nWrW0tKCa665BjfccINznmz5dCF34tSKad6npix1UQmZ8sjz8cwzzyAIAkydOtVoT8r35KLc6ejoQHd3N8IwREdHh7MbcxLQXCiVO/xvEtiI1CxJViD5+NrW1lZCYFWC3KmUWxb9TXIU+lAhd4jY5mS1tH9c7Nv+tlOkvRcHT+70Pzy5E4Pe3l7MmjUr+pykMyXtAHFIS8xoeUpKEA2EcqcSi3aZl6SDo8kNJ0l+jj32WPzgBz+wXlMJt6yOjo6y0gIqG3Pnsssuw5577pno3rhnV8otyyQ/d0GckkUaDzZyJ6liiP8tB7Lf2Nyy0p6Ek9U7M2HNmjXRQo/XST6fjxQBlc6DCS+++CLOOOOMAXl2uai0W1aW7SMLtyxq8+WQO+XE3Imbi7Kop3LdskyENo+5k8Yti+YjUxkpv0nJnUsvvRSnnnpqFP8iLeT4aHPL4n/TXmO6R5LtaeYA2a9Jhf7CCy8YCUXpluWq3AGKidesx2GyA7kbVhbKHS2fcTbmVVddhcsvvzzxs1xsjjAMMXHiRPzxj38EoLfDcohloPBuzznnnOikz3KJBtlnsoy5kwXRnbVyh//lSlKX95w1ceiKJIQbvy4IgorlyRX5vD8tywOFXbGdd945Op4tyQRLHU8qJtIOCoNBuZMk5s7ixYvR0dGROH6Gy6CWlrGWExndf9999+G0005Lnbck72LZsmVYtmyZ03PKHbR5W8uC3KmkcueDDz7A3LlzE93L86DBVo9pjJAsTstyJXeyVu7YyKKkMJV/KCl3+NG3vJ+EYRiROwO147TPPvvgiiuuGJBnl4s0fWIou2UNtHKH7iPDWY7JWfSjct2yTGNAWuWOdF3NWrlDytpyT1WhfMaNvS4LWPk+29racOaZZ1oDeZvInTRtQvZrem5jY6OxzfFFK5CM3NFcVLKC5pZVKeVO3Hj44IMP4sEHHwQA/OY3v8Ghhx4am3f+N+7axYsXR+4zlVDuvP7667j44oujuFflvivZlpLYmKa67i/lzpo1a/CFL3zB+TQtSSwOFbcs2WfiIMfhgUQu50/L8kAheFYul4sm+yzInRUrVqTKi3x2mgFL3uOaRhrlzuc//3n83//9X2LD1WXySkt+mGLuHHXUUbjyyitj78+C3HGpj6zcsvpLuVOOaxU3CNPsStrusbXvrq4u3HTTTTj33HOdn1nuaVm0U+jilmVbEKTZsZE7k+W0rTjlDm+//B27oL/IHX70LX/WYCB3Ko05c+YkIlKToL+UO1m5ZQ0G5U4lyJ0slTvlumVphDaNhaQkKccty0W5E4Yhuru7nciduHSBAglxzjnnWN+73OHOUrnzwgsv4PLLL7cGq9fmCY3cOf/88/H973/fmA5Q2q9pjGxoaDDaA/I9pSV3KuGWJcmdSsfcMZWhp6cnysO7774bSwwkGf/i3gv9v5z6tal200DmdSjF3FmwYAFeffVVTJ8+PfruhRdewMqVK6355eROko33gSJ3ZL7jQNcNtGKG1n8DnY/+gCd3YmDaiSiH3IlTbZhQrnJH7t4kSYNf79qxly1bhhUrViQ2XJMw1kkHa5mXNPdr9yWZPJKQO+VORnxCTHJUrwlyAi83n9zQTKvwSGIQc3R2duKRRx7B/fff7/zMcmPuuLTbJMqdcsidcsg++XyTgZeGhOpP5Y5m0A8mcqdSRtsPf/hDnHrqqZmkFYYhbr311qg9pRkTkpLjSdM3IQtyJ6lyh0iNfD6P2bNn49133y2L3IlbyKRJ97777is62TMLtyyZD54/IryT2jJJlDt0bRJyhz9L4sUXX8TFF1+MV199NTafWSh35JjqMkaZ5if5nClTpsSeaCjbFfX3hoYGo00q3bJsMXfoWlflThiGePbZZ1ONkZpyh57V38odTmy6qKqSbPzJNpPWLau7uxuffPKJ+ptc55Q7LpvGryzcsipN7si89vb24itf+UrkFsfBN5oprc7OzpKxohLroHJBz0uq3HElVSpl+1G6ntzxKIvcoQY90OTOj3/8Y/zxj38si9xJE1CZDLqkblkuk1daZYucyJLebxpwk7QLl/rIitzJ2i2rkqdl9Te5Q7skSQJNm2LuuObb5XpJ7tiMiDRuWfS3nKM8Zf+Tk73rokZDJcidF198sUSpYlLu5PNrY+5UmmCKQ6WMtra2tihGQrmYPXs2jj/+eDzyyCMA0hmcaZU7YRji7rvvTrUgy+fziRUjGrhh7vpc+nvaaafh5JNPTjxHaull6Zb14osv4qGHHoo+pyV3HnjgASxYsEBdQPK04kgaCUorLqAyV+4QwZAVueMytslx0ETEuPQZ+T4pjzZyx2SvyDwnIRUoj9Teq6qqMlHuUDlcyZ3nn38eBxxwAGbMmGHNtwZqj5zcSeKWlcvl8NxzzxV9NuUzbpHe09NTQnTG5d2W3urVq0velSSw5AZn3Nhz2223YYcddlDHOPmOy52zZJ6HknJHq/eenh7V9uZ5sSl3BqNbFj03qXLH1R0qqzi1Ep7c8YhgYrxdBgk5aRDKJXeSEjOTJk3Cc889p7ojuabBnyknCts9fBJ1hUue0jLWcncw6f1xOwOuE1Dcc7MatHl+BmvMHVrYuBiY3Lhw6Yu2Sb2zsxP5fD4VuWPaoYyDi8y80gGVKb1Vq1Y53xv3fDnWyUVN1v0/KfbZZx9sueWWRd/ZYu5QXxlo5U6lyB2XvuYK6j+0eE5KvPMxOakBP3XqVBx11FF46qmnUue73HpI6pbFbYn29nZ0dHQkniM54jYa0qTLXUWAdOROGIb4zne+g9///vdqPrSTHJOSO3GuIHxRQc9wIXe4zWZK24U8k/mMU+4ksXmovWWh3CGi0wYTucNd7kx2skvMHakojSN3iJx2eZ8SGrmTxC3riSeewP7774/Zs2cX5c/mlmVro0k2uGzj64oVK7DhhhviiSeeKHqmbf3iMvY0Nzejvb09qvNFixbhuOOOi8YuYHC5Zck89Be5Y8q79lwTuSPbvAvh21/kzqpVq/DAAw+UvPM40HWupEpWJ0xLeHLHI4JpUEzi75qVcse0oIzLSz6fR09PTybKnSSGeFpyx6VcaQ1XuUuRRvkj83j33XdHBgbPz2OPPYZ58+apabhO4OVORv2l3EmbT96/4gINv/zyyxg1ahQWLVpU9EwX49pE7pSr3ElL7tjKWemAylkod+Tz5WQvDbKBVu5oGApuWbwOnn/+eQwfPhzNzc1lp1sOmSAh31fSsUvbdHB5Xj6fjxYbtNufBFmRO0ndsnj++T9X9c7KlStxxhlnlCymaFc0K3KHt31KO8mOKs3/0l2P/06gunN9F9LdJ4lyhy9ITeBzQhy547K54Bpzx2Uuo2tc6syV3Emj3KH3yolA0+LWxS2LSA66No7cKad9kx1Lm0r0l8oTB5o7KRanrS3E5ZPXn8t7sM39zc3N6OzsxMKFC4uuLVe5Iwm9M888E7fddhvuu+++knpL8z74PaZNbJe52FQ3WW4YuZA7LmMwf89c/ZlGuVOpTSCJY445Bocffjg++OADAJULqOzJnfLhyZ0YmMidJEaolKQuXbo0VV7SumWRNFAO6EnToOtclDs8XZfJQ7s3jRzxjjvuwOLFi633afWQNm+TJ0/GUUcdhf/5n/8pSe8b3/gGdt9995I0+tMtaygod+hv3K7V/Pnz0d3djSVLlqh50FApcsdEtMaB5/mSSy7B6aefbnxGpY9Cz5LcMRl4aZQ7/UXuZBlQ+cEHH7Qu7g8++GBce+21ifPI6+3DDz9Ee3t7dHJjOUir3Fm4cCFOOOGEIgWd6V27vnOej6TKHeq7aWTcWSt3kpZXkjuA2zjyzDPP4IorrogCr9K9WbplyTKRMZyE7JQkhKyfLJQ7ca4gfFHB+2ccGehC7vDx+aOPPlLfXdKYO7Y2JOe7JG5ZMt1yyB36S2OAdCvS/rq6ZXEbRXNR4ShnYUs2KdmzvA5d2riJLLHl0+bex/ts0veg5UvmR44F/BlpyB0i6Uj9DKx9x2lUJLzOTbYVJwxNc62prgdKuWN79zwvXP1Z6dOy8vl86vibs2bNivIJJHfLqqmpcbq+UuSOnM/WZXhyJwblkDtZxNyZM2cOfvjDH6K3t7cscqe7u1tV7rgOetIg5d/FXS9doeJA1z722GPYYYcd1IFcK3tzczP+8z//E9/85jetaWu7BK6Q7YB20emoSVlO7WQ0mQdTPrX0koK/oywCKlfitCy6P47ckQZVkp1T7Zr+jLnz0ksv4cMPPyxSwJ199tn4v//7v+gamviGknJHvv9KKnd6e3uj41azgMktixs/LsbLBx98gG9/+9tFMUok3njjDefYECYli2tbcwEn6ZPgmWeewS233BIZeTw/aZU75ZA7NDfE9eEvfvGLJUfLJ1WLmJBU3SU3SvjiKoldIeubkNQ+IFx88cXRMcxy7CFjOKlyBzATMLzeXYgKDipznLsTfR8EQVEbiXPlcYm5Q2m/99572GqrrfDiiy+WXENljsunyzwqr8nSLSuNcsdG7sixytUti5NursqdNGQC2aTUB3m7do25w68th4TibllJbMM05I6JOIh7piT06uvrARQTcOUodzQ3SJON+cwzz2DMmDFG25rnV+Y/iw2jrJQ7/Dta3/H6TLJ5maQPHHDAAWhsbMT555/vfA+B+rG0VeOQNKByd3c3Fi1ahNdeey1xHm2gOvVHoXukInfIWJPGDTXwJOTOCSecgD/+8Y945ZVXUpM7+bzulsUVJHFpaAGVk+wYJRnwKU/vvPMOZs2aFUlfOWzpzpkzx5ouv0fe77qDIXfkpCQ+zkhznUzL9aXN2i3LZMykXXAmUe7Qb6tXr8bZZ58dGYIu92jXkHInyRHGaZU7//mf/4nf/va31glbLqAqGXMnDMOyYu7I9ikXMUli7nR0dBQRTaY+NGnSJHz5y1/Gxx9/nDrfHNz1pBzlDrVDvmC85ZZbIsKX0ndduGqqGJ7HgSR3yA2K95m05M6VV16JK6+8MjW5k8/no3zEGZnvvvsu3n///aLvslbuuOSDP4/mAT4fuLxb00KNYFocxeGcc87BP//5z6K0pTHsasxfd911UT81kTu8P5QbUNm2cKb88zabhNwxpU15JSW2pqjLUrkTR+588sknRqLPJeZOUnJHc8uSf5O4ZfX09BjJnTgi4/7770/Uj2Xf4+26HOWOLZ8mGyGpW5aLcke+q7h1TFLlTkNDA4DCO5Jzf5r5SbpFa2Whzx9//DE6OjrU/mYqj218nT9/ftFcHYdyY+6QUou/Z77RxIlHrSwccW1Lw8yZM4v+JoGMq1fJgMo777wzPv/5z1uv+/jjj7HHHntEav44yM2KdRme3IlBUnInDENUV1fjjDPOKLqHkystLS3Oz+fyR5MvatxgSjsTckdYU/IQ5s2bh5tvvrnkdz4ouSh3XALISrgYp3yCe//994vKYyIx+D1XXHEFLrzwQutuoi1v8l1IcsdmBFNeFy1aZFyoDla3rEqclkV/42Lu0G/PPfccLrnkErz00ktF39vusZE7sm/YkDbmTkdHR9Exl1p+TModmxGRRrlD9xOh4jLR3XrrrQiCIHJ5NO0Mmo5Ct7WPnXbaCaNGjYo+m/p9OfFVNNgCKichd6RCs7W1FSeccAIOPPDA6BppzC1atMjonstJt3LJnfb2drXu44hUE+gdaARU0j5x++23489//rOVbNfA03cld/hCkZBULWICv98luCsfA+g9lEPuxLkgpBmbZb9IotxZuXIlfvazn+Huu+8G4KbcsX2nIalypxLkjuz3tt35LGLumNQwvb29mDp1KjbffHPcdNNN6j2VVO7weds0FqR1y3JR7kyZMgVHHHEEnn322ej7U045pYTMlffyfsfft0sblyrYcpU7vN6SvgcO09ogjuRJS+7wGDHlnJalKXfi8u6inCHY6myzzTbDZpttljivWjtx2YCvra3F17/+9RLCRx4W4vJukswbEkmU/BdddBGCIIjm/7g6+O1vf1skYkij3HGJLXjttddi6tSpuO2225zS9eSOR4SkgyF1mOuuu864sE7SERsbG6N0TWy7K7kjFwo2o/qvf/0rfvzjH0dGEDcSpMFieib/m6TMLi4dlN7bb7+NbbfdFpdccknsQpLX36OPPopHHnkk1ugxpRGn3LG5CVAdbrLJJthiiy1i81oO+ku5kwW5E7cLQc+ixb2LMWHrI9wwSRr134WA5JC7NVoZ5QLKxS3LpX088MADmDlzZklcASJ3mpqaYtO48cYbASA6Ttw0BqVR7nz00UdFn039OEu/ecpbFm5Zcied/nLjRpI7m2yyCTbaaCM1PU7+SwMQSEbo7bDDDrj++uuj+3gdlqPcscXccX1Pixcvxty5c1Mrd1zJHbpOXsNjwZSjkDSROwsXLlTngUopd2TMnXLmkHLcsuTpaS4xd2zfaZDz7ECQO3JRq+Whksod/twPP/wQAKJTkuQ9WZA7sj1xt6w4UoHq3raoSuuWRXYN5WfJkiW49tpr8eijjxqfJdURfDxLMuablLxaPl2UOy7jsi0903swjRmuY4TNLUv2xTTjjRZzx6TcsdlsprrJ0nZIE3NHPvfJJ58sUe7w8Ymvz1zGhCTzFz03Cblz5ZVXAlhre0tyk+P111/Hr371Kxx77LHRd5UKqEybgq4qdE/ueERIqtyhxt/Q0FDU8PmklcSo5gx5lm5ZfGLT0pADmNxtjCtHpZU79B3JKZ999tlExolpEo2b2E0TjjSsbYOTrHsNUno7a9YsfPnLX44WV67gZcwy5k4WCwhAJ3dMaUlDzLRo0O7RruH+za6TSVq3LDLgNGNVBptzccuKI8I4Dj/8cOy4444l5A5NiC4TLrW74cOHF+Vf9tVKxtxJY8jYQIS3HAvCMNlR6HIHn+qABw/M5+OPGiZwQ2XSpEn49Kc/jRUrViQmynt7e/HJJ5/gk08+AQB8+ctfjvzsXRZzGjS3LPm+XAzpXC6HZcuWoa2trUhSnYTcyefd3LJMxrjLAt4F/NlUP7lcDjvuuCP++Mc/llwvF3KcXHLJh5yT6d5KBFROQ+7QNVS/lVDuSDLZVG98UZE25k4cuWMj4V3JHZdNEhNh0tvbW+Qmo+XRxS0rbnyyuWWZ5gO6xyXYblpyRz6L7olTT/N+x+stiWtlXPwrnk9bG+V9NaltqOWr0m5ZAxlzx0W5M9Dkjot9LG1A3gZ5m3BR7qRZXyVZD9D4QrApdwjcbS6NcseEv/zlL5GKfPTo0QA8uaPBkzsxkIN3nIFNRkN9fX3RBMonrSQDDB9E05I7pNyRblg2ckeWV+42Am7KHZedewmToaBdQwuo7u7uWMOQ15/JmEm6a8J3BvnvNhkyNypc8goAZ5xxBiZPnoynn37aep/EYFfu8Pv7m9zRTnqwIQxDoww77n3SZK31B4oplUS5k6ZfmZQ7LmnQolUuIGU/qORpWZVQ7tDfctyyJEFIdcD7fhIyhRsq9957L+bNm4ff/OY3ickdmosoXx988EG0w+9CMGtI4pZlS5+TVXSsKk/DBt72XMgdU+BZ3uf5c1966aWiQOdx4Oly2fqqVavUuBBZK3fi4ktoaba0tKix7AhyAZMk5o6J3JH5TDP/9vb24sUXXywhDSqp3DGN7Vm6ZZUbc4fbido9ccSabXyaNGkSPve5zxW12zAsPqnOZi/yfNnKVy65I+vaZYNNW+imUe64bESays7JsSQKqiyVO+W4ZbkSrTbwccUUc0fmPYlyJ8n46prXtDF3CFK5Y2qDLjF30thX5ZA7NuUOhRKRZBWgbyQuXbq0ZJ40zc1AgdwBgNmzZydW7lA9eXLHI7Vyx0bu9Ldyh8gdfl0ulytR8nDIgZR/rjS5Y4otxJGG3OEDP19oc8RN7KZJ06Tc0Y7+465t9Fm7RntO0kGJ18lAxdy54IILMGnSJGv+kpA7VI4s3bJcgipr0mHXPijdsuLIHU4A2sidOKOMT5I8/7lcLpbcyeVyUd5o0Ron6y5HuRMXKyTNLpXpek7U8cUJUOyu5ZJvuZjQTobh7z4O3C2L3Davvfba6B2kJXd4AOkk+eFwIXdcFqpcrcPJHZeyaeSObSFnInc09REAfOlLX8Lpp58emw8CX2RQndvmPU25k6Rtm+bWJG5Z48aNw4QJE2LLJOede++9F0EQYMGCBbH3xrllpVHu/OpXv8I+++yDqVOnAhgcAZWTuGWZ7AuXeVSOpbxdawsqnm4csWYjFY4++mi8/vrr0QIqn88X2bNJTsuytW9bQOU05E6cqybvd0mVO5qi3ZTPOAUGV2m4jMsuyh1ZN3HkW9y4K92yaG7rj6PQTWXJKuZOUqRV7tx9992YPXt2ybWUFm+DrqrSNDZREpuXQOQxwabc0Yhmm3Jno402wgYbbFD0nS0GFq2pcrkchg0bBsArdzR4cicGSQdDMhoaGhqMC+skA4xG7iRVDeTzyd2yTJMBESP8Ow3afa5w2e2i9LgrS1y98ndnmkTTKndM5I5JucPzqgWIlc9JyziXS+709PQUTUhpTsu68cYb8Y9//MOaP5OqhUPuHrmQO7b8JXXL4pNM0vYtlTv8eo3c0Z7F4bpjwxcxSZU7t99+O7bcckv09vaWkDvSwJQ7mUli7hCofZrKltZAc9k1Mync0ih3OLkzb948tLe3I59P55bF63vlypXG8migd88NMd5/05A71Fa107Jk20hD7iRR7uTzbm5Zpmtsu4NJoCl3XMkd/s90ven+OOWObZ7O5wsB1U3llnMvjU0vvPACAGDGjBnG/Nli7vz1r3/FK6+8YsxXXB+he4kAjSNFuHIniVuW5iZiStuF3InLZxLljrQxcrlcZH+kdcuyjQcaechVX7Rxwa+Vz62EW5aJ5HRR7lD8MWlTAOW5ZdlIKBsBaVpnaLDZ/SaCPWvlDicITJswSWBzy8oy5o5tnE8ad1Ebq2xrtB/96Ef4wx/+UJJXSstEMGbtlpWlcsdl3uXXu65feLqyH/M1H5Xb9ZAiPh+s61j3S1gm0ip3KhFzxzQg2wZTmsAGyi3LdRF6ww034OCDD1bLZZswqZMmUe5QGbJ0y0qi3JHkjsY6m+og6aBUrlvWPffcg1122SUaPNMod7gSQiKJckdOSlkqdyZPnow77rjDmA5gP9HBNrmSSoQTijw/ZCjzSSvuaFZX5Y6N3Imrv/nz52PFihXo6uoqOVnI1A/SBpwG1o6RpkVpGkNGPluSTpTncsgdukYuZmtra7HXXnvhmmuuMZLJGrihwtsBtfvp06ejvr4+iqUDAG+99VZJHUvlDu+HSfLDkUS5Y3tPdOoaAMyZMyf6v0ue+HMq4ZaVFBq5Y2v3UgWbz6eLuSOfQXNQ3PzJ38v06dPVZ0gSX8472rwm75Wqqnw+j7POOisKzp5m/qUxgg6aiLuvHOWOdLU2pe3ilhWXT5d5VF7D27VpN96Urja2xhFP/FpO7iQJqBxH7sjTsmybJ6ZxJ27hKd3zef74/TaY5jtbPk1kDCeZksyXadyyTL+XQ+7IvKYhdzQ3JJONmYbccSHPbW6qHEmUO7yt8X7Ky0FplaPcSVLnGqEZhyRuWbY+lSagsnzPtGHOSWWv3CmFJ3diYBoUTYMhJ3d4500bc6dctyz6rVzljibnd1HuuO7cv/rqq3jxxReLro3beeS/SdcKDS4xd+ImdpMhIQ1BzTWD54O/CxdyJ+2gxOskiQyTsHz5cvT09EQqjzQxd7hixZQ/F3KHvicDMG3MHVqYcHLn6quvxi9+8QtjOoCu3EnSB3k9aOQOV+5IFyqJLJQ7cXnXJvCkbllxsSa0vMaRO0mNR56OJieWY0dWyp2amho0NzejubnZON5o4OOBtoP1u9/9Dt3d3XjwwQcBFI4C3nXXXXHppZcWpRPnlpXGCNfInSQbIDNmzMAnn3wSKXcmTpxYlluWjHOkIalbVlLwZ8vTRLTyyDGPvwuX8pvmVlNAZZkmD8r//PPP44477sB1112nlomeQWkTbEda2wIq83HHNq6ZQG1aPj9uQ0CSO3EHE3R3d0e2l8nWS6LckfeYrrO9f9nPkpA7cSSTi3KHk7aS3IkjEQZTzB1eJ9pCtD+VO/J+F9uakITccSV5TJC2F6830zxtwtKlS0v6XxrlTm9vL8IwxDPPPGOM00Nw6V9Zkjta/XISX+alt7fXqB4bDMqdJG5Z2liUVLnD+67sx5SGJ3fs8ORODFwGw0ceeSTqKC4BlZMYkrQQ7ejoKJvc4deRUSmvI5gmHT4hJlHuxA0+q1evjurIZRdDm8jj6pUPhCbCQUtj2bJluOSSS4p2WOLInXKVO/I52qC0ZMkSTJgwAW+//TbCMMRNN91U5O7A88fTTAK+C6sRey5t0Kbc4e/aldwp1y2L6pCTOy0tLbGuWWmVOxoxGoZh1GbilDtam3QlTcmIqqqqKiGM4t6dZmjGKXfivrchTrlDaba3t+Piiy92llHzdLRAydIti4/VSZQ7mlsWtWvb4kmCG768jJSuXMTTiYHksiLT4QRIuW5Z2mlZJmNce+dHH300zjvvPCxevBgNDQ2YOHEili5dGv3ukifebuOUO4ceeijuueee6JrXX389IpMqodyRroVxxCz/x8tmg1xYxu1SyzwsW7Ys+v+LL76Iu+66KwpSSZAkvnyGi3JH9gcqp8y/VjYTyL6KIysIfH5OeloWkTtx46PLaVlx+XTZJJHv1YXcMbVD7bNcfMoy8OfHKXdku+mP07KkTRhnc/DyJg2oLJ9h6782G6GcAyrKccuia1zJAVlPnCCQZY4bwzbaaCPsuuuuRd/ZbCsTMdXT04OXXnoJX/nKVzBlypSia9OQO7SBGYe0MXek3S/tKpNyx/Zu+PteuXIlDjvssCJFr4TJFTEOptOybHWgxXssR7kzY8YM/PSnP/XkjiM8uRODuMXsvHnzcNBBB+H+++8H4BZQOZ/PY968edHJJS7Pb29vT0Xu8IEwjVuWNoG6TECm3UUT1qxZg+7u7oiN52loz5F5MCl3zjvvPDz66KNF91AZXJU7P/rRj3D22WfjueeeM044SU/Lyufz0QCTRLnDB8f77rsPCxYswPXXX4/JkyfjJz/5Cc4888yidHidlEPudHZ2WpUrtrYgF88EKUeuBLmjTer0Hd91WrVqVayyKW3MHU6U8v5Ax4qnUe4kdcuqr6/vF+UOQRraLsaqXLiZdgQvvvhinHPOOfjzn/8cm6ZMhx/fS5BjR1JyR+7gc3JHI0TjYFpomBZu1G5k+ly5I8c8nq8ksLllyTaptau2trbo+PONNtoItbW1iRU03ICPI3f++c9/4te//nWUv+OOOw4XXnghgOzInZ6eHowcORLA2vblotyhd5CU3JH9ku5xDajMyZ0VK1aobSHuFKC0bllx9kNcf5MBqwlxBAxX7gRBYCV3SBEWR+6YFvi2fJnaWZKYO3K8SaPckd8lsbU0csdkF0kiyjZfUUDlIAjQ2NiYqXJnzJgx0Ql43L7ktgDPRxykfcvz0NbWhnHjxuHhhx8uypdWBlm+JJsh5bhl0W+u5I60vXh7M83TNnz00UdFn21uWTblDrVD+htnj/QXuaO9B0me8rxk4ZZ11VVX4R//+Af++Mc/xl4PVC7mjuzz/LpylDuPPvoobrjhhqINuqTkDl3vyZ0+BEEwNwiCt4IgmBYEwRuVztRgQhy5I0+T4QGVTXEccrkcTj31VJx44onOz29ra7MOHHH3a25ZpoGG36ftEtp23uT9ruQO1SMFH+VpvP322zjwwAPVoNQm5Q4NJr/5zW/wzW9+s+geG7mjGTf0XJ43OeHYlDtXXHEFnnjiiSg9cssaMWIEgPRuWXTf6NGjo117WmTI8gRBkGoBww11bTHkEqjOpNyRxmUcEUDXl+uWxQ0VPjkkIXe0BdRDDz2kyv15H7CRO0mUO65GII1HdXV1ickdrZ/LBbzp/SdR7lCblsodeQ+lSXUc51ohywGULr6B7GLuaG5Z/HdXt6w4ckcu4uPIHd6e+KI9Ddlb7lHoVNeLFy+OyB05N8ZBI3c01Z18fm9vL9ra2krGD9fnmtDb2xuN5UnJHWn0u7wTk3LH1S2LyJ2GhgbjXCgXMEnqx0TuEJkv526tbCZI1824+/iOMeVn9OjRVnKHFv3kihA3PmbhlmUjvOS9cvHEN0bkgs02/s6ZMydS/dmer811cmfd1PelPeSi3Bk2bBjq6+udyR3ZD+TCc/Xq1WhpaYlOwNPKWu5R6HxMWrp0KZqbmzFr1qyia7Wyl+OW9cEHH2D77bfHokWLSvJlInekkjstucPboIlISQKbW5Z8z9qGblzso/5yy5LPkaEgNDuU0irXLevVV18FAGy99dbG66m+amtro00fF6RR7mh2Yxypwk8/Nj2L5jmNVI6Dto5aV5FEuXNAGIa7hWG4Z8VyMwhhGnTlBEt/42LuVFdXRzsfLkwxJ3dsA4cJ9FtStyxbuZMYIi7KBqCY3JELw5/+9Kd44okn8Pzzz0fXaxM5L4NmvEnlkqtxyY/2M5EattOyrrrqKtx9991RelSHNnLH9I41cmfUqFGRLHWrrbYqSofKWFdXl2jSfffdd4tcX+KUO6a0qaw231z6fxwRIMmdtG5Z3FDhRk93d3fszqLMO6W1ZMkSHHLIIbj33ntL7uNGCW8/NuWOq1tWXL+ixXhDQ0NJkGZO0NgMTz5OacqdO++8syRYty3mzsKFC4vUAxQc1TXmDhGp/PfFixcb64K/e025I5VlaWPuyLmA8mlz29BgMvIkuUPpSRKJwJU7msqgHOWOi1uWqU319vaipaUFY8eOjdRNMi0beNuzKXdk+Yhkloty7dok6OnpwbBhw1BVVRV74pv8jebhJEaqaePEpNyRZaO+t9FGG0XP1+rKVg5bfdli7sTZD3HvQQZ2J8QRMJzcGTNmjJXcoWfQvF9OzB3Xekuj3CknoDIAbLfddthss82KfrcRVLLdEvhGgcnuSxJQubGxMRG5YyIB6L3MmzcPAKLjlrXFZ9bKHaBUiWoiu7UyuIxHN954I2bPnl10EISsd9N7of/zPNnejcktSyN3ksR/Ibi4vMsy8HYv28lAu2Vp75Mr1fk1lFYatyyN3HG5no4Qd43DmUa5o13vSu709PQUkTj8L9lVfNwB3NqdJ3c8IphIHTnYUWeMi7lTV1cXLfKSLBw0csfFIOTMtlTqJCF3NDm/i3InbsFO0JQ7cjFnY/elckfb1ZcDq23RzMHJHdOEYzstSxpDVPekskmi3OHQyB3Te5SLqDjst99+OOecc6KTe6RyJ24BIZ8fR6QlIXfk7pGtXNqkrpE7BJthZ2t/XA0wa9YsdSeQ9/l8Po+6ujoAunJH2/XgcFXEmdyyXAw7un7FihXG577zzjs45phjIgm6TE97p0cddRR++tOfRp/JcHA9LUuSG++//z423nhjXHPNNWodaDu0UrnDr8lKuWMid+IMd9MuMqUvxxoXckcapGnInZ6enqhstoDKNkOayJ3e3l7U1taWuK265ImPwUnIHSJNNXLHtgsbBypLY2Ojk3KH/yYJj3LIHUKcfcDJHXq2SblTDrmjHYXOy2ob1+Igr4ubfyjmTk1NTYltJkHtwtUty7bJUImYO7L9cnJaKthc25ULucPT4tclccuy5YOUda7KHZONKReeRO5ssskmReXhbShtzB2N3KF5zIXcMblluYyDZD/y0xVNm25aunIN4EIKSHKno6PDOE8ngc0ty1SGNModrV6J6IhT7rS3t+Pll19OFXNH24zj9ZTWLYvKs3Llymg9YIsdSddTmV1dsyQZYlMj29ZVSZQ7UsXDVZj0HF43ce/v5JNPxuOPP16UxroM1xKGAB4PgmBKEAQnVTJDgw2mCUROWlK5U1VVpZI79fX10eSYFbnjEnAracwdk1xU7rzF7WolVRhoblk8gJbML5/Ik5A7cgEt881B5A6XMSZR7khjiJQSNMAmOQqdl4Huq66ujuI3mXZgiVSUuOWWW/Cb3/ym5HuawGix0tnZqe50xxmlrgx/GnJHS8d0j9ZXNH9x206GS8yh1tZW7LDDDjj22GOja7UFdT6/1hUjjXJHLqRNcIm5w8uhPYOTO9KIovYhd8Jl/+RlWLp0KZYvXx59lsodU5uS7if0Oy1W77zzzpIyyHRc3LKyCqjM36d2nQmmXeRyYu5oblm5XA5nn302fvKTn8SWkacH2GPuxC1m6F9NTU1FyR357kidR9/HuWW5LlKoLI2NjbGuhfw3cnFOSu5IwzpuIaOROw0NDRg5cqTRFimH3JGKnSyVO6br4uYfUu7U1dWV2Gam/PdnQOUkyh1pe3IFg6lebOlqNpftuXweq6urswZUlvZQnHJn1apVGDVqVAm5o90nnyVJG3omBZglckcrqzbmPvPMMzjttNPUvNpIBZNyRyuDifh3GQe0zcE4csfklhX3TBu5YyKXk8C2ceai3IkLbG0bX5uamgAUbLfFixdjzpw5ah6PPvpo7L333tGhJUmUOxpJydtfucqd2bNnR9/ZNijpmaQadyV3TJugSZU7caQKJ3Sk/UR/NbcsoEBwmRCGIW688Uacd955ALxyh+NLYRjuAeCbAE4OgmA/eUEQBCcFQfBGEARvcMn9UEccuSOVO/zEJ23BQItsueCPe35a5Q6fWGw79ybjTRtoJftvy7eLwiAMQ6tbFpfhyefyXawk5I50xeDfS5DCgit35KBtU+5odcYX9zZyx2ao0338hCyTcWdyy3rwwQeLXMYI/JQ2KnsatyxXhj8JuSNdgNKSO5pyR1t4T5kyBV/5yleK2pSpD9IE/dhjj0XXasodLtGlts+VHrKvSpSr3HEhd1yUO3IXmSANbZ5+e3t7kSFD5E5a5Q6Rr4sXLy4pg0zH5JalXaPlwZa+XMzKcUsaoia4umVRfZiUO9RetZg7NAa98cYbeO2110ry0N3dXUTA8fR4Xng+pDFualPUDzRyx2VhwPtcJd2yXBcpPT09RuVOHIFB/dDmytbZ2Ym33nqr5H7Zt5K4ZW2wwQaRi7hmi2hKL1MZJFzdslw3VzTIe+PsECJ36uvro3LH5b+cmDu5XA7f/OY38dRTTxXdI10zZP5t5ZfjokbupEl3zpw5xjGX8sx/4+2F3H1N82FSt6zm5maMHTu2Im5ZG220kVoeQB9zJ02ahOuvv17Nq+yDPA8058rTX3kZyM1d2rS29yChkTuSDJN1JNsoz1MStyy6r7293ThPJ4HNtjeRO3wsl+0kiVsWd48/66yz8L3vfU/N46RJkwAU9zsJU/vXNuPkeqScmDv8ehflzkCRO5JUkelq5I5U7tA8J21YG7kjN8Y8udOHMAwX9v1dCuABAJ9Trvl9GIZ7hmG4J/m2rguII3ekcofHjdAWDCQJlrtla9aswYIFC4zPLzegMlAq9TMNNFq5pZScYNrZTrITwSdxbReJd2aZXzlZEeJi7pjIHW1SJXKHE0hy8SoHcZtyRxq5tpg7pokOWCvH1YiAJ598EkuXLi0id0wLDa0eJLlTCeWOfHdxhAVdn0S5YyJEKR2Zb02589prr+GZZ54p6p+uO5WAWbkjyR3Ka5xyx7QYvOeee0r8xmlBXlNTk1q5wxf5JnJH1pvJIAMKbUojd2TMHZknqdyRhk0ScqcSyh1TzB25AIhT7sSROwQb+Q3Y3bL4HKS9+6OPPhpyHufkji2gsqtbVlrlDn9OUuVOJcgdrtyxuWU9/fTTeP/991Vyh49P7777bnR8OwDcdttt2HPPPUv6h5x7kgRU3mCDDVBVVRU9O0vljnTLkqSAbc5wJXdMmxgSfMe4u7sb9fX1UblNyMItq729HY8++mgUByMur+Uod6Stqd1jq9f33nvPiVTgz6eyE7kj7zfNh3Ex7SS5Y7MfNeLiyiuvjDYjpFuW7B+8zfOxlY+RXPG+evXqyK1DEvW8bqRblma7NTY2Yvfddy9RrLi8L4Km/I6zS7JW7nB73SUdDl4fGplhsoH5+Gd6D0nIHZo/W1tbsXr1auNBDXKOqaRyR1MyadDai23ziK4vl9yxbdrayJ2qqirMnTs3WrvIvFIf1ZQ7kiCStovtxCxu0/E01mXEkjtBEAwPgmAE/R/AgQDernTGBgviyB2TckdOuFy5Q0YO7xj/+7//i/3339/4/HIDKvM8UrqmgUYrt7Yo1e6T39tktQTuK2lT7mgTgEb4AOlj7mjfucTckYO3jLkj88l38NK6ZdFCXhIBYRjioIMOwu9+97vouSbljqkeiNwhAyXtUeiyfrTfZD5M7Tnp9/y3csgdTjzKdLXdJEAndySBJckdTlbYlDuSKASAGTNm4N///d9x0knFXrNcGp5EucfzrSl3ZPs3BfI0KXe4UZE05g73uX766aetO2kyHXoub8vlkjuSLIhzy3JR7miEtinmjimegM0ti+YgU/+nwOA8r3HkjnzXlSJ3eLmTxtzhpH5Wblmk3Bk2bJg1oPLxxx+PK664osQQl+TOzTffjB//+MfRNa2treju7i5ZWJnmVtOiiMDJHRoHKknu8HzxsroqZzWUo9xJSu7EuZ5ryh2bq5atrbmQK/K5NuVO3JwKFJM7tvrn/Y6uNyl35HNdY+6sXLkytXLnySefxGmnnRbNgVK5I9PiZdUIdbLTKP3TTjsNX//61/H222+XLG55eV0DKr/zzjslc7ILyUbQlN88H0899VS0iNbSpfLJ9DSYyJ22tjbjPB0H0yaGabNAs7VcyR3bnMTtMBtRKqH1FZPdro3VPC+S3EnqlmVa5xGIhKOyJY25o82l/K+WJw5O7nz961+PQkGYYoRpMXdkQGV52pdt00xu9ntyp4CNALwQBMF0AK8BmBSG4aOVzdbgQVJyx6Tc4TF36Dc+OKxYsQLNzc3G53PSw7ZgNd0PlErpbYs7jSWn6yTjrMG2uJPgi4akAZVNi2AXtywN2vflkjvSLYuIHUnuPPXUU5FPaBK3LFkvtJjjE29tba3R0LQpd6jdmo5Cj2uDckF79tln49JLLy1Kg/5fCXLHptzRJnJtgqAycHcdkzGrtXf+HZ/kJbnjqtzh74vuIaKPjrYl0HgklWpJlDtc7mpS7pjcsuQ4EoZhiVsWTbRxp2VRmjQevPnmm/jqV7+KJ554oiTvHLxsmnJHLo6yCqgsT3sw1ZVEZ2dnZHxpu8uu7jfaaVm9vb1FfcKk3KE65kQXjalBEKjEiDTGtd3TLMkdGuuojBLy3Unipb+UO21tbZHxLuN8Uf3Ld6L1fznGyPHS1C5kOVpbWzF69Ogi5Y5pjDH1RRdyRxsT+LNciQ+ZJ/l/2318UZE25s7TTz+NAw44wPhMjdyx7Wxrz3bZqJP1ZnLL0sb1OHInCbnE32FDQ0PR800L8yRuWWPGjElF7kiil/5SzB2ZJw5NuSNtObLP33rrLeN8nzTmjonccRl76BotoHJraysOPPBA3HbbbdH39913H959992i+/lz0rhlSVLCNe+UR4K2SSvrQo5JvN1p70HLv60dtba2ltSJrUzlKneyIHe0sVQSLp2dnRg+fDjOO++86DpS7rieliXLn1a5E4YhVq1aFdmUJnKnp6fHuDnG1T38WbZNM6/cURCG4YdhGH6m799OYRhe3B8ZGywwkTty0pIBleWiOS6gMh/YW1tbo9OPOENeLrljU+6YOq9WfrlAtj3XZbKKU+7YAipLgoCQltyhNP7whz9EixhbQGWToe3iliV3Xv7+97/jsssuK1r4m4wlfp9sR5zpdlHu2MidcpU7chK45JJL8Mtf/rLoXrq/EuSOra9oC1ttsqNy8wnCVH6be4gsI90rjUC5uJPvR+sHkggl8LgrSckdul47SUQaLSa3LM1QzufzRdfTtTJGgWlHkCZ3MhB4jDftOFONuOF1Iesm7VHoktyRbcLWRjg6OzsjVzVbzB2TAUkwKXf42Ez/XnvtNfzjH/+I7qUxj+94UVsaO3ZsKrcsboj39vaiurq6rJg7SZU7lSJ3enp6Ssgd/m4+85nP4Oqrr44WI1o/lAtnrY9ri5vm5mbjLrVpPJUKFk460/W2RQlP21Qf9ByZL552UnKHz+mmscGUXjluWT/96U/x7LPPlhDncuNCsy9cy0j3Pvfcc/jTn/6k5kuO9SZyR4sZZivvJ598YiXctLQ4uWOLuSPzGqfg7ujocFbuyIU9jYuE7u5uhGEYvTdbWppLjByvJ06cCKA4RpGmcKN5zBZzRz6Lfnch2Qh0jabc6ezsjIhl+v473/lO0amWcWsADvpNKneA0lOKbO+Yg9+nqfLlfCLbqE25I8tSLrmzaNGiku9sxIZpncDrRhIyfE51PS1Lm/slYfLss88CAB544IEScqdctyxpp1544YVFZKNmB/Lxw0QM2pQ7lGY55I4/LcvDSFLIwU4LqMw7hBZQWRpUdP3NN9+ML33pS0WDV7kBlYFSGbptYJfl5gOttkMlod1nAh/k+WKC7rXF3Emi3JExdzTQ96Qu+eijj6JBhu+6cpUFf7Ykd0xHoXMCh/JKsUg6OjoSuWWZyB0eIygtueOq3JGD/1tvvYXVq1erkwBQ2o54PvqT3JH5trllkdHMFwey/FpdckNfTnJA6aSVxC3L5MJIKEe540LuaAtl7Xf6y2M4yWuTBlSmNPiYQRJ8Dm3hI92yTIujcpQ7csdJXmcCV+7ExdxpaWlRXVQBc0Bl3napHVx99dU488wzo3uJ3NGUO+PGjbMGVDbNS7weslDuaOROe3s7giDAnXfeaSR3NEIyCbnT0tKC008/Hddee23UV21HoS9YsAALFiyIxhutXfM602wDnibdP336dIwfPz4Ktkz9Qs4d2hhXV1cXBRaWebKR+Lb6kvdrY4JGHLmmy+2EpModvlETF1CZ8k19gMAXLfyZNuWOK7lD7+zmm2+O1LsS/H3yti/nMT4eutiIWswcDS7kjmYvk4qYl1MDkfRp3bLk3NfT04OWlharqyTBNsfR+6UAxh988EHJmM7r2dUti99P+XN5DwRKTyN3pN1lGt94nu6++25cddVV1mdp5I60tW1tjb//OOWOycbiZTSROybCW8sffba5ZX300Ucl3/X09GDu3LlqWqaNF1M+pHInaUBlG8lBgaA/+9nPRtdlFVCZv7fbbrsN559/Pi688MLoOxkMmdY9JluI9zmTcofP37Zy9/T04LOf/SyefPJJ75blUQoTySF3JJIEVKbJUQ7slOaKFSvQ1dVV5FbDO38ScofnQbLBLuSONjjxa3O5HNrb27HzzjvjxRdfLLk/bsEOFE8O2u5cf7plURoTJkwAUFgs8nct61wOPNIgqK6ujnXLorZBf/lurMktyxSMLpfLFS14OLljMjS17yW5E6fckW3pC1/4Am644QajfHPhwoUlhFfcLmMacscUp4ryJMtuc++QrpVAaT3YlDuS5DMZJppbVldXF1544YWi9Pg99FdOWiZyhxYIMh0OvguolUUrA8Gk3OFkobzWNaAyjQeUL97XSYKvlYPfI4kuTWUprzOB70L96U9/wsKFCwGUugeY6kqCZNTyWqnceeaZZzBmzBj885//LCknYA6oLBfyNBfx7zXlDv3fpNyRu5OVJnfy+dLTsiio9nnnnVfy7myEZBJyZ/Lkyfi///s/nHLKKXjjjTeispjIHV6/2pgjyR0ak0071pT2J598gjAMo/bmuvnDlTtkd0glm6yXNOSOtsDiZdXSsPU3Tu7YCG/teyJD0ih3RowYAaA0Nl6W5A5fPJtcJSS5wtuHafyy1TXPqwupIElhoBAYmNsZ8nlhGBaRYrZ6X7p0KQCd3NEWuHHkTnd3d9Q3ZJ4keJw003hNf3lQdHmt5pZle67sa3E2kDZna25ZcqxwJXdMqjHplpWW3OG/8f5UrnJH2hkmtywtf1IJouVfI3fuuecebLnllnj55ZdL0pZ/tfcgx4skblktLS245JJL1HSlDUtqLd6+KhFzhzbWuA2qzYeu5I5U7kjCMk6509raijfffBPTp0/3blkepTAt4iSTnSSgMv0mOzp95pODNojKQc/FJxMo3am0DXg2UkuSJEuWLME777yDt99+u+R+F3InTrljOwpd+w5AkfpF+z2O3Nlss80AAB9//HHRIldOHnHkjmSq6RobubNixQrjroVcJPM80HVcuUPPTarcoUUXvQ+u3CFXMy1/VE/t7e1YuXJl0YDMnzN79mxj+8+K3DnrrLMwefLkoutskwGVU4Kuo/fT0NBgLH+cW5ZGRsqTRDTlzlVXXYV9990XTz31VAlRyPNhcsvKWrkjDS4JEympkTvyN9NCg64jckMS6oAun9ZIdqnc4XXT2dkZtf8kyp358+fjBz/4QXTSkWn31IXc0WLuSEOIXHeff/55Na8mtyz+rslAkvORptyhdjBmzJhUblmyH5R7FLqm3KGTDWVsG45yyR05J/f0rD0KXWvDNP7blDu8zlwXCPQseVqTac4gSPckG7ljmr9t/cKkTOPz3qc//WmccMIJJdfYyAW+iDQRdxI8/0lj7lAfMJE7trEwrXKnq6vLuODi71XGiNFsTf6sOHInbjznz+ebFC4BlXksSduim8gdirmjkVRafuhZ0i2rp6eniNyx2aJU542NjUZbjvqFFoCal1eelmUiWfn99HscGWdy65HfuSh3aGzin039Wq53+H3SLcs2hvN8aOSOVj5ZJxq5E6fcsa11ePomcoe3I4k5c+aUPEfOhdomp1yPJHHLevTRR3H22WdHMZRMdkJ3dzc+/PBDAMXryUqclkXKu3HjxkXfSXInDMMo9p7MKy+rptyRf+MCKvO24ckdjxLEkTs25Y62m2JS7vBBhfvsauRO3M4ch6nxy0Hs448/xksvvRR9lpOW3DXi18kYE9p9crB9/fXXI6UPnxxclTuaMSINHBNDz8snQd9vvPHGAIC5c+cWGdMmcke2D0nu8LyRMS8nTE25Y1o4cYNJEgGacqempsY4wWt1YVPuNDY2Gidefg/feevp6SmS4fYHuXPFFVfgwQcfLLqOX6sROS6nZXHljiy/Vpcm5Y40wCgtk3IHAB588EHVQJB9hZBFQGVbsMk4Y1COI9TGu7q6Sshp14DK0i2LjxnawlKSvjLf0i0rl8tFC7skyh05xqVxywrD0OiWZVrEaDHJgOJ3z3e/5JxAfY9/L08wA9bW3ejRo53csmR9ZK3c0cgdPqbGEfiaW5ZtEaDFDaHnuCh3bOSOtnA2zcGS3DHFmzAtFLWYOybS2zQmL1q0CIcffniJq5K8n4OnpblPannlKFe5kzbmTlNTEwCzcocgF2v8b1xe+XhpUu6kIXdcbMQ4klP+xttofX29VbnDyZ2qqirrhptU7mjuZdq9pnGRK3fGjh1rHKeBtX2blEg8XbmoXL58eZRXre9RvmXMnSTKnbj2zNPVfpdjiNYOZb/Xxib5LE25I11eXMkdzS1Ls0lsCkYTuUOE9V577YWHH37Yauvwe0zkji3wMG0oyLT4X+29yvEiiXJHxjXTxu+pU6eWuFVnRe5oyh0id8aMGRN9Z1LuUHou5I5U7tDfOLcs3k68W5ZHCUzkjpQpplHuyEW5NNqkcse0E+A6mNoCKl922WX47ne/ayy37Jz8Ohu5Y9otOffcc3H66acDMJM7dA8/+k7+JheshI6ODuMgDpjVTnIQ++ijj4oWptJAkAOPJHeIyOHpErFDaZDKyOaWJd81P8EoTrlTXV1tjDNA7bCzs7PIeOVxhugvN4BMZCeVh+7hA7Ikd0yGhak92wyP1tZWlQyQk2kcuePilsWVO9pukul+2eflYp8ThvKd0u7xc889V5SGNHxMbllSnSIX+Vrd2pQ7poWjKV+SuA7DsMQgcw2oLN2y+MStvVOejotbVi6XQ1VVFaqrqxMpdyTSuGVRW9CUOybIAMsEXi/cyOLXkYEk+4bmlsXJHd4mTES+7MdpyZ0XXngBX/nKV1QDnt63dI2xKXc0ki1uZ/W+++5DQ0MD3nnnnZKxjit3ZHwPuRDRFlCSUJJzWxxRatpg0MbmfL4QA4XHnjG9Jy1NwsUXX4wHHnhAdeMwtW+bckJ7toQt5k7cnEFtJU3MHYq1Yoq5o32OU0xIyI2nK6+8ErvuumvRNVq7p+tN5E7cnAro5L0GjYCk07JsBC+RO+PGjSsq5+zZs4vcXTi509DQUDa509PTgwULFgAoKLGzUu4ABXU1oCt3uPKeb+JpzzWtAeJIFvl/SbrIuUabFzVb3vT+bW5ZpgV6XP415Y5mk9hsLdM6IwxDtLa24o033sCbb77prNwxEVy2DRmN3JF515R8/P9kg9OYExdzx6bM6u7uRktLC/baay/cdddd0fdtbW1RfioRUJnIHZ5fOUfFxdzh75bWIK7KHTnv0G9eueOhwkTu0F+TcodPgECpcod+l3LsMAyN5I6L8WbKP2AndwD9lAXNcJeDUhrlTnt7e1ROfgoLn9DlIkHbLZaEDk/ftktkghyE586dG93HCaOkblkaycDfX0/P2mj5zc3NxndMf7mhKQ0EGVA5jtzJ5XL49a9/jf322w8dHR1YtGhRNLASeCyA4cOHGydeqifArtzhfuu8HnmaEqbvly1bhnHjxqGmpgavvPJK0fU83ffff7/IbcdVuUNpuCh34mLuaAsnTblD19XV1aG3tzeq0xkzZhSptmS7kJOWyS1L9n+b4dnV1VWiDnEld2R/4pOsVELExdwxkTtxyh2tbUq3LDmmVVVVoaamBq+99hpOOOEE5zGWw2SA2QxFqhNO7kg1lnwe/S6/X7NmTcmpd/wZlBfNqNWUO52dnaiurkZTU1PZMXdyuZwzufP666/jmWeeicYPPgabglfzsUeCvweqH+3d8PyTyvSXv/xlifFqU+5wQ5SeY2ov9EzTgkUSUGncsuhack/SyB05Zl5//fUleaYFDZ9ztfs5XMgdW92UE1A5Scwdyr9U7vBxNy4P2mLOlFdt8TZt2jTMmjVLvY/P8fSZp5k0oLKrcoenReVL4pY1bty4onxsv/322GabbaLPfOe/oaEh1jXFtnCmci1cuBCjR4/GiBEjrESXptyxkTs8mDW/ltvvpMKUNqPWTiiNJBtc/P+02SfHjiTkjrQR5LVAYQ6Qa4Ak5A6/j5M7cu7g15psCZtyh2+WyhAN+Xwe//Zv/4af/vSnRelLu5yDVH8a+BxmstddyJ2Ojo6ISJb28KRJk/DFL36x5P1q8b56enoilQ7fBObKnXJj7mjrL+q/vD1oblk2cof3OZNyh7dpG7nD24YndzxKEEfucOVOb2+vasTV1dVFjZwrd7T0eUA26ZaVhtzhv0lyxGR8a8+Syh2+oEij3Ons7IzqbvXq1aitrcWYMWPUQMjUaW1xHoC1Rk1VVVWscscEmW+u3OFuWXwhzu+TBgHVl5zE+Y4OUBgEXdyy6DMnSkwuPEQSVFdXR3EGVq1aVeL61tvbi4ULF2LhwoU46KCDsMkmm5SQOzwWgCu5w0/r6u3tjSbz6upqLFu2zEo8arCRO1S/b775ZtH1vN6POOKI6Bh2KpOEy2lZRNDyPMl2o90vjXAX5Q4RSXwRxXc7ZT4q4ZZFMWhqamqMC04JU7vl5ZCS4izcsuKUOy5HoedyuYjceeKJJ3DLLbeUuGRwxCl35KLcptyhMnG3LL4zqD1Pi0lGbYgk0nzBx/saLcwk2WdS7jQ2NqKhoaHIrc70rk3zC+241dTUlJTNtoiTz4k7vtg0jvBraAEfR+7Qdf/617+ioM1UJ6TcGTZsWNTP5DvnijGbOoIvnOSCQP6lNGW8ClN9AWvrjAdUlvXE2+ftt9+O//7v/y4hcah99Ce5o6nF4u7jbbK7uztRzB165zTe0EJp8eLFOOmkk0rKLhfK/K8trxq5s3LlyhIFJ3+fSd2yXMobdx1fONN1Sdyyxo8fb40P2dbWhurqaowcOTKqe/lsDlO/IFDMnU033bRIhanlobOzE0EQROXh6WkuKLLv8fLKIPSybuTGAi9PXOwj05xNm31y7OBkt4Qc88kWtD2XNsw0wpjgGgM0S+WORu7QO5Ibvfl8Hg8//DBuuOGGkvTjyB2alzlmzJiBIAgwefLkkryaiCmeZ/q9vb09UmhLt6w333wTr7zySklMN5NyR2uzWZI7WhmI3NE23OX60YXckcodbcPEhdzp7S11y/JHoXvEkjtcFi4nSWqMjY2NRVJf2+5cLpcrUu5og50cOFyNpTjljkbuyOuoc5JRTnJC+SxTvRG4m8/q1asxYsQIDBs2rKQTAvrug7a4pHpramqKjbljgnwvixYtitK1KXdM5A4FENPemYncWbFiRWzMHdNJA7lcqXKnpqYmUu6MHj0am2yySUl56X08++yzAKAqd2zkDi9LXMydLbfcEitWrDC2TVN7Nn0vJ0mgdFIFCgYzrzdNQeHqlmUyPGzkjiSb5GTFjT++MyrJHU2yS8+Xk5ZpUelC7nDDsKamRiV3TJO+i3KH8saDiVJ/0dKWbY2uS6Lc0YweWTf5fB5BEBTt7lC6K1aswCmnnOK0IEqj3OF9jO6NI3e0mDs0jmrkDh9HyX2Jz0mAOaByQ0MDGhoaVLe6uHlJjuOuyh3TXMINU2kAyv9z8IUP1W0cucOVklxBIpU79J00vvkcGbeA1jZU+Gd5v1TumFwigVJyR1vM8DqQcX0IA6Hcsb1bU5pplDuS3KE0iNw57bTT8Ic//AGPPPKIMe825Y5L3qm9meLnZBlQWYs9pYG3p1yuoAiura21KnfILSsIAowZMybWBhszZgyCIHAid+LmXlLubLLJJqiurjbaosDaOa62traknWrKHW3RSn8lucOfx8dNmWdpx/FyysNU5P+XL19e9J0cf7JyywIKY5nJdqN0TeD3aRuUmk3iEnNHew9cuaOtoygP/B7T+EzzsEbuUKzSBx54wEiwa+OBdAvu6OhQyR0i1IBSt3KTckfbSOJEY1JyJ5/P44ADDsBrr71W9L2m3LH1E7keMsUW6u7uLgnHIed4V3LHK3c8VMSRO9Q5pESMDxJk9AHxyp18Pm90y5LPNu2QavkHSgcMU8BL/n852NEAyI1iF+WOfBYnC1auXImRI0di2LBhJZH3AX2C0hbvnNxJq9zRBmHKkxZzJ84ti+rZRO7QAGZS7pgmCy0YHeVbBlTmyh1eHl7Ozs7OovZhU+4MGzaspDy8vkwxd4hY2XLLLdHc3FxRckcj//ikV1NTU5ZblrZDKfMi8yT7kjSWNeVOHLkj2wWftDiZ1Nvbi56eniKVB29/tp3Rzs7OEnInbocxLuYOUKrc4RJ2LW3TM6lugiCIDagsJb5Aqcojl1ur3JH3Pfvss7j22mujUypkWhxybJAGtwZNuSMJEJNyh5dBkjtavVO5qP3xetLIHVLu0G/y5JQ4ckeqWFzJHdN8R+loxxeb0uLf53JrA2cnIXd4ujKgMrCW8AHSkTsm28BEGMiFkWmhzctnI3d4+9DcGIHyyB1XAsf2mzYG/OAHP4hirMjraIFUW1ubOOYOlYcUKDK2i5YnW1n5e+XjPgcRSdrpoXwDh56lEdiAm43oSu7IRTAnd2x2cnNzM0aPHo3q6upYcmfs2LEAUELuaPfJ9q2RnkTu8AMltLrg6tRylDvcLQsoPdhDkjv8/6YNgz//+c/YcsstrRsyzz33XNF9mspBIgm5w6+T5I7s767rEVe3LNN7dlXuaG5ZhNdee60ofW08BBCp/jRyh77jRJ7MuzYeSILWptxxJXdqa2tV5U5jY2ORcqe2thZ1dXWJlDu1tbUYP3580fdcNarZ3EmUO9JOM62xvFuWGzy5EwMbCQMUK3c0EgRY67MNoOQUFptyJ4lbVhiG+OUvf4n33nuvKP/arh2lZTK+ZXn599SJuXJHi9guJ1rNgKT73n//fUycOBHDhg1T3bJoALCd0AKsXYiMGDEiM7cs/lzbaVkmcsdm9IRhGO3QS3LHZKDTZ21ipOtkQOWamhrjbiVfwPP6zTrmDrDWYN1yyy3R0tJSNLBr71bChdyRk7xcxNPvUg2h5YNAafCj0E3ljyN3bEQAJ4hsblnabgfdy5U7/H3RZ74bnc/no/es1S1fjGel3NHcsrhyx7Y4j3tmU1NTrFuWidyRxLaJ3NGMtDhyh7fJMAydlDs85k45yp3Ro0cDMJM7tDDk8xWwNo6AyS2Lp2Mi8k2bB1QfSZU7JnKnqakpkXKH59dG7pgWInKsI9LARu7wuopbQMuxS7Z7Wa44ckezAWwBlbWYCbLN0nvTyB1T+66kcufRRx/Fn/70J/zXf/2Xeg/ZLbTR4ULumJQ7poUBT1PadxxUxu9973v40Y9+pOZFI3f4+5R9mD9HWzC5lJfnTYPcjKD+a1PuELkzduxYBEHgpNwBSskdLf9x5E4+n8eCBQuw8cYbx7pluSh3eB3LRTYvf1tbW3QctFTuEMlIMCl38vk8brrpJuy22274+OOPsXz5cjz88MNFQbYp3Y033hj//Oc/i+pAU25IyDWAbWyS5I62YaJdqz2TEKfcMW0g8jLayB1bzB3CK6+8UvTubOQOud5KUFvl71r+TarckepsE7kjyUVyK5S/jxw5MpqrgIKd2NDQYCV3XnnlFVx77bVR+hS7k4OeYwoTQXaHHIddyB1uD2t/uWeCtrHH24k/LcujBKbFuYweb1PucHKHDHXTrm4ulyuKtq8tIuTAEYYhli5diksvvRT333+/mn8g3i0rDEM88sgjOOGEE0p2lwg0sbsqd/h9HKQECcMQs2bNwvbbbx/rlmUL4gmsNYRMblku5I5tEE4TUFm2H77wyefz0WThGnOHvm9tbY0GKJNbFlfu2AIqA6XyTJNyh+JkpCF36ISJLbfcsugzry+epoQLuWNT7vAdDbmo1PIh06SJqra21mjMasY8z5NJdSF3Kug6Tbmj1RU9n5M7fPFLn3mMkThyh7cNE7kTt9Mn24nmlsUX63Ks4bCphWpqalBfX5/aLUuOXUnIHZNBrBnYuVwukXIHQEkgR/m8pG5Zsq/TmC4XIYDZLYunY3rX9Pejjz7C6tWrS9qKRu7Y2qFMn7dvTZ5uei88vTRuWXKs05Q7cjxIo9wx9TV5f1ysHfr8b//2b7jssssAFMfccSF3ZJulzwPlliWvozYplb+8DvL5glIxacwdunbFihV4//33jQsDbZyxkZUff/wx5s6dq5INpBIyxc/hJJ1sUxpZYyovxc/SyiDB21dvbyGWH80JJnuA3LLGjh0bkWrvvfdeUdwqDpNyx4Xc0eaifD6P8ePHOyl3aI4zudHydi0DKvO029rasMEGGwAozy3rJz/5CaZPnx6NHZMnT8b8+fOLrgGAb3/723j99dexcOHCkvdtm2skkWHbfMrKLUsjQHk+tTnIROJx5Y4cWzi5I9cCvL1Pnz69aPw0jc9SucNtA/puzZo1Rnu9HOVOPp+PPpuUO/RcsoGknUOBmmnzvLq6Opbcuf3223HuuedG+SZinIPSX7JkScl3QLKAyvwdccJY2l78XhrTiWTm4OSQV+54lMBkWNJfrtzRBsrq6upo5xwolfpqhI2rWxYflKjTkt+jzD/lkaelDcJPPPEEbrvttpJdGnmfptyxGdaaW1ZXVxcWLlyI1atXR+ROJZU7aWLu8OfLgMoLFy4sMQRM5I42+eRyuahtrFmzpkj+TXm99dZbEQRBiTpq1apV0a68NAp4m+TKHZuhGUfukBsdXxTw/PC61mLuAAXjuKqqCpttthmA4raaFbnT2dmJnXbaCQ888AAAs0IjiXKHL2RI1i/HBZsxxSd2+Q74eMAnI+6WRbs6WnmTKHc4udPb24swDJ2UO11dXcaAyqbFgDRq8vnikwB5/jRSS0vb9sy6ujrU19dblTt1dXVOyh0buaMt2lyVO3Sfra3zI3l52Tjk87Sj0PkJhIA5oDLdJ9sm/V9T7iR1y9p3331x+eWXO5E72rs17YZSn2hqaormJVv7IXAjsVy3LE25097eXkLscaM8bgFtInNM5I7Mr2lR9OKLL0bxIWynZWnKQDmu8TlRohxyZ/78+Zg+fbr1fg30HiW5IxcVRO7Y8mBS7syaNQvbbrutMbi6NibY5lxS3JjIBqBwXPjdd99ddB+f4ymIt83O438l+DjD820rH9mEmluWRixK5c52222HjTfeWH1G1uQOULAHXZQ7tbW1qnJHG8cJmv3X3t5eRO5I+5m/Hxe3LBqDTbb9gQceCKCgtJB1YXufsu9LBZi8lkDkDs07aZU7chzlG1z897TKHZNblpwLeTuiOnnsscfwm9/8pug6Tu7wfkM2Fyd35F8XcieNckf2ATq9zkTu8ENNiNidO3cuPvnkE0h0d3cXre00cofysXTp0ug7G7lDdqA238q6kf1PU8PayB3eTjy541GCOHKHK3fkwEUdQtuFNe0E53LxbllaXkwTAB9o45Q7wFp1Cp+0+IBPEzsZ5dzQsJE78lkUPPWtt94CACflTpzrDt07fPhwNUK+bdIhaJMiV+5wQ3nTTTfFjBkziq43kTvarhEnd/iRhStWrIiumzdvXlH+6PvW1laMGjUKVVVVJQsaGVDZRbkjd2DloovInYaGhqK0tAW3SbmzfPlyjBw5MpIs8wnBhdwxLWr4oL506VK8++67OOGEEwAUv8cwDK3kTlVVldUtq729vSRmgyy/titnU+6QEZuVcodPWppyh7uF5vNubll0XRLljqay4btpQGnMHUAPBjpnzhyccsop1l1o8iG3KXf48brSlU/2oSAIMlPu8LR7e3vVmBgETbnjeloWh2tAZcqDtosL6DF3uEri+uuvj96pidxpbm7GihUrUpM7cXMvtW/NvU4DN/qSKHfI8JZjnVTufPjhhyXtjM+RcQsu04ZA3CLWtLAgg3rNmjWRIoTH3JF1rvUhaThTebJW7lx//fU4/PDD1d9spBilLTeHeNuhhQCN3zLAPkHG3JH1bdrtlgtl7V6eJxoLbJtORx55JI466qgie40TBJzcob4kyWSZNw5J7sQRj/SXkzv8mVpfXbFiRUXcsrTFvoaRI0c6B1R2Ve4QtD7a0dGBDTfcEEB8zB2TcoffQ23UZNtPnDgRQEEJJuvCptx56623MGfOnOizbWzSyB25ltGulTBtitxxxx349re/rc5BJvULJxU111STWxYfI/i8x+fBb3zjGzjvvPOi63p6CgGVyV7n7ZOezVVabW1t2HTTTTFp0iQ178Daequuro7CVCSNuSPrkpQ78v2byJ3Ozk5sueWW2HzzzSFB5A6RMZpbFqVvOuBFKtyk3WgiOvl4J/sfd8uiMb2uri76PgxD/O///m+kJlpflTs18Zes34gjd2zKHUnu8AWDtvCjdKgxS9bflAcbuWNiik1kBw0eJhk53ZdUuSMnOLqHdumI3NF2AimtOHLHFlD59NNPVw1R07O0euPkzuzZs9XyyXYRR+5Q2yByp66uDi0tLdhoo43U/NH9q1atwsiRI1FVVWVU7nC3rHKVO+SWReSOaaeO6onu4ZNQc3MzRo0aFZE7vK3yd3vbbbfh9NNPx+uvv66WXUKbTGhXyTQJygVzXV0dampqrG5ZHR0daGpqUpVLmoFJBmMa5U53d3fRjkSccoeeYVPudHR0JIq5w/NaU1NTZGSYguYSNPKHE9dA6WlZgE7uHHrooXj33Xdx0EEHleSLUFdX50TuyF2gurq6kp3vXC6ZW1accsdEvtJn/hx5WhZQSrSalDsctoDKsq9Tu9TGb3nf+PHjMWrUKADACSecgBkzZmC77bYrypdGfHKDk+DqlqWpAXg5OLnjQrrxMTmJcmf8+PFYvXp1dG1NTU1Uz1y5893vfjc6kZCr3+g5cQto2Xdsu7/yXkAPZk4LMlIguQZUJvQXuQMUx2/Q7tdA+ZHkjk25QyoRSTh0d3dHalftuaZjdLUxQXtXl156KY466iirckdLm/cDPqbR+NXY2BjNHQRtfuaQJEpackcS9XxhTsqdZcuWpQ6o7DIumPI+YsSIWLcsIsfilDtSHSqJIGp/JrcsvnHK75f/52WhPksnYslrxo0bhxEjRhS5+Lkod0444YRofqBreT/hkG5ZtAaQB3HIayVs4/PkyZNV5aVp/OfztnxnXLkjCTYZx0rrVxI25Q69tzVr1kRlb25uxsKFC/H+++8X5U9bV9TX10eKw7jTsrS1Fi+3yS2L5mwaW2ldqm2m8zJT3vk6goPS58pW/g4lGSX/upA7JuWOyS1r7ty5OOuss3DyySdH1/uj0D1KIDulafdQkjs0AdbU1BT5aUrGUE5MXFachtwxTQCUR/69Nghr5I5UP6Qhd/izuEEzbdo0NDU1YZNNNimZ0E15k+kRuFuWHNCvuuoq/O53v7Omz/OtKXc4ccLjKHFItlm+Xyk7pUUc7aiOGDHC+G7onueeew6rVq1SlTt8Vy9pQGUOU0BlE7nD07bF3Bk5cmRkwJmUO2+99RbefPPNkryajGDe1qgcJnKHJgBJ7jQ2NqKurs66sCEDUFPuyImI5yWNcoeO7aW67ujoiN6JZrzTc+Ni7vDFbBy5w/NabkBlusd2WhZQvFiUY6KMgcFRW1trdMui9DXlDi2IpFGZRcwdE7mTtXJHe3euAZXpfm5M8TJJt6yGhgbss88+GDFiRKRapPYlxzjeBmQdA5VX7vBy8jrkBqYLudPd3Y329vZo3KJr+emOfJ4HgIULF0Z54nl1IXfkHGT6rN2r/c3lctGik5/45xJQ2fRdJckdrQ/z+233ZBVzh9zWtOcmOS1Ly/P999+Pe+65JyJ3XN3FJbkCFN4jLb7IvkzilpWW3CG7gvqvLf4WqYyDIIh999THpKLIRu7EERmk3OFEnwap3NGUAzKgrmzT1M9IuZMkoLK0z6md0cLZpNypqqrCFltsgblz55bYmzblDlDcX2R9zpw5E3fddVfRb8Ba5Q6NqVkod4BCXREZwtMxuWXZlDu2gMqS3JEn2GqQAZVN5I7c3JU2gLYe08gdV7csAh8PbG5ZRO5UVVWhqanJidwhrxSbW5ZJuSPzK+cyGVSawN+FtNn4holG7sgNld5e75bloUAOKHLA4Z1YGvG9vcUxd/huEEFODnywJbcs7gLFr+V/TTF3+GDFO9Lzzz+PJ598sqS8LsodPrCnUe5w4+3DDz/Epz/96RI3CA0kEZw2bZo6CEvljs1QMUEbhPlgRYMhDcISceQOz3dvb280WRC5M3z4cOskc8stt2D//ffH5MmTS4yWIAiKlAFcuSPdsuSEKckdacCScocTDrw8vL5sMXdMyh3eNiVRSnDZ4aS2ZTLM6V3KRSXFErG5ZdF9nCizGVPaxCbbpE25U1dXV0Tu0DgiyVb+HZ+0JBHZ2dkZLVrSKHfKdcuicsj82XbVgLXtSTvym+Ci3CEih/JdXV0dyXnlrpEkd5YtW4Zbb701VrnDx3etbnp7e41+5kA6ckd7DtWjFnNHtnFaOHJDl/KluWU1Njbi29/+dsnzJblDaRLhLBcaktzh44rMH6XHP1N6tECVyh2uAOFKKN5nXcgdMlxpl5uTO/QM7pYFrN0pTUPumHaq4xQKGqlDn6WixTWgsum7NOROHNFgez5gJ3cIWcTcIRcMPod86lOfwh133AHATO7IuR0wvysiduLcsnja/P1T+jygMo1nSQIqJ3HL4v2bnhen3KG8ke1byaPQTe3DRbkDIFLuSDUOXyhLckdeS+MlHRkdF1DZptyhuk1D7kiVgwmaUob+7rLLLjj66KOjZ9F8KMkdCVdyR2trU6dOLfndNP5x5Y58D64xd/j4p42Fr7zyCn79619blTvc00La/5J45GWmPlNXVxfNI2SrSbcsU0BlWVfSLYvuk+ROdXU1mpqa1BinBBnbJo1blonc0ebbcpU73d3d+Otf/1pywuP66pblyZ0YmBbncvdQ/p9PuDbljmz0vLORcocGVRPBxJU7rm5Z06dPLwoYJsvA4yhIciefzxcZxUmVO7yempubowEtrsN1dXXh1ltvxe67744HH3yw5HdJvGguXnHQDAVeb/R+TBObHLT44P7mm28W7UzkcqXKHSJ3TEYQXQcgUu5wNQp/H7aAypJ8kka6pihI45bFvyflDrmTmcgd6fpjypMGqdyRxg29j3LIHVv5tXaTRrnT3d1dotzRyB1JssQpd2gMciF3pHJHk6zHLTQ15Q71HR5Qmfq+5pYlj9PUnmlT7nByh/dLTljJsTsIgqLx6K677sLxxx+PuXPnFpVP5ofccUzI5ezKHR4olUBHfWrP0z4Da8cpLeaOJqOXxAzVdVtbG2bOnImf/OQnaGtri4za4447LrpfGl+awsDFLau2tlYti02RAhQTpPwZceQOV+5oO4n0HFpc2ZQ7FPOJIE+Z4mOBbQHN34Vpt9O0iLVt/kjSwzWgMsHklpUkoDLBhdzR5j8Xckfex+vERO7I96Epd+g+wE25IxebWlnon8u8JhehvO1TOnSSpTa+ZK3cIduW5g+TGzr9raqqqvhR6DZyh2+Cmeoi7ih0F+UOXUs2aFrlTj6fLyF3pMqCyh0EgVG549JnZF60cSefX3uyaznkjkaAcmjkjotyR5t7TKdl8TVWLpcrWkfJ9n/nnXfiwgsvdCJ3NOWOjdzRlDtEwpjcsuLInSQBlZuamkrmBQ6p3OFjIH9uGIZFblm2mDvyfSWJuRNH7vztb3/Df/zHf+DCCy8suc8fhe5RAtNkJTsxsNagowWZRu6YlDuUrotyRzPiqPG2t7erCyTAvCPGIY1SuSDNWrmzYsWKImWTDV1dXdHgL0ksoPgodP45CTQDWrLoQPyiVlPunHjiiTjnnHOKnpWU3Nl0002j/8uYO7Q4clHuUBuh/GlxODjiYu7wtE1uWatWrcKIESNQVVWFsWPHGt2ypMSW4GIE07NNBjjlhxsmP/3pT3HhhRcalR980pHKHZOaANB3LeSkrCl3KJAdxQHq7e2NVe5oblnaaVmc0KDdGF4ODt4GslLutLe3RwY8l/Lzk4bk8+WJC1rfc425w/sG36mV5I5U7lBMLDIMeR54HcSRO7RbL/NG0JQ7fBGl3aPVB419pCCxKXeA0j7HibWjjjoKN910ExYvXhwtur761a/iiSeeAFA61nG3LG7EuZA7tnZoIlR5YE9eF3w3kccwI4O0t9ctoLJJuTN8+PAit6ytttoKp556alE60oiVmyUSfIFhIlLj5p4wDCOlCl1vUu64kjvyGnrHmnInzs6wlZ/SNqnekoK3SVoIyLmQb5gAa8kd7tqblNyJy2tS5Q5f7PA2wt2yNOVOHLmTVUBlqdyRbba6utqJ3KlEzJ2RI0cWKXdMeaATISUx50LuyPm/trYWDQ0NJYeiJDkKncYmvnDWyk/kTmtrK1asWFGUHxebiT9T5gkozB1hGBaRO3yD15Qv2zPk/wFgu+22w7Rp00p+l+SYRu6kccuSG568jxFWrVpVtCllc8uiegFKx39tM4yTO/SOTcqdJG5ZtoDKUrljOvmP58HmlkV5WbVqVTQ/8g3ULNyyJFFF13G3LB5SQbpEe+WOhwoboQIUx7GgwYQW2blcccwdvkNNkJ1fi7lDE6hpBzMMwyLjncfd4YOVC7ljCrZJICOJL0o5uROGIQ499FA88sgjRemYyJ2WlhZnckcL3sYh3VDSkDuaoWBbMErYyJ329vYStxON3OGGuQRvPzLmDrU7rtzhgzLPszzhRu628mvpFKlyAyoDaxet48aNMwZUNu16Vorc+cpXvoJjjjkmc+UO9SWuRHJR7lBQ9TjlDo9hQN/Z3LIobg8ZukmVO+XG3Jk8eTKmTp0aGQFcuWMjd+Rn7Zkup2VJtywiF4jc4W5fktwho0j2G/l/irVgAidfgbX1eO211+IHP/iBSu7QgpQg25C2kCSVDbUvW0BloLTPcXKHL7S4UUvHGWuSeJmuS8wdUh5KUB3NnDkThxxySImSK06589hjj+GXv/xlSZq5nJtblqtyp7q6GldddVXRokcbV20Lf27UmojUuLkHKFYAacqdpKdlmZCFckcztLU8pCF3eB2alDt0qgp/tqbcoXzGuWWtXr3ayR0mqXKH900TuUPuCfw+njeJJEehS3LHJeYO33RIEnNnIJQ7DQ0NsQGVTW5Zso9WVVVh+PDhZQVUJhvFpECncpNbFgB88MEHRflIAlM9kiKF2kpWblnyOdtttx0++uijknRknEqeTxu5Y3LLIqJ7+PDhRkKBQORHa2trbMwdnlepWNHKT9eMGzcumquGDRuG6urqfom5M2LECHWTnMBjd/JNYgkid6jv0jOHDRtWYgPbyB26hosj+DWyzFK5Q6A88pApsg/5gMoeTuQOdRxO7pDihcfc6enpcVbujBw5MtYtiw9kfGeOd9g4kkJCi8egDYBczs7Jne7ubvzzn//Ec889V5SOyS0rDMNEyh2b0STdUMohd0zKHYJpArORO9oCp6GhAUEQFJE7/D4JXn6KuWNS7tgCKkvljgT/nuqztbXVidzhMXfk+6LJcezYsUXtICvlDj3bZIBzFzYCTQaVjrkDlLYlTblDx2LGxdyhE6z4dy5uWVy540ru0K4mfed6WhZvF4cccggWLVpUVE661oXciVPuJA2ozOuiq6sryoNG7tC4zGOn8HzRe9xxxx3V+uBl0twmXnjhBTzzzDNOyh1Xcmf48OHqAiGJcqetra0ovhg3aqnfSMONtwkuoU5L7tB3L730Eh566CHMnz+/6Hsec0cqBYHCSYwU54unyd+bC7kTF3OHwMkdzQiPU0fIOcj0WbuX/5/bCZpyh8YW2YdNAY01yMUrUDlyx1Zvpns54cjJHZ6WK7njotxZuHAhRo4ciSuuuMKaR2qrWSh3qE3ZlDumukvilsX7N9m2cTF3+KZDOTF3tHlGzr0mQkPGCTTlobGxsWzlDi+vRu7YlDuy3UrXcQmu3JkwYQIARG7D5RChfLMQWEvuZOGWpW2KHH/88XjuueeK4h3x33l6ktCR9a4pd/j/gbXrguHDhxflRyO6ObkTp9zhzzcREhq5Q/GZKF0tKDpX0djSNZ2WZXLLso3Vrsqdnp4etLS0RPOjRu7IfpLL5dDc3Bwpzfh9tI4xKXcIRI5LcofyaIq5sz4QO4And2JhIndoFz6fz5eQO2SkUoegAaG7u9tZubPBBhuUuGXJa/mgx3fmTOSOi9EWp9yhAVBzy8rn80W78Rwm5Q6wltBwibmTRLljCxZmgpzggPKUOzw9jdypqqpCQ0NDCbljmpz59zLmjiR3KBAbMe48z5oCwVQ+qs+WlhZjQGX+fvlEKmX71BfkaWP95ZZlI3eSuGWZyK2k5I6m3CGyluqaYvDItsHdC5K4ZbnG3OFtwKTcMYGUA5rxQOnyo9DJWNICKsv7tDbrotzh5E5vb2+JWxa1TY3ciVPu0L0bbbQRwjAsCjjM0durB1Qmco/eEW+fMi82cofaUFtbG5qamqJ04tyyNDIaKJBCnNzhiy7qN3Ks05Q7WbhlmdSFnCDl74VL0OXGAc1rcW5Z7777Lo488kgAaxeelMdhw4ZFdSlJKll++VwTbMqduEWsXERyW0UeL07KHa7wIZjmWNOYKufZtOQOJ8VMJHvcghfQj+XN5Qqq4rTKHb6wsZE77733HgAUKRA0pFHu8HZA76yurq7ELUtbIGdN7tDzpG1qIlwqGXMnjvSkwzpyuRzuueceTJkyRb1u2LBhscodqXSSRBBXH5Sj3Mnn80byRJY/CIJo/tHchl0h3xm9AzriW7plmfLnehQ6PWe//fbDfvvtV6Su4teGYaiO8a7KHaB4c4OHbzDFeSEQGbJ69WrU1dVhww03RHV1ddER8ny8lAoRF3KHq32HDRtWRDBSWZIqd+h3uo/mcOmWZQPd6+qWNWrUqKLNZo3c4X/HjRuHW2+9tSgdoJTcMRFlVB9x5M766JIFeHInFiZyhwxxYC0rSp+5WxYndzTljkyfkztxbll8EDWRO3ywShJzh+dPkjtcuSPdskzP4HmVhhuPSWRDnHKnq6sLVVVV6kLRFfx9SHmfdp2ETblDxhwH5ZdiesQpd/j3ZKBLckfGgSpXuUMTw6pVq9DQ0GAlN+iZBPkO6F1L482F3HExWOKUO5SmJuNM4pYl+6K28Eqr3AHWGhPV1dVFO008vZqamhIDV1PI0SROZA6RNGEYlu2WZQKlfc899xR9v91220XB0PtLuUPXx7llceWOPL3PRu709q6NnUVzgWl3SBJefAGRy+Ui10d+v3TLMgWNpbwABYPcpNyxEZiyPUtyhy9uqH5sbllSRciRNKCyzDelF+eWRW1X3iuVO5dffjlOOumkouf+/e9/Rz6fx4QJEyIjnPLIgzTz9G3kDl+QaNCUO66LWF7vchdakjucvHAld77zne/ggQceKFrYAKUnVGVB7pjcsuIWvEAxuSPtKx5zh8phIndkzB0XtyzXQxw4ueOq3OFl4Ys5Se4MRMwd0/P4RkQcudPU1BSll6VbFj2/t7cXP//5z3HDDTeo15Sj3JFEENn9MpivJHd4niXREEdk8jZN/ULWexLIvsLJHT5Hk3Inq5g71J+kOxJvS5QXfkgHJ3rk/JXP5412KLenqJ7IZdBE7hCZdeSRR2Lq1KlFhAzPs4yPZCN3qF1tsMEG0Xeackcjd2Q/leMBb7PA2tMck5A7mnJHI0Z6enqwatUqjB49umg9YlPuaO2T55W/C26zSWjkjly3EeFJ7dWTOx4A7OQONR7NLYsmYB5zByhtWDbljsktS+aF7qPOmqVyhyS4/DMnd7hyx0buAGuNT/mMJG5ZlL7WQbu6uorItHKVOzxQp4SN3OGDMX9nJuVOY2NjNInQO3RR7qxcubIo5g4pxnh+Ozo61IDK5Idsmog1coe7ZZnIDUqbII1dejfSMOB5pv8nVe7U19fHKncI5bplyb6oqQs0Ca2Lcgco1DUpd2hM4O6dlG/plqUp5OQi1DXmDn+nSckd0+9f/epXsdNOOxXVtSnmTltbG2bPnh19ti1ukyp3pFsWkSp0vVTLUF5NblkTJ07EFVdcgcMOOwyAndyxKXfIHY/frylPOLQFArllaQsEm1uWbM981xRI7pbF67tctyx5Pz3HpNzhEnRNucPJglwuh5dffrkk/VWrVqGpqQmffPJJEfkHlJ5oxstByEK5I8sft7Eg0wFQEjiTNgbkfcDadybb8IgRI/Dtb3+7ZO7tT3LHRblDGyV0D1C8EKaNDprXeGB/ejaN85QGd8uyzZmuG0pE7EhS3wS+k82Ju0qSO5MmTcKxxx5b8hsnd2S/MhEuLjF3SBkHpDsKneddtl2a82wbhI2NjYlj7pj6KI+5w/PFbcMgCIryUo5bliQ905A7ctyR5A65Zmd9WhYndzTyh9sHnNzhRLl8Z1K5E0fuUPuQYysfN+vq6lBbW4tddtmlqN0nIXc0W5CTOxRzR/bhJModYK0dxRXdPMBxVVVVKnJHs6uJ3CHlDle2piF3NOVOLpdTx4COjo5Y5Q6tA11PZV5X4MmdGNjIHancMcXckcExOWSjp4aouWVpeSC0trZGJ4LwxXW55I4c8OQgIpU7tmdQfk1uWXHkDrB2YDZ10CyVO5zAMimuJCQZxicobfea5xeIV+7w+w844ABrQGWgWLkjCRjXHRaafMIwdDotS5PAEkzKHZ7ntOQOnU4BxJM72mTg6palkVvSEATSKXeoLZCPd01NTVEAQJ6eptzhdSSVO3RPmpg7aZQ7GvjOLA/CqQX+vfXWW7H99ttHnzUjnkDKnbiAyrQwkW5ZcTF3CCa3rJqaGpx22mlRbBcTuUMLOoJU7pCiyKbc0dIkSHJHM8BtAZW1nV9eVptbVpxyRy6qkip34tyy5PjKlTtyoUSxTrhyh5MC9NyWlhaMGjUKQRBEY4pG7pjcsiRcyB2TzRGnUHAld0iVZmqj9M7kGE3Xl0vuyPJTeloweA5X5Q5/j9qim+ZCqp84tyxy55JkplYu1w0l3t9dlDv8fdJ9QOEd0SKX2rkp5pAGG7nzzDPP4O677y7KA/3lykeZT54OV+7ExdyhuDFAsRqFp+vyLAAlJAwpdzQbjN/DlTtSMWhyy+KbZLy8cW5Z0g0prVuWrCvATeVsgskti0IIZBFzh9uj1M80Ah5Ya3cC7sodGWeH2xXcnqL7pVqEwMdNOVcR+HuTJLqLW5Ykd8p1y+Ll5ZvhtbW10TOTKHd4QGU+D/Iy0jzJ55S4mDsSJnLn7bffxte//nU1j6TckW7sPD2pfPfkjkd0pCiQXrnDAyoDyZQ75C7gQu6sXr0ao0ePBlDojDfccAPefvttdRfVhjhyh6tE6LOrcofyYlLuuHQ6qh/TBM3JtHIDKvOdRFffdBO5A+inR6Uld95//33sscceRUSDjLkDrFXuaG5ZNgOA/8YlqKaYO5I44s/hcFHupI25w5UZ/aXcsRmYaWLucHKH6pog2wZX7mh1JmNQUf658SBVgQSp6pLkTtyCxNS2aCyrr6/Htddei//+7/82Knck4tyy+HGYHFQOHniXlDtazJ18Pp+I3KGFDodpLDMpd8iAyufzJb7tccodnhdK20buJFHuUFoETbnD1Y5Aupg7RDjYFGSaixOQTrlD9cSVO3LXlZQ7NK9KNyYX5Y5WFtuuejnKHZtbFl901NfXIwgCYxulupELWRO5IwmNODtDlp/S09qxvC8puWNT7nByp6enJ+rbPT09Jeo5rtwxvT+tDZnACQPTvGaKs8VtMgqoTBuJJuWOCTa7Rqrt+MKZbFtJ7sjxQyp3TPPG1Vdfjb/97W/GvNnIHWmjA6XkDikzbOQOKXeobfCxGVjbLmT759fGxdzhC3UbuWMjT/g1QHbKHXkvjauk3AmCICJ3bOSTC7nDiXyu3JHX0rtNo9wx2aFtbW2oqamJVG/A2sMp5NjKP/Py8r7J+5vcIHBxy4oLqEwbw0D5yh1O7nB3aw1cuUM2Ed3L0dnZiba2NowePbrot3KUO5Joe+qpp9Q8dnZ2loxD1J64LQR4cseDQVOsaOQOdRITuePilkXpE3lBEtU1a9bEnpZF11EAze7ubpxyyim4/fbbYw0mibjTsqgDmtyybModeUwgIYlyh+rHNIFUV1eXpdzR3LIANx9w+t7mUy0HtSAIioxoaitxblk00HIDNM4ti6fZ0dHhTO7wnQXulqWRn5Q2DbZpYu4Q0pA7hDTkjokckOROnFsaIQvljpwseXquyh0+/tCxsJQPk3JHliepcseF3AGA66+/vqjctj5r2/VxdcsC1hIN/AQwHnMHgHHxK4/8pfTltWli7tAiQSp1TESTLB+lA6wNqMyNHr77KSHfaxJyR+aD140ruaOdYiTTNSl3TKdlUb61mDtUB1zVqCl3SG4OwKrcIdUskJ1yR76T5cuX48Ybb3Talbcpd6jvmdoo1Y0k4E3GvWxPccodF3LHFDvLhdzRlMt83qTxm35rbW3F5z73uaKDL9KQO/l8PjoYwTWPNuUOP+FNEjaU97q6uiKVuAyoHDdO25Q78j3yOY/GPPk+5LzoSu5suOGG2HTTTYu+iyN3ZD/g70WWizZCNAUhv4fGCVK2AsVuWaT25OAEnXTLssXckTFmynHLkn3V1Ebvv/9+PPHEE9Z0TW5ZtBFJtqNpTnIld6Qtq5FmcmMmScwdOpwAKJ7D1qxZg8bGxqL4MDJ+nAYTucPvkSrGpModaoNp3bKkApq3NX4aWdqYO0DpvEEnXrkod2ykIyd3NKJNgy2gsim2qyd3PFRyh0sFqaESOaGdliVj7pjcsmhQam9vj4JfAWsnFP5suWMBFAYV2qmlwZd2gwGUyHWT1IGm3EkaUJnnuxxyxyR7poE3K+VOLlccMM418KAkd2wGCLD2tCygUKc2Nxl+vzbQxrll8faSVrlD5A7lUSMaOzo6omCV8h1QPdqUO4Sk5A5PM+64Q5NyJ+lpWTbCoRzlDrU/Tbkjd5v4d1K5Q4oWQlNTk6rckXUr26k8Cj3uXZgmcXoejwuWlXLHJaAysJYEkModvpgoV7ljc8syxdzhGwJZuWXxwNB88WpCnHKH15FNRk+IC6hMi2YioOVzZ8+eXbLIks8zBVSmtqQpDPgOJu3qa8odkpsDKFncc0Uudx+0ERB8t1mD5uJBn++9916cfPLJRcfH2tIxKXcofza3LGkwA7r7FFCqxO3p6bGS6/I90rs3LXQJrsodfq9NucPb+rRp04ru5wGVeR61/BM0gtAEflKgaazkO/rSppBtn+INSoI7boEk7RrbXMXtTxqnTJteklQjlw7TvKG1RVfljpZ3Gus04sDmlkVtnhMHXLnDyR0ecFsr77Bhw6zKHZpPpRsJkIzcSeKWtfvuu2PjjTe2puvqlmUKsCvLO3z4cJx//vlF+Uqr3KFnUz5N5A4pd2iTXMbcIYUMlZW/dxNcyB2TupSXiSDJHdrMswVUdjkKHTArdwgauXPNNdfg3HPPLSmLidyhz2TLUcwdQmNjo1G5o4GTO/yzDT09PSVl4zYHB83XcWuDdQXrRylTIk65IxcN/LQsvptiU+5IY7q7u7vkKFx5Wpa2Q9ra2hqRO0SAdHd3R9c0NjY6xdzR6oAPJJLcSaLc+etf/4onn3yyxBh0PS0LMJM7PBI6vY+sAioD2ZE7csDiblm0m+CSP025Y3PLku0uiXKH70pzcieXy5W0SaAwsZArw2BV7mjR9RsaGkpOiwOyVe7I/iGVO7wMUrlji7ljcsvihCGwltwZaOUO1Q0Z/C7kjotyR1NGyHGadm+JRJengQDJyJ0slTtp3LK0BRmdlgWsNUq5tN0E3p6pLSZR7lx00UVYsGBBSX5MAZWBtX1KkjuHH344tt9++6Kdc63cJrcsHm/AptwhY1rGS5BuWTblDpe4Z63coc9J5jOp3OGnZcUpdyigsKlNu5A7cnznkOXXFjRZkTvaolvG3OHtsrW1NVa5YyN3XJU7vM5MZCtX7sgxw0TuyE08bZzW1Hy8DNozeVqc3JGbNKTmlYtuqj+TcidrcofGKUrD5OLGQW5ZdA0fm4mE4+SO6aRLoNgti9dpPp8vGpd6enqK0uHXxdkwVJdBEJQQQbbNlTjlAidCAbNblmleImJl0003xf3334/29nZceOGFRWm7KHd4H9Xcsvh4r7lldXZ2RpuM0i2LbG1uS9nqjfJMMLllyfuTkDt8HSRdbJPG3KE5Wyp3CFpA5YcffhgPPfRQSbkkucOV7gCiMY+7ZZFHgiSjbHZjkndBSELu+IDKHhHiyB1qfNQpuVsWUGh4MuZOnHKHyB0+aVJ61OE1tQSdllVXVxdJA7lyx+SyEAduSPD81tbWIgiCEnLH9ozf/OY3uP76643KHZdOJw1wAjdYK6HckUaQTZqdVLlD5R4zZkwsuSMnXl5ntLjl9UvqMZluEnJnzJgxReoWjdwhNy1KO47cke3bFivF9FmCp5nGLWv8+PFobm62vrO4mDscVPY0yh0qj43c4TF3TG5ZDQ0NRRMfkTt8cSvvk3mm67Ikdwg777xzUcDEcpU7QLz7DnfLampqwvLlywGgRGGpGa6m07LKjbnDlTt8IUl5sY2NPC+dnZ045JBDsGrVqqi9kFEjNyFsaeVyOdVVzkbuAMC5556Lm266Kfoc55YFrCV3ZFywxx9/vCiNJMod3v+1dynJHW1RLt2yZMwdqt/NNtus6D4bASHnU9vvsq9pxLMtHemWRWNy3JGwruQOlZO3J9ptl5shNtDYwueAcgIqa/0rTrlDmDdvXlluWUmVOzK/HC7KHaqP7u5uZ+UOr8Mk5A6f82jM00g8rQ2TcsdE7mjzdVJyh+ddkju8LZvIOQpmS9fwDVdu98rj2jU1HrlldXR0FLUX6ZbFNxHle4tTXRGCICgZ40zzsy2QOkG60HC3LFflzpw5c7Bw4UL87Gc/i76fNWtWlC9SS1L+6TsOTbnDyR1eR83Nzdhhhx0wf/58AG7KHW7HuRAKLsqdJOQOKSSHDRuG+vr6qIyyTl2OQqd6Kke5097eHuVXi/MjlTtUB6R4HTlyZNGmM7UVmzqRoxzlDn83ccodT+54OCt34sidJDF3uLyWYDpukg/gtFMrlTuaDDIJ5CTD2XZy7+ADjm1XmIzvctyyTIYQd8uiBXga5Q43SmzKHdPkyUk/nh5BI3foKNbf/va3mSl3pKRcphvnlsXLV1dXFy0MTModYK3EvLe3NxG5ExcrRfscZwymIXc23nhj5PP5kqNxpVuWptzRjFYiIm3kjinmDuWR94mkp2Vpyh2KuSN3Sfh9zz//PH71q18V5ZOTOxRgzwZXcofKQG5fackdUu4ApQtD6bfPAyo3NTVFbi4uyh0tD0mUO729+mlZNFZTWmmVOwsXLox236g+SIbv4pbFyUq6no+j2mlZNsS5ZQFrCVOp3JFpmAxlvsCVxAstKk0BlWmBQgTf5z//eUycODFKn7tlSeUO1cUWW2xRlHYcARFX/7JP0/NsfUNLh9flqlWrIvdaV+WOydWQ3jstDjhRQW07CbmjLRJNyh2Xo9C1xRZ3YZExd3p7e6N3+fHHHxvJHRe3rCyVO5tssol6DY0TXLHB7ca4mDu8DsuNuWMid+S8GBdzJ065YzsKXftM9+65554AylPu8Dg9ruQO39Tl46cWUFnblJC2lQ1Udy7EZ1pyx+SWpc1L+Xy+KN4Z4YEHHkik3OHtSIu5Q58Js2bNiv5Pyp04ckfaQTZk7ZZF5E4QBBg7dqwxJoxLQGWCidyRyp3q6tKAym1tbdH1nFijda0kd/jYQ5+5uoervGw44IADivLK83nkkUfipJNOMt6ruRB7cqcAT+5Y4EruaG5ZQKHR8/g5gLtyJym5AyAid7hyh67hg2kS5HJ6QGXqUEmUO7QbQvVEZXQhd4gs0MA7Nxnz0offFUndsn7xi19gjz32iL5PqtwJggA33ngj7rjjDnzve9/LjNyRx1+X45ZVW1sbyVtt5A5/t7QgcnHLMsWDcjHeOGyuABImcgcAFi1aVHStptyxKXYItKMx0Mod6ZalGTX8vv32269IfUHX1dTUYN68eWhoaMBbb71lLDcQT+4sWLAAn/vc5yLFFwWFTOuWxWMLdXd346OPPioal4DiuqZ4RMOHDy8xIAGohICpfJpyx+aWZVLu8EUTf+8mook/n0CE4uc+9zmceOKJAErJHRsBzxe8aZQ7EtzQkuMxlYnHGtDeLeXXxS2LvqN8cgJJS5PGRiL4zjrrrOj45/b2dvT09BhPy6KFfFJyJ84tTrZzl7FGS0e6ZZF7bRy5Q+7kccodGo94eegdJRmLbeTOU089hWOPPTbaOLDVLeXPplzhyh1OolEw308++USNuePqluWq3OEw2Uy/+MUvcNZZZ5U8k48T1L5NMXe0hRXVoRaI11W5E0fuaGRHOeSOi3KHY8MNN8RTTz2Fv//979HzCTZyR1Pu0GYAUEzuxLllaeQ4tw8poK+J3OF1aAt+S+00a3KH8qO5ZZHC1KTc4ScVEpYvX65uNqc9LQswq1Dz+XyRckcehS4DKruQxpWIuUNlHjduXMmcxcsS55ZFMLllSRtCig7oHkqXjyE8dh3/K4NQ8zGSiEDthGCOK664Av/zP/9TVCaez6233hr//u//XnIftRnNLYvmI0/ueBhhI3e4XzF1Sh5QGVgrleWNKU65Y4u5QzCpBTRyR2PKk4BPMnzHnwwLTu7k8/lY5Q6pe4IgiCasLMgdupcPQGnLS39djkI/9thjixY8fCDWJltNubPXXnvh6KOPjj7bYCN36OSMrq6uIlfANModG7lDackFBCd3Ro4cCaA85Q6lvXz5cvztb3/LlNzRYu7QTiknd2RgUq7ckceFc4wZMyYy9CW5wxcN1EZMyh0buUOql7lz5yaKucOJT0numMgVeeKQzbUHMC9EKY1NNtkEm266adSGqqqqYsmdOOUOP7p1l112iQgqjdyhgMPcaHZxy9LKl0S5k8vl0NraGj2X6p3GRqoLqdyxGSS8fRGheMYZZ+DTn/40gLXkTtKYOzJ22SabbFIUf8vFSOLtUj5Xi7mj9Scet4dDvleqQ1qE8DzKd8ml5ly5w11jqf9K5Q7lkUj94447rijtrJU7rjv4pnSAwjhMyp24gMq9vb3qwi1r5c7BBx+Mp59+Wm3f1Fa+9rWv4fbbb48WCbZFGI2XthgYJrcsinvByR3ZB7N0y+KQ7YGIpU033RT77bcfgNKFPxHK1K7pZCC5UWIiwoHinXbtetnfeFiApOQOr3cNadyybPZLTU0NvvKVr0R2iAu5wwMql6vcIbcsoPgEJa7ckYSl6UQ0YO3puRqSkjtx47aLW1Y+b465k8/nI7KcP4sTM9rpRtraiPIglTuc1NQgY+5wO5Q20eR6Jg5ZkDscpDoBCuSOiXzQ3LLKVe5Qf+TrBNrMkOWQ5I5U7vBn8Gu0mH0SfAzSiDYeZoOD280mckf2yfWN3InXoq3HiFPuyJ1DGpypoRG5w2FS7nByJ41bFlAwturr66PgiSa3rD322ANvvvmmSxUUkTtSSkluWa7KHRqsSVFAnd+l0yVR7tDfNOROUuWOXIhJckfWh2ZscrhOvJJFB8zKHc14roRyhy/gOLlDBgGgK3eklJynt3Tp0mhByWNbaEZlkpg7mnFBi+CFCxdGv8n3RcodAFZyZ9y4capyhxQj/LQeMsg15Q4Z1LW1tRH5xNvAlClTsOWWW0b3uCh3bG5ZTz/9tFoe7gLmAlPfk/XOlTsNDQ1Oyh3TbjSNmcuXL0dbWxvmzJkT3cf9snt6elRyx8UtS+aFyprktKzm5maMHz8ea9asMSp3pEuIa15ox5QbRNRuTG5rHBq509HRgf322w/PPfdcSRltu/FULoKMGcPJHTLStDEpacyd6uq1gV7pGSa3LFrsELkzevToEmWOKebOXnvtFbkwcbiQO/xYWg7pLsSfx8HjX2nQFtgUoDdOuWPalZexFmje1lyMXIj2b37zmzjggAPURaKcN8mWIeWByd0oCAIn5Q6wtj319q49jpzIHT7O031xblm9vb2ZkDtNTU3o7OyMYqnIZ2oKvyTKnZqamsilS4tzQjCVk54vY0PyNAZauSPLxT+b8iCVO5x455shktyxBVQGiskdbh9SPfJ5iSBtq7Fjx+KTTz4pybO2UWRD1m5ZJuUOjacu5I6m3Kmursb06dOjsVeSO/X19dFaQgPZwXS/vM4Uc8cGnmfTesREiGqgzTcAuOiii0o23LQ0k5I7BC3mDlAYa4h8IVsdsCt3TOSOptwB7AcB8A1ME7nDCSh+X1dXlzWgssT6Ru545Y4Frm5ZUlbJJwjZkEzKHZtbljQeTYucYcOGGU/LIlUHAPzud7/Ddddd51IFRSQWJ3doUZrELYt2OGhQk+SObYAlckGDRu5wyWUSmJQ7JvlyHLkjEUfuZOGW1dXVVeKWJdNNS+6YAirTZ2oLNKl2dHQUGWkm5Y6GfD6PO++8syjPhDjlTpqYO5/61KcAFCt3NHKHl99UhzZyRx7ZTgs9m3Jnq622ivLMDRJpPGjKHRlQmfcNSe5QEFsJbSFgQ5xbFrCW3ClHuUPvnCt3aFG+ePHi6HpOmJuUOyZyR9s5ilPumAyI3t5etLS0RMFS+QKCdnJpIcTzklS5w/NMpGVLSwtqamoSB1QGzH00zlDSjERC0pg7pvFcnpbF50/KX5xbFi3KuXKHnwIClMbckUYlwZXc0eCq3NEW1Rwa8UyEe1xAZVI/mYKE8zqtq6tTlTtan5HgO/aacufjjz+OPlPMJtvilN57nHKHnsWJTLrOFnMnzi2rpaXFOqea8i1tppEjR5bE39BcdjgpxjcF42LuUH1rY7rNhZinSeOUtglpUu4kJXekIlrLhwmuAe7l87hyh2+4lhNzh4OrMKiv881ggrQtTMqdSpA7klw2uWWZlDsAInKH5y+JckemK92yONmjgeYaqXYmDBs2rMg9Palb1je/+U28+eabxs16F1AfAoB99tknij1jUjHy/5ueYzqYQip3pPqS7qG2ydsi2d3yPcUpd6SLmAZOoidR7vBn8407mXcOfxS6RwSN3OHECv1fyir5DmmcckemqwVUNrll5fP5okGwoaHB6pbF8+AymFG+eBrSh1NGr3d1y6qvr486v8tR6K7KHT4AleOWJZU7Jvky310Diskwl8k2S3KHTsvSlDtZumW5kDu0kKB75P+lckdDPp9Xj1WW9xPKPS2rrq4O48aNiyV3TG5pHGPHjlXJnTAMi55NExOVzRRzZ+utty5xCzDtmhE6OzuNAZVN5M4bb7yhlke6ZWngdZ6U3KH4D2QIaGXT+jM/7lsezWkid0i509TUVNRPeJvi5A5vy1r5kih3mpubEYZhRO7wBQTlTe6Q2YxomRcbubN8+XLU1NQ4B1SWcbE0ZEXukGFYLrljUu6YDGZJLPDjXKVbllTumMaYcskdubhKQ+5oC+z6+vroZBYgvXKHu7rV19cXzfnaOGYCJ4vks7q7uzF58uSiz67kji3mndyMAYrdPxYuXBipKUwxd0x2BbmimGBqF7I/nnLKKdHmmyu5Y1LuaP2J6lsb012UO+SqTCS0PG5cmxeJ3DHNC1mclsVhU+6YIE/L0pQ7Wswd7sJFkOQOd+k0kTuaOouw9dZbo6mpqSQIrrT/4mBS23C4uGXFjRHLli2L7iXwgxj4+9BOy5LjI49rGoZhVPemjQoiJBoaGtR2x8MLyGebIO223Xff3bhZ7wptLLOpGLNW7tDcBqAoPo5G7ki7RIu5w8d0qdzRyso3MLWYO+W4ZUl45Y5HBFfljpRVciaeGurrr7+O999/P5VyRxt4aGdOI3eoY3NyRw6mrrvwfFdLc8virGwatyxtl1UDV/pI8IVYucodk1uW5gdLzzEpd+IMcKDUqMlCueMSULkS5E4+ny+JuQOsnZgbGxuj8sqj0DXk8/logQ7YyZ0gCIr6TBpyByi4sHByR7YhTu7I8nOYlDtAKQlVW7v2KHgT6TVx4sSoTNokSJDKnTi3LL6L3dvbi2nTphXFVSG4kDv8PZqMDzlxk1sWGY4ybtkmm2wSKaq09koTNj8ti8gdeo9kmFL+Ojs70d7ebo25Q0HZAZ3c4fXMd+B42TSQ0aspd+iv7Ntp3LL4woLcspYvX17U1kxp0dzCF+hpyR1XtywioJPE3CFIckebU+Q4LZU79P/hw4eXKHdMp2WlJXe6uroSKXe0dh/n9qQtsGtrazMnd7jLLZCM3LGRl93d3Zg5c2bR5zhyh8YAl5g7HFx5QWN2mtOy4mBqFzK9z372s1EQUXomlSkIgiK3LBO5w+PjSFBZksbcIfDnA8XqSf47B/Vv07xQabespModHlCZn/SnKXf4tbw8fD7hxBePucNteJtb1kknnYT33nuvaDEOJFPukDteFm5ZFHPHpFileY76E5DMLcuk3KG1DdW9idzhNhJ3v+Lp8eclVe4QZF0mHRu0dmnbiDAdhU4wkTtSuUP5vvrqq6OAxjz/adyy+CYyd8uiNaJWx2lj7kjljnfLKoUndyzgRpxG7kiFhnTH4TF39txzT0ycONEo4+MTSZxbFj0/DMOiQbC+vr5Enka74uWQO5zEkm5ZnJmPU+7QpM+VO3wRYstTdXXp0X0EzS2rXOWOdMsyDQi0I8Xvt7llaffbPpvyl8Qt61Of+lRJum1tbYnIHZIFc3LnySefLKrjOOUOH6RdVAH5fL6IaLGRO3LyiosPYyJ3Nt54Y2e3rDjlDh0taSN3SLlD9SaVO/PnzweAorHDRu5w2bum3LG5Zc2ePRsdHR340pe+VJIujzVgAn9OWrcsed1+++2He++9t6jcHFy5o7ll8V1meucUk8zVLStOuUOuQBymfkxyda7c4e2IK3dsygYTtJg7pNxZtWoVamtrrW5ZPC88DdO7j5tHtMCM8l7uljVjxgzsvvvuRe04jtzhmynyXcvdYLpWU+6MHj26aPFDyh3ttCwbeRxH6lPcLQ1ZKnfkfXQ6nCu5Y2rTScgd25gR55bFx3taYGsqVL7zT/HLeFnkMzX1NNU19R+bW1acXWFTFmmQixFth50vfDTlDi22a2triw4BsCl3NLesrq6uiIRNS+5o7spkJ5nmyzhyx+UodI40yh0ec8cloDI/fTBOuUN1Jd2ySCVGzyHIOmxqasLGG29cMm4kJXcA9w3EuNOyNFKWPi9dujS6l75P4pYl61OSFvTZtN7g6xSNVKyvr0+s3NHGsnKJAu1daCpGoKC6zkq5Q8/dd9998fnPfz76XlPumE7L0mLucALIhdzh46yJ3NE2CmyeKJ7cKcCTOxZwUkOSO3wCTRJQOU65Q8a9C7lDEzqBlDsEbuxKQyWtW1Y5yh26hhQFDQ0NicgdbZEly5OFcod2VOQiXBuINbesJMqdpOQOH0iBteUlpUFvby96enqKFq2bbbZZSbtLSu5oMXdOPPFETJo0qegeGXMHWDuA88WiC3FG5A53gyJo5A5vP3EGuI3c4QGV45Q7pjokMmzVqlUleZHyXr4g4e4AdXV1UVBgjdzR6k1T7siYO9XVulvWlClTAMBI7sQZQLxcruQOJ9A1wo8bYUmVO+3t7fjLX/4SKSYpTVq0x5E7lJ7cLZV50ZQ7pjatKXfkaRtyhyxOucOhuWWR8glYa2SbwBe6/Rlzhy/wp02bho8++qgkDVOfpvmSK3e0uDI1NTUlJ4Zx5Q6NcS7KHRu5U45bFo+VY1PupHHLqq2txeWXX46f/exnAMxzTW+v22lZLuSOrS5M5GVVVRW6u7tVckdT7lBdypg7Wtwhk3JH1rXttCzeX7X2b9qEMvWVpOQOJ1d4mtxupDSTxty5+eabsf/++5eUk4M2NulefvoW/a4pd5K6Ze28887YfffdjeXIWrnDT8viahyTWxa9Zy1+oSR3NOWOzS1L2hY0N8r+xNto1uQO/e3o6Chxy6I5T9YrlYXmOcKoUaMSKXdoA4ZA7dVVucM3QbWN3nLdsghxdRkH7X6NcAUK7Y027ONi7khw+0e+M81uTntaVjnKHZNbluk+0zWmccuTOx4RbOSOzS1LBrnjMJE7fCB3Ue7Qjm+cckeTWCdR7vCBhJM7tJhOotyh8pKiIIlyp6qqqt+UO5K0o/S0/EljkU/elVDuUPuTAy1NtDTZSXJHkntJyJ2amhoceOCB+P73v4+JEydayRh6/9ItrKamxqjcsaW3ePFiTJgwoeQ36ecvlTtx714zoAFggw02KIqdkFa5QyfTtLS0qIYFQSp3uIKlvr4ee++9NwBgt912c1LuSHJHKnco/zKN3t5e3HvvvRg2bBh22223knSTumUlJXekcof6DjfCtHdK7Ywrd/j7+/73v49JkyYVLXxM5I58L8ceeyxuv/32TJU7ktzhSj/A7JblapAQIaERqVtvvXWsWxaf17Jwy+IL17a2thJik9LmYxeAkngrgNloo/FfC6jM3wuREUAxuUPX0HHYlC9aYFAb4SSjbZzOKuaOtDk44sidH/3oR5g6dWpJvr773e9GO7W2cZe7JRIkuVNdXYhtNHfuXIwfPx4zZsyI3hW1P1tdmJQ79fX1JeQOJ+5k3fN3zckdbbzQ+pIWENfVLUt7D6ZNKFdyh7dZV+UOXStt0ThyR8vThx9+WJSGhFTuyL6WJuaO1p9+8IMfRBsOWjls9ku5blkuyh2yRzVbqqqqSrVttYDKmluWrEMTuZNGuRNXF5LcIVddvmCneSpOuUMYNWpUpPgB3I5C5yD7gIgGmptMAbq5XcHtDEJW5I5UsySFdp+mYgTWtjceG0eC2zMc/F2ZCDlg7bt2ccuyxdzhbYXWiCbljjbG8Xxr0OJW2sA3Dz254+FM7nClDl0PFBprnNFPShE+SEnljjbwPProoyWLCknukFxUKncozodrHfBy8o5cW1ubWrlTX1+PzTffvOgYZ1uni1PuyF0JTYrpAr5znZbcqWRAZXmdXABS++SD/Kc//emidEeOHBlL7nDDora2Fpttthn++Mc/Fg3GErlcLlqg1dfXRxMwyeWTKnfa29uxcuXKoiPQCVIKLskHW9n4ffL5w4YNQ0dHR9Qf6V2SEeUac4eUO0Tu8Odpp2Vxcocbcueddx4WLlyIjTfeOLFyh2JbyTalKXcuu+wyPPTQQzj//PNVA6FSMXe4ckcjd7hyRzPkbAGVOfiYSuqWuJg7EyZMwDHHHKP24zjljqkfS7esfD5folDQiFtXMp5iVEk/9XfffRevvPJK5LZhAh/vs465097eXkTK8j5VXV2NN998M7qWH7uehNyRAZWlckce9cqJhQ033DBKj64hxQHQP8odV7esuJg7zzzzDGbNmmXNl6mNJnXLevnll7FixQpcf/31qZU7/FlEdstAzWTvmJQ7pN6jdmLaCZaKHD7n8zy4BFTWymfbhNLgotzhqnDKr6w3TmrS9Uncsqi8tBhzJXd4/fPf5TNtto3pNx6IWMuHCWncsnh8uiTKnfb29li3LFPMHVe3rCzJnTgbU44/9H+prtWUO/QMOQePHDkyVrlje0dkH0hyx4SkblnlxtxxsfU1uCh3qF2QgrStrc1oX9lUg1RG+Uz5vN7eXmtAZdnvy1XuSEWkC7ljU+5o4KfGenLHo4Tc4UaxJHeCIFAbZ5xyR1sgclm5TI9w2GGHYc2aNUXpNTQ0FN1nc8tKE3NHU+5IcidOuUMEUG1tLa677jr84x//KCq3CdXVyWPupHXLMpE72qAg3bL4zkwlAirL60y7+3zRuummmxb9NmrUKJXcMZ12JNufjdyh919fXx+5OaQld+ikLE7uUBuR5E5S5Y7JLYufygCsnXB4DAkX5Y4kd/jztF0HqjdOctTV1aGmpiaKmZJWuSPBjR1KY+bMmdhiiy1wxhlnqAtHTcKvXaPlw3QNzwc3BgCd3NGguWWRMoeDtw9XtyyZFw5O9svYZ/J+DlLukLJLumVp5E5S5Q53nSTssMMOGD9+fOw7NCl3snDL6ujoKCJ8CWTgUX//8pe/XBHlTm1trarcMZE7cnMmK+WOLaAytzPKccvSIJ9pKkNStyxCY2NjCbljy2cS5Y7NLStOuSPHHNm3+ZzPvzcpd/i12nu07Z5rcFHu8PGa+qgk3PkYZ3PLorLIMZ3qkdxukip36Nm2mDsmxP1G4yQPsp21Wxa3I12UO7TZqB1OUV1dHYUe4PmxHYXu4pb1H//xHyV5JlTKLYv+X1VVHP9E23QwjWujR482npaVRrnDbUmt3PQc6u+aepo/z/ZsmwpREh1J4aLcob686aabAijYECb7lhOKMk0TuSHz3tPTU7Zyh9o/4K7cMZHxGrTQBjZwcsd1jTXUsX6UMiUkucMnE67QIOPEhdzRlDsauRPnlqWlXwm3LL6rxRVM1KGo4w4bNqxkF1oDSaBpMcYniyzJnXKUO/y9k9FhWmBJYxFYu3CopHJH+ilLAo8bmHKBPHLkSKxZs6bEIOETZi6Xw8iRI/GDH/ygJOaIKY/cLauhoSEiOMgISOqWRbFvuFsWLRY0cidtzB1eHsoj7VbI+CNSuWNSCEm3LL7IiVPucLcsjrTKHYnq6lK3LHpeEARqn9QWVRLluGVJ5Q5dpymPOLbZZhtstdVW2HHHHVW3LP4sqexpamoqMobSkDvkwhBH4hOWLVtW5I7q4pblotzh46N2uoStLBxJlTtx+ZJGojyiHCjEBNpggw1w/vnnY5tttsHee++Njz/+uCSNcpU7o0ePjlyviLzlCiKTcoeQlXLHFlBZLqgAfRErF9QuSKrcMbVpE7nT0NCQyi1r4403xsYbb4wjjzwSY8eOjcjurMgdOdZr5E4ulyshMyW5o9VXpZU7clebbDDXmDs25Y4kh3hZ2trajLYcJ5d4frk6xRRzh3DQQQfhyCOPLCmnhqqqQuD9e+65B7vuums0fmet3OHXuRyFTuROW1tbiRqSyCxSaPINIRe3LLneoHfz85//HK2trVG6/eGWRf+XGzB8jIgjaJLG3NEUSiZyJ45Arq6uLnk/SQIqk/2rjbWURlpyR2v3sg7JNiQ7eOnSpap9SyfGmjwMXGLuAMXKnaampqjOpV2iKXd4e5DKHS1ffLO00sod1/a/rsCTOxZIcodPWDygMjVqGVCZfuOQnzV/bz5JA/aBh/+mBVSupFsWV+4MGzbMWbnDg/GZyiJRXZ0soHIWyh0+WJkWWDZyJ01AZdcdJvl/m3JHpjty5EiEYVikugKKWf9cLoc99tgDf/jDH5zzKJU7RO6Uq9zh5M6ECRNQVVUV7WLwATstuaMpd2hC08idJMqdlStXlih3bKdlcemzaTHmotwhtwZtocH7hlQ28LLyvEqVnoY05I5JucOfbzP8N954Y3zwwQfYaaedisib2traIlKSj6ncLYurXJKSO2+99Ra22WYbAKXvwpTn9vZ2jB07VjX2Af20LBelZVVVVaSUK4fc+fvf/46TTz4ZgNuJdnHjlc0ti3DNNdfgvvvuwwUXXID33nsPY8aMUduPyZ2Mxn9OAFA5ef7+9a9/4Te/+Q2AYrcsOiGpv5Q7+Xze6FagkTtaXWgnEMahXHJHU+7wsayxsbFE6WibAymdm266Cffccw/++te/YsWKFRFB43paFicXqB0ApaeXUhnkhhj1QT5WchKf8qrVl1a+SgRUlm5ZlYi5I8mduIDKJuWOLeYO4Wtf+xr23HPPot9NIHKHFAs0fmet3OFl4HZ5XMwdHndSPo/IZK7ckQG/pdqKypbL5bDzzjvjrLPOiuonCAKMGDGiaNOREDfuuCoXKFQE35Tq6elR3bKoXCalJMFG7mjE0KRJk3DeeecVXdPQ0ODslsXzqJVXbhrZ1kN0WqJNuVMJtyx6x2Qbks27ZMkSdU6gtDT1TlrlTlNTU9luWdQ/tDrKKuZO3HqWk16e3PGIVe5wcqempkZtnC4xd8pR7thi7nC3rCyUO5pbFpWZyB2XmDsmcidOlmnbEZMdl6sTxo8f7yxj52QWl+zbyB25i1tp5Q6vJxO5Iyc/qdwBSk8kkOSOaXfaRu7wmDukXqEdQp4nF+WO5pb1+c9/HkuXLo0W1WndskwErFTuaG5ZfJHnGlC5v5U7S5YsAVB8UhJPR5NHx5E7mhHLoY1Tp556Kh577DH1Gqnc4eQt5S+O3OF9jNpUc3MzRowYgZaWFuy1114AzG5ZQRBERKjcldSeQcjlctFJZpQ+hy3PY8aMKWpDLqdlxRkkVVVVkRFqkmabysLxyCOP4J577gEA1U1OIgm5w5VkvN2NGDEiIqaAtca0K4ggJbcsvsjlz9l6660jAoe7ZZHSq7+UO4B5ccLry6bcoX6ZhNxJ4l6r2QgauZOFW1ZjY2OJ0pTIHcqDi3KH1MBJlTuUPrcvtJg7Wn1p77oSblmcjOduUXKTRCp3kpyWxcuyZs0aI7lDG5tykRen3JEbjPKzCVVVVQjDMJqTaYFfCXKHtzf+mZ7NyR2aP2zkDilstJg7UgGluWV98YtfxMUXX1ySvowDBmTrlkXvj5M7cgOGrytk3CVgrZ1ZW1uLxsbGRMqdYcOGRcQYXWNS7tg2lE2KuyTKHZqb+tsti89DgJtyB9Dn/6TKHWqLXLkj8yXJHankcnXLkupEF7csr9yJhyd3LHAld4gE0HaK0ih3pFy2XHJHDnAunYEgyykDKhNclTukjKmUcocviGgAfPzxx/GrX/3Kmi/CrFmz8Ic//CF6pkkZQpBGClAaWNsGV3LHNOBxg1vbhdt8881L7iFFA+1YEyS5Y8qL6XvuliWVO8cccwwOOeSQ6FoX5Q6RT5QOPXvcuHGqJJzXkc3wA7JT7pgUKiNHjkRVVVV0FHramDscScgdCqyrkTv8Pv5/qj9uwJVL7nzuc5/DRhttVPIMKg9X7nClDZUjzi2LP3Ps2LERYURtmZ+QoCl3gIIBI409bjRrC9R8Ph8RaLJcVB4TxowZU9SGpEJByp9NYw9HEAQRKVKOcofnhW8ypFXuyIWrRu5IcKLHBTT+xx2Fzj9Tf6uuro7aQ38pdwBzQGRXtywqn+sOtpavcpU71dXVJWNFErcsU9vhbllElGjkDv2VblkyoHJczB2g0B64fTFY3LK0XW1O7nAiSSN3kgRUdiV34gIqu8Tc0WxSE2gzgjaOaG42zb1AercsKgN3LweKg8Jym2PYsGFYvXp1STom5Y7mlkXElzwRjX7TUGlyR8ad1NyyNHKH1zPN5xQLNIlyR57WR5temnLHtqFs6rdZKXcoDdf2Zbqfg7+nqqqqqC1+6lOfQlVVFZYsWWI8CRCIV+6YlI8ErtwZMWJEpJiV5I4kJaVyh9qO61HoJqWlBtl+4tq+J3c8isB3fjo6OnDAAQdEv2kxdzS3LGnEuSh3qqurI/9JIL1bFu0Kl6vc4Tt5vKPzNLJQ7sSRO2mPQnfZ+eY455xzovzw9LQ0tMkja+XOEUccgXfffTda9PBr+IAr5eavvvoq3njjjaLrALNyh2SfgH13WgtYS/dwo4jUK9XV1bjkkkuKggFq5Iqc0LXA1HJiMSl34mAid5LG3DGRSDS50bGVmsKEruPKHS59til3NMUYUKizPffcEzfffDOAeHKn0sod2T9syh1O7tA4E6fckSpJWqBLNY5JuUPXNjQ0lCw+CCbljo3csY03REJROlkpd7Jwy+LjN18s9ie5k0a5Q+SOLaAyzy9X7hAkucOvB/pfudPS0oK3337bGlC5HOVOueSOVO5wlxOX07JMz+fKHZrzNXKHylNOzB2+eJXKnYEidzTljrQtaZzgCzk+xsUFVCYXNv4s7vZoI3e4Wxigu2W5kDuaAlmDJHdobq6kckeSO0QqcHKnurraSO5QeWTMHR5QmdyyqC74piDVoale0rhluZI7XK1vc8uidsSfHUfu0DvTlDuSSJdjb5LTskzpEOShAy7zkTYHxq0P4qDdQ/N4EAQIgiBqFw0NDRg/fnwqciepcoeTOzKvktAzBVSmNsFVbxL95Za1PpI76ejG9QRcuQMAr732WvSbdMviyh0bueOq3KHn2o5NlelJ5Q5QkMJLw8SV3Kmuri4iY6Sx09/kTpKYO2QQAKWkgSu4GsQ0SdjInaxOy6qrq8M222yjTuimBWBdXR0+97nPqenSgC3JnfPOOw+HHHIITj75ZOsChi9qOTi5U1dXFylutDbB0+YTQhAEUX+wMfl8gUGfk+yemCYQWiyREZHmtCy+o0yLFE0NQ9dy5Q7f8dCCCgJryR2traxatQpvv/02pkyZAgCRauaqq67CTjvtVFJezYVUW/xIV1ENJnKHt0tbzB1NuZOE3AEKMXgWL14ckTv03igfVVVVKrlDwaR53ggmcmfp0qXRZ9NCWMO4ceOK2lAcuaO1bT6+0ecslDtceelC7lC+ampqVINTLhC1gMoS5Sh3wjBEdbUeUJl/diV3KqXccYm588gjj+CRRx4pck8g7L777pg5cyY233xzvP3227HP0/JlKgONLy5uWbx++aIgSUBlLZ80blI/pl1/bofU1NQUBaeWblmmxYKmcAVQotxJ65ZViZg7sizkMsiVO3yMThNzh8gToGAbmMgTGfNNc8sybVgSaOHKP5tAYx3VUxq3LNt4c80110QbUUmUOzZyh+qVyB3KqxzvOZEqlTucQJMoR7kTB025QySUSbmjzRPUnzTljkbsye9syh0+xx1zzDFYvnw5Fi1ahOnTpxeVhatJOKRyx4XcsSl3qC5sajINWt5IbU9uqdQWa2trsdFGG2HRokVqWja3LG5DyDYVp9zhafA8SxdGqdyh32lcMSl3iFSutFuWa8ypdQXrRylTQpI7HBq5o8mA45Q7moTVNmBKyJ13mdeOjo4SVtmlM1B6fJKRxk7Wblk2AiaNcof/npZV5+m5umVlrdzRdvLl9fIdy2e7KHdGjRoV+fXaFjDcvYqDdtfIMCaDST7HVD45EVdSuVNTUxO9O96uadHlotw59thj1ZOZ+M4GnbRhOlY6jXKHDC3NIJYLbFqwnnrqqfh//+//FeWP6oGg1R9fjF966aWRqk1DGuUOTexSuZPGLQtAdGy85pYFFOqeCAB6D6Tc0foV3QMUv4845Y4tzxtttFFi5Y5Mf5999sETTzwRlZeTO+XE3DEpd+Ji7pj6niR3iDAfKOWO3JnnfYHI6IFU7mgEmfbdFltsgZdffjl6/y7I2i1LKnf4oiBJQGUtn+SWRW2Zn25mmgNIjZIm5g4QT+5kodxZvXo17rvvvqLvsyJ3eH+Nc8v6+c9/ju9///tF/YPnw6TO5XlJqtyxKaFs4yUdhe4ac6eurq7IFZjnVcORRx6Jo48+GkDp+EDtuFy3LHL95ModYG0sm6qq/nPLioNG7gBQyR2bckeSO2EYoqurq8TO02LuyA1Zm3Jn/PjxeOSRR/DpT3+6pCymfpsk5o5LQGXTc0yQdgkHkTuULid3Ntxww+gUWYlKKHfk+MKfExdzh363kTtcuWNyo+Wg9ijHyK233hoTJkwwjr3ro3LHkzsWmMgdWpRogXeB4oYnjThNuaMFVObPdVXuyOOEgULH0gZTl4GeYg1pp1RIsqM/lDt77703Dj744JLBiy9+5ABE/0+r3Ikjd2zKnf4kd2QZTcQAsHbSlTF3pMuRaQGzzTbbqIQNkXv0bFosyeeYyifVYbYYVhq5k0S5Q21Gtgup3LGRO6+88opVHst3oDVXJ7pWKljiYu7w+yS44Th27Fh1cZWG3KmtLZw+ddZZZ5VcQzDtbJmeJ8uThXJnk002AaC7ZfHrm5qaovatuWVpRjM3dCS5E6fc4elttNFGRuUOEXc8TUnc0ndf+9rXou+DIMjELSupcieO3NFcTuLInaTKHTr9kZ+oRPlOotzR5o6BVO4QtA0TbRNDA2938h2Z7jWRO/KZNnLHdOIfh6kOuVsW9TkajzVyJwu3LAAlblk8jyYFgNY/bOROU1NTif1C+ZXloucCOrlD9c8Xx5zc+eCDDyJChKO6uhonnngivvWtbxU9i7czOm5cgyR3XGLuaJuDmnpDAyl3ZMwdjdxpbGzEBx98gG9961tF39vGG23zweaWxW3NYcOGqe7KlCYpd/gJXzJoOo3v2nH3pnopxy0rDjygLu8f2mlZsg3Y3LKAQj26kDtyQ8Om3LHNQSY7yTXmTn19Pb75zW/iRz/6kTpemjZX46BtJBA4ucPdski5QweNSMQpd5LE3KH3n8Qti9cBr3dOzEtoMXeSkjtVVYXTc+fNm4cddtih5Bl0nyd3PCKYyJ2amho15g7BVbkTBEGsWxb/rEH+pil35ABnIndkR+K+v65uWV1dXdaJOm1A5aqqKkyYMAEPPfRQyc4uZ4nlwoi+S6vc4QOCaXCX5dViL5ngQu5IdxnNKJJltBEDNOlKgobULEBhhynJohpYS+5Qm3dV7nCChj9Tcw/Qdo/pbxLlDvUB+U5lQGWacKhMUsquge9suLhlVVevVf1xgtZG0FVVmWPuELR4OzIdXrda/5NuNLY+mka5Q+ABgYHylTsmcof6BTeADjjgAHzta18zumWNHj0aVVVVRYG9k8bc4Z833HDDIqJAEiAuyh1J9FZVZeOWxeNuJCF3TOmmIXdM7rcayL2DAunKgMqm98Kl5BJyE4M/C+i/mDsEjdxx6Y9A8XtLotxJc1oWJ3eoDWrGOMFFuSPJHT5HaModLaByWuUOL7PJhtCUbXExd2S9cncEjQTh18iYO0EQRHXNF3Dvv/8+Jk6ciA8//NCYD5kX7paVRLmjuWXFxdxJelqWK7kTBAEmTJhQ0rZtc7YLuWNyyxo+fLiTcofyHoZhERlENo603WTQaq2c/C/dZ0PWyh0+L7nE3AEK9SjtPBebnYglmp/4+Gmbg0x2e0NDQ9H3pnoeN24c9tlnH/zud79T26htA8YGLX4mQZI7UrljCotAaSVV7sixmSt3+Jjootzh4yVturgqd2xuWVQm2d74NVrZCJ7c8SiCidyprq5W3bIIpuCpdC2hvr7eGFCZp+Oq3NHyqrllUaeTkAZnfX19kVuW7ERSoUQLFdNRoEB5yh3t//j/7H15mB1F1f7bd5t9zcxkX8nGkhAghEAIhB3ZRXZBRAUEFERERHHFDfXDHRARFTdAP7cfioqAgIALn8qi7FsCIRIIhOyZmdu/P6pP9+m6VdXVffvO3Jn0+zzzzMy9vVRXV5069dZ7TkFtuOTBo1rlju4acuw4IBwDmQzTwYbc4avz8jG6wUUmBvh3urAsrkrh91NBpfhIqtyZP3++Xx5+f1UycJ1jn1S5I9e3LqEyfS7XkQq8/9J1dNtKU7viA6Ru1Vt+76py8JV/WZoulw+IF5YlnytDdb4sr9aRO7JyJ0lCZaCS3CGbZiJ3LrroInzzm99UOpwAcPLJJ+P+++/3Q9yAypw7Mkz92qTc4WU1TSxV5E5c5Y5NO6b6TRqWlYTcKRQK2gmyDO5oqrZCl+tNDrsoFAp4+umn8fDDD1dcUz6fE3I25I7pmDjkji60hspvgql/m8gdU5vjYw4fZ4jcURHUpnwVMlQ5d2yUO9Xm3DEpd3QKABV5pXu3unfGt93WkZG6sCx+P97uf/zjHyvLwK/J/87lciFiNwm5Q+frtkKvZrcs13WtEirrrmNL7sj2wZRQ2SYsi5Q7hMHBwZAqSrWbbZywLB1RyaFS1ZgQJyxLHldVYVkNDQ2xlTu63bIIKnInjnLHJizr/e9/P+66666Kz+XrUxmSKHdU5/Aca7lcrkK5E1WWKOWObkwkcJKeq2hln4PO0yl36JionDt0rCks67777sNll13mP1tG7kQjI3cMoAFKniiTckdH7piUOypyJ0q5Y5tzh59DUIVlqVaCVWVNEpa1ZcuWxOSOqdPFJXdMqwA6XHrppbj++uv9/7mzpSMPVIPHli1brEgAoHICoCqnarLH70+/dU6rfI6O3OHKHVXZdOXksfabN2/2+wspd1TOD8e+++6rLCc5sComX0XuxFHuUB+Q6zvOVuima9OxtOKXhnJHdo6jcu7olDs6csdkE7h6TNeuq1XuxAnLmjhxIi688EIsXrw49DmFZck5d+TVJpUDpFPuNDY2YtGiRaGyb9y4MURaypOhKHKHEwXyhF7u5yrbIx/DlU+2OXei+gspIUzHRil3dDl3ovqQbWiWTO7YKne4VHzGjBnYaaedKq4pn8+VOybbrsrRJCNOWJYKqnFOhaTKHT6e6FSxOuUOX401tR/d/XlYFrVlmtDXMiyrqanJ/86W3FGRn7r3LpebwMkdXeicTrkDhNWJVI6//vWvyjLwa/Iyye3IhtyR65/8WFVCZRWZoyqHCrlcTplzR0V66q5TbVgWqW1KpVLI5+DkDn93ZCvkZOjr1q3DwMCA7xtRzh3Zn7INy7JR7tjMIzh05I4qLMuk3JFz7gBqckfVL+Q6kVNOcBtjGoN0/dYmLKurqwszZ86s+JwjavFXByJ3dOoz/jdP8yCTharzdL5NXOVOPp+PvVuWbE+4ckf1frgvYArLmj9/Pi6//HIlkWdD7pRKpYzcyRAgKiyLvlcZZ4IpLCtKuWOzs4htWFYS5Q6P/ZUn/nwFg56TdjQwrbrSSnXa5I7ccU2OhA5NTU2hpGyctEtC7pjIEX6+6X+6N/8uCbljUu7wlR3b1TS+skL1Iit3bCdou+66q/Ke5MCawrJqlXOHHElVWFYUaccnCOQUcjsgS5vz+bxvA/gKlWlCpHNaqgnLslHuAPp+qitvEuVOVFhWa2srrrzyygqVii4sS560xSF3CLwOaMeKiRMnAkBFYm1Tv+7r64ut3Ek7LMtmB0E+WdRNDKLCg+TdHokcjeqrtkmV6Z3ZKnfo/yRhWfR5uVy2Uu6YJlOyX0DQbT8tw1a5YyJ3dO9fDhWR+5ANuWOzQGVyxCksq6mpCfl8XknuyLleiBzZunUrXNeNHZZVLBZDCk0grJBQlVcVlhVF7sh1YUPuyKHeUcodE2RfUDUOJkmoTP4wqU74s8jKnVqFZenGZlvlDh0n73hK4zjfdYfIHfqOk6D0PDSJp/GIxgm+i5a8OMz97ijljg25E1e5o9oKnZ4rKucOf+cqckeXJoKux+9lIndUNl7us3IZOWyUOzaLs3x+kITciVr4lMOyTHMrKq/s2+hIeoJOuVMqlULXUqly6Hj6Xj7GFJZFcySZ3FH5+/L/OuWOro3L/XZbQEbuGKAjd4rFou880KCVVLmjSj5Xa+WOLueOvEuPPMjInYiXs1AoWCl3APgZ82XYKkXiKndsJjJ0HCe4uNOjM96q3bIo2aHN4BCH3FFJcbnDLbct3XWbm5uRz+d95QEnE2wdLqBSPbPffvvhzjvv9K/H1S4m6Cb8ppw7CxYswGmnnYY99tgDQHrKnWJRJEw0KXei6oU7vTbKHbnOe3t7MWbMGOV9+GQ+KucOV0Spyif/bUvu6AZQG+WOjiSSCeco5Y7uHejIHXqPpOzhIQiqa6quz8tHO1ZQSKGcVFNlowg9PT3+/2+88UYFMSQ7SCrbQ99xmxAnLMuG8OYOdFRYlm4L2P7+/lDb7+3tjQzLAqpX7ujy1cmTtyinXqXckY+RoQtFsgndiavciUPuxA3LomtTWeOQO9Uod4jcoW3OS6VSrITKQFh9YKvcock6P4c/s6q8NEnjE8uovpJ2WBZX7owZMwYdHR047bTTsMMOOxjLQZDHwcbGRj8BsAq6sCyyAbpJWjVhWdWSO7bKHfKNZeXOunXr/LYnkzv8/dHn/L4PPfQQfv7znwMIyB1S7tD9ZRWJastwjjhhWVEEvAyeUFnuO7LvIvuA/B485w4Pb5P7kmxX6Pnk9iP3VfpftjV8bNctgtkod2zmDKYx2gSyG7p8lORX5HK5ULiSKR+dTrkjk8oqn5eDlDtR5I5JucNtNBHz8n1kG2uTUDmucoer4FWL46MZ28ZTJoSNckdFJpicONmxqHa3LBvljmx4VCvB/FwqMyV2I4dZNoZ0jYaGBuTzeT/nDrHLunLrlDsmmDqwSnJoWgXQQSZ3+Aqzrs5Ug0fayh1eBvkYbnBNyh35nJaWFn9g4c6x7QSGl4u/5zVr1oQc/p/+9Kd46KGHjNcBgPvvvx8/+MEPQvc05dxpa2vDDTfc4K+A2UwYOXTkDiDav5xzZ8GCBZg5cyYmTJiAVatWRV4bCIdlmbZCl5/vggsu0MrqeRtQvR8aIH/0ox/h6KOPVl5Ddj5VfxPSJHfk1VuTc89361A9p65tTpw4EWeddRYOPfRQAEG983o5//zzccEFFxivqbq+3M4B4MILL8R73vOeii3idU4J/U3/f/CDH8Q555yjPJf3bZ0ykf+mEACe+FkGX+1MQ7lD19CRErKt33fffa36qq1yRyZ3SGUqO/4Euq9qK3T5mvL3tsS3jhDT5YzgsFXu0ASmGuWOyb7TmMpDMVTkDn8mPimoRrnT3NyMTZs2+dfSkTvz5s3D0qVL/ZC6YrHo3492TpPvLdtNXieFQkFL7sjkxD777IMHHnjAvzcfQ2sZlkXnqsidQkHkqlqzZg1uuOEGrR1Q2Sb+2fjx4xPl3CF/+cQTT6x4ThW5o+tbMhzHCZE7aefckfsyJ3eon65fv75CUcvbC/2vute8efN8okNH7sjjA58wm8pcC+WOKSyLkyx8DKkmLCupckdH7nDo/CSasxDiRhLIx+juo0MUufOXv/wFr7zySkW0RDXKHV37VCl3+vv7USqVlLuSyf2+v7/fzz0aV7nDr2ezFbrKhprmhlywoFMpjlZk5I4BfKtzjjjkjkm5UyqVlDl3TAYzKkeLLixLNqYqZ0teqTMpd3hYFhlKSnpHq+ZxSKko2Cp3VASILbmTy+UqBuso5Y6J3LEx9jbkjmolXz5efkZTQmWZ3KlWuSO/S37v4447DnPnzlWeP2/ePN/JWbx4MU499VQluWMj0zQpd1TPQQOR6p02NzdX7Ja155574sknn0R7e3tkvVCZdGFZqpw7vFytra3YbrvtjNeOIndM7T2Jcod/F5fcUa220HcEuU5poNeFZen6Vi6Xw7e+9S0sWLDAPx8IJlCTJk3CV7/6VZxwwgkV58YJy+K7SXz9618POeuq8236OUFF4MhjgmznHMfBuHHj8Kc//QknnXSS9to8LCvKPsUhd3SkBKkYCUuXLq2JcodyrchboQ+HckdH7vD/q1Xu2PRzIFx+E7kj979cLoczzjgDP/jBDyp8HP47SrlDNjZO2APZ3/7+fjQ0NITIHe6HTJgwAXfffbcffsqVO3w7XxOZHpfc4ePNbrvtVtEv+aRT97xJyB3bsCx+H90qv3wPecLT3d1t3ApdVqrQ9ajt0uKHHJYltzdbXyOXG7qwLCq3KueObAvJjyLIi3Ac9Hwqcof7IfS3KWyU36PW5I4cQs4/iyJ3bLdC1/m18v+2YVkcJnJHZ+flcpnAyx6HNKDxTVb8EpqamirU27JyR+dP6VKB6JQ78rMPDAxg48aNaGxstFLu0JirOqZQKFTYC4L8/uIod3gbMpE7cv7KjNzJACAgd2ilgEBEhqpRA9Xn3DGteskNU0fu0H0pLEueQKqMmY7cUSVUVil3AFFXUcodfi9bRJE7MittGih0UIVl8euqyqyaJMVR7qicahm61Xr+t/yObZQ7PPQFSEe5w68XhYceegivvPKKtpwqckf17ulzXZvSDdw2yh3VwPT2t78dP/nJT7R5M7izYZtQWX4eHXQryQR6p7bOLHd+ah2WZSJ35OfmK4cmZzkKVO82ioiotq96Pp1DHUXm2JBvqratI7Hp97777qslDnh5TTux8HLYkjuLFy/G7NmzK76XyZ3Ozs5hVe7YkDt8B0Rd36xWudPY2BiZEN0EOi6tsCzVeDF9+nS89a1v1S4u2IRl5fMidEUVqq2zdS0tLb4yyaTcUZVHpdyxzblDZeU2WQ5/0d2bT6Dj5NyhHEF0rvw+6RxVQmU6lk+2OXgOM8IVV1yB973vfaHP5HGwtbU1Uc4d2Y+VJ2lJlTtE7sgJlWsRlkXHqsKy5IUOai/yPXSLgEBlzh36Tvbjosjb4dotCwjn4KJrkr3m5IMtuUPPIi/iyXOWJMod20XZapU7cckdk7qWQy4jV+7I/ifVo25hV5dzR05b0d/fj1WrVmHcuHFWOXcofQL/TkW46cKyZHLHZjGXtw8TuSOnGsnIHQUcx8k7jvNPx3FuqWWB6glE7sjsKm/UKoOetnKHX0+Xn4fOoUZMzpSKmIlS7vDVK1NCZe5c8A6aBrljUiipJvgyKy1PHG3JHVmGaDLeKmksIAi1Wih3TDl35GfUrQDS39zZJsmibtDVoVpyRwV5kOEOBJBMuaMb3HWGnit3Vq1ahWKxGBqM8/k8TjrpJO0kmqsJVOSOaSU5qs55GzC1LxvyQL5OrcKy+ORL/o4gP3fSsCwZsnLHhDg5dwhR+TUINv1c/o7/tiV3osCTn8Yhd6LyOXR0dODxxx/H9ttvH/qeyJ0lS5bgQx/6kH/dKPt/+umn46KLLop8HpnckZU7Kpudy+WMYVn8ukmUO+3t7Tj++OMrcl5xu6hr13GVO9WEZcVZDOCfmcgdkvNzv+S3v/0tzj///Iqy6eqej8Emckcm4Lia+KMf/SiefPLJimczkTuFQgFNTU3KZJ1Rv3l7s825Q0odsk0HHXSQMpQ2l8tVhCzwvIWycoegIneOPPJIzJs3r6JcMrlDKhkV5HwwclgWQV6UMSVUjrKHfCv0JLtl2S52AHrlDvXfpUuX4oQTTsD06dOtyR2TckeeqHJyR/c8QxWWxa9J91Ipd3bccUfcc889OOCAA/zjaR6QRLkjEw65XK5i56ZqlDv8vrpzbeYMOv87CrbkDrdxjuOEyDNdXk2dLdcpd/h3gHj/K1euxPjx45Xkjqq+5e9U46cuLIuOjROWxdueiaSTlTv7778/Fi5cWPH8oxFx5BMXAHgUgD6j0yhDFLnDt6PkE3DeeaKUOxs2bIhU7vAGKycClY+l321tbb4qQjZwOoNXbVgWIQ1yJ5/PhxzduOSOPDDYGmpTQmXd5FSuS5rQDFfOHRWxJJ/DjTZ3xqMmuKpyyfWiU7TYgJeTJmqmFT5u7Guh3Fm+fDkmTZqkfC9Rqg2acNI1CaaEylGTdN4GbBOQm77j96+Fcoc78UmUO42Njb4zL1/XBlTvNuRO1HtQPbetciefz+Of//yn79TbvB+VI6Mjd2zrg6+yR7U1HiYSRZyqHC0gSKj85z//2f+sp6enIoxNxoIFCzBhwgT8z//8j/E4FbnD1RO6FdkohVsul6sgwGyJ73w+j5tvvhnf/e53cd111/mfq8gdeYIapTBraWnBhg0bQnlWTLBRPQLJyZ1p06ahqakJW7duRX9/P1zXRbFYDLWLvffeW7krjK79yWNTlHKHTyToOa699lr/GqacO/ydUFiWidyR27u8EMcVBYR8Ph/a1lpH7px99tnYZZddKuqDT/bpvXPlnY7cUYVlqdptoSDSDHzhC1/Aww8/bGUr+f14EmsO/s7l8Up+D7UOy4qj3NGRO5SMf9q0abjpppsAhIlInTKCf/bqq6+iUCiE3g333cjPjgrLUpE7ukW1uOSOabcsIEzucN907733xi9+8Qv/eKobebeszs5O5buXba1MrI8dO9b/n/czuR1y8HbmOE5FsmV+PRk2cwbT4q8JcZU7VBc2yh0diW8id3hd9/f3Y+XKldhrr71Ctlhlb3XfqXw+nXKHjo0TlsXbh+k9trW14aMf/SiOOeYYAMCvfvWrimcfrbBa7nMcZxKAwwFcF3XsaAIZWHlyYSJ35E5uE5YVtVuWvOrBIXdcOocrM+Qy6a6VRlgWv3c15I6uU8t/07FpKXd0YVnyYMPvIZe11jl3dKseUYMc/5sbbb5NYByioRbKHX5P6l+cINDVlynUIy65w5U7zz//PKZMmaK8btTEnn8ftRU6wTafj+y0/PjHP/YHMMB+pZJfp9bKnTg5dyh0gE/mdOeaUE9hWQsWLMDkyZO11yeoCJMZM2agvb3dJ0V0zlQU5DAUE/jqaBS5oyuHKnn+zTffjK985SuRZY2zcsrDslSruqrrmvIOqfpEnJBV1TFpKHeuuuoqXH/99Vi2bBmA6DoyjcHyeK77zkTuTJ06FRs3bsSCBQtCYVkNDQ0YM2YMJk6cWHE9+boyVOSOait0eVLIyR0Ok1JStVuWitzRtXPZthUKBfT29uKSSy7BYYcdpjyXkzuFQpCTQtem8vkgB4u84QOgX0xRKXd05E4+n8fFF1+MG264IXK3U/n+RxxxBAChtuPgyWJVag3Tog0HkaDDkVBZF05NUJE7qntx5U5XV1dF/5L9OFtyR0XQqnIq8d9R4LtlRZE7JnKfjpPJHVVbkJ9V1T6iyB2d+kZ1ffl/nY8YBZX/bQPbPibbOG6bdMod3ee2yp0NGzbglVdewYQJE6zCsvh3pmN0yh0ah2sRlpXP5/GpT30KO+64Y8Uzj3bYeYTAVwB8EEClNfXgOM5ZjuM84DjOA6tXr06jbMOOlStXolQq+TsiEEzkjmyU5NANHs/f1taG9evXVwxSqtWgqBUJmdyRJYymidT06dND1+JhWZQ4Wp5kmcgdWpUwDSY2yh0OUwdWsbjyhFm3sswhO/vy4CWXWTXAAtXtlmWajJjIHd7uVE6ITI7J5E41yp1akTs8p5WKpef/5/PxwrIo75SqPXByZ/ny5bHJHZWzUcucO/l8HieffHIFoauDfD+6Di/vJz/5SQBq9aDcD2bOnIkzzzwT+++/v7K8qlUc/iz871//+tf4wAc+gCuvvBKDg4PaCfhIz7ljQ+7wtn3QQQdh7dq1fm4DeYKblNwx2WFOkkS1dZ0DLefcAYCxY8daJUxOQu5EbYXOr2uzmq/rm3FsOx8nCTpyJ6qddnZ24owzzlDmqVDBluSV3y9/Pt3YKjvunNwpFAp47rnn/Al/HIJWDssqFouxlTscccKyZHJH7l+69i4vxH3+85/HnDlzQvUURe6YFvBk5Q6/N/mYciiVSrmjqnN5kcM23xU9x3bbbQfXdbFo0aLQ97NmzQrdt5qwrC1btvh+ctrKHbneVcodQG0Dk+Tc6e7u1hJd5HNWE5alm0SbiF4O025ZgDrnjso3bWtrw1577YXddtutgkRV2VKdz07/y+SOTODrFjV1vr+J3JbLo4NuUT8KtuO1yc7bhmXJ/qiqnPz6K1asACAS1pt2y+LvUffbRrlDx6vCsnSEHK/vKHJnW0VkC3Mc5wgAL7uu+3+m41zXvdZ13YWu6y6kLVlHOlasWIFJkybh05/+NH7961/7kyfqCNxxVTV4QL2qQseOHz8eq1evrkq5owvL4hO9qAnkgw8+iJUrV1Y4C0nDslpbW5HL5YyDSVynVCYnOPi9VB1eHjx1kOuFq0FUxls1oAHxlDtxEiqbknzyZ4xS7qjInWqUO/K7VMXC20JHdtmQO2mGZf31r3/FvHnzsGLFCkydOlV53TjKHd1W6ConxgT+vuXJgmmQ45DbDznJvL9+7GMfg+u6yncs12dDQwOuvfZaJQnG26VO+QYEz33kkUfii1/8Ysj5V9WJrXOUNOeObhIkw5ZQtCFx5WOjyFzdMSbI5I6uDcvfJ1XuqMgdW9icx/ua67rYsmVLKCzL5MjakDtpKHeoDrkzXiqVlOONjtzRkWxRdaSaAMvlU11XZUtkQoPXrUzuAIEfAMTrw3HDsvjkpVpy59BDD8Vxxx1XUUbdPeUxV7XqLB/LVT48dFdXH3yyr5ps8c0zOGyVO7JvYxsyYvLR1q9fjwsuuCD0nSksK4rc4er5jRs3VqQniLqOzt7q7HwS5Y7OR+GflcvlCuWOTEDkcoGKQVduk3JHtglJlDu2YVly2+bvOJ/P495778Whhx5qJHd0RLr8fxLlDm9nJl/btAhggvzebGG7OKUahwhye6Rrzpw5E0Bl2JMpbJDX3fLlywEgUrmjmlfJx9god+g8m7As1b1N7zEjd8xYAuAox3GeA3AjgP0dx/lhTUtVJ1i+fDkmT56MxsZGHHnkkRUhCjbKHRMjPH78eLzxxhsVYV+q1aCoFYlqlDttbW0YP368MSyrUNAnVJZXIRsbG/H1r38db3vb25Rl5uXWoVpyRzdQmDq7KomziZlPg9wxrSYQTKsjfGDUMff0Pf+bk3/V5tyR27gqv4ItdPVRjXInSVgWADzyyCMAoFXuRO2KogvLqoVyRz7XlsR0HMcn41T1tPPOO2O33XYLTRR0JI2OWFWt4qiOsykvL7cNkubcqTahclS/Nj2viTDRTTbj5tyhc6sld0y2CbDblct0/yjIbYt2h7RR7tj0EV3ftKlveRyPUu7k83ltWFYtyB3+bPvss09IeaGyJfJvmfAdGBgIkTscqvauq38TucPHOduwLNucO/l8Hqeeeiq++tWvVpQ7iuRR5TXR2eckyh2bsCwb5Y4uLIvXCc+HZVqgMxHYLS0tFXZLJndsFzUcx6kgd3TtOg1yR6fcUY33fIdAk3KHP193d7eW6CI/M0nOHZ2NoLrW2Qq57uOQO1H2n2Cj3JGJUbl9yMSmjXJHZS/k5wH0xFAU5Pdmi1wuhxtvvBF33HGH8TjVgi5Bl/Jj2rRp+O9//4tLLrkkdK6pffK65+SOat7F33UU6W2r3OHkjiksSzXPNvm9GbljgOu6l7quO8l13WkATgJwh+u6p9a8ZHWAFStWhCZ2Mrmj2grdZtCixkhM9KpVq0Lfq5Q7dM6pp56KP/7xj3jLW96iPDaNnDtkRBsaGrTKHW7YZeVOY2Mjzj333IpdGVT30qFW5E4ceS6f/OdyuYpJvi4kIu2EyqbJnqrdqZwQ+Rwb5Y4tuSNPnNMkd+T3ZiJ3dO9WZeCpvlTfyYOQjtyJUjPYhmXFqXPVwBqX3JH7BpE7quc54IAD8MADD4S+05E7qtUaW3InKqGmzWcqUL3bqMlqGZYlvw8enitDfp8q262bbEYhqXInKizLRJgkVe4kIXdslDs2Y4GqLcchvuVr5HI5Jbkj96so5Y5um1sdbJU7F198Ma666irld7r2aArLMt3L9Bmg3i2LniMqLCuqj8o2X1bu6Mqos7Uyka8iA03kDk+obCImVIn5ZXKHctEQ4ubcIXDljm5HSH5/gsmXUY1zcch9IndaWlqwadMmbbvW9UtdeK/qszjKnRkzZuBd73pX6Hudv0FQ5dyRFxGThGVNnDgREyZM8MMB+b3b2tpCC7433HADbrnlFv95OWzDsuRym8qbJCwrapIv1/eMGTMqrqMiIFTXrFa5w+vCJvzNcRyceOKJ2G+//SKP05VPp9wBgL6+vgqf2TbnzvPPPw8AfgJxgmmuq7OLJnJHJt3JR1PlPJP/tyV3bP2i0Yht98kjMDg4iBdeeMFPgAlUkjtcuWMK35FBx/T19QGwI3e4ATzggAP8ssidSKXckQdWXYOn+44fPx7t7e0oFovKhMqUT0YXlsVjcnWIcvhNkyMbckd+3jgOPS8jf6+f+MQn8KMf/QgHHnhg6HjZoahVQmUVmaQydknCslTKnajy0zmUP4MSSPJEinGhe++6yWOUY8/LyUHOg6qv/uc//wn9n3ZCZVOCz6g65+87qXJHvh9J3G0n4XHJHSIyhkO5E2fnNtuwLP7+dG3Opl/rYCJudN/VmtyJG5alytkSF6bz5PvKEm+Tcse0gklQEWu27VU+hvq4itxRqV9UqHVYlslHqBW5o6t/eWySCRi5jfHyVBuWpSu3rp3LY25ccqdQKPjvyCbnjrzhA//MJixLZ9d05A4n2lTnqa5N95XbUBphWd3d3di4caOWrDddJ4p0IcRR7gDANddcgz/84Q/+NuCq+/Bnb2tr0/qn9HeShMpjxozBiy++iN133z10bD6fxx133IHzzz/f/+y0007DwQcfrLyH7W5Z3H5FjUM2O4TqFmT58/JnksmdpUuX4q9//Ss++MEPKq+jugYhKbmj8r/jhBPbHqeyK7qcOwTZZ44id8juLl++HMVisWJHS5UN1NlDlS8YpdxRHafzo/g82+T32rzD0YpY5I7run9yXfeIWhWmnrBq1SoMDg4alTt8JxDe0GwmaPl8HpSbSCZ36FrceMkODXUMGvTllb1qlDvnnnsuHn74Yd/xkBMqyxJkHbljmoBFGUCd40DPI38nDzK6gcL0bqLCskqlEk455RTt6jmhv78/cUJl/r9u8q4bGOV2Iz8L/5s70IsXL8bSpUu199eB6nzatGlYtWoVPvvZzwKoDbljo9zp7u7G2WefXZHYUW4vRDZ0d3crk0fSc59wwgkAoM25ExWWxQcp7pSnvVtWXHJH51TbJlzU9c2mpibkcrmKfF9UnuFU7tjAVrnD7bKunm1WkHR5I0yEpjwOqMgUE2Qn20Tu8MlyXHInynG2gc1ETdXXeLlV7ycO0Z+Gciefz4fURIBaucMn8bJdSkru6NoYLx/9rXu+WpE7uverCssimJQ7SXLuyIoCGbqxXjdxUqkNdP2ZLx7x42XoyB26DvmRssrGNixLtmO25I7OxtH5sj1NGpbFyZ0xY8Zg69at2jBbk80wkXfycUTuyHmyVMjnRcJ7OceJ7j6NjY1aYpV8fducOza5rHK5HHbZZZeKJPa6BbMku2VFkTucQOTzGf4MJnJH5ReqbPyiRYsqztONkfyaQ63csfVfVModvsW86lj5HvIYqWvzZHcHBwcxduxYrS+uIrR07YDviCXXpy5xcjVhWfJ7zMidDBWgjOEm5c7WrVuVjTuqQZFxI+XOSy+9FPreFJZFHZg6Bg16fKXyk5/8JE466ST/erJDE0XutLa2YsqUKcjn86GwLPnetVTumBhY1eBlq9yRnQwOVViWapVXHlh1E0EbA24id+QJhonF588YR7mTz+dx4YUX4oYbbgg9k+65OHi5xo4d6xOhxx57rPE8E3T1YUPu5HI5XHPNNdh5550rjlFNXL7xjW/4z81x00034eabb8ZPfvITvPzyy1oHN2rCq1PuyCsTprYtg7/vtJQ7qnKZoHPqOzs7cffdd+OUU07xv+N9xETYxiFddZ+pYAorMN3LlHOHxgETMWLq11GwIXN1v6MQV7lD7TbublkymZwUUaGWKnKHVnVPP/10pew9Drmj6ytxbDu1/SjlTj4f5Nz56U9/invuucf/Lim5YwpHNJE7Kod5qMgdVVgWL4uOaNEpd0w5d6oNyzKtiuvGLlKNympTnU3L5YKdZFTkzmmnnYYvf/nL+PCHPxw6L42cO3HInfXr14fOl8cjeQy27U+c3CHiaMOGDdpjbcurO54rd7gPGLULqMpmqO7T0NCgJbqIJEgSliV/p7q3/LlqXOZhWaqd45KEZQHqxWp+juxjq3xuav9k4+Xz5DKo5k6q49LMuWOzmGHrv6jIHaqDtJU7vK+rdrNUkdRR9pGrFOVnrka5oyN3TPPGbQ3JltS2AVBSKRW5Qw1GlVDZtJpLoIapC8uSJ4d8YJSVOzTIyTvd0DFEQMUhdzihwRMqy450lHIn7kDLoTNUqnNV5I5uoKABbXBwELlcLiRbV91TZUCilDtAELoWBdOAQ/VvmuypSMU4CZVNA6OtcofO6erqwuuvvx4KCYwL3Xu3IXd01+DvnH9P/U/GtGnTMG3aNADBqqgKUWFZqpw7MuknkztJlDuquklC7iQNy+JlXrJkCW699daK6+fzyZU7pLTiKgRbMiOOcofu5bqu8vqykx+H3InjZJiIm7TJnaiEqaeeeirGjh0bUmNx6GT5upW4uOCEh+m+8iTdcRx873vf015TLqMMuW8B8ZU7/D6c3KGxVCYaCoWCP4ltbGwM2Sddm4tqV7YJleWy6MYY+bkItQrLMpE7tmFZJjI9Lrmja+dxwrLoXnGUOwQVuZPP5/G+972v4rw45A6/R1LlDtnavffeG0Cl3Yr6XwdO7tCkMwm5owvPVh3HlTPFYhEDAwOR5I5KgaK6j5xMXSZJbIhx08KizkdSQRWazskdWWXMPyO7xr8z3au1tRUbN27UzkfkvqPqS2PGjMEL/DpWSwAAu0JJREFUL7wAQN3n+PXoPF3ZovwlmzFbnlcAtQ/L0il3onzjqK3QSVFWLpeVPrzKBurIPdU9TeSOjmjT+VEZuRMNuxa2DYKUOzwsS47jt0morAIZ8NbWVjQ0NGhz7nR1daFQKPjhDnR9XhYa5ExyZHnA0BkWui/vkKqEymmQO9Uod2zIHd0EOpfLKdlwwJxQWVV/pgFNXg3TwWbAod+mnDv8HcdJqGwaGOOSO4CIt7cduFSoBbkTZ5UwDpLslqUid+KUjbeB4VLumBQ4vIz8O95HVedFPbfpHnHKGoUoNR4QvHdTfSUlXwC9o6S6nqm8KsgOlYmEzufzGD9+vHHXwyjnDqie3DF9Lo9JpnPk8gylcufzn/98ReLVXE6fc0f+Ts6pJz+LDrZhWbLzbUPu1Eq5k8/nQ2RWnLAslY0xkVi61WO5jLrfkyZNQrFY9BcDVGSNSv1LvoWNcieK3NGhoaEB999/Py6++GLjPeQJPifX4pA7O++8M+6880584QtfqLgXH6/of9tFDU7ukCpIF/pt6pcf+tCH/FBr0/Hy5FKXzFx376i2bgrLkttnFLlTjXIHqHz3juNY75almtybykWhpjK5oyJFdO2D8hrlcjmtckfXzmqZc4f730Ol3JFtne755DFS9VwU0kr3UZE78vV4W9X5AXys5e/8iCOOwLJlyyquLT+ryd+3WdRMy9cfidh2nzwCxx13HH75y1+GktKRMaFOpMq5IxMpKpDBcRwHfX19WnLnlFNOwd///ne0t7dXOPM04MydOxcA8IEPfKDiPuPHj/fvxxt5lBPBO5Drur5CSTYS9ZRQWfUe+PfcwKsGE/pOLmM1YVlJcu6onlNlVOXz+TOanGtSQejInTgTGB3hUg109aEiMPj/UQMCdy7TKm9UqAoffEmKXQvljo7QjLqG/HdS5Y7unfC/5YlMnPJG3SNNmBz0JMqdJH3E1N91qkFbZ1G1c5Bpkh23rCoHtxpyR3euXK+m3dxkqMYIGSq7Uk3OnbPOOgu77747HMcJkTtyP5YVA4SZM2eis7OzQi4/FGFZcn1FkTsq0lOX60UHIhVkcoe3V5VyR1YqRZHpfPJqUnboJjEzZszApk2bsNNOO1Vcw2Sf01TumLB48eJQm1HdQ/ZZ+bsyhbWq6mvZsmUVuT1UNirOggtXbY4bNw4AsGbNGuWxputccskloQml7niZ3JFJfR3k980hK3dMYVk2bSJN5Q6/X6lUQn9/v3G3LB4apVPuqGyAjtxRKXcodFF+hm9961u4++67MXXqVCvljsle8DIkDctSzfvSVO6o6oZsI+Wd0vkYsq2KyrlTKpX8Y0zkDq8/3fivIpT4e7n66qtx1llnVVybn2N6JtW96TlUx2+LyMgdDaZOnYqjjz469BkZOnLAdDl3ojouNwR9fX145ZVXKr4HBEmyYMEC/7pAZc6dtrY2uK6Lww8/vOI+O+64Y8X9+DVkkKGXDQqRWHKHraVyx9RJVZMfnUNBf6vej2mySfdRTQR0EyzZQNkYcNNALA8WtuSOKaEyHV8r5U610BnzNMKyTAkPkyDOblnFYtGfsJlWMG1JDj4ZU61MmQY13f1qrdwxnWdLaun+N2HatGn41Kc+ZXVsHOVOVL4afo6pvN/4xjdw++23V5xratu631FQkTu6tpKE3FGVuRoHS3duNeSOnDtPBdVz6MiPqGvINl1W1HIiSafcOfLII7FmzZqK8LioZ026W5bKlkQReAMDA6kod4BgfLJR7nBVk7zDi5zrQX7OanfLou90i0q6c8jXqqVyR3Vd1T3e+ta3atV5cZQ7MuS6M70HU1vg39H2zK+++mrksTZl1k10+fG6fFcyTGFZMqEot5+VK1cCEGNVlJqMX0/1PuOSO/wera2t2LRpkzLnDl3HJueOqi8Ryah797oFWf6cDQ0N/uYftsod3v/uuusuPPPMM6H76sobZ/zjdWG7FboNVPU5a9YsAJVhaTrFlopokdHV1eVHigDJlTvyPXXKHV0blXMt6Y7TkTvys2XkTgYrkFEjckeVc0c2SirwTqHK6WFaQZKVO1QWFXbYYQcAInzMZiCVnQ06jkgsmViKIndMBixuWFYU6RE3547qHnGVO/IAK69k2ky2bcidKMeSftPfJueajrHJuRPlKNlMXONCVx+q98C/Nw0ItEpI9WI7sEYhTs6dUqnkt1O5jm2IV/napoGVf6aC7n5xyR0dEaKaBBOxpXoW1TVkmO4RhWeffRYf/ehHrY41ycqTJFS26SPTpk3zHTZ+rOq9pknuEIGgOzctcqfewrJogmhD7ujUnXGVO4RCoaBV7nDlST5fuYKvuueCBQvwnve8B/PmzQvdkxAn5069hGUBduQOlXf+/Pm46qqrcPDBB6OnpwebN2/G/vvvX3E83VNH7pgm5bb9TUfuyPctFotDotwBosmdM844A+eee67yXFPOsqj76xbB6G/bcY+fR2p0eUFUdaypzKaFqWqVO6pr8udThWWdfvrpOP744/GjH/3IitypVVhWe3s71q1bZ7VbFo0f/DsTuRNHuWPTPrgd1T0vv47jONhnn338VBtyf5Rh079UZEMtlDv8mt/+9rdxww03+Fve2yp3dD40AFx11VW4/vrrrZQ7JoJFNy7LJLsphMyGBNIR6hm5EyAjd2JAVu4kTajMj5FXmvi1OGSHhjqNbktIIFDuPP3001YDaWdnZygMjc6RkzLXS1gWNwi6nDuy0aGwJHmAoe/ke6omWLqwLNnZlQcPG7m6idzRDYL020TuyNeqV+WOzjinodzROQNJERWWxckkaktE8vCymcoug79veeC3JXd0g2fcsKyodyKXN2lC5ah7pAmTcqeasCzT+2hsbFTaN1Pbln8nCctaunQp9t57b23ZbNqD3AZUDlctwrJM5E7U/WiTBJs+oiOTbdqf6th8Pm8kd/hxJnk6oampCV//+tf9iZNM5sRR7tiSO6o2XSwWsWnTJgDRuzXK11VBF5alUu7kcjmcc845vt/R0NAQUmeZlJJRYVk6P0J+Hgp/U5E1tLgg9wmZ3LFR7nBbYduv4thZGVEJ123uq7JRccKyVOROUuVO1HsE9MqdNHfLkp/94osvxs0334yuri4loSIjrbAseb7S0dFhTe6oJtgqe0wg5Y7se+vGuSi/KGlYluqa9arcUfn8bW1tOO2007R1T1DZSbleCRMmTMCkSZP870wJlfl9df1Jbge6d07g55veu+o4k9+bkTsZrECGjggVVc4dXefh4MfwlRh50iSfw3+TYbMhdx577DGrAf6iiy7CnXfe6f/PyZ2hDssysc0qo9bV1YXddtsN8+fPr/hO/l92qgn0/913341zzjkn9J5U5Jj8TmSZOq9nvmKruqfqf53x1k2e6TjTfaLInTiO4FCGZUUNYDbkznCFZZVKJd+ZVYVlJSE5+Hk2gxyHrq7iKndk0pGgIgijyB1bp1x1jzRhmrzPmTMHY8eOxcSJEwGkp9xRyfR1ZdGRO7Ztmpf5M5/5DD73uc9pz43r3PJypEXuyGXQEYtxlDu0cmsaO1U2HYin3NGtLHJyR+Ug03fyJNMEXXniJFTW9Ue5rpcuXYp3vetd2H777f1j+PbRaSt3ZOLOps1TGaLsbdycO7oJPIWxqPwEWlCS61SnlJYh+1a6cBQd4izYyDCNCaaQLaCyrtIIy6qW3NEtmHFUm1DZhtwxhXjy++jesckOxSF3ZOVOR0cHyuWyv629KueOzW5Z1Sp35PAck3LHRO7wdqbzLXXltRlT+TuPQ7rajtem+pTHQp0aRibhTXaDSD1O7lx++eWh66vmHCriRy6jqc3L8zPdcaZ78/vpzt+WsO0+eQLYKneiBlBuCDi5I5MmHLqwLJODOmfOHADA+vXrIw0lIIwvJWjmz7Rly5aQQVaRO6pJYjXkjmzUVOQOH1gaGhrwwAMPYJmXME8evFXkjm6QWbp0Ka666irt/XUTK9mI8mt/8pOfxDnnnKN9TtX/queUj1ENLjZhWTbKnSjDWA85d1SyVdUxw0HuyAo3Wo2X31/UCpXq2qZVE/6ZCrr7xVXu6Ag3VRtqa2ur2Jo3Caml+z8tmFZE99hjD6xatQo9PT0A7Fa1bWyhrNwxETe6yWYSckcuq+3nqmNMtrCa1TP5XNmhl4lUm/uRckfeyIBDN1GLU9+qY2VyhzuzSZQ7BLn/zJgxA0C6CZXps76+Pnz7298OhezIIaime8nXVYHGJ3kizH0Nk83gO5vKz8n/Tyssi+pZ14/l+8ZR7vC2WCwWqyJ3bO3E+eefD8dxjGOCnP9Jd1/V+5LHPduwrDFjxqBQKFSt3KH2rjpeJoptw7JMY4cpLEtn41TfyddLIyyLv2Mao1977bUK4oeuc+ihh+KSSy7BjBkztPbfRO5s2bJF6cPy95HL5Yw+HRB/K/RaKndUdtx0fjXKHfneUQuf8tzM1B5Ifcl9tcsuuyy0SCDbNVUZZB9YnhdHlVX3TCoRhaovxbWRoxEZuRMDl112GQ444ACccsopAPQ5d6IQRe6YnAzqIJQ3gHYPUKGxsRHvf//78atf/crKIVKVExDGmDtJZLx0YVmmQY4QNZGUWWnVZMFU5yZjIf/I5eZQsd82yh15xfDtb3879ttvP205Vf/rVplUAyM3dqaEylHkTpwJ91CQO3Ld6r43EST0noeK3JFJNn7fNHPuqIhLuQyma8h/J1XumNow/f3rX/+6Iu+NrhxRZQZqp9yxsV0mEpUgOzem60Upd0zEj015OWwn3vJ9ddCVVVYxJoV8ruy0yWHKAEL5i1Qgcke34w6gJ3fiKHdU15Bz7qhIWvouiXJnu+22w/e+9z384he/ABAoSkzlo79tyR0VkpA7puvxsKy+vj4AwKWXXoru7m4twcJhCsvSkTum1fson4Pq2RSWpSJ34ih3eNgZ/zwKSZQ7X/3qV1Eul41jQhS5Y5qoJQ3LKpVK6Orqqlq5o/L/CJy05H5DVEJlnc2Qy6UKy+JQJTGWEScsy9ROVDl3gIDcUZVz7Nix+PznP498Pl/hC6jsMYHCst544w2tn6mavMvHEHT9QOdfmcidanPu8Dkd1YmNf2B7/bSUO729vcpUIITNmzcDUIdlyddVESzy/7yMprmFjhwy+fsmv1dH/G1LSO51bYOYNGkS/vjHP+KHP/whgGhyR9eBeaewVe7Izvyb3/xm3HzzzTjmmGOMZf6f//kfAMB3vvOd0PmA2Cbz3//+t/ZcKg89pzyZpEGwubk5ckCTETcsS9WBTeSCzujIOXfy+bzvmKkGENU9kuTckQcr/rnuf13b0jnepkmnXA8kdzdNmqMGoHpQ7tiQO8Ot3OEDTZq7ZZmcZ9OgprtfXHInSk3Fv+MhHKp715tyx1SeJOSO6X1E5dxRjSk6kicKJieWkuLK9zJBV1aZ0EgKud5ozDGFZS1ZssR4TQrLMkHnVMepb1VdxFHuxCE/ebs9/fTT8fLLLwMwkzuyHYiacJnacBS5YxM+wkGLD8ViEe985zux//77+0pkm3dgUu7w54ibc0c36bAhd/h929ra0NLSEivnDrX9uBOXWoRl5XI5Y7JlOob/Tjrucb8zn89XRe7wMumUOzzcjMZsIN2t0Hm/lMsw3GFZAPD6669XfGda/JTftWkr9Chyp7+/vyIsS/UMNmFZ3I7a+Nq673XgtpGPo4Dop6SEkRFXuWNSFun8MJVfeM899/jvQQVS6JjIHZMPKt/TVrmjmq/19/cbj8vIHTNq4yGPclBjUuXcod/f/e53tcQJNzic3DE1SNmYO46D448/3npCpmKv77//frzxxhvac+RBRnZC586di+uvvx6HH364scwqI5l2WJbufNUEmH64QdZdR+XY2ih3VHHVquubVhNsyB0Vk22TUBkQDrRp1aMelDtpkDtU90O9WxbP+0Cf09+8DUU5UKprk3MKxE+orKurWoZlqRBn8mq6R5rQOYIcccgdmz6iC8tS9VnbyWZU2VVllccSmzrWhYwmaVcqyOXdZZddsMsuu1TseBJFLnBQziQTdBO1OEopVV2YyB35PTuOY70joewf8IWZqPLx+6m+0/VzDl7/qkm/qv+a2ldLS4tPhJdKJZ/Y4dcylcc2505UriZdu9bZV1UC2lwuh+bm5pCv993vfhdXXHFF3Sp3CLq+29LSEnkteTHQ9B5swrLouU3kTlSZeFl0Phl/T9xvsA3LiiIKGhsbjWNkHHInrk8pQw7LInLHFJbFofNRVe2GlDvr1q3Tjv2yn657JiDdsCxVPdv0LxXZwNNV6GA7XqcRlsXPnThxok+cm2BD7vA5h85O6pQ7urKq5mu641S+layaysidDLFAHY52kQIqG+Pb3/72UP4aDm4I4ip3kk6kk5wvr2rJHc9xHJxxxhloamoyDmiq72yVO6qV7yijpvpORe7YTKxtyB2VAW5vb68YnGxWB1QDjmzEdJM9+twmoTKgJnfiOIJZQmVzWJZOucPLIDsxtpM41aTQltzR3a+WYVlR5ag35U615I7cBkzXG8qEyqayJiFh5P6pulY1DpZcpp122gn/+Mc//NVHeeL3tre9LfKaUZM0ft1qlDuqsSOfz4fsEC2ayCoCuR6j6lAuF6kPTjrppMhz6Po2Ey4deD2pVobjttexY8eiu7vbWO6oPkXlkp9L99w2CZV1dXHkkUfi/PPPx9e+9rWKcjqOg9/85jd4//vf73+3ww47YNq0abGUO+QrxiV34owvMnRjQlQyZX4vVZ+R23tccqfardC5DyhDJnfS3gpdZ+8JccKy0lbuUFjWK6+8glKpFEnuyD4gKT9MOXdswrKI/DXV6aJFi3DYYYdVzLNsyR352WQS0qZ/qYgOm7AsW5LVZJvmzJmDcePG+WHGUaFOcWAbliXbRfl3UuWO7r2biCV+nSznThaWlQjckNkQDTLy+by/RWnSsKy4UK2eREGn3LFlufnx8gpiGsodU52bmGD5h2AKy1INcqawLFXyWFM5Vf/Lz6maeKrIHZuwLCBauRPVlocj546uvMORUJnyXsmg68ux+vLEbnBwMPTeqKwm8PctOyG25I7OYcuUO8OTc2cow7JMZY1SvKigKyt/njSVO7r7zZw5E7fffjuWLl1qfW1TvjoVMQPUTrkjjw+yXYu6nzzZa2howJo1a9DW1oaZM2di33331Z5D19f1W5sycBKAVug5+PPwRTEdPvCBD2iJOps2z8OyTIqRfD7vl6masKxisYivfvWr2nN33313ZTmjksZSGYHhUe4sWbIERx99NH71q1+FPo9D7qjGCXkyHZfc0SnObccRk3JHDssa6pw79RCW9cILL2Du3LmR/jGVleqGws1MYVkm5Y7cX6lvqu49ceJE/OY3v6n4XCaLdHMfuQzkkxWLRSsbRdeX79PX14c5c+ZgxowZeOGFF5Tn2fqgJqJpp512wksvvYQTTjhBeU1Te4yCbVhWUuVOFBGl8y355ya/N21ffyRi233yKmCagNuyvfJqDBAvoXLSMg8HuWNKBhZV3qRhWTpjwVcEuEGOuo5qAi4bMj6gtbW1KQcP3XOqPtORWLr219HRgQ9/+MM46qijIq8JpKfcSXOirTPm9ZhQ+bjjjsONN95YsQua3B7khMpA2DmLaoOqa/P2VA25w99dLbZCtyV36k25YypPHHLHRnmhc/ZVbVtne6qpD3mVLcm5tSJ3opQz/Ln3339/62dYu3YtnnrqKe33urErzjiqqovzzjvP35CBxiAiVlT91yZnEy8PL1dXVxcKhQI+8IEPaMkF/t7SCsuKIneirgWI8XO77baLLLMOtjl3iNyhv6PuFWcRz6at8HqLUu4MR86dQw89FL/85S8rPrcJ7dApnOlv276kIncIcn+Pei/yIp/q3knDsmzJHXm3rGrIHdXzxgnLmjlzZqifEblTLpfR29sb6ZssXboU119/PfbYYw8A6txTBHpv5XJZO/bzeQb9H/UMMnR+t8mH4HYhzqIlf+d0fltbGx577DHss88+2vNs+yEdZxpDdT5LrZQ7Jh9UvicXLMRR7ujKbiKW+HlZWFZG7iSCzDQD8ZU7JnLHJA8eSuUOj53nndOU3IvDNNgNlXJHpW7gTnXUBNOUUFnHUgNCuSM7M1GDvvyZDbkjO+ef+cxnMHv27IprEqnFy9Da2lr3yp20yJ0FCxZg5513TrW8juPgxBNPrJjkywOMvBU6lUH1ToZTuRM3f5dNQmWbFVnVNUzHRl23GthMHG3IENm5iZqIVqvcqaZNy2X9/ve/jwcffNDq3OFS7sRR0KjQ3t5unKDqxq5qlTsXXXQRjj76aP9z+YcQd6KR1D+Qx0X5evIxOqRN7phgQwrQBDkq545qUqe6l27yUW05q1Hu2Parasgd3XlJlDu692Br+1Xkjsl/MV1L1ecI3CfnY3ZaW6HL+RjlZ1DlbdJdr1rlzo9//GN8+9vf9v/nivO+vr5IcqdQKOCMM87wjyOFvqptEnEkX0u2eyo7GKfd2oZlyfbONOfQQWU/5d8270gH1dxCd4yNb2yLuLtl6fyApFuh654pSrkj5zvKyJ0MsaAyPnGVO3SNoVbuxHGq0gzLsjmeQyaS4jq+JiaY/0RNrE3KHXkw58fIOXd0joTJ8Mcld6IgD5zd3d0VExydg6/CSCF3HMfBr3/9a1x66aWpl5euryqTbit0+tum7DJk5xSIn1BZ51ilFZZlS9rEaWtxHNZqkFZYlkwAma6nU22Y2kea5I7cx/r6+jB//nyrc3X5wHiZVZP9uGWT/0/juU3QOdVxxuEo59pE7sQlQ5KSO/x5hlK5U43TbfPuTcod+ZlNZapGKWdzrI1yR/YVhzIsSy4DodqcO7rJmek6KnJH1z914GSBDbmTy6Wv3ImzFXqUcqdacke+honcsWk7RO6oFj50ycNloisusSyD141JISa/A3nMjkPg8gV7ud2b7Irts5jKovNZqlHutLa2ar9T+aCqeRZgn1A5rnJHVd/8vEy5k+XcSQQTuWM7yabEY0OVUDkJAx6VUFl1fQ6dg6z7jEMmd1Qr22nslhU1ETWRO/I9+DOlGZYlhz+pymw7EPHjrrzySmzZsiV0jG1IDS/XUJA7NsZedw35mLRVH7r7yWFZLS0t/ooIdzqSkBy5XKVyx7QayFGtcmdbz7lD36WVc4fu6zhOZB433YSpmvrg9hCIp7SxcWj32muvxGWLE5aVJnR2tVrljvw9jUHy+BB3olENuaObfMtlMZXBdresoVbuyOEA3O7y9i4TrPK9oiYdScs51Dl3ktZ7EuWOzk+i72zqh5+XhnKHvz/dO6/1VuhDFZZVKBQwMDAQa2xtaGhAQ0MDtmzZgr6+vlhjNAD09PQAAHbbbTfl96eccgoOPfRQ7XXTVO5E9VnZD5LH7DjKHRXZwO0Lhavx+8V5lmrCspIQHDb+D39mnc+iU+7IZeV56FTXIUQpd+izTLmTkTuJoJpExR306fihCsuyHUg5qlXumFhnnbFauHAhHnjgAStyx1TnOkPL2XyZ7DCFZdmsng9VWJaqLuK2OwCYMWOGsTz1oNyJmhSo6l53TNRkKyl0g6oclvWjH/0otMuPDTElgz+DaSBMQu4MZ0LlelHu2LQRG+VOXHKHrsudcVP7SLNNk22vhtzRrdgB0OZ7iXN9cpBlZ7/W7SDt3bLk76kfyxPNuGRINeSOiUTk5TeVIYoYpnLFmTjpYNPmVcod3biaz+e1bV7Xz+Ks6pveSZKcO9XsllVtWFY+n8fg4OCQhmXRefTcfBe1uMod/k5kQpWgU+5EJVQ22SSZQLEld3TPY6PcsSV35PfS2trqkztxfBMAmDdvHu6//34tufOjH/0IAHD77bcr768jd+LYeV2fjVLuxLW58j10Y5NpPmd7fVNf1/WjWvm78kK56l6yLxGl3Onr66u4tuo4VX2r2kuWUDkjdxJB1ZjisKRLlizxmdyRFJZlImtU5TMZJp0zdfvtt2PlypW44IILAIgM+/l8vmI1hf+OQ+7IBinK8eGx+/Jx8m+Z3NENHhxJyB2VkbSdPNquAMt/qzCSwrL4MbUmd+SJPQ008+bNCx2jKns1yh3eB03X0Q2ycZU7Ue9E9V2S46LukSZsJslJyJ04k0FTf68mTITw61//OrRdNdkFkzOqg87O8udVKTniXr9UKimJr1qTO7VU7rznPe9BoVDAJZdcUjE+yHYvjt2OA37ftMKydPcB0lXu2JA7POeOru0UCoXY5I5N+W3aShLlzlAmVJbPS0Lu6NqX7XhM35MvNmbMGP+7apQ7NuROLZQ7sppMPt4mLMs0H4jb1+T30tbWhldffRW9vb2R/rEKixcvjjxGN/bLJECSTTt0/d1E7vDF17SUO7btweZZhlq5Y4KKYKlWuUPkjs4Hku+tqm9+XqbcycidRFAZnzhOy2c/+1n/73reCl1OqGxy/mXDCJiZa52xam9vDxEjJ554Ir70pS8pB/RqwrLkv3XXOe6449DS0uIbH0A/eMgTGp1yhxykqLLLBjPtsCzdMaq/VRgp5E6tJ4S6+5lW/DgBk7ZyJ+od6+43knbLGs6wLBtyx8ZG6c6JQ+YmGReOPPLI0P9kF6pR7uhsIe2ikhRUFtnhHgnKnaj+eNJJJwEALr30UuRy6t2q4oT12ZaLg5N6un5r04ajjon7PCbYvAMelqXrM7y9696RbKPjLBDYlJO3ryjlTj3k3KF7xgnLUo0TScKyyN5yXzCpcofGT9W9+bPlcunn3JH/l79LKyzLtq/JZaHQ8b6+Pv89yTtcVQvd2C+TAEkW5Gz7rPzcKkIiCjbkjtxGkxBVNuROlN9rg3/+859Ys2aN8RiVD6r7rVLuqBYfaX61adMmAPp3xj9XjTn0WZZzJyN3EkHFFCZVBXByh0uJZYyUsCw+4TEdb5tzp6GhAbvuuqvyXnGUOzwci37TQOI4IteFqm66urr8rWvlspmMuMp54fVB5I5p5UU3QTRN9kwYDcod3UpdPSl3ZKJTRQKoyBnVtVTn0W/dcyUld+KGZdnslmVL7sRxQG2OT4q0yJ0kk1mblb8oZyoJyLbrnFETdLapUCjgoYce0m5nHads/D5DTe7UUrnDj+P1z+87FGFZqnLGnWBRO9cl4pTbVjVOt029cl8qajEmn48flpUWuaNLMsshK3eGM+cOKc6TKHd0k2lb25+mcidJWNZwkDu657ENy9KVR3UtOpf6MA+VGUpyR2UHa6HckcfVJAsy/J3r7EtcAlJ3/ahj0lDuLFiwIPIYPh4kUe6o3iW1tVdeeSV0vomwMil3MnInI3cSwUTuxG1MccOykhrYJBPbJOSOylkxHa+DyYmOo9xRrQrkcjm4rhtyqgcHB2NLJeUy6hh6m8ma6vmSrOSbwA2i6RhT2ThqQe7wZ+UOha79brfddnjLW96CPffcU1tu+Rppqz50cl855458jOl96sDPkdudLbmju5+t7aqH3bJqpdyx6U8qW6e7ThxyRzXplM+tBblDdsHmuWToiEXHcUJhiEkhlynN5zZBZ9NtJuzysTb9kS8GqO5vO15WQ+6kEZZlS+5U217l8sow5dxRTb5qmXPH9KxxlDtpkDvVhmXRbkhp5Nyx9UllcieNnDvc95NR663Q5WNM5E4S5U6SsYefy5U7/Pw0x1wTIRHX9uiuHUXuyP5HXJsrl89mcUZVDhOGWrljA5UPqvvNFwJNbZbaGi162yh3TOROFpaVkTuJoFrVSkO5U8uwrCTny7tl0Q5f1ZI7svRSBRtyJ45yhz4jg0TkDpcI2r47G9KGf66SJ8vkheo83XPy+8QhFW3InXpR7hSLRWzZsiWSyGtqasLPfvYz5TXk/2s12Omchlood3jbS0O5k8TxNynJ4lwzCalle3xS2EySkyh34jiKpvdZC3KnGuWOrsxpTQSGOyyrGuVOnMmrPNHUkVo6VEPu6CbfhL6+PrS0tBhzJ9WbcofnytON1/wd68qjG+Nt3r9NX7BR7tDncs4d236aJrlDaGlpsT5HVQ9JwrJU+Q/jkjtJlDvd3d1oamqKzB1mGn/lZ1TNIQjVboUeNyyLjiF/tK2tDYVCwc/LlnRuE3U/1TXluUE1yp2oMYl/7jiOPw7G9anpWFtyJ049ys9iOkZHhKRNcKieOUq5oyLZOXjaC9V1TPdWjZtZQmVg233yKsAbjOysVKPcMTk+1YZlJTGS8gqCzKqqoEo8LA/ANk6J6XnlujZJU3XkDhkHPkm2rZsollp+BtXxNisvuudUTYptJxtxVB3DTe4Adiot3TUI/P3KK+RpIGp1QZdzJwm5w9+3bsIRh9xJUhe1CMuqF+VOWmFZNu127dq1eP311/3/bYgb+bNqxwW6FrcNaeTcSauP6UiOoSJ3hkq5oyN3hlK5oyNbTz31VDzxxBPGyS2VbyiVO6ZrmJQ79Hc1yp0445DpWBvljrz5xnDsliU/w1DuliWTOxxJyR1VnyPIm3eceeaZeOCBB6yThschBVRlTjssy6atcnva29uLyZMna0mSNKCzo7VIqBxFFPDjVX6VCdxHl9u5bjyN8yy6ccj0HHLZ0h4jZ86ciZ6eHnR3d2uJLHnc5qSdDbmjewdRyp0s506AjNxJAJUTlrQjqYx8vSh3+KoXbauoKx/JdU0JlWlybTN5MA38NhMnHavLJ/hEssR1jOXjoyYCqkHG9Hzy8Wnl3OEGMerZbK45UsgduQ2kPdiZ5L7FYjEyLCsO2cLfd1LljsmxtEEtwrJsn9v2+KRIi9zh70JHKLa3t6Ojo6PiujrCRFW+NMgUTnQD6eyWVWvlTtokkgxdX4ozVsSZvMoTTXlhJOoaSckd/t51tr9QKGDChAnG62zevBmAntyJ+zwm2Pgyppw79LcNuVNNf7Mpp81W6Fu3bgVQH2FZhDRy7tj6zHRvG3InzuKIzi7LeSObm5uxww47GK/L7x3XRzGRO1HKHdW9kip36NxPfvKT+O1vf1tRhjQXVEyEhGp+Fcde6Pqszk/jtj7KN9c9h6o9R80L4jxLNWFZaRMcy5Ytw+rVq9He3h7ps+gSKsuIq9zR2Q/6LAvLysKyEiFNcofDtHJarTOb1Eg2NDRg8+bNkcodkukuW7asosxyHcVR7iQld1T1xY23TPDormNTNp0B1pFA3KmwIXdME5o4BnwkK3fitF9+jf7+/or6Gkpy5+ijj8bee++tLKPq3Q21cieJwxZF7thOJuK0NdM90oSNnaXvbMgd1cRdB11bV5F/aZI7chnTSKic1vtRrQDy69ea3Iki7G2uYWNzdUq+uDveVKPcqYYEoF1OKJRDV744YYo62LwD025Z9Devb115dGNxHHWGqS5tlAlpkTvV9Mkk5I7OT6K/bctF56uU5HIdRPUTbj90dpmXJ047jeOLmcidasOy5L5mY6v4u+rr6wtNttMYY3RllK/JFR70Py+fDXSLXlHKFmoPcfq4SrmjIo1U5bOBKRWGfL2hUu5w6J5VZe9NY7asCs3IneqRkTsJYCJ30nBaVNeo1mlOer4tuTN27Fg89NBDmD17dsU9ed3YKndMg5fNhF/X8Tmhw5U8unuZyhZlxE0TMZvBedGiRXj99df9BHc6wkr+TAebSWYc5U4a29rK0DkmSZQ7hUIB/f39Fc5C2oMdfy9yHqUbb7xReY6O4LOdxKkm43HJnaT1oNu5iGA78U7y3LbHJ0UtlDu2bU5uD2PGjAmtjqmOScPxpjLGId/5uWmXR3X94QrLGg7lDj++HsKybHDAAQfgve99Lz784Q9r7wOkM+Gw8WWilDtyex/usCzTs5AqWs65M5TkjvwMaYRl0W9b28/Jna6uLrzyyis12Qo9zvVUx9q8FxOBZKPktfEfk4ZlyagFSWAiXJL6t/K1o/qs6nt+/zgknYrcUS1Cx911rFwuh66hwlArdziSKHeqsZ0mMo1/n4VlIQvLSgKT81Wtk82vyVHtAF0NuUNlisq5M2/ePGW8MK+jfF6/7ShHrZU7MtHD7xm3bKrnVB3H24jJ0NFn++67L+64444KB1DlhNu0u5Gk3JGNc5xEnDqCiP5OW/UhK7dsy6gafOPKyvn5Q03u6N5/nEktoV6UOza2wIbckft63FVAADjzzDPxyCOPWJE71U7cuFNbTULlJCutJshtTedEpg1dXcS1t/y36V65nMh1IV+7HhIq26BYLOJrX/saxo0bp70P/bbtDzrYvAO+U6GOvBoqcscmLMt0PSJ3kubcqYVyh4eT6mCyUXyssm1rMrlD56vuqYNsl+OMUVFI0jaijo9S7qjOrSYsy+Rv1yIsSzV5V03Wkyh3ZH9Ip7DmflSUby7D5NPr5gVx2pRNnlMbIqRWkOs4qXJHRhzlDn8+qusxY8agq6sL06dPT/BUowOZcicBuCNgoyKxhakzVuvEJy0fdyTI0Ng6/6qOH1e5YyI/bJQ78goJKXX44B53FVh+F7JxpRA13XFctWFaedEZNtVkz5ZQsF0B1pWNo95z7qjOqYVyh5M7W7dutS6j6X2aztOdb9sWqp0YR9k82/6U5Lltj0+KOModm63QZeLEBPn9NTQ0YPLkydrrqn4nAZUviXJH3sEmDbJJLpvp+rUmd4ZSuTNz5kwAwJYtW/zvaq3c4W3TdsKZBLLdSsNPMl2DL0rpFkVUq8AyTAs0UbDpC0mUO8NJ7lxwwQUYP348li1bFnmOqc54vUaVi3I9qsgd3T114H5h2uSOvNhie91qyJ3RqNzhnyXxMXV+uY7ckW1THFLEpCSR279KSR6FOMqdqOerBWyemX5H2aF3v/vd/gYTujbHP9f594DIZ7hmzZoqnmzkIyN3EmDMmDH+37qVyySggc5EaAyXciefz/uDbFyngnfIuMqdWoVlUVnSDMuiujrxxBOVx/Ey0X1Ng7ONsY5jwG3InTgOfq3IHdUAWw25M1RhWXGdKdUEcjQod5KEZdWLcsemP9kod/i7sJ3M2owhOttTTZuWCag45M7OO++Ma6+9FgcccECoHLUid2TSvtbkzlDm3Nluu+0qvqt1QmXeNqtR7tjch35Xa4Nt2pjNbln8HcdV7tj4QDZt1EZdkVbOnWrqnK7R09ODSy65JNF9kyp3iNzi9pbInTfeeCN0rO2YY0syxulPcdqGakIadZyqXKa5QtKcO7rr1YLcka8p75ZVjXInSuWp6tO8PcZ5j5zcke9fjQCgmrCselPuRLWjq6++2v87Sm2lItNU5diWkZE7CTB27Fj/77SVO7a7NiS5Nr+OLcih4GFZtmWQHWTqkHGMpom4qSYsK5fLYf78+RgzZgyefvppANWTO+3t7XjhhRf89qFzDKMcC927UrWBOO3Opu7rQbmjGgiSrJjqwrLSnrgkDcuiZHlxCDW5DfH72a4cynX5zne+E//3f/8XWWaC7W5ZcQibOESQzfFJEUe5Y5tQube31w+5McGmjetsSjVkiqzcidryVy7PmWeeGboWL1e12BZ2y6L6J+UOx1CEZanaUNrkKe9XvK0lgU29xsm509zcjI0bNxrvlaTd2fTNOModCsvad9998fa3vz2U37DactTiGrrxiX9nMx7T83PlDoX/USLvXM4un0lc5U4cJCH++HkqJFHujKSwLJVyJ6l/K187aoxU9WneJmx9avqtsxHyYlgS5Y4NARj1fLWAbmFR9exx5rC6vikLBeTrkY2UEzRvi8jInQTg233KE51qnRbd+dUO0EnPV4Vl2T6jitWNq9xJSu7YKHc+85nPAAB+/vOfa69jKpuK5Jk4cWLkceRY6N6FbnBRfR7HoTj88MMjY1DjrN4mGayiIJM7SZQ78jm1Vu7oVopM4OROEuUOqc6A+AmVZVtw3XXXRZaXY9GiRTj55JOx44474uabb6543pGs3LGZJMdV7vzlL3/RbhGtOyfqGJ1tSQK5z8VR7kSVr1rIhJPu+dOG7l0kmdzb9MdcLocpU6ZUfDdSEipHgVSq8gQqCWzaGA8X1D0f1ekXvvCFUCic6l66yYtNOU3H2oxrcljWhAkT8N3vfjfy/oQ0+kqSa5gm19xuRV1TRe58+ctfRm9vLwYGBvCVr3wF+XzeitzRTeTTQNzJq+pvGUnIHbpeY2MjOjs7Q4vROtj427UgwuRrcr+G3zuJcsdELqrKwBc5bJ+VX8OW3IlTj/WeUFlnF+k3jdtxEyrr3oGpvgFg5syZuOmmm3D44Ycnep7RhIzcSQDHcdDY2OjvIgWkYwBbW1u1k4Bqneak5eOx6zYsMoeK3DFJoDmqDctSGRIaOBzHUU6okyp3ooyrygDysBxd2dMOy/rSl74UecxwK3fa29vR3t5eFbkjD6pyfaU9ITTdz3SOityJ65zy820H82pt1ZgxY/DjH/8Y3/jGNwDYKcxUiDOZ1PWttGFT9jlz5mDatGmYMWOG8TrXXHMN9t9/fyvVDmC3QCD39zj933RfvvJYj+ROVLL6tKEjupIod2zIHcdxlIqpuGFZccGd5FqGZdH1yW7VWrnDyR2VopHbzzlz5hjLrLpnnHHI9G7onZuOkcOy4mK4yB3Tooc8oTaBnl/OufPFL34RH/rQh/zr9Pf3R15L9sFqoUaJo0yX/zYdx2ETRlUqlfDUU09ZJb+28bdrQe7UQrmja3c6f5rXpaxijYKNcse02BgFmwV1XVuoB+WOKqGyzfPr7MLYsWPR3NyMlpYW5fM5joMTTjgh6eOMKtTurY9y9PT0AEg3LOvCCy/E73//e+V31Q7QSZ3uNBIq00CatnLHVOcm5U5jY2PIUYpbN7YTLJNjyJ1L3fXTJndsYVsftSB3LrzwQtx11101I3dqqdyJE5bFyb04yh3ViudQkzuqsth8LkMelG2Ptbl2Utj0p1mzZuHZZ5/F+PHjjdc6++yzMWvWrFTvXQty5y1veQtOO+20SNLZBmk7lNSnSCWV5nOboOtLce5rWxemsUAOR9MhqX/A7WGtyR0+earm+jbjU3NzM6677jq89a1v1Y6bcSbhcnuIc66pnDbj2kgld+S6U7Uvm7agUu7I14nT16gs1bbDqLLI+NjHPoY//OEPFeU0taUkyh0+/o4ZM8bK3zbZtSTqmaT3k/2zJISILSGr6tvUJpIsYOv8sGp2y7JR7kTNF4ZTuVPNVuiq497ylrfgueeeQ1tb25CQVyMZmXInIXp6evDCCy9UDPZJOlIuJ+KFe3p6fNJIdQxQvXKnGnInbkJlmdwhozmUW6Hz5y2VSigUCvjqV7+qnFjaGgldSETUygB/B0nIHd0KpPxZtbCtj1qQO52dnejs7EyF3FGtig4FuROnjFQm1eem83j7iUvupDUxtnWaos6PeyxQ+7Cs4XAYqlHuVFMfp512GgDhOFWj2kmrPBxyWNZoVe7Qsaeeeip+/etf+9+NloTK/F5pkTtR13jnO98JAHjssccqjueTsTj3ijtBiTrWRrmjSigcB2n0lST92mSjeJmiyqXaLUu+jm0/4WPlm9/8ZnR3d0c+hy2i3vcnP/lJ/2/bvhbXR7Qphwo2SqBaEGFy+adPn47XXnvN/78a5Y5sf6MWSzmxY3s/Xj75mVQEh6ocJqSRULmWvozO91SN23HskO4d5PN5XwmdkTtmZOROQtCOWdWwsoTly5dj9erVxmOqNbBJz1clVI5L7sjETpyVhKRhWarvvvzlL6Orqws77bRT6Nhqw7J0dVutcsdm0lwryezg4GBkfbS3twOAVT6RJGXgvw844ACcd955xlAY+VxdWFbag4HpflHnyH/bKlhyudGl3IlL7tRqQK93cqeaMJEo5HK5uiN3ZMd4qMgd3btIe3JP39MxP/jBD0LfDSW5Q+e7rltzcqfWYVlRx9uWIQ1yp1rlDpE7cRKdc9RLWJaK0OCTPh3SVO7wsnz0ox+NfIY4UC2+6RBn7DPdKy0yxmYxtRbkjnzNz3/+88p7JyEVo8KyVGqTuPaJ38tGvcI/t4FNKgxd37T1B6uBrq7lZ0+L3JGPsb3etoiM3EkIWWFTTUObOHFiKBGvCtU6zUnPryahsrw6k3ZYVtzdsvbff3/jvdImd0yOoePo471170rnpKruXQ1sjfDcuXNx9913Y8mSJandmyAPGhMmTPBzvNieq9otot7DsmxJDj4pGy5yx5bUjDqfn2NzrM3xSZE2OZHk3qZ6q2ayGQVb2xx1jbTKw68n9+Va2D0O2TklxBkrbCd7nKiVUeuwLB25U4v2zydP1by3uH1UZY9syyDb1Djt26acNsqd9vZ2vPTSS0pywwZp2LQkZKqpr8ZR7pjIHd0Ch22ZdPjd736He+65x3iM7tpDSe5EhWXZwlQvtQzL0vm48r2rIaqi/HPehsg/tJ3jcLsQpWJJUo82qTB0zzccyp1JkyZh3rx5/gJ6U1MTPvOZz+DYY4/1k9bbql4zcqc6ZOROFDZtAu66Czj0UPF/uQzceit6POVOzx13ABs3YtyKFWgBsNOKFcC11wJvfjPQ2wvccw/wyCPAmDHA1q3AunXAsmVAsQhs2QLsuKO47qpVwIsvApMnA7/4hbjPlCnAPvsAN92EvV96CbcCmP7oo4DrAtRBbr0VOPBA4C9/AZ57TtzjzW8GHnsMmDVLlOH++9Fxxx2YDmDu1q3A1VeLc/fcE+jqAl57DejrA154AZgxA3j8cWDePOAf/0BDQwMaAPT98Y94a7GIqwHsOn8+cMMNoownnyyuddttwAEHAE1Nor7mzUPu8cfRB2DG2rWYBGDOwAAG8nlsLhSABx4QddvUBPz970F9b789sPPOmL16NVoBdD/0ELDXXsBNN4myHn44+v7yFzgAdnj+eeQAzPrXv4DvfAc48URx7+XLMfHpp9ELYOd164Dnnxd1O26cqPNSSdThwADQ1IQdBgfxNIC2Rx8V9wKANWuAn/0MGD8e2H9/cf+WFuD447HDs88iB2CfF14Arr4a45YvRxOA6c89B2zYII677Tb03XEHxgKY198PvPgi8qtXYyqAGf392FwuY6PrBu+CYdH69XgaQMs//wlMnSra0L77YsdnnkERwLx//tM/r2f1ajQAmPHPfwLXXQeccAKQzwO/+x2wZAnQ3w/cfTfwxhuiDa9bBzz1FLDffqI+n3tOfNfZCaxYAcyYgX0A/B3A5MceE+3y//0/oKEB2Hln4G9/Azo6gLe8BSiXsXTp0qAN0srib38LLF8u2sOsWcD//Z9oXytXAv/4R/CgS5YAzc3i/rvuCrz0kjhv5kzM37gRfwVQGhgAfv5zoK0N4Pe6+27xPLfeChxyCPCrXwGvvw6ccAJ6/vMfdACYv3EjngEw9ZVXxLVXr8akwUFst369qL+99xbPdfvtQZnGjgUOOwzg+Q02bhTtatky4Cc/EeecfDLgDSrTHn8cBQBLNm/GswBmPvGEsAHHHCOeGwDuvx+YOFHU/333YcbWraINXn01CrvuCgCYCKDlBz8Adt8d2GknYQc6OkS9//WvwJYtWPTss/g+gF0feQQT+/tRADDl+eeBchl9Dz2EZkjO5e9/Dzz7LHDkkaL+H3gAjutiDoDeclnUwx57iPoHgGeeEbZk3Djx2b/+JX7ffDPQ0yPq/PbbgcMOw5RHHgEAzH3mGdGXbr8d2Gcf9D36KNoBLB0YEPd8/nlhV559FvjnP/2itb7+OnYCsBFA2w9/KN7H0qXAyy8DTz4JzJ0r3m97OxatXo3bAOwH4A4AO9A9b71VnHfrrcKeHHecqLO//AXYbjth/wDg3nuBhx4SbWXGDODBB4H77hPf7buvOOfXv8bOGzbgJQATb7kF+O9/xTVeeUUc86c/AQcfLGzdm94U1LHrCvvA5OTI50Ufeeopcb/nnhPvf8IEUY777hPX3H57cfzy5Zi7eTNeBND0/e+L9jdrFrDbbsIO9fYChx2GGU88gSKACQ88ABx+OOY8/jjyAHZ87jnRfydPFn1h5UqEsGiReI5p04A5c4BHHwUKBTGObN0KtLZi6tq1gRP53/8Cv/ylsD977inevxceEUJLi+gLt90GHHQQJj/xBJoBzP3vf0W/bm8XbX/GDNG3/vEPYXfWrhWfA6J9tbUBq1cL+7t5s/j/tdcw3RtXd/TsXO+//gUceCBmP/UUcgAWPfmkaMONjaIcjY3Ar38t7NZxxwnb89RT4jkPPFDUwcSJop4efhj4859F+995Z/H3f/4j2vR//wsAaHj6aeA3vxHvGMCBa9fi3wD2ovuqUCgIO/zgg5jR2Ykd160Tz7Z8uaiDSZNEPXR2AuUypmzcCGdgQJRn3jxR9uXLgUWLUCgU0AageO+9wmbffbf4/dvfir74u98BRx6J+StXIg9g99dfF2McTYTvvVf0oyefFO1uxgzxXrznm7F5MzYMDAD//jfQ3o5dHQcrAex4zz3AzJnAwoWiz5IN6O8Xvswxxwhb6DjAKaeIZybcdptoi8ceK/rZTjsBTz2FcQCmr12Lp1wXczZuDOpvt92EH7B2raiftWvFfbdsAe64Q7zT/fYD1q8HbroJsx58EHkAvX/7mxibf/MbYbN/9jNxzEkniXYJAGvXovX229ELYLdNm/x7ThscxHT6f++9xf3vv1+0v4YG4JZbANfFDitXohNA509/CowZg3lPPYUSgPH/+hdw9NHi3kccId7DIYeI9vbEE8DRR6PzwQfRAWDOmjWi3T3xhGh3L70kbOnmzWjy+ul01xXvv61NjDUtLWKs2LwZf7r0Uty8di16mpr8foYtW4AbbxRj0AknCJ/mppvE8598cvD8ALoffxxjAMwfHBT3fuEFoLtb2Mt77hE2t6lJjHOkanYc0WafegrYYQfssWULXgEw5amnxPdbtoj3v2mTGMPf+lYx1hx8sO8HNP/tb2gHsPPq1cDVV2Psc89hAoAxAPJPPAGUy5hWLmNMf7+6L40ZA0yYgHlr1uAvUJA75TJmP/00AOCggQH8BMAOzz4rynTHHaIPERwHOPpoFJ94AhMAzNy0Sdjk9evFd6US8Mc/imN32QWHHHIIDtl1V/HZ/vuLel67VvSBri7gqKOA//1fYP584cO7Lrr/8hcUAMx++GHxPLmcGCemTBH9cNYs3x8oPfIIpgFoAtDywx+KtjNvnijzmjUAgHEA8Ic/iM//+EdRz64L7L035v/3v/gFgCVPPx3UXWMjcMop6HvwQTQC2OHll8Xz/fOf4r0++KCw+4CwHRs2iGsODGDm4CCaARQee0zYe0DYofvuQ5frohNA369+JezG4YcH779UEu3tt78Vz/qHPwi/L58X7WHffUUbe/55YTPGjgXuvx9tf/oTpgOYPjAQfvd77CGO8XyGKRs2YAyAwne/K8auI44Q7/c3vxFzo+5u8fchh4g+ePjhGPvgg2gAsP3mzcD112P2ffehA8AO69YJ32LDBuDee5FbuxYzAUwZHASuvhozN25EeXAQ09etwxOA8CFmzxZzqR13BH76U2H/GKasWIE8gPG3347GV15BEcDUxx4Dtm5F94MPogXADq+9hjsB7NLfj9cA7NbfL55v6lTxbv7yl3C7XrhQ+ID//S/mb9iAxwE0bt4sxrVDDxV1vnWr8LcOOQRzn3kGDoA9nnkG+Pa3RZ3Pno3G++5DC4BZ1EZyOWGznn5ajAXUxwYHgQULxHU3bw7mpC++KHy2vfcGXn1VtPfBQTEnOvhg4KabsNsTT6AAYJdHHgGuvhrtTU146O9/F/bz738HjjsOH/7wh4HbbsMLd9+NPgDz+/uFf/Lii2IeCIgy77CDsDe33Ya5a9ficQC45hrR5qdPF7b5b38DFi8GbroJM//5TxQBjP3d78R48+Y3i/Z3yCHChu6zTzC+33uveJZjjhG+7bYA13VT/9ltt93cUYMrr3RdwHUfecR1BwZc949/dF3Ave6MM9zZotn5P+fy/z/6Ude9+WbXdZzQMS7gugcdFPxNOP981x03znUvuyx87Kc+5f99OX32xS+67pFHuu5tt4n/r7gifM6ll7puqeS6F10krj1xousC7g8A94GmpuC4hQtd98QTxX3f+lbXHTPGdT/4QdctFsU1cjn3/Le9zT2RXXv13Xe77ne+E1zjJz9x3UmTxN/f+Y7rrl7turmc615wgesC7t8A96XWVve6fN59uLHR/X1np/ud6dMr64R+2ttdd9ddXRdwb6LPPv3p8PeA+x7v/7P4uZ/7XOhan5CvfdhhrrvnnqLu3vQm1917b/+7P9ExK1a47muvue7llwfnURsAXPeYY1wXcC+Vrn024JYdx3W/9S3X3brVdQsF1wXczyqe8b6mJveOxkb3lmJRWQfrczn3NtWze/eRjw+1u69+1X/f7vTprsvf99veJt454LoHHCDaxnHHue7MmeJ/dp0P0t+5nPo9LV0avHPAdd/7XnG9V18N2nwu57pf+5rrNjeLtpXPh68xa5br7rWX63Z0iPPo8x12cF3A7QTcl3lb+cY3wufvtZf4/fGPB59dconfdtbn8+6n6POWFtedO9f9VXu7++/OTvHZokWue+ihlc+2yy6uWy4HffOznxWfv/WtwTG/+Y347ve/d13A/a73+c38Ottv77qvvOK699wT9PsFC1wXcO9ub3fv9tpyubXVnQq4X6PzikW/nal+zmR/n09/L17suoD7GcDdb8kSUf5zzw3OmzpV2ATv/18D7h8bGsT/u+7qutde67pjxwbHF4uiDzuOX6cuELzDq65yXcB9H32+//7i94c+5LoQtiZU7tNPd91p0yqe5U+A+yP+2Re/6Lpnny3uf955oWPf5f2+gLcfKit9tmCB6/7976LcY8e6bne3615zjev29orv993XdQcHw2Xp6hLvCnBXlkrKPut2dIjfp5wift95p+tu2OC6n/+86/7v/6rf1RVXiHZ36aXBZw8/LPoDtQeC9/1X+PmlkqgP+n/JEtcF3PdL9wnZhK98Rdtu/Lq68UZhB3mfP+00d0VHh7uou9t1f/e7oMy5nOt+7GPma37pS6H3cSV9vt9+oh1SHW/c6LqdnaLPzZ5dWS5AfF8ouO4557judtu5LuC+F3AHHce9ho497DDXBdyPyuW46SbXXbUq+P/DH/btsF+fgOsecYSoc7L/vb0V/W1dQ4P7Ns3znqn5PPRz1lmuC7jrjzxS/D9hguv29bnuO97huv/zP+KzpUtd9+CD3Ye6u93fdna67k47iXLNnSu+HxhwP/axjwX946MfFb+pX5CdPfts1wXcb9JxJ50k7MFvfiPq9bLLRD9QlPMvZAvZj//+CgVRZiAoM2/b/P13dYm2/dJLwefnny/sxemnuy7g/i2fd1e2tbk/bmpyH/Nsn/8c9J6KRdFn1q0Lj8EbNoix1fv/w/T5jBni98UXB8d++9uiHstl4dsA7qcAdxVrC79qbXUfojIsW+a6b3lLcL3TTvOP25rPBzbO+/HbHdkQ6ivf/GZw3DnnuC7gfo/+nzkzXH8LF7ruokXuxiVLXADuBvr8iCPU7WnrVt/mug8/7Lq33BJ8t//+wRgDuO5nPhPYlU2bXBdw7+bXGjPGdadMcd22NvH/kiXCH5o61XU/8AExLrqu8AUbG/1x9od0/mOPVdq8Cy8Uv3/yE3HuM8/4z7+V+RBXAu5/2Xl3NTa697S2RvYnB3Af+uUvRfkGBkK+OfnF74q4hvve97ou4L4IuE+0tAjfd8wY8V1PT3Dc1KnB89M7cV3Xfc97Kt6vC7juP/4hfH3AvUK+Z6nkul//evg6Xh95GHDvpOMWLxbfnXii606d6gJsLOA+HPtRPq9Xrq9A2Ex/PJgzJ3yd444TNpjsE/8heO/0v8ViuA9861tiDKH/aXzwfBv3i1903QMPFH+/732BD3nhheK6kye7LoSP8Ffmk/jXOPVU//9Bxwn6OuC6zz3nutddF/zP5yKA677zna4LuF8C3AH2ue/P9vYKv9j7/2bA/bPnBz3c2en+qq3Nfb6jw72bxqM99hD2yZuTqH7uZX7hh+jzww93XQjfpj+Xcz8IuK/n82HfYvVq3+8I/RSLrnvHHf7/ANwnjzpK/D97tuuuWeO6P/iB+P8d73BdwP04P5+9929RO6Dv3v9+3yaGfvbZR4zLCxcG7//444Vdfuyx0DzUBcQ8w/v7LPla3E9fvFjYcq8NfIYf543vLhCejwDui21t7jXkn9IzKe59Dr8e9WGar33ta+L3jjsGbfBd73JHGwA84LqVPEymZ4rCnXeK3xdeKFanvFX+8cUiOumYb35TfMbPW74cOOccsWK6YoVYAf/PfwTz6q2aAQj+fvVV8bNmjVgZ+PGPxee//KV/aDP9cfHFQk3x/e+L/3/+c/H7Jz8RK80//algdh99VPz2VodaADSXy4J1Pe00wR4/95xg6H/5S3H/Bx4Q7PQPfwiUyxjb3x88J4CefB740pfEChQgVsxfeEH8/fLLgqUvl4WyB8A0AM1bt6IDQFu5jI7BQey5Zo1YrbvuOuBrXxP1s2oV8OEPi1VFT92xI92UWOaFC8X3AD5/3nkAgMm8zu+4Q/z+7ncx2NCAFkjYtCmoZ/rx4NIfkyeL1U1aoeL3Z+/DT2345S8DACYAcFxXrHatWuWvcjejEm3lMtrKZbQCgqFftSr4ufBCNJbL8LMCvfhi6Nyx9Mdtt/kr31P4Af/4hzjn5JPFKt0OO4jVgcmTRdt6+WVx3N13i5XBF18Uz8oUFaFyv+Ut4pr/939CjfL448Dxx4v3DPgr3fjd74L/XVesICxcCFxwgVgR3LpV9IXnnxfP+T//I1aT77tPrIhdfHFwc++apwPoffZZsUoGCFUJIFYZx44NlBd8he5HPwIgVr1aBgfRTQq3DRuAFSvQWS6jhRQIDz8snvvEE4P6/8hHxGcvvRRc8//9v+Da8+YJNcJVV4k+dPfdAIDp3qH70DlHHSXq6utfB848U3y2fr3/PlsHB9E2OOiv5H0FCNprf79oZ6ecIq7/9a+LOrn+egBAH3tP/t/e6k8jgN/dd59YUbzqKlH/v/ud6KPjxol3vXgxWh0HLa7X6l99VbSngQHgyivFakl/v+ifrit+T50q7IYnE8bjjwMAOuj+1Pe8fu+3U8Lzz4v3dOGFfl1vXboUTRBtbbCvT6iLPvIRseLX3y/UeAsXAu96V+hZ/Xp68kmhLjjoILGq9KtfCQXCQQeJcs+aFSjwVq8W6pe77hK27bnnxGrWPfcIxdejjwItLWgaHAyuf++9wPe+J1aq1q4Vn914o/i9YQPw1a8CH/qQaAcdHYEdW7VKrIK99JI4jtkZvPWtov3svrsok4Q2AO748cAnPiH6zHPPBV/eey8AhOwxAIQChN//frFC95//BGVZvhz41rfEuDBxonj+l14K27hXXkHr1q24a80aMUaR3SmXxXnjxoXt1KpVoi7p3dL7AJMD3323eH5AqJp+9jNR17ffLlQMn/60aJcXXCDs1cc/Lu597LHi3Xir8ifsuSdyrovt6bq//S0AoQAA4Lc5vPBCuL5++lPRpq+7Tqy+kh3xrosXXhCqltWrRX/74AfFc59/Ppr6+zEXgFsoiBW/Vav8cb6Lrn/nnZV1smqVsHPXXQcAaPGk6Fi5UtjeF14Q/dmrc6xciTGOg3ENDUF78BIA46yz8KEvfMG3LfjNb8Tvb39b/Kb+67XJNjruxhuFPbjxRtGPHntM9INLLxV1d8894v0vW4ZW6s8MTfTHwEAwXnj9HbfcIn7fdltwwgc+IN7vz38etsXXXivshfd+elxX+AKui+aBAbGKumIF8J73AG97myjf/vuLNvOLXwTvid4Ve7f+s9KY8Oc/B8c+8YT4vWGDeB8AWgE0ua6wJfPno8110ULP/uqr4l3Q9W65RSgELrkExcHB4H17780fG6k8t94qflN/oGfn5eR9DRDv47nnUPSu4V/zt78FTj9d+BzPPivaJD2L17/whz/46g68//3C9n72s+L/KVOA735X+Do/+IE/Lkzl93711eA9HXywqK/HHxf9+Oqrxer48uWi/jdv9sdZf8P4l14K3s1jjwn1x1VXif///W/x2zunF0CxXAY+9CH0d3WhCWKMIrS6LlrKZaEOkPvRvfcKO+md03vnncL3JJ/DU6FQ/wj537vvLspJ19plF3+sngDhG+DVV0U9AeI5//53UZ8rVoi2T3V8yy2iPq67TrQLxxH21Ms5iLvu8q/dS/e/9lpRN3PnAu99r/iMbMHHPw4AcCDaJYDg3T72GPD88/64CEDYk3vvFX7Y//6v7zP49u8PfxB2q7HRV8H0Asi5btBGH39cPOvVVwuFw5/+JN47+e4c5B95baSvvz9ox/vvD5x/vvAHCfQ+/vUv8fuRRwIV1LXXijEEEP6W6/p9shlAs+uKNrhqlfArX3tNjBEecq4b+BiAeBekPgICv4Pwne8AEGNiHgA+9zm4jhPU8+rVQb+aPh1tgO8HtQ4MoKNcRsvWrcEY9te/ivf9j38An/lMuH3+7/8CAPZiu2b6/phnq3cEUCiX0QWgg/sWAHD22eJZPvzh4JrPPivUQscdF3qs8RTd8NRTov2QjfPmfn67IxWr18Yme3WIyy8X1338cXGfD31I9I8HHxTt6/HHxQ/5vRs3imcYGBB19fTTwg/y7Bp/B74OhmzAs8+K38ceK+zP1Vf7bSA0H3rmGeH3H3ZYcF/PV2rx5oyYNEn0DZ7Xk64PNvfZZZfAhlObpzr697/F/adPF7aaxs1RjozcMWFwMJjEkjPjOTcH7r47TjriCPHZzJkAmNMHiAb16qtCMjtpkpB9b7+9kOVTIwQCp2TdOuGIvfpq4PwDoTCWDXL5aHL7t7+J3/vsIyZ15Eg8+aRwKr3GXAJQBMT1Z84MDB0QOOF0zRUrAAB9W7cGzh4gjPajjwpjUSyGJYVr1/qDHIUoPAygNDiIRgANrovxHR1CXjxlCvDOd4qBb9IkMVmfNi30eFvoj2efFVLpH/9YyAcBtKxbB0Cqcyr7/PlwSyU0AOh3HOCSS0TdlMuintevF789oggAlvPrvPZacC1AGL2GhmAw55gwIVyON94IETKqFIgl76fBdYXRGjs2+OnqQp4/+4oVwlh7Dr1v4saPF3UIZtiBYIA98URhkO+9V9TZ9OnimTduFGEZ/f1igCfnfc0aMdgccQQGIAxD2XGEDHmXXYQs/8wzxQBxzTXAqaeK82hC8uSTwBVXiIk5ICbYl10WTLJfekn0pSlTxHO+9a1BaOGyZT5xweE7L+94h/jtOQVYsUJMCAnUhtvbfWeFpgUtPL53wwY0ui4ayanftEmQqwsXBvV/8MHiuwcfDJ7vL38RMmNAON6HHy4Gvh/+UEjhARAV1En3OvZYIXl96aVgArJ5sz94NbiueP+77w4ceSR2hGgTg9OmifoGgLPOEpL597xH2I6xgjLhDkKo/UPYiJLrCgdr5kzgC18QMtW77xY/220HtLaiEUAjDXLUF2bOFOTLSSeJd0M24dVXhQPwwx+K5wf8Nl5BoHpEsj+V+fSnxSTuX/8S/W/OHL+u3ZYWFCH6SHncOOFYc0Jj82Yx2d9779C9+OQAn/qUeBfHHism7+99r3AOly0T7e1Nbwps0sc+Jp7r3HPFhOTUU8W1L7lEnP+OdyDvlae/s1PIgE8/PZg4AYGj+sYbwBe/KIiUjRuFY0p2bOxYEV5BdoCTOw89BHzuc8Lpf+01YVuoz3rP5ra0CBIGEBOuMWP8dw8wQu3AAwEwm7DHHqJ8++0XtJexYwWxe9ZZwmGcNUv0+Vde8W08AGDdOjR6dhqAsB1dXut69FEh1eZ2auxYYVOA8HUgwuwAiPGT7AsAnHGG6EckbT/qKPGcX/mKWKj4xCfEAsXXvx70NwB7z54dvq6HTfTHzJnCPr/0UlCWqVODCdOee4p299OfionJihXBJOOIIwSxsHSpcN4nTAB6epAvl9EOiHYybZp4Xm9nHT84pK+vsk7GjhX2gdoJe28AxD0pZG7jRuCVVzChowO7zJoVEIi0cHL99WjavBlNkyaJ/4mA37pVyOtvuknUk3fe81L9+BMwOm/OHNEf9t5bkHVdXWikcjIUAGzs7BRthfD008K+7rab+P+++0Td8PDE7u6gze+3n+i/gO9/PJTPo8EL/2gsl8XxkyaJBZ7vfEf0s1tvFe3q+98PE+wvvBBqZ22QQD7Q+PEB8eD5CIDo00XXFaFwHR1oct3g2detEz8UQvvaayJMwMur2AXALZV8OX/IHwKC/kshBvvt55Pgz8rHnniisKXr1wMvv4z8ypUoAniK2nu5LI6ZN0+0u+22E59v2BCMc3/4Q+C7XHih8MN+/3ux6PGJTwj/b+JE0a7POgsA8CQvQ2OjINxuuUWE0KxeHdhcsvm3317RrzfTH+vXi+M7O0WbWrIkIC4ee0y0jT/9CQBrk729cItFFAC8AWDwjDOAgw9GDp4f1NFR2Y/22ksQ5xCTwkZqD0QmeO2LWrDfJubNE77iuHHBtXbc0R/T1wMolcviOXp7xfh/773i99y54h2sXCn6J+Hqq8X9vvAFMWaXy2LxZcoU4R94hOxWOn7cOFE3n/tccI3eXnENL2n6c2D+4auvin7std3t4I1zM2aIutxrL+EXHHusTzL7C4x9fcJuzZ/v38rvOdLiIPbdV1yL5iB8oZlAxB/rf60AXMcBLrpIvGvy84Dw4icQkP0zZog6LJXEPOg//xHPSLu/wfNV2trEO+rtFe9kyxZhsy+/HIC34EH26JZbxKSddo6jPnfjjUE4EQA/KLm3F+V8vtIP3203YOJENDiOaH8AGsplNLkuGgYHsZb646xZYnHv3e8WRARvn2SXWR2WpNvQGEVjdhHA65Qjhhbl99svuOa0aWKcWr/ev8ZcAC0vvSTIk7POEu//r38VX3pEmD+T2X57EbbkjQmtNKcaN06MiXTenDnis/nzxTj83/+K9/bqq8Je//734t0tWiSe//HHhV2e6tHEjGDx+x3di/x08tvJ/4U0H3Jd0aYnTQrmEV65S4ODws62tYnx/UMfCs5jIef+wtb11wu/DgjaPF9o6OoSiwcvvihIxm0AGbljwiOPiM7TxIZzb2WitHkzLvQGTnR1wXWc8ESLBvwpIV2FcBheeSWIUSfyiDrzypWiQU+bFuQw8VjLRkigTuO6IsZ6/PiQgcMzz4Q6Vgmeg1MqBZMH7nwDgUPmYczmzZVsKyCMwoQJATvqOKKu7r475JivcRyUBgfRAGHIp3R3o7epKRQTHqobXhT64+mnxQRn1iyx+gv4BrWbjikUAsdk6lSf3Cm6rnBm2tpEHROxI5E7BQBbxo8PVuaffDJwbp94QpSNJt1g78J7N530/xtvhIyPityhiX0DEORGIHiDlp/d4vnnhWHyjGorP86rw5mcdKKVs3HjxPuh67e1ibKtWycmzE1Nwknk73/ZMmDXXVHw6sPVJSrr7hYraEBgyAFhgD/3OVEnU6aISc4RR4jPW1pC7QJjxwrHcsECMZDQ6haD/6ydneF7tbWJQY6IBiIzly3zz6Wh0W8fHnqamtCSywU5ZgDhDBLIQSJyx8u9gM99Tky6Tj5ZqJkI3r1p+u6/zebmYLKz1XP5nnzSn/Q10uSiuRlOQ4NP+LnFIvDRjwpnaOnScOE9e8B7jvx8oUnP9dcHTtBeewUDc0MDGsEckTfeEIMqtaOOjqAeiBxbtEg4X5ddJv4nBRLU8KdVxaKoN1qNY/bQKRRQhKf0KBbFJKHDc4PavCfZd1//GSrInRtuEEQUx0c+Ioj0971P/D9rVlD/y5aJyX1HhyDMaDXoc58Tq3ClEgquiyI8xQbh7W8X1+D94Y9/FJPAq64SNpccC0JbW2AHuIPd2yvK3N0tzp87t8KuOI2NQT08/7xo/6xtd9IfntrNbw/nnCPu6ymdlKDcauvXBwQEAKxbhwau4njwQeFgU/vZYYfKa1F7kVZ/Q5ad25fBQaHYy+VEOaRdC0NlPOSQimtskg7z20GxKMY+Tu4Q6VAshkmK7bYTz/7UU2KsGz9eqLP+9KdgTPbG+24ALh/7PfvVIP1fgcMOC/6WJ09PPBG0pdWrhS+wYYP42bxZTGwkn2HB4sXiD77iePjhwkbM8fUUwcSSQAogIjvYCjMAoLkZjYODWC3twpQHUM7nA+ccEON+V5d4Nw0NwvGfOlWQ+ITXXw/a/CmniN+s3ZQgcqg1ui5Kg4Nhv4rgOIIM/vOfRbui8194IeSs+3avVBLj++Cg8BF23VW82298I7S6XIKnICmVgObmMLlD4+LSpcJuA6Lte3nXugC4DQ3+/5100XnzAj+K6ggQCxceKrycd7xDkLJem3ZcF1PBJnyNjaG+7tuo9euDCdVddwX9qrdX2OpyWdTViScK4o/av0dwPg3WPt70JkGa7LNPQB5RXTiOsDe33x6eHIH1PyJ3aDLHy3vbbcKOk3KNPm9ogJvP+76Fk88DjY1ogLfIoGoLgP8+mgE0kB2VyB2yWv7Y96Uv+QuuPlg/eQ4euUMLfYsWBeMdEasrVoQm2HjkEWEf5s4N+vdhh4mJ9P33+33Nb5fUjt70JkEaA+J+d93lq4VoYcOlMfavf/XvOdN7Zv86HJ7dKUn/+8QrmEKCjz3NzWJxjh0X8t8I5E8xcqcNHsFJ9frQQ6L9O05AohPIByU/aeFCcc+VK0PH+ovNNMa0tgb+eWtryA6jqUkoe++4Q8ytqM09/rjo9yeeGOQKQnhsKOfzkLxsMSZ4+USpHhsHBwW5MzAgFEWLFwt7fdhhgtyTd6ujvslsvGqxzX8GiLZfcN1w3hdayCdsv72wXx6O8P84QjxnuRzOEwmmli0U/MUwAFhK9rOlRbRtIvWonQNhG7B5s7DtN98sfJT3eAkw/v53Ye9pwYfNK/1+5+Wh9eujs1O8N9aOKuZDc+YIG/bqq+L+jNxpAYL2f+mlwdxPRe5MmCBU54Ca3DnwQDFmAoHScpQjI3dMIHaaO8s0CK5dG5AJLS1wGxqCjj1+fCBtVJE7W7cG39OqskzuFApBp/M6qGb4E5g1Sxha7jAz5VG5tzdgyjm5Q9Bss9m9cWOY3OETbLpGY6Mo66pVwvAef7x/eAdEI2t0HDGgbtggnlVF7lDyUw++emXNmoD4IYPqOTf+YEqDd0sL0N0Nt1gMEyGtrcKJo8GDfnvIw3PgyJEGgkHw1VcjyR2Vcqfc0FBhzLZCvAOazOvIHX+a9fzzYqLnfR56Js/w7UNJWXt6gnYlO/Lt7cKwb9ki6nmvvcSEhtUB5s3zHYUiDOQOe+4K52BwUAxOjiMmMP/v/4UnCRw33ywIplJJrN5+73vhWwAYLBSCif5LL4l2WiqJgeR73xODJE0umZNJrStMFwIzxo4NwuEIvM90dopB7Ic/FE7i1VcLh+iii8RgMWGCWD27667Qu6tY/25pEQMhDYIdHUEfLxTQUC6LyUVTE1AqhR2dN79ZrMrL9S+RHICB3Ln++kpyiNDYGFqxwsCAGJA5SUgOAg2IVF/0LlTkzs47B7egPwqFsCPB//ZWcot0XLEonOGmJuC880Qb23PPIEGnfO099gjIJ0J3t7BBRx8t/vdUHwDESuJXviLKfsUVFdWCYjEgd2R7WCz6Kj0AgeOw997C8We2AUCY3KH22dMjVn4bG0XbYOQyoQkQz0+E5vLlou0wssNX7njvwm8P06YJWyUTXhy9vZXJlgHgjTfgcHJn/XrxvGRXVeQO2QAiVLywFSW5c9JJYkXygguEk37yyZXvjuMznxHEHrtGQTrEH5dkcqe5OWi/c+aExzYaU70QN4wbF9gq/8LiymPY36IAogQl6f8K7LqrIBkdp5LcocWTefOEEz0wIOqafIm1a8OkGxCEhwBiTGpuDtrDjBn+VxUjuHwdOZFkczOaAJRZ/awHI/Y/+MHAP1m+XLTZXC4giadNE/2MJvmvvy76VqEgbGRjoyARvvc9oLMTnRC+QBPgE9tKzJolxqnHHhMTQ0C81xUr/Pfnj7cvvRRMtrfbTvw89JBYLKBwWAibUQCEDW1qQlO5HJCZb7whfsaMERP9YlHUMyNz3IYG3977/Y+SonM0NQlyyuubFQqjCRMqfK8Z8NpULifCvDnRIZM7Y8aINvSvf4lnaWgI/JSddhJ1+stfihBitmCyBd4YdcQR4QS2nAQ58kixYHLooWJclsZ2n9zZsEH4JfTeqS3uskugPvPg11VjI+CRO3lA1HFTkyD6yuXwBgYcjNwpkh0l8sAjSSqUO6pdxRi58zKYcmfdumBMA/TkDi3wOY5YWLr4YqHyXbxY9A2vbL7amN6h44iwm6VLxb1uvRVoaMDArrv6Y75LYxQLdbzmoouw94IF6j7i2Z0KkpktWPlPxMmd+fPFsXxhi9mI5+iPp54SE/qXXvLfSxu8MXHatMBWzpghxhMiK668Usx3SIVB5M7SpcH4wUIXfTUdkTttbaLfv/aa+Fsmd444QsydVqwQbZ7eG/m6bAzmY4NbKFSmR5g5E/AWgEueH9RYLqMtn0exXMbU7m69fSLQHIbZeNkf2yR97i9mHX64qMe5c4OFHI4zz/R9lMkAyuRHeZELkMJpfYshkTv+u2hpCZRGQPhvGhMJzzwjbMhJJwW+8eBgmNxRhch2dor2TnajtVX8MDtSMUbNni3aULksxjnPJ8q7LtpdN3gHjhMoK1XkTnOz6PelUiW509cnokQmTBDkWLf8lkYnMnJHB9cVsct77SXk/J/+dGiAwBtvBLJNmdzhnUVF7hAmTxaD9CuvBJNsIneAYGLiqXFC5I58XTqWOiN978WklqdPD5Q7DQ3hzv2pTwW7QfEylkro3LAhfF9iYdvagsnOpEmiY3s7IOCAA8QPAieskcJQaJUyjnKHf0fneQ6/X+dz5wbP7ThwS6XwYN/aKgyd6wbKHYYCvNX6SZOCwYavcIwZE/rfd0UaG+HmcpXKnUIBA729FcbsZccR5I5OueM52r5yh3ayUpE7xaL4IQPOHP2KcIC2tvC7mzkzWAGj682eHXIcXNN2kM3NYUMOBO+Iq8dM6OoKE3ptYVe4Fd6gxsmd1hCdEDbUjMygo3rkCeSGDaLf9vWJ+urqCk/aAfH/v/8tHLi//12sXvDJn+OIVU/WBytcUyJ3aLWVkwxTpqCxXBb9gZE7JUBLsoJ9pyR3HnsM5Vwu3D508FasQi3vxRfD5M6llwrZ8DveIZ6T2j4d4w2w/v0uvzxYOYHkbHBbxcmdUslfvfSf+0tfEhOLT3xCtM+WlornDhFHUSDVxvjx0c5asYiiSrlDoEktEBA2KscMEPVETgb1u299S6iAgMBJktAKSbmzdq3o/6efLsgRMOWA9y789kA2wQSJQPehkuePHRs45SpyJ58XfZPOPeOMcH4DICB3rrgiyCPwy18Kks2EBQvEpIiVrVM6REvuTJ4cjIOyLaKxmcKhVTtnsEmFk0S5k8sJf2HhQnW9AqEQCmzcGEwk164NTyqBsPrpnHPEMdSnTOSODJnwb2lBV6mEXjYW98Nb6MjnxXMQETE4GLRZ6gdTpwa74I0fHyh3xo8Xdvk//xGhlqefDuy2G8Z4E0kKfdCqNegdDQ6Kv8eMCSbQnh317V5TU3A8kTsKhPqIp9xpGBwUtnxgQJCibW3C5n/602JSy5U6TLkTIiy4XQfE2DFmDLByJfq7uqzJnQbXFaTQu98tFdwr+YoVwhcgO/z884EtJgJMVsIxJUMe3qRy/vywb8Dr69prhc+7997KfGA+ubNuXVi5s9tuQe4kQJD811wDt1AI+mxjI9x83i+HUygAjY1oLJfNyh3v8zYAebK5knKHxgPj2Ef+IRipsGaNv1OgDxqfli8XvgJNKJ95JvBvxo8XJH1DQ1ilB0buyGNNW5uot9tuA5Ytg9veHih3aIz6wx/Eb8fB2LVrMbatTe0ny3aIxqo99vAP8Z+IE/nUdkiZSwsIHvzg4aeeEjZm82b/HfvKnVIpsD1TpgT2c/JkESJIobqAIFt+8QuxMEZ22AvXK3d2BgtaUoSC7+fJ5M7eewfj4uzZQT8i/436AVh/95Q7obcxbpx4H6TcIXJncBBTvXc8taUl2l+QFpqBSuUOtcQQueO6og+efnqQ3kAFb27QDqBMbbqzM1zHHkIq1r33FguEvGwyucNtkGwzKQTx9NPD9o2TO2zXMN/GNTaK+3Byh887IIWtTZokjiGfZPVq0e68Z53a1BR+BzQPYG16CtkN+t3eHtyPFkXuuy8goG+8MUT6j2Zk5I4OFEf7jncIo/6Rj4Qbukm5Q52loSEYHAicwDj5ZEE23H574NBt2hRN7jz6aLCiTsaOBgja/vyMM8T/d98tjunuDsKUZOXO4YcLY0CD9eWXi+fdcUd0rFuHZggVCgC1cmfyZGF0SM7Z1+dvIUl10kz3Xr9eOLMW5E5oWk6SP+rssnKHBm/P8XSLxcDolEqivBQasmVLBfNdAIQzm88H72/HHQNHoadHKJK8UAg+wSzzd792re/gUmgYxyaIVSMieCKVO4BauUPnNTcHjhiVu7u78rrt7YGqp71dvGtatdl/fzHIFIshx8Go3HGcgDADRD/5+c/F+bKKwRYKcsctFIJB9PXX9eROZ6dwWLx+Qa1runRNP6a4uVkce/jhlQqC448Xn11wgbgmhX/JYINlBbnT3CwGQlqpl8gdX7nT3OyTHCXATMqYyJ2WlnCbV61esu98p4ZWTLduDRMVEycKm/DmN4uJBNW7twJLbcl/G8ccI9Q9nqOlVO60t4fuURGWRfelLerpPEm5EyKOokD1Lsv0VeAroqqJOyd3SK2iI3fa2oJwMLI1nDzTrB51AqKO+XU7OoRt8hR6nfweCDuykdCROzy/BGHs2EB6ryJ3gOCZ8nmguRnlQkGt3NHd1wRpZVR2nI3kzpw5wiawCS4A4Rg7jhW5M4aXAbAndwitrWFihqsTeCio6wZ5mdaurVh4CBFEM2eG2z1zzI1vv1AIxlBCczOcjRuR27hRkDDvf38QkkvPxklIarOc3CF0dgbKHZpsTZ8e2LPmZvR61+yk/qCbPPFFivHjxfv8xz/EhMLrz36pGhqC42fMCE9U2AREVryObWoKh0cMDIi2fNhhQRJjrx10wQvPY+SOm8uJOqUFP7KP9OytreEFJiprV1fI93JzuUC5o7LZdN1HHhG/VeTOsmXiXFmpyVTIPrkjt9nWVlEHLS0B6SP3GQ/+Ytvzzwvfl7//SZMCH+zII4Gzz8bgpEkhcgc8LMtT7jSQciciLGs2IJSF48aJ0J6tW/2xlXpoyN+TMXOmP863QigD/H7GfQQao0i5Q0TGwIDahs2ZI/y9H/0IbqFQqdwhELmzfDmwww5wikWf3HH6+kTfeughUUYKLaStymXowrLmzQPuvx+u4wR1QX3gq18NQqoBMeegRPQefPvx5JPBBNmbE7SDqVlpLOXkDv2mOurqEu/hmGPEZ1OnimfxIgncCRPUyh1AkJgSueM0NQk7f+ih4pjZs4O+RqT17rv7oTuycic0JtFcSQrLcsplODRHeOUV9RyFgy80e21LHqNoxKd2EfJ3rr9ezLN0YOSOy9u0wr8O+UTNzcIX33FHNbnT2Rn2o7u7w0TfT34i3vvuu4fPmzo17Jd49sL3amgRnUK/iNxhC8ChXkG2k+bIL7wg+rX3Psd6PoUPah+bN/vtbEpTk7gv9YG2tmCeQ9D5aKMcGbmjw/33i4ZywgnBZ9zpkMmdxsagY5Pxmzy5MrSCExiHHio61W23hVfrqBEfeKAYMD1H0O8YY8YEg85BB4kOTVLLUkmspH3sY2FlDYV+kDGleEggcDaI3Hnzm8Xq1eTJaPfIHZfKvXq1eKampuA8Uu7QZIYMfFMTujxWvJ1CQOg5VYazqys00Q4dQfene3uDVkVYFjkcKuWOAb5yBwje37RpgUM8Zoy4ztveJi7pn1iA29AQVu68+CIwcSLcQqEilI4INq1yR865A+jDsgBRj+SkkGMrr9ACYQeGcjoRLr/c32WAT3DLJnIHEHVK7/Xaa4Vz+fDDIjFzEkhETAu8kAH+7mSyhiYc3d2iTm65BZsp9ApAUQ59obpqaRGhYF5ywxDe9z4xgHzlKyL3iCqRNmAmd0i5Q5BWQBrL5SDvBA/LMk3QTWFZTU0oF4vBQBul3CFykTusuufkcJzQcRUE6osvor+lJexsEEnDVTsAnFIpCMuK8dz+tW3IjMmTRR+zIXe86zXBQrmzYYOwRTq7IrdT+TONcqcLCIdlAYFzwsNEcjnf8QmpEqLA37cpLAoQDvt73iMmAhLxXlG29nbAcSrJnVWrQhL7WKAxwrP1keTO66+LicmkSaKunn5ahPdxkGqVdn+KIHdCyh3bsCxefiLQly0TK9gETu4AQUJaUu6ce26wUyc5y8WiT/D50Cl35En8uHGVvkhzs3CESUnLJt8+sd/REbQTarM0dvAxhMidlSsrQ769e3V5pE4n1YmO3JkyJSjrhAnifVHiVEbulEslcZxOueOt8JabmyvInRKNA/z9y/ZPVu6wsCx/JX3pUjHZIj+Rq0B5aDh95zhB/TQ0ALNnC3JHtdADBPaFkoCSn/f660F5Z80S4xpTbgAQ7+djHxNFoc9UhOSsWUFYPyAWNOjdMJuylf6gsChpAwwsXCjGThr/GxvDyp1CwW9f8JQ7Da6LBouwrPdSyPXhh4s2++STenJHVY9NTYLALBRQMdWTbfiUKYKE4eQOoLeB48cDp5yCwebmsKKMo61NKAnWrxe+ikfu+As6NLZMniyI9Gee0S+C6sgdAFi8GOWGhspceEcdFW7rPT0VY6LfRp56KiB3vHL5yh0g6GMmckde1KbFCc8fcydM0Ct3AHVYFiCUr1OnCpWarNxxHBEOivDYUJbDsmihgi9ykb0hcuf116OVO0QqDA4C7e0oF4sVYxS1NWoXsVTHKnsDBDaAjQXKBa+WlmA3OMq5A1T4YQDEO6V+8/rroo7IHvCFc66k9955yAfk77ClRfzvzQtdxwm/B8pNRm2GxAE0f3ntNTW5AwRE9GuvhfuIyofNyJ0MIbz//cLA8wbF5XBvvCGcIscRAxcnd7jxk8FXz8aNEzHbf/6zmtw55BCh0vEabMgw0LUXLxYrKSTDA0Rjz+UCx3bNGjiNjSL0g5IKOo5wmorFoHPNni3uTYZ50iR0bdyIg5YsQb69XQzAtMMTd1JIuUOg6zU2+sbNJ3d4GWXk86H6CQ1QvN6YAfHvKit3OLkjGx0F8kBgGGkiPmVKmNxhx/B34ZZKlWFZEyYAiljfEuCrdkzKndC0SxeWBYTrkRx9FbnDjR4pdwh8ILZV7gBBnTY3Bw7G9tubVSMm6MKy+DPqlDtMCRFaqVHsBuOXWQfHsZsoM3KnYuoqkzs8pHPyZBRcVzi5nnKnBFHnjoVyh5e80y9AUzjPlK1yJy65A4Tek8qhdvP5ymS3uVylU+E5uCHljgq6nDs2DlI+L1aiLrkk+lh2n4qcO0ClPecTXxmquuSf8bbxxS/6IUhdQKVyh2wrm2yWSSKPKpQ7ciJHGWPHinLst5/+GHom73c5nw+TO089Veno20IaIzqlryvIHUBMoKiddXer2wgpEwoFNcnm2YZ2IDxJS6LcIXz1q4Gatr09TBRyvP56kFCUznddsYjz+OOVdblkif8eQ2+f8l/RhFRFYnEb2NwMFArIg4VlAaLfUvuzIXe4cke+l0fuUBiElvArlYJ3OH58OJzAW3XvAlMT77WXmPS96U3Czl5xhTjPu5/b3V2Zq061m5lMyHJyRwrT8idbEycKVdFee4n/2bNXKHfou74+0XYmToQzYQLGwgvLUtls6gOUHJrlNQvZE1Xfdxzgk5/Epr6+yhAejq9/PdjimI4h28Du59+ByB25DZdKImcQvS++6CUpd5DPi5w7PDxZBa+NLqZ3Q4r1//zHn7hSK44MSf7Sl4Djjqskd+T3PnlyoNwZNy6oMx25Q2hqCmySKiyLSFpP1eIrd4pFkZtq2TLg858XvuZrr+mVO7qcOx7KhUIluaNabGA2YaPjBGTR888HoS8sLMuvVxvljsrmU2hWayuctrbwYrP3uQ+m3OkAgvZx6KEiJLC9vVK5A4gFBh6GJefcee97RRoKAKCNLFzXOA5o4ThB/2xpCSv4PfjzH+93rIUpnXLnlFNE3ifa2VV3XT5+cgUOt6eEyy4Tmx0QuJCBfFfyf6iuPNupXURvbg61u3JnZ+AbfPnLwXioI3d4zh0g3IapvcnHyH4XKb62QWTkjglSTKof09zbGyh3KPdIY2Mw+JnIHT5AkAJnxYqQhLjCEHvGPMTOcqafVoRk0A4uixb5O+QUgMCYTpwozqVJ/Ic/LBKe0bUmTYKzZg3GF4viOclYUPlk5Q6BKXdo2GmzIXeAUP20aj7n5/oNeIcdhHHylDWQyR3V4MYQkqKff76IzWxpCe5Lv1XkTkNDYLS4cqdYDAYVz5lp8cocRe6Eakel3CGDxQ0bkY8qR15W7nDHjE/42KqQNbkTQZxZQxWWVSyK9knv3IbcKRYrdymRESW5tQEjKypcUwrLIhBh2N4e7ive4JOja5jIHaltDHKHuLHRPiyrsVEox5KSO+w4lUPtFgphAqZQEKvbPIcVEJamx8g1FIvcAQTxzcm1iPs066593HHAxz8eOD6mFSGVvdGRO/Pn+6uJLUAwkaR3KCl3/JU82VbYEJLkdDsO8Nvfil3CdJDzdqlAZfN+V0jgH300Obkj9XW5xyrJHSBapXXUUeL3wIB63OT9itvXuOSO7FxTfY4fr58ovvqqUPHI+Tba25W5FtDVJXIYQSJ3aLckWuVVEf4yucMn39z2U1ul38ceK8iARYuCYzo7xbi3dq1auaOavJsmT9THJkwIxqpCwe8nebAJT0uLyBVDybE/+MFQGKHb1aXciABAuI1rlDuhPgkvLEvua1S/PORKR+7k8+L4iRMFIUATa5NyZ/ly8WycULO0124up1Z5EHbeuZLoPeEEsfDI2px/DZr4q/xbft+GhlB+Itoty19IY/5hlHLH31572TJRD//+dzzlDrtPp/yZ7FNMmiTa8vr1oh+SbxFB7hS4zZfbuxQG4xSLvnLFKZVEvpo77xSpGjo6gk0/DGFZOsKuot0B6vGILRo6YO9306Zge3OVcudNbxJRBfPm6ckd1dhBfbKvzye3CqqwLPpbZ4cJ1J8kMjlE5sjkzq67BuORNycquq46TNrGR2Q+cLmhoSLpv9xDY4WUM+WOy9v09Okifx/z4ZU+ES9/c7NoV7qFhWOOCSvuuQLyvPMEAUzPSuOASblDAgNO7nR0qBeiqF8RucPnL/wZKCUAINoZjd06AgjYZlU7QEbuxMP++4vY5yVLAuWO1/hCW6b29opjvaTCIXR2Bo5Td7do2HK+A7mBsnAB//899xSJr8hRVaGzU7DcN9wAhxMQZCjOPFPkFSFIO0L5ne7FF0UHoo5L5Zs3Txy/zz5BJyoWA6eD1UlFQ9MRAmwAVYZlAWqjS7Jbcq5KpbCKwSIsyzeMU6aIrOqAnXKHG17K+D5hAsDJna99DTj3XJ/kykMkltSRO6HSSsqdwXw+MGxUF01NgYNpo9wZOzbILs+/s02oDATtIII4s4Yu5w6gJ5I05E4k3RS1KmODqLAsKlM+H0xWenvDTosXlgV47T0GyVH26qvsEWBujLCsRnhONp942w6E7D0pyZ1crtKJ+ctfghUzD6GwLJOzU03OnTiIUu709IhEz0TOVUPucGeytzc8uaG/JeKEH1NmyZMTKXe6uoRNU0m0CTbkjqzckfvec8+lptyRoSV3TLuFAQHxoYPcPwnyinlU+5NXoltbxTNNmFARguyDknBz5Q5grgu2wyEAsdPf+98v/qbdVVRjguz8FwrIedcJ2X5y5qnNNjUJ34ETQJ2dQW4FTVhWBUyhetttJ56rt1fc63vfE84/c/zLOkKAygOIzRU6O8OLIrwsFmFZ/t/eWF2AFCYBqPMQyX2Bk44nnyxIsqYmNMEiLGvtWvEeisXg2WzHXb4VdNSYTjj3XJH8lrWb0BM3NETvOiPXX6GAEjxf0AvL8hGRUBmrVgXPPmOGUO7EybnDylFRA3I99vYGO4u2tlqTO1q7Id9DUu5U2G1qh3LICcEUlgXhM1XkelLVieP4Np52ifT7w/33i37iPXOI3NlxR5FKoqUlmXKntxdOqRQsNqvCsphyB4C6ffAFbgajcofXdUMDWmnROYlyBwgpdyoIX6DCD1WWQwe+O5/KNjA7qw3L4n87jti9ljYqkFEoBLaFkzvbbx8kSweUyp2y44jz5UVY9k7LHR3q5y+VhI8jK3eA8DtwnPCcg74zKXe2YXInZe94lMNxhIHq6AiUO15jduSB7Pbb1dfI5YRTPTAgOoNqwNAod0LsbC5XubOCCp4BdDjZQUbolFPM51LHWLVKsMUyudPdLSTJQJAfoK8vcFpNzpvOWWVKgtARPCxLOnewUEBeVpk0NIQH+4j8EkVA7fholDuhnDv83dNgMXUqkM+Hja5koHNeOUNQkTtMudMGIbv1S0qGraVFGPvW1sq8DEAluUNb2m7cGK6bJGFZNVLutIFNsltbRWJRU84dgqweUCEN5c68eeL9bdlSSe40NQWDYGenqHMKgZRXzL18G62AedWRkUCu46DY2Qm8/DIcIphjhGX5GArlDv/NQMqdkJpQBe+7CnInbbktJ5FM16Y2KCs7Vcdw8DbHncm+vvCuJpzcefnl4D489K1UAvJ5lB0HLWRzbJQ7HR3i2cie6SaITU12jpFE7rhyWBaQKrmzacECNN16KzB+fNhR3HFHQWice250PXR3CwWAyk4CkcodowpCV376e84c8UMhYXybcyDYhU1W7pjslWen/Rbb0yOe8eqrRWj31q1BOAsHf7aWljCxz/sr2VZNnigA4b4g7yAl38v0GeG888QqO4Vqs6T25XweucHB8KKKDGq73q44VsodTVgWANEm2P8V995xR7HbEQthdLkP8v3v+/lAAIjdlgDg//7PTO40NIj3Wy4HPlBXVzjnTgTcfN6+zcp405tESCEUO91E5ezi9eflJwkRo1GTdyB4V6tXi+cnHzxJWBaVQ4bsv/T0BGF7ScgdemccvG15yh0/54481nC7myAsq2Jxy0QCTp0KrFiBIl1v1izh799/f+DfQPhim1X1utNOIUWdkdzhxzQ0BPbbRrmjah+HHy52kJRUZyEyp1TSkzve4hIA9Vhu4yMyMiNENu+1F9y//x0Oj8hATN/Fq5d2QN1ujzxSLDZ94hPRYVnUjvbZx3zPnh5hW3hYlgyJ3GkBMOAtDFTMCeidFgpwW1r05FZvr8iRB+jJHbre668H5I4cupiROz4y5U4StLcHW6Fz5QQhKnlkT08lacARFZYVd4AG4Hh5NgDYTQKAoKOsWxcOy1JN5sk48smiaWWtmrAs6f451QDIn7GhIXKVqwSo69VGuaN6zilTwsqdYlHvvCnKHaqdzk7/uEZ4eWgIVI+krHrmmSA0jUMeOAGRp0iWVsdR7qRN7kireX5YFhCUOWlY1nveE952Mg1yZ9o0YP16DHIyAwhCNXk4g+OIvhGh3LHJudMCMcGh61DSV7dYDGyErYPL+1UCckdlU0I5dyIUOf7qpYVyJ3FYli24csd0bWqLJsdBrsu2trDDzyeZPT1q5Y6sECoU/CTntEoYyq9gY9cdJzz+qOziBz8oVviiJm+8bN7vsqrv6fLLRCGfr7SPLLFtyLYWiyIsV7MVdgXuvBO46ir1d3w8SSvnDl3ztttE7g9APfYTuZNAuVOQ/se73y0WZb78ZRFGIUMRlgV4z2cKy1KBT45oNxrdvQgmP2nBArHtuwKUaydnqhOubimVKrZC92FS7sh2mpM7qr5Gm1sQvHBbAKJOVONkUxNaoVnoAYJdKYFKki1GWJa12kzGIYeICTQU5E4UJJsWIv3zebXNk0HvynWD591hB+CJJ/zNEeht+r0xIiyrArINlH1NW3KHyqpq1xK5Q+pZAHrlDmC3Fbqcc8cbV5XXk3HDDf4icTMQqMsGB0UYNa9LVZufN09MsCnnpYncmTbND09VLjbHVe4UCiJUWhqnKsgcg3LHR1LlDg9D8q7X39gI3HsvNkth6ECysKwmKMhkQNSJp6hR+kRUfh7OFAWa58QgdwBPPQ5Uzgn4b3k+xNHXF2wqwG2yityh35lyx4iM3EmCjg5B7qxf7xvfULytidQAROOlBmxD7rCwrDIPyYkDbphtyR3eMZqaKtlYDhW5k0S5w1ZTQ+bIEJblqO4jD0oRBEQjNBM6uq9tWBZh6tRwzh0duSO3Fe9dhzomU+4AGnKHfvf2qg05N3pUF9/6lkg2yxFHuaMjXKoBa1tFMCc6ZlhWRU1//OPhne/SCMsC/Al36C3Su5DDGS66SCSRkydVMcOymhEmd+i3KxOaOshODbWXBGFZctkAQe7YrFA5pZLIP4WEOXcSkNxGxFXuxAnLUjnY3d3i84YGu7AsAINeuWiCW7as6xBmzAhyd6je5Zw56pBiFWTljqrvnXWW3bVUkGy929hYmVg8bQVXWmFZnHjnodj0+ZQplcQXV+6USsE9TPZKVu5E2W2CIqEyoCD2VepIGZzcUR0XV7ljQMEbA0qmSatE7viIQ+6wPuk0NoYJbBsfysbnampCBynvdDZbJnfoty0Zz8mdJDZTVqwBVuROhZq9UIiv3OGf0/PuuKPIUempIio8yYiwrAqkRe5QWVXtWgrLcjgJLpc3Srkjvw9Fzh3tvWVMn+4T4g2AmGTT8bvuatfm+edz54rcLYcdVnlcLieIwksugVMqBW1BXsCjv+MsmDMYc+7ofCRbmyWD+d+Nnr+X9/qral6QJCwLgH5OqcrHqiibNXp6RNiwqb6ZXzvojTVlsis65U5rq55kA8Ihq1HKHfqtIneynDs+srCsJGhvF7LNl1/2J/0h9UjUbkHf/GYg+4wZllXO5ZIxclEMvAryICMnVOZIi9w5/3yRT8jb0WTLjBloOO+8sBMsn6u6j2zEIwiIBkDtrB97rIi/psmQKkROft+UMLEK5U4InZ3hybOKnY8aiOideVveAlAnXuaOw1CHZQGinKtX+/+6uhUBgmrioRo4m5srwxBSQojMoHsBlSvelAfjj38MjmXZ/PPQrNAQvLaRA7A1n0dRJnfkOGYdZLl8e7uI70+g3AGAwVwuFBbpFgp2K1TcMbFQLNERvh1MQnKbwEiDzSbni57fJizLccKrzhxdXcG7MCl32H3KxSKwZYvfTkJEr61d/9//Va+WEuL0Z0VC5RAuvTQ8JsRFS0s4dEkid8q5HHK2ZIYt0grLirKP3/++SJRLu3cB4Zw79Pv11+OFZdlO4BU5dwDFQkcc5Y7OmY6r3DFBsnvG8qjIHb65Q09PEPZkCMtympoAx8FAoYDCwIBarSshZMt1dr2pKXhvtuROXOVONWFZ7JyQdVHlVZIh2zRZ0Wmj3Mnl/NDnkHKHocJSm2y3bVgW/y5t5U5nJ5xSSU/K8/dqQ+4owrK091ZB9humThW5RXfdNVxfNrugFovANdfov6d8Zyrikz+rjXJHgwpyR6cYiVLuxEyo7Hjl9+eCijYdayGGlc/R1b0qHyshCbnznveEQ8RVYONBuVBAfutWf9FJS+60tJiVOwsWCBUZkI5yp6dHzNsycidDLFCDeemlIJkXd9KjJh081p8PGDong02CIkNldEii3JEHmaFQ7uTzoclM//jxaKBJse5cxX1Cq0Y65Q5t3wqP3FHV7dSpwGc+E/yviHfOy+WZNElcy9sRAUB15E5Hh8ivkcshVy4HEkjA3oBLK+xaDGdYFqBVrWnb3rx5Ij/DIYf4HylXmBob1THIKaAskzuyckd2HDRhWQCslDuA925kcsfWEZNXhOKSO9I7KBeLIZVdnLAsQEzSjcod6X2SgjF12alXhlCSRxXihGWNHStyGKhs5vjxIh8KoHZkVcodry5cptyRyx8JnmeEdqLbsCH4LI4zKCt3+Pt+5hn1Dk9xIJUlx8jQHID+pIsdJugmFXI4RBSpFGWbvaT7PtraRC4/+pvOjSJ35ITKSZQ7jPSvCMsaP16fH5BAY7Yuv1KKyh0lIaorj4rc4X2mpSWo9yhyBx65OjCQnnJHJtpVoHdfT+SOTViWtJtjBbljO3mnvHRkB+fODUhzCYOFAvIm/1tuM45T2Q5lcmfsWFEHaSh3PF8u1O9NOXdU/d4i504IUe1EbqdTp4qcRvPnhyf6tvMGG6j6Bh+LqiF3uG/ghWWF/iekEZbFbbxslxT1lSQsK3RNGarFZlXZbHHoodHHML92sFBAcetW/cYnUliWDxW5QzAp12zJncmTt3lyJwvLSgJqQC+/XJlzJ+5qFE8UTI5RVFhWEtiGbHDIYVmmnDt0bLU5d4Doya58fxvlDh9cHSecVBSecbQxuF79c2PaIBsQvnUroVrlDrxdsoAwucMTKptgu7MVD8uqA3LHd6J1IWClEvCNb4TZfvk9NjUJx6GGyh1lWBaFXMmOgyYsCzCs0ABAPg/Xc1wHuXNMv22VOzIJRPbM9j3Kyh25ndiSO953BdiFZRGaAD/3TKrg96k25w4dQyvcKgf7W98Crr9e/G0ZlkV9n8gdcqrKuZz9pF6G/N6rIXeSjDMmSGVp9HabLHv9IPF4aII3IQAQ7qtsUjVoU9c29pF/x7f0lc+No9xJIyyLt/93vAO4917zJJHuORTkTrXKHfm529tDz+9DQe74K9Q2bdumL/BniFLukK8YNyxLTmQcFymGZSUmd4DgeZubtaRxxVgkQ/ZJW1srF2Nlcufd7xY5uqLaq6ld8k1IAHtyx2YrdPmZ4yp35P5xzDEigXlzczLFvw10z8/9VL4RSkzlDr+2q7tXmjl3OBnFE2tL8EsWV7kzVGFZNjj0UDEmbLedrxzW5tzhfrupze+8c/C3bJ85yAbwDQf489H9yD5l5E6GWFAx69T54pI7nHigWEONciePIVbu0PZ5QLRyZ/x44C1vAQ4+OPgsiXJHLp/KCMrnKgyfVrnT1ib+bmsLOUfW5I6KKZefk5IUy8+hMtAGcsctFsWg4zkbviHVJVSOKndTU7rKnRrn3AFQqdyxuZfcvlUEWIrKndDW3/zajgN87WtiK18Ok3LH1Dcdx59UugpyJ1HOHbI/bW32q7qyckeO+bcNyzIN9rrjINpmzcmdtHLumMidadOCSYqK3Jk0SbRZdh9KIkv5Rsqc3EkKSvZME+I4zqApLCuNXDgaIp9InZqQO+w+KuVOCfBJViNsnGuu9OWhLly5E3UNWbljWyemhMqyumXRIvO1aJvjSy9Vf6/yBYYiLKu9PZykXkXukP2TkcsF9lgid2zCsqwmxnHInYTKnZrk3LEIy8pRX83lxDjAc+7YJlQGKskdoCI0iyCPRRWQ61i3qyGVp7VVtKWlS83X5eVUtUt6h/TuTGONZULlEoAyUEHmVqjKotqJXJZ3vStYdEibrCfofB7u53FVVQxbUZafRzcmpRGWZVLumOrLZq7B60X3/N5CRwHemCTbbSBVXxeAyNn3ne8AxWLgf+hyY9qSOzytQi4X1KH8DuIod4CM3DHBcZxGx3H+5jjOg47j/NtxnE8ORcHqGtwQyModm0FfBq0U0MqdboKLlJQ7tuROLhd0Fq7cUQ2IhQLws5+FtyU0GCSbLZ8BTYiNTc4deQLrbcXpO3KtrZW7/iRU7vjvnFbWSLkjG7OYyp0tJ5wgQv+kCU1oJSIOO69zYjmGcyt0oLJ8UQmVFahoMzK5wxOVpoCKPsnfxdlnA7vsoi4P/W0blgVpUpsWudPebj9RACqOrSB3bEOF2HdxlDsV90gLcckdm5w7Y8eG7agOqonOu98N/OtfofbR7I09jbQ7lVf3g9W0Z7KHSRMwst/WeZ9soSHyh5PcsSYXbWyW4wTPyCeQCZQ7Ben/SGhy7mhDlE0YP16Eyai2XKfry0hK7sQNy5JJFnk3NJP9k+5FE0ejwpLfS/U3hw25I4dlxVTupB6WNW9esDuSAaR2ylEdJkmozL/jz0tkooRIO6hS7sigHQV13+tgE5Zlo9xpaQn6cETOHZWPFiuhMmBupzZ5X5JA5/O0talJnVood3gZSNHPUa1yx1TmtJQ7CMaiijHJVtVfBXyyu9qwLECkK6E5sC6XKLVlUlvKx5CN2GknsYHJUUfFfKLRAxsvYAuA/V3X3RnAAgCHOo6zuKalqnfMnh38Xa1yBxADiePow7KYsRpS5Q4QlsKalDsq6OqCK4JUsFXuGCSbOVm54zjBJIaUO2zb3EbAzvHxmPI8+z+00v6ud4kkzHLZk4RllUqh78lxCQ1WtgmVAbtJfD0kVGaoCMuyaXtym5HrKOXBrqJPRl0/qXIHgexcpdyxDiuSY7lnzgzvVhAFUmlQaIx8L/6/RVgWALOzo6iTmit3TO8hTs6djg7hsPCQGxVU5E5TU+V7kSabflhWNSQHkb5JyJ0FC4BbbglyXo02ckcTlmXV/mzrk74/+ujgM9lWmex7UuUOb3MtLeGEymnXq1z+QiG5sitmWJZWuVMsip/eXn3Sb6m/tXgLOK1R+VcgTYbTCMsiYmDBAuEr2uaz4uROlWFZ63t6gIcesiOWZBJODg+Lq9zh9lZD7kQqd/h9cjm9P1ENuWNS7tiQO44T1G9Ezh2lgrDanDsccRMq20I3TtAiA9nXJOkuZL/CJiyroaHST4ybc0cqq5EMi0nu5AzPT2NgRahwrcKyGNw4yp0oJeNDD4kNBoBocken3KF+29cHfOlL1ef8G8GItPSu67oA1nv/Fr2fykxm2xK6u4VK49VX01PutLaKBtnZWemccXIn6aQmaexsRwewYoXoQJTAzpbc0dVF1IAZNdnlEtc1a+yUO3ReW5tIWl0sil3LFi4EzjlHfG/p0JZzOeQGB8UuQY4TPGdzM/Dtb/vHOfIkNwm5w+9L5E7SuNrPfjacTFUF5jj0RzlKtQjL6u0VZRgYAIDAMU8jLCvOoB0DFRMh29h8+jvGhNif1KrIHe/c/nweRdukkg0NwJVXAoOD5jJzHHAAcOWVWPvtb6Pz0UeVYVk+bMOybEkgDzXLteLBqCSaPl044PI21hxNTSKUZeFC4L3vNW8jDdglT+THSbtlVVUf7e3hrWfj9GfHCas1bNVPtiC7ls+LNjpU5I4qHCCOqhGwt1ktLeJ98qSSdH0b5Y7X12Pn3MnlxPNt2RJKNFwEkik8TKD6LJVEEvFqbLDNhI/U1Z2dcHiycE7u0O+vfS1IbC5DIijy3jmmlXQfNj6XTUJlmRhYuBD473+j7+/B5aRKlcqdWP6nTO7I40LSnDsAcPzxwNatWHfppWhju2vGInfGjNH3TZokxpkYm/yLXC6881aUj9vRIRKpR+TcUZHMtCC2pVRCwxlnqLcl5zCVJemicBR015XV5Skod2KROxs2CB/0+edj75ZV0d5NNiJmQmXH8Pzkf1a0hSEgd7TKHTnKo7UVzpYtwYkq38BmF2BK7j9mjPqYWbOA3/8e2G+/BE8zumBlqR3HyTuO8y8ALwO4zXXdvyqOOctxnAccx3lgNTO2oxbECFabUBkQE+7OTiEj+8MfKr9nA3Lk4KVD0thZWi1paoqv1NDF2UcZm6jJLp1PA7DCiFYod4BAubL//sCyZeFksvJ9DagwpjpjXqVyR2b+fXInqXLn+OOBffYxHzPcYVkXXwz8+c/B/2mSO4WC+K6elTtRYVmc4NMk8ItMKik7Nbp8UKbzL7wQg1Kogg9+/zSUOyzXEGFYw7IWLRI7YM2Zoz/GcYC//hU48USRgyuq3fIdVEzvohbKnYsvFsRvCso2sk1lIB2CQA5JkcidmrQDQK3ciRuWFUe5Q2pWOVGt5TXKjhNfuQOI52tuFve2JWWTgOqRxuykIVmA3eSptRX4+c9F4k+dcod+T5sWVmOb7kW208JehsZvnT2JE5bFN9+IgxRz7tSM3Imbc6exEXjnOyv84Vg5d3bYQb+y39MTjI22iJoDXH01cO654u+osYb87oiwLKUd8tr7YKEgtiXnqRJUMJUln8egRx5bEZq20N1z3LjwAmQScke+ti25Q4ohIoarVe6Y6sumXTG7ZVTuUB5GnR9aS+UOkTtUVnnTnThhWRy6ec0pp4hQ9c5O/TEHH5zOwtIIh9UI7rruIIAFjuN0AviF4zg7ua77iHTMtQCuBYCFCxeOfmXPtGnAAw9UKnaSGMCPflQ4ITp5sDe5yZfLwxuWRfeuJiyruzva2PABOorceeIJ5X0c1YrY174mDPeuu6rvZenQVsS4akLyHHnSXi5XXkx26AzElpLcSduA8zqIamszZ4rQtvnz07k3INrHwoXB/1Q/Rx4JfOITdrH+urAsIJhIpYjY5E4+H4QK8qSdQCrKnci8AylJrX1iIQ3lTsRgTPbPv8dwJlQG9LsCVYPGRqC/Pxm5U81knAjfa66JzocWAXLyBvN55GySDkeBT2xXr073uU1QTSq8e+VQA+UOPee//y1W7OVr2JA7pKyN0zfYFuhDQu6MGSO2V661cgcA3vxmABLJUioFSVZtyqBbjY+h3BnI51HQ9YU4YVmqpK82KBRSy7ljtUscwUTuyAmVbXLuKMJgZVI7lnLnllv07XzWrCApqy2i2uWppwZ/R401prAs9j42RZE7NojwPwbzeeQHBoZGuXPFFUI9Q6hCuVMGkDNtO69S7jQ3x0tEbEioXDTZfRvyIZfDVoj3bFLu+D7hMCh3/N076Xn6+oRfS7vXtreLedqMGcB//hOcmJTcKZWCkMwaKfFHC2KN4K7rvu44zp8AHArgkYjDRzemTRO/X31V/K5GuTNjhvgxoDxc5A5X7syZI1Y6dKtcMuS6KJXCuR10cBzhEA0Omgc+mmCpyB1uuGmQO+CAymux+nRihGXx39qQPHlQUTl4ccgd2ZAC6eeRYXUQ2db6+oCnnkrnvlIZyo6DnOsGddDTA3z843bn65Q79Pdwh2XRMdQeEiRUVip32MTaCNsQoAj4qzayE5kgoXLUZHIwn/dD9YAhCMtK05G1RVMTsG6deQypBblD4AqShCBiNbX3o1FpDkvOHW4bbeqoVBLJ1Hm4lQqc3JHzolk66C5X7sSZgPNFG16XtQrLIvXJUJA7HirIHbq/TRnke9nshEP3ZfZY2zttyJ0TThC/o0I7NQiNT1Xm3NlYK+WOqT5Vyh0PFcqdqEkjlaVYNJOuH/kIcOGF5mvJiDPZrEa5w565rLBDRLBb28aIucFAPo/SwEBVvoL1PXt6wlvRV5FzZzCXE6EpOv/KRO7QBixRmDFDHDd1akBeeGXNm8ps2Q+3wCN3DHVPCw3ajT2GUrkzdapQ1uy0k/i/WASeew5oaoLzkY8EJ9qSOzbhmhm5o0RkC3McpxdAv0fsNAE4EMAVNS9ZvWPPPcVvMsLVKHcsMJjLiWRHw6ncmTsXeOYZ+3OpLjo6gLVrxf+WO/MMErmjKuuSJcA3viEM5C9/aSR3IvOPJFit9I0pXVf37uVBRfXuZKfGk8HmXVcflsWvm/Z2h7yMtVBHWGIgl0NpcDDRJLsikV29KXcA0WZV5E6UcscmLGuolDs0kZcH6rQTKqPScal5WNZwkDs2Y4hm955USI4U+oZrSzDa4pBDgKefDlZz5bCsWil3DDl3+P0j8Y9/RB+z664il4AKXV3ivhHqHzeXQ5HyZsUNyxoK5Q7fWrqhofZhWRwq+2pL7lQTluUdM8Bz3uiuz68tY/Zs4LLLosuqAx/Hq1TubKiG3JFJpkIhUK6afDQDuSP3/0hyx/b90Q6rcRCHiLBR7ugIhqgFOO961sqdiLIMFgrAli3p7pZlq5KtIizLt9E2u2VRuCb/scHOOwOvvSYWre+8U3ymUvlt3hw+zzJsyM8EZtotS6fcoZ3HargduHLBWVby811qCTbkThTBlpE7Rtj0/vEAvu84Th5CkXyz67q31LZYIwDHHgvcdluQuKka5Y4FtHGVtqhWuZOkA1FddHYKcqehAfj6160GTX9yoDq2UADOOw/48Y/F/yrDRxNdjxTTIklYlmXOHcdmAFM8n04GqyR3Jk8WRtyU/yMOePLuWk2cLDDoJVBN5FB4dT2QywkDJydcMyXCTYBQn9xhBzHgR4GTOwkSKpuUO7Gk6SmQO3I7qVVYFkdNFBusrM5wtP0E5I7vyKZR3ne/W+QjqwY0sUjr/eyxh/h529vE/15b9/Oe1Vq5owjLAiyVO7b4whf03515JrDbbpGTUddx4m+FDmiVO6mTp7SrZFNT9erJuL6WalKRlNyJQyzZEJ226pVqUK0iK+mCT5Ryx3Eqc86pYCJ3ZNLfduyrRV2nqdzp6dFPyjnJbFCDW48JFmFZQA23Qje9/yrCsshn0Prhqpw7/McWlKZCs9iGzk6Ro4/D8t209fQAr7xibK9aFWt7O/Db3wKLF1vdKwl8H9BGyRg3505UPxoCZdJIRmQLc133IQC7DEFZRh4OPDD4u8bKnarJnaS7ZdGAmoS0onO6ukT2+cZGkYzUAv5kLqnh984biDKiMdQDBG3OHZnckZ1KlWOkMIoDuZyIkZee3XdceDl7eoA33rAqtxXqRLnjOxQJ+hM5IYP5PAo8ETgA/OpXVYWdqEBOVH8+j+K//213UtKwLJ53SbP15ohR7sQIy8qUO+pjUg3LWrJE/FQBsnmpky6yYkm1c2CaiAjLskqonAa6u8N+hgZu0oTKJ58c7BRVS+UOEDjsTU3VLYIlJHcGHQd5qpuWlurIHZvJjHes0R4PAbkT6iNVkjux7K60s5+jsveNjdG21pRzJ2lYVi389DSVO5dcIvqmCpxkNviUqeXc8a4zUsgdSOSOdc6dt71N5HXbc0/g6KPt76crK12/o6OS3LGca5RaWwW5YwrLMm0ucOihVvdJCl+xY/E8FfMhE6ZMic55NXmysE061es2juFbmh9tqLVyhydSTYI0dsuKC07uALEGVH9yn9TwM+WOEQly7tCA6kaQOxWDCr07xwm2ldcodwDFblkqCWTaqCflDpDM4SViL5dDw49/DGy/ffBdLUgB75qRKjGOhGFZ/kBuCMuKtWNICslzK9pJDcKy5NXvWpM7xq3Qa4XhVu6kgbTDsggyuWNyaNOAamyppbKlSpST5tyh3XuA2ivXTjlFkIf33jssYVkDuRz8t/b5z9uFK6SwW1ZdKXeqyLkDpLhbFl3TRrkzcaJYZFS8r9hhWfVC7kSN+RMnih8Vot5H3FDdCKKJrjMku2XJqCIsSzmHMJE7J50U/M839bCFzlZ0dlYea+tfSASpCq682DyEyHsEecFGPRNHufPJTwKXXmo+Zp99RHL+WmxuMQpQJx7hKMAQKXcST06ThmUddRSwfLlgUuOCYqoTkDtlDcERgoVyx7SFoF9GgqXBrQgJ0JSjYqWKnqW5Gdi0SeyeZSJ3dMqdWqoKaplUMwaoDnIJHF6eyBKHH55quVTw20OcwZWHhqWs3Imc6NP9CoWq3rFPLOjaKZBaQuWhVu4MS0LlBOQO1VvdkA1DpNyhNlbTnDuFQriNJp3kDgWSKnc4am37v/518XvjRv3E1QZJlTv8mQ47zO7cKsKycqTcqSdyp1rlTpphWYAdufPOdwolheI4Y0iwChaT5cTo7Q3vFGRCnImuDPYOlD5HymFZ7X19wOrV5gTBcTEEyh1lzh1eJ9QGHCcdpaIpLEtTxkhYkMk1X+gwYKynrum1sOexlDuNjdE21nEyYseAjNxJC6M1587UqcAXv5jsnhRT3dYWxNxbYtCGyDA5Wp5RbKPdOXRIsFppG5ZVMYBzx6JcFgSPIcbZKudO2qhWwp0Sqonz9kmOIZqAJSJ3vv/94O+kCZVpJdP7XfJivwtRWy87TrAlcDXQKXdsV4rrOKHysJE79G5Mx7Df2tC4YcJQhWX5fa5W5M473ynyZ3HkcmJ7XaSccycFlHO5IGlvUrtX67AswjveUd35CcmdgST1opuwxQjLajRtIJFS/jMj6jXnDn0XZWuLRW3ohdz/i5T/xLZMaWLKFODRR0VevyhUQ+4A/qYbKrLNepGHEOF/NFCd1iosy/T8vb2CHIljj2hMpJw7rP8XuN3mfTkNe64jgNvagM5OlJuakHvppVAZI2Gj3Kl1/jkDfDVXjN0DAVTve2aIRJ0tP41g1Fq5U+3uIOR0A0M7aR8zRuQNaGhIPyxr/HjhbEyaVPkdnRflOFSh3IkKywoRExSWlcsFOzFodonwByXJYNJErqYTz3pT7iTJuVOr0BAN/LwncROZUs6HGLleXL4atWCB2C3Oy8lBTpjNKgoaG6t21vxkenK/sVXkxHBwtwlyp6kpIHh0kJy9mitY4oL6Xq12WxqqnDvbb68kISiB6XA40ia4jhOEHNU7uVMt4u4QWc14UIVyh8bvjt5e/UG0i2Za6gEV0lTuxDlfqitH1b6qzL8kjz1jVb4gB/lgNfLTMWeOXf+rktzxF3sV9yp6/cOxrdeostBYWKvdskxj7QUXAPffH+vSfkJlaQ5RkaIhbRWXjghubAT+7//Q/973BsfavnOqmzpV7sRqGxm5M6So4xF8hKG7W2wVLm8DlxLSSqhcLhSQG8pVx1tvFdK5730vlhG1CsuaMgV44QW1DNbWcFeRc8d38G13y3KcgNgplwHaulaCLoGda0tYVYM6ySth9f41sMp1kCaSKHc4cjl/JS6S5OCEiuOEE/957cKqzhoaqh5gSRXXKqvjbNVfccKyZPn9aCR3bKTIsq2x3SFtqDBawrI0KOdywOBg3YVlhcqTRlhWvbQnFd70JuCb3wTmzbM7fpjIHWsfpKkJGBhIPdG/j3rJuaOy90uWVDUOyeNAZF4Y8sFqRe7YokpyxzXYoRYvDcIE211Bo5TD1H7THBNt1cptbWJeFQdSziEtuZPPCyIureeaMwc44giRkBkI9/8ZM+BwBV/csKw6Ve7EaRu+gspxwgqqDDVBHY/gIwylkpBk1ghpKXcK1WxBmgQkbbeZuDDoQpMqoMuUnkS5YxuWJU/mKQxGCocJTRB5guAockdDbEycPh0AMDZJ/iNb1Mnq7Ugid3zlThX3G8jlkB8cjFbu8Jw7MuKQfymQO+09PQCAMTK5SvUBIGeaDFQRllUTMiOXQ9lxkHPd4SN3otp7nZAcOlCOrNQdTWlVtObKHQ38rYfrjdzhznIKyp2aJFROC83N4UTQUdBN7mzQ1RUmA+Ks9tva46amYMeyWqBOwrIcFel/5ZXxy8NQ0f9t7HYKqtWqUa1yx+vvSrKN8k3GTDhe8bf8Wa2UO2mrOOSE0qb+T/54GmhtBf7f/wtfm/1OFJZkkXPHuFtWrREnTJW9hzoeXUYNsjoeIXCrVe4MherDhJjkTjWTewD2Roc7tEnDsiZNAm68sSJJozLWtwpyh+S2SUKVrJHUkUsZpF6qJixrqFYyEuXckTCQy6EhBrmjdA7iTDxSIHe0NsUr40AuB+PTxHDwKuxejd7tgOOgNFzkzjvfGb0VuUa5Uy/kTs3Cst7yFmDzZj8Rr98ehvi53ToOy/Ix2pU7cRFH0Sjj7LOB/fYL54jhvy3ua0Xu1HAle9i2QrfJuVMl/NBgx4HjuvbkzmhQ7kBD7sQNN4oqSw2VO4MA8mnbUlm5I+XgCSFNckd1bfY75FPUQLkzrGFZNm2DdjGrs4WR0YpRNIKPbqSl3Bk2cuecc4AZM6wPp5XZxOROLXPuqAbWE0+sOC4kQ6QPOblTLiuv39LZCbz4YmXZh+Id1slW6C2dncDzzydSmvnKnaEqf5KcOxL8ZJ9RJIeJ3ImzwpYGuaNzPGzrI4azPyQ5d+A5HuXy8JA7S5eKHxMOPhj4yEeAnXYS/9Pkpk4m4zUjVvv6gAsv9P8dLsXSoGlSNYwIlWe0K3fiwusjPRMmxD+3owPYfffg/zjJnG0n2Y2NgOvGL5sthmu3rIYG4LrrgAMOAKDJuVMl/PwqjY0obNpkN/a1tVWorIccSTc48WDKuRPbT8znBcmiO6cWyh228Jn2SO5K5I5x17pakjsmctO2H9rslpWCcjwx4hCJGbkzpBhFI/joRrnalUqayA0XuXPxxbEOHztlCvDkk0Gm/rioZc4dW6ZcZcwsyJ0x48cD//738JA7CeqjFuiZMAF48MFE5B4N5kO9W1Y1Ez6/jVQTlhVnha2xsXoHO0K5Mxi1Gh0noXLcLW8Tgt7DsJA7NujsBD79af9fysOlbA/DAMp5UescQMZ+UMv7UjjEaFTuJFjoGBGgMJU0JnFHHQV85SvAzJnW97VS7mh8gTTgVKvIqkbN+853BuWIkWPNGt41B4jcsbHb3/++2IVpOJFSWJZyQp/ATxzI5ZAvl4cu504tJ/oS2aHNuQMMrXLH+93vOCjaKvVihGUNywYomXKnbpGROyMEVYdlOY7oXPU6aZHQTNs8J3UyEyh3bMOy/Jw7NSJ3tGUfYnJnOHfLquZZffXAEE1Q0si5468qRSl36Ps0lDu1IndMMmjFcRV/KzAkCZXB+upImdzqdiwbJhQ9pV0qE2kDfHJvOBIqY5Qqd+qE2E8daY6bnZ1iBx8bxE2oXCukmXOnmnZRbXiYAr5yp6kJeO01u3e8eHEq964KtQzLStDey55itepwb1sw5U7qoAUPidwZ9rAsW7/IcA0V6iKhcqbcqTtk5M4Iga8OqMaZpe23RwKqVRrVMueOrYNPk37O0k+bJkIMnn9ef95wkjt1klC5mmcl5c54290iqkUKYVmN7e3A+vXV5dyJU2cXXFC9g12tcidGW5PtXq2VOyOO3KmTMBoi5adst11N7zOsu2UBoz6h8mjMuTPkvk+9JFROM+dONe2+BsodIrUH5YTX9Q4qN4B8gnxLrimxewIypqG9HVizRv1eatF/vGslVuabQP4YpXYg5c5Qh2V1dABnnAEceKAoRxJyw2LBLpW5YVJkyp26xSgawUc3qg7LAkQHHCmDX7UDSg13yyJnJ1FY1s9+JlRUDzwA9Perz6MyyxPMTLkT69ymWjgOCqSh3Gnr7gZWrowmd0z1EsepU+SIio0IcidN5c5QJVQeaeQOEZi0k96ww2sLtd6V0Uhy1hDlbSQsy3ahY0Sg3smdffYB1q6tXTmqHdPTUIUByNWC3OHKHWDELV4mzTlDY2taYVk5WvhVEU21UO54z9/gbRKSKmjBg3LuUA7GoVbu5HLA9df7//okUxwyr7FRPI+h3w1rWFam3KlbZOTOCEEqGdFLpZEz+FXrkBUKYqCKo9yxvFc5LrnDj6Py7LVX5HnDrdwZVjVANcqtoc4vlULOHS2hJ6HRSwTphy2qrjFUzx0RlhU5iMdw9uUQu0y5I0BhUMr2MBwYojY4bDl3tpWwrEy5Uz1sJz6f+ERty5Fizp2q/M9aKMMo1GWkkjuOgyTUQqoJlak8Oltai/6Ty4l2VYv3RWFYtOOq1/+URNgnPwnUeCHCRxJy4/TTIzeiGdawrATKnaHKhbmtYxSN4KMbqWz9ui2RO45j97xDnVDZBnWSc2dY8y6koNwZqraehnLHtsyTvXCXXm9LaOU1hkqdp0m06Ngqd2IkcB0q5U55hJE7wzZx1cGUEypF+FsgZzl3AKQflpUpd0bwfWWkmXOninZf092yiNwZKcr0KlUMvv1Ji9wx+cq1qttSqTbjhKTcMSZUPvzw9O+vA7XVOO983jzxY4C/wDEchHx7u/hto5JXLXZnqBkycmeEoJzGSuW2RO4AwFvf6m/DqUWSnDvVJFS2ge7Za7FrgQxe1pFK7gzRBJNATutQKHcc03FD0T44dGSS7QpNLhdswRr1rlhfypfLtVPuWCa2rhsMcVuPxBArd4YtLKvOlC1ke8oAcglyeACon5DctDGcyp1cbuiUARqE/Jok79VxRLtClcqdasuhAIWlto8fLz4YKf4tC8tKAiPJnCSMyjQ3eNvbgClT0re1NdrghXwk10a5M5QgvyipfdYglaiOpFi2DPjVr4Bdd40+1pT7KEPqqC8PJYMWvhHfVsKy0pi0fOc70cdUQe5ErmLVSrlTy9Upx8Gg4yDvuiM2ofKQK3fSkMUWi6Jv24YyqdpqXx9w+eXAm9+cvBxxUG3OHcC6rU2bPRv44x8xUCohv3lzzdrmSAvLqht1AGGUkzvGFfPhBJFO1UwcRqtyJ58PlLxDfd9f/hLYffehva+qHKq/Y6DsOMi5bt3l3Gnp7AQANC5eDHR3A/vum8p1a458HoMQYVlJYNw9l8jEOKSiKSxr6lQRHpQ2ajUfIRJFSqg87OFANco5M6zkTj4PHHWU3bFZzp0hRUbujBCksjvIUUcBtMJR7xiqScsQJFSOPajont1zZFDj/BplmnCPVOXOUE9401Lu2JTX9GyOA1x2WfIyxMV224mf7bcPFyPGID7gOCi5buQkvcmT/w6WSsDmzVnOHUK9KXeGqjzDpdwxTaqGEb5yx3ESJWgFMHrJHdsQ7VrgyCOH/p4yqs25A09tUKVPULWCSAW6ZmsrcPXV6VxziOAWCmimsJa455rsUF8f8ItfRKvWOYajf5gIpWqvi8oFgGFX7iQJy4px3aHOPxcbwxk+tg0iq+URglRy7nzuc+kUZigwVBP0BKtasXPuxB1UdKE3Bx0E3HsvMGdOvOvFxKDjoAiMXOXOUE94pZWiRLCNP6+nyfy4ccBTT1V+HoPUHMzlgHLZOixrgNpDjdqmT9CNFAdkG1XuFL1dVkq12G3FALded8uiyV41yp3RmlAZECoGyh2yraHardCR0i5xNLkFkE9rgmsZzlyPKDQ2orWrK9G5xq3QAeCYY+JdcDjInRrdk+q0fcwY8YHXNrr6+lK/VyzUKKHwsCp34sB7/skRCaIzpINRNoKPXqRC7owkNDfb7XZVLRKsalkb0xjhKSHMny+k3PL1cznzLlspgRy5YXXwd9pJJJLLlDuVx/HfdYg4OYj8bUGj2hoRpbUmd+qJPLNBvZV3iNrn9FmzAAy9o1ivYVk02asqnwMnd+qlPaWFm24CZs8e7lIMD1Ikd6pq9969B6tRl8kY6p0x00QVyhXrHVuHoCyJUSNyh1S+U7zNJ+i5xk6alPq9YqFWYVnUv+t9bug9f4O342uG2qK+PJQMWpSHSYY+bHj724W0tNarbV6OGQDxyR3LCWnsd3bSScDf/hbvnBThO3LDuRJw/PHAQw8lcyaHKedOVY6WraNTb5N5BeKEZdUbueOTBXVcvyHUG9k3RO2T8ig4Q/zc9R6WVZVyh42Fo065c9BBInfINojQrpdJd2dKwyegxa40E8qOgPFQiyoIFb+/p6mAGuo6rHFYVsXv4R4jky72Wl633sakCozkvjoCMcpG8FGMkSK9Swu9vcDRRw/JreImEI4bljVt5syqyjfU8B38kWqECwXgxBOBffYZkttRPVUV07399sDLL0cfNxIGSIp5t8m5k8thAEAhyuGnnRZoJ4wa2cFGWlWq5/rlqLf20NMDnHMOcPDBtb3PMD13Khsb1AIUNlPlxIF2rxuxtj9DBYioG3Qc5BMSK+U02j0rR2qoN/sXB9WQO2mHh15wAbBhQzrXssUBBwATJ6Z/XR2pM9xtpEZbgY+YqI6R3FdHIOq8NWQgjJgOPAIRW6lCx1mSO0OdF6Ja1IVypxo4DnDjjUN2OzeNlZOPfET8RKHelBoKxCG7yo6DAceJHogo5w6FadbKDo40B2TePOAd7wD23nu4SyKQywFXXVX7+wzTe6p75U6V16HEuRm5M4rAFDNJW22qOXfSJHdGwHioRRVhSamTzIcdls514uBrX6vNdXXKneG2aTVKqOz7n8P9fFGol/ewjSBjCkYIhmvr120BicOyLMmdkfbOyiMtqexww5bsSwMLFwpVRI2TalcDn9yxVe7YOPtyWFat+tRI67NNTcB3vjPcpRh6DLdyp95y7qQRloVgLMyNlPafIRreuFSu4hKp5NypRVjKSLPXHCnk3Kk3O1QXqFdyJ4aiORbIT6+zBYcK5HLiZ7jfwzaCbPY2QjBikmaNQMQmd2yVGiOUkKuLhMojCJT3Y0i22pw8Gfj972t/n2pATryNcieXs1vJbWwEAAwOhXKHnJAM9YthJnci860NNSihcpXttpzLAYODmXJnFMFJQTGTipqXJVRODbNmiTHRS7A+opBGzp16n9APB+qc3EldZTaS5obDkbh7G0XmwY4UZGFZNUNccsdaqeE44pojzJiN+LCsocZISWg3RHBirFD1Tpjg725hxNFHA9/4BtaPGyf+ryW5M8L66zaJ4Vbu1FlfT125MxLDXDKoQcqdKoi/uk2oPHUqsHw5MG1aetccKrS0iJ8EqFc7VBeQx4Z6WWT1yrFdyqprf7F5JMwNM/9qyDACWkMGIAvLqiXikhmx8h+NQGNGTmC2emuJjNwJIY6Sqbm9HVi/PvqiXV3AeefBvesu8X9G7mzbGKYkmfUaDkGTvWonzhmxP/rgh8lWo9xJMaFyquTOSMa11ybeDTb13bJGE4gwo80RHEck+u/tHb4yAUEOzubmdK87kpQ7hx4K7LnncJdim8AIaA0ZAIysDjzCEDfHTKytr0fgZDFz8GNiJK2cDAF85Y5N+ykU4tm0Wis2WlsTr6ZmGEIsXQpcdhmwaNGQ3rZew7Iy5U4GHZwUSJVUEypnhITAzjsnPtWN6bNuUxg7FvjDH4AlS4LP/vEPQfAMJ/J5QTSl7buMpLnhT3863CXYZjACWkMGYARlRB+BiB2WFceYTp4MTJqUrGDDBD/nTtbW7JApd8KIkzgzJvlZ810DL7oIOO642lw7Q3pobgYuv3zIb1u34RA0+U5JuZMlVB5FSCHXTZphWanm3NlGkSl3InDQQeH/J08ennLIiLuYZYEsqiODChm5M0Iwc/vtAQBTpk8f5pKMPpRrSe78/e8jzuj6YVkjYSWgHkBKlay+AASr/jVR7njH1ox4nDhR/GTIoEC9kju+mrRacicLyR11oHG8mrbht4sUEiqnulvWNop6tUMZIlADJX/v+PGh3xkyABm5M2LQ2tEBACh4u8ZkSA9x48l9R9pmUpowpno4kW2FHg+xwpC2Afg5HmoQtlhz5U6GDAbU7aSKlDtVTpxjq1gz1D9SCMuq24TK2yj8cbDe7FAGMzJyJ8MQIRvBRwrqZTu/UYikW6GP1oE12wo9JkZ5e4iNOGFqXV3Axo3Wlx7rhTj20K5ZGTIMIep1UpVWQuVUJvEZ6gpOnDBZDVJNqJwpd6pGthX6CMW4ceInTWRzwwwKZLO3kYKRlDRrhCG2UmWUK1vcjNyJhbauLgBAa2fn8BakTlD0doPo7uuLPvjKK4GtW62vPd6Lne8Y7uSIGbZJ1G0i07QSKtfr82VIjjrLuZMpd6pH3SoIM5hx330iX1yayOaGGRTIWsNIQdaBa4aaJlQegcjyLsRDp0c0zN5hh2EuSX2Acu5sN2dO9MFxpcSjvO9lqG/Uu3Kn2rCs2PnnMtQ9/BxodbIVerZbVvWoVzuUIQLd3elfM/OJMiiQWdmRAtqaNNuiNHXEXpUa5WE4qThy2xJqneR3pKGWzkbmyGQYRtTtinlKyp3M9o9CpJDIOBXlTpZQOTXUrR3KMPTIwrIyKJBZ2ZGCQw4BrrgC2HHH4S7JqMNg3IFylE8wM+VOTGSDaxgZuZNhlKJeE3q7aZE7mXJn1MFJIRwqTeVOtW00Qx2Hh2YYesyfDxx8sPidIYOHzDKMFLS1AR/84HCXYlSi7DgYBJC3XFGqVwc/LWQ5d2IiIxzCqGV9tLWJ362t6V87Q4YI1O2KOe2WlSl3MkhIg9xJU7mThWVVD3/H1qyfZujpAX7/++EuRYY6Q2ZlM2zzKOdyGIhzwiifzNMEJiN3LEGEQ3v78JajXtDQIHbBqsXWnAcdBNx6K7D99ulfO0OGKNRprgt/spfl3MkggRS4w75bluNgoMpyZPBAdZjVZYYMGRTIRvAM2zzKjhNrVWvy9Omh36MNflhWlt/JDhMnAvffD+y223CXpD6QzwNPPgl0dKR/7UIBOPTQ9K+bIYMF6jYcIm3lTr09X4bkIOKvGuVOSoqujNxJB6NdPZ4hQ4bqkFmGDNs84ip3OsaMARDskjTa4Ct36mx1uq6xePFwl6C+4PWRDBlGFepduZOFZWWQkEtBuZNWqHbZcbKcOymgbsNDM2TIUBfIKPQM2zwGc7lgO3QbjPKwrCyhcoYMGTJUolyn5I6v3EkhLGuQXS/DyEcqOXfSIhMKBX9xLENyZOROhgwZTMjInQzbPCZOmYJSc7P9CaOc3CHHIZeROxkyZMgQoF6J7xS3Qo+Vfy5D3aNucu4AaG5rw8y5c6u6Rgb47yHLi5ghQwYVMnInwzaPcRMnojnO7juzZwNz54rfoxCZND9DhgwZFKhT5Q6FZVWbz6ScywnlToZRAyeFLchTyzVVKIzaRbGhRLZbVoYMGUzIrGyGDHEdjokTgUcfrV15hhm+cidLqJwhQ4YMPtw6XTF3aPKdgnInVohyhvpHCjup+Tl3UgjLqjdidCQiC8vKkCGDCfXloWTIMBzo7KzNzj4jFNlW6BkyZMhQCX/FvM5so5tSWFbv2LHIvfhiGkXKUC/w2uxgNeROWmTChz8M7LhjddfIULckc4YMGeoDmWXIkOFTnwLWrh3uUtQNsoTKGTJkyKAATarqbcWcyJ0qw7Kmz5oFPPVUGiXKUC9IMyyr2nZ/3nnVnZ9BoE7DQzNkyFAfyMidDBl6esRPBgDBqlCWUDlDhgwZAvg5LupsxTytrdCznCijECnkY8rUvHUGskOZj5YhQwYFMkudIUOGEPz4+syRy5AhQ4YA9RoOkZJyB3vsAWzalEKBMtQNUsi5k22yUF/wybbsfWTIkEGBbLesDBkyhFCvq9MZMmTIMKyoVwWDR8hXTe6cfTbwk5+kUKAMdQOvrWbKnVGELCwrQ4YMBmTkToYMGUIoNDaKPzLHIUOGDBkC0OS23ia5KagzMoxSpBGylyl36grZAlyGDBlMyDyBDBkyhLBg4ULxR+bIZciQIYOPulUwpLRbVoZRiHraCj1DOsjInQwZMhiQkTsZ/n979xZqaVnGAfz/4B5HHU+JJjkjzTRpOSbOlIVlVKTQkewmKOhABUFlaQhR3XTbRUQFFkh2IilChSSolAoiCM08kOMUSsepKSekki6qsaeL9U3tmdmzD+Psvdbn/v1gsb/1rvXt/QzMs/fLf73f+8EhNm7aNJk8+BQY4H+esWVLkuTsc8+dciWH+t8n+cIdDjf8HT8el2X5wGc22HMHWIzYFzjUu96VXHTRtKsAmCnP3L49SXL6WWdNuZLDHK89d3jqqcqBPMmVO7O6Ym29mtWN3YGZ4DcDcKht2yYPAP5vx47k2c9Ozjtv2pUc6njdLYunpCeSJ3UJT9vAd6ZsOuOMJMmpZ5453UKAmSTcAQBYys6dycMPT7uKI9lQmUXMbdyYSy699JjPt3Jntuy45JIk/79MFGA+MwEAgLE6GOrYc4cFnLB1a06/+OJjPt+eO7Oldu5MXvjCZOvWaZcCzCAxPADAWB2P213z1PXAA0/usiwrd2bLhRcmd9897SqAGbXkyp2qOr+qflhVe6pqd1VduxaFAQCwBHvusJiNG5/cqhvhDsBoLOc39YEk13f3vVV1WpKfVdWd3f3QKtcGAMBi7LnDKrJyB2A8lpwJdPe+7r53OH48yZ4km1e7MAAAlmDlDquo3XobYDRWNBOoqq1JdiW5a1WqAQBg+WyozGqyoTLAaCw73KmqU5PcmuS67v77Aq+/p6ruqap79u/ffzxrBABgIS7LYhW5LAtgPJY1E6iqDZkEOzd3920Lvae7b+zuy7r7snPOOed41ggAwEIOhjrCHVbBSZs2JUlOPPnkKVcCwFKWjOGrqpLclGRPd39q9UsCAGBZrNxhFe285po8/thjOW2z7TYBZt1yZgJXJHlbkldW1f3D47WrXBcAAEuxcodVdOJLX5rT7rjDnjsAI7Dkyp3u/nESu/QBAMyYOrhyx4bKALCu+ZgHAGCsDoY7VlYAwLom3AEAGCu3QgcAItwBABgvK3cAgAh3AADGy8odACDCHQCA0aq54d4Y7pYFAOuamQAAwFgNoU4LdwBgXTMTAAAYK+EOABDhDgDAaLksCwBIhDsAAON1MNQR7gDAumYmAAAwVgdvhS7cAYB1zUwAAGCsrNwBACLcAQAYrRpW7gh3AGB9MxMAABgrl2UBABHuAACMlpU7AEAi3AEAGK+DK3cOhjwAwLok3AEAGCsbKgMAEe4AAIxWzc1NDoQ7ALCumQkAAIxUn3JKkuSJk06aciUAwDQJdwAARuqJLVvy8iR/3LVr2qUAAFMk3AEAGKnt27fnee97X15+1VXTLgUAmKK5aRcAAMCx2bBhQ2644YZplwEATJmVOwAAAAAjJtwBAAAAGDHhDgAAAMCICXcAAAAARky4AwAAADBiwh0AAACAERPuAAAAAIyYcAcAAABgxIQ7AAAAACMm3AEAAAAYMeEOAAAAwIgJdwAAAABGTLgDAAAAMGLV3cf/m1btT/Lb4/6N197ZSf4y7SJghPQOrJy+gWOjd2Dl9A2s3Kz0zTO7+5zDB1cl3HmqqKp7uvuyadcBY6N3YOX0DRwbvQMrp29g5Wa9b1yWBQAAADBiwh0AAACAERPuLO7GaRcAI6V3YOX0DRwbvQMrp29g5Wa6b+y5AwAAADBiVu4AAAAAjJhw5yiq6tVV9cuqeqSqPjLtemBWVNX5VfXDqtpTVbur6tph/KyqurOqHh6+Pm3eOR8deumXVfWq6VUP01VVJ1TVfVX17eG5voElVNWZVXVLVf1i+NvzYr0Di6uqDw3ztAer6utVdZK+gSNV1Rer6tGqenDe2Ip7papeUFU/H177bFXVWv9bhDsLqKoTktyQ5DVJdiR5S1XtmG5VMDMOJLm+uy9KcnmS9w/98ZEk3+/uC5J8f3ie4bU3J7k4yauTfG7oMViPrk2yZ95zfQNL+0yS73b3c5NcmkkP6R04iqranOSDSS7r7uclOSGTvtA3cKQvZ/L/fr5j6ZXPJ3lPkguGx+Hfc9UJdxb2oiSPdPevuvtfSb6R5Oop1wQzobv3dfe9w/HjmUyyN2fSI18Z3vaVJG8cjq9O8o3u/md3/zrJI5n0GKwrVbUlyeuSfGHesL6BRVTV6UleluSmJOnuf3X3X6N3YClzSU6uqrkkpyT5Y/QNHKG7f5TkscOGV9QrVfWMJKd39096sqnxV+eds2aEOwvbnOT3857vHcaAeapqa5JdSe5Kcm5370smAVCSpw9v008w8ekkH07yn3lj+gYW96wk+5N8abik8QtVtSl6B46qu/+Q5JNJfpdkX5K/dfcd0TewXCvtlc3D8eHja0q4s7CFro9zWzGYp6pOTXJrkuu6+++LvXWBMf3EulJVr0/yaHf/bLmnLDCmb1iP5pI8P8nnu3tXkn9kWB5/FHqHdW/YH+TqJNuSnJdkU1W9dbFTFhjTN3Cko/XKTPSQcGdhe5OcP+/5lkyWMgJJqmpDJsHOzd192zD852FJYoavjw7j+gmSK5K8oap+k8mlvq+sqq9F38BS9ibZ2913Dc9vySTs0TtwdFcl+XV37+/ufye5LclLom9guVbaK3uH48PH15RwZ2E/TXJBVW2rqhMz2TTp9inXBDNh2Pn9piR7uvtT8166Pck7huN3JPnWvPE3V9XGqtqWyQZjd69VvTALuvuj3b2lu7dm8jflB9391ugbWFR3/ynJ76vqOcPQlUkeit6BxfwuyeVVdcowb7sykz0S9Q0sz4p6Zbh06/GqunzoubfPO2fNzK31DxyD7j5QVdck+V4mu8t/sbt3T7ksmBVXJHlbkp9X1f3D2MeSfCLJN6vq3ZlMKt6UJN29u6q+mclk/ECS93f3E2teNcwmfQNL+0CSm4cP3H6V5J2ZfECpd2AB3X1XVd2S5N5M+uC+JDcmOTX6Bg5RVV9P8ookZ1fV3iQfz7HNz96byZ23Tk7yneGxpmqymTMAAAAAY+SyLAAAAIARE+4AAAAAjJhwBwAAAGDEhDsAAAAAIybcAQAAABgx4Q4AAADAiAl3AAAAAEZMuAMAAAAwYv8FlP1bkvw4LI0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "Note that our formula for LB of distance profile is different than what provided in the paper." + "LB = _calc_LB_dist_profile(T, D, m, Σ_T[idx], m_target, Σ_T_target[idx])\n", + "\n", + "plt.title(f'distance profile of subseq at {idx} with length {m_target}')\n", + "plt.plot(D[np.isfinite(D)], 'k', label='True D')\n", + "plt.plot(LB[np.isfinite(LB)], 'r', label='Lower-Bound D')\n", + "plt.legend()\n", + "plt.show()" ] }, { "cell_type": "code", "execution_count": null, - "id": "6b4e19c1", + "id": "11e07596", "metadata": {}, "outputs": [], "source": [] From ca04fae03f5a9e4c803c7a69a6921dcef9b8e264 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 10 Apr 2022 17:21:46 -0600 Subject: [PATCH 03/64] improve markdowns --- docs/Tutorial_VALMOD.ipynb | 26 ++++++++------------------ 1 file changed, 8 insertions(+), 18 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index e3e266d0e..e84e7ad1f 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -61,7 +61,7 @@ "source": [ "We would like to find set $S^{*} = \\bigcup\\limits_{m=min\\_m}^{max\\_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", "\n", - "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index in two different motif sets. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty. " + "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty because both these two subsequences start from the same index." ] }, { @@ -85,11 +85,9 @@ "id": "3826e0a5", "metadata": {}, "source": [ - "**$n^{th}$ best match**: Given a subsequence $T_{i,m}$, the $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", + "**$n^{th}$ best match**: For the subsequence $T_{i,m}$, its $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", "\n", - "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?) best match.
\n", - "\n", - "**Top-k $n^{th}$ discord**: This is k-th value of $P^{n_{th}}$, sorted in ascending order. $P^{n_{th}}$ is the matrix profile that is constructed based on $n^{th}$ best match rather than 1NN.\n" + "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?) best match.
" ] }, { @@ -137,9 +135,9 @@ "id": "a8e87bc0", "metadata": {}, "source": [ - "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors (maybe the second, third, ...or n-th neighbor as well.) \n", + "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors rather than just 1NN. \n", "\n", - "For further details, see Fig. 2 of the paper (Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" + "For further details, see Fig. 2 of the paper. Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" ] }, { @@ -148,7 +146,7 @@ "metadata": {}, "source": [ "**Variable-length Top-k $n^{th}$ Discord Discovery:**
\n", - "Given a time series `T`, a subsequence length-range `[min_m, max_m]`, and `K` and `N`, we want to find **top-k $n^{th}$ discord** for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." + "Given a time series `T`, a subsequence length-range `[min_m, max_m]`,`K`, and `N`, we want to find **top-k $n^{th}$ discord** for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." ] }, { @@ -164,7 +162,7 @@ "id": "5f999789", "metadata": {}, "source": [ - "The idea goes as follows: \"given the distance profile of $T_{j,m}$, how can I find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` but is longer by `k` elements ?" + "The idea goes as follows: \"given the distance profile of $T_{j,m}$, how can I find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`?" ] }, { @@ -172,15 +170,7 @@ "id": "03836054", "metadata": {}, "source": [ - "In other words, can I find lower bound for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$?" - ] - }, - { - "cell_type": "markdown", - "id": "be9e2963", - "metadata": {}, - "source": [ - "(Note: It is more common to consider `i` as the main index and `j` as the neighbor. Here, however, we choose `j` as the start index of subsequene of interest so to be consistent with the paper.)" + "In other words, can I find lower bound for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$? If I can find that, it means I can find the lower-bound for the distance profile of the query $T_{j,m+k}$." ] }, { From c6b0868cee07b4332918e3ce24a75d4902b55754 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 10 Apr 2022 22:33:06 -0600 Subject: [PATCH 04/64] Major Revise of Notebook --- docs/Tutorial_VALMOD.ipynb | 498 ++++++++++++++----------------------- 1 file changed, 186 insertions(+), 312 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index e84e7ad1f..079fc4831 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -41,9 +41,7 @@ "id": "aa1d847c", "metadata": {}, "source": [ - "Some important notations that we may use later:\n", - "* subsequence $T_{i,m}$ --> a subsequence of `T` that starts at index `i` and has length `m` \n", - "* Motif set $S^{m}_{r}$ (for a given motif pair $\\{T_{idx,m},T_{nn\\_idx,n}\\}$) --> is a set of subsequences of length `m` that has `distance < r` to either $T_{idx,m}$ or $T_{nn\\_idx,n}$. And, the cardinality of set is called the frequency of the motif set." + "**Notation:** $T_{i,m} = T[i:i+m]$, a subsequence of `T` that starts at index `i` and has length `m` " ] }, { @@ -59,6 +57,8 @@ "id": "fd1568ab", "metadata": {}, "source": [ + "For a given motif pair $\\{T_{idx,m},T_{nn\\_idx,n}\\}$, Motif set $S^{m}_{r}$ is a set of subsequences of length `m` that has `distance < r` to either $T_{idx,m}$ or $T_{nn\\_idx,n}$. And, the cardinality of set is called the frequency of the motif set.\n", + "\n", "We would like to find set $S^{*} = \\bigcup\\limits_{m=min\\_m}^{max\\_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", "\n", "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty because both these two subsequences start from the same index." @@ -162,7 +162,7 @@ "id": "5f999789", "metadata": {}, "source": [ - "The idea goes as follows: \"given the distance profile of $T_{j,m}$, how can I find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`?" + "The idea goes as follows: \"given the distance profile of $T_{j,m}$, how can we find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`?" ] }, { @@ -170,485 +170,359 @@ "id": "03836054", "metadata": {}, "source": [ - "In other words, can I find lower bound for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$? If I can find that, it means I can find the lower-bound for the distance profile of the query $T_{j,m+k}$." + "In other words, can we find **Lower Bound (LB)** for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$? (So, the last `k` elements of $T_{i,m+k}$ are unknown)" ] }, { "cell_type": "markdown", - "id": "4fc93b47", + "id": "a1429322", "metadata": {}, "source": [ - "$d^{(m+k)}_{j,i} \\ge \\min{(d)}$, where $d$ is:\n", - "\n", - "$d = \\sqrt{\n", - "\\sum\\limits_{t=1}^{m}{\n", - "(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}})^{2}\n", - "}\n", - "}$, where ($\\mu_{i,m+k}$, $\\sigma_{i,m+k}$), and ($\\mu_{j,m+k}$, $\\sigma_{j,m+k}$) are (mean, standard deviation) of subsequences $T_{i,m+k}$ and $T_{j,m+k}$, respectively." + "### Derving Equation (2)" ] }, { "cell_type": "markdown", - "id": "ff38394a", + "id": "982235e5", "metadata": {}, "source": [ - "**Note:** The values $\\mu_{j,m+k}$ and $\\sigma_{j,m+k}$ are known. The goal is to find its lower-bound distane to its neighbor `i` (i.e. $T_{i,m+k}$) without using its last `k` elements! The value $d$ shown above is the z-normalized distance between $T_{j,m+k}$ and $T_{i,m+k}$ considering only the `m` first elements. We know that it is already less than $d^{(m+k)}_{j,i}$. So, by minimizing the Right Hand Side of inequation, we can get the Lower Bound (LB)." + "\\begin{align}\n", + " d^{(m+k)}_{j,i} ={}& \n", + " \\sqrt{\\sum\\limits_{t=1}^{m+k}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}} \n", + " \\\\\n", + " \\geq{}&\n", + " \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}}\n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "49b2a8fc", + "id": "a86c6f4d", "metadata": {}, "source": [ - "Factoring out $\\frac{1}{\\sigma_{j,m+k}}$ --> Therefore: $d = \\frac{1}{\\sigma_{j,m+k}}\\sqrt{\n", - "\\sum\\limits_{t=1}^{m}{\n", - "(\\frac{T[i+t-1] - \\mu_{i,m+l}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{1})^{2}\n", - "}\n", - "}$ " + "\\begin{align}\n", + " LB ={}& \n", + " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}} \n", + " \\\\\n", + " ={}&\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}}\n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "4fa6a3a9", + "id": "31a8cf32", "metadata": {}, "source": [ - "mulitply by $\\frac{\\sigma_{j,m}}{\\sigma_{j,m}}$ --> Therefore: $\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\n", - "\\sum\\limits_{t=1}^{m}{\n", - "(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}})^{2}\n", - "}\n", - "}$\n", - "\n" + "Note that the variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$." ] }, { "cell_type": "markdown", - "id": "1634ef47", + "id": "4595f60b", "metadata": {}, "source": [ - "Now, we replace $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$, so we have:" + "\\begin{align}\n", + " f(\\mu^{'}, \\sigma^{'}) ={}& \n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", + " }^{2}} \n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "a86bc201", + "id": "0be3b76a", "metadata": {}, "source": [ - "$d = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\n", - "\\sum\\limits_{t=1}^{m}{\n", - "(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}})^{2}\n", - "}\n", - "}$\n", - "\n" + "\\begin{align}\n", + " X_{t} \\triangleq{}& \n", + " {\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " } \n", + " \\\\\n", + "\\end{align}" ] }, { - "attachments": { - "image.png": { - "image/png": "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" - } - }, "cell_type": "markdown", - "id": "5b0f6217", + "id": "c42a67de", "metadata": {}, "source": [ - "**Important Note:**
\n", - "Note the typo in eq(1) of the paper...\n", - "![image.png](attachment:image.png)" + "To find critical point(s):" ] }, { "cell_type": "markdown", - "id": "9682721a", + "id": "9ee22e69", "metadata": {}, "source": [ - "Somehow, the authors change $\\mu_{j,m+k}$ to $\\mu_{j,k}$...However, these two values can be different." + "\\begin{align}\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0 \\quad \\text{(1)}\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0 \\quad \\text{(2)}\n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "4e1ac4d6", + "id": "97afedeb", "metadata": {}, "source": [ - "**>>> In this notebook:
\n", - "we try to calculate LB after correcting such typo. The problem becomes...**" + "\\begin{align}\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", + " \\sum \\limits_{t=1}^{m} {\\frac{-2}{\\sigma^{'}}X_{t}} \\Rightarrow \\text{with (1):}\n", + " \\sum \\limits_{t=1}^{m} X_{t} = 0 \\quad (3)\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "51f452b1", + "id": "94efd415", "metadata": {}, "source": [ - "**To find the minimum value of d, we need to minimize the following function:**" + "\\begin{align}\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", + " \\sum{\\frac{-2}{\\sigma^{'2}}\\left(T[i+t-1] - \\mu^{'}\\right)X_{t}} \\Rightarrow {\\text{with (2) and (3)}}:\n", + " \\sum \\limits_{t=1}^{m} T[i+t-1]X_{t} = 0 \\quad (4)\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "4c09667d", + "id": "0a5b427d", "metadata": {}, "source": [ - "$f(\\mu^{'}, \\sigma^{'}) = \\sum\\limits_{t=1}^{m}{\n", - "(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}})^{2}\n", - "}$
\n", - "\n", - "**Let's take its partial derivatives and put them equal to 0...**
\n", - "$\\frac{\\partial f}{\\partial \\mu^{'}} = 0$
\n", - "$\\frac{\\partial f}{\\partial \\sigma^{'}} = 0$" + "Exapanding (3):" ] }, { "cell_type": "markdown", - "id": "37330b9c", + "id": "870b5eb8", "metadata": {}, "source": [ - "**First, let us first provide some guidelines:**
\n", - "\n", - "(1) We use $T_{i}$ to represent $T[i+t-1]$, and $T_{j}$ to represent $T[j+t-1]$. Since we use them inside $\\sum$, the notation should suffice.
\n", - "(2) We use $\\sum$ without limits. It is alway from $t=1$ to $m$.
\n", - "(3) We define: $X_{t} = \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}$
\n", - "(4) Similar to paper, We define: $q = \\frac{\\sum{T_{i}T_{j}} - m\\mu_{i,m}\\mu_{j,m}}{m\\sigma_{i,m}\\sigma_{j,m}}$ (note: $q=1$ for $i=j$)
\n", - "(5) Note that: $\\sum{T_{i}} = m\\mu_{i,m}$, and $T_{j} = m\\mu_{j,m}$.\n", - "(6) We use $\\mu_{j}$ and $\\sigma_{j}$ to represent $\\mu_{j,m}$ and $\\sigma_{j,m}$, respectively. If we want to show $\\mu$ for length `m+k`, we use $\\mu_{j,m+k}$" + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m} X_{t} = 0\n", + " \\\\\n", + " \\sum \\limits_{t=1}^{m} {\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}} = 0\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m}T[i+t-1] - \\sum \\limits_{t=1}^{m} \\mu^{'}\\right) - \n", + " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=1}^{m}T[j+t-1] - \\sum \\limits_{t=1}^{m} \\mu_{j,m+k}\\right) = 0\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\\left(m\\mu_{i,m} - m\\mu^{'}\\right) - \n", + " \\frac{1}{\\sigma_{j,m}}\\left(m\\mu_{j,m} - m\\mu_{j,m+k}\\right) = 0\n", + " \\\\\n", + " \\sigma_{j,m}\\left(\\mu_{i,m} - \\mu^{'}\\right) - \n", + " \\sigma^{'}\\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) = 0\n", + " \\\\\n", + " \\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} = 0 \\quad (5)\n", + "\\end{align} " ] }, { "cell_type": "markdown", - "id": "0df0e59b", + "id": "ebdb4516", "metadata": {}, "source": [ - "**Let us solve it...**" + "Expanding (4):" ] }, { "cell_type": "markdown", - "id": "e330f0c1", + "id": "5c3fd3cb", "metadata": {}, "source": [ - "(1) $\\frac{\\partial f}{\\partial \\mu^{'}} = 0$
\n", - "\n", - "Therefore: $\\sum{\\frac{-2}{\\sigma^{'}}X_{t}} = 0$
\n", - "Therefore: $\\sum{X_{t}} = 0$ (eq: I)" + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m} T[i+t-1] \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right) = 0\n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "30f65c15", + "id": "5864dd2f", "metadata": {}, "source": [ - "(2) $\\frac{\\partial f}{\\partial \\sigma^{'}} = 0$
\n", - " \n", - "Therefore: $\\sum{\\frac{-2}{\\sigma^{'2}}(T_{i} - \\mu^{'})X_{t}} = 0$
\n", - "Therefore: $\\sum{(T_{i} - \\mu^{'})X_{t}} = 0$
\n", - "Therefore (using eq I): $\\sum{T_{i}X_{t}} = 0$ (eq II)
" + "\\begin{align}\n", + " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m} T[i+t-1]T[i+t-1] - \\sum \\limits_{t=1}^{m} T[i+t-1] \\mu^{'}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}}\\right) = 0\n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "abe3ee87", + "id": "c311fdc2", "metadata": {}, "source": [ - "Also, let us find out the value we are trying to minimize:" + "\\begin{align}\n", + " r \\triangleq \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}} \\quad (6)\n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "91096e9d", + "id": "4ea25765", "metadata": {}, "source": [ - "$f(\\mu^{'}, \\sigma^{i}) = \\sum{\n", - "(\\frac{T_{i} - \\mu^{'}}{\\sigma^{'}} - \\frac{T_{j} - \\mu_{j,m+k}}{\\sigma_{j}})^{2}\n", - "} \n", - "= \n", - "\\sum{\n", - "[(\\frac{T_{i} - \\mu^{'}}{\\sigma^{'}} - \\frac{T_{j} - \\mu_{j,m+k}}{\\sigma_{j}})X_{t}]\n", - "} \n", - "= \n", - "{\n", - "\\frac{\\sum{T_{i}X_{t}} - \\sum{\\mu^{'}X_{t}}}{\\sigma^{'}} - \\frac{\\sum{T_{j}X_{t}} - \\sum{\\mu_{j,m+k}X_{t}}}{\\sigma_{j}}\n", - "} $" + "\\begin{align}\n", + " \\frac{1}{\\sigma^{'}}\\left(m(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) - m \\mu_{i,m} \\mu^{'}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({m(r\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m}) - m\\mu_{i,m}\\mu_{j,m+k}}\\right) = 0\n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "7d6213b9", + "id": "1bdc9010", "metadata": {}, "source": [ - "And, with help of eq I and II, we can see:
\n", - "$f_{optim} = - \\frac{\\sum{(T_{j}X)}}{\\sigma_{j}} $" + "\\begin{align}\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + \\left(r\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\left(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}\\right) = 0 \\quad (7)\n", + " \\\\ \n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "c63a0492", + "id": "30a6adeb", "metadata": {}, "source": [ - "Therefore:
\n", - "$f_{optim} = - \\frac{1}{\\sigma_{j}}F$, where:
\n", - "\n", - "$F = \\sum{T_{j}X} = \\frac{\\sum{T_{i}T_{j}} - \\sum\\mu^{'}T_{j}}{\\sigma^{'}} - \\frac{\\sum{T_{j}T_{j}} - \\sum{\\mu_{j,m+k}T_{j}}}{\\sigma_{j}}$" + "Solving (5) and (7) gives:" ] }, { "cell_type": "markdown", - "id": "0fe24576", + "id": "fae1014d", "metadata": {}, "source": [ - "**We need to find $\\mu^{'}$ and $\\sigma^{'}$:**" - ] - }, - { - "cell_type": "markdown", - "id": "b028fd7f", - "metadata": {}, - "source": [ - "eq I: $\\sum{X} = 0$,
\n", + "\\begin{align}\n", + " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma^{'}}{\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k}) \\quad (8)\n", + "\\end{align}\n", "\n", - "Therefore: $\\sum{\\frac{T_{i} - \\mu^{'}}{\\sigma^{'}} - \\frac{T_{j} - \\mu_{j,m+k}}{\\sigma_{j}}} = 0$
" - ] - }, - { - "cell_type": "markdown", - "id": "e6e9ce06", - "metadata": {}, - "source": [ - "Therefore: ${\\frac{\\sum{T_{i}} - \\sum{\\mu^{'}}}{\\sigma^{'}} - \\frac{\\sum{T_{j}} - \\sum{\\mu_{j,m+k}}}{\\sigma_{j}}} = 0$
" - ] - }, - { - "cell_type": "markdown", - "id": "89ac2637", - "metadata": {}, - "source": [ - "Therefore: ${\\frac{m\\mu_{i} - m{\\mu^{'}}}{\\sigma^{'}} - \\frac{m{\\mu_{j}} - {\\mu_{j,m+k}}}{\\sigma_{j}}} = 0$" - ] - }, - { - "cell_type": "markdown", - "id": "dd128a9f", - "metadata": {}, - "source": [ - "Therefore: $\\sigma_{j}(\\mu_{i}-\\mu^{'}) - \\sigma^{'}(\\mu_{j}-\\mu_{j,m+k}) = 0$ (eq III)" - ] - }, - { - "cell_type": "markdown", - "id": "6784d89e", - "metadata": {}, - "source": [ - "---\n", "\n", - "And, with eq II:
\n", - "$\\sum{T_{i}X} = 0$," + "\\begin{align}\n", + " \\sigma^{'} = \\frac{\\sigma_{i,m}}{r} \\quad (9)\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "cc05e3e1", + "id": "4d83a448", "metadata": {}, "source": [ - "Therefore: $\\frac{\\sum{T_{i}T_{i}} - \\sum\\mu^{'}T_{i}}{\\sigma^{'}} - \\frac{\\sum{T_{i}T_{j}} - \\sum{\\mu_{j,m+k}T_{i}}}{\\sigma_{j}} = 0$" + "We can try to simply $f_{min}(\\mu^{'}, \\sigma^{'})$ first with help of (3) and (4) before plugging in the values $\\mu^{'}$ (8) and $\\sigma^{'}$ (9)." ] }, { "cell_type": "markdown", - "id": "b6f83405", + "id": "14904456", "metadata": {}, "source": [ - "Therefore: $\\sigma_{j}(m\\mu_{i}^{2} + m\\sigma_{i}^{2} - m\\mu_{i}\\mu^{'}) - \\sigma^{'}(m\\mu_{i}\\mu_{j} + mq\\sigma_{i}\\sigma_{j} - m\\mu_{i}\\mu_{j,m+k}) = 0$ (eq IV)" + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", + " }^{2}} \n", + " \\\\\n", + " ={}&\n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", + " }X_{t}}\n", + " \\\\\n", + " ={}&\n", + " {\n", + " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]X_{t} - \\sum\\limits_{t=1}^{m}\\mu^{'}X_{t}}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]X_{t} - \\sum\\limits_{t=1}^{m}\\mu_{j,m+k}X_{t}}{\\sigma_{j,m}}\n", + " } \n", + " \\\\ \n", + " ={}&\n", + " {\n", + " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}T[j+t-1]X_{t}\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {\n", + " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}{T[j+t-1]\\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)}\n", + " } \n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "71dbe617", + "id": "32e63873", "metadata": {}, "source": [ - "**solving eq (III) and eq (IV) give us optimal values for $\\mu^{'}$ and $\\sigma^{'}$ as follows:**" + "with (6), (8), and (9), we can get:" ] }, { "cell_type": "markdown", - "id": "32537233", + "id": "d717fbad", "metadata": {}, "source": [ - "$\\sigma^{'} = \\frac{\\sigma_{i}}{q}$ (thus, q must be positive.)
\n", - "$\\mu^{'} = \\mu_{i} - \\frac{\\sigma^{'}}{\\sigma_{j}}(\\mu_{j}-\\mu_{j,m+k})$" - ] - }, - { - "cell_type": "markdown", - "id": "bd4a023d", - "metadata": {}, - "source": [ - "Now, we plugged back in the values to find LB:\n", - "\n", - "$LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{f_{optim}}$, where:
\n", - "\n", - "$f_{optim} = - \\frac{1}{\\sigma_{j}}F$, where:
\n", - "\n", - "$F = \\sum{T_{j}X} = \\frac{\\sum{T_{i}T_{j}} - \\sum\\mu^{'}T_{j}}{\\sigma^{'}} - \\frac{\\sum{T_{j}T_{j}} - \\sum{\\mu_{j,m+k}T_{j}}}{\\sigma_{j}}$ in which we should use the optimal value for $\\mu^{'}$ and $\\sigma^{'}$." + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", + " m (1 - r^{2}) \n", + " \\\\\n", + "\\end{align}" ] }, { "cell_type": "markdown", - "id": "bec46a29", + "id": "4d0f0609", "metadata": {}, "source": [ - "* If $q \\gt 0$: $LB = \\frac{\\sigma_{j}}{\\sigma_{j,m+k}} \\sqrt{m(1-q^{2})}$\n", - "* If $q \\le 0$: $LB = \\frac{\\sigma_{j}}{\\sigma_{j,m+k}} \\sqrt{m}$ (proof not provided in this notebook)" + "**Therefore, the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" ] }, { "cell_type": "markdown", - "id": "3bcf8519", - "metadata": {}, - "source": [ - "### LB dist profile function" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "928fc18c", + "id": "8c3c9bbd", "metadata": {}, - "outputs": [], "source": [ - "def _calc_LB_dist_profile(T, D, m, σ, m_target, σ_target):\n", - " \"\"\"\n", - " This function finds the lower-bound of a distance profile for a subsequence with window size `m_target` based\n", - " on the distance profile of a subsequence with window size `m` starting from the same index `idx`.\n", - " (note: this is for z-normalize case)\n", - " \n", - " Parameters\n", - " ----------\n", - " T: numpy.ndarray\n", - " a time series of interest\n", - " \n", - " D: numpy.ndarray\n", - " Distance profile for a subsequence with length `m` located at an index `idx` in time series `T`\n", - " \n", - " m: int\n", - " length of subsequence for which the the distance profile D is provided. \n", - " \n", - " σ: float\n", - " standard deviation of subsequence `T[idx : idx + m]`\n", - " \n", - " m_target: int\n", - " new length of subsequence whose lower-bound distance profile will be returned.\n", - " \n", - " σ_target: float\n", - " standard deviation of subsequence `T[idx : idx + m_target]`\n", - " \n", - " Return\n", - " --------\n", - " LB : numpy.ndarray\n", - " Lower_Bound of distance profile for subsequence with length `m_target`, starting at index `idx`.\n", - " \n", - " \"\"\"\n", - " if m_target <= m:\n", - " raise ValueError(f\"m_target, {m_target} should be larget than m, {m}\")\n", - " \n", - " if len(D) != T.shape[0] - m + 1:\n", - " raise ValueError(f\"length of distance profile D, {len(D)}, should be T.shape[0]-m+1, {T.shape[0]-m+1}\")\n", - " \n", - " excl_zone = int(np.ceil(m_target / config.STUMPY_EXCL_ZONE_DENOM))\n", - " \n", - " k = T.shape[0] - m_target + 1\n", - " T_is_finite = core.rolling_isfinite(T, m_target)\n", - " \n", - " R = 1 - np.square(D[:k])/(2 * m)\n", - " \n", - " LB = (σ/σ_target) * np.sqrt(m) * np.sqrt(1 - np.square(np.maximum(R,0)))\n", - " core.apply_exclusion_zone(LB, idx, excl_zone, np.inf)\n", - " LB[~T_is_finite] = np.inf\n", - " \n", - " return LB" - ] - }, - { - "cell_type": "markdown", - "id": "52a327c7", - "metadata": {}, - "source": [ - "**Example:**" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "df09cc41", - "metadata": {}, - "outputs": [], - "source": [ - "T = np.random.uniform(-100,100, size=1000)\n", - "idx = 500 #start index of subsequence" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "2f0a21d3", - "metadata": {}, - "outputs": [], - "source": [ - "m = 10\n", - "_, Σ_T = core.compute_mean_std(T, m)\n", - "Q = T[idx:idx+m]\n", - "excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", - "\n", - "#################################################\n", + "\\begin{align}\n", + " LB ={}& \n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m (1 - r^{2})} \\quad \\text{if} \\, r > 0\n", + " \\\\\n", + "\\end{align}\n", "\n", - "m_target = 11\n", - "_, Σ_T_target = core.compute_mean_std(T, m_target)\n", - "Q_target = T[idx:idx+m_target]\n", - "excl_zone_target = int(np.ceil(m_target / config.STUMPY_EXCL_ZONE_DENOM))" + "\\begin{align}\n", + " r ={}& \n", + " \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", + " \\\\\n", + "\\end{align}" ] }, { - "cell_type": "code", - "execution_count": 94, - "id": "35649122", + "cell_type": "markdown", + "id": "1112e11c", "metadata": {}, - "outputs": [], "source": [ - "D = core.mass(Q, T)\n", - "core.apply_exclusion_zone(D, idx, excl_zone, np.inf)\n", + "**Note:**
\n", + "* Note that eq(9) is valid only for $r > 0$. Therefore, we can use the formula above to calculate $LB$ only if $r > 0$. \n", + "* The pearson correlation, `r`, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", "\n", - "D_target = core.mass(Q_target, T) #true distance profile for length m_target\n", - "core.apply_exclusion_zone(D_target, idx, excl_zone, np.inf) " - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "09669a7a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "LB = _calc_LB_dist_profile(T, D, m, Σ_T[idx], m_target, Σ_T_target[idx])\n", + "**Pending...**
\n", + "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function.\n", "\n", - "plt.title(f'distance profile of subseq at {idx} with length {m_target}')\n", - "plt.plot(D[np.isfinite(D)], 'k', label='True D')\n", - "plt.plot(LB[np.isfinite(LB)], 'r', label='Lower-Bound D')\n", - "plt.legend()\n", - "plt.show()" + "* For $r \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$." ] }, { "cell_type": "code", "execution_count": null, - "id": "11e07596", + "id": "7c1c6e20", "metadata": {}, "outputs": [], "source": [] From d21061aee318130a3d7560029093883296e74aa0 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 11 Apr 2022 00:37:11 -0600 Subject: [PATCH 05/64] Use 2662 to enclose math equations written in latex to resolve rendering issue --- docs/Tutorial_VALMOD.ipynb | 148 +++++++++++++++++++++++++------------ 1 file changed, 101 insertions(+), 47 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 079fc4831..1933799ec 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 14, "id": "6534d116", "metadata": {}, "outputs": [], @@ -175,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "a1429322", + "id": "746512bd", "metadata": {}, "source": [ "### Derving Equation (2)" @@ -183,9 +183,11 @@ }, { "cell_type": "markdown", - "id": "982235e5", + "id": "38b7f91c", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " d^{(m+k)}_{j,i} ={}& \n", " \\sqrt{\\sum\\limits_{t=1}^{m+k}{{\n", @@ -197,14 +199,17 @@ " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", " }^{2}}}\n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "a86c6f4d", + "id": "ec4e090c", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " LB ={}& \n", " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", @@ -214,12 +219,13 @@ " ={}&\n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}}\n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "31a8cf32", + "id": "57e5b64c", "metadata": {}, "source": [ "Note that the variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$." @@ -227,35 +233,41 @@ }, { "cell_type": "markdown", - "id": "4595f60b", + "id": "3773968e", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " f(\\mu^{'}, \\sigma^{'}) ={}& \n", " \\sum\\limits_{t=1}^{m}{{\n", " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", " }^{2}} \n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "0be3b76a", + "id": "37918e15", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " X_{t} \\triangleq{}& \n", " {\n", " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", " } \n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "c42a67de", + "id": "d1f33f26", "metadata": {}, "source": [ "To find critical point(s):" @@ -263,44 +275,53 @@ }, { "cell_type": "markdown", - "id": "9ee22e69", + "id": "caebf383", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0 \\quad \\text{(1)}\n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0 \\quad \\text{(2)}\n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "97afedeb", + "id": "c8749eee", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", " \\sum \\limits_{t=1}^{m} {\\frac{-2}{\\sigma^{'}}X_{t}} \\Rightarrow \\text{with (1):}\n", " \\sum \\limits_{t=1}^{m} X_{t} = 0 \\quad (3)\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "94efd415", + "id": "22345d05", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", " \\sum{\\frac{-2}{\\sigma^{'2}}\\left(T[i+t-1] - \\mu^{'}\\right)X_{t}} \\Rightarrow {\\text{with (2) and (3)}}:\n", " \\sum \\limits_{t=1}^{m} T[i+t-1]X_{t} = 0 \\quad (4)\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "0a5b427d", + "id": "6e390cf4", "metadata": {}, "source": [ "Exapanding (3):" @@ -308,9 +329,11 @@ }, { "cell_type": "markdown", - "id": "870b5eb8", + "id": "4915e55a", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\sum \\limits_{t=1}^{m} X_{t} = 0\n", " \\\\\n", @@ -327,12 +350,13 @@ " \\\\\n", " \\sigma_{j,m} \\mu^{'} + \n", " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} = 0 \\quad (5)\n", - "\\end{align} " + "\\end{align} \n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "ebdb4516", + "id": "194a1fc1", "metadata": {}, "source": [ "Expanding (4):" @@ -340,62 +364,77 @@ }, { "cell_type": "markdown", - "id": "5c3fd3cb", + "id": "f230b5a7", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\sum \\limits_{t=1}^{m} T[i+t-1] \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right) = 0\n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "5864dd2f", + "id": "1ed1ca80", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m} T[i+t-1]T[i+t-1] - \\sum \\limits_{t=1}^{m} T[i+t-1] \\mu^{'}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}}\\right) = 0\n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "c311fdc2", + "id": "041a0e1e", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " r \\triangleq \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}} \\quad (6)\n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "4ea25765", + "id": "7db261d9", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\frac{1}{\\sigma^{'}}\\left(m(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) - m \\mu_{i,m} \\mu^{'}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({m(r\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m}) - m\\mu_{i,m}\\mu_{j,m+k}}\\right) = 0\n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "1bdc9010", + "id": "9117f549", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + \\left(r\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\left(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}\\right) = 0 \\quad (7)\n", " \\\\ \n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "30a6adeb", + "id": "f43f2aba", "metadata": {}, "source": [ "Solving (5) and (7) gives:" @@ -403,22 +442,26 @@ }, { "cell_type": "markdown", - "id": "fae1014d", + "id": "f795e3f1", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma^{'}}{\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k}) \\quad (8)\n", "\\end{align}\n", + "$$\n", "\n", - "\n", + "$$\n", "\\begin{align}\n", " \\sigma^{'} = \\frac{\\sigma_{i,m}}{r} \\quad (9)\n", - "\\end{align}" + "\\end{align}\n", + "$$" ] }, { "cell_type": "markdown", - "id": "4d83a448", + "id": "91ee02c3", "metadata": {}, "source": [ "We can try to simply $f_{min}(\\mu^{'}, \\sigma^{'})$ first with help of (3) and (4) before plugging in the values $\\mu^{'}$ (8) and $\\sigma^{'}$ (9)." @@ -426,9 +469,11 @@ }, { "cell_type": "markdown", - "id": "14904456", + "id": "2879d93c", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", " \\sum\\limits_{t=1}^{m}{{\n", @@ -455,12 +500,13 @@ " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}{T[j+t-1]\\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)}\n", " } \n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "32e63873", + "id": "bc4bbf39", "metadata": {}, "source": [ "with (6), (8), and (9), we can get:" @@ -468,19 +514,22 @@ }, { "cell_type": "markdown", - "id": "d717fbad", + "id": "afa56247", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", " m (1 - r^{2}) \n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "4d0f0609", + "id": "5b734817", "metadata": {}, "source": [ "**Therefore, the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" @@ -488,25 +537,30 @@ }, { "cell_type": "markdown", - "id": "8c3c9bbd", + "id": "a6b23cfe", "metadata": {}, "source": [ + "\n", + "$$\n", "\\begin{align}\n", " LB ={}& \n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m (1 - r^{2})} \\quad \\text{if} \\, r > 0\n", " \\\\\n", "\\end{align}\n", + "$$\n", "\n", + "$$\n", "\\begin{align}\n", " r ={}& \n", " \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", " \\\\\n", - "\\end{align}" + "\\end{align}\n", + "$$\n" ] }, { "cell_type": "markdown", - "id": "1112e11c", + "id": "b3bfd88c", "metadata": {}, "source": [ "**Note:**
\n", @@ -522,7 +576,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7c1c6e20", + "id": "8b78d721", "metadata": {}, "outputs": [], "source": [] From 88503ed043eaeedff1fd23cc66f7bcec38e0eb17 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 11 Apr 2022 01:06:44 -0600 Subject: [PATCH 06/64] modify math eq latex in markdown --- docs/Tutorial_VALMOD.ipynb | 60 +++++++++++++++++++------------------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 1933799ec..888ed3cca 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -175,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "746512bd", + "id": "d60acabc", "metadata": {}, "source": [ "### Derving Equation (2)" @@ -183,7 +183,7 @@ }, { "cell_type": "markdown", - "id": "38b7f91c", + "id": "1d3734ed", "metadata": {}, "source": [ "\n", @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "ec4e090c", + "id": "ade9e7e4", "metadata": {}, "source": [ "\n", @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "57e5b64c", + "id": "d410ec5a", "metadata": {}, "source": [ "Note that the variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$." @@ -233,7 +233,7 @@ }, { "cell_type": "markdown", - "id": "3773968e", + "id": "a293197c", "metadata": {}, "source": [ "\n", @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "37918e15", + "id": "6722cf8a", "metadata": {}, "source": [ "\n", @@ -267,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "d1f33f26", + "id": "e7564257", "metadata": {}, "source": [ "To find critical point(s):" @@ -275,7 +275,7 @@ }, { "cell_type": "markdown", - "id": "caebf383", + "id": "c2de39a8", "metadata": {}, "source": [ "\n", @@ -291,7 +291,7 @@ }, { "cell_type": "markdown", - "id": "c8749eee", + "id": "8b7c8a81", "metadata": {}, "source": [ "\n", @@ -306,7 +306,7 @@ }, { "cell_type": "markdown", - "id": "22345d05", + "id": "4eae27d8", "metadata": {}, "source": [ "\n", @@ -321,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "6e390cf4", + "id": "2dd7d048", "metadata": {}, "source": [ "Exapanding (3):" @@ -329,7 +329,7 @@ }, { "cell_type": "markdown", - "id": "4915e55a", + "id": "848e6f89", "metadata": {}, "source": [ "\n", @@ -356,7 +356,7 @@ }, { "cell_type": "markdown", - "id": "194a1fc1", + "id": "4a34e737", "metadata": {}, "source": [ "Expanding (4):" @@ -364,7 +364,7 @@ }, { "cell_type": "markdown", - "id": "f230b5a7", + "id": "de3f6023", "metadata": {}, "source": [ "\n", @@ -378,7 +378,7 @@ }, { "cell_type": "markdown", - "id": "1ed1ca80", + "id": "1ce7c9be", "metadata": {}, "source": [ "\n", @@ -392,7 +392,7 @@ }, { "cell_type": "markdown", - "id": "041a0e1e", + "id": "3a87f16d", "metadata": {}, "source": [ "\n", @@ -406,7 +406,7 @@ }, { "cell_type": "markdown", - "id": "7db261d9", + "id": "1543b1f4", "metadata": {}, "source": [ "\n", @@ -420,21 +420,21 @@ }, { "cell_type": "markdown", - "id": "9117f549", + "id": "1d37830b", "metadata": {}, "source": [ "\n", "$$\n", "\\begin{align}\n", - " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + \\left(r\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\left(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}\\right) = 0 \\quad (7)\n", - " \\\\ \n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (r\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (7)\n", + " \\\\\n", "\\end{align}\n", "$$\n" ] }, { "cell_type": "markdown", - "id": "f43f2aba", + "id": "6adaea06", "metadata": {}, "source": [ "Solving (5) and (7) gives:" @@ -442,7 +442,7 @@ }, { "cell_type": "markdown", - "id": "f795e3f1", + "id": "631d7d57", "metadata": {}, "source": [ "\n", @@ -461,7 +461,7 @@ }, { "cell_type": "markdown", - "id": "91ee02c3", + "id": "a0e36dfc", "metadata": {}, "source": [ "We can try to simply $f_{min}(\\mu^{'}, \\sigma^{'})$ first with help of (3) and (4) before plugging in the values $\\mu^{'}$ (8) and $\\sigma^{'}$ (9)." @@ -469,7 +469,7 @@ }, { "cell_type": "markdown", - "id": "2879d93c", + "id": "b51d32b2", "metadata": {}, "source": [ "\n", @@ -506,7 +506,7 @@ }, { "cell_type": "markdown", - "id": "bc4bbf39", + "id": "cfd5a617", "metadata": {}, "source": [ "with (6), (8), and (9), we can get:" @@ -514,7 +514,7 @@ }, { "cell_type": "markdown", - "id": "afa56247", + "id": "a5c3b9e8", "metadata": {}, "source": [ "\n", @@ -529,7 +529,7 @@ }, { "cell_type": "markdown", - "id": "5b734817", + "id": "64dc1027", "metadata": {}, "source": [ "**Therefore, the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" @@ -537,7 +537,7 @@ }, { "cell_type": "markdown", - "id": "a6b23cfe", + "id": "98db40a5", "metadata": {}, "source": [ "\n", @@ -560,7 +560,7 @@ }, { "cell_type": "markdown", - "id": "b3bfd88c", + "id": "8cbad624", "metadata": {}, "source": [ "**Note:**
\n", @@ -576,7 +576,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8b78d721", + "id": "448cd8ce", "metadata": {}, "outputs": [], "source": [] From 1a87295637d91d5eb8a7cfce30497c477042aea1 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 11 Apr 2022 22:49:52 -0600 Subject: [PATCH 07/64] add twin_freak explanation --- docs/Tutorial_VALMOD.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 888ed3cca..4e1c902e5 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "id": "6534d116", "metadata": {}, "outputs": [], @@ -101,13 +101,13 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 2, "id": "3d9db678", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -135,7 +135,7 @@ "id": "a8e87bc0", "metadata": {}, "source": [ - "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors rather than just 1NN. \n", + "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors rather than just 1NN. In discord discovery, it is called twin-freak problem (see [Tutorial](https://cci.drexel.edu/bigdata/bigdata2017/files/Tutorial4.pdf)). It happens when the (same) anomally occurs more than once. In our example above, the anomaly occurs twice. Therefore, we should be able to detect it if we consider 2nd nearest neighbor. \n", "\n", "For further details, see Fig. 2 of the paper. Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" ] From 00377bda6cd0c62dc18f4d912e47c1eb7bb9cc03 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 11 Apr 2022 23:15:57 -0600 Subject: [PATCH 08/64] add lower-bound calculation for non-normalized p-norm distance --- docs/Tutorial_VALMOD.ipynb | 93 +++++++++++++++++++++++++++++++++++++- 1 file changed, 92 insertions(+), 1 deletion(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 4e1c902e5..7b32f6b96 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -154,7 +154,7 @@ "id": "27b8effd", "metadata": {}, "source": [ - "# 2- Lower-Bound Distance Profile (for z-normalize case)" + "# 2- Lower-Bound Distance Profile" ] }, { @@ -173,6 +173,97 @@ "In other words, can we find **Lower Bound (LB)** for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$? (So, the last `k` elements of $T_{i,m+k}$ are unknown)" ] }, + { + "cell_type": "markdown", + "id": "e8ac29f2", + "metadata": {}, + "source": [ + "## 2-1 Non-normalized distance" + ] + }, + { + "cell_type": "markdown", + "id": "f4719164", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " d^{(m+k)}_{j,i} ={}& \n", + " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", + " \\sum\\limits_{t=1}^{m+k}{\n", + " \\bigg\\lvert{\n", + " T[i+t-1] - T[j+t-1]\n", + " }\\bigg\\rvert\n", + " }^{p}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", + " \\sum\\limits_{t=1}^{m}{\n", + " \\bigg\\lvert{\n", + " T[i+t-1] - T[j+t-1]\n", + " }\\bigg\\rvert\n", + " }^{p}\n", + " +\n", + " \\sum\\limits_{t=m+1}^{m+k}{\n", + " \\bigg\\lvert{\n", + " T[i+t-1] - T[j+t-1]\n", + " }\\bigg\\rvert\n", + " }^{p}\n", + " }\n", + " \\\\\n", + " \\geq{}&\n", + " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", + " \\sum\\limits_{t=1}^{m}{\n", + " \\bigg\\lvert{\n", + " T[i+t-1] - T[j+t-1]\n", + " }\\bigg\\rvert\n", + " }^{p}\n", + " }\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "6e1b4c8a", + "metadata": {}, + "source": [ + "Therefore:" + ] + }, + { + "cell_type": "markdown", + "id": "f3e5e6de", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " d^{(m+k)}_{j,i} \\geq{}&\n", + " d^{(m)}_{j,i}\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "22bb5bd4", + "metadata": {}, + "source": [ + "In other words, we can simply use the p-norm distance between $T_{i,m}$ and $T_{j,m}$ as the lower-bound value for the distance between $T_{i,m+k}$ and $T_{j,m+k}$." + ] + }, + { + "cell_type": "markdown", + "id": "99f37c41", + "metadata": {}, + "source": [ + "## 2-2 Normalized distance" + ] + }, { "cell_type": "markdown", "id": "d60acabc", From f38e2e56c47eca62e87066b08ebcb0d10556603e Mon Sep 17 00:00:00 2001 From: ninimama Date: Tue, 12 Apr 2022 02:48:27 -0600 Subject: [PATCH 09/64] major improvement in derivation of lower-bound --- docs/Tutorial_VALMOD.ipynb | 504 ++++++++++++++++++++++++++++++++++--- 1 file changed, 464 insertions(+), 40 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 7b32f6b96..81c8af1fa 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -175,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "e8ac29f2", + "id": "c3b87441", "metadata": {}, "source": [ "## 2-1 Non-normalized distance" @@ -183,7 +183,7 @@ }, { "cell_type": "markdown", - "id": "f4719164", + "id": "9ecaf914", "metadata": {}, "source": [ "\n", @@ -228,7 +228,7 @@ }, { "cell_type": "markdown", - "id": "6e1b4c8a", + "id": "0fcfe4a4", "metadata": {}, "source": [ "Therefore:" @@ -236,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "f3e5e6de", + "id": "7e435c6b", "metadata": {}, "source": [ "\n", @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "22bb5bd4", + "id": "cac0c884", "metadata": {}, "source": [ "In other words, we can simply use the p-norm distance between $T_{i,m}$ and $T_{j,m}$ as the lower-bound value for the distance between $T_{i,m+k}$ and $T_{j,m+k}$." @@ -258,12 +258,20 @@ }, { "cell_type": "markdown", - "id": "99f37c41", + "id": "ce92bccd", "metadata": {}, "source": [ "## 2-2 Normalized distance" ] }, + { + "cell_type": "markdown", + "id": "d0773bc3", + "metadata": {}, + "source": [ + "In z-normalized distance, one should note that $d^{(m+k)}_{j,i} \\geq d^{(m)}_{j,i}$ is not necessarily correct. In other words, the distance between two subsequences does not necessarily increase by making them longer. However, it seems there is a very nice relationship between $d_{j,i}^{(m)}$ and the lower-bound value of $d_{j,i}^{(m+k)}$." + ] + }, { "cell_type": "markdown", "id": "d60acabc", @@ -285,6 +293,17 @@ " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", " }^{2}}} \n", " \\\\\n", + " d^{(m+k)}_{j,i} ={}& \n", + " \\sqrt{\n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}\n", + " +\n", + " \\sum\\limits_{t=m+1}^{m+k}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}\n", + " } \n", + " \\\\\n", " \\geq{}&\n", " \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", @@ -294,6 +313,14 @@ "$$\n" ] }, + { + "cell_type": "markdown", + "id": "6447a1c8", + "metadata": {}, + "source": [ + "So, the Lower-Bound (LB) value for $d_{j,i}^{(m+k)}$ can be obtained by minimizing the right-hand side:" + ] + }, { "cell_type": "markdown", "id": "ade9e7e4", @@ -308,6 +335,46 @@ " }^{2}}} \n", " \\\\\n", " ={}&\n", + " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", + " \\left[\\frac{1}{\\sigma_{j,m+k}}\n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{1}\n", + " \\right)\n", + " \\right]\n", + " }^{2}}}\n", + " \\\\\n", + " ={}&\n", + " \\min \\sqrt{\n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left[\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m}}\n", + " \\frac{1}{\\sigma_{j,m+k}}\n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", + " - \n", + " \\frac{T[j+t-1] - \\mu_{j,m+k}}{1}\n", + " \\right)\n", + " \\right]\n", + " }^{2}\n", + " }\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\min \\sqrt{\n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left[\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{j,m}\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", + " - \n", + " \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " \\right]\n", + " }^{2}\n", + " }\n", + " }\n", + " \\\\\n", + " ={}&\n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}}\n", " \\\\\n", "\\end{align}\n", @@ -319,7 +386,9 @@ "id": "d410ec5a", "metadata": {}, "source": [ - "Note that the variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$." + "**Note:** that the variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. Also, note that all $\\mu$ and $\\sigma$ values are **constant** regardless of them being known or unknown.
\n", + "\n", + "We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$." ] }, { @@ -347,7 +416,7 @@ "\n", "$$\n", "\\begin{align}\n", - " X_{t} \\triangleq{}& \n", + " \\alpha_{t} \\triangleq{}& \n", " {\n", " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", " } \n", @@ -361,7 +430,8 @@ "id": "e7564257", "metadata": {}, "source": [ - "To find critical point(s):" + "Please note that the unknown variables are now $\\mu^{'}$ and $\\sigma^{'}$.
\n", + "**To find critical point(s):**" ] }, { @@ -380,6 +450,14 @@ "$$\n" ] }, + { + "cell_type": "markdown", + "id": "ec55a584", + "metadata": {}, + "source": [ + "**Deriving $\\frac{\\partial{f}}{\\partial{\\mu^{'}}}$:**" + ] + }, { "cell_type": "markdown", "id": "8b7c8a81", @@ -389,12 +467,59 @@ "$$\n", "\\begin{align}\n", " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m} {\\frac{-2}{\\sigma^{'}}X_{t}} \\Rightarrow \\text{with (1):}\n", - " \\sum \\limits_{t=1}^{m} X_{t} = 0 \\quad (3)\n", + " \\sum \\limits_{t=1}^{m}{\n", + " \\frac{\\partial{(\\alpha_{t}^{2})}}{\\partial{\\mu^{'}}}\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", + " \\sum \\limits_{t=1}^{m}{\n", + " 2\\frac{\\partial{(\\alpha_{t})}}{\\partial{\\mu^{'}}}\\alpha_{t}\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}&\n", + " \\sum \\limits_{t=1}^{m} {\n", + " 2\\left(\n", + " \\frac{-1}{\\sigma^{'}}\n", + " \\right)\n", + " \\alpha_{t}} \n", + " \\\\\n", + " 0 ={}&\n", + " \\frac{-2}{\\sigma^{'}}\\sum \\limits_{t=1}^{m}{\\alpha_{t}}\n", + " \\\\\n", "\\end{align}\n", "$$\n" ] }, + { + "cell_type": "markdown", + "id": "4e757a6e", + "metadata": {}, + "source": [ + "Please note that $\\sigma^{'}$ is constant and thus it was factered out of the summation.
\n", + "This gives us:" + ] + }, + { + "cell_type": "markdown", + "id": "ced6809c", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m}{\\alpha_{t}} = 0 \\quad (3)\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "14e92a2e", + "metadata": {}, + "source": [ + "**Deriving $\\frac{\\partial{f}}{\\partial{\\sigma^{'}}}$:**" + ] + }, { "cell_type": "markdown", "id": "4eae27d8", @@ -404,8 +529,82 @@ "$$\n", "\\begin{align}\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", - " \\sum{\\frac{-2}{\\sigma^{'2}}\\left(T[i+t-1] - \\mu^{'}\\right)X_{t}} \\Rightarrow {\\text{with (2) and (3)}}:\n", - " \\sum \\limits_{t=1}^{m} T[i+t-1]X_{t} = 0 \\quad (4)\n", + " \\sum \\limits_{t=1}^{m}{\n", + " \\frac{\\partial{(\\alpha_{t}^{2})}}{\\partial{\\sigma^{'}}}\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", + " \\sum \\limits_{t=1}^{m}{\n", + " 2\\frac{\\partial{(\\alpha_{t})}}{\\partial{\\sigma^{'}}}\\alpha_{t}\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\sum \\limits_{t=1}^{m} {\n", + " 2 \\left(\n", + " \\frac{-\\left({T[i+t-1] - \\mu^{'}}\\right)}{\\sigma^{'2}}\n", + " \\right)\n", + " \\alpha_{t}} \n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=1}^{m}{\\left({T[i+t-1] - \\mu^{'}}\\right) \\alpha_{t}}\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=1}^{m}{\\left({T[i+t-1]\\alpha_{t} - \\mu^{'}\\alpha_{t}}\\right)}\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\n", + " {\\left(\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " - \n", + " \\sum \\limits_{t=1}^{m}{\\mu^{'}\\alpha_{t}}\n", + " \\right)\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\n", + " {\\left(\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " - \n", + " \\mu^{'}\\sum \\limits_{t=1}^{m}{\\alpha_{t}}\n", + " \\right)\n", + " }\n", + " \\\\\n", + " 0 ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\n", + " {\\left(\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " - \n", + " \\mu^{'} (0)\n", + " \\right)\n", + " }\n", + " \\\\\n", + " 0 ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\n", + " {\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " }\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "00f650c2", + "metadata": {}, + "source": [ + "And, this gives:" + ] + }, + { + "cell_type": "markdown", + "id": "b8578a82", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} ={}&\n", + " 0 \\quad (4)\n", "\\end{align}\n", "$$\n" ] @@ -415,7 +614,7 @@ "id": "2dd7d048", "metadata": {}, "source": [ - "Exapanding (3):" + "**Exapanding (3):**" ] }, { @@ -426,21 +625,27 @@ "\n", "$$\n", "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m} X_{t} = 0\n", + " \\sum \\limits_{t=1}^{m} \\alpha_{t} ={}& \n", + " 0\n", " \\\\\n", - " \\sum \\limits_{t=1}^{m} {\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}} = 0\n", + " \\sum \\limits_{t=1}^{m} {\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}} ={}& \n", + " 0\n", " \\\\\n", " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m}T[i+t-1] - \\sum \\limits_{t=1}^{m} \\mu^{'}\\right) - \n", - " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=1}^{m}T[j+t-1] - \\sum \\limits_{t=1}^{m} \\mu_{j,m+k}\\right) = 0\n", + " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=1}^{m}T[j+t-1] - \\sum \\limits_{t=1}^{m} \\mu_{j,m+k}\\right) ={}& \n", + " 0\n", " \\\\\n", " \\frac{1}{\\sigma^{'}}\\left(m\\mu_{i,m} - m\\mu^{'}\\right) - \n", - " \\frac{1}{\\sigma_{j,m}}\\left(m\\mu_{j,m} - m\\mu_{j,m+k}\\right) = 0\n", + " \\frac{1}{\\sigma_{j,m}}\\left(m\\mu_{j,m} - m\\mu_{j,m+k}\\right) ={}& \n", + " 0\n", " \\\\\n", " \\sigma_{j,m}\\left(\\mu_{i,m} - \\mu^{'}\\right) - \n", - " \\sigma^{'}\\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) = 0\n", + " \\sigma^{'}\\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) ={}& \n", + " 0\n", " \\\\\n", " \\sigma_{j,m} \\mu^{'} + \n", - " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} = 0 \\quad (5)\n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} ={}& \n", + " 0 \\quad (5)\n", "\\end{align} \n", "$$\n" ] @@ -450,7 +655,7 @@ "id": "4a34e737", "metadata": {}, "source": [ - "Expanding (4):" + "**Expanding (4):**" ] }, { @@ -475,26 +680,54 @@ "\n", "$$\n", "\\begin{align}\n", - " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m} T[i+t-1]T[i+t-1] - \\sum \\limits_{t=1}^{m} T[i+t-1] \\mu^{'}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}}\\right) = 0\n", + " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m} T[i+t-1]T[i+t-1] - \\sum \\limits_{t=1}^{m} T[i+t-1] \\mu^{'}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}}\\right) \n", + " ={}& 0\n", " \\\\\n", + " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m} T[i+t-1]T[i+t-1] - \\mu^{'}\\sum \\limits_{t=1}^{m} T[i+t-1]\\right) - \\frac{1}{\\sigma_{j,m}}\\left({\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]}\\right) \n", + " ={}& 0\n", "\\end{align}\n", "$$\n" ] }, { "cell_type": "markdown", - "id": "3a87f16d", + "id": "817b3066", + "metadata": {}, + "source": [ + "Now, recall the pearson correlation $\\rho$:" + ] + }, + { + "cell_type": "markdown", + "id": "c1337680", "metadata": {}, "source": [ "\n", "$$\n", "\\begin{align}\n", - " r \\triangleq \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}} \\quad (6)\n", + " \\rho = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}} \\quad (6)\n", " \\\\\n", "\\end{align}\n", "$$\n" ] }, + { + "cell_type": "markdown", + "id": "62a33c70", + "metadata": {}, + "source": [ + "**Note:** The pearson correlation, $\\rho$, is 1 when $i=j$.
\n", + "**Note:** Also: $\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$" + ] + }, + { + "cell_type": "markdown", + "id": "2ff6215e", + "metadata": {}, + "source": [ + "Therefore:" + ] + }, { "cell_type": "markdown", "id": "1543b1f4", @@ -503,7 +736,42 @@ "\n", "$$\n", "\\begin{align}\n", - " \\frac{1}{\\sigma^{'}}\\left(m(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) - m \\mu_{i,m} \\mu^{'}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({m(r\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m}) - m\\mu_{i,m}\\mu_{j,m+k}}\\right) = 0\n", + " \\frac{1}{\\sigma^{'}}\\left(m\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2} - \\mu^{'}(m \\mu_{i,m})\\right) - \\frac{1}{\\sigma_{j,m}}\\left({m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu_{j,m+k}(m\\mu_{i,m})}\\right) ={}& 0\n", + " \\\\\n", + " \\sigma_{j,m}\\left(\n", + " m\\sigma_{i,m}^{2} \n", + " + \n", + " m\\mu_{i,m}^{2} \n", + " - \n", + " \\mu^{'}(m \\mu_{i,m})\n", + " \\right) \n", + " - \n", + " \\sigma^{'}\\left(\n", + " {m\\rho\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " m\\mu_{i,m}\\mu_{j,m}) \n", + " -\n", + " \\mu_{j,m+k}(m\\mu_{i,m})}\n", + " \\right) ={}& 0\n", + " \\\\\n", + " m\\left[\n", + " \\sigma_{j,m}\\left(\n", + " \\sigma_{i,m}^{2} \n", + " + \n", + " \\mu_{i,m}^{2} \n", + " - \n", + " \\mu^{'}(\\mu_{i,m})\n", + " \\right) \n", + " - \n", + " \\sigma^{'}\\left(\n", + " {\\rho\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " \\mu_{i,m}\\mu_{j,m}) \n", + " -\n", + " \\mu_{j,m+k}(\\mu_{i,m})}\n", + " \\right)\n", + " \\right]\n", + " ={}& 0\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -517,7 +785,7 @@ "\n", "$$\n", "\\begin{align}\n", - " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (r\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (7)\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (7)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -528,7 +796,7 @@ "id": "6adaea06", "metadata": {}, "source": [ - "Solving (5) and (7) gives:" + "**Solving (5) and (7) gives:**" ] }, { @@ -539,13 +807,13 @@ "\n", "$$\n", "\\begin{align}\n", - " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma^{'}}{\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k}) \\quad (8)\n", + " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma^{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k}) \\quad (8)\n", "\\end{align}\n", "$$\n", "\n", "$$\n", "\\begin{align}\n", - " \\sigma^{'} = \\frac{\\sigma_{i,m}}{r} \\quad (9)\n", + " \\sigma^{'} = \\frac{\\sigma_{i,m}}{\\rho} \\quad (9)\n", "\\end{align}\n", "$$" ] @@ -574,21 +842,91 @@ " ={}&\n", " \\sum\\limits_{t=1}^{m}{{\n", " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", - " }X_{t}}\n", + " }\\alpha_{t}}\n", " \\\\\n", " ={}&\n", " {\n", - " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]X_{t} - \\sum\\limits_{t=1}^{m}\\mu^{'}X_{t}}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]X_{t} - \\sum\\limits_{t=1}^{m}\\mu_{j,m+k}X_{t}}{\\sigma_{j,m}}\n", + " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]\\alpha_{t} - \\sum\\limits_{t=1}^{m}\\mu^{'}\\alpha_{t}}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\sum\\limits_{t=1}^{m}\\mu_{j,m+k}\\alpha_{t}}{\\sigma_{j,m}}\n", + " } \n", + " \\\\ \n", + " ={}&\n", + " {\n", + " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]\\alpha_{t} - \\mu^{'}\\sum\\limits_{t=1}^{m}\\alpha_{t}}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}\\alpha_{t}}{\\sigma_{j,m}}\n", + " } \n", + " \\\\ \n", + " ={}&\n", + " {\n", + " \\frac{0 - \\mu^{'}(0)}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}(0)}{\\sigma_{j,m}}\n", " } \n", " \\\\ \n", " ={}&\n", " {\n", - " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}T[j+t-1]X_{t}\n", + " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t}\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {\n", + " - \\frac{1}{\\sigma_{j,m}} \n", + " \\sum\\limits_{t=1}^{m}{\\left[\n", + " T[j+t-1]\\left(\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " \\right]\n", + " }\n", " } \n", " \\\\\n", " ={}&\n", " {\n", - " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}{T[j+t-1]\\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)}\n", + " - \\frac{1}{\\sigma_{j,m}} \n", + " \\sum\\limits_{t=1}^{m}{\n", + " \\left(\n", + " \\frac{T[i+t-1]T[j+t-1] - \\mu^{'}T[j+t-1]}{\\sigma^{'}} - \\frac{T[j+t-1]T[j+t-1] - \\mu_{j,m+k}T[j+t-1]}{\\sigma_{j,m}}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{1}{\\sigma_{j,m}} \n", + " {\n", + " \\left(\n", + " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\mu^{'}\\sum\\limits_{t=1}^{m}T[j+t-1]}{\\sigma^{'}} \n", + " - \n", + " \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]T[j+t-1] - \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}T[j+t-1]}{\\sigma_{j,m}}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{1}{\\sigma_{j,m}} \n", + " {\n", + " \\left(\n", + " \\frac{(m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - m\\mu_{j,m}\\mu^{'}}{\\sigma^{'}} \n", + " - \n", + " \\frac{(m\\sigma_{j,m}^{2} + m\\mu_{j,m}^{2}) - m\\mu_{j,m}\\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma^{'}} \n", + " {\n", + " \\left(\n", + " {\\sigma_{j,m}(\\rho\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{j,m}\\mu^{'})} \n", + " - \n", + " {\\sigma^{'}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma^{'}} \n", + " {\n", + " \\left(\n", + " {\\rho\\sigma_{i,m}\\sigma_{j,m}^{2} + \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \\mu_{j,m}\\sigma_{j,m}\\mu^{'}} \n", + " - \n", + " {\\sigma^{'}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right)\n", + " }\n", " } \n", " \\\\\n", "\\end{align}\n", @@ -600,7 +938,93 @@ "id": "cfd5a617", "metadata": {}, "source": [ - "with (6), (8), and (9), we can get:" + "with (8), and (9), we can get:" + ] + }, + { + "cell_type": "markdown", + "id": "7a2c7400", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", + " {- \\frac{m\\rho}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {\n", + " \\left[\n", + " {\\rho\\sigma_{(i,m)}\\sigma_{j,m}^{2} + \n", + " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \n", + " \\mu_{j,m}\\sigma_{j,m}\\left({\n", + " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " }\n", + " \\right)} \n", + " - \n", + " {(\\frac{\\sigma_{i,m}}{\\rho})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m\\rho}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {\n", + " \\left[\n", + " {\\rho\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " + \n", + " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", + " - \n", + " {\n", + " \\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", + " + \n", + " \\mu_{j,m}\\sigma_{j,m}\\frac{\\sigma_{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " }\n", + " } \n", + " - \n", + " {(\\frac{\\sigma_{i,m}}{\\rho})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {\n", + " \\left[\n", + " {\\rho^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " + \n", + " \\rho\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", + " - \n", + " {\n", + " \\rho\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", + " + \n", + " \\mu_{j,m}\\sigma_{i,m}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " }\n", + " } \n", + " - \n", + " {(\\sigma_{i,m})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", + " \\left( \n", + " {\\rho^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " - \n", + " \\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " }\n", + " \\right)\n", + " } \n", + " \\\\\n", + "\\end{align} \n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1cd8f37", + "metadata": {}, + "source": [ + "Therefore:" ] }, { @@ -612,7 +1036,7 @@ "$$\n", "\\begin{align}\n", " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", - " m (1 - r^{2}) \n", + " m (1 - \\rho^{2}) \n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -635,14 +1059,14 @@ "$$\n", "\\begin{align}\n", " LB ={}& \n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m (1 - r^{2})} \\quad \\text{if} \\, r > 0\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m (1 - \\rho^{2})} \\quad \\text{if} \\, \\rho > 0\n", " \\\\\n", "\\end{align}\n", "$$\n", "\n", "$$\n", "\\begin{align}\n", - " r ={}& \n", + " \\rho ={}& \n", " \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", " \\\\\n", "\\end{align}\n", @@ -655,13 +1079,13 @@ "metadata": {}, "source": [ "**Note:**
\n", - "* Note that eq(9) is valid only for $r > 0$. Therefore, we can use the formula above to calculate $LB$ only if $r > 0$. \n", - "* The pearson correlation, `r`, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", + "* Note that eq(9) is valid only for $\\rho > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho > 0$. \n", + "* The pearson correlation, $\\rho$, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", "\n", "**Pending...**
\n", "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function.\n", "\n", - "* For $r \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$." + "* For $\\rho \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$." ] }, { From 9c1e9fd05c4d85d54eda9b1abc4d37e12c077316 Mon Sep 17 00:00:00 2001 From: ninimama Date: Tue, 12 Apr 2022 02:59:22 -0600 Subject: [PATCH 10/64] minor changes to improve clarity --- docs/Tutorial_VALMOD.ipynb | 62 ++++++++++++++++++++++++-------------- 1 file changed, 39 insertions(+), 23 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 81c8af1fa..662b182b8 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -175,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "c3b87441", + "id": "3b5c8c5a", "metadata": {}, "source": [ "## 2-1 Non-normalized distance" @@ -183,7 +183,7 @@ }, { "cell_type": "markdown", - "id": "9ecaf914", + "id": "1f7e294e", "metadata": {}, "source": [ "\n", @@ -228,7 +228,7 @@ }, { "cell_type": "markdown", - "id": "0fcfe4a4", + "id": "5a4d2b3a", "metadata": {}, "source": [ "Therefore:" @@ -236,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "7e435c6b", + "id": "dc578dbd", "metadata": {}, "source": [ "\n", @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "cac0c884", + "id": "b51f7143", "metadata": {}, "source": [ "In other words, we can simply use the p-norm distance between $T_{i,m}$ and $T_{j,m}$ as the lower-bound value for the distance between $T_{i,m+k}$ and $T_{j,m+k}$." @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "ce92bccd", + "id": "0b539ca8", "metadata": {}, "source": [ "## 2-2 Normalized distance" @@ -266,7 +266,7 @@ }, { "cell_type": "markdown", - "id": "d0773bc3", + "id": "91ab346f", "metadata": {}, "source": [ "In z-normalized distance, one should note that $d^{(m+k)}_{j,i} \\geq d^{(m)}_{j,i}$ is not necessarily correct. In other words, the distance between two subsequences does not necessarily increase by making them longer. However, it seems there is a very nice relationship between $d_{j,i}^{(m)}$ and the lower-bound value of $d_{j,i}^{(m+k)}$." @@ -315,7 +315,7 @@ }, { "cell_type": "markdown", - "id": "6447a1c8", + "id": "72a47d5c", "metadata": {}, "source": [ "So, the Lower-Bound (LB) value for $d_{j,i}^{(m+k)}$ can be obtained by minimizing the right-hand side:" @@ -452,7 +452,7 @@ }, { "cell_type": "markdown", - "id": "ec55a584", + "id": "a3656f16", "metadata": {}, "source": [ "**Deriving $\\frac{\\partial{f}}{\\partial{\\mu^{'}}}$:**" @@ -492,7 +492,7 @@ }, { "cell_type": "markdown", - "id": "4e757a6e", + "id": "6ef98f3f", "metadata": {}, "source": [ "Please note that $\\sigma^{'}$ is constant and thus it was factered out of the summation.
\n", @@ -501,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "ced6809c", + "id": "cdc74b21", "metadata": {}, "source": [ "\n", @@ -514,7 +514,7 @@ }, { "cell_type": "markdown", - "id": "14e92a2e", + "id": "393ddb8f", "metadata": {}, "source": [ "**Deriving $\\frac{\\partial{f}}{\\partial{\\sigma^{'}}}$:**" @@ -589,7 +589,7 @@ }, { "cell_type": "markdown", - "id": "00f650c2", + "id": "c3b80336", "metadata": {}, "source": [ "And, this gives:" @@ -597,7 +597,7 @@ }, { "cell_type": "markdown", - "id": "b8578a82", + "id": "c398718a", "metadata": {}, "source": [ "\n", @@ -691,15 +691,15 @@ }, { "cell_type": "markdown", - "id": "817b3066", + "id": "0c839937", "metadata": {}, "source": [ - "Now, recall the pearson correlation $\\rho$:" + "**Now, recall the pearson correlation $\\rho$:**" ] }, { "cell_type": "markdown", - "id": "c1337680", + "id": "82bc9b8e", "metadata": {}, "source": [ "\n", @@ -713,7 +713,7 @@ }, { "cell_type": "markdown", - "id": "62a33c70", + "id": "4880c751", "metadata": {}, "source": [ "**Note:** The pearson correlation, $\\rho$, is 1 when $i=j$.
\n", @@ -722,10 +722,10 @@ }, { "cell_type": "markdown", - "id": "2ff6215e", + "id": "a01fd0cc", "metadata": {}, "source": [ - "Therefore:" + "**Therefore:**" ] }, { @@ -818,6 +818,14 @@ "$$" ] }, + { + "cell_type": "markdown", + "id": "b266cfb2", + "metadata": {}, + "source": [ + "**Note:** eq(9) is valid if $\\rho \\gt 0$. Therefore, the rest of this calculation is based on the assumption that $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" + ] + }, { "cell_type": "markdown", "id": "a0e36dfc", @@ -938,12 +946,12 @@ "id": "cfd5a617", "metadata": {}, "source": [ - "with (8), and (9), we can get:" + "plugging in (8) and (9):" ] }, { "cell_type": "markdown", - "id": "7a2c7400", + "id": "f3e25620", "metadata": {}, "source": [ "\n", @@ -1015,13 +1023,19 @@ " \\right)\n", " } \n", " \\\\\n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", + " (\\rho^{2} - 1)\n", + " \\sigma_{i,m}\\sigma_{j,m}^{2}\n", + " } \n", "\\end{align} \n", "$$\n" ] }, { "cell_type": "markdown", - "id": "f1cd8f37", + "id": "d836a69d", "metadata": {}, "source": [ "Therefore:" @@ -1082,6 +1096,8 @@ "* Note that eq(9) is valid only for $\\rho > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho > 0$. \n", "* The pearson correlation, $\\rho$, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", "\n", + "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho)}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", + "\n", "**Pending...**
\n", "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function.\n", "\n", From ffee3db4ae63bbc1e5d4680012a3672c25ae248f Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 13 Apr 2022 09:36:32 -0600 Subject: [PATCH 11/64] explain system of equations in finding critical points --- docs/Tutorial_VALMOD.ipynb | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 662b182b8..366f0128c 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -107,7 +107,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -425,13 +425,37 @@ "$$\n" ] }, + { + "cell_type": "markdown", + "id": "5b946c7f", + "metadata": {}, + "source": [ + "Therefore, we can write $f(\\mu_{'},\\sigma_{'})$ as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "6a6a8e13", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f(\\mu^{'}, \\sigma^{'}) ={}& \n", + " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, { "cell_type": "markdown", "id": "e7564257", "metadata": {}, "source": [ "Please note that the unknown variables are now $\\mu^{'}$ and $\\sigma^{'}$.
\n", - "**To find critical point(s):**" + "**To find extrema points, we first need to find the critical point(s) by solving the single system of equations below.** In other words, we are looking for $\\mu^{'}$ and $\\sigma^{'}$ that satisfies both equations below.\n", + "\n" ] }, { From 7554bff7810263b5efb6735cc77af3bece569bc8 Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 13 Apr 2022 09:46:46 -0600 Subject: [PATCH 12/64] use cdot in latex math to improve readability --- docs/Tutorial_VALMOD.ipynb | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 366f0128c..8f3258acb 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -427,7 +427,7 @@ }, { "cell_type": "markdown", - "id": "5b946c7f", + "id": "0a4166f2", "metadata": {}, "source": [ "Therefore, we can write $f(\\mu_{'},\\sigma_{'})$ as follows:" @@ -435,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "6a6a8e13", + "id": "f0cc2aa1", "metadata": {}, "source": [ "\n", @@ -598,7 +598,7 @@ " {\\left(\n", " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", " - \n", - " \\mu^{'} (0)\n", + " \\mu^{'}\\cdot 0\n", " \\right)\n", " }\n", " \\\\\n", @@ -760,14 +760,15 @@ "\n", "$$\n", "\\begin{align}\n", - " \\frac{1}{\\sigma^{'}}\\left(m\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2} - \\mu^{'}(m \\mu_{i,m})\\right) - \\frac{1}{\\sigma_{j,m}}\\left({m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu_{j,m+k}(m\\mu_{i,m})}\\right) ={}& 0\n", + " \\frac{1}{\\sigma^{'}}\\left(m\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2} - \\mu^{'} \\cdot m\\mu_{i,m}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu_{j,m+k} \\cdot m\\mu_{i,m}}\\right) ={}& 0\n", " \\\\\n", + " \\frac{\n", " \\sigma_{j,m}\\left(\n", " m\\sigma_{i,m}^{2} \n", " + \n", " m\\mu_{i,m}^{2} \n", " - \n", - " \\mu^{'}(m \\mu_{i,m})\n", + " \\mu^{'} \\cdot m\\mu_{i,m}\n", " \\right) \n", " - \n", " \\sigma^{'}\\left(\n", @@ -775,8 +776,10 @@ " +\n", " m\\mu_{i,m}\\mu_{j,m}) \n", " -\n", - " \\mu_{j,m+k}(m\\mu_{i,m})}\n", - " \\right) ={}& 0\n", + " \\mu_{j,m+k} \\cdot m\\mu_{i,m}}\n", + " \\right)\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}} ={}& 0\n", " \\\\\n", " m\\left[\n", " \\sigma_{j,m}\\left(\n", @@ -784,7 +787,7 @@ " + \n", " \\mu_{i,m}^{2} \n", " - \n", - " \\mu^{'}(\\mu_{i,m})\n", + " \\mu^{'} \\mu_{i,m}\n", " \\right) \n", " - \n", " \\sigma^{'}\\left(\n", @@ -792,7 +795,7 @@ " +\n", " \\mu_{i,m}\\mu_{j,m}) \n", " -\n", - " \\mu_{j,m+k}(\\mu_{i,m})}\n", + " \\mu_{j,m+k} \\mu_{i,m}}\n", " \\right)\n", " \\right]\n", " ={}& 0\n", @@ -888,7 +891,7 @@ " \\\\ \n", " ={}&\n", " {\n", - " \\frac{0 - \\mu^{'}(0)}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}(0)}{\\sigma_{j,m}}\n", + " \\frac{0 - \\mu^{'} \\cdot 0}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}\\cdot 0}{\\sigma_{j,m}}\n", " } \n", " \\\\ \n", " ={}&\n", From b8ea16cf41054fff51dc3d57f239bbebb756f71d Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 13 Apr 2022 09:52:03 -0600 Subject: [PATCH 13/64] restructure the notebook to keep the flow --- docs/Tutorial_VALMOD.ipynb | 92 +++++++++++++++++++------------------- 1 file changed, 46 insertions(+), 46 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 8f3258acb..4c5eb56a2 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -427,7 +427,7 @@ }, { "cell_type": "markdown", - "id": "0a4166f2", + "id": "27796250", "metadata": {}, "source": [ "Therefore, we can write $f(\\mu_{'},\\sigma_{'})$ as follows:" @@ -435,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "f0cc2aa1", + "id": "e7b2a98e", "metadata": {}, "source": [ "\n", @@ -536,6 +536,47 @@ "$$\n" ] }, + { + "cell_type": "markdown", + "id": "5c39469f", + "metadata": {}, + "source": [ + "**Exapanding (3):**" + ] + }, + { + "cell_type": "markdown", + "id": "25a3cf35", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m} \\alpha_{t} ={}& \n", + " 0\n", + " \\\\\n", + " \\sum \\limits_{t=1}^{m} {\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}} ={}& \n", + " 0\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m}T[i+t-1] - \\sum \\limits_{t=1}^{m} \\mu^{'}\\right) - \n", + " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=1}^{m}T[j+t-1] - \\sum \\limits_{t=1}^{m} \\mu_{j,m+k}\\right) ={}& \n", + " 0\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\\left(m\\mu_{i,m} - m\\mu^{'}\\right) - \n", + " \\frac{1}{\\sigma_{j,m}}\\left(m\\mu_{j,m} - m\\mu_{j,m+k}\\right) ={}& \n", + " 0\n", + " \\\\\n", + " \\sigma_{j,m}\\left(\\mu_{i,m} - \\mu^{'}\\right) - \n", + " \\sigma^{'}\\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) ={}& \n", + " 0\n", + " \\\\\n", + " \\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} ={}& \n", + " 0 \\quad (4)\n", + "\\end{align} \n", + "$$\n" + ] + }, { "cell_type": "markdown", "id": "393ddb8f", @@ -628,49 +669,8 @@ "$$\n", "\\begin{align}\n", " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} ={}&\n", - " 0 \\quad (4)\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "2dd7d048", - "metadata": {}, - "source": [ - "**Exapanding (3):**" - ] - }, - { - "cell_type": "markdown", - "id": "848e6f89", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m} \\alpha_{t} ={}& \n", - " 0\n", - " \\\\\n", - " \\sum \\limits_{t=1}^{m} {\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}} ={}& \n", - " 0\n", - " \\\\\n", - " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m}T[i+t-1] - \\sum \\limits_{t=1}^{m} \\mu^{'}\\right) - \n", - " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=1}^{m}T[j+t-1] - \\sum \\limits_{t=1}^{m} \\mu_{j,m+k}\\right) ={}& \n", - " 0\n", - " \\\\\n", - " \\frac{1}{\\sigma^{'}}\\left(m\\mu_{i,m} - m\\mu^{'}\\right) - \n", - " \\frac{1}{\\sigma_{j,m}}\\left(m\\mu_{j,m} - m\\mu_{j,m+k}\\right) ={}& \n", - " 0\n", - " \\\\\n", - " \\sigma_{j,m}\\left(\\mu_{i,m} - \\mu^{'}\\right) - \n", - " \\sigma^{'}\\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) ={}& \n", - " 0\n", - " \\\\\n", - " \\sigma_{j,m} \\mu^{'} + \n", - " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} ={}& \n", " 0 \\quad (5)\n", - "\\end{align} \n", + "\\end{align}\n", "$$\n" ] }, @@ -823,7 +823,7 @@ "id": "6adaea06", "metadata": {}, "source": [ - "**Solving (5) and (7) gives:**" + "**Solving (4) and (7) gives:**" ] }, { @@ -858,7 +858,7 @@ "id": "a0e36dfc", "metadata": {}, "source": [ - "We can try to simply $f_{min}(\\mu^{'}, \\sigma^{'})$ first with help of (3) and (4) before plugging in the values $\\mu^{'}$ (8) and $\\sigma^{'}$ (9)." + "We can try to simply $f_{min}(\\mu^{'}, \\sigma^{'})$ first with help of (3) and (5) before plugging in the values $\\mu^{'}$ (8) and $\\sigma^{'}$ (9)." ] }, { From d3f9617b926585ab5ef97ab65382d5da9272bc54 Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 13 Apr 2022 10:06:45 -0600 Subject: [PATCH 14/64] minor changes --- docs/Tutorial_VALMOD.ipynb | 65 ++++++++++++++++++++++++++++++-------- 1 file changed, 52 insertions(+), 13 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 4c5eb56a2..fe12d0fe0 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -427,7 +427,7 @@ }, { "cell_type": "markdown", - "id": "27796250", + "id": "a8f1ad87", "metadata": {}, "source": [ "Therefore, we can write $f(\\mu_{'},\\sigma_{'})$ as follows:" @@ -435,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "e7b2a98e", + "id": "3b2d48d7", "metadata": {}, "source": [ "\n", @@ -538,7 +538,7 @@ }, { "cell_type": "markdown", - "id": "5c39469f", + "id": "4f30398f", "metadata": {}, "source": [ "**Exapanding (3):**" @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "25a3cf35", + "id": "49cf48c5", "metadata": {}, "source": [ "\n", @@ -708,8 +708,8 @@ " ={}& 0\n", " \\\\\n", " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m} T[i+t-1]T[i+t-1] - \\mu^{'}\\sum \\limits_{t=1}^{m} T[i+t-1]\\right) - \\frac{1}{\\sigma_{j,m}}\\left({\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]}\\right) \n", - " ={}& 0\n", - "\\end{align}\n", + " ={}& 0 \\quad (*)\n", + "\\end{align} \n", "$$\n" ] }, @@ -740,8 +740,8 @@ "id": "4880c751", "metadata": {}, "source": [ - "**Note:** The pearson correlation, $\\rho$, is 1 when $i=j$.
\n", - "**Note:** Also: $\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$" + "**Note:** The pearson correlation, $\\rho$, is 1 when $i=j$. Becauses, the correlation of any subsequence with itself is 1.
\n", + "**Note:** we can rewrite (6) as: $\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$" ] }, { @@ -749,7 +749,7 @@ "id": "a01fd0cc", "metadata": {}, "source": [ - "**Therefore:**" + "**Therefore, continue from eq(*)...**" ] }, { @@ -760,7 +760,26 @@ "\n", "$$\n", "\\begin{align}\n", - " \\frac{1}{\\sigma^{'}}\\left(m\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2} - \\mu^{'} \\cdot m\\mu_{i,m}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu_{j,m+k} \\cdot m\\mu_{i,m}}\\right) ={}& 0\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left[\n", + " \\left(\n", + " m\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2}\n", + " \\right)\n", + " - \n", + " \\mu^{'} \\cdot m\\mu_{i,m}\n", + " \\right] \n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left[\n", + " \\left(\n", + " m\\rho\\sigma_{i,m}\\sigma_{j,m} \n", + " + \n", + " m\\mu_{i,m}\\mu_{j,m}\n", + " \\right)\n", + " - \n", + " \\mu_{j,m+k} \\cdot m\\mu_{i,m}\n", + " \\right]\n", + " ={}& 0\n", " \\\\\n", " \\frac{\n", " \\sigma_{j,m}\\left(\n", @@ -774,14 +793,17 @@ " \\sigma^{'}\\left(\n", " {m\\rho\\sigma_{i,m}\\sigma_{j,m} \n", " +\n", - " m\\mu_{i,m}\\mu_{j,m}) \n", + " m\\mu_{i,m}\\mu_{j,m} \n", " -\n", " \\mu_{j,m+k} \\cdot m\\mu_{i,m}}\n", " \\right)\n", " }{\n", " \\sigma^{'}\\sigma_{j,m}} ={}& 0\n", " \\\\\n", - " m\\left[\n", + " \\frac{m}{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\left[\n", " \\sigma_{j,m}\\left(\n", " \\sigma_{i,m}^{2} \n", " + \n", @@ -793,13 +815,30 @@ " \\sigma^{'}\\left(\n", " {\\rho\\sigma_{i,m}\\sigma_{j,m} \n", " +\n", - " \\mu_{i,m}\\mu_{j,m}) \n", + " \\mu_{i,m}\\mu_{j,m}\n", " -\n", " \\mu_{j,m+k} \\mu_{i,m}}\n", " \\right)\n", " \\right]\n", " ={}& 0\n", " \\\\\n", + " \\sigma_{j,m}\\left(\n", + " \\sigma_{i,m}^{2} \n", + " + \n", + " \\mu_{i,m}^{2} \n", + " - \n", + " \\mu^{'} \\mu_{i,m}\n", + " \\right) \n", + " - \n", + " \\sigma^{'}\\left(\n", + " {\\rho\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " \\mu_{i,m}\\mu_{j,m}\n", + " -\n", + " \\mu_{j,m+k} \\mu_{i,m}}\n", + " \\right)\n", + " ={}& 0\n", + " \\\\\n", "\\end{align}\n", "$$\n" ] From 7d26c9721a800ecac2cff241a2e45c8f8f308681 Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 13 Apr 2022 11:24:51 -0600 Subject: [PATCH 15/64] improve readibility of equations --- docs/Tutorial_VALMOD.ipynb | 227 +++++++++++++++++++++++++++++-------- 1 file changed, 180 insertions(+), 47 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index fe12d0fe0..82657ab11 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -375,7 +375,7 @@ " }\n", " \\\\\n", " ={}&\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}}\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}} \\quad (1)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -402,7 +402,7 @@ " f(\\mu^{'}, \\sigma^{'}) ={}& \n", " \\sum\\limits_{t=1}^{m}{{\n", " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", - " }^{2}} \n", + " }^{2}} \\quad (2)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -418,7 +418,7 @@ "\\begin{align}\n", " \\alpha_{t} \\triangleq{}& \n", " {\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}} \\quad (3)\n", " } \n", " \\\\\n", "\\end{align}\n", @@ -427,7 +427,7 @@ }, { "cell_type": "markdown", - "id": "a8f1ad87", + "id": "c5e7e16d", "metadata": {}, "source": [ "Therefore, we can write $f(\\mu_{'},\\sigma_{'})$ as follows:" @@ -435,14 +435,14 @@ }, { "cell_type": "markdown", - "id": "3b2d48d7", + "id": "a55134cb", "metadata": {}, "source": [ "\n", "$$\n", "\\begin{align}\n", " f(\\mu^{'}, \\sigma^{'}) ={}& \n", - " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \n", + " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \\quad (4)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -466,9 +466,9 @@ "\n", "$$\n", "\\begin{align}\n", - " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0 \\quad \\text{(1)}\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0 \\quad (5)\n", " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0 \\quad \\text{(2)}\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0 \\quad (6)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -531,22 +531,22 @@ "\n", "$$\n", "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m}{\\alpha_{t}} = 0 \\quad (3)\n", + " \\sum \\limits_{t=1}^{m}{\\alpha_{t}} = 0 \\quad (7)\n", "\\end{align}\n", "$$\n" ] }, { "cell_type": "markdown", - "id": "4f30398f", + "id": "0a3dd808", "metadata": {}, "source": [ - "**Exapanding (3):**" + "**Exapanding (7):**" ] }, { "cell_type": "markdown", - "id": "49cf48c5", + "id": "91c2bf00", "metadata": {}, "source": [ "\n", @@ -572,7 +572,7 @@ " \\\\\n", " \\sigma_{j,m} \\mu^{'} + \n", " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} ={}& \n", - " 0 \\quad (4)\n", + " 0 \\quad (8)\n", "\\end{align} \n", "$$\n" ] @@ -669,7 +669,7 @@ "$$\n", "\\begin{align}\n", " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} ={}&\n", - " 0 \\quad (5)\n", + " 0 \\quad (9)\n", "\\end{align}\n", "$$\n" ] @@ -679,7 +679,7 @@ "id": "4a34e737", "metadata": {}, "source": [ - "**Expanding (4):**" + "**Expanding (9):**" ] }, { @@ -729,7 +729,7 @@ "\n", "$$\n", "\\begin{align}\n", - " \\rho = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}} \\quad (6)\n", + " \\rho = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -741,7 +741,7 @@ "metadata": {}, "source": [ "**Note:** The pearson correlation, $\\rho$, is 1 when $i=j$. Becauses, the correlation of any subsequence with itself is 1.
\n", - "**Note:** we can rewrite (6) as: $\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$" + "**Note:** we can rewrite (6) as: $\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (**)" ] }, { @@ -749,7 +749,7 @@ "id": "a01fd0cc", "metadata": {}, "source": [ - "**Therefore, continue from eq(*)...**" + "Therefore, with help of (\\*\\*), we continue eq(*):" ] }, { @@ -851,7 +851,7 @@ "\n", "$$\n", "\\begin{align}\n", - " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (7)\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (10)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -862,7 +862,7 @@ "id": "6adaea06", "metadata": {}, "source": [ - "**Solving (4) and (7) gives:**" + "**Solving (8) and (10) gives the values of critical point:**" ] }, { @@ -873,13 +873,13 @@ "\n", "$$\n", "\\begin{align}\n", - " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma^{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k}) \\quad (8)\n", + " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma^{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k}) \\quad (11)\n", "\\end{align}\n", "$$\n", "\n", "$$\n", "\\begin{align}\n", - " \\sigma^{'} = \\frac{\\sigma_{i,m}}{\\rho} \\quad (9)\n", + " \\sigma^{'} = \\frac{\\sigma_{i,m}}{\\rho} \\quad (12)\n", "\\end{align}\n", "$$" ] @@ -889,7 +889,7 @@ "id": "b266cfb2", "metadata": {}, "source": [ - "**Note:** eq(9) is valid if $\\rho \\gt 0$. Therefore, the rest of this calculation is based on the assumption that $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" + "**Note:** Eq(12) is valid if $\\rho \\gt 0$. Therefore, the rest of this calculation is based on the assumption that $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" ] }, { @@ -897,7 +897,17 @@ "id": "a0e36dfc", "metadata": {}, "source": [ - "We can try to simply $f_{min}(\\mu^{'}, \\sigma^{'})$ first with help of (3) and (5) before plugging in the values $\\mu^{'}$ (8) and $\\sigma^{'}$ (9)." + "---\n", + "\n", + "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. Note that we solved the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all discovered equations (5), (6), (7), (8), (9), and (10) throughout the solution.
" + ] + }, + { + "cell_type": "markdown", + "id": "2b12d914", + "metadata": {}, + "source": [ + "**Start with equation (4):**" ] }, { @@ -908,29 +918,109 @@ "\n", "$$\n", "\\begin{align}\n", - " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", - " \\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", - " }^{2}} \n", + " f(\\mu^{'}_{c},\\sigma^{'}_{c}) ={}&\n", + " \\sum \\limits_{t=1}^{m}\\alpha_{t}^{2}\n", " \\\\\n", " ={}&\n", - " \\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", - " }\\alpha_{t}}\n", + " \\sum \\limits_{t=1}^{m}\\alpha_{t} \\cdot \\alpha_{t}\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "4324a2d7", + "metadata": {}, + "source": [ + "And, we replace one of $\\alpha_{t}$ with its equivalent term provided in eq(3)..." + ] + }, + { + "cell_type": "markdown", + "id": "b07d7917", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}&\n", + " \\sum\\limits_{t=1}^{m}{\n", + " {\\alpha_{t}\n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " }}\n", " \\\\\n", " ={}&\n", " {\n", - " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]\\alpha_{t} - \\sum\\limits_{t=1}^{m}\\mu^{'}\\alpha_{t}}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\sum\\limits_{t=1}^{m}\\mu_{j,m+k}\\alpha_{t}}{\\sigma_{j,m}}\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]\\alpha_{t} \n", + " - \n", + " \\sum\\limits_{t=1}^{m}\n", + " \\mu^{'}\\alpha_{t}\n", + " \\right)\n", + " - \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[j+t-1]\\alpha_{t} \n", + " - \n", + " \\sum\\limits_{t=1}^{m}\n", + " \\mu_{j,m+k}\\alpha_{t}\n", + " \\right)\n", " } \n", " \\\\ \n", " ={}&\n", " {\n", - " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]\\alpha_{t} - \\mu^{'}\\sum\\limits_{t=1}^{m}\\alpha_{t}}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}\\alpha_{t}}{\\sigma_{j,m}}\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]\\alpha_{t} \n", + " - \n", + " \\mu^{'}\\sum\\limits_{t=1}^{m}\\alpha_{t}\n", + " \\right)\n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} \n", + " - \n", + " \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}\\alpha_{t}\n", + " \\right)\n", " } \n", - " \\\\ \n", - " ={}&\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "fe63f99b", + "metadata": {}, + "source": [ + "And, now with help of eq(7), $\\sum\\limits_{t=1}^{m}{\\alpha_{t}}=0$, and the eq(9), $\\sum\\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}}=0$, we will have:" + ] + }, + { + "cell_type": "markdown", + "id": "6b89c8a5", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}&\n", " {\n", - " \\frac{0 - \\mu^{'} \\cdot 0}{\\sigma^{'}} - \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}\\cdot 0}{\\sigma_{j,m}}\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " 0 - \\mu^{'} \\cdot 0\n", + " \\right) \n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}\\cdot 0\n", + " \\right)\n", " } \n", " \\\\ \n", " ={}&\n", @@ -970,13 +1060,33 @@ " }\n", " } \n", " \\\\\n", - " ={}&\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "0e8a53c1", + "metadata": {}, + "source": [ + "And, now with help of Pearon Correlation eq(\\*\\*)..." + ] + }, + { + "cell_type": "markdown", + "id": "eed98832", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", " {- \\frac{1}{\\sigma_{j,m}} \n", " {\n", " \\left(\n", - " \\frac{(m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - m\\mu_{j,m}\\mu^{'}}{\\sigma^{'}} \n", + " \\frac{(m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu^{'} \\cdot m\\mu_{j,m}}{\\sigma^{'}} \n", " - \n", - " \\frac{(m\\sigma_{j,m}^{2} + m\\mu_{j,m}^{2}) - m\\mu_{j,m}\\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\frac{(m\\sigma_{j,m}^{2} + m\\mu_{j,m}^{2}) - \\mu_{j,m+k} \\cdot m\\mu_{j,m}}{\\sigma_{j,m}}\n", " \\right)\n", " }\n", " } \n", @@ -1012,7 +1122,7 @@ "id": "cfd5a617", "metadata": {}, "source": [ - "plugging in (8) and (9):" + "And, finally, we plug in the values $\\mu^{'}$(11) and $\\sigma^{'}$(12):" ] }, { @@ -1024,10 +1134,12 @@ "$$\n", "\\begin{align}\n", " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", - " {- \\frac{m\\rho}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\n", + " (\\frac{\\sigma_{i,m}}{\\rho})\n", + " } \n", " {\n", " \\left[\n", - " {\\rho\\sigma_{(i,m)}\\sigma_{j,m}^{2} + \n", + " {\\rho\\sigma_{i,m}\\sigma_{j,m}^{2} + \n", " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \n", " \\mu_{j,m}\\sigma_{j,m}\\left({\n", " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", @@ -1050,11 +1162,11 @@ " {\n", " \\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", " + \n", - " \\mu_{j,m}\\sigma_{j,m}\\frac{\\sigma_{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " \\frac{\\sigma_{i,m}}{\\rho\\sigma_{j,m}}{\\mu_{j,m}\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", " }\n", " } \n", " - \n", - " {(\\frac{\\sigma_{i,m}}{\\rho})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " {\\frac{\\sigma_{i,m}}{\\rho}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", " \\right]\n", " }\n", " } \n", @@ -1074,11 +1186,32 @@ " }\n", " } \n", " - \n", - " {(\\sigma_{i,m})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " {\\sigma_{i,m}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", " \\right]\n", " }\n", " } \n", " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {\n", + " \\left[\n", + " {\\rho^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " + \n", + " \\rho\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", + " - \n", + " {\n", + " \\rho\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", + " + \n", + " \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m} - \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m+k})\n", + " }\n", + " } \\\\\n", + " - \n", + " {\\sigma_{i,m}\\sigma_{j,m}^{2} - \\sigma_{i,m}\\mu_{j,m}^{2} + \\sigma_{i,m}\\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " \\\\\n", " ={}&\n", " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", " \\left( \n", @@ -1159,7 +1292,7 @@ "metadata": {}, "source": [ "**Note:**
\n", - "* Note that eq(9) is valid only for $\\rho > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho > 0$. \n", + "* Note that eq(12) is valid only for $\\rho > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho > 0$. \n", "* The pearson correlation, $\\rho$, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", "\n", "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho)}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", @@ -1167,7 +1300,7 @@ "**Pending...**
\n", "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function.\n", "\n", - "* For $\\rho \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$." + "* **For $\\rho \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$.**" ] }, { From 32f12a11503485ddfaebed092c51f2df74803412 Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 13 Apr 2022 12:14:54 -0600 Subject: [PATCH 16/64] Polish math equations --- docs/Tutorial_VALMOD.ipynb | 84 +++++++++++++++----------------------- 1 file changed, 33 insertions(+), 51 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 82657ab11..e5751d9d3 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -375,7 +375,7 @@ " }\n", " \\\\\n", " ={}&\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}} \\quad (1)\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}}\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -399,6 +399,10 @@ "\n", "$$\n", "\\begin{align}\n", + " LB ={}& \n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + " \\sqrt{f(\\mu^{'},\\sigma^{'})} \\quad (1)\n", + " \\\\\n", " f(\\mu^{'}, \\sigma^{'}) ={}& \n", " \\sum\\limits_{t=1}^{m}{{\n", " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", @@ -427,7 +431,7 @@ }, { "cell_type": "markdown", - "id": "c5e7e16d", + "id": "d4ad4a6b", "metadata": {}, "source": [ "Therefore, we can write $f(\\mu_{'},\\sigma_{'})$ as follows:" @@ -435,7 +439,7 @@ }, { "cell_type": "markdown", - "id": "a55134cb", + "id": "07223500", "metadata": {}, "source": [ "\n", @@ -538,7 +542,7 @@ }, { "cell_type": "markdown", - "id": "0a3dd808", + "id": "0aad71e0", "metadata": {}, "source": [ "**Exapanding (7):**" @@ -546,7 +550,7 @@ }, { "cell_type": "markdown", - "id": "91c2bf00", + "id": "0d3f4dfa", "metadata": {}, "source": [ "\n", @@ -718,7 +722,7 @@ "id": "0c839937", "metadata": {}, "source": [ - "**Now, recall the pearson correlation $\\rho$:**" + "Now, recall the pearson correlation $\\rho$:" ] }, { @@ -740,8 +744,8 @@ "id": "4880c751", "metadata": {}, "source": [ - "**Note:** The pearson correlation, $\\rho$, is 1 when $i=j$. Becauses, the correlation of any subsequence with itself is 1.
\n", - "**Note:** we can rewrite (6) as: $\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (**)" + "Note: The pearson correlation, $\\rho$, is 1 when $i=j$. Becauses, the correlation of any subsequence with itself is 1.
\n", + "Note: we can rewrite pearson correlation equation as: $\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" ] }, { @@ -749,7 +753,7 @@ "id": "a01fd0cc", "metadata": {}, "source": [ - "Therefore, with help of (\\*\\*), we continue eq(*):" + "**Therefore, with help of (\\*\\*), we continue our calculation from eq(\\*):**" ] }, { @@ -889,7 +893,7 @@ "id": "b266cfb2", "metadata": {}, "source": [ - "**Note:** Eq(12) is valid if $\\rho \\gt 0$. Therefore, the rest of this calculation is based on the assumption that $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" + "**Note:** Since standard deviation is positive, eq(12) is valid only if $\\rho \\gt 0$. Therefore, the rest of this calculation is based on the assumption that $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" ] }, { @@ -899,12 +903,12 @@ "source": [ "---\n", "\n", - "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. Note that we solved the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all discovered equations (5), (6), (7), (8), (9), and (10) throughout the solution.
" + "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. Note that we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" ] }, { "cell_type": "markdown", - "id": "2b12d914", + "id": "92abd2a2", "metadata": {}, "source": [ "**Start with equation (4):**" @@ -930,7 +934,7 @@ }, { "cell_type": "markdown", - "id": "4324a2d7", + "id": "7afe0a3d", "metadata": {}, "source": [ "And, we replace one of $\\alpha_{t}$ with its equivalent term provided in eq(3)..." @@ -938,7 +942,7 @@ }, { "cell_type": "markdown", - "id": "b07d7917", + "id": "bfb10bce", "metadata": {}, "source": [ "\n", @@ -996,7 +1000,7 @@ }, { "cell_type": "markdown", - "id": "fe63f99b", + "id": "4a9e3f03", "metadata": {}, "source": [ "And, now with help of eq(7), $\\sum\\limits_{t=1}^{m}{\\alpha_{t}}=0$, and the eq(9), $\\sum\\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}}=0$, we will have:" @@ -1004,7 +1008,7 @@ }, { "cell_type": "markdown", - "id": "6b89c8a5", + "id": "650cae87", "metadata": {}, "source": [ "\n", @@ -1066,7 +1070,7 @@ }, { "cell_type": "markdown", - "id": "0e8a53c1", + "id": "9f2ca2da", "metadata": {}, "source": [ "And, now with help of Pearon Correlation eq(\\*\\*)..." @@ -1074,7 +1078,7 @@ }, { "cell_type": "markdown", - "id": "eed98832", + "id": "35db152a", "metadata": {}, "source": [ "\n", @@ -1122,7 +1126,7 @@ "id": "cfd5a617", "metadata": {}, "source": [ - "And, finally, we plug in the values $\\mu^{'}$(11) and $\\sigma^{'}$(12):" + "And, now we are at a good position to plug in the values $\\mu^{'}$(11) and $\\sigma^{'}$(12):" ] }, { @@ -1194,7 +1198,7 @@ " ={}&\n", " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", " {\n", - " \\left[\n", + " \\left(\n", " {\\rho^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", " + \n", " \\rho\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", @@ -1202,16 +1206,15 @@ " {\n", " \\rho\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", " + \n", - " \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m} - \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m+k})\n", + " \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m} - \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m+k}\n", + " }\n", " }\n", - " } \\\\\n", " - \n", - " {\\sigma_{i,m}\\sigma_{j,m}^{2} - \\sigma_{i,m}\\mu_{j,m}^{2} + \\sigma_{i,m}\\mu_{j,m}\\mu_{j,m+k})}\n", - " \\right]\n", + " {\\sigma_{i,m}\\sigma_{j,m}^{2} - \\sigma_{i,m}\\mu_{j,m}^{2} + \\sigma_{i,m}\\mu_{j,m}\\mu_{j,m+k}}\n", + " \\right)\n", " }\n", " } \n", " \\\\\n", - " \\\\\n", " ={}&\n", " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", " \\left( \n", @@ -1222,36 +1225,15 @@ " \\right)\n", " } \n", " \\\\\n", - " \\\\\n", " ={}&\n", " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", " (\\rho^{2} - 1)\n", " \\sigma_{i,m}\\sigma_{j,m}^{2}\n", - " } \n", - "\\end{align} \n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "d836a69d", - "metadata": {}, - "source": [ - "Therefore:" - ] - }, - { - "cell_type": "markdown", - "id": "a5c3b9e8", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", - " m (1 - \\rho^{2}) \n", + " }\n", " \\\\\n", - "\\end{align}\n", + " ={}&\n", + " m(1-\\rho^{2})\n", + "\\end{align} \n", "$$\n" ] }, @@ -1260,7 +1242,7 @@ "id": "64dc1027", "metadata": {}, "source": [ - "**Therefore, the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" + "**Finally, with eq(1), the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" ] }, { From 5405d476493846d2e9eaf566c3d6fd13c747cd1a Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 13 Apr 2022 13:30:51 -0600 Subject: [PATCH 17/64] imporve readability --- docs/Tutorial_VALMOD.ipynb | 38 +++++++++++++++++++++++++++++++------- 1 file changed, 31 insertions(+), 7 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index e5751d9d3..338f043c0 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -708,10 +708,34 @@ "\n", "$$\n", "\\begin{align}\n", - " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m} T[i+t-1]T[i+t-1] - \\sum \\limits_{t=1}^{m} T[i+t-1] \\mu^{'}\\right) - \\frac{1}{\\sigma_{j,m}}\\left({\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}}\\right) \n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", + " -\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1]\\mu^{'}\n", + " \\right) \n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " {\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] \n", + " -\\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}\n", + " }\n", + " \\right) \n", " ={}& 0\n", " \\\\\n", - " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m} T[i+t-1]T[i+t-1] - \\mu^{'}\\sum \\limits_{t=1}^{m} T[i+t-1]\\right) - \\frac{1}{\\sigma_{j,m}}\\left({\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]}\\right) \n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " \\sum \\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", + " -\n", + " \\mu^{'}\\sum\\limits_{t=1}^{m} T[i+t-1]\n", + " \\right) \n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1]\n", + " -\n", + " \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", + " \\right) \n", " ={}& 0 \\quad (*)\n", "\\end{align} \n", "$$\n" @@ -785,7 +809,8 @@ " \\right]\n", " ={}& 0\n", " \\\\\n", - " \\frac{\n", + " \\frac{1}{\\sigma^{'}\\sigma_{j,m}}\n", + " \\left[\n", " \\sigma_{j,m}\\left(\n", " m\\sigma_{i,m}^{2} \n", " + \n", @@ -801,8 +826,7 @@ " -\n", " \\mu_{j,m+k} \\cdot m\\mu_{i,m}}\n", " \\right)\n", - " }{\n", - " \\sigma^{'}\\sigma_{j,m}} ={}& 0\n", + " \\right] ={}& 0\n", " \\\\\n", " \\frac{m}{\n", " \\sigma^{'}\\sigma_{j,m}\n", @@ -903,7 +927,7 @@ "source": [ "---\n", "\n", - "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. Note that we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" + "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of the crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. Note that we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" ] }, { @@ -1073,7 +1097,7 @@ "id": "9f2ca2da", "metadata": {}, "source": [ - "And, now with help of Pearon Correlation eq(\\*\\*)..." + "And, now with help of the fact that $\\sum{T} = m\\mu$ and also the Pearon Correlation equation (\\*\\*)..." ] }, { From cd6be19decc37fb2a9af6484d83b6ba508fb90ef Mon Sep 17 00:00:00 2001 From: ninimama Date: Wed, 13 Apr 2022 13:37:06 -0600 Subject: [PATCH 18/64] minor change --- docs/Tutorial_VALMOD.ipynb | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 338f043c0..3e3b453e9 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -714,14 +714,15 @@ " -\n", " \\sum\\limits_{t=1}^{m}T[i+t-1]\\mu^{'}\n", " \\right) \n", - " - \n", + " - \\\\\n", " \\frac{1}{\\sigma_{j,m}}\n", " \\left(\n", " {\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] \n", " -\\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}\n", " }\n", " \\right) \n", - " ={}& 0\n", + " ={}& \n", + " 0\n", " \\\\\n", " \\frac{1}{\\sigma^{'}}\n", " \\left(\n", @@ -729,14 +730,15 @@ " -\n", " \\mu^{'}\\sum\\limits_{t=1}^{m} T[i+t-1]\n", " \\right) \n", - " - \n", + " - \\\\\n", " \\frac{1}{\\sigma_{j,m}}\n", " \\left(\n", " \\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1]\n", " -\n", " \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", " \\right) \n", - " ={}& 0 \\quad (*)\n", + " ={}& \n", + " 0 \\quad (*)\n", "\\end{align} \n", "$$\n" ] From 06480c1fdd25b9df0b934b16678abfb8194e8abc Mon Sep 17 00:00:00 2001 From: ninimama Date: Fri, 15 Apr 2022 20:18:52 -0600 Subject: [PATCH 19/64] minor changes to improve readability --- docs/Tutorial_VALMOD.ipynb | 73 ++++++++++++++++++++------------------ 1 file changed, 39 insertions(+), 34 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 3e3b453e9..d38012c3e 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -107,7 +107,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -386,35 +386,16 @@ "id": "d410ec5a", "metadata": {}, "source": [ - "**Note:** that the variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. Also, note that all $\\mu$ and $\\sigma$ values are **constant** regardless of them being known or unknown.
\n", + "**Note:** that the unknown variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. Also, note that all $\\mu$ and $\\sigma$ values are **constant** regardless of them being known or unknown.
\n", "\n", - "We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$." - ] - }, - { - "cell_type": "markdown", - "id": "a293197c", - "metadata": {}, - "source": [ + "We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$. Note that the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$.
\n", "\n", - "$$\n", - "\\begin{align}\n", - " LB ={}& \n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - " \\sqrt{f(\\mu^{'},\\sigma^{'})} \\quad (1)\n", - " \\\\\n", - " f(\\mu^{'}, \\sigma^{'}) ={}& \n", - " \\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)\n", - " }^{2}} \\quad (2)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" + "Also, we define $\\alpha_{t}$ as:" ] }, { "cell_type": "markdown", - "id": "6722cf8a", + "id": "caffa72c", "metadata": {}, "source": [ "\n", @@ -422,7 +403,7 @@ "\\begin{align}\n", " \\alpha_{t} \\triangleq{}& \n", " {\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}} \\quad (3)\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", " } \n", " \\\\\n", "\\end{align}\n", @@ -431,22 +412,39 @@ }, { "cell_type": "markdown", - "id": "d4ad4a6b", + "id": "91be5280", "metadata": {}, "source": [ - "Therefore, we can write $f(\\mu_{'},\\sigma_{'})$ as follows:" + "Therefore, we have:" ] }, { "cell_type": "markdown", - "id": "07223500", + "id": "a293197c", "metadata": {}, "source": [ "\n", "$$\n", "\\begin{align}\n", - " f(\\mu^{'}, \\sigma^{'}) ={}& \n", - " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \\quad (4)\n", + " LB ={}& \n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + " \\sqrt{\\min f(\\mu^{'},\\sigma^{'})} \\quad (2)\n", + " \\\\\n", + " f(\\mu^{'}, \\sigma^{'}) ={}&\n", + " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \\quad (3)\n", + " \\\\\n", + " \\alpha_{t} ={}& \n", + " \\frac{\n", + " T[i+t-1] - \\mu^{'}\n", + " }{\n", + " \\sigma^{'}\n", + " } \n", + " - \n", + " \\frac{\n", + " T[j+t-1] - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " } \\quad (4)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -457,8 +455,7 @@ "id": "e7564257", "metadata": {}, "source": [ - "Please note that the unknown variables are now $\\mu^{'}$ and $\\sigma^{'}$.
\n", - "**To find extrema points, we first need to find the critical point(s) by solving the single system of equations below.** In other words, we are looking for $\\mu^{'}$ and $\\sigma^{'}$ that satisfies both equations below.\n", + "**To find extrema points, we first need to find the critical point(s) by solving the single system of equations below.** In other words, we are looking for $\\mu^{'}$ and $\\sigma^{'}$ that satisfies both equations below:\n", "\n" ] }, @@ -483,7 +480,7 @@ "id": "a3656f16", "metadata": {}, "source": [ - "**Deriving $\\frac{\\partial{f}}{\\partial{\\mu^{'}}}$:**" + "**Solving $\\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0$:**" ] }, { @@ -586,7 +583,7 @@ "id": "393ddb8f", "metadata": {}, "source": [ - "**Deriving $\\frac{\\partial{f}}{\\partial{\\sigma^{'}}}$:**" + "**Solving $\\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0$:**" ] }, { @@ -656,6 +653,14 @@ "$$\n" ] }, + { + "cell_type": "markdown", + "id": "f9c281a2", + "metadata": {}, + "source": [ + "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." + ] + }, { "cell_type": "markdown", "id": "c3b80336", From 0ccf8d5e8e28b8955cd1424a007792c5f4351d14 Mon Sep 17 00:00:00 2001 From: ninimama Date: Fri, 15 Apr 2022 20:30:39 -0600 Subject: [PATCH 20/64] improve readibility of expansion of eq 9 --- docs/Tutorial_VALMOD.ipynb | 35 +++++++++++++++++++++++++++++++---- 1 file changed, 31 insertions(+), 4 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index d38012c3e..694a23205 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -375,7 +375,7 @@ " }\n", " \\\\\n", " ={}&\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}}\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}} \\quad(1)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -395,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "caffa72c", + "id": "ce5f5ca3", "metadata": {}, "source": [ "\n", @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "91be5280", + "id": "dc3e9f2a", "metadata": {}, "source": [ "Therefore, we have:" @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "f9c281a2", + "id": "a1cb6776", "metadata": {}, "source": [ "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." @@ -713,6 +713,32 @@ "\n", "$$\n", "\\begin{align}\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}}\n", + " \\right)\n", + " - \n", + " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\left(\n", + " \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " ={}& 0\n", + " \\\\\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\left(\n", + " T[i+t-1] - \\mu^{'}\n", + " \\right)\n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\left(\n", + " T[j+t-1] - \\mu_{j,m+k}\n", + " \\right)\n", + " ={}& 0\n", + " \\\\\n", + " \\\\\n", " \\frac{1}{\\sigma^{'}}\n", " \\left(\n", " \\sum\\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", @@ -729,6 +755,7 @@ " ={}& \n", " 0\n", " \\\\\n", + " \\\\\n", " \\frac{1}{\\sigma^{'}}\n", " \\left(\n", " \\sum \\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", From 9d61b8fc0f85ed8e68ebcbd500cbe859750265fe Mon Sep 17 00:00:00 2001 From: ninimama Date: Fri, 15 Apr 2022 21:02:18 -0600 Subject: [PATCH 21/64] Elaborated calculations --- docs/Tutorial_VALMOD.ipynb | 103 ++++++++++++++++++++++++++++++++----- 1 file changed, 90 insertions(+), 13 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 694a23205..0d9d1faa0 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -395,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "ce5f5ca3", + "id": "0405ac3d", "metadata": {}, "source": [ "\n", @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "dc3e9f2a", + "id": "e16169fc", "metadata": {}, "source": [ "Therefore, we have:" @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "a1cb6776", + "id": "bd343f99", "metadata": {}, "source": [ "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." @@ -780,7 +780,7 @@ "id": "0c839937", "metadata": {}, "source": [ - "Now, recall the pearson correlation $\\rho$:" + "Now, recall that the pearson correlation $\\rho_{ij}$ between two subsequenes of lenght $m$ is defined as follows:" ] }, { @@ -791,7 +791,7 @@ "\n", "$$\n", "\\begin{align}\n", - " \\rho = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", + " \\rho_{ij} = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -802,8 +802,8 @@ "id": "4880c751", "metadata": {}, "source": [ - "Note: The pearson correlation, $\\rho$, is 1 when $i=j$. Becauses, the correlation of any subsequence with itself is 1.
\n", - "Note: we can rewrite pearson correlation equation as: $\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" + "Note: we can rearrange the pearson correlation equation as:
\n", + "$\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" ] }, { @@ -825,7 +825,7 @@ " \\frac{1}{\\sigma^{'}}\n", " \\left[\n", " \\left(\n", - " m\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2}\n", + " m\\rho_{ii}\\sigma_{i,m}\\sigma_{i,m} + m\\mu_{i,m}\\mu_{i,m}\n", " \\right)\n", " - \n", " \\mu^{'} \\cdot m\\mu_{i,m}\n", @@ -834,7 +834,7 @@ " \\frac{1}{\\sigma_{j,m}}\n", " \\left[\n", " \\left(\n", - " m\\rho\\sigma_{i,m}\\sigma_{j,m} \n", + " m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", " + \n", " m\\mu_{i,m}\\mu_{j,m}\n", " \\right)\n", @@ -843,6 +843,38 @@ " \\right]\n", " ={}& 0\n", " \\\\\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left[\n", + " \\left(\n", + " m\\cdot1\\cdot\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2}\n", + " \\right)\n", + " - \n", + " \\mu^{'} \\cdot m\\mu_{i,m}\n", + " \\right] \n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left[\n", + " \\left(\n", + " m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " + \n", + " m\\mu_{i,m}\\mu_{j,m}\n", + " \\right)\n", + " - \n", + " \\mu_{j,m+k} \\cdot m\\mu_{i,m}\n", + " \\right]\n", + " ={}& 0\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "5c9c05b6", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", " \\frac{1}{\\sigma^{'}\\sigma_{j,m}}\n", " \\left[\n", " \\sigma_{j,m}\\left(\n", @@ -854,7 +886,7 @@ " \\right) \n", " - \n", " \\sigma^{'}\\left(\n", - " {m\\rho\\sigma_{i,m}\\sigma_{j,m} \n", + " {m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", " +\n", " m\\mu_{i,m}\\mu_{j,m} \n", " -\n", @@ -875,7 +907,7 @@ " \\right) \n", " - \n", " \\sigma^{'}\\left(\n", - " {\\rho\\sigma_{i,m}\\sigma_{j,m} \n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", " +\n", " \\mu_{i,m}\\mu_{j,m}\n", " -\n", @@ -893,7 +925,26 @@ " \\right) \n", " - \n", " \\sigma^{'}\\left(\n", - " {\\rho\\sigma_{i,m}\\sigma_{j,m} \n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " \\mu_{i,m}\\mu_{j,m}\n", + " -\n", + " \\mu_{j,m+k} \\mu_{i,m}}\n", + " \\right)\n", + " ={}& 0\n", + " \\\\\n", + " \\sigma_{j,m}\\left(\n", + " \\sigma_{i,m}^{2} \n", + " + \n", + " \\mu_{i,m}^{2}\n", + " \\right)\n", + " - \n", + " \\sigma_{j,m}\\left(\n", + " \\mu^{'} \\mu_{i,m}\n", + " \\right) \n", + " - \n", + " \\sigma^{'}\\left(\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", " +\n", " \\mu_{i,m}\\mu_{j,m}\n", " -\n", @@ -901,6 +952,24 @@ " \\right)\n", " ={}& 0\n", " \\\\\n", + " - \\sigma_{j,m}\\left(\n", + " \\sigma_{i,m}^{2} \n", + " + \n", + " \\mu_{i,m}^{2}\n", + " \\right)\n", + " + \n", + " \\sigma_{j,m}\\left(\n", + " \\mu^{'} \\mu_{i,m}\n", + " \\right) \n", + " + \n", + " \\sigma^{'}\\left(\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " \\mu_{i,m}\\mu_{j,m}\n", + " -\n", + " \\mu_{j,m+k} \\mu_{i,m}}\n", + " \\right)\n", + " ={}& 0\n", "\\end{align}\n", "$$\n" ] @@ -913,12 +982,20 @@ "\n", "$$\n", "\\begin{align}\n", - " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (10)\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (10)\n", " \\\\\n", "\\end{align}\n", "$$\n" ] }, + { + "cell_type": "markdown", + "id": "922bb7a8", + "metadata": {}, + "source": [ + "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." + ] + }, { "cell_type": "markdown", "id": "6adaea06", From 52204e0f3605418a6ad2ed383b042f4ff4338dea Mon Sep 17 00:00:00 2001 From: ninimama Date: Fri, 15 Apr 2022 21:52:10 -0600 Subject: [PATCH 22/64] show calculations for two equations with two unknowns --- docs/Tutorial_VALMOD.ipynb | 197 +++++++++++++++++++++++++++++++++++-- 1 file changed, 187 insertions(+), 10 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 0d9d1faa0..8b8514630 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -395,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "0405ac3d", + "id": "c6ac3737", "metadata": {}, "source": [ "\n", @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "e16169fc", + "id": "7df0ca7f", "metadata": {}, "source": [ "Therefore, we have:" @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "bd343f99", + "id": "b1eafd1e", "metadata": {}, "source": [ "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." @@ -869,7 +869,7 @@ }, { "cell_type": "markdown", - "id": "5c9c05b6", + "id": "6f947d20", "metadata": {}, "source": [ "\n", @@ -990,7 +990,7 @@ }, { "cell_type": "markdown", - "id": "922bb7a8", + "id": "f386eb7a", "metadata": {}, "source": [ "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." @@ -1001,7 +1001,131 @@ "id": "6adaea06", "metadata": {}, "source": [ - "**Solving (8) and (10) gives the values of critical point:**" + "**Now, it is time to solve equations (8) and (10), provided below:**" + ] + }, + { + "cell_type": "markdown", + "id": "bedf9fb0", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "\\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} \n", + " ={}& 0 \\quad(8)\n", + " \\\\\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) \n", + " ={}& 0 \\quad(10)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "b808d61d", + "metadata": {}, + "source": [ + "Note that in the system of equations above, the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$, and the remaining ones are known." + ] + }, + { + "cell_type": "markdown", + "id": "c777af36", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "-\\mu_{i,m}\\left[\n", + " \\sigma_{j,m} \\mu^{'} \n", + " + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} \n", + " - \n", + " \\sigma_{j,m}\\mu_{i,m} \n", + " \\right]\n", + " ={}& 0 \\quad(8')\n", + " \\\\\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) \n", + " ={}& 0 \\quad(10)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "62a5b4d2", + "metadata": {}, + "source": [ + "$(8')+(10)$ gives:" + ] + }, + { + "cell_type": "markdown", + "id": "4ff2f712", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "-\\mu_{i,m}\\sigma_{j,m} \\mu^{'} - \n", + " \\mu_{i,m}\\mu_{j,m}\\sigma^{'} + \\mu_{i,m}\\mu_{j,m+k}\\sigma^{'} \n", + " + \\sigma_{j,m}\\mu_{i,m}^{2} +\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}\\sigma^{'} + \\mu_{i,m}\\mu_{j,m}\\sigma^{'} - \\mu_{i,m}\\mu_{j,m+k}\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m}^{2} - \\sigma_{j,m}\\sigma_{i,m}^{2}\n", + " ={}& 0\n", + " \\\\\n", + " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}\\sigma^{'} - \\sigma_{j,m}\\sigma_{i,m}^{2} \n", + " ={}& 0\n", + " \\\\\n", + " \\sigma_{i,m}\\sigma_{j,m}\n", + " \\left(\n", + " \\rho_{ij}\\sigma^{'} - \\sigma_{i,m}\n", + " \\right)\n", + " ={}&\n", + " 0\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "2d2810c3", + "metadata": {}, + "source": [ + "Hence:" + ] + }, + { + "cell_type": "markdown", + "id": "0b83e765", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sigma^{'} = \\frac{\\sigma_{i,m}}{\\rho_{ij}} \\quad (11)\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "f0811a01", + "metadata": {}, + "source": [ + "Note that we assumed $\\sigma_{i,m}$ and $\\sigma_{j,m}$ cannot be zero. Also, since standard deviations are positive, eq(11) is valid only if $\\rho_{ij} \\gt 0$." + ] + }, + { + "cell_type": "markdown", + "id": "f031e59c", + "metadata": {}, + "source": [ + "And, subsituting eq(11) in eq(8):" ] }, { @@ -1012,15 +1136,68 @@ "\n", "$$\n", "\\begin{align}\n", - " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma^{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k}) \\quad (11)\n", + "\\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}}) - \\sigma_{j,m}\\mu_{i,m} \n", + " ={}& 0 \n", + " \\\\\n", + " \\frac{1}{\\sigma_{j,m}}\\left[\n", + " \\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}}) - \\sigma_{j,m}\\mu_{i,m} \n", + " \\right]\n", + " ={}& 0 \n", + " \\\\\n", + " \\mu^{'} + \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}) - \\mu_{i,m} \n", + " ={}& 0 \n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "00754de4", + "metadata": {}, + "source": [ + "Hence:" + ] + }, + { + "cell_type": "markdown", + "id": "f7840fd6", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}} \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) \\quad(12)\n", "\\end{align}\n", "$$\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "679375e8", + "metadata": {}, + "source": [ + "**Therefore, the critical point of function $f(\\mu^{'},\\sigma^{'})$ is:**" + ] + }, + { + "cell_type": "markdown", + "id": "d6807479", + "metadata": {}, + "source": [ "\n", "$$\n", "\\begin{align}\n", - " \\sigma^{'} = \\frac{\\sigma_{i,m}}{\\rho} \\quad (12)\n", + " \\sigma^{'} ={}& \n", + " \\frac{\\sigma_{i,m}}{\\rho_{ij}} \\quad (11)\n", + " \\\\\n", + " \\mu^{'} ={}& \n", + " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}} \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) \\quad(12)\n", + " \\\\\n", "\\end{align}\n", - "$$" + "$$\n" ] }, { @@ -1028,7 +1205,7 @@ "id": "b266cfb2", "metadata": {}, "source": [ - "**Note:** Since standard deviation is positive, eq(12) is valid only if $\\rho \\gt 0$. Therefore, the rest of this calculation is based on the assumption that $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" + "**NOTE:** It is worthwhile to reiterate the fact that the solution is valid when $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" ] }, { From ad1e79dd96876f416658831d7eaa53babfb28010 Mon Sep 17 00:00:00 2001 From: ninimama Date: Fri, 15 Apr 2022 22:52:13 -0600 Subject: [PATCH 23/64] Discussed the advantage of discovered solution --- docs/Tutorial_VALMOD.ipynb | 44 +++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 8b8514630..ae5739a65 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -395,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "c6ac3737", + "id": "be1e17a8", "metadata": {}, "source": [ "\n", @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "7df0ca7f", + "id": "876440a6", "metadata": {}, "source": [ "Therefore, we have:" @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "b1eafd1e", + "id": "ec9d15db", "metadata": {}, "source": [ "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." @@ -869,7 +869,7 @@ }, { "cell_type": "markdown", - "id": "6f947d20", + "id": "e4c3d081", "metadata": {}, "source": [ "\n", @@ -990,7 +990,7 @@ }, { "cell_type": "markdown", - "id": "f386eb7a", + "id": "7559b895", "metadata": {}, "source": [ "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." @@ -1006,7 +1006,7 @@ }, { "cell_type": "markdown", - "id": "bedf9fb0", + "id": "db952d92", "metadata": {}, "source": [ "\n", @@ -1025,7 +1025,7 @@ }, { "cell_type": "markdown", - "id": "b808d61d", + "id": "8074e1d6", "metadata": {}, "source": [ "Note that in the system of equations above, the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$, and the remaining ones are known." @@ -1033,7 +1033,7 @@ }, { "cell_type": "markdown", - "id": "c777af36", + "id": "16abecf4", "metadata": {}, "source": [ "\n", @@ -1057,7 +1057,7 @@ }, { "cell_type": "markdown", - "id": "62a5b4d2", + "id": "d0cf405e", "metadata": {}, "source": [ "$(8')+(10)$ gives:" @@ -1065,7 +1065,7 @@ }, { "cell_type": "markdown", - "id": "4ff2f712", + "id": "b2242352", "metadata": {}, "source": [ "\n", @@ -1093,7 +1093,7 @@ }, { "cell_type": "markdown", - "id": "2d2810c3", + "id": "18eecb94", "metadata": {}, "source": [ "Hence:" @@ -1101,7 +1101,7 @@ }, { "cell_type": "markdown", - "id": "0b83e765", + "id": "923ba0e2", "metadata": {}, "source": [ "\n", @@ -1114,7 +1114,7 @@ }, { "cell_type": "markdown", - "id": "f0811a01", + "id": "8a5e0e53", "metadata": {}, "source": [ "Note that we assumed $\\sigma_{i,m}$ and $\\sigma_{j,m}$ cannot be zero. Also, since standard deviations are positive, eq(11) is valid only if $\\rho_{ij} \\gt 0$." @@ -1122,7 +1122,7 @@ }, { "cell_type": "markdown", - "id": "f031e59c", + "id": "cc154e80", "metadata": {}, "source": [ "And, subsituting eq(11) in eq(8):" @@ -1154,7 +1154,7 @@ }, { "cell_type": "markdown", - "id": "00754de4", + "id": "2ca19873", "metadata": {}, "source": [ "Hence:" @@ -1162,7 +1162,7 @@ }, { "cell_type": "markdown", - "id": "f7840fd6", + "id": "d1d78325", "metadata": {}, "source": [ "\n", @@ -1176,7 +1176,7 @@ }, { "cell_type": "markdown", - "id": "679375e8", + "id": "c296d6f7", "metadata": {}, "source": [ "**Therefore, the critical point of function $f(\\mu^{'},\\sigma^{'})$ is:**" @@ -1184,7 +1184,7 @@ }, { "cell_type": "markdown", - "id": "d6807479", + "id": "916c22b8", "metadata": {}, "source": [ "\n", @@ -1205,6 +1205,8 @@ "id": "b266cfb2", "metadata": {}, "source": [ + "**NOTE:** It is important to note that eq(11) and eq(12) are favorable to us as it gives the $\\mu^{'}$ and $\\sigma^{'}$ of the critical point of `f` as a function of known parameters $\\mu_{i,m}$, $\\sigma_{i,m}$, $\\mu_{j,m}$, $\\sigma_{j,m}$, $\\rho_{i,j}$, and $\\mu_{j,m+k}$. Therefore, we can calculate the lower-bound LB as a function of the aforementioned parameter. \n", + "\n", "**NOTE:** It is worthwhile to reiterate the fact that the solution is valid when $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" ] }, @@ -1213,8 +1215,6 @@ "id": "a0e36dfc", "metadata": {}, "source": [ - "---\n", - "\n", "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of the crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. Note that we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" ] }, @@ -1223,7 +1223,7 @@ "id": "92abd2a2", "metadata": {}, "source": [ - "**Start with equation (4):**" + "**Start with equation (3):**" ] }, { @@ -1234,7 +1234,7 @@ "\n", "$$\n", "\\begin{align}\n", - " f(\\mu^{'}_{c},\\sigma^{'}_{c}) ={}&\n", + " f(\\mu^{'},\\sigma^{'}) ={}&\n", " \\sum \\limits_{t=1}^{m}\\alpha_{t}^{2}\n", " \\\\\n", " ={}&\n", From e5c3d381c905af7abc12190d0036666f9b319edb Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 00:48:03 -0600 Subject: [PATCH 24/64] add subscript ij to correlation rho --- docs/Tutorial_VALMOD.ipynb | 128 ++++++++++++++++++++++--------------- 1 file changed, 78 insertions(+), 50 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index ae5739a65..bbe2edc09 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "6534d116", "metadata": {}, "outputs": [], @@ -388,14 +388,14 @@ "source": [ "**Note:** that the unknown variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. Also, note that all $\\mu$ and $\\sigma$ values are **constant** regardless of them being known or unknown.
\n", "\n", - "We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$. Note that the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$.
\n", + "We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$. Note that the unknown variables are now $\\mu^{'}$ and $\\sigma^{'}$.
\n", "\n", "Also, we define $\\alpha_{t}$ as:" ] }, { "cell_type": "markdown", - "id": "be1e17a8", + "id": "327fbe20", "metadata": {}, "source": [ "\n", @@ -412,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "876440a6", + "id": "6a8b3359", "metadata": {}, "source": [ "Therefore, we have:" @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "ec9d15db", + "id": "c0924610", "metadata": {}, "source": [ "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." @@ -869,7 +869,7 @@ }, { "cell_type": "markdown", - "id": "e4c3d081", + "id": "041482b8", "metadata": {}, "source": [ "\n", @@ -990,7 +990,7 @@ }, { "cell_type": "markdown", - "id": "7559b895", + "id": "53f8c4b4", "metadata": {}, "source": [ "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." @@ -1006,7 +1006,7 @@ }, { "cell_type": "markdown", - "id": "db952d92", + "id": "9d89eca8", "metadata": {}, "source": [ "\n", @@ -1025,7 +1025,7 @@ }, { "cell_type": "markdown", - "id": "8074e1d6", + "id": "44d1afe8", "metadata": {}, "source": [ "Note that in the system of equations above, the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$, and the remaining ones are known." @@ -1033,7 +1033,7 @@ }, { "cell_type": "markdown", - "id": "16abecf4", + "id": "35c4e371", "metadata": {}, "source": [ "\n", @@ -1057,7 +1057,7 @@ }, { "cell_type": "markdown", - "id": "d0cf405e", + "id": "5cb0edfe", "metadata": {}, "source": [ "$(8')+(10)$ gives:" @@ -1065,7 +1065,7 @@ }, { "cell_type": "markdown", - "id": "b2242352", + "id": "0e192a68", "metadata": {}, "source": [ "\n", @@ -1093,7 +1093,7 @@ }, { "cell_type": "markdown", - "id": "18eecb94", + "id": "02003455", "metadata": {}, "source": [ "Hence:" @@ -1101,7 +1101,7 @@ }, { "cell_type": "markdown", - "id": "923ba0e2", + "id": "70c325aa", "metadata": {}, "source": [ "\n", @@ -1114,7 +1114,7 @@ }, { "cell_type": "markdown", - "id": "8a5e0e53", + "id": "e087b39f", "metadata": {}, "source": [ "Note that we assumed $\\sigma_{i,m}$ and $\\sigma_{j,m}$ cannot be zero. Also, since standard deviations are positive, eq(11) is valid only if $\\rho_{ij} \\gt 0$." @@ -1122,7 +1122,7 @@ }, { "cell_type": "markdown", - "id": "cc154e80", + "id": "024639e5", "metadata": {}, "source": [ "And, subsituting eq(11) in eq(8):" @@ -1154,7 +1154,7 @@ }, { "cell_type": "markdown", - "id": "2ca19873", + "id": "a745e0a1", "metadata": {}, "source": [ "Hence:" @@ -1162,7 +1162,7 @@ }, { "cell_type": "markdown", - "id": "d1d78325", + "id": "853400dd", "metadata": {}, "source": [ "\n", @@ -1176,7 +1176,7 @@ }, { "cell_type": "markdown", - "id": "c296d6f7", + "id": "15f7a1e4", "metadata": {}, "source": [ "**Therefore, the critical point of function $f(\\mu^{'},\\sigma^{'})$ is:**" @@ -1184,7 +1184,7 @@ }, { "cell_type": "markdown", - "id": "916c22b8", + "id": "a7ff8c57", "metadata": {}, "source": [ "\n", @@ -1205,9 +1205,9 @@ "id": "b266cfb2", "metadata": {}, "source": [ - "**NOTE:** It is important to note that eq(11) and eq(12) are favorable to us as it gives the $\\mu^{'}$ and $\\sigma^{'}$ of the critical point of `f` as a function of known parameters $\\mu_{i,m}$, $\\sigma_{i,m}$, $\\mu_{j,m}$, $\\sigma_{j,m}$, $\\rho_{i,j}$, and $\\mu_{j,m+k}$. Therefore, we can calculate the lower-bound LB as a function of the aforementioned parameter. \n", + "**NOTE:** It is important to note that eq(11) and eq(12) are favorable to us as it gives the $\\mu^{'}$ and $\\sigma^{'}$ of the critical point of `f` as a function of known parameters $\\mu_{i,m}$, $\\sigma_{i,m}$, $\\mu_{j,m}$, $\\sigma_{j,m}$, $\\rho_{ij}$, and $\\mu_{j,m+k}$. Therefore, we can calculate the lower-bound LB as a function of the aforementioned parameter. \n", "\n", - "**NOTE:** It is worthwhile to reiterate the fact that the solution is valid when $\\rho \\gt 0$. (We will discuss $\\rho \\leq 0$ later...)" + "**NOTE:** It is worthwhile to reiterate the fact that the solution is valid when $\\rho_{ij} \\gt 0$. (We will discuss $\\rho_{ij} \\leq 0$ later...)" ] }, { @@ -1215,7 +1215,9 @@ "id": "a0e36dfc", "metadata": {}, "source": [ - "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of the crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. Note that we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" + "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of the crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. \n", + "\n", + "**NOTE:** we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" ] }, { @@ -1249,7 +1251,7 @@ "id": "7afe0a3d", "metadata": {}, "source": [ - "And, we replace one of $\\alpha_{t}$ with its equivalent term provided in eq(3)..." + "And, we replace one of $\\alpha_{t}$ with its equivalent term provided in eq(4)..." ] }, { @@ -1400,18 +1402,41 @@ " {- \\frac{1}{\\sigma_{j,m}} \n", " {\n", " \\left(\n", - " \\frac{(m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu^{'} \\cdot m\\mu_{j,m}}{\\sigma^{'}} \n", + " \\frac{(m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu^{'} \\cdot m\\mu_{j,m}}{\\sigma^{'}} \n", " - \n", - " \\frac{(m\\sigma_{j,m}^{2} + m\\mu_{j,m}^{2}) - \\mu_{j,m+k} \\cdot m\\mu_{j,m}}{\\sigma_{j,m}}\n", + " \\frac{(m\\rho_{jj}\\sigma_{j,m}^{2} + m\\mu_{j,m}^{2}) - \\mu_{j,m+k} \\cdot m\\mu_{j,m}}{\\sigma_{j,m}}\n", " \\right)\n", " }\n", " } \n", " \\\\\n", " ={}&\n", + " {- \\frac{1}{\\sigma_{j,m}} \n", + " {\n", + " \\left[\n", + " \\frac{\n", + " m\\left(\n", + " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu^{'} \\cdot \\mu_{j,m}\n", + " \\right)\n", + " }{\n", + " \\sigma^{'}\n", + " } \n", + " - \n", + " \\frac{\n", + " m\\left(\n", + " 1\\cdot\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m+k} \\cdot \\mu_{j,m}\n", + " \\right)\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma^{'}} \n", " {\n", " \\left(\n", - " {\\sigma_{j,m}(\\rho\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{j,m}\\mu^{'})} \n", + " {\\sigma_{j,m}(\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{j,m}\\mu^{'})} \n", " - \n", " {\\sigma^{'}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", " \\right)\n", @@ -1422,7 +1447,7 @@ " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma^{'}} \n", " {\n", " \\left(\n", - " {\\rho\\sigma_{i,m}\\sigma_{j,m}^{2} + \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \\mu_{j,m}\\sigma_{j,m}\\mu^{'}} \n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} + \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \\mu_{j,m}\\sigma_{j,m}\\mu^{'}} \n", " - \n", " {\\sigma^{'}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", " \\right)\n", @@ -1451,38 +1476,38 @@ "\\begin{align}\n", " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", " {- \\frac{m}{\\sigma_{j,m}^{2}\n", - " (\\frac{\\sigma_{i,m}}{\\rho})\n", + " (\\frac{\\sigma_{i,m}}{\\rho_{ij}})\n", " } \n", " {\n", " \\left[\n", - " {\\rho\\sigma_{i,m}\\sigma_{j,m}^{2} + \n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} + \n", " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \n", " \\mu_{j,m}\\sigma_{j,m}\\left({\n", - " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", " }\n", " \\right)} \n", " - \n", - " {(\\frac{\\sigma_{i,m}}{\\rho})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " {(\\frac{\\sigma_{i,m}}{\\rho_{ij}})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", " \\right]\n", " }\n", " } \n", " \\\\\n", " ={}&\n", - " {- \\frac{m\\rho}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {- \\frac{m\\rho_{ij}}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", " {\n", " \\left[\n", - " {\\rho\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", " + \n", " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", " - \n", " {\n", " \\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", " + \n", - " \\frac{\\sigma_{i,m}}{\\rho\\sigma_{j,m}}{\\mu_{j,m}\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}{\\mu_{j,m}\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", " }\n", " } \n", " - \n", - " {\\frac{\\sigma_{i,m}}{\\rho}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " {\\frac{\\sigma_{i,m}}{\\rho_{ij}}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", " \\right]\n", " }\n", " } \n", @@ -1491,12 +1516,12 @@ " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", " {\n", " \\left[\n", - " {\\rho^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", " + \n", - " \\rho\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", + " \\rho_{ij}\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", " - \n", " {\n", - " \\rho\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", + " \\rho_{ij}\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", " + \n", " \\mu_{j,m}\\sigma_{i,m}(\\mu_{j,m}-\\mu_{j,m+k})\n", " }\n", @@ -1511,12 +1536,12 @@ " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", " {\n", " \\left(\n", - " {\\rho^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", " + \n", - " \\rho\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", + " \\rho_{ij}\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", " - \n", " {\n", - " \\rho\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", + " \\rho_{ij}\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", " + \n", " \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m} - \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m+k}\n", " }\n", @@ -1530,7 +1555,7 @@ " ={}&\n", " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", " \\left( \n", - " {\\rho^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", " - \n", " \\sigma_{i,m}\\sigma_{j,m}^{2} \n", " }\n", @@ -1539,12 +1564,12 @@ " \\\\\n", " ={}&\n", " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", - " (\\rho^{2} - 1)\n", + " (\\rho_{ij}^{2} - 1)\n", " \\sigma_{i,m}\\sigma_{j,m}^{2}\n", " }\n", " \\\\\n", " ={}&\n", - " m(1-\\rho^{2})\n", + " m(1-\\rho_{ij}^{2})\n", "\\end{align} \n", "$$\n" ] @@ -1554,7 +1579,7 @@ "id": "64dc1027", "metadata": {}, "source": [ - "**Finally, with eq(1), the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" + "**Finally, with eq(2), the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" ] }, { @@ -1566,14 +1591,17 @@ "$$\n", "\\begin{align}\n", " LB ={}& \n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m (1 - \\rho^{2})} \\quad \\text{if} \\, \\rho > 0\n", + " \\frac{\n", + " \\sigma_{j,m}\n", + " }{\\sigma_{j,m+k}\n", + " } \\sqrt{m (1 - \\rho_{ij}^{2})} \\quad \\text{if} \\, \\rho > 0\n", " \\\\\n", "\\end{align}\n", "$$\n", "\n", "$$\n", "\\begin{align}\n", - " \\rho ={}& \n", + " \\rho_{ij} ={}& \n", " \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", " \\\\\n", "\\end{align}\n", @@ -1586,10 +1614,10 @@ "metadata": {}, "source": [ "**Note:**
\n", - "* Note that eq(12) is valid only for $\\rho > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho > 0$. \n", + "* Note that eq(12) is valid only for $\\rho_{ij} > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho_{ij} > 0$. \n", "* The pearson correlation, $\\rho$, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", "\n", - "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho)}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", + "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho_{ij})}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", "\n", "**Pending...**
\n", "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function.\n", From 1e2b64c1f67d9c7f83d073ba3d747bfab718a62d Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 01:00:13 -0600 Subject: [PATCH 25/64] Create Tutorial Notebook for VALMOD --- docs/Tutorial_VALMOD_notebook.ipynb | 178 ++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 docs/Tutorial_VALMOD_notebook.ipynb diff --git a/docs/Tutorial_VALMOD_notebook.ipynb b/docs/Tutorial_VALMOD_notebook.ipynb new file mode 100644 index 000000000..86e244817 --- /dev/null +++ b/docs/Tutorial_VALMOD_notebook.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c7a27406", + "metadata": {}, + "source": [ + "In this tutorial, we would like to implement VALMOD algorithm proposed in paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf), and reproduce its results as closely as possible.\n", + "\n", + "The **VAriable Length MOtif Discovery (VALMOD)** algorithm takes time series `T` and a range of subsequence length `[min_m, max_m]`, and find motifs and discords." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0adbe18a", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import stumpy\n", + "from stumpy import core, config\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" + ] + }, + { + "cell_type": "markdown", + "id": "e9d48c97", + "metadata": {}, + "source": [ + "# 1- Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "b0423978", + "metadata": {}, + "source": [ + "**Notation:** $T_{i,m} = T[i:i+m]$, a subsequence of `T` that starts at index `i` and has length `m`. " + ] + }, + { + "cell_type": "markdown", + "id": "4a4af7fd", + "metadata": {}, + "source": [ + "## Motif discovery" + ] + }, + { + "cell_type": "markdown", + "id": "78ac5b0f", + "metadata": {}, + "source": [ + "For a given motif pair $\\{T_{idx,m},T_{nn\\_idx,n}\\}$, Motif set $S^{m}_{r}$ is a set of subsequences of length `m` that has `distance < r` to either $T_{idx,m}$ or $T_{nn\\_idx,n}$. And, the cardinality of set is called the frequency of the motif set.\n", + "\n", + "We would like to find set $S^{*} = \\bigcup\\limits_{m=min\\_m}^{max\\_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", + "\n", + "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty because both these two subsequences start from the same index." + ] + }, + { + "cell_type": "markdown", + "id": "7fc09927", + "metadata": {}, + "source": [ + "## Discord Discovery" + ] + }, + { + "cell_type": "markdown", + "id": "0f4ee615", + "metadata": {}, + "source": [ + "First, we need to provide a few definitions...\n", + "\n", + "**$n^{th}$ best match**: For the subsequence $T_{i,m}$, its $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", + "\n", + "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?) best match.
\n", + "\n", + "**NOTE**:
\n", + "Why should we care about $n^{th}$ discord (n>1)? We provide a simple example below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37fdbb26", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "T = np.random.uniform(-100,100,size=3000)\n", + "m = 200\n", + "i, j = 100, 1500\n", + "\n", + "T[i:i+m] = 0\n", + "T[j:j+m] = 0\n", + "\n", + "plt.plot(T)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cb3a3940", + "metadata": {}, + "source": [ + "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors rather than just 1NN. In discord discovery, it is called twin-freak problem (see [Tutorial](https://cci.drexel.edu/bigdata/bigdata2017/files/Tutorial4.pdf)). It happens when the (same) anomally occurs more than once. In our example above, the anomaly occurs twice. Therefore, we should be able to detect it if we consider 2nd nearest neighbor. \n", + "\n", + "For further details, see Fig. 2 of the paper. Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" + ] + }, + { + "cell_type": "markdown", + "id": "45eeecf5", + "metadata": {}, + "source": [ + "**Variable-length Top-k $n^{th}$ Discord Discovery:**
\n", + "Given a time series `T`, a subsequence length-range `[min_m, max_m]`,`K`, and `N`, we want to find **top-k $n^{th}$ discord** for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." + ] + }, + { + "cell_type": "markdown", + "id": "e503fb0a", + "metadata": {}, + "source": [ + "# 2-Lower Bound of Distance Profile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71517d38", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 3ed76c332f58d1acbe9275026f8706b7165e24a5 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 01:11:57 -0600 Subject: [PATCH 26/64] Removed old notebook --- docs/Tutorial_VALMOD.ipynb | 1658 ------------------------------------ 1 file changed, 1658 deletions(-) delete mode 100644 docs/Tutorial_VALMOD.ipynb diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb deleted file mode 100644 index bbe2edc09..000000000 --- a/docs/Tutorial_VALMOD.ipynb +++ /dev/null @@ -1,1658 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "482a2e9b", - "metadata": {}, - "source": [ - "In this tutorial, we would like to implement VALMOD algorithm proposed in paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf), and reproduce its results as closely as possible.\n", - "\n", - "The **VAriable Length MOtif Discovery (VALMOD)** algorithm takes time series `T` and a range of subsequence length `[min_m, max_m]`, and find motifs and discords." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6534d116", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import stumpy\n", - "from stumpy import core, config\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" - ] - }, - { - "cell_type": "markdown", - "id": "1bc907ef", - "metadata": {}, - "source": [ - "# 1- Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "aa1d847c", - "metadata": {}, - "source": [ - "**Notation:** $T_{i,m} = T[i:i+m]$, a subsequence of `T` that starts at index `i` and has length `m` " - ] - }, - { - "cell_type": "markdown", - "id": "8c36e21f", - "metadata": {}, - "source": [ - "### Motif discovery" - ] - }, - { - "cell_type": "markdown", - "id": "fd1568ab", - "metadata": {}, - "source": [ - "For a given motif pair $\\{T_{idx,m},T_{nn\\_idx,n}\\}$, Motif set $S^{m}_{r}$ is a set of subsequences of length `m` that has `distance < r` to either $T_{idx,m}$ or $T_{nn\\_idx,n}$. And, the cardinality of set is called the frequency of the motif set.\n", - "\n", - "We would like to find set $S^{*} = \\bigcup\\limits_{m=min\\_m}^{max\\_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", - "\n", - "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty because both these two subsequences start from the same index." - ] - }, - { - "cell_type": "markdown", - "id": "c0455171", - "metadata": {}, - "source": [ - "### Discord discovery" - ] - }, - { - "cell_type": "markdown", - "id": "71cfdcf0", - "metadata": {}, - "source": [ - "First, we need to provide a few definitions..." - ] - }, - { - "cell_type": "markdown", - "id": "3826e0a5", - "metadata": {}, - "source": [ - "**$n^{th}$ best match**: For the subsequence $T_{i,m}$, its $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", - "\n", - "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?) best match.
" - ] - }, - { - "cell_type": "markdown", - "id": "5167292f", - "metadata": {}, - "source": [ - "**NOTE**:
\n", - "Why should I care about $n^{th}$ discord (n>1)? We provide a simple example below:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3d9db678", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "T = np.random.uniform(-100,100,size=3000)\n", - "m = 200\n", - "i, j = 100, 1500\n", - "\n", - "T[i:i+m] = 0\n", - "T[j:j+m] = 0\n", - "\n", - "plt.plot(T)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a8e87bc0", - "metadata": {}, - "source": [ - "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors rather than just 1NN. In discord discovery, it is called twin-freak problem (see [Tutorial](https://cci.drexel.edu/bigdata/bigdata2017/files/Tutorial4.pdf)). It happens when the (same) anomally occurs more than once. In our example above, the anomaly occurs twice. Therefore, we should be able to detect it if we consider 2nd nearest neighbor. \n", - "\n", - "For further details, see Fig. 2 of the paper. Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" - ] - }, - { - "cell_type": "markdown", - "id": "1be2fecb", - "metadata": {}, - "source": [ - "**Variable-length Top-k $n^{th}$ Discord Discovery:**
\n", - "Given a time series `T`, a subsequence length-range `[min_m, max_m]`,`K`, and `N`, we want to find **top-k $n^{th}$ discord** for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." - ] - }, - { - "cell_type": "markdown", - "id": "27b8effd", - "metadata": {}, - "source": [ - "# 2- Lower-Bound Distance Profile" - ] - }, - { - "cell_type": "markdown", - "id": "5f999789", - "metadata": {}, - "source": [ - "The idea goes as follows: \"given the distance profile of $T_{j,m}$, how can we find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`?" - ] - }, - { - "cell_type": "markdown", - "id": "03836054", - "metadata": {}, - "source": [ - "In other words, can we find **Lower Bound (LB)** for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$? (So, the last `k` elements of $T_{i,m+k}$ are unknown)" - ] - }, - { - "cell_type": "markdown", - "id": "3b5c8c5a", - "metadata": {}, - "source": [ - "## 2-1 Non-normalized distance" - ] - }, - { - "cell_type": "markdown", - "id": "1f7e294e", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " d^{(m+k)}_{j,i} ={}& \n", - " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", - " \\sum\\limits_{t=1}^{m+k}{\n", - " \\bigg\\lvert{\n", - " T[i+t-1] - T[j+t-1]\n", - " }\\bigg\\rvert\n", - " }^{p}\n", - " }\n", - " \\\\\n", - " ={}&\n", - " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", - " \\sum\\limits_{t=1}^{m}{\n", - " \\bigg\\lvert{\n", - " T[i+t-1] - T[j+t-1]\n", - " }\\bigg\\rvert\n", - " }^{p}\n", - " +\n", - " \\sum\\limits_{t=m+1}^{m+k}{\n", - " \\bigg\\lvert{\n", - " T[i+t-1] - T[j+t-1]\n", - " }\\bigg\\rvert\n", - " }^{p}\n", - " }\n", - " \\\\\n", - " \\geq{}&\n", - " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", - " \\sum\\limits_{t=1}^{m}{\n", - " \\bigg\\lvert{\n", - " T[i+t-1] - T[j+t-1]\n", - " }\\bigg\\rvert\n", - " }^{p}\n", - " }\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "5a4d2b3a", - "metadata": {}, - "source": [ - "Therefore:" - ] - }, - { - "cell_type": "markdown", - "id": "dc578dbd", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " d^{(m+k)}_{j,i} \\geq{}&\n", - " d^{(m)}_{j,i}\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "b51f7143", - "metadata": {}, - "source": [ - "In other words, we can simply use the p-norm distance between $T_{i,m}$ and $T_{j,m}$ as the lower-bound value for the distance between $T_{i,m+k}$ and $T_{j,m+k}$." - ] - }, - { - "cell_type": "markdown", - "id": "0b539ca8", - "metadata": {}, - "source": [ - "## 2-2 Normalized distance" - ] - }, - { - "cell_type": "markdown", - "id": "91ab346f", - "metadata": {}, - "source": [ - "In z-normalized distance, one should note that $d^{(m+k)}_{j,i} \\geq d^{(m)}_{j,i}$ is not necessarily correct. In other words, the distance between two subsequences does not necessarily increase by making them longer. However, it seems there is a very nice relationship between $d_{j,i}^{(m)}$ and the lower-bound value of $d_{j,i}^{(m+k)}$." - ] - }, - { - "cell_type": "markdown", - "id": "d60acabc", - "metadata": {}, - "source": [ - "### Derving Equation (2)" - ] - }, - { - "cell_type": "markdown", - "id": "1d3734ed", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " d^{(m+k)}_{j,i} ={}& \n", - " \\sqrt{\\sum\\limits_{t=1}^{m+k}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", - " }^{2}}} \n", - " \\\\\n", - " d^{(m+k)}_{j,i} ={}& \n", - " \\sqrt{\n", - " \\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", - " }^{2}}\n", - " +\n", - " \\sum\\limits_{t=m+1}^{m+k}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", - " }^{2}}\n", - " } \n", - " \\\\\n", - " \\geq{}&\n", - " \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", - " }^{2}}}\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "72a47d5c", - "metadata": {}, - "source": [ - "So, the Lower-Bound (LB) value for $d_{j,i}^{(m+k)}$ can be obtained by minimizing the right-hand side:" - ] - }, - { - "cell_type": "markdown", - "id": "ade9e7e4", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " LB ={}& \n", - " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", - " }^{2}}} \n", - " \\\\\n", - " ={}&\n", - " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", - " \\left[\\frac{1}{\\sigma_{j,m+k}}\n", - " \\left(\n", - " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{1}\n", - " \\right)\n", - " \\right]\n", - " }^{2}}}\n", - " \\\\\n", - " ={}&\n", - " \\min \\sqrt{\n", - " \\sum\\limits_{t=1}^{m}{{\n", - " \\left[\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m}}\n", - " \\frac{1}{\\sigma_{j,m+k}}\n", - " \\left(\n", - " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", - " - \n", - " \\frac{T[j+t-1] - \\mu_{j,m+k}}{1}\n", - " \\right)\n", - " \\right]\n", - " }^{2}\n", - " }\n", - " }\n", - " \\\\\n", - " ={}&\n", - " \\min \\sqrt{\n", - " \\sum\\limits_{t=1}^{m}{{\n", - " \\left[\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - " \\left(\n", - " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{j,m}\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", - " - \n", - " \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", - " \\right)\n", - " \\right]\n", - " }^{2}\n", - " }\n", - " }\n", - " \\\\\n", - " ={}&\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}} \\quad(1)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "d410ec5a", - "metadata": {}, - "source": [ - "**Note:** that the unknown variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. Also, note that all $\\mu$ and $\\sigma$ values are **constant** regardless of them being known or unknown.
\n", - "\n", - "We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$. Note that the unknown variables are now $\\mu^{'}$ and $\\sigma^{'}$.
\n", - "\n", - "Also, we define $\\alpha_{t}$ as:" - ] - }, - { - "cell_type": "markdown", - "id": "327fbe20", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\alpha_{t} \\triangleq{}& \n", - " {\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", - " } \n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "6a8b3359", - "metadata": {}, - "source": [ - "Therefore, we have:" - ] - }, - { - "cell_type": "markdown", - "id": "a293197c", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " LB ={}& \n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - " \\sqrt{\\min f(\\mu^{'},\\sigma^{'})} \\quad (2)\n", - " \\\\\n", - " f(\\mu^{'}, \\sigma^{'}) ={}&\n", - " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \\quad (3)\n", - " \\\\\n", - " \\alpha_{t} ={}& \n", - " \\frac{\n", - " T[i+t-1] - \\mu^{'}\n", - " }{\n", - " \\sigma^{'}\n", - " } \n", - " - \n", - " \\frac{\n", - " T[j+t-1] - \\mu_{j,m+k}\n", - " }{\n", - " \\sigma_{j,m}\n", - " } \\quad (4)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "e7564257", - "metadata": {}, - "source": [ - "**To find extrema points, we first need to find the critical point(s) by solving the single system of equations below.** In other words, we are looking for $\\mu^{'}$ and $\\sigma^{'}$ that satisfies both equations below:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c2de39a8", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0 \\quad (5)\n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0 \\quad (6)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "a3656f16", - "metadata": {}, - "source": [ - "**Solving $\\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0$:**" - ] - }, - { - "cell_type": "markdown", - "id": "8b7c8a81", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m}{\n", - " \\frac{\\partial{(\\alpha_{t}^{2})}}{\\partial{\\mu^{'}}}\n", - " }\n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m}{\n", - " 2\\frac{\\partial{(\\alpha_{t})}}{\\partial{\\mu^{'}}}\\alpha_{t}\n", - " }\n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}&\n", - " \\sum \\limits_{t=1}^{m} {\n", - " 2\\left(\n", - " \\frac{-1}{\\sigma^{'}}\n", - " \\right)\n", - " \\alpha_{t}} \n", - " \\\\\n", - " 0 ={}&\n", - " \\frac{-2}{\\sigma^{'}}\\sum \\limits_{t=1}^{m}{\\alpha_{t}}\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "6ef98f3f", - "metadata": {}, - "source": [ - "Please note that $\\sigma^{'}$ is constant and thus it was factered out of the summation.
\n", - "This gives us:" - ] - }, - { - "cell_type": "markdown", - "id": "cdc74b21", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m}{\\alpha_{t}} = 0 \\quad (7)\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "0aad71e0", - "metadata": {}, - "source": [ - "**Exapanding (7):**" - ] - }, - { - "cell_type": "markdown", - "id": "0d3f4dfa", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m} \\alpha_{t} ={}& \n", - " 0\n", - " \\\\\n", - " \\sum \\limits_{t=1}^{m} {\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}} ={}& \n", - " 0\n", - " \\\\\n", - " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m}T[i+t-1] - \\sum \\limits_{t=1}^{m} \\mu^{'}\\right) - \n", - " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=1}^{m}T[j+t-1] - \\sum \\limits_{t=1}^{m} \\mu_{j,m+k}\\right) ={}& \n", - " 0\n", - " \\\\\n", - " \\frac{1}{\\sigma^{'}}\\left(m\\mu_{i,m} - m\\mu^{'}\\right) - \n", - " \\frac{1}{\\sigma_{j,m}}\\left(m\\mu_{j,m} - m\\mu_{j,m+k}\\right) ={}& \n", - " 0\n", - " \\\\\n", - " \\sigma_{j,m}\\left(\\mu_{i,m} - \\mu^{'}\\right) - \n", - " \\sigma^{'}\\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) ={}& \n", - " 0\n", - " \\\\\n", - " \\sigma_{j,m} \\mu^{'} + \n", - " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} ={}& \n", - " 0 \\quad (8)\n", - "\\end{align} \n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "393ddb8f", - "metadata": {}, - "source": [ - "**Solving $\\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0$:**" - ] - }, - { - "cell_type": "markdown", - "id": "4eae27d8", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m}{\n", - " \\frac{\\partial{(\\alpha_{t}^{2})}}{\\partial{\\sigma^{'}}}\n", - " }\n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m}{\n", - " 2\\frac{\\partial{(\\alpha_{t})}}{\\partial{\\sigma^{'}}}\\alpha_{t}\n", - " }\n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", - " \\sum \\limits_{t=1}^{m} {\n", - " 2 \\left(\n", - " \\frac{-\\left({T[i+t-1] - \\mu^{'}}\\right)}{\\sigma^{'2}}\n", - " \\right)\n", - " \\alpha_{t}} \n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", - " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=1}^{m}{\\left({T[i+t-1] - \\mu^{'}}\\right) \\alpha_{t}}\n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", - " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=1}^{m}{\\left({T[i+t-1]\\alpha_{t} - \\mu^{'}\\alpha_{t}}\\right)}\n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", - " \\frac{-2}{\\sigma^{'2}}\n", - " {\\left(\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", - " - \n", - " \\sum \\limits_{t=1}^{m}{\\mu^{'}\\alpha_{t}}\n", - " \\right)\n", - " }\n", - " \\\\\n", - " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", - " \\frac{-2}{\\sigma^{'2}}\n", - " {\\left(\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", - " - \n", - " \\mu^{'}\\sum \\limits_{t=1}^{m}{\\alpha_{t}}\n", - " \\right)\n", - " }\n", - " \\\\\n", - " 0 ={}&\n", - " \\frac{-2}{\\sigma^{'2}}\n", - " {\\left(\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", - " - \n", - " \\mu^{'}\\cdot 0\n", - " \\right)\n", - " }\n", - " \\\\\n", - " 0 ={}&\n", - " \\frac{-2}{\\sigma^{'2}}\n", - " {\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", - " }\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "c0924610", - "metadata": {}, - "source": [ - "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." - ] - }, - { - "cell_type": "markdown", - "id": "c3b80336", - "metadata": {}, - "source": [ - "And, this gives:" - ] - }, - { - "cell_type": "markdown", - "id": "c398718a", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} ={}&\n", - " 0 \\quad (9)\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "4a34e737", - "metadata": {}, - "source": [ - "**Expanding (9):**" - ] - }, - { - "cell_type": "markdown", - "id": "de3f6023", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m} T[i+t-1] \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right) = 0\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "1ce7c9be", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", - " \\left(\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}}\n", - " \\right)\n", - " - \n", - " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", - " \\left(\n", - " \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", - " \\right)\n", - " ={}& 0\n", - " \\\\\n", - " \\\\\n", - " \\frac{1}{\\sigma^{'}}\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", - " \\left(\n", - " T[i+t-1] - \\mu^{'}\n", - " \\right)\n", - " - \n", - " \\frac{1}{\\sigma_{j,m}}\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", - " \\left(\n", - " T[j+t-1] - \\mu_{j,m+k}\n", - " \\right)\n", - " ={}& 0\n", - " \\\\\n", - " \\\\\n", - " \\frac{1}{\\sigma^{'}}\n", - " \\left(\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", - " -\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1]\\mu^{'}\n", - " \\right) \n", - " - \\\\\n", - " \\frac{1}{\\sigma_{j,m}}\n", - " \\left(\n", - " {\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] \n", - " -\\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}\n", - " }\n", - " \\right) \n", - " ={}& \n", - " 0\n", - " \\\\\n", - " \\\\\n", - " \\frac{1}{\\sigma^{'}}\n", - " \\left(\n", - " \\sum \\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", - " -\n", - " \\mu^{'}\\sum\\limits_{t=1}^{m} T[i+t-1]\n", - " \\right) \n", - " - \\\\\n", - " \\frac{1}{\\sigma_{j,m}}\n", - " \\left(\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1]\n", - " -\n", - " \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", - " \\right) \n", - " ={}& \n", - " 0 \\quad (*)\n", - "\\end{align} \n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "0c839937", - "metadata": {}, - "source": [ - "Now, recall that the pearson correlation $\\rho_{ij}$ between two subsequenes of lenght $m$ is defined as follows:" - ] - }, - { - "cell_type": "markdown", - "id": "82bc9b8e", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\rho_{ij} = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "4880c751", - "metadata": {}, - "source": [ - "Note: we can rearrange the pearson correlation equation as:
\n", - "$\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" - ] - }, - { - "cell_type": "markdown", - "id": "a01fd0cc", - "metadata": {}, - "source": [ - "**Therefore, with help of (\\*\\*), we continue our calculation from eq(\\*):**" - ] - }, - { - "cell_type": "markdown", - "id": "1543b1f4", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\frac{1}{\\sigma^{'}}\n", - " \\left[\n", - " \\left(\n", - " m\\rho_{ii}\\sigma_{i,m}\\sigma_{i,m} + m\\mu_{i,m}\\mu_{i,m}\n", - " \\right)\n", - " - \n", - " \\mu^{'} \\cdot m\\mu_{i,m}\n", - " \\right] \n", - " - \n", - " \\frac{1}{\\sigma_{j,m}}\n", - " \\left[\n", - " \\left(\n", - " m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", - " + \n", - " m\\mu_{i,m}\\mu_{j,m}\n", - " \\right)\n", - " - \n", - " \\mu_{j,m+k} \\cdot m\\mu_{i,m}\n", - " \\right]\n", - " ={}& 0\n", - " \\\\\n", - " \\frac{1}{\\sigma^{'}}\n", - " \\left[\n", - " \\left(\n", - " m\\cdot1\\cdot\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2}\n", - " \\right)\n", - " - \n", - " \\mu^{'} \\cdot m\\mu_{i,m}\n", - " \\right] \n", - " - \n", - " \\frac{1}{\\sigma_{j,m}}\n", - " \\left[\n", - " \\left(\n", - " m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", - " + \n", - " m\\mu_{i,m}\\mu_{j,m}\n", - " \\right)\n", - " - \n", - " \\mu_{j,m+k} \\cdot m\\mu_{i,m}\n", - " \\right]\n", - " ={}& 0\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "041482b8", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\frac{1}{\\sigma^{'}\\sigma_{j,m}}\n", - " \\left[\n", - " \\sigma_{j,m}\\left(\n", - " m\\sigma_{i,m}^{2} \n", - " + \n", - " m\\mu_{i,m}^{2} \n", - " - \n", - " \\mu^{'} \\cdot m\\mu_{i,m}\n", - " \\right) \n", - " - \n", - " \\sigma^{'}\\left(\n", - " {m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", - " +\n", - " m\\mu_{i,m}\\mu_{j,m} \n", - " -\n", - " \\mu_{j,m+k} \\cdot m\\mu_{i,m}}\n", - " \\right)\n", - " \\right] ={}& 0\n", - " \\\\\n", - " \\frac{m}{\n", - " \\sigma^{'}\\sigma_{j,m}\n", - " }\n", - " \\left[\n", - " \\sigma_{j,m}\\left(\n", - " \\sigma_{i,m}^{2} \n", - " + \n", - " \\mu_{i,m}^{2} \n", - " - \n", - " \\mu^{'} \\mu_{i,m}\n", - " \\right) \n", - " - \n", - " \\sigma^{'}\\left(\n", - " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", - " +\n", - " \\mu_{i,m}\\mu_{j,m}\n", - " -\n", - " \\mu_{j,m+k} \\mu_{i,m}}\n", - " \\right)\n", - " \\right]\n", - " ={}& 0\n", - " \\\\\n", - " \\sigma_{j,m}\\left(\n", - " \\sigma_{i,m}^{2} \n", - " + \n", - " \\mu_{i,m}^{2} \n", - " - \n", - " \\mu^{'} \\mu_{i,m}\n", - " \\right) \n", - " - \n", - " \\sigma^{'}\\left(\n", - " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", - " +\n", - " \\mu_{i,m}\\mu_{j,m}\n", - " -\n", - " \\mu_{j,m+k} \\mu_{i,m}}\n", - " \\right)\n", - " ={}& 0\n", - " \\\\\n", - " \\sigma_{j,m}\\left(\n", - " \\sigma_{i,m}^{2} \n", - " + \n", - " \\mu_{i,m}^{2}\n", - " \\right)\n", - " - \n", - " \\sigma_{j,m}\\left(\n", - " \\mu^{'} \\mu_{i,m}\n", - " \\right) \n", - " - \n", - " \\sigma^{'}\\left(\n", - " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", - " +\n", - " \\mu_{i,m}\\mu_{j,m}\n", - " -\n", - " \\mu_{j,m+k} \\mu_{i,m}}\n", - " \\right)\n", - " ={}& 0\n", - " \\\\\n", - " - \\sigma_{j,m}\\left(\n", - " \\sigma_{i,m}^{2} \n", - " + \n", - " \\mu_{i,m}^{2}\n", - " \\right)\n", - " + \n", - " \\sigma_{j,m}\\left(\n", - " \\mu^{'} \\mu_{i,m}\n", - " \\right) \n", - " + \n", - " \\sigma^{'}\\left(\n", - " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", - " +\n", - " \\mu_{i,m}\\mu_{j,m}\n", - " -\n", - " \\mu_{j,m+k} \\mu_{i,m}}\n", - " \\right)\n", - " ={}& 0\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "1d37830b", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (10)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "53f8c4b4", - "metadata": {}, - "source": [ - "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." - ] - }, - { - "cell_type": "markdown", - "id": "6adaea06", - "metadata": {}, - "source": [ - "**Now, it is time to solve equations (8) and (10), provided below:**" - ] - }, - { - "cell_type": "markdown", - "id": "9d89eca8", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - "\\sigma_{j,m} \\mu^{'} + \n", - " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} \n", - " ={}& 0 \\quad(8)\n", - " \\\\\n", - " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) \n", - " ={}& 0 \\quad(10)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "44d1afe8", - "metadata": {}, - "source": [ - "Note that in the system of equations above, the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$, and the remaining ones are known." - ] - }, - { - "cell_type": "markdown", - "id": "35c4e371", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - "-\\mu_{i,m}\\left[\n", - " \\sigma_{j,m} \\mu^{'} \n", - " + \n", - " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} \n", - " - \n", - " \\sigma_{j,m}\\mu_{i,m} \n", - " \\right]\n", - " ={}& 0 \\quad(8')\n", - " \\\\\n", - " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) \n", - " ={}& 0 \\quad(10)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "5cb0edfe", - "metadata": {}, - "source": [ - "$(8')+(10)$ gives:" - ] - }, - { - "cell_type": "markdown", - "id": "0e192a68", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - "-\\mu_{i,m}\\sigma_{j,m} \\mu^{'} - \n", - " \\mu_{i,m}\\mu_{j,m}\\sigma^{'} + \\mu_{i,m}\\mu_{j,m+k}\\sigma^{'} \n", - " + \\sigma_{j,m}\\mu_{i,m}^{2} +\n", - " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}\\sigma^{'} + \\mu_{i,m}\\mu_{j,m}\\sigma^{'} - \\mu_{i,m}\\mu_{j,m+k}\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m}^{2} - \\sigma_{j,m}\\sigma_{i,m}^{2}\n", - " ={}& 0\n", - " \\\\\n", - " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}\\sigma^{'} - \\sigma_{j,m}\\sigma_{i,m}^{2} \n", - " ={}& 0\n", - " \\\\\n", - " \\sigma_{i,m}\\sigma_{j,m}\n", - " \\left(\n", - " \\rho_{ij}\\sigma^{'} - \\sigma_{i,m}\n", - " \\right)\n", - " ={}&\n", - " 0\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "02003455", - "metadata": {}, - "source": [ - "Hence:" - ] - }, - { - "cell_type": "markdown", - "id": "70c325aa", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\sigma^{'} = \\frac{\\sigma_{i,m}}{\\rho_{ij}} \\quad (11)\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "e087b39f", - "metadata": {}, - "source": [ - "Note that we assumed $\\sigma_{i,m}$ and $\\sigma_{j,m}$ cannot be zero. Also, since standard deviations are positive, eq(11) is valid only if $\\rho_{ij} \\gt 0$." - ] - }, - { - "cell_type": "markdown", - "id": "024639e5", - "metadata": {}, - "source": [ - "And, subsituting eq(11) in eq(8):" - ] - }, - { - "cell_type": "markdown", - "id": "631d7d57", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - "\\sigma_{j,m} \\mu^{'} + \n", - " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}}) - \\sigma_{j,m}\\mu_{i,m} \n", - " ={}& 0 \n", - " \\\\\n", - " \\frac{1}{\\sigma_{j,m}}\\left[\n", - " \\sigma_{j,m} \\mu^{'} + \n", - " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}}) - \\sigma_{j,m}\\mu_{i,m} \n", - " \\right]\n", - " ={}& 0 \n", - " \\\\\n", - " \\mu^{'} + \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}) - \\mu_{i,m} \n", - " ={}& 0 \n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "a745e0a1", - "metadata": {}, - "source": [ - "Hence:" - ] - }, - { - "cell_type": "markdown", - "id": "853400dd", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}} \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) \\quad(12)\n", - "\\end{align}\n", - "$$\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "15f7a1e4", - "metadata": {}, - "source": [ - "**Therefore, the critical point of function $f(\\mu^{'},\\sigma^{'})$ is:**" - ] - }, - { - "cell_type": "markdown", - "id": "a7ff8c57", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\sigma^{'} ={}& \n", - " \\frac{\\sigma_{i,m}}{\\rho_{ij}} \\quad (11)\n", - " \\\\\n", - " \\mu^{'} ={}& \n", - " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}} \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) \\quad(12)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "b266cfb2", - "metadata": {}, - "source": [ - "**NOTE:** It is important to note that eq(11) and eq(12) are favorable to us as it gives the $\\mu^{'}$ and $\\sigma^{'}$ of the critical point of `f` as a function of known parameters $\\mu_{i,m}$, $\\sigma_{i,m}$, $\\mu_{j,m}$, $\\sigma_{j,m}$, $\\rho_{ij}$, and $\\mu_{j,m+k}$. Therefore, we can calculate the lower-bound LB as a function of the aforementioned parameter. \n", - "\n", - "**NOTE:** It is worthwhile to reiterate the fact that the solution is valid when $\\rho_{ij} \\gt 0$. (We will discuss $\\rho_{ij} \\leq 0$ later...)" - ] - }, - { - "cell_type": "markdown", - "id": "a0e36dfc", - "metadata": {}, - "source": [ - "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of the crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. \n", - "\n", - "**NOTE:** we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" - ] - }, - { - "cell_type": "markdown", - "id": "92abd2a2", - "metadata": {}, - "source": [ - "**Start with equation (3):**" - ] - }, - { - "cell_type": "markdown", - "id": "b51d32b2", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " f(\\mu^{'},\\sigma^{'}) ={}&\n", - " \\sum \\limits_{t=1}^{m}\\alpha_{t}^{2}\n", - " \\\\\n", - " ={}&\n", - " \\sum \\limits_{t=1}^{m}\\alpha_{t} \\cdot \\alpha_{t}\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "7afe0a3d", - "metadata": {}, - "source": [ - "And, we replace one of $\\alpha_{t}$ with its equivalent term provided in eq(4)..." - ] - }, - { - "cell_type": "markdown", - "id": "bfb10bce", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " f_{min}(\\mu^{'},\\sigma^{'}) ={}&\n", - " \\sum\\limits_{t=1}^{m}{\n", - " {\\alpha_{t}\n", - " \\left(\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", - " \\right)\n", - " }}\n", - " \\\\\n", - " ={}&\n", - " {\n", - " \\frac{1}{\\sigma^{'}}\n", - " \\left(\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]\\alpha_{t} \n", - " - \n", - " \\sum\\limits_{t=1}^{m}\n", - " \\mu^{'}\\alpha_{t}\n", - " \\right)\n", - " - \\frac{1}{\\sigma_{j,m}}\n", - " \\left(\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[j+t-1]\\alpha_{t} \n", - " - \n", - " \\sum\\limits_{t=1}^{m}\n", - " \\mu_{j,m+k}\\alpha_{t}\n", - " \\right)\n", - " } \n", - " \\\\ \n", - " ={}&\n", - " {\n", - " \\frac{1}{\\sigma^{'}}\n", - " \\left(\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]\\alpha_{t} \n", - " - \n", - " \\mu^{'}\\sum\\limits_{t=1}^{m}\\alpha_{t}\n", - " \\right)\n", - " - \n", - " \\frac{1}{\\sigma_{j,m}}\n", - " \\left(\n", - " \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} \n", - " - \n", - " \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}\\alpha_{t}\n", - " \\right)\n", - " } \n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "4a9e3f03", - "metadata": {}, - "source": [ - "And, now with help of eq(7), $\\sum\\limits_{t=1}^{m}{\\alpha_{t}}=0$, and the eq(9), $\\sum\\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}}=0$, we will have:" - ] - }, - { - "cell_type": "markdown", - "id": "650cae87", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " f_{min}(\\mu^{'},\\sigma^{'}) ={}&\n", - " {\n", - " \\frac{1}{\\sigma^{'}}\n", - " \\left(\n", - " 0 - \\mu^{'} \\cdot 0\n", - " \\right) \n", - " - \n", - " \\frac{1}{\\sigma_{j,m}}\n", - " \\left(\n", - " \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}\\cdot 0\n", - " \\right)\n", - " } \n", - " \\\\ \n", - " ={}&\n", - " {\n", - " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t}\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {\n", - " - \\frac{1}{\\sigma_{j,m}} \n", - " \\sum\\limits_{t=1}^{m}{\\left[\n", - " T[j+t-1]\\left(\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", - " \\right)\n", - " \\right]\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {\n", - " - \\frac{1}{\\sigma_{j,m}} \n", - " \\sum\\limits_{t=1}^{m}{\n", - " \\left(\n", - " \\frac{T[i+t-1]T[j+t-1] - \\mu^{'}T[j+t-1]}{\\sigma^{'}} - \\frac{T[j+t-1]T[j+t-1] - \\mu_{j,m+k}T[j+t-1]}{\\sigma_{j,m}}\n", - " \\right)\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{1}{\\sigma_{j,m}} \n", - " {\n", - " \\left(\n", - " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\mu^{'}\\sum\\limits_{t=1}^{m}T[j+t-1]}{\\sigma^{'}} \n", - " - \n", - " \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]T[j+t-1] - \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}T[j+t-1]}{\\sigma_{j,m}}\n", - " \\right)\n", - " }\n", - " } \n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "9f2ca2da", - "metadata": {}, - "source": [ - "And, now with help of the fact that $\\sum{T} = m\\mu$ and also the Pearon Correlation equation (\\*\\*)..." - ] - }, - { - "cell_type": "markdown", - "id": "35db152a", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", - " {- \\frac{1}{\\sigma_{j,m}} \n", - " {\n", - " \\left(\n", - " \\frac{(m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu^{'} \\cdot m\\mu_{j,m}}{\\sigma^{'}} \n", - " - \n", - " \\frac{(m\\rho_{jj}\\sigma_{j,m}^{2} + m\\mu_{j,m}^{2}) - \\mu_{j,m+k} \\cdot m\\mu_{j,m}}{\\sigma_{j,m}}\n", - " \\right)\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{1}{\\sigma_{j,m}} \n", - " {\n", - " \\left[\n", - " \\frac{\n", - " m\\left(\n", - " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu^{'} \\cdot \\mu_{j,m}\n", - " \\right)\n", - " }{\n", - " \\sigma^{'}\n", - " } \n", - " - \n", - " \\frac{\n", - " m\\left(\n", - " 1\\cdot\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m+k} \\cdot \\mu_{j,m}\n", - " \\right)\n", - " }{\n", - " \\sigma_{j,m}\n", - " }\n", - " \\right]\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma^{'}} \n", - " {\n", - " \\left(\n", - " {\\sigma_{j,m}(\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{j,m}\\mu^{'})} \n", - " - \n", - " {\\sigma^{'}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", - " \\right)\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma^{'}} \n", - " {\n", - " \\left(\n", - " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} + \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \\mu_{j,m}\\sigma_{j,m}\\mu^{'}} \n", - " - \n", - " {\\sigma^{'}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", - " \\right)\n", - " }\n", - " } \n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "cfd5a617", - "metadata": {}, - "source": [ - "And, now we are at a good position to plug in the values $\\mu^{'}$(11) and $\\sigma^{'}$(12):" - ] - }, - { - "cell_type": "markdown", - "id": "f3e25620", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", - " {- \\frac{m}{\\sigma_{j,m}^{2}\n", - " (\\frac{\\sigma_{i,m}}{\\rho_{ij}})\n", - " } \n", - " {\n", - " \\left[\n", - " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} + \n", - " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \n", - " \\mu_{j,m}\\sigma_{j,m}\\left({\n", - " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", - " }\n", - " \\right)} \n", - " - \n", - " {(\\frac{\\sigma_{i,m}}{\\rho_{ij}})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", - " \\right]\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{m\\rho_{ij}}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", - " {\n", - " \\left[\n", - " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", - " + \n", - " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", - " - \n", - " {\n", - " \\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", - " + \n", - " \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}{\\mu_{j,m}\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", - " }\n", - " } \n", - " - \n", - " {\\frac{\\sigma_{i,m}}{\\rho_{ij}}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", - " \\right]\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", - " {\n", - " \\left[\n", - " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", - " + \n", - " \\rho_{ij}\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", - " - \n", - " {\n", - " \\rho_{ij}\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", - " + \n", - " \\mu_{j,m}\\sigma_{i,m}(\\mu_{j,m}-\\mu_{j,m+k})\n", - " }\n", - " } \n", - " - \n", - " {\\sigma_{i,m}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", - " \\right]\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", - " {\n", - " \\left(\n", - " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", - " + \n", - " \\rho_{ij}\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", - " - \n", - " {\n", - " \\rho_{ij}\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", - " + \n", - " \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m} - \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m+k}\n", - " }\n", - " }\n", - " - \n", - " {\\sigma_{i,m}\\sigma_{j,m}^{2} - \\sigma_{i,m}\\mu_{j,m}^{2} + \\sigma_{i,m}\\mu_{j,m}\\mu_{j,m+k}}\n", - " \\right)\n", - " }\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", - " \\left( \n", - " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", - " - \n", - " \\sigma_{i,m}\\sigma_{j,m}^{2} \n", - " }\n", - " \\right)\n", - " } \n", - " \\\\\n", - " ={}&\n", - " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", - " (\\rho_{ij}^{2} - 1)\n", - " \\sigma_{i,m}\\sigma_{j,m}^{2}\n", - " }\n", - " \\\\\n", - " ={}&\n", - " m(1-\\rho_{ij}^{2})\n", - "\\end{align} \n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "64dc1027", - "metadata": {}, - "source": [ - "**Finally, with eq(2), the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" - ] - }, - { - "cell_type": "markdown", - "id": "98db40a5", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " LB ={}& \n", - " \\frac{\n", - " \\sigma_{j,m}\n", - " }{\\sigma_{j,m+k}\n", - " } \\sqrt{m (1 - \\rho_{ij}^{2})} \\quad \\text{if} \\, \\rho > 0\n", - " \\\\\n", - "\\end{align}\n", - "$$\n", - "\n", - "$$\n", - "\\begin{align}\n", - " \\rho_{ij} ={}& \n", - " \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "8cbad624", - "metadata": {}, - "source": [ - "**Note:**
\n", - "* Note that eq(12) is valid only for $\\rho_{ij} > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho_{ij} > 0$. \n", - "* The pearson correlation, $\\rho$, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", - "\n", - "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho_{ij})}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", - "\n", - "**Pending...**
\n", - "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function.\n", - "\n", - "* **For $\\rho \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "448cd8ce", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 9d8753810d6d1ab2f3e77612ad8a706b92765383 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 01:12:29 -0600 Subject: [PATCH 27/64] ADD new notebook for deriving equation in VALMOD --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 1507 +++++++++++++++++ 1 file changed, 1507 insertions(+) create mode 100644 docs/LowerBound_Dist_Profile_Derivation.ipynb diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb new file mode 100644 index 000000000..09d5202ca --- /dev/null +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -0,0 +1,1507 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3b23610b", + "metadata": {}, + "source": [ + "In this notebook, we would like to derive the eq(2) of the paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf)." + ] + }, + { + "cell_type": "markdown", + "id": "5f999789", + "metadata": {}, + "source": [ + "The idea goes as follows: \"given the distance profile of $T_{j,m}$, how can we find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`?" + ] + }, + { + "cell_type": "markdown", + "id": "03836054", + "metadata": {}, + "source": [ + "In other words, can we find **Lower Bound (LB)** for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$? (So, the last `k` elements of $T_{i,m+k}$ are unknown)" + ] + }, + { + "cell_type": "markdown", + "id": "3b5c8c5a", + "metadata": {}, + "source": [ + "## 2-1 Non-normalized distance" + ] + }, + { + "cell_type": "markdown", + "id": "1f7e294e", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " d^{(m+k)}_{j,i} ={}& \n", + " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", + " \\sum\\limits_{t=1}^{m+k}{\n", + " \\bigg\\lvert{\n", + " T[i+t-1] - T[j+t-1]\n", + " }\\bigg\\rvert\n", + " }^{p}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", + " \\sum\\limits_{t=1}^{m}{\n", + " \\bigg\\lvert{\n", + " T[i+t-1] - T[j+t-1]\n", + " }\\bigg\\rvert\n", + " }^{p}\n", + " +\n", + " \\sum\\limits_{t=m+1}^{m+k}{\n", + " \\bigg\\lvert{\n", + " T[i+t-1] - T[j+t-1]\n", + " }\\bigg\\rvert\n", + " }^{p}\n", + " }\n", + " \\\\\n", + " \\geq{}&\n", + " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", + " \\sum\\limits_{t=1}^{m}{\n", + " \\bigg\\lvert{\n", + " T[i+t-1] - T[j+t-1]\n", + " }\\bigg\\rvert\n", + " }^{p}\n", + " }\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "5a4d2b3a", + "metadata": {}, + "source": [ + "Therefore:" + ] + }, + { + "cell_type": "markdown", + "id": "dc578dbd", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " d^{(m+k)}_{j,i} \\geq{}&\n", + " d^{(m)}_{j,i}\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "b51f7143", + "metadata": {}, + "source": [ + "In other words, we can simply use the p-norm distance between $T_{i,m}$ and $T_{j,m}$ as the lower-bound value for the distance between $T_{i,m+k}$ and $T_{j,m+k}$." + ] + }, + { + "cell_type": "markdown", + "id": "0b539ca8", + "metadata": {}, + "source": [ + "## 2-2 Normalized distance" + ] + }, + { + "cell_type": "markdown", + "id": "91ab346f", + "metadata": {}, + "source": [ + "In z-normalized distance, one should note that $d^{(m+k)}_{j,i} \\geq d^{(m)}_{j,i}$ is not necessarily correct. In other words, the distance between two subsequences does not necessarily increase by making them longer. However, it seems there is a very nice relationship between $d_{j,i}^{(m)}$ and the lower-bound value of $d_{j,i}^{(m+k)}$." + ] + }, + { + "cell_type": "markdown", + "id": "d60acabc", + "metadata": {}, + "source": [ + "### Derving Equation (2)" + ] + }, + { + "cell_type": "markdown", + "id": "1d3734ed", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " d^{(m+k)}_{j,i} ={}& \n", + " \\sqrt{\\sum\\limits_{t=1}^{m+k}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}} \n", + " \\\\\n", + " d^{(m+k)}_{j,i} ={}& \n", + " \\sqrt{\n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}\n", + " +\n", + " \\sum\\limits_{t=m+1}^{m+k}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}\n", + " } \n", + " \\\\\n", + " \\geq{}&\n", + " \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}}\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "72a47d5c", + "metadata": {}, + "source": [ + "So, the Lower-Bound (LB) value for $d_{j,i}^{(m+k)}$ can be obtained by minimizing the right-hand side:" + ] + }, + { + "cell_type": "markdown", + "id": "ade9e7e4", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " LB ={}& \n", + " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", + " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " }^{2}}} \n", + " \\\\\n", + " ={}&\n", + " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", + " \\left[\\frac{1}{\\sigma_{j,m+k}}\n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{1}\n", + " \\right)\n", + " \\right]\n", + " }^{2}}}\n", + " \\\\\n", + " ={}&\n", + " \\min \\sqrt{\n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left[\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m}}\n", + " \\frac{1}{\\sigma_{j,m+k}}\n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", + " - \n", + " \\frac{T[j+t-1] - \\mu_{j,m+k}}{1}\n", + " \\right)\n", + " \\right]\n", + " }^{2}\n", + " }\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\min \\sqrt{\n", + " \\sum\\limits_{t=1}^{m}{{\n", + " \\left[\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{j,m}\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", + " - \n", + " \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " \\right]\n", + " }^{2}\n", + " }\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}} \\quad(1)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "d410ec5a", + "metadata": {}, + "source": [ + "**Note:** that the unknown variables are $\\mu_{i,m+k}$ and $\\sigma_{i,m+k}$. Also, note that all $\\mu$ and $\\sigma$ values are **constant** regardless of them being known or unknown.
\n", + "\n", + "We subtitute $\\mu_{i,m+k}$ with $\\mu^{'}$, and $\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}$ with $\\sigma^{'}$. Note that the unknown variables are now $\\mu^{'}$ and $\\sigma^{'}$.
\n", + "\n", + "Also, we define $\\alpha_{t}$ as:" + ] + }, + { + "cell_type": "markdown", + "id": "8a778de1", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\alpha_{t} \\triangleq{}& \n", + " {\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " } \n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "18656992", + "metadata": {}, + "source": [ + "Therefore, we have:" + ] + }, + { + "cell_type": "markdown", + "id": "a293197c", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " LB ={}& \n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + " \\sqrt{\\min f(\\mu^{'},\\sigma^{'})} \\quad (2)\n", + " \\\\\n", + " f(\\mu^{'}, \\sigma^{'}) ={}&\n", + " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \\quad (3)\n", + " \\\\\n", + " \\alpha_{t} ={}& \n", + " \\frac{\n", + " T[i+t-1] - \\mu^{'}\n", + " }{\n", + " \\sigma^{'}\n", + " } \n", + " - \n", + " \\frac{\n", + " T[j+t-1] - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " } \\quad (4)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "e7564257", + "metadata": {}, + "source": [ + "**To find extrema points, we first need to find the critical point(s) by solving the single system of equations below.** In other words, we are looking for $\\mu^{'}$ and $\\sigma^{'}$ that satisfies both equations below:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c2de39a8", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0 \\quad (5)\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0 \\quad (6)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "a3656f16", + "metadata": {}, + "source": [ + "**Solving $\\frac{\\partial{f}}{\\partial{\\mu^{'}}} = 0$:**" + ] + }, + { + "cell_type": "markdown", + "id": "8b7c8a81", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", + " \\sum \\limits_{t=1}^{m}{\n", + " \\frac{\\partial{(\\alpha_{t}^{2})}}{\\partial{\\mu^{'}}}\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", + " \\sum \\limits_{t=1}^{m}{\n", + " 2\\frac{\\partial{(\\alpha_{t})}}{\\partial{\\mu^{'}}}\\alpha_{t}\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}&\n", + " \\sum \\limits_{t=1}^{m} {\n", + " 2\\left(\n", + " \\frac{-1}{\\sigma^{'}}\n", + " \\right)\n", + " \\alpha_{t}} \n", + " \\\\\n", + " 0 ={}&\n", + " \\frac{-2}{\\sigma^{'}}\\sum \\limits_{t=1}^{m}{\\alpha_{t}}\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "6ef98f3f", + "metadata": {}, + "source": [ + "Please note that $\\sigma^{'}$ is constant and thus it was factered out of the summation.
\n", + "This gives us:" + ] + }, + { + "cell_type": "markdown", + "id": "cdc74b21", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m}{\\alpha_{t}} = 0 \\quad (7)\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "0aad71e0", + "metadata": {}, + "source": [ + "**Exapanding (7):**" + ] + }, + { + "cell_type": "markdown", + "id": "0d3f4dfa", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m} \\alpha_{t} ={}& \n", + " 0\n", + " \\\\\n", + " \\sum \\limits_{t=1}^{m} {\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}} ={}& \n", + " 0\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m}T[i+t-1] - \\sum \\limits_{t=1}^{m} \\mu^{'}\\right) - \n", + " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=1}^{m}T[j+t-1] - \\sum \\limits_{t=1}^{m} \\mu_{j,m+k}\\right) ={}& \n", + " 0\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\\left(m\\mu_{i,m} - m\\mu^{'}\\right) - \n", + " \\frac{1}{\\sigma_{j,m}}\\left(m\\mu_{j,m} - m\\mu_{j,m+k}\\right) ={}& \n", + " 0\n", + " \\\\\n", + " \\sigma_{j,m}\\left(\\mu_{i,m} - \\mu^{'}\\right) - \n", + " \\sigma^{'}\\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) ={}& \n", + " 0\n", + " \\\\\n", + " \\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} ={}& \n", + " 0 \\quad (8)\n", + "\\end{align} \n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "393ddb8f", + "metadata": {}, + "source": [ + "**Solving $\\frac{\\partial{f}}{\\partial{\\sigma^{'}}} = 0$:**" + ] + }, + { + "cell_type": "markdown", + "id": "4eae27d8", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", + " \\sum \\limits_{t=1}^{m}{\n", + " \\frac{\\partial{(\\alpha_{t}^{2})}}{\\partial{\\sigma^{'}}}\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", + " \\sum \\limits_{t=1}^{m}{\n", + " 2\\frac{\\partial{(\\alpha_{t})}}{\\partial{\\sigma^{'}}}\\alpha_{t}\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\sum \\limits_{t=1}^{m} {\n", + " 2 \\left(\n", + " \\frac{-\\left({T[i+t-1] - \\mu^{'}}\\right)}{\\sigma^{'2}}\n", + " \\right)\n", + " \\alpha_{t}} \n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=1}^{m}{\\left({T[i+t-1] - \\mu^{'}}\\right) \\alpha_{t}}\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=1}^{m}{\\left({T[i+t-1]\\alpha_{t} - \\mu^{'}\\alpha_{t}}\\right)}\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\n", + " {\\left(\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " - \n", + " \\sum \\limits_{t=1}^{m}{\\mu^{'}\\alpha_{t}}\n", + " \\right)\n", + " }\n", + " \\\\\n", + " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\n", + " {\\left(\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " - \n", + " \\mu^{'}\\sum \\limits_{t=1}^{m}{\\alpha_{t}}\n", + " \\right)\n", + " }\n", + " \\\\\n", + " 0 ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\n", + " {\\left(\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " - \n", + " \\mu^{'}\\cdot 0\n", + " \\right)\n", + " }\n", + " \\\\\n", + " 0 ={}&\n", + " \\frac{-2}{\\sigma^{'2}}\n", + " {\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " }\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "c6084fa1", + "metadata": {}, + "source": [ + "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." + ] + }, + { + "cell_type": "markdown", + "id": "c3b80336", + "metadata": {}, + "source": [ + "And, this gives:" + ] + }, + { + "cell_type": "markdown", + "id": "c398718a", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} ={}&\n", + " 0 \\quad (9)\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "4a34e737", + "metadata": {}, + "source": [ + "**Expanding (9):**" + ] + }, + { + "cell_type": "markdown", + "id": "de3f6023", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sum \\limits_{t=1}^{m} T[i+t-1] \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right) = 0\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "1ce7c9be", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}}\n", + " \\right)\n", + " - \n", + " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\left(\n", + " \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " ={}& 0\n", + " \\\\\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\left(\n", + " T[i+t-1] - \\mu^{'}\n", + " \\right)\n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\left(\n", + " T[j+t-1] - \\mu_{j,m+k}\n", + " \\right)\n", + " ={}& 0\n", + " \\\\\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", + " -\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1]\\mu^{'}\n", + " \\right) \n", + " - \\\\\n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " {\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] \n", + " -\\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}\n", + " }\n", + " \\right) \n", + " ={}& \n", + " 0\n", + " \\\\\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " \\sum \\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", + " -\n", + " \\mu^{'}\\sum\\limits_{t=1}^{m} T[i+t-1]\n", + " \\right) \n", + " - \\\\\n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1]\n", + " -\n", + " \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", + " \\right) \n", + " ={}& \n", + " 0 \\quad (*)\n", + "\\end{align} \n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "0c839937", + "metadata": {}, + "source": [ + "Now, recall that the pearson correlation $\\rho_{ij}$ between two subsequenes of lenght $m$ is defined as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "82bc9b8e", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\rho_{ij} = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "4880c751", + "metadata": {}, + "source": [ + "Note: we can rearrange the pearson correlation equation as:
\n", + "$\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" + ] + }, + { + "cell_type": "markdown", + "id": "a01fd0cc", + "metadata": {}, + "source": [ + "**Therefore, with help of (\\*\\*), we continue our calculation from eq(\\*):**" + ] + }, + { + "cell_type": "markdown", + "id": "1543b1f4", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left[\n", + " \\left(\n", + " m\\rho_{ii}\\sigma_{i,m}\\sigma_{i,m} + m\\mu_{i,m}\\mu_{i,m}\n", + " \\right)\n", + " - \n", + " \\mu^{'} \\cdot m\\mu_{i,m}\n", + " \\right] \n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left[\n", + " \\left(\n", + " m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " + \n", + " m\\mu_{i,m}\\mu_{j,m}\n", + " \\right)\n", + " - \n", + " \\mu_{j,m+k} \\cdot m\\mu_{i,m}\n", + " \\right]\n", + " ={}& 0\n", + " \\\\\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left[\n", + " \\left(\n", + " m\\cdot1\\cdot\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2}\n", + " \\right)\n", + " - \n", + " \\mu^{'} \\cdot m\\mu_{i,m}\n", + " \\right] \n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left[\n", + " \\left(\n", + " m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " + \n", + " m\\mu_{i,m}\\mu_{j,m}\n", + " \\right)\n", + " - \n", + " \\mu_{j,m+k} \\cdot m\\mu_{i,m}\n", + " \\right]\n", + " ={}& 0\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c6d53dd", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\frac{1}{\\sigma^{'}\\sigma_{j,m}}\n", + " \\left[\n", + " \\sigma_{j,m}\\left(\n", + " m\\sigma_{i,m}^{2} \n", + " + \n", + " m\\mu_{i,m}^{2} \n", + " - \n", + " \\mu^{'} \\cdot m\\mu_{i,m}\n", + " \\right) \n", + " - \n", + " \\sigma^{'}\\left(\n", + " {m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " m\\mu_{i,m}\\mu_{j,m} \n", + " -\n", + " \\mu_{j,m+k} \\cdot m\\mu_{i,m}}\n", + " \\right)\n", + " \\right] ={}& 0\n", + " \\\\\n", + " \\frac{m}{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\left[\n", + " \\sigma_{j,m}\\left(\n", + " \\sigma_{i,m}^{2} \n", + " + \n", + " \\mu_{i,m}^{2} \n", + " - \n", + " \\mu^{'} \\mu_{i,m}\n", + " \\right) \n", + " - \n", + " \\sigma^{'}\\left(\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " \\mu_{i,m}\\mu_{j,m}\n", + " -\n", + " \\mu_{j,m+k} \\mu_{i,m}}\n", + " \\right)\n", + " \\right]\n", + " ={}& 0\n", + " \\\\\n", + " \\sigma_{j,m}\\left(\n", + " \\sigma_{i,m}^{2} \n", + " + \n", + " \\mu_{i,m}^{2} \n", + " - \n", + " \\mu^{'} \\mu_{i,m}\n", + " \\right) \n", + " - \n", + " \\sigma^{'}\\left(\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " \\mu_{i,m}\\mu_{j,m}\n", + " -\n", + " \\mu_{j,m+k} \\mu_{i,m}}\n", + " \\right)\n", + " ={}& 0\n", + " \\\\\n", + " \\sigma_{j,m}\\left(\n", + " \\sigma_{i,m}^{2} \n", + " + \n", + " \\mu_{i,m}^{2}\n", + " \\right)\n", + " - \n", + " \\sigma_{j,m}\\left(\n", + " \\mu^{'} \\mu_{i,m}\n", + " \\right) \n", + " - \n", + " \\sigma^{'}\\left(\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " \\mu_{i,m}\\mu_{j,m}\n", + " -\n", + " \\mu_{j,m+k} \\mu_{i,m}}\n", + " \\right)\n", + " ={}& 0\n", + " \\\\\n", + " - \\sigma_{j,m}\\left(\n", + " \\sigma_{i,m}^{2} \n", + " + \n", + " \\mu_{i,m}^{2}\n", + " \\right)\n", + " + \n", + " \\sigma_{j,m}\\left(\n", + " \\mu^{'} \\mu_{i,m}\n", + " \\right) \n", + " + \n", + " \\sigma^{'}\\left(\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", + " +\n", + " \\mu_{i,m}\\mu_{j,m}\n", + " -\n", + " \\mu_{j,m+k} \\mu_{i,m}}\n", + " \\right)\n", + " ={}& 0\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "1d37830b", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) = 0 \\quad (10)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9a99369", + "metadata": {}, + "source": [ + "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." + ] + }, + { + "cell_type": "markdown", + "id": "6adaea06", + "metadata": {}, + "source": [ + "**Now, it is time to solve equations (8) and (10), provided below:**" + ] + }, + { + "cell_type": "markdown", + "id": "7878d61c", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "\\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m} \n", + " ={}& 0 \\quad(8)\n", + " \\\\\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) \n", + " ={}& 0 \\quad(10)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "ff88e0e5", + "metadata": {}, + "source": [ + "Note that in the system of equations above, the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$, and the remaining ones are known." + ] + }, + { + "cell_type": "markdown", + "id": "5fd91d3d", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "-\\mu_{i,m}\\left[\n", + " \\sigma_{j,m} \\mu^{'} \n", + " + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)\\sigma^{'} \n", + " - \n", + " \\sigma_{j,m}\\mu_{i,m} \n", + " \\right]\n", + " ={}& 0 \\quad(8')\n", + " \\\\\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + (\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{i,m}\\mu_{j,m+k})\\sigma^{'} - \\sigma_{j,m}(\\mu_{i,m}^{2} + \\sigma_{i,m}^{2}) \n", + " ={}& 0 \\quad(10)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "5b103a95", + "metadata": {}, + "source": [ + "$(8')+(10)$ gives:" + ] + }, + { + "cell_type": "markdown", + "id": "99e96a66", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "-\\mu_{i,m}\\sigma_{j,m} \\mu^{'} - \n", + " \\mu_{i,m}\\mu_{j,m}\\sigma^{'} + \\mu_{i,m}\\mu_{j,m+k}\\sigma^{'} \n", + " + \\sigma_{j,m}\\mu_{i,m}^{2} +\n", + " \\mu_{i,m}\\sigma_{j,m}\\mu^{'} + \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}\\sigma^{'} + \\mu_{i,m}\\mu_{j,m}\\sigma^{'} - \\mu_{i,m}\\mu_{j,m+k}\\sigma^{'} - \\sigma_{j,m}\\mu_{i,m}^{2} - \\sigma_{j,m}\\sigma_{i,m}^{2}\n", + " ={}& 0\n", + " \\\\\n", + " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}\\sigma^{'} - \\sigma_{j,m}\\sigma_{i,m}^{2} \n", + " ={}& 0\n", + " \\\\\n", + " \\sigma_{i,m}\\sigma_{j,m}\n", + " \\left(\n", + " \\rho_{ij}\\sigma^{'} - \\sigma_{i,m}\n", + " \\right)\n", + " ={}&\n", + " 0\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "bb19c9d4", + "metadata": {}, + "source": [ + "Hence:" + ] + }, + { + "cell_type": "markdown", + "id": "3675507e", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sigma^{'} = \\frac{\\sigma_{i,m}}{\\rho_{ij}} \\quad (11)\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "11ce066e", + "metadata": {}, + "source": [ + "Note that we assumed $\\sigma_{i,m}$ and $\\sigma_{j,m}$ cannot be zero. Also, since standard deviations are positive, eq(11) is valid only if $\\rho_{ij} \\gt 0$." + ] + }, + { + "cell_type": "markdown", + "id": "21f83530", + "metadata": {}, + "source": [ + "And, subsituting eq(11) in eq(8):" + ] + }, + { + "cell_type": "markdown", + "id": "631d7d57", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "\\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}}) - \\sigma_{j,m}\\mu_{i,m} \n", + " ={}& 0 \n", + " \\\\\n", + " \\frac{1}{\\sigma_{j,m}}\\left[\n", + " \\sigma_{j,m} \\mu^{'} + \n", + " \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}}) - \\sigma_{j,m}\\mu_{i,m} \n", + " \\right]\n", + " ={}& 0 \n", + " \\\\\n", + " \\mu^{'} + \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right)(\\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}) - \\mu_{i,m} \n", + " ={}& 0 \n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "c6c67de5", + "metadata": {}, + "source": [ + "Hence:" + ] + }, + { + "cell_type": "markdown", + "id": "2aa3b77a", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\mu^{'} = \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}} \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) \\quad(12)\n", + "\\end{align}\n", + "$$\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "cb7cdb37", + "metadata": {}, + "source": [ + "**Therefore, the critical point of function $f(\\mu^{'},\\sigma^{'})$ is:**" + ] + }, + { + "cell_type": "markdown", + "id": "e87fc766", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\sigma^{'} ={}& \n", + " \\frac{\\sigma_{i,m}}{\\rho_{ij}} \\quad (11)\n", + " \\\\\n", + " \\mu^{'} ={}& \n", + " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}} \\left(\\mu_{j,m} - \\mu_{j,m+k}\\right) \\quad(12)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "b266cfb2", + "metadata": {}, + "source": [ + "**NOTE:** It is important to note that eq(11) and eq(12) are favorable to us as they give the $\\mu^{'}$ and $\\sigma^{'}$ of the critical point of `f` as a function of known parameters $\\mu_{i,m}$, $\\sigma_{i,m}$, $\\mu_{j,m}$, $\\sigma_{j,m}$, $\\rho_{ij}$, and $\\mu_{j,m+k}$. Therefore, we can calculate the lower-bound LB as a function of the aforementioned parameters. \n", + "\n", + "**NOTE:** It is worthwhile to reiterate the fact that the solution is valid when $\\rho_{ij} \\gt 0$. (We will discuss $\\rho_{ij} \\leq 0$ later...)" + ] + }, + { + "cell_type": "markdown", + "id": "a0e36dfc", + "metadata": {}, + "source": [ + "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of the crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. \n", + "\n", + "**NOTE:** we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" + ] + }, + { + "cell_type": "markdown", + "id": "92abd2a2", + "metadata": {}, + "source": [ + "**Start with equation (3):**" + ] + }, + { + "cell_type": "markdown", + "id": "b51d32b2", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f(\\mu^{'},\\sigma^{'}) ={}&\n", + " \\sum \\limits_{t=1}^{m}\\alpha_{t}^{2}\n", + " \\\\\n", + " ={}&\n", + " \\sum \\limits_{t=1}^{m}\\alpha_{t} \\cdot \\alpha_{t}\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "7afe0a3d", + "metadata": {}, + "source": [ + "And, we replace one of $\\alpha_{t}$ with its equivalent term provided in eq(4)..." + ] + }, + { + "cell_type": "markdown", + "id": "bfb10bce", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}&\n", + " \\sum\\limits_{t=1}^{m}{\n", + " {\\alpha_{t}\n", + " \\left(\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " }}\n", + " \\\\\n", + " ={}&\n", + " {\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]\\alpha_{t} \n", + " - \n", + " \\sum\\limits_{t=1}^{m}\n", + " \\mu^{'}\\alpha_{t}\n", + " \\right)\n", + " - \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[j+t-1]\\alpha_{t} \n", + " - \n", + " \\sum\\limits_{t=1}^{m}\n", + " \\mu_{j,m+k}\\alpha_{t}\n", + " \\right)\n", + " } \n", + " \\\\ \n", + " ={}&\n", + " {\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]\\alpha_{t} \n", + " - \n", + " \\mu^{'}\\sum\\limits_{t=1}^{m}\\alpha_{t}\n", + " \\right)\n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} \n", + " - \n", + " \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}\\alpha_{t}\n", + " \\right)\n", + " } \n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "4a9e3f03", + "metadata": {}, + "source": [ + "And, now with help of eq(7), $\\sum\\limits_{t=1}^{m}{\\alpha_{t}}=0$, and the eq(9), $\\sum\\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}}=0$, we will have:" + ] + }, + { + "cell_type": "markdown", + "id": "650cae87", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}&\n", + " {\n", + " \\frac{1}{\\sigma^{'}}\n", + " \\left(\n", + " 0 - \\mu^{'} \\cdot 0\n", + " \\right) \n", + " - \n", + " \\frac{1}{\\sigma_{j,m}}\n", + " \\left(\n", + " \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}\\cdot 0\n", + " \\right)\n", + " } \n", + " \\\\ \n", + " ={}&\n", + " {\n", + " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t}\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {\n", + " - \\frac{1}{\\sigma_{j,m}} \n", + " \\sum\\limits_{t=1}^{m}{\\left[\n", + " T[j+t-1]\\left(\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\right)\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {\n", + " - \\frac{1}{\\sigma_{j,m}} \n", + " \\sum\\limits_{t=1}^{m}{\n", + " \\left(\n", + " \\frac{T[i+t-1]T[j+t-1] - \\mu^{'}T[j+t-1]}{\\sigma^{'}} - \\frac{T[j+t-1]T[j+t-1] - \\mu_{j,m+k}T[j+t-1]}{\\sigma_{j,m}}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{1}{\\sigma_{j,m}} \n", + " {\n", + " \\left(\n", + " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\mu^{'}\\sum\\limits_{t=1}^{m}T[j+t-1]}{\\sigma^{'}} \n", + " - \n", + " \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]T[j+t-1] - \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}T[j+t-1]}{\\sigma_{j,m}}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "9f2ca2da", + "metadata": {}, + "source": [ + "And, now with help of the fact that $\\sum{T} = m\\mu$ and also the Pearon Correlation equation (\\*\\*)..." + ] + }, + { + "cell_type": "markdown", + "id": "35db152a", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", + " {- \\frac{1}{\\sigma_{j,m}} \n", + " {\n", + " \\left(\n", + " \\frac{(m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) - \\mu^{'} \\cdot m\\mu_{j,m}}{\\sigma^{'}} \n", + " - \n", + " \\frac{(m\\rho_{jj}\\sigma_{j,m}^{2} + m\\mu_{j,m}^{2}) - \\mu_{j,m+k} \\cdot m\\mu_{j,m}}{\\sigma_{j,m}}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{1}{\\sigma_{j,m}} \n", + " {\n", + " \\left[\n", + " \\frac{\n", + " m\\left(\n", + " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu^{'} \\cdot \\mu_{j,m}\n", + " \\right)\n", + " }{\n", + " \\sigma^{'}\n", + " } \n", + " - \n", + " \\frac{\n", + " m\\left(\n", + " 1\\cdot\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m+k} \\cdot \\mu_{j,m}\n", + " \\right)\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma^{'}} \n", + " {\n", + " \\left(\n", + " {\\sigma_{j,m}(\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m} - \\mu_{j,m}\\mu^{'})} \n", + " - \n", + " {\\sigma^{'}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma^{'}} \n", + " {\n", + " \\left(\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} + \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \\mu_{j,m}\\sigma_{j,m}\\mu^{'}} \n", + " - \n", + " {\\sigma^{'}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "cfd5a617", + "metadata": {}, + "source": [ + "And, now we are at a good position to plug in the values $\\mu^{'}$(11) and $\\sigma^{'}$(12):" + ] + }, + { + "cell_type": "markdown", + "id": "f3e25620", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f_{min}(\\mu^{'},\\sigma^{'}) ={}& \n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\n", + " (\\frac{\\sigma_{i,m}}{\\rho_{ij}})\n", + " } \n", + " {\n", + " \\left[\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} + \n", + " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} - \n", + " \\mu_{j,m}\\sigma_{j,m}\\left({\n", + " \\mu_{i,m} - \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " }\n", + " \\right)} \n", + " - \n", + " {(\\frac{\\sigma_{i,m}}{\\rho_{ij}})(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m\\rho_{ij}}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {\n", + " \\left[\n", + " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " + \n", + " \\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", + " - \n", + " {\n", + " \\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", + " + \n", + " \\frac{\\sigma_{i,m}}{\\rho_{ij}\\sigma_{j,m}}{\\mu_{j,m}\\sigma_{j,m}}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " }\n", + " } \n", + " - \n", + " {\\frac{\\sigma_{i,m}}{\\rho_{ij}}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {\n", + " \\left[\n", + " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " + \n", + " \\rho_{ij}\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", + " - \n", + " {\n", + " \\rho_{ij}\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", + " + \n", + " \\mu_{j,m}\\sigma_{i,m}(\\mu_{j,m}-\\mu_{j,m+k})\n", + " }\n", + " } \n", + " - \n", + " {\\sigma_{i,m}(\\sigma_{j,m}^{2} + \\mu_{j,m}^{2} - \\mu_{j,m}\\mu_{j,m+k})}\n", + " \\right]\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}} \n", + " {\n", + " \\left(\n", + " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " + \n", + " \\rho_{ij}\\mu_{i,m}\\mu_{j,m}\\sigma_{j,m} \n", + " - \n", + " {\n", + " \\rho_{ij}\\mu_{j,m}\\sigma_{j,m}\\mu_{i,m} \n", + " + \n", + " \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m} - \\mu_{j,m}\\sigma_{i,m}\\mu_{j,m+k}\n", + " }\n", + " }\n", + " - \n", + " {\\sigma_{i,m}\\sigma_{j,m}^{2} - \\sigma_{i,m}\\mu_{j,m}^{2} + \\sigma_{i,m}\\mu_{j,m}\\mu_{j,m+k}}\n", + " \\right)\n", + " }\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", + " \\left( \n", + " {\\rho_{ij}^{2}\\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " - \n", + " \\sigma_{i,m}\\sigma_{j,m}^{2} \n", + " }\n", + " \\right)\n", + " } \n", + " \\\\\n", + " ={}&\n", + " {- \\frac{m}{\\sigma_{j,m}^{2}\\sigma_{i,m}}\n", + " (\\rho_{ij}^{2} - 1)\n", + " \\sigma_{i,m}\\sigma_{j,m}^{2}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " m(1-\\rho_{ij}^{2})\n", + "\\end{align} \n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "64dc1027", + "metadata": {}, + "source": [ + "**Finally, with eq(2), the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" + ] + }, + { + "cell_type": "markdown", + "id": "98db40a5", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " LB ={}& \n", + " \\frac{\n", + " \\sigma_{j,m}\n", + " }{\\sigma_{j,m+k}\n", + " } \\sqrt{m (1 - \\rho_{ij}^{2})} \\quad \\text{if} \\, \\rho > 0\n", + " \\\\\n", + "\\end{align}\n", + "$$\n", + "\n", + "$$\n", + "\\begin{align}\n", + " \\rho_{ij} ={}& \n", + " \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "8cbad624", + "metadata": {}, + "source": [ + "**Note:**
\n", + "* Note that eq(12) is valid only for $\\rho_{ij} > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho_{ij} > 0$. \n", + "* The pearson correlation, $\\rho_{ij}$, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", + "\n", + "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho_{ij})}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", + "\n", + "**Pending...**
\n", + "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function.\n", + "\n", + "* **For $\\rho \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$.**" + ] + }, + { + "cell_type": "markdown", + "id": "e0dcda60", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b4ef7db6b0218cdc58214a54e97cc43c8467ecd2 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 03:32:46 -0600 Subject: [PATCH 28/64] ADD calculation for LowerBound when corr is negative --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 690 +++++++++++++++++- 1 file changed, 668 insertions(+), 22 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 09d5202ca..6d202afac 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3b23610b", + "id": "78a48e7c", "metadata": {}, "source": [ "In this notebook, we would like to derive the eq(2) of the paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf)." @@ -13,7 +13,8 @@ "id": "5f999789", "metadata": {}, "source": [ - "The idea goes as follows: \"given the distance profile of $T_{j,m}$, how can we find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`?" + "**The idea goes as follows:**
\n", + "\"Given the distance profile of $T_{j,m}$, how can we find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`?" ] }, { @@ -246,7 +247,7 @@ }, { "cell_type": "markdown", - "id": "8a778de1", + "id": "3cbc6e7f", "metadata": {}, "source": [ "\n", @@ -263,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "18656992", + "id": "7f3eea94", "metadata": {}, "source": [ "Therefore, we have:" @@ -506,7 +507,7 @@ }, { "cell_type": "markdown", - "id": "c6084fa1", + "id": "71ea26a2", "metadata": {}, "source": [ "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." @@ -720,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "4c6d53dd", + "id": "ad80f924", "metadata": {}, "source": [ "\n", @@ -841,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "f9a99369", + "id": "4ebe4a56", "metadata": {}, "source": [ "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." @@ -857,7 +858,7 @@ }, { "cell_type": "markdown", - "id": "7878d61c", + "id": "715c3679", "metadata": {}, "source": [ "\n", @@ -876,7 +877,7 @@ }, { "cell_type": "markdown", - "id": "ff88e0e5", + "id": "00d6276a", "metadata": {}, "source": [ "Note that in the system of equations above, the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$, and the remaining ones are known." @@ -884,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "5fd91d3d", + "id": "2561aacf", "metadata": {}, "source": [ "\n", @@ -908,7 +909,7 @@ }, { "cell_type": "markdown", - "id": "5b103a95", + "id": "3341901c", "metadata": {}, "source": [ "$(8')+(10)$ gives:" @@ -916,7 +917,7 @@ }, { "cell_type": "markdown", - "id": "99e96a66", + "id": "f54ab003", "metadata": {}, "source": [ "\n", @@ -944,7 +945,7 @@ }, { "cell_type": "markdown", - "id": "bb19c9d4", + "id": "0b72b310", "metadata": {}, "source": [ "Hence:" @@ -952,7 +953,7 @@ }, { "cell_type": "markdown", - "id": "3675507e", + "id": "626898d8", "metadata": {}, "source": [ "\n", @@ -965,7 +966,7 @@ }, { "cell_type": "markdown", - "id": "11ce066e", + "id": "54616fb6", "metadata": {}, "source": [ "Note that we assumed $\\sigma_{i,m}$ and $\\sigma_{j,m}$ cannot be zero. Also, since standard deviations are positive, eq(11) is valid only if $\\rho_{ij} \\gt 0$." @@ -973,7 +974,7 @@ }, { "cell_type": "markdown", - "id": "21f83530", + "id": "89cd4a74", "metadata": {}, "source": [ "And, subsituting eq(11) in eq(8):" @@ -1005,7 +1006,7 @@ }, { "cell_type": "markdown", - "id": "c6c67de5", + "id": "46693f92", "metadata": {}, "source": [ "Hence:" @@ -1013,7 +1014,7 @@ }, { "cell_type": "markdown", - "id": "2aa3b77a", + "id": "693ab21d", "metadata": {}, "source": [ "\n", @@ -1027,7 +1028,7 @@ }, { "cell_type": "markdown", - "id": "cb7cdb37", + "id": "707daf5b", "metadata": {}, "source": [ "**Therefore, the critical point of function $f(\\mu^{'},\\sigma^{'})$ is:**" @@ -1035,7 +1036,7 @@ }, { "cell_type": "markdown", - "id": "e87fc766", + "id": "9532e72b", "metadata": {}, "source": [ "\n", @@ -1066,7 +1067,7 @@ "id": "a0e36dfc", "metadata": {}, "source": [ - "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of the crtical point, we need to plug them in $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. \n", + "Now that we calculated the values $\\mu^{'}$ and $\\sigma^{'}$ of the crtical point, we need to plug them in the function $f(.)$ to find the extremum value. However, using these values directly in function $f(.)$ might make the calculation difficult. Therefore, we prefer to use higher-level equations (7) and (9) to first simplify $f_{min}(.)$. \n", "\n", "**NOTE:** we have been solving the single system of equations (5) and (6). Therefore, the calculated values $\\mu^{'}$(11) and $\\sigma^{'}$(12) should satisfy all equations (5), (6), (7), (8), (9), and (10) discovered throughout the solution.
" ] @@ -1478,8 +1479,653 @@ }, { "cell_type": "markdown", - "id": "e0dcda60", + "id": "d34924d4", "metadata": {}, + "source": [ + "So far, we derived the first sub-function (i.e. LB for $\\rho_{ij} \\gt 0$) of the piecewise function provided in the eq(2) of the paper VALMOD.
\n", + "Now, we would like to derive the second sub-function, where LB is defined for $\\rho_{ij} \\leq 0$." + ] + }, + { + "cell_type": "markdown", + "id": "27d5a98b", + "metadata": {}, + "source": [ + "Let us first visit the equation stated by the authors again:" + ] + }, + { + "cell_type": "markdown", + "id": "a3bdfcbb", + "metadata": {}, + "source": [ + "$LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$, if $\\rho_{ij} \\leq 0$" + ] + }, + { + "cell_type": "markdown", + "id": "bded35a6", + "metadata": {}, + "source": [ + "Comparing the equation above with eq(2) of notebook, i.e. $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, shows that we need to prove:" + ] + }, + { + "cell_type": "markdown", + "id": "392ab830", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "f(\\mu^{'}, \\sigma^{'}) \\geq{}& \n", + "m\n", + "\\\\\n", + "\\frac{\n", + "f(\\mu^{'}, \\sigma^{'})\n", + "}{\n", + "m} \\geq{}& 1\n", + "\\\\\n", + "\\frac{\n", + "f(\\mu^{'}, \\sigma^{'})\n", + "}{\n", + "m}\n", + "-\n", + "1 \\geq{}& 0 \\quad (13)\n", + "\\end{align} \n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2abe5321", + "metadata": {}, + "source": [ + "Therefore, we need to show (13) is correct when $\\rho_{ij} \\leq 0$.\n", + "\n", + "$F \\triangleq \\frac{f(\\mu^{'}, \\sigma^{'})}{m} - 1$ (14)" + ] + }, + { + "cell_type": "markdown", + "id": "debdb6a8", + "metadata": {}, + "source": [ + "We start with eq(3), $f(\\mu^{'}, \\sigma^{'}) = \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}}$, and we replace $\\alpha_{t}$ with its equivalent term, see eq(4). Therefore:" + ] + }, + { + "cell_type": "markdown", + "id": "5139c4a9", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "f(\\mu^{'},\\sigma^{'}) ={}& \n", + " \\sum \\limits_{t=1}^{m}\n", + " \\left(\n", + " \\frac{\n", + " T[i+t-1] - \\mu^{'}\n", + " }{\n", + " \\sigma^{'}\n", + " } \n", + " - \n", + " \\frac{\n", + " T[j+t-1] - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\n", + " \\right)^{2}\n", + " \\quad (15)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "18136d35", + "metadata": {}, + "source": [ + "Inside the summation, we use the formula: $(A+B)^{2} = A^{2} + B^{2} - 2AB$" + ] + }, + { + "cell_type": "markdown", + "id": "672654f3", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "f(\\mu^{'},\\sigma^{'}) ={}& \n", + " \\sum \\limits_{t=1}^{m}\n", + " \\left[\n", + " \\left(\n", + " \\frac{\n", + " T[i+t-1] - \\mu^{'}\n", + " }{\n", + " \\sigma^{'}\n", + " }\\right)^{2}\n", + " +\n", + " \\left(\n", + " \\frac{\n", + " T[j+t-1] - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\n", + " \\right)^{2}\n", + " -\n", + " 2\n", + " \\left(\\frac{\n", + " T[i+t-1] - \\mu^{'}\n", + " }{\n", + " \\sigma^{'}\n", + " }\\right)\n", + " \\left(\\frac{\n", + " T[j+t-1] - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\n", + " \\right)\n", + " \\right]\n", + " \\\\\n", + " \\\\\n", + " ={}&\n", + " \\sum \\limits_{t=1}^{m}\n", + " \\left[\n", + " \\left(\n", + " \\frac{\n", + " T[i+t-1]^{2} + \\mu^{'2} - 2T[i+t-1]\\mu^{'}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\\right)\n", + " +\n", + " \\left(\n", + " \\frac{\n", + " T[j+t-1]^{2} + \\mu_{j,m+k}^{2} - 2 T[j+t-1]\\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " \\right)\n", + " -\n", + " 2\n", + " \\left(\\frac{\n", + " T[i+t-1]T[j+t-1] \n", + " - T[i+t-1]\\mu_{j,m+k}\n", + " - T[j+t-1]\\mu^{'}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\right)\n", + " \\right]\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "5c6c90c2", + "metadata": {}, + "source": [ + "Now, we distribute summation into all terms..." + ] + }, + { + "cell_type": "markdown", + "id": "b68a1419", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "f(\\mu^{'},\\sigma^{'}) ={}& \n", + " \\frac{\n", + " \\sum \\limits_{t=1}^{m}T[i+t-1]^{2} + \\sum \\limits_{t=1}^{m}\\mu^{'2} - 2\\mu^{'}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " \\sum \\limits_{t=1}^{m}T[j+t-1]^{2} + \\sum \\limits_{t=1}^{m}\\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[j+t-1]\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\n", + " \\frac{\n", + " \\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] \n", + " - \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", + " - \\mu^{'}\\sum \\limits_{t=1}^{m}T[j+t-1]\n", + " + \\sum \\limits_{t=1}^{m}\\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9b131ae", + "metadata": {}, + "source": [ + "With help of Pearson Correlation equation (\\*\\*), we have:" + ] + }, + { + "cell_type": "markdown", + "id": "7cf643c5", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "f(\\mu^{'},\\sigma^{'}) ={}& \n", + " \\frac{\n", + " (m\\rho_{ii}\\sigma_{i,m}^{2} + m\\mu_{i,m}^{2}) + m\\mu^{'2} - 2\\mu^{'}\\cdot m\\mu_{i,m}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " (m\\rho_{jj}\\sigma_{j,m}^{2} + m\\mu_{j,m}^{2}) + m\\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\cdot m\\mu_{j,m}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\n", + " \\frac{\n", + " (m\\rho_{ij}\\sigma_{i}\\sigma_{j} + m\\mu_{i}\\mu_{j}) \n", + " - \\mu_{j,m+k}\\cdot m\\mu_{i,m}\n", + " - \\mu^{'} \\cdot m\\mu_{j,m}\n", + " + m\\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "2190c5b2", + "metadata": {}, + "source": [ + "Recall that $\\rho_{ii}=1$ and $\\rho_{jj}=1$. After subsituting them in the formula above, and factoring out m, we can write it down as:" + ] + }, + { + "cell_type": "markdown", + "id": "2975a04b", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "\\frac{f(\\mu^{'},\\sigma^{'})}{m} ={}& \n", + " \\frac{\n", + " \\sigma_{i,m}^{2} + \\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " \\sigma_{j,m}^{2} + \\mu_{j,m}^{2} + \\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\mu_{j,m}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\n", + " \\frac{\n", + " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m}\n", + " - \\mu_{j,m+k}\\mu_{i,m}\n", + " - \\mu^{'} \\mu_{j,m}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\\\\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\sigma_{i,m}^{2} + \\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\left(1\n", + " +\n", + " \\frac{\n", + " \\mu_{j,m}^{2} + \\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\mu_{j,m}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " \\right)\n", + " -\n", + " 2\n", + " \\frac{\n", + " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m}\n", + " - \\mu_{j,m+k}\\mu_{i,m}\n", + " - \\mu^{'} \\mu_{j,m}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "aa314371", + "metadata": {}, + "source": [ + "Therefore, we can now see:" + ] + }, + { + "cell_type": "markdown", + "id": "9812ba37", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 ={}& \n", + " \\frac{\n", + " \\sigma_{i,m}^{2} + \\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " \\mu_{j,m}^{2} + \\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\mu_{j,m}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\n", + " \\frac{\n", + " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m}\n", + " - \\mu_{j,m+k}\\mu_{i,m}\n", + " - \\mu^{'} \\mu_{j,m}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "223936b7", + "metadata": {}, + "source": [ + "Recall eq(13), $i.e. F=\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 \\geq 0$, is equivalent to what claimed in the paper for $\\rho_{ij} \\leq 0$. So, we just need to prove that the right hand side, F, is always positive. " + ] + }, + { + "cell_type": "markdown", + "id": "bec5c521", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " F ={}& \n", + " \\frac{\n", + " \\sigma_{i,m}^{2} + \\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " \\mu_{j,m}^{2} + \\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\mu_{j,m}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\n", + " \\frac{\n", + " \\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + \\mu_{i,m}\\mu_{j,m}\n", + " - \\mu_{j,m+k}\\mu_{i,m}\n", + " - \\mu^{'} \\mu_{j,m}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\\\\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\sigma_{i,m}^{2} + (\\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m})\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " (\\mu_{j,m}^{2} + \\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\mu_{j,m})\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\n", + " \\left(\n", + " \\frac{\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " +\n", + " \\frac{\\mu_{i,m}\\mu_{j,m}\n", + " - \\mu_{j,m+k}\\mu_{i,m}\n", + " - \\mu^{'} \\mu_{j,m}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\right)\n", + " \\\\\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\sigma_{i,m}^{2} + (\\mu_{i,m}-\\mu^{'})^{2}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " (\\mu_{j,m} - \\mu_{j,m+k})^{2}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\\frac{\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " +\n", + " 2\\frac{\\mu_{i,m}\\mu_{j,m}\n", + " - \\mu_{j,m+k}\\mu_{i,m}\n", + " - \\mu^{'} \\mu_{j,m}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\\\\n", + " \\geq{}&\n", + " \\frac{\\mu_{i,m}\\mu_{j,m}\n", + " - \\mu_{j,m+k}\\mu_{i,m}\n", + " - \\mu^{'} \\mu_{j,m}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " } \\quad (16)\n", + " \\\\ \n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "0074dd7e", + "metadata": {}, + "source": [ + "Note that the first two terms are non-negative (i.e. $\\geq 0$).The third term is also non-negative because $\\rho_{ij}\\leq 0$. Therefore, we just need to prove the last term is non-negative. Hence, we need to show:" + ] + }, + { + "cell_type": "markdown", + "id": "fdbf21bd", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "G \\triangleq{}& \\frac{\\mu_{i,m}\\mu_{j,m}\n", + " - \\mu_{j,m+k}\\mu_{i,m}\n", + " - \\mu^{'} \\mu_{j,m}\n", + " + \\mu^{'}\\mu_{j,m+k}\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\geq 0\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a8220bd", + "metadata": {}, + "source": [ + "Let us factorize G as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "9e49a113", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "G = {}& \\frac{\\mu_{i,m}(\\mu_{j,m}\n", + " - \\mu_{j,m+k})\n", + " - \\mu^{'} (\\mu_{j,m}-\\mu_{j,m+k})\n", + " }{\n", + " \\sigma^{'}\\sigma_{j,m}\n", + " }\n", + " \\\\\n", + " = {}&\n", + " \\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}-\\mu_{j,m+k})}\n", + " {\\sigma^{'}\\sigma_{j,m}} \\quad (17)\n", + " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "4cef4acd", + "metadata": {}, + "source": [ + "**NOTE:**
\n", + "We **cannot** prove eq(17) to be always positive! In fact, it is possible that eq(17) to be negative! For instance, consider a case where subsequence $T_{j,m+k}$ has lower mean compared to $T_{j,m}$. But, its neighbor, at index `i`, has higher mean in $T_{i,m+k}$ compared to $T_{i,m}$. We will investigate this matter shortly with help of `np.random.uniform` time series data." + ] + }, + { + "cell_type": "markdown", + "id": "952091ef", + "metadata": {}, + "source": [ + "**NOTE:**
\n", + "Let us take a better look at eq(2) of paper VALMOD. It seems there has been a typo and the authors replace $\\mu_{j,m+k}$ with $\\mu_{j,m}$. (In the paper, the authors used $l$, instead of $m$ as the subsequence length.). Having considered that typo, we can see the the term $\\mu_{j,m}-\\mu_{j,m+k}$ in the numerator of eq(15) becomes 0, and thus we can get to the equation provided in the paper." + ] + }, + { + "cell_type": "markdown", + "id": "096b25c4", + "metadata": {}, + "source": [ + "**The correct formula can be achieved as follows:**
\n", + "Based on eq(14),(16), and eq(17):" + ] + }, + { + "cell_type": "markdown", + "id": "7915ff7e", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "\\frac{f(\\mu^{'}, \\sigma^{'})}{m} - 1 \\geq{}&\n", + "\\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}-\\mu_{j,m+k})}\n", + " {\\sigma^{'}\\sigma_{j,m}}\n", + " \\\\\n", + " f(\\mu^{'}, \\sigma^{'}) \\geq{}&\n", + " m \\left[\n", + " \\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}-\\mu_{j,m+k})}\n", + " {\\sigma^{'}\\sigma_{j,m}} \n", + " \\right]\n", + " + 1\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "74bbbd59", + "metadata": {}, + "source": [ + "And, with help of eq(2), i.e. $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, we can claim:" + ] + }, + { + "cell_type": "markdown", + "id": "88052b40", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{align}\n", + " LB^{*} ={}&\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + " \\sqrt{\n", + " m \\left[\n", + " \\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}-\\mu_{j,m+k})}\n", + " {\\sigma^{'}\\sigma_{j,m}} \n", + " \\right]\n", + " + 1\n", + " } \\quad (\\rho_{ij} \\leq 0)\n", + " \\\\\n", + "\\end{align}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "02822e09", + "metadata": {}, + "source": [ + "We used superscript * to distinguish the LB proposed by the paper and the LB achieved by the calculaton of this notebook." + ] + }, + { + "cell_type": "markdown", + "id": "88edf049", + "metadata": {}, + "source": [ + "## Validating LB with a test case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58062d43", + "metadata": {}, + "outputs": [], "source": [] } ], From 9629b8ec786301292a2989bf5088ae4cbc0c144b Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 05:21:14 -0600 Subject: [PATCH 29/64] FIXed Calculation for deriving eq(2) when q is neg --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 316 +++++++++--------- 1 file changed, 156 insertions(+), 160 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 6d202afac..7d025a180 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "78a48e7c", + "id": "d8ebe111", "metadata": {}, "source": [ "In this notebook, we would like to derive the eq(2) of the paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf)." @@ -247,7 +247,7 @@ }, { "cell_type": "markdown", - "id": "3cbc6e7f", + "id": "2ade7583", "metadata": {}, "source": [ "\n", @@ -264,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "7f3eea94", + "id": "5fe5c9e3", "metadata": {}, "source": [ "Therefore, we have:" @@ -507,7 +507,7 @@ }, { "cell_type": "markdown", - "id": "71ea26a2", + "id": "1340817b", "metadata": {}, "source": [ "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "ad80f924", + "id": "182b8064", "metadata": {}, "source": [ "\n", @@ -842,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "4ebe4a56", + "id": "978473a2", "metadata": {}, "source": [ "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." @@ -858,7 +858,7 @@ }, { "cell_type": "markdown", - "id": "715c3679", + "id": "6ac05b5f", "metadata": {}, "source": [ "\n", @@ -877,7 +877,7 @@ }, { "cell_type": "markdown", - "id": "00d6276a", + "id": "b2322ecc", "metadata": {}, "source": [ "Note that in the system of equations above, the unknown variables are $\\mu^{'}$ and $\\sigma^{'}$, and the remaining ones are known." @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "2561aacf", + "id": "e40d711e", "metadata": {}, "source": [ "\n", @@ -909,7 +909,7 @@ }, { "cell_type": "markdown", - "id": "3341901c", + "id": "4dfc6b45", "metadata": {}, "source": [ "$(8')+(10)$ gives:" @@ -917,7 +917,7 @@ }, { "cell_type": "markdown", - "id": "f54ab003", + "id": "c798dc6b", "metadata": {}, "source": [ "\n", @@ -945,7 +945,7 @@ }, { "cell_type": "markdown", - "id": "0b72b310", + "id": "3627a49a", "metadata": {}, "source": [ "Hence:" @@ -953,7 +953,7 @@ }, { "cell_type": "markdown", - "id": "626898d8", + "id": "de0702cf", "metadata": {}, "source": [ "\n", @@ -966,7 +966,7 @@ }, { "cell_type": "markdown", - "id": "54616fb6", + "id": "ed3f7802", "metadata": {}, "source": [ "Note that we assumed $\\sigma_{i,m}$ and $\\sigma_{j,m}$ cannot be zero. Also, since standard deviations are positive, eq(11) is valid only if $\\rho_{ij} \\gt 0$." @@ -974,7 +974,7 @@ }, { "cell_type": "markdown", - "id": "89cd4a74", + "id": "91752bef", "metadata": {}, "source": [ "And, subsituting eq(11) in eq(8):" @@ -1006,7 +1006,7 @@ }, { "cell_type": "markdown", - "id": "46693f92", + "id": "335173da", "metadata": {}, "source": [ "Hence:" @@ -1014,7 +1014,7 @@ }, { "cell_type": "markdown", - "id": "693ab21d", + "id": "8efc2627", "metadata": {}, "source": [ "\n", @@ -1028,7 +1028,7 @@ }, { "cell_type": "markdown", - "id": "707daf5b", + "id": "4278ff7e", "metadata": {}, "source": [ "**Therefore, the critical point of function $f(\\mu^{'},\\sigma^{'})$ is:**" @@ -1036,7 +1036,7 @@ }, { "cell_type": "markdown", - "id": "9532e72b", + "id": "e0104b24", "metadata": {}, "source": [ "\n", @@ -1479,7 +1479,7 @@ }, { "cell_type": "markdown", - "id": "d34924d4", + "id": "fc19b2dd", "metadata": {}, "source": [ "So far, we derived the first sub-function (i.e. LB for $\\rho_{ij} \\gt 0$) of the piecewise function provided in the eq(2) of the paper VALMOD.
\n", @@ -1488,7 +1488,7 @@ }, { "cell_type": "markdown", - "id": "27d5a98b", + "id": "7e523470", "metadata": {}, "source": [ "Let us first visit the equation stated by the authors again:" @@ -1496,7 +1496,7 @@ }, { "cell_type": "markdown", - "id": "a3bdfcbb", + "id": "326d2300", "metadata": {}, "source": [ "$LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$, if $\\rho_{ij} \\leq 0$" @@ -1504,7 +1504,7 @@ }, { "cell_type": "markdown", - "id": "bded35a6", + "id": "86dd8eb5", "metadata": {}, "source": [ "Comparing the equation above with eq(2) of notebook, i.e. $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, shows that we need to prove:" @@ -1512,7 +1512,7 @@ }, { "cell_type": "markdown", - "id": "392ab830", + "id": "8670ed3e", "metadata": {}, "source": [ "\n", @@ -1538,7 +1538,7 @@ }, { "cell_type": "markdown", - "id": "2abe5321", + "id": "b6b9eff9", "metadata": {}, "source": [ "Therefore, we need to show (13) is correct when $\\rho_{ij} \\leq 0$.\n", @@ -1548,7 +1548,7 @@ }, { "cell_type": "markdown", - "id": "debdb6a8", + "id": "a4f11acc", "metadata": {}, "source": [ "We start with eq(3), $f(\\mu^{'}, \\sigma^{'}) = \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}}$, and we replace $\\alpha_{t}$ with its equivalent term, see eq(4). Therefore:" @@ -1556,7 +1556,7 @@ }, { "cell_type": "markdown", - "id": "5139c4a9", + "id": "1aac6ab8", "metadata": {}, "source": [ "\n", @@ -1585,7 +1585,7 @@ }, { "cell_type": "markdown", - "id": "18136d35", + "id": "bf007040", "metadata": {}, "source": [ "Inside the summation, we use the formula: $(A+B)^{2} = A^{2} + B^{2} - 2AB$" @@ -1593,7 +1593,7 @@ }, { "cell_type": "markdown", - "id": "672654f3", + "id": "f8d24612", "metadata": {}, "source": [ "\n", @@ -1668,7 +1668,7 @@ }, { "cell_type": "markdown", - "id": "5c6c90c2", + "id": "edc051ab", "metadata": {}, "source": [ "Now, we distribute summation into all terms..." @@ -1676,7 +1676,7 @@ }, { "cell_type": "markdown", - "id": "b68a1419", + "id": "9f44f100", "metadata": {}, "source": [ "\n", @@ -1711,7 +1711,7 @@ }, { "cell_type": "markdown", - "id": "f9b131ae", + "id": "c1cbd849", "metadata": {}, "source": [ "With help of Pearson Correlation equation (\\*\\*), we have:" @@ -1719,7 +1719,7 @@ }, { "cell_type": "markdown", - "id": "7cf643c5", + "id": "bb5a2896", "metadata": {}, "source": [ "\n", @@ -1754,7 +1754,7 @@ }, { "cell_type": "markdown", - "id": "2190c5b2", + "id": "f54b458f", "metadata": {}, "source": [ "Recall that $\\rho_{ii}=1$ and $\\rho_{jj}=1$. After subsituting them in the formula above, and factoring out m, we can write it down as:" @@ -1762,7 +1762,7 @@ }, { "cell_type": "markdown", - "id": "2975a04b", + "id": "755955af", "metadata": {}, "source": [ "\n", @@ -1823,7 +1823,7 @@ }, { "cell_type": "markdown", - "id": "aa314371", + "id": "96db6201", "metadata": {}, "source": [ "Therefore, we can now see:" @@ -1831,7 +1831,7 @@ }, { "cell_type": "markdown", - "id": "9812ba37", + "id": "4359532f", "metadata": {}, "source": [ "\n", @@ -1865,7 +1865,7 @@ }, { "cell_type": "markdown", - "id": "223936b7", + "id": "317e1594", "metadata": {}, "source": [ "Recall eq(13), $i.e. F=\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 \\geq 0$, is equivalent to what claimed in the paper for $\\rho_{ij} \\leq 0$. So, we just need to prove that the right hand side, F, is always positive. " @@ -1873,7 +1873,7 @@ }, { "cell_type": "markdown", - "id": "bec5c521", + "id": "d73539ec", "metadata": {}, "source": [ "\n", @@ -1944,186 +1944,182 @@ " }\n", " -\n", " 2\\frac{\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", - " +\n", + " -\n", " 2\\frac{\\mu_{i,m}\\mu_{j,m}\n", " - \\mu_{j,m+k}\\mu_{i,m}\n", " - \\mu^{'} \\mu_{j,m}\n", " + \\mu^{'}\\mu_{j,m+k}\n", " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", - " }\n", + " } \n", " \\\\\n", - " \\geq{}&\n", - " \\frac{\\mu_{i,m}\\mu_{j,m}\n", - " - \\mu_{j,m+k}\\mu_{i,m}\n", - " - \\mu^{'} \\mu_{j,m}\n", - " + \\mu^{'}\\mu_{j,m+k}\n", + " ={}&\n", + " \\frac{\n", + " \\sigma_{i,m}^{2} + (\\mu_{i,m}-\\mu^{'})^{2}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " (\\mu_{j,m} - \\mu_{j,m+k})^{2}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\\frac{\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " -\n", + " 2\\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}\n", + " - \\mu_{j,m+k})\n", " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", " } \\quad (16)\n", - " \\\\ \n", + " \\\\\n", "\\end{align}\n", "$$\n" ] }, { "cell_type": "markdown", - "id": "0074dd7e", + "id": "e0b43f1e", "metadata": {}, "source": [ - "Note that the first two terms are non-negative (i.e. $\\geq 0$).The third term is also non-negative because $\\rho_{ij}\\leq 0$. Therefore, we just need to prove the last term is non-negative. Hence, we need to show:" + "**Now, we define two new intermediate variables as follows:**" ] }, { "cell_type": "markdown", - "id": "fdbf21bd", + "id": "ed9912e4", "metadata": {}, "source": [ "\n", "$$\n", "\\begin{align}\n", - "G \\triangleq{}& \\frac{\\mu_{i,m}\\mu_{j,m}\n", - " - \\mu_{j,m+k}\\mu_{i,m}\n", - " - \\mu^{'} \\mu_{j,m}\n", - " + \\mu^{'}\\mu_{j,m+k}\n", - " }{\n", - " \\sigma^{'}\\sigma_{j,m}\n", - " }\n", - " \\geq 0\n", - " \\\\\n", + " \\beta \\triangleq{}& \\mu_{i,m} - \\mu^{'}\n", + " \\\\\n", + " \\gamma \\triangleq{}& \\mu_{j,m} - \\mu_{j,m+k}\n", + " \\\\\n", "\\end{align}\n", - "$$\n" + "$$" ] }, { "cell_type": "markdown", - "id": "8a8220bd", + "id": "961562f1", "metadata": {}, "source": [ - "Let us factorize G as follows:" + "By subsituting $\\beta$ and $\\gamma$ for their corresponding terms in eq(16), we get:" ] }, { "cell_type": "markdown", - "id": "9e49a113", + "id": "ecd9622b", "metadata": {}, "source": [ "\n", "$$\n", "\\begin{align}\n", - "G = {}& \\frac{\\mu_{i,m}(\\mu_{j,m}\n", - " - \\mu_{j,m+k})\n", - " - \\mu^{'} (\\mu_{j,m}-\\mu_{j,m+k})\n", + " F ={}&\n", + " \\frac{\n", + " \\sigma_{i,m}^{2} + \\beta^{2}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " \\frac{\n", + " \\gamma^{2}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\\frac{\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " -\n", + " 2\\frac{\\beta\\gamma\n", " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", + " } \n", + " \\\\\n", + " ={}&\n", + " \\left(\n", + " \\frac{\n", + " \\sigma_{i,m}^{2}\n", + " }{\n", + " \\sigma^{'2}\n", " }\n", - " \\\\\n", - " = {}&\n", - " \\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}-\\mu_{j,m+k})}\n", - " {\\sigma^{'}\\sigma_{j,m}} \\quad (17)\n", - " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "4cef4acd", - "metadata": {}, - "source": [ - "**NOTE:**
\n", - "We **cannot** prove eq(17) to be always positive! In fact, it is possible that eq(17) to be negative! For instance, consider a case where subsequence $T_{j,m+k}$ has lower mean compared to $T_{j,m}$. But, its neighbor, at index `i`, has higher mean in $T_{i,m+k}$ compared to $T_{i,m}$. We will investigate this matter shortly with help of `np.random.uniform` time series data." - ] - }, - { - "cell_type": "markdown", - "id": "952091ef", - "metadata": {}, - "source": [ - "**NOTE:**
\n", - "Let us take a better look at eq(2) of paper VALMOD. It seems there has been a typo and the authors replace $\\mu_{j,m+k}$ with $\\mu_{j,m}$. (In the paper, the authors used $l$, instead of $m$ as the subsequence length.). Having considered that typo, we can see the the term $\\mu_{j,m}-\\mu_{j,m+k}$ in the numerator of eq(15) becomes 0, and thus we can get to the equation provided in the paper." - ] - }, - { - "cell_type": "markdown", - "id": "096b25c4", - "metadata": {}, - "source": [ - "**The correct formula can be achieved as follows:**
\n", - "Based on eq(14),(16), and eq(17):" - ] - }, - { - "cell_type": "markdown", - "id": "7915ff7e", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - "\\frac{f(\\mu^{'}, \\sigma^{'})}{m} - 1 \\geq{}&\n", - "\\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}-\\mu_{j,m+k})}\n", - " {\\sigma^{'}\\sigma_{j,m}}\n", - " \\\\\n", - " f(\\mu^{'}, \\sigma^{'}) \\geq{}&\n", - " m \\left[\n", - " \\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}-\\mu_{j,m+k})}\n", - " {\\sigma^{'}\\sigma_{j,m}} \n", - " \\right]\n", - " + 1\n", + " +\n", + " \\frac{\n", + " \\beta^{2}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " \\right)\n", + " +\n", + " \\frac{\n", + " \\gamma^{2}\n", + " }{\n", + " \\sigma_{j,m}^{2}\n", + " }\n", + " -\n", + " 2\\frac{\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " -\n", + " 2(\\frac{\\beta}{\\sigma^{'}})(\\frac{\\gamma}{\\sigma_{j,m}})\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\sigma_{i,m}^{2}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " (\\frac{\n", + " \\beta\n", + " }{\n", + " \\sigma^{'}\n", + " })^{2}\n", + " +\n", + " (\\frac{\n", + " \\gamma\n", + " }{\n", + " \\sigma_{j,m}\n", + " })^{2}\n", + " +\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " -\n", + " 2(\\frac{\\beta}{\\sigma^{'}})(\\frac{\\gamma}{\\sigma_{j,m}})\n", + " \\\\ \n", + " ={}&\n", + " \\frac{\n", + " \\sigma_{i,m}^{2}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " +\n", + " \\left(\n", + " \\frac{\\beta\n", + " }{\\sigma^{'}\n", + " }\n", + " -\n", + " \\frac{\\gamma\n", + " }{\\sigma_{j,m}\n", + " }\\right)^{2}\n", + " \\\\\n", "\\end{align}\n", "$$\n" ] }, { "cell_type": "markdown", - "id": "74bbbd59", - "metadata": {}, - "source": [ - "And, with help of eq(2), i.e. $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, we can claim:" - ] - }, - { - "cell_type": "markdown", - "id": "88052b40", - "metadata": {}, - "source": [ - "$$\n", - "\\begin{align}\n", - " LB^{*} ={}&\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - " \\sqrt{\n", - " m \\left[\n", - " \\frac{(\\mu_{i,m}-\\mu^{'})(\\mu_{j,m}-\\mu_{j,m+k})}\n", - " {\\sigma^{'}\\sigma_{j,m}} \n", - " \\right]\n", - " + 1\n", - " } \\quad (\\rho_{ij} \\leq 0)\n", - " \\\\\n", - "\\end{align}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "02822e09", - "metadata": {}, - "source": [ - "We used superscript * to distinguish the LB proposed by the paper and the LB achieved by the calculaton of this notebook." - ] - }, - { - "cell_type": "markdown", - "id": "88edf049", + "id": "1f8c0fb5", "metadata": {}, "source": [ - "## Validating LB with a test case" + "NOTE: Since $\\rho_{i,j} \\leq 0$, the second term becoms non-negative. Therefore $F \\geq 0$ which, according to eq(14), satisfies equations (13). Therefore, LB proposed by the authors are acceptable." ] }, { "cell_type": "code", "execution_count": null, - "id": "58062d43", + "id": "665164cc", "metadata": {}, "outputs": [], "source": [] From 8b249e42d132ceeabad7dd7e0b269704a99246cc Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 05:38:41 -0600 Subject: [PATCH 30/64] Removed unnecessary variables in calculating LB --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 138 ++++++------------ 1 file changed, 47 insertions(+), 91 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 7d025a180..60123374c 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -1868,7 +1868,7 @@ "id": "317e1594", "metadata": {}, "source": [ - "Recall eq(13), $i.e. F=\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 \\geq 0$, is equivalent to what claimed in the paper for $\\rho_{ij} \\leq 0$. So, we just need to prove that the right hand side, F, is always positive. " + "Recall eq(13), $i.e. F=\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 \\geq 0$, is equivalent to what claimed in the paper for $\\rho_{ij} \\leq 0$. So, we just need to prove that the right hand side, F, is always non-negative. " ] }, { @@ -1972,138 +1972,94 @@ " - \\mu_{j,m+k})\n", " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", - " } \\quad (16)\n", + " } \n", " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "e0b43f1e", - "metadata": {}, - "source": [ - "**Now, we define two new intermediate variables as follows:**" - ] - }, - { - "cell_type": "markdown", - "id": "ed9912e4", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\beta \\triangleq{}& \\mu_{i,m} - \\mu^{'}\n", - " \\\\\n", - " \\gamma \\triangleq{}& \\mu_{j,m} - \\mu_{j,m+k}\n", - " \\\\\n", - "\\end{align}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "961562f1", - "metadata": {}, - "source": [ - "By subsituting $\\beta$ and $\\gamma$ for their corresponding terms in eq(16), we get:" - ] - }, - { - "cell_type": "markdown", - "id": "ecd9622b", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " F ={}&\n", + " ={}&\n", " \\frac{\n", - " \\sigma_{i,m}^{2} + \\beta^{2}\n", + " \\sigma_{i,m}^{2}\n", " }{\n", " \\sigma^{'2}\n", " }\n", + " +\n", + " \\frac{(\\mu_{i,m}-\\mu^{'})^{2}}{\\sigma^{'2}}\n", " +\n", " \\frac{\n", - " \\gamma^{2}\n", + " (\\mu_{j,m} - \\mu_{j,m+k})^{2}\n", " }{\n", " \\sigma_{j,m}^{2}\n", " }\n", + " +\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", " -\n", - " 2\\frac{\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", - " -\n", - " 2\\frac{\\beta\\gamma\n", - " }{\n", - " \\sigma^{'}\\sigma_{j,m}\n", - " } \n", + " 2(\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}})(\n", + " \\frac{\\mu_{j,m}\n", + " - \\mu_{j,m+k}}{\\sigma_{j,m}})\n", " \\\\\n", " ={}&\n", - " \\left(\n", - " \\frac{\n", + " \\frac{\n", " \\sigma_{i,m}^{2}\n", " }{\n", " \\sigma^{'2}\n", " }\n", " +\n", - " \\frac{\n", - " \\beta^{2}\n", - " }{\n", - " \\sigma^{'2}\n", - " }\n", - " \\right)\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " +\n", + " \\left[\n", + " \\frac{(\\mu_{i,m}-\\mu^{'})^{2}}{\\sigma^{'2}}\n", " +\n", " \\frac{\n", - " \\gamma^{2}\n", + " (\\mu_{j,m} - \\mu_{j,m+k})^{2}\n", " }{\n", " \\sigma_{j,m}^{2}\n", " }\n", " -\n", - " 2\\frac{\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", - " -\n", - " 2(\\frac{\\beta}{\\sigma^{'}})(\\frac{\\gamma}{\\sigma_{j,m}})\n", + " 2(\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}})(\n", + " \\frac{\\mu_{j,m}\n", + " - \\mu_{j,m+k}}{\\sigma_{j,m}})\n", + " \\right]\n", " \\\\\n", " ={}&\n", - " \\frac{\n", + " \\frac{\n", " \\sigma_{i,m}^{2}\n", " }{\n", " \\sigma^{'2}\n", " }\n", " +\n", - " (\\frac{\n", - " \\beta\n", - " }{\n", - " \\sigma^{'}\n", - " })^{2}\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " +\n", + " \\left[\n", + " (\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}})^{2}\n", " +\n", " (\\frac{\n", - " \\gamma\n", + " \\mu_{j,m} - \\mu_{j,m+k}\n", " }{\n", " \\sigma_{j,m}\n", " })^{2}\n", - " +\n", - " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", " -\n", - " 2(\\frac{\\beta}{\\sigma^{'}})(\\frac{\\gamma}{\\sigma_{j,m}})\n", - " \\\\ \n", - " ={}&\n", - " \\frac{\n", + " 2(\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}})(\n", + " \\frac{\\mu_{j,m}\n", + " - \\mu_{j,m+k}}{\\sigma_{j,m}})\n", + " \\right]\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", " \\sigma_{i,m}^{2}\n", " }{\n", " \\sigma^{'2}\n", " }\n", " +\n", - " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", - " +\n", - " \\left(\n", - " \\frac{\\beta\n", - " }{\\sigma^{'}\n", - " }\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " +\n", + " \\left[\n", + " \\left(\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}}\\right)\n", " -\n", - " \\frac{\\gamma\n", - " }{\\sigma_{j,m}\n", - " }\\right)^{2}\n", - " \\\\\n", + " \\left(\\frac{\n", + " \\mu_{j,m} - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\\right)\n", + " \\right]^{2}\n", + " \\\\\n", "\\end{align}\n", "$$\n" ] @@ -2113,7 +2069,7 @@ "id": "1f8c0fb5", "metadata": {}, "source": [ - "NOTE: Since $\\rho_{i,j} \\leq 0$, the second term becoms non-negative. Therefore $F \\geq 0$ which, according to eq(14), satisfies equations (13). Therefore, LB proposed by the authors are acceptable." + "NOTE: Since $\\rho_{i,j} \\leq 0$, the second term becoms non-negative. Therefore $F \\geq 0$ which, according to eq(14), satisfies equations (13). Therefore, the LB proposed by the authors is correct." ] }, { From e3874833ab4b09bf93db8deca82b971fcaac8f10 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 06:39:39 -0600 Subject: [PATCH 31/64] ADDed proof for pearson correlation --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 99 ++++++++++++++++++- 1 file changed, 97 insertions(+), 2 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 60123374c..ff96212a6 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -646,7 +646,94 @@ " \\rho_{ij} = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", " \\\\\n", "\\end{align}\n", - "$$\n" + "$$\n", + "\n", + "**Proof:**\n", + "\n", + "$$\n", + "\\begin{align}\n", + "\\rho_{ij} ={}&\n", + " \\frac{\n", + " COV(T_{i}T_{j})}{\n", + " \\sigma_{i}\\sigma_{j}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " E\\left[\n", + " (T_{i} - \\mu_{i})(T_{j} - \\mu_{j})\n", + " \\right]}\n", + " {\n", + " \\sigma_{i}\\sigma_{j}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\frac{1}{m}\\sum\\limits_{t=1}^{m}\n", + " (T[i+t-1] - \\mu_{i})(T[j+t-1] - \\mu_{j})\n", + " }\n", + " {\n", + " \\sigma_{i}\\sigma_{j}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]T[j+t-1] \n", + " -\n", + " \\sum\\limits_{t=1}^{m}\n", + " \\mu_{i}T[j+t-1]\n", + " -\n", + " \\sum\\limits_{t=1}^{m}\n", + " \\mu_{j}T[i+t-1]\n", + " +\n", + " \\sum\\limits_{t=1}^{m}\\mu_{i}\\mu_{j}\n", + " }{\n", + " m\\sigma_{i}\\sigma_{j}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]T[j+t-1] \n", + " -\n", + " \\mu_{i}\\sum\\limits_{t=1}^{m}\n", + " T[j+t-1]\n", + " -\n", + " \\mu_{j}\\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]\n", + " +\n", + " \\sum\\limits_{t=1}^{m}\\mu_{i}\\mu_{j}\n", + " }{\n", + " m\\sigma_{i}\\sigma_{j}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]T[j+t-1] \n", + " -\n", + " \\mu_{i}\\cdot m\\mu_{j}\n", + " -\n", + " \\mu_{j}\\cdot m\\mu_{j}\n", + " +\n", + " m\\mu_{i}\\mu_{j}\n", + " }{\n", + " m\\sigma_{i}\\sigma_{j}\n", + " }\n", + " \\\\\n", + " ={}&\n", + " \\frac{\n", + " \\sum\\limits_{t=1}^{m}\n", + " T[i+t-1]T[j+t-1] \n", + " -\n", + " m\\mu_{i}m\\mu_{j}\n", + " }{\n", + " m\\sigma_{i}\\sigma_{j}\n", + " }\n", + " \\\\\n", + "\\end{align}\n", + "$$" ] }, { @@ -654,7 +741,7 @@ "id": "4880c751", "metadata": {}, "source": [ - "Note: we can rearrange the pearson correlation equation as:
\n", + "Note: we can rearrange the pearson correlation equation as below:
\n", "$\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" ] }, @@ -1477,6 +1564,14 @@ "* **For $\\rho \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$.**" ] }, + { + "cell_type": "markdown", + "id": "b35090c6", + "metadata": {}, + "source": [ + "### Derving Equation (2): Continued" + ] + }, { "cell_type": "markdown", "id": "fc19b2dd", From b6b0151156beb75a5c6fab9a477324647c3e3e0e Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 07:06:03 -0600 Subject: [PATCH 32/64] proof read the notebook --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 90 ++++++------------- 1 file changed, 29 insertions(+), 61 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index ff96212a6..a24785032 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -640,40 +640,30 @@ "id": "82bc9b8e", "metadata": {}, "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " \\rho_{ij} = \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", - " \\\\\n", - "\\end{align}\n", - "$$\n", - "\n", - "**Proof:**\n", - "\n", "$$\n", "\\begin{align}\n", "\\rho_{ij} ={}&\n", " \\frac{\n", - " COV(T_{i}T_{j})}{\n", - " \\sigma_{i}\\sigma_{j}\n", + " COV(T_{i,m}T_{j,m})}{\n", + " \\sigma_{i,m}\\sigma_{j,m}\n", " }\n", " \\\\\n", " ={}&\n", " \\frac{\n", " E\\left[\n", - " (T_{i} - \\mu_{i})(T_{j} - \\mu_{j})\n", + " (T_{i,m} - \\mu_{i,m})(T_{j,m} - \\mu_{j,m})\n", " \\right]}\n", " {\n", - " \\sigma_{i}\\sigma_{j}\n", + " \\sigma_{i,m}\\sigma_{j,m}\n", " }\n", " \\\\\n", " ={}&\n", " \\frac{\n", " \\frac{1}{m}\\sum\\limits_{t=1}^{m}\n", - " (T[i+t-1] - \\mu_{i})(T[j+t-1] - \\mu_{j})\n", + " (T[i+t-1] - \\mu_{i,m})(T[j+t-1] - \\mu_{j,m})\n", " }\n", " {\n", - " \\sigma_{i}\\sigma_{j}\n", + " \\sigma_{i,m}\\sigma_{j,m}\n", " }\n", " \\\\\n", " ={}&\n", @@ -682,14 +672,14 @@ " T[i+t-1]T[j+t-1] \n", " -\n", " \\sum\\limits_{t=1}^{m}\n", - " \\mu_{i}T[j+t-1]\n", + " \\mu_{i,m}T[j+t-1]\n", " -\n", " \\sum\\limits_{t=1}^{m}\n", - " \\mu_{j}T[i+t-1]\n", + " \\mu_{j,m}T[i+t-1]\n", " +\n", - " \\sum\\limits_{t=1}^{m}\\mu_{i}\\mu_{j}\n", + " \\sum\\limits_{t=1}^{m}\\mu_{i,m}\\mu_{j,m}\n", " }{\n", - " m\\sigma_{i}\\sigma_{j}\n", + " m\\sigma_{i,m}\\sigma_{j,,m}\n", " }\n", " \\\\\n", " ={}&\n", @@ -697,15 +687,15 @@ " \\sum\\limits_{t=1}^{m}\n", " T[i+t-1]T[j+t-1] \n", " -\n", - " \\mu_{i}\\sum\\limits_{t=1}^{m}\n", + " \\mu_{i,m}\\sum\\limits_{t=1}^{m}\n", " T[j+t-1]\n", " -\n", - " \\mu_{j}\\sum\\limits_{t=1}^{m}\n", + " \\mu_{j,m}\\sum\\limits_{t=1}^{m}\n", " T[i+t-1]\n", " +\n", - " \\sum\\limits_{t=1}^{m}\\mu_{i}\\mu_{j}\n", + " \\sum\\limits_{t=1}^{m}\\mu_{i,m}\\mu_{j,m}\n", " }{\n", - " m\\sigma_{i}\\sigma_{j}\n", + " m\\sigma_{i,m}\\sigma_{j,m}\n", " }\n", " \\\\\n", " ={}&\n", @@ -713,13 +703,13 @@ " \\sum\\limits_{t=1}^{m}\n", " T[i+t-1]T[j+t-1] \n", " -\n", - " \\mu_{i}\\cdot m\\mu_{j}\n", + " \\mu_{i,m}\\cdot m\\mu_{j,m}\n", " -\n", - " \\mu_{j}\\cdot m\\mu_{j}\n", + " \\mu_{j,m}\\cdot m\\mu_{i,m}\n", " +\n", - " m\\mu_{i}\\mu_{j}\n", + " m\\mu_{i,m}\\mu_{j,m}\n", " }{\n", - " m\\sigma_{i}\\sigma_{j}\n", + " m\\sigma_{i,m}\\sigma_{j,m}\n", " }\n", " \\\\\n", " ={}&\n", @@ -727,9 +717,9 @@ " \\sum\\limits_{t=1}^{m}\n", " T[i+t-1]T[j+t-1] \n", " -\n", - " m\\mu_{i}m\\mu_{j}\n", + " m\\mu_{i,m}\\mu_{j,m}\n", " }{\n", - " m\\sigma_{i}\\sigma_{j}\n", + " m\\sigma_{i,m}\\sigma_{j,m}\n", " }\n", " \\\\\n", "\\end{align}\n", @@ -932,7 +922,7 @@ "id": "978473a2", "metadata": {}, "source": [ - "In the calculations above, we subsitute 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." + "In the calculations above, we subsituted 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." ] }, { @@ -1559,14 +1549,12 @@ "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho_{ij})}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", "\n", "**Pending...**
\n", - "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function.\n", - "\n", - "* **For $\\rho \\leq 0$, the authors claimed that: $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$.**" + "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function." ] }, { "cell_type": "markdown", - "id": "b35090c6", + "id": "2c878b7b", "metadata": {}, "source": [ "### Derving Equation (2): Continued" @@ -1757,28 +1745,8 @@ " \\right)\n", " \\right]\n", " \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "edc051ab", - "metadata": {}, - "source": [ - "Now, we distribute summation into all terms..." - ] - }, - { - "cell_type": "markdown", - "id": "9f44f100", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - "f(\\mu^{'},\\sigma^{'}) ={}& \n", - " \\frac{\n", + " ={}&\n", + " \\frac{\n", " \\sum \\limits_{t=1}^{m}T[i+t-1]^{2} + \\sum \\limits_{t=1}^{m}\\mu^{'2} - 2\\mu^{'}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", " }{\n", " \\sigma^{'2}\n", @@ -1835,7 +1803,7 @@ " -\n", " 2\n", " \\frac{\n", - " (m\\rho_{ij}\\sigma_{i}\\sigma_{j} + m\\mu_{i}\\mu_{j}) \n", + " (m\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}) \n", " - \\mu_{j,m+k}\\cdot m\\mu_{i,m}\n", " - \\mu^{'} \\cdot m\\mu_{j,m}\n", " + m\\mu^{'}\\mu_{j,m+k}\n", @@ -1852,7 +1820,7 @@ "id": "f54b458f", "metadata": {}, "source": [ - "Recall that $\\rho_{ii}=1$ and $\\rho_{jj}=1$. After subsituting them in the formula above, and factoring out m, we can write it down as:" + "Recall that $\\rho_{ii}=1$ and $\\rho_{jj}=1$. After subsituting them in the formula above, and multiply it by $\\frac{1}{m}$ :" ] }, { @@ -1921,7 +1889,7 @@ "id": "96db6201", "metadata": {}, "source": [ - "Therefore, we can now see:" + "Therefore:" ] }, { @@ -1963,7 +1931,7 @@ "id": "317e1594", "metadata": {}, "source": [ - "Recall eq(13), $i.e. F=\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 \\geq 0$, is equivalent to what claimed in the paper for $\\rho_{ij} \\leq 0$. So, we just need to prove that the right hand side, F, is always non-negative. " + "eq(13), $i.e. F=\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 \\geq 0$, is equivalent to what claimed in the paper for $\\rho_{ij} \\leq 0$. So, we just need to prove that the right hand side, F, is always non-negative. " ] }, { From 1c4d7fa96d199b68e5c7373555c1cc365c6d8f67 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 07:09:32 -0600 Subject: [PATCH 33/64] check equations and flow --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index a24785032..814324401 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -1554,7 +1554,7 @@ }, { "cell_type": "markdown", - "id": "2c878b7b", + "id": "a5370108", "metadata": {}, "source": [ "### Derving Equation (2): Continued" From 4335cbdce0a265ff7820e091d5acb3e411f8f717 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 07:23:34 -0600 Subject: [PATCH 34/64] comments are adressed --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 814324401..5ab017a8d 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -510,7 +510,7 @@ "id": "1340817b", "metadata": {}, "source": [ - "Note: In the calculations above, we substitute 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." + "Note: In the calculations above, we substituted 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." ] }, { @@ -868,9 +868,8 @@ " \\mu_{i,m}^{2}\n", " \\right)\n", " - \n", - " \\sigma_{j,m}\\left(\n", + " \\sigma_{j,m} \\cdot\n", " \\mu^{'} \\mu_{i,m}\n", - " \\right) \n", " - \n", " \\sigma^{'}\\left(\n", " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", @@ -887,9 +886,8 @@ " \\mu_{i,m}^{2}\n", " \\right)\n", " + \n", - " \\sigma_{j,m}\\left(\n", + " \\sigma_{j,m} \\cdot\n", " \\mu^{'} \\mu_{i,m}\n", - " \\right) \n", " + \n", " \\sigma^{'}\\left(\n", " {\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m} \n", From 58c88f5d35c919e7e287fb6c133b5d2cb329e6f7 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 12:19:31 -0600 Subject: [PATCH 35/64] Improve readabilty of calculations --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 158 ++++++++---------- 1 file changed, 72 insertions(+), 86 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 5ab017a8d..5420a285d 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -5,7 +5,9 @@ "id": "d8ebe111", "metadata": {}, "source": [ - "In this notebook, we would like to derive the eq(2) of the paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf)." + "In this notebook, we would like to derive the eq(2) of the paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf).\n", + "\n", + "**Notation:** $T_{i,m} = T[i:i+m]$, a subsequence of `T` that starts at index `i` and has length `m`. " ] }, { @@ -14,15 +16,7 @@ "metadata": {}, "source": [ "**The idea goes as follows:**
\n", - "\"Given the distance profile of $T_{j,m}$, how can we find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`?" - ] - }, - { - "cell_type": "markdown", - "id": "03836054", - "metadata": {}, - "source": [ - "In other words, can we find **Lower Bound (LB)** for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$? (So, the last `k` elements of $T_{i,m+k}$ are unknown)" + "\"Given the distance profile of $T_{j,m}$, how can we find a lower bound for distance profile of $T_{j,m+k}$\", where $T_{j,m+k}$ represents a sequence that starts from the same index `j` with length `m+k`? In other words, can we find **Lower Bound (LB)** for $d(T_{j,m+k}, T_{i,m+k})$ only by help of $T_{j,m}$, $T_{i,m}$, and $T_{j,m+k}$? (So, the last `k` elements of $T_{i,m+k}$ are unknown)" ] }, { @@ -1567,66 +1561,6 @@ "Now, we would like to derive the second sub-function, where LB is defined for $\\rho_{ij} \\leq 0$." ] }, - { - "cell_type": "markdown", - "id": "7e523470", - "metadata": {}, - "source": [ - "Let us first visit the equation stated by the authors again:" - ] - }, - { - "cell_type": "markdown", - "id": "326d2300", - "metadata": {}, - "source": [ - "$LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\sqrt{m}$, if $\\rho_{ij} \\leq 0$" - ] - }, - { - "cell_type": "markdown", - "id": "86dd8eb5", - "metadata": {}, - "source": [ - "Comparing the equation above with eq(2) of notebook, i.e. $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, shows that we need to prove:" - ] - }, - { - "cell_type": "markdown", - "id": "8670ed3e", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - "f(\\mu^{'}, \\sigma^{'}) \\geq{}& \n", - "m\n", - "\\\\\n", - "\\frac{\n", - "f(\\mu^{'}, \\sigma^{'})\n", - "}{\n", - "m} \\geq{}& 1\n", - "\\\\\n", - "\\frac{\n", - "f(\\mu^{'}, \\sigma^{'})\n", - "}{\n", - "m}\n", - "-\n", - "1 \\geq{}& 0 \\quad (13)\n", - "\\end{align} \n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "b6b9eff9", - "metadata": {}, - "source": [ - "Therefore, we need to show (13) is correct when $\\rho_{ij} \\leq 0$.\n", - "\n", - "$F \\triangleq \\frac{f(\\mu^{'}, \\sigma^{'})}{m} - 1$ (14)" - ] - }, { "cell_type": "markdown", "id": "a4f11acc", @@ -1818,7 +1752,7 @@ "id": "f54b458f", "metadata": {}, "source": [ - "Recall that $\\rho_{ii}=1$ and $\\rho_{jj}=1$. After subsituting them in the formula above, and multiply it by $\\frac{1}{m}$ :" + "Recall that $\\rho_{ii}=1$ and $\\rho_{jj}=1$. Therefore:" ] }, { @@ -1829,7 +1763,8 @@ "\n", "$$\n", "\\begin{align}\n", - "\\frac{f(\\mu^{'},\\sigma^{'})}{m} ={}& \n", + "f(\\mu^{'},\\sigma^{'}) ={}& \n", + " m\\left[\n", " \\frac{\n", " \\sigma_{i,m}^{2} + \\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m}\n", " }{\n", @@ -1851,9 +1786,11 @@ " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", " }\n", + " \\right]\n", " \\\\\n", " \\\\\n", " ={}&\n", + " m\\left[\n", " \\frac{\n", " \\sigma_{i,m}^{2} + \\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m}\n", " }{\n", @@ -1878,6 +1815,7 @@ " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", " }\n", + " \\right]\n", "\\end{align}\n", "$$\n" ] @@ -1887,7 +1825,7 @@ "id": "96db6201", "metadata": {}, "source": [ - "Therefore:" + "Hence:" ] }, { @@ -1898,7 +1836,10 @@ "\n", "$$\n", "\\begin{align}\n", - "\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 ={}& \n", + " f(\\mu^{'},\\sigma^{'}) ={}& \n", + " m \\left[1 + g(\\mu^{'},\\sigma^{'})\\right] \\quad (16) \n", + " \\\\\n", + " g(\\mu^{'},\\sigma^{'}) ={}& \n", " \\frac{\n", " \\sigma_{i,m}^{2} + \\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m}\n", " }{\n", @@ -1919,19 +1860,11 @@ " + \\mu^{'}\\mu_{j,m+k}\n", " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", - " }\n", + " } \\quad(17)\n", "\\end{align}\n", "$$\n" ] }, - { - "cell_type": "markdown", - "id": "317e1594", - "metadata": {}, - "source": [ - "eq(13), $i.e. F=\\frac{f(\\mu^{'},\\sigma^{'})}{m} - 1 \\geq 0$, is equivalent to what claimed in the paper for $\\rho_{ij} \\leq 0$. So, we just need to prove that the right hand side, F, is always non-negative. " - ] - }, { "cell_type": "markdown", "id": "d73539ec", @@ -1940,7 +1873,7 @@ "\n", "$$\n", "\\begin{align}\n", - " F ={}& \n", + " g(\\mu^{'},\\sigma^{'}) ={}& \n", " \\frac{\n", " \\sigma_{i,m}^{2} + \\mu_{i,m}^{2} + \\mu^{'2} - 2\\mu^{'}\\mu_{i,m}\n", " }{\n", @@ -2121,6 +2054,11 @@ " }\\right)\n", " \\right]^{2}\n", " \\\\\n", + " \\geq{}&\n", + " 2\\frac{(-\\rho_{ij})\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}} \\quad (18)\n", + " \\\\\n", + " \\geq{}&\n", + " 0\n", "\\end{align}\n", "$$\n" ] @@ -2130,13 +2068,61 @@ "id": "1f8c0fb5", "metadata": {}, "source": [ - "NOTE: Since $\\rho_{i,j} \\leq 0$, the second term becoms non-negative. Therefore $F \\geq 0$ which, according to eq(14), satisfies equations (13). Therefore, the LB proposed by the authors is correct." + "NOTE: Since $\\rho_{i,j} \\leq 0$, the second term becoms non-negative. Therefore:" + ] + }, + { + "cell_type": "markdown", + "id": "c26f0a33", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " g(\\mu^{'},\\sigma^{'}) \\geq{}& 0\n", + " \\\\\n", + " 1 + g(\\mu^{'},\\sigma^{'}) \\geq{}& 1\n", + " \\\\\n", + " m\\left[1 + g(\\mu^{'},\\sigma^{'})\\right] \\geq{}& m\n", + " \\\\\n", + "\\end{align}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d7ae9e69", + "metadata": {}, + "source": [ + "Therefore, according to eq(16), $f(\\mu^{'},\\sigma^{'}) = m \\left[1 + g(\\mu^{'},\\sigma^{'})\\right]$, we have: $f(\\mu^{'},\\sigma^{'}) \\geq m$, and according to eq(2), $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, we can see that:" + ] + }, + { + "cell_type": "markdown", + "id": "b661f3d9", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " LB ={}& \n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{m} \\quad \\text{ if } \\rho_{ij} \\leq 0\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "1365fad2", + "metadata": {}, + "source": [ + "**NOTE:** Please note that a stronger LB for $\\rho_{ij} \\leq 0$ is $2\\frac{(-\\rho_{ij})\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}$ (see eq(18) above). However, this has $\\sigma^{'}$ which is unknown. we would like to find LB that is only based on known parameters. Therefore, we are okay with the LB proposed in the paper." ] }, { "cell_type": "code", "execution_count": null, - "id": "665164cc", + "id": "52c83826", "metadata": {}, "outputs": [], "source": [] From 9c921c25980fe07224e682e3f1a39778a0f8c9cd Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 12:20:20 -0600 Subject: [PATCH 36/64] DELETED file with old name --- docs/Tutorial_VALMOD_notebook.ipynb | 178 ---------------------------- 1 file changed, 178 deletions(-) delete mode 100644 docs/Tutorial_VALMOD_notebook.ipynb diff --git a/docs/Tutorial_VALMOD_notebook.ipynb b/docs/Tutorial_VALMOD_notebook.ipynb deleted file mode 100644 index 86e244817..000000000 --- a/docs/Tutorial_VALMOD_notebook.ipynb +++ /dev/null @@ -1,178 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c7a27406", - "metadata": {}, - "source": [ - "In this tutorial, we would like to implement VALMOD algorithm proposed in paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf), and reproduce its results as closely as possible.\n", - "\n", - "The **VAriable Length MOtif Discovery (VALMOD)** algorithm takes time series `T` and a range of subsequence length `[min_m, max_m]`, and find motifs and discords." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0adbe18a", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import stumpy\n", - "from stumpy import core, config\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" - ] - }, - { - "cell_type": "markdown", - "id": "e9d48c97", - "metadata": {}, - "source": [ - "# 1- Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "b0423978", - "metadata": {}, - "source": [ - "**Notation:** $T_{i,m} = T[i:i+m]$, a subsequence of `T` that starts at index `i` and has length `m`. " - ] - }, - { - "cell_type": "markdown", - "id": "4a4af7fd", - "metadata": {}, - "source": [ - "## Motif discovery" - ] - }, - { - "cell_type": "markdown", - "id": "78ac5b0f", - "metadata": {}, - "source": [ - "For a given motif pair $\\{T_{idx,m},T_{nn\\_idx,n}\\}$, Motif set $S^{m}_{r}$ is a set of subsequences of length `m` that has `distance < r` to either $T_{idx,m}$ or $T_{nn\\_idx,n}$. And, the cardinality of set is called the frequency of the motif set.\n", - "\n", - "We would like to find set $S^{*} = \\bigcup\\limits_{m=min\\_m}^{max\\_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", - "\n", - "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty because both these two subsequences start from the same index." - ] - }, - { - "cell_type": "markdown", - "id": "7fc09927", - "metadata": {}, - "source": [ - "## Discord Discovery" - ] - }, - { - "cell_type": "markdown", - "id": "0f4ee615", - "metadata": {}, - "source": [ - "First, we need to provide a few definitions...\n", - "\n", - "**$n^{th}$ best match**: For the subsequence $T_{i,m}$, its $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", - "\n", - "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?) best match.
\n", - "\n", - "**NOTE**:
\n", - "Why should we care about $n^{th}$ discord (n>1)? We provide a simple example below:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "37fdbb26", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "T = np.random.uniform(-100,100,size=3000)\n", - "m = 200\n", - "i, j = 100, 1500\n", - "\n", - "T[i:i+m] = 0\n", - "T[j:j+m] = 0\n", - "\n", - "plt.plot(T)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cb3a3940", - "metadata": {}, - "source": [ - "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors rather than just 1NN. In discord discovery, it is called twin-freak problem (see [Tutorial](https://cci.drexel.edu/bigdata/bigdata2017/files/Tutorial4.pdf)). It happens when the (same) anomally occurs more than once. In our example above, the anomaly occurs twice. Therefore, we should be able to detect it if we consider 2nd nearest neighbor. \n", - "\n", - "For further details, see Fig. 2 of the paper. Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" - ] - }, - { - "cell_type": "markdown", - "id": "45eeecf5", - "metadata": {}, - "source": [ - "**Variable-length Top-k $n^{th}$ Discord Discovery:**
\n", - "Given a time series `T`, a subsequence length-range `[min_m, max_m]`,`K`, and `N`, we want to find **top-k $n^{th}$ discord** for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." - ] - }, - { - "cell_type": "markdown", - "id": "e503fb0a", - "metadata": {}, - "source": [ - "# 2-Lower Bound of Distance Profile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71517d38", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 4c1e2be79448d5483b6bf0cb56f8bffcbd1575a2 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 12:28:11 -0600 Subject: [PATCH 37/64] Reviewed notebook --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 5420a285d..2b91a91e2 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -2073,7 +2073,7 @@ }, { "cell_type": "markdown", - "id": "c26f0a33", + "id": "d435f4fc", "metadata": {}, "source": [ "\n", @@ -2091,7 +2091,7 @@ }, { "cell_type": "markdown", - "id": "d7ae9e69", + "id": "125c27bc", "metadata": {}, "source": [ "Therefore, according to eq(16), $f(\\mu^{'},\\sigma^{'}) = m \\left[1 + g(\\mu^{'},\\sigma^{'})\\right]$, we have: $f(\\mu^{'},\\sigma^{'}) \\geq m$, and according to eq(2), $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, we can see that:" @@ -2099,7 +2099,7 @@ }, { "cell_type": "markdown", - "id": "b661f3d9", + "id": "06f789ce", "metadata": {}, "source": [ "\n", @@ -2113,7 +2113,7 @@ }, { "cell_type": "markdown", - "id": "1365fad2", + "id": "810ab4ae", "metadata": {}, "source": [ "**NOTE:** Please note that a stronger LB for $\\rho_{ij} \\leq 0$ is $2\\frac{(-\\rho_{ij})\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}$ (see eq(18) above). However, this has $\\sigma^{'}$ which is unknown. we would like to find LB that is only based on known parameters. Therefore, we are okay with the LB proposed in the paper." @@ -2122,7 +2122,7 @@ { "cell_type": "code", "execution_count": null, - "id": "52c83826", + "id": "826bb9c8", "metadata": {}, "outputs": [], "source": [] From 2b6c9dc4296f375e139db07316d54f91e9d5e759 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 12:40:04 -0600 Subject: [PATCH 38/64] ADD heading and revise subheadings --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 2b91a91e2..d45ddb85d 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -1,5 +1,13 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "4db65758", + "metadata": {}, + "source": [ + "# Intro" + ] + }, { "cell_type": "markdown", "id": "d8ebe111", @@ -24,7 +32,7 @@ "id": "3b5c8c5a", "metadata": {}, "source": [ - "## 2-1 Non-normalized distance" + "## Non-normalized distance" ] }, { @@ -107,7 +115,7 @@ "id": "0b539ca8", "metadata": {}, "source": [ - "## 2-2 Normalized distance" + "## Normalized distance" ] }, { From b2a86e407045bbfaa70af178d8f5d469cc0fed4d Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 14:20:56 -0600 Subject: [PATCH 39/64] ADD proof for global minimum --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 175 +++++++++++++++++- 1 file changed, 167 insertions(+), 8 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index d45ddb85d..ed53f146a 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4db65758", + "id": "760a9022", "metadata": {}, "source": [ "# Intro" @@ -1557,7 +1557,8 @@ "id": "a5370108", "metadata": {}, "source": [ - "### Derving Equation (2): Continued" + "### Derving Equation (2): Continued\n", + "**How about LB for the case $\\rho_{ij} \\leq 0$?**" ] }, { @@ -2027,7 +2028,7 @@ " \\sigma^{'2}\n", " }\n", " +\n", - " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}}{\\sigma^{'}}\n", " +\n", " \\left[\n", " (\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}})^{2}\n", @@ -2050,7 +2051,7 @@ " \\sigma^{'2}\n", " }\n", " +\n", - " 2\\frac{-\\rho_{ij}\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}}{\\sigma^{'}}\n", " +\n", " \\left[\n", " \\left(\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}}\\right)\n", @@ -2060,10 +2061,10 @@ " }{\n", " \\sigma_{j,m}\n", " }\\right)\n", - " \\right]^{2}\n", + " \\right]^{2} \\quad (18)\n", " \\\\\n", " \\geq{}&\n", - " 2\\frac{(-\\rho_{ij})\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}} \\quad (18)\n", + " 2\\frac{(-\\rho_{ij})\\sigma_{i,m}}{\\sigma^{'}} \\quad (19)\n", " \\\\\n", " \\geq{}&\n", " 0\n", @@ -2124,13 +2125,171 @@ "id": "810ab4ae", "metadata": {}, "source": [ - "**NOTE:** Please note that a stronger LB for $\\rho_{ij} \\leq 0$ is $2\\frac{(-\\rho_{ij})\\sigma_{i,m}\\sigma_{j,m}}{\\sigma^{'}\\sigma_{j,m}}$ (see eq(18) above). However, this has $\\sigma^{'}$ which is unknown. we would like to find LB that is only based on known parameters. Therefore, we are okay with the LB proposed in the paper." + "**NOTE:** Please note that a stronger LB for $\\rho_{ij} \\leq 0$ is $2\\frac{(-\\rho_{ij})\\sigma_{i,m}}{\\sigma^{'}}$; see eq(19) above. **However,** this has $\\sigma^{'}$ which is unknown. we would like to find LB that is only based on known parameters. Therefore, we are okay with the LB proposed in the paper." + ] + }, + { + "cell_type": "markdown", + "id": "d7846918", + "metadata": {}, + "source": [ + "### Derving Equation (2): Continued\n", + "**Is the LB calculated for the case $\\rho_{ij} \\gt 0$ a global minimum?**" + ] + }, + { + "cell_type": "markdown", + "id": "70ddffdd", + "metadata": {}, + "source": [ + "There is still one thing left to be done. We need to show that the LB discovered for $\\rho_{ij} \\gt 0$ is actually a global minimum. In other words, we need to show that the inequation below holds true for all $\\rho_{ij} \\gt 0$:" + ] + }, + { + "cell_type": "markdown", + "id": "b59c489a", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " f(\\mu^{'},\\sigma^{'}) \\geq{}&\n", + " f(\\mu_{c}^{'},\\sigma_{c}^{'})\n", + "\\end{align}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "26564b7a", + "metadata": {}, + "source": [ + "where, $\\mu_{c}^{'}$ (eq(11)) and $\\sigma_{c}^{'}$ (eq(12)) are the values of the critical point." + ] + }, + { + "cell_type": "markdown", + "id": "2a694b0b", + "metadata": {}, + "source": [ + "We replace left-hand side $f(\\mu^{'},\\sigma^{'})$ with its equivalent term (16), and we replace $f(\\mu_{c}^{'},\\sigma_{c}^{'})$ with $m(1 - \\rho_{ij}^{2})$ as calculated before. Therefore:" + ] + }, + { + "cell_type": "markdown", + "id": "1a1f44e4", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " m \\left[1 + g(\\mu^{'},\\sigma^{'})\\right] \\geq{}& m(1 - \\rho_{ij}^{2})\n", + " \\\\\n", + " 1 + g(\\mu^{'},\\sigma^{'}) \\geq{}& 1 - \\rho_{ij}^{2}\n", + " \\\\\n", + " g(\\mu^{'},\\sigma^{'}) + \\rho_{ij}^{2} \\geq{}& 0 \\quad (20)\n", + "\\end{align}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2d2fc60e", + "metadata": {}, + "source": [ + "Therefore, we need to show inequation (20) is satisfied for all $\\rho_{i,j} \\geq 0$.
\n", + "We now subtitute eq(18) for $g(.)$. Thus:" + ] + }, + { + "cell_type": "markdown", + "id": "9054a4f6", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " \\frac{\n", + " \\sigma_{i,m}^{2}\n", + " }{\n", + " \\sigma^{'2}\n", + " }\n", + " +\n", + " 2\\frac{-\\rho_{ij}\\sigma_{i,m}}{\\sigma^{'}}\n", + " +\n", + " \\left[\n", + " \\left(\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}}\\right)\n", + " -\n", + " \\left(\\frac{\n", + " \\mu_{j,m} - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\\right)\n", + " \\right]^{2}\n", + " + \n", + " \\rho_{ij}^{2} \n", + " \\geq{}& 0\n", + " \\\\\n", + " \\left[\n", + " \\left(\\frac{\n", + " \\sigma_{i,m}\n", + " }{\n", + " \\sigma^{'}\n", + " }\\right)^{2}\n", + " +\n", + " \\rho_{ij}^{2} \n", + " -\n", + " 2\\left(\\frac{\\sigma_{i,m}}{\\sigma^{'}}\\right)\\rho_{ij}\n", + " \\right]\n", + " + \n", + " \\left[\n", + " \\left(\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}}\\right)\n", + " -\n", + " \\left(\\frac{\n", + " \\mu_{j,m} - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\\right)\n", + " \\right]^{2}\n", + " \\geq{}& \n", + " 0\n", + " \\\\\n", + " \\left(\\frac{\n", + " \\sigma_{i,m}\n", + " }{\n", + " \\sigma^{'}\n", + " }\n", + " -\n", + " \\rho_{ij}\n", + " \\right)^{2} \n", + " + \n", + " \\left[\n", + " \\left(\\frac{\\mu_{i,m}-\\mu^{'}}{\\sigma^{'}}\\right)\n", + " -\n", + " \\left(\\frac{\n", + " \\mu_{j,m} - \\mu_{j,m+k}\n", + " }{\n", + " \\sigma_{j,m}\n", + " }\\right)\n", + " \\right]^{2}\n", + " \\geq{}& \n", + " 0\n", + "\\end{align}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b2d7da6e", + "metadata": {}, + "source": [ + "The above inequation is always satisfied. Therefore, the critical point gives global minimum." ] }, { "cell_type": "code", "execution_count": null, - "id": "826bb9c8", + "id": "10851222", "metadata": {}, "outputs": [], "source": [] From 53e740b8c2a500acf413b38757c92878e5a35e3e Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 14:25:34 -0600 Subject: [PATCH 40/64] Correct some notes --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index ed53f146a..952819e1e 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "760a9022", + "id": "0e440d53", "metadata": {}, "source": [ "# Intro" @@ -1549,7 +1549,7 @@ "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho_{ij})}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", "\n", "**Pending...**
\n", - "* The proof is not complete. We need to take the second derivatives and make sure the discovered values give local minimum and not maximum or saddle point. Also, we need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function." + "* We need to analyze the behavior of function `f` to verify that this local minimum is actually the global minimum for this function (more on this later after we derive LB for case $\\rho_{ij} \\leq 0$.)" ] }, { @@ -1566,7 +1566,7 @@ "id": "fc19b2dd", "metadata": {}, "source": [ - "So far, we derived the first sub-function (i.e. LB for $\\rho_{ij} \\gt 0$) of the piecewise function provided in the eq(2) of the paper VALMOD.
\n", + "So far, we have derived the first sub-function of the piecewise function provided in the eq(2) of the paper VALMOD, that is LB for $\\rho_{ij} \\gt 0$.
\n", "Now, we would like to derive the second sub-function, where LB is defined for $\\rho_{ij} \\leq 0$." ] }, @@ -1612,7 +1612,7 @@ "id": "bf007040", "metadata": {}, "source": [ - "Inside the summation, we use the formula: $(A+B)^{2} = A^{2} + B^{2} - 2AB$" + "Inside the summation, we use the formula: $(A \\pm B)^{2} = A^{2} + B^{2} \\pm 2AB$" ] }, { @@ -2130,7 +2130,7 @@ }, { "cell_type": "markdown", - "id": "d7846918", + "id": "a8b816ff", "metadata": {}, "source": [ "### Derving Equation (2): Continued\n", @@ -2139,7 +2139,7 @@ }, { "cell_type": "markdown", - "id": "70ddffdd", + "id": "fc7711bb", "metadata": {}, "source": [ "There is still one thing left to be done. We need to show that the LB discovered for $\\rho_{ij} \\gt 0$ is actually a global minimum. In other words, we need to show that the inequation below holds true for all $\\rho_{ij} \\gt 0$:" @@ -2147,7 +2147,7 @@ }, { "cell_type": "markdown", - "id": "b59c489a", + "id": "cc4dc1b6", "metadata": {}, "source": [ "\n", @@ -2161,7 +2161,7 @@ }, { "cell_type": "markdown", - "id": "26564b7a", + "id": "81e012b9", "metadata": {}, "source": [ "where, $\\mu_{c}^{'}$ (eq(11)) and $\\sigma_{c}^{'}$ (eq(12)) are the values of the critical point." @@ -2169,7 +2169,7 @@ }, { "cell_type": "markdown", - "id": "2a694b0b", + "id": "372a014e", "metadata": {}, "source": [ "We replace left-hand side $f(\\mu^{'},\\sigma^{'})$ with its equivalent term (16), and we replace $f(\\mu_{c}^{'},\\sigma_{c}^{'})$ with $m(1 - \\rho_{ij}^{2})$ as calculated before. Therefore:" @@ -2177,7 +2177,7 @@ }, { "cell_type": "markdown", - "id": "1a1f44e4", + "id": "e10ed8a8", "metadata": {}, "source": [ "\n", @@ -2194,7 +2194,7 @@ }, { "cell_type": "markdown", - "id": "2d2fc60e", + "id": "7988c834", "metadata": {}, "source": [ "Therefore, we need to show inequation (20) is satisfied for all $\\rho_{i,j} \\geq 0$.
\n", @@ -2203,7 +2203,7 @@ }, { "cell_type": "markdown", - "id": "9054a4f6", + "id": "ea2a5a7e", "metadata": {}, "source": [ "\n", @@ -2280,7 +2280,7 @@ }, { "cell_type": "markdown", - "id": "b2d7da6e", + "id": "fe8f12ec", "metadata": {}, "source": [ "The above inequation is always satisfied. Therefore, the critical point gives global minimum." @@ -2289,7 +2289,7 @@ { "cell_type": "code", "execution_count": null, - "id": "10851222", + "id": "ebf2b75e", "metadata": {}, "outputs": [], "source": [] From a39e98b097a2bda928d50e65dbdb3030b4f70526 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sat, 16 Apr 2022 14:35:02 -0600 Subject: [PATCH 41/64] Rename the notebook --- docs/Tutorial_VALMOD.ipynb | 178 +++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 docs/Tutorial_VALMOD.ipynb diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb new file mode 100644 index 000000000..86e244817 --- /dev/null +++ b/docs/Tutorial_VALMOD.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c7a27406", + "metadata": {}, + "source": [ + "In this tutorial, we would like to implement VALMOD algorithm proposed in paper [VALMOD](https://arxiv.org/pdf/2008.13447.pdf), and reproduce its results as closely as possible.\n", + "\n", + "The **VAriable Length MOtif Discovery (VALMOD)** algorithm takes time series `T` and a range of subsequence length `[min_m, max_m]`, and find motifs and discords." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0adbe18a", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import stumpy\n", + "from stumpy import core, config\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" + ] + }, + { + "cell_type": "markdown", + "id": "e9d48c97", + "metadata": {}, + "source": [ + "# 1- Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "b0423978", + "metadata": {}, + "source": [ + "**Notation:** $T_{i,m} = T[i:i+m]$, a subsequence of `T` that starts at index `i` and has length `m`. " + ] + }, + { + "cell_type": "markdown", + "id": "4a4af7fd", + "metadata": {}, + "source": [ + "## Motif discovery" + ] + }, + { + "cell_type": "markdown", + "id": "78ac5b0f", + "metadata": {}, + "source": [ + "For a given motif pair $\\{T_{idx,m},T_{nn\\_idx,n}\\}$, Motif set $S^{m}_{r}$ is a set of subsequences of length `m` that has `distance < r` to either $T_{idx,m}$ or $T_{nn\\_idx,n}$. And, the cardinality of set is called the frequency of the motif set.\n", + "\n", + "We would like to find set $S^{*} = \\bigcup\\limits_{m=min\\_m}^{max\\_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", + "\n", + "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty because both these two subsequences start from the same index." + ] + }, + { + "cell_type": "markdown", + "id": "7fc09927", + "metadata": {}, + "source": [ + "## Discord Discovery" + ] + }, + { + "cell_type": "markdown", + "id": "0f4ee615", + "metadata": {}, + "source": [ + "First, we need to provide a few definitions...\n", + "\n", + "**$n^{th}$ best match**: For the subsequence $T_{i,m}$, its $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", + "\n", + "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?) best match.
\n", + "\n", + "**NOTE**:
\n", + "Why should we care about $n^{th}$ discord (n>1)? We provide a simple example below:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37fdbb26", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "T = np.random.uniform(-100,100,size=3000)\n", + "m = 200\n", + "i, j = 100, 1500\n", + "\n", + "T[i:i+m] = 0\n", + "T[j:j+m] = 0\n", + "\n", + "plt.plot(T)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cb3a3940", + "metadata": {}, + "source": [ + "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors rather than just 1NN. In discord discovery, it is called twin-freak problem (see [Tutorial](https://cci.drexel.edu/bigdata/bigdata2017/files/Tutorial4.pdf)). It happens when the (same) anomally occurs more than once. In our example above, the anomaly occurs twice. Therefore, we should be able to detect it if we consider 2nd nearest neighbor. \n", + "\n", + "For further details, see Fig. 2 of the paper. Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" + ] + }, + { + "cell_type": "markdown", + "id": "45eeecf5", + "metadata": {}, + "source": [ + "**Variable-length Top-k $n^{th}$ Discord Discovery:**
\n", + "Given a time series `T`, a subsequence length-range `[min_m, max_m]`,`K`, and `N`, we want to find **top-k $n^{th}$ discord** for each `k` in $\\{1,...,K\\}$, for each `n` in $\\{1,...,N\\}$, and for all `m` in $\\{min\\_m,...,max\\_m\\}$." + ] + }, + { + "cell_type": "markdown", + "id": "e503fb0a", + "metadata": {}, + "source": [ + "# 2-Lower Bound of Distance Profile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71517d38", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 46ed1f654f90df73abd37ab0a3e9c0724b8b9950 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 17 Apr 2022 13:43:09 -0600 Subject: [PATCH 42/64] Removed redundant equation --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 19 +++---------------- 1 file changed, 3 insertions(+), 16 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 952819e1e..aecfbb8d4 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -257,7 +257,7 @@ "\\begin{align}\n", " \\alpha_{t} \\triangleq{}& \n", " {\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}} \\quad (2)\n", " } \n", " \\\\\n", "\\end{align}\n", @@ -282,23 +282,10 @@ "\\begin{align}\n", " LB ={}& \n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - " \\sqrt{\\min f(\\mu^{'},\\sigma^{'})} \\quad (2)\n", + " \\sqrt{\\min \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}}} \\quad (3)\n", " \\\\\n", " f(\\mu^{'}, \\sigma^{'}) ={}&\n", - " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \\quad (3)\n", - " \\\\\n", - " \\alpha_{t} ={}& \n", - " \\frac{\n", - " T[i+t-1] - \\mu^{'}\n", - " }{\n", - " \\sigma^{'}\n", - " } \n", - " - \n", - " \\frac{\n", - " T[j+t-1] - \\mu_{j,m+k}\n", - " }{\n", - " \\sigma_{j,m}\n", - " } \\quad (4)\n", + " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \\quad (4)\n", " \\\\\n", "\\end{align}\n", "$$\n" From a2754b56e5e8dd05c740e466ed2e2a9b4fe4b2fe Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 17 Apr 2022 13:52:33 -0600 Subject: [PATCH 43/64] FIXED spelling errors --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index aecfbb8d4..7eb2d8304 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -621,7 +621,7 @@ "id": "0c839937", "metadata": {}, "source": [ - "Now, recall that the pearson correlation $\\rho_{ij}$ between two subsequenes of lenght $m$ is defined as follows:" + "Now, recall that the pearson correlation $\\rho_{ij}$ between two subsequences starting at locations $i$ and $j$, respectively, and both of length $m$ is defined as follows:" ] }, { @@ -909,7 +909,7 @@ "id": "978473a2", "metadata": {}, "source": [ - "In the calculations above, we subsituted 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequenec with itself is 1." + "In the calculations above, we subsituted 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequence with itself is 1." ] }, { From 87c53321425d8696ca975f65b62501fbe5212d0e Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 17 Apr 2022 14:59:01 -0600 Subject: [PATCH 44/64] minor changes --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 61 +++++++++---------- 1 file changed, 29 insertions(+), 32 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 7eb2d8304..4afba90a6 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -761,6 +761,26 @@ " \\right]\n", " ={}& 0\n", " \\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "51235736", + "metadata": {}, + "source": [ + "In the calculations above, we subsituted 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequence with itself is 1." + ] + }, + { + "cell_type": "markdown", + "id": "182b8064", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", " \\frac{1}{\\sigma^{'}}\n", " \\left[\n", " \\left(\n", @@ -781,18 +801,7 @@ " \\mu_{j,m+k} \\cdot m\\mu_{i,m}\n", " \\right]\n", " ={}& 0\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "182b8064", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", + " \\\\\n", " \\frac{1}{\\sigma^{'}\\sigma_{j,m}}\n", " \\left[\n", " \\sigma_{j,m}\\left(\n", @@ -904,14 +913,6 @@ "$$\n" ] }, - { - "cell_type": "markdown", - "id": "978473a2", - "metadata": {}, - "source": [ - "In the calculations above, we subsituted 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequence with itself is 1." - ] - }, { "cell_type": "markdown", "id": "6adaea06", @@ -1141,7 +1142,7 @@ "id": "92abd2a2", "metadata": {}, "source": [ - "**Start with equation (3):**" + "**Start with equation (4):**" ] }, { @@ -1167,7 +1168,7 @@ "id": "7afe0a3d", "metadata": {}, "source": [ - "And, we replace one of $\\alpha_{t}$ with its equivalent term provided in eq(4)..." + "And, we replace one of $\\alpha_{t}$ with its equivalent term provided in eq(2)..." ] }, { @@ -1485,7 +1486,7 @@ " }\n", " \\\\\n", " ={}&\n", - " m(1-\\rho_{ij}^{2})\n", + " m(1-\\rho_{ij}^{2}) \\quad (13)\n", "\\end{align} \n", "$$\n" ] @@ -1495,7 +1496,7 @@ "id": "64dc1027", "metadata": {}, "source": [ - "**Finally, with eq(2), the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" + "**Finally, with eq(3), the lower-bound `LB` for distance profile of `T[j:j+m+k]` is as follows:**" ] }, { @@ -1510,13 +1511,9 @@ " \\frac{\n", " \\sigma_{j,m}\n", " }{\\sigma_{j,m+k}\n", - " } \\sqrt{m (1 - \\rho_{ij}^{2})} \\quad \\text{if} \\, \\rho > 0\n", + " } \\sqrt{m (1 - \\rho_{ij}^{2})} \\quad \\text{if} \\, \\rho > 0 \\quad (14)\n", + " \\\\\n", " \\\\\n", - "\\end{align}\n", - "$$\n", - "\n", - "$$\n", - "\\begin{align}\n", " \\rho_{ij} ={}& \n", " \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", " \\\\\n", @@ -1562,7 +1559,7 @@ "id": "a4f11acc", "metadata": {}, "source": [ - "We start with eq(3), $f(\\mu^{'}, \\sigma^{'}) = \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}}$, and we replace $\\alpha_{t}$ with its equivalent term, see eq(4). Therefore:" + "We start with eq(4), $f(\\mu^{'}, \\sigma^{'}) = \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}}$, and we replace $\\alpha_{t}$ with its equivalent term, see eq(2). Therefore:" ] }, { @@ -2159,7 +2156,7 @@ "id": "372a014e", "metadata": {}, "source": [ - "We replace left-hand side $f(\\mu^{'},\\sigma^{'})$ with its equivalent term (16), and we replace $f(\\mu_{c}^{'},\\sigma_{c}^{'})$ with $m(1 - \\rho_{ij}^{2})$ as calculated before. Therefore:" + "We replace left-hand side $f(\\mu^{'},\\sigma^{'})$ with its equivalent term (16), and we replace $f(\\mu_{c}^{'},\\sigma_{c}^{'})$ with eq(13), i.e. $m(1 - \\rho_{ij}^{2})$. Therefore:" ] }, { From 8a3501051b186eb47dc3baed6d557e3d5919d275 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 17 Apr 2022 15:42:11 -0600 Subject: [PATCH 45/64] convert equations to base-zero indexing --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 284 +++++++++--------- 1 file changed, 141 insertions(+), 143 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 4afba90a6..b6591b9cb 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -45,33 +45,33 @@ "\\begin{align}\n", " d^{(m+k)}_{j,i} ={}& \n", " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", - " \\sum\\limits_{t=1}^{m+k}{\n", + " \\sum\\limits_{t=0}^{m+k-1}{\n", " \\bigg\\lvert{\n", - " T[i+t-1] - T[j+t-1]\n", + " T[i+t] - T[j+t]\n", " }\\bigg\\rvert\n", " }^{p}\n", " }\n", " \\\\\n", " ={}&\n", " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", - " \\sum\\limits_{t=1}^{m}{\n", + " \\sum\\limits_{t=0}^{m-1}{\n", " \\bigg\\lvert{\n", - " T[i+t-1] - T[j+t-1]\n", + " T[i+t] - T[j+t]\n", " }\\bigg\\rvert\n", " }^{p}\n", " +\n", - " \\sum\\limits_{t=m+1}^{m+k}{\n", + " \\sum\\limits_{t=m}^{m+k-1}{\n", " \\bigg\\lvert{\n", - " T[i+t-1] - T[j+t-1]\n", + " T[i+t] - T[j+t]\n", " }\\bigg\\rvert\n", " }^{p}\n", " }\n", " \\\\\n", " \\geq{}&\n", " \\sqrt[\\leftroot{5}\\uproot{5}p]{\n", - " \\sum\\limits_{t=1}^{m}{\n", + " \\sum\\limits_{t=0}^{m-1}{\n", " \\bigg\\lvert{\n", - " T[i+t-1] - T[j+t-1]\n", + " T[i+t] - T[j+t]\n", " }\\bigg\\rvert\n", " }^{p}\n", " }\n", @@ -143,24 +143,24 @@ "$$\n", "\\begin{align}\n", " d^{(m+k)}_{j,i} ={}& \n", - " \\sqrt{\\sum\\limits_{t=1}^{m+k}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " \\sqrt{\\sum\\limits_{t=0}^{m+k-1}{{\n", + " \\left(\\frac{T[i+t] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", " }^{2}}} \n", " \\\\\n", " d^{(m+k)}_{j,i} ={}& \n", " \\sqrt{\n", - " \\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " \\sum\\limits_{t=0}^{m-1}{{\n", + " \\left(\\frac{T[i+t] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", " }^{2}}\n", " +\n", - " \\sum\\limits_{t=m+1}^{m+k}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " \\sum\\limits_{t=m}^{m+k-1}{{\n", + " \\left(\\frac{T[i+t] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", " }^{2}}\n", " } \n", " \\\\\n", " \\geq{}&\n", - " \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " \\sqrt{\\sum\\limits_{t=0}^{m-1}{{\n", + " \\left(\\frac{T[i+t] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", " }^{2}}}\n", " \\\\\n", "\\end{align}\n", @@ -184,29 +184,29 @@ "$$\n", "\\begin{align}\n", " LB ={}& \n", - " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", - " \\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", + " \\min \\sqrt{\\sum\\limits_{t=0}^{m-1}{{\n", + " \\left(\\frac{T[i+t] - \\mu_{i,m+k}}{\\sigma_{i,m+k}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m+k}}\\right)\n", " }^{2}}} \n", " \\\\\n", " ={}&\n", - " \\min \\sqrt{\\sum\\limits_{t=1}^{m}{{\n", + " \\min \\sqrt{\\sum\\limits_{t=0}^{m-1}{{\n", " \\left[\\frac{1}{\\sigma_{j,m+k}}\n", " \\left(\n", - " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{1}\n", + " \\frac{T[i+t] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t] - \\mu_{j,m+k}}{1}\n", " \\right)\n", " \\right]\n", " }^{2}}}\n", " \\\\\n", " ={}&\n", " \\min \\sqrt{\n", - " \\sum\\limits_{t=1}^{m}{{\n", + " \\sum\\limits_{t=0}^{m-1}{{\n", " \\left[\n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m}}\n", " \\frac{1}{\\sigma_{j,m+k}}\n", " \\left(\n", - " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", + " \\frac{T[i+t] - \\mu_{i,m+k}}{\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", " - \n", - " \\frac{T[j+t-1] - \\mu_{j,m+k}}{1}\n", + " \\frac{T[j+t] - \\mu_{j,m+k}}{1}\n", " \\right)\n", " \\right]\n", " }^{2}\n", @@ -215,13 +215,13 @@ " \\\\\n", " ={}&\n", " \\min \\sqrt{\n", - " \\sum\\limits_{t=1}^{m}{{\n", + " \\sum\\limits_{t=0}^{m-1}{{\n", " \\left[\n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", " \\left(\n", - " \\frac{T[i+t-1] - \\mu_{i,m+k}}{\\sigma_{j,m}\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", + " \\frac{T[i+t] - \\mu_{i,m+k}}{\\sigma_{j,m}\\frac{\\sigma_{i,m+k}}{\\sigma_{j,m+k}}} \n", " - \n", - " \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", " \\right)\n", " \\right]\n", " }^{2}\n", @@ -229,7 +229,7 @@ " }\n", " \\\\\n", " ={}&\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=1}^{m}{\\left(\\frac{T[i+t-1] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}} \\quad(1)\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}} \\times \\min \\sqrt{\\sum\\limits_{t=0}^{m-1}{\\left(\\frac{T[i+t] - \\mu_{i,m+k}}{\\frac{\\sigma_{j,m} \\sigma_{i,m+k}}{\\sigma_{j,m+k}}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right)^{2}}} \\quad(1)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -257,7 +257,7 @@ "\\begin{align}\n", " \\alpha_{t} \\triangleq{}& \n", " {\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}} \\quad (2)\n", + " \\frac{T[i+t] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m}} \\quad (2)\n", " } \n", " \\\\\n", "\\end{align}\n", @@ -282,10 +282,10 @@ "\\begin{align}\n", " LB ={}& \n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - " \\sqrt{\\min \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}}} \\quad (3)\n", + " \\sqrt{\\min \\sum \\limits_{t=0}^{m-1} {\\alpha_t^{2}}} \\quad (3)\n", " \\\\\n", " f(\\mu^{'}, \\sigma^{'}) ={}&\n", - " \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}} \\quad (4)\n", + " \\sum \\limits_{t=0}^{m-1} {\\alpha_t^{2}} \\quad (4)\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -333,24 +333,24 @@ "$$\n", "\\begin{align}\n", " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m}{\n", + " \\sum \\limits_{t=0}^{m-1}{\n", " \\frac{\\partial{(\\alpha_{t}^{2})}}{\\partial{\\mu^{'}}}\n", " }\n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m}{\n", + " \\sum \\limits_{t=0}^{m-1}{\n", " 2\\frac{\\partial{(\\alpha_{t})}}{\\partial{\\mu^{'}}}\\alpha_{t}\n", " }\n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\mu^{'}}} ={}&\n", - " \\sum \\limits_{t=1}^{m} {\n", + " \\sum \\limits_{t=0}^{m-1} {\n", " 2\\left(\n", " \\frac{-1}{\\sigma^{'}}\n", " \\right)\n", " \\alpha_{t}} \n", " \\\\\n", " 0 ={}&\n", - " \\frac{-2}{\\sigma^{'}}\\sum \\limits_{t=1}^{m}{\\alpha_{t}}\n", + " \\frac{-2}{\\sigma^{'}}\\sum \\limits_{t=0}^{m-1}{\\alpha_{t}}\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -373,7 +373,7 @@ "\n", "$$\n", "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m}{\\alpha_{t}} = 0 \\quad (7)\n", + " \\sum \\limits_{t=0}^{m-1}{\\alpha_{t}} = 0 \\quad (7)\n", "\\end{align}\n", "$$\n" ] @@ -394,14 +394,14 @@ "\n", "$$\n", "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m} \\alpha_{t} ={}& \n", + " \\sum \\limits_{t=0}^{m-1} \\alpha_{t} ={}& \n", " 0\n", " \\\\\n", - " \\sum \\limits_{t=1}^{m} {\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}} ={}& \n", + " \\sum \\limits_{t=0}^{m-1} {\\frac{T[i+t] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m}}} ={}& \n", " 0\n", " \\\\\n", - " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=1}^{m}T[i+t-1] - \\sum \\limits_{t=1}^{m} \\mu^{'}\\right) - \n", - " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=1}^{m}T[j+t-1] - \\sum \\limits_{t=1}^{m} \\mu_{j,m+k}\\right) ={}& \n", + " \\frac{1}{\\sigma^{'}}\\left(\\sum \\limits_{t=0}^{m-1}T[i+t] - \\sum \\limits_{t=0}^{m-1} \\mu^{'}\\right) - \n", + " \\frac{1}{\\sigma_{j,m}}\\left(\\sum \\limits_{t=0}^{m-1}T[j+t] - \\sum \\limits_{t=0}^{m-1} \\mu_{j,m+k}\\right) ={}& \n", " 0\n", " \\\\\n", " \\frac{1}{\\sigma^{'}}\\left(m\\mu_{i,m} - m\\mu^{'}\\right) - \n", @@ -436,50 +436,50 @@ "$$\n", "\\begin{align}\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m}{\n", + " \\sum \\limits_{t=0}^{m-1}{\n", " \\frac{\\partial{(\\alpha_{t}^{2})}}{\\partial{\\sigma^{'}}}\n", " }\n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}& \n", - " \\sum \\limits_{t=1}^{m}{\n", + " \\sum \\limits_{t=0}^{m-1}{\n", " 2\\frac{\\partial{(\\alpha_{t})}}{\\partial{\\sigma^{'}}}\\alpha_{t}\n", " }\n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", - " \\sum \\limits_{t=1}^{m} {\n", + " \\sum \\limits_{t=0}^{m-1} {\n", " 2 \\left(\n", - " \\frac{-\\left({T[i+t-1] - \\mu^{'}}\\right)}{\\sigma^{'2}}\n", + " \\frac{-\\left({T[i+t] - \\mu^{'}}\\right)}{\\sigma^{'2}}\n", " \\right)\n", " \\alpha_{t}} \n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", - " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=1}^{m}{\\left({T[i+t-1] - \\mu^{'}}\\right) \\alpha_{t}}\n", + " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=0}^{m-1}{\\left({T[i+t] - \\mu^{'}}\\right) \\alpha_{t}}\n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", - " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=1}^{m}{\\left({T[i+t-1]\\alpha_{t} - \\mu^{'}\\alpha_{t}}\\right)}\n", + " \\frac{-2}{\\sigma^{'2}}\\sum \\limits_{t=0}^{m-1}{\\left({T[i+t]\\alpha_{t} - \\mu^{'}\\alpha_{t}}\\right)}\n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", " \\frac{-2}{\\sigma^{'2}}\n", " {\\left(\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " \\sum \\limits_{t=0}^{m-1}{T[i+t]\\alpha_{t}} \n", " - \n", - " \\sum \\limits_{t=1}^{m}{\\mu^{'}\\alpha_{t}}\n", + " \\sum \\limits_{t=0}^{m-1}{\\mu^{'}\\alpha_{t}}\n", " \\right)\n", " }\n", " \\\\\n", " \\frac{\\partial{f}}{\\partial{\\sigma^{'}}} ={}&\n", " \\frac{-2}{\\sigma^{'2}}\n", " {\\left(\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " \\sum \\limits_{t=0}^{m-1}{T[i+t]\\alpha_{t}} \n", " - \n", - " \\mu^{'}\\sum \\limits_{t=1}^{m}{\\alpha_{t}}\n", + " \\mu^{'}\\sum \\limits_{t=0}^{m-1}{\\alpha_{t}}\n", " \\right)\n", " }\n", " \\\\\n", " 0 ={}&\n", " \\frac{-2}{\\sigma^{'2}}\n", " {\\left(\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " \\sum \\limits_{t=0}^{m-1}{T[i+t]\\alpha_{t}} \n", " - \n", " \\mu^{'}\\cdot 0\n", " \\right)\n", @@ -488,7 +488,7 @@ " 0 ={}&\n", " \\frac{-2}{\\sigma^{'2}}\n", " {\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} \n", + " \\sum \\limits_{t=0}^{m}{T[i+t]\\alpha_{t}} \n", " }\n", "\\end{align}\n", "$$\n" @@ -499,7 +499,7 @@ "id": "1340817b", "metadata": {}, "source": [ - "Note: In the calculations above, we substituted 0 for $\\sum \\limits_{t=1}^{m}{\\alpha_{t}}$ according to eq(7)." + "Note: In the calculations above, we substituted 0 for $\\sum \\limits_{t=0}^{m-1}{\\alpha_{t}}$ according to eq(7)." ] }, { @@ -518,7 +518,7 @@ "\n", "$$\n", "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}} ={}&\n", + " \\sum \\limits_{t=0}^{m-1}{T[i+t]\\alpha_{t}} ={}&\n", " 0 \\quad (9)\n", "\\end{align}\n", "$$\n" @@ -540,7 +540,7 @@ "\n", "$$\n", "\\begin{align}\n", - " \\sum \\limits_{t=1}^{m} T[i+t-1] \\left(\\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right) = 0\n", + " \\sum \\limits_{t=0}^{m-1} T[i+t] \\left(\\frac{T[i+t] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m}}\\right) = 0\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -554,43 +554,43 @@ "\n", "$$\n", "\\begin{align}\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\sum\\limits_{t=0}^{m-1}T[i+t-1] \n", " \\left(\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}}\n", + " \\frac{T[i+t] - \\mu^{'}}{\\sigma^{'}}\n", " \\right)\n", " - \n", - " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\sum\\limits_{t=0}^{m-1}T[i+t-1] \n", " \\left(\n", - " \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", " \\right)\n", " ={}& 0\n", " \\\\\n", " \\\\\n", " \\frac{1}{\\sigma^{'}}\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\sum\\limits_{t=0}^{m-1}T[i+t] \n", " \\left(\n", - " T[i+t-1] - \\mu^{'}\n", + " T[i+t] - \\mu^{'}\n", " \\right)\n", " - \n", " \\frac{1}{\\sigma_{j,m}}\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1] \n", + " \\sum\\limits_{t=0}^{m-1}T[i+t-1] \n", " \\left(\n", - " T[j+t-1] - \\mu_{j,m+k}\n", + " T[j+t] - \\mu_{j,m+k}\n", " \\right)\n", " ={}& 0\n", " \\\\\n", " \\\\\n", " \\frac{1}{\\sigma^{'}}\n", " \\left(\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", + " \\sum\\limits_{t=0}^{m-1}T[i+t]T[i+t]\n", " -\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1]\\mu^{'}\n", + " \\sum\\limits_{t=0}^{m-1}T[i+t]\\mu^{'}\n", " \\right) \n", " - \\\\\n", " \\frac{1}{\\sigma_{j,m}}\n", " \\left(\n", - " {\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] \n", - " -\\sum \\limits_{t=1}^{m}T[i+t-1]\\mu_{j,m+k}\n", + " {\\sum\\limits_{t=0}^{m-1}T[i+t]T[j+t] \n", + " -\\sum \\limits_{t=0}^{m-1}T[i+t]\\mu_{j,m+k}\n", " }\n", " \\right) \n", " ={}& \n", @@ -599,16 +599,16 @@ " \\\\\n", " \\frac{1}{\\sigma^{'}}\n", " \\left(\n", - " \\sum \\limits_{t=1}^{m}T[i+t-1]T[i+t-1]\n", + " \\sum \\limits_{t=0}^{m-1}T[i+t]T[i+t]\n", " -\n", - " \\mu^{'}\\sum\\limits_{t=1}^{m} T[i+t-1]\n", + " \\mu^{'}\\sum\\limits_{t=0}^{m-1} T[i+t]\n", " \\right) \n", " - \\\\\n", " \\frac{1}{\\sigma_{j,m}}\n", " \\left(\n", - " \\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1]\n", + " \\sum\\limits_{t=0}^{m-1}T[i+t]T[j+t]\n", " -\n", - " \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", + " \\mu_{j,m+k}\\sum \\limits_{t=0}^{m-1}T[i+t]\n", " \\right) \n", " ={}& \n", " 0 \\quad (*)\n", @@ -648,8 +648,8 @@ " \\\\\n", " ={}&\n", " \\frac{\n", - " \\frac{1}{m}\\sum\\limits_{t=1}^{m}\n", - " (T[i+t-1] - \\mu_{i,m})(T[j+t-1] - \\mu_{j,m})\n", + " \\frac{1}{m}\\sum\\limits_{t=0}^{m-1}\n", + " (T[i+t] - \\mu_{i,m})(T[j+t] - \\mu_{j,m})\n", " }\n", " {\n", " \\sigma_{i,m}\\sigma_{j,m}\n", @@ -657,40 +657,40 @@ " \\\\\n", " ={}&\n", " \\frac{\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]T[j+t-1] \n", + " \\sum\\limits_{t=0}^{m-1}\n", + " T[i+t]T[j+t] \n", " -\n", - " \\sum\\limits_{t=1}^{m}\n", - " \\mu_{i,m}T[j+t-1]\n", + " \\sum\\limits_{t=0}^{m-1}\n", + " \\mu_{i,m}T[j+t]\n", " -\n", - " \\sum\\limits_{t=1}^{m}\n", - " \\mu_{j,m}T[i+t-1]\n", + " \\sum\\limits_{t=0}^{m-1}\n", + " \\mu_{j,m}T[i+t]\n", " +\n", - " \\sum\\limits_{t=1}^{m}\\mu_{i,m}\\mu_{j,m}\n", + " \\sum\\limits_{t=0}^{m-1}\\mu_{i,m}\\mu_{j,m}\n", " }{\n", " m\\sigma_{i,m}\\sigma_{j,,m}\n", " }\n", " \\\\\n", " ={}&\n", " \\frac{\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]T[j+t-1] \n", + " \\sum\\limits_{t=0}^{m-1}\n", + " T[i+t]T[j+t] \n", " -\n", - " \\mu_{i,m}\\sum\\limits_{t=1}^{m}\n", - " T[j+t-1]\n", + " \\mu_{i,m}\\sum\\limits_{t=0}^{m-1}\n", + " T[j+t]\n", " -\n", - " \\mu_{j,m}\\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]\n", + " \\mu_{j,m}\\sum\\limits_{t=0}^{m-1}\n", + " T[i+t]\n", " +\n", - " \\sum\\limits_{t=1}^{m}\\mu_{i,m}\\mu_{j,m}\n", + " \\sum\\limits_{t=0}^{m-1}\\mu_{i,m}\\mu_{j,m}\n", " }{\n", " m\\sigma_{i,m}\\sigma_{j,m}\n", " }\n", " \\\\\n", " ={}&\n", " \\frac{\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]T[j+t-1] \n", + " \\sum\\limits_{t=0}^{m-1}\n", + " T[i+t]T[j+t] \n", " -\n", " \\mu_{i,m}\\cdot m\\mu_{j,m}\n", " -\n", @@ -703,8 +703,8 @@ " \\\\\n", " ={}&\n", " \\frac{\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]T[j+t-1] \n", + " \\sum\\limits_{t=0}^{m-1}\n", + " T[i+t]T[j+t] \n", " -\n", " m\\mu_{i,m}\\mu_{j,m}\n", " }{\n", @@ -721,7 +721,7 @@ "metadata": {}, "source": [ "Note: we can rearrange the pearson correlation equation as below:
\n", - "$\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" + "$\\sum \\limits_{t=0}^{m-1}T[i+t]T[j+t] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" ] }, { @@ -767,7 +767,7 @@ }, { "cell_type": "markdown", - "id": "51235736", + "id": "538ba69e", "metadata": {}, "source": [ "In the calculations above, we subsituted 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequence with itself is 1." @@ -1154,10 +1154,10 @@ "$$\n", "\\begin{align}\n", " f(\\mu^{'},\\sigma^{'}) ={}&\n", - " \\sum \\limits_{t=1}^{m}\\alpha_{t}^{2}\n", + " \\sum \\limits_{t=0}^{m-1}\\alpha_{t}^{2}\n", " \\\\\n", " ={}&\n", - " \\sum \\limits_{t=1}^{m}\\alpha_{t} \\cdot \\alpha_{t}\n", + " \\sum \\limits_{t=0}^{m-1}\\alpha_{t} \\cdot \\alpha_{t}\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -1180,10 +1180,10 @@ "$$\n", "\\begin{align}\n", " f_{min}(\\mu^{'},\\sigma^{'}) ={}&\n", - " \\sum\\limits_{t=1}^{m}{\n", + " \\sum\\limits_{t=0}^{m-1}{\n", " {\\alpha_{t}\n", " \\left(\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\frac{T[i+t] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", " \\right)\n", " }}\n", " \\\\\n", @@ -1191,18 +1191,18 @@ " {\n", " \\frac{1}{\\sigma^{'}}\n", " \\left(\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]\\alpha_{t} \n", + " \\sum\\limits_{t=0}^{m-1}\n", + " T[i+t]\\alpha_{t} \n", " - \n", - " \\sum\\limits_{t=1}^{m}\n", + " \\sum\\limits_{t=0}^{m-1}\n", " \\mu^{'}\\alpha_{t}\n", " \\right)\n", " - \\frac{1}{\\sigma_{j,m}}\n", " \\left(\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[j+t-1]\\alpha_{t} \n", + " \\sum\\limits_{t=0}^{m-1}\n", + " T[j+t]\\alpha_{t} \n", " - \n", - " \\sum\\limits_{t=1}^{m}\n", + " \\sum\\limits_{t=0}^{m-1}\n", " \\mu_{j,m+k}\\alpha_{t}\n", " \\right)\n", " } \n", @@ -1211,17 +1211,17 @@ " {\n", " \\frac{1}{\\sigma^{'}}\n", " \\left(\n", - " \\sum\\limits_{t=1}^{m}\n", - " T[i+t-1]\\alpha_{t} \n", + " \\sum\\limits_{t=0}^{m-1}\n", + " T[i+t]\\alpha_{t} \n", " - \n", - " \\mu^{'}\\sum\\limits_{t=1}^{m}\\alpha_{t}\n", + " \\mu^{'}\\sum\\limits_{t=0}^{m-1}\\alpha_{t}\n", " \\right)\n", " - \n", " \\frac{1}{\\sigma_{j,m}}\n", " \\left(\n", - " \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} \n", + " \\sum\\limits_{t=0}^{m-1}T[j+t]\\alpha_{t} \n", " - \n", - " \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}\\alpha_{t}\n", + " \\mu_{j,m+k}\\sum\\limits_{t=0}^{m-1}\\alpha_{t}\n", " \\right)\n", " } \n", " \\\\\n", @@ -1234,7 +1234,7 @@ "id": "4a9e3f03", "metadata": {}, "source": [ - "And, now with help of eq(7), $\\sum\\limits_{t=1}^{m}{\\alpha_{t}}=0$, and the eq(9), $\\sum\\limits_{t=1}^{m}{T[i+t-1]\\alpha_{t}}=0$, we will have:" + "And, now with help of eq(7), $\\sum\\limits_{t=0}^{m-1}{\\alpha_{t}}=0$, and the eq(9), $\\sum\\limits_{t=0}^{m-1}{T[i+t]\\alpha_{t}}=0$, we will have:" ] }, { @@ -1254,21 +1254,21 @@ " - \n", " \\frac{1}{\\sigma_{j,m}}\n", " \\left(\n", - " \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t} - \\mu_{j,m+k}\\cdot 0\n", + " \\sum\\limits_{t=0}^{m-1}T[j+t]\\alpha_{t} - \\mu_{j,m+k}\\cdot 0\n", " \\right)\n", " } \n", " \\\\ \n", " ={}&\n", " {\n", - " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=1}^{m}T[j+t-1]\\alpha_{t}\n", + " - \\frac{1}{\\sigma_{j,m}} \\sum\\limits_{t=0}^{m-1}T[j+t]\\alpha_{t}\n", " } \n", " \\\\\n", " ={}&\n", " {\n", " - \\frac{1}{\\sigma_{j,m}} \n", - " \\sum\\limits_{t=1}^{m}{\\left[\n", - " T[j+t-1]\\left(\n", - " \\frac{T[i+t-1] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t-1] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", + " \\sum\\limits_{t=0}^{m-1}{\\left[\n", + " T[j+t]\\left(\n", + " \\frac{T[i+t] - \\mu^{'}}{\\sigma^{'}} - \\frac{T[j+t] - \\mu_{j,m+k}}{\\sigma_{j,m}}\n", " \\right)\n", " \\right]\n", " }\n", @@ -1277,9 +1277,9 @@ " ={}&\n", " {\n", " - \\frac{1}{\\sigma_{j,m}} \n", - " \\sum\\limits_{t=1}^{m}{\n", + " \\sum\\limits_{t=0}^{m-1}{\n", " \\left(\n", - " \\frac{T[i+t-1]T[j+t-1] - \\mu^{'}T[j+t-1]}{\\sigma^{'}} - \\frac{T[j+t-1]T[j+t-1] - \\mu_{j,m+k}T[j+t-1]}{\\sigma_{j,m}}\n", + " \\frac{T[i+t]T[j+t] - \\mu^{'}T[j+t]}{\\sigma^{'}} - \\frac{T[j+t]T[j+t] - \\mu_{j,m+k}T[j+t]}{\\sigma_{j,m}}\n", " \\right)\n", " }\n", " } \n", @@ -1288,9 +1288,9 @@ " {- \\frac{1}{\\sigma_{j,m}} \n", " {\n", " \\left(\n", - " \\frac{\\sum\\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - \\mu^{'}\\sum\\limits_{t=1}^{m}T[j+t-1]}{\\sigma^{'}} \n", + " \\frac{\\sum\\limits_{t=0}^{m-1}T[i+t]T[j+t] - \\mu^{'}\\sum\\limits_{t=0}^{m-1}T[j+t]}{\\sigma^{'}} \n", " - \n", - " \\frac{\\sum\\limits_{t=1}^{m}T[j+t-1]T[j+t-1] - \\mu_{j,m+k}\\sum\\limits_{t=1}^{m}T[j+t-1]}{\\sigma_{j,m}}\n", + " \\frac{\\sum\\limits_{t=0}^{m-1}T[j+t]T[j+t] - \\mu_{j,m+k}\\sum\\limits_{t=0}^{m-1}T[j+t]}{\\sigma_{j,m}}\n", " \\right)\n", " }\n", " } \n", @@ -1515,7 +1515,7 @@ " \\\\\n", " \\\\\n", " \\rho_{ij} ={}& \n", - " \\frac{\\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", + " \\frac{\\sum \\limits_{t=0}^{m-1}T[i+t]T[j+t] - m\\mu_{i,m}\\mu_{j,m} }{m\\sigma_{i,m}\\sigma_{j,m}}\n", " \\\\\n", "\\end{align}\n", "$$\n" @@ -1559,7 +1559,7 @@ "id": "a4f11acc", "metadata": {}, "source": [ - "We start with eq(4), $f(\\mu^{'}, \\sigma^{'}) = \\sum \\limits_{t=1}^{m} {\\alpha_t^{2}}$, and we replace $\\alpha_{t}$ with its equivalent term, see eq(2). Therefore:" + "We start with eq(4), $f(\\mu^{'}, \\sigma^{'}) = \\sum \\limits_{t=0}^{m-1} {\\alpha_t^{2}}$, and we replace $\\alpha_{t}$ with its equivalent term, see eq(2). Therefore:" ] }, { @@ -1571,16 +1571,16 @@ "$$\n", "\\begin{align}\n", "f(\\mu^{'},\\sigma^{'}) ={}& \n", - " \\sum \\limits_{t=1}^{m}\n", + " \\sum \\limits_{t=0}^{m-1}\n", " \\left(\n", " \\frac{\n", - " T[i+t-1] - \\mu^{'}\n", + " T[i+t] - \\mu^{'}\n", " }{\n", " \\sigma^{'}\n", " } \n", " - \n", " \\frac{\n", - " T[j+t-1] - \\mu_{j,m+k}\n", + " T[j+t] - \\mu_{j,m+k}\n", " }{\n", " \\sigma_{j,m}\n", " }\n", @@ -1607,19 +1607,19 @@ "\n", "$$\n", "\\begin{align}\n", - "f(\\mu^{'},\\sigma^{'}) ={}& \n", - " \\sum \\limits_{t=1}^{m}\n", + " f(\\mu^{'},\\sigma^{'}) ={}& \n", + " \\sum \\limits_{t=0}^{m-1}\n", " \\left[\n", " \\left(\n", " \\frac{\n", - " T[i+t-1] - \\mu^{'}\n", + " T[i+t] - \\mu^{'}\n", " }{\n", " \\sigma^{'}\n", " }\\right)^{2}\n", " +\n", " \\left(\n", " \\frac{\n", - " T[j+t-1] - \\mu_{j,m+k}\n", + " T[j+t] - \\mu_{j,m+k}\n", " }{\n", " \\sigma_{j,m}\n", " }\n", @@ -1627,12 +1627,12 @@ " -\n", " 2\n", " \\left(\\frac{\n", - " T[i+t-1] - \\mu^{'}\n", + " T[i+t] - \\mu^{'}\n", " }{\n", " \\sigma^{'}\n", " }\\right)\n", " \\left(\\frac{\n", - " T[j+t-1] - \\mu_{j,m+k}\n", + " T[j+t] - \\mu_{j,m+k}\n", " }{\n", " \\sigma_{j,m}\n", " }\n", @@ -1641,18 +1641,18 @@ " \\\\\n", " \\\\\n", " ={}&\n", - " \\sum \\limits_{t=1}^{m}\n", + " \\sum \\limits_{t=0}^{m-1}\n", " \\left[\n", " \\left(\n", " \\frac{\n", - " T[i+t-1]^{2} + \\mu^{'2} - 2T[i+t-1]\\mu^{'}\n", + " T[i+t]^{2} + \\mu^{'2} - 2T[i+t]\\mu^{'}\n", " }{\n", " \\sigma^{'2}\n", " }\\right)\n", " +\n", " \\left(\n", " \\frac{\n", - " T[j+t-1]^{2} + \\mu_{j,m+k}^{2} - 2 T[j+t-1]\\mu_{j,m+k}\n", + " T[j+t]^{2} + \\mu_{j,m+k}^{2} - 2 T[j+t]\\mu_{j,m+k}\n", " }{\n", " \\sigma_{j,m}^{2}\n", " }\n", @@ -1660,9 +1660,9 @@ " -\n", " 2\n", " \\left(\\frac{\n", - " T[i+t-1]T[j+t-1] \n", - " - T[i+t-1]\\mu_{j,m+k}\n", - " - T[j+t-1]\\mu^{'}\n", + " T[i+t]T[j+t] \n", + " - T[i+t]\\mu_{j,m+k}\n", + " - T[j+t]\\mu^{'}\n", " + \\mu^{'}\\mu_{j,m+k}\n", " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", @@ -1670,25 +1670,23 @@ " \\right)\n", " \\right]\n", " \\\\\n", + " \\\\\n", " ={}&\n", " \\frac{\n", - " \\sum \\limits_{t=1}^{m}T[i+t-1]^{2} + \\sum \\limits_{t=1}^{m}\\mu^{'2} - 2\\mu^{'}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", + " \\sum \\limits_{t=0}^{m-1}T[i+t]^{2} + \\sum \\limits_{t=0}^{m-1}\\mu^{'2} - 2\\mu^{'}\\sum \\limits_{t=0}^{m-1}T[i+t]\n", " }{\n", " \\sigma^{'2}\n", " }\n", " +\n", " \\frac{\n", - " \\sum \\limits_{t=1}^{m}T[j+t-1]^{2} + \\sum \\limits_{t=1}^{m}\\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[j+t-1]\n", - " }{\n", - " \\sigma_{j,m}^{2}\n", - " }\n", + " \\sum \\limits_{t=0}^{m-1}T[j+t]^{2} + \\sum \\limits_{t=0}^{m-1}\\mu_{j,m+k}^{2} - 2\\mu_{j,m+k}\\sum \\limits_{t=0}^{m-1}T[j+t]}{\\sigma_{j,m}^{2}}\n", " -\n", " 2\n", " \\frac{\n", - " \\sum \\limits_{t=1}^{m}T[i+t-1]T[j+t-1] \n", - " - \\mu_{j,m+k}\\sum \\limits_{t=1}^{m}T[i+t-1]\n", - " - \\mu^{'}\\sum \\limits_{t=1}^{m}T[j+t-1]\n", - " + \\sum \\limits_{t=1}^{m}\\mu^{'}\\mu_{j,m+k}\n", + " \\sum \\limits_{t=0}^{m-1}T[i+t]T[j+t] \n", + " - \\mu_{j,m+k}\\sum \\limits_{t=0}^{m-1}T[i+t]\n", + " - \\mu^{'}\\sum \\limits_{t=0}^{m-1}T[j+t]\n", + " + \\sum \\limits_{t=0}^{m-1}\\mu^{'}\\mu_{j,m+k}\n", " }{\n", " \\sigma^{'}\\sigma_{j,m}\n", " }\n", From 94dd218713d2efc91bd2407239b3cefee5d43f22 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 17 Apr 2022 16:21:40 -0600 Subject: [PATCH 46/64] minor changes --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index b6591b9cb..3436c5288 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -720,7 +720,7 @@ "id": "4880c751", "metadata": {}, "source": [ - "Note: we can rearrange the pearson correlation equation as below:
\n", + "We can rearrange the pearson correlation equation as follows:
\n", "$\\sum \\limits_{t=0}^{m-1}T[i+t]T[j+t] = m\\rho\\sigma_{i,m}\\sigma_{j,m} + m\\mu_{i,m}\\mu_{j,m}$ (\\*\\*)" ] }, @@ -767,7 +767,7 @@ }, { "cell_type": "markdown", - "id": "538ba69e", + "id": "3ab1a478", "metadata": {}, "source": [ "In the calculations above, we subsituted 1 for $\\rho_{ii}$ as the Pearson Correlation of a subsequence with itself is 1." @@ -1527,7 +1527,7 @@ "metadata": {}, "source": [ "**Note:**
\n", - "* Note that eq(12) is valid only for $\\rho_{ij} > 0$. Therefore, we can use the formula above to calculate $LB$ only if $\\rho_{ij} > 0$. \n", + "* Note that eq(12) is valid only for $\\rho_{ij} > 0$. Therefore, we can use the formula (14) to calculate $LB$ only if $\\rho_{ij} > 0$. \n", "* The pearson correlation, $\\rho_{ij}$, can be also obtained with help of $ED_{z-norm}$ between subsequences `T[i:i+m]` and `T[j:j+m]`.\n", "\n", "In fact: $d_{i,j}^{(m)} = \\sqrt{2m(1-\\rho_{ij})}$, where $d_{i,j}^{(m)}$ is the z-norm euclidean distance between two sequences of length `m` that start at index `i` and `j`.\n", @@ -2085,7 +2085,7 @@ "id": "125c27bc", "metadata": {}, "source": [ - "Therefore, according to eq(16), $f(\\mu^{'},\\sigma^{'}) = m \\left[1 + g(\\mu^{'},\\sigma^{'})\\right]$, we have: $f(\\mu^{'},\\sigma^{'}) \\geq m$, and according to eq(2), $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, we can see that:" + "Therefore, according to eq(16), $f(\\mu^{'},\\sigma^{'}) = m \\left[1 + g(\\mu^{'},\\sigma^{'})\\right]$, we have: $f(\\mu^{'},\\sigma^{'}) \\geq m$, and according to eq(3), $LB = \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\\sqrt{\\min f(\\mu^{'},\\sigma^{'})}$, we can see that:" ] }, { @@ -2179,7 +2179,7 @@ "id": "7988c834", "metadata": {}, "source": [ - "Therefore, we need to show inequation (20) is satisfied for all $\\rho_{i,j} \\geq 0$.
\n", + "Therefore, we need to show that inequation (20) is satisfied for all $\\mu^{'}$ and $\\sigma^{'}$ when $\\rho_{i,j} \\geq 0$.
\n", "We now subtitute eq(18) for $g(.)$. Thus:" ] }, From 86e0a33d24a87b8d7203bca025dfbf27bd30e3cc Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 17 Apr 2022 18:43:43 -0600 Subject: [PATCH 47/64] proof read --- docs/LowerBound_Dist_Profile_Derivation.ipynb | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/LowerBound_Dist_Profile_Derivation.ipynb b/docs/LowerBound_Dist_Profile_Derivation.ipynb index 3436c5288..e93c9cccf 100644 --- a/docs/LowerBound_Dist_Profile_Derivation.ipynb +++ b/docs/LowerBound_Dist_Profile_Derivation.ipynb @@ -282,7 +282,11 @@ "\\begin{align}\n", " LB ={}& \n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - " \\sqrt{\\min \\sum \\limits_{t=0}^{m-1} {\\alpha_t^{2}}} \\quad (3)\n", + " \\sqrt{\\min \\sum \\limits_{t=0}^{m-1} {\\alpha_t^{2}}} \n", + " \\\\\n", + " ={}&\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + " \\sqrt{\\min f(\\mu^{'}, \\sigma^{'})} \\quad (3)\n", " \\\\\n", " f(\\mu^{'}, \\sigma^{'}) ={}&\n", " \\sum \\limits_{t=0}^{m-1} {\\alpha_t^{2}} \\quad (4)\n", @@ -2124,7 +2128,7 @@ "id": "fc7711bb", "metadata": {}, "source": [ - "There is still one thing left to be done. We need to show that the LB discovered for $\\rho_{ij} \\gt 0$ is actually a global minimum. In other words, we need to show that the inequation below holds true for all $\\rho_{ij} \\gt 0$:" + "We need to show that the LB discovered for $\\rho_{ij} \\gt 0$ is actually a global minimum. In other words, we need to show that the inequation below holds true for all $\\rho_{ij} \\gt 0$:" ] }, { From 92e7840579f13c67673e30efc6231201189bca5b Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 17 Apr 2022 19:07:10 -0600 Subject: [PATCH 48/64] ADD LowerBound formula --- docs/Tutorial_VALMOD.ipynb | 61 ++++++++++++++++++++++++++++++++++---- 1 file changed, 56 insertions(+), 5 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 86e244817..366206b60 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -146,12 +146,63 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "71517d38", + "cell_type": "markdown", + "id": "8538f0e3", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Lower Bound (LB) for $d(T_{j,m+k}, T_{i,m+k})$ can be calculated as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "a7f08024", + "metadata": {}, + "source": [ + "**Non-normalized distance (p-norm):**" + ] + }, + { + "cell_type": "markdown", + "id": "297e8f9e", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "LB_{j,i}^{(m)} ={}& \n", + "d_{j,i}^{(m)}\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "7ff2e666", + "metadata": {}, + "source": [ + "**Normalized distance:**" + ] + }, + { + "cell_type": "markdown", + "id": "0f192dfa", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "LB_{j,i}^{(m)} ={}& \n", + "\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + "\\sqrt{\n", + "m\\left(\n", + "1 - \\max(\\rho_{j,i},0)^{2}\n", + "\\right)\n", + "}\n", + "\\\\\n", + "\\rho_{j,i} ={}& 1 - \\frac{d_{j,i}^{2}}{2m}\n", + "\\end{align}\n", + "$$\n" + ] } ], "metadata": { From bd5c133813809c85ec5b0c228b44d1722bf4ec5d Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 24 Apr 2022 09:03:16 -0600 Subject: [PATCH 49/64] Add intro on VALMOD algorithm --- docs/Tutorial_VALMOD.ipynb | 41 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 366206b60..216f5605a 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -24,6 +24,7 @@ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import heapq\n", "\n", "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" ] @@ -203,6 +204,46 @@ "\\end{align}\n", "$$\n" ] + }, + { + "cell_type": "markdown", + "id": "3b82e805", + "metadata": {}, + "source": [ + "**NOTE:** A better notation might be $LB_{j,i}^{m+k, m}$ as it shows the lower bound is for subsequence of length `m+k`, and it is calculated based on $\\rho_{j,i}$ of subsequences with length `m`. However, to simplify the notaton, we avoided using `m+k`. We expect the reader to remember that the calculated lower bound LB is for subsequences of length `m+k`, as shown in the $\\sigma_{j,m+k}$." + ] + }, + { + "cell_type": "markdown", + "id": "833c4f6b", + "metadata": {}, + "source": [ + "# 3-VALMOD algorithm\n", + "The valmod algorithm discovers motifs / discords for subsequence length range `(min_m, max_m)`. The algorithm starts with performing complete scan for length `min_m`, and as we see shortly, it extracts more infomation than just Matrix Profile values and their corresponding indices. Then, it uses those information to accelerate constructing matrix profile for length `(min_m+1, max_m)`. \n", + "\n", + "The main algorithm `VALMOD`, shown as Algorithm1 of the paper (see page 13). However, before implementing this function, we first implement the functions that are being called inside this algorithm. To be consistent with the paper, we use the paper's proposed name of function as title of each section. However, we may use a different name for the function in the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "f6cbecbd", + "metadata": {}, + "source": [ + "## 3-1- ComputeMatrixProfile (see page 15)\n", + "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `top-p` smallest values of each distance profile and their indices. More precisely, it just need those indices, the first smallest value and the p-th smallest value. (The parameter `p` should be set by the user.) \n", + "\n", + "In the paper, the authors used the formula LB to convert distances to LB on the go. So, as they scan pairs of subsequences, they keep track of the matrix profile and its index for each subsequence. They also convert distances to lb using LB formula to calculate the LB values for subsequences with length `m+1`, then, they use heap structure to discover `top-p` LB values for each distance profile. However, in this notebook, we simply return the `top-p` distances as their corresponding LB values can be calculated without losing the order. This can help us avoid performing such conversion on all elements of each distance profile, and instead, just calculate `LB` for the `top-p` smallest distances.\n", + "\n", + "**NOTE:** In STUMPY, parameter `p` is used to calculate the p-norm distance. To this end, we use a different name for the parameter. Let us call it `n_LB`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a6144a1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 34441e092dafe2ddd9ad07be61aab2cb8f811b47 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 24 Apr 2022 18:57:00 -0600 Subject: [PATCH 50/64] Add section 3 (core idea) and section 4 (implementation) --- docs/Tutorial_VALMOD.ipynb | 287 +++++++++++++++++++++++++++++++++++-- 1 file changed, 272 insertions(+), 15 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 216f5605a..e71274dcb 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 280, "id": "0adbe18a", "metadata": {}, "outputs": [], @@ -24,6 +24,7 @@ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import time\n", "import heapq\n", "\n", "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" @@ -170,8 +171,8 @@ "\n", "$$\n", "\\begin{align}\n", - "LB_{j,i}^{(m)} ={}& \n", - "d_{j,i}^{(m)}\n", + "LB_{j,i}^{(m+k)} ={}& \n", + "d_{j,i}^{(m)} \\quad (1)\n", "\\end{align}\n", "$$\n" ] @@ -192,25 +193,157 @@ "\n", "$$\n", "\\begin{align}\n", - "LB_{j,i}^{(m)} ={}& \n", + "LB_{j,i}^{(m+k)} ={}& \n", "\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", "\\sqrt{\n", "m\\left(\n", - "1 - \\max(\\rho_{j,i},0)^{2}\n", + "1 - \\max(\\rho^{(m)}_{j,i},0)^{2}\n", "\\right)\n", - "}\n", + "} \\quad (2)\n", "\\\\\n", - "\\rho_{j,i} ={}& 1 - \\frac{d_{j,i}^{2}}{2m}\n", "\\end{align}\n", "$$\n" ] }, { "cell_type": "markdown", - "id": "3b82e805", + "id": "f3c414f7", "metadata": {}, "source": [ - "**NOTE:** A better notation might be $LB_{j,i}^{m+k, m}$ as it shows the lower bound is for subsequence of length `m+k`, and it is calculated based on $\\rho_{j,i}$ of subsequences with length `m`. However, to simplify the notaton, we avoided using `m+k`. We expect the reader to remember that the calculated lower bound LB is for subsequences of length `m+k`, as shown in the $\\sigma_{j,m+k}$." + "And, the pearson correlation $\\rho^{(m)}_{j,i}$ can be calculated as follows: " + ] + }, + { + "cell_type": "markdown", + "id": "117f52c8", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "\\rho^{(m)}_{j,i} ={}& \n", + "\\frac{\\sum \\limits_{t=0}^{m-1}{T[i+t]T[j+t]} - m\\mu_{i,m}\\mu_{j,m}}{m\\sigma_{i,m}\\sigma_{j,m}} \\quad (2a)\n", + "\\end{align} \n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "9d25f29d", + "metadata": {}, + "source": [ + "Alternatively, $\\rho^{(m)}{j,i}$ and $d^{(m)}_{j,i}$ are related to each other according to the following formula:" + ] + }, + { + "cell_type": "markdown", + "id": "ce0de3e3", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "d^{(m)}_{j,i} ={}& \n", + "\\sqrt{\n", + "2m \\left(\n", + "1-\\rho^{(m)}_{j,i}\n", + "\\right)\n", + "} \\quad {(2b)}\n", + "\\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb030645", + "metadata": {}, + "source": [ + "# 3- Core Idea" + ] + }, + { + "cell_type": "markdown", + "id": "7e44c53b", + "metadata": {}, + "source": [ + "The core idea of VALMOD can be explained as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "419329a4", + "metadata": {}, + "source": [ + "## 3-1: Ranked Lower Bound (LB) of Distance Profile \n", + "Ranked LB of distance profile refers to the values of the LB of a distance profile sorted in ascending order. It is important to note that such ranking is preserved for all subsequence length range `(min_m+1, max_m)` having assumed that they are all being calculated based on the distance profile for subsquence with length `min_m`.\n", + "\n", + "In other words...
\n", + "IF:" + ] + }, + { + "cell_type": "markdown", + "id": "33011ed1", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "LB^{(m+k_{1})}_{j,i} \\leq{}& \n", + "LB^{(m+k_{1})}_{j,i^{'}}\n", + "\\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "6f9db92c", + "metadata": {}, + "source": [ + "THEN:" + ] + }, + { + "cell_type": "markdown", + "id": "49a09cf9", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + "LB^{(m+k_{2})}_{j,i} \\leq{}& \n", + "LB^{(m+k_{2})}_{j,i^{'}}\n", + "\\\\\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "61ab5384", + "metadata": {}, + "source": [ + "where, the lower-boundns are calculated based on the distances for length `m`. " + ] + }, + { + "cell_type": "markdown", + "id": "f46bf2bc", + "metadata": {}, + "source": [ + "**Also:**
\n", + "$LB^{(m+k)}_{j}$ denotes the distance profile of subsequence `T[j:j+m+k]`, calculated based on the distance profile of `T[j,j+m]`." + ] + }, + { + "cell_type": "markdown", + "id": "f623aa60", + "metadata": {}, + "source": [ + "## 3-2: Accelerating Matrix Profile calculation\n", + "Storing all \"ranked LB\" for all indices needs a significant ampunt of memory. Instead, we can just store the `p` smallest values of the ranked $LB^{(m+k)}_{j}$ and their corresponding indices. The parameter `p` is set by the user (e.g. see Table 2 on page 28). As we will see in the next section, we can use this meta information to skip some unnecessary calculation of distances for length larger than `min_m`." ] }, { @@ -218,10 +351,10 @@ "id": "833c4f6b", "metadata": {}, "source": [ - "# 3-VALMOD algorithm\n", - "The valmod algorithm discovers motifs / discords for subsequence length range `(min_m, max_m)`. The algorithm starts with performing complete scan for length `min_m`, and as we see shortly, it extracts more infomation than just Matrix Profile values and their corresponding indices. Then, it uses those information to accelerate constructing matrix profile for length `(min_m+1, max_m)`. \n", + "# 4-VALMOD algorithm\n", + "The VALMOP algorithm (see Algorithm1 of the paper (see page 13)) discovers the matrix profile and the matrix profile indices for subsequence length range `(min_m, max_m)`.\n", "\n", - "The main algorithm `VALMOD`, shown as Algorithm1 of the paper (see page 13). However, before implementing this function, we first implement the functions that are being called inside this algorithm. To be consistent with the paper, we use the paper's proposed name of function as title of each section. However, we may use a different name for the function in the notebook." + "In this section, we implement the functions that are being called by VALMOD algorithm, followed by the implementation of VALMOD algorithm." ] }, { @@ -229,18 +362,142 @@ "id": "f6cbecbd", "metadata": {}, "source": [ - "## 3-1- ComputeMatrixProfile (see page 15)\n", + "## 4-1- ComputeMatrixProfile (see page 15)\n", "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `top-p` smallest values of each distance profile and their indices. More precisely, it just need those indices, the first smallest value and the p-th smallest value. (The parameter `p` should be set by the user.) \n", "\n", "In the paper, the authors used the formula LB to convert distances to LB on the go. So, as they scan pairs of subsequences, they keep track of the matrix profile and its index for each subsequence. They also convert distances to lb using LB formula to calculate the LB values for subsequences with length `m+1`, then, they use heap structure to discover `top-p` LB values for each distance profile. However, in this notebook, we simply return the `top-p` distances as their corresponding LB values can be calculated without losing the order. This can help us avoid performing such conversion on all elements of each distance profile, and instead, just calculate `LB` for the `top-p` smallest distances.\n", "\n", - "**NOTE:** In STUMPY, parameter `p` is used to calculate the p-norm distance. To this end, we use a different name for the parameter. Let us call it `n_LB`." + "**NOTE:** In STUMPY, parameter `p` is used to calculate the p-norm distance. To this end, we use a different name for the parameter. Let us call it `h`." + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "id": "279e54e3", + "metadata": {}, + "outputs": [], + "source": [ + "def naive_VALMODstump(T, m, h = 5):\n", + " \"\"\"\n", + " Explain\n", + " \"\"\"\n", + " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", + " \n", + " distance_matrix = np.array(\n", + " [core.mass(Q, T) for Q in core.rolling_window(T, m)]\n", + " )\n", + " \n", + " n_T = T.shape[0]\n", + " l = n_T - m + 1\n", + " \n", + " P = np.full(l, np.NINF, dtype=np.float64)\n", + " I = np.full(l, -1, dtype=np.int64)\n", + " \n", + " DP = [] #distance profile\n", + " for i in range(l):\n", + " tmp = [(np.NINF,-1)] * h #to use max_heap, I flipped the sign.\n", + " heapq.heapify(tmp)\n", + " DP.append(tmp)\n", + " \n", + " diags = np.arange(excl_zone + 1, l)\n", + " for k in diags:\n", + " for i in range(0, n_T - m + 1 - k):\n", + " D = -1 * distance_matrix[i, i + k]\n", + " \n", + " if D > DP[i][0][0]: \n", + " heapq.heapreplace(DP[i], (D, i+k))\n", + " if D > P[i]:\n", + " P[i] = D\n", + " I[i] = i+k\n", + " \n", + " if D > DP[i+k][0][0]:\n", + " heapq.heapreplace(DP[i+k], (D, i))\n", + " if D > P[i+k]:\n", + " P[i+k] = D\n", + " I[i+k] = i\n", + " \n", + " \n", + " # post-processing\n", + " P = -1 * P \n", + " DP_larget_dist = np.asarray([-1 * item[0][0] for item in DP], dtype=np.float64)\n", + " DP_all_indices = np.asarray([[pair[1] for pair in item] for item in DP], dtype=np.int64)\n", + " \n", + " return P, I, DP_larget_dist, DP_all_indices" ] }, + { + "cell_type": "code", + "execution_count": 284, + "id": "915eeabb", + "metadata": {}, + "outputs": [], + "source": [ + "seed = 0\n", + "np.random.seed(seed)\n", + "T = np.random.uniform(low=-100, high=100, size=1000)\n", + "m = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "a025035b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running time: 1.074126958847046\n" + ] + } + ], + "source": [ + "tic = time.time()\n", + "P, I, DP_larget_dist, DP_all_indices = naive_METAstump(T, m, h=5)\n", + "toc = time.time()\n", + "print('running time: ', toc-tic)" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "f3e1d82d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[557, 281, 279, 157, 667],\n", + " [827, 282, 280, 158, 668],\n", + " [828, 283, 669, 281, 159],\n", + " ...,\n", + " [687, 316, 410, 218, 886],\n", + " [587, 887, 219, 244, 411],\n", + " [530, 588, 193, 412, 220]], dtype=int64)" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DP_all_indices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9739437", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, - "id": "9a6144a1", + "id": "10b4684a", "metadata": {}, "outputs": [], "source": [] From ab36c8aa3e34c639949bd51faeed31e272c21e57 Mon Sep 17 00:00:00 2001 From: ninimama Date: Sun, 24 Apr 2022 22:02:04 -0600 Subject: [PATCH 51/64] Elaborate sections 2,3,4 --- docs/Tutorial_VALMOD.ipynb | 190 +++++++++++++++++++++++++++++-------- 1 file changed, 153 insertions(+), 37 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index e71274dcb..e281850e8 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -207,7 +207,7 @@ }, { "cell_type": "markdown", - "id": "f3c414f7", + "id": "530e86c4", "metadata": {}, "source": [ "And, the pearson correlation $\\rho^{(m)}_{j,i}$ can be calculated as follows: " @@ -215,7 +215,7 @@ }, { "cell_type": "markdown", - "id": "117f52c8", + "id": "6d015362", "metadata": {}, "source": [ "\n", @@ -229,7 +229,7 @@ }, { "cell_type": "markdown", - "id": "9d25f29d", + "id": "b792cb2b", "metadata": {}, "source": [ "Alternatively, $\\rho^{(m)}{j,i}$ and $d^{(m)}_{j,i}$ are related to each other according to the following formula:" @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "ce0de3e3", + "id": "4a905185", "metadata": {}, "source": [ "\n", @@ -256,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "eb030645", + "id": "40da3ca9", "metadata": {}, "source": [ "# 3- Core Idea" @@ -264,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "7e44c53b", + "id": "f83325aa", "metadata": {}, "source": [ "The core idea of VALMOD can be explained as follows:" @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "419329a4", + "id": "77748a27", "metadata": {}, "source": [ "## 3-1: Ranked Lower Bound (LB) of Distance Profile \n", @@ -284,7 +284,7 @@ }, { "cell_type": "markdown", - "id": "33011ed1", + "id": "c2e1782a", "metadata": {}, "source": [ "\n", @@ -299,7 +299,7 @@ }, { "cell_type": "markdown", - "id": "6f9db92c", + "id": "a49074ff", "metadata": {}, "source": [ "THEN:" @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "49a09cf9", + "id": "ce9d6f16", "metadata": {}, "source": [ "\n", @@ -322,7 +322,7 @@ }, { "cell_type": "markdown", - "id": "61ab5384", + "id": "01c13220", "metadata": {}, "source": [ "where, the lower-boundns are calculated based on the distances for length `m`. " @@ -330,7 +330,7 @@ }, { "cell_type": "markdown", - "id": "f46bf2bc", + "id": "a3edd469", "metadata": {}, "source": [ "**Also:**
\n", @@ -339,7 +339,7 @@ }, { "cell_type": "markdown", - "id": "f623aa60", + "id": "ee30da36", "metadata": {}, "source": [ "## 3-2: Accelerating Matrix Profile calculation\n", @@ -363,23 +363,140 @@ "metadata": {}, "source": [ "## 4-1- ComputeMatrixProfile (see page 15)\n", - "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `top-p` smallest values of each distance profile and their indices. More precisely, it just need those indices, the first smallest value and the p-th smallest value. (The parameter `p` should be set by the user.) \n", + "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `p-th` smallest value of each distance profile and all the indices of `top-p` smallest values. \n", "\n", - "In the paper, the authors used the formula LB to convert distances to LB on the go. So, as they scan pairs of subsequences, they keep track of the matrix profile and its index for each subsequence. They also convert distances to lb using LB formula to calculate the LB values for subsequences with length `m+1`, then, they use heap structure to discover `top-p` LB values for each distance profile. However, in this notebook, we simply return the `top-p` distances as their corresponding LB values can be calculated without losing the order. This can help us avoid performing such conversion on all elements of each distance profile, and instead, just calculate `LB` for the `top-p` smallest distances.\n", + "In the paper, the authors used the LB formula to convert distances to LB on the go. So, as they scan pairs of subsequences, they calculate LB for each pair of subsequences. The authors used heap data structure to store `top-p` smallest LB values for each distance profile. " + ] + }, + { + "cell_type": "markdown", + "id": "15cce626", + "metadata": {}, + "source": [ + "**NOTE (1): Our implementation is slightly different than what proposed in the Algorithm3 of the paper**\n", + "
\n", + "In addition to matrix profile (P) and matrix profile indices (I), we just return the p-th smallest value of each \"distance profile\" and the indices for all top-p smallest valus of each \"distance profile\". \n", + "* In addition to storing the \"indices\" of the top-p smallest distances for each distance profile, we just need to store the p-th (not all top-p) smallest distance of each distance profile (see line 8 of the algorithm 4 provided in the paper). Therefore, at the end of algorith3, we return p-th smallest value of each distance profile rather than all top-p smallest values of each distance profile.\n", + "* We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ corresponding to each $d^{(m)}_{j,i}$. As proved below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the p-th smallest value of distance profile and then calculate LB with help of eq(2)." + ] + }, + { + "cell_type": "markdown", + "id": "2064289b", + "metadata": {}, + "source": [ + "IF: \n", "\n", - "**NOTE:** In STUMPY, parameter `p` is used to calculate the p-norm distance. To this end, we use a different name for the parameter. Let us call it `h`." + "$$\n", + "\\begin{align}\n", + "d^{(m)}_{j,i} \n", + "\\geq{}&{}\n", + "d^{(m)}_{j,i'}\n", + "\\\\\n", + "\\end{align}\n", + "$$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "da181848", + "metadata": {}, + "source": [ + "THEN:\n", + "\n", + "$$\n", + "\\begin{align}\n", + "\\rho^{(m)}_{j,i} \n", + "\\leq&{}\n", + "\\rho^{(m)}_{j,i'}\n", + "\\\\\n", + "\\max(\\rho^{(m)}_{j,i}, 0) \n", + "\\leq&{}\n", + "\\max(\\rho^{(m)}_{j,i'},0)\n", + "\\\\\n", + "\\left(\\max(\\rho^{(m)}_{j,i}, 0)\\right)^{2}\n", + "\\leq&{}\n", + "\\left(\\max(\\rho^{(m)}_{j,i'},0)\\right)^{2}\n", + "\\\\\n", + "1 - \\left(\\max(\\rho^{(m)}_{j,i}, 0)\\right)^{2}\n", + "\\geq&{}\n", + "1 - \\left(\\max(\\rho^{(m)}_{j,i'},0)\\right)^{2}\n", + "\\\\\n", + "\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + "\\sqrt{m\n", + "\\left[\n", + "1 - \\left(\\max(\\rho^{(m)}_{j,i}, 0)\\right)^{2}\n", + "\\right]\n", + "}\n", + "\\geq&{}\n", + "\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + "\\sqrt{m\n", + "\\left[\n", + "1 - \\left(\\max(\\rho^{(m)}_{j,i'}, 0)\\right)^{2}\n", + "\\right]\n", + "}\n", + "\\\\\n", + "LB^{(m)}_{j,i} \\geq{}& \n", + "LB^{(m)}_{j,i'}\n", + "\\\\\n", + "\\end{align}\n", + "$$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e10dc4a", + "metadata": {}, + "source": [ + "Therefore, if the ranked distance profile and its ranked lower bound have the same order. we can skip line19 of Algorithm3 and calculate LB once we discover p-th smallest value of distance profile at the end of algorithm 3. In our implementation, we simply return the p-th smallest value as it can be easily calculated outside of the function." + ] + }, + { + "cell_type": "markdown", + "id": "57fbdf38", + "metadata": {}, + "source": [ + "**NOTE (2):** \n", + "
\n", + "In STUMPY, parameter `p` is used to denote the kind of p-norm distance. To this end, we use the name `h` to denote the number of smallest LB values that should be considered for each distance profile." ] }, { "cell_type": "code", - "execution_count": 263, - "id": "279e54e3", + "execution_count": 304, + "id": "7313cb44", "metadata": {}, "outputs": [], "source": [ - "def naive_VALMODstump(T, m, h = 5):\n", + "def VALMODstump(T, m, h = 5):\n", " \"\"\"\n", - " Explain\n", + " This function takes the input time series `T`, window size `m`, and, \n", + " in addition to matrix profile and matrix profile indicecs, it returns the indices of top-h smallest value \n", + " and the h-th smallest value for each distance profile.\n", + " \n", + " This is a naive implementation in a sense that it calculate the whole distance_matrix right in the beginning \n", + " of the algorithm. The structure of this code is based on the stump function available in stumpy/test/naive.py.\n", + " \n", + " Parameters\n", + " ----------\n", + " T : numpy.ndarray\n", + " \n", + " m : int\n", + " \n", + " h : int\n", + " \n", + " Returns\n", + " ---------\n", + " P :\n", + " \n", + " I :\n", + " \n", + " DP_larget_dist : \n", + " \n", + " DP_all_indices : \n", + " \n", " \"\"\"\n", " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", " \n", @@ -390,12 +507,12 @@ " n_T = T.shape[0]\n", " l = n_T - m + 1\n", " \n", - " P = np.full(l, np.NINF, dtype=np.float64)\n", + " P = np.full(l, np.NINF, dtype=np.float64) \n", " I = np.full(l, -1, dtype=np.int64)\n", " \n", - " DP = [] #distance profile\n", + " DP = []\n", " for i in range(l):\n", - " tmp = [(np.NINF,-1)] * h #to use max_heap, I flipped the sign.\n", + " tmp = [(np.NINF,-1)] * h\n", " heapq.heapify(tmp)\n", " DP.append(tmp)\n", " \n", @@ -405,18 +522,17 @@ " D = -1 * distance_matrix[i, i + k]\n", " \n", " if D > DP[i][0][0]: \n", - " heapq.heapreplace(DP[i], (D, i+k))\n", + " heapq.heappushpop(DP[i], item=(D, i+k))\n", " if D > P[i]:\n", " P[i] = D\n", " I[i] = i+k\n", " \n", " if D > DP[i+k][0][0]:\n", - " heapq.heapreplace(DP[i+k], (D, i))\n", + " heapq.heappushpop(DP[i+k], item=(D, i))\n", " if D > P[i+k]:\n", " P[i+k] = D\n", " I[i+k] = i\n", - " \n", - " \n", + " \n", " # post-processing\n", " P = -1 * P \n", " DP_larget_dist = np.asarray([-1 * item[0][0] for item in DP], dtype=np.float64)\n", @@ -427,8 +543,8 @@ }, { "cell_type": "code", - "execution_count": 284, - "id": "915eeabb", + "execution_count": 305, + "id": "46815234", "metadata": {}, "outputs": [], "source": [ @@ -440,15 +556,15 @@ }, { "cell_type": "code", - "execution_count": 285, - "id": "a025035b", + "execution_count": 306, + "id": "63e9acea", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "running time: 1.074126958847046\n" + "running time: 1.078115463256836\n" ] } ], @@ -461,8 +577,8 @@ }, { "cell_type": "code", - "execution_count": 286, - "id": "f3e1d82d", + "execution_count": 307, + "id": "d0953028", "metadata": {}, "outputs": [ { @@ -477,7 +593,7 @@ " [530, 588, 193, 412, 220]], dtype=int64)" ] }, - "execution_count": 286, + "execution_count": 307, "metadata": {}, "output_type": "execute_result" } @@ -489,7 +605,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f9739437", + "id": "e0f83c16", "metadata": {}, "outputs": [], "source": [] @@ -497,7 +613,7 @@ { "cell_type": "code", "execution_count": null, - "id": "10b4684a", + "id": "7b4a04dd", "metadata": {}, "outputs": [], "source": [] From 4ae4d26ea7edbf4a68e57d2a44c0d1c7c8f05857 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 25 Apr 2022 00:24:33 -0600 Subject: [PATCH 52/64] Improve readability --- docs/Tutorial_VALMOD.ipynb | 227 ++++++++++++++++++++++--------------- 1 file changed, 135 insertions(+), 92 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index e281850e8..a6b897202 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -207,7 +207,7 @@ }, { "cell_type": "markdown", - "id": "530e86c4", + "id": "6fc1437c", "metadata": {}, "source": [ "And, the pearson correlation $\\rho^{(m)}_{j,i}$ can be calculated as follows: " @@ -215,7 +215,7 @@ }, { "cell_type": "markdown", - "id": "6d015362", + "id": "be0504c8", "metadata": {}, "source": [ "\n", @@ -229,7 +229,7 @@ }, { "cell_type": "markdown", - "id": "b792cb2b", + "id": "de7efb95", "metadata": {}, "source": [ "Alternatively, $\\rho^{(m)}{j,i}$ and $d^{(m)}_{j,i}$ are related to each other according to the following formula:" @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "4a905185", + "id": "30baf843", "metadata": {}, "source": [ "\n", @@ -256,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "40da3ca9", + "id": "58f786c0", "metadata": {}, "source": [ "# 3- Core Idea" @@ -264,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "f83325aa", + "id": "1ac2547c", "metadata": {}, "source": [ "The core idea of VALMOD can be explained as follows:" @@ -272,11 +272,11 @@ }, { "cell_type": "markdown", - "id": "77748a27", + "id": "91b69e91", "metadata": {}, "source": [ "## 3-1: Ranked Lower Bound (LB) of Distance Profile \n", - "Ranked LB of distance profile refers to the values of the LB of a distance profile sorted in ascending order. It is important to note that such ranking is preserved for all subsequence length range `(min_m+1, max_m)` having assumed that they are all being calculated based on the distance profile for subsquence with length `min_m`.\n", + "Ranked LB of distance profile refers to the values of the LB of a distance profile sorted in the ascending order. It is important to note that such ranking is preserved for all subsequence length range `(min_m+1, max_m)` having assumed that they are all being calculated based on the distance profile for subsquence with length `min_m`.\n", "\n", "In other words...
\n", "IF:" @@ -284,7 +284,7 @@ }, { "cell_type": "markdown", - "id": "c2e1782a", + "id": "3f4fcb84", "metadata": {}, "source": [ "\n", @@ -299,7 +299,7 @@ }, { "cell_type": "markdown", - "id": "a49074ff", + "id": "573a90f3", "metadata": {}, "source": [ "THEN:" @@ -307,14 +307,52 @@ }, { "cell_type": "markdown", - "id": "ce9d6f16", + "id": "c0a4e7df", "metadata": {}, "source": [ "\n", "$$\n", "\\begin{align}\n", + "\\frac{\n", + "\\sigma_{j,m+k_{1}}}\n", + "{\\sigma_{j,m+k_{2}}\n", + "}\n", + "LB^{(m+k_{1})}_{j,i} \n", + "\\leq{}&\n", + "\\frac{\n", + "\\sigma_{j,m+k_{1}}}\n", + "{\\sigma_{j,m+k_{2}}\n", + "}\n", + "LB^{(m+k_{1})}_{j,i'}\n", + "\\\\\n", + "\\frac{\n", + "\\sigma_{j,m+k_{1}}}\n", + "{\\sigma_{j,m+k_{2}}\n", + "}\n", + "\\left[\n", + "\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k_{1}}}\n", + "\\sqrt{\n", + "m\\left(\n", + "1 - \\max(\\rho^{(m)}_{j,i},0)^{2}\n", + "\\right)\n", + "}\n", + "\\right]\n", + "\\leq{}&\n", + "\\frac{\n", + "\\sigma_{j,m+k_{1}}}\n", + "{\\sigma_{j,m+k_{2}}\n", + "}\n", + "\\left[\n", + "\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k_{1}}}\n", + "\\sqrt{\n", + "m\\left(\n", + "1 - \\max(\\rho^{(m)}_{j,i'},0)^{2}\n", + "\\right)\n", + "}\n", + "\\right]\n", + "\\\\\n", "LB^{(m+k_{2})}_{j,i} \\leq{}& \n", - "LB^{(m+k_{2})}_{j,i^{'}}\n", + "LB^{(m+k_{2})}_{j,i'}\n", "\\\\\n", "\\end{align}\n", "$$\n" @@ -322,7 +360,7 @@ }, { "cell_type": "markdown", - "id": "01c13220", + "id": "5328138d", "metadata": {}, "source": [ "where, the lower-boundns are calculated based on the distances for length `m`. " @@ -330,20 +368,11 @@ }, { "cell_type": "markdown", - "id": "a3edd469", - "metadata": {}, - "source": [ - "**Also:**
\n", - "$LB^{(m+k)}_{j}$ denotes the distance profile of subsequence `T[j:j+m+k]`, calculated based on the distance profile of `T[j,j+m]`." - ] - }, - { - "cell_type": "markdown", - "id": "ee30da36", + "id": "ad925832", "metadata": {}, "source": [ "## 3-2: Accelerating Matrix Profile calculation\n", - "Storing all \"ranked LB\" for all indices needs a significant ampunt of memory. Instead, we can just store the `p` smallest values of the ranked $LB^{(m+k)}_{j}$ and their corresponding indices. The parameter `p` is set by the user (e.g. see Table 2 on page 28). As we will see in the next section, we can use this meta information to skip some unnecessary calculation of distances for length larger than `min_m`." + "Storing all \"ranked LB\" for all indices needs a significant ampunt of memory. Instead, we can just store the `top-p` smallest values of the ranked $LB^{(m+k)}_{j}$ and their corresponding indices. The parameter `p` is set by the user (e.g. see Table 2 on page 28). As we will see in the next section, we can use this meta information to skip some unnecessary calculation of distances for length larger than `min_m`." ] }, { @@ -352,9 +381,7 @@ "metadata": {}, "source": [ "# 4-VALMOD algorithm\n", - "The VALMOP algorithm (see Algorithm1 of the paper (see page 13)) discovers the matrix profile and the matrix profile indices for subsequence length range `(min_m, max_m)`.\n", - "\n", - "In this section, we implement the functions that are being called by VALMOD algorithm, followed by the implementation of VALMOD algorithm." + "The VALMOP algorithm (see Algorithm1 and Algorithm2 on page 13) discovers variable-length matrix profile and the matrix profile indices. In this section, we implement the functions that are being called by VALMOD algorithm, followed by the implementation of VALMOD algorithm itself." ] }, { @@ -362,27 +389,24 @@ "id": "f6cbecbd", "metadata": {}, "source": [ - "## 4-1- ComputeMatrixProfile (see page 15)\n", - "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `p-th` smallest value of each distance profile and all the indices of `top-p` smallest values. \n", + "## 4-1- ComputeMatrixProfile (Algorith3 on page 15)\n", + "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `top-p` smallest value of each distance profile and their indices as well.\n", "\n", - "In the paper, the authors used the LB formula to convert distances to LB on the go. So, as they scan pairs of subsequences, they calculate LB for each pair of subsequences. The authors used heap data structure to store `top-p` smallest LB values for each distance profile. " + "In the paper, the authors used the LB formula to convert distances to LB. So, as they scan pairs of subsequences, they calculate LB for each pair of subsequences. The authors used heap data structure to store `top-p` smallest LB values for each distance profile. " ] }, { "cell_type": "markdown", - "id": "15cce626", + "id": "cb41990b", "metadata": {}, "source": [ "**NOTE (1): Our implementation is slightly different than what proposed in the Algorithm3 of the paper**\n", - "
\n", - "In addition to matrix profile (P) and matrix profile indices (I), we just return the p-th smallest value of each \"distance profile\" and the indices for all top-p smallest valus of each \"distance profile\". \n", - "* In addition to storing the \"indices\" of the top-p smallest distances for each distance profile, we just need to store the p-th (not all top-p) smallest distance of each distance profile (see line 8 of the algorithm 4 provided in the paper). Therefore, at the end of algorith3, we return p-th smallest value of each distance profile rather than all top-p smallest values of each distance profile.\n", - "* We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ corresponding to each $d^{(m)}_{j,i}$. As proved below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the p-th smallest value of distance profile and then calculate LB with help of eq(2)." + "We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ corresponding to each $d^{(m)}_{j,i}$. As proved below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the top-p smallest value of distance profile and then calculate their corresponding LB value." ] }, { "cell_type": "markdown", - "id": "2064289b", + "id": "6b1327a0", "metadata": {}, "source": [ "IF: \n", @@ -400,7 +424,7 @@ }, { "cell_type": "markdown", - "id": "da181848", + "id": "41714628", "metadata": {}, "source": [ "THEN:\n", @@ -447,104 +471,123 @@ }, { "cell_type": "markdown", - "id": "7e10dc4a", + "id": "06f3b5d2", "metadata": {}, "source": [ - "Therefore, if the ranked distance profile and its ranked lower bound have the same order. we can skip line19 of Algorithm3 and calculate LB once we discover p-th smallest value of distance profile at the end of algorithm 3. In our implementation, we simply return the p-th smallest value as it can be easily calculated outside of the function." + "This proves that the ranked distance profile and its ranked lower bound have the same order." ] }, { "cell_type": "markdown", - "id": "57fbdf38", + "id": "29f02f99", "metadata": {}, "source": [ "**NOTE (2):** \n", "
\n", - "In STUMPY, parameter `p` is used to denote the kind of p-norm distance. To this end, we use the name `h` to denote the number of smallest LB values that should be considered for each distance profile." + "In STUMPY, parameter `p` is used to denote the kind of p-norm distance. To this end, we use the name `n` to denote the number of smallest LB values that should be considered for each distance profile." ] }, { "cell_type": "code", - "execution_count": 304, - "id": "7313cb44", + "execution_count": 396, + "id": "12c7d922", "metadata": {}, "outputs": [], "source": [ - "def VALMODstump(T, m, h = 5):\n", + "def _VALMOD_stump(T, m, n = 5):\n", " \"\"\"\n", - " This function takes the input time series `T`, window size `m`, and, \n", - " in addition to matrix profile and matrix profile indicecs, it returns the indices of top-h smallest value \n", - " and the h-th smallest value for each distance profile.\n", - " \n", - " This is a naive implementation in a sense that it calculate the whole distance_matrix right in the beginning \n", - " of the algorithm. The structure of this code is based on the stump function available in stumpy/test/naive.py.\n", + " This function takes the input time series `T`, window size `m`, and, the number of elements `n` that \n", + " should be stored for each distance profie. In addition to the matrix profile and the matrix profile indicecs, \n", + " this function returns the indices of top-n smallest value and their corresponding indices for each distance profile.\n", " \n", " Parameters\n", " ----------\n", " T : numpy.ndarray\n", - " \n", + " The time series or sequence for which to compute the matrix profile\n", + " \n", " m : int\n", - " \n", - " h : int\n", + " Window size\n", + " \n", + " n : int\n", + " The number of elements stored for each distance profile\n", " \n", " Returns\n", " ---------\n", - " P :\n", + " P : numpy.ndarray\n", + " The matrix profile\n", + " \n", + " I : numpy.ndarray\n", + " The matrix profile indices\n", + " \n", + " Partial_DP : numpy.ndarray\n", + " The partial distance profiles that contain their `n` smallest values \n", + " \n", + " Partial_DP_indices : numpy.ndarray\n", + " The indices corresponding to Partial_DP\n", + " \n", + " Notes\n", + " -----\n", + " https://doi.org/10.48550/arXiv.2008.13447\n", " \n", - " I :\n", + " see Algorithm 3\n", " \n", - " DP_larget_dist : \n", " \n", - " DP_all_indices : \n", + " This is a naive implementation in a sense that it calculates the whole distance_matrix right in the beginning \n", + " of the algorithm. The structure of this code is based on the naive implemention of function stump, \n", + " available in stumpy/test/naive.py.\n", " \n", + " In contrast to the original paper, we simply return the `h` smallest values for each distance profile as their order \n", + " is the same as their corresponding LB values. This can help us to compute the P and I in a clean way.\n", " \"\"\"\n", " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", " \n", + " #naive calculaton of distance_matrix\n", " distance_matrix = np.array(\n", " [core.mass(Q, T) for Q in core.rolling_window(T, m)]\n", " )\n", " \n", - " n_T = T.shape[0]\n", - " l = n_T - m + 1\n", + " k = T.shape[0] - m + 1\n", " \n", - " P = np.full(l, np.NINF, dtype=np.float64) \n", - " I = np.full(l, -1, dtype=np.int64)\n", + " P = np.full(k, np.NINF, dtype=np.float64) \n", + " I = np.full(k, -1, dtype=np.int64)\n", " \n", " DP = []\n", - " for i in range(l):\n", - " tmp = [(np.NINF,-1)] * h\n", + " for _ in range(k):\n", + " tmp = [(np.NINF,-1)] * n\n", " heapq.heapify(tmp)\n", " DP.append(tmp)\n", " \n", - " diags = np.arange(excl_zone + 1, l)\n", - " for k in diags:\n", - " for i in range(0, n_T - m + 1 - k):\n", - " D = -1 * distance_matrix[i, i + k]\n", + " diags = np.arange(excl_zone + 1, k)\n", + " for i in diags: \n", + " for j in range(0, k - i): \n", + " D = -1 * distance_matrix[j, j + i] \n", " \n", - " if D > DP[i][0][0]: \n", - " heapq.heappushpop(DP[i], item=(D, i+k))\n", - " if D > P[i]:\n", - " P[i] = D\n", - " I[i] = i+k\n", + " if D > DP[j][0][0]: \n", + " heapq.heappushpop(DP[j], (D, j+i)) \n", + " if D > P[j]:\n", + " P[j] = D\n", + " I[j] = j+i \n", " \n", - " if D > DP[i+k][0][0]:\n", - " heapq.heappushpop(DP[i+k], item=(D, i))\n", - " if D > P[i+k]:\n", - " P[i+k] = D\n", - " I[i+k] = i\n", + " if D > DP[j+i][0][0]:\n", + " heapq.heappushpop(DP[j+i], (D, j)) \n", + " if D > P[j+i]: \n", + " P[j+i] = D \n", + " I[j+i] = j \n", " \n", " # post-processing\n", " P = -1 * P \n", - " DP_larget_dist = np.asarray([-1 * item[0][0] for item in DP], dtype=np.float64)\n", - " DP_all_indices = np.asarray([[pair[1] for pair in item] for item in DP], dtype=np.int64)\n", " \n", - " return P, I, DP_larget_dist, DP_all_indices" + " DP = np.array(DP)\n", + " Partial_DP = -1 * DP[:,:,0].astype(np.float64)\n", + " Partial_DP_indices = DP[:,:,1].astype(np.int64)\n", + " \n", + " return P, I, Partial_DP, Partial_DP_indices" ] }, { "cell_type": "code", - "execution_count": 305, - "id": "46815234", + "execution_count": 397, + "id": "82fe0538", "metadata": {}, "outputs": [], "source": [ @@ -556,29 +599,29 @@ }, { "cell_type": "code", - "execution_count": 306, - "id": "63e9acea", + "execution_count": 398, + "id": "b758fbac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "running time: 1.078115463256836\n" + "running time: 1.08510160446167\n" ] } ], "source": [ "tic = time.time()\n", - "P, I, DP_larget_dist, DP_all_indices = naive_METAstump(T, m, h=5)\n", + "P, I, Partial_DP, Partial_DP_indices = _VALMOD_stump(T, m, n=5)\n", "toc = time.time()\n", "print('running time: ', toc-tic)" ] }, { "cell_type": "code", - "execution_count": 307, - "id": "d0953028", + "execution_count": 399, + "id": "52e54a1f", "metadata": {}, "outputs": [ { @@ -593,19 +636,19 @@ " [530, 588, 193, 412, 220]], dtype=int64)" ] }, - "execution_count": 307, + "execution_count": 399, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "DP_all_indices" + "Partial_DP_indices" ] }, { "cell_type": "code", "execution_count": null, - "id": "e0f83c16", + "id": "f237c078", "metadata": {}, "outputs": [], "source": [] @@ -613,7 +656,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7b4a04dd", + "id": "72365af4", "metadata": {}, "outputs": [], "source": [] From 450ddc6cb757cb13bab1555934dc50390b1e31b6 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 25 Apr 2022 00:44:37 -0600 Subject: [PATCH 53/64] Correct grammer and typo --- docs/Tutorial_VALMOD.ipynb | 144 ++++++++++++++++++------------------- 1 file changed, 71 insertions(+), 73 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index a6b897202..75827d222 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -182,7 +182,32 @@ "id": "7ff2e666", "metadata": {}, "source": [ - "**Normalized distance:**" + "**Normalized distance (see eq(2) of the paper):**" + ] + }, + { + "cell_type": "markdown", + "id": "8b8cc26a", + "metadata": {}, + "source": [ + "\n", + "$$\n", + "\\begin{align}\n", + " LB={}& \n", + " \\begin{cases}\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + "\\sqrt{\n", + "m\\left(\n", + "1 - \\rho^{(m)^{2}}_{j,i}\n", + "\\right)\n", + "}, & \\text{if $\\rho^{m}_{j,i}>0$}\\\\\n", + " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", + "\\sqrt{\n", + "m\n", + "}, & \\text{otherwise}\n", + " \\end{cases}\n", + "\\end{align}\n", + "$$\n" ] }, { @@ -190,6 +215,7 @@ "id": "0f192dfa", "metadata": {}, "source": [ + "Or, equivalently:\n", "\n", "$$\n", "\\begin{align}\n", @@ -207,7 +233,7 @@ }, { "cell_type": "markdown", - "id": "6fc1437c", + "id": "a6b6a92f", "metadata": {}, "source": [ "And, the pearson correlation $\\rho^{(m)}_{j,i}$ can be calculated as follows: " @@ -215,7 +241,7 @@ }, { "cell_type": "markdown", - "id": "be0504c8", + "id": "06f74a00", "metadata": {}, "source": [ "\n", @@ -229,15 +255,15 @@ }, { "cell_type": "markdown", - "id": "de7efb95", + "id": "1e23d962", "metadata": {}, "source": [ - "Alternatively, $\\rho^{(m)}{j,i}$ and $d^{(m)}_{j,i}$ are related to each other according to the following formula:" + "Alternatively, $\\rho^{(m)}_{j,i}$ and $d^{(m)}_{j,i}$ are related to each other according to the following formula:" ] }, { "cell_type": "markdown", - "id": "30baf843", + "id": "bd2e70a1", "metadata": {}, "source": [ "\n", @@ -256,7 +282,7 @@ }, { "cell_type": "markdown", - "id": "58f786c0", + "id": "1dd3b3a4", "metadata": {}, "source": [ "# 3- Core Idea" @@ -264,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "1ac2547c", + "id": "9b0ebd60", "metadata": {}, "source": [ "The core idea of VALMOD can be explained as follows:" @@ -272,19 +298,19 @@ }, { "cell_type": "markdown", - "id": "91b69e91", + "id": "d3c23204", "metadata": {}, "source": [ "## 3-1: Ranked Lower Bound (LB) of Distance Profile \n", "Ranked LB of distance profile refers to the values of the LB of a distance profile sorted in the ascending order. It is important to note that such ranking is preserved for all subsequence length range `(min_m+1, max_m)` having assumed that they are all being calculated based on the distance profile for subsquence with length `min_m`.\n", "\n", - "In other words...
\n", - "IF:" + "In other words,
\n", + "**IF:**" ] }, { "cell_type": "markdown", - "id": "3f4fcb84", + "id": "33bc22e8", "metadata": {}, "source": [ "\n", @@ -299,15 +325,15 @@ }, { "cell_type": "markdown", - "id": "573a90f3", + "id": "02b333a3", "metadata": {}, "source": [ - "THEN:" + "**THEN:**" ] }, { "cell_type": "markdown", - "id": "c0a4e7df", + "id": "3fc03958", "metadata": {}, "source": [ "\n", @@ -360,19 +386,11 @@ }, { "cell_type": "markdown", - "id": "5328138d", - "metadata": {}, - "source": [ - "where, the lower-boundns are calculated based on the distances for length `m`. " - ] - }, - { - "cell_type": "markdown", - "id": "ad925832", + "id": "8f1df704", "metadata": {}, "source": [ "## 3-2: Accelerating Matrix Profile calculation\n", - "Storing all \"ranked LB\" for all indices needs a significant ampunt of memory. Instead, we can just store the `top-p` smallest values of the ranked $LB^{(m+k)}_{j}$ and their corresponding indices. The parameter `p` is set by the user (e.g. see Table 2 on page 28). As we will see in the next section, we can use this meta information to skip some unnecessary calculation of distances for length larger than `min_m`." + "Storing all \"ranked LB\" for all indices requires a significant amount of memory. Instead, we can just store the `top-p` smallest values of the ranked $LB^{(m+k)}_{j}$ and their corresponding indices. The parameter `p` is set by the user (e.g. see Table 2 on page 28). As we will see in the next section, we can use this meta information to skip some unnecessary calculation of distances for length larger than `min_m`." ] }, { @@ -381,7 +399,7 @@ "metadata": {}, "source": [ "# 4-VALMOD algorithm\n", - "The VALMOP algorithm (see Algorithm1 and Algorithm2 on page 13) discovers variable-length matrix profile and the matrix profile indices. In this section, we implement the functions that are being called by VALMOD algorithm, followed by the implementation of VALMOD algorithm itself." + "The VALMOP algorithm (see Algorithm1 and Algorithm2 on page 13) discovers variable-length matrix profile and the matrix profile indices. In this section, we implement the functions by taking a bottom-top approach. So, we first implement the functions that are being called by VALMOD algorithm, and then we implement VALMOD algorithm." ] }, { @@ -389,7 +407,7 @@ "id": "f6cbecbd", "metadata": {}, "source": [ - "## 4-1- ComputeMatrixProfile (Algorith3 on page 15)\n", + "## 4-1- ComputeMatrixProfile (see Algorith3 on page 15)\n", "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `top-p` smallest value of each distance profile and their indices as well.\n", "\n", "In the paper, the authors used the LB formula to convert distances to LB. So, as they scan pairs of subsequences, they calculate LB for each pair of subsequences. The authors used heap data structure to store `top-p` smallest LB values for each distance profile. " @@ -397,19 +415,19 @@ }, { "cell_type": "markdown", - "id": "cb41990b", + "id": "eb51f0f6", "metadata": {}, "source": [ "**NOTE (1): Our implementation is slightly different than what proposed in the Algorithm3 of the paper**\n", - "We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ corresponding to each $d^{(m)}_{j,i}$. As proved below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the top-p smallest value of distance profile and then calculate their corresponding LB value." + "We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ corresponding to each $d^{(m)}_{j,i}$. As we prove below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the `top-p` smallest value of distance profile and then calculate their corresponding LB value." ] }, { "cell_type": "markdown", - "id": "6b1327a0", + "id": "3a1ba5e4", "metadata": {}, "source": [ - "IF: \n", + "**IF:**\n", "\n", "$$\n", "\\begin{align}\n", @@ -424,10 +442,10 @@ }, { "cell_type": "markdown", - "id": "41714628", + "id": "f7f22edc", "metadata": {}, "source": [ - "THEN:\n", + "**THEN:**\n", "\n", "$$\n", "\\begin{align}\n", @@ -471,7 +489,7 @@ }, { "cell_type": "markdown", - "id": "06f3b5d2", + "id": "ec7b8819", "metadata": {}, "source": [ "This proves that the ranked distance profile and its ranked lower bound have the same order." @@ -479,18 +497,18 @@ }, { "cell_type": "markdown", - "id": "29f02f99", + "id": "3db70f03", "metadata": {}, "source": [ "**NOTE (2):** \n", "
\n", - "In STUMPY, parameter `p` is used to denote the kind of p-norm distance. To this end, we use the name `n` to denote the number of smallest LB values that should be considered for each distance profile." + "In STUMPY, parameter `p` is used to denote the kind of p-norm distance. To this end, from this point onwards, we use `n` to denote the number of elements that should be stored for each distance profile." ] }, { "cell_type": "code", - "execution_count": 396, - "id": "12c7d922", + "execution_count": 402, + "id": "be7b439d", "metadata": {}, "outputs": [], "source": [ @@ -498,7 +516,7 @@ " \"\"\"\n", " This function takes the input time series `T`, window size `m`, and, the number of elements `n` that \n", " should be stored for each distance profie. In addition to the matrix profile and the matrix profile indicecs, \n", - " this function returns the indices of top-n smallest value and their corresponding indices for each distance profile.\n", + " this function returns the top-n smallest values and their corresponding indices for each distance profile.\n", " \n", " Parameters\n", " ----------\n", @@ -531,13 +549,12 @@ " \n", " see Algorithm 3\n", " \n", - " \n", - " This is a naive implementation in a sense that it calculates the whole distance_matrix right in the beginning \n", + " This is a naive implementation. It calculates the whole distance_matrix right in the beginning \n", " of the algorithm. The structure of this code is based on the naive implemention of function stump, \n", " available in stumpy/test/naive.py.\n", " \n", - " In contrast to the original paper, we simply return the `h` smallest values for each distance profile as their order \n", - " is the same as their corresponding LB values. This can help us to compute the P and I in a clean way.\n", + " In contrast to the original paper, we simply return the `n` smallest values for each distance profile as their order \n", + " is the same as their corresponding LB values. \n", " \"\"\"\n", " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", " \n", @@ -586,8 +603,8 @@ }, { "cell_type": "code", - "execution_count": 397, - "id": "82fe0538", + "execution_count": 403, + "id": "f431a4fb", "metadata": {}, "outputs": [], "source": [ @@ -599,15 +616,15 @@ }, { "cell_type": "code", - "execution_count": 398, - "id": "b758fbac", + "execution_count": 404, + "id": "0b3c14c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "running time: 1.08510160446167\n" + "running time: 1.1100335121154785\n" ] } ], @@ -620,35 +637,16 @@ }, { "cell_type": "code", - "execution_count": 399, - "id": "52e54a1f", + "execution_count": null, + "id": "b5e0fe7e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[557, 281, 279, 157, 667],\n", - " [827, 282, 280, 158, 668],\n", - " [828, 283, 669, 281, 159],\n", - " ...,\n", - " [687, 316, 410, 218, 886],\n", - " [587, 887, 219, 244, 411],\n", - " [530, 588, 193, 412, 220]], dtype=int64)" - ] - }, - "execution_count": 399, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Partial_DP_indices" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", "execution_count": null, - "id": "f237c078", + "id": "6df35afc", "metadata": {}, "outputs": [], "source": [] @@ -656,7 +654,7 @@ { "cell_type": "code", "execution_count": null, - "id": "72365af4", + "id": "154664aa", "metadata": {}, "outputs": [], "source": [] From 15e5a1432def2d5fbc016a8b8677cca781c27d26 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 25 Apr 2022 01:01:09 -0600 Subject: [PATCH 54/64] proof read --- docs/Tutorial_VALMOD.ipynb | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 75827d222..7eb2a79ea 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 1, "id": "0adbe18a", "metadata": {}, "outputs": [], @@ -97,7 +97,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -182,7 +182,7 @@ "id": "7ff2e666", "metadata": {}, "source": [ - "**Normalized distance (see eq(2) of the paper):**" + "**Normalized distance(see eq(2) of the paper):**" ] }, { @@ -193,7 +193,7 @@ "\n", "$$\n", "\\begin{align}\n", - " LB={}& \n", + " LB^{(m+k)}_{j,i}={}& \n", " \\begin{cases}\n", " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", "\\sqrt{\n", @@ -223,7 +223,7 @@ "\\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", "\\sqrt{\n", "m\\left(\n", - "1 - \\max(\\rho^{(m)}_{j,i},0)^{2}\n", + "1 - \\left(\\max(\\rho^{(m)}_{j,i},0)\\right)^{2}\n", "\\right)\n", "} \\quad (2)\n", "\\\\\n", @@ -236,7 +236,7 @@ "id": "a6b6a92f", "metadata": {}, "source": [ - "And, the pearson correlation $\\rho^{(m)}_{j,i}$ can be calculated as follows: " + "And, the pearson correlation, $\\rho^{(m)}_{j,i}$, can be calculated as follows: " ] }, { @@ -302,7 +302,7 @@ "metadata": {}, "source": [ "## 3-1: Ranked Lower Bound (LB) of Distance Profile \n", - "Ranked LB of distance profile refers to the values of the LB of a distance profile sorted in the ascending order. It is important to note that such ranking is preserved for all subsequence length range `(min_m+1, max_m)` having assumed that they are all being calculated based on the distance profile for subsquence with length `min_m`.\n", + "Ranked LB of distance profile refers to the values of the LB of a distance profile sorted in the ascending order. It is important to note that such ranking is preserved for all subsequence length range `(min_m+1, max_m)` having assumed that they are all being calculated based on the $\\rho_{j,i}$ values for length `min_m`.\n", "\n", "In other words,
\n", "**IF:**" @@ -407,10 +407,10 @@ "id": "f6cbecbd", "metadata": {}, "source": [ - "## 4-1- ComputeMatrixProfile (see Algorith3 on page 15)\n", + "## 4-1- ComputeMatrixProfile (see algorith3 on page 15)\n", "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `top-p` smallest value of each distance profile and their indices as well.\n", "\n", - "In the paper, the authors used the LB formula to convert distances to LB. So, as they scan pairs of subsequences, they calculate LB for each pair of subsequences. The authors used heap data structure to store `top-p` smallest LB values for each distance profile. " + "In the paper, the authors used the LB formula to convert distances to LB. So, as they scan pairs of subsequences, they calculate LB for each pair of subsequences. The authors used max_heap data structure to store `top-p` smallest LB values for each distance profile. " ] }, { @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 402, + "execution_count": 4, "id": "be7b439d", "metadata": {}, "outputs": [], @@ -538,7 +538,7 @@ " The matrix profile indices\n", " \n", " Partial_DP : numpy.ndarray\n", - " The partial distance profiles that contain their `n` smallest values \n", + " The partial distance profiles that contain the `n` smallest values of each distance profile\n", " \n", " Partial_DP_indices : numpy.ndarray\n", " The indices corresponding to Partial_DP\n", @@ -558,7 +558,7 @@ " \"\"\"\n", " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", " \n", - " #naive calculaton of distance_matrix\n", + " # naive calculaton of distance_matrix\n", " distance_matrix = np.array(\n", " [core.mass(Q, T) for Q in core.rolling_window(T, m)]\n", " )\n", @@ -568,6 +568,7 @@ " P = np.full(k, np.NINF, dtype=np.float64) \n", " I = np.full(k, -1, dtype=np.int64)\n", " \n", + " #create nest list of heapified lists\n", " DP = []\n", " for _ in range(k):\n", " tmp = [(np.NINF,-1)] * n\n", @@ -603,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 403, + "execution_count": 5, "id": "f431a4fb", "metadata": {}, "outputs": [], @@ -616,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 404, + "execution_count": 6, "id": "0b3c14c2", "metadata": {}, "outputs": [ @@ -624,7 +625,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "running time: 1.1100335121154785\n" + "running time: 2.506328582763672\n" ] } ], From c8653e2f6c9ac0c41d54cf52bdfded77b8f194a3 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 25 Apr 2022 01:12:37 -0600 Subject: [PATCH 55/64] Removed unrecognized latex code --- docs/Tutorial_VALMOD.ipynb | 26 -------------------------- 1 file changed, 26 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 7eb2a79ea..57cb9047c 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -185,37 +185,11 @@ "**Normalized distance(see eq(2) of the paper):**" ] }, - { - "cell_type": "markdown", - "id": "8b8cc26a", - "metadata": {}, - "source": [ - "\n", - "$$\n", - "\\begin{align}\n", - " LB^{(m+k)}_{j,i}={}& \n", - " \\begin{cases}\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - "\\sqrt{\n", - "m\\left(\n", - "1 - \\rho^{(m)^{2}}_{j,i}\n", - "\\right)\n", - "}, & \\text{if $\\rho^{m}_{j,i}>0$}\\\\\n", - " \\frac{\\sigma_{j,m}}{\\sigma_{j,m+k}}\n", - "\\sqrt{\n", - "m\n", - "}, & \\text{otherwise}\n", - " \\end{cases}\n", - "\\end{align}\n", - "$$\n" - ] - }, { "cell_type": "markdown", "id": "0f192dfa", "metadata": {}, "source": [ - "Or, equivalently:\n", "\n", "$$\n", "\\begin{align}\n", From d924fc1847c58322f64391cb0441fd3cff5f8494 Mon Sep 17 00:00:00 2001 From: ninimama Date: Mon, 25 Apr 2022 01:22:00 -0600 Subject: [PATCH 56/64] minor changes --- docs/Tutorial_VALMOD.ipynb | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 57cb9047c..d11c2eebe 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "0adbe18a", "metadata": {}, "outputs": [], @@ -393,7 +393,7 @@ "metadata": {}, "source": [ "**NOTE (1): Our implementation is slightly different than what proposed in the Algorithm3 of the paper**\n", - "We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ corresponding to each $d^{(m)}_{j,i}$. As we prove below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the `top-p` smallest value of distance profile and then calculate their corresponding LB value." + "We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ for each $d^{(m)}_{j,i}$. As we prove below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the `top-p` smallest value of distance profile and then calculate their corresponding LB value." ] }, { @@ -481,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "be7b439d", "metadata": {}, "outputs": [], @@ -490,7 +490,7 @@ " \"\"\"\n", " This function takes the input time series `T`, window size `m`, and, the number of elements `n` that \n", " should be stored for each distance profie. In addition to the matrix profile and the matrix profile indicecs, \n", - " this function returns the top-n smallest values and their corresponding indices for each distance profile.\n", + " this function returns the top-n smallest values and their corresponding indices for each distance profile as well.\n", " \n", " Parameters\n", " ----------\n", @@ -501,7 +501,7 @@ " Window size\n", " \n", " n : int\n", - " The number of elements stored for each distance profile\n", + " The number of elements stored for each distance profile.\n", " \n", " Returns\n", " ---------\n", @@ -523,9 +523,9 @@ " \n", " see Algorithm 3\n", " \n", - " This is a naive implementation. It calculates the whole distance_matrix right in the beginning \n", + " This is a naive implementation of stump. It calculates the whole distance_matrix right in the beginning \n", " of the algorithm. The structure of this code is based on the naive implemention of function stump, \n", - " available in stumpy/test/naive.py.\n", + " available in stumpy/test/naive.py. \n", " \n", " In contrast to the original paper, we simply return the `n` smallest values for each distance profile as their order \n", " is the same as their corresponding LB values. \n", @@ -578,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "f431a4fb", "metadata": {}, "outputs": [], @@ -591,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "0b3c14c2", "metadata": {}, "outputs": [ @@ -599,7 +599,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "running time: 2.506328582763672\n" + "running time: 2.5790293216705322\n" ] } ], From df84ded6a17ce9b679fbd41b56d2427bcf9b9b91 Mon Sep 17 00:00:00 2001 From: ninimama Date: Tue, 26 Apr 2022 17:03:55 -0600 Subject: [PATCH 57/64] minor changes - Fix typos - replace variable name n with k --- docs/Tutorial_VALMOD.ipynb | 45 +++++++++++++++++++------------------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index d11c2eebe..9b501a521 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "0adbe18a", "metadata": {}, "outputs": [], @@ -373,7 +373,7 @@ "metadata": {}, "source": [ "# 4-VALMOD algorithm\n", - "The VALMOP algorithm (see Algorithm1 and Algorithm2 on page 13) discovers variable-length matrix profile and the matrix profile indices. In this section, we implement the functions by taking a bottom-top approach. So, we first implement the functions that are being called by VALMOD algorithm, and then we implement VALMOD algorithm." + "The VALMOD algorithm (see Algorithm1 and Algorithm2 on page 13) discovers variable-length matrix profile and the matrix profile indices. In this section, we implement the functions by taking a bottom-up approach. So, we first implement the functions that are being called by VALMOD algorithm, and then we implement VALMOD algorithm." ] }, { @@ -476,21 +476,21 @@ "source": [ "**NOTE (2):** \n", "
\n", - "In STUMPY, parameter `p` is used to denote the kind of p-norm distance. To this end, from this point onwards, we use `n` to denote the number of elements that should be stored for each distance profile." + "In STUMPY, parameter `p` is used to denote the kind of p-norm distance. To this end, from this point onwards, we use `k` to denote the number of elements that should be stored for each distance profile." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "id": "be7b439d", "metadata": {}, "outputs": [], "source": [ - "def _VALMOD_stump(T, m, n = 5):\n", + "def _VALMOD_stump(T, m, k = 5):\n", " \"\"\"\n", - " This function takes the input time series `T`, window size `m`, and, the number of elements `n` that \n", + " This function takes the input time series `T`, window size `m`, and, the number of elements `k` that \n", " should be stored for each distance profie. In addition to the matrix profile and the matrix profile indicecs, \n", - " this function returns the top-n smallest values and their corresponding indices for each distance profile as well.\n", + " this function returns the top-k smallest values and their corresponding indices for each distance profile as well.\n", " \n", " Parameters\n", " ----------\n", @@ -500,7 +500,7 @@ " m : int\n", " Window size\n", " \n", - " n : int\n", + " k : int\n", " The number of elements stored for each distance profile.\n", " \n", " Returns\n", @@ -512,7 +512,7 @@ " The matrix profile indices\n", " \n", " Partial_DP : numpy.ndarray\n", - " The partial distance profiles that contain the `n` smallest values of each distance profile\n", + " The partial distance profiles that contain the `k` smallest values of each distance profile\n", " \n", " Partial_DP_indices : numpy.ndarray\n", " The indices corresponding to Partial_DP\n", @@ -527,7 +527,7 @@ " of the algorithm. The structure of this code is based on the naive implemention of function stump, \n", " available in stumpy/test/naive.py. \n", " \n", - " In contrast to the original paper, we simply return the `n` smallest values for each distance profile as their order \n", + " In contrast to the original paper, we simply return the `k` smallest values for each distance profile as their order \n", " is the same as their corresponding LB values. \n", " \"\"\"\n", " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", @@ -537,21 +537,21 @@ " [core.mass(Q, T) for Q in core.rolling_window(T, m)]\n", " )\n", " \n", - " k = T.shape[0] - m + 1\n", + " l = T.shape[0] - m + 1\n", " \n", - " P = np.full(k, np.NINF, dtype=np.float64) \n", - " I = np.full(k, -1, dtype=np.int64)\n", + " P = np.full(l, np.NINF, dtype=np.float64) \n", + " I = np.full(l, -1, dtype=np.int64)\n", " \n", " #create nest list of heapified lists\n", " DP = []\n", - " for _ in range(k):\n", - " tmp = [(np.NINF,-1)] * n\n", + " for _ in range(l):\n", + " tmp = [(np.NINF,-1)] * k\n", " heapq.heapify(tmp)\n", " DP.append(tmp)\n", " \n", - " diags = np.arange(excl_zone + 1, k)\n", + " diags = np.arange(excl_zone + 1, l)\n", " for i in diags: \n", - " for j in range(0, k - i): \n", + " for j in range(0, l - i): \n", " D = -1 * distance_matrix[j, j + i] \n", " \n", " if D > DP[j][0][0]: \n", @@ -578,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "f431a4fb", "metadata": {}, "outputs": [], @@ -586,12 +586,13 @@ "seed = 0\n", "np.random.seed(seed)\n", "T = np.random.uniform(low=-100, high=100, size=1000)\n", - "m = 50" + "m = 50\n", + "k = 5" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "0b3c14c2", "metadata": {}, "outputs": [ @@ -599,13 +600,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "running time: 2.5790293216705322\n" + "running time: 2.5206298828125\n" ] } ], "source": [ "tic = time.time()\n", - "P, I, Partial_DP, Partial_DP_indices = _VALMOD_stump(T, m, n=5)\n", + "P, I, Partial_DP, Partial_DP_indices = _VALMOD_stump(T, m, k)\n", "toc = time.time()\n", "print('running time: ', toc-tic)" ] From 7d38e39c0b01c5d25589971e6e9c487cc644f1f5 Mon Sep 17 00:00:00 2001 From: ninimama Date: Tue, 26 Apr 2022 17:59:21 -0600 Subject: [PATCH 58/64] replace heapq with np.searchsorted --- docs/Tutorial_VALMOD.ipynb | 73 ++++++++++++++------------------------ 1 file changed, 26 insertions(+), 47 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 9b501a521..965824fbb 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -481,16 +481,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 50, "id": "be7b439d", "metadata": {}, "outputs": [], "source": [ "def _VALMOD_stump(T, m, k = 5):\n", " \"\"\"\n", - " This function takes the input time series `T`, window size `m`, and, the number of elements `k` that \n", - " should be stored for each distance profie. In addition to the matrix profile and the matrix profile indicecs, \n", - " this function returns the top-k smallest values and their corresponding indices for each distance profile as well.\n", + " An extended version of stump. While stump returns the smallest valus of each distance profile \n", + " and its corresponding index, this function returns top-k smallest values of each distance profile \n", + " and their corresponding indices.\n", " \n", " Parameters\n", " ----------\n", @@ -505,18 +505,12 @@ " \n", " Returns\n", " ---------\n", - " P : numpy.ndarray\n", - " The matrix profile\n", + " P_topk : numpy.ndarray\n", + " The i-th row contains the top-k smallest values of the distance profile corresponding to `T[i:i+m]`.\n", " \n", - " I : numpy.ndarray\n", - " The matrix profile indices\n", + " I_topk : numpy.ndarray\n", + " The indices that correspond to P_topk\n", " \n", - " Partial_DP : numpy.ndarray\n", - " The partial distance profiles that contain the `k` smallest values of each distance profile\n", - " \n", - " Partial_DP_indices : numpy.ndarray\n", - " The indices corresponding to Partial_DP\n", - " \n", " Notes\n", " -----\n", " https://doi.org/10.48550/arXiv.2008.13447\n", @@ -539,46 +533,31 @@ " \n", " l = T.shape[0] - m + 1\n", " \n", - " P = np.full(l, np.NINF, dtype=np.float64) \n", - " I = np.full(l, -1, dtype=np.int64)\n", - " \n", - " #create nest list of heapified lists\n", - " DP = []\n", - " for _ in range(l):\n", - " tmp = [(np.NINF,-1)] * k\n", - " heapq.heapify(tmp)\n", - " DP.append(tmp)\n", + " P_topk = np.full((l,k), np.inf, dtype=np.float64)\n", + " I_topk = np.full((l,k), -1, dtype=np.int64)\n", " \n", " diags = np.arange(excl_zone + 1, l)\n", " for i in diags: \n", " for j in range(0, l - i): \n", - " D = -1 * distance_matrix[j, j + i] \n", + " D = distance_matrix[j, j + i] \n", " \n", - " if D > DP[j][0][0]: \n", - " heapq.heappushpop(DP[j], (D, j+i)) \n", - " if D > P[j]:\n", - " P[j] = D\n", - " I[j] = j+i \n", + " if D < P_topk[j,-1]:\n", + " idx = np.searchsorted(P_topk[j], D, side='right') \n", + " P_topk[j] = np.insert(P_topk[j], idx, D)[:-1]\n", + " I_topk[j] = np.insert(I_topk[j], idx, j+i)[:-1]\n", " \n", - " if D > DP[j+i][0][0]:\n", - " heapq.heappushpop(DP[j+i], (D, j)) \n", - " if D > P[j+i]: \n", - " P[j+i] = D \n", - " I[j+i] = j \n", - " \n", - " # post-processing\n", - " P = -1 * P \n", - " \n", - " DP = np.array(DP)\n", - " Partial_DP = -1 * DP[:,:,0].astype(np.float64)\n", - " Partial_DP_indices = DP[:,:,1].astype(np.int64)\n", + " if D < P_topk[j+1,-1]:\n", + " idx = np.searchsorted(P_topk[j+i], D, side='right') \n", + " P_topk[j+i] = np.insert(P_topk[j+i], idx, D)[:-1]\n", + " I_topk[j+i] = np.insert(I_topk[j+i], idx, j)[:-1]\n", + " \n", " \n", - " return P, I, Partial_DP, Partial_DP_indices" + " return P_topk, I_topk" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 51, "id": "f431a4fb", "metadata": {}, "outputs": [], @@ -592,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 52, "id": "0b3c14c2", "metadata": {}, "outputs": [ @@ -600,13 +579,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "running time: 2.5206298828125\n" + "running time: 2.5092923641204834\n" ] } ], "source": [ "tic = time.time()\n", - "P, I, Partial_DP, Partial_DP_indices = _VALMOD_stump(T, m, k)\n", + "P_topk, I_topk = _VALMOD_stump(T, m, k)\n", "toc = time.time()\n", "print('running time: ', toc-tic)" ] @@ -630,7 +609,7 @@ { "cell_type": "code", "execution_count": null, - "id": "154664aa", + "id": "c7a09d91", "metadata": {}, "outputs": [], "source": [] From cbe649f5092962ccccca9179f519cc5f0884b71b Mon Sep 17 00:00:00 2001 From: ninimama Date: Tue, 26 Apr 2022 18:19:17 -0600 Subject: [PATCH 59/64] Fix typo --- docs/Tutorial_VALMOD.ipynb | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 965824fbb..0dd91dd99 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -481,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 64, "id": "be7b439d", "metadata": {}, "outputs": [], @@ -506,7 +506,8 @@ " Returns\n", " ---------\n", " P_topk : numpy.ndarray\n", - " The i-th row contains the top-k smallest values of the distance profile corresponding to `T[i:i+m]`.\n", + " The i-th row contains the top-k smallest values of the distance profile corresponding to `T[i:i+m]` sorted \n", + " in ascending order.\n", " \n", " I_topk : numpy.ndarray\n", " The indices that correspond to P_topk\n", @@ -546,7 +547,7 @@ " P_topk[j] = np.insert(P_topk[j], idx, D)[:-1]\n", " I_topk[j] = np.insert(I_topk[j], idx, j+i)[:-1]\n", " \n", - " if D < P_topk[j+1,-1]:\n", + " if D < P_topk[j+i,-1]:\n", " idx = np.searchsorted(P_topk[j+i], D, side='right') \n", " P_topk[j+i] = np.insert(P_topk[j+i], idx, D)[:-1]\n", " I_topk[j+i] = np.insert(I_topk[j+i], idx, j)[:-1]\n", @@ -557,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 65, "id": "f431a4fb", "metadata": {}, "outputs": [], @@ -571,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 66, "id": "0b3c14c2", "metadata": {}, "outputs": [ @@ -579,7 +580,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "running time: 2.5092923641204834\n" + "running time: 2.3726773262023926\n" ] } ], From 34edea87f7aa90f6a4f38aeb89e77a47f6d99d3d Mon Sep 17 00:00:00 2001 From: ninimama Date: Tue, 26 Apr 2022 18:38:53 -0600 Subject: [PATCH 60/64] Test P and I of _VALMOD_stump --- docs/Tutorial_VALMOD.ipynb | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 0dd91dd99..dd6f377f6 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -593,11 +593,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "b5e0fe7e", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "P = P_topk[:,0]\n", + "I = I_topk[:,0]\n", + "#just test the P and I\n", + "mp = stumpy.stump(T, m)\n", + "np.testing.assert_allclose(mp[:,0].astype(np.float64), P)\n", + "np.testing.assert_allclose(mp[:,1].astype(np.int64), I)" + ] }, { "cell_type": "code", From b4b9f691a7f598d0b197af802d64b59a25cff515 Mon Sep 17 00:00:00 2001 From: ninimama Date: Tue, 26 Apr 2022 18:50:34 -0600 Subject: [PATCH 61/64] minor changes --- docs/Tutorial_VALMOD.ipynb | 42 +++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index dd6f377f6..50c4e7c05 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -382,7 +382,7 @@ "metadata": {}, "source": [ "## 4-1- ComputeMatrixProfile (see algorith3 on page 15)\n", - "This algorithm scans all pairs of subsequences. However, instead of just returning the matrix profile and its indices, the algorithm returns the `top-p` smallest value of each distance profile and their indices as well.\n", + "This algorithm scans all pairs of subsequences. However, instead of returning the matrix profile and its indices, the algorithm returns the `top-p` smallest value of each distance profile and their corresponding indices.\n", "\n", "In the paper, the authors used the LB formula to convert distances to LB. So, as they scan pairs of subsequences, they calculate LB for each pair of subsequences. The authors used max_heap data structure to store `top-p` smallest LB values for each distance profile. " ] @@ -481,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 77, "id": "be7b439d", "metadata": {}, "outputs": [], @@ -522,8 +522,8 @@ " of the algorithm. The structure of this code is based on the naive implemention of function stump, \n", " available in stumpy/test/naive.py. \n", " \n", - " In contrast to the original paper, we simply return the `k` smallest values for each distance profile as their order \n", - " is the same as their corresponding LB values. \n", + " Unlike the original paper that returns matrix profile and indices and the LB values for the next length (i.e. m+1), \n", + " we return the `k` smallest values and their indices for each distance profile.\n", " \"\"\"\n", " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", " \n", @@ -534,23 +534,23 @@ " \n", " l = T.shape[0] - m + 1\n", " \n", - " P_topk = np.full((l,k), np.inf, dtype=np.float64)\n", - " I_topk = np.full((l,k), -1, dtype=np.int64)\n", + " P_topk = np.full((l, k), np.inf, dtype=np.float64)\n", + " I_topk = np.full((l, k), -1, dtype=np.int64)\n", " \n", " diags = np.arange(excl_zone + 1, l)\n", " for i in diags: \n", " for j in range(0, l - i): \n", " D = distance_matrix[j, j + i] \n", " \n", - " if D < P_topk[j,-1]:\n", + " if D < P_topk[j, -1]:\n", " idx = np.searchsorted(P_topk[j], D, side='right') \n", " P_topk[j] = np.insert(P_topk[j], idx, D)[:-1]\n", - " I_topk[j] = np.insert(I_topk[j], idx, j+i)[:-1]\n", + " I_topk[j] = np.insert(I_topk[j], idx, j + i)[:-1]\n", " \n", - " if D < P_topk[j+i,-1]:\n", - " idx = np.searchsorted(P_topk[j+i], D, side='right') \n", - " P_topk[j+i] = np.insert(P_topk[j+i], idx, D)[:-1]\n", - " I_topk[j+i] = np.insert(I_topk[j+i], idx, j)[:-1]\n", + " if D < P_topk[j + i, -1]:\n", + " idx = np.searchsorted(P_topk[j + i], D, side='right') \n", + " P_topk[j + i] = np.insert(P_topk[j + i], idx, D)[:-1]\n", + " I_topk[j + i] = np.insert(I_topk[j + i], idx, j)[:-1]\n", " \n", " \n", " return P_topk, I_topk" @@ -558,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 78, "id": "f431a4fb", "metadata": {}, "outputs": [], @@ -572,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 79, "id": "0b3c14c2", "metadata": {}, "outputs": [ @@ -580,7 +580,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "running time: 2.3726773262023926\n" + "running time: 2.096430778503418\n" ] } ], @@ -588,22 +588,22 @@ "tic = time.time()\n", "P_topk, I_topk = _VALMOD_stump(T, m, k)\n", "toc = time.time()\n", - "print('running time: ', toc-tic)" + "print('running time: ', toc - tic)" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 80, "id": "b5e0fe7e", "metadata": {}, "outputs": [], "source": [ - "P = P_topk[:,0]\n", - "I = I_topk[:,0]\n", + "P = P_topk[:, 0]\n", + "I = I_topk[:, 0]\n", "#just test the P and I\n", "mp = stumpy.stump(T, m)\n", - "np.testing.assert_allclose(mp[:,0].astype(np.float64), P)\n", - "np.testing.assert_allclose(mp[:,1].astype(np.int64), I)" + "np.testing.assert_allclose(mp[:, 0].astype(np.float64), P)\n", + "np.testing.assert_allclose(mp[:, 1].astype(np.int64), I)" ] }, { From c6dd58d68be3033f7ca4caa360fee81b7fcea2ce Mon Sep 17 00:00:00 2001 From: nimasarajpoor Date: Mon, 27 Feb 2023 08:34:14 -0500 Subject: [PATCH 62/64] Implement VALMOD-draft version --- docs/Tutorial_VALMOD.ipynb | 399 +++++++++++++++++++++++++++++-------- 1 file changed, 311 insertions(+), 88 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 50c4e7c05..f67df115a 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 175, "id": "0adbe18a", "metadata": {}, "outputs": [], @@ -20,12 +20,12 @@ "%matplotlib inline\n", "\n", "import stumpy\n", - "from stumpy import core, config\n", + "from stumpy import stump, core, config\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import math\n", "import time\n", - "import heapq\n", "\n", "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" ] @@ -43,7 +43,7 @@ "id": "b0423978", "metadata": {}, "source": [ - "**Notation:** $T_{i,m} = T[i:i+m]$, a subsequence of `T` that starts at index `i` and has length `m`. " + "**Notation:** $T_{i,m} = T[i:i+m]$ is a subsequence of `T` that starts at index `i` and has length `m`. " ] }, { @@ -63,7 +63,7 @@ "\n", "We would like to find set $S^{*} = \\bigcup\\limits_{m=min\\_m}^{max\\_m}{S^{m}_{r}}$, and $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$. In other words, we want to find motif sets for different length `m` and we want to make sure there is no \"common\" (see note below) subsequence between any two motif sets. \n", "\n", - "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty because both these two subsequences start from the same index." + "**NOTE:** The subsequences in motif set of length m and m' are indeed different because they have different length. However, by the constraint $S^{m}_{r} \\cap S^{m'}_{r'} = \\emptyset$, the authors meant to avoid considering two subsequences (of different length) that start from the same index. For instance, if $T_{200,m}$ is in one set and $T_{200,m'}$ in another set, the authors consider the intersection of their corresponding set to be non-empty because both of these two subsequences start from the same index." ] }, { @@ -83,7 +83,7 @@ "\n", "**$n^{th}$ best match**: For the subsequence $T_{i,m}$, its $n^{th}$ best match is simply the $n^{th}$ smallest distance in the distance profile.
\n", "\n", - "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequence and its ($n^{th}$ ?) best match.
\n", + "**$n^{th}$ discord**: a subsequence $T_{i,m}$ is the $n^{th}$ discord if it has the largest value to its $n^{th}$ best match compared to the distances between any other subsequences and their ($n^{th}$) best match.
\n", "\n", "**NOTE**:
\n", "Why should we care about $n^{th}$ discord (n>1)? We provide a simple example below:" @@ -127,7 +127,7 @@ "source": [ "Here, the subsequences at index `i` and `j` can be considered an anomaly. However, the 1NN distance is 0 for them. Therefore, we may need to investigate other neighbors rather than just 1NN. In discord discovery, it is called twin-freak problem (see [Tutorial](https://cci.drexel.edu/bigdata/bigdata2017/files/Tutorial4.pdf)). It happens when the (same) anomally occurs more than once. In our example above, the anomaly occurs twice. Therefore, we should be able to detect it if we consider 2nd nearest neighbor. \n", "\n", - "For further details, see Fig. 2 of the paper. Notice that `Top-1 2nd discord` subsequence has a close 1-NN; however, it is far from its 2nd closest neighbor.)" + "For further details, see Fig. 2 of the paper." ] }, { @@ -152,7 +152,7 @@ "id": "8538f0e3", "metadata": {}, "source": [ - "Lower Bound (LB) for $d(T_{j,m+k}, T_{i,m+k})$ can be calculated as follows:" + "Lower Bound (LB) for $d_{j,i}^{(m+k)} = d(T_{j,m+k}, T_{i,m+k})$ can be calculated as follows:" ] }, { @@ -393,7 +393,7 @@ "metadata": {}, "source": [ "**NOTE (1): Our implementation is slightly different than what proposed in the Algorithm3 of the paper**\n", - "We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ for each $d^{(m)}_{j,i}$. As we prove below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the `top-p` smallest value of distance profile and then calculate their corresponding LB value." + "We can skip line19 of Algorithm 3 provided in the paper. We do NOT need to calculate $LB^{(m+k)}_{j,i}$ for each $d^{(m)}_{j,i}$. As we prove below, the ranked distance profile, $DP^{(m)}_{j}$, is in the same order as its corresponding ranked Lower Bound, $LB^{(m+k)}_{j}$. Therefore, we can simply return the `top-p` smallest value of distance profile and then calculate their corresponding LB value all at once." ] }, { @@ -481,135 +481,358 @@ }, { "cell_type": "code", - "execution_count": 77, - "id": "be7b439d", + "execution_count": 164, + "id": "a010e37e", "metadata": {}, "outputs": [], "source": [ - "def _VALMOD_stump(T, m, k = 5):\n", + "def _VALMOD_stump(T, m, k):\n", " \"\"\"\n", - " An extended version of stump. While stump returns the smallest valus of each distance profile \n", - " and its corresponding index, this function returns top-k smallest values of each distance profile \n", - " and their corresponding indices.\n", + " Computes the top-1 matrix profile and matrix profile indice, and also computes the lower bound component\n", + " and their coresponding indices.\n", " \n", " Parameters\n", " ----------\n", " T : numpy.ndarray\n", " The time series or sequence for which to compute the matrix profile\n", - " \n", + " \n", " m : int\n", " Window size\n", - " \n", + " \n", " k : int\n", - " The number of elements stored for each distance profile.\n", + " Number of nearest neighbors to consider in constructing the profiles and lower bounds.\n", " \n", " Returns\n", - " ---------\n", - " P_topk : numpy.ndarray\n", - " The i-th row contains the top-k smallest values of the distance profile corresponding to `T[i:i+m]` sorted \n", - " in ascending order.\n", + " -------\n", + " out 1: np.ndarray\n", + " A 1D array containing the exact matix profile values\n", + " \n", + " out 2: np.ndarray\n", + " A 1D array containing the exact matix profile indices\n", " \n", - " I_topk : numpy.ndarray\n", - " The indices that correspond to P_topk\n", + " out 3: np.ndarray\n", + " A 2D array, with k columns, containing the core component of lowerbound values,\n", + " \n", + " out 4 : np.ndarray\n", + " A 2D array, with k columns, containing the indices that correspond to the lowerbound values\n", + " \"\"\"\n", + " mp = stump(T, m, k=k)\n", + " P_TopK = mp[:, :k].astype(np.float64)\n", + " I_TopK = mp[:, k:2*k].astype(np.int64)\n", + " \n", + " # In VALMOD paper, LB has the following component:\n", + " # np.sqrt(m * (1 - np.square(ρ_clip))). Here, we\n", + " # show it by `LB_σr`\n", + "\n", + " ρ = 1.0 - np.square(P_TopK) / (2 * m)\n", + " ρ_clipped = np.clip(ρ, a_min=0.0, a_max=1.0)\n", + " r = np.sqrt(m * (1.0 - np.square(ρ_clipped))) \n", + " _, σ = core.compute_mean_std(T, m)\n", + " LB_σr = σ.reshape(-1,1) * r\n", " \n", - " Notes\n", - " -----\n", - " https://doi.org/10.48550/arXiv.2008.13447\n", + " return P_TopK[:, 0], I_TopK[:, 0], LB_σr, I_TopK\n", " \n", - " see Algorithm 3\n", " \n", - " This is a naive implementation of stump. It calculates the whole distance_matrix right in the beginning \n", - " of the algorithm. The structure of this code is based on the naive implemention of function stump, \n", - " available in stumpy/test/naive.py. \n", " \n", - " Unlike the original paper that returns matrix profile and indices and the LB values for the next length (i.e. m+1), \n", - " we return the `k` smallest values and their indices for each distance profile.\n", + "def _VALMOD_stump_partial(T, m, k, LB_σr, LB_I):\n", " \"\"\"\n", - " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", + " Compute partial matrix profile for subsequence length `m`, \n", + " with help of lowerbound. \n", + " \n", + " Parameters\n", + " ----------\n", + " T : numpy.ndarray\n", + " The time series or sequence for which to compute the matrix profile\n", + " \n", + " m : int\n", + " Window size\n", + " \n", + " k : int\n", + " The number of nearest neighbor to consider for constructing lowerbound \n", + " profiles\n", + " \n", + " LB_ar : np.ndarray\n", + " The array that contains the main component of lowerbound values\n", + " \n", + " I_TopK : np.ndarray\n", + " The array that corresponds to the indices of lower bound values\n", + " \n", + " Returns\n", + " -------\n", + " P : np.ndarray\n", + " A 1D array containing the exact matix profile values\n", " \n", - " # naive calculaton of distance_matrix\n", - " distance_matrix = np.array(\n", - " [core.mass(Q, T) for Q in core.rolling_window(T, m)]\n", - " )\n", + " I : np.ndarray\n", + " A 1D array containing the exact matix profile indices\n", + " \n", + " LB_σr : np.ndarray\n", + " A 2D array, with k columns, containing the core component of lowerbound values,\n", " \n", - " l = T.shape[0] - m + 1\n", + " LB_I : np.ndarray\n", + " A 2D array, with k columns, containing the indices that correspond to the lowerbound values\n", + " \"\"\"\n", + " n = len(T) - m + 1\n", + " P = np.full(n, np.inf,dtype=np.float64)\n", + " I = np.full(n, -1,dtype=np.int64)\n", + " is_mp_valid = np.full(n, 0, dtype=bool)\n", " \n", - " P_topk = np.full((l, k), np.inf, dtype=np.float64)\n", - " I_topk = np.full((l, k), -1, dtype=np.int64)\n", + " # may add support for `T_B` (AB-join)\n", + " Q, μ_Q, σ_Q, Q_subseq_isconstant = core.preprocess(T, m)\n", + " T, M_T, Σ_T, T_subseq_isconstant = core.preprocess(T, m)\n", " \n", - " diags = np.arange(excl_zone + 1, l)\n", - " for i in diags: \n", - " for j in range(0, l - i): \n", - " D = distance_matrix[j, j + i] \n", + " σ_Q_inv = 1.0 / σ_Q\n", + " LB = σ_Q_inv.reshape(-1, 1) * LB_σr[:len(σ_Q_inv)]\n", + " \n", + " maxLB_profile = np.full(n, np.NINF, dtype=np.float64)\n", + " isin_excl_zone = np.full(LB.shape[1], 0, dtype=bool)\n", + " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", + " for i in range(n):\n", + " isin_excl_zone[:] = False\n", + " \n", + " excl_zone_start = max(i - excl_zone, 0)\n", + " excl_zone_stop = min(i + excl_zone + 1, n)\n", + " excl_zone_range = range(excl_zone_start, excl_zone_stop)\n", + " \n", + " min_dist = np.inf\n", + " idx = -1\n", + " for enum, j in enumerate(LB_I[i]):\n", + " if j >= n:\n", + " isin_excl_zone[enum] = True # just to exclude...\n", + " \n", + " elif j in excl_zone_range:\n", + " isin_excl_zone[enum] = True\n", " \n", - " if D < P_topk[j, -1]:\n", - " idx = np.searchsorted(P_topk[j], D, side='right') \n", - " P_topk[j] = np.insert(P_topk[j], idx, D)[:-1]\n", - " I_topk[j] = np.insert(I_topk[j], idx, j + i)[:-1]\n", - " \n", - " if D < P_topk[j + i, -1]:\n", - " idx = np.searchsorted(P_topk[j + i], D, side='right') \n", - " P_topk[j + i] = np.insert(P_topk[j + i], idx, D)[:-1]\n", - " I_topk[j + i] = np.insert(I_topk[j + i], idx, j)[:-1]\n", + " else:\n", + " QT = np.dot(T[i:i+m], T[j:j+m])\n", + " d_square = core._calculate_squared_distance(\n", + " m,\n", + " QT,\n", + " μ_Q[i],\n", + " σ_Q[i],\n", + " M_T[j],\n", + " Σ_T[j],\n", + " Q_subseq_isconstant[i],\n", + " T_subseq_isconstant[j],\n", + " )\n", + " d = np.sqrt(d_square)\n", + " if d < min_dist:\n", + " min_dist = d\n", + " idx = j\n", + " \n", + " eligible_LB = LB[i, ~isin_excl_zone]\n", + " if len(eligible_LB) > 0:\n", + " maxLB = eligible_LB[-1]\n", + " else:\n", + " maxLB = np.NINF\n", + " \n", + " if min_dist < maxLB:\n", + " P[i] = min_dist\n", + " I[i] = idx\n", + " is_mp_valid[i] = True\n", + " else:\n", + " maxLB_profile[i] = maxLB\n", + " is_mp_valid[i] = False\n", " \n", + " n_invalid = np.sum(~is_mp_valid)\n", + " time_complexity_threshold = (n * np.log2(k) / np.log2(n))\n", " \n", - " return P_topk, I_topk" + " global_min_dist = np.min(P)\n", + " global_min_maxLB = np.min(maxLB_profile[~is_mp_valid])\n", + " if global_min_dist > global_min_maxLB:\n", + " if n_invalid < time_complexity_threshold:\n", + " for idx in np.flatnonzero(~is_mp_valid):\n", + " if global_min_dist <= maxLB_profile[idx]:\n", + " continue # Q: so, are we considering approx. best match?\n", + "\n", + " QT = core.sliding_dot_product(T[idx:idx+m], T)\n", + " D = core._mass(\n", + " T[idx:idx+m], \n", + " T, \n", + " QT, \n", + " μ_Q[idx], \n", + " σ_Q[idx], \n", + " M_T, \n", + " Σ_T, \n", + " Q_subseq_isconstant[idx], \n", + " T_subseq_isconstant\n", + " )\n", + " core.apply_exclusion_zone(D, idx, m, np.inf)\n", + "\n", + " arg = np.argmin(D)\n", + " if D[arg] < np.inf:\n", + " P[idx] = D[arg]\n", + " I[idx] = arg\n", + "\n", + " args_topk = np.argsort(D)[:k]\n", + " LB_I[idx] = args_topk\n", + "\n", + " ρ = 1.0 - np.square(D[args_topk]) / (2 * m)\n", + " ρ_clipped = np.clip(ρ, a_min=0.0, a_max=1.0)\n", + " r = np.sqrt(m * (1 - np.square(ρ_clipped)))\n", + " LB_σr[idx] = σ_Q[idx] * r\n", + "\n", + " else:\n", + " mp = stump(T, m, k=k)\n", + " P_TopK = mp[:, :k].astype(np.float64)\n", + " I_TopK = mp[:, k:2*k].astype(np.int64)\n", + "\n", + " ρ = 1.0 - np.square(P_TopK) / (2 * m)\n", + " ρ_clipped = np.clip(ρ, a_min=0.0, a_max=1.0)\n", + " r = np.sqrt(m * (1 - np.square(ρ_clipped)))\n", + " _, σ = core.compute_mean_std(T, m)\n", + " LB_σr = σ.reshape(-1,1) * r\n", + " LB_I = I_TopK\n", + " \n", + " return P, I, LB_σr, LB_I" ] }, { "cell_type": "code", - "execution_count": 78, - "id": "f431a4fb", + "execution_count": 165, + "id": "be7b439d", "metadata": {}, "outputs": [], "source": [ - "seed = 0\n", - "np.random.seed(seed)\n", - "T = np.random.uniform(low=-100, high=100, size=1000)\n", - "m = 50\n", - "k = 5" + "def _update_PIM(P, P_new, I, I_new, M, m_new):\n", + " \"\"\"\n", + " Update P (profile values), I (profile indices), M (length of subsequences), in place, \n", + " by using the new values `P_new`, `I_new`, `m_new`\n", + " \n", + " Parameters\n", + " ----------\n", + " P : np.ndarray\n", + " The matrix profile value containing the scaled distance between a subsequence to the nearest neighbor\n", + " \n", + " P_new : np.ndarray\n", + " The matrix profile value containing the scaled distance between a subsequence to the nearest neighbor, \n", + " computed for a subsequence length that is longer than the one used for `P`\n", + " \n", + " I : np.ndarray\n", + " The matrix profile indices containing the nearest neighbor index of each subsequence\n", + " \n", + " I_new : np.ndarray\n", + " The matrix profile indices containing the nearest neighbor index of each subsequence, computed \n", + " for a subsequence length that is longer than the one used for `I`. These indices correspond to \n", + " the matrix profile `P_new`\n", + " \n", + " M : np.ndarray\n", + " For a subequence at index `i`, `M[i]` is the lenght of subsequence for which the lowest distance \n", + " between `i` and its nearest neighbor is discovered.\n", + " \n", + " m_new : int\n", + " The new subsequence length that is used for computing P_new, I_new\n", + " \n", + " Returns \n", + " -------\n", + " None\n", + " \"\"\"\n", + " n = len(P_new)\n", + " mask = P_new < P[:n]\n", + " P[:n][mask] = P_new[mask]\n", + " I[:n][mask] = I_new[mask]\n", + " M[:n][mask] = m_new" ] }, { "cell_type": "code", - "execution_count": 79, - "id": "0b3c14c2", + "execution_count": 166, + "id": "94eceff1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "running time: 2.096430778503418\n" - ] - } - ], + "outputs": [], "source": [ - "tic = time.time()\n", - "P_topk, I_topk = _VALMOD_stump(T, m, k)\n", - "toc = time.time()\n", - "print('running time: ', toc - tic)" + "def VALMOD(T, m_min, m_max, k):\n", + " \"\"\"\n", + " This function finds the matrix profile of T_A while considering different length of subsequences in \n", + " range `[m_min, m_max]` inclusive. To be able to compare distances across different subsequence length, \n", + " each distance is scaled by a factor of `1 / sqrt(m)`. \n", + " \n", + " Parameters\n", + " T : np.ndarray\n", + " The timeseries of interest\n", + " \n", + " m_min : int\n", + " The smallest window size\n", + " \n", + " m_max : int\n", + " The largest window size\n", + " \n", + " k : int\n", + " The number of nearest neighbors to capture for speeding up the computaion.\n", + " \n", + " Return\n", + " ------\n", + " PIM : np.ndarray\n", + " A 2D array, with exactly three columns, representing the ensembled matrix profile. The first column \n", + " contains the ensembled matrix profile value. The second column contains their corresponding nearest\n", + " neighbor index, and the third (last) column contains the corresponding subsequence length. Hence, \n", + " for instance, when `dist = PIM[i, 0]`, `j = PIM[i, 1]`, and `m = PIM[i, 2]`, then `dist` is a (scaled) \n", + " distance between subsequence `S_i` and subsequence `S_j`, each with length `m`. `dist` is the lowest \n", + " scaled distance between `S_i` and all of its neighbors considering all values of `m`.\n", + " \"\"\"\n", + " n = len(T) - m_min + 1\n", + " out_P = np.full(n, np.inf, dtype=np.float64)\n", + " out_I = np.full(n, -1, dtype=np.int64)\n", + " out_M = np.full(n, -1, dtype=np.int64)\n", + " \n", + " # out_P, out_I, out_M = _update_PIM(out_P, P_TopK[:,0] / np.sqrt(m), out_I, I_TopK[:, 0], out_M, m)\n", + " LB_σr = None\n", + " for m in range(m_min, m_max + 1):\n", + " if LB_σr is None: # only runs for the first iteration, i,e, lowest `m` \n", + " P, I, LB_σr, LB_I = _VALMOD_stump(T, m, k)\n", + " else:\n", + " P, I, LB_σr, LB_I = _VALMOD_stump_partial(T, m, k, LB_σr, LB_I)\n", + " \n", + " _update_PIM(out_P, P/np.sqrt(m), out_I, I, out_M, m)\n", + " \n", + " out = np.empty((n, 3), dtype=object)\n", + " out[:, 0] = out_P\n", + " out[:, 1] = out_I\n", + " out[:, 2] = out_M\n", + " \n", + " return out" ] }, { "cell_type": "code", - "execution_count": 80, - "id": "b5e0fe7e", + "execution_count": 172, + "id": "9557a1ad", "metadata": {}, "outputs": [], "source": [ - "P = P_topk[:, 0]\n", - "I = I_topk[:, 0]\n", - "#just test the P and I\n", - "mp = stumpy.stump(T, m)\n", - "np.testing.assert_allclose(mp[:, 0].astype(np.float64), P)\n", - "np.testing.assert_allclose(mp[:, 1].astype(np.int64), I)" + "import time\n", + "\n", + "seed = 0\n", + "np.random.seed(seed)\n", + "T = np.random.rand(1000)\n", + "m_min=5\n", + "m_max=10\n", + "k=10\n", + "\n", + "T1 = time.time()\n", + "valmod_mp = VALMOD(T, m_min, m_max, k)\n", + "T2 = time.time()" ] }, { "cell_type": "code", "execution_count": null, - "id": "6df35afc", + "id": "f80bf53e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cff941a8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb7e87da", "metadata": {}, "outputs": [], "source": [] @@ -617,7 +840,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c7a09d91", + "id": "0b9d7321", "metadata": {}, "outputs": [], "source": [] @@ -639,7 +862,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.9" } }, "nbformat": 4, From ada4fdf3c620792c270bacba933cab24998c4a74 Mon Sep 17 00:00:00 2001 From: nimasarajpoor Date: Mon, 27 Feb 2023 22:52:09 -0500 Subject: [PATCH 63/64] implement naive valmod and minor changes --- docs/Tutorial_VALMOD.ipynb | 255 ++++++++++++++++++++++++++++--------- 1 file changed, 193 insertions(+), 62 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index f67df115a..886237e0b 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 1, "id": "0adbe18a", "metadata": {}, "outputs": [], @@ -30,6 +30,34 @@ "plt.style.use('https://raw.githubusercontent.com/TDAmeritrade/stumpy/main/docs/stumpy.mplstyle')" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "44d283f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.8774290881094438, 3, -1, 3],\n", + " [0.22840038810292498, 4, -1, 4],\n", + " [0.012465907727357997, 5, 0, 5],\n", + " [0.8774290881094438, 0, 0, 6],\n", + " [0.1871064481158026, 6, 1, 6],\n", + " [0.012465907727357997, 2, 2, 7],\n", + " [0.1871064481158026, 4, 4, -1],\n", + " [0.23027056533433626, 5, 5, -1]], dtype=object)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stump(np.random.rand(10), 3)" + ] + }, { "cell_type": "markdown", "id": "e9d48c97", @@ -97,14 +125,12 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABlEAAAH5CAYAAAARAhhCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9d7wkR3Xof2aDFklIMlmSLYL9jN/PCNvYpAfYYAwCTBJgoo0RyegRZcwDBAYECAkhJGTliLJWQjmstKvNOeecw91w9266aW++078/ZmdudXeFU1WnOsw9Xz5i5850V1VXVz6pEkVRBAzDMAzDMAzDMAzDMAzDMAzDMEyMMXkXgGEYhmEYhmEYhmEYhmEYhmEYpoiwEIVhGIZhGIZhGIZhGIZhGIZhGEYCC1EYhmEYhmEYhmEYhmEYhmEYhmEksBCFYRiGYRiGYRiGYRiGYRiGYRhGAgtRGIZhGIZhGIZhGIZhGIZhGIZhJLAQhWEYhmEYhmEYhmEYhmEYhmEYRgILURiGYRiGYRiGYRiGYRiGYRiGYSSMy7sAWVCtVmH//v1w2mmnQaVSybs4DMMwDMMwDMMwDMMwDMMwDMPkSBRF0NXVBWeffTaMGaO2NxkVQpT9+/fDOeeck3cxGIZhGIZhGIZhGIZhGIZhGIYpEC0tLfAnf/Inyt9HhRDltNNOA4BaZZx++uk5l4ZhGIZhGIZhGIZhGIZhGIZhmDzp7OyEc845pyE/UDEqhCh1F16nn346C1EYhmEYhmEYhmEYhmEYhmEYhgEAMIYA4cDyDMMwDMMwDMMwDMMwDMMwDMMwEliIwjAMwzAMwzAMwzAMwzAMwzAMI4GFKAzDMAzDMAzDMAzDMAzDMAzDMBJYiMIwDMMwDMMwDMMwDMMwDMMwDCOBhSgMwzAMwzAMwzAMwzAMwzAMwzASWIjCMAzDMAzDMAzDMAzDMAzDMAwjgYUoDMMwDMMwDMMwDMMwDMMwDMMwEliIwjAMwzAMwzAMwzAMwzAMwzAMI4GFKAzDMAzDMAzDMAzDMAzDMAzDMBJYiMIwDMMwDMMwDMMwDMMwDMMwDCOBhSgMwzAMwzAMwzAMwzAMwzAMwzASWIjCMAzDMAzDMAzDMAzDMAzDMAwjgYUoDMMwDMMwDMMwDMMwDMMwDMMwEliIwjAMwzAMwzAMwzAMwzAMwzAMIyGoEGXOnDnw4Q9/GM4++2yoVCrwxBNPxH6PogguueQSOPvss+Hkk0+Gd73rXbB+/frYNf39/fCtb30LXvrSl8Kpp54KH/nIR2Dv3r0hi80wDMMwDMMwDMMwDMMwDMMwDBNWiHL8+HH467/+a7j++uulv//mN7+Bq6++Gq6//npYunQpnHnmmfDe974Xurq6GtdcdNFF8Pjjj8ODDz4I8+bNg+7ubvjQhz4Ew8PDIYvOMAzDMAzDMAzDMAzDMAzDMMwopxJFUZRJRpUKPP7443D++ecDQM0K5eyzz4aLLroIfvCDHwBAzerkFa94BVxxxRXwta99DTo6OuBlL3sZ3HvvvfDpT38aAAD2798P55xzDjz77LPwvve9D5V3Z2cnnHHGGdDR0QGnn356kOdjGIZhGIZhGIZhGIZhGIZhGKYcYOUGucVE2blzJ7S2tsJ5553X+G7ChAnwzne+ExYsWAAAAMuXL4fBwcHYNWeffTace+65jWtk9Pf3Q2dnZ+w/hmHCMThchaHhaiZ5bT/UDQ8u2QPD1UzkvwySg519MH/bYchILs8wDMOUhJ6BITh6fCDvYjAMU3Bun7sDHli8J+9iNJi4ZA9c/NhaqPKeg2GCsedIDwxmdI7AMAzjS25ClNbWVgAAeMUrXhH7/hWveEXjt9bWVjjppJPgRS96kfIaGZdffjmcccYZjf/OOecc4tIzIVi7twPectk0eHQ5x7wpE1EUwUevnw//8JuZmSyA/umq2fDDx9bCQ0tbgufF4HnLZdPhX29fDLO2HMq7KE1HFEUwMMSbC4Zhyslf//x5+NtfToWOnsG8i8IwTEE52NkHl07aCD96fG1hFKUufmwtTFyyB6ZtPJh3UZgmZjQroE3feBD+4cqZ8IXfL5H+PjhcZSUMxosp61vhg9fOhW1t3XkXhWkSchOi1KlUKrG/oyhKfZfEdM3FF18MHR0djf9aWviwtQx8c+IKONjZD//18Oq8i8JYMDBchQ0HOmF/Rx9sOdhlvoGIFXuOZZYXg2fh9iN5F6Hp+PLdy+Cvfj4F2nt4E8EwjJqVe47BJ25aACsDzo/Hjg/AM2v2Q/8QPjbh4HDtgGj9gY5QxWIYJhDVagTLdx+F4/1DQfMR068W7FC5o5cFwHlSrUbQ1tmXdzGCcPvcHfDmy6bDzsPH8y5KLtyzcDcAACxQ7B8/ev18+NtfToXth/gAPG+q1QiumLwJpp8QKm880AkfvHYuzNrclnPJ9Hzt3uWwfn8nfOfBlXkXhWkSchOinHnmmQAAKYuStra2hnXKmWeeCQMDA3Ds2DHlNTImTJgAp59+euw/pvgMDRdrwczgqMCIQLO1ozkXuAwevQiccWHGpjboG6zClPVqC8zRAAuR8DSr65GBoSo8t/YAHBtFWok2Gqofv2kBLN99DP7l5oXByvOZWxfBNx9YCVdO3mx9b4VnCIZJMVyN4LEVe2HPkR7nNFo7+uDCe5fDgu2HUdevammHHz66Bg539xuvfXh5C3zipoXwmVsXOZcPg6ggWTAZChSsOF6o5pRJaw4EFcD78J2HVsGbL5veOLxtJi6dtBEOdfXDT59cl3dRcsHUtzYcqLnln7TmQPjCMDE6egdhX3tv4++n1+yHm2Zthy/fvQwAAL5y9zJYv78TLrhzaV5FtKKdraEZInITorzmNa+BM888E6ZOndr4bmBgAGbPng1ve9vbAADg7/7u72D8+PGxaw4cOADr1q1rXMM0DwYDJKagRMLy5wALURiGCcANM7fB3/xiKkxcUhxf6UXl+4+shrdePh06+5pvs/A/07fA/71/BXzqlnBCgiLR1tkHb718Olz1PE5gUT8bC+kKZ/MJi9NJa+0PNHidxzBpJi7ZA9/9w2r4hytnOqfxq2c3wuT1rfC52xajrj//hvnw4NIW+O/HzQe3j67YBwAAa/eFtSQbI4wPRbNEaRZumb0d3nr5dGg5GhfYrd/fAd94YAV87EZ1zNk8eXr1fgCorQWble7All4MY8tf//x5ePuvZ8DBE1Zg+9vj5zxlU27jeYWhIqgQpbu7G1atWgWrVq0CgFow+VWrVsGePXugUqnARRddBJdddhk8/vjjsG7dOrjgggvglFNOgc997nMAAHDGGWfAl7/8Zfiv//ovmD59OqxcuRL+7d/+DV7/+tfDe97znpBFZxjGgYNNamrNWJDRIdnQcBU+fctCuOSp9dlkWAB+8OhaWLbraJC0fztlM/zgkTWF9ct85ZTaIfLFj63NuSTF5w/L9kJbVz88sXJf3kUh5+nVtYP7raPEr/H1M7fBwc5+uG5G8x4cMcxoZ/FO/3nd1ToP4yJnwrhsdC5FS7XCHXYVrDiuXP7cJjjY2Q+XPbsx9n1ZXEn5voarn98M779mDnQVUMmkpx/vIrOZKOq+gxlhdUt73kUgwUbBqBmFmrfP3QFfuXsZx1klIOiqaNmyZfCGN7wB3vCGNwAAwHe/+114wxveAD/96U8BAOD73/8+XHTRRfD1r38d3vjGN8K+ffvg+eefh9NOO62Rxu9+9zs4//zz4VOf+hS8/e1vh1NOOQWefvppGDt2bMiiMznAGorl51iGGgncXEY387YdhsU7j8JdC3blXZQGWQRCVbnpiaIIhobdFkXVagTXz9wGDy1rKc1GOm9c6zpLmtFsPcroJKtajeArdy/NXUhbuINET3jeZkYrXX2D8POn1wdzl/TiU09yug+z95owLps9dyVmiZJJlmh0c08URZms/yhJzi0lK74z187YBptau+CBxcWzau4ZbL5DW6a5KduwgR2np204COf+bAr8doq929oic+mkjTBt40F4clXzKdllTVAhyrve9S6Ioij131133QUANd+nl1xyCRw4cAD6+vpg9uzZcO6558bSeMELXgDXXXcdHDlyBHp6euDpp5+Gc845J2SxGUIWbDsMOzgQWFPTZGc8ow4xdsKCbYfh+hlbveIpUPm8b+vsg6MazcqixVBavOMI/H8/mQz3LNyVS/6fvHkhvPXyGdA3qNZkm7PlEMyUBP/rFe5Zt78TvvuHVRzfSMPWg13wlz+dAr+ZvCnvomi5euoWbR8azQwMVWHZrqNKYdiqve0wbWOblZC2f2gYnlq9H44gYgyMViqsLcOMUq6YvAnunL8rmLskVyEKhqwsUUSKJpTQ7XU+d9tieOeVM720e7/z4Er43sOrne+3Jfk8rtYAw9UI1u3ryOx9Ue05hwrWvgAAegdGpyUKlhCrh82tXaQWB3cv2AWPLN9Lll5RaJa1G7bf1+MTXd+k7gN7NWcFDI7cYqIwzc+G/Z3wudsXw7uvmt34rlqNYP3+DunBBQccZcpGy9Ee2NbWDZtbu2DvMfeAoKHZcrBLGjz0keV74XU/mwILttUCkX7u9sXw2+e3OPm6p+R4/xC8+bLp8Le/nFoaM++LHloFA8NV+OmT+WiuL9t9DA5398OavXKf5b0Dw/Dvv18CX7xzaWrDcHxg5O9vT1wJj63YB//18KqQxS01V07ZDAPDVbhx1va8i2JEdP+29WAXdPQ2n3WKCz94dA38y80L4QqFIGzQ4TDs6qlb4NsTV8InCeO1lGT4Y5hRRVtXn/XaZMvBsAplLxGEKDplCheyEqKMGSMGlncf/ELMc7rSLNxxBPYe64W1+9qd0j7Y2QdPrtoPjyzfC8czciFDNbVcOmkDfOi6efDLZzYQpain2abEh5aOWMQcH6XuvPJi0Y4j8L5r5sB7r55tvhjBgY5e+NlT6+F7D68uzd7Vl7I9JlZJtGSPxeQAC1GYYGw40Jn67vqZ2+CD186DH0r82jeJkJsZJURRBH//m5nwnqtnw/uumQPvuMI9IGhIdhzqhvN+NwfeeOm01G/fe3g19A4Ow1fvWRb7fs/RfAVCe4/1Nj4XUFmslIiHKj2JTbpM+217G7v1KgJr9rbD5+9YDBv2p+dTDPX+s25fB7z3d3PgLZelx4HRyOMn4sXcNncnWZrPnhA+7zhE13eaYfgTDxOKsM7rGxyGC+9dDhOXFM+dC1N8/rC0Bd78q+mpmBImdE2f4sDt1AnjGp+PEFsgThifVUyUEVzXfldM3gR//fPnYdIaWmUgzCtyfY2idnRWY37aEsUtnTvn7wIAyMy1brMdTv/g0ZHzkNGqHZ7XK528rhUAAA4oLO93HT5uFTunq29kb1WkZrpk59HGs1Ijc3M4WGB3x8NFejFMqWEhCpMp10zbAgAgNXUswN6aYZqO5bvN/reTSwqfTQrFIdlQdWQBVpZxoejrMl3xWPvNj+FqBJ+8eQF896FV5Gl/5Pr5MHfrYfjOgyud7q/7PZ+95RAAAPQNxjc3v5+3E+5fvNuvkAyjoWhj48Qle2Dy+la4WKLMwzAm6hr3tgLYLAWI1JYoJ43N/rjANR7UTScsRC95Onur4HIp/SRjopSq8N40mzCmSLR29MENM7eRuzZNjqFHjw8YXehFUQTzth6WurYdO0Y9KG9r64J3/XYW/ONvZ+HLh74yWz51y0K48L7lsOeIu4Jk/dmS7yDZja56fjP8+Y+fg3X75F4RXNjW1gXff2S1V/nrYN0Ohhgepm04CN95cGVTBqwfjbAQhckU3djVLP4WRzNFWZMODVfhmTX74WAnx3VweSU+75GiFw/noJU3qki8pF5JMEufAN6zNrfBNx5YAccyjsfR2tEH9y/enblf6dV722HprmPw2Mpwgfpc3ZPUD0Zk02tbVx/84pkN8OPH10H/EAvSGD82tXbCl+9aCrM2t0GnoL0pHs4VYZXHcYKYZkScsW1ixmFcKU8Yn01gebHUPrH5QoBZE9kezHf1DcI107bAtrb8Y4cWZf9moizlHM382x2L4copm+FbE92UfzDsb++Fv/3lVHiPwRXXE6v2wb/dsRjO+136Oo0MBRbuqLnCPdzttl4oYjNt9TgTUR3RJZ/zuhm1GCK2lpo6PnHTQvjDsr3wxbuWeKeVZ6ytr9yzDJ5ctR+um7E1tzIwdLAQhWEYL8QF7f2L98CvJmXjF1cnc7tz/i745gMr4X3XzAmWf2kW8mUpp4BrwMeh4SrM33Y4M5/SGI509yuDVxcFakuUC+5cCpPWHIDLn6NbRGP4yPXz4MePr4NfZ5yvzYGVK+e8+BSn++pdaYxkwBSFTbrxbGi4Cl+/fzn8fl5N83pwuAozN7dZuTkoC/WDu6yFz1mmGYrP3bYYpm9qgwvuXAp/dcnzje/F4bwIujKDGfRXprlYvOMI/OLpDbUxM4c2PDhchfsX74Ydh2qH7XVNb5VAkNqdihgTJeR6RhRCZHHWZXOghhmLbct82bOb4JppW+ELvx85HMyqeYnPE0UR/FeGQe198FHwKRKzNrfBm3/VnO5V60LBBduPpH6Logi2tXXHhKQu73Tm5jYAMLuffn79QQCQC0PGaKQoZ53+gsZnF8Ws0W7pRPn4dSWy7QSucrHuvEKOM22dtBZaTD6wEIUpDAXYWzMEUPqYd2Xaxtqiqb2n+Q75bHFZCOS99LPVFFnV0g5RFMG107fCv96+GL5899JAJbNjU2sn/N2l0+BThIGmQ9ATyHJD5Wc4FG1dtYXpjBObK2qW7z4Kuw6nF/Gh3GCIh1V/8qKToXdgGL7xwAp4wsLipb5RNc2vukeYtPYAPLu2FX5xwo3NddO3whfvXAqfv8NfK8wWWTmP9w/BZ29dBHd7+mT/4aNr4B1XzGhK4VAWqA5043NQdiu9o8cHYFVLe+r7ogu1meLx6VsXwe/n74SbZm/PJf+7F+yCHz++Dt59VU2b+nO3L4Irp2yOuXkUD+1sFFEwgk1RiNJncJ/jgzi++86rptv3HuuB118yBX7xNJ3il+16e+WetLvdrATNYknXa2KuXfzYGvjavcukh8KHid01YaBabuV9xn3BnUsba9bRxO+mboH3XD2b1FJBh24cGavpbC98wUiMKawFh5hc3vvozCjZg+bd7wGyFJRH8J8PrcpcoXG0wEIUJjf++wn2R90MFFErKItJsnhPLSfrBQNJTBRBUxijzXP+DfNh0toDcP/iWqDgRSfMsPPmD0trsZ9W7GnPtyCgr8eegeJY7lAQos3vONQNn7hpIbxL4h85lNsR8SDs5PFjYeKSPTBpzQG4yCL2is6dF5ak/95HV9SEOLID6hCYxoA75++EhTuOwM+e8vOB/+DSFtjf0WclpBIJM9aWZaZRI9ZLlpYob71sOpx/w3xYtCOuDetq6cgwu4+4a8Ji3GapSMa223FCI3fu1sPS66kFheOFmChZucsMHaPjplnboWdgGH4/H6f4pSpNbH6yLHKeB3piuWWKS21dffDNB1bAxCUtMGX9wZQW+HNrD8AbL21OSwomHNeecPd0+7yRfoftB6Lbd+x4qpvuZRbaMlozUAjb1tYFX7t3GazfTxdLhBJMVTWT9Y3uUfoGhzNpE75sau2Cx1fug1tm78i7KE0JC1GY3Lhv0Z74wk0yQPcODMOF9y6Hx1bshY7eQbhu+laSwFJMczPaAiRSk3f1ufgsfXLV/gAlceNARy8AhGuHm1o74VeTNkB7j5uv3uTmQ2aJkncbKBqbWruUv2HNw32oRhH0OgQLrnclnwO8PHlg8R74m19MhTV725XXHPc81Eu5viE66e8dGIb3XzNn1Acwp+geV0zeBN99aJXVJn3gxHudu/VQ7PuhKluiMO64jg4+w4rO7UwdsWtQu6wTU6MOWh/LR7REKVo3VYw9ceuZjMpCQMw+UNK8fvz4OnhmzYHG38n17JXPbw5UMj1klig0yTAaiuC+U9dedOOqeJ/LXsu2nX7+jiUwZf1B+NgNC6zzyhNRkfanT/opMhUJ3et7929nwVsvn95wr2lNRv1C52qa9/j+sBCFKQyyMeWuBbtg8vpW+O4fVsNPnlgHV03dAh+5YV7mZWPKBc8NIzj59s+5BgcLt3s2I9bZv962GADCCVHef81cuG3uTvgJ0YI160DszUaog5O4z3KA0wX3Alga7rwkE2wZBCs/enwtdPQOwkUPrgqWxz9dlQg4StRvV+9th02tXTBxyZ6GYHU0ggks3zswDM+uPRALSC9y06zt8NjKfbDxQFyY6aL5mEUMI2Z0EkURDARwd6VyOxN3HyO686Itg9jN+ocCClGEZ8Cun9q6+mB1BlaRSksU4bPtmi/PtXbMQlAyMrcYYk3kdQjHo3d5wFp6uLQlvIDGzZ2XCF5RaiQ9275dd388UDJ3o2LV3Lto98j3Je+pule+/8S7mrHJzXV0VnuvIggxmxkWojCFoSLp7e29I9L/hSdcMnCci2JRRGm27mClu38INrWq/f9S5FEkXA7yk7cMDFXhZ0+ugxmbDhrvpVgcDIvuvJD3RFFxNlc7TsTNCG0RtX4f3uxbV5IsLCmyJOsA36HceSU3Iae9YHzjM9Zaq9ms8kI8jikwqSvjx46Mhct2pX3fYzA9b//QMPxm8iZYsrMYLgxlYF7Zz55aB1+/fwVceO9y7XWi1dCeIz3w5sumw82WcSrYnRcTigvuXAqvv2QKdEj2KboDDVOLVClMqw4pqQWF4jwSsvu4xER586+mw0dvmA9r94Z1g6MqjrgXsK0aWV1mdcBmskQx3l+QtcUlT61nn/8FBdusQh6468arschTUBfvCM2GalxSuzkMVxYGR0zJIvFCWMDiDwtRmFwRO7WsP4+J+b9kysjeYz2w5aDaFY4ruo2Gbu5+79Wz4f3XzIUF2+W+pJk09y/eDXcv3A1fumuZ8VqKidntUD8qzKauTskUimIUqyaLTRYbrAgAXjhhxBKluw8Xx6bel2RKCqVCU3zqJ4vAbQOYHH/Eg8yUyzB0mvrf716wC26ctR0+dctCp/QpUR30xixRFO3wD8tq8aMWbD8i/V3GZc9uhENd/fDr5zZpr0uuFfhAhPFBN5bO3nII+oeqMGVDK2meKrcz4rdxd17ZBH8nT1v4bNtNl+zSC5JXtbTDR2+YTy5wFstpuwaVXZ+VBnds/42YRJOX5DWMiuVu6+qDuxbsgltm74Dj/Xax/Qq2XWhKQlqiYNEJY7XuvETLPgehdLO2r5LvJCwI9wKz2o6Ja99mbY95wkIUptAg3AAzOWMal99xxUw473dz4Eh3fyblAdBPFnWT2Snr/Da5ZZmPnA4DE38fsAigdt2Mbd7uLJplwRrKQkGHm0YhfTmy5NjxAbgTGRw2BKGsPZJauePHjSzZVG6PktSboGkupT64Ga5GsG5fB3mA4zIhCoNDHdzvOOQe6JqasYpGFglNgHJJ59rvQh4wMwwAkC8QVYeR4vdiltTWVuJaJuQhv3hAbtu/xxoGl4seXAmrW9qdBc4qAYlYH2VdSsmU0kyKF0Wzcu0P4EYPoPbef/H0Bpi4ZE+Q9MtKN0JoFfKwGG3lorNEwbrzQo6nZddV0qJ4tqIpL2aJal5eu7cDHl+5V3mfazM52NlnFZNsjHDKX7TxuhlgIQqTKyZzYqwWA1N8QrlMkTEapopNrZ2wYo/ZRQy2LijjYjyxap/X/aI/7zLP+3ksWjBZYobVMtX71+9fAT9/ekPj7xCLet3BUTAhSuIP8bmwQpT6PVnPpL99fjN86Lp58JMn12Wcsz9Ur1PceLu2EdOBZZH6qVKIQjgji2OX6/KQY6IweeDjpkl52Bdz1zHymVqIErMQCSiDjFui2D2D6dB/MNHvqWooHrvM0hLFkF5WjHE4DcotJoqQ73ih4CFiEQHUXIn/fv5OuPixtUHSLyN3zt8J5/5silGwFPIMB5u01hIFmQh2PFVZBvpy+9wdcPFjawspsCheiWjw2Ut/+Pp58J8PrSb1eLL7yHF4y2XT4fwb5qPvEdccySZcwKZUOliIwqBwGbgpps7SuyBhGmT5LkMuNAaGqtDWibfMCMX7r5kLH79xgdnCB1kXH/ifOcp7bOvTVyDjFMcFireYa7ZYI0WlHi8rL0Iptut8rXch3XmNBJbPdi69aVYtTsXEJS0k6WW9EnA5+E/eIQpRRkMcDtlB7+HufvjiXUsbf1M2Q9dDaeqg28zoAqWEQLwaUR1yq4pCbQFYjc1FIS1RhDwtHyEtw42X85wXn+xUJnlqcqyHlhynBVNg+STJdp/Xga7K8sdWiIJtx7L4RqOdutKSSbCE9SaSVzfQufMSCzWc85rh0kkbYeKSPbDUMbZenSAKZs2/tFViat7b2rrl9zksXedtqwlkNrV2oa2pxebNlij0sBCFMXKoqx/+z+Uz4LdTNpOnbVrEsQil+Eh9+uY8WNtmf9mzG9HBaT9y/Tx482XTYeMB/+D0roj1e8ggRMFWxa4jI5ZCvm/P9/0nNQYPdvbBu6+aBbfP3REszxDk4c7rP+5dpuiT6ntCHYoU8JU4ow0sn8GDJt9pL9Kku94EZYt21lEIS8wSZRQIUWQHEr+atBFW7mk33otti0PVCD5z60K49JkN5os1aTCMCz4uZHzGW5UQvP51FNXcJ9axsbbCCNjj1hbopKUMDVdhuBrBlPWtsGF/ch3tbr1neo5zXnRK47OLSz91YHnhs3Wq+SGu+3CH3fGLksNoVu63k25O6wxYvlNs86J4p32Dw/DFO5fAvQt3EaSWD5PWHICfPLHOSkBbBG8iendeuDRc1gwh9lW2cX8osX2TZRoLZWDKn6Vy2itfPDJ/bW7FxRkWi9fZNxjbkxSga5aeceZLmNHOjbO2QWtnH1w/cxt8731/ESwfdufVPFQj/OIkBDaLl+2HuuHWObXD+Qvf+WfG6zedmLyeWrXfrXAEiOs5Ux9xC5Bsfw8lwwn/21c/vwV2HDoOl07aCF/5+z9V3pdnuWV553FW194zCPs7+uCP/8hP67LMZF3tWbjzcs2hXjaeSfEkBVZRFFlvls6/YT68/X+9pPG3a0wU16aVx9JJ5s7rILHV5qzNh2DRjqOwaMdReP/rznRKgwPLM648kdO6T+XOq658NmntAZi09kDj+0FizWkqJZXhagTvvHIW7GvvbXy369cfFPIZudZ2XjWthcWYYi5jgOoOsZy2ZZa687JKwZ2YEqNLLL1ESbM6UIyti4Q/QrnzsnmlS3cdhSU7j8KF7/yz2Hz4wOI9MHPzIZi5+RB8/v+8mr6QJxgcrsLz6w/CW/70xfDSF04gTfsbD6wAALXbThnoJhGw0ev6pO5ZxLvwMVHCBvL2Fcywdxc8mLHctTpdrKjFew529sG5f3wG6q46b/7VdPibc/7IOl9GDQtRGCOuCxPMUG+aEMT5jcf+8lCNIhibmCSoX5+uPdgsXlw1O/I8hhEXdGYhSvYldclxc2sXnPPik+GUk8altH4wGmYRFM8aJS/zWZp6KFZd5o1OoBHqUDap/RvLBZllow028QQa+tG+8cAKuPFf/87qnlUt7bCqpb3xt2sYDtNtiFAJmSE76E2Ogb5Dk7gexb735HUcWJ4JDfWMoDrrq3//yPJ4EFsrSxTENaqDa1v2HeuNCVB0+dhOq2MtfWtYa1YrA8uL19CkmQXxrO1njPwsUeJKVnX6h+jiOsbys+jNn7x5IQAAvPjUk+CsM14Ab3r1i+HUCeMysyC4ceZ2+N20LXD2GS+ABRf/U5A87lqwC30t9aF9LCYass1qY6IIjbZajZTuvZLeEcoK9Xij3fs0R5VpKfquKtmcxT0J4w+782KMhNTai2vCpIcjrb9KJhembTgIX7l7KRw+4UZK1jpki5Ysz/Bs1gkFO3dHIdavqYu4PJ6vtottnc7degjed80c+OC18wAAYJjQ1UKe5CdEkXwnvNNkkyli3fkQ+nmS6QebIgnchDTceZmy0u2FAjzf5HWt8Gji4E9HpjG1En8/u7YVd5+mnkK581LlmYcVr0yrM1k+p/mIOB6DeMBcNME7kz9bD3bB+TfMh1mb25zToG5Wqr1QfVxMDi/UgkKbmCiT1x2A59fjxswkGEuUvcd6pHEJbbR7U0oJHsTHkPKMJyZ3XrZrBpc52redimXoD2SJ4jJ1X/zYWrjgzqXwtXuXA4D9/rdajZzqZsqJfre/I/+4nQA2MVHC9Rvd+xPXSTqXXS4xUcozEuCQ9e/fz9uZQ0myATWHB/AEggGbrm4fwEtff1iIwhhxlcDTBJYnSIQh5Sv3LINpG9vgsmc3AoDiwDbnwdnm8Nr1oFvWNH0PZAaGqnDjrG0x39YyxDKbTKsxRcr7fT15wkXGzsPHASC+mM3Sb7ELHb2D8JW7l0FbVzo2TZnjF+fdJnwIuSEDSPfzPGKiYKkrQRTNNeaF9y2H/3p4NRzoUGsllw1duxt2fH9FdueVzEIqREn+TdhX0JYoib/F+YVdezFJvnbfcljV0g4X3LnUeO2x4wPodH0EwUp3XkJMFBHquD9x4Yb6us6+QbjwvhXwH/cuhz5J3C5TFcSChkvGis6+QXjHFTPhzZdNt047FD7WM7LLsxLsmpQYk5gCy9tW/4Jth+HPf/yc9UGsyvInlBBFfM6v3rMM2nvwfb4eDNqWj94wH9546TRpH8qSo8cHYPK6A87CrkKsOzXdSRxXdVazTjFRCriJolBGEpNIWkAWmRBN0VXPm8J9Yqh8GDwsRGGMDGV0Eijr6+IE7OJDkAnHoROHxl++K73RzHvtYJO/uDayWfTINz/4fGXcPm8H/GbyZvjQdfO011m583Ioh+9z2N6eXLw6xw8QPr/n6tmwfPdR9L0/e3IdvPfq2dA7YLdp+Z9pW2HaxoPS31wPTn0Z7QunIL6INX7Pg1kZiAdKjf9L/6ZNox4TxcNizaY9Henuh/X79UJgkY7eQXziGRFFQC6VbcbD+uQTyeaiVHwZl3yIFb3FYuY1RjPF5bBEIULFf9y7TPq99YGH4XKlJcqJf5Nzkk3wZwzYdXJP/8j6yTbQdy3tkc+y2/ceVQvdbWI1uBxIoQLLWyabl7VyEpclY7Lstgfm//mHVQAA8ItnNthlrFgH9VsKHFxqfuqGg3DV81us77M9vF67rwM6egdhrUGhLjT/ctMCuPC+FXD9jG1O92Of26kbIKsU685Lt0ZDx0QRPofo2d57c48EbPfGoZXZbBlnKfHA1JXpXFLpbjejPTqfm4aFhSiMEWqNJhWyQYW7f3GpL46W7T6W+q2+aBEnoSwHc5vJO+YqJOc5f/2+TtR1olzTeDiaR0wUa8fQ8T8ptH62tXXDZ29bjL7/7oW7YWtbNzy5ap9VvnW3djJCHa6boHjlxVr+Fovkaw31mikOj7HuvKj4u0unNdzyYcC21bKvBbIeC/IIIJo8xIyiKO3OS1ENutJS15yofZr3nM/QcsXkTfCPv53lJZy16apLd6XXvwDyduXTI82x7+J/U/vwTwn0XdMx3Ixx56XCZshz6feqfYVOwcKlHFkNSWI+LhYDyXImk9hysAvO+91seHbtAeu0sfmKfTWcJUr87yPH8ULWrKFuOztOeAdwfYfUHtnFcwSKmPVi+ZL7TrG/Z3UOZiLP9cqF9y23FqQUCRshOxbz+QtdXjFhPfKe0a5QGRoWojBGqDWaROLmxOnfC2EKylgzIkQZ+S7PmChDw1X43G2L4OdPr09dS7k28k0KuwETtWfFhUEURbCqpR26PYMYZr1Oo7JESRZ8wGFTpcp5/f4OmLzOzs93UbQMGVpS7dXhPW9u7YJpG+QWTHXiMpTEBg+ZZb0vFUnYWkQ3BxToHsvZnZfjaJzFdJvMI6npJ487QPfu0YoZicYvriub0UKoDERRBDsOdZPX/02ztsPOw8fhvkW7ndPIpU0YmrIqaHpdkzpliWLwICCOwRsOdMLHbpyvdVOEtbbwjqcn3G9aPyXnkbz2iy6HW4XAsrDJ2k0qBiTr/9sTV8KWg93w9ftXOBQOh9gGrIUoyDm5aBr1VKzb1wHfuH8F7D5y3Hitaw0U4QhHt94cmwgsr8JlTvBZ5l45ZZO1Qh8GX+Wa7Ye6iUqSPSqXmCowr8+9NgvQMRhvWIjCGMlzkymaWhZhMmZGyFJzVJq/pgDJ/BdsPwILth+BO+fvSl0bD5iJRx4LxnMDaXkwChA/SHp6zQE4/4b58LEb5lunqStH6DPP5BDjqvUTspgfvHYeXHjfcljd0o6+p1Dnc7qDjyY71A79NMnqcrEyeN81c+Ar9yyDNXvb0Xm6uSA5IUQhXLTn2V5keVNbOUZA34ayt0QJn0fyiZIuhyLQ+xlH56Oaown8UbM7r3y4Z+FuePdVs+H/Pbw6SPo+YxRFm7BOwXCDSkBQ/zalOKQYb6IoggvuXAKfvmVR7PuVe9rhhplqlz1ZBU+3cY2V/N1GiOLyBEp3XrFr/C1RssK0nrDVsk5ef3zAT5krnlek+DxyTf9QGC15incka5sYxfiQ7eND182DSWsPwH/cszxYHsnn/sPSFul1Lo+JFQjoll5iGrp95xDSsi9WJI93d8PM7fCdB1e5J6CA+oyiTOdyKpeYShBVVabnZ+hhIQqj5Xj/EEzb2BYsfXERJzsI4fGpuGiFGCcUgrLaH0RRBJc9uxHuXbir8beITiuvqlic54FtnIMkT53QXNnaNqIt4nTw6qtNaHl72hKluBHZNx/siv2te9SiWqIUs1Rxfj9vJ3zy5gWFjJkxf3s8WKjPe97c2qX8zcdNSJ3GgaBHTBRqCtotvNE9lvPBrONtefhClmn6pQTynnmIfWLVnnanNLDap0w4rpuxFQAAHlup1rbtGxyG455WtS6QtAmZsNejS6oEBPXvk7mpDv0Ghqswa/MhWLIrHS+uR+OqJR4TRV9WKjAKfOJ4kKwjrcWMw0Oo7qB2CZxV/cYtaPzrI1n/4t8PLN6T0qy3eU6xLcSFViOf+wfD7BuSzdBlbpW6K0cMCFnsSXceNluiuJJsE99/dE2wvFRg61Cn8IHdk4ptI4QFU9arFR+lpaKt87HuvPa198Lz61tRey7XdbZbYHnhc9Eqd5TCQhRGy+OaDQ4FZndeQbNnAiGLiRKSdfs64dY5O+AnT64/kW/8d91EJ177mymb4fn1di6bYmk531kDu3cXD+TEhdq4MekhPY+51nbxqNOixJa/SBv7OnkLUXoGhuCnT66DBdsOmy9OkPci7RfPbIClu47BU6v3W9+rKnpH7yDM3Nzm7aIymb6PtabuTtVhgexvFbqYKKqF/NXPb4bP3rpI6Q6PMt5GETW5QrT9epM70t0P+9rVwZGpyKNeU5YoEc0xgqofuNYju/MqB2/4xVR43c+mQJ9lwGhfimidpLREOfF1SnFIMcc5y3KF+7BdxmUIiuejzyiCeP/Na78oljLvNZ8NtiVNzvvpwPKJ64XPP3p8LXznwVXOc6vYJ6uK/c+A5boOW5JkmSetPQDvv2YO9Hha2hRw6UMOdh0Scr+hXWML+WotUUbpOiGtBONeDx09g/DU6v25xVXBBpZ/+69nwH/cuxyOY8rpag3tdhtTMFiIwhSamDuvHMvBpNG9j4YQJZuipGKA2OQrLshvnbMD/uNed9Nm33WgizsvkXFjJZrAmHyTV2W8XkzmP+wQFDWCyLgQP9zdD5c+swG2tbn7dU0FH9TkGfyAzjAo3jhzO9yzcDd87vbFJAfyWXHs+Ihv9hedMt4hBfkDffbWRfDFO5fCLXN2OJZMTqiDEwpf6/WymQQfYl7XztgGC3ccgefWyYOJ2rssieCu+TthmUT7GZvU1rZuuGLyJuuDkqJQfw9/d+k0ePuvZ2jjD1CQR3yAVEwUSLcVCveSvohVM0rPRkpB7wnhyS6Ez35KQs2HYu8YtBzHVDFR6qkmyzzo0LDvX7xH6RKJymLbdAgXC2BvyCaKotjhuo1w3+UJVOUR68Z2zSety4KOSbM2t8HKPccaf5vcqcneh+t4qzIEyMISRVbkTa1d8PCyveg0ZC0TM0cXtCmgCbkOQQeWxyocpQLLj5B1TBR1mvm2CJ/sv3z3Uvj2xJXwi2fSsWmzIERbzHKdnfe7Z9KwEIXRcpJ65U6COCTINWVF08gRWHuw2MjeTsi5JmmmmZpsNHkXqynhCqPaUIwP3F+xWLvzSjyPi9YPJs/v/mE13D5vJ3zw2rnW6dcZtjj8CO6VzPDMu4/2hEo6KKI7uhdOGEeW7oYDnQDgZmGpa19xVyehBCqRk1ClvjGMuWpGlpHqUGL6xja45OkN8C83L6zl75jOTbO2w+HusMIHH3TVmlyzYF1ouNZVHkonMp/TaYGt/ImwB6DUvatMmuOjlTKu900ltg22rfLnXu82qcDyjsLmPyAOhkO+jbgLHbMlini9jSUKttvfvWCXkJ/iptj8jy9D4tbMsXVD9vOnN8DHblzQ+DvZ5pJDuOx1uI63VUVZVVYpMlqO9qQU7lAokvW1kCuiFS41aEuUgGXAtjkKSxTxecs3a6VJPoPNMyWvXba7JoB9ZDle+EhJiP7mmqRvWbDvgZe3YaE7nWCakpPGZXgoawi8Jk6Eg8NVGDtmbBalYhwYceeVTX7JzVMyW4zVDAVZxRJRbShkPj+dNH/tb/G6Px0TBb8xssm3rkXXr3BThCFliaK5NqsDOsyheBncutURA5KGKLabP3T1PWJ77RkYhh8+thY+cO6Z8M+vPwuTMCpPZw3OE/eJnv6iSDLdStJXtV9bd14hfW6XBdeD4DJpnyUNIaMo3W+cNMAttNNlpJq6xUEtEwqTZZww9pXT+EzL1A0Hra4XNV7FumkElk9c7zpsdCpikKnWnEm8rbEVeaqIu/OiPyX72VMjWtOq4sQU/KytNB0KRYT3Oj+pq4aQogxXIxjvsHUfVrQ/sQi6utxzpAf+4cqZMEE400C7ClbUlI2ylzwmCvr2sKAC3Lu1Fuo+6ZKc7jVh1wLr9nXA5HWt8P5zz7QvQIlJWxL7D1h5uUajihMYm38TSc7c1BaL/aRUGmLfOk1BMdSWmcISWrNdthkQEQcacdwdrf4pi4TuIK3+WkMEVhNK0Pg0JhEo1mqeJywi5QZSh2qjNl7izsvlID/rg7tkd3bxSZ5VkW3GniJpOccOIgkONoPi248M97skrzvIE/vL9TO3wdOr96M1jSOI4FBXv+rHkfw9NTjjAS+x9yqKVaB2HQrqR0xu0Kniyqjm2CwOaJJZJGNyRRCl+o2vOy/q52IhSvEpYowSE7Ii+/T5sRX5Xqh+SJlsxtQ1Fj+4Dvc+YsIz0zweQcKdV+J37c32ZdOVo461Oy9JQcLum4R8vPcriTkt8TupJUpV/p6x1rmLdhwBADcFKlWRhyzcDssOTVHuvDIa+lRu/HwJuQ6hiLcSV1RSCww2tXbBhfctj7mz88nXlaxnwpRwniLN8k3nMXTr0S/etRSeWGUfxxOVb5BUGR9YiMJoydQSRULMEkVYRPkGBmb8QcVEESebgJJ3cYM5HEVWC3VnDeEAUxp20ZX03VpHFljerRzZ3k+x2Mxq45lqL5psHUK72NEEyixRFMG6fR3KIOZFWTnqDvLE6WjrQbt4OzfN2g5v+tU0uGX29tRvca3cxIGB5VgRjwOBO7gJdXhJvbksjEanzlrJ8Zlda4pKSKMjWbbk9FOzREne4/fuqZtk2Tf0zUrZrYVkY5yuR5r6hdidhxMHyhv2d8LqlvZE/qpyabNRUsWeVhuwUXAwzRMRRBAJc29o//Sq8sQt5SyFKDk27VUt7bBkZy1OmZNiSeKmZP3L3odrVxbbvGhxH8UXRcr7Ve7wMKjKPGRhIie1REHcl8W+ZmCoCn/x35NTYwgF2D7psrbFgk0OIxTbcrALny/6SjxlWq80q7KVOBeazrVU63Bvd17Iqs3qXGS0wkIURotMs50SsXvLBhVxAhY1wMsaXHa0IFt0ZhUTZRhpiVK/pFjWAjhipu3C97LA8nlgO3Hr3oHN6zFeS/CqbQLCBl9ERvV8MGVJ34e+noDLn9sIP386HVDwjnk74UPXzYOv37+8IRzXWc1gMN7h8Gy6dyn2R9uA4buO1OLWXP7cJuf8dcjG4npS5mDz8jyzOKBvNpKCb6oaVG3i8nhFcneSeKEzBpfxYMamNmW/LKOVw2ijSGs0F4aGq95rAbFvifUxplKBf5bEeKM4PBmuRnDxY2vhkeV7UwJ9kdlbDsGlz2ywWhupiAnPZIKohBWCeI3NQTnl4ZJYH/aWKJLvMmzun7plofI33RxSrUapZ01Wv+x+V4GoapzGpiZrGvU2sKm1E370+Fo42NmnyEOey6CnllTR1lFXTtlMnqapSz64ZA8s3H4k9p2u/YvJ4eOt4PaXmHnGRhmUoh/3Dw3DbXN2+CfkSGr5lvi7aG1YB1VRxSHMlGazCpKYGixEYbSE1uwRkQeWH/ksaipTBbxl3NE1jawnDrGd1oQoiQMrTWEplRwpLThe/cNJMHfrIel1qo2IzP2eW/wH/d/G+y1vSBl3OB1yyxfLN8zcBvO3Ha5dY59simTdY2Nl5I2uJKG7a+/AMNwyewfcOX8XtCU2qrfP3QkAANM2tsE/XjXL3h2fBLMGqz268yExv2OWQhQdOiVLW4GrOAZKLVE0wpb093QNpoh7DNcy6e4LNRYo3XkFyU2fh2y9SCFD8W0ja/d1wCdvHjksFOusSGM0I6eM76he4s6+QXjTr6bBNx5YoV0zmw7nkopC6AJ48OzaAzBxyR743sOrY2N+csz5wu+XwO3zdsIDi/f4Zxpzr2O+OmaVY5ML6brfrsxFxHZOf3BpS+q75D5L1qadlUFUhsriGklzvy5e5Af+Zy48sHgPfHviSmMeIjYeMWR7UNV4kNfBa5jA2/pEf/jYWvjsbYusYyHZoEtO/M0m3mVW3DJ7B/zq2Y255Z9eX0YFsvzOh5glimNlON1VhAbJxGAhCqPFZzLDjC2m5MVNuWh90tU3JLucKQj195rVWlB0IzIcpad9ebydGkXSFEj2t8/fsUR6narIVIHls4Zi0axK4copm+Ffb1+MTsc0bNnERAl++HOisCFyoewXohZhsv7EJttytBeOHB/QCg8ocHk2XRsV3/OxHnlgXheS/prdDqBPCFFi36Wv65P4xA538D/6SGrS6tZHDy9rgUeW7wUA9/afhzuv5PwjE2xn7SqyztY2uZu9sls5NCuxQzWHcShvDdl6s3pu7QE41jMIz65t1V9vdOcVd1k78r0qPbd8RETrLcycvK+9F1UGHSa3PmnNaFG4g09b9rdN2VTf244nZR1+nt+Qbs9Jd44mSxSbR1dZooj1rbVgQMTw3HigU/674j7f2KyoEaqk7aNO3HIM9zDUy05sn0Stdy2mlQXbD+MvVpB2sRbBb6dshvsW7fZO24WyjlehyG+VEX8RA0NV6TqJ31dYWIjCaAneAYX0ZYuc2OG4MEB09dEdVDGu6Kw7au9K3LAFdeclallbarK7Ltikmts5xOUQF6bjZUIUh/SLNPGiixJlU26bg+W86zF+cO62eQ1NctwfUwlfPpf0VRuxlqM9sQ1+KEuUdNBLXBp1Dc7YZlZSA//n8hnpe+vjeOLyvA8oQxOd+B8l2IPgjt5B+H+PrIHvPbwaegaGnEvh4QLemXEIIb6bez75fEcBC1GKST1WAwC7XAOI92cxDojtUGxVlQrrRV0SRkGGxe+mvpl052UzNjitixHX2CseSA6+AKC7fwi+es8yeHLVPsv06NBZEYyVWVYgjhRd+3I1tucB+WfNG/KZD1XtyiomiuQ7lfs5rHWN/F75HcPVCO5ZuEspKAIIs64bExtDNBfGrLnUF4pFxLrW0lqiCJ9TcRkl99nU0HceXGVxNY6NB7rg+pnb4L+fWIe+x2fN5GNJXLRZm6p1xy1RiBJFoBrfjvcPwV///Hn4pMY1IxMGFqIwWrLcYMqtBeQjVHc/W6LkjdZf7olmIzYf6sDyscVUInaOzUEN5WFZVlq2qo3IOKk7L4dyCHWy50iP9cGw7aKtTAdZmOCDdWyfq6tv0C6mhiF5bO6yPpDVG0mOI5VKJS7wcWq/9KgOwf/+NzPhQPuIi7JQTdk5IOuJAqE3swKh3HmVqLtboXus5LChmg97B0Ysgvw0XcPv7kzuvCKI0GNgljIfsUhldBU1GvjsbYsan8v4iuRFdm/lY1WWKIo0KYSNKutFbNpOc3esb0rKlFAGsOm/LlrxqrKpvqdqqzfN2gZTNxz0OogNKVTCxJ+RHcq7NkvVe44L2NX3S11NIvNWu/Pye9kqd+khhruHl7XAT59cDx/4n3T8pDq6N+qszCFsSbFrAWwbwcdE0eU18utnb1sEHYSW5CHozFmBOIoA7hWsYJpbnUoOxRhPKbBcsP0I9A4Ow/Ldx8jSZHCwEIXR4jNYYCZCV0sFdudVbOoLkzz2vlPWt8LBzn709UXaoGMFOiqtLDGwfH3T4aT5e+KWg5198A9XzoTHVoTVhrNQ6FKCeU7XgwXxvmGLwtpq3b3+kufhb34xFXoG7MY3zHOFdpHlSnIjmbRECVFUl+fXjRMLdxxR/+hBpPwDT33jqrJKQt0bcKdEkTZ18Vz7h65eq5JYXdLrhGt8YtJloSGXfBq5Oy9IfWedj4dmrgmKuYcJi4s7r6K4aaVSHhL7s3igrDrLVj29Ta2IeVYzWj/E44voM+odGLYaG7yVnBQ5VC3KnEpTcnkURXD0uN9h6ZVTNsGbfjUNWjvkwdJ9kVodJupH1jaHqxH0DgzDJU+th0NduH3atyeuhO88tEr6G/b928ylR48PwIb9IxYbqrHEJrC8rC4wJaLqa2v2dRivcV0ztBztUf42RmHNlkT8hVqpzia9p9bs1/5uOvwOPe1kPa0l81ux5xjsPqJ+36MBqjW6D1ilhmKsgpoXFqIwWvLWEFctWrvYEiV3dFOHbM8bdq4ZyfDHj6fNXHV5kwZJ9r0fmYDqTEHc2AyeUOPzeby1e80Lbxm2eWoX14TadK5VIWoDpoIPahJV/dbeMwDPrT0A/UIcCvE59x3rld2WYsfh49DRq95sF1FLyHTwWoFKcIsuF8Giro1aWQ9ZkDxQivcFO60+2YGYqX2oxhlKLaqCnHUGZxjpZjLmKgDc56c8+n5SOzlq/F/iO0tCNhF2FVV8ymgtJOu3yWGzWo3g2xNXwnXTtyLSE+6LuRMxuwQylUuFKACKafwremQqbReBqS69BH936TSYv02IO6C5fMWeYzBlfWvsUipXaOLXtm01VMu+YeZ2ONw9ANfPNLctFzDxF1UxUW6YuQ3uWrALlc+hrn54avX+WGwIlfWJ3hIFlR0AAPzdpVPhn6+dC+tOCB5UyVq585IGlg9nRZZO03yN65rh738zEzr7BmOCJ1ma+P1tdkIUXU4UXip85y6KpTZldWL3ptT5FomYO03itCevOwDfuH+F1NsOZv5hsoWFKIwWn8ncNrC8TGNLlT3HRCk2DUuUgsyiOm1AygWb7/P6BsAbJ9hON4QoHuVxXcDZ5pl+bBfNU6fbUDy1ekQ7yWZRrLr2X29fDP/3/hXw2ymbG9+Jl9rU+zuuSMeysCVUN0Vt3JLPWgGA2CaZXgvZzRJFfZONRqINqoMzG0baYMwPCgpVnt7uvEqw7E+dB3o+83AUf2plQGjHcSBJFgpyySxk2snpWD75v3uKfsVkh4ugq4hxm5IlWr7nGDy1ej9cNXWLVTriusI+sDyeuPsr+WdbbOZmzFLrkqfXo/L9+I0L4OjxEWWHKKJb99hYz+juzRPbYqCEKIo9/fZD3RblMrQXhHAPACv0qcS+X7j9iPS6Ot6B5S37bpF5929nwT9fOxcWbD8csxwUx2FfJUFXbNo29awxKPNJWCKSfcpn7Z73lEy1JrDxoKOqLdV9F963AiatPQA3ztyGL08ZB4wmgYUojJZMFcAkg4pqcOhmd16FphETJaP8fCaRIrnz8NXUGS+487I52FXlm9WiJ6tDVYrFho0likqIsv6ExpYonIlvFvEV39WHCz4dd3uQ36pLKjMReHT53kKaxOe9F0o2JduxIm6JgrvZ9oCnKAdCLnx74srGZ9lTYNZCukuwLokIlLkBgD4GGYa0O6/0SOPyPL4xknS4uIpisqVZ35HYW2zWazEhiuIaCtelKheQ2KRtY62t398BC7aPWJZgFFb6Bkcm5tDrGoxgStVWtxzsgi/dtTRl3S27mvIpQk3JMqFEEqklShSRKp1ghwYftzuqLIYsFoWy7NV9V8w7u7HP55D5cHdNQPn8+oMxobfYTnTrTrGvU68jtcmlBGnI+5TJxW+iFqJkvcamzG78mOY4co6POWHW2Ye7064O4+NCjU2tnfCtiSuU6ZR5T1YGmqNFM8EQ+9+rXnIKffqO9/UIwVeZfNC6yDrxZuOWRvkhK2u9aLKFnfMG1Oku+/tVm0txEdxY4AfQ5Dffb3e9brOs+uXh5S2o63xJboptLFFs6lFM1sb1ALostjUUqEKTySY3t794ZkMWxbAmtMY6RqPJpQTSmCjoe+Xf27qQKQOiQFMGiSUKIg2qQ5M8tP5OGhvfUkQgaRNOBxPhKKOrqGbApn02q8u1U04a1/hsUgyLHdSjxhHF9zZa2aqYKPgkrPL/4LXz4LoZ24Rrw713p3FWaZk58lk1nHzutsUwY1MbfOqWhZgkc8CuIGOlQePjaci6eDWKrOIKyohbReEaps98qGqHNoHlZdljBDt07cOcEMWSIYoipZAXK0TRTcmicghW6KOPGWFXwbZ1RG2l7qSE4pFf0u2ezXIpeakYs7XMxN1p6q9V/Uyl5PQvNy0M5gmBMcNCFEaLOFjIXDX4otJ6avyuuI9dMBSbrK07qDd1rucqvs0Su2lUHSqI3w6cEKK4PEv9FteJ3nZhalvG/e29MGX9wXieURREa6s7EeQ9qVmky9P0XGL9Yvyc24JNJs/RVLaRFMvjpg1m+D0HwaIJaT2oDgssqI/FYpvCzp9XT90C/3b7YrRrK0y6P396PSzbdQyVPxpiiYHsMXzf/nAVNzqlxgzHjPMIeDl+bHpLkXLnpXgg/FhlrpCnVu+HlXvUbUxMo1kP6JsJnaDrd1O3wM2ztwfJ12fLE40sohok27iomNvVj3dRHA8sH86fl2p9gp2L5JfZK5b8YVkLfHviShgY0m8srFwwEnZ7MR/VeFLXLO4djCsAhl5XhEpddiCaykvSNqvVyNsNlipPXapeliiKhAc9n0M1vuRlKa6tIosiqQ6ZddUl7qvoY6LgrzXteW2bEb0lCmlyRq4UXE7X8k8ISi3qA2O9Vgbic6H+WtXPVMtzWewUJjvGmS9hRjOxwSJwXlJrAYTmD5MPqDgjAd+TzRwku7b+nWzBVo0iGJuD7QzaTYJqEhf+sNGSSnLPwt3wqTee46yaZNs/dZfL0mrvSR84hGpqnYnA7VYxURwHKttqV2UTP4Q3Xx+717IMWFLuvCzcOFLhknxod16y/YW4mU4fRuPQ1S8mjXnbDsO5f3wGKi9M17hz/i64c/4uVHpFwrdNYg8GbDZoRSeKZAebbulg71+ztz3mms1Ekdx5MnJUfedARy/8z4mg7F96+2vgpHG0eoGhD33EttdlskQRqgCjCasM/m4nRRHuE/NX30IZj6z+nN9/ZA0AALz1T18Cb3jlH5Gk7QJGLkXheo7yOXzS0h30mZQ+AOTr2OEo8tqbpPPECfekMVGQfUF1nY07L1llZmnRK6Z52bMbYevBLslV/uNdlMhLPCfQvR9Rm55aiKJ7z6k26+vOK3GPSfA7mpAp2ZSRyetac8lXcdSDvoehJ/cW/epXvxoqlUrqv2984xsAAHDBBRekfnvrW9+ac6lHD1kuSqVB6JT38dCQN1p3Xo1DOvNmjwKfdirb8zi7+PBsltjbVWt38f6RwPJuhfrQdfMyEyNZx15QHRAYtULs8ukZGEodbtho0dm58xq5NoQWubYkGZ7WJnOSbSTj78m+bF19Q3YbXATh3Xnp33m16veaMIcNmPba1tmnPERzqaMizuWyMqFc6GguGRqOcMJLWwG04vosDFGMRY3SbcqpDUfSj1J2Hj5ulTRbNRcf1VAeOh6GXxyFdHmSexyx7ZmEKCJifajKSNGsxZQnrTkgpo66X3aVTbGS6/COXry1jgmX6rluxjbY1pYOiq4ScIUqRwhs2wtGKCGTQVar/i4UXZSDXLpy/R5Vur7uvDBlomof4jPcOmcHzNx8yKk8NvkAxPsD1hIF2xaxxaU0SMvdEsWhRVBau/kk1SyWKD99cr3wl1uFNEdNMLkLUZYuXQoHDhxo/Dd16lQAAPjkJz/ZuOb9739/7Jpnn302r+KOOmILwjxWe4o8ed9bbOrtJv6ewk0bPhtombWAa/vy3cij3XkhNiE+7rzquLqVwmZZrUawr703rWXvqAFkug2Tbv2RfztlM/zlT6ckDg/S2me6NE3vSWXqHteGQhzeWrrIwVRvVgHppBYYBFl/9Ib5yt9c0g992GqqB4o+MpKW/HvMgfybL5sOrZ19VveXEaezfk0FVKO4u0Fl30xZHDlu0gqwS5M5WPRtIhRtTEyD3XkVH5V2f+g5yufQZ962I/D3v5kBC7YdVl4jFj9p8QoAMG/rYfjULQth+6H4wT1m/YexTnXFT5gfLh+by12f4WOSdUXMPaDhzPSUk8Ymb9am50+YPiIVoiSyku0fqlEEQ4Tmf1h1G1kMlySySwaHq7BC4RrS5jmkgeUxQpQM5yeSmCgaFSjdGtrXEkW/9sKnIzZr3W1RFMEd83bC3K1pYZSIf7yK+FuJCw2zX7tYZZm4eHyTCFFEbOpji2D95bs+L6Ly2Wgkd3deL3vZy2J///rXv4Y/+7M/g3e+852N7yZMmABnnnlm1kVjwO8AFkPM7LP5xtemRh9YPv5v7mitZtKlxGkd66+Joprm8RjEwuHhZS1w7Yyt0H4cp2kXd7MnfBaKRGEy79wlkSuL7z2yGh5bsc+QlsQFlIXVmivXz9wW+7eOjSWKjcadTbA6E7LqL8qCO/loIWKiAACs39+pLpdDawkvRAnbpuMb27qQO3lg74dLHVEFWKRE3n/80hyuxi1RVM+dDODsmm8W9VrPoW9wGF4wfqyzBY+JkBtGCvc7TFjyEnRhDl5VzNlSO1xrOdrb+C6ZnNg3ZOuKf7tjMQAAfOP+FfD5//Mq6X1Kl0BKd1564nsyVdrme2t/SxSULPqytVVHBu2kS+KD3sYSJSVECUyoKpHFSE02YVnrqQlR6Aoljt+6Z5XuwxDF+NFja1PxF+vYPIdsPlbN0XnJ9UOcwYh9Utc3RIGUNrC8IsZUFOnKr04wJfgzxUQ58fvC7Ufgl89sAACAXb/+oDIneksU4bP2mbMBcw5UZ1zG7rz2tffCxY+thS+/4zXwzte+zHyDAzZd9bzfzfHLy0m46JUlYyB3SxSRgYEBuO++++BLX/pSbPE2a9YsePnLXw6vfe1r4atf/Sq0tbVp0+nv74fOzs7Yf4wbebs6cN0IMOFBxUQRry+oOy/ZQTdm0y67IopqE/fcrYfgwvuWw3nXzEH5RP1/j6yBlqO90g2aDFW/jLnsEcrkSugFmlGAcoIi9fdke9GVzWavqDq8yGIIzrJ+k3nJLTDClsjNEoW+HCJyH+N+2nkqbN1xYYeBvNcLIfE9yMfKtKkEBlko/UUAsHZvB/zvn0yGS55an/49wh2s5omvaxkmPKp35GopiwWjAOMDdrw8cnwg9rdYH6oSqi1RbA5+7dLGYHOvb9fU3U4pmK1azNMvGB8XohRl9LEth7xvxFORdc/hakSgnS/PUfdOXcf5h5fvVf7m+xyY4YWqprLSXK/N+arBR33fsGiJ4vCuXPdhqXpBDvl723vNF8GIRwgqXJzDmK57fn0r3DRru5fXAwzjxmYr8fnho2tgzpZD8IXfLwGA/AVOIqHXLkw2FEqI8sQTT0B7eztccMEFje8+8IEPwP333w8zZsyAq666CpYuXQrvfve7ob+/X5nO5ZdfDmeccUbjv3POOSeD0jcnskNZ0vQN7i1CmqQz4ai3m6IcmOgEPrLFdYRY96i2EG//9Qz4/B1LYMr6g7CtrRuW7DyKLyiSYYX2lfgkDZdqHj3XVZs55FvfcrALlsvM66PIbB1EkL+N9pnNobJYdqxJ+ci98b+lY6kir9wxLCbDzDv2hNZYH1OpHUj/dspm6BlIC1Nr2Yvzsa2WrvhZJYS1SjJFM51Hu8Ty0G7kkZUjegjB9FNV98lqk3bV1M0AAHDXgl2p3yJI14nqiZJzTRRFMHldK+w+cjxR9/6NLFJ8ZrLDpnXmNV8FDyyPfKxkKXCB5f0ZozghwAZqVikaYUmun0xDWl592cYS5dST4g5ApG2b8EGytETBaPVXowiGKd15RfLPIn2Dw/CZWxeR5VnH5jnk7rzsrMhCo9vvuR7Yx/ek6vsGq/YxUUTW7uuArj65JwdX5R5Z37SOiRIwsDzVvPgf9y6HKyZvUrqtE/FZ449XTSiBONSlPiemIsulSXwPn12+jJrc3XmJ3HHHHfCBD3wAzj777MZ3n/70pxufzz33XHjjG98Ir3rVq2DSpEnw8Y9/XJrOxRdfDN/97ncbf3d2drIgxZEsO6qdix4eQXKnop7E6xNtyLeUNOXFXptEtsCidB8R4ixLbYkifvZ/CaayDw1X4Wv3LofXnX26shy+JDcUKpNY2YFdCFKWKJpMTYt3sXpjMVE8XfFQuCMKVZdpd16SvBEbYwxRFCk3qtvaumBzazf88+vPRB04B3fnNaYC33lwJew4fBzW7e+Au774ZrQ/aVsaQ0MiyaxiOlHmmRW+9Z9y56U8/LQT3uS5mUo+AsaNILa80za2wYX3LQcAgBU/eS++TJYTbqEEyowUH2uh4WoEn79jMbz2FafBJR95XeP7q6dugQPtvfCbf/krZZvxCSwvQ+fOy3SfSjhhW0abmsS4HLSlKON9qG5vSvfkhDuv4C6zkfUtK7faEimSurpLJSFJoBrZKSKZwIzfS3fJFdl8S2FjiWITWD72SERVhWnvYQLLi5/VhRDHePS4KNTq+TfMh1e++BSY8/1/NJZJ95trFWw/1A3/+dAq+NBfnRX7ntLqKgl1yu09ZnfiPuNmswSWFzGNr6r6MtUE3fxUjPm2WSmMJcru3bth2rRp8JWvfEV73VlnnQWvetWrYOvWrcprJkyYAKeffnrsP8aN4JqliEMF6W08LhQa1SFdkagXTWZti4qJIvsuowfGWAgTyFCMLN55FKZvaoNrZ2wzXxwYVNUTVIadJQo+XbVAkv4Npl3skGehzjvxt+wQiOqZ33r5dDiacIUCUHve91w9B77xwAqYu1Ud+FeE2Co/xZhKBXYcPg4AALM21/zp64RJIVyqKBf8yLk5L0uULLZmqEczCFRlFkapJIjqsAjeAupxwRLfou5dtltx8EXcxoq8RmFqqM6hMOuthduPwILtR1KWUtdO3woPL98LGw90yW8Eepd4SaEE1jotOUeKz406iEV8bwN2neurzGG7nrZK2yplfL6mV4qJiRJBMcZvHTJ3Xsn3Jes/w9XIO14jlYeMZHltq9xmnSp7nyoBaIgpCZMmTZtLHCsLdUQdWD5Z3j1He+Ql0qSX/MXVgveiB1fBmr0dcNmzm2LfDw5XYdfh4zBjkzyujj1C20c2FL0QaeTHpKtBDDYKf+MzdueVfJdFH1OZ8lEYIcqdd94JL3/5y+GDH/yg9rojR45AS0sLnHXWWdrrGBpiAaw9V9/yIIOme+y+Z7JDNx9RuJKywScf2YLN1XVPVs0yFkxRkX/DGsijs5jWHOMVgeL8Ndq9blenS/GGUoXTrVDxycYsUYSK//Ldy/CJaAl3IKGjWo2gb3BY+bvREsXjnR3s7Ie7JW6GxLpYu68DlRalMKtFstkbU5G5yIjPv35FMG9S1VpTuN1HU8dE8Xy0KAJ406+mGa+rJg6IXNt/VvtF7QYd0qMOuh5jAkT7gwNs2s3kgq5Z8XGlOGhwuaPzWS9zWUSJjTuv2DovtlZQ2gsovvU7+DWR7xSQT+ZRYp7WcXIqJordXKxj1+HjcOWUTXCke8SNTYj3EUUKd16Jv6XuvKoRaRwqqfW95hpKbJ5DHljeTJaWW74W8DJU4xb2HpIyOCYou60x3iZ+7OiVW3EMDFfhXb+dBV+6axks2nHEugwp60XR3StBTfUL7sZeMN58JGxznpCy8mlCKYaPFxTb+1TWqEx+FMKdV7VahTvvvBO+8IUvwLhxI0Xq7u6GSy65BD7xiU/AWWedBbt27YIf/ehH8NKXvhQ+9rGP5Vji0UPerg7UgeV5CCkCquYhO0wr2vlafY6SbdCLfrCCCSzfEGQhnkV5iWEBoNIsoXzX+IW37yEzPaZxKh5AXn5QOGfLIef8xbeTV9186paFsGz3scbfyRYjW1iTaonKvlMIrHT4CgimbTgILzr1JPi7V70I/v43M1O/VyqVmnanYvChHJNUSfnOq9RClHVIAVcmSB5t1+Hj8Oy6A/Dv/+fV8MIJ+uV0BJFSUBq7LtL/jaUIG9YoSrcJl8eJKwrQtjFeSxYflXtVijauSyF0YHns/ir5nOJdIYK/m7BZkxUVyr2tOK7bHqpRvqfzb5wP7T2DsGF/p/W9tu9KbokS/1vWPWvuvOjMepNKB1niGydPvQagfxJUkkTuvGzOBeRp0D6/Nl9Pa6RGMorWJ1pdrWpph7f+6Uscc5DkSVBNooLb4HAET63eD529g3Dn/J1w27+/0T+DHMnSQr1ajVBjYoOMludFOxNpNgohRJk2bRrs2bMHvvSlL8W+Hzt2LKxduxbuueceaG9vh7POOgv+8R//ER566CE47bTTcirt6CK2OAyQvkmjii1Ryknj/WT0nowbF81vsg26a0yUrNolpnwj1kDumDSTVP5eqash/+NANRbrcy15Cu5kGwCqAxBRgFJLN47s3VJqnrtYQMrwERDsOdIDX7mnZlG069dya9uxlQqMG1MB0flY3E2I32G0Lq2R7+X3Ys8qqca/rQe74KdProeFSM09anmBrM3I6ux918yB/qEqtBztgcs//lfad4KtmypR28/MEkXxufEdkVAIe7+9SxbnojAZodL4pjhs040d1DFRkqAtUTQH79aB5S2qzDbwdaT5rXGNRf6ya6nWW7SKPmnlJb/03Oa0ekyDZbuOGa70IwJVYPnEgbTMnVdE4M5L9dkyWd9XZbVPjMXwrMXqy9Kdly87T7iatSW+7sTdY2OhhyoD8joAc6zVSuqDnsHQfoAR6MbkXkGI8u2JK6FNCMb+w8fWStKiydeXpbuOwmteeiq89IUTlNdkpUf0zJr98P1H1sAN//q36Hvumr8LWo72wLWfeQOMU3j0SIOvz+FqBCv3HIMJ4+xdtDF4CiFEOe+886SL4ZNPPhmmTJmSQ4mYOiGC2VrdQ5Y7Q02lUlFrNJ/4oSjvT6exKLVEcXbnlc0Tx9x5KQ7dGu8gYJFCLRBd6rHsglWftuNlbZQzso0k1my5q28Qbpu7U5t+PS1Vn+npH4bD3f3axTiA32Hr3na5r2aRMRVIBWtNHhBQvUO12wv599i9CNV64av3LINdR8x1FpLkkzy1ej984W2vjn1Xd4WweKc8focuvRBBm2PpF0DyXLMOjD/Q4p1H4J6Fu+CX558Lr32FoAylKS+VG4PJ6w7A+8+NuwLO29qaMYNZjzlbbGkaHrU7r0lrD8T+xo6XYxKR5WMxURT3KMd4VI6mtNX3xH6TCUF8Xb1q7lfvR5S/eJVFloq1coPqez/tJ2P6uHTU7X/smPTBH2aOq0ZRsMDyWVs+2Wx/xJp4ctV+2HAAZy2U5fREMdolyyuOcdjxTnedi/WhLj3n6kXeqHMX6QKVkk2dvsGR8okCFIC4qy9Z/gD5rDPnbDkE//77JXDSuDGw5dIPKK/LomxRFME3J64CAIAv3rkUfV//UBWeXdsK73vdAfjo3/yx8fobZm6LWeab1q03ztwGV03dAn/8Ryejy8TYU5iYKEwxcVnrbGvrhm9NXAnb2rqN14qLHpvxjre9+YOKiRJ7UbRvjcp/q0ybCHUonVEjlE2WGF+8FAeapkWIaoFIquWHva5A76yRn8W1cas/+oLKBGx5kGxSkr04+vkvfWYjXDt9q/aaCGo+uD/wP3OF70a4fuY2eOOl05Q+jev4uG7AjFUNd14CWksUy5co09hMWQlYpZhGVUW7jthpMR7s7DdflDE/e2q98jfc2IMeyTR/4QmtRV9H+1xRum7uWbgbFu88Cl9xjPXkM3ZdeN+KE8WiPYhgwuJqGawCE5gdILw7L9cAylHsNzttdlOW8bTl1/icgVtZi7hn45ynb/qmd5r6OYCgqatvSJ2fAjsrnghknnyTaUjdeQWNiUKWLAq7wPIjlXHRQ6vg1jk7YFNrl/TaEM+EaVMU7hGrCW2f+LMghSjEunk21mumKrCtogFBEEHdPin2ib0D6niVci8B3ll6M2tzzcX1wFAVth9SnzOGiPFDTY+m/uus3dsBV07ZDM+ta0Wne/fCXQAAsK+9V3kNKxD5w0IURouLW5V/vX0RPL16P9w4a3s8LcN90slJqTGLKwuTD1kHGDa6+NDMpTI5gPOmPcBj/+0vp8LcrfG4GKo9iOxwyGehZVqCDEo0VXzzdMU1x/3tvdrA5z7YLFJ8Dum9tTszfF3JrGSHvVXkxmvZbrMFQDWqHeJvPjiyYZUlufWgfEM7ko5HP0Ks5ceMARirObRz1rRW+CWX5qHavCJ3jqo2fP/iPcp75m87nC5HzmoS9m5Boti/0muQacWqsGTrHKn7HcW1h7rwgrLYvEZcKXm3NcaMb+yBJNj+HfoIBi1EgXg/Eu9TTRkUc7rqEAo7zsmu8i2WizsvaTkoFX2ItcMBimFJqEPu/z/+8LJD+eFq5G3BHrdMFOcGy3QSf9sKEayEKDYJh5iSEGmGaHIy5R0T1OcHNqnFxzzNncjYRtSv0qU+ddf1Dan3vr46BLYCKixiOv901WzUdQBhhCpZ7J2PHHdRKiv4BNIksBCF0eIyQNhokYrpS2Uoqvt445s7NQ8DeiFXGd4T1v89Ki3fwkg41jMIn79jSew7VTBFmUaczyRvulUVEyUPMAKL5BVbD3bB2349A97921n4jIq+u1Vg2xcpFoeu2m+mvD96/Ty4ZtoWlEYjeqNh+N1n349pMWMkPrLF+nMek6L4v8l0Vfn55GXDZc9ugn7NRi43LJ4FdSlyQ5myDnIWnuU/TkWgHpetiqfQbKWAWuuVoYc6sLyYmi6J0F0I2/ZSigaxfZOlJYrFwIaJt5Ls36Z1mI1iiW9fH5n7wq5TdfVhvjd02XDp21oIYa6XNZ9qhLOkR5clYPWZxhfK51CR5Q6LYryLQN3msNWFFnKjY/VphL6WDagx3mJvI7eiFD57ptXeMwA9/TohimRv5pknBdhmmv8K2B1WFC8HhYiJwhSX+GGt7wFL+v74ZsbiMI0HmNyxdecVclLwaZuyhTCl5mOIiVwZaFX8TPAIpoNbpUYZsZZfqKbz/IaDAACwv6PPOQ1d2UzlFoc8al+3OvIUbqbceUktJfTlW723A1bv7YA/eZHZ32sEUSpPl0OV0AcxYysVSMYXjAtF6dpF/fAu2Q5U6YeOiTIwVC1UAERrjdaG0oB/muk6xJdmsmDuX4QNZBTRB26n6IeUBxGMGzYHdsplhmNbEPtYni4/0OOlxp2XqvgmBSeHbFMF6OobhA9dN095v41lGhatJYpSOUB1Lc27T87TrvfqvnMm0ABn2s8DqKxgaWOixK2WyZLF5W3xHDbjXRB3vohrgliiCC+FIiZKHKSFtHbMSKRoCCyvLAmiKC7vVZes0z7mxL/b2rrgPVfP0ectddkXzzOP2RPdlzJQJMpiL22TQ8/AEOxvdz/PYOxgIQqjJQNFCy0UwRGZ7MEcKmWJzspJpuXo2u6zWsQr3fHENnN1QRaiUI7CSmVMFOFzd/8QfOrmhfCev3wFfPe9rzWXxRHUYyIuqkAFdmj8rNpg0x5C+xnXHz7gvqMgma5J20lXbldLlKzHJYzGdKUCMC4RICYuhHYrtXQjpNQUlH+P3Ytk7caxKGCeG+0P3ENx5cL7ljc+Z2WIEn+shFDuxP+881DmkMb2uUdrmy0TuP4l/97kJVhriRL4iAg751dArZBk63LFprWrLVFqqfxh2V7YfaTHKn2l2xvU2swtbVn7qX1F0/fjwc3tKOP4g61n2fuisN6Iuy02zw7K7Y1nUWzcPlsJUQjWfek0Ef2LYNGgs6SlEKL88pkN8MoXnwLv/ctXoMsUpI/l5s7LfawReXj5XuM1RbBmloEtV/KqEI8Tavh2Let7r56jjYMSz6OY77dMsDsvRkvoBV4swKPjfUw+VCoV5SRudYBPgE82Mm2i+kLf1ndvVhr+mMC49cfyKZHpXkz9PLS0BTYc6DQGAI/l61Boyrr/+E0L8PkStfHwY638s45DXf3w2VtrMa5CITsEwtYpRqORbBPqcS9mrbr90HHt4jcdWL72bLsOH0e6sjOPGb41lbfSBRVXT90CX7xrKfp6Fzm1Scsbnbfi+6wCy5vwtW5KpkHexpqkzTYz5DFRhJeeZzfBzk3Jw45Y+ZVxS9zLJeSsTVv2XuJWXnj1DLmygyR9hw4b3rJ3BLnAxq4AlMXFpiWrV60WvOy7xJfyeHfUfVmdf2hCrXe89mweN1MMhRFEyjEAWzZTvX71nmXQNzhs4c4L/5vpYLnxs8selaK9OOzjRGzesXxvhr8/mIABe10xlsD+yOZGRd1iBSi1NHjx6wsLURgtlAHzXG5XapD4FIQJjmwRtHz3MRjyDCgoEjO7tbi28d2Jf+WWKBHM3XoI/vzHz8Fd83dK0yxivBdZDAWffmu6VxlYPjZu+NUTehNI+DraewbpEtNQb5fD1Qi6+4Ya3/s+ClX8k8uf2wgLdxyBb01cGft++6FuuH3uDugbtI9lke6KejeOumfBWqKkNkbF67opam7s4lpnybq4YeY2eNdvZ8GlkzZaph3/N/m96npzuvlUbJ4ueUTsNuvy66jck5g2kL0Dw/CB/5kLlz1r13ZsqAn6HO9VpunfxkyHnky+JN+xjcY3Lv2Rz/m688JdlzzMEu8zWYukvifQnh9R0MleoGFrUUtxrYmqx3o39OgTYk5OHpSP5BX/W+XOi7QshGcUtlSrEaxuaYe1ezu011327EZnpYbSzU6aNSX23WPa7KEul2Dbkrwcf0OnT/wCI+UfDvcbkM2NhTj3QHalLGZ23/eLKaOszoeqEXT3D0muZrKEhSiMltCapXGzenxMFN735g/GV6f4nn742Fp4269nwAevnQu7jxwPWzgLZG28GkVw0YOrAADgkqc3oNPKql2qXKjIrA18Fj3mmCiqjfrI53G2/iYk4BYaNNfYQpHmx26cDx+9YX7QMri0gw6FMOmfrpoNl07aCNfNwFsXjZQjjpclCkIoK+vfNjVxyVPr4cJ7l3tt/l16QDIGikzr97fPbwEAgDvmyQW9AHKBiepZfA9cmsUSxZbGfKdpWZj+N23DQbhj3o5E2n5lU/Hkqn2w8UAn3Dpnh/liDTpN0whoDs2oXFjI02aKDkpYbvEm4/sO9XXBA8tjLVGgolRMUQpRCPZOqsev17U8Lf2htlI4KvvOsnPaxIEhFS4YBN82wvW8oChHuj1LLFEUS7alu46i81FZVVsrD3mO/kPVCD56w3z48PXztApFt87ZAf0KhTNpuYIIvsyQuPPS5IVdH2Kuy8vKoRjqOjWw7VdWVxjlgTGSE+J0F8++RlwVH4LE/Mnp/u89vBrO/dkUz9wZX1iIwmih1BoxJSX33c4UGVu3MG1d/bB+fyf88NG1xOXwEBTIAstHbmuDIrVXmSDLOg3D74OKHZF437hktGxLKLWXirJhTbLGoMlGQXyzibnBfMny3cdci9MgeEwU8Assf9eCXTB5fSts2N+JvidJPFglUkCU6FvpA2p64ajNAZdNus0OSoiJuOgr9yyDmZsPWactw3QgkoXAq2ZNJcfmwCZWd8TlHq1ttsiEd78kCCHCZqUFLUTRGFJSu+0zKbaJ17gIPlQCjV6kVatL0witPS2mTiI0ptx3B8gviuTppmKiSJqPyqrskzcvhJ6BuGZ13+AwdPTqrcKxa0UVOjfDNj2rf5DOy4LvM7kSJGaE8JkysLxsXe9CKlC6Y2B5tdDa7wXq3knotiHdm9nUCWFZRLDttFlifvAStbiwEIXRQuVagpoCFYWRYHIldXygOGaIsoPYWvmLOQEPDlfh6PEB6W/igo0kJorJEmXInPr4sfb1KKb69l/PgMdW7rNOA8OdCldtNoQYF6k20dhFpOqQIYs1qEzbCTvv4GKiSL5DlCuJbXykOCMViT28To5L1UjuQgML5hxamT7h5leavFt2DfLeK7kFvjYX2mcUMKXuKdtuYD40lV/Q3T8EV0/dAr0Ddi4BqQ8pR6v1VJFxHQ9Wt7SjrsO4w8oCvBAlEX8wI01tVdUMVyNYu7fDqMQgF7LI73nrZdO97pfmr9mHUHb7uJUnYcIEYKpLaq3r0ICS6cjajy6+0fH++FzwjitmwHt/N8eQ50h6tiXuGxyGq6duafwdciiwSXvN3nbhL5oGlZ2XhPS6VfWbCrf1VDbkvdaMJO29taMPLrx3OSzacSR8/gU4fcM6t8jGnVf4+ghmdZV3Y24CxuVdAKbgEPXepH93GbLurDbPzn8gH/VU1BNq1hsJc3bqyUIaE6UaGSfq4C4CFLzvmjmw49CIOzRVliQxUQy/qw6WxTzHyU7JLVC5DEvlaZnu4h1H4HC3XBiVFVT+2G3HSdtA5CGR+t1FNlpcTJT0Ndg+EeKwFW+JEmkPZ7z6dX1sSHzve0Dd7NPy0l1H4S/OPA1Of8H42PcNzewCPb9pfxRiA5Wylor0/eba6VthcLgKP3j//0YHMTZaNNuOqUV6aaMI6lgkUQRSl5hyN8F44UVIVK6NUuVI3ieUX2ktooyJgsuzlrb8+589tV55jyl5uWAE0P7dXXpr6B5uCp6NUWDIE9W8r9eCV68t621SHhMFXy7V+lw1H9gO5UPIvUXWfOmuZY3PZOtOxDU0geUTbcPh/WCuC/XmyOcl8TPBWiOuYFb74/uProE5Ww7B5PWtsOvXH5SUwS1faitHKrDvKIvim2IKmmpe6TIzg6GJz1H9YSEKoyXLmChW99EWg3FAN5FhfMRniW4yJXXnlcHjigIUXf4jGyP3QhktUVRCFCHPcYIlirjB0ueLLGA8Uyv2tfc6ZFIO5AK+7MuBQerGUb4HS4GyRJHkgRmXLnlqPUzdcLDxt1dMFNFFAPKe4eFkYHm3/EcO+OMHPQc7+1LP5NtEmt010idvXgh/+tJTYcb33hX7HvPUrhuWUBudsUQ7TN/ibTpg5yaPek1RNM1xJt3mXd659gA4dl2egeWxwpz436LygErZR+lexqIubasmipKH2jgFBqouaFIowlzrguxg04esp1HKebsaAdSX/LJDWJU7L1d8UiMtSSClBUx1DQ5XYfeR4/BnL3uhUx51KIbClCLFiX+vm74VrhKsfnTYWOj5kswqvlaXlSNfwYJMILsnUIxZjBAUExuXGrQ7r0TpQsz1xxRxQ5nRAQtRGC1UiyvMgt50mJa4kSkwdc2ykJsBsbn45CNTRhquRnRaGBmuuWw0dlHpGdIYQLg4Gi/4jRmqRk7uvTDYPi5V2wzRxG3LprocvWh0FLpQaG3JY6LQ1aqrQOmuBbus73EtjwyTJYoPU9a3wm1zd8IbX/WiRNkU2qXIdEfDgfSOw+kNayjNSZ9NqKlnehoIkoETqscFgL5Qa4cytFDoUOheayQsW/I8EsM2vTGVikJBRhf83S/PWtoBrNUkJYu7LxIvthN+2FnfxL/cefg4/PKZDfCNf/xf8HeJedGEzMWO6veswQn4cd9hfhORHsISLxLidW+XNiaGCxWuSWOe6ev3r4CpGw7C7z7914651AjR3+tgBSgA2MDy+fQpWRu5YeY2pVIedTFlayGMMlkSTFv3jYkSCnQrNVjSFcGdlXrvnmkxGEdYiMJooVzvuAy+ykUxS1Fyp1JRv9N1+zrgoodWwd++8o8yLZMN9aLLtdRogtbFMgqESnBS7yN+bn/0v2PceYlClMHhauxvZb4OlWYdHNM6B3pCLZSkG/mYVUNxkGnSUmp20ikC0BxoY9t20lVZMkg32jWCJL/b5u4EAIBlu48l8sClqaLZLVHUmJ87XTXxL1SHS6FqNISrhmRbwzQHjH/rUMLEEOkxxQc7Boc+x8COl8k+Ii67lAdBFO3axRrb4NpKVq6P3bgAV5yKIgFzoYz8xz3LYGtbN8zY1CZ1iYNNvoxzoEu8NdfHpIlppSiHSllTrcWZGa7TLaa66hbTN83arhSEYNavJJYo4K+kkLKQlu3Rgcr9mH8juHLKZlxeFAogkj2+STDpmm9hz/GRDVVvJVMeQUX5ZpTRAwtRGC1xqbd7V8ZoRdls6ku4Th1V3D6vdki3Yk97Rjm6NwjZAmQ4oaUwXI3gG/evgNf/yRnaHPNul+KCsO5v22fTYroTE1hedOc1OBQBnORcHC22T1mEw/Xi47fKxB2i6rWdfGu3ttmqpL6zxeewVRxL8JYo1cThjFvesvzGjqlI48n4NuXm7gtqGi7THO//1aQN8OiKfel0IdycQiFE2d/RB698ySnK3zEHFPbagMSazKSpMRSEHkZc9x3UoMf0RGB50SWS2hLFTSgb8w6AK50VNq/WthnYuDAbGIpge9uIVWHLsR7L3GIZNJAGaXdP2RvMnGy7tohAHeO0ll89JorEnRe1JUpA5SCrocGQues4Y/NMnb1DcMbJ480XKqBx55VQDnGReSLabCjLcKWA7gQ+qxWKIlclmyMXSxQMcu8w+a+YsO+gDEISTBGLUOeMHBaiMFqy1KqxcefFY0r+FGl+cmkPq/d2wLNrD0gX9dUo7u5k5qY2mLy+FSavb7XPKKeKoui7sjTW7G2Hv/qTPwIAgEFEZNRxgholxv0XALgpG1rvuO3zAChWu6+Dciek2RzkOpxKLVHoSiRdgDok7xUTJVYe3D3D1ShWdsr4JaeeNBY6+9KBfH2DEVMJeso2vTfejabgOoFC3TIoS8ZiTEAQLNpxVPkbziWgHeTanLyYLAxdfYPw9ftXwHmvOzP2PW5+w7/HeGB29XWhD2HQvv+T91XN5VfvnfD15OLuxJS8v6De/jfZ91+4cwkc6ur3K0w9/Zj1jf4Bsx5vMLlVo7RQhKKUstZD8/hpZbFa2naJk64zA61abJ6ps29QKURxG2nckFlL2JDcVmLc8VFRdC8nYpuNJN9RUgR3VzJcY6KIFPstM2WhIF6RmaISPLC85+9MfhR0fpUiW2OsbmmHr9+/Atbt60j9Vq1GsQm4Z3A4dY10g5BDi1UdjjfO9TyKJLv3I9fPFy5Q3SduKke+V7n/St2Puip5T34bqKJRr/+4G6liYtTQC3Do4rSxo9J8Q+Y9VE2773I5gKnfId76wgly/RnVM2LL7OzvvKiNEwmm+DqNxzwI4s7L4Znq5UgWRzWHmLJwCYbNFINbZu+AuVsPw0+eWOedFjawfJ64u/MShSgK9z2KtEI/u1GIYlECIv0H6T1JAYpPPIj4Qb4kf8tCk45JiLRU87bJHY4pO6klCvGAGyk+295bp38ovddDpRWoY9mk2zOgKTtGoYHInZcIQs8uBWZcpHLHlMzJtNawFSz4Ck2T45Ksq5qsu6TPgchbpmdThLkTO1YnX1Vcmc3uSS5+bA1cMXmT1T2+rNvXAXO3HipEnTNyWIjCaKFyqxJFZp+rsoHRxjybKQ/Uxzc+rWF/R1/qu2oUGX21y/L81sSVHiXxRyxTfSHqd/hrvzhLfi9+HhoO12+tN6qa36i0tF0hM1XP6V4bumUWEaSWKOnFtFufcC+TmL+NJUooXvgCuRDFP/6M1+3O5C3PR2nKO6YbqkrHxNpkKK1ORDksdyHUZW1mYXrZaO8dyCSforxyrNC5UqnE2j3mIHr57mPw2Iq9zmWjgEKgYHO/cj0a+IXH17sFaVwWWLvzQs5LsnmZerxVCdtdONw9AH/x35PdymH83a1wpWtPBMVNtkdZklS1UjYraJmiZKi9gvxMzmMfZHl9Z98gbGvrds+PcGMwcUkL3DRrO12CCD503Tz4/B1LYN+x3kzzZfCwEIXREnqD6TogF2UTNJqpQCXX92CnEWJX0GqUjGOAu3/N3rRVS6bE3P+QJkdyH9adVxYuD3Rj29iM4jORrfEwm45YQSPNb9kxf9thqYs8X3cAIrL3LPvOVAVeMVGEN41NZmhYr3iAfWcjnqZGrj/V0hIFi/N8Xvitq576c+ueo2hrFlFQHEr4hQtmax4FxXql8P/to73M0CB77WpLODOqa7r6BiXX4g5ew7vzwl2XLIbYr3RF/O4fVqc060OPQ6axPI/+FtyjgrjudtC8T3Lvol3+iZwAM7fKAstTrAlD9R+l9b1lOpTtwuzGzTVdy+tVLlkRtUMVqN3GvZ00DaQlSghMefvUEbUru3o9hxKiyBRcbHLyfd4v/H4JvOfq2bC6pT32fQgLpKIhvtKDXWlFX6YYsBCF0UKl5YG6VWo6qFoQMMwI1Auq4WpUGndlqsCK9e99DihNazPcYnfkGqw7LxeoXCZ8/9E1+NgtgaA6VPZx5xVF4fvAb5/fLP1ebHfemquy7xzS9NLActD6r22Mwsx0p56kCofnK7Byu69oAgZbcOWPNH/p0g5zOCO686LahLukYjvEzN162CEXNWyJUhyoX0XvwDB884GVzvn4uHjCgHfnZXbpoiLdt/E3ux2AijnJlBUs8re8P/lb1PgXsU71mPvEO6UKGpZp3zZ3J1nLw1S37RgYadIVvw8XE0UsC+bdKr6ntHgmSymRriHhHYfcNfWThFjvuywtUpYoBH1KhV7xJf1b3ucC4njesEQxrhHd6kqm4JLlcmnlnnYAALhlTtwCRPYKHlq6Bx5e1pK4Tu3Pq+jLvoGhkTOICePG5lgSRgcLURgtpgmwvWcAZm5uQ23CpQti4bPMXyFmocYw1ERRFMRnfJY0uiSir/hoL5nuE1MIGRPFliIPIf92x+Kg1iFUSft2EdXtlJYJVM9KFxMFx1C1qo2jYXMUlcQ+GDEup9F6IN0YajWP79J+QlrojIlZotDkk3RPhEkVM8/aFC+PQPUMDT5znuzWPUd7pOkX5Z2jy5GKF+SeR97P7pu97n7lb4Gf2WZeRgvPXQvjQKg2kcUeKm6VYi8MClEOl99VDA5XlW7/OnoG4d1XzXZLWAKF0DhK6P64xR80ryOiiKbdhh4fqdOrSsYaCus3GfK4r/q/Q7Bid3vs7+Sw0tEzCD94dC38v0fWQM/AkPK6MtEnxOGdMI6P6osKvxlGi2lz/ZHr58MX71wKdy/Y5ZR+TGsl5j7JeKdTfgwdlUrYQx4bqLV1h6OIXAfxeH869gMFStP2E3/4HJAZNwaY+4TPYS1RLDdQeZ8mgNqVTcvRXui2aC/Jfih7NL1rKFma/pjSUD2/bKPgXAYiixqqg+YI2QVkigku423DnZdinBDxFRS51lHqLstkdO93cLjq5VcZA2YsKZqASYyJQmWJ8tjKfbG/MY+MCT9FXnMxC+tivZfRjKqPuL6i1GFcfSxUKHkkCe/OC/dgyWKI94Uso221Y9YhNolSdc3QPTwyjCdulq8+JbJLpxqlVxZaYVUU4dYiodx5CZ+91oqEDSOUG7sfPrYW/vnaudLf9rb3SL+X5o95XRRukhL5uFmimG8K5s4r4GjhknbyncS21icqARMjy5SuDJkQ1NeS0IW+hEvK5L6xZ3BkrzyIjLtalLMr1XvoFYQo43KO0cqoYSEKoyW+QEkPOnVNr+fWHdCmg9EakLmeUQ3YvO9lREI0B5MWlU0bvGLyJnjdz6bArM1tnqXSI/ZRbNBSfXqG3y2zGBhCLnBcNp2214+6MaR4D6zsYUV0s+CRUMydFzKhoarhSo/yqA7Nfd1nur62kAfZ/3HPMnjP1bODBlmul143ZThpPAbssmLcJ5dNOA5zuvV5NrTrJBWjbx4oLtSu3VWHcaFjZGDBlmNMJR5/0MfNcniBgil/9xIY4zAqfgvdx+MHmzRp2q7flXtlRH2rstLFq1JarQr5ZTGmx57Psu5JFRsCtrFNrV3S78fJglYoQAlR0Klp8sEIUk1pINZKoQ7BQ7qbo7GcSadnUoJxzbYoZ/emsTCujC1+jj9A3NuNW1miKIJXv+QU6W+y4dJVwbd/cETbriDLFUYCC1EYLeG19FRCkjCTAkNHmUwlXdoL5fPdNKvm0/OXz2ygS1SC2G3q6w6fvuIeKFr8PPJXnjFRks9CZllQgNEo/SjlGD9VfYw0JkoUoYJXm6A6aMYmM1yVBXt1yE9yr63GNzbfollbAADM3HwIAAB+P39nsDzqj607NHK20glUpTF3XjmeKmP65q1zdgTLvygH6oyujyBekuygLTl+AkDPwBC0HMVrcIcEbYmS6CL9Q+7rKKvxxHfu9cxfer+mUNRuabFg5lZbBi199Fw3Y5tzXtVqZKlhjsN22eVjaet6vw6b4lPEj7RlLPEpN8U6OYrk1hI2YC1RKGo0FUfJkGje5x5ZrrHzjomiylNnnRO7LkhpsslPtL4p4r6KqaGKLsowABDvvD79GGP8G9fW9c+ToWdVS7vwl0ErLEPItUcijHs55AZYMbV29w/BM6v3w3mvO9OhhEIpFIpYFH3IrFWIKJPweQi5MXQpstGcnuBA2pfD3f0wcfGext9Zrsd1z5vlIlTctKj6BuVmmCyWiUc64nNikxmqRjBujL9Wnwy1Oy+/DJwDy3vlmj95up9wJURg+SRULkQmLtljvsiRIgjBmRqUfeQ3kzfDW//0JanvP3fb4tg6Nn2IJs5PYcEKL5N95NJJG5W/mQguULBch2VBeEuUkQyohlLbMfnqqVuk32OeXarpT/AcoTTZVe7TrK2yCNtFKHdeOmzc/OQ1z7nkmgosn2HZ4wIggvQCnE2ESjuJ1LIi+S6clYPwym1JBTadspL4SwiBV1bzV+/AiBClaHsHZgQWojBagi8+DZqv6kNaHlXy4Pwb5uddhMygmn9V/rd/8OgamLTmADwQ6ICoEURV+O72uTvgK3//p/g0CBavYlcd8NCgtMkHA5klikUy33xgBSzacRSXLuR7uJnJEIuxRPFsg1SP4dNeYgoCyHSGq1VIGgvHhaS4dGTjgMqqBuOiQ0fRrC2yolF8zaSRDpaKELwgr/MllDsvTKqyc6DOvkHnPDEbZ7FcbIlSHKhjokxaE3czHEVRQhGILi8XrNx5ZXSQ6KvZHz/kS9/v/RQ6ZRDl/BUWlSKT7HcsodZ+Mmzn7ShSr2NirnUkEyL13t3n0JtSw9uYVIDXaWOJktW4FoF5DDChimWV/I6iLaWV6/Rp5uV2tE5MsRm9B3DLS95/8fdTtblke0hZooiKD8KPujcVQrm0UqlYJ4yJicLnncWF3XkxWrLU4o3HRIm09/GQkj95m7WKUGu/RQBg4W7WifoGf83eDrI0ZW4FxAlY1GC0TU/+u1LMKf22bxBpieKwaMhhD2MNVoBS54I7l6CuSz6bdNOhuz+nylENIZTaVlEUobY99y7aDY+vVMfNoKoibDrVquy90vSLUItyd/d/bveFjHNixYni69pZkU3yLT3HkCLbqP/XH1ZnNyYV+L2MNlT7DZTQXzKGHOsZSFyDSUcg8CI3izEBszbIEiu3UZbrGBUYix/s2lSavsIywochZIBkCoYjyxk4Ajg+MGy8LIs9oo/Qj7KG8+hW9O68/NNINn+XMyRMH6IQKh/vH0p956KopCNSfG58F0XScqgwuTpevOMIKh2MMEjWvKyEKLrfLNJJrk+TxYoLboXPRTqkUqCqh75B0Z1XRoVhrGEhyiinWo1gX3uv8veYqSwivfbEpkWVlixNmfskpY9bHlRypwLFOJgOhSmwfFGIW7qMfKaJieJ/n5iEuDDIGlvft/h0PW42NLG5Ww97JB6nKGOm64GYc37I655evR/+86HV6nQytlxKavTVv7NNZ+R681yelzuvJNj3/90/rIZjx9VrjqzwCd7rw8xNbfCooyBJbA/BLFEQycqUFaZuOEhfGAW8QS0OSksUx/mgF3HQm+frx84plYq769xQ6x5lforPZOlrEqXOz0lpgagQQ0QDE0pwKN2bq5VPLnpoZSPOo45QWyixtD4uxymVSQ539cPkdQeUv3tbVGewgKexskiON/blFpv+cDWSuqrzrY5rpm2B1/1sCjyfXGuY0vWoos2tnTCUiAv6H/cuh9f9bArsONSNSsO0jv/0rYu053A2FOUMxGSJIhI7U9Sk6dofs1oviEL9IitgjXZYiDLK+c5Dq+Dtv54BT6/eL/3dpvNO33gQ/uYXU63yV0mQjRrwVrkwTY+pvThMQjLLKG9CN1wh/UbfxRxaK64x9X/bDVovUojiUk22Y4brO/VZV4byEZ3E9GTYZzc9q+8aW3m/QgjnBNHG2udMw8W9QRRhIonZ5V1n5Z529LW1H3B5OS/2PR6zu38od42z+mNrN3cOh5k1lxXq379411JE6cyECiyPab/1d5flK5RZbDL5Y7tOM12d1JbH9bns2gPenZd7Hlkq0WBc7NgkmRw/TGOE0nMBgTIQ9jrZeBJTdEKmOUxkHojJz3b4n7n5EPLK8IGpUekRtwsZH71hPlx43wp1EXKeZjDZU1miuLR3EbEPPbFyH9w6Z0c6H/Ab266ZthUAANbui3uCoHabKD7/lPUH4dsProz9XlcYeWAxzq03Zi9xrCfhDtXxkWTtYWHC0sW1tmzus4mJEruukvzbFOcWB2UbUfU5tkQpByxEGeXUhSc3KrRKbDrvFZM3aX83JhULLF+7Wr0o5lElb2ruH/1m5yiKYHA4R18iCqIoMq4osY+elw9VkzVX7FrL77F5J9PAClEcc9X/mvg5a615AHrz+0YZVI0RGYsjr6CTqr5BebBpm5aqXZDF0PG5LvBrunfRbugfyt5arOyzOab8WW+EbARLecZeykqwrKLsba+syF676twYK3BM0pNwk2K7FgoeWB5rieKRR9qaMbsWL3XH5amMYFV+izWw5nbEdSNXUg2lOkuUajWC62dshYXbca57TFSjKMghfybuvBSfMZj6X97KGSJUru10hHhalzKKTX/P0R55uhlYz4bI4tm1rdLvsU0tHhMlLEVp/8n3kI6JIn4OWyu69ClrSxz/Qyk5Mf5wYHlGShRF0NbVb2Uq6zJ2mdx18dDR3Hz2tkWwfl8nLPzRP8ELJ9gNR7FgzcTlAgi/gaZCJbDo6huCJ1ftg64+s79V5cLAVLHK2+TjBtrvtMtYYhqfEonmoYE8dkwFBgVf16EC30nTo02OJFPVGj327N4upuyOUKpRBGMDalBaabdqDsBshTGY6x9Zvhde+eJTkClL8hqlE3Z9/NQJzF3GmwjcD7dsNMJDufPCkLfLCLZEKQ7K/YDielPLwcRtSAkZMmwO2H5XCyzviMfzue3p/K/QXap356XfT4bCdIjnkr9OsP30mv3w2+drLo52/fqDptIZ85IJLymsd0IJyFX1bW05RVSeouUlzR9RABJLFDD3B2MaiHtC1acxXes2hh/jMZhiomDBZBdSwaX2jmkyUNexOn3Xqku27yzIc33O6GEhCgMA6Unr+hnb4CqJH0ptGtr0zfeLBxCNyxU38pjSHNQDbc/behjef+6Zzuno2sPx/iF4bp1c+0OHaQHhsuAM3WzFfnzzbLPP4sZ9yu8dNfgUt4WMiWJb0jzGkHFjxgBAeMsrKoF3aCsqVR+KbRQ887B9zyEOVVVxi/B3nfgmgza7ZOdRSUnC4qudjCFkW64XQe/OyzXt8C89mDsv1LqPGnOKsTrltWRh8Assn6Z3IGGJgkpHUHIILN9Dj10BxhVleoFTC3nw56oLpE4Pd6fNwSa2LAs0ViYtCu18aX6IDKsyjQ0CZHMudS7x57NLPcs9QIi87AR2+Ux0LksLzHokikLVqfxzcBRjW9rqQiwUroCyNSSVUE0r1A5UgUkLGZUiacqdV+wet7L97S+nKhVTQ60X2BKluLA7L0aKXIBi0Gw0aT5aaBWZtSh5UMmbCnhox2XIRQ+tgrsW7LK+z+Q/s4iCPArtMRHT3I3rhyPXYAK94tNN3GM5/mQdKBwgoDsvzDXaxS5ZUbzK0biGcGQpmnDNxhKF4p012rmXoAK7WXNMvxQziRonje2cH1nM36TpdsXkTfDP/zMXegbMVo2xPBDvNW+XEWyJUhyoD116kOuNvsFh+NTNC+G66Vtj/SK4Oy/04YhHYHmPsdXlTiohAuX9ru0Kre4Qc7EjObB0yl3N+LG0RzehhsBs3HkJdV8AhZlQZFFSirk4FfvNoeS4YTGQ4ofpnCuQoA6r5BMToTien2HJQvFI+bvmGZKlUl0ZovQYzx7UhHK3y/jDQhSGDN9uLnPPxEMHg0G3sKkHbrMlxHn3ke5++kQFwmp54X9XLfSKZImyr703SDl0JIUooQ4Q5Rt5981mKFSLdEptsCiKrDYVQSxRFJpS2nuQ32VB3gfdOookgKGupaz6qWmTdtOs7bDhQCc8unwved55N62ijIWMxhLF0s1XHZwQJYJHV+yFJbuOWlvf+0IRWN58GKX/mx79OsPKnZjku2fXHLC6HsA9TglaUYEgLxtOGoc/usEUh3LNE7Pkss3LVjEEaK2Wk1DOTSHWKdR9meJxq5F/f8C0x1AChLzWA1nHRMHk59v+IwDo7h+Cm2Zth91HjqPvu2X2dnjTr6Y7lSumBOExbxYJlqEUFxaiMABANHFo10aR9HeV1ld0wuPNs2vlC2be+OZP3ocfoTFZorjQ2TcEC7YdpkksAyjceYkpYAPLh9DsTv4+cUmLfSaydC2uDRdY3u76303bAk+s3Oedr6+mkjomCuGm3jKp0Fo/WO3jKEr3Py+3Vz4ayVhLFFeN3yaZz0MIm1zrxlSWWDBkpIdBXbBjaR6Iy/OIiSKWizeoxUF5eIZ4R7Kx5zjSnVc/NlYbMejA8h7uvJI/5y109sm/f6gKDy1zWbcFfubYeCJRYCGe4GwsUTB5D0ehAsunGy71eBtXuFEJWxXfZ9gVKJSBgudfkMjyGKWjSPGrb/uKFJ9Dg616ca1mY9XuAkVz+NWkDXDF5E1w3u/moMt0+XOb4LBG4TRliaJILLk/jSlqhxjvAlnulMlibrTBQhQGTeh+HLdEiWDJzqOw/ZBces1jSv5UIOx76B/CWy1QlyOKzAsI1zxvmbPD7UZEOULUg8/vSbCWKG5CFP1NeR8eAACMtTkwpC6ukN7UDQfhoodWESRpX0jMPXEf456bR8vri3KoWpUcbLjEVinDXJk66LPRVi7B88kIPR7Z9Jt8A8sDPLV6P9r1EjVFmBeYGmpLFDkh1mhxTdawAj5sH625znUVULsL4t2CQgufDb/b5j8wZBB2qQ7RHbs4eo4Fw0MTc5KDOy/du1T95BT30bhn0JTDPrvEmsiOLA8nyzDLUBwCR5C0FAqzR1Al6/1OTfvIQC9SrVCW+Dv22b0wmDeNmf9MZajHve0fqpLNp6mYKIo/mkXRN5Ri36wth4KkO5pgIQoDADQbSV0KMq3aZL6xwPIRwObWTk1eZViSMK6s398Bf/Hfk+HnT69XXhM66HUoDdmQEzt1v3D3JS1sbIQk+nLS+EyWgzZh/KWhLFGSUD2r2FaHhquwcs8xGByme4eqRbXPxjiJTBihI1QwxJH0kdeBn3DBNV/qe3Hpu2dQpJUAuTsvyOb5Qm3SsJYo3564Mn2vdV4RXP7cRrhuxlbL+ywzYkiQjf2244B1G5HtQTSC6tCg3Xlpd+om5ZFsMeXnUx5jYHnF3655Ypuj+B6zOJgfP07cK5vf/8LtR+BNv5oOU9a3aq5KfEPwGLI9FMVcEztIRiSn2itmquGd80SDyb0oh86YJhIp1vXeMhS/29PpIQukOm/QBZb3s0ynQWvFrMnESwCUqhPcdYkC0BOo/4RScpq1+RBsP9QdJO3RAgtRGBKiyF9rKfY9gHYE5I1v/oTU0rv6+Zpv6jvn70JdT77wgagwC0obyC1RjL+bVUfFa7CBid0sHPx+z4JxYxPmxUTpojS3PPO4dNJG+NiNC+CnT44INr3deSm+p2zHtmmFOE+OFP1Bf49M6UCepjadQrR8JmuM83PGB38qqNYRk9e1wi2zd8D6/WrlmzoYFzBM9ij3A4h3hHmLmFcds0RBpBm/164t4d15ufcRnSaz8V7nXOV5177Dp5q81HW9EV4JQPgs+504P9Gd1+CwWXP+c7cvgsPd/fC1e5crrwlypih5Xbr1la8Fuvo8Qf5DmYZ+absiVg6imIkp5lNUTBSPe7XpGtYG9a+o19XYvVAV0d6zJI8i6GUjkXCd5gyxRPuikGtUm1g1TBoWojAAkB6MZYHrfA4pbbUgoigyDJRM3ty1YBcs23U072IEQ9QModSeo9dWDtcbKOZuMY2g8SZKMCjYWKL4xbCo/Zu07lNfb87rrgW7AABg4pI9qd92HT4OX7t3GaxqaUeXDUBjwq643gXb20MfKGdtFUPSh/2T0Kfvo1FXhJ3kCawOKJHals5lsXHnZRGnx6oMiBqhUlZYvNNtLVIU932jnWfW7Id5inhxzfqK0EIU0AmY9Pem42oVZ37LKm+K2H7a64T0Te+Uov5FIcoAwjLY3EbkuAitxLRkd1O3P5MAC3tvaMowhlHNxT7vJHm/zzUuhGyf1Onh3Q2mwbxqjDeOXOIgpsqlSCx19hJWMzZU6qHjdDLusBCFAYD0EGTrc7VSQSzUbA6PIAdTPMaaL9+9zOk+oy9rxff3LNwlvz7AiirEgrJsGF0FKDet8s8Yjh4fgJajvZZ3mfM50G6fJiUdPYOwQxHjKRRZadt844EVMGX9QTj/hvkk6dEGlg+jHYxlX3svXDppw0h5kPdJtUNzcnuFPkzKaaxLKmA0FaEOC4TP1VzdedHk5brRLJNGYjPzzQdWKn+jOlSTanMjrgmF1hWKAKXVN7WgN32PoClN7CbKaFynSDxLSxTZO6XOX9yfG+PEIKh5kkh8R9ATZO9LN06j8xTrWzxUznmtp8NbGUhyv807wlz5+Ip98J0HV1rFI03lQ1Cl1fhLleejeHpvSxTF5+R31Afy2DE+S0sUTJGyEBonSQeWl3/WHiEWZNmHaUchZSihBUvNDgtRGCnjx6Y7VujAzcmBsFlM8RgaVuw5FnMlJELdGqIovqiRu9YpXhukXrC4Tt7KzSzi3r/95VTYcMDskgWbZ533/m6OdZqm/K6eugWWIK2xfvzE2tR3ukWq1atEHSCFa6+7j/Q43YcxYfcttbX2PHE1fenOpTEteewmL4K0FMVFOFmUzUIoko/X3T8Et8/dAXuPubVJr7IEqGxnLWqLa/MMLE+1iRvCnkhDQoDU5P2DGQHlFky4xlZ2YduNsHOBTtBoTMG2fYe2VPE4+HUtmuvBqo+md0jEdmmKUYcpWyjFA9nBMPV462WxTViOZmB/Rx88uWo/PLS0xTkNCss3VBtRXOPbvlysz/TX4cDONUVbz2vd8wXKU7tvRqZRsGrUEkrJifEndyHKJZdcApVKJfbfmWee2fg9iiK45JJL4Oyzz4aTTz4Z3vWud8H69epg0wwNMndeJkzuYqRSfcU9UVTOmBQMDbKF1+Gu/kzLYNKQHTL4Iq6TcudF3LApD5xTaTv+HjvszWjVl9Uyo/72pm9sg2un44MYL9pxJEyBJNRHW7Q7L8X32KaajPWiQ8xL1Rfi2la+mmX5aiduPtgV+9vHqsPP7ZXHvZo6fGDxHrjw3uVw25wdZG5TfN7AJU9tgEsnbYSPXE9jFWVDCGF+KFRuFmduboN3XDFDOl5Zn8cirqGyRMHOx0mKdigxGlm555j2d+U6I8C7y7I5YM9G9Bq1JuW25PW4POV3291BPYfZ5E2RJ36uHrlQun4gFxyMQGKJ4p2CkJYohJT8rltfuVk+ud+fpdVqEAWLQE3tSPcAQSru+MRE8d8v1BgcrsL3H1njlZYNKmWS5OO4WKKIdTJ/22FYvhun+IdZmxktUVA52ZGsK/X+tfiHiAc7++CaaVugtaNPeU2eSk6MntyFKAAAr3vd6+DAgQON/9auHdHY/c1vfgNXX301XH/99bB06VI488wz4b3vfS90dXVpUmRsSQ6E4y3deQH4L47FA5gIDNpXPKaMOl74gnGxv2PzY4D2YJp+H1u5jz5TT+j9r5ano2Vd1NZO9aKHgvLUPMC4MfL5wnSonkVg+WrVLr3w/meRGmyRof5sLFossL3+R4+vhcnrW+FXz260us8nz9i9iVvnbTsEADW3gBsdLNqKhuo1m4SyNm1eXP998c6lsPdYL3z2tkX4BJRlMBeCaqPr7M6rRHNcs/KxGxdof8cFlnc7eEsJcIW/Q7u6wLY9ysDyoVcWpkey6W55d020VrPHQb4v/QYhCs76Kn0dyXNIA8v7J6xS0rJXmNH/XgFCt87e93sKB6z6nXte87cdgeW79UJxivxlLugAKCxRaglM23BQ+zt2bsBWpaqdJb+PtX2HNnHdjG3wiZsWIsuEiYliXQSv+wAkdRIbf/VCXNl1VMiqy5TNVVO3wDXTtsIFdy5RXsMxUYpLIYQo48aNgzPPPLPx38te9jIAqDXya665Bn784x/Dxz/+cTj33HPh7rvvhp6eHnjggQdyLnVzsePQcbhcOARxCSyvIwL5oKWaBMzuvJjmwvxGXzhhnPEaKqIIF1TNhZDb8qxdjKktycyfycuS9bMTpKEd4ywqC6NtqkvN973I3D9iUHUxyjWjtRAhcDPCuwuQHfB5CBsy6B/5HHqpM/3A/8zNsBzZPf/+9l74zK16IYepKGJ7kHmEkWuR2wrkzFDNs5gAy3ViB2+8mCw86lcU4OWJSdq687LMCh1YvqLuezb9HCDb9k6tKW+awzDrUav8sIoKBksU8rlXSI7GEiVMo5Ctb7WWKA55xAUqimtU3zvkVyRk5VcqJlmk66v5ftFDq2L52seqGfmsulPZZoleap8hLgx1n8EqlFHFRBki2mR19w8pf4uiKMh8kw4rL88k7QVEvKdYbGpVGwZkGbuJsaMQQpStW7fC2WefDa95zWvgM5/5DOzYsQMAAHbu3Amtra1w3nnnNa6dMGECvPOd74QFC9SaS/39/dDZ2Rn7jzFzy5wdjc+2geUB3A541IubSLuBYe3B0YfusEW3oHE9oynyhCsSsmzuvqSzJ/MhoWRjUMgx08qdV6wcChN2whZk+9ihF6zY1GUbEKdDBoLHKYpgqZnweea9x3rpCgL5arpRufNyPUzkDerooWivGu/Oi9ISJey9ZkGHjXIIjdA2tAKBmHoWQ6n4PKaYKDLGYgddh2Znenxs/eAFWPLPVHlQKb15e+qwUL7xhTxuTYA1uOoS78DykT5929SxY88YZJ8UQ7+h9xKS7zBrPkyJDjm6WfcZk7ExUbJ25iWbpyl0hIKuz4vv8azQ5C5Eectb3gL33HMPTJkyBW677TZobW2Ft73tbXDkyBFobW0FAIBXvOIVsXte8YpXNH6Tcfnll8MZZ5zR+O+cc84J+gzNiMydl25uoj6oifQylEIfajMumH1curYxF03XCMrhTzMJvTsv4xXmNGJu+pqn54Z+Epv0Ve8pdBOupz9e4c7LFUrrJaNbrAShD0F8NsOxesHmZ5tvxoduAJ7ayQUZUu6cvzPI+CbVNsX0a4uKCSVIwCRLNUbZWKKIFKT5MDowL8nxmpSlRoYtAqvxXbNEkf9mdJ9l+Dsksrosk4INXuHB5S53xPxM457s2VNDbpRdO9EFSaay/MGCm59oJijq+oyiyOpw1UZ4SRnIOpK0rSRJ+YGYvc6yRja+eAtRkG8qtKtHFXErWvdnHcTEkCvoEUgqJopy/6vz9EBZonp+YfJxXNoyGZC7EOUDH/gAfOITn4DXv/718J73vAcmTZoEAAB3331345pkR6gFHVd3josvvhg6Ojoa/7W0tIQpfBOD1lQR0I0VqolUrTmU/QDIlBdde3DVdI3dRtjeqA+24z6BidM25q36PjJeQ03WQwLFc4UWcmAP3ZXm0MhVtI0lSix9xW0+fq5TaVneH94SBX9QkOcBWJaUzbL0D8ta4OjxePDVnz+9IYgQW1Y3FNYbYrLhNN0Q2o6KQcC2LtkSpXnRuf0lzwtxgKe+165AuuvF37TxIS3zsCmiU/UaC+SS6IlbTQKj1LNGXlli6uqOeTvhF89saPwtG0op2umG/Z2wuqU99b1p3HONFeSK6Vmx462LskmItR6dJQrtYPX5O5bAe66enfqeorzUc6IpveR5E2ZP6+re0ITREqX+O3odj8sXuxfEuTozM1Q1r5d8BUWmM0FnUkI3+XlDyu1XrO7Ks+4r2/5oNJFdkAEkp556Krz+9a+HrVu3wvnnnw8AAK2trXDWWWc1rmlra0tZp4hMmDABJkyYELqoTY1tl9VpSrnkGUURW6KMYmRtSTfp6dpe7ZDGvsWEiokSkuwtURT3EaThlWkW2VkfmNimb3e9CyaFBCyqwPLSPIXPFP6bjflZ13vYikfsXU6UQ30oVP8dlxDyOgKosrKNB5S1VuD3H1kD5/7x6V5p+G3iEAE/LVLLU5BANc26uLUBYIWcMkD1jrI+UDahmwvih0F041vo+c209rMZ95L3uwtDCBayCn4pCFC88tJQrUbwz9fW4nut/tl5cUsUB+FxSjkjCiSQlFSgbph2srSN5J8xlDle87xth6XfU8zlWbvzGjumErOMQLnzUqTrb4mSD6oxXmcp6WNdjrFEoVibhRBW6IQjqgvLLIhgRZ/ikrslSpL+/n7YuHEjnHXWWfCa17wGzjzzTJg6dWrj94GBAZg9eza87W1vy7GUowBJpzUNQtrBUrFI06WpHcB5UGkyEIumxCXY+d1lHVA7XHa4EUXY0PKUmCZvTG5ZCVTKGFhen77NQXLysL32b6wNB9KQBKAPLO+zMU5SjeyCG1JuIL/5wIrUd2gNNiAWJmGvs5ynKfBJPa+lwLp96Vh7WRUFMzcZtbZpiuJVBgDCwPKOlihl3lyPFpRawYrPNumkDuqFL0Lr0ejWV9VEOZRax6Y1mq0Sgce9qPRz6G6uc7rLmlJqieKWfYNBQdp27PhALD1TgGjMfC51uxboRVGkG+/3kfR7krJUKsHHAGooXhu1ZappH5lUwopZWygHPsvvkYxYmiivsEtP8l17z0DqOxdLFIAIth/qll9XjeD2uTtgxZ5j0t+HsdpcgZDVS7Uawa7Dx433ar3UCCmLgqlqlBjnSrTsw3heY/Ihd0uU733ve/DhD38YXvnKV0JbWxtceuml0NnZCV/4whegUqnARRddBJdddhn8+Z//Ofz5n/85XHbZZXDKKafA5z73ubyL3tS49FlvS5TExFm2xQsTFl3z0v3m4poOoDztr5Dzaw6Fynpzbptfv+MhHxU6/+S+defcxxQCRUrNG9uUKPN+Zs2B1Hc+2mMuRaMQLmKtlVwPSPgcW47q3VFbSfq0SR2YwxiqwPKu42uZtZGZYmE/1+DSoozPF1z5I0fhrTrv7Dq5UfHIZQ4X7tG5PnLNj3T+NaSFd+fltNhRpGWfVJ3aGpVA8EN4PqK9jiD/rBUL0jFRzPmr1ka+8/mWg13wlbuXwjkvPsUvIQ1fvnsZPPp/3xYX2CPvTZ6T/dNVaZduAADPrD0Al07aCAAAS3/8ntTvQ4iTeWq3sSqOHR+AiUv3wOIdR2H2lkOINOOJqtpLRWOJEqKFhzoyooxRlKQkx1yFJXchyt69e+Gzn/0sHD58GF72spfBW9/6Vli0aBG86lWvAgCA73//+9Db2wtf//rX4dixY/CWt7wFnn/+eTjttNNyLnlz43KQpD/kjrSHeDJ0Juy87202wg3lYx03oOLG1ecgMstJit6dl2mDJv89bnIsfqYplzzPbLHNr7t/KGwGsVtxN1ejCMYStNBxY0c0yUwHpzE3JSpLFMVnF2zXnzkraDWIIG1Bo+pXsWtSLsCSH8z5mtIsEsny5hXwEwCsGqvPoQgqrryhMJRxh1TghChEligW7rxc3MYw+YFyw4U5LEbllR26cTVpieKeh/5v7b22WtdRlJijzOWxzCDL25T3HTs+AC869STFTbJ0/FpVyiopNnbriSBKudtO3hOB/F17j8ySwmkFhy7VFHsuuwRQ5xxEy4msLOUp1mrDxOs9+5goI5911uqyVH2VoB5fuc/r/hSS8izffQwA4usj7Dqoiuz729q6hevSV9qsl1xJPvoOwWpGbKff/cMqmLnZLDxB5SO2HeHrMivLhItZyPiSuxDlwQcf1P5eqVTgkksugUsuuSSbAjFKjIs14n6um1MKfKbDOIHZJKuv0S4cHRbBkdttKEJauNALUfzvy6qrZn3QW6SDZVdtQ+N9yLZ6kiBE6RscRqePCizvW82R3fY1eGB57OF5BJDHEa+0nSDvvW7GNtdcHe8rFtQHJVEkfx/Uc0ioPDCHMVRa9hxYvnlRuvOyfHWyOVsnZAjdNHRtT/xpjEfMSZ1P/Tywi4mSLLtd2lHiX1tU9339/hUw8T/eKv0txHhSjbWFSlzJhCC7Wj0nDrL9k5WCtkTR/WY5HmDcxjYLqvNWm35HfWZrSm9syp2Xef2v/B4AFm4/Ar94ZgP86mPnwt++8kUWJTVD2WbEesEug7B7I3EfJgNzME/dDo71DEq/x1ifiMiEwDJiligJpbQQfZ/SYlSEWqjJ0FG4mChMMciqz8YPW+OTA9bvIdN8yN6u6xt31XQtS2B5VR+iwLSIQuWW1ViSTTawYk87zN3qrjUjoh/jaJGN6fXNim9eYwRNMpvDTGUwRcKHt00quBAFWaIIDAd86PzsrvcZ9vYc7XG6z6fKa2sF9/vLCGZu8m3GFFWK2ahTvTrXwPK8lCw+VK/oB4+uReTlnpttn9N5VIlpY+us8k1rtIzbt2k9GtQamUjYNnKf/MaFO46o73HLSou4JhlTqSSeR58jRnmGssxmC0j3e+X3yD9j8sTkRjU/+bZ7/LpRfp2VBVrmlih21wOo3121GsFnb1sEGw90wmduWYQsYTh0T4J6Tk1fVb2nq6dugVvn7NCmiwksHxIxd9+zFpV1jjhv6qz6i05Id16MHyxEYaRIx+YIoH9IJ902aFVJf1bfw3HlGSy65uDq1zPUwVzQsPLE/cJ1oeGzaXElyzHh83csCZ6H78FyltgFKYydDkmJL4r9HsZWKBJ6vYpOX6Y17XabFUWYW32KkKdAxc5VDu11qfssBOBySxT/isT0Par3ZSO8FccUtkTJB3JrKkRPmbbxICahzLBx56U8HDUdWhu/0NzrUBemW3yql1oYEgLZeOKbezXm9ieRNiJxY1dT7s/9kCWJdU2TxSvLcn7KqgVS1Bu1S9vIkF7S5TamiURRZLQsDOGyivI9xsd4XEMT+4+qLJOSsRglF2L2bBSCP0wa1kIUjXKZ8paovPoyvEYtLixEYaTIOm1X/xD8fz+ZDIe7+6X3+LtdET9G7M6riTHNmRi3C9h51+kwKIpP7EVpb9m7rHL7PWZVJlm6zNh0ML3QKxnmusnuXaEO1yXfUS3OxCCFFCmSWqJEdu8ivDsvrEZhui6dDrXq1kYFFsrlcbBWBlSPRn8ALclDep1dZWPOMKjen6s7r2ZuP80CZp3hnLbCBVTycwh0c03S+oAKo5CDeO71ycC2KKrrnQXRDveYrbftUxUPTmv7GeL1FqHFkOk+rAs7bR4xrxXyz8iEtFSALsZaVsotFP0365goY1IxUcz5q/t6sSZ03aPE3HkprkkO/VQuqYYQkqqQe9iYpSVh8CVVusk2WKZ1X0ijoVAuyEYLucdEYcpFNQJ4evV++OLbX5P6TdfPVYouOvNrDiw/msCFylX+omkQRbNE8UH+nLQbKnnKHmnEFn0RVKsRfOmuZQQp54uP2wKK9PX3Sr7TCCZ9y2oT+C7upkRONdZm3Mrken9o4Rc29ZrwR/97VoTemHoJeAq0GihOSRBlSYzLSUhiomTohsBV65S1/MpAOd6RdWBrTZPFdh1bZY4Q1nK6/CjSHLnXbc3l2sedbpPc4zvEJNuCzUFqBCcOyhrKFPJ1oFSQHmAPhG7XDi3F9g5Tu6AcdfIOLG/TBqndB5lSG5fYpIvjoq17stBLDsrlQtLaEJOfl9BQAOP+NKtZ11ZJIKX4EMl/i8dESaZBD4XikYwixV9l4rAlCiPF9vDGVttXmm7ib70lCg8qzQVGK8LtfhctvgiimBCvLC4pqbuF64Y4vqjB3VM2bNzlqKByWeg6Hpo2kdieI2o12ZRFrQUzkkb/4DAcUVg/YrDfWDtnhQKvURhJtKTDHyxID1OCOiEsliCkcCCsMuW3RejDkFDvHCNEUc4hlm3Cxse3bn5iiodO2cp0jVdegRuHdv4VlQ00geUp1iGUmKrPbl3jWxpNQVC32d9oPJiPAPa398IflrWgree8BL2oucI9eVu0lijiZ+QZhM8YgJkei6hMp4PiVVIrFphjoiTdeWHyVwlXijWj68aQuJs+XEMT2yxWcURWgiHEesn7TE93u/Cbq6JrHXU/Fs9w5O7fSAk0VmSpjMTYwZYojJQQhzVS4YsmLd+BlWEAfALLj3wuiraqaVNKfRjpbNJPkEbRMY932T046lAV+Z0Lrv0DY4ly7YxtcO2MbU7pA9SFEXjCB/HDpS8rBubwNyW0jOr/uj9XkYUcRRpfbOoY5bJC0XYx2Wxq7YK/+vnzcO+X3wxveOWL0mmAoTERrL+GqJ2rB6Bohy5MtqQ0fTMc6/SuXnAHbLbWGTbPF6JveM1DRoGR4mDVOUP9z1c9vxl1S7Jc7/vdHOjqH4J9x3pRxUjGiIsLquyeDr0PJ1n7pxMJuZeyVxjR30F5BOFvmY5DVb827YR6CWwvRDGnmZclCiUxd17Ixia+x75B9/XVIMqdl3PyVlhboiTntJh1zsj3MUsUzT11OnoG4YxTxluVxURWykhMPrAlCiNF12cjqA1A+9pHFoA6TSkA+4Vz7XqWoowezO9aq9Sg+ZHCnRflZtJHs8lYDuK5tm9wWJ+dQ700y6FV6Ffhc3+9jk1NbSTgozw3rL9UVUwUk+BGbcJO10ZCaCf6gLZEsbiWFIPwJkiWnukXZaWQ1evCHoh09w/Bd/+w2njd9x9dAzM2xYNuDwxV4fa5O2DPkZ6RfK37UvHH+hIUcdST5SvyUvon7B9xIYq6DszzVfyCkO09SmTn752AprAhnrlajeA6iXIHxhKlq38IAADmbD2Eyit2iBbZWV/o6vA1Lz31RBq4ANAU6A4EnfYT8QTM15gvj1GUtYWIrp4o3iP1vG1KLnmI7hUTJXBDphBa1okJR5HJit2nf0i/P9eBCizvnLoZldstt7TkiMkmFZKS9X3NtC3w1794Hh5fude5HKHceYVcRxdxfCsTLERhpJgmol9P3gRv//UMq3vk+ajv58DyjIjrO3cJnBVF8YVdURQBsi7G7fN2Sr9/YPEe7X0665iCVKU3FDFRqFwF4Nz7SL4zPoNBU+/EA6g2xsb7Vflq77LDftMTtoXifd2nS+5zyFDkfsfzuRz3g9MRML6vAUAap+rSSRvhH66caSyPClxg+exfvphjGQQ9ox2Mn3+qtxhXAgjbNnQBnG3mCf3vib9xyTpjXFN4pW34XXGB63vU3fWxmxbgy6BZD2NInnfGAqsb7o2ixGGi8Lm+/iyLkLKRhuX3ynRQa/RiHDNi50nVuGFT71m78xo31t6dVxTJn6lMs3kV4QZZtwfoR7oDlCWNc+eFSt6QhjmRpCWSMc3E33FLPVU59GleM20rAAD89+PrnPflodpeUc6fmDTszouRouuzURTBLbN32N0D8sWjbuLQxgso1VTJUJAWso20EF1rcJ0QxfuKYk5pWjRmVcofPb4WXnf26Zor5Bo2o+m8yncjYuUaCCWoSn/7jitmwhff/mqrcskQ3fbYPLZqg0rZTlSbLRW6gy0KfFw+6ZQOXNJHl4M8RTqKNKbYlAV7qXTMt8hItVnOot7Cu8bzp/glZJr1Hekt/nFPnbFxshVSeUIOBXLNU3ff6pZ2zX0R6eG77pAQMxeorOrrimIuCjYqVK516lDERFHnbS4T5nsRqreYlXU19jBeB7UXTtMyYGwlKUQZ+WwrGA2tFGGbvO7yhIEZirglioc7L1Rged89LO4377FSkY+YLLZdVCo1B1xOTx6o6ZVhHT1aYUsURk4eC13xc6QfWIt0cMJQoD4s9MU1JkolZolCV6CQQZqz1Oo90NGr2bQInzW/lZnQz5FFPXX3D8F1M7Yp88Iubm2EjLHFs+Iayv5mm1Zwd17Y66jKcSIhbHp5KCikhIBNMkaEwqZ68oxLMlSCzV8JitiUUJwxxzTyiQaNbGOZqfNKdVvHw9GUJUrOh4w2uVMV1TUZ17lQKxxzSDKp2EElrBfV0LKa96kV0nRW7yPfyzEVhdIIJathhSIbakUi0yHwGIfA8lGkUsy1K1ueuLnzGrmw3+BuWwemH2LLdMpJY9VpIO63dbmus65UzW/VKH6h7tmKYn1WJ7RiH+MOC1EYKVptEdVPhoWjreml3hKFGW3o3rlujrE1Fa3nVczA8vpyZFlK14VGs1iR2brRCAkmr5DlGVRpu5tuzGCtGgn/j6EommzSDaJLfg73ZI1PlZd9PDH5N7ex4JWh7JsZVFtRN39xbelilpEZIa+5NHS+urlmGHnAZh1v0uZaz+f3taJLYo43ojpAc8vTtajJ/HybUdK6I35oiL83WRadJYortkI91W/aeZ1MuIZIiMrlLuH9+r2wf+VQz4mm5MZphCi2x03B1++212tuEAUZ2HTF9NDuvCSpYxRcsO3ApYvIxiFXUEK3RB109g3CT55YB8t2HY19XwH3Lh+q5RXFEwqThoUojJQ8Dmvi42BkiInCg0pzgQgsr11Tq390nRBFixHKOcwvsLz+uyy7xZhKBaf5FVsQj55+m+WzpjbtlgJrpSUKMv/4ZsD/uUk3QpZJFSUwpUzxACUss/xeli/muyKBGVPX7uuAWZvbwhcGSb1OzYdguO9UYGOihADjhiDvtpV3/oyZkK+IdKqxTEtnJDaM8FkPYF6bpuabwO09x6lbPV9m3Mf1exV7dEO4z3pLjIkiqztfq3lZyciDlses0RTXWH4vUiyddDPK8cCi2qnPbE3v3OUQHRMnqwjo+md8725fcB9LFExMFCy694d5LNv3r7Nc130W77p66ha4d9Fu+JebF6bSL5ghStA2XbRnLRssRGGk2B5Yux7u6E3q7NJiyoy9JgEW10mCyhKlaKahWRDTnEv9lm1ZQuFy+Jmk6E0D+6pUWk1yYc7Il6pNOmUbse27PQPD8K2JK+kKkARZnAgkmwXEgUEqHZK6LG6ntXm+C+5cGq4gjoRWWFHGRHHI15YyuPMaTYL9soIKLE/0GrNcn+jmpqQbPtez0ayt/JIzVur3HLpbiMDyOrRKLQ6JxjXzo5Rlio4I4uss8fqKxhLlEzcvgEPd/faFNaC3TrRf38TuVt4j/yHL6YnSqktvver/UNSa77pxrlJJ74NibVSTbh4KP5RKVhh3XrrswsdEQeKwj42i2ji2fn8HDHgq+mDmxmQ97jp8XJleSJfrLrAlSnHhwPKMFKz0XEQv9LAbBIxaMDymNDUu7UWFm5ZLInC9z0Y0w11jlt3iq/csQ12Xl6VMaIyHF77pWyQgOarwSg9LvYfEDnwi6Uf5/YquSRsTxe7Z716wC5buOkaWv6w8GKhcidAQdlPhd9AXp1jbHzP6+UFx+GOxqRokjIlir2lfzAFfLFWOIWOYAhITVIfOS5MB2tWLoVOmLAwMT+UjVMQp0+HTT6WHEBig0kHnX4zxS3eIZqvMI9b/mEr6uzo7DqkPGW3yS0IxJaiUSWyTRgWWJ9J0ykpYrzyMt8ifeg2pe+djKhXtWY9tny6K6+0GujHe00sD2p2XJGmMggs6GDvqqjRPrNoH//nQauv70lZz9s8yfqzChkAi1POBojmWQRlptMJCFEaKrTlypeJ4WK3YsEQA5TsNYdA4vdqM5xGxORdFE0CueRNJP+dJMUoRFvPm1a8WvA4bLNNT/YZ25+VoGq5Kn9YliF1ih7sH6DKXgHbnBZGTO69/u32xND+/9ljcHl2UMc+eSPh/zVWyMd8mlxz7EiYmitIlZEavlS1RRje69z9cjeBjN86HP/6jk73TkqG3RIn/pj40tCuTlXKGQ9cwWUnkYoniLERxuy8dE8XvsDSpsW5dLmGh9dDSlpGv60KUKMwMb9qv6K53KY+tiydMHkW0FrdXu7BrM+Qu1zTpja1UtJYoyjSV1kXlmc9tLMpk+FiiDGEsUZBlUgkatX0dACYuaVH+bgPGiidKlGfcWHXHdu3zofYiZWrTow1258VI0QpRFAtjbfzuyH6A4cDyo4etB7th68Guxt+Yg6Nb5+yAY8cHpL/p7sOQXKBRuvN6bl0r3DBzm1NaRQosr0Nnet4s6wGKd0FnNqwQgmS0AYxrVOmJu5MwX+OLraZ5YQLLSw5L4ooG8oR8rWhkqQZ3keCj/UxYjqxZt68DLn1mg/L3kNqWWQifiqJ8oKMERRz1ZLlmEPNat68DVu5ph2fWHAiSl07IiO07xnVIVLMIe3r1fthzpCeDsdzvd6+0PQ7LKdG9Opf616W3cMcRq7R++uT6xudGYHn7IjmDnbso5ydVSqay6G0kLMvg+zjI+ynWBtTWmbr2K3PnhUElTMyqLaPX8ZrfxHqhFBqi7iW6BkB/9qedn4heltrN8cgfSavocQpLlFpgebqNM8UevAzr6NEKC1EYMlwsUWKuXxJSeZ0ZbXm1TxkZV03dAu/93RzoH7ILlHbXgl3GayjaCrVbkiunbHa6z/goBewWUeLzaNH89W12dppj8nt9Xalhh3Rl2o6VQN1GbFILLkTBXhdJasGhbLa35DG3+mRZ5qXAh66bB3cv3K29Rvp4JXlmzOYvl/enEfIzxSPLNYOYU54CddESRXcd5sBzztZD8K2JK+EfrpxpUTp//AXySQUck+JKJP076z5OrTiUdO0mPucjy/fCYo0gRZd1fXlHK7DQp6Vrr9hSDA5HsG5fR/p+67WO3fU+FO1wXwe9Oy+zsEoEM96rrQ/KM5+L/dqlzikEORTpKy1RCNLGpOtSD+M1kp+iufMK6Ra3aPFfygYLURgpejO89G81bQJ9Z7TRGkgGz8Pex5Sb3gG1EEVnGo41ES870k1p5qUwExOcFLGABJieq0yLeW+Bj1ITSE8WgeVtCR0jAdsuTPOljUVL0fErYgkeUALlJjKvNExghCh5K9mVoX8wcnyVBGT3eWn3Wt6qOzgbTgWWl19r1KuJAPa19zb+PnIcHyzcpSqM66IcxuvQh4g2+bkkaRLKLN11VHu/amcej/tI/15k7xqtRGL4/UPXzatdF7OCVvQRxwP3CCKymCiUaD2FOI4T2PRdMAWWdymLKsXQ64l6efAKZuoCYQLLa9MOPpbi0td6jXFoq7bELXrkdZpsgyp3XpWKu1gh1NvAuMVl8oGFKIwUl4lIb9JnJs9JnikGYzSNSDfhHu/XCF8cyqEyFS46RbHyiG9s4t+XsV5lGDdgiOfULcR9xsP6n2L6eVS7VOgnfKl050VYBlsNr9DCL5tDBKxGK2ZD16zWHkUumy/qg4J8HprykLiRZs5zFvubLj6yV7R011F4bh29m60sW4M2JgoyzhhmvnrZCyc0Pm852I1K1x39waBNd6OyLHCd013HpohYESNmiUJobpGHfCAPhTefuFtkVeT5cNi2SCFEWLLrKCwzCOZsMD26U0wUmaU24MdNXyjaKi4NnRDGvwzanJHpuzmhoduQqGLLxIUo8STGjVEffxdNcMqB5YsLC1EYKdogxIT9Wb3oNWnz8KDSjOhcwqk0s/sGh+FHj6/V3OffVorS2mTPQqGNmSUlKCIJvs9p025x5u/haj7WBi2sUlTdnT6wJf7a4OtVZPqmOZA4O0MagQVLPtrf0Nwm6aYx3z1d/zRMYDZ/ecxZkeIzU0xkbeSTNy+EG2dt90/bOwV3dFaPcRdOauUTW8sPk3VYfC4vFuZndbvPNT91ORKHfLE07RNNaiInk9C6vtakO+LOi/Bdm96RxfrHOmvhnoOdffCtiSu1VjqmLKKITtBE2Zdc4kzYtrt/uXmh1fU6dFm7rt1USQ5qAqZTvEvbtbDu2b2Eo+AXXwgnqMKWJF+PAphskgqc45WWKISCUyJCuvNi/GAhCiOFUNnFKT3f/JhyY/N+RVcFoShKe8Ms+otGWpu+gIV0wPwusntO36weXr7XL3/nO8MvvkNoz/uA3vhA+mQjJqCyzBe7+ZNdFVpI4VPjZR1OvJ6ZrBRhKcPmrwRFbEpsxhSUkgBRr/ARIlhfr7NEIQws73uIb0NcqUd2eGehHEJU1Ky7uN6FjV96lAKPkcDyYWpIrvimzsulHCqB+C+e2QBPr94Pn7x5IbmFUh6ghU8E7ryo8XHnpSSSvz+tEMUxK2laBIn5uvMK/VKx/dGpLjzKnrxV6flCs1/SBZYvmhSF3XkVFxaiMFJ0XVa18NDd09k3KJ1IY4N0YqGoVbbgMaUpsY2DE0XmgxqKtUlRLJ9stQ7zol7Ow91x/9vFKB0NpsNwzLNqfclalCVZFKrx0bSWNPozljyF+J3SnRfhAG/tzossZ0X62M2w7ABCca/eJ7H/ExVlXJGRfL6imeL7oHp1NIK+8O8UF1g+57aVd/5MwfA82LJA1z2wBye21hk2BzIufTOkoo/xVtXe1DFP16LqDvlcWJQIHG8riFJNifXvSZVWEOVB/eZQJrFeDnWN7D1USZn6Qu1XmvVEVopBqjFl5Z52ugJYEmINrlqTDmrceeWxNtQ9i/iuXJQ5fNaBmDU9tkyu+1gyITkinZrweeRClSUKgHuP79HE9PUh5DqkibZLuTAu7wIwxcR20WfqiOf9bg687c9egs+/wIc2TDiixr94DTbTQth1oUF1uBM+xkLx+koEEVw9dQtcO30rvPolpyR+Gx34vnab+3HuvDwKYy6AUz6qaYO6qDZ9pFAxUbRuQahKhChLaI03z4O1Zt4ISOumJINoUd15ibAlSvEJ2UawcadCoFu7DguHgTrrA/OhdfyKLNs7xdCl8ndvV45s9wDJPccTK/cJadqnd92MbU7lAKg9u8rqq36obFJapIR8bSeuPYXvX3zKSVb3ul2AJ6t9mmy/u2D74UzyVqEbcypgckenEYxKbtNZolBAOT/ELFF0z6kqCzKf0HOaW0wUDyF14sb4vkg1X8RvEmOiiPdUKpWm3k8wtLAlCiMFrS0ifGcaqBdsP5L6Lr4ASh4Y8e52tGG7YIggsvLx7FqQvA96GhjKUZhyAsC107cCAMCuIz0jXyLGibIQ3irITtsw/nf63nX7OjzLo8nfYQOgg9Kllm1K4YM1IjOI0n7wlZYoiFW/jwVMeDw06ko6nqC055SuOewe+vN3LIaO3kGre6T5WlY2LrB89sTdP5S0AY0iMG+oCK6fbPsHhTsvjEWseImNiz2CpbP17/H8LeuTIE9Mesb7Ejf+9vktjikp0idKpxEThSg9DNjA8k7jsnDLi184IkRx8Z5Rhywmimcl+xyYr24Jt/bHYBoXk1WMWxvJ6yQrd15YtFZDsZgo8mt07c9nr4Jxp4lts6q0dLG8KFHlIX5dTZw9jBMsUTAW0wwjg4UojBTdkEI53LguenN3AcF4oTz0c3ituuCcADSb66K0NqP/64zKYWL+trTAtNkw1rXnDu3jNy6wKY6R2+ftJE1PhY1yn9qdF1lxSA+2KEBvhiXXqvq/3p0XMkNDWYrKaDwEN815SeZuPQw3zoprM2exhMIcBIeOQWTMP6ziKkNBhk0ky+aodeeFPdixFFpk2d+kCne2ghGLg/UBxQEqlTAEfZ8mR2/VGtXpse56BY2YKJRKK0RCNPR1QmWIt7zolPGNz0MKF0+m546A7uA9z1luTM6a9VpLFEcplerViUKUSoV+PU9riSKk65Bf6LEcW3cu7cvL+jxlXSkfA3T5jRcsUUQXcGyEwtjAQhRGim5wlv1Umwf9BnSs1q1/TkyRmbmpDRbtOJr+QbYhiwCGAp2CiNn5upuhwuTapRSyxTKUEYHZEgWbjvzKzr4h57LU/wwdEFyVPxalBhNxG7FJL7wlisd1igMlzD7URniTNV7vu6TjyXceXIm6Th5XyJ5OAksUWzBa73nPWSVtPqWnKC4z8nz/un1Wcl2rutR8kBY/dQ8+v5kUfYgEADJumb0DuvvT66bMle60Woie+2QPIVSS+jli5JCuK3oFTbsyDA0n+8jI/X908oglyrGeAXl+iOwKM04p3RSZyfsZtIHlQVY+xLpBcU0yJkr+6wt1AWL14lJQr3MJTB3jyDsOYUrZLIqgb3BYK4AXLVEGhtTjCMPo4JgojBS9AEOyqXfV2HG8kce45iSCCL5411LlbzKM7gwcGktqUuajFhKy3KyFxvQcmWp8BqpT7OI4LnDEb/YIDdKU2L4GG3cnLmDHIypT+DL0t1EoQ4EDHX3Ga9wPTs1kUW+owPIZlEOXJ2+Yiw/lgQ9FXlRl0M01WEsUyjx9E0/OWRQCYNvr5245NHJvFP/XHsf9qWt26PTtclAGlq8rsWQ4BOLXP2YGhquJ9jaC+MzH++UBn817R7qzhjynmTE5H3Kb6tmldCqLLK07L4JqqGdJ8T7Fesk8sDziVt+1kekc0fn8L5VPfM958WNr4cGlLfC6s0+PfS9mN1YwnxlIWC+F4qwzXoBa92dJQWTEpYUtURgpeW8sTYuXMhwMMfZo37nktwgATHHkXFtKLD+P5jZxyR73mwWOHR+A866ZY7iK+0VRQGm5Ia+jyCskrvOFMrA84QPZbjSCm8hjr4vS81yU+N0qXx+ttcDtq8hlyxvpvFeSZ0YdDBRgrckUmyzfkUte+9p7yfMaivnLj5R7Hoz7JPEa3/nN+5DJIv/0YZn5Hln53APLO92m9+TgluTI/ZK9se6d6PKrNGQodLELYu61JInq5gTbMiQ1yFUMKxJGTU8W5dGnM3onGu17raSVtTB7b1WSMXdeieuysswX0bvjEq5zaB9F2avo54Tw7T5ZDQ8ubQEAgPX7O5XXiH/rBG+UnHLS2EzyYbKDhSiMFN2wp3Ln5WuNaNJgMpWBKT8ur9WksZe35P/JVftJ0rllzg441NWf+n5Taxf84ukNcKS7v/D9IoqyCTSXCabDi2xKIc0rzzpWaQbKUFm65OnOK7gWKTKDZCDE2r3ym3WbQ+u6lNxQ5AOArIJX5oX78Y8h3QzqLEsLAlfyjsnCmMHJ4mgOyl2S+fr9K5zy1rU9rCUKJrB8/Hp9evHzy/TFpmPISPmH8it9epYvRLamcPaU4HZbeKUDm2ujSPnOKo2YKN5FwpcHWXrMex8YqqrPEGKfXQWQxVlbmPqljrwtUYLUoSLRtDsv2szr6ZFYtVhY7cvvd78O5QIYmb5LXVAqTkWJ3iEjOU+K96TcebkXLQVCHsiUGHbnxcgpQG/XCnIyKwUTCtniRrfgCenaRJaXKmBhXtw8e7v0+188swEAAFqO9RSinCbKUEYMpuegdFtgTCOnnV597azKXd7HzelSHtrbphReuwvbLizqzlETleJ6Cnzab1EOOUKgnvPo0gqaBuL6PIQYNoJeZnThMvdsbq1pvNo2ZZWGPEDcEmXH4ePwl4JrEhGUJQoyTwwVg8acMVh34A4nmwqdhSgEwpdkffg+v+x2Vw37MQ1LFDow7ZGK/tThpzxxrEBSBtnaOseJpvAxURLfoYTmIH/f8cDylfgVhPXg2yyGhqvwnw+tGknPIQ0fz8O4fRgOZWxLQz6uxcfGC9OhtkRhB1cMHhaiMFJczJGdBsTYZtZPKs+UHxfB2e+mbglRlHjeJWiQ6/Z1wP8+87S8izFqyPuwIJZX6u9s2yv1s1KGJbGNcVKUwPIQ2WxiMMkVdwzzKVlxnyoceU1HIQSSeU+tbIlSfIq+/ho3xs2pg26uGRYOduZuPQxbD3ZLrzOvQ9w1s6UazOi75eOFzTyUFABhyi61REHnSINYzuQ7ppiHqbrDGMESJUQXk75/ZD6YywZSgeXF+9XvAEsEdPODbyoqKxsMeR8La4UojhIeVZJJ10xFnTqeXLUfjvUMNv5WlVN7JoJ8uKfXuHnDwKbvZIlif0uD/5m2tfH5BePHxN2iqeox8b14T3IcCUZB2yLjDrvzYqRoFx2O2sW2uFglMOXG9r0ODlVh2e5jYQojUIb2VooyQvEPRLBQPEZNqbM4m+okoTdfysDy1Cb4Vgc3YdunhQwl9WJdSkbyPAXusrp4AWVHFXyT5CAugzrDapTmSu4FYEys2NMO33t4tfYaD13z2DzkMlyOG+t6EKjObCixCWvtlLulNRU3qQ3so5UP4K/Vno8lilumFLFUfOs7nbb9fKc6qB6xJC7GIGj7mgaGEoHlFZ99BCFUry/PfY+roKLlaA9J/qZHTxYv5uZKmab8F9GdVy0mSvxvX+qpYatUVc6O3kHUdTM2tRnLYuLKKZuRV7ql7+ouzrVLHB8YjqWB6VvptbTQxoSvK8lAOs1O3hLWksNCFEaKkyWKw4iosj6JNPnoS8GUBdvmImtfyY0mFUlfuGVobWU5SixDGTEYDy8yfdDi1KrRnUNsUxM+JoptUuEtUXAZ1A5Lkt/J06H0b5wHPmUr8GMFI3QbpQLlMiKHZ4lrKZekMkc5jyzfGyxt3ybQsESxTEdriYLs5Lbuk2zGDtmlJtdRJo15mypKzoCYe2VGQa6vl8INWMoXf4DhRhtYXpNfiJgo5nUxTbsGUAeWHxquwhNCLEpXQRb2gBabFhW2SY1xPCj92I3z3W5MoLdEAXA5yU0Kh+uIbSKkGzPqfuySXHDXw1hLlIBpG9OBeN2pzyfVf/MakHGFhSiMFK0fQ8lvrpOV2vTOIELhMa8p0YkBZL8kTXdDMXHJnkzy8SGUST4lZSgjFhthQWhSZaHKGnU4r+mzlhpojfvM2eIpWHtDu7OQHkC5KCoUH6++UoYH9ED2eEW2XhPBufPK9wU2efNhLHGyRHE8pdT1D6yCkDmwfPIQ37PFZ2yJIl5/z8LdxutlQh5nSxRXIYpQ58kDfN/qVx0e61AHlhfS9CiTClk5dc06HofSXKKaGx5REbP2+c75u2DjgU4hT/enK8p+xWeN5GqJcrh7wDlPEd07l1qOIdJUvRcxXkYFKmkrA19sxy/sdQ4NzSsmCuYabPpKjwIZra8MgnuA2hgQJf6uI47RIQ0zCjKUMISwEIWRYrvoCOExRCvI8c+OyRGlxaSl8C6UECW5qegRzEeLSqiNECPHtKnBjImFtxwOXDjVgpVSM8jl0CEk+MDyeu2pUBzvH5KWpajUzPTzLkUYIsWg7vK8JGu0QAcISbIMhMtaiM0BhdUAgNth5VhHIYquzGhLlBP/qvrM0HAE//3EWut0VZie1HQQblW/LkWVFDDrLh5z5xUgc5sUdfXdaLYZVhClclHSEqWe8uwth2Lf28bFE9MrSkyUWFqWZSpyYHkA95gaUsXKIXVeJiu6PIki+3oI7noYmXyutRohlXWSfwtfpONW0bF+fyd85taFsHLPMcJU6ShynygDLERhpOjdecl/cxl4lJYohvuOHh+ANXvbHXJkioxtGxoazm7xX3QoTc9DUg6nY2ZMVZ3l4VxqgUiUrkkbtlKpaA+hZO/60eV7Yf62wyful6dL6vqgYH3CxhIFa+WjXQhbPv72Q8cleQWuQx+NumK93kygiYmSAYhM8nh9sQ10RjFFmXLgMp6MPxEThXJtg7VEqZdXNQM8snxvLICxlTsvybWmgz5j/VkLYi0PjCVZub4V55gowufkAb5vC/Gx5EnSCCwPdHO8KR2tJYr4G6I41UiuQJEUFPoIsqh6tHf1etzvGrOCCl2bqFTSq1dMXaljoggTeoV+fWg7JmDzr0a0fZsC7LO6WDpRWakk3Zgrzyc1ghJb18g2PL16PyzacRQ+duMC2oSZQsBCFEYKeqFzAuqBp5aHfoj9yPU0/jqZ4qBfFKR/HAjozqt8h3PFL3AERCunEoB9TIp25qppZ6I9EfwwiSxuR/z39Hd3L9wN/3r7YgBQL74pD6NsUyqKdpdNjCOtT/RGvu7P5eoOAotPjWfmMqBAlGVuwgiSQ41dzGjDZ3zzy3ncWPqtNL5f1K5TjdHJgPRG91+GbHUCe4zl5LEeGhdBKpL1kIxvaIPrfSpXMWQkCmZsvooLGu68Ag3BUkskUgUZ+d/JNu76CqIogpPGFe+YzPZx8tY1N9W/PqaP3dMOJjL7/fydVvdnRfKZXfY8Ww52EZVGjr8lSjKYexhwWcTnAXGMEJtMSMuMoin0Mf6My7sATPEwxiNR3ueQl+f9THmRtTNtTBTJT6EsUcrYFstY5mYG5c6L6HBalZVv8seOmw88kv04D7dBWaVFgY9wTaxr8efQm+SiCJak99IVozSURe7g4mYha9idVz7kfbBXp6aFH//blnpMFMqmhI+Jov99yc6jievxhZStx30tUfYc7bHI375ORc9qA0NVeMcVM62VraZtOAh//9qX2mUsoHPn5T+X2h236q4dCSyf5Rio2eOhropfE++7tT+Sj+MjqP9fL3shLNt9zPn+Onla4OdtiaINLC/5DvXuFZYbg2JgeQC4csrmkb9zqAa0+96oVj6brtjW1e9YKhwUSlyhiaJ4+1LVX2oIEMfoVNwqXhMyOIonYmdypxgb9Ob1c864IWsOQ+yLo4HLhjNrmklr3LzQQi6eKVzzIDRAXThqEKL4CoFUdUgqRClYi7NZoOtM0EPkp0zDO4VwNPuGR/Z0Ls9MEWA6hCuLPF5f7OCtuZsPY4lLv3CNiaJjGLm2rR8gYUtgStbUx80xUeSf6+w5gheiuJDUJN7X3guHLA8bv3LPMvjVpI0eY4Og5Rxgi2JTLq0rpfo1QDfHm8pGeb6gUuBJHtoHsQayxHee8bo9Z4m17tkrFTftf9U4FSpOaiPf/JsSDYRrM2chHdHeBC10i7maFn8b+SukQKhZmg4zAgtRmBQu5t41rQCHTbnCCSra5UnTzGgMgP2cOhg0Jkq52lY1snEAlB/N0mWNIpQMnzPUezcJUWp5q/92LRXluF609ob3kZx+q6p7McKsotWDiE/7LYPw2BXVc5XlcXHaxCpBajZPyZYozQHVa3RJJoQ7L+uYKMjDH2N7NwgYfRQnoiiCAwn3YqbrbaGSZz24pMX5Xp0lii/qNYB9WiOWKB4FskQf583uDKB2gJo+GE3WuY8MpYizg711VnEtUWRg+r3L2iiPWijqWRZuze1XJt0jkT4uYtxI1q/KnddoI+ehofSwEIVJkWdAZOxvdUbz4Fd2pBq2uuslP4ayRCljs1KZNycZGq5CV58+1gVjxuIsQkkFkQ6uMMk/aVpw7+CwOWufDariXlJtRU0+eWBj3o8NLK/Pz5/Q9ReiDTUzeSmP2GbrehiS5b5uFDafpqR/yG0tSGHFOb7uzsvinhedMl77O9YFUb2PYbW5ffd3RkuU2IFW+jA7+NBFNHhEVlHJ4oivLhVYnuD5k/Va0+ZXXAvq30RLlBBIFS5JM5Dnl+w6rm2+SGsLn7IU+5y04nSQmxSgNb7XWBVQuE8O1SSKeJaFbXO6evU94zMRRRGq7nQKaeL4UCEqFzM6YCEKk8J8OJi+oFLxH3hiLhYU1yTHatYibC60h4aSVjE4FO79l61pYQ/W/vnaufD6S563dnFAQdKktsyYniNbS5TioDtEQadB+US2B790OcvTRws/JBtERel0W8N6fkVqI7Q075OpKMvchHPnle/D5J0/Q8OHrpvnfK/v2ZqLO6+xY/Tbb+yhWuMytCUKMj0VGZ7I7mvvhUdX7LW6hzIwsOvQIM7T1K6kIrCc8RQXVypiYPns3GdrtdNP/FutRjB7S5s5LYUddHJMd30HEdAFxiadZyyTMgw1wTE9uj6wvFuaRQFbzKz3xVhLLwwuIy7V89aU5IQ9p9KyWX3GSG0tqKIsbZbBk7sQ5fLLL4c3velNcNppp8HLX/5yOP/882Hz5s2xay644AKoVCqx/9761rfmVOLmx9jRZdolrotN3YJK8lvSLJWFKOVF5RbO5vrBUJYoJWxXtcWE+botB7sBAGD2lkNhC6SghFUrxUXY3Iz4umKSQWuJUqz3gN5UGcbH2HMVW9XQiM8baibBbBLVIY7L01Jo3Fvnibhm2sb0YRmFxiiWZpmPGHfibcC+QYx3cOdlWmPaWCzaYDpQjh9IpfGJiWK7rl666xj8+PF1VvdQDR0VqHi4Ix35nAosH9BFjgrVeJqPeyPzAzy+ch/84NG1iLQSa6ITn5PnAkU4J8izBJSCRRd0bd45nIbqsFxMO8Bzh2pKWTdRTHbY8dpFCEaJy7PEg9GPfK5UKrwmZNDkLkSZPXs2fOMb34BFixbB1KlTYWhoCM477zw4fvx47Lr3v//9cODAgcZ/zz77bE4lbn6MGtaEea1qaYdLn9kAx/uH4nmotGeSZeHBblQzFDAmSunaluVhYhkFRUXCKGtGVC/Vhj+PA1Jl3orPPmn6ULRDdhs3XLq6RefX0M50uNkjX6v0PQqXvLOIPn6p404X4VAIA6ac+9p7MyiJmrLUJRNmzaIKTm1D3RLFpnwm7Ve0JUrDnZfd9a4YBZw6RSivnHFQDrXOlrTCbWHcefmnUYFkTBQizXCFtrfuu+S90zcdtM5LTDsZW9zHnVdRZod47Be7UuW9JtJVfwXSwg6cBat9PyhKTBSZ+72s2xnK1SoyLVX7wvR1X1SCVFlZVLoSgXRxJWUoymjCUDEu7wJMnjw59vedd94JL3/5y2H58uXwD//wD43vJ0yYAGeeeWbWxRuVGDWsJRdUHB0J3jx7ey3NxO1RJPdHOyaREW+AmwuMqbfIYHK1PIqRHboWkRIUEYWprrMcm1R55b15MqE21aerO9uUQr82q5goiWuxygXUFFngmixaEdt8pVIhaVhRFJVKU66Ifr6TlKCITUmW1kY2uLSH8WPtn8XGIkR73Yl/KxWA5//zH2DrwW74xgMrlNcb3XlFqj9G8kGTg3LHsZ6B8JkYiLnzIn5oVaQW1Ryjy70oh8pJsGND6mC0kUf8W2qXai7kOWfnPdYan52weGJeqccmyIfiIFxWjCjKNh4HJh9st0l6iGnkYbK2JHpYzF47LXDlc0SA0jsxyJ3cLVGSdHR0AADAi1/84tj3s2bNgpe//OXw2te+Fr761a9CW5vaX2Z/fz90dnbG/mPwOGm7eo5BW9u6UdelY6L45cvkh+1iRDYhsxBlhJrgsfgU+UCWErTFQYDqqCeZe1U75k9ZbNv2Fnq/iZ+z9Hqctu2ryKODT8lqLq/IihIEV0uUkEJGl/ZgL5As5osRy4UN4M3kTyauQRzycImJYupMWO3Y+gFQBSrw2lecBu/9y1dorzcKbwz5aeNvgX5cyWIOuvA+tQDJBvew8vE2lKxvihpIBZY3HIXJ1jTiwXqWawP9nFD7bSxWiJKyIqv9nXbnhS9fsjRkGvOEbtzKYIEhohXkSa0yMIfiRV7RiuBK2dE7kOnz4Kx9cCVyi4mS8bijUUhLjhdlaVlM/hRKiBJFEXz3u9+Fd7zjHXDuuec2vv/ABz4A999/P8yYMQOuuuoqWLp0Kbz73e+G/n55YOTLL78czjjjjMZ/55xzTlaPMCrQmcu5pxn3/a1avHBg+Xw57QVhjddsJ6+hgAcgZZtIy1Daoh6quTBxyZ68i9Agr1qtgETDx6Iwqj5GOa5TboRJwGoYR/hy561p6I3H+ynUu1VA/X5ye+QyVLYlzfdEzUuIdxVBfF/hskZpuPOyzFcHdg60tcSzmVuVFg/I8pRtDZ3EdbiLu/NK/kZ3mF5HH5NAk18jsHwYYYEsTd2WrX49ViaZXNvVPybzKIKgPI+psxn2W6onwJxDBTBEsUbeX9MleXZtazMurzJpg/H4Jqpr1IKTrIaHZny/o51CCVG++c1vwpo1a2DixImx7z/96U/DBz/4QTj33HPhwx/+MDz33HOwZcsWmDRpkjSdiy++GDo6Ohr/tbS0ZFH8psEc8DCLMsi/T/nOZEOEbAn88m0nGaqYKP/21leSpJMnUU21o/CMloUEym0BVEgOGlRjdhZn63rNU8O9Sk179/L4phW6fa7c044uR7IosgMDTDq+hO6yPn2gDMOJsyVK8u8o/i9FWiIHO/vsE9bgKgzN8rBjtMxHzUCoAxnfZMeOsd9Km/qG7fhen+uNgd8NCZt+9+mb5etrbgUO6c5Lheq9RIrfxFgUmWrAI3JTuQdKpaVQ4EkKTVzfgWwN5gplHdumlXe3043bFaikhAooKwmHclAos1DsJ8qi8hRSiYtyLscIQXSxz8S5uOz6aLbkPTaUndxjotT51re+BU899RTMmTMH/uRP/kR77VlnnQWvetWrYOvWrdLfJ0yYABMmTAhRzFGBy8FXd/8QdCeCw1vlKTswklyXPIxgS5RsIV0IShLTpS+7nsqd10ljx6byKlvTirwcEGRDsUtHS7amyom/c228olaQ6+a1eVvKYyv3oa6TualSKhcgFv7NWqVlaCsmdys6ZE8Xat3zlsumW5dFe31BX41YrDK0H6ZGJgpcDveMc5CSmpod2hLlRInrJTAdaJkOlE2WBDZx5cvctSrgHnsqpDsvqr1JpTLyLrNUWtH9Xv8JfygbJdpr7TOVO68i4fIIUVR7x3nPcabcndxBRSBtTOKzFsFCW7YPLECxUGD3sFpLOKKy6IhZoihyjBKlEa9KjdGBCl3m+ZCRk7sQJYoi+Na3vgWPP/44zJo1C17zmtcY7zly5Ai0tLTAWWedlUEJRx8uHX1TaxdtGRQDYVJDhYUo2RJ6MaZLX9YmqIQoZVnU6CiL4KcMZaQA+5wklgI5VmqIrKmTLGOTkwuZ5U8SfPgKXIHNPia4WqKooBkzwt/jU86smkQzHLCNFkYsseheGlZQraO+L7G513RAZbt+qB8YmoYa/z0T3p2XzW/NhPiYWe1RVXsYhDevgOpXskNu811jkYZdqb574l8qd14R0G2qRkvblxJg3YBpsSHWxRSKJD5KNVmCtkRR3a9L2yJ9E6h0NPO8+LkZzoKY7Mjdndc3vvENuO++++CBBx6A0047DVpbW6G1tRV6e3sBAKC7uxu+973vwcKFC2HXrl0wa9Ys+PCHPwwvfelL4WMf+1jOpR+dhFhupdKMFBslDiyfK6Gr2zZ9KndesnmzbE0rgvKVuZlBvYtAC7Ys20EyLxeXU6k0uSHXfPjqahCrqRwldbDsCW1V5XXYrlorFAise5IkKRcEiX+zxrYdlEHJpejWm0y2uLSHPC1RRgLL1zDGRDEFljdkazOUlaD7K/ERLIjvLqnl7DvcqCzOVQezEURSjfyaG6UT1xC/p4GhqtOcXL9lLLI/pXI48UWy76TegU2ZnO+kTSkWMxZtpVa/3itrb/QufyNDTB+772MU9UC8qOVKQOLOK4O2J85pyvaSsuwf+aMM69RQjOJHJyF3IcpNN90EHR0d8K53vQvOOuusxn8PPfQQAACMHTsW1q5dCx/96Efhta99LXzhC1+A1772tbBw4UI47bTTci59k5JDp8Jq0ScPI4p+eNJsUFW3i9aU7LcBIkuUVF5lPFgpQZEj08FwE2G70fHLiyCRHFDVUTOP6y87Dedq1M6dV0l2ZQEow3hC/Xp8DqgafzvlG/b6rBDLxYo45aHe16nbVax/OqR9yoSx5osSmIUoyHQSf5vmApv2LhtnjDFXDAemZYLCnVdSaFWk9Z4oeKFKs7WzD173s8nwnw+tkv6OsiBATpjV2oZCSPvE94k6dz0kLer8hWVEgaZcD4IpL7XS4EtOPQl3oWWjkF1NvVo//QVhnAph241K5pmVVSJmTqsmjorEe8TPtTilDIOjEO68dJx88skwZcqUjErDAJgHziDuW5Ab/HRMFPqyMGryXIzJ2l3/ELvzqlONolIcPpegiCRgHnNgqAo/e3K9f1451anM37L4l2u5qMf1IvULdFEk14n1Iv6sG74a13nUQWj3Az5vp0CvVomrkEv1aHk9s222pTi8KUERmRphXEemhdU+aVFdi1bCqF+GHGJ8tW5NWuM6S9QyjNUi7uuGkfuoA8sn5Aao66WvLBYTha6MDyzeA4PDETyxaj985k3npH7Xre3q5cAadqmKnXLn5fF8mbocwqaVY94u6F3K6VeXyvFGqYAlpo3nW+/+X3DJ0xss7sAhdahSoVV8GkPtL/YEeHderpbWTreliMdEUeSVynvkG1Ho2gxnQTaUYo1eYHK3RGGKh2lgC3EgJTNQlk8+CUsUHgAyhXQhKE1Lp8WWHVjLqCJBrZkTgtHUXzHtZ1VLOzy6Yq9/XsYvwuF6UBJFI63hLa95cSLN0dNOVNjUgIs7BBvCu/PyO+Qo+lhNtcdVBc7NiqwsUbLcyI5mVw5lJeQbc0nbpQmZ91l26WG7jNmdl/5AykegPlp6mvju0kGL6WuhUqkoG4DSehXCBJY3jd2Y58e6v0zueVTzY0fvICq9kPjWsdMYQ5S3L7rsZWvLmCBWcTP1I500biy85qWnEqeazV4m1HIJXXKnAtDVizim3LNwl/Ka2FghfM5qDVgkZT6GhtwtURgGQGKJohhr2BIlX0JXd34a9eVXP4giOo3KkJSgiCRkuWBS5xW+XaetCO2f+y2veTEs3nm08Xdzj+tYDeO0ZZlYt0mXRN95cCX85Vmne+aqKovHzYEpcNEauMZEUUHxzE4uwSxzdt2chrd8ErQQi9y4mxiXNxzqVYndM6vmYMrGtl1i17C+lhHGQ3Lxc3L+Kllfcy3tUDXs+EKXZO1l1oQRGR0kIn5DC1EUVtDJ9eOWg92o9ORlKl6bxSsqnfg3XFFQmMrrsv9WKc9o3QnW3Zv5KO0Ev8GeYOcXyLKrjYX0vYeqasT+/tiKfai81O68CAvGND1sicKkMI0fIcaXCJIaUKqwfvHh2qRVxRATuLplybcc7YGfPLEOdh85HjbzVFnK1baSWllFpWwbaVeyfMpkXkVxu6ff0Gg2V8TFX9XSTptgBtTmRMmXEo4eH4AnV+2Hy5/bJEmn+P3NT8BT/Cd03eMqtTCdtFL9a8naEsU7x/CUoYxMjZGYKIRvjUAJQJWW9lLDM4QS7pm2THEhSPp341DWROs710f5zK2LGp+TYRtJBOAEqVQqgSxRhM/SQ25EXtjA8rX00oolVH2HcpzJd02eb5807QOSbztuMSC/1ye2jmwM1BhzeSErJfXrCKVygu1HLutbyjrAlLO23xTHipHP1C4XS8UofnQK2BKFSZH3hAtQixWwYnd76vvkYF2Aoo4qKBeCJjPeOhfcuQS2HworQNEt4spCGfpCGcpIRZbPmmu9Jg+hsFpyIIwBgd00/jyAr2NXbLQIkTKU4IRuXz7pl2FIodIUHNG0JRCIuNxjmW8ZxvsylJHJDjfXXPY3mYQZtvph2CHGVFZKrfGizF+u+KxDhqsRjB1Tkbjz8iyT4n6dNrjqldW/LooaQv3ZXGOi1J+DUgBJlRSt3BeXWEPoTJe1E8ZnJzyEx9SzbAysIMtha92TxXlazoYoWsvhLJ4fM1fqyrFE8IKw60gPRZHkZQiWMpMXLERhUpg6epAxMZHmtyeuhOMDw6nL0u68eFjKkuCHaZLWF1qAoqSMTasE/aH4JaQhW2F0jlpuBi0z9W8jP6aEmKOlkWiQa7AJmlRot2Dxf93KEvqFNLcUJVDcz8yxtkQpaEdOu4+NmsKlZ7MTqjnFrScd7icriZCmrTsv5HU2whnquijocKDGo7z9Q8NwyknjMnPnpc8l3ToqUIlZolAVk2IcxcdEScQ5qFuiEHmoKF17VZHzc+iyr1mAJLVjcWmaxsiU0i0+eTKyyStUYPmAlij2t6jTwliiJP4Wx+WnV+8nLE25aJYhLi/YnRdjTYgDlWSaMgEKQHpxxUKUbKGd+AgT86VJzk+KVKUyagvfvEuRDZku1B2tQUKA15LTaJPRFadw2GiwpTUtmSQRxOspdDwNF9xjosjfuK9wYt7Ww7B81zGi0qgpi7fVspRztBPiEIwirRCxB2y7ODomiimwvOk43mIow7qjbEb6B2t+vFKWKJ6VILvfZXYRD6+L8lrqz4Zty6r1EanVB5Uliu/9MWmR3T25WxppKtG1fv0sUdLfYd151ddeRVplhlLSQVuiKPI3K2TTtEu0O6/E31kzWs4+RhNsicKkMHX0EAMBNs20EIW+LIya0JqlRZlkilIOW8pa7mYkU3de2WWVzlsjwNFboox8Trtp5IYcgbuVTzIdMKSVN15WMsV9rAY+e1xZf/JZ9xzp7od/u2Oxd1lQ13u0uWzdIUZQrGMRRkaoeSEeWL4YA4p1YHmidM3uvPS/F6T6SPB5lP6hmhCFWtHvl89sgJ2H8S5nTFYAtYvoVgexmCiOqY5FqvcmLWjqfZcqxkGzKA3mfU5iyj5tMYKxLDBfpXRxJxVEVrxcFSqvk1wYAa0LrmDuvJAP6aK4RNkfUO68Em+siPNUUdYeDB62RGFiTN94MJfDFlcNdR50siV0bef1OmWLgDK2rCIflI5QhjL6k+W7SPabL9+9DH4tCTJOT8X5KXWboGZuIeg5K0q/Vxe3MxRjang3jvncmxWuLk58tC3TN9X+OXp8wKkstSTsMnZuN4HlGcli5X3IxOBYuacdrp66BQaGquaLHXHrWvQNKFSbtOmT8tgB+s4Zv4fWCiNrfOa9vsGaNwXqmChLdx2Dw9396R8s54oKiDFRCsKJguDdecWpVzVdYPkC1Y0HZT4mUZZd1d51bywypBmAnoGh4HmEsr72tUSxScMHTH+vJvZTRfRik8c6tIDVUCpYiMLE+PLdy4yjXog+hz1YGpNosbz5zRYyv7mKw9eibLKKUo5mg9L3cuHJ1BIlndnNs7dnVwABtIxAtERJbAKauY2ghR+SK2PHU7ZWAU1ap7WDjmI/XHLd4ovPBtDHZ71ttmVZnxW9/TQlDs3w33+/BK6dvhVe97MpZMWgcDlFEXcqibUlCrI+ze68aPKRpl2ybuYzLtQtUaiFKLZEoAksf+KHor0XvDuveMHrfaaI8w6lGzdbBZq85zejdZvl9QC4OlC1I2n6WHdeI5ejmLaxDXmlO8HCuaFjotj786JskzgXbkmBfvaYnrmIgh1GDwtRmMKAGVQ5Jkpzk5slimQNUEYrpxIWuWnJ8lXk9d6nbTwIQ8NxreDYZs/TdUgzgn3majXdhmKB5dGVV/xK9nLn5fB8331olXuGDrjHRIlTf1afNzo2wyj3ZZlDS1JMJhBxCz/7xhBGscz2DmRMFM852TSW6QT9ZetmPuNC/1DNEiWLPWqlom63KoGCePBZiytGpiE3kq6toseJf8daWKIk21stlhyRJQplveTY+POe30KsA2QxA2vfI+71eBk3zdoO+9t7CzWWBZOhEOSfd9tTUsBy5VFXeQtYyw4LUZgUpi4VJCYK8rrkYM1ClOwoy4HIaKb4r2j0TNlZjk151uk9C3fH/nayRBlF4Qiw4yi11pJPGwndvrKOnfHYyn3O+blAG1YevAZ6n67WLOstnZs8hikCtmMidg71XcebstElP5r2EH2D9Zgo+ZYjiuTvrAIjbaZorwUt51ccopusrfLAt0Tx2C/YPP2VLijQ5R+BLCYKIk3ERcqYKJJ7xf6go62rHz5580LzhTqIX4iPdbEOdEwUrTsveSKUXinQgeWF6/JYyxZtnGX84cDyTApzRw+hVYC7LjlZ8KCUHdR1LdtQ5RcTJU5ZfeGWocyjpc9m+Zx5Hk6sbDkW+ztSfE4SpfQIRwdo+5EIUo1I/At7TtDs/S1ZTXuO4oPuZgWVJUodlzOi+i0+ZbF35+XW+LKWqTaLcIixJ+nuyKUphHCbUw0U9mVw2KgmN/JJdqlF5yx7r/Ipf90SJS2wpa8VvSZ4pDzlrLtRpSyRT3yGetWMQUpRDnX3w9Or98e+q0YRmeCK8lXlsUZvZJnz/GZ25+UQmNzye/E36bBWqaDLsa+9N/M1Sh5g5zNVXXT1h48HA+BmfVTEuWn0qJg2D2yJwqQwdeRQligozYLEaM2b3+zgmi4+ZdD0Gy0LhdHxlOlDGbSlhaCJNBo2JA3QWoT6LzO1dAqclVf6JRjzvOIISA41fcZQqrJgKKBCsJSSFJPJALfhhL4FWcdEIcrXNn5B4m7UIWYZSGou29J/whIlq/WuqqjK3CthLFFiAknHNLCC/iunbE59V5Y5xwfbNpV3lZjyT1miYC0LLNOtQ7F2zrtORUJZ8rsqN1OmjQEVWD7hHrmIZyW5uPMqXjWUChaiMNYE6XTIRNPuvOiLwmSD7NXldcA+mtwJ1cmjposwYf/5y1+YST5ZLtJUWWXRrgdTMVGEz5oqEH8aTf3PxhIlea3sQB2bXxH6ngovGYrn/Vng6m5BeTDm8cA+MVGsfdwXttHFy8XKOEwdl5bQEG6Saq7bXV+UOTQetyv5W8aF8cSnuH0qSxSPNFVUKhVlutVIE1i+UaZivJh6OXzCdlGO5VjlTmxaWd9fhrUfgNvYpWyzsa/xgeWx7rwooO5vocqNPV9zd1dLUw/3L95jvOb5Da3Q1TdiGZPL+UcOeTJhYSEKk8I04T60rCWXfAE4sHyeZHEgUqTXWaSyNBN51+ubXvNiePVLTgmez2hpPikhisWD590Wik26csT6ws59NON2cV9UGdoQNlCuiXdeORMeXb7XS3nEzxIl7PV1sj4QLkMbYsKQ9M1eFMGfvSUKTaeJ1YWkB/v436+nVxSBT0hGLFHyRalgA8J7iIo1BmYp6M+KPAWseY9pyfw/+jdn669HpWm+RmnhIhOilHhMohr7k2DbjZMlSsYj45T1B2N/swJ2Da4GP1iIwqQosoQ2KUTJe3EwmsiipvN6m6EWIVlT9O5Aqdnlio+WmxUZPmeemoSDQ2l7iZFP6nKJY3eowIhFxMXdmfhdHfvA8sUdHHzm8TKsAbA+3pMkn+1gZz/818Ornd5lPS2f6sK7d7C7PkvksdgKWNAmp6gjvo8lCiW2SWY1hdpkkxqnIvs08qJSAa81XP/QCeWSZFyzQEONagzTjW31dVeo0c/+sL/2r0/cLlLFyigiWzd5W6I4PFdjzvfMm5offuB/Nz7XikizPgLArXOLsBamHAPz3j7lnb8TBVzzFbBIjAEWojAp8gqAhpnY0jFRAhWISUHdLGTpFekwozglwVOExWHRqUAlk0P7LN9FqCC0GFwtUSIYne0V+8Sm+kEHlkfmp02jwK8pAih2AQFgHLXk1kcQkkHGDQVnH+GYQ0nHjzXX80eunw+HuwfieRW7+TCB8Q4sD/SHlXlZ2ZtcRpqWTs3Ul3zWJ43A8oRpuqB29VohGadT6XrcWy+Fz3RZ1OaXy7lKI+/Ms46RzN8kJMNYBvop3qS/q1SyU+aifh8+QkcdaKWZgGmHIhdl8aIOTowzLERhCgF2YZmc5KosRcmMLBb/uVmiOAS2KyJl6A55H5xn5vc2U0uU/GhoXErQxkQpQVsNAVrIZNCysx6jcjt4R6SfgXXEyPXZNzxXSxQVWR2wJoU/2Gzrm/qs5yOMG5i1+zpS37Fb2NGLbJS1TiNA87HtO9kFlsfnpIoHUgbLU5klqA31dVHeQ0sE8ndWqUCs0VCty/N+tUWNiUKJbZHyfoRk2xLbSE144ZKm4nvhh2Sy9Z9kbSRLbxTUfSRUyfHnco7p59gwi7jmy+NspKxnXUWBhShMijz6lKvEm7t/dpC2C8Wky+O5H0VcGIhEUZT7Ow6ltZMkUyFKjpWqCyyvxfOQoqxgF8qyDXxcQw+ZH0EdF3mhXdySjeAqQ1Ed4vgJnfA3v2D8WKd860Ns1vPRuDFuW5oytCEmG1wEfyHaj+2YG0IwISuB0RJF68LzRBruRcqMoWoE0ze1Od/fCL+gcedFNTxWKrpDZXMmwVyMWfYMCvdTUY5W2Tp8q9jlflUbzBqdJUoUAbzk1JPi1wufVeOa6pGW7T5mLo/ie5txaemuoxZXJ/Knfh2BBlRbpZkykU+X0Gda4G0Wo2Bc3gVgGAC81k9yj1z0Q2NGQ4FeXUo4F+W/8HShhEXOhSzebZZjU56vfShx6oR35zVyYQnX4M74CD/Er2wDy3sdTHjci0vfPYcosrs7jzGS2p2Xj4WHzfO/YPwY6O4X80VqJkItoEDWde06jvA6cnRDdbBNua7Iz52XH+J6IJlW2QLLP7B4j/O9IVy8afNTZFSN5PVdM0QZiYlCJtDJWURGaokSEb4/woaAHWe2tXXDI8tbYMK4seaLA5Is7dhEg/zaO/8MNrV2wcnjx8JTq/fH71W583Jw/z6Spvxam3HpsRX78BcHJpwlCjJ/DyWhvCjiki+PIhWwGkoFC1GYFLlYoiCvSy7QijgQNivZ1DW/UB+KfhhUhNKNqVQyKUemz5pjxQ4MJWOijBRGd9hLukFtQqoG4UCWrpJCDysFH7a8odbUcwos75BP0rIDnUYjsLzbi3U9iHOt52Zvf4yalKVAQdx55dUmTQIlXR+buKQFlUddyNrMjFgBJL5XfA6Fbk9QaYzTGRQEQb0YPuUp+h7IFZfH+sRNC+gL4oA+JkoEL5wwDm779zfC9I0Ha0IUpPWU66vO26U0NaHcI4ZUNsy7mzZXC2Dygt15MSnymmAwuaYDy/NQmBXU7UKWXm6vU7IIKWPLKkN3yLuMlUo2Zcg2JopCWyuDMgw4uvMStR/z1l7MEi+LEOFmtCWKR35lwHYznUd9YGJ1yFA9V1ZjSzpWGO6++uNmHRPF2W1as3cSRgtVYHlKrGOiZDSF2uSjc2U1WkhZ42RcB6rsYoHlQ82Kzofc7lDOORFEZAfJlHVctn6UiomiOHm0GlsQ1yiVbhU3ZxYvkzi9/C1Ryrd/y8PTiCnLMno/Ge2wEIUpBNjBIxVYnseczMjk4Dl8FiiKUg5bWKhopgJZ1VMR3kX4MqRiosSyVOcvjvklXIO7g3wlcuFAFPvdKluPNh+6FXm50ClEP9PjKkRR4fUuM6jr+uGF6zjrOh641jPPm0wdp5agsDrwKod1TBSifA014JNPI+VRNN9r32NGmypZdVcgbolCGZ/FmUY/8pnfijmWF7RYuVDR/AUQfr0pOzeqCRWzGZhe+sKTzBdZECwmCfJFuOROKaB0oYj9MZciFbAeygQLUZgUv35uUz4ZIzpzcrDmzW92ZHLsnNPrlC4CSti0it4dai6c8i3kmDGV5rNEyVFjPe2uAm8hkXdbyAN8/egt9dBzH0EVh97sUFnn4K7Pvs05u5lSWZh5lMWmz6VKjd1U1w/n0DnR4KoROfpGIaZO2lIA3xr+7GWn0hZGwHZvQ3YAGIkf02Xwyadet6NBhqJuR/Sjja6paN15NeGbILVEiejelv+yo7yzVPLZKwl3Xo3vLQQqmL2kMiZKznX5x390MqnmWM4ylHJaouTQBsrbgxkVLERhUjy3rjXzPLELjLR7CR6WsoK6rmXJ8fv0owxCxbyLmNVyL8vHVG0c86jr3oFh1HVi2cq3BHcHu8mXbRLFv2wPC/Lud6EogzCOOrC8k8shAm157K11oVHWFjOu1Vxlk+ZRjSkOiIr64VGI1tMM7rySNNx3joIJHzPe0h3Qayx+QX7IKQbSptx3+Vkq+ZejDHug0Y5pnsa9Qvv3XG9fw5LBVbTMYmpg+5KTJUrO3TTv/GXkUaai752KDgtRmEIQnfifiZQlSlV6GROAZh5qXX2/F42ibyCKICSrVCqZ1FMBHjWX9vDlu5c1PusPEAjV/JqQqCYdSHw38kUzxUTJ8rA9j/oYQyxE8enXNndWKpVEvAi7TXX2MVH4FITxw8VSK8S6Jq+1kpirrAg2PawIa6C8iBr/JhQhMq4TXTsaiYkSZl50TdOnjijXvFHj/yjS8kvIVdBbBJJtUGkBZRVvyXyNKjnZuVGWS4dk0f/4j072Si9vS5AyLru2tnXnXYQ0JevXDMC4vAvAMAAAWw7iBrTkJrnoh8bNBHVVy5Ir0tsso4S+DN0h7yJmFlg+wycN4fYnS8q4CHcFezgmdeflks6J63zaQuj+4lU2y7v3t/d65OaGswwlUlhsepXGjrGVCgzZtqEAGs4YXIUovI4cxUTugeVDuq2zbZJUU6ixzxJM1s3oRiqJyhIlRFvReY9QC7JHTFFohz8fd28n/vWoJdq4RMVMq2yk3XkZrke8f584PsOKGzPzUpDIfoynOnuwwPLI+nWLiVKe/SkVRVAiZWhhSxSmVCQnX/bCkCFZHDzn9D6bZVNXhv6Q90JiTCUbAcdoiYmiQ5v9KDVEwT6zyR93llaYwfuLR/I2m+kxFYB3XjnLPTNHihVY3u5ecc2FnV/qwgzX+ci1tlzPd/MeJ0cjeWvPqrASogRcN1rHRMmoPq0sURRWGAV99aTUn133FqnGncU7j1rfU6mIlih0QZ4nLtnjnUZRLFEARucalZpkHar6P/WwkBwT601jWLJ4rghCxazxtaANVWxsX3IuP3euGLnEaeF34AULUZhSkdy05H0gO5qgHOBVU25RrD9qm4q8S2FP0ftDEbRPKpBNYPkstZtVORWlP8kQS9YsQkwMPvHgxXsnr8fFLmu4FfEUVDQD1MIMLNRuppxioiT+xSL2TbQ7rxO3ZG3hwZYojC3JOdKmLYzElIj/S4F1TBSifEP2hHpdj4bZXmmJInyR9/os2X6psd2PjFiiuNM3SKddQvl+fFOKWSEXeF2PIb6mSP+OcV2GqQPVWD5cMHdeRRWioLvv/8/ee8dLVpR540913zh37tw7M3dm7uScM5OHYWCIA0gQRUBRQUAQxwQGEFQEV9c17uv7Kuoiu+awu7rq+luVVVdds6Krq65hUVDAAMogYcK9/fujb3fXqVPhqXjqdNfXD07f7lNVz6n41BMN2o9dVuEDqjfuwC4pPZISJaFUSJ4oxcF5OK+i45RQaBfLuCQMUoOQ4hU5riFkSIv2RJElPK3xEzwmTKCW77927S278B34skXlzDBtV6gcDaQQI4xxpu6dOvRxZKoja9d1laAPnbngN7F8MbMyK8DM06CzxkSvEKsXkg/kvHEKoIHX3QRawmxfNBXxrqe99ctO63Ml7O3kqxk2nJfOvvDR7/wGvvPrPxnRI7p3BNuV2Bwxlg13khFaO6OILaKT9yUXSDlREkqFvBIl7QChEKKnYxlNm3irRSJ6mXQE/UoICWIFU/R7AsSznnj49QOPwCd/cC8AtI8S0yV4Z5vJvHUxD73nRLFUCmCLF+WJ4rrdUHwPIYwCCNlso0xZcqJ0olVkggAmXl5eEstrFgi0tdkoQDppmWE8QIvuD9oTxQcpuu/nwmvWKWKhA3DeGbGCVSTS5zRvO8G+3+d//Dvp74N9XXDfQzQdAK/9zE/giz/9fe5ZIqAlBKqWDftia7HnmgnfVbIpnJDARVKidDDKeHFkNe5JiRIO7Sx4bhcZbjnWdMEhDCDMPAuaWF7kiFLwfJC1/obP/k8wOsoKJwqQZmz2MuwN+tB5q6I8UVzn6rAbSb3SlYwOBVe2Fc5LqylrpJwoCbpgx17nTsEKr5yG/9HNieKqXYftiJKqtwu/LYVg/GLZa+icKL54ct37+c9//zD8+N6DcIQXb6kgxBPQq7zIeaIIfnO9L/Cm37u+/L/cZ8sczssX8R6jeWnV3y5QbYdF39cT9JGUKB2MMq5X9qwo4zuUFa67mlffu7/CZ3B8o10s4dN6UKNCSBDla9jE8oILezgStBEzbTGA1z9FrW/fChib2nW8Bova5mPIiWJSlgDJWJ7jyzYSy4edsKYeP9F7cCYEg144L/0yWGjnRHG0x2RydnBosGmmWXeb8NsytLwqxANZJL9e39v90qFb79/8W1yGNS67xWUfl+24YulV7SGu3i9SZ75cWxVLVxJfdKP5aqOcKPpl2h2FhPMqoM12QsqJ0sEo4+JJ4byKQ4iu/o+f/cF/IwjUoJyW2431UFDUGiVqUDzzFConSlAlSgQ06LZPCxc6KUY6FrWamx3IRdLjqMN5afRSUcvBNlwDC7Owbg2PJD1konkhC1cshcum+4F57pnynfUJfqCrZKwXck+H7t0m1AmqE3+ffYNO8kTBJJYPBVUOilr9wuMc7bCvFs1DN1DqvszlAOH7QHFDewXKlxdyV2LJsr2ve0ssj/U87ogd3R6q+RjLXpOAR1KidDDK6DrGXpKTBWE4uGbiYpp+7SLEbayHomL/Y1D0sIcL5xUOovcpWsn80GNHhL8VPQ/KgHw4lGJ6LeaxiukcEaFiyGmLxjvUK2dDvuD3k5aFsxmlBMze0fQcH48nekzHIBYOxWaPbXmi1Lh12aCou01N8LmJWAYucrTCaLLf8z/7BE/ImU115YeSsu+rLmUkbr1aSsD0UNClVuUNZ9yupC5CwskBZDliTFC0J4oZf1uLbh4XLQaS7cPeFGWRjUHZkJQoCaVG0ULCjkKHdXUZp1bLEyXOm24MB7at6zQWId9V6IkSjAI7xDlb+XjlE9ZoPb9g2iSjdnhjZzKlnMwB354oNtaGGuWL2n/ch/Oysc7Ue57eLw8dxUnFSDOcl15btjDd2ktt5ZvgFHqeKD6h6YkSKrG8xrPsPtWM5hUpf+oSLjxAfSObWN49oe1wP3fVL7a8Rxt0JRck89nxvqCpIC8s3KttOC9P+ymef2uP/bxwuYlMyReOigQNJCVKB6OMZ3LusCjjS5QUHdXVJeVYG1R3xeyJEkHXhhCmhvVEiaBTNVFCkgEAYMeSaVrPX3n8EqN2eIKNsvaZClbvVYJOcX05s1FOaFnaQ5b2hx8Xe5bRaIbzCjw2xuG84p9CbYWv/vyP8PPf/6VoMrjQmrN0OCTH0M6J4kjMcvCxI/C7g48DAP+97LaymoM6yoFa89+cJDcKEGjNGV8kRfKqxtDJt6asy001zusKAWlYX1VZm3Y16Ai5JbkO51X0dd80J0ps89h3P9rM9U4wPCgjkhKlg1HGiyO7jbSDpUtZ4LqrY7f+jJs6PhoCgFDeFiYoWuBfISRQTpTiZ1AMNIhAU1Ym/lBXWGWep8ERHFQUdWL5QO3YwNhDQkCwyXs0hXpaMuLsbH/48aPocgAl8kSJd5tsS1x82zeLJqEJWbglFRrTrel14ICeBrRzojg6Q7/y8z/Cjtf+O/zpkcP8dmxyojQ8UQxpKyOweeGKgKvE8otHBrjfF/1+MaGTuwKdW4OXE8XGYIT1hDNo3wfyShTbcF7eAnoh228PFJ3bpQglXwdvS06QlCgdjNiF2BiknCgJLtAuDk5aOVEKeMkY+jUUo/z9ex4K0xCIma+oY1J3yK3SZrq5yInSitcfLgRUSPhKiusSrpXaNsYjukVpC7i/HBIrUU5fN2pKkjMY50SJeYInBIWBI4qfUEgFX25+dC+ff3HBP3WCVa1IsVbEqPK6m1aQ16BmfMa/8+lb4N+vOZ77W9nv5zWIh/eJhQ4T6NKefd6C12H+lp3z9fVQzL5kzR56Ihu7fo08UfSLeIf3Y0nx0kXkREmwQ1KiJJQK7EaSLr/h0A5Kt3ZHYz1UIz5xi55FJBARH/rW3f4bmUAZ12bGE6UwKvShu7SMlyLnbCvquPPerI1SQIO6ovrPuTdSoPcgkJ2/9z30ONz+n3dxn501pa/52SzRqD1MhRGJjwyHopUDKugom9np5jQRdcHd9Miho9y9VWsrizOSVRA0+y6XFyaeXiAtLYp5HSA2Ekj7agtOw3mVrFux5Lq+B+SMkGIJ58W2HW1ieawnSplucGKgjE89QsYa+TI8KNteEhuSEqWDUcbF4/PSkiCH866OaOh4TEAZp1aD5qKZARFcxhg2RahwXiEhDvtTjjctk2VqMJd/5HfKejyEmnENq3BetbjfDcD9fmyzrnXLspS/+lM/5j9HPWh9qTYsXnjovAQlHjmMCwkXCrohX2g0zi0/OVF0w3m53WP+cmiM347F2u6ocF6IczcULyzqb+85UUq+sbqk3zqxfIlPKdNXf+jRI/D5H//OvF3dPitoY7JlD33dSdDKL9OcKJFN6ZjPpZhp62SURony9re/HRYvXgx9fX2wZcsW+MpXvlI0SQkRIHKjtrZCJ3V1bIe7LmJVosSAEsnr0RBN15j3x7KuMV0hkqnQids/RXmiRDxYOpQVJYiw2XN4fW8Upo9vGC0HwSsm6Hle1B5rnlg+3vndbsDm1SkKOmemA0N+IXTrdL3kHjl01DqxPLvfNv5uRx6MRePN8yE54wAhdE4U85NRprxLnii+0D79yjt7G7Pxwnd/A/74F35uJlzd+Gdt9qTBvi6t52u1mlujE0/A9p+58Upc89g2N40KqreV8aGdcGaWEaVQonzkIx+BF77whXDDDTfAnXfeCccddxycfvrpcPfd4cKltCPKyN+wDFti0sLBtaAhppHjH1AxUagH38yADYpmnAghbSc0E3qiRPya9DyIeLraw6EA3SwnSqMuCzrMi+Lqt6ENUXbaQI95Aw7gWrgfcg/F6uPpVyzq/DENIxbzPtluiF2JohXOi0nM7XIaFZVYvgEfHkOtV2rnA7+Oxjwqmt/FIHmi8FHPFVPyl4gA6DXAbAs/ue+gXbuaShTTXcnWaNE2/KkvJYzPuW9St2+2suh7qDzcXEotHyNKoUR585vfDJdddhlcfvnlsHr1anjrW98K8+fPh3e84x1Fk1ZqlIG5Y8FuIzFbWrcbEi9ZHsTqiVKD4mPvxNkzdhDv5fEu2rLuJ9o5UQzbcXU+u7gI+R4rq6T3CEHHlXuXTLRj3IwVXOeo+tOjR4zL/uZPj6GfredEwXqi8D+bwLS8rvKmcUwmPjIcDj5uPnd9wGbofVoQFz0n6zlR7FDWM94Fmoo11hOF+jvUHZzXSt0TpTV/TcdKtgLKKGPwBdu1kJk3ZetWTXqLer+yedD6hs9wXiZgc/Q5r7/gcZAqUeKcIh0PPR+0AnD48GH47ne/C9ddd13m+1NPPRW+9rWvccscOnQIDh061Pz74EE7bXZCvHjPV++Cz/zwvqLJ6AgcOsqPkdwOyOXagVr5GFUKsSpRAIoX6xNSPA2uUUZPFFpIFO9sLQ68XB923iQ2iopyo+hLquvt+Ft3PWhU7ujYOFz1/u+inyeEoC9vxKUWBczmuu4wVwiB8VoNbvzED2Gwr1u/wQRtHHwsLiUKC7MQMG53yMcOj8Hho3ox+5x7ohwagyn9bu0smzlRHNN67LLp8J+/eMBtpY6QO8Opb0LxZ9ywbGCvQAGQj6VR2MmIUIZ8a2UAWhjv+Cag5VUY8BbCklW08F4ItAORPv2m66pCCIx52jh9i01U81F2R4t0hnQ8olei/PGPf4SxsTGYNWtW5vtZs2bB/fffzy3zute9Dl796leHIK/UiFnAJgJ71vz2z4/Bb/+Mt6xMiAOElHP+lQWxKlFiGPMKIVHQ4RL3P/Q49/s2e80ooLuyTC9IvMSLJuNZhjlgRSOicDPsjk07FqgY7seu6X3siL4hBDonCvVcUaeP7mV+9nAf3PPgY/Cz3/3FE0UJZYOOsjkXzsvRgt148+e0y7gWAv7l0FGYwon1r3OeiRQIrveHof5uWDU6CD+9/2HHNZsDE0Yz1HkkCg3nM6ePrN1OhEuvnLL1qq6nsav306qHFJebJM7bun9PFN3tgRC/I1S0sZXcE8VXyDYv1XYMoleiNMBOoHpiJv6kuv766+Gaa65p/n3w4EGYP3++V/rKiLKsnazAPTvmt168JTg9nY7v/vpBePdX7iqaDKeI1RDEFK7Dx7hE0Yd2xF1jjPd949fc72OO55yhrcSDolIIu3yzwsbTc7u2+VpU5Yu29Cv6cgZQF+LohggiUK6cKLrNvv2pW+C+hx4rPHRSp0HHG8o3ZOGW0HW4IaUJXS8UAPdH6KMec6JEsB16RytPTo37fRG00MgnljcjTDaWEbOfaLh6h3boC1NgFYmu9wV9Ib3b9kVg15qtzaMvurFKUJPmjTyNocFb+llMvu8JKqplv3fCmVlGRK9EGRkZgWq1mvM6+f3vf5/zTmmgt7cXent7Q5BXasQsYKNBb5n0RtLbVYH960aLIKmjcfeDjxRNgneU3Y3b1PI5BIqOk0wIKZyGUOiMtwwLlplVsfTGVlrgZq24sJL2PY9s6sfwMZ6i7qBhvB27ptcoRBY2JwrliWItFAhzfg31d8P6eUNB2kpoYemMAfjlH+LkI3WWSAzK0QZcU3JkTO69gIFoay7K4jskmme3xJu06Ds47Ylyw8d/ZFiHeCzL7onicnxcdkXZulWXXFf9ruVVaNGObln29WzPEV/HEHYYgsobPDZVtNhENu9TWvk4EX1i+Z6eHtiyZQt8/vOfz3z/+c9/Hnbv3l0QVQkhUbQVaYIfxCTIbrdLXTXSnV024j5DkNFbCIHyXUJMEfN7ZhxRiiMjWvAY6qKGsyzzSISiL0eml8zv/vpPcK8gVJ8JdIVatLUy5tnm56LCYiResRQQjVMMw2eSE6W1V0e8UWqi7p2QRwxjFBp7lo1ol6nxdShGXka24PES9STN9cH8w8FDud9doOxKFAB391TbetqgK5UocmvRyf/mpL2M0Umcmyp2zhp5ohjknSXELy9ftFGE3BgvzjnS6YhU1JbFNddcA3/3d38H73nPe+AnP/kJvOhFL4K7774brrrqqqJJKzXKciaL8pWWhf52g0pAcvyKGfCz15wOS0YGAlHkHvUQMeWdYdVKvFu7qFt9KlH6uqrNz+2YWF6EmN8zJiWqHrLzVMXc2vC+LkLNNMuaF/UO271WVboxRkXNOdPL2f/94i+c0mHy9ljKM3xaQfe9dM0sB0TjVMz4MeGWtKyXG/tKBHC86Fywv6JQVq73B99KW7szXNyRoeYNL1whLTS2Ohdl4bzMa40CZac/FujuJb/8wyNwypv/I3i7ocCSFat8PLYwpwSIV0WH73FQzUd5ThS3tCS4QfThvAAALrjgAnjggQfg5ptvhvvuuw/WrVsHn/nMZ2DhwoVFk1ZqxHrAsKBjIKaNpBzo6aoob8Qxzb92m1fVWN9HEiatu0LgsKdmuygFTdHWJiFRFkVgmYdERbpLAY/paNorKeKdRxjKivZEiSFH1Wd+eD+84glrtMrUL63IZ2lPlJLkRIlgWBIig5knih9adOB6Ko/X+JbCNu34Sizv+3wy2c8aZ24Mc0OZWN6PDiU6IawJYsyJEjM/xgPao4FaZz///V8ctIsHAXNeXXd/YPnxovlTEbD3BiN+z3AK++yq4mUDBYTzKtdWEh3iNVdmcPXVV8OvfvUrOHToEHz3u9+FvXv3Fk1S+ZEWT4IBCj9nAqHMy8OnV4cvzJrS561uOpwOASj34GogZgYpG86rPPM1lxNFQbpxThSewtFiQK28WCKfR0r6Cj60YjkzP/Ste7TLYGmnL6BFvW4k3ZyggGxOxbJW3dfIWAABAABJREFUTFDkPum631wIwEWelGULTWLDTku7MdB84YZlA2hOGl9klMWIRwS3ig/b8uXty6KmQcjE8itnDRqXtb3/+Lo/Yc+AYNs58avo8O6JoljDck+Ucp2ZnYLSKFES3KOMh3KZhG2divLNqjxKzvtHYFEhhuhitXD6JG9tsp4oZdz7TBDze8ZLmR58nUm8sTP3RLGjxTfswpSpCzeWf1H9EMt+/KdH9Hz9iMallX6qqGiS6aJZDsTEx+cF/RrhvDwLoYvEeK3G3Vs7cY2Zxfyf+DeCySGioeWJYk6kbD7E8O62iPEV2qFfeXC/tejs5XYtfeTKnehnyxPOy2dOFH3U8zgZFESiaD5dmhPFW5ttupkEQlKiJMSPTJiI4shI0INqqNLW7Q9FMwMi8K/ldcyd2u+t3YxnTpxd4wUxX7boi3uk05WLkKS6Hb9ivFhQ9XumrWihbSyege/7xq+1y9BCssE+SQTgjCeKpWWlYfE4ejlBBaknSjgyuNAK59UsU/xB6zxElpOcKPK/bfD+y3Zk6/Y4BCb8dARTognu/CRUODpP7ZY9sbxTAaNlVWXuSmleII/vpbeXm++gdeF+cSeXr6ax/Rfy1X32c9FsesqJUj4kJUoHo8yHckK8iOFCqQvewXz46HgBlNijQnCWwLFZIHR5NF/uYsJ5lXCKGqHsl9gyQB3Oy4z7rQ9djfOdQV1mxZyV9w3VXlbxLCxSoejLmQ2I4LP0Ocv3NbXvTxfNcqNsXg4suUXuk677zktOlIkKXZC6Z/mIfSVIGOVEmZgNsrMp1HwRhfNqCI59sYkx5ESZPWQXJthZThSHo102th5LrvtcSXoIdv6UbPxUMFUy68qK6p7R2k1p1O93/JWJ5QvxRUmwQVKidDDKuI+X7I6VIEDRlsEqfO/Xf4Kv/fKBoskwQoWQaPu3VoNCNp5qlQ3n1RmI+bJFkxbnbOWDZbSVShTDdnhDV5RSzLdi3PdrFc03VEqqRSGEZBTysveIIbF8uXaSBB5Czx0bb4mWJ4ojYizgutecnDVMHY2/it6PdWGyfTdeXdaNoQzOHhSEcXThiXJEYmwWg0Fdd9VczOU0J0rxXVEYisuJUoNpAz2oZ60NPzTKt9tUcHnHkbdDSp0TRYVxid1u0d5GCXwkJUoHIwYGRxexCocT9BHT/GNn1bd+9WAhdLhAhZDCmQEZHj8yxv3eJ81VOtRMxH3jGvGssDwiWv5eYZ5Ynm8JXHRdPmBDGuYcaVncFtMJsYZXxIDmueSeKLjnfKLE3dxRiNnjREd5ENO6DpVY3kU7ZbvHmbxzxMctANTXoItROCRVojhowBL289XNS7j0Bo4tioAruN7DapCNQiBtG5IJhjEsPPUCNIWG7/PcZtWmuRknJAGOExLiABH+kVAEYr4AJ8BErON4x+g5H/ie8DdC/Fy8qmxi+RhudyEQ8Wu2S06UkAKhosJ5xYwaqPul6PlVUkcUIJClXXauZD1R/NEkg26zRc+LhDyKHhKtOPpNS/6adtnYUavZi2ofPnQUvnXXgzBzsBd+9ruHYfrkXie0hYbJGV+rARw6OgZ//7VfiZ+xoCkkZHz54TG+URRAHOFkbQSjxVPfHihK6VOrhRlDQvTOrVqt1la8R6hXIZ5lG0Xz6bJ14kvBk/Y4OyQlSgejLIunnQ6bhBYi4K+baKc5VvEcN9QG8oifBJbPnAw/+91fnLdL51shpDx7ny1itliLlzI52L1CHc7LMCcK5PdIO4+NYsr6rr9WA+UtrnHxKi4nSqQbMgL0pRWbE6Wo9y1xN3cURMNUxPC52GNj4GVNz5mLts+HD33rntz34nwW+Hb+5t/+J/P3gX3L6jWUbJ2apeurwbu//L+uSXEGAvhxICBeF7LckTEoUWynmktvYFflI+hWLeDpdbsxaPU5CbcvuR6+oo0mjTz1DDrBt7dQ0Xx6SixfPqRwXh2Mog7idXOnGJfN7CMlYyQ6DUUf7J2KiiM3/dAgBOAdF2/xUneV0SqV7RJiirK8ZzlnbB0qyo23Qc7YmV7EY58HvpV9zcTyBfVD0ZczU7CJPGWvkfFE8UeSFGXeRzoJ2HkUO5rK2Rj2V8N+E/HpohCQNuPz7YkwuT6G2OcZYnqX+fF9B6W/RzFvAJSEyN5fGs7LmCCHsJhsNuPzktNWZusyr6r0kL27z6gAGC/lBmwziWL2iL958gaLFuKFsaGYwdD7zYnimflQJZaXKVHcUpLgCEmJ0sGI2UpZhCSYLx7tOALt9E71nChxvpGMYSYAsHTGZHjPJVudt9vFJJbvFMS8w2emQomGhL0wqNaazau5PKNt6vLNK1h5oiBoK3rJs0rcsiAfPkEWzovyWLF8XdPzS7dYrOdkuyPqbtfYjNjXKPJOZdqloq1J7IlijlZi+ZgnQB4m1EajIBGgHoII56Epe/9DRyLPiWJZ3uQVqhUCF2ybn60ngr4oCmhFhuttIWCfq0jvqhBYOmMyALTfXAiVM4oQ4jXKRtGnkox/8HVmdkxoc09ISpSE4EjWggkJ/sDGsS8bfDALtBCzZPd3K8QQTkGEMirxefA1nVz2jm1fRzyNULQVqTglAcNE+EBGhYL1REnhvBIMEfp+wO6NOlsdmxOljBD1dz0nSv69OnGJmexntZp6LherdCPNwbThEw+PRR7Oy+pQKJ7+BuKhxAQF5UTRaNt3vg2fKJrqUO3XQxD6a80sbCMeqv0+gu0yQRNJidLJKOGCLfqwSFALKsp4EJSVeeKh/irle5/GEPgQeFapOjvKEyXitUjTVqYRyU0fBfGm040XTsU4sXzN1tsjXtRDNsRLYXe1Uto9hwDJ0C57C1pg6OJtzWJml7OfOw0xj5PJvIth+zHdYkJ6ojTANrl4ZMBfYw6A7drnn7gMBnqqAFAuxZpq/srm1tIZ4rGLYl1YljflLfJeap0LfEgt1+3yQxKK2jZvn6D2X5nSfbDPPEV10eylWU4UvpLeR1tYFM2nS3PGxssydTSSEqWDUcZDPW0kCQlyVCp+XV5tIGcS6kT7oD3jieK++mgR8x4fwwU7DAzjBUN+/IoSzPhWUviuv8jL0VO3LyhtOC8A1sPE/jmvYNo9bvmIzuMJgSCaH8ROkuUEOtbzjX2lUaLIM81UMSUyIBqv1bgMhBPDfqqOxSMDMGe4z6LSeDBzSh8898RlABA/f0NajihKhRlvbh23fAT+6onr4LS1o8JyMXii2Jz9puRzW7ROLO+sqo5BTN1Erzfe+L3qrLVB6XGJYEaoxHNOFG814yANd140cQlcJCVKB6Oog5gQgOkDPfjnC9/aEnSAFfTFxAi20wFVz4lSNBXmMGGSVJaMdE6UMveNNmJaZBKU2RNMRbnLVytqOGOeRZg+KWp6vfPpW+D6M1aVloMhzKVVtjfTeiLbiy4BU0+UFvYsG4GRyb1WdCS0P2z21JiOLQwty2dORpfzkxOlXmlE3YYCdpx18owAFM+eNfguEyOGdXOH4Gk7Fkp5t6LfD8B+jZq+Qpl5WteQzYNMakTHfSbQA3NBLO/NnSynMs2Jors/EMO20PV7XrOq95UamXbw/IoZSYnSwSjS3fgjV+5EP5uxcPRAS4IesGMgDf0R2UBGRo4VKp6tNWwgYyIaFJuQTgBg47wh4e+ZkDSR9o0PRHCHbXv4S/iXXy824xnzXLATtqg5maL2w9PWjkJvVzW6804HWP4rE84rAsFVDWpRh3nrZIimB4lAVKCVE6VB7UShQj1REB3H2wdFe6No7bgYIfbMfMFJK6zr9AmTN67VDAsWAOW85bwH5tVi8ESxgUvyY8pxFxplo1cXqFBeQCilJe/38iLkqe2Tly/aY1wqH/FEWsm36MKRlCgJwUGg7vZsVLbM0oiEDNqdsSoOJfVEmaDZB5PU1anhvCJeYrSQpkxjwk5P1XQ1fTeX+6P1PPA8j6yUQxhPFIv6Q+EZuxbCkshyA7CWfzL+K6tsse9xo3jZus+XYWK0I0SCe6gVPiZae2VThxLuoF0iyUGhAq9vxTlR3L+VqG+3L56mFZ0gNLD3TqLJexerdGvt0qqRNl2SIZUoonlsIzOoK+L1y9Ghm5p1WXdFxMy8AoXlRNEwpLDNJYpTpPA/Y8tj6i0CRrQbrSu/sg3f/ah+ZUk4L5eEJDhDUqJ0MGIWsImQNpIELyj65u4QFeJGiFUUfFBerbSOuli9dHwgZkvAeCnTgzqcl9l8q4ciqDHfmfda2yaWR4Xz8rvm1QZs6vYv2DYf3nLBJhfkuAMhRvtlUVss3a5OKI+EsIjpBLbJO9UUQgecaJfsXsT9HsPz8fZB0foWhfOyWduclCjNM22yRWLlej1WxaXQeWWsYiIGNMZSmROF54mC6BQfIeFEEFmR2xiXf/Q7v4GHHjtiXoEnRMzWc1EUuTr95DtUVDvDpNs+88P7YExzg+ApJ12iaNmA3BPFU8SDEpxTMSMpUToYhS0doifiTedaXEjeQHGjrDlRGrtCxfTWI3npjCdKCfumHUEzjGUaE3b/U+2HLl/N9MyOnVG2FQqoyvueX8o5gA734Iggh6DfTZ5Y3m3IRNucKAnlAykooFdmrukI3ph5HmKfFYZC07SEVlVYq/GtuF3sUYQAbFk4FQAAzt86377CSEAI1T+1uPcjniJL/Gz+TTDrNKSwX6REiUGhD2C/N5RNcUID7YnieKxqEEbOhSW78X6+9tWiYEL7x777G3jgkcPa5bwmli/aC1byW4mnR1vDzvQjodQoZbxo2tIwcsFQpwI7rWKafu10QFUITogVU/8DtBgYH2FJq1Ri+ZDWcUUj5nel92/RdL3tmVvhoceOwDUf/UEgqvTha++o8W6AhuPJ82rRK+93IlnRhijr28JM6Y2EqSNCD0IC2f1YqkQRfDaB6Xyjx5mXU4hFbP3dKShaWCGDQTSvsDcRi87jh/Pi1+djy2+sawIE3vus7fCD3/wZdiye7r4h19Docp3E8kWjJdTFPaeLkDKGrkoFAMZz3xexx/PajDW/SltDSyFux7eo1og6ZJ7Nvl7sgRpqjfn2FvLdjar9cFxyYY+ZZ+pkJE+UhEJguumnS29CghyEEC+KiFAwZgglDEqVqjPmEFeuEbOiHEPa/GmToKsaF5vCzk5lTpQSr8UGfM8iq1BjCAWR7yFQzwEcBTHOlaxyREwgTbuL88doSjBGNvHufp0N4fQg4ddAjcn9oXNmyiyLY4R2ThRuAmT7ASIEYKC3C3YvHWl6ENjU6rv7se9MC/kwc6LIeUMr7VU8Me/tceG8YsiJEoyEbLtJVtEE9iR23Wd6OW38ewL7UrAWPdNCrTFiGF4Wi8LDeUl+8xbOqxysS7SISzqREBRFLZ6iN/wEO6gY/8bPMQqERCgTrSr4jhvqCw2ajRgZRRE6nFcnMQ0xvytNWrkvnHLabaw4HTmiwHd//Sf45+/91rC0/3lklesF8UzFM6ermr9oT5TIlgEh2f1YRl/2uWJehB6HuidKxBtgQpTQjaMP0NqDQkw30coyXXMzJvdyv/fhxVrW1WjStTVQj0mR/ZHJH6V8Nv8emC4J+X4iY5tYjlTrkKVuyCgGMuI9vljI4x+Xk6r+L1c5HctENUAofo94bsu3EkU1HaU5UZxSkuAKSYmSUAh0NgRsTO6EcqHUTGHEqHi21vAFr+G8MkqUzpl5MdtioxKCQ3zMI7u0lF4IDt/AdO6+9Y6fOaPBB8ZslCioeeTdxFD+M6L5ovJBqEDTLqMO+xwGNTDMiaLZcAmPybaATBBS9JDoTLuY+CyUopbz1EU7FnCfFXoRWLyyL9YrlmHIKCbiZb2aaCWW90OsLDyNawjXYhGTg8O4uuyJst1hsNR6yYmC7CubcKqx7D9FIeTr+2yr6HGU3dd90VayrSQ6JCVKB6MwTxQLi8sOP6sSPCFG4ZUpKjFKnjVgLJiQJZZPOVEihDonStFMbZHgXQBNh/N7d//Zkha/E8l2nip5Gc/zyJXiN7b5zlr+yfZm+pei3oMeh1BJZRP0Edk0z+yzWvciiWVxaKAUtZxneruq8MTNc3Pf18MkukWjPp4SbdnMyY5bcwcTo78IpoQU9J1nPJ9KhHmW96W6V8Imlud/H9teY4oY9hhTFKX04Xlzi2A7T5Q5UShCXPPTRfON4cJ5+fUqL9woQuqJ0i47WXshKVE6GDFbKSeUF9h5VTZrmrIA64kSW+/7ZBJoT5ROyokS87tiSSuar2XBzlMlecbhvDh55QsaTt/t2lisxpFY3j6kW5zhvJj8WkhXFBchF0z408IvwQkoCJXmUFwouAZM5l3IbVnWd8qyGu2IeAcXo8Or43XnbXBQsx8QAvDhZ+9UP6fJxUbMnmXBeSnMe4bkP7sE0tXCcqKwnigO+6Is06aBoujVbddmrqiK1qj6f3fwELz2Mz9p/VYr/tyzQTi+y2+UDf+J5RW/S34r8fRoayQlSkJw1A3lk9tkJyO2cYyNHiuQ+KyvdKwkfTBJdJ0xKxaco/SvGttMzkMdzssd2tXwYczGFQUZFq5IoOJlI58LjfOOmQcAAKtGB+U6FMHnkMgIIhCuKPH1dkIo7Fk2wv1ehz1o8hVl4Sk0eCtRYnkrSCqcMdgLPV1mIgkfXjM0CJBMXj3ps80pUYt6f6kr7Se8ZgwGGjOVQq6KqmB8ihiDmMe9CBRq/IM21vKfWJ7GwcePZtsP17RzhOw3n00VbYRTxDopCecSLZISpYNRGr4/87nMR01Cgn9UkMxgyPWPvYACWLjrIl+ok5QoMb8phjabOMW+kMuJovRCMKO/Bh6EWIbwTYdVThRQzyXflyPl9oZsPjZlPgGAU9fMgs88/zj4+NXHynNZEP5nYxhMCR/5tBLcQ7Znhh5Cm72tGAGtQFiMWHR6niiCOhws7tj2ORXQ9FIGTJhpVaRRBP1KKhsG0+EK64livi58gG3VOrG8acjByEG/io+hChfOC7P/iuaoZeOR3ZF8gVb8+kDR/KPsPChawZPAR1KidDCKPIe19gPXl/MEK+CZEvlgxcQHttO0qpD4DlydC75v2lXxn9sJMSuM6EshVjgbI3x5ovCGLuLhtILvBLS+55Bqf0OF2onQgxCg/m5r5kyB/p6qwhOl9WtR5w/dbm3ifwkRIoKJLpqiOlb5Ta+DZlk7mmxAv86KWfz8IjrLsp5PIP9CNkPXyonC/z2CaSEEtu8IOykkKPo8byl85ITwzjeMcUvInHyViDxReOjks6jIN9e5AxlHSEGUU50rVqHECp7koRSVBMwUHR+7ahesmT1FXX/Enij+Est37r7kAkmJ0sEoavHwNqqLti8ogJKEovC9X/+58AtEuwLriRISKHImiPZhDZK1uuuciVeWdxUNeWTTGADyNPmikedhUVxYBL8NW3miIMLO+J5HSkcUwYa8cd5QppaiL3Escl5XEvJceqKYhufRPjvi6u4EgELGJJv0F4+G8CyGY5Zec7dfuh2edezi/DMa9fkQgDf6KTbvUhXQChTq2eiF5tQ9QemJwnl/VJ9E6u3uGz4s5muSv2IHln/0sS9g70CEgNXZgzWUEZePZ/7qIiTlJuuqr6sKk3u7lM8VvYWknCjlQ1KiJESBJ2yYjXouE/K6XHxExwAzLo8dGYPP/PA+/8Qg0W4HFIbRCHnJ07FMNr58IMt10r4R87uGcrP3DaUXQuwvgIDvaTRm4R2Goq1gLYrwZ2ZyxD5XZBd9+gJalECAXou1Wtz7XydDqDQvwBuL5YN05kwRAnPMHjF3uB9eedYamNKXFRzp8FainCg2e1Sjn1zvc/73Tf0GyrT3+DJCCGnEE5v3PYsyzQfXKNYTJUw7qulXQzxTVoR6L0LMFB1Y+oreQ2TKxjIr2doZSYnSwSjqYKOtdZrfofeHtJG0Cz71g3uLJqEtUSHxHbcYxscmnJdOiegtBB0i5osbTZswvEeMtw5NkkxXY10I7G8AP/zsnVq0+ITNe2KK+r4cmdZOl4sxnBc7d7GWlC6622ROZMN5JcSK2LZ1mifQ4Q/Y9yiWt1B3qhaf5NETRfi7p3ptUR9nRP9SvHethhOsFoW64X3Dk8oknJcaQXOiVPkUFbXXsM129HmETu7uoWkNLxhjPs4B3VbhvOybt0IoiQOxGCVM/xbNlxThidLR+5IDJCVKByMmAZtMyFH0AZGgh4imFRrxqR3MUanghIYh1z+GnsYjxiMhs+Kg2rexei8bYo53irlg8xTuZYM5/fn+cTmemxcMO6vLFmMW5oI16v9F8O6IYuiNlAmBhagnatDvYvkepvOcVtbjwryVuL9LDGli+cKtQXWebgihvZCiBX7IJXNPN9H5bDM6rXBe5YIWvRqdXPS8aYXz0j8/Ma8ZMidKtehYPBR8nCuZkIMR7Dc6KFK5jJ2DtiHYbBLL138rL0Ie2SZt1ccW81y8WpQyz492RlKidDSKyonCsXDUKJtQLFwxcC5n3zmb5jisrdwgEGFOFA16vCeWL9sNxAIhL7HaiJk2CXSs823AywtR0i5TwiYnCgaipLOuoJoDWKFKZNt2jiCssYuL9zDKicKMcyd5HSaYgd16tHKiEH4dc4f74c5XnGJFl7BNze+zzxQr4G9WGRuD6ggEWuMQ+96TCY2t8WysiCknCgDHS62D7h0ssK/uY57Fct/DeKaVFaFeqx7OyyRSBc6DpehwXtK5mlxRokRSonQwIjlbAAAv5GjTM6gtEfI8Onn1LPjg5TvMK2ijiVVBMhohl7+OlY7vedNJl5lYLhA8PHzoqPIZbBiNIqFaa1bUswK+ohLLe94txm08UZAeTT6hql/oiZJ5JkLlN/u3hD56jy9KnsUKBiPe/joa8nkUioZWQ6ZW3i2Beb7s1IEeY9pMgEr+7cITxcEA+Rhin3wd1pK58Wydnrg5F1rhY3L+Fm61zUDEhxXhbcgjxXZ2ZkMO6mPj/GFLCnA4Zc2s3Hf00rzu9FVB6OC1rUJRM7rsCpZQygfTyATY/TsyPWwGEZPW0UhKlAQlfOyPbJ1Fa4ATyg1C3wjaHGVcKqicKBPPGFuNKzqmr7t+3O1aOmJWfwlRFhmi6KITY7gd7fVn+Aq8LnGpzCjaKpnGmEX9GGG5K4FPT1cFnr5zoXb9Qutxqlz9CItvvtOQvWYmsbxtOC8wm3OJjywHxErFco1fEfMtVOz8emL5snAQfoH2JKSiLNz70GPw6OExRYli+7ep8FE/6ZkSewjDeRWl0GcbLngpCVLGOMXk3i549zO2Cn9/0jHz4Krjl2a+o/eYIvf/EFu5ryZc037WRs3IHgGHzcgTBVmkaP6xCEeUBDt0FU1AQnHAnulE41lcffndQCY3zQga0kbSNojpglamaaVcjxFaNOswJ76sQb59w8nwwF8Ow6KRAT8NRIiY1pgMRSTUcwVXoZxY1Gq1nNKkOE8Uv7DxRMEQ52oO7Vk2Apcftxje941fZ+tXEoD7Ora5ztIjz2VBP1cMsjlR2iNMTach9JDk91T9xPIxnLO8tWmzv4i2ZJvxafRT2dadDr2NZ3/024Pwo98e9EOQA2QU3UojBL+0uEBMOVF4cLlDmGw3IfpH1EKDj419HpnS5+a1zGtxrXyKdZhMvbUrhGjlZS0KssgRvkiLPexk7EhKlA4G9iCuEOI8LAy7IWCFrGWzVOtIFLAnY2NetgNU65EAcj0FvPijkrpN/OvDGoQQgMG+bhjs63Zed8yIOicKAkUztTzkBFOe2uF7oriDTt9690SxSiyvLutyT+EKKx04z+mEjCkK0jBMVL+48PwxyokSewcmAEAcfDxNAT3XxsfN6yzyuMXxWDreh4Kd1cHQFT/6+sDQrLv9FK97qxPsK7F8SMSUE4WAh/6xnCtFno1F5kTBIoQEIYYwlhjo0hJybpnwltj1GANfIkJs4RMT6kjhvDoYWA2k87VL8huCbrzZhAQatvOiTAeU0vKd4C580eVEIdl/tVH8jTQ6xGAhi4GMzvKsTD5stpZ4hs9zThTLF1WG87KqPVsPfzzlLYguZxnFA5DoziGWbhl1rEKoCGTyXEBM6yeBBja3TgiwU0THMrNBKTvPfL6CcC/hfG16zwLwY4DR6KfY9jkVdKjVefaBRw7rkuIMNJ2qsebOrcg4s2qFL86KhUqXvLhJXUE8dQRN1OQ/R4EwWxJ+7y4TQpJvMo3x4bz063YJaUQGX20mHtkKSYmSoEQIZilZEJYHrvZctyHiys+IYIE59Ezyipy8ehbsWTYCb3jyBgOqFPTohEOImtUuFxJ/5B55wZRKgG4G3jp3yfDGtMpslCio/dDz4YBRbPN/kP4ZH9Aew/YwERTpHnvR93eHoSgezjixfMmYTj0lSo3bFy74s3L1Gn6c64pwz8Q4Au35qFIcuvS+9IVq5NIs+8Tydigy3BnaE8UvGer2DSe1i3Og6HenoUtLqL2AEFNeHrl/O/Aot4HUmDCmCZLQROTHToJPoC8LHhYvWyXWzTHtIwk8WHuiuCEjChBAeqIw639Kfxe8//IdcN4x85zThItHSiaeNWxE0kanKmasck1EAMLxWowNKupM6a9xhVh64+nq3hx1OK8aQgjkeQop5wDi+xjDeeVzosiebf1qrbQynA6Zdms184oSCkOoJeBirbFC6CCenwTgyr1LOF9zBN2IZ0TA7Ku6KHP8dR1FSlnQoLTkbCIAAHSJPFEKGA7fPKvJcBWZE6X5O+eBWKYeNoKDbRvi38xbdz3ddGkJqUQxUqEg75GmfKur15cmli/RudJJSEqUDgY+J4rbdnnVoXOixCZlSMgBfVFyzD11ysxQWz2bhYVpCNx99KMOc5LWuDuUxVW3LHQC6BkAuIZuP8kuzjrrzPfwjFnllQ87efhhc1RlcGEcYrso5eiTCQGQz2FhlhPFrnxCGMR0xrOKD5N5EzKcFwDAy/avgk8/bw/TqLqci5wdLpVPZYFWuOmSvBt91qiUf/xwXnHBxPveJ9g+a3TxkzwYqWFQLXTRFRQ6XgMheC+XLZyyZpbD2rLQpbMMOVEw24PpHuKKn5GuEk9dnPhkOyQlSgcDnRPFw+pl9xzZJiz6JS3+OFGMQNQunnzZLnUqYN6HvTjFYo1mFvO0zQbQESIZUiVEZxEhbk+fHYunOaytDmU4L8MXqNvR2wn4yrIubDymogjnpcqJIvg5l3Mk8uGS9SP9WxQ5UWqIXDmxd3ibQu7R5KfNST1VIQ30PqvDvzbmfKNIqPO2UiGwbu5QRknO6zbbvrTNVcWimRPFw50yFl6nTDtKY/8zySkW29YZU2J5Hhp7zIJpk+BtF23WL285wWMI5xWbkUgOhSpx9HD5nsWtsq7pjnSYTMMlYu+Rpq8dwhMl8u2tY5GUKAlKuHcVzH+XNogEG8TG0PuEUmAHuEOdlVs2Lsw++lLgaZ9Bo10TRrumkJh10vygUZbE8iIYM7Uex1u3bmNSOFNadzxl56oOXb7nkU04LwCMsNyq+kw9PMG70hNFUl+mbnPSvMA8sbzdm9Sa/6eHlFsvQQTszDBJLJ//3t88JKLPHuY+b1+2aaVZW8mWKdEY0dIoZqnzRqUsK8M7iZQERQjuCadduouto10anI1BwnkJXsx+R/WPEFNc2D+1ct9RQ+0PxjlLkM8Zh/Ny9Poy3sPXPlZyEUHhSEqUDkaRi0cnOS/9W5kPmnaBK4FaTInl22lemSZf87kdoHKiTDAJJJ1KzhCLd5EK0liwKgE153eM0NoVVFXaeKLYwlUIB//hvCw8URDP+PdEMSzHKB5iF1ahw3lp1vsvzz0283dPl9khkA3nVeYMDO0NsWdWeNTAJrF8o4y60JKRAT3CNNqXPGFV/9i4VfG2QeTbshGynlgm5ePqFJEnSmxjVzeWCE9UkeHOymLMFducliEjF3NMt259IXvNpC18ThSDysFd/9vcgxOKQWHiql/96ldw2WWXweLFi6G/vx+WLl0Kr3rVq+Dw4cOZ5xoXS/q/W2+9tSCqOxOuBRC8DSd5oiTYIHbhk1MorZ5xLq/seV1reqK470tcYvmJfw3q76jx14DrcBy+ICITM6yhrc9DhmDKrVHN8q76xvc08h3Oy+UY8aoyDekWezgvnZwtrFeNTTvTB3qMVCDsfO+uKsZFu4UEF5D1u6+9Bmu4pSPwa4ZDapYVP/svB44V/2gIej1i5rIun8TjH2z2qCaPaV5FIdDxQgn5bieummlVvqUENC8bC8qSE8UUmZCDBmdjiHBnojlRk/zu0kPHBraGmNg2hL9FNH1t+TdfMB0jrCehsRzBmSeKpImI5kdCC11FNfzTn/4UxsfH4Z3vfCcsW7YMfvSjH8EVV1wBjzzyCLzxjW/MPHv77bfD/v37m38PDQ2FJrctgT3UQ6xddGL50rHfnQvVgeTSOoWA3Txtt3mFYQbY/rcNqSODDgNgzshIBHxmNZYe5VChyKGaDhUCMJYrQ7gHnJf8XmpfFKN6efuj7pYpEyzoLDPfloR2yj61SMGl/MBse1JbydqeYUEgJbD1o67yztW6pJut1QBuPHMN/Pi+g3DPg485qT/BDURnfAzGEDo7UZNaRGJ5U++qXJvspiFpM68E1cNRxzyhTJgaM+qWzPhnQ2DmYC8smDbJuHyM72SDqHKiqAzdCiC1yMTyZbDlChHJQu7Jq8kzRTXdwxBTV1Lrt4Xd6wrWoUjvWd7CebWFlKA4FKZE2b9/f0YxsmTJEvif//kfeMc73pFTogwPD8Po6GhoEtse6MXjaX+k5VzYxPIxHRwJfGC3ZNdbd5obEyBmS9Zn6CdcOK/svwn2KIsbvYhKDONYFypla/A6h1jBlKcLMxtmBsBtThRdWnxijGrAjzLX767iYg5gQw4UCawlpe5b5Kx2wUzwwp4z86dNgi+9eB8sffln9CtLKATBTyybBhuW/KhHfSjw+Z+FzwsemjPcx/1+bDwfz8vqPcqSYNoQIazZm22RcPxd5McSAIjvGEXRLmrW1FgiM9QmZ2OR4bwm/lXySd4pibt9Yzgm3JZ/8wnzttQFTRWNId6/DHtwJyKq6PMPPfQQTJs2Lff9gQMHYGRkBLZt2wa33norjHMYOxqHDh2CgwcPZv5LMIfrtYuxmBKWdUtKQpvA9oAp0wGFsXvHKC1sBbQ6wPDvDZJTcmB3KIkORQhM/OiYDBB5MCWPN3YHHz+qVYfUOCGiddYI53XTJ/8btv/VHVplazX1PHfqicIZUVPhQC7EkCFNvpAPgSR5VlJO3Y7W40JkcqIEDvOWgEcM3S6iQSsnSq4WiSWpo5fWqYZ9VlT26hOWwVO2zst9z00sLyHgeScuw9EVwwTQgM5+FlJBRA/Pi09dAevmTkGXJeggN6IzL65BjMoThYOiDZqChPMSfF/0u+OAC4Nt2YLsR826/EG3H0ItPVMldaWCG1tzTxQ3HVDEMinF0owY0ShRfvnLX8Lb3vY2uOqqqzLf33LLLfCxj30M7rjjDrjwwgvh2muvhde+9rXSul73utfB0NBQ87/58+f7JL20wC4eXxYMcbM8Cb7hcvOuH35pRgHgXVdZTzSfh6kOk9FrGvZC5grboVOjPDlRzOnkKQp8WiXqxws2a/SxI2Nwx09+Z1S2AWdnt+dp1BDW/f3XfgUPPHJY8XQWKCtwZxsA/zKm2t+E4YuYOnwoE1xCnhPFvFG23lqtZuaJErkgLaEO2VTxJXCTzQy6Ta1wXg1PFIbmULMw4/3F6dRcOC9Bxw/0dsHfPHkjTJ3Unfle1yvw5NWztJ4vC+reA+pR1QmR5QI0D3/gxOXw4lNXossSDZcIH+/UU3Urfqoq8l+FRF3Yy5xp9O+WpJrskHGcjWZ8Ugi4aPqUNfL9T9RG3ViszAioRTEshilqasDpatrK7uuxKa0T6nCuRLnpppuAlwye/u873/lOpsy9994L+/fvh/PPPx8uv/zyzG833ngj7Nq1CzZt2gTXXnst3HzzzfCGN7xBSsP1118PDz30UPO/e+65x/VrtgWwB3EIT5QQZRPCwtdQnb4uH9qvXcMDmALDL7PntU+BO8oKZGIMKxUC33/lKbBqdNAbPZ0CnyHaXEIczguUGwmX8Q24HcTM3JZEh2K192CErr7lB+aeKNnPrs4xV958OUt2SbV0H2vnRHFlpU9V1Awh4qbqhDZF3phEI7F8sw78sy6hnVhes35eThSbteo1J4rPQwqrbAi42xAgwfg73lvZvunUgW71QxoQheKJ5W7Y2FZMDf5shzqEDsWEF44pJ4PtXHnX07c4okQNm/Cpyro1awwWwhDMxgifE8VQiWJUKg8Z6xGFDjQhB+c5UQ4cOAAXXnih9JlFixY1P997772wb98+2LVrF7zrXe9S1r9z5044ePAg/O53v4NZs/ha397eXujt7dWiuxOBvSz4EhLRyX+lYSIU1lYJccGn6+6q0UFYMJ2TTNHSAqyd5lXdoln9PuwojXnkZVE5UahHhif1wPTJPej6CVtBru72Gd+2hMXcCz20OcGy5vMhUWQyUR3Y5EGpgfoi7lKQ4lKglCvniMxqhThPCg2gCg/H/4wBNyeKwaaQDeelLh+LgK3zIPbM8saG5Lwy+I/psK+N9dAoIyvrigcRrjOuLUH2SxUJOZ5Qcw9R1t+47zled74dbl16CbpEzgNKJ+yYhvU7xstJF1Mn9cDvDh6yq4SCKFxVLDlR6PPM2hPFYL53VdwHnhnoqcLuZSPw+R/LPaazCiQxilxiNm033svKG7egsvz6NJ932rr7xrChC43DeTnqf9my9tXH8agwywnnSpSRkREYGRlBPfvb3/4W9u3bB1u2bIHbb78dKohN/s4774S+vj4YHh62pDQBi+FJ3fCgZogNGdzFD0zLv8wwEZJgwqK0O5SMKMGFxAqaE8Vz4Mi0E7QpEBd9nmAXY/nvCqq5XaTwxRlz7/msHfNcv/dY14oGxGEcspbkrqzNqo4q0uk3Xat4UVkbcPeCTmIOSoLYxsR0+8Gsa5/I6lDc+6Ic5VrWSBSpivq9eqJEgLrFc7iXU6SHddeOh/N56iS8kRQGcYSrqkM2B0znhy0P5iPXZDdzzxS1ILvrx2LIUI+UU2D7AUqga9Y2ggl03hGz2UIIjic23ULceaKkcF5lg3MlChb33nsvnHDCCbBgwQJ44xvfCH/4wx+av42O1sP1fOpTn4L7778fdu3aBf39/fDFL34RbrjhBnj2s5+dPE0cAHMkr587BG88fyOc9tYvO28/cwFAbhBpG0kQwcqSxBkVxQOtRGF2AJ/hvFKy+AQZRJcsDMvMY3x9TjddZnZyb2FsljNhum8lpc3WEzyBONfiW1UEofx3KIBzNe4spIYPVJO6Aq3ca9fM5kREcrQECWTDFNo2KmdMYrDbYcq4mpo6nvn5nCiKupm/XXui+IJ/JbnGs/7IyMEmFBLWYx0A4PDRvLbG5j0HeqrQY5r7UIDYEsvnvCupoSqCUscpaACg7t2CeZemJwrvN4ceOraIaR8pFnqEhsq9ScCMRyaAVaIUq0UpwhMlwQ6F3e4/97nPwS9+8Qv4xS9+AfPmzcv81tDGdXd3w9vf/na45pprYHx8HJYsWQI333wzPPe5zy2C5LaDat+bOqkbPvW8Pd7aN4rpmHaS6OE9NDGngUa+pYQ6erurymfyniieiAHcsmXHT8fmpD4vZNZOCTHDJgyKL4ExFqp5OmOwOIMP2645Z9Mc+Jfv3+tdsGkbzkt16LhU4nLHG+EdiPneFZXOPFEMn9X3RHGDTE6UWv47bw0nOEM4D3NqrmTa16iBDeflgCodZA3R9J7H4CjH1cEqJ0pJXYaxr1wX8vmkJAv22PS17x7iKVEsXnRKv9t8KAAAVd/u7pbIJpa3myRGoS498MndVQLdCO1MGda9nREm0ghYMO61mon3h97zPsH3WIwISE+UonOiSG2UfI13GRZnxChMiXLJJZfAJZdcIn1m//79sH///jAEdSSKWTz6mwGhPkV0cnQoXO25Lvdu21kRE0OigmoNECAoTxQWfhPLl6iDE6IBZtrwBOQiobmL84OtQUVjkZ4otsqDUKvWSomCyX3h28LQQUmXcfd9ef6hlZ2WAoEamHGnyeOxHBAOEyk+NKeWEkVQhvd6XqamYsnpnlUsnOdEgUYOzHKtU9Owhj5BiB3PrvNOjx8ZsyrPwsc+HZMniiqsmwmltvuij/x4XVUCvd2tuyYm1LT09wLlO3XaCmxfs20boxVl3ZoVHhkLE1dQJ49TphwQv+G8nOVESb4oZUPcqvsEr1DxX74vM5kY2ml/aDvojumq0UHjOk0P13YEIQB9KE8UJpyXRz4Iw5xYj1/aREoL0VlDQD0vdHKi+ICqrSIFRq6sD23ChmDgOxyAU08UniOKYU4U9hlXQgR3OVGy9UiVKNRnUX9ftmcxvOS0lYrS5sgkli9cHJ8gQkzGUOws0Zk3WsJ1R3uQMDQgisfSo+EoR4kiqwFtka1FhRqxGNPWhefh2ssp7zxZs/M8UWzgw2mk6PxEKricoyZ1+fDY7q5WcHfNkpzF8RrbhIUuncGUKIaGRoTglIimfImreSM3UnLTRq5NP9V2DJISJUGIWBhTGpHwQwkOwM6vf3vhXpg91CctI2SULdmTmC71KmDWgEli+aJzoujG79ZCeYY3gYFqHoQO51WmM8hV18QczgtAfRHwPWRKRRpS8OlqbrmyOmVrwQpDRK0vHhmA5TMn55/nFDDLiaJpzVmitZzgFvTY03NNNO+esGF2vg5mpoe+M+kq8F14orhYM2VbdzrCu5CvZsuzY+893HkQoF2tOiOaVHxKGl5YhvPfcm+pEPeJ07srFeauKXhzJO1FDqHNnETvDZLn9BWg8cx3nrLdF8w8UQCqVY+eKGbFcpD1YkSOdgkUkhKlg1G4joTQH4uxYEpwDxu+3sZiOCKewisw/YuzDsqiaCVKQudCNPUwFwUdJYqPaSiicUpfF1x7ygr3DWrAOpxXoHVrs/egijp6DZHHo4t8lC57uog8QXQf6M479nnTvBgiwXhCXJBOj4LHjdf87KE+mDUlb+DTeI/cfA20/Oj9GXOH0t2ndIVjynBeHsfWay5GdL+F3Xdt+lPnbN84f5hXgUXbxkXFdbqvUornn7jMuKzt+5sMuw+eoKtKtO6avD0qpnO6yFuqlVLSMeFRh/MyeFlCCCrcn6nnvrNwXlJP7yRDiRFJidLBKOrw0t1wkvy1c1BUzNQyzTHVOU8ILidK3hPFgigFcKEmEtoJVx6/BP2szMJdteZ568Gn8J+lR9TU9195KjzvpOXe6MDAlfLSN6ug44nylZfuy/xdg5pS6O42nFe+LlX9uHBeDnOiOOLsc7lKkMMkO6N4/Sez3NUBPQ4RyWYSGBTCbwkmRK2WPX94c1yk5G28Rsi5Rved6LOwrCaXxfdAENeBVaKUjdfToTfU3CYAMMbMS+28CojH/+k5u2DZjLz3oA0IuF8zofcU3UT2RSsLfIXzou+awjEowWEcYv64lF/4MsCp16dX42HH4f5kMPZEQcz/IoyPaEjvwZ5IK3pfKjuSEqWDYWrt5wqZQ8CBO2RC+YFJTCfOnxD35JijCFWGhTL+PuA8UVj43A9w4bwsLdsk9Mc+N9oRc4f70c8KPVEQZXnWQzGMtqt8JHY02JVvvIHvS5JdYnn1M0WPBHZrc6Xs8ZFEFkDDUs7SUMZ0NmiH8zJsJ8EOwvB2EE7eJh77PAWi7SmvZAx7p9K+Q2l7oujt++iIAo73J+/3QkI0PH3C7So2840AjtLlswb5ecCMW/bjmS6qs4g9nje/aW8Mk/tIVtFrZmDgui+60Z4orVBmMaMo+gh2MTrG7qXTndRzZCxuSTwhgPJE6a6aXZhcDZ3sGhT72ulUJCVKghC+LwRGCaLStTd62MwaU+baOM5so7x5UTTmTsULlWXA0IryRGFGqmhPFBska4r44EJQglnXXIGxx/mWy2Phrylr+BKmu0at5pffcCm0M7OEEwh4aL2DYd08uFPgZevBWsrptu4qzEcmsXw6FBIMwJs2ornU2FcKiubF7GscYwLLs2qMIxyzievf6EfX/RPTUg955NoklrfN8WL1nh76KDSrE9v7q2AoH5aiK5cThY8y5EQBMJcvYUtJ904LLzJT/laoeNRVtgcL50WM1g4hBMUT9yDypvDrNyqm14anTSPxyXZISpQORlFLh7cVmGwPaenHDZODRRkWxZCWGODsrEJ466CsgyJPLK+NgjnwC7bOL7T92KAjx7WZeTwX7JAzIaYEjyysaQv0amO1GlqJywsxFTKxPK9L3YTzcjeXMFZ3JsCOkaw/uPwfZ0xNQPdf4g8jhmR6hr7U18MB0n/noZr3MuWiT2grK5X7VPZ37ZwomvSUBWghqcazLmDNs6POJfdv5KOPYubDALL7mpG8Q7FHqWDr/bN90bTcd91dlcxdU9RCTfF7DAgjCI8LonfWpTNYThQwUyZgPVF6EApBQQuG5RjIXb0TIkRSonQwVPyX72tBNvyD7Dnqc9pICoerCyPvsqwcX8ED1neJEs0rZRcB1hMlC5378kXb9RQGJjlRdC5FmDBwvnHAItFkO0Lr0iaMOa9mmYu+PMe8dRQd4xeLsfGaduiYBjDnkaspYmqVqyOEcwFX4VLYamRCO9d8mpmgSK+CoveOTkUMvS4aeh5fOl6rcY+oRh2N30KrUlQ5UXQ9Udh35+dEsUfZlp2Op3vIPcXGexwrkBQphmwsowkhzpWlXjxmDNoT/dZUJBQ09+s51ywa5xTtruDyb8pAzwIb+k5dM8uKDpc56XRRq+nPC59RWXTHYftiN2HBMDCLYIPLYWQczsvRUEjDeblpIsExkhKlg1GY9ZTbczwhNiCZY95TNsKfsl3MsJjS15X5G2NN2IvJicKMk86lRpfJ8hEDOSFuOJHfI+rg8b2i+enFqjLiqW07BqHCZ9Zq5nlRxsZr8C/fv1f6jNuEnn7qIuAwsbynSYn2FpL9hhD2mvKm6ZwpP4r2IOJ6oggmvjhMX3gBF6ZF7TAtup4oivrtbPEVdfsMB6mR6yXUDsRTRPjx8ND7HlWneVFxnY6s6m3bo3Hc8pHmZ3qoilDe+2ixi8mJolJM+3rvM9aPwiuesMa6Hu+jUoBCzyfmT+uHD16+A/ZQ89wnDKN5ASEEFdq46Jwo0jY8rZ2i+a2yIylROhmq1eN5dWVjaBN40jHzlM9FLbFK0ALXsk9RRm79YzM3/M8rk+X0nBOWwsjk3sx3mCVg5oniM5wX4iHmxbRHJMX2jAo661E6copqeMyxaI38+dHDaJqE5OSse+M9k8piaT9eq6GVKOw7fftXf0IUMqEKDxfhvIC4G68uw9jOKkgFlVSTovjT2Ne758HH4Af3/BlPWLPd1ud0GsSLkEpuFdhwgPycKPyyTU8U51RhQSlhecpJ0OOp2P7neqJIx0jeQqMfS3IsNRErvTYsL9by3gd/40PZHSMf9n8vOqb5mc4HZEJpZqwNxt3HHO6uInOiSH5zQZaLq18QQbikFd32XYyn7Xk7OqUPdi8Lo0AB0PMIzJQDHE9ctCeKjL+Ob3dLAEhKlIQCINoM3vSUjXDl3iVBaUnQh09ZNYa5Fif5dE2NW5hYyq2dMwXGNMsRqDMDG+YNSZ/70Lfuyfz9ktNWcZ87YeUMbhtaNGFyomjWqdW+x7oT+HDD5Kuf4QlsRcX+8vhRO4J4iHhy0QqmLQunFkiJHDpKFBM4PRsQnhSoQh7hSkilkzcLI8QSC6PzZR94RF/hWRalYadDNkqhbCFE85XXvIgHa9Twh4cPwRPf/p9w758fc0OcBDY2ZbrrQ3dPRoe80qo1DmBD0obcgnL7sUbbOsJ8voLOHD76CGOode6mOc7ak62lxk9Dk7ph84Jh4e8hUbH0kuKV7a6yOVEEe6pkG8mE8zIjzQmIhRGLC77Ddag1m2LtxkYRggtt3NNlOP6OZq40nJenMUm2p3ZISpQORtFrh3sZUFhTtdnensDANLeFqYUCtl0XMF1vR8f0XPYb7/Ky/XylSAN//Muh5ufv3ngynMKJKTu5twuewkmYrttfQVIzFMT5XbxzAXziuccW0nbM0BHkyg3c5fXwPVFEGwWaJGN6YgJtmX/6ulG4ZPcirfKhltS4RTgvDFy9hrElnOTcUj1jAm+J5ZFpa2Tv4nNO0XsOxmihPCu5vRCdkIaaK6KcKDzQ8+3Ou/8Mr/rkfwNAuHmVuUNxWs17TepBW4mi+F0Zpq/oS6kAOv0WVonivw0f4bx8QExn64ciwz3aDlXWEUW/Nh+vXkXmRPE9TcsiCC7NmomUMyKAyZLJL4dKLF9FhEHn1e/KE0XWhpsmEhwjKVE6GIUnlucJvxQXgdgOoYQ8mgn0VK79nBmmDIsi/S3uyWHK6B0ey0qu1BYr+v0wdVIP9/v+nqog7JpeGyhPFPbC73A4fe4blx67GDbNH/bXQEmByOPXhNC7DNOOxuD6mAYx7zqVSAQIGPj1RPH77so8VZg6Jv49fkXe808XonBa+siOidQThWpSNNd8x60PoqxP8IrQuRoxrY3X+HSx8/lhH56OEmjfjTTXx1GO1lSuIFXw/G0ezqsesz/cy7H7sU7bOpb33Hu5xXv6yU0nOHOEf8jqsiYn3yQ194u4q/posUIAugxDIDWRyRVjXo3tmBUtPyjbnlgEbIyYMJ4omGd8QhrOK02QKJGUKB0M1WXFV6I+vvKk8ZuXJhNKAh3BK41azZIBMy/qHUdZJQqynM47iYRutRpfcObDE6VoJtYUae/iQ8sTRfA9Jm43b88QFXGhSPCp7HNdH/2+prGEQ+EINieKQd0u34NXl1KtrdH5f3/pNjhp1Uwtmlj4ugtiWUJR874tRjOeKH6bSrCAaH6EPENFbfFzosQzmzKKE4WXPvudLo+lq9dWe6Io6ECSt1ERqhaLeVP7Uc/VE8ZjJfAWBGkixLQsKvSWLnA6RKzSCPGM9CGFUYXl+xuNO4KXVhTPoUJIZiyF9TcTy5u37xs20Sxc3Ml1mxadA1p1IOqODaa0Fa0gsYUv6uPhbMqJpERJKBXKKmhNwEGdoJcIGcjYz0gjvhcIHGHDeSneU5fJUDEXPLp1uzp2K3gbJAuRYsGbv6Ih6ZSEpg3QXRP7GjxyFBkrygBuvdr0K8MJZUjzX9s9pcvUGkEBmeENTbHUWh0RdsgUuvVEviTaFrGdmfSs/uov/pj7XaRMEFq/B3q9rCeKulEXdLk472zo+Ovz1sPtl263psEH6l4G4WDntWWZWL5ArwF+ne7mv+7+tH7ukFCx16jr4UP6XmpXn7C0+dlWkevFE6WCUy5KKXegDKhBLbozhQdfNBorfzDeWxGBUP+vVQ7piVL0FJIu8VgHpcORlCgdjKKMq7gWUwS3PRa9ySWogZ1X3DBRVmFRzCdHzAzYkTFWuOi2F+S8RY3LvOv2F0aAaxu/W1p34kCCw0VOFALqecBvx59wK2fd63hquVR20BcHQvQv4iG3RV7oGFfwvf5dKPBdUugqnFc+sbz4WUzsed/zKZsTxW9bCe5BwN+4jQ718X9ANDheqwnCmtpjUo84Dnt31Y7XY/k039t5iPPiwu0LYNoAP/ysT/gSwNuAPTJ1mtZR+PDqtXlLAmJjOFOIjrysxb5dXZl6qdr+9sJN8LLT+Tko737w0Vw5LB3TBnrgGE5iepO+8+VRhOnfZhg/3hORnNP1u0Yx90QTLxgnymwJPVHCkDAChJs7M/+cGVx1l9xIyc+gxORlW0YkJUoHQ7V0Qi4t2fagOpgT2gemsh8bV9xgMJywrBIF/Z4OLn3O1hjqUlJONOiOWRFXBNyE81KX1fFE8ROL23F9LuvKhPOKe34ePooM52XwGl5inGfqVwg7EQIeXcgSuipkr2iwIyLLiWIDVxfEbDivxCDGCiFP76m9wb4ueOfTt2RpoIhQ5ocUeqIIvteYz2esnw3nHTOX+9sbz99oVXeurAtlr6QOZR5EjzlRfK52tAIlsBj21eeshaH+brheIMB3AR+8lB9PFHftYkaR9QLLhNajfvvDw4d4DWjDdn6bJuWmy7Ooh/NS14k9i03nRa3mhm82npfoecVHfV+Mmz+PBab8fxnCecn4kMivbx2LpETpYKg0kN4uzDImXLFRpH2keLiaFSZhouQXOHOEmFem/cZa/9r0EQ8y3qIGbmKExx5KSIW5w+LY2SV/NW9wxbOqLuuhE8vnrXvdTgCX84m2vop9mvr0RCka+kIZdZ0yC3ZXF0Z2n8de8qRr1uNEpF8bc0QlD8WCoOA5XOO9z9oOS2ZMNi4/PKmb+72L2dNVIfDmp2zKfb9x3hD0donXOACrJOf8bkucJtT71kRuhJKtu1jpXTFrEO58xSlw5fH1sE86VBJAKh4E3/dIlPjKOoldfg5unWK7+uYnNK+o2S9a/U705hPvLDXZI21ZAl7XVZhxFJ37PpWnAI7OjABL3Kkxj0eFeKwGVwTMhqlCCDKcl866NCBEAdk8LoEOqCORlCgJQugmFbRBY0NSMReR7u0JHJiMlY2wPdaD3zXUika9flD1Oc+KSHdrQLnHE/ZvHeG4333j0mMXwc3nrLWrpMMg63M61jOAWCmHmcu8+RtyJwgnDNAHnRrDyIIrYE/mwxa6g+/Lq2p/wwmr3O3bNufoDWesFv6GNazRbd3V+HQKD1B2iOa6aPSu82hpj5nR73vWDu73LuabsAqBoFnH+4V9NyWfJP0V/4wIjx8Zl1cSqfMYOvQVCXdHbbRjF7oRJ1jkvVOPhbujjy5y6YmC6VJaiVSGSAi+vH9QnijIdW01k22VRAWrSrXDedHKK/NWNb7NI3Q0GNN1RsB9TpSMEtXR4pIaKUWqyO90JCVKB0MZzivCeFnpkhw/bMJoKBPLCw4SW3feENPK1XLCKgywh64sVmhNEAtcFxhKjobU2mqiq0LgCRvmcH+zZW7adUuT7dXbFk/TqEf+e5XDxfjsU+/DpSE4U1ZFFYzdG+zImL/17/ICwk2Mbli/XWgecVkbT5RMmCPmN5kShW6xKKs5+r0jZF8TMOCM21XHL4XL9iw2rpK3Vui1J+NZbzxzNawXJo0WtadFnca34nZ4bbJ3OBVd1iGDFPX/ZSK5dtwnkR3KIuyydcqw80Rxvz8Lk2Rnvsa9NGYM+7pbXmKsAF5V2oQVs+0vAlhNIB5VQlBnfYN0XyvDtG9C8ymyeVXEriFW4AclQwsm+yshAN28iyIHqjxkrTrdd5I0J0rEY9LJSEqUTobi4PEn0xRfaPiWV2n3aEfwYsUqh5rY5U9oBzi3fJfUVwP+PqDLtGIEuI9MXLCN4VlqhpiaRohduG0K2Xth3xjTNdycKCJlK7JdHbg+n3gXO9MWbMN5hZyaR5FKFNNLlAsYK0s0rWQxrcgEADbWybJ1K4u4hg3n5XNK6b52m2690UMWRkQkSLAR2vqCi3PMRhHjWyli0mY7Aj3OUGz/6PIi+MfzD2KFkrja+LhFw/vb9szM1IV4rq8764miA53Hec8aGbhazkuuB24FF5ZNRm9mvzemsWbEm9F3BxtvokIUIDFrOjzBdH8lBJe7hwBAVwW3r/lQwBWREyUZG9khPq40IRhUHgO+cqLI0HnHQvngc1qYCpTr7vZurG99IVSiW913kQncajU+A6z9Lgia/nJoTLeIy+bl5WVCQdvLiV3xaCFjMtn+lLsxq9rB96AL78pc2DnrGtn6OEohw0lGj0HsyrojHnOiON1LuIYeqjLu+36wr0v4m8y7UAVpjixsOC+PQiUZYp/jCXXIRkmYxN1je3LhhYEGxAEI8Okmmc9yJbnucYd5GzkvVJwVr0/oCFjL8mbY8EWi97ZSoiA7c2RyL7pO0d5PEM/IyojQx+Qr0sunYDJL7HhXH/Oyvi503tsDERZtZXJKQYB9SeNOFAKiFk1kKTJ+1BWwfTRjML9vdCG1HmhPFA8rSs5f59u77vRVsH0RPqpDgnskJUoHQ8Vg+xKWc/dBhxYiCcXCZt4ohVEWZV3i2GXTtcu4C+el+r3+BD7+r/xBHtn93fKEpyKaZLD2RCnp5tCugj8t5YbFBZHrieKxS31fdrhCesO6MgpSIwsuw4YNcOSoz5wo4ccMV9C8vpPXzBIqPGys5Oi+Ys8suXcybdVpL2g1ASEAu5bUz+an7ljgrZ2ENoADoTh2JpvUgVknqnBeoWHdHxG8Aw9YgRmpW3MVBp05gFYKCR608QyrEBzP5/p9sOcihnft7Ra/v8u1yA3TZ1SPndiXV7rCWPjbG5SZVVCrmRk3BQ/nJVOiWNRl7EFjwX+yz33k2bvg+BUz4NJjF5kRg2kPcP30lK3zct9hc6LYKIdtoeuJctXxS+Gms+1ytd721busync6khIlIS7EcBNIKAzKnCiepocu87Z9kb4SZcmMydpleFBerjX7SGl5zDnY+3u64Krjl+Z/MGijAWsligY+8dxjYd/KGejnMZaIrhnZ0sPBhQHT7zyr+5Bd6nr8uDYGhm3Q+2nsyjpsTiQjd379Ik5hY/ErQleFwFsv3Mz9zS6cl/g3bMxm7dY1Cvz9pduEv1UIgdsu2QoffvZOrfMpISxkcz22EFSysmjLdgNPVrFyhf8Mrw0f3s8uDJkiP4py0BLmBzptXPQh3nMo/12PVTgv932EOfLQ5yLiMdoTRctTieBCYLmGD4VBBakzbAiHQ3pb4MI3ZQ0/SrYtNWFqoCl6Y2w/0O2umTMF/uFZ22H9XH7+MFfAzCFef+DyBBLoQudEQT2mBT0/FDd03H/wcbjnwUftKulgJCVKB6OoWHgyIZFqPyjvMdd5MBkp0wOBZ4niEyZtnbNxDrx0/0ot4b0NsCTKmM0aiC/ip6yZiacFQczDjBLF6XgylW2aPwy3X7rdR9XBy4dEX3cFHadamhOF+cnOe42jRAnYqa5bUiVB1oFtTpQQaJB4ZMynJ4rfulwo/3XHWPa8VTgvOjk78xs2T56v5XfLOWvhhJXic6dCCEzq6YKdS6YjL80JRSAmPr5Wk6sanCgNpL8JBFnE/hwLfccrclz9vytGKFse2DrNWOUoQjesQ6F4HTWAPRcxT/XSieVJNsF6VjhvVn+j3gbo+W2UEsXDPQXriYJV5hrf/yHfp7qeSaT5f/7g4izB1IWuo0wbFuDp5fGp2HBe2JwoPrxAZevapxHcQ48d8VZ3uyMpUToYMeUTwjMVXslIcACby4xSGCWZKVaHjGZRk3esVghcfcIy2GoZw9KxI4q832p8hoRotsRrY93cKZm/x7ESugJAQCbssNuUYhIoqdDbVUW7O0tzojDv/MTNcyXPyfunQO9rAHCvsOHWZtgEfR9A3g3cNKyBxt5wBJlY3gSuxsg0/IGuxR9mT5BZgdooECoZwU12TGQxm+kWpQpU7nduxifpTcoBmfeFaI7ZsXc8xTSy7ETDOvm0XOQJwKwJNqY/Cy+KBdm5bqtUMqTX1OPGR/8UeUcN2XSIxPJaHkCIZ7HnIsZjJZNYHvBrXlct5OpsJODeA6aC9KqRrTMXa5C3N+PCIZp1yPxp/Ub1SD0SLcbZuTd8pHwUdg7z8jmvGB1U1090zjD3nYT19MZ8r4Mi8l+3C5ISpYPhIsmua8S6eSeEgYp3JUSSeDScDsWqEd+eC7qMoTx8izkdqmcv2b24+XlSTxVetn8VvkJNhBhfU6aqyD3vZqRXSQM6tGKVmk/dsQBmTukzbk9HYBzhkZeHheVirlxGyBbn4doYvsPInChFv4VJP6I8UXSrlXp62ShRxL+hPVGMW1dVrDCy0HzvxG8mAOjHIm8AHR3IZJ4JywgUJ5bCTBfQVUyVBTphr8ryZjpCdd5jdjlRCGou6ioclO0iFyzmqV4mnBcmsT39vC7uvOfP1F/6C9nHkkMrURo0KJ6zIpFI/1QWqa9dHAUvOnkFmixfoPvdOJyXhQJfhBjuWDyDzJHJvfAfLzkBvn3DycJydWUorg0v4bxkfIj75pqI2H41eiQlSkJwcN1bJ75Ubd5lYVATzGDqTaLDALmAieUbaf5rRyd2jbiwjqnVamKlFap28dP0N1956T5YNDJg3EKZrf+KylXxlK3z4Mlb8gn4ZHB1qaVfWRVbW9U9vP6zDSugA9ej5+rSDcCE8zKoI8TUbOw/L//4D/035gBG4bww9WrSUZFYzblKLM9CZrFGLOaai7BIJu0mFAOjvciTpa7qRJCeYw52f1ENRNE2WzhYLg6P72xen18DFj3hWnGbkFbTlmR2I3MHcJv20JeiZ+m5gQ7nhXgu64nChPPKfOZydCg6AAB+8Ye/AADAu778v+gyoaZghVEe+TIwueYUudKCt4ejcqIY7p+m/Yu9E4VCrIZVQiDlPCKlwMLpAzBjsFdaFp3nDPWUHuRGq/7GaixpUYyRlCgdjBiXDV/BIv89ISx8CCIbUHtZmJeV16tX2C6Hg3lZF+VZyML81EAsONPpM99hVrxbv0hMDK3HE/HM+ZrKDly7RJuJdnGprbfdgipEkKpFnidKyHPCeTgvTn2mirbsxd5Pp2xfPA1uOXedcfmOCMEk3DsEgh9En0hzolh0anZtZn+ThVyky8kEWj7XZidMpfaAxHAjFAUuTE5F6zr3t+Qs1NwDsoI/+fPBow1gF2AkCxXbPTohXsp0R7URolollvfQR6Ijj27LYV556KNyotTrxipo9N7/wUcO575TzVu+YZE+v69CpYIN59XILM/7rfVZtBc+78RlcNzyEUn9+bmM8/7VL8Otx6yYJcStvueSrVZV2MlSzMsq60bWbxqeqr5G8M86hwHdLtZ0CudljqRE6WAUl1iec8Az/+qUTYgLNtPK5mCKfWa0vK30sCTnoaFqR69+lXWWaDxVFh00ir5YFt2+FAraZg72woXbF4ShRQGZ9TsLqSCXACybORkAAM7eNEf8GKIx7oVR0KlliIHO6zbTJujwFSZ1YMoc2LcMnr5zoUHtjTZiXpx5+KLWJAyjqO98ebehhY6a9eIvrvLfdd+7bHOv3eHLSEYFaSxyf81KgbOkNuPdbCDXKQWwKIkcxb6ChpELaCiHOM/ZhPPCzhOdvsS8CzqcF+IxWoly+Oi4JDSSOVwK9W2NVXh7DSGMJ4qgDdt9yEQugNk/M3cUoqMDNuvMEPfQnUumweiUfvWDEsS8DWNoM1aiAH6u+egjV+HTdZE8UcyRlCgdjfgWTtTCzgQ9GDE+iiplwlEb6wnN501WTlNRqNnYh5+9E3YtmZ6rx1U7MmazVgOu5IwQgLnD/fC2izbDPzxre+73wb4uZRsql3edfvK9b8iqb8xJn1ZMvt5Pv16NS7ri0U8/bw/8x0tOgC0LpynqkVdUVDg0X+CvBbN3pPsm1n7SvtwX+Bp1K9I8AepwXgiBKNMOhhYRbMbaPJwXrg4bKENZCn4+b/NcD9QkmMLAucMbVPKWBq2mIWPqlWiR1GxX6ZmtaEJXloTzgLMrX6/DrbTV1DteZ/iwfFqkx6xzWCWWJ7i5qXcHQAjN0d4i6uf6KCXSoaNjGYE89o7mCzqGRbbtoLq06Yii4pPMUIP8XMHU5dsLeuO8oczfco9EvbpdGBwIH4t0E8Pur+da8HtoRRr1oCvjPHlOFD5lLoZK5mGeIEdSonQwQnqi3HrxMc3PvEXf+E4lTI10b09wBKUwSnr5Djg5TNwuG3Nck86ZU/rgqTtangjuwweJf6tBDWYM8pN+AwCctXEOHL9iRu57dhzbYdn62ntU1nFEw0JKBybvU6cFV1DuiEKgr7sKC6fLvawwLfHu8iHPCddN8eozbUMUpxtNS4B+jFW5owOlsNPDKxKJAMNCvpWZa6xwEnvXUq19XxDVfeDEZd7aTNCHRJTkZT/g3jmwZSVPitaZCwMQodBEox5d5QLN1pqEazIRQBUJfDivSAh2CK3QrJyR7bIJGYlsW+eahVkXLhPLd1HrY85wvzixPEe4bzufVN3CC9HsQ8HHesOKqvctauKGLUS9K0W7Rv/kxlRQ7l8O7MFVCG75InxoOb0zBgOfcsV6uEQ1dccsmArzpup74xCsthz8nAlYIyXXGEvhvIyRlCgJ3rF14VRYP2+4aDISHEG139rEYcYc/ryLoS2DqFvWzBOFGLXFllFaOWk2oPJEOXXNLHjOCUvh1ou3oOvMM5l2Fki7lkyHiyxCWtkyqC6sfoTl7YoHhQ6t0pwoOkImxe+8i7Gvy9zsobxCMYh8xbANum98Wd1Zz/8yLQAwC7cmFPDQnx1aImKFRap62aNcHvaInmt67aNDKDhWVpVt7rU/al7C4iCaFcKFx5fsKRsL0yxfmC/AY8W/cf1J6ooBYFJvlfu9nBdyJ6TWRZG5GpvPUf9fBHT5Krs7k8UZg3zu0SNj+DqFAuHW99hcYdhX+6+bToXv3ngyDPR2iXOycN7W9wzhe6LYgVe+QkjmvUUrsCET8GlswhbF7M1MNC/vhpgu+Q3pmaK1X7UnjJQoGjMgNO/os7kUzsscSYnSwQi1bDCRlmxD4iS0B1wyUb5R2FxVMaK4x5pQMZuVCoGX7V8F+9eNImvMt22kOKI+rxwdhIt3FpsXRCUINZ0OmFBAPqxeTBSPOpY6svdyyaiG9GS4/ozVue9cX7y43piGdWX7Js7DVVfgX3QeC5Nwa5j1Sz+DC/8lfsounFfrcy6xPFLY7FPpbIJ2tCQvM+Sxv0XSSBuhrcRQRFlW8psD5Z/NWjHZC0c5hgC89gZ6uoTPdSJcKXpdoehz0AbYPnr00FF8nYhnaFajuyougT0/p/R1w/TJ9dyQmfNbakBkP25mieWtm+XUmb2biOiSketLDhXb3UEFl6TY578ppiymbvTd0XB/dGEYYQqTnCgu6EiJ5c2RlCgdjFDrplarKS/Yje/UMa/jOfQS3EMdzssPc6p74NZq+odXc44b0IlxmcY/wDwued50i2Df0cWFr8hLo9cQNBgBq6+2NWvWuXS4oFkWsqgBXpxrEZ02XnIA/HdyfSTxz0ezRui+MblYhVhzMV1kTaE2EnEP2XhiY7/zIBU4I9ePfoJ3N8+1wVTqcBBvHnO5lmhlIfI5FqJ1xn492Nctrl/YrqhuWlgrbhPATkA5qUfgiSJZhS6NI3Tg3YJc48EityCdtusCyWIUQ2x1PA9fAIBHDuM9UURnzpT+Lu4zLuYxpn1u/chHTbudX7/dKuHVWSXAeKLwd5wG26Dmk8worNXy+6W2JwrirkE/awKXvG5GeZX/FVmH4PtIFbQYI77+bv65ha3f9xyQQR7Oy9+YHB1LShRTJCVKB8On+3O2HTtmLdYNPcE9bC7QIWdJDWrahxph/hXhuOUj8nocW8RJXdwNtwi2Rm4IHIUAQPa8Nj0+J4dnqx8TjxFXbeee13hW6oni8IW401dQve2JJzMAcAWXihr2khgjqxxKaKqL1523Hv0sZg2blDNt0xVvZ54Txc+gqqr1pbxJcAuZLi5UThR82XphHs2qtCG3X7INVsyaDO955jb9dh08b2MzIFKiSGlA85xuRQ/e77JEJ8yLA4OhAhSJOniiReJmgPwaP2vjHO5zWp4onHc5f8s8OGXNrObfmeTvknc3GUMhH2Mh0xDRoZrvvD3UB59VqWA9UcJznpjXzXr/+odLD13Z49ixFivw9WgJCdXa+cAVO7zW33ouLHzcIRpInijmSP66Cd5RtxJo/a1rARLzhp4ghsmw2eTOCC1krxAAvJ0UNIlXtTVrijzvgmtrHmlOFAHzq/YYk/+NgXYdnhkBZcxlB4LusNBvWCsZqaMLg3I+F3xA8Ogb5axhLFzGs6bHy8gTJUDX/vEvh/03YoBCxfEYpTKILSePWsQ4lo059rIlTyyv16YOfIY8SHAJAV9B/IyVVOGomNNynlNO7b5VM2Hfqpny+iXCEVU+hUz73IrM94FJgnBe0nMdOXrHLBg2oKg4oBUoJTL5M6Xz08/bA+vmDtm1zfL2gucOHR03rhMA4A3nb4Qv/c/vm39jlSgmECaWZ/8OMEH4Rms+lNNZHkSoRGl4oqgvr0aoQc2on1ldLn6ds3/jSspDHLsbnyITy/uEjOcFAPjQFTvhmAVT7drAKqAyykNHsgdZuFyPo5JSopgjeaJ0MGJUPiotOsOQkVAQXFgMhkA9nJeumI1M/KuuW1qPY5myHyF+tlJb61JCig3747NppUIK8Yx52/7KuMiJggFXKCt62PLMw47Dl15ygnkbDi/APoUGRcHne1xx3GKjcmq+xYcAQ/zbuKdbkexsyspzfe1Xqr2yTSZ5m8PEE8X1yNL1mQpBhOG8NKi1EWQRwecGbO54A6LE8liCJNi+eJo2Pb6xcf6w8Dcto48CtyA9IxeN8EXUZzd8OMkYaInofubuRRo1ZuvgVYml3cRRSie3m20Xqu+IfOWrXcjrPKqEZPrUmTDZCdTvmgmTTcD74sXWjjHCcmGkVjZuyffearrPu5r18nBejhrhICWWN0dSonQwCksszxUSNX4q27ae4BLqnChiBtJKAWNSJuBUzV6W1UL3fCnJ8zJPFNNwXnlTnfwzGUoxDK8cz9ojFoC6ufYJvresHFPex1wjRP9SpVPGVcQOkxA+4jAIHmhhvps91Ad9FnF5eTBVdGbCeZl4HqGeaZ8z+8C+5c3PXGWW4F2VPaDZRah+r9/6ub+N4Q14BfXWobP/032jO1/Rc8jQgED8ffvM3XaBl7POYo9qzJHBvrxnhoszTsxXIHgixSN2OVH4nigyYTF27AZ64wuCcdwyVRhddR1B7wQO2irq7MZ4mX/uRXthxmCvcZ2t71s/ZIxKJO9u0i+iZRHLEeODjgphc6LwIeMjaMWLKY1stJMGbSqY8semdGIVH7ddstWsgQm4MFr0gf1rR6HL0mpTVtrJnljggmXXSV93i8HwSVUK52WOpETpYASzGtDIws0XXFBVOSEowSdsppXqfJUeoObNaqMGBvHXJx4/YpDESxm1wQImKVF0PcbMBLjZMqo2z9k0F/7pObu128HR4g+qumMS9OkllrcXtmBQdE4N181z9TSGHVZpQ0+UGGHqHWgzJLJ5PzZurkVxMU2k+4THeZimeDkg9UQRTGyf+5fUw2ri3yuPXwq7lkyHK/cuoWjyRxShG6e/1xDW2tzxerr44gEXHqY2yX+LgJaAtcBdSKdlrWeJWz6CXeK8Ja+viNdr14Ulf7Zufh/l7jEa9ZsrFTipxr0oUbIB7JQeMp7WBq9dXDiv/Nj4hOy8IJnPCAW6C2otjUt4w71hnjjU38U7F8KTt8xD1S2CT9ZSJxyjj3sn25/nb5nf/Jw8UeJEUqIkeAcmsXw7WbMmmMPGgsLqQqtZlGf5gm3i8SNamVS0oUuXLLG86SXcxCpIWl/96qF8bumMAbuGDECa/5q9pAuLU1PoVksIvox8zN29ED9sg7Pqs21x2/d/dpl7otAX+3Bn7AkrZ8DWhVPhlU9Y47xur29hamGI9g50B5kwZv60Seb1UnXq7P50OW0LfaxQyVE9CcVClmg4VGJ5XY/Kof5u+NCzd8KF2xc0fxOG83Igzdb1fODtQTaiEdGZI+MXsXDtqQngNzy1TnicMhkrmNDqwwOGm/tH20hNsBapzxl+SFKXyR6k6wVpA9VU5/1um6+HGyKMZN9PZNGOvUfy6PvSi09Als2WXjFrUFmGVXxhxmqov1vbwC/bJv5ZU2BDy/mQuy2bOQj/8txjxW1aynh8ygqxcwAg23fOUqIw9dhGEsAiKVHMkZQoCd7BCpxFFw8A9QZbIv40wQBqi16cJUcI6Aug6yV0kiWatNbMvYK2dvIgtEAwmZnvNAUG2vTYKnEkzJVt/xV16Ta58GvJhqT7vF67pu2wsPW+VHlKugDvfUwFizQTbpZY3qzdpTMmwz8+ZzfsXTHDqHzZYHpu6X5PQ3RRnjvcD3OH+5XlUTAO51jMpibOp5E4x5ggDMkK4v20sNBDHEvyBlwoFMTvpVbQ5GL6O4QwdKGkHey6d9FvrtHfI1bs1OdlfDRbocDXISS7B3AToWvWydbBK+9z3snkGhkoBMEnrZppTQsvJ5ovT5RMThTBc43vTWjo7VaLKVml/HWnr4Kh/u7m36J2dadDV4XAZ15wnF4hBmKeT7ceyW8O6rCBq/xSuuVdeMxh71p+lKPZeezaA1CEFM7LHIUqURYtWlTXLFL/XXfddZln7r77bjjrrLNgYGAARkZG4PnPfz4cPny4IIrbC8GieUEt66ooEaiq9om01ONH4yAws3IyV6Jh23vmroWcevWIrUENdi2Vx1DOtTHRhK0nimsBlUk4LxXynij2ll2YOuhx5MUw14FteSxQFqeebryurP14cHVfjcuyM09MCPpMm8haXkbVkVFCZdmNKRcKIoHzjiXT7BTOtIWdxgmQ8USRGjuYE+fayCatiGIgm1V+jDrEqNXc8TmY9tB1EHU9qq7Sv+OphTa0sPj2S7bBMyh+uozr6cYzV8O2RVOlScx1DJJCnQW8ZnwaxbTKOFAcslU4MBzBeILQShT5HqTVNABk6VUnfhf/dtsl24S/NYyAVMZA/PBWduMmUkrR1drKlGQGRPK8KlkCp03qwRmiMM+o5vbFOxfC3OF+S/5K/xcVZk3J5g6KNScKgN0ZITOyMK6TZD/jPVHcI++JQt/f/MEmh2Kno3BPlJtvvhnuu+++5n833nhj87exsTE488wz4ZFHHoGvfvWr8OEPfxj+6Z/+Ca699toCKW4f6FyQbUEUmwFpPicvmxAn5gz1OanHVPC6bs4QirkfntQNr3ARYqYG8MbzN2gVaVBn4omSFe7hn8VAxnC5UrTa0owRKLCgY6/q7iGnrJkF73z6llZ5iTO8bO/CQBkKSIOx02rXRMmpVb+P60IevPkrmtM+TrwQp5NxThSqXITGv+WDUGhqJty3GhJPXheyqbZEEi4xYxXvsM1sG6p60iQvA6Q5SAoUQnOfI+K/0dbnBnRgaiaCzw3oe16qkzzTZ8qq2YNKA7nYcflxS+BjV+2GyY6S3av23+2LpiHq8A8C4RQ+vLZpcD1RdBVCmkJzTB4kHWTrrlGfOfUb9js26g7Pspw0/88dCGF5bVE4L4oG9jdEG/qEsaGQRHWrlcaC6o2BMgZENnDbM7fCeZvnwtUnLGPawJUnANx9z3fILF9lrb1cNN48eBhn0Z0WuScsnzk58/eMwZbibSx5ohijcCXK4OAgjI6ONv+bPLk10J/73Ofgxz/+Mbz//e+HzZs3w8knnwxvetOb4N3vfjccPHhQWOehQ4fg4MGDmf8S8lCtm49f7SZJM5tX3kViwoRy4qyNc6S/qxgM3s83nrkahiZ1Bw33VAOA4Uk9Rm3Ze6Lgfse+kpGXiObvJgyHTHiBKm+xmzx950KYM4QLiWPLS2EYXjOvLv0y6koBjl2G88CSvZfOfFCNo45ywN5SjvddAGbakFOrYm6SlpC9vh/ln58XqbCKWi2NoeJnwe+i6YhpukL4fWF/kRTj9ku2oTz0pDyex+UiFpb4azPBLUT7uS/+rgY1hVInW5g+j7Cx52VoVL+AyWMk9nIh3Id4z1sGr+R+m0vG3AHejliFA8bg5axN4jvQvpUzoFoh8HfP3KpJ4UT7AbwnXeylOd6eQ7e2J4rw+9YvND8kC2FjdF+heDRa2ZEL0aNTJ/N3rVaD3/75MXjBh78vLcdTtvg4A+uJ5VsQdSnWYJdHImYe1IA3p9TIhYALo73kf23Q9kmrZ8GbL9gEA6wiBKtEIQBfv/5EuOr4pda0YGF9RjiWH+bKIF8+44HlyDyPNXqgz1rbIdm5ZDq86qyWAXEXvRemnCjGKFyJ8vrXvx6mT58OmzZtgr/6q7/KhOr6+te/DuvWrYM5c1pMx2mnnQaHDh2C7373u8I6X/e618HQ0FDzv/nz53t9h3bF5gVTndWV2ZckgqhkSRg/eNZt/ER2eZygiJGvZpjyv0+hYp9iUNQcazT7uIknCqce3NNquBAE5ChgBQ8OhM8hL+iEAEySxMh2CRPFIQaedCgwOtQHzz9xmfLZUMrykMuZ11SI5t3kRLFXZIq+awdYJbxU/s5/wqYrfdQJwFwOmYN94fQBeMOT+R6YdDldpR+WZteWiO06l2OHSOiQt2ymfnNNBFbQxP6N0UtrTKzG+372hXuZdolyT8ryhS60KHKlDEA+2e1TdywAAIBjl03PlLnu9FWwalSd3LkUMJx8p68bhR/edCpMG8AZXD1pyzz4n1v2w4mrZpk1qA19gaEJTl49E950/kaqPpKZmi6uIBj+hm5HGs7Lsn1VjgH0XYZ5pxoAvPITP1IW43ui2HUyr3srJKtcFCeWl1SiAIpv5TSbTRovOFOYvcwVHyKD0EhAsx4ZDei8HkBgsK8bloyIPYxdw8oYAtzxiqIyRbKE7DTOzE/HhNFz5GhSohijUCXKC17wAvjwhz8MX/ziF+HAgQPw1re+Fa6++urm7/fffz/MmpVlJqZOnQo9PT1w//33C+u9/vrr4aGHHmr+d88993h7hzKDd96tmT3FezvNxNecZ9Oltpygx9gmVIPJ+DeUOraHs0mbJq0cUniiqKwasKweVigYIsyPabgb+nfU6zhkOuhEo4fGxnGWoSZAFDdpQy2AMa8TIxRwtZer6uFdGETvbmsxxK3X8foRXVbN6qIukqYEWdBSpuO8vseY9ZdyrXkQ7sst9u3UM7YwSUqNqlfFP5RqxnUwBNtwV6XiaQQtFKSSooTYJ6tulO7vqUJPV+tKjvV8kMHmtBO9Fvu+K2YNwg9eeSq871k7Mt/vWTYCH7pipwUF8QAtuANGmUzqQkqdurqqYcQyaJ6aU04XF21fANMnt3hGmWJS9p0MQv6cGY8mJIvDhN+il8W45D6M8VYSoVYDePBRdU5gUQgx13sr6w2rSiyvAp/3RZaVfCGqIqtoQbThoAMxPIpNO+M1g3Em0j+dwoozJXKPQJN+mznYCkWvsyfa5p3hQSQrZT+bQlTD2HhKimIK56f1TTfdBGyyePa/73znOwAA8KIXvQiOP/542LBhA1x++eVw6623wm233QYPPPBAsz7e5bRWq0kvrb29vTBlypTMfwl5sAfb2RvneFFi1HN+5Q8run3C/Auc3xLihSt3RhOr/BWz6tZuaOWCNlV5mOhQbDxRePWY/s7CBzPAVmkiZyhSIEaAwKSelov044fVIdhMqVUrkMxiVxe9bxYV0gdA/O5lDf1q2l+0wMvE44y3BnMCEO1a7eCrvZBKeBcQCUVc7ucCfwHlt6KpxoZ2bZZ1RDMp3Lc+AQPevOrtqsA7Lj4muKdwPbG8+cFga4Qiel/hOtZoz8zYp9GMSBGa/35oUjf3fPHBWxYB4/CjHl/fRyjHUGDplCUUR9eJ6OxsYvn82lg3ty4rOn/rvNxvKohyg2CjNGBQgxqqLNcTxXpu8OZb9jthOC9JThRlqwjCeWOJyz1CmL/RVGEfzEFs/KI53wXPP3LoKN4TZeIxjFLTFWzPd9dygQu2ZSMV6fYdAD5XkQrsus1GErCrmzAKT9pbPHmimMNNJjUKBw4cgAsvvFD6zKJFi7jf79xZt1r5xS9+AdOnT4fR0VH45je/mXnmT3/6Exw5ciTnoZKgD5bBroEfIVONuTlL94IAlr4J7jGG1Auorcr12n3n07c0w87ZHDKYg/3vnrEVLn/vd8zbmPh3+czJ8OWf/UGrrM6y1O0GH54o+UuSfZ26QmDbJukL16MyJYplQxjFoW4T737GVrj6A+KQl416ddEogpmPUoshjTcy8UTxBb6S3237Lj1RfKRE0SGlTOE52XAOXCGVqKypYttKcSMScloqhKiyOkJYupytdb6wDUuPxtzzJZqf7Y5vvvwkGJ7UIwwF19ttriHjXytwY8/uvXwBsEXeA4u9Ibtf5X9nV6+OskhEV1VivZ37O6BSMwYDCdu9NyR0+EqeEaRuWzRUa8qkHZFiJqPcp5UonPnyj1fthl8/8CgM9FbhNf/6E632s+G8tIqiUavhziwf4bx4YM95Mb+AzYnCU6bhaMnnrqJ/45dh+WNTfkBLySp41tXoHHz8CHrtNJUoIe9QVjyv/RM0nrJ1Xoav0NsT/UPXU0oFkpnvrT/GxiI4PEsK5yzOyMgIrFq1SvpfX18ft+ydd94JAACzZ88GAIBdu3bBj370I7jvvvuaz3zuc5+D3t5e2LJli2vSEzwis3glDFNJ+M8EBjTzJI81qxIY6wlJTls7ii4L0GBClY9xsZKK72xz5LzolBXaZWjeVEW+7lrywUCx48y2sXhkQCmwzJbHvY9on7F9xcckIdhs6/ahBDhlzSzlWvMtdAjlieLiAu6yLV7T//epm9Fx0XlgL5LHLR9BlnPLhNfrac9TOiZPFOxex6fZjhq6tOkWITSiF31vWW/r9/acm+0GnrCtcV6x58bOJdMAAOBpOxbChnlDzml579d/Df/y/XuFv6s4Ult9oW4OGBHfxDvvbc54Ufuy92Vp4L3bIJsEuQTQUooZlgsNTM4dp+1l5ipkDhfePNH2REE8TisAeYqGvu4qrBwdNOqXbDivTGb5DHQ8y9nHPv1f9yE9UTh1EffnI7sXCFUoDU8Ug+ZRieVzfYwrl1UI85//56t3U/VOKOZseEVEWVTIL8H3Dz9+VGOc84pGbPumsKlbpaQWKQlk6MpMYntlmA3Y9ZNR8jluj64ueaKYozDn969//evwlre8Bb7//e/DXXfdBR/96EfhyiuvhLPPPhsWLKgnqjv11FNhzZo18PSnPx3uvPNO+Pd//3d48YtfDFdccUUK0eUAvGXjYymxIRyKDPOSYA/exWwMeVszttilfhdZu9hMHd15ZxbOq97I5N4umCy7SHLrprUobhdJ1aA+Wyuxf7xqF6IQvk1+aAM7Zo3GY4ePIsqYtaec84hnTOo1CWGi54WgXT2/HoOVLWrbh+KIbYrXxBM2zIHv3ngyzJvab9SGuSeK+pIoA69IiBxKMvhUkJnW7SP/kGmbtv1jnt8FN9d8Cu6EgufEV0YF2TZM71m3X7oN/u6Z2wAAYKC3Cz55YI9Re7Lh/+2fH4MHHxHnGRBZuDdgawQhEuFg1kn2+fzvNmHKhGHGqAMgJ3xj+4pT/v974XHGNBUF/NmQFZC3i1I3+xoO3gkxT3RbEZ6x1NfZcF5imPA4GU8USijJX4Nmffjij/3AqJx5i1R5Li+I1KJYtIFZQvWQ8eJyormB2bunMDmNbOGbR3noMbwniqht1d8NTKLyhpq2pV0e+Rz27GPngEk4L1f3Sla25fL8ICDmNcaSEsUYhSlRent74SMf+QiccMIJsGbNGnjlK18JV1xxBXzoQx9qPlOtVuFf//Vfoa+vD4499lh4ylOeAueeey688Y1vLIrs9gJn3djE0JUhY4UiEXimxKDlBM8d0Myax53gW/yc/RwzEkDbtKfjiaLZkih8hg7ef1k2qWj+Mp39YvrkXu02itwbHj08Zm1FbQf3ii4jRaAGHSHDbH3/lafAWy/Y1Pzb11yxs0Azy20jqguDqkTgZdy2jrWWozZDwMpCTvW7cO8wb1Mk6LGOnUx9NmUHdRXz7taFm3oS/II7r0jmHwAA2LdyptzgxAL4uUIkf/HPORfGBqIqjlK8dhHzXWoEh3h23tRJjinyD71+lp+5UmMgWy9CzXln0pqRQU9uDWUb568h92cI9myzNdzx6eXty9BDWR5BC8+7B0CuW1H1lemaQL0v4X5k6pEWk5blQRSWWvc9ZYZi2jlRFI/z6ls7Zwq89onrUe1k2tQukS0t3z/1kbkjsZoGKSXuwa4FTCQBra1GIIdNnijmKMyv9phjjoFvfOMbyucWLFgAn/70pwNQ1HlwlQwc005GAyrbBBEHVkJ8EDFPulAJgGSMhpXnAeYZW+EUtjznObp3sd48IZVKe5aPwGlrZ8Fn//t33N9545q1FJIDK4AWWVrYXk4flYbzsr34ysubCt+V4bz0q9SCK49DjDJoeFIPTJ/cCpflzVuB06eulUWYNtBbiUJQoaYlj6I9UXyhLlQylVBY/cyhRV2CEEHYDsvdzlxIY1+HLZIRTnmhy7fo1W3BGyruK74SyxPCN2r7/cOHWs8oBPb5MDd4YkXvXaWNbphnssZynafUZN+5Oad9tOWijoDjk5MBKJzrdWkT8UiZOY+s02RN03Ofvg/n1qDlujBXKrgfb7bPxeG86r+49Cpn62efw0RpqjBrNYT8KYSsAZ1Hpvk8s3Zy3hkA9A14z7IReP/lWcNJLOwM0fBnOWau1Wp5QzMT8uYM98Ef/3JI/SCCHhpZGYnre2YLY+PIpMYJORTmiZJQPEIm4sMKTHm/LZw+4JqcBMcYyzCN9c8vPHl57jnVAYhJsl0UaNpla2cVlTslU15x4ZVBzxNFDyE8BsyUAPZ1mIJt6jFZYnlBGSwwDK9J3T48UbTal/5WzEK29rScIPuq45e2vtJRCCHem1efqbdYkJwoCiFjWWCqqKo/q1aEukaFEH5uCc99Lqqe/t5XyFbTUGPtElanXYAN52WK09eNwuvO07eS5UHFh4gsi9H1C5SPolofeuwI9yFet9kcd6Jx0BmfkN6oPqGTPyTUG5+1cY5VeT2+hf/ZFOyS4fWtbU4UvrObpnTZEDXBZyfVGxb2YfySU6II9hsbrrvRhszol9vHiPetMp2C9+Aw70xhDizNKtm5PNTfLfxNWAdaqehu8vg0dnUR/cSkT0Ym98LzT1ym3TYLdo6zSj4bEMLMCuqP5IlijqRESfCOek4UnDCH/e30daNwy7nrPFGW4Ao8RfZxy2fA9195CpyyZlbzO9cKAF0I86k4bPhtF23OMWe2oA9Xl5YYAH6Ya7ZKF0yYtuDdwXsdu2w6AAA8bccCYfu2zSg9i0zrVfxuolDQGUZXjLfyPZrPmSspdXHd6atabeF1CsYwZfTpfcjIE4VTRFRLdzWM+MhbqDYwPwdcr2Hs87y7j3WOBqq46dVKWwCGvfSrfm8PmW3bg59Yvg4X58Y7Lt4Ce1fMyNXNa08Flt/CJE/XeQOxUI0vuH/K1nncdriJ5TVXMEaJoxPOq13WI/Y9CDCGC37Igb97xlY4wBHa6Z2Ntj6LWk3lWpb8LPzOqGmDimz3IFmOgTqf4VbIjYP7sc4nlheE8+Iklj9nU10JeOXxS5rf0b8vmzkZXn32WujpwokpZfuy6M2zcinCDUPKDT+fewZFovazOvW877LtsHLWINx+6TaN/YpM1CVfj3mPIzPOkBC/dzNs3WdPKKAvO25xJrF8XdGgvzYJAGxfPB3ZuriuvCcKfX/jl9W5xovO9xvPXIOvJCGDwsJ5JRQPbmJ5DwpJNtRTc+PmPMtuYG9+yqaMFa6vnC0JdhCF8xqe1KO5ycsPsEI9UTSeZd1fARjaZZVxcxXh6dC1xjBLLO9WKYIRQmKazIST0KhfhL+/dDvc++fHYOH0ATh81M7ldcO8Ifiv3zyU+x7nneB+jLx7okjuPi4vHY0zoaaKDwH2Icx4tU4fyOb3kbWBmsPccmYT2EeoGREpX37pPtj1ui/YNVgkWEGPzhxV/S54wN4rw/0idhGDXGooY1Q7koaCPNwS9MC3HG4IdNy04UwIy/7NfGGyx/Z2VeDQBD8hKl6t5D3NPvvCvcJ8crx+0zUwramPUEY4yQjfMkIaEswTJcS1EPsmWe9PP+9/MmWYFhqm7yTyuALgryF9Rbz8b1672Lp0Qc9HG5kF7zwzpc1WgM3rO9YLT+dV3/DkjfCMXYtg0/xh7u9X7l0C52+dj6qL1y7mXXMGUJxCrlcwKlSrQb0b5g3DZ1+0FwAADh1VR06Qtadan1bRn6x4Xjf8wd9euAled956GOjtyt3JdRVQAPV1YENXV4XAkbG8akp21tqCrtu10W8nIXmidDB4B4+vPCn0BtNYr5iW6gd/WuAxgTdumXBemmVpYBhnTzMU5g73o5+WMcaEuA9joJMTRRchQp2YHNIq6xh1ee0mc411VyvNcIK2gtD1c4e436u6plox2wFVZWz2esxlyVdIHwxE1VtH86II/z8XbYZLdi+CMzfMtqsUAVMelx4DZ54ognpmD+X3zzKd3bhLN/+pIpT7FSLIiUIs93S6KDdcmLpu21Asps8J92gtahK8Q5bU2cNislNWSn4DM3oxHoJdnE2/ytzYsQYGJhApRVkaRGUA2id/Ft5zICtM4wryPe5GusYprvZdXRCC8K73oEzFVmm7B9Hv5jqclylpPmadfk6UFnq6KrBl4VQmJ4UZlTXgKUT4+xcNln7sPdUVe2VTp+zxEInlTWFTF1F4z8kUtdnnCAz01n0IaCNSokEfPVdMDFF5dbHHNX3WuhgCF4alCVkkJUoHgydE82HRw1YpZVC4Qhtk2YTCIJ83+Ellk1jeFp88cCxsXjAsbptqWv5GfAs8m6lLX4bdxzs1oUgPKubUlzdGq35/aLlEG5ZXFOyuVszqVgpYDKrUIET2pG7YCRmaoQICM4hnb5wDN529Nje35e9tBtN8HWxOFN0x51pCFn0Ee2o/p7TVEUgpiNLeuxCPEyDc8bQO50V91pkudDmZoNUGyn7202xCADTGzhU/YqpwUz3HFuPRq/SoFtRPz28ez5Q/B+TtsEpWleGESAGQpRd3zlYq4e5rhZ9JFAin4+ZNxRtoxQ4XrChG2aa7D7icA7Z7kMwDzJU1vS5s1yJvr8n1k8OcKNb0CvZVGiz9qnu7E0G2oA6XeyVaWdj8V36/yHkcWRjg2b6l67WTUeJprM2MIUTF7r26JsLt5HOiuH3ZrDF7RIdmiZGUKAlNeAuVVdO4sPihIKFA0Aylao4pk3QaTpA9y0YAAOCGM1fzqyUA0yf3wmlrR1H1yV6DEP4F2JkCyJFwoAEfh2leIGkpjCX6Cdht3sp2rPq680er6H0H++RRNbuRMYFZqMbVdwSMYKE8vL9JC7Zv9MAjh43KYRKxcstRU8eVJwoGvBjYscNGuFGEdWjdE0WcW8IU9Nwyjbcso8LnnEhGNuUAb89uDJ27XFqE+9mmnokvMjCxQsWEfOJ7oogVvc7nvkDgonrfJ2+ZByesnAErZw26padAEMDtW/Xn8g++7aJj4KRVM+GjV+5yTpsptNhvB3NLmrOOez9wtA8g1lqujOUpKgpv7aJ+43Cb4H6PWDQykPlb+N5IftCYPE67mHOEfYarEEcYEunQjaELVZ9MgY32RCGo9lwaWlp58RCVkZrBWWz4chWENym6rkZxSU6UxNvGiZQTpYPBD+flB/jE8vlLAv1NyolSArDWbxpj5uucuOHM1TB9oAdmTunjtzvxr1Q5onFA895DZOXHwjZXkS4jUQmgSjfKu5L7WyKY433nT6agbEdnDF5+xmp4zvu/C7964FHu7z1VswSgqvdXXfS4dTpqX8vK32AcRUVsFS78scbj4cePItpwJ1BgPVF0wV1Xub/bg7m3ea8iLjhEpERBkHLtKSvgTZ//mVm7mGc8dYfpZT/dP8uD6EJAKe4rRmsfwQtWOYxZzhMFyVOaQHRnU/GLbzx/o2NKioeekJQqN9GHC6ZPgtsu2QYAAL/4/V9ckpaBLl9lNHUdTDS2DheeKOwKUN3BpDVZ3omyOVHM67F5BxbWHqpM8eefuAxGBPmZWBh5orB1SCrhhvOiPwtenRWgmwrUdSC8R7psQ7My1ePsGWc1p20UiIo9y2SKZxLLA/48r1KPVSsK7Q4Hu5dOh6/98oE6DdWGJ0oWRPDZBKwsNSll3CB5onQw+MJa90oK9oCTxsrnfZcWe8dAxegRMA1DBEIFimsQsEvUpWRoVL/rMlAB1pcqvIvSQgn0mRSd+l3ANJzUrCl98InnHiv83TScl7KI4TpyAZfDwU0q6WnAQ4S34lXnIieKSRX88AbteR4TYqasrJd1TAtOVSGc+6rSe1fMkNRqBtbS2LeilF9Pe87NdgM/EXB97JShsLCCUML/zLanrEdRjqdU0OHhRHR2VfO15ENH+pvvPGVA/Xu7M8UHfNvW6YTQLVPc+XDh1thcMdmzi59A3Q1tXnhnC9QFtRblDcu6HuoVo3lPM5ESopkTBXG/b3626iOCUoiwT2DDcNspAvhl9RUfEjmaZmV5zxr2nMnCZruNbU9kDc2w5JnmRBnoqcL3XnEKvO+yHXDe5rmwds4U2L10OgDk5a+ZMziyfkuoIylROhmBvDp6uypojWremoAwB2vaSYqG7rShH1eVPWfTHAAA2LlkGvd30/F3EbOefkRm0U4I4R6qNlM3m4TRvB4ebJOi8YBhTrXrRFTBXtRctuVPMC+ntdswwYCKXt+7vzNPFMXvOok7/Rx5/s8k05wo2Xi/+nTGeNz6oqkIC3ibdxGG80LUKTVkQZ5z5ohwUiUEBV/5l/1XBF6YK59Q5Uoy4W0woUCqFZJbffJwXtpkSCG03o7xUAgALD+ZVT5x6nHEF3HL63hPaj1rVk6EfC4KTpu6QmXM/QBZV7vOcd9vJbpLmuVEwT9bq+XnJcZLj23Dx11Y1abgKd9k1FtpNpNtz2fEF5s3UyWWN4HsTJWB3iMqFT26pg30QLVC4M0XbIJ/ff5x0DNxx2dzKVUyXjL2oKtvzx0uPJISpYPB9URBlLvhDH5eCRHecsGmzEWkeVniPJvXiDP0pXBepYMsyR6L2UP98N+vPg0+ePlOfwRJIFWO0M8p3okbksfi2NIL56WHEDIJF/kYdGsIdQ/iWyrxvpNYIEloNVaiKH432Uv1lB9Sdbl22yLovIbt6VHU3Vp1LmLKubLIbF/m29xC1PUeilMYCzxRECMkFeQpDQ5w3/NpM2vTFu0Scq5dwOOzGsIMFa+AjveuT5agPXm9JrzNtkUtIyFRcZ6ySNeL3wbCcF6Zu1w868rn3fDo+DjqOdZjOqLu4YIlb/sivvFapoyDd1IpJl21A8AogAz4JucIqCjLlLNtF3G+iyIwYJem2+Tq6rpYutDNW5Ap2sN1x9XJOmx6f2oWNNxql8wYsCOceDAiZa7XJopWHeWbzhx3mROFAMkYXcV+NpUFSYmSkAVic7xi7xJ49zO2oqqbP60fVs+ekvlOfhHgCJ/TYi8VZFMIY9k60NsldMU1nQqqOeSShyDAD1+VFWjqtagTYxdr0WlKC67S7J885lqnVUL06cxcnkII0QzHV0VbT5eZcFdVpizqaLVHjZk1vhEtvO8iVUKy5cwUmXbncZnOboyiqiglAA82nigyZDxRJB4D8jpKNPAJwcGbV439SaWQdJZ4HssfqZSKGnV/7boT4YOX74Adi2klCv9hHs8kCxPres2JFPDR5awJgMePjKGfLYsXA+EIJBdOn+SvPSktvPuBvdDQFL7H0EqOTJXdsXhaM3qDslyA/hTtDSYerTLPEl79eQ9Buizu3VXhvAjzb+t7HcE4+tFg0CXJJJ/mp5+3B2YP9XvllE36NhfOC1kJfRZXKnYh+hqdwvYqJpwXdm3VoJbhu8pyTsWOpETpYNgklh9F5pdoGPBklqtk7fKsz9OFvNxwaSFmOhWczCCFcKn5GBEwY8hmVB5iqt7UXS8uvEQA5H3iImEfpgrdi8KuJdMF9eC+E7WpRYXCuqaeE8Wk/xTKB4tliSkqtXb3vKWL5oH1VhTiKOIJ73MdxieEFYJULd3BYzx6fZFkkBvSGrYhB3nTuaLYTxplxTS14MO4u4g5FeM87mSI5i2Amn9Bh/NCCCBMkKNPo+45w/2we9mIMK8I/Zn3nqzFK8/D3xXo6kTeJ66X1cU7FwIAwL6VM+ADl++Ak1fPdNyCGR4/Mo5UHoc/Q9j2vZS3rZfTjiq0jO6VgX28IVzMvlMcBwGWn1c9Nbm3C/avHUW26f8cFOdEwZWXkSdNLG9o7MHCJpepCPnQdfw2REprEVxQ2jK49L8u1s0dAgA74b1KHmjCT3cxSc2wU4Ce6zrBIviyhfq3rHLKZf6xWo3Zc+PYCkuPpETpYLDCbZ37su4CzGrzdbT2aaWXHTpeFDRCWrw1phmePlnYL6JkFHSntVHoJeQ6C9HPJrFm82Jji01HgH941na9OrHNCJo+b/NcbnkZpd3VihED5cMTxVmyT4fPcpdGyL0jQBvYNbplwVSYN7WfW85IWcr1DGUFeZLyJTq/7SxXHRICuDlVD+fF8URBWYziCPbJE7oqm9AeaOwVSk8U5GLzlZg6q2SsWZ/N4pwo+es5+5XXZSMQ4PjkF687fRW877Lt8PanbYFjl43AU3cs8NeYBkw9UWKOPk0gv0ZCeVSy7fA9ANzTgq3Sp5U2Afy6xRhDDE/qQZULccaq7nk+vZjZxzEC6Mm9Xco2+d7YeOJyOTcEzxXBAhHmXyyKSizvY0+gz9R6/fpykyqx2zkbr/Xln/0h+z3vIUOM12qZ+0KZ7mYxIylROhhci3fHXF+jPqzFVE5A45SahBCQzSGd6SWMr2riGuxgIukck5z7rxUNNeEfnHY063bhJQLAKkqZNhRMvfKygHhGWl5Qtqcr7BH45gs25b5TWdfUPVGYMg4slfznRJHV43dnp2t/5RPWOKzX/4nEa0FH6L10xmRuOTNX9/x37Xom67jy58uG7xUCYgtMF6EFxD+rK5eufT1qEtoQUk9exQwxYVdswhIaKUmU76Del7meKLLE8njyUKDro/vPFb/IQ09XBY5bPgP6e6oAALBv5Ux459O3eA0zhcHjR3A5UWo19Vlt23sLpon7QqduQvIhaLDeNkbIKA7VdWp7ouRkB6Yi4riV+ux77lwyDa7cuwT2rZwhLWetGEIUF3qiYJswJDGX2wTk5B67bDp84drjc/c/VdhpEX1aMi1Hc8sFz9nyRMl+rxovGzmhLdXye6V+fbTiT0fOkCmn0fDDh46Kf3s8+5tLT5S6EqX1d8RbXKmQlCgJGbg2nJEZCRdsQJxgCF0lhonSAyCsYMqlcJQQkUWOKy2KigC9qkPExuQz1+7bzVp42tSDF7oQzu/al1nJ7z1VOWMvgmpcrcJ5BTSxtJ2elx67iPrLju6iLtcYoQNAflxslSi2Fo1lOs9ZWmO31CKEwLhG8nYaspjW5olrzfvLd0/HPZKdCMn8UwwWz0PDpB4sVEIwk3lfEfAKdF08gR57pmf+crxfiQQ4tmeKDgghcNraUaniIAQePzqG2kPGarVMZ7nmki7ZvQg+cPkOx7W2IOZx7QZapawJ5omCfC7mnCi//P1f6JqAEALXn7EanrRlXvNbcThp84av3LtE+YzQE6XWoAHfvs741zjPZ/5mqloyMhmWzJiMmodc2tCU8Xh3xUVSs34bNGgJyepaGUNalucB6ynEIhvOi3jhf3UMTVUYGwfGE8WuvoQ6khKlgxFCDqab+NTEMiYhboxTRlw6U47HkBUppqHJUa2dp2ybLy2vC1oRpVJKNRhVtEWFh1OAZSiUbt7WYYPwtLmGrbu3StlTD+eVfQjD7KseMUv2qPGshACXw8UNaZRhPgn1rMOGOfX7qw/H6OcTE9ICL306Y1Qk+KJJpcysP6P3vTkt6mcqRLCGEYVVOb1az+EXTHwzJSFWSPOnKeavCb9iMzdzdxKqtpqobo2NRPS+XRzjibwnCl/R4QKiYWiHxPJzh+shL09C5lzBhvMaG69l+sc1v3HT2WthvkOFkslQurGAJ/Dqs9dCb1cFXrp/paNIAaK2su3a1OUCdT7DvIXf/vkx7vdjlEUFl4O0fKlVo1PgxzefJn3GNnqEbHz2rpB72uTroj4zv7XIZO9V8npMwO7vov1Td064nKO6bduF87KQ4hD5e5usdVoZQgh+vDPhvCwPRfFZS5/vdm3UatlV2AbHeBRISpQOBj+cF64sdqPh1dfYGPiiIok1QUJHgXcu1SCOZLcqGq7cuzTfhuCzi/ZsYJavRK+MCyZD1zrMNmlz/jt8fToW/ATyYRVodHflw3mhlCiK330r0bHMbgMrZw0K6pG/SeM9spcmT4J2L7WqgV0+7Jhm4v06oiVU3PSioaUwNPbeMCrWbJPniYKZK/JwSsqGlZArvO2UzgntDdVUcGct7KYek6mbWaOC8jyeKZdYXr9pKURk0c26FOwUhY9fvRv++rz1cP3pq1HPs7kTRBgbr2WNNpz7osihPRdzz/MrcKE4o+cKgXqi6R/fvB+uPmFZdPu/fw8r5HMa64tWojx910IAABjMzFv7O9ikHvk6EDqicHh0ZXvM35ftWQxvuWAj/Pu1x3Pqz+emkp0TIg8MvgcPj1+R0yqrM6Y9s0FJWTxR6uXdEkuHzSSAV3DS53OFyO/wpqDvb6I9GHuPHxtnwnlFtueWFTjOIKEtkbMyrLln+rghu6SHm9PmEwoAO+YZLwoNya3L2MuuLZ1k64SQ+gG7ffE0+NZdD1Lfu3kfVRfqNuMzxnUDOkqFBo4yEkLt+2GgvYQw/4ogGjdCAKAmLs1LLI95N9V8M1KiIDv1hjPkwgkeo/rey7bDP373N3Dw8SPwzv/4XzRJ/DMG/6wOimI8sUpImSeKSYgKfpgNfPkyneeVSjz04pLDA3cRYy6CPoR7sfQdFzHT1oGQzT61JwpS+eFoQubqyf1pt6+KwmZ11V3NsuVkOVEcz3GRVW+I8K8m0NnRZk7pgwu345LWn7x6JlyxdwnKG4X1RIkZOjx5tcKfo+i2BMaRjXq9dBnJ/KPVjmrv+PyL9hqRBODWk4smk1aiXHvqSjh59SwgBOCp7/5m81m5cYPenYD3qK2xnAzd1Qo8cfM8eOwwzissqwTm06WjeDFF7vgQNKHrReGE1MYa0azMxgDPyrgRFHPY8iwG4OezVZWzjeYhottl6Myx5IniBckTJSEDtCcKcglyE59K601oN9CycJ2zt4jLmkzJg3YFn3jOZd6ImuAzt/0mHbi6TTxReJC9roq55pHw9V8+wDzjZz6ctXGO8zpdKnx6u/LHtIuu8BnO64q9S7RpnDWlD567bxlMH+jJtqmoR5Rc2we4ggfHbexcMk3ZrtjiL9sZ1YodE45S1ulXm8E5m/TWn69Toe4RZlZ7IYIzAvycKAhaeOWKROL7OgtyPkte1kRQZxd+RPy30DBCVSfiWWzul1Y9blcRvRdOn9xDfZ9ptK2xdeFU+LtnboMpfd2o58fGa9lx0Nyf7bsTXwNP2S4q7fouZuJZ7aNdEyyaPgmWC7ymsTT4eFs6dHpvVwWOXTYCA5TniM6eZArRXbJBmU4bYkMo/qabm1OSc6IZDYV5hJtYnsvz49/ExHMyVM7JZuhvzXJWieUtJpqKPzepm70jYcc2o0QhdqevWLFGK6/tVmjdEyUyxr8NkJQoCZ5hnhOlLBY9nQbtfdhw33Zp1eJ6KpmcRVgaeAedz7PPxBNFLdhmhLkG3M2hoy2LI5MowhhLJACA/3PhJmlZZTs8JlvDgl/VNd3V/K3LSU6UAvkp19ZE2DpCMJG2yj5eiJFcfiDR+4H4DCVEX3HGa8X1Xvrmp2yCTz9vj+Na9WF3uQvfZoUQfs45RFnZOsgo7DWMYLDrNrF1CbJdaHRKn7Ssq3Be2LXHPpb7m1OP6pjJeKIIbuFdKL7MTknOQkT2taeshH0rZ8D/fermaD1RXGDJjIHM37rcwlitllWy2ZMUFKKhdWERnbG0R7bbjqgwWpQD+5ZxDaUwoLttjMo9yrtvqdatUkCNoEd0lzThu3XuASpDKpEiPO8hxaOD/l1/ovqSY7lUmivvis5asu8PbHHsczkvO2TBTDgvT4NMhH/o4ygTzqtsZ1OsSEqUDkYIIRr/Ei7bDaiNqZM4qzZCPkqc2c7NzYliOGddWDBkLEUQz7G06rrr0tAJiaa7bFwpq2Tt8pgMVatjbDgvzBhJGGdxGbv3b8XXNatHxQz7SyyvD51XDBX6SkcxwD65f+0oN8ayCLYCah42zhtqfl43dwoMcOKvo8/C3J5jZ8nED+elrmewr/4OmNAL1QqBxSMDyud8Ixe7ukD2A3secXOiIPZz2QzNXrQ0EstnhGR6nee7r2OKQ54gx8v2r4JT18yCdz9jK/d3nyFjeDCZmzzlpqhO0dzEvKdMMG0Lej+cOqkbbr90Ozxhwxx0uJMy4h8u3Q7PnMgnAZA9yzF7yNj4OOOpFG9OFEI4wmXhXFQ/o0NX3hNFu8ocRD3t0prbBVj+adnMyfCqs9Za1ztGzbUGD8Deh2Rv72IMlJ4omvMTi0m9VbShUf03fhv4UJGqL1rI85X8Z7XvSw7GS6RMUsHKs9OC8LqSQz2uOsjnrNEvZ5sTBaW8Nq8eAADGx2vBc3R1AtqYHUpQgbegnCeW1yxrIgRNiBum94iyKtEaZLOvbcM86PRhy0UX156PcF6y3Aw88H49OsYqUSyZr4DQaY93maXRXc0nlsfUr07IXhxD5XJp24TzmjrQDUtnTEa3xfc6Qhfn4l8OtLwwREoH9m4napM907OMvn/h9uuftB6OWz4CV+xdAgAAi0cG4PR1o8pyMez1uDXFR1H0C9ewSoEq9URJF60Ev5AdPVMHeuBdz9gKp6yZxf0dy6+4W5IyIViNy5eMKc5WoWU/9RmlRFE+oYcMKQK6XMZpjw3zp02CV5+zrvm3tifKeLZ/Yt9JsbJg15bWeT4kzERi32/fyhkGddjRyvJhUv5f0RRdblwRo1PF+7kwehJ7olhXzcVL96+ElbMG4Y3nb8x8Twjjhc2UayqZ2O9Vrijir4RAhwGnPofaN0TKpJwBKMh/12rTYpo99NgR5ztFFzNRsONFF7M37OCXx1Rrmlg+hfZyg6RE6WCE8UTJNyLbGLJMfJtx6B2KbD4P/KTjMTTjNTMRj2omYYSLWQszdV3s3LdRPGrlRNFcNl5cURkiTRKvHR1v+aebbAVWShfLLtGyuAL5/Ouu5n/D1K8aVp8h6VwipKu5Gn574PEj49zvsUJ6dkyz4bz8j94F2xbA+y7bAZMnvGkIIfCOi7coy/myUNRCTlGpuYhdNKpTkhBjBaJ07WcuWvx2ufSom50oj3wwoW3hMkeJcT2G7WWEXTV+PUqBJmJf1vZEcbyu6OpYZXyngN7/MP07Nj4ega8DDjw6RbTTikvTeZadT9nffM4pWdXvuWSbv4YFYK3WRec4gB53wHru18vjx001Bhj+0TbBdqY9xDNPWD8HPvuivVxDKKmxbvMZVkHOe9ZeacZr2xYu9vuWJ4ocsYjb73rgEbQRNhbsOYutIhPOixCNkry61G3YYmy8Fl0uxHZAUqIkZCDSTmItYXP1cb6THcakQ5n1doJMzaEjuOUdIL7PACx9GFWOU1pNYsoi1w8u9rZdeyaW2iYHfgzu+gB6tBOFK3BXpSK0pFLVK4NZOC9Xc8XhOGmsjdiNb+g8QDTQ523OgszuPOWH89KvpwyI6bUwfUwIP2wQZq8dr0EmbA0NF0tEOzKF50nVrnO2rLCxguziGBXw4IoXMKlFdf5jwnR0VYiSz8wISR3vYBlBb+Z7Ne2hsWnBsJd6rT1RtMNZ20Gn5roHBCtENlfoSdviNY5o1zeKaJcVt9pQQM8lbn40DbmNi3mp8hJ00UYPJXEeGewRPpf1mMNpMtBRGTReIyekF5QtlEfRbNvGW9lmzbHGCfmq9JW99F2aAEHfk+hytspD0bzr667aVUxhrJYN5xX5Vbg0yAffTugY6CwiURJTZRuaRbLMRSwseoINTC/MvHPFZV06vwMw81HmiYKqy+w3APeCYB+eKCyJvEsYkTG5DuDTSpPbHvVZZ56qbFgqFc5lV5MeHgoN5yX7LXfx01cG+bocYyzVTNoe7OuChx8/ChvnDfPbzbXBr4e93GQFYWqhXK5dlDDfvq9jEHJjrBJFKCycF+c7FSUzBnthw7wh2LZoKlxz6krY+OrPZX6n+Tyt2YKWP0Qw2AmlhQ9Bl7Qaxb7AI0cnJ4pIoVKtqO9cdD09iOTUJmFh6+3Qyvh41u8d1+yFL//sj3DxzoXwvq//2nn9ujzS9sXTsh7rjulxDexIuojNL1O++TCYbFTp+h5gWwWPX3JB11EN7zeT3zFwyXeLquqqVuCrL9sHY+M1mNRjJsIU7WHc3J2Wr4TdL7VD7ZoQI2iTbTsfijwL3avjeZvnCuvSwXiNNRyw32NZLzuTcF62OVFEMphexJmORT6cl7OqOxpJidLB4C0i0bqqHwR6SfbqbehZSmYvF6gmEgJDd++leTudsjzB+zg/2o0XXLJ7Efz9136lXa5BtSyxvC50wnnpHo4mOVF4JWTxNm0v3sXYi1mU5l2UhM+CdFArJL/bovpT8UiRPJRvRxRR9baKIwzZJm186sAe+Mh37oHL9izm/p4PCcCnRBpm0IAf57XiQ4amc4H0JYS3qdVcsGReIwGx9ams9NevOxG6Jkznhvq7c7+rpq9wH0vKkQQk7GKqu5lnproYep6LXoMXWifbdlZww0NXpQJHxvAMr0uBC4BY+Bw6RCSAeL4smzkIy2YOSp9xBdWb3vmKU2DqQNYyPnxieY1zlPOsULjseJh1PCTaDRWSX/+m04TuN5XCVXVn0L2j8ea2ymNJrwnxw/OmTlKWlin+eAo2toycMkaxLnvWcq6vmDUZHj8yDnc/+KheQQQatKhoYkdad76+6SmtnDU2a31svJYPhUkRY1J3xfDYpOUmtp56omggGMMILFieJOlQ3CCF8+pg6CSWN934+FbC4udFFloJnQfe+Ju6kaqT6jXqb+Gms9fC+Vvm0ZVQdKiRs+ZAXJyFdWm8dqOPsE24jGPboiGLUGuZtbyPCaIhrIfzEtPKs3JBhfNS0VOWnCgOn4uVcVw0MgAv278KRib3cn9H50QBmZBbH3xloPtZgOUJfKJs7IZI+KKaK12KDT+jsG8Tc7WSDW2CBNjwo67Ws4kFt2rZZK1Y+c9UMeG8qLK93f6u8zSJnXQvw2x/PdUKXHHc4pwCxQShu5Ztr7uLT0BGSOiARnYOuVDGic4qVci7Nzx5AwAA/O2Fm6xpwIBnFCWCTr9g8jDJWlbucwgaTAzyhO1ZViXzQGoqD5i34u3Ftm+Efg9avoAwwHTiAZ5vmvs3C12u0FUIyFqtxpxFFpU166CMImr48cqG87K7FYnu87QSxXa8654oyRXFNZInSkITMoY9z/Qg69SwEq7/Zi5oTogD7JjXJL/JwDtXTBNjuXFVbn2WCpdI8yHu15yflPB5+Lm6FMuq4YbzEnx2haxCxbysSRld4aOsOW5eChQ98qdMwjO6gktBfOPcot/GlxcjhpH1YZ2LtZbKJZan4/0a0MW9VHroWzlPEEb5pWNdyKIoweJ0geDOZg7Se5eOEQzKOY7kn7vu9FV44hLaAjYx1X2EH5VBFUqRd5aNaVmF89+nq0oAjuBp6+1yFz8dgM0pQH3fQSECVDzS/Gn98MVrTxAqpkNzWDojQyA/d3sE72E95kTOj4eaUbwz6vyt8+GcTXPxVt+WxNbD89LVmYcCosvxwnnpGJXp35HyJUyt+n1AxpM1fsN4ovAUADrjlZOdCXq6EBay2Q9u54YMNnvJOKPkIEwYB5OaacXfeK2GvptKc+5oQmQYgjnTR4f6mp/nT+uHGZN74Xt3/zn3HBvOK8ENItryEkKDt6CmT+Zfyk33Pd1wXpDbIBPKjqxQBr+Lcz1Ran4OApdC3UZdpmTyyvk8+2xdURuQ6XlsvV3a4eyXCoolP1YIz2IKIcxH0qWsh2PRZbsGdbZ2pas5T1EvKmRLN++7AMcUNjEm+4LWpAXzIMO344sknBIgzAWYV98d1xzPPgVP2jIPLtw2H/7PRZupsnbE1IR/yIFplV2rH3n2Trjq+KX4RhLaAjbnh4pdaVkZK55D7o6y5SSyXFVahVOfReGxWL5s1hSOlyL1SJ9jT5S8sKoOmqx28VTDQLSvqjz78hU5IEYALV6e82iXQAqeyRvg4AXyngHuO6W5D+Tayj/rKmxOdxXDl8uVsqbg7Tk6Rqm6wm1ZOK9XPmGNVl082HZL9n35BjJsG6Z3YakBoeUAE8GKc7liWBJz4bvYvy32fRu6xxglR04Za2IoRo35eK2GXo/0XKkqokko6xKUxYTonDbQAx+/ejf82wuPg6+89ERYNnMy97l6YvkWOufk9oukREnI4G0XbYbti6bBe5+1PfM9e8BitwvtcF50m0mHEiUu3DY/SDtLZ+QPA9OzWxWjGlM/PR1lTLdI0CxzL9ahC5sTxYQZcIV8gmuFpYuBoJxXnq6mXbaPSl2Lkv0OJfCV/45dS9k+ddOrLsem8RooIa5lW0Xp9fM5UfiQ7TkmFx9eOz68LuQKRn+dfvM5a4O0I4JOizOYUG+EAHRXK/DXT9oAZ2+cY1QnFwFvVzzBzdzhfuftJIOc9oFI0NuAy5FePXsKrBodzNaP2IvVCeEJ9zMN1jr1C9eekK+H+uzaE0XES9H7v6lneFmgzg+lYrLc0bJwujoPhPY2xzwvCueV9WjVbIPXLFNJqLs+ybPSTtGNUKjVachS4UIXqfJ+I6Ay1rK7o9F1PGvPYvjWy0/i1KFjLGM+UnWPV5mgCW8MY6vAwCjx8nWG2VhFyiT6rvDxq3c7btR8XFlFoYu7CL331BPXY8vRBg92NFQFPA02ROfmBVNh1egU6TPj47UMX9JB9g9ekZQoHQyeUGXJjMnw0at2wd4VMzLfm25WuiFjshZP6eIbI+YM98NPb9kv/D0XzsswEtVrnrgu951pCKLHj45Jf8dOtVefvRZWzhqEa09dqU2DjQBaJySabg+ZrDMus5kR2GZ/41la6DSr8mBaxlG4iSwpMbDdeWxixrLg5URBhZVSvAXWK6xoAaTqPY5bPsIp017QyYlCgxY4moXz4qxb9m+Lzv7olbuUdfgUsDxj16LmZ5tmYlLA2PaXUgAsDEeB2JMQyuCn7ligrCeh3LC5wD9z9yLp77x5aKJAftP5G+Ezz9+j72kAauUCJsUEa9zCM9yh39VFYnkRzyTK59funihqZZi8vE3YOhqLpk+Cf36OWpipYxDFCyPVjfBEMQFrTZ9XRIY5P+vvbPsuYmA8WioONTl0v42Nc37XsCpzQRLNL86c0id50j+yHn7Z34jge27YaY0+5NOBKyTzcuaHGdOnBQDghjNW5+qQ1bV5wVS3Xi8WZcdqWS2HzKDBRBkyPo73RMnmNbPbwUQshkvDiKMpnJcXJCVKB8M2P4VpGzImRsZoJcSDvm785m6q+BiZ3Avzp2WtUk0t3x4/wuEwNUEIgWfuXgSffdFemCVhEBvTNu+NwXsqD153+byshgnnZdeG6vVfddZa+QMB4OsiaJqXwpUnihdodJXsPWYO9sLupRwliqezAzPGxy6b7rxdbCJWdp+YMdgLT9+5EC49dhFM7tVPgef7DN6+eNpEO8Uf9liLQW5Zt6Tw60M2QiwvdDpej7bgxlWPYC4k+IWNcPmUNbOkv2Nnj2qaiayZ6dVVE8TzUnk+Z4zFKvT3rc+sxw2PXPo7FwIXeu1nFCrUMzRdoXgIV8oIG6j6nwdX/XPmhtkwfTInnBsDW2VHlyAcVWaOWrXQqCNbiw9DCdEpiG3rPZds1W5T5SVXb5/h5QDrnZAHvS5492y6XrWnif0gqO55Oi3YUiPjglo5Udh5aKbwkJXijbc2HK6P9fOGqGqb6iRpmXw4L/P2babZ+Hg2sbysqg3zhlF10sYHw5O60ff4TDgvyw1M5IlCe6NiWxCNzdg4G86r+DO1HZASyyegwG4S2I2QG85L8ryN9XhCcXjmroXwD1//Nfc3G6EMeyCYKmTmDMmtYpxaWkzM23FGb2PTxvlb5sOt//FL2LdyJvzk/oNYSlBPhRBaqeLtqta6bNSHJ3XD1IkEy9kkgMXtHy6FC5VK/k1QOVFUfWoQzsuHBV3uN435uG3RNK12bZWRKg+sa09ZAZfuWWzVBrZdHmqQnxu3nJv36EO3i/4yDFbM4sf7dYEi2I1N84fhA9+826isr/wspivEpFmXFpYyJE4yLtiejwunT4JfP/Ao9zdX80co2KS+F72G6pzJeqLwG8IIZmhanOdEEbQzbaAHnnTMPKjVak2+q10RQkn0nBOWwju+9EsndenkteA5RIgSHLu+I+Sq87BBN14lK1NonJvqgT1x1Szo665oGeD1IHKiVEieJtE80zGE4iluM0pRUAn75W01+I2N84fhB/f8GY5nopXU63A3kLZVybz9mh4YkjKtsnY8CvZZ+jHMvuPyfqurA7ARwNvQXU8sTxkg5IzLWp+fc8JS6OuuwomrZkrr7KpW4I5r9sKRsRoM9nVreKJklSg281XkiWJSp2hk6onlk+LENZISpYOhs5yMFRpcTxRJO0BvkGZNJoTFJbsXwavOWiNWomT+sNvEdYt/8cUnwMOPH0G7FsuYA+vpaFHB0KRu+ObLT4ZqhcBbPv8z+NFvxYoU3T4ysaJQhopiiDD1pmjVh3+WV7/u9mWdnNkhs1IP56WvxNaxYpPWw7mM2FqxOLPyF/5YzOHxrD2LYcDA40MFdDivmlsLI0w4L9+o7zX1d/r0844zVqSrkH9X/LubCg+OWTgV3nfZdlgwLRvvXqWsk8Hl3sXbxzDCZRlEFu6i7647fRX89f/3U+HzCZ0HqSFW4FnSy5GAqPIT0PuFaN2wAm3euu6h2nbhiZINXSM2SHnTUzZat+UaPixrVXWaGKqwJZ5/4nJ3ShTLvV9kEY3J4SMDYRUHzO8uhO9sV/O9yBx4OEho7caE1OMor0xB80IrZw3mfs96lslbxQ7Bx5+zGw6PjWeiUVyyexH8/dd+BS88ebmTNnSf1S1Pch/q4M5Da2UOU4GB8YuvE62pTGIaz+VWZMqF8ERZN3cKvPrstXDbV++Cz/zwfgAAODI2jg4L2NddheecsBTV1rKZrbVjokTxte/SsF0PLE+S9ClukJQoHQxZ7goWxuG8OEyobMPJMlrpylwGTBvokTJoLgXK9fmEr2/xyADqOZeGVi1Bs1s0lB3P3bcMFo8MwLd+9SB8kGPF3Fhz2HeyTYrWgKw92zADZbCg8GXJXuWE58F5osh/x3uitITYZXcOrAHA685bD9f/8w+LJkUL6MTyrhuObLx7uirw+BF5fitT2Lyq6bogAHDc8rxFJ7aszvdYhNxpVZ4o/d1VuHzP4qYSxdhLJrJ53OnwOscQY11BCDKFuX+Yv/t78soLXn4CUSUi3rlaIcozenJf6wqPTUKLhSynQKdAFN6s+Z2qPGKmu+xbHT6bQH7usYq71z9pvXa9qLaR/IyLNth17HMuizx5aJgaRfFAz88nbZkHDx86CtsF3tmm+x2LSoVAXyW759109lq47vRVWmG+fUOUxwmg5bGVDyuH6wOd4ULIx9XtOfTYJZzPOSUJs2/lwnmZNQ0AAPOm9qsfgvrYbFk4DT7y7Xua3x1lvK18RKvB1knLTbqrlcxcknmXferAnnxdAc7XMSYnimlo/IQsUk6UDoaO5Y6ppnV4Ut7dW8Zn0D8lT5RyQGeYrMN52ac20Wuf+qxracsK/tlDFtMmDz1dFTh381wYdZS4z2Rtr5s7lPtOFrZNJ8wAD9h540uw6AKmDJ+3nChYAjx0nqzK3G8OLbRqNYCLtpsnri5KmIS9dNdqNWcXi49dtYvvKRC6E3CGfA7aMRdshOgRtg2ZR8iMQXX8fBFMw2+KhDDdzA1RdQ6y3sgprGsbwuMFHjNbbGKY0/OxVgOYxFGiqMN5Zed4s27qGTY/BY/iwb7u5uceV9YwzfZw/Go7QzlNHRiq4LyKsQJu1GPNdtla6XVxzIJhuGBbnVeShUYyAXvnsL0f8MDnXfTuOxhPIhrdiDXI83g1tRGjBaHVCoHL9izO5LzItKI4S22HwLUCxcaIliBLs92hTCwvrKf1UCOs4tRJ9b0ZmxMlk2tL3aQTtDxRxLS4aqOBPctGMsntVaDn+BiT+B3p5KMFbBWVifW2bdFUOGl1NmRYt2QjXjtnSu67qmDfoMcBq/SQ5UTx5cXfyUhKlAQU2IMAsxY3zBuC2y/Zlv8B6beYLs/tAZf7tr9DoD7XZNXbMhaup7Ore4eOQOGOa46Hdz19C+xaKk+enUssX4Anis3+4XPnefXZa7WeJyQfbxV1QdAMuSauxz1c7e2+8kII2+OFeCL+Lz+hj8LL9yyGbYumCUNiyP52jVCvbtOOsVWgaP5aUFMhBFbProdCWDID54mZBR3Oi0cbogZGuCOCyhMl8YDtCZ9JTTFTBsPzYGnkeqI4yomiepdBKnSka0F0igpg7wGNKZ3tW7t+tk1wLEosb7sPE+r/xb+7RdPbIFM58ZoDsgcRzov1gpOKQ5SkymcYvYep1nCIszbkLpIJmSjwRsIYpmRXJ29OZfFPz9kNJ62aCR969s6JZ9VvfcH2+cLfCCHcu77p1sTn6Rn5HjOv2BI6+2I+bwmBK/YugVWj+fBzPLAyH7o6H2tZJ5zXK56wBj521e66JwpVTsrzcn7DyEfGLV1Hconlk0LFCZISpYOhs4ZM9qpPHtjDtViXVZWxQkyzs5RgN+eaQiijVbdd8SBozGFVXFER0MJtoTm6Xns6Co5lMyfDqWtHBfSIy5lc8HupC4msS9558RY+PdothsEzdy+Cv71wE/r5aiVvVxU0nJcHYYqslrM3zYEKgWZCQGkSes3v84KxWGdJFjo5UZy0N7Fey+QN+sTNc63KY0Om8RBEAJGzaBQpYOp45u5FcMqaWdrtGM8hQRd0SRg5rtCCCcPhomc7VQjciWjsldLcBZWK8ZplS/E8UXhJnmmIcqLQpboqFeVaHOzzF5EbK+gNgaLkPRlPdJ4BhYM2XPatjlCRQF5JJxL+uQ7tlves9SAM1fzeBTCeKFyjKEOidOSqREFadPIWy4GSeSy0zgj+9zZYO2cIbrtkG6wanTJRp5guAIDzNs+FfStnKmyL/cxaIuiHXKh/kP8tg+gO0YvJH8SjhQ6xqEEHFti+lslNRMpo4fOCTqKbUBlmqMCG80pwg9i2zYSA0FlPLHNlsxaxlhfp4tseyIQHsU4s7+cUcBEeqfVg/Z+cNQdWEIpsRsTw6faQKwvGbBiYLBXckFSK9f0vB45tfhZdFt79jK2wY0nLK0bYxZrMsS0vrRoDnSSw9ZA27HcIJYri9waNn37eHngzMlFsCGHKyORe+Mkt++G2Z25VPrt3hV4+CdvtI5QwaWRyNhwTPieKm/2xUT9fyO2kCefo7arANaes0C7X4G3ofaRoYCyKZeG8mp+ZmfK8E5cp681Yq2nMJxE97LuoLAnpbyocgVNC+eHzMo+ZLhieR6ikzCg9ajDQk1dkqN5PpKCglS+yddPA7qUjAIDLxaALmRCyzPibJ29AP8sbWxpGieWR4X2yZRAPgb4QmJ3jonlk6+ECwJ5L4t92LZkO77lEzfupwBOUE8JpXALeMpZ1MRu6koe6YYCrO5fCE0VD4BxC3qKjLLOlRpp7t/lv9hl+OC9ePXjqVGty4fS8t3DRwm6XzYvGHOO1BSCPPuIqt5BJHTIegqdM/Y+XnADfuuEk7boaUBlmNCDi2cdrNa8ewJ2KpETpZLAeA5L1ZRLOSwSsZXGZrGATwsB3MiwX1aMUMg7acRUC28UFiUUunBdfiyLF8pktd1/R4S+z+Ij74q8jnOQx9qiCcgomBmnd3CE475h54mo4A+VbGdHbVRVaSQEAzB3uh3c9fQucR3sfBBD6qNz9XSl5v/zSE+CElS0FEc8lnodazZV17ETfc61vCXzr5fzLgA/krDYlwk2Td//CtcfDTWetgRectBxBjOBrw04XFeNZ6WGbEClof/5Xp8O1p65UlmcVeCaE0Pu1TMDL+ykltG5/+GTjMII6jNJBxHOw9fPCeanCzma8ragFRQtKMDQumD4J7rhmL3zDw36cyYlicaq86+l8T+Ei8A/P2g5P2SoOndPA2592DKwaHYS3XCA3LsHMIhVcemHo8PK8ZqsCdwRZaCRcW9kyLJl0/S8+bQWcuMreg1LEq/kM52WSE8WGGi1PFMV7x3bWWoeQQyjtMB5Btt1iG8qaAJ8uU4E4Tzmu8kThNI6G6P1VSpTG+7FznKbNh4wQW6Wsbd7ZvXD6AMwc5OexFW0bvV0VOG75CGxZOBXmDvcjKePjKOOJUrSirl2QlCgdDK2koewma3ENkiaWpxryyewk+IPM9ZP+PMUgFEGt5sclUTTTdC+1NEzDeWGhWh9YJtTVOhOFpTBtg+7uRl++42nHwEVU/FiMtVEhcDhH8dZRWaimK5ZEuilXCjdbq7cZg71w6tpRB15UegMVyjtyUk8XLJg2qdUu1ovNVTgvweWq8d3MKfzLQBmxcPoAXHLsYqvEqPS8WDS9Pm7nbxErJlXAWunReOHJy2H30ulwxvrZ3N9VAp5F0yfBS05bCdsWTW1+pxXyVfC9bM9Qzeu6J4oL80L7KhLcwWc8bsxQu8wfYhbOi//5qNQThU/zspmDasWnCQQ06uLUtaPwvVecAvvXjqK8S30CK9A8Y/1s+LcX7oVllCGPyR4SWlClO05sd9B3nMxd3HFmeZk3joynx3h6sLXmQ+FqVKEJnBLFncJCJ0eoqskQ8paQIh0Z3yB6V+z+kDfssX9W1nRD8TtnqMV3Y+8iK2ZNhtlDcn5d917jIpxXD9IKlJ3jmRCLOSWY/QTD1iHzFMXsA9m68s9/5aX7gBAC733WdvjHq3ZZe5HUw3klzYlr+AuoqsCXvvQl2LdvH/e3b33rW7BtWz0hOW9Cv+Md74CrrrrKK30JWbCHi5UnilSJQv9h3kZCOKjiMosYvZ6uKgAclZZlN31fniiig1MWXkFY18S/soRobtxO+ZXoHpSuQkHImjURvvPe7/T1s2HzgqnwoW/dU68XaaGv27qZtR3+WZ0h4ofzQtCj+B27luh6Jvc6Yhk0+or3qCkzWCYWMitYEP9Gw5W7NiavQCjg16Lb0E+8dx/u7xE82/r8/JOWw+rZU2DFLHXiTBG9XE+U3IUx+/cLT9YPZUbj1LWj8Nx96nBfumAvlKpzsIt63iZPTUK88LoPT0ySPmoNsUqGrgqBRw6peE9EWzWAfk7IJ1V+pmzen9bncQujHdcQ5W0xwbSBHrjVs0dKEfIhnheSLjBdOzyJf+7k6tIYKN69rFoh0N9dhceOjMGxy0aa37uYipmrfY6fbX0huyt88IqdcP6tX0e1x3V+d6WUFwCj5MF6FQPYG+1kwnkplDcRsHoZ2JKT9WjF3Qu54UUtCVEbOzZoEdBIAC7ZvQjWzR2CtXOmwC2f/jH0dFXQe8/ikQH4r9881KqPrruhaGSaVt0jbBLLN4A1FJK15cOoDR3OS/KgrryDp7ybP2FE15i74+NaVeYwznqilOo2HC8KU6Ls3r0b7rvvvsx3r3jFK+COO+6ArVuz1iq333477N+/v/n30NBQEBrbHXqCPHebFe/S0YDKIiUpUuPBa85dB1/86e/hwu0L0GWsE8v7ngBM/bQSBct8ixk25BpCvqLonG68AnbFhgjnpYp9r66P3ylYr7Yyo0JME8srnmH69HXnrYfr//mH0nqcKVEsoVIA+UvEiPvOTVutioMnlm9e7NTwvcwwIRdC0LFv5Qx4wcn8kF8s37J69hSrtjA5k0J4RXFj+gsv+63v6XKy84We10/fuRDufvBR2LxgONNahh4ptQllQYicKF3VCnz7hpNhvFbLeZlVCEHHGFdhxczJmb8n9VThNeeuk5Yx8UQJgWwIr+wv7QCbM4Iue80pK+AT3/8tvOl8ebgvzAzD0PS0Hfg7Fha8NdhVIXDHtcfDV3/+BziXUgTaKtRU1vgksx7EDWxbNE27TTaskxb5mltEFyqxPKcZQTuqvtbzRFEI89E1mSPkLiINGy+IY4Xls3Xew8RbIpdTtEJg++L63P/rJ+FzOgHIQ/yahvOyiWLTQI+Cx23QQCsP+nPnONOWBl0iYPnqfFi+1t+6ieUxZ71tYvmj47XMvdl3aPxOQWESkZ6eHhgdHW3+feTIEfjkJz8JBw4cyG0yw8PDmWcT3MAmaajJen7p/pXwvV//CU5fJx7LLDOl30ZCOFy8cyFcvHOh+kFDBlGjKmuISDlqof7P0ep4PrtSbPpx42aYQMsm6AM/s0dIQ8XYtWkD1eVGzx06b2HvwhOFpeGi7Qv4ShTq82Bft7phBHTGhnfpUMecF/xgm8tF8buv/Sk33t4VF2SiXZ5lXpwHMwG3tNE1zZvaD7dful38LOF/VrfBf7i3m5cTxW+/2xgojE7pE1KHzYlyy4Tg+Sf3HeT+boNIp2yCZ8wY5Ie56qoS5R6KXQ47lkyHW85ZC6/4l/8GgHroFZUwNeuJIn4mtNEYfSd07Tltg9iM587ZNAeej8mhxUFegSCfC0/eMs8q1KQI47V8e9UKgbnD/XDBtqzSxvX4yxJCu1Ieiu411rk2JBsHJkQRYTxmbajRCrepaCg2vm7tHHNDFELkvIPISIiXEoibF1Cjr9h5yCrvG7/66n7ZFGm27ZG3FN3Rz1w/Cp/6wb3K8vRd7x+fswt+ct/Dzb+LTCwvW+rdgtxS4roQijXsYhc85spoJCGLaHKifPKTn4Q//vGPcMkll+R+O3DgAIyMjMC2bdvg1ltvhXGFYPPQoUNw8ODBzH8JeYT2RLn6hGXwd8/cpogX2GrHh2tlQnjQh+CTt86D2UN9cNH2+ahjm52i47Wa8GLsA2MGOpTmeyESHfKAVW4KPVFwzTThLM8FVU0+yaOdJVvmck99j7Yc0mxT9PwZ61sK4NPWzoIPXr6Doot+R3mDWnsvj7FHeaLIfzcRTDRC99myY7YzzpQfzK8tPUq4nigB7OvyMcT5bbpik0UWakUAS0KRtLqeA7xwXrk2IxgbAIB/e+FxcMe1xwt/z8WOVvB49Hcuk/AmxAPr80MWAgexMKoV4mSzbFTx1B0IYyIKWQVFnLPalaA3JoR+D13FdEhlUQ1quXNLZEGdTSyvxpOOyecDo+e57EwwD2MnEE5naPB7bqLCeVXy/IIpTWpjraxSVNZOEKNVxIv+8KZT4VsvPwmmW+Z5kjUlUlyoZE4m48QqEUSGmaKqbedr/i6e/6zbhs4+Jcozc9raUfjIs3cqy9N3vbVzhrxHI8F2hUz2oOuJgtnzTGRRmfK1WmY/SPlR3CAaJcptt90Gp512GsyfPz/z/S233AIf+9jH4I477oALL7wQrr32Wnjta18rret1r3sdDA0NNf9j60zQByvI8xVPL9L7RIIF6Jkypa8b/vNlJ8LrztNzSW1gfBzgquOXwlkb58CtFx/jhkBozTt2Vo8ZeKKI6so8wxzVJoohcU6ULB0qaOZAEyIbb1OMFgOLX+yZ8z5jtSZpx8Nm8uqzW2E6zjtmHuym4kbTUAlBlzEhQGSoh/Niv1OXUwl20Xs4VY0q/xEWOmPDe1LFAIY8R4b6W945fYgwTFgYXd6chfOqN8hrNvQRjQ6j6JgyPW8p+rM9Hbx40S7CqMjAW1K8dcbWuWp0Ckzu7RK2JU8sL/8u5wEtrCmhVPCZWB4x57sqRCsUjgq6AkhdoXQoZMN54Y1COgEmPRDzfsWb/rwEx/XvNfg1AvC8E+W5tdjq6GZl3uU64M3ZfGBcOXTlHLOmyBN4A+Q9ywkx3w6VYZdoD37Fm4dILI/BYF83zET0owoywz3S5G+zP+jmPRXVT4Ot8ugYo+zjlHV7PKor0x15vSg2/NoJIbBjyXR1W5LE8ux25eIOgF0G7Hqh+QlMWL9MXcwk4U1Duv65w/3wsv2rtNqo1QCeOhF6f++KGVplE8RwHs7rpptugle/+tXSZ7797W9n8p785je/gc9+9rPw0Y9+NPfsjTfe2Py8adMmAAC4+eabM9+zuP766+Gaa65p/n3w4MGkSOFAN6RMCNCtpJwo5QQ7Rrn8GBMnhJlHAsBAbxe87aLNZsRpYsxgvjUOcvbwl73uuZvmwLu/ctdEOVw7rtYk97JhwdgDZN990/xh84oa9VGfi7zcY60zVUqUlaODcPul22D2EPbSxVquobQoUmDHl65miqNwXrYwFYDZnx/5Tu3vqcId1xwPFYJPlohrqdUWOieKMiEkru1muAOFkLsI+LLY42Hh9Enw6wcehdPW4sPJ6pAhohmVE8Xx+7pkrei6aKu8XCgTrqCrBWdnnJNaElzBJxuPGetqpaLk7fTivuvNMEzS4xDAKjjbJrRy4PfQ5TdCToXx8Vquvcm9/HNHd76yfFDeCIjhZ6knRJbruhBZ2dueKbLiVx2/FH56/8PwhA2zhc/k8jhYkKOTu0LVTrspSum3yStRBGU8dAE7344K3Oh99X9+juQV5T6H3j6Ut3iS+5BLYutkz046XFa35kvTnihD/d3w7xzPbrof/vO6E4V1sb012NsFDx86CitmTYblswbhB686FQZ7u+Dkt/yHFo0JfDhXohw4cAAuvPBC6TOLFi3K/H377bfD9OnT4eyzz1bWv3PnTjh48CD87ne/g1mzZnGf6e3thd7ecCF/ygqbcF6+lBmZZLrR+Ekl6IAV5IkEexirgbxCJqwWzcQTpYFc4CBkCApdQacteNV84LIdcPUHvwd/de56fD1URfQr/PNzdjPP6ROesaiiimMvXD5CLsnisWJiWO9bORPVToXwLqHqcqpH0EoU6sUmu/JE0XnWu6UWHqLppuNZZNIWdq276pempZ4ivEFscEkbIQD/9Jzd8OWf/QHOWC8WitSfFVs9spg20AMPPnJY+gwqnFcAiaCWIFlAT5eEkePN65jnV4IbrJw1CP/1m4e81I0L52WuiKeB9d5ikVWQW5OhjX++ejfc8ukfwyufsEb4TGZPS2rItgM7S6dO6oZlMwe5z+ry7FxPSuozK0+g14DPnCiqkFa2GOjtgnc/Y6viqfxq4gn5azWALQunSmvS8gjgtpz93TdC7iL0+LP3xBZ/my3Du0/yDOd05lDeEyUrU2h5xfBhu/fKzrnWe7DyPTfGWAD2io6czinjXZWFk7WNrCMfpo32RNE1qmg9v3nBMIxwQtmZsiv/fPVueNeX/7eZw6sZOSEZpDuBcyXKyMgIjIzwQ5zwUKvV4Pbbb4dnPOMZ0N2ttnK98847oa+vD4aHhy2oTADAC7sBwjH6GesBbkKvMHQkuINLgafLEAzZeuv/stWbJOMSWrnIymi3ImZOGuvYhvnavWwE7nzFKeYhtyiI3PP1qNPPieJjqyCCzyxceiTwLpWYcVExr9iZTQjA4pEBuOuPj8DupWr3a2ydNuDtA6JQJGUFVuhAowZuzkh5OK/c1cW+QSktuOd8UDEyuRfO48R3l7Utmnv/76nHwDfvegCqFQK3/+evpPVhPFFc82QmnmmZ7wU/yIRifEFX6ztMjPmE8uHGM9fA5L4u5TowAWZdVCsVJwY5pmGNs4K58HP8mAVT4eNXHyt9JrOntckytOlrI+Mfbj3GJDjFeK2W6Y0bzhQr1HTCzxFQ87+5nCi0wYjzHI0s72R/p7FBhajn0g9edSo89OgRmDd1kvQ5neupqs12M1rNGiEx8435V/Qcui2Zcoqp8wgivIXIaNAENYYGjHe5Tpg4FWyVojqeKG50KLha2NDWtKxIZjjEA610ERmGYmVRLF+zfNYgvOH8jVr0JODhXImiiy984Qtw1113wWWXXZb77VOf+hTcf//9sGvXLujv74cvfvGLcMMNN8Czn/3s5GniAlF6otBt5n9P4bzKB9GYGYXz8jT+ogu1iRKlVWf272wYE+NqlXXohxAQKDksiHQ9TrRDENbq26aPMWVlfFIvwhMFCzaGMgCOWVS9A1aIRADgsy/cC4eOjsGgo3BeOsIM3rNcC2CZiVLzGTuElH/Iwh6J176bhdcK56U2ZJg73O+kTRHyAhfxfuVCIHnCyhnw0/seht1L8cZAGK+hMzfMhjM3zIbXfPrHyvp6u9U5UXx4P7Ewj9XeKphPIkx95vQVzWuysaVNR7fdQpWUHUOTuuFVZ62Fv//arzzwdOqxrhI73s4WFSQPUyRiogujrKKfmDWlF9bMngJf/J8/+CMKgZiT99Zq+H1RN8RWD7tvs/xr7m+1EFGF4Uk9mb9jyfHBgs1xyONZpvR1o0LnTmXemQU9/1i+5K+euA7ufuBReOeX/7dJl2+EHBLZHtv4WzUvAQTGvBqcSM4TRZRY3lPfYLYgn2NvrQSS0M/WfcTBmY6ld7g/uz7pcdVVHGXnKr+sawPieE+mcqFw3fNtt90Gu3fvhtWrV+d+6+7uhre//e2wa9cu2LBhA/zt3/4t3HzzzfCmN72pAEo7G7qb7I1n5scTg5TMMIEGe3ny7YnCwkqJonNMGUz1WC8JAPLLownVNYEnilSJAmIm2gVkjHSfgSfKl158Ar8djuUaZuxdvnNPVyWjQJEtw3lT/QrVAWxyotjtHyHPpExoS2SzrnbHRtOydj/z/OPgw8/eCaOIvD42cBXeA4vbL9kG/3ndidKQfEtnDGT+NrXaFj3LCqFY3PbMrdHzRyevrocrvOK4JcJneHsoHR+6ixn7dPFrL/hg5zDLoqtSUVpxm54VmFKuDWp8wDf/5BPffPnJsIvjNVu29wgJWdfozlfe+UWXkynWTTwi3v2MrbkQOE1vA4b2oj0ucry7xZxk+RAW2ZwoJHOPOn3dbDhn09zm3ytm8UO5lRaS+db0tOaE+frA5Tvg2XuXUN/ZkZHLiSLwRPGWE0XymyiUmMuz0VRO0WiCvetlZAFM3Y8dHjNqiwaWWlZpS4+r7ivTfK4wyoBjXilmBX+ZULgnygc/+EHhb/v374f9+/cHpKazoLOEWMZDJiD+6S37UTkBeMBYdCaUC6LN2kiY7m3fr038f7YBm3BeOU8U5BtjlS+qywD2IC/DMhOGCpOF8/KhOMnG4cj+Rn02Cee1aIR/IeIJkTEXQV7fVEiLQcYqIrDM/f976jHwtV/+MXMBEdeJqnLi4fxXvGVp5oWgt76LWiuyRKw0XO2PrUtm/rfGV2vmTHHTGJIWFerKRvv2CCGgiiL1D8/aDuff+nW476HHm2WoGhT1q2ngeqIUMPt4ZxF2P7j14i1w30OPw/xp4pAkPB6P3u9CK9ASyg/MjKlWiJtwXoZV0HtarMYwMk/IMuCM9bPhtZ/5qbP6iuoBX3ceNpyXDNlwXupSqpBcOX7G0hPllDWc/Lg83kXTW9VH17viUwD0eTDaCaLKeLhfsnsRzJ86Cd5yx8/cEMdBSB5GtsfKqDh22Qj0dFXgXRMeOjzkxk9SIdv2kTF1nlWR0aAJZkzuhV/+/i/c+niKRtewZeFYGUwm1Bnz7IxB+whF2L4YYjxRbAxuqxU1P4C9r2OpSCoUNyjcEyWhOPjSJmMVKGOK9suW1DahDpOE1VgE90QxaK/BKMrCeYnK6IBek2+MLOal3Pol+69ufVlFK1LAim9q4nl1CdkTmJwGWHDzBiDoY624AQA+96K9zc+2ORBYLBqZBH/1xPWwcLrcOs4FTGPRl4lxzFhnMoMgev9GNiTrtpsWalxJhHX9OpBZrWa/D0fXvKmT4AbK29a1ZbnL/QML3pzSOf7o965BPRQXT4FCV8nb2+j40M7CeRmWS4gfH7tqV+ZvlCdKlSiFHqZnBS58SuuzjN4izyvZ+VMGzJs6CX7wqlMz37l6DbRBVMQMRz2cF+5Ze2V2VnnB7vv0vc5nYvn693b12ioUeZ7lpvPkzPWzpb+z9dL9zBpi9XZV4AUnLzcjJEJk91hWaZf9Vwbrmc9UcJQ5d3yx07c9cyucsmYWvPyMVcJnmv3AvKXqfqVngG33grK1Qa/xO67Zm1NsmECWz4SWs0xhcqKw40pjgcSQCABnODSp163PQ8xnU5lQuCdKQnHQWUQ+cqLwLjEqT5S08MsHmyFjx9vX8Ivmlcj11hY5QxZaAGWghFo/d4gqz6/g1ou3wNu/9Av4r9881PxusLcL5voIv+Tc9bT1OUSYCRETmbHikTSOyWmABdcbAFFXlcMMLpvZctnHDhGW7m5FCCKTOkXPCsIKt8oIvs8vDb1BCao/yE42XBnHOVG4OhQnLeCh5YnimZZMe/Q+lPleow7Bu/E82UzmXlH9IUNGkMN5qW5q3yqh7DYhMLYtmpb5G2UpT4hWUmYdYBT8mbxukc7yrNNtnDSq4EKoxgPWiIPvySd+nveTL56DNUiTtYNV+tV/x6y/7N+yHFqm4LEwBIr3qnLlefbX563PGRmoIEt+HaJbQnY9vWex3dT4TbSvie55JvQvmZHNXYfxRMkSY9ZpJ62eBSetzntouTBOtpEdYjF5QkmRD+fFB323tcGlxy6CT3z/t/DrBx7N/UZvTezao40W6Td+9dlrYf+6UWmb9J4n6q4z1o3Cv60fha0Lp/EfSCgESYmSgAK7sF3cP3heBSknSvnBjqpL7xF/nij8ek3aE01b19O5qsnc7V83Cu/88i8z333nFSdrCb9l8LlcRUnDZfct37tHXhHW+qbXIJyXCLzwBhjGVDWseE9EXE/yPF98QUW7r7MjpDApa7mJK+Nqd2zMrxhCzRQdw1wE8T4v7zPM3OwuwPSbt6R488l2StAKUMIZ26oqllpCx0M2Q1CeKBUCh3WFWRyY7rdlCFtsKzyMETZ8gUlR3esD73FfhoM1wPeHa36KVZTQ7+jTEyW0oQUPvNBSRgYSqDLi8NSVCltH0T3jFpXMHsu828Sfoj5ULTmd8XrO8Uvh4GNH4LS1dUH6EcYwk3enCGUsLFLaTp9sHxarAZPlvG7uFPibJ20AAH8yHxGGJ/XAf7xkHzzl1q/Dt371YOY3ViFG47jlI7B3xQxYO2cK/PS+g83vn7l7kbJNTDivrmoF3v60Lcq6sN1lGs0hIYukROlgaLnk5TxR7Bcgz/0ta/2U0M4wYRxVFuimEHqimOREadYpLsteSmhBObbFisFaYZ9zGTaGfl1pOC+FFZCqbizjTyea7O/Re0/M9iabv6Y5oXjgMVWYOSJzS8bWAeDJE0Vj7OknV84ahP/53cPw4tNWostjMHe4H37758fUtIS0pJNcAkXz011OlAkaFHSFADZGuqkwwgkyluUaxQTf80IgxM4PoZ2lFPG+uyX7lguhdUJ7AzPU1QqB8aOK2WQ42XDhvOQKCl8GCTqvxFrwFwkUP+afDKotXGsxR05g7ydYTxQT1ENY0fVlK6SvWa4MN1ohmwj3ewx4dzhb6ti+JIQYzRPMHJSF82L5qiCeKP6b4DYmUgJi6LF9pr+nCjedvRZRSwuuc8FN6e/m3m+aYXup5j77wr3wzbsezD1LQ0cAb7KeP/2845qffXmMmmDT/GF49zO2wuKRfHiurmoF3vus7QAAcOnt39Kql16LKQ9guRCpjV9CCORCJUk2q5zliIf2AeSMVkI5EfNFooEGc8nSapZYvj5vTeOGmoTzyoQD43wXGi6UrKL66Nfi8Rvvv2wH/L+nHgNzhlthyiY7iieKDXHR5zknypGjam2iihlzvS67NCzITefma564Dr758pPgvGPmmVUgwIeu2AnLZ4qtjIqAbJ6Lhs6ZdRHnctX8yU0LaLAKBVNPv9EpfY4o4rStQQcGIb26ZPBxdtcUAjN6HykB65BggWcduxgAAM47Zq6zOjFW89UK8WaQg4EsXj9AHIIUkmV2osc7Lt4Cw5O64Q1P3pD5/pVPWNP8HPuVMiR5Onu7znzEPMme6TTf4iycF88TBYi1XMF2Djnz6kFUw4Ycou+zbD+3m7yFfh/R1QTzynwe2E9fPXXHAjh59UxYNdoKTeWipbddtAnWzJ4Ct168hcur0nMSs/x0xCK2852VJRQtTzplzSxnocMawITzco2i+7FdkDxROhh62mSPhFDIhDBJKr5SIm/9wn/OyBPF084vqtZEiSKqU8Z4Ya2taWTXJE4JEypEnrTbDEigq8sqj/KV7Vk+AgAAP7635VI74DgpW71t5m/q84Z5Q+AKvH3w8aNjynIqQSxW0YUdLpXni0mdAMx4A8AshDBcd4otmD4JXvGENfCM9+hZEPlERoaFXLeuPVFiuFijPVEIke6xd1x7PKx71WddkSX2IlGF86I/Cx7leqJEMBYA4vemv5fNQ6USRbJvxdEDCa7w8jNWwRnrR2HDvOGg7VYrxAkvyTtDMbWK8ik1UJQSVaQ3KUNOlC0Lp8Kdrzglt09uX+wmhrxJH8QcMkVn/tP7NPZO9NYLNsELP/J9QX0MLZRC0+QuxANh/m1+X/BU5t0bTGjC8GYb5w3BqWtmNfNe0mPH8kshuiVU39ffrQVeCDUAgH6HEQNMQZP22ieu99LGspmD8JkX1L07fnDPn1ttM/82/3IoZ7GNFs5uNzHvqQ3o8up0FAfbOxe2d5ISxQ2SEiUBhRmD2RiJvhagytJ7+uQePw0neER5c6IYeaJM/OtbSakKCRE79NzqqXLU99KcKNRvup4oommGvXTMnzYJPnVgD0wd6IY9r/+iVtsseEzVoSMOPFGQ7fPGiTe3uz3lMvA5tVfM0vM+KWqdYRlrd0qUenu8VkML87GWqaqnXHmjcdvOShyt4UyQZF0NfkKhFX10OC+elaekHuNwXiUQAnciuqoV2LrIbaJUzDTscqREMYXK456nRA2NLI3F0aED3t7RQ+Wns3kNV8K7WPYi9mojo4u+/2Pz/a2bO4WqO4tceFKqb13NfZ5NDyF6/e9jh3AWrgzzDCHwrmdsbf4tu86W8Q4pAz2PckqUib/P3zofPv1f98HxK2cI63GRiD0m8Hmu7N+qeR8isXwDsnM61DiMTumDm8/RC8mmA9r7uixnbUIdSYnSwdDZCF9y2iq4/6HH4fyt8wEAYMSTMkNkpfm2izbDN+96AM7ZNBeu+egPvLSd4AcYgbSwLPO3r/iYNebfBszCeU3UpcNoVAisnzsEP/ztQ/DkLfPgjp/8DlGGapP6vnEhKdJyWXbhNKEq64lC1yWxWqZ+8uOJIn+T9Y68UbhKFIQniqtwXthLJxs6QFqn4dzEFlM994nnHgtf/fkf4OKdC43oCAFZYnlhThRFnTMHcSGtmu3xLlyoGtxBdAHOgYS93Ir2IRck8Nau71ejrepfdPIKeMsdP4PXnLvOQzutz8rxSuZyCZrA7AHVSkXJS/q0eK0ownfEEs6vgVi84EzQY2sKbYGYty8d2vq6q/CN60+CSgXP58nmDHumL5s5Gfq7qzBtwJ1coZl7MXNO2/MItuVdLW0TOsZkAulIlHuukDG2E0zZvu4qfOTKXUHoEUHV61633qaxlL9GbM+OGHKifOPlJ3mtn/ZEGQsUZtR1yPVORVKiJKAwbaAHbr90e/PvhdMHYPOCYbjz7j87bUdkoXXWxjlw1sY5TttKCAPRVm1ytvra+OcM8YWLVuG8qM8nrZqZsYhjUSEEPnbVLrj7wUdhxSxcvE1RWKsYzkbXNIisUWRziC6i7YkimrX0hSzQnYN3ATjkIicKUkiEfU8dTxSdrvPRz5vmD8Om+cPa5UIKk1TWyjyo9sdn710Cv37gETht3aiibaLVrk9sWTgVfnzfQeVz4YUA/PaUcwShBC4iJ8KOJdObn19w8nK47LjF3H1TosNCgZ6huvPLtFcimMYJgYDZB6oVN17NvBp0k6Dz1kDVUSzjGYO98IeHDxmVFXn/lg3dtCeKxYuYhfOKFzqJ5QEARgV3JBaNemTelOzx1ttVhTtfeQpXefjX562H6/75h3DjmatR7bN05NsudjbbJLq3LTc+Lh7zEN0SkkfL5vlg+ty6bvnfMYMeg8YaZeeB+gwLlwogtpwoPkDfnW35kqQcCYukROlg5DYnzfLnb5nvXIlCIzJjrARDuNzUdaq67vRVymfOO2YuPG3HQpgpyLMgs9wRoZlYnip62yXbpGUqpG4Vg1Wg1MvoWz/HsKQaZGvRQvUlfQ+Quf4/dqTlreEqnBcNtmVfjDTv0ncYoURR50TBtc+rhVe2Wycnioe+sq0TF8s+HOi28u72Zi7u/T1VePMFm5Rty8N5KYs7xYrRQfjEc4+FWVN61Q8XBFEuAVPwBFC++v2rL9sHP773IJyyZlbme/GeiSFErISmL4lFC7QS2g9oTxQHJq7TJplZzqvmfcMw1ZZ3/tSBPfDvP/0d3PDxH1nVU/QytekFV54oRp5JEcizLtg6H75795/gF7//S+Z736Rlww2TzBziGQn0CfJTXLh9AZy5YTYM9nVrtU+aPEz2cDYNI+wK7KubKhZMzk6pJ0qbHcUVxXwzgQslEGt8XGS/N+6ILAnsuXPp7kXwps//jPod34ZtaFpWqRDBluocWU+UMG/Yjv1YBFLq7g5GnIuIb12fUF4IPVEwZZnCWC393hUz4KrjlyqfWzFrELYsnCr8fWwszCoxYfLoItw4pxb02ELWayaXWvoCW8sI4cRlHjl0tPm5r9v9Uae7P5ky4DwmFOeJIn9nLCP8p0ePoJ7TiWWt03cm/dYWJ4fA0wwAhAvM1aW/0Ro3HnTg3q0SApvmD8PsoX7pc0WyCzKFl7ScyFpWsZZcvuu8qZPg1LWj6DW5ZvYU7vd08W7RHl/D798TjyckaAEzi+s5UeTPyPbS/3PRZnjxqStgI9ebUT1rVbybq5xIo0N98LQdZiErsWFTYwftAX4kED8fE17/5A3w+RftzX3v22JZNoV1eWeeAmX7ommwb+UMeO+ztnNKCAxAgFifnbZrQffdRwUGfiaQDXkQeUvAbUQUqcFJ3RYv8u5nbIULJsLi49ryh6bcQWEEdPW+ZfBPz9nd/Ftn57DPiWJVvBSgDR5NDHdNkBxW3CApURKiQjaECf+ZNzx5AwAA3HrxMQEoStAHzv3ShLHBHqimFwS2mN2BJi67Y3E2mapJX2Q9UVqfZRQHEzRKiOjp4lucSauj6qMt1ob7xZagxEIhiwlBJ6/SHYfCaweXE8VN+7RHT/HAjWM7KODpN8Dqp1zF8W/o37jdGLhrsfOYQHHjriNwNL2AxzKn+3uq8KbzN+a+J4TAVccvhYu2L4CF0weE5bM5UeJ4p4T2gWxOPX3nQuiuEnj23iVWYTPO3jgHDpy43Lg8ZO45eXqrGqExgyAycnRAG+0cCRVwfgK889ilcgELXr2+hZMywzAXngF7V4zA7Zduh70r+EnBm/Jhwn5f7GTWffV/uno393v3ioH2AYFsP+v2ue7RoMPTjUzuhWftWYwuO2zo7SgCzyNMRUO1QjLGpjryFdtpmjunQ0n/LejWLUqvZRcesg184PIdzupK4COF8+pg2O5FXkKyZOrnN3D+1vlw7ua5YmvHhEKRP/PcKSJUdV2yexH8/dd+BdecssKizRaO2uREkRS96Zy1MG2gB/7uq3cBAIDJnVkk0G+0q1qfV+5dot8oEjJhbu+EZaDORYruy+5qBb704hNgrFaD/h6xQmbX0ulw5obZsHYO33Ja3p6eNSmAeL86dc0s+NyPfwdP27lAmw5RvY8fEQsDGrGjVZ4oLmLCm4BnFSlDp8pZzXKi6LXRiDfOhhhotMdboqGHA5sfIPQ8Ecdc16jDDSnB0Svw7FOG0CR66uVkLZegC9mauuXcdfDKs9ZAdxWTWN4fsqGO8r+78kRxhTKHVs56ogRWomhOopDx7NmWXAvl83koCPWbeb0fv3o3fOGnv4fLj5PfX7hetKT4MzfXLwqC5g7zPXBdrMmsoU7RPeMWvNwfzd8MX7UZitrWmwlR/h1POwbe8593wS3nrrNrTIKmEkVXyaTxrO68Wjc3e1/vNB7QNpwXXfrYZSOS5zqsYz0hKVE6GDEuoowLpuS5pEApD2zCeeXqUkzZm85eCy/bv0oqXNeBq8TyLKb0dcOBE5e1lChG4bzsOLnrz9BL1OgKjUvt1kXTYPuiabB4RGy13AC7Vy1ClKlWCPy/p7r1VmOvhZgn3/n0LfDo4TEY0MzLYoIf33waTOqpt2OSE2X+tH6458HHfJDWxHKNvD8sZFN+JVWvr+tgUfdMdihFe4vubnXh9gVw9qY58K//dR8Tp7nRIF8QERJ4TxS+Pd2CaZOc0tNqL9s294eELGpuLe0SEnJQrL/G3cGXwBpTrciLuAFXMfxt0C4eY3RfHj4adu+JeafzZUTTmM8VyZFoc3fZvGAqbF4wVfmc0ACk4LmcS0oOZoJi1yH2GnT941W74Mm3ft1p3U0EWhBLZ0zOzG9X+1cjd6ptbZjyp6+fDaevn23ZkhyNvZFejzVwO0zYs+zFp66Avxwag0t2L8p8X5SxX1EIlRMlwQ2SEqWDYe2J4oYMYZ3tZhnRsXB4JmAOVBsFCiust1KiKGi1CTcFoLZmLBKyV2+EV6hWCHz0ql2o+kLzFagk48g+J4QEUaAAQFOBAqBmXnlK9A9dsRM+8u17YOXoIBz44J3O6fOJqQM98I3rT4L+7iq8+lP/XTQ51pBdkkV7i4lgkJ4zDYhCYajo8gEsH8A+9tYLNsGX/uf38IKT3XglyhunP8rpzdDpuStjzGXgJ0CmHLGdjwn+gB3qIoUVNI28c7gL6X0XCu2yfFx5ovjaT4IqqzxPfzavl4lnrQ1EZ5+tftKWdFfv7roLG9VtXTQNBvu64OHHj0qfN4HvfA+fef5xcO+fH4M1c6bAf9/7UPN72zF/59O3wFd+/ge4cBs/l4nuWODDQvtFK5xXFqph0hlG7PvNGe6H846Zl/u+qMTyG+YOwbfuejBQay2knCjlQlKiJBjDSzgvmtGK6x6RYAjhXo2YP7nQYLbEBIQOrfaJ5bPXcoDsJeJfn78n951PSBPLdxkklg+tREG0F5r3/d4rToEXfPhO+MrP/whP27EAPvDNu6XPK5UonHecN3USXHvqSvjG/z5gQ6o3qPp8dMg8CSetgNi+aBrc+9Bj8Js/+fXKkYFe0ljZj6tl0grnVbz4DLs3Esj22YpZg3Du5rl+iAKxwCuCLosXRM+yMEZv6YS4gRVE+7Jw1fVE4aGx58Uy+9tlT3OlRMFOnZDhuXTB7q2uh5i1bqcRwtOKawBCSOE8Da91E5JcvIXIoMPXtPWtuF4zZwqsmQjfTI8zO990+/u0taNw2tpR4/KxohHmjA0HvmzmZGdtWCeWZ7bsUFvqNaeugIHeLti/blT9sENYrxHs2WTXSsIEkpi6gxHjIrK1zk8oD0xGt11dO03uFKLQd7wumjbgNjmdDbqNkqbGMe6ZPg+8P00b6IHbnrkNPvLsnfCqs9Yqn1d7oogR085r1M2WL/DRq3bBjMFeu0osQb8C2jqp5uhyzaGh+VvgyYHOD1AgvxDQuSQKmB7D0wd6rLbyTujbBDtg54gvVhKj+COKm3cM4bxoxOjRZoLQOVFiBiucdA16CrP3thDTm+9FW/wZ0hAqN8KMbpg/bFSPzxw2vu7ZIe/voT2fdFCkbCuTWL7a8ETJGmEet3wE/vq89fCJ5x7LrUNHOWy71otSRE/q6YIXnbICVs/Wz6lqg1BrpE1FacGRlCgdDPtwXu4Pgoybo/PaE0KAnVYuD0HvERiY+p9/4jIAAHjmroX6ValoFXqS4EBftFXFm2s10KKSjXlvl364tfAHPkIQEoAKFj1dFdixZDrKm8ckJ0oDsYZlxa6TdhD6yM5CWU6Ui3fW96qdS6ZZtJ23UOPRFQJsWBAZihp1el52KZK4ZBUu5Z+nGPzthZvg8j2L4cRVMwtRh3dKP3cMJMOJ3Z9UimmfPIfK6EUUZsU3MntTJCFnAMCZDY2rMcX2h4vmfHnj+fbyo8/tWq3GRJnwP6FEgnPrpOB2xZvtf+Ha4+EnN++HyYahfn2F8wLwJ8gNGULRb7hrO88W1d0sFJoGSownCiEELty+ADYJFHw+E8uzYKdMzN59Ddi8crg1En8/lgFJidLRYGINRramYrMeSDCD6EzACER9KmQweOHJK+CzL9yLsvxnocOIoq2tKWQTN9IXlonvCrwEy/gAo3BeFrS4hEjIECPU1qziXn386JhbYixQlAD0lnPWQV93BZ5zwtJC2j881hof7Jqp1Wqwd8UM+OrL9sH7L9th3HZMltDYvZF9yvf6FFVv5mlXLuj27Tmb5sKNT1gDhBDo1dj/Y+NJE+IH9rwoMpyXCl0pnJdTXHX8Ulg7Zwqcs8k8vCN9Jk5C5l2Mef9ieXTXY5z1bBD/5gsiA5CiI1w02u+qVqzyd7p4C3p+0v0ypa/bQe15hFSi0P2TC+dVsFEFbWgTej7S7944ZzLhvDCVaAyjrcL0zRdshAoBuOmsNVb1lAW2HoJY5XjMZ1OZkHKidDBiWUQiYW9EcpwEC4g2dZPhDW0hX6kQWDk66L0ds5woPi1t/KFbYanNQ2jlGS4nStydrppTsrV06Eg8ShQaPnuc7Y51c4fgv1+9Hx5+/Ai840u/nGg/3JjTY9DDrBnR/Gx8PW/qJKu2G6HMeO8bet6jc6IEFpCIPIV0kkJ7V/R4qt9mO96ycCo8cfNcWDwy4I6ghIQJYOe8SlhRZD4eX5b6qzR42YyANXJeR4XrTl8F152+yqqO3q4qvPWCTXDo6BhMn4wL9eliBun2/Vsv2AQv/Mj3lc/5Zqkz4bwYZtPEaEwXIt6l3DO5BRe8Dj0qdG3vuWQbvPQf/wt2LZ0Ot331Lut2Gggbzqv1RrpKu7lT+xV1G5HURCyeKBVDj0c9TxTNyhnsXjoCP3vN6VwP76IVoj5w1HecxQSnSJ4oCeZwtH9VM8Jg/ueE8sIl37RshruEZwCQs4p1yuIpKrP1FBEJ8XiXf8J5rijoWCI3EFp5hmku9u1JxKhfcdxiAAC48czVwrKPH4mHkbNdGzawTUhpg8cpJQrbLj0/P3h5y+PE1V47Z7iP267oO5/AChRZ8QiPTrqvXIJuqxM8UWxACIG3XLAJnn/S8oBtBmsqoSTw5omCeEZ1t3EtZPvUgT3w0v0r4Zm7FxmVT+unjnM3z4ULti3AF3Awx3SVeedungtXHa/2nvVmmDQxV1hPlGy+Uz9NZ8gQtGHrBbMoEuW/iy0i64nS+rxu7hB85gXHwXHLR+wboRAyJVGXRrhrFnOH++EDl++ATx3Y45iqOrqYUHdFoeWJko9kIYNeThS8EZQItAIlEttvbxizfMGnbq+Hc966cKr0uXbvx1BInigdDPucKG5AC0nKFC4ngQ/2gBVNM8z40nWdvXEOvPjUlRaUtfCS01bCF376e7hQ50KkCZ3lZeuJwgMR/lEsSh3Oy3M//u2Fm+AFH/4+nLRqJvz7T39vVVdVYBH/8jNWw5XHL4URiTXlY7F6okQ0j33j0NHWjZMn8Hvvs7bDrx94BHYva110Ta2nWaHeyADO0jYE0OG8iHp+7F42AtMHeuCBRw5b0yVqS5UTJVOHNRVyrPGUFDPUOmSn/cLpcQiwEuIF1vhKpUTxKduiFa0DPflruOtwiuvnDcH6eUPG5Tvo2C0UIfuZnf+ufTQqRCwoDhEutLEPZJpG8Agi/ONVu+Aj374Hrj9DbHwUEi7OYJpfDGG0GtITpYvaY1keEvOqxy4TK5DY4rprh/ZWDtknAEy0F44nimsPTNeh+2KJoCOH+TuzXnu62LN8BL76sn0wa0qfVT0JOCQlSgeD3SzXzzVnsm2Q9URpfZ9yopQTuSMAcSbMGVJv+P/nos1G9PDw3H3L4Ln7ljmrjwcf1hrCMtRHXrMNJi+GJcWGJsIgynBeHvrynE1zYf+6UagQApf9w3dgsyCxHwYia1ZCiFSBApD1gnCJJQZWfCbdbDQ0kTHn9Bhsnp+3Ktq7YgYAzMh852qZhEj8igU2OlaRFNN7QSyhGgAA9q8bhb950gZY55i3C7Udd08o3D/x3GPh3j8/Bmvm+FEKJbQPsKuPJ6t47RPXw8s//kOjdqdO6oY/PXoETl49S/lsb1cV3v2MrXBkbByGJuXzD8S0hwCkqACm4G2TIfoSZaDmmQZaUVJEThTeEiLEvO2ti6bB1kXTLKlyBxdKL5Enii+EzIlCKypikyXRCp6jocMs0HRwcqJgoBXOK7KzLHa4WCOYcM5FekC1E5ISpYPRWEPnbJoDq0anwKXHLtIq74oZrGY8UVqf097bHhDnRGkN8D9ffWwocqRwebCoaqKnt5kSha6Ld2OgPka0low8UUKH8xI0mAlJ4Els29tVTzb53mdtt6rHxtpvx+Lp0t9118mTjpkHK0cnw9kbzRO7FgWXc++GM1bDO/7jl9BdJfC7g4ekz9Ih1aYO9MD3X3kKbLr58xM08YlyQevtl26T/h5aoCbzRBns7YKHDx0FgAlPFOo30fr0QT7dlknOJxUu3mnmMUkIgadsm++YGv94/ZPWw1vv+Dm8/knrAQBg0/xh2GShUI7o+EvwDHxOFLdMxReuPQF+9ruHYftinKD1lDViZUsIS30VROFii0CR+WlsUJScCtOur8TyjWoyOVFqNcYC3k1bGDpy3xc9mTkwmieuw3nZV6dEUCUKpahwPd9s34Kmbcw2fpMmeIbKNP+KC+eFby+Co6xUCOWZVM4TNT6knCgJsGp0CjznhKXQ110tpP2KQNjbPingOhuiM4Ee61GBJ0qnbPQmjIYopwrX+k2/em8wC+cV30wIcRG0gY0gZs2cKTC5F2dj0VUh8Nx98hjcUyd1w7P3LhWucxlisoK1peSKvUvguzeeDMtmqnM7HTqa9QYantTT/Ox6NdBy/30rZzqu3Q6yeTx7WDyfvCdtF8yGLo2cKJi5vX3RNHjNuetRbYeC7769YNsC+Pr1J8Gq0eR5kpCHbPqhlSgcxtQmn9HUgR7YsWS6k/OqsefFYiwa0RFcKsTEt65mQju6tkQ+a+McAIBmPhZ6HYzXapm5HMYTJd8Ggbj4SRu46ENVOC/XfRUydFU3dUFjlTe273XUUvFBe8kU6olS9R+hwnk4L6e1xYdQisZYeIuyI3IxUIJPXH/GavjKS/fBU3cYWjkyfy+bORn+6onrtOupCnKixC6kTMChU/dq1SFFM3JmjAZ/3cSOgR59Za3vA//9l+3IKAJQyWEj73Vba9a1yNA5P7llP7zktFXSZ9xZOeIqivmejL3A0Z4ovnH6utmwevYUuIRJPMxNLB+GpCZk4QDmDPc3PxMmKUqvgbJWCxRZ9MWnW8G46G5lMQniGojxArYBmfPh2XuXeKYkoUhgzwierKK3IEMyFl0RXH6yoX4iPlAjxjEL8mE4Q4A3XP9w6TZ42f4Wn+Z6D3/zUzbCJw8cCy84aXnut1otK0DH5jmzAZd3IXFy7arueM2562D6QA9ccdziVhmDds7cMBsAAPon9rmsYiv//KwpbnPjFeWJ4rrZI2N2vDl9NwvZJwDZ85G3DlGeKBo8qc+lHuNatkU4JUqETHwJkcJ5dTCmDfTAtIEe9YMCsJvjHdccb1RPNRuXiKq/HbfIzkOZNmuXpOowGiYCbyJYK8134FyCi7xCXHvKCvjXH94Hlx2nL8TyPYX2LB+BPctH4P998ZfS9kTePzHCNq66zKKenm+Y8EU2e7mZelG/VGzC6scM8tKY7rV93VX4/15wXO57Xj+GnvcygcvsoX7hbyE9a4+Oty7VWp4oiGdCX7LLiM0LhuH9l+0Q/k7vPyevngXv+vL/wtaFxQg4E/zCJpxXH6V4LXLVpTjy5ca/X3s8/NuP7s8ZJYQCjw2YOaUPnnPCUnj9v/20/gwzw21nXHe1AhvmDXN/G6/VMkqUEJ4oIp4zdr6dh4t3LoSn7VgAn/zBvc3vTN7j9U/aAMP93XD2hNcQDR6vt2p0Cvz1eeuNPMh5GCsosbxrDxjWe8RmThXpiVLl5ETB3IMw3Xn2xjnwyR/cC1cY3PflbbcnPzxjsBf+8PAhOH7lDPXDDrBq9hT41l0PBmmrnZGUKAmFg2aoSEmt6xPEEB15GMFqmc/LmYN9cPeDj6KeNbk0E8HnBmqK30PjeScth+dxrNQwiE3ADRBHn8pg64kis4b9/9u7+/ioqnvf499JSCYhZIZACJMHHgLIYyAlIBBEEZAICj5QrQhSOG21VNBSsD2KrVArlfZU7ulRq231Fn1db+l9vSo97amloCJWBUWEilKrrVC0EqnIQ0RNIFn3D8wwk8wk87Bn7z3J5/168TLO3tl7zWSvvdes31rrd934ftqw86BmjAjEdCyrPisnvgA7deXVn25jtFuUQqWyrFeOLtXG3f+MONI0HhMG9NCgom66ekxsuTrauo79uWeTMns8UmPICMGcLqkNooSWKvSLcDxBlFjYvGR2Wjq3fw/lxbj8YEHXLL3xvRnKTkHuGjgv1toXqWPNPTNR3N66sFeRz5pOXLsM7NVNS6YMirgt3r+sLyer/Z0S0PLyT2XbqrGpxXJeNuZECVuySu5LMi6dGQTQHo/HE9YmT2SgUDdvF625cmTEbdE+lrnjElupRJKWTBkYHJgmWZ+Hqi2hn5XV521ssm6WuJXHilcwiBLntRTLp/njuZ/T3XNGxtwui9XFIwK66/d/0cBeeZYe10qJ3GJ+t3SSnnrjfc0ZXWZ9gSL4r7mj9Z9PvqkvVve35XwdFUEUJMyqtkjYcl5pNNIbkbVsnKdzICQZDy0cqzv++zV946LBEbeHLV2XwLUeGoRqbyRJutcl2xPLx9BMdPtnmnwQpY3O665ZevabU2KeYZLUTJQEftUFq6EkrT6hmSgpKMhnvndFhdZcWaGu2ck1G7vnZrfK8dGWtjo9crLCOxVCA0/eLPsugniW8woVy7XdUUfeOcmp/H+wQYwPjEj9aqEzUZxsuLohsbybrJ49QvWnmjR/QuIduukg9NL90dWV+vWudxMatBBLFbAzP0VTi+W87JmJEvk1N1atoQGffrPkvHaXz+qWc7btZcX7SPUl8M2Lh+qGCwaq8rubJVm/rFZbQu+hLc+b7Ed3ysKRLVYeKxah9SLiTJRYihPDPh6Px/IAiiT16dFVr3xnuvJzuujy+563/PhOCfhzNH98P1vPt/bzo2w7X0fVAboakO7CZ6Io5GcXtnZgGTf+deNtznzz4iGSpP49u7baNrh3vjbcUK3xA3q2e5xE1ghur66ErWntyk87dm7sR3T7coPJdsS0Nzsqnvdv90d187RzVNjNq5unRh4NGkmqRnwmavyAHpIkX07rLyLRgnxWz9gKG9QgJR1ASURb13FuSGd4kzFheWSi5UT5xvQzQe0vjLVuxFfoF2Grl+JhOS8gXGWUZYOk2DsXIy0t45bgmlMzUdzapumV79VDC8dqypAip4uSUqGX5FVjyvTLGyaoIIElt2PKa9Bqn1T+7U1YR7YdQcLogRp3XuOf69O9zeVJpRZtQQvehh2BrdDZwk61ZawOGJYVtP13ioeT7buzM1HSS4+8bGVlZrh+ICM6PmaiwHGhAzejja5H5+T2Ubg3XjhQV44u1Uv7P9SyX+1J+DgJLecVbT2vNj6ydK1Tti/nFctIm9SXIinJJqe1siMnmUMl0rFT7M/VztunxfW7Y/oV6IYLBqi8MHyauAmPRtpm5SXD1L9nni4ZWdxqW7TbYipvl1bdO+I9TlurLoV2etafbtLpkOW8ov3t54/vpwvO6aXS7sl9EQ49fiqXZHBjEKXUwk4Eu6XrMxBnrbpshAL+HF0xurTVtlj/vJHalqGz18iJgo6s5eWf501dALGxKbwj2457cMSZKPKk9f3fF7p8qcWNUTs+l44TROmqDTdM0JfX79TJhkZdc25sS9NGYndOlNA/c3MQJd4AmhuX13aTNL7FIE4EUZCw0If46tnDEz5OZtSZKEhHsQY+0rkx28zj8agkwc640PefyCigsPxBYct5Rdw5rdm/nFf73Dpqs1lOkssZWTla0KovfPF85PH+fTwej1ZeMqztfWysSPk5Wfrq5IEx7TugME9vf3BS04f3Tll5nJjNNrpvdw0o7BZ1e+g1fqqxSQ1t5ZEJ0adH65mD8YqWEyW+Y7T/mdq57EqsqvoW6HtXVKi8p3vXpQ6VmeHR9OG9dfzjU21eT0gP/twsfWvG0IjbYr3vR17Oy10zUSr7+B0uCexk50CF5o7Qb186TG/U1um8gYXWnDyCJmPCvhcmMvM+XpHuA/Eu5+XxuGsWvNWzpe3Om2lnYvlQLce4WHH5TRjQU7u+M10HjpzUkN75CR/H7pwoYTPCPIkt55WKP2O6r5YRqmu2O9oRSD2CKLDEovPKE/7djCg5UdCx8bc+K7GcKCE/R9gellDR0/p30omLvscEuf2jTHZpEitnoqTrdZcufvXVam3962HNHlVi6XGdzFF21xUVmj++b5udolkh01ROnW5S/en488hY4XQc61rH+zG6cSaKJC2YYN/6zVb4+RfHOl0E2CDW+hWpXtmZR6ktzQMYiv252vbNC1231CQSZ8dzNJ6O0K+cPyC1hdGZ9nvIJNGULR2VnZmhhs9OFO0M8Zz7l9dP0LINe3TXFRUWlO5Msu1k5Ics5/Vxw+kkSxM+4NGOQWF2JpYPlargTU5WpoYGfEkdw+6ZKKGDcppXKwj929s9y6Si1KfX/nlCFwzuZet5U+nWmcP05vsfaUF1erWRET+CKEhYss/cqr7d9crBY5obMhUyfHQ9PW8dwYPXVWnx/3lFa+fEnky4mTu7j6yX9HJeISLNBEr3mmT3sm6xnM7tt6fcJEfDZFqYnd3lH1Wb8kM6sHy51jSZkh111fL67JXv1RfGJr6kQDSpGB0Wa73xeNpvA4R2iJxqbArLiZJqoUWL54tw2Ci1WEYMd5aHIGCBZJ7LbpmJEjpSv1+azPZCerE1sXyTCc+/kaLl6p6+ZbIm/WDrmXN8Vodavs147g8TBvTUjpXTLCmbxyNd/rnWyw/GI3RgVN2nFgRRQn62YwVBp2bVunlZcLsHyYSeL9JXvJgCsBaW57+XTFL96UZH8i2mSsCfoye+fr7TxYANOs5VC9sV5eck9fuPfnm89r57XOPKe5x9MZ172yCp9QN2RkWx3rxrprJbJPp14/RNO9taoe8/oeW8wkaPtBbpvbjxM4+F3U3gaKNxwtZ1dvlnmeuimShWRZzsSEjaUnaXDO24bZqMjLwu6WTriJ5aMVkv7f9Qtz2+N+bfCe2MaWg0qo9xOS+rxbMkQ543vma3U0tgAOko1ufyzIqA/vBabdhrbkksn5npTNuiZkRv/eL5A+prwXKHcE4szS07+24H987XsU8aUn6esoKz123EnCge97fbY1X36amkjxGW7s+GUWGNzjTPWl3rbroG7J6JEhpEiZQ3M5bSWBmUyszwdKgACjoXrlwkbMKAHrqlZrAGFSW2HmQ3bxdVD+wZ9prbR3cjMS0DKJK05soKXfXAdi2vGexAiSIr6Z5cYDBRiawRHJZLPqRNE6l509xAXjJlkJ772weaU5XciCi72d2PWOyPnOcmtBO9yOdttT0vO1MnGxo1JJD4GrlWSbZDyMqOnGRjH1eNKdO/6uqTWns4GQG/M/eFaJxI7JjqZ/PAXt00sFe3+IIoIWU6MxPF/ct55YV8YYzlM3Xrcl6AK8V4n/rBVaM06ZxCnfjktH6w6Q1Jkjeknepk7NLSAQxx+PcZQzWixK/Jg3vpz+8cc6QMsIcdo/OfXD5Z7x37RMNLfHr+bx+k/Hyhog1Mc6qPwarTjirz69V3j+siS/LfObeUlJ3c3IZqjKPtaMn5TNszUWLhlk+T/kI4jSAKEubxeLR06jnWHjPs+JYeGi4zqqy79t15sbpktvEkt/lpvaC6nw5++LGmDCmK6/cS6dRMNt9A2O+EnL5HXnbU36ke2FN/vqPGsmWJ7GLXZbDhhgk6XFevQUWRkw9nZnj051U1amoyEYMUu74zXacam9QtztHmqZDsTJRRpX79X4vKkuzIrx9dXWlRSRCP8LxL9j6QYzlfZovlvOyciRJavnhGE8Y7EyXiOuK0jYCIYq0avpwszR/fTxt3vxt8LVXLDMXLjsTbkeRkZeqqMWWOnBv26paT+jbqoKJuwba03R3ozTUoPHm6x7Flwq067+Nfm6iT9Y3yd00+T5LdMQ2nghlNxqhmeG9t3ve+JHf1LdmeE6W9mSgxXBRMjgbOcEcWPeAzoQ0NN3REIrXaDKA4wNslU3deXqEpQ+MLoiQrkWWKuueeDZbkZGfo/nlVumhYkZZNOzOzJ1o7x981K+VfJKYMOZMk7tz+BZYcz641bScM6KnLKttOzu3PzVJBlEBVTlZmWA4NJ3kjzP6Kx9Vj+2jV7OH6/c2Tki6LS/qmOgwnvsS46Ytns9D7WMPpJp2yMYgS8J+diRZP50CeN7bg5lcmlUuSVl46LL6CdQJ/+Pr5WjJloNPFgAvFe5/q3jXys9zJdfStzEcGd3F6KaH75o1WRalP378y/hyVybC7/zzadxwXNmPi0iUzw5IAimT/jAKnZugbI/10wZjg/7vpGjgdx1KwVgjLiRLhgyiI8jx0o2vH9ZVkXT8DEC96qeEqoff0dLqZI4SFLbPKPt313N8+iLgcWEeSSBAlNztTW75xgTwej7xdMnXpqGJdOqo4uN2E5e+w13/OHa3/efU9XVJR3P7OMWDkS/ySHVWbmeHRv51XbklZWi7biMQMK/bpL4dOaE6V/aOF3fTFs1nL5bxWXTZc1/x0h5ZOGZTycw8qytd/XDVKvX052ryvtv1f+Ezo4JC2PtPbLx2mG6cManNmYWc1rNinYcU+/f3wSW16vVbzPvsyDcTbST35nF6aXVmioS5YgrNZF4dyooSKNlAEyTm3PHqHnx0DFWaNKtGsUW0PFEqFiDMqU2DRxP56dPuBqEF2pwb0ZLmgTrdk1/eq3y2dpGf+elhfOd+a7xPxamwyjs1Aao/tieVD+wVCPpMH5lfpyMkG9S/Ms7U8yZg3rq8qSvyuWD4bnRNBFLhK6HOOzoP0ZOUXwHXXVOrBZ97WvPF9LDumGyXasD8nxhwRdrcf/blnlsqwihM5INzOn5ul45/Enlzyc326p64wUby4cpoOfvixxvbvYfu5O6KNN07UgSMnNTTgs+V84ctrWnMTibWTM5bTZbZILD804NPu70y3bVmeq8eeeS49sfdQzL8T63JeHo+HNlA7HriuSp+ealJutjsSgsN58d6mMjI8uvfa0akpTIISGVRjtaq+3XXztHNUXkiSeSsV5efoq5MH6Kfb3m61rSMPFqoo9dtyntWXjdDKS4ZFHHjn8UTPlZIqD15XpVW/fV33Xltl63ljYddsu5Flfo0ss+fvH4lTuVhiEU8+PStEm/gyc6Q1Ax7t5PF4VOnA91qgWcce3o20E9rBwkio9HL/vCoFfDl6aOG5lh2zKD9Hd8werkFF7h5pkGwbLRWjZFzcboybzTOe08KW5RfoweuqNKZf+1OZLxpWpF9eP8GGUoXr7cvRuS4IoNx5+Yiw/6arnKxM2wIokjuX8AoVGixpXsrLibwGVTHUwWahieWRHI/HQwAFYay6ZznZfBrlYIdjM4/Ho+XTB+vK0eRIsdrAXpFz7nVkvfK92n7bVP15VU3Kz9XWygXNs/VLu+emvBySNKOiWC+uvEjjyp1vB3dWLYMobmrXOjkTJV6LJ5+Z3fVtlpgFJDETBS4T+nArsGjdT9ij5XJSiJ1TiUTTRQeKB1mmKD9HMyqK9b+fP9DuvoOK8jt1Z+MXq/vritGl8rkkX026CPjPdjTYHZuI5XQZLRLLO+XzVWXKyvSoqm/7wZTQ9cy5rwHWcjrnRDKeXD5Zf//XR5o4sNDpoiCFrhxdqu1/P9JqmdOO/jWg2G9P4CJUwJcT/DnT49HUoUX63dJJ6s8Mq07T/nAon31M7M6Jkp1EHtp/nzFEC6r72RaABNwupTNR1qxZo4kTJ6pr167q3r17xH0OHjyo2bNnKy8vT4WFhbr55pvV0NAQts/evXs1efJk5ebmqrS0VHfeeaejSf+QOqGdIgF/Tht7Ah2H3VPM0w33++hiuXJ8uYyXIIASv27eLvrTt6boxZXTXLmmdMucKE7JzPDoytFl6tez/fWk/blZWnnJUH1rxpC4r8khny3fODaOmS9AR/f4jRODP7vwNhWzQUXddPGIgNPFQIplZWbof13zOX1hbJ9Wr1thypAiSVLXTjxwplludqZeWjlNu759kTIyPPJ4PBpZ5lc+7UFXL3NlJTe/z57dvLaeb3x5D11WWaIV0wfH/bsej4cAChAipT0rDQ0Nuvrqq1VdXa2HH3641fbGxkZdeuml6tWrl5577jkdOXJECxculDFG9957ryTpxIkTmj59uqZMmaKdO3fqzTff1KJFi5SXl6cVK1aksvhwQHaXDP3HVaPUZIyK8gmioOMKncabkeKFFe0Ynfm1Cwdq8773deXo0pSfC2e11Wn0k/lV2vRarRZN7G9bedCx9OmR/IjNcf176KUDH575nxhvRTHlRAnZyQ15BGJ1wwWRk96254mvn69TjU3KyaJzDGhW1bdAAwrz9PYHJzW70v6k2UAylk4ZpDdq6yybgTSuvIf+56ZJKiugw1OSinz0JURS4sDMICc0uXAqyi/+7Vytf/6A7UsMZ2R49F8uywEGpKuUBlG++93vSpLWr18fcfvmzZu1b98+vfPOOyopOdPwveeee7Ro0SKtWbNGPp9Pjz32mD799FOtX79eXq9XFRUVevPNN7Vu3TotX7484ujI+vp61dfXB///xIkT1r85pMzVYzt2EnFAUlhHWGGU0SjDSxLPfxB6/FQHaSRpdN8Cvbq6RvkxJk6OhcdzJrfL6BiWyems+hR01Q59GHHbJSOLdUkaJgzs6NJ5tHQiHv3yOK34f3/Wi/s/1K0zhsb0O7EkovXlZmndFyp179N/09rPj0q2mK6XmeFRZgYBFKCl3yw9T28cqtO5/a1pK7h48DI6mFsuHmL5Me1K5I701b8wTw9eN0Y9u3Xs/LMtYyhOLCvX0pQhRcEZYwDSk6NrfGzfvl0VFRXBAIokXXzxxaqvr9euXbs0ZcoUbd++XZMnT5bX6w3b57bbbtOBAwdUXl7e6rh33313MIADAKk2fXhvde+aFVcS7cwMj/bcMV1NRq1GFj9/61Qd+ahe5YXtLw8TTa98r7596TB5szLl7WJPx5vVSyY9tXyynth7SIvOa32fxxm3XzpMjU1GV40hAWy6WH3ZCM392Y5gosaOLicrU/fPr5Ixpt1lwf70rSl679gnGlESvRNozZUVOnTsU1WU+lVR6tecKq59oDPz5WRZmry5Zb4KAOhoZlR03OUDb5s5VOu2vKm7rqiQJD3ypXE6+OHHquzT3dmCAegQPMaGxebXr1+vZcuW6dixY2Gv33DDDTpw4IA2b94c9rrX69X69et17bXXqqamRv3799fPfvaz4Pb33ntPpaWleuGFF1RdXd3qfJFmovTp00fHjx+Xz5f4yG4AiOZ0Y5MyP1tzFwDaEktAAQBgn6MnG/R+3acaGuC7IgCks9ONTepiUa4hAJ3DiRMn5Pf7240bxH1nWb16tTweT5v/Xn755ZiPF6kToWXnQst9muM+0TogvF6vfD5f2D8ASKUumRl0igKICfcKAHCXgrxsAigA0AEQQAGQKnEv57V06VLNnTu3zX369+8f07ECgYBefPHFsNeOHj2qU6dOqXfv3sF9amtrw/Y5fPiwJAX3AQAAAAAAAAAAsFrcQZTCwkIVFhZacvLq6mqtWbNGhw4dUnHxmeS3mzdvltfr1ZgxY4L7rFy5Ug0NDcrOzg7uU1JSEnOwBgAAAAAAAAAAIF4pned28OBB7dmzRwcPHlRjY6P27NmjPXv26KOPPpIk1dTUaPjw4VqwYIF2796tp556Srfccouuv/764BJc8+bNk9fr1aJFi/Taa69p48aN+v73v6/ly5ezHAYAAAAAAAAAAEiZlCaWX7RokR555JFWr2/dulUXXnihpDOBlhtvvFFPP/20cnNzNW/ePP3oRz+S1+sN7r93714tWbJEL730kgoKCrR48WLdcccdMQdRYk0QAwAAAAAAAAAAOr5Y4wYpDaK4BUEUAAAAAAAAAADQLNa4QUqX8wIAAAAAAAAAAEhXBFEAAAAAAAAAAAAiIIgCAAAAAAAAAAAQAUEUAAAAAAAAAACACAiiAAAAAAAAAAAAREAQBQAAAAAAAAAAIAKCKAAAAAAAAAAAABEQRAEAAAAAAAAAAIiAIAoAAAAAAAAAAEAEBFEAAAAAAAAAAAAiIIgCAAAAAAAAAAAQAUEUAAAAAAAAAACACLo4XQA7GGMkSSdOnHC4JAAAAAAAAAAAwGnN8YLm+EE0nSKIUldXJ0nq06ePwyUBAAAAAAAAAABuUVdXJ7/fH3W7x7QXZukAmpqa9N577yk/P18ej8fp4rjGiRMn1KdPH73zzjvy+XxOFwdAFNRVID1QV4H0QF0F0gN1FUgP1FUgPVBXIzPGqK6uTiUlJcrIiJ75pFPMRMnIyFBZWZnTxXAtn89H5QHSAHUVSA/UVSA9UFeB9EBdBdIDdRVID9TV1tqagdKMxPIAAAAAAAAAAAAREEQBAAAAAAAAAACIgCBKJ+b1erVq1Sp5vV6niwKgDdRVID1QV4H0QF0F0gN1FUgP1FUgPVBXk9MpEssDAAAAAAAAAADEi5koAAAAAAAAAAAAERBEAQAAAAAAAAAAiIAgCgAAAAAAAAAAQAQEUQAAAAAAAAAAACIgiAIAAAAAAAAAABABQZRO7Cc/+YnKy8uVk5OjMWPG6E9/+pPTRQI6jdWrV8vj8YT9CwQCwe3GGK1evVolJSXKzc3VhRdeqNdffz3sGPX19brppptUWFiovLw8XXbZZXr33XftfitAh/Lss89q9uzZKikpkcfj0W9+85uw7VbVzaNHj2rBggXy+/3y+/1asGCBjh07luJ3B3Qc7dXVRYsWtXrOTpgwIWwf6iqQWnfffbfOPfdc5efnq6ioSFdccYX++te/hu3DcxVwXix1lecq4LwHHnhAo0aNks/nk8/nU3V1tf7whz8Et/NMTS2CKJ3Ur371Ky1btky33367du/erfPPP18zZ87UwYMHnS4a0GmMGDFChw4dCv7bu3dvcNsPf/hDrVu3Tvfdd5927typQCCg6dOnq66uLrjPsmXLtHHjRm3YsEHPPfecPvroI82aNUuNjY1OvB2gQzh58qQqKyt13333RdxuVd2cN2+e9uzZo02bNmnTpk3as2ePFixYkPL3B3QU7dVVSZoxY0bYc/aJJ54I205dBVJr27ZtWrJkiXbs2KEtW7bo9OnTqqmp0cmTJ4P78FwFnBdLXZV4rgJOKysr09q1a/Xyyy/r5Zdf1tSpU3X55ZcHAyU8U1PMoFMaN26cWbx4cdhrQ4cONbfeeqtDJQI6l1WrVpnKysqI25qamkwgEDBr164Nvvbpp58av99vHnzwQWOMMceOHTNZWVlmw4YNwX3++c9/moyMDLNp06aUlh3oLCSZjRs3Bv/fqrq5b98+I8ns2LEjuM/27duNJPPGG2+k+F0BHU/LumqMMQsXLjSXX3551N+hrgL2O3z4sJFktm3bZozhuQq4Vcu6agzPVcCtCgoKzEMPPcQz1QbMROmEGhoatGvXLtXU1IS9XlNToxdeeMGhUgGdz1tvvaWSkhKVl5dr7ty5evvttyVJ+/fvV21tbVgd9Xq9mjx5crCO7tq1S6dOnQrbp6SkRBUVFdRjIEWsqpvbt2+X3+/X+PHjg/tMmDBBfr+f+gtY6JlnnlFRUZEGDx6s66+/XocPHw5uo64C9jt+/LgkqUePHpJ4rgJu1bKuNuO5CrhHY2OjNmzYoJMnT6q6uppnqg0IonRCH3zwgRobG9W7d++w13v37q3a2utLpREAAAUBSURBVFqHSgV0LuPHj9ejjz6qP/7xj/r5z3+u2tpaTZw4UUeOHAnWw7bqaG1trbKzs1VQUBB1HwDWsqpu1tbWqqioqNXxi4qKqL+ARWbOnKnHHntMTz/9tO655x7t3LlTU6dOVX19vSTqKmA3Y4yWL1+uSZMmqaKiQhLPVcCNItVViecq4BZ79+5Vt27d5PV6tXjxYm3cuFHDhw/nmWqDLk4XAM7xeDxh/2+MafUagNSYOXNm8OeRI0equrpaAwcO1COPPBJM0JdIHaUeA6lnRd2MtD/1F7DONddcE/y5oqJCY8eOVb9+/fT73/9ec+bMifp71FUgNZYuXapXX31Vzz33XKttPFcB94hWV3muAu4wZMgQ7dmzR8eOHdOvf/1rLVy4UNu2bQtu55maOsxE6YQKCwuVmZnZKoJ4+PDhVhFLAPbIy8vTyJEj9dZbbykQCEhSm3U0EAiooaFBR48ejboPAGtZVTcDgYDef//9Vsf/17/+Rf0FUqS4uFj9+vXTW2+9JYm6Ctjppptu0m9/+1tt3bpVZWVlwdd5rgLuEq2uRsJzFXBGdna2Bg0apLFjx+ruu+9WZWWlfvzjH/NMtQFBlE4oOztbY8aM0ZYtW8Je37JliyZOnOhQqYDOrb6+Xn/5y19UXFys8vJyBQKBsDra0NCgbdu2BevomDFjlJWVFbbPoUOH9Nprr1GPgRSxqm5WV1fr+PHjeumll4L7vPjiizp+/Dj1F0iRI0eO6J133lFxcbEk6ipgB2OMli5dqscff1xPP/20ysvLw7bzXAXcob26GgnPVcAdjDGqr6/nmWoHW9PYwzU2bNhgsrKyzMMPP2z27dtnli1bZvLy8syBAwecLhrQKaxYscI888wz5u233zY7duwws2bNMvn5+cE6uHbtWuP3+83jjz9u9u7da6699lpTXFxsTpw4ETzG4sWLTVlZmXnyySfNK6+8YqZOnWoqKyvN6dOnnXpbQNqrq6szu3fvNrt37zaSzLp168zu3bvNP/7xD2OMdXVzxowZZtSoUWb79u1m+/btZuTIkWbWrFm2v18gXbVVV+vq6syKFSvMCy+8YPbv32+2bt1qqqurTWlpKXUVsNHXvvY14/f7zTPPPGMOHToU/Pfxxx8H9+G5CjivvbrKcxVwh9tuu808++yzZv/+/ebVV181K1euNBkZGWbz5s3GGJ6pqUYQpRO7//77Tb9+/Ux2drapqqoy27Ztc7pIQKdxzTXXmOLiYpOVlWVKSkrMnDlzzOuvvx7c3tTUZFatWmUCgYDxer3mggsuMHv37g07xieffGKWLl1qevToYXJzc82sWbPMwYMH7X4rQIeydetWI6nVv4ULFxpjrKubR44cMfPnzzf5+fkmPz/fzJ8/3xw9etSmdwmkv7bq6scff2xqampMr169TFZWlunbt69ZuHBhq3pIXQVSK1IdlWR+8YtfBPfhuQo4r726ynMVcIcvfelLwX7cXr16mWnTpgUDKMbwTE01jzHG2DfvBQAAAAAAAAAAID2QEwUAAAAAAAAAACACgigAAAAAAAAAAAAREEQBAAAAAAAAAACIgCAKAAAAAAAAAABABARRAAAAAAAAAAAAIiCIAgAAAAAAAAAAEAFBFAAAAAAAAAAAgAgIogAAAAAAAAAAAERAEAUAAAAAAAAAACACgigAAAAAAAAAAAAREEQBAAAAAAAAAACI4P8D/cQ5DswK3qAAAAAASUVORK5CYII=\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -479,9 +505,81 @@ "In STUMPY, parameter `p` is used to denote the kind of p-norm distance. To this end, from this point onwards, we use `k` to denote the number of elements that should be stored for each distance profile." ] }, + { + "cell_type": "markdown", + "id": "4711a892", + "metadata": {}, + "source": [ + "First, let us implement the naive version of VALMOD, that is we do not take advantage of previously-calculated top-k profiles, and we just iteratively call `stump`." + ] + }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 15, + "id": "4a17e969", + "metadata": {}, + "outputs": [], + "source": [ + "def naive_VALMOD(T, m_min, m_max):\n", + " # out_P is the scaled version of matrix profile value. \n", + " n = len(T) - m_min + 1\n", + " out_P = np.full(n, np.inf, dtype=np.float64)\n", + " out_I = np.full(n, -1, dtype=np.int64)\n", + " out_M = np.full(n, -1, dtype=np.int64)\n", + " \n", + " for m in range(m_min, m_max + 1):\n", + " mp = stump(T, m)\n", + " P = mp[:,0].astype(np.float64)\n", + " I = mp[:,1].astype(np.int64)\n", + " \n", + " P[:] = P / np.sqrt(m)\n", + " \n", + " l = len(P)\n", + " mask = P < out_P[:l]\n", + " out_P[:l][mask] = P[mask]\n", + " out_I[:l][mask] = I[mask]\n", + " out_M[:l][mask] = m\n", + " \n", + " out = np.empty((n, 3), dtype=object)\n", + " out[:, 0] = out_P\n", + " out[:, 1] = out_I\n", + " out[:, 2] = out_M\n", + " \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "62a300d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computing time: 6.5180253982543945\n" + ] + } + ], + "source": [ + "# Example\n", + "seed = 0\n", + "np.random.seed(seed)\n", + "T = np.random.rand(5000)\n", + "m_min = 50\n", + "m_max = 100\n", + "\n", + "t_start = time.time()\n", + "naive_VALMOD(T, m_min, m_max)\n", + "t_stop = time.time()\n", + "\n", + "print(\"The computing time: \", t_stop - t_start)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "a010e37e", "metadata": {}, "outputs": [], @@ -583,61 +681,48 @@ " σ_Q_inv = 1.0 / σ_Q\n", " LB = σ_Q_inv.reshape(-1, 1) * LB_σr[:len(σ_Q_inv)]\n", " \n", - " maxLB_profile = np.full(n, np.NINF, dtype=np.float64)\n", - " isin_excl_zone = np.full(LB.shape[1], 0, dtype=bool)\n", + " global_min_maxLB = np.inf\n", " excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))\n", " for i in range(n):\n", - " isin_excl_zone[:] = False\n", - " \n", " excl_zone_start = max(i - excl_zone, 0)\n", " excl_zone_stop = min(i + excl_zone + 1, n)\n", " excl_zone_range = range(excl_zone_start, excl_zone_stop)\n", " \n", " min_dist = np.inf\n", " idx = -1\n", + " maxLB = LB[i, -1]\n", " for enum, j in enumerate(LB_I[i]):\n", - " if j >= n:\n", - " isin_excl_zone[enum] = True # just to exclude...\n", - " \n", - " elif j in excl_zone_range:\n", - " isin_excl_zone[enum] = True\n", + " if j >= n or j in excl_zone_range:\n", + " continue\n", " \n", - " else:\n", - " QT = np.dot(T[i:i+m], T[j:j+m])\n", - " d_square = core._calculate_squared_distance(\n", - " m,\n", - " QT,\n", - " μ_Q[i],\n", - " σ_Q[i],\n", - " M_T[j],\n", - " Σ_T[j],\n", - " Q_subseq_isconstant[i],\n", - " T_subseq_isconstant[j],\n", - " )\n", - " d = np.sqrt(d_square)\n", - " if d < min_dist:\n", - " min_dist = d\n", - " idx = j\n", - " \n", - " eligible_LB = LB[i, ~isin_excl_zone]\n", - " if len(eligible_LB) > 0:\n", - " maxLB = eligible_LB[-1]\n", - " else:\n", - " maxLB = np.NINF\n", + " QT = np.dot(T[i:i+m], T[j:j+m])\n", + " d_square = core._calculate_squared_distance(\n", + " m,\n", + " QT,\n", + " μ_Q[i],\n", + " σ_Q[i],\n", + " M_T[j],\n", + " Σ_T[j],\n", + " Q_subseq_isconstant[i],\n", + " T_subseq_isconstant[j],\n", + " )\n", + " d = np.sqrt(d_square)\n", + " if d < min_dist:\n", + " min_dist = d\n", + " idx = j\n", " \n", " if min_dist < maxLB:\n", " P[i] = min_dist\n", " I[i] = idx\n", " is_mp_valid[i] = True\n", " else:\n", - " maxLB_profile[i] = maxLB\n", + " global_min_maxLB = min(global_min_maxLB, maxLB)\n", " is_mp_valid[i] = False\n", " \n", " n_invalid = np.sum(~is_mp_valid)\n", " time_complexity_threshold = (n * np.log2(k) / np.log2(n))\n", " \n", " global_min_dist = np.min(P)\n", - " global_min_maxLB = np.min(maxLB_profile[~is_mp_valid])\n", " if global_min_dist > global_min_maxLB:\n", " if n_invalid < time_complexity_threshold:\n", " for idx in np.flatnonzero(~is_mp_valid):\n", @@ -688,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 7, "id": "be7b439d", "metadata": {}, "outputs": [], @@ -735,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 8, "id": "94eceff1", "metadata": {}, "outputs": [], @@ -794,48 +879,94 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 9, "id": "9557a1ad", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing time: 98.4311773777008\n" + ] + } + ], "source": [ - "import time\n", + "k=20\n", "\n", - "seed = 0\n", - "np.random.seed(seed)\n", - "T = np.random.rand(1000)\n", - "m_min=5\n", - "m_max=10\n", - "k=10\n", - "\n", - "T1 = time.time()\n", + "t_start = time.time()\n", "valmod_mp = VALMOD(T, m_min, m_max, k)\n", - "T2 = time.time()" + "t_stop = time.time()\n", + "\n", + "print(\"Computing time: \", t_stop - t_start)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, + "id": "a6ff78e0", + "metadata": {}, + "outputs": [], + "source": [ + "ref = naive_VALMOD(T, m_min, m_max)\n", + "comp = VALMOD(T, m_min, m_max, k=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, "id": "f80bf53e", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# np.testing.assert_almost_equal(ref, comp)\n", + "# results in error as the paper's proposed method is approx. \n", + "# However, the global min is exact. " + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "cff941a8", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.8414941313430254, 2031, 51], dtype=object)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx=np.argmin(ref[:,0])\n", + "ref[idx]" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "bb7e87da", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.841494131343025, 2031, 51], dtype=object)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx=np.argmin(comp[:,0])\n", + "comp[idx]" + ] }, { "cell_type": "code", From f6126ca76c6fe4ded11df8b222d8a51a7f767098 Mon Sep 17 00:00:00 2001 From: nimasarajpoor Date: Tue, 7 Mar 2023 04:36:40 -0500 Subject: [PATCH 64/64] minor changes --- docs/Tutorial_VALMOD.ipynb | 299 ++++++++++++++++++------------------- 1 file changed, 145 insertions(+), 154 deletions(-) diff --git a/docs/Tutorial_VALMOD.ipynb b/docs/Tutorial_VALMOD.ipynb index 886237e0b..f5c58265d 100644 --- a/docs/Tutorial_VALMOD.ipynb +++ b/docs/Tutorial_VALMOD.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 43, "id": "0adbe18a", "metadata": {}, "outputs": [], @@ -23,6 +23,8 @@ "from stumpy import stump, core, config\n", "import pandas as pd\n", "import numpy as np\n", + "import numba\n", + "from numba import njit, prange\n", "import matplotlib.pyplot as plt\n", "import math\n", "import time\n", @@ -32,24 +34,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 44, "id": "44d283f8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.8774290881094438, 3, -1, 3],\n", - " [0.22840038810292498, 4, -1, 4],\n", - " [0.012465907727357997, 5, 0, 5],\n", - " [0.8774290881094438, 0, 0, 6],\n", - " [0.1871064481158026, 6, 1, 6],\n", - " [0.012465907727357997, 2, 2, 7],\n", - " [0.1871064481158026, 4, 4, -1],\n", - " [0.23027056533433626, 5, 5, -1]], dtype=object)" + "array([[0.7444217828807693, 2, -1, 2],\n", + " [1.5382980393045818, 4, -1, 4],\n", + " [0.19836142937718138, 5, 0, 5],\n", + " [0.44958674269840077, 7, 0, 7],\n", + " [1.5382980393045818, 1, 1, 7],\n", + " [0.19836142937718138, 2, 2, 7],\n", + " [0.9901822253111079, 2, 2, -1],\n", + " [0.44958674269840077, 3, 3, -1]], dtype=object)" ] }, - "execution_count": 4, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -119,13 +121,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 45, "id": "37fdbb26", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -515,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 46, "id": "4a17e969", "metadata": {}, "outputs": [], @@ -550,36 +552,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "62a300d8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computing time: 6.5180253982543945\n" - ] - } - ], - "source": [ - "# Example\n", - "seed = 0\n", - "np.random.seed(seed)\n", - "T = np.random.rand(5000)\n", - "m_min = 50\n", - "m_max = 100\n", - "\n", - "t_start = time.time()\n", - "naive_VALMOD(T, m_min, m_max)\n", - "t_stop = time.time()\n", - "\n", - "print(\"The computing time: \", t_stop - t_start)" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 48, "id": "a010e37e", "metadata": {}, "outputs": [], @@ -615,23 +596,24 @@ " A 2D array, with k columns, containing the indices that correspond to the lowerbound values\n", " \"\"\"\n", " mp = stump(T, m, k=k)\n", - " P_TopK = mp[:, :k].astype(np.float64)\n", - " I_TopK = mp[:, k:2*k].astype(np.int64)\n", + " P = mp[:, :k].astype(np.float64)\n", + " I = mp[:, k:2*k].astype(np.int64)\n", + " is_mp_valid = np.full(len(T) - m + 1, 0, dtype=bool)\n", " \n", " # In VALMOD paper, LB has the following component:\n", " # np.sqrt(m * (1 - np.square(ρ_clip))). Here, we\n", " # show it by `LB_σr`\n", "\n", - " ρ = 1.0 - np.square(P_TopK) / (2 * m)\n", - " ρ_clipped = np.clip(ρ, a_min=0.0, a_max=1.0)\n", - " r = np.sqrt(m * (1.0 - np.square(ρ_clipped))) \n", + " ρ = 1.0 - np.square(P) / (2 * m)\n", + " # clipping ρ\n", + " ρ[:] = np.clip(ρ, a_min=0.0, a_max=1.0)\n", " _, σ = core.compute_mean_std(T, m)\n", - " LB_σr = σ.reshape(-1,1) * r\n", - " \n", - " return P_TopK[:, 0], I_TopK[:, 0], LB_σr, I_TopK\n", - " \n", + " LB_σr = σ.reshape(-1,1) * np.sqrt(m * (1.0 - np.square(ρ))) \n", + " is_mp_valid[:] = True\n", " \n", + " return P[:, 0], I[:, 0], LB_σr, I, is_mp_valid\n", " \n", + "\n", "def _VALMOD_stump_partial(T, m, k, LB_σr, LB_I):\n", " \"\"\"\n", " Compute partial matrix profile for subsequence length `m`, \n", @@ -652,7 +634,7 @@ " LB_ar : np.ndarray\n", " The array that contains the main component of lowerbound values\n", " \n", - " I_TopK : np.ndarray\n", + " LB_I : np.ndarray\n", " The array that corresponds to the indices of lower bound values\n", " \n", " Returns\n", @@ -678,7 +660,7 @@ " Q, μ_Q, σ_Q, Q_subseq_isconstant = core.preprocess(T, m)\n", " T, M_T, Σ_T, T_subseq_isconstant = core.preprocess(T, m)\n", " \n", - " σ_Q_inv = 1.0 / σ_Q\n", + " σ_Q_inv = 1.0 / σ_Q # add code to handle `σ_Q==0` cases\n", " LB = σ_Q_inv.reshape(-1, 1) * LB_σr[:len(σ_Q_inv)]\n", " \n", " global_min_maxLB = np.inf\n", @@ -690,7 +672,6 @@ " \n", " min_dist = np.inf\n", " idx = -1\n", - " maxLB = LB[i, -1]\n", " for enum, j in enumerate(LB_I[i]):\n", " if j >= n or j in excl_zone_range:\n", " continue\n", @@ -710,7 +691,8 @@ " if d < min_dist:\n", " min_dist = d\n", " idx = j\n", - " \n", + " \n", + " maxLB = LB[i, -1]\n", " if min_dist < maxLB:\n", " P[i] = min_dist\n", " I[i] = idx\n", @@ -719,61 +701,61 @@ " global_min_maxLB = min(global_min_maxLB, maxLB)\n", " is_mp_valid[i] = False\n", " \n", - " n_invalid = np.sum(~is_mp_valid)\n", - " time_complexity_threshold = (n * np.log2(k) / np.log2(n))\n", - " \n", " global_min_dist = np.min(P)\n", - " if global_min_dist > global_min_maxLB:\n", - " if n_invalid < time_complexity_threshold:\n", - " for idx in np.flatnonzero(~is_mp_valid):\n", - " if global_min_dist <= maxLB_profile[idx]:\n", - " continue # Q: so, are we considering approx. best match?\n", - "\n", - " QT = core.sliding_dot_product(T[idx:idx+m], T)\n", - " D = core._mass(\n", - " T[idx:idx+m], \n", - " T, \n", - " QT, \n", - " μ_Q[idx], \n", - " σ_Q[idx], \n", - " M_T, \n", - " Σ_T, \n", - " Q_subseq_isconstant[idx], \n", - " T_subseq_isconstant\n", - " )\n", - " core.apply_exclusion_zone(D, idx, m, np.inf)\n", - "\n", - " arg = np.argmin(D)\n", - " if D[arg] < np.inf:\n", - " P[idx] = D[arg]\n", - " I[idx] = arg\n", - "\n", - " args_topk = np.argsort(D)[:k]\n", - " LB_I[idx] = args_topk\n", - "\n", - " ρ = 1.0 - np.square(D[args_topk]) / (2 * m)\n", - " ρ_clipped = np.clip(ρ, a_min=0.0, a_max=1.0)\n", - " r = np.sqrt(m * (1 - np.square(ρ_clipped)))\n", - " LB_σr[idx] = σ_Q[idx] * r\n", - "\n", - " else:\n", - " mp = stump(T, m, k=k)\n", - " P_TopK = mp[:, :k].astype(np.float64)\n", - " I_TopK = mp[:, k:2*k].astype(np.int64)\n", - "\n", - " ρ = 1.0 - np.square(P_TopK) / (2 * m)\n", - " ρ_clipped = np.clip(ρ, a_min=0.0, a_max=1.0)\n", - " r = np.sqrt(m * (1 - np.square(ρ_clipped)))\n", - " _, σ = core.compute_mean_std(T, m)\n", - " LB_σr = σ.reshape(-1,1) * r\n", - " LB_I = I_TopK\n", + " if global_min_dist <= global_min_maxLB:\n", + " return P, I, LB_σr, LB_I, is_mp_valid\n", " \n", - " return P, I, LB_σr, LB_I" + " if np.sum(~is_mp_valid) < (n * np.log2(k) / np.log2(n)):\n", + " for idx in np.flatnonzero(~is_mp_valid):\n", + " if global_min_dist <= maxLB_profile[idx]:\n", + " continue \n", + " \n", + " QT = core.sliding_dot_product(T[idx:idx+m], T)\n", + " D = core._mass(\n", + " T[idx:idx+m], \n", + " T, \n", + " QT, \n", + " μ_Q[idx], \n", + " σ_Q[idx], \n", + " M_T, \n", + " Σ_T, \n", + " Q_subseq_isconstant[idx], \n", + " T_subseq_isconstant\n", + " )\n", + " core.apply_exclusion_zone(D, idx, m, np.inf)\n", + "\n", + " arg = np.argmin(D)\n", + " if D[arg] < np.inf:\n", + " P[idx] = D[arg]\n", + " I[idx] = arg\n", + " global_min_dist = min(global_min_dist, P[idx])\n", + " \n", + " args_topk = np.argsort(D, kind='mergesort')[:k]\n", + " LB_I[idx] = args_topk\n", + "\n", + " ρ = 1.0 - np.square(D[args_topk]) / (2 * m)\n", + " ρ[:] = np.clip(ρ, a_min=0.0, a_max=1.0)\n", + " LB_σr[idx] = σ_Q[idx] * np.sqrt(m * (1 - np.square(ρ)))\n", + " is_mp_valid[idx] = True\n", + "\n", + " else:\n", + " mp = stump(T, m, k=k)\n", + " P = mp[:, :k].astype(np.float64)\n", + " I = mp[:, k:2*k].astype(np.int64)\n", + "\n", + " ρ = 1.0 - np.square(P) / (2 * m)\n", + " ρ[:] = np.clip(ρ, a_min=0.0, a_max=1.0)\n", + " _, σ = core.compute_mean_std(T, m)\n", + " LB_σr = σ.reshape(-1,1) * np.sqrt(m * (1 - np.square(ρ)))\n", + " LB_I = I\n", + " is_mp_valid[:] = True\n", + "\n", + " return P[:,0], I[:,0], LB_σr, LB_I, is_mp_valid" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 49, "id": "be7b439d", "metadata": {}, "outputs": [], @@ -820,11 +802,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 50, "id": "94eceff1", "metadata": {}, "outputs": [], "source": [ + "def print_verbose(msg, verbose=False):\n", + " if verbose:\n", + " print(msg)\n", + "\n", + "\n", "def VALMOD(T, m_min, m_max, k):\n", " \"\"\"\n", " This function finds the matrix profile of T_A while considering different length of subsequences in \n", @@ -861,12 +848,17 @@ " \n", " # out_P, out_I, out_M = _update_PIM(out_P, P_TopK[:,0] / np.sqrt(m), out_I, I_TopK[:, 0], out_M, m)\n", " LB_σr = None\n", + " is_exact = np.full(n, 1, dtype=bool)\n", " for m in range(m_min, m_max + 1):\n", " if LB_σr is None: # only runs for the first iteration, i,e, lowest `m` \n", - " P, I, LB_σr, LB_I = _VALMOD_stump(T, m, k)\n", + " idx = 1232\n", + " P, I, LB_σr, LB_I, is_mp_valid = _VALMOD_stump(T, m, k)\n", " else:\n", - " P, I, LB_σr, LB_I = _VALMOD_stump_partial(T, m, k, LB_σr, LB_I)\n", - " \n", + " P, I, LB_σr, LB_I, is_mp_valid = _VALMOD_stump_partial(T, m, k, LB_σr, LB_I)\n", + " \n", + " l = len(is_mp_valid) # which is: len(T) - m + 1 \n", + " is_exact[:l] = is_exact[:l] & is_mp_valid\n", + " \n", " _update_PIM(out_P, P/np.sqrt(m), out_I, I, out_M, m)\n", " \n", " out = np.empty((n, 3), dtype=object)\n", @@ -874,107 +866,106 @@ " out[:, 1] = out_I\n", " out[:, 2] = out_M\n", " \n", - " return out" + " return out, is_exact" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "9557a1ad", + "execution_count": 51, + "id": "d0800ab7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Computing time: 98.4311773777008\n" + "The computing time: 5.127043724060059\n", + "Computing time: 34.47108221054077\n" ] } ], "source": [ - "k=20\n", + "# Input\n", + "seed = 0\n", + "np.random.seed(seed)\n", + "T = np.random.rand(10000)\n", + "m_min = 50\n", + "m_max = 60\n", "\n", + "#####################\n", + "\n", + "# naive valmod: a simple for-loop, computing full mp for each `m`\n", "t_start = time.time()\n", - "valmod_mp = VALMOD(T, m_min, m_max, k)\n", + "mp_ref = naive_VALMOD(T, m_min, m_max)\n", "t_stop = time.time()\n", + "print(\"The computing time: \", t_stop - t_start)\n", + "\n", + "#####################\n", "\n", + "# valmod\n", + "t_start = time.time()\n", + "mp_comp, is_exact = VALMOD(T, m_min, m_max, k=20) # k=20 is provided by user\n", + "t_stop = time.time()\n", "print(\"Computing time: \", t_stop - t_start)" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "a6ff78e0", + "execution_count": 52, + "id": "b3f9d40b", "metadata": {}, "outputs": [], "source": [ - "ref = naive_VALMOD(T, m_min, m_max)\n", - "comp = VALMOD(T, m_min, m_max, k=10)" + "np.testing.assert_almost_equal(mp_ref[is_exact, 0], mp_comp[is_exact,0])" ] }, { "cell_type": "code", - "execution_count": 25, - "id": "f80bf53e", - "metadata": {}, - "outputs": [], - "source": [ - "# np.testing.assert_almost_equal(ref, comp)\n", - "# results in error as the paper's proposed method is approx. \n", - "# However, the global min is exact. " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "cff941a8", + "execution_count": 53, + "id": "8aabf529", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([0.8414941313430254, 2031, 51], dtype=object)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "ename": "AssertionError", + "evalue": "\nArrays are not almost equal to 7 decimals\n\nMismatched elements: 5771 / 9915 (58.2%)\nMax absolute difference: 0.0592546542833442\nMax relative difference: 0.06382009396320394\n x: array([0.9452865772913406, 0.9344991685815902, 0.9092612749994912, ...,\n 0.9612889703718848, 0.9425392112609088, 0.9439425333188787],\n dtype=object)\n y: array([0.9678644071613989, 0.9574466161924685, 0.9319605844727593, ...,\n 0.9612889703718848, 0.9425392112609088, 0.9478057237030245],\n dtype=object)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[53], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtesting\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_almost_equal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmp_ref\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mis_exact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmp_comp\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mis_exact\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[0;31m[... skipping hidden 2 frame]\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/stumpypy39/lib/python3.10/site-packages/numpy/testing/_private/utils.py:844\u001b[0m, in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m 840\u001b[0m err_msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(remarks)\n\u001b[1;32m 841\u001b[0m msg \u001b[38;5;241m=\u001b[39m build_err_msg([ox, oy], err_msg,\n\u001b[1;32m 842\u001b[0m verbose\u001b[38;5;241m=\u001b[39mverbose, header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[1;32m 843\u001b[0m names\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m), precision\u001b[38;5;241m=\u001b[39mprecision)\n\u001b[0;32m--> 844\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(msg)\n\u001b[1;32m 845\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m 846\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtraceback\u001b[39;00m\n", + "\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 7 decimals\n\nMismatched elements: 5771 / 9915 (58.2%)\nMax absolute difference: 0.0592546542833442\nMax relative difference: 0.06382009396320394\n x: array([0.9452865772913406, 0.9344991685815902, 0.9092612749994912, ...,\n 0.9612889703718848, 0.9425392112609088, 0.9439425333188787],\n dtype=object)\n y: array([0.9678644071613989, 0.9574466161924685, 0.9319605844727593, ...,\n 0.9612889703718848, 0.9425392112609088, 0.9478057237030245],\n dtype=object)" + ] } ], "source": [ - "idx=np.argmin(ref[:,0])\n", - "ref[idx]" + "np.testing.assert_almost_equal(mp_ref[~is_exact, 0], mp_comp[~is_exact,0])" ] }, { "cell_type": "code", - "execution_count": 24, - "id": "bb7e87da", + "execution_count": 54, + "id": "9557a1ad", "metadata": {}, "outputs": [ { "data": { + "image/png": "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\n", "text/plain": [ - "array([0.841494131343025, 2031, 51], dtype=object)" + "
" ] }, - "execution_count": 24, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "idx=np.argmin(comp[:,0])\n", - "comp[idx]" + "plt.figure(figsize=(30,3))\n", + "plt.plot(mp_ref[:,0], c='k', label='naive')\n", + "plt.plot(mp_comp[:,0], c='r', label='valmod')\n", + "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b9d7321", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {