ajout de mes solutions 1-2-3

Benoit-Muller · Oct 11, 2022 · b32276d · b32276d
1 parent 242d079
commit b32276d
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Showing 11 changed files with 525 additions and 140 deletions.
diff --git a/labs/ex01/template/taskA.ipynb b/labs/ex01/template/taskA.ipynb
@@ -2,11 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "import numpy as np\n",
@@ -25,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": true
    },
@@ -36,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "collapsed": true
    },
@@ -56,18 +65,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.77132064 0.02075195 0.63364823 0.74880388 0.49850701]\n",
+      " [0.22479665 0.19806286 0.76053071 0.16911084 0.08833981]\n",
+      " [0.68535982 0.95339335 0.00394827 0.51219226 0.81262096]\n",
+      " [0.61252607 0.72175532 0.29187607 0.91777412 0.71457578]\n",
+      " [0.54254437 0.14217005 0.37334076 0.67413362 0.44183317]\n",
+      " [0.43401399 0.61776698 0.51313824 0.65039718 0.60103895]\n",
+      " [0.8052232  0.52164715 0.90864888 0.31923609 0.09045935]\n",
+      " [0.30070006 0.11398436 0.82868133 0.04689632 0.62628715]\n",
+      " [0.54758616 0.819287   0.19894754 0.8568503  0.35165264]\n",
+      " [0.75464769 0.29596171 0.88393648 0.32551164 0.1650159 ]]\n"
+     ]
+    }
+   ],
    "source": [
     "print(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
@@ -76,24 +102,49 @@
     "def standardize(x):\n",
     "    ''' fill your code in here...\n",
     "    '''\n",
+    "    (n,d) = np.shape(x)\n",
+    "    x = x - x.mean(axis=0)\n",
+    "    x = x / np.sqrt(np.sum(x**2,axis=0)/n)\n",
     "    return x\n",
     "\n",
     "std_data = standardize(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.0775774  -1.34411605  0.31525355  0.80665878  0.24128662]\n",
+      " [-1.81711634 -0.77630186  0.74088404 -1.25592235 -1.42276759]\n",
+      " [ 0.62228127  1.64254169 -1.797091   -0.03521894  1.51565143]\n",
+      " [ 0.23651339  0.90075228 -0.83122987  1.40786459  1.11788073]\n",
+      " [-0.13414844 -0.95529104 -0.55795449  0.54097769  0.01136005]\n",
+      " [-0.70898541  0.56774371 -0.08900028  0.45652209  0.65726018]\n",
+      " [ 1.2571441   0.25993298  1.23775021 -0.72176808 -1.4141686 ]\n",
+      " [-1.41508984 -1.04555188  0.96949701 -1.69076861  0.75969247]\n",
+      " [-0.10744434  1.21308427 -1.14296098  1.19109415 -0.35450368]\n",
+      " [ 0.98926822 -0.46279408  1.15485183 -0.69943932 -1.11169162]] \n",
+      "\n",
+      " [-1.66533454e-16  4.99600361e-17 -2.22044605e-17  1.11022302e-17\n",
+      "  3.33066907e-16] \n",
+      "\n",
+      " [1. 1. 1. 1. 1.]\n"
+     ]
+    }
+   ],
    "source": [
     "print(std_data, \"\\n\\n\", np.mean(std_data, axis=0), \"\\n\\n\", np.std(std_data, axis=0))"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.8.8 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -107,7 +158,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.8.8"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "233a531365d7bd5abb8382eb032c18c305e1c6b951add6f6a5c925475bc609cb"
+   }
   }
  },
  "nbformat": 4,

diff --git a/labs/ex01/template/taskB.ipynb b/labs/ex01/template/taskB.ipynb
diff --git a/labs/ex01/template/taskC.ipynb b/labs/ex01/template/taskC.ipynb
diff --git a/labs/ex02/template/costs.py b/labs/ex02/template/costs.py
@@ -11,14 +11,10 @@ def compute_loss(y, tx, w):
 
     Args:
         y: shape=(N, )
-        tx: shape=(N,2)
-        w: shape=(2,). The vector of model parameters.
+        tx: shape=(N,D)
+        w: shape=(D,). The vector of model parameters.
 
     Returns:
         the value of the loss (a scalar), corresponding to the input parameters w.
     """
-    # ***************************************************
-    # INSERT YOUR CODE HERE
-    # TODO: compute loss by MSE
-    # ***************************************************
-    raise NotImplementedError
+    return np.sum((y-tx@w)**2)/(2*len(y))