Lectura_8_Cluster_Jerarquico.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"StbihtfAUwSm"},"source":["# ***Clusters jerárquicos.***\n","\n","## ***Universidad Central***\n","\n","## ***Maestría en analítica de datos***\n","\n","## ***Métodos estadísticos para analítica de datos.***\n","## ***Docente: Luis Andrés Campos Maldonado.***"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":2206,"status":"ok","timestamp":1663728475211,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"kNdDRNj1eqL1"},"outputs":[],"source":["import warnings\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import plotly.express as px\n","#\n","warnings.filterwarnings(\"ignore\")\n","#\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.decomposition import PCA\n","from factor_analyzer import FactorAnalyzer\n","from sklearn.cluster import AgglomerativeClustering         # Calcular Agrupamiento jerarquico\n","from scipy.cluster import hierarchy as sch                  # Calcular Agrupamiento jerarquico\n","from scipy.spatial.distance import pdist, cdist             # Distancias entre individuos.\n","from utils.plots_clustering import *\n","\n","\n","plt.style.use(\"ggplot\")\n","plt.rcParams[\"figure.figsize\"] = (15,6)\n","url_base = \"https://raw.githubusercontent.com/lacamposm/Metodos_Estadisticos/main/data/\""]},{"cell_type":"markdown","metadata":{"id":"UR35pVhdIDKd"},"source":["## ***Análisis de conglomerados.***\n","\n","El análisis de conglomerados busca particionar un conjunto de objetos en grupos, de tal forma que los objetos de un mismo grupo sean similares y los\n","objetos de grupos diferentes sean disímiles. Así, el análisis de conglomerados\n","tiene como objetivo principal definir la estructura de los datos colocando las\n","observaciones más parecidas en grupos.\n","Los propósitos más frecuentes para la construcción y análisis de conglomerados\n","son los siguientes:\n","\n","1. La identificación de una estructura natural en los objetos; es decir, el desarrollo de una tipología o clasificación de los objetos.\n","\n","2. La búsqueda de esquemas conceptuales útiles que expliquen el agrupamiento de algunos objetos.\n","\n","3. La formulación de hipótesis mediante la descripción y exploración de los grupos conformados.\n","\n","4. La verificación de hipótesis, o la confirmación de si estructuras definidas mediante otros procedimientos están realmente en los datos."]},{"cell_type":"markdown","metadata":{"id":"tfwLb8ZSL2Jw"},"source":["Para alcanzar los propósitos anteriormente ilustrados se deben considerar los\n","siguientes aspectos:\n","\n","1. ¿Cómo se mide la similaridad? Se requiere de un “dispositivo” que permita comparar los objetos en términos de las variables medidas sobre\n","éllos. Tal dispositivo debe registrar la proximidad entre pares de objetos de tal forma que la distancia entre las observaciones (atributos del\n","objeto) indique la similitud.\n","\n","2. ¿Cómo se forman los conglomerados? Esta inquietud apunta a la arquitectura de los métodos; es decir, al procedimiento mediante el cual se\n","agrupan las observaciones que son más similares dentro de un determinado conglomerado. Este procedimiento debe determinar la pertenencia al grupo de cada observación.\n","\n","3. ¿Cuántos grupos se deben formar? Aunque se dispone de un amplio número de estrategias para decidir sobre la cantidad de conglomerados a construir, el criterio decisivo es la homogeneidad “media” alcanzada\n","dentro de los conglomerados. Una estructura simple debe corresponder a un número pequeño de conglomerados. No obstante, a medida que el número de conglomerados disminuye, la homogeneidad dentro de los conglomerados necesariamente disminuye. En consecuencia, se debe llegar\n","a un punto de equilibrio entre el número de conglomerados y la homogeneidad de éstos. La comparación de las medias asociadas a los grupos o conglomerados construidos, desde el enfoque exploratorio, coadyuvan\n","a la decisión acerca del número de éstos, generalmente, es de la incumbencia del especialista asociado con el estudio en consideración."]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":29,"status":"ok","timestamp":1663728475222,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"-ilG51erKUk_","outputId":"7437730e-dac9-4d11-9162-f4028bcf1ee7"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Edad</th>\n","      <th>Estatura</th>\n","      <th>Peso</th>\n","    </tr>\n","    <tr>\n","      <th>Persona</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>A</th>\n","      <td>30.0</td>\n","      <td>1.69</td>\n","      <td>66.0</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>32.0</td>\n","      <td>1.70</td>\n","      <td>69.0</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>35.0</td>\n","      <td>1.65</td>\n","      <td>72.0</td>\n","    </tr>\n","    <tr>\n","      <th>D</th>\n","      <td>33.0</td>\n","      <td>1.68</td>\n","      <td>67.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         Edad  Estatura  Peso\n","Persona                      \n","A        30.0      1.69  66.0\n","B        32.0      1.70  69.0\n","C        35.0      1.65  72.0\n","D        33.0      1.68  67.0"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["# Ejemplo.\n","columns = [\"Edad\", \"Estatura\",\"Peso\"]\n","data = np.array([30, 1.69, 66, 32, 1.70, 69, 35, 1.65, 72, 33, 1.68, 67]).reshape(4,3)\n","index = [\"A\",\"B\",\"C\",\"D\"]\n","example = pd.DataFrame(data, columns = columns, index = index)\n","example.index.name = \"Persona\"\n","example"]},{"cell_type":"markdown","metadata":{"id":"2Ak0FiLvNtN4"},"source":["### ***Métodos jerárquicos.***\n","\n","Estos métodos empiezan con el cálculo de la matriz de distancias entre los objetos. Se forman grupos de manera aglomerativa o por un proceso de división.\n","Una de las características de esta técnica es la localización irremovible de cada\n","uno de los objetos en cada etapa del mismo. Con los procedimientos aglomerativos cada uno de los objetos empieza formando un conglomerado (grupos\n","unitarios). Grupos cercanos se mezclan sucesivamente hasta que todos los objetos quedan dentro de un mismo conglomerado. Los métodos de división\n","inician con todos los objetos dentro de un mismo conglomerado, éste es dividido luego en dos grupos, éstos en otros dos hasta que cada objeto llega a ser\n","un conglomerado. Ambos procedimientos se resumen en un diagrama de árbol que ilustra la conformación de los distintos grupos, de acuerdo con el estado,\n","de fusión o división, jerárquico implicado por la matriz de similaridades; este diagrama se conoce con el nombre de ***dendrograma***. Por su amplia aplicación,\n","se explican solo los métodos aglomerativos."]},{"cell_type":"markdown","metadata":{"id":"tmoMM1PtOVQL"},"source":["#### ***Métodos aglomerativos.***\n","\n","Son los más frecuentemente utilizados. Una primera característica de estos métodos es que buscan una matriz de similaridades de tamaño $n×n$, ($n$ número de objetos), desde la cual, secuencialmente, se mezclan los casos más cercanos; aunque cada uno tiene su propia forma de medir las distancias entre grupos o clases. Un segundo aspecto es que cada paso o etapa en la conformación de grupos puede representarse visualmente por un ***dendrograma***. En tercer lugar, se requieren $n−1$ pasos para la conformación de los conglomerados de acuerdo con la matriz de similaridades. En el primer paso cada objeto es tratado como un grupo; es decir, se inicia con $n$ conglomerados, y, en el paso final, se tienen todos los objetos en un solo conglomerado. Finalmente, los métodos jerárquicos aglomerativos son conceptualmente simples.\n","\n","Aparte de las características y bondades anotadas, estos métodos adolecen de algunas fallas; por una parte, los cálculos requeridos en los algoritmos son muy numerosos, aunque aritméticamente simples, por ejemplo con 500 casos se requieren cerca de 125.000 valores en la matriz de similaridades, situación que demanda el uso de una gran cantidad de cómputo; otra falla es que pasan sólo una vez a través de los datos; así, una partición pobre de los datos es irreversible en las etapas posteriores. A excepción del método de asociación simple, los demás métodos tienen el inconveniente de que generan diferentes soluciones al reordenar los datos en la matriz de similaridad; por último, estos métodos son muy inestables cuando se extraen casos del análisis; en consecuencia son bastante sensibles a la presencia de observaciones atípicas."]},{"cell_type":"markdown","metadata":{"id":"0SvBv9SmeSJj"},"source":["El algoritmo procede de la siguiente manera: \n","\n","1. Preparar los datos\n","2. Cálculo de información de similitud entre cada par de individuos en los datos.\n","3. Uso de la función de vinculación para agrupar objetos en un árbol de clúster jerárquico, basado en\n","la información de distancia generada en el paso 2. ***Objetos/cluster*** que están próximos se vinculan haciendo uso la función de vinculación.\n","4. Determinar dónde cortar el árbol jerárquico en grupos. Esto crea un partición de los datos."]},{"cell_type":"markdown","metadata":{"id":"zAavDkaidavl"},"source":["- **Paso 1:** El primer paso es considerar cada individuos como un cluster\n","<p align=\"center\"><img src=\"https://d1rwhvwstyk9gu.cloudfront.net/2021/02/image-25.png\" width=\"250\">\n","\n","    \n","- **Paso 2:** Identificar los dos clusters que son similares y convertirlos en un cluster\n","\n","<p align=\"center\"><img src=\"https://d1rwhvwstyk9gu.cloudfront.net/2021/02/image-26.png\" width=\"500\">\n","\n","- **Paso 3:** Repetir el proceso hasta que sólo queden un cluster con todos los individuos.\n","\n","\n","<p align=\"center\"><img src=\"https://d1rwhvwstyk9gu.cloudfront.net/2021/02/image-14.png\" width=\"600\">"]},{"cell_type":"markdown","metadata":{"id":"oOcKr3SBpaYB"},"source":["### ***Distancias usadas entre individuos.***\n","\n","Sea $x=(x_1, \\ldots, x_n)$ y $y=(y_1, \\ldots, y_n)$ dos individuos con sus vectores de variables observadas (todas cuantitativas) se definen las siguientes distancias:\n","1. Distancia [Euclidiana](https://es.wikipedia.org/wiki/Distancia_euclidiana):\n","$$D(x,y)=\\sqrt{\\sum_{i=1}^n (x_i-y_i)^2}=\\sqrt{(x-y)^T(x-y)}$$\n","2. Distancia de [Mahalanobis](https://es.wikipedia.org/wiki/Distancia_de_Mahalanobis)\n"," $$D(x,y)=\\sqrt{(x-y)^T\\Sigma^{-1}(x-y)}$$\n","3. Distancia de [Manhattan](https://es.wikipedia.org/wiki/Geometr%C3%ADa_del_taxista)  \n","$$D(x,y)=\\sum_{i=1}^n |x_i-y_i|$$\n","\n","4. Distancia de [Minkowski](https://es.qwe.wiki/wiki/Minkowski_distance)\n","$$D(x,y)=\\left(\\sum_{i=1}^n |x_i-y_i|^p\\right)^{\\frac{1}{p}}$$\n","\n","5. Distancia de [coseno]()\n","$$D(x,y) = \\cos(\\theta) = \\frac{x \\cdot y}{{\\|x|}\\| \\|y\\|}$$\n","\n","6. Distancia de [Chebyshev]()\n","$$D(x,y) = \\max_{i} |x_i - y_i|$$"]},{"cell_type":"markdown","metadata":{},"source":["#### ***Nota.***\n","\n","- La distancia Euclidiana es adecuada cuando los datos son numéricos y están en la misma escala, ya que mide la distancia en línea recta entre dos puntos. Sin embargo, si las variables tienen diferentes escalas, es recomendable estandarizarlas antes de aplicar esta medida, ya que es sensible a magnitudes.\n","\n"," - La distancia de Mahalanobis es útil cuando las variables están correlacionadas, ya que toma en cuenta la matriz de covarianza entre ellas. Esta distancia refleja la relación entre las variables y es adecuada en situaciones donde las correlaciones entre las variables son importantes. Sin embargo, requiere conocer la matriz de covarianza y es más compleja computacionalmente.\n","\n","- La distancia Manhattan es útil para datos dispersos o que contienen muchos ceros, como en datos de frecuencia. Suma las diferencias absolutas entre las variables, por lo que es ideal cuando se buscan diferencias absolutas entre individuos. Es importante estandarizar los datos si las variables están en escalas distintas.\n","\n","- La distancia de Minkowski es una generalización de las distancias Euclidiana y Manhattan. Permite ajustar la importancia de las diferencias grandes o pequeñas mediante el parámetro p. Es útil cuando necesitas flexibilidad en la forma de calcular las distancias.\n","\n","- La distancia de Coseno mide la similitud en la dirección de los vectores y es ignorante de la magnitud. Es ideal para datos de alta dimensionalidad, como en análisis de texto, donde la orientación de los vectores es más relevante que su tamaño.\n","\n","- La distancia de Chebyshev es adecuada cuando te interesa la mayor diferencia entre las variables, ya que se enfoca en la distancia máxima entre dos puntos a lo largo de cualquier dimensión individual. Es útil si hay una variable crítica en el análisis."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":24,"status":"ok","timestamp":1663728475222,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"GARob3H_Vwp0","outputId":"21f11c7b-1bfd-4a13-d1d3-5531f9183170"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Edad</th>\n","      <th>Estatura</th>\n","      <th>Peso</th>\n","    </tr>\n","    <tr>\n","      <th>Persona</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>A</th>\n","      <td>30.0</td>\n","      <td>1.69</td>\n","      <td>66.0</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>32.0</td>\n","      <td>1.70</td>\n","      <td>69.0</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>35.0</td>\n","      <td>1.65</td>\n","      <td>72.0</td>\n","    </tr>\n","    <tr>\n","      <th>D</th>\n","      <td>33.0</td>\n","      <td>1.68</td>\n","      <td>67.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         Edad  Estatura  Peso\n","Persona                      \n","A        30.0      1.69  66.0\n","B        32.0      1.70  69.0\n","C        35.0      1.65  72.0\n","D        33.0      1.68  67.0"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["example"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1663728475223,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"ebiRpYnBWd_3","outputId":"a717d57e-3a37-4735-ef13-b29a5b1f2d7a"},"outputs":[{"name":"stdout","output_type":"stream","text":["[3.60556514 7.8103521  3.16229347 4.2429353  2.23615742 5.38524837] \n","\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>Persona</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","    <tr>\n","      <th>Persona</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>A</th>\n","      <td>0.00</td>\n","      <td>3.61</td>\n","      <td>7.81</td>\n","      <td>3.16</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>3.61</td>\n","      <td>0.00</td>\n","      <td>4.24</td>\n","      <td>2.24</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>7.81</td>\n","      <td>4.24</td>\n","      <td>0.00</td>\n","      <td>5.39</td>\n","    </tr>\n","    <tr>\n","      <th>D</th>\n","      <td>3.16</td>\n","      <td>2.24</td>\n","      <td>5.39</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Persona     A     B     C     D\n","Persona                        \n","A        0.00  3.61  7.81  3.16\n","B        3.61  0.00  4.24  2.24\n","C        7.81  4.24  0.00  5.39\n","D        3.16  2.24  5.39  0.00"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# Vector de las distancias.\n","print(pdist(example, metric=\"euclidean\"),\"\\n\")\n","# Matriz de distancias euclidiana\n","eucli = cdist(example, example, metric=\"euclidean\")\n","# Visto como un pd.DataFrame\n","pd.DataFrame(eucli, columns=example.index, index=example.index).round(2)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":319,"status":"ok","timestamp":1663728475523,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"cBZkbgiIDhIw","outputId":"5ffd5c7b-9c1c-4776-dfee-4dceefd4a8c7"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>Persona</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","    <tr>\n","      <th>Persona</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>A</th>\n","      <td>0.00</td>\n","      <td>2.65</td>\n","      <td>2.65</td>\n","      <td>2.65</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>2.65</td>\n","      <td>0.00</td>\n","      <td>2.65</td>\n","      <td>2.65</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>2.65</td>\n","      <td>2.65</td>\n","      <td>0.00</td>\n","      <td>2.65</td>\n","    </tr>\n","    <tr>\n","      <th>D</th>\n","      <td>2.65</td>\n","      <td>2.65</td>\n","      <td>2.65</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Persona     A     B     C     D\n","Persona                        \n","A        0.00  2.65  2.65  2.65\n","B        2.65  0.00  2.65  2.65\n","C        2.65  2.65  0.00  2.65\n","D        2.65  2.65  2.65  0.00"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Matriz de distancias Mahalanobis\n","maha = cdist(example,example,metric=\"mahalanobis\")\n","# Visto como un pd.DataFrame\n","pd.DataFrame(maha, columns=example.index, index=example.index).round(2)"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":38,"status":"ok","timestamp":1663728475525,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"nyagp22PC2C1","outputId":"39a4cd2c-cab4-4e18-e5c5-258a94377c4d"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>Persona</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","    <tr>\n","      <th>Persona</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>A</th>\n","      <td>0.00</td>\n","      <td>5.01</td>\n","      <td>11.04</td>\n","      <td>4.01</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>5.01</td>\n","      <td>0.00</td>\n","      <td>6.05</td>\n","      <td>3.02</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>11.04</td>\n","      <td>6.05</td>\n","      <td>0.00</td>\n","      <td>7.03</td>\n","    </tr>\n","    <tr>\n","      <th>D</th>\n","      <td>4.01</td>\n","      <td>3.02</td>\n","      <td>7.03</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Persona      A     B      C     D\n","Persona                          \n","A         0.00  5.01  11.04  4.01\n","B         5.01  0.00   6.05  3.02\n","C        11.04  6.05   0.00  7.03\n","D         4.01  3.02   7.03  0.00"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# Matriz de distancias Manhattan\n","minko = cdist(example,example,metric=\"cityblock\",)\n","# Visto como un pd.DataFrame\n","pd.DataFrame(minko, columns = example.index, index = example.index).round(2)"]},{"cell_type":"markdown","metadata":{"id":"J2Z0-HsMEnoA"},"source":["### ***Medida de similaridad***\n","\n","Hay muchos métodos de aglomeración de clústeres (es decir, métodos de vinculación). Los métodos comunes de enlace se describen a continuación.\n","\n","Sea $r$ y $s$ dos conjuntos (cluster) de $k$ y $l$ individuos, entonces las siguientes medidas de similaridad se definen entre los grupos (cluster):\n","\n","• ***Vinculación simple o única:*** Se define la distancia entre dos clústeres como el valor mínimo de todas las distancias por pares entre los elementos en\n","el grupo 1 y los elementos del grupo 2. Tiende a producir grupos largos y \"sueltos\":\n","\n","$$D_{rs}=min\\{D(X_r, Y_s)\\}$$\n","\n","• ***Vinculación máxima o completa:*** La distancia entre dos clústeres se define como el valor máximo de todas las distancias por pares entre los elementos del grupo 1 y los elementos del grupo 2. Tiende a producir grupos más compactos:\n","\n","$$D_{rs}=max\\{D(X_r, Y_s)\\}$$\n","\n","• ***Enlace medio o promedio:*** La distancia entre dos conglomerados se define como la distancia media entre los elementos del grupo 1 y los elementos del grupo 2.\n","\n","$$D_{rs}=\\frac{1}{lk}\\sum_{rs}D(X_r, Y_s)$$\n","\n","• ***Enlace centroide:*** La distancia entre dos clusters se define como la distancia entre el centroide del conglomerado 1 (un vector medio de variables de longitud $p$) y el centroide para el grupo 2.\n","\n","$$D_{rs}=D(\\bar{X}_r, \\bar{Y}_s)$$\n","\n","• ***Método de la varianza mínima de Ward:*** En cada paso, el par de grupos con una distancia mínima entre grupos se fusionan, lo que ayuda a mantener la varianza total dentro de los conglomerados lo más baja posible. Este enfoque es útil en situaciones donde se desea que los grupos formados sean lo más homogéneos posible en términos de la varianza interna.\n","\n","$$D_{rs}=\\sqrt{\\frac{2kl}{k+l}}\\cdot||\\bar{X}_r- \\bar{Y}_s||$$"]},{"cell_type":"markdown","metadata":{"id":"OBuUv6VUsDfW"},"source":["***NOTA:***\n","\n","Tenga en cuenta que, en cada etapa del proceso de agrupación, los dos grupos, que tienen la\n","menor distancia de vinculación, están vinculados entre sí.\n","\n","***Por lo general, se prefieren la vinculación completa y el método de Ward.***"]},{"cell_type":"markdown","metadata":{"id":"F8S4nwX9rbMl"},"source":["## ***Linkage y dendograma.***\n","\n","- ***Linkage:***\n","\n","Recuerdemos que la función de enlace toma la información de la distancia, y agrupa pares de objetos en grupos en función de su similitud. Seguido, estos grupos recién\n","formados se vinculan entre sí para crear grupos más grandes. Este proceso es iterado hasta que todos los objetos en el conjunto de datos original estén vinculados en una jerarquía de árbol.\n","\n","- ***Dendograma:***\n","\n","Estos corresponden a la representación gráfica del árbol jerárquico generado por el algoritmo de ***clustering***."]},{"cell_type":"markdown","metadata":{"id":"Vm7AqTSrfdxg"},"source":["***Información:***\n","\n","<img src=\"https://d1rwhvwstyk9gu.cloudfront.net/2021/02/image-15.png\" width = \"200\">\n","\n","***Dendograma:***\n","\n","<img src=\"https://d1rwhvwstyk9gu.cloudfront.net/2021/02/image-16.png\" width = \"700\">"]},{"cell_type":"markdown","metadata":{"id":"SCWSrPkOgeks"},"source":["***Para tener en cuenta***\n","\n","- La altura de los bloques representa la distancia entre clusters.\n","- ¿Cómo  encontrar el número de clusters utilizando un dendrograma o dónde debemos dejar de fusionar los clusters? Las observaciones se asignan a los clusters trazando una línea horizontal a través del dendrograma.\n","Por lo general,  se corta  el dendrograma de forma que se corte la línea vertical más larga.\n","\n","<p align=\"center\"><img src=\"https://d1rwhvwstyk9gu.cloudfront.net/2021/02/image-17.png\" width=\"200\">\n","\n","\n","- En la anterior gráfica, un cluster tiene las observaciones A y B, y un segundo cluster tiene C, D, E y F."]},{"cell_type":"markdown","metadata":{"id":"MWhupJWbrjv4"},"source":["## ***Ejemplo 1.***"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":36,"status":"ok","timestamp":1663728475527,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"l7-zVRxtrOSD","outputId":"6717b407-62ac-43df-d8fe-e4b543021527"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>Persona</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","    <tr>\n","      <th>Persona</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>A</th>\n","      <td>0.00</td>\n","      <td>3.61</td>\n","      <td>7.81</td>\n","      <td>3.16</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>3.61</td>\n","      <td>0.00</td>\n","      <td>4.24</td>\n","      <td>2.24</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>7.81</td>\n","      <td>4.24</td>\n","      <td>0.00</td>\n","      <td>5.39</td>\n","    </tr>\n","    <tr>\n","      <th>D</th>\n","      <td>3.16</td>\n","      <td>2.24</td>\n","      <td>5.39</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Persona     A     B     C     D\n","Persona                        \n","A        0.00  3.61  7.81  3.16\n","B        3.61  0.00  4.24  2.24\n","C        7.81  4.24  0.00  5.39\n","D        3.16  2.24  5.39  0.00"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["pd.DataFrame(eucli, columns=example.index, index=example.index).round(2)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["array([[1.        , 3.        , 2.23615742, 2.        ],\n","       [0.        , 4.        , 3.16229347, 3.        ],\n","       [2.        , 5.        , 4.2429353 , 4.        ]])"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# A partir de la matriz de distancias se realiza la formación de los clusters.\n","# Hacemos uso de: linkage y dendogram de scipy.\n","hc = sch.linkage(\n","    example,                         # Dataset estandarizado\n","    method=\"single\",                 # Medida de similaridad entre cluster\n","    metric=\"euclidean\"               # Medida de similaridad entre individuos.\n",")\n","\n","hc"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABL4AAAH9CAYAAAAUFrVpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAunklEQVR4nO3df5DU9X348deet3ccGu+QAAfhh4dw5FuVX9EkJE2C2jaMYUz4xnRQM4YmMYnSmnGqJhNsRlJtPMyY8C3amIaOIRWVLyktRoZg05hCJNXUViAoRIwDvXjH0bBHE7jjjtvvH5H7cnCH7AG38N7HY8aZ/Xz283FfN7P33vXpZ/cy+Xw+HwAAAACQmLJiDwAAAAAAp4PwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIUnmxByjE3r17o7Ozs9hjcJYaNmxYtLS0FHsMoMRZi4Bisw4BZwJrESejvLw8hgwZcmLHnuZZTqnOzs7o6Ogo9hichTKZTET87jmUz+eLPA1QqqxFQLFZh4AzgbWIgeSjjgAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJKm82APAQMjn89HVdiDy7W2Rz+eLPQ5QqjIZaxEMhIrKyGQyxZ4CADgDCF8kL5/PR1fDF6Nxx0vFHgUgGos9AJSCCf8ryu68T/wCAHzUkRJwsD1C9AKA0vHKS797/QcASp4rvigp5zzw3chXVBZ7DKBEZTKZqK2tjaamJh91hNOhvS26/vzGYk8BAJxBhC9KS+WgyAhfQJFkMpkoG1QVmcpBEcIXnHJ+qwCAo/moIwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkspP5uR//Md/jOXLl8fVV18d8+bN6/O4jRs3xhNPPBEtLS1RW1sbN9xwQ0yfPv1kHhoAAAAAjqvfV3y98sor8fTTT8e4ceOOe9y2bdti8eLFceWVV0ZDQ0Ncfvnlcf/998fOnTv7+9AAAAAA8Kb6dcVXW1tb/PVf/3V89rOfjX/4h3847rFr1qyJqVOnxjXXXBMREXPnzo3NmzfH2rVr4zOf+Uyv53R0dERHR0f3diaTiaqqqu7bUJAjnjOZTKbHNsBAOvwa5rUMTpOjXvP9rh3LOgScCaxFDKR+ha9vf/vbMW3atJg8efKbhq/t27fH7Nmze+ybMmVKPP/8832es2rVqli5cmX3dl1dXTQ0NMSwYcP6My4lrqvtQDS+cXvEiBFRNqiqqPMA1NbWFnsESNKRr/m1tbVe84/DOgScCaxFDISCw9dPfvKT+OUvfxlf/epXT+j4XC4X1dXVPfZVV1dHLpfr85w5c+b0iGWHK3BLS0t0dnYWOjIlLt/e1n27ubk5oqKyiNMApSyTyURtbW00NTVFPp8v9jiQnCNf85uamiJTOaiI05yZrEPAmcBaxMkqLy8/4YujCgpfe/bsiUceeSTuuuuuqKio6NdwJyKbzUY2m+31Pr8UFOrI50w+n4/wHAKKLJ/Pez2D08Br/omzDgFnAmsRA6Gg8PXqq69Ga2trfOELX+je19XVFS+99FKsXbs2li9fHmVlPb8vv6amJlpbW3vsa21tjZqamv5PDQAAAABvoqDwdemll8bXvva1Hvv+5m/+JkaNGhUf/vCHj4leERH19fWxefPm+NCHPtS9b9OmTTFx4sR+jgwAAAAAb+7YUnUcVVVVMXbs2B7/VFZWxlve8pYYO3ZsREQsWbIkli9f3n3O1VdfHS+++GI8+eST0djYGCtWrIgdO3bErFmzTu1PAgAAAABH6NdfdTyePXv29PiTpJMmTYpbb701Hn/88Xjsscdi5MiRcccdd3SHMgAAAAA4HU46fN19993H3Y6ImDFjRsyYMeNkHwoAAAAATlhBH3UEAAAAgLOF8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIUnkhB69bty7WrVsXLS0tERExevTouPbaa2PatGm9Hv/MM8/EQw891GNfNpuNRx99tJ/jAgAAAMCJKSh8XXDBBXH99dfHyJEjI5/Px49//ONYtGhRLFq0KMaMGdPrOVVVVbF48eJTMiwAAAAAnKiCwtdll13WY/u6666LdevWxS9+8Ys+w1cmk4mampqChuro6IiOjo4e/46qqqru21CQI54zmUymxzbAQDr8Gua1DE6To17z/a4dyzoEnAmsRQykgsLXkbq6umLjxo3R3t4e9fX1fR7X1tYWt9xyS+Tz+airq4vrrruuz0h22KpVq2LlypXd23V1ddHQ0BDDhg3r77iUsK62A9H4xu0RI0ZE2aCqos4DUFtbW+wRIElHvubX1tZ6zT8O6xBwJrAWMRAy+Xw+X8gJO3fujAULFkRHR0cMGjQobr311pg+fXqvx27fvj1ef/31GDduXOzfvz9Wr14dL730UjzwwAMxdOjQPh+jryu+WlpaorOzs5BxIfLtbXFo/sciIqL8oZURFZVFnggoVZlMJmpra6OpqSkKfPkFTsCRr/nnPPh/I1M5qMgTnXmsQ8CZwFrEySovLz/hi6MKvuJr1KhRcf/998f+/fvjpz/9aTz44IOxcOHCGD169DHH1tfX97garL6+Pm677bZ4+umnY+7cuX0+RjabjWw22+t9fiko1JHPmXw+H+E5BBRZPp/3egangdf8E2cdAs4E1iIGQlmhJ5SXl0dtbW2MHz8+rr/++rjwwgtjzZo1J3xuXV1dNDU1FTwoAAAAABSi4PB1tK6urh4fS3yzY3fu3BlDhgw52YcFAAAAgOMq6KOOy5cvj6lTp8Zb3/rWaGtriw0bNsTWrVtjwYIFERGxZMmSuOCCC+L666+PiIiVK1fGxIkTo7a2Nn7729/G6tWro6WlJa666qpT/5MAAAAAwBEKCl+tra3x4IMPxt69e2Pw4MExbty4WLBgQUyePDkiIvbs2dPjz5H+5je/iYcffjhyuVyce+65MX78+Ljnnnt6/T4wAAAAADiVCgpfN99883Hvv/vuu3tsz5s3L+bNm1foTAAAAABw0k76O74AAAAA4EwkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJJUXuwBAKBU5PP56Go7EPn2tsjn88UeB5KTb2/r9TZHyGSsQ5waFZWRyWSKPQXAmxK+AGAA5PP56Gr4YjTueKnYo0BJyP/5jSHr9K6x2AOQhgn/K8ruvE/8As54PuoIAAPhYHuE6AVAKl556XevbQBnOFd8AcAAO+eB70a+orLYYwAlKJPJRG1tbTQ1NfmoI/3T3hZdf35jsacAOGHCFwAMtMpBkRG+gCLIZDJRNqgqMpWDIoQv+sGzBjjb+KgjAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEhSeSEHr1u3LtatWxctLS0RETF69Oi49tprY9q0aX2es3HjxnjiiSeipaUlamtr44Ybbojp06ef3NQAAAAA8CYKuuLrggsuiOuvvz7uu++++OpXvxqXXHJJLFq0KHbt2tXr8du2bYvFixfHlVdeGQ0NDXH55ZfH/fffHzt37jwlwwMAAABAXwoKX5dddllMnz49Ro4cGaNGjYrrrrsuBg0aFL/4xS96PX7NmjUxderUuOaaa2L06NExd+7cGD9+fKxdu/aUDA8AAAAAfSnoo45H6urqio0bN0Z7e3vU19f3esz27dtj9uzZPfZNmTIlnn/++eP+uzs6OqKjo6N7O5PJRFVVVfdtKMgRz5lMJtNjG2DAWIuAM8Dh99LeU9NvR72eeS7RH9YiBlLB4Wvnzp2xYMGC6OjoiEGDBsXtt98eo0eP7vXYXC4X1dXVPfZVV1dHLpc77mOsWrUqVq5c2b1dV1cXDQ0NMWzYsELHhehqOxCNb9weMWJElA2qKuo8QGmyFgFnktra2mKPwFnqyNez2tpar2ecFGsRA6Hg8DVq1Ki4//77Y//+/fHTn/40HnzwwVi4cGGf8as/5syZ0+NKscMVuKWlJTo7O0/Z41Aa8u1t3bebm5sjKiqLOA1QqqxFwJkgk8lEbW1tNDU1RT6fL/Y4nIWOfD1ramqKTOWgIk7D2cpaxMkqLy8/4YujCg5f5eXl3VV2/PjxsWPHjlizZk185jOfOebYmpqaaG1t7bGvtbU1ampqjvsY2Ww2stlsr/f5paBQRz5n8vl8hOcQUATWIuBMks/nva+mX7yecSpZixgIBX25fW+6urp6fB/Xkerr62Pz5s099m3atCkmTpx4sg8LAAAAAMdVUPhavnx5bN26NXbv3h07d+7s3n7f+94XERFLliyJ5cuXdx9/9dVXx4svvhhPPvlkNDY2xooVK2LHjh0xa9asU/tTAAAAAMBRCvqoY2trazz44IOxd+/eGDx4cIwbNy4WLFgQkydPjoiIPXv29PirDJMmTYpbb701Hn/88Xjsscdi5MiRcccdd8TYsWNP7U8BAAAAAEcpKHzdfPPNx73/7rvvPmbfjBkzYsaMGQUNBQAAAAAn66S/4wsAAAAAzkTCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJ5YUcvGrVqnjuueeisbExKioqor6+Pj7+8Y/HqFGj+jznmWeeiYceeqjHvmw2G48++mj/JgYAAACAE1BQ+Nq6dWt88IMfjIsuuigOHToUjz32WNxzzz3xwAMPxKBBg/o8r6qqKhYvXnzSwwIAAADAiSoofC1YsKDH9vz58+PTn/50vPrqq/F7v/d7fZ6XyWSipqamXwMCAAAAQH8UFL6Otn///oiIOO+88457XFtbW9xyyy2Rz+ejrq4urrvuuhgzZkyfx3d0dERHR0f3diaTiaqqqu7bUJAjnjOZTKbHNsCAsRYBZ4DD76W9p6bfjno981yiP6xFDKR+h6+urq545JFHYtKkSTF27Ng+jxs1alTcfPPNMW7cuNi/f3+sXr067rrrrnjggQdi6NChvZ6zatWqWLlyZfd2XV1dNDQ0xLBhw/o7LiWsq+1ANL5xe8SIEVE2qKqo8wClyVoEnElqa2uLPQJnqSNfz2pra72ecVKsRQyEfoevpUuXxq5du+IrX/nKcY+rr6+P+vr6Htu33XZbPP300zF37txez5kzZ07Mnj27e/twBW5paYnOzs7+jkyJyre3dd9ubm6OqKgs4jRAqbIWAWeCTCYTtbW10dTUFPl8vtjjcBY68vWsqakpMpV9f9cz9MVaxMkqLy8/4Yuj+hW+li5dGi+88EIsXLiwz6u2jjdcXV1dNDU19XlMNpuNbDbb631+KSjUkc+ZfD4f4TkEFIG1CDiT5PN576vpF69nnErWIgZCWSEH5/P5WLp0aTz33HPx5S9/OYYPH17wA3Z1dcXOnTtjyJAhBZ8LAAAAACeqoCu+li5dGhs2bIg777wzqqqqIpfLRUTE4MGDo6KiIiIilixZEhdccEFcf/31ERGxcuXKmDhxYtTW1sZvf/vbWL16dbS0tMRVV111an8SAAAAADhCQeFr3bp1ERFx991399h/yy23xMyZMyMiYs+ePT3+MsNvfvObePjhhyOXy8W5554b48ePj3vuuSdGjx59cpMDAAAAwHEUFL5WrFjxpsccHcXmzZsX8+bNK+RhAAAAAOCkFfQdXwAAAABwthC+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIUnmxBwAAAAZGPp+PAwcPRVtnV+Tz+WKPw9mosyuyb9xs7+yKOKerqONwdspkMtYgBozwBQAAJSCfz8cXn94ZL7e8XOxROItVHjoYj71x+8bvvRLt51QUdR7OXlPe1hRfmTmy2GNQAoQvAAAoAe2H8vFyy4Fij8FZrv2civjfMxcVewwS8GJja7Qfqo3KczLFHoXECV8AAFBill070X9sAkXR1tkVn/jeK8UegxIifAEAQIkZVF4mfAFQEvxVRwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJCk8kIOXrVqVTz33HPR2NgYFRUVUV9fHx//+Mdj1KhRxz1v48aN8cQTT0RLS0vU1tbGDTfcENOnTz+pwQEAAADgeAq64mvr1q3xwQ9+MO69996466674tChQ3HPPfdEW1tbn+ds27YtFi9eHFdeeWU0NDTE5ZdfHvfff3/s3LnzpIcHAAAAgL4UFL4WLFgQM2fOjDFjxsSFF14Y8+fPjz179sSrr77a5zlr1qyJqVOnxjXXXBOjR4+OuXPnxvjx42Pt2rUnPTwAAAAA9KWgjzoebf/+/RERcd555/V5zPbt22P27Nk99k2ZMiWef/75Ps/p6OiIjo6O7u1MJhNVVVXdt6EgRzxnMplMj22AAWMtAorsyPfRmch4Xw0UhbWIgdbv8NXV1RWPPPJITJo0KcaOHdvncblcLqqrq3vsq66ujlwu1+c5q1atipUrV3Zv19XVRUNDQwwbNqy/41LCutoOROMbt0eMGBFlg6qKOg9QmqxFQLEdOHgoIrZFxO/WoaqKc4o7EFCSrEUMtH6Hr6VLl8auXbviK1/5yqmcJyIi5syZ0+MqscMFuKWlJTo7O0/545G2fPv//w665ubmiIrKIk4DlCprEVBsbZ1d3bebm5ujstxVFsDAsxZxKpSXl5/wxVH9Cl9Lly6NF154IRYuXBhDhw497rE1NTXR2traY19ra2vU1NT0eU42m41sNtvrffl8vuB5KW1HPmfy+XyE5xBQBNYioNh6rEORtwwBRWEtYqAV9OX2+Xw+li5dGs8991x8+ctfjuHDh7/pOfX19bF58+Ye+zZt2hQTJ04sbFIAAAAAKEBB4Wvp0qWxfv36+PznPx9VVVWRy+Uil8vFwYMHu49ZsmRJLF++vHv76quvjhdffDGefPLJaGxsjBUrVsSOHTti1qxZp+6nAAAAAICjFPRRx3Xr1kVExN13391j/y233BIzZ86MiIg9e/b0+KsMkyZNiltvvTUef/zxeOyxx2LkyJFxxx13HPcL8QEAAADgZBUUvlasWPGmxxwdxSIiZsyYETNmzCjkoQAAAADgpBT0UUcAAAAAOFsIXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAklRd6wtatW2P16tXxy1/+Mvbu3Ru33357vPOd7+zz+J///OexcOHCY/Z/61vfipqamkIfHgAAAABOSMHhq729PS688MK48sor42tf+9oJn/eNb3wjBg8e3L19/vnnF/rQAAAAAHDCCg5f06ZNi2nTphX8QNXV1XHuuecWfB4AAAAA9EfB4au/7rzzzujo6IgxY8bExz72sXj729/e57EdHR3R0dHRvZ3JZKKqqqr7NhTkiOdMJpPpsQ0wYKxFQJEd+T46Exnvq4GisBYx0E57+BoyZEjcdNNNcdFFF0VHR0f88Ic/jIULF8a9994b48eP7/WcVatWxcqVK7u36+rqoqGhIYYNG3a6xyVBXW0HovGN2yNGjIiyQVVFnQcoTdYioNgOHDwUEdsi4nfrUFXFOcUdCChJ1iIG2mkPX6NGjYpRo0Z1b0+aNCmam5vjqaeeij/7sz/r9Zw5c+bE7Nmzu7cPF+CWlpbo7Ow8vQOTnHx7W/ft5ubmiIrKIk4DlCprEVBsbZ1d3bebm5ujstxVFsDAsxZxKpSXl5/wxVED9lHHI02YMCFefvnlPu/PZrORzWZ7vS+fz5+usUjUkc+ZfD4f4TkEFIG1CCi2HutQ5C1DQFFYixhoZcV40Ndeey2GDBlSjIcGAAAAoEQUfMVXW1tbNDU1dW/v3r07XnvttTjvvPPirW99ayxfvjx+/etfx5/+6Z9GRMRTTz0Vw4cPjzFjxsTBgwfjX/7lX2LLli1x1113nbqfAgAAAACOUnD42rFjRyxcuLB7e9myZRER8YEPfCDmz58fe/fujT179nTf39nZGcuWLYtf//rXUVlZGePGjYu/+Iu/iEsuueQUjA8AAAAAvSs4fF188cWxYsWKPu+fP39+j+0Pf/jD8eEPf7jwyQAAAADgJBTlO74AAAAA4HQTvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJKi/0hK1bt8bq1avjl7/8Zezduzduv/32eOc733ncc37+85/HsmXLYteuXTF06ND46Ec/GjNnzuzvzAAAAADwpgq+4qu9vT0uvPDC+NSnPnVCx+/evTvuu+++uPjii2PRokXxoQ99KL75zW/Gf/7nfxb60AAAAABwwgq+4mvatGkxbdq0Ez5+3bp1MXz48LjxxhsjImL06NHx8ssvx1NPPRVTp04t9OEBAAAA4IQUHL4K9Ytf/CIuvfTSHvumTJkSjzzySJ/ndHR0REdHR/d2JpOJqqqq7ttQkCOeM5lMpsc2wICxFgFFduT76ExkvK8GisJaxEA77eErl8tFdXV1j33V1dVx4MCBOHjwYFRUVBxzzqpVq2LlypXd23V1ddHQ0BDDhg073eOSoK62A9H4xu0RI0ZE2aCqos4DlCZrEVBsBw4eiohtEfG7daiq4pziDgSUJGsRA+20h6/+mDNnTsyePbt7+3ABbmlpic7OzmKNxVkq397Wfbu5uTmiorKI0wClyloEFFtbZ1f37ebm5qgsd5UFMPCsRZwK5eXlJ3xx1GkPXzU1NdHa2tpjX2tra1RVVfV6tVdERDabjWw22+t9+Xz+lM9I2o58zuTz+QjPIaAIrEVAsfVYhyJvGQKKwlrEQCv4rzoWauLEibF58+Ye+zZt2hT19fWn+6EBAAAAKGEFh6+2trZ47bXX4rXXXouIiN27d8drr70We/bsiYiI5cuXx5IlS7qP/6M/+qPYvXt3/P3f/300NjbGD37wg9i4cWN86EMfOjU/AQAAAAD0ouCPOu7YsSMWLlzYvb1s2bKIiPjABz4Q8+fPj71793ZHsIiI4cOHxxe/+MX4zne+E2vWrImhQ4fG5z73uZg6derJTw8AAAAAfSg4fF188cWxYsWKPu+fP39+r+csWrSo0IcCAAAAgH477d/xBQAAAADFIHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAklTen5PWrl0bTz75ZORyuRg3blx88pOfjAkTJvR67DPPPBMPPfRQj33ZbDYeffTR/jw0AAAAAJyQgsPXs88+G8uWLYubbropJk6cGE899VTce++98Y1vfCOqq6t7PaeqqioWL1580sMCAAAAwIkqOHx9//vfj6uuuiquuOKKiIi46aab4oUXXogf/ehH8ZGPfKTXczKZTNTU1JzwY3R0dERHR0eP86uqqrpvQ0GOeM5kMpke2wADxloEFNmR76MzkfG+GigKaxEDraDw1dnZGa+++mqPwFVWVhaXXnppbN++vc/z2tra4pZbbol8Ph91dXVx3XXXxZgxY/o8ftWqVbFy5cru7bq6umhoaIhhw4YVMi5ERERX24FofOP2iBEjomxQVVHnAUqTtQgotgMHD0XEtoj43TpUVXFOcQcCSpK1iIFWUPjat29fdHV1HXP1Vk1NTfzqV7/q9ZxRo0bFzTffHOPGjYv9+/fH6tWr46677ooHHngghg4d2us5c+bMidmzZ3dvHy7ALS0t0dnZWcjIEPn2tu7bzc3NERWVRZwGKFXWIqDY2jq7um83NzdHZbmrLICBZy3iVCgvLz/hi6P69eX2haivr4/6+voe27fddls8/fTTMXfu3F7PyWazkc1me70vn8+fljlJ15HPmXw+H+E5BBSBtQgoth7rUOQtQ0BRWIsYaGWFHHz++edHWVlZ5HK5HvtzudwJf4dXeXl51NXVRVNTUyEPDQAAAAAFKSh8lZeXx/jx42PLli3d+7q6umLLli09ruo6nq6urti5c2cMGTKksEkBAAAAoAAFf9Rx9uzZ8eCDD8b48eNjwoQJsWbNmmhvb4+ZM2dGRMSSJUviggsuiOuvvz4iIlauXBkTJ06M2tra+O1vfxurV6+OlpaWuOqqq07pDwIAAAAARyo4fL3nPe+Jffv2xYoVKyKXy8WFF14YX/rSl7o/6rhnz54ef470N7/5TTz88MORy+Xi3HPPjfHjx8c999wTo0ePPmU/BAAAAAAcrV9fbj9r1qyYNWtWr/fdfffdPbbnzZsX8+bN68/DAAAAAEC/FfQdXwAAAABwthC+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJKELwAAAACSJHwBAAAAkCThCwAAAIAkCV8AAAAAJEn4AgAAACBJwhcAAAAASRK+AAAAAEiS8AUAAABAkoQvAAAAAJIkfAEAAACQJOELAAAAgCQJXwAAAAAkSfgCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkq789Ja9eujSeffDJyuVyMGzcuPvnJT8aECRP6PH7jxo3xxBNPREtLS9TW1sYNN9wQ06dP7/fQAAAAAPBmCr7i69lnn41ly5bFtddeGw0NDTFu3Li49957o7W1tdfjt23bFosXL44rr7wyGhoa4vLLL4/7778/du7cedLDAwAAAEBfCr7i6/vf/35cddVVccUVV0RExE033RQvvPBC/OhHP4qPfOQjxxy/Zs2amDp1alxzzTURETF37tzYvHlzrF27Nj7zmc/0+hgdHR3R0dHRvZ3JZKKqqirKy/t1gRolLx+HLpoUERHnVFZGlGeLPA9QmqxFQHHly/Ixafh5ERFRma2I7DlFHggoSdYiToVC+lBBJamzszNeffXVHoGrrKwsLr300ti+fXuv52zfvj1mz57dY9+UKVPi+eef7/NxVq1aFStXruzefu973xuf//znY8iQIYWMC//f/3m02BMAWIuAovv7Twwv9ggA1iIGVEHha9++fdHV1RU1NTU99tfU1MSvfvWrXs/J5XJRXV3dY191dXXkcrk+H2fOnDnHxLKOjo7IZv3fcQAAAABOzBn52cFsNityAQAAAHBSCvpy+/PPPz/KysqOuVorl8sdcxXYYTU1Ncd88X1ra2ufxwMAAADAqVBQ+CovL4/x48fHli1buvd1dXXFli1bor6+vtdz6uvrY/PmzT32bdq0KSZOnNiPcQEAAADgxBQUviIiZs+eHT/84Q/jmWeeif/6r/+Kb3/729He3h4zZ86MiIglS5bE8uXLu4+/+uqr48UXX4wnn3wyGhsbY8WKFbFjx46YNWvWKfshAAAAAOBoBX/H13ve857Yt29frFixInK5XFx44YXxpS99qfuji3v27IlMJtN9/KRJk+LWW2+Nxx9/PB577LEYOXJk3HHHHTF27NhT9kMAAAAAwNEy+Xw+X+whAAAAAOBUK/ijjgAAAABwNhC+AAAAAEiS8AUAAABAkoQvAAAAAJJU8F91hLNNU1NTrF69OjZt2hR79+6N8vLyGDt2bMyYMSP+4A/+ICoqKoo9IlACfvCDH8TSpUtjwoQJ8Vd/9VfFHgcoIc8880w89NBDPfadf/75MWbMmLjmmmti2rRpRZoMKBVHr0PZbDbOO++8GDt2bEybNi2uuOKKqKqqKuKEpEz4ImkvvPBCPPDAA5HNZuP9739/jBkzJjo7O+Pll1+O7373u7Fr16747Gc/W+wxgRKwYcOGGDZsWLzyyivR1NQUtbW1xR4JKDF//Md/HMOHD4+IiFwuFz/+8Y/jq1/9anzhC1+Id7zjHUWeDigFh9ehQ4cORS6Xi61bt8Z3vvOdeOqpp+LOO++McePGFXtEEiR8kazdu3fHN77xjRg2bFh8+ctfjiFDhnTfN2vWrGhqaooXXnihiBMCpWL37t2xbdu2uP322+Nb3/pWrF+/Pj72sY8VeyygxEybNi0uuuii7u0rr7wybrrppvjJT34ifAED4uh1aM6cObFly5a47777YtGiRfH1r3/dJ3I45XzHF8n6p3/6p2hra4vPfe5zPaLXYbW1tXH11VcXYTKg1Kxfvz7OPffcmD59erz73e+ODRs2FHskgDj33HOjoqIiysr8JwFQPJdcckl89KMfjZaWlvjXf/3XYo9DgrzKkax///d/jxEjRsSkSZOKPQpQ4jZs2BDvete7ory8PN773vfG66+/Hq+88kqxxwJKzP79+2Pfvn2xb9++2LVrV/zt3/5ttLW1xfvf//5ijwaUuMPr0KZNm4o8CSnyUUeStH///vj1r38dl112WbFHAUrcq6++Go2NjfEnf/InERHx9re/PYYOHRobNmyICRMmFHk6oJT85V/+ZY/tbDYbN998c0yePLlIEwH8ztChQ2Pw4MHR3Nxc7FFIkPBFkg4cOBAR4S+DAEW3fv36qK6ujksuuSQiIjKZTMyYMSPWr18fN954o48YAQPmU5/6VIwcOTIiIlpbW2P9+vXx8MMPR1VVVbzrXe8q8nRAqRs0aFD3f8fBqeTdNkk6HLwsnEAxdXV1xbPPPhsXX3xx7N69O5qamqKpqSkmTpwYra2tsXnz5mKPCJSQCRMmxOTJk2Py5Mnxvve9L774xS/G6NGj4+/+7u+is7Oz2OMBJa6trc2FC5wWrvgiSYMHD44hQ4bErl27ij0KUMK2bNkSe/fujWeffTaeffbZY+5fv359TJkypQiTAUSUlZXFxRdfHGvWrInXX389xowZU+yRgBL13//937F///4YMWJEsUchQcIXyXrHO94R//zP/xzbt2+P+vr6Yo8DlKDDH3P81Kc+dcx9//Zv/xbPP/98HDx40J/tBorm0KFDEfG7Ky0AiuXwX3OcOnVqcQchST7qSLKuueaaqKysjG9+85uRy+WOub+pqSnWrFkz8IMBJeHgwYPx3HPPxfTp0+Pd7373Mf/MmjUrDhw4ED/72c+KPSpQojo7O2PTpk1RXl4eb3vb24o9DlCitmzZEt/73vdi+PDh8fu///vFHocEueKLZNXW1sbnP//5+PrXvx633XZbfOADH4gxY8ZEZ2dnbNu2LX7605/GzJkziz0mkKif/exnceDAgT7/uuzEiRPj/PPPj/Xr18d73vOeAZ4OKEX/8R//EY2NjRERsW/fvtiwYUO8/vrr8ZGPfCQGDx5c5OmAUnB4Herq6opcLhc///nPY9OmTfHWt7417rzzTlfBc1oIXyTtsssui6997WuxevXqeP7552PdunWRzWZj7NixceONN8ZVV11V7BGBRK1fvz6y2WxMnjy51/vLyspi+vTpsX79+vif//mfeMtb3jLAEwKlZsWKFd23s9lsvO1tb4tPf/rT8Yd/+IdFnAooJYfXofLy8jjvvPNi7Nix8YlPfCKuuOIKX2zPaZPJ5/P5Yg8BAAAAAKea7/gCAAAAIEnCFwAAAABJEr4AAAAASJLwBQAAAECShC8AAAAAkiR8AQAAAJAk4QsAAACAJAlfAAAAACRJ+AIAAAAgScIXAAAAAEkSvgAAAABIkvAFAAAAQJL+HzhlGzS8SGbnAAAAAElFTkSuQmCC","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Dendograma con method=\"single\", metric=\"euclidean\"\n","# El agrupamiento jerárquico codificado como una matriz de vinculación.\n","dendograma = sch.dendrogram(hc, labels=example.index)"]},{"cell_type":"markdown","metadata":{"id":"l11S4HktzuCy"},"source":["En el ***dendrograma*** que se muestra arriba, cada hoja corresponde a un registro, mientras nos movemos\n","arriba del árbol, los objetos que son similares entre sí se combinan en ramas, que se fusionan a una altura mayor.\n","La altura de la fusión, proporcionada en el eje vertical, indica la (dí)similitud/distancia\n","entre dos registros/clusters. Cuanto mayor sea la altura de la fusión, menos similaridad hay entre los cluster que se fusionan. Esta altura se conoce como la distancia ***cofenética*** entre los dos objetos."]},{"cell_type":"markdown","metadata":{"id":"LGKjsytdDnxX"},"source":["Definamos varios métodos y metricas:"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":31,"status":"ok","timestamp":1663728475532,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"CmbbwpCADthU"},"outputs":[],"source":["hc  = sch.linkage(example, method=\"single\", metric=\"cityblock\",)\n","hc1 = sch.linkage(example, method=\"complete\", metric=\"mahalanobis\")\n","hc2 = sch.linkage(example, method=\"centroid\", metric=\"euclidean\")\n","hc3 = sch.linkage(example, method=\"ward\", metric=\"euclidean\")"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":861,"status":"ok","timestamp":1663728476363,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"QrAFYwBKGgbH","outputId":"f151ece4-2c0b-46e6-d851-8ada6e499c9f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x600 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Distintos dendogramas.\n","fig, axes = plt.subplots(2,2, sharex=True)\n","#\n","sch.dendrogram(hc,  ax=axes[0,0], above_threshold_color=\"yellow\", orientation=\"top\", labels=example.index)\n","sch.dendrogram(hc1, ax=axes[0,1], above_threshold_color=\"red\", labels=example.index)\n","sch.dendrogram(hc2, ax=axes[1,0], above_threshold_color=\"blue\", color_threshold=4, labels=example.index)\n","sch.dendrogram(hc3, ax=axes[1,1], above_threshold_color=\"black\", labels=example.index)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"nHghkSCBfFdh"},"source":["***Notas importante:*** \n","\n","1. Tenga en cuenta que, por lo general, se recomienda estandarizar las variables en el conjunto de datos antes\n","de realizar análisis posteriores. La estandarización hace que las variables sean comparables, cuando\n","se miden en diferentes escalas. Por ejemplo, una variable puede medir la altura en metros y otra variable puede medir el peso en kg.\n","\n","2. El agrupamiento aglomerativo es el tipo más común de agrupamiento jerárquico. Úselo para agrupar registros en clusters en función de su similitud. También se le conoce como AGNES (Anidamiento Aglomerativo). \n","\n","3. El clusterin aglomerativo funciona \"de abajo hacia arriba\". Es decir, cada objeto es inicialmente considerado como un cluster de un solo elemento (hoja). En cada paso del algoritmo, los dos clusters que son más similares se combinan en un nuevo cluster más grande\n","(nodos). Este procedimiento se repite hasta que todos los registros son miembros de un solo gran\n","clúster (raíz). El resultado es un árbol basado en la\n","representación de los registros, denominado ***dendograma.***\n","\n","Tenga en cuenta que el agrupamiento aglomerativo es bueno para identificar pequeños grupos."]},{"cell_type":"markdown","metadata":{"id":"-OI5SJ41FpN7"},"source":["## ***Ejemplo 2.***"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":37,"status":"ok","timestamp":1663728476364,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"7vZCUUcwfAeY","outputId":"72f74afe-147c-41f8-ce61-f0a17861d5e6"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Murder</th>\n","      <th>Assault</th>\n","      <th>UrbanPop</th>\n","      <th>Rape</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>13.2</td>\n","      <td>236</td>\n","      <td>58</td>\n","      <td>21.2</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>10.0</td>\n","      <td>263</td>\n","      <td>48</td>\n","      <td>44.5</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>8.1</td>\n","      <td>294</td>\n","      <td>80</td>\n","      <td>31.0</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>8.8</td>\n","      <td>190</td>\n","      <td>50</td>\n","      <td>19.5</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>9.0</td>\n","      <td>276</td>\n","      <td>91</td>\n","      <td>40.6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            Murder  Assault  UrbanPop  Rape\n","Alabama       13.2      236        58  21.2\n","Alaska        10.0      263        48  44.5\n","Arizona        8.1      294        80  31.0\n","Arkansas       8.8      190        50  19.5\n","California     9.0      276        91  40.6"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df_usarrests = pd.read_csv(url_base + \"USArrests.csv\", index_col=0)\n","df_usarrests.head()"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":33,"status":"ok","timestamp":1663728476365,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"Gu15NgGdvG_u","outputId":"1bbeacfa-4fcc-4625-eaee-e56b76217ed3"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>count</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>min</th>\n","      <th>25%</th>\n","      <th>50%</th>\n","      <th>75%</th>\n","      <th>max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Murder</th>\n","      <td>50.0</td>\n","      <td>7.788</td>\n","      <td>4.355510</td>\n","      <td>0.8</td>\n","      <td>4.075</td>\n","      <td>7.25</td>\n","      <td>11.250</td>\n","      <td>17.4</td>\n","    </tr>\n","    <tr>\n","      <th>Assault</th>\n","      <td>50.0</td>\n","      <td>170.760</td>\n","      <td>83.337661</td>\n","      <td>45.0</td>\n","      <td>109.000</td>\n","      <td>159.00</td>\n","      <td>249.000</td>\n","      <td>337.0</td>\n","    </tr>\n","    <tr>\n","      <th>UrbanPop</th>\n","      <td>50.0</td>\n","      <td>65.540</td>\n","      <td>14.474763</td>\n","      <td>32.0</td>\n","      <td>54.500</td>\n","      <td>66.00</td>\n","      <td>77.750</td>\n","      <td>91.0</td>\n","    </tr>\n","    <tr>\n","      <th>Rape</th>\n","      <td>50.0</td>\n","      <td>21.232</td>\n","      <td>9.366385</td>\n","      <td>7.3</td>\n","      <td>15.075</td>\n","      <td>20.10</td>\n","      <td>26.175</td>\n","      <td>46.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          count     mean        std   min      25%     50%      75%    max\n","Murder     50.0    7.788   4.355510   0.8    4.075    7.25   11.250   17.4\n","Assault    50.0  170.760  83.337661  45.0  109.000  159.00  249.000  337.0\n","UrbanPop   50.0   65.540  14.474763  32.0   54.500   66.00   77.750   91.0\n","Rape       50.0   21.232   9.366385   7.3   15.075   20.10   26.175   46.0"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df_usarrests.describe().T"]},{"cell_type":"markdown","metadata":{"id":"ml27Hds_ONAu"},"source":["Recuerde que debemos estandarizar los datos."]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":29,"status":"ok","timestamp":1663728476365,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"uTqSTc7sORoo","outputId":"9f7647aa-974c-41a9-a5d6-ba5d32d93a83"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Murder</th>\n","      <th>Assault</th>\n","      <th>UrbanPop</th>\n","      <th>Rape</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>1.255179</td>\n","      <td>0.790787</td>\n","      <td>-0.526195</td>\n","      <td>-0.003451</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>0.513019</td>\n","      <td>1.118060</td>\n","      <td>-1.224067</td>\n","      <td>2.509424</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>0.072361</td>\n","      <td>1.493817</td>\n","      <td>1.009122</td>\n","      <td>1.053466</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>0.234708</td>\n","      <td>0.233212</td>\n","      <td>-1.084492</td>\n","      <td>-0.186794</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>0.281093</td>\n","      <td>1.275635</td>\n","      <td>1.776781</td>\n","      <td>2.088814</td>\n","    </tr>\n","    <tr>\n","      <th>Colorado</th>\n","      <td>0.025976</td>\n","      <td>0.402909</td>\n","      <td>0.869548</td>\n","      <td>1.883901</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              Murder   Assault  UrbanPop      Rape\n","Alabama     1.255179  0.790787 -0.526195 -0.003451\n","Alaska      0.513019  1.118060 -1.224067  2.509424\n","Arizona     0.072361  1.493817  1.009122  1.053466\n","Arkansas    0.234708  0.233212 -1.084492 -0.186794\n","California  0.281093  1.275635  1.776781  2.088814\n","Colorado    0.025976  0.402909  0.869548  1.883901"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["scaler_data_usarrets = StandardScaler().fit_transform(df_usarrests)\n","##\n","df_scaler_usarrets = pd.DataFrame(scaler_data_usarrets, columns=df_usarrests.columns, index=df_usarrests.index)\n","df_scaler_usarrets.head(6)"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":26,"status":"ok","timestamp":1663728476366,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"njhrfqk3pvbz","outputId":"8db8e6e1-7205-4187-d887-b6192e0499f0"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Alabama</th>\n","      <th>Alaska</th>\n","      <th>Arizona</th>\n","      <th>Arkansas</th>\n","      <th>California</th>\n","      <th>Colorado</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>0.000000</td>\n","      <td>4.419320</td>\n","      <td>3.173451</td>\n","      <td>1.424984</td>\n","      <td>3.541993</td>\n","      <td>3.365494</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>4.419320</td>\n","      <td>0.000000</td>\n","      <td>3.893855</td>\n","      <td>3.523499</td>\n","      <td>3.577636</td>\n","      <td>2.812180</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>3.173451</td>\n","      <td>3.893855</td>\n","      <td>0.000000</td>\n","      <td>2.713884</td>\n","      <td>1.752951</td>\n","      <td>2.660469</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>1.424984</td>\n","      <td>3.523499</td>\n","      <td>2.713884</td>\n","      <td>0.000000</td>\n","      <td>3.274534</td>\n","      <td>2.898161</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>3.541993</td>\n","      <td>3.577636</td>\n","      <td>1.752951</td>\n","      <td>3.274534</td>\n","      <td>0.000000</td>\n","      <td>1.439765</td>\n","    </tr>\n","    <tr>\n","      <th>Colorado</th>\n","      <td>3.365494</td>\n","      <td>2.812180</td>\n","      <td>2.660469</td>\n","      <td>2.898161</td>\n","      <td>1.439765</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             Alabama    Alaska   Arizona  Arkansas  California  Colorado\n","Alabama     0.000000  4.419320  3.173451  1.424984    3.541993  3.365494\n","Alaska      4.419320  0.000000  3.893855  3.523499    3.577636  2.812180\n","Arizona     3.173451  3.893855  0.000000  2.713884    1.752951  2.660469\n","Arkansas    1.424984  3.523499  2.713884  0.000000    3.274534  2.898161\n","California  3.541993  3.577636  1.752951  3.274534    0.000000  1.439765\n","Colorado    3.365494  2.812180  2.660469  2.898161    1.439765  0.000000"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["# Distancia Mahalanobis.\n","pd.DataFrame(\n","    cdist(df_scaler_usarrets, df_scaler_usarrets, \"mahalanobis\"),\n","    columns=df_scaler_usarrets.index,\n","    index=df_scaler_usarrets.index\n",").iloc[0:6, 0:6]"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":216,"status":"ok","timestamp":1663728476560,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"b1rE-32WmnAS","outputId":"82067fd9-746e-4597-8b33-01eaeffb7ac5"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Alabama</th>\n","      <th>Alaska</th>\n","      <th>Arizona</th>\n","      <th>Arkansas</th>\n","      <th>California</th>\n","      <th>Colorado</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>0.000000</td>\n","      <td>2.731204</td>\n","      <td>2.316805</td>\n","      <td>1.302905</td>\n","      <td>3.296239</td>\n","      <td>2.677982</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>2.731204</td>\n","      <td>0.000000</td>\n","      <td>2.728061</td>\n","      <td>2.854730</td>\n","      <td>3.043126</td>\n","      <td>2.350139</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>2.316805</td>\n","      <td>2.728061</td>\n","      <td>0.000000</td>\n","      <td>2.745350</td>\n","      <td>1.323789</td>\n","      <td>1.378889</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>1.302905</td>\n","      <td>2.854730</td>\n","      <td>2.745350</td>\n","      <td>0.000000</td>\n","      <td>3.801851</td>\n","      <td>2.859794</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>3.296239</td>\n","      <td>3.043126</td>\n","      <td>1.323789</td>\n","      <td>3.801851</td>\n","      <td>0.000000</td>\n","      <td>1.300691</td>\n","    </tr>\n","    <tr>\n","      <th>Colorado</th>\n","      <td>2.677982</td>\n","      <td>2.350139</td>\n","      <td>1.378889</td>\n","      <td>2.859794</td>\n","      <td>1.300691</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             Alabama    Alaska   Arizona  Arkansas  California  Colorado\n","Alabama     0.000000  2.731204  2.316805  1.302905    3.296239  2.677982\n","Alaska      2.731204  0.000000  2.728061  2.854730    3.043126  2.350139\n","Arizona     2.316805  2.728061  0.000000  2.745350    1.323789  1.378889\n","Arkansas    1.302905  2.854730  2.745350  0.000000    3.801851  2.859794\n","California  3.296239  3.043126  1.323789  3.801851    0.000000  1.300691\n","Colorado    2.677982  2.350139  1.378889  2.859794    1.300691  0.000000"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["# Distancia Euclidiana.\n","pd.DataFrame(\n","    cdist(df_scaler_usarrets, df_scaler_usarrets, \"euclidean\"),\n","    columns=df_scaler_usarrets.index,\n","    index=df_scaler_usarrets.index\n",").iloc[0:6, 0:6]"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1663728476561,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"Jb_ifPVBwupK","outputId":"15f0702a-d03b-4df3-ad56-6bfdafcd5042"},"outputs":[{"data":{"text/plain":["array([[14.        , 28.        ,  0.2079438 ,  2.        ],\n","       [12.        , 31.        ,  0.35377437,  2.        ],\n","       [13.        , 15.        ,  0.43312429,  2.        ],\n","       [22.        , 48.        ,  0.49909939,  2.        ],\n","       [19.        , 30.        ,  0.54082482,  2.        ],\n","       [35.        , 52.        ,  0.55914837,  3.        ]])"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["hc = sch.linkage(\n","    df_scaler_usarrets,     # Dataset estandarizado\n","    method=\"ward\",          # Medida de similaridad entre cluster\n","    metric=\"euclidean\"      # Medida de similaridad entre individuos.\n",")   \n","\n","hc[0:6, :]"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":1900,"status":"ok","timestamp":1663728478457,"user":{"displayName":"Luis Andres Campos 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","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# dendogram\n","dendogram = sch.dendrogram(hc, labels=df_scaler_usarrets.index)"]},{"cell_type":"markdown","metadata":{"id":"JjYbvAWqSVc3"},"source":["## ***Coeficiente aglomerativo.***\n","\n","Sea $l_i$ la distancia entre el valor maximo y el valor donde se une un dato al arbol, se define el coeficiente aglomerativo como:\n","\n","$$AC_{arbol}=\\frac{1}{d_{maxima}}\\sum_{i=1}^n l_i$$\n","\n","El coeficiente aglomerativo mide la eficiencia de los clústeres formados. Un valor bajo indica que los puntos se agrupan a distancias pequeñas, lo que implica clústeres compactos. Un valor alto indica que los clústeres se forman a distancias mayores, lo que sugiere una estructura más dispersa."]},{"cell_type":"markdown","metadata":{},"source":["## ***Coeficiente Cophenético.***\n","\n","\n","Después de vincular los objetos en un conjunto de datos en un árbol de ***clúster jerárquico***, es posible que se desee evaluar que las distancias (es decir, las alturas) en el árbol reflejan las distancias originales de buena manera.\n","\n","Una forma de medir qué tan bien el dendograma refleja los datos es calcular la ***correlación entre las distancias cofenéticas*** y los datos de distancia originales. Si la agrupación es válida, la vinculación de objetos en el árbol de clúster debe tener una fuerte correlación con las distancias entre objetos en la matriz de distancia original.\n","\n","Cuanto más se acerque a 1 el valor del coeficiente de correlación, con mayor precisión será la solución de agrupamiento reflejada es sus datos. Los valores superiores a 0,75 se consideran buenos."]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1663728478459,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"Ou9fFGsLTThb","outputId":"36021604-7fd3-4a1c-fed2-6ff1ae5f54ce"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>single</th>\n","      <th>complete</th>\n","      <th>average</th>\n","      <th>weighted</th>\n","      <th>centroid</th>\n","      <th>median</th>\n","      <th>ward</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>braycurtis</th>\n","      <td>0.45419</td>\n","      <td>0.579994</td>\n","      <td>0.587999</td>\n","      <td>0.555646</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>canberra</th>\n","      <td>0.684276</td>\n","      <td>0.711447</td>\n","      <td>0.853879</td>\n","      <td>0.829601</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>chebyshev</th>\n","      <td>0.554387</td>\n","      <td>0.66185</td>\n","      <td>0.678834</td>\n","      <td>0.588439</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>cityblock</th>\n","      <td>0.54621</td>\n","      <td>0.703594</td>\n","      <td>0.71184</td>\n","      <td>0.709585</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>correlation</th>\n","      <td>0.714172</td>\n","      <td>0.734769</td>\n","      <td>0.857805</td>\n","      <td>0.72666</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>cosine</th>\n","      <td>0.796175</td>\n","      <td>0.695941</td>\n","      <td>0.804943</td>\n","      <td>0.639452</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>euclidean</th>\n","      <td>0.541272</td>\n","      <td>0.697944</td>\n","      <td>0.718038</td>\n","      <td>0.621264</td>\n","      <td>0.715281</td>\n","      <td>0.555451</td>\n","      <td>0.697527</td>\n","    </tr>\n","    <tr>\n","      <th>jaccard</th>\n","      <td>0.460865</td>\n","      <td>0.824278</td>\n","      <td>0.871284</td>\n","      <td>0.866515</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>mahalanobis</th>\n","      <td>0.675893</td>\n","      <td>0.640368</td>\n","      <td>0.714444</td>\n","      <td>0.606974</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>minkowski</th>\n","      <td>0.541272</td>\n","      <td>0.697944</td>\n","      <td>0.718038</td>\n","      <td>0.621264</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>seuclidean</th>\n","      <td>0.541272</td>\n","      <td>0.697944</td>\n","      <td>0.718038</td>\n","      <td>0.621264</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>sqeuclidean</th>\n","      <td>0.42189</td>\n","      <td>0.628639</td>\n","      <td>0.636384</td>\n","      <td>0.600807</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               single  complete   average  weighted  centroid    median  \\\n","braycurtis    0.45419  0.579994  0.587999  0.555646        -1        -1   \n","canberra     0.684276  0.711447  0.853879  0.829601        -1        -1   \n","chebyshev    0.554387   0.66185  0.678834  0.588439        -1        -1   \n","cityblock     0.54621  0.703594   0.71184  0.709585        -1        -1   \n","correlation  0.714172  0.734769  0.857805   0.72666        -1        -1   \n","cosine       0.796175  0.695941  0.804943  0.639452        -1        -1   \n","euclidean    0.541272  0.697944  0.718038  0.621264  0.715281  0.555451   \n","jaccard      0.460865  0.824278  0.871284  0.866515        -1        -1   \n","mahalanobis  0.675893  0.640368  0.714444  0.606974        -1        -1   \n","minkowski    0.541272  0.697944  0.718038  0.621264        -1        -1   \n","seuclidean   0.541272  0.697944  0.718038  0.621264        -1        -1   \n","sqeuclidean   0.42189  0.628639  0.636384  0.600807        -1        -1   \n","\n","                 ward  \n","braycurtis         -1  \n","canberra           -1  \n","chebyshev          -1  \n","cityblock          -1  \n","correlation        -1  \n","cosine             -1  \n","euclidean    0.697527  \n","jaccard            -1  \n","mahalanobis        -1  \n","minkowski          -1  \n","seuclidean         -1  \n","sqeuclidean        -1  "]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["metricas = [\n","    \"braycurtis\", \"canberra\", \"chebyshev\", \"cityblock\", \"correlation\", \"cosine\",\n","    \"euclidean\", \"jaccard\", \"mahalanobis\", \"minkowski\", \"seuclidean\", \"sqeuclidean\"\n","]\n","\n","\n","metodos = [\"single\", \"complete\", \"average\", \"weighted\",\"centroid\",\"median\", \"ward\"]\n","\n","cophenetic_coef(df_scaler_usarrets, metodos, metricas)       "]},{"cell_type":"markdown","metadata":{"id":"Bv7LiIRpANAq"},"source":["## ***Cortar el dendograma en diferentes grupos.***\n","\n","Para identificar subgrupos, podemos cortar el ***dendrograma*** a cierta altura.  como se describe en las siguientes secciones.\n","\n","\n","Uno de los problemas con el agrupamiento jerárquico es que no nos dice cuántos clústeres hay, o dónde cortar el dendrograma para formar clusters.\n","Puede cortar el árbol jerárquico a una altura determinada para dividir los datos en clusters. Vamos a usar a [`sch.cut_tree()`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.cut_tree.html) para esta tarea."]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1663728478459,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"J2PZdVcGExGf","outputId":"72db0471-1737-4a82-9d6a-3f0889e4c3a0"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            label\n","Alabama         0\n","Alaska          1\n","Arizona         1\n","Arkansas        2\n","California      1"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["# Seleccionar 4 clusters.\n","labels = sch.cut_tree(hc, n_clusters=4)\n","pd.DataFrame(labels, index=df_scaler_usarrets.index, columns=[\"label\"]).head()"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":1079,"status":"ok","timestamp":1663728479529,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"Vked4VVvnjij","outputId":"6edbf622-fdf9-4dd7-b172-c0f87075189e"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["dendogram1 = sch.dendrogram(\n","    hc,                               # El agrupamiento jerárquico codificado \n","    labels=df_scaler_usarrets.index,  # como una matriz de vinculación.\n","    color_threshold=4,\n","    orientation = \"right\"\n",")"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1663728479531,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"Tcxt1eM-nV-c","outputId":"9008909b-5103-4571-bbf7-c286754da8cf"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            label\n","Alabama         0\n","Alaska          0\n","Arizona         0\n","Arkansas        1\n","California      0"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["# Seleccionar 2 clusters.\n","labels = sch.cut_tree(hc, n_clusters=2)\n","pd.DataFrame(labels, index=df_scaler_usarrets.index, columns=[\"label\"]).head()"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":1012,"status":"ok","timestamp":1663728480529,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"GwXmpfFdJLxQ","outputId":"7d5252ca-1f20-454e-a787-325a49fb6d5f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["dendogram2 = sch.dendrogram(hc, labels=df_scaler_usarrets.index, orientation=\"right\")"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"data":{"image/png":"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HWTPl5PCpVdkVowZSbmzdQd2XAgOIAUL5Kegw92rGyOB6PKuUN3sqKQ2kFwpgwKK2K1IIHxx9aNh0bf6MoT6ZbNy0ocpDrkmdXtBZMl/Y+UV4LM69FOqg4A4oDQPkr6zG06GNl0ZLrZ+vcCxNKuWQoCwpfFQRjwgCozGjZBFQ8Fa11U0VrwRQT61GMpcIXg4qjvJkYy85tLT6B0ojGMdS387BycxOsHIsQroKceoCWEwAqM1o2ARWbjdZNJW3ZRAsmILpsjGXnthafQGmYPoZyXIw+Cl8VEC0nAACASTZaN9GyqWiMqxZ9rPNwFa21p5S3PNnZTolafDL2ESoaWy2EET0UviogWk4AAICKjkF/C2NctehjnR+djdaejuPoy8WZyvi9ZGMNBzH2EYDyUqbC1/z58zV37lxlZGTo9NNP1y233KKUlJRiX79ixQq99dZb2rNnj5KTk3XDDTeoffv2ZV5oAChKSSeIkEo/SQQTRADA/zDob9EqWkub46GVTUVc524fy87vV6mLXqXF2EcATCr1Lnn58uWaMWOGBg8erKZNm2revHkaM2aMJkyYoBNPPLHQ6zds2KBnn31W119/vdq3b68vvvhC48ePV2pqqho0aGDkHwEAZZ0gQir5JBFMEAEAeRj099hMt7Shlc2x2Zqps3btekpLSzvmWHafzftDkvtbNubH2EcAKoNSF74++OADdevWTRdddJEkafDgwfruu+/0+eefq3fv3oVe/+GHH6pt27a68sorJUkDBgzQmjVrNH/+fN12222RLT0A/H9MEAEA5YML36KZbmlDK5tjM73O/9eycX+p3nc8FRsZ+whAZVCqwpff79fmzZvDClxer1dt2rTRzz//XOR7fv75Z/Xq1SvsubPOOktff/11sZ+Tk5OjnJyc0GOPx6OkpCTFHqPNsON11LxuNUlSQly84kz3Z7eYT3b088mOfn60skdfcrriDRanjuQGNHLhNkmsczdk284nO/r5NrO9Xql23URJUnx8nLzeypFtOz9/dlKVeKPZubms82Nld7joBHkMZgdyHa1YlCmJdZ5fbq4U441T7bpx5kIL8PsdxcSwzskuv3yyo59Ptpn8Y9WH8vM4pWhfu3fvXt1xxx16/PHH1axZs9Dz//nPf7Ru3To98cQThd5z3XXXaciQIerUqVPouY8//lizZs3SlClTivycmTNnatasWaHHHTt21F133VXSxQQAAAAAAAAq5qyOV199daFWYjk5OYqLs3e3BQAAAAAAAO5SqsJX9erV5fV6lZGREfZ8RkaGatSoUeR7atSoof37w/vF79+/v9jXS1JcXBxFLgAAAAAAAESkVL0yY2Nj1bhxY61duzb0XCAQ0Nq1a8O6PubXrFkzrVmzJuy51atXq2nTpmVYXAAAAAAAAKBkSj0cWa9evfTpp59q0aJF2r59u6ZOnars7Gx17dpVkvT888/r9ddfD73+8ssv1w8//KC5c+dqx44dmjlzpn755Rf17NnT2D8CAAAAAAAAKKjUY3x16NBBBw4c0MyZM5WRkaGGDRtq+PDhoa6L6enp8nj+N5ta8+bNdeedd+rNN9/UG2+8oZNPPln333+/GjRoYOwfAQAAAAAAABRUqlkdAQAAAAAAgMqi1F0dAQAAAAAAgMqAwhcAAAAAAABcicIXAAAAAAAAXInCFwAAAAAAAFyJwheAMHv37tXevXut5R86dEjbtm2zlg8AAAAAQBCzOpZCsBhQs2bNcl4SwLzt27fr6aef1r59+yTlbef33nuvTjnllIizx4wZo7vvvlsxMTH65z//KUnq3Lmzrr322oizbVq1apXatm0b9tz8+fPVs2dPI/lHjhzRRx99pC1btignJyf0/H333WckH+7y5ZdfaufOnerTp4/27t2rzMxMNWjQoLwXCzDK7/dr9+7dYfvE008/3Vh+Tk5OWHaVKlWMZdu0adMmbdmyRUeOHAk9d/nll5fjEqGscnNz9eGHH8rn82nw4MHy+XxKT09X69aty3vRAESJjbpCUdctFd3ixYuP+vsuXboY+6xYY0nlzOYJgc2CAI6usp6gVkYvv/yy+vTpo06dOkmSli1bpqlTp2rUqFERZ+/fv19Vq1bV8uXLdc4552jgwIEaOnRohS98vfbaa6pRo4YaNmwoSVq0aJEWL15srPD10ksvqUqVKvr555/Vq1cvLVq0SC1atDCSbZuti9Ndu3bplVde0datW8P256+88krE2bbZLGS+9dZb+uWXX5SWlqY+ffrI4/Fo8uTJevzxxyPO9vv9io3NOx1IS0vT9u3b1a5dO3m9laNRuO1CiZR3LIqLizOaWdTJXtWqVdW4ceNKc4PN9Hr59ttvNWnSJB08eFAJCQk6ePCgateurRdeeCHi7J9//lkvvviidu7cGfb8W2+9FXG2be+8846++uorpaenq0WLFlqzZo1at25t5Dx33LhxeuCBB475HMx5+eWXFQgEtGHDBknSCSecoAkTJujJJ58s5yUrXwcPHtSCBQuUlpam3Nzc0PN///vfy3GpSmb27Nnq27fvMZ873uTm5uqrr76Sz+dTIBAIPX/NNddEnF1ZtxebdYXZs2frlVde0aWXXqquXbtWiuvmb7/9VpJ0+PBhrVu3TmeccYYkaf369WrZsiWFr4JsnhBIdgsCttm6ELN9cRqNE9R9+/Zp9+7dYTvLli1bGsuvbDIzM0PbuCR17NhR7777rpHs4Dr+6aef1LZtW8XGxiomJsZItiQtWLBAHTt2VJUqVTR16lRt2rRJN910U8Tf5z333KNnnnlGI0aM0IYNGzRv3jw9/PDDhpZa2rp1q5566indd999uuyyy9S1a1cjJ75ZWVlKTEzUoUOHivx9pAdCmxenkyZNUo8ePTRr1iyNGDFCH330kerUqRNxbn629os2C5nffPONUlNTNWzYMEnSSSedpKysLCPZI0eO1MMPPyy/36+HH35YdevW1bfffqvbbrvNSL4kvf7667r00ktVq1YtY5mS3W1Ryvsbfe6553Tw4EG99NJL2rx5s5YvX64bb7wx4uwvvvgidJLn8Xi0fv16paSkaNeuXbr55pvVoUOHiD9j06ZNWr16tSTpzDPPVEpKSsSZkr318tZbb2nMmDEaP368xo0bpyVLlmjr1q1Glnn69On6+9//rilTpujRRx/VRx99ZLyYaWvfsmzZMo0dO1YjRozQfffdp507d+r111+PdHElSb///nuh53bv3m0kO8h2a7Xly5cXWuc333yzsXzTNm7cqPHjx4eKi1WrVg07F42UrXOi/Gys86efflonnHCCmjVrZu3Gi61tZeXKlYWKXEU9Vxb79+/XzJkztXXr1rDlTk1NjTh71apVmj59utLS0sIKU6autyZMmKCMjAylpKQY/05tbS9ffPGFOnXqpA8//LDI30e677JZVxg9erR+/fVXffzxx7rrrrt0/vnnq2fPnjr11FMjzpbs7FvuvfdeSdL48eM1duzYUE+G3377zfiNKVcUvmyeEEj2CgITJkzQ3XffrQceeEAej6fQ703s0GxdiNm+OLV9gvrOO+/o/fffV7169cJ2lmPHjo0420ZR8Fgto0zsGLxer7Zv3x7aOW7fvt3YgeS0007TE088oR07dujGG29Udna2kdygjz/+WN27d9f69ev122+/acCAAXrttdci/j5PPfVUDRo0SKNHj5bX69XIkSN1wgknGFpqKT4+XpIUExOjrKwsJSUl6cCBAxHnjho1SqmpqfrrX/9a5O8j3V5sXpwePnxYHTp00OzZs9WgQQPddtttGj58uPr06WMkX7K3X7RVyJTytpWCf4+mRirIzc1VUlKSFi9erC5duuj6668PdUk2afjw4UpJSVHPnj3Vpk0bI5k2t0Upb789ePBgTZs2TZLUqFEjPf/880YKXwkJCRo3blzoLm/w3GX06NEaN25cxIWvhQsXavbs2Tr//PMl5V0k9O3bV926dYt42W2tF4/Hozp16oSKAJ07d9bQoUMjXl4pbztv2rRpaHvv06ePHnzwQV1xxRVG8iV7+5a4uDjFx8fLcRw5jqP69esrLS0toswFCxZowYIF2rVrV9g6PnTokLGLJMn+zelp06Zp9+7d+vXXX9WxY0etWLFCZ555ppFsWzd5g8f+oEAgEFZ0iJStc6IgW+t83759GjlypIElLJqN5V61apVWrVqlvXv36tVXXw09X9yNx7J48cUXdcYZZ2jt2rUaOHCgFi5cGOqJEKlXXnlFf/3rX60VG7dt26YJEyYUeZ0bKVvbS7DRxZYtW4xnS3YbGkh5x+M77rhDW7ZsUWpqqhYuXKhWrVrp5ptvjnh4DJv7Fp/PF7Z8p512mnw+X8S5+bmi8GXjhCA/WwWBK6+8UpL0l7/8JeKs4ti6ELN9cWr7BPXzzz/XxIkTjRYxgmwUBYMH03nz5unIkSPq0aOHpLwT14InUGV13XXXadSoUaGdzrZt23TnnXcayf773/+uVatWqWHDhkpISNDevXt1/fXXG8mWFGo9tnbtWnXp0kVt27bVG2+8Uea8/CcvkhQbG6t69erpvffek2TuTnK1atWUmZmpdu3aacyYMTrhhBOMdHEKFs1tdeGxeXEa/C6TkpK0e/du1ahRQ3/88YeR7CBb+0VbhUxJql27tn766Sd5PB75/X7NmTPH2Imv3++XJP3444+hkzHTJ8DXX3+9+vfvr+XLl+utt94KNcXv0qWLEhMTy5xrc1uU8lpPBpvdBz8v2C00Urt27Qrr2lC/fn35fD7VrVvXyEXC/PnzlZqaqurVq0uS+vTpo8cee8xI4cvWeglm1KpVSytXrlSdOnV08ODBiHOl/+1bTjjhBG3evFm1a9c29vcZZHPf4vf71bBhQ7322muqWbNmxIWStm3bqn79+po6dWrYMS0pKcloV2HbN6d//PFHjR8/XkOHDtVNN92kK6+80liLT1s3eRs0aKAlS5bIcRz5fD69++67atWqlYElzmP6nKggW+u8Xr16OnjwoKpWrWpgKQuzsdzx8fGqWrWqPB5PWGv6WrVqGevm+Pvvv6t3795aunSpzjnnHLVt21ajRo3SgAEDIs5OSkqyOiZU7dq15ff7jbeulextL/3795dkr8ukzYYGkrR69WrNnz9f27Zt06WXXqqLL75Y69at0/jx4zVx4sSIsm3uW6pUqaJFixaFujYuXrw4ovPDorii8GXjhCA/WwWBxo0bKxAI6NNPP9U//vGPiPOKYutCzPbFqe0T1BNPPNFK0UuyUxQM/uGvXLkyrCXggAEDNHToUCMFx7Zt2+qZZ57Rxo0bJUlNmzYNXTBFKj4+Xuedd17occ2aNY2OYePxeLR8+XKtWLEi1BUseDFfFgW7AuZfdpMefPBBeb1eXXvttVq6dKkOHTqkzp07G/2M9PR0rVu3Th6PRy1atFDt2rUjzrR5cdqiRQv98ccf6tmzp4YNG6bY2FgjXb7ys7VftFXIlKS//vWveuGFF7Rt2zYNHDhQrVu3NnbcaNWqle655x4FAgHddtttyszMNNoVOSj/d/nGG2/ok08+0TvvvKMbbrihzNu9zW1RyttG/H5/qBCVnp5u7AQ12Mou+G9fsmSJ8ZO8/PtwU/tzyd56ueyyy5SZmakBAwZowoQJOnjwoLGbgx06dNAff/yhq6++Wo888ohyc3ONjzNpa99y6623yu/366abbtLrr7+uPXv2RPz3X6dOHdWpU0fPPPNMxMt3NLZvTudvDev3+1WjRg1jM1Pbusl78803a8aMGcrIyNDIkSN17rnn6oYbbjCyzJL5c6KCbK3zhIQEDR06VG3btg0rlJi62WhjuVu2bKmWLVvq3HPPNXYzqqDgcS4+Pl5//PGHqlatauyaq3379lq5cqXx89xgN8F69erpkUce0XnnnRf2nZpo8Wlre1m3bp1atmypb775psjfn3POORHl22xocM899+iEE07QZZddpvPPPz+0vV9wwQX67LPPIs63uW/529/+pokTJ2rSpEnyeDxq1KiRhgwZYiQ7yBWFLxsnBPm1bdtWTz/9tDZt2iTJbEHA6/Vq165dRrKKYutCzPbFqe0T1DPPPFPTp09Xp06dwnaWJu5y2iwKZmVlaf/+/TrxxBMl5fX7NzXOz6ZNm3TKKafo7LPPlpTXTPuXX35RkyZNIs62PSbcLbfconfffVfdunVTnTp1tHPnzojunvbr10+BQEDz58+3OmtW8IDk8XiMF7ykvHEKpk2bFupqM336dN1yyy3q2LFjRLk2L04HDhwoSerUqZPOOOMMHTp0yPjMhbb2izYLmTVq1NCIESOUnZ0tx3GMFkhuueUWbd26VXXr1lVsbKwCgYBuv/12Y/lS3uxFH3/8sZYsWaIWLVro3nvvVdOmTbV3716NGDGizOvJ5rYoSZdeeqnGjx+vAwcO6M0339TSpUuNXaD+/e9/18SJE/XSSy/J4/Ho9NNP15AhQ5SVlRX6O4hEcnKy3njjDXXv3l1SXtfH5OTkiHMle+vlzDPPVLVq1VStWjU999xzkmTsplevXr0kSWeddZamTZumnJwcJSUlGckOsrVv2bt3rxo0aKDExETdcccdkvK6WJmwe/duvffee4UGiDY1jq3tm9OJiYnKzs7WGWecoYkTJ6pGjRpKSEgwkm3jfC4QCGjWrFm67bbbjI6jmN+gQYM0Z84cY+dEBdla56eccorVicNsbivffPNNkYUSE4O4n3zyyfrjjz904YUXavjw4apSpYoaN24cca4kffTRRzp06JDi4+PDWu1Gen6ev5vgKaecoh07dkSUVxRb28uSJUvUsmVLzZs3r8jfR1r4sllX+Mc//lHstjF8+PCI823uW+rXr6+xY8fq8OHDkmT8+CxJHsfUICHlaPPmzYW+ZBvVa1v++9//KjMzU127dg27mDFRhAkEAvJ6vXIcJ+xCzOQsD+np6VYuToP8fr/xE9SiKsgej0fPP/98xNmvvfaaevfurR9++EHTpk0LFQVNXIwtWLBAb7/9ttq1aycp78S3X79+uuSSSyLOHjp0qMaOHRsqxuTm5mrEiBFGumk88sgjoe4Cd999d6i7gMlxm2wYNmyY1VmWNm/erDfeeKPQJAsmtkNJuvvuuzV8+HDVrVtXUt4FzhNPPKEJEyYYybclEAgoIyMj7OLIREu1/Pm294um5ebm6sMPP1RaWppuvfVW+Xw+paenq3Xr1sY+w+Ysurfddpsuvvhi9ejRo1AhYObMmaGuBRXRhg0b9PXXX0vKO+HN38XPBFsneQcOHNC0adO0Zs0aSVKbNm3017/+NXTjJFI21svQoUMLjW9a1HOlsWvXLp188snFjv1mslufrX2LjfUS9OCDD6pNmzaFxvhp3759xNlSXmuGunXr6siRI3r99dd18OBB9e3b11jrmIyMDFWrVk2BQEAffPCBMjMz9ec//9nIRBq2zucefPBBY+NtlQeb69ymjIwMVa1aVY7jhJb78ssvN3J+8dprr4V+zsnJ0ffff6+mTZsaa8kTtH79eh08eFBt27Y10jJ7z549RT5velIhREfwXNHn82nw4MHGzxVXrVpVqGtsUc+Vle2J51zR4uv555/X8OHDQzuuH374QW+88UbEha9oDCgu5c0wIik085Jkrghjs0VJ8OJUyrtASk9Pj/jgEa0TVFPjPxTFZouV7t27q3nz5vrxxx8l5d3BPu2004xkB0/Yg2JiYozNMmSru8DcuXN1xRVXFBqTKyjS5s5t2rTR0qVLdeGFF0aUU5wXXnhBPXv2tDaoaEJCQqjoJUl169Y1dnfz+++/165du8KKU8EWFZFYtGiRXnnlFcXExIS6UXk8Hk2dOjXi7CDT+8VoTFTy8ssvKxAIaMOGDZLyuoFPmDDBSGE2GrPo3nvvvYUKI8EbVGUpetnuiiDl7RP/+c9/6plnnlHz5s0jziuKzZO86tWr6+677zaSVZTmzZsbWy9+v19HjhxRIBDQ4cOHQxM3HDp0KOJWza+++qqGDRumcePGFfqdqXOtINP7lp07d2rnzp06dOhQ2LZ+6NAhY5PE5OTkGB1zs6Dg+U/+1mombdmyJXTRFTynmD9/vnr27Blxtq3zuXbt2umdd94pdMPb5M0GmzNdfvfdd7r44osl/W+df/bZZ6HnSsv2LHpBNWrUCP1s+sZrwVa6f/zxh/FrjfwTTxw+fFjVqlWLOLNOnTrKzs4OtdAKjsVr0t69e/Xbb7+FbYsmjtGHDx/Wf//739DNnbPOOkvXXXed0ZtIfr9fu3fvDlv2sl6Ljho1So8++mixE0+Z6AVj81xRyhumomCRq6jnysLmxHNBrih8DR48WOPHj9eoUaO0detWvfzyyxoxYkTEudEYUFyyU4QZMmTIUQfHjfREz9bFaTRPUCWzLRzS09OLfN5UUTCoQYMGVlrXxcbGhgqPUt4Jt6lBnG11/wyeMNpqqbNw4UIdOnRIL730UtjfvKkuml6vN9QNyYZ27dpp5syZ6tatmxzH0aJFi3T22WeHZhsq63p77rnntH37djVs2DDsQs+E2bNna+zYsapfv76RvKKYbmkXjYlKNm7cqPHjx+uBBx6QJFWtWtVYYdr2LLqSNGXKFD344IOh/eCqVasiukFluyuClPf3Wb16dWVnZxu/EJCic5K3cePGQl3YggPHRsJ097g5c+Zo1qxZksL/jpKSkiIuqAfHIbF5wyvI9L7l559/1uLFi7V///6wbT0pKUk33XRTxMsr5c2cZfIcpaCDBw9qwYIFhbYVUwNHv/baa6pRo0aoBdmiRYu0ePFiI4Wv2bNnq02bNkpJSTG6fmbPni2p8M0FUzcbbM50KeXN7FawyFXUcyVlexa9GTNm6KabbtL48eOLPFe57777jH/mCSecoN27dxvJWr58uaZNm1Zo/EoTg4pv2LBBTz31VKgouH//fv3zn/9Us2bNIs6W8gqis2fPVmZmppKTk7V161Y1bdrUyDF66tSpSkhI0D333CMp75x96tSpxoY7+vbbbzVp0iQdPHhQCQkJOnjwoGrXrl3mY0nwRlRR17im2DpXjMZNGJsTzwW5ovDVokULXXXVVRozZowyMzM1bNgw1atXL+LcaAwoLhVfMInkIBs80Vu2bJn27NkTNsaHiearti5Oo3WCaqOFw9ChQ0MH1D/++CNUNPL7/TrhhBOMtFix2TWuX79+evjhh0NV+x9++MHYiamtMeGC23W/fv0iziqKzYOTlNdiwtQ4akWZM2eOpP+dZAcVd9JdUps3b9bTTz9tpZXaCSecYLXoJZlvaRfsam+yOXZBBW+2BAIBY+Pk2J5FV8obizP/Dapp06ZFdIMq2HLE1DhExUlOTtbIkSN1wQUXhLXMMNECwfZJ3pQpU/TDDz+EFaglM4WvZ555Rm3atFHPnj2N/A3169dP/fr105QpUzR48OCI84pjuxu16X1L165d1bVr14ha0xzLgQMHdP/996tZs2ZhBW9TxYCnn35aJ5xwgrWWzffcc4+eeeYZjRgxQhs2bNC8efP08MMPG8l2HEf//e9/tX37djVr1kxt2rRRmzZtIm5pb2vG5SBbsy5u2rRJP//8sw4cOBDWOuvQoUMRDXBtexa94LH53HPPtZIvKWx9BAIBbdq0KayFWSRef/11Pfjgg1bOFWfMmBHWInvDhg169dVXNWbMGCP58+bNU2pqqh577DGlpqZq3bp1WrRokZHsbdu2afz48aHHt956q+6//34j2VLe3+mYMWM0fvx4jRs3TkuWLCm2R1JJnHTSSZLsdiO1da4YjZswNieeC6rUha+CTWJzc3N1xhlnaNWqVVq1apWxprE2BxSX/lcwcRxHOTk5ys7OjrhQEjwo//DDD2F3j5s3b67hw4dHPFB8NC5ObZ6g2mjh8PLLL0uS/vOf/yg5OTl0kvr5558bm8HIZte49u3b69FHHw01Ge7bt6+xgZBtD1huq7mz7SbgP/30kz799FMlJyeHHaxMdI2T7J1c16lTRzk5OVZawZx77rmaN29eoYknTLbqs9XS7tFHHy30XNWqVdWsWTNddtllEe1jGjRooCVLlshxHPl8Pr377rvGBhS1PYuuZO8GlWS3u6DjOGrYsKF8Pp+RvPxsn+StWbNGTz/9tNEW6kG2uscNHjxY6enpWr9+vaS879HUzKjR6kZtY99y8cUXa8WKFaEhMc466yxdcMEFRrIvvPBCa935pby/z5EjR1rLP/XUUzVo0CCNHj1aXq9XI0eONPZ3dc011+iaa65RVlaWvvrqK82cOVOvvvqq9cJVpGzNurhv3z5t2bIl7LxIyrv4NVG0WrBggTp27KgqVaro5Zdf1saNG3XTTTdFvD8Pti7q2rVrxMtYnPzrw+v1qmHDhkbG35XyumjaukF65MiRsGEImjdvHjYJVaRiY2NVrVq10PG5ZcuWxQ5PUlrBrvHBc/2srCyjE2d4PB7VqVMntOydO3fW0KFDI8612YjB1rliNG7C2Jx4LqhSF74KNolt0KCBHMcx3lS2V69euv/++wsNKG5KsGAS9NVXX0VUUc4vOD5G8E51dnZ2qJtTJGxfnNo+QbXZwuGHH37QjTfeGHrcrVs3PfDAA0YuEmx3jatfv76Vgub69etDB9Zg8XLFihX605/+ZCTfVnNn203ABw0aZCTnaNLT07Vu3Tp5PB61aNHCSPH4pptu0ujRo9WyZcuwi2oTsxe9+eabkvLuQuZn8kLDVku7Ro0aadu2berSpYs8Ho8WL16sk046Sb/88oumTZsW0UyJN998s2bMmKGMjAw99NBDOvfcc40VHmzOomv7BpXt7oK2WiBI9k/yTjrpJONdVoNsdY/7+uuv9dJLL6l58+byeDyaPn267rjjDiPdYqLRjdrWvmXWrFn6+uuv1blzZ3k8Hr377rvasWOH+vbtG3F2sBgQHFfNVLf1oHr16ungwYOqWrWq0dyCF8+xsbGqV6+e3nvvPUlmxrNavXq11qxZo7Vr18pxHP3pT3+KqMtgNMb4kezNXnjuuefq3HPP1ffffx+6JjLp448/Vvfu3bV+/Xpt27ZNAwYM0GuvvWZsf26z263NY8Ull1yid955RxdccEHYOZeJ/W9iYqJWr14d2q7XrFljdNbouLg4OY6jk08+WfPmzVOdOnWMNR7p0qWLRowYEepBsmLFCqPFzWDPnVq1amnlypWqU6dOoe6mZWGzEYPNc0UprzhdsPBlarKOxYsXS1Jo0hzJ/DBHlbrwZXMnk5/NAcWLcv7552vOnDlGZrnq2LGjRowYESowfPnll0bu7Nm+OLV9gmqzhYPf79fOnTtDy75z586wccQiYeOkOhoDc7/wwgu65JJLdNVVV8nv92v69On65ZdfjBW+bDV3tt0E3GbXOClvwNhp06apRYsWkvJaOt5yyy3q2LFjRLmvv/66YmNjlZOTY2ycqaBo3Em31dLu559/1mOPPRY6kbngggs0atQoPfbYYxF3G0pMTNRtt92m2267LaKcogTHUDrrrLM0bdo0o7Po2r5BFY0xIWwNzGvrJC84/kZKSoqeeuopdejQIWw7N7HstrrHzZo1S2PGjAm1Nvb5fHrmmWeMLHM0Wqrb2rd8+eWXGjNmTKh4cfHFF+uhhx4yUvjat2+fXnrpJa1du1ZS3qQut99+e6hbTqQSEhI0dOhQtW3bNmxbibQwVfBGq42Z3MeMGaNmzZrppptuCh1HIxGNMX4k6a677lJMTIwGDhwYmr3w3nvvNZbfpk0bvf/++2EtEC+//PKIZxkMvn/t2rXq0qWL2rZta2QcqyCb3W5nzpypyy67LHQsOnDggD7++GMjDSVycnL0zjvvaO7cuWHjqppoDPCXv/xFTz31VCjXcRz985//jDg3aMCAATp8+LBuvPFGTZkyRYcOHTJ20/fKK69UgwYNQtvhwIEDjc0uKEmXXXaZMjMzNWDAAE2YMEEHDx4stmhdGjYbMdg8V5RUqEWd3+83VsiMxjiclbrwFWSraWx+tgYUlxTWAivYLzw4vXmk+vfvr5SUlNAJzQ033GDkLo3ti1PbJ6g2WzjccMMNGjlyZOiu/datW/W3v/3NSLaNk+poDMw9duxYvfDCC1q3bp3279+vpk2bavTo0cbybTV3ttUEPFoDrc6aNUtPPvlkaGbH3bt364knnoi48LVz5049++yzJhaxXNhqaVdwwgaPx6PMzEzFxMRE3PLG5nHum2++UcuWLVWlShV9+OGH2rhxo/r162fkmGf7BpXt7oI2B+a1dZJXcMD/BQsWhD02sey2uscFAoGwLvbJycnGuq5Eoxu1rX2L4zhhLXYSExNDLbQiNWnSJDVv3lx33nmnpLztZdKkSaExVyN1yimn6JRTTjGSlZ+tsT3ze+qpp7R69Wq9//77mjx5slJSUnTmmWeWeduPxhg/UvjshT179jTeOvO1116Tz+dTjx495PF49Omnn2rPnj265ZZbIsr1eDxavny5VqxYEdr+Ihk7rCCb3W6//vrrsAYL1atX1zfffGNkO50zZ47+9a9/GRt+JL8mTZroueeeC415XL9+fWMTW0l513NVqlRRlSpVQuveVK+mYLe7/MUuk13xOnXqJEmqVq2annvuOSOZkt3xfW2dK7777rt67733lJWVFVb8O3LkiJFxQ/MzOfFcQa4ofNluGrt//37NnDlTW7duDfsiTI3Dk38D8nq9Sk5ONlJRDmrfvr3at29vLC/I1jgckv0TVJstHM455xw9/fTT2rhxoySpWbNmql69upFsGyfVwYG5mzZtWujiPDMz08hnVKtWTV26dNFLL72kxMRE9ezZ0+iB1VZzZ1tNwKMx0KqUd6c9WPSSpLp16xrp7lC/fn0dOnTI6MEoWl1AJHst7dq0aaOxY8eGLoq++OILtW7dWllZWREXvmwe5958803961//0pYtW7R06VL16NFDU6dO1WOPPRZxdpCtu+G2uwvaGJg3OPxAccMORPp3ZXvAf8neWDnVq1fXp59+qosuukhS3rAHpo6f0ehGbWvfkpKSookTJ6pbt26S8i7uUlJSjGT//vvvYZM19e7d2+gA0bYLVG+99ZZ69eqlKlWq6Mknn9SmTZs0ePBgI2OgnXrqqTr11FN1zjnn6Pvvv9f777+vpUuXRlz0tTnGj5TXUu3uu+9WTExM6Lvs3LmzsRu8P/74o8aNGxdqJdS+fXsjYx8NGjRIc+bMUbdu3VSnTh3t3LnT2FiWkr1ut5KKLESbKtrVqlXLStFL+t8Ea8HjTkZGRqhQZcK///3vQtfLRT1XFqZnFy3oscce05lnnqnWrVurSZMmEXcDD/as8fv91sb3tXWu2KNHD3Xo0EFTp04Nm4AmKSlJ1apVi3SxJdmZeK4gVxS+bDeNffHFF3XGGWdo7dq1GjhwoBYuXBiaNtkEm62njhw5oo8++khbtmwJK9pF2qLE5jgckv0T1HXr1hV6rmrVqjr55JONDAR84oknGlsX+dnsGjdixAjde++9oYPrhg0b9Pzzz2vixIkRZ0+bNk0bNmzQk08+qR07duiJJ57Qtddeq86dO0ecLeW1WjvttNNCg9tH2tz5q6++0vnnn2+tCXg0BlqVpHbt2mnmzJnq1q2bHMfRokWLdPbZZ4cuuMt6YhMfH6+hQ4fqrLPOMtZ1JVpdQCR7+8W//vWvWrhwoVauXCkp72Kge/fuiomJibh7rM3jXDB79erVuuSSS9S9e3ctXLjQSHaQrbvhtseEsDEw76hRo5SamlpskdfkOYHf79fu3bvDtvNIioJz587VFVdcUew6iLT72uDBgzVx4sTQ2KeNGjUKtUSKVDS6Udvct8yePTt0TtSmTRsjYyoGZWRkhFoJZWRkGMsN2rRpk7Zs2RLWYtrUBFTffPONrr32Wq1evVoxMTEaPXq0JkyYYKTwNXny5NB5RZs2bXTjjTeqTZs2EefaHONHyrthX7VqVS1fvlznnHOOBg4cqKFDhxorfDmOE1boKfi4rJo2baoHHngg9Lh+/foRtyLLz1a3WylvWd977z1dccUVchxHc+fONdZzpVWrVpoxY4Y6dOhg/AbP0KFDlZmZGTYTfWJiomrVqqU777yzzNe7+/fvV0ZGho4cOaJt27aFto/g2NORsDW7aEH9+vXT6tWrNX36dPl8Pp1xxhlq06aNLr300jLl5e9ZExxex9SNnSBb54rBYujw4cO1b9++UFE6NzdXfr/fSGMGGxPPFeSKwpftprG///67evfuraVLl+qcc85R27ZtNWrUKA0YMMDYZ9hqPfXSSy+pSpUq+vnnn9WrVy8tWrTIyDgFNsfhCAQCGj16tLEBxIvyyiuvaNu2bapXr548Ho98Pl+oFcs//vEPtW7dutSZ0WixYuukWpL69OmjRx99VAMHDlR6eroWLFgQ8eDwQTk5OXr88ccVFxen5ORknX766Xr22WeNFb6kvCKPqcFWZ8+erZUrV2rQoEFWmoAf68LZxEmYlNc8Xsr79+QXfFzWC8HgXXCTotUFRLKzXwwEAnriiSc0cuTIMp8UHY3N41wgENDGjRv11VdfhbplmzyGSvbuhtseE8LGwLzBu7q2CzHffvutJk2apIMHDyohIUEHDx5U7dq1I1pnwRavJlt75le1alWNGTMmtI4TExML3f2tyGydc+3cuVM33HBD2HMrV640Mq7VFVdcoQceeCB0s6jgBD2Reuedd/TVV18pPT1dLVq00Jo1a9S6dWtjha9gK4x169bpggsuUP369Y0N0N+4cWP17t07rOW0CbYnKgoW6n/66Se1bdtWsbGxEY+/lV/btm31+OOPh27eLVmyxMj5l83B5yV73W6lvKLGxIkT9eabb4YmE/q///s/I9lLly6VlHdTNsjUDZ6LL75Yp5xyirp06SLHcbR06VL99ttvat68uV5++eUyD0uybNkyzZs3T/v27QtryVSlShVdddVVES2z7dlFg1q0aKEWLVroiiuu0MqVK/X222/r+++/L/M5XsuWLfX555/r7bff1u+//y4pb4KCvn37GmulZrsm8uWXX2rGjBnyeDx64YUX9Ntvv+mNN97Qgw8+GHG2zYnnglxR+LLdNDZ4oRsfH68//vhDVatWLTSWSyRstp7aunWrnnrqKd1333267LLL1LVrVz355JMR59och8Pr9WrKlClhg5Wb1rhxY/3lL38JbSfB7iuXXnqppkyZUqZ1FI0WK7ZOqqW8gbhr166tkSNHqnr16ho/fryxOxG33367/H6/fD6fkpOTVbt2bT366KNGsqW8savee++9QidLZe3yM3bsWL399tt64IEHdNttt0U0i1NRbF00FmTrwtpG15XiJlcIMtW1XLKzX/R6vcrOzlYgELByB/+WW27Ru+++a+U4d+2112ry5Mlq06aNTj31VO3cuVMnn3yykewg03fDbXcXDLI5MG/QoUOHwo6fproNvPXWWxozZozGjx+vcePGacmSJRGPrRK8WLfVfW38+PF6+OGHQwW2PXv2KDU1NaIxBaPZjdrWOdfzzz+v4cOHh8Zp+uGHH/TGG28YKXx17txZDRs2DLWEv+KKK4xO4LRs2TKNHTtWI0aM0H333aedO3fq9ddfN5afkJCgd999V8uWLdPo0aPlOI6xC71LLrlE6enp+uKLLyTlXQjXqlUr4lybY/xIebOuPvHEE9qxY4duvPFGZWdnG82/4YYbtHDhwtBEGueff74uueSSiHNtDj4v2e12W7NmTY0aNSqsaG+KzRs8P/zwQ6io7vF41KVLFw0dOlQ33nhjqPdNWVx++eW6/PLLNWvWLKOtUyX7s4sGvfnmm1qzZo38fr9atWqlQYMGRdTzZvHixXr//fc1ePDgUOOODRs26LXXXpPX6zXSC8R2TWTOnDlKTU0NFUQbNmyoPXv2GMm2OfFckCsKX7abxp588sn6448/dOGFF2r48OGqUqVKaFwkE2y2ngp224uJiVFWVpaSkpKMbEQ2x+GQ8gppwSKJDZs3bw4bcL5ly5Z65ZVX1KRJkzLPUnfSSScpEAjopZdesjZ4pq2Taimv6fBzzz2n/v3769dff9Vzzz2nO++808j3um7dOj377LPyer168cUXtWnTJn344YfGurA888wzatOmjXr27GnkZCkmJkYDBgzQeeedp9GjR6t27dphuZEWYaIxKK9tpruu2JxcoSBb+8UmTZooNTVVF154YdhJr4l9ebNmzULHOcdxdNJJJxk7zp1zzjlhy1i/fn1jEywEmb4bHq3ugsHWv/kH5jVl+fLlmjZtmjIzM8OKvqa6sHo8HtWpUyd0TOvcubORcXiCbHRf+9Of/qQJEybovvvu0759+/TEE09E3AI2mt2obe1bBg8erPHjx2vUqFHaunWrXn75ZY0YMSLi3CCbEzjFxcUpPj4+1B2ufv36SktLM5Y/ZMgQzZ8/XzfeeKNq1Kghn89nbOIF0zemozHGj5TXQmrVqlVq2LChEhIStHfvXl1//fVGsqW8Gz09evRQjx495DiOsrKyjJx72Rx8Psj0fmvXrl06+eSTi72pYGq8yU2bNoXNommqaJqTkxP6N0h5/57guonkOw2uj3PPPVdbt26Vx+NR9erVwyZeiNSBAwdCwx0EVa1aVY0bNzbSc+rTTz9VvXr11K1bN5155pkRX5N+8MEHGj58eFjPhvbt2+vUU0/VuHHjIi58BQIBffXVV1ZrIl6vt9CEQqbGbLY58VyQKwpfUt4JZMHuX6a6CwUvzi+//HI1btxYBw8eNDpdqs3WU9WqVVNmZqbatWunMWPG6IQTTjCyMyhqHA5T3eKkvEHV77//fjVv3jzsAtLUxZjH49G6detClft169YZaRpvu9WHrZNqKa949I9//CM0i+H8+fM1fPhwI02p//vf/+rRRx/V008/LSlvoN78zZMjlZOTY/SkTsrrfvzf//5XzZo10xVXXGHl+zx8+LD++9//hsYQOeuss3TddddFPNGC7RYONrquFLyLtnfvXkkyOmlGkK394rZt2yTlnSzlZ6Lw9eKLL+qmm25SQkKCHnzwQe3atUsDBw400q0yNzdXX331lXw+X9ixx+RdWtN3w6PVXfAf//iH2rRpozZt2qh169ZGZ5B8/fXX9eCDD1pr9RE8Ga1Vq5ZWrlypOnXq6ODBg0aybXVfu+yyy7Rnzx5NnjxZGzdu1LXXXhvx5DzR7EZta9/SokULXXXVVRozZowyMzM1bNgw1atXz8AS553/BGfpCwQCchxHHo8n4rHsguLj4+X3+9WwYUO99tprqlmzprFzXCnvnDn/jZPk5GT17t3bSLbpG9PRusETHx8f1hqwZs2aRo+lto5HNgefl+zst1599VUNGzasyMK6qe6ICxcu1OzZs3X++edLyptttG/fvqHJLiJx/fXX66GHHgqbif6OO+5QVlZWaMKosihqffzxxx+qV6+e7r33XiOtyr/44gutW7dOZ5xxhjwej9avX6+UlBTt2rVLN998c0TLL0lTpkzR1q1btWbNGr3yyivas2ePmjVrpjvuuKNMebm5uUUeh+rWrWtkn+j1evXjjz9GnHM0SUlJysjICF0zr1mzxlgrdZsTzwW5ovA1bdo07d69W7/++qs6duyoFStWGOuaFAgE9M9//lPPPPOMJIWKAibZbD314IMPyuv16tprr9UXX3yhgwcPGhlXKTk5udA4HCZ16dLF+PSo+Q0aNEgTJkwIG8zx7rvvVlZWlv785z9HlG2z1Yetk2opr3tf/u2uZ8+eat68uZHsgsVdydwdAimvWb/J6boXLlwYap5tovl+caZOnaqEhATdc889oc+dOnVqxEVk2y0cbHZd2b59u55++mnt27dPUt4J+7333mt0XI78+8WlS5fq0KFDRvaLNmfT27x5s6pWrapvvvlGDRs21KOPPqqHH37YSOFrwoQJysjIUEpKipUCr1T0hCKS3Qk7TBg1apRWr16tlStXavr06apRo0ZokOtI1ahRw1rRS8orImVmZmrAgAGaMGGCDh48aOzC2/Q+IH9riQsvvFCTJ0/WmWeeGWpJYaLVxJAhQwrd4KpataqaNm2qAQMGGDl5L+qcK5JzmfwDN0t5F05nnHGGVq1apVWrVhkZJ2vSpEm67rrrrP3933rrrfL7/brpppv0+uuva8+ePUZvlNoc+9T0jemi9neHDh1Senq60RZ3q1at0vTp05WWlha2vKZuFNg6HtkcfF6yc+4SHEfJZnfE+fPnKzU1NXSO3qdPHz322GNGCl/nnnuumjdvrp9//llS+Ez0V199dZlzi1sfixcv1vTp042MCZWQkKBx48aFzg+D3+fo0aM1bty4iAtfUt51V9WqVVW1alVt3rxZv/zyS5mz/H6/jhw5UmgStSNHjoTtuyLRrl07vfPOO+ratWvYNaip4R9uuOEGjR07VmlpaRo5cqR2794d8Xd5rCEYTLWalFxS+Prxxx81fvx4DR06VDfddJOuvPJKYzsgr9er6tWrKzs7WwkJCUYyC7LZeuq7775T+/bt5fV6jTX9lvKmeO3bt29Yv+FJkybp9ttvN5IfbO4ZHBDZ1EClQc2bN9fEiROLHLQ80qamNlt92Lpgl/IKUS+//LL27NmjYcOGafv27dqxY4caNWoUcXZcXJyysrJC3+O2bduMzJ4ZdODAAd1///1q1qxZ2MlSWU98ly5dqscee8z4gLYFbdu2LWwsu1tvvdXINPLBFg4nnXRSaLtOS0vT9u3bjYyHYLPryssvv6w+ffqoU6dOkvJOVKdOnWq0qBS8uPN4PEYnWJCkFStWhHVHMDGzWH4//fSTzj77bFWpUsXYReq2bds0YcIE4/vZ/F577bXQz0eOHNHOnTvVoEGDiLv2FNUMvkqVKmrWrJkGDRoU8d9w7dq1dfHFFyslJUVNmjTRRx99pGXLlhkpfF1yySV65513dMEFF4TtD00V8IN/Q9WqVdNzzz1nJDPI9D6gqCL9l19+qS+//NJYq4kLL7xQe/fuDQ0ivGjRotDFwOTJk3XvvfdG/Blz5sxR37595fF4Qudcs2fPVt++fcuUV7BldIMGDeQ4jtEW01WqVDG+n8ovWNBJTEwsc0uJo7E59mnBG9Off/65kRvTY8aM0d13362YmJjQMb9z587GuvW88sor+utf/2ptrKwg08cjm4PPS3bPXcaNGxfWxay458oq/3ZneibA9PT00PXEwYMHtW/fvtB5pGldunQpVNAvq127doVtL/Xr15fP51PdunWNnM/cdddd8vv9atOmjdq3b6+bb75ZJ554YpnzzjvvPD3//PO6/fbbQ60aMzMzNXny5FBrvkgVN3mVqaJ3kyZNNGrUKG3YsEGO46h58+YRt9A82k16k7N0Sy4pfMXHx4d2uH6/XzVq1Ah1kzEhOTlZI0eO1AUXXBBWPTU1I43N1lPz5s3TlClTdOGFF6pr167GZmLbsWOHJk+erH79+oVOrjdv3mwkW8rr5//SSy9p7dq1kvKmkb799tsj3hEXHBA5eIFx5MgRHTlyxEhF3GarD5sX7JMnT9Zpp50WaplRt25dPfvss6HvtywmTJigu+++W3369NHjjz+uvXv36rnnntOaNWt01113mVp0XXjhhUYLu4888ojVIkBQIBDQ4cOHQ015s7KyjHYBGTlypB5++GH5/X49/PDDqlu3rr799lvddtttEeXa7LqSmZkZts117NhR7777rpHs4PZY3ED6kRZhZs2apa+//lqdO3eWx+PRu+++qx07dpT5oje/GjVqaMqUKVq1apX69Okjv99vbJ3Xrl1bfr/f+LTR+Y0dOzbs8aZNm7Ro0aKIc/v376/c3NxQy8zPPvtMOTk5OvHEEzVlypSIx0F67rnn9Msvv+iUU05RmzZt9OCDDxo7jubk5Oidd97R3Llzw/btU6dOjSi34JgnBZloTW16H2B7dk5JWr16tZ544onQ4+bNm2v48OEaO3ZsqNVtpFauXFno772o50rK5AxlxTn//PO1ZMkSdejQwWhL7Llz5+qKK67Q9OnTi9zfmmrFY3Ps06JuTJsYm3T//v2qWrWqli9frnPOOUcDBw7U0KFDjRW+kpKSjA7JUpCt45Ht8U9tnrsEZ+nLz1RRLTk5WW+88UZocpGFCxcaGwP5448/1sKFC5WVlaXOnTvrjz/+0KRJk6xey5ha50lJSVq8eHHoemjJkiVGr6GHDRtmdKKfAQMG6IUXXtAdd9wRNqba2WefrQEDBhj5DNvDP6Snp6tGjRpq166d1q9fr6VLl6pLly4RdUmMxvE/yBWFr8TERGVnZ+uMM87QxIkTVaNGDaOtsxzHUcOGDeXz+Yxl5vfNN9+oZcuWqlKlit5//31t3LhR/fr1M9LsedSoUUpLS9PixYv15JNP6sQTT9RFF10UcfetGjVq6MEHH9TYsWOVnp6u3r17FzldfVlNmjRJzZs3D51gLFiwQJMmTQo1KS6raA2IbLrVRzRmvNu1a5fuvvvu0HTJJlpkBVvUtW3bVvXr19eqVavkOI769+9vdOICEzOh5BeNopeUdwE6YsSIUHPsFStWGP23BKcEXrx4sbp06aLrr79e//znPyPOtdl1xev1avv27aHiwvbt243dsb7yyisl2Rtn5csvv9SYMWNCx5+LL75YDz30kJHC1z/+8Y/QCUbVqlW1e/fu0HgIkapXr54eeeQRnXfeeWHFL1M3d4qSkpKiyZMnR5zz9ddfh13kXnPNNRo2bJiefPLJQq1uy2LXrl1KSEhQ/fr1dcoppxgbV0nKax30r3/9y/gkLt9++62kvDEEg+OfSNL69evVsmVLI4WvgvuA9PR0o93XcnJywrp+mLgplZmZGdZ6Pzs7O3QTLNLjXbDr4d69e8PGxipu1tGy2Lt3r3777bew9WKiJfkpp5yiiRMnFrr4iPR8KHgBamu8piAbY5/m73pz22236fDhw5LyLrSDN6kjEZxw4qefflLbtm0VGxsbmtHMhPbt22vlypVGZv0siq3jka1xT4NsnLssWLBACxYs0K5du8ImDzl06JCxmyS33Xabpk2bFspv06aNBg8ebCR74cKFGjNmjB566CFJeUU2E2MHF7Xvy8zM1IIFC4x1Xfv73/+uiRMn6qWXXpLH49Hpp5+uIUOGKCsrSwMHDow4/+STT5bf79fu3bvD9rtlXf7Y2Fjddddd8vl8+vXXXyXlFdNNnwNs3rxZ27dvD7XgO3LkiLEWfOPHj9fo0aO1d+9ePfvss2revLnWrVtnpMX0pk2bdNpppykhIUHLly/Xpk2b1KtXL6PjE7qi8HXXXXfJ6/Vq4MCB+uCDD5SZmWnkCwiyfcftzTff1L/+9S9t2bJFS5cuVY8ePTR16lQ99thjRvLr1aun/v37q3fv3nr11Vc1ZcoUI+MW1ahRQ4888oieeeYZ7d6922jh6/fff1efPn1Cj3v37m2kC1g0BkS20eojGgOiFrzTe+TIkYi/0/wFpLp166pHjx4R5RUUvKNc3CC8pu4o23LllVfqtNNOC53kDRw40Ohd2uCU7j/++GOoFZWJIlKwKJ+QkKDbb7/daKHwuuuu06hRo0KfsW3bNmOzfwZn480/zorJ8VUcxwm76ZKYmGhsv1i9enV16NAhVEyuWbNmRK0x8/P7/TrllFO0Y8cOI3lFyX8hGQgEtHHjxjLPoJvf4cOHdeDAgVDXjwMHDoQuUk1cSI4dO1YHDx7U2rVrtXLlSr388suqXbu2kZnHatWqZWXm4uD5z/jx4zV27NjQtv3bb79FfOwral+blJSkxMRELV68WA0bNowo/+eff9aLL74Y2s6DTByzO3XqpBEjRoRuRH311Vfq2LGjsrKyIh74Pj4+XlWrVpXH4wkr0tWqVctI4fuzzz7T7NmzlZmZqeTkZG3dulVNmzY1Uvh69dVX9cADD6hx48ZGu8UFW6fYbsVjY+zT/F1v8h/fggP/R9r15rTTTtMTTzyhHTt26MYbb1R2dnZEeQV99NFHOnTokOLj48PO7SKd2CbI1vHI1rinQSeddJISExONdrsN3tidOnVq2DlnUlKSsQJP9erVQ2O3mhbs/pmfif1AUQ0MqlevrrPOOsvYNU39+vU1duzYsMJ0kImxvr/99ltNmjRJBw8eVEJCgg4ePKjatWtH3EIpOTnZyrFfik4Lvvj4eH333Xe65JJL1LdvXyPX51Jeo5fU1FTt2rVLb775pi644AK9+OKLRmcwdkXhK//UqPmLJabk5uZq3rx5YS14Lr/8cmN3Z4I5q1ev1iWXXKLu3btr4cKFRrKlvMrvokWLtGLFCjVp0sRIk/7gTjEpKUnDhg3TpEmTQmNbmZKRkRH6bjMyMoxm22Sj1UfwQn3Hjh3Wxj9o1aqV3nnnHeXk5Gj16tWaN29exHcLt27dWmzrOinyk7DgHWVTgzZGU/6JM0yMu1WUVq1a6Z577lEgENBtt92mzMxMI/ut9PR0TZo0ST/++KM8Ho9atWql2267LeKxiQKBgKpWrapnnnlGGzdulCQ1bdrU+HgWtsZXSUlJ0cSJE0MDzn722WdKSUmJeHmlvP3KjBkz5PF49MILL2j79u164403jAwQG43uVPkvJGNiYnTyySdryJAhEef26tVL999/f6hg/MMPP6hPnz7KysoyMjlHIBDQb7/9pq1bt2rr1q3KzMw0tg9u1aqVZsyYoQ4dOoS1tDN1seTz+cIKuqeddlrELdc//PBDNWnSRG3btrXSMnb69On6+9//rilTpujRRx/VRx99ZKwLbv/+/ZWSkhIaQuG6664LzRgZ6UDoLVu2VMuWLXXuuedGXPwryrx585SamqrHHntMqampWrdunZGuwpJ04oknqnXr1kay8ps1a9ZRf29q1lgbkzjZ7nrz97//XatWrVLDhg2VkJCgvXv3Gp2Z2tbENkG2jke2xj0Nuuuuu9S6dWt17dpVbdu2NVLgqVOnjurUqaPU1FTFxcWF9ouO4ygnJ8fYeLYbN25UWlpa2A0jE613q1evrp07d4aWe9GiRUbGmbTd5S5o37592r17d9h6MTVpzltvvaUxY8Zo/PjxGjdunJYsWXLMgdjLm60WfEHB1tirV6823ivA6/XK6/Xq+++/V48ePdSrVy9jY+QFVerC1/jx44964mViRhcp725YWlqaevToIY/Ho08//VR79uzRLbfcYiQ/ePf7q6++0t/+9jdJ/2upEan77rtPfr9fXbt21bhx44w1dcw/XovX69Xf/vY3o3f1rrjiCj3wwANhFzMmBhMO+v7770Pfq+kZb2y2+njiiSeUnJysyy67TGeffbbRC49rr71W77//vpKSkvTGG2/o3HPPjXhK8Pr16xu5MC+O7TvKjz32mM4880y1bt1aTZo0Mbq+ozFxxi233KKtW7eqbt26io2NleM4RiageP7559W+fXvdc889chxHn376qZ5//nk98sgjEeV6vV5NnjxZ48eP19lnnx3xchbH1vgqf/3rXzVr1izNmDFDUl53BBOtPaS8bnGpqakaPXq0JKlhw4bas2ePkWzJXjeqIBsXko7j6JxzztEZZ5wRKmb8+c9/DhV7Bg0aFPFn3HrrrWrQoIFat26tgQMHGp35bunSpZIU6l4umR3ItUqVKlq0aFHo4mjx4sURj38ycuRIff755/riiy/0pz/9SRdddJHRO9e5ublq2rRpqJt2nz599OCDD+qKK66IKDf/jYZgscuG0047TcuXL5fP5ws7t4i0yBMbG6tq1aqFLu5atmxZbEvn0jr77LM1f/58/elPfworMkZ6QynYCuP333/X2rVrQ/uTb7/91lihLRAIaMyYMRo5cmTYhAIVXXx8vBo3bqx169Zpw4YNatGihdHW3pG2YDwWW8cj2+Oe/vvf/9aXX36p999/P2z84/r160ec/dhjj2n48OGhv5vDhw9r7NixoXUUiSlTpuiHH35Qw4YNw44/Jgpff/nLX/Tss89qx44d+tvf/hZq0FAZvPPOO3r//fdVr169sPVScEzRsvJ4PKpTp05ov9u5c+ew7qwVka0WfEEdO3bUbbfdppNPPlnNmzfXvn37jBV3c3JylJGRoW+//VY33HCDJHPjwQVV6sLXueeeW+Tzubm5RvrgB61bt07jxo0LbTjt27c3uuFfe+21mjx5stq0aaNTTz1VO3fuNDaY3uDBg43c9Q5at26dWrZsqW+++abI30d6lyAzM1PVqlVT586d1ahRI/3444+S8gphp512WkTZ+U2fPt3ajDc2W31MnDhR33zzjT766CNNnz5d3bt3V7du3YxMwx4TE6Orr746oumLC4qLi7N6AnasmWEivRvRr18/rV69WtOnT5fP59MZZ5yhNm3aRDxld5DtiTN++eWXIvvLR+rAgQOh8bKkvC6bxxpQu6SSk5Pl8/msNQOX7I2vkpiYaLRAn5/X69UJJ5wQ9pypgahtdqMKsjXj1eOPP66nnnrK6PEhvxdffNFaYdp2q5K//e1vmjhxYmgstUaNGkXcyq5169Zq3bq1Dh06pGXLlmnixImKj4/X9ddfr6ZNm0a8zMG/wxNOOEGbN29W7dq1jdytjsaNBilvAo2MjAyjBVIp71jqOI5OPvlkzZs3T3Xq1DF2nhu84VewBXakNwKDY+w8/vjjGjduXKj74b59+/Tvf/87ouwgr9er7OxsBQIBq7MXmvbFF19o2rRpatmypRzH0fTp03XLLbeoY8eORvL379+vmTNnauvWrWE3M0yMByvZOx7ZHvc0MTFRXbt2VdeuXbV7927NmTNH99xzj5Gb3tnZ2WHF4ipVqhj7G12zZo2efvppo7OhS3lFhS1btmjMmDFhs9xXlr+lzz//XBMnTiy0LZoS3KZr1aqllStXqk6dOjp48KCVzzLFVgu+oL59+6pnz55KSkqSx+NRUlKSkbGDpbwbl3fffbfatGmjxo0by+fzGR8jslIXvgruDHfu3KlPP/1US5YsUa1atfTnP//ZyOcEp70t7nGkzjnnnLALjPr16xtrrZaSkqK5c+fK5/Np8ODB8vl8Sk9PL/PdtiVLlqhly5aaN29ekb+P9EJp9OjRSk1N1dNPP617773X2sWMzRlvbLb68Hq9Ou+883Teeedp8+bNGj9+vN5++2116tRJ/fv3L9O4FjaLRyb/Topickr3orRo0UItWrTQFVdcoZUrV+rtt9/W999/b6zwZXviDFv95ZOTk7Vz587QXdKdO3caK1RlZmbq/vvvV/PmzcOKgab2iZK98VXS09M1depU/f777xo/fry2bNmitWvXGik2JiUlKSMjI3Qys2bNGiMFb8luN6ogGzNeeTwe1axZM2yML9OC3ZBstYbbtGlT2DAKTZo0iTgzf1eMO+64IzTIcJUqVcL+DZGoUqWKzj33XGVmZuqjjz7Sjh07jBS+OnTooD/++ENXX321HnnkEeXm5hqb6c72jQYpr6vWhAkTjHcDHTBggA4fPqwbb7xRU6ZM0aFDh4y0aAwEAho9erSaNWtmYCmLtm/fvrBzk5NOOsnozOtNmjRRamqqLrzwwrDv1WTh3rRZs2bpySefVN26dSVJu3fv1hNPPGGs8PXiiy+GWsIOHDhQCxcuNNoF19bx6Morr1SDBg1C+0TT455KeT1qvvnmG33++ef65ZdfjI096zhOaOZ4KW/8UFOtVU466SQrsy57vV7NmTNHF1xwgbGB+KPpxBNPtFb0kqTLLrtMmZmZGjBggCZMmKCDBw8edfiWisB2C75169YV+byJAei7desWajQi5Y0NbWI81fwqdeFLyquwL1++XJ999pl2796tI0eO6PHHHzc6DlLbtm31+OOPhwptS5YsMTomz1tvvaVevXqpSpUqevLJJ7Vp0yYNHjw44pkAJenll19WIBDQhg0bJOXdRZ0wYUKZp3q+4447FAgEdN1111k5UTpy5Ig2btyo3377Tdu2bStUODE19onNGW9stvqQ8mYZ+/jjj7VixQqdc8456tatm9auXasnnnhC//rXv0qd9+qrr6pJkyZWDnq2x5mIxsQTa9askd/vV6tWrTRo0CBjYwdI9pffdH/5YPfyrKysUHFKyhuQ2lTL0i5duhhpvn80tsZXmTJlijp27Ki5c+dKyiuwTZw40Ujh64YbbtDYsWOVlpamkSNHavfu3ca6EdvsRmV7xqvExEQ98MADateuXdhFr6mJLWy2hlu4cKFmz56t888/X5L01FNPqW/fvmEnfmVhc2DuQCCgb775Rp999pn27Nmjzp07KzU11dgwCsG/lbPOOkvTpk1TTk6OsRndbN9okPJavfv9fuMXqcGblVWqVDF6IeD1ejVlypSwcZVMq1mzpmbOnKmLL75YUt7flMlZuoLjyxacxbUiF74SEhJCRS8p7wLPZEvE33//Xb1799bSpUt1zjnnqG3btho1apQGDBhgJN/W8ejIkSM666yzQsWuQCCgI0eOGGvpNG3aNK1YsUKNGjVS165ddf/99xtrOd2pUyeNHj06NATHggULIj6XCfauSUlJ0VNPPaUOHTqErQsT23ijRo20fv360Oy/lcmZZ56p6dOnq1OnTkbHyQyu98TERK1fv17S/1qwRmvW97JKTk622oLvtddeC/185MgR7dy5Uw0aNIioNWlxvciCTO7LK3Xh66WXXtLKlSvVokULXXXVVWrXrp3uvPNO44N/33DDDVq4cGHoizn//PONzIoY9M033+jaa6/V6tWrFRMTo9GjR2vChAlGCl8bN27U+PHjQxe7VatWjXgmLZsnSpdffrn+/e9/a/fu3YX+iEyMfZK/Um96xhvb3e6kvEG5d+3apR49eujpp58ONQFt3Lhxmbua3XHHHVq0aJF27Nihiy66SJ06dYp4HJhosb2z/PTTT1WvXj1169ZNZ555ppXudzZbk5juL5+/e3n+wYMjHUg4P5PdGooTHx8fVvSuWbOmkQux/fv3q3Pnzvrggw8k5XXbMjUJSpMmTTRq1Cht2LBBjuOoefPmxpqA2+xGZXvGqwYNGhiZkbM4NlvDzZ8/X6mpqaHWan369NFjjz0WceHLZhfK22+/XbVr19ZFF10UKnYfOHAg1B0xku/08OHDWrp0qX777TdJed+tqZlLpehM4lCvXj098sgjOu+888IuxCI5/m/atElz587V9u3bJeUV1K+44gojrQMl+93LhwwZomnTpoXOQ88880wjE1sEmZytLFratWunmTNnqlu3bnIcR4sWLdLZZ58d1jozEsHz2vj4eP3xxx+qWrWq/vjjj4iXO8jW8ajgOFlZWVnGxsmS8iZDe/LJJ1WrVi0jefn17t1bNWrU0HfffSdJuvTSSyM+NyrYu2bBggVhj02cK27cuFGLFi1SvXr1ws79TXWLtSl43fP111+HnjNxrZh/vW/evDk0I3hQRSyqB/cdQcFz2qysLMXFxRm7GVNw/LRNmzZFfE5UXC+yIApf/9/y5cvVpEkTde/eXWeddZY8Ho+VSqzX61WPHj2MNYctKLjM69at0wUXXKD69esb+3cUvEsSCASMNL21daLUvXt3de/ePdTV0TSbLZBsd7tzHEedOnXShRdeWGT1/qmnnipT7kUXXaSLLrpIu3bt0ueff64HHnhAzZs3V+/eva3NIGmK7Z3llClTtHXrVq1Zs0avvPKK9uzZo2bNmhmbBtv22Eqm+8tHoygl5e3bt2zZElYMNNGCZ9SoUXr00UeLbaoe6SyjMTExYa1UMzMzjXb3rVKlipUZQIvqRnXrrbcayQ7OePXMM88YySvI1sQWQTZbw0kK66Jpq7umSfHx8Tpw4IDee+89eTyesO07kguOvXv36qGHHlLNmjWVkpIix3G0ePFizZkzR48//nhEheljjU1q8qTa7/frlFNO0Y4dO4zk/fzzzxo7dqy6d+8e6ga3adMmPf744xo+fLiR7qW2u5fXqFHDyvlcfsuWLdOaNWsk5RXWgmNEVVRz5syRJM2ePTvs+eDjso45lZubG5o1948//tCFF14YKiQVvHiPlI3jkc1xsqS8mws2de3aVR07djRWZIhGUXfQoEGhGxeV4RiUn62bPPnX+wMPPFApiuvH6oJZu3ZtDRo0yPjkLikpKaFxRMsqmuu3Uhe+Jk+erOXLl2vWrFmaNGmSunTpEnFrpvyOdXJrqitFQkKC3n33XS1btkyjR4+W4zjGZnVs0KCBlixZIsdx5PP59O6776pVq1YR59o+UbJ1khQcaD09PV3Vq1cPFQaPHDkS8QC6MTExoRnzvv7662InX4jE+++/b60b2Mknn6z+/fvr1FNP1YwZM9S4ceMKX/i6+eabrTc7rlatmqpWraqqVatq8+bN+uWXX4xl2x5byWZ/eVvFqWnTpmn37t369ddf1bFjR61YsUJnnnlmxLmSdPfdd0uyVwC/4IILNHnyZB0+fFiffvqpFixYEHHrnaBVq1Zp+vTpVmairVu3rqpUqRLWjernn3+OODe/3bt367333is0HXtZT3i++OILderUqdiWtqbGbbLZGi45OVlvvPFGqGvMwoULrU7qYIKtC41Zs2apa9eu6t+/f9jzb7/9tt5+++2IZqO1PTZpfqZblb333nv629/+FtZC9bzzzlPTpk01Z84cI1O9R6N7+caNGwv97Zv6zBkzZuinn34Kzej4wQcfaPPmzVaHnIiUif12UZ577jndc889uvPOOyXl7QcbN26stLQ0vfnmm8Y+x9bxyNY4WccqCkR600vKG1vxueee08GDB/XSSy9p8+bNWr58ubHt0O/3a/fu3WHnXJG2mv7888/19ttvh8bgrF27tvr27RvqllxRBbeRgq2cgiJtMZlfRe/aGHS0v71AIKDNmzfrxRdfjLjwlX8M0UAgoI0bNxqtvdhWqQtfiYmJuvjii3XxxRdr+/bt+uyzz+T3+/XQQw/pwgsvjHgAapN/OEczZMgQzZ8/XzfeeKNq1Kghn89nbErmm2++WTNmzFBGRoZGjhypc889N9TlKRIFT5RMzaQ5YcIE3X333XrggQeK3NmYanr71FNP6dFHHw177umnn9YTTzxR5szNmzeHfp41a5bxwpfNgZx//fVXffbZZ1q1apXOOussDRs2zNhMlDYFx5wqyMRYNpJ01113yf//2rvzsCjL/X/g7wEFBDFUBMKNI7IO4Eoukbskfc0S3DvCseOWZabkfkzR1BQVyx1cMZcWzDTpeFQwEBOjDQQjRAE3EATEccRhGH5/8JunGUArnvue4YHP67q6Lhiu7rmjYeZ5PvdnUavh7e2Nnj17IiQkBM8995yoNXXxyibhneHAMziVnp6O8PBwLFy4EMHBwRg1ahSzm21tHyJek0ZfffVVXLhwAUqlEqmpqRg5ciSzUq19+/Zxm0S7YcMGLF++XMgGzMnJwZYtW7BlyxZmzxEREQFvb2+MGDGCyf6//vpr9O/fn3umLa+m4gAwffp07N27V+h95u3tjWnTpjFZW2quXr1aZ4/K0aNHY/78+aLWHjFiBADDnCpXVlbi1KlTegMLXnnllXqXPN+6davOXqS+vr749NNPRe1VS5vJW1FRwaWBdlRUFH799Vc4OTnp/e2zCnz9+OOPCA8PFw4yhw0bhvnz5zfowJdWzcCO2AbxFRUViIqK0nsfsbW1xbZt2/Daa6+JWlsXr88jHn2yAP79ZoHq38m0adOwd+9eANW9s7Zu3crkdfjjjz9i165dePToEczNzfHo0SPh/2t9fffddzhx4gSmTZsm9GzOzMzEwYMHYWJiYrAM//pYvnw51q1b99SAJq/AslSZmJiga9euTAZF6P4tabNLWZau8ybpwJeuDh06IDg4GG+88QZ++OEHxMfHiw588S6h0HJwcMC//vUvve9ff/11UWvqTl3w8/PTu/nKzs4W3aBb+4bIepLmqFGjAEDv98GDWq3WKwM1MzMTPe2q5uRPHng0cl64cCFMTEwwePBgBAUFCb8XVr0meOLZywYAFi1ahOeff57b+ryySXhnOPAMTpmZmQkX0mq1GjY2NkwngAHVhw11BUzrGyjNzc3FnTt30K9fP/j5+SE1NRWPHj3CpUuXYG9vz6Qcieck2sDAQKxfvx7Lli1Dfn4+Nm7cKGTHsVJRUcFkgICuGTNmYODAgRgyZIgwYZQ1Xk3FgeqyEta/Z6l6Wj+8Zs2aib6x/vDDD9G2bVsMHToUfn5+XD/TDhw4gIKCAvj7+0Mmk+HcuXMoLCzEm2++Wa/1ntXUm1Uz9Ly8PHz88cfcMlXS0tKwadMmZg3Ka7K0tNTr1Wpqasqs/yEvFy9exN69e6FQKPQ+i44cOSJq3blz52LNmjU4cuQIJk6ciPv372PlypUICAgQAsAs8Po84tEnC/jjsOuXX37h9jlaXl6u1yBeJpMxa5z/2WefYfXq1QgPD8f69euRkJCgl3lTH9988w2WLl0KW1tb4bGePXuiQ4cOWL9+fYMOfGmTIHgFuHR/tyqVqtawNVaD1gxN26BfDN73Xbw1msCXlqmpKfr27cukMbzW48ePcejQIaF/QLdu3TBx4kTRk4aio6MRHBz81KwVMWWDulMXdJWUlKCkpETUmwXPSZraHgQsJ+fVRSaT4cGDB0L2Tmlpqeg1KyoqhDdH3a+1GmojZ23GxPXr17Fnz55aP2/KJyd2dnY4efIkCgoKMHXqVOTn56OoqEi4GRaLVzaJtgcZrwwHnsEpCwsLPHnyBO7u7tiyZQtsbGyYTrsCoDfauaKiAgkJCaJO2r/44gu9m4rff/8do0ePRnl5Ob7++msmJeA8J9H27dsXRUVF2LBhA+7evYu3336bWeNsrY4dO6KoqEjvIluM8PBwZGdnIz4+HkuXLkWHDh0wdOhQ9OvXj8nrxRDDSpYuXYqAgAD069eP2RAEqXrWf7/Ym8edO3ciJSUF8fHxOHToEHr16oUhQ4Ywex/XlZGRgfXr1wvvjz179tSbZvp3qdXqOidcAxB9WKe1d+9ebpkqQHWmLY9MMm1Gs4uLC1avXi1kBiUmJnKZOs7S4cOHsXjxYubvs82bN8eCBQuwcuVKAMClS5fg7+/PrPRbi+fn0aBBgzBo0CCh7JGlmJgY7Nu3Dy+//DIGDRrENAhuamoKtVot3M8VFRUxy4aTyWRo166dUB0wYMAAUe8rQHV2al2fx3Z2dkzKS6WsZoagbrURi2oSqdNoNCgtLdV7nbC6tgP4ZR8DjTDwxcPu3bthbm6OuXPnAqjuw7F7927Mnj1b1Lra4E5dJXFia4prTl1QKBSIiYlBYmJirR4afwfvSZpPCwJqseohFhAQIJTEAtUXSmIz/FQqld6bI483Sh5ZiE05sPVn9uzZA41Gg8zMTACAtbU1Nm/ejI8++ojJ+jyzSYDqGwNPT09YWlrixIkTyMrKwrhx49CxY0dR6/IMTs2ZMwempqaYPHkyvvnmGygUCuY9/2r+93fp0gX/+c9/MGbMmHqtV1hYqHcTbWZmJtyEXbx4sf4b1fHtt98yn0SrWwrr4OCApKQkdOvWDQqFAikpKUx7H5WVlWH+/PlwdXXVu6AR857u7OwMZ2dnhISEIDk5GfHx8di/fz/69esnqicUwH9YCQCMGzcOp0+fxsGDBzF48GD4+/szmS4qRbm5uU8tWxF789usWTPhQLS4uBjnz59HVFQUNBoNBg8ezLThdVVVVa3sbzEZ4DWvK3Sx6j3DM1MFqG5+vHHjRvTv318v60vs+0vNjOb4+Hjha7HZMLzZ2NgwD3oBf/x3v/HGG4iIiECvXr3g7e0tPM4qU4XH5xEA7NixA8HBwTA3N8fixYtx9+5dTJ48WXQFj9aqVatw48YNnD59GnPmzEGfPn0wYsQIdOjQQfTaL7/8MsLDw1FWVoajR48iMTGRSWsZ4I/gf9u2bXH58mW0a9cOjx49ErWmWq2GSqWqlYmpUqmYBdV5+/nnn4UsW5a95qSe1cTT+fPnsW/fPpiamgqfQTKZDLt37xa9Nu8+eQAFvv6SvLw8hIeHC99PnTpVdM8JoLpcKD09/ak/Z1HXrlKpcOrUKcTGxsLPzw8RERGwtrau93q8J2nyaAhfl8GDB8POzg4///wzgOqGtB4eHqLWNNQb5bVr15CTkwOVSiU8xvo0j1TLyspCeHi40EDYysqKSRPH48ePw9zcHAEBAXqPf/vtt1CpVMx6cRw9ehQbNmxATk4OEhMT4e/vj6ioKOE0uL7mzJkDExMTpsEppVIJhUIBOzs74bHAwEDcu3ePe7ntw4cPRWV91hxGEhoaKnytUCjqva4uHj1Kat44WlhY4ObNm7h58yYAtk2/X3rpJWa9K2tq3rw5+vXrh+bNm+Orr77CxYsXRQe+WDcpr0u3bt3QrVs3FBUV4X//+x8WL14MNzc3vPLKK3qBiKaAZT+5Z2nTpg0CAwPRvXt3REVF4fPPP2ca+OrevTs+/PBDoUwoISFBVGmVIa4reGaqAH/0Pz1z5oze42LfX6Qwae1phg0bhmPHjqFv3756gQexWRO6nxMWFhZ69xksM1V49cy6fv06rKyskJKSAicnJ4SFheGDDz5gFvgCqjMaZ86ciZycHKxbtw5nz56FXC5HSEiIqIqKAQMGwN7eHj/88APUajVmz57N7H08ICAACoUCEyZMwObNm/Ho0SPRLWFeeOEFbN26FTNmzBBKgxUKBSIjI9GnTx8Gu+Zv//793HqfStXKlSvh4+MDLy8vODs7M2/OHxMTg7Vr13JpL8GzT54WBb7+Ao1Gg8ePHwuljY8fP2aSBhobGwtnZ2d0796d+QtTo9EgLi4OMTExkMvlWLNmDZOGzrwnaRqyplwulzOZcGlIx44dQ3JyMoqKiuDh4YG0tDR4eXlR4IuTmidhGo2Gyd/+pUuX6gw+DR06FEuXLmUW+NKWD6WmpmLYsGEYPnw4zp49K3pdGxsb4WtWN42ffvopfHx89AJfQPXghdTUVKZNv3WHZ2g0GhQWFgr9BetDo9FAqVQKATp7e3sA1cE8Vu+PPBryL1++HBqNBteuXeNeGsSrgXZeXh7i4uKQlJQEBwcH+Pv7o3///szWVyqVOHLkCAoLC7Fo0SLcunULOTk5zIYWANU3Gw8ePIBMJoONjQ327t0LNzc3Zk30pYDXwAldCoUCCQkJOH/+PB4+fIhBgwYJmfysvPHGGzh79ixSUlIgk8nQt29fDBs2jOlzsHL58mX07t2bW6aKNsuId8/WyspKxMbGcmtJwENFRQWOHTuGkydPCjfsLLImDHUAy/vv9erVq+jVqxcsLS2ZBzRSU1Px3//+F3l5eXj55ZcxZMgQZGRkIDw8XHQA3s3NDW5ubox2+gft503Lli3xySefMFlzwoQJ2LZtG2bOnCn0sr179y569eqFCRMmMHkO3nj2PpWqsWPHIjU1Ffv370d+fj7c3d3h7e3NLHhsbW3Nracq7+xjgAJff8nAgQOxdOlS4WL6+++/ZxKgWbZsGeLj43HhwgX069cPgwcPZjbKPDQ0FGq1GhMnTkTnzp2hVCr1Ur/rm+7Me5KmrosXLyInJ0cv5VZME3eAb181Q0hKSsLatWuxdOlSvP/++7hz5w4OHz5s7G01Wp06dUJCQgKqqqqQn5+P48ePMwmWVlVV1dnol3XzX+2o4eTkZLz11lsAamcn/R08S5Gzs7Mxffr0Wo/36dOHeTmu7o2YiYkJ7O3thYmP9dG/f39s374ds2bNEoJfSqUSO3bsYBaEuX79Oo4cOYJ79+7pBdPEnuCbmJggKipKL6uZB9YNtE+fPo34+Hjcv38fAwYMwPLly5mUq9QUGRmJjh07CgNj7Ozs8PHHHzMJfF24cAGnT5+GUqlEQEAA/v3vf8PMzAwajQazZ89uUoEvnn766SfEx8cjNTVV6NHarVs3pjfUNafxtm3bFkD1AKDo6GjR1y48fPHFF4iKisLAgQMRFBSEy5cvM81U0WYF1fzMYDV1WYt3SwIevvrqK2zYsIHZNb+h8fo8srGxQVRUFH755RcEBgZCrVYz7Tc1d+5cWFtbIyAgAH369BHeA/r27Yu4uDhRa9+7dw9ff/01CgoK9H4nYjITnzaZW0tM1mSzZs0wZ84c5Ofn48aNGwCqM2yk9Jrk2WtOqjw8PODh4YFXX30Vly9fxhdffIGff/5Z9P25duCZr68vTp06BT8/P71DTBaVGbyzjwEKfD2TtvRm1KhR6Nixo9Dc3t/fn0nJhpeXF7y8vKBUKpGUlIQtW7bAzMwMkyZNEj0FTFsGV9cNI6sLDh6TNLX27t2Le/fu4caNG3jxxRfx/fffw8fHR/S6PPuqGULz5s1hZmYm9A1xdHREQUGBsbfVaIWEhCA6OhqlpaVYtmwZfH19mUymUyqVwsW/Lm12KSvjx49HZGQkvL290aFDB9y5c0fUlMqnlSJXVlaK7sPzrMwo1n+b2vcBbUN+MUEvoDrrTXtyqr1ozM/PR+/evREUFCRus//ftm3bMGLECC4p/Q4ODsjPz+d6wcu6gfZPP/2E0aNHo3fv3lwbw9+9exfvvfcekpOTAbAJTn/++efw8vLC+fPnMW7cOHh7e+v93MTE5Kn9rsjf9+mnn2LIkCGYNm0aWrVqxeU5eGbw86I7IGLt2rXo0KEDhgwZgn/84x9M1jdU9hGvlgQ8tW3bVlIBhpp4fR7Nnj0biYmJGDhwIKysrHDv3j2MHDmS6fraIVo1LVmyRNTaERER8Pb2xogRI5j9Tp42mVuLRTsCBwcHyb0WdT8fefSak7KjR48iLS0NarUacrkc//73v5kMjKt5TRIdHa33PYsDap598rQo8PUMuqU3PXr0QI8ePQAAycnJOHToELPSG0tLS/j6+kKhUODbb7/F7du3RQe+DNmYj8ckzfT0dISHh2PhwoUIDg7GqFGjmPw3GaKvGk9mZmZQq9VwcnLCwYMH0aZNG2anYbzrwqXIwsIC06dPrzMTSQwPDw/ExMTUaqZ+/PhxphNNe/furXdh5OjoKCorq2am6507d3Du3DkkJCSgbdu2+L//+796r11ZWalXLqjFslxQ69atW9i0aRNKSkoAVPf9mTdvXr2HdJiamuLdd9/lenJqYmKC4cOHM1tPl0KhwPz58+Hm5gYLCwvhcZYZsKxT2BcvXsxiW3+q5h5VKpWoZuUA8ODBA0RGRqKoqAhVVVXIysqCl5cXunbtKtwwseyv1tRt2rSJ+3PwzODnqa4BEQcOHGAyIMJQeLUk4EkulyM6Ohr9+/fXy5pg1XyeN16fR61atdK7jrCzs6vV/kCM4uJiODg46A38GTt2LJNp6RUVFUwORnU9K1usob/GeeLVY64xOHfuHOzt7TF06FD4+Pgw+xwyxCA0nn3ytCjw9Qy8S280Gg1SUlIQFxeHwsJCDBgwAOvWrROdfdAYmJmZCTcAarUaNjY2QnaGGFI8ldU1depUqNVqBAcH4/DhwygqKhI9XVSLd124lGjLmp5GbHDqn//8J5YvX46ff/5ZCHJnZWVBqVRixYoVotbW9dlnn2HkyJGwtLTERx99hGvXrmHatGmigtRPnjzBxYsXERcXh3v37kGlUuHDDz8UPdm1f//+2Lp1K2bNmoWWLVsCqA7I7Ny5k2nPJqC6NCYwMFAoV0tKSsLu3btFN0vmeXLq5uaG7OxsLpPABg4cqBf0Z5HBV5MhUth5kMvliImJQUVFBVJTU3Hq1CnRZRXaQ7PS0lJcuXIFGRkZ2LZtG0pLS+Hu7m6woF5DVVJSUquEiuWBAA88M/iB6r/J5ORk5Ofn693w1ncSbU08BkQYCq+WBDwlJiYCgJBJCrBtPs8b68+jzZs347333tPrv6nraZNN/666Bv7s3r1b9MAfoHpadFFRkegBBXWJiYnRyx6vqqrC1q1b8e677zJ/LinQ9phTq9XC4VRBQQFu3bolJKk0VVFRUcjNzUVaWhr27duHwsJCuLq6YubMmUzWv3btGtq3b6/X9/zOnTvM3gt49cnTosDXM/AuvZkxYwZsbW0xePBg4X9yWVkZysrKAEjn5IcHCwsLPHnyBO7u7tiyZQtsbGxgbm4uel2pnsrW7B8CVDd1tLCwwHfffQcnJyfRz8GrLlyKDh48WOfjJSUlKCkpER34btWqFdatW4cLFy4IE6+GDh0KPz8/pn2+UlJSMH78eKSmpsLU1BSrVq3C5s2b6x342rlzJy5fvgwPDw+89tpr6NGjB959913RQS8ACAoKwvbt2/HWW2/pNVrt06cPs5s7LYVCodej6cUXX8Tx48eZPgdrV69exblz5+Dg4KD3GmFxQ6DN5GOZwadVWVkJU1PTWinsCQkJ8Pf3F70+b+PHj8eJEyfQokULHD16FL1798brr7/OZG0bGxv07dsXrVu3ho2NDZKSkpCTk8Nkbak6duwYTpw4AXt7e73A6Nq1a424q7+ORwY/UB0YKC0t1csKZIX3gAje6mpJwLo8hjVDVmXwwPrz6PHjx8jIyMCkSZOY9zrVxWvgD1B9/zZ//ny4urrqZfGxyJz+7bffkJiYKLTZ2bVrF/Om31K0bNkyfPDBB1Cr1fjggw9gZ2eHH3/8kXmVhtS0bNkSVlZWsLKywvXr15Gdnc1s7aioKL3PYzMzM0RFRYnqqRgWFvbMn7Oc4Et/Nc/Au/TGzMwMZWVl+PrrryGTyfTKJ6R08sOStq/anDlzYGpqismTJ+PkyZO4ceMGQkNDRa/P+1SWF0NkqvGqC5eimjdZCoUCMTExSExMxLhx45g8h5mZmTAoghftayUjIwN9+/aFo6OjqNfPxYsX4ezsjOHDh6Nbt26QyWTMXo8mJiZ45513MGbMGO6NVk1MTHDr1i2hGfqtW7cafPYRr0bnvDL4tD755BPMnTu3Vgr7O++8g+3btzMLIvFw7do1nDx5Ejdv3gRQnVni4+Mj+rWSkZGBjIwMpKeno7i4GC4uLvDw8MCiRYtE9eBrDOLj47FlyxZYW1szX7usrKxWj6+6HqsP3hn8eXl52Lx5M9PPf0MNiOBFNzPbz89P7zAjOztbEtcvFRUVegOcWDSINgTWn0e2traIjIzE/fv34erqCrlcXqv8mwXWA390vfTSS0z6P9dl7ty5WLlyJVq3bo0ff/wR5eXlTTbbS1dlZSVatGiB7777DgMHDsSkSZOY3CtK2Zw5c6BWq+Ht7Y2ePXsiJCQEzz33HLP1NRqN3t+kqamp6JjIq6++CgC4cuUKbty4gcGDB0Mmk+H8+fNMEjt0UeDrGXiX3kj9xIcHbV813YyUoKAgJCcn49ixYw26rxpPhshU41UXLmUqlQqnTp1CbGws/Pz8EBERweWGjBdzc3McP34cSUlJWLVqFaqqqkRd5EVGRuLixYv48ssvsWvXLgwcOJB5/y1DNFqdOHEili9fLvT1yMvLa/DNxD09PaFWq1FUVMTs98Mzg0+roqICUVFRmDZtmpDCXlRUhLCwMKZNi1n7/fffsXbtWgwfPhwvvvgigOpA2OrVq7FkyRJRnxdhYWFwcXHBmDFjaBR7Dc899xy399jVq1fXykip67H64J3Bb2trC7VarZdJIpahBkTwwjszm6fff/8dO3bswJ07d/Qeb8h71sU6qKi9ti8pKUF6ejq38m/WA3901eyBypKlpSXmz58vXLe8//77Df6wzhC017Pp6elC4Lup/154H6A1a9YMd+/eFZ7jzp07orMPe/bsCaC6pHflypXC51G/fv2YZnsBgKxKbJfWRkyj0WD79u1ITk6uVXoza9asJv/HxcPChQufehE6b9480U1q6zqVHTBggGT6qmkz1c6fP88lU01bF56Wlsa8LlxKNBoN4uLiEBMTA7lcjvHjxws9BaQkPz8f//3vf+Hh4YE+ffogPz8fly5dYpJlc+vWLcTFxSExMRH29vZ46aWXJFUWW1ZWhqysLACAi4sLFi5ciB07dhh5V0+Xnp6OTz75BCYmJtixYweuXbuG2NhYUae+wcHBcHZ2xmuvvSZk8L3zzjtMs40rKiqwZs0auLq6YuLEibh//z7CwsIwYsQIvPLKK8yeh7Xw8HAMHDiwVj8v7QRj7QS5+vjtt9+Em7t79+6ha9eukMvl8PT0hKOjo9itS1Jubi6A6p5HSqWy1qh0MYEjtVoNlUqF5cuXY+XKlUJ2vVKpxKpVq/Dxxx+L2zyAt99+W/iaZQZ/bGwsgOr329zcXLzwwgt6v5eG/DdkaLqZ2QEBAcwm6vKwZMkSTJkyBVFRUQgLC8O3336L5s2bC5kPDVV0dDSCg4MRHh5eZ/Yhi7I+tVqNzMxMpKenIykpCeXl5di1a5fodXl79OgRzpw5g4KCAr0DwVmzZtV7zZo9z0pLS2FpaSmUg7LqfSZVe/bswZUrV6DRaLBx40aUl5fjww8/FFV2J3WVlZWIjY1FQUEBpk6divz8fBQVFcHLy4vJ+j/99BN27NghHNr9+uuvmDVrFpNDvDlz5iAiIkKIr1RWVmLevHlMPqO1KOPrGQxZekOqUV+1Z+OdqcazLlxKQkNDoVarMXHiRHTu3BlKpVK4MQMa/utEy8HBAf/617/0vmdVWtahQwcEBwfjjTfeEIIBUgp8tWrVCr169TL2Nv6yw4cPIywsTAj+d+3aVXQ/KENk8DVv3hwLFiwQmgdfunQJ/v7+Df6G/datW3U2sff19cWnn34qam13d3e4u7sjKCgIarUaWVlZSE9Px/r161FeXo6dO3eKWl+Kak7p+uGHH4SvxbZ++Oqrr/Dll18CgN77YYsWLZhlHfLK4Nf9G2/fvj1u377N5XmkTIqZ2ZWVlXBxcRFKtQIDA7F48eIGH/jSZnr5+vrW+pmYa3RDlX/zHBKxadMmWFtbw9XVlVlihO77lfY+hUVpdmPx5ptvIjc3F3Z2dmjWrBk0Go1khnLwsmfPHmg0GmRmZgIArK2tsXnzZmbBwJ49eyIsLAxpaWkAqquyWMVFvL29sXr1amHYUmJiIry9vZmsrUWBr7/AEKU3pBr1VaubISaA8q4LlxKVSgWg7rID1q8THhPMDHEqq2Vqaoq+ffuKmhRJ/pxGo6n1OSQ2vdzCwkLoM6fN4FOr1fjPf/7DLINPGzB+4403EBERgV69esHb21t4vKEGkZ/VYJnFoBUAKC4uRnp6uvDPgwcPmI/ulgqerR/Gjh2LsWPHCiW3UqLNFnlaf7KmrGZm9po1aySTma0t5bG2tsb169dha2srif+f2veqp9GdDvx3GKr8m+eQiJKSEixbtozpmp6enoiPj8cXX3yB+/fvA6guew4KCuLaH1YqZDIZnJycUFFRAaVSiWbNmsHe3t7Y2zKqrKwshIeHC1npVlZWzA80HR0duWSnT5kyBWfOnMHly5cBVAfYhw4dyvQ5KPBFGhTqq1Y3Q2SqUWPlPxjqdcJrghmvU1lD4Hkiq5u1V9fzNmTNmzdHeXm58P8vLy+P6fQrXhl8upk8FhYWejdODfmwQa1WIy8vD3V1g9BtRl0fO3fuxNWrV1FcXCw0ch4yZAicnZ0l2WuJpfXr19cqI63rsfoYPXo01Go1mjVrht9++w05OTkYOHCgMJa9IePZn0yqpJyZ3b9/fzx8+BCjR4/GihUrUFlZifHjxxt7W3+K16ClsLAwpKen4+TJk9izZw+38m8eQyK07O3t8ejRI1hZWTFb87vvvsOJEycwbdo0uLq6AgAyMzNx8OBBmJiYcO0rJgVS75XHQ83rQo1Go3ctLdaDBw/w+eefIzc3V+9aiMVnkampKUaMGCFce/L4O6XAF2lQgoKCsH37drz11lu1+qqxuPGVKkNkqtnZ2eHkyZPc6sJJbbwmmPE6lTUEnieyNcupdPEcoS7G5s2b8d577yEwMBAffvghiouL8cknnyAtLQ1z5sxh/nysM/iketigUqmeeiEn9mLM1tYWM2fOhIuLC42kr0Gb1aCroKCAydrh4eFYtWoViouL8fHHH8PNzQ0ZGRmYN28ek/V50PYn02g0ePz4sV5/svLyciPvzrgMmZnNmrbEtlu3bti7dy8qKiokEYDlNWjJUOXfPIZEaJmbm2PhwoXo3r273vohISH1XvObb77BkiVL9DIZe/bsiQ4dOmD9+vVNPvC1f/9+zJo1q1avvKasU6dOSEhIQFVVFfLz83H8+HHI5XJm6+/YsQPu7u64cuUKJk+ejLNnzzKbvFhcXIxdu3bhypUrAKpLH2fMmMG0uomuuEiDQn3V6maIm0fedeGkNl4TzHidyhoCzxNZKQZhtCeZ3bt3h6OjI3755RdUVVVh3LhxTf59kSeer5WmfIjzNGfOnMGZM2dw9+5dLFy4UHhcqVSiQ4cOzJ7HzMwMP/30E4YNG4agoCDMnz+f2do8GKI/mVRJ8f38WVnHQMPOUgMALy8veHl5CYOWtmzZwnTQEu/yb3t7e6xYsYLLkIj27dsznYgMVGei11W+a2dnxzSLR6qk2iuPp5CQEERHR6O0tBTLli2Dr68vJk2axGz9+/fv4/XXX0diYiJ69+6N7t27Y/ny5ZgwYYLotSMjI+Hm5iYMbTpz5gx27dqFRYsWiV5biwJfpEGivmqGZ4i6cFJNe/Hr4+OD/fv3M51gBvA7lTUEnieyUqQbALSzs4O/v78Rd0MIH9rA7u7du/UyJFq0aMEsGFBRUYGKigqkpqY2+OEKWlLuT0ZqW79+vZC1X1paChsbG+FnDT1LTRfrQUuGKv9Wq9XchkSMHTuW+ZrajM+aGekqlUp0yX1joK0KkFqvPJ4sLCwwffp0TJ8+ncv62ix1MzMzPHz4EFZWVnj48CGTte/fv4/AwEDh+9dff5354RQFvgghAPjXhZM/8JxgBvA/leUhNjYWAN8TWSnKzc3FlClTnvrzffv2GXA3hPDRrl07tG3bFl26dBE93ONpXnzxRUyfPh3PP/883NzcUFJS0mBLnGuioFfjoJultmDBgmeW3zdEvAYtGar8WzssgofHjx/j0KFDwrS7bt26YeLEiaJKWF944QVs3boVM2bMEHqHKRQKREZGok+fPkz2LUWff/45vLy80KdPHzx8+BCBgYGS6pXHQ0ZGxjN/zupz9fnnn8fDhw/x0ksvYcmSJbC0tESXLl2YrA1A70CgtLSU2bpaFPgihADgXxdO/mCoEg3Wp7I85eTkCF/zOpGVIkdHRyxevNjY2yCEOxMTE9y9e5fb+kFBQRgxYgRatGgBmUyGFi1aIDQ0lNvzsfS0m7mm3MRZ6qTWhgDgN2jJkOXfWVlZKCgo0KtoYNH3dPfu3TA3N8fcuXMBAGfPnsXu3bsxe/bseq85YcIEbNu2DTNnztTre9yrVy8mpWVS9eDBA0RGRqKoqAhpaWmQy+VYvHgxOnfuDEtLS2NvzygOHjxY5+MlJSUoKSlh9lmhLUN85ZVX0KVLFzx69IjZJNZXX30VCxYsENb79ddf8c9//pPJ2lqyqrpGFhFCmpzy8nJER0cL2Ue+vr4IDg6GhYWFkXfWePGaYFbXqeyAAQOYNojkpaysDK1atfrTx5qKhQsXNunJbaRpOXToEBQKBQYNGqT32cOi3LGyshKxsbGSHOCi28hepVIhISEBGo0Go0aNMuKuiBhSfG9/++23ha95DVriKSoqCr/++iucnJz0huewGHAxf/58hIeH/+lj9ZGfn099j+tQWlqKK1euICMjA+np6SgtLYW7uzsdFqI6MzAmJgaJiYkICAhAUFAQk3WLiopgY2PDbTpyXl6ekL0ml8vRsWNHJutqUcYXIQQA/7pwUhuvCWa8TmUNYfXq1bVuBup6rKmgsynSlFy8eBEAkJqaKjzG6oZaygNcdIOAFhYWGDlyJJYsWUKBL4lRKpXC1zUndQJo8NkqUhwooCstLQ2bNm3iUuKs/f+pDQA8fvyYWbsQ6ntcNxsbG/Tt2xetW7eGjY0NkpKS9KoHmiKVSoVTp04hNjYWfn5+iIiIYDpEi/d05E6dOqFTp05M1qoLBb4IaeIMVRdO/sB7gpmZmRnKysrw9ddfS+ZUVtvEtebNgFKp1Mt2aGqk1gOGEDF43lg3pgEut2/fZtZQmBhOzX6NupM6ASpd5a1169bcBucMHDgQS5cuRf/+/QEA33//PQYNGsTluZq6jIwMIcuruLgYLi4u8PDwwKJFi4SS0KZGo9EgLi4OMTExkMvlWLNmTZ0TQVngNR35+vXrOHLkCO7du6f32czynoUCX4Q0cYaqCyd/4D3BTIqnsl999RW+/PJLAPo3Ay1atMDIkSONtCtCiKFdu3ZNyPjq1q0bnJ2dmawr5QEuugET7Z6fNfSCNEx0PWVcXbt2xcaNG9G/f3+994PevXvXe02lUgmFQoFRo0ahY8eOQnN7f39/vPTSS6L3TGoLCwuDi4sLxowZw6y/lNSFhoZCrVZj4sSJ6Ny5M5RKpTBBHmBX6cFzOvK2bdswYsQIuLq66pUis0SBL0KauLVr1+p9r1sXPm7cOCPtqnEzxAQzqRk7dizGjh2LqKgommBGSBN19uxZxMTECBPLNm7ciKCgIAwdOlT02lIe4KKb+WlqagobGxtuNwaENFbXr18HUJ11r0tM4OvTTz+Fj48P7Ozs0KNHD/To0QMAkJycjEOHDtH1DAdhYWFIT0/HyZMnsWfPHnTt2hVyuRyenp5wdHQ09vaMQqVSAag7uM6y0oPndGQTExMMHz6cyVpPQ83tCSEAateFBwYGMq0LJ7UtWbIEa9asMfY2CCGkQXj//ffxwQcfCMMsysrKsHLlSmzYsEH02nUNcAkJCYG5ubnotQ3hyZMnQv8aJycnyeybkMbsWUMK5s2bh02bNhl4R02LWq1GVlYW0tPTceHCBZSXl2Pnzp3G3laj9ujRI7Ro0QImJiYoLy+HUqlEmzZtRK+7e/duDB48mFmWd10o44uQJs6QdeFEn1wux65du7hMMJOq8ePH1/k4lYgQ0jToTnBlMc31888/h5eXF1xdXSU7wCUzMxMbN26EjY0NAODBgwcIDQ2Fq6urcTdGiITwmOz6rD6BMpms3uuSP1dcXIz09HThnwcPHsDd3d3Y22rUUlJS4OnpCRMTE5w4cQJZWVkYO3asqMDXggULIJPJoFarce7cOTg4OOhlkbEcbkWBL0KaOEPVhZPaeE4wk6oDBw4IX6tUKiQkJEimDw8hRBwHBwccOXJEKHc4e/as6GlmDx48QGRkJO7fvw9XV1fI5XJ4eXmha9eukikXjI6Oxrx584SbuszMTBw4cACrV6828s4IkQ4ek10rKyuhVCprTeRUKpWSHZ7R0O3cuRNXr15FcXGx8J4+ZMgQODs7w9TU1Njba9SOHj2KDRs2ICcnB4mJifD398fu3buxcuXKeq9Zc8gHTxT4IqSJM1RdOKlNik3oedPNfLOwsMDIkSOxZMkSjBo1yoi7IoQYwvTp07F3715h2q23t7foHjnaf7+kpATp6enIyMjAtm3bUFpaCnd3dyxevFj0vnlTqVR6mQxubm7CZzeRnpUrV8LHxwdeXl5wdnamzCAD4THZtX///ti6dStmzZqFli1bAqjulbtz505hwiNhy9bWFjNnzoSLiwuaNaNQhiFpA4upqakYNmwYhg8fjrNnz4pa81m9jm/cuCFq7Zro1UJIE0fBF+PiNcGssbh9+zYePnxo7G0QQgygVatWeO+997is3bp1a/Tt2xetW7eGjY0NkpKShJ5ZDZ2FhQVSU1Ph4+MDAEhLS9M7JCDSMnbsWKSmpmL//v3Iz8+Hu7s7vL298fLLLxt7a40aj8muQUFB2L59O9566y08//zzAIC7d++iT58+GDNmjKi1Sd3o92o8Go0GWVlZSE5OxltvvQWgus+aWNnZ2SgsLIRcLoe1tTVu3ryJI0eOIDMzE3v27BG9vhY1tyeEECOpOcHs8uXLzCaYSdWUKVOEr7UXpFOmTMGgQYOMtCNCCG8ZGRnP/LmY6bcZGRnIyMhAeno6iouL4eLiAg8PD3h6ego3qg1ddnY2Nm7cCBMTE2gv20NDQ9GlSxcj74yIoVQqcfnyZXzxxRcoLS3FoUOHjL2lRm3Xrl3w8PDAyZMnERoaiuPHj8PMzAxvvvmm6LXz8/OF7JR//OMfoku0CWmIUlJS8Nlnn8Hb2xvBwcG4c+cODh8+jPfff7/eax4/fhzHjx9H+/btoVAoMGLECBw6dAj+/v4ICgqClZUVs/1T4IsQQoyE5wQzqSosLBS+NjU1hY2NjWT68BBC6udp5YYlJSUoKSkRNdxi/PjxcHFxwZgxY9C9e/d6r2NsarUad+7cAQA4OjpSiY+EHT16FGlpaVCr1ULPOU9PT8ri4ygvLw85OTk4f/48bt68CUB6k10JaYzmzp2LZcuWoU2bNrh9+zZCQ0OxdOlSeHt7M38u+tQkhBAjYj3BTOratWuHJ0+eCCVIVlZWdFFKSCO3du1ave8VCgViYmKQmJiIcePGiVo7LCwM6enpOHnyJPbs2YOuXbtCLpfD09MTjo6OotY2pGbNmsHc3BwpKSkoLCxEr169jL0lUk/nzp2Dvb09hg4dCh8fH8oO4uz06dM4cuQIHB0dcfv2bcyaNUvItCeE/D3Xrl1DTk6OXp/JV155pd7rNW/eXJgK2b59ezz//PNcgl4ABb4IIcRoeEwwk7rMzExs3LgRNjY2AKonsoWGhsLV1dW4GyOEcKdSqXDq1CnExsbCz88PERERsLa2FrWmu7s73N3dERQUBLVajaysLKSnp2P9+vUoLy/Hzp07Ge2evVWrVmHy5MlwcnJCcXExFi1aBBcXF5w5cwY3b97E66+/buwtknqIiopCbm4u0tLSsG/fPhQWFsLV1RUzZ8409tYapf/973/YsGEDbG1tkZeXh6ioKAp8EVIPx44dQ3JyMoqKiuDh4YG0tDR4eXmJCnxVVFQgLy8PukWIut937txZ9L61KPBFCCFGwmOCmdRFR0dj3rx5wgSzzMxMHDhwAKtXrzbyzgghvGg0GsTFxSEmJgZyuRxr1qxBu3btmD5HcXEx0tPThX8ePHigNymxISouLoaTkxMA4MKFC/D09MT8+fOhUCiwYsUKCnxJWMuWLWFlZQUrKytcv34d2dnZxt5So2VqagpbW1sAQKdOnfDkyRMj74gQaUpKSsLatWuxdOlSvP/++0KPLzFUKhXWrVun95j2e5lMhq1bt4paXxcFvgghxEh4TjCTKpVKpXcz6ubmppdOTQhpfEJDQ6FWqzFx4kR07twZSqUSubm5ws/FnPju3LkTV69eRXFxMVxdXSGXyzFkyBA4OzsLo9kbKt0pdL///jt69OgBoDpo0tD3Tp5uzpw5UKvV8Pb2Rs+ePRESEoLnnnvO2NtqtGpmlNT8nmVGCSGNWfPmzWFmZoaqqipUVVXB0dERBQUFotbctm0bo939OQp8EUKIgfGcYCZ1FhYWSE1NhY+PDwAgLS2NGv4S0shpg9t1NbEXe+Jra2uLmTNnwsXFRXIN4WUyGe7fvw8rKytkZGRg0qRJws8oa0W6Fi1aJJmJoo2BITNKCGnMzMzMoFar4eTkhIMHD6JNmzbCBHYpoKmOhBBiYDwnmElddnY2Nm7cCBMTE+E0NjQ0FF26dDHyzgghxLCSk5MRGRkJU1NTuLm5ITQ0FEB1CfiXX36JpUuXGnmHpD4qKysRGxuLgoICTJ06Ffn5+SgqKoKXl5ext0YIIU+Vl5cHOzs7qFQqHD58GEqlEoGBgUJJfkNHgS9CCDEy3QlmAQEBCAoKMvaWjEqtVuPOnTsAAEdHR8llaRBCCCulpaUoLS1F586dIZPJAFT3/tJoNELfIiItkZGR0Gg0yMzMREREBB49eoRVq1bho48+MvbWCCGklgMHDtT5eFVVFWQyGUJCQgy8o/oxMfYGCCGkqVKpVPjqq68wd+5cAEBERESTD3oBQLNmzWBubo60tDT8+uuvxt4OIYQYjY2NDZycnISgFwC0adOGgl4SlpWVhZkzZ6J58+YAACsrK1RWVhp5V4QQUrfY2FhkZmaiRYsWsLS0FP6xsrKCpaWlsbf3l9ExOiGEGJghJphJzapVqzB58mQ4OTmhuLgYixYtgouLC86cOYObN2/S9DJCCCGNgu7QAqD6mkBKfXIIIU3LsmXLEB8fjwsXLqBfv34YPHgwHBwcjL2tv40CX4QQYmA8J5hJVXFxsdAj4MKFC/D09MT8+fOhUCiwYsUKCnwRQghpFDp16oSEhARUVVUhPz8fx48fh1wuN/a2CCGkTl5eXvDy8oJSqURSUhK2bNkCMzMzTJo0CS4uLsbe3l9GgS9CCDEwnhPMpEr3BPz3339Hjx49AAAtW7aEqampsbZFCCGEMBUSEoLo6GiUlpZi2bJl8PX11ZvYSQghDZGlpSV8fX2hUCjw7bff4vbt2xT4IoQQ8nTbtm0z9hYaHJlMhvv378PKygoZGRl6NwFPnjwx4s4IIYQQdiwsLDB9+nRMnz7d2FshhJA/pdFokJKSgri4OBQWFmLAgAFYt24dWrdubeyt/S0U+CKEEGJ0o0ePxoIFC2Bqagq5XA5HR0cAQGZmZpPvf0YIIUT6MjIynvlzT09PA+2EEEL+uhkzZsDW1haDBw+Gm5sbAKCsrAxlZWUApNOiRVZVVVVl7E0QQgghpaWlKC0tRefOnYUJZsXFxdBoNDTBjBBCiKQtXry4zsdLSkpQUlJSZ/sDQggxtrffflv4WiaTQTd8JKUWLRT4IoQQQgghhBADUigUiImJQWJiIgICAhAUFGTsLRFCSKNFgS9CCCGEEEIIMQCVSoVTp04hNjYWfn5+CAwMhLW1tbG3RQghjRoFvgghhBBCCCGEI41Gg7i4OMTExEAul2P8+PHUw5IQQgyEAl+EEEIIIYQQwtHcuXOhVqsxduzYOptBS6VBNCGESBFNdSSEEEIIIYQQjlQqFQDU2cReSg2iCSFEiijjixBCCCGEEEIIIYQ0SibG3gAhhBBCCCGEEEIIITxQ4IsQQgghhBBCCCGENEoU+CKEEEIIIYQQQgghjRIFvgghhBBCCCGEEEJIo0SBL0IIIYQQQgghhBDSKFHgixBCCCGEEEIIIYQ0ShT4IoQQQgghhBBCCCGNEgW+CCGEEEIIIYQQQkij9P8AbbhWoWOvmVkAAAAASUVORK5CYII=","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["hc_def = sch.linkage(df_scaler_usarrets, method=\"average\", metric=\"cosine\")\n","dendo_def = sch.dendrogram(hc_def, labels=df_scaler_usarrets.index)"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>link_height</th>\n","      <th>diff</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>48</th>\n","      <td>1.455229</td>\n","      <td>0.461014</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>0.788468</td>\n","      <td>0.159465</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>0.994216</td>\n","      <td>0.113933</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>0.490798</td>\n","      <td>0.104382</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>0.880283</td>\n","      <td>0.091815</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    link_height      diff\n","48     1.455229  0.461014\n","45     0.788468  0.159465\n","47     0.994216  0.113933\n","42     0.490798  0.104382\n","46     0.880283  0.091815"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["# Opción para el número de cluster #\n","# Calcular las diferencias entre las alturas de enlace en el dendrograma.\n","# Se ordenan en orden descendente.\n","# Se realiza el corte a la altura del dendograma dada por el promedio de las alturas donde se presentan las \n","# mayores difencias.\n","cut_diff = pd.DataFrame(hc_def[:,2], columns=[\"link_height\"])\n","cut_diff[\"diff\"] = cut_diff.diff(1)\n","cut_diff = cut_diff.sort_values(by=[\"diff\"], ascending=False)\n","cut_diff.head()"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1663728482118,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"03i_Uchwcx8p","outputId":"ee0b65e2-0a4c-4c1b-9e23-861903a9c2fe"},"outputs":[],"source":["\n","# Determinar la máxima distancia vertical que no se intersecta con ninguno de los otro grupos.\n","# Dibujar una linea horizontal en ambos extremos.\n","# El número de clusters es igual al número de lineas verticales que pasan por la recta horizontal.\n"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":1215,"status":"ok","timestamp":1663728483320,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"qyCjxw5kk_JN","outputId":"16ea1e2f-a82d-4452-995c-5e454c19e43f"},"outputs":[{"name":"stdout","output_type":"stream","text":["1.455229438091773\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Primera opción\n","# Punto 1 de corte.\n","point_cut1 = cut_diff.loc[cut_diff[\"diff\"].idxmax(), \"link_height\"]\n","print(point_cut1)\n","\n","dendogram1 = sch.dendrogram(\n","    hc_def,                           # El agrupamiento jerárquico codificado\n","    labels=df_scaler_usarrets.index,  # como una matriz de vinculación.\n","    color_threshold=point_cut1,\n",")\n","\n","plt.axhline(y=point_cut1, color='black', linestyle='--')\n","plt.title(\"Hierarchical Clustering Dendrogram\")\n","plt.show()"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.7884684908073105\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Punto 2 de corte.\n","point_cut2 = cut_diff.loc[45, \"link_height\"]\n","print(point_cut2)\n","# Segunda opción\n","dendogram1 = sch.dendrogram(\n","    hc_def,                          # El agrupamiento jerárquico codificado\n","    labels=df_scaler_usarrets.index, # como una matriz de vinculación.\n","    color_threshold=point_cut2,\n",")\n","\n","plt.axhline(y=point_cut2, color='black', linestyle='--')\n","plt.title(\"Hierarchical Clustering Dendrogram\")\n","plt.show()"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["1.1218489644495417\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Punto 3 de corte. Promediamos los 2 primeros.\n","point_cut3 = cut_diff[\"link_height\"].head(2).mean()\n","print(point_cut3)\n","# Segunda opción\n","dendogram1 = sch.dendrogram(\n","    hc_def,                          # El agrupamiento jerárquico codificado\n","    labels=df_scaler_usarrets.index, # como una matriz de vinculación.\n","    color_threshold=point_cut3,\n",")\n","\n","plt.axhline(y=point_cut3, color='black', linestyle='--')\n","plt.title(\"Hierarchical Clustering Dendrogram\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"Ib9XETs9o2DM"},"source":["## ***sklearn***\n","\n","Vamos a usar [`AgglomerativeClustering`](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html) del módulo [`sklearn.cluster`](https://scikit-learn.org/stable/modules/clustering.html#)"]},{"cell_type":"code","execution_count":30,"metadata":{"executionInfo":{"elapsed":6659,"status":"ok","timestamp":1663728491090,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"h_mmGZPVK_Gk"},"outputs":[],"source":["#%%capture\n","#!pip install mglearn\n","import mglearn"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":1761,"status":"ok","timestamp":1663728492818,"user":{"displayName":"Luis Andres Campos 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","text/plain":["<Figure size 2000x800 with 10 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["mglearn.plots.plot_agglomerative_algorithm()"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ward</th>\n","      <th>complete</th>\n","      <th>average</th>\n","      <th>single</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>braycurtis</th>\n","      <td>-1</td>\n","      <td>0.579994</td>\n","      <td>0.587999</td>\n","      <td>0.45419</td>\n","    </tr>\n","    <tr>\n","      <th>canberra</th>\n","      <td>-1</td>\n","      <td>0.711447</td>\n","      <td>0.853879</td>\n","      <td>0.684276</td>\n","    </tr>\n","    <tr>\n","      <th>chebyshev</th>\n","      <td>-1</td>\n","      <td>0.66185</td>\n","      <td>0.678834</td>\n","      <td>0.554387</td>\n","    </tr>\n","    <tr>\n","      <th>cityblock</th>\n","      <td>-1</td>\n","      <td>0.703594</td>\n","      <td>0.71184</td>\n","      <td>0.54621</td>\n","    </tr>\n","    <tr>\n","      <th>cosine</th>\n","      <td>-1</td>\n","      <td>0.695941</td>\n","      <td>0.804943</td>\n","      <td>0.796175</td>\n","    </tr>\n","    <tr>\n","      <th>correlation</th>\n","      <td>-1</td>\n","      <td>0.734769</td>\n","      <td>0.857805</td>\n","      <td>0.714172</td>\n","    </tr>\n","    <tr>\n","      <th>euclidean</th>\n","      <td>0.697527</td>\n","      <td>0.697944</td>\n","      <td>0.718038</td>\n","      <td>0.541272</td>\n","    </tr>\n","    <tr>\n","      <th>mahalanobis</th>\n","      <td>-1</td>\n","      <td>0.640368</td>\n","      <td>0.714444</td>\n","      <td>0.675893</td>\n","    </tr>\n","    <tr>\n","      <th>minkowski</th>\n","      <td>-1</td>\n","      <td>0.697944</td>\n","      <td>0.718038</td>\n","      <td>0.541272</td>\n","    </tr>\n","    <tr>\n","      <th>seuclidean</th>\n","      <td>-1</td>\n","      <td>0.697944</td>\n","      <td>0.718038</td>\n","      <td>0.541272</td>\n","    </tr>\n","    <tr>\n","      <th>sqeuclidean</th>\n","      <td>-1</td>\n","      <td>0.628639</td>\n","      <td>0.636384</td>\n","      <td>0.42189</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 ward  complete   average    single\n","braycurtis         -1  0.579994  0.587999   0.45419\n","canberra           -1  0.711447  0.853879  0.684276\n","chebyshev          -1   0.66185  0.678834  0.554387\n","cityblock          -1  0.703594   0.71184   0.54621\n","cosine             -1  0.695941  0.804943  0.796175\n","correlation        -1  0.734769  0.857805  0.714172\n","euclidean    0.697527  0.697944  0.718038  0.541272\n","mahalanobis        -1  0.640368  0.714444  0.675893\n","minkowski          -1  0.697944  0.718038  0.541272\n","seuclidean         -1  0.697944  0.718038  0.541272\n","sqeuclidean        -1  0.628639  0.636384   0.42189"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["## En AgglomerativeClustering tenemos los siguientes linkages:\n","## linkage: {\"ward\", \"complete\", \"average\", \"single\"}, default=\"ward\"\n","## El criterio de vinculación (linkage) determina qué distancia usar entre conjuntos de observación.\n","## El algoritmo fusionará los pares de clústeres que minimizan este criterio.\n","## \"ward\": Minimiza la varianza de los grupos que se fusionan.\n","## \"average\": Usa el promedio de las distancias de cada observación de los dos conjuntos.\n","##  El enlace \"complete\" o \"máximo\" utiliza las distancias máximas entre todas las observaciones de \n","## los dos conjuntos.\n","## \"single\": Utiliza el mínimo de las distancias entre todas las observaciones de los dos conjuntos.\n","linkages = [\"ward\", \"complete\", \"average\", \"single\"]\n","## metric: str or callable, default=\"euclidean\"\n","## Metric used to compute the linkage. Can be “euclidean”, “l1”, “l2”, “manhattan”, “cosine”,\n","## or “precomputed”. If linkage is “ward”, only “euclidean” is accepted. If “precomputed”,\n","## a distance matrix (instead of a similarity matrix) is needed as input for the fit method.\n","metrics = [\n","        \"braycurtis\", \"canberra\", \"chebyshev\", \"cityblock\", \"cosine\", \"correlation\",\n","        \"euclidean\", \"mahalanobis\", \"minkowski\", \"seuclidean\", \"sqeuclidean\"\n","]\n","## Haremos uso de esto para calcular las \"mejores\" elecciones:\n","cophenetic_coef(df_scaler_usarrets, methods=linkages, metrics=metrics)"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"data":{"text/plain":["{'n_clusters': 2,\n"," 'metric': 'correlation',\n"," 'linkage': 'average',\n"," 'optimal_threshold': 1.102466314178095,\n"," 'cophenetic': 0.8578051897177195}"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["metrics = [\n","        \"braycurtis\", \"canberra\", \"chebyshev\", \"cityblock\", \"cosine\", \"correlation\",\n","        \"euclidean\", \"mahalanobis\", \"minkowski\", \"seuclidean\", \"sqeuclidean\"\n","]\n","\n","optimal_clusters_hierarchy(df_scaler_usarrets, metrics)"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-1 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-1 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-1 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-1 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-1 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-1 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-1 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-1 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-1 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-1 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-1 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-1 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-1 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-1 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-1 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-1 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering(linkage=&#x27;average&#x27;, metric=&#x27;cosine&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AgglomerativeClustering<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.AgglomerativeClustering.html\">?<span>Documentation for AgglomerativeClustering</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AgglomerativeClustering(linkage=&#x27;average&#x27;, metric=&#x27;cosine&#x27;)</pre></div> </div></div></div></div>"],"text/plain":["AgglomerativeClustering(linkage='average', metric='cosine')"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["n_clusters = 2\n","cluster1 = AgglomerativeClustering(\n","    n_clusters=n_clusters,            # El número de clústeres a buscar.\n","    metric=\"cosine\",                  # Métrica entre individuos.\n","    linkage=\"average\",                # El criterio de vinculación.\n",")\n","#\n","cluster1.fit(df_scaler_usarrets)"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":64,"status":"ok","timestamp":1663728492820,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"S5frpj-aAtK6","outputId":"177b2d8b-6672-4990-caed-0b4ba1b8949c"},"outputs":[{"name":"stdout","output_type":"stream","text":["2\n","[1 1 1 1 1 1 0 0 1 1 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0\n"," 0 0 1 0 1 1 0 0 1 0 0 0 0]\n"]}],"source":["# Atributos: n_clusters_, labels_\n","print(cluster1.n_clusters_)\n","print(cluster1.labels_)"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":58,"status":"ok","timestamp":1663728492821,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"ZpgrllZqAvRB","outputId":"4c08d8e8-3c23-443a-86cf-687470304f65"},"outputs":[{"name":"stdout","output_type":"stream","text":["4\n","['Murder' 'Assault' 'UrbanPop' 'Rape']\n"]}],"source":["# Atributos: n_features_in_, feature_names_in_,\n","print(cluster1.n_features_in_)\n","print(cluster1.feature_names_in_)"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":52,"status":"ok","timestamp":1663728492822,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"pTxldnjgApCG","outputId":"a91c52de-d505-4694-edfa-cc3c79310a0a"},"outputs":[{"data":{"text/plain":["cluster1\n","0           27\n","1           23\n","Name: count, dtype: int64"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["labels1 = cluster1.labels_\n","pd.DataFrame({\"cluster1\":labels1}, index=df_scaler_usarrets.index).value_counts()"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":50,"status":"ok","timestamp":1663728492823,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"aesKhAjoOMAU","outputId":"0f11b94b-a4cf-452f-f327-7b9a65463585"},"outputs":[{"data":{"text/plain":["array([1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n","       0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n","       0, 1, 0, 0, 0, 0])"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["# \"Ajuste y predicción del cluster sobre la data (no necesita de fit)\"\n","cluster1.fit_predict(df_scaler_usarrets)"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":46,"status":"ok","timestamp":1663728492823,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"UKbi-RkTFZ7u","outputId":"ba383596-9157-4062-e36f-125829fb5579"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Murder</th>\n","      <th>Assault</th>\n","      <th>UrbanPop</th>\n","      <th>Rape</th>\n","      <th>cluster</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>13.2</td>\n","      <td>236</td>\n","      <td>58</td>\n","      <td>21.2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>10.0</td>\n","      <td>263</td>\n","      <td>48</td>\n","      <td>44.5</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>8.1</td>\n","      <td>294</td>\n","      <td>80</td>\n","      <td>31.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>8.8</td>\n","      <td>190</td>\n","      <td>50</td>\n","      <td>19.5</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>9.0</td>\n","      <td>276</td>\n","      <td>91</td>\n","      <td>40.6</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            Murder  Assault  UrbanPop  Rape  cluster\n","Alabama       13.2      236        58  21.2        1\n","Alaska        10.0      263        48  44.5        1\n","Arizona        8.1      294        80  31.0        1\n","Arkansas       8.8      190        50  19.5        1\n","California     9.0      276        91  40.6        1"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["# DataFrame final con las clasificaciones.\n","df_usarrests_result = df_usarrests.copy()\n","df_usarrests_result[\"cluster\"] = labels1; df_usarrests_result.head()"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1663728492824,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"4lvP7ydurKbr","outputId":"44ede3d0-97b9-46d2-b605-e39b8436314e"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>cluster2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            cluster2\n","Alabama            3\n","Alaska             2\n","Arizona            1\n","Arkansas           0\n","California         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  }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Murder</th>\n","      <th>Assault</th>\n","      <th>UrbanPop</th>\n","      <th>Rape</th>\n","      <th>cluster</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>13.2</td>\n","      <td>236</td>\n","      <td>58</td>\n","      <td>21.2</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>10.0</td>\n","      <td>263</td>\n","      <td>48</td>\n","      <td>44.5</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>8.1</td>\n","      <td>294</td>\n","      <td>80</td>\n","      <td>31.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>8.8</td>\n","      <td>190</td>\n","      <td>50</td>\n","      <td>19.5</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>9.0</td>\n","      <td>276</td>\n","      <td>91</td>\n","      <td>40.6</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            Murder  Assault  UrbanPop  Rape  cluster\n","Alabama       13.2      236        58  21.2        3\n","Alaska        10.0      263        48  44.5        2\n","Arizona        8.1      294        80  31.0        1\n","Arkansas       8.8      190        50  19.5        0\n","California     9.0      276        91  40.6        1"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["# DataFrame final con las clasificaciones.\n","df_usarrests_result2 =  df_usarrests.copy()\n","df_usarrests_result2[\"cluster\"] = labels2; df_usarrests_result2.head()"]},{"cell_type":"markdown","metadata":{"id":"2wA8SRw4GDuG"},"source":["## ***Visualización de los cluster.***\n","\n","Vamos a hacer uso de `PCA` para lograr visualizar los cluster."]},{"cell_type":"code","execution_count":42,"metadata":{"executionInfo":{"elapsed":34,"status":"ok","timestamp":1663728492826,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"8l9JE6WXGHSZ"},"outputs":[{"data":{"text/plain":["array([0.62006039, 0.86750168, 0.95664248, 1.        ])"]},"execution_count":42,"metadata":{},"output_type":"execute_result"}],"source":["pca = PCA(n_components=0.99)\n","pca.fit(df_scaler_usarrets)\n","np.cumsum(pca.explained_variance_ratio_)"]},{"cell_type":"code","execution_count":43,"metadata":{"executionInfo":{"elapsed":31,"status":"ok","timestamp":1663728492828,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"3d995YiHGZPD"},"outputs":[],"source":["n_clusters = 2\n","cluster1 = AgglomerativeClustering(n_clusters=n_clusters, metric=\"cosine\", linkage=\"average\")\n","cluster1.fit(df_scaler_usarrets)\n","# Cluster de cada registro.\n","labels1 = cluster1.labels_"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":564,"status":"ok","timestamp":1663728493362,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"CdF2po-5G6KW","outputId":"d9685d7e-0f1a-4db0-d9ae-c2082f5725a6"},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"<b>%{hovertext}</b><br><br>cluster=1<br>x=%{x}<br>y=%{y}<br>Indice=%{text}<extra></extra>","hovertext":["Alabama","Alaska","Arizona","Arkansas","California","Colorado","Florida","Georgia","Illinois","Kentucky","Louisiana","Maryland","Michigan","Mississippi","Missouri","Nevada","New Mexico","New York","North Carolina","South 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Biplot."},"xaxis":{"anchor":"y","domain":[0,1],"range":[-3.342225615017864,3.363042270287223],"title":{"text":"Dim 1 (62.01%)"}},"yaxis":{"anchor":"x","domain":[0,1],"range":[-2.7437959866443897,1.9204600120431201],"title":{"text":"Dim 2 (24.74%)"}}}}},"metadata":{},"output_type":"display_data"}],"source":["# Plot con 2 clusters.\n","cluster_biplot(pca, df_usarrests, labels1, size_text=5)"]},{"cell_type":"code","execution_count":45,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1663728493364,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"0MCbejziHPry","outputId":"3084653f-2ea3-43e3-ef53-899895db9b68"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-2 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-2 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-2 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-2 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-2 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-2 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-2 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-2 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-2 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-2 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-2 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-2 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-2 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-2 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-2 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-2 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-2 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-2 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-2 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-2 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-2 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering(linkage=&#x27;average&#x27;, metric=&#x27;cosine&#x27;, n_clusters=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AgglomerativeClustering<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.AgglomerativeClustering.html\">?<span>Documentation for AgglomerativeClustering</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AgglomerativeClustering(linkage=&#x27;average&#x27;, metric=&#x27;cosine&#x27;, n_clusters=4)</pre></div> </div></div></div></div>"],"text/plain":["AgglomerativeClustering(linkage='average', metric='cosine', n_clusters=4)"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["# Con 4 clusters\n","n_clusters = 4\n","cluster2 = AgglomerativeClustering(n_clusters=n_clusters, metric=\"cosine\", linkage=\"average\",)\n","cluster2.fit(df_scaler_usarrets)"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1663728493364,"user":{"displayName":"Luis Andres Campos Maldonado","userId":"15478348060554261231"},"user_tz":300},"id":"LzLdPlixHzjz","outputId":"581b04a1-84d1-434e-b7cd-559d7cbf1407"},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"<b>%{hovertext}</b><br><br>cluster=1<br>x=%{x}<br>y=%{y}<br>Indice=%{text}<extra></extra>","hovertext":["Alabama","Arkansas","Georgia","Kentucky","Louisiana","Mississippi","North Carolina","South Carolina","Tennessee","Virginia"],"legendgroup":"1","marker":{"color":"#636efa","symbol":"circle"},"mode":"markers+text","name":"1","orientation":"v","showlegend":true,"text":["Alabama","Arkansas","Georgia","Kentucky","Louisiana","Mississippi","North 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Biplot."},"xaxis":{"anchor":"y","domain":[0,1],"range":[-3.342225615017864,3.363042270287223],"title":{"text":"Dim 1 (62.01%)"}},"yaxis":{"anchor":"x","domain":[0,1],"range":[-2.7437959866443897,1.9204600120431201],"title":{"text":"Dim 2 (24.74%)"}}}}},"metadata":{},"output_type":"display_data"}],"source":["# Cluster de cada registro.\n","labels2 = cluster2.labels_\n","# Plot con 4 clusters.\n","cluster_biplot(pca, df_usarrests, labels2, size_text=6.5)"]},{"cell_type":"markdown","metadata":{},"source":["## ***Ejemplo: FA + Cluster Jerarquico***"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Factor1</th>\n","      <th>Factor2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>SS loadings</th>\n","      <td>1.950982</td>\n","      <td>0.857294</td>\n","    </tr>\n","    <tr>\n","      <th>Portion_Var</th>\n","      <td>0.487746</td>\n","      <td>0.214323</td>\n","    </tr>\n","    <tr>\n","      <th>Cumulative_Var</th>\n","      <td>0.487746</td>\n","      <td>0.702069</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 Factor1   Factor2\n","SS loadings     1.950982  0.857294\n","Portion_Var     0.487746  0.214323\n","Cumulative_Var  0.487746  0.702069"]},"execution_count":47,"metadata":{},"output_type":"execute_result"}],"source":["df_usarrets = pd.read_csv(url_base + \"USArrests.csv\", index_col=0)\n","scaled_data_df_usa_arrests = StandardScaler().fit_transform(df_usarrets)\n","\n","fa_vari = FactorAnalyzer(n_factors=2, rotation=\"varimax\")\n","fa_vari.fit(scaled_data_df_usa_arrests)\n","fa_transform = fa_vari.transform(scaled_data_df_usa_arrests)\n","\n","# Varianza factores, variabilidad y variabilidad acumulada.\n","pd.DataFrame(\n","    fa_vari.get_factor_variance(), columns=[\"Factor1\", \"Factor2\"],\n","    index=[\"SS loadings\", \"Portion_Var\",\"Cumulative_Var\"]\n",")"]},{"cell_type":"markdown","metadata":{},"source":["1. ***factor1:*** El primer factor parece ser compuesto principalmente de asesinato, asalto y violación, con cargas de 0,84, 0,91 y 0,78, respectivamente (lectura anterior). Esta variable latente se podría pensar como ***indice delictivo***.\n","\n","2. ***factor2*** El segundo esta compuesto por poblacion urbana y violacion, con cargas de 0,64 y 0,59. Esta variable latente se podría pensar como ***Dinámica urbana y crimen***."]},{"cell_type":"code","execution_count":48,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["'Optimos parametros': {'n_clusters': 2, 'metric': 'correlation', 'linkage': 'complete'}\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>factor_1</th>\n","      <th>factor_2</th>\n","      <th>cluster</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Alabama</th>\n","      <td>1.157124</td>\n","      <td>-0.651535</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Alaska</th>\n","      <td>0.807827</td>\n","      <td>0.770150</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>Arizona</th>\n","      <td>0.374417</td>\n","      <td>1.354160</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Arkansas</th>\n","      <td>0.293548</td>\n","      <td>-0.604472</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>California</th>\n","      <td>0.464488</td>\n","      <td>2.007143</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            factor_1  factor_2  cluster\n","Alabama     1.157124 -0.651535        1\n","Alaska      0.807827  0.770150        1\n","Arizona     0.374417  1.354160        0\n","Arkansas    0.293548 -0.604472        1\n","California  0.464488  2.007143        0"]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["metricas = [\n","    \"braycurtis\", \"canberra\", \"chebyshev\", \"cityblock\", \"correlation\",\n","    \"cosine\", \"euclidean\", \"mahalanobis\", \"minkowski\",\n","]\n","\n","params = optimal_clusters_hierarchy(fa_transform, metricas, to_sklearn=True)\n","print(\"'Optimos parametros':\", params)\n","\n","clustering = AgglomerativeClustering(**params)\n","clustering.fit(scaled_data_df_usa_arrests)\n","\n","df_factor = pd.DataFrame(\n","    fa_transform,  # Puntuaciones factoriales\n","    columns=[\"factor_1\", \"factor_2\"],\n","    index=df_usarrets.index\n",")\n","\n","df_factor[\"cluster\"] = clustering.labels_\n","df_factor.head()"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"alignmentgroup":"True","hovertemplate":"cluster=1<br>factor_1=%{y}<extra></extra>","legendgroup":"1","marker":{"color":"#636efa"},"name":"1","notched":false,"offsetgroup":"1","orientation":"v","showlegend":true,"type":"box","x0":" 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col in df_factor.drop(columns=[\"cluster\"]).columns:\n","    px.box(df_factor, y=col, color=\"cluster\", title=col).show()"]},{"cell_type":"markdown","metadata":{},"source":["#### ***Como caracterizamos cada cluster?***"]},{"cell_type":"markdown","metadata":{"id":"NVM-_dKtQhuR"},"source":["## ***Ventajas y desventajas de la agrupación jerárquica***\n","\n","1. No se necesita especificar el número de clusters necesarios para el algoritmo.\n","\n","2. No funciona bien en los conjuntos de datos grandes. En general, es aplicable a los  pequeños. \n","\n","3.  En comparación con K-means, el clustering jerárquico es computacionalmente pesado y tarda más tiempo en ejecutarse.\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"fh2iUIw2QoLg"},"source":["## ***Conclusión.***\n","A pesar de las limitaciones del clustering jerárquico cuando se trata de grandes conjuntos de datos, sigue siendo una gran herramienta para tratar con conjuntos de datos pequeños y medianos y encontrar patrones en ellos. "]},{"cell_type":"markdown","metadata":{"id":"EPPEihczQs_q"},"source":["## ***Referencias:***\n","\n","1. _Análisis estadístico de datos multivariados. Díaz L & Morales M. Editorial: Publicaciones Facultad de ciencias. UNAL._\n","\n","2. _Multivariate Analysis I. Kassambara A. Disponible en: http://www.sthda.com/english/_"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyNufJFEvDG44h8u2uqzYeMi","provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.10.12"}},"nbformat":4,"nbformat_minor":0}