Computer Vision/Cancer_paper/Cancer_Proyecto_Miguel_Gutierrez.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "liquid-secondary",
   "metadata": {},
   "source": [
    "## Analisis Cancer\n",
    "\n",
    "Miguel Gutierrez\n",
    "\n",
    "El presente trabajo tratamos de replicar el articulo en la obtencion de descriptores, luego finalmente unimos todos los descriptores para encontrar el mejor modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adjacent-class",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "import skimage.exposure as exposure\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from skimage import io\n",
    "from os.path import join\n",
    "from skimage import transform ,feature, measure,img_as_ubyte,data\n",
    "from skimage.color import label2rgb, rgb2gray\n",
    "from skimage.measure import regionprops, regionprops_table,moments_central,label, find_contours, moments_normalized, moments_hu\n",
    "from scipy.spatial import distance_matrix\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from sklearn.decomposition import PCA\n",
    "from scipy.stats import skew, kurtosis, entropy\n",
    "from skimage.transform import rotate\n",
    "from skimage.feature import local_binary_pattern,ORB,hog,greycomatrix\n",
    "from skimage.filters import gabor_kernel\n",
    "from scipy import ndimage as ndi\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "figured-clear",
   "metadata": {},
   "source": [
    "## Importamos las Imagenes\n",
    "\n",
    "Primero cargamos las imagenes, solo vamos a trabajar con las de validación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "sticky-guatemala",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['01_TUMOR', '02_STROMA', '03_COMPLEX', '04_LYMPHO', '05_DEBRIS', '06_MUCOSA', '07_ADIPOSE', '08_EMPTY'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "imgs = []\n",
    "DIR = 'colorectal_cancer/validation'\n",
    "files = os.listdir(DIR)\n",
    "print(files,'\\n')\n",
    "Lista = ['Tumor','Stroma','Complex','Lympho','Debris','Mucosa','Adipose','Empty']\n",
    "Tumor =[]\n",
    "Stroma = []\n",
    "Complex = []\n",
    "Lympho = []\n",
    "Debris = []\n",
    "Mucosa = []\n",
    "Adipose = []\n",
    "Empty = []\n",
    "# Iterando Sobre SubCarpeta\n",
    "for i in range(len(files)):\n",
    "    files1 = os.listdir(join(DIR, files[i]))\n",
    "    if i==0:\n",
    "        for j in range(len(files1)):\n",
    "            Tumor.append(io.imread(join(DIR, files[i],files1[j])))\n",
    "    if i==1:\n",
    "        for j in range(len(files1)):\n",
    "            Stroma.append(io.imread(join(DIR, files[i],files1[j])))\n",
    "    if i==2:\n",
    "        for j in range(len(files1)):\n",
    "            Complex.append(io.imread(join(DIR, files[i],files1[j])))\n",
    "    if i==3:\n",
    "        for j in range(len(files1)):\n",
    "            Lympho.append(io.imread(join(DIR, files[i],files1[j])))\n",
    "    if i==4:\n",
    "        for j in range(len(files1)):\n",
    "            Debris.append(io.imread(join(DIR, files[i],files1[j])))\n",
    "    if i==5:\n",
    "        for j in range(len(files1)):\n",
    "            Mucosa.append(io.imread(join(DIR, files[i],files1[j])))\n",
    "    if i==6:\n",
    "        for j in range(len(files1)):\n",
    "            Adipose.append(io.imread(join(DIR, files[i],files1[j])))\n",
    "    if i==7:\n",
    "        for j in range(len(files1)):\n",
    "            Empty.append(io.imread(join(DIR, files[i],files1[j])))\n",
    "            \n",
    "            \n",
    "# Pasando imagenes a grises\n",
    "Tumor = [rgb2gray(im) for im in Tumor]\n",
    "Stroma = [rgb2gray(im) for im in Stroma]\n",
    "Complex = [rgb2gray(im) for im in Complex]\n",
    "Lympho = [rgb2gray(im) for im in Lympho]\n",
    "Debris = [rgb2gray(im) for im in Debris]\n",
    "Mucosa = [rgb2gray(im) for im in Mucosa]\n",
    "Adipose = [rgb2gray(im) for im in Adipose]\n",
    "Empty = [rgb2gray(im) for im in Empty]\n",
    "\n",
    "Todo = [Tumor,Stroma,Complex,Lympho,Debris,Mucosa,Adipose,Empty]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extended-surfing",
   "metadata": {},
   "source": [
    "Verificamos que cargo correctamente las imagenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "trained-check",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x25d087202e0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Empty[0],cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exempt-invasion",
   "metadata": {},
   "source": [
    "# Low Histogram\n",
    "\n",
    "Los estadisticos bajos que utilice fueron la media,variance, skewnes,curtosis,minimo,maximo,contraste "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "macro-attachment",
   "metadata": {},
   "outputs": [],
   "source": [
    "def low_histogram(class1):\n",
    "    # Class1: Imagen\n",
    "    # Descriptores : Lista estadisticos bajos\n",
    "    descriptores = []\n",
    "    for img in class1:\n",
    "        # Obtencion de estadisticos\n",
    "        aux = [img.mean(), img.std(), skew(img.ravel()),\n",
    "               kurtosis(img.ravel()), img.min(), img.max(),\n",
    "               1.0 - (1.0/(1+ img.std()**2))]\n",
    "        descriptores.append(aux)\n",
    "    return descriptores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "undefined-attention",
   "metadata": {},
   "source": [
    "Aplicamos el low histogram en todas las imagenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "swedish-spanish",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La clase Tumor analizada\n",
      "La clase Stroma analizada\n",
      "La clase Complex analizada\n",
      "La clase Lympho analizada\n",
      "La clase Debris analizada\n",
      "La clase Mucosa analizada\n",
      "La clase Adipose analizada\n",
      "La clase Empty analizada\n"
     ]
    }
   ],
   "source": [
    "df_t_l = pd.DataFrame(columns=[\"media\",\"desviacion estándar\", \"skewness\",\n",
    "                                              \"curtosis\",\"minimo\", \"maximo\",\"contraste\",\"clase\"])\n",
    "for i in range(0,len(Todo)):\n",
    "    des= low_histogram(Todo[i])\n",
    "    df = pd.DataFrame(des, columns=[\"media\",\"desviacion estándar\", \"skewness\",\n",
    "                                              \"curtosis\",\"minimo\", \"maximo\",\"contraste\"])\n",
    "    df['clase']=Lista[i]\n",
    "    df_t_l = pd.concat([df_t_l,df], ignore_index=True)\n",
    "    print(f\"La clase {Lista[i]} analizada\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "painful-cycling",
   "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>media</th>\n",
       "      <th>desviacion estándar</th>\n",
       "      <th>skewness</th>\n",
       "      <th>curtosis</th>\n",
       "      <th>minimo</th>\n",
       "      <th>maximo</th>\n",
       "      <th>contraste</th>\n",
       "      <th>clase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.342303</td>\n",
       "      <td>0.302688</td>\n",
       "      <td>1.038178</td>\n",
       "      <td>-0.597964</td>\n",
       "      <td>0.035210</td>\n",
       "      <td>0.950916</td>\n",
       "      <td>0.083930</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.284395</td>\n",
       "      <td>0.132290</td>\n",
       "      <td>1.533068</td>\n",
       "      <td>3.513061</td>\n",
       "      <td>0.037509</td>\n",
       "      <td>0.933012</td>\n",
       "      <td>0.017200</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.300449</td>\n",
       "      <td>0.220826</td>\n",
       "      <td>1.110602</td>\n",
       "      <td>0.225319</td>\n",
       "      <td>0.021488</td>\n",
       "      <td>0.917824</td>\n",
       "      <td>0.046497</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.344726</td>\n",
       "      <td>0.197159</td>\n",
       "      <td>1.586735</td>\n",
       "      <td>1.801484</td>\n",
       "      <td>0.067795</td>\n",
       "      <td>0.969140</td>\n",
       "      <td>0.037417</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.426256</td>\n",
       "      <td>0.257157</td>\n",
       "      <td>0.493997</td>\n",
       "      <td>-1.126756</td>\n",
       "      <td>0.030187</td>\n",
       "      <td>0.939517</td>\n",
       "      <td>0.062028</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>0.953664</td>\n",
       "      <td>0.011838</td>\n",
       "      <td>-14.605011</td>\n",
       "      <td>297.483326</td>\n",
       "      <td>0.586165</td>\n",
       "      <td>0.989910</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.953490</td>\n",
       "      <td>0.012690</td>\n",
       "      <td>-11.839159</td>\n",
       "      <td>195.084762</td>\n",
       "      <td>0.663456</td>\n",
       "      <td>0.982364</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.954638</td>\n",
       "      <td>0.004596</td>\n",
       "      <td>-3.201485</td>\n",
       "      <td>48.847362</td>\n",
       "      <td>0.843703</td>\n",
       "      <td>0.979827</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>0.954122</td>\n",
       "      <td>0.019744</td>\n",
       "      <td>-13.293419</td>\n",
       "      <td>210.103112</td>\n",
       "      <td>0.511363</td>\n",
       "      <td>0.985720</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.946399</td>\n",
       "      <td>0.006527</td>\n",
       "      <td>-3.915970</td>\n",
       "      <td>58.486529</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        media  desviacion estándar   skewness    curtosis    minimo    maximo  \\\n",
       "0    0.342303             0.302688   1.038178   -0.597964  0.035210  0.950916   \n",
       "1    0.284395             0.132290   1.533068    3.513061  0.037509  0.933012   \n",
       "2    0.300449             0.220826   1.110602    0.225319  0.021488  0.917824   \n",
       "3    0.344726             0.197159   1.586735    1.801484  0.067795  0.969140   \n",
       "4    0.426256             0.257157   0.493997   -1.126756  0.030187  0.939517   \n",
       "..        ...                  ...        ...         ...       ...       ...   \n",
       "974  0.953664             0.011838 -14.605011  297.483326  0.586165  0.989910   \n",
       "975  0.953490             0.012690 -11.839159  195.084762  0.663456  0.982364   \n",
       "976  0.954638             0.004596  -3.201485   48.847362  0.843703  0.979827   \n",
       "977  0.954122             0.019744 -13.293419  210.103112  0.511363  0.985720   \n",
       "978  0.946399             0.006527  -3.915970   58.486529  0.779000  0.980392   \n",
       "\n",
       "     contraste  clase  \n",
       "0     0.083930  Tumor  \n",
       "1     0.017200  Tumor  \n",
       "2     0.046497  Tumor  \n",
       "3     0.037417  Tumor  \n",
       "4     0.062028  Tumor  \n",
       "..         ...    ...  \n",
       "974   0.000140  Empty  \n",
       "975   0.000161  Empty  \n",
       "976   0.000021  Empty  \n",
       "977   0.000390  Empty  \n",
       "978   0.000043  Empty  \n",
       "\n",
       "[979 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "personalized-adolescent",
   "metadata": {},
   "source": [
    "## High Histogram\n",
    "\n",
    "Probamos que funciona con una imagen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "accomplished-farmer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.40403777e+25 1.49531130e+26 9.20146854e+28 3.18396786e+30\n",
      " 4.76579301e+32 2.85179354e+34 2.98461384e+36 2.23140872e+38\n",
      " 2.07098370e+40]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "central = moments_central(Tumor[0],order=11)\n",
    "central = central[:,2:11]\n",
    "central = np.mean(central,axis=0)\n",
    "plt.imshow(Tumor[0],cmap='gray')\n",
    "print(central)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "antique-movie",
   "metadata": {},
   "source": [
    "El histograma retorna la media de los momentos centrales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "immune-department",
   "metadata": {},
   "outputs": [],
   "source": [
    "def high_histogram(class1):\n",
    "    # Class 1 Imagen\n",
    "    # Descriptores\n",
    "    central = moments_central(class1,order=11)\n",
    "    central = central[:,2:12]\n",
    "    central = np.mean(central,axis=0)\n",
    "    return central"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modern-sacramento",
   "metadata": {},
   "source": [
    "Creamos la base de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "southwest-moderator",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La clase Tumor analizada\n",
      "La clase Stroma analizada\n",
      "La clase Complex analizada\n",
      "La clase Lympho analizada\n",
      "La clase Debris analizada\n",
      "La clase Mucosa analizada\n",
      "La clase Adipose analizada\n",
      "La clase Empty analizada\n"
     ]
    }
   ],
   "source": [
    "df_t_h = pd.DataFrame(columns=[str(x)+' Momento' for x in range(2,12)])\n",
    "for i in range(0,len(Todo)):\n",
    "    clase = Todo[i]\n",
    "    print(f\"La clase {Lista[i]} analizada\")\n",
    "    for img in clase:\n",
    "        central = high_histogram(img)\n",
    "        df = pd.DataFrame([central], columns=[str(x)+' Momento' for x in range(2,12)])\n",
    "        df['clase']=Lista[i]\n",
    "        df_t_h = pd.concat([df_t_h,df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "given-enough",
   "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>2 Momento</th>\n",
       "      <th>3 Momento</th>\n",
       "      <th>4 Momento</th>\n",
       "      <th>5 Momento</th>\n",
       "      <th>6 Momento</th>\n",
       "      <th>7 Momento</th>\n",
       "      <th>8 Momento</th>\n",
       "      <th>9 Momento</th>\n",
       "      <th>10 Momento</th>\n",
       "      <th>11 Momento</th>\n",
       "      <th>clase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.404038e+25</td>\n",
       "      <td>1.495311e+26</td>\n",
       "      <td>9.201469e+28</td>\n",
       "      <td>3.183968e+30</td>\n",
       "      <td>4.765793e+32</td>\n",
       "      <td>2.851794e+34</td>\n",
       "      <td>2.984614e+36</td>\n",
       "      <td>2.231409e+38</td>\n",
       "      <td>2.070984e+40</td>\n",
       "      <td>1.698009e+42</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.024494e+25</td>\n",
       "      <td>6.213925e+26</td>\n",
       "      <td>3.657138e+28</td>\n",
       "      <td>2.389448e+30</td>\n",
       "      <td>1.628867e+32</td>\n",
       "      <td>1.105846e+34</td>\n",
       "      <td>8.002568e+35</td>\n",
       "      <td>5.568161e+37</td>\n",
       "      <td>4.125002e+39</td>\n",
       "      <td>2.923090e+41</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.567811e+25</td>\n",
       "      <td>-8.027656e+25</td>\n",
       "      <td>-1.700597e+29</td>\n",
       "      <td>-1.498905e+30</td>\n",
       "      <td>-7.236907e+32</td>\n",
       "      <td>-1.142376e+34</td>\n",
       "      <td>-3.317133e+36</td>\n",
       "      <td>-7.371632e+37</td>\n",
       "      <td>-1.594170e+40</td>\n",
       "      <td>-4.468822e+41</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.113272e+25</td>\n",
       "      <td>-8.454881e+26</td>\n",
       "      <td>-1.217710e+29</td>\n",
       "      <td>-4.431659e+30</td>\n",
       "      <td>-5.575508e+32</td>\n",
       "      <td>-2.502299e+34</td>\n",
       "      <td>-2.801839e+36</td>\n",
       "      <td>-1.479809e+38</td>\n",
       "      <td>-1.503980e+40</td>\n",
       "      <td>-8.974465e+41</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.131477e+25</td>\n",
       "      <td>-1.355277e+26</td>\n",
       "      <td>1.057623e+29</td>\n",
       "      <td>4.158094e+29</td>\n",
       "      <td>4.332257e+32</td>\n",
       "      <td>7.220768e+33</td>\n",
       "      <td>2.015840e+36</td>\n",
       "      <td>6.186984e+37</td>\n",
       "      <td>1.030140e+40</td>\n",
       "      <td>4.497641e+41</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>1.864395e+24</td>\n",
       "      <td>4.648701e+24</td>\n",
       "      <td>6.311105e+27</td>\n",
       "      <td>2.002565e+28</td>\n",
       "      <td>2.539999e+31</td>\n",
       "      <td>9.511471e+31</td>\n",
       "      <td>1.110900e+35</td>\n",
       "      <td>4.719931e+35</td>\n",
       "      <td>5.104471e+38</td>\n",
       "      <td>2.402237e+39</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>2.322414e+24</td>\n",
       "      <td>3.104447e+24</td>\n",
       "      <td>8.046245e+27</td>\n",
       "      <td>2.092607e+28</td>\n",
       "      <td>3.307442e+31</td>\n",
       "      <td>1.171650e+32</td>\n",
       "      <td>1.471603e+35</td>\n",
       "      <td>6.188783e+35</td>\n",
       "      <td>6.851918e+38</td>\n",
       "      <td>3.202940e+39</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>1.608005e+24</td>\n",
       "      <td>-3.149427e+24</td>\n",
       "      <td>5.456131e+27</td>\n",
       "      <td>-1.139403e+28</td>\n",
       "      <td>2.208910e+31</td>\n",
       "      <td>-4.475492e+31</td>\n",
       "      <td>9.733105e+34</td>\n",
       "      <td>-1.855352e+35</td>\n",
       "      <td>4.507047e+38</td>\n",
       "      <td>-8.018024e+38</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>3.383597e+24</td>\n",
       "      <td>2.039174e+24</td>\n",
       "      <td>1.168574e+28</td>\n",
       "      <td>1.143907e+28</td>\n",
       "      <td>4.772406e+31</td>\n",
       "      <td>6.001953e+31</td>\n",
       "      <td>2.114610e+35</td>\n",
       "      <td>3.106298e+35</td>\n",
       "      <td>9.831334e+38</td>\n",
       "      <td>1.595383e+39</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2.021011e+24</td>\n",
       "      <td>-6.360130e+24</td>\n",
       "      <td>6.612442e+27</td>\n",
       "      <td>-3.344089e+28</td>\n",
       "      <td>2.570834e+31</td>\n",
       "      <td>-1.736866e+32</td>\n",
       "      <td>1.091639e+35</td>\n",
       "      <td>-9.071531e+35</td>\n",
       "      <td>4.893085e+38</td>\n",
       "      <td>-4.764978e+39</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        2 Momento     3 Momento     4 Momento     5 Momento     6 Momento  \\\n",
       "0    2.404038e+25  1.495311e+26  9.201469e+28  3.183968e+30  4.765793e+32   \n",
       "1    1.024494e+25  6.213925e+26  3.657138e+28  2.389448e+30  1.628867e+32   \n",
       "2   -4.567811e+25 -8.027656e+25 -1.700597e+29 -1.498905e+30 -7.236907e+32   \n",
       "3   -3.113272e+25 -8.454881e+26 -1.217710e+29 -4.431659e+30 -5.575508e+32   \n",
       "4    3.131477e+25 -1.355277e+26  1.057623e+29  4.158094e+29  4.332257e+32   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "974  1.864395e+24  4.648701e+24  6.311105e+27  2.002565e+28  2.539999e+31   \n",
       "975  2.322414e+24  3.104447e+24  8.046245e+27  2.092607e+28  3.307442e+31   \n",
       "976  1.608005e+24 -3.149427e+24  5.456131e+27 -1.139403e+28  2.208910e+31   \n",
       "977  3.383597e+24  2.039174e+24  1.168574e+28  1.143907e+28  4.772406e+31   \n",
       "978  2.021011e+24 -6.360130e+24  6.612442e+27 -3.344089e+28  2.570834e+31   \n",
       "\n",
       "        7 Momento     8 Momento     9 Momento    10 Momento    11 Momento  \\\n",
       "0    2.851794e+34  2.984614e+36  2.231409e+38  2.070984e+40  1.698009e+42   \n",
       "1    1.105846e+34  8.002568e+35  5.568161e+37  4.125002e+39  2.923090e+41   \n",
       "2   -1.142376e+34 -3.317133e+36 -7.371632e+37 -1.594170e+40 -4.468822e+41   \n",
       "3   -2.502299e+34 -2.801839e+36 -1.479809e+38 -1.503980e+40 -8.974465e+41   \n",
       "4    7.220768e+33  2.015840e+36  6.186984e+37  1.030140e+40  4.497641e+41   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "974  9.511471e+31  1.110900e+35  4.719931e+35  5.104471e+38  2.402237e+39   \n",
       "975  1.171650e+32  1.471603e+35  6.188783e+35  6.851918e+38  3.202940e+39   \n",
       "976 -4.475492e+31  9.733105e+34 -1.855352e+35  4.507047e+38 -8.018024e+38   \n",
       "977  6.001953e+31  2.114610e+35  3.106298e+35  9.831334e+38  1.595383e+39   \n",
       "978 -1.736866e+32  1.091639e+35 -9.071531e+35  4.893085e+38 -4.764978e+39   \n",
       "\n",
       "     clase  \n",
       "0    Tumor  \n",
       "1    Tumor  \n",
       "2    Tumor  \n",
       "3    Tumor  \n",
       "4    Tumor  \n",
       "..     ...  \n",
       "974  Empty  \n",
       "975  Empty  \n",
       "976  Empty  \n",
       "977  Empty  \n",
       "978  Empty  \n",
       "\n",
       "[979 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_h"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mature-texture",
   "metadata": {},
   "source": [
    "## Local Binary Patterns (LBP)\n",
    "\n",
    "LBP mira los puntos alrededor del punto central y testear si lo puntos de alredero son mayores o menores que el punto central ,es decir es un resultado binario. Uno puede decidir el tamaño del radio y el numero de puntos a considerar alrededor. En el artículo hacen algo similar pero con una transformada de fourier invariante (Un poco mas complicada). Como consecuencia solo reducimos el numero de bins a 38. Estos serían nuestros descriptores. Se hizo con un radio de tamaño 1 y con 8 vecinos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adult-investing",
   "metadata": {},
   "source": [
    "## Ejemplo Imagen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "accessory-lithuania",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hist(lbp1):\n",
    "    n_bins = int(lbp1.max() + 1)\n",
    "    freq1 = plt.hist(lbp1.ravel(), bins=38, range=(0, 38),\n",
    "                   facecolor='0.5')\n",
    "\n",
    "    return freq1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aboriginal-leisure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YnH30O0Pr2xtd36uAfwbuAX7Umt/JYF177GO4RH1vYALGMMnPMXjD9AwGE8tbqupP22vlkwyWPL4E/FabJU9KfXcCUwzOprkb+N2hN14n1qYOd0nSwjbzsowkaRGGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SerQ/wGCWt5EO5mtzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "radius = 1\n",
    "n_points = 8 * radius\n",
    "\n",
    "lbp = local_binary_pattern(Tumor[0], n_points, radius)\n",
    "\n",
    "freq1 =hist(lbp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "international-buddy",
   "metadata": {},
   "outputs": [],
   "source": [
    "def LBP(Image):\n",
    "    radius = 1\n",
    "    n_points = 8 * radius\n",
    "\n",
    "    lbp = local_binary_pattern(Tumor[0], n_points, radius)\n",
    "\n",
    "    freq1 = hist(lbp)\n",
    "    return freq1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "logical-conflict",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La clase Tumor analizada\n",
      "La clase Stroma analizada\n",
      "La clase Complex analizada\n",
      "La clase Lympho analizada\n",
      "La clase Debris analizada\n",
      "La clase Mucosa analizada\n",
      "La clase Adipose analizada\n",
      "La clase Empty analizada\n"
     ]
    },
    {
     "data": {
      "image/png": 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YnH30O0Pr2xtd36uAfwbuAX7Umt/JYF177GO4RH1vYALGMMnPMXjD9AwGE8tbqupP22vlkwyWPL4E/FabJU9KfXcCUwzOprkb+N2hN14n1qYOd0nSwjbzsowkaRGGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SerQ/wGCWt5EO5mtzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_t_lp = pd.DataFrame(columns=['Bin'+str(x) for x in range(0,38)])\n",
    "for i in range(0,len(Todo)):\n",
    "    clase = Todo[i]\n",
    "    print(f\"La clase {Lista[i]} analizada\")\n",
    "    for img in clase:\n",
    "        lpi = LBP(img)\n",
    "        df = pd.DataFrame([lpi], columns=['Bin'+str(x) for x in range(0,38)])\n",
    "        df['clase']=Lista[i]\n",
    "        df_t_lp = pd.concat([df_t_lp,df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "optimum-basketball",
   "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>Bin0</th>\n",
       "      <th>Bin1</th>\n",
       "      <th>Bin2</th>\n",
       "      <th>Bin3</th>\n",
       "      <th>Bin4</th>\n",
       "      <th>Bin5</th>\n",
       "      <th>Bin6</th>\n",
       "      <th>Bin7</th>\n",
       "      <th>Bin8</th>\n",
       "      <th>Bin9</th>\n",
       "      <th>...</th>\n",
       "      <th>Bin29</th>\n",
       "      <th>Bin30</th>\n",
       "      <th>Bin31</th>\n",
       "      <th>Bin32</th>\n",
       "      <th>Bin33</th>\n",
       "      <th>Bin34</th>\n",
       "      <th>Bin35</th>\n",
       "      <th>Bin36</th>\n",
       "      <th>Bin37</th>\n",
       "      <th>clase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>903.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Bin0   Bin1  Bin2   Bin3   Bin4  Bin5   Bin6   Bin7  Bin8  Bin9  ...  \\\n",
       "0    903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "1    903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "2    903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "3    903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "4    903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "..     ...    ...   ...    ...    ...   ...    ...    ...   ...   ...  ...   \n",
       "974  903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "975  903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "976  903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "977  903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "978  903.0  254.0  32.0  180.0  426.0  31.0  193.0  514.0  38.0   6.0  ...   \n",
       "\n",
       "     Bin29  Bin30  Bin31  Bin32  Bin33  Bin34  Bin35  Bin36  Bin37  clase  \n",
       "0     14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Tumor  \n",
       "1     14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Tumor  \n",
       "2     14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Tumor  \n",
       "3     14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Tumor  \n",
       "4     14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Tumor  \n",
       "..     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "974   14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Empty  \n",
       "975   14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Empty  \n",
       "976   14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Empty  \n",
       "977   14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Empty  \n",
       "978   14.0  365.0  378.0   22.0    9.0    0.0    9.0   17.0    8.0  Empty  \n",
       "\n",
       "[979 rows x 39 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_lp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confidential-semester",
   "metadata": {},
   "source": [
    "# GCLM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secure-battle",
   "metadata": {},
   "source": [
    "Se utilizaron las cuatro direcciones 0,45,90 y 135, y cinco vectores de desplazamiento de 1 a 5px. Luego se hace el promedio por rotacion para hacerlo invariante a rotaciones. Se obtuvieron los estadisticos de contraste, correlationc, energia, homogeneidad, obteniendo 20 descriptores por cada imagen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "smooth-blair",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w=10\n",
    "h=10\n",
    "fig=plt.figure(figsize=(15, 15))\n",
    "columns = 2\n",
    "rows = 5\n",
    "angulos = [0,45,90,135]\n",
    "final = []\n",
    "\n",
    "\n",
    "\n",
    "def gclm(img):\n",
    "    df1 = pd.DataFrame()\n",
    "    for d in range(1,6):\n",
    "        \n",
    "        contraste = []\n",
    "        disimilaridad = []\n",
    "        homogeneidad = []\n",
    "        ASMl = []\n",
    "        energia = []\n",
    "        correlacion = []\n",
    "        matriz_angulos = []\n",
    "        \n",
    "        for j in angulos:\n",
    "            m_angulos = feature.greycomatrix(img_as_ubyte(img),angles = [j],distances=[d],symmetric=True)\n",
    "            matriz_angulos.append(m_angulos)\n",
    "\n",
    "        # Contraste\n",
    "        contrast_propiedad = []\n",
    "        dissimilarity_propiedad = []\n",
    "        homogeneity_propiedad = []\n",
    "        ASM_propiedad = []\n",
    "        energy_propiedad = []\n",
    "        correlation_propiedad = []\n",
    "\n",
    "        for k in range(0,4):\n",
    "\n",
    "            contrast= feature.greycoprops(matriz_angulos[k],'contrast')\n",
    "            dissimilarity= feature.greycoprops(matriz_angulos[k],'dissimilarity')\n",
    "            homogeneity= feature.greycoprops(matriz_angulos[k],'homogeneity')\n",
    "            ASM = feature.greycoprops(matriz_angulos[k],'ASM')\n",
    "            energy= feature.greycoprops(matriz_angulos[k],'energy')\n",
    "            correlation= feature.greycoprops(matriz_angulos[k],'correlation')\n",
    "\n",
    "            contrast_propiedad.append(contrast)\n",
    "            dissimilarity_propiedad.append(dissimilarity)\n",
    "            homogeneity_propiedad.append(homogeneity)\n",
    "            ASM_propiedad.append(ASM)\n",
    "            energy_propiedad.append(energy)\n",
    "            correlation_propiedad.append(correlation)\n",
    "\n",
    "        contraste.append(np.mean(contrast_propiedad))\n",
    "        disimilaridad.append(np.mean(dissimilarity_propiedad))\n",
    "        homogeneidad.append(np.mean(homogeneity_propiedad))\n",
    "        ASMl.append(np.mean(ASM_propiedad))\n",
    "        energia.append(np.mean(energy_propiedad))\n",
    "        correlacion.append(np.mean(correlation_propiedad))\n",
    "\n",
    "        if d == 1:\n",
    "            df1 = pd.DataFrame({f'contrast {d}':contraste,f'dissimilarity {d}':disimilaridad,f'homogeneity {d}':homogeneidad,f'ASM {d}':ASMl,f'energy {d}':energia,f'correlation {d}':correlacion})\n",
    "        else:\n",
    "            df2 = pd.DataFrame({f'contrast {d}':contraste,f'dissimilarity {d}':disimilaridad,f'homogeneity {d}':homogeneidad,f'ASM {d}':ASMl,f'energy {d}':energia,f'correlation {d}':correlacion})\n",
    "            df1 = pd.concat([df1,df2],axis=1)\n",
    "    return df1\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "atlantic-firmware",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La clase Tumor analizada\n",
      "La clase Stroma analizada\n",
      "La clase Complex analizada\n",
      "La clase Lympho analizada\n",
      "La clase Debris analizada\n",
      "La clase Mucosa analizada\n",
      "La clase Adipose analizada\n",
      "La clase Empty analizada\n"
     ]
    }
   ],
   "source": [
    "df_t_gc = pd.DataFrame()\n",
    "for i in range(0,len(Todo)):\n",
    "    clase = Todo[i]\n",
    "    print(f\"La clase {Lista[i]} analizada\")\n",
    "    for img in clase:\n",
    "        gclmi = gclm(img)\n",
    "        gclmi['clase']=Lista[i]\n",
    "        df_t_gc = pd.concat([df_t_gc,gclmi], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "affected-client",
   "metadata": {
    "scrolled": true
   },
   "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>contrast 1</th>\n",
       "      <th>dissimilarity 1</th>\n",
       "      <th>homogeneity 1</th>\n",
       "      <th>ASM 1</th>\n",
       "      <th>energy 1</th>\n",
       "      <th>correlation 1</th>\n",
       "      <th>contrast 2</th>\n",
       "      <th>dissimilarity 2</th>\n",
       "      <th>homogeneity 2</th>\n",
       "      <th>ASM 2</th>\n",
       "      <th>...</th>\n",
       "      <th>ASM 4</th>\n",
       "      <th>energy 4</th>\n",
       "      <th>correlation 4</th>\n",
       "      <th>contrast 5</th>\n",
       "      <th>dissimilarity 5</th>\n",
       "      <th>homogeneity 5</th>\n",
       "      <th>ASM 5</th>\n",
       "      <th>energy 5</th>\n",
       "      <th>correlation 5</th>\n",
       "      <th>clase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>191.684687</td>\n",
       "      <td>7.921120</td>\n",
       "      <td>0.233476</td>\n",
       "      <td>0.001957</td>\n",
       "      <td>0.044157</td>\n",
       "      <td>0.983833</td>\n",
       "      <td>499.569238</td>\n",
       "      <td>12.822576</td>\n",
       "      <td>0.170890</td>\n",
       "      <td>0.001340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.030766</td>\n",
       "      <td>0.892567</td>\n",
       "      <td>1692.072369</td>\n",
       "      <td>22.762355</td>\n",
       "      <td>0.122391</td>\n",
       "      <td>0.000855</td>\n",
       "      <td>0.029162</td>\n",
       "      <td>0.854325</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>203.095830</td>\n",
       "      <td>10.521734</td>\n",
       "      <td>0.103318</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.016740</td>\n",
       "      <td>0.909581</td>\n",
       "      <td>499.450329</td>\n",
       "      <td>16.693400</td>\n",
       "      <td>0.065742</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.012662</td>\n",
       "      <td>0.587762</td>\n",
       "      <td>1011.858673</td>\n",
       "      <td>23.689789</td>\n",
       "      <td>0.046921</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.012474</td>\n",
       "      <td>0.532178</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>145.101481</td>\n",
       "      <td>7.988929</td>\n",
       "      <td>0.156975</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.018380</td>\n",
       "      <td>0.977231</td>\n",
       "      <td>396.891131</td>\n",
       "      <td>13.323500</td>\n",
       "      <td>0.099364</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.012904</td>\n",
       "      <td>0.854489</td>\n",
       "      <td>1168.394161</td>\n",
       "      <td>22.973819</td>\n",
       "      <td>0.063548</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.012453</td>\n",
       "      <td>0.819145</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>282.152954</td>\n",
       "      <td>11.734243</td>\n",
       "      <td>0.105356</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>0.944291</td>\n",
       "      <td>698.440674</td>\n",
       "      <td>18.389282</td>\n",
       "      <td>0.069475</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.012132</td>\n",
       "      <td>0.726771</td>\n",
       "      <td>1675.987500</td>\n",
       "      <td>27.643935</td>\n",
       "      <td>0.050310</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.011875</td>\n",
       "      <td>0.672105</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>334.106565</td>\n",
       "      <td>12.416593</td>\n",
       "      <td>0.114287</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.014622</td>\n",
       "      <td>0.961245</td>\n",
       "      <td>895.461914</td>\n",
       "      <td>20.574530</td>\n",
       "      <td>0.073608</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.010603</td>\n",
       "      <td>0.786678</td>\n",
       "      <td>2208.349198</td>\n",
       "      <td>33.196751</td>\n",
       "      <td>0.049426</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.010212</td>\n",
       "      <td>0.745735</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>2.621036</td>\n",
       "      <td>0.456630</td>\n",
       "      <td>0.842452</td>\n",
       "      <td>0.181342</td>\n",
       "      <td>0.425595</td>\n",
       "      <td>0.855793</td>\n",
       "      <td>7.039149</td>\n",
       "      <td>0.776005</td>\n",
       "      <td>0.762345</td>\n",
       "      <td>0.142922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111898</td>\n",
       "      <td>0.334415</td>\n",
       "      <td>0.215120</td>\n",
       "      <td>16.239309</td>\n",
       "      <td>1.240890</td>\n",
       "      <td>0.653523</td>\n",
       "      <td>0.104404</td>\n",
       "      <td>0.323073</td>\n",
       "      <td>0.114847</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>4.452738</td>\n",
       "      <td>0.665401</td>\n",
       "      <td>0.781487</td>\n",
       "      <td>0.114930</td>\n",
       "      <td>0.338613</td>\n",
       "      <td>0.791168</td>\n",
       "      <td>11.219356</td>\n",
       "      <td>1.095328</td>\n",
       "      <td>0.683820</td>\n",
       "      <td>0.083261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063362</td>\n",
       "      <td>0.251668</td>\n",
       "      <td>0.114865</td>\n",
       "      <td>18.770469</td>\n",
       "      <td>1.586698</td>\n",
       "      <td>0.566269</td>\n",
       "      <td>0.059468</td>\n",
       "      <td>0.243843</td>\n",
       "      <td>0.065382</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.614844</td>\n",
       "      <td>0.311076</td>\n",
       "      <td>0.864377</td>\n",
       "      <td>0.188445</td>\n",
       "      <td>0.433828</td>\n",
       "      <td>0.801824</td>\n",
       "      <td>1.280202</td>\n",
       "      <td>0.522706</td>\n",
       "      <td>0.785472</td>\n",
       "      <td>0.147533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114469</td>\n",
       "      <td>0.338179</td>\n",
       "      <td>0.323915</td>\n",
       "      <td>2.288974</td>\n",
       "      <td>0.840047</td>\n",
       "      <td>0.670631</td>\n",
       "      <td>0.106563</td>\n",
       "      <td>0.326388</td>\n",
       "      <td>0.260025</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>2.542426</td>\n",
       "      <td>0.454815</td>\n",
       "      <td>0.843523</td>\n",
       "      <td>0.152748</td>\n",
       "      <td>0.390510</td>\n",
       "      <td>0.950481</td>\n",
       "      <td>7.766307</td>\n",
       "      <td>0.799419</td>\n",
       "      <td>0.761552</td>\n",
       "      <td>0.115940</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086340</td>\n",
       "      <td>0.293694</td>\n",
       "      <td>0.621218</td>\n",
       "      <td>25.325838</td>\n",
       "      <td>1.426008</td>\n",
       "      <td>0.637520</td>\n",
       "      <td>0.079298</td>\n",
       "      <td>0.281565</td>\n",
       "      <td>0.519554</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>1.316912</td>\n",
       "      <td>0.527677</td>\n",
       "      <td>0.784196</td>\n",
       "      <td>0.105201</td>\n",
       "      <td>0.323888</td>\n",
       "      <td>0.778375</td>\n",
       "      <td>2.828420</td>\n",
       "      <td>0.871494</td>\n",
       "      <td>0.678132</td>\n",
       "      <td>0.074374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056346</td>\n",
       "      <td>0.237304</td>\n",
       "      <td>0.260300</td>\n",
       "      <td>4.733707</td>\n",
       "      <td>1.296940</td>\n",
       "      <td>0.555433</td>\n",
       "      <td>0.052754</td>\n",
       "      <td>0.229665</td>\n",
       "      <td>0.203890</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     contrast 1  dissimilarity 1  homogeneity 1     ASM 1  energy 1  \\\n",
       "0    191.684687         7.921120       0.233476  0.001957  0.044157   \n",
       "1    203.095830        10.521734       0.103318  0.000281  0.016740   \n",
       "2    145.101481         7.988929       0.156975  0.000339  0.018380   \n",
       "3    282.152954        11.734243       0.105356  0.000250  0.015786   \n",
       "4    334.106565        12.416593       0.114287  0.000214  0.014622   \n",
       "..          ...              ...            ...       ...       ...   \n",
       "974    2.621036         0.456630       0.842452  0.181342  0.425595   \n",
       "975    4.452738         0.665401       0.781487  0.114930  0.338613   \n",
       "976    0.614844         0.311076       0.864377  0.188445  0.433828   \n",
       "977    2.542426         0.454815       0.843523  0.152748  0.390510   \n",
       "978    1.316912         0.527677       0.784196  0.105201  0.323888   \n",
       "\n",
       "     correlation 1  contrast 2  dissimilarity 2  homogeneity 2     ASM 2  ...  \\\n",
       "0         0.983833  499.569238        12.822576       0.170890  0.001340  ...   \n",
       "1         0.909581  499.450329        16.693400       0.065742  0.000192  ...   \n",
       "2         0.977231  396.891131        13.323500       0.099364  0.000223  ...   \n",
       "3         0.944291  698.440674        18.389282       0.069475  0.000176  ...   \n",
       "4         0.961245  895.461914        20.574530       0.073608  0.000148  ...   \n",
       "..             ...         ...              ...            ...       ...  ...   \n",
       "974       0.855793    7.039149         0.776005       0.762345  0.142922  ...   \n",
       "975       0.791168   11.219356         1.095328       0.683820  0.083261  ...   \n",
       "976       0.801824    1.280202         0.522706       0.785472  0.147533  ...   \n",
       "977       0.950481    7.766307         0.799419       0.761552  0.115940  ...   \n",
       "978       0.778375    2.828420         0.871494       0.678132  0.074374  ...   \n",
       "\n",
       "        ASM 4  energy 4  correlation 4   contrast 5  dissimilarity 5  \\\n",
       "0    0.000949  0.030766       0.892567  1692.072369        22.762355   \n",
       "1    0.000160  0.012662       0.587762  1011.858673        23.689789   \n",
       "2    0.000167  0.012904       0.854489  1168.394161        22.973819   \n",
       "3    0.000147  0.012132       0.726771  1675.987500        27.643935   \n",
       "4    0.000112  0.010603       0.786678  2208.349198        33.196751   \n",
       "..        ...       ...            ...          ...              ...   \n",
       "974  0.111898  0.334415       0.215120    16.239309         1.240890   \n",
       "975  0.063362  0.251668       0.114865    18.770469         1.586698   \n",
       "976  0.114469  0.338179       0.323915     2.288974         0.840047   \n",
       "977  0.086340  0.293694       0.621218    25.325838         1.426008   \n",
       "978  0.056346  0.237304       0.260300     4.733707         1.296940   \n",
       "\n",
       "     homogeneity 5     ASM 5  energy 5  correlation 5  clase  \n",
       "0         0.122391  0.000855  0.029162       0.854325  Tumor  \n",
       "1         0.046921  0.000156  0.012474       0.532178  Tumor  \n",
       "2         0.063548  0.000155  0.012453       0.819145  Tumor  \n",
       "3         0.050310  0.000141  0.011875       0.672105  Tumor  \n",
       "4         0.049426  0.000104  0.010212       0.745735  Tumor  \n",
       "..             ...       ...       ...            ...    ...  \n",
       "974       0.653523  0.104404  0.323073       0.114847  Empty  \n",
       "975       0.566269  0.059468  0.243843       0.065382  Empty  \n",
       "976       0.670631  0.106563  0.326388       0.260025  Empty  \n",
       "977       0.637520  0.079298  0.281565       0.519554  Empty  \n",
       "978       0.555433  0.052754  0.229665       0.203890  Empty  \n",
       "\n",
       "[979 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_gc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comic-alcohol",
   "metadata": {},
   "source": [
    "## Filtros Gabor\n",
    "\n",
    "Se aplico filtros de gabor en seis direcciones 0,30,60,90,120 y 150 y seis wavelenghts 2,4,6,8,10,12 como en el articulo. Para estar el descriptor de la textura invariane respecto a la rotacion, se promedio los resultados obtenidos del filtro de gabor sobre todas las iteraciones obteniendo 6 descriptores por imagen. Este ya de por si es un algoritmo demorado, por eso no decidi poner mas descriptores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "double-depth",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_feats(image, kernels):\n",
    "    feats = np.zeros((len(kernels), 2), dtype=np.double)\n",
    "    for k, kernel in enumerate(kernels):\n",
    "        filtered = ndi.convolve(image, kernel, mode='wrap')\n",
    "        feats[k, 0] = filtered.mean()\n",
    "        feats[k, 1] = filtered.var()\n",
    "    return feats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-drill",
   "metadata": {},
   "source": [
    "Generamos los kernels que vamos a utilizar para todas las imagenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "theoretical-popularity",
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = 1\n",
    "kernels = []\n",
    "for wave in (range(2,14,2)):\n",
    "    for theta in range(6):\n",
    "        theta = theta*30\n",
    "        frequency = 1/wave\n",
    "        kernel = np.real(gabor_kernel(frequency, theta=theta,\n",
    "                                          sigma_x=sigma, sigma_y=sigma))\n",
    "        kernels.append(kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "mobile-stationery",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gabor_method(Image,kernels):\n",
    "    gabors = compute_feats(Image,kernels)\n",
    "    gabors = gabors[:,0]\n",
    "    invariant = []\n",
    "    for i in range(0,36+6,6):\n",
    "        inv = np.mean(gabors[i:i+6])\n",
    "        invariant.append(inv)\n",
    "    invariant = np.array(invariant)\n",
    "    invariant = invariant[np.logical_not(np.isnan(invariant))]\n",
    "    return invariant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "separate-rocket",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La clase Tumor analizada\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miguel Gutierrez\\AppData\\Roaming\\Python\\Python38\\site-packages\\numpy\\core\\fromnumeric.py:3372: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "C:\\Users\\Miguel Gutierrez\\AppData\\Roaming\\Python\\Python38\\site-packages\\numpy\\core\\_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La clase Stroma analizada\n",
      "La clase Complex analizada\n",
      "La clase Lympho analizada\n",
      "La clase Debris analizada\n",
      "La clase Mucosa analizada\n",
      "La clase Adipose analizada\n",
      "La clase Empty analizada\n"
     ]
    }
   ],
   "source": [
    "df_t_g = pd.DataFrame(columns=['Wavelengts'+str(x) for x in range(2,14,2)])\n",
    "for i in range(0,len(Todo)):\n",
    "    clase = Todo[i]\n",
    "    print(f\"La clase {Lista[i]} analizada\")\n",
    "    for img in clase:\n",
    "        invariant = gabor_method(img,kernels)\n",
    "        df = pd.DataFrame([invariant], columns=['Wavelengts'+str(x) for x in range(2,14,2)])\n",
    "        df['clase']=Lista[i]\n",
    "        df_t_g = pd.concat([df_t_g,df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "biblical-promotion",
   "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>Wavelengts2</th>\n",
       "      <th>Wavelengts4</th>\n",
       "      <th>Wavelengts6</th>\n",
       "      <th>Wavelengts8</th>\n",
       "      <th>Wavelengts10</th>\n",
       "      <th>Wavelengts12</th>\n",
       "      <th>clase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003386</td>\n",
       "      <td>0.099639</td>\n",
       "      <td>0.197890</td>\n",
       "      <td>0.251517</td>\n",
       "      <td>0.281003</td>\n",
       "      <td>0.298430</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002813</td>\n",
       "      <td>0.082783</td>\n",
       "      <td>0.164412</td>\n",
       "      <td>0.208967</td>\n",
       "      <td>0.233465</td>\n",
       "      <td>0.247944</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002972</td>\n",
       "      <td>0.087456</td>\n",
       "      <td>0.173694</td>\n",
       "      <td>0.220764</td>\n",
       "      <td>0.246644</td>\n",
       "      <td>0.261941</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003410</td>\n",
       "      <td>0.100345</td>\n",
       "      <td>0.199291</td>\n",
       "      <td>0.253297</td>\n",
       "      <td>0.282992</td>\n",
       "      <td>0.300542</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.124077</td>\n",
       "      <td>0.246424</td>\n",
       "      <td>0.313204</td>\n",
       "      <td>0.349921</td>\n",
       "      <td>0.371623</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>0.009434</td>\n",
       "      <td>0.277597</td>\n",
       "      <td>0.551326</td>\n",
       "      <td>0.700732</td>\n",
       "      <td>0.782880</td>\n",
       "      <td>0.831433</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.009433</td>\n",
       "      <td>0.277547</td>\n",
       "      <td>0.551225</td>\n",
       "      <td>0.700604</td>\n",
       "      <td>0.782737</td>\n",
       "      <td>0.831281</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.009444</td>\n",
       "      <td>0.277881</td>\n",
       "      <td>0.551889</td>\n",
       "      <td>0.701448</td>\n",
       "      <td>0.783680</td>\n",
       "      <td>0.832283</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>0.009439</td>\n",
       "      <td>0.277731</td>\n",
       "      <td>0.551590</td>\n",
       "      <td>0.701068</td>\n",
       "      <td>0.783256</td>\n",
       "      <td>0.831833</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.009362</td>\n",
       "      <td>0.275483</td>\n",
       "      <td>0.547126</td>\n",
       "      <td>0.695394</td>\n",
       "      <td>0.776917</td>\n",
       "      <td>0.825100</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Wavelengts2  Wavelengts4  Wavelengts6  Wavelengts8  Wavelengts10  \\\n",
       "0       0.003386     0.099639     0.197890     0.251517      0.281003   \n",
       "1       0.002813     0.082783     0.164412     0.208967      0.233465   \n",
       "2       0.002972     0.087456     0.173694     0.220764      0.246644   \n",
       "3       0.003410     0.100345     0.199291     0.253297      0.282992   \n",
       "4       0.004217     0.124077     0.246424     0.313204      0.349921   \n",
       "..           ...          ...          ...          ...           ...   \n",
       "974     0.009434     0.277597     0.551326     0.700732      0.782880   \n",
       "975     0.009433     0.277547     0.551225     0.700604      0.782737   \n",
       "976     0.009444     0.277881     0.551889     0.701448      0.783680   \n",
       "977     0.009439     0.277731     0.551590     0.701068      0.783256   \n",
       "978     0.009362     0.275483     0.547126     0.695394      0.776917   \n",
       "\n",
       "     Wavelengts12  clase  \n",
       "0        0.298430  Tumor  \n",
       "1        0.247944  Tumor  \n",
       "2        0.261941  Tumor  \n",
       "3        0.300542  Tumor  \n",
       "4        0.371623  Tumor  \n",
       "..            ...    ...  \n",
       "974      0.831433  Empty  \n",
       "975      0.831281  Empty  \n",
       "976      0.832283  Empty  \n",
       "977      0.831833  Empty  \n",
       "978      0.825100  Empty  \n",
       "\n",
       "[979 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "operational-pressure",
   "metadata": {},
   "source": [
    "## HOG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "running-prize",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La clase Tumor analizada\n",
      "La clase Stroma analizada\n",
      "La clase Complex analizada\n",
      "La clase Lympho analizada\n",
      "La clase Debris analizada\n",
      "La clase Mucosa analizada\n",
      "La clase Adipose analizada\n",
      "La clase Empty analizada\n"
     ]
    }
   ],
   "source": [
    "df_t_ho = pd.DataFrame(columns=['hog'+str(x) for x in range(1,9)])\n",
    "for i in range(0,len(Todo)):\n",
    "    clase = Todo[i]\n",
    "    print(f\"La clase {Lista[i]} analizada\")\n",
    "    for img in clase:\n",
    "        fd, hog_image = hog(img, orientations=8, pixels_per_cell=(100, 100),\n",
    "                    cells_per_block=(1, 1), visualize=True,feature_vector=True)\n",
    "        df = pd.DataFrame([fd], columns=['hog'+str(x) for x in range(1,9)])\n",
    "        df['clase']=Lista[i]\n",
    "        df_t_ho = pd.concat([df_t_ho,df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "moderate-particle",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hog1</th>\n",
       "      <th>hog2</th>\n",
       "      <th>hog3</th>\n",
       "      <th>hog4</th>\n",
       "      <th>hog5</th>\n",
       "      <th>hog6</th>\n",
       "      <th>hog7</th>\n",
       "      <th>hog8</th>\n",
       "      <th>clase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.400346</td>\n",
       "      <td>0.400346</td>\n",
       "      <td>0.368167</td>\n",
       "      <td>0.292895</td>\n",
       "      <td>0.286753</td>\n",
       "      <td>0.292874</td>\n",
       "      <td>0.360322</td>\n",
       "      <td>0.400346</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.361299</td>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.301797</td>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.303766</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.351050</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.409298</td>\n",
       "      <td>0.320179</td>\n",
       "      <td>0.409298</td>\n",
       "      <td>0.285847</td>\n",
       "      <td>0.409298</td>\n",
       "      <td>0.271392</td>\n",
       "      <td>0.409298</td>\n",
       "      <td>0.268370</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>0.435807</td>\n",
       "      <td>0.256034</td>\n",
       "      <td>0.435807</td>\n",
       "      <td>0.177085</td>\n",
       "      <td>0.435807</td>\n",
       "      <td>0.298250</td>\n",
       "      <td>0.435807</td>\n",
       "      <td>0.233285</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.313174</td>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.340003</td>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.344325</td>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.260582</td>\n",
       "      <td>Empty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         hog1      hog2      hog3      hog4      hog5      hog6      hog7  \\\n",
       "0    0.400346  0.400346  0.368167  0.292895  0.286753  0.292874  0.360322   \n",
       "1    0.353553  0.353553  0.353553  0.353553  0.353553  0.353553  0.353553   \n",
       "2    0.353553  0.353553  0.353553  0.353553  0.353553  0.353553  0.353553   \n",
       "3    0.353553  0.353553  0.353553  0.353553  0.353553  0.353553  0.353553   \n",
       "4    0.353553  0.353553  0.353553  0.353553  0.353553  0.353553  0.353553   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "974  0.371517  0.361299  0.371517  0.301797  0.371517  0.366071  0.371517   \n",
       "975  0.353910  0.353910  0.353910  0.353910  0.353910  0.353910  0.353910   \n",
       "976  0.409298  0.320179  0.409298  0.285847  0.409298  0.271392  0.409298   \n",
       "977  0.435807  0.256034  0.435807  0.177085  0.435807  0.298250  0.435807   \n",
       "978  0.387252  0.313174  0.387252  0.340003  0.387252  0.344325  0.387252   \n",
       "\n",
       "         hog8  clase  \n",
       "0    0.400346  Tumor  \n",
       "1    0.353553  Tumor  \n",
       "2    0.353553  Tumor  \n",
       "3    0.353553  Tumor  \n",
       "4    0.353553  Tumor  \n",
       "..        ...    ...  \n",
       "974  0.303766  Empty  \n",
       "975  0.351050  Empty  \n",
       "976  0.268370  Empty  \n",
       "977  0.233285  Empty  \n",
       "978  0.260582  Empty  \n",
       "\n",
       "[979 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_ho"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "royal-lancaster",
   "metadata": {},
   "source": [
    "# Tecnicas de Machine Learning\n",
    "\n",
    "Ahora utilizaremos los modelos plantados por el articulo para encontrar cual es el mejor prediciendo uniendo todos los datasets. Aca si nos desviamos del articulo original."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "immune-burns",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total = pd.DataFrame()\n",
    "df_total = pd.concat([df_t_l,df_t_h,df_t_lp,df_t_gc,df_t_g,df_t_ho],axis=1)\n",
    "df_total = df_total.loc[:,~df_total.columns.duplicated()] # Elimina columnas repetidas -> clase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aware-representative",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>media</th>\n",
       "      <th>desviacion estándar</th>\n",
       "      <th>skewness</th>\n",
       "      <th>curtosis</th>\n",
       "      <th>minimo</th>\n",
       "      <th>maximo</th>\n",
       "      <th>contraste</th>\n",
       "      <th>clase</th>\n",
       "      <th>2 Momento</th>\n",
       "      <th>3 Momento</th>\n",
       "      <th>...</th>\n",
       "      <th>Wavelengts10</th>\n",
       "      <th>Wavelengts12</th>\n",
       "      <th>hog1</th>\n",
       "      <th>hog2</th>\n",
       "      <th>hog3</th>\n",
       "      <th>hog4</th>\n",
       "      <th>hog5</th>\n",
       "      <th>hog6</th>\n",
       "      <th>hog7</th>\n",
       "      <th>hog8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.342303</td>\n",
       "      <td>0.302688</td>\n",
       "      <td>1.038178</td>\n",
       "      <td>-0.597964</td>\n",
       "      <td>0.035210</td>\n",
       "      <td>0.950916</td>\n",
       "      <td>0.083930</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>2.404038e+25</td>\n",
       "      <td>1.495311e+26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.281003</td>\n",
       "      <td>0.298430</td>\n",
       "      <td>0.400346</td>\n",
       "      <td>0.400346</td>\n",
       "      <td>0.368167</td>\n",
       "      <td>0.292895</td>\n",
       "      <td>0.286753</td>\n",
       "      <td>0.292874</td>\n",
       "      <td>0.360322</td>\n",
       "      <td>0.400346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.284395</td>\n",
       "      <td>0.132290</td>\n",
       "      <td>1.533068</td>\n",
       "      <td>3.513061</td>\n",
       "      <td>0.037509</td>\n",
       "      <td>0.933012</td>\n",
       "      <td>0.017200</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>1.024494e+25</td>\n",
       "      <td>6.213925e+26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233465</td>\n",
       "      <td>0.247944</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.300449</td>\n",
       "      <td>0.220826</td>\n",
       "      <td>1.110602</td>\n",
       "      <td>0.225319</td>\n",
       "      <td>0.021488</td>\n",
       "      <td>0.917824</td>\n",
       "      <td>0.046497</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>-4.567811e+25</td>\n",
       "      <td>-8.027656e+25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.246644</td>\n",
       "      <td>0.261941</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.344726</td>\n",
       "      <td>0.197159</td>\n",
       "      <td>1.586735</td>\n",
       "      <td>1.801484</td>\n",
       "      <td>0.067795</td>\n",
       "      <td>0.969140</td>\n",
       "      <td>0.037417</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>-3.113272e+25</td>\n",
       "      <td>-8.454881e+26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.282992</td>\n",
       "      <td>0.300542</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.426256</td>\n",
       "      <td>0.257157</td>\n",
       "      <td>0.493997</td>\n",
       "      <td>-1.126756</td>\n",
       "      <td>0.030187</td>\n",
       "      <td>0.939517</td>\n",
       "      <td>0.062028</td>\n",
       "      <td>Tumor</td>\n",
       "      <td>3.131477e+25</td>\n",
       "      <td>-1.355277e+26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.349921</td>\n",
       "      <td>0.371623</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>0.953664</td>\n",
       "      <td>0.011838</td>\n",
       "      <td>-14.605011</td>\n",
       "      <td>297.483326</td>\n",
       "      <td>0.586165</td>\n",
       "      <td>0.989910</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>Empty</td>\n",
       "      <td>1.864395e+24</td>\n",
       "      <td>4.648701e+24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.782880</td>\n",
       "      <td>0.831433</td>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.361299</td>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.301797</td>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.303766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.953490</td>\n",
       "      <td>0.012690</td>\n",
       "      <td>-11.839159</td>\n",
       "      <td>195.084762</td>\n",
       "      <td>0.663456</td>\n",
       "      <td>0.982364</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>Empty</td>\n",
       "      <td>2.322414e+24</td>\n",
       "      <td>3.104447e+24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.782737</td>\n",
       "      <td>0.831281</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.353910</td>\n",
       "      <td>0.351050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.954638</td>\n",
       "      <td>0.004596</td>\n",
       "      <td>-3.201485</td>\n",
       "      <td>48.847362</td>\n",
       "      <td>0.843703</td>\n",
       "      <td>0.979827</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>Empty</td>\n",
       "      <td>1.608005e+24</td>\n",
       "      <td>-3.149427e+24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783680</td>\n",
       "      <td>0.832283</td>\n",
       "      <td>0.409298</td>\n",
       "      <td>0.320179</td>\n",
       "      <td>0.409298</td>\n",
       "      <td>0.285847</td>\n",
       "      <td>0.409298</td>\n",
       "      <td>0.271392</td>\n",
       "      <td>0.409298</td>\n",
       "      <td>0.268370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>0.954122</td>\n",
       "      <td>0.019744</td>\n",
       "      <td>-13.293419</td>\n",
       "      <td>210.103112</td>\n",
       "      <td>0.511363</td>\n",
       "      <td>0.985720</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>Empty</td>\n",
       "      <td>3.383597e+24</td>\n",
       "      <td>2.039174e+24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783256</td>\n",
       "      <td>0.831833</td>\n",
       "      <td>0.435807</td>\n",
       "      <td>0.256034</td>\n",
       "      <td>0.435807</td>\n",
       "      <td>0.177085</td>\n",
       "      <td>0.435807</td>\n",
       "      <td>0.298250</td>\n",
       "      <td>0.435807</td>\n",
       "      <td>0.233285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.946399</td>\n",
       "      <td>0.006527</td>\n",
       "      <td>-3.915970</td>\n",
       "      <td>58.486529</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>Empty</td>\n",
       "      <td>2.021011e+24</td>\n",
       "      <td>-6.360130e+24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.776917</td>\n",
       "      <td>0.825100</td>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.313174</td>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.340003</td>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.344325</td>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.260582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        media  desviacion estándar   skewness    curtosis    minimo    maximo  \\\n",
       "0    0.342303             0.302688   1.038178   -0.597964  0.035210  0.950916   \n",
       "1    0.284395             0.132290   1.533068    3.513061  0.037509  0.933012   \n",
       "2    0.300449             0.220826   1.110602    0.225319  0.021488  0.917824   \n",
       "3    0.344726             0.197159   1.586735    1.801484  0.067795  0.969140   \n",
       "4    0.426256             0.257157   0.493997   -1.126756  0.030187  0.939517   \n",
       "..        ...                  ...        ...         ...       ...       ...   \n",
       "974  0.953664             0.011838 -14.605011  297.483326  0.586165  0.989910   \n",
       "975  0.953490             0.012690 -11.839159  195.084762  0.663456  0.982364   \n",
       "976  0.954638             0.004596  -3.201485   48.847362  0.843703  0.979827   \n",
       "977  0.954122             0.019744 -13.293419  210.103112  0.511363  0.985720   \n",
       "978  0.946399             0.006527  -3.915970   58.486529  0.779000  0.980392   \n",
       "\n",
       "     contraste  clase     2 Momento     3 Momento  ...  Wavelengts10  \\\n",
       "0     0.083930  Tumor  2.404038e+25  1.495311e+26  ...      0.281003   \n",
       "1     0.017200  Tumor  1.024494e+25  6.213925e+26  ...      0.233465   \n",
       "2     0.046497  Tumor -4.567811e+25 -8.027656e+25  ...      0.246644   \n",
       "3     0.037417  Tumor -3.113272e+25 -8.454881e+26  ...      0.282992   \n",
       "4     0.062028  Tumor  3.131477e+25 -1.355277e+26  ...      0.349921   \n",
       "..         ...    ...           ...           ...  ...           ...   \n",
       "974   0.000140  Empty  1.864395e+24  4.648701e+24  ...      0.782880   \n",
       "975   0.000161  Empty  2.322414e+24  3.104447e+24  ...      0.782737   \n",
       "976   0.000021  Empty  1.608005e+24 -3.149427e+24  ...      0.783680   \n",
       "977   0.000390  Empty  3.383597e+24  2.039174e+24  ...      0.783256   \n",
       "978   0.000043  Empty  2.021011e+24 -6.360130e+24  ...      0.776917   \n",
       "\n",
       "     Wavelengts12      hog1      hog2      hog3      hog4      hog5      hog6  \\\n",
       "0        0.298430  0.400346  0.400346  0.368167  0.292895  0.286753  0.292874   \n",
       "1        0.247944  0.353553  0.353553  0.353553  0.353553  0.353553  0.353553   \n",
       "2        0.261941  0.353553  0.353553  0.353553  0.353553  0.353553  0.353553   \n",
       "3        0.300542  0.353553  0.353553  0.353553  0.353553  0.353553  0.353553   \n",
       "4        0.371623  0.353553  0.353553  0.353553  0.353553  0.353553  0.353553   \n",
       "..            ...       ...       ...       ...       ...       ...       ...   \n",
       "974      0.831433  0.371517  0.361299  0.371517  0.301797  0.371517  0.366071   \n",
       "975      0.831281  0.353910  0.353910  0.353910  0.353910  0.353910  0.353910   \n",
       "976      0.832283  0.409298  0.320179  0.409298  0.285847  0.409298  0.271392   \n",
       "977      0.831833  0.435807  0.256034  0.435807  0.177085  0.435807  0.298250   \n",
       "978      0.825100  0.387252  0.313174  0.387252  0.340003  0.387252  0.344325   \n",
       "\n",
       "         hog7      hog8  \n",
       "0    0.360322  0.400346  \n",
       "1    0.353553  0.353553  \n",
       "2    0.353553  0.353553  \n",
       "3    0.353553  0.353553  \n",
       "4    0.353553  0.353553  \n",
       "..        ...       ...  \n",
       "974  0.371517  0.303766  \n",
       "975  0.353910  0.351050  \n",
       "976  0.409298  0.268370  \n",
       "977  0.435807  0.233285  \n",
       "978  0.387252  0.260582  \n",
       "\n",
       "[979 rows x 100 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "committed-marathon",
   "metadata": {},
   "source": [
    "Al probrar todos los descriptores suscede que la precision de las predicciones disminuye significativamente incluso a 10 %."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stable-reducing",
   "metadata": {},
   "source": [
    "Seleccionamos nuestras variable dependiente e independientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "guided-innocent",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_t_l['clase']\n",
    "X = df_t_l.copy()\n",
    "X = X.drop(columns=['clase'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "celtic-window",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total.isnull().sum().sum() # No tiene NaNs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "original-replica",
   "metadata": {},
   "source": [
    "Probamos los modelos que funcionen correctamente"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "divine-glenn",
   "metadata": {},
   "source": [
    "## K-NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "delayed-commons",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "offshore-details",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "#scaler = MinMaxScaler()\n",
    "#X_train = scaler.fit_transform(X_train)\n",
    "#X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "palestinian-tattoo",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 1.00\n",
      "Accuracy of K-NN classifier on test set: 0.60\n"
     ]
    }
   ],
   "source": [
    "n_neighbors = 1\n",
    " \n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors)\n",
    "knn.fit(X_train, y_train)\n",
    "print('Accuracy of K-NN classifier on training set: {:.2f}'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of K-NN classifier on test set: {:.2f}'\n",
    "     .format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continent-breathing",
   "metadata": {},
   "source": [
    "## SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "confident-aircraft",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import plot_confusion_matrix, accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "wireless-tuition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of linSVM classifier: 0.6204081632653061\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(random_state=42, kernel='linear')\n",
    "svm = svm.fit(X_train, y_train)\n",
    "\n",
    "y_pred = svm.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy of linSVM classifier: {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floral-toilet",
   "metadata": {},
   "source": [
    "## Ensemble Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "subjective-ridge",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from collections import OrderedDict\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "everyday-joshua",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7714285714285715\n",
      "Error rate:  0.22857142857142854\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth=7, random_state=0)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "preds = clf.predict(X_test)\n",
    "accuracy = accuracy_score(y_test,preds)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Error rate: \", 1 - accuracy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classical-kitty",
   "metadata": {},
   "source": [
    "## Creamos el pipeline para encontrar el mejor modelo en cada base de datos creada anteriormente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "designed-dublin",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mejor_modelo(basedatos):\n",
    "    y = basedatos['clase']\n",
    "    X = basedatos.copy()\n",
    "    X = X.drop(columns=['clase'])\n",
    "    scaler = preprocessing.StandardScaler().fit(X)\n",
    "    X = scaler.transform(X)\n",
    "    \n",
    "    \n",
    "    Modelos = [\"K-NN\",\"SVC\",\"Random Fortest\"]\n",
    "    Accuracies = []\n",
    "    \n",
    "    # K-NN\n",
    "    # X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "    \n",
    "    n_neighbors = 1\n",
    " \n",
    "    knn = KNeighborsClassifier(n_neighbors)\n",
    "    \n",
    "    Accuracies.append(cross_val_score(knn,X,y).mean())\n",
    "    \n",
    "    # SVC \n",
    "    svm = SVC(random_state=0, kernel='linear')\n",
    "\n",
    "    Accuracies.append(cross_val_score(svm,X,y).mean())\n",
    "    \n",
    "    # Random Forest \n",
    "    clf = RandomForestClassifier(max_depth=7, random_state=0)\n",
    "    # clf.fit(X_train, y_train)\n",
    "\n",
    "    #preds = clf.predict(X_test)\n",
    "    Accuracies.append(cross_val_score(svm,X,y).mean())\n",
    "    \n",
    "    # Selecciona el mejor\n",
    "    maxi = Accuracies.index(max(Accuracies))\n",
    "    \n",
    "    return Modelos[maxi],Accuracies[maxi]\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "willing-veteran",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('SVC', 0.6874149659863945)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mejor_modelo(df_t_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "humanitarian-protection",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El mejor modelo de la base de datos Low Histogram es SVC con un accuracy de 0.6874149659863945\n",
      "El mejor modelo de la base de datos High Histogram es K-NN con un accuracy de 0.2523024594453166\n",
      "El mejor modelo de la base de datos Local Binary Patters es SVC con un accuracy de 0.15424385138670854\n",
      "El mejor modelo de la base de datos GLCM es SVC con un accuracy de 0.608770277341706\n",
      "El mejor modelo de la base de datos Gabor Filters es SVC con un accuracy de 0.4932810047095761\n",
      "El mejor modelo de la base de datos HOG es SVC con un accuracy de 0.28190999476713763\n"
     ]
    }
   ],
   "source": [
    "Bases = [df_t_l,df_t_h,df_t_lp,df_t_gc,df_t_g,df_t_ho]\n",
    "Basess = [\"Low Histogram\",\"High Histogram\",\"Local Binary Patters\",\"GLCM\",\"Gabor Filters\",\"HOG\"]\n",
    "\n",
    "for i,base in enumerate(Bases):\n",
    "    Modelo,Accuracy = mejor_modelo(base)\n",
    "    print(f\"El mejor modelo de la base de datos {Basess[i]} es {Modelo} con un accuracy de {Accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollywood-tongue",
   "metadata": {},
   "source": [
    "Se obtiene un acurracy de 0.68 con validacion cruzada esto pues no utilizamos toda la base de datos ya que toma su tiempo. Esto es un buen accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "related-fusion",
   "metadata": {},
   "source": [
    "Luego hacemos un PCA sobre la base de datos total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "useful-image",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=0.790):\n",
      "{'logistic__C': 10000.0, 'pca__n_components': 64}\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tbdu2Zf311y86hiRJTeYYZEmSpBbutw+9wm8feqXoGHXDHmRJkqQW7tUJ04qOUFcskCVJklq43xy6XdER6opDLCRJkqQKFsiSJEkt3K8fGMGvHxhRdIy64RALSZKkFm78lJlFR6grFsiSJEkt3C+/sE3REeqKQywkSZKkChbIkiRJLdzP7nuJn933UtEx6oZDLCRJklq4ydNnFx2hrlggS5IktXA//dzWRUeoKw6xkCRJkipYIEuSJLVw5909nPPuHl50jLrhEAtJkqQWbuac+UVHqCsWyJIkSS3cjw7csugIdcUhFpIkSVIFC2RJkqQW7pw7h3HOncOKjlE3LJAlSZKkCo5BliRJauHO2m+LoiPUFXuQJUmSpAoWyJIkSS3cD297gR/e9kLRMeqGQywkSZJauA5t7TOtZIEsSZLUwv1g382LjlBX/HVBkiRJqmCBLEmS1MKdfsvznH7L80XHqBsOsZAkSWrhunVqV3SEumKBLEmS1MJ9b59Ni45QVxxiIUmSJFWwQJYkSWrhTrnpOU656bmiY9QNh1hIkiS1cH1W71B0hLpigSxJktTCnbz3JkVHqCsOsZAkSZIqWCBLkiS1cCfe8Awn3vBM0THqhkMsJEmSWrgNeq1WdIS6YoEsSZLUwn3n4wOKjlBXHGIhSZIkVbBAliRJauGOv/5pjr/+6aJj1A2HWEiSJLVwm/fpWnSEumKBLEmS1MJ9c/eNio5QVxxiIUmSJFWwQJYkSWrhjrtmCMddM6ToGHXDIRaSJEkt3Pb9uhUdoa5YIEuSJLVwx350w6Ij1BWHWEiSJEkV6q5Ajoh9ImJERIyMiNOW0O7zEZERMWhl5pMkSWpuvnb1U3zt6qeKjlE36mqIRUS0Bi4G9gLGAk9FxB2ZObxRuy7ACcB/V35KSZKk5mXXDXsWHaGu1FWBDOwIjMzMVwEi4gbgAGB4o3Y/An4GnLpy40mSJDU/X91t/aIj1JV6G2KxDjCmYnlsed0CEbE90Dcz717SgSLi2IgYHBGDJ0yYUP2kkiRJapbqrUBeoohoBfwa+O7S2mbmpZk5KDMH9erVq/bhJEmSVlFHXvEkR17xZNEx6ka9DbEYB/StWF63vK5BF2BL4JGIAFgLuCMi9s/MwSstpSRJUjPyic16Fx2hrtRbgfwUMCAi1qdUGB8KfLFhY2ZOARaMIo+IR4BTLI4lSZKW35d26V90hLpSV0MsMnMucDxwP/Ai8NfMHBYR50bE/sWmkyRJUktQbz3IZOY9wD2N1p25mLa7r4xMkiRJzdnhlz8BwHVf27ngJPWh7gpkSZIkrVyf2bpP0RHqigWyJElSC3fYjusVHaGu1NUYZEmSJKloFsiSJEkt3CGXPM4hlzxedIy64RALSZKkFu6ggesWHaGuWCBLkiS1cF8Y1HfpjVoQh1hIkiS1cHPmzWfOvPlFx6gbFsiSJEkt3BGX/5cjLv9v0THqhkMsJEmSWrhDd3SIRSULZEmSpBbus9t5k16lmhXIEbE/8NHy4qOZeWetziVJkqTlN2P2PAA6tmtdcJL6UJMxyBHxU+AEYHj54zsR8ZNanEuSJEkr5qgrn+SoK58sOkbdqFUP8r7Atpk5HyAirgaeAb5fo/NJkiRpOR2xc7+iI9SVWo5B7gZMKr9evYbnkSRJ0grYb5s+RUeoK7UqkH8KPBMRDwNBaSzyaTU6lyRJklbA1JlzAOjaoW3BSepDTQrkzPxLRDwC7FBe9b3MfKsW55IkSdKKOebqwQDc+PVdCk5SH6paIEfEppn5UkRsX141tvy5T0T0ycynq3k+SZIkrbivfKR/0RHqSrV7kE8GjgV+tYhtCexZ5fNJkiRpBe2z5dpFR6grVS2QM/PY8stPZebMym0R0aGa55IkSVJ1TPpgNgDdO7crOEl9qMk8yMBjTVwnSZKkgn3j2iF849ohRceoG9Ueg7wWsA7QMSK2ozSDBUBXoFM1zyVJkqTqOOb/Nig6Ql2p9hjkTwJHAesCv65Y/z4+JESSJKkufWLzNYuOUFeqPQb5auDqiPh8Zv6tmseWJElSbbzzfunWsd5dvGUMajcP8t8iYl9gC6BDxfpza3E+SZIkLb9vX/8M4DzIDWpSIEfEHymNOd4DuBw4CHiyFueSJEnSivnG7hsWHaGu1OpR07tm5tYR8XxmnhMRvwLurdG5JEmStAJ236R30RHqSq2meWuYA3l6RPQB5gDOQC1JklSHxk+ewfjJM4qOUTdq1YN8Z0R0A34BPE3pKXqX1ehckiRJWgEn3fgs4BjkBlUvkCOiFfBQZk4G/hYRdwEdMnNKtc8lSZKkFfftPQcUHaGuVL1Azsz5EXExsF15eRYwq9rnkSRJUnXsNqBn0RHqSq3GID8UEZ+PiFh6U0mSJBXpjYnTeWPi9KJj1I1aFchfB24CZkXE1Ih4PyKm1uhckiRJWgGn3vwcp978XNEx6katHhTSpRbHlSRJUvWdtNfGRUeoK7WaxUKSJEmriJ036FF0hLpSqyEWkiRJWkWMmjCNUROmFR2jbtiDLEmS1MJ9/5ahgPMgN6hZgRwRuwEDMvPKiOgFrJaZr9XqfJIkSVo+/2+fTYqOUFdqUiBHxFnAIGAT4EqgLXAt8JFanE+SJEnLb2C/7kVHqCu1GoP8WWB/4AOAzBwPOLOFJElSHRrx1vuMeOv9omPUjVoVyLMzM4EEiIjONTqPJEmSVtCZt7/Ambe/UHSMulGrMch/jYhLgG4RcQzwVeCyGp1LkiRJK+D7n96s6Ah1pVYPCvllROwFTKU0DvnMzHywFueSJEnSitmmb7eiI9SVWt2ktz7wr4aiOCI6RkT/zHy9FueTJEnS8hs2fgoAW/RZveAk9aFWY5BvAuZXLM8rr5MkSVKdOffO4Zx75/CiY9SNWo1BbpOZsxsWMnN2RLSr0bkkSZK0As7cb/OiI9SVWhXIEyJi/8y8AyAiDgDerdG5JEmStAIcWrGwWhXIxwHXRcTvgADGAF+u0bkkSZK0Ap4bMxnwZr0GtZrFYhSwc0SsVl6eVovzSJIkacX95J4XAbjx67sUnKQ+1GoWi/bA54H+QJuIACAzz63F+SRJkrT8zj1gy6Ij1JVaDbG4HZgCDAFm1egckiRJqoJN1upSdIS6UqsCed3M3Gd5doyIfYALgdbA5Zl5fqPtxwHfojR13DTg2Mx0XhJJkqTlNGT0JAAG9utecJL6UKt5kB+LiK2WdaeIaA1cDHwK2Bw4LCIazztyfWZulZnbAj8Hfr2iYSVJklqyn983gp/fN6LoGHWjVj3IuwFHRcRrlIZYBJCZufVS9tsRGJmZrwJExA3AAcCCHuLMnFrRvjOQ1QwuSZLU0vzkc8vcr9ms1apA/tRy7rcOpSnhGowFdmrcKCK+BZwMtAP2XNSBIuJY4FiA9dZbbznjSJIkNX8b9lqt6Ah1pSZDLDJzdGaOBmZQ6uFt+KjW8S/OzA2B7wFnLKbNpZk5KDMH9erVq1qnliRJanaeeHUiT7w6segYdaMmBXJE7B8RrwCvAY8CrwP3NmHXcUDfiuV1y+sW5wbgwOVLKUmSJIALHnyZCx58uegYdaNWQyx+BOwM/D0zt4uIPYAjmrDfU8CAiFifUmF8KPDFygYRMSAzXykv7gu8giRJkpbbLw7apugIdaVWBfKczJwYEa0iolVmPhwRv1naTpk5NyKOB+6nNM3bFZk5LCLOBQZn5h3A8RHxCWAO8B5wZI3egyRJUouwXo9ORUeoK7UqkCeXHzP9T+C6iHgH+KApO2bmPcA9jdadWfH6hGoGlSRJaun+/cq7AOw2oGfBSepDrQrkA4CZwEnA4cDqgI+ZliRJqkMX/aM0YtUCuaQmBXJmVvYWX12Lc0iSJKk6Ljhk26Ij1JWqFsgR8e/M3C0i3mfhad0aHhTStZrnkyRJ0orr061j0RHqSlUL5Mzcrfy5SzWPK0mSpNp5ZMQ7AOy+Se+Ck9SHqg+xiIjWwLDM3LTax5YkSVL1/eGRUYAFcoOqF8iZOS8iRkTEepn5RrWPL0mSpOq66IvbFR2hrtRqFos1gGER8SQV07tl5v41Op8kSZKWU+8uHYqOUFdqVSD/sEbHlSRJUpX9ffjbAHxi8zULTlIfajXN26O1OK4kSZKq77J/vQpYIDeoSYEcETsDFwGbAe0oPTb6A6d5kyRJqj9/OGJg0RHqSq2GWPwOOBS4CRgEfBnYuEbnkiRJ0gro3rld0RHqSqtaHTgzRwKtM3NeZl4J7FOrc0mSJGn53ffCm9z3wptFx6gbtepBnh4R7YBnI+LnwJvUsBiXJEnS8rvyP68DsM+WaxcbpE7UqkD+EqWC+HjgJKAv8PkanUuSJEkr4LIjBxUdoa7UqkAeCNydmVOBc2p0DkmSJFVB1w5ti45QV2o17GE/4OWIuCYiPhMRtSrEJUmStILufG48dz43vugYdaMmBXJmfgXYiNIsFocBoyLi8lqcS5IkSSvm2idGc+0To4uOUTdq1rObmXMi4l4ggY7AgcDXanU+SZIkLZ+rvrJj0RHqSk16kCPiUxFxFfAKpZvzLgfWqsW5JEmStGI6tmtNx3ati45RN2rVg/xl4Ebg65k5q0bnkCRJUhXc+sxYAD673boFJ6kPNSmQM/OwWhxXkiRJ1XfDk2MAC+QGzi4hSZLUwl37tZ2KjlBXLJAlSZJauLatfeBxJa+GJElSC3fT4DHcNHhM0THqRlV7kCNiKKVp3RYpM7eu5vkkSZK04m4eUrpJ7wuD+hacpD5Ue4jFZ8qfv1X+fE358+FVPo8kSZKq5Mav71J0hLpS1QI5M0cDRMRembldxabTIuJp4LRqnk+SJEmqtlqNQY6I+EjFwq41PJckSZJWwF+efIO/PPlG0THqRq1msTgauCIiVi8vTwa+WqNzSZIkaQXc9fx4AA7bcb2Ck9SHWj0oZAiwTUOBnJlTanEeSZIkrbjrvrZz0RHqSk2GPUTEmhHxJ+CGzJwSEZtHxNG1OJckSZJUTbUaF3wVcD/Qp7z8MnBijc4lSZKkFXDN469zzeOvFx2jbtSqQO6ZmX8F5gNk5lxgXo3OJUmSpBXw9xff4e8vvlN0jLpRq5v0PoiIHpQfGhIROwOOQ5YkSapDV391x6Ij1JVaFcgnA3cAG0bEf4BewEE1OpckSZJUNbWaxeLpiPgYsAkQwIjMnFOLc0mSJGnFXPHv1wD46m7rF5ykPtSqBxlgR6B/+RzbRwSZ+ecank+SJEnL4bFR7wIWyA1qUiBHxDXAhsCz/O/mvAQskCVJkurM5UfuUHSEulKrHuRBwOaZmTU6viRJklQTtZrm7QVgrRodW5IkSVV06T9Hcek/RxUdo27Uqge5JzA8Ip4EZjWszMz9a3Q+SZIkLaenR08uOkJdqVWBfHaNjitJkqQq++OXBhYdoa7Uapq3R2txXEmSJKnWqlogR8S/M3O3iHif8lP0GjYBmZldq3k+SZIkrbjfPzISgG/uvlHBSepDVQvkzNyt/LlLNY8rSZKk2hk+fmrREepKLR8UQkT0Bjo0LGfmG7U8nyRJkpbd7764fdER6kpNpnmLiP0j4hXgNeBR4HXg3lqcS5IkSaqmWs2D/CNgZ+DlzFwf+DjwRI3OJUmSpBXw24de4bcPvVJ0jLpRqyEWczJzYkS0iohWmflwRPymRueSJEnSCnh1wrSiI9SVWhXIkyNiNeCfwHUR8Q7wQVN2jIh9gAuB1sDlmXl+o+0nA18D5gITgK9m5uhqhpckSWpJfnPodkVHqCu1GmJxADADOAm4DxgF7Le0nSKiNXAx8Clgc+CwiNi8UbNngEGZuTVwM/DzKuaWJElSC1erB4VU9hZfvQy77giMzMxXASLiBkrF9vCKYz9c0f4J4IgViCpJktTi/fqBEQCcvPcmBSepD9V+UMgiHxBC0x8Usg4wpmJ5LLDTEtofzWJmx4iIY4FjAdZbb72lnFaSJKnlGj9lZtER6kq1HxSy0h4QEhFHAIOAjy0my6XApQCDBg3KRbWRJEkS/PIL2xQdoa7U7EEhEbE9sBulHuR/Z+YzTdhtHNC3Ynnd8rrGx/4E8APgY5k5qwpxJUmSJKB2Dwo5k9LY4x5AT+CqiDijCbs+BQyIiPUjoh1wKHBHo2NvB1wC7J+Z71Q3uSRJUsvzs/te4mf3vVR0jLpRqx7kw4FtMnMmQEScDzwL/HhJO2Xm3Ig4Hrif0jRvV2TmsIg4FxicmXcAvwBWA26KCIA3MnP/Gr0PSZKkZm/y9NlFR6grtSqQxwMdgIYR3+1ZxFCJRcnMe4B7Gq07s+L1J6qUUZIkScBPP7d10RHqSq0K5CnAsIh4kNIY5L2AJyPitwCZ+Z0anVeSJElaIbUqkG8tfzR4pEbnkSRJ0go67+7SIyd+sG/j57O1TLUqkO9tfANdRGySmSNqdD5JkiQtp5lz5hcdoa7UqkD+V0T8MDP/ChAR36X0UA9/LZEkSaozPzpwy6Ij1JVaFci7A5dGxBeANYEXKT1GWpIkSaprNZkHOTPfBO4DdgH6A1dn5rRanEuSJEkr5pw7h3HOncOKjlE3atKDHBF/pzTV25aUnoz3p4j4Z2aeUovzSZIkSdVSqyEWv8vM28qvJ0fELsD3a3QuSZIkrYCz9tui6Ah1pVZDLG6LiN0i4ivlVWsA19biXJIkSVI11aRAjoizgO8Bp5dXtcMCWZIkqS798LYX+OFtLxQdo27UaojFZ4HtgKcBMnN8RHSp0bkkSZK0Ajq0rUmf6SqrVgXy7MzMiEiAiOhco/NIkiRpBfkEvYXV6teFv0bEJUC3iDgG+DtwWY3OJUmSJFVNTXqQM/OXEbEXMBXYBDgzMx+sxbkkSZK0Yk6/5XkAfvq5rQtOUh9qNcSCckFsUSxJklTnunVqV3SEulKzAlmSJEmrhu/ts2nREeqKtyxKkiRJFVZKgRwRfSPi1JVxLkmSJC2bU256jlNueq7oGHWjZkMsIqIX8AXgMKAPcGutziVJkqTl12f1DkVHqCtVLZDLDwP5HPBFYGPgFmD9zFy3mueRJElS9Zy89yZFR6gr1e5Bfgd4EjgD+Hf5YSGfrfI5JEmSpJqp9hjk04H2wO+B0yNiwyofX5IkSVV24g3PcOINzxQdo25UtUDOzN9k5s7AAeVVtwF9IuJ7EbFxNc8lSZKk6tig12ps0Gu1omPUjcjM2p4gYktKN+odkpkb1fRkizFo0KAcPHhwEaeWJElSgSJiSGYOWpZ9qtqDHBEbRcRHKtdl5gvAvcA+1TyXJEmSVAvVHoP8G2DqItZPAS6o8rkkSZJUBcdf/zTHX/900THqRrVnsVgzM4c2XpmZQyOif5XPJUmSpCrYvE/XoiPUlWoXyN2WsK1jlc8lSZKkKvjm7oXcJla3qj3EYnBEHNN4ZUR8DRhS5XNJkiRJVVftHuQTgVsj4nD+VxAPAtoBPjBEkiSpDh13Tals++OXBhacpD5UtUDOzLeBXSNiD2DL8uq7M/Mf1TyPJEmSqmf7ft2KjlBXqlogR0QH4DhgI2Ao8KfMnFvNc0iSJKm6jv2oDz+uVO0xyFdTGlIxFPgU8MsqH1+SJEmqqWqPQd48M7cCiIg/AU9W+fiSJEmqsq9d/RQAlx+5Q8FJ6kO1C+Q5DS8yc25EVPnwkiRJqrZdN+xZdIS6Uu0CeZuIaHiSXgAdy8sBZGY6C7UkSVKd+epu6xcdoa5UexaL1tU8niRJkrSyVfsmPUmSJK1ijrziSY68wlvHGlR7iIUkSZJWMZ/YrHfREeqKBbIkSVIL96Vd+hcdoa44xEKSJEmqYIEsSZLUwh1++RMcfvkTRceoGw6xkCRJauE+s3WfoiPUFQtkSZKkFu6wHdcrOkJdcYiFJEmSVMECWZIkqYU75JLHOeSSx4uOUTccYiFJktTCHTRw3aIj1BULZEmSpBbuC4P6Fh2hrjjEosouePDloiNIkiQtkznz5jNn3vyiY9SNuiuQI2KfiBgRESMj4rRFbP9oRDwdEXMj4qAiMi7JhQ+9UnQESZKkZXLE5f/liMv/W3SMulFXQywiojVwMbAXMBZ4KiLuyMzhFc3eAI4CTln5CSVJkpqfQ3d0iEWluiqQgR2BkZn5KkBE3AAcACwokDPz9fK2uvk7wAUPvrxQz3H/0+4G4ISPD+CkvTYuKpYkSVKTfHY7b9KrVG8F8jrAmIrlscBOBWVpspP22nhBIdz/tLt5/fx9C04kSZLUdDNmzwOgY7vWBSepD3U3BrlaIuLYiBgcEYMnTJhQdBxJkqS6ddSVT3LUlU8WHaNu1FsP8jigchDMuuV1yywzLwUuBRg0aFCueLSmOeHjA1bWqSRJkqriiJ37FR2hrtRbD/JTwICIWD8i2gGHAncUnGmZNGXMsVPBSZKkerLfNn3Yb5s+RceoG3VVIGfmXOB44H7gReCvmTksIs6NiP0BImKHiBgLfAG4JCKGFZd4+TgVnCRJqidTZ85h6sw5RceoG/U2xILMvAe4p9G6MyteP0Vp6IUkSZKq4JirBwNw49d3KThJfai7Arm5cio4SZJUr77ykf5FR6grkbnS7l8rzKBBg3Lw4MFFx1jAqeAkSZJWjogYkpmDlmWfuhqDLEmSpJVv0gezmfTB7KJj1A2HWBTAqeAkSVI9+ca1QwDHIDewQC6AY44lSVI9Oeb/Nig6Ql2xQJYkSWrhPrH5mkVHqCuOQZYkSWrh3nl/Ju+8P7PoGHXDHmRJkqQW7tvXPwM4BrmBBbIkSVIL943dNyw6Ql2xQJYkSWrhdt+kd9ER6opjkCVJklq48ZNnMH7yjKJj1A0L5Dp1wYMvFx1BkiS1ECfd+Cwn3fhs0THqhgVynbrwoVeKjiBJklqIb+85gG/v6YPMGlggr8LsZZYkSdWw24Ce7DagZ9Ex6oY36dWRCx58eaGe4/6n3Q2UHk29qKfvXfjQKz6VT5IkrbA3Jk4HYL0enQpOUh8skOvISXttvKDg7X/a3bx+/r4FJ5IkSS3BqTc/BzgPcgML5FXMsvYyS5IkLY01xMIskOvUCR9f9EB5e5klSVK17bxBj6Ij1BVv0qtT/iYnSZJWllETpjFqwrSiY9QNe5BXYYvrZZYkSVoW379lKOAY5AYWyKswe5klSVI1/L99Nik6Ql2xQJYkSWrhBvbrXnSEuuIYZEmSpBZuxFvvM+Kt94uOUTfsQZYkSWrhzrz9BcAxyA0skCVJklq47396s6Ij1BULZEmSpBZum77dio5QVxyDLEmS1MINGz+FYeOnFB2jblggS5IktXDn3jmcc+8cXnSMuuEQC0mSpBbuzP02LzpCXbEHuZm74MGXi44gSZLq3BZ9VmeLPqsXHaNuWCA3cxc+9ErRESRJUp17bsxknhszuegYdcMCWfYyS5LUwv3knhf5yT0vFh2jbjgGuRm64MGXF+o57n/a3QCc8PEBnLTXxh9qf+FDryxyvSRJahnOPWDLoiPUFQvkZuikvTZeUPD2P+1uXj9/34ITSZKkerbJWl2KjlBXLJBbqGXtZb7gwZftZZYkqZkaMnoSAAP7dS84SX2wQG7mTvj4gEWuX9ZeZodhSJLUfP38vhEA3Pj1XQpOUh8skJs5i1pJkrQ0P/ncVkVHqCsWyFpsL/OyDsOQJEmrpg17rVZ0hLoSmVl0hpobNGhQDh48uOgYqzRv9pMkqfl64tWJAOy8QY+Ck1RfRAzJzEHLso/zIEuSJLVwFzz4ss9FqOAQCzXJ4oZhVHKmC0mSVk2/OGiboiPUFXuQ1SRNKXx9rLUkSaum9Xp0Yr0enYqOUTcskCVJklq4f7/yLv9+5d2iY9QNh1hohTjThSRJq76L/lH6v3y3AT0LTlIfnMVCVdOUmS4cpyxJUv0ZP3kGAH26dSw4SfU5i4XqXlPGKXsXrSRJK1efbh2bZXG8vCyQVTVNmemiKSyiJUlauR4Z8Q6PjHin6Bh1wwJZVbO4oRMXPPgy/U+7e8H45IbXK1LkNnXGjKacw2JbktTS/eGRUfzhkVFFx6gb3qSnmjtpr40XFM+LG6dcq5v9LnzolaXu35Q2TRk77fhqSdKq6qIvbld0hLpiD7Lqwkl7bczr5++7oHhueF1ZcNaiJ7qpmtJjXa2hISuzjSRJAL27dKB3lw5Fx6gbdVcgR8Q+ETEiIkZGxGmL2N4+Im4sb/9vRPQvIKaW04qMU25KEQ1NK6SLKrarVWg314LdNiveph4z2cY2K9KmHjM1xzZ/H/42377+6brJU802rbv07LPURo3UVYEcEa2Bi4FPAZsDh0XE5o2aHQ28l5kbARcAP1u5KbUimjIEYUVv9mtKIV2tHusie7Wrod4KdtuseJt6zGQb26xIm3rM1BzbXPavV7nz+TfrJk8127Tu3G3tpTZqpN7GIO8IjMzMVwEi4gbgAGB4RZsDgLPLr28GfhcRkS1hQucWYmUU0U3NsbSx09UaX70y20iS1NgfjhjI9j96sOgYdaOuHhQSEQcB+2Tm18rLXwJ2yszjK9q8UG4ztrw8qtzm3UbHOhY4FmC99dYbOHr06JX0LlRPqnVzXVMegrKqtGlcRDdYUqFtm/puU4+ZbGObFWlTj5lss+q2efPqE5n15ivxoR2WJDPr5gM4CLi8YvlLwO8atXkBWLdieRTQc0nHHThwYEor4tcPjKhKm37fu8s2tqlpm3rMZBvbrEibeszUHNvcO3R8XeWpZpt2a22UuYw1aV2NQQbGAX0rltctr1tkm4hoA6wOTFwp6dRiNWV4QrWGhqzMNpIkAVz5n9eLjlBX6q1AfgoYEBHrR0Q74FDgjkZt7gCOLL8+CPhHZh2NE5GWoFqFdnMt2G2z4m3qMZNtbLMibeoxU3Nsc9mRgzjuYxvUTZ5qtpn3weSl333YSF2NQQaIiE8DvwFaA1dk5nkRcS4wODPviIgOwDXAdsAk4NAs39S3OIMGDcrBgwfXOLkkSZLqTUQMycxBy7JPvc1iQWbeA9zTaN2ZFa9nAl9Y2bkkSZKaqzufGw/Aftss85TBzVLdFciSJElaua59ojTblwVyiQWyJElSC3fVV3YsOkJdsUCWJElq4Tq2a110hLpSb7NYSJIkaSW79Zmx3PrM2KJj1A17kCVJklq4G54cA8Bnt1u34CT1wQJZkiSphbv2azsVHaGuWCBLkiS1cG1bO+q2kldDkiSphbtp8BhuGjym6Bh1wwJZkrRSfepTn+Lqq68uOkbN7b777lx++eUAXHfddey99941PYe0Im4eMpabh3iTXoO6e9R0LUTEBGD0Upr1BN5dCXFaOq/zylN5rbei9AvxUGB+xfYewIjF7B/A2kB3oC0wF3gfGA/Mrk3kutIFWB94fintlud7eitK1zQpfT2mAG/wv69NV0rXvlN53UzgrXK7ANah9HVpTenrMhmoVdfPWsDqfPj7pA2wNTC8nG9lWNV+fmwCTKS2mWtxjlXtOq/KvNYrxyaZ2WVZdmgRY5Azs9fS2kTE4GV9TreWndd55am81hHxOqWC7+bM/El53deAIzJz98XsfwelYuwA4BmgM3AEMDMz/1TzN1CwiNgduHZp36/L8z1d/np8LTP/HhHrAPcDD2TmaRFxEHAFcBJwE6VfSv6P0tfqmIg4C9gTOAx4E+gHfDQz/7wsGZYh67rA68AXMvO1ivXHA1/JzIHLcKyg1DEzf6mNF73/KvXzIyIeofQ9VLMu3lqcY1W7zqsyr/XKERGDl3Ufh1hILccvgFMiotvSGkbEJ4C9gAMy86nMnJuZUzLz4obiOCL6RMQdETEpIkZGxDEV+58dETdFxLUR8X5EDI2IjSPi9Ih4JyLGRMTeFe0fiYifRsSTETE1Im6PiO4V2/ePiGERMbncdrOKba9HxCkR8XxETImIGyOiQ8X2z0TEs+V9H4uIrZe2b0R0Bu4F+kTEtPJHn4hoFRGnRcSoiJgYEX+l1ItLeb9ry+snR8RTEbHm0q51Zo4rn2vLcgH5a+BHmXl5+ZrPz8xHM7Ph+u4A3JqZ47Pk9cUVxxHxh4j4ZaN1t0fEyeXX34uIceWv0YiI+Pgi8o0F/gF8qdGmLwN/jog1IuKuiJgQEe+VXy+YJ6r89TovIv4DTAc2KK/7Wnn7hhHxj/J1ezcirqv8Hq38GgHbVn59I+LOiq/PtIiYHxFHlbddWP4+mxoRQyLi/xb3NYiI9hHxy4h4IyLejog/RkTH8rZ7IuJXFW1viIgryq+Pioj/RMTvyt8/Ly3qGla0/XfF8hYR8WD538/bEfH98vodI+Lx8vfQm+Vjt6vYb6/yeaZExO8o/RLbsG2J11JS01kgSy3HYOAR4JQmtP0E8GRmLunP9jcAY4E+wEHATyJiz4rt+wHXAGtQ6oG+n9LPnHWAc4FLGh3vy8BXKQ0tmAv8FiAiNgb+ApwI9ALuAe6sLBqAg4F9KA2J2Bo4qrzvdpR6Y79OaTjJJcAdEdF+Sftm5gfAp4Dxmbla+WM88G3gQOBj5ff9HrBe+ThHUhqK0Ld8ruOAGUu4fpQz9gU+Xb5Gm5T3v3kJuzwBnBwR34yIrcpF9eL8BTikoU1ErAHsDdwQEZsAxwM7lP/0+ElKPcWLcjUVBXJ5322B6yl9Ta+k1JO9HqX3/LtG+38JOJbSXzEaD3cL4KeUrudmlN7/2Y3aNHyNhlLx9c3M/Rq+PsAXKA1Deai8z1PljN3LOW+Kil+cGjkf2LjcfiNK36Nnlrd9FfhSROwZEYcDOwInVOy7EzCK0p/KzwJuiYpf7hYlIroAfwfuK7/vjSpyz6P014OewC7Ax4FvlvfrCdwCnFHePgr4SOWhWfq1lNQUmelHaRz2sUVnaAkfXudirjWlwucTwJaUxrH2Ar4GPLKYfS8DbljCsftS+o+8S8W6nwJXlV+fDTxYsW0/YBrQurzchdL4227l5UeA8yvab05pnHNr4IfAXyu2tQLGAbtXvLcjKrb/HPhj+fUfKPXGVmYfAXysCfvuDoxttO+LwMcrlhuK+TaUCqnHgK2b8LV5vXw9JlMqGH8PdKRU7CTQYQn7tga+BfwHmEVpTPiRi2kblMY2f7S8fAzwj/LrjYB3yt8XbZeStxMwFdi1vHwecPti2m4LvFex/AhwbqM2j1AaYrKo/Q8Enml0rY5o+J6u/BpVtNm4/F52W8J7eA/YZjHX6ANgw4p1uwCvVSx/ntIY73crz0GpUB9P+X6e8rongS81fp/ltv8uvz6s8j0u5dqfSOkvBlD6JfKJRtnHNvVaNvUDf06vtA+vdf1eZ3uQyzLz0qIztARe55VnUdc6M18A7gJOW8ruEykVf4vTB5iUme9XrBtNqeetwdsVr2cA72bmvIplgNUq2lT2Vo+mdBNbz/K5FvQ6Zmn86phG53qr4vX0iuP2A75b/nP15IiYTKm479OEfRelH3BrxbFepFQgr0mpt/x+Sr2z4yPi5xHRdgnHOjAzu2Vmv8z8ZmbOoHTdYQnXPjPnZWmoy0eAbpSK1SuiYthJRduk1NN/WHnVF4HryttGUiq+zgbeKQ8d6NP4GOW20ymNh/5yuTf6cODPABHRKSIuiYjRETEV+CfQLSJaVxxisX+JiIg1y+ceV97/Wkpf90pvlXNcSqOvUUSsDtwOnJGZlUMYTomIF8tDESZT6t1vfFwo/bLYCRhS8XW9r7y+wZ2UfjEZUXmOsnHl69xgNAt/fy1KX0q9vx8SpaFId0XEW+Xr8ZOK3H2ouJbl846p2Lcp13Kp/Dm98nitV47luc4WyFLLcxalnsR1ltDm78COUTGWtJHxQPfyn4obrEepZ3d59W10rDmUeuzGUypMgQU3evVt4rnGAOeVC9GGj06Z+Zcm7LuoKX7GAJ9qdLwOmTkuM+dk5jmZuTmwK/AZSj1+y2JE+Ryfb0rjzJyRmRdT6h3dfDHN/gIcFBH9KA0H+FvF/tdn5m6Urm8CP1vC6a6mNNRhL0p/AbizvP67lIaG7JSZXYGPltdXDv1Y0nRJPylv36q8/xGN9l2siGhFafjEw5X/AZbHG/+/ct41MrMb/5sBpLF3Kf3CtkXF13T1LA3baHAepV+G1o6Iwxrtv06jYS7rUfqeXZIxwAaL2fYH4CVgQPl6fL8i95tU/Dup+LfQYLmvpaSFWSBLLUy55/BG4DtLaPN34EFKvaUDI6JNRHSJiOMi4qtZGpv8GPDTKN2ctjVwNKUeq+V1RERsHhGdKI1Rvrnc4/xXYN+I+Hi5R/a7lIYWPNaEY14GHBcRO0VJ54jYt1FhvzhvAz3KPZQN/gicVy42iYheEXFA+fUe5THBrSkNR5jD/6Zta5Jyj+DJwA8j4isR0TVKNwbuFhGXls9zYkTsHhEdy1+XIykVrM8s5pjPUCoCLwfuz8zJ5eNsUh5X257SNG0zlpL3X5SGhFxKafhNw1R/Xcr7Ti6PvT1rWd5zef9pwJQozehx6jLsex6l2VVOaLS+C6We/QlAm4g4k9LUeR9S/ovEZcAFEdEbICLWiYhPll9/FPgKpV92jgQuKuds0Bv4TkS0jYgvUBr7e89Sct9Fqdg+MUo3CHaJiIbn/Hah9P0zLSI2Bb5Rsd/dwBYR8bmIaEPp3/Bajd738l5LSRUskKWW6VxKhcWSHETpP/obKfW+vQAMotS7DKU/2/en1Ft2K3BWubBeXtcAV1H6c3oHygV8Zo6g1BN2EaVCbz9gv4oCbbEyczCl3vLfUeplHUn5Bq8m7PsSpd7XV8t/eu8DXAjcATwQEe9TumGuobBZi9LNdVMp9TY+Wn5PyyQzbwYOoTSmeTylQv3HlIYRQGmIwa8oXad3KY1H/nxmvrqEw15Paazx9RXr2lO6Oe3d8rF6A6cvIVdSGlbRr/y5wW8ojZ9+l9L1uG/p73Ih5wDbU/oeu5vSTWhNdRiwM/Be/G8mi8MpDXW5D3iZ0pCHmSx5nujvUfreeKI8NOHvwCYR0ZXSez2+/FeCfwF/Aq6s6DX+LzCA0vs/DzgoMyd+6AwVykOT9qL0vfwW8AqwR3nzKZSGwrxPqXC/sWK/dyndjHg+peE4AyiNRW+wItdSUoUW8aAQSfUtVsJ8sVK1RWlKua+Vh6lIakbsQZYkSZIqWCBLkiRJFRxiIUmSJFWwB1mSJEmq0KboACtDz549s3///kXHkCRJ0ko2ZMiQdzOz19Jb/k+LKJD79+/P4MGDi44hSZKklSwiRi+91cIcYiFJkiRVsECWJEmSKlggS5IkSRUskCVJkqQKFsiSJElSBQtkSZIkqYIFsiRJklTBAlmSJEmqYIEsSZIkVbBAliRJkipYIEuSJEkVLJAlSZKkChbIkiRJUgULZEmSpBbukEse55BLHi86Rt2wQJYkSZIqWCBLkiRJFSyQJUmSpAoWyJIkSVIFC2RJkiSpggWyJEmSVMECWZIkSapggSxJkiRVsECWJEmSKlggS5IkSRUskCVJqgEf3SutuiyQJUmSpAo1LZAjYp+IGBERIyPitEVsXy8iHo6IZyLi+Yj4dMW208v7jYiITzb1mJIkSdKKqFmBHBGtgYuBTwGbA4dFxOaNmp0B/DUztwMOBX5f3nfz8vIWwD7A7yOidROPKUmSJC23WvYg7wiMzMxXM3M2cANwQKM2CXQtv14dGF9+fQBwQ2bOyszXgJHl4zXlmJIkSdJyq2WBvA4wpmJ5bHldpbOBIyJiLHAP8O2l7NuUY0qSJEnLreib9A4DrsrMdYFPA9dERFUyRcSxETE4IgZPmDChGoeUJElSC1DLAnkc0Ldied3yukpHA38FyMzHgQ5AzyXs25RjUj7epZk5KDMH9erVawXehiRJklqSWhbITwEDImL9iGhH6aa7Oxq1eQP4OEBEbEapQJ5QbndoRLSPiPWBAcCTTTymJEmStNza1OrAmTk3Io4H7gdaA1dk5rCIOBcYnJl3AN8FLouIkyjdsHdUZiYwLCL+CgwH5gLfysx5AIs6Zq3egyRJklqemhXIAJl5D6Wb7yrXnVnxejjwkcXsex5wXlOOKUmSJFVL0TfpSZIkSXXFAlmSltMhlzzOIZc8XnQMSVKVWSBLkiRJFSyQJUmSpAoWyJIkSVIFC2RJkiSpggWyJEmSVMECWZIkSapggSxJkiRVsECWJEmSKtT0UdOSJEmqvTnz5jN91jw+mD2X6bPn8kHD6wXr5vHBrPLnyvXlz8PGT6VTu9ZFv426YYEsSZK0kmQms+bOX6hgnTbrf0Xt9Nlz+WD2PKbPavR5Edsri93Z8+Y3OUP7Nq3o3L4Nndq1pnO7NnRq35pWAa1bRQ3f+arFAlmSJGkR5s9PZsxZQk/sEnpkF7t99jzmzc8mZ+jcrjWd2rcpfW7Xhs7tW9OtY1vW6dahtLyI7Qt9LhfADZ87tW1Nm9YfHmF7yCWPV/PSrfIskCVJ0ipv7rz5H+5pXcYe2cbrp8+e1+Tztwro3L5RQdquNb26tKdfu04fKlQbti/oyW3Uo7ta+zZ0aNOaVvbqFsICWZIkrTSZyeyFxssuYajBUgrYyu2z5zZ9iEG7Nq0W2eO6Rud2y9YjW1HYtm/TigiL2ebCAlmSJC1SZnmIwXIUrKXtix6SMHcZhhh0bNt6QXHaUJB26dCGtbp2WHKPbMX6xsVt20UMMZAqWSBLktQMzJ03n+lz5i1iLOzy9chOnzWX6XPmkU2sZVsFHx7v2q4NPTq3o2/3Th/ukV3QU/vhArdhW8e2rb1xTIWwQJYkaSWbPXf+IsfGTlvMTV1LvCmsvH7WMgwxaNMqFipEG4rXPt3aLqaIXfpwgw5tHWKg5sMCWZKk5TB33nwmTZ/NpA9mM3HabCZ+MJuJ02aVlj+Yzctvv8+8+ckBv/v3h3ps58xr+hCDDm1bfaiXdbX2bejdpX2j9YsqYCsK3IpCt10bhxhIS2KBLEkSpYL3velzmPjBLCY1Knjf/WA2k6bNLr8urZs8fc4ijxMB3Tu1Y+ac+bRuFXTr1I511lj2HtklTcklqbYskCVJzdK8+cl70xt6d2ctuqe3YtvkGXMWOd42Atbo1I7undvRo3M7Nl2rS/l1e3qsVvrcvXO78ut2dOvUjtatYsG8sld/dceV/M4lrSgLZEnSKqGh4G1c2L47bTaTFnpd+nhv+uzFFrzdOrYtF7Xt2XjNLvRYrR3dO7enR7nQ7d65HT1XKxW+a5QLXkkthwWyJKkQ8+Ynk8sF7/8K21kLXk/8YBYTF7xefMEL0K1T21Jx27k9A3qvtqC3t8dqlb27DQVvW4ctSFoiC2RJUlXMn59MnjFn4SJ32iwmfrBwr29D0fve9Nksbjrcbp3aLihyN+y1Gjus346enUs9u91Xa196XS56LXglVZsFsiRpkebPT6bMmLNQUdtws9rED8qFb8VQh0kfLL7gXb1j2wXDFzbo1ZlB/bvTszyUocdqpaENDT29a3Rq54McJBXKAlmSWoj585OpM+csejjDh3p6Sz288xZT8Xbt0GZBYdu/R2cG9uu+UJH7vxvY2rFGZwteSasWC2RJWkVlNvTwNipyG2ZqaDQv76QPFl/wdunQZsFNaf16dGL7ft0Wmqmh8vUando5j66kZs0CWZLqRGYydcbcRrMz/K/wnVju9V3Qw/vBbOYuoeBt6NHt270T2/btttiZGix4JWlhFsiSVCOZydSZcxcucstTkv3v9f96et+bPnuxT1jr0r5N+aa0dqy7Rie2Wbfbh6YjW1Dwdm5L+zatV/K7laTmwwJZkpZT6VHDc7j44ZGLnZd3cQXvau3bLBivu063Dmy9zuoLCuBF9fRa8ErSymOBLEnLKDO57dlxPDd2CnPnJ7+4fwSd27VeMOdun9U7sNU6Xeneuf2CmRoa9/R2aGvBK0n1qqYFckTsA1wItAYuz8zzG22/ANijvNgJ6J2Z3SJiD+CCiqabAodm5m0RcRXwMWBKedtRmfls7d6FJP3P6IkfcMZtL/CvV96lc/vWbNKjM3/7xq4WvJLUjNSsQI6I1sDFwF7AWOCpiLgjM4c3tMnMkyrafxvYrrz+YWDb8vruwEjggYrDn5qZN9cquyQ1NmfefC7/12v85u8v07Z1K87Zfwvufn48EWFxLEnNTC17kHcERmbmqwARcQNwADB8Me0PA85axPqDgHszc3pNUkrSUjzzxnucfstQXnrrfT65xZqcs/+WrLV6B+4Z+mbR0SRJNVDLAnkdYEzF8lhgp0U1jIh+wPrAPxax+VDg143WnRcRZwIPAadl5qxFHPNY4FiA9dZbb5nDS9L7M+fwi/tHcM0To1mzSwcu+dJAPrnFWkXHkiTVWL3cpHcocHNmzqtcGRFrA1sB91esPh14C2gHXAp8Dzi38QEz89LydgYNGrSYh59K0qLd98JbnH3HMN5+fyZH7tKf7+69MV06tC06liRpJahlgTwO6FuxvG553aIcCnxrEesPBm7NzDkNKzKz4W+asyLiSuCUKmSVJADenDKDs24fxgPD32bTtbrwhyO2Z7v11ig6liRpJaplgfwUMCAi1qdUGB8KfLFxo4jYFFgDeHwRxziMUo9xZfu1M/PNiAjgQOCFKueW1ALNm59c8/jr/PKBl5k7fz6nfWpTjt5tfdq29glzktTS1KxAzsy5EXE8peERrYErMnNYRJwLDM7MO8pNDwVuyMyFhkFERH9KPdCPNjr0dRHRCwjgWeC4Wr0HSS3D8PFTOf3WoTw3ZjL/N6An5x24Fev16FR0LElSQWo6Bjkz7wHuabTuzEbLZy9m39cp3ejXeP2e1UsoqSWbMXsev3noZS7/12t069iWCw/dlv236UPpD1SSpJaqXm7Sk6SV6tGXJ3DGbUMZM2kGBw9al+9/ejO6dWpXdCxJUh2wQJbUorw7bRY/ums4tz87ng16deaGY3dm5w16FB1LklRHLJAltQiZyU2Dx3LePS8yffZcTvj4AL65x4a0b+NT8CRJC7NAltTsjZowje/fMpT/vjaJHft35yef25KNencpOpYkqU5ZIEtqtmbNnccfH3mVix8eSYe2rTj/c1tx8KC+tGrlTXiSVOnGr+9SdIS6YoEsqVl68rVJnH7L84ya8AH7bdOHH35mM3p36VB0LEnSKsACWVKzMmX6HH5674vc8NQY1unWkSu/sgN7bNK76FiSpFWIBbKkZiEzufP5Nzn3zuG8N302x350A078xAA6tfPHnCRp2fg/h6RV3phJ0/nh7S/wyIgJbL3u6lz91R3Yos/qRceSJK2iLJAlrbLmzpvPFf95jQsefIUIOPMzm3Pkrv1p7U14qgPe9CStuiyQJa2SnhszmdNvGcrwN6fyic16c84BW7JOt45Fx5IkNQMWyJJWKdNmzeVXD4zg6sdep+dq7fnD4duzz5ZrEWGvsSSpOiyQJa0y/j78bc68/QXenDqTI3bqx6n7bELXDm2LjiVJamYskCXVvbenzuTsO4Zx7wtvscmaXbjoi9szsN8aRceSJDVTFsiS6tb8+cl1T77Bz+99idnz5nPqJzfhmP/bgHZtWhUdTZLUjFkgS6pLI956n9NveZ6n35jMRzbqwXkHbkX/np2LjiVJagEskCXVlZlz5nHRP17hkkdfpUuHNvz64G347HbreBOeJGmlsUCWVDf+M/JdfnDrUF6fOJ3Pb78uP9h3M7p3bld0LElSC2OBLKlwE6fN4ry7X+SWZ8bRv0cnrv/aTuy6Uc+iY0mSWigLZEmFyUz+9vQ4zrt7OO/PnMvxe2zE8XtuRIe2rYuOJklqwZZYIEdEB+AzwP8BfYAZwAvA3Zk5rPbxJDVXr737AT+4dSiPjZrIwH5r8NPPbcXGa3YpOtYy8VHCktQ8LbZAjohzKBXHjwD/Bd4BOgAbA+eXi+fvZubzKyGnpGZi9tz5XPrPUfz2HyNp37oVPz5wS76443q0auVNeJKk+rCkHuQnM/OsxWz7dUT0BtarQSZJzdSQ0ZM4/ZahvPz2NPbdam3O2m9zenftUHQsSZIWstgCOTPvXtKOmfkOpV5lqVk75JLHAf+cviKmzJjDz+97iev++wZ9Vu/An44cxMc3W7PoWJIkLdKShljcCeTitmfm/jVJJKnZyEzuGfoWZ985jInTZnH0butz8l4b07m99wdLkurXkv6X+uVKSyGp2Rk3eQZn3vYCD730Dlv06coVR+7AVuuuXnQsSZKWaklDLB5dmUEkNQ/z5idXPfY6v3pgBJlwxr6bcdSu/WnTulXR0SRJapKl/p0zIgYAPwU2pzSLBQCZuUENc0laBb0wbgqn3zKUoeOmsMcmvTj3gC3p271T0bEkSVomTRkIeCVwFnABsAfwFcCuIEkLfDBrLhc8+DJX/Oc1unduz+++uB37brU2EU7dJkla9TSlQO6YmQ9FRGTmaODsiBgCnFnjbJJWAQ+/9A5n3PYC4ybP4LAd1+O0fTZl9U5ti44lSdJya0qBPCsiWgGvRMTxwDhgtaYcPCL2AS4EWgOXZ+b5jbY39EoDdAJ6Z2a38rZ5wNDytjcaZs2IiPWBG4AewBDgS5k5uyl5JFXPO+/P5Jw7h3P382+yUe/VuOm4Xdihf/eiY0mStMKaUiCfQKl4/Q7wI0oF7ZFL2ykiWgMXA3sBY4GnIuKOzBze0CYzT6po/21gu4pDzMjMbRdx6J8BF2TmDRHxR+Bo4A9NeB+SqmD+/OSGp8Zw/r0vMnPOfE7ea2O+/rENaN+mddHRJEmqiqYUyPMycxowjdL446baERiZma8CRMQNwAHA8MW0P4zSWOfFitKAxj2BL5ZXXQ2cjQWytFK88vb7fP/WoTz1+nvsvEF3fvLZrdigV5P+oCRJ0iqjKQXyryJiLeBm4MbMfKGJx14HGFOxPBbYaVENI6IfsD7wj4rVHSJiMDAXOD8zb6M0rGJyZs6tOOY6iznmscCxAOut5xOxpRUxc848fv/wSP7w6Cg6t2/Dzw/ami8MXNeb8CRJzdJSC+TM3KNcIB8MXBIRXSkVyj+uYo5DgZszc17Fun6ZOS4iNgD+ERFDgSlNPWBmXgpcCjBo0KDFPhFQ0pI9PmoiP7h1KK+++wGf3W4dzth3M3qs1r7oWJIk1UyTpmvLzLcy87fAccCzNG0Gi3FA34rldcvrFuVQ4C+Nzjmu/PlV4BFK45MnAt0ioqGwX9IxJa2A9z6Yzak3Pcdhlz3B3PnJNUfvyAWHbGtxLElq9pryoJDNgEOAz1MqUG8EvtuEYz8FDCjPOjGOUhH8xcaNImJTYA3g8Yp1awDTM3NWRPQEPgL8PDMzIh4GDqI0k8WRwO1NyCKpiTKT254dx4/uepGpM+bwjd035Dt7DqBjO2/CkyS1DE0Zg3wFpWL0k5k5vqkHzsy55Wnh7qc0zdsVmTksIs4FBmfmHeWmhwI3ZGblMIjNKA3nmE+pl/v8itkvvgfcEBE/Bp4B/tTUTJKWbPTEDzjjthf41yvvsm3fbvz0c1ux2dpdi44lSdJK1ZQxyLss78Ez8x7gnkbrzmy0fPYi9nsM2Goxx3yV0gwZkqpkzrz5XP6v1/jN31+mbetWnHvAFhy+Uz9at/ImPElSy7PYAjki7qR0k9t9mTmn0bYNgKOA1zPzipomlFRTz7zxHqffMpSX3nqfT26xJufsvyVrrd6h6FiSJBVmST3IxwAnA7+JiEnABKAD0B8YBfwuMx3/K62i3p85h1/cP4JrnhjNml06cOmXBrL3FmsVHUuSpMIttkDOzLeA/wf8v4joD6wNzABezszpKyeepFq474W3OPuOYbz9/kyO3KU/p3xyE1Zr35RbEiRJav6a9D9iZr4OvF7TJJJq7s0pMzjr9mE8MPxtNlu7K3/80kC27dut6FiSJNUVu4ykFmDe/OSax1/nlw+8zNz58zntU5ty9G7r07Z1k6ZClySpRbFAlpq54eOncvqtQ3luzGQ+unEvfnzAlqzXo1PRsSRJqlsWyFIzNWP2PH7z0Mtc/q/X6NaxLRceui37b9OHCKdukyRpSZryJL2PAGcD/crtA8jM3KC20SQtr0dfnsAZtw1lzKQZHDKoL6d/elO6dWpXdCxJklYJTelB/hNwEjAEmFfbOJJWxLvTZvGju4Zz+7Pj2aBXZ244dmd23qBH0bEkSVqlNKVAnpKZ99Y8iaTllpn8dfAYfnLPS8yYPY8TPj6Ab+6xIe3btC46miRJq5ymFMgPR8QvgFuAWQ0rM/PpmqWS1GSjJkzj+7cM5b+vTWLH/t35yee2ZKPeXYqOJUnSKqspBfJO5c+DKtYlsGf140hqqllz5/GHR0bx+4dH0aFtK87/3FYcPKgvrVp5E54kSStiqQVyZu6xMoJIaronX5vE6bc8z6gJH7DfNn344Wc2o3eXDkXHkiSpWWjKLBarA2cBHy2vehQ4NzOn1DKYpA+bMn0OP733RW54agzrrtGRq76yA7tv0rvoWJIkNStNGWJxBfACcHB5+UvAlcDnahVK0sIykzuff5Nz7xzOe9Nnc+xHN+DETwygUzunMpckqdqa8r/rhpn5+YrlcyLi2RrlkdTImEnTOeO2F3j05Qlsve7qXP3VHdiiz+pFx5IkqdlqSoE8IyJ2y8x/w4IHh8yobSxJc+fN54r/vMavH3yZ1hGc+ZnNOXLX/rT2JjxJkmqqKQXyN4Cry2ORA5gEHFXLUFJL99yYyZx+y1CGvzmVT2y2JucesAV9unUsOpYkSS1CU2axeBbYJiK6lpen1jqU1FJNmzWXXz0wgqsfe52eq7Xnj0dszye3WIsIe40lSVpZFlsgR8QRmXltRJzcaD0AmfnrGmeTWpQHh7/NWbe/wJtTZ3LETv04dZ9N6NqhbdGxJElqcZbUg9y5/NlHckk19PbUmZx9xzDufeEtNlmzCxd9cXsG9luj6FiSJLVYiy2QM/OS8udzVl4cqeWYPz+57sk3+Pm9LzF73nxO/eQmHPvRDWjbulXR0SRJatGa8qCQq4ETMnNyeXkN4FeZ+dUaZ5OarRFvvc/ptzzP029M5iMb9eC8A7eif8/OS99RkiTVXFNmsdi6oTgGyMz3ImK72kWSmq+Zc+bx24de4dJ/vkrXjm359cHb8Nnt1vEmPEmS6khTCuRWEbFGZr4HEBHdm7ifpAr/Gfku3791KKMnTueggevy/U9vRvfO7YqOJUmSGmlKofsr4PGIuInSPMgHAefVNJXUjEycNovz7n6RW54ZR/8enbj+azux60Y9i44lSZIWoynzIP85IoYAe5RXfS4zh9c2lrTqy0z+9vQ4zrt7OO/PnMvxe2zE8XtuRIe2rYuOJkmSlqBJQyUyc1hETAA6AETEepn5Rk2TSauw1979gB/cOpTHRk1kYL81+OnntmLjNZ0xUZKkVUFTZrHYn9Iwiz7AO0A/4EVgi9pGk1Y9s+fO59J/juK3/xhJ+zatOO+zW3LYDuvRqpU34UmStKpoSg/yj4Cdgb9n5nYRsQdwRG1jSaueIaMncfotQ3n57Wnsu9XanLXf5vTu2qHoWJIkaRk1pUCek5kTI6JVRLTKzIcj4je1DiatKqbMmMPP73uJ6/77Bn1W78CfjhzExzdbs+hYkiRpOTWlQJ4cEasB/wSui4h3gA+acvCI2Ae4EGgNXJ6Z5zfafgH/u/mvE9A7M7tFxLbAH4CuwDzgvMy8sbzPVcDHgCnl/Y7KzGebkkeqpszknqFvcfadw5g4bRZH77Y+J++1MZ3bOwuiJEmrsqb8T34AMAM4CTgcWB04d2k7RURr4GJgL2As8FRE3FE5A0ZmnlTR/ttAwwNIpgNfzsxXIqIPMCQi7q94YMmpmXlzE7JLNTFu8gzOvO0FHnrpHbbo05UrjtyBrdZdvehYkiSpCppSIPcG3szMmcDVEdERWBOYuJT9dgRGZuarABFxA6Vie3FTxB0GnAWQmS83rMzM8eVe617A5CbklWpm3vzkqsde51cPjCATzth3M47atT9tWrcqOpokSaqSpvyvfhMwv2J5Xnnd0qwDjKlYHlte9yER0Q9YH/jHIrbtCLQDRlWsPi8ino+ICyKi/WKOeWxEDI6IwRMmTGhCXGnJXhg3hQMv/g8/ums4O63fnQdO+ihf+78NLI4lSWpmmvI/e5vMnN2wUH5d7efjHgrcnJnzKldGxNrANcBXMrOhSD8d2BTYAegOfG9RB8zMSzNzUGYO6tWrV5XjqiWZNz8ZPXE6+//u37w5ZSa/++J2XHHUDvTt3qnoaJIkqQaaMsRiQkTsn5l3AETEAcC7TdhvHNC3Ynnd8rpFORT4VuWKiOgK3A38IDOfaFifmW+WX86KiCuBU5qQRVou4ybPYOi4KcyaO58v7rQe39tnU1bv2LboWJIkqYaaUiAfR2n2it8BQWnYxJebsN9TwICIWJ9SYXwo8MXGjSJiU2AN4PGKde2AW4E/N74ZLyLWzsw3IyKAA4EXmpBFWmaz5s7jm9cOYc68+Wy2dhd+8tmtio4kSZJWgqUWyJk5Cti5PNUbmTmtKQfOzLkRcTxwP6Vp3q4oP7L6XGBwQ480pcL5hszMit0PBj4K9IiIo8rrGqZzuy4ielEq1p+lVMBLVXfOncN5buwUBvReja4d7DWWJKmlWGyBHBFHZOa1EXFyo/UAZOavl3bwzLwHuKfRujMbLZ+9iP2uBa5dzDH3XNp5pRV10+AxXP/fNzjuYxvyzBvvFR1HkiStREu6Sa/hDqQui/mQmqVh46dwxm0vsMsGPThl742LjiNJklayJQ2x2LD8eXhmNmVaN2mVN2X6HI67dghrdGrHRV/czincJElqgZb0v/+nyzfCnb6ywkhFmj8/OfHGZ3hrykx+f8T29FxtkVNsS5KkZm5JPcj3Ae8Bq0XE1Ir1AWRmdq1pMmkl+93DI3l4xAR+dMAWbL/eGkXHkSRJBVlsD3JmnpqZ3YC7M7NrxUcXi2M1N4+MeIcL/v4yn91uHY7YuV/RcSRJUoGWOsAyMw9YGUGkooyZNJ0Tb3yWTdYszXXcMFOLJElqmRZbIEfEv8uf34+IqeXPDR9TF7eftCqZOWce37zuaebNT/54xEA6tmtddCRJklSwxY5Bzszdyp+d0k3N1tl3DGPouClc9uVB9O/Zueg4kiSpDix1iEVEbBgR7cuvd4+I70REt5onk2rshiff4IanxnD8Hhux1+ZrFh1HkiTViaZM8vo3YF5EbARcCvQFrq9pKqnGho6dwpl3DOP/BvTkpL18GIgkSfqfphTI8zNzLvBZ4KLMPBVYu7axpNp574PZHHftEHqt1p4LD92O1q28KU+SJP1PUwrkORFxGHAkcFd5XdvaRZJqZ9785IQbn2XC+7P4/eHb071zu6IjSZKkOtOUAvkrwC7AeZn5WkSsD1xT21hSbVz40Cv88+UJnL3/FmzTt1vRcSRJUh1a0pP0AMjM4cB3KpZfA35Wy1BSLfzjpbf57UOvcNDAdTlsx75Fx5EkSXVqqQVyRAwAfgpsDnRoWJ+ZG9Qwl1RVb0yczok3PMvma3flxwdu6cNAJEnSYjVliMWVwB+AucAewJ+Ba2sZSqqmmXPmcdy1QwD44xED6dDWh4FIkqTFa0qB3DEzHwIiM0dn5tnAvrWNJVVHZvKDW19g+JtTufDQ7VivR6eiI0mSpDq31CEWwKyIaAW8EhHHA+OA1WobS6qO6598g789PZbvfHwAe2zau+g4kiRpFdCUHuQTgE6UbtQbCHyJ0pRvUl17dsxkzrljOB/buBcnfHxA0XEkSdIqoimzWDxVfjmN0pRvUt2bOG0W37x2CL27tufCQ7f1YSCSJKnJFlsgR8SdQC5ue2buX5NE0gqaNz854YZnefeD2dzyjV3p1smHgUiSpKZbUg/yL1daCqmKfv3gCP498l1+/vmt2XKd1YuOI0mSVjFLKpCHA73KDwpZICI2BybUNJW0nB4Y9hYXPzyKQ3foy8E7+DAQSZK07JZ0k95FQM9FrO8BXFibONLye+3dD/juX59jq3VW5+z9tyg6jiRJWkUtqUDeKDP/2XhlZv4L2Lp2kaRlN332XL5x7RBatw7+cMT2PgxEkiQttyUNseiyhG1tqx1EWl4NDwMZ8fb7XPWVHVl3DR8GIkmSlt+SepBHRsSnG6+MiE8Br9YukrRsrnliNLc+M46TPrExH9u4V9FxJEnSKm5JPcgnAndHxMHAkPK6QcAuwGdqnEtqkiGj3+NHdw1nz017c/weGxUdR5IkNQOL7UHOzFeArYBHgf7lj0eBrTPz5ZURTlqSd6fN4lvXPc3aq3fkgoO3pZUPA5EkSVWwxCfpZeYs4MqVlEVqsrnz5vPt65/hvemzueWbu7J6J4fFS5Kk6ljSGOQVFhH7RMSIiBgZEactYvsFEfFs+ePliJhcse3IiHil/HFkxfqBETG0fMzfRoTdhi3QLx4YweOvTuQnn92KLfr4MBBJklQ9S+xBXhER0Rq4GNgLGAs8FRF3VD54JDNPqmj/bWC78uvuwFmUxjwnMKS873vAH4BjgP8C9wD7APfW6n2o/tz3wptc8uirHL7Tenx+4LpFx5EkSc3MUnuQI2K/iFienuYdgZGZ+WpmzgZuAA5YQvvDgL+UX38SeDAzJ5WL4geBfSJibaBrZj6RmQn8GThwObJpFTVqwjROuel5tunbjTP327zoOJIkqRlqSuF7CPBKRPw8IjZdhmOvA4ypWB5bXvchEdEPWB/4x1L2Xaf8uinHPDYiBkfE4AkTfDJ2c/DBrLkcd80Q2rVpxR8O3572bXwYiCRJqr6lFsiZeQSloQ+jgKsi4vFy8bmkB4ksq0OBmzNzXrUOmJmXZuagzBzUq5dz467qMpPv/e15Rk2YxkWHbUefbh2LjiRJkpqpJg2dyMypwM2UhkmsDXwWeLo8bnhxxgF9K5bXLa9blEP53/CKJe07rvy6KcdUM3Llf17nruff5Lt7b8JHNupZdBxJktSMNWUM8v4RcSvwCKVHTO+YmZ8CtgG+u4RdnwIGRMT6EdGOUhF8xyKOvymwBvB4xer7gb0jYo2IWAPYG7g/M98EpkbEzuXZK74M3N6E96lV2FOvT+In97zIXpuvyTc+tmHRcSRJUjPXlFksPg9ckJn/rFyZmdMj4ujF7ZSZcyPieErFbmvgiswcFhHnAoMzs6FYPhS4oXzTXcO+kyLiR5SKbIBzM3NS+fU3gauAjpRmr3AGi2bsnfdn8q3rnmbdNTryq4O38WEgkiSp5ppSIJ8NvNmwEBEdgTUz8/XMfGhJO2bmPZSmYqtcd2aj5bMXs+8VwBWLWD8Y2LIJubWKmzNvPsdf/wxTZ87hz0fvSNcOPgxEkiTVXlPGIN8EzK9YnldeJ9XUz+59iSdfm8T5n9uaTdfqWnQcSZLUQjSlQG5TnscYgPLrdrWLJMHdz7/J5f9+jSN36ceB2y1yJj9JkqSaaEqBPCEi9m9YiIgDgHdrF0kt3ch33ufUm59j+/W68YN9fRiIJElauZoyBvk44LqI+B0QlB7g8eWaplKLNW3WXL5+zRA6tWvN7w8fSLs2y/MQR0mSpOW31AI5M0cBO0fEauXlaTVPpRYpM/l/Nz/H6xOnc+3RO7HW6h2KjiRJklqgpvQgExH7AlsAHUrTD0NmnlvDXGqBLv/Xa9wz9C1O/9Sm7LJhj6LjSJKkFqopDwr5I3AI8G1KQyy+APSrcS61ME+8OpHz73uJfbZYi2M/ukHRcSRJUgvWlAGeu2bml4H3MvMcYBdg49rGUkvy9tSZHH/9M/Tr0YlffGFrGv5KIUmSVISmFMgzy5+nR0QfYA6wdu0iqSWZPXc+37zuaabPnssfjxhIFx8GIkmSCtaUMch3RkQ34BfA00ACl9UylFqOn9zzIkNGv8dFh23Hxmt2KTqOJEnSkgvkiGgFPJSZk4G/RcRdQIfMnLIywql5u/3ZcVz12Ot89SPrs982fYqOI0mSBCylQM7M+RFxMbBdeXkWMGtlBFPzNuKt9zntb0PZof8anP7pTYuOs0Q3fn2XoiNIkqSVqCljkB+KiM+Hd06pSqbOnMM3rh3Cah3acPEXt6dtax8GIkmS6kdTKpOvAzcBsyJiakS8HxFTa5xLzVRmcspfn2P0pOlc/MXt6d3Vh4FIkqT60pQn6XnnlKrmj4++ygPD3+aMfTdjx/W7Fx1HkiTpQ5ZaIEfERxe1PjP/Wf04as4eG/kuv7j/Jfbdem2O3m39ouNIkiQtUlOmeTu14nUHYEdgCLBnTRKpWXpzygy+/Zdn2KDXavz88z4MRJIk1a+mDLHYr3I5IvoCv6lVIDU/s+bO4xvXPs3MOfP44xED6dy+Kb+XSZIkFWN5KpWxwGbVDqLm68d3vcizYybz+8O3Z6PeqxUdR5IkaYmaMgb5IkpPz4PSrBfbUnqinrRUtzw9lmueGM2xH92AT2/lE8olSVL9a0oP8uCK13OBv2Tmf2qUR83Ii29O5fu3DmWn9bvz/z65SdFxJEmSmqQpBfLNwMzMnAcQEa0jolNmTq9tNK3KpsyYw3HXDmH1jm353Re3p40PA5EkSauIJj1JD+hYsdwR+Htt4qg5mD8/+e5fn2XcezP4/eHb06tL+6IjSZIkNVlTCuQOmTmtYaH8ulPtImlV94dHR/H3F9/hjH03Y2A/HwYiSZJWLU0pkD+IiO0bFiJiIDCjdpG0KvvnyxP45QMjOGDbPhy5a/+i40iSJC2zpoxBPhG4KSLGAwGsBRxSy1BaNY19bzon3PAMG/fuwk8/t5UPA5EkSaukpjwo5KmI2BRomIZgRGbOqW0srWpmzpnHN697mrnzkj8csT2d2vkwEEmStGpa6hCLiPgW0DkzX8jMF4DVIuKbtY+mVck5dw7n+bFT+OXB27BBLx8GIkmSVl1NGYN8TGZObljIzPeAY2qWSKucvw4ew1+efINv7L4hn9xiraLjSJIkrZCmFMito2IwaUS0BtrVLpJWJS+Mm8IPb3uBXTfswXf32rjoOJIkSSusKQNF7wNujIhLystfL69TCzd5+my+cd0Qundux28P286HgUiSpGahKRXN94B/AN8ofzwEnNqUg0fEPhExIiJGRsRpi2lzcEQMj4hhEXF9ed0eEfFsxcfMiDiwvO2qiHitYtu2TcnS3BxyyeMccsnjhZ1//vzkpBuf5a0pM/n94dvTczUfBiJJkpqHpsxiMR/4Y/mDiPg/4CLgW0varzwU42JgL2As8FRE3JGZwyvaDABOBz6Sme9FRO/yOR8Gti236Q6MBB6oOPypmXlzE9+jauCif4zk4RET+NGBW7LdemsUHUeSJKlqmvQ38YjYLiJ+HhGvA+cCLzVhtx2BkZn5ambOBm4ADmjU5hjg4vKNf2TmO4s4zkHAvZk5vSlZVXuPjHiH3zz0Mp/bfh2O2Gm9ouNIkiRV1WIL5IjYOCLOioiXKPUYjwEiM/fIzIuacOx1yvs0GFteV2ljYOOI+E9EPBER+yziOIcCf2m07ryIeD4iLoiIRf5tPyKOjYjBETF4woQJTYirphgzaTon3PAsm6zZhfMO9GEgkiSp+VlSD/JLwJ7AZzJzt3JRPK/K528DDAB2Bw4DLouIbg0bI2JtYCvg/op9Tgc2BXYAulMaI/0hmXlpZg7KzEG9evWqcuyWaeaceXzjuiHMz+SSLw2kY7vWRUeSJEmquiUVyJ8D3gQejojLIuLjlB413VTjgL4Vy+uW11UaC9yRmXMy8zXgZUoFc4ODgVsrn9yXmW9mySzgSkpDObQSnHn7C7wwbiq/OWRb+vXoXHQcSZKkmlhsgZyZt2XmoZR6ax8GTgR6R8QfImLvJhz7KWBARKwfEe0oDZW4o1Gb2yj1HhMRPSkNuXi1YvthNBpeUe5Vpjw384HAC03IohV0w5Nv8NfBY/n2nhvx8c3WLDqOJElSzSz1Jr3M/CAzr8/M/Sj1Aj/DYoY1NNpvLnA8peERLwJ/zcxhEXFuROxfbnY/MDEihlMqwk/NzIkAEdGfUg/0o40OfV1EDAWGAj2BHy/9bWpFPD92MmfePoz/G9CTEz/hw0AkSVLz1pQHhSxQnm3i0vJHU9rfA9zTaN2ZFa8TOLn80Xjf1/nwTX1k5p7LklkrZtIHs/nGtU/Tq0t7Ljx0O1q38qY8SZLUvC1TgayWZd785IQbnmHC+7O46bhd6N7ZJ4xLkqTmzwJZi3Xh31/mX6+8y08/txXb9O1WdBxJkqSVokkPClHL89CLb/Pbf4zkCwPX5dAd+i59B0mSpGbCAlkfMnriB5x047Ns0acrPzpwSx8GIkmSWhQLZC1kxux5HHft00QEfzxiIB3a+jAQSZLUsjgGWQtkJj+4bSgvvTWVK47agb7dOxUdSZIkaaWzB1kLXPffN7jl6XGc8PEB7LFJ76LjSJIkFcICWQA888Z7nHPnMHbfpBff2XPA0neQJElqpiyQxcRps/jmdU+zZtcO/OaQbWnlw0AkSVIL5hjkFm7e/OQ7NzzDxA9mc8s3dqVbJx8GIkmSWjZ7kFu4Xz0wgv+MnMiPD9ySLddZveg4kiRJhbNAbsEeGPYWv39kFIft2JeDB/kwEEmSJLBAbrFee/cDvvvX59h63dU5a78tio4jSZJUNyyQW6Dps+dy3DVDaN06+P3h2/swEEmSpArepNfCZCan3zKUl995n6u/siPrruHDQCRJkirZg9zC/Pnx0dz+7Hi+u9fGfHTjXkXHkSRJqjsWyC3IkNGT+NFdw/nEZr355u4bFR1HkiSpLlkgtxAT3i89DGSdNTryq4N9GIgkSdLiWCC3AHPnzefbf3maKTPm8IfDB7J6x7ZFR5IkSapb3qTXAvzi/hE88eokfvWFbdi8T9ei40iSJNU1e5CbuXuHvskl/3yVI3Zej88PXLfoOJIkSXXPArkZG/nONE69+Xm27duNH35m86LjSJIkrRIskJupD2bN5bhrh9CuTSt+f/j2tG/jw0AkSZKawgK5GcpMvve353l1wjQuOmw7+nTrWHQkSZKkVYYFcjN0xX9e567n3+SUT27CRzbqWXQcSZKkVYoFcjPz5GuT+Ok9L7L35mvyjY9tWHQcSZKkVY4FcjPyztSZfOv6p+nbvRO/PHgbInwYiCRJ0rJyHuRmYs68+Xzr+qeZNnMu1x69E107+DAQSZKk5WGB3Eycf+9LPPX6e1x46LZsslaXouNIkiStshxi0Qzc9fx4/vTv1zhq1/4csO06RceRJElapdW0QI6IfSJiRESMjIjTFtPm4IgYHhHDIuL6ivXzIuLZ8scdFevXj4j/lo95Y0S0q+V7qHevvP0+/+/m5xnYbw2+/+nNio4jSZK0yqtZgRwRrYGLgU8BmwOHRcTmjdoMAE4HPpKZWwAnVmyekZnblj/2r1j/M+CCzNwIeA84ulbvod7NnZ98/dohdGrXmou/uD3t2vgHAUmSpBVVy4pqR2BkZr6ambOBG4ADGrU5Brg4M98DyMx3lnTAKE3LsCdwc3nV1cCB1Qy9qshMXp0wjdETp3PRYduz1uodio4kSZLULNSyQF4HGFOxPLa8rtLGwMYR8Z+IeCIi9qnY1iEiBpfXH1he1wOYnJlzl3BMACLi2PL+gydMmLDCb6bevDV1Ju9Nn8P39tmEXTbsUXQcSZKkZqPoWSzaAAOA3YF1gX9GxFaZORnol5njImID4B8RMRSY0tQDZ+alwKUAgwYNymoHrwfdO7fjmP/boOgYkiRJzUote5DHAX0rltctr6s0FrgjM+dk5mvAy5QKZjJzXPnzq8AjwHbARKBbRLRZwjFbhLVX78hGvTr7MBBJkqQqq2WB/BQwoDzrRDvgUOCORm1uo9R7TET0pDTk4tWIWCMi2les/wgwPDMTeBg4qLz/kcDtNXwPdc3iWJIkqfpqViCXxwkfD9wPvAj8NTOHRcS5EdEwK8X9wMSIGE6p8D01MycCmwGDI+K58vrzM3N4eZ/vASdHxEhKY5L/VKv3IEmSpJanpmOQM/Me4J5G686seJ3AyeWPyjaPAVst5pivUpohQ5IkSao6J86VJEmSKlggS5IkSRUskCVJkqQKFsiSJElSBQtkSZIkqYIFsiRJklTBAlmSJEmqYIEsSZIkVbBAliRJkipYIEuSJEkVLJAlSZKkChbIkiRJUgULZEmSJKmCBbIkSZJUwQJZkiRJqmCBLEmSJFWwQJYkSZIqWCBLkiRJFSyQJUmSpAoWyJIkSVIFC2RJkiSpggWyJEmSVMECWZIkSapggSxJkiRVsECWJEmSKlggS5IkSRUskCVJkqQKFsiSJElSBQtkSZIkqYIFsiRJklTBAlmSJEmqUNMCOSL2iYgRETEyIk5bTJuDI2J4RAyLiOvL67aNiMfL656PiEMq2l8VEa9FxLPlj21r+R4kSZLUsrSp1YEjojVwMbAXMBZ4KiLuyMzhFW0GAKcDH8nM9yKid3nTdODLmflKRPQBhkTE/Zk5ubz91My8uVbZJUmS1HLVsgd5R2BkZr6ambOBG4ADGrU5Brg4M98DyMx3yp9fzsxXyq/HA+8AvWqYVZIkSQJqWyCvA4ypWB5bXldpY2DjiPhPRDwREfs0PkhE7Ai0A0ZVrD6vPPTigohov6iTR8SxETE4IgZPmDBhxd6JJEmSWoyib9JrAwwAdgcOAy6LiG4NGyNibeAa4CuZOb+8+nRgU2AHoDvwvUUdODMvzcxBmTmoVy87nyVJktQ0tSyQxwF9K5bXLa+rNBa4IzPnZOZrwMuUCmYioitwN/CDzHyiYYfMfDNLZgFXUhrKIUmSJFVFLQvkp4ABEbF+RLQDDgXuaNTmNkq9x0RET0pDLl4tt78V+HPjm/HKvcpERAAHAi/U7i1IkiSppanZLBaZOTcijgfuB1oDV2TmsIg4FxicmXeUt+0dEcOBeZRmp5gYEUcAHwV6RMRR5UMelZnPAtdFRC8ggGeB42r1HurZjV/fpegIkiRJzVJkZtEZam7QoEE5ePDgomNIkiRpJYuIIZk5aFn2KfomPUmSJKmuWCBLkiRJFSyQJUmSpAoWyJIkSVIFC2RJkiSpggWyJEmSVMECWZIkSapggSxJkiRVsECWJEmSKlggS5IkSRUskCVJkqQKFsiSJElSBQtkSZIkqUJkZtEZai4iJgCjl9KsJ/DuSojT0nmdVx6v9crhdV55vNYrh9d55fFarxybZGaXZdmhTa2S1JPM7LW0NhExODMHrYw8LZnXeeXxWq8cXueVx2u9cnidVx6v9coREYOXdR+HWEiSJEkVLJAlSZKkChbI/3Np0QFaCK/zyuO1Xjm8ziuP13rl8DqvPF7rlWOZr3OLuElPkiRJaip7kCVJkqQKFsiSJElShRZfIEfEPhExIiJGRsRpRedpTiLiioh4JyJeqFjXPSIejIhXyp/XKDJjcxARfSPi4YgYHhHDIuKE8nqvdZVFRIeIeDIinitf63PK69ePiP+Wf47cGBHtis7aHERE64h4JiLuKi97nWsgIl6PiKER8WzDdFj+/Ki+iOgWETdHxEsR8WJE7OJ1rr6I2KT8vdzwMTUiTlzWa92iC+SIaA1cDHwK2Bw4LCI2LzZVs3IVsE+jdacBD2XmAOCh8rJWzFzgu5m5ObAz8K3y97HXuvpmAXtm5jbAtsA+EbEz8DPggszcCHgPOLq4iM3KCcCLFcte59rZIzO3rZiT158f1XchcF9mbgpsQ+l72+tcZZk5ovy9vC0wEJgO3MoyXusWXSADOwIjM/PVzJwN3AAcUHCmZiMz/wlMarT6AODq8uurgQNXZqbmKDPfzMyny6/fp/RDdx281lWXJdPKi23LHwnsCdxcXu+1roKIWBfYF7i8vBx4nVcmf35UUUSsDnwU+BNAZs7OzMl4nWvt48CozBzNMl7rll4grwOMqVgeW16n2lkzM98sv34LWLPIMM1NRPQHtgP+i9e6Jsp/9n8WeAd4EBgFTM7MueUm/hypjt8A/w+YX17ugde5VhJ4ICKGRMSx5XX+/Kiu9YEJwJXlYUOXR0RnvM61dijwl/LrZbrWLb1AVoGyNMeg8wxWSUSsBvwNODEzp1Zu81pXT2bOK//pbl1Kf4XatNhEzU9EfAZ4JzOHFJ2lhdgtM7enNNzwWxHx0cqN/vyoijbA9sAfMnM74AMa/Ynf61xd5XsU9gduarytKde6pRfI44C+Fcvrltepdt6OiLUByp/fKThPsxARbSkVx9dl5i3l1V7rGir/efRhYBegW0S0KW/y58iK+wiwf0S8Tmno256Uxm96nWsgM8eVP79Daazmjvjzo9rGAmMz87/l5ZspFcxe59r5FPB0Zr5dXl6ma93SC+SngAHlO6PbUeqKv6PgTM3dHcCR5ddHArcXmKVZKI/N/BPwYmb+umKT17rKIqJXRHQrv+4I7EVpzPfDwEHlZl7rFZSZp2fmupnZn9LP5X9k5uF4nasuIjpHRJeG18DewAv486OqMvMtYExEbFJe9XFgOF7nWjqM/w2vgGW81i3+SXoR8WlKY91aA1dk5nnFJmo+IuIvwO5AT+Bt4CzgNuCvwHrAaODgzGx8I5+WQUTsBvwLGMr/xmt+n9I4ZK91FUXE1pRu7mhNqYPhr5l5bkRsQKmnszvwDHBEZs4qLmnzERG7A6dk5me8ztVXvqa3lhfbANdn5nkR0QN/flRVRGxL6abTdsCrwFco/xzB61xV5V/23gA2yMwp5XXL9D3d4gtkSZIkqVJLH2IhSZIkLcQCWZIkSapggSxJkiRVsECWJEmSKlggS5IkSRUskCW1GBGREfGriuVTIuLsAiM1SUS8HhE9i85RlIg4MCI2LzqHpJbDAllSSzIL+FxRxWbFU+C0bA4ELJAlrTQWyJJakrnApcBJjTdExFURcVDF8rTy590j4tGIuD0iXo2I8yPi8Ih4MiKGRsSG5Xa9IuJvEfFU+eMj5fVnR8Q1EfEf4JqI6B8R/4iI5yPioYhYbxFZekTEAxExLCIuB6Ji2xHlcz8bEZdEROtF7L9DRDwWEc+V23aJiA4RcWU58zMRsUe57VERcVtEPFjuqT4+Ik4ut3kiIrqX2z0SEReWz/tCROxYXt+9vP/z5fZbV7zvK8r7vRoR31nae4iIaRFxXjn3ExGxZkTsCuwP/KLcfsPyx30RMSQi/hURm5b3/0I523MR8c9l+9aQpP+xQJbU0lwMHB4Rqy/DPtsAxwGbAV8CNs7MHSk9Fevb5TYXAhdk5g7A58vbGmwOfCIzDwMuAq7OzK2B64DfLuJ8ZwH/zswtKD3lbD2AiNgMOAT4SGZuC8wDDq/cMSLaATcCJ2TmNsAngBnAt4DMzK0oPYL16ojoUN5tS+BzwA7AecD0zNwOeBz4csXhO5XP+03givK6c4Bnyu/n+8CfK9pvCnwS2BE4KyLaLuU9dAaeKOf+J3BMZj5G6RGxp2bmtpk5itIvOd/OzIHAKcDvy/ufCXyyvP/+i7iuktQk/rlPUouSmVMj4s/AdygVjk3xVGa+CRARo4AHyuuHAnuUX38C2DxiQWdv14hYrfz6jsxsONculIpRgGuAny/ifB9taJOZd0fEe+X1HwcGAk+Vz9MReKfRvpsAb2bmUw3vt5x7N0rFOZn5UkSMBjYu7/NwZr4PvB8RU4A7K97f1hXH/kt5/39GRNeI6AbsRukXAjLzH+Xe767l9neXHwU9KyLeAdZcynuYDdxVfj0E2KvxhSlf012Bmyqudfvy5/8AV0XEX4FbGu8rSU1lgSypJfoN8DRwZcW6uZT/qhYRrYB2FdtmVbyeX7E8n//9HG0F7JyZMytPVC7iPqhS7qDU+3x6lY7XoCnvDyAb7dd4eUnHnVc+1pLew5zMzEbtG2sFTC73Pi8cJvO4iNgJ2BcYEhEDM3PiUjJK0oc4xEJSi5OZk4C/AkdXrH6dUs8mlP4833YZD/sA/xtuQURsu5h2jwGHll8fDvxrEW3+CXyxfJxPAWuU1z8EHBQRvcvbukdEv0b7jgDWjogdym26ROnmwH+Vz0dEbExp2MaIZXh/UBoa0dAbPSUzpzQ67u7Auw291ovRlPfQ2PtAF1jQI/5aRHyhvH9ExDbl1xtm5n8z80xgAtB3Gd+fJAEWyJJarl8BlbNZXAZ8LCKeozQMYll7fb8DDCrfrDac0pjlRfk28JWIeJ7SeOYTFtHmHOCjETGM0lCLNwAyczhwBvBAef8HgbUrd8zM2ZQK2YvK7+VBoAOlcbqtImIopTHKR5WHPyyLmRHxDPBH/vfLxdnAwHKe84Ejl3SApryHRbgBOLV84+CGlAryo8vvbxhwQLndL8o3Ib5A6ReR55bx/UkSAPG/v2ZJkrRoEfEIcEpmDi46iyTVmj3IkiRJUgV7kCVJkqQK9iBLkiRJFSyQJUmSpAoWyJIkSVIFC2RJkiSpggWyJEmSVOH/A12rz/8tInCsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df_total['clase']\n",
    "X = df_total.copy()\n",
    "X = X.drop(columns=['clase'])\n",
    "scaler = preprocessing.StandardScaler().fit(X)\n",
    "X = scaler.transform(X)\n",
    "\n",
    "pca = PCA()\n",
    "\n",
    "# set the tolerance to a large value to make the example faster\n",
    "logistic = LogisticRegression(max_iter=10000, tol=0.1)\n",
    "pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n",
    "\n",
    "param_grid = {\n",
    "    'pca__n_components': [5, 15, 30, 45, 64],\n",
    "    'logistic__C': np.logspace(-4, 4, 4),\n",
    "}\n",
    "search = GridSearchCV(pipe, param_grid, n_jobs=-1)\n",
    "search.fit(X, y)\n",
    "print(\"Best parameter (CV score=%0.3f):\" % search.best_score_)\n",
    "print(search.best_params_)\n",
    "\n",
    "# Plot the PCA spectrum\n",
    "pca.fit(X)\n",
    "\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(10, 10))\n",
    "ax0.plot(np.arange(1, pca.n_components_ + 1),\n",
    "         pca.explained_variance_ratio_, '+', linewidth=2)\n",
    "ax0.set_ylabel('PCA explicada por el radio de la varianza')\n",
    "\n",
    "ax0.axvline(search.best_estimator_.named_steps['pca'].n_components,\n",
    "            linestyle=':', label='Numero de componentes')\n",
    "ax0.legend(prop=dict(size=12))\n",
    "\n",
    "# For each number of components, find the best classifier results\n",
    "results = pd.DataFrame(search.cv_results_)\n",
    "components_col = 'param_pca__n_components'\n",
    "best_clfs = results.groupby(components_col).apply(\n",
    "    lambda g: g.nlargest(1, 'mean_test_score'))\n",
    "\n",
    "best_clfs.plot(x=components_col, y='mean_test_score', yerr='std_test_score',\n",
    "               legend=False, ax=ax1)\n",
    "ax1.set_ylabel('Accuracy Clasificacion (val)')\n",
    "ax1.set_xlabel('Numero de componentes')\n",
    "\n",
    "plt.xlim(-1, 70)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.title(\"N Componentes PCS vs Varianza explicada\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complicated-reset",
   "metadata": {},
   "source": [
    "Parece ser que con cerca de 10 componentes ya podemos explicar la mayoria de la variabilidad de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "engaged-valuable",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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