Multimodal-Emotion-Recognition/01-Audio/Notebook/SVM/02 - Train [SVM].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Speech Emotion Recognition - SVM Classifier\n",
    "\n",
    "A project for the French Employment Agency\n",
    "\n",
    "Telecom ParisTech 2018-2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this notebook is to set up all speech emotion recognition preprocessing and audio features extraction.\n",
    "\n",
    "### Audio features:\n",
    "The complete list of the implemented short-term features is presented below:\n",
    "- **Zero Crossing Rate**: The rate of sign-changes of the signal during the duration of a particular frame.\n",
    "- **Energy**: The sum of squares of the signal values, normalized by the respective frame length.\n",
    "- **Entropy of Energy**: The entropy of sub-frames' normalized energies. It can be interpreted as a measure of abrupt changes.\n",
    "- **Spectral Centroid**: The center of gravity of the spectrum.\n",
    "- **Sprectral Spread**: The second central moment of the spectrum.\n",
    "- **Spectral Entropy**: Entropy of the normalized spectral energies for a set of sub-frames.\n",
    "- **Spectral Flux**: The squared difference between the normalized magnitudes of the spectra of the two successive frames.\n",
    "- **Spectral Rolloff**: The frequency below which 90% of the magnitude distribution of the spectrum is concentrated.\n",
    "- **MFCCS**: Mel Frequency Cepstral Coefficients form a cepstral representation where the frequency bands are not linear but distributed according to the mel-scale.\n",
    "\n",
    "Global Statistics are then computed on upper features:\n",
    "- **mean, std, med, kurt, skew, q1, q99, min, max and range**\n",
    "\n",
    "### Data:\n",
    "**RAVDESS**: The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 7356 files (total size: 24.8 GB). The database contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes *calm*, *happy*, *sad*, *angry*, *fearful*, *surprise*, and *disgust* expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. (https://zenodo.org/record/1188976#.XA48aC17Q1J)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. General import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:27.045712Z",
     "start_time": "2019-04-15T13:26:24.216475Z"
    }
   },
   "outputs": [],
   "source": [
    "### General imports ###\n",
    "from glob import glob\n",
    "import os\n",
    "import pickle\n",
    "import itertools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "### Warning import ###\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "### Graph imports ###\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "### Sklearn imports ###\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:27.056304Z",
     "start_time": "2019-04-15T13:26:27.048682Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load datas from pickle\n",
    "[features, labels] = pickle.load(open(\"../Datas/Pickle/[RAVDESS][HAP-SAD-NEU-ANG-FEA-DIS-SUR][GLOBAL_STATS].p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. Train and test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:27.066244Z",
     "start_time": "2019-04-15T13:26:27.059625Z"
    }
   },
   "outputs": [],
   "source": [
    "# Build Train and test dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=123)\n",
    "\n",
    "# Encode Label from categorical to numerical\n",
    "lb = LabelEncoder()\n",
    "lb.fit(y_train)\n",
    "y_train, y_test = lb.transform(y_train), lb.transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV. Scale features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:27.086683Z",
     "start_time": "2019-04-15T13:26:27.070301Z"
    }
   },
   "outputs": [],
   "source": [
    "# Scale train and test dataset\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V. Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:27.294007Z",
     "start_time": "2019-04-15T13:26:27.090748Z"
    }
   },
   "outputs": [
    {
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YuKVaa79fVZ+Soc3t8zOEgR+ZIdR6Q5JvTfJD66fetdZeWFWflOHn92EZKqF+I0MY9qwMIchJn+stVfXnxq/vczJU4twnQzXQTyf5t7f667terbV7qurRSZ6S5P9M8qEZ/m5+L0Pl0dfnKByanvMNVfXaDAPyH5Xhd+VXknx+a+27qurvn/Lp/mWGU+c+L8mnZ/heJMlXj58vGX7HfyfDrKOPy1B99JNJ/mGufJrdtXyt3f49AMA2VGtXOmQFADhNVbUkaa3dqhO+AADgzNIuBwAAAMBNEzIBAAAAcNOETAAAAADcNDOZAAAAALhpKpkAAAAAuGkHu17ArfI+7/M+7Y477tj1MgAAAADOjVe84hVvbq3dfi2PPTch0x133JG77rpr18sAAAAAODeq6rev9bHa5QAAAAC4aUImAAAAAG6akAkAAACAmyZkAgAAAOCmCZkAAAAAuGlCJgAAAABumpAJAAAAgJsmZAIAAADgpgmZAAAAALhpQiYAAAAAbpqQCQAAAICbJmQCAAAA4KYJmQAAAAC4aUImAAAAAG6akAkAAACAmyZkAgAAAOCmCZkAAAAAuGlCJgAAAABumpAJAAAAgJsmZAIAAADgpgmZAAAAALhpQiYAAAAAbpqQCQCA/MG7D/Pk7/iFvP5tf7TrpQAAZ5SQCQCAvPbNf5iXvvpNeeU9b9/1UgCAM0rIBABAFsuWJFm2tuOVAABnlZAJAIAsxnBpCpsAAK6XkAkAgLSmkgkAuDlCJgAAslgOr4VMAMCNEjIBALBqk5vCJgCA6yVkAgBAuxwAcNOETAAArAZ/Lw3+BgBukJAJAICjdjmVTADADRIyAQCQKVtSyAQA3CghEwAAq0om7XIAwI0SMgEAsBr4bfA3AHCjhEwAAKzCpYVKJgDgBm01ZKqqx1TVq6vq7qp62gn336eqvme8/2VVdcfafX+mqn6+ql5VVa+sqvtuc60AAPtssRxeq2QCAG7U1kKmqponeVaSxyZ5ZJLPqapHbjzsKUne2lp7RJJnJvma8bkHSZ6f5Itaa49K8olJLm1rrQAA++6oXW7HCwEAzqxtVjI9OsndrbXXtNYuJnlBksdtPOZxSZ47vv3CJJ9SVZXkzyf5b621X06S1trvtdYWW1wrAMBe0y4HANysbYZM75/kdWvv3zPeduJjWmuHSd6e5MFJPihJq6qXVNV/raov3+I6AQD2ntPlAICbdbDFj10n3Lb5r5bTHnOQ5OOSfFSSdyb58ap6RWvtx489ueqpSZ6aJA972MNuesEAAPtqypZkTADAjdpmJdM9SR669v5DkrzhtMeMc5gemOQt4+0/1Vp7c2vtnUlenOQjNj9Ba+3ZrbU7W2t33n777Vv4EgAA9sNUwbQw+BsAuEHbDJlenuQDq+rhVXVbkicmedHGY16U5Enj249P8hOttZbkJUn+TFW9xxg+fUKSX93iWgEA9toULmmXAwBu1Nba5Vprh1X1pRkCo3mSb2+tvaqqnpHkrtbai5I8J8nzquruDBVMTxyf+9aq+sYMQVVL8uLW2n/Y1loBAPbd0elyQiYA4MZscyZTWmsvztDqtn7b09fefleSJ5zy3Ocnef421wcAwEC7HABws7bZLgcAwBkxdcnJmACAGyVkAgAgi6mSyUwmAOAGCZkAAFjNYhIyAQA3SsgEAMAqZGr65QCAGyRkAgAgi+X4WsgEANwgIRMAAGvtcjteCABwZgmZAADIcqldDgC4OUImAABWbXIGfwMAN0rIBADAqpLJTCYA4EYJmQAAyFTAJGMCAG6UkAkAAO1yAMBNEzIBAKBdDgC4aUImAACybE6XAwBujpAJAIAsltNrIRMAcGOETAAArCqZZEwAwI0SMgEAcBQySZkAgBskZAIAYNUmZ/A3AHCjhEwAAKza5BQyAQA3SsgEAMCqTU67HABwo4RMAACs2uScLgcA3CghEwAAa6fLCZkAgBsjZAIA4KhdTsgEANwgIRMAAFmM2ZJ2OQDgRgmZAABYa5fb8UIAgDNLyAQAgHY5AOCmCZkAAFi1yWmXAwBulJAJAIBVm5yMCQC4UUImAACOZjJJmQCAGyRkAgBgbfC3kAkAuDFCJgAAjmYyCZmAzvz6G38/3/zjv7HrZQDXQMgEAIB2OaBbP/LKN+Ybfuy/O5gAzgAhEwAAWS7H1/ZwQGemEFzIBP0TMgEAsGqTs4kDejNdl8yMg/4JmQAAWLXJ2cQBvRGCw9khZAIAwOlyQLeWDiaAM0PIBABAFuPebbHc7ToANk3XJQcTQP+ETAAAaJcDumXwN5wdQiYAALTLAd1aaJeDM0PIBADA0SZOpQDQmSlcWmrnhe4JmQAAyFQgoFAA6I3B33B2CJkAAHBEONCt6bpk8Df0T8gEAIBKAaBbQnA4O4RMAACsBn43IRPQGSE4nB1CJgAAVAoA3VqMlyXtctA/IRMAAKtTm5ZNNRPQF5VMcHYImQAAWLXLJU6YA/oyVViqtIT+CZkAADi2eVMtAPRkuiZNFZdAv4RMAAAcq2RSLQD0RLscnB1CJgAAsp4r2ccBPZlC8KWLE3RPyAQAgHY5oFtOl4OzQ8gEAMCxzZt2OaAnS4O/4cwQMgEAkGVrOZhVkqSpZAI6sjCTCc4MIRMAAFm0loP5EDKpFgB64nQ5ODuETAAAZNmSC7PZ6m2AXjhdDs4OIRMAAFkujyqZnOAE9OSoksm1CXonZAIAIIvWcmE+/NNQuxzQE4O/4ewQMgEA7LnWWlrLKmRSyQT0ZKpk0i4H/RMyAQDsuak4YNUuZ7gu0JHFeE3SLgf9EzIBAOy5qQVl1S6nWgDoiMHfcHYImQAA9tzUHncwM/gb6M+qXU4lE3RPyAQAsOemUGk1k8lGDujIdE0SgEP/hEwAAHvuqF1uqGTSkgL05KiSaccLAa5KyAQAsOeOBn9PlUw7XAzAhikIV2UJ/RMyAQDsueVGJZOWFKAnBn/D2SFkAgDYc0eDv2fH3gfogcHfcHYImQAA9tyibcxkspEDOjLNYhKAQ/+ETAAAe26awbQ6Xc5GDujIUiUTnBlCJgCAPbdql1uFTLtcDcBxU7gkZIL+CZkAAPbctHG7MNMuB/RnGvytyhL6J2QCANhzy9VMprGSScgEdGS6Rrk0Qf+ETAAAe27auB2Mg79t5ICeOF0Ozo6thkxV9ZiqenVV3V1VTzvh/vtU1feM97+squ4Yb7+jqv6oqn5pfPnWba4TAGCfrdrlxkqmhZYUoCPT4QSqLKF/B9v6wFU1T/KsJJ+a5J4kL6+qF7XWfnXtYU9J8tbW2iOq6olJvibJXx7v+83W2odva30AAAyO2uXGSiYbOaAjq0omATh0b5uVTI9Ocndr7TWttYtJXpDkcRuPeVyS545vvzDJp1RVbXFNAABsuPx0ORs5oB9TtaUAHPq3zZDp/ZO8bu39e8bbTnxMa+0wyduTPHi87+FV9YtV9VNV9ee2uE4AgL3mdDmgV+vBkkom6N/W2uWSnFSRtHlVOO0xv5vkYa2136uqj0zyg1X1qNba7x97ctVTkzw1SR72sIfdgiUDAOyfad7JBZVMQGfWg6XFcocLAa7JNiuZ7kny0LX3H5LkDac9pqoOkjwwyVtaa+9urf1ekrTWXpHkN5N80OYnaK09u7V2Z2vtzttvv30LXwIAwPl3ebvcLlcDcGS9slIADv3bZsj08iQfWFUPr6rbkjwxyYs2HvOiJE8a3358kp9orbWqun0cHJ6q+oAkH5jkNVtcKwDA3lpsDP7WLgf0Ynmsksm1CXq3tXa51tphVX1pkpckmSf59tbaq6rqGUnuaq29KMlzkjyvqu5O8pYMQVSSfHySZ1TVYZJFki9qrb1lW2sFANhnbapkGmcyqRYAerEeLAmZoH/bnMmU1tqLk7x447anr739riRPOOF535fk+7a5NgAABtOcE6fLAb1Zrs1hcm2C/m2zXQ4AgDNgqg64bQyZDNcFerHQLgdnipAJAGDPrdrl5trlgL4Y/A1ni5AJAGDPLTZPl1MtAHTC4G84W4RMAAB77qhdbjxdTrUA0Injg793uBDgmgiZAAD23JQpHcymwd87XAzAGu1ycLYImQAA9ty0iVvNZJIyAZ3QLgdni5AJAGDPTe1xR6fL2cgBfTjWLqeSCbonZAIA2HNtc/C3jRzQifXrkSpL6J+QCQBgz03DdFftckImoBPrw75VWUL/hEwAAHvu8na5Xa4G4IjB33C2CJkAAPbcUbucSiagL8fa5VyaoHtCJgCAPbc6XW42zmSykwM64XQ5OFuETAAAe27at11YVTLtcDEAa7TLwdkiZAIA2HNT5dJ8NoRMjgkHeqGSCc4WIRMAwJ6bQqX5rDIr7XJAP5wuB2eLkAkAYM9NlQLzqsxnpSUF6MYULFVpl4OzQMgEALDnlqtNXKWqtMsB3ZiCpQvzmUomOAOETAAAe26xNpNpXqVdDujGdH26bT7LwqUJuidkAgDYc1OmdNQut9v1AEwWq0omATicBUImAIA9N7Wj1GyYe6IlBejFFCxpl4OzQcgEALDnVu1yBn8DnVmshUyuTdA/IRMAwJ5btctNM5ls5IBOTNej2w5UMsFZIGQCANhzq3a5Gk6YWyx3vCCA0XQ9ujB38iWcBUImAIA9tzzWLpc0GzmgE0eDv2cGf8MZIGQCANhz0yZuVpVZlZYUoBvHBn8LwKF7QiYAgD03beJmszFkspEDOjGF3rfNZ1lq5YXuCZkAAPbcsg1Dv5PhtYwJ6MWqXe5AlSWcBUImAIA9t2gtY8aUWcVGDuiGdjk4W4RMAAB7brlsmdWQMs1m2uWAfhj8DWeLkAkAYM8tWztql6tyuhzQjeXaTCYBOPRPyAQAsOcWyxxVMjldDujIdD06mLs2wVkgZAIA2HPL9ZlMs8rCCU5AJ6Zc6WA2cygBnAFCJgCAPXesXW4W7XJAN5bj9eg2p8vBmSBkAgDYc4v1wd9l8DfQj4XT5eBMETIBAOy5ZWuZzcxkAvrjdDk4W4RMAAB7brkcTpVLkvmszD0BurFUyQRnipAJAGDPLdYHf1dUMgHdmA4iuG0+BOBmxkHfhEwAAHtus11uaRMHdGKqXjqYD1tXITj0TcgEALDnlhuDv4VMQC+G61NWJ2BqmYO+CZkAAPbcoh1t4OYzg7+Bfixay3xWqyB8udzxgoArEjIBAOy55fpMpllFxgT0Yqq0HLvlVDJB54RMAAB77ni7XLTLAd1YLI9XMqm0hL4JmQAA9ty0iUuSeWmXA/qxaC3zqtU1aun6BF0TMgEA7Llly1Elk3Y5oCPL5XD6pcHfcDYImQAA9tyytczGfxXOSqUA0I/LB3+7PkHPhEwAAHtusRzaUZLxdDmVAkAnFsuMg79VMsFZIGQCANhzQyXTNPi7DP4GurFctsxnWQXhZsZB34RMAAB7btnWT5cr7ShAN6bB37PV4O8dLwi4IiETAMCeWy5zrF1OxgT04mjw9/C+djnom5AJAGDPLVrLmDGlSjsK0I/Nwd+uT9A3IRMAwJ4bZp6MlUxmMgEdmQ4mmK5Rrk/QNyETAMCeW7a1kGkmZAL6MR1MYPA3nA1CJgCAPbdoSY0buKrKwmBdoBPLZTKro2uUEBz6JmQCANhzy2XLfJzJNJ/ZxAH9WIynX86dLgdngpAJAGDPHWuXM5MJ6Mg0M87pcnA2CJkAAPbcYtk22uVs4oA+OF0OzhYhEwDAnlu2thqqO59VljZxQCcWy412OZVM0DUhEwDAnlu2bJwut+MFAYymdl6ny8HZIGQCANhzy2XLuH9LlZknQD8Wy6HScrYa/O36BD0TMgEA7LnF5uBvmzigE8tlMpsdVVsKwaFvQiYAgD132UwmmzigEwZ/w9kiZAIA2HPLZY6dLrdsSRM0AR0w+BvOFiETAMCeGwbrDm9PFU32cUAPLh/8veMFAVckZAIA2HNTpUCSjMUC5p4AXTga/H30PtAvIRMAwJ5btrY6uWl6bSMH9GCxHK5P2uXgbBAyAQDsuWXLscHfiXY5oA/TwQRzg7/hTBAyAQDsuaFdbnhbuxzQk8VyPF1OJROcCUImAIA9t1yutcupFgA6smxDG69KJjgbhEwAAHtuakdJ1tvlbOSA3RsGfx9dm4RM0DchEwDAnls0lUzRXmCuAAAgAElEQVRAn6bB39rl4GzYashUVY+pqldX1d1V9bQT7r9PVX3PeP/LquqOjfsfVlV/UFV/d5vrBADYZ8vlUbi0Ol3ORg7owOWDv3e8IOCKthYyVdU8ybOSPDbJI5N8TlU9cuNhT0ny1tbaI5I8M8nXbNz/zCQ/sq01AgAwbuLGfxVOGzkZE9CDo8Hf4/suTtC1bVYyPTrJ3a2117TWLiZ5QZLHbTzmcUmeO779wiSfUjX8y6aqPjPJa5K8aotrBADYe4vWjiqZptPltMsBHViO7bxTAL50bYKubTNkev8kr1t7/57xthMf01o7TPL2JA+uqvsn+Yok/3iL6wMA2HuttbR2QrucjRzQgcWyZVZH1ygzmaBv2wyZ6oTbNq8Ipz3mHyd5ZmvtD674CaqeWlV3VdVdb3rTm25wmQAA+2vKkqaTm7TLAT1ZtuG6JACHs+Fgix/7niQPXXv/IUnecMpj7qmqgyQPTPKWJB+d5PFV9bVJHpRkWVXvaq39y/Unt9aeneTZSXLnnXe62gAAXKdpwza1yZl7AvRkOZ4uN3e6HJwJ2wyZXp7kA6vq4Ulen+SJST534zEvSvKkJD+f5PFJfqK11pL8uekBVfVVSf5gM2ACAODmTRu2qUpASwrQk4XT5eBM2VrI1Fo7rKovTfKSJPMk395ae1VVPSPJXa21FyV5TpLnVdXdGSqYnrit9QAAcLlVyFQbIZOWFKADm6fLCcChb9usZEpr7cVJXrxx29PX3n5Xkidc5WN81VYWBwDAql1uqhKYWlK0ywE92Dxdzkwm6Ns2B38DANC5ab92WbuclhSgA4vl2C5n8DecCUImAIA9ttwc/D2+1pIC7FprLcs2hOBVlSrXJuidkAkAYI9NbXFTlYBqAaAX02Vo1c5b5doEnRMyAQDsscsGfzsmHOjEambcuGudzcq8OOickAkAYI9Ns5cuO13ORg7YsVUIPjuqZHLyJfRNyAQAsMeO2uWG949OcNrVigAGJ51+6doEfRMyAQDssaPB31O73Hi7SiZgxzZnxs0M/obuCZkAAPbYZTOZpnY5LSnAjm2G4EMlk2sT9EzIBACwx1anN22cLmcfB+za0eDvtZBJJRN0TcgEALDHpk3cWCiQcS9nIwfs3GJj8PfM4G/onpAJAGCPLS+beaJdDujDdPrlXLscnBlCJgCAPbYKmWqzXc5GDtitzdMvZ6VdDnonZAIA2GNH7XLHK5lUCwC7dtLgb1WW0DchEwDAHlu1o2y2y6kWAHbs5MHfu1wRcDVCJgCAPbbcaEdxuhzQi8VlM+PMi4PeCZkAAPbYtIk7apcbb7eRA3ZseUI7rypL6JuQCQBgj02buGnw98zgb6ATU9btdDk4O4RMAAB7bLWJm2aemMkEdOJoJtPwvkom6J+QCQBgjx2dLje8f3S63K5WBDCYAqWZSiY4M4RMAAB7bDX4e9UuN95uIwfs2ObpcjOny0H3hEwAAHts2S4/Inz9doBdmQ4mmK3aeQXg0DshEwDAHluccHpTcrS5A9iVzYMJtMtB/4RMAAB7rG0M/p6tBn/vakUAg8va5aoE4NA5IRMAwB6bNnGz1eDv4bWWFGDXFicM/nZtgr4JmQAA9thJm7gkWlKAnVuOp1yuz4xTyQR9EzIBAOyx1i4/vSkx+BvYvcXq+jS8PyuVTNA7IRMAwB5bjJUCs9qcyWQjB+zWcnl5paVKJuibkAkAYI9tVgpMpzhN4RPArpw4+Nu1CbomZAIA2GNtYybTbPzXoUomYNcunxnnUALonZAJAGCPLTbaUVbtcjZywI4tNyqZtMtB/4RMAAB7bLMdZdUuZyMH7Nhi82ACg7+he0ImAIA9NmVJs8tOl9vVigAGm5WWKpmgf0ImAIA9djTz5Oi2WWmXA3Zv2S6vtFy4NkHXhEwAAHtstYmro5RpPiuDv4Gdm06Sm9dRpaUAHPomZAIAuBf8yuvfnre/89Kul3GZacNWayFTlZYUYPem69N06uXctQm6J2QCALgXfM6z/0u+4+d+a9fLuMzm4O9k2MipFgB27bLB37NaVTcBfRIyAQBsWWst73j3Yf7gXYe7Xsplpizp8na5HS0IYLQ5+HtWw/UU6JeQCQBgy6aN0mGHyc00e6nW/lVYFcN1gZ1rzelycNYImQAAtuxwFTL11+exapcz+BvozGY778zpctA9IRMAwJatQqZFf5ujVbvc5kwmIROwY4uNdt650+Wge0ImAIAtOxwn1V7qMmSaTpc7uq3KcF1g9y47XU67HHRPyAQAsGVTuHR22uWiWgDYuctOl6tKh5dRYI2QCQBgyxZdt8sd38Ql2uWAPmyeLjefRSUTdE7IBACwZZdW7XL9/S/4qWKp1iqZqrSkALu33Bj8PTf4G7onZAIA2LKj0+X62xwtWjtWxZQYrgv0YdUuN4bgs/Fa5foE/RIyAQBs2WHPlUzt+DymZAyZ7OGAHTsa/H1UyZRomYOeCZkAALbssOeZTMuWjYwpVTZxwO5tVlpOYZOWOeiXkAkAYMsOOz5dbnlSu1xVmpAJ2LHFcvPky7FdzvUJuiVkAgDYskvLqV2uv43RYnl0ctNkZrgu0IFla5mt7VhX7XKuT9AtIRMAwJYtln1XMm0UMmU2q3Q4PgrYM4tlO1bJdDT4e1crAq7mmkOmqvq4qvqr49u3V9XDt7csAIDzYxr43eVMphNPl4t2OWDnFsu2CpaSZD6+aWYc9OuaQqaq+r+TfEWSrxxvupDk+dtaFADAeXI0k6m/jdFi2U5ul7OJA3ZsMwSfG/wN3bvWSqbPSvIZSf4wSVprb0jynttaFADAebJql+uwB22YeWImE9CfU9vlhODQrWsNmS62oWa6JUlV3X97SwIAOF+mdrkeB38vN05vSoZqAXs4YNc2Q3CDv6F/1xoyfW9VfVuSB1XVFyb5T0n+9faWBQBwfhx2PPh7cdLg77KJA3bvtEom1yfo18G1PKi19vVV9alJfj/JByd5emvtx7a6MgCAc6Lrwd/LU9rllDIBO7ZY5vhMptIuB727ppApScZQSbAEAHCdpv/rfqnTmUyXny5XXa4V2C9Du9zR+wZ/Q/+uKWSqqndknMeU5LYMp8v9YWvtAdtaGADAedH16XItJ58u1+Fagf2yefrl9KbLE/TrWtvljp0kV1WfmeTRW1kRAMA5c2nZebvc5kymWaXDpQJ7ZtmOz2SaO10Ounetg7+Paa39YJJPvsVrAQA4l1btch0O/j6xXa6SZhMH7JjT5eDsudZ2uf9j7d1Zkjtz1D4HAMAVXBrLglobT0vaLB3aoc12lES7HNAHp8vB2XOtg7//0trbh0lem+Rxt3w1AADn0OHaEO1Li2Xms/kOV3Pc8qSZTLMy8wTYucUyJ1YyaZeDfl3rTKa/uu2FAACcV+sDv3sb/r15elOSzGqY1QSwS0M779H7TpeD/l0xZKqqb84V2uJaa3/zlq8IAOCcWR/4vV7V1IPNdpRk2MgtVAoAO3Zau5xKJujX1SqZ7rpXVgEAcI4drg387rOS6fKZTDZxwK6dPvh7VysCruaKIVNr7bn31kIAAM6rS8cqmfoKb5bt5MHf2uWAXbu8kunodqBP13q63O1JviLJI5Pcd7q9tfbJW1oXAMC5sVgeH/zdE+1yQK8Wy5MrmVRaQr9mV39IkuS7kvxakocn+ccZTpd7+ZbWBABwrhyrZOrs/8AvW04Y/F1Z9pWFAXto2Y6H4AZ/Q/+uNWR6cGvtOUkutdZ+qrX215J8zBbXBQBwbhybydRZJdNyeVK7nEoBYPcWy7YKlpKjwd8qLaFf19Qul+TS+Pp3q+ovJnlDkodsZ0kAAOfL+v91v9TZTKZFO76JS8Z2OZUCwI4tWk5ul3N9gm5da8j01VX1wCR/J8k3J3lAkr+9tVUBAJwjx9vlOqtkarm8kmlWsYcDdm25bJmvXZ60y0H/rrVd7mWttbe31n6ltfZJrbWPbK296GpPqqrHVNWrq+ruqnraCfffp6q+Z7z/ZVV1x3j7o6vql8aXX66qz7qurwoAoCPrLXK9VTIN7XLHb9MuB/TgsnY5g7+he9caMv1cVf3HqnpKVb3XtTyhquZJnpXksRlOpfucqnrkxsOekuStrbVHJHlmkq8Zb/+VJHe21j48yWOSfFtVXWvVFQBAV9aHfXc3k+mkdrkqmzhg55bt+My4o0qmXa0IuJprCplaax+Y5B8meVSSV1TVD1fV51/laY9Ocndr7TWttYtJXpDkcRuPeVyS545vvzDJp1RVtdbe2Vo7HG+/bxL/ygEAzqzDjk+XWyxbaqNdrspMJmD3NiuZ5uPu1eBv6Ne1VjKltfYLrbUvyxAevSVH4dBp3j/J69bev2e87cTHjKHS25M8OEmq6qOr6lVJXpnki9ZCJwCAM2V9DtOlzv4X/OYR4clQLWCwLrBri9aODf6eGfwN3bumkKmqHlBVT6qqH0nyc0l+N0PYdMWnnXDb5tXg1Me01l7WWntUko9K8pVVdd8T1vXUqrqrqu5605vedNWvAwBgFy4tWqYc57C3mUwtJ54uZw8H7Now+PukdjkXKOjVtVYy/XKSD0/yjNbaB7XWvqK19oqrPOeeJA9de/8hSd5w2mPGmUsPzFAltdJa+7Ukf5jkQzY/QWvt2a21O1trd95+++3X+KUAANy7FsuW+12YJ+nwdLnlUQA2qdKOAuzeop08+Nv1Cfp1rcO0P6C14S+5qv54a+2N1/Cclyf5wKp6eJLXJ3liks/deMyLkjwpyc8neXySn2ittfE5r2utHVbVn0zywUlee41rBQDoyqXFMve9MM87Ly76m8l02uDvztYJ7J/lMicO/nZ9gn5dU8g0BUyjFyf5iGt4zmFVfWmSlySZJ/n21tqrquoZSe5qrb0oyXOSPK+q7s5QwfTE8ekfl+RpVXUpyTLJF7fW3nytXxQAQE8O1yuZumuXO2Umk0oBYMcWy5b1DHw1k8nlCbp1rZVM606ao3Si1tqLM4RS67c9fe3tdyV5wgnPe16S593A2gAAunO4bLnvhWFKQXeDv5c58XS5ZUtau/zkOYB7y3KzXc7pctC9az5dbs2/vuWrAAA4xw7Hdrkk/bXLLdvqWPDJXLUA0IHlxulyc6fLQfeuWMk0nuj2RUkekeSVSZ7TWvtX98bCAADOi8NFy3ve92B8u7NKppNmMs3W7rv2InaAW2rhdDk4c65WyfTcJHdmCJgem+Qbtr4iAIBz5nC5zP1uGyqZLnU4k+mkdrnERg7YraHScr1dbqqydG2CXl1tJtMjW2sfmiRV9Zwkv7D9JQEAnC+Hy5b7HEztcn1VMm1WCiRrJzjZyAE7tGwbp8sJwKF7V6tkujS90Vo73PJaAADOpcPF+uDvvjZHy5bL2+XMZAI6sDkzbtUuJwCHbl2tkunDqur3x7cryf3G9ytJa609YKurAwA4By4tlrnfNPi7t5Bp2bJ5gNz0vmoBYJcWG4O/ZwZ/Q/euGDK11ub31kIAAM6rxbLlPmMlU2/tcst2ertcUy0A7NDy1MHfu1oRcDVXa5cDAOAmXVosczCb5cK8umuX26wUSI6qBVQyAbu02Dj9cnpTuxz0S8gEALBlh8uWg1nlYDbLYWf/C365PD5YNzk6wclGDtiV1lraxuDvqsqstMtBz4RMAABbdrhsOZjPcjCvHHa2OVq244N1k6PB3zImYFemSsrLDiaYlQAcOiZkAgDYssPFMhfmlQvzWXczmRatXV7JZPA3sGNTkLQZMs2qVDJBx4RMAABbtFy2LNuwUTqYVVeny53UjpKstcvZyAE7MuXxm9en+axcm6BjQiYAgC2a2uMuzGe5MJ91Nfh72qdd1o6iXQ7YsaNKpuO3z0u7HPRMyAQAsEVTe9zBrMaZTP20y03VABsZU2bjvxBt5IBdObo+XV5pqV0O+iVkAgDYoqly6WA+665dbjmGSLMTZp4k2uWA3Vka/A1nkpAJAGCLDhdHlUxDu1w/lUxTyDQ/YeZJMsxsAtiFKw3+7ugyCmwQMgEAbNFUDXQwn9rl+gluTm1HmSqZhEzAjixPuT7NZ9EuBx0TMgEAbNGlafD3bJaDWWeVTNPpTdrlgM6cVslk8Df0TcgEALBFU7vcfFb9zmTaHPw9vm8fB+zKFHJvtvMa/A19EzIBAGzR4WXtcv1UMp1aKTBTyQTs1nSprMtC8FoF5EB/hEwAAFs0VS5dmM/Gwd/9bI6OKpkurxRYvx/g3ra8Qgje0WUU2CBkAgDYokub7XIdVTKtZjKdMvhbyATsyumnyxn8DT0TMgEAbNHUcnZhXjmYz7qayXS0iTt++3w1+PveXhHA4PTT5UorL3RMyAQAsEVT5dLBbJYL81rNaOrBaZu42fgvRJVMwK6cXsnkdDnomZAJAGCLphlMB/PKwWy2Om2uB6fOZJra5ToKxOAk77q0yMXDfv6muHUWV6hkcm2CfgmZAAC2aGqPO5jNcjCvrgZ/r44IP+10OdUCdO4LnvML+X9+5Nd3vQy2YJoZd/Lgb9cm6NXBrhcAAHCerdrl5pULs1lfg7/HfdrshHaU9fuhV69/2x/lve9/266XwRacNjNuVmYyQc9UMgEAbNFUyXRhrGTqafD3Ubvc8dun97Wk0LtLi+XqBEfOlyu2y6lkgm4JmQAAtmiqXJrPKhfms642xKt2uRM2cev3Q68uLpa52NHfFLfO8pTB33OVTNA1IRMAwBZNp8ldmFcOZp2dLjdVMp3aLtfPWuEklw5VMp1Xp4Xgs9nRvCagP0ImAIAtWg3+ns9yMJ/11S43btROPV1OyETnLi6WTpc7p6Z23c0Q3OBv6JuQCQBgi6Yqi4NZ5cK8cqmj/wW/PGWw7tSe0lHRFVymtZZLi9bViY3cOotT2uUM/oa+CZkAALZo2gwdzCvzWaW1fmYdTZu4uqySaby/k3XCSaZwSbvc+WTwN5xNQiYAgC26NIVMs1kujCVDvWyKl6fOPNEuR/+mgd/a5c4ng7/hbBIyAQBs0eG4EZ4GfyfpZvj3tIyTNnHD/X2sE05yaQyXnC53Pk0/1pNCcCET9EvIBACwRdOg7/mscjBWMi06mSEzbdQ29nCr9hR7d3o2hUu9VAZya63a5TZnxpV2OeiZkAkAYIumqqUL81kuzIfwppfh36t2lBOOCE+O2umgR1ObnMHf59Op7XIqmaBrQiYAgC06XDtd7mBMbw472RRfaRO3fj/0yEym821xhZlxMibol5AJAGCLpsHfQ7vcWMnUSXvPUbvc5UeEJ0enz0GPpr8jM5nOpynknl02M87Jl9AzIRMAwBYtlssczCpVtWqX62fw98mVTFPIpF2Onl06HH4/Ly2WaQLRc2c1k+mEEFzIBP0SMgEAbNHhoq0qmI7a5fqovFiecnrTUbvcvb0iuHYXF4skSWv9BLfcOldqlxMqQr+ETAAAW3Rp0Vbh0mrwdyczmaZ2uMtPlxvvt3GnYxcPj34/e2lB5daZcqSTTpfTygv9EjIBAGzRYrm8vJKpl9Pllqe0yxn8zRmwPovp0qHf1fNmcVo776wiU4R+CZkAALbo0vKokumgs0qmqVDpstPlSshE/y6tnSpn+Pf5c1q73Hzm2gQ9EzIBAGzR4WK5apO7MO9rJtNUKTC7rF1uPF2uj2XCidaDJSHT+XP66XIGf0PPhEwAAFt0uGirSqHpdS9Dils75fSm8V+IqgXo2aVj7XJCpvPmSoO/nXwJ/RIyAQBs0eGyrSqYjgZ/97EhvtIR4Uls5Ojau9eCpV7+prh1VtenkyqZBODQLSETAMAWHS6XOZhtDP7uZCbT4pTB31PlgI0cPVsPlt6tkuncWZ4y+Hs+0y4HPRMyAQBs0aW1drlp8Hc/7XLD681KgaPT5e7tFcG1u6SS6VybfqQntssJwKFbQiYAgC1aHGuXGyuZln1siE8b/D3dpl2Onq0P++7lxEZunaPB38dvN/gb+iZkAgDYokuL5aqCaWqb665dri5PmeYzc0/o23qwpJLp/Lni4O92dHAB0BchEwDAFh0u2ipcmiqZetkQt1OOCE+G4d9aUujZ+hymi2YynTtXmxmnmAn6JGQCANiiYfD38E+u3mYynXa63HSbdjl6th7WXuwkuOXWWbaWqqRqc/D38FrLHPRJyAQAsEWHy7bWLjedLtfHhnjqNjq1Xa6PZcKJLhr8fa4tlu3Ea9PRwQRCJuiRkAkAYIsOF+uDv4fNUS9Ditspg3WTcfC3TRwdO1bJpF3u3Fm0dmIr7xQ8qWSCPgmZAAC26NJiuZopctDb6XJXapdzTDidu7RQyXSeLU+pZJqupw4mgD4JmQAAtmixbKsKpmkAeC+VTFMhwOZg3WSoFhAy0bN3Hy5zn4NhO3Oxk78pbp3F8uRr0xSKmxkHfRIyAQBs0eGyrWYxTW1zh51siKcQ6YRigVSZyUTfLi1a/th9Doa3tcudO8vWckLGdFTJJGSCLgmZAAC26NJiuapgms8qVf21y53ckqJSgL5dPFzk/mPI5HS582exbCdXMmmXg64JmQAAtmixdrpcMrTM9dMuN4ZM2uU4gy4tWt7jtvnwtkqmc2fRTg6Z5qt2uXt7RcC1EDIBAGzRpUVbDfxOkoPZLIedVF1MlUp1QiVTVakUoGsXD5dHIVMnf1PcOstlO/HaNOVOrk/QJyETAMAWHS6P2uWS5GBeOeykDe20SoFkqG7SLkfPLi6Wue1gltvms7xbyHTuLE45XW5ql3N9gj4JmQAAtuhwcTT4OxmGf/cyk2nZTp7HlIwhkz0cHbt4uMxtB/PcdjDLpUO/rOfNsp3eyjvc72cOPRIyAQBs0eFymQsbM5m6OV1u2U48WS4ZTpzTjkLPLi2WuW1euTAv7XLn0LK1zE7YrTpdDvomZAIA2KLDxfHB3xfms24Gf592elMyDv62iaNjl8Z2ueFvSsh03ly1XU4IDl0SMgEAbElrLYfLlvna/44fZjL1sSG+erucTRz9uni4zIX5LLcdzHLR6XLnzqK1VaC0brpmyRWhT0ImAIAtmdo5Lsw6bZdrV2qXK5s4unZp0YaQaT7LRb+s587ylEqm6bBO7XLQJyETAMCWTKfIHcyPD/7upbXniu1yM+0o9O3dh9rlzrPTrk8zg7+ha0ImAIAtmTa+B+uVTPNahU+7tmxXmclkE0fHhsHf2uXOq2Vrq0BpncHf0DchEwDAlixWlUzr7XL9VF0M7XInh0xDu5xNHP26uKpkqm6G6XPrnFrJNIVMQnDo0lZDpqp6TFW9uqrurqqnnXD/farqe8b7X1ZVd4y3f2pVvaKqXjm+/uRtrhMAYBumje/xdrmOZjItrzz42x6Onl1aLHNhXrlgJtO5tGi54uBvp19Cn7YWMlXVPMmzkjw2ySOTfE5VPXLjYU9J8tbW2iOSPDPJ14y3vznJX2qtfWiSJyV53rbWCQCwLdMpcsfa5Wazbk6XW7SWU7rlMivtKPRruRxObrxtPs9tB/1UB3LrDIO/L79duxz0bZuVTI9Ocndr7TWttYtJXpDkcRuPeVyS545vvzDJp1RVtdZ+sbX2hvH2VyW5b1XdZ4trBQC45aaKpc2ZTL209iyXJx8RngzDdbWj0KupcunCQQ2ny5nJdO5cbfC36xP0aZsh0/sned3a+/eMt534mNbaYZK3J3nwxmM+O8kvttbevaV1AgBsxTTg+8Jau9zBrLqpZLri4O9ZpdnE0ampcum2udPlzqvFVQZ/d3IZBTYcbPFjn/Qvls1/qVzxMVX1qAwtdH/+xE9Q9dQkT02Shz3sYTe2SgCALTmcTpdbH/w9n3Uzk2nRcuImLhkrmbSj0Kmpcum2g1kuHMy6qQ7k1lkuW247uLwmYsrsVTJBn7ZZyXRPkoeuvf+QJG847TFVdZDkgUneMr7/kCQ/kOQLWmu/edInaK09u7V2Z2vtzttvv/0WLx8A4OZcOqFd7sK8VhVOu7ZcXmEm06xi306vpr+tC/OZdrlzanFKpeXM4G/o2jZDppcn+cCqenhV3ZbkiUletPGYF2UY7J0kj0/yE621VlUPSvIfknxla+1nt7hGAICtmSqBDmbr7XKzVYXTrl2xXa6iXY5urSqZ5rPcdlBOlzuHhhD89HY5lZbQp62FTOOMpS9N8pIkv5bke1trr6qqZ1TVZ4wPe06SB1fV3Um+LMnTxtu/NMkjkvyjqvql8eV/2dZaAQC24dLypHa5fgZ/L07ZxCXa5ejb0eBvM5nOq6tVMmmXgz5tcyZTWmsvTvLijduevvb2u5I84YTnfXWSr97m2gAAtu3odLmj/693YTbravD3qSHTTMhEv9YrmS7MZ7mkXe7cWSxPnhl3NPjb9Ql6tM12OQCAvXZ4SiVTL4O/ly1XaJerKBSgV6vT5Q4qtx3MtMudQ8tly/yE3eqqXc4FCrokZAIA2JLD1XDi9cHf/bT2LK44+Nsmjn6tQqb5fPybamaInTNXbZdTyQRdEjIBAGzJVMk0Pzb4u6PT5VrL7JSUaValHYVuTe1yF+aV28YQt5dZZ9way2VLndAuN12yZIrQJyETAMCWXFrNZFpvl5t11C7XMj9lJtN8VlnaxdGp9cHftx3Mjt3G+bA45frkdDnom5AJAGBLpk3QhbXBIhfmtTp1bteuerqckIlObQ7+TmL49zmzWDpdDs4iIRMAwJZMc2OODf6ezdJaH/8XftmG2UsnGdrl7t31wLWaqgRvO1gLmVQynSutOV0OziIhEwDAlhye2C43zY/Z/YZ4ecVKpmiXo1sXF4skQyWTdrnzaeF0OTiThEwAAFsyVSsdzI8P/k7SxfDv005vSoaNXA/VVnCSS4djK+rBLLeNf18XtcudK1c7XU4lE/RJyAQAsCXT7KULG4O/k2TRwfDv5SntKEkym1Xs4ejVuxcnzGTq4G+KW+e0SkuDv6FvQiYAgC2Z2uXW/2/8haldroOBR8Mm7uT7tMvRs0vHBn/304LKrXNaJdP/3/FrhSUAACAASURBVN6dh0l23/W9//xOLb1Oz3T3rJJG0mhG1kgWkm1ky7KD4wVjG4OF89hcGxIMlwRMbO7N5bkEQxJiSAiLA3nCdUIeiLlhsbywWhhsATZcY7AkS7IkjzZ7NCPNjGbR9DLTW3VXnXN+94+zdHV3narT0+d0be/X8+jp7qoz00dTVaf7963v9/MrxMHf231GANKgyAQAAJATt+G4XPC52wErpKTdm6RgIUenADpVVFAqF1czmVYYl+spSbtfRpsVMC4HdCaKTAAAADlxw4VwqdChwd+2SfC3Y+hkQseK8pdKBRNnMnXCawrZ8ROK4AR/A52NIhMAAEBOok6mRuNynRD83bTIZAydAuhYNc+XMcFrq1SkyNSLWgV/02kJdCaKTAAAADmJFr0lp9G4XPsXxE3H5RxDpwA61ornq1xwZAydTL3K9xtvTBBdsyiCA52JIhMAAEBOvDBY22kU/N0BmUzWrj23eo5hdzl0rppr4+JStLtclUymnhJ0Mm28fTX4mwsU0IkoMgEAAOSk5tk1od9SXSdTB+wu59kWu8tRZUKHqnpeHPhdLprwNp6vvcTzbVxQqufQyQR0NIpMAAAAOXE9X8V1VZxiB3Uy+bbxIk4KRlII/kanqrk27mCKPtboZOoZUQEpqdOScV6gc1FkAgAAyInr2w1FpmhB3AmZTL4vmYQikwnH5SwLOXSgmufXdTKF43Id8JpCNqICUmIR3BjxcAOdiSITAABATlzfj4tKkajo1Am7ywXB343vixZ3HXCawAYrnh/nm5UI/u45XotOJscRnZZAh6LIBAAAkBPX27h7W7GDFsR+whbhkuLiEws5dKKa66tcLEgi+LsXRdedxOuTMXEhCkBnocgEAACQE9e3GzqZou4Lt0MymZqNy0liIYeOVPV8lcPX0kAxKtzyXO0V0XUnaVzOcSgyAZ2KIhMAAEBOXM+Pg74jhU4bl2sS/C3RyYTOVPP8DcHfdDL1jmjzzWbB31ybgM5EkQkAACAnNX/jOFoc/O23f0Hs2+bjKNExyN6K6+np83PtPo2uVXVXg78LjpFjOmMEFdlYDf5ufD/jckDnosgEAACQE9fzVXISgr87YLTH960SGpni21nI5eMzj57Vd/36l3W5Umv3qXSlqrd2FLVUcCgy9ZBWwd/R7pcAOg9FJgAAgJx4vt0wLtdJO2F5NsW4HCu5XEwvVOX6VvPLFJmuRH0nkySVi45WGJfrGdEonJN4feLaBHQqikwAAAA5qXk23k0uEhWdOiGTqfnucmQy5Wm55oUfKYxciZrnq1z32irTydRT4uDvZrvLcW0COhJFJgAAgJy4vh+Px0WK4fic2wELYt9X693lWMjlYrXI5LX5TLpTzVvbycS4XG/xbevd5ehkAjoTRSYAAICcuJ7dUGQqhZ1MnbDdumetCgm/DcbB36zbc1EJi0sVikxXpOr68WtJCsblOuE1hWyk2V2OAjjQmSgyAQAA5MT114YTS4rH5zpjd7lmmUyrxyB7laq35iM2p+b564K/japkMvWMeHe5JkVwNiUAOhNFJgAAgJy4nr8hUyTqbGp314W1VtamGJdjIZcLOpm2ZsXdOC5XZVyuZ8S7yzUbl6MADnQkikwAAAA5CTqZGu8u57a5yBTVjpoF60oS67h8kMm0NeuDvweKZDL1kjiTqVnwNwVwoCNRZAIAAMhJkMm09tetgmNkTPvH5VY7BRrfH502uSf5iDuZGJe7ItVGnUyMy/WMeHe5Jp1M1BSBzkSRCQAAICc131exsHGRVHLaH1IcdQokBes6jMvlKs5kopNp0zzfyrdal8lEJ1MviYvgicHf5MUBnYoiEwAAQE4a7S4nScWCkdvmBXGrLcKjMRXLQi4XlZoffqTItFlRMWlNJ1PRUZXd5XpGy+sT43JAx6LIBAAAkBPPt/FucvUKjpHb5gVSy2DdqJOJIlMu4kwmxuU2bSUci6vvZCozLtdT4nG5pE5Lgr+BjkWRCQAAICfBNusNxuUKTtszmaJvz7hcezAud+UadTKVi4ZxuR7SapyXTiagc1FkAgAAyInr24bvxBcd0wG7y0XjKI3vXx2X264z6i9x8DdFpk2LOpbKdU9eMpl6S/RQNg/+5uIEdCKKTAAAADlxPX/D7nJStCBu87hcy+Dv8DgWcrlY3V2OwshmRcWk9eNyNcblesZq8Hfj+wuGcTmgU1FkAgAAyInr24bjcsWC6YBxuRaZTA6ZTHnxfBt34yzTybRpcSfThuBviky9Is3GBBTAgc5EkQkAACAnrmdVaPBWfGeMywUfk4J1o8Udu8tlr76wxLjc5lUTOpkI/u4daYK/2UwQ6EwUmQAAAHLi+snB3+3Oj4nH5RIymVaDv7frjPpHfWGpwu5ym9awk6lg2j6Ciuy0GuctmNVuTACdhSITAABADnzfyrdqmMkUjMu1uZOp5bhceBydTJmrLyzRybR5UTGpXN/JxLhcT4muT4zLAd2HIhMAAEAOamHmUrFRJpPT/k6meIvwpCJTeDvdAtmrH5cjk2nzotfO2k4mR55vKTz0iJbjcgR/Ax2LIhMAAEAOosylYoNFUqnQ/kymVou4AsHfuYm6l8YGi3QyXYFoXK4+kyn6vN3FW2SjVRGcTiagc1FkAgAAyEE0DlcsNAr+dtq/u1y4PkvKPIk7mVjHZS4al5sYKZPJdAWisbj6cbmBIkWmXhI9jA2mjYPb6WQCOhZFJgAAgBy48Q5YDcblOiCk2G8Z/B0eR5Upc1H30vhImU6mK7Aa/L365I06mdhhrjdEHZRJmUyOYyiAAx2KIhMAAEAO3CbjaFF+TDt5KYJ1649DdqIcponhMplMV6DmNRuX4/naC+KNCZrsLse1CehMFJkAAAByEBWZSo12l3NM28d6/BZbhK+Oy7GQy1p9J1PNs21/LnSb1U6mtbvLSYzL9Qo/RScTRSagM1FkAgAAyEE0Ltdwd7mCiYtQ7RJFQrXcXY4iU+Yq1eAff2KkLIkd5jarcSdT8HxdYVyuJ7TcmIBMJqBjUWQCAADIQTS202iRVHScuAjVLnHmScJvg6vjctt1Rv0j7mQaLq/5GumsNOpkYne5ntKq05Ld5YDORZEJAAAgB9ECqNRod7mOCv5OWsStPQ7ZiTOZRkrB11UKI5sRvXbKDTOZ+LfsBdHD2Dz4m2sT0IkoMgEAAOQgWuwWGwV/O45cv82ZTH7zIpNhXC43laqnomO0YzAoMtHJtDmNxuXIZOotXtzJ1Pj+gqGTCehUFJkAAABy4LboZHLb3MmUJvOk/jhkp1LzNFQqaKhUiL9GelXXV8Exa5670euMTKbe4KfY/ZJrE9CZKDIBAADkIMpcalTEKRWctndcROuz5HE5s+Y4ZKdS8zRYLmgwKjJVKTJtRs3z46DvSLlowvt4wvaCVkVwxxiuTUCHosgEAACQg6iTqeHuck4H7C4XZzI1vj+qPfms5DK3XA07mcpBkYnd5TZnxfXX5DFJUrkQ/FvW6GTqCa2Dv+myBDoVRSYAAIAcRONwjcflnLaPy/m2xbicQyZTXhiX25qa56/ZWU6SSmEnU5VMpp7gtRiXcxwT5zYB6CwUmQAAAHJQ85uNy5n4/naJFnEmaREXZTKxkMtcNC43xLjcFak26GRid7ne4rUqghtDlyXQoSgyAQAA5MCLOpkabI9UdBxZ295xj1adTFGRiYVc9ipVT0MlR4Pl4LlBJ9Pm1DxfpeL6cbng6yrjcj2h1e6XBTqZgI5FkQkAACAHbtip1DCTqRCFFLdvQRw1UjXbvUki+DsPy+vG5chk2pyaZzdmMhWd+D50v+jS2KwIbq1kKTQBHYciEwAAQA6ixW4xYVxOam8nU9QFkFBjigPBCdfNXqXmaYjd5a7YiutvyDorxZ1M/Fv2Aq/FxgRR8YnrE9B5KDIBAADkwIt3l2s8LiepreHffqstwgn+zk2l5mmwVFCp4KhUMIzLbVLD4O+4O5Dnay/wfSvHJGfGxUUmrk9Ax6HIBAAAkINoFK5ZJ1M7w7+jBoBmwbrBcSzislap+vGo3GCpQJFpkxoFf0dFJ3aX6w2etYnXJqk+M267zghAWhSZAAAAcuCGVZz1Yz2SVOiATqZW4yjx7nIs4jIXZTJJ0lCpQCbTJgXB32ufuFHAPsHfvSHoZEouMkWXVTqZgM5DkQkAACAHblidafRufGcEfzffvSnaFI9OpmxZa+NMJkkaKhfIZNqkqrexk8lxjIqOaetrCtnx/HSdTGQyAZ2HIhMAAEAOVjuZksfl3DYukKLiUctxORZxmap5Vp5v49DvIcblNq3aIPhbCroGKTL1Bs/axJ0vpbrdL7k+AR2HIhMAAEAOolG45sHf7VsQe606mQzBunmICkprM5kojGxGtUHwtxTkMhH83Rt83ybufCnVZTJxfQI6DkUmAACAHESh3k2Dv9u5u1yUydRqdzk6BTIV5S/F43KlgpYZl9uUWoNxOSnoZFohk6kntAz+Znc5oGNRZAIAAMiBF3UyNcpkijqZOmF3uRYjKdSYshXlL8XB32XG5Tar5trGnUwFMpl6hecnj/JK9eO823VGANKiyAQAAJCDmp+ceVTsgE6m1XG55GMcQ6dA1qKC0mAp+DWcTKbNq3qNM5mCcTmqDr3AWnaXA7oVRSYAAIAcuJ6vomNkGiyUogVyOzOZbItxOSnIPSHzJFurRaa6TCbG5Tal5jbOZCoVHFUZl+sJaXeXY5wX6DwUmQAAAHLg+TbuWFovGqFr5+5yrYK/o/tYxGVrecO4nBPnNCGdlYROJnaX6x1ey06mMJOJ6xPQcXItMhlj3mqMecYYc9wY86EG9w8YYz4V3v+AMeb68PZJY8zfGGMWjDEfzfMcAQAA8lDzrEpO41+1oh3n2rkg9lJmMrFmz1alQfA343LpWWvD4O+Nz9ty0VGV3eV6gt+ik6lA8DfQsXIrMhljCpL+m6S3SbpF0nuNMbesO+yHJc1aa49I+i+Sfjm8fVnSv5P0f+d1fgAAAHlyfT+xkynaXa7Ru/AnpxZ1emYp13OT6sflko9xDFuEZy0uMpXWFpks/86puL6VtUoI/nZUY1yuJ3i2efA343JA58qzk+lVko5ba09Ya6uSPinp7nXH3C3pd8LP/1DSm4wxxlq7aK39soJiEwAAQNepeVaFpE4mx4mPWe8n/+Ax/exnjuV6blLKcTmHTKasRflLcSZTuSBrpRWKI6lE3X8Nx+WKRlVa73qC79ummxLQyQR0rjyLTFdLOl339ZnwtobHWGtdSZclTeZ4TgAAANvC8/24Y2m96Ha3wf7b5+eWNbVQzfXcpNXFWattwsk8ydZyg3G5+tvRXM0Nno9Jwd9kMvWGtMHfXJ+AzpNnkanRVWH9VSDNMcnfwJgfMcY8ZIx56OLFi5s6OQAAgDy5XnLwd7R4cht0Ml1aqmluuZbruUlS1ADQupMp91PpK43G5epvR3MrXvDv1KiTqczucj0jbfB3gzo9gDbLs8h0RtLBuq+vkXQ26RhjTFHSTkkzab+BtfY3rbV3WGvv2LNnzxZPFwAAIDs138ZjceuVEoK/q66vhRVXc5X8i0yr43LJxziGzJOsVarBYz4Y7y4XFpmqFJnSiEZMG3YyFR3G5XpE6+Dv4CPjckDnybPI9FVJNxpjDhljypLeI+nedcfcK+l94efvkvRFS+ohAADoAZ7vq5iwSCrG43Jrf+25VAnG5OaW3dyDoKMiU8txOX41y1Sl5qlcdOJ/90E6mTYl6lQqJ3QyMS7XGzzLuBzQrYp5/cXWWtcY80FJ90kqSPpta+0Txpifl/SQtfZeSR+T9HvGmOMKOpjeE/15Y8xzksYklY0x3yPpO6y1T+Z1vgAAAFmqeVbFBgthaTX42123IJ5dDDqYPN9qqeppZCC3X9VkrZUxkiH4e1st17x4RE4ik2mzmgV/B7vL8XztBZ6fclyO6xPQcfL7zUWStfYvJP3Futt+tu7zZUnvTviz1+d5bgAAAHlyveROpij4e/3ucrNLq4Hfc8u1XItMrTJPpKBbgHG5bFWq64pM8bgcHThpxJ1MDcflDJ1MPcJv0clUoJMJ6Fh5jssBAAD0LddPDv6OOpzW7y53qa7IdDnnXCbfri7UkhQI/s5cpebFhSWJ4O/NqsadTBufuyWCv3uG59um1ycnDv7mAgV0GopMAAAAOXA9q1JC8HfU4bSxk2m1sDRXcfM7OQWLsxY1JhlDsG7WKjUvzmGSyGTarGadTOUCwd+9wvelhMunpNVxOa5PQOehyAQAAJAD1/cTO5miPBm32bhczp1MXovdm6Sg04lOgWwFmUyrv4JHXU3L7C6XSjQO1zD4u0jwd68g+BvoXhSZAAAAclDzkhdJBcfImEbjcnWdTMudMi7HIi5LlSrjclsRF5kaZTIVHPl2Y6A+ug/B30D3osgEAACQA8+3DXfAipQcZ8O43MxiVYNhl0venUy+TTMuZ8R6PVuVhN3lKDKlE43LNXptRbetf12h+6QP/t6uMwKQFkUmAACAHNSa7C4nScWCkdcg+Pvg+LAkaW4530ymVONyDp0CWVufyTQQduRUGJdLpRoWkBpmMoW3kcvU/VoHf68eB6CzUGQCAADIQbPd5aQg/LtR8Pfu0QGNlAvbsLtcykwmikyZWq6u7WRyHKPBkqNlOplSiYO/G2UyFaJAfYpM3c7zrUyzIlN4n+X6BHQcikwAAAA58HyrYpPtkUoFZ0Mm0+xSVRMjZY0NlbZpXK55kSkYl2MRl6VKbW0mkxSMzDEul05UQGo2LhcVotC9giJ48v3sLgd0LopMAAAAOah5ybvLScG43Prd5S4t1bRruKSxwVLuwd+txlEkgr/zsD6TSQqLTIzLpRJ3MjUZl6OTqfu1Gudldzmgc1FkAgAAyIHr2eaZTOuCv33f6tJSVePDZY0NFTVXyTeTybdKNy7Hej0zvm+1XPPXZDJJ0mCZTqa0VjuZNj53V4O/edJ2O2vF7nJAl6LIBAAAkIMgkyn5V61iwawZl5tbrsm32rZOJt9Ps7sc4yhZWgm7cBqNy5HJlM5Kk06mqMi0wrhc1/PYXQ7oWhSZAAAAcuD6vkpNO5nWjsvNLgVFpfHhsnYObcO4XJrgb8fIZxwlM1G3UsNxOYpMqcSdTA3yzsrFKPib52y3S7u7HNcnoPNQZAIAAMiB6zXvZCoVnDVjPbNLVUmKg78vL+Ud/C0ymbZZYpGpTCZTWjXPV6lg5DQokJYLhfgYdDfftw0f4wjB351huebp3OVKu08DHYYiEwAAQA5qnt88k6lg5Na9C38pLDIF43JFza+4ub5Ln25czoimkOxEhaTBdeNyg6WCKjUKI2lUXb/hznLSak5TjXG5rufZ5p1MBYK/O8L//LsT+s7/+neyFPtQhyITAABADjzfNt9dzlnbyTSzuDouNzZUkrXSQjW/8G8/zbicEYuHDC03GZcjkymdmmcb5jFJUim8fYVOpq7n+WrayeQQ/N0Rnp9e0uxSTfMr+W5Uge5CkQkAACBj1tog+LtBbkykVFibyRR1Mo0PlzU2WJIkzVXyG5nzfNt09yYp2N2JToHsNM1kYlwulZUmnUzlaHc5Opm6XlAET76fTqbOMLMY/NyaWai2+UzQSSgyAQAAZCwag2s6Luc4a3aXm12qyjHSjsGixoaKkqS5Sr6dTC2LTA5FpixFhaSh8tpfwYfKBH+nVfP8uJi0XtThRPB392sd/E2RqRNMhUWm6cWVNp8JOglFJgAAgIxFC59mwd/FglmzGJ5dqml8uCzHMaudTDnuMOdbpRiXM2IaJTtRIWmw1CiTiSJTGlXXTx6XC19vVY9/y26XNvibcbn2mgmLS9N0MqEORSYAAICMxdusN8lkKhWcNe/CX1qqatdwUFwaG9qucbnmxzgOuzdlqVkmU9X16cpIIdpdrpHV4G/+Hbtd+uDv7TojNBIVl6YXKTJhFUUmAACAjEVZS806hYqOWRf8XdX4cFmStDMsMl3Oscjk2+adAlKQyZTnDnf9ZnVcbl2RKRyfI/y7tZqX3MkU3V6l8tD1PL/5xgRR3B2dTO1TqXpaCq9p0wuMy2EVRSYAAICMuSnG5UoFJz5Oki4t1bQrLDKtjsvlvLtci0ymgmNYxGWoWfB3/f1Ilir4myJT12tVBCf4u/3qc5joZEI9ikwAAAAZiwK9S806mQpGrrc2+Hs8HJcbHYyCvztgdzmKTJlplskkiR3mUmgW/B1nMrG7XNdrFfxdIPi77epzmMhkQj2KTAAAABmLxuWaBn87Thz8ba3V7FJNEyNBJ1PBMdoxUMw3+NtfHTlJEozL5XYKfWe56skYaaC4cXc5iXG5NJoFf6/uLseTtptZa+VbNe1kMsbIGMbl2mkm7F4qF5z4c0CiyAQAAJC5aJFbbJHJFHU8VWqeqq4fj8tJQfj3XCXncblWu8s5LOKyVKl5GioVZNZ1aDAul17Ns4njctHrrerxnO1mUXNSy3FeY+hkaqOpMIfphj0j8eeARJEJAAAgc16cydRqXC44bnYp6FiKxuWksMiUYyeTZ1OOy7GIy0xUZFpviHG51Kpu8ricMUblgsO4XJeLrjlNGkElBZ1OjPO2T9S9dOO+HXQyYQ2KTAAAABmLxuCKTebRSgUn7niaDX9BX9PJNFjMeXc5tS4yOUbUmLJTqfob8pgkabBMJ1NaNc9XKWFcTpJKBcO4XJeLuidb7X5ZYPfLtpperGqg6Ojg+JBmFquyFPwQosgEAACQsWgMrvW4XNTJFBSZNnQy5Vlk8q1arOHkkHmSqeWaF+cv1Ys6mchkam2lSSeTFOQyUWTqbnEnU4rdL3mo22d6oardowOaHB2Q69tcx7vRXSgyAQAAZMxNNS7nbBiXi4K/JWlssKT55fx+aff8FJlMjMtlquW4HEWmlmqer3Ix+Xlb3yGI7hSNwLXqtCT4u72mF1c0MVLWZPhza2qRXCYEKDIBAABkLCoeJQUUB/cZ1cKOp0tLDcblhor5djKlyWRyDIu4DFWqCUWmaFyuSnGklZrXvJOpVHC0QiZTV4tG4FqOy3F9aquZxaomR8uaHA1+bk0vkMuEAEUmAACAjLlhJ0WzTqGi48jaoKNodjEoJu2qH5cbLGl+xc2tkyhVkYnMk0xVal6cv1RvkE6m1Kqu37R4O1B04kw0dKfVcbnmx9Fp2V7TC1VNjJTjDtwZOpkQosgEAACQsWhcrtRidzkp6MyYXapqx0BxzeJ5bCgoOC3kNDKXalyO3ZsytVzzNFTa+Os3mUzp1TyrctPgb0c1Opm6WlQ3anV9otOyfay1mlpY0e7RAe0eHZAkTdHJhBBFJgAAgIytBn83H5eTwk6mpap2jZTW3L8zLDLltcOcta3HURzD7nJZSspkKhWMCo5RpUqRqRlrrape806mUtGoSiZTV9vM7nJ0MrXHUtXTiutrYqSs8eGok4kiEwIUmQAAADIWjes0Df4OC1CuZzW7VNNEXR6TJI0NFiVJc8v5FJk8m3J3ORZxmalUG+8uZ4zRYNFhXK6F6HXVrJOpTPB312N3uc4X5S9NjpRVLjoaGyxqeoFxOQQoMgEAAGQsCv5O08lU831dWqquCf2WVsfl8gr/9nybbhHHOEpmKjUvzl9ab6hcSCwyLa6wNbikuEOpVfB3lXG5rualDP52HHaXa5fpMH8pGpXbPTqgaTqZEKLIBAAAkLF4XK5pJlN9J1NV48Nrx+XGBsMiU06dTGnH5awNxpSwdcsJ43JSEP693GBc7viL87rt5/5Sj5+5lPfpdbwoa6lZ1lm5SCdTt4sKRy2L4IzLtU3UyRSFfk+MlNldDjGKTAAAABmLOplKTTqZik5d8PdirUEnUzguV8kv+Lv1uFxwAOu4rat5vmqeTSwyDZUadzI9cXZOnm917IW5vE+x40XFo3Kx8b+hFHYyUWTqavG4XIrgbzot2yPKX5ocLccfyWRChCITAABAxqJOpkKK3eWWa54WVtz4HeFIPC6XYyZT693lwmOpMm1ZtHNco0ym6PZGRabTM0vBx9ml/E6uS6yk6WQqOKq5PF+72WaCv8mMa4+pcFxuciQYl5sYGYhH6ACKTAAAABlz/aiTqXXw98UwLHX9uNxouSjH5JfJZK2NO5WSRIs8ck+2brkWFEiSMpkGS4WGu8udnqmEHykyrXYyNdtdjnG5bhc9fOmCv7k2tcPMQlXD5UJcNN8ddjJR9INEkQkAACBzcfB304DiYAF1cT4oMq0fl3Mcox2DJV3OMfi7ZZHJUGTKStzJ1GRcbrlRJ9Ns1MlUye/kukS64G8TdzyhO62OyzU/zjGGa1ObTC9W13TfToyU5VvpUk4/r9BdKDIBAABkLOqkaDaOFncyzUedTOUNx4wNFTW3nE8mk29bZ54UyGTKTKXVuFypEHc71YuKTGfoZIrH4Jp1Mg3QydT14nE5Opk61tTCiibDneUkxZ9PLzAyB4pMAAAAmYsWPs2yY4obOplKG44ZGyzlNi7n+1Yt1nDx/Szkti4ahUvsZGqQyeR6vs5eWtZA0dH0YlWLK/kUHLtF1Qv+fUpNO5m2r8jk+5bxoBxsLvh7O84I680sVjVZ18kUfT5N+DdEkQkAACBzUSZTscnuctFCOSoyrQ/+lsIiU57B3yk6BSSxkM5AVEBqmsm0rsh07vKyPN/qjuvHJUln+nxkrupGxdtWRab8n69/f3xKt/z7z+uJs+z6lzUvdfA316Z2mV5YV2QKd5mbXmhcZFquefqzx87KMt7YFygyAQAAZCzqpCg2HZcLO5kWWozLVfIal0uzuxyZTFlJNS63Lvg7GpV7zeHdwdd9PjJXTRP8XXBU3YZMpn1jA1qu+Tp+cT7379VvosIRwd+dyVqrmcWqJkbXZjJJ0kzCDnN//vg5/fgnvqbHz1zelnNEe1FkAgAAyJjrWTmm+TvxxbpOpoGi07D4kGcnk+9LpsUiLrrfo8i0HVIkxwAAIABJREFUZcstx+WcDZ1MZ8Kd5e46PClptejUr2pu6+DvctFR1fNz75i4dmJEBcfo2RcXc/0+/Sj1uJwxXJvaYH7FVdXztXtkNZNpInyTZCqhk+kbF+bXfERvo8gEAACQMde3TXeWk9buLteoi0mSdg7luLuctS13b4qDv8lR3rJKit3lXN+uyRM6Pbskx0jfcvVODZUKOj3T3+NytRSdTOXwdeXm3OFSLjq6bnJYx19cyPX79CNvE8HfjMttv5mwkDRZ18lULDgaHy5pJiGTKXqdHL/I66UfUGQCAADImOv5KrV4Fz7Ka5pZqjYM/ZaksaGSlqpeLkHGfqpMptVjsTVxJlO58a/fUVZTfTfT6ZklHdg5pFLB0cGJob7vZIrG5ZoF6kd5TdsxMnd4z6ieZdGcuaionWacd30n06Wlqv7DZ5+Mg/aRvenFxjmCEyPl+L71ouLSsxRl+wJFJgAAgIy5fuu8o2ihbG3j0G9JGhssSpLml7PNZbLWytpNjMvRLbBlaXaXk7Qml+n0bEUHJ4YkSQfHh8lkclN0MoX3bccOc0f2juq56UW527SbXb+ICketOi0ds7GT6b4nzutjXz6pr5yYyuv0+l4U7r17dGDN7ZOjAw2Dv5drXnztovOvP1BkAgAAyJjr+013wJK0ZpwuaVxubCjocJrLeGQubeZJPC5HJ9OWLbfYXW4ooZPp4PiwJOngRFBk6ufdmeLg7xa7y9Ufm6fDe0ZV86xO9XnxL2tR4SjNuNz6Tqanzs2v+YjsTYcjcevfHJkcKcf31Ts5tSjfSod2j+jUzFJ8LUTvosgEAACQMdezKjYZ6ZHW7jyXOC43GBaZMg7/jt78T7+7XKbfvi9Vap5KBZNYfFxfZFqueXpxfkUHJ1aLTItVT7NL+WR0dYMo+LtZATcqQNW8/J+0h/eMSJKevUj4d5a8lEUmx2zMi3vq3Nyaj8jeTFKRabSs6YWN43JR99JbXrpfvpWem+b10usoMgEAAGSs5tk4cylJaVOdTNmOy0WdSS3WcPH9jMttXaXqJ3YxSdJgOC4XjdWdmQ1CvlfH5YKP/TwyV00R/F0qBk/abclk2jsqiRGgrK2Oy7XeXa6+y9JaS5FpG0wtrGh0oLjhejYxMqBLldqG8dHjLy7IGOnNt+yLv0Zvo8gEAACQMc/3W3YyFVJ0Mu0Mi0xZ7zAXj8ulGEeRGJfLQqXmJeYxSRs7maJiUv24nKS+Dv+OupOadzIVwmPzLzKNDZa0d8cA4d8Z29S4XF0B/NzlZc0tu9qzY0AnpxYZy8rJ9EJ1zc5ykd2jZVmrDd2Wxy8u6OD4sF561ZiMocjUDygyAQAAZKzm2zXjcI3U75CVGPw9FAR/Zz8uRybTdluueXG4dyNRkSlaGEfFpPpxOUk6PVPJ8zQ7WtVNs7vc9nUySUH4N4vmbKUd53XWZTJF3Ut3336VfCt94wK5THmYWaw2/JkV3TazLpfp2RcXdGTvqAZLBV0zPsTrpQ9QZAIAAMiY62UU/D2YT/B3lGPC7nLbp1Jt0ckUj8sFD87pmSWVi472hDs4jQ4UNT5c6utOpqrnq1xwmj5vS8XtC/6WgvDvZy8u9HUge9bS7i5XWLe73NPng6LS97z86uBrwr9zMbWwosmRgQ23R7fV5zJ5vtWJqUUdCUdLj+yhKNsPKDIBAACkcPzFef3jj/yNTk23XuS7nm35Lnya4O/hckEFx+TXydQikyn6f2D9vHWVmtc0k2njuFxF14wPyal7nkQ7zPWrmus3zWOSpIEo+HubOpkO7xnR/LKri/MbA49xZa50d7knz83p2olh3XJgTEOlgp4klykXM4tVTTboZIpG6Op3mDs9s6Sq668WmfaO6sTUIm9c9DiKTAAAACnc98QFPT+9pL9+6kLLY13frulUaiRN8LcxRmODxcyDv6OFmdMyWDc8ngXBlrXKZBpcX2SaXYrzmCIHx4fjQPB+VPX8pqNy0mon03bsLidJR/bukBTkziAbcWZcmuDvulriU+fmdHT/DjmO0U37dxD+nQNrbVBkapDJFBWe6juZoq6l+iJT1fV1po87MvsBRSYAAIAU7j8xveZjM67vq9Qq78gx8e5tSUUmKdhhLvNOprRbhIf/Dx6tTFvWMpMpvG+5uhr8He0sF7lmYkgvzFbWjAj1k5rXupMpKt5uR/C3JB3eOyJJevYi27JnJS6Ct+xkWi1IVaqenpta1M0HxiRJNx8Y09Pn5xljzNhcxZXr24aZTLuGyzJmbSZTVHytLzJJhH/3OopMAAB0mLOXKnrXb/xDX4/FdJqq6+uh52YlSQ+cnGm5yK+lGJeTpJLjqOAY7RgsJh6zc6iUfSZTymDdKPibhdrWtcpkGgyLJ5Wap8uVmuaW3YadTFXP14X55VzPtVOtuK2zzsrh/SvbNC63f2xQI+WCnmXRnBk/ZSdT/bjcNy7My7fSzQeCzrJbDuzQ5UpN5y7352slL1OLQZfS7tGNmUwFx2hiuKyp+iLTiwvau2Mgzhc8smdHfDt6F0UmAAA6zJ987QU99PysPvPoC+0+FYS+/sIlVWqe3vrS/bpcqcUBs0k837ZcDEtSsWC0a6jUdGxtbLCkyxkXmVY7BZof58TB35l++77UKpOpWHBULjiq1Ly4wBztKBfp9x3map6Ni0hJykUTHrs9T1pjjA7vDcK/kQ0v5e6XTl3wdzQaF3UyHQ0/Pn2ekbksRV1KjcblotvXj8tF3UuStHO4pN2jAxSZehxFJgAAOsznjp2TJH3h6RfbfCaIfOXZYETu/3jTjcHXLUbmXM9XsVWqtoLw76TQ78jYUFFzy9lmMqUflws+ksm0dcG4XPNfvQdLjipVL84r2djJFIzP9WuXY9X1Om5cTmLHrKxdSfD30+fnNVIuxK+Zo/uDjpmn2GEuU1EBqdG4XHR7VIiy1urZdUUmSTqyd4QMsx5HkQkAgA5yanpJx16Y076xAT16+pKmFtixqBPcf2JGR/fv0C1Xjem6yeGWuUw1z67ZPS5JqeA0zWOSgk6m7MflUo6jMC6XmUrV02AxuZNJCnKZlmte3Km0PpPp6vEhGROEgvejmte6QzC6v7pN43KSdHjvqM5dXtbCSrbF4H61meDv6Ngnz83ppjD0W5J2DJZ0cGKIHeYyFu0c12hcTpImRwc0vRAc8+L8iuZX3AZFpqAoy8+V3kWRqYdYa1mMAECX+/wTQRfTh7/7pbJW+ttnLrb5jFB1fT30/IxefcOkJOnVhyb1YItcJs+3Kjopx+VaFZlyCP72CP7eVtbaYHe5JsHfkjRUKgTjcrNL2jFY1M6htV1uA8WC9u0Y7ONxudbB3+Xi9ncyHd4ThH+foDsjE9HGgIUUnUy+b2Wt1dPn5uJRucjR/WN6miJTpqICUtKbI5Mj5bgQFe8st2ddkWnPqOaXXV2cZ93aqygy9ZDfv/95veYXv6jnp9ndAgC61eeOndetV4/prbfu176xAf0NI3Nt99iZS1qu+brrcFBkuuvwpC5Xak3fIa/56cblDo4Px2MdScYGi1qu+Vpxvc2deBNRJ1OzLCipPpOJItNWVD1fvlXTTCYpuL9SDTKZDo4PyzRYZB+cGOrbTqYg+Lv5czbuZPK27zkbdWqQy5SNeFyuxUo1Gpc7e3lZc8tunMMUufnAmE5OLWq5lt21s99NL6xobLCYWOydHBnQ5UpNNc9fLTJt6GQi/LvXUWTqEdZa/b//8Jyqnq97HjzV7tMBAFyBc5cr+tqpS3rbrQdkjNEbj+7Vl75xcVvHPrDR/c9OyxjpzkMTkqQ7bwg+NhuZc1OM9UjSp3/0Lv3Em1/S9JixsJtlPsNcJn8TnQKSRCPT1ixXg9dws93lpGBcLuhkqmwYlYscHB/u20ymoJOp+b9huQ2ZTNdOjKjgGD37Im/0ZmFzwd/SU2eDgv8tB9YW7G/ev0O+lZ5psVED0pterGoyYVROkibCQPDZxaqOv7igHYNF7dmx9vio6EQuU++iyNQj7j8xoxMXFzU+XNIfPHQm03c7AQDb4/PHzkuS3nrrfknSG4/u0/yKq4eem2nnafW9+09O6+j+sXis7cDOIV0/Oaz7TyQ/Lq7nt1wgSUEnUatuomhkKssd5lbH5ZofF91PJ9PWVMJOilTjcmHw9/rQ78g1E8M6P7fcl7/rVV1f5RadTFGHxXYW58tFR9dNDtOZkZG047wFJyhIRTvI3bR/YyeTxA5zWZpeqGoyIfRbknaH900tVOOd5dZ3ZO4bG9DoQJHXSw+jyNQjfv+B57VzqKRfedftmlmsxgsVAED3+Nyx87pp3w4dDvMLXntkUuWiwy5zbbTienr4+Vm9Ouxeirz6hkk9eHI6sfji+rblWE9aY4NBkSnL8O/Njsv5tDJtSVxkatXJVCro9OySlmu+Dk40LjIdHB+StdLZS8uZn2enS5PJVHCMHLO9nUySdHjPKONyGfFTBn8XwuDvp87N69qJYY0OFNfcf+3EsIbLBXaYy9DMYjVxZzlpdde5mcWqjl9c2JDHJEnGGB3ey46MvYwiUw+4OL+i+46d17u+9Rq96eheXTc5rI/fz8gcAHSTi/Mr+upzM3EXkyQNl4u664ZJfZEiU9s8dvpykMcUhn5H7jo8qbllV08l5DK5KYO/0xgbChZOc1mOy4Xr75bB3xSZMlGpBkWmlplM5YIuzAVhuInjcmHxqR9H5qqun2oMtVRwVG1Dkem56UW52/x9e1E8LpdyY4Inz83p5gMbs+0cx+im/TsSr9PYvOnFlabjctF9J6cWdHF+ZUMeU+TIHopMvYwiUw/49EOn5fpW33fntXIco+971bV68LkZfeMCVXsA6Bb3PXFe1kpv+5b9a25/0817dXJqkV2L2uT+E0Ee06sOre1kuvPQZHx/I7WU43Jp5NHJtJp50vy46P+BdfPWbGZcLpI0LhcXmfow/LuWMuusXHC2PcvuyN5R1TyrU31Y/EvrwZMzevzMpZbHrQZ/t+5kkqSTU4s6um5ULnLzgTE9dW5OlkL5lvm+1cxi83G56L4HTgbj5IlFpr2jenF+JfOdU9EZKDJ1Oc+3+sSDp3TXDZPxeMW7vvUalQuO7nmAbiYA6BafP3Zeh3aP6KZ9a9+NfcNNeyWJbqY2uf/EtG6uy2OK7N85qEO7RxKLTF6W43JhJlOWv4zH43IpMk/qj8eVWd7EuFzkmoQi0/6xQZUKRqdnKtmdYJdYcVuPy0lBRtL2j8uNSGLHrCRPnL2sf/qxB/R9v/WAnptqHpDuWZs60y5y84HkItPcsquzl/tvvDRrlyo1+VaaHE0uMu0cKqngmJZFphuj8G9eLz2JIlOX+9I3L+rMbEX/9NXXxbdNjg7obd+yX3/0yBktVbNrrQcA5GN2saqvnJjW227dvyEg8+DEsG7at4MiUxus5jFNNrz/1TdM6IGTMw1zmVzPqpii4yKN1U6mLMfl0hWZDONymYjG5dLsLidJu0cHErueCo7RVbuG+q6TyVqrquvFu8c1Uyo4qrnb+5w9HC6an73IDnPrLay4+uA9X9P4cFCA+OAnHmkaXO/5rTclkNZevxqNy0nBDnOS9DQjc1s2sxiM8jYbl3Mco4mRsi7Or6hcdBKL5UcoMvU0ikxd7uP3P6/dowN68y371tz+/Xdep/llV3/22Nk2nRkAIK2/evKCPN/qbbceaHj/G47u1YMnZ2gr32aPnrqkFdfXXYeTikyTml929eTZjYuXmu+rlNG43GDJUbng5LK7XJpg3frjcWVWx+Wa/+odZTYl5TFFDo4P60wfjWVZa/WLn3tac8uurp9svGitVyqabc9kGhssae+OAcK/17HW6mf++Ot6fnpRv/6el+s/v/t2HXthTv/pz59K/DO+tS0L4NJqp+VIuZA4XnpTWGTqxlym+544r3f/j3/omB1mpxaqktR0XK7+/ht2jyT+jDk4Maxy0dGzFJl6Uq5FJmPMW40xzxhjjhtjPtTg/gFjzKfC+x8wxlxfd99Ph7c/Y4x5S57n2a1euFTRF59+Uf/bK6/Z0Dr8yuvH9ZJ9o/o4I3MA0PE+d+ycrhkf0q1XN273f9PNe+X6Vn/3jaltPrP+dv+JmSCP6fqJhvdHHU7rR+Y838paqZBR8LcxRmNDxYzH5YKPrcflok6mzL51X4qKTK2Cv6NOp6QFc+TgxJBOz/bHuJzvW/2bPz2m3/zSCf3AXdfpB+66vuWfKbch+FsKwr/pzFjrk189rXsfO6ufePNLdOcNk3rzLfv0w//okH7nK8/rc18/1/DPeH7Kcbnw+nX0wFhiftOOwZIOTgzpqfPdk1VrrdX/84Vv6kd/72F97dQlvfe37tenHzrd7tPSdFRkajIuV39/0qicFPxsuWH3CK+XHpVbkckYU5D03yS9TdItkt5rjLll3WE/LGnWWntE0n+R9Mvhn71F0nskvVTSWyX99/DvQ51PPXhKVtJ7X3XthvuMMfr+O6/T42cupwrYAwC0x+VKTV8+PtVwVC7y8oO7tGu4pC88fWGbz66/3X9iWrccGNPO4VLD+/eNDeqGBrlMbrh1WzGjTCYp6JLIMvh7NZOp+XHRU9KnyrQl6TOZgl/NW3UyXTM+rJnFqhZXejsWwfV8/cSnH9U9D5zSj73+sH7uHS9tGQYtReNy219kOrJ3VM9eXOjYkOmlqqtPP3RaP/MnX9efPXZWCzk/f546N6cP3/uEvu3G3fqXrz8S3/5Tbz2q2w/u0r/+o8d1anpjR57n25Y7y0mrRfCj+xuPykVu3j/WNZ1MlaqnD37ia/rVv/qG3vnyq/UPH3qj7jw0qX/9h4/rP3z2ybbuXhiNy0206GSaGAnG6ZoVmaRgxPQ4nX89qZjj3/0qScettSckyRjzSUl3S3qy7pi7JX04/PwPJX3UBL9h3y3pk9baFUknjTHHw7/vKzmeb1epeb4++dXTesNNexNnXd/5iqv1S597Wvc8cEq3XbNrm88QAHrf4oqrk1OLOjm1qBXX16Hdwzq0e1Tjw6XEgpE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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of p-values > à 1% : 6\n"
     ]
    }
   ],
   "source": [
    "# k-highest scores analysis on features\n",
    "Kbest = SelectKBest(k=\"all\")\n",
    "selected_features = Kbest.fit(X_train, y_train)\n",
    "\n",
    "# Plot P-values\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(selected_features.pvalues_)\n",
    "plt.title(\"p-values for each features\", fontsize=22)\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"P-value\")\n",
    "plt.show()\n",
    "\n",
    "# Display Comment\n",
    "alpha = 0.01\n",
    "print(\"Number of p-values > à 1% : {}\".format(np.sum(selected_features.pvalues_ > alpha)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:27.300764Z",
     "start_time": "2019-04-15T13:26:27.296069Z"
    }
   },
   "outputs": [],
   "source": [
    "# Remove non-significant features\n",
    "X_train = X_train[:,np.where(selected_features.pvalues_ < alpha)[0]]\n",
    "X_test = X_test[:,np.where(selected_features.pvalues_ < alpha)[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-15T15:50:31.107065Z",
     "start_time": "2019-01-15T15:50:31.016122Z"
    }
   },
   "source": [
    "## VI. Feature dimension reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:27.487360Z",
     "start_time": "2019-04-15T13:26:27.302394Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Covariance matrix\n",
    "cov = pd.DataFrame(X_train).cov()\n",
    "\n",
    "# Eigen values of covariance matrix\n",
    "eig = np.linalg.svd(cov)[1]\n",
    "\n",
    "# Plot eigen graph\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "plt.title('Decrease of covariance matrix eigen values', fontsize = 22)\n",
    "plt.plot(eig, '-*', label = \"eig-value\")\n",
    "plt.legend(loc = 'upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:27.533172Z",
     "start_time": "2019-04-15T13:26:27.489459Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Initialize PCA\n",
    "pca = PCA(n_components=140)\n",
    "\n",
    "# Apply PCA on train and test set\n",
    "X_train = pca.fit_transform(X_train)\n",
    "X_test = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII. Cross-Validation and hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:32.091592Z",
     "start_time": "2019-04-15T13:26:27.534926Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/msbgd/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters set found on train set:\n",
      "{'C': 3, 'gamma': 0.005, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "# Set C and Gamma parameters list\n",
    "G_list = [0.001, 0.005, 0.01]\n",
    "C_list = [1, 2, 3, 4, 5, 7, 10, 20, 50]\n",
    "\n",
    "# Set the parameters for cross-validation\n",
    "parameters = [{'kernel': ['rbf'], 'C': C_list, 'gamma': G_list}]\n",
    "\n",
    "# Initialize SVM model\n",
    "model = SVC(decision_function_shape='ovr')\n",
    "\n",
    "# Cross Validation \n",
    "cv = GridSearchCV(model, parameters, cv=3, verbose=0, n_jobs=-1).fit(X_train, y_train)\n",
    "\n",
    "# Print Best parameters\n",
    "print(\"Best parameters set found on train set:\")\n",
    "print(cv.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-10T07:46:30.172241Z",
     "start_time": "2018-12-10T07:46:30.165057Z"
    }
   },
   "source": [
    "## VIII. Best model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:32.101642Z",
     "start_time": "2019-04-15T13:26:32.094160Z"
    }
   },
   "outputs": [],
   "source": [
    "# Confusion matrix plot function\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=22)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label', fontsize=18)\n",
    "    plt.xlabel('Predicted label', fontsize=18)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:33.187421Z",
     "start_time": "2019-04-15T13:26:32.104604Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score on test dataset: 66.91%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit best mode\n",
    "model = SVC(kernel='rbf', C=3, gamma=0.005, decision_function_shape='ovr').fit(X_train, y_train)\n",
    "\n",
    "# Prediction\n",
    "pred = model.predict(X_test)\n",
    "\n",
    "# Score\n",
    "score = model.score(X_test, y_test)\n",
    "\n",
    "# Reverse label encoder\n",
    "pred = (lb.inverse_transform((pred.astype(int).flatten())))\n",
    "actual = (lb.inverse_transform((y_test.astype(int).flatten())))\n",
    "\n",
    "# Build dataFrame\n",
    "df_pred = pd.DataFrame({'Actual': actual, 'Prediction': pred})\n",
    "\n",
    "# Print Score\n",
    "print('Accuracy Score on test dataset: {}%'.format(np.round(100 * score,2)))\n",
    "\n",
    "# Compute confusion matrix\n",
    "confusion = confusion_matrix(actual, pred)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(15, 15))\n",
    "plot_confusion_matrix(confusion, classes=set(actual),normalize=True,\n",
    "                      title='Confusion matrix on train set with gender differentiation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:33.574749Z",
     "start_time": "2019-04-15T13:26:33.189572Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score on test dataset: 68.03%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute prediction without gender differentation\n",
    "PRED = list(map(lambda i:i[2:], pred))\n",
    "ACTUAL = list(map(lambda i:i[2:], actual))\n",
    "\n",
    "# Compute related prediction score\n",
    "SCORE = accuracy_score(ACTUAL, PRED)\n",
    "\n",
    "# Print Score\n",
    "print('Accuracy Score on test dataset: {}%'.format(np.round(100 * SCORE,2)))\n",
    "\n",
    "# Compute confusion matrix\n",
    "confusion = confusion_matrix(ACTUAL, PRED)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(15, 15))\n",
    "plot_confusion_matrix(confusion, classes=set(ACTUAL),normalize=True,\n",
    "                      title='Confusion matrix on test set without gender differentiation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-05T15:32:56.519212Z",
     "start_time": "2018-12-05T15:32:56.516534Z"
    }
   },
   "source": [
    "## IX. Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-15T13:26:33.588570Z",
     "start_time": "2019-04-15T13:26:33.576755Z"
    }
   },
   "outputs": [],
   "source": [
    "# save the model to local\n",
    "pickle.dump(model, open('../Model/MODEL_CLASSIFIER.p', 'wb'))\n",
    "\n",
    "# Save label encoder\n",
    "pickle.dump(lb, open(\"../Model/MODEL_ENCODER.p\", \"wb\"))\n",
    "\n",
    "# Save PCA\n",
    "pickle.dump(pca, open(\"../Model/MODEL_PCA.p\", \"wb\"))\n",
    "\n",
    "# Save MEAN and STD of each features\n",
    "MEAN = features.mean(axis=0)\n",
    "STD = features.std(axis=0)\n",
    "pickle.dump([MEAN, STD], open(\"../Model/MODEL_SCALER.p\", \"wb\"))\n",
    "\n",
    "# Save feature parameters\n",
    "stats = ['mean', 'std', 'kurt', 'skew', 'q1', 'q99']\n",
    "features_list = ['zcr', 'energy', 'energy_entropy', 'spectral_centroid', 'spectral_spread', 'spectral_entropy', 'spectral_flux', 'sprectral_rolloff']\n",
    "win_step = 0.01\n",
    "win_size = 0.025\n",
    "nb_mfcc = 12\n",
    "diff = 0\n",
    "PCA = True\n",
    "DICO = {'stats':stats, 'features_list':features_list, 'win_size':win_size, 'win_step':win_step, 'nb_mfcc':nb_mfcc, 'diff':diff, 'PCA':PCA}\n",
    "pickle.dump(DICO, open(\"../Model/MODEL_PARAM.p\", \"wb\"))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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   "title_cell": "Table of Contents",
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