keras-model-comparison-iris-flower.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning - Comparison Models \n",
    "- Random Forest                   \n",
    "- Logistic Regression              \n",
    "- Tree Classifier      \n",
    "- Support-Vector Machine(SVM)   \n",
    "- Gradient Boosting Classifier (GBC)\n",
    "- Kmeans\n",
    "- Naive Bayes    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier , GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa\n",
       "5   6            5.4           3.9            1.7           0.4  Iris-setosa\n",
       "6   7            4.6           3.4            1.4           0.3  Iris-setosa\n",
       "7   8            5.0           3.4            1.5           0.2  Iris-setosa\n",
       "8   9            4.4           2.9            1.4           0.2  Iris-setosa\n",
       "9  10            4.9           3.1            1.5           0.1  Iris-setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "location = '../input/iris/Iris.csv'\n",
    "iris_dataset = pd.read_csv(location)\n",
    "iris_dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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16EXXCgDMlk8HAwAYl1uSPLO731xVd0xyVVVdPtz2gu7+uQXWBgDM0XFnAlXVi6rqhqp656p1X1JVl1fVe4bvd5lvmQAAzEJ3H+ruNw8/35Tk6iTnLLYqAGArTDMT6OIkv5TkN1ate3aS13f386vq2cPyD8++PAAA5qWq9iS5f5Irkjw4yfdV1ZOTXJnJbKFPrbPNhUkuTJLdu3dnZWVl5nUdPu20rOzdO/P9zs0c+mBahw8fnssxWDb66RjWPNd23PNvgabuK797noNT2op+Om4I1N1vGAYIqz0+yf7h50uSrEQIBACwY1TVGUlekeQZ3f2ZqvqVJD+ZpIfvP5/kqWu36+6LklyUJPv27ev9+/fPvLaVgwez/5prZr7fubnggoU99MrKSuZxDJaNfjqGAweOWlzZu3dnPf8WaOq+WuBrxHbhOTidreink70w9O7uPpRMphQnudvsSgIAYJ6q6tRMAqCXdvcrk6S7r+/uL3b33yX5tSQPXGSNAMDszf3C0POaMnzUNKkln644pimZY2prMq72autyGlNbkwW119RpZqyqKskLk1zd3b+wav3ZR97kS/Ivk7xzve0BgJ3rZEOg648MFKrq7CQ3bHTHeU0ZPmqa1JopjMtmTFMyx9TWZFzt1dblNKa2JgtqrynkzN6Dk3xHkndU1VuHdT+S5IKqul8mp4N9MMl3L6Y8AGBeTjYEenWSpyR5/vD9d2ZWEQAAc9Pdb0xS69z02q2uBQDYWtN8RPzBJG9Ksreqrquqp2US/jyiqt6T5BHDMgAAAADb1DSfDrbRPPSHzbgWAAAAAObkZD8dDAAAAIAdRAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCABgRKrqvKr646q6uqreVVVPH9Z/SVVdXlXvGb7fZdG1AgCzJQQCABiXW5I8s7vvneRBSb63qu6T5NlJXt/d90ry+mEZAFgimwqBquo/DO8gvbOqDlbV7WZVGAAAs9fdh7r7zcPPNyW5Osk5SR6f5JLhbpckecJiKgQA5mXXyW5YVeck+YEk9+nuz1XVy5M8KcnFM6oNAIA5qqo9Se6f5Ioku7v7UDIJiqrqbhtsc2GSC5Nk9+7dWVlZmXldh087LSt79858v3Mzhz6Y1uHDh+dyDJaNfjqGNc+1Hff8W6Cp+8rvnufglLain046BFq1/e2r6m+T3CHJRzZfEgAA81ZVZyR5RZJndPdnqmqq7br7oiQXJcm+fft6//79M69t5eDB7L/mmpnvd24uuGBhD72yspJ5HINlo5+O4cCBoxZX9u7dWc+/BZq6rxb4GrFdeA5OZyv66aRDoO7+cFX9XJIPJflcktd19+vW3m9e7xYdlZAteVI9pjR+TG1NxtVebV1OY2prsqD2eteMOaiqUzMJgF7a3a8cVl9fVWcPs4DOTnLD4ioEAOZhM6eD3SWTc8fvkeTGJL9dVd/e3S9Zfb95vVt0VEK2Jr1eNmNK48fU1mRc7dXW5TSmtiYLaq93D5mxmkz5eWGSq7v7F1bd9OokT0ny/OH77yygPABgjjZzYeiHJ/lAd3+su/82ySuTfO1sygIAYE4enOQ7kjy0qt46fD06k/DnEVX1niSPGJYBgCWymWsCfSjJg6rqDpmcDvawJFfOpCoAAOaiu9+YZKMLAD1sK2sBALbWSc8E6u4rklyW5M1J3jHs66IZ1QUAAADADG3q08G6+7lJnjujWgAAAACYk81cEwgAAACAHUIIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAjsWnQBAAAAsLQOHFh0BSdmp9XLCTETCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAMCIVNWLquqGqnrnqnUHqurDVfXW4evRi6wRAJiPTYVAVXVmVV1WVX9VVVdX1dfMqjAAAObi4iSPXGf9C7r7fsPXa7e4JgBgC+za5Pa/mOT3u/uJVXXbJHeYQU0AAMxJd7+hqvYsug4AYOuddAhUVXdK8vVJvjNJuvsLSb4wm7IAANhi31dVT05yZZJndven1rtTVV2Y5MIk2b17d1ZWVmZeyOHTTsvK3r0z3+/czKEPpnX48OG5HINlo5+OYc1zbcc9/xZoaftqHq/rnoNT2Yp+2sxMoH+c5GNJXlxV901yVZKnd/fNM6kMAICt8itJfjJJD99/PslT17tjd1+U5KIk2bdvX+/fv3/mxawcPJj911wz8/3OzQUXLOyhV1ZWMo9jsGz00zEcOHDU4srevTvr+bdAS9tXc3hN8xyczlb002ZCoF1JHpDk+7v7iqr6xSTPTvKfVt9pXu8WHZWQLWP6usrSJszrGFNbk3G1V1uX05jamiyovd41Ywt09/VHfq6qX0vymgWWAwDMyWZCoOuSXNfdVwzLl2USAh1lXu8WHZWQrUmvl83SJszrGFNbk3G1V1uX05jamiyovQucYcB4VNXZ3X1oWPyXSd55rPsDADvTSYdA3f3Rqrq2qvZ29zVJHpbk3bMrDQCAWauqg0n2Jzmrqq5L8twk+6vqfpmcDvbBJN+9sAIBgLnZ7KeDfX+Slw6fDPb+JN+1+ZIAAJiX7l5vetkLt7wQAGDLbSoE6u63Jtk3o1oAAAAAmJPbLLoAAAAAAOZPCAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARmDXogsAAIAd7cCBxT323r0n/viLrBeAhTITCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACuxZdAAAAALBNHDgw+33u3Tuf/Sbz2++SMhMIAAAAYASEQAAAAAAjIAQCAAAAGAEhEADAiFTVi6rqhqp656p1X1JVl1fVe4bvd1lkjQDAfAiBAADG5eIkj1yz7tlJXt/d90ry+mEZAFgymw6BquqUqnpLVb1mFgUBADA/3f2GJJ9cs/rxSS4Zfr4kyRO2tCgAYEvMYibQ05NcPYP9AACwGLu7+1CSDN/vtuB6AIA52LWZjavq3CTfnOQ/J/nBmVQEAMC2VVUXJrkwSXbv3p2VlZWZP8bh007Lyt69M9/vMjqpvprDMdvuDh8+PJff1aWw5vfH8296+mp6c+2rJXpub8VrVXX3yW9cdVmSn05yxyTP6u7HrHOf1QOFr7r00ktP+vFWO3z4cM4444zJwqFDM9nndnX4tNNyxuc/v+gytsSY2pqMq73aupzG1NZkQe09++y57PYhD3nIVd29by47Z9urqj1JXtPd5w/L1yTZ392HqursJCvdfdzR+r59+/rKK6+ceX0rBw9m/zXXzHy/y2hl794T76sDB+ZSy3a2srKS/fv3L7qM7WnN78NJ/U6NlL6a3lz7aole0zbzWlVVU43tTnomUFU9JskN3X1VVe3f6H7dfVGSi5LJQGFWL75Hdc4SHfT1jOnFZUxtTcbVXm1dTmNqa7Kg9l5wwdY+HmP16iRPSfL84fvvLLYcAGAeNnNNoAcneVxVfTDJpUkeWlUvmUlVAADMRVUdTPKmJHur6rqqelom4c8jquo9SR4xLAMAS+akZwJ193OSPCdJhplAz+rub59RXQAAzEF3bzS97GFbWggAsOVm8elgAAAAAGxzm/p0sCO6eyXJyiz2BQAAAMDsmQkEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZg16ILAAAAWBoHDiy6AoANmQkEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIzArkUXAADA9lBVH0xyU5IvJrmlu/cttiIAYJZOOgSqqvOS/EaSL0vyd0ku6u5fnFVhAAAsxEO6++OLLgIAmL3NzAS6Jckzu/vNVXXHJFdV1eXd/e4Z1QYAAADAjJz0NYG6+1B3v3n4+aYkVyc5Z1aFAQCw5TrJ66rqqqq6cNHFAACzVd29+Z1U7UnyhiTnd/dn1tx2YZILk2T37t1fdemll2768ZLk8OHDOeOMMyYLhw7NZJ/b1eHTTssZn//8osvYEmNqazKu9mrrchpTW5MFtffss+ey24c85CFXud4La1XV3bv7I1V1tySXJ/n+7n7DmvvMZWy32uFPfnJUry2bcVKvS3N6XdnOjvrfYd52+P8mY/vbvhn6anr6apVjvAZv5rVq2rHdpi8MXVVnJHlFkmesDYCSpLsvSnJRkuzbt6/379+/2YdMkqysrOTv93XgwEz2uV2t7N2b/ddcs+gytsSY2pqMq73aupzG1NZkQe294IKtfTxGrbs/Mny/oapeleSBmbzRt/o+cxnbrbZy8OCoXls246Rel0b4unLU/w7ztsP/Nxnb3/bN0FfT01erHOM1eCteqzb1EfFVdWomAdBLu/uVsykJAICtVlWnD9d5TFWdnuQbk7xzsVUBALO0mU8HqyQvTHJ1d//C7EoCAGABdid51WSIl11Jfqu7f3+xJQEAs7SZ08EenOQ7kryjqt46rPuR7n7t5ssCAGArdff7k9x30XUAAPNz0iFQd78xSc2wFgAAAADmZFPXBAIAAABgZxACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAR2LXoAgAAADZ04MDm97F372z2A7DDmQkEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIzApkKgqnpkVV1TVe+tqmfPqigAALaesR0ALLeTDoGq6pQkv5zkUUnuk+SCqrrPrAoDAGDrGNsBwPLbzEygByZ5b3e/v7u/kOTSJI+fTVkAAGwxYzsAWHKbCYHOSXLtquXrhnUAAOw8xnYAsOR2bWLbWmdd3+pOVRcmuXBYPFxV12ziMVc7K8nHZ7Sv7U5bl9eY2quty2lMbU0W0d7nPW9ee/7yee2YHWvRY7vVxvbashkn3lfze13ZzvxOTU9fTU9fTU9fHXHs1+DN9NNUY7vNhEDXJTlv1fK5ST6y9k7dfVGSizbxOOuqqiu7e9+s97sdaevyGlN7tXU5jamtyfjay+gsdGy3mufa9PTVdPTT9PTV9PTV9PTVdLainzZzOthfJrlXVd2jqm6b5ElJXj2bsgAA2GLGdgCw5E56JlB331JV35fkD5KckuRF3f2umVUGAMCWMbYDgOW3mdPB0t2vTfLaGdVyouY6DXmb0dblNab2autyGlNbk/G1l5FZ8NhuNc+16emr6ein6emr6emr6emr6cy9n6r7Vtf7AwAAAGDJbOaaQAAAAADsEDsyBKqqR1bVNVX13qp69qLrmaWqOq+q/riqrq6qd1XV04f1X1LlbwLPAAAgAElEQVRVl1fVe4bvd1l0rbNSVadU1Vuq6jXD8j2q6oqhrS8bLk6541XVmVV1WVX91XB8v2ZZj2tV/Yfh9/edVXWwqm63TMe1ql5UVTdU1TtXrVv3WNbEfx9er95eVQ9YXOUnboO2/uzwe/z2qnpVVZ256rbnDG29pqq+aTFVn5z12rrqtmdVVVfVWcPyjj6usF0d63nIP9hovMitDWOQv6iqtw19dczPZh67teNy1ldVH6yqd1TVW6vqykXXs52t9z/Qomvajqpq7/D7dOTrM1X1jHk81o4LgarqlCS/nORRSe6T5IKqus9iq5qpW5I8s7vvneRBSb53aN+zk7y+u++V5PXD8rJ4epKrVy3/TJIXDG39VJKnLaSq2fvFJL/f3f80yX0zafPSHdeqOifJDyTZ193nZ3Jx0SdluY7rxUkeuWbdRsfyUUnuNXxdmORXtqjGWbk4t27r5UnO7+5/luT/JHlOkgyvVU9K8pXDNv9jeM3eKS7OrduaqjovySOSfGjV6p1+XGG7ujjrPA+5lY3Gi9za55M8tLvvm+R+SR5ZVQ9acE3b2dpxORt7SHffz8eeH9d6/wOxRndfM/w+3S/JVyX5bJJXzeOxdlwIlOSBSd7b3e/v7i8kuTTJ4xdc08x096HufvPw802ZPEnOyaSNlwx3uyTJExZT4WxV1blJvjnJrw/LleShSS4b7rIUba2qOyX5+iQvTJLu/kJ335glPa6ZXHT+9lW1K8kdkhzKEh3X7n5Dkk+uWb3RsXx8kt/oiT9PcmZVnb01lW7eem3t7td19y3D4p8nOXf4+fFJLu3uz3f3B5K8N5PX7B1hg+OaJC9I8h+TrL6I3o4+rrBdHeN5yCrHGC+yxvA6fXhYPHX4clHUdawdl8NmHeN/II7tYUne191/PY+d78QQ6Jwk165avi5L+kevqvYkuX+SK5Ls7u5DyeQPf5K7La6ymfpvmfxz9XfD8pcmuXHVP5jLcnz/cZKPJXnxMMX216vq9Czhce3uDyf5uUxmTRxK8ukkV2U5j+tqGx3LZX/NemqS3xt+Xrq2VtXjkny4u9+25qalayuwM60ZL7KO4RSntya5Icnl3a2v1rd2XM7GOsnrquqqqrpw0cVsYxv9D8SxPSnJwXntfCeGQLXOuqVL86vqjCSvSPKM7v7MouuZh6p6TJIbuvuq1avXuesyHN9dSR6Q5Fe6+/5Jbs4SnPq1nuFaOI9Pco8kd09yeianzqy1DMd1Gsv6O52q+tFMTkl46ZFV69xtx7a1qu6Q5EeT/Ph6N6+zbse2FdiZxjBenIXu/uJwisW5SR5YVecvuqbtZoNxORt7cHc/IJMx7vdW1dcvuqBtajT/A83KcN3UxyX57Xk9xk4Mga5Lct6q5XOTfGRBtcxFVZ2ayR/0l3b3K4fV1x851WD4fsOi6puhByd5XFV9MJPT+h6ayTsQZw6nESXLc3yvS3LdqneeLsvkBXEZj+vDk3yguz/W3X+b5JVJvjbLeVxX2+hYLuVrVlU9Jcljknxbdx8JP5atrffMJMx82/A6dW6SN1fVl2X52grsMBuMFzmG4TSUlbju1HpuNS6vqpcstqTtq7s/Mny/IZPrtuyY09+32Eb/A7GxRyV5c3dfP68H2Ikh0F8muVdNPmnotplMlXr1gmuameGaOC9McnV3/8Kqm16d5CnDz09J8jtbXdusdfdzuvvc7t6TyXH8o+7+tiR/nOSJw92Wpa0fTXJtVe0dVj0sybuzhMc1k9PAHlRVdxh+n4+0demO6xobHctXJ3ny8GlSD0ry6SOnje1UVfXIJD+c5HHd/dlVN706yZOq6rSqukcmF03+i0XUOAvd/Y7uvlt37xlep65L8oDh+bx0xxXYOY4xXmSNqrprDZ9iWVW3z+TNqr9abFXbzwbj8m9fcFnbUlWdXlV3PPJzkm9M4hMN13GM/4HY2AWZ46lgyWR61o7S3bdU1fcl+YNMPnXoRd39rgWXNUsPTvIdSd4xnLucJD+S5PlJXl5VT8vkn+xvWVB9W+GHk1xaVT+V5C0ZLiS2BL4/yUuH8PL9Sb4rkyB2qY5rd19RVZcleXMmpwq9JclFSf5XluS4VtXBJPuTnFVV1yV5bjZ+jr42yaMzuUjyZzM57jvGBm19TpLTklw++T8kf97d39Pd76qql2fyx/2WJN/b3V9cTOUnbr22dvdGv6c7+rjCdnWCz8MxW3e82N2vXWBN29XZSS4ZPq3yNkle3t0+/pzN2J3kVcMYaFeS3+ru319sSdvaev8DsY7hUgSPSPLdc32cf5jFDwAAAMCy2omngwEAAABwgoRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRCMTFXtqaquql0nuf27qmr/Brftr6rr5vXYAAAcrao+WFUPX3Qdq1XVgap6yaLrAG5NCATbyPBH/HNVdbiqrq+qF1fVGcfZ5jur6o0n+XgXVNW716y7fIN1z06S7v7K7l6Zcv8nPCipqn9SVb9dVR+vqk9X1dur6ger6pQT2Q8AwFarqq+rqj8bxjCfrKr/XVVfvYWPv+Xhy/HeBDzGdt9aVVcO495DVfV7VfV186gR+AdCINh+HtvdZyR5QJKvTvJjc3ysP0ly76q6a5IMM3Tum+QOa9Z9TZI3zLGODI91zyRXJLk2yf/V3XdO8i1J9iW547wfHwDgZFXVnZK8Jsn/m+RLkpyT5HlJPr/IurajqvrBJP8tyX9JsjvJP0ryP5I8fpF1wRgIgWCb6u4PJ/m9JOdX1Z2r6oXDuyQfrqqfqqpTqureSX41ydcM76LcmCRV9c1V9Zaq+kxVXVtVBzZ4jI8keX+Srx9WPSDJuzIJh1avu02SK4d9//3snqq6fVVdXFWfGmYP/f07XVX1m5n8Qf/dobb/uOqhv62qPjTM9vnRVeufl+TPuvsHu/vQUOM13f2t3X3jqtPJvmto16eq6nuq6quHGUM3VtUvnVyPAwBsyj9Jku4+2N1f7O7PdffruvvtSVJVT62qq4fxyx9U1Zcf2XAY3/xAVb1/GB/9bFXdZrjtnlX1R1X1ieG2l1bVmSdaXFXdvapeUVUfq6oPVNUPrLrtQFW9vKp+o6puGk7/37fq9gcMY8ubhhnbLxvGo6dnMl69+zDeO1xVdx82u+16+6uqOyf5iSTf292v7O6bu/tvu/t3u/uHVtXz21X1kmH7dwyzxZ9TVTcM48BvPNE+AIRAsG1V1XlJHp3kLUkuSXJLkq9Icv8k35jk33b31Um+J8mbuvuM7j4yILg5yZOTnJnkm5P8u6p6wgYP9Yb8Q+Dz9Un+NMkb16z78+7+wjrbPjfJPYevb0rylCM3dPd3JPlQhplN3f1fV233dUn2JnlYkh8fwqwkeXiSy47VL4N/nuReSf5NJu8i/eiw7Vcm+ddV9Q1T7AMAYJb+T5IvVtUlVfWoqrrLkRuGcdiPJPlXSe6ayXjr4Jrt/2Ums58fkMmMmKce2TzJTye5e5J7JzkvyYETKWwIlH43ydsymaH0sCTPqKpvWnW3xyW5NJPx46uT/NKw7W2TvCrJxZnMcDo41JruvjnJo5J8ZBjvnTG8ybjh/jKZYX67YZ/H8tgkv5nkLpmMh/8gk/9fz8kkRPr/TqQPgAkhEGw//3OY0fPGTGbk/Homf1yfMbxTckOSFyR50kY76O6V7n5Hd//d8O7TwSQbBSOrZ/38i0wGJX+6Zt2fbLDtv07yn7v7k919bZL/PmUbnze8O/a2TAYj9x3Wf2mSQ1Ns/5Pd/Tfd/bpMAq+D3X3DMHvqTzMJygAAtkx3fyaTN7o6ya8l+VhVvbqqdif57iQ/3d1Xd/ctmZwGdb/Vs4GS/MwwpvpQJm9yXTDs973dfXl3f767P5bkF7LxuG4jX53krt39E939he5+/1Dj6vHkG7v7td39xUzClyPjswcl2ZXkvw8zdl6Z5C+meMyN9velST4+9MOx/Gl3/8Fwv9/OJDx7fnf/bSbh0p6TmREFY+cTemD7eUJ3/+GRhap6YJJTkxyqqiOrb5PJdXPWVVX/PMnzk5yf5LZJTsvkj+d63pDkhcO7VQ9K8m3dfbiqzh7WfV0mA5H13H1NHX99nLYd8dFVP382yZGLX38iydlTbH/9qp8/t87yMS+mDQAwD8Ms7e9Mkqr6p0leksk46suT/GJV/fyqu1cms1qOjJ/WjqnuPuznbpm80fYvMrlG4m2SfOoES/vyTE7ZunHVulMyefPsiLXjs9sN14a8e5IPd3evun3DcegU+/tEkrOqatdxgqC147uPD4HSkeVkMua7McDUzASC7e/aTC4oeFZ3nzl83am7v3K4vdfZ5rcymXZ73nBx5V/NZKBxK8M7QR9JcmGSD3X34eGmNw3rzkjy5xvUdiiTKclH/KO1uz9my27tD5P83ye4DQDAttPdf5XJKVTnZzKe++5VY7kzu/v23f1nqzZZO6Y6clrVT2cypvpn3X2nJN+eDcZ1x3Btkg+sefw7dvejp9j2UJJzatW7kWtqPdHx3puS/E2SjS5VAMyREAi2ueECya9L8vNVdaequs1wgcAj04CvT3LucL72EXdM8snu/pthJtG3Hudh/jTJD+bod4PeOKy7srs/t+5WycuTPKeq7lJV5yb5/jW3X5/kHx+vjas8N8nXDhdD/LIkqaqvGC4KaLovALBtVdU/rapnDmOiI9d3vCCTN9N+NZMx01cOt925qr5lzS5+aBhTnZfk6UleNqy/Y5LDSW6sqnOS/NBxSrlNVd1u1ddpmZy+9Zmq+uGafLDHKVV1fk338fVvSvLFJN9XVbuq6vFJHrjq9uuTfOlwwefj6u5PJ/nxJL9cVU+oqjtU1anDdZT+6/G2BzZHCAQ7w5MzOa3r3ZlM/70s/3Da1B9l8oleH62qjw/r/n2Sn6iqmzL5I/vy4+z/T5LcLZPg54g/HdYd66Phn5fJdOUPZBJU/eaa2386yY/V5FO7nnWcGtLd78vkYoF7kryrqj6d5BWZfDLZTcfbHgBggW7K5MMrrqiqmzMJf96Z5Jnd/aokP5Pk0qr6zLD+UWu2/50kVyV5a5L/leSFw/rnZXKx6E8P6195nDouyOR0qSNf7xtOo3pskvtlMm77eCbXnTxucDN8OMi/SvK0TE69+vYkr8lkpvqRGU8Hk7x/GPPdfaN9rdrnL2TyZuOPJflYJjOVvi/J/zzetsDm1NGndgIAALCVqqqT3Ku737voWqZRVVck+dXufvGiawFOjJlAAAAAbKiqvqGqvmw4HewpSf5Zkt9fdF3AifPpYAAAABzL3kwuL3BGkvcleeJw3Upgh3E6GAAAAMAIOB0MAAAAYAS29HSws846q/fs2fP3yzfffHNOP/30rSxh1PT31tHXW0t/by39vbXm1d9XXXXVx7v7rjPfMaOydmy3U4z1dWyM7dbmcRhjm5NxtlubNzbt2G5LQ6A9e/bkyiuv/PvllZWV7N+/fytLGDX9vXX09dbS31tLf2+tefV3Vf31zHfK6Kwd2+0UY30dG2O7tXkcxtjmZJzt1uaNTTu2czoYAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMwK5FFzATBw4suoITs9PqBQCARdnM2Hnv3q0fexvrA9uYmUAAAAAAIyAEAgAAABiBqUOgqjqlqt5SVa8Zlu9RVVdU1Xuq6mVVddv5lQkAAADAZpzITKCnJ7l61fLPJHlB9//P3v1HWXrXdYJ/fyYJDdIgaKC2DWiYlakVXQ1aG3Fw3Qo/XGQcgR2cpXcGYWBP4x4ZYRd1lD07FqKr7iLouHNwGoNkZpDAAhmQBTQCV2TVaAIxBEMviBFDWiK/hEJPnOBn/6ibnkqluut21b23qu7zep1Tp+59ft3P9/s899btd3+f5+lHJvlskudOszAAAAAApmeiEKiqHpbkHyT55fHzSvK4JG8cL3JVkqfOokAAAAAA9m7SkUA/n+RHkvzt+PlXJvlcd981fn5bkkumXBsAAAAAU7LjLeKr6ruT3NHdN1TV6t2Tt1m0z7L+iSQnkmRpaSmj0ejMvPX19Xs837Xl5b1vY56m0eZdmFp/syN9PV/6e77093zpbwAApmXHECjJY5N8T1U9Ocl9kzwwGyODHlRVF45HAz0sye3brdzdJ5OcTJKVlZVeXV09M280GmXz811bW9v7Nubp+PF9edmp9Tc70tfzpb/nS3/Pl/4GAGBadjwdrLt/rLsf1t2XJnlGknd39z9J8p4kTx8v9qwkb5lZlQAAAADsySQjgc7mXyS5uqp+MskHklw5nZIAAACYi8N2VsVhqxcOmPMKgbp7lGQ0fvyxJJdPvyQAAAAApm3Su4MBAAAAcIgJgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCABgQKrq4VX1nqq6pao+VFUvGE9fq6pPVNWN458n73etAMB0ndct4gEAOPTuSvKi7n5/VT0gyQ1Vde143iu6+2X7WBsAMENCIACAAenu00lOjx9/oapuSXLJ/lYFAMyD08EAAAaqqi5N8ugk140nPb+qbqqqV1fVg/etMABgJowEAgAYoKo6muRNSV7Y3Z+vqlcmeWmSHv/+uSTP2Wa9E0lOJMnS0lJGo9Hcap6W9fX1Q1n3Xh3adi8v73rV9SNHMtrD+ruyz3183vt53v2zV9u07dAe23s0xHZr894JgQAABqaqLspGAPTa7n5zknT3JzfNf1WSt223bnefTHIySVZWVnp1dXXm9U7baDTKYax7rw5tu9fWdr3qaHk5q6dOTa+WSRw/Pt/X2+K89/Me+ndfbNO/h/bY3qMhtlub987pYAAAA1JVleTKJLd098s3TT+2abGnJbl53rUBALNlJBAAwLA8Nskzk3ywqm4cT3txkuNVdVk2Tge7Ncnz9qc8AGBWhEAAAAPS3e9LUtvMevu8awEA5svpYAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABkAIBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGYMcQqKruW1W/X1V/WFUfqqqXjKe/pqr+pKpuHP9cNvtyAQAAANiNCydY5s4kj+vu9aq6KMn7quod43k/3N1vnF15AAAAAEzDjiFQd3eS9fHTi8Y/PcuiAAAAAJiuia4JVFUXVNWNSe5Icm13Xzee9VNVdVNVvaKqjsysSgAAAAD2ZJLTwdLdX0pyWVU9KMk1VfUNSX4syZ8nuU+Sk0n+RZKf2LpuVZ1IciJJlpaWMhqNzsxbX1+/x/NdW17e+zbmaRpt3oWp9Tc70tfzpb/nS3/Pl/6GQ2Btbb8rmMzy8kath6VeAKZuohDobt39uaoaJXlSd79sPPnOqvqVJD90lnVOZiMkysrKSq+urp6ZNxqNsvn5rh22P2THj+/Ly06tv9mRvp4v/T1f+nu+9DcAANMyyd3BHjIeAZSqul+SJyT5cFUdG0+rJE9NcvMsCwUAAABg9yYZCXQsyVVVdUE2QqM3dPfbqurdVfWQJJXkxiTfP8M6AQAAANiDSe4OdlOSR28z/XEzqQgAAACAqZvo7mAAAAAAHG5CIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwABfudwHAIbS2tt8V3NPy8rlrOmj1AgAA7AMjgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAADIAQCABiQqnp4Vb2nqm6pqg9V1QvG07+iqq6tqo+Mfz94v2sFAKZLCAQAMCx3JXlRd39dksck+YGqelSSH03yru5+ZJJ3jZ8DAAtkxxCoqu5bVb9fVX84/t+il4ynP6Kqrhv/b9Hrq+o+sy8XAIC96O7T3f3+8eMvJLklySVJnpLkqvFiVyV56v5UCADMyoUTLHNnksd193pVXZTkfVX1jiT/S5JXdPfVVfVLSZ6b5JUzrBUAgCmqqkuTPDrJdUmWuvt0shEUVdVDz7LOiSQnkmRpaSmj0WgutU7T+vr6dOteXp7etmZo/ciRjJaXk8O2z/bQv2faPE/73L/nfXwfkuP3jG3aNvX39CExxHZr897tGAJ1dydZHz+9aPzTSR6X5H8YT78qyVqEQAAAh0JVHU3ypiQv7O7PV9VE63X3ySQnk2RlZaVXV1dnVuOsjEajTLXutbXpbWuGRsvLWT11Kjl+fL9LOT976N8zbZ6nfe7f8z6+D8nxe8Y2/Tv19/QhMcR2a/PeTXRNoKq6oKpuTHJHkmuT/HGSz3X3XeNFbsvGMGIAAA648ejuNyV5bXe/eTz5k1V1bDz/WDa+9wEAC2SS08HS3V9KcllVPSjJNUm+brvFtlv3XEOGpzasaQGGMM7DEIfO7ZeF7+sD9p7bcaj3Iu+LfbDwx/cBo7+ZttoY8nNlklu6++WbZr01ybOS/Mz491v2oTwAYIYmCoHu1t2fq6pRNu4k8aCqunA8GuhhSW4/yzpnHTI8tWFNCzCEcR6GOHRuvyx8Xx+w99yOQ70P27D3A27hj+8DRn8zA49N8swkHxyP9E6SF2cj/HlDVT03yceTfO8+1QcAzMiOIVBVPSTJfxwHQPdL8oQkP5vkPUmenuTq+N8iAIBDobvfl+RsFwB6/DxrAQDma5KRQMeSXFVVF2TjGkJv6O63VdUfJbm6qn4yyQeyMawYAAAAgANokruD3ZSNW4dunf6xJJfPoigAAAAApmuiu4MBAAAAcLgJgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMwIX7XQAAAABMZG3t3tOWl7efflAc5NoYHCOBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABkAIBAAAADAAQiAAAACAARACAQAAAAzAjiFQVT28qt5TVbdU1Yeq6gXj6WtV9YmqunH88+TZlwsAAADAblw4wTJ3JXlRd7+/qh6Q5IaqunY87xXd/bLZlQcAAADANOwYAnX36SSnx4+/UFW3JLlk1oUBAAAAMD3ndU2gqro0yaOTXDee9PyquqmqXl1VD55ybQAAAABMySSngyVJqupokjcleWF3f76qXpnkpUl6/Pvnkjxnm/VOJDmRJEtLSxmNRmfmra+v3+P5ri0v730b8zSNNu/C1PqbHS18Xx+w99z6kSMZnaumRd4X+2Dhj+8DRn8DADAtE4VAVXVRNgKg13b3m5Okuz+5af6rkrxtu3W7+2SSk0mysrLSq6urZ+aNRqNsfr5ra2t738Y8HT++Ly87tf5mRwvf1wfsPTdaXs7qqVNnX2Cf3nOLauGP7wNGfwMAMC2T3B2sklyZ5Jbufvmm6cc2Lfa0JDdPvzwAAAAApmGSkUCPTfLMJB+sqhvH016c5HhVXZaN08FuTfK8mVQIAAAAwJ5Ncnew9yWpbWa9ffrlAAAAADALE18YGgAAgB3s97UTl5f3vwbgwDqvW8QDAAAAcDgJgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAANSVa+uqjuq6uZN09aq6hNVdeP458n7WSMAMBtCIACAYXlNkidtM/0V3X3Z+Oftc64JAJiDC/e7AAC2WFvb7wrObXn5njUe9HqBe+ju91bVpftdBwAwf0IgAACS5PlV9X1Jrk/you7+7HYLVdWJJCeSZGlpKaPRaH4VTsn6+vp0615ent62Zmj9yJGMlpeTw7bP9tC/Z9o8INp8AM3oPTf1z7JDQJv3TggEAMArk7w0SY9//1yS52y3YHefTHIySVZWVnp1dXVOJU7PaDTKVOs+JCMiR8vLWT11Kjl+fL9LOT976N8zbR4QbT6AZvSem/pn2SGgzXvnmkAAAAPX3Z/s7i91998meVWSy/e7JgBg+oRAAAADV1XHNj19WpKbz7YsAHB4OR0MAGBAqup1SVaTXFxVtyX58SSrVXVZNk4HuzXJ8/atQABgZoRAAAAD0t3bXZziyrkXAgDMndPBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYADcHQwAAIZkbW2/KwBgnxgJBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAHYMgarq4W+pKXQAACAASURBVFX1nqq6pao+VFUvGE//iqq6tqo+Mv794NmXCwAAAMBuTDIS6K4kL+rur0vymCQ/UFWPSvKjSd7V3Y9M8q7xcwAAAAAOoB1DoO4+3d3vHz/+QpJbklyS5ClJrhovdlWSp86qSAAAAAD25ryuCVRVlyZ5dJLrkix19+lkIyhK8tBpFwcAAADAdFw46YJVdTTJm5K8sLs/X1WTrnciyYkkWVpaymg0OjNvfX39Hs93bXl579uYp2m0eRem1t/saOH7+oC959aPHMnoXDUdtn1xwPp3q3v192Hr30Nm4T9PAACYm4lCoKq6KBsB0Gu7+83jyZ+sqmPdfbqqjiW5Y7t1u/tkkpNJsrKy0qurq2fmjUajbH6+a2tre9/GPB0/vi8vO7X+ZkcL39cH7D03Wl7O6qlTZ19gn95zu3bA+nere/X3YevfQ2bhP08AAJibSe4OVkmuTHJLd79806y3JnnW+PGzkrxl+uUBAAAAMA2TjAR6bJJnJvlgVd04nvbiJD+T5A1V9dwkH0/yvbMpEQAAAIC92jEE6u73JTnbBYAeP91yAAAAAJiF87o7GAAAAACHkxAIAAAAYACEQAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABkAIBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAGBAqurVVXVHVd28adpXVNW1VfWR8e8H72eNAMBsCIEAAIblNUmetGXajyZ5V3c/Msm7xs8BgAWzYwh0lv8tWquqT1TVjeOfJ8+2TAAApqG735vkM1smPyXJVePHVyV56lyLAgDmYpKRQK/Jvf+3KEle0d2XjX/ePt2yAACYo6XuPp0k498P3ed6AIAZuHCnBbr7vVV16exLAQDgoKuqE0lOJMnS0lJGo9H+FrQL6+vr0617eXl625qh9SNHMjoktU6LNg/DgW/zjD4np/5Zdgho895Vd++80EYI9Lbu/obx87Ukz07y+STXJ3lRd3/2LOtu/qLwLVdfffWZeevr6zl69Ohe6t9w+vTetzFPx47ty8tOrb/Z0cL39QF7z60fOZKjd9559gX26T23awesf7e6V38ftv49ZGb1eXLFFVfc0N0rU98wh8I23+1OJVnt7tNVdSzJqLt3/BfVyspKX3/99TOtdRZGo1FWV1ent8G1telta4ZGy8tZPXVqv8uYK20ehgPf5hl9Rkz9s+wQ0Oazq6qJvtvtOBLoLF6Z5KVJevz755I8Z7sFu/tkkpPJxheFzcVPbQcekj+8Zxw/vi8vO8Q3zH5Z+L4+YO+5Hf/w79N7btcOWP9uda/+Pmz9e8gs/OcJB8Vbkzwryc+Mf79lf8sBAGZhV3cH6+5PdveXuvtvk7wqyeXTLQsAgFmoqtcl+d0ky1V1W1U9NxvhzxOr6iNJnjh+DgAsmF2NBKqqY3dfPDDJ05LcfK7lAQA4GLr7bMP3Hj/XQgCAudsxBBr/b9Fqkour6rYkP55ktaouy8bpYLcmed4MawQAAABgjya5O9h2/1t05QxqAQAAAGBGdnVNIAAAAAAOl93eHQwADqcDfve1e3FnMAAApsRIIAAAAIABMBIIAICDZdYj9paXD9+oQACYAiOBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABkAIBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAA7BjCFRVr66qO6rq5k3TvqKqrq2qj4x/P3i2ZQIAAACwF5OMBHpNkidtmfajSd7V3Y9M8q7xcwAAAAAOqB1DoO5+b5LPbJn8lCRXjR9fleSpU64LAAAAgCm6cJfrLXX36STp7tNV9dCzLVhVJ5KcSJKlpaWMRqMz89bX1+/xfNeWl/e+jXmaRpt3YWr9zY4Wvq8P2Htu/ciRjM5V02HbFwesf7e6V3/r35la+M8TAADmZrch0MS6+2SSk0mysrLSq6urZ+aNRqNsfr5ra2t738Y8HT++Ly87tf5mRwvf1wfsPTdaXs7qqVNnX2Cf3nO7dsD6d6t79bf+nanR6upif54AADA3u7072Cer6liSjH/fMb2SAAAAAJi23YZAb03yrPHjZyV5y3TKAQAAAGAWJrlF/OuS/G6S5aq6raqem+Rnkjyxqj6S5Inj5wAAAAAcUDteE6i7z3axh8dPuRYAAAAAZmTmF4YGAACAwZrVTSmWl2ez7UN2Ew31nh8hEAB7c9j+8AIAwEDt9sLQAAAAABwiQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYALeI3w/7dTvl5eXdvbbbP8+W/gXggKiqW5N8IcmXktzV3Sv7WxEAME1CIAAANruiuz+130UAANPndDAAAACAATASCACAu3WS36iqTvJvuvvk1gWq6kSSE0mytLSU0Wg0/SqWl6e/zU3WjxzJaMavcRANsd3aPAxDbHMyw3a/7nXT3+aUrB85ktHW+g7bvj/Pv5vr6+tT/VsrBAIA4G6P7e7bq+qhSa6tqg9393s3LzAOhk4mycrKSq+urk6/ihlfL2+0vJzVU6dm+hoH0RDbrc3DMMQ2J8Ns90K0+fjx81p8NBplmn9rnQ4GAECSpLtvH/++I8k1SS7f34oAgGkSAgEAkKq6f1U94O7HSb4zyc37WxUAME1OB2PxHIRbri8vH4w6AGByS0muqapk4zvir3b3O/e3JABgmoRAAACkuz+W5Jv2uw4AYHacDgYAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAAD4O5gwOJbW9vvCgAAAPadkUAAAAAAAyAEAgAAABgAIRAAAADAAOzpmkBVdWuSLyT5UpK7untlGkUBAAAAMF3TuDD0Fd39qSlsBwAAAIAZcToYAAAAwADsdSRQJ/mNquok/6a7T25doKpOJDmRJEtLSxmNRmfmra+v3+P5ri0v730bA7B+5EhGu+mraeyjeToAx8Ou+5pd0d/zpb/na2p/KwEAGLy9hkCP7e7bq+qhSa6tqg9393s3LzAOhk4mycrKSq+urp6ZNxqNsvn5rq2t7X0bAzBaXs7qqVPnv+Lx49MvZpYOwPGw675mV/T3fOnv+Rqtrk7nbyUAAIO3p9PBuvv28e87klyT5PJpFAUAAADAdO06BKqq+1fVA+5+nOQ7k9w8rcIAAAAAmJ69nA62lOSaqrp7O7/a3e+cSlUAAAAATNWuQ6Du/liSb5piLQAAAADMiFvEAwAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABuDC/S6AQ2Btbb8rAAAAAPbISCAAAACAARACAQAAAAyAEAgAAABgAPYUAlXVk6rqVFV9tKp+dFpFAQAwf77bAcBi23UIVFUXJPnXSb4ryaOSHK+qR02rMAAA5sd3OwBYfHsZCXR5ko9298e6+2+SXJ3kKdMpCwCAOfPdDgAW3F5uEX9Jkj/b9Py2JN+6daGqOpHkxPjpelWd2jT74iSf2kMNnB/9PT/6er7093zp73l6yUtm1d9fM4NtcrhN47vdYTHUz7Ehtlubh2GIbU6G2e7D3+aXvOR815i0zRN9t9tLCFTbTOt7Teg+meTkthuour67V/ZQA+dBf8+Pvp4v/T1f+nu+9DdztOfvdofFUN9XQ2y3Ng/DENucDLPd2rx3ezkd7LYkD9/0/GFJbt9bOQAA7BPf7QBgwe0lBPqDJI+sqkdU1X2SPCPJW6dTFgAAc+a7HQAsuF2fDtbdd1XV85P8epILkry6uz90nps51EOJDyH9PT/6er7093zp7/nS38zFlL7bHRZDfV8Nsd3aPAxDbHMyzHZr8x5V971O9QYAAABgwezldDAAAAAADgkhEAAAAMAAzDwEqqonVdWpqvpoVf3oNvOPVNXrx/Ovq6pLZ13TIpugv59dVX9RVTeOf/7H/ahzUVTVq6vqjqq6+Szzq6r+1Xh/3FRV3zzvGhfFBH29WlV/uenY/pfzrnGRVNXDq+o9VXVLVX2oql6wzTKO7ymZsL8d47ALVXVBVX2gqt62zbyF+15UVbdW1QfH7bl+m/kL+dk9QbsX7jO0qh5UVW+sqg+P/35825b5C7evJ2jzQu3nqlre1JYbq+rzVfXCLcss4n6epN0Lta+TpKr+5/H3wJur6nVVdd8t86eSnez6wtCTqKoLkvzrJE/Mxm1H/6Cq3trdf7Rpsecm+Wx3f21VPSPJzyb572dZ16KasL+T5PXd/fy5F7iYXpPk/0ryb88y/7uSPHL8861JXjn+zfl7Tc7d10ny29393fMpZ+HdleRF3f3+qnpAkhuq6totnyeO7+mZpL8TxzjsxguS3JLkgWeZv4jfi67o7k+dZd4if3afq93J4n2G/kKSd3b302vjjn5ftmX+Iu7rndqcLNB+7u5TSS5Lzvxb7xNJrtmy2MLt5wnbnSzQvq6qS5L8YJJHdfdfV9UbsnGXztdsWmwq2cmsRwJdnuSj3f2x7v6bJFcnecqWZZ6S5Krx4zcmeXxV1YzrWlST9DdT1N3vTfKZcyzylCT/tjf8XpIHVdWx+VS3WCboa6aou0939/vHj7+QjX9AXbJlMcf3lEzY38B5qqqHJfkHSX55v2s5QHx2L4CqemCS70hyZZJ099909+e2LLZQ+3rCNi+yxyf54+7+0y3TF2o/b+Ns7V5EFya5X1VdmI2A8/Yt86eSncw6BLokyZ9ten5b7v2l9swy3X1Xkr9M8pUzrmtRTdLfSfKPxkMF31hVD59PaYM16T5hOr6tqv6wqt5RVV+/38UsivFQ00cnuW7LLMf3DJyjvxPHOJyvn0/yI0n+9hzLLNr3ok7yG1V1Q1Wd2Gb+on5279TuZLE+Q/9ukr9I8iu1cbrjL1fV/bcss2j7epI2J4u1nzd7RpLXbTN90fbzVmdrd7JA+7q7P5HkZUk+nuR0kr/s7t/YsthUspNZh0DbpVJb70k/yTJMZpK+/LUkl3b3Nyb5zfynJJHZcHzPz/uTfE13f1OSX0zyH/a5noVQVUeTvCnJC7v781tnb7OK43sPduhvxzich6r67iR3dPcN51hsEb8XPba7vzkbp4j8QFV9x5b5i/rZvVO7F+0z9MIk35zkld396CRfTLL1eqCLtq8nafOi7eckyfjUt+9J8n9vN3ubaYd5P5+xQ7sXal9X1YOzMdLnEUm+Ksn9q+qfbl1sm1XPe1/POgS6Lcnm/1F5WO49pOnMMuNhT18ep3zs1o793d2f7u47x09fleRb5lTbUE3yHmAKuvvz3b0+fvz2JBdV1cX7XNahVlUXZSOQeG13v3mbRRzfU7RTfzvG4bw9Nsn3VNWt2ThF/nFV9e83L7CI34u6+/bx7zuycQ2Ny7csspCf3Tu1ewE/Q29Lclt33z1q9I3ZCEi2LrNI+3rHNi/gfr7bdyV5f3d/cpt5i7afNztruxdwXz8hyZ909190939M8uYkf3/LMlPJTmYdAv1BkkdW1SPGKd4zkrx1yzJvTfKs8eOnJ3l3dy9EcrkPduzvLeeHfk82rjvB7Lw1yfeNr9r/mGwM6zu930Utoqr6z+4+J7aqLs/G59un97eqw2vcl1cmuaW7X36WxRzfUzJJfzvG4fx0949198O6+9JsfCd6d3ff439VF+17UVXdf3xx+YxPk/nOJFvvqrlwn92TtHvRPkO7+8+T/FlVLY8nPT7J1psJLNS+nqTNi7afNzmes58StVD7eYuztnsB9/XHkzymqr5s3K7H595/k6aSncz07mDdfVdVPT/Jrye5IMmru/tDVfUTSa7v7rdm40vvv6uqj2YjxXrGLGtaZBP29w9W1fdk4040n0ny7H0reAFU1euSrCa5uKpuS/LjSS5Kku7+pSRvT/LkJB9N8ldJ/tn+VHr4TdDXT0/yP1XVXUn+OskzBMp78tgkz0zywaq6cTztxUm+OnF8z8Ak/e0YhylY8O9FS0muGf+76MIkv9rd76yq708W+rN7knYv4mfoP0/y2vF//n4syT8bwL7eqc0Lt5+r6suycffn522atuj7eZJ2L9S+7u7rquqN2TjN7a4kH0hychbZSR3ifgIAAABgQrM+HQwAAACAA0AIBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABkAIBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABkAIBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIBiwqrq1qp6wy3V/qar+t3PM76r62lm8NgAAAOdPCAQHRFV9e1X9TlX9ZVV9pqr+36r6r+b02sfGoc3Spmn/61mmvTNJuvv7u/ulE27/NVX1k+dZ0wOr6uer6uNVtV5VHx0/v/h8tgMAAMAGIRAcAFX1wCRvS/KLSb4iySVJXpLkznm8fnefTvLRJN+xafJ3JPnwNtPeO+t6quo+Sd6V5OuTPCnJA5P8/SSfTnL5rF8fAABgEQmB4GD4e0nS3a/r7i9191939290901JUlXPqapbquqzVfXrVfU1d684Hq3zg1X1sar6VFX9n1X1d8bz/vOqendVfXo877VV9aCz1PDejAOfqrogyaOT/MKWad82Xu5eo3uq6oer6nRV3V5Vz9k0/USSf5LkR8Yjen5t02teVlU3jUc/vb6q7jue/n1JvjrJ07r7j7r7b7v7ju5+aXe/fbzdW8eveVNVfbGqrqyqpap6R1V9oap+s6oevKu9AQAAsICEQHAw/H9JvlRVV1XVd20OL6rqqUlenOS/S/KQJL+d5HVb1n9akpUk35zkKUnuDmEqyU8n+aokX5fk4UnWzlLDmRAoGwHQh7MxGmfztIuS/P7WFavqSUl+KMkTkzwyyZlr/XT3ySSvTfJ/dPfR7v6Hm1b9x9kY6fOIJN+Y5Nnj6U9I8s7uXj9LrXf7R+PX/HtJ/mGSd2Sjry7OxufbD+6wPgAAwGAIgeAA6O7PJ/n2JJ3kVUn+oqreOr4ez/OS/HR339LddyX537MxguZrNm3iZ7v7M9398SQ/n+T4eLsf7e5ru/vO7v6LJC9P8t+cpYzfSvIN4wDqv07y2939kSQXb5r2e939N9us+4+T/Ep339zdX8zZg6at/lV3397dn0nya0kuG0//yiSnJ1j/F7v7k939iWyEY9d19we6+84k12QjuAIAACBCIDgwxiHPs7v7YUm+IRujd34+ydck+YWq+lxVfS7JZ7IxwueSTav/2abHfzpeN1X10Kq6uqo+UVWfT/LvszFKZrvXvzXJbdkIo74jG6FKkvzupmlnux7QV21TwyT+fNPjv0pydPz400mOTbD+Jzc9/uttnh8NAAAASYRAcCB194eTvCYbYdCfJXledz9o08/9uvt3Nq3y8E2PvzrJ7ePHP52N0UXf2N0PTPJPsxEgnc1vZyPs+bYkv7Nl2rfn7CHQ6W1quEeTzvGa2/nNJP9tVd3/PNcDAADgLIRAcABU1X9RVS+qqoeNnz88G6d0/V6SX0ryY1X19eN5X15V37tlEz9cVQ8er/eCJK8fT39AkvUkn6uqS5L88A6lvDcbF2W+fXyKWpK8bzzty7MxKmg7b0jy7Kp6VFV9WZIf3zL/k0n+7g6vvdm/y0b49aZx3/ydqvrKqnpxVT35PLYDAADAmBAIDoYvJPnWJNdV1RezEf7cnORF3X1Nkp9NcvX4lK6bk3zXlvXfkuSGJDcm+X+SXDme/pJsXCz6L8fT37xDHb+V5KHZCH7udmOS+yW5obv/aruVuvsd2Th17d3ZuNX8u7cscmWSR41PafsPO9SQ8TV9npCNi1Nfm+Tz2bgg9cVJrttpfQAAAO6tus/3LA3gIKmqTvLI7v7oftcCAADAwWUkEAAAAMAACIEAAAAABsDpYAAAAAADYCQQAAAAwAAIgQAAAAAG4MJ5vtjFF1/cl1566dS3+8UvfjH3v//9p75dtqe/50t/z5f+ni/9PT9b+/qGG274VHc/ZB9LAgBgzuYaAl166aW5/vrrp77d0WiU1dXVqW+X7env+dLf86W/50t/z8/Wvq6qP92/agAA2A9OBwMAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwABfudwEweGtr556/vLzzMvN20OoBAABgR0YCAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYAB2DIGq6r5V9ftV9YdV9aGqesl4+muq6k+q6sbxz2WzLxcAAACA3bhwgmXuTPK47l6vqouSvK+q3jGe98Pd/cbZlQcAAADANOwYAnV3J1kfP71o/NOzLAoAAACA6ZromkBVdUFV3ZjkjiTXdvd141k/VVU3VdUrqurIzKoEAAAAYE9qY6DPhAtXPSjJNUn+eZJPJ/nzJPdJcjLJH3f3T2yzzokkJ5JkaWnpW66++uoplH1P6+vrOXr06NS3y/b095SdPn3O2etHjuTonXfOqZgJHTu23xXMjON7vvT3/Gzt6yuuuOKG7l7Zx5IAAJiz8wqBkqSqfjzJF7v7ZZumrSb5oe7+7nOtu7Ky0tdff/1u6jyn0WiU1dXVqW+X7envKVtbO+fs0fJyVk+dmk8tk9qh5sPM8T1f+nt+tvZ1VQmBAAAGZpK7gz1kPAIoVXW/JE9I8uGqOjaeVkmemuTmWRYKAAAAwO5NcnewY0muqqoLshEavaG731ZV766qhySpJDcm+f4Z1gkAAADAHkxyd7Cbkjx6m+mPm0lFAAAAAEzdRHcHAwAAAOBwEwIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABkAIBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABuHC/CwAOobW1/a7g/By2egEAAGbASCAAAACAARACAQAAAAyAEAgAAABgAHYMgarqvlX1+1X1h1X1oap6yXj6I6rquqr6SFW9vqruM/tyAQAAANiNSUYC3Znkcd39TUkuS/KkqnpMkp9N8orufmSSzyZ57uzKBAAAAGAvdgyBesP6+OlF459O8rgkbxxPvyrJU2dSIQAAAAB7NtE1garqgqq6MckdSa5N8sdJPtfdd40XuS3JJbMpEQAAAIC9qu6efOGqByW5Jsm/TPIr3f214+kPT/L27v4vt1nnRJITSbK0tPQtV1999TTqvof19fUcPXp06ttle/p7yk6fPufs9SNHcvTOO+dUzII6dmziRR3f86W/52drX19xxRU3dPfKPpYEAMCcXXg+C3f356pqlOQxSR5UVReORwM9LMntZ1nnZJKTSbKystKrq6t7Kng7o9Eos9gu29PfU7a2ds7Zo+XlrJ46NZ9aFtXx4xMv6vieL/09P/oaAIBJ7g72kPEIoFTV/ZI8IcktSd6T5OnjxZ6V5C2zKhIAAACAvZlkJNCxJFdV1QXZCI3e0N1vq6o/SnJ1Vf1kkg8kuXKGdQIAAACwBzuGQN19U5JHbzP9Y0kun0VRAAAAAEzXRHcHAwAAAOBwEwIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABgAIRAAAADAAAiBAAAAAAZACAQAAAAwAEIgAAAAgAEQAgEAAAAMgBAIAAAAYACEQAAAAAADIAQCAAAAGAAhEAAAAMAACIEAAAAABkAIBAAAADAAQiAAAACAARACAQAAAAyAEAgAAABgAIRAAAAAAAMgBAIAAAAYACEQAAAAwAAIgQAAAAAGQAgEAAAAMABCIAAAAIABEAIBAAAADIAQCAAAAGAAhEAAAAAAAyAEAgAAABiAHUOgkcPoOQAAEUdJREFUqnp4Vb2nqm6pqg9V1QvG09eq6hNVdeP458mzLxcAAACA3bhwgmXuSvKi7n5/VT0gyQ1Vde143iu6+2WzKw8AAACAadgxBOru00lOjx9/oapuSXLJrAsDAAAAYHrO65pAVXVpkkcnuW486flVdVNVvbqqHjzl2gAAAACYkuruyRasOprkt5L8VHe/uaqWknwqSSd5aZJj3f2cbdY7keREkiwtLX3L1VdfPa3az1hfX8/Ro0envl22p7+n7PTpc85eP3IkR++8c07FLKhjxyZe1PE9X/p7frb29RVXXHFDd6/sY0kAAMzZRCFQVV2U5G1Jfr27X77N/EuTvK27v+Fc21lZWenrr79+d5Wew2g0yurq6tS3y/b095StrZ1z9mh5OaunTs2nlkW1Qx9v5vieL/09P1v7uqqEQAAAAzPJ3cEqyZVJbtkcAFXV5v9af1qSm6dfHgAAAADTMMndwR6b5JlJPlhVN46nvTjJ8aq6LBung92a5HkzqRAAAACAPZvk7mDvS1LbzHr79MsBAAAAYBbO6+5gAAAAABxOQiAAAACAAZjkmkBwuJzHnaAAAOD/b+9+Yyy7yzqAf59062octCC6rm2lJDYTsRG0k4rhzSyIKUgoRoxtIhbFrDGikJAY5IUO+gbfoNEYtQihKrIQ/khtiqZWJpVE0G2tUqwbGiRQu6FCC2WiWVN4fLG3sjvd7dzOvXNnZ36fT3Jz7znnd8998uS3J7vfPX8ARuFMIAAAAIABCIEAAAAABiAEAgAAABiAEAgAAABgAEIgAAAAgAEIgQAAAAAGIAQCAAAAGIAQCAAAAGAAQiAAAACAAQiBAAAAAAYgBAIAAAAYgBAIAAAAYABCIAAAAIABCIEAAAAABnBgtwtgD1hbO3t5efmJ6wAAAIALmjOBAAAAAAYgBAIAAAAYgBAIAAAAYABCIAAAAIABCIEAAAAABiAEAgAAABiAEAgAAABgAEIgAAAAgAEIgQAAAAAGIAQCAAAAGIAQCAAAAGAAW4ZAVXV5VX2kqu6rqk9W1esm659RVbdX1acm70/f+XIBAAAA2I5pzgR6LMkbuvt7kzw/yS9V1XOSvDHJHd19ZZI7JssAAAAAXIC2DIG6+2R33z35/JUk9yW5NMl1SW6eDLs5ySt2qkgAAAAAZlPdPf3gqiuS3JnkqiSf7e5Lztj2SHc/4ZKwqjqa5GiSHDp06Opjx47NWPITbWxsZGlpae77ZeLkybMWNw4ezNKpU7tUzHj0ew4OH556qOPJYun34mzu9ZEjR+7q7pVdLAkAgAU7MO3AqlpK8v4kr+/uR6tqqu91901JbkqSlZWVXl1d3UaZT259fT07sV8m1tbOWlxfXs7qiRO7U8uA9HsObrhh6qGOJ4ul34uj1wAATPV0sKq6OKcDoHd19wcmqz9fVYcn2w8neWhnSgQAAABgVtM8HaySvD3Jfd391jM23ZLkxsnnG5N8aP7lAQAAADAP01wO9oIkr0ryiaq6Z7LuTUnekuS9VfWaJJ9N8pM7UyIAAAAAs9oyBOrujyY53w2AXjTfcgAAAADYCVPdEwgAAACAvU0IBAAAADAAIRAAAADAAIRAAAAAAAMQAgEAAAAMQAgEAAAAMAAhEAAAAMAAhEAAAAAAAxACAQAAAAxACAQAAAAwACEQAAAAwACEQAAAAAADEAIBAAAADEAIBAAAADAAIRAAAADAAIRAAAAAAAMQAgEAAAAMQAgEAAAAMAAhEAAAAMAAhEAAAAAAAxACAQAAAAxACAQAAAAwACEQAAAAwACEQAAAAAADEAIBAAAADEAIBAAAADAAIRAAAADAAIRAAAAAAAMQAgEAAAAMYMsQqKreUVUPVdW9Z6xbq6r/rKp7Jq+X7myZAAAAAMximjOB3pnk2nOs/53uft7kddt8ywIAAABgnrYMgbr7ziQPL6AWAAAAAHZIdffWg6quSHJrd181WV5L8uokjyY5nuQN3f3Ieb57NMnRJDl06NDVx44dm0PZZ9vY2MjS0tLc98vEyZNnLW4cPJilU6d2qZjx6PccHD489VDHk8XS78XZ3OsjR47c1d0ru1gSAAALtt0Q6FCSLyTpJL+V5HB3/9xW+1lZWenjx4/PUu85ra+vZ3V1de77ZWJt7azF9eXlrJ44sTu1DEi/52DTHH4yjieLpd+Ls7nXVSUEAgAYzLaeDtbdn+/ur3b315K8Lck18y0LAAAAgHnaVghUVWdeW/HjSe4931gAAAAAdt+BrQZU1buTrCZ5ZlU9kOQ3kqxW1fNy+nKwzyT5hR2sEQAAAIAZbRkCdfcN51j99h2oBQAAAIAdsq3LwQAAAADYW4RAAAAAAAMQAgEAAAAMQAgEAAAAMAAhEAAAAMAAhEAAAAAAAxACAQAAAAxACAQAAAAwACEQAAAAwACEQAAAAAADEAIBAAAADEAIBAAAADAAIRAAAADAAIRAAAAAAAMQAgEAAAAMQAgEAAAAMAAhEAAAAMAAhEAAAAAAAxACAQAAAAxACAQAAAAwACEQAAAAwACEQAAAAAADEAIBAAAADEAIBAAAADAAIRAAAADAAA7sdgEAbLK2ttsVPDV7rV4AABiUM4EAAAAABiAEAgAAABiAEAgAAABgAFuGQFX1jqp6qKruPWPdM6rq9qr61OT96TtbJgAAAACzmOZMoHcmuXbTujcmuaO7r0xyx2QZAAAAgAvUliFQd9+Z5OFNq69LcvPk881JXjHnugAAAACYo+rurQdVXZHk1u6+arL8pe6+5Iztj3T3OS8Jq6qjSY4myaFDh64+duzYHMo+28bGRpaWlua+XyZOnjxrcePgwSydOrVLxYxHv+fg8OGph14Qx5NNf+YueE+hv5tdEP0exOZeHzly5K7uXtnFkgAAWLADO/0D3X1TkpuSZGVlpVdXV+f+G+vr69mJ/TKxtnbW4vryclZPnNidWgak33Nwww1TD70gjieb/sxd8J5Cfze7IPo9CL0GAGC7Twf7fFUdTpLJ+0PzKwkAAACAedtuCHRLkhsnn29M8qH5lAMAAADATpjmEfHvTvIPSZar6oGqek2StyR5cVV9KsmLJ8sAAAAAXKC2vCdQd5/vZg8vmnMtAAAAAOyQ7V4OBgAAAMAeIgQCAAAAGMCOPyIeYNc9lUeuLy/vvUe0AwAATMGZQAAAAAADEAIBAAAADEAIBAAAADAAIRAAAADAAIRAAAAAAAMQAgEAAAAMQAgEAAAAMAAhEAAAAMAAhEAAAAAAAxACAQAAAAxACAQAAAAwACEQAAAAwACEQAAAAAADEAIBAAAADODAbhcwpLW13a4AAAAAGIwzgQAAAAAGIAQCAAAAGIAQCAAAAGAAQiAAAACAAQiBAAAAAAYgBAIAAAAYgBAIAAAAYABCIAAAAIABCIEAAAAABiAEAgAAABiAEAgAAABgAAdm+XJVfSbJV5J8Nclj3b0yj6IAAAAAmK+ZQqCJI939hTnsBwAAAIAd4nIwAAAAgAFUd2//y1X/keSRJJ3kj7v7pnOMOZrkaJIcOnTo6mPHjm37985nY2MjS0tLc9/vjjl5crcrmMnGwYNZOnVqt8sYhn4vln5vw+HD2/7qnjt+72Gbe33kyJG7XMYNADCWWUOg7+ruB6vqO5LcnuSXu/vO841fWVnp48ePb/v3zmd9fT2rq6tz3++OWVvb7Qpmsr68nNUTJ3a7jGHo92Lp9zbMcEzbc8fvPWxzr6tKCAQAMJiZLgfr7gcn7w8l+WCSa+ZRFAAAAADzte0QqKq+uaqe9vjnJD+a5N55FQYAAADA/MzydLBDST5YVY/v5y+6+6/nUhUAAAAAc7XtEKi7P53kuXOsBQAAAIAd4hHxAAAAAAMQAgEAAAAMYJZ7Al04Tp7c849dBwAAANhJzgQCAAAAGIAQCAAAAGAAQiAAAACAAQiBAAAAAAYgBAIAAAAYgBAIAAAAYABCIAAAAIABCIEAAAAABiAEAgAAABiAEAgAAABgAEIgAAAAgAEIgQAAAAAGIAQCAAAAGIAQCAAAAGAAB3a7AAD2uLW17X93eXm2749AfwAAmBNnAgEAAAAMQAgEAAAAMAAhEAAAAMAAhEAAAAAAAxACAQAAAAxACAQAAAAwACEQAAAAwACEQAAAAAADEAIBAAAADEAIBAAAADCAmUKgqrq2qk5U1f1V9cZ5FQUAAADAfG07BKqqi5L8QZKXJHlOkhuq6jnzKgwAAACA+ZnlTKBrktzf3Z/u7v9NcizJdfMpCwAAAIB5miUEujTJ585YfmCyDgAAAIALzIEZvlvnWNdPGFR1NMnRyeJGVZ2Y4TfP55lJvrAD++Xc9Hux9Hux9Hux9Hsrb37zvPa0udfPmteOAQDYG2YJgR5IcvkZy5cleXDzoO6+KclNM/zOlqrqeHev7ORv8HX6vVj6vVj6vVj6vTh6DQDALJeD/VOSK6vq2VX1DUmuT3LLfMoCAAAAYJ62fSZQdz9WVa9N8jdJLkryju7+5NwqAwAAAGBuZrkcLN19W5Lb5lTLLHb0cjOeQL8XS78XS78XS78XR68BAAZX3U+4lzMAAAAA+8ws9wQCAAAAYI/YMyFQVV1eVR+pqvuq6pNV9bpzjKmq+r2qur+q/rWqfnA3at0Ppuz3alV9uarumbx+fTdq3Q+q6hur6h+r6l8m/X7CM6Gr6mBVvWcyvz9eVVcsvtK9b8pev7qq/uuMuf3zu1HrflJVF1XVP1fVrefYZm7P2Rb9Nr8BAAY10z2BFuyxJG/o7rur6mlJ7qqq27v7384Y85IkV05eP5TkDyfvPHXT9DtJ/r67X7YL9e03p5K8sLs3quriJB+tqg9398fOGPOaJI909/dU1fVJfjvJT+1GsXvcNL1Okvd092t3ob796nVJ7kvyLefYZm7P35P1OzG/AQCGtGfOBOruk9199+TzV3L6L7eXbhp2XZI/7dM+luSSqjq84FL3hSn7zZxM5uzGZPHiyWvzDbuuS3Lz5PP7kryoqmpBJe4bU/aaOaqqy5L8WJI/Oc8Qc3uOpug3AACD2jMh0Jkmlwr8QJKPb9p0aZLPnbH8QAQXM3uSfifJD08uq/lwVX3fQgvbZyaXb9yT5KEkt3f3eed3dz+W5MtJvm2xVe4PU/Q6SX5iclnp+6rq8gWXuN/8bpJfTfK182w3t+drq34n5jcAwJD2XAhUVUtJ3p/k9d396ObN5/iK/+GfwRb9vjvJs7r7uUl+P8lfLrq+/aS7v9rdz0tyWZJrquqqTUPM7zmZotd/leSK7v7+JH+br5+lwlNUVS9L8lB33/Vkw86xztzehin7bX4DAAxqT4VAk/t3vD/Ju7r7A+cY8kCSM/9H87IkDy6itv1oq35396OPX1bT3bclubiqnrngMved7v5SkvUk127a9P/zu6oOJPnWJA8vtLh95ny97u4vdvepyeLbkly94NL2kxckeXlVfSbJsSQvrKo/3zTG3J6fLfttfgMAjGvPhECT+0O8Pcl93f3W8wy7JcnPTJ4S9vwkX+7ukwsrch+Zpt9V9Z2P37ejqq7J6fn0xcVVuX9U1bdX1SWTz9+U5EeS/PumYbckuXHy+ZVJ/q67nS3xFE3T6033Ent5Tt8Ti23o7l/r7su6+4ok1+f0vP3pTcPM7TmZpt/mNwDAuPbS08FekORVST4xuZdHkrwpyXcnSXf/UZLbkrw0yf1J/jvJz+5CnfvFNP1+ZZJfrKrHkvxPkuv9w23bDie5uaouyukw7b3dfWtV/WaS4919S06Hcn9WVffn9FkS1+9euXvaNL3+lap6eU4/Je/hJK/etWr3KXN7scxvAACSpPybHQAAAGD/2zOXgwEAAACwfUIgAAAAgAEIgQAAAAAGIAQCAAAAGIAQCAAAAGAAQiAAAACAAQiBAAAAAAYgBAIAAAAYwP8B3fQ0xGdjQYAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_dataset.hist(alpha=0.5, figsize=(20, 20), color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separating elements\n",
    "columns = list(iris_dataset.columns)\n",
    "X = iris_dataset[[columns[1], columns[2], columns[3], columns[4]]]\n",
    "y = iris_dataset[columns[5]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "def machine_learning_algorithms(X, y):\n",
    "    train_X = X\n",
    "    train_y = y\n",
    "    \n",
    "    modelForest = RandomForestClassifier(n_estimators=100)\n",
    "    modelForest.fit( train_X , train_y)\n",
    "    \n",
    "    modelLogistic = LogisticRegression()\n",
    "    modelLogistic.fit(train_X , train_y)\n",
    "    \n",
    "    modeltrees = ExtraTreesClassifier(n_estimators=100)\n",
    "    modeltrees.fit(train_X , train_y)\n",
    "    \n",
    "    modelSVC = SVC()\n",
    "    modelSVC.fit(train_X , train_y)\n",
    "\n",
    "    modelgbc = GradientBoostingClassifier()\n",
    "    modelgbc.fit( train_X , train_y)\n",
    "    \n",
    "    modelKNN = KNeighborsClassifier(n_neighbors = 3)\n",
    "    modelKNN.fit( train_X , train_y)\n",
    "    \n",
    "    modelGaussian = GaussianNB()\n",
    "    modelGaussian.fit(train_X , train_y)\n",
    "\n",
    "    models = [modelForest, modelLogistic, modeltrees, modelSVC, modelgbc, modelKNN, modelGaussian]\n",
    "    \n",
    "    return models\n",
    "\n",
    "np.random.seed(1000)\n",
    "algorithm = machine_learning_algorithms(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['RandomForest', 'Logistic Regression', 'Tree Classifier', 'SVC', 'GBC', 'KMeans', 'N. Bayes']\n",
    "scores_train = []\n",
    "scores_test = []\n",
    "for k in range(0, 7):\n",
    "    scores_train.append(algorithm[k].score(X_train, y_train))\n",
    "    scores_test.append(algorithm[k].score(X_test, y_test))\n",
    "                \n",
    "RandomForest = 'Train:{}  Test:{}'.format(\"%.4f\" % scores_train[0], \"%.4f\" % scores_test[0])\n",
    "LogisticRegr = 'Train:{}  Test:{}'.format(\"%.4f\" % scores_train[1], \"%.4f\" % scores_test[1])\n",
    "TreeClassifier = 'Train:{}  Test:{}'.format(\"%.4f\" % scores_train[2], \"%.4f\" % scores_test[2])\n",
    "SVC = 'Train:{}  Test:{}'.format(\"%.4f\" % scores_train[3], \"%.4f\" % scores_test[3])\n",
    "GBC = 'Train:{}  Test:{}'.format(\"%.4f\" % scores_train[4], \"%.4f\" % scores_test[4])\n",
    "Kmeans = 'Train:{}  Test:{}'.format(\"%.4f\" % scores_train[5], \"%.4f\" % scores_test[5])\n",
    "NBayes = 'Train:{}  Test:{}'.format(\"%.4f\" % scores_train[6], \"%.4f\" % scores_test[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest                     : Train:1.0000  Test:0.9111\n",
      "Logistic Regression               : Train:0.9524  Test:0.9111\n",
      "Tree Classifier                   : Train:1.0000  Test:0.9111\n",
      "Support-Vector Machine(SVM)       : Train:0.9810  Test:0.9778\n",
      "Gradient Boosting Classifier (GBC): Train:1.0000  Test:0.9111\n",
      "KMeans                            : Train:0.9714  Test:0.9333\n",
      "Naive Bayes                       : Train:0.9810  Test:0.9111\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Forest                     :\", RandomForest) \n",
    "print(\"Logistic Regression               :\", LogisticRegr)\n",
    "print(\"Tree Classifier                   :\", TreeClassifier)\n",
    "print(\"Support-Vector Machine(SVM)       :\", SVC)\n",
    "print(\"Gradient Boosting Classifier (GBC):\", GBC)\n",
    "print(\"KMeans                            :\", Kmeans)\n",
    "print(\"Naive Bayes                       :\", NBayes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.title(' Different model comparison: Train Score')\n",
    "plt.ylabel(' Score')\n",
    "bar = plt.bar(columns, scores_train)\n",
    "bar[0].set_color('red')\n",
    "bar[1].set_color('green')\n",
    "bar[2].set_color('blue')\n",
    "bar[3].set_color('pink')\n",
    "bar[4].set_color('orange')\n",
    "bar[5].set_color('gray')\n",
    "bar[6].set_color('black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.title(' Different model comparison: Test Score')\n",
    "plt.ylabel(' Score')\n",
    "bar = plt.bar(columns, scores_test)\n",
    "bar[0].set_color('red')\n",
    "bar[1].set_color('green')\n",
    "bar[2].set_color('blue')\n",
    "bar[3].set_color('pink')\n",
    "bar[4].set_color('orange')\n",
    "bar[5].set_color('gray')\n",
    "bar[6].set_color('black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}