example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello, Ignite ML!\n",
    "\n",
    "This document contains example of Ignite ML Python API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "from sklearn.datasets import make_classification \n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Regression\n",
    "\n",
    "Lets prepare a regression dataset using scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, y = make_regression(n_targets=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Linear Regression\n",
    "\n",
    "Now lets train a linear model using Ignite ML and estimate the quality of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6471527323872958"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.regression import LinearRegressionTrainer\n",
    "trainer = LinearRegressionTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And just to compare lets do the same for scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6262931608562785"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "reg.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, reg.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Decision Tree Regression\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.28917343138640295"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.regression import DecisionTreeRegressionTrainer\n",
    "trainer = DecisionTreeRegressionTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.47770754907762747"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "reg = DecisionTreeRegressor()\n",
    "reg.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, reg.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. KNN Regression\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04401214262097286"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.regression import KNNRegressionTrainer\n",
    "trainer = KNNRegressionTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04401214262097286"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "reg = KNeighborsRegressor()\n",
    "reg.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, reg.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Random Forest Regression\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.45867543901273433"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.regression import RandomForestRegressionTrainer\n",
    "trainer = RandomForestRegressionTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gridgain/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.1860416725443843"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "reg = RandomForestRegressor()\n",
    "reg.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, reg.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 MLP Regression\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rrr: <class 'py4j.java_gateway.JavaObject'> : Matrix [rows=25, cols=1] : -76.67392577714574\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.653808152297644"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.common import MLPArchitecture\n",
    "from ignite_ml.regression import MLPRegressionTrainer\n",
    "\n",
    "trainer = MLPRegressionTrainer(MLPArchitecture(100).with_layer(1, activator='linear'))\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1107107707631827"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "reg = MLPRegressor(max_iter=1000)\n",
    "reg.fit(x_train, y_train)\n",
    "\n",
    "r2_score(y_test, reg.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Classification\n",
    "\n",
    "Lets prepare a classification dataset using scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, y = make_classification()\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Decision Tree Classification\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.classification import DecisionTreeClassificationTrainer\n",
    "trainer = DecisionTreeClassificationTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, clf.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 ANN Classification\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.classification import ANNClassificationTrainer\n",
    "trainer = ANNClassificationTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 KNN Classification\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.classification import KNNClassificationTrainer\n",
    "trainer = KNNClassificationTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier()\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, clf.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 2.4 LogReg Classification\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.classification import LogRegClassificationTrainer\n",
    "trainer = LogRegClassificationTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression()\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 SVM Classification\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.classification import SVMClassificationTrainer\n",
    "trainer = SVMClassificationTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "clf = LinearSVC()\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, clf.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Random Forest Classification\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.classification import RandomForestClassificationTrainer\n",
    "trainer = RandomForestClassificationTrainer()\n",
    "model = trainer.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "accuracy_score(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.7 MLP Classification\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rrr: <class 'py4j.java_gateway.JavaObject'> : Matrix [rows=25, cols=2] : 0.0916495643140068\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.88"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ignite_ml.common import MLPArchitecture\n",
    "from ignite_ml.classification import MLPClassificationTrainer\n",
    "\n",
    "def encode_label(x):\n",
    "    if x:\n",
    "        return [0, 1]\n",
    "    else:\n",
    "        return [1, 0]\n",
    "\n",
    "def decode_label(x):\n",
    "    if x[0] > x[1]:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "trainer = MLPRegressionTrainer(MLPArchitecture(20).with_layer(2, activator='sigmoid'))\n",
    "model = trainer.fit(x_train, [encode_label(x) for x in y_train])\n",
    "\n",
    "accuracy_score(y_test, [decode_label(x) for x in model.predict(x_test)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Clustering\n",
    "\n",
    "Lets prepare a clustering dataset using scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x, y = make_blobs(n_samples=2000, n_features=2, cluster_std=1.0, centers=[(-3, -3), (0, 0), (3, 3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C8O+yLF/X+X49gPUAEBcX56/LCvqS/rRQfImavYEKrq0DgAQs+oHz+5yyErh4\nmHyy6DwjjTXgp2foIGp3riWiPm8zzCFmZYERtVzeXPqmQyEuHNnudBDadmwbTjWTYmMJoxIUoWQ3\nA6GeNwCNJWQKNGkKgKVFpyEpIkmzbyx7DzTzhm1Pb46Aj87Z7+iuU4CI1Hn8IuySJAWBiPr/yLL8\nod4xsiznAsgFgIyMDNkf1xX0Mf1loXS0AHnfAyo+J+KZ/ba+cPvzDYIKra0TOPg6sOwVtAbchjxO\nRBTOf0r6F78AiGGurfMGkFf9sUbwsmc/6rdUOI2otRwFGg5hXWSS7gIjhyX2dz6IvNbTyL7zQdDe\nsIPQhhkbUNFaARky7hl/j+a6tECZYV84L5yfCOYnNdk67kb11vWKi7Hf8QONEYM1JbEv8UdWjATg\n/wL4uyzLb/jeJcGIo2QPEc3oFPJZskdfuP35BsEKcnAoEWRqCdg6sc7ao30zcOXNM4NO9uxHAfRN\nRGkkauynUYGtvIZDeKPlKKw1n8HUYFKyXWgaoynQhCudVwAA75W/hwnhE5RrsXuOUqF21Rd2Ipi/\nB2dFzVj8UYVxJHrw/ojY5wN4BMAZSZJK7H/bLMvyp35oWzDYYcUN8M4qoedOWUkiYyPhdvcNoqkC\n+HwzsHwrEBrp2Cfe0rG3qYiI5SqwfyvQ0Qw0lQELnwdqCx3vi32DYITf04jSG3hRY3/XK7BF88/p\n39jv6SfNTz/TdAZ3xdylGRjYbBXeHnHWF3cKmDmLpD3JOjFqb7CmJPYl/siKOQxlHlww4mDFDTC2\nSpz546xgx/ihDO/nm0nkDxDr5IsfaS0ePUunowXmkj1Yl74GSAAQEAKc/wy4dARoqQQsFxzvi43i\ndSwZc1hUv0WIbGaJpcuC2WNnw9Jl0RyjN+BQe8UUaII5xIzIkEgcv3oci2IXOXjaVDD5yUt3S++y\n/eQzffzxnIzaG6wpiX2JWHkq8A12UjHUnrOsF3Hz0a2vk6DO/PblW9XP0EjSP2rxpK8hvjo/WXrk\nl0Dhr4GOJmD5q3aRbibCnrQEmP2Y433xbxAdLcCf/g24cACwdaL13u8Zi52fF0qx+4RSy+R443Fl\no2mj1EpaWphaOHqLnShtN9s0aYls5A+4V7jLIW0T/oukR2JkboQQdoFvsJOK83MchY6KFxvdejoJ\nqieC6WtIRoutk3zPimNMCvCv76u/Z7+t9cLtk6Wac66Uaj8BYOajxIqZu4G06YqSPUTUAQAy8s79\nHm+ceZt49rP+P8dj3XwG7lgWrK+dFp2G1huXUdlQBGunBSum/6vj+fSZ3vmg0oZRnjkV48L6QqVu\nzBPpT6D6WjVKm0sxa8wsHGngICQLAAAgAElEQVQ44lbhLj76d7bIyNNJz5EYmRshhF3gG84mNHnx\nogLm6STo0R3AwZ8RIV+8mfwtLAoIDiPt2yc/nVo99Huarshfe8U21ZenHn10qn4mjBH0bQAyMHcD\nso//DrC0kpxzvWPdfAbuWBasqD2R/gR2ffJveK/rCoor/oCqzgYlilfOt//b5LWeJtk1cKzJDkAT\nxbM7LgFqqV8AyupXvYVHrDAbia/RJt/9Pek5XDJohLALCN5aA84mNI3Ey+M0Son7BOmvrQNY9EP3\n3gJcfc9G+e9+E7iQD9jaSWTv7irVsChg8QvKr+bZ38W6gBByLHde6802knp4sw1mF8/bG4she95m\nWA+/DIybgRVT/8kxO8V+T9l3Pgg0HNKUMGBTJtnUxU1zNml2fXKV/eKJMOsNKgNhrQybDBpZlvv9\nv9mzZ8uCQcbhX8nyj0fJ8v6t5Of25oHukUp7s2Of9r9m7+9rxse4asOIz14ibX/2kuN39Dkd/pVs\nsVrknWd2yharxXWbzHmyLMs7//dxOW13mrzzfx93fW4/Qe/nQtsF5b4sVou88YuNpK9ndnrVnjvP\nZ+eZnV5dw1/o3ftgBECx7IbGiohdQFAW7HQMvjICuhE+F8W7egtw9T2NqKesBGQAiVlA6kPAgZ+C\nWisANBOvHkV33NtL9rzNQOFW8ukmfW0TGC0KYjeh9qY9dxjoic9hE6nbEcIuIGgW7ITpe799XfLW\nk4Jac9er3ro3bfFQH79qP7FhACAgWE2bDA4jn8zEa/aE+4Cq/ci+chFIaHF+HW5gMUcmYd2D/9ej\n/vpTfJwNErzIjoQqiQM9sPgbIewCLc4i274sCsaWFWDbN1jd6ZCBw7ZTskctFWDUFr9YyUYW6mDs\nDCA2E4AMpK0CJswiP09ZCZR+AMx7mrzVNFXAvO/7WEcHgcZzxqUQnN2z0b3p4E/x0Rsk+O329PD0\nrWGoTEYO9MDib4SwC9ynL4uC0bICKcu17Rus7tSgJ5CLfkAi6ykrnVdxPJpLBoB5T5OJWMhA2sMk\njTM0Up0MPbKdHJeynPSz4aQa2UfEk78dzdVMnrp1z67ujcGf4qM3SLjzRmB0jJGAs/n1ry14bVCL\n+3BCCLvAffxdFMwoz52NeqesJPZIRzP5XW9FKxvpsymHvJjriqe9Hl2QiVg7X/yIiDb/5kDb7e4g\nUXzaw8CEmQAk8rfCX6ttuYveylX+uXhheRmJLFvIa3XqagfxdueNgD+GXouWLgC0bwDWHisyx2Vq\ntshz1keBfxDCPhwZBNu/udUfd6yV85+SyPhCPhAW7XgMH+mHRakCzc4V0O/YfPbznxK7hZ9TmLKS\niDZd/ESvAxABX/YKSY+kOfVNFUBTOWnL2bPnvzMaKL21vOzt55kC8caZtx2iZJq6CMCh4iLg3hsB\nfwyNyPVWuNLrbbx7I+ZNnOfx24HAe4SwD0cGeoOMjhYyGQmJRLZs7RajWitG0BWmkPTzyfkFRwe2\nEr+clgzga7h0NBFxZidJ+T6V7CHXO/g6GSQArb3D95euvp0wUz/ap/D/LkaDAPNcHCJbZwOHvf3s\n+zfjmL1kLhsl8+Vy/RE18ytJvfnOW0TUb4wQ9uFIf26QwcNbIw0nHL1zVpxcDTxhUWpkzJ5r6yBZ\nLIAqxrZO9W8py7XtUFFNXEx+HzcDSLrf8RnxHj37vdEbEFvbXW+egD+OLljSmyym92z/PY9NPUx4\nyPgctv07H0RazWfq3qL2Z2ZOX6Mpl6tX69xTnEX53n7nLiLqN0YI+3BkIPcYpdZIYhbJLmG9aPYY\nT6pA6k2O3vs0EemOJjVVcdEPyH+1X2uLftHIvqMJqDsBzPk/xFPXE2ojr9+oBg5rqTRVkIFs+Vb9\nAYD9dzmy3XgQYNrXRLbH33U+cNjbz+O3lTPYOWmgU/x8jbh97f9wjviFsAsc8cWj1xNGWtMFINZG\n7DwiTvw2c4Aq3LTMLuA4OQoQkb5wgPw372nSXtoqtcLkuBlAezPwp8dVy6WpHKg5AnReBZorSL/c\nWbRkNDfA9vFoLlBbpF5r+Va1tryzKN+JpQIA5vk5ajTq7BwGB8EzeIPrixQ/T8TS14jb1/4P54hf\nCPtIwROx9sWj13tb4Fe10pTBCbOI0E9ZCZS+D8WTZ8vsAo6To/Nz7CtCQd4MgkLVYl0Aid7pNQD1\n3Nh5QGs1cN9m4Mxe7cBC5wVqj5LB4uJh0j82F5719Wkfj+aqdhOg7gJF+230DD2tsePBv5+D4PXj\nG5w7YsnWjgcG7o1hoN9Y+hIh7CMFT8Sa94Jd7UDkCn5V65SVRISp0FORBADI2jK7bJ/Ya6U9rNoe\noZGOq1CnrFQXF83doG6w0VxBRJ2v2ng0V/XnIxPJ9zFTtT47+wxpH20d5NjJ84GAICDrJbLbEr2+\nrUNbVtidZ6cnxAM9IW4AH6G7I5aDJVIebouSWISwjxQ8mVDlvWA+k8PZBJ677cZwQt9rs9sYkqOw\n6fnw+75Pjo+ZCgSFQckhZ89d/ALxvfO+RwYAeu90YGEXL3V3kO8mzSWflguALGmvzW4qQlMnj+YS\nX59m0STdr57Dp10arYil98SmYTrJlDFkANJceZF2RyyHc6Q8WBDCPlLw9nWcFxSjFaLOcCU4oZHA\nP+1UhY2KrZE4lexRvey6E8Q3Bxw9844W4A+rSZQOqH445eS7QOF/EbENClPboJtlSPY2juaCDByS\nGumHrlEHuEU/JMezuzIZlRU2SplkfXsXmTKGDEBU741ID+dIebAghF3gHF5Q3JnA44XcSHD0FijR\nN4SLh0mUTb33uevVc6asVPcjvdVNMmSCTap1dHQHyWVvPE1EPTSabEhNr1dln3SdPN/eEbtdQ/3+\nT3JI2/XH1YwbQCvKR3Pt2T+LyfkHf6bdlalkj/ZvRs+N7e+iH5AJ4PgF3qWq+ujNUzyZABUiPTgR\nwi7wDE8iR5o1YiQ4rbXEz46dp/49fQ0R3orPyUYXlwrJ38+8D9wxVv09fgER37qjZHs3PZsIIJOZ\nzRXE9+YzaiISgY6rwMS5WvGbOIu0fekIEL+QqSGzCjj6NvDb+wGTPfsmdq59UOBWr7K2z5HtjhO1\ndIBiF28t+qHzTBpX+MmbHyweuMB7hLAL/E/6Gm1mi165gJI9QPFvyc8fbwTWfab+fVya3Q6x576b\nIgFLFfkvOoUMBFVfAumPEBuGDgzUJpo8D+jtAQICgSWvqKLO++K1XxPR//yHql0zP0e1ZRIXkzcF\nKrJHtgPHfkd+bruo+vH0nvnc9ikrVSuIPg9bp5pFQ/+WmKVWlPziR8TCoQMFI/Ctlirk2Wu4myOT\n3P+3YD/dQHjgQ5+ALVu29PtFc3Nzt6xfv77fryvwkY4W4NhvgagUdam93jEle4C53wMiJtknJq3k\nPAQAn/w7MH4mEHsPAJlEzy1VRMCunCbCNikTMI0GMp8CutqAJS8D1+oB6TYyqdl+BTjxDonoLReA\nazXA3atJv8KigDsmAGf+AFyrBaKTiVjT/h75FbFI4hcA858hAr38p+S4KSuJ6Hd3ErGlNgq97ykr\nAdwiJXrlHiDwdqDsEyAohAj+sd+RnxtOkL785Rmg7hgQMZn8PeUBoKGYzA8kZgEP/Iw8o/v/A5iy\nHBgzjVyvtwfY/xPyc1ym8mjf+2oT3mg5CvPVMsxsqXX+70AJDiVtuDqOwRRowswxM2EKNLl9jqB/\nePnlly9v2bIl19VxfonYJUl6AMB2AAEAfifL8uv+aFfQj7jjxbrzWq9XD4XaI9QWAey1y6OB1X9U\n7QcKTSFsqSTCPWEmMPUfSLuh0cBdq8lxoWPJ9+NmkN/ZiHze0+rqUhZad91mJZO28QsAUwT5W+kH\nasYK75d/8SNSX2bcDGDWI0BLhZraSEsJxM0nlpHlAjlv+Vb1fi4dIYMYvf/YTHUxFUWTFuq4iYiy\n61LIpEGZ+igYPPgs7JIkBQD4bwDLANQBOCZJ0seyLJ/ztW1BP2Ik2kaldY0wyqJJzAKikoFRk4jg\nGV2PChtbTIsWADv5/xNhPLSVfC76oWOGSckedZcjtvhX+hqg0wKUf0KOCzY5ZqLMe4rYL+PSHP1y\negzNxln2ChkQzv+VRNjzngKunCGiHp2iDpD/+j5Judz3fWBULBAWA0zIIBbPkV+RrJyOZmD5T9Tr\nGcxjKLsudbQAASGOOfICgR1/ROxzAVTKsnwBACRJeg/ANwEIYR9KGIm2O6V1WfhqinTSkOZvL3uF\nRKp079ApK7X7inZayITiwufVGjPU537oLSBvPbFqUlZAqbnO12+nueb8ZOrFw0R4Q6O15QeUBVOd\nxNu/XgfMXKu2GxZF3jAO/4qUDQgIUic+6WRucBjZfQkAVvyCfNIBJSaF5LfT5zjlAdV6AtRPwHU+\nO+0PmyMvonYBhz+EfSKAWub3OgD3+KFdQX9ilO3i6eQbzftm66bw9gIbVZ//VLU/gsO0edz/+r7a\n5pHtRHgtF8h/iVmkfVbYOlqAT54mYhszlUTHbM59zHTSfmczuS47UNEFU2f/ZJ9Q3axenz6f8GiS\nhQMAJ94FYlKB9quAtZW8ifztv0jq5flPHRcipa9RNwChmTJZL5F9VallAxjns/NWmcG/y3AubCVw\nH38Iu6TzN4etZCRJWg9gPQDExcX54bKCfsHThU1UtAHtIia2HV6U2B2PaISvJ3bzniLpkdRXp2V3\n2X1OaQR96WsgeTGxa9IeJt9XHSATo9QqobCi+c97iajT67MLlJKWkYnQtktkcVNXG7nHyyXAdXts\n03ga+Fu+vdLkD7V2yeIXtG8Ry14hgwdrF6WvQWtvF/Juvw3ZsRkw65UzmJ/j+GZkF3yRqigA/CPs\ndQBimd8nAWjgD5JlORdALgBkZGR4uIeYYLBg6bDh/eJarMqIRWRYsOMBbGRKa7QAzi0GKnhUXGkd\nltA1atrgxcNkib/lAhHT+f+uZqwoK0B/oG52IfeoC4TOf0pEMX0NsWHu2+x8ARUbqbMDVe3XRNQB\nIurRKWQAoOUJSt8nk7Kx95B7Zxc30Zry/MpdnRINeeHheOPM28Bd38M6+ozctMpEqqIA8I+wHwOQ\nIklSAoB6AP8C4Dt+aFfQl3hTV6SjBec/3I63z94JANiwSCeXmkamPFSA6ASorVO1ZnhxBez53PZj\naNbJhJnE6mg8Tbx4eh7NuJk4Fzhqz43vbNEu8bd1AGc+IDYMnXyt2q/WjAf07SZ2oLJZif0zaS7p\n14pfEP88hpvwVTb5kLhP5hpTVmqLiCVmKVvxZbe3A5ZW8ulqUxJO8EfCSlBhN7nGZ2GXZblHkqQn\nAXwGku64U5blsz73TNC3eFNXpGQPMqu2463pOZiS8U+eXY/NLElZDmUxDv2OTqbyaY98fZW87xFx\n3fd9YsXQza4v5AN/3QRYLcBtQSSyDg7T1oSnC5weegso+Lma5XJqLzDtIfW6/KBHB6qOFuKz65UF\nYFM6Kz5XC4/xaYvUQqGlExKz1FozB18HGk7AvPB5rKstBu78luMqXn4Q7rRoC5ONAITd5Bq/5LHL\nsvwpgE/90Zagn/BiRSI9NjN9DUBtGHcjf5pZQo/ttJAIl0audDKVtkH9YwBKumOnhSxKmjyfeOx0\nlWZvNzksdj5wWwCJxqNT1MwYfm9U2he6CUfbJbIPalAoGQDotnu8mLKbYdN2WFGPTAQSsoCwMWq9\neb23F9oPOtDRAersn7S13GnNGH4VLwtbkoC1kDz5txliCLvJNaKkwBDGpd/tDCeToobtuluLxB1B\noRtAswWv+EEmLErdfSk4lAjcpUIioDLUcrmX7NUdQ83A9G8RL/xCPrkGoP9mEhZFKkoe+ClQSd8i\nJPsbwg/VjTqO7tDWgaEiTouUUWGli68sF0hJA0AtBawHP9DRWvEpy1Xfnj4/OpGrt+MU/Y6dbKYM\n0hruvjIS7CZfEcI+hHm/uBY/3VcGwMDvtuPpAOBuuwD0RdlVNUebXfBoSVt3dxOKnQfUnyDiSVML\ng03kUwLJB7+QT9rlN6rWezMJiwLMsSRij4glx7DWCZu2ydZ44XdJomJMK1F227NzaM0ZQH+wM8oU\nCotSNwABtINgDPecYlIcI3W9ZycYUQhh18GnSLgfWZURq/k0wiOh9qBdAPr2hJGgsOmNvPXCwopg\npwUo/4ys8Jw4m0x+miKJl95wnETri35IBJnWYKGpk50W4sXTkgN68JOZbHphd4e2TVbEz3+qFh9b\nvpUILM186Wgh5QxsncCBrSRDRhnUuElj9jm6GtzcqVXPMpCbmgsGFCHsOngqhANFZFiw/4Xag3YV\njHKsAcdI1UktFN32Lh5WN9IIuF21SBKzyK5LABQhB9RBo/ZroLmSrCK9kE+OCYvReuO04FeQSVsn\nBrBPbC4mP8dmktWvdLen859qS+7ykTS1kGiqI2vl0Alh+qzcXWnKTrgaDQ4CgR0h7Dp4KoR9gT/f\nGjwWak9x9sqvZ8voRflG7U1ZCdzsACQZWLGNlAGgaYIHf0by0pOWqecmLSWTkHTVK4XdaWl+jjY/\nHdDf2YgVW5p90tFM6rsc+S/y9kAnafWeCbvwit/3lV94ZLRzkt5z4QcHdxmmk6kCR4Swcwy0DUOv\n32nrwfavKtFp60VocMDgtoXc9cih3t8jtz5C6MGXyXf8uWx7YVHA4/ZJUFaYAODsh2TCMf81Ur+F\nWiXNFSRzprcbuNEAXKsDxk4Dbg9TM2VsnaQCJEAidnYxFdsnGonTSdNemzpRGmgin6UfaLNf6K5I\nkBzb5Qc11mZxtXOS0eDgLsN0MlXgiBB2Dmc2jJHo+3MwoNfPWZKCF1akotPWMyRsIUM40af3F7Lk\nPqzll9y7ghcmuvw/ZipJV4xOAeZsIN/HpJLIet7TQFMZqYdOI+IJs7QVIJ1BB5OFz5PfafYJu5cq\nX0GDbosHEMvEVSYR/T7UyVsMi7/2rxUMW4SwczizYYxE35+ePHv9yLBgWDpsCA0OHFBbyCXu+MR2\n6H38Y0YsUHLEswqF6WvIQGBfoalkhHS0AE3l9oVBG4hFMmEmEW5l1eos1Z9vKie103kLpalCrRMT\nk0L+RssCLPqBNj1x3WdMVL5ev580/17vPthPiicRtTe2iphMHTEIYedw5kcbib4/PXn++n3uj/sD\nVz4xI0KRYVHq/ehle9D29FZ3luwhPx98XRsJ05zwXf+gLk6iFgg/UUuPabtEBiF20pNf7NPRAtTa\nqzlCclwFSrNgeMKijL+j3+s9H3YRlSuErSJwwm0D3YGhBBVZ3m4x+vtAYOmwYcfBKlg6bPoH0GqA\nyqpOP5C+hkTHy7eSTyrU9BpUhKgwU6jA0SJdJXuMj1WETHKc5KRt3beZrB69b7Njrjjdh/Sf95KM\nmnufduzn8q3ku+hUVWwvHCCRftrDJApPXKyuAjXC02dM7+3zze5H4PSZC1tFoIOI2IcZ1BYqutCC\n//x2uvI3xf/3U6SnnVeIgiV9o/33jYgseUt7DVqdMXaefh62njVBKx/qTTKeN6hecWgrSV88tBVI\nWqSW22UnMGNSgEc/Ij/T9EG6ivT8pyS18eDrak0Y2hfqmy/6AZC0WF9QlfLBHarH7s4znrKSFEcz\nKhugh7BVBE4YscI+0NkvfcWqjFgUXWjBgfImvFNYjdN113CgvEnJrvn2tG/DDHgX6TFC+35xm2Ze\nQTPPkMEJNV05CehbNbxIUSHlN6tgc7n5NgBg+evEY1/+ujadkWaQ8BZP+hpSo53dX5VNe2T7NWUl\ncP4zMgg8uJ38jR+k6KCpt2WfM85/qpYTEBG4wA+MWGEfiEVIzgYTTwcao+Mjw4Lxn99Ot6dM9uJA\neRMWJEej+KIFR6paAKRiwyIiVq1Nl1H+17cw9YGNMMeMVxs3mphjov1VGRvtn7GOn2HBjkINqCs1\n+UlL/nqKQP5Aa+2wkTsvgB0twLEdZOK0/hgAWS05QEVdL59+XBqxW+iuTHo15NPXEPGlNWk+30xS\nE/n2+LIA7uLtef5E5LgPK0assA/EIiRng4mnA42z46nnTzJqApSc+MVTYzT3W/7Xt5BZtR1FfwUy\nH3lFbcDIrmEEyNUkL2/VKNG23kpN/nq80DlbccnaH2xJ4IM/U5f/O5uYZOu5xGYaDmRIXwO0N5Na\n8Mu3qvulsu15a48YndefYismY4cVI1bYByLbxNlg4ulA487xWoEPdIjupz6wEUV/JZ8anFVbdPP/\n9LoDj1HNE/56etYMoL/ikrc/pqwkC4YSs5zbPxQlVVHWT1tkP8OjgQU7taWFAY/SPT2iP8VW5LgP\nL2RZ7vf/Zs+eLQuGNy3tN+W38yvllvabjl8e/pUs/3gU+bRjudog/+3d/5AtVxvU49qbyTHtzfq/\n2//Wsf8/5d2fHyPXom3v30p+vnpePced9ozQ6bPDd3seNj7GGzzpn2BEAKBYdkNjR2zEPpygtsfS\naWPx5bnGQTEhrPtG5CRfm7eFLB02nP9wOzKrtqvH8raEvb2PrXej/sjv8L/4LtbO0/Grqe2jWDod\n2g01ANcRsbOI1pOyAJ4gMl8EXiKEfYBwZ7KUP8bSYcM7hRcByFg7L0H523N/LMGB8iYUVDThcGUL\nOm09WDsvweP2+xyjTTmO5mJGZA+Ke9YrttD7xbV4++ydeGt6DtmxSe9c+9++lbgUwUFfojNwChCW\n4UYRrU7Ps1eciSz7HV8vXSAYAEaUsA+mFEd3Jkv5Y94vrsX2ryqU70ODA9Fp68GB8iYkxYQhMToc\nhytb8FFJA6y2XuQWVCvnkkGhGoCEtfPiERkWbJjzbhT5+/z89KJee1piKICMZa8A9uwcOncwdVqW\n8eSn/edgu7cd6kqg9coGO/PCfZ28dOd8kY0i6ANGlLAPpjrrRpOfrHjqpRJ22nphtfXg+KU2HK5s\nRs6SFCyeGoMD5U0YP9qE+KhQXGzpxOfnGpGzJEU5lwwKlQCA0OAAbFiUpMl5f7+4FgAUoT9Q3gRA\nvyYOHQgiw4I9EyZWWNn0Rba8rR3FynGWt+5tpOyuxeFqI2nAeOK0owX407+RVEpbJ6n+qPesRDaK\noA8YUcI+GOqsU4yycvjBh08pfGbZFOw4WIXDldVYPDUGa+fFA4Bix6xfmICvyq6iqqkDp+valHNX\nZcSipf0mzl2+gaXTxirt0Zx3+kw6bT2wdt/CjEmjdWvisAPBqoxYrQ/u7h6qnJhZ5j5HvscdiORP\ndjdbw9UAQzfW4FeiOiN9jfONpAHjOjm0HAEApfqjnoiLbBRBHxCwZcuWfr9obm7ulvXr17s+0M+Y\nggOQER8JU3BAn1/L0mHDu3+7iMSYcI+ulxgTjsiwYKzKiDU8LzEmHCFBARg76nakx5oRGRaM+6bE\nIDIsGGvnJeDbGbEorb+GQxXNCAm6DfcmRcPa3Yu9R2twqKIZEyNMyIgnEso+E1NwAE7XXcP2rypw\nf+oYLEyJ0VzXFBygXGdVRizeL67Fy4U2pE9NwqTF61F17Rae+2MJ7po0WmPVvPu3i/jpvjJEhgWT\n60alqGmOwaGO37MEhwJxmeTTGcd+S0QzLIocr/f9/p+QRUZhUaQPx35LPrut6s/sdYJDgaQlal/1\njqP3Mvd7JLe91waMmUa24wsyAfHzyUASHOpw34b319Gi3x/BiOfll1++vGXLllxXx42oiN0X9Pxl\nZ56ztxtNs7nnOw5W6bZNfpcVa+WZZVMd3gAy4iPtK00lpT8HypuweGoMlk4bq7RNv6PXWTptLIou\ntChRPQ97HXr+lIx/AsKC8eofj9otnHPYtW6uJluHPZ63Qjx+k9KLzl1Fvrzlw0bPgL4dwl9HzxZi\n7yU4VFuGmN18Q+e+DXHHAhIInDDkhH2gJkD1/GVn4u2OWLEZLfRYem+07U5bj8PiIkuHDUUXLACA\nogsWWDpsDs9i7bx4Zeclvj9svzttvdj+VQU6bb14ZtkUfFzSgAPlTZgxqQHPLJuitFfV1I5XPzmH\nlx6chqSYcACOdtJLD04DcM7+6f7g5vMeq4Br0QyL0gqtUeExZ9dxd/DwZPMQo3ZcWUACgRN8EnZJ\nkrYB+EcANgBVANbJstzm/CzfGKgJUN5fppOPADQRMC+wrZ3GkT6t5UKj6Kf3nsThymZ02noV77zT\n1utwv2/nV+LraiLsX1dblP6wOFvyz4o8yZQB1F2A+E/Cq5+cU6Jx1pdn7zcpJhy71s3VPDP202/4\nw5fmBwI98dRbEauXT8+2yUft3vaN3dRDIPAQXyP2LwC8IMtyjyRJPwPwAoAf+N4tY/pKLFy9CehN\nNAJEeH/8USkOV5La21Q82QifzzBRt79LxgsrUpUo+nBlMwDAauvRXMdq60FBRTOWThuLpJhwnKq7\nBgCYGBGCh2fHevwsWJFfOy8BocGByuD0UPpEEPtGVt4ELB02pIy9A7aeW3jpwWl9F4m7CyfKXr3F\nuZPNo/cW4CqLxV+ToWJxksAHfBJ2WZY/Z34tAvCwb91xTV+JhTtixV/7ncKLSl45X2CLjeYzExt1\nv2OFiKYykkhZ0tgw5y7fwOHKZqx/txi5j2Yo7Uwyhyo56TyeVJLcsCgJOw5WKfcfGhyAn+4rQ2hw\noJI/n3voAnKWpODLc41YOm0sOm296LT16NpA/Y1Xb3HephlOWUlK/XY0OdotIiddMEjwp8f+OIA/\nGH0pSdJ6AOsBIC4uzo+X9Q/evQkQu2J+UpSa122HHQSSFoVrzmKzSthJU+ppq1UZiQ2zfmECLl+z\noqqpA69+cg5fV1sQGRaEr6steO6PJZprU9Gub7Pi3b9dQku7DZu/cafm+rwQWjps6LT1ImdJsuEA\nBEDZWLvoQgtmTBqN7V9VKuJvRH+UO/Dq346PrN1dTPT5ZpLGeOEAEBajHRSO5pIa8DRvXSAYIFwK\nuyRJXwIYp/PVi7Isf2Q/5kUAPQD+x6gdWZZzAeQCQEZGhmx03EDh6k1ALwKmNoYnYqX666SUrt7k\nKF92d+m0sTAFBwKQ7YlG0z0AABzpSURBVFbJOWW1KZtTzrY7OZKkyRVfsmDdrqN46v4UHLtoURY+\nddp60NJhwy+/KAcgYftXFXhhRarSB3p9+j31/OnGHTMmRSg2kjOcWVL+wqu3ON7qcCeCL9lDJjQT\nFwOxc3XsFvv/rGuLfJs8FQh8xKWwy7K81Nn3kiStBfAggCX26mN9xkCWBNB73XcnNZGH2jfr70vE\nCytSlai86EILXnpwmiaqZW2S7V9VKFbIU/enAIBGrPl258RH4o0vynGppRMnatpQ3dyBiy2dSvZL\naHAgtn9F7idnSTJylqQ4WCt6q1WNJk6NcGZJKQwGC4ON4I3642pDjLkbgIaTIptFMOD4mhXzAMhk\n6SJZljv90yVjBrIkgLPXfWf9chyMyNhnCrpNGRSOX2q1b193Bl9XWxyKePFWCC0hAICxYUi7XbZe\nFDW2YE58JIICbkNtqxVJMWGYnxSNiy2XlONYT/+h9IlK1gt9e6D2SaetB4CEVRmxukXJnIk8/z1v\nSSn4uqzeHzVZ2AjeqIyBOymVIptFMAjw1WP/DYDbAXwhSRIAFMmy/D2fe2VAX2TEuPsWwL7u876x\nwwIcBl70WfuGtjt7coQ9I4aIrtV2S5PfTksL0A0zyPXOKfuahgYH2i0a4KOSBlxs6USNpRNVTR2Y\nnxSFjPhIPJQ+AeawIACSEpVTT3/HwSol7ZLNc9fUhLEfZ7jPKfds+Hx5w4G4o4XkfS/6ofdiSAcG\nvR2W+GMA30r0umKgs1kGw9uPYMDxNSsm2V8dcYe+yIjx5i3AHd/YaNWl3j2wKYdfnmtU8tuTYsKU\n8/nB5KUHpyEzsVGT5x4aHIiLLZ1IignDtofvxrGLFmUBUmhwAEKDA+3ZLgGaPiydNhYFFc1IGROu\n9LWgokkZOJ5ZNlVzDzQ1Ui87hn2ezgZiej+P3PoIoQd/RsrnhkXhxKVWbPrgFLY9fDdmTTa79e/h\ndIcl/hhfS/SyDEYRFUXFBBiCK0/9jadvAWwGyUPpEw19Y08GDDZLZum0sfjD0VpMjgxFVVMHvjzX\niKRF4YqH/mZ+Ja5Ze9Bp68Ezy6YqE6x62SzHLlrwUPoEZQL245J6TcVHypfnGnG4shmHK5sRFX47\n972k3LdeJM6nRrr7PGkbpcl34WeLfqyU3N30wSlUNXVg0wen8NVzWU7bUNCU4w3TF+++iKQHo4iK\nomICCGF36y2AFzWaQZIUE27oGxvVXDGyfvTeAhYkR6HT1gtLhw3Uprlm7QEAWLtvKRO26gpStdY6\ntU2oR/6jvFIcqWrBCytSAUAz2cv67epiqRZN9Ui9SFxvsGCfJ2/dsNCVvP9b3oS0lG9igz3i3fbw\n3UrE7jH9bYMMRhEdaCtIMCgY8cJOcaegFxXJnCXJWDptrCYVkD/ny3ONOFDehMzERo34G9WAYcVy\nxiSyUcbZBlJp8filVkwbfwcezZyM8sYbuDs2Aqag2xTRBKBba73T1ovjlyzKqtj5SVGYEx+JVW8X\noqqpA4BaO4a9B6MFVLR/2kGuUpMmyT7PTluP7hsCYLySd9Zks/uRuiv62ioRIioYpIwIYXdngtSd\ngl4tHTbNCkxeTPlzOm29aGknueB0Kzs1w0VbA4aNdGnN9SNVLUiKCVNskpwlKbg/dYzSBjsJy2av\nUE7XteFwZYuy+UZGfCR+vb8CVU0dSIoJ44qN9SqWjlEaJ79C1VWmkJHoU/qs5ABlMFolAkE/MCKE\n3R2/25lIUUF+6vcn7H+RlUU+RqmAkWHBCA0OUEoOsGmEqzJi0dppQ9GFFuwva1RqwOj1h9gdDSBW\njOwgqrR/dIKT8k5hNQ6UN2FuvBnpsWaYggOwdl48WjttoBUY2TK9VluP0lcq6k/vPYHDlS0oqGjC\nf62epbFtyL179zw9xev1C4PRKhEI+oERIex8NoeeQDiLHml53SNV1HdOcBBT1dNW6708lD7Bwbtm\n7RNaoXHzh6dxf+pY3X6ZQ4Oxdl68JsOm09aLdwqrlTcGvX5bbbcAAJIkIbfgghI5sxaIOSNYsYy6\ne2WlpAAR9ZOKhXO4ssW+iTaUe9MrJ6BXg8Yf6JVAcEvohVUiGKGMCGHXsxGciTgvGuwmFbS64Ry7\nrUHrk7OWCGvRsDXN+UnHWXEROFHTBlr0i/aLr9MOgPPTK3BPghk5S1I0qYeaWiwkmQVTx96hsW/o\n/bCRP52wXZgSrUy80kqTc+PNuDcpGuzGHrQqJX9ttUyCOrjpzT94GoHz0f9g2rtWIBiMjAhhp7hj\nD+iJBnse/T4pJsw+AUl2C2J9aYLkcB1qZVDRzlmSjH+YPk4jyrQP7IIhtv80cv66uhX3p5Lz2Gya\nPxbXIvfRDJiCbgMAmMOCDOcM5sRH4um9J5AYE64pAMZmydC3E3JfkvI3QN1n9Y/Ftahq6kDOkhR7\nmQTt4MZaUGyuO78AyghN9N/RgkdufYSQJffhH/24UE0gGE6MKGF3xx7QE3+9TSrYiB3QRqE0v9wo\nrZGKNhVI/jg+K4UOFsQfl7H+vkSYgm7T9HHptLGosRSjqqkDP/7oLBKjwxAfFYqZsWYH+0n196/i\n62qSNZOzJFm51juFF2G19dgLj6nPgH37oKtV6QBHUyPZ/tLBjR8s2bcEvU1CnFKyB6EHX8baZa8A\nYRmujxcIRiAjStjdwZX4s5F5ZmIUzKGqXeNquT3A1113z1bgc9xzliQj1C66bH9zH83Aq5+cQ8qY\ncOQWkJ2RfvKXc6hq6tAUGSuoaMLhyhbck2BWMmaOX2pTBiM6iQqQzBq9ssDU79crx8vPP/CDpVGq\nI4uhXZO+hswxXMtE1xfnDevRO21DIBjmBGzZsqXfL5qbm7tl/fr1/X5df/Lu3y7ip/vKEBkWjIz4\nSCTGhCMk6DZ098qYOu4OTJ8wWvO7KTgAAGAKDkDRhWZs/6oSpBamjMzEKHznnsnKMbTtsw3XUHb5\nBlo7bAi/PRD/vnQKEmPC0N0r4xefn8f5xhu4b0qMcl5kWDDumxKDsw3XMXVsOO4ICcKP/3E6rt7o\nwoHyJhw634RPTl9GdPjtiIs04e5JZmz+xp2otXTiUEWzYhWFBAVg6thwXLN2o6T2mnKPbN/MocEI\nCrgNsydHYmFKDKzdvXj3bxeRGBOu9IdiCg5ARnyk5u96f3P2fBWCQ7GzZgxe/6rGXpee+96dNgSC\nIcrLL798ecuWLbmujhMRu5foRaGOtVgkJfplM1voRtT7Si+jttXqkOu9dNpY7D1ag8OVLUpmCgBM\nL70MU3AgrLYeLEiO0hQBY+vM0JWxr2TfBYBsMk1tGgA4UdOGxVNjkFtwAVHhwQ6leJ9ZNgW//OI8\nLrZ0YkFytG65AlppEnD+huKv58t/p5e370kbAsFwZsgL+0C9butZNo5Com4MzdopNM2xttWqbGT9\nyy/Og05MflxSj4stpAryPQkk0vy62qJskQcAOUtSsDAlRlPPndo07AYYlg4bXv2E2DHm0CC0dnZj\nQXKUUtN9TnykxhKhfjzt++zJEbo7Q9FKk/w9syLqaZlfV8+X/Y61eoza7fMFUCyDsSCYYMQy5IV9\nMKe+PZQ+EafrrmHRlDE4eL5JKRxGSgaQyUkaydPI/qOSBowKCQJARP2tNbMBQIn26WIl6i1XNbXj\ndF0bnro/BZmJUUrkTqELlaiXvnhqDP7z2+nK37t7b+FwZYt9u7sIbP+qAgUVzZg2fhRyliQrE7w8\nvGjqiai78w7O8HXVcL8hVrkKBhFDXtj763Xbnc2h2TS/DYuSlMU/AHCgvAkvrEi1T7bKiqgDxNZ4\nNHMyPjqlRuoAkJkY6bBVHbvsH1Br0qSMvQMVjTeUsgf0HJrQPnZUCL6ZPkFJX6R/T4wOx+VrXYrI\nz0+KUkoYOCsHQLJnSOGxh9InKHXp2YlUo2jekwlTX1cN9xtilatgEDHkhb2vXrd5kXEmMPQ7msfN\nLtwBtNvC8dvNAaSA1+KpMUrlxllxEViYolZW5K/D5n9TMTt0vglHqlpg67mlsWLWzovH6bo2HChv\nwv2pY9DaSRY/PXV/irJhdlVTB+KjQnG4sgWPZk6GJAHTxo92KJVA+8DfB7WXCiqaFauIr3/jDkZr\nCPia7zz9arkYIVa5CgYRQ17YjfDVe9fLvQaMJ/PYT373IwAwZwTjncJqWLtvYf3CBJgYfxpQqzry\ni4LoAPFxSQNaO22YHBmKA+VNeHrvCcyeHIm18+KxYVES5sRH4soHp/Dc8qmIjw7T3Ds7OUpKBTSj\nu1fGnu/eo7wFtLTfRG5BNaqa2nGkqgWzJ5sd3kIAaFas0rRJ6sdPG38HFqZEG0bOrqJvozUEfM13\ngUDgnCEr7K6E21ffVS/rhV9BSWEnFKmos5OitATvkSqS4ZKzJEWJ1vmqjmyeOK2hvr+sEV9XtyrX\nmxwZqmTM0AycYxctqGrqwK/3V2DGpNG6dWRaO22w9fQCICLM990UHABr9y1kxJtByxzQtxA+MyYy\nLBj/tXqWZlcnV4OoK8uEj7yNdqESCATOGbLC7k305wnuTAbq9YmKOo2S2cU+C5KjMHtyJNgqjUbX\nKLrQogwE1A+fMDoEDde6sGhKjLJ/KbVLOm09WJAcjQPlTZgxKUIjxmybRy+2YvHUGHwvS7urIU3X\n3P4VEXNARs6SFIcFQHp7mzrdqNrFM3WnDr7R8xYIBPoMWWH3NPrzFD3BcXVNtsjX2werUFLTikcz\nJyMkiC5Ouk3xzdlUQX4iEiBpiLaeW5g+cTT+eU4svjzXqNgl5rAgPJQ+Ea9+cg6tnTalNnzOkmRM\nmzAKxRcteCU7Tek3Lc1LM2d4EWUj45Z2Gz48UYfaVqtSZkAPulVfS7sNUeHBfrO89J6niNQFAs8Y\nssLui3C7479T4eq09So1UtyJODcsSsIvvyhXMlPCbg/ErnVzmcqS9rKLSo47NBORAJlUPXj+Ko5U\nteC+KTHKFnzULmG3uquxFCP3UVIzZVUGqRl/pKoFP8orxf/8n0wA7G5OUbp2EiuuFVdvoLbVau+J\n5ORZkf6fu3xNSZd0p6AXjzPx9jTCFwgEhCEr7L7g3is+ES6rrdewhrtxW0S8J0eGKkXC2BWbVMSt\ntl5Ehd9uj5Rv4tzlG7DaV47qedvs6tb19yXiyvUuZcNreu3pE0fjSFULpk8crZzHiqcSaXfY7BUg\n1beEVRmxWDptLLp7SzFt/Gglx16vEuPaeQnKitdXPzmHA+VNeO6PJR6Lu7O5Cz28rs0uEIwgRqSw\nu/OKT4WLXzrvqi268TT1pwF1NScVouOX2nC4stm+krRaWRx0uLIZsyer/jibGcPmhtPl9LmPZmjK\n/QLA9xYlwRQUAEBWqiyywme1kcnTU7WtyoQsu7VfZFgw9nyXRPpVTe04dL4J9yREaiox8imQMyaN\nRnev7F21RnjmpfPPW/jwAoEjfhF2SZK+D2AbgBhZlpv90WZf4o6Nw9dX77T16uZRs22xWTEvrEgF\n4Jj6GBkWjJe/OR2vfnIOT92fgqAASVkcxK70ZPPF+b1R2Zo0fLTLpwcC0JxvCiZ12rt7ZbJtXpzZ\ncLOOVz85hyNVLbgnIVLJ8qF94zfSjo8KxfqFCV754Z546fy/nfDhBQJHfBZ2SZJiASwDUON7d/oX\nd1/jT9ddw4HyJk1k62qnJSq4B8qbsCA5WrPA5uOSerJadEw4Usbegcqr7ThcSXLHI8OC8csvziv+\nPo369QpxEWul2r5rUY9SP4X9ntZeXzptLKqa2nH8UhvuSTAr0fqSOx0366AbW9N6MrQMcGZiI5IW\nhTu0v+NQFS62dKLiartXdogv8yWDYnGSQDDI8EfE/ksAzwP4yA9t9RneLlc32s3I1U5L7KrQlvab\niq/+UPpEfFTSAACaol4EfmJV1vWgtWImcZ9asaP9L6kthCkoAA3XujA/KUpJaeQXSWUmNir20wsr\nUrFr3VxYOmyICr9dNyr+uKQBlo5uxEep8wn+QHjnAoH3+CTskiQ9BKBeluVTkiS5OnY9gPUAEBcX\n58tlvcKVEBvBi7Xe3ylGhbFI5UYAkPDqJ+dwsaUTSTFhePmb0/FxibYgGAC7HUO2oaOpkHxUTlk7\nL16xZADii7/6yTnNXqw7DlXB0tGNVnQjKSYMr2SnISlGm3fODh6ANh3T2bOknv3yaeMc2vQF4Z0L\nBN7jUtglSfoSwDidr14EsBnAcncuJMtyLoBcAMjIyJBdHO533BFiPYyO8cQCYMWXbG+nCi+73RxL\n8UULjlS14PilVkwbTzNcJIeJS77krlqvRd2L9XePzsEzfyjBIvv2dWqFSLV0AU2TpIOH3rwB4Fhe\ngeTfQ/Hu9fAm+nZWI0ZE8wKBc1wKuyzLS/X+LknSXQASANBofRKAE5IkzZVl+Ypfe+kHnOVEu1oS\n76uQsBOxX55rdJoSSIWUrjol3nukkinDT1zyP69fmICgAElji8yabMbB5xcDIOJPV8PS2ivvF9cq\n17N239Jk4ehZUeyzpNlDzt56vIm+ndWI0VtjIBAIVLy2YmRZPgNgDP1dkqSLADKGQlYMhd9LFDAu\nFeBMmNwVfqOccPZ8dcI1CtPGj4YpOECzrN9oYpTuvUoHKfo738c58ZFYkByNaePv0LRFUyj5cgdG\nVhTF2ZuLr7VejK0ymfsUCAQsIzKPncKWAKBldZ0d547n7CwiXZURqwwibL43e74nQkoHgZQxNai4\n2q5sVv3TfWXMhtmSZqHR4qkxOFzZjKAACa2daikD1pZhI3Bvs070LBxPMbq2O28JAsFIxm/CLsty\nvL/a6i9Y4XBWxIpmprxTeBGsN01hxZit+0InQ/VK6NLc8VUZsQ5i7o4IksJfvchZkqwseALO4T+/\nnQ5Au8KVnVwlK0vP4kB5Ey40HcMlS6dyjKsa6p5YUnoWjr+8cZHiKBA4Z0RG7J4IjN7uSKfrrmms\nFFZoiIet3UiDX2C0YVGSUjuG5ozTvvBZLUbQypEvrEhVFjy99OA0h4VVtAIk28dp40fhcGUzLlk6\nMTkyFP8wfZwyKPmrFLLem4fIdBEI+ocRKezOBMZo56ScJSnIWZKC4osWxUrRq3HC+tV6i4r43/mS\nBbTuCs1qMYLNGjGHBjscGxmmbvjMlyWg3nSs2YRLlk5EhZPceLVQme+lkPVqq3faepCzJEVYKAJB\nHzMihZ23TpxtgcdHnvxEJy+ErKBSnKVLkshaUtL6SDbLOU1Wi14kbZQ1oncsn0VispcaWJE2Xim5\nyz8XHnYi1Bs7hVawZPdRFWmLAkHfMCKFnbdO3N0Cjz/XH3VKWIEGSAoinw5JBxDettG7vn7mjTaL\nhM2r5wuNOVuB604GkRHO+uppWwKBwDkjUthZeMHhLQRn4uOvSTzVlunVvRbdKIOW9KXf611fL/PG\nVRaJO9aU3sbczuCjcaO+sp8CgcA/jHhhdyXO/SE+rC3DZrBQ6EYZMyaNdqjRzkLF9KUHp2nE19Vg\n5ewe9eqfG13X04lSkd0iEPQNI17YXcGX5e1LT9ho0wleePVKCvDzA3rtUFy9pTg7Vk/o9fLVfR0Q\nhf8uEHiPEHYP6Kul7O5MyPJzAp22XpyuazOs4eIvC8lV/XOj6pee7ozEY9R/IfgCgWuEsHuE/5ay\nG4m5q0iXTZN0VsNFrx1/iKIzoefb9GVy1Og5iAlXgcA1Qtg9wNkkpKei6ayMgDPB0vrxgYbph3rt\n9IUoemLj+KNdMeEqELhGkuX+L6SUkZEhFxcX9/t1+xJqkbywItXtkgCeDAR6x/Nb8bm6rrAxBIKh\njSRJx2VZznB1nHERbYFHrMqIxQsrUpUaMHrZIyw0InVXYGm0/X5xreZvev62p9ekeeyu+iwQCIYG\nworxE3wNGMC/HrCeBeGqEqS7eFJiwR3Em4FAMLAIYfczfeUBG6Vd+nOBlDt57O7gr/r1AoHAO4Sw\n+5n+WHTjz0lQVyLrzUDl6hyR2SIQ9C1C2Icg/nwr0BNZXuw9nZQdDKt5BYKRjBD2IYg/3wrcLc7l\nLLL3NAIXpQQEgr5FCPsIx93iXM7EW0TgAsHgQgj7MMbbSUpPKzGKCFwgGFyIPPZBjK/55Xq5795e\nz9O8e4FAMHCIiH0Q42v2iKcWichWEQiGB0LYBzG+eteeWiTCKxcIhgeiVoxAIBAMEfqtVowkSU9J\nklQuSdJZSZJ+7mt7AoFAIPANn6wYSZIWA/gmgBmyLN+UJGmMf7olEAgEAm/xNWLfCOB1WZZvAoAs\ny1d975JAIBAIfMFXYZ8CYKEkSV9LknRQkqQ5/uiUQCAQCLzHpRUjSdKXAMbpfPWi/XwzgEwAcwD8\nUZKkRFlnRlaSpPUA1gNAXFycL30WCAQCgRNcCrssy0uNvpMkaSOAD+1CflSSpFsAogE06bSTCyAX\nIFkxXvdYIBAIBE7x1YrJA3A/AEiSNAVAMIBmXzslEAgEAu/xVdh3AkiUJKkUwHsA1urZMILBQX9v\ngSe23BMIBgaf0h1lWbYBWOOnvgj6mP4uGSBKFAgEA4MoKTCC6O+SAaJEgUAwMPy/9u4txKo6juL4\nd2FZUIiBReBIKmRlZSQohRSVFVZirwaF1FNSYlCUF3qPikqol1B7SYgwuxDdjKI37WKZ2VSIUE4X\n9CUKgkJcPZwd+KDOOWdk/nv/W5+nOZeB9WPOXvM//zmzd04pEBHREZN2SoGIiGiXFHtERGVS7BER\nlUmxR0RUJsUeEVGZFHtERGVS7BERlSnyOXZJR4AfB/iWGdRxDpoa5qhhBsgcbVLDDDA5c1xk+/zx\nnlSk2Acl6fN+PpTfdjXMUcMMkDnapIYZoF1zZCsmIqIyKfaIiMp0pdhfLB3gNKlhjhpmgMzRJjXM\nAC2aoxN77BER0b+urNgjIqJPnSp2SWskfS9pv6QnS+cZlqRHJFnSjNJZhiHpKUnfSfpa0uuSppfO\nNAhJy5rX0QFJ60rnGZSkWZI+ljTaHAtrS2eaCElTJH0p6e3SWYYlabqk7c1xMSrp2pJ5OlPskm4E\n7gQW2L4ceLpwpKFImgXcAvxUOssE7ASusL0A+AFYXzhP3yRNAV4AbgPmA3dJml821cCOAg/bvgy4\nBniggzMcby0wWjrEBG0C3rN9KXAVhefpTLEDq4EnbP8NYPtw4TzDehZ4FOjsHzdsf2D7aHNzFzBS\nMs+AFgMHbB9sLu34Cr0FQ2fY/tX2nubrP+mVyMyyqYYjaQS4A9hcOsuwJE0Drge2QO+SobZ/L5mp\nS8U+D7hO0m5Jn0haVDrQoCStAH62vbd0ltPoPuDd0iEGMBM4dNztMTpaigCSZgNXA7vLJhnac/QW\nOsdKB5mAucAR4KVmS2mzpHNKBmrVNU8lfQhceIKHNtLLeh69t56LgFclzXXLPtYzzgwbgFsnN9Fw\nTjWH7Teb52ykty2wbTKzTZBOcF+rXkP9knQu8BrwkO0/SucZlKTlwGHbX0i6oXSeCTgDWAissb1b\n0iZgHfB4yUCtYfvmkz0maTWwoynyTyUdo3duhiOTla8fJ5tB0pXAHGCvJOhtX+yRtNj2b5MYsS+n\n+lkASFoFLAeWtu2X6zjGgOOvrj0C/FIoy9AknUmv1LfZ3lE6z5CWACsk3Q6cDUyT9LLtuwvnGtQY\nMGb7v3dN2+kVezFd2op5A7gJQNI8YCodOnGQ7X22L7A92/Zsei+GhW0s9fFIWgY8Bqyw/VfpPAP6\nDLhY0hxJU4GVwFuFMw1EvZXBFmDU9jOl8wzL9nrbI83xsBL4qIOlTnMMH5J0SXPXUuDbgpHatWIf\nx1Zgq6RvgH+AVR1bKdbkeeAsYGfz7mOX7fvLRuqP7aOSHgTeB6YAW23vLxxrUEuAe4B9kr5q7ttg\n+52Cmf7v1gDbmsXCQeDekmHyn6cREZXp0lZMRET0IcUeEVGZFHtERGVS7BERlUmxR0RUJsUeEVGZ\nFHtERGVS7BERlfkXENdN40D8Er0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f429c13d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    plt.scatter(x[y == i][:, 0], x[y == i][:, 1], s=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 KMeans Clustering\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JHkO1QXCbeKTXonu5xkwMeuZIBQ0JOwEg+eoW15JIJ/FKOLJyKNNT0wbJTBRS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\nsBMEQeQY/z/gomk8MeM3IAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f428917f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ignite_ml.clustering import KMeansClusteringTrainer\n",
    "trainer = KMeansClusteringTrainer(amount_of_clusters=2)\n",
    "model = trainer.fit(x)\n",
    "\n",
    "y_predicted = np.array(model.predict(x))\n",
    "for i in range(3):\n",
    "    plt.scatter(x[y_predicted == i][:, 0], x[y_predicted == i][:, 1], s=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AEnExLH5c1EofMUlY7Hp0il3kioxOUkMm1Ro2ix83S/g6HUMlHmG2m1TS4VrJ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\nThAEkWWQsBMEQWQZ/z8YuBwA/VcgrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f428885dba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "clr = KMeans(n_clusters=2)\n",
    "clr.fit(x)\n",
    "\n",
    "y_predicted = np.array(clr.predict(x))\n",
    "for i in range(2):\n",
    "    plt.scatter(x[y_predicted == i][:, 0], x[y_predicted == i][:, 1], s=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 GMM Clustering\n",
    "\n",
    "Using Ignite ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lbdUsfIRY6gLKPCUSYmV5Pg41+7CyPN9xdIiZqyNW7M71+F3lAMIW+6FmH/ac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\nOi089eov4g8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f428812f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ignite_ml.clustering import GMMClusteringTrainer\n",
    "trainer = GMMClusteringTrainer(count_of_components=2, max_count_of_clusters=2)\n",
    "model = trainer.fit(x)\n",
    "\n",
    "y_predicted = np.array(model.predict(x))\n",
    "for i in range(2):\n",
    "    plt.scatter(x[y_predicted == i][:, 0], x[y_predicted == i][:, 1], s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}