Lec_23_ML3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Physics 300 \n",
    "## Computational Physics I (Fall 2018)\n",
    "## BPB-248, Mon/Wed 02:30 - 03:45 pm \n",
    "\n",
    "|Instructor| Prof. Qiang Zhu|\n",
    "|--|-------------------------------|\n",
    "|Email | qiang.zhu@unlv.edu|\n",
    "|Website|http://www.physics.unlv.edu/~qzhu/|\n",
    "|Office| BPB 232|\n",
    "|Office hours | Mon/Wed 03:45 - 05:00 pm |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 23 Machine Learning (III)\n",
    "\n",
    "## 23.1 Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import decomposition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 23.2 Choose the estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.68412563  0.31939725]\n",
      " [-2.71414169 -0.17700123]\n",
      " [-2.88899057 -0.14494943]\n",
      " [-2.74534286 -0.31829898]\n",
      " [-2.72871654  0.32675451]\n",
      " [-2.28085963  0.74133045]\n",
      " [-2.82053775 -0.08946138]\n",
      " [-2.62614497  0.16338496]\n",
      " [-2.88638273 -0.57831175]\n",
      " [-2.6727558  -0.11377425]\n",
      " [-2.50694709  0.6450689 ]\n",
      " [-2.61275523  0.01472994]\n",
      " [-2.78610927 -0.235112  ]\n",
      " [-3.22380374 -0.51139459]\n",
      " [-2.64475039  1.17876464]\n",
      " [-2.38603903  1.33806233]\n",
      " [-2.62352788  0.81067951]\n",
      " [-2.64829671  0.31184914]\n",
      " [-2.19982032  0.87283904]\n",
      " [-2.5879864   0.51356031]\n",
      " [-2.31025622  0.39134594]\n",
      " [-2.54370523  0.43299606]\n",
      " [-3.21593942  0.13346807]\n",
      " [-2.30273318  0.09870885]\n",
      " [-2.35575405 -0.03728186]\n",
      " [-2.50666891 -0.14601688]\n",
      " [-2.46882007  0.13095149]\n",
      " [-2.56231991  0.36771886]\n",
      " [-2.63953472  0.31203998]\n",
      " [-2.63198939 -0.19696122]\n",
      " [-2.58739848 -0.20431849]\n",
      " [-2.4099325   0.41092426]\n",
      " [-2.64886233  0.81336382]\n",
      " [-2.59873675  1.09314576]\n",
      " [-2.63692688 -0.12132235]\n",
      " [-2.86624165  0.06936447]\n",
      " [-2.62523805  0.59937002]\n",
      " [-2.80068412  0.26864374]\n",
      " [-2.98050204 -0.48795834]\n",
      " [-2.59000631  0.22904384]\n",
      " [-2.77010243  0.26352753]\n",
      " [-2.84936871 -0.94096057]\n",
      " [-2.99740655 -0.34192606]\n",
      " [-2.40561449  0.18887143]\n",
      " [-2.20948924  0.43666314]\n",
      " [-2.71445143 -0.2502082 ]\n",
      " [-2.53814826  0.50377114]\n",
      " [-2.83946217 -0.22794557]\n",
      " [-2.54308575  0.57941002]\n",
      " [-2.70335978  0.10770608]\n",
      " [ 1.28482569  0.68516047]\n",
      " [ 0.93248853  0.31833364]\n",
      " [ 1.46430232  0.50426282]\n",
      " [ 0.18331772 -0.82795901]\n",
      " [ 1.08810326  0.07459068]\n",
      " [ 0.64166908 -0.41824687]\n",
      " [ 1.09506066  0.28346827]\n",
      " [-0.74912267 -1.00489096]\n",
      " [ 1.04413183  0.2283619 ]\n",
      " [-0.0087454  -0.72308191]\n",
      " [-0.50784088 -1.26597119]\n",
      " [ 0.51169856 -0.10398124]\n",
      " [ 0.26497651 -0.55003646]\n",
      " [ 0.98493451 -0.12481785]\n",
      " [-0.17392537 -0.25485421]\n",
      " [ 0.92786078  0.46717949]\n",
      " [ 0.66028376 -0.35296967]\n",
      " [ 0.23610499 -0.33361077]\n",
      " [ 0.94473373 -0.54314555]\n",
      " [ 0.04522698 -0.58383438]\n",
      " [ 1.11628318 -0.08461685]\n",
      " [ 0.35788842 -0.06892503]\n",
      " [ 1.29818388 -0.32778731]\n",
      " [ 0.92172892 -0.18273779]\n",
      " [ 0.71485333  0.14905594]\n",
      " [ 0.90017437  0.32850447]\n",
      " [ 1.33202444  0.24444088]\n",
      " [ 1.55780216  0.26749545]\n",
      " [ 0.81329065 -0.1633503 ]\n",
      " [-0.30558378 -0.36826219]\n",
      " [-0.06812649 -0.70517213]\n",
      " [-0.18962247 -0.68028676]\n",
      " [ 0.13642871 -0.31403244]\n",
      " [ 1.38002644 -0.42095429]\n",
      " [ 0.58800644 -0.48428742]\n",
      " [ 0.80685831  0.19418231]\n",
      " [ 1.22069088  0.40761959]\n",
      " [ 0.81509524 -0.37203706]\n",
      " [ 0.24595768 -0.2685244 ]\n",
      " [ 0.16641322 -0.68192672]\n",
      " [ 0.46480029 -0.67071154]\n",
      " [ 0.8908152  -0.03446444]\n",
      " [ 0.23054802 -0.40438585]\n",
      " [-0.70453176 -1.01224823]\n",
      " [ 0.35698149 -0.50491009]\n",
      " [ 0.33193448 -0.21265468]\n",
      " [ 0.37621565 -0.29321893]\n",
      " [ 0.64257601  0.01773819]\n",
      " [-0.90646986 -0.75609337]\n",
      " [ 0.29900084 -0.34889781]\n",
      " [ 2.53119273 -0.00984911]\n",
      " [ 1.41523588 -0.57491635]\n",
      " [ 2.61667602  0.34390315]\n",
      " [ 1.97153105 -0.1797279 ]\n",
      " [ 2.35000592 -0.04026095]\n",
      " [ 3.39703874  0.55083667]\n",
      " [ 0.52123224 -1.19275873]\n",
      " [ 2.93258707  0.3555    ]\n",
      " [ 2.32122882 -0.2438315 ]\n",
      " [ 2.91675097  0.78279195]\n",
      " [ 1.66177415  0.24222841]\n",
      " [ 1.80340195 -0.21563762]\n",
      " [ 2.1655918   0.21627559]\n",
      " [ 1.34616358 -0.77681835]\n",
      " [ 1.58592822 -0.53964071]\n",
      " [ 1.90445637  0.11925069]\n",
      " [ 1.94968906  0.04194326]\n",
      " [ 3.48705536  1.17573933]\n",
      " [ 3.79564542  0.25732297]\n",
      " [ 1.30079171 -0.76114964]\n",
      " [ 2.42781791  0.37819601]\n",
      " [ 1.19900111 -0.60609153]\n",
      " [ 3.49992004  0.4606741 ]\n",
      " [ 1.38876613 -0.20439933]\n",
      " [ 2.2754305   0.33499061]\n",
      " [ 2.61409047  0.56090136]\n",
      " [ 1.25850816 -0.17970479]\n",
      " [ 1.29113206 -0.11666865]\n",
      " [ 2.12360872 -0.20972948]\n",
      " [ 2.38800302  0.4646398 ]\n",
      " [ 2.84167278  0.37526917]\n",
      " [ 3.23067366  1.37416509]\n",
      " [ 2.15943764 -0.21727758]\n",
      " [ 1.44416124 -0.14341341]\n",
      " [ 1.78129481 -0.49990168]\n",
      " [ 3.07649993  0.68808568]\n",
      " [ 2.14424331  0.1400642 ]\n",
      " [ 1.90509815  0.04930053]\n",
      " [ 1.16932634 -0.16499026]\n",
      " [ 2.10761114  0.37228787]\n",
      " [ 2.31415471  0.18365128]\n",
      " [ 1.9222678   0.40920347]\n",
      " [ 1.41523588 -0.57491635]\n",
      " [ 2.56301338  0.2778626 ]\n",
      " [ 2.41874618  0.3047982 ]\n",
      " [ 1.94410979  0.1875323 ]\n",
      " [ 1.52716661 -0.37531698]\n",
      " [ 1.76434572  0.07885885]\n",
      " [ 1.90094161  0.11662796]\n",
      " [ 1.39018886 -0.28266094]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import decomposition\n",
    "decomposition\n",
    "pca = decomposition.PCA(n_components=2)\n",
    "pca.fit(X)\n",
    "X2 = pca.transform(X)\n",
    "print(X2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 23.3 Visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "#fig = plt.figure()\n",
    "\n",
    "labels = ['Setosa', 'Versicolour', 'Virginica']\n",
    "values = [0, 1, 2]\n",
    "#for index in range(len(labels)):\n",
    "#    value = values[index]\n",
    "#    label = labels[index]\n",
    "#for i, label in enumerate(labels):\n",
    "#    print(i, label)\n",
    "#for i in range(len(labels)):\n",
    "#    print(i, labels[i])\n",
    "\n",
    "    \n",
    "#for i in range(len(labels)):\n",
    "#    print(values[i], labels[i])\n",
    "    \n",
    "#for value, label in zip(values, labels):\n",
    "#    print(value, label)\n",
    "\n",
    "for value, label in zip(values, labels):\n",
    "    data = X2[y==value]\n",
    "    #print(data)\n",
    "    plt.scatter(data[:,0], data[:,1], label=label)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.68412563  0.31939725 -0.02791483]\n",
      " [-2.71414169 -0.17700123 -0.21046427]\n",
      " [-2.88899057 -0.14494943  0.01790026]\n",
      " [-2.74534286 -0.31829898  0.03155937]\n",
      " [-2.72871654  0.32675451  0.09007924]\n",
      " [-2.28085963  0.74133045  0.16867766]\n",
      " [-2.82053775 -0.08946138  0.25789216]\n",
      " [-2.62614497  0.16338496 -0.02187932]\n",
      " [-2.88638273 -0.57831175  0.02075957]\n",
      " [-2.6727558  -0.11377425 -0.19763272]\n",
      " [-2.50694709  0.6450689  -0.07531801]\n",
      " [-2.61275523  0.01472994  0.10215026]\n",
      " [-2.78610927 -0.235112   -0.20684443]\n",
      " [-3.22380374 -0.51139459  0.06129967]\n",
      " [-2.64475039  1.17876464 -0.15162752]\n",
      " [-2.38603903  1.33806233  0.2777769 ]\n",
      " [-2.62352788  0.81067951  0.13818323]\n",
      " [-2.64829671  0.31184914  0.02666832]\n",
      " [-2.19982032  0.87283904 -0.12030552]\n",
      " [-2.5879864   0.51356031  0.21366517]\n",
      " [-2.31025622  0.39134594 -0.23944404]\n",
      " [-2.54370523  0.43299606  0.20845723]\n",
      " [-3.21593942  0.13346807  0.29239675]\n",
      " [-2.30273318  0.09870885  0.03912326]\n",
      " [-2.35575405 -0.03728186  0.12502108]\n",
      " [-2.50666891 -0.14601688 -0.25342004]\n",
      " [-2.46882007  0.13095149  0.09491058]\n",
      " [-2.56231991  0.36771886 -0.07849421]\n",
      " [-2.63953472  0.31203998 -0.1459089 ]\n",
      " [-2.63198939 -0.19696122  0.04077108]\n",
      " [-2.58739848 -0.20431849 -0.07722299]\n",
      " [-2.4099325   0.41092426 -0.14552497]\n",
      " [-2.64886233  0.81336382  0.22566915]\n",
      " [-2.59873675  1.09314576  0.15781081]\n",
      " [-2.63692688 -0.12132235 -0.14304958]\n",
      " [-2.86624165  0.06936447 -0.16433231]\n",
      " [-2.62523805  0.59937002 -0.26835038]\n",
      " [-2.80068412  0.26864374  0.09369908]\n",
      " [-2.98050204 -0.48795834  0.07292705]\n",
      " [-2.59000631  0.22904384 -0.0800823 ]\n",
      " [-2.77010243  0.26352753  0.07724769]\n",
      " [-2.84936871 -0.94096057 -0.34923038]\n",
      " [-2.99740655 -0.34192606  0.19250921]\n",
      " [-2.40561449  0.18887143  0.26386795]\n",
      " [-2.20948924  0.43666314  0.29874275]\n",
      " [-2.71445143 -0.2502082  -0.09767814]\n",
      " [-2.53814826  0.50377114  0.16670564]\n",
      " [-2.83946217 -0.22794557  0.08372685]\n",
      " [-2.54308575  0.57941002 -0.01711502]\n",
      " [-2.70335978  0.10770608 -0.08929401]\n",
      " [ 1.28482569  0.68516047 -0.40656803]\n",
      " [ 0.93248853  0.31833364 -0.01801419]\n",
      " [ 1.46430232  0.50426282 -0.33832576]\n",
      " [ 0.18331772 -0.82795901 -0.17959139]\n",
      " [ 1.08810326  0.07459068 -0.3077579 ]\n",
      " [ 0.64166908 -0.41824687  0.04107609]\n",
      " [ 1.09506066  0.28346827  0.16981024]\n",
      " [-0.74912267 -1.00489096  0.01230292]\n",
      " [ 1.04413183  0.2283619  -0.41533608]\n",
      " [-0.0087454  -0.72308191  0.28114143]\n",
      " [-0.50784088 -1.26597119 -0.26981718]\n",
      " [ 0.51169856 -0.10398124  0.13054775]\n",
      " [ 0.26497651 -0.55003646 -0.69414683]\n",
      " [ 0.98493451 -0.12481785 -0.06211441]\n",
      " [-0.17392537 -0.25485421  0.09045769]\n",
      " [ 0.92786078  0.46717949 -0.31462098]\n",
      " [ 0.66028376 -0.35296967  0.32802753]\n",
      " [ 0.23610499 -0.33361077 -0.27116184]\n",
      " [ 0.94473373 -0.54314555 -0.49951905]\n",
      " [ 0.04522698 -0.58383438 -0.2350021 ]\n",
      " [ 1.11628318 -0.08461685  0.45962099]\n",
      " [ 0.35788842 -0.06892503 -0.22985389]\n",
      " [ 1.29818388 -0.32778731 -0.34785435]\n",
      " [ 0.92172892 -0.18273779 -0.23107178]\n",
      " [ 0.71485333  0.14905594 -0.32180094]\n",
      " [ 0.90017437  0.32850447 -0.31620907]\n",
      " [ 1.33202444  0.24444088 -0.52170278]\n",
      " [ 1.55780216  0.26749545 -0.16492098]\n",
      " [ 0.81329065 -0.1633503   0.0354245 ]\n",
      " [-0.30558378 -0.36826219 -0.31849158]\n",
      " [-0.06812649 -0.70517213 -0.24421381]\n",
      " [-0.18962247 -0.68028676 -0.30642056]\n",
      " [ 0.13642871 -0.31403244 -0.17724277]\n",
      " [ 1.38002644 -0.42095429  0.01616713]\n",
      " [ 0.58800644 -0.48428742  0.4444335 ]\n",
      " [ 0.80685831  0.19418231  0.38896306]\n",
      " [ 1.22069088  0.40761959 -0.23716701]\n",
      " [ 0.81509524 -0.37203706 -0.61472084]\n",
      " [ 0.24595768 -0.2685244   0.18836681]\n",
      " [ 0.16641322 -0.68192672 -0.06000923]\n",
      " [ 0.46480029 -0.67071154 -0.02430686]\n",
      " [ 0.8908152  -0.03446444 -0.00994693]\n",
      " [ 0.23054802 -0.40438585 -0.22941024]\n",
      " [-0.70453176 -1.01224823 -0.10569115]\n",
      " [ 0.35698149 -0.50491009  0.01661717]\n",
      " [ 0.33193448 -0.21265468  0.08320429]\n",
      " [ 0.37621565 -0.29321893  0.07799635]\n",
      " [ 0.64257601  0.01773819 -0.20539497]\n",
      " [-0.90646986 -0.75609337 -0.01259965]\n",
      " [ 0.29900084 -0.34889781  0.01058166]\n",
      " [ 2.53119273 -0.00984911  0.76016543]\n",
      " [ 1.41523588 -0.57491635  0.29632253]\n",
      " [ 2.61667602  0.34390315 -0.11078788]\n",
      " [ 1.97153105 -0.1797279   0.10842466]\n",
      " [ 2.35000592 -0.04026095  0.28538956]\n",
      " [ 3.39703874  0.55083667 -0.34843756]\n",
      " [ 0.52123224 -1.19275873  0.5456593 ]\n",
      " [ 2.93258707  0.3555     -0.42023994]\n",
      " [ 2.32122882 -0.2438315  -0.34830439]\n",
      " [ 2.91675097  0.78279195  0.42333542]\n",
      " [ 1.66177415  0.24222841  0.24244019]\n",
      " [ 1.80340195 -0.21563762 -0.03764817]\n",
      " [ 2.1655918   0.21627559  0.03332664]\n",
      " [ 1.34616358 -0.77681835  0.28190288]\n",
      " [ 1.58592822 -0.53964071  0.62902933]\n",
      " [ 1.90445637  0.11925069  0.47963982]\n",
      " [ 1.94968906  0.04194326  0.04418617]\n",
      " [ 3.48705536  1.17573933  0.13389487]\n",
      " [ 3.79564542  0.25732297 -0.51376776]\n",
      " [ 1.30079171 -0.76114964 -0.34499504]\n",
      " [ 2.42781791  0.37819601  0.21911932]\n",
      " [ 1.19900111 -0.60609153  0.51185551]\n",
      " [ 3.49992004  0.4606741  -0.57318224]\n",
      " [ 1.38876613 -0.20439933 -0.06452276]\n",
      " [ 2.2754305   0.33499061  0.28615009]\n",
      " [ 2.61409047  0.56090136 -0.20553452]\n",
      " [ 1.25850816 -0.17970479  0.0458477 ]\n",
      " [ 1.29113206 -0.11666865  0.23125646]\n",
      " [ 2.12360872 -0.20972948  0.15418002]\n",
      " [ 2.38800302  0.4646398  -0.44953019]\n",
      " [ 2.84167278  0.37526917 -0.49889808]\n",
      " [ 3.23067366  1.37416509 -0.11454821]\n",
      " [ 2.15943764 -0.21727758  0.20876317]\n",
      " [ 1.44416124 -0.14341341 -0.15323389]\n",
      " [ 1.78129481 -0.49990168 -0.17287519]\n",
      " [ 3.07649993  0.68808568 -0.33559229]\n",
      " [ 2.14424331  0.1400642   0.73487894]\n",
      " [ 1.90509815  0.04930053  0.16218024]\n",
      " [ 1.16932634 -0.16499026  0.28183584]\n",
      " [ 2.10761114  0.37228787  0.02729113]\n",
      " [ 2.31415471  0.18365128  0.32269375]\n",
      " [ 1.9222678   0.40920347  0.1135866 ]\n",
      " [ 1.41523588 -0.57491635  0.29632253]\n",
      " [ 2.56301338  0.2778626   0.29256952]\n",
      " [ 2.41874618  0.3047982   0.50448266]\n",
      " [ 1.94410979  0.1875323   0.17782509]\n",
      " [ 1.52716661 -0.37531698 -0.12189817]\n",
      " [ 1.76434572  0.07885885  0.13048163]\n",
      " [ 1.90094161  0.11662796  0.72325156]\n",
      " [ 1.39018886 -0.28266094  0.36290965]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import decomposition\n",
    "\n",
    "pca = decomposition.PCA(n_components=3)\n",
    "pca.fit(X)\n",
    "X3 = pca.transform(X)\n",
    "print(X3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 23.3 Different decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"576\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "\n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "plt.cla()\n",
    "\n",
    "for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:\n",
    "    ax.text3D(X3[y == label, 0].mean(),\n",
    "              X3[y == label, 1].mean() + 1.5,\n",
    "              X3[y == label, 2].mean(), name,\n",
    "              #horizontalalignment='center',\n",
    "              bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))\n",
    "# Reorder the labels to have colors matching the cluster results\n",
    "y1 = np.choose(y, [1, 2, 0]).astype(np.float)\n",
    "ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y1, #cmap=plt.cm.spectral,\n",
    "           edgecolor='k')\n",
    "\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 23.4 A overview of datasets\n",
    "\n",
    "http://scikit-learn.org/stable/datasets/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _wine_dataset:\n",
      "\n",
      "Wine recognition dataset\n",
      "------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 178 (50 in each of three classes)\n",
      "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      " \t\t- Alcohol\n",
      " \t\t- Malic acid\n",
      " \t\t- Ash\n",
      "\t\t- Alcalinity of ash  \n",
      " \t\t- Magnesium\n",
      "\t\t- Total phenols\n",
      " \t\t- Flavanoids\n",
      " \t\t- Nonflavanoid phenols\n",
      " \t\t- Proanthocyanins\n",
      "\t\t- Color intensity\n",
      " \t\t- Hue\n",
      " \t\t- OD280/OD315 of diluted wines\n",
      " \t\t- Proline\n",
      "\n",
      "    - class:\n",
      "            - class_0\n",
      "            - class_1\n",
      "            - class_2\n",
      "\t\t\n",
      "    :Summary Statistics:\n",
      "    \n",
      "    ============================= ==== ===== ======= =====\n",
      "                                   Min   Max   Mean     SD\n",
      "    ============================= ==== ===== ======= =====\n",
      "    Alcohol:                      11.0  14.8    13.0   0.8\n",
      "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "    Ash:                          1.36  3.23    2.36  0.27\n",
      "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "    Magnesium:                    70.0 162.0    99.7  14.3\n",
      "    Total Phenols:                0.98  3.88    2.29  0.63\n",
      "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "    Hue:                          0.48  1.71    0.96  0.23\n",
      "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "    Proline:                       278  1680     746   315\n",
      "    ============================= ==== ===== ======= =====\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners: \n",
      "\n",
      "Forina, M. et al, PARVUS - \n",
      "An Extendible Package for Data Exploration, Classification and Correlation. \n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science. \n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  Comparison of Classifiers in High Dimensional Settings, \n",
      "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Technometrics). \n",
      "\n",
      "  The data was used with many others for comparing various \n",
      "  classifiers. The classes are separable, though only RDA \n",
      "  has achieved 100% correct classification. \n",
      "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
      "  (All results using the leave-one-out technique) \n",
      "\n",
      "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
      "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Journal of Chemometrics).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(datasets.load_wine().DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "boston = datasets.load_boston()\n",
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = boston.data\n",
    "y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  6.32000000e-03   1.80000000e+01   2.31000000e+00 ...,   1.53000000e+01\n",
      "    3.96900000e+02   4.98000000e+00]\n",
      " [  2.73100000e-02   0.00000000e+00   7.07000000e+00 ...,   1.78000000e+01\n",
      "    3.96900000e+02   9.14000000e+00]\n",
      " [  2.72900000e-02   0.00000000e+00   7.07000000e+00 ...,   1.78000000e+01\n",
      "    3.92830000e+02   4.03000000e+00]\n",
      " ..., \n",
      " [  6.07600000e-02   0.00000000e+00   1.19300000e+01 ...,   2.10000000e+01\n",
      "    3.96900000e+02   5.64000000e+00]\n",
      " [  1.09590000e-01   0.00000000e+00   1.19300000e+01 ...,   2.10000000e+01\n",
      "    3.93450000e+02   6.48000000e+00]\n",
      " [  4.74100000e-02   0.00000000e+00   1.19300000e+01 ...,   2.10000000e+01\n",
      "    3.96900000e+02   7.88000000e+00]]\n",
      "[ 24.   21.6  34.7  33.4  36.2  28.7  22.9  27.1  16.5  18.9  15.   18.9\n",
      "  21.7  20.4  18.2  19.9  23.1  17.5  20.2  18.2  13.6  19.6  15.2  14.5\n",
      "  15.6  13.9  16.6  14.8  18.4  21.   12.7  14.5  13.2  13.1  13.5  18.9\n",
      "  20.   21.   24.7  30.8  34.9  26.6  25.3  24.7  21.2  19.3  20.   16.6\n",
      "  14.4  19.4  19.7  20.5  25.   23.4  18.9  35.4  24.7  31.6  23.3  19.6\n",
      "  18.7  16.   22.2  25.   33.   23.5  19.4  22.   17.4  20.9  24.2  21.7\n",
      "  22.8  23.4  24.1  21.4  20.   20.8  21.2  20.3  28.   23.9  24.8  22.9\n",
      "  23.9  26.6  22.5  22.2  23.6  28.7  22.6  22.   22.9  25.   20.6  28.4\n",
      "  21.4  38.7  43.8  33.2  27.5  26.5  18.6  19.3  20.1  19.5  19.5  20.4\n",
      "  19.8  19.4  21.7  22.8  18.8  18.7  18.5  18.3  21.2  19.2  20.4  19.3\n",
      "  22.   20.3  20.5  17.3  18.8  21.4  15.7  16.2  18.   14.3  19.2  19.6\n",
      "  23.   18.4  15.6  18.1  17.4  17.1  13.3  17.8  14.   14.4  13.4  15.6\n",
      "  11.8  13.8  15.6  14.6  17.8  15.4  21.5  19.6  15.3  19.4  17.   15.6\n",
      "  13.1  41.3  24.3  23.3  27.   50.   50.   50.   22.7  25.   50.   23.8\n",
      "  23.8  22.3  17.4  19.1  23.1  23.6  22.6  29.4  23.2  24.6  29.9  37.2\n",
      "  39.8  36.2  37.9  32.5  26.4  29.6  50.   32.   29.8  34.9  37.   30.5\n",
      "  36.4  31.1  29.1  50.   33.3  30.3  34.6  34.9  32.9  24.1  42.3  48.5\n",
      "  50.   22.6  24.4  22.5  24.4  20.   21.7  19.3  22.4  28.1  23.7  25.\n",
      "  23.3  28.7  21.5  23.   26.7  21.7  27.5  30.1  44.8  50.   37.6  31.6\n",
      "  46.7  31.5  24.3  31.7  41.7  48.3  29.   24.   25.1  31.5  23.7  23.3\n",
      "  22.   20.1  22.2  23.7  17.6  18.5  24.3  20.5  24.5  26.2  24.4  24.8\n",
      "  29.6  42.8  21.9  20.9  44.   50.   36.   30.1  33.8  43.1  48.8  31.\n",
      "  36.5  22.8  30.7  50.   43.5  20.7  21.1  25.2  24.4  35.2  32.4  32.\n",
      "  33.2  33.1  29.1  35.1  45.4  35.4  46.   50.   32.2  22.   20.1  23.2\n",
      "  22.3  24.8  28.5  37.3  27.9  23.9  21.7  28.6  27.1  20.3  22.5  29.\n",
      "  24.8  22.   26.4  33.1  36.1  28.4  33.4  28.2  22.8  20.3  16.1  22.1\n",
      "  19.4  21.6  23.8  16.2  17.8  19.8  23.1  21.   23.8  23.1  20.4  18.5\n",
      "  25.   24.6  23.   22.2  19.3  22.6  19.8  17.1  19.4  22.2  20.7  21.1\n",
      "  19.5  18.5  20.6  19.   18.7  32.7  16.5  23.9  31.2  17.5  17.2  23.1\n",
      "  24.5  26.6  22.9  24.1  18.6  30.1  18.2  20.6  17.8  21.7  22.7  22.6\n",
      "  25.   19.9  20.8  16.8  21.9  27.5  21.9  23.1  50.   50.   50.   50.\n",
      "  50.   13.8  13.8  15.   13.9  13.3  13.1  10.2  10.4  10.9  11.3  12.3\n",
      "   8.8   7.2  10.5   7.4  10.2  11.5  15.1  23.2   9.7  13.8  12.7  13.1\n",
      "  12.5   8.5   5.    6.3   5.6   7.2  12.1   8.3   8.5   5.   11.9  27.9\n",
      "  17.2  27.5  15.   17.2  17.9  16.3   7.    7.2   7.5  10.4   8.8   8.4\n",
      "  16.7  14.2  20.8  13.4  11.7   8.3  10.2  10.9  11.    9.5  14.5  14.1\n",
      "  16.1  14.3  11.7  13.4   9.6   8.7   8.4  12.8  10.5  17.1  18.4  15.4\n",
      "  10.8  11.8  14.9  12.6  14.1  13.   13.4  15.2  16.1  17.8  14.9  14.1\n",
      "  12.7  13.5  14.9  20.   16.4  17.7  19.5  20.2  21.4  19.9  19.   19.1\n",
      "  19.1  20.1  19.9  19.6  23.2  29.8  13.8  13.3  16.7  12.   14.6  21.4\n",
      "  23.   23.7  25.   21.8  20.6  21.2  19.1  20.6  15.2   7.    8.1  13.6\n",
      "  20.1  21.8  24.5  23.1  19.7  18.3  21.2  17.5  16.8  22.4  20.6  23.9\n",
      "  22.   11.9]\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-119.81884272   -5.56005586]\n",
      " [-168.89015548   10.11620863]\n",
      " [-169.31170747   14.0805323 ]\n",
      " ...\n",
      " [-138.38716306    0.9380922 ]\n",
      " [-137.50517338    4.2518251 ]\n",
      " [-139.19033295    1.00906423]]\n"
     ]
    }
   ],
   "source": [
    "pca = decomposition.PCA(n_components=2)\n",
    "pca.fit(X)\n",
    "X2 = pca.transform(X)\n",
    "print(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "506\n",
      "506\n"
     ]
    },
    {
     "data": {
      "image/png": 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NYb33eCqy1roQQoguxXU1P//F8zQ0xIjHE3PIo1GLmh0NPPjwDI+jO3CSyIUQQnQpjz05k6qq+hbljqP5bPYaDyI6NJLIhRBCdBlaa155bcEeVyjz+8x2jac1SB+5EF3Ups1VfP7FRrIzw0wYN5BgQP4ciM7Ptl0iURtILDeqdjt+5plHtXtMh0reuUJ0MVpr/nH/dN6ZsQwFmKaBaRrcc8clDBnQY5/XC9GR+f0mPXtks3VrNcptvna4cl2OHd7Ls9gOljStC9HFfPTZl7z74XLicZtY3KYxEqeuPsov73gF1z3815UQ4lDdfOPJoDXaIPGlQKPxrd7KlL++7nV4B0wSuRBdzNR3FhONWS3KGxpjrFq7zYOIhGhfw/oVkL6+HFUXActB1TYSWLkZoz7Kxi873ntAmtaF6GLWrivb4zE7xXaOQnQ22QWZ+CNxgiu3tDjWZ3BPDyI6NFIjF6ILKS+vpb46AimWZrZtl2GDCj2ISoj2FQj6uegHpxPcbdezYDjA1bed71FUB09q5EJ0IevXVxA2TSzHBlODUruSet+8bPz+jjf1Roj9obVm6pQ5vDD5E+pqIgwZ0YuzbpjIjGc+YUdlPX2GFPLdOy/hqOOHeB3qAZNELkQX0qtXN2zLxYy7aBO0qUCDXyvGjOztdXhCtJk/3f4q0xeswQ0rCIVYULKNL5dv4d63f0m/gd1JbMzZMUnTuhBdSO/euRx1VG+CfhPDATOuMS1NwGfyrW8d63V4QrSJhV+UMG3hWlxDYVguZtTBCvuozfTxzIMfdugkDpLIhehybv/jhZxyygj8fhPTVPTvn89f/3opRUU5XocmRJt4/PnPUJZDsDJGaHuUYGWMtO1RjKjDyuWlXod3yKRpXYguJhwO8ItfnM1PfzoJy3I65LaNQhyIbeW1+OsczKjTbCU3X9QhmtHx106QGrkQXZTPZ0oSF11CXkYYX9RusRyrAhqqI+gUszg6EknkQgghOrUJxwxkT7ukOK6LZXXs9RMkkQshhOjUzj5zFPhTp7v87lkEOviGQZLIhRBCdGp5eRl866rjgeYVc9NncOuvzvEmqFbUsT+GCCGEEPtQVVWPC+QNyKO2uhHDchkwsDs33XI6I0b18Tq8QyaJXAghRKdVVVXPjdc/Qn1dFNt2AQiG/Jx67phOkcRBmtaFEEJ0Ys8/N7tZEgeIRS0efOAD4jHbw8hajyRyIYQQnda8ueuaJfGdlIINJRUeRNT6JJELIYTotPLyMlKW27ZDTre0do6mbUgiF0II0WldctlxBIP+ZmU+n8nwEUUUdM/yKKrWJYlcCCFEpzXu2AHccNPJhEJ+0tICBAI+Ro4s4g+3X+R1aK2mVUatK6UeA84ByrTWRybL/gDcCJQnT7tNa/1W8tivgOsBB7hFaz2tNeIQQgghdnfhReP4xtlj2LChnJycdHr0yPY6pFbVWtPPngDuA57crfwerfXfmxYopUYAlwEjgV7Ae0qpIVrrjr1GnhBCiMNWKORn2LBeXofRJlolkWutZyql+u/n6ecDU7TWMWC9UmoNcCzwWWvEIrqWdWvLeOA/77F86WYys8JcfMmxXHDROAyjY+8vLIQQ+6ut+8h/qJRaopR6TCm1c7PjImBTk3NKk2VCHJDS0ip+9IPJfD5/A9GoRXlZLY8+8hEP/uc9r0MTQoh205aJ/H5gIDAG2Ar840AuVkrdpJSar5SaX15evu8LRJfz3NOfEtttQYdY1OL11xZSVxfxKCohhGhfbbZEq9Z6+87HSqmHgTeSTzcDTdfF650s2/36h4CHAIqLizv2ZrGiTaxauRXXbfmr4fObbC6tZtjwsAdRCSEOB0vmref152ZTXxvhhNNHcvoFYwnsNg2ts2izRK6UKtRab00+/SawNPl4KvCsUupuEoPdBgNz2yoO0Xn17ZdHyYZy9G653Irb9OjZuUalCiH234uPzuTp+z8gFrEAWL5wI2+/NJ+7n76pUybzVmlaV0o9R2Kw2lClVKlS6nrgr0qpL5RSS4BTgB8DaK2XAS8Ay4F3gB/IiHVxMC6/8vgW+wgHgj5O/PpQcnLSPYpKCOGl2ppGnrzv/V1JHBJdbqXry/nwrSUeRtZ2WiWRa60v11oXaq39WuveWutHtdZXaa2P0lqP0lqf16R2jtb6Tq31QK31UK31260Rg+h6Bg/pye3/dzFFRTkYhiIY9HHWN0bzs192/P2FhRAHZ9nnJZj+lqktGrH4ZPoyDyJqe7KNqejQjik+gsnPfp9IJE4g4MM0ZbFCIbqyKitKY8xqUUtVStEtt3O21EkiF51COBzwOgQhxGHg+Q1f4voUytY0XU1Cm3D2peM9i6stSfVFCCFEp7F44zbKJ2ThhA1cn8L1gWtC1Yg0hhzZOZcskRq5EEKITiMt4Kc202XraTkEamyUpYnn+gilBVGqc674KIlcdDhaa6ZOXciLz8+htjbCkUf15rvfm0i/fvlehyaE8Ngl40fx9KyFRG2beE5iqlnQ5+NbxUd6HFnbkaZ10eE89OAMHnrgA7ZuraGhIcbcOWv54c2T2bKl2uvQhBAe+8FpEzh5+AACPpOMUICgz+SEof34yVkneB1am5EauehQ6uuj/PfVBcTjXy3NqjXEYhZTnpvNT356lofRCSG8FvCZ/OOKs9lSU8uG8mr65+fQKyfL67DalCRy0aFs2liJ3282S+QAjqNZvrzFSr9CiC6qV7csenXr3Al8J2laFx1K9x7ZxC27RblS0KdPngcRCSGEtySRiw4lLy+DCccNark0a8DH5VdM8CgqIYTwjjStiw7nV78+j3/d+y7vTV+K62ry8zP50Y/PZMiQnl6H5okFJZu5Z/onrCmrpE9uN2459XhOHNzf67CEEO1E6d23jjoMFRcX6/nz53sdhjjMWJZDNBonIyPUaeeH7sucdZv43tP/JdqkuyHk93HXhWdy5pFDPIxMCHGolFILtNbF+zpPmtZFh+X3m2RmhrtsEgf427SZzZI4QNSy+cs7Mz2KSAjR3iSRC9GBrd5embJ8W209cbvloEAhROcjiVyIDqx7VurdnDKCAfym2c7RCCG8IIlciA7seyeNJ+RvPmY17PdxwwnFXbrLQYiuREatC9GBXTh2JPWxOP+eMZu4bWMaBtd97Rhu/Po4r0MTQrQTSeRCdGBKKa45fixXjh9DTSRCdjgkTepCdDGSyIXowLTWVDVGCPv95Gek7i8XQnRuksiF6KBmrSvht2+8R3l9Axo4fehA7jjndDKCAa9DE0K0I0nkQnRAX5ZVcPMLU5vNIX9v1VqqGyM8cdW3PIxMCNHeZNS6EB3Q47MXELedZmVxx2Fh6VY2VtV4FJUQwguSyIXogNZVVuOmWF7Zbxps3lHrQURCCK9IIheiAyruW4TfbPn2jdkOQ7rnexCREMIrksiF6ICuOfZown4/RpNFX8J+HxcffSR56WkeRiaEaG+SyIXogLpnZvDKDVdy1ojB5IRD9Mvpxs9PO5HfTDrF69CEEO1MRq0L0UH1ycnm7gvP9joMIYTHpEYuhBBCdGCtksiVUo8ppcqUUkublOUqpaYrpVYn/81Jliul1L1KqTVKqSVKqbGtEYMQQgjRFbVWjfwJYNJuZb8E3tdaDwbeTz4HOAsYnPy6Cbi/lWIQQgghupxWSeRa65lA1W7F5wOTk48nAxc0KX9SJ8wGuimlClsjDiGEEKKracs+8h5a663Jx9uAHsnHRcCmJueVJsuEEEIIcYDaZdS61lorpVouQ7UXSqmbSDS907dv3zaJSxw+Pli0hiemz6OqtpEJI/pxw6Tx9MjJ9DosIcRhZlt9HZ9u2kh6IMBJ/foT8vm9DslzbZnItyulCrXWW5NN52XJ8s1Anybn9U6WNaO1fgh4CKC4uPiAPgSIjuWxaXN5+O05ROOJDUBenbWU9z5fzQu/uYqC7AyPoxNCHC7unfMZ98+fg2kYKBSGgsfPv4ixhb28Ds1Tbdm0PhW4Jvn4GuC1JuVXJ0evHwfsaNIEL7qYhmich976KokDOK6mIRrnyekLPIzs8LO6tJyb736Zr/3gX5z1vw/z9LsLcF35jCu6hrmbS3lwwVxijkOjZdFgxamLx7l+6ivEHWffN+jEWmv62XPAZ8BQpVSpUup64C7gdKXUauC05HOAt4B1wBrgYeDm1ohBdEzrtlbiS7FmuOW4zF21KcUVXdOmshq+c9fzzFmxkWjcpqy6nvtf+5S7X/jQ69CEaBcvLPuCqG23KHe0Zk5p1/5b0SpN61rry/dw6NQU52rgB63xuqLjy8tKx97Dp+nCXOkj3+nxt+cRs5r/EYvGbV6e+QU3njuB7PSQR5EJ0T4aLYs9tT+lSvBdiazsJjzVKy+L0QN6tdjJK+T3cc3pxR5FdfhZtmEbTopm9IDPZOP2ag8iEqJ9nTNkKGn+lgPbbMdlfO8+Ka7oOiSRC8/9/cZzGD+sLwGfSTjoJzMc5NdXnMbRg2RW4k4DCnNRTXY62yluOfTKz/IgIiHa15kDBzOuV+9dydxUipDPxx9OnkhWMOhxdN6STVOE5zLTQvzrB9+kqq6RHQ1Rehdk4zdNr8M6rFw7aRwzF69rNigw6Pdx0pgB5GWlexiZEO3DNAwePe+bzNiwjnfXriE7GORbI45kSF6+16F5TiW6rA9vxcXFev78+V6HIYSn5q7YyJ+f+YDS8hr8psn5J4zkxxd/nYBfPo8L0RkppRZorffZxyiJXIgOJhKzCPhNTKN1e8bqG2I0NMYoyMvEMFo24wsh2tf+JnL5KC8OC9u21fDW20uoKK+juPgITjxxKH6/NK+nEg627kpWDY0x/nzvO3w2fy2GUqSnBfnZzWdwwvhBrfo6Qoi2ITVy4bm5c9fx+z+8guNobNshHPZTVJTLvf/8NqGQLL/Y1n7yuxdYvLwUy/pqGmAw6OO+/7ucoYN6ehiZEF3b/tbIZdS68JTjuNz5f1OJxm3i2sU1oDFisWlTJa+99rnX4XV6W7fvYMmKzc2SOEA8bvPcq/M8ikoIcSAkkQtPrV27nYaYhfapxG+jqdB+RcSy+WDGcq/D6/TKKurw+1p2YWgNW7bJ/HQhOgLpIxee+nJtGTYadp8jbZJy6VbRugb0y8eyWq6K5feZjB7ZtRfZEKKjkL+UwlNLlpWmPqAUw0bKgjBtLTMjxMXnHUOoyQA6w1CEQn4uPX+ch5EJIfaX1MiFp/ZU6/aZBqNHS42wPdx01dfp1zuP516dR21dhOIx/bj+ihPIz5MtZIXoCCSRC0+dedqRfPDRSqIxq1m5z2cy7pgjPIqqa1FKMWnikUyaeKTXoQghDoI0rQtPjT6qD+efM4ZAwIffbxIK+gkGffzhtvMIhwJehyeEEIc9qZELz33/hlM4e9Io5sxbTzjk58SvDSE7K+x1WEII0SFIIheHhb698+jbO8/rMIQQosORpnUhhBCiA5Ma+WFOa80X67exdksFfbvnMHZwUcp9qYUQorPQWrNk5WaWfbmV/Jx0vj5+cLMpkqI5SeT7SWvd7gk0ErO4+V+v8OWmcjQaQyl65WXx8E8uITs91K6xiPahteb92at45o35VNc2Mn5Uf66/cALd8zK9Dk2IdmFZDv/751dYunILcdsh4Df5f499wH23X8aAvrL3eCrStL4Pn81fx5U3P8JJF/yd86/5Ny+9voD22mjmvtdmsaJkO5G4RTRu0xizKNlezf899367vL5of4++8hl3PvQuK9dvZ3tlHW/OXMrVv3qSiup6r0MTol288s5ClqzYTCRm4TgukahFXX2U3/59qtehHbYkke/FgsUl/O4vr7Fpc2LN6eqaRh56aibPvDSnXV7/jdnLidvNN7OwHJcZi9bguG67xCDaT31jjKemzm02p95xNI3ROM+9tcDDyIRoP2+8/wWxePNlg7WGbeW1bNle41FUhzdJ5Hvx8NMft/iFisZsnn55DvZuCbYtWE7q13BdjdsBtp8VB2ZdaUXKDUws22XBso0eRCRE+3Pd1H/blNrzsa5OEvlelG5JvfuTbTvU1kfb/PVPPPIITKN5v7xSMGZgL/xmyz/4omOrcyzqo/EW5QooLMhu/4CE8MCkk0cSDLQcvpXbLZ2int08iOjwJ4l8L/oU5aYs9/vbf+lwAAAgAElEQVRMsjLafrDZTy8+mZyMMKHkL3Uo4CMzHOQ3V57W5q8t2t+fp3+EHYTd6xyGz+Db58oGJqJruOScYxjUv4BwKDFKPRjwkRYOcPtPzpUZO3sgo9b34sZvn8gv7ni5WfN6KOjjyouPw5eiCbS1de+Wwat/vI635qxgecl2BvbK49wJI2XEeie0paaW0uodxHoq0so0/kZAgVaQ1j+NkYMKvQ5RiHYRDPi4/84rmLNoPV+s3EJBbgannjCsXSpPHZUk8r0YO6ovf/rVBdz32Aw2llaR0y2Nqy4+jgu/cXS7xZAeCnDxSaPb7fWENxK1cAWmorFQoRyNcsH1QW5u0OPohGhfhqGYMHYAE8YO8DqUDkES+V6sLikj5jr89fcXSR+laFO9sjMpzM5gQ2ViVK42FdqEoM/HBUcP9zg6IcThrM0TuVJqA1AHOICttS5WSuUCzwP9gQ3AJVrr1CPLPFBbH+XHf3mZdRsrME0Dy3aYOH4Iv/n+JExDhhWI1qeU4p5Lz+Hqx17Edhwilk1awM+QHvlcc/wxXocnhDiMtVeN/BStdUWT578E3tda36WU+mXy+S/aKZZ9+r+HpvHlhjJs+6u52h/OXc3g/t254uxiDyMTndmwwgI++On1vL30S8pq6xndp5DjB/bDMGSAjxBiz7xqWj8fODn5eDLwIYdJIo9ELT5duK5ZEgeIxm1emrZQErloUxmhIBcXH+V1GEKIDqQ9ErkG3lVKaeBBrfVDQA+t9dbk8W1Aj3aIY7/E4tYejzVG93yso4rELf67aDkfrFhLXkYaV4wfw6jePb0OSwghxH5qj0R+gtZ6s1KqOzBdKbWy6UGttU4m+WaUUjcBNwH07du3HcJMyEwPkdctnW0Vdc3KDUNx/Jgj2i2O9tAYt7j0wecord5B1LIxlGLastXcdtbJXDxOaoVCCNERtPnILa315uS/ZcCrwLHAdqVUIUDy37IU1z2ktS7WWhcXFBS0dZgALFq7mYm/fICSeD2arxbmCPp9ZGeE+N6lJ7RLHO3lpflf7EriAK7WRC2bP7/9IY17aZkQQghx+GjTRK6USldKZe58DJwBLAWmAtckT7sGeK0t49gfVXWN3PjPF6nbEcWwwA4r7BDYfph4wlCe+/t1nW4ryenL1+xK4k2ZhsEXpds8iEgIIcSBauum9R7Aq8ll9XzAs1rrd5RS84AXlFLXAyXAJW0cxz69NXcFTtTFsBNrWwOgFNoHs9Zv5HeZYS/DaxPZaalXSnK0JjMki5AIIURH0KaJXGu9DmixLJnWuhI4tS1fe2+01i3W7C2t2NE8iTdRWdE594K+cvwYPl1TQqRJrVwp6J6ZzvDC9unOEEIIcWi61Mpui8q38rvPpvNF5TbS/QGuGnY0Pxl7An7DZHCvvGabVagm/yrdOefxThjYl++fPJ5/fTAbw1AoIC89jQev+qZsTiCEEB1El0nka3dUcvk7U4jYiUFc9Vacx5cvYHtjPXd//WyyM8NYmezK4EYcfMmdSgf0Sr0L2u7qY0vYWPP/aLRWEPIdQZ9ut5IdOrYNvpvW0WjFeb9qPVYPB2UZuMqlR99MemSnH9T94pbN2zOW8cGsVaSnBbjgzDEcO6Z/6wYthBCimS6TyB9YMpe403xgV9SxeWP9Sr5ZOILbnp0GTVbQcgMaW0E4bvDrq0/f5/1rowtYXnY1ro4AEHe2s6LsWobk/4vcNM96EfbqjlkfsnD7FmLaAZ8DwPxtm/nLZx/z+xMnHtC9bNvhh799nnUl5URjiZ/z3EUlXHbeMdxweeca7d9avli1mUde/JQNm6sY0CefGy4+npGDZZcz0TW4rsubL8xl6nOzaWyIM/7rQ/n2zRPJze9cg4rbQ5dZOHx51XYc3WK6OkHTxwMfzSZm2Wg08SyX+r429f0cYvma3994JqMG9drn/TdU37krie/k6ijrq+9ote+hNWmteXXVcmKO06w85ji8uHLpAd9vxmdfsn5jxa4kDhCNWTz733lUVHXOMQaHYu6SDdzyp5eY98VGyqvqmbN4Az+8/QU+X7bJ69CEaBf//ON/eeSeaWxaX0FlWS3TXl3ADy/5D/W1kX1fLJrpMon8yLwemCn6fSOWxeJVW3HRNPZ2ifRycTLByYRIkctT25egU3wA2F1DfHnK8phdiqtjhxx/W4i7TsryqNNyStq+fDJvLZEUK9/5fAaLlpce8P06u3uemNFsn3uAWNzmn0/O8CgiIdpP2dYaPnhzCbHIV38zHMeloS7C2y/P8zCyjqnLJPLvHjWeoNm8JyFgmJg7FMpW2GGwMzWYXx3XBiyo2MLC8q3si9/MS1luqjQUgUOKvS0opSjuWdRilL4Cji868JX0crLCe9jcQ5GZLlPZmtJaU7K5KuWxdRsrUpYL0ZmsWbEFv99sUR6L2Syeu96DiDq2LpPIB2TnMuWsyzi6oBemUmQFgvR3u+HfpECBk65Tzj2LOjazNpfs8/69s2/GUM3nmhsqTGHmdYftCPA7TzqNjECQoJl4QwVNk8xAkD8cYP84wHmnj8Lva/nGDAZ8HDOq3yHH2pkopcjKSD2HP7sTrlcgxO4KenbDdd0W5aZpUNQv34OIOrYuM9gNYFR+Ia+e8+1dz6+6dwpbqaV7Zj03nziHk/qVEHF8PFUykkfWHYWjDbSGyobGfd67R8YVWE4Vm2sfIPGJwKVHxuX06XZL231Dh2hwbj7vX/Ednl22mGXl2zmqe0+uGDGK/LQDH7U+oF8BP//eGfz9wXcxTQPX1WSkB/nbry8kHrMwQgHZjrOJy88pZvKrs5uNKQgFfXz7vHEeRiVE+xg0vJCifvlsWLMdp8lOkz6/yXmXj/cwso6pSyXy3Z1y5EBKKzby3NUvkR2K4jc1EOOWwQsYmVXBLQsTo83nrimFfQy8brQ20ODsIOA/nuzgcHpnfZugr3vKc12tE/PTD4Oaeve0dG4dd3yr3GvSySM4ecJglq7aguk3WLqylJ//+Bkad8QIhf1cfsUELr1iwmHxfXspatlkFaXTfUQe69eVkxYzUBouO7uYS88+xuvwhGhzSinufOBa/nbbiyyZtx5lKLJz0vnJ7RdKjfwgdOlEfunxo9ENj5ARiCWTeEKaz+H0niX0C++gtCyHEl2z1/tUNH7M4rJbcLUFOFTH5rOl/g3GF71MwMzZdd6CTZu5fdoMVm4vJxzwc8Uxo/nxScfjN1s2SXcEG7dXM3XWMmrqI5wwagAnjjqCQMDHjOhGHp49H8uy4VTIWQ45y2JMfvxjlKG49PIJu+6xIxblwaVzmLbxS7ICIa4bfgznHjG80yb7itoGLvvXs9Q2xmiMW4TyfURNkye/fzGDe8lqeqLr6Jabzp0PXEtdbYRoY5z8Hlmd9n3f1rp0Ik/zV3HNuEUYtOyrsRyDI33VbLFz6b6XBVK0dllWcRuuju4qc3WUqFPO+yvvomf4FsYc0Ys1FZVc++zLxOzESPHGuMXT8xZRUd/AX8+b1PrfXBubPv9Lfv/YNBzXxXZc3pm7ipH9ezDq5N489sUC4toBX+JNWT3CxIhDt9UOT0+exSWXHYdSinorxtmvP0FZY/2uEfQrq8tZUrGN3xx74P30HcE/3pxJRW0Dtpv44Bi1bGK2zZ/+O4PJN3u+5YAQ7S4zK0xmlowNORRdNpFrHUdXXozS1SkHuZlKs7k+C2W4nD9mz6O4S+pXsKAmkzQTjghX7FpTprbez2sfNfDF6hfolh4m1Du8K4nvFLVt3lr+Jb849evkpacd1PdhuQ6PLlnAsysWEXVszuw/mB8Xf43c0MHdb39E4zZ/fOJdYk3WaI/ELJat385cdyuR7ObTqrRfUT3SpNtql0gkTixmEwr5eWH1F1REGppNg4vYFk+u/JybjjyW7mkZbfY9eOWDZet2JfGdtIaFG7YQt20Cvi77lhRCHKSu+1cjOh3t1pGqJcd2Fetqu7GsJo9QfoSzhg9qcY7Wmn+ueoNXS2eDHg5AyLC5rvcsyjfn8Oh/z9q1oXlVXSOsiEA+LX7ipqHYVLPjoBP5D997nY82rd8193vKyiV8sHEd0y++jjR/20x7W7J2C0aqOflxC3ubhuyW1zjJQdqmoQgGEz+EmZvXpZyzHjBNFlVs5Yy+g1s17sOBuYcBf0opaVYUnV59Q4x3PlrGmg3lDD6iO5NOGkF6mkxPPVRdN5E7JUDLFYS0hpJoJt9ZORHVN4KlNIMz+rc478Oypby2eW6yNukHwHJMnt48nm1v9EdrUMmqvkKh0QR2gNVNYUbAsMD1QSzdJj+zkSWVDxFzauiV/jV6pU1AqX3PDPyyqqJZEgewXJfqaIRXVy/nyhFjDupHA7C2oorVZRX0z81hWM/mfbcBvwmkXiQnLeinjpYL4ARqNGg46qg+uxJWUXo2Bgp3t3u5WtM9fHDrvR/uzh07nBdnf0G8yYp6PsPgxGH9O+xYCSH2x5btNdz0y2eIxiyiMZtQ0M/jL3zKw3/5NoXdU3z6F/uty8wjb8E3BGjZL9Po+Lh7w9HU6iBKQbdAGCNFUn1p42dEnaYrmWkUmvVrCrEsc1cS30mhMC0IVoMvAqad2JRlVM5GPi67lmXVk1ld+zKztv2aGVt+hKv3vbrakoptKWvGjbbF3G37t5pazLKxmjT5x22b7035Lxc+/Ay/fn06lz0xhcsfn0J97KvkfNSAQoIBf4t7hYN+rjmtmNBuC+8oW5P/uU3Qb/KzX52zq/ya4WMJ7Ja8DBS90rMYnd/51hzXWjNx7CC652QQ9PsI+kzSgn6KcrP4w8X7Xs9fiI7sHw+9R219dNeUy2jMorY+yt2PvO9xZB1f162RB09GmT2w7BL8KjHYLe4alMXT+LCqd+IUw+Q7Q1pOzdJas7GhvFmZqVx2VGQQaQiQCeBqzDgoJ7FCnBsAbQJa70ryfmXxjYlz0XyVtG0doTy6hJL66RyRedZev4XeGdkpuwaChkmfzEzKojXkBbMwU3wQKdlezR+fepcv1m9FKcWEEf343bdPZ/L8hXy2fhNR+6uYlm4t4/a3Z/DXCxKD8kzD4N5bLuDmu1/GcTWuq3G0y0UnHcX3Jx7H2K29uXveLFaWlWFWOvRY6nL8kCF853sT6VnYbdd9h+QUcO9J5/GLWW8RdxxsrRmeU8ADp3S+bVTrYjGueeVlVlVWotI0rqHpHc7iVxNP5KRhA2WOvejUtNbMW1KCu9v4ENfVzFu0wZugOpEum8iV8kHe8xg77iISeR1Xa96t7M1d68ZhGCYmBhf2H8MNQye0uPa5kk+ojjckms8VgMZAU18TTvxEXcABVyWOGzb4bLDCgJlM4rUuA7qXo1sOmMfRUTbUTdtnIj+2sDfd0zLYWFuDozUh0+bkHiVkpcd4bVM5U7Z8QI8suHXYeZxe+NX85LpIjGv/NoXaxiiJZeQ1ny7bwHV/eZ7SjMZmSRwg7ji8tXwVfz7vDN5YsIL73vmU7TvqKSzI5IwRgxnUPY/ioX0oKkg0jx3Xqw8vnH/Zfv0/nNF3MBN7/w9rd1SS4Q9SlJG1X9d1NH+cMYPl5eVfNan7YJNbyydlpZwyouUYDCE6G59ppNzfwefrug3DraXLJnIAZXTDn3MX/py7iDkxJuTUMnWgn62ROvpn5pEbTD0A7an1H2EnM/BXyTzxJHOVH18UVNMErRLn+aMQD2jMCATqNG7unn+Bjd3WZ6+NR/m8aiOZvhBH5/XBUApDKZ4/9zJu/eBNlDWX+4vfQStQaHxK84/VR/N46Ujucl4mP5jN0bmD2FZbx63PvsG29CiEwdeQaOp34y5VK6toHGWm7HCxHJeT/vIQO+qj6HhiQZstNXVMmbeEOy87k6KCbGpiER5eMZt3N39Jtj/EtUPHcXbffc8J9xkGQ3M67xxqrTWvr1qJtduSlDHH4cVlS/ntySd7E5gQ7UQpxaknDOO9j1c268rz+0xOO2G4h5F1Dl06kTcVNIP0Skskk8L0bns9tybeSGLOWqKZSGtQBoS2mfgiiSS+e+rSydONKPgaXBy/YsumXBzLhFDzGrDlmDy4PMaUBa8SNoKk5bq8UbYYv2Gi0WT6Qzxy/LcZmFVA97QMnjn7PNztf0bRfPexnwxeyOzqQkpqMnh6w/v0DxVx4YPPUN0YQRuAAVYmuH7IXueCm2gpiGepZnuzoxNflY2RxDcWBDMGSifmQf/z7VlMGNGPc6c9RnnkqznhK+aUsbRqG788unPOCd9fVTUNWE6KXwoS6wkI0RX86LqJrC0pZ9OWarTWKKXo2yuX/7n2ZK9D6/AkkR+EIzK6s65+O1/9ZVbEbY2vJrHU5t4oB5ywARps/EydcgLfvHImytAoM7Fxy4JVA5izqg8Y61BBF3+0EQyIuYmE32jHueHTp3n/zFsTg91iM9G0zBN+5XJRr7XcuXocW6NVPDNvMQ3xePMx4gY4YZ2syUNmqaZqWGKUPYbaNThd7fwQnfxA4vrATOagLVW1PL9mEZXR3eaEOxaTv5zH9cOOpSDc+eaE76+//WsaAVsT7wbNBjW4Gl+j9I2LriEjPcijf72KJSs2s6G0kv598hg1rKjTjYfxgiTyg3DrsHP4+edPEnOb7qW796lDimStfOcvbfKfDbV53PPUOQzov41Mn0VJaQ82p2XgjwKWhqGxXbV5kpdroM6KsqhqE2Pz+hK1q9loKRRBisw4YSNxss/QhE2LsGmTpbrzwLK5NGRYGBb4IgrlJoPQ4ITBZ4EZh7xlLpF8RTxTY6UZLVsYVGIA304Bv8OzCxZibVOYhh+3m40OJ2LwGz6WVG3l1KLONyd8f8RiFvM+30Bu0GX7MQba0GAqlKNRDmRWHnj/oOO6zFq2gYVrN1OQncFZ44aRkyErY4nDn1KK0SN6M3pEb69D6VQkkR+EY/MGcW/xd3h4zXusry+jeodF7YYQbo5LoD71H2YNuMZXCdFFEx0ZJ+43cF0fS41epPkjxJ00wpWAculWXE2V34/bJI3u7JNXKOqsGOtqp/Fp2QMoHQSCzCWDccF6BgdiNNg+3i3vxzH5pcxank6Dq8BMxBEPaAI1oHRiIZKccJhIXRStNYYD6ds12TUm2wfvYcZ4sjBgWoSz6tmyBkztQ6Mxak2cAgs316ExGiMv0HarzAFEohbTZi1n6eqt9C3M5dxTjiQnq21fc3/ZjoujwPEpclYlPsw5QY1yNellmhPPOrAPODHL5qZ/vsSaLRU0xiyCfpN/vz6L+//nIkYd0fmm7Akh9k2GCx6k0Tn9uW/cDbx5ym28OvGnZJsZOAGFlbarS3lXAtSQyOBNqrXRQRYxn4nrmoDCsU3qG9NIG1YHKM48ZQV1ATOxlaqr0Lp581PMtamOlDNr+59xdBwbAxsDB8W8WAZl8QAzK4pY3JhDnj9KfaTJ9clY7JDGbxiM7lPIY7+/km5ZYcIhP6ahCIf89Ombm3J6G1pj2pq8zFpGDF1HWTQr2dyQ+IChtMJX7gcbVAMsfmfdPn+ey9Zu5fYH3ubWv7zMy+8tIhrbv77jqpoGLvvZ4/zzqY9486NlPPryp1x866OsLinbr+vbWiDgI55v4oQSUxgUCl800SISTPfzv5eefED3m/LhIlaVltOY/PnELIfGmMUvHn0TrffRryOE6JSkRt4KemRl8OkPb2bhli0sq9jCJwvWs2jeZrSlyU4Lk5buZ1NVLbhqV192PEOh3eafo7Q2qG8MM7RXOUt0DvW1YZxYssne0PgzLMyAg9Zgx0xeWf8UX+9l49+tVd/Rike3DWJ67QCK8neweGMfjGhiUBs7z1Wg/TCmoJD7LjuP7HCIFx+6iY/nrGFb2Q6GDerJsvpKFk//mLjjNq+WK03/4aWsDuVQs7lv8sNIc0HTwmowyF6keTeynGuuOjHlz05rzYNvfMaTb8zFbXBBaxatLOXl6Yt49I9XEg61XHimqfuf/4SqmgZsJ7kWgOUQtxzuuP8dnrzr6r1e2x4+WrQWw2eA1WTEukq0jPzPdRPJyTywloM35ixvtsb9TjsaImzYXs0RPXMPNWQhRAcjibyVKKUYW1TE2KIirho9Dr7z1bG6xhhn//oR6iNxcEDjpkx+AJblo6iwhvcripJJPFkldhVWbQDVLYrtGGgHLNtKObjO0bBpezeMEGz7tJDGqjCh5HlWOlj5iSdGDFavL8dMVruDAR+nnThs132Wf1aZmGe++1x3rVhb2x0rqBmeXsWqukL0bo07htL0CNejKjII5qb+NVtbXsn3nn2N0qod6B6JmNK2AQ02W8p2MPXDL7h00tiU1+708fw1u5J4U+tLK6lvjJHh8TrO85aWEI3bLUYiKkNRVd9yieB9Mc09d92YhjSwicNLWUUdT035jAULN5CXm8Hl3zqW48fLugmtTd757SAzLciH//g+P7v4JMaN6MOogb0wzZYLIwAEgjYNDX6smJ9U85XsBj/aSZSvLu+Zsv/adU2+XFdExbzuNFaGUW6iuVtpRaAegtsAV2PEoCEa57UFy1PGcurwQbjJuc+K5r0DbiSRnM8YsoyAL8UiD4bL4EAZvnrFZZeMb3bsozXruf7plzn/nifZVrIDsyHRV4+paOyliGUqInGbGXO/TBlXM3tZEc23h6TXXrTWvP/JSnSKT1t+06Bfz5wUV+3dueNHpNx4pUe3TPoUyHrV4vBRXlHH9T94nDenLWHz1hqWLCvlj3e9zguvzvM6tE5HauTtxDAMrjh1LFecmqhh/nX2ezwwfyG6yfBvpVwyQhEWbi6EvFR3UbiOgQokNiCptNOZvXgYx41ehWkkkqnjmHy+YiCbd+TgqzMSCZKmHwkSfbTGVoUTAtvU3P/+bPLNME+9NY/q2kaKR/Tlpm8eT1FBNgHTR9S1d4tCYdSboC1q/X6uG/sJjy04EV8yBp/hcueZr/DWf4vJOrKIM047cte1/545m4c/mYtb5mDaX83GN6PJOe1BRTxLY1iK7H2MxF6zrYIqI4ZB8488SsG4o/oRCu69Wb6tLVxZypYeFqFyBU1G/ms0uHDS6IEHfM9lK7YkZjMYzfo6uP70cTKNRxxWnntpDg2NcZwmLWbRmMWjT33CeWeNIbSPbjOx/ySRe+R/jzsNS9s8uWQxlmXgD9gUZLtUl4RwGn2Ql2LtVjSY+qv+ahNmlgxlzaZCRgzYhFKwcl0ftlbmYBVa+GoU4e026dvi4EK0u4+GXgEwkovWOBo3APVlEe54dBpxK5GIp322ko8XreU3t07C3dMAquTSrmvoxm9HzeDMoctYvLUPaf44I3psoTGaTuHAGD+9+opdl1Q1Rnhw1lzsWgd/k9bmnf/66yEWBG0kPmRccOqovf4M/zH9Y2r6umS4mkBdk5H9Jvz42lP29V/Q5t5dtRptQKRHYrMcM5ood0KKvuEsfL4D2+2spi7CzIVrUZbGNBLfp9KJTWne+3QV5x4/sg2+CyEOzoJFJc2S+E6GodhYWsmQQT09iKpz8iyRK6UmAf8kMfzqEa31XV7F4pVfT5jErydMalb2RdUm/vLpe8wtKyduN13mJZFQi3LSqY5EiTo2GvD1UWwtzaJswShwQQc08b4Wrt8ld0WUYLWDkXwv+UrihCpsKkenoUMu7lERqPHRaATQ5Q7B5EBxV2vqGuP89ImpRNITteNdu7mpRI1Sh1xG5m+hMHMHd686iQI7woSe6zkir4JtsW4clWFRX5/H3EUbmDB2AACLS7fiN010zEm1yFni9vHEQjOmqRg6oMcef3YLtm3mnYa16ByoygXfDo2/Hnx1irT0IA0p9jlvbx+t3oBOrq0fzYfkwvYoVzF0yIFPFSuvrsdnmsQtJ7EEcJO/kZvLdrRCxEK0noKCLDZsrGxRblsOOd065zbFXvEkkSulTODfwOlAKTBPKTVVa526s7YLOSq3D0+fcx2O63LN9OeZtWkT2gXl05x8RD/uP/EiVtSU8fSXn1MVbeSE7kfw/0o+Ixa2cfMsnDCgIW+FbpbEAQw3sTxssNKmoZdOjJDoltxSUAfxxRMLwkCyP7xaY+e6hKvNZBJSaEOj/UDcIGODyZvvnkA8SxMPwzMLJ9CroIoTj11Kf7WBaYu68+Hs17jtxjOYdNJIssNB4tpJLIqSKpXrRA3TiEFWRohu6XtuWv/FR9NoOiPPzgY7C8x6jb/KpbBb5qH9RxyimroI2zfvgCz9VT9+k8WAzj3xqAO+Z+8e3XaNWWjKMBSjBssccnF4ueKiY/li6aZd25ZCYm310Uf1oSDf2/dnZ+NVjfzY/9/efcfHUV0LHP+dma3qkntvGNxwBwzGNGMMxEAcwDEQAoRAILRA3kuBkLxQXl5IgSSEBAhJgCR0CDWAjQHbNBds496r3GRZtmRp28zc98eu5ZW17khrSefLZz/s3pndOXM/Wp+9d+7cC6wwxqwCEJFngYuAFp/Id7Mti3+MvSzjtiGtOzGkdScAHM/jT2/NJO54+DcGyd0JGPCVxYH6rVLLg8AOh8o2qS7zVDK3VwaIFQg525LFBoPrN4Q327XX2QHwwI5AzjZYtbYbAU/wbTMEcw0VQxKUlhexYnVH7nNKcI2PGp/Hw099yAnDe/CTyZOJWg5+PxBNX21mz732/ioIB3zcMf60fS7tWRmLsaZyR/0NAl4uXNC9L4U5of3Wb0NbtWEbhREfNQV73Q/vGXLEz9BuHQ/5M8NBP1dfcCJ/f31GciQ8ySoMBXxcc+FJB3i3Uo1r6OBu3HLDaB55/H08Y3Adj6GDu3H3Dy7IdmjNTrYSeSdgfdrrDUCdf4lE5HrgeoCuXbs2XmRNjM+y+O6ok/j9h58QMXsStxeQjI1ez4JEnkA4baS5gd13Lrk22C4gghMWJAFW3OCvTm5P5BgCuwQxpjbBW64guyCn1CLSyWPp2k6U+Hcl32BBVU2UO99+l1XbK7B3Qe5mcP3gBXZffxf8tpAbDtKldRHXn3cSpw3ouc9zDtj2nq7+vSyDvggAACAASURBVOT6A9w9fvTBVF2Dat+6AMuK09lUssXNJ5GqYH+NMKZH98MemHbNBSfRoVUBT745k+07axh0bEduunQUXdod+gh4pRrauLGDGHvWADZsrKCoMKxd6g3kqB3sZox5DHgMYPjw4Tpl1X5cM2IYecEAj0ybwbZ4FfZOqGltU7CS+oupCFT3scFOa617QDy5V7wAQhWQyAEM+GsMoYo9u/prwA0YEnmCHduzcIp4QmijTU3vONFdNpFtuXuuCQt8sHo1rjEUbE52n/viYBLJAVsYQ6ucEG//3w0HleBCPh/ndO/Fu2tWkkhbpCVk+/jusJOyftsZwML4W7QfvYrS0hI6hcvo0GY7K9/piRXP5dpbRhz254oI543sx3kj+32J0SrVcPx+mx7dWmc7jGYtW4m8FOiS9rpzqkwdBhFhwtCBTBiaHOW9uaKK+156j+nRFZQsjGAlUsut2sK2EwM4vWN73uyCvTZQ28I1tiFamMzzViyZ1PdOrXYcYgWGWGvBTkB4W2qf1Ly0waIYiY9zCXgu5PoYN/p4/rx9fvK9aYdOjrhOPq+sjhKNO4QP8paxX5w2li01L7Nw2xZ8YhH3XM7pcQzXDzrhEGvvyzd326fc/Y9VVFf3SC2m41G6oQ0DRq3n1gHfo3e35rv2ulK7bVm/ndnTlhDKCXLS6P7k5mf3cldzlq1EPhPoLSI9SCbwicDl+3+LOljti/N5+NtfZfboDfzqrQ9ZtKwUU+gQ7eGBHceqsjG5HhIX7A0B7B3JP4PkHWWSHBEN+GImfdn1Ovw1YHyC5zPE8yFQBa5tIdXCjqpcOqa64kePOpYRY3vz6BtzkHACb1UQu6Z+i9myrENqSRcEg7x40WUsKS9jfdVO+rRqQ5f8o2NClEenv8eu6iBe7Yp4Fq4LC5d1odtX9d5Z1fw9/dv/8OKjUxBLsCyLP9z5PD99/FqGjDw226E1S1npgzTGOMDNwDvAYuB5Y8zCbMTSnA3r0Zlnb7qCSfd9lxP79yF3WyHB0gKslSH8c3LwL8ipTeK7iQviGcwBerglleMtR0jkghuERBgC6wMEtlqIMYSDfob16cKjK6ZitYli5btE+8Uwdt1fBkYgYXs8/u9PDvkc+7Rqw5juxxw1SRxgyRqbAn+c/m23kh/Y0wUhYlhQqh1PqnlbOGsVLz3+AfGYQyySIFIdI1oT597r/0osGs92eM1S1q6RG2PeAt7K1vFbknb5eTzx9a/Vvl67eTvff/YtFm/cWncedQ8kkZy5zQmA8bGPNUzBCdcdcR4tTF4ntyMQ3JH8IWBZwpzAWmZuX1177dvt5BKJxwktCiBOsrs/0hqirQx//2g2N3xt5D5Hq0OyC/73//iA9z5bhmcMpw3txfeuPJNWR8kgGmMS3H3KJ5zccRlx18Zvu7ywoB8PTBuJ5wld87tlO0SlGtSkF2YQj9ZfvVBEmDNtGSPGDMjwLnUkjtrBbqrhdGtfwovf+wZxx+W+/7zHc7PmY8XAFxXwC44f8MCzhUixIVxR9/2JnNSo+BQrQbJvx0veY+6LGhI5gvEbXqmeW+/40bBNpA/JpnjaBO4VbVxiiQThYCBj3K7n8Z2fP8v6LRU4TvIXyJSZy5i/fCPP/+ZbBPyN/+e8szrKp4vX4rMtTunXnWDs14zqshJbXIKpOegv7r+YzVW5TF43guPa73uSG6Wag5pogkjbEAgEtkWxnD0LOicyrNynjpwm8hYs4LO554JzuPv80cxZU8rGiioGdG5HeSTC/W+9z4pN5SSCFk6+IbADLM/gBAXjTw2MAxBDoDKVuG3wfF5ymdUci7YXllBG1Z6EnWKidnIo+14Nb+OHGtchTOZEPmP+WraUV9YmcQDXNeysjvL+jOWMHdn3S66h/Xtl+nweeP59bMtK9jgYjye/8waPzB7FrI0daZNTzXVD53B2z9V8a9h8rhj9B50PXTVrnyxZy2tWBbGhrcAkv/qF87aTs7EGJ+ExZORx2Q6xWdJErvDbNif22nOv/jHAazftWct7R02URyd9yjNT5mCMwZjkZC5iDBIB159MyrE2HsULPZwcG6dAoEigJoaYACY9gVkGvPoJzbYscv37Hgy2asO22vng00WiCVas38bYwzv9w7J2SwUPPP8BsYQLJGMSn8OEly+gJu7Hw2JzdR4/fG80t1TN4OpBS7FKjp7r+Ep92Sprotz++GtEHRd8e4ZfVQ4uIa/G46a7xpNXuP+FkNTh0USuDqgoJ8QPLzqDEcd25da/vYrsMliuQTxI+A34BTffkL/awwsmW9tOR2F+9TrOKVnKmx8Pw+mZqL3eboXjWJtCWHHAAicMdthiXJ8+hFKJfN32HXy0fC2LN2xFBMYM6E2X9sUE/Ha99cfDQT89OpU0aB1ML5vDC+snUxGv5Pii3rCiHe5e06X2OqachRVt8NLGkEYcPw/POIFxfSzaHsRx1m3dwepN5XRtV0yP9g17Tkp9maZ8sTLjRE1iW5z1wzGcc+mJWYiqZdBErg7a6X178uStE7n2Xy/jxF3cGjfZa24bQusM+Gw8n+B0sjh7RBcW1XyK7UCwwoaZQUyhA65gV9up3vbk8p7+KuhWWMi9Y0ZTGa3kR68/yYylNUSq93SxvzlvKSN7d6OoIEw07uB5qXvjAVcMI4fuWRK0xonzyqoFfLZ1Pd3zi7ms92A65BQc9nm/tP49/rn2P8S85IjbD7fOxsqzIdQeqpM/PLqW7GCHE8Q19W8EEYGXl3yVG/Yzzi2ecPjh42/VXm93XI/Bx3TktzdeSDigt6ypo18kFscz9dcC8ABfjv4NNyRN5OqQDO7cgWnfu573l61iZzTKyT260r2kiCXrtjJrXSnt2+Vzao/u3PfFWxQ6leTmRnA9C3EF2e7Hs0guo5r2mQJs3FjJ9NLnmVv1D9p0DRCfO6rOcSPxBB8tX8tV5w7hyX9/hl2ZLE/kQrQT/Pyd9/jt177C9mgNF779N7bHIkScBAHL5oklM3jqrIkMa9P5kM836sbrJHFIrf5muRQMqGT7Z8mF40f0Ws+iRDHrKovqfUbM8VGcv/8lRv/0+id8ungNsYRLLDXgd87yUn77wofcdcXZhxy3Uo3tlL7deejVafXKwwH/fqdcVkcu+3NZqiYnJ+DnKwOO4/Lhg+jRqhgRoW+3dlw5aihjju1N2O/nxDbdiTj55ObG6NSxDMtKXdtOG6WezuDy7qo38Plctm0rRqz6971F4glemreQqo7CjuOSj+ouFlHLY9KSlWyvifC7+dPZGtlFxElmw7jnUuMk+P7HbySv7R+i0shWrAwD1DwMBZ3d2tZyJB7gmkHzCPnq3nYTsB26F1Ry4fD9T6n6yvT5qevte8Qdl9c/XXRYcSvV2Lq1LWbiaUMIB3y1X/FwwM+ZA3sxuOehLxKkDp62yFWDOK9Tfx5b2p2qxBJGn/k5U6cPYs2a9hgRTIZsbjDk5dUAYNsukuEGdkuEmvjuyd3rvt9v22yt2sU765eSyLDU5+aaSrZGdtEuZ//LJ26vquH1TxexaXslQ4/pxJB+7Yi59e+JBQiTy91XjuHTRWvID/bm1G538tNRU/m/j0eScG1cTzij21puPPPbB+wej8Qz35aTcFw8Y7B1tLtqAm7/6ihG9e/Ba58tJOF6nD+8D6f2O/xFgtTB0USuGkTA9vHcGdfx+LJulEWf5eyzPice9zF/UTdmzO1bN48baNuqmoL8CABdOpWRqdke8NkM7dmJD1etwdurleoaj67FRYTszAnTAEF7/3/uX6zexI1/eAnX84glXF77bBG5nYLE+obA5+B4yQlecgNxjCesnOnj17GpvPzDKykIhzCxVny1702MO+5FSqtyKAxGKGrzc6ycwfs97mcr15OTG2Dnzki9bQO6d8C2tONMHf2qq2N88P4iNm3ayTl9unPyyb2xffq32xg0kasGk+cPcnv/C4AL8IzL2vLtvPjyC/iqYzg5qZ0ECgNBbvtqmGU1gu0z+HweY86azbtThiFAwrGxEG4ZczJn9j+GGY//g5p4orbNHvb7uOHUk8gJ+PlG7yH85oupRN09LVxbhKFtOlEU3PetL8YYfvy3t6iJ7RldH4kl2BqsQapzsFLTygoG2zJYMTDlARyp4YWP5nPt2ScgwZOh7Sf445/QvdiFwAjE2v+Mc898Mo9fvzmVmOuw+yeIAD7bIuC3ufPysw612pVqdGtWl3HrzU8RiyVwHI9g0EenziX87g9XkpMTzHZ4zZ40hetvw4cPN7Nmzcp2GOpLEEs4vDp7EVPnrsJ4Hhec2J+xw44l6lbyp0XX4Eo1Pp/BGIhEA7w/dSBb1rXhb9+5hOHHJRfMW7ltOw9Omc7s9RtplZvDd0aeyLgBxyEiOJ7HbdP/zXulKxAEEaF9Tj7PjrmCtuG8fca1YdsOLrnnKeJRd8+0tAKVp1Vjhb06PfnGgBe3CM3NwYoJJx7bhb9895JDrotIPMGoex8lsvtygZdcHc724Pge7XngmnG0L9n/pQCljgYTJzxM2dbKOmWWJVw64SSuv0F/jB4uEZltjBl+oP20Ra4aVdDvY8KIgUwYMbBOedhXyI39/s4D7/2SeGgp1btCLJrXg20bS/j+uafUJnGAXq1LeHjChRk/3/M8ZIMP/9oAhA0mLvTu2JZC376XUFy9dTuPT5pBjXERX3KNdSGZsK2Qt/fleETACnhI3CBGCNh2xs89kGWbttW99m2BG05OL1PjczWJqyZh7dqyekkcwPMM77z9hSbyRqCJXB01wr4Cfjb2fnZUR/j3tAUMGyaM/97x5B1C19xDkz9m+oo1OI6BCIDhs1Xr+c2kadx5/pn19p+2eDV3/P0NYgkH408uFOMGk+uxE9w96C7DJBeApJaIG955Md62v4G7EXz9kYL/QvzHHzDW4rwwToaBeQCt8ut2yX++aD1PvTaDzWWVDOnXmW9eeBId2hz+vfFKfVnmzF6zz23xfQziVF8uTeTqqFOUG+bqc084rPe+MHs+MafubVwxx+XF2QvqJXLX8/jJM+8STS3kYEjO944FTtCAT/DNz8XpUwNpvyWMAXu7jSCMH7iIK4dMh93HTHyCKZ8IrZ49YDLv2qqIriVFLN+yrc4Y/bDfx9Wjhta+/s+0RTzwxCSiqX8UN2ytYPInS3nyf6+kY1ud9lVlVzgcwJJkC3zva1DdurXOXmAtiA4pVM1KJJH5VrGo49S7H3tt2Q5qYmkTvdgkvxGpf4vsaiFQJoRmhbHiBjAIBjHgXx7CEo8fnDMd26r7w8GQwOz8yQFjnTpvJZtXVSAOqV8RyXXerz/zRE7unZwGznE9HnxqSm0Sh+RCMTXROH956eMDHkOphjbi5N74dt9ZYUztQyzhOzdqt3pj0ESumpVhXTtlLB/SpWO9e1lzgn7ibloS3p3EPUN4kyFnsyFYDuENFgVv5JK/y6UwXEPQc5GYRZu86tqlStMJYJxl+43T9TzufXIy8ZhLoBICOyFQBTmVws6ymtr9Nm+rJOFkmPbSM8xeuG6/x1CqMRQW5XDHf59PwGfjsy0sS/DbNpddNoLjB3Y98AeoI6Zd66pZuesrZ3L5488Rd10SrovftvDbNnePq98yaF+UTzDso6Y6npz3XQADwXKDP5p8nipC4oK8m0/o6mri0QADu3dgUNueXPSAoWJXmK5td/DDi6YysOtWAMw+rn17niHhuGypqCKS1htgeYAHLoapc1fx3xOTlwEKckO47j6uoxftexS+Uo3F9Tza92zNj+4bT9m6ClzXMOKU3nTrrt3qjUUTuWpWjm3Xmjdu+SZPfzqHhRu30rdDG64cMYSORZkHhl10+gCeeXsOuAYjgjgGX4TaJA5pQ93iQmJjACfqY2jHDjw3aQ4JNzmyfMXGVlz35/HcNv4jLj9hARvLWtE1bVZKx3H587+m8co784jHHdq1KcCNuRn7xApy94ywL8gLMbh/Z2bPX1e7UAxAKOjnmxfqalIqu75YWsp//foV4nEX27awRLj31nGaxBuZ3keuWrRt1TWc89e/kVgWI1EE/l2Qu8kkW8h7MUB0cDWx/CCF84L1llM1gAl5vHjzM7w2+evcdtsParf94pG3mTR9CbG0a91iCYl8CyctmQf8NoN7dKRr60LOGdGHT0s38MT7M7E3J7CjyX38tsUNE07linGHNyBQqS/D3FUbufHuZ5LLm6UJBXy88NC1tC7WHqMjpfeRK3UQWufm8MZVV/KrD6fx9sxleH6DZ4N4Gdd2wW7tYK8M15siFlLd8Anhlr+M4yeXXVxbXlkV4d1pi4nvtSgKniHf+IgEwLYsovEEXsJl9qJ1fG7g9RmLSSRi5Je6WA54ftjVwSLe3sdpp/T+citCqUMQdxxueuhFrBoPO578Lng+cEMWrmd456PF+kOzEelgN9XidSwo4MELvsKfvnERJiwkMvTCGwDLIB0dcqKBjCuSGcCzhS3bchlx4p710beUV+H31Z80xpD8IfH3H09kwpmD8DyDYwxucpl23J0x8ta52E7yR4KdgIL1Hr4KlymLVn1JZ6/UoZu+ZC2yPoodS97FIQasBPh3eSQSDjurotkOsUXRRK5UyhnH9uS/zxlFvEiI59feEVabxJ2v7MRU+ggF/Azq1SHD+mzghCBcFq9T1rFdEYkMA9YsSziuZzs2lVfy9Luzk58nAhZ4liFc7iJ7HUQMhNcmdDU0lVULF23Ajpo6vVYCYJI/OE84XkerNyZN5EqluWbkcKb/z3fwj60mOixC4pgYzulVuFdth9Yux5T34G/f/zoP3T4ecd3aRO9ZEM8D/y6HYE3dJJsbDnDJeUMIBeteyQr4fVx18Qjuf/q9utfbRTCS7N7PxIobRvc/5ss9caUOwdypmXuEBPAbYXh/TeSNSa+RK7WX4mAuj465iu9//hQRN4IxBlt8/Kz/pZx93p7Z2k4tbMus5Ruo6eAHAwWrEoTLHC799qh6n/ndb5xGm5I8nnltFjurIvQ9pj23XHUGxcU5lFdW19vfV+MhZq+ZslKsoEWHIp2HXWXPptIdmTcYQ6viXF1/vJFpIlcqg0HF3Xj7zDtZsHM9nvE4vqgrfqvu1+XBP1zFvT97iWmTFoHjISEfl153Gt++rv6c7iLChK8MY8JXhtUpr6yJ4hmTvI89TXCnC64Bm3rTXg4arK0dlV05QT/V0XhyFrfdf5+pcSPjxg7czztVQ9BErtQ++CybwcXd97vP3T+/GH5+8X732Z+gP/UVTP8H0fMQFxJhG9sD2X3/uGcQzzB8QLfDPp5SX4bzxg7kXy98iuPVjiIBIBTwc8X4k7IXWAul18iVyiLbsvbc57Z7nuoYeCEL200ObkteMxeMbWE8GKm3nqksu3ziCHp2aU3IZ2N54EPIDwX5y8NX4fcf3rK+6vA1WCIXkf8RkVIRmZt6nJ+27cciskJElorI2IaKQamjnW0JVqhut7rlGXyZRgQDXtCiW1edNUtlz4oFG/ju2f/H1vcX4V9XTknc4borRvLyczfTpUurbIfXIjV0i/xBY8zg1OMtABHpB0wE+gPnAo+IiP6EUy2SiHD+iH51ZseyXZNxNhoBrIB2oqnsqa6KcsfVj7Asz2NL/2Kq8i2qV5fx4i9ex2nEtcdd12Nr6XaqqyKNdsyjWTb+VbgIeNYYEzPGrAZWADpptGqxfvb1MRQXhBE3ecuZsYRMN6kboHPnkkaPT6nd/vTEJDYOakW0Sz5OqzC7+pWw7YxOJIzH9DfnNkoM017/nCuG3MX1p9/HZQPv5H9v+CuR6lijHPto1dCJ/GYR+UJE/ioixamyTsD6tH02pMqUapFs2+LFe6/m1EE98FsW2ILnl4wTzvz0v8Y1dniqhYsnHF79eCG3/fHfPLdwOYjU3mUhRjB+i00D8infUtngsSyauYrf3P4PdpbvIhZJkIg7fPrufH55098b/NhHsyMatS4ik4H2GTbdBfwJuJdkQ+Je4DfAtw7hs68Hrgfo2lVvt1HNW1F+mIe+N55oPIHjeFxz19NsXrG9dh5rY8FVV51K716Zvm6ZLVtfRmnZDnp3bkPntkUNFbpqxmIJh2/96jnWbN5OJJZITse61z5ihESrHPqf0KPB43nhkUnEIok6ZYmYw+dTl1C+eSet2hc2eAxHoyNK5MaYsw9mPxF5HHgj9bIU6JK2uXOqbO/Pfgx4DJKrnx1JnEo1FaGAHwLwwoPfZtuOXTz1ymd0blfEpecNPehJNqpqotz64Css31CGbVk4rstpg3tx73Xn47P1Grs6eK9/spDVm7cTPcD1b88Px49o+NkGN60tz1juD/go39JyE3lDjlrvkPZyPLAg9fw1YKKIBEWkB9AbmNFQcSjVVLUuyuOOa0Yz4fxhhzRT1v1PTmLJui1E4w7V0TixhMu0eat46j8zGzBa1RxN/nz5niSe+hvcu1VlxNCqZ36jzOY24KRe2L76actJuHTu1bbBj3+0asif5w+IyHwR+QI4E7gdwBizEHgeWAS8DdxkjHH3/TFKqYMVSzh8MGclCafuRO3RuMOLH8zLUlSqqcoPh+q8Nqn7i5LrCxiMGBJthXu+dk6jxDPh5jGEcoKItedHQygcYMLNY8jJC+3nnc1bg83sZoy5cj/b7gfub6hjK9VSJRyXDCusAhCJJTJvUGofJpwxiOnzVxFzUm0tEYxl8MQQbefROjfIH68Yz4jOXfb/QV+Stp1K+MPbP+DpX73JvI+WUdQ6n0tvOpvTLxp24Dc3YzpFq1LNSF44SLf2RazauL1OuSXCyQO6Zyco1WSdcFwXxvTozBtLViOuwQhYCZfiySvxVcY48dxBjPhh4yTx3Tp0a80PHr6qUY95tNORL0o1M3dffQ7hoB9f6lpi0O+jIDfIrZfUX5VNqQP59rgRtH9rGfmfrKPw/VWUvLwIX2UMf9DPsUMbfqS6OjBtkSvVzBzfqyPP33MVz0+Zy6pN5Qzq1ZGvnTGQorxwtkNTTVDX4zoydMSxzPtwMfFo8vKMiBAM+Rl37VlZjk4BiNnXBbWjyPDhw82sWbOyHYZSSrVIsUg1T97zLG8/NYt4JM6g0/tx4wNX0Ln3wc9roA6diMw2xgw/0H7aIldKKZWRMQZT/Sf81Y9x5Q3wjRtsQq2uRXJvbJTbzdTB0USulFIqI1PzJFtXP8Vvf3cy8xe2A6Bvn8V8/84n6NT721mOTu2mg92UUkplFCl/lNt/cAZfLGiH61q4rsWixa24/abVRKN6O+PRQhO5UkqpemqicT76OJ+dO4N43p5U4XkWsajFtPcWZTE6lU671pVSStXz7KTPeftfw3Ecu962SNTHptKKLESlMtEWuVJKqTpqYnF+P2k65VU5GZfTtf1Cz97tGj0ulZkmcqWUUnX87JVJ+HZ5eGKDVXehFAMEQkFGjDq2UWIpXbmF2VMWUr55R6McrynSrnWllFJ1vLNmBfkVBkRwc/xYMRcSyYV4PL/FlTecic9Xv8v9y1RTFeWeK//I4pkr8fl9xGMJzp54Mrf85htYlrZB02ltKKWUqsMTg+Wlli0VwQv5cPMDOPkBvJCP08/s2+Ax/P6Op1n42QpikQTVlRESMYcpz3/Gq49OafBjNzWayJVSStUxsnM3nLDghSyQZHe6ARDwFwZoVZLXoMePRxNMf/1zEjGnTnksEuffj05u0GM3RZrIlVJK1fGLi8billgYC7ywjRe28MIW5Pu56vKRWFbDzuoWi8bZ13q81ZWRBj12U6SJXCmlVB1P/PkDWq8FK2bASyZU22dx3TdO5fKLT2zw4+cV5tC2S6t65ZYlDDmj4bv1mxpN5EoppWotW7aZKVMWEY0msFyDFfWwIh6BuOGEgd0aZY51EeG2h75JMCeAZSfTlD/gI6cgzLd+enGDH7+p0VHrSimlas2YsZBEIgEkE/butO06HjNnrqZ3I614NujU43h4yk94+ZFJrF++mX4nHcNF159Fq/ZFjXL8pkQTuVJKKQBWbChj0uJPqOoQxIpCcKeLlRpvZtuGnJxAo8bT5dgO3PbQNxv1mE2RJnKllGrhKnZFeHryLP757my8RAgTsHD9hkSuRd6GOJYLgsvpp/fJdqgqA03kSinVgv37kwX84vkpxBMuRgwELKwEyfvILUO02KZwU5Sf/Pcsiotzsx2uykAHuymlVAtVum0nv3j+fWIJN3WfuIAInp/a106uTQCHoqIB2Q1W7ZMmcqWUaqHenr0Uz/MybjOpGVjFNcRiNjPmnNKIkalDoYlcKaVaqLjj4mZI5MYCN2jANYTKEghCTl5xFiJUB0MTuVJKtVBnDOxZe5/23nb1dME1BCodjDGcOVa71o9WmsiVUqqF6tulHf72PoxlqP3PMkTbebg5YIIQax0gz2/Ttl1htsNV+6Cj1pVSqgUrOi6XLTk15GwQQmUQ3O6Rv86wow+IASfXxyXnnpDtMNV+HFGLXEQuFZGFIuKJyPC9tv1YRFaIyFIRGZtWfm6qbIWI/OhIjq+UUurIDMhti7/Ko2ShS94mF3/M4ItCyXwIbPfAwNeuGZXtMNV+HGnX+gLga8DU9EIR6QdMBPoD5wKPiIgtIjbwR+A8oB9wWWpfpZRSWVAQDVG8ABJ5FjWtbeK5ggEsF/wRQyvLIRTyZztMtR9H1LVujFkMZJpE/yLgWWNMDFgtIiuA3UvmrDDGrEq979nUvouOJA6llFKHp7IqQqSNHyMkJ1b3LOy4oWCtAwZuvOa8bIeoDqChBrt1Atanvd6QKttXuVJKqSyYsWgdxgKs5GQw2IIbFCIlFmI8zj1TR6sf7Q6YyEVksogsyPC4qCEDE5HrRWSWiMwqKytryEMppVSLNHvZeip2RdmzxlmKJcQLLXyrNlG6bGNWYlMH74Bd68aYsw/jc0uBLmmvO6fK2E/53sd9DHgMYPjw4eYwYlBKKZXB5ys2cOujr7IrEid5RVySM7mlNe0M4Fu3lRlvfk636SBdeQAACKRJREFUvp2zFKk6GA3Vtf4aMFFEgiLSA+gNzABmAr1FpIeIBEgOiHutgWJQSim1l53VEa793QvsisQBEAQBxCU1wTrgGfzVBttnE8wJZitUdZCOaLCbiIwH/gC0Ad4UkbnGmLHGmIUi8jzJQWwOcJMxxk2952bgHcAG/mqMWXhEZ6CUUuqg/f71jzAZ+zgNuIAFdtwQrHABGHXJiMYMTx2GIx21/grwyj623Q/cn6H8LeCtIzmuUkqpQ+d5humLV6c60+sSBHENgXKPQKWHXR3jR0/fSnFbndHtaKczuymlVAvxm9ensrVyF2RK5cYgDtguBCvi3HnPeEaNPj4bYapDpHOtK6VUC7ArGuO5j+bhAOJBnf51Y8Aky92AYOExWpN4k6GJXCmlWoDS8kp8tg2SHNgmDuAZ8FIt8XjyNnLPB76aWLbDVYdAu9aVUqoFaF+cT9xxADB2KnG7dfcxBjxj8OsNv02KtsiVUqoFKMwJEbRtDOAGkmXp+doAnm3wxyC2uTwLEarDpYlcKaVaCkm2xo0lxHPBs5MJ3AjE88GOgS/iQsLJdqTqEGjXulJKtRADu3Rg2tp1ODkkp2Hd3ZQzBjsC/mrw74jR5+Q+DR6L5xnWrCnD77Pp3KUk0+Jb6iBpIldKqRbivsvHctb/Pg4x8PxgfAAGKwoFKw1W3MH+Yi3/s+yhBo1j7ty13HfPq0SjcYyBVq3zuOeei+neo02DHre50q51pZRqIdoU5fHqHd+kfTgXOw6+akOoDPI2GuxIjPDsdby0/EGKW+c3WAzl5bu468cvUFFRTSSSIBpNsLG0gjtu/yfxuHbpHw5tkSulVAvSs30rpvzs+qwd/9135uO6Xp0yYyCRcPn0kxWcdnrDd+s3N9oiV0op1WjWrt1GIuHWK3ccj4qK6ixE1PRpIldKKdUolqzYxH/mLqOmtY9YoY1JG98mAgOO77LvN6t90q51pZRSDW7HrgjXPvgCkSIByweuIdLGR/7aOLZjOGVkb3r1apvtMJskbZErpZRqcA+/+hER1wUr1Qy3BWNDdQcfXtDmju+fn90AmzBtkSullGpwkz9flmHtVMENW/hybIJBTUeHS1vkSimlGty+J3wRxo7uj21rOjpcWnNKKaUa3Hkn9sHvs+sWGkObnDC3XndWdoJqJjSRK6WUanA3XTiSnh1KyAn6sS2LkN9Hm6I8nrr7CgJ+7VY/Elp7SimlGlxuKMA/f3QFM5auY3npNjq1LmTU8T3w2/aB36z2SxO5UkqpRmFZwoi+3RjRt1u2Q2lWtGtdKaWUasI0kSullFJNmCZypZRSqgnTRK6UUko1YZrIlVJKqSZME7lSSinVhGkiV0oppZowTeRKKaVUE6aJXCmllGrCNJErpZRSTZgYY7IdwwGJSBmwNttxZNAa2JbtII5SWjeZab1kpvWSmdZLZi2lXroZY9ocaKcmkciPViIyyxgzPNtxHI20bjLTeslM6yUzrZfMtF7q0q51pZRSqgnTRK6UUko1YZrIj8xj2Q7gKKZ1k5nWS2ZaL5lpvWSm9ZJGr5ErpZRSTZi2yJVSSqkmTBP5QRKRX4nIEhH5QkReEZGitG0/FpEVIrJURMamlZ+bKlshIj/KTuQNS0QuFZGFIuKJyPC9trXYetlbSzzndCLyVxHZKiIL0spKRGSSiCxP/b84VS4i8vtUXX0hIkOzF3nDEZEuIvK+iCxKfYduS5W36HoBEJGQiMwQkXmpuvl5qryHiHyWqoPnRCSQKg+mXq9Ibe+ezfgbnTFGHwfxAM4BfKnnvwR+mXreD5gHBIEewErATj1WAj2BQGqfftk+jwaol77AccAHwPC08hZdL3vVUYs75wx1cBowFFiQVvYA8KPU8x+lfafOB/4DCDAC+Czb8TdQnXQAhqae5wPLUt+bFl0vqXMVIC/13A98ljrn54GJqfI/Azemnn8X+HPq+UTguWyfQ2M+tEV+kIwx7xpjnNTLT4HOqecXAc8aY2LGmNXACuDE1GOFMWaVMSYOPJvat1kxxiw2xizNsKlF18teWuI512GMmQps36v4IuDJ1PMnga+mlT9lkj4FikSkQ+NE2niMMZuMMZ+nnlcBi4FOtPB6AUid467US3/qYYCzgBdT5XvXze46exEYLSLSSOFmnSbyw/Mtkr+MIfnFW5+2bUOqbF/lLYXWyx4t8ZwPRjtjzKbU881Au9TzFldfqa7gISRbnlovgIjYIjIX2ApMItmrtSOtQZV+/rV1k9q+E2jVuBFnjy/bARxNRGQy0D7DpruMMa+m9rkLcIB/NmZs2XQw9aLUkTDGGBFpkbfQiEge8BLwPWNMZXpDsiXXizHGBQanxiO9AvTJckhHLU3kaYwxZ+9vu4hcDYwDRpvUxRigFOiStlvnVBn7KW9SDlQv+9Ds6+UQ7K8uWrItItLBGLMp1UW8NVXeYupLRPwkk/g/jTEvp4pbfL2kM8bsEJH3gZNJXk7wpVrd6ee/u242iIgPKATKsxJwFmjX+kESkXOBHwAXGmNq0ja9BkxMjZrsAfQGZgAzgd6pUZYBkgMwXmvsuLNI62WPlnjOB+M14KrU86uAV9PKv5kapT0C2JnW1dxspK7hPgEsNsb8Nm1Ti64XABFps/vOIBEJA2NIjiF4H7gktdvedbO7zi4BpqQ1tpq/bI+2ayoPkoO11gNzU48/p227i+T1m6XAeWnl55McibqSZDd01s+jAeplPMlrVTFgC/CO1kvGempx57zX+T8DbAISqb+Xa0lew3wPWA5MBkpS+wrwx1RdzSftbojm9ABOJTmA64u0f1fOb+n1kjrXgcCcVN0sAH6aKu9JskGwAngBCKbKQ6nXK1Lbe2b7HBrzoTO7KaWUUk2Ydq0rpZRSTZgmcqWUUqoJ00SulFJKNWGayJVSSqkmTBO5Ukop1YRpIldKKaWaME3kSimlVBOmiVwppZRqwv4fvCHbEkilOr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "print(len(X2))\n",
    "print(len(y))\n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "plt.scatter(X2[:,0], X2[:,1], c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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