12. KMeans Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### k-Means Clustering\n",
    "* Unsupervised Machine Learning\n",
    "* Searches for a pre-determined number of clusters within an unlabeled multidimensional dataset\n",
    "* The centroid/custer center is mean of all the points belonging to the cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2064b142f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "X, y_true = make_blobs(n_samples=300, centers=4,\n",
    "                       cluster_std=0.60, random_state=0)\n",
    "plt.scatter(X[:, 0], X[:, 1], s=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.98258281,  0.86771314],\n",
       "       [-1.58438467,  2.83081263],\n",
       "       [-1.37324398,  7.75368871],\n",
       "       [ 0.94973532,  4.41906906]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(X)\n",
    "y_kmeans = kmeans.predict(X)\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 3, 2, 0, 0, 1, 3, 2, 2, 1, 2, 3, 2, 0, 3, 3, 0, 1, 1, 0, 0, 3,\n",
       "       1, 1, 3, 0, 3, 1, 3, 2, 2, 3, 2, 2, 2, 2, 2, 1, 0, 3, 1, 3, 3, 1, 1,\n",
       "       2, 1, 2, 0, 1, 0, 2, 0, 0, 1, 2, 1, 2, 0, 2, 3, 2, 1, 1, 1, 2, 0, 2,\n",
       "       1, 3, 1, 2, 1, 1, 2, 1, 3, 0, 2, 0, 3, 0, 0, 2, 3, 0, 3, 2, 2, 3, 0,\n",
       "       2, 1, 1, 3, 0, 0, 3, 1, 2, 0, 2, 0, 3, 0, 0, 3, 2, 3, 1, 1, 0, 2, 0,\n",
       "       3, 2, 0, 0, 3, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2, 1, 1, 0, 2, 1, 1, 2,\n",
       "       3, 2, 2, 1, 3, 1, 3, 1, 2, 3, 2, 2, 2, 3, 2, 3, 0, 1, 2, 1, 0, 3, 2,\n",
       "       3, 3, 0, 3, 1, 1, 3, 0, 3, 3, 2, 0, 3, 1, 2, 0, 0, 3, 1, 0, 3, 1, 1,\n",
       "       3, 3, 3, 3, 0, 2, 3, 1, 3, 3, 1, 1, 1, 3, 1, 2, 3, 1, 0, 1, 3, 2, 1,\n",
       "       2, 3, 2, 3, 1, 3, 3, 2, 1, 1, 0, 0, 3, 2, 0, 0, 1, 0, 1, 3, 2, 2, 3,\n",
       "       3, 2, 3, 0, 1, 3, 0, 1, 2, 1, 0, 3, 0, 2, 2, 2, 2, 1, 1, 2, 3, 1, 0,\n",
       "       3, 1, 1, 1, 0, 0, 2, 3, 3, 1, 0, 2, 1, 3, 2, 3, 0, 0, 1, 1, 3, 0, 0,\n",
       "       0, 3, 2, 2, 0, 0, 3, 0, 0, 0, 2, 1, 2, 3, 0, 0, 2, 2, 2, 0, 0, 3, 2,\n",
       "       1])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUCiKGf8H2FwtbrKD+vwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2064d3bdc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')\n",
    "\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### k-means on digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=10, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clusters = kmeans.fit_predict(digits.data)\n",
    "# predicting which cluster the data belongs to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 7, ..., 7, 3, 3])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.00000000e+00,  -9.99200722e-16,   3.07228916e-01,\n",
       "          7.16265060e+00,   1.19457831e+01,   1.93373494e+00,\n",
       "          1.50602410e-01,   5.42168675e-02,  -2.25514052e-17,\n",
       "          1.20481928e-02,   3.28313253e+00,   1.37951807e+01,\n",
       "          8.46385542e+00,   1.53012048e+00,   9.75903614e-01,\n",
       "          2.77108434e-01,  -1.12757026e-17,   6.44578313e-01,\n",
       "          1.07108434e+01,   1.15602410e+01,   4.33734940e+00,\n",
       "          5.25903614e+00,   3.87349398e+00,   3.25301205e-01,\n",
       "          6.02409639e-03,   4.75903614e+00,   1.47590361e+01,\n",
       "          5.86144578e+00,   6.87951807e+00,   1.07530120e+01,\n",
       "          6.21686747e+00,   1.80722892e-02,   0.00000000e+00,\n",
       "          8.92771084e+00,   1.48072289e+01,   9.35542169e+00,\n",
       "          1.28132530e+01,   1.43915663e+01,   5.48192771e+00,\n",
       "          0.00000000e+00,   9.63855422e-02,   6.50602410e+00,\n",
       "          1.16867470e+01,   1.23433735e+01,   1.48373494e+01,\n",
       "          1.07590361e+01,   1.56626506e+00,  -4.16333634e-17,\n",
       "          6.02409639e-02,   1.11445783e+00,   3.00000000e+00,\n",
       "          7.77710843e+00,   1.40843373e+01,   4.13253012e+00,\n",
       "          1.80722892e-02,  -4.71844785e-16,  -1.40946282e-18,\n",
       "          2.40963855e-02,   3.73493976e-01,   7.96385542e+00,\n",
       "          1.23253012e+01,   1.77108434e+00,  -5.77315973e-15,\n",
       "         -6.66133815e-16],\n",
       "       [  0.00000000e+00,   5.96590909e-01,   8.71590909e+00,\n",
       "          1.46306818e+01,   1.40454545e+01,   7.00568182e+00,\n",
       "          5.45454545e-01,  -2.77555756e-16,   1.13636364e-02,\n",
       "          4.20454545e+00,   1.26022727e+01,   9.07386364e+00,\n",
       "          1.12386364e+01,   1.19602273e+01,   1.84659091e+00,\n",
       "          1.13636364e-02,   5.68181818e-03,   1.85795455e+00,\n",
       "          3.53977273e+00,   3.47159091e+00,   1.17897727e+01,\n",
       "          9.92613636e+00,   8.57954545e-01,   6.93889390e-17,\n",
       "         -2.81892565e-18,   3.97727273e-02,   9.09090909e-01,\n",
       "          8.30681818e+00,   1.38295455e+01,   6.76704545e+00,\n",
       "          3.06818182e-01,  -5.63785130e-18,   0.00000000e+00,\n",
       "          6.25000000e-02,   6.81818182e-01,   4.60227273e+00,\n",
       "          1.17613636e+01,   1.23125000e+01,   2.32954545e+00,\n",
       "          0.00000000e+00,  -2.25514052e-17,   4.54545455e-01,\n",
       "          1.44318182e+00,   6.93181818e-01,   4.17613636e+00,\n",
       "          1.23693182e+01,   6.36363636e+00,   5.68181818e-03,\n",
       "         -2.34187669e-17,   9.26136364e-01,   7.28409091e+00,\n",
       "          6.59090909e+00,   8.59659091e+00,   1.37215909e+01,\n",
       "          6.09090909e+00,   1.76136364e-01,  -1.40946282e-18,\n",
       "          4.77272727e-01,   9.52840909e+00,   1.49829545e+01,\n",
       "          1.41420455e+01,   8.88068182e+00,   1.85227273e+00,\n",
       "          4.20454545e-01],\n",
       "       [  0.00000000e+00,   1.10738255e+00,   1.00268456e+01,\n",
       "          1.34093960e+01,   1.41610738e+01,   1.25369128e+01,\n",
       "          4.37583893e+00,   4.02684564e-02,   6.71140940e-03,\n",
       "          4.55704698e+00,   1.49328859e+01,   1.25637584e+01,\n",
       "          8.70469799e+00,   7.03355705e+00,   2.47651007e+00,\n",
       "          3.35570470e-02,   1.34228188e-02,   6.07382550e+00,\n",
       "          1.45302013e+01,   5.95302013e+00,   1.97315436e+00,\n",
       "          1.02684564e+00,   2.01342282e-01,   1.38777878e-16,\n",
       "          6.71140940e-03,   5.30201342e+00,   1.43355705e+01,\n",
       "          1.23624161e+01,   7.85906040e+00,   2.26174497e+00,\n",
       "          1.47651007e-01,  -5.20417043e-18,   0.00000000e+00,\n",
       "          1.94630872e+00,   8.15436242e+00,   1.00939597e+01,\n",
       "          1.02684564e+01,   5.51006711e+00,   6.37583893e-01,\n",
       "          0.00000000e+00,  -2.08166817e-17,   3.02013423e-01,\n",
       "          1.39597315e+00,   4.87248322e+00,   9.87248322e+00,\n",
       "          7.02013423e+00,   7.78523490e-01,  -7.63278329e-17,\n",
       "         -1.99493200e-17,   8.05369128e-01,   5.06040268e+00,\n",
       "          9.47651007e+00,   1.21275168e+01,   5.27516779e+00,\n",
       "          4.42953020e-01,  -3.88578059e-16,  -1.30104261e-18,\n",
       "          1.05369128e+00,   1.08926174e+01,   1.45369128e+01,\n",
       "          7.83892617e+00,   1.08724832e+00,   2.01342282e-02,\n",
       "         -6.10622664e-16],\n",
       "       [  0.00000000e+00,   1.96000000e-01,   6.47600000e+00,\n",
       "          1.24800000e+01,   1.18560000e+01,   5.69600000e+00,\n",
       "          6.84000000e-01,   8.00000000e-03,   4.00000000e-03,\n",
       "          2.59200000e+00,   1.39640000e+01,   9.25200000e+00,\n",
       "          9.44000000e+00,   1.04560000e+01,   1.33600000e+00,\n",
       "          4.00000000e-03,  -1.60461922e-17,   4.28000000e+00,\n",
       "          1.28560000e+01,   4.50800000e+00,   6.89200000e+00,\n",
       "          1.11400000e+01,   1.90400000e+00,  -1.52655666e-16,\n",
       "         -6.50521303e-19,   2.31200000e+00,   1.03960000e+01,\n",
       "          1.17840000e+01,   1.31640000e+01,   1.20560000e+01,\n",
       "          2.45600000e+00,  -1.30104261e-18,   0.00000000e+00,\n",
       "          3.00000000e-01,   3.17200000e+00,   6.17200000e+00,\n",
       "          6.84000000e+00,   1.12400000e+01,   4.23200000e+00,\n",
       "          0.00000000e+00,  -5.20417043e-18,   2.28000000e-01,\n",
       "          2.37200000e+00,   1.98400000e+00,   1.76000000e+00,\n",
       "          1.10040000e+01,   6.37600000e+00,   1.60000000e-02,\n",
       "         -3.03576608e-17,   7.60000000e-01,   8.10400000e+00,\n",
       "          5.66400000e+00,   4.76400000e+00,   1.22440000e+01,\n",
       "          5.97600000e+00,   1.12000000e-01,  -3.25260652e-19,\n",
       "          1.68000000e-01,   6.38800000e+00,   1.34680000e+01,\n",
       "          1.44960000e+01,   9.94800000e+00,   2.30000000e+00,\n",
       "          1.12000000e-01],\n",
       "       [  0.00000000e+00,   1.60194175e-01,   4.88834951e+00,\n",
       "          1.28689320e+01,   1.40631068e+01,   1.09805825e+01,\n",
       "          4.98543689e+00,   9.41747573e-01,  -2.86229374e-17,\n",
       "          1.12135922e+00,   1.06553398e+01,   1.15145631e+01,\n",
       "          1.04320388e+01,   1.25825243e+01,   5.52427184e+00,\n",
       "          5.38834951e-01,  -1.43114687e-17,   1.17961165e+00,\n",
       "          5.45145631e+00,   2.30097087e+00,   6.78640777e+00,\n",
       "          1.15339806e+01,   3.42233010e+00,   1.11650485e-01,\n",
       "         -3.25260652e-18,   9.75728155e-01,   5.04368932e+00,\n",
       "          6.50000000e+00,   1.21553398e+01,   1.21067961e+01,\n",
       "          4.79611650e+00,   4.85436893e-03,   0.00000000e+00,\n",
       "          1.44660194e+00,   8.63592233e+00,   1.30679612e+01,\n",
       "          1.46796117e+01,   1.06893204e+01,   3.93203883e+00,\n",
       "          0.00000000e+00,  -2.60208521e-17,   1.08252427e+00,\n",
       "          5.10194175e+00,   1.14611650e+01,   1.10388350e+01,\n",
       "          3.73300971e+00,   5.38834951e-01,   1.38777878e-17,\n",
       "         -2.68882139e-17,   1.01941748e-01,   2.98058252e+00,\n",
       "          1.22572816e+01,   6.39805825e+00,   4.56310680e-01,\n",
       "          9.70873786e-03,  -5.82867088e-16,  -1.62630326e-18,\n",
       "          1.26213592e-01,   6.09223301e+00,   1.19660194e+01,\n",
       "          2.70388350e+00,   2.86407767e-01,   3.39805825e-02,\n",
       "         -7.21644966e-16],\n",
       "       [  0.00000000e+00,   2.23463687e-02,   4.22905028e+00,\n",
       "          1.31396648e+01,   1.12681564e+01,   2.93854749e+00,\n",
       "          3.35195531e-02,  -2.77555756e-16,  -2.51534904e-17,\n",
       "          8.82681564e-01,   1.26201117e+01,   1.33687151e+01,\n",
       "          1.14078212e+01,   1.13687151e+01,   9.60893855e-01,\n",
       "          3.60822483e-16,  -1.25767452e-17,   3.72625698e+00,\n",
       "          1.42122905e+01,   5.25139665e+00,   2.10614525e+00,\n",
       "          1.21173184e+01,   3.53072626e+00,   5.55111512e-17,\n",
       "         -2.81892565e-18,   5.29608939e+00,   1.26424581e+01,\n",
       "          2.03351955e+00,   2.29050279e-01,   9.07821229e+00,\n",
       "          6.47486034e+00,  -5.63785130e-18,   0.00000000e+00,\n",
       "          5.88268156e+00,   1.14916201e+01,   8.65921788e-01,\n",
       "          3.35195531e-02,   8.81005587e+00,   7.15083799e+00,\n",
       "          0.00000000e+00,  -2.25514052e-17,   3.51396648e+00,\n",
       "          1.32849162e+01,   1.65921788e+00,   1.49162011e+00,\n",
       "          1.13519553e+01,   5.84357542e+00,  -2.08166817e-17,\n",
       "         -2.42861287e-17,   8.04469274e-01,   1.31117318e+01,\n",
       "          9.96089385e+00,   1.03519553e+01,   1.32960894e+01,\n",
       "          2.47486034e+00,   2.23463687e-02,  -1.40946282e-18,\n",
       "          5.58659218e-03,   4.19553073e+00,   1.35865922e+01,\n",
       "          1.33407821e+01,   5.48044693e+00,   3.18435754e-01,\n",
       "          1.67597765e-02],\n",
       "       [  0.00000000e+00,   2.77555756e-16,   3.33333333e-02,\n",
       "          1.68888889e+00,   1.10333333e+01,   1.25000000e+01,\n",
       "          4.14444444e+00,   2.44444444e-01,   2.60208521e-18,\n",
       "          5.55555556e-02,   1.87777778e+00,   8.85555556e+00,\n",
       "          1.39333333e+01,   1.23888889e+01,   5.10000000e+00,\n",
       "          3.22222222e-01,   1.30104261e-18,   1.62222222e+00,\n",
       "          8.74444444e+00,   1.27555556e+01,   1.23000000e+01,\n",
       "          1.29111111e+01,   3.82222222e+00,   1.44444444e-01,\n",
       "         -1.30104261e-18,   3.62222222e+00,   1.21222222e+01,\n",
       "          1.19111111e+01,   1.31222222e+01,   1.35000000e+01,\n",
       "          2.50000000e+00,  -2.60208521e-18,   0.00000000e+00,\n",
       "          1.92222222e+00,   6.85555556e+00,   7.23333333e+00,\n",
       "          1.16777778e+01,   1.25666667e+01,   1.84444444e+00,\n",
       "          0.00000000e+00,  -1.04083409e-17,   8.11111111e-01,\n",
       "          1.97777778e+00,   3.76666667e+00,   1.16666667e+01,\n",
       "          1.18666667e+01,   1.21111111e+00,  -1.38777878e-17,\n",
       "         -4.33680869e-18,   6.66666667e-02,   3.22222222e-01,\n",
       "          2.91111111e+00,   1.24777778e+01,   1.17111111e+01,\n",
       "          1.67777778e+00,   8.32667268e-17,  -6.50521303e-19,\n",
       "          5.55111512e-16,   5.32907052e-15,   1.76666667e+00,\n",
       "          1.12888889e+01,   1.07666667e+01,   1.74444444e+00,\n",
       "         -3.33066907e-16],\n",
       "       [  0.00000000e+00,   1.11607143e-01,   3.99107143e+00,\n",
       "          1.18839286e+01,   1.23214286e+01,   5.36160714e+00,\n",
       "          4.33035714e-01,  -3.33066907e-16,   8.92857143e-03,\n",
       "          8.57142857e-01,   8.24553571e+00,   1.35446429e+01,\n",
       "          1.25625000e+01,   9.87500000e+00,   1.56696429e+00,\n",
       "          4.30211422e-16,  -1.51788304e-17,   1.21428571e+00,\n",
       "          8.37053571e+00,   1.18839286e+01,   1.23348214e+01,\n",
       "          9.46428571e+00,   1.02678571e+00,  -9.02056208e-17,\n",
       "         -3.25260652e-18,   9.41964286e-01,   7.26339286e+00,\n",
       "          1.40848214e+01,   1.41785714e+01,   4.95535714e+00,\n",
       "          2.00892857e-01,  -6.50521303e-18,   0.00000000e+00,\n",
       "          7.67857143e-01,   8.00446429e+00,   1.47812500e+01,\n",
       "          1.29107143e+01,   2.18750000e+00,   1.78571429e-02,\n",
       "          0.00000000e+00,  -2.60208521e-17,   1.22767857e+00,\n",
       "          1.04553571e+01,   1.20178571e+01,   1.21294643e+01,\n",
       "          4.02678571e+00,   2.00892857e-01,   3.46944695e-17,\n",
       "          1.33928571e-02,   8.75000000e-01,   9.57142857e+00,\n",
       "          1.15714286e+01,   1.20758929e+01,   5.57589286e+00,\n",
       "          6.16071429e-01,   4.46428571e-03,   4.46428571e-03,\n",
       "          1.11607143e-01,   4.20089286e+00,   1.20089286e+01,\n",
       "          1.25982143e+01,   4.85714286e+00,   8.08035714e-01,\n",
       "          8.92857143e-03],\n",
       "       [  0.00000000e+00,   9.42857143e-01,   1.01885714e+01,\n",
       "          1.44400000e+01,   7.77142857e+00,   9.82857143e-01,\n",
       "         -1.33226763e-15,  -2.77555756e-16,   2.28571429e-02,\n",
       "          5.24000000e+00,   1.37200000e+01,   1.26228571e+01,\n",
       "          1.16914286e+01,   3.23428571e+00,   1.71428571e-02,\n",
       "          3.60822483e-16,   1.14285714e-02,   4.56000000e+00,\n",
       "          8.11428571e+00,   6.13714286e+00,   1.21600000e+01,\n",
       "          3.56000000e+00,   1.71428571e-02,   7.63278329e-17,\n",
       "         -2.81892565e-18,   9.65714286e-01,   2.81714286e+00,\n",
       "          7.00571429e+00,   1.25371429e+01,   2.56000000e+00,\n",
       "          4.00000000e-02,  -5.63785130e-18,   0.00000000e+00,\n",
       "          4.57142857e-02,   1.57142857e+00,   9.89714286e+00,\n",
       "          1.06971429e+01,   1.45142857e+00,  -7.10542736e-15,\n",
       "          0.00000000e+00,  -2.25514052e-17,   2.51428571e-01,\n",
       "          4.45714286e+00,   1.12457143e+01,   7.74285714e+00,\n",
       "          2.37142857e+00,   8.45714286e-01,   1.14285714e-02,\n",
       "         -2.34187669e-17,   1.19428571e+00,   1.09942857e+01,\n",
       "          1.37314286e+01,   1.19257143e+01,   1.11600000e+01,\n",
       "          7.66857143e+00,   1.10285714e+00,  -1.40946282e-18,\n",
       "          9.31428571e-01,   1.03885714e+01,   1.44685714e+01,\n",
       "          1.35028571e+01,   1.23542857e+01,   8.96571429e+00,\n",
       "          2.95428571e+00],\n",
       "       [  0.00000000e+00,  -1.16573418e-15,   1.15934066e+00,\n",
       "          1.12252747e+01,   9.53296703e+00,   1.41758242e+00,\n",
       "          5.49450549e-03,  -3.05311332e-16,  -2.51534904e-17,\n",
       "          6.04395604e-02,   7.18131868e+00,   1.45604396e+01,\n",
       "          6.19230769e+00,   8.29670330e-01,   2.74725275e-02,\n",
       "          3.74700271e-16,  -1.25767452e-17,   7.69230769e-01,\n",
       "          1.24560440e+01,   9.47252747e+00,   9.34065934e-01,\n",
       "          1.09890110e-01,   0.00000000e+00,   4.16333634e-17,\n",
       "         -3.03576608e-18,   2.29670330e+00,   1.36208791e+01,\n",
       "          8.09340659e+00,   3.87362637e+00,   1.92857143e+00,\n",
       "          1.04395604e-01,  -6.07153217e-18,   0.00000000e+00,\n",
       "          3.52747253e+00,   1.46758242e+01,   1.29175824e+01,\n",
       "          1.22527473e+01,   1.02857143e+01,   2.71978022e+00,\n",
       "          0.00000000e+00,  -2.42861287e-17,   1.86813187e+00,\n",
       "          1.45164835e+01,   1.06538462e+01,   5.57692308e+00,\n",
       "          1.01923077e+01,   9.13186813e+00,   2.30769231e-01,\n",
       "         -2.42861287e-17,   1.75824176e-01,   1.02857143e+01,\n",
       "          1.26263736e+01,   5.41758242e+00,   1.13241758e+01,\n",
       "          1.08956044e+01,   6.26373626e-01,  -1.51788304e-18,\n",
       "         -6.10622664e-16,   1.44505495e+00,   1.07362637e+01,\n",
       "          1.50989011e+01,   1.31318681e+01,   4.62087912e+00,\n",
       "          1.70329670e-01]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAckAAACxCAYAAACvOUZpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2064ef86e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ig, ax = plt.subplots(2, 5, figsize=(8, 3))\n",
    "centers = kmeans.cluster_centers_.reshape(10, 8, 8)\n",
    "for axi, center in zip(ax.flat, centers):\n",
    "    axi.set(xticks=[], yticks=[])\n",
    "    axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above\n",
    "* The data is ordered by cluster number\n",
    "* label is the right information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = np.zeros_like(clusters)\n",
    "# create a zero array lemgth data of cluster size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 7, ..., 7, 3, 3])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False, ..., False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "clusters == 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels\n",
    "mask = (clusters == 0)\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[mask][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    mask = (clusters == i)\n",
    "    # First time : All the zeros are marked True\n",
    "    # Second time : All the ones are marked True\n",
    "    labels[mask]= mode(digits.target[mask])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79354479688369506"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(digits.target, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 8, 8, ..., 8, 9, 9])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAFXCAYAAAAWMQ0YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdcleX/x/HX4TAVkJjKEFFxm9bXrNzbhubKtOFKy5Ej\nUwMFSVFRDPe2LEPNiZp7Re6vuVLTyhyooEzBwZID5/z+8Cs/F1F5rnMUPs/Hwz/OfeP9vq77vvmc\n+9yc+7o0BoPBgBBCCKOyMHcDhBCiKJLiKoQQCkhxFUIIBaS4CiGEAlJchRBCASmuQgihgKW5G/A4\nz/s2Nkvu0V/XmiUXc3wbTqMxfWZxZK5vOsrxNQlrR5cC18mVqxBCKCDFVQghFJDiKoQQCkhxFUII\nBaS4CiGEAlJchRBCASmuQgihgBRXIYRQQIqrEEIo8FQ+ofV3jIsI5PyfMXy3cCVT5o3Fx9crf52X\nTxmO/XyS6eELmDRjdP5yrVaLf5XyDO0bzI/b9hmlHXv3H2D6nPnocnT4+1cgNHgU9vYljbLtwhgM\nBoJDJ+BfoTw9P3jPJJnm6m9xywU5vqpzVWc+c1eufhV9+Xr5NFq1aZq/bFj/L3jnjT6880YfxgZG\ncPtWOhNGT+Piucv5y995ow8H9x1hyw+7jFZYU9PSGB06gWnhYWyMWoG3lyfTZ881yrYLczHmEn0G\nDGbHrmiT5IH5+lvcckGOr+pcU2QqL656vd6o2+vavT3rV21lx6afHllnaWXJ+KkjmRw6m8T45AfW\nvfjS87R8vTHjRk0xWlsOHjpM9WpV8S3rA0CXTh3ZvG0Hppg5Z/maKNq3fZNWLZopz7rHXP0tbrkg\nx1d1rikyldwWiI2NZeLEiZw+fRpLS0v0ej2VKlVi5MiR+Pn5PdG2J4bMAODl+i8+sq5jlzdJTkwh\nevujV6bDgvozK+JrMtIznyj/fgmJiZT28Mh/7eHuRnpGBhkZmco/0gSNGAbAoSNHlebcz1z9LW65\nIMdXda4pMpUU16CgIIYNG0atWrXyl504cYKRI0eyYsUKFZEAfNC7M6EjIx5ZXus/1XFyLsWW9buM\nmlfQu5yF9pm72/K3mKu/xS3XXIrTfjZFppLW5+TkPFBYAWrXrq0iKl+V6v5YWmo5eujEI+tea9OM\njVHbjf4xo7SHB8kpKfmvk5KTcXR0oISdnVFznhbm6m9xyzWX4rSfTZGppLhWrlyZkSNHsmXLFvbt\n28e2bdsYOXIklStXVhEHQJ2Xa3H44PHHrvvPy7X4+cDj1z2Jeq/U5dTpM1y+EgvAqqj1NG3U0Og5\nTwtz9be45ZpLcdrPpshUcltgzJgx7Nq1i2PHjpGeno69vT1NmzalZcuWKuIAKOvnzdW4hMeu8/Xz\n5loB656Ei7Mz40KC+CwwCJ1Oh4+3F2FjQoye87QwV3+LW665FKf9bIpMjcEUf/r8h2QmAhOQkepN\nQ2YiKNJkJgIhhDAxKa5CCKGAFFchhFBAiqsQQiggxVUIIRSQ4iqEEApIcRVCCAWkuAohhAJSXIUQ\nQgEprkIIoYAUVyGEUOCpHFsg59Z1s+ReWv/o7Aam4PtWI5Nnaiy0Js8EyM3KMEuupZ1p5r16RDEb\nWyDvTrZZcu8kJxf+Qwo4VSt4KFW5chVCCAWkuAohhAJSXIUQQgEprkIIoYAUVyGEUECKqxBCKCDF\nVQghFJDiKoQQCkhxFUIIBZRMrW0Oe/cfYPqc+ehydPj7VyA0eBT29mqeylm0awv7fz+Ng60dAF4u\nbgR07Mr70ybgYu+Y/3MdX21IkxoFP8HxJDZt3c7ipcvRaDTY2toSOGwI1atWUZJ1P1Pu53s2btvJ\n0lVr8l+np2eSlJzMtqjluDg/pzTbHP29x2AwEBw6Af8K5en5wXsmyTRXf6fMmsfOn/bg6OgAQLmy\nPkwONf702lt372PpDxvQoMHWxoZhfXpSyc+P6d9G8vOJk+Tl5fF+u7Z0fK3lE2cVieKampbG6NAJ\nRH69AN+yPkydNYfps+cSHDhCSd7vcVf4vEMXqnr75i+Lu56Mva0dMz8apCTzfjGXrzB11lxWRi7C\nzdWVfQf+y9CAIHZsiFKaa+r9fE/b11rS9n8nuy43l96DPqPX+12UF1Zz9RfgYswlJkyewqnTZ/Cv\nUF55Hpi3vydPn2HS2GBq16yhLOPy1WvMilxKZMQkXJ2f48CxXwgIn0L3ju2IjY/n+xkRZGZl0Sdw\nNJXL+1G9UsUnyisStwUOHjpM9WpV8S3rA0CXTh3ZvG0HKoZN0OXmcjEhnnWH9jPoq1mErfmepJs3\n+CPuChYaC0Yt/ZpBX81k+b5o8vR6o+cDWFtZMWZUAG6urgBUq1qFlOup6HQ6JXn3mHI/F2TxspU4\nOznxdrs2yrPM2d/la6Jo3/ZNWrVopjzrHnP1Nycnhz/OnSNy+Sre6dGHYUFfEJ+QaPQcKytLRg3o\ni+v/3pSrVijP9Rs3+PHgIdo2a4KlVoujvT0tG9Rj2959T5xXJIprQmIipT088l97uLuRnpFBRkam\n0bOup9/i+XLl6d60FTP7DKSKlw8TVi8lT6+ntl9FxnbtycRuH/HLxXNsOvpfo+cDeHmWoVGDesDd\nj44RM2bRpGF9rKyslOTdY8r9/DhpN26yZOUaRgzqb5I8c/Y3aMQw2r7xmvKc+5mrv8kp13npxRcY\n3K8PKxd/Rc3qVfl05GijF3VPd3ca1HkRuPt7M+PbSBq+VIfraTdwd3XJ/zl3F2eSUp588KgiUVwL\nOggWWuN3r7STM2O69sDbxQ2NRkOHVxoQn5ZKLb8K9G3dBitLS+xt7Wj3cn3+e/Y3o+ffLzMri+Gj\nQrgSe5UxQQFKs8C0+/lx1m7cTJMGr+LlWcYkeebur6mZq79enmWYEzGJcmXLotFo6PFuF+KuXuNa\nfIKSvKzsbEZ9OY3YhASCPumLXv9ovy0snrzPSu65duvW7ZGPqAaDAY1Gw4oVK4yeV9rDg1Onz+S/\nTkpOxtHRgRJ2dkbPiklMICYpnmY1X7hvqYHfYi+TdScHP4/S9xZhqXBYv/iERAYNC6B8OV8WzZ2J\nra2Nsqx7TLmfH2d79B4+HzLAJFlg/v6amrn6++f5C/x5/gJtXmuVv8xgMGBpafzylJCcwrCwcMp5\nezE39Atsbawp7ebC9bQb+T+TnJr6wJXsv6XkLWn48OFkZGQwefJkpkyZwpQpU5g6dSpTpkxREUe9\nV+py6vQZLl+JBWBV1HqaNmqoJMtCo2Hhjk0k3EgFYMuxnynnXporyYks27uLPL2eOzodm44eomG1\nmkracPPmLXr1G0Tzpo2ZPGGsSQormHY/P+zW7dvEXr1GrRrVTZIH5u2vOZirvxYWFoRPn83Va/F3\nc9dtwL9ieTzc3Yyac/N2Ov2Cx9D0lbpMGPYptjbWADSqW4eNP/5Ebl4etzMy2LnvII3rvvTEeUqu\nXGvVqkW7du04e/YsLVs++VcaCuPi7My4kCA+CwxCp9Ph4+1F2Bjjf40DwNfdg76t2jJu1RL0egOu\njo4Mb98FxxIlWLBtI4O+mklunp4GVWvQqnYdJW1YuXY9CYmJRO/eS/TuvfnLv5ozHadSpZRkgmn3\n88Ni467h5uKMlYKrmYKYs7/mYK7+VizvR+DQQQwOCEKv1+Pu5sakL4KNnrN22w4SU1LYfegIuw8d\nyV8+44tRxCUk8sHQz9Hl5tKhVQterFHtifNkJoL7yEwE6slMBCYiMxGYhMxEIIQQJibFVQghFJDi\nKoQQCkhxFUIIBaS4CiGEAlJchRBCASmuQgihgBRXIYRQQIqrEEIoIMVVCCEUkOIqhBAKFIlpXozF\n9fmyZsnt03q0yTMX7QwzeSaY8Rl/czHTM/7mYq4xK0p4+5gl96/IlasQQiggxVUIIRSQ4iqEEApI\ncRVCCAWkuAohhAJSXIUQQgEprkIIoYAUVyGEUECKqxBCKFBkntDau/8A0+fMR5ejw9+/AqHBo7C3\nV/M00LZ9B1i2YTMajQYbG2s+69mNqhXK568PjJiBq7MTwz/sYZS8j0f3IO7iNbYs2/nA8sGT+nEj\n5QaRESsA8PQrw4cjP8DWzgaDwcCqOev49effjNKGe0y5nyW3+OQuX7OWVet+QIMGHy9PQgJH4OL8\nnNJM1X0tEleuqWlpjA6dwLTwMDZGrcDby5Pps+cqybp8LZ7ZS5czbdTnRE6eQK+O7Rg5ZWb++qU/\nbOLkH2eNkuVZrjQj5wylbos6j6x784NWVK5d8YFlPUe8y96NBwjuNp6vx0cyMOxjLLTGO8Sm3M+S\nW3xyf/vjLJHfryRywRzWLltMWR9v5ny1SGmmKfpq0uKak5OjZLsHDx2merWq+Ja9+3xxl04d2bxt\nBwYFc8ZbW1oysm8fXJ9zAqBKeT+u37iBLjeXY6d/49DJU7Rv2cwoWS3ebsLeTQc5vOvoA8ur/qcS\nz79aneh1ex9YbqG1oKRDCQBsS9iiy9EZpR33mHI/S27xya1WpTIbVi3Dwd6eO3fukJScjFMpR6WZ\npuirkuIaHR1N06ZNadmyJVu2bMlf3qdPHxVxJCQmUtrDI/+1h7sb6RkZZGRkGj2rjLsb9V+sDYDB\nYGBm5Pc0rPMiN27dZtp3SxkzaABaC+Ps1siIFRzY+vMDy5xcS/HB0C7MDVmEPk//wLrvvlxO2x6v\nM2PjJAJnf8ri8O8f+ZknYcr9LLnFJxfAytKS6D37aNW+M8dOnKLdm28ozTNFX5Xcc50/fz7r169H\nr9czZMgQ7ty5Q4cOHZS9Axa0XWN+JH5YVnY24+YuJOl6Kl8GfMaoKTP5tMf7+Ve0Kmi1Fnwy/iOW\nTVvFzeu3HlhnZW3JJ+M/YmHoYk4c+JUKNfz4LOITLv52idSkNKPkm2M/S27Rz72nWeOGNGvckKgf\nNtJ/6HA2rfoeCyNdqDzMFH1VUlytrKwoVaoUAHPnzqVHjx6UKVMGjaLh10p7eHDq9Jn810nJyTg6\nOlDCzk5JXkJKCiPCp1LOy5PZX4zi3KXLXEtOZmbk9wBcv3ETvV5PTo6OUf2Md7XuV7Ucbp4uvPdp\nZwBKuThiYWGBlbUV0Wv3YmNrzYkDvwJw4XQMVy9eo0INP1KjjVNcTb2fJbd45F6JiyPleiov1noe\ngPZt3mD8l1O5dfs2Tv+rI8Zmir4qeVvw8vJi4sSJZGZmYm9vz+zZswkNDeXixYsq4qj3Sl1OnT7D\n5SuxAKyKWk/TRg2VZN1MT2fAmAk0qVuHcZ8OxNbampqV/Plh7gwiJ08gcvIEOrRsRvN6Lxu1sAKc\nP32RT98aSXC38QR3G0/02r38vOsoi8KWkBiXhJ29Hf41735rwd3LFU+/Mlw+e8Vo+abcz5JbfHJT\nUq4TEBJK2o0bAGzZsZOK5f2UFVYwTV+VXLmGhYWxYcOG/CvVMmXKEBkZyYIFC1TE4eLszLiQID4L\nDEKn0+Hj7UXYmBAlWet2/EhiynX2HDnGniPH8pfPGh1IKQcHJZl/R2Z6FjMC5vHBZ12wsrYiLzeP\nbycuJelqitEyTLmfJbf45L5YuxYf9fiA3p98iqWlFjdXF6ZNmqA00xR91RhU/ynwX8i5dd0suekX\nL5gld0hftV87eRxzzUQgija9zrjfUPm7LKyszJJr7ehS4Loi8T1XIYR42khxFUIIBaS4CiGEAlJc\nhRBCASmuQgihgBRXIYRQQIqrEEIoIMVVCCEUkOIqhBAKSHEVQggFpLgKIYQCMrbA/cy0K+6kmb6/\nPTtONHkmwPLdU8ySK0zDoM8zS64+545Zcu3cfQpcJ1euQgihgBRXIYRQQIqrEEIoIMVVCCEUkOIq\nhBAKSHEVQggFpLgKIYQCUlyFEEIBKa5CCKGAkqm1zWHv/gNMnzMfXY4Of/8KhAaPwt6+pEmyDQYD\nwaET8K9Qnp4fvKc8a+yUGVTw9aVb5w6kZ2QQOnUWl2LjMBgMvNmiGT27dDJK1sAxfbhyIY4NS7YB\n8M2PM0lNupG//ofIrezb+t/81+6erkxeNoZxAyK48Pslo7ThHnMdX8lVn7tp63YWL12ORqPB1taW\nwGFDqF61itJMgHMXLjJp+mzSMzLQWmgJHvEp1SpXMtr2i8SVa2paGqNDJzAtPIyNUSvw9vJk+uy5\nJsm+GHOJPgMGs2NXtPKsmCux9A8IZufe/fnL5n23DA9XF1YtnE3krClEbd7Kqd/+eKIcL78yjFnw\nOfVavpS/zNO3NBm3Mhn+bkj+v/sLq5W1FUPG98XSyvjv1+Y6vpKrPjfm8hWmzprLvBkRrF76LR/3\n6s7QgCClmQBZ2dn0/yyQnu91YeU3C/iox/uMCjXuI+EmKa7Z2dnk5OQo2/7BQ4epXq0qvmXvPufb\npVNHNm/bgSmGTVi+Jor2bd+kVYtmyrNWbdhM21YtaNmoQf6y4f0/YsjHHwKQcj2VHJ0O+5Ilnijn\n9Xea89OG/RzceSR/WeVaFdHr9YxdEMDUlePo/NFbWFho8td/FNiNnzbu5/aN9CfKfhxzHV/JVZ9r\nbWXFmFEBuLm6AlCtahVSrqei0+mUZQL89/AxvL3K0PDVlwFo0qAek8eONmqGktsC58+fZ+rUqZQq\nVYq2bdsSHByMhYUFQUFBNG3a1Oh5CYmJlPbwyH/t4e5GekYGGRmZyj/SBI0YBsChI0eV5gAEDOwH\nwJETJ/OXaTQaLLVaRodP4cd9B2lS/xV8vb2eKOfr8KUA1HypWv4yrVbLyUNniJy+Emsba4JmDiUz\nI5vN3++geftGaC217Fq3h0692z5R9uOY6/hKrvpcL88yeHmWAe7e8oqYMYsmDetjZWWlJO+ey7Fx\nuDo7M2ZSBH+ev4iDfUk+7f+xUTOUXLl+8cUX9OzZk7p16zJ48GBWr17N+vXrWbBggYq4At9ZLbRF\n4q7H3zIuYBi7Vi/l1u10vl620ujb37VuD998uYxcXS6Z6ZlsXLqdl5u+iF8VX1q93ZQFYd8ZPfMe\ncx1fyTVNLkBmVhbDR4VwJfYqY4IClOfl5uWy/9BhOrV9k++/nkvXTu0Z+Pkoo37CVrLX9Ho9devW\npUOHDrRo0QIXFxfs7e2xtFTz97PSHh4kp6Tkv05KTsbR0YESdnZK8p4m/z16nOTrd4csLGFnR+sm\njfjj/AWj5zR+sx6+/t7/v0ADebl5NGlTnxIl7Qj7NpiI5aE85+bEkAl9qdOottGyzXV8Jdc0ufEJ\niXTv0x+thQWL5s7E0cFBaR6Am4sL5cr6ULN6VQCaNqyPPk9P3LV4o2UoKa5+fn4EBQWh1+uZNGkS\nAAsXLsT1f/dVjK3eK3U5dfoMl6/EArAqaj1NGzVUkvW02bl3PwuXrsBgMJCTo2Pn3v3Uqf280XN8\nKnjRtV9HLCw0WNtY8XqXFhzYcZhvI75nUIfA/D9ypSXfYEbQAo7uPWG0bHMdX8lVn3vz5i169RtE\n86aNmTxhLLa2Nkrz7mnwSl2uJSTy29k/ATh24hRoNHiVKWO0DCWXkuPHjyc6OhoLi/+v3R4eHnTr\n1k1FHC7OzowLCeKzwCB0Oh0+3l6EjQlRkvW0Gfrxh4TNnEeXvoPQaDQ0qfcy77Y3/n3PVQt/oE/A\nB0xdNR5LSy0Hdx1h17o9Rs95HHMdX8lVn7ty7XoSEhOJ3r2X6N1785d/NWc6TqVKKct1dXFmWthY\nwqbMJCs7G2srK6ZO+AIbG2ujZchMBPeTmQiUk5kIijaZieD/FZ+/+AghhAlJcRVCCAWkuAohhAJS\nXIUQQgEprkIIoYAUVyGEUKDA77keOXKkoFUAvPTSS3+5XgghirMCi+vMmTML/E8ajYbIyEglDRJC\niKKgwOK6ZMkSU7ZDCCGKlELvuV69epVevXrRqlUrkpOT6d69O3FxcaZomxBCPLMKLa4hISH07t2b\nEiVK4OrqSps2bQgIUD8kmBBCPMsKHbglLS2NBg0aEBERgUaj4Z133mHZsmVqW2Wu4Q40msJ/RgEb\nZzWjhf0Vcz3j/8arfc2Su/mAaab9eZjGQmuWXHMxV3+1tk82+4YKhV652trakpCQgOZ/hefo0aNY\nWxtv5BghhCiKCr1yHTlyJH379uXKlSu0a9eOmzdvMmPGDFO0TQghnlmFFteaNWuyZs0aLl26hF6v\nx8/PT65chRCiEIUW19u3bzNnzhwOHz6MpaUl9erVo2/fvtgVgylUhBDi3yr0nmtQUBBarZaJEycS\nGhpKRkYGo0cbdwpaIYQoagq9cr18+fIDT2sFBQXRtq3xpxERQoiipNArVz8/P3755Zf813/88Qfl\nypVT2SYhhHjmFXjl2qxZMzQaDXfu3GH79u2UL18eCwsLLl68iK+vrynbKIQQzxwZW0AIIRQosLh6\neXkBkJOTw549e8jIyAAgLy+PuLg4hgwZYpoW/gMGg4Hg0An4VyhPzw/eM0nm3v0HmD5nProcHf7+\nFQgNHoW9fUnJfQIjJnzCpXOxrF68AYC3urbm9U7Nsbax5txvF5kyei46XS616lbn42HdsbTSkpOd\nw+yJ33D21/NGbcumrdtZvHQ5Go0GW1tbAocNoXrVKkbNeJyifHyfllzVmYXecx04cCCRkZFMmzaN\nffv2MWPGDC5cuGC0BhjLxZhL9BkwmB27ok2WmZqWxujQCUwLD2Nj1Aq8vTyZPlv9Y5ZFNbdseS++\n/OYLGreul7+sQYuXaffe63zeJ5Q+7YZiY2tNpx5tsLSyJDjiM6Z9MZ++HYezdEEUgRMHGa0tADGX\nrzB11lzmzYhg9dJv+bhXd4YGBBk143GK6vF9mnJNkVlocY2JiSEyMpKWLVvSp08fVq9eTVJS0t8O\nuH79+hM18O9aviaK9m3fpFWLZibJAzh46DDVq1XFt+zducu7dOrI5m07MCgeG6Go5r717mtsX/cT\ne7YfzF/W8q3GrPluI7dvpmMwGJg+diE7N+wlV5dL12Yfc/6PGADKeHtw60a6Udpxj7WVFWNGBeDm\nenfsh2pVq5ByPRWdTmfUnIcV1eP7NOWaIrPQr2K5uLig0Wjw8/Pj7NmztG/fnpycnAJ/PiYm5oHX\nAQEBhIeHA3e/eaBK0IhhABw6clRZxsMSEhMp7eGR/9rD3Y30jAwyMjKVfqQpqrmzJywC4IVXauYv\n8y5XBqdfSzFxQRAubs78evx3vppy9+8Bebl5OLmUYv7qyTg+58j4YVOfuA338/Isg5dnGeDuLaeI\nGbNo0rA+VlZWRs15WFE9vk9TrikyCy2u/v7+jBs3jnfffZfhw4eTlJT0l+/cvXr1wtbWFnd3dwwG\nAzExMYSEhBTJ2QsKepez0Kqdmqw45WotLflPvecJGRhOTo6Oz8MG0mvIu8ybtBiAG9dv0rVZXypW\n9ePLRV8w8N2RXL0cb9Q2ZGZlMTo0jITEJObNiDDqth+nOB1fc+WaIrPQLY0ZM4bXX3+dihUrMnjw\nYJKSkpgypeDh6qKioqhYsSJ9+/ZlyZIlVKlShSVLlhS5wgpQ2sOD5JSU/NdJyck4OjpQQvGjwcUp\n93pSKvt3HSYzI4tcXS4/btxLtVqVKWlfgvrN6+b/3PnfY7h49jLlK5U1an58QiLd+/RHa2HBorkz\ncXRwMOr2H6c4HV9z5Zois8DieuTIEY4cOcLx48cxGAwcOXIEBwcHWrduzc2bNwvcoIuLC9OnT2f3\n7t3Mnz/faA19GtV7pS6nTp/h8pVYAFZFradpo4aSa0T7dhyicetXsba5O1hQ/eZ1OXv6PHl6PcPH\nDaD6C5UB8K3gjU95T34/dc5o2Tdv3qJXv0E0b9qYyRPGYmtrY7Rt/5XidHzNlWuKTCUTFFpaWhIU\nFMTatWuV3ww3JxdnZ8aFBPFZYBA6nQ4fby/CxoRIrhFtWLEdh1L2zFsdjoWFBed+j2H+5O/Izszm\ni8GTGRDYC62lFl1OLmGfzyAlMdVo2SvXrichMZHo3XuJ3r03f/lXc6bjVKqU0XIeVpyOr7lyTZGp\nMTyF1S/nZkrhP6SCmWYiKE5kJgJRlFg7uhS4Tu2daiGEKKakuAohhAJSXIUQQoEC/6DVrVu3/EkJ\nH6cofrVKCCGMpcDiOmjQ3ee0V61aha2tLe3bt8fS0pJNmzZx584dkzVQCCGeRQUW17p1735BOzw8\nnKioqPzltWvXpmPHjupbJoQQz7BC77neuXPngfECzp49S25urtJGCSHEs67QsQUCAwPp1q0bHh4e\n6PV6UlNT//LxVyGEEH+juDZo0IDo6Gj+/PNPNBoNlStXxtKy0P8mhBDFWqG3BW7evEloaCiTJ0/G\n09OT0aNH/+XYAkIIIf5GcR09ejQ1a9bkxo0blCxZEnd3d0aMGGGKtgkhxDOr0M/3cXFxdOnSheXL\nl2Ntbc3QoUN566231LbKXM/4m2uYBTP016DPM3kmwJb/LjBLbmD7iWbJnbR+pFlyzUWveJaGghjy\nzJPLk4wtoNVquX37dv4DBZcuXcLCQh7sEkKIv1LoleugQYPo1q0b8fHxDBgwgBMnThAWFmaKtgkh\nxDOr0OLaqFEjatSowalTp8jLyyM0NBRHR0dTtE0IIZ5ZhX6+79KlC87OzjRp0oTmzZvj7OxMp06d\nTNE2IYR4ZhV45dq9e3cOHz4MQJUqVfLvuWq1Wpo1M9301UII8SwqsLjeG/Vq/PjxBAcHm6xBQghR\nFBR6W6Bz584MHToUgAsXLvD+++9z8eJF5Q0TQohn2d96iKB9+/YAVKhQgQEDBhAUFKS8YUII8Swr\ntLhmZWXRuHHj/Nf169cnKytLaaOEEOJZV+hXsZydnVm+fHn+U1lbtmzBxaXgpxLMZe/+A0yfMx9d\njg5//wqEBo/C3r6kSbINBgPBoRPwr1Cenh+8Z5JMc/V309btLF66HI1Gg62tLYHDhlC9ahXluabo\nb9fPu5BwKYHdq/ZgW9KWLsPfwb2sOxqNhqM7jhK94icASjjY0WFQBzx8PbCysWLXsl0c23ncqG0x\n1/E15+9R9J59BI8L4+CurSbJO3fhIpOmzyY9IwOthZbgEZ9SrXIlo22/0CvXiRMnsnv3bho0aEDT\npk3ZvXs3EyZMMFoDjCE1LY3RoROYFh7GxqgVeHt5Mn22aaZSvhhziT4DBrNjV7RJ8sB8/Y25fIWp\ns+Yyb0YwWFZ+AAAgAElEQVQEq5d+y8e9ujM0QP0tItX9dS/rTv8p/ajdpFb+std7teZmyg2+7B3B\n9AEzqPfWq/hW8wWga0BXbibfZGrfacwfvoAOA9tTyrWU0dpjruNrzt+jy7FxTJ09D72JHkHPys6m\n/2eB9HyvCyu/WcBHPd5nVKhxH5EutLh6enqyYMECfvnlFw4fPsycOXMoXbr03w7Q6/UkJiai1+uf\nqKF/5eChw1SvVhXfsj4AdOnUkc3bdmAwwYFaviaK9m3fpFUL0309zVz9tbayYsyoANxcXQGoVrUK\nKddT0Sl+nlx1fxu0r8/hbYc5sftk/rJ1s39gw7xNADg6O2BpZUl2RjYlHOyo/J9KbI/cAcDNlJvM\n+GQmmbczjdIWMN/xNVduVnY2o8aOZ/jgT5Tm3O+/h4/h7VWGhq++DECTBvWYPHa0UTMKvC3Qt29f\nFixYQLNmzR47UeGPP/5Y4EZHjRpFWFgYJ0+eZPjw4Tg5OZGRkUFYWBi1a9c2Tsvvk5CYSGkPj/zX\nHu5upGdkkJGRqfwjTdCIYQAcOnJUac79zNVfL88yeHmWAe7eComYMYsmDetjZWWlLBPU93ftzHUA\n+L/g/8ByvV7P+yPf5fnGz/Pr/tMkxSbhU8mbW9dv0bhzI6rWrYKllSU/rdpDclzKE7fjHnMdX3Pl\njguP4O32bfGvWF5ZxsMux8bh6uzMmEkR/Hn+Ig72Jfm0/8dGzSiwuI4bNw6AJUuW/OONxsXFATBt\n2jS++uorypUrR2JiIsOGDWPp0qX/sqkFK+id1UJbNAeYMXd/M7OyGB0aRkJiEvNmRCjPM2d/l01c\nzuppUfQc24NW3Vpy9uifuHi6cCfjDrMGz8HV04WBMz4hJS6ZuHNXjZJprv6aI3dl1Dq0Wi0d2rzJ\n1fh4ZTkPy83LZf+hw3w1I4Ka1avy074DDPx8FFtXL8Pa2tooGQXutYMHD7J+/XqOHDny2H9/h1ar\npVy5cgD508SoUNrDg+SU/79ySEpOxtHRgRJ2dkryzM2c/Y1PSKR7n/5oLSxYNHcmjg4OyjPN0d/K\ndSrh6HJ3DI2c7Bx+if4F7/9dtQIc3n73dyDl2nUuno6hbNWyRss21/E1R+4PW7Zx5vezvNOjNwOH\nBXDnzh3e6dGbpGTjfRJ4HDcXF8qV9aFm9aoANG1YH32enrhrxivwBRbXn3/+mZ9//pnVq1cTERHB\nkSNHOH78ODNnzmTLli1/udH09HQ6duzI1atXWb16NXfu3GHs2LF4enoareH3q/dKXU6dPsPlK7EA\nrIpaT9NGDZVkPQ3M1d+bN2/Rq98gmjdtzOQJY7G1tVGeCebpb+0mtWjdvSUAWisttZvU4twv50hN\nSCX2zzheal0HAPvn7PGrXo7Ys3FGyzbX8TVH7veLFrB22WJWfbeI2VPCsbGxYdV3i3B3c1Wa2+CV\nulxLSOS3s38CcOzEKdBo8CpTxmgZBd4WmDjx7l/OunXrxoYNG3B2dgbuTvvyySd/feN57dq15OTk\n8Mcff2Bra4tGo6FSpUq8/fbbRmv4/VycnRkXEsRngUHodDp8vL0IGxOiJOtpYK7+rly7noTERKJ3\n7yV699785V/NmY5TKeP9tfxh5ujvD/M20nloJ0YsGg4GA78eOM2+qP0AfBuymE5DOlKv7atoNBp2\nRO4k9mys0bLNdXyL0++Rq4sz08LGEjZlJlnZ2VhbWTF1whfY2BjnlgCAxlDInwJbt27N1q1b8wfI\nzsnJoW3btmzfvt1ojXhYzq3ryrb9l2QmAuU0Flqz5MpMBKZR3GYisHP3KXBdoQ8RNGnShF69etGq\nVSv0ej3btm3j9ddfN2oDhRCiqCm0uI4cOZLt27dz+PBhNBoNH374Ic2bNzdF24QQ4plVaHEFcHV1\npWLFinTs2JFTp06pbpMQQjzzCv0C23fffcf06dNZvHgxWVlZhISEsGjRIlO0TQghnlmFFtd169ax\naNEi7OzscHJyYs2aNURFRZmibUII8cwqtLhaWFg88MSCjY0NWq15/uIrhBDPikLvudatW5fw8HCy\nsrLYtWsXK1eu5JVXXjFF24QQ4plV6JXr559/jq+vL5UrV2b9+vU0btyYgIAAU7RNCCGeWYVeufbp\n04dvvvmGrl27mqI9QghRJBR65ZqdnU28CUerEUKIoqDQK9e0tDSaNWuGi4sLNjY2GAwGNBrNX47n\nKoQQxV2hYwtcvfr4MSq9vLyUNAjMN7ZAcXvevlgx07gRdZ7vZJbco7+uNUtucfsdsnYseD7BQq9c\n3d3dWbZsGYcOHcLS0pLGjRsrG91KCCGKikKLa3BwMNnZ2bzzzjvo9Xp++OEH/vzzT4KC1E9MJ4QQ\nz6pCi+vJkyfZtm1b/utmzZrRpk0bpY0SQohnXaHfFihTpgyXL1/Of52SkoLHfZOYCSGEeFShV665\nubm0a9eOOnXqYGlpybFjx3Bzc6N79+4AREZGKm+kEEI8awotroMGDXrg9YcffqisMUIIUVT8rbEF\nhBBC/DOmmeheCCGKGSmuQgihwN+a5uVZsHf/AabPmY8uR4e/fwVCg0dhb19See6mrdtZvHQ5Go0G\nW1tbAocNoXrVKspzzdXf4pYLYDAYCA6dgH+F8vT84D2jb39cRCDn/4zhu4UrmTJvLD6+///0o5dP\nGY79fJLp4QuYNGN0/nKtVot/lfIM7RvMj9v2Ga0t5tjPRfV3qNDHX83hnz7+mpqWRocu7xP59QJ8\ny/owddYcMjMyCQ4c8Y+2808f3Yu5fIXe/QexMnIRbq6u7DvwX8aFR7Bjwz+bqeGfPrpnrP7+U890\n7r88zS/GXGLC5CmcOn2GT/r2+cfF9a8ef/Wr6EvQuE+p+UI15k79hu8WrnxgffXnqzBl3lh6vD2Q\nxPjkB9YNCx6Am7sLgYPHPXbb/+bxV2Ps5+L2O/RXj7+a5LZAamoqKmv4wUOHqV6tKr5l784h3qVT\nRzZv26E0E8DayooxowJwc3UFoFrVKqRcT0WneO52c/W3uOUCLF8TRfu2b9KqRTOjb7tr9/asX7WV\nHZt+emSdpZUl46eOZHLo7EcK64svPU/L1xszbtQUo7bHHPu5KP8OKbktEBUVRXx8PE2bNmXYsGHY\n2NiQnZ3NF198Qb169Yyel5CYSOn7HmzwcHcjPSODjIxMpR9pvDzL4OVZBrj70TFixiyaNKyPlZWV\nskwwX3+LWy5A0IhhABw6ctTo254YMgOAl+u/+Mi6jl3eJDkxhejtj37kHxbUn1kRX5ORnmnU9phj\nPxfl3yElxfX7779nyZIl9O/fn3nz5uHn50diYiIDBgxQUlwLerex0Jrm73WZWVmMDg0jITGJeTMi\nlOeZq7/FLdecPujdmdCRj55Ltf5THSfnUmxZv8vomebcz0Xxd0jJXrOysqJEiRKULFkSH5+7l90e\nHh5oNBoVcZT28CA5JSX/dVJyMo6ODpSws1OSd7/4hES69+mP1sKCRXNn4ujgoDzTXP0tbrnmUqW6\nP5aWWo4eOvHIutfaNGNj1HYlH9XNtZ+L6u+QkuLarFkz+vfvj7+/P3379mXx4sX07t1b2cSG9V6p\ny6nTZ7h8JRaAVVHradqooZKs+928eYte/QbRvGljJk8Yi62tjfJMMF9/i1uuudR5uRaHDx5/7Lr/\nvFyLnw88ft2TMsd+Lsq/Q0puC3z88cccPnyY/fv34+npyfXr1+nWrRtNmjRREYeLszPjQoL4LDAI\nnU6Hj7cXYWNClGTdb+Xa9SQkJhK9ey/Ru/fmL/9qznScSpVSlmuu/ha3XHMp6+fN1biEx67z9fPm\nWgHrnpQ59nNR/h0qEl/FMpbiNop6sSIzEZhEcfsdMvtXsYQQoriR4iqEEApIcRVCCAWkuAohhAJS\nXIUQQgEprkIIoYAUVyGEUECKqxBCKCDFVQghFJDiKoQQCkhxFUIIBWRsgaeAXvGo64/NzM0xeaY5\nWdqZZr6tp0Wdmh3Nkvvz0e/NkmuuMQ1KeJQtcJ1cuQohhAJSXIUQQgEprkIIoYAUVyGEUECKqxBC\nKCDFVQghFJDiKoQQCkhxFUIIBZTM/moOe/cfYPqc+ehydPj7VyA0eBT29uq/OG6u3OVr1rJq3Q9o\n0ODj5UlI4AhcnJ9Tmrlx206WrlqT/zo9PZOk5GS2RS1Xmm2uXCja59W4iEDO/xnDdwtXMmXeWHx8\nvfLXefmU4djPJ5kevoBJM0bnL9dqtfhXKc/QvsH8uG2f0doyZdY8dv60B0dHBwDKlfVhcqjamWdV\nn1dF4gmt1LQ0OnR5n8ivF+Bb1oeps+aQmZFJcOAIRS00bu4/fULrtz/OMmxUCKsiF+Fgb8+UWXPJ\nyMwkJGD43898wie0dLm59B70GW+91oq327V5om2ZIvffPKH1LJ9Xf/WEll9FX4LGfUrNF6oxd+o3\nfLdw5QPrqz9fhSnzxtLj7YEkxic/sG5Y8ADc3F0IHDzusdv+t09ode87kM8G9qN2zRr/6v8/6RNa\n//a8KvJPaB08dJjq1ariW9YHgC6dOrJ52w5Uv2+YK7dalcpsWLUMB3t77ty5Q1JyMk6lHJVmPmzx\nspU4OzmZtLCaOreonlddu7dn/aqt7Nj00yPrLK0sGT91JJNDZz9SWF986Xlavt6YcaOmGKUd9+Tk\n5PDHuXNELl/FOz36MCzoC+ITEo2aURgV55WS4pqenq5iswVKSEyktIdH/msPdzfSMzLIyMgskrkA\nVpaWRO/ZR6v2nTl24hTt3nxDeeY9aTdusmTlGkYM6m+yTHPkFtXzamLIDDat2/HYdR27vElyYgrR\n2x/9yD8sqD+zIr4mI924/U9Ouc5LL77A4H59WLn4K2pWr8qnI0crfxO7R9V5paS41q9fn9WrV6vY\n9GMVdBAstGovzM2Ve0+zxg3Zs3UD/Xv3pP/Q4ej1epPkrt24mSYNXsXLs4xJ8syVWxzPqw96d2bh\nrCWPLK/1n+o4OZdiy/pdRs/08izDnIhJlCtbFo1GQ493uxB39RrX4hOMnvU4qs4rJUerSpUq/P77\n73Tv3p3Dhw+riHhAaQ8PklNS8l8nJSfj6OhACTu7Ipl7JS6O4ydP5b9u3+YN4hMSuXX7ttLce7ZH\n7+GtN1qbJMucucXtvKpS3R9LSy1HD514ZN1rbZqxMWq7kqvJP89fYNO2B6+kDQYDlpam+Xu7qvNK\nSXG1sbEhJCSEESNGsGTJEtq2bcuECROIjIxUEUe9V+py6vQZLl+JBWBV1HqaNmqoJOtpyE1JuU5A\nSChpN24AsGXHTiqW98OpVCnl2bdu3yb26jVq1aiuPMvcucXtvKrzci0OHzz+2HX/ebkWPx94/Lon\nZWFhQfj02Vy9Fg/AqnUb8K9YHg93NyV591N5Xil5a7j37lazZk1mzZrF7du3OXLkCDExMSricHF2\nZlxIEJ8FBqHT6fDx9iJsjNqvcZgz98Xatfioxwf0/uRTLC21uLm6MG3SBOW5ALFx13BzccbKRFcV\n5swtbudVWT9vrsY9/qO4r5831wpY96QqlvcjcOggBgcEodfrcXdzY9IXwUqyHqbyvFLyVax169bR\noUOHf/3/ZbBsE2TKYNlFmgyWbRom/yrWkxRWIYQoCorE91yFEOJpI8VVCCEUkOIqhBAKSHEVQggF\npLgKIYQCUlyFEEIBKa5CCKGAFFchhFBAiqsQQiggxVUIIRSQ4iqEEAoUiTm0jOWHgEcHCTaFN0PM\nMBaDhcb0mYCFpbV5cq2szJJrrgFFzOWHQPMM3NJu0ntmybVxci9wnVy5CiGEAlJchRBCASmuQgih\ngBRXIYRQQIqrEEIoIMVVCCEUkOIqhBAKSHEVQggFTDs/skJ79x9g+pz56HJ0+PtXIDR4FPb2amb8\nrNikFhWb1CIvJ49bCakcXxENwH/ebY6Tjyu5d3K59N8znN99Ukk+QPS+/cz/dgkajQZHBwdCRgzF\nx8tTWR7Axm07WbpqTf7r9PRMkpKT2Ra1HBfn55Rm3xO9Zx/B48I4uGurSfJMeV7db9PW7SxeuhyN\nRoOtrS2Bw4ZQvWqVIpH7Uo9W3LqWwtmdx9FoNNTq3IjS1XzRWFjw585jXNj3KwD27k681K0l1va2\n5N7Rcfjb7dxOTDNaO1T3tUgU19S0NEaHTiDy6wX4lvVh6qw5TJ89l+DAEUbPcqvkTeVWdYievJKs\nG+mUrVuFOu+3IDdHR+6dHLaNXYLGQkP9fm3JSLlF/OkYo7ch+84dgiaEs/Lr+ZT19mLp6igmz5rL\nrEnjjZ51v7avtaTtay0B0OXm0nvQZ/R6v4vJCuvl2Dimzp6H3kQPFZryvLpfzOUrTJ01l5WRi3Bz\ndWXfgf8yNCCIHRuinulch9LP8WLXZriUL82ZaykAlG9UEwd3J7aHLsHS1prmn3chLTaJ1EuJvPzh\na5z78ReuHDlL6erlqNe3DdtDjfMUpSn2sUluC+Tk5JCdna1s+wcPHaZ6tar4lvUBoEunjmzetgMV\nT/Y+V9adpD9iybqRDsDVE+cpU9MPZ18PLv/8BxgMGPL0xJ+OwftFf6PnA+jz9GCA9IwMADKzsrC2\nNu1jpYuXrcTZyYm327UxSV5Wdjajxo5n+OBPTJIHpj2v7mdtZcWYUQG4uboCUK1qFVKup6LT6Z7p\n3IpNanHpv2eIPXYuf5lX7QrEHPwNg96ALvMOsUf/xLduFeycSuLo8RxXjp4FIOHMJSxtrHDycTNK\nW0yxj5VcucbExDBt2jSsrKzo1q0bAQEB5ObmMmzYMN544w2j5yUkJlLawyP/tYe7G+kZGWRkZBr9\nI1zqpUT8m9amhLMDmam3KfdqdbRWlly/GI/vy1VIuXANCyst3i/43y2CCpQoYceozwbTc+BQSjk6\noNfr+XbWNCVZj5N24yZLVq5h+ddzTZY5LjyCt9u3xb9ieZNlmvK8up+XZxm8PMsAYDAYiJgxiyYN\n62OleHwE1bm/rNgNgHuVsvnLSjznQGba7fzXmWm3KeXlit1zDmTdzID73scy025T4jkHbsQmP3Fb\nTLGPlRTX0aNHM2DAAG7fvk3fvn3ZsGEDDg4O9OrVS0lxLehKwkJr/AvzlPNXObP5Z+r1bYvBYODS\nwTPcSc/i1Lr91OzQgJZB75F9M4PE36/gUr6M0fMBzl2M4avvlhG1+Ct8vDz5Pmodw78Yx8qv56HR\nqB+QZe3GzTRp8Gr+yanayqh1aLVaOrR5k6vx8SbJBNOeV4+TmZXF6NAwEhKTmDcjwiSZps593Plq\n0OsLPI8NeuNesKjsq5KzJDc3l3r16tGqVSucnJzw8PCgRIkSWFqqucVb2sOD5JSU/NdJyck4OjpQ\nws7O6FmWNlYkn4tj18Tv+XHScuJ+OZ+//NTafewYt5S9M9dhMBhIT75h9HyA/x4+Sq2a1fL/gNWl\n/VtciLnEjZu3lOQ9bHv0Ht56o7VJsgB+2LKNM7+f5Z0evRk4LIA7d+7wTo/eJCWnFP6fn4Apz6uH\nxSck0r1Pf7QWFiyaOxNHBwflmebIzUy9jV2p//8UYOdkT9aNdDJTb2Pr+OCnAzsnezLT0o2Wrbqv\nSoqrl5cXQ4cOZciQIZQsWZJp06bx1Vdf4eZmnPslD6v3Sl1OnT7D5SuxAKyKWk/TRg2VZNk62dNk\n6NtY2t69x1ntjbrEHj1L+YY1qdH2VQBsHEpQvn4Nrhw5q6QNVSpV5NiJX7meevcvpz/tP4hX6dI8\n51RKSd79bt2+TezVa9SqUV151j3fL1rA2mWLWfXdImZPCcfGxoZV3y3C3c1Vaa4pz6v73bx5i179\nBtG8aWMmTxiLra2N8kxz5V49eQG/etXRWGiwsrOhbJ3KXD1xgawb6aQn38CnTiUAPKr5gsHAzWvG\neUM1RV+VXEqGh4ezZ88eypUrR8mSJVm8eDG2traEhYWpiMPF2ZlxIUF8FhiETqfDx9uLsDEhSrLS\nE9P4Y/tRmn/eFY0FpJy/xvGVP2FhYUHdnq/RavQHgIYzmw+RdjlRSRvqvvgCPbp25qNPh2NpZUUp\nBwemTRijJOthsXHXcHNxxkrRp5CniSnPq/utXLuehMREonfvJXr33vzlX82ZjlMpdW+g5si9sPcU\n9m5OtAr+AAtLCy7u+5Xkc1cBOLRoK3U+aEG11+uSl5vHwYWbH7gH+yRM0VcZLPs+Mli2CWJlsOwi\nTQbL/n/yhJYQQiggxVUIIRSQ4iqEEApIcRVCCAWkuAohhAJSXIUQQgEprkIIoYAUVyGEUECKqxBC\nKCDFVQghFJDiKoQQCsjYAk8Bczx/nnPDeHMR/RM2zmpHsipI4t6fzZLr0ehls+Say5VNe8ySO33B\n3sJ/SIHZe6YXuE6uXIUQQgEprkIIoYAUVyGEUECKqxBCKCDFVQghFJDiKoQQCkhxFUIIBaS4CiGE\nAlJchRBCgSIzP/Le/QeYPmc+uhwd/v4VCA0ehb19ySKbu2nrdhYvXY5Go8HW1pbAYUOoXrWKsjyD\nwcDYKTOo4OtLt84dSM/IIHTqLC7FxmEwGHizRTN6dumkLN/U+/nAr78SvnwZG8ImkZ6VxZRVK4hN\nSkJvMNCqzkt0bdZcWTYU/fP56+2b2f/br9jblQDA29WVQW06MmNDFHEpyegNBprXepHODRobLfOD\nwPeIj4nnx5U/YVvSlvc/74pHWQ80Fhp+3naEXct/BMD/hYp0HNAOC62WjFsZRM1ax9UL1/5xXpG4\nck1NS2N06ASmhYexMWoF3l6eTJ89t8jmxly+wtRZc5k3I4LVS7/l417dGRoQpC7vSiz9A4LZuXd/\n/rJ53y3Dw9WFVQtnEzlrClGbt3Lqtz+U5Jt6P8clJ7Ng4wb0/3syfPG2rbiVcuLrEQHMGTKUjQcP\n8NulS8ryi8P5/HvcZT5/+11m9xvM7H6DCXz7PZb8tANXR0fmDviU6R99wpajh/g99vITZ3n4ejBo\n2gBebFo7f1mb3m9wI/kmYb3C+bLvVBq2q49f9XLYlrTlo3Efsm7eBiZ+OJmVU1fz4ZieWFpp/3Gu\n8uJqiqELDh46TPVqVfEt6wNAl04d2bxth/Jsc+VaW1kxZlQAbq53n9OvVrUKKddT0el0SvJWbdhM\n21YtaNmoQf6y4f0/YsjHHwKQcj2VHJ0O+5IllOSbcj9n5+Qw6ful9HurXf6yT9p3oG/btwBIvX0L\nXW4uJW1tjZ59T1E/n3W5uVyIj2ftwX0MnD+DCauWknTzBn1fa0vvVm8AkJp+C11enlH2c6P2DTi0\n9TDHfzqRv2zNzLWsm/cDAI4ujlhaa8lKz8Ld242s9Gz+PH4OgMQrSWRnZuNX3e8f5yq5LXDlyhXG\njh3LxYsXSUpKonr16vj4+BAYGIibm5vR8xISEynt4ZH/2sPdjfSMDDIyMpV+lDJXrpdnGbw8ywB3\n37wiZsyiScP6WFlZKckLGNgPgCMnTuYv02g0WGq1jA6fwo/7DtKk/iv4enspyTflfp6+ZhVvvlKP\n8p6e+cs0Gg1arZaJy5ay99RJGtSoibe7u1Fz71fUz+frt29Ry688PZu3xsvFlbUH9zFuRSQzPx6E\n1kLLl2tXcuC307xatRpeLk9eL1bPiAKg8ouVHliuz9PTPegDXmhci5P7fyUxNgkbW2ts7KypUqcy\nfxw9S9kqPpQpVxpHF8d/nKvkynXs2LEEBwfz008/sWzZMl5++WV69epFUJCaj64FvbNaaNVemJsr\n957MrCyGjwrhSuxVxgQFmCTzYeMChrFr9VJu3U7n62UrlWSYaj//cGA/Wgstr7/8+JGsRr7/AWtD\nx3MrM5OlO7YbNft+Rf18Lv2cM2Pf74W3qxsajYaO9RoSn5pK4v9GahvRsQvLPw8mPSuL5Xt+NGr2\nwyInLCWgXRAlHUrweo/WZGfeYWHQIlp/0JLARSN4ufVL/Hn8HHm63H+8bSVHKz09HT+/u5fRtWvX\n5vjx49SoUYNbt26piKO0hwfJKSn5r5OSk3F0dKCEnZ2SPHPnAsQnJNK9T3+0FhYsmjsTRwcH5Zn3\n++/R4yRfvzs0ZAk7O1o3acQf5y8oyTLVft5x5AhnY6/Qd8qXjPp6ITk6HX2nfMmOI4dJuXkTADsb\nG5q98CLnrsYZNft+Rf18jkmMJ/rk8YeWGjh9OYbrt+/WCDtrGxrVqMWFhH/+h6S/o+pLVSj1v6vR\nnKwcjv54HJ9K3mg0Gu5k3WHGp7OZ1PtLVs9Yi6uXK8lXUwrZ4qOUFFdvb29CQkLYtWsXwcHB1KhR\ng927d2On6OSo90pdTp0+w+UrsQCsilpP00YNlWQ9Dbk3b96iV79BNG/amMkTxmJra6M882E79+5n\n4dIVGAwGcnJ07Ny7nzq1n1eSZar9POfToXw9IoAFw0YQ1udjrK2sWDBsBKcuXmTJju13+5qby56T\nJ6hd0d/o+fcU9fNZo9Ewf9tGEtJSAdh89BDlPMpw+vIlvt/9IwaDAV1uLvvPnOL5chWMng/wQtPa\nvN7zNQAsrbS82LQ2fx4/h8FgoH/4x5StfPe+8wtNapGXm/evvi2gZLDsnJwcVq9ezfnz56latSqd\nOnXi119/xdfXl+eee67w//8vBsvee+AgM+bMR6fT4ePtRdiYEEqV+uf3ScyR+08Hy174bSRzFy7C\nv0L5B5Z/NWc6TqVK/a1t/JvBssdETM//Ktbt9HTCZs7jwqXLaDQamtR7mb7d3sPC4q/fr//tYNlP\nup//6WDZCamp9PkynE0Tw0nPymL6mtVcSogHNNSvUYMerV8rtK/w7wfLflbP5787WHb0qV9Ys38P\neQY9ro6lGPJWJ0rY2DBn03ouJyWCBl6tXI33m7bAQlP4fv47g2Xf/1UsO3s7un7WmTJ+ZQADJ/f9\nypZvt2EwGKhYqwKdBnXA0lLLzeu3WB6xiuvxj69JfzVYtsxE8BSQmQjUk5kITENmIvh/ReJ7rkII\n8bSR4iqEEApIcRVCCAWkuAohhAJSXIUQQgEprkIIoYAUVyGEUECKqxBCKCDFVQghFJDiKoQQCkhx\nFTll23QAAAgDSURBVEIIBZ7KsQWEEOJZJ1euQgihgBRXIYRQQIqrEEIoIMVVCCEUkOIqhBAKSHEV\nQggFLM3dAGPR6/WMGTOGs2fPYm1tzfjx4/H19TVJ9smTJ4mIiGDJkiUmydPpdIwaNYqrV6+Sk5ND\n//79ad68uUmy8/LyCA4OJiYmBo1Gw9ixY6lUqVLh/9EIrl+/TseOHfnmm2+oUEHNxHUP69ChA/b2\n9sDdiTcnTpxoktwFCxYQHR2NTqfj3XffpXPnzsoz165dy7p16wC4c+cOv//+OwcOHMDRUe3cXTqd\njsDAQK5evYqFhQXjxo0zyfHNyclh5MiRxMbGYm9vT0hICOXKlTNegKGI2L59uyEgIMBgMBgMv/zy\ni6Ffv34myV24cKGhTZs2hs6dO5skz2AwGNasWWMYP368wWAwGNLS0gyNGzc2WfbOnTsNgYGBBoPB\nYDh06JDJ9nNOTo5hwIABhlatWhnOnz9vkszs7GxDu3btTJJ1v0OHDhn69u1ryMvLM6Snpxtmzpxp\n8jaMGTPGsGLFCpNk7dy50zB48GCDwWAw7N+/3zBw4ECT5C5ZssQQHBxsMBgMhgsXLhg+/PBDo26/\nyNwWOHbsGA0b3p0GuHbt2pw+fdokuWXLlmXWrFkmybrntddeY8iQIQAYDAa0Wq3Jslu0aMG4ceMA\nuHbtmvKrmnvCw8Pp2rUr7u7uJskD+OOPP8jKyuLDDz+ke/funDhxwiS5+/fvp1KlSnzyySf069eP\nJk2amCT3nl9//ZXz58/TpUsXk+T5+fmRl5eHXq8nPT0dS0vTfKA+f/48jRo1AqB8+fJcuHDBqNsv\nMrcF0tPT8z++AWi1WnJzc5UfqNatWxMXF6c042ElS5YE7vZ58ODBfPrppybNt7S0JCAggJ07dzJz\n5kzleWvXrsXZ2ZmGDRuycOFC5Xn32Nra0rt3bzp37sylS5f46KOP2LZtm/JzKi0tjWvXrjF//nzi\n4uLo378/27ZtQ6PRKM29Z8GCBXzyyScmyQIoUaIEV69e5fXXXyctLY358+ebJLdq1ar89NNPtGjR\ngpMnT5KYmEheXp7RLlaKzJWrvb09GRkZ+a/1er3J3gHNIT4+nu7du9OuXTvatm1r8vzw8HC2b9/O\n6NGjyczMVJoVFRXFwYMH6datG7//X3v3ExLVGodx/Dsh/UPHCanFVMTkStrlZgxdjFgMCoI5hY4M\naZtASWoRjSKuVPqzCIlEa6OWRKShQYvBhaBI1GzEAjUIFyk4GYWWpDly7kIaipvdK/mOc+c+n+Wc\nd37POcPw45wz875nYoKrV68yPz9vNBPWz6iKi4ux2Wy4XC4cDkdcch0OB7m5uezcuZOjR4+ya9cu\nPn78aDwXYHFxkenpadxud1zyADo7O8nNzSUUCjEwMEAwGGRlZcV4bmlpKampqfj9fgYHBzl27NiW\nXgUmTXM9fvw4w8Przy4fGxuL248s2+HDhw+cP3+eK1eu4PP54prd399PR0cHAHv27MFms7Fjh9mv\nUU9PDw8ePOD+/ftkZWVx/fp19u/fbzQToLe3l2vXrgEQiUT48uVLXHKzs7MZGRnBsiwikQhfv37F\n4XAYzwUIh8Pk5OTEJes7u91OWloaAOnp6USjUdbW1oznvnr1ipycHB4+fIjX6+Xw4cNbWj9pTu1O\nnjzJ6OgoZWVlWJZFS0vLdu+SMe3t7SwuLtLW1kZbWxsA9+7dY/fu3cazT506RV1dHRUVFUSjUerr\n6+OSux18Ph91dXWUl5djs9loaWmJy9WQx+MhHA7j8/mwLIvGxsa43Vefnp7m0KFDccn6rrKykvr6\nevx+P6urq1y+fJm9e/cazz1y5Aitra20t7eTlpZGc3PzltbXqlgiIgYkzW0BEZFEouYqImKAmquI\niAFqriIiBqi5iogYoOYqCeXz589UV1dved2ZmRny8/N/O+b27dubmsr8b2rK/5eaqySUhYUFJicn\nt3s3RP6YmqsklKamJt6/f09NTQ0zMzN4vV7Ky8uprKzkyZMnBIPB2NhAIMCLFy8AuHv3LiUlJRQX\nF3Pjxg1+9/ftN2/eEAgEKC0txePx0N3dHds2Pj7OmTNnKCoqoqurK/b6ZuqLgJqrJJiGhgYOHDjA\nnTt3gPUZQzdv3qSzs3PD9wwPD/P69Wt6e3vp7+8nEonw9OnTDcc/fvyY6upq+vr66O7u5tatW7Ft\n8/PzdHV18ejRI3p6epiYmNh0fRFIoumvkpwyMjL+cTrm8+fPGR8f5/Tp0wAsLy/jdDo3HB8MBhkZ\nGaGjo4OpqamfFp4pLCyMTb30eDy8fPmSubm5X9bPzs7+08OTJKbmKgntx3ULbDbbT5fjq6urwPrT\nEc6dO0dVVRWwvrLT7+biX7p0CbvdjsfjobCwkGfPnsW2/bh2gGVZpKSkbFj/06dPW3OQkpR0W0AS\nSkpKCtFo9Jfb9u3bx9u3b7Esi3fv3jE1NQWA2+1mYGCApaUlotEoNTU1hEKhDTNGR0epra2loKCA\ncDgMEFuFKRQK8e3bNxYWFhgaGsLtdm+6vgjozFUSTEZGBk6nk0Ag8LfnVZ04cYK+vj68Xi8ulyt2\nWZ6fn8/k5CRnz55lbW2NvLw8SkpKNsy4ePEifr8fu92Oy+Xi4MGDsQXPnU4nZWVlrKyscOHCBTIz\nM8nMzPxl/dnZWXMfhPznaVUsEREDdFtARMQANVcREQPUXEVEDFBzFRExQM1VRMQANVcREQPUXEVE\nDFBzFREx4C8W22l5ScdzWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2064fc3d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "mat = confusion_matrix(digits.target, labels)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
    "            xticklabels=digits.target_names,\n",
    "            yticklabels=digits.target_names)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}