{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Machine Learning (and Numpy Arrays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Machine Learning is about building programs with **tunable parameters** (typically an\n",
    "array of floating point values) that are adjusted automatically so as to improve\n",
    "their behavior by **adapting to previously seen data.**\n",
    "\n",
    "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n",
    "algorithms can be seen as building blocks to make computers learn to behave more\n",
    "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n",
    "like a database system would do.\n",
    "\n",
    "We'll take a look at a very simple machine learning tasks here: the **clustering** task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## *Data* for Machine Learning Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Data in machine learning algorithms, with very few exceptions, is assumed to be stored as a\n",
    "**two-dimensional array**, of size `[n_samples, n_features]`.\n",
    "\n",
    "The arrays can be\n",
    "either ``numpy`` arrays, or in some cases ``scipy.sparse`` matrices.\n",
    "The size of the array is expected to be `[n_samples, n_features]`\n",
    "\n",
    "- **n_samples:**   The number of samples: each sample is an item to process (e.g. classify).\n",
    "  A sample can be a document, a picture, a sound, a video, an astronomical object,\n",
    "  a row in database or CSV file,\n",
    "  or whatever you can describe with a fixed set of quantitative traits.\n",
    "\n",
    "- **n_features:**  The number of features or distinct traits that can be used to describe each\n",
    "  item in a quantitative manner.  Features are generally real-valued, but may be boolean or\n",
    "  discrete-valued in some cases.\n",
    "\n",
    "The number of features must be fixed in advance. However it can be very high dimensional\n",
    "(e.g. millions of features) with most of them being zeros for a given sample. \n",
    "\n",
    "This is a case where `scipy.sparse` matrices can be useful, in that they are much more memory-efficient than numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Addendum\n",
    "\n",
    "There is a dedicated notebook in the training material, explicitly dedicated to `scipy.sparse`: [07_1_Sparse_Matrices](./07_1_Sparse_Matrices.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## A Simple Example: the Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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T3Mjj93NOUgsM9KrBuaeCSa+sR0s3zpAuFQ25wNucnvVSfRrqHJKhh7Vt2lt5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\nR+NS3ibZDjvXJWjaVwZScYNRMasSDjNQMKhEn//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Setosa\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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HB+4waiiwuCwBIJUsfQdQfp/Sjsx2tGlAe5ooJxtLq3KKPPjz35FAfFd1zMFI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\n9a9XqXUjhf/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Versicolor\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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mg6Z4s0pPD2uskFoH/wBC1Zk3S6c2OAD1MecZXI+vFfM/iPwTrHhOSbTNQ0Qw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naQdh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\nEzRnY6lWLDA/WqdzfXEc0Vtp9oUWNtkcRGWZvU47V9ArWOuHu6n/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Virginica\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import Image, display\n",
    "display(Image(filename='images/iris_setosa.jpg'))\n",
    "print(\"Iris Setosa\\n\")\n",
    "\n",
    "display(Image(filename='images/iris_versicolor.jpg'))\n",
    "print(\"Iris Versicolor\\n\")\n",
    "\n",
    "display(Image(filename='images/iris_virginica.jpg'))\n",
    "print(\"Iris Virginica\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Features in the Iris dataset:\n",
    "\n",
    "  1. sepal length in cm\n",
    "  2. sepal width in cm\n",
    "  3. petal length in cm\n",
    "  4. petal width in cm\n",
    "\n",
    "- Target classes to predict:\n",
    "\n",
    "  1. Iris Setosa\n",
    "  2. Iris Versicolour\n",
    "  3. Iris Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Try by yourself one of the following commands where *'d'* is the variable containing the dataset:\n",
    "\n",
    "    print(iris.keys())           # Structure of the contained data\n",
    "    print(iris.DESCR)            # A complete description of the dataset\n",
    "    print(iris.data.shape)       # [n_samples, n_features]\n",
    "    print(iris.target.shape)     # [n_samples,]\n",
    "    print(iris.feature_names)\n",
    "    datasets.get_data_home() # This is where the datasets are stored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['DESCR', 'target_names', 'feature_names', 'target', 'data'])\n"
     ]
    }
   ],
   "source": [
    "print(iris.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\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 iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(iris.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600 (150, 4)\n"
     ]
    }
   ],
   "source": [
    "data = iris.data\n",
    "print(data.size, data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Spatial and Clustering Analysis\n",
    "\n",
    "**Spatial** and **Clustering** analysis are key to identifying patterns, groups, and clusters in data. \n",
    "In astrophysics, for example, these analysis techniques are used to seek and identify star clusters, galaxy clusters, and large-scale filaments (composed of galaxy clusters). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/cluster_0.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/cluster_1.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"images/modeling_data_flow.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Machine Learning with SciPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**SciPy** provides a spatial analysis class (`scipy.spatial`) and a cluster analysis class (`scipy.cluster`). \n",
    "\n",
    "* The spatial class includes functions to analyze distances between data points (e.g., k-d trees). \n",
    "\n",
    "* The cluster class provides two overarching subclasses: \n",
    "\n",
    "    - vector quantization (`vq`); \n",
    "    - hierarchical clustering (`hierarchy`). \n",
    "    \n",
    "**Vector quantization** groups large sets of data points (`vectors`) where each group is represented by centroids. \n",
    "\n",
    "The **hierarchy** subclass contains functions to construct clusters and analyze their substructures.\n",
    "\n",
    "(*We won't see examples of hierarchical clustering in this notebook*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Simple Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.cluster import vq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Creating randomly generated data\n",
    "c1 = np.random.randn(100, 2) + 5 \n",
    "c2 = np.random.randn(30, 2) - 5 \n",
    "c3 = np.random.randn(50, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Pooling all the data into one 180 x 2 array \n",
    "data = np.vstack([c1, c2, c3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Calculating the cluster centroids and variance # from kmeans\n",
    "centroids, variance = vq.kmeans(data, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# The identified variable contains the information \n",
    "# we need to separate the points in clusters\n",
    "# based on the vq function.\n",
    "identified, distance = vq.vq(data, centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Retrieving coordinates for points in each vq # identified core\n",
    "vqc1 = data[identified == 0]\n",
    "vqc2 = data[identified == 1]\n",
    "vqc3 = data[identified == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# %load utility/plot_clustering.py\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_kmeans_clustering_results(c1, c2, c3, vq1, vq2, vq3):\n",
    "\n",
    "    # Setting plot limits\n",
    "    x1, x2 = -10, 10\n",
    "    y1, y2 = -10, 10\n",
    "\n",
    "    fig = plt.figure()\n",
    "    fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
    "\n",
    "    ax1 = fig.add_subplot(121, aspect='equal')\n",
    "    ax1.scatter(c1[:, 0], c1[:, 1], lw=0.5, color='#00CC00')\n",
    "    ax1.scatter(c2[:, 0], c2[:, 1], lw=0.5, color='#028E9B')\n",
    "    ax1.scatter(c3[:, 0], c3[:, 1], lw=0.5, color='#FF7800')\n",
    "    ax1.xaxis.set_visible(False)\n",
    "    ax1.yaxis.set_visible(False)\n",
    "    ax1.set_xlim(x1, x2)\n",
    "    ax1.set_ylim(y1, y2)\n",
    "    ax1.text(-9, 8, 'Original')\n",
    "\n",
    "    ax2 = fig.add_subplot(122, aspect='equal')\n",
    "    ax2.scatter(vqc1[:, 0], vqc1[:, 1], lw=0.5, color='#00CC00')\n",
    "    ax2.scatter(vqc2[:, 0], vqc2[:, 1], lw=0.5, color='#028E9B')\n",
    "    ax2.scatter(vqc3[:, 0], vqc3[:, 1], lw=0.5, color='#FF7800')\n",
    "    ax2.xaxis.set_visible(False)\n",
    "    ax2.yaxis.set_visible(False)\n",
    "    ax2.set_xlim(x1, x2)\n",
    "    ax2.set_ylim(y1, y2)\n",
    "    ax2.text(-9, 8, 'VQ identified')\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MFNuK8UzRM/i31n8LWUZclV2li3IYR7o6F+lEVH8pYbZ1dsiI1h/cJY4SnKw8iVJ7qer1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\n0iUi0hCTLhGRhph0iYg0xKRLRKSh/w+UZ32BI7djUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d152dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_kmeans_clustering_results(c1, c2, c3, vqc1, vqc2, vqc3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Iris Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "X = iris.data\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Creating Data\n",
    "c1 = X[iris.target == 0]\n",
    "c2 = X[iris.target == 1]\n",
    "c3 = X[iris.target == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 4) (50, 4) (50, 4)\n"
     ]
    }
   ],
   "source": [
    "print(c1.shape, c2.shape, c3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "data = np.vstack([c1, c2, c3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Calculating the cluster centroids and variance # from kmeans\n",
    "centroids, variance = vq.kmeans(data, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "identified, distance = vq.vq(data, centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "vqc1 = data[identified == 0]\n",
    "vqc2 = data[identified == 1]\n",
    "vqc3 = data[identified == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: Sepal Length (Cm) - Sepal Width (Cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10feac4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: Sepal Length (Cm) - Petal Length (Cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd7a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: Sepal Length (Cm) - Petal Width (Cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7sMNPKC+go6KmC48Htu5t1Jf81T6SLQroppTwRV6B65A0oHuYftctTJseTlZ0j3C/t5XQ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\n+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d2050b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: Sepal Width (Cm) - Petal Length (Cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d2151d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: Sepal Width (Cm) - Petal Width (Cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d218b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: Petal Length (Cm) - Petal Width (Cm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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6MX7wwDuBK90VExRAV8CHIzoJp8up5HPJGn1mtJLPJft8c+mf1Zww3pz33UGSevcIUlSf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc49978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "\n",
    "for cols in combinations(range(4), 2):\n",
    "    print(\"Features: {} - {}\".format(iris.feature_names[cols[0]].title(), \n",
    "                                     iris.feature_names[cols[1]].title()))\n",
    "    plot_kmeans_clustering_results(c1[:,cols], c2[:,cols], c3[:,cols], \n",
    "                                   vqc1[:,cols], vqc2[:,cols], vqc3[:,cols])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Note about `kmeans` in `scipy.cluster`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In `scipy.cluster` we have **two** routines to divide data into clusters using the **k-means technique**:\n",
    "\n",
    "* `kmeans`;\n",
    "* `kmeans2`. \n",
    "\n",
    "They correspond to two different implementations. \n",
    "\n",
    "The former has a very simple syntax:\n",
    "   \n",
    "       kmeans(obs, k_or_guess, iter=20, thresh=1e-05)\n",
    "       \n",
    "The obs parameter is an `ndarray` with the data we wish to cluster. \n",
    "\n",
    "If the dimensions of the array are m x n, the algorithm interprets this data as m points in the n-dimensional Euclidean space. \n",
    "\n",
    "If we know the number of clusters in which this data should be divided, we enter so with `the k_or_guess` option. \n",
    "\n",
    "**Note**: The data we pass to kmeans need to be *normalized* with the `whiten` routine. \n",
    "\n",
    "The second function is much more flexible, as its syntax indicates:\n",
    "   \n",
    "       kmeans2(data, k, iter=10, thresh=1e-05, minit='random', missing='warn')\n",
    "       \n",
    "The `data` and `k` parameters are the same as `obs` and `k_or_guess`, respectively. The difference in this routine is the possibility of choosing among different **initialization algorithms**, hence providing us with the possibility to speed up the process and use fewer resources if we know some properties of our data. \n",
    "\n",
    "We do so by passing to the `minit` parameter, one of the strings such as:\n",
    "\n",
    "* `'random'` (initialization centroids are constructed randomly using a Gaussian);\n",
    "* `'points'` (initialization is done by choosing points belonging to our data)- **Kmedoids**; \n",
    "* `'uniform'` (if we prefer uniform distribution to Gaussian).\n",
    "\n",
    "In case we would like to provide the initialization centroids ourselves with the `k` parameter, we must indicate our choice to the algorithm by passing 'matrix' to the minit option as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Machine Learning with scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### What is scikit-learn?\n",
    "\n",
    "<img src=\"images/scikit-learn.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray, numpy.ndarray)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "blobs, classes = make_blobs(500, centers=3)\n",
    "\n",
    "type(blobs), type(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blobs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Plotting the (randomly generated) data\n",
    "\n",
    "#### Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f4ac438>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JJs1Eg1INWDto7VWu5OuZJc/w+T+fX7HcX2FbYaKfi87wNYDi/yvO6YTT3sc2\ns40P2n/AppOb+GHXD9iddoyakXBrOL/1/Y1GpRtdd9vyuj1n97A6ajUFbQXpXqX7FUsw5kVSyk8I\ncVOKTYrlv4v/y9bTW6lRpAYf3fcRBW0Fr3i8y+OixZQW7Dm7B4fbgclgIswSRtPSTalboi4T1k3A\no3uoWrgqcx6ck6m2TGg7AZvZxg+7fiA2KZaLjos+yUQx9hhcHtcVg0OcI87ncYorhXP2c3zR+Quq\nFa7G8iPLKZ+/PK/d89pV3+PNqGrhqlQtXPXaB94ipAcqID4evv4aYmKgXTu4557cbpG4jbg9bmp/\nUZvIc5GkuFOwGC2Uz1eeOT3nMPK3kRyPO849Ze/ho/s+8i5LudIuLLuH76Z8/vK4PW6SXEmEWkJv\nqG3rj6/n3mn3ejNgAcrnK8/hpw5f8ZwuP3Rh6aGl3mHlYFMwKx9eSYOSDW6oLSJ7yTIWkXkJCVC7\ntlofOn48dOwIU6bkdqvEbSTyXCSHYw97M01T3CmciDtBk2+a8FfUXxyKPcSMnTP4z+z/eM9x626f\nzatB/QJMLchuNBhvOHgCNC7dmJdbvIzFaCHUEkqRkCIs7LPQ55jzSeeZv28+Sw4uweFyMKPHDO6t\ncC9BpiAK2QoxpduUbAueB84doOV3LSk7qSy95/TmQvKFa58ksoz0QG93X38NTz2VVlge1FKXV19V\ngbVWLShy9T0ZhbgR+8/tp/YXtX16eVajFaPB6POcyWDiwgtqw2mHy0GNz2pw9MJRnB4nVqOV6kWq\ns+mxTX5LOn478BtLDi2heGhxhtcfTrg13PuaruvEp8QTagm96lKQ2KRYYuwxlM1X1qcy0oFzB2j8\nTWOcHie6rlM2X1nWP7o+S4L3tcQmxVLp40rEJsfi0T1YjBZqF6vN+kfX33RVgPKCPNUD1TTtPU3T\n9mqatk3TtLmapoVf+yyR4+LiwOVbyBq7HV58UQ3nliwJPXqA8+o7TwgRqEoFKlG/RH2CTWrNZJAp\niLL5yvoHNB3vvGPqMpae1Xpyd9G7eajWQ6wYuMLvnA83fMiDPz3IRxs/4rWVr1H3i7okpqjNrPec\n3UPZSWUp+F5Bwt4OY/6++VdsY/7g/FQqWMmvrODjvz5ObHIscY444lPiOXDuAP9b978b/Uiuy9rj\na3G6nd752RR3CttOb/PbMFxkn+wcwv0DqKbr+t3AAeClbLyXCFS7dhkvZUkdFXC5YMkSmDAhZ9sl\nbhuaprFjKMqyAAAgAElEQVS4/2JGNRlFx0odeabRM6wftJ4SYSV8isAPrTcUqyltLWQhWyGm95jO\n1qFb+arLVz49y1SjV4z29mIdbgenEk4xd+9cPLqHttPacjzuOC6PC7vTTt95fTNduejohaM+CUYO\nt4MD5w9c8fhT8aeYum0qs3bN8pm/DUSwKdiv2INH9/h8RiJ7ZVsWrq7ry9I93ADcn133EjegenX4\n5RcYNgz+/VdtsH05ux3WXv8yACEyK3VfzPQ2Dd7Ee2vf48iFI7Qq34rBtQdn6pq67l/Bx+lx8ueR\nP7mr0F2cs/uu9XR73NT5og5B5iCeafwMoxqPuuZQaJPSTfg3/l9vwpDNbKNF2RYZHrvn7B6afNNE\nLbfRNAoGF2TL41soEFwgU+8rVYuyLahUoBJ7Y/aS7Er2Fl/I6IuEyB45MgeqadoCYJau6zMzeE3m\nQPOKtWtVjzT9fCiA1QojRsD/cmZoSois0vWHriw9vNSnoHuIOQSP7sHlcV1xU2yzwcybrd7k+WbP\nZ/j60QtHOZ90nuKhxek5pyebTmzCg4dBtQfxeafPMwy8Lb9ryaqoVd51qgbNwNONnmZiu4kBvz+7\n086kDZM4cO4Azco0Y1DtQTL/GaAcL6SgadpSIH2tJg21K98YXdcXXjpmDFBH1/UMe6Capumvvvqq\n93HLli1p2bJlwG0SN2jGDBg5EmJjwWRSZf/KloV16yD80jfbpUth8WKVXDRsWNrzQuQxiSmJPP7r\n4yzcv9C7rViqCGsETo8Tg2bA7rT7FY43akZ2DNvhs65R13VG/j6SLzd/iYaGxWhh7aNrKR1eGovR\nctXdX8p8UIbjccd9nqtUoBL7R8qGDrlh5cqVrFy50vt43LhxeasSkaZpDwOPAa11Xc+wltYt1wNN\nSYG5c9WaynvugZo1c7tFgUlMhPXrVRBt0gQsl5InPv8cnn1W9VKtVihRArZvh7CwrLmvrqtrhwS2\nDZUQGZmwdgJjVozx6XGm7rm55dQWJqybwM7onX7nda/SnZ97/ex9/PuB3+n6Q1dcelriXag5lLiX\n4q7Z86vzRR22nt7q81yoOZT40fFXOEPkpLyWhdsBeA7oeqXgectJSYFmzVTR9uefh0aN1PxiTtJ1\nmDcPXn4Zpk5VW5YFIiQE7r0XWrZMC54AL7yQNsTrcMCZM/DjjzfcbEB9VmFhEBEB5ctDZGTWXFfc\n9hqXbuxTk9WkmahbvC7VilSjSekmHIo9lOF5l2e0zto1yyd4AiQ4E9h9dvc129C1cle/tav5g/Nf\n71sQeVB2ZuF+DIQCSzVN26Jp2qfZeK+84ccfYc8e1XtLTlYF2wdnLvHhqpKTYcwYNU85apQqgnC5\n//4XBgyAN9+E4cOhW7e0jNqsakN6bnfG7cisQ4egXz/12bndEBUFbdtmbdvFbatZmWa8e++7WIwW\nTAYTVYtUZW7PuQDMj5yf4QbZZoOZB6s+6H18NvEss3fPzvD6F5MvXrMNQ+sPpaCtoHf7tWBTMO/e\n+26Gx6a4U9h/br9fopPIW7IzC7dSdl07zzp71n+95MVr/8e6LroOHTrAxo0qiK1eDX/+CZs2pS1D\nOXtWDbGmXPplYLfDypWweTPUq5c17ejcGX77LS2QGo3Qvv2NX3fLFjVcnErXVe/2/HkomIv1Qi9e\nhN27oXBhqHT7/ZO+lTzR4AmG1RvmV+LPbDD7zX8CFAguwMgGI3G6nVxIvsD209uxmqx+O7+EW8Kp\nWfTaUzXFQouxY+gOPt70MReTL3J/1ftpXb6133G7o3fTamorklxJON1OxrYYy9gWYwN4xyK7SSm/\nrNSihe+aSrMZmmbRlkOHDqlgmRq4kpNh/37Yti3tmPh4dc/0jMasC+IA06bBgw+qgFK6tOoNT50K\nx49f+9yrKV484+Fmay6uafvnH5VAdd99qiLTE09IjziLrDiygvGrx/P99u99NmHOLquiVlF2UlnC\n3g6j04xOnEk4432tV/VemA3+W27dUeAOZuycQfjb4ZT+oDR95/XNsKc6r9c8wqzXlwNQPKw449uM\nZ3KnydQuVptf9v3C7wd+98kS7vpDV87az5KQkoDD7eDtNW+z9pgsI8uLpJRfVps1C4YOVcOaTZqo\n+chChW78upGRUKeO7xKTsDBYtgwaXKqz6XJB5cpw7JgKRpoG+fLB4cPqz6w0fz707avaYzSqTNzt\n21VQDYSuq6Hnn39WvfiUFBU8w8Jg+fLcScYqU8b3i0FIiGpf27Y535ZbyIS1E3jtr9dwuBwEmYKo\nV6Ieywcsx2jInr1poy5EUe3TaiQ6VQUik8FE9SLV2fp4WkLPyiMraTu9rTeY20w2Puv8GUN/Heqz\nrVmQMYhkd1qwM2pG2lRow5L+S667PfvP7Wf6jul8sOED75xoyfCSbBy8kRBzCOY3zD7ZwhoadxW6\ni2ebPEv/mv39qiF5dA+v//U6U7dNxWa28fa9b9P1zq6Z+IQEyH6geYuuqwCWVTweqF9fDSc6HCqx\np3x52LHDN8nn+HHo3Rt27lS9p5kzoUaNrGtHqjvvVD3gVEajSpwaPz7wa+o6fPSRuk5Kum/6ZcvC\n0aOBXzcQHo8aUk7/bzMoSFVkeuKJnG3LLcTpdhIyPsQnGzbUEsrcnnNpV7Fdttxz5s6ZDP11KPEp\nadmuJoOJ2BdifYZy95/bz5ebv8TpdjKg1gAiz0X6nWfAgMVk8ekxGjUjiaMTM6wAdCH5Aj1/6snK\noysJtYQyssFIJq6fSJIzySdIWo1Wnm38LG+1eYtiE4txJvGM37VMmom6Jeqy+pHV3oSoC8kXeOK3\nJ5i7d663TcGmYJb0X0Lzss1v4FO7/ch+oHlJVi9mNhjUnOeoUWposUYNeP993+AJqgeYE1WDLi+2\n4HarIeQboWnq/VxeWjC1R51RycHsYjBAuXJwJF1pN6MRqlXLuTbcglJ7gelpaFm6i8ih84cY+MtA\nDpw/QM2iNeleubt3l5b090ytvZuqcsHKPkUNMlwbajDi9vheS9d1Jv89GZfHRfcq3alcsLL3tT5z\n+vBX1F84PU5ik2N5Y9UbPoEzlcPt4OD5gwDM7TmXNt+38Ztrdekudp/dzcL9C+lxVw/2nt1L8ynN\nOZ903ueaSa4kftj1gwTQHCBzoHldcrIaEi5dWs2xDhyoEm6mTg08uebECRVkz54NvF0PPaR2bUkV\nHAy9egV+vVSVK6vglV6xYjkbPFP98ov6jMPC1HDyyJHQqlXOt+MWki8oH1UKVcGkpX1393g81C1e\nN0uun5CSQNNvm7L+xHqiE6NZfng5Tyx+gmRXMhoaBgzYzDbebfsuRoORFHcKSw8t5df9v/oF8WZl\nmtGrei9CzCGEW8IJNgXzTKNn/OZsdXRGLx/NmBVjqPNFHTad3OR9bWXUSp9504yCJ6gSgPeUU/vw\nNi3TlBebvujN1k3Po3s4n3QegIfnP+wXPEFVOMqJ3WCEDOHmff36qXm3pEvzMCEhKoBWrnz1867k\n44/VEKnVquYaZ85US10yy+2GV16B6dNVIH3nncCuk5EnnoBvv1W9UV1XxewbNcqaa2dWUhIcOKDm\nsUuUyJ023GJOxZ+i55yebP53M+HWcBKdiSSmJFIxf0V+7fsrdxa6M+Brrzm2hk4zOxHniPN7zaAZ\nKB1emindptCqfCsSUhJo/E1joi5EqapCJgsbHt1AxQIVvefous6GExs4GX+S2sVqM237NMavGX/F\nEoAATUo1Ye2jahSo6MSiRCdGp7UBA2j49GyNmpFHaj/CF52/8O4mcyLuBNU/rc5Fh28CoM1sY+vj\nW6lcsLLftVNFWCPYNnQb5fKVu74P7QbEOeKYtWsWdqedjpU6+vS+bzYyB3orCgpSc56prFYVrP77\n38xf69AhNfSblJYUgc2meqLpe5N5QWQkREerYvf5ZbH5rej4xeNUmVzFu1uKhkbJsJJEPR111b05\nr2brqa00n9I8w6FiUD3g2BdiAXht5Wu8s+Yd71CpQTPQunxrlj60lCRnEqOXj2bt8bVULliZ99u/\nz5ebv+SNVW9kmImbXpVCVdg7Yi8A8/fNp++8vjjdTty6m9TfdTq6KqDf8nWG1x+eYQnAw7GHGb18\nNEsPLyUuOY4iIUWY0n2Kd66444yOLD281NsjNmpG2lVsx6edPs2R4BmbFEutz2txLukcbo8bo8HI\n4n6Lb9qhY5kDvRVZrb4B1GgMPNgdOuQ/N6vraheWO+4IvI3Z4c471Y+4ZW05tcW7vyeooBKTFMOZ\nhDMUDyse0DVrFatF0zJNWRO1BrvLd55eQ6N8vvLex/vP7feZZ/ToHo7EHkHXdbr80IW1x9eS7Epm\n6+mtrDiyggvJF/yCp81ow6N5vAk8NrONHlV6eF/vVqUbawet5f317/Pj7h997mc2mHmu6XNXfC8V\n8ldg1gOzrvj6d92/o/XU1hyOPYzL42JwncFM7jg5x4rJf7zpY84knkn7TNww4rcR7Bi2I0funxfI\nHGhe98YbaQHTbFa9sUDnGkND/ZN/kpPVHGNWOXZMlTPMl08tu9m3L+uuLW4pxUKL+SXkeHRPpsvb\nHTp/iNVRqzlnP4dBM7Co7yImtpvIsHrDqFm0JiHmECKsERQILsCMHjO85zUv2xybOe3LqNVopWnp\nppy1n2XNsTXeoOjyuLiQfCHDwFQyoiRPN3raZ77y8uIIdxe7m0alGvn1qhOdiRkWcLheRUKKsGPY\nDvaP3M+ZUWf4tNOnOboTy5mEM35fKM4l3V6Vk6QHmtc9+SRUqAC//qoC3ciRqlZsRhITVeWe4sV9\nq/qkOndOzSumXyJiNsOFCyq43iinUyU6nTih5ki3bYPmzdU61KwqNi9uGQ1KNuA/Vf7DL5G/qECi\nw8R2EwkyBV33NV5e8TIT10/EarTi1t0s6L2AVuVbMaz+MEDNYW49vZV4Rzy1i9f22Svz8bqPs/nf\nzXy//Xs0TaNeiXp83PFjHC6HX2KOpml+wR7gZNxJpmyd4j3e7rTTbVY39j2xj1LhpbzHtSjbwqcO\nrtlgpmHJhgEPVacyaAaf++SkTpU78d3277xD8MGmYO67475caUtukTnQW8UXX8BTT6kh3tBQVWDh\n8vWfgwfDN9/4Pmc0wooVKvDdqMhIqFtXBfJU4eGwaJHqlQpxGV3XWXp4KccuHqNO8TrUKV7nus/d\ndHITraa28v4CBwi3hnPhhYx7i1cS74gnxZ1CgeAC3vO6zOzC8iPLSXIlYTFaKBtRlsfrPs6opaN8\nzrWZbKR4Unwyc4NMQZTLV47y+crzeqvXqVdCldGcv28+gxcO5mLyRRqXbszcnnMpZMuCIiu56JNN\nnzBmxRgcLgfdqnTju27fEWwOvvaJeZAkEd2uduyAxo19h2dLllRFFVJ/kezYoTJZ0ycQgVoyEhQE\nkyfDww/fWDtOnVLFHdLP2dpsai/RWrXU44QE9VO0aNavlRW3lRk7ZjBs0TCfQgdmg5no56LJFxR4\n5a31x9czaMEgoi5EEWIJoWvlrkxsN5H8wflpPqU5G05s8KlY5PQ4r5iVG2IOYcPgDVQvUj3g9oic\nkae2MxM5aPt2/7WTZ874FjY4csS/Ti6oijt2u9q55Ua/yBQvrsrxpe7labOpLdFSy/CNHg0FCqgg\nW726CrhCBKhq4ap+w6rh1nAirFeY4rgOUReiaDutLfti9pHkSiLOEcfh2MPeedlf+/xK50qdCTIF\nYdSMhFnD6FmtJzazDYvR4rddWaIzkXF/jQu4PelFxkTScUZH7v78bsYsH4PTfeWlNCJnyBzozSgx\nUS1l2bNH1dutW9c/+BkMvvOa1ar57xSTnsOhXr+8slFmffEFtGmj5j/vvFMVXNA0WLBAlelzOtXP\n/v2qlu6ff97Y/cRtIcYeww87f8DhdtD1zq5ULliZ2sVrM67VOMauGIvFaPEmEN1IIs2fR//0OT/F\nncKqY6tIcadgMVqICIqgWpFq/HH4D9y6mzOJZ5i3dx5vtX4LTdMYvXy0T+1cgJ/3/szRC0cDXlpy\n6PwhVkWt4snFT5KYkoiOzoHzBzgZf5Lvun8X8HsVN06GcG82TqcqHr93rwp6Nht06qT+nDo17Tir\nVfUqX345bR3l1KmqqpGm+Q7lmkxqiPWff7Kv3WPG+NfJDQ/P2p1ixC3pVPwp7v78buIccbh1Nxaj\nhWUDltGolCquEZ0YzZmEM1TIXyHD9ZSZMXfPXB6e/zAJKWl73FqMFpLGJHkTfip+VJHDsYd9znu0\n9qN83fVrQt4K8Vs+Y8RI/1r9aVSqEV0qd+G7bd+x/cx26havyzONn/HZ6Pty07ZP4/FfH0dH96m/\nC6qer2Os44YTkYQiQ7i3gw0b4ODBtHlGu13tjHLXXb5bfzkc8MEHaq5x2DDVQx04UBVN2LVLlakr\nUkQFzwYNVKJPdipf3n/9aqncyR4UN5cJ6yZwPvk8ye5knB4nic5E/rs4rZBIkZAi1ChagzhHHMcu\nHuNGvpB3rtyZ8vnKezOBbWYbr7d83SdIhVl8M8qNmtE75xoR5D987MbN1O1TGb5oOGUnleXNVW/y\n056fGPfXODr/0PmK7U1MSWTIr0NIciX5BU9QVY0uHzIWOUsC6M0mJcV/vtNg8E3cSc/phO+/hxmX\n1r+FhqplMd26qXlSp1PVxS1a1Pe8pCQ1RBwT4/t8cjI8+6wKuv36wenTV27rmTNqK7I9e1TwbthQ\n3T88XP1Mn565957dli9XG4Z37qwyk0WeEJ0Y7Vd/Nv16Q5fHRfdZ3Sn/YXmqfFKFhl83zLCU3/Ww\nmqxsGLyBd9q8w6jGo/jxgR95odkLPsdMbDfRu37UZDARbg3nqYZPATCu5TgshoynQXR03Lrbux1a\nkiuJ1VGrOXj+IAkpCew8s5Nz9rT3FZ0YfcXeZbApmCcbPZmj6z6FPxnCvdkkJKi5xTNn1FpLq1Ul\n6fz4o/ozISHjZKDhw1Wm7fXYvFltlJ26L+cbb8BzlyqmdOgAf/2lAqnZrNam7t2bljiUasUKFaSN\nRhWMa9dW86GVKqlg36iR2pQ7r1i2DLp2TRvaDg5W87b33pu77RLM2zuPh35+yLtcxWa2Mbz+cCa0\nnQDAu2vfZdzKcd65R6vRSp/qfZjSfQrfbv2W55c+T5IriS6VuzCl25QsWWax+d/N/LTnJ2xmG4Pr\nDKZEWFqd5AWRC3hx2YsciT3is3doRjQ07r/rfhYfXIymaaS4U/iow0cMqTeEFHcKxSYWIzY51u88\nq8HKlse3ULVI1Rt+L0KRZSy3ixMnVECMjFSB6KOPVHGFnTtV73DtWt8lLcHBKunoySevfe0DB1QF\noYS0OSBsNli1Sm3vVby4bzJSWJgK3h06pD2n6yrb9kIGW1QVK6YKKwTnsbVi7dvDH3/4PteunSpk\nL3Ldhxs+ZNxf43B6nPSt0ZdP7vvEO3fYfVZ35kfO9zn+rkJ38UnHT+jyQxdv4DUbzNQpXoeZ98+k\nQv4K2dbWmTtn8tjCx3zWp2ZGsCmY7UO3U6lgJTae2Mh9M+7jouOiT9UiDY3e1Xsz8/6ZWdXs257M\ngd4uSpVSvaPISJUYlFqZqEYNFQQ2b1bbcEVEqCHTunVV8tC17NwJd9/tGzxBDRHv2qWevzyTNyXF\nfz2n3X7lvUFPn07rzeaE9etV8tKXX/qvgU3Pk0FJtYyeE7niqUZPcf6F88S/FM8Xnb/wSbypWrgq\nQca06kUmzcSdhe7k9wO/+wQxp8fJxpMbqflZTTae2Jhtbf1myzcBB08As9HMvhhVArNhqYZEPxdN\nzSI1fY7R0bN0D1URGAmgt6IqVVSi0ezZ8PvvsHLl9S1PGT3av1YuqKBZqRIsXuw//5pavi/945Ur\nr166b/Xq63kXN27aNDUE+8or8PTTag42+QpDak8/7dsrDg6GZ57JmXaKGzKywUgsJvXvW0MjX3A+\nJnecTEFbQSxG/3/3ic5ERvw2IsNrJbuS+Xnvz8zYMYNT8Vdep7zhxAYmb5rMov2L/JKAwqw3VrbS\n6XZyR4G0zR1MBhODag/yqdtrM9voX7P/Dd1H3DhZB3qrypdPDUte7vRp+Ptv1UNt3Ni39xjrP9cC\nqODZpIkaGjYafXtmFkta4HE4VMm+ffuu3nurWPHKr2WlkSPTvhDY7Wro+Kef1Fzs5Tp2VK9NUPNq\nPP883Hd71fW8WQ2aP4hkp/pipKOT4EjgfNJ5htYbymf/fMbJuJO4dd+CC2ft/pvJxzviafB1A07E\nnQBUndlVD6+iVrFaPsd9sOEDxq4Yi0f3YNSMdL2zKzN6zPAm9LxyzyssO7zsiluqgQqKqfV/U4O/\n1WjF4XbwWsvXuKvwXd7ArGkaTzR4griUOD7Z9AlGzcgLzV6gb42+AX5iIqtIAM0rPvsM3n5bBZ4R\nI+DFF69d6u7AAZXgc/489O4N/fvDyZPw/vuqcPyDD6o1oqnWrlVzlQaDSkBq00Zt1p3aq+zTR5Xd\nS/+NWtNUcAEVkF97LW0YNyhIZaym+vpr2L3bd6i0RIm0hCdQ86nvvef/Xo4eVT3T/PlVGzMqhp9Z\nlw9FJydnPC+bqlMn389L3BSWHVnmk6Wro7P88HKeavQUO4ft5Pk/nufbbd96y+0FmYIyLHr+4cYP\nORJ7xGfLsSG/DmHj4LTh3sSURF5c9qLPLiQLIhew8eRG77rUOsXrsGHwBr7e8jW6rjM/cj5RF6O8\nxxs1I0PqDKGgrSAA/Wv2J19QPiJjIikTUYZS4aV4dP6jTNmmitQXDSnKkv5LGNN8DGOaj8miT01k\nBQmgecHs2TBqVFpv6c031dzlyJFXPicqCurVU0HC41EVfY4cUQlFsbEqYP30E0yaBI89ps7p08c3\nqCxfDnPnqkALKjFpyxb49lv12GRSyUBPP60e16wJ8+ap+dSYGFV84fXXfdt0+TxjTAzMnKlq8ZrN\nqt5u2bK+x6xcqQJx6heGmjXVcxmVHsyM4GDf9+t2572Nw8UNCzGHcNGRVpDDZDB512OGW8P5vMvn\nVCxYkXErx5HiTqFr5a5M6jDJ7zrHLh7zCZ6gdltJ79D5Q34l9IwGo7eYw6mEU1TMX5HqRap771Gn\neB0eXfAobt2NAQMR1gjGtBjjk7kLaj0rwPjV4/lu+3feHV7OJJ6h0TeNiPpvlPcYkTfIHGheMG2a\n79yj3a7Wbl7N9OnquNShUrtd9ezi4tJ6e3a7qkSU6vI1mykpKuil0jS1W8uuXSrxZsIE9ff0+4W2\nbQtVL6XO79gB9eurQAxq67LLA1RKCjzyCHTvDq++6h88Qa0RTUxMKzS/fXvautUbcXkwNxqvPEwt\nblof3vchNrMNAwaCTcGUiShDr2q+e+YOqzeMuJficIx1MPvB2Rlumda6fGtCzGnLsaxGK+XyleON\nv95gYeRCdF3n6SVP+211luJOYeuprZSbVI57vruHku+XZM2xNQCcTjjNU4uf8g4he/AQag317sLi\n9rh5a9Vb1P+yPh1ndGTP2T38vPdnv31CnW6n95oi75AeaF4QEaGCV/qh0/DwKx8PKkhevvzH4UgL\nnqnS7/1ZvboKTqlB12xWGbqXq1ZN/WTk55/VOtD0W5alFlTo0gVeeEEN86Zvm92ultHMmZPxNTMq\n1vDvvxkfmxklS6oNvlMFBakiEuKWMrDWQCrmr8iKIysoZCvEw3c/7F3reeziMdpPb8/B8wcxakY+\n6/QZj9R+JMPr9KrWix1ndjBh3QR0XSfMGsbWU1tZf3w9weZghtQdwt///u133r0V7uW9te+R7E72\nrvvs8kMXzj1/juk7pvv0jlPb9O3WbxlabyjPLHmGr7d+jd1pR0NjzbE1NCzZ0O8ebt3NP//+Q4+7\netzoxyWykPRA84KxY1UhAoNBBVKbzb9u7OV69fLPrNV11ctKZbOpedFUP/+seoDBwercMWOgVavM\ntfX4cXD5VoUhJiYtYL7yiuqVXi41+3XqVChUSLWtZ08VXOvX953ztFpVgtPVJCfDwoUqKF8egFPN\nnq2ygSMi1OfbsaPqCYs8JcmZxHtr32PwgsFM3TY1oFJ8zco045V7XmF4/eE+2aqdZ3Zm/7n9uDwu\nHG4HT/z2BFtObcnwGpqmMb7NeJLGJLH6kdUkOZOwu+x48JDoTGTy35P99u+0mW2UCC2ByejbF0ly\nJnHOfo4kZ8ZLp0YsGkG1ydX4csuX3iUvqfVuqxSqgtVo9Ttn0oZJLIhckKnPRWQvKaSQVxw6BFOm\nqODUv7/qLV5Lv35qfjG9YsVU4s6FC/DAA/DWW77ByeNRvcXUoHK9Vq5M63l+8kna8KjRqNafbt2a\nduz336v6u6nD0jYbzJqletX33Zd2blAQ3H+/Snrq2FHt4JL6BSIhQS3HmT8f7rjDpynExalSgqm9\nVLNZrfesXNm/3WfPpq2LrVdP9iDNY5xuJ42/aczus7tJdiVjM9t4uNbDTO50nVWzrsLtcWN5w4KH\ntOHQYFMw/2v3P4bVH3bVc/888ifdZ3f3KQkYYg5hSrcpDFowCA0NHZ0aRWrw0X0fcc939/is/Qyz\nhBH7Qix7Y/ZS6/NafkOygLeO7eVDwiaDiYG1BrIwciHR9mif19pXbM/i/ouv/0MQ1y2QQgoyhJtX\nVKyokocyI/3cZKrgYBUwrsRgUAE2Mz77TCU5JSWp6xcoANHRKhiVK6eCXHoDBqhM3Q8+UAF29GhV\nyu+FF3znJZOT1bnTp6udYA4fVolJcZd+ae3dC61bq+So9D3r995TWbup9X8NBhWwU+di0ytc2LdK\nkshTVkWtIvJcpLdYut1p56stX/Fu23cJtYRe4+yrMxqMhAeF+xQcMGpGSoaXvOp5xy4e44ddP3iX\nxoBa0lLQVpD/3PUfGpduzJpja4iwRtC2YltMBhMvNXuJt1a/hdVoxa27+bnXzxgNRqoXqc4vvX6h\n99zefsUVdHTvNmzpi8W7PC5m7ZpFpQKV/AKo1eTfMxW5R4Zwb2aDBvn2Ii2WwBf/OxywZo1aSpK+\nMH3p9aMAACAASURBVL2uq2va7ervdrvaguy771RPdt8+KFPG/3qPPqoSkLZvV0GzcmWVwXu5xEQV\nPH/6SS3JST8aoetqePbyjbcPH/Zto8fjmwwlbhp2p92vYPrlAeVGTPvPNGxmG8GmYCxGC3cUvIMO\nd1z5C9XJuJPc/fndfLv1W1I8KWhoWIwW6hSvw18P/4XJYKJUeCl6V+/NfZXuw2RQfZDRzUezY+gO\nfuv3G1H/jaJNhTbea3a5swuJoxP557F/fComgeqFvnLPK367qjg9ThqXbozN5Fs84aVmL2XFxyKy\niATQm1m1amoINH3v7PffMy4mf7mzZ1VAW7xYBcKaNdUwaqdO6u/nz6vj3G7fRCRQASsxUa3ZvNaQ\n6NmzanlMUlLGVYB0XQ1ZP/KIykZOvGzxudutikKk16qVb7av1epbDSmV3a6GuQsUUD3lX3+9eltF\njmtapikmg8kbQCxGCzWL1qRgcMEsuX7nyp15rslzuHU3bo+b/TH7aT+9PW6PO8Pjp2ybQnxKvDdr\nVkenkK0Qfz/29xU3xP5gwwcEvxVMlclVeGHZC1fcYqxuibr0rdmXEHMIJoOJEHMIQ+oO4aVmL/nV\n5jUbzHSu3JmlA5bSt0Zf+tfoz58D//SuNRV5g8yB3sxiY9UwbvoAFxKihjIb+mfyee3erSoGpWby\nWq1q2DS1QILFooZhv/pKPb7nHrUPaep9bDY1X1mp0rXbuGWLCnhxV9heymRSQ7CXB2mrVX0xePVV\nVRUoPV1Xa2Q//1wF8BYt1FBw6GVDfr17q+dTA3dwsComUbv2tdstckxkTCSDFgwi6kIUjUs15ssu\nX5I/OH+WXNvtcWMbb/MpfBBqCeXHB37kvkr+xRTGrhjL+NXjfeYlC9sKE/1ctN+xbo+bKVunMHLx\nSG+P2WKw0LZiW37tm/GXNV3XWbh/Ifti9lGtcDU6VuqIpmlsP72d1t+3xuVxkexKplW5VkztPpWi\noUUzvI7IerIby+0mKkptpJ1+XjEiQmWfZlTGL1WjRrBpU1pP9fIlNJBWug9Ub7R/fzW8W6iQWiva\nunXamtP8V/llFxurit9nVGPXbFYB3+32LT5vsah50zZtVKC/kuRkFfSvVHc3LMy3kIKmQcuWKhs5\nwn/jY3HrsTvthL8d7lPKL9QSymedPsuwluz209tp8m0Tn63TRtQfwXttVfWsJGcSpxNOYzPbaD+9\nPXvO7vFWOEoVbg3n4osX/a59LdEJ0bT4rgXH445j0kxomsbqR1ZTo2iNTF9LZJ4kEd0OHA41r5ia\n/Vq8uAqk6dd/1qt39WscP+4/12g0pl0jKMh3GUmBAvDbb2mPXS7Vu5s7V53boIHaTzOjKj/586sM\n3N691T1cLlWm8Nw59dqDD/reKzXJaezYtKFph0Mtj1m9WvV6J0yAIkVUO4P8F8R7hYT4BlBdV9uy\n1a2rPsPMZCGLm5LNbKN6kersjt6NS1fLrzy6hyalm2R4fK1itfi93++M+mMUFx0X6VO9Dy+3UMVI\n5uyZw4CfB2DQDN4e7eXBE1SPNRDTdkwj6mKUz/zvoPmD+HuI/9pTkTdID/Rmcvas6hmeOaOCwV13\nqQzWhx9WVYHKlFEVfK41RPngg2o7tNRh06AgtT40NRGncWNYtOjKe3a+/bYq4Zd+TrNIEdU7jYlR\n+2h+/rlvgIqPV4G7VCn/IhEbN6oA+++/avnO3LlqzjJVp06qVGFSkuq1liihhqGvFQB/+EElM11e\nkSg0VPWie/a8+vnilnAq/hT3/3g///z7D4VshZj2n2k+ST7X42TcSSp/XBm76+rblJkNZpYPWE7z\nss0z3c4Ri0bw6T+f+jxXNKQop0edvsIZIitJD/RW99RTKsilzlXu3KnWjq5bl7nrfPWVKjq/caNK\nCPJ41CbdX36phjhLlbp6ctDKlf4JQdHR6gdU4IqMVAlKBQqo58LCVAnAHTtU769CBWjaVL3WsKFa\nqpKR8+dV7zY12Dud6rnVq9OWp7jdqn7vzp1qGcwjj6iebJ8+Kti2auXf404fVE+dUte84w419ypu\nKcXDirPu0Uz+H7nMnrN71B6krqsfZzKYvLuyZFaLsi2Yun2qdxcXi8FC49LXKCgicpVk4WaVJUug\naFHVQ2rc2H/pRVbYvdt3Q+vkZBWQMitfPjVPmlrJKCVFZb8OG5ZxZu2qVarYe4cOaij3WslDbrfa\nMu2OO9Qyl1Sffqo+m+HD1Rzt1YrlX0tqG3VdZSL/97/w8cfw5JOqN5saMO+5R2UXpx/qNRjUPqGg\nEpTKl1ftKlNGrT0Vt7S9Z/ey5OAS/o2//nKR5fKV80lEAjLMttXR2Xpqq9/z16NntZ4Mqz8Mk+H/\n7J13eFNl+8e/J03TJl1sBARkK+JABGRJEZkqIA5EcL84cY9XFBEEREQU9aevE1FBwIXiYE9FRTYo\nMmXv0UJ3m+T5/fH1mJycpE3apGnL/bmuXDQnJ895Tqy9c6/vbUWsJRYta7XE5D6Ti7VWOHC5XVh/\neD3WHFxjEtAX/kEpFdUHt1DO2b5dKYdDKf7ZVspqVerii8N/ndtvVyouznMdu12pRx5R6pZblOra\nValJk5RyuYJba9EipVJSPGsBSiUmKrV5s/G8n34y3pvDodTUqcZjhT3at+c6p08b966vtWFD0Xu9\n+mreK6BUbKxSDRoolZXF1zZvNu/Fbldq507P+7OylLrzTqXq11fqssuUWr+ex+fNUyohwfM+TVOq\nWbPgPj+hXFHgKlCT105WZ088W2EklGWURcWNjlOzt8wOeo0xy8co+xi7ShmXouxj7GrE4hHKNtqm\nMBL/PhLHJqrvtn5Xor3mFOSotJy0Eq1RUjLzMlXr91qrhLEJKvHFRHXe/52nTmSfiOqeIs0/tigk\n+yUh3HCwYoVnpibAQplNm+ghFlbkEiqvvcb2kW3b+Cf/0kvZO5meTq/v118Z4n31VbZvvPMO85jD\nhpn1aRs18t/fefbZ/HfTJr7+6qvmSTFvv83XO3Tgtd1u81o6el71xAljvypAb/3gQfadFsZXXzHn\nunw5BRleeslTsJSVZZ4darUai4ccDuY8ffnjD6NHrxSwY0fhexHKHW7lRq+pvbB091JDIVGeKw83\nfnkjMoZl/CuIUBjPdnoW1513Hf5O+xtutxsDvhpg8ErjYuLQp1kfXNWkZDNl463xfqfFlCYjl43E\npiOb/hXH33lyJx6b9xim9JsS1X2VNcSAhoOqfpq+Y2PDn0+rVIkyfTt20BgtXszQpff4srfeorH8\nz388hm/ePKoMeRcX1a9P4/joo9xnQQGNcWwsx5Jt2MAvBW6zhifcbuYw9+xhrtNuB/r3p0HyzjVa\nrZ4K2zp1WPTjbYydThrPggKKOVSv7v8Lh80WWObw/POZX83K4ucQE8Mw9LnnFv15NmnC+/U2/nXr\nFv0+oVyxfM9y/Hbgt3+NpzcFrgKcyD4RdL/ludXOxbnVzsXt39xukuY7p9I5mNp/arFzoGWJDYc3\n/Gs8ASDfnY9NRzZFcUdlE8mBhoNevdgakZDAP/YOB/NxkfgfyWKhF9aokf/XleIkF1+v8f/+z3zu\nvffSGP/4I4t4+vend7d2LQ1SRgZbSLy9a4cDePJJ/myzsZWmcWNW7TZr5rnnmBga7Pfe4/PYWGDB\nAhb1WK00el99RSNcowYNXpUqPBYKdju/HHTowHU6dQLGj2eF7bXXstgoEFdfzak2djv7QitVCv36\nQpknLSfNJBeoExcTh+oJobed+MsJxsXEVQjjCQCX1r7U4AXHxcShZS0RIPFF2ljChdNJPddDh9hq\nclkEJLdycuih6f+THj7MVpbTp+kV6uPLfvmF3qA3d9zBStWiuPZa4JtvjMfq1aO35nSyEjgjg/da\nowYHdnu3nGRkcC8FBfTMff+gKMXwamIiPb+aNamtq2O3M0R99tlBfywGFizgyDLvSTBz5viX+tP5\n6y+GmFu0MMsGCuWeQxmH0Oz/miEjP8NwXIOG7wd+j95Ne4e85k97fkKPqT2Q42Q1tyPWgTd7vYk7\nW94Zlj1Hm5yCHPSY2gNrDq2BBg1NqjbB0tuWIiW+4gqQFKeNRYqIygObNilVt65SFgsLf+bP97y2\nfbtS/fop1batUi+8oJTTqdSHH5oLf377LbhrjRnjKdgBlLLZlBo40PP6Sy951rZYlKpUSakDB4p3\nXzt2GIt4AN7f3LnFW08ppbp0MRcyXX118dcTKgS/7ftNNXq9kYobHaeqvVxNDfxyoNp5YmfRbyyE\n+Tvmq46TO6rW77VWH637KDwbLUO43W615dgWtfnoZuV0OaO9nYiDYhQRiQda1nE66Y0dOeI5lpBA\nL62wsWSffgq8+y491hEj/HtgJ05QUEEpihXUrEmv8OqrGRa1WOhdLl/u6eesUoXyfDo2G0PGjz8e\n+r1lZjLv6d1TarczhBxMDtMfnTtzv9707EkvVBCC5FjWMYxcOhLbT27HiewTyMjPwHnVzsP/rv4f\naieFOA5QKBeIkEJF5MABo04swBzihg2FG9BbbuEjEPv3A5dc4hlT9sQT7N1s1IhFRzt30pg2bcrr\nZWUBn39uVvVRyigjGAqJicyR3nsvr+F0ch/FNZ4Ai6JWrzaGcB9+uPjrCWccGXkZaPVeKxzKOGQo\nPNqVtgvtPmyHLQ9sgT02gEqXcEYhRUSlxRdfsNjm7LMplB6s0alShYbFm4ICauCWhOHDqb6TleWZ\n8al7kZrGvTZvTsOWkQFcfDEwdKi5XcVmo5BBcbnlFopBfPoplZFGjSr+WgDzn59+yurfDh2owysD\ntYUQmLtjLk7mnDRV7TqVE2k5aVh7aG2Ra7jcLjy14CnUmlgLDV9viJl/zIzUdoUoIh5oabBkCfVq\nda/o9ddZpTp6dNHvTUoCXnmFAux6Qc6gQTRoJWH/fqMRd7vZk+mPDz7g+d6hVpuNFa8TJpgrgrds\nYc9qVhbvW1f9CUSjRoGriotD//58eLN6NT/DU6eAm29m+08FqZgUwkuBuyDgQG8FBVuMrcg1RiwZ\ngbdWvfVvq8uds+9ENUe1kDV4hbKNGNDSYMYMc1vJtGnBGVCAkncdO1JEoUED5vlKylVXUXjBO9R5\nVYAG8CNH2M7iTWIiNWp92bqV7Su6sZ05k9q4119f8j0Xl7/+osavPqx782Z61SNGRG9PQpnlVO4p\nw/gznXhrPM6vfj4uqXWJ3/dl5mfi1lm3Yu6Ouchz5cGtPD3U2QXZ+Hzz52JAKxgSwi0NkpLMKjyh\njtJq2ZKtKKmp4fGcHn6Yk0piYxmmHTgQePZZ/+d2726czBIXB3Tr5v/c5583eqpOJ3OcAIdyf/op\ni4RKk+nTjblbXU1JEPyw99Re0zGrxYphHYdhyW1LEGOJ8fMujh77cfuPyHHmGIwnAMRoMUiJq7gt\nIGcqYkBLg4ceMhpRu53N/rt3MxzbpQswcaJ/1Z9IYbEAb7xBzzIvj2FaX0k8nSuuACZNotiAzUYh\n+A8+4P5Hj6bR1EXYvcXjddLTgf/+lwOy77+fod+JE0u2//R0ikAEkhD0xmo1f+mwyK++4J8Lal6A\nhFjPF1yLZsGltS7FiM4jCi0emrdzHvJceabjVosVyXHJeLBNCYYnCGUSaWMpLfbtY1tJZianhTRs\nSBEEXUvW4QCGDKGhCkRmJg1U9eqU4osEe/dyXujGjazynT6dA7N92baNWrx6Fa/dzjFn775L4+pN\nUhI9UW8vMC6OedVq1ULf46RJNMixsbzuggWF54R376ZkYGYm9+pwUHGpJNNghAqLUgq3f3s7Pv/z\nc8RaYpESl4LldyxHg8oNCn1f3dfqYv/p/f8+j4uJQ4e6HdCxXkfc3epu1EmuE+mtCyWgTAopAHgc\ngBtAlQCvh7kdtpzw3nvmKSI2m1Jut//z169XqkoVpZKTlYqP5xSWcHLypFLTpilVowYFEvQ9JSUp\ndeSI+fzBgzm9xHv/XbsqtX8/p7rox2JjlXrqKfPkl6Qkpf78M/R9rllj/txq1y76fVu2KDVokFJX\nXcVpMoJQBLvTdquNhzeq3ILcoM6fvWW2so+xK+sLVuUY41CNX2+sMvIyIrxLIVygrE1j0TTtbADd\nAOyJ5HXKJfqf/2C59lq2nei8/z41eLt3L/q9Tif7Iz/+mF7biBHG3sj9+6nlm5XlKbTR0TTg998p\nruDNyZPm/aenUzT+zz8Zok1PpyZt27bmnKPVyoKoUNm0yRyOPXKE+y4sr9ysGTB1aujXEyoM+07t\nwxebv4BSCtc3vx71KxUexSnqdV+uaXYNfrnrF8zfOR8pcSkYdOEgJNoSS7JloawTqsUN5QHgCwAX\nANgF8UCNHDqkVOXKHm/P4VDqgQf8n+t2G71CgLM1J00yn7tunVKdOyvVvLlSw4YpVVCg1DPPmKX9\nvvjC855Bg8zre5+7ZIn5Or4zQR0OpSZODHy/ixfTC42JUapqVaVWrQrl0/Lw00/+5f8Cee6CoJTa\nenyrSh6XrGyjbcr2gk0lvZikNh/dXPQbhTMGFMMDjVglhaZpfQDsU0rJDBx/nHUWlX/696f4/PDh\n7A/1h6aZc55WK3Oo3uzaxQKdZcvYqvH66yza+eorcxvN1197nu/c6b+AyWJh3rJbN2PLxx9/MAfa\npQsF5WvU4ISWRx8NfL9WK/tOHQ7mIufNC3xuYXTsyOphh4NFTYmJvBfp6RQKYfji4cjMz0S+Kx/5\n7nxk5mdi2KJh0d6WUM4pUQhX07QFALwH6WkAFIDhAJ4Bw7fer/ll5MiR//6cmpqK1NTUkmyr/NCo\nERWKguHrr1nF6nSy8vSuu8zh29mzjQOis7MZtmzpM4bIYqEO7smTVDoKJAmo+3hOJ2eHtmrFop/u\n3dmqYrHQgK1fX3hRk9sN9OljHHL94otA797mvQXD668Dd9/NyTcXXEANX0EohGPZxwytJQoKx7OP\nR3FHQrRZunQpli5dWqI1IlKFq2laCwALAWSDhvNsAAcAtFFKHfU5V0ViDxUSvQq3WjXjCDGdt9+m\nlqx3tasueNC1K9tVdFlAh4M5wzVrgPnz2avpKxnoy3XXUZv3t988xywWvvettwK/Ly2NHrd3y0lS\nEvDOO1QFEoQI8/bvb+PJhU/+qwzkiHVg7BVj8chlj0R5Z0JZoThVuBEJ4Sql/lBKnaWUaqiUagBg\nP4CWvsZTCJHERLaO+DOeAAt24uKMx5Ti+WvWcN6lHurMzqYH+tRTwDXXAMnJRYdBZ8/mAGxv3G5j\ncZM/UlLMBT4ulzkEXZZxuxm23rq1dPt1hbBwX+v78ES7J5ASl4KUuBQ80vYRPNy2/AwZyMrPwoHT\nB0wCDUJ0Ka1ucoVCQrhCmKhWzTyIOjeX4ddmzej1eXv7Lhf7PmvUoIxfUeICBQWUwHM4PMccDmDA\ngMLfZ7EA337L6ycnc8Tas88WL3xbFEuWsLo3OZn35D16rbhkZTH32rIlJ9i0a2cMRwtlHk3TMKrL\nKKQ/nY70p9MxtutYaOUkb/7KL6+gystV0OTNJmj4ekP8nfZ3tLck/EOpGNB/PNEi3BQhLPi2obhc\nwLFj/LlHD6Px0zQWHr3/PjVrg5kQY7UCDzxAY12rFo1zv37G8LA/OnWimMSiRVQQeuaZ4O/p6FGK\n048b51E88sf27Wy32b2bhn7hQqOovFKUEVy0KDTD+txzwLp19NqzsykyEcr+BaGYrNi7As8vfR75\nrnzkOHOw7/Q+9Jnex3BOrjMXQ38cisZvNEbHyR2x8cjGKO32zEP0zCoa/foZdWsdDh4DOI3kpps8\noVqlWIjz8MPmnk5/384dDo4fe/llGuWDBylF2KMHQ7R2OzBsWOD+1pQUjkk7coRGMRgOHgTOP5/r\nPvccQ9i//OL/XN+CgPx84KefaNjdbhrTyy9nLrdhQxY/FcaSJcA993AOqre+b24up7sIQoRZfXA1\nXG7PF1u3cuOv43/Bu27kllm3YPK6ydiZthMr9q1Ax8kdDYpIQuQQA1rRGD+eRi0xkRW2L7/MCliA\nWrwffsj2E29ycuhZ6lq4mgZUrkwjt3QpQ5cNGnAe6KuvGt87dCiwfDm9V6eT+rrTp/vf27x5DDF3\n6cKq3ffeK/p+XnmF3mJeHq+Rnc1RZP5ISTGHoa1W3vfMmZT8y8riSLP0dAroB+KLLxgCfu8985i3\nuDhKAwpChDmn0jmItcQajtVIqPFv+Nmt3Jj11yzkOD2Fgy7lwtwdc0t1n2cqYkDLC4cOcdj0k09y\nDFkgYmMZks3IYKvKAw+Yz4mPNx+77DKGWStXBi66iEaxenWOTlu5kv2k+/ZRYch7tNnixUbvLDub\nfae+5OTQ88vKAk6f5nseeQT4208+Jy8P+OQTGuutW82h5UDh17596Vl6G1G3my1AO3YYe2EB5n8D\n8cwznmpm/dt+bCy/mDRpwi8qQtT4YdsP6PpJV1z5yZWYv3N+tLcTMrnOXNz7/b1o+HpDtPuwHdYd\nWuf3vD7N+qBnk55IiE1AclwyEm2JmHm9Zzi3Bs00HUaDhriYON+lhEgQqvJCuB+o6EpETicVgc4+\nW6n69ZV64w2lXK7Q1jh4kOo9VqtH9Wf27OLv6eeflbLbPUo+drtSy5f7P9ftVqp3b4/qkN2uVGqq\n5x7OOcesXnT22eZ1du70ryA0Z47xvNxcpVq25Lk2m+fhrXj01FOB7+2bb6jS5H2dxER+Xt7Xt1iU\nat068Dp16pjv6+abqaCUn1/45ytElO+3fq/sY+wKI6EwEso+xq7m75gf7W2FRP+Z/VX8mPh/7yHx\nxUS1J32P33Pdbrf6dd+vavaW2erg6YOm159b/JxKGJugMBLK9oJN1X21rjqVeyrSt1DhQDGUiMSA\nRppnnjEaK0CpCy9UKicn+DWee85jPPVH06ae1w8eVGrkSKWeeEKpX34Jbs1ff6Ug/M03e96zbBkF\n4du3V+qTT3hs3ToKwntfOyFBqQ0b+PqAAWZDU62a+XrZ2WYDarcrtX278bxPPjGf53AoVb06De4D\nD1CeMBAffmh+v8XCz/vxx2mMExL4ZWb37sDrDBtmlCq02/mZCVEndUrqv4ZHf/Sa2iva2woal9ul\nrC9YDft3jHWo91a/V6z13G63+nTDp2rglwPVE/OfUMezjod5x2cGxTGgERWTF8AB0t7CBgDF1l96\nCfBSYCqUU6fMFa56te2hQ8zHpafznLffBmbMYG9nYVx2GR86K1cCPXt69rp+PUOlL75oVDcCGCLV\nw7hXXcX+UP19VisLfbzJyQH+8x9PKFYPIb/8MouKvDlxwnyv+fnm6uJAtGlj7tNMSuK+XnmFxUin\nTgH16gWefwpwzqnFAkybxgKpl182fl4l4c03WVHscgH33cd5quWkpaIsoPnpiCsvLSnAP2FXLQZO\nOA3H4q1+UivBrKdpGHzhYAy+cHC4tigEieRAI41324iOy0Vhg2C5/npzZe1NN/Hnt9/2GE+Aeb4n\nngi81qlTZoMIcI6nt6HPzgZeeMG/SELVqp4imsGD+bDZuMemTYEpU4zn33sv85B6rlQp4LPPWIDk\nS2qqMYcZG8s8LEAVpHbteJ0GDfznglu0MPel5uV5vqxUrco8aWHGE2Dh0ZgxbPP54w/KDoaD6dNZ\nDX3oEIu0JkwwF2YJhfJk+ydht3r+f7Bb7Xi83eNR3FFoaJqGYR2HwRHLvw0aNDjdTjjdRSiBCWUO\nMaCR5uWX+cfYm7g4NuQHS6dO1LRt2JCSePfcQw8WYEFOIO/UG91TrV6dHtVrrxlf9/cN3uk0G1uH\ng20kuuKRprFS9cABygxu2mTWpv3+e2OhUV5e4DaQiy+m1161Ko3n5ZezjUQpyhGuWsW1du+mJu+h\nQ+Y1jhwxPs/NBX780f/1SpvPPjML+3/2WfT2Uw7p1aQXZg2YhR6NeqBno574buB3uKLBFdHeVkiM\n6DwCI1NHwmqxQkEhz5WHoXOG4v0170d7a0IIiAGNNH36UIWncmUaBLsdaN2aocRQ6N+fU1MOHaLH\nontQ111n9k79KQNdfz0ntBQU8DF8uLFvcuhQs8LQ4MHGY3Y7cPvtFFDwpVo1hkX9qRmlpBifx8Wx\nxSYQ110HHD/O0O3ChTz32DEaTe+KXIuFoWdf6tQxepiaxi8eZYHKlc2fUaVK0dlLOaZH4x6YO3gu\n5gyeg64Nu0Z7OyGjaRoOZRwyeJ3ZBdkYv0Kqu8sTYkBLg6uuole0ahUfy5b5byUpDpdfTo/N2zv1\nbbFYt44C8N7GJz/faHxatqRB7dePudBp05ire/pp7tVqZV514sTQ93jnnZ6fNY3e5V138fnevcwt\nJiRQbnDtWv9rJCWZc5tuNw2SL6NG0egmJPALQHKy2eOOFs89x1aYmBga0oQETzRBOKPwp2vLWhah\nvBCRaSwhbUCmsUSWdesYAvYN6yYkcBrK4CAKD7ZsoYh88+ZA3bqhXf/vvzlyTA9bahr7KPX+zqZN\nubZu3FNS2LNZrZp5rdGjWXyTl0dvuGNHhmb9eb0nTwLffMN1r77av9ccLXbvZp+r00kxh/Ikqi+E\njQ2HN6D95PaGCTHjrxyPoW381AYA2J2+G7/u+xVVHVVxZcMrYdHE/wknxZnGIm0sZZlt25Rq3lyp\nmBil6tY1tlHs36/U4sVK7dpV+Bq33mpuM7FYlOrSpfB2EJ0XXmALR0oK2zo+/zy0e5g2TamkJOP1\nrValTp1iG4lvi09KilJz55rXSUtTqlUr9njGxLCHMzc3tL0IQhnjt32/qR6f9lAdPuygJq+drNxu\nt9/zFv29SCWMTVBJLyapxBcT1ZUfX6mcLmcp77Zig2K0sYgHWlYpKGCl6cGDHiWcpCR6dAsXMixq\nszEUO24c9Wz9MXAg21q8adiQo7l8i5t8+esvDtH2rs6129lq4p13LYwFC5i/9Z5eYrPRI83IYMGR\n95zQhASKvbdta1xn0CDgyy895zoc9Egfeyy4fQhCOab2xNo4lOkpmEuITcAHfT7ATS1uiuKuWz/Q\nYgAAIABJREFUKhZlZh6oEAb27mV7iveXC4sF+PlnGs+cHLak5OSwIGnXLv/r3HOPucjomWeKNp4A\n14w16nBC05jPdbuZD23bljneP/7wv0bXrgy1JiSweMjhAF5/ndevVIkGMCHBkw/s0oW9nL6sWmU0\ntNnZhUsaCkIF4nj2ccPzAncBDpw+EKXdCDoipFBWqVLF3EJSUMCcnq/xs9nomTZoYF4nNZU9mKNG\n0QA98ICxqKcwmjc37yE2Fqhdm/M833iDhkzTqJ27caN5DxYL8MMPwKxZbHVp29boXY4bRwO7di09\nY+9pMd40bcp79BZjaN48uPsQhHJOy1otsebgGrgUf/+tFisuOztMwh5CsZEQbllm1Cj2kTqdNFx9\n+1LwoFYtY0jUbmdI1neYdjiYOhUYMoRG22plT2fHjqx+TU/3nBcbC4wdS7H7SLB/P6t1MzLo/TZr\nRqPtT6hCECoYB04fQI+pPbD1xFZYYMHEHhMDFhsJxaM4IVwxoJHC6aRBWbCAo7vGjy+egVuyhLJ6\njRqxjUTTPHlFTeN1PvgAuPnm8N+DTmYmsGEDw7QOB3tbGzQwTkWx2Xi/hakglZSsLIZybTaGeYtS\nExJKTG4ugwenTwNXXMECaiF6nM47DUesA1aL/O6HGzGgZYlbbmHoNDub3lvVqmwH8de3WByyspgn\nrVOHfY7B4HJR2/arr9gmMmEC+z+LYvNmSug5nTTalSqx/eXNN3l/FgsLnP74g60jEyfynp97Drjt\ntpLdpxA1srOp+bF3L391NI1dQ7qyoiBUJKSIqKyQn0/NU733UR8EPW9e4Pe43SyoSUykMRo+3FhA\n5EtCAvsHCzOeSvGakydTYu+JJ9i0v2EDK10vv5zqRkXx0EMMnWZn03AfPcripUmTgG7dmLdcvZq6\nvA8+yD7HnTsplvDJJ4Wv/fnnzG/Wr8+Qta9YghA1PviAdWSZmfzPnZ3NmQCCIBCJA5QmhU2MePll\n5jd1o/vaazQmcXE0lrff7l9cIBBKUb5PN9puN4/5atLOmlV02NW7lQZgYdH+/aymHTKEx3JzWRDk\njcsF3HEHtXPfeIOhV28WLuTr+j2PHw/8/jvQvj3Qq1doesFC2Dl82PjrArCDSRAEIh5oJLDZmJPU\nC1ysVhrBHj0Cv+ebb8wi4+PHcyLKs89yysjRo8HvYfFiYP58eoxZWXQhfP8aalpwecR27czHfEO/\n993n/71uN71Qf8VF06YZ7zknB5gzh+O9OnUqOwLwZyhduhg7oGw2FnULgkDEgEaKyZOpI9uxI0Oc\na9YULhpevbrZQ3W7+cjP51f/t94K/P7t2yk0sGoVnx8+XPSMSU3zjEUrDN95poCxBzM72yzW4Pv+\nr782H09KMsvwKeUJeT/4YNF7EyJGt278Dme3M6XduTPw0UfR3pUglB2kiKis8NdfbNPQB1Xn55tz\noA88APzf/5nfO20aQ6mxsSz0uesuTle5+GL/xk/nvPNYIFQU3buz8teb1q0ZbnW76S3+8kvha5x/\nvllsYdcu7jEz03/us2pVTmUJhMtFIf2dOxnu7ddPBlNHAKX4nycY7Q1BKK8Up4hIcqBlhfPOo4H5\n+mt6ZevW0avTDaDDAVx7rfl9eXms7MjN9Zz74Yesfp02Dbj1Vnpzycn8V1fzsVop8h4M/foBK1Z4\nwq0OB48BrCzesMH8Hj087HJR9GDSJPM5DRrwve+8QwH577/3fIGw29m2Ewi3m68vX84QdUICcPfd\nMpw6AmiaGE9B8Id4oGWV/HyGML/4ggbopZdoDH05eBBo3NjoaSYnA1Om0OAqRaOUn89c5r59nlaU\nlSuDm5OpFFtSJk2i4RoyhIYqJobVve3aGae92Gysrt2xg8f79KGnWRTTpwOPPkpD3bcvB3UH0txd\nuZIygb7XPXSo8FmjgiAIfpA+0PKOUjSYixezrePBB9nWUhguF6X1vAuMHA4atoYNjefm5zN36XIx\nXFyYik9BAQXqZ8ygAR87lhWzvjidDJ9u3cr1bTZ2269fX3yhA6WKDsUuWADccAP1gHUcDoak69cv\n3nWFYrF2LevEYmMZBBCxBaE8IuPMyjvPPsuRYQDHdjVvrlROTtHvW7dOqRo1+B67PfSRY/549FHj\nqDG7Xal58/yfm5am1B13KNWypVK3367UyZPFu+bcuUpVrcpxaxddpNSePYHPPXFCqcqVPfuLiVGq\nUSOlnGEa8eRyKTV+PO/piiuUWr06POtWMJYt8/zKappSiYlKbd4c7V0JQuhAxpmVY5xOhiudTs+x\nxEQWyej5xsJwu1lwU7myeYJKcahTh+Fhb269Ffj445Kv7Q/fwdsWC12ZLVsCv2fjRo4527MHuOgi\nhoDDpQf87LMMWev7SUhgJXWzZuFZv4LQsSPT4zqaxl+TKVOitiVBKBaiRFSecTr9Kw9590kWhsUC\n1KgRHuMJUHnIl0Ajy8LBypXGShW3m9W1p08Hfs+FFzJUffo08NNP4RXT9xa1AFik9fnn4Vu/guCd\nggb4K+zvV0cQKiJiQMsK8fFstIuL8xyzWIrXuT5rFttGGjdmI19xPPwaNczHfPtYMzPZ7/rmm5wG\nUxKqVzfvU58RGg18+1MtFhGv98MddxhT6Q7HmS1/vO3ENizetRhHMo9EeytCaRBqzDfcD0gO1MOp\nU0oNHKhU7dpKtW7N3GaoLFrkSUoB/Hn8+NDXeewx5lS9c6Cvvup5PT1dqQYNuH58PP9dtiz06+i4\nXEr17q1UQgLXs9uVevfd4q9XUl591fM5WixKpaQotXdv9PZTRnG7+etVvz5T0FOmRHtH0WP4ouHK\nPsauUsalqISxCWrO9jnR3pIQApAcqIDbbzfnKZs18+QSlQJ+/pn5zUsuCVwymZcHDBjA3kyAbsX7\n73s8s5deAkaO9PRtAsELMwTC7Qa+/ZaDt9u04SOaTJ0KzJxJDeLhwzlSThD8sPbQWnT6qBOyCzxh\n/0RbItL/m44YizTRlgdESEFgyFPTjOFQvZdSKRpCXazB6WT/wfXXm9eJi6M+b04Oz/UOLQPAkSNG\n4wkAJ0+WbO8Wi3+xiGgxeDAfglAEO0/uRIxmNJQFrgKczDmJ6gnVo7QrIdJIDrSi8cgjrN7V+yjt\ndvZwAlTt+fprVn5kZNA43nZb4SPE7Haz8QSAnj2Nya/4+MLF8gWhAnN+jfPhdDsNxxJsCajqqBql\nHQmlgRjQikaTJuxsf/hh4J57OJGld2++pqsQeZOfby6lDIYePTiCLSmJlb+9enEeqCCcgTSv3hwT\nu09EXEwcEm2JqBRfCT/e/CMsmvyJrchIDrQsceAAx6CtX081nalT2aoRLv76C2jVyiP7p2lAvXoc\ngC0IQolJy0nDkawjqJ9SH/bYADKUQplEpPzKM243cO65FBRwuXisUiX2QoZT23XaNIrPu93UwZ0/\n3ywOcOAAw75Hj1Iub8CA8F1fEAShDCIGtDyzdy8NqK8o/Oefhz+36HJRfKBSJSA9HXj9dYqw9+7N\nwd3Nm3umtgAcj/bBB+HdgyAIQhlCDGh5Ji2NHqG34UpIABYupPB7YRQUUCUoJoYCCsHOnjp9mvJ5\nhw/zug4HQ8d//WU8z2Jh/rR27dDuSRAEoZwgUn7lmcqVgfvv9yjvOBxAhw5F90KmpXFU2OWXA+3b\n8xGs/N+XX1I/Vzfa2dmcquKPwgZbC4IgnIFIH2hZ4tVXgU6dgNWrKcN3661mSTlfHn2Uczd1I7hx\nIzBqFCX8iiI7u/AWFp34eJlRJQiC4IOEcMs7l1wCrFtnPNatG4uDimLnTk4x0dtY4uOBa65hH+mU\nKRReSE4Gli0LbiC2IAhCOUVCuGciF19sFDqIj2erSjA0asQca8uWnGQyeDBlACdPpmfqdnNgtRhP\nIURycoANG1jQLQgVFfFAyzvp6ZzisnMnPcZLLqH3aZceNCE6bNoEdOnCrEJBATB0KDBhQrR3JQiF\nI1W4ZyouF8XiY2KApk2LzpsKQgRp3Jjf53QSEiirfOWV0duTIBSFhHDPVPT2lXPPLbnxzMoKvopX\nEHxQCti1y3jM6QT+/DM6+xGESCIGNBAuFzB9OjBuHLBgQbR3E3ny84HrrqO4QkoKcNNNjL8JQgjo\n6pDeWK1msStBqAiIAfWHUkDfvsCQIcBzzwH9+rE1pCIzahQwZw7dBacT+O474MUXS75uejrw4IOM\n340YYR6BJlQ4vvqKbc0pKUzF33abDOoRKiaSA/XHihUc15WZ6TkWG8t5l4mJ0dtXJGnXDvjtN+Ox\nyy9nC0txyc9nBe/ff9Nw2u0sePrxR/NUGKFCcfo0Z6tXq8acqCCUdSQHGi7S0sy5xJgY/lWoqDRs\nyFibjtXKNheAMoHnnccWmfPPN0v9BWLlSmD/fo/XmZMDLFlC6UChQpOcDLRty4yAtzqlIFQkImpA\nNU17UNO0vzRN26Rp2kuRvFZYad2aYVydmBj2SZ51VvT2FGleeQWoXp3zPZOSeK/jxtELT01llW9e\nHo1n585G0ftA+FM50rTg1I+Ecs327UCDBvzfJjkZeP/9aO9IEMJPxAyopmmpAK4BcIFS6gIAr0Tq\nWmGnZk0KDDRsyLBjq1bA4sUVuz2kVi0ayU8+AT79lPG3mjX5r3cxkVI0pFu2FL1mmzaM4cXG8nl8\nPL+ciCh9heeqqzhgKC+Pj0ceMQtmCUJ5J5IW4T4ALymlnACglCpfauRt2rCZLTuboci6daO9o8iT\nnMyCqb596YUCnEXqW42bnx/cjFK7nZ/dgAH8EvKf/7BQ6QzIf+bnA2vWUJq4Ijrc//sfUKMGQ7QP\nPsi6M538fMoz+5Y2rFlTunsUhEgTSTH5pgAu1zTtRQA5AJ5USq2O4PWESNC4MTBoEFt68vIoG3jb\nbRx7FgzVq9OjPYM4fpyDdA4epBG56CIGNCqKONTs2cATT3jahSdPpljCS/8kaWJj+V3s1CnPeyyW\nM+M7qHBmUSIDqmnaAgA1vQ8BUACG/7N2ZaXUZZqmtQbwOYCG/tYZOXLkvz+npqYiNTW1JNsS/KFU\n8T2/994Drr6aYdvmzfmzEJChQykmoDvua9cynfzCC9HdV7j48kuj1kZ2NltXdAOqacBnnwE33MBa\nNJeLId3u3aOzX0Hwx9KlS7F06dISrRGxNhZN034EMF4pteyf5zsAtFVKnfA5r+y1sVQkDh5kSHbt\nWsbbpkzhxBUhYrRoYVbeufpqttYWlxMngN27gXPOAapWLcnuSs5jjwFvvEHDqHPppcCqVcbzdu/m\nZL6zzqJHfgZE7oVyTFlrY/kGwBUAoGlaUwCxvsZTKAWuuorVG243+1gHDAiuACgUcnKMf03PcC66\nCLDZPM/tdhqY4jJjBsOfV1zBfz//vOR7LAlPPEGhBJuNBeoOBzBpkvm8c84Brr8e6NhRjKdQMYmk\nBxoLYDKAiwHkAXhc90Z9zhMPNFLk5fGvm3cVS0IC3Yc77yz5+mlp9G5/+YVJruefB559tuTrlnNO\nnmSnz65djJy3bcvaKe+pc8Fy9CgNkXfXkMPBCtdoeqJHjgDTpvFXrG9fRvYFoTxTHA80YkVESqkC\nALdEan0hCGw2/tX2/uuraSzsCQd33MEqW5eLjxdfBC688IwPEVepQqd/61bmAJs2Lb4HtmsXi3K8\n/xNarTweTQNasyZDuYJwJlOBGxsFaBrwzjt0WeLjKUPYujXQu3d41v/5Z6PMTHY2sHx5eNYu51it\nFG1q1qxk4ctzzjF3ERUU8LggCNElkm0sQlng1ltZ1fLLL6zmuPZaJq7CQc2arG7RiY+n9IwQNmrW\nZBH03XczoJCfD3z4IfUpBEGILiImLxSflSuNU5IbNQJ+/bXiNDyWIY4cYdi2YUMKGAiCEF6KkwMV\nAyqUjH37OLElMZGhYe/yU0EQhHKCGFBBEARBKAZlrQ9UEM5IDh+m7G/37sDEiRVTC1cQBCkiEoSw\nkp4OXHIJcOwYBdZXrOBor3feifbOSocTJ/g45xyJ5gsVH/FABSGMzJkDZGR4ppNkZwMffEApu6Qk\nziVfuza6e4wUL74I1KnDwTt165rlDAWhoiEGVBDCiD9FQ5cL+O03zibfsoWSfMeOlf7eIsmKFcDY\nsVQmysykglKfPtHelSBEFjGgghBGevak+JM+ez0+nm233nlQpSgsv3IlvdWyjFKcqb5+vVnQwZuN\nG83zP3ftEolkoWIjBlQQwki1apxK0qcPBeQfeMCsRJSTA9x3H4uM6tUru4Om8/PZ5tu6NdCpE5WV\njh71f26jRp4vDTo1aoRPs0MQyiLSxiIIEWbYMODNN5kPjYujYfL2SOvWpTh8WWPcOGD0aI8Or9VK\nmeOvvzafqxRw772cnW6z8f7mzGHuVxDKA9IHKghllO+/52zMnTuBL75grlBH01h05OvBFcaJE8CI\nEcCOHcypPv44vb1PPuG1atcGnnmGUoDF5cYbuVdvGjdmVXEg/vyTqkkXXihyg0L5QgyoIJRxliyh\nF5eV5TlWvz6HTwdLVhbljQ8cYF7S4QD69QOaNAEmTKCna7XSgG3ezNmd69YBX31FlcU77wRq1Sr6\nOr4eaGws9/7VVyHdsiCUC8SACkI54L//5UhWm42GbtEi4OKLg3//d98BgwYZC5BiYmjgcnM9xxwO\nXqdBAxq+nByel5wMbNhQtO5/fj7VGX/9ld5xrVocwCNavEJFRJSIBKEcMH48sG0bvdG9e0MznkDg\nylbf40rRCD72GL1SpRgqPnUKmDTJeG5uLnOWs2dTDAKggV+wgKHnn39meFaMpyB4ECUiQYgCdevy\nURy6dKF3mZ1No2m3Mw9apQrw5ZfGop+rrgJeecX4fpfLYyQBGtS2bYGDB/k8Pp4tNg0aMD973nnF\n26cgVHTEAxWEckZKCltl+vWjbOC999JwfvAB22bOPRfo3JleY716wIABNLg6Dgdwww2e5y++yBxs\nRgYfJ08C999f6rclCOUOyYEKQpC43cCsWRQIaNkS6No12jsKDpeLeddPPmEbzZgxwG23eV7v35/3\n5U2zZlRNEoQzheLkQCWEKwhBoBQ9uTlzmFeMjQWeegp4/vlo76xoYmIYxvUN5eqkpgLz5jEkDDCE\n26lTqW1PEMot4oEKQhCsWsXco3f7ic1GZZ6UlOjtKxy43Ry/9umnzHl27MhK34SEaO9MEEoPqcIV\nhAhx4gSLcryxWo3FOMGyYwe9vnr1mIs8eTIsWyw2FgsweTLv5ehRYPFiMZ6CEAwSwhWEIGjVyii/\nZ7FQqKCoXkpf0tOBdu1oNN1uqvbs2kUP11czt7TQQ9JiNAUhNMQDFYQgqF4dmD+fXqPVSiWgJUtC\nF0v/9VejFm5+vkf+rrQ5dgy47DK2wTgcwPvvl/4eBKE8Ix6oIATJZZcBe/aUbA273ejJAqySjY8v\n2brF4YYbOAnG7aaQwiOPcOJK+/alvxdBKI+IByoIpUjHjuzT1A2mwwHccgtQqVLp7+W336hMpJOf\nD/z0U+nvQxDKK+KBCkIpYrUCy5cDr79OOb927TgX9O+/gXPOCW0iSzAsXcoez0qVKI6gT2fJzqZy\n0aFDnnPj4oITmddRCvj2W+49OZlrVq7MKS5xcWG9DUEok0gbiyBEiYICihgsXEjD2awZK2DD5Y3O\nmAHcdZdnOkvlyhx1NngwjZ7VSiMYG8sCphYtaNxjY4teWymuM3s2w79OJ/PB8fEcefbbb9EJSwtC\ncZFpLIJQjhg/Hhg1yqNda7PRe/v00/CsX68esG+f53lsLD3FkydpAAEaueHDgebNgauvNhrP48cp\nEVhQAPTpw7FrOuvWMRytiy9443BwgPidd5Zs/ytXsuiqdm3guutCL9gShFAQJSJBKEesXOkxngBz\nkKtXh29977UBeonexhOgF1q/PnDttcZzDxygXGFmJs9/9llgxQrggguoWjR6NA2rP/LzWeFbEt57\nD3j0URZYWa3Au++yClqMqFCWkCIiQYgSLVoYw5xWKwuMisu333L6yg030EO86SajiLzd7j886y/v\nOWYMkJZGI5ybS0P66KPMp/bvT2MayIDGxlIoori43cBDD9G7zcuj+tPvv9OACkJZQjxQQQgjhw8z\nfJmWRkNz5ZWBz336aWrrbtnCHGjlysDbbxfvup99BgwZ4gmpzpkDLFtGj+3LL4HEROC117ivIUM8\nog09enAUmr/78K7QVYoqRS+84D9sq5OcDLz1FsejFRc9p+rL8ePFX1MQIoHkQAUhTBw5whBnWhoN\nQHw88PjjwMMPU4jBH04nsHYtw56tWtFLLA7nnw9s3mw8ds89wDvvmM/9808qH9WuDXTr5l8B6aOP\ngKFDPcbS4aAHOns2sGmT/z0kJbFwqXfv4t2DNxddxH3qQ8IdDmDDBhYoCUIkEC1cQYgiU6ZQqk/3\nnnJzOWuzQQNg7lz/77FagTZtWJBTXOMJeAxNUccAGtvbb2f7TCD5wNtv57SZhAR+ERg0CBg5kqFV\n77CwLzZbiBsPwJw5/EIREwPUqAF8/bUYT6HsIR6oIISJ554Dxo41FunoJCaygCeYFpFQUQqYOBEY\nMcJTOGS3swe0TRs+z8kBFi1i3jI1leHi4pCZyTX/+ovPLRbmLOPigEaN6E1LD6hQHpE2FkGIImvX\nco6mvxyh3c4pLLVrF72OUsELy+fksMVk+XJ6nMnJDCM//TSwcydw+jTQoQPznocP8z1xcSzKadAg\n+HvTue02YOZMFvcA/EJw0UXM9T7zDMO4glAekTYWQYgil1wCfPMNcN99FCrw/l5oszEUWRgLFwI3\n38zRaeedx7WKClv+97/Azz8zhwowbNypE/dw9CiPaxofetVsVhZViebMCf0ely3zGE+Aa55/PjBu\nXOhrCUJ5R3KgghBGunWjp/nmm/T0kpL4mD3bPE/Um717gX792D/pdrOApkkToG5dhl4DsWIFjaZO\nTg7wxRf0NnNy6JU6ncaWE7e7+KL4deoYveO4OEoQCsKZiBhQQYgADzxAMYIVK4CDB4HLLze+npfH\nsOvSpTS4jz3mv69y/36gVy+e64/GjY2G2WZjGFf3SP0RH2/eT7C89x7XT0zk45xzWGlcUrKyKGP4\n00+B+0sFoawhOVBBKGX0odoHDtAbzMlhuLew/w2uv56epS+HDrGo59QpPj/rLOCNN9iDqhcUxcVx\n8LfudXbpwvCw3U7DlZAQ2jDvw4c5C9VuZx+pb/XwiRP8glCrVnDrHjjAUXGnT/MzaNCAXzwSE4Pf\nkyCUFGljEYRywPDhzJFmZNCAud2FG08gsCGqVYtCDJ9/Dnz1FbBxI9CzJ/s4a9akEbr2Wh4/dYrG\nbf58er1167IaNyUlcJuNUuZ2mLPOAgYOZMjZ23i63cCtt7JQqlEjGva0tKI/j6FD+UXg9Gl+Jlu3\nsppZEMo6YkAFoZT566/CQ6y+xMdTmN13ELdOQgKNZrduHmnAAQPoKWZkANOns3fT4WD4taAA6NqV\nnp/TyXP692eoWUcpGvr4eHqw3h5tIN55h0Y8P5952Y0bWcxUFNu2GY10Xp6nTUYQyjJiQAUhQhw8\nyBynb8FO+/aBRRMcDooVPP4420PatmWrSN++9BSvuII/f/ttyfZ18qTxWE6OcZj21KmU/svPp3Gb\nM6foXOcvvxhbePLz2S5TFG3bGntHHQ623ghCWUfaWAQhAkyfzlmcNhsNyfjxwIMP8rXhwzl1ZckS\nhmYbNqTXlZsL3HILhdxjYhjerVOHHiLAdZYs4c8LFwL/+x9DpsGSk8P8K+Dfmz1wwPPz3LlGY5ib\nW7SY+7nn0mPVq4ItFoZyi2LSJMoQbthAz7dnT+Zsq1fnvXftSq3flJSi1xKE0kSKiAQhzJw6xdyk\nd8jTbmdYUp+pqRTzfkoxZ+gvx7lxIyX+dAPqS9OmzBd6c/QojV9MDOd76kbnnXeARx7hdSpXZnjX\n+3+72Fi22vTsyedPPw28+qqnIlbT6BV6e6m+5OSwB3XrVhpPu53zPIMRbFCK97txI79MPPSQcU5q\nt24cBi4IkUKUiAShDLB5M6tKvQ1fSgorX1NT6QX+/DPDlp07B9aPPXqUQ7G9hQu8adSIxUA627cz\nHKrnV5OTOdbs0CEaP92j1DT2puq5SpsNaNaMXrG+l5MnKQxx4gSNW0wM93zBBYXfu9PJUG5eHveS\nnFzkxwWA1+7alT/n5tJwe/9ZsNsLnwIjCCVFlIgEoQxQr545RJqfT4/x779pXPPyaCDq16eXFhfH\nnOOqVVT2SUoCvvuOYVHdo/M2IA6HJySs88gj9H71a+flUQD+0kuNHq5SNO7vvstrN2rE93ob8ipV\nOHXlu++4TvfuDCcXhdXqv8c0J4eh2u3baczvvNO4p5tvZhVuIEQiUCiLiAcqCBFg/nzguutoJJxO\nYPJkDrju3p3KQrqRi48HnnySOrqLF9PQxMTQyLndNJwOB3OosbEs7snOpgG6916jEbr4YuYRvend\nmyINffsyp6qTnExPOJT+z+JSUMC+1z//pHfpcNATT01l/rd/f7bb+HqYuvC+1QpMm8Z2HEGIFOKB\nCkIZoXt3hk737qXnpuci//7b6J3m5gLr17MoSA/Verd0uN00wC4X9WuHDAl8zR492BKi5w4dDoZF\nFyygB5eXR2PkdNLjy88vnckpP/1EL1ovLsrOBn78kV8y4uLYw3rRRazY9Z7/OWQIi4m6dgVatoz8\nPgUhVMSACkKESEwEmjc3HuvQgfJ8urG02WjgAuU5gaJVinRGjwb27aNB0jR6qd9+S8OUm0tvVg8d\nf/wxvd4VKwrX6A0HOTm8ti9OJx8//MA9P/oov3C4XPx5zJjI7ksQSoqEcAWhFDl9mmHVVas8CkSB\nBl/rJCWxgjeYHCRAo2Sx0Nu98MLAAggOBwubunUL7R5CJT2dmr1paf7bZ1JSaECvvBI4coT3KzJ+\nQmlTpqT8NE27SNO0XzVNW6dp2u+apl0aqWsJpU92NnDHHWzXuPBCejJC0SQnM6R54ADlvWiYAAAg\nAElEQVTwyivBhVBr1+bnHCxWKw3okSPGSS2+5OTQG44Eu3czTPvXX0ClSixW6tiR8oF2u7mo6ZJL\nuOdatcR4CuWHSCoRvQzgeaVUSwDPA5gQwWsJpczgwcCMGewn3LSJ+TfvlgqhcDZuZEVqMMGXrVvp\noYXCnj2cCFPY+koBx4+Htm4wTJ3K0PXNNwOtWjEU26QJZ4nu3cvflwsvZJHQOecwhF2tWvj3IQiR\nJpLZDzcAXTukEoADhZwrlCOUYnuD0+k55nIB8+YVPQBaYHHMjBn8ubDcpzd33cWq1bPOKvw8pRgZ\nmDmzcO8ToPfrcAR3/cJYuRJYs4YtOZdfzvvLzfWEjl98EbjxRrbxAGybWb++5NcVhGgTSQP6KIB5\nmqZNBKABaB/BawmliKbxj6+3AY2Joai5YMTppB5uRgY1cPfvp/H0binxxWIx5wpjYymK0KtX4deb\nMQP48suijaf+37Bv36BuIyBvvAEMG0bDbbFQGMK3KMlmY0hXN6CCUFEokQHVNG0BgJrehwAoAM8C\nuBLAw0qpbzRNux7AZAB+yxVGjhz578+pqalITU0tybaEUmDsWOCZZ5gLtdmAGjU4s1LwkJ/P2Zsb\nN3qqUEeP5pcNbzSND91oxsXRM/U2ogUFHE9WFJs2FW6ck5PZe1qpEkOtZ58d2j15k5PDHlbvyTJL\nl5p7S/PyzNXIghBtli5diqVLl5ZojYhV4Wqalq6UquT1/JRSyiQHLVW45ZcffmDY9qyzmG87k8S+\ns7MZJk1PZ/WoP4m7//2PE0y8q2CbNWPu09s4Wizsg/zjDwqoT53KFpMRIzye3fXXc8ZnUcIHn37K\nEWL+jKjVSuOdl0cjXbcuQ6nFjRwcOkQhBG9vNyWFXu0nn3iO2e0UUQhGE1cQokVZE1I4oGlaZ6XU\nMk3TugLYFsFrCVHgqqv4ONPIzmZxzL599AytVuCLL9ie4s2uXeYWksOHacR8Deill1JYQC+m6dKF\nVavr1tHwdO8enGrQoEHMT//wA/cVG8vcqdXKlhU955qXRwP49decAFMcatZk5GHfPk+xktPJnKg3\neXkM9b72WvGuIwhllUhW4Q4BMFHTtHUAxgC4O4LXEoRS4cQJ4IYbWHGclcXwZXY2cM895nPbtzd6\nd7GxVNTxFRVwOuldtmgBHDvmOd62LeX6evQo2ngWFDDParHQM169mgZ5zx7mRKdPN+asARrxzMzQ\n7t8bi4WyhM2acX/VqlG4wXdYuNtt9Ii//JL31L8/i48EodyilIrqg1sQhLJPWppSdeooZbHo2kCe\nR2Ki//cMG6ZUbKxSNptSF1+s1NGjSvXurZTdbl4jNlapMWOM79+zR6kZM5SaN08pp9P/NUaNUspq\n5aNNG6WOHze+npenVEaGUtdco1RcnOd6CQlKbd9e8s9FKaVcLuN+HA7Pdex2pRYv5msff2x8LSFB\nqQ0bwrMHQSgJ/9iikOxXJD1QQahQTJ/OMV++FbJxcZ5RXL68+CInpBw6xLxm9erArFnAU095xNJ1\nCgo8A68BYPlyFt8MGUJh+m7dzF7kd99RaF6XxVu3zjNkWylWyCYkcAbo8ePMT9asyYkv4Ww78vaq\nhw/n/dWvz2ky06YxJA0A48YZReOzsjirVBDKI6KFKwhBkp1tNmAAjad30YwvdjsfOjYbx4wdPQpM\nmWIUf/duKxk82Bj6/P13hmcHDfIcW7HCaJAKCqj6A1B84c03PXtetYrG8/DhYO6WuUuXK/ReUYsF\neP55PnzxVy/oT95PEMoD4oEKQpD07m30Gu12Grkffgh+cLQ3kybRW6xalTNEp0xh4ZDO0aPG8/Py\nzNJ79eoZjTPgkf1btsxogJ1OFhI9+qh/Q5aZSaWgggLOGk1MZFVtr17hG2b92GNGg6xPXRGE8oiI\nyQtCEMyeTY8qPd1TJHPNNTSC8fHBr7NzJ71IiwUYOJBhzkB06MCKVu8RXz/8wKpanbw8oFMnas7q\nhUaLFgGtWwMvvww895y5qAcAnn3WOO3kjTfY06lX7ubledpT4uP5ReH994O/z8L4+GOu5XDwM+3Q\nITzrCkJJKE4bixhQQSiChQsZWtW9sIQEYMIE9luGwsaNNBa5uTR2djvDqoEUeg4dYrXqli18/tJL\n9OB8KSgA3n4bmDiRHmfnzhzgbbOxQtafYHxSEifDAKzY7dy5cC+zSRPOGg0Wlwv47DO28rRsyS8b\nglCWEQMqCBHgppvoNXpz4YXAhg2hrdO7NzBnjue5pgEDBrA4qTBOnaK35h0+zs+nx/jddzSG27d7\ncqmaxlznxo00vldcYc7dehvQDz4AHn44sAHVZf/i4xnOff/9wsUXlKLBXLqUazocHAb+8suF36cg\nRJMyNc5MECoKvuO3gODGkPly8qTxuVLsKy2KlBRzxe4999CQ7dpFQ+kt2KAUC4Vq12ao9uabzXvv\n3p19ogUFnIjie38WC42sw8H1cnMZvp41i4O6C+P332k8s7L43qws4PXXOQ9UECoSYkAFoQj0whfd\nyNjtwKhRoa9z443GfKnDQe9WZ/9+tr2MGgVs3lz4WjNnBh6UreN0Aj//zPztrFnMnV50Ee9j/ny2\nxrRvz7DyjTdyPykp9C5nz2Zlcd++RnH43FzmYQsjPd2s92u1ejxeQagoSBuLIBTBBRcAv/3GgqG8\nPOA//2HOMBSUouqO0+kxxPfcA9x+O/B//0fd3K1bPedOmMDc62WX+V/PZjMaUE3zX1mrFK+ZkwMs\nWQK0aWMsEPrjD1578mTqGR8+zJxl7dp8/eBBVu56h4CL0s699FJjX2hMDPWSSyJcLwhlklCVF8L9\ngCgRCWcAM2dSdUdX4NE0pS64QKkJE4zKPN6Pjh0Dr/faa573xcYqVbOmUs2aKRUT418l6csv+b6a\nNc2vt2ih1N69/q9z+rRSDRooFR/PPTscSn32WdH3u3atUuedx3u+7DIqKvnidiu1Y4dS69crlZvr\nf53du5VatMj/+wUhnKAYSkRSRCQIpcDo0WzZ8P5VdzhY7LNrl//3tGjB8WSBmDWL2rNVqlBc4dxz\n2bby9tse0XiLhTnOTZt4vWuvBX780djaYrFwvNmff/of2H36ND3UtDTmTsPRduJ2Mzc7ezbDuykp\nwE8/ca86770HPPIIve38fIpC3HVXya8tCP6QIiJBKEMsX85JJ/ffTyk9bwEBTWOLSSAcDlboFsa1\n1wLt2tFgXn45p7YcP24On953n+faH37IPKg3uth7oGrg5GQaslGjwtez+emnrCDOyaEI/qFDxqkw\nhw+zMjgnh1XIOTnA0KFmcQlBiCaSAxWECPDxx8Add3g8zthYGrzZs/lzYiKl9m691eyBxsayDWTw\n4MKvsWkTVYV0bzM72zx1paCAnt0TT/B5lSoUZ0hJoeHScbn8Cy74sns3sHgx99+nT2giEr57926b\ncbkoBqGzZw89T+9ZozYblZJq1CjeNQUh3IgHKggR4L77jOHaggI+tm2jfu2uXRRy95e9cLmAuXMp\nJP/xx4GvsWGDudrV6TR6oLGxZrUjTaNx9/aI4+Jo4Avjl18YVn7oIYZSL720+BJ/559vLEayWIwe\neePG5t5Vp5MDvAWhrCAGVBAigL8Wk/37gbp1WdWr95F27mzWsnW7PWHLe+8N3D/ZoIHZAOutKImJ\nfNSsybyoL6++Cjz+OFCnDr3SLl3MxhigoR8zhjncW29lqDcri7q5O3cyT1kcbrsN6NmT+01OZu51\n6lTP61WrUsnI4eB9OBzAjBncqyCUFSSEKwhhJDubxUIxMR4NW51evczn6z2f33/v3xuNjaXhrVzZ\n/FqHDmyD+egjnldQwGHVl17KPk+rlepHSUnm98bEcLRaWhr3/OOPDPVu3EiBeoAqRm3a0JArZb6f\n3FzgwIGgPhYTFgvwxRf0yDMz6W37fpHo2xc4coT3f/bZNKSCUJaQKlxBCBNuN43a+vXG3J2msXBn\n9Wr/Xt7BgwxN6rlMbxIT+bo/I6izfj3PufDC0Hota9UyjjazWmnQn3mGzwcMoJHz/t/TYvGMH3M4\n+Hrv3sFfMxiUYgh7+3Z66/osUUGIJMWpwhUPVBDCxNatLI7xNp5xcZSxGzLEmJv0JjbWv/dptwNf\nfVW48QSAiy/mI1R8c4xut/HYyZPmfSUm0mPUjW24jScA3H23pxjKaqUS1AsvhP86glBSJAcqCGEi\nUAi2bdvAxhNgKLV/f09RT1wccN55bO3o3t3/e06dYhvI3LlGgx0Kd99tLCSy24EbbvA8HzjQ+HpC\nAnOhubl86JW93qSlASNGUC/3yy9D39Pmzcx9ZmXRI8/Kogj9sWOhryUIkUY8UEEIE82acTTZ5s38\n4x8XR2GAFi0857jdzFX6itFPncqZnL/8QkGEp5+mRzthAttL7rnHY0x37+a8z7Q0rpeQwIrcUCtU\nR49mAc/06cyxjh9Pw61zxx3sK33lFX45GDqUg7Z9hed1MjIoA3joEPc8cybzqMOHB7+nY8fMwvmx\nsRTdr149tPsThEgjOVBBCCOnTwNPPQWsXcu854QJVPkBaIiefZahyfbt2RPqrzgIANatAzp29LSJ\nOBw0dH36sBhp7lzj+VWqsODGGsWvxJ98QtGIrCzPsfh43kMgo+tLWhq/dOjC85pGw7l3b/Em4AhC\nsIgSkSBEmeRk4J13ONLr/fc9xnPuXFbn5ufTa/z9d7aFBGLSJGOPZXY2MHIkf9YHbHtz6hSwY0fY\nbqNY6NW63hQU+A9tB6JyZWDBArb7WCzsB12yRIynUDYRAyoIpcDy5UaDmJ/PUWOBKCgwH9u8mZ7Z\nJZeYX9M0KvX4sn07PeH4eIaYvYeAb9/OPtS6dYHrrzfPKw2Vnj2Nud74eLaiFJb/9UebNvQ4nU62\nuTRvXrJ9CUKkEAMqCKVA7dpmA1eYJN0995gNj8vFvOXEicaeyZgY5kcbNDCen5EBtGrF3s68PBqj\nLl3oraanM4z888/ss/zuO65RkmxK/focpN26NXtJBw82iiOESrBhX0GIFlJEJAhBsm8fw6iHDwP9\n+nEuaLB/5PfsMbaIWCwM8Qaic2f2dO7d6znmdNKDbNOG17Va+e8TT7DNw3svSgFXXWXUuwXYgrJ2\nLato9XAywJ///JN5VH8TWYKlVSuGp4tCH7pdVIuOIJRlxAMVhCA4epQVph9/TNWeRx9lu0YwZGay\nF1Q3VgDDm0V5ez17GsXabTaGXU+cYDjY6aRXun27uXho/37/hqyggMVIDodxPwDXio/nv5Gq68vJ\n4X3VqEG5vptvNvejCkJ5QQyoIATBV1/REOpydllZ1JMFaGxmzGDryZQpZsm7rCxzONZq9VSaBuLV\nV9lDqod+XS56pN6Gz+2msfSloMC/6hHACt8OHdiyohtoh4PG7JZbWLBjtwMvvVT4/kIhPZ3FT48/\nDixb5hHX//ZbVicLQnlEDKggBMGhQ2apPX3815AhDOeOHw888ABFEbw9uBo1gEaNjF6ipgGXXVb4\nNRMSWIF6440ebV1fr9Hh8K8GdM455iksOvXrcy/LljEkfccd7EF1u1kB63LxXkePBr75pvA9BsOH\nH1I2sHVr4N13jcIP2dnchyCUR8SACkIQZGaaj8XGUoN26lRP72N2NrBoESX9dDSNxzp14qSU88+n\nYQxGGEDTmJf09WoBGsGbbvJo13ozbBinpfgSE8MiJIBe5n//C0yezPFkixYZvyRkZ1OUviTs2EHx\nhdxcfoa+XwBsNraqCEJ5RAyoIARBpUrmMGxyMsOwvso5MTHm4p2zzuIg6vR04I8/mE8Nlm7dzJJ7\n//0vjd2HH5pDtZs3A2++6X9Attvtmbbii29VcFycWZx+zRoWOLVoQe/Vn2H33Yvv5wPQu05KYguN\n6NwK5RWpwhWEIBg8mKpCWVkMzzocrH5t1IjFMDk5NCZ6P+aFF4bv2o89Rlm/KVP4/JpraHQC9Vfu\n3889+JtJWrNm4Mrhd98FunalkbVYeO7QoZ7Xd+yg8dS97V272Dv6xhuB996woblIyG6nalF8PNtq\nfMeYhUpmJvDBBwyzd+sGXHllydYThGARKT9BCJLNm+l1nTwJDBrEWZyaxsKeQYPoWTZqBEybRtGC\ncJOfT+MdSJUnLY2v5+ZSk9dbUg+g1zdzJmX/atb0r527ezewcCG/IPTty/foTJhAKUJvkYfkZPaV\nFsbo0cC4cTTqBQUUi+/bN6hbLpLsbHrze/fyvh0O7vP++8OzvnDmUBwpPzGgglDOyc9nodGPP9Kg\nX3EFhRgGD6bBsts5eqxxY1baahrDv0OH0tgEy6RJrDT2zpNWrhycgtH27eyjPe88FhSFi6lTgXvv\nNX5ZSEw0h9AFoShkHqggnCE4nQxZVqnCcV/z53s8w2XLmKM8dYpeaZUqDMnWqmVsnfnf/4Crr2ZY\nNhhuuonepN5/6nCwWCkYmjThI9ycPm3Ow+qavKJkJEQaMaCCUM7YtIl5Pr0vtV49Y74zJwf46ScW\nF1WrxmMFBazm9UYp5laDNaBnnQWsXw+MHcuxY9ddR482K4tFS7t2Mac5YEB4jNeRI8znNmrkEeX3\n5corjbnguDh64GI8hdJAQriCUIbYvJmqRxdcwOIknf372ZbSsCH7Rw8e9LwWE0ODoRfrWCzsuVy4\nkOFMnbPPBg4c8Dx3OOi5duhQ/P3m5fFa27bx54QE9sKOH1/8NQHg7bcpumCz8UvC118HHi6+dCmH\ng584wSKoDz8UiUAhdCQHKghlgLw8qv3ExLDAJZgZnUoB993H6lSbjc/nzgXatQM++ohGyWbj2t4a\ntgCNlsNBj1T3RB0Oeozr1rHQB6AGbrdu9Ebz89kKM2pU8Pe1Zg1zmeefTwMPUIT+5puNfbJWK439\nq68yzNy3LzBwIMOt27ZxX3XrBr7Ojh2sYvb2qhMTOdxbxpoJkaI4BhRKqag+uAVBqBgcPapU48ZK\nJSUplZioVKtWSmVmFv2+efOUSkhQiqaTj1q1lDpyRCm73Xjc95GQoNTChUrVrm08Hhen1IQJxutk\nZSm1fr1SBw+Gdl/PPaeUw8H7cjiUmjSJx2fM4DHv68bEKFWjhlJWK587HEr95z88LzlZqfh4pUaN\nCnytH35QKiXFuKbDodSuXaHtWRBC4R9bFJL9EiEFQQgjDz/MySsZGfTK/vwzOKGAHTvMKj2HDzOv\n6CtEYLezhzI5mZ7mbbcxdOkrnJCXB6xcCfzwg6cq1eHgfNBQKmF37KBebXY218nOpvd64gSQmmrM\nN9psHKuWmekJKWdns08zI4NeaG4uQ7yrV/u/XpMm5nvRtJJNiRGESCAGVBDCyObNxj7J3FyjrF8g\nLrjALIzQoAGNia8QgabRME6bxnmeb73F4927G6e3aBqN58CBwLnnGvOmoXDggHmWqc3GIp+aNVmw\n1KYNZ55eey3zkb5fBnyxWCgu748mTShkr39JSEgAvvjCeG+CUBYQAyoIYaRVK2Oezm6ncSmKTp2A\nJ5/kexMTqZP77bdsQfnsM3qOSUlcb+pU5givvtooCfjee0CvXlzDZqORysmh53f0KEewFYfmzc1G\nPCaGgvUA97JyJQ3tjBnADTcY8752u9kAu93sCQ3EQw/R8503jx59r17F27sgRBIpIhKEMHLqFNso\ntmxh9q5DB+D774Mvfjl2jMUyDRsa33P6NIUI6tb1FAX54t372L07J6t4c8klLAQCWNU7dy6v0bdv\n4DV1Fi7klJm8PBr4H34ofJrMunU0gseO0dD36UMJQoDh2eHDqWokCGUFqcIVhDKA280q1JgYhmEj\n3ZP45580gn//zTDqV1/ReI4bx/wjwPDnPfdQTWjjRqBjR492b5UqNHjebTOB7istjepDerjZ5WKV\n8KZNzK3efntgjd7Tp1nFe9ZZQJ06Ybt9QQgLYkAF4QwjL49e6bFjnmPJyTRUDz4IzJpFI9mlC3+2\n22k8V6zwnG+zAY88EnrvplLMeS5YQEPtcNDbnDFDhAyE8odI+QnCGcbOneapK5rGEPLMmQwpu1z0\nMnUOHzaen5/PkG6obNniMZ4A//3uO1YO+xOqjyYbNnC/555LT1kQwoEUEQlCOaZaNWPVL0CDqA/r\nTkkxGk8A6NHDOEIsIaF4RTpZWeZZpFYrW1h++gm4+GKgfn2K1nsL0Jc248cD7dsDQ4bw35deit5e\nhIqFhHAFoZwzYgRVf1wuGrRBgzjbMxC5ucAttwDffMN8pa5IFGrYNSeHE14OH2Z+NCaGuc3vvqOC\nku6Z2u2cFqPPMy1NDh6klm5urudYfDwrfCUPK3gjOVBBOENZtoyFPE2asAI3kDHURQ0OHqQIQo8e\nJctX7tpFIYctWyjx9/HHwOefA888Y/SMHQ7zfNLSYPVqikx4T6FJTmZVcevWpb8foewiBlQQhIDk\n5gKXXsq8qT58euxYFhCFk7feYk+rd2422Lmh4ebUKRZZec8HTUriAO5AE16EM5PiGFDJgQrCGcLs\n2RQl0MOZ2dmc51nY99e9e1m9++uvhZ/nzc030zjpEoQOB+eIRoOUFPbhpqSw5zUlhSFmMZ5COJAq\nXEE4Qzh92iyxV1DA3Km/iTFz53Lmp9XKc/r1Az79tOiQb+XKrHp9/XUqIPXpw/aWaHH55fR+T570\nDBcXhHBQohCupmnXAxgJ4DwArZVSa71eGwbgTgBOAA8rpeYHWENCuIIQgFOnmNusUoXSd7rxWrOG\nogUHD1IR6OOPPcOzA/H335Td03ORNhslBBcuNJ+rFK+Znu45lpBAb7Rbt7DcmiCUKaIRwt0E4FoA\ny3w2ch6AG0HD2gvA25omrdWCANA4rV9PMQPvOZq+bNhAJaOrr2bBy2238b2HD1MY4Y8/6FUtWBBc\nG0rDhvQqmzall3jVVVQt8kdBAY2377737An+PgWholOiEK5SaisA+DGOfQHMUEo5AezWNG07gDYA\nVpbkeoJQ3nE6GdJcvpxtH/HxwC+/sNXClxtvpHSeztdfM4zqLwy7YQMNXkpK4dfv2BHYurXofdps\nNLh//23MfbZqVfR7BeFMIVLZgDoA9nk9P/DPMUGocMybx/aRGjWAu+4y9hz68uGHbDnJymJO8vhx\n4NZb/Z/r6+3l5QHbtrGK1DfroVT4x339+CN7JePjaVAnTjROfxGEM50iPVBN0xYAqOl9CIAC8KxS\n6rtwbGLkyJH//pyamorU1NRwLCsIEWfDBk4p0UUDpk+nR/jJJ/7P37zZcy5Ab3LbNv/nNm3KMK1u\nLOPiODe0a1eOGNu0ia0iCQnAY48FP/ElWJo2pRE/epRVqzKPU6hILF26FEuXLi3RGmHpA9U0bQmA\nx/UiIk3TngaglFLj/3k+F8DzSilTCFeKiITyzLhxwHPPsUpVJzHR2HfozccfAw884CnkiYkBOncG\nFi0yn7ttG1/LyqI83333UXFI0+iNfvABDVz79gztCoJQfKItJu994dkApmma9hoYum0M4PcwXksQ\nygSJiex39DagDkfg82+5hcbyiy/YHlKjRmBvtWlTYPduCh9UrgzUquV5LS6OhlgQhOhRohyopmn9\nNE3bB+AyAN9rmjYHAJRSmwF8DmAzgB8B3C9uplAeOH2axTs1a3Jqx6pVhZ9/yy1sH7HZ+NzhAF5+\nOfD5FgsN5vbtlJnburVwTda4OIZrvY1nWWDfPraznH020LMn22kE4UxDpPwEwYsrrmBVrD49JDGR\necu6dQO/5+RJirefPAn07s0Wk4rCn3+ybeaCC+gtAyySatqURlMXYahXj3q4uvqQIJQ3RAtXEEpA\nfj4nh3i3iSQkUNv1ttuit69oMXQo8NFHnhD17Nn8crBqFQuZvPO8iYmU+2vRInr7FYSSEO0cqCCU\na6xWFvV4G1BNo3E401i2jOPHvCuGr7sOOHGCYWrvnC/A594zRgXhTEBUIQXhHywW4Pnn6XUCbNuo\nV4+KPWcaO3eaj506xdB28+bUl9UNpsPBEWoNG5buHgUh2kgIVxB8+PZbYPFiFsjcf///t3f/oX7V\ndRzHny+dw+XKfqGil81Z9INFs/1hmgT9UByW00DCisr2j1BLKQlrQoEiRCAhJYJkYqIILimTLH9l\n0B+Wy19TlwmSzo2mkS1G0vzx7o9zdNfpdvue790933P3fMDlfr9n33u/7w+73/M653M+5/PZFaj7\nkw0bmltopp+BTk01g4egmVHpiiua+2BXroRzzmnO3qWh8hqo9itbtzb3Si5bBkuX9lvLjh2wfn0T\nOKec8sZT8w3NpZc2C2MvXNicjd95ZzMZvTQfGaDab9xwA6xZ0+zcd+5sbh1Zu7afWrZvb6a4e+aZ\n5lrggQc2E7yfcEI/9cym555rphtcunTXrTrSfGSAar+wfXtzX+Tzz+/atmgRbNrUz5noJZfARRc1\nQf6KFSuaFVckDUMfy5lJc27LltcvAL1wYbNySB+2bn1teAI8+2w/tUiaOwaoBmfJktcv6bVzZ3Nz\nfx9WrXrt9H0HH9yMSpU0vxmgGpzFi5uFoA85pFnaa9GiZpmwvU2Jty+ddhpcfHETogsWNItbX355\nP7VImjteA9Vg7djRrEYyNTXzQtJzpaqZfEHSsDiISJKkDhxEJEnSHDFAJUnqwACVJKkDA1SSpA4M\nUEmSOjBAJUnqwACVJKkDA1SSpA4MUEmSOjBAJUnqwACVJKkDA1SSpA4MUEmSOjBAJUnqwACVJKkD\nA1SSpA4MUEmSOjBAJUnqwACVJKkDA1SSpA4MUEmSOjBAJUnqwACVJKkDA1SSpA4MUEmSOjBAJUnq\nwACVJKkDA1SSpA4MUEmSOjBAJUnqwACVJKkDA1SSpA4MUEmSOjBAJUnqwACVJKkDA1SSpA4MUEmS\nOjBAJUnqwACVJKmDsQI0yZlJHk7yUpKV07aflGRDkgeT3Jvk4+OXOhx333133yXMKtsz+eZbm2zP\n5JuPbRrVuGegG4HPAL/fbfuzwKeragVwNnDtmO8zKPPtD8v2TL751ibbM/nmY2Vs9ZQAAASrSURB\nVJtGtWCcH66qxwCSZLftD057/EiSg5McVFUvjPN+kiRNin1+DTTJmcB9hqckaT5JVe39BcntwOHT\nNwEFXFhVv2pf8zvg/Kq6b7efXQ78Aji5qv62h9+/9wIkSZoDVZWZX7XLjF24VXVyl0KSTAE3AV/c\nU3i2v3+kgiVJmgSz2YX7ahAmORS4Bbigqu6ZxfeQJGkijHsbyxlJNgPHA7ckubX9p7XAu4DvJrk/\nyX1J3jlmrZIkTYwZr4FKkqTXm6iZiJKcn+TlJG/vu5ZxJPlBkk1JHkjy8yRv6bumLpKsSvKXJH9N\nckHf9YwryVSSu5I8kmRjknP7rmk2JDmg7eW5ue9axpXk0CQ3tp+fR5J8uO+axpXkG+2EMw8luS7J\nwr5rGkWSq5JsS/LQtG1vS3JbkseS/La9bDcYe2jTyPvtiQnQdtDRycCTfdcyC24DllfVscDjwHd6\nrmdkSQ4AfgycAiwHPpfkff1WNbYXgW9W1XLgBOBr86BNAOcBj/ZdxCy5DPh1Vb0fWAFs6rmesSQ5\nEvg6sLKqPkgzcPOsfqsa2dU0+4Hpvg3cUVXvBe5iePu4N2rTyPvtiQlQ4IfAt/ouYjZU1R1V9XL7\n9B5gqs96OjoOeLyqnmzv4b0BOL3nmsZSVX+vqgfaxztods5H9VvVeNoDz1OBn/Rdy7jaI/6PVtXV\nAFX1YlX9u+eyZsOBwCFJFgBvArb2XM9IquoPwHO7bT4duKZ9fA1wxpwWNaY3alOX/fZEBGiS1cDm\nqtrYdy37wBrg1hlfNXmOAjZPe/40Aw+b6ZIcDRwL/LHfSsb2yoHnfBjMsAz4R5Kr2y7pK5Ms6ruo\ncVTVVuBS4ClgC/Cvqrqj36pmxWFVtQ2aA1PgsJ7rmW3/1357zgI0ye3tNYBXvja231cD64DvTX/5\nXNXV1V7ac9q011wIvFBV1/dYqnaTZDGwHjivPRMdpCSfAra1Z9VhAJ+bGSwAVgKXV9VK4D80XYWD\nleStNGdrS4EjgcVJPt9vVfvEfDiAA0bbb481F+4o9jQhQ5IPAEcDD7Zz6k4Bf05yXFU9M1f1jWqm\nCSaSnE3TtfaJOSlo9m0Blkx7PtVuG7S2G209cG1V/bLvesZ0IrA6yanAIuDNSX5WVV/qua6unqbp\nidrQPl8PDH3w2knAE1X1T4AkNwEfAYZ+UL0tyeFVtS3JEcDE7qtHMep+u/cu3Kp6uKqOqKpjqmoZ\nzYfoQ5McnjNJsoqmW211Vf2373o6uhd4d5Kl7ajBs4DBj/IEfgo8WlWX9V3IuKpqXVUtqapjaP5/\n7hpweNJ2CW5O8p520ycZ/uCop4Dj2wU1QtOmIQ6M2r2H42aalbYAvgwM8WD0NW3qst+eszPQERTD\n74r6EbAQuL1dqOaeqvpqvyWNpqpeSrKWZmTaAcBVVTXED/6rkpwIfAHYmOR+mr+1dVX1m34r0zTn\nAtclOQh4AvhKz/WMpar+lGQ9cD/wQvv9yn6rGk2S64GPAe9I8hTN5bbvAzcmWUNz58Rn+6twdHto\n0zpG3G87kYIkSR303oUrSdIQGaCSJHVggEqS1IEBKklSBwaoJEkdGKCSJHVggEqS1MH/AAsiZQb2\n0+rtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f94dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, ax = plt.subplots(figsize=(7.5, 7.5))\n",
    "rgb = np.array(['r', 'g', 'b'])\n",
    "\n",
    "ax.scatter(blobs[:, 0], blobs[:, 1], color=rgb[classes])\n",
    "ax.set_title(\"Blobs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now we can use **KMeans** to find the centers of these clusters.\n",
    "\n",
    "(In this example, we'll pretend we know that there are three centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.16197712, -3.8468895 ],\n",
       "       [ 7.63371311, -2.20876453],\n",
       "       [ 4.2308647 , -8.24137105]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmean = KMeans(n_clusters=3)\n",
    "kmean.fit(blobs)\n",
    "kmean.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nBu//530iEyNpPbO1J2DFJMdQPrg8RSxFiEmO4cTVEz7BMC2Hy0GpCaW0nV1U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\nyO++y6IiTy5f9r1Xm823ujgQd97pK0sYH899vfcei5GuXgUqVXLnfP3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JiSydO/M1VYXI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DSGjO12zhqNi2NVbadO4Rtm/dJLWvEGdeqKRFIYkTlQiSQIFi+mV5Wc7C6Suf9+GsGYmODXOXKE\nXqROB/TrR/WeQLRowYpWzxFfv/5KRSKVzEygVSt6epw9ugYZGffA//8pBcAqAHdj1CjttJOPPmJP\np17PLwmZme72lJgYflGYMSP4+8yKr77iWmYzP9MWLcKzrkSSG3KSA5UGVCLJhlWrGFr1LHyZPJnF\nMKGwaxeNRUYGjZ3JxKksgRR6zp5lter+/Xz+zjv04Lyx24FPPwWmTAGSk9fBau0Gh8NPRRPiQVWi\nNoiP52QYANi6lQpCWXmZNWty1miwOJ3AN9+wladhQ37ZkEgKMgWtD1QiKRJ8/rnWuKSnA9OmhW5A\nX3tN2zbicNAD+/Zb/+eXK0eje/UqvTXP8LHNRo/x5585/uvQIbWq9xtQwo+/EKKjTcjIsIK1ROnX\nXm+juU52urSKwl7P4sUZzp0xI2vxBSH4hWPdOn5uZjOHgb/7btbXkUgKGzIHKpFkg/f4LSC4MWTe\nXLmifS4E+0qzIzFRazwB9p3OmEEPb9cu1Xi6ACy49qcJQlRG1apzEBtbCcyN8nWj0YX77mNLjd3O\niSje96fT0TCbzdxnRgbD1z/8wEHdWfHXXzSe6el8b3o68OGHnAcqkRQlpAGVSLJBLXxRjYzJBIwb\nF/o6Dz6ozZeazcBDD7mfnzrFySXjxgF792a91nff+RuUvRlAKigc3xfAHuzf3wt6/V60a9cXOp0Z\nQAqAv7BiBdC7N8UeWrTg3sxmGuvYWOZ8Z8+mJ+kpDp+RwTxsViQn+/ar6vXukLFEUlSQIVyJJBtu\nuw3YtIkFQ5mZbD9p0yb793kiBFV3HA63IX7ySYoq/O9/1M09cMB97uTJzL3edZf/9YxGrQFVFECI\nHwFEA5gLoOf1tZxOM4YM+RLDht2PBx54FDbbD7DZuPA///DaM2dSz/jcOeYsy5fnumfOAD/+qG3f\nyU47t3FjrdpSVBT1knMjXC+RFEhCbRwN9wNSSEFyA/DddxRFUMUDFEWI224TYvJkIcxmX3EBQIiW\nLQOv98EH7vcZDEKULStE1aoHhE530meduDghFi7k+0qVOimAA5rX69UT4sQJ/9dJSRGialUhYmK4\nZ7NZiG++yf5+t20T4pZbeM933SXE8eO+57hcQhw+LMSOHUJkZPhf59gxIVav9v9+iSScIAdCCrIK\nVyLJA956iwVDnv/UzWYKsh896v899epxPFkgfviB2rMlSlBEvk4d4PXXWZGrisbrdMxx7t7N6/Xs\nCSxZotWr1ek43mzPHv8Du1NS6KEmJVHUPhxtJy4X8PDDDBXr9Qwdb9jAvapMnw688AK9bZsN+Phj\nDhaXSCJBTqpwZQ5UIokQ69dz0skzz7CC1VNAQFEowh4Isxno2zfr9Xv2BJo1o8Fs3ZpTWy5d8g2f\nPv20+9pffMHRaZ6oYu+BqoETEmjIxo0LX8/mnDmsILZaqbR09qx2Ksy5c8Dzz/P1q1f557BhvuIS\nEkl+InOgEkkE+OorYNAgt8dpMNDgLV7Mn+PigPnzqUTk7YEaDOyb7N8/62vs3g28+KLb27RYaAQ9\n85V2Oz274cP5vEQJijMkJmrF751O3ykq/jh2DFizhvvv1i00EQnvvXu2BjmdFINQOX6cnqfnrFGj\nkUpJZcrk7JoSSbiRHqhEEgGeflobrrXb+Th4ENi4kUazRg3/2rBOJ7BsGae3fPVV4Gvs3Olb7epw\naD1Qg8FX7UhRaNw9PeLoaK1mrT/++INh5eeeYyi1ceOcS/zdequ2GEmn03rkNWr46g47HBzgLZEU\nFKQBlUgigG+LCdtUKlZkVa/aR9qmjVZQHmBIVQ1bPvVU4P7JqlV9DbDaihIXx0fZssyLevP++8DL\nLwM330yvtF07X2MM0NCPH88c7oABDPWmp1MQ4sgR5ilzwmOPAR07cr8JCcy9zp3rfr1kSSoZmc28\nD7MZmDePe5VICgoyhCuRhBGLhcVCUVFuDVuVTp18z1d7Pn/5xb83ajDQ8BYv7vtaixZsg/nyS55n\nt3NYdePGFEnQ6ymAHx/v+96oKKB0aRpni4WFRRs2UJShUiWes38/cOedNORsh9GukZEBnD4d1Mfi\ng04HLFhAjzwtjd629xeJ7t2B8+d5/xUq0JBKJAUJWYUrkYQJl4tGbccObe5OUVi4s3Wrfy/vzBmG\nJtVcpidxcXzdnxFU2bGD59SvH1qvZblyLNZR0etp0EeO5PO+fWnkPP976nTu8WNmM1/v3Dn4awaD\nEAxhHzpEb71du/CuL5H4Q2rhSiT5yIEDLI7xNJ7R0ZSxGzJEm5v0xGDw732aTMCiRVkbTwC4/XY+\nQsU7x+hyaY9dueK7r7g4eoyqsQ238QSAJ55wF0Pp9VSCevPN8F9HIsktMgcqkYSJQCHYpk0DG0+A\nodRevdxFPdHRwC23sLXjvvv8v+fqVbaBLFumNdih8MQT2kIikwl44AH38379tK/HxjIXmpHBh1rZ\n60lSEjBmDPVyFy4MfU979zL3mZ5Ojzw9nSL0Fy+GvpZEEmmkByqRhInatTmabO9e/vKPjqYwQL16\n7nNcLuYqvcXo587lTM4//qAgwmuv0aOdPJntJU8+6Tamx44BTZrQWLlcNGw7d4ZeofrWWyzg+fZb\n5lgnTaLhVhk0iH2l773HLwfDhnHQtrfwvEpqKmUAz57lnr/7jnnU0aOD39PFi77C+QYDRfdLlw7t\n/iSSSCNzoBJJGElJAf77X2DbNuY9J0+myg9AQzRqFEOTzZuzJ9RfcRAAbN8OtGzpbhMxm2nounVj\nMdKyZdrzS5RgwY0+H78Sz55N0Yj0dPexmBjeQyCj601SEr90qMLzikLDeeJEzibgSCTBIpWIJJJ8\nJiGBs0L/+ovjxlTjuWwZq3NtNnqNf/3FtpBATJ2q7bG0WICxY/mzOmDbk6tXgcOHw3YbOUKt1vXE\nbvcf2g5E8eLAypVs99Hp2A+6dq00npKCiTSgEkkesH691iDabMDvvwc+3273PbZ3Lz2zO+7wfU1R\nqNTjzaFD9IRjYhhi3rlT+1qbNjRWffr4zisNlY4dtbnemBi2omSV//XHnXfS43Q42OZSt27u9iWR\nRAppQCWSPKB8eV8Dl5Uk3ZNP+hoep5N5yylTtD2TUVHMj1atqj0/NRVo1Ii9nZmZNEbt2tFbTU5m\nGPn339ln+fPPXCM32ZTKlTlIu0kT9pL2768VRwiVYMO+Ekl+IYuIJJIgOXmSYdRz54AePTgXNNhf\n8sePa1tEdDqGeAPRpg17Ok+ccB9zOOhB3nknr6vX88/hw9nm4bkXIYAuXbR6twBbULZtYxWtGk4G\n+POePcyj+pvIEiyNGjE8nR3q0O3sWnQkkoKM9EAlkiC4cIEVpl99RdWeF19ku0YwpKWxF1Q1VgDD\nm9l5ex07asXajUaGXS9fZjjY4aBXeuiQb/HQqVP+DZndzmIks1m7H4BrxcTwz0jV9VmtvK8yZSjX\n9/DDvv2oEklhQRpQiSQIFi2iIVTl7NLTqScL0NjMm8fWk1mzfCXv0tN9w7F6vbvSNBDvv88eUjX0\n63TSI/U0fC4XjaU3drt/1SOAFb4tWrBlRTXQZjON2aOPsmDHZALeeSfr/YVCcjKLn17+//buPTjq\n8twD+PchcUk2gRSOA6gRFISjotWD1eO1pSqiFkHH1qpIsbaOVan26HSstlat41jq2Gp7qKMjpVSx\nWK0VvBQkWKinFysIiERugoKkXCoYDCGEy3P++O52b9kk+9slv2zy/cwwJL9sdt9fK/vs+77P+zy3\nAwsXJorrz5rF7GSRYqQAKtIO//xnZqm9ePuv66/ncu7kycDNN7MoQvIMrl8/YMiQ1FmiGXD66a2/\nZkUFM1CvuCJRWzd91hiNtlwN6KijMruwxA0axLEsXMgl6a9/nWdQDxxgBuz+/bzX++8HXnyx9TG2\nx9SpLBt46qnA44+nFn5obOQ4RIqRAqhIOzQ0ZF475BDWoH366cTZx8ZGYP58lvSLM+O1c85hp5Th\nwxkY21MYwIz7kumzWoBB8MorE7Vrk915J7ulpCspYRISwFnmHXcAv/oV25PNn5/6IaGxkUXp87F2\nLYsvNDXxf8P0DwCRCI+qiBQjBVCRdvjMZzKXYXv35jJseuWckpLM5J0BA9iI+pNPgHff5X5qe40a\nlVly7447GOymTs1cqq2tBX7xi5YbZB84kOi2ki49K7hnz8zi9IsXM8HphBM4e20psKePJf1/H4Cz\n6169eIRGdW6lWCkLV6QdrrmGVYV27eLybDTK7NchQ5gMs3s3g0n8POZnP1u4177tNpb1+/Wv+f0l\nlzDoZDtf+dFHHENLPUn798+eOfz448B55zHI9ujBx06alPj52rUMnvHZ9vr1PDv6859nH/vgwZlJ\nQuXlrFpUVsZjNeltzHLV0AA8+SSX2UeNAs4/P7/nE2kvlfITaafaWs66tm8Hxo9nL04zJvaMH8+Z\n5ZAhwIwZLFpQaM3NDN7ZqvLs2MGfNzWxJm9yST2As75nn2XZv/79W66d+8EHQE0NPyCMG8ffiXvo\nIZYiTC7y0Ls3z5W25v77gQcfZFDfu5fF4seNa9ctt6mxkbP5DRt439Eox3nTTYV5fuk+gpTyUwAV\nKXLNzUw0evVVBvRzz2UhhmuuYcAqL2frsWOOYaatGZd/J01isGmvRx5hpnHyPmmfPu2rYLRmDc/R\nHnccE4oK5emngW99K/XDQmVl5hK6SFvUD1Skm9i3j0uWffuy3ddrryVmhgsXco+yvp6z0r59uSR7\n2GGpR2ceewwYM4bLsu1x5ZWcTcbPn0ajTFZqj6FD+afQdu7M3IeN1+RVJSM52BRARYrM8uXc54uf\nSx04MHW/c/du4I03mFx06KG8tncvs3mTuXNvtb0BdMAAYOlS4IEH2Hbs8ss5o921i0lL69dzT/Or\nXy1M8Nqyhfu5Q4YkivKnO//81L3gnj05A1fwlI6gJVyRTqS2llWPTjyRyUlxH33EYymDB/P8aF1d\n4mclJQwY8WSdHj145rKmhsuZcdXVwKZNie+jUc5czzor+Hj37OFrrV7NrysqeBZ28uTgzwkAv/wl\niy5EIvyQ8MIL2ZuLL1jA5uAff8wkqKlTVSJQcqc9UJFOYM8eVvspKWGCS3t6dLoDN97I7NRIhN/P\nmQOccQYwbRqDUiTC506uYQswaEWjnJHGZ6LRKGeMS5Yw0QdgDdxRozgbbW7mUZj77mv/fS1ezL3M\n4cMZ4AEWob/66tRzsqWlDPY//SmXmceNA666isutq1dzXEcemf111q5lFnPyrLqyks291dZMDpYg\nARTuHuofDkGka9i61f2YY9x79XKvrHQ/5RT3hoa2f2/uXPeKCneGTv457DD3LVvcy8tTr6f/qahw\nr6lxP/zw1Os9e7o/9FDq6+za5b50qXtdXW73dffd7tEo7ysadX/kEV6fOZPXkl+3pMS9Xz/30lJ+\nH426f/ObfFzv3u5lZe733Zf9tV55xb2qKvU5o1H39etzG7NILmKxKKf4pUIKIgV0663svPLpp5yV\nrVjRvkIBa9dmVunZvJn7iumFCMrLeYayd2/ONCdO5NJleuGEPXuAN98EXnklkZUajbI/aC6ZsGvX\nsl5tYyOfp7GRs9ePPwZGjkzdb4xE2FatoSGxpNzYyHOan37KWWhTE5d4Fy1q+fWGDs28F7P8usSI\nHAwKoCIFVFubek6yqSm1rF82J56YWRjh6KMZTNILEZgxMM6YwX6eU6bw+gUXpHZvMWPwvOoq4Nhj\nU/dNc7FpU2Yv00iEST79+zNh6bTT2PP0ssu4H5n+YSBdjx4sLt+SoUNZyD7+IaGiAnjuudR7E+kM\nFEBFCuiUU1L36crLGVzacs45wHe/y9+trGSd3FmzeATlmWc4c+zVi8/39NPcIxwzJrUk4BNPABdd\nxOeIRBikdu/mzG/rVrZgC+L44zODeEkJC9YDHMubbzLQzpwJfOUrqfu+5eWZAfjAAZ4JzeaWWzjz\nnTuXM/qLLgo2dpGDSUlEIgVUX89jFCtXcvfurLOAl19uf/LLtm1Mlhk8OPV3ds2LTOEAAA5ESURB\nVO5kIYIjj0wkBaVLPvt4wQXsrJJsxAgmAgHM6p0zh68xblz254yrqWGXmT17GOBfeaX1bjJLljAI\nbtvGQD92LEsQAlye/cEPWNVIpLNQFq5IJ3DgALNQS0q4DHuwzySuWMEguG4dl1F//3sGzwcf5P4j\nwOXPG25gNaF33gHOPjtRu7dvXwa85GMz2e5rxw5WH4ovN+/fzyzh5cu5t3rttdlr9O7cySzeAQOA\nI44o2O2LFIQCqEg3s2cPZ6XbtiWu9e7NQPXtbwN/+AOD5Be/yK/Lyxk8//KXxOMjEeA738n97KY7\n9zznzWOgjkY525w5U4UMpPiolJ9IN/P++5ldV8y4hPzss1xS3r+fs8y4zZtTH9/czCXdXK1cmQie\nAP9+6SVmDrdUqD5My5ZxvMcey5mySCEoiUikiB16aGrWL8CAGG/WXVWVGjwBYPTo1BZiFRXBknR2\n7crsRVpayiMsb7wBnHwyMGgQi9YnF6DvaJMnA2eeCVx/Pf/+8Y/DG4t0LVrCFSlyP/whq/7s38+A\nNn48e3tm09QETJgAvPgi9yvjFYlyXXbdvZsdXjZv5v5oSQn3Nl96iRWU4jPT8nJ2i4n3M+1IdXWs\npdvUlLhWVsYMX+3DSjLtgYp0UwsXMpFn6FBm4GYLhvGiBnV1LIIwenR++5Xr17OQw8qVLPE3fTrw\nu98Bd92VOjOORjP7k3aERYtYZCK5C03v3swqPvXUjh+PdF4KoCKSVVMT8LnPcd803nz6gQeYQFRI\nU6bwTGvy3mx7+4YWWn09k6yS+4P26sUG3Nk6vEj3FCSAag9UpJuYPZtFCeLLmY2N7OfZ2ufXDRuY\nvfu3v7X+uGRXX83gFC9BGI2yj2gYqqp4Dreqimdeq6q4xKzgKYWgLFyRbmLnzswSe3v3cu+0pY4x\nc+aw52dpKR9z6aXAU0+1veTbpw+zXh99lBWQxo7l8ZawfP7znP1u355oLi5SCHkt4ZrZlwHcC+A4\nAKe6+9tJP7sTwHUA9gG41d1fy/IcWsIVyaK+nnubffuy9F08eC1ezKIFdXWsCDR9eqJ5djbr1rHs\nXnwvMhJhCcGamszHuvM1P/kkca2igrPRUaMKcmsinUoYS7jLAVwGYGHaQI4DcAUYWC8C8EszHa0W\nARicli5lMYPkPprpli1jJaMxY5jwMnEif3fzZhZGePddzqrmzWvfMZTBgzmrHDaMs8QvfYlVi1qy\ndy+Dd/q4P/yw/fcp0tXltYTr7qsAoIXgOA7ATHffB+ADM1sD4DQAb+bzeiLFbt8+Lmn++c889lFW\nBvz1rzxqke6KK1g6L+6FF7iM2tIy7LJlDHhVVa2//tlnA6tWtT3OSIQBd9261L3PU05p+3dFuouD\ntRtwBICNSd9vil0T6XLmzuXxkX79gG98I/XMYbqpU3nkZNcu7kn+61/A177W8mPTZ3t79gCrVzOL\nNH3Xw73w7b5efZVnJcvKGFAffji1+4tId9fmDNTM5gHon3wJgAP4vru/VIhB3Hvvvf/+euTIkRg5\ncmQhnlbkoFu2jF1K4kUDfvtbzgh/85uWH19bm3gswNnk6tUtP3bYMC7TxoNlz57sG3reeWwxtnw5\nj4pUVAC33db+ji/tNWwYg/jWrcxaVT9O6UoWLFiABQsW5PUcBTkHamZ/AnB7PInIzL4HwN19cuz7\nOQDucfeMJVwlEUkxe/BB4O67maUaV1mZeu4w2fTpwM03JxJ5SkqAL3wBmD8/87GrV/Nnu3axPN+N\nN7LikBlno08+yQB35plc2hWR4MIuJp/8wrMBzDCzn4FLt8cA+EcBX0ukU6is5HnH5AAajWZ//IQJ\nDJbPPcfjIf36ZZ+tDhsGfPABCx/06QMcdljiZz17MhCLSHjy2gM1s0vNbCOA0wG8bGZ/BAB3rwXw\nOwC1AF4FcJOmmVIMdu5k8k7//uza8dZbrT9+wgQeH4lE+H00CvzkJ9kf36MHA+aaNSwzt2pV6zVZ\ne/bkcm1y8OwMNm7kcZbqauDCC3mcRqS7USk/kSTnnsus2Hj3kMpK7lseeWT239m+ncXbt28HLr6Y\nR0y6ihUreGzmxBM5WwaYJDVsGINmvAjDwIGshxuvPiRSbFQLVyQPzc3sHJJ8TKSigrVdJ04Mb1xh\nmTQJmDYtsUQ9ezY/HLz1FhOZkvd5KytZ7u+EE8Ibr0g+wt4DFSlqpaVM6kkOoGYMDt3NwoVsP5ac\nMXz55cDHH3OZOnnPF+D3yT1GRboDVYUUienRA7jnHs46AR7bGDiQFXu6m/ffz7xWX8+l7eOPZ33Z\neMCMRtlCbfDgjh2jSNi0hCuSZtYs4PXXmSBz002JgNqdLFrEIzTJM9DqaiYPAayo9NhjPAc7YgRw\nww2cvYsUK+2BSrdSV8ezkkcfDQwaFO5YGhqA559nwBk9uuXSfMXm4YfZGDsS4Wx8/nwWoxfpihRA\npduYORO47jq+uTc38+jIpEnhjKW+niXutm7lXmBJCQu8n3FGOOMppB07WG5w0KDEUR2RrkgBVLqF\n+nqei9y9O3GtvBx4771wZqIPPAD86EcM5HEnncSOKyJSHMJoZybS4TZtymwAHYmwc0gY6upSgycA\nbNsWzlhEpOMogErRGTgws6VXczMP94fhwgtTy/eVlTErVUS6NgVQKTqVlWwEXVHB1l7l5WwT1lpJ\nvIPpkkuA++9nEC0tZXPrKVPCGYuIdBztgUrRamhgN5Lq6rYbSXcUdxZfEJHioiQiERGRAJREJCIi\n0kEUQEVERAJQABUREQlAAVRERCQABVAREZEAFEBFREQCUAAVEREJQAFUREQkAAVQERGRABRARURE\nAlAAFRERCUABVEREJAAFUBERkQAUQEVERAJQABUREQlAAVRERCQABVAREZEAFEBFREQCUAAVEREJ\nQAFUREQkAAVQERGRABRARUREAlAAFRERCUABVEREJAAFUBERkQAUQEVERAJQABUREQlAAVRERCQA\nBVAREZEAFEBFREQCUAAVEREJQAFUREQkAAVQERGRABRARUREAlAAFRERCUABVEREJAAFUBERkQAU\nQEVERAJQABUREQkgrwBqZl82s3fNbL+ZjUi6fr6ZLTKzZWb2lpl9Mf+hFo8FCxaEPYSC0v10fl3t\nnnQ/nV9XvKdc5TsDXQ7gMgAL065vAzDG3U8CcC2Ap/J8naLS1f7D0v10fl3tnnQ/nV9XvKdclebz\ny+6+CgDMzNKuL0v6eoWZlZnZIe6+N5/XExER6SwO+h6omX0ZwNsKniIi0pWYu7f+ALN5APonXwLg\nAL7v7i/FHvMnALe7+9tpvzscwIsARrn7B1mev/UBiIiIdAB3t7YfldDmEq67jwoyEDOrBvACgAnZ\ngmfs+XMasIiISGdQyCXcfwdCM6sC8DKAO9z97wV8DRERkU4h32Msl5rZRgCnA3jZzP4Y+9EkAEMA\n/NDMlpjZ22Z2aJ5jFRER6TTa3AMVERGRTJ2qEpGZ3W5mB8ysb9hjyYeZ/cTM3jOzpWb2ezPrHfaY\ngjCzC81spZmtNrM7wh5Pvsys2sxeN7MVZrbczG4Je0yFYGY9Yqs8s8MeS77MrMrMnov9+1lhZv8d\n9pjyZWb/Eys4846ZzTCzSNhjyoWZTTWzLWb2TtK1Pmb2mpmtMrO5sW27opHlnnJ+3+40ATSWdDQK\nwIdhj6UAXgMw3N1PBrAGwJ0hjydnZtYDwP8CGA1gOICrzOzYcEeVt30AbnP34QDOAHBzF7gnALgV\nQG3YgyiQRwG86u7HATgJwHshjycvZnY4gG8DGOHunwUTN68Md1Q5mwa+DyT7HoAad/9PAK+j+N7j\nWrqnnN+3O00ABfAzAN8NexCF4O417n4g9u3fAVSHOZ6ATgOwxt0/jJ3hnQlgXMhjyou7b3b3pbGv\nG8A35yPCHVV+Yh88LwbwZNhjyVfsE/857j4NANx9n7vvDHlYhVACoMLMSgFEAdSFPJ6cuPv/AdiR\ndnkcgOmxr6cDuLRDB5Wnlu4pyPt2pwigZjYWwEZ3Xx72WA6C6wD8sc1HdT5HANiY9P1HKPJgk8zM\njgJwMoA3wx1J3uIfPLtCMsPRAP5lZtNiS9JPmFl52IPKh7vXAXgYwAYAmwB84u414Y6qIPq5+xaA\nH0wB9At5PIXWrvftDgugZjYvtgcQ/7M89vdYAHcBuCf54R01rqBauZ9Lkh7zfQB73f2ZEIcqacys\nEsDzAG6NzUSLkpl9CcCW2KzaUAT/btpQCmAEgCnuPgJAI7hUWLTM7DPgbG0QgMMBVJrZ1eGO6qDo\nCh/gAOT2vp1XLdxcZCvIYGYnADgKwLJYTd1qAIvN7DR339pR48tVWwUmzOxacGnt3A4ZUOFtAjAw\n6fvq2LWiFltGex7AU+4+K+zx5OksAGPN7GIA5QB6mdlv3P1rIY8rqI/AlahFse+fB1DsyWvnA1jn\n7tsBwMxeAHAmgGL/UL3FzPq7+xYzGwCg075X5yLX9+3Ql3Dd/V13H+Dug939aPAf0X915uDZFjO7\nEFxWG+vue8IeT0BvATjGzAbFsgavBFD0WZ4AfgWg1t0fDXsg+XL3u9x9oLsPBv//eb2IgydiS4Ib\nzWxY7NJ5KP7kqA0ATo811DDwnooxMSp9hWM22GkLACYCKMYPoyn3FOR9u8NmoDlwFP9S1C8ARADM\nizWq+bu73xTukHLj7vvNbBKYmdYDwFR3L8Z/+P9mZmcBGA9guZktAf9bu8vd54Q7MklyC4AZZnYI\ngHUAvh7yePLi7v8ws+cBLAGwN/b3E+GOKjdm9gyAkQD+w8w2gNttPwbwnJldB56cuCK8EeYuyz3d\nhRzft1VIQUREJIDQl3BFRESKkQKoiIhIAAqgIiIiASiAioiIBKAAKiIiEoACqIiISAAKoCIiIgH8\nP92hV6QZ1NyqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fee2358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7.5, 7.5))\n",
    "ax.scatter(blobs[:, 0], blobs[:, 1], color=rgb[classes])\n",
    "ax.scatter(kmean.cluster_centers_[:, 0],\n",
    "           kmean.cluster_centers_[:, 1], marker='*', s=250,\n",
    "           color='black', label='Centers')\n",
    "ax.set_title(\"Blobs\")\n",
    "ax.legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 0, 1], dtype=int32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clustering Results\n",
    "kmean.labels_[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 0, 1])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected Results\n",
    "classes[:5]\n",
    "\n",
    "## Please note that KMeans does not know in advance cluster ids, thus it cannot assign the same as in `classes`.\n",
    "## In any case, please note that same elements correspond to the same numbers (IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The `transform` function is quite useful in the sense that it will output the distance between\n",
    "each point and centroid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.08788508,  5.74849328,  1.23920211],\n",
       "       [ 7.06583518,  6.27968678,  1.50240838],\n",
       "       [ 1.90056363,  7.67616254,  7.59739209],\n",
       "       [ 0.49609409,  7.45254061,  6.28322215],\n",
       "       [ 7.78782316,  1.59477252,  5.81406349]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmean.transform(blobs)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying Iris Dataset with scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.9016129 ,  2.7483871 ,  4.39354839,  1.43387097],\n",
       "       [ 5.006     ,  3.418     ,  1.464     ,  0.244     ],\n",
       "       [ 6.85      ,  3.07368421,  5.74210526,  2.07105263]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmean = KMeans(n_clusters=3)\n",
    "kmean.fit(iris.data)\n",
    "kmean.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmean.cluster_centers_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YZGVBYWHNdGZm4NjFzVl5OQwaBOvWQVmZ/Qb6xz/g+uvjHZlSDaYhd/MFC+Ds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\n6n0U5hVy0diLGL7P8GyH1KNu+uNNLF2/lOH7DOeO0+9gv6L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UGm1MfvZbkp/9EwTzhb4M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\nRUREQqZkKyIiEjIlWxERkZD9H13qBK/vooIxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f964cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rzAw94VB01qKoKKhTx3z5aNUqPO2Jqq/KjtoNOAhJpELUagMGwJIlhdtKwXXX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\niIhIDEqkIiIiMSiRioiIxPD/AWgxTDAy6v4QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a869e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n6ZtQ+fnOSW8LF0LTps4x/OOPj3VU8c1qWQdYQjbGGBNLdlKXMcYY08BZQjbG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112bfdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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j1gG9gdXhPKZSkbBsGYwbZ1t+gwYFTnKlMXAg7Nxpu2yrV4c33wxe/1132dtL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\ndhmrV6+mf//+uFwukpKSWLRoUbHqi3E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5ntloC3efDXQAuHsb0L45B5PB4dalt7K2fu2m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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113bf5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4vacbboDBg+Pb/sSJ3jmQ09PhttvgkEPi275E5/fDI49455TeZx+4915o2zbR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\n1s/MKoBTgOmZAWa2fcbtAwgfIpoVYxEpsDvvhG9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CZr2BR4B/BpYCJ7v7p2bWF7jD3Y8z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\nEUkAFWQREZEEUEEWERFJgP8HtLxDW6RmZpYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a1aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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E0fbtLvXjEUe4rFpbt9a87ZQpcOyxcNxxMH167OoYDR9+6PJKDxgAL70U79rs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\nSZJq9v8BADz9A7YdbxsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffc2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "X = iris.data\n",
    "for cols in combinations(range(4), 2):\n",
    "    f, ax = plt.subplots(figsize=(7.5, 7.5))\n",
    "    ax.scatter(X[:, cols[0]], X[:, cols[1]], color=rgb[iris.target])\n",
    "    ax.scatter(kmean.cluster_centers_[:, cols[0]],\n",
    "               kmean.cluster_centers_[:, cols[1]], marker='*', s=250,\n",
    "               color='black', label='Centers')\n",
    "    feature_x = iris.feature_names[cols[0]]\n",
    "    feature_y = iris.feature_names[cols[1]]\n",
    "    ax.set_title(\"Features: {} vs {}\".format(feature_x.title(),\n",
    "                                            feature_y.title()))\n",
    "    ax.set_xlabel(feature_x)\n",
    "    ax.set_ylabel(feature_y)\n",
    "    ax.legend(loc='best')\n",
    "\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
