Part1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.86x - Introduction to ML Packages (Part 1)\n",
    "\n",
    "This tutorial is designed to provide a bird's eye view of the ML packages landscape.\n",
    "The goal is not to give an in-depth explanation of all the features of each packages, but rather demonstrate the purpose of a few widely used ML packages. For more details, we refer the reader to the packages' documentation and other online tutorials.\n",
    "\n",
    "You can go through the Jupyter, Numpy and Matplotlib sections before the course starts, and then start studying the next sections after you have completed unit 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/varal7/ml-tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter\n",
    "\n",
    "[Jupyter](https://jupyter.org) is not strictly speaking an ML package. It provides a browser front-end connected to an instance of IPython which allows REPL for quick testing, allows to create documents that intertwines code, output, images, and text. This is great for prototyping, demonstrations and tutorials, but terrible for actual coding. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.485220Z",
     "start_time": "2019-02-20T16:35:28.476294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6*7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.492012Z",
     "start_time": "2019-02-20T16:35:28.487024Z"
    }
   },
   "outputs": [],
   "source": [
    "def tokenize(text):\n",
    "    return text.split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.497368Z",
     "start_time": "2019-02-20T16:35:28.494171Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['In', 'a', 'shocking', 'finding,', 'scientist', 'discovered', 'a', 'herd', 'of', 'unicorns', 'living', 'in', 'a', 'remote,', 'previously', 'unexplored', 'valley']\n"
     ]
    }
   ],
   "source": [
    "text = \"In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley\"\n",
    "print(tokenize(text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.564975Z",
     "start_time": "2019-02-20T16:35:28.499321Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Numpy](http://www.numpy.org) is desiged to handle large multidimensional arrays and enable efficient computations with them. In the back, it runs pre-compiled C code which is much faster than, say, a Python `for` loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T16:57:10.540510Z",
     "start_time": "2019-02-19T16:57:10.496709Z"
    }
   },
   "source": [
    "In the Numpy tutorial, we have covered the basics of Numpy, numpy arrays, element-wise operations, matrices operations and generating random matrices. \n",
    "In this section, we'll cover indexing, slicing and broadcasting, which are useful concepts that will be reused in `Pandas` and `PyTorch`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing and slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy arrays can be indexed and sliced like regular python arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.569555Z",
     "start_time": "2019-02-20T16:35:28.566928Z"
    }
   },
   "outputs": [],
   "source": [
    "a_py = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "a_np = np.array(a_py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.575416Z",
     "start_time": "2019-02-20T16:35:28.571576Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 6] [4 6]\n",
      "[3, 5, 7] [3 5 7]\n",
      "[9, 8, 7, 6, 5, 4, 3, 2, 1] [9 8 7 6 5 4 3 2 1]\n"
     ]
    }
   ],
   "source": [
    "print(a_py[3:7:2], a_np[3:7:2])\n",
    "print(a_py[2:-1:2], a_np[2:-1:2])\n",
    "print(a_py[::-1], a_np[::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But you can also use arrays to index other arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.581665Z",
     "start_time": "2019-02-20T16:35:28.577506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 3])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = np.array([7,2])\n",
    "a_np[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.585548Z",
     "start_time": "2019-02-20T16:35:28.583493Z"
    }
   },
   "outputs": [],
   "source": [
    "# a_py[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which allows convenient querying, reindexing and even sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.591506Z",
     "start_time": "2019-02-20T16:35:28.587586Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[33 54 32 43 52 55 49 51 40 40]\n",
      "[151 185 178 186 188 186 188 199 151 192]\n"
     ]
    }
   ],
   "source": [
    "ages = np.random.randint(low=30, high=60, size=10)\n",
    "heights = np.random.randint(low=150, high=210, size=10)\n",
    "\n",
    "print(ages)\n",
    "print(heights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.596276Z",
     "start_time": "2019-02-20T16:35:28.593558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False  True  True False False  True False  True  True]\n"
     ]
    }
   ],
   "source": [
    "print(ages < 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.602746Z",
     "start_time": "2019-02-20T16:35:28.598538Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[151 178 186 188 151 192]\n",
      "[33 32 43 49 40 40]\n"
     ]
    }
   ],
   "source": [
    "print(heights[ages < 50])\n",
    "print(ages[ages < 50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.610474Z",
     "start_time": "2019-02-20T16:35:28.605443Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 5 7 6 0 2 9 4 8 1]\n",
      "[43 55 51 49 33 32 40 52 40 54]\n",
      "[186 186 199 188 151 178 192 188 151 185]\n"
     ]
    }
   ],
   "source": [
    "shuffled_idx = np.random.permutation(10)\n",
    "print(shuffled_idx)\n",
    "print(ages[shuffled_idx])\n",
    "print(heights[shuffled_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.617215Z",
     "start_time": "2019-02-20T16:35:28.613422Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 0 8 9 3 6 7 4 1 5]\n",
      "[32 33 40 40 43 49 51 52 54 55]\n",
      "[178 151 151 192 186 188 199 188 185 186]\n"
     ]
    }
   ],
   "source": [
    "sorted_idx = np.argsort(ages)\n",
    "print(sorted_idx)\n",
    "print(ages[sorted_idx])\n",
    "print(heights[sorted_idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When Numpy is asked to perform an operation between arrays of differents sizes, it \"broadcasts\" the smaller one to the bigger one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.624048Z",
     "start_time": "2019-02-20T16:35:28.619908Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 10, 12])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([4, 5, 6])\n",
    "b = np.array([2, 2, 2])\n",
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.630303Z",
     "start_time": "2019-02-20T16:35:28.626019Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 10, 12])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([4, 5, 6])\n",
    "b = 2\n",
    "a * b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T17:16:34.417254Z",
     "start_time": "2019-02-19T17:16:34.411064Z"
    }
   },
   "source": [
    "The two snippets of code above are equivalent but the second is easier to read and also more efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.634813Z",
     "start_time": "2019-02-20T16:35:28.632236Z"
    }
   },
   "outputs": [],
   "source": [
    "a = np.arange(10).reshape(1,10)\n",
    "b = np.arange(12).reshape(12,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.639328Z",
     "start_time": "2019-02-20T16:35:28.636394Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 3 4 5 6 7 8 9]]\n",
      "[[ 0]\n",
      " [ 1]\n",
      " [ 2]\n",
      " [ 3]\n",
      " [ 4]\n",
      " [ 5]\n",
      " [ 6]\n",
      " [ 7]\n",
      " [ 8]\n",
      " [ 9]\n",
      " [10]\n",
      " [11]]\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.643927Z",
     "start_time": "2019-02-20T16:35:28.640936Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  2  3  4  5  6  7  8  9]\n",
      " [ 0  2  4  6  8 10 12 14 16 18]\n",
      " [ 0  3  6  9 12 15 18 21 24 27]\n",
      " [ 0  4  8 12 16 20 24 28 32 36]\n",
      " [ 0  5 10 15 20 25 30 35 40 45]\n",
      " [ 0  6 12 18 24 30 36 42 48 54]\n",
      " [ 0  7 14 21 28 35 42 49 56 63]\n",
      " [ 0  8 16 24 32 40 48 56 64 72]\n",
      " [ 0  9 18 27 36 45 54 63 72 81]\n",
      " [ 0 10 20 30 40 50 60 70 80 90]\n",
      " [ 0 11 22 33 44 55 66 77 88 99]]\n"
     ]
    }
   ],
   "source": [
    "print(a * b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.850451Z",
     "start_time": "2019-02-20T16:35:28.645580Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:28.855206Z",
     "start_time": "2019-02-20T16:35:28.852980Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Matplotlib](https://matplotlib.org) is the go-to library to produce plots with Python. It comes with two APIs: a MATLAB-like that a lot of people have learned to use and love, and an object-oriented API that we recommend using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:29.059343Z",
     "start_time": "2019-02-20T16:35:28.857510Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x115dcc160>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2*np.pi, 2*np.pi, 400)\n",
    "y = np.tanh(x)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot multiple subplots in the same figure, or multiple functions in the same subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:29.335327Z",
     "start_time": "2019-02-20T16:35:29.061314Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11813fef0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2*np.pi, 400)\n",
    "y1 = np.tanh(x)\n",
    "y2 = np.cos(x**2)\n",
    "fig, axes = plt.subplots(1, 2, sharey=True)\n",
    "axes[1].plot(x, y1)\n",
    "axes[1].plot(x, -y1)\n",
    "axes[0].plot(x, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib also comes with a lot of different options to customize, the colors, the labels, the axes, etc.\n",
    "\n",
    "For instance, see this [introduction to matplotlib](https://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn (read this after you have completed unit 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Scikit-learn](https://scikit-learn.org/) includes a number of features and utilities to kickstart your journey in Machine Learning. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A toy example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:29.678896Z",
     "start_time": "2019-02-20T16:35:29.337354Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:29.686217Z",
     "start_time": "2019-02-20T16:35:29.681106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.4666179 , 3.86571303],\n",
       "        [2.84382807, 3.32650945],\n",
       "        [0.61121486, 2.51245978],\n",
       "        [3.81653365, 1.65175932],\n",
       "        [1.28097244, 0.62827388]]), array([0, 0, 0, 1, 1]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = make_blobs(n_samples=1000, centers=2, random_state=0)\n",
    "X[:5], y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:29.877682Z",
     "start_time": "2019-02-20T16:35:29.688363Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for label in [0, 1]:\n",
    "    mask = (y == label)\n",
    "    ax.scatter(X[mask, 0], X[mask, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:29.892563Z",
     "start_time": "2019-02-20T16:35:29.879625Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:29.897027Z",
     "start_time": "2019-02-20T16:35:29.894282Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.099709Z",
     "start_time": "2019-02-20T16:35:29.898998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for label in [0, 1]:\n",
    "    mask = (y_train == label)\n",
    "    ax.scatter(X_train[mask, 0], X_train[mask, 1])\n",
    "for label in [0, 1]:\n",
    "    mask = (y_test == label)\n",
    "    ax.scatter(X_test[mask, 0], X_test[mask, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sklearn uses a uniform and very consistent API, making it easy to switch algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For instance, training and predicting with a perceptron."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.134116Z",
     "start_time": "2019-02-20T16:35:30.101899Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.138638Z",
     "start_time": "2019-02-20T16:35:30.136452Z"
    }
   },
   "outputs": [],
   "source": [
    "clf = Perceptron(max_iter=40, random_state=0)\n",
    "# clf = LinearSVC(max_iter=40, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.147233Z",
     "start_time": "2019-02-20T16:35:30.140773Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.9250\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print('Test accuracy: %.4f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.151712Z",
     "start_time": "2019-02-20T16:35:30.149388Z"
    }
   },
   "outputs": [],
   "source": [
    "theta = clf.coef_[0]\n",
    "theta_0 = clf.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.361222Z",
     "start_time": "2019-02-20T16:35:30.153768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11acccba8>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAGfCAYAAAB2nSf9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsvX94k/d97/3+SrptyQJLBmQQBpKQpqSjAZzQLAe6tEk2e5lLyiFZctaznrZ7nna7du0ZoX1ITNoQk6SLV3aa0Oese0734yynzVoIoSTUzcwW0nYlT9JADGS0YW1IEzAGm2DZYMv2Len7/CHf8n3f+n7vH9ItWbY+r+vKhSPp/inZ37c+P94fxjkHQRAEQRAEURy+6T4BgiAIgiCI2QCJKoIgCIIgCA8gUUUQBEEQBOEBJKoIgiAIgiA8gEQVQRAEQRCEB5CoIgiCIAiC8AASVQRBEARBEB5AooogCIIgCMIDSFQRBEEQBEF4QGA6DrpgwQJ+9dVXT8ehCYIgCIIgXHH06NGLnPOY3eumRVRdffXVOHLkyHQcmiAIgiAIwhWMsXedvI7SfwRBEARBEB5AooogCIIgCMIDSFQRBEEQBEF4AIkqgiAIgiAIDyBRRRAEQRAE4QEkqgiCIAiCIDyARBVBEARBEIQHkKgiCIIgCILwABJVBEEQBEEQHkCiiiAIgiAIwgNIVBEEQRAEQXgAiSqCIAiCIAgPIFFFEARBEAThAYHpPgGCIKqX/T292Nl9CucSSSyOhrC1dQU2NjdN92kRBEEUBIkqgiCmhf09vdi2700k1TQAoDeRxLZ9bwIACSuCIGYklP4jCGJa2Nl9KieoNJJqGju7T03TGREEQRQHiSqCIKaFc4mkq8cJgiAqHRJVBEFMC4ujIVePEwRBVDokqgiCmBa2tq5ASPEbHgspfmxtXTFNZ0QQBFEcVKhOEMS0oBWjU/cfQRCzBRJVBEFMGxubm0hEEQQxa6D0H0EQBEEQhAeQqCIIgiAIgvAAElUEQRAEQRAeQKKKIAiCIAjCA0hUEQRBEARBeACJKoIgCIIgCA8gSwWCqGD29/SSjxNBEMQMgUQVQVQo+3t6sW3fm7mhw72JJLbtexMASiKsSMARBEEUB+Ocl/2ga9eu5UeOHCn7cQliJrG+8xB6BcOFm6IhHG6/3dNjmQUcACg+hjnBABKjKoksgiCqGsbYUc75WrvXUaSKICqUcwJBZfV4MezsPmUQVACgZjgGR1UApY+SEQRBzAaoUJ0gKpTF0ZCrx4vBiVBLqmns7D7l+bEJgiBmCySqCKJC2dq6AiHFb3gspPixtXWF58dyKtRKESUjCIKYLZCoIogKZWNzE57YdAOaoiEwZGupnth0Q1Hpt/09vVjfeQjXtHdhfech7O/pBSAWcCJKESUjCIKYLVBNFUFUMBubmzyrYXLSTah1/0VCCkYmUlDTU40sbqNkXae7sOuNXTg/ch6Lwouw+cbNaFve5sm1EARBVCIkqgiiShAVo2t1Upp40wu4YiwWuk53oeOVDoylxwAAfSN96HilAwBIWBEEMWshUUUQVYLbbsJiomS73tiVE1QaY+kx7HpjF4kqgiBmLVRTRRBVQjm7Cc+PnHf1OEEQxGyAIlUEUSVsbV2RZ/DpVTehOVVYvyyGIbU/73WLwouKPlYxkGs8QRClhEQVQVQJ5mJ0r0SFqAC+jt2BYHwfVD6ee13QH8TmGzcXdaxiKPfYH4Igqg8SVUTRUJfXzMHLbkINUQH86OBqhGsDWLDkXyvmc2FXqE8QBFEsJKqIoqAuL0JW6H7x/Eq8fv8DZT4bOeUc+0MQRHVChepEUVh1eRHVgVUBvMxsdDooZ6E+QRDVCYkqoigqrcurkhbxakE2Tue262PYtu9N9CaS4JiqYZqu96ScY38IgqhOSFQRRSHr5pqOLi+tELlSFvFqQTZO5+W3BqQ1TJV0nlRPRRCEV1BNFVEUm2/cbKipAorr8iqm6L2YQmRqtS8OUQH8lt3HhK+dzhqmUhTqEwRBaJCoIopCEzxedP8VW/ReaCGyl632XouzmSz2FkdD6BXce6phIghitkKiiiiatuVtnnT6FTvapNBF3KtWe699kGa6r1IpzUZnEjNZGBME4Q6qqSIqhmKL3gstRPaq1d5KnBWC1/srN1TDRHV+BFFtUKSKqBgWhRehb6RP+LgTCnUM9ypN5bUPkmy73kQS17R3zYioR7XXMJHhKEFUFySqiIrBi6L3QhZxr9JUi6MhXMi8gtpYN5iSAFejGB9oxULfOlf70e9PJPYAGKIewMxIB1YjZDhKENUFpf+IiqFteRs61nUgHo6DgSEejqNjXUfJndm9SlO13NyLYHwffDUJMAb4ahIIxveh5ebCUj2idKaZUqQDyevLO8hwlCCqC4pUERWFV0XvbvEiTXX40rfBfKrhMeZTcfjStwF8uqBzAqbSmVzyOi+jHjO9OL7SoGJ9gqguSFQRhEeUwl1eL/bWdx4quUWBFzVATrvdqqErrtA6P4IgZiaepP8YY1HG2F7G2FuMsV8wxv6TF/sliJlEqd3lSzVmRZ/uk9VwOY2GOe12q6auuI3NTTjcfjv+xxeA8Ac6sf3EnWjZ24Ku013TfWoEQXiMVzVVuwD8M+f8egCrAfzCo/0SRMVgV2u0+cbNCPqDhsesCu27TnehZW8LVj29ytEiWwqLArO4keE0GubUBqLjhZMz2i7CLZqxbd9IHzh4ztiWhBVBzC6KTv8xxiIAbgXwWQDgnE8AmCh2vwThJcWmmpzUGrlxl99x6NvY++6TwGQNllP3eK8tCkQiyIwsGia6p0663fb39CKRVG1fN5so1tiWIIiZgRc1VdcAGADwvxhjqwEcBbCZcz7iwb4Jomi8KL52WmvkpNB+f08vnj39LTDFKCymY5G1EjEMkApQ2T2N1ikYHM0XTPpIl1U0arZ2xZWi3o4giMrDi/RfAMCNAP6Gc94MYARAu/lFjLEvMMaOMMaODAwMeHBYgnCGF87kXvoN7ew+BQQSwufKvcjKRExTNIR3OttwuP12ofCU3VPOYVv3ZXXPZmtXXKnr7QiCqAy8EFVnAZzlnL82+f97kRVZBjjn3+Kcr+Wcr43FYh4cliCc4YUg8tJv6FwiCa5Ghc+Ve5H1erTPUFK1rfuS3bOGOmXWdsW5rbcjCGJmUnT6j3N+njF2hjG2gnN+CsAdAH5e/KkRhDd4MYbGS7+hxdEQLgy0IhjfZ/S1yihlX2RFLf+3XR/Dzu5T2LL7mDT9Z3VP7eq+ZPfykQ0rPbqqysNNvR1BEDMXxrlVz4/DnTC2BsDfAagBcBrA5zjng7LXr127lh85cqTo4xKEE8z1P0B2EXfbOedFsfvO7lM5MRKo78mNtEE6hHCtgmT68rQuuE7vVbH3tBo8qgiCmD0wxo5yztfavs4LUeUWElVEuZnuRVwkQjSU+h6Em76PtK5pNugPFj2ip5BrlhmMNkVDONx+e9H7JwiCmIk4FVXkqE5UBV5bEbjFyrqgJtZtEFRA8Z2AhXY8uqk/m+57SkxBApcgKgMSVQRRBiytCxTvOwELHTfjpP7M7QJOC/4UpbgXNK+RICoHElUEUQZkYgUAuBoFq8kXVsV0Ahba8WhXkG+3gJtFw23Xx/Dc0V5a8FE68ePFvEaCILzBqzE1BEFYILIu0OCX7oTCag2PFdtuX6gFhN0oHKsFXDTP75lX36uIcTRuRwKVAi/80kR46aFGEERxUKSKIMqAXpT0JpLwM4Y052iKhrC15TNQIqs9bbcvxgLCqlZKtlD3JpL40p7jSJsaX2RtMOVc8LW5e9qYGKcjgbymVOLHC8sQgiC8gUQVQZQJ68LuJk8XeJH/lGzcjJsaH9kCzoA8QWVFKRb8rtNdecJUHVqD7W/8JXhg+ufulUr8eOmhRhBEcZCoIohZil13XiE1PqIFnEEekRJRigVfFI16+KePYKxvE3wLB8EE2/SVeSRQqcSPUwEtgpoICMJbSFQRBLwz9pxJi1MhBc6iBVxWgK9HE15NRd4b2X3e9caunKDSUPk42LwXpY0AXI1gf09v2d6nYsSPk3273Q91DRKE95CoIqoG2YJc7OJSyPaiVFW5HdQLrfExL+Ayw1ANzTnepyQQnhOHEtkMwP2ibXWfZfYTTElg7Nx9eSOBeEbBWH+rQUCWQxhXkrcXdQ0ShPdQ9x9RFYg607btezO3kBbTleV2ey1V1TfSBw6eK5wud0eaV0OirTobA/U9CMb3wVeTABiKular+yyzn+BqFKnhZoz1bUJmIgrOgcxEFGN9m5Aabs4JSKvPx2yFugYJwntIVBFVgdWCXMzi0nW6C4n5j2DO9e0IX9uJQH2P7faiVJVWOF1ORGKokBofsw2Dn01VMNXGuo1Do1H4tVq9T5tv3IygP2h4XGG14JfuBACkhpsx8nY7rrzViZG325EabgYwJSBLZXdQyXglqgmCmILSf0RVYLUg23VlydJCWsTJV5MVSKwmgWB8H8aQXcRli5MsVeXGQd2L9KGXNT76tJY+TeelW7zV+9S2PDuXUNT9p9lYmAvq9QLSibCeiXVzVlDXIEF4D4kqoiqwWpCtFherOp5vvp0fcWI+FbWxbijJtdLFaVF4EfpG+oSPO8FL36VS1PjoxVrCQ7d4OxHQtrxNeP1OaqacCOvZVtRdysJ5gqhWGHfhLeMVa9eu5UeOHCn7cYnqxbwoAtkFWXMLly24siLspmgIl+P3g4vMBDjw2Op/tixS14siIOug3rGuw5EoatnbIhRl8XAcB+85aLt9OSn2Ws2UKlpk9/mw+hwcbr+96OMTBFHZMMaOcs7X2r2OIlVEVWD3rVwWsbFKC133AXHEKT4nbrnQa2JClKpa33nIVjAUmz78yv438d3XziDNOfyM4Q9+cyke33iDo23dIrvWQjsdnUbW3Iovu88HFXUTBOEEElVE1VBIqssqLbT5xs15URhkFKyf92nb/ZpTVW7SS8WkD7+y/01859X3cv+f5jz3/6UUVuW0iyg0VWf1+aBRMARBOIG6/wjCAqsOubblbfjE4j8HV6da9ZN9m/C9l2O5Vvz9Pb1Y33kI17R3YX3nIWmLvqz77P7dx/K2E3W6OR3A/N3Xzkgfd3quegrZptR40clnvq7bro950ilJEMTshiJVBGFDUPHlFuloSEHHXStzEY2DP2vClUS74fUpTC3gTiMmVmkk83b6lFrfyHmwVBSJ3hb8xdkQ1FZrh3DZfL40566jO14Vb3tdJ1Vsqk50Xc8d7cXdNzXh5bcGqKibIAgpJKoIQoKoeHk8lTG8xmoBd+NYbTfuxbxd2/I2qENrjIs/iutIc+uu7YUjtxfCzGwvsWDRb2Pg/Mq81zlN1cmu6+W3BqgonSAISyj9RxAS7NJI+3t64WOiUb3ZBdxNxMTKlVy2XTFprkB9D8LXdgpNS+3O1e45vUu5XWqw2FSdyJ0+PW8P6hqOG17nJlVHRekEQRQKiSqCkGC1uGoRFlE6TVvA3ThW613JZZi3K2Txb4qGDKNjGAN8k6alImFlFd2RPRcJKY7HvhQrYGSDlOctfSnn8N4UDeWsEZxQSU7jXae70LK3BaueXoWWvS1lH2VEEIQ7KP1HVBX6+p1onQLOgaGkKqyRser4EkVYgOyIFv0CbmVWKaolOtx+u9QzyRxpKaQjbWvrCnzl6CN5o2OYT0WwsRtXJse3yI5p3tfWZ49DzRiF5chECh0vnIQaOoLwsm4wJQGuRjE+0Iqd3TWO77ETZDYSw+oAThSYqqsUp3EvTV4JgigPFKkiqgZz9GRwVEUiqUojKVadf7JISoZzg/eVfiaeFjEBgDU7DuL+3ceEkRzZduZIy9bWFahrOG5I49U1HLdc/Dc2N4EpQ8LnmDLkKrqzsbkJc4L538vUNMcV5WfCaFh/5pW8a7DqqrNLIcpsJApxbNdfl5P7X2oqZUaknkrs9iSISoIiVUTVIIsuaWgWBju7TxmiVqLONG2enBlzhMXsfSSKQumPrxV5O/HUUiLHEIzvg8rHAWRnDyrxfVAiqwHIt41LfK7i4UU46DK6kxhVhY+LBikzn4rQwoMAvpJ7zOoef2X/m3jm1fdynvWiInaRV5hTewkrSjG+xy12Jq9ezH90w2wc1UMQXkOiiqganNbpmBcL0YJRaIrITti5KYbe9caunKDSUPk4dr2xy7C4mhffW5fciud/9bwnQkSWvvNJBinzQP7j5mHMO7tP4f7dx4Tbi7ogAe8c2ysJK5PX6UgNetHtSRCzHRJVVUq5v+VOB+aapUhIQSI5FT0J1PdkIyq6mp/UZE2R3WJR6DBaO9Hkphjaybga0eL7/K+exyc/8En85OxPbN9/Ow8pmbiM1DRiSO3P21/cIi1nFcXTo7+H2fML4VxiMxZHQ/jT1hVoW145C3wxv2dWUTir1GCpfo+pK5Ig7CFRVYVUQwGsKFWh+BkUH4Oa4bkOOC1FxSZrfsaAnLDSWwOIhIWXY28A98XQTsbVyBbfn5z9iXD4srmQ/8pYKleILkr3yMTlv4/fht2ndhv2bRcNs4viaWjCU/Qeb9l9DEfevVSykTtuKPb3zCoKt+3ftgm3cTr/sRBoVA9B2EOiqgqZjm+5pcYcERg8+9tIqkYDSDXN0VCnoK4mgMR8cc1Pbaw7J6oWR0Oe15GIIjsA0FCn4JENK13tUxbJuHXJrWjZ24LzI+fBIXZQFy2+5msdFNRLiSJ4ZnHZdboLz7/yfN62n/zAJy0/X04jHiPjKVzT3gUfY3mWFhzAM6++h7VXzZv2lJQXv2eyuYnFzH8slErpiiSISoa6/6oQJ2mjmYTIADIZ+Z7QdykxquKhe5Pw1YhrfpiuFmhwZBwdL5wseo6cHlFn2VP3rUHP9hbXIqBteRs61nUgHo6DgSEejuOTH/gknv/V87l7IUO0+JojRTKDUDvxIxITAPCTsz+x3M5JxMMH5Do2ZSN3OFDw++MFmreUSPQA3vyeFTP/sVAqpSuSICoZilRVIdPxLbeUiBZxc9RJY8Gik+h45Vnpvrgazf08qmYwqmaEryumjsTLzjJzJKNlb4tQ0OiRLb76a7JKjy70rbPcf6GiXRQJYciKpKZoCCPjKUNNnBXTVedjTvmJ8OL3bLoK9CuhK5IgKhkSVVVIqdrQpwvZYs1MHWghxY/axm4MqeIFj2cUjA+0OjrmdNeRyOq8rIQLA7NcfPU1MzJLhGBjN7be9DnLcytUtNsV/1/T7txNfLreH1mUTsPL3zNZapAgiOmDRFUVMtva0GWLeLSmEZHJGXzaAr39xIBwH5wDY32b8iJbIqa7jsSqzkt2L+LhuLAwXc/W1hXYuvc41DTPE6QaTBmyjVSIRDsyCt75j1uxvvOQZZekVSRkcTSEC5lXpB2bGtP5/liJ2ng4PqN/zwiCsIdEVZUym77lyiJv2275Ito+ZTSz/ObbYtHhSzdIBVVDnQJgqnC7NlBcKaKbUTkirPyCHrq38CjkxuYmdLxwMluzpEbBBHVnVpYIGnrR3jdyHlyNYKw/K356kcTWZ49jx4GTSIw6v2YAaLm5F8++m5+SvOkD8/Gr0ytcWVuUimJErRk7OwuCICoPElWziGr9I+wm8iYTYJ9Y+nl85zSDmjYWPys+hrZVcTx3dGocRyKpFtwBaNVhp484AfI0mJVfULFRyKHJmqXxgVZDTRXgLnWlifb1nYfy2vDVDM9dt5tuysOXvi1MSfb5vo/D7e4Eiwgvfn+8Sq2TezlBzEwYl3TQlJK1a9fyI0eOlP24sxnZEF7qzslHZsi4v6cXOw6czC340ZCCjrtWSkfSNEVDOOxyrItIZJiJhhSMpzKuC7YLOR+r89Obo/rSDXjitgdz4sypqeU17V0WPYjuzn3V06uEHY0MDCc+c8LBUeR4+fvjhbGu7HPixXtMEIR7GGNHOedr7V5HkaoZiOgbdTWMkPDKBV6W+pTV82yRjEwppMPMyTYiwaSff6c3MdXwqo5I34GXGm5GargZIcWPxzfdkHMqtzO11L9Pc66LInmhxbZWzcl9KWXXqpe/P16k1sm9nCBmJiSqZhiytIDMiXq2/BGeThd4L52krRzVnaI3MfU61SvrwAOy0ZNziSTmXveX4AGxqSUAY/orMJjnVC/Cyb20S61Zpe/sUnuVJmLIvZwgZiYkqmYYsm/UfoG7NDB7/ghPpwt8sU7S5sJ0c5RJT0jxI6j4hG7mehKjKnq2tzi/CBeYI3ZmIZ/xD4IJtjs/ct6VZ5iG03tpVS9mVYMEwLY+qRwixk3NFrmXE8TMhETVDEP2zTnNOUKKf9b+EZ5OF/hChycD2YX0oYNPg81/EeFFCUyoUfjeb0V0/GYMJVVh9x8A28HCpRTL5sV/ZDxlOBdZZ+Ci8CLHnmF+xpDh3HWUTZZas0rfaT+LntOOW0oRs7+nN9dVqWFXeF7MZ44giOmDRNUMQ/aNuklXWzUb/whPtwt8oU7SX/3xM/A17jXYACgL90EZqsE7jzxgua1WIK8VqWuUUizrIz6B+p7sjEQlgbDOE8qqMzBro5D/Pumd6kvRQFFI+k7/nF7E9CaS8DNmEGWFnquoAF7DrmaL3MsJYuZBomqGYfWNejb/EZ6pLvCj4QPwCWwARsMHADwgTQnp30tzpCOolG5kpxbxsRpTkxpuxhiAuoUHgUAir2nA/D7pner9jJWkI9Uufecktaedk5dWBqIImp7ZUvNIEEQWElUzjNmYFnBWYAwsWPT7iDR2Y1gdyC3k6tCaXAF1Jd4Ln8SZ3KckXHkRjaemZhAOjjr3yXLrvaQt8rIxNVptlJJci0dv/Jzhfcq+D9n3KRM+IHQ9z3BuefxCOzzt0ndOU3ted9HaiabZUvNIEEQWElUzkNkUkXJTYDxwfiXqxlOYt/QlnB85jyde/TounbkDo4nVeduW4/44ESyRmkYMqf1520ZqGm3rgLR9+wRNCE4WercGkvt7enPHko+pSeRSzXpBZX6fgJXC7a1ExI5D38bed58EJsWcmw5PJ182nIhLr7sArbo9Z1PNI0EQWUhUEXmU05ndTYFxoL4HvsZ9GFKzi+6Q2g9f414E0plcJKRc3lxOBcu2W76Ih3/6CFQ+nntMYbXYdssX8WcnxIut2SZD1NWpvc4K2b3teOGkoXYozTmiIQUjE6ncsWTF6NGaRnBkvbt2dp/C1tYV2bqxZQcwx2IeH2AtIvb39OLZ098CU4zRMX2Hp93n0osvG151AWrnKqqJA7Kjjx7ZsNLzz2m1TlUgiEqhdMUZxIxEEwu9iSQ4phb4/T29jrZd33kI17R3YX3nIUfbyCIAvYlk3uJmlZJysk8vkQmW+3cfM1x72/I2PPbRHYiH42BgiIfjeOyjO9C2vE26UGtF0k642uJey+5DIqnm7q0mohJJ1TCiZ3ygFTyjGLZTWC0unbnD8NnY+uL/QjLyPfhqEmAM8E3WXgXqe/KOqwle0bnu7D4FBMTRsfMj54v+XDrddmvrCoQUv+ExtxEl/fGArKDSLCiaoiE8dd8a9GxvKYmgKvQeEQThDRSpIgwUWlNS6KyyaJ1i68mkYZWS0lOOOhUr4Wa+dpkNgKwOyKmgkh1PoxijUa0YvTbWDV/NEOLhRRg8+9sYHTSm9Wpsaq+cnuu5RBJ188XRMaSi2HHgpPBzuePASdvIjJvPtBc1i6LjaSOGSjliphqmKhBEpUORKsJAoTUldmk8Eft7enFlLOX43PRt+bLHy1GnotUeWWF37Rq1galfwYY6BU9sugFNBYhC0fFEURc3pIabMfJ2O+r7nsLBew7i4vn8OimnQtd8rh0vnETzowdxdXsXrm7vApg4OsYzCkYvtEiF9+CoahmZ2d/TKxWWssc3NjfhcPvteKezLWdT4kX0tdQR1EpzhSeIaoREFWFAFuWxi/64+YPedboLLXtb8PDx30XNNU8IU0VmGIC6kQ1QWK3hcYXVom5kAxiykYBSD5DWInKyOic9VouZth+9IeSYmu3wE4khawknPt7G5qacSNPuT0NdVrQE6nsQvrYTc65vR/jaTsv3oDeRxDXtXUIhydN1wm1kj2skkqpBKHE+GR3r24TMRBScA5mJKMb6NtnODdSjF5f7e3qx9dnj0tf6bYRxoem0Qn+HimW6jksQxBSU/iMMFOos7bTA1zDDT1eDYzUbbipt0oau0ysLHqrsRRGvne+QHqvFTBbZ23HgJOpqAobRQ03REG67Pobdr58x1D05OZ5o5MxDB5+Gr1HuQSWCQ1YwLz4fH8sKQVHnokagvidbJ6crcOeXb8SICxEl4lwiif09vfjSnuOW4tdOGBeaTpuuETM02oYgph8SVQSA/Pl0tQGfYXSKnfhw+gfd7Ww48z5k9UlOrs8LU0d9NChQ34PahS+A+ScLktN1GL+wAanhZoQUP267Pib10JJFsQZHpyI42ugh7fp3/+yM9LycLp4bm5vwV794KddBqcF8KoKN3Ri/cqN0LqEI7dpFj7/TmT+TT0NsLroXfOEL8PmTyAi6CKMhBeHaqSHSI+MpQ6RPIxJSHEUT7dKshabTpstLbjZ62BHETINEFZG38A2Oqggpfjx53xrHf5Cd/kG3mw2n+BjmBANIjDoXdE7wqohXi8hlRcGzYL4pU04WGEUwvhehuhq0Xn0nnjvaKxVxTovI9eksmdgx+0bZMawOiJ8IJKBmuNACwExs0UmMhg9I05La+CDR52J0IoUJYYF7GsyXvSfmCGZI8aPjrpV5UTeRkGcsf9afGQYUHH2N1im2hrPT5SU3mzzsCGImwriD2hCvWbt2LT9y5EjZj0uIWd95SDpPsNBuJVmqrWVvi3A2XGYiiuj7O0r2zfqa9i6hUGAA3ul0HvnSFnLfsq/CJ+pUAxAPxzHyq3bhPdUGCUfrFFwZSzmKCmnCxYvzByB9DzhnGDt3b07E1AZ8wkhQbNFJYMGzeRFHjaA/iI51HYaIov7zEAkpSF31JdiUNAGw/1yIPmdbdh+zFYV/eMsyPL7xBsvXiESb4mcANwpcTYS6FbcEQcwcGGNHOedr7V5HkSrC864hq1SbbIZfxx3taFteunZzr0wdtQXzK8fl3W19I+dxRXLvtJTU4KiarTti2SJ4UOjJAAAgAElEQVRtP2OoCTAk1UzeNlbz6yIhJe8xO0TvAQAwxnMpOOZPIp1uQN3F38Xo4Orca0KKH7WN3RhSxYIqHo7n1bmZPw+JpIqwxFzUjL9myFLYiyIzmummCKemm12nu/DNt3ch8IHzqE9FkbzQgkbfOmHKUZNXxTr6k3EnQcx8qPuP8LxryCrV1ra8DR3rOgxmmOaoRinwwtRRY2NzE3zpBunzLBV1dO8yPCuogKzYSmU4FJ8xfKOd49bWFXnPAUCy9me4+X/fhhv+8Qas/Nvfwkee+pptd5r2HoDn//ozXxq+QDIbRQoMIhjfh9iik4buSln6kIHh4D0H895L0edBZJ8gQksjukH2Xjs13dSaKbLRPA4eGER02fN46N4khgSROz1OrTTMkHEnQcwOKFJFuO4asvtGbRf5KrTYvBi8LuK9+5rP49l3/8pQUwUAPOPH2IUWPHpn/j21Q01zNNQpqKsJCM9xx4GTBhuCQH0PlIX7kORqrpMyGfgeHjqYAvAZy2trW96G9n/bZn9OfBwLlvwrXr//gdxj33x7kTB9KBNAoqiR3lyUKQnwdB2Yb8xwP4P+IDbfuFm4T/0YGH2XpP5+Ffpei5optHE5i6PitK6eQiK8ZNxJELMDElWEq0XISRedV6k2r/GyiPeR2z+N5/7qLNIN38/r/lvoW5d3T62sBfQkRlX0bG+RPqdHNraHzXsRX9qzBkD2ms0i+LbrY3j5rQFk5kekdWF6+kb60HW6KyeEN9+4WTjPUCaA/JJrTw03G7r79BYL0ZpGbLvli0Lxbf4Mavs2fxbN77XT9JqsmeL8yHk8KvgCYqaQzzkZdxLE7IBEFQHAueBw8o261H45Xae7Cvaq8pJH7/hv2LavOf86N2WvU39PZdYCZqwWZLNYtXIzT3OObfvexJF3L+V1IX7n1fcAAIFMq8HWwIqOVzoAZCNc6tAajPVtApv3Ys5jauzSnVCH1gi3dSImAaPISit+qB8SF5KLPoN6QfblI1F89ccb8OWP/Vfp/beqf1oUFkfikIpiy+5jiIQUBBUfBkfVvE7JQj/nlfpFhCAId1BNFeEKJ9+oRU7eXjmd6+tdODj6RvrQ8UoHuk53Fb1vPU6GQ2vXGdUViwcV8a+UEjmG+R/aibmTDub1C05kO8l02C3IW1tXGCwMZGN7ACB8bSfU0BF897UzUiGndzAHB6K1UQSY+HuWlv4CsqJmdHA1Rt5ux5W3OjHydjtGB1dLa4m8GrujYf4Map5X+qHOycj38NDBp3Pvm5sxSptv3IygP2h4TBuXw5EttB9TM3jqvjV48r41nnzOvaz5Iwhi+qBIVZVSaKeR02/UpfLLsap38Spa5dYodDw1VQd0OfAzfOXoY3j4RHYIsZYSM7vIBxqfQwiAenEVgKyxpd6HSVQzFFt0EnXXHshFh1JXrocSPZqfAmRGl3RYOJTro0OP3bcGxwcPYe97XxPOxdHSYm5TVaLIpRNk+1scDeFC5pVcZApgYMwYDdPSoDu7b8HG5iZX56x9jrRoKCa7//SpSk2QHW6/3ZPPORl3EsTsgERVFVKMu/h0j8KwqnfxCjdFw/rXml3CtSharb82TwiqfByZyA+BSVGlF2aimqFAfQ+SkX3w6RzIlehRqImbEJjzFpiSyPN9Yj4VtY1ip3oR9+8+BmAewtdGhbVWWiG621RVofVlov3t7+nF5cBrCM7Xpy3F+2JKIiea3J6zvplC5nHmdb0TGXcSxMyH0n9ViJtUiJlSpvacIOswEz3uJIUnwk1UQ/+YqHB8LD2GoYkh4f70NVH6+y96f2RF6YE5b2Hk7XbptfiUoby0kh0iuwN9J16xqar6UEBoD2G3v9ww68gPHdWBcTWa8/G67fpYXvDN6TlX26DiQn9vCIKgSFVVUmynUTm/UZvTlC03fxo/GPtGnnmoufOsmGicm6iG/rWywnEZ5pqo3kRS6m5vVZQOAL50A3hgMO/5eHgR/nTTDXndf1qxuog8uwM1OmnOmo3c6CNPWnpSLwpFXXfmMUiKnyEaUpBI5hd7MwB33yQ29UyqacxxcJ95RsH4QCtqWPb4zx3tdXQMEdMdnS2UQho6vJqRSRDVComqKmSmdBqJ/sB/7+UY/sttf47Dl75tuVgU4/sjW0RFA5L1r+USl/A6fz1G1KQhuqIt+noYxJ5OAKT7XjwnjoOdbeg6DaFTffbe5IuHl98asPRb0tda+RmDuna14Xltf04WYNF7oaY5wrUBhGsDeefBJ8/PjCb6ZfeC86w847phzAmowuNzAD843oeX3xqwrWGaifVOWkOH9nnQUtEALIVVufyyyD2emK3Q7L8qRDaItpxpPCv0RdoinMwkLHbWn8jbSW9NAEzdMyC7GPVnXkEwvg/QiSeF1eLK2Y3IwBj50RZ9p5jrtYD8GXuyyIRoAQPgqnhc9PmQRdVii06iYcm/5s7jnf+4FargWvUzDfWWCFyNYmKgFb986CuG12vHE90LZBQk+zbl3VOZR5bTa7SjUsWBbL5jPBzHwXsOSrfzakamFZX+94cgRDid/Ueiqkqp1MXAiZ+Tkz/wXg+Jdro/vbCpV2K4dOYOw+y8YtCEh09JID4nf8aeCKsFDAC+/P03MTLhTFiZr1W0ALsRPE3REEYnUrgc+Jlwm86PPSadIWg2Cv2dRZ/F916Oue4wtLtGK8olDgr5XV319CpwgTxiYDjxmRPS7UoxXH06jkEQXkMDlQlLKrXTSJR+MOMkTel1HYzTOjR919j6zkMY9bBDTEvJMQAHHUYNrNI5h9tvx87uUxiZcHaO5msVpZFFBfXwqQgu3gMs3p2L0rGRG7G1dQU6Xjgp3cZsk7GxuQlH3r2E7752BqnhZvDLN+IPfnMpHt+YFYirG3pddxjaXaMV5UiVFVrjJDMwtZulWI76MXKPJ2Yz1P1HVBR2f1id/oH3uktRJuSidYq0U6rYRULWH+dEVGodXLIUqnZubs4xWmfsCBR1AUoL6hnPGXMG4/vAw29gZ/epbKG6ZBuzTYZWcK6JpTTneO5ob+6eb2xuwuH22/FOZxsyJkEVqO9B+NpOzJk0Xw3U9wiPab63Xae70LK3BaueXoWWvS0Gk9lyiINCO3VFBqZWsxQ1ytHdW23dlER1QZEqoqKQFdFr1Aacfw/wMhon+gav+BmujKVyQ47NUQS7azETDSkI1wYs67gAYGQ8hf09vdJrc5JC1RYwN+c4OKriK/vfzEWGRAXcrKYRQ2q/5X6YT0VtrBu9b2ejbrLC8/REBOs7D+XSXW4iQ/rrMqck9cao+pSkWbDbFXu7afgoNN0uE2j9mVfQsrdT2qxhNjB1M86p1FHsmdpNSRBOIFFFVBR27tuJpDotLd4iATEynkIiaUxb6Rd5t07in1gdN3S9rb1qHtZeNQ87DpzMCTfAeA/05xStU8A58s7JjH4Bky1wDByjaiZv22defQ9rr5qXux/mBbjrdDKvC1GEFp3iyPpimWuqtO7I3uEktu49DsBdZEh/XTKPr9pYdy6dqonYnd2nsGX3saxAXPZ1S/d+p+LAa3sPTST2jRhNZgHkCavpmIlpx0zspiQIp1ChOlFx6L/Vy2pjKqGo1UmnlDlCMTqRMggkjXCNHxkOYdGzrBMyGlIwnsq4Ks5uEixg+3t60fHCyZwYa6hThOeo34fVvdcX6wMMHPniLDMRNZiWmrv/zN2RDXUK6mry7RdE52Me8RNa8WCe2zwAcA4kT/0l3n7i94TRvTnXtwu30xd7O4lAydKw2jVZbSs8rw90ClOmdp19BEEUTtkL1RljfgBHAPRyzj/h1X6J6kMf/bimXTwouRKKWp2kf8yRnP09vdi69zjU9JQcU/wMit8njXrJrtUuImXGSgyNTKRyP1sJKsD+3usjJF2nu/DwTx+Bysdzz5s9uvyMGXyxRAyOqnhkw0rbyJBoxA9PRYUihKtR/MFvLgUgrl2SpSX1xd5OUmWy+zU4qkpTx/r9a+enia9hRezQ7+WoJoIgCsPL9N9mAL8AUO/hPokqp5RGpSIvKidmkBoF14aYw1sWKTvtXNzUZskYnUjlirr1133xyrhB5NkRCSl5Jqiy+6Sv7ekbOQ+uRjDWPxWFCil+3H1Tk7B2zIyTtJFIHI33i9OL/NLv4vHPZ+vDRMJHlJa0K/YWRa6cvn+y+jCzcGvZ66yzr1JtUwhiNuNJ+o8xtgTA0wC+CuCLdpEqSv8RTimVF5CTYm6r42gprr6R82CpKJIXWtDoW2crzGSpIJlJpZauc1ObZYXiZwAH1Iyz33vzCBnFxwAGgwhz837IFno7w9doSMGxR1ps9y9LyYrSi+nh5lya1qmRqVWxt+yz6lQ0As482MwF9EC+ESwZbBKEt5Q7/fcUgAcAzPVofwQBoHRFrU78sPSRA70YWLDoJNLz9uRSWjwwiOiy59Gy+Cp87+UJy4JkWSpIJKi0qJe27f27jxV2sTrcRKQA4Mn71tjWhCXVNL7642fwzbftxYc+6qLdU60wPFc8/+zxPNH3idVxR+criwqJ0otNuminLOr45Y/9V2xsfsDRsWXdiS+/NZCrjbNqctDO3w4nnX3lGjdDEISRokUVY+wTAPo550cZYx+3eN0XAHwBAJYtW1bsYYkqohQt3k5rsnoTybxv/aPhA/DpaoSAbFfYc+/8LZLqg4bHzQuZedGXFWhHQwo67lpp6LKziuSUgmhIMdz7rtNdeOClTswxnWugvgfJyD6M2XSj6ZF1xD2x6Qbcd/NSPPPqe4aI03NHs2lLu/SszPrCHJ0zp2m9EO9W3Ymi2rpibAXsOvvIYJMgpgcvIlXrAdzFGPs9AEEA9Yyx73DO/1D/Is75twB8C8im/zw4LjHLKWVNiNM6Fz9jed/6ZWaVGf+g8HH9Qra1dQW27D6Wm3cn808K+9Y5EgxmtHShFwKs466VuZ+1lJOvZkx3rs+CLzwA5h/N65LTWw+IsDO1NP+BSKppg9ByU9itCRW7z1Kx4n1xNIQLmVfyRPJC37q815baVqBShqZTXRdRbRQtqjjn2wBsA4DJSNX/bRZUBOGWYrx9nOC0TinNed63e1lXGFejwn2YuwG1NJ6Vf9K5t7OpKvOidPdNTXj5rQHhgqn4WW7R0lKWhdZi1Sk+w33e9cauPM8m5suA+Ual+7DqRiskkiISWk4Ku/WPl5KWm3vx7Lv5IrnlqquEry+lyWYlGGyW+neYICoRGlNDVCSFjueQoY1t0cbJADCM4xD5EQFZL6FIyDieZXygFTxjfMxsE6DBkF1M9CNstFoe6UgXJQEfY7i6vQtbdh9DbyIJPrmf5472YmvrCjx135ps0bjhJIz/q40cKYSkyfizkHZ9qzlzVqNK3ERTzk2mZ2WjgpzixT4OX/q2UCQfvvRt1/sqlnKMm7HD699hgpgJeOqozjn/EYAfeblPYnahN4a0KmgutCZElG4AIK3f0Xyb1uw4KCwc5jxfcKWGmzEGWJpVAsbOOf23dC2KYBXx0grXZdEZIL+DT83wvMhNobVYZmEjG9Arw856wC6S4jTCFlR8RUdDvIqoyITndPlHTffQdKrrIqoRilQRZUOry+kb6QMHzxU064fUahQydFVbHPWRnW373kTHCyctvzHv7+mV+kQNJVUkBGaYqeFmjLzdjitvdWLk7XahcaVVuuqJTTcAg3c6jnjpOZdIuh7ZYh58rKH4WV7ES5QmEg3olREPxw3t/SKsIimi5+oU8Z8qkaO822iIVxGVeiUmfNwqYjebocHJRDVCs/8IT3ASgRLV5cgKmgupCZEtjrKIh5Y60s/RM6MtAKJIj9nDyQn6/Uwk1oCnMrYRL9E5yVryzalKwFgUrY1uSXOeK2rXnrMqJja38dfX1OOKOoI0n3JiV1gtHvvoDsfz5qwiKebnZM76Mrsts7i0+nx6EVHZ39OLS2fugK9xryEFqLBay4jdbKYS6roIotyQqCKKxmxGKGupd5MeKaQ7ym1aYXE0ZOlXZZWO0kwdf3C8L0/chBQ/agP5Y2eArBDTUpRqhgM241lk57TjwEnh87LaMLtUkJM0kbmN32kq1wvcusrroyGyz+eRX1/CwZ81SYWxm4jKzu5TGE2sRiBtFMn+kQ0VOdS4HNDgZKIaoYHKRNG07G0R1tuYB7w6fV2hyFyx9cOK9b5Q0ZpGXPj1bVAlouap+9bkGVWKFgftuf7MKwgtPAgeGARSDUheaBEKpqZoCOcmU5RO0CJi+mHIMudw83nPFOxa70WdjLJIIUPWtFTbXva542oUV37Vnvc44N593MlwbYIgZi5lH6hMVC9OI1Cbb9wsHK/hVXpka+uKvGHFADCRyuC+m5ei+9cvIhmZankfUvsRjO8DB4Ru2+aCb6tUlRI5ho5Xnp+6tsBgznPKvG8n8/yiIQVDSVX67d5q+5nWtu6kUFwU9ZBdP4fx2qWF4gFx92VTARGVSvGFIghieiFRRRSNrDPMXKDrZLxGMWxsbkLHCyfz0m5qhuPltwbQ8IF/zbl+5/CpCDZ244pO+BRS9yH2ccp6TplFlSaURONYAOAPb1mGxzfmWyHoozmRkALFz4RjZ2baOBKnI1XMwlYWmWyoMw58rl8Ww5Dan/c6ma+YHqfmlVQ/RBAEQKKK8ABRBEphtRg8+9u4pr3LsBjZjdcoliFJF9+5RBJzJRELpgzlUnKF1n3IoiFmLyrzPD+9CGyoU/DIhpXCY5ujOYmkmu9TpcNJfVk5a6KsKLRQXDaS5srY1HzC3kQSdewOBOP7crMaAQAWXZZapOzIu5cMg5CtrBaofoggCIBEFeEBeZ1hSgyXztyB0cHsmJNSOynrxcHc66IYFdQyLY6GEJZE1OLhRTg46VdVKLJonT4aIprn5/R+iKI5aobnOvnM2KWddhz6Nva++yTgcz6vzwtEkZ9CU2ciISPqihwdXI1wbQALlvxr7vM58O5tlg0CSTWN7752Ju/eyqKANI6FIAiAfKoIj2hb3oaD9xzEic+cAH/vyxgdXG14vlROymbvKz5ZyxSo78m9RosOibyWvKrpEu3b7Dk1nsqYN3OMLGqT5jzPg0rxM4yMp6Tu4Pt7evHs6W/lBJWGZm9RKmQ+YrddH8u7BlHqrOt0F1r2tmDV06vQsrcFXae7sLG5CYfbb8c7nW043H67NFJ58fxKw+dzfMi+41IkVoH890J2XYW4shMEMbMhUUV4TjmdlGW1THULD+aZSrYtb0PHug7Ew3EwMKlJZSEjS/T7BhgyahRjfZsM0ZBihKUsauNnDHff1JQzymyoUwCeTQ/KFvid3aekRdp9V/qKGtVihax26uW3BmxHqjg1jtXuU6C+B+FrOzHn+naEr+3EgkVTFhROP4d+iT+F+b2gcSwEQWhQ+o/wnHJ2Qll1dr3T2ZZLy2zZfWwyLbPG0r6hmJEl+noxWYt9ocJSNgA6zTmeO9qbEyEfeeprqIkfQK3OTDQ53GxIWZ1LJFE3XzwiJ6NGDWIMkFz3iT3AS48CQ2eByBLgju3Aqnstr8FKbNulQp0ax25tXYGHDj4NX6NxsHG6dg+6Tq9E2/I2R55Xmg+ZvqZKe9wcQaNxLARBaFCkivAc0ViUUnVCyUaALAovKigt4zTqYBfN8npEhza6RRQ9SappfGnPcVz3F48jGfkefDUJMAb4ahK5VKh+gV8cDTkaCi2NtpzYAxz4c2DoDACe/ffAnwMn9ljel2LuiVPbjo3NTZi39KW8wcYqH8+lNkWfT8XH0FCnGCJlj2+8wdFQYhrHQhCEBkWqCM8pZyeUlffVX+xx1qqvx0nUwUk0SxRZYpOvvXbbDw1jYqzui7lDzzf3VqQFBdZpzhGMdeeJCc3WoSZ1c85mIBJSwCZuxFif/VBo4f146VFANT2uJjH64nZsu/KU9L4UYzvg1LYDAIbVAeE+NAHm5vPppJmA7BQIgtAgUUWUBDedbcVg5X31ZwnxvDirtIyT1KUTXyXzvD29+7dWAG2XYhONV5GZlQL59g36x/U2A5odw9zUzUi83ZzrmhsRFHkLoy1DZ4XHCSbPC+/Ll/YcN1xjIWLbjXFsvSL2pdIPPPby80l2CgRBaJCoImY8Mu+rQmq7nEQdnNbQaAu3zKQSsI6cieqIIDEUBbL2DaI6KYCh9oMPokYXjVIzHHU1AfRsbwEgHgMjjbZElkym/oycy8wXXmOac4N4LERsmMVz2y/n4g9+koHy1a34ZfzraNxyPyIbNgAAxvtbwSPfM0TteEbBeL/Yl8oLyvUlgiCIyoZEFVFxmD1/brs+hpffGnAdBSgkLeMk6mAl1kSGmr3iAFIOmUhzaiiqMT7QimB8n1FMcICxbGSMTdZYaaNz9Md1FW25Y3u2hkqXAkz5g/gr9T7pNXrh8q6J56EDB9D3g+3gY1nBmTp3Dn0PbwcARDZswMXzK+Ef3ZSX2hwZXlnwsWVUioEqQRCVAYkqoiQUaoYoqlf6zqvv5Z53041XaFrGLuogE2stN/ei45VvGNJ1D//0ESj1G6VDmwF55MyJoaie1HAzxjBVJwWwnKDS0I/OMR/XcbRF6/Kb7P4bDS3C9pG7sT+9znIzr7rh+p98KieoNPjYGPqffAqRDRsmRW+zcJ6jl4jSs+UwUCUIonIhUUV4TjG2BKJ6JTNuoh6lSMvIxNo33/5cXrpO5eOoiXVLRZVV5ExUR2Tu0DOTGp4SE3Oubxe+himJ4gupV92bE1e/03kIvRP2gsmrbrhUX77Q1D9ersJxpzYPBEFUDySqCM9xOiBXhNNoxnR7AInE2vYT7tJ1dt1/6tAa4OLvIxM+AJ+SQMi3AFf6fwep4dXC1wuODAjcshh8QmuAQnHyXsgc0gtJnQXicaTOnRM+DpSvcNypzUOpoNE4BFF5kKgiPKcYM0Qnxoza6yoNN+m6pmgIhy3mDU5F+1YCyNYCpSYNKbtO9OU6+WREQwpSQvtRACyDjc1Nni3Kdu8ZA6QO6YWkzhq33I++h7cbUoAsGETjlvtz/+80QlnMPXDSZVgqiokGEwRROsj8kzAgmq/mlmLMEEXGjGYq1QNINP9PYbXgl+40PObk/K1GuvRsb8FT962RbssAHHukBdGaRuHz8XDc03l1W1tXQPGJR7oAQMCf/5xV6syOyIYNiD/2KAKLFwOMIbB4MeKPPZrr/nNKsfdgvF9ioFrCLkMNGo1DEJUJiSoih9P5anYU46iuOYfrXaz/8JZltq7WlYBotuBjH92Bv2j5jOvzl0X1tIjQxuYmaeG1Jl633fJF6QBpzxdluaaCmuZ5+y02dRbZsAHXHXoJH/rFz3HdoZdcCyqgeGFy8fxKjPVtQmYiCs6BzER23uPF8953GZqh0TgEUZlQ+o/I4VXhbbE1LTPZ80fmmeX2emQpNYZshMWJQ7lXxqh2KbKd3aegpiWpRsl+3Tik6/HSwsBKuGru81af3XJ1GYoo13xNqtsiCHdQpIrI4WXh7cbmJhxuvx3vdLbhcPvt9IfYJVtbVwiDPxzIRVJEUT2nUTynKVonKTIn0ZFonYLmRw/i6vYuXN3ehfO/vg0KqzW8RuaQriGKpD7444fxkae+VlDaUnYPtHFCdinBcs64nI5je5kiJohqgSJVRI5CoweEGLff8s2vl8V+zKad5n1q++nPvIJgfB8waQaqLwbf2rrGke2Ak05Ou0J1xc8wlFSR0V3Q8MVVqFXTiF31MobVAUdRJ1EklflUjIYPYNu+Vbn74RTZfEbzfZd1rnrWZXhiT87zC5ElWXNVzQtMQjk6HIvp4iWIaoVE1SzBizC9m/lqsw2vnbHddmeJXi82RLBO8ej3E762OyeoNLR07sF7DgKwX5Sd1O5YiZOmybmCCcFcwfGhZvD31uGERRekHiuH+UIWe5EwkYlD2X0oOlV9Yo/RnX7oTPb/AUfCqpTihuq2CMI9JKpmAV61V1vV4MxmSuGM7fZbvuj1HMDHzxzFZ3/+ImLJBAZCUXx75e9hw31/JBXR+v3I/LE0caJflLOi8nPYfsL4vjup3bGLmlzTLm90cLNA21lWFLLYm4VJx+OP4P+c+A4Ws4s4xxfga6l78ULmo6Wz8HjpUcO4HwDZ/3/pUVtRVWrKVbdFELMJqqmaBXjZydW2vA0H7zmIE585gYP3HJz1ggoorr1fhttv+aLHP37mKDYf24uFyQR8ABYmE/iznmcxsP95aa2Lfj+ycTbmdK5V16eT2h27KKnVIuxmgRZZVugd5jmA9Z2HCq/5+cEX8UjqKSzxXYSPAUt8F9Gp/B3uqXmldHVSQ2fdPe6UE3uAJz8MdESz/57Y43oX01kzRhAzFRJVswAK02cX9vWdh3BNe5frhbUUzthWheCicxW9/rM/fxHBtDFtFkyr+OAL3xGK6B0HThr2Mz6Q76MkSudaiUq7YngnxcxbW1dAEXhVKT7maoHWLCsiSqPBwkDffVdwMfWJPcCRf8hrDqhjE9jp+x/Y+KPWgoSJLZEl7h53gpZSHDoDgE+lFF2efzGNEARRrVD6bxZQ7WH6YtOfpSjQl9kd3HZ9THiud9/UhOeO9hpeH0uK03cLkoPCxwdHVbStiuf2ow1YDjZ2gylDiEvSuXai0qp2x0maU/t3x4GTOSf4aEhBx10rXS/QmmXFmh0HhXVaouM74qVHIa5gm7TgclHr5Io7thtrqgBACWUfLxQPU4oz2d6EIKYDElWzgHINkK1Uiu1SKqZAX1bgLqszsnJKf2LTDbnXcwADoSgWCoTVxVCD9HzM+1noW4etN33O8j4UIyqdRkndLM5Omi6GJILK7rykOEm3laLWSduXy+4/S0qQUiS/KoJwBomqWUC5BsiWimI774pNfxZaoG9X4C4SElt2H5Oeq/716zsP4R9/405sPrbXkAIc8yv4j7v+EBgXn5N5P04oRlR6HSV1GnW0s3FwffzIksl0mQ3F1jqJWHWvt0JNdi0FphRpziBBOIdqqmYJMy427YIAACAASURBVNVs04vROMXMGtQopEC/kAJ3p+e6tXUFXlt+M3atuQcXQlFkAFwIRXHknj/B53f8KaIhxdF+nKAOrQEu/j4yE1GAAxGlER3rOhzdA6+LmZ02XVjNiCzo+Hdsz6bd7Cim1qlciK6liJQizRkkCOdQpIqYVrwYjWOX/ixV6qKQAnenqdqp6GMNPrf0ptx5f37y8Y67VubtBwBGxlO5MTZOmIpCrASQnVmXUvxQP3SDo+29jpKao4uB+h7UxroxpCTQsjcuTK/2JpLwM4Y052gq9Pi6NFxm6CwGM2HMZWOoYancS5KoRaiYWqcSIP5se5tSpEYYgnAOiSpiWvGi885qYS9l6sJtLZK2ACbVtCMRYJXG0xd/Xw78DLWxbjAlAVWN4qGDdwL4jKPrc1qPNnTgAPqffAqpvj4E4nE0brk/N8TYytXdrdDSp/UC9T0IxveBCRzh266MYOPBB7Fx7BIQBBCaB9z5l8CqIixAJtNwL0x+Zn4n9WM8ENiDxex99GE+zt30AD4yzd5Reqw/296lFKu9EYYg3ECiiphWvOq8kwmQUo7asKpFMouK266PGbr70pznIlSFnsfG5iZ89cfPIBiZEh6sJgHeuBdf/XEAG5sfsN2HkyjE0IED6Ht4O/hY9jpT586h7+FsxEYTVnqKEbL6SF5trDt3XRpj6THsevUJtL19CkhPTD2RvATs/9Psz0WKCX2U8LcSH63YGsVyjZGp9kYYgnADiSpiWin1aBwvUheyqIuswF0dWpMnKp559T3HM+XcnMNo+AB8JuGhzcMD7EWVkyhE/5NP5QSVBh8bQ/+TTwlFVTGLvT7qOCRzhJ9IGAWVRkb1rDvPKkpoFbUrJ+VKy830RhiCKCckqohppdSjcYpNXdhFXTTfJD3rOw8JR86IcLIAWp2DzyQ81p9M41M/4pg/fBG//O4dtgu+kyhEqi8/kmj1eLGLvSZoWvbGxVHMVFqw1SSl6M7T795l1K6UlDItJxLxhx3OaCSIaoa6/4hpp5SjcYrtThNFXdTQEWx/4w+w6ulVaNnbktep6CZS4GQBtIr8RGoac4+tP5nGH/+QIzac/cXWFvyhAwek+3bimh2Ix4Xbyh73ohsTEI+lCfqD2Dwu7voDUPLuPKuonRQPRsaIKNUYGScu+QRBiCFRRcxqih21IepGC8b3gQcGpRYQ0Tqx3YEZpwugVeRn2+J1CGaycbBP/YgjmDK+xnbBh70dR+OW+8GCRnHDgkE0brlfuD+vFnttLE08HAcDQzwcz1o9/NZ2wF+Tv4FPKc6J3AGy6Jzad068gUcjY0SUaowMWSgQROFQ+o+Y9RQzasOcYpEWT+ssILgs12fC6QJoleZp6/k+kHofuxqimD8s3l4mBJyipbWc1hE5rcFx0iEoSq/mePHBbIE6oOv+K213XiAeR+pcvoB6v56h63RX/rl6ODJGRCnGyJCFAkEUDokqgrDAXHPEZMXTkxYQ+3t6pTPp9DRFQ44XQ8u6p+fPog0cbSOj+GVdI1Kj+b/SsjSdGyIbNriqGbJb7Iu2uvDahVyHVSF645b78euHHkSNOqWcxwLAMx8DTou81UowMqbUkIUCQRQOpf+IimZ/Ty/Wdx7CNe1dWN95qOx1HfoUCwBwNSp83aLwopxQsMNtKswyzaOrIWpcdRnMnzFsa5Wms6OU995NiqnrdBda9rZIa9i8RCtET507B3A+VZf2118GnvwwIkc/jf/3d4GBeiCD7L//8/cYDq/0i73VZDVeFezMXqpaLYKoBhh3mqvwkLVr1/IjR46U/bjEzMIczQCyf9y9qBsphPWdh3Ah84rBkBIAkFHQ+bHH8Bd75PPoGLIdgHlmnyf2FOd8rdXsTKaYhn4dQv+b9UiN+hGILy643b/ge+/weq5p7xJ2RDIA73RORXt2HPo29r77JKC730F/0PEYHTfnBAC/vP0OYXovEE7jug0XAAAtSxajT8mPCMbDcRy852D+sXXvD4DsyJgN3yhdqtLmep2kXWmAMkEYYYwd5ZyvtXsdpf+IiqVc5oZOOZdIgqMZY0DOwZyrUUwMtKJteRv+LCGPoDx535r8czYvuFoRM+B8wV11L17/9SCWvrETjfwiklfPQWLTl/CRu/7Y/QXqKOjeu7geJymm/T29ePb0t8AU6xo2S1zeY6l9xMhUUH/zYAIdC+ZhzDf1mNRbbZW3I2Nssblep2nXUtRqzWYqxbuMmH5IVBEVS6UVzGpCIDXcjNRwc+5xLTUoEwrS+ikPipj39/Ri2+tXIalODXEOve7HE0ut5//ZRSIKuvcurseJP9bO7lPAInENW58u1dZ1ukvuc+byHssK0QN1U+fZNjIKANjVEMV5RbH3Vith/VceNtdbaV9UZgOV5F1WTaQzHO9fGUf/5XEsaQghWifoCJ4GSFQRFUulFczaCQHX4zw8KGKWLZJf2nMcgLjo20m0IlqnYHA0v+De0i5Cej1nshEUnbBw0iF4LpFE3fwoWE2+sGKpbG1b1+kugyO/YT7g8jbX97hxy/2GBRIAWCBbr6anbWQUbYH5wJYT4v1PFzbXW2lfVGYDbicOEHI45xiZSGPg8jj6h8cwcGU8+/Pl7L/6ny+NjGPSTQZ//akb0baq+IYcLyBRRVQslTZzzE4IuB7nEVky6V8keNwhssUwzbm0m85JtEJWamlZgim7HkCYcrNLMS2OhnBhoDWvho1nFIxdaAGQdeLXjzgCTOlBl/dYaB9x9y2IJP4emDyFqbq1NAIH7F3rzZQ0VWRzvZX2RWXGc2KPMLIJFG9lMptIpTN4f2RCJ4zG8kTSwJVx9A+P5/1tAoCAj2HBnFo01tdicSSI1UsiaJxbi9jkf83LGqbhqsSQqCIqlkqcOWYnBLTnsympTmw/cR7ffFuSHrpju7iI2YWBpWyRBORpHUdDlHW2EB8/cxSf/fmLiCUTGAhFMXTjuFgEiK5HowBvpq2tK7BldzKvhm18oBULfesAQNxxp3+8gHsstI84sRp46VEMHb+IviMN4JMmq25TPSVPFdlcb6V9UZnRTNavBermlszKpJLhnOPKeEoqjrQo08DlMbw/MiH8QjY3GMiJo9VLojmRpBdMjXODiIYU+Hys/BdZACSqiIpmJhbM2qakNDwoYhYtknpEAspJtEJ7zcfPHMXmY3sRTGdF1sJkQi4CtPPe93nxybr0ZtrY3IQj717CM68CI7oatpDix9ZNWRGwKLxIPB8wvMh4TsUWik/WRfXffgd4yhiZcJPqKXmqyOZ6K/GLyoxlsn6tcRXQ93oEPD3VuFCMlcl0o0WVssJobDIVNy5MxcmiSpogaooGsWZpBLE5tYjVB7P/6kRTULEYOTVDIVFFzFgqte3bNiWlp8giZiVyDPM/9HUkJvpzURx9Eb0oreMkWqG95rM/fzEnqDT0IkBYJB5ZWnRaU+PxjTdg7VXzpO/z5hs3GwQsIOjE87BQ3O1waa+3d4TN9c7ELyoVyeSXhMjV2S8o/SfmZq1M6tJo3PFoRdVTaVElUW2SPhU3cHkcl0bFUaX6YACNk8JozdJoXjRJ+3kmRZVKAYkqYkZStCN3CbFNSXmEPiLGGMBqEgjG92EMQGq4WZrWcRKt0H5ufFbcfZfq65NH5Jr/M9oO/21RaU3z+creU02kSrv/PEbaHRjmecX4rraf5amiWYmufi1ydTInrhBZCpRJUKnpDN6/MmFdpzT5+Jiaydte8bNc9GhJQwjNyxryUnCNc2uxYM7sjCqVAhJVxIykklvDbVNSHiGKiDGfitpYNxb61llG7pxEK5TIMbwfYVgwlP+1NRCPyyNyF19D24ZvFG1A6RThfMBiTVUl+2m8+5Po+9sfGrsD/Rk0fnhI7H/lZPsZnCqqajyoiRTBOcflyVolLe2m74RzElWKhJScILpxWQNik0Xesbm1iM0JZn+eU4tIlUeVSgGJKmJGUsmt4Y5SUh4gi3z5a4ZwuP32ove/641dWP4x4I9/CARTU49PKAyLt9yP85e+LDmvPltBVdIooxemqpL9RPDXwJqgMdWz6nI2SqHCWIwv2l75e+Dz/wf6n3uVjCJnOi7r9dR0Bhev5KffDFGmySLv8ZRFVKk+iCUNdbjxqoa8GiXtv9oARZWmCxJVxIykklvDvUhJOYnklDoidn7kPPpW+gGk8akfccwfBt6vB777cYa/2bABi/b+P+Ljp9LA0OScQIGgKXmU0QNTVel+wI2pHjP6WjLJeUQmnkfk0L87Pw+iYuE3/D6Gr/vPRmH0b6enokm6TrhLIxPCfUTrlJw4usmQfgsaUnGRkALGKKpU6ZCoImYkld4aLkxJOcQ2kjOZUtqcuoSO2DyM6f7QehkR00Tb4ZV+HF459Xg8nK3/EUbkOMfmS4PGHZkETcmjjB6Yqhb0egAAm6qt8uo8iLIzkcrg/ZFJQWQQR2N5USZRVKnG70Nsbi0WzK3Fsvl1uOnqhqlo0pzabMH33FosmFNDUaVZBokqYkYym1vDLSM5/sO5lFJWsnHsmteA8wE/FoXjnhZp26UxhRG5d/49N8bFgE5IlDzK6IGpquV+LOFTAtKr83CDV7VksxDOOYaTKQxcGTOIIlEqTjRNAMhGlTRxtPaqhlw3nDkFR1Gl6oVEFTFjcdoaXqnWCzK0iM1dvp/igcAeLGYXcY4vwM7he4GXDhhSSm0jo1kRE1kKfOagp+fhJI2ZF5F78sMARvJ3phMSJY8yelVAbGVmaoUmIEtUyCzFq1qyYs+hzKJuIjVVqySqU9J3wk1YRJVic2tx1fw6rL26QZh+m09RJcIBJKqIWU25rBe8FG6LoyHcNPwv6FT+DnUsW4exhF1EZ83fA0Pj4o1EKSUPFjiDaDqxB3j+QWDoUwDzAzydFXP6/ToQEq6ijIVcg1eGnwAQCLkXVZqA9PI8nOBVLVmheCjqtKiSuYB7yoByyj5AFlVqqFNy4uiaBWFjNEnrhpsTRH0oQFElwjMYtxzmVRrWrl3Ljxw5UvbjEtXH+s5DwlRTUzTkrkPOYnE3CzcgG3l5YtMN7oXViT0YfXE7QqN9EP6d18SMmchSYIuu+Nm8wAFZcbPhG4VbC8iiNub9emln4OU1FHtsfw1QMwdIDgKhBiA1jqFfZoydgM1jiGz+79OTcuuIAhD9PWdAh9hvzFOe/LAk3Tn12ZxIZQzWAEJvpcn/JtKCqFLAJxRG5vTbgjm1qAn48rYniEJhjB3lnK+1ex1FqohZjSdF0TbfwD3rZps8Tp2aBGRfnHk6KyzsUkpeRy2EnXCS/XrlYF6KyItTwSc6dnoCqAkDD74DYHKO3+4vg09kIyWp0QD6Xm8A3g0hsqqw0yuK6ajhQjaqNJRUMTDI0c9XYgBRDPAI+nkUAzyKgYEo+r/+YwxcGUdCElWaF67J1SYtn4wq6f/TUnH1QYoqEZUNiSpiVuNJUbTN4u5ZN5uVcNHQ0m12wsDrzjO77UrR0eb1NZjEcVfqfex6vQPnex7LL/J3cOz+J5/KCSoNPqEK5/iVpa7P4xqu8VRaXMytS8VdNESVvmbYvgYTaGQJNAaSWB4L4zeXz5uqU9KZUc4PU1SJmD2QqCJmNUUVReeiGpIOsMkF1rNuNjuxoC2QTiJBXkctQg1A8pL18bzG62vQidaucB06FszDmC+7mOcNvZYdO9QwmeY6i9Q58WgZ8xy/so1UclDDxTlHYlQV1iaZ58INJeVRJS3Vdm0sPCWSBnsQ6/kGYul+NLJBzEUSrEZL19pmTQhiVkCiipjVFGy9YFVDpDG5uHvWzWbVwm8uCLejnJ1npdqv19egE627GqI5QaVhGHotOrZPASau5MRloC6F1Gj+n1DzHL+i08MOUpZjahoXr4yjP/o7GGi5dUok/WocAz1HMKAr+FbT+XVXtQFfbnTJtbE5uGX5/Lw6pca5QcyfUwPFL4sqLQeuTk+e65j7zyxBzAJIVBGzHqfWCwbsUnG6xd0zzyyZiHBbmK0twmpS3qXnluSg/LlSFY573T2nE63nJa3xudE/omNPjBiidY2rLqPv9Qh4ekpkiOb4FZoe5pwjceRZ9L/4lxhQIxjAMvS/34CBZ3+KgcO16PcvzM2FGx5LCfcxP1yTE0XXNs7JswnQfp5b61Gtklf1dAQxQyFRRRAirFJxAoFSkHAz44WIMEfYtML2YiMG0lTc0uy/kykxz20DvFykdaJ1USqN5f/BDON3/unjDKc/yLPXIkqzdkQNu9NG1WS7/wIILF4snOMnSw8vmFOLf/7387puOGMq7uKVcajpMIBHDdsFMY7Gs0OILYnhA7E5WHft/CkDyvqpgbnzwlZRJYIgSgFZKhCECAft4RVJqc5bZm+w+lPA8X/y3vagVCaSk/v90VuXET1ch1pdgGc8ACTWj+Dj8SHxNcjuLYAM9yHxwAVhnVLPe4M4diaBjMWfWsayUaUF2ggTTSQdfgQxlkCMJdCIBGJsCHOQzEaVymGTQBAEALJUIIjiKLcbtg2Ou8dKNW9OFkUrle1BqZzBJ6NPTbffgVTqnOGp2hTQ9HotcBdy1zD2obun6pM+uAMD/993McAjGEB00jIgkrUNQBSpx/4l73BBxYfG2hTW+N5FIx9AHcbwTuAarFi9Di0rF+bScfPDNQiIokpvnXRXrE9jaghiWiFRRRAiyuSG7UQsueoeK6VXkSgVt+8L4tcWI+JK7AyeyXBcfH8Yg/WLcKm2HoPBuRisnYtLwezP4xM16OcNGLgQweWH/1m3ZRDA58CQwXwMT0aQhvBB31nE4lchtub3csXe2VRcEOGDD4Ad/QdA0YWplBBw3TeA6x2YWbkR99M8pmamjYMiiFJA6T+iohg6cAD9Tz6FVF8fAvG4sEZltuDUid2VK3y5XchLkW6UOoMDALPsgLOzCRiYrFVKCXJxtakJzJ8YwuLwJTSyBGK1KTTe+kc5kaQVd8/78ZcROPr3xnMU3eMTeyZFp+Ba3Nwfp9GnaUxZezpVgCAqkLKl/xhjSwH8bwALkf3r8S3O+a5i90tUH0MHDqDv4e3gY2MAgNS5c+h7OPuNvCBhVeGpEKet9q66x8o9b64UaVJdtC3DGS5hbi7N1o8oBt6PYuDZw+j/aS0G/I3ZQu/EFVxW87vXsrVKU51uKxbNRePcWsx595cIfH8PGi6/j4axy2gYv4wwTyL+kSFE5iV1IukD+ef3q27kCSU1CXz/T7I/698DmTi0iuSJPrdORFGpUr8O8GyqAEHMcLxI/6UAfIlz/gZjbC6Ao4yxf+Gc/9yDfRNVRP+TT+UElQYfGxM6VNsyzakQJzgVS67NRcvZ1i4Qca9f+3/h/h8uwLl/6pKmgZITk27dV8aMg3KHxzHg/yoGJs6iP1OPi4ggjXz7gzqMobE3gdjSGD4UGsKtiR8hFngfMWSLumPKGBpbt2Le2rvFtUq4HkOLs5+51KX3EZhfj8ZVE4g0OvBXkokUnjZ+xiw7SC1qoqw+t1ZfFKZpTA3g0TgogpgFFC2qOOd9APomf77MGPsFgCYAJKoIV5idqO0et6TEdTle4FQseWYuWiomRVwmw/HMa+/i8a5fYDyVva7eRBJfevY4nn7l11ACvtxYk8vj+b5KPgbMn1OL2Jx5aFwIXD/Yg5h6Do1sEDE2pOuASyDMxgEw4E8S2bSX3yQmMgBe2QH85u9LTzuyYUNhEVArk1b9Z0z6OiaP5Fl9boF8wbXvC8B7rwKf+Pq0Nld4NlWAIGY4nhaqM8auBtAM4DUv90tUB4F4HKlz54SPu2YaUyFOcSqWPDMXLZDkRFpYp9Svc+nuHx7H+yMTSAtqldIZjhO9Q7hpWQM+FK/HrR+sFRpQzg/Xwv/vz4o7DK0iMDbvteM6Pbt0sWFsEYNtak8kcsCAtX/kPgo2dFZiSMuBI/8ALLslu8/3XgWO/mM2asb8WcuLEn+JGDpwAH/zg7+C/2I/BkJR/ONv3IkfLb2psoQ/QZQJz0QVY2wOgOcA3M85HxY8/wUAXwCAZcuWeXVYYhbRuOV+Q00VIHaodsQ0pkKc4kYseWIuqiOd4bg0MiEUR1NmlNn/roiiSshgge8KYpEwYo2L8BvxesTm1uKvX35beLxMhmPPn/wn65OSpb5kXlhaBMbivXZcp+ck7WYQSBYNPqGGqe3MIuemz2ajSjKsPrfSLwR8KpJ1/J+yxwKy/x7/pynBVQK0+6tM3t+FyQQ2H9uLeXU1uPVPP031VETV4Un3H2NMAfADAN2cc4u/GFmo+4+Q4Vn3X7m74CqE0YmUMZo0PKYbnjsllGRRpTlIIua/gtj8eYgtWpIdlKvZBFz8GRp/9gRi6QuYh8vwMz51TwHgpUeRGTqLc5n5+FrqXryQ+Whuv3mdiqKokDQitVT3vCCKZPFe//L+/ymOfi5ejOsOvTT1gF3nnIXxZx6hecCD7xT2GbTaxmq4t9YVWebuv1/efoez+0sQM5xydv8xAH8P4BdOBBVBWFFwnYuZcnfBlZB0huP9kXxrAMN/kzPgRibSedv7fQzzwzVorM+m2z68OGJMv/UfRuyVHYilLqCOjWc3SoaAD5sdxe8EMmeymS8NNQm8+CCQSgJqEj4AS3wX0an8HaACL2Q+mp8GkkWFZLMWh85aF99bvNepvg7hJnl1enbpYjdp4+Ql4/xFPVqX4L4viD+Tdp9bqUWDRSSrhClvT+sgCWIWUHSkijH2UQD/BuBNZMtDAeAhzvkPZdtQpIoggJHxlEkkjU39rEvFvX9lXDjiZG5tALG5tVhgqk3KDc2djDI11NXA77MYluvU38jSPyqfs5kFuK/ub/NTmrLjacOfRUSWAte1AL886EokO46kWJ5TBmA++bmJUELWA7n1r3MTPf3BF7M1VCKPLKtInzlS5ZHdCEWqiGqhbJEq/v+3d+5RclT3nf/e6Z7R9IykGgnRZgYJRoknRlgMBgtHiZ04ByXEshnAsNZ6fdZZ26wJazvhkY1lrF17EEtY2bs8fAJrIBCfJCZkbMRDGNY4IrHjeJVIMmaCUewhRhihthtZmkbStDTTPXf/qK7uetxbj67q1+j7OYcDU1NddevB3G//fr/7/Un5XTi/uxJyymJFlVS1SW5jSl1UacXiHpy+ZBHOMHpx3pmGmX6ziaTTF/dixZIe9PUkVBIZNsLht+pNwcquX3jNSf3OZzV/VomRwqvAngecP4ewyAhdp6csKoezPslNqgeQEpif8/5urgjfYnb7flFWpF56u1kjpRNEYVb/JWU3MjmB7Dk55H4+D1mu2VbUXQdJyAKAbWpIY2lzA86wHKtElYIcuw8f94kqVWqT1p5p1KJJrijT8r4edPlFlRpB2KJ+3ZL9dMZMedko7M8g/8IylP7mXG9tnF/tj19tlZsQgsQ4uwj8+knkd5VQmkkjvcJAdvMWb4p5dBMK//A88n++HaVjEum+eWRH34Ax7BJZIoXCyz3mtR0H0qcZyL55v3c/AIAEurrVosuOQmT61hbqUqFhU95J2I1UhJmRLQIXZZCfXILSTArpFQPq+0vIKQJFFWkcbW7AWSrP4/DxWWeN0rFacbc92jSjiCqluwRWVFqYDBq9GF1peERSdkkvVixehEyP18CybQjrb6SbtAHH5wv7M8jtHqgEd6R3xZ3f+SzBEDbVGORMXpn4jcus8xwBzvYKoMKOHcjd/xTkCQAQKM2kkNttmGO2CabCyz3I/WCwtprwUAG56WWe/aosWgL09Jvj1KUQXeI1VmeBMMavSdRe2YSZMVysXbuRBiioyCkMRRVpHC0w4JRS4vhs2RRGCpsA5wq4k1CVFC7pTVfF0ejKAecKuCW1/17WjKhSMyJ9UYr6/SbtyufzLyzzaAeHM36Y82WWeaJfSvwsMiK8f69s+19VW4DqmMtdyE8ucYil/AvLvK7/JXj2q1I8Yq4EBPQr+1ziNdHOAiqSsBvpAB84QloBRRVpHAn+4S2V5/GLiq+SPQWnapjr7kEGmFElK3o0ZPTibasMW6PcXkeEqbe7BVEllXgCmhfpi9vaxvb50t+cC1WUybEiLIlWOkFu4SHfv8eeew0jh/LKXUsztnehO4PScfUhHfvZsQuVkOK14SvqknBe7wAfOEJaAUUVaRwBf3illNVaJZU4slJxh46ZvkqqqNLSSlTp9CWL8LZVA8r02+lLFmEg0938WqWw6NKkaUXRdpu12lGRiDN+8YhnU2G/rXbHqnmyTC9V90MX7bLMOSt88Zs/wq2ZAbypOO0d82KBqgfUhs8hvVPje7ViAOieDpdCDXh2iXYWUJGE3UgLW+IQ0s5QVJHEsaJK+QvG8fq370N+LoPXMYDX5QDy4jS8Xn4bXv/C3yF/9AROzM17Pm9FlbJLFmHlsgwuOGugGk1yp+JaElVKGl2ays+3qY2J7Yw/OeGpPzLrtIzqKrNazdMhGDMRo3elk44fD04X8ZVzN+K6H3wdvWVbUXlKIrt22iE6sjdk1Ne2eYtZq5VAqjbRzgIWqkhoHEPQRvvA6dLeC2ThC1m4UFSRUEgpcdSxAk5tE/D60ZM4PGNFlTIArqseY6mYMSNHS0/H22wRJcsmwBJSRjtHlRpB4YArClNGdvSoZkUZ2j7FYtX91OWMb0XtXEVZ+ckljmX7gL3mKe+J3j323Gu4rHgEzk9UmDtunqey/9BABn+PtwMAPvLi08gWp5HuK+NN1uo/W9rVGNvkf20JTPCx7p+KRi0YSSKFq0I33p/ucrYrarOFL4QACbWpiQrNP9uHufI8fnFs1scmoNbmRBVV6k4JT22SNwW3CCsWL5CoUgMofOJc5L5ddnr9pOYx+M4SjLOPn1qtdjQmnPseHoTaDk9izQdz5u/GzfTdY8+9hpu2/wu+JT6JlV2H1OexGWJa+1u1eN/t+UP15xrV7qXR0RfbPXWI98UC2c9/of3sD6KawzawDQ8hFk0z/yTthxVVyr/htQl4/ajbV2lWeQwj010VCjIaYQAAIABJREFURheetUzr2D3Q1w2zUxGpl/zkUshywbFNlruQf/F0GJ+67dRKd2hSm+m+Mkoz3j9X6b7KJGuL3n3xmz9Cca6ML3Rtwl3d90D5etrO42ls3fUL/7ElKYIaaTtSHWdNUB38JwOQlRTqMeDgTZ8FEMKqoZn4mcNG2Z+QFkBR1UHMledxyLIGcNgEeKNMJ0veqFJPqqva1mTlsj5cePaymk1ANRXXixWLe7AozahSsyj94g399kalWBIksSbYgHZxQ3Z9N3LfW+SsM0rNIzt61FMgfXDaFChPzL8L4/gLLMcxfKO/D3ctG8DP0imcUSrjupMpvM92/CsuOLPWSucOnXO8BLatBk4erRl6qkTQ5ITZD9Eqks8sBzZuUz/HRtmOKOwbcnuXVgVVlVIJuVv/pL1ElW6BizZSVUc6nLVZpEFQVLUYKSXeOFFS1ia5fZZ0UaWBvu5qAfe6s5c5o0k2wWRkGFVqRxq+2quBRDGqfOy512rRoIGMtycgoF1VZlw7DmzMVMTbQaT7gezaAozzV3gmxKGBDF6rCKvxud/Dbw38FW5bsRQnukxBketOY7ynG/jJN/C+X7JLK5iT7azGNwFQrya0i6DJCeCxTzhd1IuHgcc/af63e+JO2u/JFZ2yI+eUFWaQ095Vjw0jjJjRrSw8/0POmipre9QVh21uSkw6G9ZUNYjZUi2q5LQJOOHpC+cXVTrdVZtUWwFniiZGlToftzABzNVeg7dsba8IggJtQ90VBkauOladPHf/8h/g93af7fAQy3SncNuV53mFVZiJ12cfd43UijffjJPd3qL/wf5BPPPvnnEe8/FPAmX1l5dAjFWmINOZlqpqf8I2sw6DylzUhr4uDVjzr/tCnSJWVFJnfqqqEWzk6r8k7zk5ZQhbU0VRFQEpJd4oljzCSLUS7siMut/Xsr5uZW2Sw7F7SS+WZtKMKnUCMf/IVyepgweBVAool5EeGoqXQmsi+9acC6WBWLWA3KSIRdg8ezWemH+XY68zBzLqpst+hJic7VGxxed8RqklBAQm/9NkbcO21eFc3OumVkxfxe9agGjvlk4sVPjx9jehPOv9ApZaBPzKX94c+N7GFv/tIma0LZAUz4eQCixUj4AVVdLZBNjNKGdVUaV0V1UQDZ/Wj4uGlztEkxVhOo1RpYVFzDTC3z+wFcvufBg9c5U/8OVy1Y+oEwQV4JO67HPWvmRwEp9OT+CJWaeosuqfArGLV1UPPVcdkr1G6pKv34Xcca8b+Rn9Zzg3NFRQQV37E7Kfov3dKrySUUeLAlKGb7rwDeT+eQByvqYwRZfEmy6YDvXexm6f0y6tbegGTxrIghVVUkoUinNal257lGnaJ6pkCaPVK/odK+CqYmkxo0qnLDGKjL/xk29g6X02QVVBO0nVExFrdDHu5ASy5+SQ+/m8xw4iO3rUs/uQ8K6qGxrIhDqPQ2DIssbXSz05X3fhdRj/3jhOlGuCoDfVi+suvE65fyC6gmk/ulLAzGFg3GzS7CheVy1GuGOt8t0qfHncUbBfOngQuc9uQe7WP4GcHkS6r6T1ODOGi5h5vRvTP+k3AzUCMFYfN/edQ+B7G7t9TruIGbrBkwayIEXVX/y//fgfT+7DbFkdVbLE0eoV/XjH6uU4fXGvp2Huaf2L0JNWF3YSAiDWN++7vn8X7iioU++eSSpCRKxW83LQFBvnvQFjWCZfjFsZk5EtAhfZBM6KAbN9TNY7qedwmuPnTHcKf/y7bwk+l0u8et3V06a7et9yGIqPW8Xod33/Lvzs+M9wRv8ZuO7C67xF6qEQQFcaKPuIqu5+IL2oFvnq6QfmTpimoxbFw2ZBO6B+Hpp3KL9rDvKES4jPzQGVYvPqvQA8wqqwP4PC/j5AVr4ASqCwvw99p89VTE7939vYCyraRcw02g2enNIsSFF1zhlL8dF3DruiSmbEaWkvo0okIWJ88/7Z8Z/hF0uB0xVuCp5JKmREzLMS73jKOcHGXaqvScEZw8XaBG6kgQ3jysnz4HmfxpkvZtSr//yiaq7JXu+uvlQpqgBTWAWKqO5+p/BRIoHySf9d5ueAjXfWxn/HWmBW8Z7MzwHbP25et3tS17xbKq8uzwirTvPOd8bflb7o/97qopJR2ue0k5jpAKsS0pksSFH1jtXL8Y7Vy1s9DLLQifHN+4z+M/DQbx3A7z8l0VuqbZ/tFhhyT1IhI2LKmhf3BBtnqb4rBacdk2byvGh0E/7xshDHdkfVXAKjNKOuS9T5fYUmvSiEqApBedYpXoPuuSqKqHm30isMlA4VvMdwobpH2vs2kwK6uvXvrV9UcvOWWqo6TLq5DcRMor5qhLhgfouQehndZK7SMlbBLFBZFbqFzHUXXoe9o/24970Cry8F5gEcMgSOXP9B7x94XQTBtV1b82KfTOutX1FFy/zGNLrJXNE1Pm3+2++e+EXiAHNy7q7VXrmL4Kvb4/p6FY+E3DFEpNsupMLcc/v1Atp3K7t5C0Rvb+Dh0v3e1LL2vvWVgUVL9M/I9nyM4SJGLstjzQdzGLnqmFNQ7fjDivi1pZsnJwLHGsjkhBntGx8w/x3jmFY0t3TwICBl1VetsGNH/HESggUaqSKkaUT55m37Jv8+YyVwwftxV+8/4VNvDajzCRkRC1yJZ/9M1CL2MBGueutjgiJxrshXdn03ct9NQ87WFphESkPp0KVz7VgmlN//C4fBp6dwfn13LRW54XPh/K8Kr5qiwXoWinfLGAXw013IP/g1lI53IdUzj/KccDili9Q8sud5o1nZ0aOOWrTqvqNHgWLRFC2qdyFMpFQnjJ/enKwzfMzawNgrGAkJgKKKkGagmBze94/3431hIlt+y+7vWFvdlr3qcuTuf8rpI5Q2J1MYq5wGilEnKt/WIfPx6mPC1KbZBIYBAFFSOGEFpEq8dqWA+XkA0rzW8z8EXHo7cNb6qnO5snD+u2lgxw5zTNa57K1rdIR4Fsbs4zDGfl77iHIlpHr1HwCffTULGsI8H53wKh4277/qWsI8l4Tb+MRewUhIADT/JKQZJGx8uPuJe7H2+/8dGdiKprszKAxcjfwju/zFRj1jieKGHZUkDTDjjts+0WeWOfv8aT479a71yjqn9NAQRp7dGW5Mbvyehda80jbGdEYt4ESXKQ7n1TYyyvOHuYd+xqOqawn7XBI26tR2ANA9K0IqhDX/ZE0VIc0gQePDx557DUN7v+AUVAAwV4Qx+zhGnt2JNftexMizO9XRm3rG4lM/VtixA1MXb8C+Nedi6uINketTCq9kMPX0MPY9PISpJ7Io5M+sCaq4dTpB9Vr2ep1tq81okiXgAK/4cNc+wachti76MbrJjHj51Wb5PYugGq10Bnjr+x11aADMn99/L3DFPbXnqD3/q7X7HKZ2cOSSaNcS9FwsQtYThiV7w/WemrREUseEVGD6j5BmkKDx4Re/+SP8Aw6pfxlGpNU7FkWNj29D5bOLgVEmz+dn0sh9bxGwMQPjpZvip378BKQ7WmKP7PjVVlmfrVxbun8QpWOK/bq6ULBSgG6mnoFvtMnvWajSlHaKh83Gw+d/yDyP6v6HiTDZ04B+tYOTE+b5olyL9rm86kwXJuxtZT0Lrv4jjYKiipBmkODkcHC6iIM9K7BSKIRVGJE2cgmw50E4JvU6x6It/N12K4yN+wPrtnwLh39TM9lHie75CcinN4db0egms8zxLLNrjyC3ewCy7Ir8lMs1gemxHQgoiPeL/Lhr7HRte6aeCU4t+wm0sALWb2Wo7r3yWxTgFnPWORLytjLGxiiiSMNg+o+QZhDDfsHN0EAGXyhtwozscWwvYlGwMKpGFexREmFGNZIs/D1UwNQjS7Dv4UEzpbc/o06daQuHD0KbnooS3XPZMQAwfx65pL5ef9axbCLCGC5i8KJp5XAtgYjJCTO9uP3jwYIKqESyfLBbVkhv5wgA4cSn9V7qCHMMv31077jquVio7CXC2nMQ0mIoqghpFglNDn/8u2/Bt1Lvxmfm/jMOzK/AvBR4Ta7ACxfeUmdUQQZP4hr03lCy4v4tqq1TCvsznglY9/l0f2VcHkS0iJpOzNZzvdZnFX5WxnAR0Cz6KeUOmtGXKCIuSMzYa8F0HSIs8Rnk8zS6qXJ/fI7hh7buaZX+fUxCzBHShlBUEdJhXHHBmbjtyvOwd+nv4Ddmv4TfyGzH7iu+g4su+/3gDydYMA+oC3/NSd450VvO7jBWOib57Dk5iJ5u58d7e5FdqzPilNHFqErMhokW6dCICJXhprkd0dOMAS1jHAX8qkhVqscUn2FNOXURvTACtt7PxhVzhLQhrKkipAO54oIza33zopBgwTzgLvw9iHQ/UDo2D1UurDSTMtNuNiNMI/sasG4x8j9ehdIv3kD6tKXahszmBzSTcFRESt9qR4clSBTmnwCQPf8YcntO85qSrg3yQBKIVN8Wxt3eapUzezxcsX+c2qU4n22XJssLkagGvyQRKKoIaSMa3pesAZOYMTZmrvSrHHfqiayy8W96xQDww0c9zuLGWcdgvOXnwMZt/qvakpxsowoqC6sAfNESTzrPOOsYkO5F/qVh5/N76SafyJgA1n3MvC/W8dKaWiOLsFFF3xWMr3od1GP05cs9ug/TEwDKg0AKGHhjHwZHQ3ywnZosLyQSdqIn4aGoIqRN8LUnSEpYNWISm5wAHr22KlRU7VAAYPHvbASKt6uPUTzsH4GxO8IngbGq/hSgj6gxhg7B+NRtznFOFs0CdSXSdGe3WxIUD/tPgGHa6YRC46AekdzNN2P6rx+ubSiXqz8Pfv7zwQdogybLC46EnehJeOioTkib0K5uz77RM407eG73Ukz/Wz/saUDR24vBt+WULVQqeyBJ92zfa7hqPYzpB+qzVOjuB0pF/ao7lYP4ttXqQvXMcqCnP5rDfRhH9qjU6ewPAPveuhYoKyJ/qRTW/LC+Y7aahkeMG03CTvSEjuqEdBzt2JescPcW5G76tCn2pKxGz6qu6Zro0rFcLzzF6idOmMXqOnR1XaJLv3ItzDVUIoCOa7j/KRQGrjZFTe1E4Q44d1wvqAB1JGvjNqCr27t99pg+6qSLiLlXNGaWu67DRWZ5CAf1GKvtVILKtj2u436zUb4v9ne+E0jYiZ6Eh6KKkDZBay+gtS1ICN2S+8kJ5B/8GmTJuXvVewnQTsalmVSk7ejp13sXyTLqblMDIL/tVrXB6F/vNCNOta2RjqtHegXg6CazDstNedYsmldhTYBP3gjcvBwYN8x/P3mjc0Xj5pfNf668X70Kb+O22r6NWG2X0owfwI/X/xoO3vTZjhIofoa0HUOc1ZwkFhRVhLQJDe1L5iOctEvud25F6bj6T0Q1eqazF+hTR3J0tgNILfJGYFRiQ9e3TxfFmpxA6ZA63VE6VPBPoYkYfx5VAlDhbwXAFI26CfDJG4E9D9QK62XZ/PnJG73HCWMw24DJdmDTB7S/K09PAyWnKm93gdKOEePIJGg2TKLBQnVC2gRPXzLLXmDvh4GXYhSU+60E8itoLRxAuu909Uo+K3qmWk3Y1Y3sujJy/ygcxeqitxfZ8zQTkyU47EXL44Z6X1XfPl3B9c6tSPeV1dfQV/Jsc6BM8enqvhS4C4O1dhaVInzV4oFHr1Ufe+9XgEsVRf9BRd8NWKhgFaNPT3xNnwp00c4CJT04qK5tbHTEOGm4AKAlsFCdkHZEVYzcnQn+tqnyptH1mjNWVdJ3mr8BPf0o/Hjes5JPpIHB276g6Gd3wOyLN3sMKM+isD+D/OQSlGZSSK8YQHbzFr29gLtQenIC2H6NemxWCkt1nMxyMxVmMT6Awv5e7zWk5jH47pTpkxWWzHLgre83I0WhEcCV99megcKTyu+Z6oQlAIwXIowjQXz8j/atOVfrLG8nkcUXDfJhcq/CBSqLLG7Z2lnF6iRRwhaqM1JFSDtSz5JoXfRGl+KyJiNdofTscRi/lAJQqImj/nlkP/YB5+Ri/0Z8x9rqKjdjuFhb6WekgbEx014gjE/Wzq3wbVOjsygoHjbvgy06ZAyb11e9hr4ysuu7YVw77hmLQwj2lZEdPVq7hp7+WnQorLCqNF8uTAH5yazzuOevCBYCOpNSXR2WRaOMHwMihLooj2PoSaS0G+jD5IkYd+LqP9IyGKkipB2pZ0n0HWvVAkk3MVtpp6Dl+VZEK8zkHGbcYSZ87XGs8fj4NNmjXkERv+pYXkVhf0Yd0bqoUBFWPtcwconpNeU+TzqDwr5icLRPh1VTpbtO1b2rN8oZBt07VrnnyihPdzfQ3w9ZKNQvUNz3e/a42qIihjUEIX4wUkVIJ1NPOxndsnirEFoVHbImWa05JaJNUmHG7VfrYU2e2rolEWx8ab8Po5uAn+4ya5Bk2RSY53+odn5rLE/eiPwTOzyGpVbPQmO4aBauW1Ewd23S1DPmcaeecYrF7dcgP3m697glIP/5G810qJ9QtSJj1vgd16mvI1NGOR+91kyp1hO5solPJZV73pAojyoqpSNMI2q6t5MGQlFFSLvgrk1K9ThbugSt0qqnEBow/+0nquzptCDitMEJNLUMWSRuF3CTE2YEyb567vmHTBdzV6SqNKMuRK7aQMhyTcQA3on++Ye80aCdW1GaURfEl2ZS4dJWl95u/qOKElliyf55P3FtjTVKqiyM2ajtnhtjY8mmysL0OlSMwwNbt5AmQFFFSDvg/oNvpTZEl7kKLUybFj9BE7QSqKffTKmoiDLxxFldFtSmJkxrllSPQ8AVvjyO/K4lKM0MOGukLFsG2/3SrxK0RYjslg6qaNDTm81/rOfX3Y9032KlP5d13MIUkP/Y51E6Pu4f2fETS/ZnFKaNjb0+Lyh6EyRqGu1/FNaYtJ5G1GzdQhKGPlWENIIwHkp2dBOXnPem6nTE8aa59E6gS1P8bBcSYbAbU97wQvgJSzt5CvM4OuNKi8xy4PK7q+cr7NiB3LctoSRQmkkjt9tAYX/GPJfrnmdHj0KknDYKIjWP7OhR7zh1Yy0edtb6zB2vHNcZYbOOa9VxlY4h2BzTLwpjf0YbPodQ7vB2awqVT5l9Pz+CGkDHRXfddqf4MO+67jriuMkT4oKiipCkCTNRufH7wx5F1NQraEY3AVd8ub7xJUVQaw2dceWV95v2Aptfdlxv/o47tTVSZjTHeU3GcBGDFxUq/lUS6b6SrUjdNZ4IDuTG8AwG3/GG8rj5ySXeMerMMXWO8xbW9YxuQqg0aWaZf/QGlRYzTw5i38ODmHoiawpSN1YD6JBO95Hb1uieu90p3kpv+32JaXXrlqhftEhHwvQfIUlTT5ohKGXTDFEzusnH00oz8ahSR0B96b+geqyIqUWtM/ZMSuvf5bCBUFIplM8sN3v5zc8FXxcA4+zjMM72ple17XxUY7eu89FrNas5bc8oTLr05FH1CjoAKBywreQD7JE+AN57FDKN5l4daEXmAOjrsIKee9haqTj1fm6iFryznuuUgZEqQpKmnjRDUBSiWd+mo7QxUUXkHvsE8Pgno0XpLMKkL61I3JX3mT9vv8bbdqcSDdC1xEmvGDCPE3TPIYDV73alHSvHLB42U7OZ5bWx+jU11vhKadv5ZOa80QxrIpdleNJ77mcUeG0wBaFP30FlDzwr0qcihPCvu6+eXwQ2INrmOEYSrVvqiUSHHSPpeCiqCEmaetIM1h981cRsnzAbnUKIMvGoJor5OeeKRaBWwB32/H6iCdBPak/e6NieXXvEW8uUBrJv3m8eE3Bea2Z5TSRllpvpsZe/Y+7X3e8dqxUtsib6jdvMQnk3Xd3A2z+iFKvZqz/g7fdo1XHZJ2vHNQP29F7hlX5M7RjEvn8/bqbT7t5SezZBJqE+fQf1kT5NgiOE8G9IX70oX2LqTY/bqUcgsZ7rlIHpP0KSpt40g7VCT5daaFYKIWzPsCgTgtvp3I2m1Y15Htd16iY1l5eTlaLKv7AMpePmarvseW+Y261jjn3J68MVxRfJSp9Z4y/P1lZsAqY427jNHPdZ6z3P1ahsz99xJ0oHDyLdV3K6uFuT9exx5UIGs9B9CWTZlk675xHgomkYw6iJpnRGb5apsdtID96r7oG3wgC6j9SVRgvTV6+wY0c0n6t6PN3iUI9AavYYScugozohjaARJoMBbtaJE3QNuvHo0I0zjA8SYAqUnv5o5wRgRtw0k1r1mDZndJXRph9X3h+/T2Nmmb6+yYepJ7LaZtEjl+VrGzLLgVIx0hi1PfA+/l4Yxx6qjdcuHAMI6qtXV9+9RjrIq6jn/8Nmj5EkDh3VCWkljegQ38wUQpiomCoi51e8rRtnWHNHt11BWBQr/ZTHLLwasVkyTEERZWGCw5ncZmbqd126NkPwKXR3by8esTV2Dif0le7oV62HMf2A83pLIY05dce0RaL8aq7qLmRPmnoi0c0eI2kZjFQR0ik0M1IV9lyqaJbd/DLMOP36/CXBuqsr7WOiRrgCSPWYvljbr0GoPo1hI3J23O2FXISOVCX1jjT4Hdy35lxANScJgTX7Xox9/MRgu5tTjrCRKhaqE9IpRFmZF5ewUTFV4e/GbeHGaRXdN1JQAaagCrMazo/ujCnO7AX8l99t9hXUNn521ctEabdiUS2kVxPKsDTJd0T7XryayKIJe22VY3u/bC9fpyQK3smChKKKkE4hqSXhYYhjlBhmnJ7VbC66up12BarVd2EpHFCPyc8CwY5ImZ+99HbnRPrTXfp0oUrIRI2UGasCrR+UhqW/egzGmgys6ywMXI2p6+8Nb7bpOyaf5x/BAFRH9obr1ash1x5J5PiENBrWVBHSSbhrtaxoT9JpiLhGiUE1ZX5RG1Xh87bVwJyqN6FAYX8v8pNLUJpJOfv7WVhCwD2mJ28E9jyIwEhZd696+96v6D+jErs+tVHecypMTzVNrx2GpcYqYMOtzlY9Uc02/VC9FxYJ9NGr1lbd/GmUjknn85wD+/SRtoeiipBOpZEWC40urPUrrlcVPhePqA+zvxe53QOQZdMM0+P67Wdc+vxDCJV6nD2uvq9BAsktdv32t6JmxSPqe+3ndm+hqGuqq/DbjwCBl8SiCWNsDMbeD0P5bOjrRNocFqoT0o64l9sD3gk3TtFwqwttg+wY3NewbbWy+H1qxxkoHfdWMaT7Shj58CLTImHqGe91RrWDUI3p5uVqoSS6gPQi76pIWa75V/kdV0dgoburMB4NLPxu9KKJZtuHEBIAC9UJ6VTcjuHVZf+ulhh+RcN+buv1tNkISehmuWGaA9vd409MK3crHRfq7cVu8xzPP6S+znoiHu7PvP0j6v3SihV783NqQZXqMSNhYRzyrbown9YynqHoCr/d26M69Td60UQzF2UQkiCMVBHSboSJolgrwvz205kL6o5vN8LURcd8CDRudEfH/Iw2VWaVCrSWAkNDGBn7uf46TxSiGXwC6ijJkzfWrkGkTKEVpk4LMCNaIuX09QprGhrSSDKUmWa9xpRxop1hPtvqaCohNsJGqiiqCGk3Qvk2CdPMMcj3yC4EHMaTEenqBhYt8RVZUxdvULcgGRrCyJ2/r564z/+QGU1yb9e1VXFRyJ+J3PcWqUWDri6nHiK4Xxc+cS7yu+b0hfNBJJy+DWz70gqnfrqLkw6DooqQTiVspOqGF0IIpUqdTT3Gk34oJkFt/U4FT1876zpUvee0hpreMRReyahFQz11UyqitmHZsgVythZ9Eql5DF5UiCasrNY6zYjOaEW8t0YrEVgvRToQ1lQREoeoNSZJElRv5F5uf8MLeoNIq86mHuNJP6zl8zZ09TsW1sq8wn7btVkeUm4jxSA/rMzyqqgzxsYw8uxOrNn3Ikae3VmLwsQ1/LTo6Q8tbPJ33OkQVAAgy13ITy6NeNJKDdj2j5tF+o18/+J4ktVDM9stEdJkKKoIcdPAQu5QuI0qM8udRpiqNElQYa/fhBXFCNOO65gq40Y3psBYYju3ZuIOEkR22wWdAI5j+Gmj8PyhcMX3AEq5nHr7TIw/tcXDjX3/ml0UbtXruWmUiCOkidCnihA3URrkNoqoDZmDfKWMlf4pl3rSg65J0N0sV5cKrDb79Zu4HdejGLc9Uqby6vrpLqeVwpX3mceMeJ2F/Rnk9iyDLJm1YkHmmenBQXVdWZ9PUbxIofByj7+BadD7F6eou5nNficngJNHvdtTPVzZRxYErKkixE0jakxavZIpTHGw2xvrxLTaBkD1WQXawnXLQyrsPfB7HpllmoJ24fyMfbyTE8Cj1wYLGQSsLnx2Z21D5d4Vnj9UEWG2kfT2YvDXT8LIvqa8vML+DHI/GHQW2yvrsDTvXzMKv5N6f/1Wnm5+Of44CWkQrKkipF6SrjGJmE4M7fUUhTD9+Oy1TZtfBt5/rzoF19MfasJW9nHr7UX25tujNaHV3feePp8Vgi4RZo9sjW5CYcV/QW73QEUwCXW9V2W7Ckeaz/Z8jeEiBtcdQbq/DAhTfA3eshXGteMwhZ6X/ORSr+u5O00K6O+DX2TVGl+c+kDN+1u4e0v091SXhtY45hPSaTD9R4ibuH3v3ERIJybeq81OPSnFn+7y+i7polcu3OlA5XL+MKieh2WaGQXbhJ5/ZFe1tY2FJWSM4WJ1xV/6O/eqo232onzX86324jNWATfYolmqewl9vVU1TQr4v39+hd9JtDJSvL+FKSC3Z3s1Ihf6PdWmoRdgPVWro9OkJTBSRYibMFGdKERY7eTXq60lTD0D36hPANqVeVFQPY+exdGPY5u49QXlFSFTKYTXRttuuL62IezzvfR2s7bL5Yiuq7dKLxbQvn/26JPQ/Bk3VgZHscKgek8nlzhSnEDI9zSoKL6Vq26TpNWLXUjLYKSKEBVRozp+6L6diy7zj6ztPNrJXrM9kLjflttl+bu7mNrPw6q7H8C8b6QxsKC8IjyMim+Sb7QtSvRldFPFg6tGdvQocrsNyHJNHIneXmRxZzA7AAAXQElEQVQ/vxVQiVB39EnpDC9Mx/o9Dyp+h2jPT3F9jiiafXuY99TexsfuAdbIBuHNph0Wu5CWwEgVIY1GZw8gy55vr6F7tYUhiW/LjfQwihKVcF+LFgGM3em1pEhnTDFTOY8yApWaR3bUtjKt0kPR2PthjIz9HGv+ZlwdbYtqSeBeNTlcxOBFBaQXAxCiVoeli+ppPMcK+zOYeiKLfQ8PYuqJ01F49GvJ2Bcori/dr04B+76n1jO018EVDwNPb66J/7hRtXahXb6MkKZDUUVIo/FrhOuaNEKlm8ISd5KanABmNIXgI5dEH4/72AGCr3D3FkytW4N955yDqY/8NxSmgg4qgHUfq0UZb3jBTLeVip6G1MbZRQzeshXpoSEA5opEpet5GEEaNV2sECnG8AmMjG8MlyZVTMyF/RnkdhvOwvtdfSj8W1d8DyrF9WU/9oHo76nOgNby4dK533eiEGm2oSppGxIRVUKI9wghfiSEeEkI8ZkkjknIgmJ0k77A2zZpGGNjtck+TNTCjzjflqupGE0x+NQz4YehWs0YIPgKd29B7p5HUDoGmCIhpVidZ1ERMlfeZ9Yt2fE5T7Xea+JmjFwVoj+fnyBVucLrGN1k9jx0rAaUZg/EMFFExcScn1ziSB8ClcL7PV3J1AeObjKFmLESKByAMfs4Bj/+3mjvqd97N1dUf+kAOlOINNtQlbQNsWuqhBApAHcD+B0ABwDsFkI8IaV8Me6xCVlQhKy9McbG4q/0i3A+JUFtbfx66tnquAr5IeS+m662bqmuEnvbIRjDquOaE2/+z7f7r86rXktAv7gwwtISGI9eq6lPCnG8qPgtAAgSPIrVkNoaJ2t7mJ56fvV3inono/sBGHdGEGi699FClk3hkdSq21bSTENV0lYkEal6B4CXpJQ/kVLOAngYwOUJHJeQhUWzv73GOV+geBDeqMrkhNmnbvvHq2mz/K45by+8EyeQf8G/1qd0LMCNHQh3LWHTMH6RxDDHi0qcKKIiHZceULf0SfeVw6V7g9KxSdQ7jVwCnVcXgFoULalVt60mSvSSLBiSWP13JgD7148DAH41geMSsrBo9rfXOOcLiipAOqMqkxPAY58A5p0CShtBOQ5lVKLQcznyF2+AbvJN982jsD+D/AvLUDoOpHfei+wNGX1kL8hzzB6dEV3+kaqubtMba3wg3rObnNCfK6xoc61Ozb55B3Kf+a/OFYRW4X3h9eDjBa1Wi1t4PTlhpjd1iwysZ5LkqltCWkDTLBWEENcAuAYAzjrrrGadlpD2otmTRr3nU4kRN/YJ9enNHkEFmJESZZuXwSFg7GaH4Cv0XI7c/U95fLosREpi8bt+FbnvvBDeHNVPWIayJqhc6qsDyD+3CKWZLqT7Tkd29BCMmTqW+1vnVJ3LJiqjmqUaY2PA05uR3zXnbbtjrAoeV5Boimva6ZdONlYxNUYWDEmIqtcA2P+vXVnZ5kBKeR+A+wCz918C5yWENIqghsaAc0LVtIvRejDdcD0wOuaYSPMXb9AIKon0YoHsR68yndA15qha8aETlkF1YxXMlXW91WsozaSR++cB5PbOQ/7l55Eeuje8U7zunCKFwsDVDlEZ1U3fuHYcxpl1dgIIEk1xuwxoI1oiXL0XIR1CEjVVuwGMCCFWCyF6AHwQwBMJHJcQ0kqsFV9dmlVZIWwVjOEiBtfPIL3CCFwlpjWOFF0Y2bMPxidvTdYcNWTqSrmybl5Azpn3xRI/sXrfyXlfwRiKCNYOnhWZPZf719/F7TJAiwFyihA7UiWlLAkhPgXgmwBSAB6UUv4w9sgIWYg0uB9YYceO+L327OzcCsxr0mJ2W4XMcnW0SnTBuO5/wwhxjVqXc5uhZJh9fHnyRmDvV4JX+dnQ1YXZCYyWWegiQpllyusCgFJOvV1JiHSvsr/k/U8BH78axuzj+nczTuo66X6ahLQpifhUSSmfklL+ipTyl6WUtyZxTEIWHA3uB2ZNlqWDBwEpo0VQNMeb+suTFYfurNcjyh512bjNbHJsJ9UDvP/e0BNxGOPTWOaoT94I7HnAX1ClesyCdBs693A3oaJlqhWZXd3A7DGk+0rKj6T7yon2jNP2l3xkV+NWqyXdT5OQNoWO6oQ0iwa34UiyGXNVoNkdut3mm/bUzegm4PK7zYiVRaXpsdL8U0OXTTCJgYFaqrDS0sbY+2EM/vrJUOlED3u/4vPLykR/+d3AFfcEuoerCBUtU4mLRUuA8iyyo0chUk4BJ1LzyJ73RqKtWhLvLxkWWgyQUwA2VCakWTS4H1iSk6VSoNnNN7u61ambkk00Fg+jcNcfIbd7mdf886e7HKkm5co/679dq/SM7GswNh6OHunwi1CNTzt/th3XAICz1lfTqinDQPnYMaBUiyxFaiXkTqOND5jnqZia5ieXeFfwJdiqJXYKlRCihZEqQppFg4t1k2zGrBVoMykzGnXFPV5Bo4jE5Z/rVZt/Pvg1Rxo0/8CEPsoWIsIXKhqma4Oi226j2tJm34v4lftuwNBvlqvpuvQKo/5WQoDj+RvDRYxclseaD+Ywclm+5h6fYEF3ov0lwxClcTYhHQ5FFWkboqSJOpIGO6onOVlqBdrQmcDml9URIkU0RW/+2RVuv1wuMMIXupbs7R9RH0e3XUUlamZkX6uIn4MY2bgfxtnB1gxaVO+FnYQLuhPtLxlEg+sICWk3KKpIW5B0kXVb0uBi3XonS5WYrUugKaIp6T51ys29Xbvf4GBghC90LdmltwPrrq5FpkTK/NndhNmPRtTFud+LzPJKbVqId6TOKJA98jby7M7GCCqg4XWEhLQbQsrm+3CuW7dO7tmzp+nnJe3L1MUb1HUeQ0MYeXZnC0Z0auBeXg+Y4mnwFnPSi2TP4HYoh2Wc6TL/TAOD6444GiMr96uMwzi7qF6OXxEb+9acC6j+jgmBNfterF5nIlYT4wPQtloZL+g/1wgrDcX9tt+XtkB7v4S3jo2QNkYIsVdKuS5oPxaqk7agZSuSOpWEJmm/KE/kCIbChd1TfL1YIPvRK2FMPwDYSq2c+6WRHhryCh/X9RZeySB//Qa1oEIthan0ZYrgVO5A2xOx0mBa9Qzc4sdKgQHxxE9Qv752IG57G0I6DEaqSFvASFUEEoxQhIny1EVQhGJywuwX6DYMDXkdqgib4yxWlGtsTP9uLQZGLs1FE6WTE8D2a9TXZqxSt1y5Y61GWGj2D0snRIE6IZpGSAjCRqpYU0XagqavSOpkEqxTSXLFoIOglY6jm8yC9yvvr6vGTBVhc2B7l7RR0GMSkYunRzdBm/6LapkR1yahE1q/0PSTnGIw/UfaAisNk2iLlYWKrsGxbrsP2avWI3fPI5BlUd0mUhLZq9arPxA27Ri2LUmdrU+C0sJyerqa4tP6MtmL46OkzYxV0VJajUqBdUrrlzjtbQjpMBipIm1D01YkdQq6lV0x/JbcGLOPY/Ci6YrnkkS6r4TBi6ZNY07VeDTL4z0rCF/JNDRCESaSZtWGKaOgqXlkR486PxA2chTVGqNRVhqMAhHSdjBSRUg74lfcrHMGj9AkuErhAIxh6ViJZ233oEk7Fr48jtz3FnkLwW/ZCiNOzZAP2Ruu962psijlct4oaL9Edm3Be81hI0eOgvwQCwWi7h8FnyhQ4s21CSGBUFQR0o741U1p00+rop8nSmpKE8nJ75qDPOGsM7KiRI2axN1CCV1dQNkrKq2IljE2VhtLVbDadgyKHKnSnlEEY5NTYImseGyEDUQrz0NIE2D6j5B2xK+4Ocl0kt+x3OnHzDLlIbRu6AcPovCJcxvWnsSeLh76n7eFX+hgpc3szZ/TPo7mHegKHru5drOuuQPvLSF+UFQR0o74FT0nWUujOxbgnexOHgVSPZ5D6NzQASD37TIK+3uTmSx93MPrcpN3NX/Wjq8DXcFj+74165o78N4S4gfTf4S0I0Eru5JMJ6mOdcda72Q3P2dGd04UHPVb2dGjHjd0C1nuQn5yiVm/FMeYMoSBpiPFp8BRY9QvkV0LGMO2HXTja5QlQhQipsi0Kx7DWmU065rb4d4SkiCMVBHSjrR6ZZduUiseAeS8Y5MxXMTgRQXo/Jsc6UH3ccP2rosZ0fD0ljwG5HYbKOx3pf1U191qP6g6UmSxfd+adc2tvreEJAxFFSHtyugmsxh6fNr8dzOLd/0mO8XvjOEi0ouF4gOu9KD9s1HEQsyIhrLGqBJFc6CqG2uUJUJY6hCU9TbXrtKsax65BIDrvWlHry1CQkJRRQjx4jepan6X/eiV/n5Q7skyiliIGdHQ1hi5i+xPHvWKunaNGgYIyki+b+6IIdD4a56cAJ5/CM4IpwDO/xBX/5GOhTVVhHQyjVqOHsZbyfU7Y3QTcNb6Wt3SaUuRHX0DRvaEOSm7Px9FLMR0Dw/lqg6YdWOquqpWuoI3uimxrl5t7EvxehMGoRLVkMDUM407JyENhqKKkE4lRPF2LPyEhOZ3QcXizp0jiIWYBpoqs1ClqzrQfkXSjW5H4xcxbKSQZJE6WYBQVBHSqbRqMoyCXyQtqliIES1S9pY8Jwcj646UoLVF0n73q1EGmXHFTb3RUp2oFl3mMdvlHSYkAhRVhHQq7f5NPyiS1mix4MITRXOPD4gUAUq8DUzY+5U0cdKLcaKlKlENmHYdSUZcCWkiLFQnpFNp9+XoYQrRW7nCMUYBuseiodIGprBjR/3jaZURZpyVfnHGbN1/VSNwGoCSDoWRKkI6BXeaZeQSc/VUo2pt4tLqSFqYtFSdESC/NjB1R6tadb/iRAzjjnl0E7D9mnjHIKSNoKgipBNQpVmef8hcfj71THs2o01q1VqAOFKm4c4uNrSIP3YbGBWNXuXnR73pxSTG3MrrJiRhmP4jpN1QuYzr0ixTz7QufRZEEgaSAQah2jTcl8cbmkrTtXtRbS/s2IGpizdg35pzMXXxBn2KsNUmo/WQxJg78boJ0UBRRUg7oRMRqm/yQHunSJIwzQyo2dGm4XbNqY+X0P0K2wYmUu1Vq01G6yGJMXfidROigek/QtoJnYgQKUcT4yrtniKJu2otoGZH75Su+dOW0P1SWjQoVv+Fqb3ypi9vq78uq1FmsH4ksTKxleaqhCQIRRUhQTRzotKJCFk2UyLtWpTeKALqbbRO6SsMoPtItPsV8TmHMToNqr2yIlmW8LIiWdbxA7GPObMMmD0GlGfN3yVtBksICYTpP0L8iNL0Nwm0NgmrTs0USUC9jTYNt3lLtPvVoOccVHvlF8kKxD3m4uGaoLKgNQEhTYWRKkL8aLZruZ/L+KmYIglY7h+Yhgt7vxr0nJXtcWy1V7FWESp75ylo57o7QhYYFFWE+NFs76Amu4x3BAFiMlK/QR0Nes5Bok+bvtREuOoaW7vX3RGygKCoIsSPVnjonIoRqVbTwOfsJ/qCIln+B9aM2c6pUHdHSBvBmipC/KCHjh6Vn1anEvScJyeAbauBccP8Z9vqRK7XGBvD4C1bkR4aAoRAemgIg7dsDRd5U425qxvILMcpVXdHSBvBSBUhfjAdpyZOI91GjSfOM/J7zpMTwGOfAOZt3lfFw8Djn3R+tk7qTl/y3SSk7RBSyqafdN26dXLPnj1NPy8hJCHuWKtJl60ynd2biVvgAWYEJ6koje5agdZcLyGk6Qgh9kop1wXtx/QfISQ6rW6WbCfAdT02ftfElXWEEBsUVYSQ6Gj9tFqw0qzRAs/vmriyjhBig6KKEBKddirgb7TA2/A5swDcTarnlFmwELopNCGnOBRVhJDotFMT3EYLvNFNwBX3VFbVVcgsBy6/+5QoCo/UFNr1OQoxcqrBQnVCSOfTikbCpwhTF29QG5QODWHk2Z3Kz7h7GgKm/1ZouwhC2oywheq0VCCEdD40TG0Y9bTS8etpSFFFFjJM/xFCCNES1BRaRayehoR0MBRVhBBCtGRvuB6it9exLaiVTj1CjJCFAEUVIYQQLfW00qlHiBGyEGBNFSGEEF+ittKx9s3fcSdKuRzSg4PI3nA966nIgoeiihBCSOLU3dOQkA6G6T9CCCGEkASgqCKEEEIISQCKKkIIIYSQBKCoIoQQQghJAIoqQgghhJAEoKgihBBCCEkAiipCCCGEkASgqCKEEEIISQCKKkIIaVcmJ4A71gLjA+a/JydaPSJCiA90VCeEkFYwOQHs3AoUDgDGSmDD54DRTc7f7/hDYK5o/lx41fwZcO5HCGkbGKkihJBmYwmmwqsAZE0w2SNRO7fWBJXFXNHcTghpSyiqCCGk2YQRTIUD6s/qthNCWg5FFSGENJswgslYqd5Ht50Q0nIoqgghpNmEEUwbPgd0Z5y/786Y2wkhbQlFFSGENJswgml0EzD2JcBYBUCY/x77EovUCWljuPqPEEKajSWM/Fb/WftRRBHSMVBUEUJIK6BgImTBwfQfIYQQQkgCUFQRQgghhCQARRUhhBBCSAJQVBFCCCGEJABFFSGEEEJIAsQSVUKILwoh/lUIMSmEeFQIMZDUwAghhBBCOom4kapvAVgrpRwF8GMAN8UfEiGEEEJI5xFLVEkpn5FSlio/7gLAplSEEEIIOSVJsqbqYwCeTvB4hBBCCCEdQ6CjuhDibwGcofjVFinl45V9tgAoAfiqz3GuAXANAJx11ll1DZYQQgghpF0JFFVSyt/2+70Q4iMALgWwQUopfY5zH4D7AGDdunXa/QghhBBCOpFYvf+EEO8B8GkA75ZSziQzJEIIIYSQziNuTdWfAlgC4FtCiB8IIb6cwJgIIYQQQjqOWJEqKeWbkxoIIYQQQkgnI3zKoBp3UiFeB/BK00/cPqwAcKjVgyCx4XNcGPA5Lgz4HBcG7focz5ZSnh60U0tE1amOEGKPlHJdq8dB4sHnuDDgc1wY8DkuDDr9ObL3HyGEEEJIAlBUEUIIIYQkAEVVa7iv1QMgicDnuDDgc1wY8DkuDDr6ObKmihBCCCEkARipIoQQQghJAIqqFiGE+KIQ4l+FEJNCiEeFEAOtHhMJjxDiPUKIHwkhXhJCfKbV4yHREUKsEkL8nRDiRSHED4UQ17V6TKQ+hBApIcRzQognWz0WUh9CiAEhxNcr8+I+IcSvtXpM9UBR1Tq+BWCtlHIUwI8B3NTi8ZCQCCFSAO4GsBHAuQD+gxDi3NaOitRBCcAfSSnPBbAewCf5HDuW6wDsa/UgSCzuAvB/pZTnADgfHfo8KapahJTyGSllqfLjLgArWzkeEol3AHhJSvkTKeUsgIcBXN7iMZGISClzUsrvV/77KMw/4me2dlQkKkKIlQDeB+DPWj0WUh9CCAPAbwJ4AACklLNSyunWjqo+KKrag48BeLrVgyChORPAq7afD4CTcUcjhBgGcAGAf2rtSEgd3Ang0wDmWz0QUjerAbwO4M8radw/E0L0t3pQ9UBR1UCEEH8rhHhB8c/ltn22wExDfLV1IyXk1EUIsRjAIwCul1K+0erxkPAIIS4FkJdS7m31WEgs0gAuBPB/pJQXADgOoCNrVWM1VCb+SCl/2+/3QoiPALgUwAZJb4tO4jUAq2w/r6xsIx2GEKIbpqD6qpRye6vHQyLzTgCXCSHeC6AXwFIhxF9JKf9ji8dFonEAwAEppRUp/jo6VFQxUtUihBDvgRmyvkxKOdPq8ZBI7AYwIoRYLYToAfBBAE+0eEwkIkIIAbOGY5+U8vZWj4dER0p5k5RypZRyGOb/h89SUHUeUsqfAXhVCPGWyqYNAF5s4ZDqhpGq1vGnABYB+Jb5tx27pJTXtnZIJAxSypIQ4lMAvgkgBeBBKeUPWzwsEp13AvgwgH8RQvygsu2zUsqnWjgmQk5V/gDAVytfVH8C4KMtHk9d0FGdEEIIISQBmP4jhBBCCEkAiipCCCGEkASgqCKEEEIISQCKKkIIIYSQBKCoIoQQQghJAIoqQgghhJAEoKgihBBCCEkAiipCCCGEkAT4/0c2a4pXsk/zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for label in [0, 1]:\n",
    "    mask = (y_train == label)\n",
    "    ax.scatter(X_train[mask, 0], X_train[mask, 1])\n",
    "for label in [0, 1]:\n",
    "    mask = (y_test == label)\n",
    "    ax.scatter(X_test[mask, 0], X_test[mask, 1])\n",
    "x_bnd = np.linspace(X[:, 0].min() - 1, X[:, 0].max() + 1,  400)\n",
    "y_bnd = - x_bnd * (theta[0] /theta[1]) - (theta_0 / theta[1])\n",
    "ax.plot(x_bnd, y_bnd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another toy example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.548447Z",
     "start_time": "2019-02-20T16:35:30.363336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_blobs(n_samples=500, centers=3, random_state=7)\n",
    "y[y==2] = 0\n",
    "fig, ax = plt.subplots()\n",
    "for label in [0, 1]:\n",
    "    mask = (y == label)\n",
    "    ax.scatter(X[mask, 0], X[mask, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.553418Z",
     "start_time": "2019-02-20T16:35:30.550315Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.564550Z",
     "start_time": "2019-02-20T16:35:30.555270Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# clf = SVC(kernel=\"linear\", random_state=0)\n",
    "clf = SVC(kernel=\"rbf\", random_state=0)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.570590Z",
     "start_time": "2019-02-20T16:35:30.566597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print('Test accuracy: %.4f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.836667Z",
     "start_time": "2019-02-20T16:35:30.572440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x11ae7f390>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = X[:, 0].min()\n",
    "x_max = X[:, 0].max()\n",
    "y_min = X[:, 1].min()\n",
    "y_max = X[:, 1].max()\n",
    "\n",
    "XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]\n",
    "Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for label in [0, 1]:\n",
    "    mask = (y == label)\n",
    "    ax.scatter(X[mask, 0], X[mask, 1])\n",
    "    \n",
    "Z = Z.reshape(XX.shape)\n",
    "ax.contour(XX, YY, Z, colors=\"black\",\n",
    "    linestyles=['--', '-', '--'], levels=[-.5, 0, .5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classify digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.841115Z",
     "start_time": "2019-02-20T16:35:30.838828Z"
    }
   },
   "outputs": [],
   "source": [
    "# from sklearn.datasets import load_breast_cancer\n",
    "# breast_cancer = load_breast_cancer()\n",
    "# X, y = breast_cancer.data, breast_cancer.target\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.848232Z",
     "start_time": "2019-02-20T16:35:30.842930Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:30.924328Z",
     "start_time": "2019-02-20T16:35:30.850181Z"
    }
   },
   "outputs": [],
   "source": [
    "digits = load_digits()\n",
    "X, y = digits.data, digits.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.114710Z",
     "start_time": "2019-02-20T16:35:30.926361Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11a634ac8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.matshow(digits.images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.119708Z",
     "start_time": "2019-02-20T16:35:31.116440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1437, 64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.124124Z",
     "start_time": "2019-02-20T16:35:31.121711Z"
    }
   },
   "outputs": [],
   "source": [
    "clf = Perceptron(max_iter=40, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.191239Z",
     "start_time": "2019-02-20T16:35:31.126079Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9389\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print('Accuracy: %.4f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.195327Z",
     "start_time": "2019-02-20T16:35:31.192983Z"
    }
   },
   "outputs": [],
   "source": [
    "clf = LinearSVC(C=1, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.287245Z",
     "start_time": "2019-02-20T16:35:31.197110Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9361\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print('Accuracy: %.4f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.291955Z",
     "start_time": "2019-02-20T16:35:31.289158Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.299243Z",
     "start_time": "2019-02-20T16:35:31.293825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 29,  0,  0,  0,  0,  1,  0,  5,  0],\n",
       "       [ 0,  1, 33,  2,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 29,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0, 30,  0,  0,  0,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0, 38,  1,  0,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0, 43,  0,  0,  0],\n",
       "       [ 0,  2,  0,  0,  0,  0,  0, 37,  0,  0],\n",
       "       [ 0,  2,  1,  0,  0,  0,  0,  0, 35,  1],\n",
       "       [ 0,  0,  0,  2,  0,  1,  0,  0,  2, 36]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn also includes utilities to quickly compute a cross validation score..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.653085Z",
     "start_time": "2019-02-20T16:35:31.301137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.9443, Std: 0.0127\n"
     ]
    }
   ],
   "source": [
    "clf = LinearSVC(C=1, random_state=0)\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores =  cross_val_score(clf, X_train, y_train, cv=5)\n",
    "print(\"Mean: %.4f, Std: %.4f\" % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:31.997193Z",
     "start_time": "2019-02-20T16:35:31.654977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.9555, Std: 0.0101\n"
     ]
    }
   ],
   "source": [
    "clf = LinearSVC(C=0.1, random_state=0)\n",
    "scores =  cross_val_score(clf, X_train, y_train, cv=5)\n",
    "print(\"Mean: %.4f, Std: %.4f\" % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... or to perform a grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:32.001507Z",
     "start_time": "2019-02-20T16:35:31.998962Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:32.010342Z",
     "start_time": "2019-02-20T16:35:32.004094Z"
    }
   },
   "outputs": [],
   "source": [
    "clf = LinearSVC(random_state=0)\n",
    "param_grid = {'C': 10. ** np.arange(-6, 4)}\n",
    "grid_search = GridSearchCV(clf, param_grid=param_grid, cv=5, verbose=3, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.503447Z",
     "start_time": "2019-02-20T16:35:32.012143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] C=1e-06 .........................................................\n",
      "[CV] ................ C=1e-06, score=0.8424657534246576, total=   0.0s\n",
      "[CV] C=1e-06 .........................................................\n",
      "[CV] ................ C=1e-06, score=0.8719723183391004, total=   0.0s\n",
      "[CV] C=1e-06 .........................................................\n",
      "[CV] ................ C=1e-06, score=0.9090909090909091, total=   0.0s\n",
      "[CV] C=1e-06 .........................................................\n",
      "[CV] ................ C=1e-06, score=0.8426573426573427, total=   0.0s\n",
      "[CV] C=1e-06 .........................................................\n",
      "[CV] ................. C=1e-06, score=0.897887323943662, total=   0.0s\n",
      "[CV] C=1e-05 .........................................................\n",
      "[CV] ................ C=1e-05, score=0.9075342465753424, total=   0.0s\n",
      "[CV] C=1e-05 .........................................................\n",
      "[CV] ................ C=1e-05, score=0.9134948096885813, total=   0.0s\n",
      "[CV] C=1e-05 .........................................................\n",
      "[CV] ................ C=1e-05, score=0.9440559440559441, total=   0.0s\n",
      "[CV] C=1e-05 .........................................................\n",
      "[CV] ................ C=1e-05, score=0.9020979020979021, total=   0.0s\n",
      "[CV] C=1e-05 .........................................................\n",
      "[CV] ................ C=1e-05, score=0.9330985915492958, total=   0.0s\n",
      "[CV] C=0.0001 ........................................................\n",
      "[CV] ................ C=0.0001, score=0.934931506849315, total=   0.0s\n",
      "[CV] C=0.0001 ........................................................\n",
      "[CV] ............... C=0.0001, score=0.9411764705882353, total=   0.0s\n",
      "[CV] C=0.0001 ........................................................\n",
      "[CV] ................ C=0.0001, score=0.965034965034965, total=   0.0s\n",
      "[CV] C=0.0001 ........................................................\n",
      "[CV] ............... C=0.0001, score=0.9405594405594405, total=   0.0s\n",
      "[CV] C=0.0001 ........................................................\n",
      "[CV] ............... C=0.0001, score=0.9683098591549296, total=   0.0s\n",
      "[CV] C=0.001 .........................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................. C=0.001, score=0.958904109589041, total=   0.0s\n",
      "[CV] C=0.001 .........................................................\n",
      "[CV] ................ C=0.001, score=0.9653979238754326, total=   0.0s\n",
      "[CV] C=0.001 .........................................................\n",
      "[CV] ................. C=0.001, score=0.965034965034965, total=   0.0s\n",
      "[CV] C=0.001 .........................................................\n",
      "[CV] ................. C=0.001, score=0.965034965034965, total=   0.0s\n",
      "[CV] C=0.001 .........................................................\n",
      "[CV] ................ C=0.001, score=0.9788732394366197, total=   0.0s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] ................. C=0.01, score=0.9554794520547946, total=   0.1s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] ................. C=0.01, score=0.9619377162629758, total=   0.1s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] ................. C=0.01, score=0.9685314685314685, total=   0.1s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] ................. C=0.01, score=0.9615384615384616, total=   0.1s\n",
      "[CV] C=0.01 ..........................................................\n",
      "[CV] .................. C=0.01, score=0.971830985915493, total=   0.1s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] .................. C=0.1, score=0.9452054794520548, total=   0.1s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] ................... C=0.1, score=0.972318339100346, total=   0.1s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] .................. C=0.1, score=0.9475524475524476, total=   0.1s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] .................. C=0.1, score=0.9615384615384616, total=   0.1s\n",
      "[CV] C=0.1 ...........................................................\n",
      "[CV] .................. C=0.1, score=0.9507042253521126, total=   0.1s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] .................. C=1.0, score=0.9417808219178082, total=   0.1s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] .................. C=1.0, score=0.9619377162629758, total=   0.1s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] .................. C=1.0, score=0.9370629370629371, total=   0.1s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] .................. C=1.0, score=0.9545454545454546, total=   0.1s\n",
      "[CV] C=1.0 ...........................................................\n",
      "[CV] ................... C=1.0, score=0.926056338028169, total=   0.1s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] ................. C=10.0, score=0.9383561643835616, total=   0.1s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] ................. C=10.0, score=0.9653979238754326, total=   0.1s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] ................. C=10.0, score=0.9335664335664335, total=   0.1s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] .................. C=10.0, score=0.951048951048951, total=   0.1s\n",
      "[CV] C=10.0 ..........................................................\n",
      "[CV] .................. C=10.0, score=0.954225352112676, total=   0.1s\n",
      "[CV] C=100.0 .........................................................\n",
      "[CV] ................. C=100.0, score=0.928082191780822, total=   0.1s\n",
      "[CV] C=100.0 .........................................................\n",
      "[CV] ................ C=100.0, score=0.9446366782006921, total=   0.1s\n",
      "[CV] C=100.0 .........................................................\n",
      "[CV] ................ C=100.0, score=0.9335664335664335, total=   0.1s\n",
      "[CV] C=100.0 .........................................................\n",
      "[CV] ................ C=100.0, score=0.9475524475524476, total=   0.1s\n",
      "[CV] C=100.0 .........................................................\n",
      "[CV] ................ C=100.0, score=0.9401408450704225, total=   0.1s\n",
      "[CV] C=1000.0 ........................................................\n",
      "[CV] ............... C=1000.0, score=0.9383561643835616, total=   0.1s\n",
      "[CV] C=1000.0 ........................................................\n",
      "[CV] ............... C=1000.0, score=0.9550173010380623, total=   0.1s\n",
      "[CV] C=1000.0 ........................................................\n",
      "[CV] ............... C=1000.0, score=0.9300699300699301, total=   0.1s\n",
      "[CV] C=1000.0 ........................................................\n",
      "[CV] ............... C=1000.0, score=0.9545454545454546, total=   0.1s\n",
      "[CV] C=1000.0 ........................................................\n",
      "[CV] ............... C=1000.0, score=0.9436619718309859, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.5s finished\n"
     ]
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.508680Z",
     "start_time": "2019-02-20T16:35:34.505328Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 0.001}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.513596Z",
     "start_time": "2019-02-20T16:35:34.510724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9665970772442589\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.519424Z",
     "start_time": "2019-02-20T16:35:34.515521Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9639\n"
     ]
    }
   ],
   "source": [
    "y_pred = grid_search.predict(X_test)\n",
    "print('Accuracy: %.4f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a lot more features! \n",
    "We have only looked at some classification models and some model selection features, but sklearn can also be used for regression, "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.766901Z",
     "start_time": "2019-02-20T16:35:34.521399Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pandas](http://pandas.pydata.org) is a library that provides a set of tools for data analysis (Python Data Analysis Library). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas dataframes can be created by importing a CSV file (or TSV, or JSON, or SQL, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.771137Z",
     "start_time": "2019-02-20T16:35:34.768606Z"
    }
   },
   "outputs": [],
   "source": [
    "# df = pd.read_csv(\"file.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas dataframes can also be created directly from a dictionary of arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.783033Z",
     "start_time": "2019-02-20T16:35:34.774533Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mean_fit_time': array([0.01142702, 0.01115093, 0.01050925, 0.02054195, 0.07160912,\n",
      "       0.06751108, 0.06907425, 0.06755571, 0.07172804, 0.07606635]), 'std_fit_time': array([0.00053882, 0.00038095, 0.00030056, 0.0004086 , 0.00281235,\n",
      "       0.00261869, 0.00117984, 0.00326013, 0.00573768, 0.0030746 ]), 'mean_score_time': array([0.00030556, 0.00029874, 0.00025778, 0.00029097, 0.00029759,\n",
      "       0.00030074, 0.00031796, 0.00030222, 0.00032935, 0.00035524]), 'std_score_time': array([4.90965395e-05, 1.10162491e-04, 3.79735462e-05, 3.75066905e-05,\n",
      "       4.90331443e-06, 1.30740366e-05, 3.27037297e-05, 1.36716789e-05,\n",
      "       3.10957060e-05, 4.44746199e-05]), 'param_C': masked_array(data=[1e-06, 1e-05, 0.0001, 0.001, 0.01, 0.1, 1.0, 10.0,\n",
      "                   100.0, 1000.0],\n",
      "             mask=[False, False, False, False, False, False, False, False,\n",
      "                   False, False],\n",
      "       fill_value='?',\n",
      "            dtype=object), 'params': [{'C': 1e-06}, {'C': 1e-05}, {'C': 0.0001}, {'C': 0.001}, {'C': 0.01}, {'C': 0.1}, {'C': 1.0}, {'C': 10.0}, {'C': 100.0}, {'C': 1000.0}], 'split0_test_score': array([0.84246575, 0.90753425, 0.93493151, 0.95890411, 0.95547945,\n",
      "       0.94520548, 0.94178082, 0.93835616, 0.92808219, 0.93835616]), 'split1_test_score': array([0.87197232, 0.91349481, 0.94117647, 0.96539792, 0.96193772,\n",
      "       0.97231834, 0.96193772, 0.96539792, 0.94463668, 0.9550173 ]), 'split2_test_score': array([0.90909091, 0.94405594, 0.96503497, 0.96503497, 0.96853147,\n",
      "       0.94755245, 0.93706294, 0.93356643, 0.93356643, 0.93006993]), 'split3_test_score': array([0.84265734, 0.9020979 , 0.94055944, 0.96503497, 0.96153846,\n",
      "       0.96153846, 0.95454545, 0.95104895, 0.94755245, 0.95454545]), 'split4_test_score': array([0.89788732, 0.93309859, 0.96830986, 0.97887324, 0.97183099,\n",
      "       0.95070423, 0.92605634, 0.95422535, 0.94014085, 0.94366197]), 'mean_test_score': array([0.87265136, 0.91997216, 0.94989562, 0.96659708, 0.9638135 ,\n",
      "       0.95546277, 0.94432846, 0.94850383, 0.93876131, 0.94432846]), 'std_test_score': array([0.02746696, 0.01590452, 0.01381196, 0.00656282, 0.0057433 ,\n",
      "       0.0101387 , 0.0126865 , 0.01142328, 0.00715855, 0.00956827]), 'rank_test_score': array([10,  9,  4,  1,  2,  3,  6,  5,  8,  6], dtype=int32), 'split0_train_score': array([0.88296943, 0.92925764, 0.96331878, 0.98253275, 0.99388646,\n",
      "       0.99825328, 0.99737991, 0.99475983, 0.99563319, 0.99650655]), 'split1_train_score': array([0.88066202, 0.93118467, 0.96428571, 0.9825784 , 0.99303136,\n",
      "       0.99651568, 0.99738676, 0.99216028, 0.98954704, 0.98432056]), 'split2_train_score': array([0.86967854, 0.92267593, 0.96003475, 0.98349262, 0.99478714,\n",
      "       0.99826238, 0.99739357, 0.99478714, 0.99565595, 0.99565595]), 'split3_train_score': array([0.88184188, 0.93223284, 0.96090356, 0.98001738, 0.99218071,\n",
      "       0.99652476, 0.99565595, 0.9913119 , 0.98783666, 0.99391833]), 'split4_train_score': array([0.8725065 , 0.92541197, 0.95836947, 0.98178664, 0.99392888,\n",
      "       0.99566349, 0.98091934, 0.99045967, 0.99132697, 0.98785776]), 'mean_train_score': array([0.87753168, 0.92815261, 0.96138246, 0.98208156, 0.99356291,\n",
      "       0.99704392, 0.99374711, 0.99269576, 0.99199996, 0.99165183]), 'std_train_score': array([0.00538275, 0.00359355, 0.00215879, 0.00116527, 0.00088661,\n",
      "       0.00103936, 0.00644882, 0.00177967, 0.00317394, 0.00475144])}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.824275Z",
     "start_time": "2019-02-20T16:35:34.784975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.011427</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>{'C': 1e-06}</td>\n",
       "      <td>0.842466</td>\n",
       "      <td>0.871972</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.842657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.872651</td>\n",
       "      <td>0.027467</td>\n",
       "      <td>10</td>\n",
       "      <td>0.882969</td>\n",
       "      <td>0.880662</td>\n",
       "      <td>0.869679</td>\n",
       "      <td>0.881842</td>\n",
       "      <td>0.872507</td>\n",
       "      <td>0.877532</td>\n",
       "      <td>0.005383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011151</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>{'C': 1e-05}</td>\n",
       "      <td>0.907534</td>\n",
       "      <td>0.913495</td>\n",
       "      <td>0.944056</td>\n",
       "      <td>0.902098</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919972</td>\n",
       "      <td>0.015905</td>\n",
       "      <td>9</td>\n",
       "      <td>0.929258</td>\n",
       "      <td>0.931185</td>\n",
       "      <td>0.922676</td>\n",
       "      <td>0.932233</td>\n",
       "      <td>0.925412</td>\n",
       "      <td>0.928153</td>\n",
       "      <td>0.003594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010509</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>{'C': 0.0001}</td>\n",
       "      <td>0.934932</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.965035</td>\n",
       "      <td>0.940559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.949896</td>\n",
       "      <td>0.013812</td>\n",
       "      <td>4</td>\n",
       "      <td>0.963319</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.960035</td>\n",
       "      <td>0.960904</td>\n",
       "      <td>0.958369</td>\n",
       "      <td>0.961382</td>\n",
       "      <td>0.002159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.001</td>\n",
       "      <td>{'C': 0.001}</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>0.965398</td>\n",
       "      <td>0.965035</td>\n",
       "      <td>0.965035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.966597</td>\n",
       "      <td>0.006563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.982533</td>\n",
       "      <td>0.982578</td>\n",
       "      <td>0.983493</td>\n",
       "      <td>0.980017</td>\n",
       "      <td>0.981787</td>\n",
       "      <td>0.982082</td>\n",
       "      <td>0.001165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.071609</td>\n",
       "      <td>0.002812</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 0.01}</td>\n",
       "      <td>0.955479</td>\n",
       "      <td>0.961938</td>\n",
       "      <td>0.968531</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>...</td>\n",
       "      <td>0.963814</td>\n",
       "      <td>0.005743</td>\n",
       "      <td>2</td>\n",
       "      <td>0.993886</td>\n",
       "      <td>0.993031</td>\n",
       "      <td>0.994787</td>\n",
       "      <td>0.992181</td>\n",
       "      <td>0.993929</td>\n",
       "      <td>0.993563</td>\n",
       "      <td>0.000887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.067511</td>\n",
       "      <td>0.002619</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'C': 0.1}</td>\n",
       "      <td>0.945205</td>\n",
       "      <td>0.972318</td>\n",
       "      <td>0.947552</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>...</td>\n",
       "      <td>0.955463</td>\n",
       "      <td>0.010139</td>\n",
       "      <td>3</td>\n",
       "      <td>0.998253</td>\n",
       "      <td>0.996516</td>\n",
       "      <td>0.998262</td>\n",
       "      <td>0.996525</td>\n",
       "      <td>0.995663</td>\n",
       "      <td>0.997044</td>\n",
       "      <td>0.001039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.069074</td>\n",
       "      <td>0.001180</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 1.0}</td>\n",
       "      <td>0.941781</td>\n",
       "      <td>0.961938</td>\n",
       "      <td>0.937063</td>\n",
       "      <td>0.954545</td>\n",
       "      <td>...</td>\n",
       "      <td>0.944328</td>\n",
       "      <td>0.012686</td>\n",
       "      <td>6</td>\n",
       "      <td>0.997380</td>\n",
       "      <td>0.997387</td>\n",
       "      <td>0.997394</td>\n",
       "      <td>0.995656</td>\n",
       "      <td>0.980919</td>\n",
       "      <td>0.993747</td>\n",
       "      <td>0.006449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.067556</td>\n",
       "      <td>0.003260</td>\n",
       "      <td>0.000302</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>10</td>\n",
       "      <td>{'C': 10.0}</td>\n",
       "      <td>0.938356</td>\n",
       "      <td>0.965398</td>\n",
       "      <td>0.933566</td>\n",
       "      <td>0.951049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.948504</td>\n",
       "      <td>0.011423</td>\n",
       "      <td>5</td>\n",
       "      <td>0.994760</td>\n",
       "      <td>0.992160</td>\n",
       "      <td>0.994787</td>\n",
       "      <td>0.991312</td>\n",
       "      <td>0.990460</td>\n",
       "      <td>0.992696</td>\n",
       "      <td>0.001780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.071728</td>\n",
       "      <td>0.005738</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>100</td>\n",
       "      <td>{'C': 100.0}</td>\n",
       "      <td>0.928082</td>\n",
       "      <td>0.944637</td>\n",
       "      <td>0.933566</td>\n",
       "      <td>0.947552</td>\n",
       "      <td>...</td>\n",
       "      <td>0.938761</td>\n",
       "      <td>0.007159</td>\n",
       "      <td>8</td>\n",
       "      <td>0.995633</td>\n",
       "      <td>0.989547</td>\n",
       "      <td>0.995656</td>\n",
       "      <td>0.987837</td>\n",
       "      <td>0.991327</td>\n",
       "      <td>0.992000</td>\n",
       "      <td>0.003174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.076066</td>\n",
       "      <td>0.003075</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>1000</td>\n",
       "      <td>{'C': 1000.0}</td>\n",
       "      <td>0.938356</td>\n",
       "      <td>0.955017</td>\n",
       "      <td>0.930070</td>\n",
       "      <td>0.954545</td>\n",
       "      <td>...</td>\n",
       "      <td>0.944328</td>\n",
       "      <td>0.009568</td>\n",
       "      <td>6</td>\n",
       "      <td>0.996507</td>\n",
       "      <td>0.984321</td>\n",
       "      <td>0.995656</td>\n",
       "      <td>0.993918</td>\n",
       "      <td>0.987858</td>\n",
       "      <td>0.991652</td>\n",
       "      <td>0.004751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       0.011427      0.000539         0.000306        0.000049   1e-06   \n",
       "1       0.011151      0.000381         0.000299        0.000110   1e-05   \n",
       "2       0.010509      0.000301         0.000258        0.000038  0.0001   \n",
       "3       0.020542      0.000409         0.000291        0.000038   0.001   \n",
       "4       0.071609      0.002812         0.000298        0.000005    0.01   \n",
       "5       0.067511      0.002619         0.000301        0.000013     0.1   \n",
       "6       0.069074      0.001180         0.000318        0.000033       1   \n",
       "7       0.067556      0.003260         0.000302        0.000014      10   \n",
       "8       0.071728      0.005738         0.000329        0.000031     100   \n",
       "9       0.076066      0.003075         0.000355        0.000044    1000   \n",
       "\n",
       "          params  split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0   {'C': 1e-06}           0.842466           0.871972           0.909091   \n",
       "1   {'C': 1e-05}           0.907534           0.913495           0.944056   \n",
       "2  {'C': 0.0001}           0.934932           0.941176           0.965035   \n",
       "3   {'C': 0.001}           0.958904           0.965398           0.965035   \n",
       "4    {'C': 0.01}           0.955479           0.961938           0.968531   \n",
       "5     {'C': 0.1}           0.945205           0.972318           0.947552   \n",
       "6     {'C': 1.0}           0.941781           0.961938           0.937063   \n",
       "7    {'C': 10.0}           0.938356           0.965398           0.933566   \n",
       "8   {'C': 100.0}           0.928082           0.944637           0.933566   \n",
       "9  {'C': 1000.0}           0.938356           0.955017           0.930070   \n",
       "\n",
       "   split3_test_score       ...         mean_test_score  std_test_score  \\\n",
       "0           0.842657       ...                0.872651        0.027467   \n",
       "1           0.902098       ...                0.919972        0.015905   \n",
       "2           0.940559       ...                0.949896        0.013812   \n",
       "3           0.965035       ...                0.966597        0.006563   \n",
       "4           0.961538       ...                0.963814        0.005743   \n",
       "5           0.961538       ...                0.955463        0.010139   \n",
       "6           0.954545       ...                0.944328        0.012686   \n",
       "7           0.951049       ...                0.948504        0.011423   \n",
       "8           0.947552       ...                0.938761        0.007159   \n",
       "9           0.954545       ...                0.944328        0.009568   \n",
       "\n",
       "   rank_test_score  split0_train_score  split1_train_score  \\\n",
       "0               10            0.882969            0.880662   \n",
       "1                9            0.929258            0.931185   \n",
       "2                4            0.963319            0.964286   \n",
       "3                1            0.982533            0.982578   \n",
       "4                2            0.993886            0.993031   \n",
       "5                3            0.998253            0.996516   \n",
       "6                6            0.997380            0.997387   \n",
       "7                5            0.994760            0.992160   \n",
       "8                8            0.995633            0.989547   \n",
       "9                6            0.996507            0.984321   \n",
       "\n",
       "   split2_train_score  split3_train_score  split4_train_score  \\\n",
       "0            0.869679            0.881842            0.872507   \n",
       "1            0.922676            0.932233            0.925412   \n",
       "2            0.960035            0.960904            0.958369   \n",
       "3            0.983493            0.980017            0.981787   \n",
       "4            0.994787            0.992181            0.993929   \n",
       "5            0.998262            0.996525            0.995663   \n",
       "6            0.997394            0.995656            0.980919   \n",
       "7            0.994787            0.991312            0.990460   \n",
       "8            0.995656            0.987837            0.991327   \n",
       "9            0.995656            0.993918            0.987858   \n",
       "\n",
       "   mean_train_score  std_train_score  \n",
       "0          0.877532         0.005383  \n",
       "1          0.928153         0.003594  \n",
       "2          0.961382         0.002159  \n",
       "3          0.982082         0.001165  \n",
       "4          0.993563         0.000887  \n",
       "5          0.997044         0.001039  \n",
       "6          0.993747         0.006449  \n",
       "7          0.992696         0.001780  \n",
       "8          0.992000         0.003174  \n",
       "9          0.991652         0.004751  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(grid_search.cv_results_)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas columns are also Numpy arrays, so they obey to the same indexing magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:34.851638Z",
     "start_time": "2019-02-20T16:35:34.826575Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.011427</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>{'C': 1e-06}</td>\n",
       "      <td>0.842466</td>\n",
       "      <td>0.871972</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.842657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.872651</td>\n",
       "      <td>0.027467</td>\n",
       "      <td>10</td>\n",
       "      <td>0.882969</td>\n",
       "      <td>0.880662</td>\n",
       "      <td>0.869679</td>\n",
       "      <td>0.881842</td>\n",
       "      <td>0.872507</td>\n",
       "      <td>0.877532</td>\n",
       "      <td>0.005383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011151</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>{'C': 1e-05}</td>\n",
       "      <td>0.907534</td>\n",
       "      <td>0.913495</td>\n",
       "      <td>0.944056</td>\n",
       "      <td>0.902098</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919972</td>\n",
       "      <td>0.015905</td>\n",
       "      <td>9</td>\n",
       "      <td>0.929258</td>\n",
       "      <td>0.931185</td>\n",
       "      <td>0.922676</td>\n",
       "      <td>0.932233</td>\n",
       "      <td>0.925412</td>\n",
       "      <td>0.928153</td>\n",
       "      <td>0.003594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010509</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>{'C': 0.0001}</td>\n",
       "      <td>0.934932</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.965035</td>\n",
       "      <td>0.940559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.949896</td>\n",
       "      <td>0.013812</td>\n",
       "      <td>4</td>\n",
       "      <td>0.963319</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.960035</td>\n",
       "      <td>0.960904</td>\n",
       "      <td>0.958369</td>\n",
       "      <td>0.961382</td>\n",
       "      <td>0.002159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.000409</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.001</td>\n",
       "      <td>{'C': 0.001}</td>\n",
       "      <td>0.958904</td>\n",
       "      <td>0.965398</td>\n",
       "      <td>0.965035</td>\n",
       "      <td>0.965035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.966597</td>\n",
       "      <td>0.006563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.982533</td>\n",
       "      <td>0.982578</td>\n",
       "      <td>0.983493</td>\n",
       "      <td>0.980017</td>\n",
       "      <td>0.981787</td>\n",
       "      <td>0.982082</td>\n",
       "      <td>0.001165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0       0.011427      0.000539         0.000306        0.000049   1e-06   \n",
       "1       0.011151      0.000381         0.000299        0.000110   1e-05   \n",
       "2       0.010509      0.000301         0.000258        0.000038  0.0001   \n",
       "3       0.020542      0.000409         0.000291        0.000038   0.001   \n",
       "\n",
       "          params  split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0   {'C': 1e-06}           0.842466           0.871972           0.909091   \n",
       "1   {'C': 1e-05}           0.907534           0.913495           0.944056   \n",
       "2  {'C': 0.0001}           0.934932           0.941176           0.965035   \n",
       "3   {'C': 0.001}           0.958904           0.965398           0.965035   \n",
       "\n",
       "   split3_test_score       ...         mean_test_score  std_test_score  \\\n",
       "0           0.842657       ...                0.872651        0.027467   \n",
       "1           0.902098       ...                0.919972        0.015905   \n",
       "2           0.940559       ...                0.949896        0.013812   \n",
       "3           0.965035       ...                0.966597        0.006563   \n",
       "\n",
       "   rank_test_score  split0_train_score  split1_train_score  \\\n",
       "0               10            0.882969            0.880662   \n",
       "1                9            0.929258            0.931185   \n",
       "2                4            0.963319            0.964286   \n",
       "3                1            0.982533            0.982578   \n",
       "\n",
       "   split2_train_score  split3_train_score  split4_train_score  \\\n",
       "0            0.869679            0.881842            0.872507   \n",
       "1            0.922676            0.932233            0.925412   \n",
       "2            0.960035            0.960904            0.958369   \n",
       "3            0.983493            0.980017            0.981787   \n",
       "\n",
       "   mean_train_score  std_train_score  \n",
       "0          0.877532         0.005383  \n",
       "1          0.928153         0.003594  \n",
       "2          0.961382         0.002159  \n",
       "3          0.982082         0.001165  \n",
       "\n",
       "[4 rows x 21 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['param_C'] < 0.01]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They also provide most functionality you would expect as database user (`df.sort_values`, `df.groupby`, `df.join`, `df.concat`, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-20T16:35:35.071086Z",
     "start_time": "2019-02-20T16:35:34.853988Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df['mean_test_score'], label=\"validation error\")\n",
    "ax.plot(df['mean_train_score'], label=\"training error\")\n",
    "ax.set_xticklabels(df['param_C'])\n",
    "ax.set_xlabel(\"C\")\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T20:36:16.683621Z",
     "start_time": "2019-02-19T20:36:16.679694Z"
    }
   },
   "source": [
    "## Other packages "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other packages that didn't make the cut:\n",
    "- [Plotly](https://plot.ly) and [Seaborn](http://seaborn.pydata.org): two other plotting libraries\n",
    "- [Scipy](https://www.scipy.org): a science library built on top of Numpy\n",
    "- [Scrapy](https://www.scipy.org): a web crawling library\n",
    "- [pdb](https://docs.python.org/3/library/pdb.html): a debugger for python (not ML-specific but terribly useful)\n",
    "- [tqdm](https://github.com/tqdm/tqdm): a progress bar (not ML-specific)\n",
    "\n",
    "Next time:\n",
    "- [Pytorch](https://pytorch.org)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "164px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}