BeginnerTutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data science intro\n",
    "\n",
    "Welcome to the Data Science Beginner Tutorial!  We'll be discussing the Data Science process, \n",
    "provide some examples, and finish up with two interesting models.\n",
    "\n",
    "Recommended Data Science Process:\n",
    "1. Get data\n",
    "2. Explore your data (Exploratory Data Analysis)\n",
    "3. *Clean* data (Preprocessing)\n",
    "4. Extract vector features from your data (Feature Extraction)\n",
    "5. Model your data\n",
    "\n",
    "In reality, you'll end up going back and forth between steps 2-5 until you are satisfied with the\n",
    "quality of your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## brief background on data science and machine learning\n",
    "\n",
    "Fundamentally, data science is about deriving insights or knowledge about\n",
    "your data.  This could be understanding the difference between two subsets\n",
    "of a distribution of data (think male / female heights, there exists a bi-modal\n",
    "distribution that can be modelled well factoring in birth sex), or as complex\n",
    "as a predictive task (think forecasting rain, stock market, image classification).\n",
    "\n",
    "Generally, we view data as a $n \\times d$ matrix $X$:\n",
    "\n",
    "$$\n",
    "X \\in \\mathbb{R}^{n\\times d}\n",
    "$$\n",
    "\n",
    "where $n$ is the number of **examples** we have, and $d$ is the number of \n",
    "**features** we have.  In the height example, $d$ might simply be 1, the measured\n",
    "height of a person.\n",
    "\n",
    "### supervised learning\n",
    "\n",
    "In supervised learning, **for each example, we have an output label**.  We usually view\n",
    "this as an $n$ dimensional vector $y$:\n",
    "\n",
    "$$\n",
    "y \\in \\mathbb{R}^n\n",
    "$$\n",
    "\n",
    "and for each $i = 1, \\ldots n$, we have pairs of data $(X_i, y_i)$, with $X_i \\in \\mathbb{R}^d$.\n",
    "In classification tasks, $y_i$ is discrete ($y_i \\in \\{0, 1, \\ldots, k\\}$), and regression tasks\n",
    "$y_i$ is continuous ($y_i \\in \\mathbb{R}$).\n",
    "\n",
    "The goal of supervised learning is to learn a function $f$:\n",
    "$$\n",
    "y = f(X) + \\epsilon\n",
    "$$\n",
    "\n",
    "such that $\\epsilon$ is *minimized*.\n",
    "\n",
    "### unsupervised learning\n",
    "\n",
    "In unsupervised learning, we don't have any labels with which to learn a \n",
    "function from.  Our task usually is to learn some sort of structure in the data, \n",
    "such as finding similar sub-groups in a dataset (clustering).  An example might\n",
    "be looking at a social network graph of Rice students and trying to identify \n",
    "which students belong to the same college."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up our environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# sensible defaults\n",
    "plt.rcParams[\"figure.figsize\"] = 10, 6\n",
    "plt.rcParams[\"figure.dpi\"] = 150\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "\n",
    "import os\n",
    "import PIL\n",
    "from skimage.io import imread\n",
    "from glob import glob\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting & Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This allows us to have the same \"random\" generation of numbers\n",
    "seed = hash(sum([ord(char) for char in \"hackrice8 is awesome!\"]))\n",
    "np.random.seed(seed)\n",
    "NUM_CLASSES = 10 \n",
    "NUM_EXAMPLES = 42000\n",
    "KEEP_NUM = 10000\n",
    "HEIGHT = 28\n",
    "WIDTH = 28\n",
    "\n",
    "def load_data(file_path='data/', keep_num=KEEP_NUM):\n",
    "    x, y = np.zeros((NUM_EXAMPLES, HEIGHT, WIDTH), dtype=np.float32), np.zeros(NUM_EXAMPLES, dtype=np.uint8)\n",
    "    img_num = 0\n",
    "    for label in tqdm(range(NUM_CLASSES)):\n",
    "        class_img_paths = glob(os.path.join(file_path, str(label), '*.jpg')) \n",
    "        for img_path in class_img_paths:\n",
    "            # This will bring the data to between 0. and 1. (makes machine learning models easier to train)\n",
    "            x[img_num] = imread(img_path, as_grey=True) / 255.\n",
    "            y[img_num] = label\n",
    "            img_num += 1\n",
    "            \n",
    "    random_idxs = np.random.permutation(NUM_EXAMPLES)\n",
    "    x = x[random_idxs[:keep_num]]\n",
    "    y = y[random_idxs[:keep_num]]\n",
    "            \n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.5, random_state=seed, stratify=y)\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=seed, stratify=y_train)        \n",
    "    \n",
    "    return (X_train, y_train), (X_val, y_val), (X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally to perform supervised learning you need two types of data sets:\n",
    "\n",
    "* In one dataset (your \"gold standard\") you have the input data together with correct/expected output, This dataset is usually duly prepared either by humans or by collecting some data in semi-automated way. But it is important that you have the expected output for every data row here, because you need for supervised learning.\n",
    "* The data you are going to apply your model to. In many cases this is the data where you are interested for the output of your model and thus you don't have any \"expected\" output here yet.\n",
    "\n",
    "While performing machine learning you do the following:\n",
    "\n",
    "* Training phase: you present your data from your \"gold standard\" and train your model, by pairing the input with expected output.\n",
    "* Validation/Test phase: in order to estimate how well your model has been trained (that is dependent upon the size of your data, the value you would like to predict, input etc) and to estimate model properties (mean error for numeric predictors, classification errors for classifiers, recall and precision for IR-models etc.)\n",
    "* Application phase: now you apply your freshly-developed model to the real-world data and get the results. Since you normally don't have any reference value in this type of data (otherwise, why would you need your model?), you can only speculate about the quality of your model output using the results of your validation phase.\n",
    "\n",
    "The validation phase is often split into two parts:\n",
    "\n",
    "* In the first part you just look at your models and select the best performing approach using the validation data (=validation)\n",
    "* Then you estimate the accuracy of the selected approach (=test).\n",
    "Hence the separation to 50/25/25.\n",
    "\n",
    "In case if you don't need to choose an appropriate model from several rivaling approaches, you can just re-partition your set that you basically have only training set and test set, without performing the validation of your trained model. I personally partition them 70/30 then.\n",
    "\n",
    "Check out [this question](https://stats.stackexchange.com/questions/9357/why-only-three-partitions-training-validation-test) for more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ZIPFILE = \"./data.zip\"\n",
    "EXTRACTDIR = './'\n",
    "DATADIR = './data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import zipfile\n",
    "\n",
    "# Extract all the files \n",
    "zip_ref = zipfile.ZipFile(ZIPFILE, 'r')\n",
    "zip_ref.extractall(EXTRACTDIR)\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e428bfc19f84205afe571a378aa8c3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_val, y_val), (X_test, y_test) = load_data()\n",
    "NUM_TRAIN = len(X_train)\n",
    "NUM_VAL = len(X_val)\n",
    "NUM_TEST = len(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring our Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at what our images are of:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ae8003128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "for image_idx in range(i, i + 9):\n",
    "    plt.subplot(3, 3, image_idx + 1)\n",
    "    plt.imshow(X_train[image_idx], cmap='gray', interpolation='none')\n",
    "    plt.title(\"Class {}\".format(y_train[image_idx]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining the Pixel Values\n",
    "**Note that these images aren't actually black and white (0,1). They are gray-scale (0-255).**\n",
    "\n",
    "A [histogram](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist) of this image's pixel values shows the range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ae7ff69e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X_train[i].ravel())\n",
    "plt.title('Histogram of Pixel Intensities of image {}, class {}'.format(i, y_train[i]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intensity Comparison\n",
    "\n",
    "For each class, let's look at a very simple statistic: the overall intensity of the image,\n",
    "measured as the sum of the values of the image.  Then, we'll compare the average intensities\n",
    "of each class, hopefully seeing some sort of difference between the classes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ae3e66b00>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GuHA6/i+SfDjJGVX1njHG9iV9JgAAAABYmqV8NXWM8btjjN8cY7x7jHHdJpb8xjS+djXCTdf5aJJzkhye5AVr1vxyksOSvHM1wk1rvpzkZdPLM3b3MwAAAADA3tT+sIaqOjDJk6aXF6wzZfXYqWuOn7yDNRcluSPJSVV1wB5vEgAAAACWbI6npj4kK19b/eoY49p1zl8+jcetOf6INee/Y4zxzSSfzsrvzD14SfsEAAAAgKWZI8TdfxrXi3AZY9ya5MYk96qqQ5Okqg5Lcs8drVs4fvSS9gkAAAAAS7OshzXsikOm8bYdzLk1K78Td2iSmxfW7GjdrdN46GY2UVVXbHBq62bWAwAAAMCumCPEAQAszR/+yiVzb6HNr53zpJ1PAgBgnzVHiLtlGg/awZyDp/HmNWtW1920iTU7NMY4dr3j051yx2zmGgAAAACwWXP8Rtw103jUeier6uCsfC3162OMm5NkjHFTkm/saN3C8S8uaZ8AAAAAsDRzhLjPJrkzyRFV9cPrnH/kNH5yzfFPrDn/HVV19yQPT3JHks8taZ8AAAAAsDTtIW6McXuS1R9zedY6U7ZN47vXHL9ozflFpyQ5IMnFY4w79niTAAAAALBkc9wRlyRnT+Mrq+pBqwer6nFJXpjkxiR/tmbNG7Py23CnVdUzFtYcmeSs6eXr99qOAQAAAGAPLOVhDVV1cpJXLRy6x3T8YwvHXjPGuChJxhgXV9XvJ3lRko9X1funNU9JUkmeN8a4cfE9xhg3VNXzk5yf5IKq2p7k+iQnZeU35c4eY2xfxucBAAAAuKu5719/fO4ttLjuiT829xZ227KemnpEkuPXOX78mjnfMcZ4cVV9PMmvZyXAfTPJxVkJdh9Z703GGG+rqhOSvDLJY7MS765M8gdjjPP2+FMAAAAAwF6ylBA3xjg3ybkd68YYlyZ52q6+FwAAAADMaa7fiAMAAACA7ytCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADbbMvQEAAADuGl796lfPvYUW3y+fE+jnjjgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADSYPcRV1WOq6vyq+seq+lZV3VhVH6qq51VVrTN/v6p6SVV9qqpur6qvTusfNsf+AQAAAGAztsz55lX1zCRvSbJfksuTfCjJEUmekORfJTkpyS8uzL9bkrcm+dkkNya5KMl9kmxLcnJVPXGMcVnnZwAAAACAzZjtjriq2pLkj7IS4X5xjPGoMcbPjTGelOS4JDckOb2qnriw7PlZiXCfT/LQMca2McaJSZ6V5KAkfz5dFwAAAAD2KXN+NfWhSY5M8tkxxl8snhhjfCbJf5tePmbh1G9M48vGGF9emP+2JO9K8i+TnLbXdgwAAAAAu2nOEHfnJuddnyRV9cAkD0tye1a+krrWBdN46p5vDQAAAACWa84Q97+TXJ3kIVV1+uKJ6cELv5Tk60nePh1+xDR+eozxrXWud/k0HrcX9goAAAAAe2S2EDfG+HaS52TloQt/XlV/W1X/o6ouSfLJJNcmefIY44Zpyf2n8doNLrl6/Oi9tWcAAAAA2F2zPthgjHFpVf1UVu56e+T0lyTfTPL+rNw1t+qQabxtg8vdOo2Hbua9q+qKDU5t3cx6AAAAANgVc341NVX1C0kuS/KlJMdnJbY9OMm5Sc5IcklV7T/bBgEAAABgSWa7I66qHpTkvCRfSXLKGOOW6dTnk7ywqn4oySlJnp/kj5Osnj9og0sePI03b+b9xxjHbrCvK5Ics5lrAAAAAMBmzXlH3M8nuXuS9y1EuEXnT+MJ03jNNB61wfVWj39xOdsDAAAAgOWZM8SthrNvbHB+9fi9pvET0/jwqrr7OvNXf1/uk0vYGwAAAAAs1Zwh7rppfPQG5x8zjV9IkjHGPyT5TJIDk5y8zvxt0/juJe0PAAAAAJZmzhD3zmk8oar+7eKJqnpskpdMLy9YOHX2NJ5VVUcuzH9GkqcnuWrhugAAAACwz5gtxI0xLk/yuunlH1XVp6vq/Kr6cJJLs/Lwhf86xrh4Ydmbkrw9yYOS/H1VvbWq/jorse72JL80xvinvk8BAAAAAJsz5x1xGWOcmeQZSf4yyX2T/GxWnlj6gSSnjzFeuGb+/03yrCRnJPnHrDxV9UeTvC3Jo8cY/7Nv9wAAAACweVvm3sAY4+1Zuctts/O/nZWvqJ69s7kAAAAAsK+Y9Y44AAAAAPh+IcQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACgwZa5NwDfs159z7l30OPV35h7BwAAAHCX4I44AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQYJ8IcVV1RFW9rqo+W1W3V9UNVXV5Vf3nDeafWlUfqKqbpr/tVXVy974BAAAAYLNmD3FV9agkn0lyRpJvJXlnko8luXeSl6wz/8VJ3pXk8UkuTXJJkp9I8p6q+vWmbQMAAADALtky55tX1RFJ3pfkwCSnjTHeteb8T6x5/ZAkr0tyZ5InjjE+Oh1/cJKPJPm9qnrfGOOqjv0DAAAAwGbNfUfcbyW5T5Iz10a4JBljXLbm0IuS7JfknNUIN837XJLfzkpYfNHe2y4AAAAA7J7ZQlxVHZjkl5LcmuTNm1y2+jtwF6xzbvXYqXu4NQAAAABYujm/mvroJIcm+fAY4/aqelqSpyQ5IMnnkpw/xvjH1clVdXiS+08v/27txcYYX6qqryU5uqoOG2PctNc/AQAAAABs0pwh7php/EpVvSPJaWvO/6eqesEY479Pr1cj3NfHGLducM1rs/JV16OTfGpHb15VV2xwauuOtw0AAAAAu27O34i71zQ+PclPJ/m1JEcmeUBWHshwYJLzqurHpnmHTONtO7jmaqA7dKk7BQAAAIA9NOcdcasRcEuSV4wx/mjh3JlVdXSSZyU5M8kvLvvNxxjHrnd8ulPumPXOAQAAAMDumvOOuFsW/l/vYQ2rx35qzfyDdnDNg6fx5j3YFwAAAAAs3Zwh7ovTeNsY46vrnP/CNB45jddM472q6uB/Pj1JctSaawMAAADAPmHOELf65NMDq2r/dc7fexpvSZIxxo35boz78bWTq+p+WXlQwxc9MRUAAACAfc1sIW6McU2STySpfPfrp4tWj/3dwrGLpnHbOvNXj717KRsEAAAAgCWa8464JDlrGl9XVT+4enB6UuoZ08tzFub/fpJvJ/mVqnrswvwHJXlFkn+a5gAAAADAPmXOp6ZmjPEXVfXUJM9JcmVVfSTJgUken2T/JH86xnjrwvzPVtWZSc5O8qGqen+SbyZ56rTu348xrur+HAAAAACwM7OGuMnzklya5IVJTkwyklye5E/GGOetnTzG+L2quirJmUmeMB3+X0nOGmO8p2XHAAAAALCLZg9xY4yR5E+nv82ueXf8FhwAAAAAdyFz/0YcAAAAAHxfEOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAAN9pkQV1U/UFVfqapRVVftZO5zq+qyqrqlqm6oqvdW1eO79goAAAAAu2qfCXFJXp/kPjubVFVvSPLmJA9PcnGSy5I8JckHq+pn9uoOAQAAAGA37RMhrqqenOQ5Sf50J/NOSvKiJNcnecQY42fGGD+d5IQk307y5qo6fG/vFwAAAAB21ewhrqoOTPInSa5M8rqdTP+NaXztGOPzqwfHGB9Nck6Sw5O8YG/sEwAAAAD2xOwhLsl/SPIjSX4lybc2mjQFuydNLy9YZ8rqsVOXujsAAAAAWIJZQ1xVHZfkjCRvHmN8aCfTH5Jk/yRfHWNcu875y6fxuCVuEQAAAACWYstcb1xVd0vyxiQ3JnnZJpbcfxrXi3AZY9xaVTcmuVdVHTrGuHkn73/FBqe2bmIvAAAAALBL5rwj7t8leUySM8cY129i/iHTeNsO5tw6jYfuycYAAAAAYNlmuSOuqu6f5LVJPjDGOHeOPYwxjl3v+HSn3DHN2wEAAADge9xcd8T9YZJ7ZOUBDZt1yzQetIM5B0/jDr+WCgAAAADd5vqNuFOy8ttw51TV4vEDpvGHq2r79P/PjzGuS3LN9Pqo9S5YVQcnOTzJ13f2+3AAAAAA0G22hzVkJZr91AbnDlg4txrnPpvkziRHVNUPjzH+z5o1j5zGTy51lwDNHLmtAAAbmElEQVQAAACwBLN8NXWMUev9JXngNOXqheNfmNbcnuSS6fyz1rnstml8917dPAAAAADshjmfmro7zp7GV1bVg1YPVtXjkrwwK193/bM5NgYAAAAAO3KXCnFjjIuT/H6SH0jy8ap6R1W9N8kHs/I12+eNMW6cc48AAAAAsJ67VIhLkjHGi5M8L8lnkjwlyeOSXJzkhDHGO+bcGwAAAABsZM6HNfwz0+/B1SbmnZvk3L28HQAAAABYmrvcHXEAAAAAcFckxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABoIcQAAAADQQIgDAAAAgAZCHAAAAAA0EOIAAAAAoIEQBwAAAAANhDgAAAAAaCDEAQAAAEADIQ4AAAAAGghxAAAAANBAiAMAAACABkIcAAAAADQQ4gAAAACggRAHAAAAAA2EOAAAAABoIMQBAAAAQAMhDgAAAAAaCHEAAAAA0ECIAwAAAIAGQhwAAAAANBDiAAAAAKCBEAcAAAAADYQ4AAAAAGggxAEAAABAAyEOAAAAABrMFuKq6qCq+pmq+rOq+mxV3VFVt1bVJ6rqN6vqkB2sfW5VXVZVt1TVDVX13qp6fOf+AQAAAGBXzHlH3OlJ3p7k+Um+neRdST6U5IFJfivJ39T/a+/Oo3Qp6zuBf39uiGAEFJWoEAEFUVHBiGJQY9QRDHGJaxzHPWrcEpfxZAYNLpnJUYM4o3EZBVxOXOIyaCbiEhcUcQMEQ0QRo4BGBQkqO+Jv/qhqaZvue/veS9fb3ffzOadPdVfV8/bv3jrv21Xfeup5qm6+sFFVHZnk6CR3SvKpJF9J8sAkx1fVw6YpHQAAAAA2zSyDuCuTvDXJPt29T3c/ursfnGSvJKck2TvJkfMbVNUDkjw/yU+T3KW7Hza2uU+GMO/oqtphyn8EAAAAACzHzIK47n5Hdz+ju7+5YP2/J3n2+OMjquoG8za/YFy+qrvPnNfmxCRvTrJDkqeuYNkAAAAAsFlW62QNp47LbZLcNEmqatsk9x/Xf2CRNnPrDl3Z0gAAAABg063WIG73cXllkgvG7/fKEMyd193nLtLm5HG57wrXBgAAAACbbLUGcc8fl8d19+Xj97uOy8VCuHT3xUkuTLJjVd14hesDAAAAgE1yvVkXsFBVHZJhnLcrk7x03qbtx+UlG2h+cYZx4m6c5Bcb+T2nL7Fpj+VVCgAAAADLt6p6xFXV3knenaSSvLi7T91IEwAAAABYE1ZNj7iqulWS45LsmOSI7n79gl0uGpc32sDLbDcuN9gbLkm6+45L1HF6kn021h4AAAAANsWq6BFXVTsl+USS3ZIcneRFi+x29ri89RKvsV2Gx1L/o7s3GsQBAAAAwJRmHsRV1fZJPpahF9qHkjy9u3uRXb+V5PIkO4+95xbab1yetiKFAgAAAMAWmGkQV1XbJDk2yT2SfDzJ47r7qsX27e5Lk3x6/PFRi+zyyHH50Wu7TgAAAADYUjML4qrquknek+T+ST6f5BHdfcVGmh0xLg+rqtvNe617JXlGkguTvH0FygUAAACALTLLyRqek+Th4/fnJ/m7qlpsvxd19/lJ0t2fqqrXJ3l+kq9X1SeT3CDJAzPMtPrk7r5wxSsHAAAAgE00yyBux3nfP3zJvZLDMwR1SZLu/vOq+nqGIO+BSa5I8qkkr+zuL65AnQAAAACwxWYWxHX34RlCts1pe0ySY669agAAAABgZc181lQAAAAA2BoI4gAAAABgAoI4AAAAAJiAIA4AAAAAJiCIAwAAAIAJCOIAAAAAYAKCOAAAAACYgCAOAAAAACYgiAMAAACACQjiAAAAAGACgjgAAAAAmIAgDgAAAAAmIIgDAAAAgAkI4gAAAABgAoI4AAAAAJiAIA4AAAAAJiCIAwAAAIAJCOIAAAAAYAKCOAAAAACYgCAOAAAAACYgiAMAAACACQjiAAAAAGACgjgAAAAAmIAgDgAAAAAmIIgDAAAAgAkI4gAAAABgAoI4AAAAAJiAIA4AAAAAJiCIAwAAAIAJCOIAAAAAYAKCOAAAAACYgCAOAAAAACYgiAMAAACACQjiAAAAAGACgjgAAAAAmIAgDgAAAAAmIIgDAAAAgAkI4gAAAABgAoI4AAAAAJiAIA4AAAAAJiCIAwAAAIAJCOIAAAAAYAKCOAAAAACYgCAOAAAAACYgiAMAAACACQjiAAAAAGACgjgAAAAAmIAgDgAAAAAmIIgDAAAAgAkI4gAAAABgAoI4AAAAAJiAIA4AAAAAJiCIAwAAAIAJCOIAAAAAYAKCOAAAAACYgCAOAAAAACYgiAMAAACACQjiAAAAAGACgjgAAAAAmIAgDgAAAAAmIIgDAAAAgAkI4gAAAABgAoI4AAAAAJiAIA4AAAAAJiCIAwAAAIAJCOIAAAAAYAKCOAAAAACYgCAOAAAAACYgiAMAAACACQjiAAAAAGACgjgAAAAAmIAgDgAAAAAmIIgDAAAAgAkI4gAAAABgAoI4AAAAAJjAmgziqmrbqnpFVX27qi6rqh9W1VFVdatZ1wYAAAAAi1lzQVxV3TDJp5O8NMn2SY5Nck6SJyc5pap2n2F5AAAAALCoNRfEJTksyT2TnJjk9t39mO4+IMkLk+yc5KhZFgcAAAAAi1lTQVxV3SDJc8Yfn93dF81t6+4jkpyW5L5Vtf8s6gMAAACApaypIC7JvZPcJMlZ3X3KIts/MC4Pna4kAAAAANi4tRbE3WVcnrzE9rn1+05QCwAAAAAs21oL4nYdl+cusX1u/W4T1AIAAAAAy1bdPesalq2q3prk6Un+ursPW2T7nknOTHJmd99+I691+hKb9t5mm22us8cee2xxvZvizB9ftPGd1onb3WL7WZcwjfPOmHUF09h571lXMImzLjxr1iVMYo8dpv3sm5XLv/OdWZcwiW323HPWJUzign+/eNYlTGanXbabdQmT+Om5Z8+6hEnc9Na7bnyndeCXP75k1iVM4nq3uNGsS5jEeeedN+sSJrHzzjvPuoRJXHzxmbMuYTLbbXe7WZcwiW9dfNmsS5jEXtvdcNLfd9ZZZ+Xyyy//RXf/1pa+liDumm6f5NIk52xhuavd3NX21pEubB0c0/XF8VxfHM/1xfFcfxzT9cXxXF8cz/XF8Vx/tpZjepskl3T3Lbf0ha53LRQzpbluY0vdbpq7TfyLjb1Qd9/xWqlojZoLIrf2/4f1xDFdXxzP9cXxXF8cz/XHMV1fHM/1xfFcXxzP9ccx3XRrbYy4uecUbr3E9rn135+gFgAAAABYtrUWxJ06LvdbYvvc+tMmqAUAAAAAlm2tBXEnJPlZkj2q6q6LbH/kuPzodCUBAAAAwMatqSCuu69I8obxxzdW1a+nDquqFyTZN8nnuvukWdQHAAAAAEtZa5M1JMmrkjwgyYFJzqyqzyfZLckBSc5L8pQZ1gYAAAAAi6runnUNm6yqtk3yl0n+JMMUshckOS7JS7v73FnWBgAAAACLWZNBHAAAAACsNWtqjDgAAAAAWKsEcQAAAAAwAUEcAAAAAExAEAcAAAAAExDEAQAAAMAEBHEAAAAAMAFBHAAAAABMQBAHAAAAABMQxAEAbKKqunlV7TrrOgAAWFuqu2ddAyusqu6W5NAk+ybZLcmNx02/SPL9JKcl+Wh3nzKbCmHrVVXXS3LTJBd095Ub2XenJNt399mTFMcmq6rrJNknyRVJzux5f2Srav8Mn8U7Jzkryfu6+wczKZQtVlWfSXJQd19v1rWwZapqmyQHJNklycVJTu7uH862KjZHVe2b5MVJ7pvk5kkuSnJykjd394dmWRtLq6rDkny+uz8361q49lTVfZP8bpKfJvlwd184rr9+kqckuU+SHZN8L8kHuvvTMyqVZaqqOyd5aJK75Opc4VdJ/iPJ6Uk+m+RD3X35rGpcKwRx61hV/U6SozKcjCRJbWD3zvDGeWp3f28l62JaVXVwkp27+52zroWrVdXNkhyZ5BFJtklyZZKPJXlZd39jiTZHJ3mCC//VqaoOSfK2JLcYV307yeO6++tV9ZdJXpnhc7gyfOZemuSJ3f3BWdTLlhmDuPt093VnXQsbVlUPSvKD7j59kW3PTXJ4kh0WbDo2yZ929/krXyGboqpeneTPkxzY3V+bt/6xSY5Jcv1c85y3k7y1u581VZ0sX1X9KsMxOifJu5O8u7vPmG1VbImqeleSP5m36oIkf5AhdPvnJPvlN9+nneQt3f1nU9XI8lXVbyd5e5IHza0al51rHsfzk7ywu989XYVrjyBunRrfLCdnuBt4WpIPjD+fm+Fub5Jsl+TWGT4IH5Xkzkl+nGR/d4LXj6o6Mck9XCyuHlW1XZKvJtkr17xYuCLJi7r7DYu0OzrJf3EsV5+qulOSkzJcAJ6VIVjdK8kPMpyIHj9uf1+GE5T7JfnPSS5Pcufu/u70VbOYqvr2Mne9VZIbZjjec7q797r2q2JLjBf5R3f3UxesPyzJyzN8Dn8tyZkZemcclOEc6RsZ/n66s7+KVNVJSXbs7t3nrdslyXeSbJvkH5IcneGCf6cMn7cvyHBsn+jicPUZ36Nz5i5OT07yriTv7e6fTF8Vm6uqnpShM8gFGYLVZDjnOSfJJ5O8KMk/Jnl/kp8luUeGcP1GGW5gvn/iktmAqrppklMy5AbfS3JChl6Ot03ywCTXzXBD6ydJ7p3kj5Nsn+QV3f3y6SteGwRx61RVvT3Jk5O8oLuPXGabFyR5bZKjuvtpK1kf0xHErT5V9dIMF3+nJHl2klOT7J7hJOQp425/293/dUE7QdwqVVXvSPKEJH/W3W8e1z0hyTsynLScmeQh3f3LeW2em+T1SV7f3X8xedEsal7PjA31Il9Ke3+uPuMxPaa7nzJv3W0yBDdXJnl4d39y3radk3w4yb0ynEe9fuKS2YCq+mmSE7v7D+ete36S1yV5TXe/ZJE2d01yYpKvdfdBkxXLsozv0XcleU+Gv6V/lCEM7yRXJfnEuP3Y7r5sVnWyPFV1fIbH/fft7m+N6+6Q4Xz3qgyfx89a0OaeSb6Q5DPd/cCJS2YDqup/Z7heeU2S/77gXPZWGZ7ouVmSfbr7wqq6ZYZe5XdPcr/u/vwMyl71TNawfj04yZeXG8IlSXcfkeTLSQ5esaqAZLhT9PMkh3T3l7r70u4+vbufnmEMsZ8leWFV/Z+q2pwwgOndN8m35kK4JOnud2UYL2O3DHcFf7mgzZuS/HuGRzVYPc7P+IhMkttluOO72NeXx/3mr9t9kddjdXpYhh6sr5ofwiVJd5+Xq3usPnoGtbFh22b4Gzrf7TO8H6/RmzxJuvvrSb6YYVwjVqeruvu47n58hiEenpjkUxluihyS5O+T/Liqjqqq+8+wTjbuzhnG/PvW3Iru/maSzye5QYaOH7+hu7+UISy/21RFsmx/mOEc9yULz2XHsY6fkeSWGR9F7u4fZbjWuSJDJwMWIYhbv3bK0AtjU31/bMsqU1VXbc5XhjtSrC57Jvlid/944Ybu/qckB2bovv+UJO8bJ3RgdbtlhtBtoW+Oy2uM+zeezHwjye+sXFlshr0zPErzjAy9om7V3d9f+JXksiRZZD1rw1xw84HFNo7j5Z6U5A4T1sTynJ3kTgvWzfWSumQD7S7N5vV0ZWLdfUl3v6u7/1OS22R4lPHUDAPDPynJJ6vq7Kr6n+PQEKwu2yU5b5H1c48YLzUE0g9z9aSCrB67JPmXDWw/bVz++r3Y3edm6OF44ArWtaYJ4tavs5McVFU3Wm6Dcd+DMgQArD5zg7xfsYlfnj9ffa6ba97N/7VxgOJ7Jzkjwx2lY6vqhhPVxuZZ6lGZi5Oku5c63udnuDvMKtHdF3T3kzKMe3KjJMdX1VurasfZVsa1bO4ceEPnPN/PcEHJ6vKPSe5YVQ+Zt26u59QjF2swPip1YK6+OcIa0d0/6u4junu/DLOS/02G65xbJ3lJkq/Psj4WdV6Gm84L3W5c7rNEu30yPBXC6nJBlj5mmbft0gXrf5JhbE4WIYhbv96X5LeTfHycyn2Dxn0+nqFXx9+vcG1snh9kCNV26+5tl/uV5Cszrptr+n6ueTf/N4xdvX8vwwDiD05yXJLfWvnS2Ew/yTB4/0KnJ/n0BtrdNEMYxyrT3f+c4X362gw9MM4Yx/1jbdq+qnad+8ow0HQy3Olfyg5J/mPlS2MT/U2G4/LeqnpGVW3T3R/LMCbR66rqhePg4qmq61fVAzL8Db1JhkfOWaO6+4zu/m/dfdsMQ0K8LYKb1eiEJPtV1WPmVlTV4zJMEPhvSV69sLNIVT0zyR0z9ERmdflMkr2r6mULN1TVTknemOEa9YsLNu+Sq//WsoDJGtapsffMZzI8ltgZZnWbmzV1rtv+jXL1rKl7ZLiT+KUkv2+GsNWnqj6YYUybQ8fHF5fbzmQNq8w4mcqTktyhuzc4Q+M4w+pHkvx+xt6NjuXqM74/H5xhJr8rltmmMsxUfUZ332cl62PLjDer3pZk/ySfTfKsDBf09/F+XP3mTcCxmCd09zVuQFbVdTKcM/2gu393Jetj01XV/hnCtZ0y9Dw+JcMNkYdm6HWeDOe72+bqJwp+Y8IOVo/FJlTZhLY3WO7fXaZRVXfPMN7bdZLMjRO3V5ILM8xi/JUMveaOyxCk3j3J3HnQI7r72CnrZcOqau8MHQO2zXA8j89wM2S3JA/J8DjxvyS5a3f/amyzTYZz3C929yGzqHu1M+7QOtXdl1XV/ZK8NMMsJ3vm6i7Ccyej88fJ+FmGAW5fJYRbtb6S5OEZpvhedhAX46GsRh/JMKvxX2S4oF9Sd19cVQcneW+GINbdk9XphAwnkftnOPlcjgdlmGXqcytVFNeO7j6tqg5I8rwkr8wwVtGGxqJidTk+S3923n6J9YdmeErgQytSEVuku08aZ2F8RZLHZxhaZaG5x4pPTfLq7n7PVPUxHSHc6tPdX6uqxyb5uwzjriZDp5Andvc3quopSY5J8rT85izlrxHCrT7dfUZV/VGGp+b2Hr/mH7eTMwSov5rXbM8M4+x+eMpa1xI94rYCVXX9DONN3SXJrkm2HzddlGGMhVOTnNDdV86mQpajqvbLEKx+sbtfswntDkmyc3e/Y8WKY5NU1bYZZha6srvfucw210nynAw9rl6+kvUxjao6MMN4KZ8bB4ZnDRgfa3xjhrvArUfc+lRV98oQ0n15HLeTVWp8CuTuGcYp2jFDL5yLMgwDcUp3G/t4lauq+yb50fxZNln7quq6GUKby5N8d35QU1V7JnlshqezfpTk2O4+ZSaFsizj9csjMtx0npuQ4/ju/sRMC1ujBHEAAAAAMAGTNQAAAADABARxAAAAADABQRwAAAAATEAQBwAAAAATEMQBAAAAwAQEcQAAAAAwAUEcAMBWoKoOr6quqifNuhYAgK2VIA4AAAAAJiCIAwAAAIAJCOIAAAAAYAKCOACANa6qblNV/6uqvl1Vl1bVBVX1tar6q6r6rY203XMcP+7EqvpRVV1RVedW1Tur6vZLtNmtqt40/r5Lxt93elW9par2WrDvnarq3VX13aq6rKrOq6qvV9WRVbXLtfn/AACw2gniAADWsKo6KMlpSZ6b5PpJPprkhCQ3SXJ4kt038hJPS/KyJNsl+WqSjyT5eZInJPlqVe274PfdJsnJSZ45rvqnJJ9LcnmSpye517x99x9f8/FJfpHk2CRfGut8fpLfCO0AANa76826AAAANk9V7ZTkg0l2SPLiJEd096/mbb9Xkh9u5GX+b5K3dPe/LXjtJyc5KsmRSe4/b9PTkuyU5A3d/dwFbXbNELLNeV6SGyZ5UXf/7YJ9907ys439GwEA1hNBHADA2vW0JDsnOa67X7twY3efuLEX6O4vLbH+6Kp6apL7VdVNunsuNNt5XH5qkTZnL1i1oX3P2FhtAADrjSAOAGDtesC4fMuWvEhVbZ/k0CR3zdDbba5X2y5JKskeGR5HTZKTxuX/qKqrknyquy9b4qVPSnJwkjdW1WFJvtDdv9ySWgEA1jJBHADA2nWbcXnW5r5AVd0/yXtzde+1xdx43vfHJHlQkkdnGI/usqr6apLjkhzV3T+at+9rkvxekvsl+UySi6rqxCT/L8kx83rZAQBsFUzWAACwlRp7wr0/yc2SvCLJPhkmbbhOd1eS98ztOtemu6/q7sck2S/JyzNMxnBAkr9O8u2qOnDevj/PML7cQUleneRfx5+PTPKtqrrdiv4DAQBWGUEcAMDadc643GMz2x+U5KZJPtjdf9Xd3+zuS7q7x+1Lzrja3ad09+HdfZ8Mvelel6Hn3JEL9uvu/kJ3v6S7D0jy2xkCvltkCO8AALYagjgAgLVrbhKEP93M9juOy3MXbqiqPTP0etuosefbXybpJHfayL4/SXL4+OMG9wUAWG8EcQAAa9fbkpyf5OCq+vOqqvkbq+qeVXXzDbT/9rh8RFX9eoy4qtohydtz9aQN81/zCVW1WIB2cIZHWM+Zt+8zq+q2i+x7yLg8Z5FtAADrlskaAADWqO6+oKoeleQjGR4Nfd44ccK2Se6QZM8kd0vykyXaf62qPpnkgRnGd/vsuOl+GQK+Y5M8dEGzP07yzqo6K8k3klya5LYZxon7VZLD5u37zCRvqqp/TfLNJL9MsneSuyS5LMO4dAAAWw094gAA1rDu/myGYOvNGXqkPSzJvZP8LMnLsvEZVR+aYay28zL0ats/wyyq90xy4SL7H5HkjUl+kWGMuYcnuXmS9yU5oLv/Yd6+L01yVIZHVv8gyaEZQsK3Jblrd5+wSf9YAIA1rq4eixcAAAAAWCl6xAEAAADABARxAAAAADABQRwAAAAATEAQBwAAAAATEMQBAAAAwAQEcQAAAAAwAUEcAAAAAExAEAcAAAAAExDEAQAAAMAEBHEAAAAAMAFBHAAAAABMQBAHAAAAABMQxAEAAADABARxAAAAADABQRwAAAAATEAQBwAAAAATEMQBAAAAwAQEcQAAAAAwgf8PA7eI+4RXyvsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ae3e8c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intensities = pd.DataFrame({\n",
    "    \"intensity\": X_train.sum((1, 2)),\n",
    "    \"class\": y_train\n",
    "})\n",
    "intensities.groupby(\"class\").intensity.mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIPELINE STEP 2: CREATE MODEL - START SIMPLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training our model\n",
    "* First, we use the [sklearn.svm](http://scikit-learn.org/stable/modules/svm.html#svc) module to create a [vector classifier](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html).\n",
    "* Next, we pass our training images and labels to the classifier's fit method, which trains our model.\n",
    "* Finally, the test images and labels are passed to the score method to see how well we trained our model. Fit will return a float between 0-1 indicating our accuracy on the test data set\n",
    "\n",
    "### Aside on SVM's (if you don't know what they are)\n",
    "We have 2 colors of balls on the table that we want to separate.\n",
    "![](http://i.imgur.com/zDBbD.png)\n",
    "\n",
    "We get a stick and put it on the table, this works pretty well right?\n",
    "\n",
    "![](http://i.imgur.com/aLZlG.png)\n",
    "\n",
    "Some villain comes and places more balls on the table, it kind of works but one of the balls is on the wrong side and there is probably a better place to put the stick now. What a troll. \n",
    "\n",
    "![](http://i.imgur.com/kxWgh.png)\n",
    "\n",
    "SVMs try to put the stick in the best possible place by having as big a gap on either side of the stick as possible. \n",
    "\n",
    "![](http://i.imgur.com/ePy4V.png)\n",
    "\n",
    "Now when the villain returns, the stick is still in a pretty good spot.\n",
    "\n",
    "![](http://i.imgur.com/BWYYZ.png)\n",
    "\n",
    "There is another trick in the SVM toolbox that is even more important. Say the villain has seen how good you are with a stick so he gives you a new challenge.\n",
    "\n",
    "![](http://i.imgur.com/R9967.png)\n",
    "\n",
    "There’s no stick in the world that will let you split those balls well, so what do you do? You flip the table of course! Throwing the balls into the air. Then, with your pro ninja skills, you grab a sheet of paper and slip it between the balls.\n",
    "\n",
    "![](http://i.imgur.com/WuxyO.png)\n",
    "\n",
    "Now, looking at the balls from where the villain is standing, they balls will look split by some curvy line.\n",
    "\n",
    "![](http://i.imgur.com/gWdPX.png)\n",
    "\n",
    "Boring people the call balls data, the stick a classifier, the biggest gap trick optimization, call flipping the table kernelling and the piece of paper a hyperplane."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIPELINE STEP 2.1: TRAIN MODEL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC score 0.925\n",
      "10.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 1 -r 1\n",
    "clf = svm.SVC(C=1.0, kernel='rbf', degree=1)\n",
    "clf.fit(X_train.reshape((NUM_TRAIN, -1)), y_train.ravel())\n",
    "score = clf.score(X_val.reshape((NUM_VAL, -1)), y_val.ravel())\n",
    "print('SVC score {}'.format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIPELINE STEP 2.2: EVALUATE MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis: How did our model do?\n",
    "\n",
    "**You should have gotten around 0.92, or 92% accuracy. This is pretty good. 10% accuracy is what you would've gotten if you didn't divide the images by 255 (that is as good as randomly guessing a number).** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TODO:\n",
    "Try playing with the parameters of svm.SVC (C, kernel type, degree, etc.) to see how the results change. Check out the SVC [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) for more info. \n",
    "\n",
    "**You should be able to do MUCH MUCH MUCH better by switching the parameters. Focus on the kernel type and degree ;) **\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many ways to improve this, including not using a vector classifier, but here's a simple one to start. Let's just simplify our images by making them true black and white.\n",
    "* To make this easy, any pixel with a value simply becomes 1 and everything else remains 0.\n",
    "* We'll plot the same image again to see how it looks now that it's black and white. Look at the histogram now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIPELINE STEP 2.3: ITERATE as necessary (feature engineering, model engineering, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ztv9o4EzgQUkr0rCLgTmSZgABrAY+3pEKzawjmjnb/2NAw4y6o/pyzKxbfIefWaYcfrNM\nOfxmmXL4zTLl8JtlyuE3y5TDb5Yph98sUw6/WaYcfrNMOfxmmXL4zTLl8JtlyuE3y5S6+e+XJQ0B\nT5YGTQI2da2AndOrtfVqXeDaWlVlbQdHRFP/L6+r4X/dwqXBiBiorYBR9GptvVoXuLZW1VWbd/vN\nMuXwm2Wq7vAvrHn5o+nV2nq1LnBtraqltlqP+c2sPnVv+c2sJg6/WaZqCb+kkyT9QtJjki6qo4aR\nSFot6cHU7PhgzbUskrRR0srSsP0lLZX0aPo5bBuJNdXWE822j9KsfK3rrteau+/6Mb+kccAjwPHA\nWmA5MCciHu5qISOQtBoYiIjabwiR9F7gReCaiHhnGvbPwOaIuDz94ZwYEZ/ukdoWAC/W3Wx7ak1q\nSrlZeeAU4CxqXHej1HUaNay3Orb8M4HHIuLxiPgNcAMwu4Y6el5E3ANsbhg8G1icuhdT/PJ03Qi1\n9YSIWB8R96furcD2ZuVrXXej1FWLOsI/FVhT6l9LjStgGAHcKek+SfPrLmYYkyNifep+BphcZzHD\nGLPZ9m5qaFa+Z9ZdK83dV80n/F7vmIiYAZwMnJd2b3tSFMdsvXSttqlm27tlmGblX1Pnumu1ufuq\n1RH+dcC0Uv9BaVhPiIh16edG4FZ6r+nxDdtbSE4/N9Zcz2t6qdn24ZqVpwfWXS81d19H+JcD0yUd\nImkv4AxgSQ11vI6kvdOJGCTtDZxA7zU9vgSYm7rnArfVWMsOeqXZ9pGalafmdddzzd1HRNdfwCyK\nM/6/BC6po4YR6joU+Hl6PVR3bcD1FLuBv6U4NzIPOABYBjwK3Ans30O1fRt4EHiAImhTaqrtGIpd\n+geAFek1q+51N0pdtaw3395rlimf8DPLlMNvlimH3yxTDr9Zphx+s0w5/GaZcvjNMvX/akmKP/w2\nqjcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f6f05f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_bin_test = np.where(X_test > 0.1, 1, 0) \n",
    "X_bin_val = np.where(X_val > 0.1, 1, 0)\n",
    "X_bin_train = np.where(X_train > 0.1, 1, 0) \n",
    " \n",
    "\n",
    "img = X_bin_train[i]\n",
    "plt.imshow(img, cmap='binary')\n",
    "plt.title('Binary image from label {}'.format(y_train[i]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a9acbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Histogram of pixel intensities of Binary Image {}'.format(i))\n",
    "plt.hist(img.ravel())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retraining our model\n",
    "\n",
    "We follow the same procedure as before, but now our training and test sets are black and white instead of gray-scale. Our score still isn't great, but it's a huge improvement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC score 0.934\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVC(C=1.0, kernel='rbf', degree=1)\n",
    "\n",
    "clf.fit(X_bin_train.reshape((NUM_TRAIN, -1)), y_train.ravel())\n",
    "score = clf.score(X_bin_val.reshape((NUM_VAL, -1)), y_val.ravel())\n",
    "print('SVC score {}'.format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIPELINE STEP 3: GET FINAL MODEL PERFORMANCE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labelling the test data\n",
    "\n",
    "Now for those making competition submissions (or just any final evaluation), we can load and predict the unlabeled data from the withheld test set. We then output this data to a results.csv. This will allow us to see how well our trained model performs on the final test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final test accuracy: 91.46%\n"
     ]
    }
   ],
   "source": [
    "predicted_classes = clf.predict(X_bin_test.reshape(NUM_TEST, -1))\n",
    "svc_acc = np.mean(predicted_classes == y_test) * 100\n",
    "print('Final test accuracy: {}%'.format(svc_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Check which items we got right / wrong\n",
    "correct_indices = np.nonzero(predicted_classes == y_test)[0]\n",
    "incorrect_indices = np.nonzero(predicted_classes != y_test)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CORRECTLY CLASSIFIED EXAMPLES\n"
     ]
    },
    {
     "data": {
      "image/png": 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L87ceBab0UUyIjiA9irohTYp2UXkqlZmNBX4KnBRCWN74eDmEEPp6HGNmM4Fi\nHkshBoD0KOqGNCnaSaU7ZzNbk0x0PwghXJxvXpT7Wlb7XBanyoYQZoUQduq030cMH6RHUTekSdFu\nqgSEGZm/ZEkI4aSG7V8GnmgIdpgUQvhwSV3B/Zos7FMW3JUKsvL17LvvvpGdCmrxSd194oRUgMT+\n++8f2T6oao899ohsH/wFcMwxxzRtN5WE5dZbb41sn/g9lZQklaylkSqLjrRIpwPC2qrHTvVzMCgL\naPFBgVC+0ExKRz7Ix9upJBhlCwb4ALG+2m6BbgSEtU2TI0aMCI1Jk1IJXVKBfWX4gEK/yMOGG25Y\nWsfChQsje+rUqaVlfPITr5XUGOCvzf7z+gQ9AOedd15kn3HGGZF9zz33FMr488Fr3/cD2pMsp2pA\nWJXH2v8IvAv4i5mtThnzMeB04Mdm9l7gAeCwVjoqRD+RHkXdkCZF2ykdnEMIvwX6Gunf2t7uCNEc\n6VHUDWlSdAJlCBNCCCFqRlcXvqiykLj3hVbxg3p/1fvf//7IXn/99QtlvC/Ek0oG8vvf/z6yvU/C\nT573iU1SeB+6950AXHzxxZG9YMGCyE75j73/0fv8UslcRH3wCXpS/le/rSxpQpWFCjw+AQTAokWL\nIttra9y4cYUyK1asiGyvv7LYl+FCCCFKZpRaoMYfO5+UJJWU6Kc//Wlk+8WALrzwwkIZf42s4mP2\n37PXsb8upfCxPDfeeGNk++s7FBek8LE7qXa9bvt7PnUa3TkLIYQQNUODsxBCCFEzNDgLIYQQNaN0\nnnNbGzMLjT7n1NxHP/cs5XPxeJ/y4sXxXP/UAtk+0b73vaXmF3o/jfeveF9bqu+33357ZP/617+O\n7FmzZhXK+MUJPN7nBOn50o2kfM5t0kLH55W2izrNc/ba8dpKUTa/uIpf18dr+POvTEdQ9CuWLV4D\nRf93yk/aJnpGj1DUZGpOr59rWzaHHIrXLh8vk/Lj/tM//VNkpxap8PhrnteTv876+dYAX/7ylyPb\na3DChAmFMn4RC39NTOXH8MfNn0+pOI92XCPbtvCFEEIIIbqLBmchhBCiZmhwFkIIIWqGBmchhBCi\nZnQ9IKwxYUYqwYgPYvFO+tTEcB/s8O53vzuyTzjhhEKZ1KIUjXzta18rbPOT8i+//PLI9oFbvu8A\njz32WGS3Kbl/neiZAJw6BYSJjtEzegRpcjiggDAhhBCiR9HgLIQQQtSM0sHZzDYys+vM7E4zu8PM\nTsy3n2oHs3cRAAAgAElEQVRmC8zstvxvv853Vwx3pEdRN6RJ0QlKfc5mNhWYGkK41czGAbcAB5Gt\nTboyhHBG0wriuvrtT6myAEBZgvLUotk+ubpPSlIlcYlPBuCPZWqyvE+2MNjJ1TtAR318g61H0XN0\n3OcsTYr+UNXnXGU954XAwvz1CjO7C5g2sO4J0RrSo6gb0qToBP3yOZvZDGBH4KZ803+Y2e1mdr6Z\nTeyjzEwzm2tmcwfUUyEc0qOoG9KkaBeVB2czGwv8FDgphLAcOAfYDNiB7FfjmalyIYRZIYSdemk6\ng6g/0qOoG9KkaCelj7UBzGxNMtH9IIRwMUAIYVHD++cCV1Spyyfa93ifcpUk+o1zp6E4f9r7l1P9\nqJI8PlVPM3wydtEe2qlHIdqBNCnaTZVobQPOA+4KIXylYfvUht0OBua1v3tCxEiPom5Ik6ITVLlz\n/kfgXcBfzOy2fNvHgCPNbAcgAPOB4zrSQyFipEdRN6RJ0Xa6nr6zv4+1q1D2WDuF74efspVaX9an\nCfXToLp5LGtMz6RL1LSVYUHP6BGkyeGA0ncKIYQQPUqlgLB20nhnnFoYYuzYsZHtA8JSgVv+jnXN\nNdcs7Ye/603dKXuee+650n0a8XfjULxjr9KuEEKI4YXunIUQQoiaocFZCCGEqBkanIUQQoia0e1o\n7ceAB4DJwONda3hgqK/9Y5MQwvqD3IdKNOgR6nHsqqK+Vqdn9Ai6RnaBwe5rZT12dXB+qVGzub0y\nvUF9HR700rFTX4c+vXTc1NfOoMfaQgghRM3Q4CyEEELUjMEanGcNUrutoL4OD3rp2KmvQ59eOm7q\nawcYFJ+zEEIIIfpGj7WFEEKImqHBWQghhKgZXR+czWwfM7vHzO4zs1O63X4zzOx8M1tsZvMatk0y\ns2vM7N78/8TB7ONqzGwjM7vOzO40szvM7MR8ey37W1ekx/YgPbaHOusRekeTQ0GPXR2czWwk8E1g\nX2BrsvVOt+5mH0qYDezjtp0CXBtC2BK4NrfrwAvAB0MIWwO7Ah/Ij2Vd+1s7pMe2Ij0OkB7QI/SO\nJntej92+c94ZuC+EcH8I4Tngh8CBXe5Dn4QQbgCWuM0HAnPy13OAg7raqT4IISwMIdyav14B3AVM\no6b9rSnSY5uQHttCrfUIvaPJoaDHbg/O04CHGuyH8211ZkoIYWH++lFgymB2JoWZzQB2BG6iB/pb\nI6THDiA9tkwv6hFq/h33qh4VENYPQjbvrFZzz8xsLPBT4KQQwvLG9+rYX9E+6vj9So/Dm7p9x72s\nx24PzguAjRrs6fm2OrPIzKYC5P8XD3J/XsLM1iQT3g9CCBfnm2vb3xoiPbYR6XHA9KIeoabfca/r\nsduD883Alma2qZmNAo4ALutyH/rLZcDR+eujgUsHsS8vYWYGnAfcFUL4SsNbtexvTZEe24T02BZ6\nUY9Qw+94SOgxhNDVP2A/4K/A34CPd7v9kr5dBCwEnifz97wXWI8squ9e4FfApMHuZ97X3cgeydwO\n3Jb/7VfX/tb1T3psW1+lx/Ycx9rqMe9fT2hyKOhR6TuFEEKImqGAMCGEEKJmaHAWQgghaoYGZyGE\nEKJmaHAWQgghaoYGZyGEEKJmaHAWQgghaoYGZyGEEKJmaHAWQgghaoYGZyGEEKJmaHAWQgghaoYG\nZyGEEKJmaHAWQgghasaQG5zNbIaZBTNbI7evNLOjy8q1od1TzeyCTrfTR9uzzey0wWhbNEd6FHVC\neuwdBmVwNrP5Zva0ma00s0X5wRvbibZCCPuGEOZU7NOenehDwwmxsuHvv/pR3szsBDObZ2ZPmdnD\nZvYTM9uuE/2t0J+Q92P1Z/nOYPSjXQxDPW5tZnPNbGn+9ysz27of5WujRzObbGa/M7MnzOxJM/uD\nmf1jt/vRToahHnc1s2vMbImZPZZraWo/ytdJj680s0vzz7HEzK42s61aqWsw75wPCCGMBV4D7AR8\nwu+QH/ShdHc/IYQwNv/7bD/KfR04ETgBmAS8EvgZsH8H+liVVzd8lmMHsR/tYjjp8RHgcGBy/ncZ\n8MN+lK+THlcCxwJTgAnAF4HLV98Z9jDDSY8TgVnADGATYAXw3X6Ur5MeJ5CdT1uRafJPwKUt1TRI\nC2HPB/ZssL8MXJG/vh74HPA74GlgC2Bd4DyyRb4XAKcBI/P9RwJnAI8D9wMfIFtke42G+o5taOt9\nwF1kAriTTPzfB1bl7a0EPpzvuyvwe2AZ8Gdg94Z6NgV+k9dzDXAWcEEfn3dGY5/6eay2BF4Edm6y\nz2zgtPz1ROAK4DFgaf56esO+x+THaQXwf8C/5tu3yD/Pk/mx/FGT9gKwxWAvRi49tqZH99nXyPv4\n917VY0NdI4AD8uO9wWDrSnrsvx7zsq8BVvS6HvNyk/LjvV6/dTDY4gM2Au4APtsglgeBbcguHGsC\nlwDfBsYAG5D9Gjku3/944O68nknAdX2JDzg0F+/rAMsP+CZ9nBDTgCeA/chO+r1ye/38/T8AXwHW\nAt6Uf5llg/MC4GGyX4WTKx6r44EHSvZpFN96wCHAOsA44CfAz/L3xgDLga1yeyqwTf76IuDj+Wcd\nDezWpL1Advf1KHAxMGMwdCQ9tqbHhjqXAS+QXXg/0at6zPe/HXguP9bnDrampMf+67Gh7pOAP/ay\nHhvaPghY2JIOBlF8K8kuDg8AZwNrN4jlMw37TgGeXf1+vu1I4Lr89a+B4xvee1sT8V0NnFh2QuT2\nR4Dvu32uBo4GNia7qI1peO/CvsQHjCV7NLVG/nn+B7i64rH6eJlQG8WXeG8HYGmD+Jbl4lzb7fc9\nskdL0yv06U3AKLJHOGcB82jhqUBd/oabHl0dY4D3A/v3qh4byozOv4ujB1tT0mPLetweWAK8cQjo\ncTrZj50jW9HBYPorDgohTAghbBJCeH8I4emG9x5qeL0J2a/DhWa2zMyWkf1K3CB/f0O3/wNN2twI\n+FvF/m0CHLq6zbzd3ch+TW1I9oU+VaXdEMLKEMLcEMILIYRFwL8DbzOzcRX68UTeZiXMbB0z+7aZ\nPWBmy4EbgAlmNjLv7+FkvzYXmtnPzexVedEPk/1a/pOZ3WFm72nyeW4IITwXQlhG5uuZAfxD1T7W\nlGGjx0byMt8CvmdmG5TtTw312PBZngkhXAScYmavrtrHmjLs9GhmWwBXkv1AuLFiP2qpRzNbH/gl\ncHauyX5T12CC0PD6IbJfhpNzsU4IIYwPIWyTv7+QTFSr2bhJvQ8Bm1doc/W+329oc0IIYUwI4fS8\nzYlmNqZiu321VeX4XwtMN7OdKtb9QbJghF1CCOPJ7nIhExYhhKtDCHuRCfpu4Nx8+6MhhPeFEDYE\njgPOzk+Wqlg/9u01hroeR5A95ptWYd9e0OOawGYV9+1FhpwezWwT4Fdkj++/32xfR+30aGYTyQbm\ny0IIn+vHZ4mo6+D8EiGEhWQf9EwzG29mI8xsczN7c77Lj4ETzGx6flBOaVLdd4APmdlr80jHLXJR\nACwiPqEvAA4ws73NbKSZjTaz3c1segjhAWAu8GkzG2Vmu5EFoiQxs13MbKu87+sB3wCuDyE8mb9/\njJnN7+Pz30v2WOuivP1ReV+OMLPUZx1HFrixzMwmAZ9q6McUMzswP2meJXt0tip/71Azm57vupTs\nZFyV+CzbmNkO+TEZS+ZXWkAWRDLkGSJ63MvMdszrGU/2HS4l/w57TI+7mtlueT/WNrOPkD3qvamv\nzz+UGCJ6nEb2+P2sEMK3Eu/3kh7Hkz3e/10IodmxLqX2g3POUWQ+zjvJDsz/8PKjjHPJDsafgVvJ\nApSShBB+QhbpeCFZgMLPyIIkAL4AfMKyRzQfCiE8BBwIfIwssu8h4P/x8jH7F2AXMv/Ip8h8En2x\nGXBV3uY8si/+yIb3NyKLvuyLE8h8u98k84n8DTgYuDyx79eAtckiCv+Yt7uaEcDJZMFcS4A3A/+W\nv/c64CYzW0k2FeDEEML9ifqnAD8iC5y4n+zx1ttDCM836f9Qo9f1OIEswOVJMi1tDuwTQngmf7+X\n9LhW3o8nyH4k7kfmP3+kSf+HGr2ux2PJrpGnWkMuiIb3e0mPB+f7vtvivBb9eZIFgOWOazGImNkv\nyb7sYXH3KeqN9CjqxHDVowZnIYQQomb0ymNtIYQQYtigwVkIIYSoGQManM1sHzO7x8zu6yMyToiu\nIk2KOiE9ilZp2edsZiOBv5KlbXsYuJksE8qdTcrIwT3EMIunN4cQHg8hrD9IfemXJqvoMfH5qvSj\n32U6UUcrVGl3sI5Ji/SMHvMytb1Gjhw5MrJffPHFftcxYkR8P7hqVWE2UqV9yhhEvZUSQqiUE2Ig\nK7fsDNy3OpzczH5IFlrfp/BEkVZE5E+SlHjL6vHtVimzxhpFufh6nn/++UqZqTpE2zU5atSoyH7+\n+XjGWOrYr7XWWk3LVLmorbnmmpH93HPPFfbp70Us9f298MIL/W7XH5Nnn322abtQPCbPPPNMH3u2\nnSGlx8Fk3Lg4oeGyZctKy/jrwzrrrBPZK1euxDNmzJjIfuqppyK7yoDudVxFo/78SLXTyg+FVhnI\nY+1pxGnhHiaRYcjMZlq2duzcAbQlRBVKNSk9ii6ia6RomY6veRpCmEWWMLzWj2wGiyp3Kp5WHid5\nUnfJ/u7G/9r0d1m9SKMeR44cGUaPHv3Se3//+98L+1f5xe3x36E/1v44p8p4O1XG66DsV33qaUlZ\nP1L4dqs87iy7U/Z19FXPUKRXrpH+TrnK0zf/lMXfKW+8cTE3x4MPPljaTlm7VWg891N08elOkoHc\nOS8gztm6egUOIQYLaVLUCelRtMxABuebgS3NbFMzGwUcQZbWTIjBQpoUdUJ6FC3T8mPtEMILZvbv\nZHlbRwLnhxDuaFvPhOgn0qSoE9KjGAgD8jmHEH4B/KJNfRFiwEiTok5Ij6JVOh4QJprTjtD81BQZ\nP7WgzIbilB8frJYq4wMxqgQU1YVVq1ZFQWCpz1c21S0ViFL2naaCzPwUEx+cViUYz+vAB5H5KSlQ\nDNjx318qUMv3xR+jVoK7qgT9iO7iteFJnevjx4+P7OXLl0e214YP/krhzzE/pQtgxYoVkV1lKpU/\nT/3nqdJOJ1H6TiGEEKJmaHAWQgghaoYGZyGEEKJmyOc8yHhfW8rvWebD7FRyEN+3lF+wm+nsOo33\n+0LRT+v9X6lj4n113v+V8r+WpdH08QApfP9TPmZPmc957bXXLpTxiSSqHBOP1/lQSHAz1CiLH0nF\nFngfs9eXj4tI+YJ9cpCnn346slN+38mTJ0f2448/HtmpJD6+7QkTJkR2lfSknUR3zkIIIUTN0OAs\nhBBC1AwNzkIIIUTNkM95kPG+xJS/zvsfvY+vypKRVeY5+23eT5Na3q3XafSbpfy6ZQn1U74snzC/\nlbnf/jtP4f13vl3//gYbbFCo45FHHmnaRmoxkDJS/uMq8QuiXpQtaOJ9tFCMc9hqq60i++yzz47s\nnXbaqVDH3Lnx4lw/+9nPIvvMM88slPE+5rK8ASmqnHPdRHfOQgghRM3Q4CyEEELUDA3OQgghRM0Y\nkM/ZzOYDK4AXgRdCCEUHghBdRJoUdUJ6FK3SjoCwt4QQHi/fTVQhFYDkAzFaSfzhy5QtRADpBAE9\nQmVNNh6HKsfEB8E8+eSTpWVe9apXRfaUKVMK+1x//fVN60h9Fz4Y7dFHH43ssWPHRnZKN/vuu29k\n33zzzZGdCpJLJZ9oJHUcva79Pq0sltFD9OQ10h9/r7cnnniiUMYHhKUS+zTiE4wAbL/99pH9hje8\nIbInTpxYKPO5z32uaT+q6Ouxxx6LbC18IYQQQoiIgQ7OAfiVmd1iZjNTO5jZTDOba2ZzU+8L0Waa\nalJ6FF1G10jREgN9rL1bCGGBmW0AXGNmd4cQbmjcIYQwC5gFYGbNJ40KMXCaalJ6FF1G10jREgMa\nnEMIC/L/i83sEmBn4IbmpUQjPjF8yi/YyqIA3sfifX5lyTUA1ltvvchOLaTgE18MNgPRZBW/lE/s\nnzqO2267bWT/93//d2S//vWvL5TxPmWfNCHVjvcBvuIVryjsU8bOO+8c2ZtttllkX3DBBYUy/phU\nWdijbCGPoepz7uVr5JgxYyL7ta99bWR7Py8UExn5BDz+enf66acX6vjnf/7nyN56660j+6Mf/Wih\nzA477BDZ++23X2SnfN/ef+xjNLrpX07R8mNtMxtjZuNWvwbeBsxrV8eE6C/SpKgT0qMYCAO5c54C\nXJKn4VsDuDCEcFVbeiVEa0iTok5Ij6JlWh6cQwj3A69uY1+EGBDSpKgT0qMYCFbF99i2xhTs0BbG\njx8f2TNmzCjs47f5OXsp3/Z2220X2Ztuumlk33TTTYUy559/fmQvX778ll5JtOD1mPJLeZ+6P24p\nX+nMmXFQ7le/+lXfbmnfvG8utXhGmU/WzyFde+21C/v4bTfcELtD3/GOdxTKlM3tTn2+ddddN7L9\nQvZ+kQ5oLdYiQc/oEep1jfS+3c9//vORnZqj7PX0ute9LrJvu+22yE59x7vuumtke7/0m9/85j56\n/DJ/+MMfItvPlYbiOebPp07FPIQQKq36onnOQgghRM3Q4CyEEELUDA3OQgghRM3Q4CyEEELUjHYs\nfNEvyoJh2hGg5oN0UsEmPsDGt+snpEMxeOHb3/52ZL/mNa+J7IceeqhQhw/M8okkHn+8mB9/9OjR\nkf2Wt7wlslOLE3h8EgifTAOKgWY+GOqee+4plOnhxTEws0gbPvHH6n0a8cfRLxQBMGnSpKbtVkko\n4vuSClbzGvYBLL4fK1euLNThNf3+97+/aZ0pfOKJVF99AJgPxvF1pPo2HEkdy5ROG/HJQ6CYQMgv\nHnHMMccUynzqU59q2s6NN95Y2HbQQQdFdiporBGf6Ajgj3/8Y2T7612qTn/++KQkXm9QPH+qjBvd\n1KTunIUQQoiaocFZCCGEqBkanIUQQoia0XWfc6O/LeV/9tta8UF7P1kq4UZZvSn/3Lvf/e7IPuqo\noyL7kUceKa3D+499mdQx8YuAX3zxxZH9pz/9qVBmyZIlTfuS8h+X+Q7vvPPOQple9jmHECJ/vfdb\nQfHz+WP0zne+s1DmBz/4QWT7xAyp47h48eLIPvTQQyP7m9/8ZqHMu971rsj28Qz+O0/FUey4446R\nfccddxT28fgEN/Pnz4/sKn5qfx6kYiBEa4kwUr5Sz9KlSyP7wgsvLOzj9eSTEP32t78tlPH+YO/b\n9r7vJ554orSvnj322KOw7Xe/+11k+3Mulbjkmmuuiey6LbSiO2chhBCiZmhwFkIIIWqGBmchhBCi\nZpQ6J8zsfODtwOIQwrb5tknAj4AZwHzgsBDC0r7q6IuU39f79LwPNuU/bqUdj58bOHXq1MI+fr7d\nww8/HNmHHHJIZKd8wX4unfdzpObj+f57n1Jq3p/fx8/PLZt/WGfapUkzi45Lag6j16OfCz5vXnF5\n3pNPPjmy/XfufV1Q/I632GKLyE7NbfU+QT9vs8r8d4/3S3sfIRR9zJ6UtjbYYIPI9j729ddfv1DG\nx1rUlW5fI8tIxbqUXXcWLVpUKOMXuqgyx9fHqaxYsaLfdUyYMCGy/Rz5VFyE14rX0047Fdc+8efh\ntGnTItvHA0F78nBUpcqd82xgH7ftFODaEMKWwLW5LUS3mI00KerDbKRH0WZKB+cQwg3AErf5QGBO\n/noOcBBCdAlpUtQJ6VF0glanUk0JISzMXz8KTOlrRzObCczs630h2kQlTUqPokvoGikGxIDnOYcQ\nQrMFwkMIs4BZUK+FxMXQpZkmG/U4YsQI6VF0HF0jRSu0OjgvMrOpIYSFZjYVWFxaoiLdcrj7QLMt\nt9wysr/1rW8Vyuyyyy6RPWfOnMhOJZfw+EAM3w8f1JOiSqCPD7yoMsHeBwOlAks8ZYEmXaTfmgwh\nVDrejfjAEx9oB3DttddGtg/yq6Lx++67L7J90g4oXxzDt5sKsProRz8a2X7RgXPPPbdQpoouPD4A\nzCd86ZXgr37Qlmtk6nwqO+dSZXxgow+ATV1TyhaGSC1W4hfL8QG8VerwAWCeVOIjHwDmgxJTgbb+\nGCxYsKBpu92m1alUlwFH56+PBi5tT3eEaBlpUtQJ6VEMiNLB2cwuAv4AbGVmD5vZe4HTgb3M7F5g\nz9wWoitIk6JOSI+iE5Q+1g4hHNnHW29tc1+EqIQ0KeqE9Cg6QdcXviijHT5nn4DD+zmgmDz9M5/5\nTGSnJq17X5tPOuL9K+edd16hDu/T8z7nKpP0fZlUovuyxT9Svp5WfImphTp6iUZtTJlSDKj1vlL/\n/aR8dd6/5X13KT91ma9+3XXXLW3nySefjGyfpCSV6OPAAw+M7MMOOyyy3/GOdxTKfPGLX4zsMh87\nFJOZlC0oAq0lHBpqpHTh9eNt7/eF4rH0+6Ta8TEMPsahSnxJ6nttpMr1wy+eceyxx5aW8df8M844\no7BP6jg1st566xW2tbJQR6sofacQQghRMzQ4CyGEEDVDg7MQQghRM6ybibw7NcHe+y28Dyblk/31\nr38d2X6e86OPPloo4/2N3qfnF8tI+XBnzowTAV16aTzDIuVn84stdGpheu8f8rafmwrFY/Lss8/e\nEkIoOuxrSCt69FpKxQj4hd69zznlq/PzmMv8YQCzZ8+O7KOPPjqyy+appvri/Yp+EYIUXucpP/V1\n113XtA4/5xTSi260QM/oEbLEOI3Xryrz8FPfq8df59vhz/d5EaCoW39trpKjwV93fF9T55w/Trfc\ncktkv/GNbyyU8eOEbzc1n7odhBAqBerozlkIIYSoGRqchRBCiJqhwVkIIYSoGbWb59wK3p/i/Wgp\nv/quu+4a2WVz+qqw1157RfZnP/vZwj7f/e53I9vPt7711lsLZVKL1/eXsrm3UPTtVPH19DIjR46M\n/PlLly4t7OP9Ut5nlprH6b+vVMyDp8wHmJqjfMIJJ0S29z0eeuihTd+H4ufxPuYquZ29ncpLf9ZZ\nZ0X25ZdfHtnz588vlBHVqDLf2OvYf2cp/fl6fSxFK3kRys4nKPqpff73lI7/+te/Rrb3MVfRft1y\nNujOWQghhKgZGpyFEEKImqHBWQghhKgZVValOt/MFpvZvIZtp5rZAjO7Lf/br7PdFOJlpElRJ6RH\n0QlKk5CY2ZuAlcD3Qgjb5ttOBVaGEIrZxJvXVZr0ocwp366kKX4hAb9oQCqAwG/zQVU+YOKggw4q\n1HHhhRdG9he+8IXITgWRlQWE+QQWUJ7EokrwQ5VjnVj4veNJH9qlSa/H1HH037EPnEktYuGPmw+k\nSx17X8YnnkkF7PiAHB/MdeSR8WJJqQCez3/+85E9ffr0yF6xYkWhjMcn40np1QfFnXzyyZHtA8ba\nSM/oMS8XCSG1iIjXT5VkGb5MK9dR/z2ntFF2XS1LMAKw9957R/ZVV11VWsYHgPkFhlJlfF/8taxK\nwpRWaFsSkhDCDcCSAfdIiDYhTYo6IT2KTjAQn/N/mNnt+SOdieW7C9FxpElRJ6RH0TKtDs7nAJsB\nOwALgTP72tHMZprZXDOb22JbQlShkialR9EldI0UA6LSwhdmNgO4YrU/pep7iX1L/SlliS6qJGz3\ni3Onkkt4fOL9VNL9hH+1aZ1vf/vbC9t+9KMfRfbChQsje6uttiqUKWsn5cP0vtAqCfTbRFcWGmiH\nJju1EEsr+HgFHzOQOk/LFibxvt8pU6YU6vBlXve610X2//zP/xTK+AQ9Zb47KPpFvf/bJ/ABmDdv\nXmFbC/SMHvN9Q+PxrHK9K1uwBsqvq6lrsb9mlC0UkSpTNr6kFs/wcTkHHHBAZHufNMAvf/nLpn2t\n4j9uRyKqKnR04Qsza1x+6WCgLWeREK0iTYo6IT2KgVKaV9DMLgJ2Byab2cPAp4DdzWwHIADzgeM6\n2EchIqRJUSekR9EJSgfnEMKRic3ndaAvQlRCmhR1QnoUnaCrC1+YWeTbqDI/z8+P9D4yKPrWvI95\nk002KZTxc5Df9KY3RXbK3+D9gH4xAu/XSM2b9X4N71NK+ZwXLFjQtB+p41jmY04txjDUFrboJbyG\nvQ86FXfgv2Nfh9ffokWLCnX4+Ay/IMU73/nOQpnzzz8/slP+So+Pi/Dncdm8/OFEmc+5zDeaKvOK\nV7wisr1W/HzkKu1UoWxe849//ONCmX333Teyvc632267Qpnly5dH9r333hvZqc/n/dL+86XGmipj\nVrtQ+k4hhBCiZmhwFkIIIWqGBmchhBCiZmhwFkIIIWpGVwPCQgilDnWfwMAHkpQtAgHFBQD8BHUo\nBsLcf//9kf3YY48Vyvigip12ap7boCx5CMBnPvOZyL777rsL+1RJRFBGOxLfi+5RJQmJZ9KkSZHt\n9Zk6D/bcc8/Ivvjii5vWmeqbDyT0gURQDOrx+xx11FGFMp/85CcL24YDjcczldCllcCsRx99tOn7\nqQQ1PoDQByl++tOfLpTxC1DsuuuuTdtNBaH6bT7g8IwzytcS8dfMSy+9tLDPN77xjcj+3//938hO\nBZF1E905CyGEEDVDg7MQQghRMzQ4CyGEEDWj0sIXbWuswkIDKR9LI1X8uN5HkfJzH3HEEZF99dVX\nl5YpW0jgox/9aGSfeOKJhTq8b9tPqK/iT6qSgL6bk+UdXVlooB3UaeELnxTG+90OPvjgQhm/IMD4\n8eMje/PNN4/sm266qbSOK6+8MrLXW2+9QhmfwMcvYpE6h/3n80kjUu2kFnRpgZ7RI8CIESNC47FK\nLdjgY2r8Ij2pMl4b/vqQul6ceWa8kNYHPvCBPnrddz2+XT/epL5jf43/xS9+EdmTJ08ulHn961/f\ntI6ycQXgQx/6UGSfe+65hX28bluhowtfCCGEEKJzaHAWQgghaoYGZyGEEKJmVFkyciPge8AUsuXP\nZoUQvm5mk4AfATPIlkQ7LISwtK96quJ9ElXm+Pr5d943XDbHD4p+G7+oBcD+++8f2f/yL/8S2d4v\nuBMHUyIAACAASURBVHDhwkIdBx54YNO+jhs3rlBmxYoVke19SlV8c2U+zV6h23rsFmXfh9cewLvf\n/e7I9ueKj1/YbLPNCnV4n+B73vOeyE7FpPhtY8eOjewnnniiUMbnFfA+5sMPP7xQpldopyZDCEmf\ncSPLli1r+n7Kv+p9pXvttVdkp+bAe/9xaiEIT9kiKH7u8O9///vCPu94xzua1pGKO/Ltli38A3DV\nVVdFtp8//fWvf720jk5S5c75BeCDIYStgV2BD5jZ1sApwLUhhC2Ba3NbiE4jPYq6IU2KtlM6OIcQ\nFoYQbs1frwDuAqYBBwJz8t3mAAelaxCifUiPom5Ik6IT9Ct9p5nNAHYEbgKmhBBWP7d9lOyRTqrM\nTGBm610UIo30KOqGNCnaReWAMDMbC/wUOCmEEDkwQuaESs4ZDSHMCiHs1EtzDUX9kR5F3ZAmRTup\ndOdsZmuSie4HIYTVmfEXmdnUEMJCM5sKLO5v4z6ZBlRLMuIpCxr7yU9+Utg2a9asyD777LMj2wfK\nQDGoyidfuOyyyyL7vPPOK9Qxb968pn1tJXlIKmjH99Xv49+H3gkS65QeBxN/LnhNp5Lw+8ArH2Tl\nA3hS59uSJUsi2wd3pcr44Ekf1JgKpjz55JMj+2tf+1pk9/pCLO3S5IgRI6IAV3+sAcaMGRPZ/prp\nFyYBWHfddSN7++23j+zUgkI+0NZfH1LXEB+c6oNZzznnnMj++Mc/XlqH13Gqr2VJR1KJmg455JDI\nTh3rwaT0ztmyI3UecFcI4SsNb10GHJ2/PhooLvshRJuRHkXdkCZFJ6hy5/yPwLuAv5jZbfm2jwGn\nAz82s/cCDwCHdaaLQkRIj6JuSJOi7ZQOziGE3wJ9TaR9a3u7I0RzpEdRN6RJ0Qm6uvBFlaTuZaSS\ndHh/Q5XFI2bMmBHZ73rXuyLbJ5cHuPnmmyP71ltvjWyfdMT7W6Dctzh69OhCGe+H9r6elI+pLDFL\nB+mZhQbqtPCF97N5v+Lb3/72Qpk99tgjsn1SEu93S53r3hfnz8mUz9njz7/PfvazhX0+/elPN+1b\nKpFOm2IgekaPUNRkKqFIWVxOlXgSf/37zne+Uyjz1rfGvyv8dSjVN38tOuyw+GGBX1gl5QsuiyHa\ncMMNC9seeeSRpmVS11XfV5/IJHW+tDJmebTwhRBCCNGjaHAWQgghaoYGZyGEEKJmdNXnXMWf4vF+\ngCq+U59kP5Uo3n9u35eUX8f731rxP/g5e63Ma67SV++z9HP4qvhgWqRnfHx18jl7Wlmo5Pjjj49s\nr8+UL+9jH/tYZPvFMa6++upCmUsvjWcE+fnWP/3pTwtl1llnncju4pzSntEjFDXZSj4Cf+5D8brp\ntZAq47/HvffeO7JTsT1+rr2/plTRtd/H+6VTZfzn8XWktF9WplN5H+RzFkIIIXoUDc5CCCFEzdDg\nLIQQQtQMDc5CCCFEzRjUgDAxJOmZAJxe0mMqWYNP3FGWnMIvagHF4ElvL126tFDmsccei+wqgYS+\nr94uSzwxAHpGj9BbmhStoYAwIYQQokfR4CyEEELUjCpLRm5kZteZ2Z1mdoeZnZhvP9XMFpjZbfnf\nfp3vrhjuSI+ibkiTohOU+pzzRcKnhhBuNbNxwC3AQWTLn60MIZxRuTH5U4YDHfXxDVc9tsPnnFpc\nwicH8YlLnnvuuapdbEpZUohe9jkPV02K1qjqc66yZORCYGH+eoWZ3QVMG1j3hGgN6VHUDWlSdIJ+\n+ZzNbAawI3BTvuk/zOx2MzvfzCb2WVCIDiA9irohTYp2UXlwNrOxwE+Bk0IIy4FzgM2AHch+NZ7Z\nR7mZZjbXzOa2ob9CANKjqB/SpGgnleY5m9mawBXA1SGEryTenwFcEULYtqQe+VOGPt3w8Q07Pab8\nxd4PXeZz7hapBW38daaDPmZPV+Y5D0dNitZo2zxny64K5wF3NYouD4JYzcHAvP52Uoj+Ij2KuiFN\nik5QGhAG/CPwLuAvZnZbvu1jwJFmtgMQgPnAcR3poRAx0qOoG9KkaDtK3ynaTc+kS+wlPeqxdsv0\njB6htzQpWkPpO4UQQogepcpjbSHEIJN6wuW3tSPRh7/rTd2xl90F1+UOXoheRnfOQgghRM3Q4CyE\nEELUDA3OQgghRM3ots/5ceABYHL+uhdQX/vHJoPcfn9YrUeox7GrymTg8U4sHtEBf/FgH9de0iPo\nGtlpBruvlfXY1alULzVqNrdXpjeor8ODXjp26uvQp5eOm/raGfRYWwghhKgZGpyFEEKImjFYg/Os\nQWq3FdTX4UEvHTv1dejTS8dNfe0Ag+JzFkIIIUTf6LG2EEIIUTM0OAshhBA1o+uDs5ntY2b3mNl9\nZnZKt9tvhpmdb2aLzWxew7ZJZnaNmd2b/584mH1cjZltZGbXmdmdZnaHmZ2Yb69lf+uK9NgepMf2\nUGc9Qu9ocijosauDs5mNBL4J7AtsTbbe6dbd7EMJs4F93LZTgGtDCFsC1+Z2HXgB+GAIYWtgV+AD\n+bGsa39rh/TYVqTHAdIDeoTe0WTP67Hbd847A/eFEO4PITwH/BA4sMt96JMQwg3AErf5QGBO/noO\ncFBXO9UHIYSFIYRb89crgLuAadS0vzVFemwT0mNbqLUeoXc0ORT02O3BeRrwUIP9cL6tzkwJISzM\nXz8KTBnMzqQwsxnAjsBN9EB/a4T02AGkx5bpRT1Czb/jXtWjAsL6QcjmndVq7pmZjQV+CpwUQlje\n+F4d+yvaRx2/X+lxeFO377iX9djtwXkBsFGDPT3fVmcWmdlUgPz/4kHuz0uY2ZpkwvtBCOHifHNt\n+1tDpMc2Ij0OmF7UI9T0O+51PXZ7cL4Z2NLMNjWzUcARwGVd7kN/uQw4On99NHDpIPblJczMgPOA\nu0IIX2l4q5b9rSnSY5uQHttCL+oRavgdDwk9hhC6+gfsB/wV+Bvw8W63X9K3i4CFwPNk/p73AuuR\nRfXdC/wKmDTY/cz7uhvZI5nbgdvyv/3q2t+6/kmPbeur9Nie41hbPeb96wlNDgU9Kn2nEEIIUTMU\nECaEEELUDA3OQgghRM3Q4CyEEELUDA3OQgghRM3Q4CyEEELUDA3OQgghRM3Q4CyEEELUDA3OQggh\nRM3Q4CyEEELUDA3OQgghRM3Q4CyEEELUDA3OQgghRM0YcoOzmc0ws2Bma+T2lWZ2dFm5NrR7qpld\n0Ol2+mh7tpmdNhhti+ZIj6JOSI+9w6AMzmY238yeNrOVZrYoP3hjO9FWCGHfEMKcin3asxN9cO18\nMj85KrdlGSeY2Twze8rMHjazn5jZdp3sa4V+HZV/lmMHsx8DZTjq0czeamZ3m9nfzew6M9ukH2Vr\npUcz28HMbsk/yy1mtsNg9KNdSI/SIwzunfMBIYSxwGuAnYBP+B3ygz5k7u7NbHPgULL1UPvD14ET\ngROAScArgZ8B+7e1g/3AzCYCHwPuGKw+tJlho0czmwxcDPwXmZ7mAj/qRxW10aOZjQIuBS4AJgJz\ngEvz7b2M9FidoanHQVoIez6wZ4P9ZeCK/PX1wOeA3wFPA1sA6wLnkQ1qC4DTgJH5/iOBM4DHgfuB\nD5Atsr1GQ33HNrT1PuAuYAVwJ5n4vw+syttbCXw433dX4PfAMuDPwO4N9WwK/Cav5xrgLOCCks99\nFdmC39HnLymzJfAisHOTfWYDp+WvJwJXAI8BS/PX0xv2PSY/TiuA/wP+Nd++Rf55nsyP5Y9K+vUt\n4P3++Pbi33DTIzAT+H2DPSZv61W9pkfgbfl3YA3bHgT2GWxdSY/S40D0OOjiAzYiu/v6bINYHgS2\nAdYA1gQuAb6df2kbAH8Cjsv3Px64O69nEnBdX+Iju2tdALwOsPyAb9LHCTENeIJsMB0B7JXb6+fv\n/wH4CrAW8Kb8y+xzcM7bvjTVVsmxOh54oGSfRvGtBxwCrAOMA34C/KxB9MuBrXJ7KrBN/voi4OP5\nZx0N7NakvZ3Jft2OYIgNzsNBj2R3Gue4bX8BDuk1PQL/CVzptl0OfHCwdSU9So8D0eMaDB4/M7MX\nyH6J/Bz4fMN7s0MIdwCY2RQyAUwIITwNPGVmXyX7tfVt4DDgayGEh/L9vwDs3kebxwJfCiHcnNv3\nNenfO4FfhBB+kdvXmNlcYD8zu45MwHuGEJ4FbjCzy/uqyMzG5Z9vrybt9cV69OMxeAjhCeCnDW1/\njuyEXM0qYFszezCEsLCh7ueBTYANQwgPA79N1W9mI4GzgX8PIawys/58ljozbPQIjCW7c2hkOdnF\nqoxa6ZHsszzptlX9LHVGehzmehxMf8VBIYQJIYRNQgjvz4W1mocaXm9C9utwoZktM7NlZKLbIH9/\nQ7f/A03a3Aj4W8X+bQIcurrNvN3dyH5NbQgsDSE8VbHdU4HvhxDmV2y7kSfyNithZuuY2bfN7AEz\nWw7cAEwws5F5fw8n+7W50Mx+bmavyot+mOzX8p/M7A4ze08fTbwfuD2E8McWPkudGU56XAmMd9vW\nJbu7KaNuehzIZ6kz0uMw12NdgwlCw+uHgGeByblYJ4QQxocQtsnfX0gmqtVs3KTeh4DNK7S5et/v\nN7Q5IYQwJoRwet7mRDMbU7HdtwInmNmjZvZo3t8fm9lHmpRZzbXAdDPbqcK+AB8EtgJ2CSGMJ3uk\nBJmwCCFcHULYi0zQdwPn5tsfDSG8L4SwIXAccLaZbdHHZzm44bO8ATjTzM6q2L9eZKjp8Q7g1auN\nvNzmVAvuq5se7wC2t/gRzvYVP0uvIj2+zJDVY10H55fIHy38kmwAGG9mI8xsczN7c77Lj8kGvul5\nBPEpTar7DvAhM3ttHum4hb0csr8I2Kxh3wuAA8xsbzMbaWajzWx3M5seQniAzOf6aTMbZWa7AQc0\nafetwLbADvnfI2Rf8DcBzOwYM5vfx+e/l+wx8kV5+6PyvhxhZqnPOo4smGKZmU0CPrX6DTObYmYH\n5uJ/luxX3qr8vUPNbHq+61Kyk3FVov5jgH9o+CxzgU+T+WOGPENEj5eQPbo7xMxGk2nkzyGEu6Hn\n9Hg9WUDQCWa2lpmdkO/76yaff8ggPQ5hPfbXSd2OP5oERJEIMCJ7LHAO8DDZ8/z/BY7I31sD+CrZ\n443/ozwa8XjgnvzAzwN2zLcfSBZosQz4UL5tF7IIvSVkPpGfAxvn720G3JjXUylau6/PTzaF4AdN\n9jeyqQJ3AH8nC9r4ES8HK8zm5YCHDfPPvBL4K9mPgJAfp6m8HHG4LN9v67zcl/J6V5I92ppZ8bMU\nvq9e+xuOegT2JLszeDrv04xe1SOwI3BL/lluXX0Me/VPepQeQwhZuLcYXMzsl8CJIYS7BrsvQkiP\nok4MVz1qcBZCCCFqRu19zkIIIcRwQ4OzEEIIUTMGNDib2T5mdo+Z3ddHZJwQXUWaFHVCehSt0rLP\n2bJMUX8ly3r1MHAzcGQI4c4mZeTgdpjLsJX6PkaOHBnZL774YkfaaROPhxDW71TlzeivJqXHruqi\nX/2AtvWlZ/SYl6mNJlvRRjf01IpWOqivfhNCqJRWcSDpO3cG7gsh3A9gZj8kC7fvU3h1oh0i8nVU\nqdNvW3PNNSP7ueeeK5QZPz5OOLN06dLI9oM3wKpV8RS8UaPiRVGeffbZQpk20SwTUKfpWU228gMs\ndcFppIqmq+ivE/i+r7FG8VL0/PPPt6Mp6bFFWrlmrLXWWpH9wgsvRLbXdUqjI0bED3T9tWz06NGF\nMk8//XRhWyP+s0Dx8/h2q1y/O8lAHmtPI04L93C+LcLMZprZXMvyrgrRSUo1KT2KLqJrpGiZji98\nEUKYBcyCej2yEcMT6VHUDWlSpBjI4LyAOGfr9HxbT+AfT/hHGt5O4R+3eLsK/jGifywExcfY6667\nbmSvWFHMqe4f/fjHPmuvvXahTNmjoR6gZzXp9VjFRdKOR9K+TOrxsqesrynKzpU2PcKuGz2rRyi/\nnqW+92eeeabj7bYSc5N6JF/mlmzlet5OBvJY+2ZgSzPb1MxGAUcAl7WnW0K0hDQp6oT0KFqm5Tvn\nEMILZvbvwNXASOD8kK8xKsRgIE2KOiE9ioEwIJ9zyBba/kXpjkJ0CWlS1AnpUbSKMoQJIYQQNaPj\n0dq9gnf+txLk0gqpOXtlPPnkk6X7+OCuSZMmRfaSJf+/vbOPsao61/jzDnqT8iVfBVER/IrVVoVI\n8aZYKxVotaSUtJrbaFVaxURspTW5Em2qNTahatVYLelYCWh7tbSVVDR+0EpCb2OriFhpEZUWPwhC\nAVGBP0Bn3T/m4D3rWYuz9uw558w6M88vmcy8e/bae83ez9lrznne9a6dXT6vaBxl9MfJW5zcxUl/\nsWSdVOJgGWJJZam5qyI/OEmPExBjSYo8r5nbsK55/xh8niKJj3we7kfsOJxo1tNJs3rnLIQQQmSG\nBmchhBAiMzQ4CyGEEJkhz/kglJnoXoYik/aPP/54L37ttde8OFZbmz0X9phjxU4aWG9bJOB7yHHM\nZ0t5b0X8sUZ4aEV8xCJFf4ocRzQPrk+9Z8+eZBu+h/VYw6AIfJ4yz/OeLoyjd85CCCFEZmhwFkII\nITJDg7MQQgiRGX3Wc+a5mGX8LfbJ2CeM+SupBQBGjBgRtGGPmSnjyYi8KOOJsd5YwzyHOZbfkFpH\nukg+Q5kFYDTPOX/4+VZmjfGURgcMGJA8Ly/sE2vDx+X8mVjf+Ticw9HTGtU7ZyGEECIzNDgLIYQQ\nmaHBWQghhMiMbnnOZrYJwPsAPgTwgXNuYj06JURZpEmRE9KjKEs9EsKmOOe21+E4TaUeRUZSCQOx\nhDDexhP7t28PLyUnWRx22GFevGvXrpr9AICRI0d68bZt25JtWpiW1GQ1RRKzOIGF9ylS4Cb1Oijy\nOuEEnliCYqr4RE8n3zSYltQj35MiRYq4uFFKg7FCOqniH0WKnzCxRSzKHKeZ6GNtIYQQIjO6Ozg7\nAH8ws+fNbE5sBzObY2arzWx1N88lRBFqalJ6FE1Gz0hRiu5+rH2mc26zmY0EsMLMXnbOrarewTnX\nDqAdAMys64VVhegaNTUpPYomo2ekKEW3Bmfn3ObK921mtgzAJACrarfKA/bA2DeLLc7NbdgbKeOb\nsedy+OGHB/v86U9/8uLZs2d78TPPPBO0Yd+PPWYuUAEU8yhzp1U1yXpLLXQPhPcrVYxm4MCBwTb2\nEfkYMf+PXwdFdM9+OLcpsyBCK9CqeozBGizjFw8bNsyLTzrppGCfF154wYv37t3rxTNmzAjaTJzo\n59mdf/75Xjx48OCgzbJly7z48ccfrxk3m9Ifa5vZADMbdOB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12dddba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCORRECTLY CLASSIFIED EXAMPLES\n"
     ]
    },
    {
     "data": {
      "image/png": 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sghpLaEGKrPMjtLCHTbwSm97yPNb+JoAfAXhRRJ5z205CIri/ishBAN4GsG91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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a6b2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_outputs(correct_indices, incorrect_indices):\n",
    "    print('CORRECTLY CLASSIFIED EXAMPLES')\n",
    "    fig = plt.figure(figsize=(7,7))\n",
    "    for i, correct in enumerate(correct_indices[:9]):\n",
    "        plt.subplot(3,3,i+1)\n",
    "        plt.imshow(X_test[correct], cmap='gray', interpolation='none')\n",
    "        plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], y_test[correct]))    \n",
    "    plt.tight_layout()\n",
    "    plt.show() \n",
    "    \n",
    "    print('INCORRECTLY CLASSIFIED EXAMPLES')\n",
    "    plt.figure(figsize=(7,7))\n",
    "    for i, incorrect in enumerate(incorrect_indices[:9]):\n",
    "        plt.subplot(3,3,i+1)\n",
    "        plt.imshow(X_test[incorrect], cmap='gray', interpolation='none')\n",
    "        plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], y_test[incorrect]))\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "show_outputs(correct_indices, incorrect_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# So, I think we can do better. Let's return to step 2, but with something more complicated: DEEP LEARRRRNNIIINNGGGGG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out some of these cool resources\n",
    "1. https://www.youtube.com/watch?v=P2HPcj8lRJE\n",
    "2. https://www.youtube.com/watch?v=h3l4qz76JhQ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://memegenerator.net/img/instances/74873992/let-me-sprinkle-some-deep-learning-magic.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import goodies \n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Reshape\n",
    "from keras.optimizers import SGD\n",
    "from keras.utils import to_categorical "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modify the target matrices to be in the one-hot format, i.e. ,\n",
    "\n",
    "``` \n",
    "0 -> [1, 0, 0, 0, 0, 0, 0, 0, 0] \n",
    "1 -> [0, 1, 0, 0, 0, 0, 0, 0, 0] \n",
    "2 -> [0, 0, 1, 0, 0, 0, 0, 0, 0] \n",
    "etc. \n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_cat = to_categorical(y_train, NUM_CLASSES)\n",
    "y_val_cat = to_categorical(y_val, NUM_CLASSES)\n",
    "y_test_cat = to_categorical(y_test, NUM_CLASSES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the neural network\n",
    "Build the neural-network. Here we'll do a simple 3 layer fully connected network.\n",
    "![](https://raw.githubusercontent.com/wxs/keras-mnist-tutorial/8824b7b56963a92ef879f09acd99cf3a210db2b8/figure.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Reshape((HEIGHT*WIDTH,), input_shape=(HEIGHT, WIDTH))) # Input image, flatten the image\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu')) # An \"activation\" is just a non-linear function applied to the output\n",
    "                              # of the layer above. Here, with a \"rectified linear unit\",\n",
    "                              # we clamp all values below 0 to 0.\n",
    "                           \n",
    "model.add(Dropout(0.2))   # Dropout helps protect the model from memorizing or \"overfitting\" the training data\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax')) # This special \"softmax\" activation among other things,\n",
    "                                 # ensures the output is a valid probaility distribution, that is\n",
    "                                 # that its values are all non-negative and sum to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the model\n",
    "Keras is built on top of some backend (you should use [Tensorflow](https://www.tensorflow.org/)), which will be a backend that allow you to define a computation graph in Python, which they then compile and run efficiently on the CPU or GPU without the overhead of the Python interpreter.\n",
    "\n",
    "When compiling a model, Keras asks you to specify your loss function and your optimizer. The loss function we'll use here is called categorical crossentropy, and is a loss function well-suited to comparing two probability distributions.\n",
    "\n",
    "Here our predictions are probability distributions across the ten different digits (e.g. \"we're 80% confident this image is a 3, 10% sure it's an 8, 5% it's a 2, etc.\"), and the target is a probability distribution with 100% for the correct category, and 0 for everything else. The cross-entropy is a measure of how different your predicted distribution is from the target distribution. More detail at Wikipedia\n",
    "\n",
    "The optimizer helps determine how quickly the model learns, how resistent it is to getting \"stuck\" or \"blowing up\". We won't discuss this in too much detail, but \"adam\" is often a good choice. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TODO: Try messing with the parameters:\n",
    "1. Number of Dense neurons \n",
    "2. Number of layers\n",
    "2. Dropout parameters\n",
    "3. [Activations](https://keras.io/activations/)\n",
    "4. [Optimizer](https://keras.io/optimizers/)\n",
    "5. Batch size\n",
    "6. Number of epochs\n",
    "7. Go crazy! (you probably don't need to for this dataset...) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optim = 'adam'\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer=optim, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to train! :D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 4000 samples, validate on 1000 samples\n",
      "Epoch 1/10\n",
      "4000/4000 [==============================] - 1s 307us/step - loss: 0.6524 - acc: 0.8003 - val_loss: 0.2381 - val_acc: 0.9260\n",
      "Epoch 2/10\n",
      "4000/4000 [==============================] - 1s 209us/step - loss: 0.2474 - acc: 0.9245 - val_loss: 0.1906 - val_acc: 0.9360\n",
      "Epoch 3/10\n",
      "4000/4000 [==============================] - 1s 204us/step - loss: 0.1701 - acc: 0.9467 - val_loss: 0.1451 - val_acc: 0.9510\n",
      "Epoch 4/10\n",
      "4000/4000 [==============================] - 1s 224us/step - loss: 0.1186 - acc: 0.9635 - val_loss: 0.1541 - val_acc: 0.9560\n",
      "Epoch 5/10\n",
      "4000/4000 [==============================] - 1s 237us/step - loss: 0.0789 - acc: 0.9742 - val_loss: 0.1905 - val_acc: 0.9460\n",
      "Epoch 6/10\n",
      "4000/4000 [==============================] - 1s 228us/step - loss: 0.0600 - acc: 0.9802 - val_loss: 0.1701 - val_acc: 0.9500\n",
      "Epoch 7/10\n",
      "4000/4000 [==============================] - 1s 258us/step - loss: 0.0450 - acc: 0.9845 - val_loss: 0.1677 - val_acc: 0.9590\n",
      "Epoch 8/10\n",
      "4000/4000 [==============================] - 1s 253us/step - loss: 0.0335 - acc: 0.9888 - val_loss: 0.1741 - val_acc: 0.9530\n",
      "Epoch 9/10\n",
      "4000/4000 [==============================] - 1s 242us/step - loss: 0.0285 - acc: 0.9900 - val_loss: 0.1167 - val_acc: 0.9650\n",
      "Epoch 10/10\n",
      "4000/4000 [==============================] - 1s 210us/step - loss: 0.0182 - acc: 0.9950 - val_loss: 0.1446 - val_acc: 0.9620\n"
     ]
    }
   ],
   "source": [
    "# Why do we train on bin? Play around and see if you can do better. \n",
    "num_epochs = 10\n",
    "batch_size = 32\n",
    "\n",
    "fit = model.fit(X_train, y_train_cat,\n",
    "          batch_size=batch_size, \n",
    "          epochs=num_epochs, verbose=1,\n",
    "          validation_data=(X_val, y_val_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_val_train(metrics, fit):\n",
    "    plt.figure(figsize=(14,3))\n",
    "    for i, m in enumerate(metrics):\n",
    "        train_measure = fit.history[m]\n",
    "        val_measure = fit.history[\"val_\" + m]\n",
    "        eps = np.arange(len(train_measure))\n",
    "\n",
    "        plt.subplot(1, 2, i + 1)\n",
    "        plt.plot(eps, train_measure, '-r', label='Training ' + m)\n",
    "        plt.plot(eps, val_measure, '-b', label='Validation ' + m)\n",
    "        plt.legend(loc='lower right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ElHpsQtW6NZx+unX3ex/p1oiIiATWrjnzTGjVKtKtERGJqJCCTQgrPQ/NWeV5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\nHGwkSZIkTZ6DjSRJkqTJ+wW4sp24SHJO6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13b586ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_val_train(['loss', 'acc'], fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://cs231n.github.io/assets/nn3/accuracies.jpeg)\n",
    "The gap between the training and validation accuracy indicates the amount of overfitting. Two possible cases are shown in the diagram above. The blue validation error curve shows very small validation accuracy compared to the training accuracy, indicating strong overfitting (note, it's possible for the validation accuracy to even start to go down after some point). When you see this in practice you probably want to increase regularization (stronger L2 weight penalty, more dropout, etc.) or collect more data. The other possible case is when the validation accuracy tracks the training accuracy fairly well. This case indicates that your model capacity is not high enough: make the model larger by increasing the number of parameters.\n",
    "\n",
    "** What does this mean for your generated train/validation accuracy?**\n",
    "\n",
    "Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data.  Intuitively, overfitting occurs when the model or the algorithm fits the data too well.  Specifically, overfitting occurs if the model or algorithm shows low bias but high variance.  Overfitting is often a result of an excessively complicated model, and it can be prevented by fitting multiple models and using validation or cross-validation to compare their predictive accuracies on test data.\n",
    "\n",
    "Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data.  Intuitively, underfitting occurs when the model or the algorithm does not fit the data well enough.  Specifically, underfitting occurs if the model or algorithm shows low variance but high bias.  Underfitting is often a result of an excessively simple model.\n",
    "\n",
    "Both overfitting and underfitting lead to poor predictions on new data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/5000 [==============================] - 0s 57us/step\n",
      "Test loss: 0.225407455571\n",
      "Test accuracy: 0.9474\n"
     ]
    }
   ],
   "source": [
    "loss, acc = model.evaluate(X_test, y_test_cat, verbose=1)\n",
    "print('Test loss:', loss)\n",
    "print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The predict_classes function outputs the highest probability class\n",
    "# according to the trained classifier for each input example.\n",
    "predicted_probs = model.predict(X_bin_test)\n",
    "predicted_classes = np.argmax(predicted_probs, axis=-1)\n",
    "\n",
    "# Check which items we got right / wrong\n",
    "correct_indices = np.nonzero(predicted_classes == y_test)[0]\n",
    "incorrect_indices = np.nonzero(predicted_classes != y_test)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CORRECTLY CLASSIFIED EXAMPLES\n"
     ]
    },
    {
     "data": {
      "image/png": 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L87ceBab0UUyIjiA9irohTYp2UXkqlZmNBX4KnBRCWN74eDmEEPp6HGNmM4Fi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\n59mdf/75Xjx48OCgzbJly7z48ccfrxk3m9Ifa5vZADMbdOB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13b586e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCORRECTLY CLASSIFIED EXAMPLES\n"
     ]
    },
    {
     "data": {
      "image/png": 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T5P3hnlmzsh6ID37wgx2WKUPdfc5CCCGEqA8anIUQQojI0OAshBBCRIYGZyGEECIyarHw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\nHdOQxxiUuXPnxrZNnz7diYcPH+7EGzZscGJfftlcsI9Jc8+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JSHI7mcj3APYA6Aug/Ez4\n4WBbKzNIVf++nduQSkk+AmE8d2mxrenVTT4CvEbWQHu3NXU+1rRz/uWkImvrZao6trUx1NNzx7Z2\nfPX0vLGtxeDH2kRERIFh50xERBSY9uqcF7TTebNgWxtDPT13bGvHV0/PG9tagHapORMREVHb+LE2\nERFRYNg5ExERBabmnbOI3CoiX4vIDhF5stbnL0dEXhWRgyKyqWRbHxFZISLbo++927ONZ4nIQBFZ\nKSJbRGSziDwabQ+yvaFiPuaD+ZiPkPMRqJ+c7Aj5WNPOWUQ6A/h3AKMBXIHW9U6vqGUbEiwEcKvZ\n9iSAj1T1UgAfRXEIfgYwTVWvADASwD9Hz2Wo7Q0O8zFXzMcq1UE+AvWTk3Wfj7V+5/wrADtUdZeq\ntgB4C8AdNW5Dm1T1UwBHzOY7ACyKfl4E4M6aNqoNqrpfVb+Kfj4BYCuAAQi0vYFiPuaE+ZiLoPMR\nqJ+c7Aj5WOvOeQCAb0vivdG2kPVT1f3Rz80A+rVnY3xEZDCA4QBWow7aGxDmYwGYj5nVYz4Cgf+N\n6zUfOSCsAtp631lQ956JSA8A7wKYqqrHS38XYnspPyH+fZmPjS20v3E952OtO+d9AAaWxBdG20J2\nQET6A0D0/WA7t+cXItIFrYn3Z1X9S7Q52PYGiPmYI+Zj1eoxH4FA/8b1no+17py/BHCpiFwsIn8H\nYDyAJTVuQ6WWALgv+vk+AB+0Y1t+ISIC4BUAW1V1XsmvgmxvoJiPOWE+5qIe8xEI8G/cIfJRVWv6\nBWAMgG0AdgL4l1qfP6FtiwHsB/C/aK33PADgfLSO6tsO4L8A9GnvdkZt/Qe0fiSzEcCG6GtMqO0N\n9Yv5mFtbmY/5PI/B5mPUvrrIyY6Qj5y+k4iIKDAcEEZERBQYds5ERESBYedMREQUGHbOREREgWHn\nTEREFBh2zkRERIFh50xERBSY/wNR0/r0EIilJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11eba7128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_outputs(correct_indices, incorrect_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Final test accuracy: 91.46\n",
      "Neural Net Final test accuracy: 94.74000000000001\n"
     ]
    }
   ],
   "source": [
    "print('SVM Final test accuracy: {}'.format(svc_acc))\n",
    "print('Neural Net Final test accuracy: {}'.format(acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite only being 3% better, the margin of error grows increasingly small the closer we get to perfect scores. With some tuning on this model, and some more data (increase `KEEP_NUM`), you should be able to achieve accuracies on the order of 97%. With other [models](https://elitedatascience.com/keras-tutorial-deep-learning-in-python) you should be able to do even better. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try it yourself!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Here](https://www.kaggle.com/datasets) are some cool datasets to play around with\n",
    "* [Bitcoin historical data](https://www.kaggle.com/mczielinski/bitcoin-historical-data): Try predicting prices! (Make a billion dollars)\n",
    "* Use [full MNIST data](http://yann.lecun.com/exdb/mnist/) to read handwriting and if you want to be [different](https://www.kaggle.com/jwjohnson314/notmnist)\n",
    "* Build a cool [facial recongizer](http://www.face-rec.org/databases/): train on your friends and see if you can predict\n",
    "* Analyze enron with this [dataset](http://www.cs.cmu.edu/~enron/) and find anomalous and/or S C A N D A L O U S emails\n",
    "* Check out Kaggle [competitions](https://www.kaggle.com/competitions), particularly the playground competitions if you don't feel super comfortable diving in immediately. Make sure to use the kernels to help you start! \n",
    "* ** Do something cool with machine learning! ** "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}