classifier_3class_rectangle_ndim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from tensorflow import set_random_seed\n",
    "# for reproducibility \n",
    "np.random.seed(1)\n",
    "set_random_seed(1)\n",
    "\n",
    "from keras.layers import Dense, Input, concatenate\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Lambda, Dropout, Activation, Lambda\n",
    "from keras.layers import Reshape, Conv2DTranspose\n",
    "from keras.models import Model, Sequential\n",
    "from keras.optimizers import Adam\n",
    "from keras.datasets import mnist\n",
    "from keras.datasets import fashion_mnist\n",
    "from keras.losses import mse, binary_crossentropy\n",
    "from keras.utils import plot_model\n",
    "from keras import backend as K\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils import np_utils\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a classifier with outlier samples from the rectangle\n",
    "\n",
    "class Classifier:\n",
    "\n",
    "    # 3 Class classifier - class 2 represents outliers and class 0,1 represent inliers.\n",
    "    # Class 0 sampled from a circle centered at (x, y) = (-2, 0) with radius 0.5\n",
    "    # Class 1 sampled from a circle centered at (x, y) = (+2, 0) with radius 0.5\n",
    "    # Class 2 sampled from rectagle with origin (x, y) = (-3, -1) with h=2, w=6 excluding regions covered by inliers.\n",
    "\n",
    "    # Generate n points uniformly within a circle of radius r, centered at (x, y)\n",
    "    # return as a numpy array n x 2 of dtype = float32\n",
    "    def __init__(self):\n",
    "        self.number_of_classes = 3\n",
    "        self.input_dim = 2\n",
    "        self.epochs = 200\n",
    "        self.batch_size = 32\n",
    "        # There are 2 inlier circles\n",
    "        # Inlier circle centers on the 1st axis; for all other dimensions, the centers are at 0.\n",
    "        self.x1 = -10\n",
    "        self.x2 = +10\n",
    "        self.r = 5\n",
    "        # Number of inlier samples for each class\n",
    "        self.n = 1000\n",
    "        # width of the outlier outer square side\n",
    "        self.s = 100\n",
    "        self.data, self.labels = self.get_data()\n",
    "        print(self.data.shape, self.labels.shape)\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.data, self.labels, test_size=0.5, shuffle=True, stratify=self.labels,\n",
    "                                                            random_state=1)\n",
    "        self.model = self.get_model()\n",
    "\n",
    "        \n",
    "        self.mu = np.mean(self.X_train, axis=0)\n",
    "        self.sigma = np.std(self.X_train, axis=0)\n",
    "\n",
    "        self.data = self.preprocess_data(self.data)\n",
    "        self.X_train = self.preprocess_data(self.X_train)\n",
    "        self.X_test = self.preprocess_data(self.X_test)\n",
    "\n",
    "\n",
    "\n",
    "    # return True if point p (a list) lies inside a hypersphere centered at the origin with radius r\n",
    "    def is_in_hypersphere(self, r, p):\n",
    "        p = np.array(p)\n",
    "        return np.sum(p*p) <= r**2\n",
    "\n",
    "\n",
    "    # return True if point p (a list) lies inside a hypersphere centered at the (x, 0, 0, ...) with radius r\n",
    "    def is_in_hypersphere_centered_at(self, x, r, p):\n",
    "        p = np.array(p)\n",
    "        p[0] -= x\n",
    "        return np.sum(p*p) <= r**2\n",
    "\n",
    "\n",
    "    # uniformly sample a point from a square centered at the origin with side = s\n",
    "    def sample_point_from_square(self, s):\n",
    "        p = []\n",
    "        # first point\n",
    "        for i in range(0, self.input_dim):\n",
    "            p.append(np.random.uniform(-1*s/2, s/2, 1))\n",
    "        return p\n",
    "\n",
    "\n",
    "    # sample points using rejection sampling from a sphere centered at the origin\n",
    "    def sample_point_from_hypersphere(self, r):\n",
    "        p = self.sample_point_from_square(2*r)\n",
    "        while not self.is_in_hypersphere(r, p):\n",
    "            p = self.sample_point_from_square(2*r)\n",
    "        return p\n",
    "            \n",
    "\n",
    "    def preprocess_data(self, x):\n",
    "        #return (x-self.mu)/self.sigma\n",
    "        return x\n",
    "\n",
    "    # sample from a hypersphre with rejection sampling\n",
    "    def get_hypersphere_data(self, x, r, n):\n",
    "        data = np.zeros((n, self.input_dim))\n",
    "        count = 0\n",
    "        while count<n:\n",
    "            data[count, :] = self.sample_point_from_hypersphere(r)\n",
    "            # update the point with offset for the 1st dimension\n",
    "            data[count, 0] += x\n",
    "            count += 1\n",
    "\n",
    "        data = data.astype(np.float32)\n",
    "        return data\n",
    "        # plt.scatter(x, y, s = 4)\n",
    "    \n",
    "\n",
    "    \n",
    "    # generate n outlier points uniformly sampled within square centered at the origin\n",
    "    # with side=s, excuding the inlier circles\n",
    "    def get_outlier_data_sub(self, s, x1, x2, r, n):\n",
    "        count = 0\n",
    "        data = np.zeros((n, self.input_dim))\n",
    "        while count < n:\n",
    "            p = self.sample_point_from_square(s)\n",
    "            if (not self.is_in_hypersphere_centered_at(x1, r, p)) and (not self.is_in_hypersphere_centered_at(x2, r, p)):\n",
    "                data[count, :] = p\n",
    "                count += 1\n",
    "        return data\n",
    "\n",
    "    def get_outlier_data(self):\n",
    "        data = self.get_outlier_data_sub(self.s, self.x1, self.x2, self.r+0.3, self.n)\n",
    "        return data.astype(np.float32)\n",
    "        \n",
    "    def get_data(self):\n",
    "        # class 0 data\n",
    "        data = self.get_hypersphere_data(self.x1, self.r, self.n)\n",
    "        labels = np.ones((self.n, 1))*0\n",
    "        print(labels.shape)\n",
    "        # class 1 data\n",
    "        data = np.concatenate((data, self.get_hypersphere_data(self.x2, self.r, self.n)), axis=0)\n",
    "        labels = np.concatenate((labels, np.ones((self.n, 1))*1), axis=0)\n",
    "        print(labels.shape)\n",
    "        # outlier data\n",
    "        data = np.concatenate((data, self.get_outlier_data()), axis=0)\n",
    "        labels = np.concatenate((labels, np.ones((self.n, 1))*2), axis=0)\n",
    "        print(labels.shape)\n",
    "        return data.astype(np.float32), labels.astype(np.float32)\n",
    "    \n",
    "    def get_model(self):\n",
    "        model = Sequential()\n",
    "        model.add(Dense(500, input_shape=(self.input_dim,)))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Dense(500))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Dense(self.number_of_classes))\n",
    "        # model.add(Convolution2D(10,3,3, border_mode='same'))\n",
    "        # model.add(GlobalAveragePooling2D())\n",
    "        model.add(Activation('softmax'))\n",
    "        model.compile(loss='binary_crossentropy', optimizer=Adam(0.001), metrics=['accuracy'])\n",
    "        return model\n",
    "\n",
    "\n",
    "    def train(self):\n",
    "        # fit the model\n",
    "        Y_train = np_utils.to_categorical(self.y_train, self.number_of_classes)\n",
    "        Y_test = np_utils.to_categorical(self.y_test, self.number_of_classes)\n",
    "        self.model.fit(self.X_train, Y_train, epochs=self.epochs, validation_data=(self.X_test, Y_test), batch_size=self.batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 1)\n",
      "(2000, 1)\n",
      "(3000, 1)\n",
      "((3000, 2), (3000, 1))\n",
      "Train on 1500 samples, validate on 1500 samples\n",
      "Epoch 1/200\n",
      "1500/1500 [==============================] - 0s 249us/step - loss: 0.4021 - acc: 0.8320 - val_loss: 0.3224 - val_acc: 0.9369\n",
      "Epoch 2/200\n",
      "1500/1500 [==============================] - 0s 79us/step - loss: 0.2674 - acc: 0.9284 - val_loss: 0.2377 - val_acc: 0.9562\n",
      "Epoch 3/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.1577 - acc: 0.9711 - val_loss: 0.1153 - val_acc: 0.9933\n",
      "Epoch 4/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0791 - acc: 0.9853 - val_loss: 0.0600 - val_acc: 0.9856\n",
      "Epoch 5/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0493 - acc: 0.9889 - val_loss: 0.0463 - val_acc: 0.9876\n",
      "Epoch 6/200\n",
      "1500/1500 [==============================] - 0s 80us/step - loss: 0.0307 - acc: 0.9916 - val_loss: 0.0363 - val_acc: 0.9893\n",
      "Epoch 7/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0263 - acc: 0.9924 - val_loss: 0.0297 - val_acc: 0.9907\n",
      "Epoch 8/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0258 - acc: 0.9918 - val_loss: 0.0319 - val_acc: 0.9947\n",
      "Epoch 9/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0282 - acc: 0.9918 - val_loss: 0.0259 - val_acc: 0.9940\n",
      "Epoch 10/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0206 - acc: 0.9936 - val_loss: 0.0259 - val_acc: 0.9920\n",
      "Epoch 11/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0178 - acc: 0.9947 - val_loss: 0.0226 - val_acc: 0.9911\n",
      "Epoch 12/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0218 - acc: 0.9924 - val_loss: 0.0232 - val_acc: 0.9944\n",
      "Epoch 13/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0247 - acc: 0.9933 - val_loss: 0.0227 - val_acc: 0.9944\n",
      "Epoch 14/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0212 - acc: 0.9924 - val_loss: 0.0238 - val_acc: 0.9942\n",
      "Epoch 15/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0147 - acc: 0.9962 - val_loss: 0.0218 - val_acc: 0.9922\n",
      "Epoch 16/200\n",
      "1500/1500 [==============================] - 0s 75us/step - loss: 0.0126 - acc: 0.9962 - val_loss: 0.0229 - val_acc: 0.9931\n",
      "Epoch 17/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0131 - acc: 0.9958 - val_loss: 0.0168 - val_acc: 0.9944\n",
      "Epoch 18/200\n",
      "1500/1500 [==============================] - 0s 75us/step - loss: 0.0129 - acc: 0.9969 - val_loss: 0.0234 - val_acc: 0.9949\n",
      "Epoch 19/200\n",
      "1500/1500 [==============================] - 0s 79us/step - loss: 0.0132 - acc: 0.9967 - val_loss: 0.0388 - val_acc: 0.9936\n",
      "Epoch 20/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0127 - acc: 0.9953 - val_loss: 0.0216 - val_acc: 0.9927\n",
      "Epoch 21/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0166 - acc: 0.9967 - val_loss: 0.0284 - val_acc: 0.9940\n",
      "Epoch 22/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0131 - acc: 0.9958 - val_loss: 0.0205 - val_acc: 0.9944\n",
      "Epoch 23/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0152 - acc: 0.9967 - val_loss: 0.0334 - val_acc: 0.9936\n",
      "Epoch 24/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0111 - acc: 0.9971 - val_loss: 0.0251 - val_acc: 0.9944\n",
      "Epoch 25/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0108 - acc: 0.9969 - val_loss: 0.0313 - val_acc: 0.9913\n",
      "Epoch 26/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0162 - acc: 0.9960 - val_loss: 0.0209 - val_acc: 0.9936\n",
      "Epoch 27/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0109 - acc: 0.9978 - val_loss: 0.0126 - val_acc: 0.9967\n",
      "Epoch 28/200\n",
      "1500/1500 [==============================] - 0s 79us/step - loss: 0.0085 - acc: 0.9978 - val_loss: 0.0159 - val_acc: 0.9944\n",
      "Epoch 29/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0076 - acc: 0.9987 - val_loss: 0.0147 - val_acc: 0.9951\n",
      "Epoch 30/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0151 - acc: 0.9969 - val_loss: 0.0384 - val_acc: 0.9818\n",
      "Epoch 31/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0107 - acc: 0.9969 - val_loss: 0.0216 - val_acc: 0.9964\n",
      "Epoch 32/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0074 - acc: 0.9982 - val_loss: 0.0148 - val_acc: 0.9947\n",
      "Epoch 33/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0097 - acc: 0.9969 - val_loss: 0.0126 - val_acc: 0.9978\n",
      "Epoch 34/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0057 - acc: 0.9982 - val_loss: 0.0277 - val_acc: 0.9938\n",
      "Epoch 35/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0151 - acc: 0.9969 - val_loss: 0.0113 - val_acc: 0.9964\n",
      "Epoch 36/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0143 - acc: 0.9951 - val_loss: 0.0181 - val_acc: 0.9936\n",
      "Epoch 37/200\n",
      "1500/1500 [==============================] - 0s 75us/step - loss: 0.0159 - acc: 0.9960 - val_loss: 0.0304 - val_acc: 0.9920\n",
      "Epoch 38/200\n",
      "1500/1500 [==============================] - 0s 80us/step - loss: 0.0121 - acc: 0.9964 - val_loss: 0.0211 - val_acc: 0.9951\n",
      "Epoch 39/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0198 - acc: 0.9942 - val_loss: 0.0201 - val_acc: 0.9951\n",
      "Epoch 40/200\n",
      "1500/1500 [==============================] - 0s 75us/step - loss: 0.0076 - acc: 0.9978 - val_loss: 0.0109 - val_acc: 0.9969\n",
      "Epoch 41/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0115 - acc: 0.9978 - val_loss: 0.0360 - val_acc: 0.9922\n",
      "Epoch 42/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0157 - acc: 0.9964 - val_loss: 0.0229 - val_acc: 0.9947\n",
      "Epoch 43/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0124 - acc: 0.9960 - val_loss: 0.0306 - val_acc: 0.9936\n",
      "Epoch 44/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0115 - acc: 0.9973 - val_loss: 0.0135 - val_acc: 0.9973\n",
      "Epoch 45/200\n",
      "1500/1500 [==============================] - 0s 79us/step - loss: 0.0091 - acc: 0.9982 - val_loss: 0.0224 - val_acc: 0.9960\n",
      "Epoch 46/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0094 - acc: 0.9978 - val_loss: 0.0090 - val_acc: 0.9978\n",
      "Epoch 47/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0049 - acc: 0.9987 - val_loss: 0.0204 - val_acc: 0.9942\n",
      "Epoch 48/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0161 - acc: 0.9947 - val_loss: 0.0233 - val_acc: 0.9933\n",
      "Epoch 49/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0133 - acc: 0.9973 - val_loss: 0.0269 - val_acc: 0.9884\n",
      "Epoch 50/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0108 - acc: 0.9982 - val_loss: 0.0191 - val_acc: 0.9933\n",
      "Epoch 51/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0093 - acc: 0.9973 - val_loss: 0.0202 - val_acc: 0.9942\n",
      "Epoch 52/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0080 - acc: 0.9987 - val_loss: 0.0104 - val_acc: 0.9964\n",
      "Epoch 53/200\n",
      "1500/1500 [==============================] - 0s 75us/step - loss: 0.0088 - acc: 0.9969 - val_loss: 0.0179 - val_acc: 0.9947\n",
      "Epoch 54/200\n",
      "1500/1500 [==============================] - 0s 80us/step - loss: 0.0106 - acc: 0.9987 - val_loss: 0.0233 - val_acc: 0.9969\n",
      "Epoch 55/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0076 - acc: 0.9969 - val_loss: 0.0138 - val_acc: 0.9960\n",
      "Epoch 56/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0085 - acc: 0.9973 - val_loss: 0.0188 - val_acc: 0.9947\n",
      "Epoch 57/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0064 - acc: 0.9987 - val_loss: 0.0120 - val_acc: 0.9960\n",
      "Epoch 58/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0058 - acc: 0.9987 - val_loss: 0.0131 - val_acc: 0.9960\n",
      "Epoch 59/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0057 - acc: 0.9987 - val_loss: 0.0143 - val_acc: 0.9956\n",
      "Epoch 60/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0229 - acc: 0.9951 - val_loss: 0.0096 - val_acc: 0.9969\n",
      "Epoch 61/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0083 - acc: 0.9978 - val_loss: 0.0104 - val_acc: 0.9969\n",
      "Epoch 62/200\n",
      "1500/1500 [==============================] - 0s 79us/step - loss: 0.0152 - acc: 0.9947 - val_loss: 0.0353 - val_acc: 0.9867\n",
      "Epoch 63/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0124 - acc: 0.9978 - val_loss: 0.0277 - val_acc: 0.9920\n",
      "Epoch 64/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0090 - acc: 0.9973 - val_loss: 0.0179 - val_acc: 0.9982\n",
      "Epoch 65/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0161 - acc: 0.9960 - val_loss: 0.0296 - val_acc: 0.9938\n",
      "Epoch 66/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0110 - acc: 0.9978 - val_loss: 0.0228 - val_acc: 0.9964\n",
      "Epoch 67/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0085 - acc: 0.9982 - val_loss: 0.0127 - val_acc: 0.9956\n",
      "Epoch 68/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0141 - acc: 0.9960 - val_loss: 0.0301 - val_acc: 0.9947\n",
      "Epoch 69/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0248 - acc: 0.9933 - val_loss: 0.0326 - val_acc: 0.9933\n",
      "Epoch 70/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0095 - acc: 0.9969 - val_loss: 0.0189 - val_acc: 0.9933\n",
      "Epoch 71/200\n",
      "1500/1500 [==============================] - 0s 80us/step - loss: 0.0080 - acc: 0.9978 - val_loss: 0.0104 - val_acc: 0.9969\n",
      "Epoch 72/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0077 - acc: 0.9987 - val_loss: 0.0167 - val_acc: 0.9973\n",
      "Epoch 73/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0056 - acc: 0.9987 - val_loss: 0.0140 - val_acc: 0.9951\n",
      "Epoch 74/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0056 - acc: 0.9982 - val_loss: 0.0244 - val_acc: 0.9929\n",
      "Epoch 75/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0066 - acc: 0.9982 - val_loss: 0.0180 - val_acc: 0.9938\n",
      "Epoch 76/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0040 - acc: 0.9991 - val_loss: 0.0140 - val_acc: 0.9951\n",
      "Epoch 77/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0112 - acc: 0.9982 - val_loss: 0.0147 - val_acc: 0.9951\n",
      "Epoch 78/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0075 - acc: 0.9964 - val_loss: 0.0088 - val_acc: 0.9987\n",
      "Epoch 79/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0080 - acc: 0.9978 - val_loss: 0.0221 - val_acc: 0.9942\n",
      "Epoch 80/200\n",
      "1500/1500 [==============================] - 0s 82us/step - loss: 0.0067 - acc: 0.9987 - val_loss: 0.0162 - val_acc: 0.9978\n",
      "Epoch 81/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0054 - acc: 0.9987 - val_loss: 0.0109 - val_acc: 0.9973\n",
      "Epoch 82/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0044 - acc: 0.9982 - val_loss: 0.0120 - val_acc: 0.9964\n",
      "Epoch 83/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0071 - acc: 0.9978 - val_loss: 0.0133 - val_acc: 0.9978\n",
      "Epoch 84/200\n",
      "1500/1500 [==============================] - 0s 79us/step - loss: 0.0102 - acc: 0.9960 - val_loss: 0.0107 - val_acc: 0.9978\n",
      "Epoch 85/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0055 - acc: 0.9982 - val_loss: 0.0294 - val_acc: 0.9924\n",
      "Epoch 86/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0117 - acc: 0.9982 - val_loss: 0.0100 - val_acc: 0.9969\n",
      "Epoch 87/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0077 - acc: 0.9973 - val_loss: 0.0109 - val_acc: 0.9969\n",
      "Epoch 88/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0101 - acc: 0.9969 - val_loss: 0.0111 - val_acc: 0.9973\n",
      "Epoch 89/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0037 - acc: 0.9991 - val_loss: 0.0098 - val_acc: 0.9973\n",
      "Epoch 90/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0055 - acc: 0.9987 - val_loss: 0.0249 - val_acc: 0.9933\n",
      "Epoch 91/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0132 - acc: 0.9960 - val_loss: 0.0282 - val_acc: 0.9947\n",
      "Epoch 92/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0128 - acc: 0.9964 - val_loss: 0.0312 - val_acc: 0.9947\n",
      "Epoch 93/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0119 - acc: 0.9964 - val_loss: 0.0114 - val_acc: 0.9973\n",
      "Epoch 94/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0088 - acc: 0.9973 - val_loss: 0.0144 - val_acc: 0.9973\n",
      "Epoch 95/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0097 - acc: 0.9982 - val_loss: 0.0107 - val_acc: 0.9973\n",
      "Epoch 96/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0043 - acc: 0.9987 - val_loss: 0.0137 - val_acc: 0.9969\n",
      "Epoch 97/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0085 - acc: 0.9969 - val_loss: 0.0243 - val_acc: 0.9911\n",
      "Epoch 98/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0067 - acc: 0.9978 - val_loss: 0.0109 - val_acc: 0.9969\n",
      "Epoch 99/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0055 - acc: 0.9982 - val_loss: 0.0165 - val_acc: 0.9942\n",
      "Epoch 100/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0073 - acc: 0.9978 - val_loss: 0.0167 - val_acc: 0.9969\n",
      "Epoch 101/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0081 - acc: 0.9964 - val_loss: 0.0192 - val_acc: 0.9942\n",
      "Epoch 102/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0123 - acc: 0.9978 - val_loss: 0.0093 - val_acc: 0.9973\n",
      "Epoch 103/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0053 - acc: 0.9982 - val_loss: 0.0125 - val_acc: 0.9973\n",
      "Epoch 104/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0051 - acc: 0.9973 - val_loss: 0.0097 - val_acc: 0.9978\n",
      "Epoch 105/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0067 - acc: 0.9978 - val_loss: 0.0288 - val_acc: 0.9907\n",
      "Epoch 106/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0066 - acc: 0.9982 - val_loss: 0.0159 - val_acc: 0.9964\n",
      "Epoch 107/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0060 - acc: 0.9973 - val_loss: 0.0146 - val_acc: 0.9964\n",
      "Epoch 108/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0053 - acc: 0.9978 - val_loss: 0.0173 - val_acc: 0.9956\n",
      "Epoch 109/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0072 - acc: 0.9973 - val_loss: 0.0253 - val_acc: 0.9960\n",
      "Epoch 110/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0121 - acc: 0.9956 - val_loss: 0.0524 - val_acc: 0.9911\n",
      "Epoch 111/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0201 - acc: 0.9964 - val_loss: 0.0188 - val_acc: 0.9960\n",
      "Epoch 112/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0074 - acc: 0.9987 - val_loss: 0.0083 - val_acc: 0.9987\n",
      "Epoch 113/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0049 - acc: 0.9982 - val_loss: 0.0389 - val_acc: 0.9862\n",
      "Epoch 114/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0118 - acc: 0.9978 - val_loss: 0.0129 - val_acc: 0.9969\n",
      "Epoch 115/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0095 - acc: 0.9964 - val_loss: 0.0278 - val_acc: 0.9956\n",
      "Epoch 116/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0131 - acc: 0.9969 - val_loss: 0.0132 - val_acc: 0.9960\n",
      "Epoch 117/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0108 - acc: 0.9960 - val_loss: 0.0122 - val_acc: 0.9973\n",
      "Epoch 118/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0084 - acc: 0.9982 - val_loss: 0.0138 - val_acc: 0.9969\n",
      "Epoch 119/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0066 - acc: 0.9982 - val_loss: 0.0113 - val_acc: 0.9969\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 120/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0058 - acc: 0.9991 - val_loss: 0.0106 - val_acc: 0.9982\n",
      "Epoch 121/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0034 - acc: 0.9991 - val_loss: 0.0217 - val_acc: 0.9964\n",
      "Epoch 122/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0081 - acc: 0.9973 - val_loss: 0.0117 - val_acc: 0.9978\n",
      "Epoch 123/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0050 - acc: 0.9973 - val_loss: 0.0132 - val_acc: 0.9978\n",
      "Epoch 124/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0065 - acc: 0.9973 - val_loss: 0.0304 - val_acc: 0.9938\n",
      "Epoch 125/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0116 - acc: 0.9969 - val_loss: 0.0097 - val_acc: 0.9973\n",
      "Epoch 126/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0075 - acc: 0.9973 - val_loss: 0.0109 - val_acc: 0.9991\n",
      "Epoch 127/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0064 - acc: 0.9973 - val_loss: 0.0216 - val_acc: 0.9916\n",
      "Epoch 128/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0037 - acc: 0.9987 - val_loss: 0.0201 - val_acc: 0.9973\n",
      "Epoch 129/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0060 - acc: 0.9982 - val_loss: 0.0111 - val_acc: 0.9978\n",
      "Epoch 130/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0038 - acc: 0.9991 - val_loss: 0.0117 - val_acc: 0.9978\n",
      "Epoch 131/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0027 - acc: 0.9996 - val_loss: 0.0135 - val_acc: 0.9973\n",
      "Epoch 132/200\n",
      "1500/1500 [==============================] - 0s 80us/step - loss: 0.0078 - acc: 0.9982 - val_loss: 0.0211 - val_acc: 0.9947\n",
      "Epoch 133/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0103 - acc: 0.9982 - val_loss: 0.0205 - val_acc: 0.9951\n",
      "Epoch 134/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0069 - acc: 0.9991 - val_loss: 0.0163 - val_acc: 0.9956\n",
      "Epoch 135/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0068 - acc: 0.9973 - val_loss: 0.0176 - val_acc: 0.9964\n",
      "Epoch 136/200\n",
      "1500/1500 [==============================] - 0s 79us/step - loss: 0.0051 - acc: 0.9991 - val_loss: 0.0122 - val_acc: 0.9973\n",
      "Epoch 137/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0055 - acc: 0.9982 - val_loss: 0.0131 - val_acc: 0.9978\n",
      "Epoch 138/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0062 - acc: 0.9982 - val_loss: 0.0177 - val_acc: 0.9964\n",
      "Epoch 139/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0022 - acc: 0.9991 - val_loss: 0.0150 - val_acc: 0.9964\n",
      "Epoch 140/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0069 - acc: 0.9987 - val_loss: 0.0128 - val_acc: 0.9978\n",
      "Epoch 141/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0134 - acc: 0.9973 - val_loss: 0.0167 - val_acc: 0.9964\n",
      "Epoch 142/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0055 - acc: 0.9982 - val_loss: 0.0137 - val_acc: 0.9960\n",
      "Epoch 143/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0062 - acc: 0.9991 - val_loss: 0.0121 - val_acc: 0.9969\n",
      "Epoch 144/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0060 - acc: 0.9978 - val_loss: 0.0209 - val_acc: 0.9938\n",
      "Epoch 145/200\n",
      "1500/1500 [==============================] - 0s 79us/step - loss: 0.0137 - acc: 0.9978 - val_loss: 0.0202 - val_acc: 0.9960\n",
      "Epoch 146/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0055 - acc: 0.9978 - val_loss: 0.0196 - val_acc: 0.9938\n",
      "Epoch 147/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0078 - acc: 0.9978 - val_loss: 0.0114 - val_acc: 0.9964\n",
      "Epoch 148/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0057 - acc: 0.9982 - val_loss: 0.0106 - val_acc: 0.9978\n",
      "Epoch 149/200\n",
      "1500/1500 [==============================] - 0s 81us/step - loss: 0.0065 - acc: 0.9982 - val_loss: 0.0119 - val_acc: 0.9960\n",
      "Epoch 150/200\n",
      "1500/1500 [==============================] - 0s 75us/step - loss: 0.0033 - acc: 0.9991 - val_loss: 0.0116 - val_acc: 0.9982\n",
      "Epoch 151/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0038 - acc: 0.9987 - val_loss: 0.0116 - val_acc: 0.9969\n",
      "Epoch 152/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0065 - acc: 0.9991 - val_loss: 0.0162 - val_acc: 0.9956\n",
      "Epoch 153/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0042 - acc: 0.9987 - val_loss: 0.0089 - val_acc: 0.9982\n",
      "Epoch 154/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0056 - acc: 0.9987 - val_loss: 0.0086 - val_acc: 0.9978\n",
      "Epoch 155/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0029 - acc: 0.9987 - val_loss: 0.0147 - val_acc: 0.9964\n",
      "Epoch 156/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0045 - acc: 0.9991 - val_loss: 0.0140 - val_acc: 0.9960\n",
      "Epoch 157/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0062 - acc: 0.9987 - val_loss: 0.0128 - val_acc: 0.9964\n",
      "Epoch 158/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0045 - acc: 0.9987 - val_loss: 0.0182 - val_acc: 0.9964\n",
      "Epoch 159/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0051 - acc: 0.9982 - val_loss: 0.0183 - val_acc: 0.9973\n",
      "Epoch 160/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0065 - acc: 0.9978 - val_loss: 0.0135 - val_acc: 0.9978\n",
      "Epoch 161/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0023 - acc: 0.9991 - val_loss: 0.0118 - val_acc: 0.9973\n",
      "Epoch 162/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0047 - acc: 0.9982 - val_loss: 0.0174 - val_acc: 0.9960\n",
      "Epoch 163/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0171 - acc: 0.9964 - val_loss: 0.0188 - val_acc: 0.9951\n",
      "Epoch 164/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0058 - acc: 0.9978 - val_loss: 0.0123 - val_acc: 0.9951\n",
      "Epoch 165/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0081 - acc: 0.9982 - val_loss: 0.0094 - val_acc: 0.9982\n",
      "Epoch 166/200\n",
      "1500/1500 [==============================] - 0s 85us/step - loss: 0.0028 - acc: 0.9996 - val_loss: 0.0119 - val_acc: 0.9969\n",
      "Epoch 167/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0039 - acc: 0.9987 - val_loss: 0.0165 - val_acc: 0.9942\n",
      "Epoch 168/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0036 - acc: 0.9987 - val_loss: 0.0109 - val_acc: 0.9969\n",
      "Epoch 169/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0040 - acc: 0.9991 - val_loss: 0.0101 - val_acc: 0.9982\n",
      "Epoch 170/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0038 - acc: 0.9996 - val_loss: 0.0098 - val_acc: 0.9982\n",
      "Epoch 171/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0074 - acc: 0.9982 - val_loss: 0.0251 - val_acc: 0.9947\n",
      "Epoch 172/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0092 - acc: 0.9978 - val_loss: 0.0125 - val_acc: 0.9969\n",
      "Epoch 173/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0043 - acc: 0.9982 - val_loss: 0.0124 - val_acc: 0.9987\n",
      "Epoch 174/200\n",
      "1500/1500 [==============================] - 0s 75us/step - loss: 0.0084 - acc: 0.9982 - val_loss: 0.0137 - val_acc: 0.9973\n",
      "Epoch 175/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0057 - acc: 0.9987 - val_loss: 0.0296 - val_acc: 0.9951\n",
      "Epoch 176/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0091 - acc: 0.9978 - val_loss: 0.0238 - val_acc: 0.9924\n",
      "Epoch 177/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0177 - acc: 0.9964 - val_loss: 0.0214 - val_acc: 0.9964\n",
      "Epoch 178/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0047 - acc: 0.9982 - val_loss: 0.0115 - val_acc: 0.9973\n",
      "Epoch 179/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0039 - acc: 0.9982 - val_loss: 0.0105 - val_acc: 0.9973\n",
      "Epoch 180/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0076 - acc: 0.9978 - val_loss: 0.0138 - val_acc: 0.9964\n",
      "Epoch 181/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0112 - acc: 0.9969 - val_loss: 0.0078 - val_acc: 0.9969\n",
      "Epoch 182/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0048 - acc: 0.9982 - val_loss: 0.0127 - val_acc: 0.9973\n",
      "Epoch 183/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0059 - acc: 0.9987 - val_loss: 0.0090 - val_acc: 0.9982\n",
      "Epoch 184/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0036 - acc: 0.9991 - val_loss: 0.0107 - val_acc: 0.9973\n",
      "Epoch 185/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0041 - acc: 0.9987 - val_loss: 0.0107 - val_acc: 0.9973\n",
      "Epoch 186/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0064 - acc: 0.9982 - val_loss: 0.0073 - val_acc: 0.9982\n",
      "Epoch 187/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0057 - acc: 0.9982 - val_loss: 0.0114 - val_acc: 0.9960\n",
      "Epoch 188/200\n",
      "1500/1500 [==============================] - 0s 78us/step - loss: 0.0125 - acc: 0.9978 - val_loss: 0.0099 - val_acc: 0.9964\n",
      "Epoch 189/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0036 - acc: 0.9987 - val_loss: 0.0103 - val_acc: 0.9987\n",
      "Epoch 190/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0040 - acc: 0.9991 - val_loss: 0.0135 - val_acc: 0.9973\n",
      "Epoch 191/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0033 - acc: 0.9991 - val_loss: 0.0088 - val_acc: 0.9987\n",
      "Epoch 192/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0022 - acc: 0.9996 - val_loss: 0.0103 - val_acc: 0.9978\n",
      "Epoch 193/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0040 - acc: 0.9987 - val_loss: 0.0118 - val_acc: 0.9973\n",
      "Epoch 194/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0040 - acc: 0.9982 - val_loss: 0.0080 - val_acc: 0.9987\n",
      "Epoch 195/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0034 - acc: 0.9987 - val_loss: 0.0093 - val_acc: 0.9978\n",
      "Epoch 196/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0051 - acc: 0.9982 - val_loss: 0.0178 - val_acc: 0.9964\n",
      "Epoch 197/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0048 - acc: 0.9991 - val_loss: 0.0233 - val_acc: 0.9956\n",
      "Epoch 198/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0053 - acc: 0.9973 - val_loss: 0.0142 - val_acc: 0.9964\n",
      "Epoch 199/200\n",
      "1500/1500 [==============================] - 0s 76us/step - loss: 0.0037 - acc: 0.9996 - val_loss: 0.0115 - val_acc: 0.9969\n",
      "Epoch 200/200\n",
      "1500/1500 [==============================] - 0s 77us/step - loss: 0.0035 - acc: 0.9987 - val_loss: 0.0411 - val_acc: 0.9938\n"
     ]
    }
   ],
   "source": [
    "classifier = Classifier()\n",
    "\n",
    "classifier.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot of input data\n"
     ]
    }
   ],
   "source": [
    "# Plot input data\n",
    "plt.figure()\n",
    "plt.scatter(classifier.data[:, 0], classifier.data[:, 1], s=4, c=classifier.labels.squeeze())\n",
    "plt.show()\n",
    "print(\"Plot of input data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 1)\n",
      "(400, 1)\n",
      "(600, 1)\n",
      "(300, 2)\n"
     ]
    }
   ],
   "source": [
    "# Create image for paper\n",
    "classifier.n = 200\n",
    "data, labels = classifier.get_data()\n",
    "labels = labels.squeeze()\n",
    "ind_0 = np.where(labels == 0)[0]\n",
    "\n",
    "ind = np.random.randint(0, ind_0.shape[0], 50)\n",
    "ind_0 = ind_0[ind]\n",
    "\n",
    "ind_1 = np.where(labels == 1)[0]\n",
    "\n",
    "ind = np.random.randint(0, ind_1.shape[0], 50)\n",
    "ind_1 = ind_1[ind]\n",
    "\n",
    "ind_2 = np.where(labels == 2)[0]\n",
    "\n",
    "ind = np.concatenate((ind_0, ind_1, ind_2), axis=0)\n",
    "data = data[ind, :]\n",
    "labels = labels[ind]\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "ind = np.where(labels == 0)[0]\n",
    "plt.scatter(data[ind, 0], data[ind, 1], s=10, c='r', marker='D')\n",
    "ind = np.where(labels == 1)[0]\n",
    "plt.scatter(data[ind, 0], data[ind, 1], s=10, c='b', marker='o')\n",
    "ind = np.where(labels == 2)[0]\n",
    "plt.scatter(data[ind, 0], data[ind, 1], s=10, c='y', marker='*')\n",
    "#plt.xlim(-18, +18)\n",
    "#plt.ylim(-18, +18)\n",
    "#plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.legend(('$class\\ 0$','$class\\ 1$','$class\\ 2\\ (OOD\\ sample)$'), fontsize='x-small');\n",
    "plt.show()\n",
    "plt.savefig(\"exp2_a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f21cdd122d0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot classified data - for test data\n",
    "\n",
    "y_pred = classifier.model.predict(classifier.X_test)\n",
    "y_max = y_pred.max(axis = -1)\n",
    "y_labels = y_pred.argmax(axis = -1)\n",
    "\n",
    "fig = plt.figure()\n",
    "#ax = fig.add_subplot(111, projection='3d')\n",
    "#ax = Axes3D(fig)\n",
    "\n",
    "#ax.scatter(classifier.X_test[:, 0], classifier.X_test[:, 1], y_max, c=classifier.y_test.squeeze())\n",
    "plt.scatter(classifier.X_test[:, 0], classifier.X_test[:, 1], s=4, c=y_labels.squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 1)\n",
      "(400, 1)\n",
      "(600, 1)\n",
      "(300, 2)\n"
     ]
    }
   ],
   "source": [
    "# Create image for paper\n",
    "classifier.n = 200\n",
    "data, labels = classifier.get_data()\n",
    "labels = labels.squeeze()\n",
    "ind_0 = np.where(labels == 0)[0]\n",
    "\n",
    "ind = np.random.randint(0, ind_0.shape[0], 50)\n",
    "ind_0 = ind_0[ind]\n",
    "\n",
    "ind_1 = np.where(labels == 1)[0]\n",
    "\n",
    "ind = np.random.randint(0, ind_1.shape[0], 50)\n",
    "ind_1 = ind_1[ind]\n",
    "\n",
    "ind_2 = np.where(labels == 2)[0]\n",
    "\n",
    "ind = np.concatenate((ind_0, ind_1, ind_2), axis=0)\n",
    "data = data[ind, :]\n",
    "labels = labels[ind]\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = classifier.model.predict(data)\n",
    "y_labels = y_pred.argmax(axis = -1)\n",
    "c_list = ['r', 'b', 'y']\n",
    "marker_list = ['D', 'o', '*']\n",
    "plt.figure(figsize=(5,5))\n",
    "for i in range(0, data.shape[0]):\n",
    "    marker = marker_list[int(labels[i])]\n",
    "    c = c_list[y_labels[i]]\n",
    "    plt.scatter(data[i, 0], data[i, 1], s=10, c=c, marker=marker)\n",
    "\n",
    "plt.legend(('$class\\ 0$','$class\\ 1$','$OOD\\ sample$'), fontsize='x-small');\n",
    "import matplotlib.lines as lines\n",
    "dimond_line = lines.Line2D([0], [0], color='w', marker='D', markerfacecolor='g',\n",
    "                          markersize=5, label='TC 0')\n",
    "\n",
    "circle_line = lines.Line2D([0], [0], marker='o', color='w', label='TC 1',\n",
    "                          markerfacecolor='g', markersize=5)\n",
    "\n",
    "star_line = lines.Line2D([0], [0], color='w', marker='*', markerfacecolor='g',\n",
    "                          markersize=10, label='TC 2')\n",
    "\n",
    "\n",
    "red_line = lines.Line2D([0], [0], marker='s', color='w', label='PC 0',\n",
    "                          markerfacecolor='r', markersize=5)\n",
    "\n",
    "\n",
    "blue_line = lines.Line2D([0], [0], marker='s', color='w', label='PC 1',\n",
    "                          markerfacecolor='b', markersize=5)\n",
    "\n",
    "\n",
    "yellow_line = lines.Line2D([0], [0], marker='s', color='w', label='PC 2',\n",
    "                          markerfacecolor='y', markersize=5)\n",
    "\n",
    "# Create a legend for the first line.\n",
    "first_legend = plt.legend(handles=[dimond_line, circle_line, star_line], loc=1)\n",
    "\n",
    "\n",
    "# Add the legend manually to the current Axes.\n",
    "ax = plt.gca().add_artist(first_legend)\n",
    "\n",
    "# Create another legend for the second line.\n",
    "plt.legend(handles=[red_line, blue_line, yellow_line], loc=4)\n",
    "\n",
    "plt.show()\n",
    "plt.savefig(\"exp2_c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 2)\n"
     ]
    }
   ],
   "source": [
    "print(classifier.X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}