├── .gitignore
├── .ipynb_checkpoints
    └── Convolution_Op-checkpoint.ipynb
├── Class1
    └── June_ML_Intro.pptx.pdf
├── Class10
    ├── .ipynb_checkpoints
    │   └── PCA-checkpoint.ipynb
    └── PCA.ipynb
├── Class11
    ├── .ipynb_checkpoints
    │   └── Linear_Regression-checkpoint.ipynb
    └── Linear_Regression.ipynb
├── Class12
    ├── .ipynb_checkpoints
    │   └── Logistic_Regression-checkpoint.ipynb
    └── Logistic_Regression.ipynb
├── Class13
    ├── .ipynb_checkpoints
    │   ├── Intro_to_NN-checkpoint.ipynb
    │   └── Non_Linear_Decision_Boundary-checkpoint.ipynb
    ├── Intro_to_NN.ipynb
    └── Non_Linear_Decision_Boundary.ipynb
├── Class15
    ├── .ipynb_checkpoints
    │   ├── Classification_on_MNIST-checkpoint.ipynb
    │   └── Simple_Autoencoder-checkpoint.ipynb
    ├── Autoencoder_Sequential.ipynb
    ├── Classification_on_MNIST.ipynb
    └── Simple_Autoencoder.ipynb
├── Class16
    ├── .ipynb_checkpoints
    │   ├── Conv_Op-checkpoint.ipynb
    │   └── Convolution_Networks-checkpoint.ipynb
    ├── Conv_Op.ipynb
    ├── Convolution_Networks.ipynb
    └── img.jpg
├── Class17
    ├── Transfer_Learning.ipynb
    └── convolutional_network_tutorial.ipynb
├── Class18
    ├── .ipynb_checkpoints
    │   ├── Markov_Chain-Text_Gen-checkpoint.ipynb
    │   └── Markov_Chains_text-generation-checkpoint.ipynb
    ├── Markov_Chains_text-generation.ipynb
    └── input.txt
├── Class19
    ├── .ipynb_checkpoints
    │   └── LSTM_SentimentAnalysis-checkpoint.ipynb
    └── LSTM_Sentiment-Analysis.ipynb
├── Class2
    ├── .ipynb_checkpoints
    │   ├── Intro_to_Python-checkpoint.ipynb
    │   ├── Libraries-checkpoint.ipynb
    │   └── Statistical_Computation-checkpoint.ipynb
    ├── Intro_to_Python.ipynb
    ├── Libraries.ipynb
    ├── Statistical_Computation.ipynb
    └── movie_metadata.csv
├── Class21
    ├── .ipynb_checkpoints
    │   ├── Add_Boundary-checkpoint.ipynb
    │   └── Working_with_Segmentation_Masks-checkpoint.ipynb
    ├── Add_Boundary.ipynb
    ├── Working_with_Segmentation_Masks.ipynb
    ├── images
    │   ├── anf_64540_01_model1.jpg
    │   ├── anf_64540_01_model1_Mask_front_inspect.png
    │   ├── anf_65268_01_model1.jpg
    │   └── anf_65268_01_model1_Mask_front.png
    └── mask_wo_index
    │   ├── anf_64540_01_model1_Mask_front_inspect.png
    │   └── anf_65268_01_model1_Mask_front.png
├── Class3
    ├── .ipynb_checkpoints
    │   ├── Doubts_and_Discussions-checkpoint.ipynb
    │   └── K_Nearest_Neighbors-checkpoint.ipynb
    ├── Doubts_and_Discussions.ipynb
    └── K_Nearest_Neighbors.ipynb
├── Class4
    ├── .ipynb_checkpoints
    │   ├── Assignment-Color_Extraction-checkpoint.ipynb
    │   ├── Doubts-checkpoint.ipynb
    │   └── KMeans_Clustering-checkpoint.ipynb
    ├── Assignment-Color_Extraction.ipynb
    ├── Doubts.ipynb
    ├── KMeans_Clustering.ipynb
    └── im.jpg
├── Class5
    ├── .ipynb_checkpoints
    │   └── Doubts-checkpoint.ipynb
    └── Doubts.ipynb
├── Class6
    ├── .ipynb_checkpoints
    │   ├── Doubts_and_Discussions-checkpoint.ipynb
    │   ├── Untitled-checkpoint.ipynb
    │   └── Web_Scraping_to_CSV-checkpoint.ipynb
    ├── Doubts_and_Discussions.ipynb
    ├── Web_Scraping_to_CSV.ipynb
    └── imdb.csv
├── Class7
    ├── .ipynb_checkpoints
    │   └── Decision_Tree_and_RF-checkpoint.ipynb
    ├── DT_Dataset.png
    ├── Decision_Tree_and_RF.ipynb
    └── titanic.csv
├── Class8
    ├── .ipynb_checkpoints
    │   └── Naive_Bayes-checkpoint.ipynb
    ├── NB_dataset.png
    ├── Naive_Bayes.ipynb
    └── mushrooms.csv
├── Class9
    ├── .ipynb_checkpoints
    │   ├── CamScanner-checkpoint.ipynb
    │   └── Challenge-checkpoint.ipynb
    ├── CamScanner.ipynb
    ├── CamScanner.py
    ├── CamScanner_Modular.ipynb
    ├── Challenge.ipynb
    ├── page.jpg
    ├── receipt.jpg
    ├── sales_data_test.csv
    ├── sales_data_training.csv
    └── test_s1.jpg
├── Project
    ├── Gesture_Record.py
    └── README.md
├── README.md
└── assignments
    └── python_assignment.pdf


/.gitignore:
--------------------------------------------------------------------------------
1 | .DS_Store
2 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Convolution_Op-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class1/June_ML_Intro.pptx.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/coding-blocks-archives/machine-learning-june-2018/f0772da9a9a2aec974c1c1daf7aa1f482d764962/Class1/June_ML_Intro.pptx.pdf


--------------------------------------------------------------------------------
/Class10/.ipynb_checkpoints/PCA-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class11/.ipynb_checkpoints/Linear_Regression-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "%matplotlib inline\n",
 12 |     "import pandas as pd\n",
 13 |     "from sklearn.datasets import make_regression"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 2,
 19 |    "metadata": {
 20 |     "scrolled": true
 21 |    },
 22 |    "outputs": [
 23 |     {
 24 |      "name": "stdout",
 25 |      "output_type": "stream",
 26 |      "text": [
 27 |       "(500, 1)\n",
 28 |       "(500,)\n"
 29 |      ]
 30 |     },
 31 |     {
 32 |      "data": {
 33 |       "image/png": 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\n",
 34 |       "text/plain": [
 35 |        "<Figure size 432x288 with 1 Axes>"
 36 |       ]
 37 |      },
 38 |      "metadata": {},
 39 |      "output_type": "display_data"
 40 |     }
 41 |    ],
 42 |    "source": [
 43 |     "(X, y) = make_regression(n_samples=500, noise=6.5, bias=7.5, n_features=1)\n",
 44 |     "print X.shape\n",
 45 |     "print y.shape\n",
 46 |     "\n",
 47 |     "plt.scatter(X, y)\n",
 48 |     "plt.show()"
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "code",
 53 |    "execution_count": 8,
 54 |    "metadata": {},
 55 |    "outputs": [],
 56 |    "source": [
 57 |     "'''Functions Needed'''\n",
 58 |     "def hypothesis(x, w):\n",
 59 |     "    x0 = 1\n",
 60 |     "    h = w[0]*x0 + w[1]*x\n",
 61 |     "    return h\n",
 62 |     "\n",
 63 |     "def get_error(x, w, y_true):\n",
 64 |     "    error = 0.0\n",
 65 |     "    m = x.shape[0]\n",
 66 |     "    for ix in range(m):\n",
 67 |     "        error += (hypothesis(x, w) - y_true)**2\n",
 68 |     "    error = error/2*m\n",
 69 |     "    return error"
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "code",
 74 |    "execution_count": 4,
 75 |    "metadata": {},
 76 |    "outputs": [],
 77 |    "source": [
 78 |     "def get_grads(x, w, y_true):\n",
 79 |     "    pass"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": 6,
 85 |    "metadata": {},
 86 |    "outputs": [],
 87 |    "source": [
 88 |     "def gradient_descent(x, w, y_true, learning_rate=0.01):\n",
 89 |     "    error = get_error(x, w, y_true)\n",
 90 |     "    \n",
 91 |     "    [grad_0, grad_1] = get_grads(x, w, y_true)\n",
 92 |     "    \n",
 93 |     "    w[0] = w[0] - learning_rate*grad_0\n",
 94 |     "    w[1] = w[1] - learning_rate*grad_1\n",
 95 |     "    \n",
 96 |     "    return w, error"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "code",
101 |    "execution_count": null,
102 |    "metadata": {},
103 |    "outputs": [],
104 |    "source": [
105 |     "num_epochs = 100\n",
106 |     "lr = 0.01\n",
107 |     "error_logs = []\n",
108 |     "W = np.array([0.1, 0.1])\n",
109 |     "for ix in range(num_epochs):\n",
110 |     "    W, error = gradient_descent(X_train, W, Y_train, learning_rate=lr)\n",
111 |     "    error_logs.append(error)"
112 |    ]
113 |   },
114 |   {
115 |    "cell_type": "code",
116 |    "execution_count": null,
117 |    "metadata": {},
118 |    "outputs": [],
119 |    "source": [
120 |     "plt.plot(error_logs)\n",
121 |     "plt.show()"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "code",
126 |    "execution_count": null,
127 |    "metadata": {},
128 |    "outputs": [],
129 |    "source": []
130 |   }
131 |  ],
132 |  "metadata": {
133 |   "kernelspec": {
134 |    "display_name": "Python 2",
135 |    "language": "python",
136 |    "name": "python2"
137 |   },
138 |   "language_info": {
139 |    "codemirror_mode": {
140 |     "name": "ipython",
141 |     "version": 2
142 |    },
143 |    "file_extension": ".py",
144 |    "mimetype": "text/x-python",
145 |    "name": "python",
146 |    "nbconvert_exporter": "python",
147 |    "pygments_lexer": "ipython2",
148 |    "version": "2.7.15"
149 |   }
150 |  },
151 |  "nbformat": 4,
152 |  "nbformat_minor": 2
153 | }
154 | 


--------------------------------------------------------------------------------
/Class12/.ipynb_checkpoints/Logistic_Regression-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class13/.ipynb_checkpoints/Intro_to_NN-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class13/.ipynb_checkpoints/Non_Linear_Decision_Boundary-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class13/Intro_to_NN.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "%matplotlib inline\n",
 12 |     "from sklearn.datasets import make_circles\n",
 13 |     "from keras.layers import Dense, Activation\n",
 14 |     "from keras.models import Sequential\n",
 15 |     "from keras.utils import np_utils"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 2,
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "X, y = make_circles(n_samples=1000, factor=0.4)"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 3,
 30 |    "metadata": {},
 31 |    "outputs": [
 32 |     {
 33 |      "name": "stdout",
 34 |      "output_type": "stream",
 35 |      "text": [
 36 |       "(1000, 2) (1000,)\n"
 37 |      ]
 38 |     }
 39 |    ],
 40 |    "source": [
 41 |     "print X.shape, y.shape"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 4,
 47 |    "metadata": {},
 48 |    "outputs": [
 49 |     {
 50 |      "data": {
 51 |       "image/png": 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\n",
 52 |       "text/plain": [
 53 |        "<Figure size 432x288 with 1 Axes>"
 54 |       ]
 55 |      },
 56 |      "metadata": {},
 57 |      "output_type": "display_data"
 58 |     }
 59 |    ],
 60 |    "source": [
 61 |     "for ix in range(X.shape[0]):\n",
 62 |     "    if y[ix] == 0:\n",
 63 |     "        plt.scatter(X[ix, 0], X[ix, 1], c='r')\n",
 64 |     "    else:\n",
 65 |     "        plt.scatter(X[ix, 0], X[ix, 1], c='b')\n",
 66 |     "        \n",
 67 |     "plt.show()"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "code",
 72 |    "execution_count": 7,
 73 |    "metadata": {},
 74 |    "outputs": [
 75 |     {
 76 |      "name": "stdout",
 77 |      "output_type": "stream",
 78 |      "text": [
 79 |       "(700,)\n",
 80 |       "(300,)\n"
 81 |      ]
 82 |     }
 83 |    ],
 84 |    "source": [
 85 |     "split = int(0.7*X.shape[0])\n",
 86 |     "X_train = X[:split]\n",
 87 |     "X_test = X[split:]\n",
 88 |     "\n",
 89 |     "y_train = y[:split]\n",
 90 |     "y_test = y[split:]\n",
 91 |     "\n",
 92 |     "print y_train.shape\n",
 93 |     "print y_test.shape"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 9,
 99 |    "metadata": {},
100 |    "outputs": [
101 |     {
102 |      "name": "stdout",
103 |      "output_type": "stream",
104 |      "text": [
105 |       "[0 1]\n"
106 |      ]
107 |     }
108 |    ],
109 |    "source": [
110 |     "print np.unique(y_train)"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 10,
116 |    "metadata": {},
117 |    "outputs": [
118 |     {
119 |      "name": "stdout",
120 |      "output_type": "stream",
121 |      "text": [
122 |       "(700, 2)\n",
123 |       "(300, 2)\n"
124 |      ]
125 |     }
126 |    ],
127 |    "source": [
128 |     "Y_train = np_utils.to_categorical(y_train)\n",
129 |     "Y_test = np_utils.to_categorical(y_test)\n",
130 |     "print Y_train.shape\n",
131 |     "print Y_test.shape"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 12,
137 |    "metadata": {},
138 |    "outputs": [
139 |     {
140 |      "name": "stdout",
141 |      "output_type": "stream",
142 |      "text": [
143 |       "0 [1. 0.]\n",
144 |       "0 [1. 0.]\n",
145 |       "0 [1. 0.]\n",
146 |       "1 [0. 1.]\n",
147 |       "1 [0. 1.]\n",
148 |       "0 [1. 0.]\n",
149 |       "0 [1. 0.]\n",
150 |       "0 [1. 0.]\n",
151 |       "0 [1. 0.]\n",
152 |       "0 [1. 0.]\n"
153 |      ]
154 |     }
155 |    ],
156 |    "source": [
157 |     "for ix in range(10):\n",
158 |     "    print y_test[ix], Y_test[ix]"
159 |    ]
160 |   },
161 |   {
162 |    "cell_type": "code",
163 |    "execution_count": 29,
164 |    "metadata": {},
165 |    "outputs": [
166 |     {
167 |      "name": "stdout",
168 |      "output_type": "stream",
169 |      "text": [
170 |       "_________________________________________________________________\n",
171 |       "Layer (type)                 Output Shape              Param #   \n",
172 |       "=================================================================\n",
173 |       "dense_12 (Dense)             (None, 5)                 15        \n",
174 |       "_________________________________________________________________\n",
175 |       "activation_7 (Activation)    (None, 5)                 0         \n",
176 |       "_________________________________________________________________\n",
177 |       "dense_13 (Dense)             (None, 2)                 12        \n",
178 |       "_________________________________________________________________\n",
179 |       "activation_8 (Activation)    (None, 2)                 0         \n",
180 |       "=================================================================\n",
181 |       "Total params: 27\n",
182 |       "Trainable params: 27\n",
183 |       "Non-trainable params: 0\n",
184 |       "_________________________________________________________________\n"
185 |      ]
186 |     }
187 |    ],
188 |    "source": [
189 |     "model = Sequential()\n",
190 |     "# model.add(Dense(2, input_shape=(2,)))\n",
191 |     "# model.add(Activation('relu'))\n",
192 |     "model.add(Dense(5, input_shape=(2,)))\n",
193 |     "model.add(Activation('relu'))\n",
194 |     "model.add(Dense(2))\n",
195 |     "model.add(Activation('softmax'))\n",
196 |     "\n",
197 |     "model.summary()"
198 |    ]
199 |   },
200 |   {
201 |    "cell_type": "code",
202 |    "execution_count": 30,
203 |    "metadata": {},
204 |    "outputs": [],
205 |    "source": [
206 |     "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": 31,
212 |    "metadata": {},
213 |    "outputs": [
214 |     {
215 |      "name": "stdout",
216 |      "output_type": "stream",
217 |      "text": [
218 |       "Train on 700 samples, validate on 300 samples\n",
219 |       "Epoch 1/15\n",
220 |       "700/700 [==============================] - 0s 38us/step - loss: 0.6311 - acc: 0.5529 - val_loss: 0.6124 - val_acc: 0.5633\n",
221 |       "Epoch 2/15\n",
222 |       "700/700 [==============================] - 0s 37us/step - loss: 0.6133 - acc: 0.5643 - val_loss: 0.5977 - val_acc: 0.5700\n",
223 |       "Epoch 3/15\n",
224 |       "700/700 [==============================] - 0s 36us/step - loss: 0.5962 - acc: 0.5914 - val_loss: 0.5820 - val_acc: 0.5867\n",
225 |       "Epoch 4/15\n",
226 |       "700/700 [==============================] - 0s 38us/step - loss: 0.5783 - acc: 0.6600 - val_loss: 0.5648 - val_acc: 0.6867\n",
227 |       "Epoch 5/15\n",
228 |       "700/700 [==============================] - 0s 35us/step - loss: 0.5600 - acc: 0.7400 - val_loss: 0.5467 - val_acc: 0.7633\n",
229 |       "Epoch 6/15\n",
230 |       "700/700 [==============================] - 0s 38us/step - loss: 0.5410 - acc: 0.8214 - val_loss: 0.5278 - val_acc: 0.8600\n",
231 |       "Epoch 7/15\n",
232 |       "700/700 [==============================] - 0s 36us/step - loss: 0.5214 - acc: 0.9257 - val_loss: 0.5080 - val_acc: 0.9367\n",
233 |       "Epoch 8/15\n",
234 |       "700/700 [==============================] - 0s 36us/step - loss: 0.5013 - acc: 0.9443 - val_loss: 0.4878 - val_acc: 0.9433\n",
235 |       "Epoch 9/15\n",
236 |       "700/700 [==============================] - 0s 36us/step - loss: 0.4808 - acc: 0.9543 - val_loss: 0.4669 - val_acc: 0.9567\n",
237 |       "Epoch 10/15\n",
238 |       "700/700 [==============================] - 0s 38us/step - loss: 0.4599 - acc: 0.9586 - val_loss: 0.4462 - val_acc: 0.9600\n",
239 |       "Epoch 11/15\n",
240 |       "700/700 [==============================] - 0s 35us/step - loss: 0.4388 - acc: 0.9700 - val_loss: 0.4256 - val_acc: 0.9767\n",
241 |       "Epoch 12/15\n",
242 |       "700/700 [==============================] - 0s 38us/step - loss: 0.4180 - acc: 0.9771 - val_loss: 0.4046 - val_acc: 0.9933\n",
243 |       "Epoch 13/15\n",
244 |       "700/700 [==============================] - 0s 37us/step - loss: 0.3978 - acc: 0.9900 - val_loss: 0.3849 - val_acc: 1.0000\n",
245 |       "Epoch 14/15\n",
246 |       "700/700 [==============================] - 0s 36us/step - loss: 0.3777 - acc: 0.9986 - val_loss: 0.3649 - val_acc: 1.0000\n",
247 |       "Epoch 15/15\n",
248 |       "700/700 [==============================] - 0s 38us/step - loss: 0.3582 - acc: 1.0000 - val_loss: 0.3454 - val_acc: 1.0000\n"
249 |      ]
250 |     },
251 |     {
252 |      "data": {
253 |       "text/plain": [
254 |        "<keras.callbacks.History at 0x10b4a3810>"
255 |       ]
256 |      },
257 |      "execution_count": 31,
258 |      "metadata": {},
259 |      "output_type": "execute_result"
260 |     }
261 |    ],
262 |    "source": [
263 |     "model.fit(X_train, Y_train, batch_size=10, epochs=15, validation_data=[X_test, Y_test])"
264 |    ]
265 |   },
266 |   {
267 |    "cell_type": "code",
268 |    "execution_count": null,
269 |    "metadata": {},
270 |    "outputs": [],
271 |    "source": []
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": null,
276 |    "metadata": {},
277 |    "outputs": [],
278 |    "source": []
279 |   }
280 |  ],
281 |  "metadata": {
282 |   "kernelspec": {
283 |    "display_name": "Python 2",
284 |    "language": "python",
285 |    "name": "python2"
286 |   },
287 |   "language_info": {
288 |    "codemirror_mode": {
289 |     "name": "ipython",
290 |     "version": 2
291 |    },
292 |    "file_extension": ".py",
293 |    "mimetype": "text/x-python",
294 |    "name": "python",
295 |    "nbconvert_exporter": "python",
296 |    "pygments_lexer": "ipython2",
297 |    "version": "2.7.15"
298 |   }
299 |  },
300 |  "nbformat": 4,
301 |  "nbformat_minor": 2
302 | }
303 | 


--------------------------------------------------------------------------------
/Class13/Non_Linear_Decision_Boundary.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "%matplotlib inline\n",
 12 |     "from sklearn.datasets import make_circles"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 3,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "X, y = make_circles(n_samples=1000, factor=0.4)"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 8,
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "name": "stdout",
 31 |      "output_type": "stream",
 32 |      "text": [
 33 |       "(1000, 2)\n",
 34 |       "(1000,)\n"
 35 |      ]
 36 |     }
 37 |    ],
 38 |    "source": [
 39 |     "print X.shape\n",
 40 |     "print y.shape"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 12,
 46 |    "metadata": {},
 47 |    "outputs": [
 48 |     {
 49 |      "name": "stdout",
 50 |      "output_type": "stream",
 51 |      "text": [
 52 |       "(1000, 1)\n"
 53 |      ]
 54 |     }
 55 |    ],
 56 |    "source": [
 57 |     "y2 = y.reshape((1000, 1))\n",
 58 |     "print y2.shape"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "code",
 63 |    "execution_count": 4,
 64 |    "metadata": {},
 65 |    "outputs": [
 66 |     {
 67 |      "data": {
 68 |       "image/png": 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\n",
 69 |       "text/plain": [
 70 |        "<Figure size 432x288 with 1 Axes>"
 71 |       ]
 72 |      },
 73 |      "metadata": {},
 74 |      "output_type": "display_data"
 75 |     }
 76 |    ],
 77 |    "source": [
 78 |     "for ix in range(X.shape[0]):\n",
 79 |     "    if y[ix] == 0:\n",
 80 |     "        plt.scatter(X[ix, 0], X[ix, 1], c='r')\n",
 81 |     "    else:\n",
 82 |     "        plt.scatter(X[ix, 0], X[ix, 1], c='b')\n",
 83 |     "\n",
 84 |     "plt.show()"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "code",
 89 |    "execution_count": 13,
 90 |    "metadata": {},
 91 |    "outputs": [
 92 |     {
 93 |      "name": "stdout",
 94 |      "output_type": "stream",
 95 |      "text": [
 96 |       "(1000, 3)\n"
 97 |      ]
 98 |     }
 99 |    ],
100 |    "source": [
101 |     "data = np.hstack((X, y2))\n",
102 |     "print data.shape"
103 |    ]
104 |   },
105 |   {
106 |    "cell_type": "code",
107 |    "execution_count": 14,
108 |    "metadata": {},
109 |    "outputs": [
110 |     {
111 |      "data": {
112 |       "text/plain": [
113 |        "array([[-0.38865269,  0.0945996 ,  1.        ],\n",
114 |        "       [-0.03514048, -0.39845344,  1.        ],\n",
115 |        "       [-0.19270147,  0.35052267,  1.        ],\n",
116 |        "       [-0.27013312, -0.29500525,  1.        ],\n",
117 |        "       [ 0.98865174, -0.15022559,  0.        ],\n",
118 |        "       [ 0.65658576,  0.75425138,  0.        ],\n",
119 |        "       [ 0.1140077 , -0.38340872,  1.        ],\n",
120 |        "       [-0.1140077 , -0.38340872,  1.        ],\n",
121 |        "       [ 0.38194582,  0.11881663,  1.        ],\n",
122 |        "       [ 0.56726895, -0.8235326 ,  0.        ],\n",
123 |        "       [-0.96538164,  0.26084151,  0.        ],\n",
124 |        "       [ 0.3954607 , -0.06009024,  1.        ],\n",
125 |        "       [ 0.25882238, -0.304977  ,  1.        ],\n",
126 |        "       [-0.02009773,  0.39949478,  1.        ],\n",
127 |        "       [-0.77051324,  0.63742399,  0.        ]])"
128 |       ]
129 |      },
130 |      "execution_count": 14,
131 |      "metadata": {},
132 |      "output_type": "execute_result"
133 |     }
134 |    ],
135 |    "source": [
136 |     "data[:15]"
137 |    ]
138 |   },
139 |   {
140 |    "cell_type": "code",
141 |    "execution_count": 5,
142 |    "metadata": {},
143 |    "outputs": [
144 |     {
145 |      "name": "stdout",
146 |      "output_type": "stream",
147 |      "text": [
148 |       "(700, 2) (700,)\n",
149 |       "(300, 2) (300,)\n"
150 |      ]
151 |     }
152 |    ],
153 |    "source": [
154 |     "split = int(0.7*X.shape[0])\n",
155 |     "X_train = X[:split]\n",
156 |     "X_test = X[split:]\n",
157 |     "\n",
158 |     "Y_train = y[:split]\n",
159 |     "Y_test = y[split:]\n",
160 |     "\n",
161 |     "print X_train.shape, Y_train.shape\n",
162 |     "print X_test.shape, Y_test.shape"
163 |    ]
164 |   },
165 |   {
166 |    "cell_type": "code",
167 |    "execution_count": 15,
168 |    "metadata": {},
169 |    "outputs": [],
170 |    "source": [
171 |     "from sklearn.linear_model import LogisticRegression"
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "code",
176 |    "execution_count": 16,
177 |    "metadata": {},
178 |    "outputs": [
179 |     {
180 |      "data": {
181 |       "text/plain": [
182 |        "0.5866666666666667"
183 |       ]
184 |      },
185 |      "execution_count": 16,
186 |      "metadata": {},
187 |      "output_type": "execute_result"
188 |     }
189 |    ],
190 |    "source": [
191 |     "ls = LogisticRegression()\n",
192 |     "ls.fit(X_train, Y_train)\n",
193 |     "ls.score(X_test, Y_test)"
194 |    ]
195 |   },
196 |   {
197 |    "cell_type": "code",
198 |    "execution_count": null,
199 |    "metadata": {},
200 |    "outputs": [],
201 |    "source": []
202 |   }
203 |  ],
204 |  "metadata": {
205 |   "kernelspec": {
206 |    "display_name": "Python 2",
207 |    "language": "python",
208 |    "name": "python2"
209 |   },
210 |   "language_info": {
211 |    "codemirror_mode": {
212 |     "name": "ipython",
213 |     "version": 2
214 |    },
215 |    "file_extension": ".py",
216 |    "mimetype": "text/x-python",
217 |    "name": "python",
218 |    "nbconvert_exporter": "python",
219 |    "pygments_lexer": "ipython2",
220 |    "version": "2.7.15"
221 |   }
222 |  },
223 |  "nbformat": 4,
224 |  "nbformat_minor": 2
225 | }
226 | 


--------------------------------------------------------------------------------
/Class15/.ipynb_checkpoints/Classification_on_MNIST-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class15/.ipynb_checkpoints/Simple_Autoencoder-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class15/Classification_on_MNIST.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "%matplotlib inline\n",
 12 |     "import pandas as pd\n",
 13 |     "from keras.utils import np_utils\n",
 14 |     "from keras.models import Sequential\n",
 15 |     "from keras.layers import Dense, Activation, Input"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 2,
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "ds = pd.read_csv('/Users/ayush/Downloads/all/train.csv')"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 3,
 30 |    "metadata": {},
 31 |    "outputs": [
 32 |     {
 33 |      "data": {
 34 |       "text/html": [
 35 |        "<div>\n",
 36 |        "<style scoped>\n",
 37 |        "    .dataframe tbody tr th:only-of-type {\n",
 38 |        "        vertical-align: middle;\n",
 39 |        "    }\n",
 40 |        "\n",
 41 |        "    .dataframe tbody tr th {\n",
 42 |        "        vertical-align: top;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe thead th {\n",
 46 |        "        text-align: right;\n",
 47 |        "    }\n",
 48 |        "</style>\n",
 49 |        "<table border=\"1\" class=\"dataframe\">\n",
 50 |        "  <thead>\n",
 51 |        "    <tr style=\"text-align: right;\">\n",
 52 |        "      <th></th>\n",
 53 |        "      <th>label</th>\n",
 54 |        "      <th>pixel0</th>\n",
 55 |        "      <th>pixel1</th>\n",
 56 |        "      <th>pixel2</th>\n",
 57 |        "      <th>pixel3</th>\n",
 58 |        "      <th>pixel4</th>\n",
 59 |        "      <th>pixel5</th>\n",
 60 |        "      <th>pixel6</th>\n",
 61 |        "      <th>pixel7</th>\n",
 62 |        "      <th>pixel8</th>\n",
 63 |        "      <th>...</th>\n",
 64 |        "      <th>pixel774</th>\n",
 65 |        "      <th>pixel775</th>\n",
 66 |        "      <th>pixel776</th>\n",
 67 |        "      <th>pixel777</th>\n",
 68 |        "      <th>pixel778</th>\n",
 69 |        "      <th>pixel779</th>\n",
 70 |        "      <th>pixel780</th>\n",
 71 |        "      <th>pixel781</th>\n",
 72 |        "      <th>pixel782</th>\n",
 73 |        "      <th>pixel783</th>\n",
 74 |        "    </tr>\n",
 75 |        "  </thead>\n",
 76 |        "  <tbody>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>0</th>\n",
 79 |        "      <td>1</td>\n",
 80 |        "      <td>0</td>\n",
 81 |        "      <td>0</td>\n",
 82 |        "      <td>0</td>\n",
 83 |        "      <td>0</td>\n",
 84 |        "      <td>0</td>\n",
 85 |        "      <td>0</td>\n",
 86 |        "      <td>0</td>\n",
 87 |        "      <td>0</td>\n",
 88 |        "      <td>0</td>\n",
 89 |        "      <td>...</td>\n",
 90 |        "      <td>0</td>\n",
 91 |        "      <td>0</td>\n",
 92 |        "      <td>0</td>\n",
 93 |        "      <td>0</td>\n",
 94 |        "      <td>0</td>\n",
 95 |        "      <td>0</td>\n",
 96 |        "      <td>0</td>\n",
 97 |        "      <td>0</td>\n",
 98 |        "      <td>0</td>\n",
 99 |        "      <td>0</td>\n",
100 |        "    </tr>\n",
101 |        "    <tr>\n",
102 |        "      <th>1</th>\n",
103 |        "      <td>0</td>\n",
104 |        "      <td>0</td>\n",
105 |        "      <td>0</td>\n",
106 |        "      <td>0</td>\n",
107 |        "      <td>0</td>\n",
108 |        "      <td>0</td>\n",
109 |        "      <td>0</td>\n",
110 |        "      <td>0</td>\n",
111 |        "      <td>0</td>\n",
112 |        "      <td>0</td>\n",
113 |        "      <td>...</td>\n",
114 |        "      <td>0</td>\n",
115 |        "      <td>0</td>\n",
116 |        "      <td>0</td>\n",
117 |        "      <td>0</td>\n",
118 |        "      <td>0</td>\n",
119 |        "      <td>0</td>\n",
120 |        "      <td>0</td>\n",
121 |        "      <td>0</td>\n",
122 |        "      <td>0</td>\n",
123 |        "      <td>0</td>\n",
124 |        "    </tr>\n",
125 |        "    <tr>\n",
126 |        "      <th>2</th>\n",
127 |        "      <td>1</td>\n",
128 |        "      <td>0</td>\n",
129 |        "      <td>0</td>\n",
130 |        "      <td>0</td>\n",
131 |        "      <td>0</td>\n",
132 |        "      <td>0</td>\n",
133 |        "      <td>0</td>\n",
134 |        "      <td>0</td>\n",
135 |        "      <td>0</td>\n",
136 |        "      <td>0</td>\n",
137 |        "      <td>...</td>\n",
138 |        "      <td>0</td>\n",
139 |        "      <td>0</td>\n",
140 |        "      <td>0</td>\n",
141 |        "      <td>0</td>\n",
142 |        "      <td>0</td>\n",
143 |        "      <td>0</td>\n",
144 |        "      <td>0</td>\n",
145 |        "      <td>0</td>\n",
146 |        "      <td>0</td>\n",
147 |        "      <td>0</td>\n",
148 |        "    </tr>\n",
149 |        "    <tr>\n",
150 |        "      <th>3</th>\n",
151 |        "      <td>4</td>\n",
152 |        "      <td>0</td>\n",
153 |        "      <td>0</td>\n",
154 |        "      <td>0</td>\n",
155 |        "      <td>0</td>\n",
156 |        "      <td>0</td>\n",
157 |        "      <td>0</td>\n",
158 |        "      <td>0</td>\n",
159 |        "      <td>0</td>\n",
160 |        "      <td>0</td>\n",
161 |        "      <td>...</td>\n",
162 |        "      <td>0</td>\n",
163 |        "      <td>0</td>\n",
164 |        "      <td>0</td>\n",
165 |        "      <td>0</td>\n",
166 |        "      <td>0</td>\n",
167 |        "      <td>0</td>\n",
168 |        "      <td>0</td>\n",
169 |        "      <td>0</td>\n",
170 |        "      <td>0</td>\n",
171 |        "      <td>0</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>4</th>\n",
175 |        "      <td>0</td>\n",
176 |        "      <td>0</td>\n",
177 |        "      <td>0</td>\n",
178 |        "      <td>0</td>\n",
179 |        "      <td>0</td>\n",
180 |        "      <td>0</td>\n",
181 |        "      <td>0</td>\n",
182 |        "      <td>0</td>\n",
183 |        "      <td>0</td>\n",
184 |        "      <td>0</td>\n",
185 |        "      <td>...</td>\n",
186 |        "      <td>0</td>\n",
187 |        "      <td>0</td>\n",
188 |        "      <td>0</td>\n",
189 |        "      <td>0</td>\n",
190 |        "      <td>0</td>\n",
191 |        "      <td>0</td>\n",
192 |        "      <td>0</td>\n",
193 |        "      <td>0</td>\n",
194 |        "      <td>0</td>\n",
195 |        "      <td>0</td>\n",
196 |        "    </tr>\n",
197 |        "  </tbody>\n",
198 |        "</table>\n",
199 |        "<p>5 rows × 785 columns</p>\n",
200 |        "</div>"
201 |       ],
202 |       "text/plain": [
203 |        "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
204 |        "0      1       0       0       0       0       0       0       0       0   \n",
205 |        "1      0       0       0       0       0       0       0       0       0   \n",
206 |        "2      1       0       0       0       0       0       0       0       0   \n",
207 |        "3      4       0       0       0       0       0       0       0       0   \n",
208 |        "4      0       0       0       0       0       0       0       0       0   \n",
209 |        "\n",
210 |        "   pixel8    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
211 |        "0       0    ...            0         0         0         0         0   \n",
212 |        "1       0    ...            0         0         0         0         0   \n",
213 |        "2       0    ...            0         0         0         0         0   \n",
214 |        "3       0    ...            0         0         0         0         0   \n",
215 |        "4       0    ...            0         0         0         0         0   \n",
216 |        "\n",
217 |        "   pixel779  pixel780  pixel781  pixel782  pixel783  \n",
218 |        "0         0         0         0         0         0  \n",
219 |        "1         0         0         0         0         0  \n",
220 |        "2         0         0         0         0         0  \n",
221 |        "3         0         0         0         0         0  \n",
222 |        "4         0         0         0         0         0  \n",
223 |        "\n",
224 |        "[5 rows x 785 columns]"
225 |       ]
226 |      },
227 |      "execution_count": 3,
228 |      "metadata": {},
229 |      "output_type": "execute_result"
230 |     }
231 |    ],
232 |    "source": [
233 |     "ds.head()"
234 |    ]
235 |   },
236 |   {
237 |    "cell_type": "code",
238 |    "execution_count": 4,
239 |    "metadata": {},
240 |    "outputs": [
241 |     {
242 |      "name": "stdout",
243 |      "output_type": "stream",
244 |      "text": [
245 |       "(42000, 785)\n"
246 |      ]
247 |     }
248 |    ],
249 |    "source": [
250 |     "data = ds.values\n",
251 |     "print data.shape"
252 |    ]
253 |   },
254 |   {
255 |    "cell_type": "code",
256 |    "execution_count": 8,
257 |    "metadata": {},
258 |    "outputs": [
259 |     {
260 |      "name": "stdout",
261 |      "output_type": "stream",
262 |      "text": [
263 |       "6400\n",
264 |       "(6400, 784) (6400,)\n",
265 |       "(1600, 784) (1600,)\n"
266 |      ]
267 |     }
268 |    ],
269 |    "source": [
270 |     "## data Preperation\n",
271 |     "split_data = data[:8000]\n",
272 |     "split = int(0.8*split_data.shape[0])\n",
273 |     "print split\n",
274 |     "X_train = split_data[:split, 1:]\n",
275 |     "y_train = split_data[:split, 0]\n",
276 |     "\n",
277 |     "X_test = split_data[split:, 1:]\n",
278 |     "y_test = split_data[split:, 0]\n",
279 |     "print X_train.shape, y_train.shape\n",
280 |     "print X_test.shape, y_test.shape"
281 |    ]
282 |   },
283 |   {
284 |    "cell_type": "code",
285 |    "execution_count": 9,
286 |    "metadata": {},
287 |    "outputs": [
288 |     {
289 |      "name": "stdout",
290 |      "output_type": "stream",
291 |      "text": [
292 |       "(6400, 10)\n",
293 |       "(1600, 10)\n"
294 |      ]
295 |     }
296 |    ],
297 |    "source": [
298 |     "## One Hot Encodings\n",
299 |     "Y_train = np_utils.to_categorical(y_train)\n",
300 |     "Y_test = np_utils.to_categorical(y_test)\n",
301 |     "print Y_train.shape\n",
302 |     "print Y_test.shape"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "code",
307 |    "execution_count": 10,
308 |    "metadata": {},
309 |    "outputs": [
310 |     {
311 |      "name": "stdout",
312 |      "output_type": "stream",
313 |      "text": [
314 |       "1 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
315 |       "0 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
316 |       "0 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
317 |       "8 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
318 |       "9 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n"
319 |      ]
320 |     }
321 |    ],
322 |    "source": [
323 |     "for ix in range(5):\n",
324 |     "    print y_test[ix], Y_test[ix]"
325 |    ]
326 |   },
327 |   {
328 |    "cell_type": "code",
329 |    "execution_count": 13,
330 |    "metadata": {},
331 |    "outputs": [],
332 |    "source": [
333 |     "def to_categories(y_test):\n",
334 |     "    rows = y_test.shape[0]\n",
335 |     "    cols = len(np.unique(y_test))\n",
336 |     "    \n",
337 |     "    mat = np.zeros((rows, cols))\n",
338 |     "    for ix in range(y_test.shape[0]):\n",
339 |     "        indx = y_test[ix]\n",
340 |     "        mat[ix][indx] = 1.0\n",
341 |     "    return mat"
342 |    ]
343 |   },
344 |   {
345 |    "cell_type": "code",
346 |    "execution_count": 18,
347 |    "metadata": {},
348 |    "outputs": [
349 |     {
350 |      "name": "stdout",
351 |      "output_type": "stream",
352 |      "text": [
353 |       "_________________________________________________________________\n",
354 |       "Layer (type)                 Output Shape              Param #   \n",
355 |       "=================================================================\n",
356 |       "dense_8 (Dense)              (None, 256)               200960    \n",
357 |       "_________________________________________________________________\n",
358 |       "activation_6 (Activation)    (None, 256)               0         \n",
359 |       "_________________________________________________________________\n",
360 |       "dense_9 (Dense)              (None, 100)               25700     \n",
361 |       "_________________________________________________________________\n",
362 |       "activation_7 (Activation)    (None, 100)               0         \n",
363 |       "_________________________________________________________________\n",
364 |       "dense_10 (Dense)             (None, 10)                1010      \n",
365 |       "_________________________________________________________________\n",
366 |       "activation_8 (Activation)    (None, 10)                0         \n",
367 |       "=================================================================\n",
368 |       "Total params: 227,670\n",
369 |       "Trainable params: 227,670\n",
370 |       "Non-trainable params: 0\n",
371 |       "_________________________________________________________________\n"
372 |      ]
373 |     }
374 |    ],
375 |    "source": [
376 |     "model = Sequential()\n",
377 |     "model.add(Dense(256, input_shape=(784,)))\n",
378 |     "model.add(Activation('relu'))\n",
379 |     "model.add(Dense(100))\n",
380 |     "model.add(Activation('relu'))\n",
381 |     "model.add(Dense(10))\n",
382 |     "model.add(Activation('softmax'))\n",
383 |     "\n",
384 |     "model.summary()"
385 |    ]
386 |   },
387 |   {
388 |    "cell_type": "code",
389 |    "execution_count": 17,
390 |    "metadata": {},
391 |    "outputs": [],
392 |    "source": [
393 |     "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=)"
394 |    ]
395 |   },
396 |   {
397 |    "cell_type": "code",
398 |    "execution_count": null,
399 |    "metadata": {},
400 |    "outputs": [],
401 |    "source": []
402 |   },
403 |   {
404 |    "cell_type": "code",
405 |    "execution_count": null,
406 |    "metadata": {},
407 |    "outputs": [],
408 |    "source": []
409 |   }
410 |  ],
411 |  "metadata": {
412 |   "kernelspec": {
413 |    "display_name": "Python 2",
414 |    "language": "python",
415 |    "name": "python2"
416 |   },
417 |   "language_info": {
418 |    "codemirror_mode": {
419 |     "name": "ipython",
420 |     "version": 2
421 |    },
422 |    "file_extension": ".py",
423 |    "mimetype": "text/x-python",
424 |    "name": "python",
425 |    "nbconvert_exporter": "python",
426 |    "pygments_lexer": "ipython2",
427 |    "version": "2.7.15"
428 |   }
429 |  },
430 |  "nbformat": 4,
431 |  "nbformat_minor": 2
432 | }
433 | 


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/Class16/.ipynb_checkpoints/Conv_Op-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class16/.ipynb_checkpoints/Convolution_Networks-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class16/Convolution_Networks.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "%matplotlib inline\n",
 12 |     "import pandas as pd\n",
 13 |     "import cv2\n",
 14 |     "from keras.models import Sequential\n",
 15 |     "from keras.layers import Dense, Activation, Convolution2D, Flatten, MaxPooling2D\n",
 16 |     "from keras.utils import np_utils"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "code",
 21 |    "execution_count": 22,
 22 |    "metadata": {},
 23 |    "outputs": [
 24 |     {
 25 |      "data": {
 26 |       "text/html": [
 27 |        "<div>\n",
 28 |        "<style scoped>\n",
 29 |        "    .dataframe tbody tr th:only-of-type {\n",
 30 |        "        vertical-align: middle;\n",
 31 |        "    }\n",
 32 |        "\n",
 33 |        "    .dataframe tbody tr th {\n",
 34 |        "        vertical-align: top;\n",
 35 |        "    }\n",
 36 |        "\n",
 37 |        "    .dataframe thead th {\n",
 38 |        "        text-align: right;\n",
 39 |        "    }\n",
 40 |        "</style>\n",
 41 |        "<table border=\"1\" class=\"dataframe\">\n",
 42 |        "  <thead>\n",
 43 |        "    <tr style=\"text-align: right;\">\n",
 44 |        "      <th></th>\n",
 45 |        "      <th>label</th>\n",
 46 |        "      <th>pixel0</th>\n",
 47 |        "      <th>pixel1</th>\n",
 48 |        "      <th>pixel2</th>\n",
 49 |        "      <th>pixel3</th>\n",
 50 |        "      <th>pixel4</th>\n",
 51 |        "      <th>pixel5</th>\n",
 52 |        "      <th>pixel6</th>\n",
 53 |        "      <th>pixel7</th>\n",
 54 |        "      <th>pixel8</th>\n",
 55 |        "      <th>...</th>\n",
 56 |        "      <th>pixel774</th>\n",
 57 |        "      <th>pixel775</th>\n",
 58 |        "      <th>pixel776</th>\n",
 59 |        "      <th>pixel777</th>\n",
 60 |        "      <th>pixel778</th>\n",
 61 |        "      <th>pixel779</th>\n",
 62 |        "      <th>pixel780</th>\n",
 63 |        "      <th>pixel781</th>\n",
 64 |        "      <th>pixel782</th>\n",
 65 |        "      <th>pixel783</th>\n",
 66 |        "    </tr>\n",
 67 |        "  </thead>\n",
 68 |        "  <tbody>\n",
 69 |        "    <tr>\n",
 70 |        "      <th>0</th>\n",
 71 |        "      <td>1</td>\n",
 72 |        "      <td>0</td>\n",
 73 |        "      <td>0</td>\n",
 74 |        "      <td>0</td>\n",
 75 |        "      <td>0</td>\n",
 76 |        "      <td>0</td>\n",
 77 |        "      <td>0</td>\n",
 78 |        "      <td>0</td>\n",
 79 |        "      <td>0</td>\n",
 80 |        "      <td>0</td>\n",
 81 |        "      <td>...</td>\n",
 82 |        "      <td>0</td>\n",
 83 |        "      <td>0</td>\n",
 84 |        "      <td>0</td>\n",
 85 |        "      <td>0</td>\n",
 86 |        "      <td>0</td>\n",
 87 |        "      <td>0</td>\n",
 88 |        "      <td>0</td>\n",
 89 |        "      <td>0</td>\n",
 90 |        "      <td>0</td>\n",
 91 |        "      <td>0</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <th>1</th>\n",
 95 |        "      <td>0</td>\n",
 96 |        "      <td>0</td>\n",
 97 |        "      <td>0</td>\n",
 98 |        "      <td>0</td>\n",
 99 |        "      <td>0</td>\n",
100 |        "      <td>0</td>\n",
101 |        "      <td>0</td>\n",
102 |        "      <td>0</td>\n",
103 |        "      <td>0</td>\n",
104 |        "      <td>0</td>\n",
105 |        "      <td>...</td>\n",
106 |        "      <td>0</td>\n",
107 |        "      <td>0</td>\n",
108 |        "      <td>0</td>\n",
109 |        "      <td>0</td>\n",
110 |        "      <td>0</td>\n",
111 |        "      <td>0</td>\n",
112 |        "      <td>0</td>\n",
113 |        "      <td>0</td>\n",
114 |        "      <td>0</td>\n",
115 |        "      <td>0</td>\n",
116 |        "    </tr>\n",
117 |        "    <tr>\n",
118 |        "      <th>2</th>\n",
119 |        "      <td>1</td>\n",
120 |        "      <td>0</td>\n",
121 |        "      <td>0</td>\n",
122 |        "      <td>0</td>\n",
123 |        "      <td>0</td>\n",
124 |        "      <td>0</td>\n",
125 |        "      <td>0</td>\n",
126 |        "      <td>0</td>\n",
127 |        "      <td>0</td>\n",
128 |        "      <td>0</td>\n",
129 |        "      <td>...</td>\n",
130 |        "      <td>0</td>\n",
131 |        "      <td>0</td>\n",
132 |        "      <td>0</td>\n",
133 |        "      <td>0</td>\n",
134 |        "      <td>0</td>\n",
135 |        "      <td>0</td>\n",
136 |        "      <td>0</td>\n",
137 |        "      <td>0</td>\n",
138 |        "      <td>0</td>\n",
139 |        "      <td>0</td>\n",
140 |        "    </tr>\n",
141 |        "    <tr>\n",
142 |        "      <th>3</th>\n",
143 |        "      <td>4</td>\n",
144 |        "      <td>0</td>\n",
145 |        "      <td>0</td>\n",
146 |        "      <td>0</td>\n",
147 |        "      <td>0</td>\n",
148 |        "      <td>0</td>\n",
149 |        "      <td>0</td>\n",
150 |        "      <td>0</td>\n",
151 |        "      <td>0</td>\n",
152 |        "      <td>0</td>\n",
153 |        "      <td>...</td>\n",
154 |        "      <td>0</td>\n",
155 |        "      <td>0</td>\n",
156 |        "      <td>0</td>\n",
157 |        "      <td>0</td>\n",
158 |        "      <td>0</td>\n",
159 |        "      <td>0</td>\n",
160 |        "      <td>0</td>\n",
161 |        "      <td>0</td>\n",
162 |        "      <td>0</td>\n",
163 |        "      <td>0</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>4</th>\n",
167 |        "      <td>0</td>\n",
168 |        "      <td>0</td>\n",
169 |        "      <td>0</td>\n",
170 |        "      <td>0</td>\n",
171 |        "      <td>0</td>\n",
172 |        "      <td>0</td>\n",
173 |        "      <td>0</td>\n",
174 |        "      <td>0</td>\n",
175 |        "      <td>0</td>\n",
176 |        "      <td>0</td>\n",
177 |        "      <td>...</td>\n",
178 |        "      <td>0</td>\n",
179 |        "      <td>0</td>\n",
180 |        "      <td>0</td>\n",
181 |        "      <td>0</td>\n",
182 |        "      <td>0</td>\n",
183 |        "      <td>0</td>\n",
184 |        "      <td>0</td>\n",
185 |        "      <td>0</td>\n",
186 |        "      <td>0</td>\n",
187 |        "      <td>0</td>\n",
188 |        "    </tr>\n",
189 |        "  </tbody>\n",
190 |        "</table>\n",
191 |        "<p>5 rows × 785 columns</p>\n",
192 |        "</div>"
193 |       ],
194 |       "text/plain": [
195 |        "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
196 |        "0      1       0       0       0       0       0       0       0       0   \n",
197 |        "1      0       0       0       0       0       0       0       0       0   \n",
198 |        "2      1       0       0       0       0       0       0       0       0   \n",
199 |        "3      4       0       0       0       0       0       0       0       0   \n",
200 |        "4      0       0       0       0       0       0       0       0       0   \n",
201 |        "\n",
202 |        "   pixel8    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
203 |        "0       0    ...            0         0         0         0         0   \n",
204 |        "1       0    ...            0         0         0         0         0   \n",
205 |        "2       0    ...            0         0         0         0         0   \n",
206 |        "3       0    ...            0         0         0         0         0   \n",
207 |        "4       0    ...            0         0         0         0         0   \n",
208 |        "\n",
209 |        "   pixel779  pixel780  pixel781  pixel782  pixel783  \n",
210 |        "0         0         0         0         0         0  \n",
211 |        "1         0         0         0         0         0  \n",
212 |        "2         0         0         0         0         0  \n",
213 |        "3         0         0         0         0         0  \n",
214 |        "4         0         0         0         0         0  \n",
215 |        "\n",
216 |        "[5 rows x 785 columns]"
217 |       ]
218 |      },
219 |      "execution_count": 22,
220 |      "metadata": {},
221 |      "output_type": "execute_result"
222 |     }
223 |    ],
224 |    "source": [
225 |     "ds = pd.read_csv('/Users/ayush/Downloads/all/train.csv')\n",
226 |     "ds.head()"
227 |    ]
228 |   },
229 |   {
230 |    "cell_type": "code",
231 |    "execution_count": 23,
232 |    "metadata": {},
233 |    "outputs": [],
234 |    "source": [
235 |     "data = ds.values[:8000]"
236 |    ]
237 |   },
238 |   {
239 |    "cell_type": "code",
240 |    "execution_count": 24,
241 |    "metadata": {},
242 |    "outputs": [
243 |     {
244 |      "name": "stdout",
245 |      "output_type": "stream",
246 |      "text": [
247 |       "(6400, 784) (1600, 784)\n",
248 |       "(6400,) (1600,)\n"
249 |      ]
250 |     }
251 |    ],
252 |    "source": [
253 |     "split = int(0.8*data.shape[0])\n",
254 |     "X_train = data[:split, 1:]\n",
255 |     "X_test = data[split:, 1:]\n",
256 |     "\n",
257 |     "y_train = data[:split, 0]\n",
258 |     "y_test = data[split:, 0]\n",
259 |     "\n",
260 |     "print X_train.shape, X_test.shape\n",
261 |     "print y_train.shape, y_test.shape"
262 |    ]
263 |   },
264 |   {
265 |    "cell_type": "code",
266 |    "execution_count": 25,
267 |    "metadata": {},
268 |    "outputs": [
269 |     {
270 |      "name": "stdout",
271 |      "output_type": "stream",
272 |      "text": [
273 |       "(6400, 10)\n",
274 |       "(1600, 10)\n"
275 |      ]
276 |     }
277 |    ],
278 |    "source": [
279 |     "Y_train = np_utils.to_categorical(y_train)\n",
280 |     "Y_test = np_utils.to_categorical(y_test)\n",
281 |     "print Y_train.shape\n",
282 |     "print Y_test.shape"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 26,
288 |    "metadata": {},
289 |    "outputs": [
290 |     {
291 |      "name": "stdout",
292 |      "output_type": "stream",
293 |      "text": [
294 |       "[0 1 2 3 4 5 6 7 8 9]\n"
295 |      ]
296 |     }
297 |    ],
298 |    "source": [
299 |     "print np.unique(y_train)"
300 |    ]
301 |   },
302 |   {
303 |    "cell_type": "code",
304 |    "execution_count": 27,
305 |    "metadata": {},
306 |    "outputs": [
307 |     {
308 |      "name": "stdout",
309 |      "output_type": "stream",
310 |      "text": [
311 |       "(6400, 784)\n"
312 |      ]
313 |     }
314 |    ],
315 |    "source": [
316 |     "X_train = X_train/255.0\n",
317 |     "X_test = X_test/255.0\n",
318 |     "print X_train.shape"
319 |    ]
320 |   },
321 |   {
322 |    "cell_type": "code",
323 |    "execution_count": 28,
324 |    "metadata": {},
325 |    "outputs": [
326 |     {
327 |      "name": "stdout",
328 |      "output_type": "stream",
329 |      "text": [
330 |       "(6400, 28, 28, 1)\n",
331 |       "(1600, 28, 28, 1)\n"
332 |      ]
333 |     }
334 |    ],
335 |    "source": [
336 |     "## Because layer 1 is convolution\n",
337 |     "X_train = X_train.reshape((-1, 28, 28, 1))\n",
338 |     "X_test = X_test.reshape((-1, 28, 28, 1))\n",
339 |     "print X_train.shape\n",
340 |     "print X_test.shape"
341 |    ]
342 |   },
343 |   {
344 |    "cell_type": "code",
345 |    "execution_count": 29,
346 |    "metadata": {},
347 |    "outputs": [
348 |     {
349 |      "name": "stdout",
350 |      "output_type": "stream",
351 |      "text": [
352 |       "_________________________________________________________________\n",
353 |       "Layer (type)                 Output Shape              Param #   \n",
354 |       "=================================================================\n",
355 |       "conv2d_10 (Conv2D)           (None, 26, 26, 32)        320       \n",
356 |       "_________________________________________________________________\n",
357 |       "activation_14 (Activation)   (None, 26, 26, 32)        0         \n",
358 |       "_________________________________________________________________\n",
359 |       "conv2d_11 (Conv2D)           (None, 24, 24, 16)        4624      \n",
360 |       "_________________________________________________________________\n",
361 |       "activation_15 (Activation)   (None, 24, 24, 16)        0         \n",
362 |       "_________________________________________________________________\n",
363 |       "max_pooling2d_3 (MaxPooling2 (None, 12, 12, 16)        0         \n",
364 |       "_________________________________________________________________\n",
365 |       "conv2d_12 (Conv2D)           (None, 10, 10, 8)         1160      \n",
366 |       "_________________________________________________________________\n",
367 |       "activation_16 (Activation)   (None, 10, 10, 8)         0         \n",
368 |       "_________________________________________________________________\n",
369 |       "conv2d_13 (Conv2D)           (None, 8, 8, 8)           584       \n",
370 |       "_________________________________________________________________\n",
371 |       "activation_17 (Activation)   (None, 8, 8, 8)           0         \n",
372 |       "_________________________________________________________________\n",
373 |       "flatten_3 (Flatten)          (None, 512)               0         \n",
374 |       "_________________________________________________________________\n",
375 |       "dense_5 (Dense)              (None, 256)               131328    \n",
376 |       "_________________________________________________________________\n",
377 |       "activation_18 (Activation)   (None, 256)               0         \n",
378 |       "_________________________________________________________________\n",
379 |       "dense_6 (Dense)              (None, 10)                2570      \n",
380 |       "_________________________________________________________________\n",
381 |       "activation_19 (Activation)   (None, 10)                0         \n",
382 |       "=================================================================\n",
383 |       "Total params: 140,586\n",
384 |       "Trainable params: 140,586\n",
385 |       "Non-trainable params: 0\n",
386 |       "_________________________________________________________________\n"
387 |      ]
388 |     }
389 |    ],
390 |    "source": [
391 |     "model = Sequential()\n",
392 |     "\n",
393 |     "## Shape Here - (b, 28, 28, 1)\n",
394 |     "model.add(Convolution2D(32, kernel_size=(3, 3), input_shape=(28, 28, 1)))\n",
395 |     "model.add(Activation('relu'))\n",
396 |     "## Shape Here - (b, 26, 26, 32)\n",
397 |     "model.add(Convolution2D(16, kernel_size=(3, 3)))\n",
398 |     "model.add(Activation('relu'))\n",
399 |     "## Shape Here - (b, 24, 24, 16)\n",
400 |     "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
401 |     "## Shape Here - (b, 12, 12, 16)\n",
402 |     "model.add(Convolution2D(8, (3, 3)))\n",
403 |     "model.add(Activation('relu'))\n",
404 |     "## Shape Here - (b, 10, 10, 8)\n",
405 |     "model.add(Convolution2D(8, (3, 3)))\n",
406 |     "model.add(Activation('relu'))\n",
407 |     "## Shape Here - (b, 8, 8, 8)\n",
408 |     "model.add(Flatten())\n",
409 |     "## Shape Here - (b, 512)\n",
410 |     "model.add(Dense(256))\n",
411 |     "model.add(Activation('relu'))\n",
412 |     "## Shape Here - (b, 256)\n",
413 |     "model.add(Dense(10))\n",
414 |     "## Shape Here - (b, 10)\n",
415 |     "model.add(Activation('softmax'))\n",
416 |     "model.summary()"
417 |    ]
418 |   },
419 |   {
420 |    "cell_type": "code",
421 |    "execution_count": 30,
422 |    "metadata": {},
423 |    "outputs": [],
424 |    "source": [
425 |     "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
426 |    ]
427 |   },
428 |   {
429 |    "cell_type": "code",
430 |    "execution_count": 31,
431 |    "metadata": {},
432 |    "outputs": [
433 |     {
434 |      "name": "stdout",
435 |      "output_type": "stream",
436 |      "text": [
437 |       "Train on 6400 samples, validate on 1600 samples\n",
438 |       "Epoch 1/20\n",
439 |       "6400/6400 [==============================] - 9s 1ms/step - loss: 0.3764 - acc: 0.8794 - val_loss: 0.2260 - val_acc: 0.9325\n",
440 |       "Epoch 2/20\n",
441 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.1243 - acc: 0.9622 - val_loss: 0.1975 - val_acc: 0.9450\n",
442 |       "Epoch 3/20\n",
443 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0738 - acc: 0.9770 - val_loss: 0.1271 - val_acc: 0.9606\n",
444 |       "Epoch 4/20\n",
445 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0580 - acc: 0.9819 - val_loss: 0.1729 - val_acc: 0.9550\n",
446 |       "Epoch 5/20\n",
447 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0399 - acc: 0.9859 - val_loss: 0.1514 - val_acc: 0.9569\n",
448 |       "Epoch 6/20\n",
449 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0271 - acc: 0.9906 - val_loss: 0.1355 - val_acc: 0.9650\n",
450 |       "Epoch 7/20\n",
451 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0324 - acc: 0.9888 - val_loss: 0.2425 - val_acc: 0.9425\n",
452 |       "Epoch 8/20\n",
453 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0188 - acc: 0.9947 - val_loss: 0.1501 - val_acc: 0.9669\n",
454 |       "Epoch 9/20\n",
455 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0210 - acc: 0.9936 - val_loss: 0.1515 - val_acc: 0.9694\n",
456 |       "Epoch 10/20\n",
457 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0171 - acc: 0.9948 - val_loss: 0.1614 - val_acc: 0.9669\n",
458 |       "Epoch 11/20\n",
459 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0149 - acc: 0.9959 - val_loss: 0.1401 - val_acc: 0.9719\n",
460 |       "Epoch 12/20\n",
461 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0158 - acc: 0.9958 - val_loss: 0.1594 - val_acc: 0.9681\n",
462 |       "Epoch 13/20\n",
463 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0139 - acc: 0.9953 - val_loss: 0.1701 - val_acc: 0.9637\n",
464 |       "Epoch 14/20\n",
465 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0127 - acc: 0.9967 - val_loss: 0.2112 - val_acc: 0.9613\n",
466 |       "Epoch 15/20\n",
467 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0120 - acc: 0.9964 - val_loss: 0.1560 - val_acc: 0.9731\n",
468 |       "Epoch 16/20\n",
469 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0097 - acc: 0.9966 - val_loss: 0.2243 - val_acc: 0.9619\n",
470 |       "Epoch 17/20\n",
471 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0153 - acc: 0.9955 - val_loss: 0.1684 - val_acc: 0.9613\n",
472 |       "Epoch 18/20\n",
473 |       "6400/6400 [==============================] - 11s 2ms/step - loss: 0.0124 - acc: 0.9969 - val_loss: 0.1564 - val_acc: 0.9712\n",
474 |       "Epoch 19/20\n",
475 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0032 - acc: 0.9994 - val_loss: 0.1899 - val_acc: 0.9681\n",
476 |       "Epoch 20/20\n",
477 |       "6400/6400 [==============================] - 10s 2ms/step - loss: 0.0044 - acc: 0.9984 - val_loss: 0.1879 - val_acc: 0.9706\n"
478 |      ]
479 |     },
480 |     {
481 |      "data": {
482 |       "text/plain": [
483 |        "<keras.callbacks.History at 0x1145e2150>"
484 |       ]
485 |      },
486 |      "execution_count": 31,
487 |      "metadata": {},
488 |      "output_type": "execute_result"
489 |     }
490 |    ],
491 |    "source": [
492 |     "model.fit(X_train, Y_train, epochs=20, batch_size=8, validation_data=[X_test, Y_test])"
493 |    ]
494 |   },
495 |   {
496 |    "cell_type": "code",
497 |    "execution_count": null,
498 |    "metadata": {},
499 |    "outputs": [],
500 |    "source": []
501 |   }
502 |  ],
503 |  "metadata": {
504 |   "kernelspec": {
505 |    "display_name": "Python 2",
506 |    "language": "python",
507 |    "name": "python2"
508 |   },
509 |   "language_info": {
510 |    "codemirror_mode": {
511 |     "name": "ipython",
512 |     "version": 2
513 |    },
514 |    "file_extension": ".py",
515 |    "mimetype": "text/x-python",
516 |    "name": "python",
517 |    "nbconvert_exporter": "python",
518 |    "pygments_lexer": "ipython2",
519 |    "version": "2.7.15"
520 |   }
521 |  },
522 |  "nbformat": 4,
523 |  "nbformat_minor": 2
524 | }
525 | 


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/Class16/img.jpg:
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https://raw.githubusercontent.com/coding-blocks-archives/machine-learning-june-2018/f0772da9a9a2aec974c1c1daf7aa1f482d764962/Class16/img.jpg


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/Class18/.ipynb_checkpoints/Markov_Chain-Text_Gen-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class18/.ipynb_checkpoints/Markov_Chains_text-generation-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import numpy as np\n",
 12 |     "import random"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "metadata": {
 19 |     "collapsed": true
 20 |    },
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "f = open('input.txt')\n",
 24 |     "ds = f.read()\n",
 25 |     "f.close()"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 5,
 31 |    "metadata": {},
 32 |    "outputs": [
 33 |     {
 34 |      "name": "stdout",
 35 |      "output_type": "stream",
 36 |      "text": [
 37 |       "3382026\n",
 38 |       "<type 'str'>\n"
 39 |      ]
 40 |     }
 41 |    ],
 42 |    "source": [
 43 |     "print len(ds)\n",
 44 |     "print type(ds)\n",
 45 |     "\n",
 46 |     "start = 2500\n",
 47 |     "length = 50000\n",
 48 |     "\n",
 49 |     "data = ' '.join([ix.strip() for ix in ds[start:start+length].split('\\n')]).lower()"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 8,
 55 |    "metadata": {},
 56 |    "outputs": [
 57 |     {
 58 |      "name": "stdout",
 59 |      "output_type": "stream",
 60 |      "text": [
 61 |       "45844\n",
 62 |       "48\n",
 63 |       "set(['!', ' ', '\"', \"'\", ')', '(', '-', ',', '.', '1', '0', '3', '2', '5', '4', '7', '6', '9', '8', ';', ':', '?', 'a', 'c', 'b', 'e', 'd', 'g', 'f', 'i', 'h', 'k', 'j', 'm', 'l', 'o', 'n', 'q', 'p', 's', 'r', 'u', 't', 'w', 'v', 'y', 'x', 'z'])\n"
 64 |      ]
 65 |     }
 66 |    ],
 67 |    "source": [
 68 |     "print len(data)\n",
 69 |     "\n",
 70 |     "x = set()\n",
 71 |     "for ix in data:\n",
 72 |     "    x.add(ix)\n",
 73 |     "print len(x)\n",
 74 |     "print x"
 75 |    ]
 76 |   },
 77 |   {
 78 |    "cell_type": "code",
 79 |    "execution_count": 16,
 80 |    "metadata": {
 81 |     "collapsed": true
 82 |    },
 83 |    "outputs": [],
 84 |    "source": [
 85 |     "## Transition Matrix - Frequency Values\n",
 86 |     "T = {}\n",
 87 |     "\n",
 88 |     "## order context\n",
 89 |     "order = 3\n",
 90 |     "\n",
 91 |     "for cx in range(len(data) - order):\n",
 92 |     "    cntx = data[cx:cx+order] ## Current State\n",
 93 |     "    \n",
 94 |     "    future_val = data[cx+order] ## Next State\n",
 95 |     "    if T.get(cntx) is None:\n",
 96 |     "        T[cntx] = {}\n",
 97 |     "        T[cntx][future_val] = 1\n",
 98 |     "    else:\n",
 99 |     "        if T[cntx].get(future_val) is None:\n",
100 |     "            T[cntx][future_val] = 1\n",
101 |     "        else:\n",
102 |     "            T[cntx][future_val] = T[cntx][future_val] + 1"
103 |    ]
104 |   },
105 |   {
106 |    "cell_type": "code",
107 |    "execution_count": 17,
108 |    "metadata": {},
109 |    "outputs": [],
110 |    "source": [
111 |     "## Transition Matrix Probabilities\n",
112 |     "count = 0\n",
113 |     "for key in T.keys():\n",
114 |     "    #if count>4:\n",
115 |     "        #break\n",
116 |     "    count+=1\n",
117 |     "    possible = T[key] ## Dict of all next states for this \"key\" current state\n",
118 |     "    \n",
119 |     "    total = float(sum(T[key].values()))\n",
120 |     "    #print key, \"---\", T[key], \"---\", total\n",
121 |     "    \n",
122 |     "    for k in T[key].keys():\n",
123 |     "        T[key][k] = T[key][k]/total\n",
124 |     "    \n",
125 |     "    #print key, \"---\", T[key], \"---\", total\n",
126 |     "    #print \"---------------\""
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "code",
131 |    "execution_count": 35,
132 |    "metadata": {
133 |     "collapsed": true
134 |    },
135 |    "outputs": [],
136 |    "source": [
137 |     "def generate_next_character(cntx):\n",
138 |     "    '''Returns next state given an input current state'''\n",
139 |     "    possible = T.get(cntx) ## Dict of possible next states for the input current state\n",
140 |     "    \n",
141 |     "    if possible is None:\n",
142 |     "        return ' '\n",
143 |     "    shuffled_keys = possible.keys()\n",
144 |     "    \n",
145 |     "    ## Only exploration\n",
146 |     "    random.shuffle(shuffled_keys)\n",
147 |     "    \n",
148 |     "    ## For exploitation also\n",
149 |     "    ## shuffled_keys = sorted(shuffled_keys, key=shuffled_keys.values(), reverse=True)\n",
150 |     "    \n",
151 |     "    r = np.random.random()\n",
152 |     "    #print r\n",
153 |     "    #print shuffled_keys\n",
154 |     "    key_probs = [possible[kx] for kx in shuffled_keys]\n",
155 |     "    #print key_probs\n",
156 |     "    for k in range(len(key_probs)):\n",
157 |     "        if r< key_probs[k]:\n",
158 |     "            return shuffled_keys[k]\n",
159 |     "    return random.choice(shuffled_keys)"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": 36,
165 |    "metadata": {},
166 |    "outputs": [
167 |     {
168 |      "data": {
169 |       "text/plain": [
170 |        "'p'"
171 |       ]
172 |      },
173 |      "execution_count": 36,
174 |      "metadata": {},
175 |      "output_type": "execute_result"
176 |     }
177 |    ],
178 |    "source": [
179 |     "generate_next_character('sco')"
180 |    ]
181 |   },
182 |   {
183 |    "cell_type": "code",
184 |    "execution_count": 39,
185 |    "metadata": {},
186 |    "outputs": [
187 |     {
188 |      "name": "stdout",
189 |      "output_type": "stream",
190 |      "text": [
191 |       " entrial marked; \"it of nose ove could facumstable tes glaned the save deten.  ween to me so the plained\n"
192 |      ]
193 |     }
194 |    ],
195 |    "source": [
196 |     "## Main Loop\n",
197 |     "index = int(np.random.random()*(len(data)-order))\n",
198 |     "initial = data[index:index+order]\n",
199 |     "context = initial\n",
200 |     "sentence = ' '+context\n",
201 |     "num_chars = 100\n",
202 |     "for ix in range(num_chars):\n",
203 |     "    next_char = generate_next_character(context)\n",
204 |     "    sentence+=next_char\n",
205 |     "    context = sentence[-order:]\n",
206 |     "print sentence"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": null,
212 |    "metadata": {
213 |     "collapsed": true
214 |    },
215 |    "outputs": [],
216 |    "source": []
217 |   }
218 |  ],
219 |  "metadata": {
220 |   "kernelspec": {
221 |    "display_name": "Python 2",
222 |    "language": "python",
223 |    "name": "python2"
224 |   },
225 |   "language_info": {
226 |    "codemirror_mode": {
227 |     "name": "ipython",
228 |     "version": 2
229 |    },
230 |    "file_extension": ".py",
231 |    "mimetype": "text/x-python",
232 |    "name": "python",
233 |    "nbconvert_exporter": "python",
234 |    "pygments_lexer": "ipython2",
235 |    "version": "2.7.15"
236 |   }
237 |  },
238 |  "nbformat": 4,
239 |  "nbformat_minor": 2
240 | }
241 | 


--------------------------------------------------------------------------------
/Class18/Markov_Chains_text-generation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import random"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 2,
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "f = open('input.txt')\n",
 20 |     "ds = f.read()\n",
 21 |     "f.close()"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 3,
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "name": "stdout",
 31 |      "output_type": "stream",
 32 |      "text": [
 33 |       "3382026\n",
 34 |       "<type 'str'>\n"
 35 |      ]
 36 |     }
 37 |    ],
 38 |    "source": [
 39 |     "print len(ds)\n",
 40 |     "print type(ds)\n",
 41 |     "\n",
 42 |     "start = 2500\n",
 43 |     "length = 50000\n",
 44 |     "\n",
 45 |     "data = ' '.join([ix.strip() for ix in ds[start:start+length].split('\\n')]).lower()"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 4,
 51 |    "metadata": {},
 52 |    "outputs": [
 53 |     {
 54 |      "name": "stdout",
 55 |      "output_type": "stream",
 56 |      "text": [
 57 |       "45844\n",
 58 |       "48\n",
 59 |       "set(['!', ' ', '\"', \"'\", ')', '(', '-', ',', '.', '1', '0', '3', '2', '5', '4', '7', '6', '9', '8', ';', ':', '?', 'a', 'c', 'b', 'e', 'd', 'g', 'f', 'i', 'h', 'k', 'j', 'm', 'l', 'o', 'n', 'q', 'p', 's', 'r', 'u', 't', 'w', 'v', 'y', 'x', 'z'])\n"
 60 |      ]
 61 |     }
 62 |    ],
 63 |    "source": [
 64 |     "print len(data)\n",
 65 |     "\n",
 66 |     "x = set()\n",
 67 |     "for ix in data:\n",
 68 |     "    x.add(ix)\n",
 69 |     "print len(x)\n",
 70 |     "print x"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": 5,
 76 |    "metadata": {},
 77 |    "outputs": [],
 78 |    "source": [
 79 |     "## Transition Matrix - Frequency Values\n",
 80 |     "T = {}\n",
 81 |     "\n",
 82 |     "## order context\n",
 83 |     "order = 3\n",
 84 |     "\n",
 85 |     "for cx in range(len(data) - order):\n",
 86 |     "    cntx = data[cx:cx+order] ## Current State\n",
 87 |     "    \n",
 88 |     "    future_val = data[cx+order] ## Next State\n",
 89 |     "    if T.get(cntx) is None:\n",
 90 |     "        T[cntx] = {}\n",
 91 |     "        T[cntx][future_val] = 1\n",
 92 |     "    else:\n",
 93 |     "        if T[cntx].get(future_val) is None:\n",
 94 |     "            T[cntx][future_val] = 1\n",
 95 |     "        else:\n",
 96 |     "            T[cntx][future_val] = T[cntx][future_val] + 1"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "code",
101 |    "execution_count": 6,
102 |    "metadata": {},
103 |    "outputs": [],
104 |    "source": [
105 |     "## Transition Matrix Probabilities\n",
106 |     "count = 0\n",
107 |     "for key in T.keys():\n",
108 |     "    #if count>4:\n",
109 |     "        #break\n",
110 |     "    count+=1\n",
111 |     "    possible = T[key] ## Dict of all next states for this \"key\" current state\n",
112 |     "    \n",
113 |     "    total = float(sum(T[key].values()))\n",
114 |     "    #print key, \"---\", T[key], \"---\", total\n",
115 |     "    \n",
116 |     "    for k in T[key].keys():\n",
117 |     "        T[key][k] = T[key][k]/total\n",
118 |     "    \n",
119 |     "    #print key, \"---\", T[key], \"---\", total\n",
120 |     "    #print \"---------------\""
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 7,
126 |    "metadata": {},
127 |    "outputs": [],
128 |    "source": [
129 |     "def generate_next_character(cntx):\n",
130 |     "    '''Returns next state given an input current state'''\n",
131 |     "    possible = T.get(cntx) ## Dict of possible next states for the input current state\n",
132 |     "    \n",
133 |     "    if possible is None:\n",
134 |     "        return ' '\n",
135 |     "    shuffled_keys = possible.keys()\n",
136 |     "    \n",
137 |     "    ## Only exploration\n",
138 |     "    random.shuffle(shuffled_keys)\n",
139 |     "    \n",
140 |     "    ## For exploitation also\n",
141 |     "    ## shuffled_keys = sorted(shuffled_keys, key=shuffled_keys.values(), reverse=True)\n",
142 |     "    \n",
143 |     "    r = np.random.random()\n",
144 |     "    #print r\n",
145 |     "    #print shuffled_keys\n",
146 |     "    key_probs = [possible[kx] for kx in shuffled_keys]\n",
147 |     "    #print key_probs\n",
148 |     "    for k in range(len(key_probs)):\n",
149 |     "        if r< key_probs[k]:\n",
150 |     "            return shuffled_keys[k]\n",
151 |     "    return random.choice(shuffled_keys)"
152 |    ]
153 |   },
154 |   {
155 |    "cell_type": "code",
156 |    "execution_count": 8,
157 |    "metadata": {},
158 |    "outputs": [
159 |     {
160 |      "data": {
161 |       "text/plain": [
162 |        "'v'"
163 |       ]
164 |      },
165 |      "execution_count": 8,
166 |      "metadata": {},
167 |      "output_type": "execute_result"
168 |     }
169 |    ],
170 |    "source": [
171 |     "generate_next_character('sco')"
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "code",
176 |    "execution_count": 9,
177 |    "metadata": {},
178 |    "outputs": [
179 |     {
180 |      "name": "stdout",
181 |      "output_type": "stream",
182 |      "text": [
183 |       "  commontinge who what?\" i direcial. is eariosity. i obtainst ver, is kness hazine?\"  you re-agent.\"  \"q\n"
184 |      ]
185 |     }
186 |    ],
187 |    "source": [
188 |     "## Main Loop\n",
189 |     "index = int(np.random.random()*(len(data)-order))\n",
190 |     "initial = data[index:index+order]\n",
191 |     "context = initial\n",
192 |     "sentence = ' '+context\n",
193 |     "num_chars = 100\n",
194 |     "for ix in range(num_chars):\n",
195 |     "    next_char = generate_next_character(context)\n",
196 |     "    sentence+=next_char\n",
197 |     "    context = sentence[-order:]\n",
198 |     "print sentence"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "code",
203 |    "execution_count": null,
204 |    "metadata": {
205 |     "collapsed": true
206 |    },
207 |    "outputs": [],
208 |    "source": []
209 |   }
210 |  ],
211 |  "metadata": {
212 |   "kernelspec": {
213 |    "display_name": "Python 2",
214 |    "language": "python",
215 |    "name": "python2"
216 |   },
217 |   "language_info": {
218 |    "codemirror_mode": {
219 |     "name": "ipython",
220 |     "version": 2
221 |    },
222 |    "file_extension": ".py",
223 |    "mimetype": "text/x-python",
224 |    "name": "python",
225 |    "nbconvert_exporter": "python",
226 |    "pygments_lexer": "ipython2",
227 |    "version": "2.7.15"
228 |   }
229 |  },
230 |  "nbformat": 4,
231 |  "nbformat_minor": 2
232 | }
233 | 


--------------------------------------------------------------------------------
/Class19/.ipynb_checkpoints/LSTM_SentimentAnalysis-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class2/.ipynb_checkpoints/Intro_to_Python-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class2/.ipynb_checkpoints/Libraries-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class2/.ipynb_checkpoints/Statistical_Computation-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


--------------------------------------------------------------------------------
/Class2/Intro_to_Python.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 4,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "<type 'int'>\n",
 13 |       "<type 'str'>\n",
 14 |       "<type 'float'>\n"
 15 |      ]
 16 |     }
 17 |    ],
 18 |    "source": [
 19 |     "## Variables\n",
 20 |     "a = 6\n",
 21 |     "print type(a)\n",
 22 |     "a = '6'\n",
 23 |     "print type(a)\n",
 24 |     "a = 10.0\n",
 25 |     "print type(a)"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 8,
 31 |    "metadata": {},
 32 |    "outputs": [
 33 |     {
 34 |      "name": "stdout",
 35 |      "output_type": "stream",
 36 |      "text": [
 37 |       "5\n",
 38 |       "[1, 2, 3, 4, 5, 10]\n",
 39 |       "[1, 33, 2, 3, 4, 5, 10]\n",
 40 |       "<type 'list'>\n"
 41 |      ]
 42 |     }
 43 |    ],
 44 |    "source": [
 45 |     "## Lists\n",
 46 |     "a = [1,2,3,4,5]\n",
 47 |     "print len(a)\n",
 48 |     "a.append(10)\n",
 49 |     "print a\n",
 50 |     "a.insert(1,33)\n",
 51 |     "print a\n",
 52 |     "\n",
 53 |     "a = list()\n",
 54 |     "print type(a)"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 13,
 60 |    "metadata": {},
 61 |    "outputs": [
 62 |     {
 63 |      "name": "stdout",
 64 |      "output_type": "stream",
 65 |      "text": [
 66 |       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
 67 |       "<type 'list'>\n",
 68 |       "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
 69 |       "[1, 3, 5, 7, 9]\n"
 70 |      ]
 71 |     }
 72 |    ],
 73 |    "source": [
 74 |     "## Range\n",
 75 |     "x = list(range(10))\n",
 76 |     "print x\n",
 77 |     "print type(x)\n",
 78 |     "\n",
 79 |     "x2 = range(1, 10)\n",
 80 |     "print x2\n",
 81 |     "x3 = range(1, 10, 2)\n",
 82 |     "print x3"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 29,
 88 |    "metadata": {},
 89 |    "outputs": [
 90 |     {
 91 |      "name": "stdout",
 92 |      "output_type": "stream",
 93 |      "text": [
 94 |       "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
 95 |       "[2, 3, 4]\n",
 96 |       "9\n",
 97 |       "9\n",
 98 |       "[1, 2, 3, 4, 5, 6, 7, 8]\n",
 99 |       "[]\n",
100 |       "[2, 4, 6, 8]\n"
101 |      ]
102 |     }
103 |    ],
104 |    "source": [
105 |     "## Slicing\n",
106 |     "x = range(1, 10)\n",
107 |     "print x\n",
108 |     "x2 = x[1:4]\n",
109 |     "print x2\n",
110 |     "x[4]\n",
111 |     "print x[-1]\n",
112 |     "print x[8]\n",
113 |     "print x[:-1]\n",
114 |     "print x[-1:-3]\n",
115 |     "print x[1::2]"
116 |    ]
117 |   },
118 |   {
119 |    "cell_type": "code",
120 |    "execution_count": 33,
121 |    "metadata": {},
122 |    "outputs": [
123 |     {
124 |      "name": "stdout",
125 |      "output_type": "stream",
126 |      "text": [
127 |       "<type 'str'>\n",
128 |       "<type 'str'>\n",
129 |       "1233 hello\n"
130 |      ]
131 |     }
132 |    ],
133 |    "source": [
134 |     "## Strings - are Immutable\n",
135 |     "a = '1233'\n",
136 |     "print type(a)\n",
137 |     "a = \"1233\"\n",
138 |     "print type(a)\n",
139 |     "b = ' hello'\n",
140 |     "print a + b"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": 49,
146 |    "metadata": {},
147 |    "outputs": [
148 |     {
149 |      "name": "stdout",
150 |      "output_type": "stream",
151 |      "text": [
152 |       "1 3 5 7 9 "
153 |      ]
154 |     }
155 |    ],
156 |    "source": [
157 |     "## Loops\n",
158 |     "for ix in xrange(1, 11, 2):\n",
159 |     "    print(ix, end=' ')"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": 53,
165 |    "metadata": {},
166 |    "outputs": [
167 |     {
168 |      "name": "stdout",
169 |      "output_type": "stream",
170 |      "text": [
171 |       "6\n",
172 |       "8\n"
173 |      ]
174 |     }
175 |    ],
176 |    "source": [
177 |     "print(2*3)\n",
178 |     "print(2**3)"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 54,
184 |    "metadata": {},
185 |    "outputs": [
186 |     {
187 |      "name": "stdout",
188 |      "output_type": "stream",
189 |      "text": [
190 |       "30\n"
191 |      ]
192 |     }
193 |    ],
194 |    "source": [
195 |     "## Functions\n",
196 |     "def my_func(arg1):\n",
197 |     "    return arg1*10\n",
198 |     "print(my_func(3))"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "code",
203 |    "execution_count": 57,
204 |    "metadata": {},
205 |    "outputs": [
206 |     {
207 |      "name": "stdout",
208 |      "output_type": "stream",
209 |      "text": [
210 |       "set([10])\n",
211 |       "set([10, 12])\n"
212 |      ]
213 |     }
214 |    ],
215 |    "source": [
216 |     "## Sets\n",
217 |     "x = set()\n",
218 |     "x.add(10)\n",
219 |     "print(x)\n",
220 |     "x.add(12)\n",
221 |     "print(x)"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "code",
226 |    "execution_count": 71,
227 |    "metadata": {},
228 |    "outputs": [
229 |     {
230 |      "name": "stdout",
231 |      "output_type": "stream",
232 |      "text": [
233 |       "<type 'tuple'>\n",
234 |       "{}\n",
235 |       "22\n",
236 |       "<type 'tuple'>\n",
237 |       "{'a': 4, 'b': 5}\n"
238 |      ]
239 |     },
240 |     {
241 |      "data": {
242 |       "text/plain": [
243 |        "61"
244 |       ]
245 |      },
246 |      "execution_count": 71,
247 |      "metadata": {},
248 |      "output_type": "execute_result"
249 |     }
250 |    ],
251 |    "source": [
252 |     "## Arguments and Keyword Arguments\n",
253 |     "def add(a1, a2,*args, **kwargs):\n",
254 |     "    ret_sum = a1+a2\n",
255 |     "    print(type(args))\n",
256 |     "    print(type(kwargs))\n",
257 |     "    for ix in args:\n",
258 |     "        ret_sum += ix\n",
259 |     "    for ix in kwargs.keys():\n",
260 |     "        ret_sum += kwargs[ix]\n",
261 |     "    return ret_sum\n",
262 |     "print(add(10, 12))\n",
263 |     "add(10, 12, 14, 16, a=4,b=5)"
264 |    ]
265 |   },
266 |   {
267 |    "cell_type": "code",
268 |    "execution_count": 78,
269 |    "metadata": {},
270 |    "outputs": [
271 |     {
272 |      "name": "stdout",
273 |      "output_type": "stream",
274 |      "text": [
275 |       "{'k2': 100.0, 'key': 10}\n",
276 |       "['k2', 'key']\n",
277 |       "[100.0, 10]\n",
278 |       "<type 'list'>\n"
279 |      ]
280 |     }
281 |    ],
282 |    "source": [
283 |     "## Dictionary - Entirely Heterogenous\n",
284 |     "d  = dict()\n",
285 |     "d = {}\n",
286 |     "d['key'] = 10\n",
287 |     "d['k2'] = 100.0\n",
288 |     "print(d)\n",
289 |     "print(d.keys())\n",
290 |     "print(d.values())\n",
291 |     "print(type(d.values()))"
292 |    ]
293 |   },
294 |   {
295 |    "cell_type": "code",
296 |    "execution_count": 79,
297 |    "metadata": {},
298 |    "outputs": [
299 |     {
300 |      "data": {
301 |       "text/plain": [
302 |        "'This is a \\nmultiline comment\\n'"
303 |       ]
304 |      },
305 |      "execution_count": 79,
306 |      "metadata": {},
307 |      "output_type": "execute_result"
308 |     }
309 |    ],
310 |    "source": [
311 |     "# This is a comment\n",
312 |     "\n",
313 |     "\"\"\"This is a \n",
314 |     "multiline comment\n",
315 |     "\"\"\""
316 |    ]
317 |   },
318 |   {
319 |    "cell_type": "code",
320 |    "execution_count": 86,
321 |    "metadata": {},
322 |    "outputs": [],
323 |    "source": [
324 |     "## Classes in Python\n",
325 |     "class MyClass:\n",
326 |     "    class_list = [] \n",
327 |     "    def __init__(self, arg1):\n",
328 |     "        '''This is the constructor'''\n",
329 |     "        self.object_list = []\n",
330 |     "        self.class_list.append(arg1)\n",
331 |     "        self.object_list.append(arg1)"
332 |    ]
333 |   },
334 |   {
335 |    "cell_type": "code",
336 |    "execution_count": 87,
337 |    "metadata": {},
338 |    "outputs": [
339 |     {
340 |      "name": "stdout",
341 |      "output_type": "stream",
342 |      "text": [
343 |       "[10]\n",
344 |       "[10]\n",
345 |       "[12]\n",
346 |       "[10, 12]\n",
347 |       "[10, 12]\n"
348 |      ]
349 |     }
350 |    ],
351 |    "source": [
352 |     "o1 = MyClass(10)\n",
353 |     "print(o1.object_list)\n",
354 |     "print(o1.class_list)\n",
355 |     "o2 = MyClass(12)\n",
356 |     "print(o2.object_list)\n",
357 |     "print(o2.class_list)\n",
358 |     "print(MyClass.class_list)"
359 |    ]
360 |   },
361 |   {
362 |    "cell_type": "code",
363 |    "execution_count": null,
364 |    "metadata": {},
365 |    "outputs": [],
366 |    "source": []
367 |   }
368 |  ],
369 |  "metadata": {
370 |   "kernelspec": {
371 |    "display_name": "Python 3",
372 |    "language": "python",
373 |    "name": "python3"
374 |   },
375 |   "language_info": {
376 |    "codemirror_mode": {
377 |     "name": "ipython",
378 |     "version": 3
379 |    },
380 |    "file_extension": ".py",
381 |    "mimetype": "text/x-python",
382 |    "name": "python",
383 |    "nbconvert_exporter": "python",
384 |    "pygments_lexer": "ipython3",
385 |    "version": "3.7.3"
386 |   }
387 |  },
388 |  "nbformat": 4,
389 |  "nbformat_minor": 2
390 | }
391 | 


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/Class21/.ipynb_checkpoints/Add_Boundary-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class21/.ipynb_checkpoints/Working_with_Segmentation_Masks-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class3/.ipynb_checkpoints/K_Nearest_Neighbors-checkpoint.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "%matplotlib inline"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 8,
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "name": "stdout",
 21 |      "output_type": "stream",
 22 |      "text": [
 23 |       "(500, 2)\n",
 24 |       "(500, 2)\n"
 25 |      ]
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "## Input data generation\n",
 30 |     "mean_01 = np.array([0.0, 0.0])\n",
 31 |     "cov_01 = np.array([[1.0, 0.1], [0.1, 1.1]])\n",
 32 |     "\n",
 33 |     "mean_02 = np.array([5.0, 6.0])\n",
 34 |     "cov_02 = np.array([[1.3, 0.2], [0.2, 1.5]])\n",
 35 |     "\n",
 36 |     "dist_01 = np.random.multivariate_normal(mean_01, cov_01, 500)\n",
 37 |     "dist_02 = np.random.multivariate_normal(mean_02, cov_02, 500)\n",
 38 |     "\n",
 39 |     "print dist_01.shape\n",
 40 |     "print dist_02.shape"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 9,
 46 |    "metadata": {},
 47 |    "outputs": [
 48 |     {
 49 |      "data": {
 50 |       "image/png": 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\n",
 51 |       "text/plain": [
 52 |        "<Figure size 432x288 with 1 Axes>"
 53 |       ]
 54 |      },
 55 |      "metadata": {},
 56 |      "output_type": "display_data"
 57 |     }
 58 |    ],
 59 |    "source": [
 60 |     "for ix in range(dist_01.shape[0]):\n",
 61 |     "    plt.scatter(dist_01[ix, 0], dist_01[ix, 1], c='r')\n",
 62 |     "    plt.scatter(dist_02[ix, 0], dist_02[ix, 1], c='g')\n",
 63 |     "plt.show()"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 16,
 69 |    "metadata": {},
 70 |    "outputs": [
 71 |     {
 72 |      "name": "stdout",
 73 |      "output_type": "stream",
 74 |      "text": [
 75 |       "(1000, 3)\n",
 76 |       "float64\n"
 77 |      ]
 78 |     }
 79 |    ],
 80 |    "source": [
 81 |     "rows = dist_01.shape[0] + dist_02.shape[0]\n",
 82 |     "cols = dist_01.shape[1] + 1\n",
 83 |     "\n",
 84 |     "data = np.zeros((rows, cols))\n",
 85 |     "print data.shape\n",
 86 |     "print data.dtype"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 21,
 92 |    "metadata": {},
 93 |    "outputs": [],
 94 |    "source": [
 95 |     "data[:dist_01.shape[0], :-1] = dist_01\n",
 96 |     "data[dist_01.shape[0]: rows, :-1] = dist_02\n",
 97 |     "\n",
 98 |     "data[dist_01.shape[0]:rows, -1] = 1.0"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": 22,
104 |    "metadata": {},
105 |    "outputs": [
106 |     {
107 |      "name": "stdout",
108 |      "output_type": "stream",
109 |      "text": [
110 |       "(1000, 3)\n"
111 |      ]
112 |     }
113 |    ],
114 |    "source": [
115 |     "print data.shape"
116 |    ]
117 |   },
118 |   {
119 |    "cell_type": "code",
120 |    "execution_count": null,
121 |    "metadata": {},
122 |    "outputs": [],
123 |    "source": [
124 |     "## Testing Training Split"
125 |    ]
126 |   },
127 |   {
128 |    "cell_type": "code",
129 |    "execution_count": null,
130 |    "metadata": {},
131 |    "outputs": [],
132 |    "source": []
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": null,
137 |    "metadata": {},
138 |    "outputs": [],
139 |    "source": []
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": null,
144 |    "metadata": {},
145 |    "outputs": [],
146 |    "source": []
147 |   }
148 |  ],
149 |  "metadata": {
150 |   "kernelspec": {
151 |    "display_name": "Python 2",
152 |    "language": "python",
153 |    "name": "python2"
154 |   },
155 |   "language_info": {
156 |    "codemirror_mode": {
157 |     "name": "ipython",
158 |     "version": 2
159 |    },
160 |    "file_extension": ".py",
161 |    "mimetype": "text/x-python",
162 |    "name": "python",
163 |    "nbconvert_exporter": "python",
164 |    "pygments_lexer": "ipython2",
165 |    "version": "2.7.15"
166 |   }
167 |  },
168 |  "nbformat": 4,
169 |  "nbformat_minor": 2
170 | }
171 | 


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/Class4/.ipynb_checkpoints/Assignment-Color_Extraction-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class4/.ipynb_checkpoints/Doubts-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class4/Doubts.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [],
 8 |    "source": [
 9 |     "import numpy as np"
10 |    ]
11 |   },
12 |   {
13 |    "cell_type": "code",
14 |    "execution_count": 3,
15 |    "metadata": {},
16 |    "outputs": [
17 |     {
18 |      "name": "stdout",
19 |      "output_type": "stream",
20 |      "text": [
21 |       "(array([1, 2, 3]), array([2, 2, 1]))\n"
22 |      ]
23 |     }
24 |    ],
25 |    "source": [
26 |     "x = [1,2,3,1,2]\n",
27 |     "print np.unique(np.array(x), return_counts=True)"
28 |    ]
29 |   },
30 |   {
31 |    "cell_type": "code",
32 |    "execution_count": null,
33 |    "metadata": {},
34 |    "outputs": [],
35 |    "source": []
36 |   },
37 |   {
38 |    "cell_type": "code",
39 |    "execution_count": null,
40 |    "metadata": {},
41 |    "outputs": [],
42 |    "source": []
43 |   }
44 |  ],
45 |  "metadata": {
46 |   "kernelspec": {
47 |    "display_name": "Python 2",
48 |    "language": "python",
49 |    "name": "python2"
50 |   },
51 |   "language_info": {
52 |    "codemirror_mode": {
53 |     "name": "ipython",
54 |     "version": 2
55 |    },
56 |    "file_extension": ".py",
57 |    "mimetype": "text/x-python",
58 |    "name": "python",
59 |    "nbconvert_exporter": "python",
60 |    "pygments_lexer": "ipython2",
61 |    "version": "2.7.15"
62 |   }
63 |  },
64 |  "nbformat": 4,
65 |  "nbformat_minor": 2
66 | }
67 | 


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/Class4/im.jpg:
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https://raw.githubusercontent.com/coding-blocks-archives/machine-learning-june-2018/f0772da9a9a2aec974c1c1daf7aa1f482d764962/Class4/im.jpg


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/Class5/.ipynb_checkpoints/Doubts-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class5/Doubts.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class6/.ipynb_checkpoints/Doubts_and_Discussions-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class6/.ipynb_checkpoints/Untitled-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class6/.ipynb_checkpoints/Web_Scraping_to_CSV-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class6/Doubts_and_Discussions.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 3,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "abcdefigh\n"
13 |      ]
14 |     }
15 |    ],
16 |    "source": [
17 |     "x = '123(abcdefigh)'\n",
18 |     "print x.split('(')[-1].split(')')[0]"
19 |    ]
20 |   },
21 |   {
22 |    "cell_type": "code",
23 |    "execution_count": 5,
24 |    "metadata": {},
25 |    "outputs": [
26 |     {
27 |      "name": "stdout",
28 |      "output_type": "stream",
29 |      "text": [
30 |       "1+2+3+4+5\n"
31 |      ]
32 |     }
33 |    ],
34 |    "source": [
35 |     "x2 = ['1', '2', '3', '4', '5']\n",
36 |     "ans = '+'.join(x2)\n",
37 |     "print ans"
38 |    ]
39 |   },
40 |   {
41 |    "cell_type": "code",
42 |    "execution_count": 7,
43 |    "metadata": {},
44 |    "outputs": [
45 |     {
46 |      "name": "stdout",
47 |      "output_type": "stream",
48 |      "text": [
49 |       "name1\n",
50 |       "name2, name3\n"
51 |      ]
52 |     }
53 |    ],
54 |    "source": [
55 |     "my_string = 'name1, name2, name3'\n",
56 |     "part1 = my_string.split(',')[0]\n",
57 |     "part2 = ','.join(my_string.split(',')[1:]).strip()\n",
58 |     "\n",
59 |     "print part1\n",
60 |     "print part2"
61 |    ]
62 |   },
63 |   {
64 |    "cell_type": "code",
65 |    "execution_count": null,
66 |    "metadata": {},
67 |    "outputs": [],
68 |    "source": []
69 |   }
70 |  ],
71 |  "metadata": {
72 |   "kernelspec": {
73 |    "display_name": "Python 2",
74 |    "language": "python",
75 |    "name": "python2"
76 |   },
77 |   "language_info": {
78 |    "codemirror_mode": {
79 |     "name": "ipython",
80 |     "version": 2
81 |    },
82 |    "file_extension": ".py",
83 |    "mimetype": "text/x-python",
84 |    "name": "python",
85 |    "nbconvert_exporter": "python",
86 |    "pygments_lexer": "ipython2",
87 |    "version": "2.7.15"
88 |   }
89 |  },
90 |  "nbformat": 4,
91 |  "nbformat_minor": 2
92 | }
93 | 


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/Class6/Web_Scraping_to_CSV.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "import pandas as pd\n",
 12 |     "import requests\n",
 13 |     "from bs4 import BeautifulSoup"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 2,
 19 |    "metadata": {},
 20 |    "outputs": [
 21 |     {
 22 |      "name": "stdout",
 23 |      "output_type": "stream",
 24 |      "text": [
 25 |       "<class 'requests.models.Response'>\n"
 26 |      ]
 27 |     }
 28 |    ],
 29 |    "source": [
 30 |     "url = 'https://www.imdb.com/chart/top'\n",
 31 |     "\n",
 32 |     "## Fetch the webpage\n",
 33 |     "r = requests.get(url)\n",
 34 |     "print type(r)"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 3,
 40 |    "metadata": {},
 41 |    "outputs": [
 42 |     {
 43 |      "data": {
 44 |       "text/plain": [
 45 |        "200"
 46 |       ]
 47 |      },
 48 |      "execution_count": 3,
 49 |      "metadata": {},
 50 |      "output_type": "execute_result"
 51 |     }
 52 |    ],
 53 |    "source": [
 54 |     "r.status_code"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 4,
 60 |    "metadata": {},
 61 |    "outputs": [],
 62 |    "source": [
 63 |     "info = r.text"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 5,
 69 |    "metadata": {},
 70 |    "outputs": [],
 71 |    "source": [
 72 |     "soup = BeautifulSoup(r.text, 'html.parser')"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 6,
 78 |    "metadata": {},
 79 |    "outputs": [
 80 |     {
 81 |      "name": "stdout",
 82 |      "output_type": "stream",
 83 |      "text": [
 84 |       "<class 'bs4.BeautifulSoup'>\n"
 85 |      ]
 86 |     }
 87 |    ],
 88 |    "source": [
 89 |     "print type(soup)"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 7,
 95 |    "metadata": {},
 96 |    "outputs": [],
 97 |    "source": [
 98 |     "TITLES = []\n",
 99 |     "YEARS = []\n",
100 |     "RATINGS = []\n",
101 |     "URLS = []\n",
102 |     "\n",
103 |     "ACTORS = []\n",
104 |     "DIRECTORS = []"
105 |    ]
106 |   },
107 |   {
108 |    "cell_type": "code",
109 |    "execution_count": 8,
110 |    "metadata": {},
111 |    "outputs": [
112 |     {
113 |      "name": "stdout",
114 |      "output_type": "stream",
115 |      "text": [
116 |       "1\n",
117 |       "<class 'bs4.element.Tag'>\n"
118 |      ]
119 |     }
120 |    ],
121 |    "source": [
122 |     "table_inf = soup.find_all('tbody', {'class': 'lister-list'})\n",
123 |     "print len(table_inf)\n",
124 |     "print type(table_inf[0])\n",
125 |     "\n",
126 |     "doc = table_inf[0]"
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "code",
131 |    "execution_count": 9,
132 |    "metadata": {},
133 |    "outputs": [
134 |     {
135 |      "name": "stdout",
136 |      "output_type": "stream",
137 |      "text": [
138 |       "250\n"
139 |      ]
140 |     }
141 |    ],
142 |    "source": [
143 |     "rows = doc.find_all('tr')\n",
144 |     "print len(rows)"
145 |    ]
146 |   },
147 |   {
148 |    "cell_type": "code",
149 |    "execution_count": 13,
150 |    "metadata": {},
151 |    "outputs": [
152 |     {
153 |      "name": "stdout",
154 |      "output_type": "stream",
155 |      "text": [
156 |       "<td class=\"titleColumn\">\n",
157 |       "      1.\n",
158 |       "      <a href=\"/title/tt0111161/?pf_rd_m=A2FGELUUNOQJNL&amp;pf_rd_p=e31d89dd-322d-4646-8962-327b42fe94b1&amp;pf_rd_r=CSESAR7MPGRFM0ZK3S2M&amp;pf_rd_s=center-1&amp;pf_rd_t=15506&amp;pf_rd_i=top&amp;ref_=chttp_tt_1\" title=\"Frank Darabont (dir.), Tim Robbins, Morgan Freeman\">The Shawshank Redemption</a>\n",
159 |       "<span class=\"secondaryInfo\">(1994)</span>\n",
160 |       "</td>\n"
161 |      ]
162 |     }
163 |    ],
164 |    "source": [
165 |     "test_row = rows[0]\n",
166 |     "title_column = test_row.find('td', {'class' : 'titleColumn'})\n",
167 |     "print title_column\n",
168 |     "#print title_column.a.text\n",
169 |     "#print type(title_column.a.text.encode('utf-8'))"
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "code",
174 |    "execution_count": 14,
175 |    "metadata": {},
176 |    "outputs": [
177 |     {
178 |      "name": "stdout",
179 |      "output_type": "stream",
180 |      "text": [
181 |       "[<span class=\"secondaryInfo\">(1994)</span>]\n"
182 |      ]
183 |     }
184 |    ],
185 |    "source": [
186 |     "list_of_span = title_column.find_all('span')\n",
187 |     "print list_of_span"
188 |    ]
189 |   },
190 |   {
191 |    "cell_type": "code",
192 |    "execution_count": 47,
193 |    "metadata": {},
194 |    "outputs": [
195 |     {
196 |      "name": "stdout",
197 |      "output_type": "stream",
198 |      "text": [
199 |       "250\n"
200 |      ]
201 |     }
202 |    ],
203 |    "source": [
204 |     "count = 0\n",
205 |     "movie_root_url = 'https://www.imdb.com'\n",
206 |     "for row in rows:\n",
207 |     "    #if count>1:\n",
208 |     "        #break\n",
209 |     "    count+=1\n",
210 |     "    title_column = row.find('td', {'class': 'titleColumn'})\n",
211 |     "    rating_column = row.find('td', {'class' : 'ratingColumn'})\n",
212 |     "    \n",
213 |     "    movie_title = title_column.a.text\n",
214 |     "    year = title_column.span.text.split('(')[-1].split(')')[0]\n",
215 |     "    movie_rel_url = title_column.a['href']\n",
216 |     "    movie_url = movie_root_url + movie_rel_url\n",
217 |     "    \n",
218 |     "    imp_people = title_column.a['title']\n",
219 |     "    director = imp_people.split(',')[0].split('(')[0].strip()\n",
220 |     "    \n",
221 |     "    actors = ','.join(imp_people.split(',')[1:]).strip()\n",
222 |     "    \n",
223 |     "    rating = rating_column.strong.text\n",
224 |     "    \n",
225 |     "    TITLES.append(movie_title.encode('utf-8'))\n",
226 |     "    YEARS.append(year.encode('utf-8'))\n",
227 |     "    URLS.append(movie_url.encode('utf-8'))\n",
228 |     "    RATINGS.append(rating.encode('utf-8'))\n",
229 |     "    \n",
230 |     "    ACTORS.append(actors.encode('utf-8'))\n",
231 |     "    DIRECTORS.append(director.encode('utf-8'))\n",
232 |     "    \n",
233 |     "#     print movie_title\n",
234 |     "#     print year\n",
235 |     "#     print rating\n",
236 |     "#     print movie_url\n",
237 |     "#     print director\n",
238 |     "#     print actors\n",
239 |     "    #print '--------'\n",
240 |     "print count"
241 |    ]
242 |   },
243 |   {
244 |    "cell_type": "code",
245 |    "execution_count": 35,
246 |    "metadata": {},
247 |    "outputs": [],
248 |    "source": [
249 |     "print type(title_column)"
250 |    ]
251 |   },
252 |   {
253 |    "cell_type": "code",
254 |    "execution_count": 48,
255 |    "metadata": {},
256 |    "outputs": [
257 |     {
258 |      "name": "stdout",
259 |      "output_type": "stream",
260 |      "text": [
261 |       "250\n",
262 |       "250\n",
263 |       "250\n",
264 |       "250\n"
265 |      ]
266 |     }
267 |    ],
268 |    "source": [
269 |     "print len(TITLES)\n",
270 |     "print len(DIRECTORS)\n",
271 |     "print len(ACTORS)\n",
272 |     "print len(RATINGS)"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "code",
277 |    "execution_count": 69,
278 |    "metadata": {},
279 |    "outputs": [],
280 |    "source": [
281 |     "empty_df = pd.DataFrame({'Title': TITLES, 'Rating': RATINGS, 'Year': YEARS, 'URL': URLS, 'Director': DIRECTORS, 'Actors': ACTORS}, columns=['Title', 'Year', 'Rating', 'Director', 'Actors', 'URL'])"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "code",
286 |    "execution_count": 15,
287 |    "metadata": {},
288 |    "outputs": [],
289 |    "source": [
290 |     "pd.DataFrame?"
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": 70,
296 |    "metadata": {},
297 |    "outputs": [
298 |     {
299 |      "data": {
300 |       "text/html": [
301 |        "<div>\n",
302 |        "<style scoped>\n",
303 |        "    .dataframe tbody tr th:only-of-type {\n",
304 |        "        vertical-align: middle;\n",
305 |        "    }\n",
306 |        "\n",
307 |        "    .dataframe tbody tr th {\n",
308 |        "        vertical-align: top;\n",
309 |        "    }\n",
310 |        "\n",
311 |        "    .dataframe thead th {\n",
312 |        "        text-align: right;\n",
313 |        "    }\n",
314 |        "</style>\n",
315 |        "<table border=\"1\" class=\"dataframe\">\n",
316 |        "  <thead>\n",
317 |        "    <tr style=\"text-align: right;\">\n",
318 |        "      <th></th>\n",
319 |        "      <th>Title</th>\n",
320 |        "      <th>Year</th>\n",
321 |        "      <th>Rating</th>\n",
322 |        "      <th>Director</th>\n",
323 |        "      <th>Actors</th>\n",
324 |        "      <th>URL</th>\n",
325 |        "    </tr>\n",
326 |        "  </thead>\n",
327 |        "  <tbody>\n",
328 |        "    <tr>\n",
329 |        "      <th>0</th>\n",
330 |        "      <td>The Shawshank Redemption</td>\n",
331 |        "      <td>1994</td>\n",
332 |        "      <td>9.2</td>\n",
333 |        "      <td>Frank Darabont</td>\n",
334 |        "      <td>Tim Robbins, Morgan Freeman</td>\n",
335 |        "      <td>https://www.imdb.com/title/tt0111161/?pf_rd_m=...</td>\n",
336 |        "    </tr>\n",
337 |        "    <tr>\n",
338 |        "      <th>1</th>\n",
339 |        "      <td>The Godfather</td>\n",
340 |        "      <td>1972</td>\n",
341 |        "      <td>9.2</td>\n",
342 |        "      <td>Francis Ford Coppola</td>\n",
343 |        "      <td>Marlon Brando, Al Pacino</td>\n",
344 |        "      <td>https://www.imdb.com/title/tt0068646/?pf_rd_m=...</td>\n",
345 |        "    </tr>\n",
346 |        "    <tr>\n",
347 |        "      <th>2</th>\n",
348 |        "      <td>The Godfather: Part II</td>\n",
349 |        "      <td>1974</td>\n",
350 |        "      <td>9.0</td>\n",
351 |        "      <td>Francis Ford Coppola</td>\n",
352 |        "      <td>Al Pacino, Robert De Niro</td>\n",
353 |        "      <td>https://www.imdb.com/title/tt0071562/?pf_rd_m=...</td>\n",
354 |        "    </tr>\n",
355 |        "    <tr>\n",
356 |        "      <th>3</th>\n",
357 |        "      <td>The Dark Knight</td>\n",
358 |        "      <td>2008</td>\n",
359 |        "      <td>9.0</td>\n",
360 |        "      <td>Christopher Nolan</td>\n",
361 |        "      <td>Christian Bale, Heath Ledger</td>\n",
362 |        "      <td>https://www.imdb.com/title/tt0468569/?pf_rd_m=...</td>\n",
363 |        "    </tr>\n",
364 |        "  </tbody>\n",
365 |        "</table>\n",
366 |        "</div>"
367 |       ],
368 |       "text/plain": [
369 |        "                      Title  Year Rating              Director  \\\n",
370 |        "0  The Shawshank Redemption  1994    9.2        Frank Darabont   \n",
371 |        "1             The Godfather  1972    9.2  Francis Ford Coppola   \n",
372 |        "2    The Godfather: Part II  1974    9.0  Francis Ford Coppola   \n",
373 |        "3           The Dark Knight  2008    9.0     Christopher Nolan   \n",
374 |        "\n",
375 |        "                         Actors  \\\n",
376 |        "0   Tim Robbins, Morgan Freeman   \n",
377 |        "1      Marlon Brando, Al Pacino   \n",
378 |        "2     Al Pacino, Robert De Niro   \n",
379 |        "3  Christian Bale, Heath Ledger   \n",
380 |        "\n",
381 |        "                                                 URL  \n",
382 |        "0  https://www.imdb.com/title/tt0111161/?pf_rd_m=...  \n",
383 |        "1  https://www.imdb.com/title/tt0068646/?pf_rd_m=...  \n",
384 |        "2  https://www.imdb.com/title/tt0071562/?pf_rd_m=...  \n",
385 |        "3  https://www.imdb.com/title/tt0468569/?pf_rd_m=...  "
386 |       ]
387 |      },
388 |      "execution_count": 70,
389 |      "metadata": {},
390 |      "output_type": "execute_result"
391 |     }
392 |    ],
393 |    "source": [
394 |     "empty_df.head(n=4)"
395 |    ]
396 |   },
397 |   {
398 |    "cell_type": "code",
399 |    "execution_count": 71,
400 |    "metadata": {},
401 |    "outputs": [],
402 |    "source": [
403 |     "empty_df.to_csv('./imdb.csv', index=False)"
404 |    ]
405 |   },
406 |   {
407 |    "cell_type": "code",
408 |    "execution_count": 72,
409 |    "metadata": {},
410 |    "outputs": [
411 |     {
412 |      "data": {
413 |       "text/html": [
414 |        "<div>\n",
415 |        "<style scoped>\n",
416 |        "    .dataframe tbody tr th:only-of-type {\n",
417 |        "        vertical-align: middle;\n",
418 |        "    }\n",
419 |        "\n",
420 |        "    .dataframe tbody tr th {\n",
421 |        "        vertical-align: top;\n",
422 |        "    }\n",
423 |        "\n",
424 |        "    .dataframe thead th {\n",
425 |        "        text-align: right;\n",
426 |        "    }\n",
427 |        "</style>\n",
428 |        "<table border=\"1\" class=\"dataframe\">\n",
429 |        "  <thead>\n",
430 |        "    <tr style=\"text-align: right;\">\n",
431 |        "      <th></th>\n",
432 |        "      <th>Title</th>\n",
433 |        "      <th>Year</th>\n",
434 |        "      <th>Rating</th>\n",
435 |        "      <th>Director</th>\n",
436 |        "      <th>Actors</th>\n",
437 |        "      <th>URL</th>\n",
438 |        "    </tr>\n",
439 |        "  </thead>\n",
440 |        "  <tbody>\n",
441 |        "    <tr>\n",
442 |        "      <th>0</th>\n",
443 |        "      <td>The Shawshank Redemption</td>\n",
444 |        "      <td>1994</td>\n",
445 |        "      <td>9.2</td>\n",
446 |        "      <td>Frank Darabont</td>\n",
447 |        "      <td>Tim Robbins, Morgan Freeman</td>\n",
448 |        "      <td>https://www.imdb.com/title/tt0111161/?pf_rd_m=...</td>\n",
449 |        "    </tr>\n",
450 |        "    <tr>\n",
451 |        "      <th>1</th>\n",
452 |        "      <td>The Godfather</td>\n",
453 |        "      <td>1972</td>\n",
454 |        "      <td>9.2</td>\n",
455 |        "      <td>Francis Ford Coppola</td>\n",
456 |        "      <td>Marlon Brando, Al Pacino</td>\n",
457 |        "      <td>https://www.imdb.com/title/tt0068646/?pf_rd_m=...</td>\n",
458 |        "    </tr>\n",
459 |        "    <tr>\n",
460 |        "      <th>2</th>\n",
461 |        "      <td>The Godfather: Part II</td>\n",
462 |        "      <td>1974</td>\n",
463 |        "      <td>9.0</td>\n",
464 |        "      <td>Francis Ford Coppola</td>\n",
465 |        "      <td>Al Pacino, Robert De Niro</td>\n",
466 |        "      <td>https://www.imdb.com/title/tt0071562/?pf_rd_m=...</td>\n",
467 |        "    </tr>\n",
468 |        "    <tr>\n",
469 |        "      <th>3</th>\n",
470 |        "      <td>The Dark Knight</td>\n",
471 |        "      <td>2008</td>\n",
472 |        "      <td>9.0</td>\n",
473 |        "      <td>Christopher Nolan</td>\n",
474 |        "      <td>Christian Bale, Heath Ledger</td>\n",
475 |        "      <td>https://www.imdb.com/title/tt0468569/?pf_rd_m=...</td>\n",
476 |        "    </tr>\n",
477 |        "    <tr>\n",
478 |        "      <th>4</th>\n",
479 |        "      <td>12 Angry Men</td>\n",
480 |        "      <td>1957</td>\n",
481 |        "      <td>8.9</td>\n",
482 |        "      <td>Sidney Lumet</td>\n",
483 |        "      <td>Henry Fonda, Lee J. Cobb</td>\n",
484 |        "      <td>https://www.imdb.com/title/tt0050083/?pf_rd_m=...</td>\n",
485 |        "    </tr>\n",
486 |        "  </tbody>\n",
487 |        "</table>\n",
488 |        "</div>"
489 |       ],
490 |       "text/plain": [
491 |        "                      Title  Year  Rating              Director  \\\n",
492 |        "0  The Shawshank Redemption  1994     9.2        Frank Darabont   \n",
493 |        "1             The Godfather  1972     9.2  Francis Ford Coppola   \n",
494 |        "2    The Godfather: Part II  1974     9.0  Francis Ford Coppola   \n",
495 |        "3           The Dark Knight  2008     9.0     Christopher Nolan   \n",
496 |        "4              12 Angry Men  1957     8.9          Sidney Lumet   \n",
497 |        "\n",
498 |        "                         Actors  \\\n",
499 |        "0   Tim Robbins, Morgan Freeman   \n",
500 |        "1      Marlon Brando, Al Pacino   \n",
501 |        "2     Al Pacino, Robert De Niro   \n",
502 |        "3  Christian Bale, Heath Ledger   \n",
503 |        "4      Henry Fonda, Lee J. Cobb   \n",
504 |        "\n",
505 |        "                                                 URL  \n",
506 |        "0  https://www.imdb.com/title/tt0111161/?pf_rd_m=...  \n",
507 |        "1  https://www.imdb.com/title/tt0068646/?pf_rd_m=...  \n",
508 |        "2  https://www.imdb.com/title/tt0071562/?pf_rd_m=...  \n",
509 |        "3  https://www.imdb.com/title/tt0468569/?pf_rd_m=...  \n",
510 |        "4  https://www.imdb.com/title/tt0050083/?pf_rd_m=...  "
511 |       ]
512 |      },
513 |      "execution_count": 72,
514 |      "metadata": {},
515 |      "output_type": "execute_result"
516 |     }
517 |    ],
518 |    "source": [
519 |     "movie_data = pd.read_csv('imdb.csv')\n",
520 |     "movie_data.head(n=5)"
521 |    ]
522 |   },
523 |   {
524 |    "cell_type": "code",
525 |    "execution_count": 73,
526 |    "metadata": {},
527 |    "outputs": [
528 |     {
529 |      "name": "stdout",
530 |      "output_type": "stream",
531 |      "text": [
532 |       "(250, 6)\n"
533 |      ]
534 |     }
535 |    ],
536 |    "source": [
537 |     "print movie_data.values.shape"
538 |    ]
539 |   },
540 |   {
541 |    "cell_type": "code",
542 |    "execution_count": 66,
543 |    "metadata": {},
544 |    "outputs": [],
545 |    "source": [
546 |     "pd.DataFrame?"
547 |    ]
548 |   },
549 |   {
550 |    "cell_type": "code",
551 |    "execution_count": null,
552 |    "metadata": {},
553 |    "outputs": [],
554 |    "source": []
555 |   }
556 |  ],
557 |  "metadata": {
558 |   "kernelspec": {
559 |    "display_name": "Python 2",
560 |    "language": "python",
561 |    "name": "python2"
562 |   },
563 |   "language_info": {
564 |    "codemirror_mode": {
565 |     "name": "ipython",
566 |     "version": 2
567 |    },
568 |    "file_extension": ".py",
569 |    "mimetype": "text/x-python",
570 |    "name": "python",
571 |    "nbconvert_exporter": "python",
572 |    "pygments_lexer": "ipython2",
573 |    "version": "2.7.15"
574 |   }
575 |  },
576 |  "nbformat": 4,
577 |  "nbformat_minor": 2
578 | }
579 | 


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/Class7/.ipynb_checkpoints/Decision_Tree_and_RF-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class7/DT_Dataset.png:
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https://raw.githubusercontent.com/coding-blocks-archives/machine-learning-june-2018/f0772da9a9a2aec974c1c1daf7aa1f482d764962/Class7/DT_Dataset.png


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/Class7/Decision_Tree_and_RF.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import pandas as pd\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "%matplotlib inline\n",
 13 |     "from sklearn.tree import DecisionTreeClassifier\n",
 14 |     "from sklearn.ensemble import RandomForestClassifier"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 2,
 20 |    "metadata": {},
 21 |    "outputs": [
 22 |     {
 23 |      "data": {
 24 |       "text/html": [
 25 |        "<div>\n",
 26 |        "<style scoped>\n",
 27 |        "    .dataframe tbody tr th:only-of-type {\n",
 28 |        "        vertical-align: middle;\n",
 29 |        "    }\n",
 30 |        "\n",
 31 |        "    .dataframe tbody tr th {\n",
 32 |        "        vertical-align: top;\n",
 33 |        "    }\n",
 34 |        "\n",
 35 |        "    .dataframe thead th {\n",
 36 |        "        text-align: right;\n",
 37 |        "    }\n",
 38 |        "</style>\n",
 39 |        "<table border=\"1\" class=\"dataframe\">\n",
 40 |        "  <thead>\n",
 41 |        "    <tr style=\"text-align: right;\">\n",
 42 |        "      <th></th>\n",
 43 |        "      <th>PassengerId</th>\n",
 44 |        "      <th>Survived</th>\n",
 45 |        "      <th>Pclass</th>\n",
 46 |        "      <th>Name</th>\n",
 47 |        "      <th>Sex</th>\n",
 48 |        "      <th>Age</th>\n",
 49 |        "      <th>SibSp</th>\n",
 50 |        "      <th>Parch</th>\n",
 51 |        "      <th>Ticket</th>\n",
 52 |        "      <th>Fare</th>\n",
 53 |        "      <th>Cabin</th>\n",
 54 |        "      <th>Embarked</th>\n",
 55 |        "    </tr>\n",
 56 |        "  </thead>\n",
 57 |        "  <tbody>\n",
 58 |        "    <tr>\n",
 59 |        "      <th>0</th>\n",
 60 |        "      <td>1</td>\n",
 61 |        "      <td>0</td>\n",
 62 |        "      <td>3</td>\n",
 63 |        "      <td>Braund, Mr. Owen Harris</td>\n",
 64 |        "      <td>male</td>\n",
 65 |        "      <td>22.0</td>\n",
 66 |        "      <td>1</td>\n",
 67 |        "      <td>0</td>\n",
 68 |        "      <td>A/5 21171</td>\n",
 69 |        "      <td>7.2500</td>\n",
 70 |        "      <td>NaN</td>\n",
 71 |        "      <td>S</td>\n",
 72 |        "    </tr>\n",
 73 |        "    <tr>\n",
 74 |        "      <th>1</th>\n",
 75 |        "      <td>2</td>\n",
 76 |        "      <td>1</td>\n",
 77 |        "      <td>1</td>\n",
 78 |        "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
 79 |        "      <td>female</td>\n",
 80 |        "      <td>38.0</td>\n",
 81 |        "      <td>1</td>\n",
 82 |        "      <td>0</td>\n",
 83 |        "      <td>PC 17599</td>\n",
 84 |        "      <td>71.2833</td>\n",
 85 |        "      <td>C85</td>\n",
 86 |        "      <td>C</td>\n",
 87 |        "    </tr>\n",
 88 |        "    <tr>\n",
 89 |        "      <th>2</th>\n",
 90 |        "      <td>3</td>\n",
 91 |        "      <td>1</td>\n",
 92 |        "      <td>3</td>\n",
 93 |        "      <td>Heikkinen, Miss. Laina</td>\n",
 94 |        "      <td>female</td>\n",
 95 |        "      <td>26.0</td>\n",
 96 |        "      <td>0</td>\n",
 97 |        "      <td>0</td>\n",
 98 |        "      <td>STON/O2. 3101282</td>\n",
 99 |        "      <td>7.9250</td>\n",
100 |        "      <td>NaN</td>\n",
101 |        "      <td>S</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <th>3</th>\n",
105 |        "      <td>4</td>\n",
106 |        "      <td>1</td>\n",
107 |        "      <td>1</td>\n",
108 |        "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
109 |        "      <td>female</td>\n",
110 |        "      <td>35.0</td>\n",
111 |        "      <td>1</td>\n",
112 |        "      <td>0</td>\n",
113 |        "      <td>113803</td>\n",
114 |        "      <td>53.1000</td>\n",
115 |        "      <td>C123</td>\n",
116 |        "      <td>S</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <th>4</th>\n",
120 |        "      <td>5</td>\n",
121 |        "      <td>0</td>\n",
122 |        "      <td>3</td>\n",
123 |        "      <td>Allen, Mr. William Henry</td>\n",
124 |        "      <td>male</td>\n",
125 |        "      <td>35.0</td>\n",
126 |        "      <td>0</td>\n",
127 |        "      <td>0</td>\n",
128 |        "      <td>373450</td>\n",
129 |        "      <td>8.0500</td>\n",
130 |        "      <td>NaN</td>\n",
131 |        "      <td>S</td>\n",
132 |        "    </tr>\n",
133 |        "  </tbody>\n",
134 |        "</table>\n",
135 |        "</div>"
136 |       ],
137 |       "text/plain": [
138 |        "   PassengerId  Survived  Pclass  \\\n",
139 |        "0            1         0       3   \n",
140 |        "1            2         1       1   \n",
141 |        "2            3         1       3   \n",
142 |        "3            4         1       1   \n",
143 |        "4            5         0       3   \n",
144 |        "\n",
145 |        "                                                Name     Sex   Age  SibSp  \\\n",
146 |        "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
147 |        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
148 |        "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
149 |        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
150 |        "4                           Allen, Mr. William Henry    male  35.0      0   \n",
151 |        "\n",
152 |        "   Parch            Ticket     Fare Cabin Embarked  \n",
153 |        "0      0         A/5 21171   7.2500   NaN        S  \n",
154 |        "1      0          PC 17599  71.2833   C85        C  \n",
155 |        "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
156 |        "3      0            113803  53.1000  C123        S  \n",
157 |        "4      0            373450   8.0500   NaN        S  "
158 |       ]
159 |      },
160 |      "execution_count": 2,
161 |      "metadata": {},
162 |      "output_type": "execute_result"
163 |     }
164 |    ],
165 |    "source": [
166 |     "ds = pd.read_csv('./titanic.csv')\n",
167 |     "ds.head(n=5)"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": 3,
173 |    "metadata": {},
174 |    "outputs": [
175 |     {
176 |      "name": "stdout",
177 |      "output_type": "stream",
178 |      "text": [
179 |       "(891, 12)\n"
180 |      ]
181 |     }
182 |    ],
183 |    "source": [
184 |     "print ds.values.shape"
185 |    ]
186 |   },
187 |   {
188 |    "cell_type": "code",
189 |    "execution_count": 6,
190 |    "metadata": {},
191 |    "outputs": [
192 |     {
193 |      "name": "stdout",
194 |      "output_type": "stream",
195 |      "text": [
196 |       "(891, 7)\n"
197 |      ]
198 |     }
199 |    ],
200 |    "source": [
201 |     "dk = ds.drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'])\n",
202 |     "dk.head()\n",
203 |     "\n",
204 |     "print dk.values.shape"
205 |    ]
206 |   },
207 |   {
208 |    "cell_type": "code",
209 |    "execution_count": 7,
210 |    "metadata": {},
211 |    "outputs": [
212 |     {
213 |      "name": "stdout",
214 |      "output_type": "stream",
215 |      "text": [
216 |       "(714, 7)\n"
217 |      ]
218 |     }
219 |    ],
220 |    "source": [
221 |     "df = dk.dropna(axis=0)\n",
222 |     "print df.values.shape"
223 |    ]
224 |   },
225 |   {
226 |    "cell_type": "code",
227 |    "execution_count": 8,
228 |    "metadata": {},
229 |    "outputs": [
230 |     {
231 |      "data": {
232 |       "text/html": [
233 |        "<div>\n",
234 |        "<style scoped>\n",
235 |        "    .dataframe tbody tr th:only-of-type {\n",
236 |        "        vertical-align: middle;\n",
237 |        "    }\n",
238 |        "\n",
239 |        "    .dataframe tbody tr th {\n",
240 |        "        vertical-align: top;\n",
241 |        "    }\n",
242 |        "\n",
243 |        "    .dataframe thead th {\n",
244 |        "        text-align: right;\n",
245 |        "    }\n",
246 |        "</style>\n",
247 |        "<table border=\"1\" class=\"dataframe\">\n",
248 |        "  <thead>\n",
249 |        "    <tr style=\"text-align: right;\">\n",
250 |        "      <th></th>\n",
251 |        "      <th>Survived</th>\n",
252 |        "      <th>Pclass</th>\n",
253 |        "      <th>Sex</th>\n",
254 |        "      <th>Age</th>\n",
255 |        "      <th>SibSp</th>\n",
256 |        "      <th>Parch</th>\n",
257 |        "      <th>Fare</th>\n",
258 |        "    </tr>\n",
259 |        "  </thead>\n",
260 |        "  <tbody>\n",
261 |        "    <tr>\n",
262 |        "      <th>0</th>\n",
263 |        "      <td>0</td>\n",
264 |        "      <td>3</td>\n",
265 |        "      <td>male</td>\n",
266 |        "      <td>22.0</td>\n",
267 |        "      <td>1</td>\n",
268 |        "      <td>0</td>\n",
269 |        "      <td>7.2500</td>\n",
270 |        "    </tr>\n",
271 |        "    <tr>\n",
272 |        "      <th>1</th>\n",
273 |        "      <td>1</td>\n",
274 |        "      <td>1</td>\n",
275 |        "      <td>female</td>\n",
276 |        "      <td>38.0</td>\n",
277 |        "      <td>1</td>\n",
278 |        "      <td>0</td>\n",
279 |        "      <td>71.2833</td>\n",
280 |        "    </tr>\n",
281 |        "    <tr>\n",
282 |        "      <th>2</th>\n",
283 |        "      <td>1</td>\n",
284 |        "      <td>3</td>\n",
285 |        "      <td>female</td>\n",
286 |        "      <td>26.0</td>\n",
287 |        "      <td>0</td>\n",
288 |        "      <td>0</td>\n",
289 |        "      <td>7.9250</td>\n",
290 |        "    </tr>\n",
291 |        "    <tr>\n",
292 |        "      <th>3</th>\n",
293 |        "      <td>1</td>\n",
294 |        "      <td>1</td>\n",
295 |        "      <td>female</td>\n",
296 |        "      <td>35.0</td>\n",
297 |        "      <td>1</td>\n",
298 |        "      <td>0</td>\n",
299 |        "      <td>53.1000</td>\n",
300 |        "    </tr>\n",
301 |        "    <tr>\n",
302 |        "      <th>4</th>\n",
303 |        "      <td>0</td>\n",
304 |        "      <td>3</td>\n",
305 |        "      <td>male</td>\n",
306 |        "      <td>35.0</td>\n",
307 |        "      <td>0</td>\n",
308 |        "      <td>0</td>\n",
309 |        "      <td>8.0500</td>\n",
310 |        "    </tr>\n",
311 |        "  </tbody>\n",
312 |        "</table>\n",
313 |        "</div>"
314 |       ],
315 |       "text/plain": [
316 |        "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare\n",
317 |        "0         0       3    male  22.0      1      0   7.2500\n",
318 |        "1         1       1  female  38.0      1      0  71.2833\n",
319 |        "2         1       3  female  26.0      0      0   7.9250\n",
320 |        "3         1       1  female  35.0      1      0  53.1000\n",
321 |        "4         0       3    male  35.0      0      0   8.0500"
322 |       ]
323 |      },
324 |      "execution_count": 8,
325 |      "metadata": {},
326 |      "output_type": "execute_result"
327 |     }
328 |    ],
329 |    "source": [
330 |     "df.head()"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "code",
335 |    "execution_count": 9,
336 |    "metadata": {},
337 |    "outputs": [
338 |     {
339 |      "name": "stderr",
340 |      "output_type": "stream",
341 |      "text": [
342 |       "/usr/local/lib/python2.7/site-packages/pandas/core/generic.py:4401: SettingWithCopyWarning: \n",
343 |       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
344 |       "Try using .loc[row_indexer,col_indexer] = value instead\n",
345 |       "\n",
346 |       "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
347 |       "  self[name] = value\n"
348 |      ]
349 |     }
350 |    ],
351 |    "source": [
352 |     "sex_mapping = {\n",
353 |     "    'male': 0,\n",
354 |     "    'female': 1\n",
355 |     "}\n",
356 |     "df.Sex = df.Sex.map(sex_mapping)"
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "code",
361 |    "execution_count": 10,
362 |    "metadata": {},
363 |    "outputs": [
364 |     {
365 |      "data": {
366 |       "text/html": [
367 |        "<div>\n",
368 |        "<style scoped>\n",
369 |        "    .dataframe tbody tr th:only-of-type {\n",
370 |        "        vertical-align: middle;\n",
371 |        "    }\n",
372 |        "\n",
373 |        "    .dataframe tbody tr th {\n",
374 |        "        vertical-align: top;\n",
375 |        "    }\n",
376 |        "\n",
377 |        "    .dataframe thead th {\n",
378 |        "        text-align: right;\n",
379 |        "    }\n",
380 |        "</style>\n",
381 |        "<table border=\"1\" class=\"dataframe\">\n",
382 |        "  <thead>\n",
383 |        "    <tr style=\"text-align: right;\">\n",
384 |        "      <th></th>\n",
385 |        "      <th>Survived</th>\n",
386 |        "      <th>Pclass</th>\n",
387 |        "      <th>Sex</th>\n",
388 |        "      <th>Age</th>\n",
389 |        "      <th>SibSp</th>\n",
390 |        "      <th>Parch</th>\n",
391 |        "      <th>Fare</th>\n",
392 |        "    </tr>\n",
393 |        "  </thead>\n",
394 |        "  <tbody>\n",
395 |        "    <tr>\n",
396 |        "      <th>0</th>\n",
397 |        "      <td>0</td>\n",
398 |        "      <td>3</td>\n",
399 |        "      <td>0</td>\n",
400 |        "      <td>22.0</td>\n",
401 |        "      <td>1</td>\n",
402 |        "      <td>0</td>\n",
403 |        "      <td>7.2500</td>\n",
404 |        "    </tr>\n",
405 |        "    <tr>\n",
406 |        "      <th>1</th>\n",
407 |        "      <td>1</td>\n",
408 |        "      <td>1</td>\n",
409 |        "      <td>1</td>\n",
410 |        "      <td>38.0</td>\n",
411 |        "      <td>1</td>\n",
412 |        "      <td>0</td>\n",
413 |        "      <td>71.2833</td>\n",
414 |        "    </tr>\n",
415 |        "    <tr>\n",
416 |        "      <th>2</th>\n",
417 |        "      <td>1</td>\n",
418 |        "      <td>3</td>\n",
419 |        "      <td>1</td>\n",
420 |        "      <td>26.0</td>\n",
421 |        "      <td>0</td>\n",
422 |        "      <td>0</td>\n",
423 |        "      <td>7.9250</td>\n",
424 |        "    </tr>\n",
425 |        "    <tr>\n",
426 |        "      <th>3</th>\n",
427 |        "      <td>1</td>\n",
428 |        "      <td>1</td>\n",
429 |        "      <td>1</td>\n",
430 |        "      <td>35.0</td>\n",
431 |        "      <td>1</td>\n",
432 |        "      <td>0</td>\n",
433 |        "      <td>53.1000</td>\n",
434 |        "    </tr>\n",
435 |        "    <tr>\n",
436 |        "      <th>4</th>\n",
437 |        "      <td>0</td>\n",
438 |        "      <td>3</td>\n",
439 |        "      <td>0</td>\n",
440 |        "      <td>35.0</td>\n",
441 |        "      <td>0</td>\n",
442 |        "      <td>0</td>\n",
443 |        "      <td>8.0500</td>\n",
444 |        "    </tr>\n",
445 |        "  </tbody>\n",
446 |        "</table>\n",
447 |        "</div>"
448 |       ],
449 |       "text/plain": [
450 |        "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare\n",
451 |        "0         0       3    0  22.0      1      0   7.2500\n",
452 |        "1         1       1    1  38.0      1      0  71.2833\n",
453 |        "2         1       3    1  26.0      0      0   7.9250\n",
454 |        "3         1       1    1  35.0      1      0  53.1000\n",
455 |        "4         0       3    0  35.0      0      0   8.0500"
456 |       ]
457 |      },
458 |      "execution_count": 10,
459 |      "metadata": {},
460 |      "output_type": "execute_result"
461 |     }
462 |    ],
463 |    "source": [
464 |     "df.head()"
465 |    ]
466 |   },
467 |   {
468 |    "cell_type": "code",
469 |    "execution_count": 11,
470 |    "metadata": {},
471 |    "outputs": [
472 |     {
473 |      "name": "stdout",
474 |      "output_type": "stream",
475 |      "text": [
476 |       "(571, 6)\n",
477 |       "(143, 6)\n",
478 |       "(571,)\n",
479 |       "(143,)\n"
480 |      ]
481 |     }
482 |    ],
483 |    "source": [
484 |     "data = df.values\n",
485 |     "## Training and Testing split\n",
486 |     "split = int(data.shape[0]*0.8)\n",
487 |     "train_data = data[:split]\n",
488 |     "test_data = data[split:]\n",
489 |     "\n",
490 |     "X_train = train_data[:, 1:]\n",
491 |     "Y_train = train_data[:, 0]\n",
492 |     "\n",
493 |     "X_test = test_data[:, 1:]\n",
494 |     "Y_test = test_data[:, 0]\n",
495 |     "\n",
496 |     "print X_train.shape\n",
497 |     "print X_test.shape\n",
498 |     "print Y_train.shape\n",
499 |     "print Y_test.shape"
500 |    ]
501 |   },
502 |   {
503 |    "cell_type": "code",
504 |    "execution_count": 14,
505 |    "metadata": {},
506 |    "outputs": [
507 |     {
508 |      "name": "stdout",
509 |      "output_type": "stream",
510 |      "text": [
511 |       "0.7832167832167832\n"
512 |      ]
513 |     }
514 |    ],
515 |    "source": [
516 |     "## Using Sklearn DT and RF\n",
517 |     "dt = DecisionTreeClassifier()\n",
518 |     "dt.fit(X_train, Y_train)\n",
519 |     "print dt.score(X_test, Y_test)"
520 |    ]
521 |   },
522 |   {
523 |    "cell_type": "code",
524 |    "execution_count": 15,
525 |    "metadata": {},
526 |    "outputs": [
527 |     {
528 |      "name": "stdout",
529 |      "output_type": "stream",
530 |      "text": [
531 |       "0.8321678321678322\n"
532 |      ]
533 |     }
534 |    ],
535 |    "source": [
536 |     "rf = RandomForestClassifier(n_estimators=5)\n",
537 |     "rf.fit(X_train, Y_train)\n",
538 |     "print rf.score(X_test, Y_test)"
539 |    ]
540 |   },
541 |   {
542 |    "cell_type": "code",
543 |    "execution_count": null,
544 |    "metadata": {},
545 |    "outputs": [],
546 |    "source": []
547 |   },
548 |   {
549 |    "cell_type": "code",
550 |    "execution_count": null,
551 |    "metadata": {},
552 |    "outputs": [],
553 |    "source": []
554 |   }
555 |  ],
556 |  "metadata": {
557 |   "kernelspec": {
558 |    "display_name": "Python 2",
559 |    "language": "python",
560 |    "name": "python2"
561 |   },
562 |   "language_info": {
563 |    "codemirror_mode": {
564 |     "name": "ipython",
565 |     "version": 2
566 |    },
567 |    "file_extension": ".py",
568 |    "mimetype": "text/x-python",
569 |    "name": "python",
570 |    "nbconvert_exporter": "python",
571 |    "pygments_lexer": "ipython2",
572 |    "version": "2.7.15"
573 |   }
574 |  },
575 |  "nbformat": 4,
576 |  "nbformat_minor": 2
577 | }
578 | 


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/Class8/NB_dataset.png:
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https://raw.githubusercontent.com/coding-blocks-archives/machine-learning-june-2018/f0772da9a9a2aec974c1c1daf7aa1f482d764962/Class8/NB_dataset.png


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/Class9/.ipynb_checkpoints/CamScanner-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class9/.ipynb_checkpoints/Challenge-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/Class9/CamScanner.py:
--------------------------------------------------------------------------------
  1 | # coding: utf-8
  2 | 
  3 | # In[87]:
  4 | 
  5 | 
  6 | import numpy as np
  7 | import matplotlib.pyplot as plt
  8 | import cv2
  9 | #get_ipython().magic(u'matplotlib inline')
 10 | 
 11 | 
 12 | # In[88]:
 13 | 
 14 | 
 15 | im_path = './receipt.jpg'
 16 | img = cv2.imread(im_path)
 17 | print img.shape
 18 | img = cv2.resize(img, (1500, 800))
 19 | print img.shape
 20 | plt.imshow(img)
 21 | plt.show()
 22 | 
 23 | 
 24 | # ## Image Blurring
 25 | # ## Edge Detection
 26 | # ## Contour Extraction
 27 | # ## Best contour selection
 28 | # ## Projection to fixed size screen
 29 | 
 30 | # In[89]:
 31 | 
 32 | 
 33 | ## Image Blurring
 34 | orig = img.copy()
 35 | gray = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY)
 36 | plt.imshow(gray)
 37 | plt.show()
 38 | blurred = cv2.GaussianBlur(gray, (5,5), 0)
 39 | plt.imshow(blurred)
 40 | plt.show()
 41 | print blurred.shape
 42 | 
 43 | 
 44 | # In[90]:
 45 | 
 46 | 
 47 | regen = cv2.cvtColor(blurred,cv2.COLOR_GRAY2BGR)
 48 | plt.imshow(orig)
 49 | plt.show()
 50 | plt.imshow(regen)
 51 | plt.show()
 52 | 
 53 | 
 54 | # In[91]:
 55 | 
 56 | 
 57 | ## Edge Detection
 58 | edged = cv2.Canny(blurred, 0, 50)
 59 | orig_edged = edged.copy()
 60 | 
 61 | plt.imshow(orig_edged)
 62 | plt.show()
 63 | 
 64 | 
 65 | # In[92]:
 66 | 
 67 | 
 68 | ## Contours Extraction
 69 | _, contours, _ = cv2.findContours(edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
 70 | print len(contours)
 71 | contours = sorted(contours, reverse=True, key=cv2.contourArea)
 72 | 
 73 | 
 74 | # In[93]:
 75 | 
 76 | 
 77 | ## Best Contour Selection
 78 | for c in contours:
 79 |     p = cv2.arcLength(c, True)
 80 |     approx = cv2.approxPolyDP(c, 0.02*p, True)
 81 |     
 82 |     if len(approx) == 4:
 83 |         target = approx
 84 |         break
 85 | 
 86 | print target.shape
 87 | 
 88 | 
 89 | # In[94]:
 90 | 
 91 | 
 92 | ## Reorder target contour
 93 | def reorder(h):
 94 |     h = h.reshape((4,2))
 95 |     print h
 96 |     
 97 |     hnew = np.zeros((4,2), dtype=np.float32)
 98 |     
 99 |     add = h.sum(axis=1)
100 |     hnew[3] = h[np.argmin(add)]
101 |     hnew[1] = h[np.argmax(add)]
102 |     
103 |     diff = np.diff(h, axis=1)
104 |     hnew[0] = h[np.argmin(diff)]
105 |     hnew[2] = h[np.argmax(diff)]
106 |     
107 |     return hnew
108 | 
109 | 
110 | # In[95]:
111 | 
112 | 
113 | reorderd = reorder(target)
114 | print '----------'
115 | print reorderd
116 | 
117 | 
118 | # In[96]:
119 | 
120 | 
121 | ## Project to a fixed size screen
122 | input_represent = reorderd
123 | output_map = np.float32([[0,0],[800,0],[800,800],[0,800]])
124 | 
125 | 
126 | # In[97]:
127 | 
128 | 
129 | M = cv2.getPerspectiveTransform(input_represent, output_map)
130 | ans = cv2.warpPerspective(orig, M, (800,800))
131 | 
132 | 
133 | # In[98]:
134 | 
135 | 
136 | plt.imshow(ans)
137 | plt.show()
138 | 
139 | 
140 | # In[84]:
141 | 
142 | 
143 | ans2 = cv2.cvtColor(ans, cv2.COLOR_BGR2GRAY)
144 | temp = cv2.GaussianBlur(ans2, (3,3), 0)
145 | temp2 = cv2.GaussianBlur(ans2, (5,5), 0)
146 | temp3 = cv2.GaussianBlur(ans2, (1,1), 0)
147 | #plt.imshow(temp)
148 | #plt.show()
149 | 
150 | # In[83]:
151 | 
152 | 
153 | now = cv2.cvtColor(temp, cv2.COLOR_GRAY2BGR)
154 | #plt.imshow(now)
155 | #plt.show()
156 | 
157 | 


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/Class9/CamScanner_Modular.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import matplotlib.pyplot as plt\n",
 11 |     "import cv2\n",
 12 |     "%matplotlib inline"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "import pytesseract\n",
 22 |     "from PIL import Image, ImageEnhance, ImageFilter"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 3,
 28 |    "metadata": {},
 29 |    "outputs": [],
 30 |    "source": [
 31 |     "## Reorder target contour\n",
 32 |     "def reorder(h):\n",
 33 |     "    h = h.reshape((4,2))\n",
 34 |     "\n",
 35 |     "    \n",
 36 |     "    hnew = np.zeros((4,2), dtype=np.float32)\n",
 37 |     "    \n",
 38 |     "    add = h.sum(axis=1)\n",
 39 |     "    hnew[3] = h[np.argmin(add)]\n",
 40 |     "    hnew[1] = h[np.argmax(add)]\n",
 41 |     "    \n",
 42 |     "    diff = np.diff(h, axis=1)\n",
 43 |     "    hnew[0] = h[np.argmin(diff)]\n",
 44 |     "    hnew[2] = h[np.argmax(diff)]\n",
 45 |     "    \n",
 46 |     "    return hnew"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": 6,
 52 |    "metadata": {},
 53 |    "outputs": [],
 54 |    "source": [
 55 |     "#img_path is path of the image needed to \n",
 56 |     "def convert_img_text(img_path):\n",
 57 |     "    img = cv2.imread(img_path)\n",
 58 |     "    print(img.shape)\n",
 59 |     "    img = cv2.resize(img, (1500, 800))\n",
 60 |     "    \n",
 61 |     "    ## Image Blurring\n",
 62 |     "    orig = img.copy()\n",
 63 |     "    gray = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY)\n",
 64 |     "    blurred = cv2.GaussianBlur(gray, (5,5), 0)\n",
 65 |     "    \n",
 66 |     "    ## Edge Detection\n",
 67 |     "    edged = cv2.Canny(blurred, 0, 50)\n",
 68 |     "    orig_edged = edged.copy()\n",
 69 |     "    \n",
 70 |     "    \n",
 71 |     "    ## Contours Extraction\n",
 72 |     "    _, contours, _ = cv2.findContours(edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n",
 73 |     "    contours = sorted(contours, reverse=True, key=cv2.contourArea)\n",
 74 |     "    \n",
 75 |     "    ## Best Contour Selection\n",
 76 |     "    for c in contours:\n",
 77 |     "        p = cv2.arcLength(c, True)\n",
 78 |     "        approx = cv2.approxPolyDP(c, 0.02*p, True)\n",
 79 |     "    \n",
 80 |     "        if len(approx) == 4:\n",
 81 |     "            target = approx\n",
 82 |     "            break\n",
 83 |     "            \n",
 84 |     "    #reordering points        \n",
 85 |     "    reorderd = reorder(target)\n",
 86 |     "    \n",
 87 |     "    ## Project to a fixed size screen\n",
 88 |     "    input_represent = reorderd\n",
 89 |     "    output_map = np.float32([[0,0],[800,0],[800,800],[0,800]])\n",
 90 |     "    \n",
 91 |     "    M = cv2.getPerspectiveTransform(input_represent, output_map)\n",
 92 |     "    ans = cv2.warpPerspective(orig, M, (800,800))\n",
 93 |     "    \n",
 94 |     "    \n",
 95 |     "    #blurring again to increase readability\n",
 96 |     "    ans2 = cv2.cvtColor(ans, cv2.COLOR_BGR2GRAY)\n",
 97 |     "    temp = cv2.GaussianBlur(ans2, (1,1), 0)\n",
 98 |     "    now = cv2.cvtColor(temp, cv2.COLOR_GRAY2BGR)\n",
 99 |     "    \n",
100 |     "    #converting into text\n",
101 |     "    img=Image.fromarray(now)\n",
102 |     "    img.save(\"refined_receipt.jpg\")\n",
103 |     "    text = pytesseract.image_to_string(Image.open(\"refined_receipt.jpg\"))\n",
104 |     "    print(text)\n",
105 |     "    "
106 |    ]
107 |   },
108 |   {
109 |    "cell_type": "code",
110 |    "execution_count": 7,
111 |    "metadata": {},
112 |    "outputs": [
113 |     {
114 |      "name": "stdout",
115 |      "output_type": "stream",
116 |      "text": [
117 |       "(2448, 3264, 3)\n",
118 |       "WHfiLE\n",
119 |       "WHFOODS\n",
120 |       "\n",
121 |       "UHDLE FOODS HflRKET — UESTPDRT.CT 05880\n",
122 |       "399 POST RD NEST — (203) 227—6858\n",
123 |       "\n",
124 |       " \n",
125 |       "\n",
126 |       "365 BRCON LS NF 4 99\n",
127 |       "365 BHCDN LS NP 4.99\n",
128 |       "365 BQCON LS NP 4 99\n",
129 |       "365 BHCON LS NF 4 99\n",
130 |       "\n",
131 |       "BROTH CHIC NP 2 19\n",
132 |       "\n",
133 |       "FLOUR RLNOND NP 1!.99\n",
134 |       "\n",
135 |       "CHKN BRST BNLSS SK NF 18 80\n",
136 |       "HEAVY CRERH NP 3.39\n",
137 |       "\n",
138 |       "BHLSHC REDUCT NP 6.49\n",
139 |       "\n",
140 |       "BEEF ERND 85/15 NP 5.01\n",
141 |       "JUICE CUP CRSHEU L NP 8.99\n",
142 |       "DOCS PINT DRGHNIC NP 14.49\n",
143 |       "\n",
144 |       "HNY HLHDND BUTTER NP 9.99\n",
145 |       "\n",
146 |       "lI-l Yfix .00 BBL ‘01.33\n",
147 |       "\n",
148 |       ".“.l'ttlilti\n",
149 |       "\n",
150 |       "‘nn'nfl-n-n-n-nmflflwfl\n"
151 |      ]
152 |     }
153 |    ],
154 |    "source": [
155 |     "convert_img_text('./receipt.jpg')"
156 |    ]
157 |   },
158 |   {
159 |    "cell_type": "code",
160 |    "execution_count": null,
161 |    "metadata": {},
162 |    "outputs": [],
163 |    "source": []
164 |   },
165 |   {
166 |    "cell_type": "code",
167 |    "execution_count": null,
168 |    "metadata": {},
169 |    "outputs": [],
170 |    "source": []
171 |   },
172 |   {
173 |    "cell_type": "code",
174 |    "execution_count": null,
175 |    "metadata": {},
176 |    "outputs": [],
177 |    "source": []
178 |   },
179 |   {
180 |    "cell_type": "code",
181 |    "execution_count": null,
182 |    "metadata": {},
183 |    "outputs": [],
184 |    "source": []
185 |   }
186 |  ],
187 |  "metadata": {
188 |   "kernelspec": {
189 |    "display_name": "Python 3",
190 |    "language": "python",
191 |    "name": "python3"
192 |   },
193 |   "language_info": {
194 |    "codemirror_mode": {
195 |     "name": "ipython",
196 |     "version": 3
197 |    },
198 |    "file_extension": ".py",
199 |    "mimetype": "text/x-python",
200 |    "name": "python",
201 |    "nbconvert_exporter": "python",
202 |    "pygments_lexer": "ipython3",
203 |    "version": "3.6.5"
204 |   }
205 |  },
206 |  "nbformat": 4,
207 |  "nbformat_minor": 2
208 | }
209 | 


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/Class9/Challenge.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import numpy as np\n",
 10 |     "import pandas as pd"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 2,
 16 |    "metadata": {},
 17 |    "outputs": [
 18 |     {
 19 |      "name": "stdout",
 20 |      "output_type": "stream",
 21 |      "text": [
 22 |       "(1000, 10)\n"
 23 |      ]
 24 |     },
 25 |     {
 26 |      "data": {
 27 |       "text/html": [
 28 |        "<div>\n",
 29 |        "<style scoped>\n",
 30 |        "    .dataframe tbody tr th:only-of-type {\n",
 31 |        "        vertical-align: middle;\n",
 32 |        "    }\n",
 33 |        "\n",
 34 |        "    .dataframe tbody tr th {\n",
 35 |        "        vertical-align: top;\n",
 36 |        "    }\n",
 37 |        "\n",
 38 |        "    .dataframe thead th {\n",
 39 |        "        text-align: right;\n",
 40 |        "    }\n",
 41 |        "</style>\n",
 42 |        "<table border=\"1\" class=\"dataframe\">\n",
 43 |        "  <thead>\n",
 44 |        "    <tr style=\"text-align: right;\">\n",
 45 |        "      <th></th>\n",
 46 |        "      <th>critic_rating</th>\n",
 47 |        "      <th>is_action</th>\n",
 48 |        "      <th>is_exclusive_to_us</th>\n",
 49 |        "      <th>is_portable</th>\n",
 50 |        "      <th>is_role_playing</th>\n",
 51 |        "      <th>is_sequel</th>\n",
 52 |        "      <th>is_sports</th>\n",
 53 |        "      <th>suitable_for_kids</th>\n",
 54 |        "      <th>total_earnings</th>\n",
 55 |        "      <th>unit_price</th>\n",
 56 |        "    </tr>\n",
 57 |        "  </thead>\n",
 58 |        "  <tbody>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>0</th>\n",
 61 |        "      <td>3.5</td>\n",
 62 |        "      <td>1</td>\n",
 63 |        "      <td>0</td>\n",
 64 |        "      <td>1</td>\n",
 65 |        "      <td>0</td>\n",
 66 |        "      <td>1</td>\n",
 67 |        "      <td>0</td>\n",
 68 |        "      <td>0</td>\n",
 69 |        "      <td>132717</td>\n",
 70 |        "      <td>59.99</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>1</th>\n",
 74 |        "      <td>4.5</td>\n",
 75 |        "      <td>0</td>\n",
 76 |        "      <td>0</td>\n",
 77 |        "      <td>0</td>\n",
 78 |        "      <td>0</td>\n",
 79 |        "      <td>1</td>\n",
 80 |        "      <td>1</td>\n",
 81 |        "      <td>0</td>\n",
 82 |        "      <td>83407</td>\n",
 83 |        "      <td>49.99</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>2</th>\n",
 87 |        "      <td>3.0</td>\n",
 88 |        "      <td>0</td>\n",
 89 |        "      <td>0</td>\n",
 90 |        "      <td>0</td>\n",
 91 |        "      <td>0</td>\n",
 92 |        "      <td>1</td>\n",
 93 |        "      <td>1</td>\n",
 94 |        "      <td>0</td>\n",
 95 |        "      <td>62423</td>\n",
 96 |        "      <td>49.99</td>\n",
 97 |        "    </tr>\n",
 98 |        "    <tr>\n",
 99 |        "      <th>3</th>\n",
100 |        "      <td>4.5</td>\n",
101 |        "      <td>1</td>\n",
102 |        "      <td>0</td>\n",
103 |        "      <td>0</td>\n",
104 |        "      <td>0</td>\n",
105 |        "      <td>0</td>\n",
106 |        "      <td>0</td>\n",
107 |        "      <td>1</td>\n",
108 |        "      <td>69889</td>\n",
109 |        "      <td>39.99</td>\n",
110 |        "    </tr>\n",
111 |        "    <tr>\n",
112 |        "      <th>4</th>\n",
113 |        "      <td>4.0</td>\n",
114 |        "      <td>1</td>\n",
115 |        "      <td>0</td>\n",
116 |        "      <td>1</td>\n",
117 |        "      <td>0</td>\n",
118 |        "      <td>1</td>\n",
119 |        "      <td>0</td>\n",
120 |        "      <td>1</td>\n",
121 |        "      <td>161382</td>\n",
122 |        "      <td>59.99</td>\n",
123 |        "    </tr>\n",
124 |        "  </tbody>\n",
125 |        "</table>\n",
126 |        "</div>"
127 |       ],
128 |       "text/plain": [
129 |        "   critic_rating  is_action  is_exclusive_to_us  is_portable  is_role_playing  \\\n",
130 |        "0            3.5          1                   0            1                0   \n",
131 |        "1            4.5          0                   0            0                0   \n",
132 |        "2            3.0          0                   0            0                0   \n",
133 |        "3            4.5          1                   0            0                0   \n",
134 |        "4            4.0          1                   0            1                0   \n",
135 |        "\n",
136 |        "   is_sequel  is_sports  suitable_for_kids  total_earnings  unit_price  \n",
137 |        "0          1          0                  0          132717       59.99  \n",
138 |        "1          1          1                  0           83407       49.99  \n",
139 |        "2          1          1                  0           62423       49.99  \n",
140 |        "3          0          0                  1           69889       39.99  \n",
141 |        "4          1          0                  1          161382       59.99  "
142 |       ]
143 |      },
144 |      "execution_count": 2,
145 |      "metadata": {},
146 |      "output_type": "execute_result"
147 |     }
148 |    ],
149 |    "source": [
150 |     "ds = pd.read_csv('./sales_data_training.csv')\n",
151 |     "print ds.shape\n",
152 |     "ds.head()"
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 3,
158 |    "metadata": {},
159 |    "outputs": [
160 |     {
161 |      "name": "stdout",
162 |      "output_type": "stream",
163 |      "text": [
164 |       "(400, 10)\n"
165 |      ]
166 |     },
167 |     {
168 |      "data": {
169 |       "text/html": [
170 |        "<div>\n",
171 |        "<style scoped>\n",
172 |        "    .dataframe tbody tr th:only-of-type {\n",
173 |        "        vertical-align: middle;\n",
174 |        "    }\n",
175 |        "\n",
176 |        "    .dataframe tbody tr th {\n",
177 |        "        vertical-align: top;\n",
178 |        "    }\n",
179 |        "\n",
180 |        "    .dataframe thead th {\n",
181 |        "        text-align: right;\n",
182 |        "    }\n",
183 |        "</style>\n",
184 |        "<table border=\"1\" class=\"dataframe\">\n",
185 |        "  <thead>\n",
186 |        "    <tr style=\"text-align: right;\">\n",
187 |        "      <th></th>\n",
188 |        "      <th>critic_rating</th>\n",
189 |        "      <th>is_action</th>\n",
190 |        "      <th>is_exclusive_to_us</th>\n",
191 |        "      <th>is_portable</th>\n",
192 |        "      <th>is_role_playing</th>\n",
193 |        "      <th>is_sequel</th>\n",
194 |        "      <th>is_sports</th>\n",
195 |        "      <th>suitable_for_kids</th>\n",
196 |        "      <th>total_earnings</th>\n",
197 |        "      <th>unit_price</th>\n",
198 |        "    </tr>\n",
199 |        "  </thead>\n",
200 |        "  <tbody>\n",
201 |        "    <tr>\n",
202 |        "      <th>0</th>\n",
203 |        "      <td>3.5</td>\n",
204 |        "      <td>1</td>\n",
205 |        "      <td>1</td>\n",
206 |        "      <td>1</td>\n",
207 |        "      <td>0</td>\n",
208 |        "      <td>1</td>\n",
209 |        "      <td>0</td>\n",
210 |        "      <td>1</td>\n",
211 |        "      <td>247537</td>\n",
212 |        "      <td>59.99</td>\n",
213 |        "    </tr>\n",
214 |        "    <tr>\n",
215 |        "      <th>1</th>\n",
216 |        "      <td>2.5</td>\n",
217 |        "      <td>0</td>\n",
218 |        "      <td>0</td>\n",
219 |        "      <td>0</td>\n",
220 |        "      <td>1</td>\n",
221 |        "      <td>1</td>\n",
222 |        "      <td>0</td>\n",
223 |        "      <td>0</td>\n",
224 |        "      <td>73960</td>\n",
225 |        "      <td>59.99</td>\n",
226 |        "    </tr>\n",
227 |        "    <tr>\n",
228 |        "      <th>2</th>\n",
229 |        "      <td>3.5</td>\n",
230 |        "      <td>0</td>\n",
231 |        "      <td>0</td>\n",
232 |        "      <td>0</td>\n",
233 |        "      <td>0</td>\n",
234 |        "      <td>1</td>\n",
235 |        "      <td>1</td>\n",
236 |        "      <td>0</td>\n",
237 |        "      <td>82671</td>\n",
238 |        "      <td>59.99</td>\n",
239 |        "    </tr>\n",
240 |        "    <tr>\n",
241 |        "      <th>3</th>\n",
242 |        "      <td>4.0</td>\n",
243 |        "      <td>1</td>\n",
244 |        "      <td>1</td>\n",
245 |        "      <td>0</td>\n",
246 |        "      <td>0</td>\n",
247 |        "      <td>1</td>\n",
248 |        "      <td>0</td>\n",
249 |        "      <td>0</td>\n",
250 |        "      <td>137456</td>\n",
251 |        "      <td>39.99</td>\n",
252 |        "    </tr>\n",
253 |        "    <tr>\n",
254 |        "      <th>4</th>\n",
255 |        "      <td>2.0</td>\n",
256 |        "      <td>1</td>\n",
257 |        "      <td>0</td>\n",
258 |        "      <td>1</td>\n",
259 |        "      <td>0</td>\n",
260 |        "      <td>1</td>\n",
261 |        "      <td>0</td>\n",
262 |        "      <td>0</td>\n",
263 |        "      <td>89639</td>\n",
264 |        "      <td>59.99</td>\n",
265 |        "    </tr>\n",
266 |        "  </tbody>\n",
267 |        "</table>\n",
268 |        "</div>"
269 |       ],
270 |       "text/plain": [
271 |        "   critic_rating  is_action  is_exclusive_to_us  is_portable  is_role_playing  \\\n",
272 |        "0            3.5          1                   1            1                0   \n",
273 |        "1            2.5          0                   0            0                1   \n",
274 |        "2            3.5          0                   0            0                0   \n",
275 |        "3            4.0          1                   1            0                0   \n",
276 |        "4            2.0          1                   0            1                0   \n",
277 |        "\n",
278 |        "   is_sequel  is_sports  suitable_for_kids  total_earnings  unit_price  \n",
279 |        "0          1          0                  1          247537       59.99  \n",
280 |        "1          1          0                  0           73960       59.99  \n",
281 |        "2          1          1                  0           82671       59.99  \n",
282 |        "3          1          0                  0          137456       39.99  \n",
283 |        "4          1          0                  0           89639       59.99  "
284 |       ]
285 |      },
286 |      "execution_count": 3,
287 |      "metadata": {},
288 |      "output_type": "execute_result"
289 |     }
290 |    ],
291 |    "source": [
292 |     "ds_test = pd.read_csv('./sales_data_test.csv')\n",
293 |     "print ds_test.shape\n",
294 |     "ds_test.head()"
295 |    ]
296 |   },
297 |   {
298 |    "cell_type": "code",
299 |    "execution_count": null,
300 |    "metadata": {},
301 |    "outputs": [],
302 |    "source": []
303 |   }
304 |  ],
305 |  "metadata": {
306 |   "kernelspec": {
307 |    "display_name": "Python 2",
308 |    "language": "python",
309 |    "name": "python2"
310 |   },
311 |   "language_info": {
312 |    "codemirror_mode": {
313 |     "name": "ipython",
314 |     "version": 2
315 |    },
316 |    "file_extension": ".py",
317 |    "mimetype": "text/x-python",
318 |    "name": "python",
319 |    "nbconvert_exporter": "python",
320 |    "pygments_lexer": "ipython2",
321 |    "version": "2.7.15"
322 |   }
323 |  },
324 |  "nbformat": 4,
325 |  "nbformat_minor": 2
326 | }
327 | 


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/Class9/sales_data_test.csv:
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  1 | critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price
  2 | 3.5,1,1,1,0,1,0,1,247537,59.99
  3 | 2.5,0,0,0,1,1,0,0,73960,59.99
  4 | 3.5,0,0,0,0,1,1,0,82671,59.99
  5 | 4.0,1,1,0,0,1,0,0,137456,39.99
  6 | 2.0,1,0,1,0,1,0,0,89639,59.99
  7 | 3.5,1,0,1,0,0,0,1,103734,59.99
  8 | 4.0,1,0,0,0,0,0,1,96766,59.99
  9 | 3.5,1,1,1,0,1,0,0,223148,59.99
 10 | 4.0,1,1,0,0,0,0,0,149979,59.99
 11 | 2.5,0,1,0,1,1,0,1,138418,59.99
 12 | 5.0,1,0,0,0,1,0,0,96810,39.99
 13 | 4.0,1,0,0,0,1,0,0,122264,59.99
 14 | 3.5,0,1,0,0,0,1,0,101834,59.99
 15 | 4.0,0,0,1,1,1,0,0,126065,59.99
 16 | 4.5,0,1,1,0,1,1,0,138090,39.99
 17 | 4.0,0,0,0,0,1,1,0,92173,59.99
 18 | 3.5,0,0,1,0,1,1,1,109436,59.99
 19 | 3.5,0,0,0,1,0,0,0,45607,39.99
 20 | 5.0,1,0,0,0,1,0,0,97444,39.99
 21 | 2.5,1,0,1,0,1,0,0,105476,59.99
 22 | 3.0,0,0,0,1,1,0,0,85838,59.99
 23 | 3.5,0,1,0,1,1,0,0,107157,39.99
 24 | 2.5,0,1,0,1,1,0,1,141427,59.99
 25 | 4.0,0,0,1,1,0,0,0,89481,59.99
 26 | 3.5,1,0,1,0,1,0,1,146337,59.99
 27 | 3.5,0,0,1,0,1,1,1,108485,59.99
 28 | 3.0,1,1,0,0,1,0,0,139760,49.99
 29 | 3.5,1,0,0,0,1,0,1,82241,39.99
 30 | 3.0,1,0,0,0,0,0,0,70001,59.99
 31 | 4.5,1,0,1,0,0,0,1,105182,49.99
 32 | 2.5,0,0,0,1,1,0,0,75069,59.99
 33 | 3.5,1,1,0,0,1,0,0,189098,59.99
 34 | 4.0,1,0,0,0,1,0,0,82347,39.99
 35 | 4.5,0,1,0,1,1,0,0,196858,59.99
 36 | 5.0,0,0,0,1,1,0,0,82558,39.99
 37 | 3.5,0,0,0,1,0,0,0,68575,59.99
 38 | 5.0,0,1,0,0,1,1,0,124787,39.99
 39 | 2.5,0,0,0,1,1,0,1,68098,49.99
 40 | 5.0,0,1,1,0,1,1,1,162900,39.99
 41 | 5.0,0,1,0,1,1,0,1,234234,59.99
 42 | 2.0,0,0,0,1,1,0,0,42862,39.99
 43 | 3.5,0,0,0,1,1,0,0,62816,39.99
 44 | 2.5,1,1,0,0,0,0,0,106427,59.99
 45 | 3.5,0,0,1,0,0,1,0,58992,49.99
 46 | 3.5,0,1,0,0,1,1,0,95332,39.99
 47 | 3.0,1,0,1,0,1,0,0,80130,39.99
 48 | 2.0,0,0,0,1,0,0,0,46561,59.99
 49 | 5.0,1,1,1,0,1,0,0,246394,49.99
 50 | 3.5,1,0,0,0,1,0,1,81502,39.99
 51 | 4.5,0,0,0,0,1,1,0,101359,59.99
 52 | 2.5,0,0,0,1,0,0,0,54005,59.99
 53 | 5.0,1,1,1,0,0,0,1,232809,59.99
 54 | 2.5,1,0,0,0,1,0,0,87897,59.99
 55 | 4.0,1,0,0,0,1,0,0,82347,39.99
 56 | 2.5,0,0,1,0,1,1,0,65194,49.99
 57 | 4.0,1,0,0,0,1,0,0,120997,59.99
 58 | 3.0,1,0,1,0,1,0,1,131925,59.99
 59 | 2.0,0,0,0,1,1,0,1,70159,59.99
 60 | 3.0,1,1,1,0,1,0,0,202559,59.99
 61 | 4.5,1,1,0,0,1,0,0,228216,59.99
 62 | 4.0,1,0,0,0,0,0,0,72321,49.99
 63 | 4.5,1,1,1,0,1,0,0,270660,59.99
 64 | 4.5,0,0,0,1,1,0,0,114504,59.99
 65 | 3.5,0,0,0,0,0,1,0,60023,59.99
 66 | 2.5,0,0,0,1,1,0,1,82037,59.99
 67 | 4.5,1,0,1,0,1,0,0,135140,49.99
 68 | 4.5,1,1,0,0,1,0,0,191493,49.99
 69 | 4.0,1,0,0,0,1,0,0,123848,59.99
 70 | 3.5,0,1,1,1,1,0,0,129644,39.99
 71 | 4.0,0,0,1,1,1,0,0,124798,59.99
 72 | 4.0,1,0,0,0,1,0,0,100827,49.99
 73 | 3.5,0,0,0,1,1,0,1,105793,59.99
 74 | 3.5,1,1,0,0,1,0,1,206835,59.99
 75 | 2.5,0,0,0,1,1,0,0,62555,49.99
 76 | 3.5,1,0,0,0,1,0,0,74323,39.99
 77 | 3.0,0,1,1,0,1,1,0,153780,59.99
 78 | 4.0,1,1,1,0,1,0,1,272244,59.99
 79 | 4.0,0,0,0,1,1,0,1,116404,59.99
 80 | 3.0,1,0,1,0,0,0,0,55848,39.99
 81 | 5.0,1,0,0,0,1,0,0,144278,59.99
 82 | 4.5,0,0,1,0,1,1,0,119888,59.99
 83 | 4.0,0,0,0,0,1,1,0,77204,49.99
 84 | 4.5,0,0,1,0,1,1,0,119572,59.99
 85 | 3.5,0,0,0,1,1,0,1,86838,49.99
 86 | 3.5,1,1,1,0,1,0,0,225048,59.99
 87 | 3.0,1,0,1,0,1,0,1,87942,39.99
 88 | 2.5,1,0,0,0,1,0,0,87263,59.99
 89 | 2.5,1,0,0,0,1,0,0,57748,39.99
 90 | 3.5,0,1,0,1,1,0,1,146886,49.99
 91 | 2.5,0,0,0,1,1,0,0,74277,59.99
 92 | 4.5,1,0,0,0,1,0,0,89843,39.99
 93 | 2.0,0,0,0,0,1,1,0,56539,59.99
 94 | 5.0,1,0,0,0,1,0,0,96388,39.99
 95 | 3.5,1,0,0,0,1,0,0,92381,49.99
 96 | 5.0,0,1,1,0,1,1,0,149386,39.99
 97 | 5.0,0,0,0,0,1,1,1,79285,39.99
 98 | 3.5,0,1,0,0,1,1,1,128938,49.99
 99 | 3.5,0,1,0,1,1,0,0,133953,49.99
100 | 4.0,1,0,1,0,0,0,0,70206,39.99
101 | 3.5,0,0,0,0,1,1,1,61654,39.99
102 | 4.0,0,0,1,1,0,0,0,90906,59.99
103 | 4.5,0,0,0,1,1,0,1,127332,59.99
104 | 4.0,0,1,0,1,0,0,0,127174,59.99
105 | 2.5,1,0,0,0,1,0,0,86947,59.99
106 | 4.0,1,0,0,0,0,0,1,96449,59.99
107 | 4.0,1,0,0,0,1,0,0,103203,49.99
108 | 5.0,0,0,1,1,0,0,1,117829,59.99
109 | 4.0,1,0,0,0,1,0,0,121630,59.99
110 | 2.0,1,1,0,0,0,0,0,91856,59.99
111 | 2.5,1,0,0,0,0,0,0,61924,59.99
112 | 4.5,1,0,0,0,1,0,1,146653,59.99
113 | 4.0,0,0,1,1,0,0,1,99300,59.99
114 | 2.5,1,0,0,0,1,0,0,58593,39.99
115 | 3.5,1,0,0,0,1,0,0,112128,59.99
116 | 3.5,0,0,0,0,0,1,0,39484,39.99
117 | 5.0,1,1,0,0,1,0,0,245795,59.99
118 | 2.5,1,1,0,0,1,0,0,150613,59.99
119 | 3.5,1,0,1,0,1,0,0,88681,39.99
120 | 4.5,0,0,0,1,1,0,1,125590,59.99
121 | 3.5,1,0,1,0,1,0,0,131291,59.99
122 | 2.5,0,1,0,0,0,1,0,78711,59.99
123 | 3.5,0,1,0,1,1,0,0,132765,49.99
124 | 4.5,1,1,0,0,1,0,0,151075,39.99
125 | 4.5,1,0,0,0,0,0,0,96291,59.99
126 | 4.5,0,0,0,1,1,0,0,116087,59.99
127 | 3.5,0,0,0,0,1,1,0,83779,59.99
128 | 2.0,1,0,0,0,1,0,0,75385,59.99
129 | 4.5,1,0,1,0,1,0,1,148998,49.99
130 | 4.5,0,0,1,0,1,1,0,120680,59.99
131 | 3.5,0,0,0,1,0,0,0,68259,59.99
132 | 4.5,1,0,0,0,1,0,0,134775,59.99
133 | 3.5,0,1,0,1,1,0,1,175319,59.99
134 | 4.0,0,0,0,1,0,0,0,74593,59.99
135 | 4.5,0,0,0,1,1,0,0,114187,59.99
136 | 4.0,0,1,0,1,1,0,1,198442,59.99
137 | 4.0,0,0,0,0,1,1,0,91856,59.99
138 | 4.0,0,1,0,1,1,0,0,149526,49.99
139 | 4.5,0,0,0,0,1,1,0,101042,59.99
140 | 4.0,0,0,0,0,1,1,0,92648,59.99
141 | 3.5,1,0,0,0,0,0,0,78078,59.99
142 | 3.5,0,0,0,1,1,0,0,95499,59.99
143 | 4.0,1,1,0,0,1,0,0,139251,39.99
144 | 2.5,1,0,1,0,0,0,0,50569,39.99
145 | 4.5,1,0,1,0,1,0,1,175477,59.99
146 | 4.0,1,0,1,0,1,0,0,146337,59.99
147 | 4.0,1,0,0,0,1,0,0,80869,39.99
148 | 5.0,0,0,0,0,1,1,1,100827,49.99
149 | 4.0,0,1,0,0,1,1,0,102828,39.99
150 | 3.5,1,0,1,0,1,0,1,145228,59.99
151 | 3.5,0,0,0,1,1,0,0,96132,59.99
152 | 2.5,0,0,0,0,1,1,0,55164,49.99
153 | 4.0,1,1,0,0,1,0,0,138934,39.99
154 | 2.5,1,0,0,0,1,0,0,88214,59.99
155 | 4.5,0,0,1,1,1,0,1,151405,59.99
156 | 2.0,1,0,0,0,1,0,0,50675,39.99
157 | 4.0,1,0,0,0,1,0,0,122897,59.99
158 | 4.0,1,0,1,0,1,0,0,145228,59.99
159 | 3.0,1,1,0,0,1,0,1,153749,49.99
160 | 4.5,1,0,0,0,1,0,0,90054,39.99
161 | 2.0,1,0,1,0,1,0,0,92648,59.99
162 | 3.5,0,0,0,1,1,0,0,95340,59.99
163 | 3.5,0,0,0,0,1,1,0,82671,59.99
164 | 4.0,0,0,0,0,0,1,0,65725,59.99
165 | 4.0,0,1,0,1,1,0,0,147678,49.99
166 | 4.0,0,0,0,1,0,0,1,82037,59.99
167 | 4.5,1,0,0,0,1,0,0,133192,59.99
168 | 3.0,0,1,0,0,0,1,1,99141,59.99
169 | 4.0,1,0,1,0,1,0,0,147445,59.99
170 | 4.0,1,1,0,0,1,0,0,206835,59.99
171 | 2.5,1,0,0,0,1,0,0,57537,39.99
172 | 4.0,1,1,0,0,0,0,0,148079,59.99
173 | 4.5,0,0,1,0,1,1,1,88787,39.99
174 | 4.5,1,1,0,0,1,0,0,229166,59.99
175 | 4.0,0,0,1,1,1,0,1,139685,59.99
176 | 4.5,1,0,0,0,1,0,0,134459,59.99
177 | 3.5,0,0,0,1,0,0,0,56088,49.99
178 | 3.0,0,1,0,1,1,0,0,144595,59.99
179 | 4.0,1,0,1,0,1,0,1,106523,39.99
180 | 4.0,0,0,0,1,1,0,0,105793,59.99
181 | 4.5,1,0,0,0,1,0,0,89843,39.99
182 | 3.0,1,0,0,0,1,0,0,99300,59.99
183 | 2.0,0,0,1,1,1,0,0,76494,59.99
184 | 4.5,1,0,0,0,1,0,0,133508,59.99
185 | 2.5,0,0,1,1,0,0,0,63191,59.99
186 | 3.0,0,0,1,0,0,1,0,63507,59.99
187 | 4.5,1,1,0,0,0,0,1,120565,39.99
188 | 2.5,0,1,0,0,1,1,1,124164,59.99
189 | 4.0,0,0,0,1,1,0,1,116721,59.99
190 | 4.0,0,0,1,0,1,1,1,101355,49.99
191 | 5.0,0,0,0,0,0,1,1,86155,59.99
192 | 4.0,1,0,1,0,0,0,0,104684,59.99
193 | 3.0,1,0,0,0,0,0,0,71584,59.99
194 | 4.5,1,0,1,0,1,0,1,175953,59.99
195 | 2.5,1,0,0,0,0,0,1,57144,49.99
196 | 4.0,1,0,0,0,0,0,0,87580,59.99
197 | 4.5,0,0,0,1,1,0,1,127015,59.99
198 | 2.0,0,0,0,0,1,1,1,63666,59.99
199 | 4.0,0,0,0,0,0,1,0,43918,39.99
200 | 2.0,1,0,0,0,1,0,1,69418,49.99
201 | 4.0,0,0,0,0,1,1,0,91856,59.99
202 | 5.0,1,0,0,0,1,0,1,161065,59.99
203 | 3.5,0,1,0,1,0,0,0,114820,59.99
204 | 4.0,0,1,0,0,1,1,0,154731,59.99
205 | 3.5,1,0,0,0,1,0,0,111178,59.99
206 | 3.5,1,0,1,0,1,0,0,88998,39.99
207 | 2.5,0,0,0,1,1,0,0,62951,49.99
208 | 2.0,0,0,1,1,0,0,0,36739,39.99
209 | 3.5,1,0,0,0,1,0,0,74323,39.99
210 | 5.0,0,0,0,1,1,0,0,125748,59.99
211 | 4.5,0,1,0,1,1,0,0,193690,59.99
212 | 3.0,0,1,0,1,1,0,0,146495,59.99
213 | 4.0,0,0,1,0,1,1,0,110544,59.99
214 | 4.0,0,0,0,0,1,1,0,91223,59.99
215 | 4.5,1,1,0,0,1,0,1,252764,59.99
216 | 3.0,0,0,0,0,1,1,1,81879,59.99
217 | 5.0,1,0,0,0,1,0,0,144278,59.99
218 | 4.0,0,0,0,1,0,0,1,54475,39.99
219 | 4.0,0,0,0,0,1,1,0,92173,59.99
220 | 4.0,0,0,0,0,1,1,0,92648,59.99
221 | 2.0,0,1,1,1,0,0,1,69573,39.99
222 | 4.0,1,0,0,0,0,0,0,58065,39.99
223 | 3.5,0,0,0,1,1,0,0,64083,39.99
224 | 3.5,1,0,0,0,1,0,0,109277,59.99
225 | 3.5,0,0,0,0,0,1,1,65725,59.99
226 | 4.5,1,1,0,0,0,0,1,178803,59.99
227 | 4.0,0,0,0,1,1,0,0,105476,59.99
228 | 4.5,1,1,0,0,1,0,0,188854,49.99
229 | 4.0,0,0,1,1,1,0,0,83191,39.99
230 | 2.5,1,0,0,0,1,0,1,94707,59.99
231 | 3.5,1,1,0,0,0,0,0,133192,59.99
232 | 5.0,1,0,0,0,1,0,0,96810,39.99
233 | 4.0,0,0,1,1,0,0,0,59860,39.99
234 | 5.0,1,0,0,0,1,0,1,160432,59.99
235 | 4.5,0,0,0,1,1,0,0,116087,59.99
236 | 5.0,0,1,0,1,1,0,0,210636,59.99
237 | 2.5,1,0,0,0,1,0,0,73773,49.99
238 | 3.0,1,1,0,0,1,0,0,170885,59.99
239 | 3.5,0,1,1,0,1,1,0,168351,59.99
240 | 2.0,1,0,0,0,1,0,1,69418,49.99
241 | 4.5,1,1,0,0,1,0,0,228691,59.99
242 | 3.5,1,0,1,0,1,0,0,134142,59.99
243 | 3.5,0,1,0,1,1,0,0,134349,49.99
244 | 5.0,0,1,0,0,1,1,0,185613,59.99
245 | 4.5,1,1,0,0,1,0,0,151392,39.99
246 | 4.0,1,0,0,0,1,0,0,120997,59.99
247 | 2.0,0,0,1,1,1,0,0,66118,49.99
248 | 2.5,1,0,0,0,1,0,0,87580,59.99
249 | 3.0,1,0,0,0,1,0,0,100250,59.99
250 | 4.0,1,1,0,0,1,0,1,190569,49.99
251 | 3.5,0,0,1,1,1,0,0,94757,49.99
252 | 2.0,0,0,0,1,1,0,1,71901,59.99
253 | 4.5,0,0,0,1,0,0,0,81562,59.99
254 | 4.5,1,0,1,0,1,0,0,159957,59.99
255 | 3.5,0,1,0,1,0,0,1,127015,59.99
256 | 4.0,0,0,0,0,1,1,1,84595,49.99
257 | 3.0,0,0,0,0,1,1,0,48986,39.99
258 | 5.0,0,0,0,0,1,1,0,91721,49.99
259 | 3.5,1,1,0,0,0,0,0,112177,49.99
260 | 3.0,1,1,0,0,0,0,0,120839,59.99
261 | 3.0,0,1,1,0,1,1,1,111063,39.99
262 | 3.5,1,1,0,0,1,0,0,190206,59.99
263 | 4.5,0,0,0,1,0,0,0,81562,59.99
264 | 4.0,0,0,0,1,1,0,0,87762,49.99
265 | 3.0,0,1,1,1,1,0,0,173102,59.99
266 | 4.0,0,1,0,0,0,1,0,93965,49.99
267 | 3.5,0,1,1,1,1,0,1,214596,59.99
268 | 3.0,0,0,1,1,0,0,0,72851,59.99
269 | 3.5,1,0,0,0,1,0,1,123373,59.99
270 | 3.5,0,0,0,1,1,0,0,95499,59.99
271 | 3.0,1,0,0,0,1,0,0,98983,59.99
272 | 3.5,1,0,0,0,1,0,0,92117,49.99
273 | 4.5,0,0,0,1,1,0,0,115771,59.99
274 | 5.0,1,1,0,0,1,0,0,246429,59.99
275 | 4.0,0,0,0,0,0,1,1,71743,59.99
276 | 3.5,1,0,0,0,0,0,0,79820,59.99
277 | 3.5,1,0,0,0,0,0,1,58276,39.99
278 | 4.5,1,0,0,0,1,0,1,146970,59.99
279 | 3.5,0,0,0,1,1,0,1,104843,59.99
280 | 4.0,0,0,1,1,0,0,0,89481,59.99
281 | 4.5,0,1,0,1,1,0,0,196383,59.99
282 | 3.5,0,0,0,1,0,0,0,45079,39.99
283 | 3.0,1,0,0,0,1,0,1,89874,49.99
284 | 3.5,0,1,1,1,1,0,0,192265,59.99
285 | 2.0,0,1,0,1,1,0,0,111178,59.99
286 | 3.5,1,1,1,0,1,0,1,246904,59.99
287 | 4.0,1,0,0,0,1,0,1,136201,59.99
288 | 4.0,0,0,0,1,1,0,0,103734,59.99
289 | 3.5,0,0,0,0,1,1,0,55320,39.99
290 | 2.5,1,1,0,0,0,0,0,106427,59.99
291 | 3.5,1,1,0,0,1,0,0,189573,59.99
292 | 2.5,0,1,1,1,1,0,0,152672,59.99
293 | 3.5,1,0,1,0,1,0,0,131608,59.99
294 | 4.5,0,0,0,0,1,1,0,83275,49.99
295 | 4.0,1,0,1,0,1,0,0,97233,39.99
296 | 4.0,1,0,1,0,1,0,1,162966,59.99
297 | 3.5,0,0,0,1,0,0,1,75227,59.99
298 | 4.5,0,0,1,1,0,0,0,98825,59.99
299 | 4.0,1,0,1,0,1,0,1,160115,59.99
300 | 3.5,0,0,0,0,1,1,1,75092,49.99
301 | 3.5,1,0,0,0,1,0,1,101883,49.99
302 | 2.0,0,1,0,1,1,0,0,91193,49.99
303 | 3.5,1,0,1,0,1,0,1,146812,59.99
304 | 3.0,0,0,1,1,1,0,1,111970,59.99
305 | 3.5,1,0,0,0,1,0,0,110544,59.99
306 | 4.5,1,0,0,0,0,0,0,95182,59.99
307 | 2.0,0,0,1,1,1,0,0,79028,59.99
308 | 4.0,1,1,0,0,1,0,0,210478,59.99
309 | 4.0,0,1,0,1,1,0,1,129961,39.99
310 | 4.0,0,0,0,0,0,1,0,66200,59.99
311 | 3.5,1,0,0,0,1,0,0,110861,59.99
312 | 3.5,1,1,0,0,1,0,0,126054,39.99
313 | 4.0,0,0,0,1,0,0,1,81720,59.99
314 | 3.5,1,1,1,0,1,0,0,228216,59.99
315 | 4.0,0,0,0,1,1,0,0,105793,59.99
316 | 3.5,0,1,0,0,1,1,0,143011,59.99
317 | 4.0,0,0,0,1,1,0,1,115771,59.99
318 | 4.0,1,1,0,0,1,0,0,171829,49.99
319 | 4.0,1,0,1,0,1,0,0,145862,59.99
320 | 3.0,0,0,1,0,1,1,0,73905,49.99
321 | 2.5,1,0,0,0,1,0,1,97083,59.99
322 | 2.5,1,0,0,0,1,0,0,72717,49.99
323 | 4.5,0,1,1,1,1,0,0,232967,59.99
324 | 4.5,1,0,0,0,1,0,0,111517,49.99
325 | 4.0,0,0,0,1,1,0,0,106110,59.99
326 | 4.5,1,1,0,0,1,0,1,248646,59.99
327 | 4.0,0,0,0,0,1,1,0,92331,59.99
328 | 4.0,1,1,0,0,1,0,1,192945,49.99
329 | 4.0,0,0,1,0,1,1,0,109436,59.99
330 | 3.5,0,1,0,1,0,0,0,116087,59.99
331 | 4.0,0,0,1,1,0,0,0,89322,59.99
332 | 4.0,1,0,1,0,1,0,0,148237,59.99
333 | 3.0,1,0,0,0,0,0,1,77919,59.99
334 | 5.0,1,0,0,0,1,0,0,121547,49.99
335 | 2.0,1,0,1,0,1,0,0,91223,59.99
336 | 2.5,1,0,0,0,0,0,1,45185,39.99
337 | 3.5,0,0,0,1,1,0,1,103101,59.99
338 | 2.0,0,0,1,0,1,1,0,68417,59.99
339 | 2.5,1,1,0,0,1,0,0,146812,59.99
340 | 4.5,0,1,1,0,1,1,1,188194,49.99
341 | 3.0,0,1,0,0,1,1,1,136993,59.99
342 | 3.5,1,0,0,0,0,0,1,86155,59.99
343 | 4.0,1,0,1,0,1,0,0,97233,39.99
344 | 4.5,1,0,0,0,1,0,0,110725,49.99
345 | 3.5,1,0,0,0,1,0,0,110228,59.99
346 | 3.0,1,1,0,0,1,0,1,185138,59.99
347 | 3.5,1,1,0,0,1,0,1,208578,59.99
348 | 5.0,0,0,1,1,1,0,0,149663,59.99
349 | 5.0,0,0,1,0,0,1,1,68728,39.99
350 | 4.0,0,1,1,1,1,0,0,142524,39.99
351 | 3.5,1,0,0,0,1,0,0,111811,59.99
352 | 4.5,0,0,0,1,1,0,0,95416,49.99
353 | 2.0,1,0,0,0,0,0,0,36211,39.99
354 | 2.5,0,0,1,0,1,1,1,56798,39.99
355 | 2.0,1,0,0,0,1,0,0,49619,39.99
356 | 4.5,1,0,0,0,1,0,1,123263,49.99
357 | 3.0,0,1,0,1,1,0,0,146020,59.99
358 | 2.5,0,1,0,0,0,1,0,53525,39.99
359 | 3.5,1,1,0,0,1,0,1,205093,59.99
360 | 3.5,0,0,0,1,1,0,0,95340,59.99
361 | 4.5,0,0,1,1,1,0,1,100928,39.99
362 | 3.5,1,0,0,0,1,0,0,110544,59.99
363 | 3.5,1,0,1,0,1,0,0,133508,59.99
364 | 4.0,1,1,0,0,1,0,0,206835,59.99
365 | 4.0,1,0,0,0,0,0,1,63872,39.99
366 | 2.5,1,1,0,0,1,0,0,151088,59.99
367 | 4.5,1,0,0,0,1,0,0,89420,39.99
368 | 5.0,0,0,0,0,1,1,0,109752,59.99
369 | 3.0,0,1,1,1,0,0,1,135567,59.99
370 | 3.0,1,0,0,0,1,0,1,110228,59.99
371 | 2.0,1,0,0,0,1,0,0,75702,59.99
372 | 2.5,1,0,0,0,1,0,1,97399,59.99
373 | 4.0,1,0,0,0,0,0,0,72189,49.99
374 | 3.5,0,0,1,1,1,0,0,113395,59.99
375 | 2.5,0,1,0,1,0,0,0,76280,49.99
376 | 2.5,0,0,1,1,1,0,1,82879,49.99
377 | 4.0,1,0,1,0,1,0,1,160590,59.99
378 | 3.5,0,0,1,1,0,0,1,88530,59.99
379 | 3.5,0,0,0,1,0,0,1,49302,39.99
380 | 4.0,0,0,0,1,1,0,0,105476,59.99
381 | 3.5,0,0,0,1,1,0,0,94232,59.99
382 | 3.5,1,0,0,0,1,0,1,120680,59.99
383 | 4.0,0,0,0,1,0,0,0,74435,59.99
384 | 4.5,0,0,0,1,1,0,0,76435,39.99
385 | 3.0,0,0,0,0,0,1,1,58123,59.99
386 | 4.5,0,1,0,0,1,1,0,171043,59.99
387 | 3.5,0,1,0,0,1,1,0,140160,59.99
388 | 3.0,0,0,0,1,1,0,0,84729,59.99
389 | 2.0,0,0,0,1,1,0,0,42546,39.99
390 | 4.0,1,0,0,0,0,0,1,95974,59.99
391 | 2.0,1,0,0,0,1,0,0,76019,59.99
392 | 4.0,0,0,0,1,0,0,0,75069,59.99
393 | 4.5,1,0,0,0,0,0,0,79448,49.99
394 | 4.0,0,0,0,0,1,1,0,61549,39.99
395 | 3.0,0,1,0,1,1,0,0,95544,39.99
396 | 2.5,0,0,1,1,0,0,1,69842,59.99
397 | 4.5,1,0,0,0,0,0,0,95816,59.99
398 | 4.0,1,0,0,0,1,0,0,121947,59.99
399 | 5.0,1,1,1,0,0,0,0,142313,39.99
400 | 2.0,1,1,0,0,0,0,0,61549,39.99
401 | 3.5,1,0,1,0,0,0,0,80107,49.99
402 | 


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/Class9/test_s1.jpg:
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https://raw.githubusercontent.com/coding-blocks-archives/machine-learning-june-2018/f0772da9a9a2aec974c1c1daf7aa1f482d764962/Class9/test_s1.jpg


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/Project/Gesture_Record.py:
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  1 | 
  2 | # coding: utf-8
  3 | 
  4 | # In[77]:
  5 | 
  6 | 
  7 | import os
  8 | import cv2
  9 | import time
 10 | 
 11 | 
 12 | # In[78]:
 13 | 
 14 | 
 15 | gesture_name = raw_input("Enter Gesture Name ")
 16 | 
 17 | 
 18 | # In[79]:
 19 | 
 20 | 
 21 | images = []
 22 | num_frames = 60
 23 | 
 24 | 
 25 | # In[80]:
 26 | 
 27 | 
 28 | capture_start = False
 29 | cv2.namedWindow("Video", cv2.WINDOW_NORMAL)
 30 | 
 31 | vc = cv2.VideoCapture(0)
 32 | vc.set(cv2.CAP_PROP_FPS, 5)
 33 | 
 34 | rval, frame = vc.read()
 35 | 
 36 | cnt = 0
 37 | 
 38 | while True:
 39 |     
 40 |     if frame is not None: 
 41 |         
 42 |         frame = cv2.flip(frame, 1)
 43 |         cv2.imshow("Video", frame)
 44 |         
 45 |     rval, frame = vc.read()
 46 |     keypress = cv2.waitKey(1)
 47 |     
 48 |     if keypress == ord('q'):
 49 |         break
 50 |     elif keypress == ord('c'):
 51 |         capture_start = True
 52 |     
 53 |     if ( capture_start ):
 54 |         img = cv2.flip(frame, 1)
 55 |         #img = cv2.cvtColor( img, cv2.COLOR_RGB2BGR )
 56 |         images.append(img)
 57 |         cnt += 1
 58 |         if ( cnt > num_frames ):
 59 |             break
 60 |         
 61 | vc.release()
 62 | cv2.destroyAllWindows()
 63 | cv2.waitKey(1)
 64 | 
 65 | 
 66 | # In[81]:
 67 | 
 68 | 
 69 | if len(images) == 0:
 70 |     print(" ERROR !!  No Frames Recorded" )
 71 |     exit()
 72 | 
 73 | 
 74 | # In[83]:
 75 | 
 76 | 
 77 | last_num = 0
 78 | folder_name = gesture_name
 79 | gestures_rec = os.listdir('./')
 80 | 
 81 | for i in gestures_rec:
 82 |     
 83 |     if i[0] == '.':
 84 |         continue
 85 |     if '_'.join( i.split('_')[:-1] ) == gesture_name:
 86 |         last_num = max(last_num, int( i.split('_')[-1] ) )
 87 | 
 88 | last_num += 1
 89 | folder_name = gesture_name + '_' + str(last_num)
 90 | try:
 91 |     print('[i] Creating directory {}...'.format(folder_name))
 92 |     os.makedirs(folder_name)
 93 | except (IOError) as e:
 94 |     print('[!]', str(e))
 95 |     exit()
 96 | 
 97 | 
 98 | # In[84]:
 99 | 
100 | 
101 | print ("[i] Creating Video")
102 | output = './' + folder_name + '/output.mp4'
103 | fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Be sure to use lower case
104 | out = cv2.VideoWriter(output, fourcc, 5.0, (images[0].shape[1], images[0].shape[0]) )
105 | 
106 | for image in images:
107 | 
108 |     f = image
109 |     #print (f.shape)
110 |     out.write(f) # Write out frame to video
111 | 
112 |     #cv2.imshow('video',frame)
113 |     if (cv2.waitKey(1) & 0xFF) == ord('q'): # Hit `q` to exit
114 |         break
115 | 
116 | # Release everything if job is finished
117 | out.release()
118 | cv2.destroyAllWindows()
119 | 
120 | 
121 | # In[ ]:
122 | 
123 | 
124 | print ("Output Video is in", output)
125 | 
126 | 


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/Project/README.md:
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1 | ## Recording Gesture
2 | 1. Run `python Gesture_Record.py`
3 | 2. Enter Gesture Name
4 | 3. In the Webcam window, Press 'c' to start capturing
5 | 4. 5 sec of video will be recorded and saved to a folder in same directory
6 | 


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/README.md:
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1 | Machine Learning Course with Python
2 | 


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/assignments/python_assignment.pdf:
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https://raw.githubusercontent.com/coding-blocks-archives/machine-learning-june-2018/f0772da9a9a2aec974c1c1daf7aa1f482d764962/assignments/python_assignment.pdf


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