├── LICENSE
├── README.md
└── notebooks
    ├── Programming Exercise 1 - Linear Regression.ipynb
    ├── Programming Exercise 2 - Logistic Regression.ipynb
    ├── Programming Exercise 3 - Multi-class Classification and Neural Networks.ipynb
    ├── Programming Exercise 4 - Neural Networks Learning.ipynb
    ├── Programming Exercise 5 - Regularized Linear Regression and Bias v.s. Variance.ipynb
    ├── Programming Exercise 6 - Support Vector Machines.ipynb
    ├── Programming Exercise 7 - K-means Clustering and Principal Component Analysis.ipynb
    ├── Programming Exercise 8 - Anomaly Detection and Recommender Systems.ipynb
    └── data
        ├── bird_small.png
        ├── ex1data1.txt
        ├── ex2data1.txt
        ├── ex2data2.txt
        ├── ex3data1.mat
        ├── ex3weights.mat
        ├── ex4data1.mat
        ├── ex4weights.mat
        ├── ex5data1.mat
        ├── ex6data1.mat
        ├── ex6data2.mat
        ├── ex6data3.mat
        ├── ex7data1.mat
        ├── ex7data2.mat
        ├── ex7faces.mat
        ├── ex8_movies.mat
        ├── ex8data1.mat
        ├── ex8data2.mat
        └── vocab.txt


/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 Jordi Warmenhoven
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Coursera Machine Learning 
 2 | <IMG src='https://coursera.s3.amazonaws.com/topics/ml/large-icon.png?auto=format&dpr=1&h=256&w=256&fit=fill&bg=FFF' width=25% height=25%><P>
 3 | This repository contains python implementations of certain exercises from the course by Andrew Ng.<P>
 4 | 
 5 | For a number of assignments in the course you are instructed to create complete, stand-alone Octave/MATLAB implementations of certain algorithms (Linear and Logistic Regression for example). The rest of the assignments depend on additional code provided by the course authors. For most of the code in this repository I have instead used existing Python implementations like Scikit-learn.<P>
 6 | 
 7 | <A href='http://nbviewer.ipython.org/github/JWarmenhoven/Machine-Learning/blob/master/notebooks/Programming%20Exercise%201%20-%20Linear%20Regression.ipynb'>Exercise 1 - Linear Regression</A><BR>
 8 | <A href='http://nbviewer.ipython.org/github/JWarmenhoven/Machine-Learning/blob/master/notebooks/Programming%20Exercise%202%20-%20Logistic%20Regression.ipynb'>Exercise 2 - Logistic Regression</A><BR>
 9 | <A href='http://nbviewer.ipython.org/github/JWarmenhoven/Machine-Learning/blob/master/notebooks/Programming%20Exercise%203%20-%20Multi-class%20Classification%20and%20Neural%20Networks.ipynb'>Exercise 3 - Multi-class Classification and Neural Networks</A><BR>
10 | <A href='http://nbviewer.ipython.org/github/JWarmenhoven/Machine-Learning/blob/master/notebooks/Programming%20Exercise%204%20-%20Neural%20Networks%20Learning.ipynb'>Exercise 4 - Neural Networks Learning</A><BR>
11 | <A href='http://nbviewer.jupyter.org/github/JWarmenhoven/Machine-Learning/blob/master/notebooks/Programming%20Exercise%205%20-%20Regularized%20Linear%20Regression%20and%20Bias%20v.s.%20Variance.ipynb'>Exercise 5 - Regularized Linear Regression and Bias v.s. Variance</A><BR>
12 | <A href='http://nbviewer.jupyter.org/github/JWarmenhoven/Machine-Learning/blob/master/notebooks/Programming%20Exercise%206%20-%20Support%20Vector%20Machines.ipynb'>Exercise 6 - Support Vector Machines</A><BR>
13 | <A href='http://nbviewer.jupyter.org/github/JWarmenhoven/Machine-Learning/blob/master/notebooks/Programming%20Exercise%207%20-%20K-means%20Clustering%20and%20Principal%20Component%20Analysis.ipynb'>Exercise 7 - K-means Clustering and Principal Component Analysis</A><BR>
14 | <A href='http://nbviewer.jupyter.org/github/JWarmenhoven/Machine-Learning/blob/master/notebooks/Programming%20Exercise%208%20-%20Anomaly%20Detection%20and%20Recommender%20Systems.ipynb'>Exercise 8 - Anomaly Detection and Recommender Systems</A><BR>
15 | 
16 | ##### References:
17 | https://www.coursera.org/learn/machine-learning/home/welcome
18 | 


--------------------------------------------------------------------------------
/notebooks/Programming Exercise 3 - Multi-class Classification and Neural Networks.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Programming Exercise 3 - Multi-class Classification and Neural Networks"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 36,
 13 |    "metadata": {
 14 |     "collapsed": false
 15 |    },
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "# %load ../../standard_import.txt\n",
 19 |     "import pandas as pd\n",
 20 |     "import numpy as np\n",
 21 |     "import matplotlib as mpl\n",
 22 |     "import matplotlib.pyplot as plt\n",
 23 |     "\n",
 24 |     "# load MATLAB files\n",
 25 |     "from scipy.io import loadmat\n",
 26 |     "from scipy.optimize import minimize\n",
 27 |     "\n",
 28 |     "from sklearn.linear_model import LogisticRegression\n",
 29 |     "\n",
 30 |     "pd.set_option('display.notebook_repr_html', False)\n",
 31 |     "pd.set_option('display.max_columns', None)\n",
 32 |     "pd.set_option('display.max_rows', 150)\n",
 33 |     "pd.set_option('display.max_seq_items', None)\n",
 34 |     " \n",
 35 |     "#%config InlineBackend.figure_formats = {'pdf',}\n",
 36 |     "%matplotlib inline\n",
 37 |     "\n",
 38 |     "import seaborn as sns\n",
 39 |     "sns.set_context('notebook')\n",
 40 |     "sns.set_style('white')"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "markdown",
 45 |    "metadata": {},
 46 |    "source": [
 47 |     "#### Load MATLAB datafiles"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 3,
 53 |    "metadata": {
 54 |     "collapsed": false
 55 |    },
 56 |    "outputs": [
 57 |     {
 58 |      "data": {
 59 |       "text/plain": [
 60 |        "dict_keys(['__version__', '__globals__', '__header__', 'y', 'X'])"
 61 |       ]
 62 |      },
 63 |      "execution_count": 3,
 64 |      "metadata": {},
 65 |      "output_type": "execute_result"
 66 |     }
 67 |    ],
 68 |    "source": [
 69 |     "data = loadmat('data/ex3data1.mat')\n",
 70 |     "data.keys()"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": 4,
 76 |    "metadata": {
 77 |     "collapsed": false
 78 |    },
 79 |    "outputs": [
 80 |     {
 81 |      "data": {
 82 |       "text/plain": [
 83 |        "dict_keys(['__version__', '__globals__', '__header__', 'Theta1', 'Theta2'])"
 84 |       ]
 85 |      },
 86 |      "execution_count": 4,
 87 |      "metadata": {},
 88 |      "output_type": "execute_result"
 89 |     }
 90 |    ],
 91 |    "source": [
 92 |     "weights = loadmat('data/ex3weights.mat')\n",
 93 |     "weights.keys()"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 5,
 99 |    "metadata": {
100 |     "collapsed": false
101 |    },
102 |    "outputs": [
103 |     {
104 |      "name": "stdout",
105 |      "output_type": "stream",
106 |      "text": [
107 |       "X: (5000, 401) (with intercept)\n",
108 |       "y: (5000, 1)\n"
109 |      ]
110 |     }
111 |    ],
112 |    "source": [
113 |     "y = data['y']\n",
114 |     "# Add constant for intercept\n",
115 |     "X = np.c_[np.ones((data['X'].shape[0],1)), data['X']]\n",
116 |     "\n",
117 |     "print('X: {} (with intercept)'.format(X.shape))\n",
118 |     "print('y: {}'.format(y.shape))"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": 6,
124 |    "metadata": {
125 |     "collapsed": false
126 |    },
127 |    "outputs": [
128 |     {
129 |      "name": "stdout",
130 |      "output_type": "stream",
131 |      "text": [
132 |       "theta1: (25, 401)\n",
133 |       "theta2: (10, 26)\n"
134 |      ]
135 |     }
136 |    ],
137 |    "source": [
138 |     "theta1, theta2 = weights['Theta1'], weights['Theta2']\n",
139 |     "\n",
140 |     "print('theta1: {}'.format(theta1.shape))\n",
141 |     "print('theta2: {}'.format(theta2.shape))"
142 |    ]
143 |   },
144 |   {
145 |    "cell_type": "code",
146 |    "execution_count": 7,
147 |    "metadata": {
148 |     "collapsed": false
149 |    },
150 |    "outputs": [
151 |     {
152 |      "data": {
153 |       "image/png": 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BrKwsCgsLiY+P5/Lly1gsFiIjI5+49hQKBUNDQzQ3N6PVatm9ezc7d+7EZrNJ0b579uwh\nJiaG3t5ezp07F/IpShAEybpVU1NDSkoK5eXlJCQkoFAo8Pl89Pb2cvjwYVQqFS+++OIPCmCDwUBl\nZeUjT7aCIDA1NUVnZyetra3U1tZy5coVRkZGGBwcZOvWrU/0VyoUCiYmJuju7qarqwuTySS97/j4\nOB6PR/I/h4rf76e/v599+/ahVquluSHuRaEwPj7O0aNH6evrQ6VSoVAoUKvVksJrNBqlbz8xMUFX\nVxd6vZ5du3aRnZ0dtAVR9H03NTUxNDTEL37xC2w2G1NTU9TU1NDY2IjdbsdgMLB27VpiYmJC3kf8\nfj+9vb2o1Wp0Oh0Oh4Px8XGMRiNRUVEhtfVd1Go1ZrOZmJgYOjs7/2/5gBUKBRaLhYmJCY4fP45G\no2FqaorDhw/T0tLCmjVrKCsrC2pzV6vV5OXlkZeXR0NDA3q9XtLmExISUKlUs0qH6OnpYWZmhoKC\nAhITEyWBUVdXh8PhIBAIhLQIBgcHuXbtGnfu3JF+z2QyoVKp6Ojo4NatW5IPLSoqioKCAnJzc4Oe\n+GLEZWxsLIsWLcJisTz07j6fj+npaZRKZchKiWjebm9vR6vVfs/05fP5JIUomDERo737+/ul0+N3\nTw8iBoOBRYsWsXPnTn7961/T2tpKamrqYxfa6OgoIyMjbNy4kaysrFkFZzyIKMTOnj3LkSNHMBgM\n7N69G61WS3R0NCaTiZSUFJKSkh4KunnSmHi9XiYmJlAoFBQUFLBkyRIp6CwuLo6Ojg4iIyNJT09n\n/fr1LFu2LOi4ABGPx8P169e5efMmsbGxVFdXk5iYSFNTE5cvX6a4uJjExMSgzM9utxun00lMTAyF\nhYWkpqZKCgGAXq9Hr9cjCAKRkZEhn9ZmZmb49ttv+fLLL+nr62Pbtm3k5uZiNBoJBAIMDg5y8uRJ\nampqWLdu3WNNj1qtltzcXLKzsx8ZOBkIBCgtLWVoaIjc3Fz27dtHY2MjWq2WwsJCMjIyHtt3lUqF\nx+OhpaWFuro6srKypG9nMBjQaDRh70Mej4fW1lZOnz7NO++8w/r168MSvuJ7Tk5O4nK5pAwRjUYj\nxR643W60Wq3Ud7vdjk6nY3p6OqxnTU9PS20qFAru3LnDuXPncDgc0vPDsc4pFAqmpqbo6+tjYGCA\nhoYG2tra8Pv9pKamUlVVhdlsDrndB9s3GAzExMTgcrmkNLJweaoEsFKpxGq1EhUVxenTpxkdHWVy\ncpLBwUEKCgrYtGnT9wTHD6FSqcjIyKC0tJQbN27g9/tpb2+ntraWrKwsUlJSpKCKcHA6nSgUCkmj\nFbXuI0eO4HQ6MZlM6HS6oE+/LS0tXL58WdpoAYaGhrh58yaNjY00NzfjdrsfMo8NDw9Lvr/HIQgC\nY2Nj9PX1SRuiXq/H6/XicrkYHx9ndHSU/v5+dDodaWlpxMfHhxR0c/fuXZqbm8nPz5dO/WKebU9P\nDzExMcTFxQUt7MbHx+no6ECj0VBSUvLYfuh0OnJyckhMTOTOnTtUVFT8oAAWx81kMpGWlvaQ/1zM\nO/Z6vWi1WmlzCAWHw0FHRwdjY2NkZmaybNkySktLH3l6fDAP9HHPeVB5EU8nolDp7e3l5s2bjI2N\nsWLFCt566y3S0tJC9neNjo5SW1vL2NgYGzZsIBAIcPz4cb766it6e3vZu3cvFoslqLYiIyMxm810\ndXVx+/ZtCgoKMJvNzMzMcPXqVU6dOoXVasXhcBAREUFqampI69Dj8XDx4kVu3bpFcnIy+fn5WCyW\nh/K6Dx06JFm9nhRd/GAU+nfXq7jhpqWlYbPZaG9vp62tjaGhIUZGRvB6vY+NddBoNMTFxeF2uzl7\n9iyLFy+mrKwMpVJJYmIikZGRuFyusAWw3W5nfHyctWvXYrVaw06tjI6OZsuWLXi9XgBJCPr9fmku\nabVayeXV1dWF0WjEYrGgVqtDijoX3ShqtZrTp0+j0+k4evQoHR0deL1e4uPjpcDQcN6lt7eXvr4+\nPB4P/f392O12ent7pXm5fv36WcV6qNVq9Hq9ZOWcTebEUyWARf+CUqmUTIpKpZLly5eze/duVq5c\nGVJ7SqVSSiEYHR3l0KFDnD9/nvz8fDZu3MjmzZsxm81hfQy1Wi35TQDJVCMuSNGH/STEYK6WlhZa\nW1ulnwFcvnyZe/fuSf5v8d9GR0c5ffo0qamp7Nmz54nPEHNqRZ9NZGQk09PT9PT0cOPGDRoaGrh9\n+zbd3d0YDAYWL17MunXrqKioCCqoCe4LzJGREUloiYu2ubmZI0eOsGzZMtasWRP0YnU4HLS1taHT\n6SS/3OPGUKVSYTQaGRgYeKw5LxAI0NXVRUxMjJSLKvZ3cnKSzs5Ouru7SU5OJjExkZiYGMkU9yQE\nQSA1NZUtW7YwNTXFpUuXGBgY4K/+6q+kTfdRfQ8VUbnxeDxcuXKF+vp6oqOj2bBhA6mpqSELX9Es\n39PTw9DQENeuXePzzz/n5s2beDwe3nrrLUpLS4PykwuCQHx8PIWFhTQ0NLB//378fj9r1qxhbGyM\nf/zHf2RwcJCEhARWr15NdXU1+fn5IfVX/FbiqUaMXBdPhF9//TU9PT38zd/8DYsXL5b6FS4PRi1H\nRkai0WhwOBz09fUxOTkpneYfhV6vp6SkhLq6Oq5evcr+/ftZsGDB905h381BDna+idalB5U7sS9i\nDrLP50OtVkv70aPa1mg0pKSkPPI54v8X18nY2Bjd3d1s374dk8n0xH5+F4PBwMKFCykoKOCLL76g\nubmZS5cuSRaRiIgIIiIiJCtlqFy7do2+vj62b9/Oc889h06n48KFC3z00Ud8+eWXLF++POS0xQcR\nlZyhoSHGx8fR6/Vht/XUCGCPx0NzczO//e1v6ezsxO12EwgEMBqN5OfnU1RUFJKmJbJ69Wqampqo\nra3F5XJJfq6Wlhba2tp4++23sVqtIbWpUCjIzMykr6+PxsZGkpKSUKlUtLS0MDg4KAm5YLR60Wfl\ndrsfEhyiKW14eFjSRuF//FJ9fX0MDQ0F1d9AIMDQ0BAej4fY2Fg8Hg+ff/45Bw8epK+vj4iICJRK\npZSTefz4cRoaGvjzP/9ztm3bFpQiIQqspqYm+vr6iI+PZ3h4mHfffZehoSEWL14ctF9YzK0OBALk\n5OQEHU0tCIJ0QvwhVCoV+fn57N+/n+HhYXw+HyqVitHRUY4fP857773H4OAgWq2W0tJS/uRP/oSS\nkpKgg7SMRiPLli0jNzeXmzdv8t577/Ev//Iv/N3f/R0LFy783kk+mIWrVqslq8KD73f37l3OnTvH\n0NAQW7ZsYcmSJWFtWIIgkJSUREVFBTdu3KCvr4+mpiYUCgWvvfYar7zySkh+crVazfr16/F6vRw6\ndIjf/e53vP/++/j9fkZGRnj++ef5oz/6I7KzszEYDGG5AMSxuHnzJv/5n/9JRUUFsbGxXLlyhYsX\nL5KVlUVJSclDyqD4p2hFChWXy8X58+dxuVxSHvOTTmlKpZL8/HxWr15Nc3MzNTU1REdH8/Of/5yJ\niQkp3kM0y/p8vpADAsWAKZVKRSAQkFK+hoeHsdvtdHV1kZmZSVVVlWQp+KF2HkUgEJBy1t1uN42N\njdy9exeHw8Hg4CDT09PodLqQLGYpKSm88cYbKBQKPvvsM/Lz83nttdfIzc3lq6++oqmpCbvdzqJF\ni0Ke0y6XS7I8WK1WYmJiKCsr4/r167S2tkpR1uEgugtmZmYYGBhgeHg46EPKo3gqBLDX6+X27dv8\n7ne/48yZMyQmJrJr1y70ej1NTU3MzMwwOTkZVmDPggUL+NM//VNWr16N2+1mZGSES5cucfPmTY4f\nPy5FzoaiEQmCQEpKCgsXLqS+vp5/+7d/w2QyMTk5SWVlJUqlMugPLAgCRqORtLQ0EhMTcTgcGAwG\nkpOTWbBgASMjI9jtdqamplAqlaSmplJRUUF5eTlLliwJ6hkqlYqkpCSMRiMtLS3MzMxw7do1fD4f\nL7/88kOpJU6nk3/4h39Ar9ej0+mCUiIEQSA7O5uqqiqOHz/Ob3/7WxITE+np6aG5uZn169ezYMGC\nkHJIx8bGGBkZIT09PagNWjQf5+fnPzEtSzQfnThxgrS0NPLy8vj222/55JNPuHv3LgqFgrGxMc6d\nO4fVasVqtZKdnR303FOr1VgsFun7/Ou//ivXr18nMTHxoRNDsPMtJiaGhQsX4vV66erqkjZtcZM1\nmUzk5+cTExMTVHuPIjo6mm3btpGZmUlNTQ3x8fGsX7+eV155RYqZCIXExESpvRMnTvDxxx9L/6ZS\nqYiOjg47IEan01FaWkp0dDROp5MrV65w584dNBoNLpcLn89HXFzcQwGQYnDY9PQ0JpMp5LHyeDyc\nOnWKzs5OZmZmSE9PJykp6QdjE0REk+uqVasYHR3liy++4PPPP6elpQWr1crg4CCCIHDq1Cm6urrI\nyMhg48aNQc0N0V1kMBjo7+/HYDBw8+ZN6urqmJycJC4uDqVSyd27d7lw4QJTU1Ps2bMn5HGfnp6m\nqamJs2fPSvWlBwYGOH36NNevX2dqaoply5axbds2bDZbUBYYtVr9UADi888/z4oVK4iOjiY1NZXB\nwUE6OjpCto7A/f3DZrNhtVqlw4MY6PZgLEK4TE1N0dvbi8PhoLu7m9zc3LBdmfMugH0+H3fu3OGT\nTz6htraWwsJCdu3aJQVbvf/++4yPjzM0NMSiRYtCbt9gMJCfn09ycjKBQACXy0VBQQEnT57k5MmT\nHDx4kNWrV5OdnR20gBAEAYPBQF5eHkqlku7ubrRaLRaLhW+//RaHwxGSMNfpdFLhkYaGBhQKBenp\n6WRnZ3P48GHu3r0L3BcyWq2WBQsWUFlZGfTJXRTcaWlpNDc3097ezsjICEuXLqWyspLc3FzgviJ0\n8OBBBEFg6dKlIVWPiYyMpLi4GI1GI20qtbW1WK1WnnnmmZD8fH6/H5fLhcPhCMoPOz09TUtLC+Pj\n4+Tk5DwxMtxsNrNgwQKuXr2K2Wxm8+bNdHd34/P52Lt3L1arlXv37nHy5Em6u7ulEoo/hGgKfLBO\ntZgqJlYra29vx+VyhWWyi4iIICMjg6ioKOrr69m6dSsGg4Guri4cDgcpKSnk5+eHZSESEc19ostj\nw4YNPPvss6Snp4el3Ws0Gmw2GwaDgeHhYT766COpTOSVK1f49NNP2b59O3l5eSHni2s0GsrKynjp\npZfo6uqis7OTe/fu4XQ6peCobdu2PXQ6FZUql8uFTqcLSgCL1qnu7m5OnDhBTU0NQ0NDFBUVsWnT\nJrKysoIaG5VKRVpaGtu3b5fiW1pbW2ltbcXpdCIIAhcvXqS/v18quRoMYuxDeXk5+/fvl/KXExIS\nKCsrw2q1otfrGRgY4Msvv6SxsZEtW7aELIBVKhVms5nc3FwSEhKA++Vsy8vLiY2NZXp6mqysrMea\n4r/LzMwMvb29NDY2snLlSpYtW0ZsbKwUFyHu+eFWYTOZTERHR0tKWVtbG3fu3CE9PX3Wte7j4uJY\nsmQJFy5coL+/H5/P90RF7IeYVwGsUNwvPtHc3Mz58+dJSUnhtddeY/Xq1ZjNZlwuF3FxcZIPI1xE\nc4RCcb8uamJioqQttrS0MDQ0xIIFC0Lue1xcHNHR0eTl5eH3+zEYDFy4cCHkSaNUKsnIyGDbtm0U\nFhbi8/kwm81ERkZy9uzZh9oSA6fE4iHBTnir1cq6dev49NNPsdvtwH1NbmxsDJVKhd/vp6mpicOH\nDxMdHc2KFStITU0N6T0sFgvLly+XomDPnTuHyWQiNTU1rIAKn8/H+Pj4Y7+/y+Xixo0bHD58mJSU\nFFJTUx9rMhcEAbPZzM6dO/n00085d+4cw8PDBAIBYmJiWLp0KVFRUcTExNDQ0CCZ3n4IMa3ixo0b\nxMXFkZmZKQVliKYq0SQWrpas1WrJyMigsrJSejfRrx8IBMjKyiItLS2stsV36Ovr48SJE1y6dAmb\nzcb27dulwifhIiok4lwtKSmhuLiY2tpajh49is/nY8+ePeTn54c0NkqlEpvNxosvvsjg4CA1NTWM\njo4yNTVFVlYWmzdvZs2aNd8LvhJ9/sEIfNEN0t3dzdGjR/n000/p7u6msLCQZ599ljVr1hAXFxd0\nn/V6PdkXDdbnAAAMyElEQVTZ2VgsFtLS0rh+/Tp9fX1cv36d3t5eAoEAaWlp5OTkBD0WKpWK5ORk\nXnrpJd577z1aW1ulUr3l5eWSItrf38+FCxeCjmX4LhqNRkrT8nq9WK1W7ty5w6pVqyQlRKVShVQV\nyul00tDQQEdHB7/85S8lN96DNR+e5E76IUSTueiua2pq4tSpUzidTsrLy2ed8x8XF0dZWRk6nY6x\nsbFZFfiY9xOweJyfnp5m586dbN26VQow6evrQxAEaYOZ7e06omAU88PEMPvZmCTEICG4bxLs7u6m\nrKws5LquKpUKm832UDWcycnJh2rlqlQqKUVI1JyDxWg0sm3bNux2O2fOnGF0dJSWlha+/vprKdDo\n/fff59atWzz//PMUFhYGVWHrUe8RFRUl1cQWTeeh9FWs5pSYmIjdbqevr4+0tLSHTuN+vx+n08m3\n337LoUOHuHXrFm+//TaxsbFP3MD0ej2bN2/G5/Px8ccfc/ToUWkzq6urY2pqikAggNPpfGI6hEJx\nv6rPwYMHSU5OlsyuarVaMo/29fWRn58ftslVrVaTmprKyy+/TCAQwGw24/f7GR8fJyYmhqysrLAK\nFohMT09TV1fHqVOniIuL49lnn52T6+sEQcDj8TA1NYXJZGLx4sVShbjf//73fPPNN1itVhITE0O6\ngUr04S5cuBCr1cqZM2fQaDRkZGSwadMmtm3bhtlsfqi9QCAglXp80vwQT75dXV0cO3aMzz77jJGR\nEfLz8/njP/5jVq9eHValMZVKRUJCAps3b2bFihUMDAzw7//+7wwPD7NgwQK2bt1KeXl5SOMQERHB\njh07sNvtUlR2T0+PdFmJz+ejs7OTkZERNm3aFNZFK2KAqZg5Ie4XBoNBih+B4APdxACm+vp6kpOT\nWblyJREREQiCwODgIL29vZJ5PRyMRiNOp5P29na8Xi+nTp3i2rVrlJSUsGrVqlkXHxJdKCkpKVKe\nf7gX2My7ABarqiiVShYsWMDU1BROpxOHw8GlS5ek/LukpKSQNxjx/4tXSInBTH19fVy8eJHW1tZZ\nXXH44DNEv+XY2BhLliwJy9T4qPcT0wBE5SEjI4OqqqqwIkczMzN544038Hg8nDlzhqGhIY4dO4bD\n4cBms/H1119TVlbG7t27fzAiMtj3EAQh7GvF1Go12dnZrF27ln379nH8+HF27Ngh5bb6fD7p0oeD\nBw/S0dHByy+/zKZNm4JWfKKioti+fTszMzPs37+f5uZmmpubuXHjhiR0xWpbT0ozEE+lFy9eRKVS\nSdXL2tra+PDDDyU/82w078jISFatWiX93el0MjIyQlRUFMnJyWGbwADsdjsnT54kJSWFF154gYKC\ngrDb+i4Wi4Xy8nJqa2sZGRnB7XazdetWhoaG+Oijjzh79iz5+fmPLcX5QwjC/ZKiFy5cIDIykj17\n9rB169YfLMEYihVtfHycs2fPSjXkd+7cye7duykpKZlViVVxHZvNZqKiosjOzub8+fPodDqp2Eyo\n+5xWq+XVV1/F4/Fw8uRJ/uM//oPk5GTi4+Px+Xy4XC4SExNZsWJF2Ckz4pp+MHXuwZ+HghjA1Nvb\nywsvvIDFYmF6epqpqSnOnDlDf38/RUVFYbkcAXJycjh+/DgHDhzA4/HgcDgoLS3l7bffZuHChXNS\nFctisbBq1Sq+/vprJiYmgq5D/13mXQBHRERIOYF/+7d/y6JFi+jv75c2l6qqKhYtWjSrykoNDQ0M\nDAxgs9mIjo6mvr6ew4cP4/V6ycnJkfLnZoNY7Uj0l8xFQfHR0VHa29ulZHdRiRCvUwzHIpCbm8uf\n/dmfkZSUxNGjR+nu7ubUqVPYbDaWLFnCX//1X5Obmzvr/ms0GhYtWsTVq1fD6mdCQgIbN26kpaWF\nf/qnf+Lrr78mNzcXg8FAb28vbW1tuN1uiouL+fWvf83y5ctDsjqIEfa7d++muLiYixcvcu3aNamA\nSmRkJPn5+axatYqMx5SyCwQCJCYm8s4773Ds2DEuXrzIkSNH8Hg8CIJAZmYmf/mXfxl0MFmwiMU5\nxBiCcBQdMW/2wIEDKJVK1q5dK8UDzBUajYasrCyqqqr4zW9+Q0tLCzt27JBySpuamrh06RKrV68O\neXwE4f6NNz6fj127drFz586gc5UfhxjtfOzYMZRKJc899xyvv/76nF0UAPfnjUajITs7G5PJJAmk\ncOrSC4KA1WrlnXfeoaqqiosXL2K325mZmSEpKYl169ZJ1oK5Kj0r+lfDTaPz+XxMTk5it9tpbGyk\nvr6eCxcuMDg4yKZNm3jhhRfCrlC3ePFiNm3axCeffML4+Djr1q3jpZdeemLJ11Dx+/3Y7XbcbnfY\nsmneBbDBYCAlJYX09HTJjGI2m1m3bh3Lly9n0aJFYZ1+4X9MSSdPnuTs2bP4fD60Wi1Op5OBgQHJ\nF5icnDzrzXF6eprbt28THx8/q7ywBxkbG5PC/P1+v1TGMNzxgPvmk4ULF7J3717Kysro6upCoVBI\naUR5eXlzdtl0Xl4edXV1Uq50KIhXiP3iF78gJiaG/v5+bt++jVarJTIykqVLl5Kdnc3ixYvJyckJ\nS7MXTXi5ubkkJiayceNGaVzF1J/o6OgnjocY5LZr1y5KS0ul/FCNRkNOTo5UZWkuEQThoXKT4Sqo\nYhGPyspKiouL50zAPNjPyMhIysvLKS8v59KlS/T09KBSqSTT64IFC8ISDJOTkzQ0NJCTk0NZWdkT\na4AH29/Lly/zxRdf4HK5+PnPf05VVZVU/3cuEQSB3NxcioqKcDqds5ojCoWC6OhoiouLSU9PZ2pq\nSlLORCvOXCmAgnD/2tiZmZmwgqS0Wi3p6ekUFxdz6NAhTp06hdFoJDMzk7Vr17Jy5UqprGg4fdPr\n9axfv16qUJiZmSm5hebi9AtIaZsVFRWzCuqaVwEsTpDi4mLefvttBgYG8Pl8mEwmaSIFU3/2cSiV\nSioqKhgfH+fmzZuMjIygUqkoLS2lqqqKDRs2zDoqDu5vBo2NjVLe6lwI4MjISNLS0qT7gZctW8by\n5cslf0m46HQ6MjIyiI+Pl25pMRqN6PX6sAM1HoXNZpM2LvHe21AwGo0UFRVhMBgYHByUXBXR0dHE\nxsaSkJBAXFzcrBQG0b+VkJAgRXg+SLDjLEb9WiwW8vLypKjoqKioORe+cH9sUlJS6O3tZWxsTMpn\nDgW/309dXR2JiYlStaofA5VKRWpqKm+88QZlZWXY7XZGR0cpKipi2bJlLFmyJOy8XLVazfLly2d9\n36uIWJWupaUFnU6H3++XqqLN1eYtEggEiI+PZ8+ePbjd7oduJgsHhUKB0Wj8nj9yLvutVCqlKnLh\nmuIVCgWJiYn87Gc/Y8GCBVLQaVZWFhkZGVit1lkrOzabTYp01+v1UsnPuSIyMpLVq1eTk5Pz2Nzq\nJzHvJ2AxwMRms0kavVi1Jdyyag+iVCqprKwkISGB5uZmSQAnJyezfPlyLBbLrM0yomk4Pj6e4uLi\nWV+sLWK1Wnnuuefo7e1FEAQKCgpYtGjRnJiRxEACcZKG48t5HIIgEBERwTPPPBNStOiDiBtKcXHx\nQ9HID9aKnas+z1U7er3+odP4XN4t/CAGg4HS0lK6u7u5cuUKixYtIjMzM6RAmLGxMQYGBli/fj1J\nSUlzZp78LqKlobKykqKiIu7du8fo6KhUv/pRis+TEAQBrVbLhg0b5lR5EP2zhYWFGI3GOb2o41Ho\n9XoqKioAwo76/S5zrSg8iBiBvmXLlllZHCIiIigvL6e4uPh7rrW56L+4v8GPMx56vZ6CggLy8/Nn\n9d0Uwo/5tb6D2+1+7L+LL/FjdCnU6i+hIAj3Lw+4ffs26enpmEymOdvMvtvvn/BzyTzFKBQKWltb\n+e///m+cTid79+5lxYoVQc8P8e7tkydPsmnTJpKSkubM8vE4HvWM2czp2cSGPApBEOjq6qK7uxu9\nXk9GRsZDmQgy95mrcf8x9/yniR9ykT1VAvh/O2Jg1P/1ySTzdOD1ehkcHMTj8WCxWEI6BQqCgMvl\neiitROY+YiEVQF7PMnPCUyGAZWRkZGRkZO7z4zh9ZGRkZGRkZB6LLIBlZGRkZGTmAVkAy8jIyMjI\nzAOyAJaRkZGRkZkHZAEsIyMjIyMzD8gCWEZGRkZGZh6QBbCMjIyMjMw8IAtgGRkZGRmZeUAWwDIy\nMjIyMvOALIBlZGRkZGTmAVkAy8jIyMjIzAOyAJaRkZGRkZkHZAEsIyMjIyMzD8gCWEZGRkZGZh6Q\nBbCMjIyMjMw8IAtgGRkZGRmZeUAWwDIyMjIyMvOALIBlZGRkZGTmAVkAy8jIyMjIzAOyAJaRkZGR\nkZkHZAEsIyMjIyMzD8gCWEZGRkZGZh6QBbCMjIyMjMw88P8AekW4kiHLlR8AAAAASUVORK5CYII=\n",
154 |       "text/plain": [
155 |        "<matplotlib.figure.Figure at 0x729ccc70>"
156 |       ]
157 |      },
158 |      "metadata": {},
159 |      "output_type": "display_data"
160 |     }
161 |    ],
162 |    "source": [
163 |     "sample = np.random.choice(X.shape[0], 20)\n",
164 |     "plt.imshow(X[sample,1:].reshape(-1,20).T)\n",
165 |     "plt.axis('off');"
166 |    ]
167 |   },
168 |   {
169 |    "cell_type": "markdown",
170 |    "metadata": {},
171 |    "source": [
172 |     "### Multiclass Classification"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "markdown",
177 |    "metadata": {},
178 |    "source": [
179 |     "#### Logistic regression hypothesis\n",
180 |     "#### $$ h_{\\theta}(x) = g(\\theta^{T}x)$$\n",
181 |     "#### $$ g(z)=\\frac{1}{1+e^{−z}} $$"
182 |    ]
183 |   },
184 |   {
185 |    "cell_type": "code",
186 |    "execution_count": 8,
187 |    "metadata": {
188 |     "collapsed": true
189 |    },
190 |    "outputs": [],
191 |    "source": [
192 |     "def sigmoid(z):\n",
193 |     "    return(1 / (1 + np.exp(-z)))"
194 |    ]
195 |   },
196 |   {
197 |    "cell_type": "markdown",
198 |    "metadata": {},
199 |    "source": [
200 |     "#### Regularized Cost Function \n",
201 |     "#### $$ J(\\theta) = \\frac{1}{m}\\sum_{i=1}^{m}\\big[-y^{(i)}\\, log\\,( h_\\theta\\,(x^{(i)}))-(1-y^{(i)})\\,log\\,(1-h_\\theta(x^{(i)}))\\big] + \\frac{\\lambda}{2m}\\sum_{j=1}^{n}\\theta_{j}^{2}$$\n",
202 |     "#### Vectorized Cost Function\n",
203 |     "#### $$ J(\\theta) = \\frac{1}{m}\\big((\\,log\\,(g(X\\theta))^Ty+(\\,log\\,(1-g(X\\theta))^T(1-y)\\big) + \\frac{\\lambda}{2m}\\sum_{j=1}^{n}\\theta_{j}^{2}$$"
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "code",
208 |    "execution_count": 9,
209 |    "metadata": {
210 |     "collapsed": true
211 |    },
212 |    "outputs": [],
213 |    "source": [
214 |     "def lrcostFunctionReg(theta, reg, X, y):\n",
215 |     "    m = y.size\n",
216 |     "    h = sigmoid(X.dot(theta))\n",
217 |     "    \n",
218 |     "    J = -1*(1/m)*(np.log(h).T.dot(y)+np.log(1-h).T.dot(1-y)) + (reg/(2*m))*np.sum(np.square(theta[1:]))\n",
219 |     "    \n",
220 |     "    if np.isnan(J[0]):\n",
221 |     "        return(np.inf)\n",
222 |     "    return(J[0])    "
223 |    ]
224 |   },
225 |   {
226 |    "cell_type": "code",
227 |    "execution_count": 10,
228 |    "metadata": {
229 |     "collapsed": true
230 |    },
231 |    "outputs": [],
232 |    "source": [
233 |     "def lrgradientReg(theta, reg, X,y):\n",
234 |     "    m = y.size\n",
235 |     "    h = sigmoid(X.dot(theta.reshape(-1,1)))\n",
236 |     "      \n",
237 |     "    grad = (1/m)*X.T.dot(h-y) + (reg/m)*np.r_[[[0]],theta[1:].reshape(-1,1)]\n",
238 |     "        \n",
239 |     "    return(grad.flatten())"
240 |    ]
241 |   },
242 |   {
243 |    "cell_type": "markdown",
244 |    "metadata": {},
245 |    "source": [
246 |     "#### One-vs-all Classification"
247 |    ]
248 |   },
249 |   {
250 |    "cell_type": "code",
251 |    "execution_count": 27,
252 |    "metadata": {
253 |     "collapsed": false
254 |    },
255 |    "outputs": [],
256 |    "source": [
257 |     "def oneVsAll(features, classes, n_labels, reg):\n",
258 |     "    initial_theta = np.zeros((X.shape[1],1))  # 401x1\n",
259 |     "    all_theta = np.zeros((n_labels, X.shape[1])) #10x401\n",
260 |     "\n",
261 |     "    for c in np.arange(1, n_labels+1):\n",
262 |     "        res = minimize(lrcostFunctionReg, initial_theta, args=(reg, features, (classes == c)*1), method=None,\n",
263 |     "                       jac=lrgradientReg, options={'maxiter':50})\n",
264 |     "        all_theta[c-1] = res.x\n",
265 |     "    return(all_theta)"
266 |    ]
267 |   },
268 |   {
269 |    "cell_type": "code",
270 |    "execution_count": 12,
271 |    "metadata": {
272 |     "collapsed": false
273 |    },
274 |    "outputs": [],
275 |    "source": [
276 |     "theta = oneVsAll(X, y, 10, 0.1)"
277 |    ]
278 |   },
279 |   {
280 |    "cell_type": "markdown",
281 |    "metadata": {},
282 |    "source": [
283 |     "#### One-vs-all Prediction"
284 |    ]
285 |   },
286 |   {
287 |    "cell_type": "code",
288 |    "execution_count": 25,
289 |    "metadata": {
290 |     "collapsed": true
291 |    },
292 |    "outputs": [],
293 |    "source": [
294 |     "def predictOneVsAll(all_theta, features):\n",
295 |     "    probs = sigmoid(X.dot(all_theta.T))\n",
296 |     "        \n",
297 |     "    # Adding one because Python uses zero based indexing for the 10 columns (0-9),\n",
298 |     "    # while the 10 classes are numbered from 1 to 10.\n",
299 |     "    return(np.argmax(probs, axis=1)+1)"
300 |    ]
301 |   },
302 |   {
303 |    "cell_type": "code",
304 |    "execution_count": 26,
305 |    "metadata": {
306 |     "collapsed": false
307 |    },
308 |    "outputs": [
309 |     {
310 |      "name": "stdout",
311 |      "output_type": "stream",
312 |      "text": [
313 |       "Training set accuracy: 93.17999999999999 %\n"
314 |      ]
315 |     }
316 |    ],
317 |    "source": [
318 |     "pred = predictOneVsAll(theta, X)\n",
319 |     "print('Training set accuracy: {} %'.format(np.mean(pred == y.ravel())*100))"
320 |    ]
321 |   },
322 |   {
323 |    "cell_type": "markdown",
324 |    "metadata": {},
325 |    "source": [
326 |     "#### Multiclass Logistic Regression with scikit-learn"
327 |    ]
328 |   },
329 |   {
330 |    "cell_type": "code",
331 |    "execution_count": 43,
332 |    "metadata": {
333 |     "collapsed": false
334 |    },
335 |    "outputs": [
336 |     {
337 |      "data": {
338 |       "text/plain": [
339 |        "LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
340 |        "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
341 |        "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
342 |        "          verbose=0, warm_start=False)"
343 |       ]
344 |      },
345 |      "execution_count": 43,
346 |      "metadata": {},
347 |      "output_type": "execute_result"
348 |     }
349 |    ],
350 |    "source": [
351 |     "clf = LogisticRegression(C=10, penalty='l2', solver='liblinear')\n",
352 |     "# Scikit-learn fits intercept automatically, so we exclude first column with 'ones' from X when fitting.\n",
353 |     "clf.fit(X[:,1:],y.ravel())"
354 |    ]
355 |   },
356 |   {
357 |    "cell_type": "code",
358 |    "execution_count": 44,
359 |    "metadata": {
360 |     "collapsed": false
361 |    },
362 |    "outputs": [
363 |     {
364 |      "name": "stdout",
365 |      "output_type": "stream",
366 |      "text": [
367 |       "Training set accuracy: 96.5 %\n"
368 |      ]
369 |     }
370 |    ],
371 |    "source": [
372 |     "pred2 = clf.predict(X[:,1:])\n",
373 |     "print('Training set accuracy: {} %'.format(np.mean(pred2 == y.ravel())*100))"
374 |    ]
375 |   },
376 |   {
377 |    "cell_type": "markdown",
378 |    "metadata": {},
379 |    "source": [
380 |     "### Neural Networks"
381 |    ]
382 |   },
383 |   {
384 |    "cell_type": "code",
385 |    "execution_count": 45,
386 |    "metadata": {
387 |     "collapsed": true
388 |    },
389 |    "outputs": [],
390 |    "source": [
391 |     "def predict(theta_1, theta_2, features):\n",
392 |     "    z2 = theta_1.dot(features.T)\n",
393 |     "    a2 = np.c_[np.ones((data['X'].shape[0],1)), sigmoid(z2).T]\n",
394 |     "    \n",
395 |     "    z3 = a2.dot(theta_2.T)\n",
396 |     "    a3 = sigmoid(z3)\n",
397 |     "        \n",
398 |     "    return(np.argmax(a3, axis=1)+1) "
399 |    ]
400 |   },
401 |   {
402 |    "cell_type": "code",
403 |    "execution_count": 46,
404 |    "metadata": {
405 |     "collapsed": false
406 |    },
407 |    "outputs": [
408 |     {
409 |      "name": "stdout",
410 |      "output_type": "stream",
411 |      "text": [
412 |       "Training set accuracy: 97.52 %\n"
413 |      ]
414 |     }
415 |    ],
416 |    "source": [
417 |     "pred = predict(theta1, theta2, X)\n",
418 |     "print('Training set accuracy: {} %'.format(np.mean(pred == y.ravel())*100))"
419 |    ]
420 |   }
421 |  ],
422 |  "metadata": {
423 |   "kernelspec": {
424 |    "display_name": "Python 3",
425 |    "language": "python",
426 |    "name": "python3"
427 |   },
428 |   "language_info": {
429 |    "codemirror_mode": {
430 |     "name": "ipython",
431 |     "version": 3
432 |    },
433 |    "file_extension": ".py",
434 |    "mimetype": "text/x-python",
435 |    "name": "python",
436 |    "nbconvert_exporter": "python",
437 |    "pygments_lexer": "ipython3",
438 |    "version": "3.4.3"
439 |   }
440 |  },
441 |  "nbformat": 4,
442 |  "nbformat_minor": 0
443 | }
444 | 


--------------------------------------------------------------------------------
/notebooks/Programming Exercise 4 - Neural Networks Learning.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Programming Exercise 4 - Neural Networks Learning"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 2,
 13 |    "metadata": {
 14 |     "collapsed": false
 15 |    },
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "# %load ../../../standard_import.txt\n",
 19 |     "import pandas as pd\n",
 20 |     "import numpy as np\n",
 21 |     "import matplotlib as mpl\n",
 22 |     "import matplotlib.pyplot as plt\n",
 23 |     "\n",
 24 |     "# load MATLAB files\n",
 25 |     "from scipy.io import loadmat\n",
 26 |     "\n",
 27 |     "pd.set_option('display.notebook_repr_html', False)\n",
 28 |     "pd.set_option('display.max_columns', None)\n",
 29 |     "pd.set_option('display.max_rows', 150)\n",
 30 |     "pd.set_option('display.max_seq_items', None)\n",
 31 |     " \n",
 32 |     "#%config InlineBackend.figure_formats = {'pdf',}\n",
 33 |     "%matplotlib inline\n",
 34 |     "\n",
 35 |     "import seaborn as sns\n",
 36 |     "sns.set_context('notebook')\n",
 37 |     "sns.set_style('darkgrid')"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "markdown",
 42 |    "metadata": {},
 43 |    "source": [
 44 |     "#### Load MATLAB datafiles"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 3,
 50 |    "metadata": {
 51 |     "collapsed": false
 52 |    },
 53 |    "outputs": [
 54 |     {
 55 |      "data": {
 56 |       "text/plain": [
 57 |        "dict_keys(['X', '__header__', 'y', '__globals__', '__version__'])"
 58 |       ]
 59 |      },
 60 |      "execution_count": 3,
 61 |      "metadata": {},
 62 |      "output_type": "execute_result"
 63 |     }
 64 |    ],
 65 |    "source": [
 66 |     "data = loadmat('data/ex4data1.mat')\n",
 67 |     "data.keys()"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "code",
 72 |    "execution_count": 4,
 73 |    "metadata": {
 74 |     "collapsed": false
 75 |    },
 76 |    "outputs": [
 77 |     {
 78 |      "name": "stdout",
 79 |      "output_type": "stream",
 80 |      "text": [
 81 |       "X: (5000, 401) (with intercept)\n",
 82 |       "y: (5000, 1)\n"
 83 |      ]
 84 |     }
 85 |    ],
 86 |    "source": [
 87 |     "y = data['y']\n",
 88 |     "# Add intercept\n",
 89 |     "X = np.c_[np.ones((data['X'].shape[0],1)), data['X']]\n",
 90 |     "\n",
 91 |     "print('X:',X.shape, '(with intercept)')\n",
 92 |     "print('y:',y.shape)"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "code",
 97 |    "execution_count": 5,
 98 |    "metadata": {
 99 |     "collapsed": false
100 |    },
101 |    "outputs": [
102 |     {
103 |      "data": {
104 |       "text/plain": [
105 |        "dict_keys(['Theta2', '__header__', 'Theta1', '__globals__', '__version__'])"
106 |       ]
107 |      },
108 |      "execution_count": 5,
109 |      "metadata": {},
110 |      "output_type": "execute_result"
111 |     }
112 |    ],
113 |    "source": [
114 |     "weights = loadmat('data/ex3weights.mat')\n",
115 |     "weights.keys()"
116 |    ]
117 |   },
118 |   {
119 |    "cell_type": "code",
120 |    "execution_count": 6,
121 |    "metadata": {
122 |     "collapsed": false
123 |    },
124 |    "outputs": [
125 |     {
126 |      "name": "stdout",
127 |      "output_type": "stream",
128 |      "text": [
129 |       "theta1 : (25, 401)\n",
130 |       "theta2 : (10, 26)\n",
131 |       "params : (10285,)\n"
132 |      ]
133 |     }
134 |    ],
135 |    "source": [
136 |     "theta1, theta2 = weights['Theta1'], weights['Theta2']\n",
137 |     "print('theta1 :', theta1.shape)\n",
138 |     "print('theta2 :', theta2.shape)\n",
139 |     "params = np.r_[theta1.ravel(), theta2.ravel()]\n",
140 |     "print('params :', params.shape)"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "markdown",
145 |    "metadata": {},
146 |    "source": [
147 |     "#### Neural Network\n",
148 |     "Input layer size = 400 (20x20 pixels) <br>\n",
149 |     "Hidden layer size = 25 <br>\n",
150 |     "Number of labels = 10"
151 |    ]
152 |   },
153 |   {
154 |    "cell_type": "markdown",
155 |    "metadata": {},
156 |    "source": [
157 |     "### Neural Networks - Feed Forward and Cost Function"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 7,
163 |    "metadata": {
164 |     "collapsed": true
165 |    },
166 |    "outputs": [],
167 |    "source": [
168 |     "def sigmoid(z):\n",
169 |     "    return(1 / (1 + np.exp(-z)))"
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "markdown",
174 |    "metadata": {},
175 |    "source": [
176 |     "#### Sigmoid gradient\n",
177 |     "#### $$ g'(z) = g(z)(1 - g(z))$$\n",
178 |     "where $$ g(z) = \\frac{1}{1+e^{-z}}$$"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 8,
184 |    "metadata": {
185 |     "collapsed": true
186 |    },
187 |    "outputs": [],
188 |    "source": [
189 |     "def sigmoidGradient(z):\n",
190 |     "    return(sigmoid(z)*(1-sigmoid(z)))"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "markdown",
195 |    "metadata": {},
196 |    "source": [
197 |     "#### Cost Function \n",
198 |     "#### $$ J(\\theta) = \\frac{1}{m}\\sum_{i=1}^{m}\\sum_{k=1}^{K}\\big[-y^{(i)}_{k}\\, log\\,(( h_\\theta\\,(x^{(i)}))_k)-(1-y^{(i)}_k)\\,log\\,(1-h_\\theta(x^{(i)}))_k)\\big]$$\n",
199 |     "\n",
200 |     "#### Regularized Cost Function\n",
201 |     "#### $$ J(\\theta) = \\frac{1}{m}\\sum_{i=1}^{m}\\sum_{k=1}^{K}\\bigg[-y^{(i)}_{k}\\, log\\,(( h_\\theta\\,(x^{(i)}))_k)-(1-y^{(i)}_k)\\,log\\,(1-h_\\theta(x^{(i)}))_k)\\bigg] + \\frac{\\lambda}{2m}\\bigg[\\sum_{j=1}^{25}\\sum_{k=1}^{400}(\\Theta_{j,k}^{(1)})^2+\\sum_{j=1}^{10}\\sum_{k=1}^{25}(\\Theta_{j,k}^{(2)})^2\\bigg]$$"
202 |    ]
203 |   },
204 |   {
205 |    "cell_type": "code",
206 |    "execution_count": 9,
207 |    "metadata": {
208 |     "collapsed": false
209 |    },
210 |    "outputs": [],
211 |    "source": [
212 |     "def nnCostFunction(nn_params, input_layer_size, hidden_layer_size, num_labels, features, classes, reg):\n",
213 |     "    \n",
214 |     "    # When comparing to Octave code note that Python uses zero-indexed arrays.\n",
215 |     "    # But because Numpy indexing does not include the right side, the code is the same anyway.\n",
216 |     "    theta1 = nn_params[0:(hidden_layer_size*(input_layer_size+1))].reshape(hidden_layer_size,(input_layer_size+1))\n",
217 |     "    theta2 = nn_params[(hidden_layer_size*(input_layer_size+1)):].reshape(num_labels,(hidden_layer_size+1))\n",
218 |     "\n",
219 |     "    m = features.shape[0]\n",
220 |     "    y_matrix = pd.get_dummies(classes.ravel()).as_matrix() \n",
221 |     "    \n",
222 |     "    # Cost\n",
223 |     "    a1 = features # 5000x401\n",
224 |     "        \n",
225 |     "    z2 = theta1.dot(a1.T) # 25x401 * 401x5000 = 25x5000 \n",
226 |     "    a2 = np.c_[np.ones((features.shape[0],1)),sigmoid(z2.T)] # 5000x26 \n",
227 |     "    \n",
228 |     "    z3 = theta2.dot(a2.T) # 10x26 * 26x5000 = 10x5000 \n",
229 |     "    a3 = sigmoid(z3) # 10x5000\n",
230 |     "    \n",
231 |     "    J = -1*(1/m)*np.sum((np.log(a3.T)*(y_matrix)+np.log(1-a3).T*(1-y_matrix))) + \\\n",
232 |     "        (reg/(2*m))*(np.sum(np.square(theta1[:,1:])) + np.sum(np.square(theta2[:,1:])))\n",
233 |     "\n",
234 |     "    # Gradients\n",
235 |     "    d3 = a3.T - y_matrix # 5000x10\n",
236 |     "    d2 = theta2[:,1:].T.dot(d3.T)*sigmoidGradient(z2) # 25x10 *10x5000 * 25x5000 = 25x5000\n",
237 |     "    \n",
238 |     "    delta1 = d2.dot(a1) # 25x5000 * 5000x401 = 25x401\n",
239 |     "    delta2 = d3.T.dot(a2) # 10x5000 *5000x26 = 10x26\n",
240 |     "    \n",
241 |     "    theta1_ = np.c_[np.ones((theta1.shape[0],1)),theta1[:,1:]]\n",
242 |     "    theta2_ = np.c_[np.ones((theta2.shape[0],1)),theta2[:,1:]]\n",
243 |     "    \n",
244 |     "    theta1_grad = delta1/m + (theta1_*reg)/m\n",
245 |     "    theta2_grad = delta2/m + (theta2_*reg)/m\n",
246 |     "    \n",
247 |     "    return(J, theta1_grad, theta2_grad)"
248 |    ]
249 |   },
250 |   {
251 |    "cell_type": "code",
252 |    "execution_count": 10,
253 |    "metadata": {
254 |     "collapsed": false
255 |    },
256 |    "outputs": [
257 |     {
258 |      "data": {
259 |       "text/plain": [
260 |        "0.28762916516131892"
261 |       ]
262 |      },
263 |      "execution_count": 10,
264 |      "metadata": {},
265 |      "output_type": "execute_result"
266 |     }
267 |    ],
268 |    "source": [
269 |     "# Regularization parameter = 0\n",
270 |     "nnCostFunction(params, 400, 25, 10, X, y, 0)[0]"
271 |    ]
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": 11,
276 |    "metadata": {
277 |     "collapsed": false
278 |    },
279 |    "outputs": [
280 |     {
281 |      "data": {
282 |       "text/plain": [
283 |        "0.38376985909092365"
284 |       ]
285 |      },
286 |      "execution_count": 11,
287 |      "metadata": {},
288 |      "output_type": "execute_result"
289 |     }
290 |    ],
291 |    "source": [
292 |     "# Regularization parameter = 1\n",
293 |     "nnCostFunction(params, 400, 25, 10, X, y, 1)[0]"
294 |    ]
295 |   },
296 |   {
297 |    "cell_type": "code",
298 |    "execution_count": 12,
299 |    "metadata": {
300 |     "collapsed": false
301 |    },
302 |    "outputs": [
303 |     {
304 |      "data": {
305 |       "text/plain": [
306 |        "[0.19661193324148185,\n",
307 |        " 0.23500371220159449,\n",
308 |        " 0.25,\n",
309 |        " 0.23500371220159449,\n",
310 |        " 0.19661193324148185]"
311 |       ]
312 |      },
313 |      "execution_count": 12,
314 |      "metadata": {},
315 |      "output_type": "execute_result"
316 |     }
317 |    ],
318 |    "source": [
319 |     "[sigmoidGradient(z) for z in [-1, -0.5, 0, 0.5, 1]]"
320 |    ]
321 |   }
322 |  ],
323 |  "metadata": {
324 |   "kernelspec": {
325 |    "display_name": "Python 3",
326 |    "language": "python",
327 |    "name": "python3"
328 |   },
329 |   "language_info": {
330 |    "codemirror_mode": {
331 |     "name": "ipython",
332 |     "version": 3
333 |    },
334 |    "file_extension": ".py",
335 |    "mimetype": "text/x-python",
336 |    "name": "python",
337 |    "nbconvert_exporter": "python",
338 |    "pygments_lexer": "ipython3",
339 |    "version": "3.4.3"
340 |   }
341 |  },
342 |  "nbformat": 4,
343 |  "nbformat_minor": 0
344 | }
345 | 


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/notebooks/Programming Exercise 8 - Anomaly Detection and Recommender Systems.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Programming Exercise 8 - Anomaly Detection and Recommender Systems"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "- [Anomaly Detection](#Anomaly-Detection)\n",
 15 |     "- [Recommender Systems](#Recommender-Systems)"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 1,
 21 |    "metadata": {
 22 |     "collapsed": false
 23 |    },
 24 |    "outputs": [],
 25 |    "source": [
 26 |     "# %load ../../../standard_import.txt\n",
 27 |     "import pandas as pd\n",
 28 |     "import numpy as np\n",
 29 |     "import matplotlib.pyplot as plt\n",
 30 |     "\n",
 31 |     "from scipy.io import loadmat\n",
 32 |     "from sklearn.svm import OneClassSVM\n",
 33 |     "from sklearn.covariance import EllipticEnvelope\n",
 34 |     "\n",
 35 |     "pd.set_option('display.notebook_repr_html', False)\n",
 36 |     "pd.set_option('display.max_columns', None)\n",
 37 |     "pd.set_option('display.max_rows', 150)\n",
 38 |     "pd.set_option('display.max_seq_items', None)\n",
 39 |     " \n",
 40 |     "#%config InlineBackend.figure_formats = {'pdf',}\n",
 41 |     "%matplotlib inline\n",
 42 |     "\n",
 43 |     "import seaborn as sns\n",
 44 |     "sns.set_context('notebook')\n",
 45 |     "sns.set_style('white')"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "markdown",
 50 |    "metadata": {},
 51 |    "source": [
 52 |     "### Anomaly Detection"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": 2,
 58 |    "metadata": {
 59 |     "collapsed": false
 60 |    },
 61 |    "outputs": [
 62 |     {
 63 |      "data": {
 64 |       "text/plain": [
 65 |        "dict_keys(['__header__', 'yval', '__version__', 'Xval', 'X', '__globals__'])"
 66 |       ]
 67 |      },
 68 |      "execution_count": 2,
 69 |      "metadata": {},
 70 |      "output_type": "execute_result"
 71 |     }
 72 |    ],
 73 |    "source": [
 74 |     "data1 = loadmat('data/ex8data1.mat')\n",
 75 |     "data1.keys()"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": 3,
 81 |    "metadata": {
 82 |     "collapsed": false
 83 |    },
 84 |    "outputs": [
 85 |     {
 86 |      "name": "stdout",
 87 |      "output_type": "stream",
 88 |      "text": [
 89 |       "X1: (307, 2)\n"
 90 |      ]
 91 |     }
 92 |    ],
 93 |    "source": [
 94 |     "X1 = data1['X']\n",
 95 |     "print('X1:', X1.shape)"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "code",
100 |    "execution_count": 4,
101 |    "metadata": {
102 |     "collapsed": false
103 |    },
104 |    "outputs": [
105 |     {
106 |      "data": {
107 |       "image/png": 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PZN8PHhxs58m8Fnd2c8udtswnSoZca+bnzp3TxIkT1b59e0VHR2vGjBn65Zdf\n8nWxVq1aqX79+pIs6H/LjQJQ0l0eKp5m74I637p1Fu4XL9rgsbg4m3YWGWk15fvus37td96x90aO\nlL7/3oJ/5kwL6qAg62s/eNBqyPHx0u9/b1PC+vWzWr/LZe8PGmRzvuvXt9p3Wpp0553Sv/9tYbl5\nsy3j6nZbDb5jRytfbKy9N2yYNHiw3UjMm2fzyBcvtulyniCXvI+Xd1Hk9ft02jKfKBlyDfOhQ4cq\nKChI06dP1+TJk3Xx4kXFxcXl62J9+vTRvn37JEnbt29XgwYN8nUeAAUXKvHxVgP3hPf69d7Bbp61\n0Pv0sSZ0z1Ko589bTblSJdtprGZNWw61Vi1r/l62zM7XvbuF9A032Nrte/faYLbPP7dR7ZINMjt9\n2vqoK1SwloBrr7WWgOPHrRyffWaD3JKSrIZ/4412o7Bunff3+PBD76pvTz9tZVmwwAbRXV5zzq6L\nIvPStJ6BgNmNQ3DaMp8oGXIN8+PHj2v48OG67bbbVK9ePcXFxenw4cP5uti4ceM0adIk9ezZU7t3\n79YzzzyTr/MAuPpQyWnAXESEhemJEzZ47cQJq31n7oP3TEs7d87WSJ8+3VZiGz/e+rIHDbJBaZUr\nW8iuWmWD05YutX3Df/7ZFolJSbHtT1991VaCK1/e5p5/9ZU115cvb9f973+taTw83K7fubPtXDZr\nlgX6P/5htfmUFAvj++6z3+f0afsdNmyw3yM21hal8fzenu8gONj68sPCvP3eUVHepWldLrsOg9vg\nFLmGeWhoqD777LOM54cOHVJoaGieL1CjRg0tWbJEklS/fn0tXrxY8+fP18svv6zy5cvno8gAroYn\nwCIirEl82bKsA+aCg60GvWCB7RUuefcp99RYM7cASDYFbeRIG5C2ZYs1b+/caSH+4IO2yppnMN3j\nj3u3H42KsuBescKCv14979Ksx49LVapYDb9DB9vVLDLSmvCDgqymXaaM1b6XLbMBauXKWW0+KsoG\nwC1ebLX2yEgbiFetmnceusvlDf+kJCvzP/+ZtUmdwW1wqlzD/OjRo+rRo4fatWunqKgoderUSbt3\n71aLFi3UsmVLf5QRKLEKYvqZp0nZ5bLa8YwZWYPK5bINRmJjpZ49rSm8ZUv7jEfmFoCxY61mfOiQ\nlefTTy0409JsyllkpAX0p5/aOueNGlk/+h/+YOHetKl9dtMm+/w119j7AQEWrB9/bEu3du1qNwBu\nty3reuBXMXJyAAAZTklEQVSA1biXLbMbgZAQC+fTp62sH35oC8ycPm3dAJ5d1RYt8o6+j4y0z95/\nv61EV7ly1i6Kgh6HAPhLrmH+5ptvauPGjZo7d67eeustJSYmatmyZVqwYIHmz5/vjzICJVZBTD/z\n1DY988QTE71BFR9vfcPVqtno8GrVrNm7cWOb+/3OO9lfMyrKVncLC5O2b7fpXI0aWVN8WJjNER8+\n3KaJrVplS7z26mXB7XbbUqzp6XbjUKOG9cfffLM14y9caH3sd90lVaxo5Th3zso5YoQ9njtn/fyp\nqdbcXq6c7XoWHm7N69WqZb+vumS/+65ddrynyd3TRcHgNjhVgNuzt2kOUlJS9Mknn+jnn3/O8ro/\nNl85duyYWrZsqY0bN6pmzZo+vx5QFHm28/RMl7panibzChWspvrEE/Z6YmL2m6xERnqb5QcNsgFr\nnub25s2lyZOtKfujjyxsjx+30eutWtniMYsXW2AfOGAbnqxaZc3nf/2rNau//LIFeXKy9Z3//vdW\nU9+3z44LDrbQHzDARreXK2flrlPHNl9xu+0mQLIgnzbNbiA+/tjKu3q193taudJq6t27Z93e1LNB\nTGIiA9hQ9OQn+3KdZz5gwACdOXNGtWvXVkBAQMbr7KQG+F5BzGn21DYvDzBPiGWeWx4S4g3AxEQL\nwEqVLPCjo21XsTZtrGY8Z44tlfrYYzYAbcsWe75pk00bq1nTFm05csTOERZmo9jvuceWed2zx65X\np46F7KVLduMwfLhde/x4G4leubLdEPzlL7a2+jvvSL17203EjBkW8Fu22M3EZ5/ZCHzJ26KQmip5\nVo5u2pSR6Cim3Llo06ZNbh/xme+//95dt25d9/fff19oZQAK09q1bvelS/bzpUv2vCBduuR2z53r\ndp8/b4+XLrndK1a43W++aa+9+aY9P3/e7b7vPrf75El7PH/ejn/rLbc7KMjt3rXL7b7tNnveo4fb\nPX68212tmts9YYLbXbeu2/3EE3ZsbKzb3bev2122rNsdHe12T53qdt9+u/3r3dvt7tLFHhs2dLvn\nzXO7e/Vyu3/6ye0ePdrt/te/3O4yZey9ChXseaNGbvfu3W531apu98GDbndUlLe8c+fasX37ut33\n3OMtM1DU5Sf7cu0zv+mmm3TixAl/3FcAuIyv5zRn10ccHW3N0m3a2KNkNfS1a23aV//+1kqQlGRN\n6mPG2IC5xo2tVh8RYf3cU6bYdLXISNvgZNs2Ww3O7bbWgI8/ttp2nTr2mUcesT72HTvsOrt32zl6\n9bLNU7p3t9e//trKdv/9tn/588/bxin9+9t1Je8KdXffbU37nl3fGNCG4irHZvbY2FgFBAQoKSlJ\n7du3V7169VSqVKmM9xn8Bjhf5psDz83C5U37R47YtLCNGy0c9++3fueePS1MFy+2QP7pJ9tPfPZs\n67vu3t07n9vT7z9woC1AM2+eNYe3bGkD3YKCbEqbZ7qYZ9OVRx+1G4wJE2wO+pw5Nghu+XJbDW7A\nALvRiI62Veo++cR+TkqyZv1x42wFO89CMPSRo7jKMcyfe+45f5YDQBGRuY/dsxLaiRM2cC021juA\nbMkSC92gIAvm997zDkzbv987UnzZMns/IcGCd8IEC/fPP/dOM/v8c6tpJyRYjfrZZ63m3r27nfem\nm2zhmfPnrT9/61brJ69d2wK7Rw/rx+/Uya47bpzNe1+50jvPPPM4AaC4ybGZPSwsTGFhYQoICMjy\nLzAwUGXLltW5c+f8WU4AfnJ5035kZM6fLV3aRsdv3uxdAvbDD71N6UlJ9l5Kim1F+uWXNnK9VStr\n2q9WzW4GbrnF5qE/9pjVrOfNs9Dv1cumqn37rTXRb9pk67Vv2WKfq1bNztmrl3cDlcREW6HOM4d8\n82ZCHMVfrn3ms2fP1jPPPKP58+frvffe07PPPqsxY8aoU6dOis/vBsoAHMOzfeiGDVeuh+4J/ogI\nm28+YYLVhqdPt9pyWJhtptKggTWhDxok/fij7UE+ZIjdKHhuCG66yYJ+3jxrRt+yxbuM7KVLVsP2\nzB0vXdpq4OfOeefNe8rD2ukoiXINc7fbrTVr1mjmzJmaNWuW1q5dq8qVK2vlypV6++23/VFGoEQr\niFXgfgtP2GYO0st5tg9t397ms0vWV+2pSQcEWLP3fffZ1LMHHrCm8Mxhm5RkU86ee86a3yXv4Lzp\n071TzjzHsMAL4JVrmJ8+fVo33nhjxvPrr79ep0+fVkhIiNy/vt4MgAJQEKvAZXa1Nwd5qelmHjSX\nlGSBHRlpI91feskWd/n7322A2r332uIyo0fb7+Ry2eYtR4/atqVPPWXN56VL2/U8q9RFRNhnV660\n16iBA165Lhpz11136fnnn1f79u2Vnp6udevW6a677tLmzZtVrlw5f5QRKNEyb/6RkPDbN/+4fNU3\nz+Ypv0XmQXNPPGGrsLVoYSPTM6+j/vrrtqb7hAlS3bq2DGy7dtbEnnmgWnR01vK+846tICdZU79n\nFTsAJtea+fjx43XXXXdp6dKlWrFihZo0aaIxY8YoICBA06ZN80cZgRKtoDf/+C07g3n2Pne5cq4l\nS9aX7RlN7nJZOO/fb03x1avbjmcnT0r//ret4tahg410T0nJvryeXd0WLPBOeQPgleva7DktGJO5\n6d1XWJsdsLCMiCi49cRdLht8Nm6chW1MTN5HfLtcVktOTbXnnlpy5nDNqbyeNeJjYiy4U1Pt3yef\nWPAPGiT17XtlUOflmkBxkp/syzXMW7RokbEme0pKin788UfdfvvtWr58+W8vcS4Ic6DgxcfbhilL\nl1qNedw4G2CW13BMTrapZZKNcL+azV8yLx4TFGQ18WPHpLfeskVj2rXL2sTuKW9KineK3Lp11p9O\nHzmKK59stLJp06Ysz7/88kstWrQofyUEUOiy22Alr0Geee9zyX7Oay05u01jVq2yDVe2bJGGDcu+\nmf3y0L487AHkoc/8co0aNdKBAwd8URYAfpLffvjERBtpnnnUeV6nhGU3laxcOdsGtVMne2RMLZA/\nudbMZ82aleX5v/71L1WpUsVnBQLge4mJNso885KteWm6/i215JzWgV+wIGttHcDVu+qa+d13363X\nXnvNF2UB4CdRUdYHvWCBBWpSUt7nrxfkIjYs/AIUjFxr5v3791dSUpL27t2rtLQ03XnnnapYsaI/\nygbAh/I7f70g56lnV1sHcPVyrZlv27ZNHTp00IoVK7Ry5Uo98sgj+uijj/xRNgA+lN9+8+BgW7I1\nIsICXfL/ErMAssq1Zj5jxgy9//77qlWrliTp+++/V//+/fXQQw/5vHAAfOfyrU7zOn/d0yzv2Z6U\nFdmAwpdrzTw1NTUjyCWpVq1aSk9P92mhAPheftc2T0y08PZsT+oZSJeTwt4oBigJcg3zG2+8Ue++\n+66Sk5OVnJysd999VzVq1PBH2QAUQZ7Q92xPeu7crzfRF/RGMQCulGuYT5w4UXv27FGrVq3UsmVL\n7d69W+PHj/dH2QAUUVczCv23rAUPIG9y7TOfP3++Xn31VX+UBYBDXM0o9OxWfiPQgYKVa838o48+\nYt9yAPnGXHLA93KtmVesWFFt27ZVgwYNVKZMmYzXJ0+e7NOCASgemEsO+F6uYR7NrgYAABRpeQrz\nixcv6uzZszS3AwBQBOVpo5W3335blSpVUkBAgNxutwICArRx40Z/lA8AAOQi1zBfsWKFNm3apEqV\nKvmjPAAA4CrlOpq9WrVquvbaa/1RFgAAkA851sw9+5hXqFBBMTExCg8PV6lSpTLe79+/v+9LBwAA\ncpVrM3ujRo38UQ4AAJBPOYZ5jRo1mJYGAIAD5NhnPn/+fH+WAwAA5FOuA+AAAEDRlmMz+5EjR9Sy\nZcsrXmeeOQAARUuOYR4aGqo5c+b4sywAACAfcgzz0qVLq0aNGv4sCwCgEMXHSxERtiGOy2U73LEx\njjPk2GfeuHFjf5YDAFDIIiKkBQuk5GR7jIgo7BIhr3IM8zFjxhTIBfbu3avY2FhJ0tGjR9WtWzf1\n6NFDf/7znwvk/ACAghEcLMXESG3a2GNwcGGXCHnl09Hsc+fO1ahRo5SSkiLJ9kAfPHiwFi5cqPT0\ndG3YsMGXlwcAXAWXS1q6VEpIsEeXq7BLhLzyaZiHhoZq9uzZGc8PHDigpk2bSpLCw8O1fft2X14e\nAHAVEhOl2FgpJMQeExMLu0TIK5+GeevWrbOs5555P/Ty5cvr/Pnzvrw8AOAqREV5m9aDgxn85iR+\nXTQmMNB7uQsXLqhChQr+vDwAAMWSX8O8fv362rVrlyRp69atatKkiT8vDwBAsZTrrmkFafjw4Ro9\nerRSUlJUu3ZttW3b1p+XBwCgWPJ5mNeoUUNLliyRJN18881asGCBry8JAECJwkYrAAA4HGEOIN/i\n471zkV0uew7A/whzAPnG8p9A0eDXAXAAipfMy38mJLD8J1BYqJkDyDeW/wSKBsIcQL6x/CdQNNDM\nDiDfMi/3yfKfQOGhZg4AgMMR5gAAOBxhDgCAwxHmAAA4HGEOAIDDEeYAADgcYQ4AgMMR5gAAOBxh\nDgCAwxHmAAA4HGEOAIDDEeYAADgcYQ4AgMMR5gAAOBxhDgCAwxHmAAA4HGEOAIDDEeYAADgcYQ4A\ngMMR5gAAOBxhDgCAwxHmAAA4HGEOAIDDEeYAADgcYQ4AgMMR5gAAOBxhDgCAwxHmAAA4HGEOAIDD\nEeYAADgcYQ4AgMMR5gAAOBxhDgCAwxHmAAA4HGEOAIDDEeYAADhcUGFc9NFHH1VISIgkqWbNmpo0\naVJhFAMAgGLB72HucrkkSfPnz/f3pQEAKJb83sx+6NAhXbx4UX369FHv3r21d+9efxcBAIBixe81\n82uuuUZ9+vRRly5d9O233+qpp55SQkKCAgPpvgcAID/8HuY333yzQkNDM36uWLGizpw5o+uvv97f\nRQEAoFjwe3V4+fLlmjJliiTp1KlTunDhgqpWrervYgAAUGz4vWbeuXNnjRw5Ut26dVNgYKAmTZpE\nEzsAAL+B38O8dOnSmj59ur8vCwBAsUWVGAAAhyPMAQBwOMIcAACHI8wBAHA4whwAUKTFx0v/txK4\nXC57jqwIcwBAkRYRIS1YICUn22NERGGXqOgplF3TAADIq+BgKSZGatNGSkiw58iKmjkAoEhzuaSl\nSy3Ily71NrnDizAHABRpiYlSbKwUEmKPiYmFXaKih2Z2AECRFhXl/Tk4OOtzGGrmAAA4HGEOAIDD\nEeYAADgcYQ4AgMMR5gAAOBxhDgCAwxHmAAA4HGEOAIDDEeYAADgcYQ4AgMMR5gAAOBxhDgCAwxHm\nAAA4HGEOAIDDEeYAADgcYQ4AgMMR5gAAOBxhDgCAwxHmAAA4HGEOAIDDEeYAADgcYQ4AgMMR5gAA\nOBxhDgCAwxHmAAA4HGEOAIDDEeYAADgcYQ4AgMMR5gAAOBxhDgCAwxHmAAA4HGEOAIDDEeYAADgc\nYQ4AgMMF+fuCbrdb48aN0+HDhxUcHKyJEyeqVq1a/i4GAADFht9r5hs2bJDL5dKSJUv0/PPPa/Lk\nyf4uAgAAxYrfw/zzzz9Xs2bNJEl33HGH9u/f7+8iAABQrPi9mT05OVnXXnuttwBBQUpPT1dg4JX3\nFWlpaZKkH374wW/lAwCgMHkyz5OBeeH3MA8JCdGFCxcynucU5JJ05swZSVL37t39UjYAAIqKM2fO\nKDQ0NE+f9XuYN27cWB999JHatm2rPXv2qG7dujl+tmHDhlq0aJGqVq2qUqVK+bGUAAAUjrS0NJ05\nc0YNGzbM8zEBbrfb7cMyXSHzaHZJmjx5sm655RZ/FgEAgGLF72EOAAAKFovGAADgcIQ5AAAOR5gD\nAOBwfh/Nnlcs++ofjz76qEJCQiRJNWvW1KRJkwq5RMXL3r17NX36dC1YsEBHjx7ViBEjFBgYqDp1\n6mjs2LGFXbxiIfN3/NVXX+mPf/yjbr75ZklS165d1a5du8ItoIOlpqbqhRde0PHjx5WSkqK+ffvq\n1ltv5e+4gGX3Pd9www1X9bdcZMM887Kve/fu1eTJk/XGG28UdrGKFZfLJUmaP39+IZekeJo7d65W\nr16t8uXLS7KZG4MHD1bTpk01duxYbdiwQa1atSrkUjrb5d/x/v379eSTT6p3796FW7BiYs2aNapU\nqZKmTZumc+fOqUOHDqpXrx5/xwUs8/d89uxZdezYUf369buqv+Ui28zOsq++d+jQIV28eFF9+vRR\n7969tXfv3sIuUrESGhqq2bNnZzw/cOCAmjZtKkkKDw/X9u3bC6toxUZ23/HmzZvVo0cPxcXF6eLF\ni4VYOudr166dBgwYIMnmPpcqVUoHDx7k77iAZf6e09PTFRQUpAMHDuijjz7K899ykQ3znJZ9RcG5\n5ppr1KdPH7399tsaN26chgwZwndcgFq3bp1lsaPMs0DLly+v8+fPF0axipXLv+M77rhDw4YN08KF\nC1WrVi3NnDmzEEvnfGXLllW5cuWUnJysAQMGaNCgQfwd+8Dl3/PAgQPVqFEjDR8+PM9/y0U2zK9m\n2Vfkz80336xHHnkk4+eKFStmLKGLgpf57/fChQuqUKFCIZameGrVqpXq168vyYL+0KFDhVwi5zt5\n8qR69eql6OhoRUZG8nfsI5d/z1f7t1xk07Fx48basmWLJOW67CvyZ/ny5ZoyZYok6dSpU7pw4YKq\nVq1ayKUqvurXr69du3ZJkrZu3aomTZoUcomKnz59+mjfvn2SpO3bt6tBgwaFXCJn+/HHH9WnTx8N\nHTpU0dHRkqTbb7+dv+MClt33fLV/y0V2AFzr1q318ccf6/HHH5ck9j33gc6dO2vkyJHq1q2bAgMD\nNWnSJFo/fGj48OEaPXq0UlJSVLt2bbVt27awi1TsjBs3ThMmTFDp0qVVtWpVjR8/vrCL5GhvvfWW\nzp07pzfeeEOzZ89WQECA4uLi9OKLL/J3XICy+55HjhypSZMm5flvmeVcAQBwOKphAAA4HGEOAIDD\nEeYAADgcYQ4AgMMR5gAAOBxhDgCAwxHmQBG2c+dOxcbGXtUxPXv29FFpft2mTZu0cOHC33SODRs2\naNGiRQVUIqDkIMyBIi4gIOCqPr9z504flSRnLpdLc+bMUdeuXX/TeVq1aqXExEQlJSUVUMmAkoEw\nBxwoLS1No0eP1uOPP67WrVvr6aef1qVLl/Tiiy9KkmJiYiTZcptdunTRo48+qj/96U86e/asJKlF\nixZ67bXX1KVLF7Vv314HDx6UJH311Vd67LHH1L59e8XGxurUqVMaNmyYPvjgg4xr9+zZU19++WWW\n8qxdu1ZhYWEqVaqUjh8/ro4dO+q5555TmzZt9Pzzz2vp0qV6/PHH9fDDD+s///mPJGnq1Knq2LGj\nHn30Uc2aNSvjXBEREdTOgatEmAMOtHv3bgUHB2vJkiVKTEzUf//7X23dulWjRo2SJC1dulRJSUl6\n5ZVXNG/ePK1YsUL333+/XnrppYxzVK5cWcuWLVNMTIzefPNNSdLQoUPVr18/rV27VpGRkZo/f746\nd+6sNWvWSJKOHz+un3/+WY0aNcpSnk2bNmVsiylJhw8fVr9+/ZSQkKB9+/bpxIkTWrJkiR5++GF9\n8MEHOnHihLZt26ZVq1ZpyZIlOnr0qFwulySpadOm2rRpk0+/P6C4KbJrswPIWdOmTVWxYkUtWrRI\n33zzjY4ePZqxy6CnWf7LL7/UyZMn1bNnT7ndbqWnp6tixYoZ53jggQckSXXq1NH69ev1888/68yZ\nM3rwwQclKWNfBEk6c+aMTpw4odWrV6tDhw5XlOe7775T9erVM55XrVpV9erVkyRdf/31uueeeyRJ\nNWrU0M6dO1W9enVdc8016tq1qx566CENHDhQwcHBGZ/57rvvCuy7AkoCwhxwoI0bN2rmzJnq3bu3\nOnXqpJ9//vmKz6SlpalJkyZ64403JFm/duZthcuUKSPJwt/tdqt06dJZjne5XDp16pRq1aqljh07\nKj4+Xv/4xz/09ttvX3GtgICALPuKX36uoKCs/6kJDAzUBx98oF27dmnLli167LHHtGjRIoWGhioo\nKIgNf4CrxP9jgCIuu72Qtm/frocfflgdO3ZU5cqVtWvXLqWlpUmSSpUqpfT0dN1xxx3as2ePvv32\nW0nS7NmzNW3atByvExISohtuuEHbt2+XJK1atUozZ86UJEVHR2vJkiW64YYbst0mNzQ0VCdOnPjV\nMmf21VdfqUePHrr77rs1bNgw3Xrrrfrmm28kSceOHdNNN930q8cDyIqaOVDEffHFF2rcuLHcbrcC\nAgL0yCOPqHv37ho8eLD+8Y9/KDg4WHfeeaeOHTsmyQa3dejQQcuXL9ekSZM0cOBApaenq3r16po+\nfbqknEfIT5s2TePGjdO0adNUqVKljPCvXr26qlevnrHX8uWaN2+uTz/9VM2aNbvi/Nld6/bbb9ed\nd96pyMhIlS1bVvXr11d4eLgkaceOHWrZsmU+vy2gZGILVAC5OnXqlHr27Kn4+PgrmtAla5Lv1q2b\nli5dmqW5PT+6deumWbNmqXLlyr/pPEBJQjM7gF+VkJCg6OhoDRkyJNsgl6Tg4GD17dtX77///m++\nVtu2bQly4CpRMwcAwOGomQMA4HCEOQAADkeYAwDgcIQ5AAAOR5gDAOBwhDkAAA73/wGAt2CUVd7j\ncgAAAABJRU5ErkJggg==\n",
108 |       "text/plain": [
109 |        "<matplotlib.figure.Figure at 0x2e6ddd0>"
110 |       ]
111 |      },
112 |      "metadata": {},
113 |      "output_type": "display_data"
114 |     }
115 |    ],
116 |    "source": [
117 |     "plt.scatter(X1[:,0], X1[:,1], c='b', marker='x')\n",
118 |     "plt.title(\"Outlier detection\")\n",
119 |     "plt.xlabel('Latency (ms)')\n",
120 |     "plt.ylabel('Throughput (mb/s)');"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 5,
126 |    "metadata": {
127 |     "collapsed": false
128 |    },
129 |    "outputs": [
130 |     {
131 |      "data": {
132 |       "text/plain": [
133 |        "EllipticEnvelope(assume_centered=False, contamination=0.1, random_state=None,\n",
134 |        "         store_precision=True, support_fraction=None)"
135 |       ]
136 |      },
137 |      "execution_count": 5,
138 |      "metadata": {},
139 |      "output_type": "execute_result"
140 |     }
141 |    ],
142 |    "source": [
143 |     "clf = EllipticEnvelope()\n",
144 |     "clf.fit(X1)"
145 |    ]
146 |   },
147 |   {
148 |    "cell_type": "code",
149 |    "execution_count": 6,
150 |    "metadata": {
151 |     "collapsed": false
152 |    },
153 |    "outputs": [
154 |     {
155 |      "data": {
156 |       "image/png": 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25ZdffjF9XgkhhLBd1pnAWJH8amCMSULNmjXx9fXl7bffJj09nS+++AInJyd+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\n8hj/H9YuPZWjlvWJAAAAAElFTkSuQmCC\n",
157 |       "text/plain": [
158 |        "<matplotlib.figure.Figure at 0xcffbb70>"
159 |       ]
160 |      },
161 |      "metadata": {},
162 |      "output_type": "display_data"
163 |     }
164 |    ],
165 |    "source": [
166 |     "# Create the grid for plotting\n",
167 |     "xx, yy = np.meshgrid(np.linspace(0, 25, 200), np.linspace(0, 30, 200))\n",
168 |     "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
169 |     "Z = Z.reshape(xx.shape)\n",
170 |     "\n",
171 |     "# Calculate the decision function and use threshold to determine outliers\n",
172 |     "y_pred = clf.decision_function(X1).ravel()\n",
173 |     "percentile = 1.9\n",
174 |     "threshold = np.percentile(y_pred, percentile)\n",
175 |     "outliers = y_pred < threshold\n",
176 |     "\n",
177 |     "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(14,5))\n",
178 |     "\n",
179 |     "# Left plot\n",
180 |     "# Plot the decision function values\n",
181 |     "sns.distplot(y_pred, rug=True, ax=ax1)\n",
182 |     "# Plot the decision function values for the outliers in red\n",
183 |     "sns.distplot(y_pred[outliers], rug=True, hist=False, kde=False, norm_hist=True, color='r', ax=ax1)\n",
184 |     "ax1.vlines(threshold, 0, 0.9, colors='r', linestyles='dotted',\n",
185 |     "           label='Threshold for {} percentile = {}'.format(percentile, np.round(threshold, 2)))\n",
186 |     "ax1.set_title('Distribution of Elliptic Envelope decision function values');\n",
187 |     "ax1.legend(loc='best')\n",
188 |     "\n",
189 |     "# Right plot\n",
190 |     "# Plot the observations\n",
191 |     "ax2.scatter(X1[:,0], X1[:,1], c='b', marker='x')\n",
192 |     "# Plot outliers\n",
193 |     "ax2.scatter(X1[outliers][:,0], X1[outliers][:,1], c='r', marker='x', linewidths=2)\n",
194 |     "# Plot decision boundary based on threshold\n",
195 |     "ax2.contour(xx, yy, Z, levels=[threshold], linewidths=2, colors='red', linestyles='dotted')\n",
196 |     "ax2.set_title(\"Outlier detection\")\n",
197 |     "ax2.set_xlabel('Latency (ms)')\n",
198 |     "ax2.set_ylabel('Throughput (mb/s)');"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "markdown",
203 |    "metadata": {
204 |     "collapsed": true
205 |    },
206 |    "source": [
207 |     "### Recommender Systems"
208 |    ]
209 |   },
210 |   {
211 |    "cell_type": "code",
212 |    "execution_count": 7,
213 |    "metadata": {
214 |     "collapsed": false
215 |    },
216 |    "outputs": [
217 |     {
218 |      "data": {
219 |       "text/plain": [
220 |        "dict_keys(['__header__', '__globals__', 'R', 'Y', '__version__'])"
221 |       ]
222 |      },
223 |      "execution_count": 7,
224 |      "metadata": {},
225 |      "output_type": "execute_result"
226 |     }
227 |    ],
228 |    "source": [
229 |     "data2 = loadmat('data/ex8_movies.mat')\n",
230 |     "data2.keys()"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": 8,
236 |    "metadata": {
237 |     "collapsed": false
238 |    },
239 |    "outputs": [
240 |     {
241 |      "name": "stdout",
242 |      "output_type": "stream",
243 |      "text": [
244 |       "Y: (1682, 943)\n",
245 |       "R: (1682, 943)\n"
246 |      ]
247 |     }
248 |    ],
249 |    "source": [
250 |     "Y = data2['Y']\n",
251 |     "R = data2['R']\n",
252 |     "print('Y:', Y.shape)\n",
253 |     "print('R:', R.shape)"
254 |    ]
255 |   },
256 |   {
257 |    "cell_type": "code",
258 |    "execution_count": 9,
259 |    "metadata": {
260 |     "collapsed": false
261 |    },
262 |    "outputs": [
263 |     {
264 |      "data": {
265 |       "text/plain": [
266 |        "array([[5, 4, 0, ..., 5, 0, 0],\n",
267 |        "       [3, 0, 0, ..., 0, 0, 5],\n",
268 |        "       [4, 0, 0, ..., 0, 0, 0],\n",
269 |        "       ..., \n",
270 |        "       [0, 0, 0, ..., 0, 0, 0],\n",
271 |        "       [0, 0, 0, ..., 0, 0, 0],\n",
272 |        "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
273 |       ]
274 |      },
275 |      "execution_count": 9,
276 |      "metadata": {},
277 |      "output_type": "execute_result"
278 |     }
279 |    ],
280 |    "source": [
281 |     "Y"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "code",
286 |    "execution_count": 10,
287 |    "metadata": {
288 |     "collapsed": false
289 |    },
290 |    "outputs": [
291 |     {
292 |      "data": {
293 |       "text/plain": [
294 |        "array([[1, 1, 0, ..., 1, 0, 0],\n",
295 |        "       [1, 0, 0, ..., 0, 0, 1],\n",
296 |        "       [1, 0, 0, ..., 0, 0, 0],\n",
297 |        "       ..., \n",
298 |        "       [0, 0, 0, ..., 0, 0, 0],\n",
299 |        "       [0, 0, 0, ..., 0, 0, 0],\n",
300 |        "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
301 |       ]
302 |      },
303 |      "execution_count": 10,
304 |      "metadata": {},
305 |      "output_type": "execute_result"
306 |     }
307 |    ],
308 |    "source": [
309 |     "R"
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "code",
314 |    "execution_count": 11,
315 |    "metadata": {
316 |     "collapsed": false
317 |    },
318 |    "outputs": [
319 |     {
320 |      "data": {
321 |       "image/png": 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ezvUjWi+ljFeutpJTU3rDL745+t47/+4jyfIRFUmPzuquDY22/TwAbolKcuJo\nFbCxc29AE/yYbxcuacfXqbAcqS2c7mw3pW1ruKY1FVpPIWdU2dzDXdBraF5sIXdsbuAcVxlKogq4\nNAgJpnenURhc+TPVla2c6HemME9Uy3Ht/ndpo6Z+G1IuXHOggXRdfVv6DLo30nWyb1VovdDN6lXw\nbZh1WXC6nemBPNGtD6NC0KRED6DDzkgrgA6ovv086L7NJ6He3BQ8c/25A/tTcB8U/m1Csqjvusal\nOeCzXYPOwsxc8MZN+jvuVaANkpat1BUc8+kyydkkWlv4I8QUN8/0HBpZgZYZbxOLR+dhtrt7YECj\n+fAF4DQ9Gwc+U0gnDOWE7Rfbq21RHOud5oNOBOh6T4935/myDSp0DyDA0ogSLq0D3+3KV6zv/47R\nPvCZ67Z3BvZOuRb+8TtTWB6fsIbtnh4fgkIQ7bf4ua8dLx6d79/jKgNX++bmN7qHkn4WArZVbja0\njSu03CRBX217GHF/aAmOEHXhXVMCsG/Ew+94I6Ad0VQB9FRTjFBA053ecXdfS+BSx+GTp4322Stf\nsX7J4EHzg2ce8UZEBx76ThQepgivoffgHiS+KY/+juVEj1vgAxB2ZryW/07fCfOPv+OmvYWZObjw\n8uf178X3R0wDDd+giZ2QfkYnC/wcpUtaFnxwQ8GDtonFo/MwveNumOhNLpEMMe2Hjj3Yfx/Tu+OG\n34WZuf6kzo8bt3Xm1WvOgekdd8PCzNzAgIoD6ca1W/thXQ7suQ96c1P9GGXoLTXRm+y3DWpqwnzh\n/hPTJlJqMuJ5pBL7ppXj/YmWTuYAS8POHNhzX/+ZfL1iesfdSyYK3CB8YM99/UggmO9uZ9rYJ7hm\n9sz15wLA2Y3R+B22t3XbOwNrMLZ+hu94YM99A+GBaH/Ce7mlhvcz3I+1ePRMWLTb9x2DqesuHlhz\ne+DEwwPtcra7e4kFAbVqbCMTvcklcfnoxG+boExjkgmqHVMWj86LjsapSinedyJNCTvhM9efOyAF\nHz55Gg6fPN1vBNjReJBNSrczDdtu2QW37zsGh07N9zsCN+0cPnl6QBujkqRJqnjgxMNw0RVnN58e\nOnV2UMVNlRO9ySUH8mG69ETdnQf3DzQM03ERh0+e7nd0qklhJ8WBEsGBZqI3OXDcAr4n3k+jbGN+\nD52a7//OWbe90zd10M68fMN4/3qsMzq58A6CE9rhk6dh49qtA/dj3rFM8HiJnQf39ycAenLs7fuO\nGScDKvHjA5ktAAAgAElEQVTRiYhey+N/0TaC6eOgi/lAoWbd9g5sWjk+EI3BFCEB4KygsW57p18+\n3c6ZmH34Hb43xkrEesFB1zTI3L7v2BJt1zWg2L7HvB46Nd8PSoztENOnMeIwLWwrNFrFkeN74dCp\nMwMbnUh3Xb2t//x12zv99PDe5RvG+/WL7QKFQiyzA3vu638OAH3BAPsRwJn2hs+0WQLOX7UC7rnj\nTL/fdfW2/gSEk1pvbqovpFy7eUs/H/hOyOo15/T71PIN4/1TBqZ33N1vGyhYYIQUGrWGthF8dx5L\n8tCpeVg8Oj/QBuhY0+1ML9lkTJ3GTIIIhqgymXGnd9yd5QgfTmsiOlCki5n0utyL6sO0ia4qsYvN\nCzNzsO2WXaLd+qHPqLIALo2cEPP8Op0HTM/K1S94urZF86qOCdLIGdKYbjR6R0jebM/IVb44CaGF\nB4UVV/QLabqSd87p6HDl838z+t7b//ZDyfIRtKYkjWVFC9cUzsdF6MI6j3pMGSU7LMBSU6n0/ddt\n7wyYJVwTUqjEVmXgs2mHiGRdwPb8mAnJ5eLuypfpWbEDpq8/8HRNR80DmPuy1Mxkeg5Cy0haxngK\ngfTZlFxmLVs+cI0HBWHbO4bWbwl78krRlIImJZN3Suhk4Ct8/j0fBE0BUwHAaE6xNZjQvVIlIfFU\ni9EwbAFpKbhQLR04JHuJXGn5PI+ohuzLU2jd+o6hALC3ZduRCSkIHbxsnnvUFMnXf6nnWyhVj9/o\nzU2JBB/XmjVA+KRgG1dM3/uulaQfyjB44C0uLsKLXvQi+PrXv+68zjkp8cnFdvgWhx+lYMIn5WL6\n9MRWtI/HpIf3U3svfQ7A2bUW3oBShZsxHfrlwiS58g5gcu+mUYr5/fi3LR+mSYe7V5uia5vs5Lyc\ncdEe4KwzBLWj83xRad5UB/Ra6s1pGgD46be0Lkx2fBpxnL+X77h5fJbtfh/cay0G/kwsE+qtR/+n\n4OQQkm+bJyStWxvYBtBZItW+QJubPf/d90zuBHHPHeFaDU/fdoKBrd5L0KSq8Oijj8KOHTvgSU96\nkvda55rSVRd0l3iG8SjDPnBhmk9UdawDYR65VBVq7zW968LMHDxz/bnRkaZt6VYhdjOtNC++9wkt\nVx45neeH2+p5+q7o2gBn2x4ADDjk2K4PxdYuTO/iirIuSVdC6FqMKVI6ekWithQyGM52dw9Eunb1\ncx5xHODs5CQdG0z5851GELOWhm0R20/MepXvnhLWlN74S78dfe/H/uaPnd/fcMMN8KIXvQg++tGP\nwnXXXQfnnXee9VqnpsS9zii0c3O4ZM87GLpj0vupBOvCZtc33UelQjohuY5JNoUqMtmtuZs4lQjp\nfi6aLs971fUSDh9gTHsqAAaPAEH4ybj0HvybHv1B/5eazmzmONMghIMAYtoHRvdjmdojtj30tOJe\nWqayodqpT1MwmYxNGz1N9ewagGybOG3ljJ9zt2cKd79eveacAY9TvGb5hnGnFmVKj0LrkntNUriG\nb9rQ7sKmhfL7bc/h8E3WFBSOTOOARBMEWKqpl0iuNaW9e/fC8uXLYdOmTeDQgfo4NaUjN985cCZP\nlVApIcQ+R3pfqCSaQqo25a2u8rRBpco68oLP48+ylS+V2inUzTa1xo1pS6RhU5lJNlr7sJWHNCzW\nrqu3FeeR6mtf9J0lWoOvLCSWjFzk9OzMqSlddUE3+t4Pf7Fn/e71r399fy/Tl7/8ZTjvvPPgwx/+\nMCxfbn6XIPNdCe7XvMFinko5PyiEkvIsNR9UyXNMZ63TddtGCe0+Na76Rq0hpp5TtGlJndtcsVPg\nm0C7nbOb/usm56T0lgt+J/reW7/4QdF1l112GUxPT8eb77jbKG4MbRKeJ2wYu67eVowLuM30xlV3\nHiGhSaSdu+5JtOkJCaCa1lMqPEYgBUMxxZCifUjrPHRCkprObG70yNR1Fw9lm6gDSfSHIJdwX2XV\ngW93vITck5etwdo6URMmBlcHLWVyLyUfualTKHHtocp1qGFoPeZqm641copvHKFhqeqiBME1BXv2\n7HFqSQCZIjqMMk2vEymDDKPpTSmHus3LOc13v/XCePPdn3xBZr6T4NWUTHHJmqBErxXqoYVSpm1C\nknhzpcIn8YaWZSoJmntcpkbq2TdquOq7dAm8dG051flLJdCagKwY8RggrPBSTyJS1Ts1vggHNBqy\nC3ptlbJJEXUiRLhIqWnQ6Bu+zYIhmDbuVk0vx7VVcUXvsMGPyOCR/yVafS6znoSqh+XVRZUzulzb\na+qklDBDXvNdCeaPEjywRoHc5tlhN/+W2k6r5qvU9xpFcprvfmfzNdH3fvDgzcny4dWUMI7XbHf3\nwDk6dTDb3T0QFt9HU7HrUsddi3lG1XRmu4PnOtENlRTJu9hMlSaPL7o5F6BZqRxxlbXprBv8XBoR\nu0oeXCGPTPAjVGzpuIidkKq2WdOG85Dn5TD52+qvypiIobHa4HxUB95JiR62h953kkkiRSHyOF0+\nmtjzQzdy2pDs5jZ9Z4p5J41UjfDrpVGz6Tk6FNtR6r70bN/RQ9IA4teAUk5mrvbG29jyDeNB8dqk\n10nqaba721te/CDAOvAFS/VBI6KETIj8edJTDUKw1V/sxI39k0ZKT5l+G/FOShh+hg5+kspOUYg0\nMGZKLSgkLd9EIg2PQq+1aRG+CQTjlfE0eLgcHjLHlBcO11hseZAMNLb2YcqXK2hmCL4j4DFt2zWu\nk4cp2HZoeBnpmoA0aDCm60PyXIzVxtOnpxT70nCFLDJ9j/XvihJP4eVCQ3jRZ9XtlBEz5mAbMwVT\nNjHRm1yyXi4tp9Qsg7Hon5QErSk1ub6krtbtJ3RNSetchiTkTtPrwinQ9pB3Temal7wt+t6bP/eB\nZPkI2jy77ZZdXtsnknp956IrynMJT02MyZPfwzWeJmzRtvZBj7ygUh/9neZXOgCZNKAqUmWotiaV\ninOwce1Wr4mOfp+ynGyEloN0rGjThGQLPkyx9c2mtr+U4n0XNCldu3mL1/ZJr42FO1SkOmOlSSST\ng+8cJBv0Or5GY1r/y93obe77mJeJ3uSA5E5/jzH7Unf7FAFRY55P/68TyboLjTrOr5WUU6hgE3oC\ncsz5RKViWge2fV/aOtHYWPxP0nz4zHelqMxNuaWqO2w688+wu4QrSk4Wj87DT734JdnSf/uFb4++\n9/0H3p8sH15Nica7azKqgtQtPDU5JqQUbqWuBerU5rvQCUlivgu910WKd+Ru6jE00T9sDiqUusy5\nqcyYqfIYaz6rAi5vcIcYE7b8mcaHbme6COWgDryakmoKaZBonKnKmj6rRO2kxDZVYp6UvJTqAGLr\ns93OtPPcoqq842W/F33v+z77B8nyIVpTwvPuc7tk2jSIqnHjuLRieo5vsdW1mZFuLLZBIwvHhthx\nnZRKiZGoJG7QVcH35WtnANWl7KpSb5UJKVX5pA6XZHsGhZd3CZuXbVqrz3mAf+frkxgYwEaVepW4\n9vNYkHR/1+LR+dqdlMYq/Euaj5A1JdvviqIodTDq405Ol/B3XfTO6HtvvOemZPkI8r6jjHLDUBSl\nGR469mDTWRhaWuMSzhsBqrR1L+qmVGVzxMjz5S80/9yMgn9zB4cSYmK5TG+SKAu2z6uYN6vQVAzF\nOqlSVrnbnM0xo9Q1IBtVTaF1m1Jb6RLeVHSHUVfZFRltG7SUMLR+85rv3vMr74q+9/rP3JgsH15N\niU4Gtg2PuQnZ2a+4iS2jFGWbI/IBTSukTeaQQtukYZXg0BBKqjGnLfU0quNZ9JpSXYQ0oCZdn+sI\nFikN5e+6xlZGvg6A94V0FH5tjsgHsWnlEKraFHWkiejhoeQyE7alnuoez1qzptQ0KRoQHxxDJSXf\n9bPd3aI9SCGbHE1sWjkujgztStMWpVyyqVfSUfB6fm1VTamOc6tiSPnc0LKRDNymNHlYphCqtmMp\nVfeNNdEebG7eLmx1GHpMTVVKcQkPmpRMhYSfuRoA/Y46SizMzBmPpeDHCFQ9GI2DE500Xd8ZQodP\nnjbupdq4dmv/GYdPnobe3FQ/j7Zn28L9b1y7FQ6dmu//7euwrjNt+L24/8MkAODhdSEdwjbgYV4O\nnRrUKmnamBe6K57fz/NHz99BUAgwpUOfGRNfkB+nECo40bSwrUvOIOKCDT02g0Pf2ZQmprVue2fJ\ncQu+fPsEE9/3vOxtbR6/902cVSYfX3/k+IKo4rvbzoIzjROHT542psuj2PB+k5pSNCWvowOlrl3v\nG9duHZg42qJulwjfHV5KedK2VLVdmd6p7kgWJUbOcFHFacD3rrbvbfXM64/+3WS5phjvbP1Nkrbp\n3XM6OlzfeU/0ve+Zuz5ZPoI0JW46ymnzjT1gS4LkoLdSCTmenB9xXuU5sZik3Nnu7qSxDPEgSIrk\nvX2H10kJHThLWMCusqbme1ebRC+tczqI20zAdZBCAK9yUm1uzahUgiYlaooCyBt6HSszRKqXVqIt\n321wO+cd22ZyAzC/p+l615pDDvjxGlWfFTtQ8SM2YvMROvHHOI2UhC/ftmNrbOZXn3ncl0ZKSvLM\nK8Gi0QS1OzqELqyGNJJRqETJUfQhnRwABta6kNADyGzXY3wx15pFVa0slQdeXW7S+JwUA2wTrt0p\nJ4Yjx/c6hcm6N4eH9h0fVQSPuifIsbGx6J+UBE1KOOhXaZShZoNRmGhCCJHmbc4XJnid2p5jq3vb\n9Sg1c+1Ici/H1cFTaHbYNnNrMCnd0WPTKiESCDLMfbzKWJl6gvRRiqODeFIyDXD0M1dHtnk/0ai/\n1JVSEtXblc9YbFKnza3a9/70/XjZxa5fce8rl3s3PZEV4esvPg8nXvahLsir15yzpE5pNGxT/due\nEdPBqXeeLY+S59jWUSXtLSbKPff2o16osW7gtmtMIZ1s7VOiQdsEBJ6mzfPNlpbr2ak0xpzmwdBI\n/JiXxaPzA568uWhNmCETuT1i2ubJBFDWeTwuDzv+HS9r/NuWRoj3XoynH3a8kPtM7UX67Ca9Eeto\n56GecKNOKd6pAOa85PS++4Nfe2/0vb/3l/H3cqIcHXJhkpikz2x6gbKkDs4nHRd8wMK/bR0zpMO6\nrrXtEzpyfG/woNCbm1oiKUvT4Hub6iDVc6p4qFZpr742FfJ+Pg2APiv0fauaYfEU2Tqhz6vbfFcK\nYk2Jbg5royZTIiFSWSrJtm5JsCTJE8Cdn7ratQYWHSS39t0UVduTakoeenNTjfjN1yW9NuGeG9K5\nJOGFOCkiYcRqoNwMJ5WGYzCtQ9ooYUBLEXcutl5StPM6rRJcU6pboMqJL6pI3ZpSKWGGotaUUhG7\nM3zYyW3vL6FcVVuQk6O+ePlXfYarPqu2Z9P9OfpIU0fkoKApiXCSU1P6wCXxAsvbPp2ufQatKaWO\neFA1hlbThJSHRJLnjTMXoeWaSlulHmy2AawtRyrk0OBtnofSWHI2TBI/L/+qfc0lYIS2Z/4+pvsl\naYbWUVOb51evOWfgfXYe3A8Adm/FXCwbi/9Jmo+Qiw/suW9JwfBBxOc6y09QNYGdE9NYmJkTDVbd\nzrR1oqAd09RYpZF66ee0EdvytzAzB93OtNVkQ9M3BUql35vyYqoPLAPbhlXTZ7b8Lx6dNwaMdHV4\nW2ih1WvOgW5nGlavOcd6Ly0nyTPps+i7I7aO7GpPkjKjUq2vLfueR9M0DbY+t33Thmqa3yPH9/b/\n5uWDQZExbanbucllHVk8Oi8aQG191bZB3CUEmiZ0VzvLRQqhCk2UyzeM17qsUMrm2UbNd1Jmu7vh\nwsufl0ySaWKxNMZcxdX4GJNFSa6/WAYuM4ma9dJDTXMlmG5zwdtOSW0/BFcd5TTf/eEr44Oq/u6n\n4oO5cloxKSnpqHtC1klGGQWqTPYh947CpBS9ptQW27+JHOsBbYgwDpDH+8xlYgidkLBdNRkGp6QQ\nPKlp87uV3MeqaJ+laK6lmO9EkxIOOgf23Nf/zOVpQ+/haYTYSHFjneQAspC0TSezuu71deTZ7u6B\nsuFpY5gQX3o8hJBpMzEXBkxrejwdjmnhe2Fmzvi5ZCBwHS/gC2lkE24mepNBx3RgSKeQUC62a0NM\nPqk3WIaGEJJsMDe1cdvaL01LKni6wgTFQO+neXjo2IPO+0LWPflzbMScUk37aci4hfk1PdMVfisV\npTg6jKT5Tk1KeUixXqF1Uw9azu0kp/nulktviL736j///WT5qP3oihJoa2cs3fRSihmiKk1spM6J\nSUNqax8YZpru36UEZA2alJqMA9V2JA3OZyozmZViGnJT64GS57oGy7o6LT/8T0pT5errJzQSy4WX\nPy84/VLWj0teU0qBz2w87O+PBJnvqMrf1O7nFOTIe5vLo0QWZubggRMPt9KlV8nHqPeznOa7P/71\nePPdb/9ZQ+Y7urHRtLDPCd1gabtPsnk2ZEFy+YbxqLhWrnumd9w98Hfooiu9xvT+UmxOBojrHVzf\nheTBtvAt0XypeSlmQkqxuO2DL8S7ztTKrd3Z3oeevyRt674N5CanFV8+JEjyh+nbNpSmKOfQNGz9\nlDqfSNOUOnPlpJRD/oI0pWHeeDcqtCnKsjJIirqrY0OpjhNnSd3fcmpKt07cFH3vW2bfmSwfQZoS\nNrQ6F+SkUl7Tdu8QKafO8ouVvlwSuIkqEZWrRmO2nS4bQtOLzBJoxPXYesUJKadU7pqQJHWN15jC\nhuXWJlKnX2VCqltzarWjw+GTp9PmwoG0UnN4E4UMVCGSoU9STRkyP1Zitd1ny3uVzldVkjxyfG/Q\ncRAmc5tp71ouqk6AR47vrRQ9AMAeXy43krrGa3pzU0vWj3JrYKnTr9KX6z4qqJXmuyZQc1NaSixP\nNfcoUtoazw4gTTvPab776GvfF33vmz7xjmT5KH6fEg6gTZlXUj23afMicuT43uyHl4VGXE81IaU+\nYqOp56cmt+ZX56F/bZ2QAOTtvNR2VBfBse98oVxSws0rdTZ++lwfkk6fyrzIJzf+bEk4kiqakqTu\ndx7cbzyiJNU+KxupzMqudFxtsO4B0+dpidDB0ObhWYXUmje+V9X+3tbBPXc/sVFK7LvizXdKPdRt\n1uP7TUo0K5poSz7bBC9TLWM7Oc13M6//g+h7t9/1e8nyUbv5TiL9mIJE+vY8pZAkYk1sde4xqCo9\n2g7gcw0COaQ0voBNPctCoNfXUQemcjIFyrVRd/BWG5jfEqIEpJqAYsrWtZ8w9cnSNmIOoszByGpK\nKgXFgYukuZwC1NlgtKjiMNCWthIz1pTuSJFTU9p1+fuj79225+3J8iHSlEJ3hrvYtFIeIgR3RkvC\n81fBl74rvD/mzybt0D0XrjRN11BwEMCJyQSu99EQ+CHlFqPFuvLB06Yh+lMcbcLvpxq1JJ/8mIaY\nPNBrU7TRUO1HcmyF6X2qrL/RU2xj8lT1eim+CcmU/9AJKVSbiX3XpqM9VOGxxx6Dd73rXfCa17wG\nXve618HXvvY15/VBmhKVPOrSeNqiWeWWHlOlb7Lfb1o5ni3vvvqTvFfMu5cs8ZaQtxx5SNVXXenk\nHg9K1wLbqCnde++98PnPfx5uuOEG+NKXvgR33HEH3Hrrrdbrg9eUcMbmA1tKYqSCKpJECimkrk19\nOXach+Q9VDpGzdh2wF5vbsrbfiSTFs9X6IBb9RC1kD6AWkod0q9NQ68yIdneNWT7BtUweHpNrjHR\nDasp1lKrrHnW7W2ca03ppS99KVx//Zmj1h966CF42tOe5s5HiKY06hF6XeihacNDCZqMYqZN/SxH\nXnNqSndc8YHoe6+4423ea97xjnfAvffeC7fccgs8//nPt14X7X1X5x6A1M9qYv+CxJOnypHsIdiO\nQ4/Ft6+EH/csYba7u7ENx01OSHW9cymbuQHk7X7x6HxQOKmmST0h5daccocZet/73gf33HMPvPvd\n74Yf/vCH9nyEZJoeV7F6zTkAEOaOTRehXZ2CD+D4LJqGLW1XOqb0fJtRXc+k39Fzpkz3HNhzn7ec\nenNTA9fQRmgbKE3lSD8zfX/k+N4ln9PObssnLxt0DkDnC5cTy8LMHEz0JgfagGswmuhNwjPXn2v9\n3nVsgKS+Fo/OW93jqYON6Rmm5wMM1pfU4YKbDddt7ww8F9uTKTip62/fpEMHTMkE5TM92cpSgss8\nS9v98g3j/b8l2zC4sMQ3G+PfvvfHNpXLfV5yNE+3M519bT2X+W7fvn3wsY99DAAAfvRHfxSWLVsG\ny5bZp57K5jufqSOVKSTlAqQkT7H5zmXilJoCUl/nIvRdeR3G1mlOE06bTHdNmtPx2dI8+Oos9F18\n1+csm5i0q7YrvD+n+e5Pt90cfe9lu66xfveDH/wA3vnOd8K3v/1tePTRR+FNb3oTvPjFL7ZeL9KU\nUKJ46NiDS76b6E06Z3nJ4C9BcmyGdCHRlSeUqug1tmea3tvWWEPDM3FVXToIh1wXahLkkudFV4Qt\n3PLI1L5jx23tKsWEZEsb6z3WVJLCLCZ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Grz5N5VJSexxGcmnEOSelY38UPymtf2u6SSmr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\n",
322 |       "text/plain": [
323 |        "<matplotlib.figure.Figure at 0x2e6dc10>"
324 |       ]
325 |      },
326 |      "metadata": {},
327 |      "output_type": "display_data"
328 |     }
329 |    ],
330 |    "source": [
331 |     "sns.heatmap(Y, yticklabels=False, xticklabels=False);"
332 |    ]
333 |   }
334 |  ],
335 |  "metadata": {
336 |   "kernelspec": {
337 |    "display_name": "Python 3",
338 |    "language": "python",
339 |    "name": "python3"
340 |   },
341 |   "language_info": {
342 |    "codemirror_mode": {
343 |     "name": "ipython",
344 |     "version": 3
345 |    },
346 |    "file_extension": ".py",
347 |    "mimetype": "text/x-python",
348 |    "name": "python",
349 |    "nbconvert_exporter": "python",
350 |    "pygments_lexer": "ipython3",
351 |    "version": "3.5.1"
352 |   }
353 |  },
354 |  "nbformat": 4,
355 |  "nbformat_minor": 0
356 | }
357 | 


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/notebooks/data/bird_small.png:
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https://raw.githubusercontent.com/JWarmenhoven/Coursera-Machine-Learning/beadaa9497ec6f7bd10b968ee7ab08fd56e2d1e5/notebooks/data/bird_small.png


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/notebooks/data/ex1data1.txt:
--------------------------------------------------------------------------------
 1 | 6.1101,17.592
 2 | 5.5277,9.1302
 3 | 8.5186,13.662
 4 | 7.0032,11.854
 5 | 5.8598,6.8233
 6 | 8.3829,11.886
 7 | 7.4764,4.3483
 8 | 8.5781,12
 9 | 6.4862,6.5987
10 | 5.0546,3.8166
11 | 5.7107,3.2522
12 | 14.164,15.505
13 | 5.734,3.1551
14 | 8.4084,7.2258
15 | 5.6407,0.71618
16 | 5.3794,3.5129
17 | 6.3654,5.3048
18 | 5.1301,0.56077
19 | 6.4296,3.6518
20 | 7.0708,5.3893
21 | 6.1891,3.1386
22 | 20.27,21.767
23 | 5.4901,4.263
24 | 6.3261,5.1875
25 | 5.5649,3.0825
26 | 18.945,22.638
27 | 12.828,13.501
28 | 10.957,7.0467
29 | 13.176,14.692
30 | 22.203,24.147
31 | 5.2524,-1.22
32 | 6.5894,5.9966
33 | 9.2482,12.134
34 | 5.8918,1.8495
35 | 8.2111,6.5426
36 | 7.9334,4.5623
37 | 8.0959,4.1164
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/notebooks/data/ex2data1.txt:
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  1 | 34.62365962451697,78.0246928153624,0
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/notebooks/data/vocab.txt:
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   1 | 1	aa
   2 | 2	ab
   3 | 3	abil
   4 | 4	abl
   5 | 5	about
   6 | 6	abov
   7 | 7	absolut
   8 | 8	abus
   9 | 9	ac
  10 | 10	accept
  11 | 11	access
  12 | 12	accord
  13 | 13	account
  14 | 14	achiev
  15 | 15	acquir
  16 | 16	across
  17 | 17	act
  18 | 18	action
  19 | 19	activ
  20 | 20	actual
  21 | 21	ad
  22 | 22	adam
  23 | 23	add
  24 | 24	addit
  25 | 25	address
  26 | 26	administr
  27 | 27	adult
  28 | 28	advanc
  29 | 29	advantag
  30 | 30	advertis
  31 | 31	advic
  32 | 32	advis
  33 | 33	ae
  34 | 34	af
  35 | 35	affect
  36 | 36	affili
  37 | 37	afford
  38 | 38	africa
  39 | 39	after
  40 | 40	ag
  41 | 41	again
  42 | 42	against
  43 | 43	agenc
  44 | 44	agent
  45 | 45	ago
  46 | 46	agre
  47 | 47	agreement
  48 | 48	aid
  49 | 49	air
  50 | 50	al
  51 | 51	alb
  52 | 52	align
  53 | 53	all
  54 | 54	allow
  55 | 55	almost
  56 | 56	alon
  57 | 57	along
  58 | 58	alreadi
  59 | 59	alsa
  60 | 60	also
  61 | 61	altern
  62 | 62	although
  63 | 63	alwai
  64 | 64	am
  65 | 65	amaz
  66 | 66	america
  67 | 67	american
  68 | 68	among
  69 | 69	amount
  70 | 70	amp
  71 | 71	an
  72 | 72	analysi
  73 | 73	analyst
  74 | 74	and
  75 | 75	ani
  76 | 76	anim
  77 | 77	announc
  78 | 78	annual
  79 | 79	annuiti
  80 | 80	anoth
  81 | 81	answer
  82 | 82	anti
  83 | 83	anumb
  84 | 84	anybodi
  85 | 85	anymor
  86 | 86	anyon
  87 | 87	anyth
  88 | 88	anywai
  89 | 89	anywher
  90 | 90	aol
  91 | 91	ap
  92 | 92	apolog
  93 | 93	app
  94 | 94	appar
  95 | 95	appear
  96 | 96	appl
  97 | 97	appli
  98 | 98	applic
  99 | 99	appreci
 100 | 100	approach
 101 | 101	approv
 102 | 102	apt
 103 | 103	ar
 104 | 104	archiv
 105 | 105	area
 106 | 106	aren
 107 | 107	argument
 108 | 108	arial
 109 | 109	arm
 110 | 110	around
 111 | 111	arrai
 112 | 112	arriv
 113 | 113	art
 114 | 114	articl
 115 | 115	artist
 116 | 116	as
 117 | 117	ascii
 118 | 118	ask
 119 | 119	asset
 120 | 120	assist
 121 | 121	associ
 122 | 122	assum
 123 | 123	assur
 124 | 124	at
 125 | 125	atol
 126 | 126	attach
 127 | 127	attack
 128 | 128	attempt
 129 | 129	attent
 130 | 130	attornei
 131 | 131	attract
 132 | 132	audio
 133 | 133	aug
 134 | 134	august
 135 | 135	author
 136 | 136	auto
 137 | 137	autom
 138 | 138	automat
 139 | 139	avail
 140 | 140	averag
 141 | 141	avoid
 142 | 142	awai
 143 | 143	awar
 144 | 144	award
 145 | 145	ba
 146 | 146	babi
 147 | 147	back
 148 | 148	background
 149 | 149	backup
 150 | 150	bad
 151 | 151	balanc
 152 | 152	ban
 153 | 153	bank
 154 | 154	bar
 155 | 155	base
 156 | 156	basenumb
 157 | 157	basi
 158 | 158	basic
 159 | 159	bb
 160 | 160	bc
 161 | 161	bd
 162 | 162	be
 163 | 163	beat
 164 | 164	beberg
 165 | 165	becaus
 166 | 166	becom
 167 | 167	been
 168 | 168	befor
 169 | 169	begin
 170 | 170	behalf
 171 | 171	behavior
 172 | 172	behind
 173 | 173	believ
 174 | 174	below
 175 | 175	benefit
 176 | 176	best
 177 | 177	beta
 178 | 178	better
 179 | 179	between
 180 | 180	bf
 181 | 181	big
 182 | 182	bill
 183 | 183	billion
 184 | 184	bin
 185 | 185	binari
 186 | 186	bit
 187 | 187	black
 188 | 188	blank
 189 | 189	block
 190 | 190	blog
 191 | 191	blood
 192 | 192	blue
 193 | 193	bnumber
 194 | 194	board
 195 | 195	bodi
 196 | 196	boi
 197 | 197	bonu
 198 | 198	book
 199 | 199	boot
 200 | 200	border
 201 | 201	boss
 202 | 202	boston
 203 | 203	botan
 204 | 204	both
 205 | 205	bottl
 206 | 206	bottom
 207 | 207	boundari
 208 | 208	box
 209 | 209	brain
 210 | 210	brand
 211 | 211	break
 212 | 212	brian
 213 | 213	bring
 214 | 214	broadcast
 215 | 215	broker
 216 | 216	browser
 217 | 217	bug
 218 | 218	bui
 219 | 219	build
 220 | 220	built
 221 | 221	bulk
 222 | 222	burn
 223 | 223	bush
 224 | 224	busi
 225 | 225	but
 226 | 226	button
 227 | 227	by
 228 | 228	byte
 229 | 229	ca
 230 | 230	cabl
 231 | 231	cach
 232 | 232	calcul
 233 | 233	california
 234 | 234	call
 235 | 235	came
 236 | 236	camera
 237 | 237	campaign
 238 | 238	can
 239 | 239	canada
 240 | 240	cannot
 241 | 241	canon
 242 | 242	capabl
 243 | 243	capillari
 244 | 244	capit
 245 | 245	car
 246 | 246	card
 247 | 247	care
 248 | 248	career
 249 | 249	carri
 250 | 250	cartridg
 251 | 251	case
 252 | 252	cash
 253 | 253	cat
 254 | 254	catch
 255 | 255	categori
 256 | 256	caus
 257 | 257	cb
 258 | 258	cc
 259 | 259	cd
 260 | 260	ce
 261 | 261	cell
 262 | 262	cent
 263 | 263	center
 264 | 264	central
 265 | 265	centuri
 266 | 266	ceo
 267 | 267	certain
 268 | 268	certainli
 269 | 269	cf
 270 | 270	challeng
 271 | 271	chanc
 272 | 272	chang
 273 | 273	channel
 274 | 274	char
 275 | 275	charact
 276 | 276	charg
 277 | 277	charset
 278 | 278	chat
 279 | 279	cheap
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 281 | 281	cheer
 282 | 282	chief
 283 | 283	children
 284 | 284	china
 285 | 285	chip
 286 | 286	choic
 287 | 287	choos
 288 | 288	chri
 289 | 289	citi
 290 | 290	citizen
 291 | 291	civil
 292 | 292	claim
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 296 | 296	clear
 297 | 297	clearli
 298 | 298	click
 299 | 299	client
 300 | 300	close
 301 | 301	clue
 302 | 302	cnet
 303 | 303	cnumber
 304 | 304	co
 305 | 305	code
 306 | 306	collect
 307 | 307	colleg
 308 | 308	color
 309 | 309	com
 310 | 310	combin
 311 | 311	come
 312 | 312	comfort
 313 | 313	command
 314 | 314	comment
 315 | 315	commentari
 316 | 316	commerci
 317 | 317	commiss
 318 | 318	commit
 319 | 319	common
 320 | 320	commun
 321 | 321	compani
 322 | 322	compar
 323 | 323	comparison
 324 | 324	compat
 325 | 325	compet
 326 | 326	competit
 327 | 327	compil
 328 | 328	complet
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 332 | 332	concept
 333 | 333	concern
 334 | 334	condit
 335 | 335	conf
 336 | 336	confer
 337 | 337	confid
 338 | 338	confidenti
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 340 | 340	configur
 341 | 341	confirm
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 349 | 349	construct
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 351 | 351	consum
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 354 | 354	content
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 356 | 356	contract
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 358 | 358	control
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 361 | 361	convert
 362 | 362	cool
 363 | 363	cooper
 364 | 364	copi
 365 | 365	copyright
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 367 | 367	corpor
 368 | 368	correct
 369 | 369	correspond
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 372 | 372	couldn
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 374 | 374	countri
 375 | 375	coupl
 376 | 376	cours
 377 | 377	court
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 379 | 379	coverag
 380 | 380	crash
 381 | 381	creat
 382 | 382	creativ
 383 | 383	credit
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 385 | 385	cross
 386 | 386	cultur
 387 | 387	current
 388 | 388	custom
 389 | 389	cut
 390 | 390	cv
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 392 | 392	dagga
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 396 | 396	danger
 397 | 397	dark
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 399 | 399	databas
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 401 | 401	date
 402 | 402	dave
 403 | 403	david
 404 | 404	dc
 405 | 405	de
 406 | 406	dead
 407 | 407	deal
 408 | 408	dear
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 413 | 413	decis
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 450 | 450	didn
 451 | 451	diet
 452 | 452	differ
 453 | 453	difficult
 454 | 454	digit
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 456 | 456	directli
 457 | 457	director
 458 | 458	directori
 459 | 459	disabl
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 461 | 461	discov
 462 | 462	discoveri
 463 | 463	discuss
 464 | 464	disk
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 466 | 466	disposit
 467 | 467	distanc
 468 | 468	distribut
 469 | 469	dn
 470 | 470	dnumber
 471 | 471	do
 472 | 472	doc
 473 | 473	document
 474 | 474	doe
 475 | 475	doer
 476 | 476	doesn
 477 | 477	dollar
 478 | 478	dollarac
 479 | 479	dollarnumb
 480 | 480	domain
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 482 | 482	done
 483 | 483	dont
 484 | 484	doubl
 485 | 485	doubt
 486 | 486	down
 487 | 487	download
 488 | 488	dr
 489 | 489	draw
 490 | 490	dream
 491 | 491	drive
 492 | 492	driver
 493 | 493	drop
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 498 | 498	dw
 499 | 499	dynam
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 501 | 501	each
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 513 | 513	echo
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 519 | 519	editor
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 522 | 522	effect
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 527 | 527	electron
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 530 | 530	email
 531 | 531	emailaddr
 532 | 532	emerg
 533 | 533	empir
 534 | 534	employ
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 539 | 539	encourag
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 554 | 554	entri
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 565 | 565	estim
 566 | 566	et
 567 | 567	etc
 568 | 568	euro
 569 | 569	europ
 570 | 570	european
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 575 | 575	everi
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 577 | 577	everyth
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 582 | 582	excel
 583 | 583	except
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 586 | 586	exclus
 587 | 587	execut
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 590 | 590	exmh
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 592 | 592	expect
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 594 | 594	experi
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 596 | 596	expir
 597 | 597	explain
 598 | 598	explor
 599 | 599	express
 600 | 600	extend
 601 | 601	extens
 602 | 602	extra
 603 | 603	extract
 604 | 604	extrem
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 606 | 606	fa
 607 | 607	face
 608 | 608	fact
 609 | 609	factor
 610 | 610	fail
 611 | 611	fair
 612 | 612	fall
 613 | 613	fals
 614 | 614	famili
 615 | 615	faq
 616 | 616	far
 617 | 617	fast
 618 | 618	faster
 619 | 619	fastest
 620 | 620	fat
 621 | 621	father
 622 | 622	favorit
 623 | 623	fax
 624 | 624	fb
 625 | 625	fd
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 630 | 630	feedback
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 657 | 657	flash
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 667 | 667	forc
 668 | 668	foreign
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 680 | 680	franc
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 684 | 684	freshrpm
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 686 | 686	fridai
 687 | 687	friend
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 690 | 690	ftoc
 691 | 691	ftp
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 693 | 693	fulli
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 695 | 695	function
 696 | 696	fund
 697 | 697	further
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 700 | 700	gain
 701 | 701	game
 702 | 702	gari
 703 | 703	garrigu
 704 | 704	gave
 705 | 705	gcc
 706 | 706	geek
 707 | 707	gener
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 710 | 710	gift
 711 | 711	girl
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 713 | 713	given
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 716 | 716	gnu
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 720 | 720	god
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 726 | 726	got
 727 | 727	govern
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 735 | 735	group
 736 | 736	grow
 737 | 737	growth
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 744 | 744	hack
 745 | 745	had
 746 | 746	half
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 748 | 748	hand
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 755 | 755	hate
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 768 | 768	height
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 794 | 794	how
 795 | 795	howev
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 797 | 797	html
 798 | 798	http
 799 | 799	httpaddr
 800 | 800	huge
 801 | 801	human
 802 | 802	hundr
 803 | 803	ibm
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 805 | 805	idea
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 819 | 819	immedi
 820 | 820	impact
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 863 | 863	internet
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 897 | 897	judgment
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 911 | 911	kill
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 913 | 913	king
 914 | 914	kingdom
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 917 | 917	knowledg
 918 | 918	known
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 920 | 920	lack
 921 | 921	land
 922 | 922	languag
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 924 | 924	larg
 925 | 925	larger
 926 | 926	largest
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 936 | 936	lead
 937 | 937	leader
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 960 | 960	linux
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 962 | 962	listen
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 964 | 964	live
 965 | 965	ll
 966 | 966	lo
 967 | 967	load
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 970 | 970	locat
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 972 | 972	lockergnom
 973 | 973	log
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 975 | 975	longer
 976 | 976	look
 977 | 977	lose
 978 | 978	loss
 979 | 979	lost
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 981 | 981	love
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 983 | 983	lower
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 987 | 987	mac
 988 | 988	machin
 989 | 989	made
 990 | 990	magazin
 991 | 991	mai
 992 | 992	mail
 993 | 993	mailer
 994 | 994	main
 995 | 995	maintain
 996 | 996	major
 997 | 997	make
 998 | 998	maker
 999 | 999	male
1000 | 1000	man
1001 | 1001	manag
1002 | 1002	mani
1003 | 1003	manual
1004 | 1004	manufactur
1005 | 1005	map
1006 | 1006	march
1007 | 1007	margin
1008 | 1008	mark
1009 | 1009	market
1010 | 1010	marshal
1011 | 1011	mass
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1013 | 1013	match
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1015 | 1015	matter
1016 | 1016	matthia
1017 | 1017	mayb
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1019 | 1019	mean
1020 | 1020	measur
1021 | 1021	mechan
1022 | 1022	media
1023 | 1023	medic
1024 | 1024	meet
1025 | 1025	member
1026 | 1026	membership
1027 | 1027	memori
1028 | 1028	men
1029 | 1029	mention
1030 | 1030	menu
1031 | 1031	merchant
1032 | 1032	messag
1033 | 1033	method
1034 | 1034	mh
1035 | 1035	michael
1036 | 1036	microsoft
1037 | 1037	middl
1038 | 1038	might
1039 | 1039	mike
1040 | 1040	mile
1041 | 1041	militari
1042 | 1042	million
1043 | 1043	mime
1044 | 1044	mind
1045 | 1045	mine
1046 | 1046	mini
1047 | 1047	minimum
1048 | 1048	minut
1049 | 1049	miss
1050 | 1050	mistak
1051 | 1051	mobil
1052 | 1052	mode
1053 | 1053	model
1054 | 1054	modem
1055 | 1055	modifi
1056 | 1056	modul
1057 | 1057	moment
1058 | 1058	mon
1059 | 1059	mondai
1060 | 1060	monei
1061 | 1061	monitor
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1064 | 1064	more
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1066 | 1066	mortgag
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1068 | 1068	mostli
1069 | 1069	mother
1070 | 1070	motiv
1071 | 1071	move
1072 | 1072	movi
1073 | 1073	mpnumber
1074 | 1074	mr
1075 | 1075	ms
1076 | 1076	msg
1077 | 1077	much
1078 | 1078	multi
1079 | 1079	multipart
1080 | 1080	multipl
1081 | 1081	murphi
1082 | 1082	music
1083 | 1083	must
1084 | 1084	my
1085 | 1085	myself
1086 | 1086	name
1087 | 1087	nation
1088 | 1088	natur
1089 | 1089	nbsp
1090 | 1090	near
1091 | 1091	nearli
1092 | 1092	necessari
1093 | 1093	need
1094 | 1094	neg
1095 | 1095	net
1096 | 1096	netscap
1097 | 1097	network
1098 | 1098	never
1099 | 1099	new
1100 | 1100	newslett
1101 | 1101	next
1102 | 1102	nextpart
1103 | 1103	nice
1104 | 1104	nigeria
1105 | 1105	night
1106 | 1106	no
1107 | 1107	nobodi
1108 | 1108	non
1109 | 1109	none
1110 | 1110	nor
1111 | 1111	normal
1112 | 1112	north
1113 | 1113	not
1114 | 1114	note
1115 | 1115	noth
1116 | 1116	notic
1117 | 1117	now
1118 | 1118	nt
1119 | 1119	null
1120 | 1120	number
1121 | 1121	numbera
1122 | 1122	numberam
1123 | 1123	numberanumb
1124 | 1124	numberb
1125 | 1125	numberbit
1126 | 1126	numberc
1127 | 1127	numbercb
1128 | 1128	numbercbr
1129 | 1129	numbercfont
1130 | 1130	numbercli
1131 | 1131	numbercnumb
1132 | 1132	numbercp
1133 | 1133	numberctd
1134 | 1134	numberd
1135 | 1135	numberdari
1136 | 1136	numberdnumb
1137 | 1137	numberenumb
1138 | 1138	numberf
1139 | 1139	numberfb
1140 | 1140	numberff
1141 | 1141	numberffont
1142 | 1142	numberfp
1143 | 1143	numberftd
1144 | 1144	numberk
1145 | 1145	numberm
1146 | 1146	numbermb
1147 | 1147	numberp
1148 | 1148	numberpd
1149 | 1149	numberpm
1150 | 1150	numberpx
1151 | 1151	numberst
1152 | 1152	numberth
1153 | 1153	numbertnumb
1154 | 1154	numberx
1155 | 1155	object
1156 | 1156	oblig
1157 | 1157	obtain
1158 | 1158	obvious
1159 | 1159	occur
1160 | 1160	oct
1161 | 1161	octob
1162 | 1162	of
1163 | 1163	off
1164 | 1164	offer
1165 | 1165	offic
1166 | 1166	offici
1167 | 1167	often
1168 | 1168	oh
1169 | 1169	ok
1170 | 1170	old
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1172 | 1172	onc
1173 | 1173	onli
1174 | 1174	onlin
1175 | 1175	open
1176 | 1176	oper
1177 | 1177	opinion
1178 | 1178	opportun
1179 | 1179	opt
1180 | 1180	optim
1181 | 1181	option
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1183 | 1183	order
1184 | 1184	org
1185 | 1185	organ
1186 | 1186	origin
1187 | 1187	os
1188 | 1188	osdn
1189 | 1189	other
1190 | 1190	otherwis
1191 | 1191	our
1192 | 1192	out
1193 | 1193	outlook
1194 | 1194	output
1195 | 1195	outsid
1196 | 1196	over
1197 | 1197	own
1198 | 1198	owner
1199 | 1199	oz
1200 | 1200	pacif
1201 | 1201	pack
1202 | 1202	packag
1203 | 1203	page
1204 | 1204	pai
1205 | 1205	paid
1206 | 1206	pain
1207 | 1207	palm
1208 | 1208	panel
1209 | 1209	paper
1210 | 1210	paragraph
1211 | 1211	parent
1212 | 1212	part
1213 | 1213	parti
1214 | 1214	particip
1215 | 1215	particular
1216 | 1216	particularli
1217 | 1217	partit
1218 | 1218	partner
1219 | 1219	pass
1220 | 1220	password
1221 | 1221	past
1222 | 1222	patch
1223 | 1223	patent
1224 | 1224	path
1225 | 1225	pattern
1226 | 1226	paul
1227 | 1227	payment
1228 | 1228	pc
1229 | 1229	peac
1230 | 1230	peopl
1231 | 1231	per
1232 | 1232	percent
1233 | 1233	percentag
1234 | 1234	perfect
1235 | 1235	perfectli
1236 | 1236	perform
1237 | 1237	perhap
1238 | 1238	period
1239 | 1239	perl
1240 | 1240	perman
1241 | 1241	permiss
1242 | 1242	person
1243 | 1243	pgp
1244 | 1244	phone
1245 | 1245	photo
1246 | 1246	php
1247 | 1247	phrase
1248 | 1248	physic
1249 | 1249	pick
1250 | 1250	pictur
1251 | 1251	piec
1252 | 1252	piiiiiiii
1253 | 1253	pipe
1254 | 1254	pjnumber
1255 | 1255	place
1256 | 1256	plai
1257 | 1257	plain
1258 | 1258	plan
1259 | 1259	planet
1260 | 1260	plant
1261 | 1261	planta
1262 | 1262	platform
1263 | 1263	player
1264 | 1264	pleas
1265 | 1265	plu
1266 | 1266	plug
1267 | 1267	pm
1268 | 1268	pocket
1269 | 1269	point
1270 | 1270	polic
1271 | 1271	polici
1272 | 1272	polit
1273 | 1273	poor
1274 | 1274	pop
1275 | 1275	popul
1276 | 1276	popular
1277 | 1277	port
1278 | 1278	posit
1279 | 1279	possibl
1280 | 1280	post
1281 | 1281	potenti
1282 | 1282	pound
1283 | 1283	powel
1284 | 1284	power
1285 | 1285	powershot
1286 | 1286	practic
1287 | 1287	pre
1288 | 1288	predict
1289 | 1289	prefer
1290 | 1290	premium
1291 | 1291	prepar
1292 | 1292	present
1293 | 1293	presid
1294 | 1294	press
1295 | 1295	pretti
1296 | 1296	prevent
1297 | 1297	previou
1298 | 1298	previous
1299 | 1299	price
1300 | 1300	principl
1301 | 1301	print
1302 | 1302	printabl
1303 | 1303	printer
1304 | 1304	privaci
1305 | 1305	privat
1306 | 1306	prize
1307 | 1307	pro
1308 | 1308	probabl
1309 | 1309	problem
1310 | 1310	procedur
1311 | 1311	process
1312 | 1312	processor
1313 | 1313	procmail
1314 | 1314	produc
1315 | 1315	product
1316 | 1316	profession
1317 | 1317	profil
1318 | 1318	profit
1319 | 1319	program
1320 | 1320	programm
1321 | 1321	progress
1322 | 1322	project
1323 | 1323	promis
1324 | 1324	promot
1325 | 1325	prompt
1326 | 1326	properti
1327 | 1327	propos
1328 | 1328	proprietari
1329 | 1329	prospect
1330 | 1330	protect
1331 | 1331	protocol
1332 | 1332	prove
1333 | 1333	proven
1334 | 1334	provid
1335 | 1335	proxi
1336 | 1336	pub
1337 | 1337	public
1338 | 1338	publish
1339 | 1339	pudg
1340 | 1340	pull
1341 | 1341	purchas
1342 | 1342	purpos
1343 | 1343	put
1344 | 1344	python
1345 | 1345	qnumber
1346 | 1346	qualifi
1347 | 1347	qualiti
1348 | 1348	quarter
1349 | 1349	question
1350 | 1350	quick
1351 | 1351	quickli
1352 | 1352	quit
1353 | 1353	quot
1354 | 1354	radio
1355 | 1355	ragga
1356 | 1356	rais
1357 | 1357	random
1358 | 1358	rang
1359 | 1359	rate
1360 | 1360	rather
1361 | 1361	ratio
1362 | 1362	razor
1363 | 1363	razornumb
1364 | 1364	re
1365 | 1365	reach
1366 | 1366	read
1367 | 1367	reader
1368 | 1368	readi
1369 | 1369	real
1370 | 1370	realiz
1371 | 1371	realli
1372 | 1372	reason
1373 | 1373	receiv
1374 | 1374	recent
1375 | 1375	recipi
1376 | 1376	recommend
1377 | 1377	record
1378 | 1378	red
1379 | 1379	redhat
1380 | 1380	reduc
1381 | 1381	refer
1382 | 1382	refin
1383 | 1383	reg
1384 | 1384	regard
1385 | 1385	region
1386 | 1386	regist
1387 | 1387	regul
1388 | 1388	regular
1389 | 1389	rel
1390 | 1390	relat
1391 | 1391	relationship
1392 | 1392	releas
1393 | 1393	relev
1394 | 1394	reliabl
1395 | 1395	remain
1396 | 1396	rememb
1397 | 1397	remot
1398 | 1398	remov
1399 | 1399	replac
1400 | 1400	repli
1401 | 1401	report
1402 | 1402	repositori
1403 | 1403	repres
1404 | 1404	republ
1405 | 1405	request
1406 | 1406	requir
1407 | 1407	research
1408 | 1408	reserv
1409 | 1409	resid
1410 | 1410	resourc
1411 | 1411	respect
1412 | 1412	respond
1413 | 1413	respons
1414 | 1414	rest
1415 | 1415	result
1416 | 1416	retail
1417 | 1417	return
1418 | 1418	reveal
1419 | 1419	revenu
1420 | 1420	revers
1421 | 1421	review
1422 | 1422	revok
1423 | 1423	rh
1424 | 1424	rich
1425 | 1425	right
1426 | 1426	risk
1427 | 1427	road
1428 | 1428	robert
1429 | 1429	rock
1430 | 1430	role
1431 | 1431	roll
1432 | 1432	rom
1433 | 1433	roman
1434 | 1434	room
1435 | 1435	root
1436 | 1436	round
1437 | 1437	rpm
1438 | 1438	rss
1439 | 1439	rule
1440 | 1440	run
1441 | 1441	sa
1442 | 1442	safe
1443 | 1443	sai
1444 | 1444	said
1445 | 1445	sale
1446 | 1446	same
1447 | 1447	sampl
1448 | 1448	san
1449 | 1449	saou
1450 | 1450	sat
1451 | 1451	satellit
1452 | 1452	save
1453 | 1453	saw
1454 | 1454	scan
1455 | 1455	schedul
1456 | 1456	school
1457 | 1457	scienc
1458 | 1458	score
1459 | 1459	screen
1460 | 1460	script
1461 | 1461	se
1462 | 1462	search
1463 | 1463	season
1464 | 1464	second
1465 | 1465	secret
1466 | 1466	section
1467 | 1467	secur
1468 | 1468	see
1469 | 1469	seed
1470 | 1470	seek
1471 | 1471	seem
1472 | 1472	seen
1473 | 1473	select
1474 | 1474	self
1475 | 1475	sell
1476 | 1476	seminar
1477 | 1477	send
1478 | 1478	sender
1479 | 1479	sendmail
1480 | 1480	senior
1481 | 1481	sens
1482 | 1482	sensit
1483 | 1483	sent
1484 | 1484	sep
1485 | 1485	separ
1486 | 1486	septemb
1487 | 1487	sequenc
1488 | 1488	seri
1489 | 1489	serif
1490 | 1490	seriou
1491 | 1491	serv
1492 | 1492	server
1493 | 1493	servic
1494 | 1494	set
1495 | 1495	setup
1496 | 1496	seven
1497 | 1497	seventh
1498 | 1498	sever
1499 | 1499	sex
1500 | 1500	sexual
1501 | 1501	sf
1502 | 1502	shape
1503 | 1503	share
1504 | 1504	she
1505 | 1505	shell
1506 | 1506	ship
1507 | 1507	shop
1508 | 1508	short
1509 | 1509	shot
1510 | 1510	should
1511 | 1511	show
1512 | 1512	side
1513 | 1513	sign
1514 | 1514	signatur
1515 | 1515	signific
1516 | 1516	similar
1517 | 1517	simpl
1518 | 1518	simpli
1519 | 1519	sinc
1520 | 1520	sincer
1521 | 1521	singl
1522 | 1522	sit
1523 | 1523	site
1524 | 1524	situat
1525 | 1525	six
1526 | 1526	size
1527 | 1527	skeptic
1528 | 1528	skill
1529 | 1529	skin
1530 | 1530	skip
1531 | 1531	sleep
1532 | 1532	slow
1533 | 1533	small
1534 | 1534	smart
1535 | 1535	smoke
1536 | 1536	smtp
1537 | 1537	snumber
1538 | 1538	so
1539 | 1539	social
1540 | 1540	societi
1541 | 1541	softwar
1542 | 1542	sold
1543 | 1543	solut
1544 | 1544	solv
1545 | 1545	some
1546 | 1546	someon
1547 | 1547	someth
1548 | 1548	sometim
1549 | 1549	son
1550 | 1550	song
1551 | 1551	soni
1552 | 1552	soon
1553 | 1553	sorri
1554 | 1554	sort
1555 | 1555	sound
1556 | 1556	sourc
1557 | 1557	south
1558 | 1558	space
1559 | 1559	spain
1560 | 1560	spam
1561 | 1561	spamassassin
1562 | 1562	spamd
1563 | 1563	spammer
1564 | 1564	speak
1565 | 1565	spec
1566 | 1566	special
1567 | 1567	specif
1568 | 1568	specifi
1569 | 1569	speech
1570 | 1570	speed
1571 | 1571	spend
1572 | 1572	sponsor
1573 | 1573	sport
1574 | 1574	spot
1575 | 1575	src
1576 | 1576	ssh
1577 | 1577	st
1578 | 1578	stabl
1579 | 1579	staff
1580 | 1580	stai
1581 | 1581	stand
1582 | 1582	standard
1583 | 1583	star
1584 | 1584	start
1585 | 1585	state
1586 | 1586	statement
1587 | 1587	statu
1588 | 1588	step
1589 | 1589	steve
1590 | 1590	still
1591 | 1591	stock
1592 | 1592	stop
1593 | 1593	storag
1594 | 1594	store
1595 | 1595	stori
1596 | 1596	strategi
1597 | 1597	stream
1598 | 1598	street
1599 | 1599	string
1600 | 1600	strip
1601 | 1601	strong
1602 | 1602	structur
1603 | 1603	studi
1604 | 1604	stuff
1605 | 1605	stupid
1606 | 1606	style
1607 | 1607	subject
1608 | 1608	submit
1609 | 1609	subscrib
1610 | 1610	subscript
1611 | 1611	substanti
1612 | 1612	success
1613 | 1613	such
1614 | 1614	suffer
1615 | 1615	suggest
1616 | 1616	suit
1617 | 1617	sum
1618 | 1618	summari
1619 | 1619	summer
1620 | 1620	sun
1621 | 1621	super
1622 | 1622	suppli
1623 | 1623	support
1624 | 1624	suppos
1625 | 1625	sure
1626 | 1626	surpris
1627 | 1627	suse
1628 | 1628	suspect
1629 | 1629	sweet
1630 | 1630	switch
1631 | 1631	system
1632 | 1632	tab
1633 | 1633	tabl
1634 | 1634	tablet
1635 | 1635	tag
1636 | 1636	take
1637 | 1637	taken
1638 | 1638	talk
1639 | 1639	tape
1640 | 1640	target
1641 | 1641	task
1642 | 1642	tax
1643 | 1643	teach
1644 | 1644	team
1645 | 1645	tech
1646 | 1646	technic
1647 | 1647	techniqu
1648 | 1648	technolog
1649 | 1649	tel
1650 | 1650	telecom
1651 | 1651	telephon
1652 | 1652	tell
1653 | 1653	temperatur
1654 | 1654	templ
1655 | 1655	ten
1656 | 1656	term
1657 | 1657	termin
1658 | 1658	terror
1659 | 1659	terrorist
1660 | 1660	test
1661 | 1661	texa
1662 | 1662	text
1663 | 1663	than
1664 | 1664	thank
1665 | 1665	that
1666 | 1666	the
1667 | 1667	thei
1668 | 1668	their
1669 | 1669	them
1670 | 1670	themselv
1671 | 1671	then
1672 | 1672	theori
1673 | 1673	there
1674 | 1674	therefor
1675 | 1675	these
1676 | 1676	thi
1677 | 1677	thing
1678 | 1678	think
1679 | 1679	thinkgeek
1680 | 1680	third
1681 | 1681	those
1682 | 1682	though
1683 | 1683	thought
1684 | 1684	thousand
1685 | 1685	thread
1686 | 1686	threat
1687 | 1687	three
1688 | 1688	through
1689 | 1689	thu
1690 | 1690	thursdai
1691 | 1691	ti
1692 | 1692	ticket
1693 | 1693	tim
1694 | 1694	time
1695 | 1695	tip
1696 | 1696	tire
1697 | 1697	titl
1698 | 1698	tm
1699 | 1699	to
1700 | 1700	todai
1701 | 1701	togeth
1702 | 1702	token
1703 | 1703	told
1704 | 1704	toll
1705 | 1705	tom
1706 | 1706	toner
1707 | 1707	toni
1708 | 1708	too
1709 | 1709	took
1710 | 1710	tool
1711 | 1711	top
1712 | 1712	topic
1713 | 1713	total
1714 | 1714	touch
1715 | 1715	toward
1716 | 1716	track
1717 | 1717	trade
1718 | 1718	tradit
1719 | 1719	traffic
1720 | 1720	train
1721 | 1721	transact
1722 | 1722	transfer
1723 | 1723	travel
1724 | 1724	treat
1725 | 1725	tree
1726 | 1726	tri
1727 | 1727	trial
1728 | 1728	trick
1729 | 1729	trip
1730 | 1730	troubl
1731 | 1731	true
1732 | 1732	truli
1733 | 1733	trust
1734 | 1734	truth
1735 | 1735	try
1736 | 1736	tue
1737 | 1737	tuesdai
1738 | 1738	turn
1739 | 1739	tv
1740 | 1740	two
1741 | 1741	type
1742 | 1742	uk
1743 | 1743	ultim
1744 | 1744	un
1745 | 1745	under
1746 | 1746	understand
1747 | 1747	unfortun
1748 | 1748	uniqu
1749 | 1749	unison
1750 | 1750	unit
1751 | 1751	univers
1752 | 1752	unix
1753 | 1753	unless
1754 | 1754	unlik
1755 | 1755	unlimit
1756 | 1756	unseen
1757 | 1757	unsolicit
1758 | 1758	unsubscrib
1759 | 1759	until
1760 | 1760	up
1761 | 1761	updat
1762 | 1762	upgrad
1763 | 1763	upon
1764 | 1764	urgent
1765 | 1765	url
1766 | 1766	us
1767 | 1767	usa
1768 | 1768	usag
1769 | 1769	usb
1770 | 1770	usd
1771 | 1771	usdollarnumb
1772 | 1772	useless
1773 | 1773	user
1774 | 1774	usr
1775 | 1775	usual
1776 | 1776	util
1777 | 1777	vacat
1778 | 1778	valid
1779 | 1779	valu
1780 | 1780	valuabl
1781 | 1781	var
1782 | 1782	variabl
1783 | 1783	varieti
1784 | 1784	variou
1785 | 1785	ve
1786 | 1786	vendor
1787 | 1787	ventur
1788 | 1788	veri
1789 | 1789	verifi
1790 | 1790	version
1791 | 1791	via
1792 | 1792	video
1793 | 1793	view
1794 | 1794	virtual
1795 | 1795	visa
1796 | 1796	visit
1797 | 1797	visual
1798 | 1798	vnumber
1799 | 1799	voic
1800 | 1800	vote
1801 | 1801	vs
1802 | 1802	vulner
1803 | 1803	wa
1804 | 1804	wai
1805 | 1805	wait
1806 | 1806	wake
1807 | 1807	walk
1808 | 1808	wall
1809 | 1809	want
1810 | 1810	war
1811 | 1811	warm
1812 | 1812	warn
1813 | 1813	warranti
1814 | 1814	washington
1815 | 1815	wasn
1816 | 1816	wast
1817 | 1817	watch
1818 | 1818	water
1819 | 1819	we
1820 | 1820	wealth
1821 | 1821	weapon
1822 | 1822	web
1823 | 1823	weblog
1824 | 1824	websit
1825 | 1825	wed
1826 | 1826	wednesdai
1827 | 1827	week
1828 | 1828	weekli
1829 | 1829	weight
1830 | 1830	welcom
1831 | 1831	well
1832 | 1832	went
1833 | 1833	were
1834 | 1834	west
1835 | 1835	what
1836 | 1836	whatev
1837 | 1837	when
1838 | 1838	where
1839 | 1839	whether
1840 | 1840	which
1841 | 1841	while
1842 | 1842	white
1843 | 1843	whitelist
1844 | 1844	who
1845 | 1845	whole
1846 | 1846	whose
1847 | 1847	why
1848 | 1848	wi
1849 | 1849	wide
1850 | 1850	width
1851 | 1851	wife
1852 | 1852	will
1853 | 1853	william
1854 | 1854	win
1855 | 1855	window
1856 | 1856	wing
1857 | 1857	winner
1858 | 1858	wireless
1859 | 1859	wish
1860 | 1860	with
1861 | 1861	within
1862 | 1862	without
1863 | 1863	wnumberp
1864 | 1864	woman
1865 | 1865	women
1866 | 1866	won
1867 | 1867	wonder
1868 | 1868	word
1869 | 1869	work
1870 | 1870	worker
1871 | 1871	world
1872 | 1872	worldwid
1873 | 1873	worri
1874 | 1874	worst
1875 | 1875	worth
1876 | 1876	would
1877 | 1877	wouldn
1878 | 1878	write
1879 | 1879	written
1880 | 1880	wrong
1881 | 1881	wrote
1882 | 1882	www
1883 | 1883	ximian
1884 | 1884	xml
1885 | 1885	xp
1886 | 1886	yahoo
1887 | 1887	ye
1888 | 1888	yeah
1889 | 1889	year
1890 | 1890	yesterdai
1891 | 1891	yet
1892 | 1892	york
1893 | 1893	you
1894 | 1894	young
1895 | 1895	your
1896 | 1896	yourself
1897 | 1897	zdnet
1898 | 1898	zero
1899 | 1899	zip
1900 | 


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