asl_recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Artificial Intelligence Engineer Nanodegree - Probabilistic Models\n",
    "## Project: Sign Language Recognition System\n",
    "- [Introduction](#intro)\n",
    "- [Part 1 Feature Selection](#part1_tutorial)\n",
    "    - [Tutorial](#part1_tutorial)\n",
    "    - [Features Submission](#part1_submission)\n",
    "    - [Features Unittest](#part1_test)\n",
    "- [Part 2 Train the models](#part2_tutorial)\n",
    "    - [Tutorial](#part2_tutorial)\n",
    "    - [Model Selection Score Submission](#part2_submission)\n",
    "    - [Model Score Unittest](#part2_test)\n",
    "- [Part 3 Build a Recognizer](#part3_tutorial)\n",
    "    - [Tutorial](#part3_tutorial)\n",
    "    - [Recognizer Submission](#part3_submission)\n",
    "    - [Recognizer Unittest](#part3_test)\n",
    "- [Part 4 (OPTIONAL) Improve the WER with Language Models](#part4_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "The overall goal of this project is to build a word recognizer for American Sign Language video sequences, demonstrating the power of probabalistic models.  In particular, this project employs  [hidden Markov models (HMM's)](https://en.wikipedia.org/wiki/Hidden_Markov_model) to analyze a series of measurements taken from videos of American Sign Language (ASL) collected for research (see the [RWTH-BOSTON-104 Database](http://www-i6.informatik.rwth-aachen.de/~dreuw/database-rwth-boston-104.php)).  In this video, the right-hand x and y locations are plotted as the speaker signs the sentence.\n",
    "[![ASLR demo](http://www-i6.informatik.rwth-aachen.de/~dreuw/images/demosample.png)](https://drive.google.com/open?id=0B_5qGuFe-wbhUXRuVnNZVnMtam8)\n",
    "\n",
    "The raw data, train, and test sets are pre-defined.  You will derive a variety of feature sets (explored in Part 1), as well as implement three different model selection criterion to determine the optimal number of hidden states for each word model (explored in Part 2). Finally, in Part 3 you will implement the recognizer and compare the effects the different combinations of feature sets and model selection criteria.  \n",
    "\n",
    "At the end of each Part, complete the submission cells with implementations, answer all questions, and pass the unit tests.  Then submit the completed notebook for review!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part1_tutorial'></a>\n",
    "## PART 1: Data\n",
    "\n",
    "### Features Tutorial\n",
    "##### Load the initial database\n",
    "A data handler designed for this database is provided in the student codebase as the `AslDb` class in the `asl_data` module.  This handler creates the initial [pandas](http://pandas.pydata.org/pandas-docs/stable/) dataframe from the corpus of data included in the `data` directory as well as dictionaries suitable for extracting data in a format friendly to the [hmmlearn](https://hmmlearn.readthedocs.io/en/latest/) library.  We'll use those to create models in Part 2.\n",
    "\n",
    "To start, let's set up the initial database and select an example set of features for the training set.  At the end of Part 1, you will create additional feature sets for experimentation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>left-x</th>\n",
       "      <th>left-y</th>\n",
       "      <th>right-x</th>\n",
       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>speaker</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video</th>\n",
       "      <th>frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">98</th>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             left-x  left-y  right-x  right-y  nose-x  nose-y  speaker\n",
       "video frame                                                           \n",
       "98    0         149     181      170      175     161      62  woman-1\n",
       "      1         149     181      170      175     161      62  woman-1\n",
       "      2         149     181      170      175     161      62  woman-1\n",
       "      3         149     181      170      175     161      62  woman-1\n",
       "      4         149     181      170      175     161      62  woman-1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from asl_data import AslDb\n",
    "\n",
    "\n",
    "asl = AslDb() # initializes the database\n",
    "asl.df.head() # displays the first five rows of the asl database, indexed by video and frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left-x         149\n",
       "left-y         181\n",
       "right-x        170\n",
       "right-y        175\n",
       "nose-x         161\n",
       "nose-y          62\n",
       "speaker    woman-1\n",
       "Name: (98, 1), dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asl.df.ix[98,1]  # look at the data available for an individual frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The frame represented by video 98, frame 1 is shown here:\n",
    "![Video 98](http://www-i6.informatik.rwth-aachen.de/~dreuw/database/rwth-boston-104/overview/images/orig/098-start.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Feature selection for training the model\n",
    "The objective of feature selection when training a model is to choose the most relevant variables while keeping the model as simple as possible, thus reducing training time.  We can use the raw features already provided or derive our own and add columns to the pandas dataframe `asl.df` for selection. As an example, in the next cell a feature named `'grnd-ry'` is added. This feature is the difference between the right-hand y value and the nose y value, which serves as the \"ground\" right y value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>left-x</th>\n",
       "      <th>left-y</th>\n",
       "      <th>right-x</th>\n",
       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>speaker</th>\n",
       "      <th>grnd-ry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video</th>\n",
       "      <th>frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">98</th>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             left-x  left-y  right-x  right-y  nose-x  nose-y  speaker  \\\n",
       "video frame                                                              \n",
       "98    0         149     181      170      175     161      62  woman-1   \n",
       "      1         149     181      170      175     161      62  woman-1   \n",
       "      2         149     181      170      175     161      62  woman-1   \n",
       "      3         149     181      170      175     161      62  woman-1   \n",
       "      4         149     181      170      175     161      62  woman-1   \n",
       "\n",
       "             grnd-ry  \n",
       "video frame           \n",
       "98    0          113  \n",
       "      1          113  \n",
       "      2          113  \n",
       "      3          113  \n",
       "      4          113  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asl.df['grnd-ry'] = asl.df['right-y'] - asl.df['nose-y']\n",
    "asl.df.head()  # the new feature 'grnd-ry' is now in the frames dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Try it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asl.df sample\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>left-x</th>\n",
       "      <th>left-y</th>\n",
       "      <th>right-x</th>\n",
       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>speaker</th>\n",
       "      <th>grnd-ry</th>\n",
       "      <th>grnd-rx</th>\n",
       "      <th>grnd-ly</th>\n",
       "      <th>grnd-lx</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video</th>\n",
       "      <th>frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">98</th>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             left-x  left-y  right-x  right-y  nose-x  nose-y  speaker  \\\n",
       "video frame                                                              \n",
       "98    0         149     181      170      175     161      62  woman-1   \n",
       "      1         149     181      170      175     161      62  woman-1   \n",
       "      2         149     181      170      175     161      62  woman-1   \n",
       "      3         149     181      170      175     161      62  woman-1   \n",
       "      4         149     181      170      175     161      62  woman-1   \n",
       "\n",
       "             grnd-ry  grnd-rx  grnd-ly  grnd-lx  \n",
       "video frame                                      \n",
       "98    0          113        9      119      -12  \n",
       "      1          113        9      119      -12  \n",
       "      2          113        9      119      -12  \n",
       "      3          113        9      119      -12  \n",
       "      4          113        9      119      -12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<font color=green>Correct!</font><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from asl_utils import test_features_tryit\n",
    "# TODO add df columns for 'grnd-rx', 'grnd-ly', 'grnd-lx' representing differences between hand and nose locations\n",
    "asl.df['grnd-rx'] = asl.df['right-x'] - asl.df['nose-x']\n",
    "asl.df['grnd-ly'] = asl.df['left-y'] - asl.df['nose-y']\n",
    "asl.df['grnd-lx'] = asl.df['left-x'] - asl.df['nose-x']\n",
    "# test the code\n",
    "test_features_tryit(asl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 113, -12, 119]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# collect the features into a list\n",
    "features_ground = ['grnd-rx','grnd-ry','grnd-lx','grnd-ly']\n",
    " #show a single set of features for a given (video, frame) tuple\n",
    "[asl.df.ix[98,1][v] for v in features_ground]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Build the training set\n",
    "Now that we have a feature list defined, we can pass that list to the `build_training` method to collect the features for all the words in the training set.  Each word in the training set has multiple examples from various videos.  Below we can see the unique words that have been loaded into the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training words: ['JOHN', 'WRITE', 'HOMEWORK', 'IX-1P', 'SEE', 'YESTERDAY', 'IX', 'LOVE', 'MARY', 'CAN', 'GO', 'GO1', 'FUTURE', 'GO2', 'PARTY', 'FUTURE1', 'HIT', 'BLAME', 'FRED', 'FISH', 'WONT', 'EAT', 'BUT', 'CHICKEN', 'VEGETABLE', 'CHINA', 'PEOPLE', 'PREFER', 'BROCCOLI', 'LIKE', 'LEAVE', 'SAY', 'BUY', 'HOUSE', 'KNOW', 'CORN', 'CORN1', 'THINK', 'NOT', 'PAST', 'LIVE', 'CHICAGO', 'CAR', 'SHOULD', 'DECIDE', 'VISIT', 'MOVIE', 'WANT', 'SELL', 'TOMORROW', 'NEXT-WEEK', 'NEW-YORK', 'LAST-WEEK', 'WILL', 'FINISH', 'ANN', 'READ', 'BOOK', 'CHOCOLATE', 'FIND', 'SOMETHING-ONE', 'POSS', 'BROTHER', 'ARRIVE', 'HERE', 'GIVE', 'MAN', 'NEW', 'COAT', 'WOMAN', 'GIVE1', 'HAVE', 'FRANK', 'BREAK-DOWN', 'SEARCH-FOR', 'WHO', 'WHAT', 'LEG', 'FRIEND', 'CANDY', 'BLUE', 'SUE', 'BUY1', 'STOLEN', 'OLD', 'STUDENT', 'VIDEOTAPE', 'BORROW', 'MOTHER', 'POTATO', 'TELL', 'BILL', 'THROW', 'APPLE', 'NAME', 'SHOOT', 'SAY-1P', 'SELF', 'GROUP', 'JANA', 'TOY1', 'MANY', 'TOY', 'ALL', 'BOY', 'TEACHER', 'GIRL', 'BOX', 'GIVE2', 'GIVE3', 'GET', 'PUTASIDE']\n"
     ]
    }
   ],
   "source": [
    "training = asl.build_training(features_ground)\n",
    "print(\"Training words: {}\".format(training.words))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training data in `training` is an object of class `WordsData` defined in the `asl_data` module.  in addition to the `words` list, data can be accessed with the `get_all_sequences`, `get_all_Xlengths`, `get_word_sequences`, and `get_word_Xlengths` methods. We need the `get_word_Xlengths` method to train multiple sequences with the `hmmlearn` library.  In the following example, notice that there are two lists; the first is a concatenation of all the sequences(the X portion) and the second is a list of the sequence lengths(the Lengths portion)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-11,  48,   7, 120],\n",
       "        [-11,  48,   8, 109],\n",
       "        [ -8,  49,  11,  98],\n",
       "        [ -7,  50,   7,  87],\n",
       "        [ -4,  54,   7,  77],\n",
       "        [ -4,  54,   6,  69],\n",
       "        [ -4,  54,   6,  69],\n",
       "        [-13,  52,   6,  69],\n",
       "        [-13,  52,   6,  69],\n",
       "        [ -8,  51,   6,  69],\n",
       "        [ -8,  51,   6,  69],\n",
       "        [ -8,  51,   6,  69],\n",
       "        [ -8,  51,   6,  69],\n",
       "        [ -8,  51,   6,  69],\n",
       "        [-10,  59,   7,  71],\n",
       "        [-15,  64,   9,  77],\n",
       "        [-17,  75,  13,  81],\n",
       "        [ -4,  48,  -4, 113],\n",
       "        [ -2,  53,  -4, 113],\n",
       "        [ -4,  55,   2,  98],\n",
       "        [ -4,  58,   2,  98],\n",
       "        [ -1,  59,   2,  89],\n",
       "        [ -1,  59,  -1,  84],\n",
       "        [ -1,  59,  -1,  84],\n",
       "        [ -7,  63,  -1,  84],\n",
       "        [ -7,  63,  -1,  84],\n",
       "        [ -7,  63,   3,  83],\n",
       "        [ -7,  63,   3,  83],\n",
       "        [ -7,  63,   3,  83],\n",
       "        [ -7,  63,   3,  83],\n",
       "        [ -7,  63,   3,  83],\n",
       "        [ -7,  63,   3,  83],\n",
       "        [ -7,  63,   3,  83],\n",
       "        [ -4,  70,   3,  83],\n",
       "        [ -4,  70,   3,  83],\n",
       "        [ -2,  73,   5,  90],\n",
       "        [ -3,  79,  -4,  96],\n",
       "        [-15,  98,  13, 135],\n",
       "        [ -6,  93,  12, 128],\n",
       "        [ -2,  89,  14, 118],\n",
       "        [  5,  90,  10, 108],\n",
       "        [  4,  86,   7, 105],\n",
       "        [  4,  86,   7, 105],\n",
       "        [  4,  86,  13, 100],\n",
       "        [ -3,  82,  14,  96],\n",
       "        [ -3,  82,  14,  96],\n",
       "        [  6,  89,  16, 100],\n",
       "        [  6,  89,  16, 100],\n",
       "        [  7,  85,  17, 111]]), [17, 20, 12])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.get_word_Xlengths('CHOCOLATE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### More feature sets\n",
    "So far we have a simple feature set that is enough to get started modeling.  However, we might get better results if we manipulate the raw values a bit more, so we will go ahead and set up some other options now for experimentation later.  For example, we could normalize each speaker's range of motion with grouped statistics using [Pandas stats](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-stats) functions and [pandas groupby](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html).  Below is an example for finding the means of all speaker subgroups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>left-x</th>\n",
       "      <th>left-y</th>\n",
       "      <th>right-x</th>\n",
       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>grnd-ry</th>\n",
       "      <th>grnd-rx</th>\n",
       "      <th>grnd-ly</th>\n",
       "      <th>grnd-lx</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speaker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>man-1</th>\n",
       "      <td>206.248203</td>\n",
       "      <td>218.679449</td>\n",
       "      <td>155.464350</td>\n",
       "      <td>150.371031</td>\n",
       "      <td>175.031756</td>\n",
       "      <td>61.642600</td>\n",
       "      <td>88.728430</td>\n",
       "      <td>-19.567406</td>\n",
       "      <td>157.036848</td>\n",
       "      <td>31.216447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>woman-1</th>\n",
       "      <td>164.661438</td>\n",
       "      <td>161.271242</td>\n",
       "      <td>151.017865</td>\n",
       "      <td>117.332462</td>\n",
       "      <td>162.655120</td>\n",
       "      <td>57.245098</td>\n",
       "      <td>60.087364</td>\n",
       "      <td>-11.637255</td>\n",
       "      <td>104.026144</td>\n",
       "      <td>2.006318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>woman-2</th>\n",
       "      <td>183.214509</td>\n",
       "      <td>176.527232</td>\n",
       "      <td>156.866295</td>\n",
       "      <td>119.835714</td>\n",
       "      <td>170.318973</td>\n",
       "      <td>58.022098</td>\n",
       "      <td>61.813616</td>\n",
       "      <td>-13.452679</td>\n",
       "      <td>118.505134</td>\n",
       "      <td>12.895536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             left-x      left-y     right-x     right-y      nose-x  \\\n",
       "speaker                                                               \n",
       "man-1    206.248203  218.679449  155.464350  150.371031  175.031756   \n",
       "woman-1  164.661438  161.271242  151.017865  117.332462  162.655120   \n",
       "woman-2  183.214509  176.527232  156.866295  119.835714  170.318973   \n",
       "\n",
       "            nose-y    grnd-ry    grnd-rx     grnd-ly    grnd-lx  \n",
       "speaker                                                          \n",
       "man-1    61.642600  88.728430 -19.567406  157.036848  31.216447  \n",
       "woman-1  57.245098  60.087364 -11.637255  104.026144   2.006318  \n",
       "woman-2  58.022098  61.813616 -13.452679  118.505134  12.895536  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_means = asl.df.groupby('speaker').mean()\n",
    "df_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select a mean that matches by speaker, use the pandas [map](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.map.html) method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>left-x</th>\n",
       "      <th>left-y</th>\n",
       "      <th>right-x</th>\n",
       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>speaker</th>\n",
       "      <th>grnd-ry</th>\n",
       "      <th>grnd-rx</th>\n",
       "      <th>grnd-ly</th>\n",
       "      <th>grnd-lx</th>\n",
       "      <th>left-x-mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video</th>\n",
       "      <th>frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">98</th>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "      <td>164.661438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "      <td>164.661438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "      <td>164.661438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "      <td>164.661438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>-12</td>\n",
       "      <td>164.661438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             left-x  left-y  right-x  right-y  nose-x  nose-y  speaker  \\\n",
       "video frame                                                              \n",
       "98    0         149     181      170      175     161      62  woman-1   \n",
       "      1         149     181      170      175     161      62  woman-1   \n",
       "      2         149     181      170      175     161      62  woman-1   \n",
       "      3         149     181      170      175     161      62  woman-1   \n",
       "      4         149     181      170      175     161      62  woman-1   \n",
       "\n",
       "             grnd-ry  grnd-rx  grnd-ly  grnd-lx  left-x-mean  \n",
       "video frame                                                   \n",
       "98    0          113        9      119      -12   164.661438  \n",
       "      1          113        9      119      -12   164.661438  \n",
       "      2          113        9      119      -12   164.661438  \n",
       "      3          113        9      119      -12   164.661438  \n",
       "      4          113        9      119      -12   164.661438  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asl.df['left-x-mean']= asl.df['speaker'].map(df_means['left-x'])\n",
    "asl.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Try it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_std\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>left-x</th>\n",
       "      <th>left-y</th>\n",
       "      <th>right-x</th>\n",
       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>grnd-ry</th>\n",
       "      <th>grnd-rx</th>\n",
       "      <th>grnd-ly</th>\n",
       "      <th>grnd-lx</th>\n",
       "      <th>left-x-mean</th>\n",
       "      <th>left-y-mean</th>\n",
       "      <th>right-x-mean</th>\n",
       "      <th>right-y-mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speaker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>man-1</th>\n",
       "      <td>15.154425</td>\n",
       "      <td>36.328485</td>\n",
       "      <td>18.901917</td>\n",
       "      <td>54.902340</td>\n",
       "      <td>6.654573</td>\n",
       "      <td>5.520045</td>\n",
       "      <td>53.487999</td>\n",
       "      <td>20.269032</td>\n",
       "      <td>36.572749</td>\n",
       "      <td>15.080360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>woman-1</th>\n",
       "      <td>17.573442</td>\n",
       "      <td>26.594521</td>\n",
       "      <td>16.459943</td>\n",
       "      <td>34.667787</td>\n",
       "      <td>3.549392</td>\n",
       "      <td>3.538330</td>\n",
       "      <td>33.972660</td>\n",
       "      <td>16.764706</td>\n",
       "      <td>27.117393</td>\n",
       "      <td>17.328941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>woman-2</th>\n",
       "      <td>15.388711</td>\n",
       "      <td>28.825025</td>\n",
       "      <td>14.890288</td>\n",
       "      <td>39.649111</td>\n",
       "      <td>4.099760</td>\n",
       "      <td>3.416167</td>\n",
       "      <td>39.128572</td>\n",
       "      <td>16.191324</td>\n",
       "      <td>29.320655</td>\n",
       "      <td>15.050938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            left-x     left-y    right-x    right-y    nose-x    nose-y  \\\n",
       "speaker                                                                   \n",
       "man-1    15.154425  36.328485  18.901917  54.902340  6.654573  5.520045   \n",
       "woman-1  17.573442  26.594521  16.459943  34.667787  3.549392  3.538330   \n",
       "woman-2  15.388711  28.825025  14.890288  39.649111  4.099760  3.416167   \n",
       "\n",
       "           grnd-ry    grnd-rx    grnd-ly    grnd-lx  left-x-mean  left-y-mean  \\\n",
       "speaker                                                                         \n",
       "man-1    53.487999  20.269032  36.572749  15.080360          0.0          0.0   \n",
       "woman-1  33.972660  16.764706  27.117393  17.328941          0.0          0.0   \n",
       "woman-2  39.128572  16.191324  29.320655  15.050938          0.0          0.0   \n",
       "\n",
       "         right-x-mean  right-y-mean  \n",
       "speaker                              \n",
       "man-1             0.0           0.0  \n",
       "woman-1           0.0           0.0  \n",
       "woman-2           0.0           0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<font color=green>Correct!</font><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from asl_utils import test_std_tryit\n",
    "# TODO Create a dataframe named `df_std` with standard deviations grouped by speaker\n",
    "asl.df['left-y-mean'] = asl.df['speaker'].map(df_means['left-y'])\n",
    "asl.df['right-x-mean'] = asl.df['speaker'].map(df_means['right-x'])\n",
    "asl.df['right-y-mean'] = asl.df['speaker'].map(df_means['right-y'])\n",
    "df_std = asl.df.groupby('speaker').std()\n",
    "# test the code\n",
    "asl.df['right-x-std'] = asl.df['speaker'].map(df_std['right-x'])\n",
    "asl.df['right-y-std'] = asl.df['speaker'].map(df_std['right-y'])\n",
    "asl.df['left-y-std'] = asl.df['speaker'].map(df_std['left-y'])\n",
    "asl.df['left-x-std'] = asl.df['speaker'].map(df_std['left-x'])\n",
    "test_std_tryit(df_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part1_submission'></a>\n",
    "### Features Implementation Submission\n",
    "Implement four feature sets and answer the question that follows.\n",
    "- normalized Cartesian coordinates\n",
    "    - use *mean* and *standard deviation* statistics and the [standard score](https://en.wikipedia.org/wiki/Standard_score) equation to account for speakers with different heights and arm length\n",
    "    \n",
    "- polar coordinates\n",
    "    - calculate polar coordinates with [Cartesian to polar equations](https://en.wikipedia.org/wiki/Polar_coordinate_system#Converting_between_polar_and_Cartesian_coordinates)\n",
    "    - use the [np.arctan2](https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.arctan2.html) function and *swap the x and y axes* to move the $0$ to $2\\pi$ discontinuity to 12 o'clock instead of 3 o'clock;  in other words, the normal break in radians value from $0$ to $2\\pi$ occurs directly to the left of the speaker's nose, which may be in the signing area and interfere with results.  By swapping the x and y axes, that discontinuity move to directly above the speaker's head, an area not generally used in signing.\n",
    "\n",
    "- delta difference\n",
    "    - as described in Thad's lecture, use the difference in values between one frame and the next frames as features\n",
    "    - pandas [diff method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.diff.html) and [fillna method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html) will be helpful for this one\n",
    "\n",
    "- custom features\n",
    "    - These are your own design; combine techniques used above or come up with something else entirely. We look forward to seeing what you come up with! \n",
    "    Some ideas to get you started:\n",
    "        - normalize using a [feature scaling equation](https://en.wikipedia.org/wiki/Feature_scaling)\n",
    "        - normalize the polar coordinates\n",
    "        - adding additional deltas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO add features for normalized by speaker values of left, right, x, y\n",
    "# Name these 'norm-rx', 'norm-ry', 'norm-lx', and 'norm-ly'\n",
    "# using Z-score scaling (X-Xmean)/Xstd \n",
    "asl.df['norm-rx'] = (asl.df['right-x']-asl.df['right-x-mean'])/asl.df['right-x-std']\n",
    "asl.df['norm-ry'] = (asl.df['right-y']-asl.df['right-y-mean'])/asl.df['right-y-std']\n",
    "asl.df['norm-lx'] = (asl.df['left-x']-asl.df['left-x-mean'])/asl.df['left-x-std']\n",
    "asl.df['norm-ly'] = (asl.df['left-y']-asl.df['left-y-mean'])/asl.df['left-y-std']\n",
    "features_norm = ['norm-rx', 'norm-ry', 'norm-lx','norm-ly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>left-x</th>\n",
       "      <th>left-y</th>\n",
       "      <th>right-x</th>\n",
       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>speaker</th>\n",
       "      <th>grnd-ry</th>\n",
       "      <th>grnd-rx</th>\n",
       "      <th>grnd-ly</th>\n",
       "      <th>...</th>\n",
       "      <th>left-y-std</th>\n",
       "      <th>left-x-std</th>\n",
       "      <th>norm-rx</th>\n",
       "      <th>norm-ry</th>\n",
       "      <th>norm-lx</th>\n",
       "      <th>norm-ly</th>\n",
       "      <th>polar-rr</th>\n",
       "      <th>polar-rtheta</th>\n",
       "      <th>polar-lr</th>\n",
       "      <th>polar-ltheta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video</th>\n",
       "      <th>frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">98</th>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>26.594521</td>\n",
       "      <td>17.573442</td>\n",
       "      <td>1.153232</td>\n",
       "      <td>1.663433</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>26.594521</td>\n",
       "      <td>17.573442</td>\n",
       "      <td>1.153232</td>\n",
       "      <td>1.663433</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>26.594521</td>\n",
       "      <td>17.573442</td>\n",
       "      <td>1.153232</td>\n",
       "      <td>1.663433</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>26.594521</td>\n",
       "      <td>17.573442</td>\n",
       "      <td>1.153232</td>\n",
       "      <td>1.663433</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>26.594521</td>\n",
       "      <td>17.573442</td>\n",
       "      <td>1.153232</td>\n",
       "      <td>1.663433</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             left-x  left-y  right-x  right-y  nose-x  nose-y  speaker  \\\n",
       "video frame                                                              \n",
       "98    0         149     181      170      175     161      62  woman-1   \n",
       "      1         149     181      170      175     161      62  woman-1   \n",
       "      2         149     181      170      175     161      62  woman-1   \n",
       "      3         149     181      170      175     161      62  woman-1   \n",
       "      4         149     181      170      175     161      62  woman-1   \n",
       "\n",
       "             grnd-ry  grnd-rx  grnd-ly      ...       left-y-std  left-x-std  \\\n",
       "video frame                                 ...                                \n",
       "98    0          113        9      119      ...        26.594521   17.573442   \n",
       "      1          113        9      119      ...        26.594521   17.573442   \n",
       "      2          113        9      119      ...        26.594521   17.573442   \n",
       "      3          113        9      119      ...        26.594521   17.573442   \n",
       "      4          113        9      119      ...        26.594521   17.573442   \n",
       "\n",
       "              norm-rx   norm-ry   norm-lx   norm-ly   polar-rr  polar-rtheta  \\\n",
       "video frame                                                                    \n",
       "98    0      1.153232  1.663433 -0.891199  0.741835  113.35784      0.079478   \n",
       "      1      1.153232  1.663433 -0.891199  0.741835  113.35784      0.079478   \n",
       "      2      1.153232  1.663433 -0.891199  0.741835  113.35784      0.079478   \n",
       "      3      1.153232  1.663433 -0.891199  0.741835  113.35784      0.079478   \n",
       "      4      1.153232  1.663433 -0.891199  0.741835  113.35784      0.079478   \n",
       "\n",
       "               polar-lr  polar-ltheta  \n",
       "video frame                            \n",
       "98    0      119.603512     -0.100501  \n",
       "      1      119.603512     -0.100501  \n",
       "      2      119.603512     -0.100501  \n",
       "      3      119.603512     -0.100501  \n",
       "      4      119.603512     -0.100501  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO add features for polar coordinate values where the nose is the origin\n",
    "# Name these 'polar-rr', 'polar-rtheta', 'polar-lr', and 'polar-ltheta'\n",
    "# Note that 'polar-rr' and 'polar-rtheta' refer to the radius and angle\n",
    "asl.df['polar-rr'] = np.hypot(asl.df['grnd-rx'], asl.df['grnd-ry'])\n",
    "asl.df['polar-rtheta'] = np.arctan2(asl.df['grnd-rx'], asl.df['grnd-ry'])\n",
    "asl.df['polar-lr'] = np.hypot(asl.df['grnd-lx'], asl.df['grnd-ly'])\n",
    "asl.df['polar-ltheta'] = np.arctan2(asl.df['grnd-lx'], asl.df['grnd-ly'])\n",
    "features_polar = ['polar-rr', 'polar-rtheta', 'polar-lr', 'polar-ltheta']\n",
    "asl.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>left-x</th>\n",
       "      <th>left-y</th>\n",
       "      <th>right-x</th>\n",
       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>speaker</th>\n",
       "      <th>grnd-ry</th>\n",
       "      <th>grnd-rx</th>\n",
       "      <th>grnd-ly</th>\n",
       "      <th>...</th>\n",
       "      <th>norm-lx</th>\n",
       "      <th>norm-ly</th>\n",
       "      <th>polar-rr</th>\n",
       "      <th>polar-rtheta</th>\n",
       "      <th>polar-lr</th>\n",
       "      <th>polar-ltheta</th>\n",
       "      <th>delta-rx</th>\n",
       "      <th>delta-ry</th>\n",
       "      <th>delta-lx</th>\n",
       "      <th>delta-ly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video</th>\n",
       "      <th>frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">98</th>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149</td>\n",
       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
       "      <td>9</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.891199</td>\n",
       "      <td>0.741835</td>\n",
       "      <td>113.35784</td>\n",
       "      <td>0.079478</td>\n",
       "      <td>119.603512</td>\n",
       "      <td>-0.100501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             left-x  left-y  right-x  right-y  nose-x  nose-y  speaker  \\\n",
       "video frame                                                              \n",
       "98    0         149     181      170      175     161      62  woman-1   \n",
       "      1         149     181      170      175     161      62  woman-1   \n",
       "      2         149     181      170      175     161      62  woman-1   \n",
       "      3         149     181      170      175     161      62  woman-1   \n",
       "      4         149     181      170      175     161      62  woman-1   \n",
       "\n",
       "             grnd-ry  grnd-rx  grnd-ly    ...      norm-lx   norm-ly  \\\n",
       "video frame                               ...                          \n",
       "98    0          113        9      119    ...    -0.891199  0.741835   \n",
       "      1          113        9      119    ...    -0.891199  0.741835   \n",
       "      2          113        9      119    ...    -0.891199  0.741835   \n",
       "      3          113        9      119    ...    -0.891199  0.741835   \n",
       "      4          113        9      119    ...    -0.891199  0.741835   \n",
       "\n",
       "              polar-rr  polar-rtheta    polar-lr  polar-ltheta  delta-rx  \\\n",
       "video frame                                                                \n",
       "98    0      113.35784      0.079478  119.603512     -0.100501       0.0   \n",
       "      1      113.35784      0.079478  119.603512     -0.100501       0.0   \n",
       "      2      113.35784      0.079478  119.603512     -0.100501       0.0   \n",
       "      3      113.35784      0.079478  119.603512     -0.100501       0.0   \n",
       "      4      113.35784      0.079478  119.603512     -0.100501       0.0   \n",
       "\n",
       "             delta-ry  delta-lx  delta-ly  \n",
       "video frame                                \n",
       "98    0           0.0       0.0       0.0  \n",
       "      1           0.0       0.0       0.0  \n",
       "      2           0.0       0.0       0.0  \n",
       "      3           0.0       0.0       0.0  \n",
       "      4           0.0       0.0       0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO add features for left, right, x, y differences by one time step, i.e. the \"delta\" values discussed in the lecture\n",
    "# Name these 'delta-rx', 'delta-ry', 'delta-lx', and 'delta-ly'\n",
    "asl.df['delta-rx'] = asl.df['right-x'].diff().fillna(0)\n",
    "asl.df['delta-ry'] = asl.df['right-y'].diff().fillna(0)\n",
    "asl.df['delta-lx'] = asl.df['left-x'].diff().fillna(0)\n",
    "asl.df['delta-ly'] = asl.df['left-y'].diff().fillna(0)\n",
    "features_delta = ['delta-rx', 'delta-ry', 'delta-lx', 'delta-ly']\n",
    "asl.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['left-x', 'left-y', 'right-x', 'right-y', 'nose-x', 'nose-y', 'speaker',\n",
      "       'grnd-ry', 'grnd-rx', 'grnd-ly', 'grnd-lx', 'left-x-mean',\n",
      "       'left-y-mean', 'right-x-mean', 'right-y-mean', 'right-x-std',\n",
      "       'right-y-std', 'left-y-std', 'left-x-std', 'norm-rx', 'norm-ry',\n",
      "       'norm-lx', 'norm-ly', 'polar-rr', 'polar-rtheta', 'polar-lr',\n",
      "       'polar-ltheta', 'delta-rx', 'delta-ry', 'delta-lx', 'delta-ly'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(asl.df.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO add features of your own design, which may be a combination of the above or something else\n",
    "# Name these whatever you would like\n",
    "\n",
    "# TODO define a list named 'features_custom' for building the training set\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "features_custom = ['polar-rr-sc', 'polar-rtheta', 'polar-lr-sc',\n",
    "       'polar-ltheta', 'delta-rx', 'delta-ry', 'delta-lx', 'delta-ly']\n",
    "\n",
    "df_means = asl.df.groupby('speaker').mean()\n",
    "asl.df['polar-rr-mean'] = asl.df['speaker'].map(df_means['polar-rr'])\n",
    "asl.df['polar-lr-mean'] = asl.df['speaker'].map(df_means['polar-lr'])\n",
    "\n",
    "df_std = asl.df.groupby('speaker').std()\n",
    "asl.df['polar-rr-std'] = asl.df['speaker'].map(df_std['polar-rr'])\n",
    "asl.df['polar-lr-std'] = asl.df['speaker'].map(df_std['polar-lr'])\n",
    "\n",
    "\n",
    "# using Z-score scaling (X-Xmean)/Xstd \n",
    "asl.df['polar-rr-sc'] = (asl.df['polar-rr']-asl.df['polar-rr-mean'])/asl.df['polar-rr-std']\n",
    "asl.df['polar-lr-sc'] = (asl.df['polar-lr']-asl.df['polar-lr-mean'])/asl.df['polar-lr-std']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1:**  What custom features did you choose for the features_custom set and why?\n",
    "\n",
    "**Answer 1:**\n",
    "\n",
    "Normalizing the ploar length and trying to combine more features \n",
    "The idea it to trace the z-score scling for the r and using theta with combination with the frame change Delat\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part1_test'></a>\n",
    "### Features Unit Testing\n",
    "Run the following unit tests as a sanity check on the defined \"ground\", \"norm\", \"polar\", and 'delta\"\n",
    "feature sets.  The test simply looks for some valid values but is not exhaustive.  However, the project should not be submitted if these tests don't pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "....\n",
      "----------------------------------------------------------------------\n",
      "Ran 4 tests in 0.018s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=4 errors=0 failures=0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "# import numpy as np\n",
    "\n",
    "class TestFeatures(unittest.TestCase):\n",
    "\n",
    "    def test_features_ground(self):\n",
    "        sample = (asl.df.ix[98, 1][features_ground]).tolist()\n",
    "        self.assertEqual(sample, [9, 113, -12, 119])\n",
    "\n",
    "    def test_features_norm(self):\n",
    "        sample = (asl.df.ix[98, 1][features_norm]).tolist()\n",
    "        np.testing.assert_almost_equal(sample, [ 1.153,  1.663, -0.891,  0.742], 3)\n",
    "\n",
    "    def test_features_polar(self):\n",
    "        sample = (asl.df.ix[98,1][features_polar]).tolist()\n",
    "        np.testing.assert_almost_equal(sample, [113.3578, 0.0794, 119.603, -0.1005], 3)\n",
    "\n",
    "    def test_features_delta(self):\n",
    "        sample = (asl.df.ix[98, 0][features_delta]).tolist()\n",
    "        self.assertEqual(sample, [0, 0, 0, 0])\n",
    "        sample = (asl.df.ix[98, 18][features_delta]).tolist()\n",
    "        self.assertTrue(sample in [[-16, -5, -2, 4], [-14, -9, 0, 0]], \"Sample value found was {}\".format(sample))\n",
    "                         \n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestFeatures())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id='part2_tutorial'></a>\n",
    "## PART 2: Model Selection\n",
    "### Model Selection Tutorial\n",
    "The objective of Model Selection is to tune the number of states for each word HMM prior to testing on unseen data.  In this section you will explore three methods: \n",
    "- Log likelihood using cross-validation folds (CV)\n",
    "- Bayesian Information Criterion (BIC)\n",
    "- Discriminative Information Criterion (DIC) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Train a single word\n",
    "Now that we have built a training set with sequence data, we can \"train\" models for each word.  As a simple starting example, we train a single word using Gaussian hidden Markov models (HMM).   By using the `fit` method during training, the [Baum-Welch Expectation-Maximization](https://en.wikipedia.org/wiki/Baum%E2%80%93Welch_algorithm) (EM) algorithm is invoked iteratively to find the best estimate for the model *for the number of hidden states specified* from a group of sample seequences. For this example, we *assume* the correct number of hidden states is 3, but that is just a guess.  How do we know what the \"best\" number of states for training is?  We will need to find some model selection technique to choose the best parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states trained in model for BOOK is 3\n",
      "logL = -2331.1138127433196\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "from hmmlearn.hmm import GaussianHMM\n",
    "\n",
    "def train_a_word(word, num_hidden_states, features):\n",
    "    \n",
    "    warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "    training = asl.build_training(features)  \n",
    "    X, lengths = training.get_word_Xlengths(word)\n",
    "    model = GaussianHMM(n_components=num_hidden_states, n_iter=1000).fit(X, lengths)\n",
    "    logL = model.score(X, lengths)\n",
    "    return model, logL\n",
    "\n",
    "demoword = 'BOOK'\n",
    "model, logL = train_a_word(demoword, 3, features_ground)\n",
    "print(\"Number of states trained in model for {} is {}\".format(demoword, model.n_components))\n",
    "print(\"logL = {}\".format(logL))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The HMM model has been trained and information can be pulled from the model, including means and variances for each feature and hidden state.  The [log likelihood](http://math.stackexchange.com/questions/892832/why-we-consider-log-likelihood-instead-of-likelihood-in-gaussian-distribution) for any individual sample or group of samples can also be calculated with the `score` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states trained in model for BOOK is 3\n",
      "hidden state #0\n",
      "mean =  [ -3.46504869  50.66686933  14.02391587  52.04731066]\n",
      "variance =  [ 49.12346305  43.04799144  39.35109609  47.24195772]\n",
      "\n",
      "hidden state #1\n",
      "mean =  [ -11.45300909   94.109178     19.03512475  102.2030162 ]\n",
      "variance =  [  77.403668    203.35441965   26.68898447  156.12444034]\n",
      "\n",
      "hidden state #2\n",
      "mean =  [ -1.12415027  69.44164191  17.02866283  77.7231196 ]\n",
      "variance =  [ 19.70434594  16.83041492  30.51552305  11.03678246]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def show_model_stats(word, model):\n",
    "    print(\"Number of states trained in model for {} is {}\".format(word, model.n_components))    \n",
    "    variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])    \n",
    "    for i in range(model.n_components):  # for each hidden state\n",
    "        print(\"hidden state #{}\".format(i))\n",
    "        print(\"mean = \", model.means_[i])\n",
    "        print(\"variance = \", variance[i])\n",
    "        print()\n",
    "    \n",
    "show_model_stats(demoword, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Try it!\n",
    "Experiment by changing the feature set, word, and/or num_hidden_states values in the next cell to see changes in values.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states trained in model for CHOCOLATE is 4\n",
      "hidden state #0\n",
      "mean =  [  2.83210545  -0.58307925  -0.5002342  -10.33228893]\n",
      "variance =  [ 9.97221102  8.57365789  9.08040501  9.22168899]\n",
      "\n",
      "hidden state #1\n",
      "mean =  [ 0.          0.          0.26411255 -0.83378972]\n",
      "variance =  [  4.39790881e-04   4.39790881e-04   2.65711431e+00   4.34840452e+00]\n",
      "\n",
      "hidden state #2\n",
      "mean =  [ 0.28571692  5.14285218  0.42857031  5.7142692 ]\n",
      "variance =  [ 17.06263607  19.26672401  15.67485365   7.06272726]\n",
      "\n",
      "hidden state #3\n",
      "mean =  [-1.79145183  2.20484802  0.          0.        ]\n",
      "variance =  [  2.69710624e+01   9.47153709e+00   1.37803730e-03   1.37803730e-03]\n",
      "\n",
      "logL = -202.2115462207163\n"
     ]
    }
   ],
   "source": [
    "my_testword = 'CHOCOLATE'\n",
    "model, logL = train_a_word(my_testword, 4, features_delta) # Experiment here with different parameters\n",
    "show_model_stats(my_testword, model)\n",
    "print(\"logL = {}\".format(logL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states trained in model for CHOCOLATE is 4\n",
      "hidden state #0\n",
      "mean =  [  2.83210545  -0.58307925  -0.5002342  -10.33228893]\n",
      "variance =  [ 9.97221102  8.57365789  9.08040501  9.22168899]\n",
      "\n",
      "hidden state #1\n",
      "mean =  [ 0.          0.          0.26411255 -0.83378972]\n",
      "variance =  [  4.39790881e-04   4.39790881e-04   2.65711431e+00   4.34840452e+00]\n",
      "\n",
      "hidden state #2\n",
      "mean =  [ 0.28571692  5.14285218  0.42857031  5.7142692 ]\n",
      "variance =  [ 17.06263607  19.26672401  15.67485365   7.06272726]\n",
      "\n",
      "hidden state #3\n",
      "mean =  [-1.79145183  2.20484802  0.          0.        ]\n",
      "variance =  [  2.69710624e+01   9.47153709e+00   1.37803730e-03   1.37803730e-03]\n",
      "\n",
      "logL = -565.1243334331168\n"
     ]
    }
   ],
   "source": [
    "my_testword = 'CHOCOLATE'\n",
    "model1, logL = train_a_word(my_testword, 4, features_ground) # Experiment here with different parameters\n",
    "show_model_stats(my_testword, model)\n",
    "print(\"logL = {}\".format(logL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states trained in model for CHOCOLATE is 4\n",
      "hidden state #0\n",
      "mean =  [  2.83210545  -0.58307925  -0.5002342  -10.33228893]\n",
      "variance =  [ 9.97221102  8.57365789  9.08040501  9.22168899]\n",
      "\n",
      "hidden state #1\n",
      "mean =  [ 0.          0.          0.26411255 -0.83378972]\n",
      "variance =  [  4.39790881e-04   4.39790881e-04   2.65711431e+00   4.34840452e+00]\n",
      "\n",
      "hidden state #2\n",
      "mean =  [ 0.28571692  5.14285218  0.42857031  5.7142692 ]\n",
      "variance =  [ 17.06263607  19.26672401  15.67485365   7.06272726]\n",
      "\n",
      "hidden state #3\n",
      "mean =  [-1.79145183  2.20484802  0.          0.        ]\n",
      "variance =  [  2.69710624e+01   9.47153709e+00   1.37803730e-03   1.37803730e-03]\n",
      "\n",
      "logL = 85.72111948347776\n"
     ]
    }
   ],
   "source": [
    "my_testword = 'CHOCOLATE'\n",
    "model2, logL = train_a_word(my_testword, 4, features_norm) # Experiment here with different parameters\n",
    "show_model_stats(my_testword, model)\n",
    "print(\"logL = {}\".format(logL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states trained in model for CHOCOLATE is 4\n",
      "hidden state #0\n",
      "mean =  [  2.83210545  -0.58307925  -0.5002342  -10.33228893]\n",
      "variance =  [ 9.97221102  8.57365789  9.08040501  9.22168899]\n",
      "\n",
      "hidden state #1\n",
      "mean =  [ 0.          0.          0.26411255 -0.83378972]\n",
      "variance =  [  4.39790881e-04   4.39790881e-04   2.65711431e+00   4.34840452e+00]\n",
      "\n",
      "hidden state #2\n",
      "mean =  [ 0.28571692  5.14285218  0.42857031  5.7142692 ]\n",
      "variance =  [ 17.06263607  19.26672401  15.67485365   7.06272726]\n",
      "\n",
      "hidden state #3\n",
      "mean =  [-1.79145183  2.20484802  0.          0.        ]\n",
      "variance =  [  2.69710624e+01   9.47153709e+00   1.37803730e-03   1.37803730e-03]\n",
      "\n",
      "logL = -142.95304877220514\n"
     ]
    }
   ],
   "source": [
    "my_testword = 'CHOCOLATE'\n",
    "model3, logL = train_a_word(my_testword, 4, features_polar) # Experiment here with different parameters\n",
    "show_model_stats(my_testword, model)\n",
    "print(\"logL = {}\".format(logL))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualize the hidden states\n",
    "We can plot the means and variances for each state and feature.  Try varying the number of states trained for the HMM model and examine the variances.  Are there some models that are \"better\" than others?  How can you tell?  We would like to hear what you think in the classroom online."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nILMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4312f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEICAYAAACeSMncAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKDXGiBgMBoOh1Pw/xQ5BNBYH2WsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42f56828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEICAYAAABBBrPDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42ef1400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNoiau6IoShvk/wOcTvOSb+zwqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42c2ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlGENv8VisZQyrOG3WCyWUsb/A58OFXc2JZXeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba429109e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUsqwht9isVhKGf8f8DPAOCv6c4kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba426d8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl4FEX6xz9vJhe5L0gCAaIIKqjcIMJiWF0UZUURWBBQ\nFhVYL7zxwB/giqKueKwi4oWIXOKteEtUFFhOQVAUMEiABHLfd/3+qE4YhkwyOSch9Xmefqa76+2q\nb9V0v11dXV0lSikMBoPB0HLwcLcAg8FgMDQuxvEbDAZDC8M4foPBYGhhGMdvMBgMLQzj+A0Gg6GF\nYRy/wWAwtDCM4zc0SUQkUkS+E5FsEXnK3XoaEhGZLSJLqwjfJSJxTsLiRCSximMXi8gj9SDTcAph\nHH8jICLXiMhmEckRkSMi8qmIDLLCKr3oRUSJyBl2211F5EMRybSc4VoRucDhGG8rvt9FJFdEEkTk\nNRGJtbMZLiL/s8JTReQtEYmxC58kIuuqyc9iESkRkWi7fQut/OWISJGIFNttfyoisVaechyWfzhJ\nZgqQAgQppe6qsoCrobGdn4i0EZHlInLY+r9+EJH+tY1PKdVNKRVfjxIbFMdz1wX7eBG5oZ7SXiUi\nQ0XER0SSHMJ8rOshS0SSROTO+kizOWIcfwNjnVzPAI8CkUAH4AXgihrE0Qn4AdgJnAa0Bd4DvhCR\nAXamq614rwGCge7AZuAiK55RwDJLTwTQDSgE1olIqIta/IGrgUxgQvl+pdQ0pVSAUirAyuvK8m2l\n1DC7KELs9gcopVY6SaojsFs1gS8MRcSzhocEAJuA3kAY8AbwiYgE1Lc2w0n0Rp/z5wE/O4TNBjqj\nz60hwL0icmmjqmsqKKXM0kAL2vnmAKOrsJkNLK1kvwLOsNbfBNZUYvMi8J21fjGQD7R3ko4AB4B7\nHfZ7oC+Qh63tScC6KvReCxwEpgM/u5onINbKk6cL5bYYKAaKrPK72NJ5H7APSAVWAWF2x7wNJKFv\nSN8B3az9Uxzi+sixfO3SfMRajwMSgRlWnG9a+4cD24EM4EfgvBqcC1lA7yrKaxWwBMgGdgF97MIT\ngIut9VaW1nRgN3APkGhn2xPYasWzElhRnq/q8mClczewwyrHlYCvE81nAN9adinoGz1W2Ssg1yrv\nfwChwMfAMUv3x0CMZT8XKAUKLPvnrf1nAV8CacAeYIwLZRwK7LfW/wU84RB+GBhqt/0wsKIxfUJT\nWdwu4FRegEuBEqpwdrjm+JOAf1ZiM8S6aFoB84Bvq0jnLCvO0yoJmwOst9YnUbXj/xp4Av30UlKZ\nM6ssT9TA8Vv2ix0c1nRgAxAD+AAvAcvtwicDgVbYM8B2Z3E5lq+jDdrxlwCPW/G1QjvUo0B/wAZc\nh3aUPi7kpYfl2IKrOAcKgMusuB8DNtiFJ3Dc8c8Dvkc/SbRH37QTrTBv9M39DsALGIW+6ZXnq8o8\nWOv/Qz9RhgG/ANOcaF4OPIi+IfsCg6oo23D0U6Kf9R+9DbxvFx4P3GC37Y+uXPwT8LR0pwBdnWi5\nCH0jy0Hf4DOs31xr/UL0TUEBkXbHXQ3sdIdvcPdimnoalnAgRSlVUo3dGBHJsF8cwiOAI5UcdwR9\n4YVZaVVmYx8HTmyO2IU7RUQ6oG82y5RSyeibwLXVHedAikNez3bxuGnAg0qpRKVUIdpZjipvhlFK\nvaaUyrYL6y4iwTXUZk8ZMEspVaiUykc/ObyklNqolCpVSr2BbiY7v6pIRCQI/cQ2RymVWYXpOqXU\nGqVUqWXf3YndGGCuUipNKXUQeM4u7Hy0w39GKVWslFqNbnIqx5U8PKeUOqyUSgM+Qt+0KqMY3WTS\nVilVoJRy+l5IKZWqlHpHKZWnlMpG1/IvdGaPfipJUEq9rpQqUUptA94BRjuJ/2ulVAjwvmXTDn0T\ni1BKhSilvkU3v4F+QiknC30janEYx9+wpAIRLrQRr7JO0IrFITwFiK7kuGi0g0q30qrMxj4OnNhE\n24VXxUTgF6XUdmv7LeAaEfFy4dhyIhzy+ouLx3UE3rO7Mf6CftqJFBGbiMwTkX0ikoW+6MGFm1kV\nHFNKFTikf5fDzbk9unZcKSLSCu08NyilHqsmPfsXkXmAr5Pzpi26NlzOAYewQ8qqzlYS7koeHHU4\ney9xL7r58H9Wr6PJTuwQET8ReUlEDlj/z3dAiIjYnBzSEejvoHM8EOUk/kTLZhz6fcpRK44jIjLf\nMsuxfoPsDg1GN4m1OIzjb1jWo2tUV9Yxnq+ovLYzBt1Ek2fZ9LPvoePAHnS79QnxiIgH+pH3axd0\nXAucbvWISALmo53rZS7lom4cBIY53DR8lVKH0C+zR6DfBQSjm5VAOybQj/iO5KGbHspxdCqOxxxE\n17Tt0/dTSi2vTKyI+KBroInAVNey6BJH0M66nA4OYe1ERJyE1ygPVaGUSlJK3aiUaovO34IqevLc\nBZwJ9FdKBQGDrf3O/p+D6GZLe50BSql/OdESg25W/cqqNC0CbraOu9OySUeXj/2TVHf0+5QWh3H8\nDYj1aP9/wAsicqVV8/ESkWEi8kQNopoDXCAic0UkTEQCReRWtCOeYaX1Ffpl2Hsi0ltEPC27aSIy\n2aoF3g3MFN291FdEooBX0LWgp+3SEyvcfhkAdAL6oR//ewDnoHsJ1bS5pzYsBOaKSEdLYGsRGWGF\nBaJvsKloZ/6ow7HJwOkO+7ajn1ZsVs+OqpoeAF4GpolIf9H4i8jlInJSU4H1BLQa/bL9OqVUmevZ\nrJZVwP0iEmrd5G+1C1uPfjdxm3WejUT/XzXOQ3WIyGi7SkY62nmX59OxvAPRZZEhImHALIfoHO0/\nBrqIyEQrH14i0reaZsHe6JfaAL3QPXscWYI+/0OtuG5Ev9tpcRjH38AopZ4C7gRmons1HARuQdcG\nXY3jd2AQuoaSgK65XA1copT6wc50FLAG3RsjE/3irw/6aQClu05ORL/8S0X3CmkFDFRKpdrFcwH6\nQrVfrgc+UErttGp7SUqpJOBZYLh1QbtChpzYj9/VvtTPAh+iu7Bmo1/0lveNX4Ju0jhk5WmDw7Gv\nAl2tZoPycp8O/B398m881fwfSqnNaEfxPNrR7UW/CK+MC9Dt1EM5Mb9/cS2rVTIHndc/gC/Q7wPK\nNRYBIy1daegeNe/WMg/V0RfYKCI56P9lulJqvxU2G3jDKu8x6JftrdDNiRuAzxziehb9viZdRJ6z\n3gMMBcaie+IkcfxFuzN6A1utp52zqLwmPwvdK+wA+oXyE0opRy0tAjmxOdBgMBgMpzqmxm8wGAwt\nDOP4DQaDoYVhHL/BYDC0MIzjNxgMhhZGTQefahQiIiJUbGysu2WcRG5uLv7+/u6WUSuMdvdgtDc+\nzVU31E37li1bUpRSrV2xbZKOPzY2ls2bK+uG617i4+OJi4tzt4xa0RK156dBViKUFkFJIfgGQ8RZ\n4NGIZ31LLHd301x1Q920i8iB6q00TdLxGwy1QZXB3s/h9zVw4Fs4uvNkG09faHMudPgL9LoeWndt\nfJ0Gg7sxjt/Q7CkpgJ/ehA3zIeVX8PKD9gOh2xgIP1M7e08fyD0KR7ZB0jb433+1fYdB0PcW6DYa\nxLzxMrQQjOM3NGt+/QDW3AzZhyCqJ4x8C7qOApt35fbnWVPH5B6D7Yth6yJ4Zyz87zkY9jxE92w0\n6QaD2zB1HEOzJPsIrBoFK68Ev3C49muYsgXOvca507fHvzUMvAdu2QMjXofU3+HlPrDmFijOa3j9\nBoM7MTV+Q7Nj72fwzjVQkg8XPQYD7gJbTQaGtkM8oMckOOtKWDtLNwEd+BZGr4aIM+tVtsHQZDA1\nfkOzQSn4/lF46zIIbg/TfoJB99Xe6dvjGwLDnoUJn0FOkq79/7yi7vEaDE0R4/gNzYLiPHh7FHzz\nIJwzFib/COFd6j+dTkNh6jaIPA/eGQffPqxvOAbDqYRx/IYmT0mOJ28OhV/eg6FP6Re43g34fU5Q\nDFwXD92vg/hZ8PFUKKtu8kyDoRlh2vgNTZrsI7B9eg/yD8KoFbqLZmNg89IvfYNi4Pu5kHMERq3U\nXUUNhuaOqfEbmiwZCfDaQMg/3IrxaxrP6ZcjAn99BC5bAL99Asv/bnr8GE4NjOM3NEkyDsAbQ6Ag\nHbrP387pF7tPS99/wZVvQEI8LLscinLdp8VgqA+M4zc0OTIOwBtxUJABE7+CoLOz3S2J7hPhqjfh\nwHfw1jAoynG3IoOh9rjk+EXkUhHZIyJ7ReS+SsJFRJ6zwneISC+7sAQR2Ski20Wk6Y28ZmhSZB60\navqW02/b292KjnPuNTByGRz8EZZfAcX57lZkMNSOah2/iNiAF4BhQFdgnIg4Dm01DOhsLVOAFx3C\nhyileiil+tRdsuFUJScZ3rwY8lNh4pdNy+mXc84/4MrFutnn7VF65E+DobnhSo2/H7BXKbVfKVUE\nrABGONiMAJYozQYgRESi61mr4RQmPw3e/JseRnn8p9C2CVcRzpsAw1/So4C+M8509TQ0P0RV83WK\niIwCLlVK3WBtTwT6K6VusbP5GJinlFpnbX8NzFBKbRaRP4BMoBR4SSm1yEk6U9BPC0RGRvZesaLp\nfTaZk5NDQECAu2XUiqasvSTPxo67upOzL4BzH9tJaO/0E8KbqvbE1e3Y90JnIv+WxJn3/Vrp6J5N\nVbsrNFftzVU31E37kCFDtrjcqqKUqnIBRgGv2G1PBJ53sPkYGGS3/TXQx1pvZ/22AX4CBleXZu/e\nvVVTZO3ate6WUGuaqvaiPKVev1CpOTalfv2gcpumql0ppb79t1KzUeqTW5QqKzs5vClrr47mqr25\n6laqbtqBzaoa31q+uPIB1yGgvd12jLXPJRulVPnvURF5D9109J1LdyXDKU1pMaweo3vKjHwLzrzC\n3Ypqzl8ehIJMWP8fPcPXXx9xtyKDoXpcaePfBHQWkdNExBsYC3zoYPMhcK3Vu+d8IFMpdURE/EUk\nEEBE/IGhwM/1qN/QTCkrhfevhd8+hstfhHPHuVtR7RCBvz0BvW7UX/j+8IS7FRkM1VNtjV8pVSIi\ntwCfAzbgNaXULhGZZoUvBNYAlwF7gTzgn9bhkcB7IlKe1jKl1Gf1ngtDs0Ip+HiaHv3y4sehz1R3\nK6obIvrmVZQNX80A7wDoe5O7VRkMznFprB6l1Bq0c7fft9BuXQE3V3LcfqB7HTUaTiGUgs9uh22v\n6GaSgfe6W1H94GGDK5for3rX3Axe/tDjOnerMhgqxwzSZmg0lIKvH9DTHJ5/Bwz5t7sV1S82Lxi9\nCpYNhw8ng1crdJcGg6GJYYZsMDQa386BH+ZB76l6eGXdAnhq4ekLYz+AmAF6lrBj30a4W5LBcBLG\n8RsaHKX0tIbfztHTHF6+4NR0+uV4++uP0GL6wy//7sov77lbkcFwIsbxGxoUpfRkJt89DD0mwxWv\nUumHTqcaPoHa+Qecmc3qMRjnb2hStIBL0OAulIKv74fv/g09r4crXm4ZTr8cnyA47/EdtO0Db4+G\nHUvdrchg0LSgy9DQmJSVwkdT4IfHofc0+PuiluX0y/EMKGXCFxB7Ibw3ETYtcLcig8E4fkMDUFII\nq/9xvMvm5QtaptMvxycQrvkEuvxdd/X87hEzgbvBvbTgy9HQEOSlwtJL4Jd3YOh8PYTBqfwi11U8\nfWHMO3DeRFj7EHx4gx6ywmBwB6Yfv6HeSPlV92HPStRj75x7jbsVNS1sXnoKx5DT9MvuzAMwZjX4\nhrhbmaGlYWr8hnph7+fwyvl62ILr1hqn7wwRGDIHRizWg9O90h+Sd7pblaGlYRy/oU6UlcDXD+p5\naIPbww0bof0Ad6tq+vS4Dq79CgqztPP/6U13KzK0JIzjN9SarEQ9P+66R6HHP7XTD4l1t6rmQ8fB\nMGUrtOunRyr98AYodP+88oYWgHH8hhqjFGx9BRacA0e2wVVvwohXwcvP3cqaH4HRuuY/6H7Y/jq8\neC7s/9rdqgynOsbxG2pE2l5YchF8dCNE9YBp2/UctIba4+EJFz0K/1wHnj56wvmPpkDuMXcrM5yq\nGMdvcIn8NPj8TnihKxzZoicbv+4bCDvD3cpOHdoPgKnbYcBdsO01+G9nWP80lBa5W5nhVMM4fkOV\nFGbBusfhuU6w8Vnofi3c/Av0ntKyP8pqKLxawdD/wL92QMz58IV1s936qrkBGOoPc+kaKiXrEHx5\nLzzdHr6+TzuhqdvhilcgsK271Z36tO6qB3m75hM9l+9HN8BzZ8CGZ6Egw93qDM0d8wGXoYKSAtjz\nEfy0GPZaE2R2HQ0X3ANte7tVWotEBDpfBmcMg32f6zl9P79d34i7jtYD33X8i3nyMtQc4/hbOPlp\n8Pun8NtH2tkXZkJgOxg4A3rdAKGnu1uhQQTOuFQvR7bClpdh51uw403wj4SzrtRLx8GmZ5XBNVyq\nK4jIpSKyR0T2ish9lYSLiDxnhe8QkV6uHmtoPMpK9bAKO5fDmltgYXd4IgLemwAJ8XD21TDhc7j9\ngO5l4k6nn5yczODBgwkMDGTBglN7SMvZs2czYYLzrlHdunUjPj4egOheMPxFuOsIjFwGOWfFM35R\nDG8Ng3kh8Nog/UHdr+9D5p8wadIkZs6c2Ug5MTQXqnX8ImIDXgCGAV2BcSLS1cFsGNDZWqYAL9bg\n2FOeZcuW0adPHwICAoiOjmbYsGGsW7cOcH7Riwh79+6t2N69ezdXXHEFwcHBBAYGMmTIEH788ccT\njiksLGLm/bM5vWNn/Fr50zYilmE9JrNwRAJbp/XisUC47eyPufSaflz5gj93/xLOunPGc9mHidx1\nWPfF//7wYgZfOKjK/EyaNAlPT0+OHDlSsW/atGkEBAQQEBCAt7c3Xl5eFdvDhg0jISEBEanYV76s\nXLmy0jQWLVpEREQEWVlZ3HTTTS6XtTO9jen8jh49yrhx42jbti3Dhw9n4MCBbNy4sdbx7dq1i7i4\nuBP2efvDueMgbrb+FmD8ZzDgTv0l9Q+Pw8qr4JmO8PNy2P4GfDAZvn8Mdi6DA99D+h9QnFe3fDrD\n8dytjri4OF555ZV6SXvMmDFs2rSJwsJCoqKiTghbtWoVF1xwAX5+fieVZ0vDlaaefsBepdR+ABFZ\nAYwAdtvZjACWKKUUsEFEQkQkGoh14dh6Y//XoMoaImZN2k+h7HPSs+KEYXbV8X2vvTufRW/PY85N\nCxk44xK8bN58v/VzXn/qQ8KPDuLYbsg4omvhqlTXystK9PE/vQnHguHA4X3c+MJA/t7zJl4eu5iy\nAi+++Pl1/jp4KHed8yUxZQPIT4VFR0aRqRL5O8uIoifFBbnsTHuL+INfMyB2JHl/Xc2HX0/m8YcW\nMunWq8jOzeSBBx5g9K2D2DZoG6GhodWWQW5uLu+88w7BwcEsXbqUe+65B4CFCxeycOFCQN/M9u7d\ny9Klx2ceSUhIACAjIwNPz+pPuwMHDtC1a1ekCQztWVJS4pLmcnJycujbty/z589n9+7d7Nu3j8sv\nv5yEhAQCAgLqX6DAGZfoBbRDT96hP6777mmQQt2Ml/P6yYd6+YFfa2gVCj7B+kWyd6Den5zeidIv\n9MiiNh+weeuB5jy89K/Y9DcIHjb9nkFsx9837P8aSn+xRma1/sKT1i3y0+Doz3q8pxP+7lr89RvW\nbeHyc25hzcs76NT2HPZ9eTys4I8wxv71dvp1/pX12785IaypkLErGOIaPh1XzuZ2wEG77USgvws2\n7Vw8FgARmYJ+WiAyMrLi0bYmfD/8L5QV2Gp8nOt0pybjaRWQyVP8H1fyOqmPjeTDipDhdGA4q96H\nXUAa8O66k4//9mEIB95lNlEM4KxN/+b3naV4+JZxls8N9ArYwYq9d3FHjw/50/dr9id/weP/+JK2\nsf54R/yMT0Qhf4vqhoeXIjt7HY/deAvjrx1HtwvasmmLroFOmDCBtWvXMn36dCZPnsyvv/5KZmam\n0/L//PPPadWqFWPGjGHBggX07dv3JJuEhASSk5NPiCMpKUnn6dtvsdmq/o/mzZvHV199hYjw1FNP\n8eCDD1JWVsaKFSv45JNPyMnJoWfPntx5550EBQUB+mazY8cOioqK6NSpE7fffjunnXYaH330EUuX\nLq2Iq2fPnjz66KMMGTKEpUuX0q5du4o0W7duzfXXX8/27duZO3cuI0eO5O2336ZPnz488MADrF+/\nnldffZWkpCRiY2O544476NSpU6V56NWrF3v27CE/P58uXbqQn5/PW2+9xZlnnllpeSUmJjJ06FDW\nrVtHmzZtuP/++ytsx44dyz333EPv3r0pLCzk6aef5ocffiA8PJxLL72UwsLCirL+/fffefLJJ0lM\nTKR///5IjBDSrpTe18dTkmfj+y83sWTlSxxNO0Lb4DMY330eYZxHUY4n9//YiwuCprIlcwXppX/S\n2WMoV/IGnqUnvzhIZS8fcj1JbMcDL07nIkazktcYDMDwad0BYQSv0omhvMtEDrGRMkpoz0CGs5Bg\nYviaB9nE92zZuYHZz95ODyZxOc9zjF/5lFs5zBb8ac0Q/s05jKnyvMknnWwUCbMGs4kXgV4sHWpv\ncTEA+3mFZHAIaxp4hpxNSI/4hk+nwVNwEaXUImARQJ8+fVRtHsU6fdOwNf5tW7fSs1cv5+PL2+0X\ngbXr11N2TwFPfncVnl7Hazzlvx42yFkIf/wJN//HqkHZdI1qdge4cSN0PhNePPNLHp37GJOv98C+\nda7v2vFcfPHFXP+lP3PmbGNAq/7c8dZfKpW2ZMkSkpOTue+++zjttNNOCLv22mv54osviIuLIyEh\ngXXr1jl9FP73v//NpEmTuOuuu1i4cCGBgYH07n1il5/4+HhKSkpOiKO8xn/hhRdWW3uOi4tj0qRJ\nxMTE8MgjjxAfH89PP/3Ejh072LhxI61bt+a2225j+fLlLF++vCIPo0ePxtvbmxkzZvDss8+yfft2\n4uLiSE1NrYjLnv79+3PGGfoLtMWLFxMTE1OhOT09nZCQEI4cOUJZWRm//vorTz/9NB999BF9+vRh\n6dKlzJo1iz179uDj4+M0L/Hx8YSEhFBaWsrYsWMJDg6u1GbFihW8++67fPrpp8ycOZPXX3+dDRs2\nAODr60v37t2Ji4vjvvvuIzc3lwMHDpCbm8uwYcPw8fEhLi6OoqIirrvuOm6//XZuueUWPvjgA8aN\nG8eMGTOIi4tj27ZtvPjWPIc8TKnIw1OxHhxt8wFb3v8cX19fevbsSbv7lzB1yjRKCvR3BKXFUFYM\nk6Y9xITOQ5k+ZS0F+UVs37mZfj1gaul3RPcU4t//idh2Z6AUpGekcvqWfzJkwCpKS0u5+5HJ7Cq9\nhVcff5/Jai5jbv6Bqy6ZwLi/3wBAXn4uQ675G3de/zAjL/mUX/fvZMIdf2Pq8+fQ5bSTW4rXbf6a\nf828mpLSEoqLi3jCK5Di4kI8Pb342XMRLz/2Aef3vLDC3vcjOPY5/PP5Kk9Dt7B9x8nNeg2BK47/\nENDebjvG2ueKjZcLx9YbDT0q5P7iLDoMdN3eY18qERERdLzAeTH7t4EPX1nFV+d/fFJYqzD96J2S\nkkLbdtEnhUdHR1NWVkZaWhqpqalER59sU05mZmbFMZXFk5KSUm1+/vzzT9auXctTTz1FZGQkF110\nEUuWLDnJ8VdFRETECdvr16/n7LPPrva4hQsX8vzzzxMTEwPoGn6HDh1488038fT0ZPLkyRW2s2fP\nJjQ0lMzMzEodrSt4eHgwZ86cCqe+aNEipk6dSv/++oH1uuuu49FHH2XDhg1ceOGFTuPJzc3l1ltv\nZdasWVVqGTRoEJdddhkAEydO5JlnnqnUbtWqVSxYsICwsDDCwsK47bbbePjhhwHYsGEDxcXF3H77\n7YgIo0aNYv78+RXHupKH2267jbZt9YcaAwYMYPv27YiHbvqx7zHkH+LF0ewDFPofpv2ZMbTvceJ7\noTbdoK31VXc7wjnnb1dXhD0S9iBDhgwh5ny97RMEYZ2g/QV6e+XKj+nUJZa7H/0nAKdf2JPRG67m\nh31vc/GEWSeVyTUDL+Ka6RlMmDCh4uY/ffp0fvrpJ1q1anWSffgv4LOBGl3LjcX+4sYZpU9UNXPA\niYgn8BtwEdppbwKuUUrtsrO5HLgFuAzdlPOcUqqfK8c6SfMYcKC2mWpAIoDqPeRxgtAvvLdUYdMW\n8AH+cNjfG/gZKAS6o5vJUh1sAoEuwDYgGggA9jhJJ9pKayfg+KairaX1V3TrUoSTeKKs8PL/Lxx9\nM99BxZsNp3nyBs6l6rKwJ9bSedjSU16BsE/Hw8pPMbpZMRRd2VCAjePlZx9XOfbl65heIHCala9y\nzrD226cv6PM0zUkeBOgGZFP1+exYXo5ldS6QYMXTC/2OrMAKC7K070DnPwr4xS7u06w8HnYhD/bp\nlOe5lJPPTdCVxnZAsGWTxPHz07FsPdD/XxDHK5sedvk70zq2/NqKtOK2f34Xy+bPSrScZ8Vns7SU\nPxaXWXEmOthHoM9dZ9eKO6mpj7Gno1KqtUuWSqlqF7RD/w3YBzxo7ZsGTLPWBd17Zx/6QuxT1bHN\ndQE219A+GMgFRlVhMxtYWsl+BZxhrS8F1lRi8yKwzlq/GMgHYpxpR79vuddhvwf6In3E2p5UHmcl\nceyx0kiylhRL54jq8oR2TgrwdLHsFttp2mylPdCJ7US0szvNOhdDHMrv9fK47I7JBc6z2/7MLr04\nINHB/qWanL9oR/452ll5VGN7Qnk5lhXaGV9srf8BXGpne2O5VuBCtIMXu/Af7PJVZR7s07G2D1d2\nblZy3CD0jai8vCvK3tp+CIgHoqztHg75WwvcYGc/Dviyhtfa+cDn1nqyfXyV2N4AxNck/sZaqKGP\nqe3iUj9+pdQapVQXpVQnpdRca99CpdRCa10ppW62ws9VSm2u6tiWglIqE/g/4AURuVJE/ETES0SG\nicgTNYhqDnCBiMwVkTARCRSRW4FrgRlWWl8BXwLviUhvEfG07KaJSHk7yN3ATBG5RkR8RSQKeAVd\nE3vaLj2xwu2XAUAndC+vHtZyDrDM0tHQLATmikhHS2BrERlhhQWia5epgB/wqMOxyYDjVwnbgWtE\nxCYil6ICw4uLAAAgAElEQVSdZlW8DEwTkf7Wdyv+InK5iAQ6GoqIF7AafZP8Q6l6ffO0CrhfREJF\nJAa41S5sPVAC3GadZyPR/1eN81AdIjLaSh8gHe3Iy/PpWN6B6LLIEJEwwLG9xtH+Y6CLiEy08uEl\nIn1FpKo2wd7AVmvdD11ZcNRsExFf9FOHh3Vee1Wb2VMQ87F3A6OUegq4E5gJHEPXum8B3q9BHL+j\na1Xd0bWyI8DVwCVKqR/sTEcBa4CVQCa6Jt8H+MqKZyW6dnwH2knuBlqha9L2zUgXoC9U++V64AOl\n1E6lVFL5AjwLDLcuaFfIEJEcu+VOF497FvgQ+EJEsoENHO8htgTdXHHIytMGh2NfBbqKSIaIlJf7\ndODvQAYwnmr+D6sycyPwPNrR7UU/HVXGBcBwYCjQ0y6vlb95rxlz0Hn9A/gCqJi7SylVBIy0dKUB\n/wDerWUeqqMvsFFEctD/y3RlddtGP8G8YZX3GOAZ9HmWgv5vPnOI61lglIiki8hzSqlsdNmNRT91\nJAGPo5+inNEb2Cq6/68vx5sj7ZmIPpdfBP5irb9co1yfIlTbxm84johMUbr3UbPDaHcPRnvj01x1\nQ+NpN47fYDAYWhimqcdgMBhaGMbxGwwGQwvDOH6DwWBoYTSZIRvsiYiIULGxse6WcRK5ubn4+/u7\nW0atMNrdg9He+DRX3VA37Vu2bElRLn7A1SQdf2xsLJs3n9QN1+3Ex8c32+FcjXb34Ip2pRRr8/LY\nWVhYse90Ly8uCwjA5sbRSZtruTdX3VA37SLi8mgHTdLxGwwthX1FRTyWmsr6/Hw80J8dl38J9WZW\nFg+Fh3Our697RRpOOYzjNxjcxKL0dF5IT6eVhwcPhIfzj6AgPEVQSrEmN5cnU1MZd/gw1wQFcV94\nOB5NYG4Cw6mBcfwGgxt4IyODZ9PTGebvz/0REYTbzVEgIlweEMCFfn48m5bGW1lZ2ESYER7uRsWG\nUwnj+A2GRmZNTg5PpKXxN39/Hm/Txmk7foD1JOABLMnMJNJmY1JISOOKNZySGMdvMDQiG/Lzuf/o\nUXr7+vJ469bVvrwVq6Z/rLSUJ9PSaOPpyWUNMX2joUVh+vEbDI3EsZIS7kxOpqOXF/+NjMTHw7XL\nz0OEx1q3po+vLw8cPcreIicTPxsMLlInxy8il4rIHhHZKyL3VRJ+loisF5FCEbm7LmkZDM0ZpRT/\nTkkhXymeiYwkuJp5hx3x8fDgqTZt8PfwYOaxY5SYMbYMdaDWjl9EbOjJV4YBXYFxIuI4IWYacBvw\nn1orNBhOAT7NzeXrvDxuDQ3ldG/vWsUR4enJAxER7CwsZIk1labBUBvqUuPvB+xVSu23xgFfAYyw\nN1BKHVVKbUJPjWcwtEhSSkqYm5LCeT4+XFfLOYDLuczfn4v8/Phvejr7TZOPoZbUelhmERmFngLu\nBmt7ItBfKXVLJbazgRyllNOav4hMAaYAREZG9l6xYkWtdDUkOTk5BDTTF2tGu3vIyclhRWwsO1u1\n4v4jR4gqKalznJkeHjwSHU2k9c6goV7UNddyb666oW7ahwwZskUp1ccV2ybTq8eafGARQJ8+fVRT\n/OS6pX4K7m6as/YXN2xgm58f00NDGdupU/1FnJ3NA8eOkd6rF1cHBdVfvHY013Jvrrqh8bTXpbJw\nCGhvtx1j7TMYDECxUqwODaW9p2e997+/IiCAHj4+PJueTnZZfU7pa2gJ1MXxbwI6i8hpIuKNnh/z\nw/qRZTA0f1ZmZZHk5cW94eF41/NwCyLCAxERpJWW8lJ6er3GbTj1qbXjV0qVoCcN/xz4BVillNol\nItNEZBqAiESJSCLWZOMikigiDfNcajA0IdJLS3khPZ2z8/MZ4ufXIGl08/HhqsBA3szMJMG86DXU\ngDq18Sul1gBrHPYttFtPQjcBGQwtiv+mpZFbVsbV6elIAw6uNj00lM+tISAWREU1WDqGUwvz5a7B\nUM/8VlTE29nZjA0KIroeevFURYSnJ/8KDeXbvDzW5eU1aFqGUwfj+A2GekQpxZOpqQR4eHBTaGij\npDk+OJj2np48mZpqvug1uIRx/AZDPfJ9fj4/5udzU2goITUclqG2eItwV3g4e4uLeTc7u1HSNDRv\njOM3GOqJYqu2H+vlxdgG6lvvjIv9/Ojj68t/09JM905DtRjHbzDUE6uysthfXMxdYWF4NfJsWSLC\nveHhpJeV8bLp3mmoBuP4DYZ6ILO0lAXp6fT39W2w7pvV0c3HhysCAliSmcnBYjM8lsE5xvEbDPXA\nc+npZJWVcW94eIN236yO6WFheIowLzXVbRoMTR/j+A2GOvJLYSGrsrIYFxTEWT4+btUSaXXvjM/L\n41vTvdPgBOP4DYY6UKYUj6SkEOLhwS2N1H2zOiYGB3O6lxePpaRQaF70GirBOH6DoQ58kJPD9sJC\n7gwPJ6iRum9Wh7cID4SHc7CkhNfMhC2GSjCO32CoJZmlpTydlkZ3Hx9GNLHx3wf4+THU35+XMzJI\nNC96DQ4Yx28w1JLHUlPJLC3loYgIPNz4QtcZ94aH4wnMPHaMMvNFr8EO4/gNhlrwZW4uH+XkMDU0\nlLPd/ELXGdGenswID2dTQQHLsrLcLcfQhDCO32CoIamlpTx87Bhdvb25sZ4nWKlvRgYGMrhVK55O\nSzNDNxsqMI7fYKgBSikePnaM7LIyHm3TptG/0K0pIsKc1q31C99jxyg1TT4GjOM3GGrEyuxsvsrL\n45awMDp7e7tbjku08fTkoYgIfios5L9mOAcDxvEbDC6zMT+fR1NSuNDPj38GB7tbTo0Y5u/PqMBA\nXs7I4OOcHHfLMbgZ4/gNBhf4s7iYO5KTifXy4ok2bbA18SYeR0SEByMi6OPry/8dO8bOggJ3SzK4\nEeP4DYZqyC4r45akJACej4oiwKN5XjbeIjwTGUmEzcatyckkNfDsYIamS/M8gw2GRiK9tJTJhw9z\noLiY+ZGRdPDycrekOhFqs/F8VBR5ZWVMOnzYfNzVQjGO32BwQnJJCdcdPsy+4mL+GxXF+a1auVtS\nvdDF25tXoqPJLCtj4uHD7DPdPFscxvEbDJWwr6iIaw8fJqmkhJeiohjspjH2G4rzfH15o21bSoHr\nDh9mu2nzb1EYx28w2FGmFG9mZjL60CFyy8p4NTqavqdITd+RLt7evNm2Lf4eHkw8fJjn0tIoMv38\nWwTG8RsMFgeKi7kxKYl5qamc36oV78XEcK6vr7tlNSgdvbxYHRPDFQEBvJSRwfhDh9hdWOhuWYYG\nxtPdAgwGd/NbURGL0tP5PDcXHxHmRERwdWCgW2fSakwCPTyY26YNQ/z9mX3sGKMPHWJwq1b09fYm\nzt3iDA1CnWr8InKpiOwRkb0icl8l4SIiz1nhO0SkV13SM7QckpOTGTx4MIGBgSxYsKDe4z9cXMyS\nzEyuPXyYqxIT+TYvj38GB/NZ+/aMCgpqVKc/e/ZsJkyY4DS8W7duxMfHVxoWHx9PTEyM02MnTZrE\nzJkzXdJxsb8/a9q357bQUHYUFvJUVBSjExNZlJ5uXgCfYtTa8YuIDXgBGAZ0BcaJSFcHs2FAZ2uZ\nArxY2/SaM8uWLaNPnz4EBAQQHR3NsGHDWLduHeD8ohcR9u7dW7G9e/durrjiCoKDgwkMDGTIkCH8\n+OOPJxxTVFTE7Nmz6dy5M/7+/sTGxjJ58mQSEhIqbD7++GP69euHv78/4eHhjB8/nsTExIrwxYsX\nM2jQoCrzM2nSJDw9PTly5EjFvmnTphEQEEBAQADe3t54eXlVbA8bNoyEhAREpGJf+bJy5cpK01i0\naBERERFkZWVx0003VamnKnLLyhh57bVcO2MGr2dkcHdyMsP+/JO/HTzI46mpZJeVcVtoKF926MCd\n4eFEeNb9Ifjo0aOMGzeOtm3bMnz4cAYOHMjGjRtrHd+uXbuIi4ursy5XCLLZmGqVx6i0NDxFeDY9\nnSsSE7n4zz+ZnpTEovR0vsnN5dfCQjJLS1EO7wUcz93qiIuL45VXXqkX/WPGjGHTpk0UFhYSFRV1\nQtjdd99N586dCQwM5KyzzmLJkiX1kmZzpC5neT9gr1JqP4CIrABGALvtbEYAS5Q+MzaISIiIRCul\njpwcXd15Ji2N4gZ8OXUwJIRNNZzE+rsFC1j73HOM/M9/uGrIEGze3uz55htmrlzJ5WefzQ95eaQW\nFvJkJfG+nJ5ORGoqqX/8wXMXX8yAyZO5c/58PDw92bx8OUOGDuXG1avp2LcvAK+PH0/m4cNcvWAB\nbc87j6K8PLatXs09H3xA9IUX8urrr/P2bbcx8j//4crLL6cgK4tP587lvAsuYPratfiFhLA5J4dD\nJSWV6gEoys1l+erV+AQFccNLLxF3660AdJo7l1lz5wLwxeOPk/rHH4xbuLDiuJf+/BOAmfv2YbNz\nrn8CT6amYv+vKeDDPXsIPO00Hk9NJTEkhPUpKZRZYWVKUQaUKEUpUKwURUqRrxSFSpFdVkZWaSmZ\nZWXkK8Wh3Fy8cnLYkpZGtKcn3by9GRMUxF/9/enoQr/8kpISPGtwQ8jJyaFv377Mnz+f3bt3s2/f\nPi6//HISEhIIaGITtjjDz8ODITk5zGnXjuSSEr7OzWVrQQG7Cgv5ymEuX18Rgj08CPLwqPi4bfax\nY0QFBuIpgidgE8GGvil4cLzGKeivoj/NySE1NRX756zaPHN9vWkTITffzD3ffUfgmWeecB7/JMKV\nb75JRKdOJG7bxrQxY9jYujWx/frVIqWG4WhQUKM0r9XF8bcDDtptJwL9XbBpB5zk+EVkCvqpgMjI\nSKePtlWxvF07ihryET0gAGowyFVpdja75s2jw7x57Bw4kJ1FRVBUBP36Qb9+LEtP50hBAUVFRSyr\nJN6PsrLwSU/nwCOP4NmjB2k33cT7ACUlMHo0gTt3svihh+i8fDnZP/zA/vh4zv7yS76PjobsbB3J\nVVcB8LtS7J45k4h//YtfLrqIXwoKwNsbNWsW+cOH8+LTTxN9++2k5uaSVlJSqR6AtPfeQwUGEnr9\n9XyzbBmHK3laqSxPhdYUgCvS0xEnTrT8nztw772kffQRIsK3Cxdy2gsvEOznR/KiRaSsWkVJdjbB\nAwbQafZsfENCsCnF7unTydy8mdLCQkK7dGHQgw/S9fTT2fv22/z6wQeICDmvv05wjx5c9eijDBky\nhKVLl/JHu3YAzJs3j9atW3P99dezfft25s6dy8iRI3n77bfp06cPDzzwAOvXr+fVV18lKSmJ2NhY\n7rjjDjp16lRpXnr16sWePXvIz8+nS5cu5Ofn89Zbb3HmmWeeZJuQkEBiYiJDhw5l3bp1tGnThvvv\nv7/CduzYsdxzzz307t2bwsJCnn76aX744QfCw8O59NJLKSwsrLhefv/9d5588kkSExPp378/IkJp\naWlFeFV5GDt2LFdddRVffPEFycnJ9OzZk4ceeghvb2/aAm2B4UCeCD8nJ/PSvHkc+u03xMuL6H79\nOP+pp/hs0iQAll90EYjQZe5cggYO5Ld77yXnp59QpaUE9OpFh4cfxis6mkPz55O8YQMJmzfz/oMP\nEjZyJO1nzSJ/3z4SH36Y/F278AwLI+r22wm97LJKy7qcksxMckpK2NSuHSlvvUVRly4nnsdTp/IV\nQGYmnH46Xr178/5339Gmc+cq421MAvz8auX7aoxSqlYLMAp4xW57IvC8g83HwCC77a+BPtXF3bt3\nb9UUWbt2bY3sP/30U2Wz2VRxcbFTm1mzZqnx48eftB9Qv//+u1JKqcjISPXaa6+dZPPNN98oDw8P\nlZeXp2bMmKEGDx7sNJ033nhDAWr//v0nhf3f//2fOv/885VSSr3++utq4MCBTuP561//qu655x6V\nlJSkbDab2rx5s0t5+uOPPxRQZVnYc91116kHH3xQKaXL/ZlnnlH9+/dXBw8eVAUFBWrKlClq7Nix\nFfavvvqqysrKUgUFBWr69Omqe/fulcZVjn35VpaezWZT9957ryooKFB5eXlq69atqnXr1mrDhg2q\npKRELV68WHXs2FEVFBRUmY+1a9eqbdu2KR8fH5WRkVGpzaxZs5SPj4/65JNPVElJibrvvvtU//79\nK8I7duyovvzyS6WUUjNmzFCDBg1Sqamp6s8//1TdunVT7dq1U0opVVhYqDp06KDmz5+vioqK1Ntv\nv608PT0r8lVdHjp27Kj69u2rDh06pFJTU1WHDh3Uiy++WKnmsWPHqkceeUSVlpaq/Px89f333zst\n25SUFLV69WqVm5ursrKy1KhRo9SIESMqwi+88EL18ssvV2zn5OSomJgY9dprr6ni4mK1detWFR4e\nrnbt2lWplq+++koFBwcrf39/5eXlVfHr5+engoODVXx8/EnH5OXlqaioKPXpp59WGqe7qKmPsQfY\nrFz036Jq2TQiIgOA2UqpS6zt+60byWN2Ni8B8Uqp5db2HiBOVdPUIyLHgAO1EtawRAApNbAPA9oD\nP1Vh0xaIAsoc9tuAn4FCoDfwO+A4jZIv0A3YYcVjA/Y7Saedlc5WwPFPbw1EWumFo/O5p5I4vIFz\n0c15+eh3NwWc+FRXnicf4I9Kji11sP3VisORWKAIOGzpiUS3DFmPMnhZ8W2t5Fgb0APYbqVnH1c5\nvTlevo7pBVp528bxsuoAlDjEcQ6QAFQ13GVra0kDkpzYtAUCgN+sbV/0e7PyvJ1rpZNtrR/g+LkQ\nYR2/w4rjdGu9nLMs28Mu5OFc4JClFXQZFKLL3ZFYdNkcBhzHfXAsW0daAWei/x+s9VSOX1uhQBtO\nPAc7ov+fqnzHaUA6+tyIBHZx8rlur98LfV01JWrqY+zpqJRq7ZKlq3cIxwXdTLQfXdjeaOfWzcHm\ncuBT9FP8+cD/apteU1iowR3Vsr8UfaF5VmEzG1hayX4FnGGtJwH/rMRmCNqx+QHzgG+rSOdnK87T\nKgmbA6y31icB65zE8SCwy277WuAY4FVdnjjuKJyWhYP9YuCR8nIH8tAOLMNuKUDf0GxW/vfZ2Sig\nk2NclZVvJenFAYcc7NdYGuzTzwPGVZGHVmhn/XI1eT2hvBzLCu2YL7bWC+yvM+ASINFaHwtscoh7\nuV2+qsyDfTrW9uHKzk0rLAp42bLZBUyuomz9gJc4fsPKsmxsVng8cIOd/b1oJ2+vMwd40YmWRMum\n1O630FqfX4n9k8AWIKgm13NjLNTQx9R2qXWvHqVUCXAL8DnwC7BKKbVLRKaJyDTLbA365rDXOklq\n3z2jebIefQJeWcd4vgJGV7J/DNph51k2/UTEWd++AvQFckI8IuIBXI1uhquOa4HTRSRJRJKA+ega\nStWNr/XDQWCYUirEbvFVSh0CrkF3JLgYCEY7Tjj+2qCyWl8e2iGVE+UQ7njMQWCuQ/p+ynqadURE\nfID30Q5sqmtZdIkj6KfIcjo4hLWTE/ui2ofXKA9VoZRKUkrdqJRqi87fAhE5w4n5XehafX+lVBAw\n2Nrv7P85iK7E2OsMUEr9y4mWGHQl6yulVAi6xnyzddyd9rYiMgfd23CoUqrFTkRcp378Sqk1Sqku\nSqlOSqm51r6FSqmF1rpSSt1shZ+rlNpcH6KbC0qpTOD/gBdE5EoR8RMRLxEZJiJP1CCqOcAFIjJX\nRMJEJFBEbkU74hlWWl8BXwLviUhvEfG07KaJyGQrnruBmSJyjYj4ikgU8AoQBDxtl55Y4fbLAKAT\nujdXD2s5B1hm6WhoFgJzRaSjJbC1iIywwgLRN9hUtDN/1OHYZHQTiD3bgWtExCYilwIXVpP+y8A0\nEelvfZ/iLyKXi0igo6GIeAGr0c1hfyilHJvx6sIq4H4RCbVu8rfaha1HP2HeZp1nI9H/V43zUB0i\nMtqukpGO1eHK2nYs70B0WWSISBgwyyE6R/uPgS4iMtHKh5eI9BWRs6uQ1JvjTWN+6KdER833oysJ\nFyulatY97xTDDNlQMxbV9ACl1FPAncBMdLPIQfST0vs1iON3YBDQHf04fgRdS79EKfWDneko9FPW\nSiAT3bzTB/00sEgptRL9Ev4OtJPcjW6OGOhwIVyAvlDtl+uBD5RSO63aXpJSKgl4FhhuXdCukCEi\nOXbLndUfwiIrnQ+BL0QkG9jA8V5kS9DNCIesPG1wOP5VoKuIZIhIeblPB/6Obg4YTzX/h1VpuRF4\nHu3o9qKbxSrjAnQHmKHAOXZ5/YsLea2OOei8/gF8Abxpp7EIGGnpSgP+AbxbyzyAbg5xRl9go4jk\noP+X6crq2o1uunrDKu8xwDPo8ywF/d985hDXs8AoEUkXkeeUUtnoshuLbkpKAh5HvzdyRm9gq/W0\no9DNT448in4C2mv3nzxQRZzuoMY+pjbU+uWuwWAwGJonpsZvMBgMLQzj+A0Gg6GFYRy/wWAwtDCM\n4zcYDIYWRpMcjz8iIkLFxsa6W8ZJ5Obm4u/v724ZtcJodw9Ge+PTXHVD3bRv2bIlRbn45W6TdPyx\nsbFs3tz0uvzHx8c32vC49c2ppF2pMlIy1lFQlExpWSFlZUWEBJ5HWFAf94l0wqlU7s2F5qob6qZd\nRFwe5qZJOn6DwRlKKZJSPyMtaxPeXmHYPFpRpoo5kvIJZWXFRIQMcLdEg6HJYxy/oVmRkrGOtKxN\nhAefT1T4JYB+Akg8+g7JaV8AGOdvMFSDcfyGZkN61jaOpn9DcMC5RIYNrdgv4kFMm5EkHoXktC/w\n8PAmLKi3G5UaDE0b06vH0CwoLErhcMrH+Lc6nbatR5w0J66IjZg2I/FvdTrJqV9SUprnJCaDwWAc\nv6FZcDQ9Hg+xEdP6KjzEVqmNiI2o8EspU0UcS/+ukRUaDM0H4/gNTR6bVw5ZubsIC+6Pp2fVc9b6\nercmJLAH6VmbKCp2fZpMg6ElYRy/ocnjH/IHHh6+RARf4JJ9m9A4EA+Opq9tWGEGQzPFOH5Dkyav\n4CA+rdKICL4Am62VS8d4eQYRHnw+mTk7yS+scpZPg6FFYhy/ocmilCI57RvKSr0ID+5f/QF2RIQM\nxObRiqNp3zSQOoOh+WIcv6HJkl94kLyCBPIyO+Dh4V2jY20evoQF9ycnfy+FRbWdu9pgODUxjt/Q\nZEnN/B8eHr7k50bX6viwoD4INlKzNtazMoOheWMcv6FJUlySRVbubkIDe4KqvPtmdXja/AkOOJfM\n7J8oLS2oZ4UGQ/PFOH5DkyQtSw/SFxbUrxrLqgkL7k+ZKiY9e2v1xgZDC8E4fkOTo6yshPSsLQT6\ndcHbK6ROcbXyicLPtyNpWZtQqqyeFBoMzZs6OX4RuVRE9ojIXhG5r5Lws0RkvYgUisjddUnL0HLI\nzP2Z0rI8wmrYk8cZ4cH9KS7JIDtvT73EZzA0d2rt+EXEBrwADAO6AuNEpKuDWRpwG/CfWis0tCiU\nUqRlbsTHqzX+vrH1Emeg35l4eQaTmmle8hoMULcafz9gr1Jqv1KqCFgBjLA3UEodVUptAorrkI6h\nBZFXeJCCoiTCgvudNBBbbRHxICyoL3kFBygoTK6XOA2G5kxdHH874KDddqK1z2CoNWlWF86QgPPq\nNd6QwJ6IeJKW9b96jddgaI40mfH4RWQKMAUgMjKS+Ph49wqqhJycnCapyxWag3YPWyFhbXeRnx3D\ndwk/VuyvL+0BYRGUlW7n992+KOVV5/hcoTmUuzOaq/bmqhsaT3tdHP8hoL3ddoy1r1YopRYBiwD6\n9OmjmuKcmS11Ls/GIjntG1IyoHu3kXh7hVbsry/t+YVnsf/QS3TrHtBos3Q1h3J3RnPV3lx1Q+Np\nr0tTzyags4icJiLewFjgw/qRZWhpHO/CeeYJTr8+0V07O5iunYYWT60dv1KqBLgF+Bz4BVillNol\nItNEZBqAiESJSCJwJzBTRBJFJKg+hBtOLbJyd+kunHX8YKs6woL6UVySTk7+3gZNx2BoytSpjV8p\ntQZY47Bvod16EroJyGBwilKK1MyN+HhF4N/qtAZNK8j/LDxtgaRm/o9Avy4NmpbB0FQxX+4a3E5+\n4UEKio7UaxdOZ4jYCAvqS27+PvILkxo0LYOhqWIcv8HtHEv/HpuHHyEB3RslvbCgvniIDykZ3zdK\negZDU8M4foNbyS84RE7+XsJDBtR4zP3aYrP5Ehbcl6zc3WasfkOLxDh+g1s5lvG9njQlqG+jphse\nfD4inqRkrGvUdA2GpoBx/Aa3UVCYRHbeHsKCz8fm4dOoaXva/AkN7E1Gzg6KijMaNW2Dwd0Yx29w\nG8cyvsdDfAgPqp9ROGtKRMgFCEJK5g9uSd9gcBfG8RvcQkHRUbJydxMW3A+bzdctGrw8gwgJ7EFG\n1jYKi1PdosFgcAfG8RsaHaUUR1I+webhS3jw+W7V0jo0DhFPjqR8ilLKrVoMhsbCOH5Do5ORvY28\ngj+JDPsbnjY/t2rx8gykTdgQcvP3kZW7261aDIbGwjh+Q6NSUpJDUtqX+Pl2JCSwp7vlALpfv693\nFEmpn1FaVuhuOQZDg2Mcv6FROZL6OaqsmLYRwxv8K11XEfEgOuJySkpzOJYe7245BkODYxy/odHI\nyv2FrNyfiQj9Cz7eEe6WcwJ+vjGEBvYmNXMj2bm/uVuOwdCgGMdvaBTyCg6SePRdWvm0JSJkoLvl\nVEpk+FB8vaNIPLqa/MIj7pZjMDQYxvEbGpyComP8mbQML88gOkRdg4c0mYnfTsDm4U2HqHHYPFrx\nZ9Iyiksy3S3JYGgQjOM3NChFxRkcOLIUEU86Rk3A0+bvbklV4uUZSIfo8ZSVFXPgiHH+hlMT4/gN\nDUZmzs/sO/QSZWWFdIwa32Aza9U3vt5taB81huKSDPYlLiQrd4+7JRkM9Ypx/IZ6p6Q0j8Sj75J4\n9B18vCI4PWYKvj5RNYojOTmZwYMHExgYyIIFCxpIqXMCWp3O6TFT8fIM5WDyCo6kfEpJaV6DpDV7\n9mwmTJjgNLxbt25OJ+COj48nJsb5XEeTJk1i5syZdZVoOMWok+MXkUtFZI+I7BWR+yoJFxF5zgrf\nISCu4RwAABujSURBVCK96pJec2XZsmX06dOHgIAAoqOjGTZsGOvW6VEhnV30IsLevcenB9y9ezdX\nXHEFwcHBBAYGMmTIEH788ccTjikqKmL27Nl07twZf39/YmNjmTx5MgkJCRU2H3/8Mf369cPf35/w\n8HDGjx9PYmJiRfjixYsZNGhQlfmZNGkSnp6eHDly/AXo1KlT+f/2zj3Op2r//8/3XFyHQcn9aBI6\nbilSwsFRHVKJwnGLJFGSRxfnpJBOt18XOnyLcCp3OuWknFSokS7kmmOokDtDYoxhzO3z/v2x9kyf\n+czFZy6fm896Ph7rMXvvtfZar7Vmfd577bXXfu+YmIpUrFie8uUrE1enN21avESLxo9w5x0D2Ldv\nHyJCTExMrrBkyZJ8y5g5cyaXXnopycnJPPjgg4XquRDFNX5lo6sRV2co1Sq34WTy9/x8YAqHf/2I\n1LTEQt/yPX78OP369aN27drcdttttGvXjvXr1xdbf0JCQkh9PNyz716ITp06MXv27FIpu0+fPmzY\nsIG0tDRq1sw92Bg7diz16tWjcuXK1K9fnxdeeKFUygxFim34RSQSeAPoBjQB+olIE49k3YCGThgO\nTC9ueaHK5MmTGTNmDOPGjePYsWMcOHCAhx56iI8+8v679Hv27KFdu3Y0b96cvXv3cuTIEXr27Mkt\nt9zCd999l5Pu7rvv5qOPPmLhwoWcPn2aH374gdatW7N69WoA3n//ffr378+YMWM4ceIECQkJlC1b\nlvbt23Pq1KkL6lBVzpw5zQcffEBsbCVmvz2ZX099xYHExTw6rhHrt41l4/aJjB7Th7t79+RsSiop\nKSmsWLEiJ4+kpCRSUlJyQt++ffMta//+/TRp0iTga/0jJIrqVW6mQd2RVIm5mtMp/+OXw2/x84HX\nOJC4hBNJ35B8diep5w+TkXkGlyuTM2fOcN1117Fp0yaWLVvG4MGD6d69OykpKQGtSziwadMmGjdu\nzLZt22jWrFmuuKFDh5KQkEBycjLffvstCxYsYOnSpQFSGlhKsryiDbBbVX8BEJHFQA/A/b33HsBc\nNcOjdSJSRURqqapP1sr9cng2Ls3wRdYAVK15lt2Hdnqd/kxyKuPHj+Ol1wbRos0xjp6aC8BVLeGq\nlnHsPjSdk8kbOHPuV3YfyntN3H90AZS7jMfHvsvV19Th3pF1OXnOjJBv7RXNug3XMnrMPSz64FG+\nWfsjn3/+KSu/mkjVWhvZl7gRFG65XYB0UlI2MGDgS4wY1YU2HU5z+MS7APx94rV8/c2njJ80gDGP\ndefYyXWkph1l18FpqLpQdQFZuFyZuDSdZUu3UDEmgiHDbmDunDn06hdDmehLqFj+CiqWjyO2YhMq\nVXiByIjif8x8yJAhLFiwABHh9ddfZ9KkSfzpT3/i5ZdfZtasWSQlJdGlSxdmzJhBtWrVAOjduzdr\n164lNTWVq6++munTp9O0aVNmzpyZK6/OnTvz8ccfIyLs2rWLK6+8MqfMunXr8txzzxEfH8/AgQN5\n+OGHmTJlCjfffDPz5s1j83p4+ukl7N23l4YN6zDxuR5c0fDHvBWQCLr1KkNy+gKq18ukXsPynH8s\nhZVrnqV5i/qA+8VMOHl6AyeTEul51/Ws/OwHatWuxsuT76H51fUB6HTj0zz/8kDadbiK86npTBi3\nmNUrf6D6ZbHc1actmVlnc/pPwvaDjHtiPvv3Hqdj56aICBUqH8uJ/2LV/5jyysccPvQbVzasybMv\n9uOqP5qpok5tn2bg4I58+MF6Dh8+yY3tGlO97lDKlovO2zf3HufJJ+azc8choqMiaduuMf+cPox+\nd00GoEULU/YLrwyg/Z+a8PiYd/lhyz6ysrK4tnUDnn2xH7VqVWXy/1vG2rVf8d26b3jkkVH06n0D\nE5/ry57diTw74T0Sth2g2iUxjHn8dm69vVWh/eZ00jnSM09Tv9HPfLh0EXENJdfvKioGjiebkHg0\nicysJNZtmkeLNscu3Cn9ROxlaUAnn5dTEsNfBzjotn8I8PSvm1+aOkAewy8iwzF3BdSoUaPAOc3C\nqHRJOiK+c7SVmRlNVqbL6/Tffr2HtLQMrr+hOclJ+Z+Xdl7JSCff+JQzSnKSi6/X7GTUI3fkSdOx\n8zUsWfg1xxPP8+WqnTRtVp+KFarkm9fuXac4cvgkHf7UkuTT7vFCpz+35KsvdzJ0WHdSz0FWFiQn\nRQJRqEaACqoRqEbyn/eWcNPNHWjftievvvg536yqTqNG2Td6ScC37Nu3j2PHjuX6HyYmmu/brlmz\nhsjIyELbbciQISQmJlK9enXuu+8+UlJSGD16NF988QUvvfQSsbGxTJs2jd69ezN+/HgA4uLiuPfe\ne4mKimLmzJn06NGD2bNn06hRI7p06ZKTF5Cja/369TnTXImJiWRlZREfH8/WrVs5evQoW7duZe7c\nuagqs2bNYuzYsTz//PM0btyYlStXcv/gd5k3/1+UL+8iIiqNiMh0JCITkSwkIgvBRWZmOtt/OEJG\nehbVqtYg+TSAex9V0tKU1Su38cqU+xk3YSBvTvuYCU8tYc6CxwFwueBciovkJBdTp/yXvb/8yof/\nfYbU1HRGj3wTdZn+k5GRyYihM+g/sDN9+nVkzZfbGPe3dxhy780kJ7n4cedB/v7YPKZMG0GTpn/g\nk+XfM3zIDJZ+PJ4yZaJxuWD5ss38840HKVM2mqGDXmPB3G+5u0+HPP+jV178mOvaXMWbM0eTkZHF\njoQDJCe5eOtfY2jVfBSL3n+Sen+obnpFUgq3dr+e514cisvlYtL4BYz/2xImTx3OsAdu5/t1e+h2\nWxt63nUjAMeOpDL4r1MZ8VB3pkwdye5dR3hw+P9Ru3ZNrmhQK4+W79f9xBOPziYrM4uMjCzaXzee\n8+cziIqOZPGCb5j8z+G0uq4hAO/M/px/zfyU1NR06tS5hM6dWxX42wwE6WlRxbJ9RUZVixWAu4HZ\nbvuDgP/zSLMcaO+2vxpofaG8W7VqpcHIl19+WaT08+fP1xo1ahSaZuLEiRodHa2xsbG5AqC7du1S\nVdXIyEhdsWJFnnN37typgB46dEiHDRumffv2LbCcqVOnKqCpqal54qZPn65XXnmlqqq+88472q5d\nu3zz2L9/v4qIbtmyRVVVb7nlFh09enS+dRowYECuY3v37lUgTz137NiRb1mDBw/Wp556SlVNu191\n1VW6atWqnPgjR45oVFSUZmRk5Dn31KlTCmhSUlKevLJxb9/8youOjs7VViNGjNCnn346Vx6NGjXS\n+Pj4fPVns3z5cm3WrJm+8MILBaaZOHGidunSJWc/ISFBy5Url7Nfv359XblypaqqxsXF5eoLb731\nltapU0dVVdesWaO1atVSl8uVE9+2bducel2oDvXr19d58+blxPXt21cfeOCBfDUPGjRI77//fj14\n8GCeOM+29WTLli1apUqVnP2OHTvqrFmzcvYXL16s7du3z3XO8OHD9ZlnnikwT1XVAQMG6Icffqif\nfPKJNmzYUM+dO5dvOpfLpZs3b9YJEyZocnJyoXn6m6LaGHeAjeql/RYtpitaEWkLPKOqf3H2n3Qu\nJC+6pXkLiFfVRc7+T0AnvcBUj4j8CuwvljDfcilQlI+0VsY839hUSJraQFlgr8fxVsB2IA24GnO3\n5Ok0vhLQCNgC1AJigILWHtZyyvofkJ6PhsrAj8AlmHrml09NJz7B2b8EqAtsI/cwNr86lQGaU3hb\nuHO5o/OIo6eec9y9nAinPhmYO8mqQLSTJpLf2889r2zc29ezvEpAnFOvbK50jruXL5h+erKAOgjQ\nFDhD4f3Zs70826o5sM/J51rMdOp5J66yo30bpv41Aff5yDinjke8qIN7Odl1ziJv3wQzW1AHiHXS\nJPJ7//Rs2wjM/68yv88yRLjVr7FzbvZvq4aTd+5bU5PmQD5aWjj5RTpasp9dupw8D+VzDpi2iiok\nPhAU1ca4U19Vq3uV0tsrhGfANNgvmI5VBvgBaOqRpjuwAvNPuwH4vrjlBUOgCFdUJ30scBa4u5A0\nzwDz8zmuwJXO9nzgk3zSTAe+drZvAlKBugVpx0y7jfU4HoH5kT7n7A/JzjOfPH5yykh0wglHZ48L\n1QljnBSI8rLt3nXTtNEpu10BaQdhjF2c09eqeLTfO9l5uZ1zFmjhtv+pW3mdgEMe6d8CnirC/74s\n8BnGWEVcIG2u9vJsK4wxvsnZ3gt0dUt7f7ZWoCPGwItb/Ddu9Sq0Du7lOPtH8uub+ZzXHnMhym7v\nnLZ39scD8UBNZ7+lR/2+BIa5pe8HrCzib+0G4DNn+5h7foWc8zSwrCjl+DpQRBtT3FDsVT2qmgmM\ncjr3TuA9VU0QkREiMsJJ9gnm4rAbmAWUbF1eiKGqp4EJwBsicqeIVBCRaBHpJiIvFyGrScCNIvK8\niFQTkUoi8jBwD/A3p6xVwErgPyLSSkSinHQjRGSok8/jwNMi0l9EyolITWA2ZiQ2xa08ceLdQ1ug\nAeahfksnNAMWOjp8zQzgeRGp7wisLiI9nLhKmNHlb0AFwHOd3jHgCo9jW4H+IhIpIl0xRrMwZgEj\nROR6Z5lyRRHpLiKVPBOKSDTwPuYiuVfNE/LS4j3gSRGpKiJ1gYfd4r4DMoHRTj/rhfl/FbkOF0JE\nejvlA5zCGPLsenq2dyVMWySJSDVgokd2numXA41EZJBTj2gRuU5E/liIpFbAZme7Amaw4K43QkQe\ncNpNRKQN8BBm+jn8CPQVLpQCxbwaAwMwHfEsZqT8X+BGJ+4ZLjDid/abYX4QyUAKZgTV3uOcMpiL\nxG6nrP0Yw/6HbO2YlVYbnPiTwCKgnlseQ5yyPcNs4IN8dLbBGN1qbsfy1InfR7EpHuHRAtrsXXKP\n+COARzEj/zPAHuAFJz4GWMbvUyr3kHvE3xBj6JOAD51jrTFTVmeAeU47FDjid453ddouCbNA4d9A\npXzSdXTKP4eZesiua4cC6pqrvSh8xF8BmOto2AE84a7VqdcWp15LnPCcN3WgCCN+4GXgsFOvPcBw\nt7gRTt5JQB/MVFa8k/Zn4AGP+rV1jp8CpjrHGmN+J79iLuhfAC0L+Y29DfTG3PFlANEe8RGYu7qT\nbjrG4XZ3FAwBP434iz3HH46IyHBVnRloHcXBag8MVrv/CVXd4D/t1vBbLBZLmGF99VgsFkuYYQ2/\nxWKxhBnW8FssFkuY4WvvnAMcr5z/E5FvReTqkpRnsVgslpJTkjd3IzFLom7GvPm2Aeinqjvc0twI\n7FTVUyLSDfOmr6c/nzxceumlevnllxdLly85e/YsFSsG9xekCsJqLyGqUAxPoUGhvZiEqvZQ1Q0l\n075p06YT6oc3d9vivCnn7D8JPFlI+qrAYW/yvlh89QQTVnsROXlSdfx41a5dVWvUUI2OVv3iiyJn\nY9vd/4SqbtXQ8NVzN+bV8WHO/iDgelUdVUD6x4GrstPnE+/unbPV4sWLi6XLl6SkpBATExNoGcXC\nai8af/zHP7gsPp6zl1/OmUaNiN2+nchz59g4axYZjitob7Dt7n9CVTeUTHvnzp03qWprrxJ7e4Xw\nDHjhndMtrjPGrcMl3uRtR/ylj9VeBFavVgVVd2+Q27apliunetNNqpmZXmdl293/hKpuVf+N+Evy\ncPcwv3tMBOOl8bBnIhFpgXndv4eqenqXtFiCi/R0GDUKrrgCxo79/Xjz5jBtGqxaBS++WPD5FksI\nUBLDvwFoKCJxIlIG+CuQ63uCIvIHYCkwSFV/LkFZFot/eP112LkTpk6F8uVzx913HwwYABMnwtat\ngdFnsZQCvvbOOQHjs/1NEdkqIhsLyM5iCTyHDsGzz0KPHtC9e954EXjjDXNBmDbN//osllKiJJ9e\nRFU/wbhedj82w217GJDvw1yLJeh49VXIyDCj/oKIjYX+/WH+fJO+alX/6bNYSgn75q7FApCWZox5\nz55woXdIRo6E1FSYO9cv0iyW0sYafosF4OOP4bffYOjQC6e95hq4/nqYMcO82GWxhBjW8FssAG+/\nDfXqQZcu3qUfORJ+/BHWrPGtLovFB1jDb7EcOgSffQZDhkBkpHfn9Olj5venT/epNIvFF1jDb7HM\nnQsulzH83lK+vEm/dCkcO+YrZRaLT7CG3xLeqJppns6dzUtbReGBByAzE+bN8402i8VHWMNvCW/W\nroU9e7x7qOtJ48bQujUsWlT6uiwWH2INvyW8mTMHKlWCXr2Kd37//rB5M/xsX0y3hA7W8FvCl/Pn\n4YMPjNGvUKF4efTta97otaN+SwhhDb8lfFmxAk6fNqP24lK7NnTqBAsX2jX9lpDBGn5L+LJwIVx2\nGfz5zyXLp18/M9WzZUvp6LJYfIw1/JbwJDkZli836/GjSuSyCu66C6KjzYXEYgkBrOG3hCcffmjm\n+EsyzZNNtWrQtSssXmzeB7BYghxr+C3hycKFxhnbDTeUTn79+8Phw/DVV6WTn8XiQ6zht4Qfx4+b\nL2n162dW5JQGt98OMTHWY6clJLCG3xJ+/PvfkJVlDH9pUbGiWdr53nuQklJ6+VosPsAafkv48c47\n5hu6zZuXbr733gtnz8L775duvhZLKWMNvyW82LTJhOHDSz/vG2+ERo2M7x+LJYixht8SXsyaZTxr\nDhxY+nmLmFH/2rWwe3fp52+xlBLW8FvChzNnYMECMxdfpYpvyrjnHoiIgHff9U3+FkspYA2/JXxY\nvNg8ePXFNE82tWvDX/5iDH9Wlu/KsVhKgDX8lvBh5kxo1qz01u4XxNChZk3/ypW+LcdiKSbW8FvC\ng82bYeNG8/GU0lq7XxC33258AL32mm/LsViKiTX8lvBg+nTfPdT1pGxZeOIJWLWKytu3+748i6WI\nWMNvufj5+Wezdn/oUN891PVk5EioXp3L58zxT3kWSxGwht9y8TNunBntjx/vvzIrVoQnnqDaxo3w\n3Xf+K9di8QJr+C0XN+vWma9sPf441Kjh37IffJD02FiYNMm/5VosF8AafsvFiyqMHWsM/mOP+b/8\nihU52LcvfPaZuQBZLEGCNfyWi5fly81btBMnGs+ZAeDInXdC9epmNVFqakA0WCyeWMNvuTg5dgwe\nfhgaNoRhwwImI6t8eZgzB7Ztg9GjA6bDYnHHGn7Lxce5c3DHHfDrr7BokfksYiDp1s08YJ492/rr\ntwQF1vBbLi5cLhg0CDZsMEa/VatAKzJMmgQdO5plnnZtvyXAWMNvuXhIS4NRo2DpUpg82Yz6g4Wo\nKHMhqlQJOnSAZcsCrcgSxpTI8ItIVxH5SUR2i8jf84kXEZnqxG8TkWtLUp7FUiBffw0tW5o3dB97\nDB55JNCK8lKrFnzzDTRoAHfeCY8+CunpgVZlCUOKbfhFJBJ4A+gGNAH6iUgTj2TdgIZOGA5ML255\nFksezp+HFSuMK+QOHcyqmU8/hVdf9b0/nuLSoIEx/qNGwZQp0LixWXW0Z0+glVnCiKgSnNsG2K2q\nvwCIyGKgB7DDLU0PYK6qKrBORKqISC1VPVqCcgsmMdGs3fYRZX77DY76RrqvCRrtBf1/VH8PLhdk\nZpqQkUGlHTuMi+PkZDh4EH75xXzoJD7efOowJsaMnidNCtiyzSJRtixMmwZdu8Lrr8M//gHPPms8\nhzZtCk2aQFyccS9RpYqZHoqOhjJlzF8R4/NfJPcFrqDtYhI0faaIhKpugOiTJ/1STkkMfx3goNv+\nIeB6L9LUAXzzX2nQwKzo8BE3+ixn3xPK2vM8nq1Y0RjGgQOhRw/o3BnKlQuEtJLRvbsJBw/C/Pnm\nTuD7780H2304gPGWUO0zoaob4LqqVaFXL5+XUxLDX6qIyHDMdBA1atQgPj6+yHnUfPBBxIcfv0hL\nS6Ns2bI+y9+XBJX2gkajIqgzitXISDQqCo2M5CwQXbUqWRUqkFa9OhmxsbnzCOK3YlNSUrzry23b\nmgBEnD9P2RMniDp7lqiUFCLPnUMyM4nIzEQyM3PujMT94lDQdgkIqj5TBEJVN8A54HQxbF9RKYnh\nPwzUc9uv6xwrahoAVHUmMBOgdevW2qlTp6IrKs45RSA+Pp5i6QoCrPbAYLX7n1DVDf7TLlrM0YGI\nRAE/A10wxnwD0F9VE9zSdAdGAbdipoGmqmobL/L+FdhfLGG+5VLgRKBFFBOrPTBY7f4nVHVDybTX\nV9Xq3iQs9ohfVTNFZBTwGRAJvK2qCSIywomfAXyCMfq7MXcx93qZt1fi/Y2IbFTV1oHWURys9sBg\ntfufUNUN/tNeojl+Vf0EY9zdj81w21bgoZKUYbFYLJbSxb65a7FYLGGGNfxFY2agBZQAqz0wWO3+\nJ1R1g5+0F/vhrsVisVhCEzvit1gsljDDGn6LxWIJM6zh9wIReUVEfnQ8jP5HRKq4xT3peB/9SUT+\nEkid+SEivUUkQURcItLaIy7YtRfq/TWYEJG3ReS4iGx3O1ZNRFaKyC7nb9VAaiwIEaknIl+KyA6n\nrzziHA96/SJSTkS+F5EfHO2TnONBrx2Ms0sR2SIiy519v+i2ht87VgLNVLUF5qW1JwEcb6R/BZoC\nXYE3Ha+lwcR2oBfwlfvBYNfupffXYOJdTDu683dgtao2BFY7+8FIJvCYqjYBbgAecto6FPSnAX9W\n1auBlkBXEbmB0NAO8Aiw023fL7qt4fcCVf1cVTOd3XUY1xNgvI8uVtU0Vd2LeVHtgm8m+xNV3amq\nP+UTFezac7y/qmo6kO39NShR1a8AT9eKPYA5zvYc4E6/ivISVT2qqpud7TMYQ1SHENCvhhRnN9oJ\nSghoF5G6QHdgttthv+i2hr/oDAVWONsFeR8NBYJde7Dr84Yabi7IE4EagRTjDSJyOXANsJ4Q0e9M\nl2wFjgMrVTVUtL8OjAVcbsf8ojtovHMGGhFZBdTMJ+opVV3mpHkKc1u8wJ/aLoQ32i2BRVVVRIJ6\n7bSIxAAfAGNUNVncPKAGs35VzQJaOs/e/iMizTzig067iNwGHFfVTSLSKb80vtRtDb+Dqt5UWLyI\nDAFuA7ro7y8/eO191JdcSHsBBIX2Qgh2fd5wLPvDQyJSCzMiDUpEJBpj9Beo6lLncMjoB1DVJBH5\nEvOsJdi1twPuEJFbgXJAZRGZj59026keLxCRrphbsjtU1f1LLx8BfxWRsiISh/nE5PeB0FgMgl37\nBqChiMSJSBnMg+iPAqypqHwEDHa2BwNBefclZmj/L2Cnqk52iwp6/SJSPXuVnYiUB24GfiTItavq\nk6paV1Uvx/TtL1R1IP7Srao2XCBgHnweBLY6YYZb3FPAHuAnoFugteajvSdmfjwNOAZ8FkLab8Ws\notqDmbYKuKZCtC7CfFkuw2nv+4BLMCszdgGrgGqB1lmA9vaYB6Lb3Pr4raGgH2gBbHG0bwcmOMeD\nXrtbHToBy/2p27pssFgsljDDTvVYLBZLmGENv8VisYQZ1vBbLBZLmGENv8VisYQZ1vBbLBZLmGEN\nv8VisYQZ1vBbLBZLmPH/AfHoVrVT7RZbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba428ddac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEICAYAAABfz4NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4283f5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42a55470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEICAYAAACEdClSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42d35780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42bab0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42c95a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsIbfYrFYChnW8FssFksh4/8BZQxRcqs64/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42cefc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4jfGmBhjid8YY2LM/wdopQGr2Gsr3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba427fe358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEICAYAAABfz4NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nu4A2w3xoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba475259e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY4nfGGOijCV+Y4yJMpb4jTEmyvx/QKvjE4PRpVoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba42948128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "from matplotlib import (cm, pyplot as plt, mlab)\n",
    "\n",
    "def visualize(word, model):\n",
    "    \"\"\" visualize the input model for a particular word \"\"\"\n",
    "    variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])\n",
    "    figures = []\n",
    "    for parm_idx in range(len(model.means_[0])):\n",
    "        xmin = int(min(model.means_[:,parm_idx]) - max(variance[:,parm_idx]))\n",
    "        xmax = int(max(model.means_[:,parm_idx]) + max(variance[:,parm_idx]))\n",
    "        fig, axs = plt.subplots(model.n_components, sharex=True, sharey=False)\n",
    "        colours = cm.rainbow(np.linspace(0, 1, model.n_components))\n",
    "        for i, (ax, colour) in enumerate(zip(axs, colours)):\n",
    "            x = np.linspace(xmin, xmax, 100)\n",
    "            mu = model.means_[i,parm_idx]\n",
    "            sigma = math.sqrt(np.diag(model.covars_[i])[parm_idx])\n",
    "            ax.plot(x, mlab.normpdf(x, mu, sigma), c=colour)\n",
    "            ax.set_title(\"{} feature {} hidden state #{}\".format(word, parm_idx, i))\n",
    "\n",
    "            ax.grid(True)\n",
    "        figures.append(plt)\n",
    "    for p in figures:\n",
    "        p.show()\n",
    "        \n",
    "visualize(my_testword, model)\n",
    "visualize(my_testword, model1)\n",
    "visualize(my_testword, model2)\n",
    "visualize(my_testword, model3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  ModelSelector class\n",
    "Review the `ModelSelector` class from the codebase found in the `my_model_selectors.py` module.  It is designed to be a strategy pattern for choosing different model selectors.  For the project submission in this section, subclass `SelectorModel` to implement the following model selectors.  In other words, you will write your own classes/functions in the `my_model_selectors.py` module and run them from this notebook:\n",
    "\n",
    "- `SelectorCV `:  Log likelihood with CV\n",
    "- `SelectorBIC`: BIC \n",
    "- `SelectorDIC`: DIC\n",
    "\n",
    "You will train each word in the training set with a range of values for the number of hidden states, and then score these alternatives with the model selector, choosing the \"best\" according to each strategy. The simple case of training with a constant value for `n_components` can be called using the provided `SelectorConstant` subclass as follow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states trained in model for VEGETABLE is 3\n"
     ]
    }
   ],
   "source": [
    "from my_model_selectors import SelectorConstant\n",
    "\n",
    "training = asl.build_training(features_ground)  # Experiment here with different feature sets defined in part 1\n",
    "word = 'VEGETABLE' # Experiment here with different words\n",
    "model = SelectorConstant(training.get_all_sequences(), training.get_all_Xlengths(), word, n_constant=3).select()\n",
    "print(\"Number of states trained in model for {} is {}\".format(word, model.n_components))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cross-validation folds\n",
    "If we simply score the model with the Log Likelihood calculated from the feature sequences it has been trained on, we should expect that more complex models will have higher likelihoods. However, that doesn't tell us which would have a better likelihood score on unseen data.  The model will likely be overfit as complexity is added.  To estimate which topology model is better using only the training data, we can compare scores using cross-validation.  One technique for cross-validation is to break the training set into \"folds\" and rotate which fold is left out of training.  The \"left out\" fold scored.  This gives us a proxy method of finding the best model to use on \"unseen data\". In the following example, a set of word sequences is broken into three folds using the [scikit-learn Kfold](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html) class object. When you implement `SelectorCV`, you will use this technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train fold indices:[2 3 4 5] Test fold indices:[0 1]\n",
      "Train fold indices:[0 1 4 5] Test fold indices:[2 3]\n",
      "Train fold indices:[0 1 2 3] Test fold indices:[4 5]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "training = asl.build_training(features_ground) # Experiment here with different feature sets\n",
    "word = 'VEGETABLE' # Experiment here with different words\n",
    "word_sequences = training.get_word_sequences(word)\n",
    "split_method = KFold()\n",
    "for cv_train_idx, cv_test_idx in split_method.split(word_sequences):\n",
    "    print(\"Train fold indices:{} Test fold indices:{}\".format(cv_train_idx, cv_test_idx))  # view indices of the folds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tip:** In order to run `hmmlearn` training using the X,lengths tuples on the new folds, subsets must be combined based on the indices given for the folds.  A helper utility has been provided in the `asl_utils` module named `combine_sequences` for this purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Scoring models with other criterion\n",
    "Scoring model topologies with **BIC** balances fit and complexity within the training set for each word.  In the BIC equation, a penalty term penalizes complexity to avoid overfitting, so that it is not necessary to also use cross-validation in the selection process.  There are a number of references on the internet for this criterion.  These [slides](http://www2.imm.dtu.dk/courses/02433/doc/ch6_slides.pdf) include a formula you may find helpful for your implementation.\n",
    "\n",
    "The advantages of scoring model topologies with **DIC** over BIC are presented by Alain Biem in this [reference](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.6208&rep=rep1&type=pdf) (also found [here](https://pdfs.semanticscholar.org/ed3d/7c4a5f607201f3848d4c02dd9ba17c791fc2.pdf)).  DIC scores the discriminant ability of a training set for one word against competing words.  Instead of a penalty term for complexity, it provides a penalty if model liklihoods for non-matching words are too similar to model likelihoods for the correct word in the word set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part2_submission'></a>\n",
    "### Model Selection Implementation Submission\n",
    "Implement `SelectorCV`, `SelectorBIC`, and `SelectorDIC` classes in the `my_model_selectors.py` module.  Run the selectors on the following five words. Then answer the questions about your results.\n",
    "\n",
    "**Tip:** The `hmmlearn` library may not be able to train or score all models.  Implement try/except contructs as necessary to eliminate non-viable models from consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "words_to_train = ['FISH', 'BOOK', 'VEGETABLE', 'FUTURE', 'JOHN']\n",
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# autoreload for automatically reloading changes made in my_model_selectors and my_recognizer\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete for FISH with 3 states with time 0.0687227400012489 seconds\n",
      "Training complete for BOOK with 15 states with time 4.393525449999288 seconds\n",
      "Training complete for VEGETABLE with 15 states with time 1.4591524580009718 seconds\n",
      "Training complete for FUTURE with 15 states with time 4.303278168999896 seconds\n",
      "Training complete for JOHN with 15 states with time 41.328735560000496 seconds\n"
     ]
    }
   ],
   "source": [
    "# TODO: Implement SelectorCV in my_model_selector.py\n",
    "from my_model_selectors import SelectorCV\n",
    "\n",
    "training = asl.build_training(features_ground)  # Experiment here with different feature sets defined in part 1\n",
    "sequences = training.get_all_sequences()\n",
    "Xlengths = training.get_all_Xlengths()\n",
    "for word in words_to_train:\n",
    "    start = timeit.default_timer()\n",
    "    model = SelectorCV(sequences, Xlengths, word, \n",
    "                    min_n_components=2, max_n_components=15, random_state = 14).select()\n",
    "    end = timeit.default_timer()-start\n",
    "    if model is not None:\n",
    "        print(\"Training complete for {} with {} states with time {} seconds\".format(word, model.n_components, end))\n",
    "    else:\n",
    "        print(\"Training failed for {}\".format(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows of transmat_ must sum to 1.0 (got [ 1.  1.  1.  1.  0.  1.])\n",
      "Training complete for FISH with 5 states with time 0.15174968199971772 seconds\n",
      "Training complete for BOOK with 8 states with time 4.426533006999307 seconds\n",
      "Training complete for VEGETABLE with 9 states with time 1.3518997889987077 seconds\n",
      "Training complete for FUTURE with 9 states with time 3.834533873001419 seconds\n",
      "Training complete for JOHN with 13 states with time 33.3537518510002 seconds\n"
     ]
    }
   ],
   "source": [
    "# TODO: Implement SelectorBIC in module my_model_selectors.py\n",
    "from my_model_selectors import SelectorBIC\n",
    "\n",
    "training = asl.build_training(features_ground)  # Experiment here with different feature sets defined in part 1\n",
    "sequences = training.get_all_sequences()\n",
    "Xlengths = training.get_all_Xlengths()\n",
    "for word in words_to_train:\n",
    "    start = timeit.default_timer()\n",
    "    model = SelectorBIC(sequences, Xlengths, word, \n",
    "                    min_n_components=2, max_n_components=15, random_state = 14).select()\n",
    "    end = timeit.default_timer()-start\n",
    "    if model is not None:\n",
    "        print(\"Training complete for {} with {} states with time {} seconds\".format(word, model.n_components, end))\n",
    "    else:\n",
    "        print(\"Training failed for {}\".format(word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete for FISH with 3 states with time 1.3268350929993176 seconds\n",
      "Training complete for BOOK with 15 states with time 6.495769947001463 seconds\n",
      "Training complete for VEGETABLE with 15 states with time 4.373158266998871 seconds\n",
      "Training complete for FUTURE with 15 states with time 6.862646920999396 seconds\n",
      "Training complete for JOHN with 15 states with time 36.05223161600043 seconds\n"
     ]
    }
   ],
   "source": [
    "# TODO: Implement SelectorDIC in module my_model_selectors.py\n",
    "from my_model_selectors import SelectorDIC\n",
    "\n",
    "training = asl.build_training(features_ground)  # Experiment here with different feature sets defined in part 1\n",
    "sequences = training.get_all_sequences()\n",
    "Xlengths = training.get_all_Xlengths()\n",
    "for word in words_to_train:\n",
    "    start = timeit.default_timer()\n",
    "    model = SelectorDIC(sequences, Xlengths, word, \n",
    "                    min_n_components=2, max_n_components=15, random_state = 14).select()\n",
    "    end = timeit.default_timer()-start\n",
    "    if model is not None:\n",
    "        print(\"Training complete for {} with {} states with time {} seconds\".format(word, model.n_components, end))\n",
    "    else:\n",
    "        print(\"Training failed for {}\".format(word))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2:**  Compare and contrast the possible advantages and disadvantages of the various model selectors implemented.\n",
    "\n",
    "**Answer 2:**\n",
    "\n",
    "# BIC \n",
    "   ### Advantage\n",
    "     * Uses al lower nomber of states than CV and DIC\n",
    "     * penalizing models that have high complexity\n",
    "   ### Disadvantage\n",
    "      * not as accurate as CV - it might require more traning data (due to only being evaluated with the train data and not using folds to simulate test data)\n",
    "\n",
    "# CV\n",
    "   ### Advantage\n",
    "     * Kfolds is a powerfull way to train on a samll data set but it leads to a more computitional power to find optimal values \n",
    "\n",
    "   ### Disadvantage\n",
    "     * With eatch fold a new model created an evaluated \n",
    "# DIC\n",
    "   ### Advantage\n",
    "     * Takes the longest Time to train and evaluate the model\n",
    "     * DIC does better with more states in comparison to BIC ref reading source \n",
    "\n",
    "   ### Disadvantage\n",
    "    * DIC is a more complex information criterion which has a more sophisicated means of finding the effective number of parameters\n",
    "    * DIC has some theoretical limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part2_test'></a>\n",
    "### Model Selector Unit Testing\n",
    "Run the following unit tests as a sanity check on the implemented model selectors.  The test simply looks for valid interfaces  but is not exhaustive. However, the project should not be submitted if these tests don't pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "....\n",
      "----------------------------------------------------------------------\n",
      "Ran 4 tests in 60.537s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=4 errors=0 failures=0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from asl_test_model_selectors import TestSelectors\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestSelectors())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part3_tutorial'></a>\n",
    "## PART 3: Recognizer\n",
    "The objective of this section is to \"put it all together\".  Using the four feature sets created and the three model selectors, you will experiment with the models and present your results.  Instead of training only five specific words as in the previous section, train the entire set with a feature set and model selector strategy.  \n",
    "### Recognizer Tutorial\n",
    "##### Train the full training set\n",
    "The following example trains the entire set with the example `features_ground` and `SelectorConstant` features and model selector.  Use this pattern for you experimentation and final submission cells.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of word models returned = 112\n"
     ]
    }
   ],
   "source": [
    "from my_model_selectors import SelectorConstant\n",
    "\n",
    "def train_all_words(features, model_selector):\n",
    "    training = asl.build_training(features)  # Experiment here with different feature sets defined in part 1\n",
    "    sequences = training.get_all_sequences()\n",
    "    Xlengths = training.get_all_Xlengths()\n",
    "    model_dict = {}\n",
    "    for word in training.words:\n",
    "        model = model_selector(sequences, Xlengths, word, \n",
    "                        n_constant=3).select()\n",
    "        model_dict[word]=model\n",
    "    return model_dict\n",
    "\n",
    "models = train_all_words(features_ground, SelectorConstant)\n",
    "print(\"Number of word models returned = {}\".format(len(models)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load the test set\n",
    "The `build_test` method in `ASLdb` is similar to the `build_training` method already presented, but there are a few differences:\n",
    "- the object is type `SinglesData` \n",
    "- the internal dictionary keys are the index of the test word rather than the word itself\n",
    "- the getter methods are `get_all_sequences`, `get_all_Xlengths`, `get_item_sequences` and `get_item_Xlengths`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of test set items: 178\n",
      "Number of test set sentences: 40\n"
     ]
    }
   ],
   "source": [
    "test_set = asl.build_test(features_ground)\n",
    "print(\"Number of test set items: {}\".format(test_set.num_items))\n",
    "print(\"Number of test set sentences: {}\".format(len(test_set.sentences_index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part3_submission'></a>\n",
    "### Recognizer Implementation Submission\n",
    "For the final project submission, students must implement a recognizer following guidance in the `my_recognizer.py` module.  Experiment with the four feature sets and the three model selection methods (that's 12 possible combinations). You can add and remove cells for experimentation or run the recognizers locally in some other way during your experiments, but retain the results for your discussion.  For submission, you will provide code cells of **only three** interesting combinations for your discussion (see questions below). At least one of these should produce a word error rate of less than 60%, i.e. WER < 0.60 . \n",
    "\n",
    "**Tip:** The hmmlearn library may not be able to train or score all models.  Implement try/except contructs as necessary to eliminate non-viable models from consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO implement the recognize method in my_recognizer\n",
    "from my_recognizer import recognize\n",
    "from asl_utils import show_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**** WER = 0.5449438202247191\n",
      "Total correct: 81 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *NEW *GIVE1                                              JOHN WRITE HOMEWORK\n",
      "    7: JOHN CAN GO CAN                                               JOHN CAN GO CAN\n",
      "   12: JOHN *WHAT *JOHN CAN                                          JOHN CAN GO CAN\n",
      "   21: JOHN *NEW *JOHN *WHO *GIVE1 *WHAT *FUTURE *WHO                JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX IX *WHO IX                                           JOHN LIKE IX IX IX\n",
      "   28: JOHN *FUTURE IX *FUTURE IX                                    JOHN LIKE IX IX IX\n",
      "   30: JOHN LIKE *MARY *MARY *MARY                                   JOHN LIKE IX IX IX\n",
      "   36: *IX *VISIT *GIVE *GIVE *MARY *MARY                            MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN *GO *GIVE *JOHN *MARY                                    JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *SEE BUY CAR *JOHN                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *GO *GO MARY                                            JOHN DECIDE VISIT MARY\n",
      "   67: *SHOULD FUTURE *MARY BUY HOUSE                                JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FUTURE *GIVE1 MARY                                      JOHN WILL VISIT MARY\n",
      "   74: *IX *GO *GO MARY                                              JOHN NOT VISIT MARY\n",
      "   77: *JOHN *GIVE1 MARY                                             ANN BLAME MARY\n",
      "   84: *JOHN *GIVE1 *GIVE1 *COAT                                     IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE *GIVE *WOMAN *IX IX *ARRIVE *BOOK                       JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: JOHN GIVE IX SOMETHING-ONE WOMAN *ARRIVE                      JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *WOMAN IX *IX *IX BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *SEE                                                     JOHN LEG\n",
      "  107: JOHN POSS *HAVE *GO *MARY                                     JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *LOVE *LOVE                                                   WOMAN ARRIVE\n",
      "  113: IX CAR *IX *MARY *JOHN                                        IX CAR BLUE SUE BUY\n",
      "  119: *MARY *BUY1 IX *BLAME *IX                                     SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: JOHN *ARRIVE WHAT *MARY *ARRIVE                               JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE JOHN WHO                                                 LOVE JOHN WHO\n",
      "  167: JOHN *MARY *VISIT LOVE MARY                                   JOHN IX SAY LOVE MARY\n",
      "  171: *IX MARY BLAME                                                JOHN MARY BLAME\n",
      "  174: *JOHN *JOHN GIVE1 *YESTERDAY *JOHN                            PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *EAT ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *GO BOY *GIVE1 TEACHER *YESTERDAY                             ALL BOY GIVE TEACHER APPLE\n",
      "  189: *MARY *GO *YESTERDAY BOX                                      JOHN GIVE GIRL BOX\n",
      "  193: JOHN *GO *YESTERDAY BOX                                       JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *STUDENT *GO                                            LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *WOMAN *JOHN BUY HOUSE                              JOHN TELL MARY IX-1P BUY HOUSE\n"
     ]
    }
   ],
   "source": [
    "# TODO Choose a feature set and model selector\n",
    "features = features_polar # change as needed\n",
    "model_selector = SelectorDIC # change as needed\n",
    "\n",
    "# TODO Recognize the test set and display the result with the show_errors method\n",
    "models = train_all_words(features, model_selector)\n",
    "test_set = asl.build_test(features)\n",
    "probabilities, guesses = recognize(models, test_set)\n",
    "show_errors(guesses, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**** WER = 0.5168539325842697\n",
      "Total correct: 86 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *BOOK *ARRIVE                                            JOHN WRITE HOMEWORK\n",
      "    7: JOHN *CAR *MARY CAN                                           JOHN CAN GO CAN\n",
      "   12: JOHN CAN *JOHN CAN                                            JOHN CAN GO CAN\n",
      "   21: JOHN *VIDEOTAPE *JOHN *MARY *CAR *CAR *FUTURE *MARY           JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *JOHN *MARY IX *MARY                                     JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX *MARY IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *GIVE *GIVE *MARY *MARY                            MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY IX *JOHN MARY *MARY                                     JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *POSS BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *VISIT BUY CAR *JOHN                                    FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *WHO BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *IX *IX *IX                                             JOHN DECIDE VISIT MARY\n",
      "   67: JOHN FUTURE *MARY BUY HOUSE                                   JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FUTURE VISIT MARY                                       JOHN WILL VISIT MARY\n",
      "   74: JOHN *IX *MARY MARY                                           JOHN NOT VISIT MARY\n",
      "   77: *JOHN *ARRIVE MARY                                            ANN BLAME MARY\n",
      "   84: *JOHN *JOHN *MARY BOOK                                        IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *MARY IX *IX *IX IX *ARRIVE *BOOK                             JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: JOHN *IX IX *IX WOMAN BOOK                                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *IX *IX BOOK                                      JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *FRANK                                                   JOHN LEG\n",
      "  107: JOHN *JOHN *BOOK *IX *MARY                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *JOHN *MARY *ARRIVE                                 IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *IX                                        SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: JOHN *ARRIVE WHAT YESTERDAY BOOK                              JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE JOHN WHO                                                 LOVE JOHN WHO\n",
      "  167: JOHN *JOHN *IX *MARY MARY                                     JOHN IX SAY LOVE MARY\n",
      "  171: *MARY *JOHN BLAME                                             JOHN MARY BLAME\n",
      "  174: *GIVE1 *GIVE1 GIVE1 *JOHN *CAR                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX *IX *GIVE1 TEACHER *IX                                    ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *JOHN *ARRIVE                                        JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *JOHN MARY *JOHN BUY HOUSE                               JOHN TELL MARY IX-1P BUY HOUSE\n"
     ]
    }
   ],
   "source": [
    "# TODO Choose a feature set and model selector\n",
    "# TODO Recognize the test set and display the result with the show_errors method\n",
    "features = features_custom # change as needed\n",
    "model_selector =  SelectorDIC# change as needed\n",
    "\n",
    "# TODO Recognize the test set and display the result with the show_errors method\n",
    "models = train_all_words(features, model_selector)\n",
    "test_set = asl.build_test(features)\n",
    "probabilities, guesses = recognize(models, test_set)\n",
    "show_errors(guesses, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**** WER = 0.5337078651685393\n",
      "Total correct: 83 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *NEW *NEW                                                JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE GO *HAVE                                           JOHN CAN GO CAN\n",
      "   12: JOHN CAN *HAVE CAN                                            JOHN CAN GO CAN\n",
      "   21: *MARY *NEW *GIVE1 *MARY *CAR *CAR *FUTURE *MARY               JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN LIKE IX IX *LOVE                                         JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX *LOVE                                     JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *IX *GIVE *MARY *MARY                              MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY IX *MARY MARY *MARY                                     JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN JOHN BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *JOHN *IX *IX                                           JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *POSS *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN NOT *MARY MARY                                           JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *NEW *GIVE1 BOOK                                        IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *MARY IX *WOMAN *WOMAN IX NEW *BOOK                           JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *IX IX *IX WOMAN BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *WOMAN *WOMAN *WOMAN *IX BOOK                            JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *FUTURE                                                  JOHN LEG\n",
      "  107: JOHN *JOHN *HAVE HAVE *MARY                                   JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX ARRIVE                                                    WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *BUY1                                   IX CAR BLUE SUE BUY\n",
      "  119: *MARY *BUY1 *GO CAR *JOHN                                     SUE BUY IX CAR BLUE\n",
      "  122: JOHN *BOOK BOOK                                               JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT YESTERDAY BOOK                                JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY *MARY                                              LOVE JOHN WHO\n",
      "  167: *MARY *LEAVE *IX *MARY MARY                                   JOHN IX SAY LOVE MARY\n",
      "  171: *MARY *JOHN BLAME                                             JOHN MARY BLAME\n",
      "  174: *HAVE *NEW GIVE1 *MARY *BLAME                                 PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *MARY *NEW                                                    JOHN ARRIVE\n",
      "  184: *IX *IX *GIVE1 TEACHER *MARY                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *MARY BOX                                            JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY MARY *WOMAN BUY HOUSE                              JOHN TELL MARY IX-1P BUY HOUSE\n"
     ]
    }
   ],
   "source": [
    "# TODO Choose a feature set and model selector\n",
    "# TODO Recognize the test set and display the result with the show_errors method\n",
    "features = features_custom # change as needed\n",
    "model_selector =  SelectorCV# change as needed\n",
    "\n",
    "# TODO Recognize the test set and display the result with the show_errors method\n",
    "models = train_all_words(features, model_selector)\n",
    "test_set = asl.build_test(features)\n",
    "probabilities, guesses = recognize(models, test_set)\n",
    "show_errors(guesses, test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3:**  Summarize the error results from three combinations of features and model selectors.  What was the \"best\" combination and why?  What additional information might we use to improve our WER?  For more insight on improving WER, take a look at the introduction to Part 4.\n",
    "\n",
    "**Answer 3:**\n",
    "\n",
    "The WER results from three combinations is as follwoing :\n",
    "\n",
    "    1. features_polar and SelectorDIC WER =  0.5449438202247191 Total correct: 81 out of 178\n",
    "    2. features_custom and SelectorDIC WER = 0.5168539325842697 Total correct: 86 out of 178\n",
    "    3. features_custom and SelectorCV WER =  0.5168539325842697 Total correct: 86 out of 178\n",
    "\n",
    "\n",
    "The best combination is features_custom and (SelectorDIC and SelectorCV) ** according to this results only ** WER(0.52) the notable thing is that SelectorDIC and SelectorCV getting the same result.\n",
    "The way is to improve is using Statistical Language Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part3_test'></a>\n",
    "### Recognizer Unit Tests\n",
    "Run the following unit tests as a sanity check on the defined recognizer.  The test simply looks for some valid values but is not exhaustive. However, the project should not be submitted if these tests don't pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "..\n",
      "----------------------------------------------------------------------\n",
      "Ran 2 tests in 46.618s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=2 errors=0 failures=0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from asl_test_recognizer import TestRecognize\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestRecognize())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='part4_info'></a>\n",
    "## PART 4: (OPTIONAL)  Improve the WER with Language Models\n",
    "We've squeezed just about as much as we can out of the model and still only get about 50% of the words right! Surely we can do better than that.  Probability to the rescue again in the form of [statistical language models (SLM)](https://en.wikipedia.org/wiki/Language_model).  The basic idea is that each word has some probability of occurrence within the set, and some probability that it is adjacent to specific other words. We can use that additional information to make better choices.\n",
    "\n",
    "##### Additional reading and resources\n",
    "- [Introduction to N-grams (Stanford Jurafsky slides)](https://web.stanford.edu/class/cs124/lec/languagemodeling.pdf)\n",
    "- [Speech Recognition Techniques for a Sign Language Recognition System, Philippe Dreuw et al](https://www-i6.informatik.rwth-aachen.de/publications/download/154/Dreuw--2007.pdf) see the improved results of applying LM on *this* data!\n",
    "- [SLM data for *this* ASL dataset](ftp://wasserstoff.informatik.rwth-aachen.de/pub/rwth-boston-104/lm/)\n",
    "\n",
    "##### Optional challenge\n",
    "The recognizer you implemented in Part 3 is equivalent to a \"0-gram\" SLM.  Improve the WER with the SLM data provided with the data set in the link above using \"1-gram\", \"2-gram\", and/or \"3-gram\" statistics. The `probabilities` data you've already calculated will be useful and can be turned into a pandas DataFrame if desired (see next cell).  \n",
    "Good luck!  Share your results with the class!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALL</th>\n",
       "      <th>ANN</th>\n",
       "      <th>APPLE</th>\n",
       "      <th>ARRIVE</th>\n",
       "      <th>BILL</th>\n",
       "      <th>BLAME</th>\n",
       "      <th>BLUE</th>\n",
       "      <th>BOOK</th>\n",
       "      <th>BORROW</th>\n",
       "      <th>BOX</th>\n",
       "      <th>...</th>\n",
       "      <th>VIDEOTAPE</th>\n",
       "      <th>VISIT</th>\n",
       "      <th>WANT</th>\n",
       "      <th>WHAT</th>\n",
       "      <th>WHO</th>\n",
       "      <th>WILL</th>\n",
       "      <th>WOMAN</th>\n",
       "      <th>WONT</th>\n",
       "      <th>WRITE</th>\n",
       "      <th>YESTERDAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-661.822553</td>\n",
       "      <td>None</td>\n",
       "      <td>-31754.126495</td>\n",
       "      <td>-343.985831</td>\n",
       "      <td>-31630.202078</td>\n",
       "      <td>-489.991662</td>\n",
       "      <td>-1734.265016</td>\n",
       "      <td>-686.309481</td>\n",
       "      <td>None</td>\n",
       "      <td>-840.368032</td>\n",
       "      <td>...</td>\n",
       "      <td>-2217.034368</td>\n",
       "      <td>-189.441443</td>\n",
       "      <td>-1629.692676</td>\n",
       "      <td>-318.449369</td>\n",
       "      <td>-344.794126</td>\n",
       "      <td>-12987.470680</td>\n",
       "      <td>-709.908129</td>\n",
       "      <td>-2543.217022</td>\n",
       "      <td>-348.091035</td>\n",
       "      <td>-342.371511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3403.665159</td>\n",
       "      <td>None</td>\n",
       "      <td>-29922.854863</td>\n",
       "      <td>-209.500143</td>\n",
       "      <td>-37448.276444</td>\n",
       "      <td>-499.722289</td>\n",
       "      <td>-4262.511377</td>\n",
       "      <td>-191.260142</td>\n",
       "      <td>None</td>\n",
       "      <td>-431.729562</td>\n",
       "      <td>...</td>\n",
       "      <td>-292.207305</td>\n",
       "      <td>-315.171671</td>\n",
       "      <td>-1531.081472</td>\n",
       "      <td>-251.313745</td>\n",
       "      <td>-230.154032</td>\n",
       "      <td>-22616.848310</td>\n",
       "      <td>-1241.226385</td>\n",
       "      <td>-2136.082825</td>\n",
       "      <td>-131.786141</td>\n",
       "      <td>-567.169426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4025.963403</td>\n",
       "      <td>None</td>\n",
       "      <td>-54511.547884</td>\n",
       "      <td>-413.173988</td>\n",
       "      <td>-65316.338832</td>\n",
       "      <td>-1071.801315</td>\n",
       "      <td>-5694.884283</td>\n",
       "      <td>-778.344832</td>\n",
       "      <td>None</td>\n",
       "      <td>-909.275079</td>\n",
       "      <td>...</td>\n",
       "      <td>-899.287838</td>\n",
       "      <td>-605.289625</td>\n",
       "      <td>-3426.557059</td>\n",
       "      <td>-570.269955</td>\n",
       "      <td>-1195.775641</td>\n",
       "      <td>-19152.555984</td>\n",
       "      <td>-2305.423687</td>\n",
       "      <td>-2774.013960</td>\n",
       "      <td>-700.712068</td>\n",
       "      <td>-1138.567048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-614.026823</td>\n",
       "      <td>None</td>\n",
       "      <td>-2819.255038</td>\n",
       "      <td>-343.843840</td>\n",
       "      <td>-5841.810791</td>\n",
       "      <td>-708.592439</td>\n",
       "      <td>-1218.761148</td>\n",
       "      <td>-802.159709</td>\n",
       "      <td>None</td>\n",
       "      <td>-855.924969</td>\n",
       "      <td>...</td>\n",
       "      <td>-2565.954189</td>\n",
       "      <td>-161.005243</td>\n",
       "      <td>-1167.810993</td>\n",
       "      <td>-170.300172</td>\n",
       "      <td>-260.486209</td>\n",
       "      <td>-2475.460912</td>\n",
       "      <td>-339.204317</td>\n",
       "      <td>-1568.381583</td>\n",
       "      <td>-487.273802</td>\n",
       "      <td>-941.930133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1241.139377</td>\n",
       "      <td>None</td>\n",
       "      <td>-55673.130979</td>\n",
       "      <td>-107.074305</td>\n",
       "      <td>-130091.192580</td>\n",
       "      <td>-183.575028</td>\n",
       "      <td>-2121.559969</td>\n",
       "      <td>-135.939344</td>\n",
       "      <td>None</td>\n",
       "      <td>-142.194844</td>\n",
       "      <td>...</td>\n",
       "      <td>-371.662089</td>\n",
       "      <td>-148.235939</td>\n",
       "      <td>-468.814030</td>\n",
       "      <td>-126.410928</td>\n",
       "      <td>-259.993349</td>\n",
       "      <td>-56492.315592</td>\n",
       "      <td>-990.270411</td>\n",
       "      <td>-3685.303486</td>\n",
       "      <td>-203.830347</td>\n",
       "      <td>-449.575235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ALL   ANN         APPLE      ARRIVE           BILL        BLAME  \\\n",
       "0  -661.822553  None -31754.126495 -343.985831  -31630.202078  -489.991662   \n",
       "1 -3403.665159  None -29922.854863 -209.500143  -37448.276444  -499.722289   \n",
       "2 -4025.963403  None -54511.547884 -413.173988  -65316.338832 -1071.801315   \n",
       "3  -614.026823  None  -2819.255038 -343.843840   -5841.810791  -708.592439   \n",
       "4 -1241.139377  None -55673.130979 -107.074305 -130091.192580  -183.575028   \n",
       "\n",
       "          BLUE        BOOK BORROW         BOX     ...         VIDEOTAPE  \\\n",
       "0 -1734.265016 -686.309481   None -840.368032     ...      -2217.034368   \n",
       "1 -4262.511377 -191.260142   None -431.729562     ...       -292.207305   \n",
       "2 -5694.884283 -778.344832   None -909.275079     ...       -899.287838   \n",
       "3 -1218.761148 -802.159709   None -855.924969     ...      -2565.954189   \n",
       "4 -2121.559969 -135.939344   None -142.194844     ...       -371.662089   \n",
       "\n",
       "        VISIT         WANT        WHAT          WHO          WILL  \\\n",
       "0 -189.441443 -1629.692676 -318.449369  -344.794126 -12987.470680   \n",
       "1 -315.171671 -1531.081472 -251.313745  -230.154032 -22616.848310   \n",
       "2 -605.289625 -3426.557059 -570.269955 -1195.775641 -19152.555984   \n",
       "3 -161.005243 -1167.810993 -170.300172  -260.486209  -2475.460912   \n",
       "4 -148.235939  -468.814030 -126.410928  -259.993349 -56492.315592   \n",
       "\n",
       "         WOMAN         WONT       WRITE    YESTERDAY  \n",
       "0  -709.908129 -2543.217022 -348.091035  -342.371511  \n",
       "1 -1241.226385 -2136.082825 -131.786141  -567.169426  \n",
       "2 -2305.423687 -2774.013960 -700.712068 -1138.567048  \n",
       "3  -339.204317 -1568.381583 -487.273802  -941.930133  \n",
       "4  -990.270411 -3685.303486 -203.830347  -449.575235  \n",
       "\n",
       "[5 rows x 112 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame of log likelihoods for the test word items\n",
    "df_probs = pd.DataFrame(data=probabilities)\n",
    "df_probs.head()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  },
  "nbpresent": {
   "slides": {
    "0a2d4faf-9fb8-4cee-853b-ed68b90f3f8a": {
     "id": "0a2d4faf-9fb8-4cee-853b-ed68b90f3f8a",
     "prev": null,
     "regions": {
      "3fb9ce83-fbb2-4995-832a-f8f400734ad3": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "1dbb9346-179b-4835-b430-6369d88f1a1b",
        "part": "whole"
       },
       "id": "3fb9ce83-fbb2-4995-832a-f8f400734ad3"
      }
     }
    },
    "1519a4fa-1588-4644-98de-9c43bf0aceb5": {
     "id": "1519a4fa-1588-4644-98de-9c43bf0aceb5",
     "prev": "8a712017-49b7-449f-8264-43a032ace902",
     "regions": {
      "29546121-ed11-44b7-8144-0c44e874098f": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "365590a4-6963-4812-a1cf-688f7b6bb9ff",
        "part": "whole"
       },
       "id": "29546121-ed11-44b7-8144-0c44e874098f"
      }
     }
    },
    "176eaccb-15dd-455d-bf07-504213e7aa01": {
     "id": "176eaccb-15dd-455d-bf07-504213e7aa01",
     "prev": "de6b30f4-2463-4901-92ed-aabad78e5e0f",
     "regions": {
      "1542aa9e-dc55-4b90-adef-bf5181872b42": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "5c242050-c1f7-4b3b-8103-2ea9d71a40dc",
        "part": "whole"
       },
       "id": "1542aa9e-dc55-4b90-adef-bf5181872b42"
      }
     }
    },
    "19091b36-b0e7-49b1-b501-ec05937e0da9": {
     "id": "19091b36-b0e7-49b1-b501-ec05937e0da9",
     "prev": "1983c02e-fb99-4c05-a728-e0c0ad7c06d8",
     "regions": {
      "6529a31c-8d45-425c-b1d7-d0ac6fca6a32": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "e766909d-9421-4aaf-9fb1-bc90d27e49e3",
        "part": "whole"
       },
       "id": "6529a31c-8d45-425c-b1d7-d0ac6fca6a32"
      }
     }
    },
    "1983c02e-fb99-4c05-a728-e0c0ad7c06d8": {
     "id": "1983c02e-fb99-4c05-a728-e0c0ad7c06d8",
     "prev": "176eaccb-15dd-455d-bf07-504213e7aa01",
     "regions": {
      "1c4e605d-7f22-4f30-b3fb-74b2937e7a4a": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "4d217204-e5c0-4568-bd30-12c2e41b681d",
        "part": "whole"
       },
       "id": "1c4e605d-7f22-4f30-b3fb-74b2937e7a4a"
      }
     }
    },
    "212b111f-4527-459c-8297-1db5580ee5c9": {
     "id": "212b111f-4527-459c-8297-1db5580ee5c9",
     "prev": "76898529-e49e-4663-8d02-8261dfe1d94b",
     "regions": {
      "2e4bd280-3cd6-47d0-9c81-17737b24053b": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "0c316996-9933-4b3d-82ec-259518dc8bc9",
        "part": "whole"
       },
       "id": "2e4bd280-3cd6-47d0-9c81-17737b24053b"
      }
     }
    },
    "23a7337f-a0cf-4ed4-baa9-ec06bfdc0579": {
     "id": "23a7337f-a0cf-4ed4-baa9-ec06bfdc0579",
     "prev": "e76e9a02-54c1-4ec9-80fb-c611ed398122",
     "regions": {
      "b5721d20-d6f8-4ddb-a5aa-eb16f0cc8893": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "313015a2-b5a9-4136-a8ea-5d011e47d840",
        "part": "whole"
       },
       "id": "b5721d20-d6f8-4ddb-a5aa-eb16f0cc8893"
      }
     }
    },
    "732f1952-ee54-46fb-8067-099512824296": {
     "id": "732f1952-ee54-46fb-8067-099512824296",
     "prev": "0a2d4faf-9fb8-4cee-853b-ed68b90f3f8a",
     "regions": {
      "f31d4597-08ad-4c46-ad52-4bd2d775c624": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "aadfec52-27ca-4541-8920-fa9253d51827",
        "part": "whole"
       },
       "id": "f31d4597-08ad-4c46-ad52-4bd2d775c624"
      }
     }
    },
    "76898529-e49e-4663-8d02-8261dfe1d94b": {
     "id": "76898529-e49e-4663-8d02-8261dfe1d94b",
     "prev": "19091b36-b0e7-49b1-b501-ec05937e0da9",
     "regions": {
      "ec1746fc-aec9-4a7c-8225-9e9ac8d45889": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "b3e539be-84e2-49ce-a183-31cfc5c7ce7c",
        "part": "whole"
       },
       "id": "ec1746fc-aec9-4a7c-8225-9e9ac8d45889"
      }
     }
    },
    "8a712017-49b7-449f-8264-43a032ace902": {
     "id": "8a712017-49b7-449f-8264-43a032ace902",
     "prev": "bed9e696-630e-4747-be1c-bc3737ba992f",
     "regions": {
      "1faab517-cd16-4c63-bb01-a67246749d7a": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "3f14ddf0-4145-4687-9c33-712c3c32520f",
        "part": "whole"
       },
       "id": "1faab517-cd16-4c63-bb01-a67246749d7a"
      }
     }
    },
    "90af992d-eb6d-4496-b2d2-6aa9a95b6a61": {
     "id": "90af992d-eb6d-4496-b2d2-6aa9a95b6a61",
     "prev": "732f1952-ee54-46fb-8067-099512824296",
     "regions": {
      "4f448bec-5be9-4553-88ae-e35ed7612f25": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "c445fbfb-b8ab-4e9a-8d13-12231a1c588f",
        "part": "whole"
       },
       "id": "4f448bec-5be9-4553-88ae-e35ed7612f25"
      }
     }
    },
    "bed9e696-630e-4747-be1c-bc3737ba992f": {
     "id": "bed9e696-630e-4747-be1c-bc3737ba992f",
     "prev": "23a7337f-a0cf-4ed4-baa9-ec06bfdc0579",
     "regions": {
      "ac1513f0-404f-492b-8b42-0313e9a753b0": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "18dd2eee-8b6c-4a5e-9539-132d00a7c7e1",
        "part": "whole"
       },
       "id": "ac1513f0-404f-492b-8b42-0313e9a753b0"
      }
     }
    },
    "de6b30f4-2463-4901-92ed-aabad78e5e0f": {
     "id": "de6b30f4-2463-4901-92ed-aabad78e5e0f",
     "prev": "e36b4639-be8c-46f7-a8c9-bcfb134f9fd0",
     "regions": {
      "55ec36e0-362f-4fd3-8060-7cee056039aa": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "c3cf461e-4c9e-4dec-99d2-07bfa79cbe23",
        "part": "whole"
       },
       "id": "55ec36e0-362f-4fd3-8060-7cee056039aa"
      }
     }
    },
    "e36b4639-be8c-46f7-a8c9-bcfb134f9fd0": {
     "id": "e36b4639-be8c-46f7-a8c9-bcfb134f9fd0",
     "prev": "1519a4fa-1588-4644-98de-9c43bf0aceb5",
     "regions": {
      "4c1e9714-9ba0-45fd-8a2f-ef80a5c85c2e": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "6534d4dc-125f-47e6-a022-cf1e0d277174",
        "part": "whole"
       },
       "id": "4c1e9714-9ba0-45fd-8a2f-ef80a5c85c2e"
      }
     }
    },
    "e76e9a02-54c1-4ec9-80fb-c611ed398122": {
     "id": "e76e9a02-54c1-4ec9-80fb-c611ed398122",
     "prev": "90af992d-eb6d-4496-b2d2-6aa9a95b6a61",
     "regions": {
      "9491b84d-193b-40ff-9321-d21eb1ba88d4": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "b64ec10e-fa9d-4f3f-907f-6799611ed6b1",
        "part": "whole"
       },
       "id": "9491b84d-193b-40ff-9321-d21eb1ba88d4"
      }
     }
    }
   },
   "themes": {}
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}