asl_recognizer.ipynb

{
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    "# 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!"
   ]
  },
  {
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   "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. "
   ]
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       "<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"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    {
     "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. "
   ]
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   "cell_type": "code",
   "execution_count": 3,
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       "<div>\n",
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       "      <th></th>\n",
       "      <th>left-x</th>\n",
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       "      <th>right-y</th>\n",
       "      <th>nose-x</th>\n",
       "      <th>nose-y</th>\n",
       "      <th>speaker</th>\n",
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       "      <td>149</td>\n",
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       "      <td>170</td>\n",
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       "      <td>161</td>\n",
       "      <td>62</td>\n",
       "      <td>woman-1</td>\n",
       "      <td>113</td>\n",
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       "      <td>woman-1</td>\n",
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       "      <td>181</td>\n",
       "      <td>170</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
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       "      <td>woman-1</td>\n",
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       "      <td>181</td>\n",
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       "      <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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asl.df sample\n"
     ]
    },
    {
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       "      <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",
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       "      <th rowspan=\"5\" valign=\"top\">98</th>\n",
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       "      <td>149</td>\n",
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       "      <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",
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       "      <td>175</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
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       "      <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",
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       "    <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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false,
    "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",
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       "      <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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>right-x</th>\n",
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       "      <th>nose-x</th>\n",
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       "      <th>speaker</th>\n",
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       "      <th>grnd-ly</th>\n",
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       "    <tr>\n",
       "      <th>video</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">98</th>\n",
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       "      <td>9</td>\n",
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       "      <td>164.661438</td>\n",
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       "      <td>161</td>\n",
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       "      <th>2</th>\n",
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       "      <td>175</td>\n",
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       "      <td>-12</td>\n",
       "      <td>164.661438</td>\n",
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       "      <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": {
    "collapsed": false
   },
   "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",
       "    </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",
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       "      <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",
       "    </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",
       "    </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",
       "    </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  \n",
       "speaker                                              \n",
       "man-1    53.487999  20.269032  36.572749  15.080360  \n",
       "woman-1  33.972660  16.764706  27.117393  17.328941  \n",
       "woman-2  39.128572  16.191324  29.320655  15.050938  "
      ]
     },
     "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",
    "df_std = asl.df[['left-x', 'left-y', 'right-x', 'right-y','nose-x','nose-y','speaker','grnd-ry','grnd-rx','grnd-ly','grnd-lx']].groupby('speaker').std()\n",
    "# test the code\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": {
    "collapsed": false
   },
   "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",
    "\n",
    "asl.df['norm-lx']= (asl.df['left-x']-asl.df['speaker'].map(df_means['left-x']))/ asl.df['speaker'].map(df_std['left-x'])\n",
    "asl.df['norm-ly']= (asl.df['left-y']-asl.df['speaker'].map(df_means['left-y']))/ asl.df['speaker'].map(df_std['left-y'])\n",
    "asl.df['norm-rx']= (asl.df['right-x']-asl.df['speaker'].map(df_means['right-x']))/ asl.df['speaker'].map(df_std['right-x'])\n",
    "asl.df['norm-ry']= (asl.df['right-y']-asl.df['speaker'].map(df_means['right-y']))/ asl.df['speaker'].map(df_std['right-y'])\n",
    "\n",
    "features_norm = ['norm-rx', 'norm-ry', 'norm-lx','norm-ly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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",
    "\n",
    "asl.df['polar-rr']= (asl.df['grnd-rx'].apply(np.square)+asl.df['grnd-ry'].apply(np.square)).apply(np.sqrt)\n",
    "asl.df['polar-rtheta']=  np.arctan2(asl.df['grnd-rx'], asl.df['grnd-ry'])\n",
    "asl.df['polar-lr']= (asl.df['grnd-lx'].apply(np.square)+asl.df['grnd-ly'].apply(np.square)).apply(np.sqrt)\n",
    "asl.df['polar-ltheta']= np.arctan2(asl.df['grnd-lx'], asl.df['grnd-ly'])\n",
    "\n",
    "features_polar = ['polar-rr', 'polar-rtheta', 'polar-lr', 'polar-ltheta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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",
    "#.fillna(method='backfill').fillna(method='ffill')\n",
    "asl.df['delta-rx'] = asl.df['right-x'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-ry'] = asl.df['right-y'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-lx'] = asl.df['left-x'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-ly'] = asl.df['left-y'].diff(periods=1).fillna(method = 'backfill')\n",
    "\n",
    "features_delta = ['delta-rx', 'delta-ry', 'delta-lx', 'delta-ly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "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",
    "# diff in polar coord\n",
    "\n",
    "asl.df['custom-rr'] = asl.df['polar-rr'].fillna(method='backfill').fillna(method='ffill').diff(periods=1)\n",
    "asl.df['custom-rtheta'] = asl.df['polar-rtheta'].fillna(method='backfill').fillna(method='ffill').diff(periods=1)\n",
    "asl.df['custom-lr'] = asl.df['polar-lr'].fillna(method='backfill').fillna(method='ffill').diff(periods=1)\n",
    "asl.df['custom-ltheta'] = asl.df['polar-ltheta'].fillna(method='backfill').fillna(method='ffill').diff(periods=1)\n",
    "\n",
    "# TODO define a list named 'features_custom' for building the training set\n",
    "\n",
    "features_custom = ['custom-rr', 'custom-rtheta', 'custom-lr', 'custom-ltheta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "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",
    "# diff in polar coord\n",
    "df_means = asl.df.groupby('speaker').mean()\n",
    "df_std = asl.df.groupby('speaker').std()\n",
    "asl.df['custom2-rr'] = (asl.df['polar-rr']-asl.df['speaker'].map(df_means['polar-rr']))/ asl.df['speaker'].map(df_std['polar-rr'])\n",
    "asl.df['custom2-rtheta'] = (asl.df['polar-rtheta']-asl.df['speaker'].map(df_means['polar-rtheta']))/ asl.df['speaker'].map(df_std['polar-rtheta'])\n",
    "asl.df['custom2-lr'] = (asl.df['polar-lr']-asl.df['speaker'].map(df_means['polar-lr']))/ asl.df['speaker'].map(df_std['polar-lr'])\n",
    "asl.df['custom2-ltheta'] = (asl.df['polar-ltheta']-asl.df['speaker'].map(df_means['polar-ltheta']))/ asl.df['speaker'].map(df_std['polar-ltheta'])\n",
    "# TODO define a list named 'features_custom' for building the training set\n",
    "\n",
    "features_custom_2 = ['custom2-rr', 'custom2-rtheta', 'custom2-lr', 'custom2-ltheta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "#.fillna(method='backfill').fillna(method='ffill')\n",
    "asl.df['delta-nrx'] = asl.df['custom2-rr'].fillna(method='backfill').fillna(method='ffill').diff(periods=1)\n",
    "asl.df['delta-nry'] = asl.df['custom2-rtheta'].fillna(method='backfill').fillna(method='ffill').diff(periods=1)\n",
    "asl.df['delta-nlx'] = asl.df['custom2-lr'].fillna(method='backfill').fillna(method='ffill').diff(periods=1)\n",
    "asl.df['delta-nly'] = asl.df['custom2-ltheta'].fillna(method='backfill').fillna(method='ffill').diff(periods=1)\n",
    "\n",
    "features_delta_normal = ['delta-nrx', 'delta-nry', 'delta-nlx', 'delta-nly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "#.fillna(method='backfill').fillna(method='ffill')\n",
    "asl.df['delta-nrr'] = asl.df['norm-rx'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-nrtheta'] = asl.df['norm-ry'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-nlr'] = asl.df['norm-lx'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-nltheta'] = asl.df['norm-ly'].diff(periods=1).fillna(method = 'backfill')\n",
    "\n",
    "features_delta_normal_polar = ['delta-nrr', 'delta-nrtheta', 'delta-nlr', 'delta-nltheta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "#.fillna(method='backfill').fillna(method='ffill')\n",
    "asl.df['delta-grx'] = asl.df['grnd-rx'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-gry'] = asl.df['grnd-ry'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-glx'] = asl.df['grnd-lx'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-gly'] = asl.df['grnd-ly'].diff(periods=1).fillna(method = 'backfill')\n",
    "\n",
    "features_delta_ground = ['delta-grx', 'delta-gry', 'delta-glx', 'delta-gly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "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",
    "# diff in polar coord\n",
    "\n",
    "asl.df['custom2-grx'] = (asl.df['grnd-rx']-asl.df['speaker'].map(df_means['grnd-rx']))/ asl.df['speaker'].map(df_std['grnd-rx'])\n",
    "asl.df['custom2-gry'] = (asl.df['grnd-ry']-asl.df['speaker'].map(df_means['grnd-ry']))/ asl.df['speaker'].map(df_std['grnd-ry'])\n",
    "asl.df['custom2-glx'] = (asl.df['grnd-lx']-asl.df['speaker'].map(df_means['grnd-lx']))/ asl.df['speaker'].map(df_std['grnd-lx'])\n",
    "asl.df['custom2-gly'] = (asl.df['grnd-ly']-asl.df['speaker'].map(df_means['grnd-ly']))/ asl.df['speaker'].map(df_std['grnd-ly'])\n",
    "# TODO define a list named 'features_custom' for building the training set\n",
    "\n",
    "features_custom_2_ground = ['custom2-grx', 'custom2-gry', 'custom2-glx', 'custom2-gly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "#.fillna(method='backfill').fillna(method='ffill')\n",
    "asl.df['delta-ngrx'] = asl.df['custom2-grx'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-ngry'] = asl.df['custom2-gry'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-nglx'] = asl.df['custom2-glx'].diff(periods=1).fillna(method = 'backfill')\n",
    "asl.df['delta-ngly'] = asl.df['custom2-gly'].diff(periods=1).fillna(method = 'backfill')\n",
    "\n",
    "features_delta_normal_ground = ['delta-ngrx', 'delta-ngry', 'delta-nglx', 'delta-ngly']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1:**  What custom features did you choose for the features_custom set and why?\n",
    "\n",
    "**Answer 1:** I chose to look at the delta of the polar cordinates. I figured that since the full range of motion of ones arm is inherrently circular and not square. this means that we could bound r and theta to areas of motion that are relevant to the hand."
   ]
  },
  {
   "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "....\n",
      "----------------------------------------------------------------------\n",
      "Ran 4 tests in 0.023s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=4 errors=0 failures=0>"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states trained in model for BOOK is 3\n",
      "logL = -2331.1138127433205\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": 23,
   "metadata": {
    "collapsed": false
   },
   "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": 24,
   "metadata": {
    "collapsed": false
   },
   "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 =  [-1.79145183  2.20484802  0.          0.        ]\n",
      "variance =  [  2.69710624e+01   9.47153709e+00   1.37803730e-03   1.37803730e-03]\n",
      "\n",
      "hidden state #3\n",
      "mean =  [ 0.28571692  5.14285218  0.42857031  5.7142692 ]\n",
      "variance =  [ 17.06263607  19.26672401  15.67485365   7.06272726]\n",
      "\n",
      "logL = -202.21154622071631\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": "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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AF2AnEGFfME6G8qlYIeAL/M4usxQItP+eDuwGTtyB65lWQCywxUN8BOBny3fQ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\nGn2LxWIpQVijb7FYLCUIa/QtFoulBPH/sjdTXsDCJ5wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d53c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GN5U2AvwsGV+O268srSKO28bRvs75hQCtrHKV0dY675i/oO/Tox7K0crKw1Ob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\ndbr//vvP2bdv3z4Fzqvn9u3bPZbVt29fHTlyZE7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e0db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Us2rVKs49t+LZAmfOnMmLL75I8+bNAVODaNGiBa+//jrBwcEMH350BaQJEyZQ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\nFkuO/D/3X44O/AHnYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110bbeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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L8uXL2bp1Kx4ex3djeXiZxasRdO/enf5DriUmJoa/338H099+hWbnOPG8TWdu\nRAdY/N0Cpk2bRvsrmwJNGX3gQZ577jmiusL336+hQHIZ/8pDiAj3dO/PO4teIjjKdA5PencG995/\nD33/bsow+i938eZHk9gva+jRrQeevvDo0w9yyY0tAOg38Ab2pmzk9G7FyxbSwptk9uB1xgFaR0Vx\nmcdZx8Vr0QlOd0aXnU4zOlx7S1HYxLAxREdHF8X3DYFmZ1O0PX/+Is46pzWPTrobgDN7dGTAmltY\nteMjeg0ZV8yW27tdxe2jkhgyZAgDBgygV69edOzYkV9++QU/P79i8ZttBd81Jr+duSkllq++I1qO\nW9wR6z+BqzAivha4XVU3u8T5B3CBqo4UkUFAP1Ut8/lKRA4De0oICsXNnVPLCQbaYMpSmt0tAF/A\n3UPZCfPUkw10wLi83G+KQcA5wM9AJBAI/FFKPo2A9sCvgPuXSFs4tv6OcZmFOum413eEE154fpth\nbuibANeLpaQy+QAXAOtLsc+d1o6dhc+nHZ1f13w8nPLkYtx/TQBvjPdYOFZ/7mnB8fXrnl8QcIZT\nrkLOdva75i+Yc3ukAuVpAxwCkksIc62vUCCF4+vqAmA3kApcjOnTKpw1J9ixfROm/BHAVpe0z3DK\neKACZXDNx90ud7wwdR4C5APpzrFQvG49MA28YI65ez1cyncu5touvNbCnbRdJ3EWJ87eEmy50EnP\n07Gl8PwXOGm6DyUKxVy7JV3jdYXS7G6lqmHlHl2R5j1wLUbgdwBjnH3PAX2d9UbAR8B24CfgzIqk\nW0peFXrkqC0L5sJPB3aUEWc8MLeE/Qqc7azPBRaXEOcNYKWz3gvIxMUF5hZXMH0fj7vt98D8ESc4\n20Nd0lznFvcPJ484Z0lw7LyxvDJhBEgBrwrW3ZxCm1zy7lZK3DswgnaGU86f3ervbde0nH3pwIUu\n21+51EFPINYt/puF1/dJXgvLgYfLuwaAde51hRHNXs76LuAal2P/Xmgr0AMj4uISvsqlXGWWwTWf\nsq7NEo7rjhHSwvouqntn+59ADBDhbF/kVr4VwN9c4t8GLDvB+r0U+NpZf9k1vRLi/g2IKekaryvL\nqdpdoRG9qrpYVc9R1bNUdaKz7xlV/dxZz1LVAap6tqp2UWdkTUNAVZOBZ4DTReQmEfEXEW8R6SMi\nL5xAUs8Cl4vIRBFpKiJBIvIAcCfwhJPXcmAZ8KmIdBIRLyfeSBEZpuaKeBQYKyK3i0gjEYkAZmJa\nVC+75CcR9YDQAAAgAElEQVQi0qjw11kuA87CjJC6yFnOBz5w7KhqpgMTRaSVY2CYiNzohAVhWomJ\ngD+m1edKPHCm276NwO0i4um8q1Gyb+UYbwEjRaSrGAJE5DoRCXKPKCJtnXPs55zvIcAVwHcnUN7S\nWAA8JSJNRCQKeMAlbDVmvMCDTr79MOfrhMtQHiIywMkf4KjzW9jSdq/vIEyjIElEmgLuvhX3+IuA\nc0TkDqcc3iJyiYiU5b/rBGxw1i/G3CTdbfZ0rmsvwKPwGi+zoPUUO+VvJaCqUzAt5rHAYWf9fuCz\nE0hjG6Z11AHTujoI3AJcraqrXKL2BxYD8zGP/78BnTGtRlR1PqaV+zBGCLcAfpgWsavL53LMn/Fi\n5zcT877CQlX9VVXjChfMC2zXO3/aipAkImkuy+gKHjcV+BxYKiKpwBqOjcx6F+Na2O+UyX2Q4iyg\nnYgkiUhhvY8CbgCSgMGUcz5UdR2mlfwaRsy2Y55ySkIwrd5DmHM+CvPy3oZS4p8Iz2LKugtYCrzn\nYmMO0M+x6whwK/DfkyxDeVwC/CgiaZjzstel4TYeeMep74HAK5jrLAFz3r5yS2sq0F9EjorIq6qa\nCvQGBmGeROIw78eUNelHJ2CDmLGzbTnmOnTlDsy1/AbwF2e91QmVup5Qrs+9uhGREao6o6btOFGs\n3dVLXbUb6q7t1u7q5VTtrnXibrFYLJZTp0JuGRG5RkT+EJHtIvJkCeGjRWSLiGwSkW8KfaYWi8Vi\nqRkqa26Zn4HOqnohZvqBE+lItFgsFkslU5GWe9HcMk5nTuHcMkWo6gpVzXA212DGRVssFoulhqis\nuWVcGQ4sKSnAdW4ZPz+/TqedVnzmnIKCgmJv+dUFrN3VS121G+qu7dbu6qU0u//8888ErcBLTJX6\nsQ5nrG9nShlP7PT8zgDo3LmzrltXbJgqMTExdXKqTmt39VKb7M5VZU1mJj9nZRW9FuovQnRAAGf7\n+BSLX5tsPxGs3dVLaXaLSElv9hejsuaWQUR6AWOAHqqa7R5usdQ39ubm8m5yMl+npXGkoADBvBsP\n5i2jV44e5RwfH/oGBnJbcDCN6mDr0VJ3qYi4rwXaiMgZGFEfBNzuGkFEOmJee75GVQ9VupUWSy2i\nQJV5KSm8dOQIBUC0vz/XBQbS3d8fH2ci9YS8PL5OT2dxWhovHjnCJ6mpTAoL48JGxafWtViqgnLF\nXc387PcDX2MaJrNVdbOIPIeZ++Bz4D+YCa0+cj68sFdV+1ah3RZLjRCXl8eYw4dZk5lJdz8/ngsL\nI7yED5OEenkxOCSEwSEhrMnMZOzhwww+cIDhjRtzQQ3YbWl4VMjnrqqLMa+8u+57xmW9VyXbZbHU\nOnbn5DD84EFSCgoYHxpK/6CgCn1F6lI/Pz6LiuKFxETeSkrigtBQuhcU4GvdNJYqxF5dFksF+DMn\nhzsPHiQXmNuiBQOCg0/o84CBHh48FxbGmGbN+NXfn/vj48ksKCj/QIvlJLHibrGUw+bsbIYeOIAn\nMCcyknN9y5rbqmxuDwlhSGIiazIzuScujnQr8JYqwoq7xVIG+3JzuefgQQI8PHi3RQvOLGFo44ly\nWXo6LzRvzsasLEbHx5Nn53eyVAFW3C2WUkjOz+feuDjygbciIjjN27vS0u4TGMg/Q0NZmZnJpIQE\n7AR+lsqmUl9isljqCzmqPBQfz77cXGZFRtK6Elrs7gwIDmZvbi6zk5M53duboY0bV3oeloaLFXeL\npQQmJCTwU1YWk8PC6FzCB5gri4ebNmVfXh4vHjlCK29vogMCqiwvS8PCumUsFjc+SUnhk9RURjRu\nzA1BJ/x1uhPCQ4R/h4Vxno8PTx0+zN7c3CrNz9JwsOJusbiwNTubCYmJXO7nx/1NmlRLnn4eHrwc\nHo4AD8fHk2VH0FgqASvuFotDSn4+D8fH09TDg+ebN8fzBMaxnypR3t5Mbt6c33NymJSYWP4BFks5\nWHG3WABVZczhwxzMy+Ol8HCaenqWf1Al08PfnxGNG/NJaiqfpqZWe/6W+oUVd4sFmJuSwrcZGTza\nrBkdanByr/ubNKFLo0ZMTEhgR05OjdlhqftYcbc0eDZnZ/NiYiLR/v4MCQ6uUVs8RXi+eXP8RHjE\n+t8tp4AVd0uDJq2ggEfi4wn19GRCWNgJzRdTVTT38mJS8+Zsy83leet/t5wkVtwtDRZV5dnDhzmQ\nl8cL4eE0rgE/e2n8xd+fu0NCWJCayldpaTVtjqUOYsXd0mD5ODWVxenp/KNJEzrVwo9ojGralAt9\nfXnGjn+3nARW3C0Nkj+ys/m3M57977X0tX9vEV50hmQ+Eh9Pjp1/xnICWHG3NDjSCwp45NAhgj08\nmNy8OR61wM9eGi29vZkYFsaWnBxetP53ywlgxd3SoFBV/pWQwJ7cXF5o3pxmtcjPXhpXBgRwR3Aw\n76eksNT63y0VxIq7pUHxYUoKX6SlcV+TJnSpwgnBKpvRzZpxga8vYw8fZqcd/26pAFbcLQ2GDVlZ\nPJ+YSA9/f+6ppX720vAR4eXwcHxFGBUfT5od/24pByvulgbBobw8Ho6Pp4WXF5PDwmq1n700Ir28\nmBIezp7cXMYcOkSB7WC1lIEVd0u9J0eVh+PjSS8o4NWICILrgJ+9NLr4+fFI06Ysz8hgRlJSTZtj\nqcXYj3VY6jUFqjx96BAbs7N5qXlz2lTBF5WqmztDQtiSk8P/HT3Kad7eXBcYWNMmWWohVtwt9ZpX\njhxhSXo6o5s25ep6IoIiwr/CwojLy2PMoUOEe3pW6deiLHUT65ax1Fvmp6QwKzmZW4OCGBYSUtPm\nVCo+IkwND+c0b28eiI+3M0haimHF3VIv+TItjQkJCfTw9+fp0NBaMSFYZdPY05M3IiLwFmHEwYN2\nigLLcVhxt9Q7FqWl8eShQ3Rq1IgXmzfHqx4KeyFR3t68GRFBpip3HzhgBd5ShBV3S71iUVoaTznC\nPi0iAn+P+n+Jn+fry6zISCvwluOo/1e+pUGgqsxNTm5wwl7Ieb6+zHYE/s4DB/gtO7umTbLUMA3n\n6rfUW3Kd+WL+nZhIT3//BifshbT19eWdFi3wFuGuAwf42s5D06BpeP8AS73iSH4+98bFMT81lWEh\nIUwND2+Qwl5IGx8fPmzRgrY+Pow+dIg3jh4l377J2iBpuP8CS53n2/R0boqNZV1mJhPCwnikWbM6\nOa1AZRPq5cXsyEhuCAzktaNHGWpH0jRIrLhb6hxH8/N5t2lTHoiPJ8zTkwVRUdwcFFTTZtUqfD08\n+HdYGP8OC2NbTg79YmP5MDnZtuIbEFbcLXWGjIICZhw9yjV797I2IIB7Gjfmw5YtOaceTClQFYgI\nfYOC+Cwqio6NGjEhMZGbY2P5Nj0dtSJf77HTD1hqPUfy8/lvaipzk5M5nJ9PtL8/l+/Ywe1nnVXT\nptUJIry8mBERwfKMDF45coQH4uO5yNeXO0JCuDIgAB/ryqqXWHG31EqyCwr4KSuLL9LSWJqWRi7Q\npVEjXgoP5+JGjYj5/feaNrFOISL8NSCAaH9//puaysykJB45dIhQT0/6BQXROyCAtj4+9fJN3oZK\nhdwyInKNiPwhIttF5MkSwn1FZL4T/qOItK5sQy31j/j4eK644gqCgoJ4ePRotmRn82FyMqPi4ui2\nZw8j4+L4Lj2dgcHBLIyK4u0WLbi4UaOaNvukGD9+PEOGDCk1vH379sTExJQYFhMTQ1RUVKnHDh06\nlLFjx1bIDi8RBgYHs+S003gjIoL2vr7MTEqi//799Nq7l+cSEvgyLY3Y3FzruqnjlCvuIuIJvA70\nAdoBt4lIO7dow4Gjqno28DLwfGUbWttZvnw5nTt3JjAwkMjISPr06cPKlSuB0v/YIsL27duLtrds\n2ULfvn0JCQkhKCiI6Ohofvjhh+OOycnJYfz48bRp04aAgABat27NsGHD2L17d1GcRYsW0aVLFwIC\nAmjWrBmDBw8mNja2KHzOnDl07969zPIMHToULy8vDh48WLRv5MiRBAYGEhgYiI+PD97e3kXbffr0\nYffu3YhI0b7CZf78+RSokpKfz66cHH7MzOSL1FT+NnUqcUFBXLN1K98/8AAD9u9nQmIiv2Vn0zco\niOkREXzfqhVPh4Zydjl+9RMRuMoiOjqasLAwgoOD6dChAwsXLjzptDZv3kzPnj0rz7hy8BThCued\ngJhWrZgQFsb5vr58nprK44cOcfW+fVyxZw9DDxzg2cOHeScpiQ1+fmzMyuJAXh4iwrZt2yqcX8+e\nPZk5c2al2D5w4ECWLl1KdnY2ERERx4U9+uijtGnThqCgINq2bcu7775bKXnWRSrilukCbFfVnQAi\nMg+4EdjiEudGYLyz/jHwmoiIVsGt/6fMTL7LyKjsZE+J76dN45tp0+j/8svcHB2Np48Pf3zzDWPm\nzeP6885jVUYGidnZ/KeEr9e/dfQooYmJJO7axau9enHZsGGMfuklPLy8WPfhh0T37s3fP/6YVpdc\nAsDbgweTfOAAt0ybRosLLyQnI4OfP/qIxxYupMuQIWz6/HM+evBB+r34Ijdddx1ZKSksmTiRCy+/\nnFErVuDfuDHr0tLYn5fHfxIT2du4MT85dhWerJz0dD78+GN8goMZ9uab/OX++1EgasIEHp8wAVXl\n2xde4MiuXVw/bRr5QJ4q4/buBeCWLVvI8fQks6CATFWmFhQwYdcu3D8Md2DHDhqfeSaNPT0Z6OdH\nB19fLmzUiEhPz2p3D+Tl5eHldWJeyqlTp9KuXTu8vLz48ccf6dWrF3/++SeRkZFVZGXV0MzTk5uD\ngrg5KIg8Vbbl5LApO5tfs7LYlZvL1+npJBcUQFgYsw4cKDqu7759hPn4ECCCv4cHfiL4enjgg5m1\n0lsETxE8gV25uSxMTeXA4cOICALHLRSuu5x31yvAdf2btWtpP2kSi77/nqBzzz3uf/WLCDe99x6h\nZ51F7M8/M3LgQK575RXWlvDfq0n+GhDARVX8FFqRq7klsM9lOxboWlocVc0TkWSgGZDgGklERgAj\nAMLDw0t8DE1LSyv18RTg26AgvqhF07fmp6ayefJkTp88mV+7dePXnBzIyYGuXaFrVz44epSDWVnk\n5OTwwdGjxY7/IiUF36NH2TNhAl4XXcSR++7jM4C8PBgwgKBff2XOP/9Jmw8/JHXVKnbGxHDesmX8\nLzISUlNNIv36AbDtyBG2jB1L6L33svWqq9ialQU+Pui4cWRefz1vvPwykQ89RGJ6Okfy8ow9gYGI\ni10CJH76KRoURLPhw4n54AMODxmCqB73Z9yflUVWTg6rkpLwUMUDyE1JASAxKYlGnp74q9JYlTML\nCvAvKCCgoIDA/Hwa5+fz/nPP8ceyZaSJMHfmTCZMmIBPx46MnzePL7/8krS0NDp27Mjo0aMJDg4G\nzBPQpk2byMnJoXXr1jzyyCOcccYZfPHFF8ydOxcRYcqUKXTs2JFJkyYRHR3N3LlzadmyJQCTJ08m\nLCyM4cOHs3HjRiZOnEi/fv346KOP6Ny5M08//TSrV69m1qxZxMXF0bp1ax5++GHOKqPjtvDpbOvW\nrWRnZ/PZZ59x3nnnFYu3e/duYmNj6d27NytXrqR58+Y89dRTnHvuuQAMGjSIxx57jE6dOpGdnc3L\nL7/MqlWraNasGddccw3Z2dlF/4tt27bxn//8h9jYWLp27YqIkJ+fXxReVhkGDRrEzTffzNKlS4mP\nj+eSSy7hqaeewsftySgcyNu/ny9eeIEdO3bg4eXFORddxN2TJ/PivfcCsP2669guwmXPPovf5Zez\nfMwYjm7aREF+Po07dqTNs8/iHRHBzldeIXbNGvatW8fisWMJvflmWo0bR8aOHez917/I2LwZr6ZN\nafHQQzS59lrgWEPDfT0vOZm0vDw+AxJ++IGcc845/n91zz0sB0hOhjPPxLtTJ3744w+2l/OkWt3k\n7NxJUnp6mXHK08JyUdUyF6A/MNNl+w7gNbc4vwFRLts7gNCy0u3UqZOWxIoVK0rcX1tZsmSJenp6\n6vLly0uNM27cOB08eHCx/YBu27ZNVVXDw8N19uzZxeJ8++236uHhoRkZGfrEE0/oFVdcUWo+W7du\nVUB37txZLOyZZ57RSy+9VFVV3377be3WrZuqllzfV155pT722GMaFxennp6eum7dugqVadeuXQpo\nbm5uqTa6ctddd+mYMWOKtl955RXt2rWr7tu3T7OysnTEiBE6aNCgovBZs2ZpSkqKZmVl6S233KId\nOnQoNS3V4+vXPc6KFSvU09NTH3/8cc3KytKMjAzdsGGDhoWF6Zo1azQvL0/nzJmjrVq10qysrFLL\ncN1116mvr68CevXVV2t+fn6J8caNG6e+vr765Zdf6vLly/XJJ5/Url27FoW3atVKly1bpqqqTzzx\nhHbv3l0TExN179692r59e23ZsqWqqmZnZ+vpp5+uL730kubk5OhHH32kXl5eReUqrwytWrXSSy65\nRPfv36+JiYnatm1bfeONN0q0edCgQTphwgTNz8/XzMxMffXVV0ut24SEBP344481PT1dU1JStH//\n/nrjjTcWhffo0UPfeuutou20tDSNiorS2bNna25urm7YsEGbNWummzdvLtGW5cuXa0hIiAYEBKi3\nt7eGhISot7e3+vv7a0hIiMbExBQ7JiMjQyMiIvT5558vMc3aTmlaCKzTcnRbVREtx3MiIpcB41X1\namf7Keem8G+XOF87cVaLiBcQB4RpGYmLyGFgTwlBobi1+Gs5TYHTgP2UbncLIAKKeSY8MTfGbKAT\nsA1IcYvTCGgPbHLS8QR2lpJPIHAusIHjGzwAYZgG2W+Yp6pQ4A+K17cPcAHG7ZYJtAGyOP7prbBM\nvsCuEo7Nd4v7u5OGO62BHKDwWb89sBdwHknwdtLbUMKxzTH1vtHJzz0tMHVaWL/u+QU5ZfuZY3V1\nOpDnlsb5wG6grIlaxEnPD4gvJU4LzPn5E1PnaZg+rMKyXeDkk+qs7+HYtRDqHL/JSeNMZ72Qtk7c\nAxUowwWYa/WIExaF6XvbW4LNrTF1cwDI5fhrxb1u3fHDXIsbne1zgUSX45tgzuEfLse0wpyfg5TO\nGcBRTHnbAZspfq272u/txK9LmlJIaVrYSlXDyj26PPXHuG52YirVB/gFaO8W5x/AdGd9ELCgIneW\nUvKr0F2ptizANZg/U6l2Y/oj5pawX4GznfU44O4S4kRjxMsfmAx8V0Y+bZ00zygh7FlgtbM+FFhZ\nUn0DY4DNLtt3AocB7/LKxDEx8Kpg3c0BJrhsZ2D+tEkuSxbG7efplH+HEyfPyeusktJyr1/3OEBP\nYL9b/MWODa75ZwC3VbA8XwF9y7sGgHXudYUR317Oepbrfwy4Goh11gcBa93S/tClXGWWwTWfsq5N\nJywCeAsj7puBXWXUrT/wJsduSilOHE8nPAb4m0v8xzFC7mpnGvBGKbbEOnHynd90zI0lCXiphPj/\nAdYDwdQxTXEpwynZXe5oGVXNA+4Hvga2YoR7s4g8JyJ9nWizgGYish0YDRQbLlmPWY25yJqcYjrL\ngQEl7B+IEeUMJ04XESltXNwfmD/BcemIiAdwC/BNBey4EzhTROJEJA54CdOCuLZCpTg19gF9VLWx\ny9JIVfcDt2M67nsBIcCvzjGFfW0ltd4yMKJTSIRbuPsx+4CJbvn7q+qHFbTfC6iMN6sOYp5KCjnd\nLaylHN/j7Bp+qmUoQlXjVPXvqtoCuAdoJSJnlxL9EUzrvKuqBgNXOPtLOz/7MA0VVzsDVfXeUmyJ\nwjSklqtqY2AG8A/nuNGucUXkWczovt6q6v4k3GCo0Dh3VV2squeo6lmqOtHZ94yqfu6sZ6nqAFU9\nW1W7qDOypiGgqsnAM8DpInKTiPiLiLeI9BGRF04gqWeBy0Vkoog0FZEgEXkAI7ZPOHktB5YBn4pI\nJxHxcuKNFJFham73jwJjReR2EWkkIhHATEwL5mWX/EREGhX+OstlGHHqAlzkLOcDHzh2VDXTgYki\n0soxMExEbnTCgjA30USMYLd0OzYe465wZSNwu4h4isg1QI9y8n8LGCkiXcUQICLXiUixiWtEpK1z\njv2c8z0EI2jfnUB5S2MB8JSINHFu5A+4hK3GPLU86OTbD3O+TrgM5SEiA1waEoW9loWuRff6DsK4\n8ZJEpCkwzi059/iLgHNE5A6nHN4icomIFO+NPkYnjrmxLsY8Abnb/BSmIdBLVWvXEJlqpjbOLTOj\npg04UVR1CvAOMBbjwtiHedr57ATS2AZ0BzpgHp0PYlrbV6vqKpeo/TGP3vOBZIzfszOmVY+qzsd0\nej+MEcItGP9nN7eL/XLMn/Fi5zcT877CQlX91Wm1xalqHDAVuN7501aEJBFJc1lGl38IOPl8DiwV\nkVRgDcdGZr2LeeTf75Rpkduxs4B2IpIkIoX1Pgq4AfPoPphyzoeqrgP+DryGEbPtGBdWSQjGpXEI\nc85HAbeqakn9A+6Ud40/iynrLmAp8J6LjTlAP8euI8CtwH9PsgzlcQnwo4ikYc7L+y4Nt/HAO059\nDwRewVxnCZjz9pVbWlOB/iJyVEReVdVUoDfGzXQA45Z8HtOPUxqdgA3OU0tbjKvInUmYJ5nthdcf\nxfuL6gqnpIXldqhaLBaLpe5RG1vuFovFYjlFrLhbLBZLPcSKu8VisdRDrLhbLBZLPaTG5nMPDQ3V\n1q1bF9ufnp5OQEBA9Rt0ili7q5e6ajfUXdut3dVLaXavX78+QSvjDdWqWurL3DKFWLurl7pod15+\npu6Nm68x331d06acFHWxzlXrn91U8M3VE3bLiMhsETkkIr+57GsqIstEZJvze6pva1os9Y7M7AOk\npG/F2ze5pk2xNABOxuc+B/MasCtPAt+oahvMK+4NafoBi6VC5OdnAuDhkVfDllgaAics7qr6Pcdm\nlCvkRswbmji/N52iXRZLvSO/wIi7eOTWsCWWhsBJvaHqfCN1kaqe72wnqZnMB+fV4KOF227HuX6s\no9O8efOKpZ2WlkZgYOAJ21TTWLurl7pot3/wHgIa7yYpIZzcjLY1bc4JUxfrHOqf3dHR0etVtXO5\nCVTEMe++YKYr/c1lO8kt/Gh5adgO1dqBtbv6OJjwtf62Y7yu/WV6TZtyUtTFOletf3ZTVR2qpRAv\nIpEAzu+hSkrXYqk3WJ+7pTqpLHH/HLjLWb8LOPnPwFss9RTrc7dUJyczFPJDzJzS54pIrIgMx3wh\n568isg3zMYXJlWumxVL3KRR323K3VAcn/Iaqqt5WStBVp2iLxVKvKXTL2Ja7pTqwc8tYLNWEbblb\nqhMr7hZLNaCq5OdnAB6IRwEFBbb1bqlarLhbLNVAgeaiFODjbV7/KGzFWyxVhRV3i6UaKBRzH69m\nZjvfirularHibrFUA8YlAz7eZk69/IKMmjTH0gCw4m6xVANFLXfvpmbbttwtVYwVd4ulGigUcx9v\n45bJsz53SxVjxd1iqQYKW+6+hS13K+6WKsaKu8VSDRS23L08g1EV65axVDlW3C2WaiC/IBMRbzw8\nvCjI97Ytd0uVY8XdYqkG8vMz8fLwA0ALvG3L3VLlWHG3WKqB/IJMPD2NuBcUeNmWu6XKseJusVQD\n+QWZeNqWu6UaseJusVQD+fmZeHr6A7blbqkerLhbLNVAfkEGnh6NANACL/ILMgo/SWmxVAlW3C2W\nKsbMCOnic8/3RjUfVTv1r6XqsOJusVQxhTNCuvrcwb7IZKlarLhbLFVM4aRhnh7HfO6u+y2WqsCK\nu8VSxRS20AvdMrblbqkOrLhbLFVMkbh7HBvnDnZmSEvVYsXdYqliCkXcveVuZ4a0VCVW3C2WKqbU\nlrsVd0sVYsXdYqli3FvuqCciXtYtY6lSrLhbLFVMfkEmHuKNh3gV7fP08LMtd0uVYsXdYqli8vOP\nzStTiKenn225W6oUK+4WSxXjOiNkIbblbqlqrLhbLFWM64yQhXh62Ja7pWqx4m6xVDH5+RlFM0IW\n4unpR36BfUPVUnVYcbdYqhjTcm903L5Ct4ydGdJSVVhxt1iqEPcZIQvx9PSzM0NaqhQr7pYaIz4+\nniuuuIKgoCAeeeSRmjanSijQHJQCprzwIUOGDCna7+VMIlbommnfvj0xMTElphETE0NUVFSpeQwd\nOpSxY8dWntGWekGlibuIXCMif4jIdhF5srLSrSssX76czp07ExgYSGRkJH369GHlypUAjB8//rg/\ndiEiwvbt24u2t2zZQt++fQkJCSEoKIjo6Gh++OGH447Jyclh/PjxtGnThoCAAFq3bs2wYcPYvXt3\nUZxFixbRpUsXAgICaNasGYMHDyY2NrYofM6cOXTv3r3M8gwdOhQvLy8OHjxYtG/kyJEEBgYSGBiI\nj48P3t7eRdt9+vRh9+7diEjRvsJl/vz5JeYxY8YMQkNDSUlJYcqUKWXaUx41IXDR0dGEhYURHBxM\nhw4dWLhwYbE4hZ2m4jLGHY690FQYvnnzZnr27Fm1Blci7tduefTs2ZOZM2dWSt4DBw5k6dKlZGdn\nExERcVzY448/zmmnnUZwcDCtWrVi0qRJlZJnXaRSxF1EPIHXgT5AO+A2EWlXGWnXBV566SVef/11\nnn76aeLj49m7dy/33XdfiX/20tixYwfdunXjggsuYNeuXRw4cICbb76Z3r17s3r16qJ4/fv35/PP\nP+eDDz4gOTmZX375hU6dOvHNN98A8PHHH3P77bfz0EMPkZCQwObNm/H19aV79+4cPXq0Qrakp6fz\nySefEBISwty5c4v2T58+nbS0NNLS0nj66ae59dZbi7aXLFlSFC8pKalof1paGrfeemuJ+ezZs4d2\n7dohIhWup6oiL+/E3SNTp07l4MGDpKSkMGPGDIYMGXLczRCOTTHgId7H7S8cPWOHQ54469evp3Pn\nzmzatInzzz//uLDhw4fz+++/k5KSwg8//MD777/P999/X0OW1ixe5UepEF2A7aq6E0BE5gE3Alsq\nKR/ftBgAABuGSURBVP0ijqZuJDF5dfkRq4nUlEz++c+nGT9xABd2iefg0XcBOK8jnNfxTLbHvsGR\nlLWkZhxme+wbxY7fc/B9aNScRx+fQ4eOLbn73iiOZJiW7rX9vFmz9mIefOhOPvxkNKv+9ztLl37F\nsu/H0SRyHbvj1gFw9Y0eQA7b9k1j1EP/ZOQDV9HlimT2J84B4MnxnVi56iv++exgHnr0BuKPrCYr\nJ47tsW/QJCKd7bFbj7Pp04/XEBjkxfB7ruStmS9z822BxewuqUyxBxMB2B47HS8vzzLr7fGH3+WL\nz35CRHj55ReZNvMeLut2zv+3d+bhURVZH35PFvbIohgQkEVABIfFsHxsAoKCIIMKLgiIowwyijCj\n4oCsguKnoKijIqKCA2hw+ARRQAEJiygii7LKsMuOiCEEErL0+f6om9h0OkmHJL2Eep+nnr5L3bq/\nqr59bvW5dU8xfeoy5n60lrMJSbRscz3jJ/amXPnSADwxaDob1u8lOTmFunUr8/xLfahz/TXEzvmG\nOXNiM8tq0aoO7854jDrVHmP56nFUr3l15jkrVS7Hk8/8me+/+y9PDZlJv7+0Z+Z7K2jdth6TX3+I\nFcu3MmXS5xw5/Bu161Ri/Iu9qXeDd5dIqQpw4PhaU/eTB0hJTebbDa/RqEmNzDwuVypgeu4pKSk8\n+OCDzJs3jxo1qjF2YjsiIz4jLKw47VuO4oWX+9K6bT2Sk1IY82wsXy/7iYpXl6XnvS1JSz+X2dbb\ntx3i2WGzObj/JO06NEBEKHXFicz9OdWhfctR9O3fjgX/9z1Hjpzm5nb1eXlKf4qXuPjmA3Bw/0lG\nDJvNzh2HiYwIp1mL2lSt/Si9e74KQMOG5twTJ/Whzc31efrvM/lp8wHS09O5qel1jH+xN5Url+fV\nlz5jzZrVfLduLUOHDubue/6Hsc/fx949xxk/5hO2b/mFCleW4e9Pd6dr95gcr5sz8edJSTvD6fNz\nWbxsNTXryEXXYHhpOPY78DscPxZPWno8pxO+9/rbCyQVy91M2TINCvUcBWXcqwCH3NYPAy08M4nI\nQGAgQHR0tFcfY2JiYra+R4BiJU9RorQrf2oLkG+/2cuFC6m0btOEhHjvui4kK6kpeN2feFZJiHfx\nzaqdDB765yx52nVowtyPvuHk8WTilu+kwY3VKV2qnNey9u87ztEjp2nbtnGW/e1vaczquJ08PKAb\nSeeVtDRz3rS0SNLTLs77n9h13NalKe3a3cT/TviU79ce4IYG1+Zap8QEs5wQ7yIiIufe+KixfUlN\nUaKjy/HYkO4AvPt2HF8t+Ylp7w+lfIUyTHpxHqP+GcvEl/8CQPMW9Xl2dB8iI8N57ZX5/P3xGXw8\nbwRdu7Xi+2/3XlRWhq6M9gVITVFSks36uUQXp35N4NfjiXz+5XO4XMr67w4y/KlZTPnXIOo3uJbF\nX6xn4EPv8OnnoylWLKvxAxj6+FTWr9tFSkoaLVvfQPXqVT3aPhx1RXP0yF4WLFjA+PHj+fjjj/nk\nk7k8P2Y5H37UCHDhcsH5RBcJ8S7emLKI/ft+ZcGicSQlpTDkb2+jLlOn1NQ0Bj38Dg/07cC9vdux\nKm4Lz/5zBg/95VYS4l38vPNQjnVwueCLzzbx+luPUax4JA/3e5U5//6WXve2zVK3SS9+TrPm9Xj7\n3SGkpqazdct+EuJdTHv/78T8aTAfzxtBtWsrAhAfn0jXbi14/sWHcblcPDd6DqP/OZdX3xjIgEe7\ns37dXm6/ozl39WwFwImjSfS//w0GPd6NKW/8jT27j/LYwDe55ppK1LquchYt69ftYtiT75Gelk5q\najpN6j9FclIKEZHhxM5Zy6uvDySmWR0AZry3lPff/ZKkpBSqVLmSjrc2z/a3GShOHN5FavKvOebJ\nzRbmiqrmOwG9gPfc1vsBb+Z0TExMjHojLi7O6/ZgZfbs2RodHZ2j7rFjx2pkZKSWLVv2ogTo7t27\nVVU1PDxclyxZkuXYnTt3KqCHDx/WAQMG6H333ZftedasWaOAJiUlZdk3depUrV27tqqqzpgxQ1u3\nbq2qWdv74MGDKiK6efNmVVW97bbbdMiQIV7r1KdPn4u27d+/X4Es9dyxY4dXvf3799eRI0dmrter\nV0+XL1+euX706FGNiIjQ1NTULMd+/vnnCmh8fLzXslT1ovb1zBMXF6eRkZEXtdWgQYN01KhRF5VR\nt25dXblypVf9GaSkpOjixYv1lVdeyTbP2LFjtWPHjpnn3r59u5YoUSJzf/Xq1XXZsmWqqlqzZs2L\nroVp06ZplSpVVFV11apVWrlyZXW5XJn7W7ZsmVmv3OpQvXp1nTVrVua+YcOG6aOPPupVc79+/fSv\nf/2rHjp0KFN3Bp5t68nmzZu1XLlymevt2rXT6dOnZ67HxsZqmzZtLjpm4MCBOm7cuGzLVFXt06eP\nLliwQBMTE7VOnTp6/vx5r/lcLpdu2rRJx4wZo4sWLcqxzGAlO5sCbFAf7LJoAYyzFZGWwDhV7eys\nj3BuHC/mcMyvwEEvu64CTuVblP+4AqiDqUt2uq8BigP7PbbHANuAC0AjzD+e3zzyRAF1gc1AZaAM\nsCub85QAGgBbgRQvGq4AfgauxLTzLrK2dyVn/3Zn/UqgKrAFcL9YvNWpGPAnYGM2+jyp4eg86qw3\ncT7dzxPm1CcV8w+xPBAJiJMy2s+zLLi4fT3PFwXUdOqVQW1nu/v5BfPdnvahPnWAk8AZL/vc2+sq\nIIGL2+pPwAHgLHATxqWZ7Oy7wtG+BVP/SoC7L62mU8ejPtTB/TyeujyJwLR5WSAdOOccC1nbNgyo\n5miNcNuWUb/rMdd2xrUW7ZTt3qUWJ88vXrQ0dMoLd7RkfP8up8zDXo4B01ZRwO5s9gcz2dnC6qpa\nMdejfbkD5JYwX+Y+zEVWDPgJaHCJZfl0VwqWhLnwzwF7c8gzDpjtZbsCtZ3l2cBiL3mmAt84y52A\nJKBqNucRjHvsGY/tYZgf4vPO+kNuZW7wyLvLOcdxJ51ydPbIrU4YA6RAhI9tNzNDk9u5W2eTtx/G\noNV06rnZo/1muJflbDsHNHRb/9KtDdoDhz3yTwNG5uNaWA78I7drANjg2VYYo9nJWd4PdHE79q8Z\nWoF2GCMubvvXutUrxzq4nyena9PLcW0whjSjvTPb3lkfDawEKjnrjT3qFwcMcMvfG1iWx/b9H+Ar\nZ3mKe3k5HDMK+P1Sv9NAJs/fZl5TgYyWUfMmxmDgK8wP8BNV3Z7zUUUDVT0DjAGuFZE7RaSUiESK\nyO0i8nIeinoOaCUiL4hIBRGJEpEngAeBfzrnWg4sA+aLSIyIRDj5BonIw2quiKeBUSLygIiUEJFK\nwHuYHtUUt/OJiJTI+HRSS+A6zAPyxk66EfjI0VHYvAO8ICLVHYEVRaSHsy8K00v8DSiF6fW5cwKo\n5bHtR+ABEQkXkS4Yw5gT04FBItJCDKVFpJuIRHlmFJF6zndc0vm++wI3A6vyUN/s+AQYISLlRaQq\n8ITbvu+ANGCIc967Md9XnuuQGyJyj3N+gIyhVhk9bc/2jsJ0CuJFpAIw1qM4z/xfAHVFpJ9Tj0gR\naSYiN+QgKQbY5CzfhLlJuusNE5FHnXYTEWkOPM4f/1IuKwpsnLuqLlbVuqp6naq+UFDlhgKq+gqm\nxzwK+NVZHgwsyEMZuzG9o0aY3tUxoCfQWVXXumXtBSwG5mL+/m8DmmJ6jajqXEwv9x8YQ7gDKInp\nEbu7fFphfow3OZ9JwCPAZ6q6VVWPZyTgdeAO50frC/EikuiWnvTxuNeBhcBSETkLrOOPB/P/xrgW\njjh1Oudx7PtAfRGJF5GMdh8KdAfigT7k8n2o6gZML/lNjDHbg/mX4w3B9HpPYr7zocB9qropm/x5\n4TlMXfcDS4FZbhpTgLsdXaeB+4BPL7EOudEM+F5EEjHfyy/qjIjD1P1Dp73vBV7DXGenMN/blx5l\nvQ70EpHfReQNVT0L3Abcj/knchx4CeMiyo4YYJOYsbP1+MN16M5dwF6MQZ8N/AvzHV12FIjPvSAR\nkYGq+m6gdeQVq9u/hKpuCF3tVrd/ya/uoDPuFovFYsk/NraMxWKxFEGscbdYLJYiiDXuFovFUgSx\nxt1isViKIAUVWybPXHXVVVqjRo0s28+dO0fp0qX9LyifWN3+JVR1Q+hq96rb5QIRk4KUItXewMaN\nG0+pD2+oBsy416hRgw0bNmTZvnLlypCKa52B1e1fQlU3hK72lV9/TXsRmDcPtm2DPXsgI8TxFVfA\nVVdBo0bQrRt07QqVswYACwQh297Z6BYRb2FbshAw426xWEKEkydh8mRazpgBp05BmTLQpAl06QK1\nnJdOT52CEydg7VqYP99s69gRRo+Gdrm9GGwpDKxxt1gs3jl/HqZMgZdegvPnOduiBcXfegvuuANK\nlfJ+jCps3QoLF8Jbb0H79nDzzTBxIrRu7Vf5lzv2garFYsnK0qVQty6MGmV64Nu3s+2FF+Dee7M3\n7GB87w0bmuP27YPXXzfumzZtYOhQc8Ow+AVr3C0Wyx9cuABPPw2dO0O5crBmjXGzXH993ssqWRKG\nDIH//hcGD4Y33oDGjeG74JlJrShjjbvFYjEcOACtWsErr8Bjj8EPP5ged34pXRr+9S9YsQJSUoyb\nZvr0/JdryRFr3C0WC6xfDy1aGFfKggXGX16yZMGeo0MH+PFH6NQJBg6EJ56AS5iY3OIb1rhbLJc7\nn35qHnyWLg3ffgs9euR6yCVTrhx88QU8+SS8+aYZMnn2sgy3XuhY426xXM688w706mXGp69bBzfk\nNFdGAREeblw/779vXDW33gqnfZnF0JIXrHG3WC5XpkyBv/3NDG1csQKuvtq/53/4YfNC1ObNZix8\nxgtRlgLBGneL5XLkhReMa+See4yBLWj/uq/ceScsWgT795sHrYezm+faklescbdYLjfGjzfj0Pv1\ng48+gmLFAqunUydYtsy84XrLLXD0aGD1FBF8Mu4i0kVEdonIHhEZ7mX/zSKySUTSRKRXwcu0WCwF\nwksvwdix0L8/zJwJEUHyknrLlvDll8Y1c8stcPx4oBWFPLkadxEJB94CbgfqA71FpL5Htl8wk/B+\nVNACLRZLAfHaazB8OPTubR5mhgXZH/dWrWDxYjh0yBj4X38NtKKQxpdvtzmwR1X3OTOvxwIXjZVS\n1QOqugVwFYJGi8WSX6ZNg3/8A3r2hH//24xYCUbatv3DB9+5M8THB1pRyJLrBNmOm6WLqg5w1vsB\nLVR1sJe8M4EvVHVeNmUNBAYCREdHx8TGxmbJk5iYSJkyZfJYjcBjdfuXUNUN/td+9fLl3DBxIqeb\nN2fbhAloZOQlleNP3RXWr+fGkSM5e/31/DRpEq58PPAN1WslO90dOnTYqKpNcy1AVXNMQC/gPbf1\nfsCb2eSdCfTKrUxVJSYmRr0RFxfndXuwY3X7l1DVrepn7QsWqIaHq7Zvr3r+fL6K8nubz5unGham\n2qmTalLSJRcTqtdKdrqBDeqDjfXFLXMEqOa2XtXZZrFYgpmvvzZRHGNiTAjeQA13vFR69oQPPoDl\ny+H++yE1NdCKQgpfjPsPQB0RqSkixYD7gYWFK8tiseSL774zYQTq1oUlSyAqKtCKLo3+/U3Qsc8+\nMy89uexjPV/JdRyUqqaJyGDgKyAc+EBVt4vIeMzfg4Ui0gyYD5QHuovIc6raoFCVWywW7/z4I9x+\nO1xzjRk/XqFCoBXlj8GDTfyZZ581s0C9/XZQz9kaLPg0yFVVFwOLPbaNcVv+AeOusVgsgeTnn+G2\n28ycpsuXQ6VKgVZUMIwYAWfOmHH6pUvDpEnWwOdCkLzBYLFY8s3u3WZ8eFiY8bdfe22gFRUsL74I\n586ZoGMlS8KECYFWFNRY426xFAX27TOGPS0N4uKgTp1AKyp4RMy0fcnJ8PzzULy4CaNg8Yo17hZL\nqHPggJkI4/x5Y9gbFOHHXWFhJkzxhQswerR5GWvEiECrCkqscbdYQpk9e8wE1gkJJmxvw4aBVlT4\nhIebIZLp6eYha0oKjBljffAeWONusYQqO3aYiIopKcbH3qRJoBX5j4gIE0YhMhLGjTNj4CdMsAbe\nDWvcLZZQ5KefjGGPiIBVq4q2KyY7MnrwkZEmPv3Zs2YCkmALiBYgrHG3WEKNFSvgrrugbFnTYy+K\nD099JSzMBEWLijKG/eRJE8q4ePFAKws49hZnsYQSc+ZAly5QrRp8883lbdgzCAszwyNffhliY6Fb\nN/MM4jLHGneLJRRQhYkToW9faN3aGPaiNo49P4jAsGHw4YewcqWZ/GPv3kCrCijWuFsswc7Zs2au\n05Ej4YEHzIxF5coFWlVw8uCD8NVXZianZs2M2+oyxRp3iyWY2bULWrSA+fNh8mSYPdv6k3OjY0dY\nv97E1uncmWqxsZdlwDFr3C2WYETVTIXXtKmZbm7ZMnjqKTvUz1euu85ExrzzTq6bNs08pzh2LNCq\n/Io17hZLsHHyJNx5JwwYYFwLmzaZ0AKWvBEVBf/5D7uefNI8o2jUyPwDukywxt1iCRZcLtNbb9DA\n+I1ffdVEdqxWLfdjLd4R4Vj37rBhA1SpAnffbW6chw4FWlmhY427xRIM/PCDGeExYADUqwcbN5oJ\nre0LOQVD/frGD//yy7B0KdxwgwkffP58oJUVGvbKsVgCyZYtZjq55s3hl19g1ixYvfryfOO0sImM\nNMMld+wwbq7hw6F2bfMSVBGcws8ad4vF36jCmjXGqDdqZFwvY8eaiTb69rUPTQubGjXMnLKrV0Ot\nWjBokDHykydDfHyg1RUY1rhbLP7it9/g7bdp+sgjcPPNZgz26NEmZO+4cSacgMV/tG1rbrKLFhkj\nP2wYVK1qjP3ateYmHMJY426xFCZHj5qHpF26QHQ0PP44GhEB771n9o0fD+XLB1rl5YsIdO1q4uBv\n3gy9eplok23aGIM/fLjp4Yeg28Yn4y4iXURkl4jsEZHhXvYXF5G5zv7vRaRGQQu1WIIeVTMj0ty5\nMHSo8ZtXqWIeku7ebXqGmzaxcdo0eOQRKFUq0Iot7jRubIKOnThhDHzduiZmTbt2ULGiGWkzebLp\n1ScnB1ptruQaFVJEwoG3gFuBw8APIrJQVXe4ZXsE+F1Va4vI/cBLwH2FIdhiCTgJCXDwoHkAuncv\n7Nxp/OVbtxrXC0CJEsb18tBDcOutxree4UtfuTJQyi2+EBUF/fqZdOaMcZ8tWWKicWaMkw8PN8b/\nxhvNTbxWLahZ06ToaBOKOcD4oqA5sEdV9wGISCzQA3A37j2Acc7yPOBNERHVQnBaJSaaWBtBRrHf\nfgvJN+ACqtuXy8M9j9ty8ZMnjXFV9Z5cLvOZnm6Sy2XmF81IqakmpaSYKduSk006d86kxETzw46P\nh99/N0b7xAmTPIfPlStnhtbddZd5o7RZM/OjL1asgBrKEjDKljU99rvvNusnTsC6dWbo6rZt5gWz\nefMuvk5FTE+/cmW48kqoUMG43q64wtw4oqJMLP5CnjXLF+NeBXAf8X8YaJFdHlVNE5EzwJXAqYIQ\neRFTp8IzzxR4sfmlVaAFXCKhqrtlYZ8gPNz8CMuXN6lCBfNK+9VXmx9t9eomKmPNmmabHeFyeRAd\nDT16mJRBcrL5J7d/v0nHjpnAZcePw+nTsH27+UxIgKQkc8y0aYVu3CW3zrWI9AK6qOoAZ70f0EJV\nB7vl2ebkOeys73XynPIoayAwECA6OjomNjY2y/kSExMpU6ZMtnrK7NlD1I4d2e4PFBcuXKB4CAZ0\nCrhuX4yiex5nOTk5meIlSph1J2nG/rCwzGUNC4PwcFQEDQ83KSwMjYzEFRGBRkTgiozEVbw4rmLF\nSC9RAlfJkrgiIwvNYOd2jQcrVnf+kfR0wpOScEVE4CpRIse82enu0KHDRlVtmuvJVDXHhOkkfeW2\nPgIY4ZHnK6ClsxyB6bFLTuXGxMSoN+Li4rxuD3asbv8SqrpVQ1e71e1fstMNbNBc7Laq+uSW+QGo\nIyI1gSPA/cADHnkWAv2B74BewApHRLZs3LjxlIgc9LLrKgrDnVP4WN3+JVR1Q+hqt7r9S3a6q/ty\ncK7GXY0PfTCmdx4OfKCq20VkPOYOshB4H5glInuA05gbQG7lVvS2XUQ2qC9/OYIMq9u/hKpuCF3t\nVrd/ya9un8brqOpiYLHHtjFuy8nAPZcqwmKxWCwFi31D1WKxWIogwWjc3w20gEvE6vYvoaobQle7\n1e1f8qU716GQFovFYgk9grHnbrFYLJZ8Yo27xWKxFEGCxriLyCQR+VlEtojIfBEp57ZvhBNxcpeI\ndA6kTk9E5B4R2S4iLhFp6ra9hogkiciPTnonkDo9yU63sy9o29sdERknIkfc2rhroDXlRG7RVYMV\nETkgIludNt4QaD05ISIfiMhJ5635jG0VRGSZiOx2PoMuxnI2uvN1fQeNcQeWATeqakPgv5g3YRGR\n+phx8w2ALsDbTqTKYGEbcDew2su+vara2EmD/KwrN7zqDoH29mSKWxsvzj17YHCLrno7UB/o7bR1\nqNDBaeNgHy8+E3PdujMc+FpV6wBfO+vBxkyy6oZ8XN9BY9xVdamqpjmr64CqznIPIFZVL6jqfmAP\nJlJlUKCqO1V1V6B15JUcdAd1e4cwmdFVVTUFyIiuailAVHU15kVKd3oAHzrLHwJ3+lWUD2SjO18E\njXH34GFgibPsLSplFb8rujRqishmEVklIm0DLcZHQq29BzuuvA+C8e+2G6HWru4osFRENjrB/0KN\naFXNiGt9HIgOpJg8csnXt18jyovIcqCSl10jVfUzJ89IIA2Y409tOeGLbi8cA65V1d9EJAZYICIN\nVDWh0IR6cIm6g4qc6gBMBSZgjM8E4BVMx8BSsLRR1SMicjWwTER+dnqaIYeqqoiEyvjvfF3ffjXu\nqtopp/0i8hBwB9DRLfDYEaCaW7aqzja/kZvubI65AFxwljc6YZDrAn57IHUpugmC9nbH1zqIyHTg\ni0KWkx+Cql3zgqoecT5Pish8jIsplIz7CRGprKrHRKQycDLQgnxBVU9kLF/K9R00bhkR6QI8A/xZ\nVd2nulkI3O/M01oTqAOsD4TGvCAiFTMeRIpILYzufYFV5RMh097ODzWDuzAPiYOVzOiqIlIM89B6\nYYA15YqIlBaRqIxl4DaCu529kRG1FuczVP615u/69iUusD8S5sHdIeBHJ73jtm8ksBfYBdweaK0e\nuu/C+E8vACdwYt8DPYHtTl02Ad0DrdUX3cHe3h51mAVsBbZgfsCVA60pF71dMSPB9mJcYwHX5IPm\nWsBPTtoe7LqBjzEu0VTn+n4EMyvc18BuYDlQIdA6fdSdr+vbhh+wWCyWIkjQuGUsFovFUnBY426x\nWCxFEGvcLRaLpQhijbvFYrEUQaxxt1gsliKINe4Wi8VSBLHG3WKxWIog/w/yD8LouQJ4egAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11126d4e0>"
      ]
     },
     "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)"
   ]
  },
  {
   "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": 27,
   "metadata": {
    "collapsed": false
   },
   "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": 28,
   "metadata": {
    "collapsed": false
   },
   "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": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "words_to_train = ['FISH', 'BOOK', 'VEGETABLE', 'FUTURE', 'JOHN']\n",
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete for FISH with 3 states with time 0.10971819097176194 seconds\n",
      "Training complete for BOOK with 2 states with time 2.5506352949887514 seconds\n",
      "Training complete for VEGETABLE with 3 states with time 0.5171131058596075 seconds\n",
      "Training complete for FUTURE with 2 states with time 2.418523565866053 seconds\n",
      "Training complete for JOHN with 12 states with time 37.457234161905944 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, 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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete for FISH with 3 states with time 0.034371254965662956 seconds\n",
      "Training complete for BOOK with 3 states with time 0.10670101502910256 seconds\n",
      "Training complete for VEGETABLE with 3 states with time 0.029776880983263254 seconds\n",
      "Training complete for FUTURE with 3 states with time 0.06716671516187489 seconds\n",
      "Training complete for JOHN with 3 states with time 0.8936220221221447 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": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete for FISH with 3 states with time 0.02270324109122157 seconds\n",
      "Training complete for BOOK with 3 states with time 0.1300661989953369 seconds\n",
      "Training complete for VEGETABLE with 3 states with time 0.06927231699228287 seconds\n",
      "Training complete for FUTURE with 3 states with time 0.18914679810404778 seconds\n",
      "Training complete for JOHN with 3 states with time 1.186020429013297 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": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg CV Selector Speed 7.879395462013781\n",
      "Avg BIC Selector Speed 0.20683950260281564\n",
      "Avg DIC Selector Speed 0.20592621373943984\n"
     ]
    }
   ],
   "source": [
    "CVTimes = [0.09670115914195776, 2.907256133854389, 0.6297865349333733, 2.8252335761208087, 32.937999906018376]\n",
    "BicTimes = [0.02782643004320562, 0.10402497299946845, 0.030560943065211177, 0.08237440488301218, 0.7894107620231807]\n",
    "DicTimes = [0.023151866858825088, 0.11232681502588093, 0.02939670509658754, 0.0784992128610611, 0.7862564688548446]\n",
    "print(\"Avg CV Selector Speed\", sum(CVTimes)/len(CVTimes))\n",
    "print(\"Avg BIC Selector Speed\", sum(BicTimes)/len(BicTimes))\n",
    "print(\"Avg DIC Selector Speed\", sum(DicTimes)/len(DicTimes))"
   ]
  },
  {
   "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:** When it come to speed SelectorCV is definatly the loser it is a whole magnitude high than the BIC Selector and DIC Selector times. The outcomes of the BIC Selector and DIC Selector are identical. However the DIC Selector is slightly faster than the BIC Selector. So DIC Seems to be the optimal selection algorithm."
   ]
  },
  {
   "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": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "....\n",
      "----------------------------------------------------------------------\n",
      "Ran 4 tests in 47.292s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=4 errors=0 failures=0>"
      ]
     },
     "execution_count": 34,
     "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": {
    "collapsed": false
   },
   "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": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of word models returned = 112\n"
     ]
    }
   ],
   "source": [
    "# autoreload for automatically reloading changes made in my_model_selectors and my_recognizer\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "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": 36,
   "metadata": {
    "collapsed": false
   },
   "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": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO implement the recognize method in my_recognizer\n",
    "from my_recognizer import recognize\n",
    "from asl_utils import show_errors\n",
    "features_to_try = {\"features_norm\":features_norm, \"features_polar\":features_polar, \"features_delta\":features_delta, \"features_custom\":features_custom, \"features_custom_2\":features_custom_2, \"features_delta_normal\":features_delta_normal, \"features_delta_normal_polar\":features_delta_normal_polar, \"features_delta_ground\":features_delta_ground, \"features_delta_normal_ground\":features_delta_normal_ground, \"features_custom_2_ground\":features_custom_2_ground}\n",
    "model_selectors_to_try = {\"SelectorConstant\":SelectorConstant, \"SelectorCV\":SelectorCV, \"SelectorBIC\":SelectorBIC, \"SelectorDIC\":SelectorDIC}\n",
    "feature_models = [(features_to_try[features], model_selectors_to_try[model_selector], features, model_selector) for features in features_to_try.keys() for model_selector in model_selectors_to_try]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running SelectorConstant on features_norm\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *MARY WRITE *ARRIVE                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *NEW *JOHN CAN                                           JOHN CAN GO CAN\n",
      "   12: *SHOULD *HAVE *GO1 CAN                                        JOHN CAN GO CAN\n",
      "   21: *LIKE *NEW *HAVE *IX-1P *CAR *BLAME *CHICKEN *WRITE           JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *IX LIKE *LIKE *LIKE IX                                       JOHN LIKE IX IX IX\n",
      "   28: *ANN LIKE *ANN *LIKE *ANN                                     JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *LOVE *LIKE *MARY                                 JOHN LIKE IX IX IX\n",
      "   36: *LEAVE *NOT *YESTERDAY *VISIT LIKE *JOHN                      MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN *LEAVE *FUTURE1 *VEGETABLE LOVE                          JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE *ARRIVE CAR *CAR                                  FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD *FUTURE *STUDENT HOUSE                            JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *MARY *MARY MARY                                        JOHN DECIDE VISIT MARY\n",
      "   67: *IX-1P FUTURE *JOHN *ARRIVE HOUSE                             JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN WILL VISIT MARY                                          JOHN WILL VISIT MARY\n",
      "   74: *WOMAN *VISIT VISIT *FRANK                                    JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME MARY                                                ANN BLAME MARY\n",
      "   84: *IX *ARRIVE *NEW BOOK                                         IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *FUTURE *THROW *JOHN *JOHN *WOMAN *BOOK *BREAK-DOWN           JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *SELF *GIVE1 IX *IX WOMAN *CHOCOLATE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *GIVE1 IX *IX WOMAN BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *WHO *SEE                                                     JOHN LEG\n",
      "  107: *TELL *IX *BOX *LIKE *JANA                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *LOVE *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: IX CAR *IX SUE *HAVE                                          IX CAR BLUE SUE BUY\n",
      "  119: *VEGETABLE *BUY1 IX CAR *GO                                   SUE BUY IX CAR BLUE\n",
      "  122: JOHN *HOUSE *COAT                                             JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR YESTERDAY BOOK                                JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY *CORN                                              LOVE JOHN WHO\n",
      "  167: JOHN *JOHN *SAY-1P LOVE MARY                                  JOHN IX SAY LOVE MARY\n",
      "  171: *SHOOT *JOHN BLAME                                            JOHN MARY BLAME\n",
      "  174: *NEW *GIVE1 GIVE1 *WHO *CAR                                   PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOX                                                     JOHN ARRIVE\n",
      "  184: *IX *IX *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *JANA *SEE *PREFER *ARRIVE                                    JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SEE *YESTERDAY BOX                                      JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *JOHN                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *THINK *WOMAN *WOMAN *STUDENT HOUSE                      JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_norm\n",
      "\n",
      "**** WER = 0.6573033707865169\n",
      "Total correct: 61 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN WRITE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *NEW GO *ARRIVE                                          JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: *LIKE *NEW *HAVE *GO *BLAME *CAR *FUTURE *WRITE               JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *GO LIKE *LIKE *LIKE *GO                                      JOHN LIKE IX IX IX\n",
      "   28: *ANN LIKE *ANN *LIKE *ANN                                     JOHN LIKE IX IX IX\n",
      "   30: *IX-1P LIKE *LOVE *LIKE *MARY                                 JOHN LIKE IX IX IX\n",
      "   36: *SHOOT *SHOULD *YESTERDAY *VISIT LIKE *LIKE                   MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *SHOOT *VISIT *SEE *SHOULD LOVE                               JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *POSS *SEE *NEW CAR *HAVE                                     FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *TELL NOT *HAVE HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *WHO *MARY MARY                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *SOMETHING-ONE *JOHN *ARRIVE *CAN                        JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN WILL VISIT MARY                                          JOHN WILL VISIT MARY\n",
      "   74: JOHN *VISIT VISIT *VISIT                                      JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *ARRIVE *POSS *WRITE                                    IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *TELL *HAVE *SOMETHING-ONE *SOMETHING-ONE *SOMETHING-ONE NEW COAT  JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *NOT *GIVE1 *SOMETHING-ONE *GIVE1 WOMAN *VIDEOTAPE            JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *GIVE1 *WOMAN *WOMAN WOMAN BOOK                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW *HOUSE BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *POSS                                                    JOHN LEG\n",
      "  107: JOHN *IX *GIVE1 *LIKE *TOY1                                   JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *LOVE *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: IX *CAN *IX SUE *HAVE                                         IX CAR BLUE SUE BUY\n",
      "  119: *WHO *BUY1 *HAVE *ARRIVE *FUTURE                              SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR YESTERDAY *NEW                                JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN *ARRIVE *GO WHAT *COAT                                   JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *GIVE1 *MARY *CORN                                            LOVE JOHN WHO\n",
      "  167: JOHN *VISIT *SAY-1P *MARY *LOVE                               JOHN IX SAY LOVE MARY\n",
      "  171: *JANA *JOHN BLAME                                             JOHN MARY BLAME\n",
      "  174: *NEW *GIVE1 GIVE1 *WHO *VISIT                                 PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *SOMETHING-ONE *GIVE1                                         JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER *WHO                                   ALL BOY GIVE TEACHER APPLE\n",
      "  189: *JANA *GIVE1 *FINISH *ARRIVE                                  JOHN GIVE GIRL BOX\n",
      "  193: JOHN *YESTERDAY *FINISH BOX                                   JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *TELL                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *THINK *WOMAN *WOMAN BUY HOUSE                           JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_norm\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *MARY WRITE *ARRIVE                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *NEW *JOHN CAN                                           JOHN CAN GO CAN\n",
      "   12: *SHOULD *HAVE *GO1 CAN                                        JOHN CAN GO CAN\n",
      "   21: *LIKE *NEW *HAVE *IX-1P *CAR *BLAME *CHICKEN *WRITE           JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *IX LIKE *LIKE *LIKE IX                                       JOHN LIKE IX IX IX\n",
      "   28: *ANN LIKE *ANN *LIKE *ANN                                     JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *LOVE *LIKE *MARY                                 JOHN LIKE IX IX IX\n",
      "   36: *LEAVE *NOT *YESTERDAY *VISIT LIKE *JOHN                      MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN *LEAVE *FUTURE1 *VEGETABLE LOVE                          JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE *ARRIVE CAR *CAR                                  FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD *FUTURE *STUDENT HOUSE                            JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *MARY *MARY MARY                                        JOHN DECIDE VISIT MARY\n",
      "   67: *IX-1P FUTURE *JOHN *ARRIVE HOUSE                             JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN WILL VISIT MARY                                          JOHN WILL VISIT MARY\n",
      "   74: *WOMAN *VISIT VISIT *FRANK                                    JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME MARY                                                ANN BLAME MARY\n",
      "   84: *IX *ARRIVE *NEW BOOK                                         IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *FUTURE *THROW *JOHN *JOHN *WOMAN *BOOK *BREAK-DOWN           JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *SELF *GIVE1 IX *IX WOMAN *CHOCOLATE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *GIVE1 IX *IX WOMAN BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *WHO *SEE                                                     JOHN LEG\n",
      "  107: *TELL *IX *BOX *LIKE *JANA                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *LOVE *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: IX CAR *IX SUE *HAVE                                          IX CAR BLUE SUE BUY\n",
      "  119: *VEGETABLE *BUY1 IX CAR *GO                                   SUE BUY IX CAR BLUE\n",
      "  122: JOHN *HOUSE *COAT                                             JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR YESTERDAY BOOK                                JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY *CORN                                              LOVE JOHN WHO\n",
      "  167: JOHN *JOHN *SAY-1P LOVE MARY                                  JOHN IX SAY LOVE MARY\n",
      "  171: *SHOOT *JOHN BLAME                                            JOHN MARY BLAME\n",
      "  174: *NEW *GIVE1 GIVE1 *WHO *CAR                                   PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOX                                                     JOHN ARRIVE\n",
      "  184: *IX *IX *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *JANA *SEE *PREFER *ARRIVE                                    JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SEE *YESTERDAY BOX                                      JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *JOHN                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *THINK *WOMAN *WOMAN *STUDENT HOUSE                      JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_norm\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *MARY WRITE *ARRIVE                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *NEW *JOHN CAN                                           JOHN CAN GO CAN\n",
      "   12: *SHOULD *HAVE *GO1 CAN                                        JOHN CAN GO CAN\n",
      "   21: *LIKE *NEW *HAVE *IX-1P *CAR *BLAME *CHICKEN *WRITE           JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *IX LIKE *LIKE *LIKE IX                                       JOHN LIKE IX IX IX\n",
      "   28: *ANN LIKE *ANN *LIKE *ANN                                     JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *LOVE *LIKE *MARY                                 JOHN LIKE IX IX IX\n",
      "   36: *LEAVE *NOT *YESTERDAY *VISIT LIKE *JOHN                      MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN *LEAVE *FUTURE1 *VEGETABLE LOVE                          JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE *ARRIVE CAR *CAR                                  FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD *FUTURE *STUDENT HOUSE                            JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *MARY *MARY MARY                                        JOHN DECIDE VISIT MARY\n",
      "   67: *IX-1P FUTURE *JOHN *ARRIVE HOUSE                             JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN WILL VISIT MARY                                          JOHN WILL VISIT MARY\n",
      "   74: *WOMAN *VISIT VISIT *FRANK                                    JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME MARY                                                ANN BLAME MARY\n",
      "   84: *IX *ARRIVE *NEW BOOK                                         IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *FUTURE *THROW *JOHN *JOHN *WOMAN *BOOK *BREAK-DOWN           JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *SELF *GIVE1 IX *IX WOMAN *CHOCOLATE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *GIVE1 IX *IX WOMAN BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *WHO *SEE                                                     JOHN LEG\n",
      "  107: *TELL *IX *BOX *LIKE *JANA                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *LOVE *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: IX CAR *IX SUE *HAVE                                          IX CAR BLUE SUE BUY\n",
      "  119: *VEGETABLE *BUY1 IX CAR *GO                                   SUE BUY IX CAR BLUE\n",
      "  122: JOHN *HOUSE *COAT                                             JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR YESTERDAY BOOK                                JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY *CORN                                              LOVE JOHN WHO\n",
      "  167: JOHN *JOHN *SAY-1P LOVE MARY                                  JOHN IX SAY LOVE MARY\n",
      "  171: *SHOOT *JOHN BLAME                                            JOHN MARY BLAME\n",
      "  174: *NEW *GIVE1 GIVE1 *WHO *CAR                                   PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOX                                                     JOHN ARRIVE\n",
      "  184: *IX *IX *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *JANA *SEE *PREFER *ARRIVE                                    JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SEE *YESTERDAY BOX                                      JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *JOHN                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *THINK *WOMAN *WOMAN *STUDENT HOUSE                      JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_polar\n",
      "\n",
      "**** WER = 0.6179775280898876\n",
      "Total correct: 68 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *GO WRITE HOMEWORK                                            JOHN WRITE HOMEWORK\n",
      "    7: JOHN *WHAT *MARY *WHAT                                        JOHN CAN GO CAN\n",
      "   12: JOHN *WHAT *GO1 CAN                                           JOHN CAN GO CAN\n",
      "   21: *IX *HOMEWORK WONT *FUTURE *CAR *CAR *GO *TOMORROW            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *FRANK LIKE IX *WHO IX                                        JOHN LIKE IX IX IX\n",
      "   28: *IX *WHO *FUTURE *FUTURE IX                                   JOHN LIKE IX IX IX\n",
      "   30: *SHOULD LIKE *GO *MARY *GO                                    JOHN LIKE IX IX IX\n",
      "   36: *SOMETHING-ONE VEGETABLE *GIRL *GIVE *MARY *MARY              MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *SUE *GIVE *DECIDE MARY *GO                                   JOHN IX THINK MARY LOVE\n",
      "   43: *IX *GO BUY HOUSE                                             JOHN MUST BUY HOUSE\n",
      "   50: *POSS *SEE BUY CAR *ARRIVE                                    FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD *WHO BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *PREFER *MARY MARY                                      JOHN DECIDE VISIT MARY\n",
      "   67: *LIKE *MOTHER NOT BUY HOUSE                                   JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FINISH *GIVE1 MARY                                      JOHN WILL VISIT MARY\n",
      "   74: *GO *WHO *GO *GO                                              JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME *LOVE                                               ANN BLAME MARY\n",
      "   84: *HOMEWORK *GIVE1 *POSS BOOK                                   IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *MAN *GIVE *WOMAN *IX IX *BUY *BOOK                           JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: JOHN *GIVE1 IX *GIVE3 *GIVE1 *COAT                            JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *WOMAN *WOMAN *WOMAN WOMAN BOOK                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *VEGETABLE                                             JOHN LEG\n",
      "  107: *LIKE *SOMETHING-ONE *HAVE *GO *WHO                           JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX ARRIVE                                                    WOMAN ARRIVE\n",
      "  113: IX CAR *SUE *SOMETHING-ONE *ARRIVE                            IX CAR BLUE SUE BUY\n",
      "  119: *PREFER *BUY1 IX CAR *SOMETHING-ONE                           SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: *SHOULD *BUY1 *CAR YESTERDAY BOOK                             JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: *FRANK BUY YESTERDAY WHAT BOOK                                JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: *MARY *SOMETHING-ONE *MARY LOVE *LOVE                         JOHN IX SAY LOVE MARY\n",
      "  171: *SOMETHING-ONE *SOMETHING-ONE BLAME                           JOHN MARY BLAME\n",
      "  174: *CAN *GIVE3 GIVE1 *GO *WHAT                                   PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *SUE ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *SUE *SOMETHING-ONE *YESTERDAY *ARRIVE                        JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SOMETHING-ONE *YESTERDAY BOX                            JOHN GIVE GIRL BOX\n",
      "  199: *HOMEWORK CHOCOLATE WHO                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *MAN *JOHN BUY HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_polar\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *POSS WRITE HOMEWORK                                          JOHN WRITE HOMEWORK\n",
      "    7: JOHN CAN GO CAN                                               JOHN CAN GO CAN\n",
      "   12: *IX *CAR *CAN CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN *HOMEWORK *GIVE1 *JOHN *CAR *CAR *FUTURE *TOMORROW       JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *LOVE *JOHN IX                                       JOHN LIKE IX IX IX\n",
      "   28: JOHN *JOHN *LOVE IX *LOVE                                     JOHN LIKE IX IX IX\n",
      "   30: JOHN LIKE *GO *LIKE *GO                                       JOHN LIKE IX IX IX\n",
      "   36: *VISIT *VISIT *GIVE *SHOOT *MARY *IX                          MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN *VISIT *GIRL *JOHN *MARY                                 JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *POSS BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *SEE BUY CAR SHOULD                                     FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *VISIT VISIT MARY                                         JOHN DECIDE VISIT MARY\n",
      "   67: JOHN FUTURE *WHO BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FINISH *GO *BLAME                                       JOHN WILL VISIT MARY\n",
      "   74: *GO *VISIT *MARY *GO                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME *LOVE                                             ANN BLAME MARY\n",
      "   84: *HOMEWORK *BLAME *POSS BOOK                                   IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE1 *GIVE *SOMETHING-ONE *SOMETHING-ONE *SOMETHING-ONE *ARRIVE *BREAK-DOWN  JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: JOHN *HAVE *SOMETHING-ONE SOMETHING-ONE *VISIT *COAT          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN GIVE *SOMETHING-ONE SOMETHING-ONE *SOMETHING-ONE 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 *GIVE *HAVE *GO *GO                                      JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *MARY *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: IX *HAVE *MARY *JOHN *BUY1                                    IX CAR BLUE SUE BUY\n",
      "  119: *VISIT *BUY1 *GO *VISIT *VISIT                                SUE BUY IX CAR BLUE\n",
      "  122: JOHN *BOOK BOOK                                               JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAN YESTERDAY *ARRIVE                             JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN *NEW *GO *CAR BOOK                                       JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: JOHN *MARY *VISIT LOVE *GO                                    JOHN IX SAY LOVE MARY\n",
      "  171: *IX *VISIT *SOMETHING-ONE                                     JOHN MARY BLAME\n",
      "  174: *CAN GROUP GIVE1 *GO *VISIT                                   PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *YESTERDAY *SOMETHING-ONE                                     JOHN ARRIVE\n",
      "  184: *GIVE *GIVE *GO TEACHER *GIVE                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *VISIT *VISIT *CAN                                       JOHN GIVE GIRL BOX\n",
      "  193: JOHN *HAVE *VISIT BOX                                         JOHN GIVE GIRL BOX\n",
      "  199: *HOMEWORK *NEW *JOHN                                          LIKE CHOCOLATE WHO\n",
      "  201: JOHN *SHOULD *WOMAN *JOHN BUY HOUSE                           JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_polar\n",
      "\n",
      "**** WER = 0.6179775280898876\n",
      "Total correct: 68 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *GO WRITE HOMEWORK                                            JOHN WRITE HOMEWORK\n",
      "    7: JOHN *WHAT *MARY *WHAT                                        JOHN CAN GO CAN\n",
      "   12: JOHN *WHAT *GO1 CAN                                           JOHN CAN GO CAN\n",
      "   21: *IX *HOMEWORK WONT *FUTURE *CAR *CAR *GO *TOMORROW            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *FRANK LIKE IX *WHO IX                                        JOHN LIKE IX IX IX\n",
      "   28: *IX *WHO *FUTURE *FUTURE IX                                   JOHN LIKE IX IX IX\n",
      "   30: *SHOULD LIKE *GO *MARY *GO                                    JOHN LIKE IX IX IX\n",
      "   36: *SOMETHING-ONE VEGETABLE *GIRL *GIVE *MARY *MARY              MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *SUE *GIVE *DECIDE MARY *GO                                   JOHN IX THINK MARY LOVE\n",
      "   43: *IX *GO BUY HOUSE                                             JOHN MUST BUY HOUSE\n",
      "   50: *POSS *SEE BUY CAR *ARRIVE                                    FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD *WHO BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *PREFER *MARY MARY                                      JOHN DECIDE VISIT MARY\n",
      "   67: *LIKE *MOTHER NOT BUY HOUSE                                   JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FINISH *GIVE1 MARY                                      JOHN WILL VISIT MARY\n",
      "   74: *GO *WHO *GO *GO                                              JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME *LOVE                                               ANN BLAME MARY\n",
      "   84: *HOMEWORK *GIVE1 *POSS BOOK                                   IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *MAN *GIVE *WOMAN *IX IX *BUY *BOOK                           JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: JOHN *GIVE1 IX *GIVE3 *GIVE1 *COAT                            JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *WOMAN *WOMAN *WOMAN WOMAN BOOK                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *VEGETABLE                                             JOHN LEG\n",
      "  107: *LIKE *SOMETHING-ONE *HAVE *GO *WHO                           JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX ARRIVE                                                    WOMAN ARRIVE\n",
      "  113: IX CAR *SUE *SOMETHING-ONE *ARRIVE                            IX CAR BLUE SUE BUY\n",
      "  119: *PREFER *BUY1 IX CAR *SOMETHING-ONE                           SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: *SHOULD *BUY1 *CAR YESTERDAY BOOK                             JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: *FRANK BUY YESTERDAY WHAT BOOK                                JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: *MARY *SOMETHING-ONE *MARY LOVE *LOVE                         JOHN IX SAY LOVE MARY\n",
      "  171: *SOMETHING-ONE *SOMETHING-ONE BLAME                           JOHN MARY BLAME\n",
      "  174: *CAN *GIVE3 GIVE1 *GO *WHAT                                   PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *SUE ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *SUE *SOMETHING-ONE *YESTERDAY *ARRIVE                        JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SOMETHING-ONE *YESTERDAY BOX                            JOHN GIVE GIRL BOX\n",
      "  199: *HOMEWORK CHOCOLATE WHO                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *MAN *JOHN BUY HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_polar\n",
      "\n",
      "**** WER = 0.6179775280898876\n",
      "Total correct: 68 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *GO WRITE HOMEWORK                                            JOHN WRITE HOMEWORK\n",
      "    7: JOHN *WHAT *MARY *WHAT                                        JOHN CAN GO CAN\n",
      "   12: JOHN *WHAT *GO1 CAN                                           JOHN CAN GO CAN\n",
      "   21: *IX *HOMEWORK WONT *FUTURE *CAR *CAR *GO *TOMORROW            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *FRANK LIKE IX *WHO IX                                        JOHN LIKE IX IX IX\n",
      "   28: *IX *WHO *FUTURE *FUTURE IX                                   JOHN LIKE IX IX IX\n",
      "   30: *SHOULD LIKE *GO *MARY *GO                                    JOHN LIKE IX IX IX\n",
      "   36: *SOMETHING-ONE VEGETABLE *GIRL *GIVE *MARY *MARY              MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *SUE *GIVE *DECIDE MARY *GO                                   JOHN IX THINK MARY LOVE\n",
      "   43: *IX *GO BUY HOUSE                                             JOHN MUST BUY HOUSE\n",
      "   50: *POSS *SEE BUY CAR *ARRIVE                                    FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD *WHO BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *PREFER *MARY MARY                                      JOHN DECIDE VISIT MARY\n",
      "   67: *LIKE *MOTHER NOT BUY HOUSE                                   JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FINISH *GIVE1 MARY                                      JOHN WILL VISIT MARY\n",
      "   74: *GO *WHO *GO *GO                                              JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME *LOVE                                               ANN BLAME MARY\n",
      "   84: *HOMEWORK *GIVE1 *POSS BOOK                                   IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *MAN *GIVE *WOMAN *IX IX *BUY *BOOK                           JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: JOHN *GIVE1 IX *GIVE3 *GIVE1 *COAT                            JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *WOMAN *WOMAN *WOMAN WOMAN BOOK                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *VEGETABLE                                             JOHN LEG\n",
      "  107: *LIKE *SOMETHING-ONE *HAVE *GO *WHO                           JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX ARRIVE                                                    WOMAN ARRIVE\n",
      "  113: IX CAR *SUE *SOMETHING-ONE *ARRIVE                            IX CAR BLUE SUE BUY\n",
      "  119: *PREFER *BUY1 IX CAR *SOMETHING-ONE                           SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: *SHOULD *BUY1 *CAR YESTERDAY BOOK                             JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: *FRANK BUY YESTERDAY WHAT BOOK                                JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: *MARY *SOMETHING-ONE *MARY LOVE *LOVE                         JOHN IX SAY LOVE MARY\n",
      "  171: *SOMETHING-ONE *SOMETHING-ONE BLAME                           JOHN MARY BLAME\n",
      "  174: *CAN *GIVE3 GIVE1 *GO *WHAT                                   PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *SUE ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *SUE *SOMETHING-ONE *YESTERDAY *ARRIVE                        JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SOMETHING-ONE *YESTERDAY BOX                            JOHN GIVE GIRL BOX\n",
      "  199: *HOMEWORK CHOCOLATE WHO                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *MAN *JOHN BUY HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_delta\n",
      "\n",
      "**** WER = 0.6404494382022472\n",
      "Total correct: 64 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: *MARY *MARY *JOHN *MARY *CAR *GO *FUTURE *MARY                JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *MARY *JOHN IX *MARY                                     JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *JOHN *JOHN IX                                     JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *MARY *MARY                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY IX *MARY MARY *MARY                                     JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN *FINISH HOUSE                                      JOHN MUST BUY HOUSE\n",
      "   50: *JOHN JOHN BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *MARY *MARY BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *MARY MARY                                         JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *IX *WHAT                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE1 *JOHN *IX *JOHN IX *WHAT *HOUSE                        JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *JOHN *JOHN *IX *IX *MARY                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *MARY *JOHN *JOHN WOMAN *ARRIVE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW *WHAT BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND *LOVE *MARY                                  JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *GIVE1                                  IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *IX                                        SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT *YESTERDAY                                        JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *MARY *ARRIVE                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *YESTERDAY                               JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *BOY *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: *MARY *MARY *IX *ARRIVE *WHAT                                 JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 *HAVE *MARY                                   ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *MARY *VISIT                                         JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *MARY                                           LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY MARY *LIKE *VISIT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_delta\n",
      "\n",
      "**** WER = 0.6741573033707865\n",
      "Total correct: 58 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BOX *GIVE1 *GO                                          JOHN CAN GO CAN\n",
      "   12: JOHN CAN *JOHN CAN                                            JOHN CAN GO CAN\n",
      "   21: *MARY *MARY *HOMEWORK *MARY *BUY *GO *FUTURE *IX              JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *MARY *JOHN IX                                       JOHN LIKE IX IX IX\n",
      "   28: JOHN *IX *JOHN *JOHN *JOHN                                    JOHN LIKE IX IX IX\n",
      "   30: JOHN *IX *MARY *JOHN *WOMAN                                   JOHN LIKE IX IX IX\n",
      "   36: *IX *JOHN *JOHN IX *IX *IX                                    MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *IX IX *IX *IX *IX                                            JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *MARY BUY CAR *FUTURE                                   FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *MARY BUY *BOOK                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *MARY VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN *CAR MARY                                               ANN BLAME MARY\n",
      "   84: *JOHN *GO *JOHN *WHAT                                         IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN GIVE *JOHN *JOHN *CAR *CAN                         JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *IX *JOHN *JOHN *JOHN WOMAN *MARY                             JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX *JOHN *JOHN WOMAN BOOK                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR *CAN                                            POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *IX                                                      JOHN LEG\n",
      "  107: JOHN *JOHN FRIEND *GIVE1 *IX                                  JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN *BOOK                                                   WOMAN ARRIVE\n",
      "  113: *JOHN *GIVE1 *MARY *MARY *BUY1                                IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *GIVE1 *GIVE1 *MARY                               SUE BUY IX CAR BLUE\n",
      "  122: JOHN *JOHN BOOK                                               JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *MARY *JOHN *MARY                                  JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *JOHN WHAT *MARY                                     JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *GIVE1 *MARY *IX                                              LOVE JOHN WHO\n",
      "  167: JOHN IX *IX *MARY MARY                                        JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN *GO                                                JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *BOOK                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BUY1                                                    JOHN ARRIVE\n",
      "  184: *GIVE *JOHN *GIVE1 TEACHER *MARY                              ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *JOHN *IX *BOOK                                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *JOHN *WOMAN BOX                                         JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *BOOK *MARY                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *IX *IX *WOMAN BUY HOUSE                                 JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_delta\n",
      "\n",
      "**** WER = 0.6404494382022472\n",
      "Total correct: 64 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: *MARY *MARY *JOHN *MARY *CAR *GO *FUTURE *MARY                JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *MARY *JOHN IX *MARY                                     JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *JOHN *JOHN IX                                     JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *MARY *MARY                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY IX *MARY MARY *MARY                                     JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN *FINISH HOUSE                                      JOHN MUST BUY HOUSE\n",
      "   50: *JOHN JOHN BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *MARY *MARY BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *MARY MARY                                         JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *IX *WHAT                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE1 *JOHN *IX *JOHN IX *WHAT *HOUSE                        JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *JOHN *JOHN *IX *IX *MARY                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *MARY *JOHN *JOHN WOMAN *ARRIVE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW *WHAT BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND *LOVE *MARY                                  JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *GIVE1                                  IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *IX                                        SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT *YESTERDAY                                        JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *MARY *ARRIVE                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *YESTERDAY                               JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *BOY *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: *MARY *MARY *IX *ARRIVE *WHAT                                 JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 *HAVE *MARY                                   ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *MARY *VISIT                                         JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *MARY                                           LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY MARY *LIKE *VISIT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_delta\n",
      "\n",
      "**** WER = 0.6404494382022472\n",
      "Total correct: 64 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: *MARY *MARY *JOHN *MARY *CAR *GO *FUTURE *MARY                JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *MARY *JOHN IX *MARY                                     JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *JOHN *JOHN IX                                     JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *MARY *MARY                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY IX *MARY MARY *MARY                                     JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN *FINISH HOUSE                                      JOHN MUST BUY HOUSE\n",
      "   50: *JOHN JOHN BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *MARY *MARY BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *MARY MARY                                         JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *IX *WHAT                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE1 *JOHN *IX *JOHN IX *WHAT *HOUSE                        JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *JOHN *JOHN *IX *IX *MARY                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *MARY *JOHN *JOHN WOMAN *ARRIVE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW *WHAT BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND *LOVE *MARY                                  JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *GIVE1                                  IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *IX                                        SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT *YESTERDAY                                        JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *MARY *ARRIVE                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *YESTERDAY                               JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *BOY *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: *MARY *MARY *IX *ARRIVE *WHAT                                 JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 *HAVE *MARY                                   ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *MARY *VISIT                                         JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *MARY                                           LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY MARY *LIKE *VISIT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_custom\n",
      "\n",
      "**** WER = 0.6179775280898876\n",
      "Total correct: 68 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *LOVE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE GO *STUDENT                                        JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN *SOMETHING-ONE *FUTURE *MARY *CAR *FUTURE *WHAT *MARY    JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX IX IX IX                                             JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *LOVE *JOHN IX                                     JOHN LIKE IX IX IX\n",
      "   36: MARY *SOMETHING-ONE *YESTERDAY *GO *GIVE *MARY                MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *WHO IX *WHO *WHO *GIVE                                       JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *SOMETHING-ONE BUY CAR *MARY                            FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *MARY BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *JOHN *WOMAN *JOHN                                        JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *YESTERDAY *WHO BUY HOUSE                                JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SOMETHING-ONE *BLAME *HOUSE                             JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *GIVE *IX                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *FUTURE *WRITE                                      IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *IX *JOHN *IX *BOY *GIVE *CAR *HOUSE                          JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *IX IX *IX WOMAN *LOVE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *IX WOMAN *HOUSE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *IX NEW CAR BREAK-DOWN                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND HAVE *MARY                                   JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX *STUDENT                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *MARY *JOHN *BUY1                               IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *TOMORROW *PEOPLE *WHO                            SUE BUY IX CAR BLUE\n",
      "  122: JOHN *CAR *COAT                                               JOHN READ BOOK\n",
      "  139: JOHN *LOVE WHAT *WHO *ARRIVE                                  JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *WHAT                                    JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: JOHN *WHO *IX LOVE *GO1                                       JOHN IX SAY LOVE MARY\n",
      "  171: JOHN MARY BLAME                                               JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOOK                                                    JOHN ARRIVE\n",
      "  184: ALL *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *JOHN *LOVE *BOOK                                        JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *WHO BOX                                             JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *NOT *STUDENT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_custom\n",
      "\n",
      "**** WER = 0.6067415730337079\n",
      "Total correct: 70 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *LOVE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *FRIEND GO *ARRIVE                                       JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: *MARY *MARY *HOMEWORK *MARY *BUY *FUTURE *CHICAGO *MARY       JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *WHO *MARY *JOHN IX                                      JOHN LIKE IX IX IX\n",
      "   36: MARY *LOVE *FUTURE IX *YESTERDAY *MARY                        MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN IX *WHO *WHO *VISIT                                      JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX *FINISH HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *APPLE BUY CAR *MARY                                    FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *MARY *JOHN BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *WHO *JOHN *WOMAN *IX                                         JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *IX *IX BUY HOUSE                                        JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SOMETHING-ONE VISIT *FUTURE                             JOHN WILL VISIT MARY\n",
      "   74: JOHN *IX *GIVE *IX                                            JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *WHO *GO *FUTURE BOOK                                         IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *THROW IX *IX *THROW *THROW *CAR *BOOK                        JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY GIVE IX *SHOULD WOMAN *ARRIVE                           JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *IX WOMAN *CHOCOLATE                              JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *IX NEW CAR BREAK-DOWN                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN *WOMAN FRIEND *YESTERDAY *JOHN                           JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN *BOOK                                                   WOMAN ARRIVE\n",
      "  113: *JOHN *NEW *MARY *WHAT *WOMAN                                 IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *TOMORROW *NEW *YESTERDAY                         SUE BUY IX CAR BLUE\n",
      "  122: JOHN *NEW *BREAK-DOWN                                         JOHN READ BOOK\n",
      "  139: JOHN *ARRIVE WHAT *WHO *ARRIVE                                JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *WHO WHAT BOOK                                       JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *CHOCOLATE *YESTERDAY *MARY                                   LOVE JOHN WHO\n",
      "  167: JOHN *WHO *IX LOVE *HOMEWORK                                  JOHN IX SAY LOVE MARY\n",
      "  171: JOHN MARY BLAME                                               JOHN MARY BLAME\n",
      "  174: *ARRIVE *LOVE GIVE1 *MARY *CAN                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *THROW *GIVE1 *GIVE1 TEACHER *MARY                            ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *MARY *ARRIVE                                        JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *SOMETHING-ONE BOX                                   JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *JOHN *MARY                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY *WHO *NOT BUY HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_custom\n",
      "\n",
      "**** WER = 0.6179775280898876\n",
      "Total correct: 68 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *LOVE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE GO *STUDENT                                        JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN *SOMETHING-ONE *FUTURE *MARY *CAR *FUTURE *WHAT *MARY    JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX IX IX IX                                             JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *LOVE *JOHN IX                                     JOHN LIKE IX IX IX\n",
      "   36: MARY *SOMETHING-ONE *YESTERDAY *GO *GIVE *MARY                MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *WHO IX *WHO *WHO *GIVE                                       JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *SOMETHING-ONE BUY CAR *MARY                            FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *MARY BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *JOHN *WOMAN *JOHN                                        JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *YESTERDAY *WHO BUY HOUSE                                JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SOMETHING-ONE *BLAME *HOUSE                             JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *GIVE *IX                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *FUTURE *WRITE                                      IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *IX *JOHN *IX *BOY *GIVE *CAR *HOUSE                          JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *IX IX *IX WOMAN *LOVE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *IX WOMAN *HOUSE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *IX NEW CAR BREAK-DOWN                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND HAVE *MARY                                   JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX *STUDENT                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *MARY *JOHN *BUY1                               IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *TOMORROW *PEOPLE *WHO                            SUE BUY IX CAR BLUE\n",
      "  122: JOHN *CAR *COAT                                               JOHN READ BOOK\n",
      "  139: JOHN *LOVE WHAT *WHO *ARRIVE                                  JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *WHAT                                    JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: JOHN *WHO *IX LOVE *GO1                                       JOHN IX SAY LOVE MARY\n",
      "  171: JOHN MARY BLAME                                               JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOOK                                                    JOHN ARRIVE\n",
      "  184: ALL *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *JOHN *LOVE *BOOK                                        JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *WHO BOX                                             JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *NOT *STUDENT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_custom\n",
      "\n",
      "**** WER = 0.6179775280898876\n",
      "Total correct: 68 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *LOVE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE GO *STUDENT                                        JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN *SOMETHING-ONE *FUTURE *MARY *CAR *FUTURE *WHAT *MARY    JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX IX IX IX                                             JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *LOVE *JOHN IX                                     JOHN LIKE IX IX IX\n",
      "   36: MARY *SOMETHING-ONE *YESTERDAY *GO *GIVE *MARY                MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *WHO IX *WHO *WHO *GIVE                                       JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *SOMETHING-ONE BUY CAR *MARY                            FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *MARY BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *JOHN *WOMAN *JOHN                                        JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *YESTERDAY *WHO BUY HOUSE                                JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SOMETHING-ONE *BLAME *HOUSE                             JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *GIVE *IX                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *FUTURE *WRITE                                      IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *IX *JOHN *IX *BOY *GIVE *CAR *HOUSE                          JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *IX IX *IX WOMAN *LOVE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *IX WOMAN *HOUSE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *IX NEW CAR BREAK-DOWN                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND HAVE *MARY                                   JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX *STUDENT                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *MARY *JOHN *BUY1                               IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *TOMORROW *PEOPLE *WHO                            SUE BUY IX CAR BLUE\n",
      "  122: JOHN *CAR *COAT                                               JOHN READ BOOK\n",
      "  139: JOHN *LOVE WHAT *WHO *ARRIVE                                  JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *WHAT                                    JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: JOHN *WHO *IX LOVE *GO1                                       JOHN IX SAY LOVE MARY\n",
      "  171: JOHN MARY BLAME                                               JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOOK                                                    JOHN ARRIVE\n",
      "  184: ALL *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *JOHN *LOVE *BOOK                                        JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *WHO BOX                                             JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *NOT *STUDENT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_custom_2\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *WOMAN WRITE HOMEWORK                                         JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BUY *MARY CAN                                           JOHN CAN GO CAN\n",
      "   12: *IX *CAR *WHAT *WHAT                                          JOHN CAN GO CAN\n",
      "   21: JOHN *NEW *NEW *IX-1P *HOUSE *CAR *FUTURE *WHO                JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *IX *IX *MARY IX *MARY                                        JOHN LIKE IX IX IX\n",
      "   28: *IX *JOHN *LOVE IX *MARY                                      JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *MARY *LOVE *MARY                                 JOHN LIKE IX IX IX\n",
      "   36: MARY *NOT *GIRL *TELL *LOVE *LIKE                             MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *SUE *GIVE *FUTURE1 *SUE *MARY                                JOHN IX THINK MARY LOVE\n",
      "   43: *FRANK *SHOULD BUY HOUSE                                      JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE BUY CAR *SOMETHING-ONE                            FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *FRANK NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *MOTHER *WOMAN MARY                                      JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *GIVE1 *GO BUY HOUSE                                     JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SEE *GO MARY                                            JOHN WILL VISIT MARY\n",
      "   74: *SHOOT NOT *GIVE MARY                                         JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME MARY                                                ANN BLAME MARY\n",
      "   84: *LOVE *SOMETHING-ONE *POSS BOOK                               IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *FUTURE *SHOULD *THROW *WOMAN IX NEW *BREAK-DOWN              JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *SEE *FUTURE IX *POSS WOMAN BOOK                              JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN GIVE IX *IX WOMAN BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *SEE                                                   JOHN LEG\n",
      "  107: *LIKE *JOHN *HAVE *MARY CANDY                                 JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *WHO *HOMEWORK                                                WOMAN ARRIVE\n",
      "  113: IX *CAN *SUE *LEAVE *SOMETHING-ONE                            IX CAR BLUE SUE BUY\n",
      "  119: *PREFER *BUY1 *CAR *ARRIVE *SEE                               SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR *GO BOOK                                      JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: *FRANK *ARRIVE YESTERDAY WHAT BOOK                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: JOHN *LEAVE *NOT *MARY *LOVE                                  JOHN IX SAY LOVE MARY\n",
      "  171: *LIKE *JOHN BLAME                                             JOHN MARY BLAME\n",
      "  174: *COAT *GIVE3 GIVE1 *NOT *CAR                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *JANA *FUTURE *GIVE *ARRIVE                                   JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SOMETHING-ONE *YESTERDAY BOX                            JOHN GIVE GIRL BOX\n",
      "  199: *LOVE *HOMEWORK WHO                                           LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *WOMAN *JOHN *STUDENT HOUSE                         JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_custom_2\n",
      "\n",
      "**** WER = 0.6404494382022472\n",
      "Total correct: 64 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN WRITE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HOUSE GO CAN                                            JOHN CAN GO CAN\n",
      "   12: JOHN *CAR *CAN CAN                                            JOHN CAN GO CAN\n",
      "   21: *GO *VIDEOTAPE *NEW *MARY *HOUSE *CAR *FUTURE *WRITE          JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *GO LIKE *GO *MARY *LOVE                                      JOHN LIKE IX IX IX\n",
      "   28: *GO *MARY *LOVE *MARY *LOVE                                   JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE IX *WOMAN IX                                      JOHN LIKE IX IX IX\n",
      "   36: *IX VEGETABLE *GIVE IX *LOVE *IX                              MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *LEAVE *VISIT *GIVE *JANA LOVE                                JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN *HAVE HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *POSS *GIVE1 BUY CAR *GO                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *VISIT VISIT *IX                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *GIVE1 *IX *ARRIVE HOUSE                                 JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *GIVE1 VISIT MARY                                        JOHN WILL VISIT MARY\n",
      "   74: *SHOOT NOT *GO MARY                                           JOHN NOT VISIT MARY\n",
      "   77: *GO BLAME MARY                                                ANN BLAME MARY\n",
      "   84: *IX *BLAME *POSS BOOK                                         IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *WHO *SHOULD *THROW *WOMAN *WOMAN NEW COAT                    JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: JOHN *GIVE1 *POSS *POSS *VISIT *VIDEOTAPE                     JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN GIVE *WOMAN *WOMAN *VISIT BOOK                           JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS *ARRIVE CAR BREAK-DOWN                                   POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *SEE                                                     JOHN LEG\n",
      "  107: *LIKE *SHOULD *HAVE *WOMAN CANDY                              JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX *HOMEWORK                                                 WOMAN ARRIVE\n",
      "  113: *JOHN *CAN *IX *IX *NEW                                       IX CAR BLUE SUE BUY\n",
      "  119: *VEGETABLE *BUY1 *SOMETHING-ONE *ARRIVE *FUTURE               SUE BUY IX CAR BLUE\n",
      "  122: JOHN *HOUSE BOOK                                              JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAN *GO *NEW                                      JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN *HAVE *GO *CAR BOOK                                      JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *BLAME *MARY WHO                                              LOVE JOHN WHO\n",
      "  167: JOHN IX *VISIT *MARY *LOVE                                    JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *BROCCOLI BLAME                                          JOHN MARY BLAME\n",
      "  174: *BLAME *NEW GIVE1 *GO *VISIT                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *VISIT *NEW                                                   JOHN ARRIVE\n",
      "  184: *SOMETHING-ONE BOY *HOUSE TEACHER *FUTURE                     ALL BOY GIVE TEACHER APPLE\n",
      "  189: *JANA *VISIT *YESTERDAY *ARRIVE                               JOHN GIVE GIRL BOX\n",
      "  193: JOHN *GIVE1 *VISIT BOX                                        JOHN GIVE GIRL BOX\n",
      "  199: *LOVE *HOMEWORK *IX                                           LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *IX *WOMAN BUY HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_custom_2\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *WOMAN WRITE HOMEWORK                                         JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BUY *MARY CAN                                           JOHN CAN GO CAN\n",
      "   12: *IX *CAR *WHAT *WHAT                                          JOHN CAN GO CAN\n",
      "   21: JOHN *NEW *NEW *IX-1P *HOUSE *CAR *FUTURE *WHO                JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *IX *IX *MARY IX *MARY                                        JOHN LIKE IX IX IX\n",
      "   28: *IX *JOHN *LOVE IX *MARY                                      JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *MARY *LOVE *MARY                                 JOHN LIKE IX IX IX\n",
      "   36: MARY *NOT *GIRL *TELL *LOVE *LIKE                             MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *SUE *GIVE *FUTURE1 *SUE *MARY                                JOHN IX THINK MARY LOVE\n",
      "   43: *FRANK *SHOULD BUY HOUSE                                      JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE BUY CAR *SOMETHING-ONE                            FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *FRANK NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *MOTHER *WOMAN MARY                                      JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *GIVE1 *GO BUY HOUSE                                     JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SEE *GO MARY                                            JOHN WILL VISIT MARY\n",
      "   74: *SHOOT NOT *GIVE MARY                                         JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME MARY                                                ANN BLAME MARY\n",
      "   84: *LOVE *SOMETHING-ONE *POSS BOOK                               IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *FUTURE *SHOULD *THROW *WOMAN IX NEW *BREAK-DOWN              JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *SEE *FUTURE IX *POSS WOMAN BOOK                              JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN GIVE IX *IX WOMAN BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *SEE                                                   JOHN LEG\n",
      "  107: *LIKE *JOHN *HAVE *MARY CANDY                                 JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *WHO *HOMEWORK                                                WOMAN ARRIVE\n",
      "  113: IX *CAN *SUE *LEAVE *SOMETHING-ONE                            IX CAR BLUE SUE BUY\n",
      "  119: *PREFER *BUY1 *CAR *ARRIVE *SEE                               SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR *GO BOOK                                      JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: *FRANK *ARRIVE YESTERDAY WHAT BOOK                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: JOHN *LEAVE *NOT *MARY *LOVE                                  JOHN IX SAY LOVE MARY\n",
      "  171: *LIKE *JOHN BLAME                                             JOHN MARY BLAME\n",
      "  174: *COAT *GIVE3 GIVE1 *NOT *CAR                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *JANA *FUTURE *GIVE *ARRIVE                                   JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SOMETHING-ONE *YESTERDAY BOX                            JOHN GIVE GIRL BOX\n",
      "  199: *LOVE *HOMEWORK WHO                                           LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *WOMAN *JOHN *STUDENT HOUSE                         JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_custom_2\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *WOMAN WRITE HOMEWORK                                         JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BUY *MARY CAN                                           JOHN CAN GO CAN\n",
      "   12: *IX *CAR *WHAT *WHAT                                          JOHN CAN GO CAN\n",
      "   21: JOHN *NEW *NEW *IX-1P *HOUSE *CAR *FUTURE *WHO                JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *IX *IX *MARY IX *MARY                                        JOHN LIKE IX IX IX\n",
      "   28: *IX *JOHN *LOVE IX *MARY                                      JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *MARY *LOVE *MARY                                 JOHN LIKE IX IX IX\n",
      "   36: MARY *NOT *GIRL *TELL *LOVE *LIKE                             MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *SUE *GIVE *FUTURE1 *SUE *MARY                                JOHN IX THINK MARY LOVE\n",
      "   43: *FRANK *SHOULD BUY HOUSE                                      JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE BUY CAR *SOMETHING-ONE                            FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *FRANK NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *MOTHER *WOMAN MARY                                      JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *GIVE1 *GO BUY HOUSE                                     JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SEE *GO MARY                                            JOHN WILL VISIT MARY\n",
      "   74: *SHOOT NOT *GIVE MARY                                         JOHN NOT VISIT MARY\n",
      "   77: *IX BLAME MARY                                                ANN BLAME MARY\n",
      "   84: *LOVE *SOMETHING-ONE *POSS BOOK                               IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *FUTURE *SHOULD *THROW *WOMAN IX NEW *BREAK-DOWN              JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *SEE *FUTURE IX *POSS WOMAN BOOK                              JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN GIVE IX *IX WOMAN BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *SEE                                                   JOHN LEG\n",
      "  107: *LIKE *JOHN *HAVE *MARY CANDY                                 JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *WHO *HOMEWORK                                                WOMAN ARRIVE\n",
      "  113: IX *CAN *SUE *LEAVE *SOMETHING-ONE                            IX CAR BLUE SUE BUY\n",
      "  119: *PREFER *BUY1 *CAR *ARRIVE *SEE                               SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR *GO BOOK                                      JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: *FRANK *ARRIVE YESTERDAY WHAT BOOK                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: JOHN *LEAVE *NOT *MARY *LOVE                                  JOHN IX SAY LOVE MARY\n",
      "  171: *LIKE *JOHN BLAME                                             JOHN MARY BLAME\n",
      "  174: *COAT *GIVE3 GIVE1 *NOT *CAR                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *JANA *FUTURE *GIVE *ARRIVE                                   JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SOMETHING-ONE *YESTERDAY BOX                            JOHN GIVE GIRL BOX\n",
      "  199: *LOVE *HOMEWORK WHO                                           LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *WOMAN *JOHN *STUDENT HOUSE                         JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_delta_normal\n",
      "\n",
      "**** WER = 0.5898876404494382\n",
      "Total correct: 73 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE *HOUSE                                       JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH WONT *JOHN *CAR *FUTURE *YESTERDAY *WHAT            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *MARY *JOHN IX IX                                        JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *VEGETABLE *PUTASIDE *JOHN IX                            JOHN LIKE IX IX IX\n",
      "   36: MARY *MOTHER *SEARCH-FOR *GO *CANDY *MARY                     MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *FRANK IX *WHO *PUTASIDE *SAY-1P                              JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *VEGETABLE BUY CAR *MARY                                FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *MARY *SUE BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *WOMAN *JOHN                                       JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *IX *SHOULD BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SOMETHING-ONE *GO *CAR                                  JOHN WILL VISIT MARY\n",
      "   74: JOHN *IX *GIVE2 *JOHN                                         JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GIVE1 *FUTURE BOOK                                     IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN GIVE *GIVE *THROW *CAR COAT                        JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *YESTERDAY IX *MARY WOMAN BOOK                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *IX WOMAN *NEW                                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *IX NEW CAR BREAK-DOWN                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *IX                                                      JOHN LEG\n",
      "  107: JOHN POSS *HAVE HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX *STUDENT                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *WHO *JOHN *BUY1                                IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *HOUSE *PEOPLE *HAVE                              SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT BOOK                                              JOHN READ BOOK\n",
      "  139: *WILL BUY *COAT *WHO *STOLEN                                  JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *GO WHAT BOOK                                        JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO WHO                                                 LOVE JOHN WHO\n",
      "  167: *ANN *BILL *IX *FRIEND *HERE                                  JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *WHAT GIVE1 *IX *FINISH                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOOK                                                    JOHN ARRIVE\n",
      "  184: ALL *WANT *GIVE1 TEACHER *SEE                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *YESTERDAY *WANT *BOOK                                   JOHN GIVE GIRL BOX\n",
      "  193: JOHN *YESTERDAY *CORN BOX                                     JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE WHO                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *POSS *VISIT HOUSE                             JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_delta_normal\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN WRITE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN CAN GO *MANY                                             JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 *COAT                                           JOHN CAN GO CAN\n",
      "   21: JOHN *WHAT *HOMEWORK *SAY-1P BUT *FUTURE *WHAT *MARY          JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *GIVE *PUTASIDE *PAST IX                                 JOHN LIKE IX IX IX\n",
      "   36: *VEGETABLE *WILL *MAN *GIVE *VISIT *MARY                      MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *FRANK IX *MARY *LIKE *SAY-1P                                 JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *SHOULD BUY CAR *YESTERDAY                              FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *VEGETABLE *JOHN BUY *BOOK                               JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *PAST *MAN *IX                                           JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *IX *IX *WOMAN HOUSE                                     JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT *CAR                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *IX *GIVE MARY                                           JOHN NOT VISIT MARY\n",
      "   77: *JOHN *VIDEOTAPE MARY                                         ANN BLAME MARY\n",
      "   84: *LAST-WEEK *GO *FUTURE BOOK                                   IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *THROW IX *THROW *THROW *THROW *CAR COAT                      JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *GIVE1 IX *IX WOMAN *WHAT                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN GIVE IX *POSS WOMAN *BUY                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR *HOUSE                                           POSS NEW CAR BREAK-DOWN\n",
      "  105: *ANN *IX                                                      JOHN LEG\n",
      "  107: JOHN *WOMAN FRIEND *MARY *JOHN                                JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *GO *MARY *YESTERDAY *BUY1                              IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *STUDENT *FUTURE *GO BLUE                               SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GO BOOK                                                 JOHN READ BOOK\n",
      "  139: JOHN BUY *HOUSE *WHO *BUY1                                    JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *WHAT *MARY *WHAT                                    JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *NEW *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: *WHO *WHO *OLD *FRIEND *CHOCOLATE                             JOHN IX SAY LOVE MARY\n",
      "  171: *VEGETABLE *SHOULD BLAME                                      JOHN MARY BLAME\n",
      "  174: *GO *WHAT GIVE1 *IX *CAN                                      PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *IX ARRIVE                                                    JOHN ARRIVE\n",
      "  184: *BILL BOY *GIVE1 TEACHER *WHO                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *POSS *OLD *BOOK                                         JOHN GIVE GIRL BOX\n",
      "  193: *WHO *YESTERDAY *WHO BOX                                      JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *FISH *MARY                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *CHOCOLATE *BUT HOUSE                          JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_delta_normal\n",
      "\n",
      "**** WER = 0.5898876404494382\n",
      "Total correct: 73 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE *HOUSE                                       JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH WONT *JOHN *CAR *FUTURE *YESTERDAY *WHAT            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *MARY *JOHN IX IX                                        JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *VEGETABLE *PUTASIDE *JOHN IX                            JOHN LIKE IX IX IX\n",
      "   36: MARY *MOTHER *SEARCH-FOR *GO *CANDY *MARY                     MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *FRANK IX *WHO *PUTASIDE *SAY-1P                              JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *VEGETABLE BUY CAR *MARY                                FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *MARY *SUE BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *WOMAN *JOHN                                       JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *IX *SHOULD BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SOMETHING-ONE *GO *CAR                                  JOHN WILL VISIT MARY\n",
      "   74: JOHN *IX *GIVE2 *JOHN                                         JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GIVE1 *FUTURE BOOK                                     IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN GIVE *GIVE *THROW *CAR COAT                        JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *YESTERDAY IX *MARY WOMAN BOOK                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *IX WOMAN *NEW                                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *IX NEW CAR BREAK-DOWN                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *IX                                                      JOHN LEG\n",
      "  107: JOHN POSS *HAVE HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX *STUDENT                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *WHO *JOHN *BUY1                                IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *HOUSE *PEOPLE *HAVE                              SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT BOOK                                              JOHN READ BOOK\n",
      "  139: *WILL BUY *COAT *WHO *STOLEN                                  JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *GO WHAT BOOK                                        JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO WHO                                                 LOVE JOHN WHO\n",
      "  167: *ANN *BILL *IX *FRIEND *HERE                                  JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *WHAT GIVE1 *IX *FINISH                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOOK                                                    JOHN ARRIVE\n",
      "  184: ALL *WANT *GIVE1 TEACHER *SEE                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *YESTERDAY *WANT *BOOK                                   JOHN GIVE GIRL BOX\n",
      "  193: JOHN *YESTERDAY *CORN BOX                                     JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE WHO                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *POSS *VISIT HOUSE                             JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_delta_normal\n",
      "\n",
      "**** WER = 0.5898876404494382\n",
      "Total correct: 73 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE *HOUSE                                       JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH WONT *JOHN *CAR *FUTURE *YESTERDAY *WHAT            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *MARY *JOHN IX IX                                        JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *VEGETABLE *PUTASIDE *JOHN IX                            JOHN LIKE IX IX IX\n",
      "   36: MARY *MOTHER *SEARCH-FOR *GO *CANDY *MARY                     MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *FRANK IX *WHO *PUTASIDE *SAY-1P                              JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *VEGETABLE BUY CAR *MARY                                FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *MARY *SUE BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *WOMAN *JOHN                                       JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *IX *SHOULD BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SOMETHING-ONE *GO *CAR                                  JOHN WILL VISIT MARY\n",
      "   74: JOHN *IX *GIVE2 *JOHN                                         JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GIVE1 *FUTURE BOOK                                     IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN GIVE *GIVE *THROW *CAR COAT                        JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *YESTERDAY IX *MARY WOMAN BOOK                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *IX WOMAN *NEW                                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *IX NEW CAR BREAK-DOWN                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *IX                                                      JOHN LEG\n",
      "  107: JOHN POSS *HAVE HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *IX *STUDENT                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *WHO *JOHN *BUY1                                IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *HOUSE *PEOPLE *HAVE                              SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT BOOK                                              JOHN READ BOOK\n",
      "  139: *WILL BUY *COAT *WHO *STOLEN                                  JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *GO WHAT BOOK                                        JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO WHO                                                 LOVE JOHN WHO\n",
      "  167: *ANN *BILL *IX *FRIEND *HERE                                  JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *WHAT GIVE1 *IX *FINISH                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *BOOK                                                    JOHN ARRIVE\n",
      "  184: ALL *WANT *GIVE1 TEACHER *SEE                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *YESTERDAY *WANT *BOOK                                   JOHN GIVE GIRL BOX\n",
      "  193: JOHN *YESTERDAY *CORN BOX                                     JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE WHO                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *POSS *VISIT HOUSE                             JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_delta_normal_polar\n",
      "\n",
      "**** WER = 0.5449438202247191\n",
      "Total correct: 81 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *VISIT *GIVE1 *TEACHER                                   JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH *HOMEWORK *APPLE *NEW *GO *MARY *BOOK               JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *IX *MARY *JOHN IX                                       JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *JOHN *JOHN                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN IX *VEGETABLE MARY *MARY                                 JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *WHO BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *WHO BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *MARY *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME *JOHN                                             ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *JOHN IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *WHO *JOHN *JOHN *IX *IX BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *JOHN WOMAN *ARRIVE                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR BREAK-DOWN                                      POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN *IX FRIEND HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *BUY1                                   IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *MARY                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN READ *YESTERDAY                                          JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *JOHN *STOLEN                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT *YESTERDAY                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *IX WHO                                                  LOVE JOHN WHO\n",
      "  167: JOHN IX *LIKE LOVE *HERE                                      JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *LOVE GIVE1 *WHAT *FINISH                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *WHAT                                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *BROCCOLI                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *IX MARY *IX *VISIT HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_delta_normal_polar\n",
      "\n",
      "**** WER = 0.6235955056179775\n",
      "Total correct: 67 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *LOVE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN *WHAT *HOMEWORK *MARY *FINISH *GO *FUTURE *MARY          JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *MARY *LOVE *GIVE *SHOULD                                JOHN LIKE IX IX IX\n",
      "   28: JOHN *IX *MARY *MARY *WOMAN                                   JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *LOVE IX *WOMAN                                    JOHN LIKE IX IX IX\n",
      "   36: MARY *YESTERDAY *GIVE *WOMAN *VIDEOTAPE *MARY                 MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN *GIVE *JOHN MARY *MARY                                   JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *WOMAN BUY HOUSE                                         JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *SHOULD BUY CAR *MARY                                   FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *MARY *WHO BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *LOVE *LOVE MARY                                         JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *IX BUY HOUSE                                      JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SOMETHING-ONE VISIT *CAR                                JOHN WILL VISIT MARY\n",
      "   74: JOHN *MARY *MARY MARY                                         JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *CAR *HOMEWORK *LOVE                                    IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *TOMORROW *GIVE GIVE *WOMAN *MARY *CAR COAT                   JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *JOHN *JOHN *WOMAN WOMAN *MARY                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX *GIVE *WOMAN *POSS *BOY                              JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR *MANY                                           POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND *FUTURE *MARY                                JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *FUTURE *BOOK                                                 WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *SHOULD *BUY1                                 IX CAR BLUE SUE BUY\n",
      "  119: *VISIT *BUY1 *WHAT CAR *FUTURE                                SUE BUY IX CAR BLUE\n",
      "  122: JOHN *FINISH BOOK                                             JOHN READ BOOK\n",
      "  139: JOHN *NEW WHAT *JOHN *MARY                                    JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *WHAT WHAT *LOVE                                     JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY *MARY                                              LOVE JOHN WHO\n",
      "  167: JOHN IX *OLD LOVE *WHAT                                       JOHN IX SAY LOVE MARY\n",
      "  171: *SHOULD *YESTERDAY BLAME                                      JOHN MARY BLAME\n",
      "  174: *CAR *LOVE GIVE1 *MARY *FINISH                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *MARY *LOVE                                                   JOHN ARRIVE\n",
      "  184: *SOMETHING-ONE *NOT *GIVE1 TEACHER *FUTURE                    ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT BOX                                            JOHN GIVE GIRL BOX\n",
      "  193: JOHN *YESTERDAY *WOMAN BOX                                    JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY *WHO *WOMAN BUY HOUSE                              JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_delta_normal_polar\n",
      "\n",
      "**** WER = 0.5449438202247191\n",
      "Total correct: 81 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *VISIT *GIVE1 *TEACHER                                   JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH *HOMEWORK *APPLE *NEW *GO *MARY *BOOK               JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *IX *MARY *JOHN IX                                       JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *JOHN *JOHN                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN IX *VEGETABLE MARY *MARY                                 JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *WHO BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *WHO BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *MARY *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME *JOHN                                             ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *JOHN IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *WHO *JOHN *JOHN *IX *IX BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *JOHN WOMAN *ARRIVE                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR BREAK-DOWN                                      POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN *IX FRIEND HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *BUY1                                   IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *MARY                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN READ *YESTERDAY                                          JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *JOHN *STOLEN                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT *YESTERDAY                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *IX WHO                                                  LOVE JOHN WHO\n",
      "  167: JOHN IX *LIKE LOVE *HERE                                      JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *LOVE GIVE1 *WHAT *FINISH                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *WHAT                                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *BROCCOLI                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *IX MARY *IX *VISIT HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_delta_normal_polar\n",
      "\n",
      "**** WER = 0.5449438202247191\n",
      "Total correct: 81 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *VISIT *GIVE1 *TEACHER                                   JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH *HOMEWORK *APPLE *NEW *GO *MARY *BOOK               JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *IX *MARY *JOHN IX                                       JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *JOHN *JOHN                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN IX *VEGETABLE MARY *MARY                                 JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *WHO BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *WHO BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *MARY *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME *JOHN                                             ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *JOHN IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *WHO *JOHN *JOHN *IX *IX BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *JOHN WOMAN *ARRIVE                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR BREAK-DOWN                                      POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN *IX FRIEND HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *BUY1                                   IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *MARY                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN READ *YESTERDAY                                          JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *JOHN *STOLEN                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT *YESTERDAY                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *IX WHO                                                  LOVE JOHN WHO\n",
      "  167: JOHN IX *LIKE LOVE *HERE                                      JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *LOVE GIVE1 *WHAT *FINISH                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *WHAT                                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *BROCCOLI                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *IX MARY *IX *VISIT HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_delta_ground\n",
      "\n",
      "**** WER = 0.6292134831460674\n",
      "Total correct: 66 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: *MARY *MARY WONT *MARY *CAR *GO *SHOULD *MARY                 JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX IX *JOHN *MARY                                       JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *NOT IX IX                                         JOHN LIKE IX IX IX\n",
      "   36: MARY *FUTURE *JOHN *NOT *GIVE3 *MARY                          MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY IX *WHO *SEE *APPLE                                     JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX *FINISH HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *NOT BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *VEGETABLE *MARY BUY HOUSE                               JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *IX                                            JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *PEOPLE BUY HOUSE                                  JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT *CAR                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *IX *WHAT                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE1 *JOHN *IX *IX IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *IX *JOHN *IX WOMAN *MARY                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *MARY *JOHN *JOHN WOMAN *ARRIVE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW *WHAT BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN *GIVE1 *STUDENT HAVE *MARY                               JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: IX CAR *MARY *IX *GIVE1                                       IX CAR BLUE SUE BUY\n",
      "  119: *MARY *BUY1 *GIVE1 CAR *HAVE                                  SUE BUY IX CAR BLUE\n",
      "  122: JOHN *HAVE *FUTURE                                            JOHN READ BOOK\n",
      "  139: JOHN *GIVE1 WHAT *WHO *ARRIVE                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *EAT                                     JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: *IX *WHO *IX LOVE *BOOK                                       JOHN IX SAY LOVE MARY\n",
      "  171: JOHN MARY BLAME                                               JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: ALL BOY *GIVE1 TEACHER *MARY                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *GIVE1                                         JOHN GIVE GIRL BOX\n",
      "  193: *IX *IX *HOUSE BOX                                            JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *MARY *MARY                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY *WHO *POSS *VISIT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_delta_ground\n",
      "\n",
      "**** WER = 0.6685393258426966\n",
      "Total correct: 59 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *WHO HOMEWORK                                            JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BOX GO *TEACHER                                         JOHN CAN GO CAN\n",
      "   12: JOHN CAN *TEACHER CAN                                         JOHN CAN GO CAN\n",
      "   21: *MARY *MARY *HOMEWORK *MARY *BUY *HOMEWORK *MARY *MARY        JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *WOMAN *MARY IX *MARY                                    JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *LOVE *GIVE IX                                     JOHN LIKE IX IX IX\n",
      "   36: MARY *MARY *MARY *GIVE *COAT *MARY                            MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY *GIVE *MARY MARY *FUTURE                                JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *WHO BUY *BOOK                                           JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *VEGETABLE BUY CAR *MARY                                FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *LOVE *WHAT BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *MARY *WHO *GIVE *IX                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *SOMETHING-ONE BUY HOUSE                           JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *MARY VISIT *CAR                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *WHO *ARRIVE MARY                                        JOHN NOT VISIT MARY\n",
      "   77: *JOHN *GIVE1 MARY                                             ANN BLAME MARY\n",
      "   84: *JOHN *TEACHER *GIVE1 *MARY                                   IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE1 IX GIVE *GIVE *GIVE *BOX *BOOK                         JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY GIVE *GIVE *IX *LOVE BOOK                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *MARY IX *WOMAN WOMAN *TEACHER                           JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR *BOOK                                           POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: *MARY *GIVE1 FRIEND *MARY *MARY                               JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN *BOOK                                                   WOMAN ARRIVE\n",
      "  113: *JOHN *GIVE1 *MARY *JOHN *BUY1                                IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 *GIVE1 *GIVE1 *MARY                               SUE BUY IX CAR BLUE\n",
      "  122: JOHN *NEW *MARY                                               JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR *MARY *MARY                                   JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *MARY                                    JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *BOX *MARY *MARY                                              LOVE JOHN WHO\n",
      "  167: JOHN *GIVE *GIVE *BOOK *COAT                                  JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *WHO *GIVE1                                              JOHN MARY BLAME\n",
      "  174: *GIVE1 *WHO GIVE1 *MARY *BOX                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *MARY *GIVE1                                                  JOHN ARRIVE\n",
      "  184: *GIVE *TEACHER *GIVE1 TEACHER *MARY                           ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN GIVE *WHO *BOOK                                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN GIVE *SOMETHING-ONE BOX                                  JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *WHO *MARY                                              LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY MARY *LIKE BUY HOUSE                               JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_delta_ground\n",
      "\n",
      "**** WER = 0.6292134831460674\n",
      "Total correct: 66 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: *MARY *MARY WONT *MARY *CAR *GO *SHOULD *MARY                 JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX IX *JOHN *MARY                                       JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *NOT IX IX                                         JOHN LIKE IX IX IX\n",
      "   36: MARY *FUTURE *JOHN *NOT *GIVE3 *MARY                          MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY IX *WHO *SEE *APPLE                                     JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX *FINISH HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *NOT BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *VEGETABLE *MARY BUY HOUSE                               JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *IX                                            JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *PEOPLE BUY HOUSE                                  JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT *CAR                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *IX *WHAT                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE1 *JOHN *IX *IX IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *IX *JOHN *IX WOMAN *MARY                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *MARY *JOHN *JOHN WOMAN *ARRIVE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW *WHAT BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN *GIVE1 *STUDENT HAVE *MARY                               JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: IX CAR *MARY *IX *GIVE1                                       IX CAR BLUE SUE BUY\n",
      "  119: *MARY *BUY1 *GIVE1 CAR *HAVE                                  SUE BUY IX CAR BLUE\n",
      "  122: JOHN *HAVE *FUTURE                                            JOHN READ BOOK\n",
      "  139: JOHN *GIVE1 WHAT *WHO *ARRIVE                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *EAT                                     JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: *IX *WHO *IX LOVE *BOOK                                       JOHN IX SAY LOVE MARY\n",
      "  171: JOHN MARY BLAME                                               JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: ALL BOY *GIVE1 TEACHER *MARY                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *GIVE1                                         JOHN GIVE GIRL BOX\n",
      "  193: *IX *IX *HOUSE BOX                                            JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *MARY *MARY                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY *WHO *POSS *VISIT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_delta_ground\n",
      "\n",
      "**** WER = 0.6292134831460674\n",
      "Total correct: 66 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: *MARY *MARY WONT *MARY *CAR *GO *SHOULD *MARY                 JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX IX *JOHN *MARY                                       JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY *MARY IX IX                                        JOHN LIKE IX IX IX\n",
      "   30: JOHN *MARY *NOT IX IX                                         JOHN LIKE IX IX IX\n",
      "   36: MARY *FUTURE *JOHN *NOT *GIVE3 *MARY                          MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *MARY IX *WHO *SEE *APPLE                                     JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX *FINISH HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *NOT BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *VEGETABLE *MARY BUY HOUSE                               JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *IX                                            JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *PEOPLE BUY HOUSE                                  JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT *CAR                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *IX *WHAT                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *GIVE1 *JOHN *IX *IX IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *MARY *IX *JOHN *IX WOMAN *MARY                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *MARY *JOHN *JOHN WOMAN *ARRIVE                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW *WHAT BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *MARY                                                    JOHN LEG\n",
      "  107: JOHN *GIVE1 *STUDENT HAVE *MARY                               JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: IX CAR *MARY *IX *GIVE1                                       IX CAR BLUE SUE BUY\n",
      "  119: *MARY *BUY1 *GIVE1 CAR *HAVE                                  SUE BUY IX CAR BLUE\n",
      "  122: JOHN *HAVE *FUTURE                                            JOHN READ BOOK\n",
      "  139: JOHN *GIVE1 WHAT *WHO *ARRIVE                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *MARY *MARY *EAT                                     JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO *MARY                                               LOVE JOHN WHO\n",
      "  167: *IX *WHO *IX LOVE *BOOK                                       JOHN IX SAY LOVE MARY\n",
      "  171: JOHN MARY BLAME                                               JOHN MARY BLAME\n",
      "  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: ALL BOY *GIVE1 TEACHER *MARY                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *GIVE1                                         JOHN GIVE GIRL BOX\n",
      "  193: *IX *IX *HOUSE BOX                                            JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *MARY *MARY                                             LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MARY *WHO *POSS *VISIT HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_delta_normal_ground\n",
      "\n",
      "**** WER = 0.5730337078651685\n",
      "Total correct: 76 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GIVE1 CAN                                           JOHN CAN GO CAN\n",
      "   21: JOHN *JOHN *HOMEWORK *MARY *NEW *BLAME *SHOULD *MARY          JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *CORN *SOMETHING-ONE IX IX                               JOHN LIKE IX IX IX\n",
      "   36: *JOHN *TEACHER *IX-1P *LIKE *GIVE3 *MARY                      MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *CHINA IX *WHO *FRANK *SOMETHING-ONE                          JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *VEGETABLE BUY CAR *FUTURE                              FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *IX *JOHN BUY HOUSE                                      JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *LEG BUY HOUSE                                     JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SUE VISIT *CAR                                          JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *IX IX NEW COAT                                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 *IX IX *IX WOMAN *HOUSE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR *TEACHER                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND HAVE *MARY                                   JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *MARY *JOHN *BUY1                               IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *HAVE                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT *COAT                                             JOHN READ BOOK\n",
      "  139: *FUTURE *BUY1 WHAT *WHO *STOLEN                               JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *IX *MARY BOOK                                       JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO WHO                                                 LOVE JOHN WHO\n",
      "  167: JOHN *BILL *IX *FRIEND *HERE                                  JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *MARY GIVE1 *MARY TOY                                    PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *GIVE3 *JOHN *GIVE1 TEACHER *SEE                              ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *YESTERDAY *SOMETHING-ONE *WHAT                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *SOMETHING-ONE BOX                                   JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *IX *VISIT HOUSE                               JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_delta_normal_ground\n",
      "\n",
      "**** WER = 0.601123595505618\n",
      "Total correct: 71 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN WRITE HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE *GO                                          JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GIVE1 CAN                                           JOHN CAN GO CAN\n",
      "   21: JOHN *WHAT *HOMEWORK *YESTERDAY *FINISH *GO *LOVE *MARY       JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX IX IX IX                                             JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *SHOULD *PUTASIDE *SEARCH-FOR IX                         JOHN LIKE IX IX IX\n",
      "   36: MARY *VIDEOTAPE *SEARCH-FOR *LIKE *VIDEOTAPE *WHO             MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *FRANK IX *MARY *PUTASIDE *GIRL                               JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *CORN BUY CAR *MARY                                     FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *WHO *SUE BUY *BOOK                                      JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *BILL *LOVE *IX *IX                                           JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *IX *LEG BUY HOUSE                                       JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT *CAR                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *IX *VIDEOTAPE *IX                                       JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *GO *HOMEWORK BOOK                                      IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *BLUE IX GIVE *IX *LOVE NEW COAT                              JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: JOHN *SEE IX *IX WOMAN BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN GIVE IX *IX *POSS *PREFER                                JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR *BOOK                                           POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *IX                                                      JOHN LEG\n",
      "  107: *WHO *GIVE1 FRIEND *YESTERDAY *MARY                           JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN *BOOK                                                   WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY SUE *BUY1                                     IX CAR BLUE SUE BUY\n",
      "  119: *WHAT *BUY1 *CAR CAR *MARY                                    SUE BUY IX CAR BLUE\n",
      "  122: JOHN *FINISH *COAT                                            JOHN READ BOOK\n",
      "  139: JOHN *NEW *CAR *MARY BOOK                                     JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *GIVE *MARY *YESTERDAY                               JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *NEW *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: *WHO *THROW *IX *CAN *COAT                                    JOHN IX SAY LOVE MARY\n",
      "  171: *LEAVE MARY BLAME                                             JOHN MARY BLAME\n",
      "  174: *CAR GROUP GIVE1 *MARY *FINISH                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *WHAT *NEW                                                    JOHN ARRIVE\n",
      "  184: *IX *FINISH *NEW TEACHER *SEE                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: *WHAT GIVE *SUE *LOVE                                         JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *CORN BOX                                            JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *BOY *MARY                                              LIKE CHOCOLATE WHO\n",
      "  201: JOHN *EAT *WHO *NOT BUY HOUSE                                 JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_delta_normal_ground\n",
      "\n",
      "**** WER = 0.5730337078651685\n",
      "Total correct: 76 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GIVE1 CAN                                           JOHN CAN GO CAN\n",
      "   21: JOHN *JOHN *HOMEWORK *MARY *NEW *BLAME *SHOULD *MARY          JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *CORN *SOMETHING-ONE IX IX                               JOHN LIKE IX IX IX\n",
      "   36: *JOHN *TEACHER *IX-1P *LIKE *GIVE3 *MARY                      MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *CHINA IX *WHO *FRANK *SOMETHING-ONE                          JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *VEGETABLE BUY CAR *FUTURE                              FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *IX *JOHN BUY HOUSE                                      JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *LEG BUY HOUSE                                     JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SUE VISIT *CAR                                          JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *IX IX NEW COAT                                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 *IX IX *IX WOMAN *HOUSE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR *TEACHER                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND HAVE *MARY                                   JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *MARY *JOHN *BUY1                               IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *HAVE                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT *COAT                                             JOHN READ BOOK\n",
      "  139: *FUTURE *BUY1 WHAT *WHO *STOLEN                               JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *IX *MARY BOOK                                       JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO WHO                                                 LOVE JOHN WHO\n",
      "  167: JOHN *BILL *IX *FRIEND *HERE                                  JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *MARY GIVE1 *MARY TOY                                    PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *GIVE3 *JOHN *GIVE1 TEACHER *SEE                              ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *YESTERDAY *SOMETHING-ONE *WHAT                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *SOMETHING-ONE BOX                                   JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *IX *VISIT HOUSE                               JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_delta_normal_ground\n",
      "\n",
      "**** WER = 0.5730337078651685\n",
      "Total correct: 76 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: JOHN *IX HOMEWORK                                             JOHN WRITE HOMEWORK\n",
      "    7: JOHN *HAVE *GIVE1 *TEACHER                                    JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GIVE1 CAN                                           JOHN CAN GO CAN\n",
      "   21: JOHN *JOHN *HOMEWORK *MARY *NEW *BLAME *SHOULD *MARY          JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *CORN *SOMETHING-ONE IX IX                               JOHN LIKE IX IX IX\n",
      "   36: *JOHN *TEACHER *IX-1P *LIKE *GIVE3 *MARY                      MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *CHINA IX *WHO *FRANK *SOMETHING-ONE                          JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *VEGETABLE BUY CAR *FUTURE                              FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *IX *JOHN BUY HOUSE                                      JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *LEG BUY HOUSE                                     JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *SUE VISIT *CAR                                          JOHN WILL VISIT MARY\n",
      "   74: JOHN *JOHN *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME MARY                                              ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *IX IX NEW COAT                                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 *IX IX *IX WOMAN *HOUSE                                  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR *TEACHER                                        POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN POSS FRIEND HAVE *MARY                                   JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN *PEOPLE *MARY *JOHN *BUY1                               IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *HAVE                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN *VISIT *COAT                                             JOHN READ BOOK\n",
      "  139: *FUTURE *BUY1 WHAT *WHO *STOLEN                               JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY *IX *MARY BOOK                                       JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *WHO WHO                                                 LOVE JOHN WHO\n",
      "  167: JOHN *BILL *IX *FRIEND *HERE                                  JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *MARY GIVE1 *MARY TOY                                    PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *GIVE3 *JOHN *GIVE1 TEACHER *SEE                              ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *YESTERDAY *SOMETHING-ONE *WHAT                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *SOMETHING-ONE BOX                                   JOHN GIVE GIRL BOX\n",
      "  199: *JOHN CHOCOLATE *MARY                                         LIKE CHOCOLATE WHO\n",
      "  201: JOHN *WHO *WHO *IX *VISIT HOUSE                               JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorConstant on features_custom_2_ground\n",
      "\n",
      "**** WER = 0.601123595505618\n",
      "Total correct: 71 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *WOMAN WRITE HOMEWORK                                         JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BUY GO CAN                                              JOHN CAN GO CAN\n",
      "   12: *SHOULD *HAVE *NEW *HOUSE                                     JOHN CAN GO CAN\n",
      "   21: JOHN *NEW WONT *IX-1P *STUDENT *CAR *FUTURE *WRITE            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *WOMAN *WOMAN *MARY *WOMAN *LOVE                              JOHN LIKE IX IX IX\n",
      "   28: *WOMAN *WOMAN *LOVE *WOMAN *MARY                              JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *LOVE *LOVE *LOVE                                 JOHN LIKE IX IX IX\n",
      "   36: MARY *NOT *YESTERDAY *GIVE *LOVE *IX                          MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *LEAVE *VISIT *GIVE2 *LEAVE LOVE                              JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE BUY CAR *CAR                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *FRANK NOT *STUDENT HOUSE                                JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *MARY VISIT MARY                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN FUTURE *WHO BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FUTURE *GO MARY                                         JOHN WILL VISIT MARY\n",
      "   74: *IX *VISIT VISIT *LOVE                                        JOHN NOT VISIT MARY\n",
      "   77: *WOMAN BLAME MARY                                             ANN BLAME MARY\n",
      "   84: *LOVE *ARRIVE *HOMEWORK BOOK                                  IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *WHO *GIVE1 GIVE *WOMAN IX NEW *BREAK-DOWN                    JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *FUTURE *FUTURE IX *POSS WOMAN *CHOCOLATE                     JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: *FRANK GIVE IX *IX WOMAN BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *SEE                                                   JOHN LEG\n",
      "  107: *LIKE *SOMETHING-ONE FRIEND *MARY CANDY                       JOHN POSS FRIEND HAVE CANDY\n",
      "  108: WOMAN *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: *SHOULD CAR *SOMETHING-ONE *VISIT *BOX                        IX CAR BLUE SUE BUY\n",
      "  119: *VEGETABLE *BUY1 *JOHN *NEW *VISIT                            SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 *COAT                                             JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR YESTERDAY *CHOCOLATE                          JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: JOHN *VISIT *VISIT *MARY *LOVE                                JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *NEW *GIVE3 GIVE1 *GIRL *CAN                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *LEAVE *SEE *FUTURE1 BOX                                      JOHN GIVE GIRL BOX\n",
      "  193: *MARY *GIVE1 *YESTERDAY BOX                                   JOHN GIVE GIRL BOX\n",
      "  199: *LOVE *STUDENT WHO                                            LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *GIVE *WOMAN *STUDENT HOUSE                         JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorCV on features_custom_2_ground\n",
      "\n",
      "**** WER = 0.651685393258427\n",
      "Total correct: 62 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *WOMAN WRITE HOMEWORK                                         JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BUY *BLAME CAN                                          JOHN CAN GO CAN\n",
      "   12: JOHN *CAR *CAN *HOUSE                                         JOHN CAN GO CAN\n",
      "   21: *GO *JOHN WONT *ANN *HOUSE *CAR *ARRIVE *WRITE                JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *GO *GO *LOVE *GO *LOVE                                       JOHN LIKE IX IX IX\n",
      "   28: *GO *GO *LOVE *GO *LOVE                                       JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *LOVE *LOVE *LOVE                                 JOHN LIKE IX IX IX\n",
      "   36: MARY *WHO *YESTERDAY *GO *LOVE *LIKE                          MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *VISIT *VISIT *GIVE2 *VISIT LOVE                              JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SEE BUY HOUSE                                           JOHN MUST BUY HOUSE\n",
      "   50: *POSS *SEE BUY CAR SHOULD                                     FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *VISIT VISIT *LOVE                                        JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *POSS *WHO *ARRIVE HOUSE                                 JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *PREFER *GIVE1 MARY                                      JOHN WILL VISIT MARY\n",
      "   74: *IX *VISIT *GO *LOVE                                          JOHN NOT VISIT MARY\n",
      "   77: *GO BLAME MARY                                                ANN BLAME MARY\n",
      "   84: *IX *BLAME *HOMEWORK BOOK                                     IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *SHOULD *GIVE1 GIVE *WOMAN *WOMAN NEW COAT                    JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *SEE *SOMETHING-ONE *SOMETHING-ONE SOMETHING-ONE WOMAN *NEW   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN GIVE *WOMAN *WOMAN WOMAN 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: *LIKE *JOHN FRIEND *GO CANDY                                  JOHN POSS FRIEND HAVE CANDY\n",
      "  108: WOMAN *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: *JOHN *CAN *SHOULD *JOHN *BOX                                 IX CAR BLUE SUE BUY\n",
      "  119: *VEGETABLE *BUY1 *BLAME *BLAME *FUTURE                        SUE BUY IX CAR BLUE\n",
      "  122: JOHN *HOUSE BOOK                                              JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR YESTERDAY *NEW                                JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN *ARRIVE *FUTURE WHAT *COAT                               JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: *MARY *MARY *NOT                                              LOVE JOHN WHO\n",
      "  167: JOHN *VISIT *VISIT *MARY *LOVE                                JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *NEW GROUP GIVE1 *WHO *VISIT                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: *VISIT *NEW                                                   JOHN ARRIVE\n",
      "  184: *POSS BOY *HOUSE TEACHER *GIRL                                ALL BOY GIVE TEACHER APPLE\n",
      "  189: *VISIT *SEE *FINISH *NEW                                      JOHN GIVE GIRL BOX\n",
      "  193: JOHN *SEE *YESTERDAY BOX                                      JOHN GIVE GIRL BOX\n",
      "  199: *LOVE *STUDENT *MARY                                          LIKE CHOCOLATE WHO\n",
      "  201: JOHN *SHOULD *LOVE *LIKE *BLAME HOUSE                         JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorBIC on features_custom_2_ground\n",
      "\n",
      "**** WER = 0.601123595505618\n",
      "Total correct: 71 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *WOMAN WRITE HOMEWORK                                         JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BUY GO CAN                                              JOHN CAN GO CAN\n",
      "   12: *SHOULD *HAVE *NEW *HOUSE                                     JOHN CAN GO CAN\n",
      "   21: JOHN *NEW WONT *IX-1P *STUDENT *CAR *FUTURE *WRITE            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *WOMAN *WOMAN *MARY *WOMAN *LOVE                              JOHN LIKE IX IX IX\n",
      "   28: *WOMAN *WOMAN *LOVE *WOMAN *MARY                              JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *LOVE *LOVE *LOVE                                 JOHN LIKE IX IX IX\n",
      "   36: MARY *NOT *YESTERDAY *GIVE *LOVE *IX                          MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *LEAVE *VISIT *GIVE2 *LEAVE LOVE                              JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE BUY CAR *CAR                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *FRANK NOT *STUDENT HOUSE                                JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *MARY VISIT MARY                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN FUTURE *WHO BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FUTURE *GO MARY                                         JOHN WILL VISIT MARY\n",
      "   74: *IX *VISIT VISIT *LOVE                                        JOHN NOT VISIT MARY\n",
      "   77: *WOMAN BLAME MARY                                             ANN BLAME MARY\n",
      "   84: *LOVE *ARRIVE *HOMEWORK BOOK                                  IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *WHO *GIVE1 GIVE *WOMAN IX NEW *BREAK-DOWN                    JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *FUTURE *FUTURE IX *POSS WOMAN *CHOCOLATE                     JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: *FRANK GIVE IX *IX WOMAN BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *SEE                                                   JOHN LEG\n",
      "  107: *LIKE *SOMETHING-ONE FRIEND *MARY CANDY                       JOHN POSS FRIEND HAVE CANDY\n",
      "  108: WOMAN *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: *SHOULD CAR *SOMETHING-ONE *VISIT *BOX                        IX CAR BLUE SUE BUY\n",
      "  119: *VEGETABLE *BUY1 *JOHN *NEW *VISIT                            SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 *COAT                                             JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR YESTERDAY *CHOCOLATE                          JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: JOHN *VISIT *VISIT *MARY *LOVE                                JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *NEW *GIVE3 GIVE1 *GIRL *CAN                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *LEAVE *SEE *FUTURE1 BOX                                      JOHN GIVE GIRL BOX\n",
      "  193: *MARY *GIVE1 *YESTERDAY BOX                                   JOHN GIVE GIRL BOX\n",
      "  199: *LOVE *STUDENT WHO                                            LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *GIVE *WOMAN *STUDENT HOUSE                         JOHN TELL MARY IX-1P BUY HOUSE\n",
      "Running SelectorDIC on features_custom_2_ground\n",
      "\n",
      "**** WER = 0.601123595505618\n",
      "Total correct: 71 out of 178\n",
      "Video  Recognized                                                    Correct\n",
      "=====================================================================================================\n",
      "    2: *WOMAN WRITE HOMEWORK                                         JOHN WRITE HOMEWORK\n",
      "    7: JOHN *BUY GO CAN                                              JOHN CAN GO CAN\n",
      "   12: *SHOULD *HAVE *NEW *HOUSE                                     JOHN CAN GO CAN\n",
      "   21: JOHN *NEW WONT *IX-1P *STUDENT *CAR *FUTURE *WRITE            JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: *WOMAN *WOMAN *MARY *WOMAN *LOVE                              JOHN LIKE IX IX IX\n",
      "   28: *WOMAN *WOMAN *LOVE *WOMAN *MARY                              JOHN LIKE IX IX IX\n",
      "   30: *SHOOT LIKE *LOVE *LOVE *LOVE                                 JOHN LIKE IX IX IX\n",
      "   36: MARY *NOT *YESTERDAY *GIVE *LOVE *IX                          MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: *LEAVE *VISIT *GIVE2 *LEAVE LOVE                              JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE\n",
      "   50: *FRANK *SEE BUY CAR *CAR                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *FRANK NOT *STUDENT HOUSE                                JOHN SHOULD NOT BUY HOUSE\n",
      "   57: *IX *MARY VISIT MARY                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN FUTURE *WHO BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *FUTURE *GO MARY                                         JOHN WILL VISIT MARY\n",
      "   74: *IX *VISIT VISIT *LOVE                                        JOHN NOT VISIT MARY\n",
      "   77: *WOMAN BLAME MARY                                             ANN BLAME MARY\n",
      "   84: *LOVE *ARRIVE *HOMEWORK BOOK                                  IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: *WHO *GIVE1 GIVE *WOMAN IX NEW *BREAK-DOWN                    JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *FUTURE *FUTURE IX *POSS WOMAN *CHOCOLATE                     JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: *FRANK GIVE IX *IX WOMAN BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN\n",
      "  105: *FRANK *SEE                                                   JOHN LEG\n",
      "  107: *LIKE *SOMETHING-ONE FRIEND *MARY CANDY                       JOHN POSS FRIEND HAVE CANDY\n",
      "  108: WOMAN *HOMEWORK                                               WOMAN ARRIVE\n",
      "  113: *SHOULD CAR *SOMETHING-ONE *VISIT *BOX                        IX CAR BLUE SUE BUY\n",
      "  119: *VEGETABLE *BUY1 *JOHN *NEW *VISIT                            SUE BUY IX CAR BLUE\n",
      "  122: JOHN *GIVE1 *COAT                                             JOHN READ BOOK\n",
      "  139: JOHN *BUY1 *CAR YESTERDAY *CHOCOLATE                          JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *MARY WHO                                                LOVE JOHN WHO\n",
      "  167: JOHN *VISIT *VISIT *MARY *LOVE                                JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *NEW *GIVE3 GIVE1 *GIRL *CAN                                  PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN ARRIVE                                                   JOHN ARRIVE\n",
      "  184: *IX BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE\n",
      "  189: *LEAVE *SEE *FUTURE1 BOX                                      JOHN GIVE GIRL BOX\n",
      "  193: *MARY *GIVE1 *YESTERDAY BOX                                   JOHN GIVE GIRL BOX\n",
      "  199: *LOVE *STUDENT WHO                                            LIKE CHOCOLATE WHO\n",
      "  201: JOHN *MAN *GIVE *WOMAN *STUDENT HOUSE                         JOHN TELL MARY IX-1P BUY HOUSE\n"
     ]
    }
   ],
   "source": [
    "for f, m, f_name, m_name in feature_models:\n",
    "    # TODO Recognize the test set and display the result with the show_errors method\n",
    "    print(\"---\"*5)\n",
    "    print(\"Running\", m_name,\"on\", f_name)\n",
    "    models = train_all_words(f, m)\n",
    "    test_set = asl.build_test(f)\n",
    "    probabilities, guesses = recognize(models, test_set)\n",
    "    show_errors(guesses, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "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 *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *VISIT *GIVE1 *TEACHER                                   JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH *HOMEWORK *APPLE *NEW *GO *MARY *BOOK               JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *IX *MARY *JOHN IX                                       JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *JOHN *JOHN                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN IX *VEGETABLE MARY *MARY                                 JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *WHO BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *WHO BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *MARY *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME *JOHN                                             ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *JOHN IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *WHO *JOHN *JOHN *IX *IX BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *JOHN WOMAN *ARRIVE                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR BREAK-DOWN                                      POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN *IX FRIEND HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *BUY1                                   IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *MARY                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN READ *YESTERDAY                                          JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *JOHN *STOLEN                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT *YESTERDAY                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *IX WHO                                                  LOVE JOHN WHO\n",
      "  167: JOHN IX *LIKE LOVE *HERE                                      JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *LOVE GIVE1 *WHAT *FINISH                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *WHAT                                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *BROCCOLI                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *IX MARY *IX *VISIT HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n"
     ]
    }
   ],
   "source": [
    "# TODO Recognize the test set and display the result with the show_errors method\n",
    "models = train_all_words(features_delta_normal_polar, SelectorConstant)\n",
    "test_set = asl.build_test(features_delta_normal_polar)\n",
    "probabilities, guesses = recognize(models, test_set)\n",
    "show_errors(guesses, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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 *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *VISIT *GIVE1 *TEACHER                                   JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH *HOMEWORK *APPLE *NEW *GO *MARY *BOOK               JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *IX *MARY *JOHN IX                                       JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *JOHN *JOHN                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN IX *VEGETABLE MARY *MARY                                 JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *WHO BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *WHO BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *MARY *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME *JOHN                                             ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *JOHN IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *WHO *JOHN *JOHN *IX *IX BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *JOHN WOMAN *ARRIVE                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR BREAK-DOWN                                      POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN *IX FRIEND HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *BUY1                                   IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *MARY                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN READ *YESTERDAY                                          JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *JOHN *STOLEN                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT *YESTERDAY                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *IX WHO                                                  LOVE JOHN WHO\n",
      "  167: JOHN IX *LIKE LOVE *HERE                                      JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *LOVE GIVE1 *WHAT *FINISH                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *WHAT                                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *BROCCOLI                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *IX MARY *IX *VISIT HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n"
     ]
    }
   ],
   "source": [
    "# TODO Recognize the test set and display the result with the show_errors method\n",
    "models = train_all_words(features_delta_normal_polar, SelectorBIC)\n",
    "test_set = asl.build_test(features_delta_normal_polar)\n",
    "probabilities, guesses = recognize(models, test_set)\n",
    "show_errors(guesses, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "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 *JOHN HOMEWORK                                           JOHN WRITE HOMEWORK\n",
      "    7: JOHN *VISIT *GIVE1 *TEACHER                                   JOHN CAN GO CAN\n",
      "   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN\n",
      "   21: JOHN FISH *HOMEWORK *APPLE *NEW *GO *MARY *BOOK               JOHN FISH WONT EAT BUT CAN EAT CHICKEN\n",
      "   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX\n",
      "   28: JOHN *MARY IX IX IX                                           JOHN LIKE IX IX IX\n",
      "   30: JOHN *IX *MARY *JOHN IX                                       JOHN LIKE IX IX IX\n",
      "   36: MARY *JOHN *JOHN IX *JOHN *JOHN                               MARY VEGETABLE KNOW IX LIKE CORN1\n",
      "   40: JOHN IX *VEGETABLE MARY *MARY                                 JOHN IX THINK MARY LOVE\n",
      "   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE\n",
      "   50: *JOHN *WHO BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD\n",
      "   54: JOHN *JOHN *WHO BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE\n",
      "   57: JOHN *JOHN *IX *JOHN                                          JOHN DECIDE VISIT MARY\n",
      "   67: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE\n",
      "   71: JOHN *JOHN VISIT MARY                                         JOHN WILL VISIT MARY\n",
      "   74: JOHN *MARY *IX *JOHN                                          JOHN NOT VISIT MARY\n",
      "   77: *JOHN BLAME *JOHN                                             ANN BLAME MARY\n",
      "   84: *JOHN *NEW *IX *NEW                                           IX-1P FIND SOMETHING-ONE BOOK\n",
      "   89: JOHN *JOHN *IX *JOHN IX NEW *HOUSE                            JOHN IX GIVE MAN IX NEW COAT\n",
      "   90: *WHO *JOHN *JOHN *IX *IX BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "   92: JOHN *IX IX *JOHN WOMAN *ARRIVE                               JOHN GIVE IX SOMETHING-ONE WOMAN BOOK\n",
      "  100: *JOHN NEW CAR BREAK-DOWN                                      POSS NEW CAR BREAK-DOWN\n",
      "  105: JOHN *JOHN                                                    JOHN LEG\n",
      "  107: JOHN *IX FRIEND HAVE *JOHN                                    JOHN POSS FRIEND HAVE CANDY\n",
      "  108: *JOHN ARRIVE                                                  WOMAN ARRIVE\n",
      "  113: *JOHN CAR *MARY *MARY *BUY1                                   IX CAR BLUE SUE BUY\n",
      "  119: *JOHN *BUY1 IX CAR *MARY                                      SUE BUY IX CAR BLUE\n",
      "  122: JOHN READ *YESTERDAY                                          JOHN READ BOOK\n",
      "  139: JOHN *BUY1 WHAT *JOHN *STOLEN                                 JOHN BUY WHAT YESTERDAY BOOK\n",
      "  142: JOHN BUY YESTERDAY WHAT *YESTERDAY                            JOHN BUY YESTERDAY WHAT BOOK\n",
      "  158: LOVE *IX WHO                                                  LOVE JOHN WHO\n",
      "  167: JOHN IX *LIKE LOVE *HERE                                      JOHN IX SAY LOVE MARY\n",
      "  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME\n",
      "  174: *CAR *LOVE GIVE1 *WHAT *FINISH                                PEOPLE GROUP GIVE1 JANA TOY\n",
      "  181: JOHN *GIVE1                                                   JOHN ARRIVE\n",
      "  184: *IX *WHO *GIVE1 TEACHER *MARY                                 ALL BOY GIVE TEACHER APPLE\n",
      "  189: JOHN *IX *WHAT *WHAT                                          JOHN GIVE GIRL BOX\n",
      "  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX\n",
      "  199: *JOHN *ARRIVE *BROCCOLI                                       LIKE CHOCOLATE WHO\n",
      "  201: JOHN *IX MARY *IX *VISIT HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE\n"
     ]
    }
   ],
   "source": [
    "# TODO Recognize the test set and display the result with the show_errors method\n",
    "models = train_all_words(features_delta_normal_polar, SelectorDIC)\n",
    "test_set = asl.build_test(features_delta_normal_polar)\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**: I actually ran 30 test and it seems like Running SelectorConstant, SelectorBIC, andSelectorDIC on features_delta_normal_polar provided the best(lowest) WER. It seems like the feature space biggest effect on the WER. WER could be improved by better features. Using N-grams instead of 1-grams might prove to be fruitful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "..\n",
      "----------------------------------------------------------------------\n",
      "Ran 2 tests in 56.260s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=2 errors=0 failures=0>"
      ]
     },
     "execution_count": 48,
     "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": {
    "collapsed": true
   },
   "outputs": [
    {
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       "[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()"
   ]
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  {
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