magic wand training

magic-wand.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "divided-florist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#standard imports\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from sensiml import SensiML\n",
    "from sensiml.widgets import QueryWidget, AutoSenseWidget, DownloadWidget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "polyphonic-security",
   "metadata": {},
   "outputs": [],
   "source": [
    "dsk = SensiML()\n",
    "dsk.project ='Magic Wand'\n",
    "dsk.pipeline = 'pipeline 3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "stretch-vatican",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AccelerometerX</th>\n",
       "      <th>AccelerometerY</th>\n",
       "      <th>AccelerometerZ</th>\n",
       "      <th>Label</th>\n",
       "      <th>LabelNumerical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1608</td>\n",
       "      <td>-2205</td>\n",
       "      <td>3054</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1608</td>\n",
       "      <td>-2247</td>\n",
       "      <td>3054</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1647</td>\n",
       "      <td>-2247</td>\n",
       "      <td>3099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1647</td>\n",
       "      <td>-2208</td>\n",
       "      <td>2985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>-2208</td>\n",
       "      <td>2985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AccelerometerX  AccelerometerY  AccelerometerZ Label LabelNumerical\n",
       "0            1608           -2205            3054     0              0\n",
       "1            1608           -2247            3054     0              0\n",
       "2            1647           -2247            3099     0              0\n",
       "3            1647           -2208            2985     0              0\n",
       "4            1665           -2208            2985     0              0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('magickwand/WOZ_label.csv')\n",
    "df['LabelNumerical'] = pd.Categorical(df.Label)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "blessed-berry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12003\n",
       "O     4438\n",
       "W     4008\n",
       "Z     3463\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b890a8ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=1, stop=5, step=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1:4].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "altered-massage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fa52c0c0760>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sensor_columns = ['AccelerometerX','AccelerometerY','AccelerometerZ']\n",
    "ax0 = df[sensor_columns].plot(figsize=(16,4), title='Sensor Data')\n",
    "ax0.set_ylabel(\"Acceleration\")\n",
    "ax1 = df['LabelNumerical'].cat.codes.plot(figsize=(16,4), title='Label Values: W - O - Z', secondary_y=True, alpha=0)\n",
    "ax1.fill_between(df.loc[1:8000].index, 0, df.loc[1:8000]['LabelNumerical'].cat.codes * 3, alpha=0.3 )\n",
    "ax1.fill_between(df.loc[8000:17000].index, 0, df.loc[8000:17000]['LabelNumerical'].cat.codes * 2, alpha=0.3 )\n",
    "ax1.fill_between(df.loc[17000:].index, 0, df.loc[17000:]['LabelNumerical'].cat.codes * 6, alpha=0.3 )\n",
    "#ax1.set_ylabel(\"Label code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "infrared-frequency",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading file \"wand_10_movements.csv\" to SensiML Cloud.\n",
      "DATA\n",
      "{'name': 'wand_10_movements.csv', 'is_features': False}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsk.upload_dataframe('wand_10_movements.csv', df, force=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "southeast-theory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------\n",
      " 0.     Name: wand_10_movements.csv     \t\tType: featurefile              \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 1.     Name: Windowing                 \t\tType: segmenter                \n",
      "------------------------------------------------------------------------\n",
      "\tgroup_columns: ['Label']\n",
      "\twindow_size: 350\n",
      "\tdelta: 25\n",
      "\ttrain_delta: 25\n",
      "\treturn_segment_index: False\n",
      "------------------------------------------------------------------------\n",
      " 2.     Name: generator_set             \t\tType: generatorset             \n",
      "------------------------------------------------------------------------\n",
      "\t 0. Name: Mean                     \n",
      "\t 1. Name: Zero Crossings           \n",
      "\t 2. Name: Positive Zero Crossings  \n",
      "\t 3. Name: Negative Zero Crossings  \n",
      "\t 4. Name: Median                   \n",
      "\t 5. Name: Linear Regression Stats  \n",
      "\t 6. Name: Linear Regression Stats  \n",
      "\t 7. Name: Linear Regression Stats  \n",
      "\t 8. Name: Standard Deviation       \n",
      "\t 9. Name: Skewness                 \n",
      "\t10. Name: Kurtosis                 \n",
      "\t11. Name: Interquartile Range      \n",
      "\t12. Name: 25th Percentile          \n",
      "\t13. Name: 75th Percentile          \n",
      "\t14. Name: 100th Percentile         \n",
      "\t15. Name: Minimum                  \n",
      "\t16. Name: Maximum                  \n",
      "\t17. Name: Sum                      \n",
      "\t18. Name: Absolute Sum             \n",
      "\t19. Name: Absolute Mean            \n",
      "\t20. Name: Variance                 \n",
      "\t21. Name: Ratio of Peak to Peak of High Frequency between two halves\n",
      "\t22. Name: Difference of Peak to Peak of High Frequency between two halves\n",
      "\t23. Name: Shape Median Difference  \n",
      "\t24. Name: Shape Absolute Median Difference\n",
      "\t25. Name: Two Column Median Difference\n",
      "\t26. Name: Two Column Median Difference\n",
      "\t27. Name: Two Column Median Difference\n",
      "\t28. Name: Two Column Peak Location Difference\n",
      "\t29. Name: Two Column Peak Location Difference\n",
      "\t30. Name: Two Column Peak Location Difference\n",
      "\t31. Name: Abs Max Column           \n",
      "\t32. Name: Cross Column Correlation \n",
      "\t33. Name: Cross Column Correlation \n",
      "\t34. Name: Cross Column Correlation \n",
      "\t35. Name: Cross Column Mean Crossing Rate\n",
      "\t36. Name: Cross Column Mean Crossing Rate\n",
      "\t37. Name: Cross Column Mean Crossing Rate\n",
      "\t38. Name: Cross Column Mean Crossing with Offset\n",
      "\t39. Name: Cross Column Mean Crossing with Offset\n",
      "\t40. Name: Cross Column Mean Crossing with Offset\n",
      "\t41. Name: Max Column               \n",
      "\t42. Name: Min Column               \n",
      "\t43. Name: Two Column Min Max Difference\n",
      "\t44. Name: Two Column Min Max Difference\n",
      "\t45. Name: Two Column Min Max Difference\n",
      "\t46. Name: Two Column Mean Difference\n",
      "\t47. Name: Two Column Mean Difference\n",
      "\t48. Name: Two Column Mean Difference\n",
      "\t49. Name: Two Column Peak To Peak Difference\n",
      "\t50. Name: Two Column Peak To Peak Difference\n",
      "\t51. Name: Two Column Peak To Peak Difference\n",
      "\t52. Name: Total Area               \n",
      "\t53. Name: Absolute Area            \n",
      "\t54. Name: Total Area of Low Frequency\n",
      "\t55. Name: Absolute Area of Low Frequency\n",
      "\t56. Name: Total Area of High Frequency\n",
      "\t57. Name: Absolute Area of High Frequency\n",
      "\t58. Name: Absolute Area of Spectrum\n",
      "\t59. Name: Mean Difference          \n",
      "\t60. Name: Mean Crossing Rate       \n",
      "\t61. Name: Zero Crossing Rate       \n",
      "\t62. Name: Sigma Crossing Rate      \n",
      "\t63. Name: Second Sigma Crossing Rate\n",
      "\t64. Name: Threshold Crossing Rate  \n",
      "\t65. Name: Threshold With Offset Crossing Rate\n",
      "\tgroup_columns: ['Label', 'SegmentID']\n",
      "------------------------------------------------------------------------\n",
      " 3.     Name: selector_set              \t\tType: selectorset              \n",
      "------------------------------------------------------------------------\n",
      "\t 0. Name: Tree-based Selection     \n",
      "\tremove_columns: []\n",
      "\tpassthrough_columns: ['Label', 'SegmentID']\n",
      "\tlabel_column: Label\n",
      "\tnumber_of_features: 10\n",
      "\tcost_function: sum\n",
      "------------------------------------------------------------------------\n",
      " 4.     Name: Min Max Scale             \t\tType: transform                \n",
      "------------------------------------------------------------------------\n",
      "\tpassthrough_columns: ['Label', 'SegmentID']\n",
      "\tmin_bound: 0\n",
      "\tmax_bound: 255\n",
      "\tpad: 0\n",
      "\tfeature_min_max_parameters: {}\n",
      "\tfeature_min_max_defaults: None\n",
      "------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dsk.pipeline.reset()\n",
    "\n",
    "dsk.pipeline.set_input_data('wand_10_movements.csv', group_columns=['Label'], \n",
    "                                               label_column='Label',\n",
    "                                               data_columns=sensor_columns, \n",
    "                                               )\n",
    "\n",
    "dsk.pipeline.add_segmenter(\"Windowing\", params={\"window_size\": 350,\n",
    "                                \"delta\": 25,\n",
    "                                \"train_delta\": 25,\n",
    "                                \"return_segment_index\": False,\n",
    "                                })\n",
    "\n",
    "dsk.pipeline.add_feature_generator(                 # Add Feature Generators\n",
    "    [\n",
    "        {'subtype_call': 'Statistical'},\n",
    "        {'subtype_call': 'Shape'},\n",
    "        {'subtype_call': 'Column Fusion'},\n",
    "        {'subtype_call': 'Area'},\n",
    "        {'subtype_call': 'Rate of Change'},\n",
    "    ],\n",
    "    function_defaults={'columns': sensor_columns},\n",
    ")\n",
    "\n",
    "dsk.pipeline.add_feature_selector([{'name':'Tree-based Selection', 'params':{\"number_of_features\":12}},])\n",
    "\n",
    "dsk.pipeline.add_transform(\"Min Max Scale\")     # Scale the features to 1-byte\n",
    "\n",
    "dsk.pipeline.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "artificial-madrid",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing Pipeline with Steps:\n",
      "\n",
      "------------------------------------------------------------------------\n",
      " 0.     Name: wand_10_movements.csv     \t\tType: featurefile              \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 1.     Name: Windowing                 \t\tType: segmenter                \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 2.     Name: generator_set             \t\tType: generatorset             \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 3.     Name: selector_set              \t\tType: selectorset              \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 4.     Name: Min Max Scale             \t\tType: transform                \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Results Retrieved... Execution Time: 0 min. 1 sec.\n"
     ]
    }
   ],
   "source": [
    "fv_t, s_t = dsk.pipeline.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "laughing-exclusive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gen_0016_AccelerometerXLinearRegressionStdErr_0003</th>\n",
       "      <th>gen_0018_AccelerometerZLinearRegressionStdErr_0003</th>\n",
       "      <th>gen_0021_AccelerometerZStd</th>\n",
       "      <th>gen_0030_AccelerometerZIQR</th>\n",
       "      <th>gen_0042_AccelerometerZminimum</th>\n",
       "      <th>gen_0045_AccelerometerZmaximum</th>\n",
       "      <th>gen_0055_AccelerometerXVariance</th>\n",
       "      <th>gen_0057_AccelerometerZVariance</th>\n",
       "      <th>gen_0112_AccelerometerXAbsAreaAc</th>\n",
       "      <th>gen_0114_AccelerometerZAbsAreaAc</th>\n",
       "      <th>gen_0129_AccelerometerZSigmaCrossingRate</th>\n",
       "      <th>gen_0132_AccelerometerZ2ndSigmaCrossingRate</th>\n",
       "      <th>Label</th>\n",
       "      <th>SegmentID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>173</td>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>173</td>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>173</td>\n",
       "      <td>44</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>173</td>\n",
       "      <td>44</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>173</td>\n",
       "      <td>44</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gen_0016_AccelerometerXLinearRegressionStdErr_0003  \\\n",
       "0                                                 42    \n",
       "1                                                 43    \n",
       "2                                                 44    \n",
       "3                                                 45    \n",
       "4                                                 45    \n",
       "\n",
       "   gen_0018_AccelerometerZLinearRegressionStdErr_0003  \\\n",
       "0                                                 17    \n",
       "1                                                 17    \n",
       "2                                                 16    \n",
       "3                                                 16    \n",
       "4                                                 19    \n",
       "\n",
       "   gen_0021_AccelerometerZStd  gen_0030_AccelerometerZIQR  \\\n",
       "0                          24                          17   \n",
       "1                          25                          21   \n",
       "2                          25                          23   \n",
       "3                          24                          22   \n",
       "4                          24                          21   \n",
       "\n",
       "   gen_0042_AccelerometerZminimum  gen_0045_AccelerometerZmaximum  \\\n",
       "0                             173                              44   \n",
       "1                             173                              44   \n",
       "2                             173                              44   \n",
       "3                             173                              44   \n",
       "4                             173                              44   \n",
       "\n",
       "   gen_0055_AccelerometerXVariance  gen_0057_AccelerometerZVariance  \\\n",
       "0                               17                                3   \n",
       "1                               16                                4   \n",
       "2                               15                                4   \n",
       "3                               13                                4   \n",
       "4                               13                                4   \n",
       "\n",
       "   gen_0112_AccelerometerXAbsAreaAc  gen_0114_AccelerometerZAbsAreaAc  \\\n",
       "0                                38                                41   \n",
       "1                                37                                42   \n",
       "2                                35                                41   \n",
       "3                                32                                39   \n",
       "4                                35                                41   \n",
       "\n",
       "   gen_0129_AccelerometerZSigmaCrossingRate  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "\n",
       "   gen_0132_AccelerometerZ2ndSigmaCrossingRate Label  SegmentID  \n",
       "0                                            0     0          0  \n",
       "1                                            0     0          1  \n",
       "2                                            0     0          2  \n",
       "3                                            0     0          3  \n",
       "4                                            0     0          4  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fv_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "boring-mongolia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsk.pipeline.visualize_features(fv_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "confident-poultry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----  Summary  -----\n",
      "Class Map: {'0': 0, 'O': 1, 'W': 2, 'Z': 3}\n",
      "Train:\n",
      " total:  723\n",
      " by class: [371. 134. 120.  98.]\n",
      "Validate:\n",
      " total:  181\n",
      " by class: [97. 30. 27. 27.]\n",
      "Train:\n",
      " total:  0\n",
      " by class: [0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, x_validate, y_train, y_test, y_validate, class_map = \\\n",
    "    dsk.pipeline.features_to_tensor(fv_t, test=0.0, validate=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "solved-evanescence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 12)                156       \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 12)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 8)                 104       \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 4)                 36        \n",
      "=================================================================\n",
      "Total params: 296\n",
      "Trainable params: 296\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import layers\n",
    "import tensorflow as tf\n",
    "\n",
    "tf_model = tf.keras.Sequential()\n",
    "\n",
    "tf_model.add(layers.Dense(12, activation='relu', kernel_regularizer='l1', input_shape=(x_train.shape[1],)))\n",
    "tf_model.add(layers.Dropout(0.1))\n",
    "tf_model.add(layers.Dense(8, activation='relu', input_shape=(x_train.shape[1],)))\n",
    "tf_model.add(layers.Dropout(0.1))\n",
    "tf_model.add(layers.Dense(y_train.shape[1], activation='softmax'))\n",
    "\n",
    "# Compile the model using a standard optimizer and loss function for regression\n",
    "tf_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "tf_model.summary()\n",
    "train_history = {'loss':[], 'val_loss':[], 'accuracy':[], 'val_accuracy':[]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "roman-olive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2304x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import clear_output\n",
    "import sensiml.tensorflow.utils as sml_tf\n",
    "\n",
    "num_iterations=1\n",
    "epochs=100\n",
    "batch_size=32\n",
    "\n",
    "\n",
    "data  = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n",
    "shuffle_ds = data.shuffle(buffer_size=x_train.shape[0], reshuffle_each_iteration=True).batch(batch_size)\n",
    "\n",
    "for i in range(num_iterations):\n",
    "    history = tf_model.fit( shuffle_ds, epochs=epochs, batch_size=batch_size, validation_data=(x_validate, y_validate), verbose=0)\n",
    "\n",
    "    for key in train_history:\n",
    "        train_history[key].extend(history.history[key])\n",
    "\n",
    "    clear_output()    \n",
    "    sml_tf.plot_training_results(tf_model, train_history, x_train, y_train, x_validate, y_validate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "above-syria",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def representative_dataset_generator():\n",
    "    for value in x_validate:\n",
    "    # Each scalar value must be inside of a 2D array that is wrapped in a list\n",
    "        yield [np.array(value, dtype=np.float32, ndmin=2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "enormous-mozambique",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmppelh0tky/assets\n",
      "Full Model Size 2988\n",
      "INFO:tensorflow:Assets written to: /tmp/tmprj_ndijn/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmprj_ndijn/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantized Model Size 2848\n"
     ]
    }
   ],
   "source": [
    "# Unquantized Model\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(tf_model)\n",
    "tflite_model_full = converter.convert()\n",
    "print(\"Full Model Size\", len(tflite_model_full))\n",
    "\n",
    "# Quantized Model\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(tf_model)\n",
    "converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
    "converter.representative_dataset = representative_dataset_generator\n",
    "tflite_model_quant = converter.convert()\n",
    "\n",
    "print(\"Quantized Model Size\", len(tflite_model_quant))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "unlikely-details",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing Pipeline with Steps:\n",
      "\n",
      "------------------------------------------------------------------------\n",
      " 0.     Name: wand_10_movements.csv     \t\tType: featurefile              \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 1.     Name: Windowing                 \t\tType: segmenter                \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 2.     Name: generator_set             \t\tType: generatorset             \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 3.     Name: selector_set              \t\tType: selectorset              \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 4.     Name: Min Max Scale             \t\tType: transform                \n",
      "------------------------------------------------------------------------\n",
      "------------------------------------------------------------------------\n",
      " 5.     Name: tvo                       \t\tType: tvo                      \n",
      "------------------------------------------------------------------------\n",
      "\tClassifier: TF Micro\n",
      "\n",
      "\n",
      "\tTraining Algo: Load Model TF Micro\n",
      "\t\tclass_map: {'0': 1, 'O': 2, 'W': 3, 'Z': 4}\n",
      "\t\testimator_type: classification\n",
      "\t\tmodel_json: {\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential_2\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 12], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"dense_6_input\"}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_6\", \"trainable\": true, \"batch_input_shape\": [null, 12], \"dtype\": \"float32\", \"units\": 12, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": {\"class_name\": \"L1\", \"config\": {\"l1\": 0.009999999776482582}}, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_7\", \"trainable\": true, \"batch_input_shape\": [null, 12], \"dtype\": \"float32\", \"units\": 8, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dropout\", \"config\": {\"name\": \"dropout_3\", \"trainable\": true, \"dtype\": \"float32\", \"rate\": 0.1, \"noise_shape\": null, \"seed\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_8\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 4, \"activation\": \"softmax\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.5.0\", \"backend\": \"tensorflow\"}\n",
      "\t\tmodel_parameters: {'tflite': '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'}\n",
      "\t\tthreshold: 0.3\n",
      "\t\ttrain_history: {'loss': [16.73763656616211, 7.985980987548828, 5.415872573852539, 4.1726484298706055, 3.686734199523926, 2.9432384967803955, 2.4733479022979736, 2.407106637954712, 1.953098177909851, 1.8775222301483154, 1.4452852010726929, 1.4148750305175781, 1.4645764827728271, 1.5282392501831055, 1.2575699090957642, 1.4214942455291748, 1.2633838653564453, 0.8789932131767273, 0.8635414242744446, 0.7890906929969788, 0.7575526833534241, 0.6719263195991516, 0.6663805842399597, 0.6712002158164978, 0.6693887114524841, 0.6214327812194824, 0.6876628398895264, 0.6113342642784119, 0.6148282885551453, 0.6436149477958679, 0.5979775190353394, 0.6062797904014587, 0.593529999256134, 0.580503523349762, 0.5624544620513916, 0.5843545794487, 0.5492016077041626, 0.5643273591995239, 0.5213674306869507, 0.5486764907836914, 0.5070511698722839, 0.5494219660758972, 0.5291923880577087, 0.5248003005981445, 0.507095217704773, 0.5377654433250427, 0.5272640585899353, 0.5240458846092224, 0.536936342716217, 0.5145300626754761, 0.5394728779792786, 0.5031874775886536, 0.4912702739238739, 0.474837064743042, 0.4978034794330597, 0.47343868017196655, 0.5019485950469971, 0.49350035190582275, 0.44962990283966064, 0.5068372488021851, 0.45813190937042236, 0.46448689699172974, 0.5328967571258545, 0.5025261044502258, 0.48216792941093445, 0.45539405941963196, 0.5138511061668396, 0.45825743675231934, 0.48613041639328003, 0.421750009059906, 0.45771223306655884, 0.4408651292324066, 0.46192315220832825, 0.4444059729576111, 0.44015079736709595, 0.47167694568634033, 0.41625338792800903, 0.4959246516227722, 0.41968268156051636, 0.4405563175678253, 0.46486371755599976, 0.4154033362865448, 0.4309859573841095, 0.4216099679470062, 0.4506579339504242, 0.4305371940135956, 0.39049187302589417, 0.4071049690246582, 0.41688206791877747, 0.39220014214515686, 0.40977463126182556, 0.40284794569015503, 0.4072117507457733, 0.40227970480918884, 0.4079244136810303, 0.4411878287792206, 0.4125230014324188, 0.4142322242259979, 0.39040812849998474, 0.38568106293678284], 'val_loss': [7.423384189605713, 3.4547064304351807, 2.337865114212036, 1.992551565170288, 1.5628662109375, 1.3681786060333252, 1.1656463146209717, 1.0316154956817627, 0.9042732119560242, 0.8205476999282837, 0.719544529914856, 0.7283036112785339, 0.6598464846611023, 0.6748486757278442, 0.6077790856361389, 0.5963872075080872, 0.6010370254516602, 0.5671243667602539, 0.5305271744728088, 0.5321751236915588, 0.5344095230102539, 0.535815954208374, 0.5434675216674805, 0.5016570091247559, 0.48337191343307495, 0.47400012612342834, 0.4966503083705902, 0.4655057489871979, 0.4731353521347046, 0.4721851646900177, 0.4728603959083557, 0.4557156264781952, 0.4621422588825226, 0.45680245757102966, 0.4590406119823456, 0.45628273487091064, 0.44675514101982117, 0.4536133110523224, 0.4427630603313446, 0.45673179626464844, 0.4535438120365143, 0.45974209904670715, 0.4382902681827545, 0.4329352080821991, 0.4277324080467224, 0.43999409675598145, 0.455075204372406, 0.4201134145259857, 0.46310555934906006, 0.4193616509437561, 0.42819878458976746, 0.42170286178588867, 0.42049866914749146, 0.43673479557037354, 0.4078866243362427, 0.4186587631702423, 0.3965636193752289, 0.4238881468772888, 0.4023994207382202, 0.4061179757118225, 0.40689554810523987, 0.3977871835231781, 0.4172804653644562, 0.39466631412506104, 0.3810364007949829, 0.39368936419487, 0.4210931658744812, 0.3914734125137329, 0.4202319383621216, 0.39157307147979736, 0.38887545466423035, 0.3928380012512207, 0.39595144987106323, 0.3936370015144348, 0.37269675731658936, 0.38023537397384644, 0.36519619822502136, 0.3814653754234314, 0.3695208430290222, 0.39963796734809875, 0.3552105724811554, 0.35273054242134094, 0.3974425196647644, 0.3875080645084381, 0.3748341202735901, 0.3646186590194702, 0.35597658157348633, 0.34827423095703125, 0.34497135877609253, 0.349541574716568, 0.339874804019928, 0.34511038661003113, 0.3566106855869293, 0.3430188000202179, 0.33869850635528564, 0.36726459860801697, 0.3491874933242798, 0.34711650013923645, 0.3473927974700928, 0.34057146310806274], 'accuracy': [0.4744121730327606, 0.5352697372436523, 0.6417704224586487, 0.6887966990470886, 0.7607192397117615, 0.7883817553520203, 0.8063623905181885, 0.817427396774292, 0.8478561639785767, 0.8464730381965637, 0.8838174343109131, 0.8713693022727966, 0.8713693022727966, 0.8755186796188354, 0.8686030507087708, 0.8713693022727966, 0.8727524280548096, 0.9059474468231201, 0.8838174343109131, 0.8907330632209778, 0.9031811952590942, 0.9128630757331848, 0.9128630757331848, 0.9114799499511719, 0.9170124530792236, 0.9114799499511719, 0.9100968241691589, 0.9183955788612366, 0.9183955788612366, 0.9197787046432495, 0.9253112077713013, 0.9183955788612366, 0.9225449562072754, 0.9142462015151978, 0.9280774593353271, 0.9266943335533142, 0.930843710899353, 0.930843710899353, 0.9363762140274048, 0.9336099624633789, 0.9363762140274048, 0.9225449562072754, 0.9280774593353271, 0.9239280819892883, 0.9363762140274048, 0.9391424655914307, 0.9280774593353271, 0.9377593398094177, 0.930843710899353, 0.9294605851173401, 0.932226836681366, 0.9266943335533142, 0.9377593398094177, 0.9377593398094177, 0.9280774593353271, 0.9391424655914307, 0.9419087171554565, 0.932226836681366, 0.9363762140274048, 0.9377593398094177, 0.9391424655914307, 0.9529737234115601, 0.9253112077713013, 0.9377593398094177, 0.9391424655914307, 0.9377593398094177, 0.9349930882453918, 0.9446749687194824, 0.9377593398094177, 0.954356849193573, 0.9391424655914307, 0.9488243460655212, 0.9502074718475342, 0.9557399749755859, 0.9391424655914307, 0.9446749687194824, 0.9446749687194824, 0.9460580945014954, 0.9474412202835083, 0.9557399749755859, 0.9349930882453918, 0.9474412202835083, 0.9488243460655212, 0.954356849193573, 0.9391424655914307, 0.9432918429374695, 0.9612724781036377, 0.9515905976295471, 0.9529737234115601, 0.9557399749755859, 0.9460580945014954, 0.9529737234115601, 0.9488243460655212, 0.9585062265396118, 0.9529737234115601, 0.9446749687194824, 0.9432918429374695, 0.9446749687194824, 0.9529737234115601, 0.9626556038856506], 'val_accuracy': [0.6353591084480286, 0.6629834175109863, 0.7237569093704224, 0.7734806537628174, 0.8453038930892944, 0.8729282021522522, 0.8784530162811279, 0.90055251121521, 0.9171270728111267, 0.9116021990776062, 0.9226519465446472, 0.9226519465446472, 0.9226519465446472, 0.9171270728111267, 0.9281768202781677, 0.9060773253440857, 0.8950276374816895, 0.9060773253440857, 0.9171270728111267, 0.9226519465446472, 0.90055251121521, 0.9171270728111267, 0.9171270728111267, 0.9116021990776062, 0.939226508140564, 0.9447513818740845, 0.9337016344070435, 0.9447513818740845, 0.9337016344070435, 0.950276255607605, 0.9447513818740845, 0.9558011293411255, 0.9447513818740845, 0.9337016344070435, 0.9447513818740845, 0.9447513818740845, 0.950276255607605, 0.9447513818740845, 0.9558011293411255, 0.9447513818740845, 0.9447513818740845, 0.9447513818740845, 0.9668508172035217, 0.950276255607605, 0.9613259434700012, 0.9558011293411255, 0.939226508140564, 0.9613259434700012, 0.950276255607605, 0.9613259434700012, 0.9558011293411255, 0.9668508172035217, 0.9613259434700012, 0.950276255607605, 0.9613259434700012, 0.9723756909370422, 0.9779005646705627, 0.9613259434700012, 0.9723756909370422, 0.9613259434700012, 0.9613259434700012, 0.9668508172035217, 0.9558011293411255, 0.9779005646705627, 0.9723756909370422, 0.9613259434700012, 0.9558011293411255, 0.9723756909370422, 0.950276255607605, 0.9668508172035217, 0.9613259434700012, 0.9558011293411255, 0.9558011293411255, 0.9613259434700012, 0.9779005646705627, 0.9723756909370422, 0.9723756909370422, 0.9668508172035217, 0.9723756909370422, 0.9558011293411255, 0.9779005646705627, 0.9834254384040833, 0.9613259434700012, 0.9558011293411255, 0.9613259434700012, 0.9723756909370422, 0.9779005646705627, 0.9779005646705627, 0.9779005646705627, 0.9779005646705627, 0.9834254384040833, 0.9779005646705627, 0.9723756909370422, 0.9668508172035217, 0.9779005646705627, 0.9613259434700012, 0.9723756909370422, 0.9779005646705627, 0.9779005646705627, 0.9779005646705627]}\n",
      "\n",
      "\tValidation Method: Recall\n",
      "\n",
      "\n",
      "------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Results Retrieved... Execution Time: 0 min. 22 sec.\n"
     ]
    }
   ],
   "source": [
    "class_map_tmp = {k:v+1 for k,v in class_map.items()} #increment by 1 as 0 corresponds to unknown\n",
    "\n",
    "dsk.pipeline.set_training_algorithm(\"Load Model TF Micro\",\n",
    "                                    params={\"model_parameters\": {\n",
    "                                            'tflite': sml_tf.convert_tf_lite(tflite_model_quant)},\n",
    "                                            \"class_map\": class_map_tmp,\n",
    "                                            \"estimator_type\": \"classification\",\n",
    "                                            \"threshold\": 0.3,\n",
    "                                            \"train_history\":train_history,\n",
    "                                            \"model_json\": tf_model.to_json()\n",
    "                                            })\n",
    "\n",
    "dsk.pipeline.set_validation_method(\"Recall\", params={})\n",
    "\n",
    "dsk.pipeline.set_classifier(\"TF Micro\", params={})\n",
    "\n",
    "dsk.pipeline.set_tvo()\n",
    "\n",
    "results, stats = dsk.pipeline.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "greatest-arctic",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cloudy-battlefield",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING ALGORITHM: Load Model TF Micro\n",
      "VALIDATION METHOD:  Recall\n",
      "CLASSIFIER:         TF Micro\n",
      "\n",
      "AVERAGE METRICS:\n",
      "                 F1_SCORE:  98.4  std: 0.00\n",
      "                PRECISION:  98.7  std: 0.00\n",
      "              SENSITIVITY:  98.2  std: 0.00\n",
      "\n",
      "--------------------------------------\n",
      "\n",
      "RECALL MODEL RESULTS : SET VALIDATION\n",
      "\n",
      "MODEL INDEX: Fold 0\n",
      "                 F1_SCORE:    train: 98.43  validation: 98.43  \n",
      "              SENSITIVITY:    train: 98.22  validation: 98.22  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "results.summarize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ruled-rating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CONFUSION MATRIX:\n",
       "                   0         O         W         Z       UNK       UNC   Support   Sens(%)\n",
       "         0     464.0       0.0       0.0       4.0       0.0       0.0     468.0      99.1\n",
       "         O       0.0     164.0       0.0       0.0       0.0       0.0     164.0     100.0\n",
       "         W       0.0       1.0     146.0       0.0       0.0       0.0     147.0      99.3\n",
       "         Z       7.0       0.0       0.0     118.0       0.0       0.0     125.0      94.4\n",
       "\n",
       "     Total       471       165       146       122         0         0       904          \n",
       "\n",
       "PosPred(%)      98.5      99.4     100.0      96.7                        Acc(%)      98.7"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = results.configurations[0].models[0]\n",
    "model.confusion_matrix_stats['validation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "adapted-appraisal",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Knowledgepack 'Magic_WOZ_detector_2' updated.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'uuid': 'c7d4c2a2-1e22-4574-b949-fe9a6d2534c1',\n",
       " 'execution_time': '2021-06-06T20:44:44.282610Z',\n",
       " 'neuron_array': {'size': 2897,\n",
       "  'tflite': '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',\n",
       "  'threshold': 0.3,\n",
       "  'num_inputs': 12,\n",
       "  'num_outputs': 4,\n",
       "  'estimator_type': 'classification',\n",
       "  'tensor_arena_size': 4096},\n",
       " 'model_results': {'debug': {},\n",
       "  'metrics': {'test': {},\n",
       "   'train': {'y_pred': ['0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     'Z',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'O',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z'],\n",
       "    'y_true': ['0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z'],\n",
       "    'accuracy': 98.67256637168141,\n",
       "    'f1_score': {'0': 98.82854100106498,\n",
       "     'O': 99.69604863221885,\n",
       "     'W': 99.65870307167235,\n",
       "     'Z': 95.54655870445343,\n",
       "     'average': 98.43246285235239},\n",
       "    'precision': {'0': 98.51380042462846,\n",
       "     'O': 99.39393939393939,\n",
       "     'W': 100.0,\n",
       "     'Z': 96.72131147540983,\n",
       "     'average': 98.65726282349442},\n",
       "    'sensitivity': {'0': 99.14529914529915,\n",
       "     'O': 100.0,\n",
       "     'W': 99.31972789115646,\n",
       "     'Z': 94.39999999999999,\n",
       "     'average': 98.21625675911389},\n",
       "    'specificity': None,\n",
       "    'DistanceVector': [[0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.9375, 0.0, 0.0, 0.0625],\n",
       "     [0.9375, 0.0, 0.0, 0.0625],\n",
       "     [0.9609375, 0.0, 0.0, 0.0390625],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.9375, 0.0, 0.0, 0.0625],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.74609375, 0.0, 0.0, 0.25390625],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.9609375, 0.0, 0.0, 0.0390625],\n",
       "     [0.98828125, 0.0, 0.0, 0.01171875],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.9609375, 0.0, 0.0, 0.0390625],\n",
       "     [0.9375, 0.0, 0.0, 0.0625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.74609375, 0.0, 0.0, 0.25390625],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.83203125, 0.0, 0.0, 0.16796875],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.984375, 0.0, 0.0, 0.01171875],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.828125, 0.16796875, 0.0078125, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.89453125, 0.10546875, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.83203125, 0.16796875, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.01171875, 0.81640625, 0.1640625, 0.0078125],\n",
       "     [0.0, 0.9921875, 0.0078125, 0.0],\n",
       "     [0.0, 0.98828125, 0.01171875, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.83203125, 0.16796875, 0.0],\n",
       "     [0.01171875, 0.734375, 0.25, 0.00390625],\n",
       "     [0.0, 0.62890625, 0.3671875, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.00390625, 0.62890625, 0.3671875, 0.0],\n",
       "     [0.02734375, 0.71484375, 0.24609375, 0.01171875],\n",
       "     [0.0078125, 0.625, 0.3671875, 0.0],\n",
       "     [0.0, 0.62890625, 0.3671875, 0.0],\n",
       "     [0.0, 0.984375, 0.01171875, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.7421875, 0.25390625, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.1015625, 0.87890625, 0.0078125, 0.01171875],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.89453125, 0.10546875, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.9921875, 0.0078125, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.83203125, 0.16796875, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.9921875, 0.0078125, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.93359375, 0.0625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.5, 0.5, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.7421875, 0.25390625, 0.0],\n",
       "     [0.0, 0.9921875, 0.0078125, 0.0],\n",
       "     [0.0, 0.9375, 0.0625, 0.0],\n",
       "     [0.0, 0.9375, 0.0625, 0.0],\n",
       "     [0.0, 0.98828125, 0.01171875, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.98828125, 0.01171875, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0078125, 0.87890625, 0.1015625, 0.01171875],\n",
       "     [0.00390625, 0.9453125, 0.0390625, 0.01171875],\n",
       "     [0.0, 0.98828125, 0.01171875, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.0625, 0.9375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.16796875, 0.83203125, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.25390625, 0.74609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.16796875, 0.83203125, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0625, 0.9375, 0.0],\n",
       "     [0.0, 0.0390625, 0.9609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0390625, 0.9609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.00390625, 0.00390625, 0.9921875, 0.0],\n",
       "     [0.0078125, 0.0078125, 0.984375, 0.0],\n",
       "     [0.09375, 0.2734375, 0.46875, 0.16015625],\n",
       "     [0.08984375, 0.08984375, 0.765625, 0.0546875],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0078125, 0.9921875, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.00390625, 0.0234375, 0.97265625, 0.0],\n",
       "     [0.0, 0.0078125, 0.9921875, 0.0],\n",
       "     [0.0546875, 0.15625, 0.78125, 0.0078125],\n",
       "     [0.0, 0.62890625, 0.3671875, 0.0],\n",
       "     [0.0, 0.0625, 0.93359375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.0390625, 0.9609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0625, 0.9375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.74609375, 0.0, 0.0, 0.25390625],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.00390625, 0.0, 0.7421875],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.00390625, 0.0, 0.83203125],\n",
       "     [0.25, 0.01171875, 0.00390625, 0.734375],\n",
       "     [0.16796875, 0.00390625, 0.0, 0.828125],\n",
       "     [0.25, 0.015625, 0.00390625, 0.73046875],\n",
       "     [0.25390625, 0.01171875, 0.00390625, 0.734375]],\n",
       "    'UnknownPercent': 0.0,\n",
       "    'ConfusionMatrix': {'0': {'0': 464,\n",
       "      'O': 0,\n",
       "      'W': 0,\n",
       "      'Z': 4,\n",
       "      'UNC': 0,\n",
       "      'UNK': 0},\n",
       "     'O': {'0': 0, 'O': 164, 'W': 0, 'Z': 0, 'UNC': 0, 'UNK': 0},\n",
       "     'W': {'0': 0, 'O': 1, 'W': 146, 'Z': 0, 'UNC': 0, 'UNK': 0},\n",
       "     'Z': {'0': 7, 'O': 0, 'W': 0, 'Z': 118, 'UNC': 0, 'UNK': 0}},\n",
       "    'VectorInTestSet': 904,\n",
       "    'FeaturesPerVector': 12,\n",
       "    'ActualCategoryCounts': {'0': 468, 'O': 164, 'W': 147, 'Z': 125},\n",
       "    'RecognizedCategoryCounts': {'0': 471, 'O': 165, 'W': 146, 'Z': 122},\n",
       "    'positive_predictive_rate': {'0': 98.51380042462846,\n",
       "     'O': 99.39393939393939,\n",
       "     'W': 100.0,\n",
       "     'Z': 96.72131147540983,\n",
       "     'average': 98.65726282349442},\n",
       "    'ProperClassificationPercent': 98.67256637168141,\n",
       "    'ImproperClassificationPercent': 1.3274336283185841},\n",
       "   'validation': {'y_pred': ['0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     'Z',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'O',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     '0',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z'],\n",
       "    'y_true': ['0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     '0',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'O',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'W',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z',\n",
       "     'Z'],\n",
       "    'accuracy': 98.67256637168141,\n",
       "    'f1_score': {'0': 98.82854100106498,\n",
       "     'O': 99.69604863221885,\n",
       "     'W': 99.65870307167235,\n",
       "     'Z': 95.54655870445343,\n",
       "     'average': 98.43246285235239},\n",
       "    'precision': {'0': 98.51380042462846,\n",
       "     'O': 99.39393939393939,\n",
       "     'W': 100.0,\n",
       "     'Z': 96.72131147540983,\n",
       "     'average': 98.65726282349442},\n",
       "    'sensitivity': {'0': 99.14529914529915,\n",
       "     'O': 100.0,\n",
       "     'W': 99.31972789115646,\n",
       "     'Z': 94.39999999999999,\n",
       "     'average': 98.21625675911389},\n",
       "    'specificity': None,\n",
       "    'DistanceVector': [[0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.9375, 0.0, 0.0, 0.0625],\n",
       "     [0.9375, 0.0, 0.0, 0.0625],\n",
       "     [0.9609375, 0.0, 0.0, 0.0390625],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.9375, 0.0, 0.0, 0.0625],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.74609375, 0.0, 0.0, 0.25390625],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.9609375, 0.0, 0.0, 0.0390625],\n",
       "     [0.98828125, 0.0, 0.0, 0.01171875],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.00390625],\n",
       "     [0.9609375, 0.0, 0.0, 0.0390625],\n",
       "     [0.9375, 0.0, 0.0, 0.0625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.74609375, 0.0, 0.0, 0.25390625],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.83203125, 0.0, 0.0, 0.16796875],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.984375, 0.0, 0.0, 0.01171875],\n",
       "     [0.99609375, 0.0, 0.0, 0.0],\n",
       "     [0.828125, 0.16796875, 0.0078125, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.89453125, 0.10546875, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.83203125, 0.16796875, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.01171875, 0.81640625, 0.1640625, 0.0078125],\n",
       "     [0.0, 0.9921875, 0.0078125, 0.0],\n",
       "     [0.0, 0.98828125, 0.01171875, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.83203125, 0.16796875, 0.0],\n",
       "     [0.01171875, 0.734375, 0.25, 0.00390625],\n",
       "     [0.0, 0.62890625, 0.3671875, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.00390625, 0.62890625, 0.3671875, 0.0],\n",
       "     [0.02734375, 0.71484375, 0.24609375, 0.01171875],\n",
       "     [0.0078125, 0.625, 0.3671875, 0.0],\n",
       "     [0.0, 0.62890625, 0.3671875, 0.0],\n",
       "     [0.0, 0.984375, 0.01171875, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.7421875, 0.25390625, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.0, 0.62890625, 0.37109375, 0.0],\n",
       "     [0.1015625, 0.87890625, 0.0078125, 0.01171875],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.89453125, 0.10546875, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.9921875, 0.0078125, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.83203125, 0.16796875, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.9921875, 0.0078125, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.93359375, 0.0625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.9609375, 0.0390625, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.5, 0.5, 0.0],\n",
       "     [0.0, 0.74609375, 0.25390625, 0.0],\n",
       "     [0.0, 0.7421875, 0.25390625, 0.0],\n",
       "     [0.0, 0.9921875, 0.0078125, 0.0],\n",
       "     [0.0, 0.9375, 0.0625, 0.0],\n",
       "     [0.0, 0.9375, 0.0625, 0.0],\n",
       "     [0.0, 0.98828125, 0.01171875, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.98828125, 0.01171875, 0.0],\n",
       "     [0.0, 0.9765625, 0.0234375, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0, 0.99609375, 0.00390625, 0.0],\n",
       "     [0.0078125, 0.87890625, 0.1015625, 0.01171875],\n",
       "     [0.00390625, 0.9453125, 0.0390625, 0.01171875],\n",
       "     [0.0, 0.98828125, 0.01171875, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.99609375, 0.0, 0.0],\n",
       "     [0.0, 0.0625, 0.9375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.16796875, 0.83203125, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.25390625, 0.74609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.16796875, 0.83203125, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0625, 0.9375, 0.0],\n",
       "     [0.0, 0.0390625, 0.9609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0390625, 0.9609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.00390625, 0.00390625, 0.9921875, 0.0],\n",
       "     [0.0078125, 0.0078125, 0.984375, 0.0],\n",
       "     [0.09375, 0.2734375, 0.46875, 0.16015625],\n",
       "     [0.08984375, 0.08984375, 0.765625, 0.0546875],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0078125, 0.9921875, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.00390625, 0.0234375, 0.97265625, 0.0],\n",
       "     [0.0, 0.0078125, 0.9921875, 0.0],\n",
       "     [0.0546875, 0.15625, 0.78125, 0.0078125],\n",
       "     [0.0, 0.62890625, 0.3671875, 0.0],\n",
       "     [0.0, 0.0625, 0.93359375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.0390625, 0.9609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.00390625, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0625, 0.9375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.0, 0.0, 0.99609375, 0.0],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.74609375, 0.0, 0.0, 0.25390625],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.62890625, 0.0, 0.0, 0.37109375],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.37109375, 0.0, 0.0, 0.62890625],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.5, 0.0, 0.0, 0.5],\n",
       "     [0.25390625, 0.0, 0.0, 0.74609375],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.25390625, 0.00390625, 0.0, 0.7421875],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.10546875, 0.0, 0.0, 0.89453125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.0, 0.0, 0.83203125],\n",
       "     [0.16796875, 0.00390625, 0.0, 0.83203125],\n",
       "     [0.25, 0.01171875, 0.00390625, 0.734375],\n",
       "     [0.16796875, 0.00390625, 0.0, 0.828125],\n",
       "     [0.25, 0.015625, 0.00390625, 0.73046875],\n",
       "     [0.25390625, 0.01171875, 0.00390625, 0.734375]],\n",
       "    'UnknownPercent': 0.0,\n",
       "    'ConfusionMatrix': {'0': {'0': 464,\n",
       "      'O': 0,\n",
       "      'W': 0,\n",
       "      'Z': 4,\n",
       "      'UNC': 0,\n",
       "      'UNK': 0},\n",
       "     'O': {'0': 0, 'O': 164, 'W': 0, 'Z': 0, 'UNC': 0, 'UNK': 0},\n",
       "     'W': {'0': 0, 'O': 1, 'W': 146, 'Z': 0, 'UNC': 0, 'UNK': 0},\n",
       "     'Z': {'0': 7, 'O': 0, 'W': 0, 'Z': 118, 'UNC': 0, 'UNK': 0}},\n",
       "    'VectorInTestSet': 904,\n",
       "    'FeaturesPerVector': 12,\n",
       "    'ActualCategoryCounts': {'0': 468, 'O': 164, 'W': 147, 'Z': 125},\n",
       "    'RecognizedCategoryCounts': {'0': 471, 'O': 165, 'W': 146, 'Z': 122},\n",
       "    'positive_predictive_rate': {'0': 98.51380042462846,\n",
       "     'O': 99.39393939393939,\n",
       "     'W': 100.0,\n",
       "     'Z': 96.72131147540983,\n",
       "     'average': 98.65726282349442},\n",
       "    'ProperClassificationPercent': 98.67256637168141,\n",
       "    'ImproperClassificationPercent': 1.3274336283185841}},\n",
       "  'test_set': [],\n",
       "  'train_set': [0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   8,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   12,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20,\n",
       "   21,\n",
       "   22,\n",
       "   23,\n",
       "   24,\n",
       "   25,\n",
       "   26,\n",
       "   27,\n",
       "   28,\n",
       "   29,\n",
       "   30,\n",
       "   31,\n",
       "   32,\n",
       "   33,\n",
       "   34,\n",
       "   35,\n",
       "   36,\n",
       "   37,\n",
       "   38,\n",
       "   39,\n",
       "   40,\n",
       "   41,\n",
       "   42,\n",
       "   43,\n",
       "   44,\n",
       "   45,\n",
       "   46,\n",
       "   47,\n",
       "   48,\n",
       "   49,\n",
       "   50,\n",
       "   51,\n",
       "   52,\n",
       "   53,\n",
       "   54,\n",
       "   55,\n",
       "   56,\n",
       "   57,\n",
       "   58,\n",
       "   59,\n",
       "   60,\n",
       "   61,\n",
       "   62,\n",
       "   63,\n",
       "   64,\n",
       "   65,\n",
       "   66,\n",
       "   67,\n",
       "   68,\n",
       "   69,\n",
       "   70,\n",
       "   71,\n",
       "   72,\n",
       "   73,\n",
       "   74,\n",
       "   75,\n",
       "   76,\n",
       "   77,\n",
       "   78,\n",
       "   79,\n",
       "   80,\n",
       "   81,\n",
       "   82,\n",
       "   83,\n",
       "   84,\n",
       "   85,\n",
       "   86,\n",
       "   87,\n",
       "   88,\n",
       "   89,\n",
       "   90,\n",
       "   91,\n",
       "   92,\n",
       "   93,\n",
       "   94,\n",
       "   95,\n",
       "   96,\n",
       "   97,\n",
       "   98,\n",
       "   99,\n",
       "   100,\n",
       "   101,\n",
       "   102,\n",
       "   103,\n",
       "   104,\n",
       "   105,\n",
       "   106,\n",
       "   107,\n",
       "   108,\n",
       "   109,\n",
       "   110,\n",
       "   111,\n",
       "   112,\n",
       "   113,\n",
       "   114,\n",
       "   115,\n",
       "   116,\n",
       "   117,\n",
       "   118,\n",
       "   119,\n",
       "   120,\n",
       "   121,\n",
       "   122,\n",
       "   123,\n",
       "   124,\n",
       "   125,\n",
       "   126,\n",
       "   127,\n",
       "   128,\n",
       "   129,\n",
       "   130,\n",
       "   131,\n",
       "   132,\n",
       "   133,\n",
       "   134,\n",
       "   135,\n",
       "   136,\n",
       "   137,\n",
       "   138,\n",
       "   139,\n",
       "   140,\n",
       "   141,\n",
       "   142,\n",
       "   143,\n",
       "   144,\n",
       "   145,\n",
       "   146,\n",
       "   147,\n",
       "   148,\n",
       "   149,\n",
       "   150,\n",
       "   151,\n",
       "   152,\n",
       "   153,\n",
       "   154,\n",
       "   155,\n",
       "   156,\n",
       "   157,\n",
       "   158,\n",
       "   159,\n",
       "   160,\n",
       "   161,\n",
       "   162,\n",
       "   163,\n",
       "   164,\n",
       "   165,\n",
       "   166,\n",
       "   167,\n",
       "   168,\n",
       "   169,\n",
       "   170,\n",
       "   171,\n",
       "   172,\n",
       "   173,\n",
       "   174,\n",
       "   175,\n",
       "   176,\n",
       "   177,\n",
       "   178,\n",
       "   179,\n",
       "   180,\n",
       "   181,\n",
       "   182,\n",
       "   183,\n",
       "   184,\n",
       "   185,\n",
       "   186,\n",
       "   187,\n",
       "   188,\n",
       "   189,\n",
       "   190,\n",
       "   191,\n",
       "   192,\n",
       "   193,\n",
       "   194,\n",
       "   195,\n",
       "   196,\n",
       "   197,\n",
       "   198,\n",
       "   199,\n",
       "   200,\n",
       "   201,\n",
       "   202,\n",
       "   203,\n",
       "   204,\n",
       "   205,\n",
       "   206,\n",
       "   207,\n",
       "   208,\n",
       "   209,\n",
       "   210,\n",
       "   211,\n",
       "   212,\n",
       "   213,\n",
       "   214,\n",
       "   215,\n",
       "   216,\n",
       "   217,\n",
       "   218,\n",
       "   219,\n",
       "   220,\n",
       "   221,\n",
       "   222,\n",
       "   223,\n",
       "   224,\n",
       "   225,\n",
       "   226,\n",
       "   227,\n",
       "   228,\n",
       "   229,\n",
       "   230,\n",
       "   231,\n",
       "   232,\n",
       "   233,\n",
       "   234,\n",
       "   235,\n",
       "   236,\n",
       "   237,\n",
       "   238,\n",
       "   239,\n",
       "   240,\n",
       "   241,\n",
       "   242,\n",
       "   243,\n",
       "   244,\n",
       "   245,\n",
       "   246,\n",
       "   247,\n",
       "   248,\n",
       "   249,\n",
       "   250,\n",
       "   251,\n",
       "   252,\n",
       "   253,\n",
       "   254,\n",
       "   255,\n",
       "   256,\n",
       "   257,\n",
       "   258,\n",
       "   259,\n",
       "   260,\n",
       "   261,\n",
       "   262,\n",
       "   263,\n",
       "   264,\n",
       "   265,\n",
       "   266,\n",
       "   267,\n",
       "   268,\n",
       "   269,\n",
       "   270,\n",
       "   271,\n",
       "   272,\n",
       "   273,\n",
       "   274,\n",
       "   275,\n",
       "   276,\n",
       "   277,\n",
       "   278,\n",
       "   279,\n",
       "   280,\n",
       "   281,\n",
       "   282,\n",
       "   283,\n",
       "   284,\n",
       "   285,\n",
       "   286,\n",
       "   287,\n",
       "   288,\n",
       "   289,\n",
       "   290,\n",
       "   291,\n",
       "   292,\n",
       "   293,\n",
       "   294,\n",
       "   295,\n",
       "   296,\n",
       "   297,\n",
       "   298,\n",
       "   299,\n",
       "   300,\n",
       "   301,\n",
       "   302,\n",
       "   303,\n",
       "   304,\n",
       "   305,\n",
       "   306,\n",
       "   307,\n",
       "   308,\n",
       "   309,\n",
       "   310,\n",
       "   311,\n",
       "   312,\n",
       "   313,\n",
       "   314,\n",
       "   315,\n",
       "   316,\n",
       "   317,\n",
       "   318,\n",
       "   319,\n",
       "   320,\n",
       "   321,\n",
       "   322,\n",
       "   323,\n",
       "   324,\n",
       "   325,\n",
       "   326,\n",
       "   327,\n",
       "   328,\n",
       "   329,\n",
       "   330,\n",
       "   331,\n",
       "   332,\n",
       "   333,\n",
       "   334,\n",
       "   335,\n",
       "   336,\n",
       "   337,\n",
       "   338,\n",
       "   339,\n",
       "   340,\n",
       "   341,\n",
       "   342,\n",
       "   343,\n",
       "   344,\n",
       "   345,\n",
       "   346,\n",
       "   347,\n",
       "   348,\n",
       "   349,\n",
       "   350,\n",
       "   351,\n",
       "   352,\n",
       "   353,\n",
       "   354,\n",
       "   355,\n",
       "   356,\n",
       "   357,\n",
       "   358,\n",
       "   359,\n",
       "   360,\n",
       "   361,\n",
       "   362,\n",
       "   363,\n",
       "   364,\n",
       "   365,\n",
       "   366,\n",
       "   367,\n",
       "   368,\n",
       "   369,\n",
       "   370,\n",
       "   371,\n",
       "   372,\n",
       "   373,\n",
       "   374,\n",
       "   375,\n",
       "   376,\n",
       "   377,\n",
       "   378,\n",
       "   379,\n",
       "   380,\n",
       "   381,\n",
       "   382,\n",
       "   383,\n",
       "   384,\n",
       "   385,\n",
       "   386,\n",
       "   387,\n",
       "   388,\n",
       "   389,\n",
       "   390,\n",
       "   391,\n",
       "   392,\n",
       "   393,\n",
       "   394,\n",
       "   395,\n",
       "   396,\n",
       "   397,\n",
       "   398,\n",
       "   399,\n",
       "   400,\n",
       "   401,\n",
       "   402,\n",
       "   403,\n",
       "   404,\n",
       "   405,\n",
       "   406,\n",
       "   407,\n",
       "   408,\n",
       "   409,\n",
       "   410,\n",
       "   411,\n",
       "   412,\n",
       "   413,\n",
       "   414,\n",
       "   415,\n",
       "   416,\n",
       "   417,\n",
       "   418,\n",
       "   419,\n",
       "   420,\n",
       "   421,\n",
       "   422,\n",
       "   423,\n",
       "   424,\n",
       "   425,\n",
       "   426,\n",
       "   427,\n",
       "   428,\n",
       "   429,\n",
       "   430,\n",
       "   431,\n",
       "   432,\n",
       "   433,\n",
       "   434,\n",
       "   435,\n",
       "   436,\n",
       "   437,\n",
       "   438,\n",
       "   439,\n",
       "   440,\n",
       "   441,\n",
       "   442,\n",
       "   443,\n",
       "   444,\n",
       "   445,\n",
       "   446,\n",
       "   447,\n",
       "   448,\n",
       "   449,\n",
       "   450,\n",
       "   451,\n",
       "   452,\n",
       "   453,\n",
       "   454,\n",
       "   455,\n",
       "   456,\n",
       "   457,\n",
       "   458,\n",
       "   459,\n",
       "   460,\n",
       "   461,\n",
       "   462,\n",
       "   463,\n",
       "   464,\n",
       "   465,\n",
       "   466,\n",
       "   467,\n",
       "   468,\n",
       "   469,\n",
       "   470,\n",
       "   471,\n",
       "   472,\n",
       "   473,\n",
       "   474,\n",
       "   475,\n",
       "   476,\n",
       "   477,\n",
       "   478,\n",
       "   479,\n",
       "   480,\n",
       "   481,\n",
       "   482,\n",
       "   483,\n",
       "   484,\n",
       "   485,\n",
       "   486,\n",
       "   487,\n",
       "   488,\n",
       "   489,\n",
       "   490,\n",
       "   491,\n",
       "   492,\n",
       "   493,\n",
       "   494,\n",
       "   495,\n",
       "   496,\n",
       "   497,\n",
       "   498,\n",
       "   499,\n",
       "   500,\n",
       "   501,\n",
       "   502,\n",
       "   503,\n",
       "   504,\n",
       "   505,\n",
       "   506,\n",
       "   507,\n",
       "   508,\n",
       "   509,\n",
       "   510,\n",
       "   511,\n",
       "   512,\n",
       "   513,\n",
       "   514,\n",
       "   515,\n",
       "   516,\n",
       "   517,\n",
       "   518,\n",
       "   519,\n",
       "   520,\n",
       "   521,\n",
       "   522,\n",
       "   523,\n",
       "   524,\n",
       "   525,\n",
       "   526,\n",
       "   527,\n",
       "   528,\n",
       "   529,\n",
       "   530,\n",
       "   531,\n",
       "   532,\n",
       "   533,\n",
       "   534,\n",
       "   535,\n",
       "   536,\n",
       "   537,\n",
       "   538,\n",
       "   539,\n",
       "   540,\n",
       "   541,\n",
       "   542,\n",
       "   543,\n",
       "   544,\n",
       "   545,\n",
       "   546,\n",
       "   547,\n",
       "   548,\n",
       "   549,\n",
       "   550,\n",
       "   551,\n",
       "   552,\n",
       "   553,\n",
       "   554,\n",
       "   555,\n",
       "   556,\n",
       "   557,\n",
       "   558,\n",
       "   559,\n",
       "   560,\n",
       "   561,\n",
       "   562,\n",
       "   563,\n",
       "   564,\n",
       "   565,\n",
       "   566,\n",
       "   567,\n",
       "   568,\n",
       "   569,\n",
       "   570,\n",
       "   571,\n",
       "   572,\n",
       "   573,\n",
       "   574,\n",
       "   575,\n",
       "   576,\n",
       "   577,\n",
       "   578,\n",
       "   579,\n",
       "   580,\n",
       "   581,\n",
       "   582,\n",
       "   583,\n",
       "   584,\n",
       "   585,\n",
       "   586,\n",
       "   587,\n",
       "   588,\n",
       "   589,\n",
       "   590,\n",
       "   591,\n",
       "   592,\n",
       "   593,\n",
       "   594,\n",
       "   595,\n",
       "   596,\n",
       "   597,\n",
       "   598,\n",
       "   599,\n",
       "   600,\n",
       "   601,\n",
       "   602,\n",
       "   603,\n",
       "   604,\n",
       "   605,\n",
       "   606,\n",
       "   607,\n",
       "   608,\n",
       "   609,\n",
       "   610,\n",
       "   611,\n",
       "   612,\n",
       "   613,\n",
       "   614,\n",
       "   615,\n",
       "   616,\n",
       "   617,\n",
       "   618,\n",
       "   619,\n",
       "   620,\n",
       "   621,\n",
       "   622,\n",
       "   623,\n",
       "   624,\n",
       "   625,\n",
       "   626,\n",
       "   627,\n",
       "   628,\n",
       "   629,\n",
       "   630,\n",
       "   631,\n",
       "   632,\n",
       "   633,\n",
       "   634,\n",
       "   635,\n",
       "   636,\n",
       "   637,\n",
       "   638,\n",
       "   639,\n",
       "   640,\n",
       "   641,\n",
       "   642,\n",
       "   643,\n",
       "   644,\n",
       "   645,\n",
       "   646,\n",
       "   647,\n",
       "   648,\n",
       "   649,\n",
       "   650,\n",
       "   651,\n",
       "   652,\n",
       "   653,\n",
       "   654,\n",
       "   655,\n",
       "   656,\n",
       "   657,\n",
       "   658,\n",
       "   659,\n",
       "   660,\n",
       "   661,\n",
       "   662,\n",
       "   663,\n",
       "   664,\n",
       "   665,\n",
       "   666,\n",
       "   667,\n",
       "   668,\n",
       "   669,\n",
       "   670,\n",
       "   671,\n",
       "   672,\n",
       "   673,\n",
       "   674,\n",
       "   675,\n",
       "   676,\n",
       "   677,\n",
       "   678,\n",
       "   679,\n",
       "   680,\n",
       "   681,\n",
       "   682,\n",
       "   683,\n",
       "   684,\n",
       "   685,\n",
       "   686,\n",
       "   687,\n",
       "   688,\n",
       "   689,\n",
       "   690,\n",
       "   691,\n",
       "   692,\n",
       "   693,\n",
       "   694,\n",
       "   695,\n",
       "   696,\n",
       "   697,\n",
       "   698,\n",
       "   699,\n",
       "   700,\n",
       "   701,\n",
       "   702,\n",
       "   703,\n",
       "   704,\n",
       "   705,\n",
       "   706,\n",
       "   707,\n",
       "   708,\n",
       "   709,\n",
       "   710,\n",
       "   711,\n",
       "   712,\n",
       "   713,\n",
       "   714,\n",
       "   715,\n",
       "   716,\n",
       "   717,\n",
       "   718,\n",
       "   719,\n",
       "   720,\n",
       "   721,\n",
       "   722,\n",
       "   723,\n",
       "   724,\n",
       "   725,\n",
       "   726,\n",
       "   727,\n",
       "   728,\n",
       "   729,\n",
       "   730,\n",
       "   731,\n",
       "   732,\n",
       "   733,\n",
       "   734,\n",
       "   735,\n",
       "   736,\n",
       "   737,\n",
       "   738,\n",
       "   739,\n",
       "   740,\n",
       "   741,\n",
       "   742,\n",
       "   743,\n",
       "   744,\n",
       "   745,\n",
       "   746,\n",
       "   747,\n",
       "   748,\n",
       "   749,\n",
       "   750,\n",
       "   751,\n",
       "   752,\n",
       "   753,\n",
       "   754,\n",
       "   755,\n",
       "   756,\n",
       "   757,\n",
       "   758,\n",
       "   759,\n",
       "   760,\n",
       "   761,\n",
       "   762,\n",
       "   763,\n",
       "   764,\n",
       "   765,\n",
       "   766,\n",
       "   767,\n",
       "   768,\n",
       "   769,\n",
       "   770,\n",
       "   771,\n",
       "   772,\n",
       "   773,\n",
       "   774,\n",
       "   775,\n",
       "   776,\n",
       "   777,\n",
       "   778,\n",
       "   779,\n",
       "   780,\n",
       "   781,\n",
       "   782,\n",
       "   783,\n",
       "   784,\n",
       "   785,\n",
       "   786,\n",
       "   787,\n",
       "   788,\n",
       "   789,\n",
       "   790,\n",
       "   791,\n",
       "   792,\n",
       "   793,\n",
       "   794,\n",
       "   795,\n",
       "   796,\n",
       "   797,\n",
       "   798,\n",
       "   799,\n",
       "   800,\n",
       "   801,\n",
       "   802,\n",
       "   803,\n",
       "   804,\n",
       "   805,\n",
       "   806,\n",
       "   807,\n",
       "   808,\n",
       "   809,\n",
       "   810,\n",
       "   811,\n",
       "   812,\n",
       "   813,\n",
       "   814,\n",
       "   815,\n",
       "   816,\n",
       "   817,\n",
       "   818,\n",
       "   819,\n",
       "   820,\n",
       "   821,\n",
       "   822,\n",
       "   823,\n",
       "   824,\n",
       "   825,\n",
       "   826,\n",
       "   827,\n",
       "   828,\n",
       "   829,\n",
       "   830,\n",
       "   831,\n",
       "   832,\n",
       "   833,\n",
       "   834,\n",
       "   835,\n",
       "   836,\n",
       "   837,\n",
       "   838,\n",
       "   839,\n",
       "   840,\n",
       "   841,\n",
       "   842,\n",
       "   843,\n",
       "   844,\n",
       "   845,\n",
       "   846,\n",
       "   847,\n",
       "   848,\n",
       "   849,\n",
       "   850,\n",
       "   851,\n",
       "   852,\n",
       "   853,\n",
       "   854,\n",
       "   855,\n",
       "   856,\n",
       "   857,\n",
       "   858,\n",
       "   859,\n",
       "   860,\n",
       "   861,\n",
       "   862,\n",
       "   863,\n",
       "   864,\n",
       "   865,\n",
       "   866,\n",
       "   867,\n",
       "   868,\n",
       "   869,\n",
       "   870,\n",
       "   871,\n",
       "   872,\n",
       "   873,\n",
       "   874,\n",
       "   875,\n",
       "   876,\n",
       "   877,\n",
       "   878,\n",
       "   879,\n",
       "   880,\n",
       "   881,\n",
       "   882,\n",
       "   883,\n",
       "   884,\n",
       "   885,\n",
       "   886,\n",
       "   887,\n",
       "   888,\n",
       "   889,\n",
       "   890,\n",
       "   891,\n",
       "   892,\n",
       "   893,\n",
       "   894,\n",
       "   895,\n",
       "   896,\n",
       "   897,\n",
       "   898,\n",
       "   899,\n",
       "   900,\n",
       "   901,\n",
       "   902,\n",
       "   903],\n",
       "  'model_size': 2897,\n",
       "  'validation_set': [0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   8,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   12,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20,\n",
       "   21,\n",
       "   22,\n",
       "   23,\n",
       "   24,\n",
       "   25,\n",
       "   26,\n",
       "   27,\n",
       "   28,\n",
       "   29,\n",
       "   30,\n",
       "   31,\n",
       "   32,\n",
       "   33,\n",
       "   34,\n",
       "   35,\n",
       "   36,\n",
       "   37,\n",
       "   38,\n",
       "   39,\n",
       "   40,\n",
       "   41,\n",
       "   42,\n",
       "   43,\n",
       "   44,\n",
       "   45,\n",
       "   46,\n",
       "   47,\n",
       "   48,\n",
       "   49,\n",
       "   50,\n",
       "   51,\n",
       "   52,\n",
       "   53,\n",
       "   54,\n",
       "   55,\n",
       "   56,\n",
       "   57,\n",
       "   58,\n",
       "   59,\n",
       "   60,\n",
       "   61,\n",
       "   62,\n",
       "   63,\n",
       "   64,\n",
       "   65,\n",
       "   66,\n",
       "   67,\n",
       "   68,\n",
       "   69,\n",
       "   70,\n",
       "   71,\n",
       "   72,\n",
       "   73,\n",
       "   74,\n",
       "   75,\n",
       "   76,\n",
       "   77,\n",
       "   78,\n",
       "   79,\n",
       "   80,\n",
       "   81,\n",
       "   82,\n",
       "   83,\n",
       "   84,\n",
       "   85,\n",
       "   86,\n",
       "   87,\n",
       "   88,\n",
       "   89,\n",
       "   90,\n",
       "   91,\n",
       "   92,\n",
       "   93,\n",
       "   94,\n",
       "   95,\n",
       "   96,\n",
       "   97,\n",
       "   98,\n",
       "   99,\n",
       "   100,\n",
       "   101,\n",
       "   102,\n",
       "   103,\n",
       "   104,\n",
       "   105,\n",
       "   106,\n",
       "   107,\n",
       "   108,\n",
       "   109,\n",
       "   110,\n",
       "   111,\n",
       "   112,\n",
       "   113,\n",
       "   114,\n",
       "   115,\n",
       "   116,\n",
       "   117,\n",
       "   118,\n",
       "   119,\n",
       "   120,\n",
       "   121,\n",
       "   122,\n",
       "   123,\n",
       "   124,\n",
       "   125,\n",
       "   126,\n",
       "   127,\n",
       "   128,\n",
       "   129,\n",
       "   130,\n",
       "   131,\n",
       "   132,\n",
       "   133,\n",
       "   134,\n",
       "   135,\n",
       "   136,\n",
       "   137,\n",
       "   138,\n",
       "   139,\n",
       "   140,\n",
       "   141,\n",
       "   142,\n",
       "   143,\n",
       "   144,\n",
       "   145,\n",
       "   146,\n",
       "   147,\n",
       "   148,\n",
       "   149,\n",
       "   150,\n",
       "   151,\n",
       "   152,\n",
       "   153,\n",
       "   154,\n",
       "   155,\n",
       "   156,\n",
       "   157,\n",
       "   158,\n",
       "   159,\n",
       "   160,\n",
       "   161,\n",
       "   162,\n",
       "   163,\n",
       "   164,\n",
       "   165,\n",
       "   166,\n",
       "   167,\n",
       "   168,\n",
       "   169,\n",
       "   170,\n",
       "   171,\n",
       "   172,\n",
       "   173,\n",
       "   174,\n",
       "   175,\n",
       "   176,\n",
       "   177,\n",
       "   178,\n",
       "   179,\n",
       "   180,\n",
       "   181,\n",
       "   182,\n",
       "   183,\n",
       "   184,\n",
       "   185,\n",
       "   186,\n",
       "   187,\n",
       "   188,\n",
       "   189,\n",
       "   190,\n",
       "   191,\n",
       "   192,\n",
       "   193,\n",
       "   194,\n",
       "   195,\n",
       "   196,\n",
       "   197,\n",
       "   198,\n",
       "   199,\n",
       "   200,\n",
       "   201,\n",
       "   202,\n",
       "   203,\n",
       "   204,\n",
       "   205,\n",
       "   206,\n",
       "   207,\n",
       "   208,\n",
       "   209,\n",
       "   210,\n",
       "   211,\n",
       "   212,\n",
       "   213,\n",
       "   214,\n",
       "   215,\n",
       "   216,\n",
       "   217,\n",
       "   218,\n",
       "   219,\n",
       "   220,\n",
       "   221,\n",
       "   222,\n",
       "   223,\n",
       "   224,\n",
       "   225,\n",
       "   226,\n",
       "   227,\n",
       "   228,\n",
       "   229,\n",
       "   230,\n",
       "   231,\n",
       "   232,\n",
       "   233,\n",
       "   234,\n",
       "   235,\n",
       "   236,\n",
       "   237,\n",
       "   238,\n",
       "   239,\n",
       "   240,\n",
       "   241,\n",
       "   242,\n",
       "   243,\n",
       "   244,\n",
       "   245,\n",
       "   246,\n",
       "   247,\n",
       "   248,\n",
       "   249,\n",
       "   250,\n",
       "   251,\n",
       "   252,\n",
       "   253,\n",
       "   254,\n",
       "   255,\n",
       "   256,\n",
       "   257,\n",
       "   258,\n",
       "   259,\n",
       "   260,\n",
       "   261,\n",
       "   262,\n",
       "   263,\n",
       "   264,\n",
       "   265,\n",
       "   266,\n",
       "   267,\n",
       "   268,\n",
       "   269,\n",
       "   270,\n",
       "   271,\n",
       "   272,\n",
       "   273,\n",
       "   274,\n",
       "   275,\n",
       "   276,\n",
       "   277,\n",
       "   278,\n",
       "   279,\n",
       "   280,\n",
       "   281,\n",
       "   282,\n",
       "   283,\n",
       "   284,\n",
       "   285,\n",
       "   286,\n",
       "   287,\n",
       "   288,\n",
       "   289,\n",
       "   290,\n",
       "   291,\n",
       "   292,\n",
       "   293,\n",
       "   294,\n",
       "   295,\n",
       "   296,\n",
       "   297,\n",
       "   298,\n",
       "   299,\n",
       "   300,\n",
       "   301,\n",
       "   302,\n",
       "   303,\n",
       "   304,\n",
       "   305,\n",
       "   306,\n",
       "   307,\n",
       "   308,\n",
       "   309,\n",
       "   310,\n",
       "   311,\n",
       "   312,\n",
       "   313,\n",
       "   314,\n",
       "   315,\n",
       "   316,\n",
       "   317,\n",
       "   318,\n",
       "   319,\n",
       "   320,\n",
       "   321,\n",
       "   322,\n",
       "   323,\n",
       "   324,\n",
       "   325,\n",
       "   326,\n",
       "   327,\n",
       "   328,\n",
       "   329,\n",
       "   330,\n",
       "   331,\n",
       "   332,\n",
       "   333,\n",
       "   334,\n",
       "   335,\n",
       "   336,\n",
       "   337,\n",
       "   338,\n",
       "   339,\n",
       "   340,\n",
       "   341,\n",
       "   342,\n",
       "   343,\n",
       "   344,\n",
       "   345,\n",
       "   346,\n",
       "   347,\n",
       "   348,\n",
       "   349,\n",
       "   350,\n",
       "   351,\n",
       "   352,\n",
       "   353,\n",
       "   354,\n",
       "   355,\n",
       "   356,\n",
       "   357,\n",
       "   358,\n",
       "   359,\n",
       "   360,\n",
       "   361,\n",
       "   362,\n",
       "   363,\n",
       "   364,\n",
       "   365,\n",
       "   366,\n",
       "   367,\n",
       "   368,\n",
       "   369,\n",
       "   370,\n",
       "   371,\n",
       "   372,\n",
       "   373,\n",
       "   374,\n",
       "   375,\n",
       "   376,\n",
       "   377,\n",
       "   378,\n",
       "   379,\n",
       "   380,\n",
       "   381,\n",
       "   382,\n",
       "   383,\n",
       "   384,\n",
       "   385,\n",
       "   386,\n",
       "   387,\n",
       "   388,\n",
       "   389,\n",
       "   390,\n",
       "   391,\n",
       "   392,\n",
       "   393,\n",
       "   394,\n",
       "   395,\n",
       "   396,\n",
       "   397,\n",
       "   398,\n",
       "   399,\n",
       "   400,\n",
       "   401,\n",
       "   402,\n",
       "   403,\n",
       "   404,\n",
       "   405,\n",
       "   406,\n",
       "   407,\n",
       "   408,\n",
       "   409,\n",
       "   410,\n",
       "   411,\n",
       "   412,\n",
       "   413,\n",
       "   414,\n",
       "   415,\n",
       "   416,\n",
       "   417,\n",
       "   418,\n",
       "   419,\n",
       "   420,\n",
       "   421,\n",
       "   422,\n",
       "   423,\n",
       "   424,\n",
       "   425,\n",
       "   426,\n",
       "   427,\n",
       "   428,\n",
       "   429,\n",
       "   430,\n",
       "   431,\n",
       "   432,\n",
       "   433,\n",
       "   434,\n",
       "   435,\n",
       "   436,\n",
       "   437,\n",
       "   438,\n",
       "   439,\n",
       "   440,\n",
       "   441,\n",
       "   442,\n",
       "   443,\n",
       "   444,\n",
       "   445,\n",
       "   446,\n",
       "   447,\n",
       "   448,\n",
       "   449,\n",
       "   450,\n",
       "   451,\n",
       "   452,\n",
       "   453,\n",
       "   454,\n",
       "   455,\n",
       "   456,\n",
       "   457,\n",
       "   458,\n",
       "   459,\n",
       "   460,\n",
       "   461,\n",
       "   462,\n",
       "   463,\n",
       "   464,\n",
       "   465,\n",
       "   466,\n",
       "   467,\n",
       "   468,\n",
       "   469,\n",
       "   470,\n",
       "   471,\n",
       "   472,\n",
       "   473,\n",
       "   474,\n",
       "   475,\n",
       "   476,\n",
       "   477,\n",
       "   478,\n",
       "   479,\n",
       "   480,\n",
       "   481,\n",
       "   482,\n",
       "   483,\n",
       "   484,\n",
       "   485,\n",
       "   486,\n",
       "   487,\n",
       "   488,\n",
       "   489,\n",
       "   490,\n",
       "   491,\n",
       "   492,\n",
       "   493,\n",
       "   494,\n",
       "   495,\n",
       "   496,\n",
       "   497,\n",
       "   498,\n",
       "   499,\n",
       "   500,\n",
       "   501,\n",
       "   502,\n",
       "   503,\n",
       "   504,\n",
       "   505,\n",
       "   506,\n",
       "   507,\n",
       "   508,\n",
       "   509,\n",
       "   510,\n",
       "   511,\n",
       "   512,\n",
       "   513,\n",
       "   514,\n",
       "   515,\n",
       "   516,\n",
       "   517,\n",
       "   518,\n",
       "   519,\n",
       "   520,\n",
       "   521,\n",
       "   522,\n",
       "   523,\n",
       "   524,\n",
       "   525,\n",
       "   526,\n",
       "   527,\n",
       "   528,\n",
       "   529,\n",
       "   530,\n",
       "   531,\n",
       "   532,\n",
       "   533,\n",
       "   534,\n",
       "   535,\n",
       "   536,\n",
       "   537,\n",
       "   538,\n",
       "   539,\n",
       "   540,\n",
       "   541,\n",
       "   542,\n",
       "   543,\n",
       "   544,\n",
       "   545,\n",
       "   546,\n",
       "   547,\n",
       "   548,\n",
       "   549,\n",
       "   550,\n",
       "   551,\n",
       "   552,\n",
       "   553,\n",
       "   554,\n",
       "   555,\n",
       "   556,\n",
       "   557,\n",
       "   558,\n",
       "   559,\n",
       "   560,\n",
       "   561,\n",
       "   562,\n",
       "   563,\n",
       "   564,\n",
       "   565,\n",
       "   566,\n",
       "   567,\n",
       "   568,\n",
       "   569,\n",
       "   570,\n",
       "   571,\n",
       "   572,\n",
       "   573,\n",
       "   574,\n",
       "   575,\n",
       "   576,\n",
       "   577,\n",
       "   578,\n",
       "   579,\n",
       "   580,\n",
       "   581,\n",
       "   582,\n",
       "   583,\n",
       "   584,\n",
       "   585,\n",
       "   586,\n",
       "   587,\n",
       "   588,\n",
       "   589,\n",
       "   590,\n",
       "   591,\n",
       "   592,\n",
       "   593,\n",
       "   594,\n",
       "   595,\n",
       "   596,\n",
       "   597,\n",
       "   598,\n",
       "   599,\n",
       "   600,\n",
       "   601,\n",
       "   602,\n",
       "   603,\n",
       "   604,\n",
       "   605,\n",
       "   606,\n",
       "   607,\n",
       "   608,\n",
       "   609,\n",
       "   610,\n",
       "   611,\n",
       "   612,\n",
       "   613,\n",
       "   614,\n",
       "   615,\n",
       "   616,\n",
       "   617,\n",
       "   618,\n",
       "   619,\n",
       "   620,\n",
       "   621,\n",
       "   622,\n",
       "   623,\n",
       "   624,\n",
       "   625,\n",
       "   626,\n",
       "   627,\n",
       "   628,\n",
       "   629,\n",
       "   630,\n",
       "   631,\n",
       "   632,\n",
       "   633,\n",
       "   634,\n",
       "   635,\n",
       "   636,\n",
       "   637,\n",
       "   638,\n",
       "   639,\n",
       "   640,\n",
       "   641,\n",
       "   642,\n",
       "   643,\n",
       "   644,\n",
       "   645,\n",
       "   646,\n",
       "   647,\n",
       "   648,\n",
       "   649,\n",
       "   650,\n",
       "   651,\n",
       "   652,\n",
       "   653,\n",
       "   654,\n",
       "   655,\n",
       "   656,\n",
       "   657,\n",
       "   658,\n",
       "   659,\n",
       "   660,\n",
       "   661,\n",
       "   662,\n",
       "   663,\n",
       "   664,\n",
       "   665,\n",
       "   666,\n",
       "   667,\n",
       "   668,\n",
       "   669,\n",
       "   670,\n",
       "   671,\n",
       "   672,\n",
       "   673,\n",
       "   674,\n",
       "   675,\n",
       "   676,\n",
       "   677,\n",
       "   678,\n",
       "   679,\n",
       "   680,\n",
       "   681,\n",
       "   682,\n",
       "   683,\n",
       "   684,\n",
       "   685,\n",
       "   686,\n",
       "   687,\n",
       "   688,\n",
       "   689,\n",
       "   690,\n",
       "   691,\n",
       "   692,\n",
       "   693,\n",
       "   694,\n",
       "   695,\n",
       "   696,\n",
       "   697,\n",
       "   698,\n",
       "   699,\n",
       "   700,\n",
       "   701,\n",
       "   702,\n",
       "   703,\n",
       "   704,\n",
       "   705,\n",
       "   706,\n",
       "   707,\n",
       "   708,\n",
       "   709,\n",
       "   710,\n",
       "   711,\n",
       "   712,\n",
       "   713,\n",
       "   714,\n",
       "   715,\n",
       "   716,\n",
       "   717,\n",
       "   718,\n",
       "   719,\n",
       "   720,\n",
       "   721,\n",
       "   722,\n",
       "   723,\n",
       "   724,\n",
       "   725,\n",
       "   726,\n",
       "   727,\n",
       "   728,\n",
       "   729,\n",
       "   730,\n",
       "   731,\n",
       "   732,\n",
       "   733,\n",
       "   734,\n",
       "   735,\n",
       "   736,\n",
       "   737,\n",
       "   738,\n",
       "   739,\n",
       "   740,\n",
       "   741,\n",
       "   742,\n",
       "   743,\n",
       "   744,\n",
       "   745,\n",
       "   746,\n",
       "   747,\n",
       "   748,\n",
       "   749,\n",
       "   750,\n",
       "   751,\n",
       "   752,\n",
       "   753,\n",
       "   754,\n",
       "   755,\n",
       "   756,\n",
       "   757,\n",
       "   758,\n",
       "   759,\n",
       "   760,\n",
       "   761,\n",
       "   762,\n",
       "   763,\n",
       "   764,\n",
       "   765,\n",
       "   766,\n",
       "   767,\n",
       "   768,\n",
       "   769,\n",
       "   770,\n",
       "   771,\n",
       "   772,\n",
       "   773,\n",
       "   774,\n",
       "   775,\n",
       "   776,\n",
       "   777,\n",
       "   778,\n",
       "   779,\n",
       "   780,\n",
       "   781,\n",
       "   782,\n",
       "   783,\n",
       "   784,\n",
       "   785,\n",
       "   786,\n",
       "   787,\n",
       "   788,\n",
       "   789,\n",
       "   790,\n",
       "   791,\n",
       "   792,\n",
       "   793,\n",
       "   794,\n",
       "   795,\n",
       "   796,\n",
       "   797,\n",
       "   798,\n",
       "   799,\n",
       "   800,\n",
       "   801,\n",
       "   802,\n",
       "   803,\n",
       "   804,\n",
       "   805,\n",
       "   806,\n",
       "   807,\n",
       "   808,\n",
       "   809,\n",
       "   810,\n",
       "   811,\n",
       "   812,\n",
       "   813,\n",
       "   814,\n",
       "   815,\n",
       "   816,\n",
       "   817,\n",
       "   818,\n",
       "   819,\n",
       "   820,\n",
       "   821,\n",
       "   822,\n",
       "   823,\n",
       "   824,\n",
       "   825,\n",
       "   826,\n",
       "   827,\n",
       "   828,\n",
       "   829,\n",
       "   830,\n",
       "   831,\n",
       "   832,\n",
       "   833,\n",
       "   834,\n",
       "   835,\n",
       "   836,\n",
       "   837,\n",
       "   838,\n",
       "   839,\n",
       "   840,\n",
       "   841,\n",
       "   842,\n",
       "   843,\n",
       "   844,\n",
       "   845,\n",
       "   846,\n",
       "   847,\n",
       "   848,\n",
       "   849,\n",
       "   850,\n",
       "   851,\n",
       "   852,\n",
       "   853,\n",
       "   854,\n",
       "   855,\n",
       "   856,\n",
       "   857,\n",
       "   858,\n",
       "   859,\n",
       "   860,\n",
       "   861,\n",
       "   862,\n",
       "   863,\n",
       "   864,\n",
       "   865,\n",
       "   866,\n",
       "   867,\n",
       "   868,\n",
       "   869,\n",
       "   870,\n",
       "   871,\n",
       "   872,\n",
       "   873,\n",
       "   874,\n",
       "   875,\n",
       "   876,\n",
       "   877,\n",
       "   878,\n",
       "   879,\n",
       "   880,\n",
       "   881,\n",
       "   882,\n",
       "   883,\n",
       "   884,\n",
       "   885,\n",
       "   886,\n",
       "   887,\n",
       "   888,\n",
       "   889,\n",
       "   890,\n",
       "   891,\n",
       "   892,\n",
       "   893,\n",
       "   894,\n",
       "   895,\n",
       "   896,\n",
       "   897,\n",
       "   898,\n",
       "   899,\n",
       "   900,\n",
       "   901,\n",
       "   902,\n",
       "   903],\n",
       "  'training_metrics': None,\n",
       "  'feature_statistics': {'test': {},\n",
       "   'train': {'gen_0021_AccelerometerZStd': {'1': {'25%': 17.0,\n",
       "      '50%': 34.0,\n",
       "      '75%': 53.0,\n",
       "      'max': 111.0,\n",
       "      'min': 0.0,\n",
       "      'std': 21.6815181457331,\n",
       "      '4.5%': 5.015000000000001,\n",
       "      'mean': 35.93803418803419,\n",
       "      '95.5%': 73.0,\n",
       "      'count': 468.0,\n",
       "      'median': 34.0,\n",
       "      'outlier': [5,\n",
       "       5,\n",
       "       4,\n",
       "       3,\n",
       "       2,\n",
       "       0,\n",
       "       5,\n",
       "       4,\n",
       "       4,\n",
       "       3,\n",
       "       5,\n",
       "       5,\n",
       "       5,\n",
       "       5,\n",
       "       4,\n",
       "       4,\n",
       "       2,\n",
       "       2,\n",
       "       1,\n",
       "       1,\n",
       "       4,\n",
       "       5,\n",
       "       74,\n",
       "       74,\n",
       "       76,\n",
       "       80,\n",
       "       81,\n",
       "       81,\n",
       "       81,\n",
       "       80,\n",
       "       81,\n",
       "       81,\n",
       "       76,\n",
       "       75,\n",
       "       74,\n",
       "       88,\n",
       "       97,\n",
       "       111]},\n",
       "     '2': {'25%': 139.0,\n",
       "      '50%': 145.0,\n",
       "      '75%': 150.0,\n",
       "      'max': 182.0,\n",
       "      'min': 98.0,\n",
       "      'std': 13.565634806492739,\n",
       "      '4.5%': 114.33500000000001,\n",
       "      'mean': 142.8719512195122,\n",
       "      '95.5%': 161.665,\n",
       "      'count': 164.0,\n",
       "      'median': 145.0,\n",
       "      'outlier': [98,\n",
       "       99,\n",
       "       109,\n",
       "       112,\n",
       "       103,\n",
       "       110,\n",
       "       114,\n",
       "       100,\n",
       "       164,\n",
       "       178,\n",
       "       182,\n",
       "       173,\n",
       "       164,\n",
       "       162,\n",
       "       163,\n",
       "       164]},\n",
       "     '3': {'25%': 156.0,\n",
       "      '50%': 183.0,\n",
       "      '75%': 225.0,\n",
       "      'max': 255.0,\n",
       "      'min': 122.0,\n",
       "      'std': 37.40013758902921,\n",
       "      '4.5%': 131.0,\n",
       "      'mean': 186.54421768707482,\n",
       "      '95.5%': 241.0,\n",
       "      'count': 147.0,\n",
       "      'median': 183.0,\n",
       "      'outlier': [128, 129, 125, 123, 127, 122, 244, 250, 248, 254, 255, 242]},\n",
       "     '4': {'25%': 75.0,\n",
       "      '50%': 84.0,\n",
       "      '75%': 90.0,\n",
       "      'max': 118.0,\n",
       "      'min': 60.0,\n",
       "      'std': 13.090587606023181,\n",
       "      '4.5%': 64.0,\n",
       "      'mean': 83.632,\n",
       "      '95.5%': 111.0,\n",
       "      'count': 125.0,\n",
       "      'median': 84.0,\n",
       "      'outlier': [62, 62, 60, 60, 63, 118, 116, 113, 112, 112]}},\n",
       "    'gen_0030_AccelerometerZIQR': {'1': {'25%': 9.0,\n",
       "      '50%': 21.0,\n",
       "      '75%': 44.25,\n",
       "      'max': 84.0,\n",
       "      'min': 0.0,\n",
       "      'std': 22.068238107815887,\n",
       "      '4.5%': 2.0,\n",
       "      'mean': 27.664529914529915,\n",
       "      '95.5%': 71.98499999999996,\n",
       "      'count': 468.0,\n",
       "      'median': 21.0,\n",
       "      'outlier': [1,\n",
       "       1,\n",
       "       1,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       76,\n",
       "       75,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       73,\n",
       "       74,\n",
       "       73,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       75,\n",
       "       75,\n",
       "       75,\n",
       "       73,\n",
       "       72,\n",
       "       84]},\n",
       "     '2': {'25%': 91.0,\n",
       "      '50%': 102.5,\n",
       "      '75%': 115.25,\n",
       "      'max': 186.0,\n",
       "      'min': 54.0,\n",
       "      'std': 23.01599545285018,\n",
       "      '4.5%': 71.67,\n",
       "      'mean': 104.48780487804878,\n",
       "      '95.5%': 145.0,\n",
       "      'count': 164.0,\n",
       "      'median': 102.5,\n",
       "      'outlier': [69, 55, 54, 54, 62, 60, 65, 71, 182, 186, 174, 184, 165]},\n",
       "     '3': {'25%': 144.5,\n",
       "      '50%': 167.0,\n",
       "      '75%': 204.0,\n",
       "      'max': 255.0,\n",
       "      'min': 87.0,\n",
       "      'std': 41.75193660457007,\n",
       "      '4.5%': 105.71000000000001,\n",
       "      'mean': 173.48979591836735,\n",
       "      '95.5%': 241.0,\n",
       "      'count': 147.0,\n",
       "      'median': 167.0,\n",
       "      'outlier': [104,\n",
       "       104,\n",
       "       103,\n",
       "       104,\n",
       "       100,\n",
       "       87,\n",
       "       87,\n",
       "       245,\n",
       "       246,\n",
       "       255,\n",
       "       244,\n",
       "       253,\n",
       "       252]},\n",
       "     '4': {'25%': 60.0,\n",
       "      '50%': 67.0,\n",
       "      '75%': 75.0,\n",
       "      'max': 100.0,\n",
       "      'min': 37.0,\n",
       "      'std': 12.364360855088252,\n",
       "      '4.5%': 43.58,\n",
       "      'mean': 66.64,\n",
       "      '95.5%': 85.0,\n",
       "      'count': 125.0,\n",
       "      'median': 67.0,\n",
       "      'outlier': [37, 41, 43, 37, 42, 41, 87, 100, 98, 94, 90]}},\n",
       "    'gen_0042_AccelerometerZminimum': {'1': {'25%': 172.0,\n",
       "      '50%': 183.0,\n",
       "      '75%': 199.0,\n",
       "      'max': 255.0,\n",
       "      'min': 87.0,\n",
       "      'std': 24.938581191800523,\n",
       "      '4.5%': 140.0,\n",
       "      'mean': 184.67521367521368,\n",
       "      '95.5%': 223.0,\n",
       "      'count': 468.0,\n",
       "      'median': 183.0,\n",
       "      'outlier': [135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       92,\n",
       "       87,\n",
       "       87,\n",
       "       227,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       233,\n",
       "       233,\n",
       "       233,\n",
       "       233,\n",
       "       233,\n",
       "       232,\n",
       "       255,\n",
       "       255,\n",
       "       226,\n",
       "       224,\n",
       "       228,\n",
       "       224]},\n",
       "     '2': {'25%': 71.0,\n",
       "      '50%': 75.0,\n",
       "      '75%': 86.0,\n",
       "      'max': 133.0,\n",
       "      'min': 58.0,\n",
       "      'std': 14.671713091716256,\n",
       "      '4.5%': 58.0,\n",
       "      'mean': 78.7439024390244,\n",
       "      '95.5%': 108.0,\n",
       "      'count': 164.0,\n",
       "      'median': 75.0,\n",
       "      'outlier': [120, 120, 109, 110, 133]},\n",
       "     '3': {'25%': 28.0,\n",
       "      '50%': 40.0,\n",
       "      '75%': 86.0,\n",
       "      'max': 137.0,\n",
       "      'min': 0.0,\n",
       "      'std': 42.25464735606209,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 56.7891156462585,\n",
       "      '95.5%': 126.0,\n",
       "      'count': 147.0,\n",
       "      'median': 40.0,\n",
       "      'outlier': [137, 137, 137, 137, 137]},\n",
       "     '4': {'25%': 156.0,\n",
       "      '50%': 162.0,\n",
       "      '75%': 168.0,\n",
       "      'max': 183.0,\n",
       "      'min': 118.0,\n",
       "      'std': 13.922513677377882,\n",
       "      '4.5%': 118.0,\n",
       "      'mean': 159.952,\n",
       "      '95.5%': 183.0,\n",
       "      'count': 125.0,\n",
       "      'median': 162.0,\n",
       "      'outlier': []}},\n",
       "    'gen_0045_AccelerometerZmaximum': {'1': {'25%': 43.0,\n",
       "      '50%': 72.0,\n",
       "      '75%': 100.0,\n",
       "      'max': 170.0,\n",
       "      'min': 0.0,\n",
       "      'std': 37.151168417361525,\n",
       "      '4.5%': 24.075000000000003,\n",
       "      'mean': 74.58333333333333,\n",
       "      '95.5%': 152.0,\n",
       "      'count': 468.0,\n",
       "      'median': 72.0,\n",
       "      'outlier': [15,\n",
       "       15,\n",
       "       15,\n",
       "       13,\n",
       "       17,\n",
       "       17,\n",
       "       17,\n",
       "       23,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       14,\n",
       "       17,\n",
       "       19,\n",
       "       24,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170]},\n",
       "     '2': {'25%': 117.0,\n",
       "      '50%': 125.0,\n",
       "      '75%': 128.0,\n",
       "      'max': 143.0,\n",
       "      'min': 95.0,\n",
       "      'std': 11.86630597184008,\n",
       "      '4.5%': 96.34,\n",
       "      'mean': 123.97560975609755,\n",
       "      '95.5%': 143.0,\n",
       "      'count': 164.0,\n",
       "      'median': 125.0,\n",
       "      'outlier': [95, 95, 95, 95, 95, 95, 95, 95]},\n",
       "     '3': {'25%': 110.0,\n",
       "      '50%': 195.0,\n",
       "      '75%': 217.0,\n",
       "      'max': 255.0,\n",
       "      'min': 55.0,\n",
       "      'std': 55.25627723347238,\n",
       "      '4.5%': 81.0,\n",
       "      'mean': 171.156462585034,\n",
       "      '95.5%': 255.0,\n",
       "      'count': 147.0,\n",
       "      'median': 195.0,\n",
       "      'outlier': [55, 55]},\n",
       "     '4': {'25%': 127.0,\n",
       "      '50%': 134.0,\n",
       "      '75%': 151.0,\n",
       "      'max': 195.0,\n",
       "      'min': 112.0,\n",
       "      'std': 19.6991075612495,\n",
       "      '4.5%': 117.0,\n",
       "      'mean': 139.24,\n",
       "      '95.5%': 172.0,\n",
       "      'count': 125.0,\n",
       "      'median': 134.0,\n",
       "      'outlier': [112, 112, 195, 195, 195, 195, 195]}},\n",
       "    'gen_0055_AccelerometerXVariance': {'1': {'25%': 5.0,\n",
       "      '50%': 11.0,\n",
       "      '75%': 21.25,\n",
       "      'max': 108.0,\n",
       "      'min': 0.0,\n",
       "      'std': 15.042842238719663,\n",
       "      '4.5%': 1.0,\n",
       "      'mean': 15.26923076923077,\n",
       "      '95.5%': 45.0,\n",
       "      'count': 468.0,\n",
       "      'median': 11.0,\n",
       "      'outlier': [0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       46,\n",
       "       48,\n",
       "       50,\n",
       "       61,\n",
       "       63,\n",
       "       61,\n",
       "       59,\n",
       "       51,\n",
       "       46,\n",
       "       49,\n",
       "       59,\n",
       "       68,\n",
       "       74,\n",
       "       79,\n",
       "       78,\n",
       "       77,\n",
       "       80,\n",
       "       79,\n",
       "       108,\n",
       "       71,\n",
       "       53]},\n",
       "     '2': {'25%': 34.75,\n",
       "      '50%': 44.5,\n",
       "      '75%': 55.0,\n",
       "      'max': 85.0,\n",
       "      'min': 14.0,\n",
       "      'std': 16.258042819080277,\n",
       "      '4.5%': 18.0,\n",
       "      'mean': 46.08536585365854,\n",
       "      '95.5%': 79.66499999999999,\n",
       "      'count': 164.0,\n",
       "      'median': 44.5,\n",
       "      'outlier': [16, 16, 16, 16, 14, 14, 15, 80, 85, 82, 80, 80, 85, 83, 80]},\n",
       "     '3': {'25%': 36.5,\n",
       "      '50%': 115.0,\n",
       "      '75%': 161.5,\n",
       "      'max': 255.0,\n",
       "      'min': 19.0,\n",
       "      'std': 63.32999809746763,\n",
       "      '4.5%': 24.14,\n",
       "      'mean': 104.4625850340136,\n",
       "      '95.5%': 191.87000000000006,\n",
       "      'count': 147.0,\n",
       "      'median': 115.0,\n",
       "      'outlier': [23,\n",
       "       20,\n",
       "       19,\n",
       "       23,\n",
       "       23,\n",
       "       21,\n",
       "       23,\n",
       "       199,\n",
       "       200,\n",
       "       197,\n",
       "       210,\n",
       "       231,\n",
       "       255,\n",
       "       226]},\n",
       "     '4': {'25%': 53.0,\n",
       "      '50%': 65.0,\n",
       "      '75%': 77.0,\n",
       "      'max': 98.0,\n",
       "      'min': 33.0,\n",
       "      'std': 15.921898087529557,\n",
       "      '4.5%': 38.58,\n",
       "      'mean': 64.904,\n",
       "      '95.5%': 91.42,\n",
       "      'count': 125.0,\n",
       "      'median': 65.0,\n",
       "      'outlier': [36, 38, 37, 33, 38, 38, 93, 93, 95, 97, 98, 92]}},\n",
       "    'gen_0057_AccelerometerZVariance': {'1': {'25%': 2.0,\n",
       "      '50%': 6.0,\n",
       "      '75%': 13.25,\n",
       "      'max': 52.0,\n",
       "      'min': 0.0,\n",
       "      'std': 7.893726208204827,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 8.683760683760683,\n",
       "      '95.5%': 24.0,\n",
       "      'count': 468.0,\n",
       "      'median': 6.0,\n",
       "      'outlier': [25,\n",
       "       25,\n",
       "       26,\n",
       "       28,\n",
       "       29,\n",
       "       29,\n",
       "       29,\n",
       "       29,\n",
       "       29,\n",
       "       29,\n",
       "       26,\n",
       "       25,\n",
       "       25,\n",
       "       34,\n",
       "       41,\n",
       "       52]},\n",
       "     '2': {'25%': 80.0,\n",
       "      '50%': 86.0,\n",
       "      '75%': 92.0,\n",
       "      'max': 134.0,\n",
       "      'min': 41.0,\n",
       "      'std': 14.537109324367204,\n",
       "      '4.5%': 55.335,\n",
       "      'mean': 84.5609756097561,\n",
       "      '95.5%': 105.66499999999999,\n",
       "      'count': 164.0,\n",
       "      'median': 86.0,\n",
       "      'outlier': [41,\n",
       "       42,\n",
       "       51,\n",
       "       53,\n",
       "       45,\n",
       "       51,\n",
       "       55,\n",
       "       43,\n",
       "       109,\n",
       "       127,\n",
       "       134,\n",
       "       121,\n",
       "       109,\n",
       "       106,\n",
       "       107,\n",
       "       109]},\n",
       "     '3': {'25%': 99.0,\n",
       "      '50%': 134.0,\n",
       "      '75%': 200.0,\n",
       "      'max': 255.0,\n",
       "      'min': 62.0,\n",
       "      'std': 54.45180671471167,\n",
       "      '4.5%': 71.0,\n",
       "      'mean': 144.63945578231292,\n",
       "      '95.5%': 229.0,\n",
       "      'count': 147.0,\n",
       "      'median': 134.0,\n",
       "      'outlier': [68, 69, 65, 64, 68, 62, 234, 246, 242, 253, 255, 230]},\n",
       "     '4': {'25%': 25.0,\n",
       "      '50%': 31.0,\n",
       "      '75%': 36.0,\n",
       "      'max': 59.0,\n",
       "      'min': 17.0,\n",
       "      'std': 9.309738224378364,\n",
       "      '4.5%': 19.0,\n",
       "      'mean': 31.488,\n",
       "      '95.5%': 52.0,\n",
       "      'count': 125.0,\n",
       "      'median': 31.0,\n",
       "      'outlier': [18, 18, 17, 17, 59, 57, 54, 53, 53]}},\n",
       "    'gen_0112_AccelerometerXAbsAreaAc': {'1': {'25%': 24.0,\n",
       "      '50%': 44.0,\n",
       "      '75%': 68.0,\n",
       "      'max': 161.0,\n",
       "      'min': 0.0,\n",
       "      'std': 32.64310545680496,\n",
       "      '4.5%': 4.0,\n",
       "      'mean': 48.60042735042735,\n",
       "      '95.5%': 118.0,\n",
       "      'count': 468.0,\n",
       "      'median': 44.0,\n",
       "      'outlier': [2,\n",
       "       1,\n",
       "       1,\n",
       "       2,\n",
       "       3,\n",
       "       3,\n",
       "       3,\n",
       "       3,\n",
       "       2,\n",
       "       3,\n",
       "       1,\n",
       "       0,\n",
       "       1,\n",
       "       0,\n",
       "       0,\n",
       "       1,\n",
       "       2,\n",
       "       119,\n",
       "       121,\n",
       "       125,\n",
       "       128,\n",
       "       127,\n",
       "       123,\n",
       "       127,\n",
       "       140,\n",
       "       140,\n",
       "       142,\n",
       "       131,\n",
       "       151,\n",
       "       157,\n",
       "       161,\n",
       "       160,\n",
       "       160,\n",
       "       149,\n",
       "       145,\n",
       "       137,\n",
       "       120]},\n",
       "     '2': {'25%': 73.0,\n",
       "      '50%': 82.0,\n",
       "      '75%': 90.0,\n",
       "      'max': 121.0,\n",
       "      'min': 56.0,\n",
       "      'std': 12.181872796870936,\n",
       "      '4.5%': 60.33500000000001,\n",
       "      'mean': 81.34146341463415,\n",
       "      '95.5%': 100.99499999999998,\n",
       "      'count': 164.0,\n",
       "      'median': 82.0,\n",
       "      'outlier': [57,\n",
       "       60,\n",
       "       56,\n",
       "       57,\n",
       "       58,\n",
       "       60,\n",
       "       58,\n",
       "       60,\n",
       "       106,\n",
       "       108,\n",
       "       105,\n",
       "       102,\n",
       "       103,\n",
       "       119,\n",
       "       121,\n",
       "       102]},\n",
       "     '3': {'25%': 138.5,\n",
       "      '50%': 158.0,\n",
       "      '75%': 172.0,\n",
       "      'max': 255.0,\n",
       "      'min': 63.0,\n",
       "      'std': 36.784406147774014,\n",
       "      '4.5%': 73.57,\n",
       "      'mean': 154.59183673469389,\n",
       "      '95.5%': 210.29000000000002,\n",
       "      'count': 147.0,\n",
       "      'median': 158.0,\n",
       "      'outlier': [68,\n",
       "       65,\n",
       "       69,\n",
       "       67,\n",
       "       73,\n",
       "       63,\n",
       "       67,\n",
       "       225,\n",
       "       212,\n",
       "       242,\n",
       "       255,\n",
       "       229,\n",
       "       229,\n",
       "       221]},\n",
       "     '4': {'25%': 114.0,\n",
       "      '50%': 139.0,\n",
       "      '75%': 153.0,\n",
       "      'max': 189.0,\n",
       "      'min': 72.0,\n",
       "      'std': 24.016870414692445,\n",
       "      '4.5%': 98.58000000000001,\n",
       "      'mean': 134.696,\n",
       "      '95.5%': 170.42000000000002,\n",
       "      'count': 125.0,\n",
       "      'median': 139.0,\n",
       "      'outlier': [72, 79, 85, 89, 98, 98, 176, 175, 184, 189, 172, 171]}},\n",
       "    'gen_0114_AccelerometerZAbsAreaAc': {'1': {'25%': 26.0,\n",
       "      '50%': 44.0,\n",
       "      '75%': 64.0,\n",
       "      'max': 182.0,\n",
       "      'min': 0.0,\n",
       "      'std': 32.641085377485936,\n",
       "      '4.5%': 7.0,\n",
       "      'mean': 48.47435897435897,\n",
       "      '95.5%': 104.0,\n",
       "      'count': 468.0,\n",
       "      'median': 44.0,\n",
       "      'outlier': [5,\n",
       "       4,\n",
       "       6,\n",
       "       6,\n",
       "       6,\n",
       "       6,\n",
       "       6,\n",
       "       4,\n",
       "       4,\n",
       "       3,\n",
       "       0,\n",
       "       3,\n",
       "       6,\n",
       "       5,\n",
       "       105,\n",
       "       109,\n",
       "       108,\n",
       "       106,\n",
       "       112,\n",
       "       118,\n",
       "       125,\n",
       "       152,\n",
       "       173,\n",
       "       172,\n",
       "       174,\n",
       "       169,\n",
       "       167,\n",
       "       179,\n",
       "       181,\n",
       "       178,\n",
       "       182,\n",
       "       169,\n",
       "       164,\n",
       "       154,\n",
       "       106]},\n",
       "     '2': {'25%': 72.0,\n",
       "      '50%': 78.0,\n",
       "      '75%': 91.0,\n",
       "      'max': 112.0,\n",
       "      'min': 57.0,\n",
       "      'std': 13.324611217703419,\n",
       "      '4.5%': 61.335,\n",
       "      'mean': 80.84146341463415,\n",
       "      '95.5%': 105.0,\n",
       "      'count': 164.0,\n",
       "      'median': 78.0,\n",
       "      'outlier': [60,\n",
       "       59,\n",
       "       60,\n",
       "       61,\n",
       "       58,\n",
       "       57,\n",
       "       60,\n",
       "       61,\n",
       "       110,\n",
       "       112,\n",
       "       106,\n",
       "       107,\n",
       "       110,\n",
       "       108,\n",
       "       106]},\n",
       "     '3': {'25%': 107.0,\n",
       "      '50%': 136.0,\n",
       "      '75%': 163.0,\n",
       "      'max': 255.0,\n",
       "      'min': 64.0,\n",
       "      'std': 43.77950487454055,\n",
       "      '4.5%': 69.71,\n",
       "      'mean': 139.25850340136054,\n",
       "      '95.5%': 226.8800000000001,\n",
       "      'count': 147.0,\n",
       "      'median': 136.0,\n",
       "      'outlier': [68,\n",
       "       65,\n",
       "       64,\n",
       "       66,\n",
       "       66,\n",
       "       68,\n",
       "       65,\n",
       "       240,\n",
       "       241,\n",
       "       236,\n",
       "       243,\n",
       "       244,\n",
       "       255,\n",
       "       243]},\n",
       "     '4': {'25%': 143.0,\n",
       "      '50%': 150.0,\n",
       "      '75%': 159.0,\n",
       "      'max': 205.0,\n",
       "      'min': 120.0,\n",
       "      'std': 17.069145905272585,\n",
       "      '4.5%': 124.58000000000001,\n",
       "      'mean': 151.848,\n",
       "      '95.5%': 188.42000000000002,\n",
       "      'count': 125.0,\n",
       "      'median': 150.0,\n",
       "      'outlier': [120,\n",
       "       124,\n",
       "       121,\n",
       "       124,\n",
       "       124,\n",
       "       123,\n",
       "       189,\n",
       "       192,\n",
       "       191,\n",
       "       204,\n",
       "       201,\n",
       "       205]}},\n",
       "    'gen_0129_AccelerometerZSigmaCrossingRate': {'1': {'25%': 0.0,\n",
       "      '50%': 0.0,\n",
       "      '75%': 0.0,\n",
       "      'max': 146.0,\n",
       "      'min': 0.0,\n",
       "      'std': 8.935379478454335,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 0.5833333333333334,\n",
       "      '95.5%': 0.0,\n",
       "      'count': 468.0,\n",
       "      'median': 0.0,\n",
       "      'outlier': [127, 146]},\n",
       "     '2': {'25%': 36.0,\n",
       "      '50%': 45.5,\n",
       "      '75%': 73.0,\n",
       "      'max': 146.0,\n",
       "      'min': 0.0,\n",
       "      'std': 31.953455296755713,\n",
       "      '4.5%': 36.0,\n",
       "      'mean': 59.38414634146341,\n",
       "      '95.5%': 146.0,\n",
       "      'count': 164.0,\n",
       "      'median': 45.5,\n",
       "      'outlier': [0, 0, 0, 18, 18, 18]},\n",
       "     '3': {'25%': 109.0,\n",
       "      '50%': 146.0,\n",
       "      '75%': 164.0,\n",
       "      'max': 255.0,\n",
       "      'min': 0.0,\n",
       "      'std': 50.252193172506516,\n",
       "      '4.5%': 36.0,\n",
       "      'mean': 131.6530612244898,\n",
       "      '95.5%': 200.0,\n",
       "      'count': 147.0,\n",
       "      'median': 146.0,\n",
       "      'outlier': [0, 0, 0, 0, 0, 219, 237, 219, 237, 255, 237]},\n",
       "     '4': {'25%': 0.0,\n",
       "      '50%': 0.0,\n",
       "      '75%': 0.0,\n",
       "      'max': 0.0,\n",
       "      'min': 0.0,\n",
       "      'std': 0.0,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 0.0,\n",
       "      '95.5%': 0.0,\n",
       "      'count': 125.0,\n",
       "      'median': 0.0,\n",
       "      'outlier': []}},\n",
       "    'gen_0132_AccelerometerZ2ndSigmaCrossingRate': {'1': {'25%': 0.0,\n",
       "      '50%': 0.0,\n",
       "      '75%': 0.0,\n",
       "      'max': 11.0,\n",
       "      'min': 0.0,\n",
       "      'std': 0.8788169448374076,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 0.07051282051282051,\n",
       "      '95.5%': 0.0,\n",
       "      'count': 468.0,\n",
       "      'median': 0.0,\n",
       "      'outlier': [11, 11, 11]},\n",
       "     '2': {'25%': 33.0,\n",
       "      '50%': 55.0,\n",
       "      '75%': 67.0,\n",
       "      'max': 166.0,\n",
       "      'min': 11.0,\n",
       "      'std': 31.32312423157235,\n",
       "      '4.5%': 22.0,\n",
       "      'mean': 57.68292682926829,\n",
       "      '95.5%': 118.31499999999991,\n",
       "      'count': 164.0,\n",
       "      'median': 55.0,\n",
       "      'outlier': [11, 11, 155, 133, 122, 133, 144, 166, 122, 122]},\n",
       "     '3': {'25%': 67.0,\n",
       "      '50%': 89.0,\n",
       "      '75%': 122.0,\n",
       "      'max': 255.0,\n",
       "      'min': 33.0,\n",
       "      'std': 47.94128868275086,\n",
       "      '4.5%': 44.0,\n",
       "      'mean': 98.35374149659864,\n",
       "      '95.5%': 215.73000000000008,\n",
       "      'count': 147.0,\n",
       "      'median': 89.0,\n",
       "      'outlier': [33, 33, 255, 222, 222, 233, 244, 244, 233]},\n",
       "     '4': {'25%': 0.0,\n",
       "      '50%': 0.0,\n",
       "      '75%': 0.0,\n",
       "      'max': 111.0,\n",
       "      'min': 0.0,\n",
       "      'std': 30.64595537171523,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 11.104,\n",
       "      '95.5%': 111.0,\n",
       "      'count': 125.0,\n",
       "      'median': 0.0,\n",
       "      'outlier': []}},\n",
       "    'gen_0016_AccelerometerXLinearRegressionStdErr_0003': {'1': {'25%': 19.75,\n",
       "      '50%': 34.0,\n",
       "      '75%': 48.0,\n",
       "      'max': 255.0,\n",
       "      'min': 0.0,\n",
       "      'std': 24.63178421563417,\n",
       "      '4.5%': 6.0,\n",
       "      'mean': 36.401709401709404,\n",
       "      '95.5%': 74.0,\n",
       "      'count': 468.0,\n",
       "      'median': 34.0,\n",
       "      'outlier': [5,\n",
       "       5,\n",
       "       5,\n",
       "       5,\n",
       "       3,\n",
       "       2,\n",
       "       1,\n",
       "       0,\n",
       "       2,\n",
       "       4,\n",
       "       5,\n",
       "       5,\n",
       "       2,\n",
       "       1,\n",
       "       1,\n",
       "       2,\n",
       "       5,\n",
       "       5,\n",
       "       2,\n",
       "       5,\n",
       "       75,\n",
       "       78,\n",
       "       79,\n",
       "       77,\n",
       "       80,\n",
       "       90,\n",
       "       91,\n",
       "       94,\n",
       "       102,\n",
       "       107,\n",
       "       108,\n",
       "       111,\n",
       "       113,\n",
       "       114,\n",
       "       112,\n",
       "       101,\n",
       "       255,\n",
       "       167,\n",
       "       100,\n",
       "       94]},\n",
       "     '2': {'25%': 74.75,\n",
       "      '50%': 84.5,\n",
       "      '75%': 98.0,\n",
       "      'max': 123.0,\n",
       "      'min': 46.0,\n",
       "      'std': 16.74346315080033,\n",
       "      '4.5%': 53.0,\n",
       "      'mean': 85.0,\n",
       "      '95.5%': 110.0,\n",
       "      'count': 164.0,\n",
       "      'median': 84.5,\n",
       "      'outlier': [46, 52, 52, 50, 48, 49, 50, 115, 111, 111, 114, 123, 123]},\n",
       "     '3': {'25%': 83.0,\n",
       "      '50%': 127.0,\n",
       "      '75%': 167.0,\n",
       "      'max': 200.0,\n",
       "      'min': 55.0,\n",
       "      'std': 46.10665557573507,\n",
       "      '4.5%': 61.0,\n",
       "      'mean': 125.5578231292517,\n",
       "      '95.5%': 192.43,\n",
       "      'count': 147.0,\n",
       "      'median': 127.0,\n",
       "      'outlier': [58, 57, 59, 55, 55, 60, 197, 196, 199, 193, 200, 196, 195]},\n",
       "     '4': {'25%': 93.0,\n",
       "      '50%': 109.0,\n",
       "      '75%': 116.0,\n",
       "      'max': 139.0,\n",
       "      'min': 78.0,\n",
       "      'std': 15.850254907892216,\n",
       "      '4.5%': 82.58000000000001,\n",
       "      'mean': 107.208,\n",
       "      '95.5%': 131.84,\n",
       "      'count': 125.0,\n",
       "      'median': 109.0,\n",
       "      'outlier': [79, 78, 82, 80, 79, 80, 139, 133, 136, 134, 139, 133]}},\n",
       "    'gen_0018_AccelerometerZLinearRegressionStdErr_0003': {'1': {'25%': 13.0,\n",
       "      '50%': 25.0,\n",
       "      '75%': 38.25,\n",
       "      'max': 214.0,\n",
       "      'min': 0.0,\n",
       "      'std': 20.978368923742263,\n",
       "      '4.5%': 4.0,\n",
       "      'mean': 28.566239316239315,\n",
       "      '95.5%': 65.0,\n",
       "      'count': 468.0,\n",
       "      'median': 25.0,\n",
       "      'outlier': [2,\n",
       "       1,\n",
       "       0,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       3,\n",
       "       3,\n",
       "       3,\n",
       "       72,\n",
       "       73,\n",
       "       70,\n",
       "       73,\n",
       "       79,\n",
       "       81,\n",
       "       81,\n",
       "       67,\n",
       "       66,\n",
       "       66,\n",
       "       70,\n",
       "       72,\n",
       "       73,\n",
       "       73,\n",
       "       67,\n",
       "       66,\n",
       "       214,\n",
       "       130,\n",
       "       67,\n",
       "       86,\n",
       "       87]},\n",
       "     '2': {'25%': 125.0,\n",
       "      '50%': 135.0,\n",
       "      '75%': 141.5,\n",
       "      'max': 182.0,\n",
       "      'min': 57.0,\n",
       "      'std': 20.627845388862173,\n",
       "      '4.5%': 85.33500000000001,\n",
       "      'mean': 130.41463414634146,\n",
       "      '95.5%': 159.0,\n",
       "      'count': 164.0,\n",
       "      'median': 135.0,\n",
       "      'outlier': [85,\n",
       "       57,\n",
       "       61,\n",
       "       84,\n",
       "       81,\n",
       "       80,\n",
       "       83,\n",
       "       83,\n",
       "       160,\n",
       "       179,\n",
       "       182,\n",
       "       167,\n",
       "       160,\n",
       "       160,\n",
       "       165]},\n",
       "     '3': {'25%': 153.0,\n",
       "      '50%': 177.0,\n",
       "      '75%': 221.5,\n",
       "      'max': 255.0,\n",
       "      'min': 123.0,\n",
       "      'std': 38.539141133533434,\n",
       "      '4.5%': 127.0,\n",
       "      'mean': 182.82993197278913,\n",
       "      '95.5%': 242.0,\n",
       "      'count': 147.0,\n",
       "      'median': 177.0,\n",
       "      'outlier': [124, 126, 123, 123, 126, 124, 243, 252, 249, 255, 255, 243]},\n",
       "     '4': {'25%': 69.0,\n",
       "      '50%': 76.0,\n",
       "      '75%': 87.0,\n",
       "      'max': 119.0,\n",
       "      'min': 41.0,\n",
       "      'std': 15.487714173582255,\n",
       "      '4.5%': 55.16,\n",
       "      'mean': 78.392,\n",
       "      '95.5%': 111.0,\n",
       "      'count': 125.0,\n",
       "      'median': 76.0,\n",
       "      'outlier': [53, 50, 41, 54, 54, 51, 119, 117, 113, 113, 112]}}},\n",
       "   'validation': {'gen_0021_AccelerometerZStd': {'1': {'25%': 17.0,\n",
       "      '50%': 34.0,\n",
       "      '75%': 53.0,\n",
       "      'max': 111.0,\n",
       "      'min': 0.0,\n",
       "      'std': 21.6815181457331,\n",
       "      '4.5%': 5.015000000000001,\n",
       "      'mean': 35.93803418803419,\n",
       "      '95.5%': 73.0,\n",
       "      'count': 468.0,\n",
       "      'median': 34.0,\n",
       "      'outlier': [5,\n",
       "       5,\n",
       "       4,\n",
       "       3,\n",
       "       2,\n",
       "       0,\n",
       "       5,\n",
       "       4,\n",
       "       4,\n",
       "       3,\n",
       "       5,\n",
       "       5,\n",
       "       5,\n",
       "       5,\n",
       "       4,\n",
       "       4,\n",
       "       2,\n",
       "       2,\n",
       "       1,\n",
       "       1,\n",
       "       4,\n",
       "       5,\n",
       "       74,\n",
       "       74,\n",
       "       76,\n",
       "       80,\n",
       "       81,\n",
       "       81,\n",
       "       81,\n",
       "       80,\n",
       "       81,\n",
       "       81,\n",
       "       76,\n",
       "       75,\n",
       "       74,\n",
       "       88,\n",
       "       97,\n",
       "       111]},\n",
       "     '2': {'25%': 139.0,\n",
       "      '50%': 145.0,\n",
       "      '75%': 150.0,\n",
       "      'max': 182.0,\n",
       "      'min': 98.0,\n",
       "      'std': 13.565634806492739,\n",
       "      '4.5%': 114.33500000000001,\n",
       "      'mean': 142.8719512195122,\n",
       "      '95.5%': 161.665,\n",
       "      'count': 164.0,\n",
       "      'median': 145.0,\n",
       "      'outlier': [98,\n",
       "       99,\n",
       "       109,\n",
       "       112,\n",
       "       103,\n",
       "       110,\n",
       "       114,\n",
       "       100,\n",
       "       164,\n",
       "       178,\n",
       "       182,\n",
       "       173,\n",
       "       164,\n",
       "       162,\n",
       "       163,\n",
       "       164]},\n",
       "     '3': {'25%': 156.0,\n",
       "      '50%': 183.0,\n",
       "      '75%': 225.0,\n",
       "      'max': 255.0,\n",
       "      'min': 122.0,\n",
       "      'std': 37.40013758902921,\n",
       "      '4.5%': 131.0,\n",
       "      'mean': 186.54421768707482,\n",
       "      '95.5%': 241.0,\n",
       "      'count': 147.0,\n",
       "      'median': 183.0,\n",
       "      'outlier': [128, 129, 125, 123, 127, 122, 244, 250, 248, 254, 255, 242]},\n",
       "     '4': {'25%': 75.0,\n",
       "      '50%': 84.0,\n",
       "      '75%': 90.0,\n",
       "      'max': 118.0,\n",
       "      'min': 60.0,\n",
       "      'std': 13.090587606023181,\n",
       "      '4.5%': 64.0,\n",
       "      'mean': 83.632,\n",
       "      '95.5%': 111.0,\n",
       "      'count': 125.0,\n",
       "      'median': 84.0,\n",
       "      'outlier': [62, 62, 60, 60, 63, 118, 116, 113, 112, 112]}},\n",
       "    'gen_0030_AccelerometerZIQR': {'1': {'25%': 9.0,\n",
       "      '50%': 21.0,\n",
       "      '75%': 44.25,\n",
       "      'max': 84.0,\n",
       "      'min': 0.0,\n",
       "      'std': 22.068238107815887,\n",
       "      '4.5%': 2.0,\n",
       "      'mean': 27.664529914529915,\n",
       "      '95.5%': 71.98499999999996,\n",
       "      'count': 468.0,\n",
       "      'median': 21.0,\n",
       "      'outlier': [1,\n",
       "       1,\n",
       "       1,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       76,\n",
       "       75,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       73,\n",
       "       74,\n",
       "       73,\n",
       "       74,\n",
       "       74,\n",
       "       74,\n",
       "       75,\n",
       "       75,\n",
       "       75,\n",
       "       73,\n",
       "       72,\n",
       "       84]},\n",
       "     '2': {'25%': 91.0,\n",
       "      '50%': 102.5,\n",
       "      '75%': 115.25,\n",
       "      'max': 186.0,\n",
       "      'min': 54.0,\n",
       "      'std': 23.01599545285018,\n",
       "      '4.5%': 71.67,\n",
       "      'mean': 104.48780487804878,\n",
       "      '95.5%': 145.0,\n",
       "      'count': 164.0,\n",
       "      'median': 102.5,\n",
       "      'outlier': [69, 55, 54, 54, 62, 60, 65, 71, 182, 186, 174, 184, 165]},\n",
       "     '3': {'25%': 144.5,\n",
       "      '50%': 167.0,\n",
       "      '75%': 204.0,\n",
       "      'max': 255.0,\n",
       "      'min': 87.0,\n",
       "      'std': 41.75193660457007,\n",
       "      '4.5%': 105.71000000000001,\n",
       "      'mean': 173.48979591836735,\n",
       "      '95.5%': 241.0,\n",
       "      'count': 147.0,\n",
       "      'median': 167.0,\n",
       "      'outlier': [104,\n",
       "       104,\n",
       "       103,\n",
       "       104,\n",
       "       100,\n",
       "       87,\n",
       "       87,\n",
       "       245,\n",
       "       246,\n",
       "       255,\n",
       "       244,\n",
       "       253,\n",
       "       252]},\n",
       "     '4': {'25%': 60.0,\n",
       "      '50%': 67.0,\n",
       "      '75%': 75.0,\n",
       "      'max': 100.0,\n",
       "      'min': 37.0,\n",
       "      'std': 12.364360855088252,\n",
       "      '4.5%': 43.58,\n",
       "      'mean': 66.64,\n",
       "      '95.5%': 85.0,\n",
       "      'count': 125.0,\n",
       "      'median': 67.0,\n",
       "      'outlier': [37, 41, 43, 37, 42, 41, 87, 100, 98, 94, 90]}},\n",
       "    'gen_0042_AccelerometerZminimum': {'1': {'25%': 172.0,\n",
       "      '50%': 183.0,\n",
       "      '75%': 199.0,\n",
       "      'max': 255.0,\n",
       "      'min': 87.0,\n",
       "      'std': 24.938581191800523,\n",
       "      '4.5%': 140.0,\n",
       "      'mean': 184.67521367521368,\n",
       "      '95.5%': 223.0,\n",
       "      'count': 468.0,\n",
       "      'median': 183.0,\n",
       "      'outlier': [135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       135,\n",
       "       92,\n",
       "       87,\n",
       "       87,\n",
       "       227,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       236,\n",
       "       233,\n",
       "       233,\n",
       "       233,\n",
       "       233,\n",
       "       233,\n",
       "       232,\n",
       "       255,\n",
       "       255,\n",
       "       226,\n",
       "       224,\n",
       "       228,\n",
       "       224]},\n",
       "     '2': {'25%': 71.0,\n",
       "      '50%': 75.0,\n",
       "      '75%': 86.0,\n",
       "      'max': 133.0,\n",
       "      'min': 58.0,\n",
       "      'std': 14.671713091716256,\n",
       "      '4.5%': 58.0,\n",
       "      'mean': 78.7439024390244,\n",
       "      '95.5%': 108.0,\n",
       "      'count': 164.0,\n",
       "      'median': 75.0,\n",
       "      'outlier': [120, 120, 109, 110, 133]},\n",
       "     '3': {'25%': 28.0,\n",
       "      '50%': 40.0,\n",
       "      '75%': 86.0,\n",
       "      'max': 137.0,\n",
       "      'min': 0.0,\n",
       "      'std': 42.25464735606209,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 56.7891156462585,\n",
       "      '95.5%': 126.0,\n",
       "      'count': 147.0,\n",
       "      'median': 40.0,\n",
       "      'outlier': [137, 137, 137, 137, 137]},\n",
       "     '4': {'25%': 156.0,\n",
       "      '50%': 162.0,\n",
       "      '75%': 168.0,\n",
       "      'max': 183.0,\n",
       "      'min': 118.0,\n",
       "      'std': 13.922513677377882,\n",
       "      '4.5%': 118.0,\n",
       "      'mean': 159.952,\n",
       "      '95.5%': 183.0,\n",
       "      'count': 125.0,\n",
       "      'median': 162.0,\n",
       "      'outlier': []}},\n",
       "    'gen_0045_AccelerometerZmaximum': {'1': {'25%': 43.0,\n",
       "      '50%': 72.0,\n",
       "      '75%': 100.0,\n",
       "      'max': 170.0,\n",
       "      'min': 0.0,\n",
       "      'std': 37.151168417361525,\n",
       "      '4.5%': 24.075000000000003,\n",
       "      'mean': 74.58333333333333,\n",
       "      '95.5%': 152.0,\n",
       "      'count': 468.0,\n",
       "      'median': 72.0,\n",
       "      'outlier': [15,\n",
       "       15,\n",
       "       15,\n",
       "       13,\n",
       "       17,\n",
       "       17,\n",
       "       17,\n",
       "       23,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       14,\n",
       "       17,\n",
       "       19,\n",
       "       24,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       22,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170,\n",
       "       170]},\n",
       "     '2': {'25%': 117.0,\n",
       "      '50%': 125.0,\n",
       "      '75%': 128.0,\n",
       "      'max': 143.0,\n",
       "      'min': 95.0,\n",
       "      'std': 11.86630597184008,\n",
       "      '4.5%': 96.34,\n",
       "      'mean': 123.97560975609755,\n",
       "      '95.5%': 143.0,\n",
       "      'count': 164.0,\n",
       "      'median': 125.0,\n",
       "      'outlier': [95, 95, 95, 95, 95, 95, 95, 95]},\n",
       "     '3': {'25%': 110.0,\n",
       "      '50%': 195.0,\n",
       "      '75%': 217.0,\n",
       "      'max': 255.0,\n",
       "      'min': 55.0,\n",
       "      'std': 55.25627723347238,\n",
       "      '4.5%': 81.0,\n",
       "      'mean': 171.156462585034,\n",
       "      '95.5%': 255.0,\n",
       "      'count': 147.0,\n",
       "      'median': 195.0,\n",
       "      'outlier': [55, 55]},\n",
       "     '4': {'25%': 127.0,\n",
       "      '50%': 134.0,\n",
       "      '75%': 151.0,\n",
       "      'max': 195.0,\n",
       "      'min': 112.0,\n",
       "      'std': 19.6991075612495,\n",
       "      '4.5%': 117.0,\n",
       "      'mean': 139.24,\n",
       "      '95.5%': 172.0,\n",
       "      'count': 125.0,\n",
       "      'median': 134.0,\n",
       "      'outlier': [112, 112, 195, 195, 195, 195, 195]}},\n",
       "    'gen_0055_AccelerometerXVariance': {'1': {'25%': 5.0,\n",
       "      '50%': 11.0,\n",
       "      '75%': 21.25,\n",
       "      'max': 108.0,\n",
       "      'min': 0.0,\n",
       "      'std': 15.042842238719663,\n",
       "      '4.5%': 1.0,\n",
       "      'mean': 15.26923076923077,\n",
       "      '95.5%': 45.0,\n",
       "      'count': 468.0,\n",
       "      'median': 11.0,\n",
       "      'outlier': [0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       0,\n",
       "       46,\n",
       "       48,\n",
       "       50,\n",
       "       61,\n",
       "       63,\n",
       "       61,\n",
       "       59,\n",
       "       51,\n",
       "       46,\n",
       "       49,\n",
       "       59,\n",
       "       68,\n",
       "       74,\n",
       "       79,\n",
       "       78,\n",
       "       77,\n",
       "       80,\n",
       "       79,\n",
       "       108,\n",
       "       71,\n",
       "       53]},\n",
       "     '2': {'25%': 34.75,\n",
       "      '50%': 44.5,\n",
       "      '75%': 55.0,\n",
       "      'max': 85.0,\n",
       "      'min': 14.0,\n",
       "      'std': 16.258042819080277,\n",
       "      '4.5%': 18.0,\n",
       "      'mean': 46.08536585365854,\n",
       "      '95.5%': 79.66499999999999,\n",
       "      'count': 164.0,\n",
       "      'median': 44.5,\n",
       "      'outlier': [16, 16, 16, 16, 14, 14, 15, 80, 85, 82, 80, 80, 85, 83, 80]},\n",
       "     '3': {'25%': 36.5,\n",
       "      '50%': 115.0,\n",
       "      '75%': 161.5,\n",
       "      'max': 255.0,\n",
       "      'min': 19.0,\n",
       "      'std': 63.32999809746763,\n",
       "      '4.5%': 24.14,\n",
       "      'mean': 104.4625850340136,\n",
       "      '95.5%': 191.87000000000006,\n",
       "      'count': 147.0,\n",
       "      'median': 115.0,\n",
       "      'outlier': [23,\n",
       "       20,\n",
       "       19,\n",
       "       23,\n",
       "       23,\n",
       "       21,\n",
       "       23,\n",
       "       199,\n",
       "       200,\n",
       "       197,\n",
       "       210,\n",
       "       231,\n",
       "       255,\n",
       "       226]},\n",
       "     '4': {'25%': 53.0,\n",
       "      '50%': 65.0,\n",
       "      '75%': 77.0,\n",
       "      'max': 98.0,\n",
       "      'min': 33.0,\n",
       "      'std': 15.921898087529557,\n",
       "      '4.5%': 38.58,\n",
       "      'mean': 64.904,\n",
       "      '95.5%': 91.42,\n",
       "      'count': 125.0,\n",
       "      'median': 65.0,\n",
       "      'outlier': [36, 38, 37, 33, 38, 38, 93, 93, 95, 97, 98, 92]}},\n",
       "    'gen_0057_AccelerometerZVariance': {'1': {'25%': 2.0,\n",
       "      '50%': 6.0,\n",
       "      '75%': 13.25,\n",
       "      'max': 52.0,\n",
       "      'min': 0.0,\n",
       "      'std': 7.893726208204827,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 8.683760683760683,\n",
       "      '95.5%': 24.0,\n",
       "      'count': 468.0,\n",
       "      'median': 6.0,\n",
       "      'outlier': [25,\n",
       "       25,\n",
       "       26,\n",
       "       28,\n",
       "       29,\n",
       "       29,\n",
       "       29,\n",
       "       29,\n",
       "       29,\n",
       "       29,\n",
       "       26,\n",
       "       25,\n",
       "       25,\n",
       "       34,\n",
       "       41,\n",
       "       52]},\n",
       "     '2': {'25%': 80.0,\n",
       "      '50%': 86.0,\n",
       "      '75%': 92.0,\n",
       "      'max': 134.0,\n",
       "      'min': 41.0,\n",
       "      'std': 14.537109324367204,\n",
       "      '4.5%': 55.335,\n",
       "      'mean': 84.5609756097561,\n",
       "      '95.5%': 105.66499999999999,\n",
       "      'count': 164.0,\n",
       "      'median': 86.0,\n",
       "      'outlier': [41,\n",
       "       42,\n",
       "       51,\n",
       "       53,\n",
       "       45,\n",
       "       51,\n",
       "       55,\n",
       "       43,\n",
       "       109,\n",
       "       127,\n",
       "       134,\n",
       "       121,\n",
       "       109,\n",
       "       106,\n",
       "       107,\n",
       "       109]},\n",
       "     '3': {'25%': 99.0,\n",
       "      '50%': 134.0,\n",
       "      '75%': 200.0,\n",
       "      'max': 255.0,\n",
       "      'min': 62.0,\n",
       "      'std': 54.45180671471167,\n",
       "      '4.5%': 71.0,\n",
       "      'mean': 144.63945578231292,\n",
       "      '95.5%': 229.0,\n",
       "      'count': 147.0,\n",
       "      'median': 134.0,\n",
       "      'outlier': [68, 69, 65, 64, 68, 62, 234, 246, 242, 253, 255, 230]},\n",
       "     '4': {'25%': 25.0,\n",
       "      '50%': 31.0,\n",
       "      '75%': 36.0,\n",
       "      'max': 59.0,\n",
       "      'min': 17.0,\n",
       "      'std': 9.309738224378364,\n",
       "      '4.5%': 19.0,\n",
       "      'mean': 31.488,\n",
       "      '95.5%': 52.0,\n",
       "      'count': 125.0,\n",
       "      'median': 31.0,\n",
       "      'outlier': [18, 18, 17, 17, 59, 57, 54, 53, 53]}},\n",
       "    'gen_0112_AccelerometerXAbsAreaAc': {'1': {'25%': 24.0,\n",
       "      '50%': 44.0,\n",
       "      '75%': 68.0,\n",
       "      'max': 161.0,\n",
       "      'min': 0.0,\n",
       "      'std': 32.64310545680496,\n",
       "      '4.5%': 4.0,\n",
       "      'mean': 48.60042735042735,\n",
       "      '95.5%': 118.0,\n",
       "      'count': 468.0,\n",
       "      'median': 44.0,\n",
       "      'outlier': [2,\n",
       "       1,\n",
       "       1,\n",
       "       2,\n",
       "       3,\n",
       "       3,\n",
       "       3,\n",
       "       3,\n",
       "       2,\n",
       "       3,\n",
       "       1,\n",
       "       0,\n",
       "       1,\n",
       "       0,\n",
       "       0,\n",
       "       1,\n",
       "       2,\n",
       "       119,\n",
       "       121,\n",
       "       125,\n",
       "       128,\n",
       "       127,\n",
       "       123,\n",
       "       127,\n",
       "       140,\n",
       "       140,\n",
       "       142,\n",
       "       131,\n",
       "       151,\n",
       "       157,\n",
       "       161,\n",
       "       160,\n",
       "       160,\n",
       "       149,\n",
       "       145,\n",
       "       137,\n",
       "       120]},\n",
       "     '2': {'25%': 73.0,\n",
       "      '50%': 82.0,\n",
       "      '75%': 90.0,\n",
       "      'max': 121.0,\n",
       "      'min': 56.0,\n",
       "      'std': 12.181872796870936,\n",
       "      '4.5%': 60.33500000000001,\n",
       "      'mean': 81.34146341463415,\n",
       "      '95.5%': 100.99499999999998,\n",
       "      'count': 164.0,\n",
       "      'median': 82.0,\n",
       "      'outlier': [57,\n",
       "       60,\n",
       "       56,\n",
       "       57,\n",
       "       58,\n",
       "       60,\n",
       "       58,\n",
       "       60,\n",
       "       106,\n",
       "       108,\n",
       "       105,\n",
       "       102,\n",
       "       103,\n",
       "       119,\n",
       "       121,\n",
       "       102]},\n",
       "     '3': {'25%': 138.5,\n",
       "      '50%': 158.0,\n",
       "      '75%': 172.0,\n",
       "      'max': 255.0,\n",
       "      'min': 63.0,\n",
       "      'std': 36.784406147774014,\n",
       "      '4.5%': 73.57,\n",
       "      'mean': 154.59183673469389,\n",
       "      '95.5%': 210.29000000000002,\n",
       "      'count': 147.0,\n",
       "      'median': 158.0,\n",
       "      'outlier': [68,\n",
       "       65,\n",
       "       69,\n",
       "       67,\n",
       "       73,\n",
       "       63,\n",
       "       67,\n",
       "       225,\n",
       "       212,\n",
       "       242,\n",
       "       255,\n",
       "       229,\n",
       "       229,\n",
       "       221]},\n",
       "     '4': {'25%': 114.0,\n",
       "      '50%': 139.0,\n",
       "      '75%': 153.0,\n",
       "      'max': 189.0,\n",
       "      'min': 72.0,\n",
       "      'std': 24.016870414692445,\n",
       "      '4.5%': 98.58000000000001,\n",
       "      'mean': 134.696,\n",
       "      '95.5%': 170.42000000000002,\n",
       "      'count': 125.0,\n",
       "      'median': 139.0,\n",
       "      'outlier': [72, 79, 85, 89, 98, 98, 176, 175, 184, 189, 172, 171]}},\n",
       "    'gen_0114_AccelerometerZAbsAreaAc': {'1': {'25%': 26.0,\n",
       "      '50%': 44.0,\n",
       "      '75%': 64.0,\n",
       "      'max': 182.0,\n",
       "      'min': 0.0,\n",
       "      'std': 32.641085377485936,\n",
       "      '4.5%': 7.0,\n",
       "      'mean': 48.47435897435897,\n",
       "      '95.5%': 104.0,\n",
       "      'count': 468.0,\n",
       "      'median': 44.0,\n",
       "      'outlier': [5,\n",
       "       4,\n",
       "       6,\n",
       "       6,\n",
       "       6,\n",
       "       6,\n",
       "       6,\n",
       "       4,\n",
       "       4,\n",
       "       3,\n",
       "       0,\n",
       "       3,\n",
       "       6,\n",
       "       5,\n",
       "       105,\n",
       "       109,\n",
       "       108,\n",
       "       106,\n",
       "       112,\n",
       "       118,\n",
       "       125,\n",
       "       152,\n",
       "       173,\n",
       "       172,\n",
       "       174,\n",
       "       169,\n",
       "       167,\n",
       "       179,\n",
       "       181,\n",
       "       178,\n",
       "       182,\n",
       "       169,\n",
       "       164,\n",
       "       154,\n",
       "       106]},\n",
       "     '2': {'25%': 72.0,\n",
       "      '50%': 78.0,\n",
       "      '75%': 91.0,\n",
       "      'max': 112.0,\n",
       "      'min': 57.0,\n",
       "      'std': 13.324611217703419,\n",
       "      '4.5%': 61.335,\n",
       "      'mean': 80.84146341463415,\n",
       "      '95.5%': 105.0,\n",
       "      'count': 164.0,\n",
       "      'median': 78.0,\n",
       "      'outlier': [60,\n",
       "       59,\n",
       "       60,\n",
       "       61,\n",
       "       58,\n",
       "       57,\n",
       "       60,\n",
       "       61,\n",
       "       110,\n",
       "       112,\n",
       "       106,\n",
       "       107,\n",
       "       110,\n",
       "       108,\n",
       "       106]},\n",
       "     '3': {'25%': 107.0,\n",
       "      '50%': 136.0,\n",
       "      '75%': 163.0,\n",
       "      'max': 255.0,\n",
       "      'min': 64.0,\n",
       "      'std': 43.77950487454055,\n",
       "      '4.5%': 69.71,\n",
       "      'mean': 139.25850340136054,\n",
       "      '95.5%': 226.8800000000001,\n",
       "      'count': 147.0,\n",
       "      'median': 136.0,\n",
       "      'outlier': [68,\n",
       "       65,\n",
       "       64,\n",
       "       66,\n",
       "       66,\n",
       "       68,\n",
       "       65,\n",
       "       240,\n",
       "       241,\n",
       "       236,\n",
       "       243,\n",
       "       244,\n",
       "       255,\n",
       "       243]},\n",
       "     '4': {'25%': 143.0,\n",
       "      '50%': 150.0,\n",
       "      '75%': 159.0,\n",
       "      'max': 205.0,\n",
       "      'min': 120.0,\n",
       "      'std': 17.069145905272585,\n",
       "      '4.5%': 124.58000000000001,\n",
       "      'mean': 151.848,\n",
       "      '95.5%': 188.42000000000002,\n",
       "      'count': 125.0,\n",
       "      'median': 150.0,\n",
       "      'outlier': [120,\n",
       "       124,\n",
       "       121,\n",
       "       124,\n",
       "       124,\n",
       "       123,\n",
       "       189,\n",
       "       192,\n",
       "       191,\n",
       "       204,\n",
       "       201,\n",
       "       205]}},\n",
       "    'gen_0129_AccelerometerZSigmaCrossingRate': {'1': {'25%': 0.0,\n",
       "      '50%': 0.0,\n",
       "      '75%': 0.0,\n",
       "      'max': 146.0,\n",
       "      'min': 0.0,\n",
       "      'std': 8.935379478454335,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 0.5833333333333334,\n",
       "      '95.5%': 0.0,\n",
       "      'count': 468.0,\n",
       "      'median': 0.0,\n",
       "      'outlier': [127, 146]},\n",
       "     '2': {'25%': 36.0,\n",
       "      '50%': 45.5,\n",
       "      '75%': 73.0,\n",
       "      'max': 146.0,\n",
       "      'min': 0.0,\n",
       "      'std': 31.953455296755713,\n",
       "      '4.5%': 36.0,\n",
       "      'mean': 59.38414634146341,\n",
       "      '95.5%': 146.0,\n",
       "      'count': 164.0,\n",
       "      'median': 45.5,\n",
       "      'outlier': [0, 0, 0, 18, 18, 18]},\n",
       "     '3': {'25%': 109.0,\n",
       "      '50%': 146.0,\n",
       "      '75%': 164.0,\n",
       "      'max': 255.0,\n",
       "      'min': 0.0,\n",
       "      'std': 50.252193172506516,\n",
       "      '4.5%': 36.0,\n",
       "      'mean': 131.6530612244898,\n",
       "      '95.5%': 200.0,\n",
       "      'count': 147.0,\n",
       "      'median': 146.0,\n",
       "      'outlier': [0, 0, 0, 0, 0, 219, 237, 219, 237, 255, 237]},\n",
       "     '4': {'25%': 0.0,\n",
       "      '50%': 0.0,\n",
       "      '75%': 0.0,\n",
       "      'max': 0.0,\n",
       "      'min': 0.0,\n",
       "      'std': 0.0,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 0.0,\n",
       "      '95.5%': 0.0,\n",
       "      'count': 125.0,\n",
       "      'median': 0.0,\n",
       "      'outlier': []}},\n",
       "    'gen_0132_AccelerometerZ2ndSigmaCrossingRate': {'1': {'25%': 0.0,\n",
       "      '50%': 0.0,\n",
       "      '75%': 0.0,\n",
       "      'max': 11.0,\n",
       "      'min': 0.0,\n",
       "      'std': 0.8788169448374076,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 0.07051282051282051,\n",
       "      '95.5%': 0.0,\n",
       "      'count': 468.0,\n",
       "      'median': 0.0,\n",
       "      'outlier': [11, 11, 11]},\n",
       "     '2': {'25%': 33.0,\n",
       "      '50%': 55.0,\n",
       "      '75%': 67.0,\n",
       "      'max': 166.0,\n",
       "      'min': 11.0,\n",
       "      'std': 31.32312423157235,\n",
       "      '4.5%': 22.0,\n",
       "      'mean': 57.68292682926829,\n",
       "      '95.5%': 118.31499999999991,\n",
       "      'count': 164.0,\n",
       "      'median': 55.0,\n",
       "      'outlier': [11, 11, 155, 133, 122, 133, 144, 166, 122, 122]},\n",
       "     '3': {'25%': 67.0,\n",
       "      '50%': 89.0,\n",
       "      '75%': 122.0,\n",
       "      'max': 255.0,\n",
       "      'min': 33.0,\n",
       "      'std': 47.94128868275086,\n",
       "      '4.5%': 44.0,\n",
       "      'mean': 98.35374149659864,\n",
       "      '95.5%': 215.73000000000008,\n",
       "      'count': 147.0,\n",
       "      'median': 89.0,\n",
       "      'outlier': [33, 33, 255, 222, 222, 233, 244, 244, 233]},\n",
       "     '4': {'25%': 0.0,\n",
       "      '50%': 0.0,\n",
       "      '75%': 0.0,\n",
       "      'max': 111.0,\n",
       "      'min': 0.0,\n",
       "      'std': 30.64595537171523,\n",
       "      '4.5%': 0.0,\n",
       "      'mean': 11.104,\n",
       "      '95.5%': 111.0,\n",
       "      'count': 125.0,\n",
       "      'median': 0.0,\n",
       "      'outlier': []}},\n",
       "    'gen_0016_AccelerometerXLinearRegressionStdErr_0003': {'1': {'25%': 19.75,\n",
       "      '50%': 34.0,\n",
       "      '75%': 48.0,\n",
       "      'max': 255.0,\n",
       "      'min': 0.0,\n",
       "      'std': 24.63178421563417,\n",
       "      '4.5%': 6.0,\n",
       "      'mean': 36.401709401709404,\n",
       "      '95.5%': 74.0,\n",
       "      'count': 468.0,\n",
       "      'median': 34.0,\n",
       "      'outlier': [5,\n",
       "       5,\n",
       "       5,\n",
       "       5,\n",
       "       3,\n",
       "       2,\n",
       "       1,\n",
       "       0,\n",
       "       2,\n",
       "       4,\n",
       "       5,\n",
       "       5,\n",
       "       2,\n",
       "       1,\n",
       "       1,\n",
       "       2,\n",
       "       5,\n",
       "       5,\n",
       "       2,\n",
       "       5,\n",
       "       75,\n",
       "       78,\n",
       "       79,\n",
       "       77,\n",
       "       80,\n",
       "       90,\n",
       "       91,\n",
       "       94,\n",
       "       102,\n",
       "       107,\n",
       "       108,\n",
       "       111,\n",
       "       113,\n",
       "       114,\n",
       "       112,\n",
       "       101,\n",
       "       255,\n",
       "       167,\n",
       "       100,\n",
       "       94]},\n",
       "     '2': {'25%': 74.75,\n",
       "      '50%': 84.5,\n",
       "      '75%': 98.0,\n",
       "      'max': 123.0,\n",
       "      'min': 46.0,\n",
       "      'std': 16.74346315080033,\n",
       "      '4.5%': 53.0,\n",
       "      'mean': 85.0,\n",
       "      '95.5%': 110.0,\n",
       "      'count': 164.0,\n",
       "      'median': 84.5,\n",
       "      'outlier': [46, 52, 52, 50, 48, 49, 50, 115, 111, 111, 114, 123, 123]},\n",
       "     '3': {'25%': 83.0,\n",
       "      '50%': 127.0,\n",
       "      '75%': 167.0,\n",
       "      'max': 200.0,\n",
       "      'min': 55.0,\n",
       "      'std': 46.10665557573507,\n",
       "      '4.5%': 61.0,\n",
       "      'mean': 125.5578231292517,\n",
       "      '95.5%': 192.43,\n",
       "      'count': 147.0,\n",
       "      'median': 127.0,\n",
       "      'outlier': [58, 57, 59, 55, 55, 60, 197, 196, 199, 193, 200, 196, 195]},\n",
       "     '4': {'25%': 93.0,\n",
       "      '50%': 109.0,\n",
       "      '75%': 116.0,\n",
       "      'max': 139.0,\n",
       "      'min': 78.0,\n",
       "      'std': 15.850254907892216,\n",
       "      '4.5%': 82.58000000000001,\n",
       "      'mean': 107.208,\n",
       "      '95.5%': 131.84,\n",
       "      'count': 125.0,\n",
       "      'median': 109.0,\n",
       "      'outlier': [79, 78, 82, 80, 79, 80, 139, 133, 136, 134, 139, 133]}},\n",
       "    'gen_0018_AccelerometerZLinearRegressionStdErr_0003': {'1': {'25%': 13.0,\n",
       "      '50%': 25.0,\n",
       "      '75%': 38.25,\n",
       "      'max': 214.0,\n",
       "      'min': 0.0,\n",
       "      'std': 20.978368923742263,\n",
       "      '4.5%': 4.0,\n",
       "      'mean': 28.566239316239315,\n",
       "      '95.5%': 65.0,\n",
       "      'count': 468.0,\n",
       "      'median': 25.0,\n",
       "      'outlier': [2,\n",
       "       1,\n",
       "       0,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       2,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       1,\n",
       "       3,\n",
       "       3,\n",
       "       3,\n",
       "       72,\n",
       "       73,\n",
       "       70,\n",
       "       73,\n",
       "       79,\n",
       "       81,\n",
       "       81,\n",
       "       67,\n",
       "       66,\n",
       "       66,\n",
       "       70,\n",
       "       72,\n",
       "       73,\n",
       "       73,\n",
       "       67,\n",
       "       66,\n",
       "       214,\n",
       "       130,\n",
       "       67,\n",
       "       86,\n",
       "       87]},\n",
       "     '2': {'25%': 125.0,\n",
       "      '50%': 135.0,\n",
       "      '75%': 141.5,\n",
       "      'max': 182.0,\n",
       "      'min': 57.0,\n",
       "      'std': 20.627845388862173,\n",
       "      '4.5%': 85.33500000000001,\n",
       "      'mean': 130.41463414634146,\n",
       "      '95.5%': 159.0,\n",
       "      'count': 164.0,\n",
       "      'median': 135.0,\n",
       "      'outlier': [85,\n",
       "       57,\n",
       "       61,\n",
       "       84,\n",
       "       81,\n",
       "       80,\n",
       "       83,\n",
       "       83,\n",
       "       160,\n",
       "       179,\n",
       "       182,\n",
       "       167,\n",
       "       160,\n",
       "       160,\n",
       "       165]},\n",
       "     '3': {'25%': 153.0,\n",
       "      '50%': 177.0,\n",
       "      '75%': 221.5,\n",
       "      'max': 255.0,\n",
       "      'min': 123.0,\n",
       "      'std': 38.539141133533434,\n",
       "      '4.5%': 127.0,\n",
       "      'mean': 182.82993197278913,\n",
       "      '95.5%': 242.0,\n",
       "      'count': 147.0,\n",
       "      'median': 177.0,\n",
       "      'outlier': [124, 126, 123, 123, 126, 124, 243, 252, 249, 255, 255, 243]},\n",
       "     '4': {'25%': 69.0,\n",
       "      '50%': 76.0,\n",
       "      '75%': 87.0,\n",
       "      'max': 119.0,\n",
       "      'min': 41.0,\n",
       "      'std': 15.487714173582255,\n",
       "      '4.5%': 55.16,\n",
       "      'mean': 78.392,\n",
       "      '95.5%': 111.0,\n",
       "      'count': 125.0,\n",
       "      'median': 76.0,\n",
       "      'outlier': [53, 50, 41, 54, 54, 51, 119, 117, 113, 113, 112]}}}}},\n",
       " 'pipeline_summary': [{'name': 'wand_10_movements.csv',\n",
       "   'type': 'featurefile',\n",
       "   'outputs': ['temp.raw'],\n",
       "   'data_columns': ['AccelerometerX', 'AccelerometerY', 'AccelerometerZ'],\n",
       "   'label_column': 'Label',\n",
       "   'group_columns': ['Label']},\n",
       "  {'name': 'Windowing',\n",
       "   'type': 'segmenter',\n",
       "   'inputs': {'delta': 25,\n",
       "    'input_data': 'temp.raw',\n",
       "    'train_delta': 25,\n",
       "    'window_size': 350,\n",
       "    'group_columns': ['Label'],\n",
       "    'return_segment_index': False},\n",
       "   'outputs': ['temp.Windowing0'],\n",
       "   'feature_table': None},\n",
       "  {'set': [{'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Mean'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'threshold': 100},\n",
       "     'function_name': 'Zero Crossings'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'threshold': 100},\n",
       "     'function_name': 'Positive Zero Crossings'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'threshold': 100},\n",
       "     'function_name': 'Negative Zero Crossings'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Median'},\n",
       "    {'inputs': {'columns': ['AccelerometerX']},\n",
       "     'function_name': 'Linear Regression Stats'},\n",
       "    {'inputs': {'columns': ['AccelerometerY']},\n",
       "     'function_name': 'Linear Regression Stats'},\n",
       "    {'inputs': {'columns': ['AccelerometerZ']},\n",
       "     'function_name': 'Linear Regression Stats'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Standard Deviation'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Skewness'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Kurtosis'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Interquartile Range'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': '25th Percentile'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': '75th Percentile'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': '100th Percentile'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Minimum'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Maximum'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Sum'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Absolute Sum'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Absolute Mean'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Variance'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'smoothing_factor': 5},\n",
       "     'function_name': 'Ratio of Peak to Peak of High Frequency between two halves'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'smoothing_factor': 5},\n",
       "     'function_name': 'Difference of Peak to Peak of High Frequency between two halves'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'center_ratio': 0.5},\n",
       "     'function_name': 'Shape Median Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'center_ratio': 0.5},\n",
       "     'function_name': 'Shape Absolute Median Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerY']},\n",
       "     'function_name': 'Two Column Median Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Median Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerY', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Median Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerY']},\n",
       "     'function_name': 'Two Column Peak Location Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Peak Location Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerY', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Peak Location Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Abs Max Column'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerY'],\n",
       "      'sample_frequency': 1},\n",
       "     'function_name': 'Cross Column Correlation'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerZ'],\n",
       "      'sample_frequency': 1},\n",
       "     'function_name': 'Cross Column Correlation'},\n",
       "    {'inputs': {'columns': ['AccelerometerY', 'AccelerometerZ'],\n",
       "      'sample_frequency': 1},\n",
       "     'function_name': 'Cross Column Correlation'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerY']},\n",
       "     'function_name': 'Cross Column Mean Crossing Rate'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerZ']},\n",
       "     'function_name': 'Cross Column Mean Crossing Rate'},\n",
       "    {'inputs': {'columns': ['AccelerometerY', 'AccelerometerZ']},\n",
       "     'function_name': 'Cross Column Mean Crossing Rate'},\n",
       "    {'inputs': {'offset': 0, 'columns': ['AccelerometerX', 'AccelerometerY']},\n",
       "     'function_name': 'Cross Column Mean Crossing with Offset'},\n",
       "    {'inputs': {'offset': 0, 'columns': ['AccelerometerX', 'AccelerometerZ']},\n",
       "     'function_name': 'Cross Column Mean Crossing with Offset'},\n",
       "    {'inputs': {'offset': 0, 'columns': ['AccelerometerY', 'AccelerometerZ']},\n",
       "     'function_name': 'Cross Column Mean Crossing with Offset'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Max Column'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Min Column'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerY']},\n",
       "     'function_name': 'Two Column Min Max Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Min Max Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerY', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Min Max Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerY']},\n",
       "     'function_name': 'Two Column Mean Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Mean Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerY', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Mean Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerY']},\n",
       "     'function_name': 'Two Column Peak To Peak Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Peak To Peak Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerY', 'AccelerometerZ']},\n",
       "     'function_name': 'Two Column Peak To Peak Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'sample_rate': 100},\n",
       "     'function_name': 'Total Area'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'sample_rate': 100},\n",
       "     'function_name': 'Absolute Area'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'sample_rate': 100,\n",
       "      'smoothing_factor': 5},\n",
       "     'function_name': 'Total Area of Low Frequency'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'sample_rate': 100,\n",
       "      'smoothing_factor': 5},\n",
       "     'function_name': 'Absolute Area of Low Frequency'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'sample_rate': 100,\n",
       "      'smoothing_factor': 5},\n",
       "     'function_name': 'Total Area of High Frequency'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'sample_rate': 100,\n",
       "      'smoothing_factor': 5},\n",
       "     'function_name': 'Absolute Area of High Frequency'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'sample_rate': 100},\n",
       "     'function_name': 'Absolute Area of Spectrum'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Mean Difference'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Mean Crossing Rate'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Zero Crossing Rate'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Sigma Crossing Rate'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ']},\n",
       "     'function_name': 'Second Sigma Crossing Rate'},\n",
       "    {'inputs': {'columns': ['AccelerometerX',\n",
       "       'AccelerometerY',\n",
       "       'AccelerometerZ'],\n",
       "      'threshold': 0},\n",
       "     'function_name': 'Threshold Crossing Rate'},\n",
       "    {'inputs': {'offset': 0,\n",
       "      'columns': ['AccelerometerX', 'AccelerometerY', 'AccelerometerZ'],\n",
       "      'threshold': 0},\n",
       "     'function_name': 'Threshold With Offset Crossing Rate'}],\n",
       "   'name': 'generator_set',\n",
       "   'type': 'generatorset',\n",
       "   'inputs': {'input_data': 'temp.Windowing0',\n",
       "    'group_columns': ['Label', 'SegmentID']},\n",
       "   'outputs': ['temp.generator_set0', 'temp.features.generator_set0']},\n",
       "  {'set': [{'inputs': {'number_of_features': 12},\n",
       "     'function_name': 'Tree-based Selection'}],\n",
       "   'name': 'selector_set',\n",
       "   'type': 'selectorset',\n",
       "   'inputs': {'input_data': 'temp.generator_set0',\n",
       "    'label_column': 'Label',\n",
       "    'cost_function': 'sum',\n",
       "    'feature_table': 'temp.features.generator_set0',\n",
       "    'remove_columns': [],\n",
       "    'number_of_features': 10,\n",
       "    'passthrough_columns': ['Label', 'SegmentID']},\n",
       "   'outputs': ['temp.selector_set0', 'temp.features.selector_set0'],\n",
       "   'refinement': {}},\n",
       "  {'name': 'Min Max Scale',\n",
       "   'type': 'transform',\n",
       "   'inputs': {'pad': 0,\n",
       "    'max_bound': 255,\n",
       "    'min_bound': 0,\n",
       "    'input_data': 'temp.selector_set0',\n",
       "    'passthrough_columns': ['Label', 'SegmentID'],\n",
       "    'feature_min_max_defaults': None,\n",
       "    'feature_min_max_parameters': {}},\n",
       "   'outputs': ['temp.Min_Max_Scale0', 'temp.features.Min_Max_Scale0'],\n",
       "   'feature_table': 'temp.features.selector_set0'},\n",
       "  {'name': 'tvo',\n",
       "   'type': 'tvo',\n",
       "   'outputs': ['temp.tvo0', 'temp.features.tvo0'],\n",
       "   'input_data': 'temp.Min_Max_Scale0',\n",
       "   'optimizers': [{'name': 'Load Model TF Micro',\n",
       "     'inputs': {'class_map': {'0': 1, 'O': 2, 'W': 3, 'Z': 4},\n",
       "      'threshold': 0.3,\n",
       "      'model_json': '{\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential_2\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 12], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"dense_6_input\"}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_6\", \"trainable\": true, \"batch_input_shape\": [null, 12], \"dtype\": \"float32\", \"units\": 12, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": {\"class_name\": \"L1\", \"config\": {\"l1\": 0.009999999776482582}}, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_7\", \"trainable\": true, \"batch_input_shape\": [null, 12], \"dtype\": \"float32\", \"units\": 8, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dropout\", \"config\": {\"name\": \"dropout_3\", \"trainable\": true, \"dtype\": \"float32\", \"rate\": 0.1, \"noise_shape\": null, \"seed\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_8\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 4, \"activation\": \"softmax\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.5.0\", \"backend\": \"tensorflow\"}',\n",
       "      'train_history': {'loss': [16.73763656616211,\n",
       "        7.985980987548828,\n",
       "        5.415872573852539,\n",
       "        4.1726484298706055,\n",
       "        3.686734199523926,\n",
       "        2.9432384967803955,\n",
       "        2.4733479022979736,\n",
       "        2.407106637954712,\n",
       "        1.953098177909851,\n",
       "        1.8775222301483154,\n",
       "        1.4452852010726929,\n",
       "        1.4148750305175781,\n",
       "        1.4645764827728271,\n",
       "        1.5282392501831055,\n",
       "        1.2575699090957642,\n",
       "        1.4214942455291748,\n",
       "        1.2633838653564453,\n",
       "        0.8789932131767273,\n",
       "        0.8635414242744446,\n",
       "        0.7890906929969788,\n",
       "        0.7575526833534241,\n",
       "        0.6719263195991516,\n",
       "        0.6663805842399597,\n",
       "        0.6712002158164978,\n",
       "        0.6693887114524841,\n",
       "        0.6214327812194824,\n",
       "        0.6876628398895264,\n",
       "        0.6113342642784119,\n",
       "        0.6148282885551453,\n",
       "        0.6436149477958679,\n",
       "        0.5979775190353394,\n",
       "        0.6062797904014587,\n",
       "        0.593529999256134,\n",
       "        0.580503523349762,\n",
       "        0.5624544620513916,\n",
       "        0.5843545794487,\n",
       "        0.5492016077041626,\n",
       "        0.5643273591995239,\n",
       "        0.5213674306869507,\n",
       "        0.5486764907836914,\n",
       "        0.5070511698722839,\n",
       "        0.5494219660758972,\n",
       "        0.5291923880577087,\n",
       "        0.5248003005981445,\n",
       "        0.507095217704773,\n",
       "        0.5377654433250427,\n",
       "        0.5272640585899353,\n",
       "        0.5240458846092224,\n",
       "        0.536936342716217,\n",
       "        0.5145300626754761,\n",
       "        0.5394728779792786,\n",
       "        0.5031874775886536,\n",
       "        0.4912702739238739,\n",
       "        0.474837064743042,\n",
       "        0.4978034794330597,\n",
       "        0.47343868017196655,\n",
       "        0.5019485950469971,\n",
       "        0.49350035190582275,\n",
       "        0.44962990283966064,\n",
       "        0.5068372488021851,\n",
       "        0.45813190937042236,\n",
       "        0.46448689699172974,\n",
       "        0.5328967571258545,\n",
       "        0.5025261044502258,\n",
       "        0.48216792941093445,\n",
       "        0.45539405941963196,\n",
       "        0.5138511061668396,\n",
       "        0.45825743675231934,\n",
       "        0.48613041639328003,\n",
       "        0.421750009059906,\n",
       "        0.45771223306655884,\n",
       "        0.4408651292324066,\n",
       "        0.46192315220832825,\n",
       "        0.4444059729576111,\n",
       "        0.44015079736709595,\n",
       "        0.47167694568634033,\n",
       "        0.41625338792800903,\n",
       "        0.4959246516227722,\n",
       "        0.41968268156051636,\n",
       "        0.4405563175678253,\n",
       "        0.46486371755599976,\n",
       "        0.4154033362865448,\n",
       "        0.4309859573841095,\n",
       "        0.4216099679470062,\n",
       "        0.4506579339504242,\n",
       "        0.4305371940135956,\n",
       "        0.39049187302589417,\n",
       "        0.4071049690246582,\n",
       "        0.41688206791877747,\n",
       "        0.39220014214515686,\n",
       "        0.40977463126182556,\n",
       "        0.40284794569015503,\n",
       "        0.4072117507457733,\n",
       "        0.40227970480918884,\n",
       "        0.4079244136810303,\n",
       "        0.4411878287792206,\n",
       "        0.4125230014324188,\n",
       "        0.4142322242259979,\n",
       "        0.39040812849998474,\n",
       "        0.38568106293678284],\n",
       "       'accuracy': [0.4744121730327606,\n",
       "        0.5352697372436523,\n",
       "        0.6417704224586487,\n",
       "        0.6887966990470886,\n",
       "        0.7607192397117615,\n",
       "        0.7883817553520203,\n",
       "        0.8063623905181885,\n",
       "        0.817427396774292,\n",
       "        0.8478561639785767,\n",
       "        0.8464730381965637,\n",
       "        0.8838174343109131,\n",
       "        0.8713693022727966,\n",
       "        0.8713693022727966,\n",
       "        0.8755186796188354,\n",
       "        0.8686030507087708,\n",
       "        0.8713693022727966,\n",
       "        0.8727524280548096,\n",
       "        0.9059474468231201,\n",
       "        0.8838174343109131,\n",
       "        0.8907330632209778,\n",
       "        0.9031811952590942,\n",
       "        0.9128630757331848,\n",
       "        0.9128630757331848,\n",
       "        0.9114799499511719,\n",
       "        0.9170124530792236,\n",
       "        0.9114799499511719,\n",
       "        0.9100968241691589,\n",
       "        0.9183955788612366,\n",
       "        0.9183955788612366,\n",
       "        0.9197787046432495,\n",
       "        0.9253112077713013,\n",
       "        0.9183955788612366,\n",
       "        0.9225449562072754,\n",
       "        0.9142462015151978,\n",
       "        0.9280774593353271,\n",
       "        0.9266943335533142,\n",
       "        0.930843710899353,\n",
       "        0.930843710899353,\n",
       "        0.9363762140274048,\n",
       "        0.9336099624633789,\n",
       "        0.9363762140274048,\n",
       "        0.9225449562072754,\n",
       "        0.9280774593353271,\n",
       "        0.9239280819892883,\n",
       "        0.9363762140274048,\n",
       "        0.9391424655914307,\n",
       "        0.9280774593353271,\n",
       "        0.9377593398094177,\n",
       "        0.930843710899353,\n",
       "        0.9294605851173401,\n",
       "        0.932226836681366,\n",
       "        0.9266943335533142,\n",
       "        0.9377593398094177,\n",
       "        0.9377593398094177,\n",
       "        0.9280774593353271,\n",
       "        0.9391424655914307,\n",
       "        0.9419087171554565,\n",
       "        0.932226836681366,\n",
       "        0.9363762140274048,\n",
       "        0.9377593398094177,\n",
       "        0.9391424655914307,\n",
       "        0.9529737234115601,\n",
       "        0.9253112077713013,\n",
       "        0.9377593398094177,\n",
       "        0.9391424655914307,\n",
       "        0.9377593398094177,\n",
       "        0.9349930882453918,\n",
       "        0.9446749687194824,\n",
       "        0.9377593398094177,\n",
       "        0.954356849193573,\n",
       "        0.9391424655914307,\n",
       "        0.9488243460655212,\n",
       "        0.9502074718475342,\n",
       "        0.9557399749755859,\n",
       "        0.9391424655914307,\n",
       "        0.9446749687194824,\n",
       "        0.9446749687194824,\n",
       "        0.9460580945014954,\n",
       "        0.9474412202835083,\n",
       "        0.9557399749755859,\n",
       "        0.9349930882453918,\n",
       "        0.9474412202835083,\n",
       "        0.9488243460655212,\n",
       "        0.954356849193573,\n",
       "        0.9391424655914307,\n",
       "        0.9432918429374695,\n",
       "        0.9612724781036377,\n",
       "        0.9515905976295471,\n",
       "        0.9529737234115601,\n",
       "        0.9557399749755859,\n",
       "        0.9460580945014954,\n",
       "        0.9529737234115601,\n",
       "        0.9488243460655212,\n",
       "        0.9585062265396118,\n",
       "        0.9529737234115601,\n",
       "        0.9446749687194824,\n",
       "        0.9432918429374695,\n",
       "        0.9446749687194824,\n",
       "        0.9529737234115601,\n",
       "        0.9626556038856506],\n",
       "       'val_loss': [7.423384189605713,\n",
       "        3.4547064304351807,\n",
       "        2.337865114212036,\n",
       "        1.992551565170288,\n",
       "        1.5628662109375,\n",
       "        1.3681786060333252,\n",
       "        1.1656463146209717,\n",
       "        1.0316154956817627,\n",
       "        0.9042732119560242,\n",
       "        0.8205476999282837,\n",
       "        0.719544529914856,\n",
       "        0.7283036112785339,\n",
       "        0.6598464846611023,\n",
       "        0.6748486757278442,\n",
       "        0.6077790856361389,\n",
       "        0.5963872075080872,\n",
       "        0.6010370254516602,\n",
       "        0.5671243667602539,\n",
       "        0.5305271744728088,\n",
       "        0.5321751236915588,\n",
       "        0.5344095230102539,\n",
       "        0.535815954208374,\n",
       "        0.5434675216674805,\n",
       "        0.5016570091247559,\n",
       "        0.48337191343307495,\n",
       "        0.47400012612342834,\n",
       "        0.4966503083705902,\n",
       "        0.4655057489871979,\n",
       "        0.4731353521347046,\n",
       "        0.4721851646900177,\n",
       "        0.4728603959083557,\n",
       "        0.4557156264781952,\n",
       "        0.4621422588825226,\n",
       "        0.45680245757102966,\n",
       "        0.4590406119823456,\n",
       "        0.45628273487091064,\n",
       "        0.44675514101982117,\n",
       "        0.4536133110523224,\n",
       "        0.4427630603313446,\n",
       "        0.45673179626464844,\n",
       "        0.4535438120365143,\n",
       "        0.45974209904670715,\n",
       "        0.4382902681827545,\n",
       "        0.4329352080821991,\n",
       "        0.4277324080467224,\n",
       "        0.43999409675598145,\n",
       "        0.455075204372406,\n",
       "        0.4201134145259857,\n",
       "        0.46310555934906006,\n",
       "        0.4193616509437561,\n",
       "        0.42819878458976746,\n",
       "        0.42170286178588867,\n",
       "        0.42049866914749146,\n",
       "        0.43673479557037354,\n",
       "        0.4078866243362427,\n",
       "        0.4186587631702423,\n",
       "        0.3965636193752289,\n",
       "        0.4238881468772888,\n",
       "        0.4023994207382202,\n",
       "        0.4061179757118225,\n",
       "        0.40689554810523987,\n",
       "        0.3977871835231781,\n",
       "        0.4172804653644562,\n",
       "        0.39466631412506104,\n",
       "        0.3810364007949829,\n",
       "        0.39368936419487,\n",
       "        0.4210931658744812,\n",
       "        0.3914734125137329,\n",
       "        0.4202319383621216,\n",
       "        0.39157307147979736,\n",
       "        0.38887545466423035,\n",
       "        0.3928380012512207,\n",
       "        0.39595144987106323,\n",
       "        0.3936370015144348,\n",
       "        0.37269675731658936,\n",
       "        0.38023537397384644,\n",
       "        0.36519619822502136,\n",
       "        0.3814653754234314,\n",
       "        0.3695208430290222,\n",
       "        0.39963796734809875,\n",
       "        0.3552105724811554,\n",
       "        0.35273054242134094,\n",
       "        0.3974425196647644,\n",
       "        0.3875080645084381,\n",
       "        0.3748341202735901,\n",
       "        0.3646186590194702,\n",
       "        0.35597658157348633,\n",
       "        0.34827423095703125,\n",
       "        0.34497135877609253,\n",
       "        0.349541574716568,\n",
       "        0.339874804019928,\n",
       "        0.34511038661003113,\n",
       "        0.3566106855869293,\n",
       "        0.3430188000202179,\n",
       "        0.33869850635528564,\n",
       "        0.36726459860801697,\n",
       "        0.3491874933242798,\n",
       "        0.34711650013923645,\n",
       "        0.3473927974700928,\n",
       "        0.34057146310806274],\n",
       "       'val_accuracy': [0.6353591084480286,\n",
       "        0.6629834175109863,\n",
       "        0.7237569093704224,\n",
       "        0.7734806537628174,\n",
       "        0.8453038930892944,\n",
       "        0.8729282021522522,\n",
       "        0.8784530162811279,\n",
       "        0.90055251121521,\n",
       "        0.9171270728111267,\n",
       "        0.9116021990776062,\n",
       "        0.9226519465446472,\n",
       "        0.9226519465446472,\n",
       "        0.9226519465446472,\n",
       "        0.9171270728111267,\n",
       "        0.9281768202781677,\n",
       "        0.9060773253440857,\n",
       "        0.8950276374816895,\n",
       "        0.9060773253440857,\n",
       "        0.9171270728111267,\n",
       "        0.9226519465446472,\n",
       "        0.90055251121521,\n",
       "        0.9171270728111267,\n",
       "        0.9171270728111267,\n",
       "        0.9116021990776062,\n",
       "        0.939226508140564,\n",
       "        0.9447513818740845,\n",
       "        0.9337016344070435,\n",
       "        0.9447513818740845,\n",
       "        0.9337016344070435,\n",
       "        0.950276255607605,\n",
       "        0.9447513818740845,\n",
       "        0.9558011293411255,\n",
       "        0.9447513818740845,\n",
       "        0.9337016344070435,\n",
       "        0.9447513818740845,\n",
       "        0.9447513818740845,\n",
       "        0.950276255607605,\n",
       "        0.9447513818740845,\n",
       "        0.9558011293411255,\n",
       "        0.9447513818740845,\n",
       "        0.9447513818740845,\n",
       "        0.9447513818740845,\n",
       "        0.9668508172035217,\n",
       "        0.950276255607605,\n",
       "        0.9613259434700012,\n",
       "        0.9558011293411255,\n",
       "        0.939226508140564,\n",
       "        0.9613259434700012,\n",
       "        0.950276255607605,\n",
       "        0.9613259434700012,\n",
       "        0.9558011293411255,\n",
       "        0.9668508172035217,\n",
       "        0.9613259434700012,\n",
       "        0.950276255607605,\n",
       "        0.9613259434700012,\n",
       "        0.9723756909370422,\n",
       "        0.9779005646705627,\n",
       "        0.9613259434700012,\n",
       "        0.9723756909370422,\n",
       "        0.9613259434700012,\n",
       "        0.9613259434700012,\n",
       "        0.9668508172035217,\n",
       "        0.9558011293411255,\n",
       "        0.9779005646705627,\n",
       "        0.9723756909370422,\n",
       "        0.9613259434700012,\n",
       "        0.9558011293411255,\n",
       "        0.9723756909370422,\n",
       "        0.950276255607605,\n",
       "        0.9668508172035217,\n",
       "        0.9613259434700012,\n",
       "        0.9558011293411255,\n",
       "        0.9558011293411255,\n",
       "        0.9613259434700012,\n",
       "        0.9779005646705627,\n",
       "        0.9723756909370422,\n",
       "        0.9723756909370422,\n",
       "        0.9668508172035217,\n",
       "        0.9723756909370422,\n",
       "        0.9558011293411255,\n",
       "        0.9779005646705627,\n",
       "        0.9834254384040833,\n",
       "        0.9613259434700012,\n",
       "        0.9558011293411255,\n",
       "        0.9613259434700012,\n",
       "        0.9723756909370422,\n",
       "        0.9779005646705627,\n",
       "        0.9779005646705627,\n",
       "        0.9779005646705627,\n",
       "        0.9779005646705627,\n",
       "        0.9834254384040833,\n",
       "        0.9779005646705627,\n",
       "        0.9723756909370422,\n",
       "        0.9668508172035217,\n",
       "        0.9779005646705627,\n",
       "        0.9613259434700012,\n",
       "        0.9723756909370422,\n",
       "        0.9779005646705627,\n",
       "        0.9779005646705627,\n",
       "        0.9779005646705627]},\n",
       "      'estimator_type': 'classification',\n",
       "      'model_parameters': {'tflite': '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'}}}],\n",
       "   'classifiers': [{'name': 'TF Micro', 'inputs': {}}],\n",
       "   'label_column': 'Label',\n",
       "   'feature_table': 'temp.features.Min_Max_Scale0',\n",
       "   'ignore_columns': ['SegmentID'],\n",
       "   'validation_seed': None,\n",
       "   'validation_methods': [{'name': 'Recall', 'inputs': {}}]}],\n",
       " 'query_summary': [],\n",
       " 'feature_summary': [{'Feature': 'gen_0016_AccelerometerXLinearRegressionStdErr_0003',\n",
       "   'Sensors': ['AccelerometerX'],\n",
       "   'Category': 'Statistical',\n",
       "   'Generator': 'Linear Regression Stats',\n",
       "   'ContextIndex': 18,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 5,\n",
       "   'GeneratorTrueIndex': 16,\n",
       "   'GeneratorFamilyIndex': 3,\n",
       "   'GeneratorFamilyFeatures': 4},\n",
       "  {'Feature': 'gen_0018_AccelerometerZLinearRegressionStdErr_0003',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Statistical',\n",
       "   'Generator': 'Linear Regression Stats',\n",
       "   'ContextIndex': 26,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 7,\n",
       "   'GeneratorTrueIndex': 18,\n",
       "   'GeneratorFamilyIndex': 3,\n",
       "   'GeneratorFamilyFeatures': 4},\n",
       "  {'Feature': 'gen_0021_AccelerometerZStd',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Statistical',\n",
       "   'Generator': 'Standard Deviation',\n",
       "   'ContextIndex': 29,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 8,\n",
       "   'GeneratorTrueIndex': 21,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0030_AccelerometerZIQR',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Statistical',\n",
       "   'Generator': 'Interquartile Range',\n",
       "   'ContextIndex': 38,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 11,\n",
       "   'GeneratorTrueIndex': 30,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0042_AccelerometerZminimum',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Statistical',\n",
       "   'Generator': 'Minimum',\n",
       "   'ContextIndex': 50,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 15,\n",
       "   'GeneratorTrueIndex': 42,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0045_AccelerometerZmaximum',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Statistical',\n",
       "   'Generator': 'Maximum',\n",
       "   'ContextIndex': 53,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 16,\n",
       "   'GeneratorTrueIndex': 45,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0055_AccelerometerXVariance',\n",
       "   'Sensors': ['AccelerometerX'],\n",
       "   'Category': 'Statistical',\n",
       "   'Generator': 'Variance',\n",
       "   'ContextIndex': 63,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 20,\n",
       "   'GeneratorTrueIndex': 55,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0057_AccelerometerZVariance',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Statistical',\n",
       "   'Generator': 'Variance',\n",
       "   'ContextIndex': 65,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 20,\n",
       "   'GeneratorTrueIndex': 57,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0112_AccelerometerXAbsAreaAc',\n",
       "   'Sensors': ['AccelerometerX'],\n",
       "   'Category': 'Area',\n",
       "   'Generator': 'Absolute Area of High Frequency',\n",
       "   'ContextIndex': 120,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 57,\n",
       "   'GeneratorTrueIndex': 112,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0114_AccelerometerZAbsAreaAc',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Area',\n",
       "   'Generator': 'Absolute Area of High Frequency',\n",
       "   'ContextIndex': 122,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 57,\n",
       "   'GeneratorTrueIndex': 114,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0129_AccelerometerZSigmaCrossingRate',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Rate of Change',\n",
       "   'Generator': 'Sigma Crossing Rate',\n",
       "   'ContextIndex': 137,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 62,\n",
       "   'GeneratorTrueIndex': 129,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1},\n",
       "  {'Feature': 'gen_0132_AccelerometerZ2ndSigmaCrossingRate',\n",
       "   'Sensors': ['AccelerometerZ'],\n",
       "   'Category': 'Rate of Change',\n",
       "   'Generator': 'Second Sigma Crossing Rate',\n",
       "   'ContextIndex': 140,\n",
       "   'EliminatedBy': None,\n",
       "   'GeneratorIndex': 63,\n",
       "   'GeneratorTrueIndex': 132,\n",
       "   'GeneratorFamilyIndex': 0,\n",
       "   'GeneratorFamilyFeatures': 1}],\n",
       " 'device_configuration': {'classifier': 'TF Micro', 'sample_rate': 100},\n",
       " 'configuration_index': '0',\n",
       " 'model_index': 'Fold 0',\n",
       " 'transform_summary': [{'step_type': 'transform',\n",
       "   'function_name': 'Min Max Scale',\n",
       "   'parameter_name': 'feature_min_max_parameters',\n",
       "   'parameter_value': {'maximums': {'gen_0021_AccelerometerZStd': 2262.7922,\n",
       "     'gen_0030_AccelerometerZIQR': 4157.25,\n",
       "     'gen_0042_AccelerometerZminimum': 3762.0,\n",
       "     'gen_0045_AccelerometerZmaximum': 8100.0,\n",
       "     'gen_0055_AccelerometerXVariance': 3678969.8,\n",
       "     'gen_0057_AccelerometerZVariance': 5134901.0,\n",
       "     'gen_0112_AccelerometerXAbsAreaAc': 1187.231,\n",
       "     'gen_0114_AccelerometerZAbsAreaAc': 1021.5,\n",
       "     'gen_0129_AccelerometerZSigmaCrossingRate': 0.04,\n",
       "     'gen_0132_AccelerometerZ2ndSigmaCrossingRate': 0.065714285,\n",
       "     'gen_0016_AccelerometerXLinearRegressionStdErr_0003': 1.0919094999999999,\n",
       "     'gen_0018_AccelerometerZLinearRegressionStdErr_0003': 1.1944826000000002},\n",
       "    'minimums': {'gen_0021_AccelerometerZStd': 71.44216,\n",
       "     'gen_0030_AccelerometerZIQR': 90.0,\n",
       "     'gen_0042_AccelerometerZminimum': -1113.0,\n",
       "     'gen_0045_AccelerometerZmaximum': 2691.0,\n",
       "     'gen_0055_AccelerometerXVariance': 13608.954,\n",
       "     'gen_0057_AccelerometerZVariance': 5118.6235,\n",
       "     'gen_0112_AccelerometerXAbsAreaAc': 115.799995,\n",
       "     'gen_0114_AccelerometerZAbsAreaAc': 112.99091000000001,\n",
       "     'gen_0129_AccelerometerZSigmaCrossingRate': 0.0,\n",
       "     'gen_0132_AccelerometerZ2ndSigmaCrossingRate': 0.0,\n",
       "     'gen_0016_AccelerometerXLinearRegressionStdErr_0003': 0.042199213,\n",
       "     'gen_0018_AccelerometerZLinearRegressionStdErr_0003': 0.035995400000000004}}}],\n",
       " 'sensor_summary': ['AccelerometerX', 'AccelerometerY', 'AccelerometerZ'],\n",
       " 'class_map': {'1': '0', '2': 'O', '3': 'W', '4': 'Z'},\n",
       " 'cost_summary': {'neurons': 2897,\n",
       "  'sensors': {'sram': 1400,\n",
       "   'flash': 0,\n",
       "   'stack': 0,\n",
       "   'latency': 1225000000,\n",
       "   'number_of_sensors': 2,\n",
       "   'max_segment_length': 350},\n",
       "  'pipeline': [{'name': 'Windowing',\n",
       "    'sram': 8,\n",
       "    'type': 'segmenter',\n",
       "    'flash': 132,\n",
       "    'stack': 152,\n",
       "    'latency': 350.0},\n",
       "   {'name': 'generator_set',\n",
       "    'sram': 4.0,\n",
       "    'type': 'generatorset',\n",
       "    'flash': 2200.0,\n",
       "    'stack': 372,\n",
       "    'latency': 37460.9703125,\n",
       "    'per_generator_costs': {'Maximum': {'name': 'Maximum',\n",
       "      'sram': 0,\n",
       "      'type': 'generator',\n",
       "      'flash': 0,\n",
       "      'stack': 0,\n",
       "      'latency': 0.0,\n",
       "      'num_features': 1,\n",
       "      'num_iterations': 1},\n",
       "     'Minimum': {'name': 'Minimum',\n",
       "      'sram': 0,\n",
       "      'type': 'generator',\n",
       "      'flash': 0,\n",
       "      'stack': 0,\n",
       "      'latency': 0.0,\n",
       "      'num_features': 1,\n",
       "      'num_iterations': 1},\n",
       "     'Variance': {'name': 'Variance',\n",
       "      'sram': 0,\n",
       "      'type': 'generator',\n",
       "      'flash': 356,\n",
       "      'stack': 202,\n",
       "      'latency': 15400.0,\n",
       "      'num_features': 2,\n",
       "      'num_iterations': 2},\n",
       "     'Standard Deviation': {'name': 'Standard Deviation',\n",
       "      'sram': 0.0,\n",
       "      'type': 'generator',\n",
       "      'flash': 508.0,\n",
       "      'stack': 336,\n",
       "      'latency': 3.5922852,\n",
       "      'num_features': 1,\n",
       "      'num_iterations': 1},\n",
       "     'Interquartile Range': {'name': 'Interquartile Range',\n",
       "      'sram': 4.0,\n",
       "      'type': 'generator',\n",
       "      'flash': 276.0,\n",
       "      'stack': 272,\n",
       "      'latency': 1050.0,\n",
       "      'num_features': 1,\n",
       "      'num_iterations': 1},\n",
       "     'Sigma Crossing Rate': {'name': 'Sigma Crossing Rate',\n",
       "      'sram': 0.0,\n",
       "      'type': 'generator',\n",
       "      'flash': 326.0,\n",
       "      'stack': 372,\n",
       "      'latency': 10850.0,\n",
       "      'num_features': 1,\n",
       "      'num_iterations': 1},\n",
       "     'Linear Regression Stats': {'name': 'Linear Regression Stats',\n",
       "      'sram': 0,\n",
       "      'type': 'generator',\n",
       "      'flash': 0,\n",
       "      'stack': 0,\n",
       "      'latency': 0.0,\n",
       "      'num_features': 2,\n",
       "      'num_iterations': 2},\n",
       "     'Second Sigma Crossing Rate': {'name': 'Second Sigma Crossing Rate',\n",
       "      'sram': 0.0,\n",
       "      'type': 'generator',\n",
       "      'flash': 330.0,\n",
       "      'stack': 372,\n",
       "      'latency': 10150.0,\n",
       "      'num_features': 1,\n",
       "      'num_iterations': 1},\n",
       "     'Absolute Area of High Frequency': {'name': 'Absolute Area of High Frequency',\n",
       "      'sram': 0.0,\n",
       "      'type': 'generator',\n",
       "      'flash': 404.0,\n",
       "      'stack': 364,\n",
       "      'latency': 7.378027299999999,\n",
       "      'num_features': 2,\n",
       "      'num_iterations': 2}}},\n",
       "   {'name': 'Min Max Scale',\n",
       "    'sram': 0,\n",
       "    'type': 'transform',\n",
       "    'flash': 228,\n",
       "    'stack': 176,\n",
       "    'latency': 5950.0}],\n",
       "  'framework': {'sram': 1068, 'flash': 2014, 'stack': 108, 'latency': 1633683},\n",
       "  'model_size': 2897},\n",
       " 'name': 'Magic_WOZ_detector_2',\n",
       " 'sandbox_uuid': '234c0713-70dc-4ef4-b90f-9eeac860d688',\n",
       " 'sandbox_name': 'pipeline 3',\n",
       " 'project_uuid': '0739e3f0-7832-42ec-a8ad-47b1ac2670ea',\n",
       " 'project_name': 'Magic Wand',\n",
       " 'feature_file_uuid': 'bffaf5f6-1d39-4631-8fed-74f836d02eb3',\n",
       " 'knowledgepack_summary': {'sensor_columns': ['AccelerometerX',\n",
       "   'AccelerometerY',\n",
       "   'AccelerometerZ'],\n",
       "  'data_columns_ordered': ['ACCELEROMETERX', 'ACCELEROMETERZ'],\n",
       "  'recognition_pipeline': [{'name': 'Windowing',\n",
       "    'type': 'segmenter',\n",
       "    'inputs': {'delta': 25,\n",
       "     'input_data': 'temp.raw',\n",
       "     'train_delta': 0,\n",
       "     'window_size': 350,\n",
       "     'group_columns': ['Label'],\n",
       "     'return_segment_index': False},\n",
       "    'outputs': ['temp.Windowing0'],\n",
       "    'feature_table': None},\n",
       "   {'set': [{'family': True,\n",
       "      'inputs': {'columns': ['AccelerometerX']},\n",
       "      'outputs': [3],\n",
       "      'function_name': 'Linear Regression Stats'},\n",
       "     {'family': True,\n",
       "      'inputs': {'columns': ['AccelerometerZ']},\n",
       "      'outputs': [3],\n",
       "      'function_name': 'Linear Regression Stats'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerZ']},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Standard Deviation'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerZ']},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Interquartile Range'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerZ']},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Minimum'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerZ']},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Maximum'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerX']},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Variance'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerZ']},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Variance'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerX'],\n",
       "       'sample_rate': 100,\n",
       "       'smoothing_factor': 5},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Absolute Area of High Frequency'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerZ'],\n",
       "       'sample_rate': 100,\n",
       "       'smoothing_factor': 5},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Absolute Area of High Frequency'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerZ']},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Sigma Crossing Rate'},\n",
       "     {'family': None,\n",
       "      'inputs': {'columns': ['AccelerometerZ']},\n",
       "      'outputs': [0],\n",
       "      'function_name': 'Second Sigma Crossing Rate'}],\n",
       "    'name': 'generator_set',\n",
       "    'type': 'generatorset',\n",
       "    'inputs': {'input_data': 'temp.Windowing0',\n",
       "     'group_columns': ['Label', 'SegmentID']},\n",
       "    'outputs': ['temp.generator_set0', 'temp.features.generator_set0']},\n",
       "   {'name': 'Min Max Scale',\n",
       "    'type': 'transform',\n",
       "    'inputs': {'pad': 0,\n",
       "     'max_bound': 255,\n",
       "     'min_bound': 0,\n",
       "     'input_data': 'temp.generator_set0',\n",
       "     'passthrough_columns': ['Label', 'SegmentID'],\n",
       "     'feature_min_max_defaults': None,\n",
       "     'feature_min_max_parameters': {'maximums': {'gen_0021_AccelerometerZStd': 2262.7922,\n",
       "       'gen_0030_AccelerometerZIQR': 4157.25,\n",
       "       'gen_0042_AccelerometerZminimum': 3762.0,\n",
       "       'gen_0045_AccelerometerZmaximum': 8100.0,\n",
       "       'gen_0055_AccelerometerXVariance': 3678969.8,\n",
       "       'gen_0057_AccelerometerZVariance': 5134901.0,\n",
       "       'gen_0112_AccelerometerXAbsAreaAc': 1187.231,\n",
       "       'gen_0114_AccelerometerZAbsAreaAc': 1021.5,\n",
       "       'gen_0129_AccelerometerZSigmaCrossingRate': 0.04,\n",
       "       'gen_0132_AccelerometerZ2ndSigmaCrossingRate': 0.065714285,\n",
       "       'gen_0016_AccelerometerXLinearRegressionStdErr_0003': 1.0919094999999999,\n",
       "       'gen_0018_AccelerometerZLinearRegressionStdErr_0003': 1.1944826000000002},\n",
       "      'minimums': {'gen_0021_AccelerometerZStd': 71.44216,\n",
       "       'gen_0030_AccelerometerZIQR': 90.0,\n",
       "       'gen_0042_AccelerometerZminimum': -1113.0,\n",
       "       'gen_0045_AccelerometerZmaximum': 2691.0,\n",
       "       'gen_0055_AccelerometerXVariance': 13608.954,\n",
       "       'gen_0057_AccelerometerZVariance': 5118.6235,\n",
       "       'gen_0112_AccelerometerXAbsAreaAc': 115.799995,\n",
       "       'gen_0114_AccelerometerZAbsAreaAc': 112.99091000000001,\n",
       "       'gen_0129_AccelerometerZSigmaCrossingRate': 0.0,\n",
       "       'gen_0132_AccelerometerZ2ndSigmaCrossingRate': 0.0,\n",
       "       'gen_0016_AccelerometerXLinearRegressionStdErr_0003': 0.042199213,\n",
       "       'gen_0018_AccelerometerZLinearRegressionStdErr_0003': 0.035995400000000004}}},\n",
       "    'outputs': ['temp.Min_Max_Scale0', 'temp.features.Min_Max_Scale0'],\n",
       "    'feature_table': 'temp.features.generator_set0'}]},\n",
       " 'knowledgepack_description': None}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.knowledgepack.save(\"Magic_WOZ_detector_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "faced-trademark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fae91d7a307b4d4c916867d8362be98d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Tab(children=(VBox(children=(HBox(children=(Dropdown(description='Model Name', layout=Layout(wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'target_platform': 25, 'test_data': None, 'debug': True, 'application': 'qf_mqttsn_ai_app', 'sample_rate': '', 'output_options': ['simple streaming'], 'kb_description': {'Magic_WOZ_detector_2': {'uuid': '3736ddd6-7be7-49a5-9ffc-9aa2a132d72b', 'results': {'1 ': 'Report', '2 ': 'Report', '3 ': 'Report', '4 ': 'Report'}, 'source': 'Custom'}}, 'debug_level': 1, 'profile': False, 'profile_iterations': 0}\n",
      "Generating bin with configuration\n",
      "target_platform : 25\n",
      "test_data : None\n",
      "debug : True\n",
      "application : qf_mqttsn_ai_app\n",
      "sample_rate : \n",
      "output_options : ['simple streaming']\n",
      "kb_description : {\"Magic_WOZ_detector_2\": {\"uuid\": \"3736ddd6-7be7-49a5-9ffc-9aa2a132d72b\", \"results\": {\"1 \": \"Report\", \"2 \": \"Report\", \"3 \": \"Report\", \"4 \": \"Report\"}, \"source\": \"Custom\"}}\n",
      "debug_level : 1\n",
      "profile : False\n",
      "profile_iterations : 0\n",
      ".list index out of range\n"
     ]
    }
   ],
   "source": [
    "download = DownloadWidget(dsk)\n",
    "download.create_widget()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "welcome-nightmare",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "norwegian-production",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sensiml",
   "language": "python",
   "name": "sensiml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}