main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "\n",
    "import iisignature\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_stim(stims : pd.Series, shift : int):\n",
    "    # shifts all stimuli by an amount\n",
    "    stims[stims != 0] += shift\n",
    "    return stims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_subject(data_paths, col='emg'):\n",
    "    df = pd.DataFrame([])\n",
    "    tot_stim = 0\n",
    "    for i, data_path in enumerate(data_paths):\n",
    "        mat = sio.loadmat(data_path)\n",
    "        if i == 0:\n",
    "            df = pd.DataFrame(mat[col])\n",
    "            df['stimulus'] = mat['restimulus']\n",
    "            df['repetition'] = mat['rerepetition']\n",
    "        else:\n",
    "            df2 = pd.DataFrame(mat[col])\n",
    "            df2['stimulus'] = shift_stim(pd.DataFrame(mat['restimulus']), tot_stim)\n",
    "            df2['repetition'] = mat['rerepetition']\n",
    "            df = pd.concat([df, df2], ignore_index=True)\n",
    "        tot_stim += len(np.unique(mat['restimulus'])) - 1\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load database 5 and 1. 2 could also work too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>repetition</th>\n",
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       "           0          1          2         3          4          5         6  \\\n",
       "0  81.193825 -25.613001  17.336512 -6.904053  12.986491  18.831488  1.902927   \n",
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       "\n",
       "           7         8          9  ...         15         16        17  \\\n",
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       "\n",
       "          18         19        20         21  stimulus  repetition  subject  \n",
       "0  13.291741  65.754509  2.470584  24.564764         0           1        1  \n",
       "1  13.291741  65.754509  2.470584  24.564764         0           1        1  \n",
       "2  13.291741  65.754509  2.470584  24.564764         0           1        1  \n",
       "3  13.291741  65.754509  2.470584  24.564764         0           1        1  \n",
       "4  13.291741  65.754509  2.470584  24.564764         0           1        1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#exercises = [1, 2, 3]\n",
    "exercises = [1]\n",
    "databases = ['db5', 'db1']\n",
    "root = './signatures_data/'\n",
    "df = pd.DataFrame([])\n",
    "for database in databases:\n",
    "    if database == 'db5':\n",
    "        subjects = range(1, 11)\n",
    "    elif database == 'db1':\n",
    "        subjects = range(11, 38)\n",
    "    \n",
    "    for subject in subjects:\n",
    "        if database == 'db5':\n",
    "            data_paths = [os.path.join(root, f\"{database}/s{subject}/S{subject}_E{exercise}_A1.mat\") for exercise in exercises]\n",
    "        elif database == 'db1':\n",
    "            data_paths = [os.path.join(root, f\"{database}/S{subject-10}_A1_E{exercise}.mat\") for exercise in exercises]\n",
    "        cur_df = load_subject(data_paths, col='glove')\n",
    "        cur_df['subject'] = subject\n",
    "        df = pd.concat([df, cur_df], ignore_index=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4190423, 25)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df['stimulus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10], dtype=int16)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df['repetition'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original DB5 paper utilizes repetitions 1, 3, 4, 5 for training classifiers and repetitions 2, 5 for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = [1, 3, 4, 6]\n",
    "test_set = [2, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_log_signature(path, depth, dims=None):\n",
    "    if dims is None:\n",
    "        dims = path.shape[1] # take second, assumes (timesteps, dims)\n",
    "    s = iisignature.prepare(dims, depth)\n",
    "    return iisignature.logsig(path, s)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a few utilities for dealing with the Ninapro data: a normalizer, which takes sEMG data and specific repetitions, then centers and scales those repetitions to unit variance as well as the notion of a path, which can be windowed to a specified size and designated overlap between successive windows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from normalizers import *\n",
    "from paths import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def splitxy(dfs : list, x_cols, y_col):\n",
    "    x = []\n",
    "    y = []\n",
    "    for df in dfs:\n",
    "        x.append(df[x_cols].to_numpy())\n",
    "        y.append(df[y_col].to_numpy()[0]) # assume everything is of the same class\n",
    "    return np.array(x), np.array(y).reshape(-1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a SVM since it's not immediately apparent what to do with such high-dimensional data. The human action recognition example used a linear classifier with a dropout layer, which was trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(pred, true):\n",
    "    assert len(pred) == len(true), \"check lengths match\"\n",
    "    return np.sum((pred == true)) / len(pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert accuracy(np.array([1, 1, 1, 2]), np.array([1, 2, 1, 2])) == 0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try the same thing with glove data\n",
    "\n",
    "Let's stick to differentiating between stimuli $1$ and $3$ - pretty sure there should be some significant differences here. Let's look at glove data plots."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at just subject $1$, exercise $1$ vs exercise $3$'s activity so we can extract out what we think are relevant features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = './signatures_data/'\n",
    "from paths import *\n",
    "from normalizers import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_paths = [os.path.join(root, f\"s1/S1_E{exercise}_A1.mat\") for exercise in exercises]\n",
    "data_paths = [os.path.join(root, f\"db5/s1/S1_E1_A1.mat\")]\n",
    "s1_df = load_subject(data_paths, col='glove')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Glove Electrode Signals, Subject 1, Exercise 1'}>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1_df[(s1_df.stimulus==1) & (s1_df.repetition==2)][[8, 9, 10, 11, 12,15]].plot(figsize=(9,6), title=\"Glove Electrode Signals, Subject 1, Exercise 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Glove Electrode Signals, Subject 1, Exercise 3'}>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1_df[(s1_df.stimulus==3) & (s1_df.repetition==2)][[8,9,10,11,12,15]].plot(figsize=(9,6), title=\"Glove Electrode Signals, Subject 1, Exercise 3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Glove Electrode Signals, Subject 1, Exercise 5'}>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1_df[(s1_df.stimulus==5) & (s1_df.repetition==2)][[8,9,10,11,12,15]].plot(figsize=(9,6), title=\"Glove Electrode Signals, Subject 1, Exercise 5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Glove Electrode Signals, Subject 1, Exercise 7'}>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1_df[(s1_df.stimulus==7) & (s1_df.repetition==2)][[8,9,10,11,12,15]].plot(figsize=(9,6), title=\"Glove Electrode Signals, Subject 1, Exercise 7\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Using $1$ indexing...*\n",
    "\n",
    "So, looks like sensors $11$ and $6$ are active in the first exercise (an index finger flexion) and sensors $11$ and $9$ are active in the second exercise (a middle finger flexion). Looking at the image, these make sense.\n",
    "\n",
    "<img src=\"imgs/sensor_mapping.png\" width=300>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "repetitions = [1, 2, 3, 4, 5, 6]\n",
    "stims = [1, 3, 5, 7]\n",
    "train_windows = []\n",
    "test_windows = []\n",
    "for stimulus in stims:\n",
    "    for repetition in repetitions:\n",
    "        for subject in range(1, 38):\n",
    "            dbpth = DB5Path(df[(df.stimulus == stimulus) & (df.repetition == repetition) & (df.subject == subject)], stimulus, repetition, dims = 22)\n",
    "            if repetition in train_set:\n",
    "                #train_windows += dbpth.get_windows(window_size=200, overlap=100)\n",
    "                train_windows += dbpth.downsample(window_size=200)\n",
    "            elif repetition in test_set:\n",
    "                #test_windows += dbpth.get_windows(window_size=200, overlap=100)\n",
    "                test_windows += dbpth.downsample(window_size=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>index</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>stimulus</th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.234766</td>\n",
       "      <td>0.868894</td>\n",
       "      <td>-1.546296</td>\n",
       "      <td>-1.875137</td>\n",
       "      <td>-1.438579</td>\n",
       "      <td>-1.820523</td>\n",
       "      <td>-0.046354</td>\n",
       "      <td>-0.877706</td>\n",
       "      <td>1.634566</td>\n",
       "      <td>...</td>\n",
       "      <td>2.196806</td>\n",
       "      <td>2.420457</td>\n",
       "      <td>3.074958</td>\n",
       "      <td>-0.025747</td>\n",
       "      <td>-0.566038</td>\n",
       "      <td>-3.368841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.445385</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.234766</td>\n",
       "      <td>0.868894</td>\n",
       "      <td>-1.546296</td>\n",
       "      <td>-1.875137</td>\n",
       "      <td>-1.438579</td>\n",
       "      <td>-1.820523</td>\n",
       "      <td>-0.046354</td>\n",
       "      <td>-0.877706</td>\n",
       "      <td>1.634566</td>\n",
       "      <td>...</td>\n",
       "      <td>2.196806</td>\n",
       "      <td>2.420457</td>\n",
       "      <td>3.074958</td>\n",
       "      <td>-0.025747</td>\n",
       "      <td>-0.566038</td>\n",
       "      <td>-3.572600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.627654</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>-1.234766</td>\n",
       "      <td>0.868894</td>\n",
       "      <td>-1.546296</td>\n",
       "      <td>-1.875137</td>\n",
       "      <td>-1.438579</td>\n",
       "      <td>-1.820523</td>\n",
       "      <td>-0.046354</td>\n",
       "      <td>-0.877706</td>\n",
       "      <td>1.634566</td>\n",
       "      <td>...</td>\n",
       "      <td>2.196806</td>\n",
       "      <td>2.420457</td>\n",
       "      <td>3.074958</td>\n",
       "      <td>-0.025747</td>\n",
       "      <td>-0.566038</td>\n",
       "      <td>-3.776355</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.809923</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>-1.234766</td>\n",
       "      <td>0.868894</td>\n",
       "      <td>-1.546296</td>\n",
       "      <td>-1.875137</td>\n",
       "      <td>-1.438579</td>\n",
       "      <td>-1.820523</td>\n",
       "      <td>-0.046354</td>\n",
       "      <td>-0.877706</td>\n",
       "      <td>1.634566</td>\n",
       "      <td>...</td>\n",
       "      <td>2.196806</td>\n",
       "      <td>2.420457</td>\n",
       "      <td>3.074958</td>\n",
       "      <td>-0.025747</td>\n",
       "      <td>-0.566038</td>\n",
       "      <td>-3.875775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.898858</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>-1.234766</td>\n",
       "      <td>0.868894</td>\n",
       "      <td>-1.546296</td>\n",
       "      <td>-1.875137</td>\n",
       "      <td>-1.438579</td>\n",
       "      <td>-1.820523</td>\n",
       "      <td>-0.046354</td>\n",
       "      <td>-0.877706</td>\n",
       "      <td>1.634566</td>\n",
       "      <td>...</td>\n",
       "      <td>2.196806</td>\n",
       "      <td>2.420457</td>\n",
       "      <td>3.074958</td>\n",
       "      <td>-0.025747</td>\n",
       "      <td>-0.566038</td>\n",
       "      <td>-3.875775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.898858</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>585</td>\n",
       "      <td>0.823275</td>\n",
       "      <td>0.361652</td>\n",
       "      <td>0.480947</td>\n",
       "      <td>0.444622</td>\n",
       "      <td>0.897865</td>\n",
       "      <td>0.788392</td>\n",
       "      <td>-0.751150</td>\n",
       "      <td>0.915591</td>\n",
       "      <td>-1.046231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.946707</td>\n",
       "      <td>0.329396</td>\n",
       "      <td>-0.865654</td>\n",
       "      <td>-1.556534</td>\n",
       "      <td>-0.566038</td>\n",
       "      <td>0.620459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.451344</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>588</td>\n",
       "      <td>0.887142</td>\n",
       "      <td>0.082048</td>\n",
       "      <td>0.480947</td>\n",
       "      <td>0.444622</td>\n",
       "      <td>0.855108</td>\n",
       "      <td>0.765519</td>\n",
       "      <td>-0.747195</td>\n",
       "      <td>0.816667</td>\n",
       "      <td>-0.913005</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.854389</td>\n",
       "      <td>0.329396</td>\n",
       "      <td>-0.865654</td>\n",
       "      <td>-1.556534</td>\n",
       "      <td>-0.226797</td>\n",
       "      <td>0.620459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.399459</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>591</td>\n",
       "      <td>0.887142</td>\n",
       "      <td>-0.248586</td>\n",
       "      <td>0.478859</td>\n",
       "      <td>0.444622</td>\n",
       "      <td>0.816646</td>\n",
       "      <td>0.734660</td>\n",
       "      <td>-0.746654</td>\n",
       "      <td>0.698064</td>\n",
       "      <td>-0.755465</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.720322</td>\n",
       "      <td>0.329396</td>\n",
       "      <td>-0.865654</td>\n",
       "      <td>-1.550231</td>\n",
       "      <td>0.262462</td>\n",
       "      <td>0.620459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.399459</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>594</td>\n",
       "      <td>0.887142</td>\n",
       "      <td>-0.366016</td>\n",
       "      <td>0.372124</td>\n",
       "      <td>0.444622</td>\n",
       "      <td>0.737433</td>\n",
       "      <td>0.666039</td>\n",
       "      <td>-0.718979</td>\n",
       "      <td>0.573425</td>\n",
       "      <td>-0.699513</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.625746</td>\n",
       "      <td>0.329396</td>\n",
       "      <td>-0.865654</td>\n",
       "      <td>-1.227845</td>\n",
       "      <td>0.436235</td>\n",
       "      <td>0.620459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.399459</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>597</td>\n",
       "      <td>0.887142</td>\n",
       "      <td>-0.483446</td>\n",
       "      <td>0.265389</td>\n",
       "      <td>0.444622</td>\n",
       "      <td>0.658219</td>\n",
       "      <td>0.597418</td>\n",
       "      <td>-0.691304</td>\n",
       "      <td>0.448784</td>\n",
       "      <td>-0.643560</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.531170</td>\n",
       "      <td>0.329396</td>\n",
       "      <td>-0.865654</td>\n",
       "      <td>-0.905458</td>\n",
       "      <td>0.610002</td>\n",
       "      <td>0.620459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.399459</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index         0         1         2         3         4         5  \\\n",
       "0        0 -1.234766  0.868894 -1.546296 -1.875137 -1.438579 -1.820523   \n",
       "1        3 -1.234766  0.868894 -1.546296 -1.875137 -1.438579 -1.820523   \n",
       "2        6 -1.234766  0.868894 -1.546296 -1.875137 -1.438579 -1.820523   \n",
       "3        9 -1.234766  0.868894 -1.546296 -1.875137 -1.438579 -1.820523   \n",
       "4       12 -1.234766  0.868894 -1.546296 -1.875137 -1.438579 -1.820523   \n",
       "..     ...       ...       ...       ...       ...       ...       ...   \n",
       "195    585  0.823275  0.361652  0.480947  0.444622  0.897865  0.788392   \n",
       "196    588  0.887142  0.082048  0.480947  0.444622  0.855108  0.765519   \n",
       "197    591  0.887142 -0.248586  0.478859  0.444622  0.816646  0.734660   \n",
       "198    594  0.887142 -0.366016  0.372124  0.444622  0.737433  0.666039   \n",
       "199    597  0.887142 -0.483446  0.265389  0.444622  0.658219  0.597418   \n",
       "\n",
       "            6         7         8  ...        14        15        16  \\\n",
       "0   -0.046354 -0.877706  1.634566  ...  2.196806  2.420457  3.074958   \n",
       "1   -0.046354 -0.877706  1.634566  ...  2.196806  2.420457  3.074958   \n",
       "2   -0.046354 -0.877706  1.634566  ...  2.196806  2.420457  3.074958   \n",
       "3   -0.046354 -0.877706  1.634566  ...  2.196806  2.420457  3.074958   \n",
       "4   -0.046354 -0.877706  1.634566  ...  2.196806  2.420457  3.074958   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "195 -0.751150  0.915591 -1.046231  ... -0.946707  0.329396 -0.865654   \n",
       "196 -0.747195  0.816667 -0.913005  ... -0.854389  0.329396 -0.865654   \n",
       "197 -0.746654  0.698064 -0.755465  ... -0.720322  0.329396 -0.865654   \n",
       "198 -0.718979  0.573425 -0.699513  ... -0.625746  0.329396 -0.865654   \n",
       "199 -0.691304  0.448784 -0.643560  ... -0.531170  0.329396 -0.865654   \n",
       "\n",
       "           17        18        19   20        21  stimulus  repetition  \n",
       "0   -0.025747 -0.566038 -3.368841  0.0  1.445385         1           1  \n",
       "1   -0.025747 -0.566038 -3.572600  0.0  1.627654         1           1  \n",
       "2   -0.025747 -0.566038 -3.776355  0.0  1.809923         1           1  \n",
       "3   -0.025747 -0.566038 -3.875775  0.0  1.898858         1           1  \n",
       "4   -0.025747 -0.566038 -3.875775  0.0  1.898858         1           1  \n",
       "..        ...       ...       ...  ...       ...       ...         ...  \n",
       "195 -1.556534 -0.566038  0.620459  0.0 -0.451344         1           1  \n",
       "196 -1.556534 -0.226797  0.620459  0.0 -0.399459         1           1  \n",
       "197 -1.550231  0.262462  0.620459  0.0 -0.399459         1           1  \n",
       "198 -1.227845  0.436235  0.620459  0.0 -0.399459         1           1  \n",
       "199 -0.905458  0.610002  0.620459  0.0 -0.399459         1           1  \n",
       "\n",
       "[200 rows x 25 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_windows[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#axes = [8,10,11,12,15,2,20]\n",
    "axes = [8,10,11,12,15]\n",
    "train_x, train_y = splitxy(train_windows, axes, ['stimulus'])\n",
    "test_x, test_y = splitxy(test_windows, axes, ['stimulus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1241, 200, 5), (1241,), (609, 200, 5))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape, train_y.shape, test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1241/1241 [01:58<00:00, 10.50it/s]\n",
      "100%|██████████| 609/609 [00:57<00:00, 10.54it/s]\n"
     ]
    }
   ],
   "source": [
    "train_signatures = []\n",
    "for window in tqdm(train_x):\n",
    "    train_signatures.append(compute_log_signature(window, depth=4))\n",
    "test_signatures = []\n",
    "for window in tqdm(test_x):\n",
    "    test_signatures.append(compute_log_signature(window, depth=4))\n",
    "train_signatures = np.array(train_signatures)\n",
    "test_signatures = np.array(test_signatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1241, 205), (609, 205))"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_signatures.shape, test_signatures.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cs = np.linspace(0.5, 20, 20)\n",
    "training_losses = []\n",
    "testing_losses = []\n",
    "for C in Cs:\n",
    "    classifier = svm.SVC(C = C, kernel='rbf')\n",
    "    classifier.fit(train_signatures, train_y)\n",
    "    training_losses.append(accuracy(classifier.predict(train_signatures), train_y))\n",
    "    #print(\"training set accuracy:\", accuracy(classifier.predict(train_signatures), train_y))\n",
    "    pred = classifier.predict(test_signatures)\n",
    "    testing_losses.append(accuracy(pred, test_y))\n",
    "    #print(\"testing set accuracy:\", accuracy(pred, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc8363169d0>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Cs, training_losses, label=\"training\")\n",
    "plt.plot(Cs, testing_losses, label=\"testing\")\n",
    "plt.title(\"Accuracy vs. Regularization Strength (Depth 4)\")\n",
    "plt.xlabel(\"$C$ (larger is less regularization)\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set accuracy: 0.9879129734085415\n",
      "testing set accuracy: 0.8702791461412152\n"
     ]
    }
   ],
   "source": [
    "classifier = svm.SVC(C=10.0)\n",
    "classifier.fit(train_signatures, train_y)\n",
    "#training_losses.append(accuracy(classifier.predict(train_signatures), train_y))\n",
    "print(\"training set accuracy:\", accuracy(classifier.predict(train_signatures), train_y))\n",
    "pred = classifier.predict(test_signatures)\n",
    "#testing_losses.append(accuracy(pred, test_y))\n",
    "print(\"testing set accuracy:\", accuracy(pred, test_y))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "elasticnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import MultiTaskElasticNet\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "encode = OneHotEncoder()\n",
    "encoded_y = encode.fit_transform(train_y.reshape(-1, 1)).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultiTaskElasticNet(alpha=0.0001, l1_ratio=1.0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultiTaskElasticNet</label><div class=\"sk-toggleable__content\"><pre>MultiTaskElasticNet(alpha=0.0001, l1_ratio=1.0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultiTaskElasticNet(alpha=0.0001, l1_ratio=1.0)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elasticnet = MultiTaskElasticNet(alpha=0.0001, l1_ratio=1.0)\n",
    "elasticnet.fit(train_signatures, encoded_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set accuracy elastic:  0.7784045124899275\n"
     ]
    }
   ],
   "source": [
    "print(\"training set accuracy elastic: \", accuracy(train_y, np.array(stims)[np.argmax(elasticnet.predict(train_signatures), axis=1)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_pred = np.array(stims)[np.argmax(elasticnet.predict(test_signatures), axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing set accuracy: 0.7569786535303776\n"
     ]
    }
   ],
   "source": [
    "print(\"testing set accuracy:\", accuracy(task_pred, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.linspace(0.001, 0.1, 50)\n",
    "training_losses = []\n",
    "testing_losses = []\n",
    "for i, alpha in enumerate(alphas):\n",
    "    elasticnet = MultiTaskElasticNet(alpha=alpha, l1_ratio=1.0)\n",
    "    elasticnet.fit(train_signatures, encoded_y)\n",
    "    training_losses.append(accuracy(train_y, np.array(stims)[np.argmax(elasticnet.predict(train_signatures), axis=1)]))\n",
    "    testing_losses.append(accuracy(test_y, np.array(stims)[np.argmax(elasticnet.predict(test_signatures), axis=1)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc837fc7310>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(alphas, training_losses, label=\"training\")\n",
    "plt.plot(alphas, testing_losses, label=\"testing\")\n",
    "plt.title(\"Accuracy vs. Regularization Strength (1.0 Ratio)\")\n",
    "plt.xlabel(\"$\\\\alpha$ (Bigger is More Regularization)\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "svm confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EEsA2JhERWTypJzu2MYmIyOKxsiMikgCpV3ZMdkREEiD1ZMc2JhERWTxWdkREEiDA+OvkBNOEIgomOyIiCWAbk4iIyMJZRGXXeE4urOUlYodhVrYciBU7BLPTa3qY2CEQPTGpV3YWkeyIiOjxpJ7s2MYkIiKLx8qOiEgCpF7ZMdkREUmAIMggGJmsjH29mNjGJCIii8fKjohIAqT+8FYmOyIiCZD6mB3bmEREZPFY2RERSYDUJ6gw2RERSQDbmERERBaOlR0RkQSwjUlERBZPMEEb05yTHduYRERk8VjZERFJgABAMPJR43xSORER1WpayCCT8B1U2MYkIiKLx8qOiEgCOBuTiIgsnlaQQcaLyomIiCwXKzsiIgkQBBPMxjTj6ZhMdkREEiD1MTu2MYmIyOKxsiMikgCpV3ZMdkREEsDZmERERBaOlR0RkQRwNiYREVm88mRn7JidiYIRAduYRERk8VjZERFJAGdjEhGRxRNg/PPozLiLyTYmERFZPlZ2REQSwDYmPbEX/3UZ7TulooFfHkpLrJAQ74o1K5vjVrKT7pj/zDiF7n2S9V538bwLJr/xbE2HK4oNS91x8Pc6SL6qgK1Si+atCxE18zZ8AkoqPf7Tdxrg92/q4o3ZtzBwdIZu++0btlj9oTfOH3NEWakM4V1yMW7uLbjUU9fUt1LrhLTNx4tj0hEYWgg3TzWiRzXE4T/riB1WrRISno1Bo1IQEJwPN/dSzBnfHId31tXtb989E5EvpSIgOA8qFzXeGtgK1y86ihhxNZJ4H1PUNubKlSvRokULODs7w9nZGREREfjjjz/EDMkgoWF3seVnf0x+41m895/2sLISMPeTw1Ao9f8Anzjijn8930u3zJrSTqSIa97Zw47oNyITi3+7gvkbrkGjAWa83BjFhRV/9A79ocLFUw5w8yzV215cKMeMlxtDJgMW/HAVi365AnWpHB8M94dWW1PfSe2jtNfi+gU7LH+vgdih1FpKey0SLzlg5dyAyvfbaXAhzhlrF/nXcGTSsG/fPvTr1w/e3t6QyWTYtGmT3v4RI0ZAJpPpLe3a6f99LCkpwfjx41G3bl04ODjg+eefR0pKisGxiFrZNWjQAB999BECAsp/EGNjY9G/f3/ExcUhODhYzNCq5IPJEXrrn8wPw7e/bUVAk2ycP/Pg02NZqRxZ95Q1HV6tELP+ut765E9uYkhoKK6ctUNouwLd9sxUGyx/rz7mrb+OD/7dSO8154854E6yLZZvuwQHJ63uPIObh+L0AUe0eja/+r+RWujEbmec2O0sdhi12on9rjix3/WR+3dt9gAAuHsX11RI4jFBGxMGvr6goAAtW7bEyJEjMWjQoEqP6d27N9asWaNbt7W11ds/ceJEbN68GRs2bICbmxsmT56Mvn374uTJk7CysqpyLKImu379+umtz5s3DytXrsSRI0fMItk9zMGhDACQn6v/nxUalol1m/9AQb4N4uPq4qvPmyEnWyFGiKIryC3/4XSqo9Ft02qBhRN8MXhMOho2qfhHp6xUBsgAG9sHPRRbhRZyuYDzx6Sb7IgMIcYdVCIjIxEZGfnYYxQKBTw9PSvdl5OTgy+//BJff/01unfvDgD45ptv4OPjgx07dqBXr15VjqXWzMbUaDTYsGEDCgoKEBERUekxJSUlyM3N1VtqDwGjx5/HuTOuSEp88Gn7xBEP/PfDcMyY0AFfLAtBULMsxCw5CGsbzWPOZZkEAfg8uj6Cn85Hw6YPktr3y91hZSVgQFRmpa9rGl4Apb0WX87zRnGhDMWFcqye4w2tVoZ76Rx2JqppD/8dLimpfAy+Kvbs2QN3d3cEBQVh9OjRSE9P1+07efIkysrK0LNnT902b29vhISE4NChQwZ9HdGTXXx8PBwdHaFQKPDmm29i48aNaN68eaXHzp8/HyqVSrf4+PjUcLSPNmbSWTRsnIOF0a31tu/fVR/HD3siKdEZxw564oMpEajvk4+nI+6IFKl4ls+oj8QEO0xfkaTbduWsHTZ9UQ9TFt+E7BEdkjpuGry36gaObnfGgMAWeKFJKArzrBAQWgh51bsYRJJ2fzamsQsA+Pj46P0tnj9//hPFFBkZiXXr1mHXrl34+OOPcfz4cXTt2lWXPNPS0mBrawsXFxe913l4eCAtLc2gryX6x+ImTZrg9OnTyM7Oxk8//YThw4dj7969lSa86dOnY9KkSbr13NzcWpHw3px4Fm07pGHaW8/gbobdY4/NuqtEepo9vH0KHnucpVk+sz4Ob1Ph441XUc+7TLc9/qgjsjOt8a82D9rWWo0Mq2d7Y9Pqevjq2AUAQHjnPKw9nICcu1awsgYcVRoMbRkMT58n/0RJJCmCzOAxt0rPASA5ORnOzg86WArFkw3LDBkyRPfvkJAQtG7dGn5+ftiyZQsGDhz46DAEAbJHfTp+BNGTna2trW6CSuvWrXH8+HF8+umnWLVqVYVjFQrFE7+p1UPAm/+JR8SzqZg+vgPupDr84yucnEtRz70I9+7Wpu+j+ghCeaI7tFWF//vxKjx99Wdadh90D6065ultmzGsEboNykLPIfcqnE/lVt7+PX2gPEm261mbWtlE0nB/Br2peXl5wc/PD1euXAEAeHp6orS0FFlZWXrVXXp6Otq3b2/QuUVPdg8TBMGo/m9NGjv5LDp1T8Gc6W1RVGgNF9fycaiCfBuUllpBaafGK6Mu4uAeb9y7q4SHVyGGv34BuTm2OLzXS+Toa8ayGQ2we6MLotdch52jVjfG5uCkgcJOgLOrBs6u+uOX1taAi7ta71q8Pze4wjewGCo3NRJOOmDlB/XxwusZj7xeTwqU9hp4+z/4/j19S9EouBB5WdbIuG37mFdKh9JeA2/fIt26R/1iNGqaj7wca2SkKuGoKoO7Vwlc3cs/hDVoWAgAyMq0RVamZb2H5vCIn7t37yI5ORleXuV/H8PDw2FjY4Pt27fjpZdeAgCkpqbi3LlzWLhwoUHnFjXZzZgxA5GRkfDx8UFeXh42bNiAPXv2YOvWrWKGVWXPvXADALBg2UG97Z/MC8OOP3yh1cjg1ygXXXsnw8GxDFl3lTh7qi4+mtUGRUU2IkRc836LLb8EY+qgQL3tkz+5WWnl9igp1xRYM98LedlW8PApxcsT7mDg6xn//EILFtSyEP/34zXd+pvRtwEA2753wcf/8RMrrFolMDgPC2LP6tZff7f8UpjtGz3wycwmaNflLibFXNbtf3fRRQDAuuW+WLe8YY3GWu1EuKg8Pz8fV69e1a0nJibi9OnTcHV1haurK6KjozFo0CB4eXnhxo0bmDFjBurWrYsXXngBAKBSqRAVFYXJkyfDzc0Nrq6umDJlCkJDQ3WzM6tKJgjiPaEoKioKO3fuRGpqKlQqFVq0aIFp06ahR48eVXp9bm4uVCoVuvuPh7VcGm1BU9lyYJPYIZidXvXDxA7BLFmpeC2gIdRCKXZmf42cnByTtArv/530W/0+5PbGXe+rLSxG0ug5VY5tz5496NKlS4Xtw4cPx8qVKzFgwADExcUhOzsbXl5e6NKlC+bMmaM3F6O4uBhTp07F+vXrUVRUhG7dumHFihUGz9cQtbL78ssvxfzyRESSIca9MTt37ozH1VN//vnnP55DqVRi6dKlWLp0qUFf+2FVSnZLliyp8gknTJjwxMEQEVE1MuN7WxqrSsnuk08+qdLJZDIZkx0REdU6VUp2iYmJ1R0HERFVI6k/4ueJ76BSWlqKS5cuQa2W7iNWiIjMhmCixUwZnOwKCwsRFRUFe3t7BAcH4+bNmwDKx+o++ugjkwdIRERkLIOT3fTp03HmzBns2bMHSuWDaazdu3fHd999Z9LgiIjIVGQmWsyTwZcebNq0Cd999x3atWund2+y5s2b49q1a495JRERiYZPKjdMRkYG3N3dK2wvKCgw+MacRERENcHgZNemTRts2bJFt34/wa1evfqRz6EjIiKRSXyCisFtzPnz56N37964cOEC1Go1Pv30U5w/fx6HDx/G3r17qyNGIiIylgkf8WOODK7s2rdvj4MHD6KwsBCNGzfGtm3b4OHhgcOHDyM8PLw6YiQiIjLKE90bMzQ0FLGxsaaOhYiIqok5POKnOj1RstNoNNi4cSMSEhIgk8nQrFkz9O/fH9bWte7xeEREBEh+NqbB2encuXPo378/0tLS0KRJEwDA5cuXUa9ePfz6668IDQ01eZBERETGMHjM7rXXXkNwcDBSUlJw6tQpnDp1CsnJyWjRogVef/316oiRiIiMdX+CirGLmTK4sjtz5gxOnDgBFxcX3TYXFxfMmzcPbdq0MWlwRERkGjKhfDH2HObK4MquSZMmuHPnToXt6enpCAgIMElQREREplSlyi43N1f375iYGEyYMAHR0dFo164dAODIkSP48MMPsWDBguqJkoiIjMMJKv+sTp06ercCEwQBL730km7b/ceu9+vXDxqNphrCJCIio0j8ovIqJbvdu3dXdxxERETVpkrJrlOnTtUdBxERVSe2MZ9MYWEhbt68idLSUr3tLVq0MDooIiIyMSY7w2RkZGDkyJH4448/Kt3PMTsiIqptDL70YOLEicjKysKRI0dgZ2eHrVu3IjY2FoGBgfj111+rI0YiIjIWH/FjmF27duGXX35BmzZtIJfL4efnhx49esDZ2Rnz58/Hc889Vx1xEhGRMSQ+G9Pgyq6goED3pHJXV1dkZGQAKH8SwqlTp0wbHRERkQk80R1ULl26BAB46qmnsGrVKty6dQufffYZvLy8TB4gEREZ7/7twoxdzJXBbcyJEyciNTUVADBr1iz06tUL69atg62tLdauXWvq+IiIyBQ4G9Mwr7zyiu7fYWFhuHHjBi5evAhfX1/UrVvXpMERERGZgtFPW7W3t0erVq1MEQsREVG1qFKymzRpUpVPuGjRoicOhoiIqocMJnjEj0kiEUeVkl1cXFyVTvb3m0UTERHVFhZxI2hBqYBgpRA7DLMSNm+s2CGYHatRZjw6LyL3Q5lih2BeNCVAdjWcl9fZERERWTajJ6gQEZEZ4KUHRERk8SSe7NjGJCIii8fKjohIAkxxuy9zvl3YE1V2X3/9NTp06ABvb28kJSUBABYvXoxffvnFpMEREZGJSPwRPwYnu5UrV2LSpEno06cPsrOzdQ9rrVOnDhYvXmzq+IiIiIxmcLJbunQpVq9ejZkzZ8LKykq3vXXr1oiPjzdpcEREZCISr+wMHrNLTExEWFhYhe0KhQIFBQUmCYqIiEyLY3YG8vf3x+nTpyts/+OPP9C8eXNTxERERGRSBld2U6dOxbhx41BcXAxBEHDs2DF8++23mD9/Pr744ovqiJGIiIwl8duFGZzsRo4cCbVajXfeeQeFhYUYNmwY6tevj08//RRDhw6tjhiJiMhYEr+o/Imusxs9ejRGjx6NzMxMaLVauLu7mzouIiIikzHqonI+mZyIyDxIfYKKwcnO39//sc+tu379ulEBERFRNWAb0zATJ07UWy8rK0NcXBy2bt2KqVOnmiouIiIikzE42b399tuVbl++fDlOnDhhdEBERFQNTNDGNOfKzmRPPYiMjMRPP/1kqtMREZEpSfwOKiZLdj/++CNcXV1NdToiIiKTMbiNGRYWpjdBRRAEpKWlISMjAytWrDBpcEREZCKcoGKYAQMG6K3L5XLUq1cPnTt3RtOmTU0VFxERmRAvPTCAWq1Gw4YN0atXL3h6elZXTERERCZl0JidtbU1xowZg5KSkuqKh4iIyOQMnqDStm1bxMXFVUcsRERUXSQ+G9PgMbuxY8di8uTJSElJQXh4OBwcHPT2t2jRwmTBERERmUKVk92oUaOwePFiDBkyBAAwYcIE3T6ZTAZBECCTyaDRaEwfJRERGYUTVKooNjYWH330ERITE6szHiIiqi5mnKyMVeVkJwjl75Kfn1+1BUNERFQdDBqze9zTDoiIqBbjReVVFxQU9I8J7969e0YFREREpscxOwPMnj0bKpWqumIhIiKqFgYlu6FDh8Ld3b26YiEiourCNmbVcLyuoj59r+K5ftfg4VEAAEhKUuHbb5rjxHEvWFlp8erIeLR5Og2envkoKLTB6VMeWPNlC9y7aydy5OKykmnxxrPH0SfkCtwcCpGZb4/NZ5ti9YFwCCj/ObOzKcOErkfQJSgRKrti3M5xwobjofjhVIjI0YvDSq7F611OoHfLK3BzLERmnj1+i2uCL/eGQxDu/24KeL3LCbzQOgFOdiU4n+KOBb91xPV06T6NpE+/a3ju+et/+x11xrdfN8OJY15/HSHglVcvoPdziXB0KsWlBFesWBKGm0mW18GSehuzyndQuT8bkx7IzLTHmi9b4O1xPfD2uB44c9od788+CF+/HCgUagQEZOPbb5pj/NgemDu7A+o3yMOsDw+IHbboRrSPw+BWF/DRnx0xcNVQfLorAq+2O42hbeJ1x0zpcRDtG93EzF+6YeCqoVh3rCXe6XUAnYOkeenL8I5xGNTmAhb+9gxeXDIES7e1w7+fOYMhbeP/dsxpDGt/Fgu3PIPhnw3C3Xx7LB/+G+xtS0WMXFyZmXZYszoEb4/thrfHdsOZOHe8/+Eh+PrlAAAGD72EFwZfwcqlYZg4thuyspSYt3A/7OzKRI7cMuzbtw/9+vWDt7c3ZDIZNm3apLdfEARER0fD29sbdnZ26Ny5M86fP693TElJCcaPH4+6devCwcEBzz//PFJSUgyOpcrJTqvVmryFGR0dDZlMpreY0w2mjx3xxoljXrh1ywm3bjnhqzWhKC6yRtNmd1FYaIuZ73bC/n0+uJXijEsJbli5rBUCg7JQr16B2KGLqkX9O9h7uSEOXPVDao4zdlxsjCOJDdDcK+Nvx6Tht/gmOHmzPlJznPFzXHNcvuOmd4yUhPrcwd6LDXHwsh9Ss52x83xjHL3aAM3r338/BLwcEY81+1ph94VGuJbuilk/dYXSRo3eLa6KGruYjh3+63c0xQm3Upzw1f9Cyn9Hm98DIGDAwKvYsL4pDh2oj6QbKny8oA0USg06d0sWO3TTE+F2YQUFBWjZsiWWLVtW6f6FCxdi0aJFWLZsGY4fPw5PT0/06NEDeXl5umMmTpyIjRs3YsOGDThw4ADy8/PRt29fg29gYrKHtz6p4OBgpKam6pb4+Ph/flEtJJdr8Wznm1Aq1Ui44FbpMQ4OZdBqgfwC2xqOrnY5neyJpxvegq9rNgAgyD0TTzVIw8Grvg+OSfFCp8AbqOeUD0BAa79b8HPNwaFrPuIELbLTSV5o0ygFvm7ZAIBAz0y09EvDwcvl71l9lzzUdSrEkasP3p8yjRVO3fBGC980MUKudeRyAc92SYZSqUHCBTd4ehXA1a0Yp0546I5Rl1kh/kxdNAu+K2Kk1USEZBcZGYm5c+di4MCBFcMRBCxevBgzZ87EwIEDERISgtjYWBQWFmL9+vUAgJycHHz55Zf4+OOP0b17d4SFheGbb75BfHw8duzYYVAsBt8b09Ssra3Nqpp7WMOG2fh4yS7Y2mpQVGSNObM7IPlmxX6/jY0GI187iz27fFFUaCNCpLXHmsNhcFSUYuOb30KjlcNKrsXyPW2x9UKg7pgFfz6DD57bg20TvkaZRg5BAD7c0hmnU7wec2bLFbv/KTgqS/HjhA3QCnLIZVqs2Pk0/owvf8/cHAsBAHfz9ceD7+bbwatOXoXzSUlD/xx8vHQXbG215b+jsyKQnOSMZs0zAQDZWUq947OzlHD3KBQjVLORm5urt65QKKBQKAw6R2JiItLS0tCzZ0+983Tq1AmHDh3CG2+8gZMnT6KsrEzvGG9vb4SEhODQoUPo1atXlb+e6MnuypUr8Pb2hkKhQNu2bRETE4NGjRpVemxJSYne44UefsPFkJLihLfe7AFHxzJ0eCYFk6cewzuTO+slPCsrLd6deRgymYDlS8NFjLZ26NX8KvqEXsaMTd1xLcMVTTwyMaXHQWTk2WNzfPkDgF9uE4/Q+nfw9veRSM1xQivf25jeez8y8x1w9EYDkb+Dmtcz9BoiW17Gez92x7V0FzTxvItJfQ4iI9cBW0430R338NC6TCboJv1IVUqyE956vQccHUvRoeMtTJ52HO9M6qzbX/E9q7jNEphygoqPj36HZdasWYiOjjboXGlp5R0HDw8Pve0eHh5ISkrSHWNrawsXF5cKx9x/fVWJmuzatm2Lr776CkFBQbhz5w7mzp2L9u3b4/z583Bzq9gKnD9/PmbPni1CpI+mVlsh9bYTAODKZVcENrmH/i9cwbJPWwMoT3TT3zsMD88CTJ/aWfJVHQBM7HYYaw61wp9/VXJXM9zgpcrHyPZx2BzfFAprNcZ3OYpJP/bGgavlt6e7ku6GJh6Z+He705JMdhN6HUbsvjBsiw8AAFy74wavOnkY+Wwctpxugrv59gCAuk5FuJv/4Ekkrg7FuJcv7dm/arUcqbcdAdz/Hc1C/4FX8OOG8g8JLq7FyLr34D1S1SlGdray0nOZNRNeepCcnAxnZ2fdZkOrur97eKb//YcKPDaMKhzzMFHH7CIjIzFo0CCEhoaie/fu2LJlC4Dym05XZvr06cjJydEtycm1bxBZJgNsbLUAHiQ67/p5mDGtE/LynvwHwpIordUVPjlrBRnkf31stJZrYWOlrXCMRivXHSM1Shs1tIL+L7dGkEH21/txK8sJmXn2aNv4we+EtZUGrRrextmb5jtMUB1kMgE2NlqkpTrg3l0lWoWn6/ZZW2sR2jITCecrH3encs7OznrLkyS7+8NXD1do6enpumrP09MTpaWlyMrKeuQxVSX6BJW/c3BwQGhoKK5cuVLpfoVCUeFNFtPwUWcRHJIBd48CNGyYjVdHxiO0RQb27PSFXK7FjA8OITDoHv7vo3awkgtwcSmCi0sRrK2l/RikfVcaIqrDKTwTkAQvVS66NLmOfz19Brsu+QMACkptcSLJGxO7Hka47y14q3LRr8VF9A29hN1/HSM1+y/6YVSnU+gQlASvOrno3CwRr7Q/iz0J998PGb49HIqRz8ahc7NENHa/h+iBu1FcZo2tZwNEjV1Mw6PiERz61++ofw5eHXUOoS3Lf0cBGTb9HICXhl1ERIdb8GuYg0nvHEdJsRX27LTAiVC17OGt/v7+8PT0xPbt23XbSktLsXfvXrRv3x4AEB4eDhsbG71jUlNTce7cOd0xVSX6mN3flZSUICEhAR07dhQ7lCqpU6cEU6YdhatrMQoKbJCYqMIHMzoi7pQn3D0KENH+NgBg+apteq+bNrkz4s9K9040C7Y9g7GdjmFG731wsS9CRr4Dfoxrjs/3t9Yd8+7GHhjf5QhiBuyEs7IYqTlOWL6nLX44FSxi5OL5vy3P4M1ux/Fuv/1wcShCZp4Dfj7eHKv3PBgDjt3/FBTWarzbbz+clCU4l+KOt2L7orBUurN/67iUYMq7xx/8jl5X4YPpHRF3srwq+HFDEyhsNRj3dpzuovL3pnVEUZHlDTeIcVF5fn4+rl59cOlLYmIiTp8+DVdXV/j6+mLixImIiYlBYGAgAgMDERMTA3t7ewwbNgwAoFKpEBUVhcmTJ8PNzQ2urq6YMmWKrhtoWOwiXi0+ZcoU9OvXD76+vkhPT8fcuXOxd+9exMfHV+lRQrm5uVCpVOjWbAqsrdgiNERqZ7ZpDGVVLM0WqrHcD2WKHYJZUWtKsPPSIuTk5Jike3X/72TTCTGwUhg3FqkpKcbFJTOqHNuePXvQpUuXCtuHDx+OtWvXQhAEzJ49G6tWrUJWVhbatm2L5cuXIyTkwZ2SiouLMXXqVKxfvx5FRUXo1q0bVqxYUWGSzD8RtbJLSUnByy+/jMzMTNSrVw/t2rXDkSNH+Mw8IiJTE+HemJ07d37s3bdkMhmio6MfO5NTqVRi6dKlWLp0qWFf/CGiJrsNGzaI+eWJiCSD98YkIiKycLVqggoREVUTPuKHiIgsnsSTHduYRERk8VjZERFJgOyvxdhzmCsmOyIiKWAbk4iIyLKxsiMikgCpX2fHZEdEJAVsYxIREVk2VnZERFJhxpWZsZjsiIgkQOpjdmxjEhGRxWNlR0QkBRKfoMJkR0QkAWxjEhERWThWdkREUsA2JhERWTq2MYmIiCwcKzsiIilgG5OIiCyexJMd25hERGTxWNkREUmA1CeoMNkREUkB25hERESWjZUdEZEEyAQBMsG40szY14uJyY6ISArYxiQiIrJsrOyIiCSAszGJiMjysY1JRERk2SyislM7KwBrpdhhmJUCHzP+iCaSgI8uiB2CWbr5RrDYIZgVTUkxcMn052Ubk4iILB/bmERERJaNlR0RkQSwjUlERJaPbUwiIiLLxsqOiEgizLkNaSwmOyIiKRCE8sXYc5gptjGJiMjisbIjIpIAzsYkIiLLx9mYRERElo2VHRGRBMi05Yux5zBXTHZERFLANiYREZFlY2VHRCQBnI1JRESWjxeVExERWTZWdkREEsA2JhERWT7OxiQiIrJsrOyIiCSAbUwiIrJ8nI1JRERk2VjZERFJANuYRERk+Tgbk4iIyLKxsiMikgC2MYmIyPJphfLF2HOYKbYxiYjI4rGyIyKSAolPUGGyIyKSABlMMGZnkkjEwTYmERFZPFZ2RERSIPHbhTHZmdDQF+Ix6pU4/PxbM3y2tg0A4N8vnUbnDjdQz60QZWo5rlx3xdpvw3DxSj2Ro605rd1v47XgMwh2y4CHfSHG7u6FHcn+uv09fa9jSNAFhLhmwkVZjP6bByMhq65uf32HXOwetL7Sc0/Y2wNbkxpX+/dQG4SEZ2PQqBQEBOfDzb0Uc8Y3x+GdD96n9t0zEflSKgKC86ByUeOtga1w/aKjiBHXvHDv2xgZfhrN3TPg7liICZt7Y9d1/78dIWBs2xMYHHIBzsoSxKd5YO7ujrh2z1V3hI2VBlOeOYQ+Ta5CYa3G0eT6mLv7WdzJN+/3UoxLD6KjozF79my9bR4eHkhLSwMACIKA2bNn4/PPP0dWVhbatm2L5cuXIzg42LhAKyFqG7Nhw4aQyWQVlnHjxokZ1hMJapyJPt2v4NoNF73tKbedseyLp/H6pH6Y9F5v3El3xPz3dkDlXCxSpDXP3lqNi1lumHPsmUr321mrcSrdE/891bbS/amFjmj//at6y6enW6OgzBr7bvlWZ+i1itJei8RLDlg5N6Dy/XYaXIhzxtpF/pXulwI7mzJcynRDzJ6Ole4fFX4ar4adQcyejhi6YRAyC+yx+oXNsLcp1R3z7rMH0K1xIqb+0QOv/jAA9jZlWP7875DLtDX1bViU4OBgpKam6pb4+HjdvoULF2LRokVYtmwZjh8/Dk9PT/To0QN5eXkmj0PUyu748ePQaDS69XPnzqFHjx548cUXRYzKcEplGd59ez8++awdhg2O19u3+0AjvfVVsa0R2f0q/P2ycDreqybDFM2+277Yd/vRSemX60EAyiu4ymgFOTKL7fW29fBNxO83AlCotjFdoLXcif2uOLHf9ZH7d232AAC4e0vng9TDDiT54UCS3yP2Cvh32Fl8fjwcO66V/17O2N4Ve0evxXNNruCHc8FwtC3BwOCLmP5nNxxJbgAAePfP7tgx6mu080nBoZtm/OFKpNmY1tbW8PT0rHgqQcDixYsxc+ZMDBw4EAAQGxsLDw8PrF+/Hm+88YaRweoTtbKrV68ePD09dctvv/2Gxo0bo1OnTmKGZbDxrx3FsVMNEBfv/djjrK016NPjCvILbHD9oQqQqi7YNQPNXe/ix6tNxQ6FzEgD5zzUcyjEoZsNdNvKNFY4keKNp7zK22rN3TNgY6XFoZs+umMyChxw9a4rwrzTajxmU5IJgkkWAMjNzdVbSkpKHvl1r1y5Am9vb/j7+2Po0KG4fv06ACAxMRFpaWno2bOn7liFQoFOnTrh0KFDJv/+a82YXWlpKb755htMmjQJMlnlE1xLSkr03tTc3MorgZrUuUMiAvzv4a13n3vkMW3DUzBj4j4oFGrcy7LDux/2QG6esgajtCyDAxNwNdsFcRkVPy0SPUpdh0IAwN1C/S7B3UI7eDvn644pVcuRW6KocIybfVHNBGoGfHx89NZnzZqF6OjoCse1bdsWX331FYKCgnDnzh3MnTsX7du3x/nz53Xjdh4eHnqv8fDwQFJSksljrjXJbtOmTcjOzsaIESMeecz8+fMrDHaKqZ5bAcaMPI7pc7qjrMzqkcedOeeBMVP7wtmpBH26X8F7k/ZhwvRIZOfa1WC0lkFhpUY//6tYcTZc7FDITD08oVAm++dJho/4/G1etH8txp4DQHJyMpydnXWbFQpFpYdHRkbq/h0aGoqIiAg0btwYsbGxaNeuHQBUKG4EQXhkwWOMWnOd3ZdffonIyEh4ez+6FTh9+nTk5OToluTk5BqMsKLARnfhUqcYyxduwR/ffY0/vvsaLYPvYECfBPzx3deQy8t/MopLbHA7zRkXr9TDopXtodHK0LvbVVFjN1e9/a5DaaXGxmtBYodCZiazoLyiu1/h3edqV4S7hXa6Y2yttXBWlDzyGHNlyjams7Oz3vKoZPcwBwcHhIaG4sqVK7pxvPsV3n3p6ekVqj1TqBWVXVJSEnbs2IGff/75sccpFIoqv6k1IS7eC6//p5/etsnjDiH5lgrfbwqGVvvozxI2NppH7qNHGxyQgF0pDZFVYt5/eKjmpeQ6IaPAHhG+KbiYUX7pj7Vcg9YNbuOTA+VVxoX0eijTyBHhm4w/r5TPeq1rX4AAt3v4+K9j6MmVlJQgISEBHTt2hL+/Pzw9PbF9+3aEhYUBKB/O2rt3LxYsWGDyr10rkt2aNWvg7u6O55579LhXbVRUbIMbyfoTTYpLrJGbp8CNZBcoFWV4eVA8Dh/3wb0sOzg7laBfr0uo51aAfYcaihO0COyty+DnlKNbb+CYi2YumcguVSC1wAkq22J4O+TD3b4AAOCvygYAZBTZ683C9HXKQRuPVIze2adG468tlPYaePs+GDfyqF+MRk3zkZdjjYxUJRxVZXD3KoGre/k0+gYNyyuYrExbZGXaihJzTbOzKYOv6sHPWn1VLprUzUROiQJpeU74Oq4FRrc5hZvZKiRlqzC6zSkUl1ljy6VAAEB+qQI/n2+KqR0PIbtYiZxiBaZ0PIwrd111szPNlgizMadMmYJ+/frB19cX6enpmDt3LnJzczF8+HDIZDJMnDgRMTExCAwMRGBgIGJiYmBvb49hw4YZGWhFoic7rVaLNWvWYPjw4bC2Fj0ck9Jo5fCpn4senfbA2bkEeXkKXLrmhknv90ZSSh2xw6sxIW7p+KbXZt36jDaHAQA/Xw3Cu4e6oqvPDSzosEe3f/GzOwAAS8+EY+mZNrrtgwMu4k6hAw7c1h8cl4rA4DwsiD2rW3/93fJZbds3euCTmU3QrstdTIq5rNv/7qKLAIB1y32xbnnDGo1VLCHu6Vgz+Ffd+rRny2f1bbrQBO9t74r/nXwKSms13uuyH86KEpxNc8frm/qisOzBh4EF+zpArZXj48htUFhrcDS5Pt7a1gdaodaM+jwZEe6gkpKSgpdffhmZmZmoV68e2rVrhyNHjsDPr/zykHfeeQdFRUUYO3as7qLybdu2wcnJybg4KyETBHHv/7Jt2zb06tULly5dQlCQYeMwubm5UKlU6NR2JqytObvREDeet//ng0hPwEcXxA7BLN18w/R3w7BkmpJiXP5kBnJycvQmgTyp+38nn+3wvtF/J9XqYuw7OMdksdUk0Uupnj17QuR8S0Rk8fikciIisnwSvxG0mTehiYiI/hkrOyIiCZBpyxdjz2GumOyIiKSAbUwiIiLLxsqOiEgKRHrET23BZEdEJAF/v7elMecwV2xjEhGRxWNlR0QkBRKfoMJkR0QkBQKMf56d+eY6tjGJiMjysbIjIpIAqU9QYbIjIpICASYYszNJJKJgG5OIiCweKzsiIingbEwiIrJ4WgAyE5zDTLGNSUREFo+VHRGRBHA2JhERWT6Jj9mxjUlERBaPlR0RkRRIvLJjsiMikgKJJzu2MYmIyOKxsiMikgKJX2fHZEdEJAFSv/SAbUwiIrJ4rOyIiKRA4hNUmOyIiKRAKwAyI5OV1nyTHduYRERk8VjZERFJAduYRERk+UyQ7Mz4UeVmneyEv/7j1OoSkSMxP9pidrANpRZKxQ7BLGlKisUOwazcf78EM66iaiOzTnZ5eXkAgIMn/ytyJGboqNgBmJ9EsQMwV5+IHYB5ysvLg0qlMt0J2cY0X97e3khOToaTkxNkMmNvDWBaubm58PHxQXJyMpydncUOxyzwPXsyfN8MV5vfM0EQkJeXB29vb9OeWCvA6DakGc/GNOtkJ5fL0aBBA7HDeCxnZ+da98tU2/E9ezJ83wxXW98zk1Z0BMDMkx0REVWRoC1fjD2HmWKyIyKSAomP2XFKXjVRKBSYNWsWFAqF2KGYDb5nT4bvm+H4nkmPTOD8ViIii5WbmwuVSoXu9d+Etdy45K7WlmDHrc+Qk5NTK8c6H4dtTCIiKWAbk4iIyLKxsiMikgIBJqjsTBKJKJjsiIikgG1MIiIiy8bKjohICrRaAEZeFK4134vKWdnVgOTkZIwaNUrsMGqdhIQErFmzBhcvXgQAXLx4EWPGjMGoUaOwa9cukaMzD1lZWVi8eDHGjRuHuXPnIjk5WeyQaqXx48dj//79YochrvttTGMXM8Xr7GrAmTNn0KpVK2g0GrFDqTW2bt2K/v37w9HREYWFhdi4cSNeffVVtGzZEoIgYO/evfjzzz/RtWtXsUOtVby9vREfHw83NzckJiaiffv2AIDQ0FAkJCQgLy8PR44cQdOmTUWOtHaRy+WQyWRo3LgxoqKiMHz4cHh6eoodVo3QXWdXLwrWclujzqXWlmJHxpdmeZ0dk50J/Prrr4/df/36dUyePJnJ7m/at2+Prl27Yu7cudiwYQPGjh2LMWPGYN68eQCAmTNn4vjx49i2bZvIkdYucrkcaWlpcHd3x8svv4y0tDRs2bIF9vb2KCkpweDBg6FUKvHDDz+IHWqtIpfLsX37dmzevBnr1q1DTk4OIiMjMXr0aPTp0wdyueU2uXTJru4o0yS7zP8x2UnV/U+Nj3srZTIZk93fqFQqnDx5EgEBAdBqtVAoFDh69ChatWoFADh37hy6d++OtLQ0kSOtXf6e7Bo1aoQvvvhCr/o9evQoBg8ezHbmQ/7+vpWVlWHjxo343//+hx07dsDDwwMjRozAyJEjERAQIHaoJqdLdq4jTZPs7q0xy2RnuR9napCXlxd++uknaLXaSpdTp06JHWKtJpfLoVQqUadOHd02Jycn5OTkiBdULXb/2Y0lJSXw8PDQ2+fh4YGMjAwxwjIbNjY2eOmll7B161Zcv34do0ePxrp169CkSROxQ6NqxGRnAuHh4Y9NaP9U9UlRw4YNcfXqVd364cOH4evrq1tPTk6Gl5eXGKHVet26dUOrVq2Qm5uLy5cv6+27efMm6tatK1Jk5sfX1xfR0dFITEzE1q1bxQ6nWgmC1iSLueKlByYwdepUFBQUPHJ/QEAAdu/eXYMR1X5jxozRa+uGhITo7f/jjz84OaUSs2bN0lu3t7fXW9+8eTM6duxYkyGZBT8/P1hZWT1yv0wmQ48ePWowIhEIgvFPGjfjD+0csyMismD3x+y61XkV1jIjx+yEUuzM/sosx+xY2RERSYEgwOibW5pxbcRkR0QkBVotIDNyzM2Mx+w4QYWIiCweKzsiIimQeBuTlR1ZjOjoaDz11FO69REjRmDAgAE1HseNGzcgk8lw+vTpRx7TsGFDLF68uMrnXLt2rd51iE9KJpNh06ZNRp+HzI+g1ZpkMVdMdlStRowYAZlMBplMBhsbGzRq1AhTpkx57KUapvLpp59i7dq1VTq2KgmKiMwX25hU7Xr37o01a9agrKwM+/fvx2uvvYaCggKsXLmywrFlZWWwsbExyddVqVQmOQ+RRWAbk6h6KRQKeHp6wsfHB8OGDcMrr7yia6Xdbz3+73//Q6NGjaBQKCAIAnJycvD666/D3d0dzs7O6Nq1K86cOaN33o8++ggeHh5wcnJCVFQUiouL9fY/3MbUarVYsGABAgICoFAo4Ovrq7vxtL+/PwAgLCwMMpkMnTt31r1uzZo1aNasGZRKJZo2bYoVK1bofZ1jx44hLCwMSqUSrVu3RlxcnMHv0aJFixAaGgoHBwf4+Phg7NixyM/Pr3Dcpk2bEBQUBKVSiR49elS4B+bmzZsRHh4OpVKJRo0aYfbs2VCr1QbHQxZIK5hmMVNMdlTj7OzsUFZWplu/evUqvv/+e/z000+6NuJzzz2HtLQ0/P777zh58iRatWqFbt264d69ewCA77//HrNmzcK8efNw4sQJeHl5VUhCD5s+fToWLFiA999/HxcuXMD69et195Y8duwYAGDHjh1ITU3Fzz//DABYvXo1Zs6ciXnz5iEhIQExMTF4//33ERsbCwAoKChA37590aRJE5w8eRLR0dGYMmWKwe+JXC7HkiVLcO7cOcTGxmLXrl1455139I4pLCzEvHnzEBsbi4MHDyI3NxdDhw7V7f/zzz/xr3/9CxMmTMCFCxewatUqrF27VpfQiSRNIKpGw4cPF/r3769bP3r0qODm5ia89NJLgiAIwqxZswQbGxshPT1dd8zOnTsFZ2dnobi4WO9cjRs3FlatWiUIgiBEREQIb775pt7+tm3bCi1btqz0a+fm5goKhUJYvXp1pXEmJiYKAIS4uDi97T4+PsL69ev1ts2ZM0eIiIgQBEEQVq1aJbi6ugoFBQW6/StXrqz0XH/n5+cnfPLJJ4/c//333wtubm669TVr1ggAhCNHjui2JSQkCACEo0ePCoIgCB07dhRiYmL0zvP1118LXl5eunUAwsaNGx/5dcny5OTkCACErrYvCj0Vw4xautq+KAAQcnJyxP62DMYxO6p2v/32GxwdHaFWq1FWVob+/ftj6dKluv1+fn6oV6+ebv3kyZPIz8+Hm5ub3nmKiopw7do1AOVPOX/zzTf19kdERDzyHqQJCQkoKSlBt27dqhx3RkYGkpOTERUVhdGjR+u2q9Vq3XhgQkICWrZsqXePyoiIiCp/jft2796NmJgYXLhwAbm5uVCr1SguLkZBQQEcHBwAANbW1mjdurXuNU2bNkWdOnWQkJCAp59+GidPnsTx48f1KjmNRoPi4mIUFhZWuI8mSYugFSDIjGtDCmY8ZsdkR9WuS5cuWLlyJWxsbODt7V1hAsr9P+b3abVaeHl5Yc+ePRXO9aTT7+3s7Ax+jfavadarV69G27Zt9fbdv6mwKX75k5KS0KdPH7z55puYM2cOXF1dceDAAURFRem1e4EHj/epbJtWq8Xs2bMxcODACscolUqj4yQyZxyzo2rn4OCAgIAA+Pn5VWmmZatWrZCWlgZra2sEBAToLfcfX9OsWTMcOXJE73UPr/9dYGAg7OzssHPnzkr329qW3yD3709i8PDwQP369XH9+vUKcdyf0NK8eXOcOXMGRUVFVYqjMidOnIBarcbHH3+Mdu3aISgoCLdv365wnFqtxokTJ3Trly5dQnZ2Npo2bQqg/H27dOlShVgDAgIs+kncVEWC1jSLgVasWAF/f38olUqEh4dj//791fDN/TNWdlTrdO/eHRERERgwYAAWLFiAJk2a4Pbt2/j9998xYMAAtG7dGm+//TaGDx+O1q1b45lnnsG6detw/vx5NGrUqNJzKpVKTJs2De+88w5sbW3RoUMHZGRk4Pz584iKioK7uzvs7OywdetWNGjQAEqlEiqVCtHR0ZgwYQKcnZ0RGRmJkpISnDhxAllZWZg0aRKGDRuGmTNnIioqCu+99x5u3LiB//73vwZ9v40bN4ZarcbSpUvRr18/HDx4EJ999lmF42xsbDB+/HgsWbIENjY2eOutt9CuXTs8/fTTAIAPPvgAffv2hY+PD1588UXI5XKcPXsW8fHxmDt3ruH/EWRRxGhjfvfdd5g4cSJWrFiBDh06YNWqVYiMjMSFCxf0nl9ZE/hxj2odmUyG33//Hc8++yxGjRqFoKAgDB06FDdu3NDNnhwyZAg++OADTJs2DeHh4UhKSsKYMWMee973338fkydPxgcffIBmzZphyJAhSE9PB1A+HrZkyRKsWrUK3t7e6N+/PwDgtddewxdffIG1a9ciNDQUnTp1wtq1a3WVnaOjIzZv3owLFy4gLCwMM2fOxIIFCwz6fp966iksWrQICxYsQEhICNatW4f58+dXOM7e3h7Tpk3DsGHDEBERATs7O2zYsEG3v1evXvjtt9+wfft2tGnTBu3atcOiRYvg5+dnUDxEprJo0SJERUXhtddeQ7NmzbB48WL4+PhUeo1tdePz7IiILNj959l1Rn9Yy4y7YYNaKMMe/FKl59mVlpbC3t4eP/zwA1544QXd9rfffhunT5/G3r17jYrFUGxjEhFJgBplRt9ARY3yCVO5ubl62xUKBRQKhd62zMxMaDQaXTfmPg8PD6SlpRkXyBNgsiMismC2trbw9PTEgbTfTXI+R0dH+Pj46G2bNWsWoqOjKz3+4RnEgiBUOqu4ujHZERFZMKVSicTERJSWlprkfJUlq4erOgCoW7curKysKlRx6enpFaq9msBkR0Rk4ZRKZY1fa2lra4vw8HBs375db8xu+/btuglgNYnJjoiIqsWkSZPw73//G61bt0ZERAQ+//xz3Lx5s8Ldj2oCkx0REVWLIUOG4O7du/jwww+RmpqKkJAQ/P7776JcDsNLD4iIyOLxonIiIrJ4THZERGTxmOyIiMjiMdkREZHFY7IjIiKLx2RHREQWj8mOiIgsHpMdERFZPCY7IiKyeEx2RERk8ZjsiIjI4jHZERGRxft/uMbrvcdW5w0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(test_y, pred)\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "cm_display = ConfusionMatrixDisplay(cm, display_labels=stims).plot(ax=ax, xticks_rotation=\"vertical\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "elasticnet confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(test_y, task_pred)\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "cm_display = ConfusionMatrixDisplay(cm, display_labels=stims).plot(ax=ax, xticks_rotation=\"vertical\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots for paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plt\u001b[39m.\u001b[39mplot(X, y)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=0.001, early_stopping=True, hidden_layer_sizes=[500],\n",
       "              max_iter=5000, random_state=1, warm_start=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=0.001, early_stopping=True, hidden_layer_sizes=[500],\n",
       "              max_iter=5000, random_state=1, warm_start=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(alpha=0.001, early_stopping=True, hidden_layer_sizes=[500],\n",
       "              max_iter=5000, random_state=1, warm_start=True)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp = MLPClassifier(                \n",
    "                solver=\"adam\",\n",
    "                random_state=1,\n",
    "                max_iter=5000,\n",
    "                early_stopping=True,\n",
    "                alpha=1e-3,\n",
    "                hidden_layer_sizes=[500,], warm_start=True)\n",
    "\n",
    "mlp.fit(train_signatures, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950]\n"
     ]
    }
   ],
   "source": [
    "sizes = np.arange(100, 1000, 50)\n",
    "print(sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "for size in sizes:\n",
    "    mlp = MLPClassifier(                \n",
    "                    solver=\"adam\",\n",
    "                    random_state=1,\n",
    "                    max_iter=5000,\n",
    "                    early_stopping=True,\n",
    "                    alpha=1e-3,\n",
    "                    hidden_layer_sizes=[size,], warm_start=True)\n",
    "    mlp.fit(train_signatures, train_y)\n",
    "    train_pred = mlp.predict(train_signatures)\n",
    "    train_acc.append(accuracy(train_pred, train_y))\n",
    "    pred = mlp.predict(test_signatures)\n",
    "    test_acc.append(accuracy(pred, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc8364c0cd0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sizes, train_acc, label=\"training\")\n",
    "plt.plot(sizes, test_acc, label=\"testing\")\n",
    "plt.title(\"Accuracy vs. Hidden Layer Size\")\n",
    "plt.xlabel(\"Hidden Layer Size\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set accuracy: 0.9895245769540693\n"
     ]
    }
   ],
   "source": [
    "train_pred = mlp.predict(train_signatures)\n",
    "print(\"training set accuracy:\", accuracy(train_pred, train_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8702791461412152"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = mlp.predict(test_signatures)\n",
    "accuracy(pred, test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "F-testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_selection import SelectPercentile, f_classif\n",
    "from sklearn.model_selection import cross_val_score\n",
    "all_signatures = np.concatenate((train_signatures, test_signatures))\n",
    "all_labels = np.concatenate((train_y, test_y))\n",
    "clf = Pipeline(\n",
    "    [(\"anova\", SelectPercentile(f_classif)), (\"svc\", svm.SVC(C=1.0))]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentiles = [1,3,6,10,15,20,30,40,60,80,100]\n",
    "score_means = []\n",
    "score_stds = []\n",
    "for percentile in percentiles:\n",
    "    clf.set_params(anova__percentile=percentile)\n",
    "    this_scores = cross_val_score(clf, all_signatures, all_labels)\n",
    "    score_means.append(this_scores.mean())\n",
    "    score_stds.append(this_scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(percentiles, score_means, np.array(score_stds))\n",
    "plt.title(\"Accuracy vs. Percentile of Features Selected\")\n",
    "plt.xticks(np.linspace(0, 100, 11, endpoint=True))\n",
    "plt.xlabel(\"Percentile\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.axis(\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "signatures",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "bb9cc3eca2c8231e981fb1e0f6b02032d6d746c99299a8ed51289e761cd494c2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}