RPCD-TEST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e462898a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install pyts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38615cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pyts.image import RecurrencePlot\n",
    "\n",
    "\n",
    "# Create a toy time series using the sine function\n",
    "time_points = np.linspace(0, 4 * np.pi, 1000)\n",
    "x = np.sin(time_points)\n",
    "X = np.array([x])\n",
    "\n",
    "# Recurrence plot transformation\n",
    "rp = RecurrencePlot(threshold=np.pi/18)\n",
    "X_rp = rp.transform(X)\n",
    "\n",
    "# Plot the time series and its recurrence plot\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "\n",
    "gs = fig.add_gridspec(2, 2,  width_ratios=(2, 7), height_ratios=(2, 7),\n",
    "                      left=0.1, right=0.9, bottom=0.1, top=0.9,\n",
    "                      wspace=0.05, hspace=0.05)\n",
    "\n",
    "# Define the ticks and their labels for both axes\n",
    "time_ticks = np.linspace(0, 4 * np.pi, 9)\n",
    "time_ticklabels = [r'$0$', r'$\\frac{\\pi}{2}$', r'$\\pi$',\n",
    "                   r'$\\frac{3\\pi}{2}$', r'$2\\pi$', r'$\\frac{5\\pi}{2}$',\n",
    "                   r'$3\\pi$', r'$\\frac{7\\pi}{2}$', r'$4\\pi$']\n",
    "value_ticks = [-1, 0, 1]\n",
    "reversed_value_ticks = value_ticks[::-1]\n",
    "\n",
    "# Plot the time series on the left with inverted axes\n",
    "ax_left = fig.add_subplot(gs[1, 0])\n",
    "ax_left.plot(x, time_points)\n",
    "ax_left.set_xticks(reversed_value_ticks)\n",
    "ax_left.set_xticklabels(reversed_value_ticks, rotation=90)\n",
    "ax_left.set_yticks(time_ticks)\n",
    "ax_left.set_yticklabels(time_ticklabels, rotation=90)\n",
    "ax_left.invert_xaxis()\n",
    "\n",
    "# Plot the time series on the top\n",
    "ax_top = fig.add_subplot(gs[0, 1])\n",
    "ax_top.plot(time_points, x)\n",
    "ax_top.set_xticks(time_ticks)\n",
    "ax_top.set_xticklabels(time_ticklabels)\n",
    "ax_top.set_yticks(value_ticks)\n",
    "ax_top.set_yticklabels(value_ticks)\n",
    "ax_top.xaxis.tick_top()\n",
    "\n",
    "# Plot the recurrence plot on the bottom right\n",
    "ax_rp = fig.add_subplot(gs[1, 1])\n",
    "ax_rp.imshow(X_rp[0], cmap='binary', origin='lower',\n",
    "             extent=[0, 4 * np.pi, 0, 4 * np.pi])\n",
    "ax_rp.set_xticks([])\n",
    "ax_rp.set_yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9658750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000,), (1, 1000, 1000))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape, X_rp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2fc0cc6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1., ..., 1., 1., 1.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        ...,\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.]]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_rp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3df23497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pyts.multivariate.image import JointRecurrencePlot\n",
    "from pyts.datasets import load_basic_motions\n",
    "\n",
    "X, _, _, _ = load_basic_motions(return_X_y=True)\n",
    "\n",
    "# Recurrence plot transformation\n",
    "jrp = JointRecurrencePlot(threshold='point', percentage=50)\n",
    "X_jrp = jrp.fit_transform(X)\n",
    "\n",
    "# Show the results for the first time series\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.imshow(X_jrp[0], cmap='binary', origin='lower')\n",
    "plt.title('Joint Recurrence Plot', fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a8e46f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((40, 6, 100), (40, 100, 100))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, X_jrp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16d55f7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 0., ..., 0., 0., 0.],\n",
       "        [1., 1., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 1., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 1., 1., 1.],\n",
       "        [0., 0., 0., ..., 1., 1., 1.],\n",
       "        [0., 0., 0., ..., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 0., ..., 0., 0., 0.],\n",
       "        [1., 1., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 1., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 1., 1., 1.],\n",
       "        [0., 0., 0., ..., 1., 1., 1.],\n",
       "        [0., 0., 0., ..., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 0., ..., 0., 0., 0.],\n",
       "        [1., 1., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 1., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 1., 1.],\n",
       "        [0., 0., 0., ..., 0., 1., 1.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1., 1., 1., ..., 1., 0., 0.],\n",
       "        [1., 1., 1., ..., 1., 0., 0.],\n",
       "        [1., 1., 1., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [1., 1., 0., ..., 1., 1., 1.],\n",
       "        [0., 0., 0., ..., 1., 1., 1.],\n",
       "        [0., 0., 0., ..., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., ..., 1., 0., 0.],\n",
       "        [1., 1., 1., ..., 1., 0., 0.],\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        ...,\n",
       "        [1., 1., 1., ..., 1., 1., 1.],\n",
       "        [0., 0., 1., ..., 1., 1., 1.],\n",
       "        [0., 0., 1., ..., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 0., ..., 1., 1., 1.],\n",
       "        [1., 1., 0., ..., 1., 1., 1.],\n",
       "        [0., 0., 1., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [1., 1., 0., ..., 1., 1., 1.],\n",
       "        [1., 1., 0., ..., 1., 1., 1.],\n",
       "        [1., 1., 0., ..., 1., 1., 1.]]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_jrp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79de919f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyts\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9b1ad02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5400, 1000, 3)\n",
      "(5400,)\n"
     ]
    }
   ],
   "source": [
    "# datasets = [\n",
    "# \"ETCHING_Multivar\",\n",
    "# ]\n",
    "datasets = [\n",
    "\"CWRU_12k_FE_multivar\"\n",
    "]\n",
    "\n",
    "\n",
    "datasets_path = \"../datasets\"\n",
    "\n",
    "for dataset in datasets:\n",
    "    Dataset_name = dataset + \"_Dataset\"\n",
    "    Dataset = np.load(datasets_path + \"/\" + Dataset_name + \".npy\")\n",
    "    Dataset = Dataset\n",
    "    print(Dataset.shape)\n",
    "    \n",
    "    Labels_name = dataset + \"_Labels\"\n",
    "    Labels = np.load(datasets_path + \"/\"  + Labels_name + \".npy\")\n",
    "    Labels = Labels.squeeze()\n",
    "    print(Labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2676681b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5400, 3, 1000)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset = np.swapaxes(Dataset, 1,2)\n",
    "Dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45ce6ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((540, 3, 1000), (4860, 3, 1000), (540,), (4860,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(Dataset, Labels, test_size=0.9, random_state=42)\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c895a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 7., 4., 4., 6., 6., 7., 4., 4., 4., 6., 6., 1., 5., 8., 7., 5.,\n",
       "       4., 6., 7., 8., 6., 7., 7., 8., 6., 3., 7., 5., 3., 3., 6., 0., 6.,\n",
       "       5., 2., 4., 0., 3., 6., 5., 6., 3., 6., 2., 5., 0., 3., 2., 5., 7.,\n",
       "       8., 7., 7., 3., 6., 6., 0., 6., 3., 4., 8., 4., 7., 4., 8., 4., 7.,\n",
       "       6., 1., 4., 1., 8., 6., 2., 6., 6., 7., 8., 6., 7., 0., 2., 6., 5.,\n",
       "       8., 3., 3., 6., 7., 8., 6., 5., 5., 6., 6., 3., 6., 7., 2., 3., 1.,\n",
       "       7., 6., 8., 3., 8., 6., 5., 4., 5., 7., 3., 0., 8., 7., 6., 1., 6.,\n",
       "       6., 1., 7., 1., 6., 7., 4., 6., 3., 8., 0., 7., 2., 6., 4., 2., 7.,\n",
       "       4., 5., 7., 8., 6., 3., 6., 2., 7., 8., 8., 6., 7., 3., 1., 8., 0.,\n",
       "       8., 8., 1., 2., 0., 6., 6., 3., 5., 2., 8., 6., 1., 1., 1., 7., 6.,\n",
       "       6., 3., 5., 2., 6., 7., 4., 6., 5., 5., 2., 6., 6., 3., 5., 7., 8.,\n",
       "       6., 3., 3., 2., 2., 2., 2., 6., 6., 6., 5., 0., 6., 3., 5., 7., 8.,\n",
       "       7., 6., 6., 5., 1., 6., 6., 6., 2., 3., 6., 0., 6., 0., 7., 5., 0.,\n",
       "       4., 6., 6., 8., 2., 5., 7., 6., 6., 0., 6., 0., 2., 0., 0., 8., 6.,\n",
       "       6., 8., 6., 1., 4., 1., 7., 7., 2., 8., 1., 0., 6., 1., 5., 4., 6.,\n",
       "       6., 0., 6., 8., 0., 7., 6., 6., 6., 6., 2., 0., 4., 4., 7., 6., 4.,\n",
       "       6., 5., 7., 6., 5., 7., 3., 5., 5., 5., 6., 1., 1., 6., 7., 2., 3.,\n",
       "       2., 4., 8., 2., 2., 7., 6., 7., 3., 7., 1., 6., 7., 7., 2., 1., 3.,\n",
       "       5., 7., 5., 8., 6., 6., 2., 0., 7., 0., 2., 6., 2., 6., 6., 6., 0.,\n",
       "       6., 6., 7., 5., 6., 6., 3., 6., 6., 6., 0., 0., 7., 4., 6., 3., 6.,\n",
       "       5., 6., 2., 5., 8., 3., 0., 4., 5., 3., 5., 6., 3., 6., 0., 0., 6.,\n",
       "       2., 2., 1., 6., 6., 6., 8., 8., 0., 3., 4., 7., 0., 5., 4., 6., 5.,\n",
       "       0., 6., 6., 2., 5., 5., 2., 6., 4., 6., 6., 7., 4., 1., 4., 6., 4.,\n",
       "       8., 6., 6., 8., 6., 1., 6., 1., 8., 6., 6., 7., 4., 6., 8., 7., 4.,\n",
       "       8., 6., 2., 6., 6., 3., 6., 4., 6., 5., 3., 6., 3., 6., 8., 3., 6.,\n",
       "       6., 8., 3., 6., 8., 8., 6., 7., 1., 1., 5., 1., 1., 3., 3., 3., 4.,\n",
       "       0., 4., 6., 6., 0., 6., 1., 3., 7., 0., 6., 4., 1., 7., 1., 8., 6.,\n",
       "       2., 8., 4., 5., 3., 4., 1., 8., 6., 0., 5., 2., 4., 3., 0., 6., 0.,\n",
       "       8., 1., 3., 8., 6., 7., 1., 7., 8., 6., 4., 6., 6., 3., 7., 2., 7.,\n",
       "       2., 6., 6., 6., 6., 3., 3., 7., 4., 4., 0., 7., 1., 6., 5., 0., 8.,\n",
       "       6., 7., 6., 7., 4., 6., 1., 2., 0., 3., 7., 6., 6., 6., 2., 8., 6.,\n",
       "       5., 6., 7., 0., 5., 6., 4., 1., 5., 0., 0., 6., 6.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b9c0fc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(540, 1000, 1000)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyts.multivariate.image import JointRecurrencePlot\n",
    "from pyts.image import RecurrencePlot\n",
    "transformer = JointRecurrencePlot()\n",
    "#transformer.fit(X_train)\n",
    "X_train_new = transformer.transform(X_train)\n",
    "X_train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f284c9a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 2.68765445e-03, 1.97487087e-02, ...,\n",
       "        1.16064244e-03, 4.36040659e-03, 5.48134589e-03],\n",
       "       [2.68765445e-03, 0.00000000e+00, 1.50019926e-02, ...,\n",
       "        1.98810736e-03, 6.41145369e-05, 4.13082654e-04],\n",
       "       [1.97487087e-02, 1.50019926e-02, 0.00000000e+00, ...,\n",
       "        2.63888081e-03, 1.04503947e-02, 8.92745314e-03],\n",
       "       ...,\n",
       "       [1.16064244e-03, 1.98810736e-03, 2.63888081e-03, ...,\n",
       "        0.00000000e+00, 2.79108174e-03, 7.85287836e-03],\n",
       "       [4.36040659e-03, 6.41145369e-05, 1.04503947e-02, ...,\n",
       "        2.79108174e-03, 0.00000000e+00, 5.00811192e-03],\n",
       "       [5.48134589e-03, 4.13082654e-04, 8.92745314e-03, ...,\n",
       "        7.85287836e-03, 5.00811192e-03, 0.00000000e+00]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00c97833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the label you want to add to the plot\n",
    "label_text = 'Label: ' + str(y_train[5])\n",
    "\n",
    "# Show the results for the first time series\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.imshow(X_train_new[5], cmap='binary', origin='lower')\n",
    "ax.set_title('Joint Recurrence Plot', fontsize=18)\n",
    "ax.text(0, 1, label_text, transform=ax.transAxes, fontsize=12, va='top', ha='left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b47ff0bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9022494690370391, 0.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(X_train_new[1]), np.min(X_train_new[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec5fc3aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5400, 1000, 3)\n",
      "(5400,)\n"
     ]
    }
   ],
   "source": [
    "datasets = [\n",
    "\"CWRU_12k_FE_multivar\"\n",
    "]\n",
    "\n",
    "\n",
    "datasets_path = \"../datasets\"\n",
    "\n",
    "for dataset in datasets:\n",
    "    Dataset_name = dataset + \"_Dataset\"\n",
    "    Dataset = np.load(datasets_path + \"/\" + Dataset_name + \".npy\")\n",
    "    Dataset = Dataset\n",
    "    print(Dataset.shape)\n",
    "    \n",
    "    Labels_name = dataset + \"_Labels\"\n",
    "    Labels = np.load(datasets_path + \"/\"  + Labels_name + \".npy\")\n",
    "    Labels = Labels.squeeze()\n",
    "    print(Labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "807f42b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5400, 1000)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset = Dataset[:,:,0]\n",
    "Dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d0ef99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((540, 1000), (4860, 1000), (540,), (4860,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(Dataset, Labels, test_size=0.9, random_state=42)\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b7f681d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyts.image import RecurrencePlot\n",
    "\n",
    "# Recurrence plot transformation\n",
    "transformer = RecurrencePlot()\n",
    "transformer.fit(X_train)\n",
    "X_train_new = transformer.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5d249ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(540, 1000, 1000)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "56ccccc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.14740988, 0.26761188, ..., 0.10801936, 0.14050639,\n",
       "        0.36385469],\n",
       "       [0.14740988, 0.        , 0.120202  , ..., 0.03939052, 0.00690349,\n",
       "        0.21644481],\n",
       "       [0.26761188, 0.120202  , 0.        , ..., 0.15959251, 0.12710549,\n",
       "        0.09624281],\n",
       "       ...,\n",
       "       [0.10801936, 0.03939052, 0.15959251, ..., 0.        , 0.03248703,\n",
       "        0.25583533],\n",
       "       [0.14050639, 0.00690349, 0.12710549, ..., 0.03248703, 0.        ,\n",
       "        0.2233483 ],\n",
       "       [0.36385469, 0.21644481, 0.09624281, ..., 0.25583533, 0.2233483 ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "21a8c01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the label you want to add to the plot\n",
    "label_text = 'Label: ' + str(y_train[5])\n",
    "\n",
    "# Show the results for the first time series\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.imshow(X_train_new[5], cmap='binary', origin='lower')\n",
    "ax.set_title('Joint Recurrence Plot', fontsize=18)\n",
    "ax.text(0, 1, label_text, transform=ax.transAxes, fontsize=12, va='top', ha='left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "49f0dbf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting ffmpeg-python\n",
      "  Downloading ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)\n",
      "Requirement already satisfied: future in /usr/lib/python3/dist-packages (from ffmpeg-python) (0.18.2)\n",
      "Installing collected packages: ffmpeg-python\n",
      "Successfully installed ffmpeg-python-0.2.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#pip install ffmpeg-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e4dfdf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "97ec5e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "import lzma\n",
    "import ffmpeg\n",
    "import zlib\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import LeaveOneOut, cross_val_score\n",
    "\n",
    "from functools import partial\n",
    "from collections import Counter\n",
    "import glob\n",
    "\n",
    "from io import BytesIO\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "85f8f036",
   "metadata": {},
   "outputs": [],
   "source": [
    "class jpeg2000:\n",
    "    def compress(im):    \n",
    "        buffer = BytesIO()\n",
    "        im.save(buffer, \"JPEG2000\")\n",
    "        buffer.seek(0)\n",
    "        return buffer.read()\n",
    "    \n",
    "class jpeg:\n",
    "    def compress(im):       \n",
    "        buffer = BytesIO()\n",
    "        im.save(buffer, \"JPEG\")\n",
    "        buffer.seek(0)\n",
    "        return buffer.read()\n",
    "    \n",
    "class png:        \n",
    "    def compress(im):\n",
    "        buffer = BytesIO()\n",
    "        im.save(buffer, \"PNG\")\n",
    "        buffer.seek(0)\n",
    "        return buffer.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "20678d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def concat_arrays(arr1, arr2):\n",
    "    \"\"\"\n",
    "    Concatene two numpy arrays vertically\n",
    "    \"\"\"\n",
    "    new_arr = np.zeros((arr1.shape[0], arr1.shape[1] + arr2.shape[1]))\n",
    "    new_arr[:arr1.shape[0], :arr1.shape[1]] = arr1\n",
    "    new_arr[:arr2.shape[0], arr1.shape[1]:] = arr2\n",
    "    return new_arr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6cfb46ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ncd_dist(img1_path, img2_path, compressor=lzma, verbose=False):\n",
    "    \"\"\"\n",
    "    Compute NCD distance with the given compressor\n",
    "    \"\"\"\n",
    "    \n",
    "#     open images\n",
    "#     img1 = Image.open(img1_path)\n",
    "#     img2 = Image.open(img2_path)\n",
    "    \n",
    "    img1 = img1_path\n",
    "    img2 = img2_path\n",
    "    #concatene images\n",
    "    concat_img = concat_arrays(img1,img2)\n",
    "    \n",
    "    if compressor in [lzma, gzip, zlib]:\n",
    "        #for lzma and gzip we need to convert images in bytes.\n",
    "        #for others, we directly compress the images with PIL\n",
    "        img1 = img1.tobytes()\n",
    "        img2 = img2.tobytes()\n",
    "        concat_img = concat_img.tobytes()\n",
    "\n",
    "    #compress the two images and concatened image\n",
    "    img1_comp = compressor.compress(img1)\n",
    "    img2_comp = compressor.compress(img2)\n",
    "    concat_img_comp = compressor.compress(concat_img)\n",
    "        \n",
    "    if verbose:\n",
    "        print('C(img1): %f'%len(img1_comp))\n",
    "        print('C(img2): %f'%len(img2_comp))\n",
    "        print('C(concat): %f'%len(concat_img_comp))\n",
    "\n",
    "    return (len(concat_img_comp) - min(len(img1_comp), len(img2_comp)))/(max(len(img1_comp), len(img2_comp)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b2d6e6c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C(img1): 2350434.000000\n",
      "C(img2): 3355335.000000\n",
      "C(concat): 6555489.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.2532444599421517"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "ncd_dist(X_train_new[0], X_train_new[1], compressor=gzip, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b16d4bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "img1 = '../datasets/subject01.centerlight'\n",
    "img2 = '../datasets/subject01.glasses'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "db4605d6",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1275874/3766842252.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "img1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a823a17e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def VideoCompression(img1_path, img2_path, compressor='mpeg1video'):\n",
    "    \"\"\"\n",
    "    Encode two images with MPEG-1 compressor and return the compressed size.\n",
    "    \"\"\"\n",
    "    imgs = ffmpeg.concat(\n",
    "        ffmpeg.input(img1_path, format='image2', vcodec='gif'),\n",
    "        ffmpeg.input(img2_path, format='image2', vcodec='gif'),\n",
    "    )\n",
    "\n",
    "    stream = imgs.output('pipe:', format='rawvideo', vcodec=compressor)\n",
    "    stream = stream.global_args( '-loglevel', 'error').run_async(pipe_stdout=True)\n",
    "    compression = stream.stdout.read()\n",
    "    return len(compression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7a0bc4fe",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'ffmpeg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1275874/4083251819.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVideoCompression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimg2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_1275874/2303428372.py\u001b[0m in \u001b[0;36mVideoCompression\u001b[0;34m(img1_path, img2_path, compressor)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pipe:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'rawvideo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvcodec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompressor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_args\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'-loglevel'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe_stdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mcompression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/ffmpeg/_run.py\u001b[0m in \u001b[0;36mrun_async\u001b[0;34m(stream_spec, cmd, pipe_stdin, pipe_stdout, pipe_stderr, quiet, overwrite_output)\u001b[0m\n\u001b[1;32m    282\u001b[0m     \u001b[0mstdout_stream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPIPE\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpipe_stdout\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mquiet\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m     \u001b[0mstderr_stream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPIPE\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpipe_stderr\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mquiet\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m     return subprocess.Popen(\n\u001b[0m\u001b[1;32m    285\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstdin_stream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstdout_stream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstderr_stream\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m     )\n",
      "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize)\u001b[0m\n\u001b[1;32m    967\u001b[0m                             encoding=encoding, errors=errors)\n\u001b[1;32m    968\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 969\u001b[0;31m             self._execute_child(args, executable, preexec_fn, close_fds,\n\u001b[0m\u001b[1;32m    970\u001b[0m                                 \u001b[0mpass_fds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m                                 \u001b[0mstartupinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreationflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshell\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session)\u001b[0m\n\u001b[1;32m   1843\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0merrno_num\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1844\u001b[0m                         \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1845\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1846\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'ffmpeg'"
     ]
    }
   ],
   "source": [
    "A = VideoCompression(img1,img2)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1df4d751",
   "metadata": {},
   "outputs": [],
   "source": [
    "def video_dist(img1_path, img2_path, compressor='mpeg1video'):\n",
    "    \"\"\"\n",
    "    Compute CK-1 distance with the corresponding compressor\n",
    "    \"\"\"\n",
    "    Cx_y = VideoCompression(img1_path, img2_path, compressor)\n",
    "    Cy_x = VideoCompression(img2_path, img1_path, compressor)\n",
    "    Cx_x = VideoCompression(img1_path, img1_path, compressor)\n",
    "    Cy_y = VideoCompression(img2_path, img2_path, compressor)\n",
    "\n",
    "    return (Cx_y + Cy_x)/(Cx_x + Cy_y) -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a8abcb2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_dist_sklearn(a,b, compressor='mpeg1'):\n",
    "    \"\"\"\n",
    "    Adapted custom distance function to work with sklearn KNN. \n",
    "    Takes two indices and return the distance of the two\n",
    "    images at those indices based on the given compressor.\n",
    "    \"\"\"\n",
    "    \n",
    "    comp_map = {'gzip': partial(ncd_dist,compressor=gzip), 'lzma': partial(ncd_dist,compressor=lzma),\n",
    "                'mpeg1': partial(video_dist, compressor='mpeg1video'), 'jpeg': partial(ncd_dist,compressor=jpeg),\n",
    "                'jpeg2': partial(ncd_dist,compressor=jpeg2000), 'png': partial(ncd_dist,compressor=png),\n",
    "                'h264': partial(video_dist, compressor='h264')}\n",
    "    img1_path = a\n",
    "    img2_path = b\n",
    "    return comp_map[compressor](img1_path, img2_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "45409d2d",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "expected str, bytes or os.PathLike object, not ndarray",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1275874/909178433.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcustom_dist_sklearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_new\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_train_new\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_1275874/3749159935.py\u001b[0m in \u001b[0;36mcustom_dist_sklearn\u001b[0;34m(a, b, compressor)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mimg1_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mimg2_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcomp_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcompressor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_1275874/2083678032.py\u001b[0m in \u001b[0;36mvideo_dist\u001b[0;34m(img1_path, img2_path, compressor)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mCompute\u001b[0m \u001b[0mCK\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0mdistance\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcorresponding\u001b[0m \u001b[0mcompressor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mCx_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVideoCompression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompressor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mCy_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVideoCompression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg2_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompressor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mCx_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVideoCompression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompressor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_1275874/2303428372.py\u001b[0m in \u001b[0;36mVideoCompression\u001b[0;34m(img1_path, img2_path, compressor)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pipe:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'rawvideo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvcodec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompressor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_args\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'-loglevel'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe_stdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mcompression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/ffmpeg/_run.py\u001b[0m in \u001b[0;36mrun_async\u001b[0;34m(stream_spec, cmd, pipe_stdin, pipe_stdout, pipe_stderr, quiet, overwrite_output)\u001b[0m\n\u001b[1;32m    282\u001b[0m     \u001b[0mstdout_stream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPIPE\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpipe_stdout\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mquiet\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m     \u001b[0mstderr_stream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPIPE\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpipe_stderr\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mquiet\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m     return subprocess.Popen(\n\u001b[0m\u001b[1;32m    285\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstdin_stream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstdout_stream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstderr_stream\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m     )\n",
      "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize)\u001b[0m\n\u001b[1;32m    967\u001b[0m                             encoding=encoding, errors=errors)\n\u001b[1;32m    968\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 969\u001b[0;31m             self._execute_child(args, executable, preexec_fn, close_fds,\n\u001b[0m\u001b[1;32m    970\u001b[0m                                 \u001b[0mpass_fds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m                                 \u001b[0mstartupinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreationflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshell\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session)\u001b[0m\n\u001b[1;32m   1776\u001b[0m                     \u001b[0mfds_to_keep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpass_fds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1777\u001b[0m                     \u001b[0mfds_to_keep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrpipe_write\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1778\u001b[0;31m                     self.pid = _posixsubprocess.fork_exec(\n\u001b[0m\u001b[1;32m   1779\u001b[0m                             \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutable_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1780\u001b[0m                             \u001b[0mclose_fds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfds_to_keep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not ndarray"
     ]
    }
   ],
   "source": [
    "custom_dist_sklearn(X_train_new[0],X_train_new[1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afdfcd36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf_ck1 = KNeighborsClassifier(n_neighbors=1, algorithm='brute', \n",
    "                               metric=, metric_params={'compressor':'mpeg1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3c34990",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_ck1.fit(X_train_new.reshape(-1, 1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0f2d518",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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