main.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Estimation of the sample covariance matrix from compressive measurements\n",
        "\n",
        "### Project by Abhijat Bharadwaj (210020002) and Animesh Kumar (21d070012)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NwgpM_rtQCFv"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.datasets import mnist\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.io import mmread\n",
        "import cv2\n",
        "import time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## MNIST Dataset\n",
        "source: https://keras.io/api/datasets/mnist/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_OYwzwTGvnl",
        "outputId": "43cf59c6-b8d4-4551-a5a1-3e9d9fc5d24b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of X: (784, 6903)\n"
          ]
        }
      ],
      "source": [
        "def mnist_zeros_loader(x1=0,x2=28,y1=0,y2=28,n1=0,n2=6903):\n",
        "    # Load MNIST dataset\n",
        "    (X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "    # Filter out only the samples with label 0\n",
        "    X_train_zeros = X_train[y_train == 0]\n",
        "    X_test_zeros = X_test[y_test == 0]\n",
        "    X = np.concatenate((X_train_zeros, X_test_zeros), axis=0)[n1:n2, y1:y2, x1:x2]\n",
        "    X = X.reshape(-1 , (x2-x1)*(y2-y1)) / 255.0\n",
        "    X=X.T\n",
        "    print(\"Shape of X:\", X.shape)\n",
        "    \n",
        "    return X\n",
        "X = mnist_zeros_loader()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Gen4 Matrix Dataset\n",
        "source: https://www.cise.ufl.edu/research/sparse/matrices/Meszaros/gen4.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of X: (1537, 4298)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[ 0.        ,  0.36457422,  0.        , ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [ 0.        ,  0.63541615,  0.09375179, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [ 0.        ,  0.        ,  0.72900391, ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       ...,\n",
              "       [ 0.        ,  0.07270836,  0.        , ...,  0.        ,\n",
              "        -1.        ,  0.        ],\n",
              "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
              "         0.        , -1.        ],\n",
              "       [ 1.        ,  0.        ,  0.        , ...,  1.        ,\n",
              "         1.        ,  1.        ]])"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def gen4_loader(p1=0, p2=1537,n1=0,n2=4892):\n",
        "    # Load the gen4 dataset\n",
        "    X = mmread(\"./datasets/gen4.mtx\").tocsc() \n",
        "    X = X.toarray()\n",
        "    if(p1!=0 or p2!=1537 or n1!=0 or n2!=4892):\n",
        "        X= X[p1:p2, n1:n2]\n",
        "    print(\"Shape of X:\", X.shape)\n",
        "    return X\n",
        "\n",
        "gen4_loader()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Traffic Video Dataset\n",
        "\n",
        "source: https://www.kaggle.com/datasets/aryashah2k/highway-traffic-videos-dataset?select=video"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(76800, 1046)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[  42.32201823,  103.69730469,  103.57789063, ...,  109.41783854,\n",
              "         109.3968099 ,  109.3968099 ],\n",
              "       [  42.32201823,  103.69730469,  103.57789063, ...,  109.41783854,\n",
              "         109.3968099 ,  109.3968099 ],\n",
              "       [  43.32201823,  103.69730469,  103.57789063, ...,  109.41783854,\n",
              "         109.3968099 ,  109.3968099 ],\n",
              "       ...,\n",
              "       [-124.67798177, -129.30269531, -129.42210937, ..., -115.58216146,\n",
              "        -119.6031901 , -119.6031901 ],\n",
              "       [-122.67798177, -129.30269531, -129.42210937, ..., -113.58216146,\n",
              "        -120.6031901 , -113.6031901 ],\n",
              "       [-122.67798177, -129.30269531, -129.42210937, ..., -117.58216146,\n",
              "        -116.6031901 , -113.6031901 ]])"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def traffic_loader(x1=0,x2=320,y1=0,y2=240, n1=0, n2=1046):\n",
        "    X = []\n",
        "    for i in range(1,21):\n",
        "        vid = cv2.VideoCapture(f'./datasets/traffic/traffic ({i}).avi')\n",
        "        while True:\n",
        "            ret, frame = vid.read()\n",
        "            if not ret:\n",
        "                break\n",
        "            frame = frame[y1:y2,x1:x2]\n",
        "            gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
        "            # print(gray_frame.shape)\n",
        "            vectorized = np.array(gray_frame.flatten())\n",
        "            vectorized = vectorized - np.mean(vectorized)\n",
        "            # print(vectorized.shape)\n",
        "            X.append(vectorized)\n",
        "    X = np.array(X)[n1:n2].T\n",
        "    print(X.shape)\n",
        "    return X\n",
        "\n",
        "traffic_loader()\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Covariance Matrix Estimator\n",
        "\n",
        "### Biased Estimator\n",
        "$$\\mathbf{\\hat{C} }_n = \\frac{1}{(m^2+m)\\mu_2^2}\\cdot\\frac{1}{n}\\sum_{i=1}^{n}\\mathbf{R}_i\\mathbf{R}_i^T\\mathbf{x}_i\\mathbf{x}_i^T\\mathbf{R}_i\\mathbf{R}_i^T$$\n",
        "\n",
        "### Unbiased Estimator\n",
        "$$\\mathbf{\\hat{\\Sigma} }_n = \\mathbf{\\hat{C} }_n - \\alpha_1\\ \\mathrm{diag}(\\mathbf{\\hat{C} }_n) - \\alpha_2\\ \\mathrm{trace}(\\mathbf{\\hat{C} }_n) \\mathbf{I}_{p\\times p}$$\n",
        "\n",
        "where,\n",
        "$$\\alpha_1 = \\frac{\\frac{k}{m+1} }{1+\\frac{k}{m+1} }$$\n",
        "and\n",
        "$$\\alpha_2 = \\frac{1 }{(1+\\frac{k}{m+1})(m+1+\\kappa+p) }$$\n",
        "\n",
        "### Normalized Error\n",
        "For an estimation $\\mathbf{\\hat{A}}_n$ of $\\mathbf{C }_n$, the normalized error is measured as\n",
        "$$\\mathrm{error}(\\mathbf{\\hat{A} }_n) = \\frac{||\\mathbf{\\hat{A} }_n -  \\mathbf{C }_n||_2}{|| \\mathbf{C }_n||_2}$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "iwD0uTj3H9wa"
      },
      "outputs": [],
      "source": [
        "class CovarianceMatrix:\n",
        "  _m = 0\n",
        "  _s = 0\n",
        "  _p = 0\n",
        "  _n = 0\n",
        "  _X = None\n",
        "  _XT = None\n",
        "  _XXT = None\n",
        "  _R_list = []\n",
        "  biasedEstimation = None\n",
        "  unbiasedEstimation = None\n",
        "  original = None\n",
        "  biasedError = 0\n",
        "  unbiasedError = 0\n",
        "\n",
        "\n",
        "\n",
        "  def __init__(self, m, s, X, XT = None, XXT = None, runBiasedEstimator = True, runUnbiasedEstimator = True):\n",
        "    self._m = m\n",
        "    self._s = s\n",
        "    self._n = X.shape[1] \n",
        "    self._p = X.shape[0] \n",
        "    self._X = X\n",
        "    self._XT = XT if (type(XT) != type(None)) else X.T\n",
        "    self._XXT = XXT if (type(XXT) != type(None)) else X@self._XT\n",
        "    self.original = (1/self._n) *self._XXT #np.cov(X)\n",
        "    self.generate_R_list()\n",
        "    if runBiasedEstimator:\n",
        "      start_time = time.time()\n",
        "      self.biasedEstimation = self.runBiasedEstimator()\n",
        "      self.biasedError = self.calcBiasedError()\n",
        "      biased_end_time = time.time()\n",
        "      self.biasedTime = biased_end_time - start_time\n",
        "      if runUnbiasedEstimator:\n",
        "        self.unbiasedEstimation = self.runUnbiasedEstimator()\n",
        "        self.unbiasedError = self.calcUnbiasedError()\n",
        "        unbaised_end_time = time.time()\n",
        "        self.unbiasedTime = unbaised_end_time - start_time\n",
        "\n",
        "  def generate_Ri(self):\n",
        "    p = self._p\n",
        "    m = self._m\n",
        "    s = self._s\n",
        "    probabilities = [1/(2*s), 1 - 1/s, 1/(2*s)]\n",
        "    Ri = np.random.choice([-1, 0, 1], size=(p, m), p=probabilities)\n",
        "    return Ri\n",
        "\n",
        "  def generate_R_list(self):\n",
        "    self._R_list = []\n",
        "    n = self._n\n",
        "    for i in range(n):\n",
        "      Ri = self.generate_Ri()\n",
        "      self._R_list.append(Ri)\n",
        "      \n",
        "  def runBiasedEstimatorUsingLoop(self):\n",
        "    self._R_list = []\n",
        "    u2 = 1/self._s\n",
        "    m = self._m\n",
        "    n = self._n\n",
        "    size_factor = 1 / ((m ** 2 + m) *( u2 ** 2))\n",
        "    sum = np.zeros((self._p, self._p))\n",
        "    \n",
        "    for i in range(n):\n",
        "      Ri = self.generate_Ri()\n",
        "      self._R_list.append(Ri)\n",
        "      xi = np.array(self._XT[i])\n",
        "\n",
        "      # print(xi.shape)\n",
        "      RRt = Ri @ Ri.T\n",
        "      RRtxi = RRt @ xi.T\n",
        "      # print(RRt.shape)\n",
        "\n",
        "      sum_i = np.outer(RRtxi, RRtxi.T)\n",
        "      # print(sum_i.shape)\n",
        "      sum += sum_i\n",
        "    c_hat =  size_factor * (1/n) *sum\n",
        "    return(c_hat)\n",
        "  \n",
        "  \n",
        "  def runBiasedEstimator(self):\n",
        "    u2 = 1/self._s\n",
        "    m = self._m\n",
        "    n = self._n\n",
        "    size_factor = 1 / ((m ** 2 + m) *( u2 ** 2))\n",
        "    R = np.array(self._R_list)\n",
        "    X = np.array(self._X)\n",
        "    R_R_T_X = np.einsum('nab,ncb,cn->na', R, R,X)\n",
        "    X_T_R_R_T = R_R_T_X.T\n",
        "    result = np.einsum('na,bn->ab', R_R_T_X, X_T_R_R_T)\n",
        "    c_hat =  size_factor * (1/n) *result\n",
        "    return(c_hat)\n",
        "\n",
        "  def runUnbiasedEstimator(self):\n",
        "    k = self._s-3\n",
        "    c_hat = self.biasedEstimation\n",
        "    m = self._m\n",
        "    p = self._p\n",
        "    var1 = k/(m+1)\n",
        "    a1 = var1/(1+var1)\n",
        "    a2 = 1/((1+var1) * (m +1 + k + p))\n",
        "    diagC = np.diag(np.diag(c_hat))\n",
        "    traceC = np.trace(c_hat)\n",
        "    I = np.identity(p)\n",
        "\n",
        "    bias_term =  a1 * diagC + a2*traceC*I\n",
        "    sigma_hat = c_hat - bias_term\n",
        "    return sigma_hat\n",
        "\n",
        "  def calcBiasedError(self):\n",
        "    numerator = np.linalg.norm(self.biasedEstimation - self.original , ord = 2)\n",
        "    denominator = np.linalg.norm(self.original , ord = 2)\n",
        "    return numerator / denominator\n",
        "\n",
        "  def calcUnbiasedError(self):\n",
        "    numerator = np.linalg.norm(self.unbiasedEstimation - self.original , ord = 2)\n",
        "    denominator = np.linalg.norm(self.original , ord = 2)\n",
        "    return numerator / denominator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Experiment Control Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ykVsoFrKEy4Z",
        "outputId": "deefc32f-4f37-40a0-ab29-76d10d57d795"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(225, 500)\n",
            "gamma values (Y) = [0.1 0.2 0.3 0.4 0.5]\n"
          ]
        }
      ],
      "source": [
        "# CONTROL PARAMETERS #\n",
        "#---------------------------------#\n",
        "X = traffic_loader(x1=200,x2=215,y1=100,y2=115, n2=500) # data matrix X\n",
        "Y = np.linspace(0.1,0.5,5) # gamma = compression factor\n",
        "print(f'gamma values (Y) = {Y}')\n",
        "\n",
        "m = int(np.round(X.shape[0]*0.4)) # m = 0.4 * p\n",
        "s_values = m/Y\n",
        "XXT = X@X.T\n",
        "\n",
        "# n_runs = 1#5 #1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Experiment Run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Single Run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "LXYErM1GiTh8",
        "outputId": "ee327514-2148-48da-fca4-88a524f8e1d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "biased_error=[]\n",
        "unbiased_error=[]\n",
        "biased_time=[]\n",
        "unbiased_time=[]\n",
        "for s in s_values:\n",
        "  print(f'processing Y = {m/s} value')\n",
        "  CovMtx = CovarianceMatrix(m, s, X, XXT = XXT)\n",
        "  biased_error.append(CovMtx.biasedError)\n",
        "  unbiased_error.append(CovMtx.unbiasedError)\n",
        "  biased_time.append(CovMtx.biasedTime)\n",
        "  unbiased_time.append(CovMtx.unbiasedTime)\n",
        "  del CovMtx\n",
        "\n",
        "plt.plot(Y,biased_error, marker='o')\n",
        "plt.plot(Y,unbiased_error, marker='o')\n",
        "plt.legend(['biased error', 'unbiased error'])\n",
        "plt.xlabel('compression factor')\n",
        "plt.ylabel('normalized estimation error')\n",
        "plt.title('Accuracy')\n",
        "plt.text(0.83, 0.81, f'p={X.shape[0]}, n={X.shape[1]}', horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes)\n",
        "plt.show()\n",
        "plt.plot(Y,biased_time, marker='o')\n",
        "plt.plot(Y,unbiased_time, marker='o')\n",
        "plt.xlabel('compression factor')\n",
        "plt.ylabel('Time')\n",
        "plt.legend(['biased time', 'unbiased time'])\n",
        "plt.title('Computation Cost')\n",
        "plt.text(0.83, 0.81, f'p={X.shape[0]}, n={X.shape[1]}', horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Averaged Loop Run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "processing loop #0\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #1\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #2\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #3\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #4\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #5\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #6\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #7\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #8\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n",
            "processing loop #9\n",
            "----------\n",
            "processing Y = 0.1 value\n",
            "processing Y = 0.2 value\n",
            "processing Y = 0.30000000000000004 value\n",
            "processing Y = 0.4 value\n",
            "processing Y = 0.5 value\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "biased_error=np.zeros(Y.shape[0])\n",
        "unbiased_error=np.zeros(Y.shape[0])\n",
        "biased_time=np.zeros(Y.shape[0])\n",
        "unbiased_time=np.zeros(Y.shape[0])\n",
        "for i in range(10):\n",
        "    print(f'processing loop #{i}')\n",
        "    print('-'*10)\n",
        "    biased_error2=[]\n",
        "    unbiased_error2=[]\n",
        "    biased_time2=[]\n",
        "    unbiased_time2=[]\n",
        "    for s in s_values:\n",
        "        print(f'processing Y = {m/s} value')\n",
        "        CovMtx = CovarianceMatrix(m, s, X, XXT = XXT)\n",
        "        biased_error2.append(CovMtx.biasedError)\n",
        "        unbiased_error2.append(CovMtx.unbiasedError)\n",
        "        biased_time2.append(CovMtx.biasedTime)\n",
        "        unbiased_time2.append(CovMtx.unbiasedTime)\n",
        "        del CovMtx\n",
        "    biased_error = biased_error + np.array(biased_error2)\n",
        "    unbiased_error = unbiased_error + np.array(unbiased_error2)\n",
        "    biased_time = biased_time + np.array(biased_time2)\n",
        "    unbiased_time = unbiased_time + np.array(unbiased_time2)\n",
        "    \n",
        "biased_error=biased_error/Y.shape[0]\n",
        "unbiased_error=unbiased_error/Y.shape[0]\n",
        "biased_time=biased_time/Y.shape[0]\n",
        "unbiased_time=unbiased_time/Y.shape[0]\n",
        "\n",
        "plt.plot(Y,biased_error, marker='o')\n",
        "plt.plot(Y,unbiased_error, marker='o')\n",
        "plt.legend(['biased error', 'unbiased error'])\n",
        "plt.xlabel('compression factor')\n",
        "plt.ylabel('normalized estimation error')\n",
        "plt.title('Accuracy')\n",
        "plt.text(0.83, 0.81, 'p=225, n=1046', horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes)\n",
        "plt.show()\n",
        "plt.plot(Y,biased_time, marker='o')\n",
        "plt.plot(Y,unbiased_time, marker='o')\n",
        "plt.xlabel('compression factor')\n",
        "plt.ylabel('Time')\n",
        "plt.legend(['biased time', 'unbiased time'])\n",
        "plt.title('Computation Cost')\n",
        "plt.text(0.83, 0.81, 'p=225, n=1046', horizontalalignment='center', verticalalignment='center', transform=plt.gca().transAxes)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Verifying the claim in the paper that $\\mathbf{R}_i\\mathbf{R}_i^T\\mathbf{x}_i$ is a close approximation of projection of $\\mathbf{x}_i$ onto the column space of $\\mathbf{R}_i \\in \\mathbb{R}^{p\\times m}$, we visualize the  $\\mathbf{R}_i\\mathbf{R}_i^T\\mathbf{x}_i$ as images against the original $\\mathbf{x}_i$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### MNIST"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of X: (784, 1)\n",
            "m = 784\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x1000 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "X_mnist = mnist_zeros_loader(n2=1)\n",
        "plt.imshow(X_mnist.reshape(28,28), cmap='gray')\n",
        "m_mnist =28*28 #int(np.round(X.shape[0]*0.4)) # m = 0.4 * p\n",
        "print(f'm = {m_mnist}')\n",
        "# s_values_mnist = m/Y\n",
        "XXT_mnist = X_mnist@X_mnist.T\n",
        "# for s in s_values_mnist:\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "for i in range(1,6):\n",
        "    g = i/10\n",
        "    s =  m_mnist / g\n",
        "    MnistCovMtx = CovarianceMatrix(m_mnist, s, X_mnist, XXT = XXT_mnist)\n",
        "    R0 = MnistCovMtx._R_list[0]\n",
        "    v = R0@R0.T @ X_mnist\n",
        "    v = v.reshape(28,28)\n",
        "\n",
        "    plt.subplot(1,5,i)\n",
        "    plt.imshow(v, cmap='gray')\n",
        "    plt.title(f'gamma = {g}')\n",
        "\n",
        "\n",
        "        \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Traffic "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1600, 1)\n",
            "m = 1600\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x800 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "X_traffic = traffic_loader(x1=200,x2=240,y1=100,y2=140, n2=1)\n",
        "plt.imshow(X_traffic.reshape(40,40), cmap='gray')\n",
        "m_traffic =40*40 #int(np.round(X.shape[0]*0.4)) # m = 0.4 * p\n",
        "print(f'm = {m_traffic}')\n",
        "# s_values_mnist = m/Y\n",
        "XXT_traffic = X_traffic@X_traffic.T\n",
        "# for s in s_values_mnist:\n",
        "\n",
        "plt.figure(figsize=(20, 8))\n",
        "for i in range(1,10):\n",
        "    g = i/10\n",
        "    s =  m_traffic / g\n",
        "    TrafficCovMtx = CovarianceMatrix(m_traffic, s, X_traffic, XXT = XXT_traffic, runUnbiasedEstimator=False)\n",
        "    R0 = TrafficCovMtx._R_list[0]\n",
        "    v = R0@R0.T @ X_traffic\n",
        "    v = v.reshape(40,40)\n",
        "\n",
        "    plt.subplot(1,10,i)\n",
        "    plt.imshow(v, cmap='gray')\n",
        "    plt.title(f'gamma = {g}')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}