GPFA_sampling.ipynb

{
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  "metadata": {
    "colab": {
      "name": "GPFA sampling.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harvineet/py-gpfa/blob/master/GPFA_sampling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "dlQL-qs_fhnF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!git https://github.com/harvineet/py-gpfa.git\n",
        "!python py-gpfa/Seq_Data_Class.py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_OdMhV9tf7s-",
        "colab_type": "code",
        "outputId": "0811a504-cef1-4bd3-8602-154125adcdd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TOKqsh6-gOmV",
        "colab_type": "code",
        "outputId": "a603b47a-76c5-490b-d168-005055cb5bf1",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "cell_type": "code",
      "source": [
        "#I am not sure how to run python from github, so you can upload from local machine\n",
        "from google.colab import files\n",
        "file = files.upload()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b1c8f92b-6d94-4015-bc71-e0d1f1d2c20a\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-b1c8f92b-6d94-4015-bc71-e0d1f1d2c20a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Seq_Data_Class.py to Seq_Data_Class.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9seBFZYNeG5N",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import math \n",
        "\n",
        "from Seq_Data_Class import Model_Specs, Trial_Class, Param_Class\n",
        "# please put em_input into your drive\n",
        "path_to_mat = 'gdrive/My Drive/GPFA/em_input.mat'\n",
        "# Load parameters\n",
        "params = Param_Class()\n",
        "params.params_from_mat(path_to_mat)\n",
        "# Store parameters\n",
        "model = Model_Specs(None, None)\n",
        "model.params = params\n",
        "model.data_from_mat(path_to_mat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O-R__3Z86m3z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "86bfdda0-7caf-4edf-f569-cf7c57bd3f19"
      },
      "cell_type": "code",
      "source": [
        "R = params.R\n",
        "d = params.d\n",
        "C = params.C\n",
        "sigma_n = params.eps\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(53, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "metadata": {
        "id": "w1cr0lms6fc8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Initialize \n",
        "T = 20\n",
        "\n",
        "q = C.shape[0]\n",
        "p = C.shape[1]\n",
        "#R = np.zeros((q,q))\n",
        "#np.fill_diagonal(R,np.random.uniform(0,2))\n",
        "#C = np.random.uniform(0,2,q*p).reshape(q,p)\n",
        "#d = np.random.uniform(0,2,q)\n",
        "#sigma_n = np.array([0.001]*p)\n",
        "sigma_f = np.sqrt(np.array([1]*p) - np.square(sigma_n))\n",
        "tau = np.random.uniform(1,10,p)\n",
        "\n",
        "K = []\n",
        "for i in range(p):\n",
        "  K.append(np.zeros((T,T)))\n",
        "for i in range(p):\n",
        "  for t1 in range(len(K[i])):\n",
        "    for t2 in range (len(K[i][t1])):\n",
        "      if t1 != t2:\n",
        "        K[i][t1][t2] = sigma_f[i] * np.exp(-(t1-t2)**2/(2*tau[i]))\n",
        "      else:\n",
        "        K[i][t1][t2] = sigma_f[i] + sigma_n[i]\n",
        "        \n",
        "      \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8Vj3SF5-CoHk",
        "colab_type": "code",
        "outputId": "ed28cb61-a5df-4ac1-ce48-123a73327165",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        }
      },
      "cell_type": "code",
      "source": [
        "# sampling x_i, in the paper they sample by identity covirance matrix\n",
        "# building matrix X and Y\n",
        "X = []\n",
        "for i in range (len(K)):\n",
        "  X.append (np.random.multivariate_normal(np.zeros(T),K[1],1)[0])\n",
        "X = np.array(X)\n",
        "Y = []\n",
        "for i in range(len(X.T)):\n",
        "  Y.append(np.random.multivariate_normal((np.dot(C,X.T[i])+d),R))\n",
        "Y = np.array(Y).T\n",
        "Y\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.62996057,  1.52265032, -0.45327062, ...,  0.50216489,\n",
              "         0.2224561 ,  1.12162793],\n",
              "       [-0.15208904,  0.48307989,  0.3203556 , ...,  0.74629868,\n",
              "        -0.44016139,  0.45591156],\n",
              "       [ 0.82854898,  0.71041356,  0.09544357, ..., -0.34994976,\n",
              "         0.57934754,  0.33002357],\n",
              "       ...,\n",
              "       [ 0.04737559,  0.33141323,  0.13492502, ...,  0.64539115,\n",
              "         0.27259949, -0.08885315],\n",
              "       [-0.15919674,  0.58421251,  0.22888256, ...,  1.30282822,\n",
              "        -0.28238125,  0.85898397],\n",
              "       [-0.09184898, -0.57797458,  0.21710646, ...,  0.01033752,\n",
              "         0.04192825,  0.05708794]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    }
  ]
}