VAE_VectorQuantized_single_cell.ipynb

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  "metadata": {
    "colab": {
      "name": "VAE_VectorQuantized_single_cell.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dauparas/tensorflow_examples/blob/master/VAE_VectorQuantized_single_cell.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cqCt_GhvCnwY"
      },
      "source": [
        "# VQ-VAE for clustering data\n",
        "\n",
        "Neural Discrete Representation Learning: https://arxiv.org/abs/1711.00937  \n",
        "Generating Diverse High-Fidelity Images with VQ-VAE-2: https://arxiv.org/abs/1906.00446\n",
        "\n",
        "VQ-VAE example: https://github.com/deepmind/sonnet/blob/master/sonnet/examples/vqvae_example.ipynb\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bICxezUReU41",
        "colab_type": "code",
        "outputId": "47a1f986-6152-472d-c844-e9d4e77a1ce4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "!pip install scanpy"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: scanpy in /usr/local/lib/python3.6/dist-packages (1.4.3)\n",
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            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from scanpy) (3.0.3)\n",
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            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx->scanpy) (4.4.0)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTDyaAnxeZZz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import scanpy as sc\n",
        "from scipy.sparse import csr_matrix\n",
        "\n",
        "import h5py\n",
        "#Step 1: import dependencies\n",
        "from tensorflow.keras import layers\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from keras import regularizers\n",
        "import time\n",
        "from __future__ import division\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.datasets import make_blobs\n",
        "import seaborn as sns; sns.set()\n",
        "import keras.backend as K\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_probability as tfp\n",
        "tfd = tfp.distributions\n",
        "\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "95YuC82P35Of",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import os\n",
        "import subprocess\n",
        "import tempfile\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import sonnet as snt\n",
        "import tensorflow as tf\n",
        "import tarfile\n",
        "\n",
        "from six.moves import cPickle\n",
        "from six.moves import urllib\n",
        "from six.moves import xrange"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Ie56c-LWA5y",
        "colab_type": "text"
      },
      "source": [
        "# Create some data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V71hArbje-0h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def import_data_fn(string):\n",
        "    data_mat = h5py.File(string)\n",
        "    x_data = np.array(data_mat['X'])\n",
        "    y_data = np.array(data_mat['Y'])\n",
        "    return x_data, y_data, np.where(x_data > 0.0, 1, 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IE1IXGJxfA8P",
        "colab_type": "code",
        "outputId": "1a457a82-9836-490a-b784-6666ccfa2070",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "X_1, y_1, Z_1 = import_data_fn('10X_PBMC.h5')\n",
        "print(X_1.shape)\n",
        "X_2, y_2, Z_2 = import_data_fn('mouse_bladder_cell.h5')\n",
        "print(X_2.shape)\n",
        "# X_3, y_3, Z_3 = import_data_fn('worm_neuron_cell.h5')\n",
        "# X_3, y_3, Z_3 = np.delete(X_3, 2682, axis=0), np.delete(y_3, 2682, axis=0), np.delete(Z_3, 2682, axis=0)\n",
        "# print(X_3.shape)"
      ],
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4271, 16653)\n",
            "(2746, 20670)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQNjF5OhfWk5",
        "colab_type": "code",
        "outputId": "15ca2d89-b01e-4e3d-dd1b-705e911b31e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "source": [
        "plt.plot(np.sort(np.mean(X_1, axis=1)/np.median(np.mean(X_1, axis=1))), 'o');\n",
        "plt.plot(np.sort(np.mean(X_2, axis=1)/np.median(np.mean(X_2, axis=1))), 'o');\n",
        "plt.plot(np.sort(np.mean(X_3, axis=1)/np.median(np.mean(X_3, axis=1))), 'o');\n",
        "plt.xlabel('cells');\n",
        "plt.ylabel('mean over genes/median');\n",
        "plt.legend(('pbmc', 'bladder', 'neuron'))"
      ],
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-221-880f33934893>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0;34m'o'\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0;34m'o'\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;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0;34m'o'\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[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cells'\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mean over genes/median'\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;31mNameError\u001b[0m: name 'X_3' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fD1A_1gmfbas",
        "colab_type": "code",
        "outputId": "3bfe8898-6008-4d18-d95b-c633dac68096",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "source": [
        "plt.scatter(np.mean(X_1, axis=1)/np.median(np.mean(X_1, axis=1)), np.sum(Z_1, axis=1))\n",
        "plt.scatter(np.mean(X_2, axis=1)/np.median(np.mean(X_2, axis=1)), np.sum(Z_2, axis=1))\n",
        "plt.scatter(np.mean(X_3, axis=1)/np.median(np.mean(X_3, axis=1)), np.sum(Z_3, axis=1))\n",
        "plt.xlabel('mean over genes/median')\n",
        "plt.ylabel('sum of non-zero genes')\n",
        "plt.legend(('pbmc', 'bladder', 'neuron'))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-56-6df7d3433955>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZ_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZ_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZ_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mean over genes/median'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sum of non-zero genes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_2' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dbk/YtSLB+gWT8s2qr4/lm4Pqola/wIHmMQBDxw8DP1/mt4vZGICho/8NXDPDLzsrHl/j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\ngCAiIlUMEEREpIoBgoiIVP1/adoCbQ861YQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzW9kUltfeoz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p1 = []\n",
        "pn1 = []\n",
        "for i in range(50):\n",
        "  lim = i\n",
        "  pn1.append(lim)\n",
        "  n = Z_1.shape[1]-np.argwhere(np.sum(Z_1, axis=0)>lim).shape[0]\n",
        "  p1.append(n)\n",
        "\n",
        "p2 = []\n",
        "pn2 = []\n",
        "for i in range(50):\n",
        "  lim = i\n",
        "  pn2.append(lim)\n",
        "  n = Z_2.shape[1]-np.argwhere(np.sum(Z_2, axis=0)>lim).shape[0]\n",
        "  p2.append(n)  \n",
        "  \n",
        "# p3 = []\n",
        "# pn3 = []\n",
        "# for i in range(50):\n",
        "#   lim = i\n",
        "#   pn3.append(lim)\n",
        "#   n = Z_3.shape[1]-np.argwhere(np.sum(Z_3, axis=0)>lim).shape[0]\n",
        "#   p3.append(n) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-w0Ndswefg6S",
        "colab_type": "code",
        "outputId": "68a3b073-d2ad-4c03-a313-702719dd051d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        }
      },
      "source": [
        "plt.scatter(pn1, p1);\n",
        "plt.scatter(pn2, p2);\n",
        "plt.scatter(pn3, p3);\n",
        "plt.xlabel('n cells');\n",
        "plt.ylabel('number of genes with n cells or less');\n",
        "plt.legend(('pbmc', 'bladder', 'neuron'))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-58-7b9da78e7115>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp1\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;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp2\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[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp3\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      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'n cells'\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'number of genes with n cells or less'\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;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, data, **kwargs)\u001b[0m\n\u001b[1;32m   2860\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2861\u001b[0m         verts=verts, edgecolors=edgecolors, **({\"data\": data} if data\n\u001b[0;32m-> 2862\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2863\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2864\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\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   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4180\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4181\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4182\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\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   4183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4184\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: x and y must be the same size"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nT9YB8dJLL0mSzp49q+PHj+u5557L+83kYmhoyNX7wuGw6/d61fR/DTpev/Vfg/rvO3Ut\nx3png3r7C/XO/Lpc5DyLqb29XRcvXlRtba3GxsZkWZYkybIsXbt2TaFQSCtWrHBVBneYrQSgEDIG\nxOTkZLprSJI++eQTBQIBVVdXq6mpSf39/ZKk/v5+NTc3KxgMui6DS+ytBKAADNu27Qe9YHx8XG+9\n9ZYmJydlmqYCgYA2b96slpYWDQ4OKhqN6ubNm1q2bJkikUi6CeO2LFt0Md1trllMM8q13plQb3+h\n3plfl4uMAeFVfg2ITEEwl1Kvt1vU21+od+bX5YLN+kpIxqNCASCP2GqjlLD5HoAFRECUEDbfA7CQ\nCIgSwnRWAAuJgCglTGcFsIAYpC4hHBUKYCERECWGo0IBLBS6mAAAjmhBeJDbxXAAkE8EhMewGA6A\nV9DF5DUshgPgEQSEx7AYDoBXEBAew2I4AF5BQHgNi+EAeASD1B7DYjgAXkFAeBCL4QB4AV1MAABH\ntCCKiAVxALyMgCgSFsQB8Dq6mIqFBXEAPI6AKBIWxAHwOgKiSFgQB8DrMo5B3LhxQ++++66Gh4e1\nePFiNTQ06MUXX1QwGNSVK1fU19enZDKpuro6bd26VdXV1ZLkusw3numUBi7f3c3EgjgAHpKxBWEY\nhp5++mkdOHBAPT09+s53vqMTJ07IsiwdPHhQP/vZz3TgwAF973vf04kTqf5zt2V+YtatlLFtj4x1\nG6RHH5OxboMMBqgBeEjGgAgEAlqzZk36+9WrVysej2tgYECVlZVqbW2VJG3cuFHnzp2TJNdlfmPW\nrZTZtV2LXtkrs2s74QDAU3Iag7AsS6dPn9YTTzyheDyuUCiULgsGg7JtW4lEwnUZAMA7cloHcfTo\nUT300EN66qmndOHChULdU1bC4XBR3lvKqLe/UG9/KUS9sw6IY8eOaXh4WDt37pRpmgqFQorH4+ny\niYkJGYahQCDguiwXQ0NDOb1+Rjgcdv3eUka9/YV6+0u29c41RLLqYvrwww8Vi8W0Y8cOVVRUSJJa\nWlqUTCZ16dIlSdLp06e1fv36eZWVK2tkWNbhHk3vf03W4Z7UFhsA4HEZWxDffPON/vjHP6qhoUG/\n/vWvJUn19fXasWOHIpGIent7NTU1lZ6uKkmmaboqK0dsqQGgVBm2bdvFvgk3SqWLyTrcI/v8mfuu\nG+s2yFzALb1pevsL9faXonYxwT221ABQqgiIAmNLDQClioAoNM6YBlCiOA+iwDhjGkCpIiAWAGdM\nAyhFdDEBABwREAAARwQEAMARAQEAcERAAAAcERAAAEdMc80ja2SY9Q4AygYBkSfs2gqg3NDFlC9/\nOpEOh7Q7LQoAKEUERJ6wayuAckNA5Am7tgIoNwREvrBrK4AywyB1nrBrK4ByQ0DkEbu2AigndDEB\nABwREAAARwQEAMBRxjGIY8eO6fz58xoZGdH+/fvV2NgoSRoaGlI0GlUikVAgEFAkElFDQ8O8ygAA\n3pGxBbF27Vrt3r1bdXV1d13v6+tTR0eHDhw4oI6ODvX29s67DADgHRkDorW1VaFQ6K5r169fVywW\nU1tbmySpra1NsVhMExMTrssAAN7iaprr6OioamtrZZqpfDFNUzU1NYrH45LkqiwYDM67MgCA/CnZ\ndRDhcLgo7y1l1NtfqLe/FKLergJixYoVGhsbk2VZMk1TlmXp2rVrCoVCsm3bVVmuhoaG3Ny6wuGw\n6/fOKMVzH/JR71JEvf2Femd+XS5cTXOtrq5WU1OT+vv7JUn9/f1qbm5WMBh0XVYqZs59sM+fkS5/\nJvv8GdnvvJ4KDQAoI4Zt2/aDXnD06FFduHBB4+PjqqqqUlVVld5++20NDg4qGo3q5s2bWrZsmSKR\nSDqd3JblolgtCOtwTyoc7mGs2yDTw9ts8JuVv1BvfylUCyJjQHhVsQJiev9r0uXP7i949DEtemWv\n688tNP7h+Av19hdPdTH5Gec+APALAiJXnPsAwCdKdpprsXDuAwC/ICBc4NwHAH5AFxMAwBEBAQBw\nREAAABwREAAARwQEAMARAQEAcERAAAAcERAAAEcEBADAEQEBAHBEQAAAHBEQAABHBAQAwBEBAQBw\nxHbfc7BGhjnzAYCvERAOrJFh2e+8Lo0MS5JsSRq4LGvbHkICgG/QxeTkTyfS4ZB2p0UBAH5BQDiw\nx8dyug4A5YiAcGAsr83pOgCUo6KNQQwNDSkajSqRSCgQCCgSiaihoaFgP29m0Pnq7YSspYH0oLPT\nYLSe6ZQGLt/dzVS3MnUdAHyiaAHR19enjo4Otbe36+zZs+rt7dWuXbsK8rNmDzonZy4OXNb0v2yV\njh28bzDa2LZHxrY9zGIC4GtF6WK6fv26YrGY2traJEltbW2KxWKamJgozA+ca9D5gwNzDkabdStl\ndm3Xolf2yuzaTjgA8J2itCBGR0dVW1sr00zlk2maqqmpUTweVzAYzOozwuFw1j/v6u3Ety2HWYzJ\nW6lWwz0qbydUn8Pnl4pc/szKCfX2F+qdPyW7DmJoaCjr11pLA47X7SUPSzcT911PLg3k9PmlIBwO\nl12dskG9/YV6Z35dLorSxbRixQqNjY3JsixJkmVZunbtmkKhUGF+4DOdqUHm2epWSv/rX52vMxgN\nAMVpQVRXV6upqUn9/f1qb29Xf3+/mpubs+5eypVZt1LWnUHnytsJJWfPYmIwGgAcFa2L6ec//7mi\n0ahOnTqlZcuWKRKJFPTnmXUrpa7tqr+nKTZzHQBwt6IFxCOPPKLf/OY3xfrxAIAMWEkNAHBEQAAA\nHBEQAABHBAQAwJFh27bTYmIAgM/RggAAOCIgAACOCAgAgCMCAgDgiIAAADgiIAAAjggIAIAjAgIA\n4IiAAAA4KtkjR3M1NDSkaDSqRCKhQCCgSCSihoaGYt9W3h07dkznz5/XyMiI9u/fr8bGRknlX/8b\nN27o3Xff1fDwsBYvXqyGhga9+OKLCgaDunLlivr6+pRMJlVXV6etW7equrq62LecN2+++aZGRkZk\nGIaWLFmiF154QU1NTWX/zGecPHlSJ0+eTP99L/fn3d3drYqKClVUVEiSOjs79fjjjxem3rZPvPHG\nG/aZM2ds27btM2fO2G+88UaR76gwvvjiC3tkZMTesmWL/fXXX6evl3v9b9y4YV+8eDH9/bFjx+zf\n/va39vT0tB2JROwvvvjCtm3b/sMf/mBHo9Fi3WZB3Lx5M/31hQsX7F/96le2bZf/M7dt2/7qq6/s\nvXv3pv++++F53/tv27btgtXbF11M169fVywWU1tbmySpra1NsVhMExMTRb6z/Gttbb3vbG8/1D8Q\nCGjNmjXp71evXq14PK6BgQFVVlaqtbVVkrRx40adO3euWLdZEA8//HD661u3bskwDF8886mpKR05\nckRdXV3pa3543k4KVW9fdDGNjo6qtrZWppnKQ9M0VVNTo3g8XrBzsL3Eb/W3LEunT5/WE088oXg8\nfldgBoNB2bad7nYpF++9954+/fRTSdKrr77qi2f+0Ucf6cknn1R9fX36ml+e98GDB2XbtlpbW/X8\n888XrN6+CAj4y9GjR/XQQw/pqaee0oULF4p9OwvipZdekiSdPXtWx48f13PPPVfkOyqsK1euaGBg\nQJ2dncW+lQW3e/duhUIhTU1N6YMPPtCRI0e0du3agvwsX3QxrVixQmNjY7IsS1LqN8xr167d1xVT\nrvxU/2PHjml4eFjbtm2TaZoKhUKKx+Pp8omJCRmGUVa/Tc7W3t6uixcvqra2tqyf+eeff67BwUFF\nIhF1d3drdHRUe/fu1fDwcNk/75lnWFFRoY6ODl2+fLlgf899ERDV1dVqampSf3+/JKm/v1/Nzc1l\n09TOxC/1//DDDxWLxbRjx470DI+WlhYlk0ldunRJknT69GmtX7++mLeZV5OTk3f9x/DJJ58oEAiU\n/TN/9tln9f777ysajSoajWrFihV67bXX9PTTT5f9875165YkybZtffzxx2pqairY33PfHBg0ODio\naDSqmzdvatmyZYpEIgqHw8W+rbw7evSoLly4oPHxcVVVVamqqkpvv/122df/m2++0fbt29XQ0KDK\nykpJUn19vXbs2KHLly+rt7dXU1NT6el/y5cvL/Id58f4+LjeeustTU5OyjRNBQIBbd68WS0tLWX/\nzGfr7u7Wzp071djYWNbP++rVq+rp6ZFlWbIsS6tWrdJPf/pT1dTUFKTevgkIAEBufNHFBADIHQEB\nAHBEQAAAHBEQAABHBAQAwBEBAQBwREAAABwREAAAR/8fvZvam2LawmEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GC1yZLijfjC5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_genes(X, Z, n):\n",
        "  indx = np.argwhere(np.sum(Z, axis=0)>n)\n",
        "  X1 = np.squeeze(X[:, indx])\n",
        "  Z1 = np.squeeze(Z[:, indx])\n",
        "  return X1, Z1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2zBkyi4fkci",
        "colab_type": "code",
        "outputId": "96c6aabe-ca55-4a20-8766-9e101c283427",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "print(X_1.shape)\n",
        "print(X_2.shape)\n",
        "print(X_3.shape)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4271, 16653)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-7536798b7893>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_2' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzVymDVGfnNm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_1, Z_1 = remove_genes(X_1, Z_1, 5)\n",
        "X_2, Z_2 = remove_genes(X_2, Z_2, 5)\n",
        "# X_3, Z_3 = remove_genes(X_3, Z_3, 5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2MEu_j_fpse",
        "colab_type": "code",
        "outputId": "e4632cb2-0477-4c8d-8d23-321fc8c57b64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(X_1.shape)\n",
        "print(X_2.shape)\n",
        "# print(X_3.shape)"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4271, 14835)\n",
            "(2746, 14483)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNxkPsbvfwZs",
        "colab_type": "code",
        "outputId": "22b33433-0d33-4dff-e421-414e40d11208",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "source": [
        "xp = np.linspace(-2,7)\n",
        "plt.scatter(np.log(np.mean(X_1, axis=0)), np.log(np.var(X_1, axis=0)));\n",
        "plt.scatter(xp, xp);\n",
        "plt.xlabel('log mean over cells');\n",
        "plt.ylabel('log var over cells');\n",
        "plt.legend(('pbmc', 'mean=var'));"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PM336dJd7XXlyjcWZZAf17f4qEOK126vJzUOAlLK7tvDLPQGHlVXG5fPtNxD0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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QefK-lh4fzUd",
        "colab_type": "code",
        "outputId": "67d76bf2-2a10-48d5-f116-cce92fe7f70e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "xp = np.linspace(-2,4)\n",
        "plt.scatter(np.log(np.mean(X_2, axis=0)), np.log(np.var(X_2, axis=0)));\n",
        "plt.scatter(xp, xp);\n",
        "plt.xlabel('log mean over cells');\n",
        "plt.ylabel('log var over cells');\n",
        "plt.legend(('bladder', 'mean=var'));"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-64-d790fc7fbc5c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mxp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\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      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log mean over cells'\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log var over cells'\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;31mNameError\u001b[0m: name 'X_2' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piF3dHLZf1eB",
        "colab_type": "code",
        "outputId": "4fa51147-8d23-42cb-db4b-812e4c2e8d8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "xp = np.linspace(-3,2)\n",
        "plt.scatter(np.log(np.mean(X_3, axis=0)), np.log(np.var(X_3, axis=0)));\n",
        "plt.scatter(xp, xp);\n",
        "plt.xlabel('log mean over cells');\n",
        "plt.ylabel('log var over cells');\n",
        "plt.legend(('neuron', 'mean=var'));"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-42d64db748d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mxp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\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      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log mean over cells'\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log var over cells'\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;31mNameError\u001b[0m: name 'X_3' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJ25ZQJXf9sR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def adjust(X, do):\n",
        "  if do:\n",
        "    return (X-np.mean(X, axis=0))/np.std(X, axis=0)\n",
        "  else:\n",
        "    return X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOmzaBYHgHoE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s_1 = np.sum(X_1, axis=1)\n",
        "# s_2 = np.sum(X_2, axis=1)\n",
        "# s_3 = np.sum(X_3, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Jse__pEBAkvI"
      },
      "source": [
        "# Define Encoder & Decoder\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1gwD36Vr6KqA",
        "colab": {}
      },
      "source": [
        "class Encoder(snt.AbstractModule):\n",
        "  def __init__(self, name='encoder'):\n",
        "    super(Encoder, self).__init__(name=name)\n",
        "    \n",
        "  def _build(self, x):\n",
        "    h = snt.Linear(256)(x)\n",
        "    h = tf.nn.leaky_relu(h)\n",
        "    h = snt.Linear(16)(h)\n",
        "    h = tf.reshape(h, shape=(-1, 16))\n",
        "    return h\n",
        "\n",
        "class Decoder(snt.AbstractModule):\n",
        "  def __init__(self, output_dim, name='decoder'):\n",
        "    super(Decoder, self).__init__(name=name)\n",
        "    self.output_dim = output_dim\n",
        "  \n",
        "  def _build(self, x):\n",
        "    h = tf.reshape(x, shape=(-1, 16))\n",
        "    h = snt.Linear(16)(h)\n",
        "    h = tf.nn.leaky_relu(h)\n",
        "    h = snt.Linear(256)(h)\n",
        "    h = tf.nn.leaky_relu(h)\n",
        "    h = snt.Linear(self.output_dim, use_bias=False)(h)\n",
        "    h = tf.clip_by_value(tf.math.softplus(h), 1e-5, 1e6)\n",
        "    return h"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "FF7WaOn-s7En"
      },
      "source": [
        "# Build Graph and train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHNuHFHUqXuo",
        "colab_type": "code",
        "outputId": "07624ed2-f685-41ba-b7b0-5e2ceb14b64b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_2.shape"
      ],
      "execution_count": 389,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2746, 14483)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 389
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HQoNjFmJvSB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "outputId": "937353e5-da03-467f-8aa8-13b57c14bfcd"
      },
      "source": [
        "s_2.shape"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-227-9007fc64c36a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms_2\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;31mNameError\u001b[0m: name 's_2' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZlSUMtIm-_P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9616e70c-15b0-4ad4-9c8d-7a1e6e6dc58c"
      },
      "source": [
        "X_2.shape"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2746, 14483)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Loq-Qs53jQQp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "randc = np.random.choice(X_2.shape[0], size=2746, replace=False)\n",
        "X = adjust(X_2, False)[randc, :]\n",
        "s = np.sum(X_2, axis=-1)[randc]\n",
        "s = s.reshape(-1,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XN4exqDoJ9bW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9009ce09-826f-4a03-adcd-4da8a83491a5"
      },
      "source": [
        "s.shape"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2746, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V82IJIsnJ-d9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1c65b987-a0ab-4812-ea53-9a15379bee9f"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2746, 14483)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "owGEoOkO4ttk",
        "outputId": "a91ecd38-788e-4bc1-a478-fd5d2d6cbf24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "# Set hyper-parameters.\n",
        "batch_size = 25\n",
        "\n",
        "num_training_updates = 40000\n",
        "embedding_dim = 16\n",
        "# The higher this value, the higher the capacity in the information bottleneck.\n",
        "num_embeddings = 20\n",
        "\n",
        "# commitment_cost should be set appropriately. It's often useful to try a couple\n",
        "# of values. It mostly depends on the scale of the reconstruction cost\n",
        "# (log p(x|z)). So if the reconstruction cost is 100x higher, the\n",
        "# commitment_cost should also be multiplied with the same amount.\n",
        "commitment_cost = 0.25\n",
        "\n",
        "# Use EMA updates for the codebook (instead of the Adam optimizer).\n",
        "# This typically converges faster, and makes the model less dependent on choice\n",
        "# of the optimizer. In the VQ-VAE paper EMA updates were not used (but was\n",
        "# developed afterwards). See Appendix of the paper for more details.\n",
        "vq_use_ema = True\n",
        "\n",
        "# This is only used for EMA updates.\n",
        "decay = 0.99\n",
        "\n",
        "learning_rate = 9e-4\n",
        "\n",
        "# Data Loading.\n",
        "train_dataset_iterator = (\n",
        "    tf.data.Dataset.from_tensor_slices((X, X, s))\n",
        "    .shuffle(10000)\n",
        "    .repeat(-1)  # repeat indefinitely\n",
        "    .batch(batch_size)).make_one_shot_iterator()\n",
        "train_dataset_batch = train_dataset_iterator.get_next()\n",
        "\n",
        "def get_samples(sess, subset='train'):\n",
        "    return sess.run(train_dataset_batch)\n",
        "\n",
        "# Build modules.\n",
        "encoder = Encoder()\n",
        "decoder = Decoder(X.shape[1])\n",
        "\n",
        "if vq_use_ema:\n",
        "  vq_vae = snt.nets.VectorQuantizerEMA(\n",
        "      embedding_dim=embedding_dim,\n",
        "      num_embeddings=num_embeddings,\n",
        "      commitment_cost=commitment_cost,\n",
        "      decay=decay)\n",
        "else:\n",
        "  vq_vae = snt.nets.VectorQuantizer(\n",
        "      embedding_dim=embedding_dim,\n",
        "      num_embeddings=num_embeddings,\n",
        "      commitment_cost=commitment_cost)\n",
        "\n",
        "# Process inputs with conv stack, finishing with 1x1 to get to correct size.\n",
        "x_in = tf.placeholder(tf.float32, shape=(None, X.shape[1]))\n",
        "x_out = tf.placeholder(tf.float32, shape=(None, X.shape[1]))\n",
        "s_mult = tf.placeholder(tf.float32, shape=(None, 1))\n",
        "\n",
        "z = encoder(x_in)\n",
        "# For training\n",
        "vq_output_train = vq_vae(z, is_training=True)\n",
        "x_recon = decoder(vq_output_train[\"quantize\"])\n",
        "#x_recon = decoder(z)\n",
        "x_recon = s_mult*x_recon\n",
        "\n",
        "recon_error = -tf.reduce_mean(tfd.Poisson(rate=x_recon).log_prob(x_out))\n",
        "#recon_error = tf.reduce_mean(tf.square(x_out-x_recon))\n",
        "loss = recon_error + vq_output_train[\"loss\"]\n",
        "\n",
        "# For evaluation, make sure is_training=False!\n",
        "vq_output_eval = vq_vae(z, is_training=False)\n",
        "x_recon_eval = decoder(vq_output_eval[\"quantize\"])\n",
        "\n",
        "# The following is a useful value to track during training.\n",
        "# It indicates how many codes are 'active' on average.\n",
        "perplexity = vq_output_train[\"perplexity\"] \n",
        "\n",
        "# Create optimizer and TF session.\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
        "train_op = optimizer.minimize(loss)\n",
        "sess = tf.train.SingularMonitoredSession()\n",
        "\n",
        "# Train.\n",
        "train_res_recon_error = []\n",
        "train_res_perplexity = []\n",
        "for i in xrange(num_training_updates):\n",
        "  a, b, c = get_samples(sess)\n",
        "  feed_dict = {x_in: a, x_out: b, s_mult: c}\n",
        "  results = sess.run([train_op, recon_error, perplexity],\n",
        "                     feed_dict=feed_dict)\n",
        "  train_res_recon_error.append(results[1])\n",
        "  train_res_perplexity.append(results[2])\n",
        "  \n",
        "  if (i+1) % 500 == 0:\n",
        "    print('%d iterations' % (i+1))\n",
        "    print('recon_error: %.3f' % np.mean(train_res_recon_error[-200:]))\n",
        "    print('perplexity: %.3f' % np.mean(train_res_perplexity[-200:]))\n",
        "    print()\n"
      ],
      "execution_count": 424,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "500 iterations\n",
            "recon_error: 0.979\n",
            "perplexity: 3.839\n",
            "\n",
            "1000 iterations\n",
            "recon_error: 0.575\n",
            "perplexity: 4.278\n",
            "\n",
            "1500 iterations\n",
            "recon_error: 0.461\n",
            "perplexity: 4.253\n",
            "\n",
            "2000 iterations\n",
            "recon_error: 0.408\n",
            "perplexity: 4.270\n",
            "\n",
            "2500 iterations\n",
            "recon_error: 0.383\n",
            "perplexity: 4.274\n",
            "\n",
            "3000 iterations\n",
            "recon_error: 0.365\n",
            "perplexity: 4.297\n",
            "\n",
            "3500 iterations\n",
            "recon_error: 0.948\n",
            "perplexity: 4.295\n",
            "\n",
            "4000 iterations\n",
            "recon_error: 1.256\n",
            "perplexity: 5.583\n",
            "\n",
            "4500 iterations\n",
            "recon_error: 0.675\n",
            "perplexity: 7.136\n",
            "\n",
            "5000 iterations\n",
            "recon_error: 0.629\n",
            "perplexity: 7.118\n",
            "\n",
            "5500 iterations\n",
            "recon_error: 0.561\n",
            "perplexity: 7.129\n",
            "\n",
            "6000 iterations\n",
            "recon_error: 0.489\n",
            "perplexity: 7.305\n",
            "\n",
            "6500 iterations\n",
            "recon_error: 0.538\n",
            "perplexity: 7.800\n",
            "\n",
            "7000 iterations\n",
            "recon_error: 0.497\n",
            "perplexity: 7.764\n",
            "\n",
            "7500 iterations\n",
            "recon_error: 0.608\n",
            "perplexity: 6.514\n",
            "\n",
            "8000 iterations\n",
            "recon_error: 0.531\n",
            "perplexity: 6.900\n",
            "\n",
            "8500 iterations\n",
            "recon_error: 0.477\n",
            "perplexity: 7.139\n",
            "\n",
            "9000 iterations\n",
            "recon_error: 0.462\n",
            "perplexity: 7.241\n",
            "\n",
            "9500 iterations\n",
            "recon_error: 0.458\n",
            "perplexity: 7.220\n",
            "\n",
            "10000 iterations\n",
            "recon_error: 0.645\n",
            "perplexity: 7.099\n",
            "\n",
            "10500 iterations\n",
            "recon_error: 0.629\n",
            "perplexity: 9.266\n",
            "\n",
            "11000 iterations\n",
            "recon_error: 0.580\n",
            "perplexity: 9.331\n",
            "\n",
            "11500 iterations\n",
            "recon_error: 0.567\n",
            "perplexity: 9.291\n",
            "\n",
            "12000 iterations\n",
            "recon_error: 0.560\n",
            "perplexity: 9.157\n",
            "\n",
            "12500 iterations\n",
            "recon_error: 0.556\n",
            "perplexity: 9.247\n",
            "\n",
            "13000 iterations\n",
            "recon_error: 0.547\n",
            "perplexity: 9.452\n",
            "\n",
            "13500 iterations\n",
            "recon_error: 0.528\n",
            "perplexity: 9.246\n",
            "\n",
            "14000 iterations\n",
            "recon_error: 0.545\n",
            "perplexity: 9.357\n",
            "\n",
            "14500 iterations\n",
            "recon_error: 0.515\n",
            "perplexity: 9.197\n",
            "\n",
            "15000 iterations\n",
            "recon_error: 0.529\n",
            "perplexity: 8.862\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-424-491df70d1c80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     89\u001b[0m   \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mx_in\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_out\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms_mult\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m   results = sess.run([train_op, recon_error, perplexity],\n\u001b[0;32m---> 91\u001b[0;31m                      feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m     92\u001b[0m   \u001b[0mtrain_res_recon_error\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\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[1;32m     93\u001b[0m   \u001b[0mtrain_res_perplexity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\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;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    752\u001b[0m         \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m         run_metadata=run_metadata)\n\u001b[0m\u001b[1;32m    755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun_step_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_fn\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;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1336\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1337\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1338\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1339\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_PREEMPTION_ERRORS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1340\u001b[0m       \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1409\u001b[0m         \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1410\u001b[0m         \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1411\u001b[0;31m         run_metadata=run_metadata)\n\u001b[0m\u001b[1;32m   1412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1413\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1168\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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;32m-> 1169\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1171\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun_step_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraw_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_with_hooks\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;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 950\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    951\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\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   1172\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1173\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1174\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m       \u001b[0mresults\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;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1350\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1354\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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   1355\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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   1357\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1339\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\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   1340\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1341\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\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;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1427\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m   1428\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1429\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1431\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\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;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "m2hNyAnhs-1f"
      },
      "source": [
        "# Plot loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2vo-lDyRomKD",
        "outputId": "d2185c56-ed4e-4c60-949e-ef0bff8fdda6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "f = plt.figure(figsize=(16,8))\n",
        "ax = f.add_subplot(1,2,1)\n",
        "ax.plot(train_res_recon_error)\n",
        "ax.set_yscale('log')\n",
        "ax.set_title('NMSE.')\n",
        "\n",
        "ax = f.add_subplot(1,2,2)\n",
        "ax.plot(train_res_perplexity)\n",
        "ax.set_title('Average codebook usage (perplexity).');"
      ],
      "execution_count": 425,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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17sUXX9Rbb73lSW7r16+v888/X3PmzFF2drbq16+vTp06ecq3bdtWq1atUosWLUKe3G/p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\nQb3MAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lyj1CCKptCZz"
      },
      "source": [
        "# View latent space"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rM9zj7ZiPZBG",
        "colab": {}
      },
      "source": [
        "# Latent space\n",
        "z_, vq_output_eval_, x_recon_, z_d_  = sess.run([z, vq_output_eval, x_recon, vq_output_train[\"quantize\"]], feed_dict={x_in: X, x_out: X, s_mult: s})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8GF2Cv7TJuU",
        "colab_type": "code",
        "outputId": "60d18450-cd45-4c68-fdda-cc2ae8b6d02a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "plt.scatter(z_[:,0], z_[:,1], c=vq_output_eval_['encoding_indices'], cmap='tab20', s=10.0);\n",
        "plt.scatter(z_d_[:,0], z_d_[:,1], c='red', s=20.0);"
      ],
      "execution_count": 427,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ihSTp2muv1TPPPFOMIgEAwAxR9DkrYRjqySef1OrVq9XZ2amGhoaRY1VVVTLGaGBgoNjF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\nAABYjbACAACsRlgBAABW+/8BjHo0YLeqCroAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnEfprFkCZyX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "true_labels = np.concatenate((y_2[randc][:,None],y_2[randc][:,None], y_2[randc][:,None],y_2[randc][:,None], y_2[randc][:,None],y_2[randc][:,None], y_2[randc][:,None],y_2[randc][:,None], y_2[randc][:,None],y_2[randc][:,None], y_2[randc][:,None],y_2[randc][:,None], y_2[randc][:,None],y_2[randc][:,None], y_2[randc][:,None],y_2[randc][:,None]), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gB3wA4VFs8wL",
        "colab_type": "code",
        "outputId": "a12021aa-7f10-4ac8-ba61-87a7de215439",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "plt.scatter(z_[:,0], z_[:, 1], cmap='tab20', c=np.reshape(y_2[randc], -1), s=10.0);\n",
        "#plt.scatter(z_d_[:,0], z_d_[:,1], c='red', s=20.0);"
      ],
      "execution_count": 422,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4C6UU11xzDatUNYHju33rtsfYqsS0108sbBJqhJgnOl9PcnT3GCjIjFos3hAn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\nngdIOKcwk/dO+Gdqx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2OTP+UbLcHGsvjyOpP5haXcmZ3NM/86iFVHVuEwHLxR8gap7lR+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\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alE6JlpJzT5i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}