EMF_RBM_Ominglot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  EMF RBM Omniglot\n",
    "\n",
    "### Binarized, 200 Epochs\n",
    "\n",
    "Look at different ways of computing the\n",
    "\n",
    "- energy density\n",
    "- free energy density\n",
    "- entropy density\n",
    "\n",
    "We can\n",
    "\n",
    "- use the TAP S and MF Energy *U_naive   \n",
    "    - which takes an equlibration step first  \n",
    "    - S(mv, mh), U_naive(mv, mh)\n",
    "- use a simple h = means_hiddens() step\n",
    "    - S(v, h), E_MF(v, h)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:41:52.758540",
     "start_time": "2017-03-06T08:41:50.307872"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlesmartin14/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/Users/charlesmartin14/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "from sklearn import linear_model, datasets, metrics, preprocessing \n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import psutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:41:52.768512",
     "start_time": "2017-03-06T08:41:52.760113"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'divide': 'warn', 'invalid': 'warn', 'over': 'warn', 'under': 'ignore'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "from sklearn.utils.validation import assert_all_finite\n",
    "from scipy.sparse import csc_matrix, csr_matrix, lil_matrix\n",
    "from sklearn.preprocessing import Binarizer\n",
    "np.seterr(all='warn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:41:52.779372",
     "start_time": "2017-03-06T08:41:52.770753"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.io import loadmat\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "def show_image(image): \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\n",
    "    imgplot.set_interpolation('nearest')\n",
    "    ax.xaxis.set_ticks_position('top')\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:41:52.786901",
     "start_time": "2017-03-06T08:41:52.781578"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import rbm_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:41:59.221653",
     "start_time": "2017-03-06T08:41:56.977203"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_x, train_y, train_char, test_x, test_y, test_char = rbm_datasets.load_omniglot_iwae()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:42:00.328447",
     "start_time": "2017-03-06T08:42:00.176656"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import binarize\n",
    "X_train = binarize(train_x, threshold=0.5, copy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:42:02.355252",
     "start_time": "2017-03-06T08:42:00.912205"
    },
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1065b40d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD7CAYAAABKWyniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqaH/A8HLb+A3rtIuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d76bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ffca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD7CAYAAABKWyniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkbR5HOpLDRl8qSGDLzVk8KWGDL7U0P8Bkxn2bqQ6IxMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140a4dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142a4f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD7CAYAAABKWyniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZvHob7UkMGXGjL4UkMGX2rI4EsN/R9vdr4SGWQSJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114402310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11449fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1146a8990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD7CAYAAABKWyniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkBDBBxL6f/73tRBGPkMfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11448e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    show_image(X_train[i+100].reshape(28,28))\n",
    "    show_image(train_x[i+100].reshape(28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:42:12.701382",
     "start_time": "2017-03-06T08:42:12.695930"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24345, 784)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-20T17:56:14.156132",
     "start_time": "2016-10-20T17:56:14.153682"
    }
   },
   "source": [
    "### EMF RBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T08:42:29.325058",
     "start_time": "2017-03-06T08:42:29.320737"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sswap(total=7516192768L, used=6550454272L, free=965738496L, percent=87.2, sin=59727265792L, sout=415178752L)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psutil.swap_memory()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mean Entropy, Energy, and Free Energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T09:36:35.846336",
     "start_time": "2017-03-06T08:42:32.775926"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EMF_RBM(batch_size=100, decay=0.01, learning_rate=0.005, momentum=0.5,\n",
      "    monitor=True, n_components=256, n_iter=200, neq_steps=3,\n",
      "    random_state=<mtrand.RandomState object at 0x106f013c0>, sigma=0.001,\n",
      "    thresh=1e-08, verbose=True, weight_decay='L1')\n",
      "[EMF_RBM] Iteration 1, pseudo-likelihood = -173.07, time = 6.62s\n",
      "computing TAP Free Energies\n",
      "monitor:  -243.661261013 346.506989736 102.848531481\n",
      "[EMF_RBM] Iteration 2, pseudo-likelihood = -174.73, time = 17.93s\n",
      "computing TAP Free Energies\n",
      "monitor:  -243.467702702 346.482319329 103.017315966\n",
      "[EMF_RBM] Iteration 3, pseudo-likelihood = -173.46, time = 17.53s\n",
      "computing TAP Free Energies\n",
      "monitor:  -243.278085103 346.47796933 103.202884751\n",
      "[EMF_RBM] Iteration 4, pseudo-likelihood = -175.57, time = 17.72s\n",
      "computing TAP Free Energies\n",
      "monitor:  -243.079864909 346.46698537 103.391520222\n",
      "[EMF_RBM] Iteration 5, pseudo-likelihood = -173.07, time = 16.91s\n",
      "computing TAP Free Energies\n",
      "monitor:  -242.839967677 346.411359636 103.581054936\n",
      "[EMF_RBM] Iteration 6, pseudo-likelihood = -174.83, time = 16.59s\n",
      "computing TAP Free Energies\n",
      "monitor:  -242.432379233 346.173738175 103.771385839\n",
      "[EMF_RBM] Iteration 7, pseudo-likelihood = -175.46, time = 16.62s\n",
      "computing TAP Free Energies\n",
      "monitor:  -241.441872026 345.267115881 103.932176855\n",
      "[EMF_RBM] Iteration 8, pseudo-likelihood = -169.64, time = 16.72s\n",
      "computing TAP Free Energies\n",
      "monitor:  -239.046977387 342.272394668 103.565734696\n",
      "[EMF_RBM] Iteration 9, pseudo-likelihood = -168.38, time = 18.30s\n",
      "computing TAP Free Energies\n",
      "monitor:  -234.966900783 334.706265483 100.451586292\n",
      "[EMF_RBM] Iteration 10, pseudo-likelihood = -162.37, time = 18.15s\n",
      "computing TAP Free Energies\n",
      "monitor:  -230.515264552 325.59716364 96.0755995064\n",
      "[EMF_RBM] Iteration 11, pseudo-likelihood = -156.05, time = 16.03s\n",
      "computing TAP Free Energies\n",
      "monitor:  -226.116752358 318.509861026 93.6333492982\n",
      "[EMF_RBM] Iteration 12, pseudo-likelihood = -158.21, time = 15.88s\n",
      "computing TAP Free Energies\n",
      "monitor:  -221.970846378 312.063845172 91.6613904551\n",
      "[EMF_RBM] Iteration 13, pseudo-likelihood = -150.34, time = 15.76s\n",
      "computing TAP Free Energies\n",
      "monitor:  -218.693457525 305.407196543 88.6451566407\n",
      "[EMF_RBM] Iteration 14, pseudo-likelihood = -149.26, time = 16.08s\n",
      "computing TAP Free Energies\n",
      "monitor:  -216.156807071 299.250434389 85.3683478496\n",
      "[EMF_RBM] Iteration 15, pseudo-likelihood = -149.05, time = 15.84s\n",
      "computing TAP Free Energies\n",
      "monitor:  -213.959121611 293.636629036 82.2664214963\n",
      "[EMF_RBM] Iteration 16, pseudo-likelihood = -143.10, time = 15.74s\n",
      "computing TAP Free Energies\n",
      "monitor:  -211.910136767 288.531313364 79.4967256647\n",
      "[EMF_RBM] Iteration 17, pseudo-likelihood = -140.71, time = 15.80s\n",
      "computing TAP Free Energies\n",
      "monitor:  -209.948284561 283.959796783 77.1751834226\n",
      "[EMF_RBM] Iteration 18, pseudo-likelihood = -141.24, time = 16.25s\n",
      "computing TAP Free Energies\n",
      "monitor:  -208.125325882 279.580615631 74.9167094844\n",
      "[EMF_RBM] Iteration 19, pseudo-likelihood = -137.60, time = 16.11s\n",
      "computing TAP Free Energies\n",
      "monitor:  -206.529942452 275.285973058 72.5024031183\n",
      "[EMF_RBM] Iteration 20, pseudo-likelihood = -137.31, time = 16.35s\n",
      "computing TAP Free Energies\n",
      "monitor:  -205.159205415 271.311792807 70.1647989698\n",
      "[EMF_RBM] Iteration 21, pseudo-likelihood = -135.60, time = 15.77s\n",
      "computing TAP Free Energies\n",
      "monitor:  -203.989090859 267.722972026 68.0001049046\n",
      "[EMF_RBM] Iteration 22, pseudo-likelihood = -137.53, time = 16.27s\n",
      "computing TAP Free Energies\n",
      "monitor:  -203.008498075 264.433035034 65.934111406\n",
      "[EMF_RBM] Iteration 23, pseudo-likelihood = -134.53, time = 16.15s\n",
      "computing TAP Free Energies\n",
      "monitor:  -202.197306687 261.39878647 63.9421246497\n",
      "[EMF_RBM] Iteration 24, pseudo-likelihood = -132.97, time = 15.99s\n",
      "computing TAP Free Energies\n",
      "monitor:  -201.535090613 258.626078845 62.0520068247\n",
      "[EMF_RBM] Iteration 25, pseudo-likelihood = -130.97, time = 15.87s\n",
      "computing TAP Free Energies\n",
      "monitor:  -201.011401097 256.109086231 60.2718562958\n",
      "[EMF_RBM] Iteration 26, pseudo-likelihood = -125.79, time = 16.25s\n",
      "computing TAP Free Energies\n",
      "monitor:  -200.623754813 253.813714202 58.5720242162\n",
      "[EMF_RBM] Iteration 27, pseudo-likelihood = -129.38, time = 16.24s\n",
      "computing TAP Free Energies\n",
      "monitor:  -200.367766177 251.703241519 56.9200661393\n",
      "[EMF_RBM] Iteration 28, pseudo-likelihood = -127.20, time = 16.15s\n",
      "computing TAP Free Energies\n",
      "monitor:  -200.229745415 249.760697395 55.3131103594\n",
      "[EMF_RBM] Iteration 29, pseudo-likelihood = -127.35, time = 16.30s\n",
      "computing TAP Free Energies\n",
      "monitor:  -200.192035712 247.976587873 53.7612486338\n",
      "[EMF_RBM] Iteration 30, pseudo-likelihood = -125.59, time = 16.22s\n",
      "computing TAP Free Energies\n",
      "monitor:  -200.240992775 246.331240618 52.260350392\n",
      "[EMF_RBM] Iteration 31, pseudo-likelihood = -125.37, time = 16.01s\n",
      "computing TAP Free Energies\n",
      "monitor:  -200.366187463 244.799990123 50.7970825614\n",
      "[EMF_RBM] Iteration 32, pseudo-likelihood = -125.37, time = 16.14s\n",
      "computing TAP Free Energies\n",
      "monitor:  -200.558345992 243.363614277 49.3617549933\n",
      "[EMF_RBM] Iteration 33, pseudo-likelihood = -122.95, time = 16.14s\n",
      "computing TAP Free Energies\n",
      "monitor:  -200.809113427 242.011372915 47.9522606607\n",
      "[EMF_RBM] Iteration 34, pseudo-likelihood = -123.79, time = 16.26s\n",
      "computing TAP Free Energies\n",
      "monitor:  -201.11178864 240.73871133 46.5714307836\n",
      "[EMF_RBM] Iteration 35, pseudo-likelihood = -118.94, time = 16.08s\n",
      "computing TAP Free Energies\n",
      "monitor:  -201.462652307 239.543162479 45.2216511305\n",
      "[EMF_RBM] Iteration 36, pseudo-likelihood = -121.30, time = 16.01s\n",
      "computing TAP Free Energies\n",
      "monitor:  -201.860384171 238.420360667 43.9011341103\n",
      "[EMF_RBM] Iteration 37, pseudo-likelihood = -116.97, time = 15.90s\n",
      "computing TAP Free Energies\n",
      "monitor:  -202.302964716 237.36133205 42.6038248745\n",
      "[EMF_RBM] Iteration 38, pseudo-likelihood = -118.09, time = 15.91s\n",
      "computing TAP Free Energies\n",
      "monitor:  -202.788040299 236.355350647 41.3217676802\n",
      "[EMF_RBM] Iteration 39, pseudo-likelihood = -119.64, time = 15.69s\n",
      "computing TAP Free Energies\n",
      "monitor:  -203.31161851 235.391471129 40.0479076027\n",
      "[EMF_RBM] Iteration 40, pseudo-likelihood = -117.27, time = 16.35s\n",
      "computing TAP Free Energies\n",
      "monitor:  -203.869001188 234.459581608 38.7762806096\n",
      "[EMF_RBM] Iteration 41, pseudo-likelihood = -112.85, time = 16.07s\n",
      "computing TAP Free Energies\n",
      "monitor:  -204.454958803 233.550038094 37.5014420489\n",
      "[EMF_RBM] Iteration 42, pseudo-likelihood = -112.91, time = 15.69s\n",
      "computing TAP Free Energies\n",
      "monitor:  -205.06400457 232.654267817 36.2188754185\n",
      "[EMF_RBM] Iteration 43, pseudo-likelihood = -113.97, time = 15.88s\n",
      "computing TAP Free Energies\n",
      "monitor:  -205.689958015 231.765498173 34.9262737178\n",
      "[EMF_RBM] Iteration 44, pseudo-likelihood = -113.21, time = 15.94s\n",
      "computing TAP Free Energies\n",
      "monitor:  -206.326841755 230.8787371 33.6228371857\n",
      "[EMF_RBM] Iteration 45, pseudo-likelihood = -109.95, time = 15.62s\n",
      "computing TAP Free Energies\n",
      "monitor:  -206.968935954 229.992105628 32.310834966\n",
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      "[EMF_RBM] Iteration 151, pseudo-likelihood = -81.86, time = 16.50s\n",
      "computing TAP Free Energies\n",
      "monitor:  -271.897118811 192.223871661 -58.406530336\n",
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      "computing TAP Free Energies\n",
      "monitor:  -272.414423011 192.073429609 -59.0089994741\n",
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      "computing TAP Free Energies\n",
      "monitor:  -272.928815916 191.92431113 -59.6077698229\n",
      "[EMF_RBM] Iteration 154, pseudo-likelihood = -80.89, time = 16.51s\n",
      "computing TAP Free Energies\n",
      "monitor:  -273.440177587 191.776487097 -60.2027414241\n",
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      "computing TAP Free Energies\n",
      "monitor:  -273.9486135 191.629886035 -60.7940854319\n",
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      "computing TAP Free Energies\n",
      "monitor:  -274.454151163 191.484515148 -61.3818147052\n",
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      "computing TAP Free Energies\n",
      "monitor:  -274.956595319 191.340413888 -61.9656836629\n",
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      "monitor:  -275.456181908 191.197607936 -62.5458946703\n",
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      "monitor:  -275.953102311 191.056087104 -63.1226446966\n",
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      "monitor:  -276.447425724 190.915816524 -63.6960247646\n",
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      "monitor:  -276.93905736 190.776873332 -64.2658339273\n",
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      "monitor:  -277.428001436 190.639215421 -64.8321251549\n",
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      "monitor:  -277.914315449 190.50279422 -65.3950044841\n",
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      "computing TAP Free Energies\n",
      "monitor:  -278.398057943 190.367524057 -65.9546111971\n",
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      "monitor:  -278.879114494 190.233379438 -66.5108569385\n",
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      "monitor:  -279.357358421 190.100454586 -67.0635034239\n",
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      "computing TAP Free Energies\n",
      "monitor:  -279.832922088 189.968810174 -67.6126049628\n",
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      "computing TAP Free Energies\n",
      "monitor:  -280.305979558 189.838452705 -68.1583103177\n",
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      "computing TAP Free Energies\n",
      "monitor:  -280.77653649 189.709260219 -68.7007530545\n",
      "[EMF_RBM] Iteration 170, pseudo-likelihood = -80.60, time = 15.95s\n",
      "computing TAP Free Energies\n",
      "monitor:  -281.244455568 189.581167417 -69.2398652195\n",
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      "computing TAP Free Energies\n",
      "monitor:  -281.709812097 189.45419108 -69.7756982793\n",
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      "computing TAP Free Energies\n",
      "monitor:  -282.17279464 189.328302318 -70.3084605323\n",
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      "computing TAP Free Energies\n",
      "monitor:  -282.633259123 189.20350414 -70.8380043884\n",
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      "computing TAP Free Energies\n",
      "monitor:  -283.091192019 189.079835108 -71.3642650945\n",
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      "computing TAP Free Energies\n",
      "monitor:  -283.546795498 188.957235896 -71.8875080516\n",
      "[EMF_RBM] Iteration 176, pseudo-likelihood = -79.53, time = 16.10s\n",
      "computing TAP Free Energies\n",
      "monitor:  -284.000050608 188.835749299 -72.4076522024\n",
      "[EMF_RBM] Iteration 177, pseudo-likelihood = -77.82, time = 15.75s\n",
      "computing TAP Free Energies\n",
      "monitor:  -284.450903413 188.715375459 -72.9246367194\n",
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      "computing TAP Free Energies\n",
      "monitor:  -284.899381917 188.59608016 -73.4385259118\n",
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      "computing TAP Free Energies\n",
      "monitor:  -285.34557521 188.477803571 -73.949460351\n",
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      "computing TAP Free Energies\n",
      "monitor:  -285.789503745 188.360564726 -74.4574402885\n",
      "[EMF_RBM] Iteration 181, pseudo-likelihood = -77.48, time = 16.05s\n",
      "computing TAP Free Energies\n",
      "monitor:  -286.231003272 188.244392955 -74.9622515526\n",
      "[EMF_RBM] Iteration 182, pseudo-likelihood = -77.60, time = 16.21s\n",
      "computing TAP Free Energies\n",
      "monitor:  -286.670426933 188.129281952 -75.4642481267\n",
      "[EMF_RBM] Iteration 183, pseudo-likelihood = -74.90, time = 15.82s\n",
      "computing TAP Free Energies\n",
      "monitor:  -287.107860377 188.015210166 -75.9635410564\n",
      "[EMF_RBM] Iteration 184, pseudo-likelihood = -78.29, time = 15.79s\n",
      "computing TAP Free Energies\n",
      "monitor:  -287.543031138 187.902103035 -76.4599462725\n",
      "[EMF_RBM] Iteration 185, pseudo-likelihood = -79.50, time = 15.80s\n",
      "computing TAP Free Energies\n",
      "monitor:  -287.975783065 187.789921876 -76.9533479442\n",
      "[EMF_RBM] Iteration 186, pseudo-likelihood = -78.86, time = 16.09s\n",
      "computing TAP Free Energies\n",
      "monitor:  -288.406420993 187.678633994 -77.4440775222\n",
      "[EMF_RBM] Iteration 187, pseudo-likelihood = -77.83, time = 16.17s\n",
      "computing TAP Free Energies\n",
      "monitor:  -288.834924426 187.568271082 -77.9320668568\n",
      "[EMF_RBM] Iteration 188, pseudo-likelihood = -81.26, time = 16.24s\n",
      "computing TAP Free Energies\n",
      "monitor:  -289.261444452 187.458808971 -78.417494071\n",
      "[EMF_RBM] Iteration 189, pseudo-likelihood = -79.18, time = 16.05s\n",
      "computing TAP Free Energies\n",
      "monitor:  -289.685914727 187.350288612 -78.9002421046\n",
      "[EMF_RBM] Iteration 190, pseudo-likelihood = -76.49, time = 16.25s\n",
      "computing TAP Free Energies\n",
      "monitor:  -290.108273696 187.242732192 -79.3802146635\n",
      "[EMF_RBM] Iteration 191, pseudo-likelihood = -80.73, time = 16.88s\n",
      "computing TAP Free Energies\n",
      "monitor:  -290.528558461 187.136114798 -79.8574668371\n",
      "[EMF_RBM] Iteration 192, pseudo-likelihood = -79.64, time = 16.50s\n",
      "computing TAP Free Energies\n",
      "monitor:  -290.946613643 187.030406182 -80.3318713726\n",
      "[EMF_RBM] Iteration 193, pseudo-likelihood = -76.57, time = 15.69s\n",
      "computing TAP Free Energies\n",
      "monitor:  -291.362473527 186.925579388 -80.8035006254\n",
      "[EMF_RBM] Iteration 194, pseudo-likelihood = -79.85, time = 15.96s\n",
      "computing TAP Free Energies\n",
      "monitor:  -291.77630227 186.821540781 -81.272624922\n",
      "[EMF_RBM] Iteration 195, pseudo-likelihood = -79.46, time = 15.80s\n",
      "computing TAP Free Energies\n",
      "monitor:  -292.188306891 186.718317716 -81.7394103723\n",
      "[EMF_RBM] Iteration 196, pseudo-likelihood = -76.73, time = 16.18s\n",
      "computing TAP Free Energies\n",
      "monitor:  -292.598438837 186.615902241 -82.2038110412\n",
      "[EMF_RBM] Iteration 197, pseudo-likelihood = -79.03, time = 15.67s\n",
      "computing TAP Free Energies\n",
      "monitor:  -293.00662467 186.514329533 -82.665708561\n",
      "[EMF_RBM] Iteration 198, pseudo-likelihood = -77.55, time = 15.64s\n",
      "computing TAP Free Energies\n",
      "monitor:  -293.412895561 186.413603306 -83.1251299258\n",
      "[EMF_RBM] Iteration 199, pseudo-likelihood = -76.83, time = 15.59s\n",
      "computing TAP Free Energies\n",
      "monitor:  -293.817260389 186.313688975 -83.5821183403\n",
      "[EMF_RBM] Iteration 200, pseudo-likelihood = -75.72, time = 15.65s\n",
      "computing TAP Free Energies\n",
      "monitor:  -294.219674652 186.214534448 -84.0366863623\n"
     ]
    }
   ],
   "source": [
    "import emf_rbm\n",
    "rbm = emf_rbm.EMF_RBM(verbose=True, monitor=True, n_iter=200)\n",
    "print rbm\n",
    "rbm= rbm.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T10:00:18.259721",
     "start_time": "2017-03-06T10:00:18.038927"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x113321b50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EVvVDNObL8RGMCn/kGZMFMP1YMF9yMZj2DDD15XHhoZiqsHgd+bTWEzPGQiElKKXaYfpU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\nREREW6WwREREW6WwREREW6WwREREW/0D1U9HjTFvl9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a526d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rbm.entropies-rbm.entropies[0], color='red', label='Entropy')\n",
    "plt.plot(rbm.mean_field_energies-rbm.mean_field_energies[0], color='blue', label='MF Energy')\n",
    "plt.plot(rbm.free_energies-rbm.free_energies[0], color='green', label='Free Energy')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Ominglot: Free Energy, Mean Field Enegry, & Entropy vs Epochs\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T10:00:19.472251",
     "start_time": "2017-03-06T10:00:19.426630"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import binarize\n",
    "X_test = binarize(test_x, threshold=0.5, copy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-03-06T10:00:19.983505",
     "start_time": "2017-03-06T10:00:19.979043"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-03-06T18:01:19.578Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01 0.261957868649\n",
      "0.1 0.261833952912\n",
      "1 0.261586121437\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "F_train = rbm.transform(X_train)\n",
    "F_test = rbm.transform(X_test)\n",
    "\n",
    "for c in [0.01, 0.1, 1, 10, 100]:\n",
    "    classifier = LinearSVC()\n",
    "    classifier.fit(F_train, train_y)\n",
    "    test_emf_rbm_pred_y = classifier.predict(F_test)\n",
    "    emf_accuracy = accuracy_score(y_pred=test_emf_rbm_pred_y, y_true=test_y)\n",
    "    print c, emf_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-03-06T18:24:09.953Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### not as good as vanilla RBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}