EMF_RBM_Energy_Densities_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  EMF RBM Energy Densities 2\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": "2016-11-08T23:33:50.367876",
     "start_time": "2016-11-08T23:33:50.361534"
    },
    "collapsed": false
   },
   "outputs": [],
   "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": "2016-11-08T23:33:50.512461",
     "start_time": "2016-11-08T23:33:50.507597"
    },
    "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": "2016-11-08T23:33:53.684714",
     "start_time": "2016-11-08T23:33:51.186577"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('List of arrays in this file: \\n', [u'HDF5.name___X', u'HDF5.name___y'])\n",
      "(60000, 784) (60000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2117.6342254841179"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf =  h5py.File('mnist.h5','r')\n",
    "print('List of arrays in this file: \\n', hf.keys())\n",
    "X = np.array(hf.get('HDF5.name___X'))\n",
    "y = np.array(hf.get('HDF5.name___y'))\n",
    "\n",
    "print X.shape, y.shape\n",
    "hf.close()\n",
    "np.linalg.norm(X, ord=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Helper methods for simple case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T22:38:39.742812",
     "start_time": "2016-11-08T22:38:39.737075"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.utils.fixes import expit    \n",
    "from sklearn.utils.extmath import safe_sparse_dot\n",
    "\n",
    "def sig_means(x, b, W):\n",
    "    a = safe_sparse_dot(x, W.T) + b\n",
    "    return expit(a, out=a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-10-20T17:56:14.156132",
     "start_time": "2016-10-20T17:56:14.153682"
    }
   },
   "source": [
    "### Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import emf_rbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T22:42:32.963306",
     "start_time": "2016-11-08T22:42:32.959982"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sswap(total=0, used=0, free=0, percent=0.0, sin=0, sout=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psutil.swap_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T22:43:58.231469",
     "start_time": "2016-11-08T22:43:55.548293"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[EMF_RBM] Iteration 1, pseudo-likelihood = -185.85, time = 23.88s\n",
      "[EMF_RBM] Iteration 2, pseudo-likelihood = -151.51, time = 27.25s\n",
      "[EMF_RBM] Iteration 3, pseudo-likelihood = -133.08, time = 27.29s\n",
      "[EMF_RBM] Iteration 4, pseudo-likelihood = -124.07, time = 27.33s\n",
      "[EMF_RBM] Iteration 5, pseudo-likelihood = -112.83, time = 27.08s\n",
      "[EMF_RBM] Iteration 6, pseudo-likelihood = -108.44, time = 27.07s\n",
      "[EMF_RBM] Iteration 7, pseudo-likelihood = -104.34, time = 27.03s\n",
      "[EMF_RBM] Iteration 8, pseudo-likelihood = -99.38, time = 27.06s\n",
      "[EMF_RBM] Iteration 9, pseudo-likelihood = -97.35, time = 27.09s\n",
      "[EMF_RBM] Iteration 10, pseudo-likelihood = -92.24, time = 27.03s\n",
      "[EMF_RBM] Iteration 11, pseudo-likelihood = -88.51, time = 27.23s\n",
      "[EMF_RBM] Iteration 12, pseudo-likelihood = -87.37, time = 27.07s\n",
      "[EMF_RBM] Iteration 13, pseudo-likelihood = -88.47, time = 27.42s\n",
      "[EMF_RBM] Iteration 14, pseudo-likelihood = -83.94, time = 27.01s\n",
      "[EMF_RBM] Iteration 15, pseudo-likelihood = -84.19, time = 27.06s\n",
      "[EMF_RBM] Iteration 16, pseudo-likelihood = -83.16, time = 27.09s\n",
      "[EMF_RBM] Iteration 17, pseudo-likelihood = -81.97, time = 27.13s\n",
      "[EMF_RBM] Iteration 18, pseudo-likelihood = -80.05, time = 27.07s\n",
      "[EMF_RBM] Iteration 19, pseudo-likelihood = -78.93, time = 27.08s\n",
      "[EMF_RBM] Iteration 20, pseudo-likelihood = -77.99, time = 27.04s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EMF_RBM(batch_size=100, decay=0.01, learning_rate=0.005, momentum=0.5,\n",
       "    monitor=False, n_components=256, n_iter=20, neq_steps=3,\n",
       "    random_state=<mtrand.RandomState object at 0x7f5524115a00>,\n",
       "    sigma=0.001, thresh=1e-08, verbose=True, weight_decay='L1')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbm = emf_rbm.EMF_RBM(verbose=True)\n",
    "rbm.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T13:42:53.473982",
     "start_time": "2016-11-08T13:42:53.469340"
    }
   },
   "source": []
  },
  {
   "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": [
    "### Mean field TAP Energies\n",
    "\n",
    "Not exactly Gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T17:12:51.346473",
     "start_time": "2016-11-08T17:12:51.341613"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ids = np.random.choice(X.shape[0], size=6000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T14:27:59.753176",
     "start_time": "2016-11-08T14:27:58.703651"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "U = rbm._U_naive_TAP(X[ids])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T14:32:29.917164",
     "start_time": "2016-11-08T14:32:29.912768"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T14:28:01.186684",
     "start_time": "2016-11-08T14:28:00.751816"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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S5s8AYxGrTsv7odQ6L45LqdzSKG9Qp2NeJHUGAwzhh1I7OI1XUm8zwNC0nDbZTMVbGo0P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\ncUQB9Xmr1aUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54f1205fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(U,100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T13:54:45.187108",
     "start_time": "2016-11-08T13:54:45.184452"
    }
   },
   "source": [
    "### Plain Mean field Energies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T17:12:41.672691",
     "start_time": "2016-11-08T17:12:41.521671"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = sig_means(X[ids],rbm.h_bias,rbm.W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T14:31:56.272616",
     "start_time": "2016-11-08T14:31:56.214258"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Emf = safe_sparse_dot(p, rbm.h_bias) + safe_sparse_dot(X[ids], rbm.v_bias)  + (X[ids].dot(rbm.W.T)*p).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T14:32:01.904695",
     "start_time": "2016-11-08T14:32:01.901151"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Emf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T14:32:48.307072",
     "start_time": "2016-11-08T14:32:46.359981"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I2BcRd0fEBW33xqrNYFHt5rO2gIh4BsUOu3emlJ5qu+2zNo952s1n7dhOAh7s\ncN1nbWHt7TawZ20YA8Z/Ad4IvAL4rxTDJZeV7p9M50PSWvcWW2aUfAq4OqX0jQXKXELRzfVK4Hrg\n6oh4V+n+uLUZHLvdfNY6iIj/FBE/o/gXznOA17cV8Vnr4Bjt5rO2gIg4HXgXxd8JZT5rC5in3Qb2\nrA0kYETEhztMxmmfmPMCgJTSFSmlr6aUvp1S+hPg94CL2yepjLrFtllE/A7wCxwJYdHp81JK/yGl\ndFdK6S9TsRX7ZcDITXbK3W7joJvfz6bLgX8I/BOKeSv/vfx5Pmu9tds46KHNiIhfBG4G/mdK6U/L\n93zWemu3QRnUTp7/meJfiwv5q3mu30NRz+cBP6CYsPLLbWVah6Y9UPrfTgeplcsMu8W02W7gV4Gz\ngIMRc/6O/H8RcW1Kqb3LsOUe4JKIOD6l9ASj0WaQt9181o44/PuZUnqQosv1hxHxPeBvIuKlqXQy\ncptxftYW224+a0ccbrOIOBX4C4oJsW9fxOf7rHHMdhvYszaQgJFS+inw0x7f/mKKiTw/aX59F/BH\nETFRGkP6dWA/8N1SmT+OiONKY5y/DuxKKe3vsR4Dtdg2i4iLgX9XunQqxVjZGyh+2ebzYuCh5i8h\njECbQfZ281k7ttb8qFULlBnrZ20e7e3ms9am+S/wvwD+L3DhIr/F2D9ri2i3wT1rVc9wbZvt+jKK\n5VxnAKdRTITaC/xpqcwK4C8pun7OoFhisxfYUipzInA/xRKbFwK/BfwMeGvVP+MA2vC5HL0a4rXA\nWykm8DyfYobwz4AP2GYLtpvP2tw2OhO4CHgRsB74NeAOYBdwvM/aktrNZ21um51K0WP9xeaf17Ve\npTI+a72128CetcobpK1xXkyRnB6kWHP7beD3W7+EpXLPodi34GfNhrkMWNFW5u8DtwMHKI6Bf0/V\nP9+A2vC5HL2fw7nAToqE+kjzz0etZx7XNpuv3XzWOj4ftwH7mj/rj4BPAKf4rC2t3XzWjmqzf9X8\nfSy/DgFP+awtrd0G+ax52JkkScpuGJepSpKkZc6AIUmSsjNgSJKk7AwYkiQpOwOGJEnKzoAhSZKy\nM2BIkqTsDBiSJCk7A4YkScrOgCFJkrIzYEiSpOz+PwbAGVH6tM6bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55344aea10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(-Emf,100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are clearly NOT Guassian distributions, and I have a feeling that these are ground state energies, and subject to EVS, not the REM.  And that the REM has to be determined by looking at all Energies during all training steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### All energies from all steps\n",
    "\n",
    "Lets compute as many energies as we can and see what we get\n",
    "\n",
    "(very very slow)\n",
    "(took this out of code but left in the notebook for now)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### replaced, not really interesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2016-11-09T01:53:04.985Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'EMF_RBM' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-f03ed253a151>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrbm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEMF_RBM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0memf_rbm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mU_energies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0memf_rbm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_wEs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'EMF_RBM' is not defined"
     ]
    }
   ],
   "source": [
    "rbm = EMF_RBM(verbose=True)\n",
    "emf_rbm, U_energies = emf_rbm.fit_wEs(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T15:32:57.381582",
     "start_time": "2016-11-08T15:32:57.377617"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Emf = U_energies[1]\n",
    "len(Emf)\n",
    "import itertools\n",
    "merged = list(itertools.chain.from_iterable(Emf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T15:34:36.122009",
     "start_time": "2016-11-08T15:34:35.524792"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131684d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(merged,100);\n",
    "plt.title(\"all energies, for all data, for all iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T15:37:40.503157",
     "start_time": "2016-11-08T15:37:39.124523"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x128b8e290>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VWqrvA37LOXcpcKmZ1TqmNGihZqPOXZ2hjiRq1N7UXtUSRNrEggHBOfddYKzGplpdkjcC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\nCggiIgIoIIiIiE8BQUREAPj/HeY7vcyRFMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1273df850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Emf[19],100, alpha=0.25);\n",
    "plt.hist(Emf[14],100, alpha=0.25, color='green');\n",
    "plt.hist(Emf[9],100, alpha=0.25, color='purple');\n",
    "plt.hist(Emf[4],100, alpha=0.25, color='orange');\n",
    "plt.hist(Emf[0],100, alpha=0.25, color='orange');\n",
    "\n",
    "plt.title(\"all U energies, colored by iteration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T15:37:57.490063",
     "start_time": "2016-11-08T15:37:56.764304"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x140a66c50>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5afk2oEyWNJYB/RFxCNghaQhYImkncEJEbE3b3A5cBNxT/1uw2Sw/YA0etDZrpFoHqU+O\niBGAiNgj6eRUPh/4+1y94VR2CNidK9+dys3qkh+wBg9amzVSowapo0H7MTOzDlFrC2JE0ryIGJHU\nBTyTyoeBBbl63amsWnlVPT09Y8ulUolSqVRjqI3nO7iaWScol8uUy+Wm7X+qCULpZ9RdwOXA9cBl\nwNpc+R2SbiTrQloIbImIkLRP0hJgK/BR4CsTHTCfIDrN6B1cATZtGmxvMGY2a1V+ee7t7W3o/idN\nEJLuBErAXElPAdcC1wHflrQS2Ek2c4mIGJS0BhgEDgKrImK0++kK4BvA8cC6iLi7oe/EzMwaaiqz\nmFZUWXVulfp9QF9B+YPAmUcUnZmZtY1vtWEziu/TZNY4ThA2o/g+TWaN43sxmZlZIScIMzMr5ARh\nZmaFnCDMzKyQE4SZmRVygjAzs0JOEGZmVsgJwszMCjlBmJlZIScIMzMr5ARhZmaFnCDMzKyQE4SZ\nmRVygjAzs0J1JQhJfyTpUUkDku6QdJykkyStl/S4pHskzcnVXy1pSNJ2SUvrD9/MzJql5udBSHoj\n8GngrRHxoqRvAR8BzgA2RsQNkj4HrAaulnQG2aNJFwPdwEZJb849ktSsofzwILP61PvAoKOB10g6\nDLwKGCZLCOek9bcBZeBqYBnQHxGHgB2ShoAlwD/UGUNLbNiwmb17DwAwMDA09lAa61x+eJBZfWru\nYoqIp4E/A54iSwz7ImIjMC8iRlKdPcDJaZP5wK7cLoZT2bSwd+8BurpKdHWV2L//YLvDMTNrupoT\nhKTXAcuBU4E3krUkfg+o7DJyF5KZ2TRUTxfTucCTEfEsgKT/BfwmMCJpXkSMSOoCnkn1h4EFue27\nU1mhnp6eseVSqUSpVKojVJvtPB5hM1G5XKZcLjdt//UkiKeAsyUdD7wAfBDYCuwHLgeuBy4D1qb6\ndwF3SLqRrGtpIbCl2s7zCcKsXh6PsJmo8stzb29vQ/dfc4KIiC2SvgP8GDiY/v1L4ARgjaSVwE6y\nmUtExKCkNcBgqr/KM5jMzDpXXbOYIqIXqExZz5J1PxXV7wP66jmmmZm1hq+kNjOzQk4QZmZWyAnC\nzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFar3dt9m047vy2Q2NU4QNuv4vkxmU+MuJjMz\nK+QWhM1q7m4yq84JwmY1dzeZVecEMQE/h9rMZjOPQUzAz6E2s9nMCcLMzArVlSAkzZH0bUnbJT0m\n6X2STpK0XtLjku6RNCdXf7WkoVR/af3hm5lZs9TbgvgysC4iFgPvBH4CXA1sjIi3APcCqwEknUH2\n+NHFwAXATZJU5/HNzKxJak4Qkk4E/mVE3AoQEYciYh+wHLgtVbsNuCgtLwP6U70dwBCwpNbjm5lZ\nc9XTgjgd+IWkWyU9JOkvJb0amBcRIwARsQc4OdWfD+zKbT+cyszMrAPVM831GOAs4IqIeEDSjWTd\nS1FRr/L1lPT09Iwtl0olSqVSbVGamc1Q5XKZcrnctP3XkyB2A7si4oH0+rtkCWJE0ryIGJHUBTyT\n1g8DC3Lbd6eyQvkEYWZmr1T55bm3t7eh+6+5iyl1I+2StCgVfRB4DLgLuDyVXQasTct3AZdIOk7S\n6cBCYEutxzczs+aq90rqPwDukHQs8CTwMeBoYI2klcBOsplLRMSgpDXAIHAQWBURNXU/mZlZ89WV\nICLiYeC9BavOrVK/D+ir55hmZtYavpLazMwKOUGYmVkhJwgzMyvk232bJX54kNl4ThBmiR8eZDae\nu5jMzKyQE4SZmRVygjAzs0IegzAr4AFrMyeIV9iwYTN79x4AYGBgaGzQ0mYXD1ibuYvpFfbuPUBX\nV4murhL79x9sdzhmZm3jBGFmZoXcxWQ2CY9H2GzlBGE2CY9H2GzlLiYzMyvkBGFmZoXq7mKSdBTw\nALA7IpZJOgn4FnAqsAO4OCL2pbqrgZXAIeDKiFhf7/HNWik/HgEek7CZrREtiCvJHiM66mpgY0S8\nBbgXWA0g6Qyyx48uBi4AbpKkBhzfrGVGxyNGf0avmTGbiepKEJK6gQuBr+WKlwO3peXbgIvS8jKg\nPyIORcQOYAhYUs/xzcyseeptQdwIfBaIXNm8iBgBiIg9wMmpfD6wK1dvOJWZmVkHqnkMQtKHgZGI\n2CapNEHVmGBdVT09PWPLpVKJUmmiQ9Quf2sN8O01zGz6KJfLlMvlpu2/nkHq9wPLJF0IvAo4QdJf\nAXskzYuIEUldwDOp/jCwILd9dyorlE8QzTR6a41RmzYNVq9sZtZBKr889/b2NnT/NSeIiLgGuAZA\n0jnAZyLiUkk3AJcD1wOXAWvTJncBd0i6kaxraSGwpfbQzdrPV1nbTNaMK6mvA9ZIWgnsJJu5REQM\nSlpDNuPpILAqImrqfjLrFL7K2mayhiSIiPgh8MO0/CxwbpV6fUBfI45pZmbN5SupzcyskBOEmZkV\n8t1czRrEA9Y208zKBOHHilozeMDaZppZ2cXkx4qamU1uViYIMzObnBOEmZkVcoIwM7NCThBmZlbI\nCcLMzAo5QZiZWaFZeR2EWbP5ojmbCZwgzJogf9Hc+vVfHbsw08nCppNZkyB89bS1i6+wtulq1oxB\n+OppM7MjM2sShJmZHZmaE4Skbkn3SnpM0iOS/iCVnyRpvaTHJd0jaU5um9WShiRtl7S0EW/AzMya\no54xiEPAVRGxTdJrgQclrQc+BmyMiBskfQ5YDVwt6Qyyx48uBrqBjZLe7MeO2mzi2U02ndTcgoiI\nPRGxLS3vB7aT/eFfDtyWqt0GXJSWlwH9EXEoInYAQ8CSWo9vNh2NDlh3dZXGJk2YdaqGjEFIOg14\nF7AZmBcRI5AlEeDkVG0+sCu32XAqMzOzDlT3NNfUvfQd4MqI2C+pssuopi6knp6eseVSqUSpVKo1\nRLOO5O4mq1e5XKZcLjdt/3UlCEnHkCWHv4qItal4RNK8iBiR1AU8k8qHgQW5zbtTWaF8gjCbiXwx\nndWr8stzb29vQ/dfbxfT14HBiPhyruwu4PK0fBmwNld+iaTjJJ0OLAS21Hl8sxnBYxPWiWpuQUh6\nP/B7wCOSfkzWlXQNcD2wRtJKYCfZzCUiYlDSGmAQOAis8gwmM7POVXOCiIi/A46usvrcKtv0AX21\nHtPMzFrHV1KbmVmhWXOzPrPpIj+7CTxobe0zoxOE7+Bq01F+dhN4hpO1z4xOEKN3cAXYtGmwvcGY\n1ci3C7d28RiEmZkVcoIwM7NCThBmZlZoRo9BmM00vn+TtZIThNk0Um3AOj9jz4nDGsUJwmyayrcm\nBgaGWLr0E4BnOlnjeAzCbJrK3+Bv//6D7Q7HZqAZ14LwxXFmZo0x4xKEL46z2S7f9bRjx5Ocdtqb\nAI9N2JFzF5PZDJPvenr66QN+zoTVbMa1IMysmKfI2pFqeYKQdD7wJbLWyy0RcX2rYzCbjao94jTf\nDVX52olkdmtpgpB0FPDnwAeBp4GtktZGxE9q3edPf7qTRx7ZnfYPzz//PF1dDQl3nN27n2j8Tuvk\nmKauE+NqZ0z5ZLFp0yBnn50tb9tW5umnD4y97oRrLcrl8rjnLneCToypGVrdglgCDEXETgBJ/cBy\noOYE8ctf/j9eeulNzJnzBp55ZgcvvPDzBoU63vDwUFP2Ww/HNHWdGFcnxrRtW5kTTzx57HW1ay1a\neQvyTvxj3IkxNUOrE8R8YFfu9W6ypFGXo446mmOOOZajjvKYu1kjVbY0isqrJYt8i8PdVtPTtB+k\nPvposX//U7z44ggvvvhrJLU7JLNZpVqyyLc48t1Y+TowPnkULT/66A6uu+7rhQnGSai5FBGtO5h0\nNtATEeen11cDUTlQLal1QZmZzSAR0bBvya1OEEcDj5MNUv8c2AJ8JCK2tywIMzObkpZ2MUXES5I+\nBazn5WmuTg5mZh2opS0IMzObPjpi2o+kz0g6LOn16fW5kh6Q9LCkrZJ+K1f3LEkDkp6Q9KVWxZTK\nVksakrRd0tJWxSTpi+mz+LGkuyV1pfJjJH0jHfuxNKbTkpgmiiute4ekTZIeTXWOa0VcE8WU1p8i\n6XlJV+XK2vX7a9t5Psnvrl3n+Q3pmNskfVfSiam83ed5YVxpXbvO86oxpfWNOc8joq0/QDdwN/Az\n4PWp7J1AV1p+G7A7V/8fgPem5XXAeS2KaTHwY7JuudOAf+TlFlhTYwJem1v+NHBzWv4IcGdaflWK\n95QWfk7V4joaeBh4e3p9Urs/q1zZt4FvAVe16pya4HNq23k+QUxntPE8Pxc4Ki1fB/R1yHleGdd1\nHXCeF35WjT7PO6EFcSPw2XxBRDwcEXvS8mPA8ZKOTd9yToiIranq7cBFrYiJ7IK+/og4FBE7gCFg\nSStiioj9uZevAQ6PrgJeo2zw/9XAC8CvWvU5TRDXUuDhiHg01XsuIqLNnxWSlgNPAo/lytoWUzvP\n8wk+p2W07zzfGBGjcWwm+6IG7T/PK+Oan5bbeZ5X+6waep63NUFIWgbsiohHJqjzu8BDEXGQ7Bez\nO7d6Ny//spodU+VFfsOprOkxpbj+VNJTwArgP6Xi7wC/JpsRtgP4rxHxy1bFNEFci9K6u1MXymiy\nbdtnJek1wB8DvUB+GmA7f3/59S09zyeIqa3nec5K4Adpue3neUVc69JyW8/ziph+kGJp6Hne9FlM\nkjYA8/JFZN8IvgBcA3yoYl1+27cBfRV1Wh1TS0wQ0+cj4n9HxBeAL0j6HFmXQA/ZVeiHgC5gLvAj\nSRs7IK5jgPcD7wEOAH8j6QHgV22MqQe4MSJ+rSZcTFljTKPbtvo8nzSmZpksplTn88DBiLgz1Wn7\neV4R1zdTnbae5xUxjX5WPTTwPG96goiIwpNe0tvJ+jgfVvZOuoEHJS2JiGckdQN/DVyamrqQfZtZ\nkNtNdyprZkwPSVqSjnNKwbGbGlOBO4Hvk50IK4C7U1Pz/0r6O7KT9f5GxFRHXLuBv42I5wAkrQPO\nAu5oRFw1xvQ+4Hck3UDWV/ySpANk51irY1qXYqId53mVmEY/p2rHbklMki4HLgQ+kCtu+3leJa62\nnudVYmrsed7IgZN6fsgGnk5Ky3OAbcBFBfU2k32jENl/tPNbFNPo4N1xwOmMH7xrakzAwtzyp4E1\nafmPya4lgawf+THgba36nCaI63XAA8DxZF9CNowev12fVUWdaxk/eNeu39/r2nWeTxBTO8/z89M5\nPLeivN3nebW42nmeF8bU6PO8oR9knW/4SV6eMfR54HngoXSyPgS8Ia17N/AI2eDZl1sVU3q9Ov2H\n2Q4szZU3NSayPtiB9MdkLfDPUvlrgDXAo+nnqlbFNFFcad2KFNMAuRkW7fqsKupU/sdp1++vbef5\nJL+7dp3nQ8DO9Dk8BNzUIed5YVxtPs+rxtTI89wXypmZWaFOmOZqZmYdyAnCzMwKOUGYmVkhJwgz\nMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQv8f3vgEMSFPDLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d2ea850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Emf[19],100, alpha=0.25);\n",
    "plt.title(\"final U energies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T15:40:15.334031",
     "start_time": "2016-11-08T15:40:15.330359"
    }
   },
   "source": [
    "So ... what is the best interepretation ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mean Entropy, Energy, and Free Energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T22:55:48.509468",
     "start_time": "2016-11-08T22:45:04.953105"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[EMF_RBM] Iteration 1, pseudo-likelihood = -183.97, time = 23.73s\n",
      "computing TAP Free Energies\n",
      "monitor:  -376.426693209 331.539359594 -43.719583282\n",
      "[EMF_RBM] Iteration 2, pseudo-likelihood = -151.26, time = 55.30s\n",
      "computing TAP Free Energies\n",
      "monitor:  -378.740222002 258.437633708 -118.026275196\n",
      "[EMF_RBM] Iteration 3, pseudo-likelihood = -133.44, time = 55.15s\n",
      "computing TAP Free Energies\n",
      "monitor:  -387.366198494 230.797582778 -153.602552854\n",
      "[EMF_RBM] Iteration 4, pseudo-likelihood = -121.00, time = 54.83s\n",
      "computing TAP Free Energies\n",
      "monitor:  -398.796273505 214.015886664 -181.179068398\n",
      "[EMF_RBM] Iteration 5, pseudo-likelihood = -113.93, time = 55.05s\n",
      "computing TAP Free Energies\n",
      "monitor:  -410.262617564 202.180610184 -203.875258686\n",
      "[EMF_RBM] Iteration 6, pseudo-likelihood = -105.69, time = 54.97s\n",
      "computing TAP Free Energies\n",
      "monitor:  -422.039761513 192.438632297 -224.89336645\n",
      "[EMF_RBM] Iteration 7, pseudo-likelihood = -102.03, time = 54.93s\n",
      "computing TAP Free Energies\n",
      "monitor:  -432.757580323 184.567297568 -243.075293325\n",
      "[EMF_RBM] Iteration 8, pseudo-likelihood = -99.22, time = 54.83s\n",
      "computing TAP Free Energies\n",
      "monitor:  -442.347875857 178.180894189 -258.701945806\n",
      "[EMF_RBM] Iteration 9, pseudo-likelihood = -95.25, time = 54.98s\n",
      "computing TAP Free Energies\n",
      "monitor:  -451.114066907 172.905543128 -272.428982227\n",
      "[EMF_RBM] Iteration 10, pseudo-likelihood = -91.67, time = 55.53s\n",
      "computing TAP Free Energies\n",
      "monitor:  -459.290576308 168.444088967 -284.779028757\n",
      "[EMF_RBM] Iteration 11, pseudo-likelihood = -90.53, time = 54.94s\n",
      "computing TAP Free Energies\n",
      "monitor:  -466.957041675 164.587513745 -296.037232992\n",
      "[EMF_RBM] Iteration 12, pseudo-likelihood = -88.86, time = 54.63s\n",
      "computing TAP Free Energies\n",
      "monitor:  -474.130528659 161.188023729 -306.367334762\n",
      "[EMF_RBM] Iteration 13, pseudo-likelihood = -87.71, time = 54.80s\n",
      "computing TAP Free Energies\n",
      "monitor:  -480.830956661 158.153969145 -315.879065132\n",
      "[EMF_RBM] Iteration 14, pseudo-likelihood = -83.37, time = 54.69s\n",
      "computing TAP Free Energies\n",
      "monitor:  -487.098425739 155.431584543 -324.66301567\n",
      "[EMF_RBM] Iteration 15, pseudo-likelihood = -83.84, time = 54.76s\n",
      "computing TAP Free Energies\n",
      "monitor:  -492.989415371 152.979414889 -332.813952284\n",
      "[EMF_RBM] Iteration 16, pseudo-likelihood = -82.78, time = 54.58s\n",
      "computing TAP Free Energies\n",
      "monitor:  -498.557334483 150.759566968 -340.420906342\n",
      "[EMF_RBM] Iteration 17, pseudo-likelihood = -80.97, time = 54.93s\n",
      "computing TAP Free Energies\n",
      "monitor:  -503.840919142 148.738409536 -347.554852812\n",
      "[EMF_RBM] Iteration 18, pseudo-likelihood = -79.07, time = 54.64s\n",
      "computing TAP Free Energies\n",
      "monitor:  -508.8704337 146.886452851 -354.274815018\n",
      "[EMF_RBM] Iteration 19, pseudo-likelihood = -79.36, time = 54.69s\n",
      "computing TAP Free Energies\n",
      "monitor:  -513.67307819 145.179494718 -360.631641115\n",
      "[EMF_RBM] Iteration 20, pseudo-likelihood = -77.34, time = 54.63s\n",
      "computing TAP Free Energies\n",
      "monitor:  -518.266035948 143.598538099 -366.660961243\n"
     ]
    }
   ],
   "source": [
    "rbm = emf_rbm.EMF_RBM(verbose=True, monitor=True)\n",
    "rbm= rbm.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T22:57:27.868434",
     "start_time": "2016-11-08T22:57:27.697130"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f54ee28b490>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GmIqnA8ZAYJi1NvmozknACmDY6evaAv2AD88XMDJCt95hLKYeywdpysTfBJgArq90PT/e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\nIiKSiv8D31uhDFELMTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54ee488a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rbm.entropies-rbm.entropies[0], color='red')\n",
    "plt.plot(rbm.mean_field_energies-rbm.mean_field_energies[0], color='blue')\n",
    "plt.plot(rbm.free_energies-rbm.free_energies[0], color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Random Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T23:36:40.236622",
     "start_time": "2016-11-08T23:36:38.422667"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "noise = np.random.normal(size=X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T23:36:56.699727",
     "start_time": "2016-11-08T23:36:56.493978"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "N = X+noise;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T23:39:16.167442",
     "start_time": "2016-11-08T23:39:16.160125"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline  \n",
    "\n",
    "def show_image(image): \n",
    "    fig = pyplot.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",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T23:39:25.338321",
     "start_time": "2016-11-08T23:39:25.208712"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GMAAkxBAGgIQYwgCQEEMYABJiCANAQgxhAEiIIQwACVWaZcsFBQXas2dPud2nn37qPmab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\nQxgAEmIIA0BCDGEASOj/AmZmz4qXj6CeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54ee4d1890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = N[0,:]\n",
    "show_image(np.reshape(v,[28,28]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  not really binarized but it should be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T23:53:02.070102",
     "start_time": "2016-11-08T23:39:43.054020"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[EMF_RBM] Iteration 1, pseudo-likelihood = -1984.87, time = 23.61s\n",
      "computing TAP Free Energies\n",
      "monitor:  -214.27720064 345.116441193 132.482529698\n",
      "[EMF_RBM] Iteration 2, pseudo-likelihood = -2289.70, time = 54.82s\n",
      "computing TAP Free Energies\n",
      "monitor:  -212.463045855 289.551685783 80.5501395216\n",
      "[EMF_RBM] Iteration 3, pseudo-likelihood = -2665.03, time = 54.34s\n",
      "computing TAP Free Energies\n",
      "monitor:  -206.02115681 268.864411282 68.6574562623\n",
      "[EMF_RBM] Iteration 4, pseudo-likelihood = -3034.50, time = 54.28s\n",
      "computing TAP Free Energies\n",
      "monitor:  -185.211284957 258.639333632 82.7512464944\n",
      "[EMF_RBM] Iteration 5, pseudo-likelihood = -3463.36, time = 54.24s\n",
      "computing TAP Free Energies\n",
      "monitor:  -148.856764142 252.770171217 118.286400823\n",
      "[EMF_RBM] Iteration 6, pseudo-likelihood = -4108.11, time = 54.42s\n",
      "computing TAP Free Energies\n",
      "monitor:  -93.9193220053 249.195209266 176.339232284\n",
      "[EMF_RBM] Iteration 7, pseudo-likelihood = -4657.63, time = 54.12s\n",
      "computing TAP Free Energies\n",
      "monitor:  -24.2607277457 245.906035429 250.626916909\n",
      "[EMF_RBM] Iteration 8, pseudo-likelihood = -5342.02, time = 54.28s\n",
      "computing TAP Free Energies\n",
      "monitor:  56.2650659342 242.099658307 335.819239811\n",
      "[EMF_RBM] Iteration 9, pseudo-likelihood = -6130.15, time = 54.20s\n",
      "computing TAP Free Energies\n",
      "monitor:  144.88351601 237.877081281 428.724081799\n",
      "[EMF_RBM] Iteration 10, pseudo-likelihood = -6713.28, time = 54.46s\n",
      "computing TAP Free Energies\n",
      "monitor:  239.423708889 233.495963264 527.140712947\n",
      "[EMF_RBM] Iteration 11, pseudo-likelihood = -7644.31, time = 54.27s\n",
      "computing TAP Free Energies\n",
      "monitor:  338.409135556 229.275295939 629.90100881\n",
      "[EMF_RBM] Iteration 12, pseudo-likelihood = -8403.68, time = 54.30s\n",
      "computing TAP Free Energies\n",
      "monitor:  440.327323787 225.446629171 735.976316054\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "emf_rbm.py:249: RuntimeWarning: underflow encountered in multiply\n",
      "  mv = 0.5 *self.mv_update(mv, mh) + 0.5*mv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[EMF_RBM] Iteration 13, pseudo-likelihood = -9345.94, time = 54.23s\n",
      "computing TAP Free Energies\n",
      "monitor:  544.360845918 222.1033689 844.864667248\n",
      "[EMF_RBM] Iteration 14, pseudo-likelihood = -10069.54, time = 54.17s\n",
      "computing TAP Free Energies\n",
      "monitor:  650.076553401 219.285126684 956.502673307\n",
      "[EMF_RBM] Iteration 15, pseudo-likelihood = -10553.91, time = 54.12s\n",
      "computing TAP Free Energies\n",
      "monitor:  757.281913454 216.954973872 1070.94834399\n",
      "[EMF_RBM] Iteration 16, pseudo-likelihood = -11638.03, time = 54.18s\n",
      "computing TAP Free Energies\n",
      "monitor:  865.981087889 215.121119279 1188.57566047\n",
      "[EMF_RBM] Iteration 17, pseudo-likelihood = -12674.29, time = 54.18s\n",
      "computing TAP Free Energies\n",
      "monitor:  975.719433574 213.697049263 1309.08476125\n",
      "[EMF_RBM] Iteration 18, pseudo-likelihood = -12865.28, time = 54.25s\n",
      "computing TAP Free Energies\n",
      "monitor:  1086.48367587 212.62799581 1432.73051027\n",
      "[EMF_RBM] Iteration 19, pseudo-likelihood = -14503.60, time = 54.11s\n",
      "computing TAP Free Energies\n",
      "monitor:  1198.2616989 211.862113769 1559.7578149\n",
      "[EMF_RBM] Iteration 20, pseudo-likelihood = -15181.92, time = 54.09s\n",
      "computing TAP Free Energies\n",
      "monitor:  1310.92652337 211.357779593 1690.40563613\n"
     ]
    }
   ],
   "source": [
    "rbm = emf_rbm.EMF_RBM(verbose=True, monitor=True)\n",
    "rbm= rbm.fit(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T23:53:02.245349",
     "start_time": "2016-11-08T23:53:02.071777"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f54ee0c1bd0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZQ96+gTfeQNuEpzOXPVzinJsLzAUwO/vH6pw7BNRIeM3MugHhZlbMObfdzPIBbYGmzrll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\nK8tc5XJIrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54ee22c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rbm.entropies-rbm.entropies[0], color='red')\n",
    "plt.plot(rbm.mean_field_energies-rbm.mean_field_energies[0], color='blue')\n",
    "plt.plot(rbm.free_energies-rbm.free_energies[0], color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "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
}