EMF_RBM_Energy_Landscapes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  EMF RBM Energy Landscapes\n",
    "\n",
    "Comparison of Entropy and Energetics Contributions to RBM Free Energy\n",
    "\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",
    "    \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": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Helper methods for simple case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import emf_rbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import rbm_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### binarized Omniglot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24345, 784) (24345,)\n",
      "there are  (55,)  classes\n"
     ]
    }
   ],
   "source": [
    "train_x, train_t, train_char, test_x, test_t, test_char = rbm_datasets.load_omniglot_iwae()\n",
    "print train_x.shape, train_t.shape\n",
    "print \"there are \",np.unique(train_char).shape, \" classes\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0120520110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = train_x[0,:]\n",
    "image = np.reshape(image,[28,28])\n",
    "rbm_datasets.show_image(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011e4e9250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011e372090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011e2a3e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011e1d8490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011e0f6d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011e0aa990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011dfd4c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011df00f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011deaae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uR1zE8Iu376w7/jfAlwBaa1ckeTnweYZnT3wfeHdr7XebK1WS+mfS84SPavmitXYpcOkU\n9WgC87DFuSuLuPV1Hv7e5sG8/D0symfIC/hIUiFDWJIKGcKSVMgQlqRChrAkFTKEJamQISxJhQxh\nSSpkCEtSIUNYkgp5t2XNpXnZ+rpoutqqOy/jsShbkSfhTFiSChnCklTIEJakQoawJBUyhCWpkCEs\nSYUMYUkqZAhLUiFDWJIKGcKSVMhty1vEPGz3nJetr/NgHsZjEotW7yJxJixJhQxhSSpkCEtSIUNY\nkgoZwpJUyBCWpEKGsCQVMoQlqZAhLEmFDGFJKmQIS1Ihrx2hY8brD0gv5kxYkgoZwpJUyBCWpEKG\nsCQVMoQlqZAhLEmFDGFJKmQIS1IhQ1iSChnCklRoohBO8vEktyV5MsmBJNcmOW1dm6uSvLDucf1s\ny5akfph0Jrwb+CxwBvBO4Djgm0letq7dDcB2YMfoMdhknZLUSxNdwKe1ds748yQXAL8GdgE3j730\n29bao5uuTpJ6brNrwicCDXh83fE9o+WKnyXZm+SVm3wfSeqlqS9lmSTAlcDNrbW7x166AfgasB94\nHfBJ4PokZzavZShJa2zmesJ7gdcDbxk/2Fq7ZuzpT5P8BLgP2APcdLgftrKywtLS0ppjg8GAwcDl\nZEn9NVUIJ/kccA6wu7X28EZtW2v7kzwG7GSDEF5dXWV5eXmaciRpYU0cwqMAfg/w9tbaA0fR/mTg\nJGDDsJakrWjS84T3Au8H3gc8lWT76HH86PVtSa5IckaSU5KcBfwHcC+wb9bFS9Kim/TsiIuAE4Dv\nAA+NPc4bvf488AbgOuB/gH8Ffgi8rbX23AzqlaRemfQ84Q1Du7X2LPCuTVUkSVuI146QpEKGsCQV\nMoQlqZAhLEmFDGFJKmQIS1IhQ1iSChnCklTIEJakQoawJBUyhCWpkCEsSYUMYUkqZAhLUiFDWJIK\nGcKSVMgQlqRChrAkFTKEJamQISxJhQxhSSo00d2WO3I8wD333FNdhyTNxFieHX+ktmmtdVvNkQpI\n3gd8ubQISerG+1trX9mowTyE8EnA2cD9wLOlxUjSbBwPvAbY11r7zUYNy0NYkrYyv5iTpEKGsCQV\nMoQlqZAhLEmFDGFJKmQIS1IhQ1iSCv0fzsMWXkf49MUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011dddf3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import binarize\n",
    "BX_train = binarize(train_x, threshold=0.2)\n",
    "for i in range(10):\n",
    "    image = BX_train[i,:]\n",
    "    image = np.reshape(image,[28,28])\n",
    "    rbm_datasets.show_image(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = BX_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-08T22:43:58.231469",
     "start_time": "2016-11-08T22:43:55.548293"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[EMF_RBM] Iteration 1, pseudo-likelihood = -218.02, time = 9.92s\n",
      "computing TAP Free Energies\n",
      "monitor:  -276.154657235 385.088035161 108.938441768\n",
      "[EMF_RBM] Iteration 2, pseudo-likelihood = -216.72, time = 23.56s\n",
      "computing TAP Free Energies\n",
      "monitor:  -275.810058661 385.023875305 109.220864897\n",
      "[EMF_RBM] Iteration 3, pseudo-likelihood = -215.94, time = 23.33s\n",
      "computing TAP Free Energies\n",
      "monitor:  -275.032062217 384.506713751 109.505866084\n",
      "[EMF_RBM] Iteration 4, pseudo-likelihood = -205.75, time = 23.48s\n",
      "computing TAP Free Energies\n",
      "monitor:  -271.053074579 379.814871573 109.022561805\n",
      "[EMF_RBM] Iteration 5, pseudo-likelihood = -197.56, time = 23.63s\n",
      "computing TAP Free Energies\n",
      "monitor:  -263.175889527 362.183296867 99.8477143099\n",
      "[EMF_RBM] Iteration 6, pseudo-likelihood = -188.68, time = 23.55s\n",
      "computing TAP Free Energies\n",
      "monitor:  -255.034883955 345.042511063 91.2832555544\n",
      "[EMF_RBM] Iteration 7, pseudo-likelihood = -180.60, time = 23.57s\n",
      "computing TAP Free Energies\n",
      "monitor:  -249.046343826 331.336051272 84.123300503\n",
      "[EMF_RBM] Iteration 8, pseudo-likelihood = -175.21, time = 23.76s\n",
      "computing TAP Free Energies\n",
      "monitor:  -245.455003486 318.776536632 75.6711524393\n",
      "[EMF_RBM] Iteration 9, pseudo-likelihood = -177.24, time = 23.46s\n",
      "computing TAP Free Energies\n",
      "monitor:  -242.649307052 307.726481394 67.8175623666\n",
      "[EMF_RBM] Iteration 10, pseudo-likelihood = -161.86, time = 23.48s\n",
      "computing TAP Free Energies\n",
      "monitor:  -240.017758841 299.630013058 62.7307530942\n",
      "[EMF_RBM] Iteration 11, pseudo-likelihood = -162.20, time = 23.48s\n",
      "computing TAP Free Energies\n",
      "monitor:  -238.246222155 292.438312463 57.7498135217\n",
      "[EMF_RBM] Iteration 12, pseudo-likelihood = -160.32, time = 23.42s\n",
      "computing TAP Free Energies\n",
      "monitor:  -237.491695841 285.480191201 51.9727403317\n",
      "[EMF_RBM] Iteration 13, pseudo-likelihood = -155.26, time = 23.98s\n",
      "computing TAP Free Energies\n",
      "monitor:  -237.227828143 279.135228633 46.2735985241\n",
      "[EMF_RBM] Iteration 14, pseudo-likelihood = -154.97, time = 23.41s\n",
      "computing TAP Free Energies\n",
      "monitor:  -237.07718971 273.485520431 41.1068380346\n",
      "[EMF_RBM] Iteration 15, pseudo-likelihood = -150.55, time = 23.44s\n",
      "computing TAP Free Energies\n",
      "monitor:  -236.900162455 268.619535119 36.718484597\n",
      "[EMF_RBM] Iteration 16, pseudo-likelihood = -151.83, time = 23.55s\n",
      "computing TAP Free Energies\n",
      "monitor:  -236.80808705 264.423440621 32.899858379\n",
      "[EMF_RBM] Iteration 17, pseudo-likelihood = -146.43, time = 23.55s\n",
      "computing TAP Free Energies\n",
      "monitor:  -236.923362621 260.777246554 29.4181158634\n",
      "[EMF_RBM] Iteration 18, pseudo-likelihood = -141.75, time = 23.89s\n",
      "computing TAP Free Energies\n",
      "monitor:  -237.281951223 257.599258792 26.1669369442\n",
      "[EMF_RBM] Iteration 19, pseudo-likelihood = -145.80, time = 23.37s\n",
      "computing TAP Free Energies\n",
      "monitor:  -237.900137428 254.754264527 23.0004860293\n",
      "[EMF_RBM] Iteration 20, pseudo-likelihood = -139.59, time = 23.34s\n",
      "computing TAP Free Energies\n",
      "monitor:  -238.754730757 252.126973715 19.8261800112\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EMF_RBM(batch_size=100, decay=0.01, learning_rate=0.005, momentum=0.5,\n",
       "    monitor=True, n_components=256, n_iter=20, neq_steps=3,\n",
       "    random_state=<mtrand.RandomState object at 0x7f01641159b0>,\n",
       "    sigma=0.001, thresh=1e-08, verbose=True, weight_decay='L1')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbm = emf_rbm.EMF_RBM(verbose=True, monitor=True)\n",
    "rbm.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 0x7f011dd6d7d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddx5cfbX9BP/ii6Cr2aNateDll2HSJFi61Fp/DBkC2dlBVyYiEt0UMqR4PPQQ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\nTeGQYWZmZk3xXz84ur5mFtalAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011e4e9dd0>"
      ]
     },
     "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": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f011d7daa10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ELDcXZs4MT6TMmgVt28KIEfDf/8YdmYjUJpVaRyNBr5RGISJpYQannAJHHQXX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\nkiRVY6IhSZKqMdGQJEnVmGhIkqRqfgMoI5MDqUllJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011d5d6410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(-1*np.array(rbm.mean_field_energies), rbm.entropies)\n",
    "plt.ylabel(\"entropy\")\n",
    "plt.xlabel(\"mean field energy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f011d92c890>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VOBXoYWZdq1k/ERGpofbtQyvLPffA8OGhFebFF+OOSrJZdZOW9wnJQ7LzonPV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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011d8b7f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(rbm.free_energies), rbm.entropies)\n",
    "plt.xlabel(\"free energy\")\n",
    "plt.ylabel(\"entropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading test data...\n",
      "Downloading train data...\n",
      "Downloading valid data...\n"
     ]
    }
   ],
   "source": [
    "x_train, x_valid, x_test = rbm_datasets.load_mnist_binarized()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6ydbhW0+21p4b/vlK4FNJfgE8AFwO/Bq4bioVS9ICGfd0xEV0X7x9d83+vwG+AtBauyLJ\ny4Ev0l098QPgPa2132+sVElaPONeJ3xUpy9aa5cCl05Qj+bMLDzxeRZqGLeOcYxTs8uL54838JGk\nQoawJBUyhCWpkCEsSYUMYUkqZAhLUiFDWJIKGcKSVMgQlqRChrAkFfJpyzpmZmFJ7SzUMK55rFlH\nz5mwJBUyhCWpkCEsSYUMYUkqZAhLUiFDWJIKGcKSVMgQlqRChrAkFTKEJamQISxJhQxhSSpkCEtS\nIUNYkgoZwpJUyBCWpEKGsCQVMoQlqZAhLEmFDGFJKmQIS1IhQ1iSChnCklTIEJakQoawJBUyhCWp\nkCEsSYUMYUkqZAhLUiFDWJIKGcKSVMgQlqRChrAkFRorhJN8MsntSZ5Ksi/JtUlOW9PmqiQvrtmu\nn27ZkrQYxp0J7wQ+D5wBvAs4DrgpycvWtLsB2ApsG26DDdYpSQvpJeM0bq2dM/o6yQXAb4AdwC0j\nb/2utfbYhquTpAW30XPCJwINeGLN/l3D0xU/T7I7ySs3eBxJWkhjzYRHJQlwJXBLa+2ekbduAL4B\n7AVeB3wauD7Jma21tpFiJWnRTBzCwG7gDcBbR3e21q4ZefmzJD8F7gd2ATcf7oetrKywtLR0wL7B\nYMBg4OlkSYtrohBO8gXgHGBna+2R9dq21vYmeRzYzjohvLq6yvLy8iTlSNLcGjuEhwH8XuAdrbUH\nj6L9ycBJwLphLUmb0bjXCe8GPgh8AHg6ydbhdvzw/S1JrkhyRpJTkpwF/AdwH7Bn2sVL0rwb9+qI\ni4ATgO8CD49s5w3ffwF4I3Ad8D/AvwI/At7eWnt+CvVK0kIZ9zrhdUO7tfYc8O4NVSRJm4j3jpCk\nQoawJBUyhCWpkCEsSYUMYUkqZAhLUiFDWJIKGcKSVMgQlqRChrAkFTKEJamQISxJhQxhSSpkCEtS\nIUNYkgoZwpJUyBCWpEKGsCQVMoQlqZAhLEmFDGFJKjTW05Z7cjzAvffeW12HJE3FSJ4df6S2aa31\nW82RCkg+AHy1tAhJ6scHW2tfW6/BLITwScDZwAPAc6XFSNJ0HA+8BtjTWvvteg3LQ1iSNjO/mJOk\nQoawJBUyhCWpkCEsSYUMYUkqZAhLUiFDWJIK/R9uNhlaPmlZwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011d730c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = x_train[0,:]\n",
    "rbm_datasets.show_image(np.reshape(v,[28,28]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[EMF_RBM] Iteration 1, pseudo-likelihood = -199.59, time = 20.72s\n",
      "computing TAP Free Energies\n",
      "monitor:  -295.530125579 373.035873411 77.703224412\n",
      "[EMF_RBM] Iteration 2, pseudo-likelihood = -167.05, time = 49.33s\n",
      "computing TAP Free Energies\n",
      "monitor:  -275.473667127 312.996972617 38.991702377\n",
      "[EMF_RBM] Iteration 3, pseudo-likelihood = -152.92, time = 48.22s\n",
      "computing TAP Free Energies\n",
      "monitor:  -264.793610297 275.32695053 12.8482423977\n",
      "[EMF_RBM] Iteration 4, pseudo-likelihood = -142.16, time = 48.15s\n",
      "computing TAP Free Energies\n",
      "monitor:  -260.531919623 254.907852005 -2.74993197686\n",
      "[EMF_RBM] Iteration 5, pseudo-likelihood = -133.59, time = 48.34s\n",
      "computing TAP Free Energies\n",
      "monitor:  -259.512453311 241.399193965 -14.656403234\n",
      "[EMF_RBM] Iteration 6, pseudo-likelihood = -127.57, time = 48.27s\n",
      "computing TAP Free Energies\n",
      "monitor:  -259.774258382 231.219323142 -24.5728610533\n",
      "[EMF_RBM] Iteration 7, pseudo-likelihood = -125.34, time = 48.66s\n",
      "computing TAP Free Energies\n",
      "monitor:  -260.789335834 223.25905184 -33.0244738788\n",
      "[EMF_RBM] Iteration 8, pseudo-likelihood = -120.96, time = 48.29s\n",
      "computing TAP Free Energies\n",
      "monitor:  -262.479012781 216.413706723 -41.0783280126\n",
      "[EMF_RBM] Iteration 9, pseudo-likelihood = -117.30, time = 47.65s\n",
      "computing TAP Free Energies\n",
      "monitor:  -264.317069246 210.495795868 -48.4158719885\n",
      "[EMF_RBM] Iteration 10, pseudo-likelihood = -111.85, time = 48.10s\n",
      "computing TAP Free Energies\n",
      "monitor:  -266.091811461 205.436927846 -54.8850503247\n",
      "[EMF_RBM] Iteration 11, pseudo-likelihood = -109.53, time = 48.29s\n",
      "computing TAP Free Energies\n",
      "monitor:  -267.783119614 201.145164834 -60.5397239149\n",
      "[EMF_RBM] Iteration 12, pseudo-likelihood = -111.28, time = 48.42s\n",
      "computing TAP Free Energies\n",
      "monitor:  -269.465452432 197.492762175 -65.5663044426\n",
      "[EMF_RBM] Iteration 13, pseudo-likelihood = -107.15, time = 47.99s\n",
      "computing TAP Free Energies\n",
      "monitor:  -271.210887082 194.33188336 -70.1775509602\n",
      "[EMF_RBM] Iteration 14, pseudo-likelihood = -105.21, time = 48.16s\n",
      "computing TAP Free Energies\n",
      "monitor:  -273.03564988 191.537940409 -74.5141241099\n",
      "[EMF_RBM] Iteration 15, pseudo-likelihood = -104.67, time = 49.35s\n",
      "computing TAP Free Energies\n",
      "monitor:  -274.908101919 189.029986817 -78.6271374033\n",
      "[EMF_RBM] Iteration 16, pseudo-likelihood = -103.31, time = 47.82s\n",
      "computing TAP Free Energies\n",
      "monitor:  -276.784896151 186.762046175 -82.5197555984\n",
      "[EMF_RBM] Iteration 17, pseudo-likelihood = -104.62, time = 47.99s\n",
      "computing TAP Free Energies\n",
      "monitor:  -278.637799141 184.70321267 -86.1938860946\n",
      "[EMF_RBM] Iteration 18, pseudo-likelihood = -98.62, time = 48.36s\n",
      "computing TAP Free Energies\n",
      "monitor:  -280.452174053 182.827842471 -89.6593493837\n",
      "[EMF_RBM] Iteration 19, pseudo-likelihood = -97.26, time = 48.16s\n",
      "computing TAP Free Energies\n",
      "monitor:  -282.225112055 181.113956712 -92.9338242818\n",
      "[EMF_RBM] Iteration 20, pseudo-likelihood = -97.70, time = 48.70s\n",
      "computing TAP Free Energies\n",
      "monitor:  -283.958669657 179.541837875 -96.0378789008\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EMF_RBM(batch_size=100, decay=0.01, learning_rate=0.005, momentum=0.5,\n",
       "    monitor=True, n_components=256, n_iter=20, neq_steps=3,\n",
       "    random_state=<mtrand.RandomState object at 0x7f01641159b0>,\n",
       "    sigma=0.001, thresh=1e-08, verbose=True, weight_decay='L1')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = x_train\n",
    "rbm = emf_rbm.EMF_RBM(verbose=True, monitor=True)\n",
    "rbm.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f011ca04e50>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WaWzfbgtCP/wwqh9ORESkxhQyIu3CC6F586iPZgD07GmjGYMGwRFHwFNPRf1D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011cb062d0>"
      ]
     },
     "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": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f011cc50390>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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91woRqdLKu7dOxpnZ7UAvoIu7r3OlBHf/xMy+JUxnnk6Y2lzTzGqn9Z7Uj66V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\nFnpTLr889Khsu20IM7ffDvP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7XsrbXwGW8nnKYw9gl7TaviP8sN65hPt3BmoAs4pPuPsP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\nOBEREZFEUTgRERGRRPl/szs5Rm46hGYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011cae8590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(-1*np.array(rbm.mean_field_energies), rbm.entropies)\n",
    "plt.ylabel(\"entropy\")\n",
    "plt.xlabel(\"mean field energy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f011da30e50>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dd/i06dGjYf/9/evGjWG//aB/f1i0KO5KRUTWUGgRqeSqVoUOHeCxx3z8yzPPwNZbw4UX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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f011dc85f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(rbm.free_energies), rbm.entropies)\n",
    "plt.xlabel(\"free energy\")\n",
    "plt.ylabel(\"entropy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WTF ?!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Random Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 16,
   "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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dNGiQO/vEE0+4szGviddee82d7dWrlzsbMz3d83NcLWZlbylu1fDvf//77mzM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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57cf113b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = N[0,:]\n",
    "rbm_datasets.show_image(np.reshape(v,[28,28]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  not really binarized but it should be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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 = -1643.18, time = 9.54s\n",
      "computing TAP Free Energies\n",
      "monitor:  -152.444200443 414.609709171 262.185777638\n",
      "[EMF_RBM] Iteration 2, pseudo-likelihood = -1717.07, time = 22.76s\n",
      "computing TAP Free Energies\n",
      "monitor:  -145.57228175 413.865910268 268.401601757\n",
      "[EMF_RBM] Iteration 3, pseudo-likelihood = -1725.51, time = 23.00s\n",
      "computing TAP Free Energies\n",
      "monitor:  -130.578616917 404.508344729 274.795938752\n",
      "[EMF_RBM] Iteration 4, pseudo-likelihood = -1922.03, time = 22.95s\n",
      "computing TAP Free Energies\n",
      "monitor:  -110.325891454 377.9256588 270.154306443\n",
      "[EMF_RBM] Iteration 5, pseudo-likelihood = -2079.38, time = 23.00s\n",
      "computing TAP Free Energies\n",
      "monitor:  -91.4252709776 355.628547258 268.85885629\n",
      "[EMF_RBM] Iteration 6, pseudo-likelihood = -2237.11, time = 22.86s\n",
      "computing TAP Free Energies\n",
      "monitor:  -71.8145698935 340.953002613 276.298854379\n",
      "[EMF_RBM] Iteration 7, pseudo-likelihood = -2568.36, time = 22.99s\n",
      "computing TAP Free Energies\n",
      "monitor:  -49.9211576245 330.30533925 290.584655447\n",
      "[EMF_RBM] Iteration 8, pseudo-likelihood = -2689.40, time = 22.86s\n",
      "computing TAP Free Energies\n",
      "monitor:  -24.4937834109 323.02112468 312.383636218\n",
      "[EMF_RBM] Iteration 9, pseudo-likelihood = -3003.79, time = 22.79s\n",
      "computing TAP Free Energies\n",
      "monitor:  5.01388375372 317.442400316 340.625205795\n",
      "[EMF_RBM] Iteration 10, pseudo-likelihood = -3310.38, time = 22.82s\n",
      "computing TAP Free Energies\n",
      "monitor:  37.7133495675 312.178482801 372.892929769\n",
      "[EMF_RBM] Iteration 11, pseudo-likelihood = -3483.34, time = 22.77s\n",
      "computing TAP Free Energies\n",
      "monitor:  73.0191184775 306.820655473 408.017464754\n",
      "[EMF_RBM] Iteration 12, pseudo-likelihood = -4014.88, time = 22.74s\n",
      "computing TAP Free Energies\n",
      "monitor:  110.440030534 301.377126005 445.371140943\n",
      "[EMF_RBM] Iteration 13, pseudo-likelihood = -4221.20, time = 22.77s\n",
      "computing TAP Free Energies\n",
      "monitor:  149.562191943 295.95081849 484.533134084\n",
      "[EMF_RBM] Iteration 14, pseudo-likelihood = -4727.19, time = 22.74s\n",
      "computing TAP Free Energies\n",
      "monitor:  190.033760722 290.634432644 525.171384124\n",
      "[EMF_RBM] Iteration 15, pseudo-likelihood = -5000.81, time = 22.78s\n",
      "computing TAP Free Energies\n",
      "monitor:  231.520281567 285.509400644 566.998686738\n",
      "[EMF_RBM] Iteration 16, pseudo-likelihood = -5069.70, time = 22.71s\n",
      "computing TAP Free Energies\n",
      "monitor:  273.744441715 280.643682172 609.798271188\n",
      "[EMF_RBM] Iteration 17, pseudo-likelihood = -5588.80, time = 22.75s\n",
      "computing TAP Free Energies\n",
      "monitor:  316.491696104 276.103586084 653.464590548\n",
      "[EMF_RBM] Iteration 18, pseudo-likelihood = -5762.02, time = 22.72s\n",
      "computing TAP Free Energies\n",
      "monitor:  359.600904906 271.90517463 697.886998377\n",
      "[EMF_RBM] Iteration 19, pseudo-likelihood = -5757.81, time = 22.82s\n",
      "computing TAP Free Energies\n",
      "monitor:  402.975433696 268.074532077 743.06872513\n",
      "[EMF_RBM] Iteration 20, pseudo-likelihood = -6790.11, time = 22.72s\n",
      "computing TAP Free Energies\n",
      "monitor:  446.571653107 264.603738252 789.065562766\n"
     ]
    }
   ],
   "source": [
    "rbm = emf_rbm.EMF_RBM(verbose=True, monitor=True)\n",
    "rbm= rbm.fit(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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 0x7f57cf684590>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xB90u66pmzoT27SE62pnc2ayZ2xWJL1qzZg0BAQEAAdbaNTfzWh4dyTDGfAjM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\nfi2tAkpfYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57cf6ee310>"
      ]
     },
     "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
}