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Revision 1db48f3a735eb0fba06a7d503f080a7ead512604 authored by Artem Artemev on 11 July 2018, 12:50:44 UTC, committed by GitHub on 11 July 2018, 12:50:44 UTC
Update version.py file to 1.2.0 (#812)
1 parent 707b195
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Sanity_check.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Sanity Check: when model behaviours should overlap\n",
    "--\n",
    "*James Hensman and Alexander G. de G. Matthews, 2016*\n",
    "\n",
    "Many of the model classes in GPflow have overlapping behaviour in special cases. In this section, we fit some approximations to a model with a Gaussian likelihood, and make sure they're all the same. \n",
    "\n",
    "The models are:\n",
    " - `GPR` full Gaussian process regression\n",
    " \n",
    " - `VGP` a Gaussian approximation with Variational Bayes\n",
    "   (approximating a Gaussian posterior with a Gaussian should be exact)\n",
    "   \n",
    " - `SVGP` a sparse GP, with a Gaussian approximation. The inducing points are set to be at the data points, so again, should be exact. \n",
    " \n",
    " - `SVGP` (with whitened representation) As above, but with a rotation applied to whiten the representation of the process\n",
    " \n",
    " - `SGPR` A sparse GP with a *collapsed* posterior (Titsias 2009). Again, the inducing points are fixed to the data points. \n",
    " \n",
    " - `GPRFITC` The FITC approximation, again, the inducing points are fixed to the data points.\n",
    " \n",
    " In all cases the parameters are estimated by the method using maximum likelihood (or approximate maximum likelihood, as appropriate). The parameter estimates should all be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import gpflow\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (12, 6)\n",
    "plt = matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1089a7240>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AwJvtkKtqPJHkS3cdv3R67mHHAADAlXXlHg6sqmeq6qSqTs7WRwUAgKHtIzi/nOSddx2/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1088c0198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "X = np.random.rand(20,1)*10\n",
    "Y = np.sin(X) + 0.9 * np.cos(X*1.6) + np.random.randn(*X.shape)* 0.4\n",
    "Xtest = np.random.rand(10,1)*10\n",
    "plt.plot(X, Y, 'kx', mew=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "m1 = gpflow.models.GPR(X, Y, kern=gpflow.kernels.RBF(1))\n",
    "m2 = gpflow.models.VGP(X, Y, gpflow.kernels.RBF(1), likelihood=gpflow.likelihoods.Gaussian())\n",
    "m3 = gpflow.models.SVGP(X, Y, gpflow.kernels.RBF(1),\n",
    "                      likelihood=gpflow.likelihoods.Gaussian(),\n",
    "                      Z=X.copy(), q_diag=False)\n",
    "m3.feature.set_trainable(False)\n",
    "m4 = gpflow.models.SVGP(X, Y, gpflow.kernels.RBF(1),\n",
    "                      likelihood=gpflow.likelihoods.Gaussian(),\n",
    "                      Z=X.copy(), q_diag=False, whiten=True)\n",
    "m4.feature.set_trainable(False)\n",
    "m5 = gpflow.models.SGPR(X, Y, gpflow.kernels.RBF(1), Z=X.copy())\n",
    "m5.feature.set_trainable(False)\n",
    "m6 = gpflow.models.GPRFITC(X, Y, gpflow.kernels.RBF(1), Z=X.copy())\n",
    "m6.feature.set_trainable(False)\n",
    "models = [m1, m2, m3, m4, m5, m6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now optimize the models. For GPR, SVGP and GPFITC this simply optimizes the hyper-parameters (since the inducing points are fixed). For the variational models, this jointly maximises the ELBO with respect to the variational parameters and the kernel parameters.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834361\n",
      "  Number of iterations: 8\n",
      "  Number of functions evaluations: 12\n",
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834361\n",
      "  Number of iterations: 54\n",
      "  Number of functions evaluations: 70\n",
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 54\n",
      "  Number of functions evaluations: 73\n",
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 54\n",
      "  Number of functions evaluations: 73\n",
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834400\n",
      "  Number of iterations: 8\n",
      "  Number of functions evaluations: 12\n",
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: Optimization terminated successfully.\n",
      "  Objective function value: 18.834355\n",
      "  Number of iterations: 8\n",
      "  Number of functions evaluations: 12\n"
     ]
    }
   ],
   "source": [
    "o = gpflow.train.ScipyOptimizer(method='BFGS')\n",
    "_ = [o.minimize(m, maxiter=100) for m in models]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If everything worked as planned, the models should have the same\n",
    "\n",
    " - prediction functions\n",
    " - log (marginal) likelihood\n",
    " - kernel parameters\n",
    " \n",
    "For the variational models, where we use a ELBO in place of the likelihood, the ELBO should be tight to the likelihood in the cases studied here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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LZgyiAaPOiF6nP+qGL6fkkBSJnJIjlUuRklKk5XT+iq3m6yaMonHMBcHHEsvGMOgMLPUu\n1dpqjRLdiW4aQ424LK6CjlNIcd3xSLJEKpdi5cSV4/L9NGKFdsNJEASMeuF9fVlVVSWnqCQzOWIp\nFUXN7wCr7ynh0yGg0wmIgoBeFDDpRWymsX0hPR6v3YQvnkHpjrBoUsWQH8drRkYsLdETzeC1lecu\nkd2sxx/P0BFKMsNjK/VyRtgYv4MuUP+ggvfcy/UFywkpgYKCoij9Ocl9gbMgCOgEHTpBlz+OqMep\nd46ZJ3eD4TA6iGfj7PPtY4l3CRa9lq5UznJKjtZIKw5TeabuGEQD6VyahlADiz2Lx9XvVE7JDfi1\nZR0UH48gCBhEoewL3sqNx2YiEM9Q2xNl8WSntmNcxpr9CSx6seAL15HFdUf2Jv7SlRedsDfxibis\nRhp9CVw2Y8G72aOBoqj0RNIIJqvWrHkICpnqNV7ZjXbi2Tg1/hqWepeWfcHgeNab6CWn5gp+jx9Z\nXHdkb+Lr1l53wt7EJ+IwOQgkAxxOHmaSbVJB6xwtwukwdcE6BL0woA8pLaocZ/KBcZa67iiyUh6V\n45qjRVIS/ngGu7nwAOK5JzfTeLCO6vkLeez5N3js+Teonr+wv7iuEKJOwGoUx8V7KZnNsacjTH1v\nFEEY48/tNWXFbrQjqzI1gRoycqbUy9Ecg6RItMZacRgL3yXe+sRWGusbqV5QzaZXNrHplU1UL6ju\nL64rlNPspCHUMObHjEuyxKHQIfb695KSUzDAhhDabfs45LGZ6I1l0OliLJjoGHc5oeWuxZ/AYijO\nr2ZfAd25F67t3xW+f+OWQRfXHY/VqCeQyNAaSDCnamxuoPZG0xzojmISRbw2M2XTh0wzbtiNdmLZ\nWP+OsVZ8V176+mEX40lIXwHdmovX9O8Kr9u8btDFdcej1+kxiAYawg0s9S4dk7n5sWysvw2d2+wm\nko0M+GvH3k9DMyBeu4meSJoGn9ZztpxEUhKBRKao0wivuvbLR6VJeLxVRQmI+7isRlr8CSKpsdUH\nMycrHDwcZX9XhAqzoSg79xrNUDmMDjJyhtpA7aByJDXDS5Il2qJtRdkl7nP19VcflSbhqfIUJSDu\nYzfaCWfC9MR7inbMcqCqKt2Jbnb37kYn6HCaB1+PoAXF45jHZqQjmKYtMLYfo4wmrYHi7RJDvgC1\nNZBgZ1uI7khqWNIcdIKA3WSgtjuKJJfPEJ5CpCWZ3e1husNpvKNgYI9mfKgwVRCX4tQH61HUsfG7\nNtr1JnsBEHXFmfKpqiq9yV729O6hPdqeb3c4DCpNlTRGG0lIiWE5/kjLKTkOhQ9xKHgIp8k55Px7\nbetjHBMEAY/dSKM/jlGv04YxlFgsLeGPZ6myF9YuR5IVntrXzauH/DT7E2SPCFQNosB0t5Xzl0zm\n44smFC3YsxhFgokMzf448ydWFOWYpRJNS+ztCKMjPzZdoyknleZKQqkQTeEmqiurx1UXgXKTU3K0\nxdqwGwtLHVNVlXd63uG51udoijQdFagKCEy0TuT0qadzwewLirYjLepEzKKZ+mA9y6uWFy2oL4WM\nnKEuUEdciuO2uAv6ndCC4nFOJwi4rSZqu6MYRAGvQ6tuLpX2YAqzfuhBqqqqvHLIz5/ebOFw9J8F\nORMrTP3pMoFEliZfgt/9o4GN29u4bOU0zlsyqSgt+lxWI+3BFG6rcdS+j3yxNPs7o9hN+vxYa42m\nDFWaK+mKd2EQDcysmFnq5Yxb/pQfWZELyiU+FDrEn2v/TH2wvv/vKowVTLVPJZQJcThxmJ5kD5sO\nbeLppqdZM3MNF8+9uCjBsdVgJZQK0R5rZ5ZzVsHHK4WElKDGX4OCQqW5suDjaUGxBlEnUGk1sr8r\nysqZIhXjoL1WuUlkchyOpvEMsS9xMpvjrmfq2N0eBmCG28pnT5vBsqmVR+XCJrM5treE+Mv2dlqD\nSf53WxPbDvn5zicW4Clwh1oQBCotRup6YqwyG0ZdUNkRSlLfE8NlNWrpEpqyJggCLouL1kgrJtE0\nbtprlRNZkWmNtg55l1hRFdbXrmdLU36yqMPo4LJ5l/GhSR/CZXb173ZKssSh8CEeb3icPb49bGna\nwmtdr3HzKTczzzWv4O/DaXbSFmvDZXbhNDkLPt5IimQi7Pfvx6Q3YdMXp1++FhRrADCIOqwGPXs7\nwqya6R51Ac1o1xFKYhCHNl48lMjyoy01NPkTVJj1XHP6LM5ZNPGYfaitRj0fmV/FWfO8vNUU4H+3\nNVHbHeXf/rKb73xiIUunFnZRNOp1pHMCh3pjLJ0yOoYuqKpKSyBBsz+Bx2bS+ndrRoW+QqKDwYOY\nRXNRdsk0AxdMB8nK2SEFxTklx7177uXVzlcRBZELqy/kU9Wfwmqwvu+1BtHAYs9iFnsW0xhu5MGa\nBzkUOsSPXv8R1y69lnNmnFPQdVYn6LAb7dQF61g5YSUGcXRsigVSAQ4EDmA32ovajUXbDtH0sxhF\nRHTs74iMmYKp0SAtyXSF0ziG0N2gK5zilr/tocmfYLLTzC8uX8Enlkw6YWCnEwROr/byyytXsGyq\nk3BS4vbN+3ih9vBQv41+FWYDvliWznCq4GMNN0VROdQbo9mf1AJizaij1+lxmBzUBGrGTMHUaKCo\nypB3idO5NHe/czevdr6KSTRx64du5eqFVx8zIH6v6spqfnj6Dzlv1nnIqswD+x5g3f51BXeQMokm\nZEWmIdwwKrpR9SR62B/Yj8PoKHp7Qi0o1hzFbtaTkmTqeqIoY3wgQ7noDKXQ6wR0g7zbDyWy3LZp\nH4ejGeZOsHP3ZcuY5BxcLm+l1ch/fWopl548FUWFX71wiH/U9w7qGMfitho5dDhONF2+bdpkRaX+\ncIzOUBqvzTjon79GUw6MohGT3kSNv2bYOhVojhbOhElJqUEHZLIi84vtv2Cvby8Vxgr+4/T/YHnV\n8kEdQ6/Tc+3Sa/n6iq9j0Bl4rvU5/ljzx4KDWafZSW+yl+5Ed0HHGW6dsU7qQ/W4TK5h2dXWgmLN\n+1RajQTiWZp88VIvZczL5hQ6w8lBj0mWZIW7nqklkMiyaJKDO9cupdI6tDtmUSfwxTNn87kPz0QF\n/uf5g7zW4B/SsY48ps2op6YzQjZXfk8dcrJCXXeUw9E0XrtpVKR5aDTHY9FbUFCoC9YhK3KplzOm\nqapKW7QNq/HEO7vv9XDtw+zz78NpdPKfZ/wn1ZXVQ17HWdPO4turvo1ep+fZlmd5uPbhggNjl9lF\nY7iRWDZW0HGGQ9/PvTHciNvsHrZuGVpQrDkmt9VIWzBJ9yh4BD6aHY6mUVUG9dheVVV+/3IjtT0x\nvHYj37tgEVZj4eUBV66azpWrpqOocM/Wera3Bgs6nsUokpNVGnrLa0CMJCvUdEXxxzN4tJZrmjHC\nYXQQzUZpijSV1e/bWBPNRollY4Pug/ty+8s83fw0oiBy86qbmWyfXPBalk9Yzs2n3IwoiDzZ9CR/\nO/S3go4n6kSsBiv1wXokuXye8qmqSmu0leZIMy6La1in8GlBseaYBCHfo7W2O0o4qT2SGw59gzUG\nu0v89L5unjtwGKOo4/YLFuMa4g7xsXz2tBlcevJUZEXl7mfraQ8WNtil0mqkJ5opmwEx2ZzCvo4w\nkZSk9SDWjDmVpkq64910xDtKvZQxqzPWiUk/uGtHQ6iB+/fdD8AXl36Rhe6FRVvPKRNP4aaVN6ET\ndDx68FFe73q9oOOZ9WaycpaDoYNlMSBGURWaIk20RdtwW9zDPpZaC4qPYcOD9xHw+/r/HPD72PDg\nfSVcUWmIOoEKi4F9HRGSWW2saLEF4hlyijqo9l+tgQT3v9oMwDc+Npe5EwprGv9egiBw7RmzOHOu\nl5Qkc8dTB4inC/u3d1uNNPjj+GLpIq1yaDI5mb0dYZJZuag3EprysP6B9QR8gf4/B3wB1j+wvoQr\nGnl9rdqawk0EUoETf4FmUJJSkkA6gFU/8NSJdC7Nr3f9GkmROGfmOZwz85yir+u0yafx+cWfB+De\n3ffSHGku6HhOs5NAOkBrpLUYyxsyRVVoCDfQGesckYAYtKD4fTY8eB8/+cEtfOnKiwj4fQT8Pi7/\nxFn85Ae39AfG4ylINulFDKKOfR3lmRs6WimKSrM/gd104rSHvps0WVH51QuHyMRCTOvexkcXTBiW\ntQmCwL99fB5zvDa6Imnu2VpX0HhoUSfgship6YoSK1HhXVqS2d0WJpNTcFq0gHisWf/Aeu78zp1c\nt/Y6Ar4AAV+Ay86+jDu/c2d/YDxegmSdoMNpclIXrCOe1epCiqkr0YVepz9hDcKRN2gb6jbQ1dOF\n+prKtUuuHba1nTfrPD46/aNklSw/f+fnhDPhgo7nMrtoj7VzOFF4R6KhyCk56oP19CZ6C55SNxha\nUPwe5164lur5C2k8WMel55zOBWetwN97GIPRyKozVhPw+/jSlRcdFSSPdTaTnqysUH9Y60hRLOGU\nRFKSMek/uFjgyJu0h/+xl7rmDvwbb+e1P909rO8/s0Hk9gsWUWHWs7MtzENvFrZj0NcHe19nhLQ0\nsoVA8UyOHa0hFAVtMM0YtebiNVQvqKaxvpFLVl/C+avOx9/rx2A0cOqZpxLwBbhu7XVHBcljmUE0\n5DtSBGrIyJkTf4HmhDJyhu549wnbsB15g/b2obd5au9TNP+0mZr7avjr//112NYnCALXL72eeZXz\nCKQD/HLHLwsquuzrg10fqieSiRRxpScmyRK1gVqC6SAui2tEC6GLEhQLgrBOEIReQRD2F+N4peTx\nVnH/xi24PF5CAT+pRAKdKCJls1x/xYVces7pNB6so3r+Qs69cG2plztiKi1aR4piagkksA5gQMqR\nN2n/c+PFdK37Gmlf64i8/yZUmPne+YvQCfC3nR3sbA0VdDyLUURQBfa0h0csMI6kJHa2BtHrhKMm\n+2nGFk+Vh3Wb1+H2ugn6gyQTSURRRMpKXLf2Oi5ZfQmN9Y1UL6hmzcVrSr3cEWHRW1BRqQ/Wax0p\niqAn0YNO0J3wEf6RN2j/+ol/5eDtB8l0ZUbkvWcQDXxr1bdwmVzUBesKLrzT6/Q4jA72+feNWGCc\nzqXZ599HTIqVZCBNsXaKHwTOK9Kxyo6jwkmly00o4CcU8OPyeLl/4xY83qpSL21E9XWk6AppHSkK\nEU1LRJLZAXWM8HiruG/DExjtlcjJCEoyMqLvv6VTnXz2tJlAvlVbKFFY0aXdrCcnq+ztCJPJDe8H\ndW80zc7WEBaDvijdOTSji8PpoNJdSdAfJOgP4va6Wbd5HZ4qT6mXNmIcRgexbIzGSKPWkaIAkiLR\nGe/EYXSc8LV9N2jWSitSVEKOybg8rhF777nMLr5+8tcRENh0aBM1/pqCjmcUjVgNVvb79w97q7Z4\nNs4e3x4kRSrZyOmiBMWqqm4DCuvfVCb60iP6gl+Xx0skFCQaHdnHB+WoryNFXU+04OBoPOsIJk+Y\nNnGkNxoD5EqYtnLZymksm+YknJL47+cPohzx4TqUotQKiwFJVtnXPjypFIqi0uSLs68zgtNi0EaW\njwN96RF9wa/b6yYcDBONREu9tJJzmpz0xHu0jhQF8Kf8KIoy4N64hxOHjxqkMtJ90Jd4l7B23lpU\nVH6767dEs//8PRhKQapJNGHWm9nr23vUsYrJn/Sz27cbUScOaVJgsWg5xe/x3JOb+9MjHnv+De7b\nsAWDwYgiyxjNVmzO/I7x5Refx2Ov17C9NUhrIFFQIdJo0t+RojNMIqN1pBisZDbH4WhmQAV2AF3d\nPfz4q1ehJCPYne7+tJ6+QtCRIOoEvnXOfJwWA7vbw/xtZ/7D9VhFqQPNt68wG8jKCjtaQ0WdepfJ\nydR0R2gNJPHaTYPq7KEZvbY+sbU/PeKxbY/xi42/QG/Qo8gKRqsRm8tG0B/kik9ewYs1L9IYbhw3\n09+O7EjhTxY2lGc8UlSFtmjbgAO1gC/ADZfeQC6Ww1Jp6U/p6SsCHSmfnvdpFrgWEMqEuHf3vaiq\nesyC1IHm2pv1Zsx6M3t8e+hNFj71tI+syLREWqgJ1uAwOrDoLUU79lCM2DNFQRBuAG4AmDhl2kid\ndtAuv+ZLHI6msS48k5+81MmuZzciSVlEm4sJX/wNANn1t+Fvb+TX9z+EY+WFAFiNIvMnOlgypYIz\nqr3McA8/pUmLAAAgAElEQVR+2s1oYdKLyIrKvs4IJ8+oHNSu53jXFU5jEIUB7xz8z31/Iu1rxTpx\nJk888xyCIPClKy+i8WAdzz25mauu/fIwrzjPYzfxb+fM4z+3HODht9pYOcPFuReuZeNDD/QXpQKE\nAv4B5zs7zAZSWZkdLSGWTKlgQsXgmuG/ly+Wpr4nhgp47VoP4kIdec2ePK3wQQPDRVVVzr7ybGr8\nNehW6Lhl5y10PNtBTsqhd+qZ8+M5ADT/tJnDTYe567678JzjQRRE5jjnMN89n1UTV7HAvWBEWj6V\ngk7QUWmupDZUy3L9ciqMFaVe0qgRTAXJytkBB8V/fOSPhNvCmKea2fjURpwmJ9etvY7G+ka2PrGV\nq6+/ephXnCfqRL6x8ht8Z9t32Nm7kxfaXmDNxWvYsG5Df0EqQNAfHHC+s1lvRq/TUxusJSklmVEx\no6DfmXg2zsHQQZJSErd5ZFqunYhQrDwjQRBmAU+qqrr0RK9dsHSF+uizLxflvMVyOJpm64HDPH/g\nMMH3DKtQa55l6erzmD55Eia9DikRpv6Nrcw861JCySwd4RS+2NEVvrM8Vj4yv4o1iyfhtIzNivdo\nWsJi0LFsWiV6bUfuhDI5mTcbAzgtxgFNsAvEM3zl4R343nqCO/7tS5y1bG7+7/2+EQ2Ij/S/Lzfy\n5L5uprut/PKKFcTCAS4953RCgfwOlMvj5bHn3xhUvrMkK4RTEpOdZmZ5bFiMg7vJSksyTb44PdE0\nTrMRo35434sr5k49JKfj84f1JGVmyYol6kNbH8Ikls/NRkJK8ErHK7zY9iJtsbaj/r8KYwWpl1Os\nPHcl7io3Rp2RZDjJzq07mXH+DHxJH53xTlT++fnnNrs5fcrpfGzGx5hqnzrS386ISOfSZOUsy6uW\nYzWM3Y2bYlFVlV29u1BRBzTBTlEVbnvlNnY8toPPX/15rjntGiC/ezySAfGRXu96nV/v/DUm0cTP\nPvIzDEkDl6y+hKA/n/Hq9rrZ9MqmQeU7K6pCOB3GYXQwp3LOoG+yJEWiO95NS7QFi94y7O/FcCbM\n6pmr9ymSsuxErx331ScHuqP8dXs7O1pD/ZfHCQ4Tq2a5WTXTxZIpFViNZ73/C89bedQfA/EMdT0x\ndrSFeL3RT0sgScsbrWx4u52PL5rA2hVTmVJZ2scCxVZhNhBMZjh4OMbCSRXoBjGqeDzqjWZQGfhI\n5z+/1UpaUjj309f0B8SQL74rRUAM8IUzZrGrPUx7MMmf3mjhkkUnLjw5EYOow2vLdzc5HE0z22Nj\notN8wlzgeCZHZyjZv/vutZlGPHdPM/J6k71sbtjMqx2vklXyGxg2g43lVctZMWEFy7zL8lXrx9j4\nuv706/v/OyElaAg1sD+wnze63sCf8vNU01M81fQUp0w8hQvnXMhC98Ix9Z4y683IqsyBwAGWVS3D\nKGo9uz9INBslLsXxWAYWMG7r2EZLtIV5F87jylVX9v+9p8pTkoAY4IwpZ7C9Zzuvd73O73b9jm/M\n+0bBx9QJOtwWN0kpye7Du5lom8h0x3QsessH/r5k5Sw9iXx+u6IquMzDO7J5KIqyUywIwnrgo4AX\nOAz8UFXVB473+nLYKa7pirD+7Tb2dOQL6AyiwJnVXs5bOonFkysKuhBKssLOthDP7u9h+7ttrATg\nXxZO4HOnzaTKUT67LcXgi6eZ4bZRXWUbUx8gxZSTFd5sCmAz6ge0q97iT3DThl2IOoHffWbliN9Q\nbXjwPs69cG3/ju+Ru9MHD8e45dE9SPEw6lP/SWfzIVweL/DP9ImhdseQFZVIOouqgt2kZ7LTjNWo\nRxDyuZE5WSGUzBJKZIlnchhEEYdZj24E33faTnFp9CR62NywmW0d2/rHzy71LuXjMz7OqZNORa8b\n+h6Pqqo0hBv4R/s/2NaxDUnJ57kvci/ic4s/R3VldVG+h3IRzUSxG+ws8iwq6Oc21u337yeVSw1o\nJzMrZ/nmi98klAnx9RVf56xpx9hMG2brH1jPmovX9O/69u1QX/T5i7h126309vYS+J8AvmYfbq8b\n+Gf6xFC7Y6iqSjwbJ6fkMIkmJtgmUGGsQCfoEAQBRVGISTGC6SCxTAxBEHAYHQMuWiyGEd8pVlW1\nNLdAA/DeD/cDje3cfe+DBGb+C5DPBb5o+RQuWjalaGkOBlHHabM9nDbbQ1swyebdnbxU18uLdb28\nesjPRcuncMWqaWOmTZTHZqItmMAoCszw2Eq9nLLki2WQZHXAaSaPvN2GCpy3dFJJAuKf/OAWNj70\nAPdv3ALQn8cMcNW1X+aqU2fwv/c+SbD5ELPnLmDdX5886nVDTe8QdQJuaz7wSksyDb1xVPI3lQAq\nYNDpMBtEvPbCcpA15evID/eElOChtx9i06ObcH08v7P0kWkfYe3ctUyxTynK+QRBYJ5rHvNc87hi\nwRVsbdnKsy3PUhus5fZXb+f0Kadz9cKrmWAdnimSI63CVEE4HeZQ6BDzXfNHNEAZLeLZOMF0cMC7\nxM+3Pk8oE2JWxSzOmHrGMK/u/fqK6Das28C6zesA+nOZAW68+Ea+dee38DX7mD53On/e8uejXjPU\n9A5BEHCY8k8Mc0qOzngn7Wo7giqgCiqooNPpMOvNVJory37jbGxEZcdx5If7bx/azObdnay7/YtI\n/jYmnJfki1/6Cp9aPnVYm/rPcFu56WPzuPyUaTz0ZiuvHPLzt50dvFTfyw2r53BGtafs3yQnohPy\ngcyh3jgGUcfkMZYmUihFUWkJJAZ809Xoi/NGUwCjqOPyU6YP8+rebyAFdFesms47513FTuDDR9x0\n3r9xS9Hync0GUWunNg4d+eF+7W+v5fGGx9n3431kujLMqZzDD7/9QybZJg3b+Z0mJ5cvuJwL5lzA\n4w2P80zzM7zR9QY7enZw2fzL+OScT46J3dVKcyWBVIAmXRNzK+eO+s+hYuuMdw44vSSdS/N4w+MA\nXL7g8pKkBJyoiM5T5eGzX/osD/Mwc/9lLg63A6NoZN3mdUXLd9br9CXrL1wsRSu0G4yRSp/oaxHV\neLAOva0SRVVRkhEqp8zm//76JNUzRr6Y4uDhGH/Y1kT94XwT7FUzXdx4dnXBlfflQFZUgsksS4vQ\nSWAs8cXS1HRF8dgG9uj5v56s4Z2WEGtXTOH6s+YM8+qOLeD3nbCArj2U5N827CYrK3z/k4s4bfb4\nGIqgpU8Mr4AvwOcv/jxth9oQHfmbIjkmM33edP78xJ9HfPiGP+XnkdpHeL3rdQCmO6bz5WVfZr5r\n9L8FVFUlmA4y3T6dWc5ZWmD8rlQuxfae7bjMAxsx/ETDEzxS9wjVldXcceYdJfs5BnyBDyyiy8gZ\nvrvtu3QnurlwzoV8bvHnSrLOkTaY9InyynAuMp3FybIbfoHO6iSXCKMkI1S4PGx6amtJAmKA+RMd\n/OyyZdx4djU2o8j21hBfX7+LrQd6Rv3EIVEnUGkxsL8rgi+WLvVyyoKqqrT4E9gGmCpT3xPjnZYQ\nZoOOy1aWb+tCgOkuK9ecnp9299sXG4ikitdvWDM+ZeUsWwNbsd9kR3SIyDEZOSbj9rpLEhADeC1e\nblp5E9877XtMtE6kPdbOD1/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Y1r2Nu35wKxsfeoD7N+bbGn3pyotoPFgHUJaDdTTvJysy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cKar2ENDJsJ//P/wDY2+ftgzBehHu4dUHJ48scbaFQFWrcKGYjVkb2FfY5Bj5yfj9LuI7RlC5ap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ARI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\njjwJioUQQgghxJH3/wGT/CzajPNr6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a04128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(m, color, ax):\n",
    "    xx = np.linspace(-1, 11, 100)[:,None]\n",
    "    mu, var = m.predict_y(xx)\n",
    "    ax.plot(xx, mu, color, lw=2)\n",
    "    ax.fill_between(xx[:,0], mu[:,0] -  2*np.sqrt(var[:,0]), mu[:,0] +  2*np.sqrt(var[:,0]), color=color, alpha=0.2)\n",
    "    ax.plot(X, Y, 'kx', mew=2)\n",
    "    ax.set_xlim(-1, 11)\n",
    "\n",
    "f, ax = plt.subplots(3,2,sharex=True, sharey=True, figsize=(12,9))\n",
    "plot(m1, 'C0', ax[0,0])\n",
    "plot(m2, 'C1', ax[1,0])\n",
    "plot(m3, 'C2', ax[0,1])\n",
    "plot(m4, 'C3', ax[1,1])\n",
    "plot(m5, 'C4', ax[2,0])\n",
    "plot(m6, 'C5', ax[2,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here are the kernels and likelihoods, which show the fitted kernel parameters and noise variance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Parameter name:\u001b[1mGPR/kern/lengthscales\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 1.0774376245327446\n",
      "\n",
      "<Parameter name:\u001b[1mGPR/kern/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.8256105491329836 \n",
      "\n",
      " <Parameter name:\u001b[1mGPR/likelihood/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.16002167259084216 \n",
      "\n",
      "----\n",
      "\n",
      "<Parameter name:\u001b[1mVGP/kern/lengthscales\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 1.0774376485988129\n",
      "\n",
      "<Parameter name:\u001b[1mVGP/kern/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.82561040142936726 \n",
      "\n",
      " <Parameter name:\u001b[1mVGP/likelihood/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.16002069250966058 \n",
      "\n",
      "----\n",
      "\n",
      "<Parameter name:\u001b[1mSVGP/kern/lengthscales\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 1.0774407145919001\n",
      "\n",
      "<Parameter name:\u001b[1mSVGP/kern/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.82561225599881838 \n",
      "\n",
      " <Parameter name:\u001b[1mSVGP/likelihood/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.16002304036809423 \n",
      "\n",
      "----\n",
      "\n",
      "<Parameter name:\u001b[1mSVGP/kern/lengthscales\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 1.0774407146160694\n",
      "\n",
      "<Parameter name:\u001b[1mSVGP/kern/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.82561225648110581 \n",
      "\n",
      " <Parameter name:\u001b[1mSVGP/likelihood/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.16002304016499685 \n",
      "\n",
      "----\n",
      "\n",
      "<Parameter name:\u001b[1mSGPR/kern/lengthscales\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 1.0774406951137334\n",
      "\n",
      "<Parameter name:\u001b[1mSGPR/kern/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.82561274879830437 \n",
      "\n",
      " <Parameter name:\u001b[1mSGPR/likelihood/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.16002302477112654 \n",
      "\n",
      "----\n",
      "\n",
      "<Parameter name:\u001b[1mGPRFITC/kern/lengthscales\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 1.0774388272951685\n",
      "\n",
      "<Parameter name:\u001b[1mGPRFITC/kern/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.82561176748687659 \n",
      "\n",
      " <Parameter name:\u001b[1mGPRFITC/likelihood/variance\u001b[0m [trainable] shape:() transform:+ve prior:None>\n",
      "value: 0.16002122963998355 \n",
      "\n",
      "----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from IPython import display\n",
    "#_ = [display.display(m.kern, m.likelihood) for m in models]\n",
    "[print(m.kern, '\\n\\n', m.likelihood, '\\n\\n----\\n') for m in models];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here are the likelihoods (or ELBOs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-18.83436136 -18.83436136 -18.83439999 -18.83439999 -18.83439999\n",
      " -18.83435478]\n"
     ]
    }
   ],
   "source": [
    "print(np.array([m.compute_log_likelihood() for m in models]))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
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