{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "GP Regression with GPflow\n", "--\n", "\n", "*James Hensman, 2015, 2016*\n", "\n", "GP regression (with Gaussian noise) is the most straightforward GP model in GPflow. Because of the conjugacy of the latent process and the noise, the marginal likelihood $p(\\mathbf y\\,|\\,\\theta)$ can be computed exactly.\n", "\n", "This notebook shows how to build a GPR model, estimate the parameters $\\theta$ by both maximum likelihood and MCMC. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Editted by @fujiisoup\n", "\n", "In this note, I demonstrate the updated implementation where the model can be reused with different data.\n", "Please see\n", "\n", "`m.X = X_new\n", "m.Y = Y_new`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import GPflow\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# build a very simple data set:\n", "N = 12\n", "X = np.random.rand(N,1)\n", "Y = np.sin(12*X) + 0.66*np.cos(25*X) + np.random.randn(N,1)*0.1 + 3\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maximum Likelihood estimation\n", "--" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#build the GPR object\n", "k = GPflow.kernels.Matern52(1)\n", "meanf = GPflow.mean_functions.Linear(1,0)\n", "m = GPflow.gpr.GPR(X, Y, k, meanf)\n", "m.likelihood.variance = 0.01" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Here are the parameters before optimization\n" ] }, { "data": { "text/html": [ "
Namevaluespriorconstraint
model.kern.variance[ 1.]None+ve
model.kern.lengthscales[ 1.]None+ve
model.mean_function.A[[ 1.]]None(none)
model.mean_function.b[ 0.]None(none)
model.likelihood.variance[ 0.01]None+ve
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print \"Here are the parameters before optimization\"\n", "m" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compiling tensorflow function...\n", "done\n", "optimization terminated, setting model state\n", "Here are the parameters after optimization\n", "1.03348398209 (s) for compilation and optimization\n" ] }, { "data": { "text/html": [ "
Namevaluespriorconstraint
model.kern.variance[ 0.49558094]None+ve
model.kern.lengthscales[ 0.0590087]None+ve
model.mean_function.A[[-0.81910249]]None(none)
model.mean_function.b[ 3.42074855]None(none)
model.likelihood.variance[ 0.00132886]None+ve
" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = time.time()\n", "m.optimize()\n", "print \"Here are the parameters after optimization\"\n", "print time.time() - t, \" (s) for compilation and optimization\"\n", "m" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oN9lYjb2OjS8hsMyyQwoH/FHyJSiHzDiYgc2bgSZN3I9p1Ci0O9BMnQr07CnZ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\nE0oK3l9U7RpFwElJkZT1nBypVaNQKDyjatcoQoo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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot!\n", "xx = np.linspace(-0.1, 1.1, 100)[:,None]\n", "mean, var = m.predict_y(xx)\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)\n", "plt.plot(xx, mean, 'b', lw=2)\n", "plt.plot(xx, mean + 2*np.sqrt(var), 'b--', xx, mean - 2*np.sqrt(var), 'b--', lw=1.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For another data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = 12\n", "X = np.random.rand(N,1)\n", "Y = np.sin(12*X) + 0.66*np.cos(25*X) + np.random.randn(N,1)*0.1 + 3\n", "# update data\n", "m.X = X\n", "m.Y = Y" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "optimization terminated, setting model state\n", "Here are the parameters after optimization\n", "0.289180994034 (s) for optimization\n" ] }, { "data": { "text/html": [ "
Namevaluespriorconstraint
model.kern.variance[ 0.7373735]None+ve
model.kern.lengthscales[ 0.07703024]None+ve
model.mean_function.A[[-1.50149169]]None(none)
model.mean_function.b[ 3.66000445]None(none)
model.likelihood.variance[ 0.01383806]None+ve
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = time.time()\n", "m.optimize()\n", "print \"Here are the parameters after optimization\"\n", "print time.time() - t, \" (s) for optimization\"\n", "m" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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W5f+ZN5s2Gf+sfdIkonnzit7unLT9uaqxWonuvpvo0Uc9H/f8eaK4OCL7U2M2m/OPATxH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\noZ89y7XdunVDfwy9LF3KdfD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot!\n", "xx = np.linspace(-0.1, 1.1, 100)[:,None]\n", "mean, var = m.predict_y(xx)\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)\n", "plt.plot(xx, mean, 'b', lw=2)\n", "plt.plot(xx, mean + 2*np.sqrt(var), 'b--', xx, mean - 2*np.sqrt(var), 'b--', lw=1.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For another data with different shape" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# shape changed!\n", "N = 13\n", "X = np.random.rand(N,1)\n", "Y = np.sin(12*X) + 0.66*np.cos(25*X) + np.random.randn(N,1)*0.1 + 3\n", "# update data\n", "m.X = X\n", "m.Y = Y" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "optimization terminated, setting model state\n", "Here are the parameters after optimization\n", "0.115693807602 (s) for optimization\n" ] }, { "data": { "text/html": [ "
Namevaluespriorconstraint
model.kern.variance[ 0.19982176]None+ve
model.kern.lengthscales[ 0.02346412]None+ve
model.mean_function.A[[-1.53200209]]None(none)
model.mean_function.b[ 3.6978339]None(none)
model.likelihood.variance[ 0.01542315]None+ve
" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = time.time()\n", "m.optimize()\n", "print \"Here are the parameters after optimization\"\n", "print time.time() - t, \" (s) for optimization\"\n", "m" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vvfzvmTvXcR/btrHivn7dcZu9e4maN+e+lCIaOpQLlc+YQfTRR0QPPkgUHW1V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\nMYOoYUPvjo2LY41y5Ij9/SYTf1cA0bZ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Z5kMAXjFvewbA0zZtksCjr90A2ugpryeyA/gc\n7BGx0/x9b9VbZk+/e5u20xFE3jUe/Hf+Dfaw2QNgpN4ye/j/iQWwwiz7HgCP6C2zjeyzAZwGcA3A\n7+CnjpC4bt2R39trV4KhBEEQwhjJQikIghDGiJIXBEEIY0TJC4IghDGi5AVBEMIYUfKCIAhhjCh5\nQRCEMEaUvCAIQhgjSl4QBCGM+X8IsLp06lQt2wAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot!\n", "xx = np.linspace(-0.1, 1.1, 100)[:,None]\n", "mean, var = m.predict_y(xx)\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)\n", "plt.plot(xx, mean, 'b', lw=2)\n", "plt.plot(xx, mean + 2*np.sqrt(var), 'b--', xx, mean - 2*np.sqrt(var), 'b--', lw=1.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fixing the parameters" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Fix the lengthscale\n", "m.kern.lengthscales.fixed = True\n", "m.kern.lengthscales = 0.2" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compiling tensorflow function...\n", "done\n", "optimization terminated, setting model state\n", "Here are the parameters after optimization\n", "0.753976821899 (s) for compilation and optimization\n" ] }, { "data": { "text/html": [ "
Namevaluespriorconstraint
model.kern.variance[ 4.03946809e-09]None+ve
model.kern.lengthscales[ 0.2]None[FIXED]
model.mean_function.A[[-1.38171976]]None(none)
model.mean_function.b[ 3.57538357]None(none)
model.likelihood.variance[ 0.22266967]None+ve
" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = time.time()\n", "m.optimize()\n", "print \"Here are the parameters after optimization\"\n", "print time.time() - t, \" (s) for compilation and optimization\"\n", "m" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot!\n", "xx = np.linspace(-0.1, 1.1, 100)[:,None]\n", "mean, var = m.predict_y(xx)\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)\n", "plt.plot(xx, mean, 'b', lw=2)\n", "plt.plot(xx, mean + 2*np.sqrt(var), 'b--', xx, mean - 2*np.sqrt(var), 'b--', lw=1.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### With different value for fixed param" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Change the lengthscale\n", "m.kern.lengthscales = 0.1" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "optimization terminated, setting model state\n", "Here are the parameters after optimization\n", "0.131355047226 (s) for optimization\n" ] }, { "data": { "text/html": [ "
Namevaluespriorconstraint
model.kern.variance[ 4.03946809e-09]None+ve
model.kern.lengthscales[ 0.1]None[FIXED]
model.mean_function.A[[-1.38171628]]None(none)
model.mean_function.b[ 3.57539326]None(none)
model.likelihood.variance[ 0.22266935]None+ve
" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = time.time()\n", "m.optimize()\n", "print \"Here are the parameters after optimization\"\n", "print time.time() - t, \" (s) for optimization\"\n", "m" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1ZjY9RTHuTSLxrwE+MbOtwFbn3Gzga/haeNgSif8q4BYAM3vXOfce\ncDzwRkoibJt0/dwmrDWf3WSXa6YDV8buXwE0l8AfAKrN7M4kx5OIfwLHOOeOcM51AS7B/zuamg5c\nDtBSA1iKtRi7c+5woBy4zMzeDSHGvWkxfjM7KnY7En9i8H/TJMFDYu+dacAZzrmOzrke+AuAS1Mc\nZ3MSif8D4GyAWD37WGBVSqPcu5YaN9Pxc9tUs/G3+rOb5KvFBwAv4EfMzAT2i20/CJgRu3860IC/\nkr8QeBP/VyrMq9yjYjGvAH4e2zYe+EGTY0rwZ19vASeHGe++xA78CT8i4s3Y7/v1sGPe1999k2Mf\nII1G1+zDe+cn+BE2i4Frw455H98/BwHPxWJfDFwadsxNYn8Y+Aj4EliN/9aREZ/bROJv7WdXzVAi\nIllMs1CKiGQxJXkRkSymJC8iksWU5EVEspiSvIhIFlOSFxHJYkryIiJZTEleRCSL/X+L5RGXiiwZ\n+QAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot!\n", "xx = np.linspace(-0.1, 1.1, 100)[:,None]\n", "mean, var = m.predict_y(xx)\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)\n", "plt.plot(xx, mean, 'b', lw=2)\n", "plt.plot(xx, mean + 2*np.sqrt(var), 'b--', xx, mean - 2*np.sqrt(var), 'b--', lw=1.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MCMC\n", "--\n", "First, we'll set come priors on the kernel parameters, then we'll run mcmc and see how much posterior uncertainty there is in the parameters." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m.kern.lengthscales.fixed = False" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
Namevaluespriorconstraint
model.kern.variance[ 4.03946809e-09]Ga([ 1.],[ 1.])+ve
model.kern.lengthscales[ 0.1]Ga([ 1.],[ 1.])+ve
model.mean_function.A[[-1.38171628]]N([ 0.],[ 10.])(none)
model.mean_function.b[ 3.57539326]N([ 0.],[ 10.])(none)
model.likelihood.variance[ 0.22266935]Ga([ 1.],[ 1.])+ve
" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#we'll choose rather arbitrary priors. \n", "m.kern.lengthscales.prior = GPflow.priors.Gamma(1., 1.)\n", "m.kern.variance.prior = GPflow.priors.Gamma(1., 1.)\n", "m.likelihood.variance.prior = GPflow.priors.Gamma(1., 1.)\n", "m.mean_function.A.prior = GPflow.priors.Gaussian(0., 10.)\n", "m.mean_function.b.prior = GPflow.priors.Gaussian(0., 10.)\n", "m" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compiling tensorflow function...\n", "done\n", "Iteration: 100 \t Acc Rate: 96.0 %\n", "Iteration: 200 \t Acc Rate: 100.0 %\n", "Iteration: 300 \t Acc Rate: 100.0 %\n", "Iteration: 400 \t Acc Rate: 100.0 %\n", "Iteration: 500 \t Acc Rate: 100.0 %\n" ] } ], "source": [ "samples = m.sample(500, epsilon = 0.1, verbose=1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JHnJq8bDTFn/r+Of4/cwJBPjM1KkAdsJuQJY5MxhkfzpNWJIodblYFg6zN51m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\ni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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(samples)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/james/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:1744: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n", " warnings.warn(\"This figure includes Axes that are not \"\n" ] }, { "data": { "image/png": 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1c6alpYX+/n4GBweJRqPWQTL/vnbtGjdv3twVXMHeNsVkMimzVbvpAxYK/r/I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yObzcLv92NychL37t0DACwvL1tWzZS4ODTbaankwBn37XQ6K6bZURoeAZCDRRBNx2azwe/3w+Fw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\nk8NtTQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Note. All these labels are wrong (or, most probably wrong). We need some machinery for labelling posterior samples!\n", "\n", "f, axs = plt.subplots(1,3, figsize=(12,4), tight_layout=True)\n", "axs[0].plot(samples[:,0], samples[:,1], 'k.', alpha = 0.15)\n", "axs[0].set_xlabel('noise_variance')\n", "axs[0].set_ylabel('signal_variance')\n", "axs[1].plot(samples[:,0], samples[:,2], 'k.', alpha = 0.15)\n", "axs[1].set_xlabel('noise_variance')\n", "axs[1].set_ylabel('lengthscale')\n", "axs[2].plot(samples[:,2], samples[:,1], 'k.', alpha = 0.1)\n", "axs[2].set_xlabel('lengthscale')\n", "axs[2].set_ylabel('signal_variance')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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YFIdQ0JjrRlRJc7Md79qa91e5UVI0WbJkd9bItR33ddowIN7dbcDMhJ7FRQPFvj4DUjjq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\ncScstqaNGulimT+FRPGYo8dO8c7IiPHIeLhw293dLvXr6/NmWLFYh42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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#an attempt to plot the function posterior\n", "#Note that we should really sample the function values here, instead of just using the mean. \n", "#We are under-representing the uncertainty here. \n", "# TODO: get full_covariance of the predictions (predict_f only?)\n", "\n", "plt.figure()\n", "\n", "for s in samples:\n", " m.set_state(s)\n", " mean, _ = m.predict_y(xx)\n", " plt.plot(xx, mean, 'b', lw=2, alpha = 0.05)\n", " \n", "plt.plot(X, Y, 'kx', mew=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variational inference" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = 25\n", "X = np.arange(N, dtype=np.float64).reshape(-1,1)\n", "Y = np.random.poisson(np.exp(np.sin(12*X) + 0.66*np.cos(25*X)))*1.0" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#build the GP object\n", "k = GPflow.kernels.RBF(1)\n", "lik = GPflow.likelihoods.Poisson()\n", "m = GPflow.vgp.VGP(X, Y, k, likelihood=lik)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compiling tensorflow function...\n", "done\n", "optimization terminated, setting model state\n", "4.08820700645 (s) for compilation and optimization\n" ] } ], "source": [ "t = time.time()\n", "m.optimize()\n", "print time.time() - t, \" (s) for compilation and optimization\" " ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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F+Hu748mTXPv0dpjf++9zbbioVeK81a4dxxlsM6YvXyb65Rf/fy5pNXo0fwqb\nO9fqSOwL0hFprDNniK6/nkxbTMpIb7/NcXbt6l05WhPK2bO8CNTZs56f48cfOdZGjezbPCDYl1/y\nzMnvvrM6EnvSk7Rt+uHMNypVyttN/I03jOuQM9qpU3lNIyNHeleW0rgvtMsFrF/PGx142szRsydv\nMLxnj28/XvuSu00IAtHQobzf6jPP8A5JRvrtN94qT1xNknYxXnwRqFsX2L49b2q4v/nsM06cXbsC\nbdr45pyVK/OL6vx54LnnPNv9pEQJYPBg/n7cOGPiMWPLOD3S0oBXXwUaN+Yt3oJBv368l+e99xpX\n5rRpPEpk6VLjygwYnlbNC7shAJtHcsyeTbmLLyUnWx3N1U6ezJuy7uli9mfPEv39t3fn37mTz/+f\n/3h2v5MneccgI6bab9rEqzRavXvJ0qU8vfrGGz3b+EJc7YsvuMll2jSrIzEfpE3bHC5X3vT2Z56x\nOpqrvfWW/rbsvn2JOnb0Poa4OH6Rbd3q2f3+7/849iee8O78V64Q9ejBbeRWvKkmJ/NjKFGCO68D\nYeKPFZKTiQYM4CGrv/xidTS+IUnbRLt2cWJSitfR8AcHDvATXE8te9o0rukaNdZ28WLPpxYfOMDj\nwENDifbt8+78aWlE7dsTtWrF48x9aelS3mjC0zetQJeVxftQvvuutiUhPv6Y6PbbiRISzI/NX0jS\nNtnrr/MVa97cP6aI9+zJ8fTt69n99u0juu46XljKak89xY/huee8L+vMGaKmTYnatiXSsPCgoQJl\nGJ+RMjN5A47bbuM35jvuIHrxxcI35cjICJw1RbTSk7QV3897Sikyqix/deEC0LQpcOAA8N57wKhR\n1sXy66/c8Vi+PE8Jv+EGbfdzubjD6LrrgLlztY8WMcvu3Tydu2RJYP9+XmPcGykpPF0+Ohpo29aQ\nEK9CZP01sxsi4M8/gRUreL36lBR+/gpAKQUi8ugZJUnbQ8uXAw4Hr3+xdi3QqpXvY8jI4Jl1e/YA\nn3zCi+VrtXEj8MADwObNvMC+P3jsMSAuDhg+PG/tFG+YkVjXrgXGjOENHMaMMbZsEbz0JG0Z8ueh\nu+8GXnoJyMoCnnqKd/Xwtfff54R9002c6DzRqhWwb5/5CduTsdvvvMNfx48HDh70/txGJezUVF40\n7K67eOeYcuV4eJsQVpKatg6XLvG6HHv28HZMU6b47iNzfDw3byjF33fo4JvzeuLiRa6R/ve/QKdO\n2u7zxBOcvXo4AAARXUlEQVQ8OaNvX+MnaeQYPJgnTHXtyp9UilrH4vx5fmOrXJnfnAcNAurVMycu\nEbz01LSlI1KnhIS8ccZffumbc6ak8MbDAO+W7c/eeYfHTp8/r+34gwd5HQuAaMMGc2L69luiDh3y\nzlOtGlG9eoWvFrhmTfB1jAnfgkk710wGkARgazHH+eZR+pGZM/kKhoYSDRv2c7E7gWjdLcTdcePG\nxVK3bpS7jZi/74936RLvMPLSS9rvkzM6x+wFujIzeajjzz8T/fCDvnHV3uzio7csI89pZFxCP7OS\ndjsAzSVpu5ezEiBwmurVe6zQPfe07svn7rhbbokiYBIBvD/eoUPa43O5PB/DbZRVq3jCidbznzlD\nVKUKX8+ffjI3Nm94u1+mnrKMPKeRcQnvmJK0uVzUkaTt3pUrRA8+eCk7cZ+hSpW6uN3dWusO2AWP\nCw+PIOBTAojKlnXR6tWexTdtGlG5ctZNvx86lPes1LqaX0wM5W6X5q8L3nu7M72esow8p5FxCe9I\n0rZIRgZR9+45ifssAQ6KiIi45smdlJSU++TPeTG4ewHkHReSm7BLlnTR4sWexXXoEFHFitZuhHv+\nPNHatdqPv3KF6K67yC+XDMhP6//SyLKMPKeRcQn99CTtEh71WhYjOjo693uHwwGHw2Fk8X6rZElg\n/PhULFmyGOnpPQD8hosXP0FWlv4yXa5KAKYB6ALgCmJjL6JLl4oe3J9Httx1V95uMVYoXx644w7t\nx4eG8jC75s35a58+xq4eJ4SVnE4nnE6nd4VoyeyQmnaR8j5KhlJY2FgCsgggCgtbQ8uWnSxwTNEf\nN0+cSKKaNYcSsJ8AIqWSCejg8cfSf/+b24ePHTP84frEhx9ybbtuXf9rJpHmEaltGwUmNo/UBbCt\nmGN88iC9sXUr0bBhRLt3u/+73udiwU6bmTPPUGjoqeyk66LHHyd67bUfiuzYSU8nWrSIqEGDo9nN\nLETNmmXQn3+meNwBlJlJdPPN/t2ZV5yMDF6zAiDq3t2/drjx147IzExeV+bXX3lj5jFjuKN8xAj+\n+s9/cp/BnDn8WihqBJJ0RPqGKUkbwAwAxwCkA/gbwNOFHOerx+mx338natOGV+jr3Nn9inJpadz+\ne999RH/84fk5Cg6PSkxMJodjM5UqRblJuFKl89SvXxp99BHRlClEn312jrp1W0d9+/Ja3TnHlS+f\nRh9/nEoZGVyWnqFWFy96/hh8ResWZfv382gZgGve/sTqIX8uF9Hq1Sepb9/f6bnn+A0u/3NNy61s\nWe4/eOcdfs4XHJMuQ/7MpydpB/SMyCNHeMr53Lk8G2748KJnte3ZA3z6KW+fNHw48MEHQFiYdzH8\n/Tfw738Ds2cDR48WfWyTJkDv3ryFU0Xtzde2snAh8PzzvPZJlSrFH79oEa+VAgALFgD3329ufP7s\n0CHeyeW333g2bHLytcfUrAk0bMi7LYWH84zOUqV4q7zLl4Hjx/l1sWsXL9CVX0QEP/+eegq47TZZ\nGMsXZMGoAv76i5P2p5/ySnJarVwJDBzIT9pZs3hlP28R8Qpnq1YBx47xi0cp3iuxZk3gnnt46neg\ny8zkqfcVK3ISDg0t/j6jRvGqfWXL8upw7dubHqZfSE/nlfEWLgQWL+ZEm1/Vqnwt7rqLk2zz5kCF\nCtrLP3UKWLeOy54/n1evzNGkCTBkCC8vUK6cMY9HXEuStoHS0oA33+RaYePGVkcTWI4cAVq25FEt\nWpa3dbl449hvv+UlZX//HWjd2vw4rXDiBL+ZzZ/PteqLF/P+VqECv7l36sQjaho1Mq42TAQkJPDe\njNOn8/KpAL+5PvccMGwYVy6EsSRpB6mZM4E//sjbkd0Oli3jndznzMlr/ihKVhavsPfDD/yRf+7c\nwKhxE3FT0fz5wLx5wIYNV/+9aVPe4LZbN960uWRJ82PKyAB++gmIieHnFcCbMT/+OG9abMQnT8Fk\nwaggNGcOb4M2ebLVkXhuzBjeaUbryJCMDN4LkicbcWeuHV26RLRgAdELLxDVrHl152CZMkQPPEA0\nfrz3my4bYf16ot69iUJC8mLs0oVoyRJ77NZz5Qpf6zfftDoS92DWkD9NBQVJ0na5/GcvwG++4bU9\n7Nqh73IRnT7t2X2uXOFFqHISyNCh/j1SJsfRo0QTJhA99BBRWNjVibp6daJnn+XNbP31sezfz8Nl\n88feuDEPLfTHmPfs4SGONWrkxbt9u9VRXUuStg9s3py363bOkDxfy8rizVJLlyb68UdrYrDahAn8\nfwCIGjQgio+3OqKrXblCtHo1D6dr0eLa4XYtW/Lyuhs2+NcY9OKcOsXDL6tXz3ssFSsSDRlCtGWL\ntbEdO8YVmDZtrr7WDRoQ/etfvLSxv5Gk7SPLl/PH2latrNk5Oi2N14X2t0Tlaxs2EN16a96L89FH\nrf0UdPAg1zx79cobX55zCwvjSUJff61tZ3J/l57Oi5HdccfVj7NJE06Qu3aZ33zichHt3Ek0dixR\n+/Y8DyMnjnLliPr359eIPzfj6Ena0hGp0+nTwIgR3Ns+dCjw0Ufej+n2BFFgjqMl4pELkZHajs/I\nAD7+mG/p6fy7hx/m0SZdupjXcUfEW6OtXMm3ZcuuHffcsCF3IN5/P+8rWqaMObFYbcsWYMIE7hA/\nfTrv9w0b8mPv0IG3a6tWzbvzZGXxRtBr1/JQyOXLr96erlQp/p/37g306GGPoYoyesQCy5fzZrQz\nZwKlS1sdjf19/z3wyis8Pt6T3dSPHQNGj+bkkZO8q1YFHnyQh8d17Kg/abhcnBy2bQO2buURHuvX\nA0lJVx9XqRIn586d+daggb7z2VVGBrBkCY/wWbjw6gQOADVq8PDZqCigdm3+OTyck2u5cvxGmJnJ\n2/mdOgWcPMkTivbv54lv27bx3/ILD+ft47p141FInoxT9weStANIaiowYwbfFi2yR63BCFeu8Ea/\nn3/Ou56/9JJnnyiOHwemTuX9KQtORomM5EkjDRpwQo+M5Ak7OW+2ly7x7eRJTsjHjnHCOHDA/QbO\n11/PNcj27XnD55YttU0WCgZZWcCaNfwJZNUq/t6TzZ4LU6cOTyTq0IGve7Nm9r7mkrT9TEwMsHEj\nv6CbNQNuvJEnhxRm7Vp+ki9ZwuNjq1cHnn2WJzbYrQbhrTlzeGnZdu2A2Fh+sXqCssc/50z79jZp\nVKvGmwHfeisnjdtv5+QfiE1UZsjK4je/7dv5zfToUZ5kdfo0TyBKSwNCQrg5q3RprkFXqcITeurX\n52t96638RhlIJGn7maVLudlk+XKusRHxE3HyZG53LWjgQP4Y2KkT3+64w961CG8dOMCzJkNC+OO2\nN4h4HZjERODwYZ55mJzMTSk5zSlly/KtShWuhVerxgmjXj3/etN0uTj+o0c5ublbT2f8eH6eZWTw\nYw8N5QkyQ4fy2iIFnTnDydKX/TJCkrZfu3wZ2LeP2+iaNeP2PFE8IuD8ef9Kmlb46Sfgq6+4bf3w\nYW77DQkBPvwQeOuta49fs4Y/aZQuzZ8GsrL4Pq1bA61aXXv8sGH8ybBuXW5zbtKE1zJxOLzvQBSF\nk6QtgsqBA5xk7NhEkZGR18mWc9u3j5uDXnnl2uNXrABWr+bHW6cOd+RVq8a1ZyNcvAjs3Qvs3MlN\nGNu2cdIfM4Z3DyooUEcv+ZokbRE0LlzgJozq1XlYWdeuvNqdv7R5pqdzjTgrC7jppmv//t133GZf\nsWJem22DBjzKpUsXn4dbqMKSc9eu3FHbtCmPCMkZFVK3rnFvJMHAtKStlOoK4AsAIQAmE9EYN8dI\n0hY+dfw4t3UvWsSdjefO8aiC5cvNOZ/Lxc1c7tp9ExKAkSPzOthy1rp++GHuVC3owgVurqhc2ZxY\nzbZpEw993LqVa+bbt/Oom61bucOwoI0beQRUZCQ/5pAQ38fsj0xZMAqcqP8C7xNZEsBmADe7Oc6I\nCUKWiC9iaqG/7t6RP674+Hi/iUsPb69/TEwMHT+eRNu3E/32m/tj1qwhKlv2EtWtm0ktWhC1a0d0\n993pdO+9m9yeb9MmotatiRo1yqTKlVOpYkWecedwuI9/716esv7VV0Tz5vFM2dGjJ9KJE7597uh5\nvhZ1/T0558mTvIWZu3OGh5/Nna0YGkpUpUoWRUScoRMn3Jf14Ye8dsjrr5+n7t3X0OjRvMDY+fPu\n4x89mpeWeOUVosGDiQYNIurXj+jcOa8emulg0nZjbQAsyvfzmwDecHOcjx6m8UaOHOn29/66T17B\nuF599VW/iEsvb66/1v/RJ59MIKAT1ajxKn366TkaNSqVIiK+IOApt2Vt2ZJCo0en0g03vEnAIzRk\nyBzauJHo0CFt8Vvx3NF7zsKuv1HnzDnm5ptb0bp1KbRgwSmqVet5AvrR55+Pd1tW375p1KXLZSpf\n3knAYmrU6DB17Mhrn7iLv39/XsagTx+iAQOInn+eaPhw7VvbWcWspP0ogAn5fn4CwJdujvPRwzRe\nYU9af92RumBcYWFhfhGXXt5cfyN3M9f7/3YXvxXPHSPjN/KcZu847038VpOkrVNR//SkpKTcJ0/O\nk8kfEqO/xqWHt9df67Uwsiwt8VvxPzIyfiPPaeT/yOj4raQnaRfbEamUagMgmoi6Zv/8ZvaJxhQ4\nTnohhRDCQ2T06BGlVCiA3QDuBXAcwHoAjxPRTr1BCiGE0KfYEZVElKWUGgJgCfKG/EnCFkIICxg2\nuUYIIYT5DBvirpQaqZQ6opTalH3ralTZZlJKdVVK7VJK7VFKvWF1PJ5SSh1USm1RSiUopdZbHU9x\nlFKTlVJJSqmt+X5XWSm1RCm1Wyn1q1KqopUxFqWQ+G3x3FdK1VRKLVNKbVdKbVNKDcv+vS2uv5v4\nh2b/3i7Xv7RSal32a3WbUmpk9u89uv5GTmMfCeA8EX1mSIE+oJQKAbAH3F5/DMAGAH2IaFeRd/Qj\nSqn9AG4jojNWx6KFUqodgAsAphJR0+zfjQFwiog+yX7jrExEb1oZZ2EKid8Wz32lVDUA1Yhos1Kq\nPIA/ATwM4GnY4PoXEX9v2OD6A4BSKoyI0rL7ClcDGAYeoaf5+hs9mdRuS8jcDmAvER0iokwAM8FP\nAjtRMP7/aBoiWgWg4BvMwwC+y/7+OwA9fBqUBwqJH7DBc5+IThDR5uzvLwDYCaAmbHL9C4k/Z71M\nv7/+AEBEadnflgb3KRI8vP5Gv9iHKKU2K6Um+etHrAJqADic7+cjyHsS2AUBWKqU2qCUetbqYHSK\nJKIkgF+YADTuEOlXbPXcV0rVBdAcwFoAVe12/fPFvy77V7a4/kqpEKVUAoATAJYS0QZ4eP09StpK\nqaVKqa35btuyv3YH8BWA+kTUPDsgv/+oEiDaElFLAN0ADM7++G53dusdt9VzP7tpYRaA4dk11oLX\n26+vv5v4bXP9ichFRC3An3BuV0o1hofX36NFFImok8ZDJwKY50nZFjkKoHa+n2tm/842iOh49tcU\npdTP4CafVdZG5bEkpVRVIkrKbrdMtjogTxBRSr4f/fq5r5QqAU5404hobvavbXP93cVvp+ufg4hS\nlVJOAF3h4fU3cvRI/v0tHgGQaFTZJtoAoKFSqo5SqhSAPgB+sTgmzZRSYdm1DiilygHoDHtcd4Wr\n2yB/ATAg+/unAMwteAc/c1X8NnvuTwGwg4jG5fudna7/NfHb5forpcJzmm6UUmUBdAK3y3t0/Y0c\nPTIV3MbkAnAQwPM57TT+LHt40DjkTRwabXFImiml6gH4GfxxqgSA6f4ev1JqBgAHgCoAkgCMBDAH\nQByAWgAOAXiMiM5aFWNRCom/I2zw3FdKtQWwAsA2ZK/tAeBt8CznH+Hn17+I+PvCHtf/VnBHY0j2\n7Qci+kgpdT08uP4yuUYIIWzENkPFhBBCSNIWQghbkaQthBA2IklbCCFsRJK2EELYiCRtIYSwEUna\nQghhI5K0hRDCRv4fTMMIMEeQTHIAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot!\n", "xx = np.linspace(-1., N+1., 100)[:,None]\n", "mean, var = m.predict_f(xx)\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)\n", "plt.plot(xx, np.exp(mean), 'b', lw=2)\n", "plt.plot(xx, np.exp(mean + 2*np.sqrt(var)), 'b--', xx, np.exp(mean - 2*np.sqrt(var)), 'b--', lw=1.2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For another data with the same shape" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Y = 30.*np.exp(-((X-np.ones((N,1))*5.)*0.1)**2.) + np.random.rand(N,1)*3.\n", "m.Y = Y" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "optimization terminated, setting model state\n", "1.70508289337 (s) for optimization\n" ] } ], "source": [ "t = time.time()\n", "m.optimize()\n", "print time.time() - t, \" (s) for optimization\" " ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5c+zWgd4895wN/G3a2G3rjjzS+7FDh9oVqtOmBX24StWgAd1HmzfbP8dzc/Vq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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot!\n", "xx = np.linspace(-1., N+1., 100)[:,None]\n", "mean, var = m.predict_f(xx)\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)\n", "plt.plot(xx, np.exp(mean), 'b', lw=2)\n", "plt.plot(xx, np.exp(mean + 2*np.sqrt(var)), 'b--', xx, np.exp(mean - 2*np.sqrt(var)), 'b--', lw=1.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### For another data with different shape" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# shape changed\n", "N = 34\n", "X = np.arange(N, dtype=np.float64).reshape(-1,1)\n", "Y = 14.*np.exp(-((X-np.ones((N,1))*5.)*0.1)**2.) + np.random.rand(N,1)*3.\n", "m.X = X\n", "m.Y = Y" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compiling tensorflow function...\n", "done\n", "optimization terminated, setting model state\n", "1.96736907959 (s) for compilation and optimization\n" ] } ], "source": [ "t = time.time()\n", "m.optimize()\n", "print time.time() - t, \" (s) for compilation and optimization\" " ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot!\n", "xx = np.linspace(-1., N+1., 100)[:,None]\n", "mean, var = m.predict_f(xx)\n", "plt.figure()\n", "plt.plot(X, Y, 'kx', mew=2)\n", "plt.plot(xx, np.exp(mean), 'b', lw=2)\n", "plt.plot(xx, np.exp(mean + 2*np.sqrt(var)), 'b--', xx, np.exp(mean - 2*np.sqrt(var)), 'b--', lw=1.2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "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": "2.7.11" }, "toc": { "toc_cell": false, "toc_number_sections": true, "toc_threshold": 6, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }