{ "metadata": { "name": "labs_gprs13" }, "name": "labs_gprs13", "nbformat": 2, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": true, "input": "import sys as sys\nimport numpy as np\nimport pylab as pb", "language": "python", "outputs": [], "prompt_number": 336 }, { "cell_type": "code", "collapsed": true, "input": "# Loading in data: the 0:1 and 1:2 ensure we get column vectors\n# for x and y.\nolympics = np.genfromtxt('olympicMarathonTimes.csv', delimiter=',')\nx = olympics[:, 0:1]\ny = olympics[:, 1:2]", "language": "python", "outputs": [], "prompt_number": 337 }, { "cell_type": "code", "collapsed": false, "input": "print(x)\nprint(y)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "[[ 1896.]\n [ 1900.]\n [ 1904.]\n [ 1908.]\n [ 1912.]\n [ 1920.]\n [ 1924.]\n [ 1928.]\n [ 1932.]\n [ 1936.]\n [ 1948.]\n [ 1952.]\n [ 1956.]\n [ 1960.]\n [ 1964.]\n [ 1968.]\n [ 1972.]\n [ 1976.]\n [ 1980.]\n [ 1984.]\n [ 1988.]\n [ 1992.]\n [ 1996.]\n [ 2000.]\n [ 2004.]\n [ 2008.]\n [ 2012.]]\n[[ 4.47083333]\n [ 4.46472926]\n [ 5.22208333]\n [ 4.15467867]\n [ 3.90331675]\n [ 3.56951267]\n [ 3.82454477]\n [ 3.62483707]\n [ 3.59284275]\n [ 3.53880792]\n [ 3.67010309]\n [ 3.39029111]\n [ 3.43642612]\n [ 3.20583007]\n [ 3.13275665]\n [ 3.32819844]\n [ 3.13583758]\n [ 3.0789588 ]\n [ 3.10581822]\n [ 3.06552909]\n [ 3.09357349]\n [ 3.16111704]\n [ 3.14255244]\n [ 3.08527867]\n [ 3.10265829]\n [ 2.99877553]\n [ 3.03392977]]" } ], "prompt_number": 338 }, { "cell_type": "code", "collapsed": false, "input": "pb.plot(x, y, 'rx')", "language": "python", "outputs": [ { "output_type": "pyout", "prompt_number": 339, "text": "[]" }, { "output_type": "display_data", "png": 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} ], "prompt_number": 339 }, { "cell_type": "code", "collapsed": true, "input": "m = -0.4\nc = 80", "language": "python", "outputs": [], "prompt_number": 340 }, { "cell_type": "code", "collapsed": false, "input": "# This is an iterative solution for \n# finding the gradient and bias. \n# Notice how slow it is!\nxTest = np.linspace(1890, 2020, 130)[:, None]\nfor i in arange(10000):\n m = ((y - c)*x).sum()/(x*x).sum()\n c = (y-m*x).sum()/y.shape[0]\npb.plot(xTest, m*xTest + c, 'b-')\npb.plot(x, y, 'rx')", "language": "python", "outputs": [ { "output_type": "pyout", "prompt_number": 341, "text": "[]" }, { "output_type": "display_data", "png": 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8o5ZpnDcPGD9elX4cwZIOEZGbKCoCNm9W5Z5vvlGlnhdfVB8C9mBJh4jITdSv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\nOAE87QIAAAAASUVORK5CYII=\n" } ], "prompt_number": 341 }, { "cell_type": "code", "collapsed": false, "input": "print(m)\nprint(c)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "-0.0139467136584\n30.7851569022" } ], "prompt_number": 342 }, { "cell_type": "code", "collapsed": true, "input": "# Now we will do a quadratic fit.\n# First, create a matrix of basis functions in the form of a design matrix\nPhi = np.hstack([np.ones(x.shape), x, x**2])", "language": "python", "outputs": [], "prompt_number": 343 }, { "cell_type": "code", "collapsed": false, "input": "# This is the multivariate linear regression solution. \nw = np.linalg.solve(np.dot(Phi.T, Phi), np.dot(Phi.T, y))\nprint(w)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "[[ 6.43641952e+02]\n [ -6.42502986e-01]\n [ 1.61109703e-04]]" } ], "prompt_number": 344 }, { "cell_type": "code", "collapsed": true, "input": "# For plotting the result create a vector of 'test' inputs and the corresponding basis set\nxTest = np.linspace(1890, 2020, 130)[:, None]\nPhiTest = np.hstack([np.ones(xTest.shape), xTest, xTest**2])", "language": "python", "outputs": [], "prompt_number": 345 }, { "cell_type": "code", "collapsed": true, "input": "# Now use the learnt parameters to compute quadratic outputs at test locations\nfTest = np.dot(PhiTest, w)", "language": "python", "outputs": [], "prompt_number": 346 }, { "cell_type": "code", "collapsed": false, "input": "# Plot the data and the model fit.\npb.plot(x, y, 'rx')\npb.plot(xTest, fTest, '-') ", "language": "python", "outputs": [ { "output_type": "pyout", "prompt_number": 347, "text": "[]" }, { "output_type": "display_data", "png": 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} ], "prompt_number": 347 }, { "cell_type": "code", "collapsed": false, "input": "# Now we are going to fit a Gaussian process using GPy\nimport GPy", "language": "python", "outputs": [], "prompt_number": 348 }, { "cell_type": "code", "collapsed": false, "input": "# First define the covariance function. We'll use the exponentiated quadratic\n# First argument is the dimensionality of the input, other arguments are the parameters\nkernel = GPy.kern.rbf(1,variance=1.,lengthscale=1.)", "language": "python", "outputs": [], "prompt_number": 349 }, { "cell_type": "code", "collapsed": false, "input": "# We can get help about GPy kernels with:\nkernel?\n# We can see what our covariance function is using print\nprint(kernel)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": " Name | Value | Constraints | Ties \n-------------------------------------------------------\n rbf_variance | 1.0000 | | \n rbf_lengthscale | 1.0000 | | \n" } ], "prompt_number": 350 }, { "cell_type": "code", "collapsed": false, "input": "# Now we create a GP regression model using our data and the covariane function\nmodel =GPy.models.GPRegression(x,y,kernel)", "language": "python", "outputs": [], "prompt_number": 351 }, { "cell_type": "code", "collapsed": false, "input": "# Again we can print the model. We get the log likelihood and parameters.\nprint(model)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "\nLog-likelihood: -1.188e+02\n\n Name | Value | Constraints | Ties | Prior \n-----------------------------------------------------------------\n rbf_variance | 1.0000 | (+ve) | | \n rbf_lengthscale | 1.0000 | (+ve) | | \n noise_variance | 1.0000 | (+ve) | | \n" } ], "prompt_number": 352 }, { "cell_type": "code", "collapsed": false, "input": "# The plot command provides a quick way of seeing the model fit.\n# In this case the paremters are poor: the length scale is too low (for example)\nmodel.plot()", "language": "python", "outputs": [ { "output_type": "display_data", "png": 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5ZdQ8a6oi4EH3YFK6hsln4XU70B2TVk52DKSUzBH6OeTkfO/9xwdRq7NXAHN7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} ], "prompt_number": 353 }, { "cell_type": "code", "collapsed": false, "input": "# Now we fit the model by maximum likelihood and replot\n# The length scale increases considerably\nmodel.optimize()\nmodel.plot()\nprint(model)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "\nLog-likelihood: -7.790e+00\n\n Name | Value | Constraints | Ties | Prior \n------------------------------------------------------------------\n rbf_variance | 9.8062 | (+ve) | | \n rbf_lengthscale | 78.8045 | (+ve) | | \n noise_variance | 0.0478 | (+ve) | | \n" }, { "output_type": "display_data", "png": 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5zniOF9Rw+FQZibHh/PGFpy8amn35S9XbSLSvo+og25cjuBmTRhOROZoDx0vY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\nwsdIMAshhI+RYBZCCB8jwSyEED7m/wdNgkmWzk/luwAAAABJRU5ErkJggg==\n" } ], "prompt_number": 354 }, { "cell_type": "code", "collapsed": true, "input": "# Let's try a different covariance function. This time we try a combination of\n# two exponentiated quadratics and a bias (which allows an offset to be inferred).\nkernel2 = GPy.kern.rbf(1, lengthscale=4, variance=12) \nkernel2 += GPy.kern.rbf(1, lengthscale=20, variance=12)\nkernel2 += GPy.kern.bias(1)", "language": "python", "outputs": [], "prompt_number": 355 }, { "cell_type": "code", "collapsed": false, "input": "# Build the model using this covariance function\nmodel2 = GPy.models.GPRegression(x,y,kernel2)\nmodel2.plot()\nprint(model2)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "\nLog-likelihood: -5.816e+01\n\n Name | Value | Constraints | Ties | Prior \n--------------------------------------------------------------------\n rbf_2_variance | 12.0000 | (+ve) | | \n rbf_2_lengthscale | 4.0000 | (+ve) | | \n rbf_1_variance | 12.0000 | (+ve) | | \n rbf_1_lengthscale | 20.0000 | (+ve) | | \n bias_variance | 1.0000 | (+ve) | | \n noise_variance | 1.0000 | (+ve) | | \n" }, { "output_type": "display_data", "png": 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/7YuEhJgwm4yEmI2YjAb2rnuFw5+tBGD8JfO5dP69OFw6NpuDbpuDbatepGTH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\njXsqMVhCvDZZqqy6mYl58WT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} ], "prompt_number": 356 }, { "cell_type": "code", "collapsed": false, "input": "# Now optimize the new model\nmodel2.optimize()\nmodel2.plot()\nprint(model2)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "\nLog-likelihood: -6.160e+00\n\n Name | Value | Constraints | Ties | Prior \n--------------------------------------------------------------------\n rbf_2_variance | 0.0217 | (+ve) | | \n rbf_2_lengthscale | 6.7619 | (+ve) | | \n rbf_1_variance | 1.0682 | (+ve) | | \n rbf_1_lengthscale | 70.1276 | (+ve) | | \n bias_variance | 18.7005 | (+ve) | | \n noise_variance | 0.0381 | (+ve) | | \n" }, { "output_type": "display_data", "png": 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EpChunJ7I9VPiOX5o14gM70tV1TVzvryWmeMiGR05PGd9FL2jK8W2r4soq2lm\n3JiBPz8vJwHuomobWvjiQCGpCYM3r3qbxcr+Y/ls3Z/DvqP59rHRBk1jyrgYbpqZzKzJ8Xh5juyu\nhLySGtrbLMwcH0lk8MAPBROuIf98HbuOlzAqKhB/n6EZtSQB7sI2H8jDx8eMTx9W8Omt5lYLe7/O\nY+v+HA6eKMRq0wHw9DAxe8oYbpqRxOSxMRiddN7t3g637C2lFOcKq1C6zpTkMEYP4sdm4VxsNp0v\n9udhsULCqOAhPbYEuAuz2XT+kXmKiclXTs87mOobW9l+IIev9p7h5NlvbnYJ8PNi3vQk5s9KYUzs\n1educYSuZm0c6H58pRSFpbW0tLYRG+7L1KQIp31TE/2Xf76OnUeLGTMqZEgaUpeTAHdxx85VUFLd\nQrSDWnylFXW8/sbfyGvwo7SiY1UmpRT+nOdf7ruHedMTCfAb2OXnestRNyPV1rdQXF6L2d3EmCh/\nxsaFYBhBo3aGM10pvtyXR2u7GvJW96UkwIeBwbqg2RPftG4fY9nDT7J592ne+6/fUpWzlfDp38En\nfCzTJ45i/uyxTJswCjeTY9apdOTNSEopKmuaqKhuxMPdiLenibTkcIJkLnKXVFRez7YjRcTHhuDr\ngFb3pQZ0PnDhGDdNHUXm4SJS4sOG/Nid5yTvCMOqnK3cfOe3CBq3gIMnCvnqy8/ZdTgXf18v0md0\ndLGUnvt6xIxi0TSN0CAfQoM6VgFqt+psO1KC1WrD3WTAw81AaICZcXEhvV7MWgwdpRRbDubT0Gpj\nUsrQdlv2lbTAXcTm/bn4+npfc/WeaxmIi3tdtW43ffopP3r8AaLH34xpVMc+a05+Sn3eDr71g1/z\n7W/dR1x0/+aD6E19zngzklKK+qY2SspqO5YNNBrwNBkYHeHPmOgAh31qEd8orWog63ARcVFB+Pp4\nOrocO2mBDxM3XRfHe5tP9qplMBQX9xYuXszhCy30O1NjKSitIT9vB35xc9iZo9j1wt8JD/Zl6vhR\nTEyKZEJyFEH+3v0+7qUun7XxordfX8vc9PkO/ySgaRr+Pp74+3wzx41SiuLqRr7OrcJk0PBw0wjw\n9mBKcgTeI3zY5lBSSrH1cCFVjRYmJLnegh/SAnchhWX1HMypIPEaq/dcbiBapj3Zx+Ut9G//r5Xc\nvPwx9h7JZ++RPErPfY1nSJL9eBEhvni1FzNn3gLiY4KJDgsgLNjnmjMm9sRgDyMcCs2tFvJLajGg\n8HQ3EuyBu5zfAAAgAElEQVTrweTkCOl2GSSF5fXsPFZMTEQgAU56vUJa4MNIbLgfJ/OraGhqxde7\n+495mqbZW6R9vbjXl9atwWBg5qQ4Zk2OZ+tXX/DvK55k5vzlRE25ixNnSzmx9R3q83ZwIuc8XqHJ\nF2qFIH9vwkP8iAjxJSLEj7BgX8KDfQkL9iUkwKfL4XqX16FpmkuFN4DZ051xY765ztHY0sYnu85h\nNICnm4mIIDMTxoRdsfCt6J22dhtf7M1FGQyMT3S9Vvelugzw1tZW5s2bR1tbGxaLhaVLl/LrX/+6\n0zaZmZksXbqUMWPGALB8+XKeffbZwat4hFswLY73tvSuK6U/5qYv4M9vre/Uun3mudX28L5WC/3i\ndjekL7BfBH1wdBjTAnX+J28H8xb/C1NuXk5eSTXnK+upqG6kqraJqtomTuSUXlGHwaARGuhDRIgf\n8bEhJMaGkDAqlOjwAAwG1z0Bu+Lj5cG4MR134iqlqG9s5YPtZ/AwGfBwMxIV4s240SHSh95DNl1n\n2+FCympbSI4Pw30Y/Ny6DHBPT0+++uorzGYzVquVuXPnsn37dubOndtpu3nz5rFhw4ZBLVR0MBg0\n5k2OZc+pMpLjQrvctrtw7WnLo6vWbU9a6D35FGCz6VTUNFJWWc/5ynrKqxoou+RPdW2T/e9fny62\nP8/Px5O0cTGkpcZyXeooAgdwuSpnomka/r5e9hnvlFLU1Lfw0fazGA3g7mbE3aQR6ONJUmwQvmaP\nfr2x2XSF1abT0GyhoraJqtoWWtutWK0KhUJX2NdeVUph/5CvuPD/VWEwaBg0DU3TMBnB091ERLAP\nUSG+eHmYhmzMfLtVZ+vhAqrrWxkdE8yE0OEzr023XShmc8cJYbFYsNlsBAUFXbGN9G8PrahQXyLL\n6ymraiC8i7k5huLiXnct9J4yGg1EhPgREeLH5Ks8bmm3UlHTSElZHWcLKzlbUMGZ/HIqa5rI2pdD\n1r4c+xwu82enMGtyPB7DuO9Y0zSC/M2d1lfUlaKppZ2vDhdhsdowahomgwFNuzRMOwaDKvtzOs5f\nXe94PoDV9k0ge7ib8PPxxM/HjL+bsc+hq5TC0m6joKKZr89WoZSO0aDhZjRgMmq4m4yE+HsxJjoQ\nby+3foe7ritO51eSU1xLS7uNMbEhRIQP3OyezqLbi5i6rjN16lTOnj3L448/zm9+85tOj2dlZXH3\n3XcTExNDdHQ0a9asITU1tfNBNI3nnnvO/nV6ejrp6ekD9ypGqI+2nSEqIgCzp/s1t3H0xb3BHOKn\nlKK4rI5DJws5cLyAwyeL7HO4eHu5s3DuOJbOn0xwwMCOehEDT9c7hlqWVzeg2/SO4ZYGDaPRgLtJ\nw8vdRLCfFyEBZvy8v/l0oeiYlK2kooHSygZa2nUsVp22dhuhgT6EBHq7VB/3vl3b2bd7u/3rP736\n4sDciVlXV8fChQt58cUXO4VvQ0MDRqMRs9nMxo0bWbVqFdnZ2Z0PIqNQBoXNpvPulpNMSIrC5KTz\ncQzlPCX1ja1s3Z/D5l2nOJNfAYDJZGD+zBSWL0wjKqzr6Qgc/WYnrk6/0HpvaGqjsbmV1lYryv4x\nQmEwGAgJ8MbP1xM3o8GlArs7A3or/S9/+Uu8vLz40Y9+dM1t4uPjOXDgQKeuFgnwwdPS1s4/s84w\neWyU0/7iOiIYz+SXs/6zQ+w8dA6lwGgwsOjGVL51+7SrrpwylG80QvRUvwK8srISk8lEQEAALS0t\nLFy4kOeee4758+fbtykrKyMsLAxN09i7dy/33XcfeXl5nQ8iAT6oahta2Lgnl4nJzhvijlJcVss/\nPjvEJx9twCMkCbOXO/cuTGPp/Ens25llD2Znv5tTjEz9GgdeWlrKihUr0HUdXdd56KGHmD9/PuvW\nrQNg5cqVrF+/nrVr12IymTCbzbz77rsD+wpEtwJ8vVg4I56Nu3OZmBI5ZAuquoLo8ACmjbLxX/ve\nIPG622gOu5G/fLiH/3ztBUpPfGVvXQ/EmHkhhlqXAT5x4kQOHjx4xfdXrlxp//v3vvc9vve97w18\nZaJXgvy8WHZjEh9uyyYpLqzLC5sjzaUTct22LISj2SWUnvgKv7g5bM+2MW5yI8GBPo4uU4hek1vp\nhxmbTWfTnnOY3N2ICR8+41376/Lb/WfNX061z0ws7Ta8PNz49h3TOLX9Xf7njT9LF4pwGt11ochn\n7WHGaDRw+/WJxAR5cSy7hJa2dkeX5JQSR4ex9vn7mT0lnpa2dv7w57/wP2/8mcXLH+aZ51bzzHOr\n7a32HVmbB+SY2zO/7HQyKqXYnvnlgOxbjEzSAh/GrDadLQfyqG2ykDQ6DHc31791uC+6u0C572g+\n697bTt6pA3iGJHHb3FRWLJuFr7fHgI2WkVEuoi9kMqsRzGQ0cOuMMbRarGz/uoCaxnYC/c1Ehvo5\nurQh1ZM7UieNjeYfG5N4//PDfLbjJLsO5/Kdu2cx/8b5Xey55zovjPHN8R985HHmzBuYY4iRR1rg\nI4hSilMFVeQU1tLSbiU00IewLm7FH056Oha98HwNa9/ZxpEL860kjArhX++5fkAWlnbksm/CNUkL\nXNhpmsa40SGMGx2CritO5lWQW1hBi0XHz8eT6DC/YRsmPZ1uNjYikF89eSdZe8/wxge7OVtQyU9e\n2cCMSaNZsXQmo6Mdt8CtEJeTFrhAKUVBWR0n8qppsVgxmkzERwV2Of/2SNBqaefDL4+w/rODtLZZ\nAZg1OY77Fl1Hclzv1ieVG4VEX8iq9KLXquqaOXD6PI0t7Zjc3Igb4WFeU9fMexsP8PmOk1jabQBM\nTI7ithtSmT0lvkcrCclFTNEXEuCiXyprm9l/qpSmVit+vmYiQ31HbGtxU8anlLYGkbH1OC2t7R2/\n0w153LnkTuZMTSA1IaLLNzqZLEv0lgS4GDA5RdWczO/oZomLDsLby8PRJQ2ZS1vQ33v6ObbuO8Mf\nXn6ewqObCZ/+HbxCk/H19mD6hNGkpcYyPjFyxFwgFoNHAlwMOKtNZ8/xYspqWzAYDSTEBGMY5vOv\nXKsP+857V5A4+z72HMmnpLyu03NCg3xITYxkXHw4yfHhxMcED4vlz+STxNCRABeDqry2mb3HS2i2\nWIkI8RvWiyd0Nwyw6Hwte4/mcSy7hONnS2lqtnR6vpvJSOLoUFLiwkiJDydlTDihgT4u1SUlfflD\nS4YRikEVFmDmjjmJ2HTFoezznD53HquClNGhmIZBa7M38k7tZ9mC+dx9yxR0XZFXXMVHH34EfvGc\nzi2j6HwtJ8+e5+TZ8/bnBPqZSRkTztj4cFLiw6nIP8JNCxY6beu2JzckSQu9d5RS1Na3UFJRR0l5\nHaUVdZSW11FSUdftc6UFLgZcTUMru44V09Rqxd/Xk+hhMKlWd8MAd2Rt7rZl2tjURnZeGafzyjl9\nroxTuWU0NrfZj9FSkU3ZvjeImTCfux5+konJUWx+/8/8/a//6VSt264+iUgL/eo6FqFupqT8Qkhf\nCOiLgX1xmOrl8jJ+Ii1wMbQCfT1ZPDsBm644XVDN6bwy2tp1xkQH4212zWluu7sdvyctUx9vD6aO\nH8XU8aOAjpO6pLyO07llnM4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} ], "prompt_number": 357 }, { "cell_type": "code", "collapsed": false, "input": "# GPy allows us to set constraints on parameters.\n# These commands use regular expressions to constrain\n# particular parameter values.\nmodel.unconstrain('rbf.*variance')\nmodel.constrain_bounded('.*variance',.0001,5.)\nmodel.constrain_bounded('.*len',.002,10.)\nmodel.constrain_fixed('bias.*var',40.)\nmodel.optimize()\nprint(model)\nmodel.plot()", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "Warning: re-constraining these parameters\nnoise_variance\nWarning: changing parameters to satisfy constraints\nWarning: re-constraining these parameters\nrbf_lengthscale\nWarning: changing parameters to satisfy constraints\n\nLog-likelihood: -2.665e+01\n\n Name | Value | Constraints | Ties | Prior \n------------------------------------------------------------------\n rbf_variance | 4.9367 | (0.0001,5.0) | | \n rbf_lengthscale | 9.9990 | (0.002,10.0) | | \n noise_variance | 0.0346 | (0.0001,5.0) | | " }, { "output_type": "stream", "stream": "stdout", "text": "" }, { "output_type": "display_data", "png": 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OOp4hwgqbIViSscM1Xb+ODvVFdKgvZk/PgFanQ15RDQpKavDlRwdxvFB/jEgo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\n" } ], "prompt_number": 358 }, { "cell_type": "code", "collapsed": false, "input": "# We can make predictions from the Gaussian process using model.predict.\n# Here, we first place those predictions input locations in an array.\nxfuture = np.array([2015,2030,2045,2060])[:,None]\nmean,var,q1,future = np.array([2015,2030,2045,2060])[:,None]\nmean,var,q1,q3 = model2.predict(xfuture)\nprint(mean)", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "[[ 3.07209821]\n [ 3.15954177]\n [ 3.2398172 ]\n [ 3.32644406]]" } ], "prompt_number": 359 }, { "cell_type": "code", "collapsed": false, "input": "# We can also plot the form of covariance functions we use\nk = GPy.kern.rbf(1,variance=1.,lengthscale=0.2)\nprint(k)\nk.plot()", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": " Name | Value | Constraints | Ties \n-------------------------------------------------------\n rbf_variance | 1.0000 | | \n rbf_lengthscale | 0.2000 | | \n" }, { "output_type": "display_data", "png": 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lperq6tLcuXPl8XhUVFRkdWlASJjxA0CcoccPAHGG4AeAOEPwA0CcIfgBIM4Q\n/AAQZwh+AIgz/w8dDQbGmblUSwAAAABJRU5ErkJggg==\n" } ], "prompt_number": 360 }, { "cell_type": "code", "collapsed": false, "input": "print(model.kern)\nmodel.kern.plot()", "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": " Name | Value | Constraints | Ties \n-------------------------------------------------------\n rbf_variance | 4.9367 | | \n rbf_lengthscale | 9.9990 | | \n" }, { "output_type": "display_data", "png": 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} ], "prompt_number": 361 }, { "cell_type": "code", "collapsed": false, "input": "#Here we show the affect of changing the variance on the \n#covariance function.\nvariances = [.5,1.,2.,4.]\nfor v in variances:\n k = GPy.kern.rbf(1,variance=v,lengthscale = .2)\n k.plot()", "language": "python", "outputs": [ { "output_type": "display_data", "png": 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YPGyyX8eZPGwy+8q8h78qfzEKhb9IE3f4nz0LFRXQdH+JE9UnqG+sZ1TcKL+O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\nm2ZkDP5cw9LX2Gw2rr/+em6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} ], "prompt_number": 362 }, { "cell_type": "code", "collapsed": true, "input": "", "language": "python", "outputs": [], "prompt_number": 362 } ] } ] }