Created
July 10, 2013 19:45
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{ | |
"metadata": { | |
"name": "shogun_gp_classification" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"Gaussian Process Probit Regression (binary classification) with Shogun\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Import all necessary modules from Sghoun" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from modshogun import RealFeatures, BinaryLabels, GaussianKernel, Math\n", | |
"from modshogun import ProbitLikelihood, ZeroMean, LaplacianInferenceMethod, GaussianProcessBinaryClassification" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 13 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Generate some easy toy data, three isotropic 2D Gaussians, with labels +1, -1. Plot it.\n", | |
"Test data is a mesh on the 2d plane" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"n=30\n", | |
"mean_a1=asarray([0,0])\n", | |
"mean_a2=asarray([2,2])\n", | |
"mean_b=asarray([1,1])\n", | |
"std_dev=0.5\n", | |
"\n", | |
"X1=(randn(n,2)*std_dev+mean_a1).T\n", | |
"X2=(randn(n,2)*std_dev+mean_a2).T\n", | |
"X3=(randn(n,2)*std_dev+mean_b).T\n", | |
"X=hstack((X1,X2,X3))\n", | |
"Y=-ones(shape(X)[1])\n", | |
"Y[:2*n]+=2\n", | |
"\n", | |
"# generate all pairs in 2d range of training data\n", | |
"import itertools \n", | |
"n_test=60\n", | |
"P=linspace(X[0,:].min()-1, X[0,:].max()+1, n_test)\n", | |
"Q=linspace(X[0,:].min()-1, X[0,:].max()+1, n_test)\n", | |
"X_test=asarray(list(itertools.product(P, Q))).T\n", | |
"\n", | |
"# plot training data\n", | |
"plot(X[0,:2*n],X[1,:2*n], 'ro')\n", | |
"_=plot(X[0,2*n:],X[1,2*n:], 'bo')\n" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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YpVbC9t8DoA79u9bGoaHhFDyeuqhL/tH29CuXmT6IlGLip5il1qYt3+4BUAfgdQxsVX7x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\n" | |
} | |
], | |
"prompt_number": 14 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Convert data into Shogun representation, print dimensions to be sure data was passed in correct " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"labels=BinaryLabels(Y)\n", | |
"feats_train=RealFeatures(X)\n", | |
"feats_test=RealFeatures(X_test)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 15 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Specify a Shogun GP (probit GP-classification with Laplace-approx.) with fixed hyper-parameters and pass it the data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"kernel_sigma=2.5\n", | |
"kernel=GaussianKernel(10,kernel_sigma)\n", | |
"mean=ZeroMean()\n", | |
"lik=ProbitLikelihood()\n", | |
"inf=LaplacianInferenceMethod(kernel, feats_train, mean, labels, lik)\n", | |
"gp = GaussianProcessBinaryClassification(inf)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 26 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Train GP, perform inference and plot predictive distribution with decision boundary" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"gp.train()\n", | |
"predictions=gp.apply_binary(feats_test)\n", | |
"Y_test=predictions.get_values()\n", | |
"Y_test=reshape(Y_test, (n_test,n_test))\n", | |
"\n", | |
"plot(X[0,:2*n],X[1,:2*n], 'ro')\n", | |
"plot(X[0,2*n:],X[1,2*n:], 'bo')\n", | |
"contour(P,Q,Y_test, levels=[0])\n", | |
"_=pcolor(P,Q,Y_test)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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22sExGOuGzym/dRUhQ6OJfmYsIcOC6+uQeSHk7AdFXGMBWe0KX/Kr2hpsgKeA\n+9GNdm+iU0bb7XZz4403Mnz4cO6+++5A7VPQCU0OJfW+VBLv7Uvligr2/2EPexYWEXt5PLGz4wmb\nZNNzgPdUZBbKFThvoerx2aweny0OyVMUwi7pi+W8VKr/tJUSxwrCLuuLNXdU0FweHB6L+vc34NY9\n8LKX7L4wBrImtG8ckyT2udf4/eoAnTxf33zzDW+99RajR49m7Fi1TsYTTzxBVlbW0T5B1WsFaNme\nSTFiyQwnMXMINRtqOfTPMnb8aguu6mYSf5VA4o2JmFPUxUuZNhmItK/SEPlQSXkyUwDixYS+SpK+\nMnc9kX4tq6Yi08VFY8j0bxdQJ9EC6lxtw7pliZ9lWrfodlSif+894IkVHD8EXj+bmre/oWboJ5Td\nOhTzA2NRIlt9FhtDtZWSMwq0jtOnf8u46TDuAfj6k2YW/lktskANZJ0Pjok+bxCtE7jAWYnwXJrM\nkBrbtq1pv38/rZT5vN6JephbcpwkAP1a9kEyhha9XDaGqF1rKLwMUX9TkH8FOzX81KlTaW4+MdKq\nRowIZ8Aj4Qx4JI2KH+s58JcDrBv5I9FnWkman0TU2bH67LsnkJYJ9TlQ5yWRhC2E1Cz5e4JJdDjc\nOhMunUTz+x9xZPQSLI9NJOSK9KCslUw9x8DUc9S/Iz7T9t3L7Ac5tZDntcq40AhZXZD8cCdq7pI3\nvdruAHbRarh12od+ZyQgckwEg18eRP+n+6va9/1FuKp3kHRXKok3JWOw6Np3txHn0a335UKzEQwu\n1WDHdV7P7hQJVsJfPwvnyoPU3/M1jX/ZiOXZM2BK9+6WNy2ZAnN3g9GlKkpZzeDogkpt+4C/+bT9\nCbgG3WhrRTfax8AUZST5liSSbk6k8vs69uXtZt9ju4m7MoH46xKJOFWWdk4nqMQ5ut9ISzBNTiTi\nm0toenMLtZcsY+OsMAY9nnpUZutuHPE+aV7X+/cpqIcPaA22mQy0UzaXIhP09MRQ2gm60e6spt2Z\nkPfjIdOp/b5eClgmm0j8cCC12+spfqOUrRf8gsluJP66RBKvS8AU3fFDKU8RK9k/i79o2WAWG4Xm\nQAhsslOgJb2rTNMWaeCyvjJtXcslIvudFenXsgiTA+Lmyoqk1hdJyfDcFA5+/1+KR63HeO8kDDeN\nQTGpd2kNdvH5El4LEst25hlfC9tNouPUznNYcBje3wrPerXdq0CiFfq2049YhEzIcaGmSdWSk1v2\nUbTo1LKndY3LAAAgAElEQVQya7J2LZkVQmTLXAGytvp9vkbCB1sY9Ggqp+8cRfof+1D9fTWrB65l\n5wNFNOzTc5PpeBFuxpQ7lZCP59K8YifOM/9B88quiDXsOEuK4FmfBeln3bBE9mPcTgaihsp7cwdq\nZZ4vgXXAdyDIaq7jiy6PdBDFoGA/y0rEWXHU76pn/3PFrBv1E7EX20m5J4WQkfqNn46Kkm7H9O4l\nNH+wDeevPsbg6EPjCxGEJgZOMvlqGXz6FzA1gusIZGaDY7z2caol847qTvobtOQUuQH1TrYR1WBb\ngBe9+t2Barj7d25zJzT6TDsAWPpZGPj8AMZtG4u5n5kNWRv55bQfKHltP811XevyqNMzURQF40Wn\nEPLdtRAXztqRP1L8ygHczZ0vHPXVMljxIOR9Bo98DY/9BMtfh4IO5OFukOxOYwDqWw1BLaE22fPs\npK3BBnVxUqI+6XjoAk27/YKjLDeHeNxAGENxciAtvtdt9iMWon8bx5CHYylZUcOexQfY+1AhKb+K\nI+22BELTxIKq1s8i8t92RYlPZbUklwYWgTuaVidVkV+3Fp9uEOvXJRr6QmCSX7SIma4CcOYDJgh1\ngjUTInwWPWVWRbB/jRWCAlYTz6P5kjEUPrCMwtd2w9NZMERdHXSltP8rabKq182/XtvIn3e2FZzz\nDkDuSph2n/paaWda0MiNkFPSNsfJQiAyAvrLXAMPSfZP0OatGctKfkXQWt5LSziBFt1ZlvtTq9Yt\nGidM5o0ju/nW6L2jyyNBQDEoxM6KJnZWNLXb6tn7p0N8P3oDtpk2Uu9PJWq87nUSfFqSmJpQv/qZ\ntJbKleAqgMajefTUHyCnJ6+Jr+HuLEPj4f2r4a11MPttmDcWFpzeoaFCJdNjYweWWK6ZAm98Crm1\nqnzhAg6EwHX9kf+YdpDuKsDQ29HlkSATnm7hlBf6ckbRaKJOj2LThZvZdNkWqtd0cmVH5xi0JDF9\nDLUK4WOe1wXHfpuzJY9eAfCw+l6nAhV/D85uGhS45jTIvwG2HYYZr1H5hex2Qk6jWRzE4+rAsopj\nAFw7A4hBTXAdA9edAo447WMdj3TAN/nFAnQ9+3joM+0uwmQ1knpXCkk3JVL80gE2XbqFkIQQkucn\nEnl5XwxdVGnn5ECYxBS1PsqxZswmfLJWqzTeCjUFgZ9tt5AcBa9eAsu3sn3eUmLOs9Pv6QEYJZKX\nL8MXJHNPYT3PFbZOrRf2h1k3dWx3HAPA0Qn3vvYy3PM8H3X22IhqsNODv+leTdCNtkinlmm4WrRd\nmVauRRcPBGHUCtsbBQJWKKEQDrb7rQy9N4qSZUfY/cohih7YRcptiaTdk0RITOsp0XScJCJdo03s\nodBYLdBaZdqarF2LBq5F69asXVf5vJaFjzuRJhSpC0f1FhYZ/JfhkJfBl2namnzORdPgUTR+OJiD\nT6zg4LANkHceTBuk+ssJOHodZEM127j8xTVY6p1EWCoZcWcCSnYMX3n6OqavEg8iOKYFayF/p1dp\ns1Hy0mYA/SVWJEyQv8RXG04FZuKfp6QFkf+21iWM42nr3gi+FYBcf48SfTe0RhJpvCPSZ9rdhGJU\nSMi2kpBtpWyHgV15+1mV/hMptyeSdndb462jlY6m63cASyT/66I7IasFnjgfviqEhR/BGQNw/jUc\nk+3Y10NydjrJ2eocdQafdnjzBWth+ZuQd7C1Lcfzw9MzY1BPPnTL0AMIG2hh6GsDqcupP2q8k38V\nj+1XfbEM1mtcamc6anr/p7zaHkB1NjsWU4D/SP7Xzju4qgIozQe3CYOrmIF7N5MaqlBjMPNL3Ezq\nD1dAswnWOOHMTBgtMYXTBsHH8+HpT/lp1BoG/jWdmHPs4r4BJH95W4MNkFcFueuPPdvW6TqCbrRD\nBWvBWqQNrVJKICQWLchTtvrf2MlcCWtb+g6EtNeiqV4Yzo6XK9k0ZTWxZ0YyaGE81jGtN3ShkhJn\nwv2wi7d5SHR7Xie5T5Pd4oucYGRSipYrrd0ySAs+VobBQDH+5WoHoOaVOxZjgHtpG8h9HzAa+Fl9\nWZcofuv6LXjr4c2Au34u9/MxAJcXxwNvte7yrhx1c8lehtvpLe1Y4IzzaDx3OJtv+g84zPDQTIhQ\nJS/jQPGBEn0HjPHi78UZ09e0eW1aLP5oxgj8C0kefZO4OVFwLYgkEwCrRD4TnXEtIe8ytLr2xUo+\no1XkBilzjdTaLkH3HumBRA4KYfQf4sjYOQTbpDBWn1vE6uwiyr8VFP/TkTAGuBa13ve1yC2OL6NR\nl8LuQK3pcrenbWw73uuvhxeyhBcZymKGUt1isFuoyYPtK44/7JQBsPQWaHLBeX+B1bvbsS8dwykJ\n0nRp9eHXCRq6PNKDMUUaGXBfPH1vj2Xf38r56ao9hPULJfX3/Yl2BLfE1cnNaM9DtvwkQ/x1qj/W\nVKq5nVp5lAWeugBWbIE7/g0Xj6Z5cTMGc2DnXZk3Qs4uyCtqbVuYAFmz0B2oewi60e4FGC0G+s6P\nJe1XdorfqWDLvO1EjA4n9TepRE+VrXfrdD1iucJCDeLwlwKo3ARfLgKDEwa3IwBo5hA4LQ0eXsqP\nE3Yz5B/pRIwKXBUDxwz1OfcPYGxSZ9hZs8BxGrAyYJvR6QTdEsZulvxki7Q4mX4rc+0T6nnSvsFz\nGxSVEJPp31GS+lq1vn58JkieBwNnp7LrjSNsvW4LlkQjpzxgI/n8BmFlHbNM/07xbzrkFDQC1Egu\nE5HWLQv2lLk1aboCZWK3SOWUOZHJdHGRx4lME5DEbTMUVVJ55mjLIOZwJ5sB+IarqTkqkRQAb0Pj\nktZIw8M56npnsq/h9j2vkXDBHGrr17Iu4zNYMBWuGw+eSjmmvqLvgET/ThZ85+bBozN+lHxGAbJz\nKDjnQg0YsPouS3ioEpyuOon+HYjSZLIQdNl+I1objhW0gRqspKVdgj7T7oUYwwwMnB/NgJus7Pu/\nGjY9Wk7jQxUM+n0K8ZfGBKXMVe9jA7CZ1pxyQ4ERnRxzC7AD1SleNOY4z/M9QAgGDqJwkGc4jRos\nuBiNGmlpBGUzuH3cC115sOYKiMmHgZkC4+2FosBlp8KEPnDXB/BFIfzhPIgX/2puWXqIlYvrcDZY\nMJnrmbwgjCHZCR06Cjrdi260ezGKUSHtskhSZ0ewI9/A9gf2svv5Q6T/MY3oSSdzfpN1qAk+X/Zq\nu8fz3KeDY25Bjap5VTCmr+FWjXczsJ3BbBcNF5In1oibhsChRVDjyXlyLMMN0N8O/74Gnv8KznkN\nnj4Xrmvb5fDSjfx8Vxhlha0VGssKbwEOMSP72MPr9Dx075ETAEVRE1RNXDuclBviWH9pIetmbaXs\n0yrc7gDk1Ox1rAUu5mj+EB72vN7ciTF3AC/4tD3XiTFlN/MeuaImD3a0w7ME1FIp92fASxfDw8vY\nfvcumhtaE2DvW7yVssK/tHlLWeFfWPliIJzndLqaoM+0Rfq1yHdb7euvv8r0aJnWrWV7wfQX15Le\ntcG/wBkAdZLYdD+tG48uboS+N8D4q2LY8XY9G+4qxG3ZzdAnEkjIbDvzFmr/Ai0UoNjZX7zj9QIZ\nRizPi0O8AUoFbTJn2TrZ5ep7Hsvxyx9CDlCJ3MtXpHUXofp7W1A/QAH+C4VuWkPjRWNLttc4HTXp\n6eNejQsBr6ryFUZ18U9avsr3eAyE++az99P32DtxN8or56EMjKG5+hvh20vrk/iKZOH/jMn+340J\n03+R7Idk/0TrGDJtWLKebhUsTVhlnq+yVAmiy1p2TGVxBrL9FunRshgorVq3BF0eOQExmhXSrw9j\n8LUWNrwfyi93HiCsbwjDnkrAdprMIp5I1CBOGHWJhjGKPOO86dXmkSzaGO6WCcEO4FtUS9WA6us9\n9BjjT/U8L0Q1+v1QDbb32B1YEI8IQ3ntAvjbj7jPfxsePUvNCy7AYGlEL63b+9DlkRMYxaCQfImV\njF8GkXxpFKuy97D2yr1UbzvRa1nGS9pjNIxRjFpHxZs8wFuyuANIQzXY5cBfUWux/BW1KvDxpJOp\nqJkH56EaaC+DbVwIETM17G8riqKgXD8WZclluJ/9DpqthA1sW6ExbNBd9L1zUIfG1+le9Jn2SYAh\nRKH/fDtpV9vY8Wwp35xeRMTZ1aQ8OghL+omY20RWe1FLWJ/sPnkbajLRRlSDPRC1NO3/+vS7BFXz\nPujpOwZ5VKbXrDs0FHBBRBZYOpeiSRmZAMuvxv3Qf3EX7cc29SYUowWDpZG+dw4iPnso0qyHOj2W\nbsk9IvMdFvUNl6Q+1aKLa+kr66/VX1wLIp9uEKd3BagViL4VkllkGx/wSBj8W2i8N5RvFzewZcpK\n+lwWwfDfRhOWbJIeJ1lq0GKnIIOQTNOW5WcW+XrL9O89skAi3+MxETX8/HmvtrtRJQtZBkBf7Vl8\n3aliqPcBOYT/DWtLTu73vNruAvYjzxYdC8yGxmHqy0avffpeImlJmpsjvQXYCLjgUtyOeCoWrcT8\n9DRC5p7CNmBbLZjDZd8B/3aTwKcbYOwZm8Q7Irp8ZfrtTkm7V66SggOelLGN4DRAZopPcQaZ1i1S\nh2SWT9auJW+I7DNq1bol6DPtk5DQSIVhC20MvCWKTU9UsnzkfgbNjyLy/pTjpgDtHYxDneHOp9VP\nOx017b4kigOAPaiRLhbUX5mbUaWOFm5F/A3zXQET5eR+AfgVbY32j55Hyz6OAYYdY/86iKIQcv0I\nDOMTqb96Ga6v92N+eipKWO851wUHYPkvkOf1W5rj+U0LRlWdnoyuaZ/EmGONjPmDnZnrUqgrdrHu\nlB8o/tM+mhubj//mHs9w4ELgHM/z8GN3Zw+q4XwL+B/gQ1TDPRu4EbgKVV5JE7w3FrjN67XMGHrL\nNj8CG4HFwB88zxuBr4+znx3HOCqO8K/mQFUDdWe+S3Oh9tJm3UX+zrYGGyCvHlZIsgaeyHSBy5//\nbZas2ovIXU/WN1ziSiXenrivXHrxH0O2PZnboCxUXwvyVK7+OvQRSXKjCmzC9lhvX7u+MOF1+Gl9\nMqvu383G53Zy2u/DGXSFGcWgSI+T8RT/2+W9DBb2lWaT1VK5RiabCFOlysLYZe2ltPUUAbUgwpW0\nlUOa8L/fjkVduLwSVRMQ+TGC+mFbQuB/pm2gDqjG+27A5/PsGS0ebrVkM4K4qupIr+no1VfCJz9Q\nO+19Ct45HfM5/j9Eou+dVAoUew0yRiCbKFpc5+CopGCSRNUbw2i9OZFJcFrW3QPhCqg1XF1jYGqn\nZto33HADiYmJjBo1qjPD6PQQ7KNMZC2LZtprUWz6Uz3vjalg90eNJ0mATmdrQSWjRkYORo26vNXn\n/7fT9tspG7cLcqAqCpw7ER6aw5FffUPNonW4XT377sopMaauk7C0aqeM9vXXX8+yZcsCtS86PYTk\njBDO+zaacY+Fs/qhGtY4tnDkJ9nCXBAoL4CND8OGRepz+XGqqAcE2XSsI+6RKahfrSuBm4BrUFcN\n+7VjXNlCaRAY1peYNefT+OUBKs9ZQXOJLDql+8kcDzk+N40LrTBzZPfsT3fSKXlk2rRpFBUVBWhX\ndHoSiqLQ7wIzfbJD+e71SH7M3ErCZTH0z03BnBjE2eDOAti5HBq8FvLqRUEtgSYBuBx1pmxClUC2\nIvf5Ph4pnofM42UgqkeJd2j8AlqTTnUNxqRwbCtmUfPbdTQMf5KRgzYRaYFaczWuBTOIym5v8Yjg\n4vCs3+auAWO9OsPOGgmOfsd+34lI0DXtdYs+Ovp3n4wB9MkYINV7RTqaTEuWpTMV6a8yXVw+hv82\ntboeivprKYV2LBoFfsginRvkmnaCwIsiTiQaG8F+U3/qLnbz/eOHWT28hFNvVZhwv0J4tP9nNJ8i\nnkEWyjLs+crDn+W3Ndigvj6SC4MlRnu9yJdKJhTKUrOaARvwmFfbzagip++PlCxviKhdtj07qnh/\nLa1RlEmo+73Pp6/E8G/uL24XnXKJh8WhuL4ARMZs4JzmT1ny/Z6j/7u6sJJVpNOcPR3QllIZwCko\ndTd6sjgU3izTe72cdRxjwTEHuQOQbLlC5AooO4Uyi6hF65Z9Ftnvdyx8sR6+kGQJ8CXoRvv0RWcF\nexM6XUBYnELGswqn3eXmu0fcvJbezMCFpfS/3Y4hJICpYJ2SS7K9FV40sxE1YOYgbf2qQXX3uwqx\nx0gg6O95dD9D/72YJWV72rS9VbiH6S++zT6P0dYJHhmj1EcLj7wj76u7/OlowtpPYdbrBuZ+YeDg\n0moKxu2g9KsA1q40SaZAhsDcpbRlI6qr3yuohX9FyNwGTiwimsR3SGFVeibAnoZutHU6ROxwhcn5\nfTnl4TjWXrmPNXMDlNPk7EyIymnbFrUQRnQsD8ex2UZr1KTsfrnnLs4FkpoQsTdL2Zpq6r+TuTDq\ndAedkkeuuOIKvvzyS0pLS+nTpw+///3vuf7669v06azvtUx31tIeiDFkvt5RLokuXuP/uU0ym6ZF\nIgXhWXNLPMiORIvzcJQa/SP7UigW9o2V+B3blAqYA+dmw3fP1/DtlAqS5xxixCIbloS2cobIpxtg\nKz4+yFMdau6kz3NVPy+TC87MgtEOedyJ6NRsl4WLe3f29m3PRM3i562n344qjfj6wMs8PLQUvJLN\nYEXCrK/G7cEtiYveJhBPkySb82jdm8csYO6OQpYcLjz6rzl9BrHpvPuozv4eHsgk7EHxoqTs+yzS\nul3hYpMzZNwWYXuMXXCc9vg3AXL3eJH/ttb5hRatW1Z/RKZpd2Vq1nfeOYbwonPSEBoB03MamTC/\nkX8/ZmP5yH2MWKSGyRuMHdC7RzvUB8DPBfB5Pqz4DI44YWgmpAXKi8T7h7VlzFygENVQ2+gpmnOw\nqR6RzcfAhIIXiXDWU5NkYfOVd1I9PRuyDsHt73Cgwk5i3hCUjpxTnYDRe5IP6PR4wmNh7HN2Bt4Y\nydo7ytjxP9Wc9pKduCkd1IV/LoB/LocSr9nvEY90EhDDfRrwa9RIRFAN97vAWNRc2CdXBrzqEdms\nHuGpPzbD6x+DE+D/3UTdw6+xK/sH0t4agylOlklRJ9joRlsn4ESPDCXj80R2v1PDd5eVEHu6GdPj\n1YQO1li38vP8tgYboDoPNucGyGiP9TzfjarJNKL6aR+reIGcSLYylO+JwEUNRjYziWppzhPv5FT1\nqP7gHa1f2QXEhNN/xUQOLtxK4cRv6Pef8VhGilMn6ASXbkrN2n7fZpl/tBY92iZJZiFrF45RJtYg\nFVm+A5F7rijPBsj1NQ0SqSI5k9YI8bG2Rvvr131SxJp2olXsGBsr8Os+euwUOONKuORChS+fj+C/\nkz9l6M0WTs0JJySi9fY6VODX/QsT1D9ek3yoKGNrCmoQHyeJs0nkzp0M5SUiaKAGM5u5nWqeAUTp\nRdsfnRjJL5zLcpZ4CatzqeBjQqn2y9y317ODb3m13Y5qyEU6tRb9+wc4/BOtAUKZgANxdWHkWrcg\nncum2JFw30jc/TayPeNzlOezUGYOIipRfGFrSVssij0A6D+gyK8tJfaQf0fAJG4Wa92y75xs3VlL\nKldZhgIt6V2PgT7T1gkq5gg3mTnVWK/vx6pf1/Lu8HImPxdBv4tDUZTjaKOSMlmYOub+F1mzlHO5\njyW0LrbNZQcfA9UIcoNrYCiftzHYAEsoZQLfstrPaJcC//Bpewm1gk1n+MHzeNqrLfDRpMrs4TDA\nhvvG/8DNZbgXuY9/LnUChu7yp9MlRKQYOfPtKKa/EcWa3FqWn1NFxZbj3ErMy4S+Pu5/IbfAAEla\nueMwtGpxG4MNsIRChvLnDo3nTYTktkjcLtP4O1uvcSVtDTb4l0gLDMq4FJSPrsT97kYKb9japvq7\nTnDRjbZOl5KcEcLFP9pIzQxl6dRKiu/aiKtSIkNkOOD8VLDMBRYBudB0Fazapy5SaiRCck8cEQBf\n7BrJTau4Xba9zvq5y3LCBCeaVEmzovznClyVTjbO+Jmmks6nI9Y5Pt2ST1umU4v8PbX6WIt0aqmm\n7RK3Ww8JLj6ZD6gs34FI65bp37KAwgBo3Vr0NZMoLTUwQKJ1p6T7t8eFS3y6vc9BCEy/F45cY+SV\nhQaKhu3i9KdDGHKVEUVRMJ/Sevy3bdtIVf2StoOVOGB9Ltw7TX3981fqoqXTpEZUnpkJ4/zlgJqv\nzEJ7WWMOgwZJzmoh/nW+NjOXufyZJbQKq3OIZzMz8PfzTkMtCuxdOPh21ARTIsMr09Z9F05k2rdT\nnou8SNIuiNxvThaJrxHwz4tw/m4NaydtIOr9mZhGqrp8uMH/+yzL1aMpd7xVvAAqW3uxJfhr7ibZ\ndy4QsVSSGylZLEVthGjuLL9z0TXtk4yCXyD/CzC5wRkCmTPAIYvgDgA/Lj3CusXlNDYcpsFsoM+C\ndAZlpwAQFefirL+aOfC9iy9ua2Tj/ziZ/lIo3rmlmhsks8cGz5f88wL4+5tQ5SWZ7H0DRgHJbQ33\n5sELmHu4kCUurwAS4yA2R93Z6UluNRP5mAom8CkRNFFDCJuZQTWnCHr3R7WWvsmi+nduJ/xcGAEe\nAoIRTdqKYlAIf3Q8xmE2qmZ8TORrDkKz+wZ1myczutE+iSj4BZb/G/K8Zl05nolhMAz3j0uPsOGu\ngywubKLldmJBYQ2FcNRwAyRNMjJnlYVf/uLk/zLqsV6zlZTfDcRoNWEwS2aZZs9i5LN/h6ok2mTn\nq8ohbOVvmWiHGoOZzYMXUJ2cTXVyNh/HwIQjLxJBPTVY2Bx1J9Vh2QH5vNWcymq/HCay2W9/Ah+4\n0+LC+DCqJOJCNdjTArwdMeYrB2MYEMWRy/5L2G9OVTPP6gQcxR3EsiSKovBH921+7dqkjfJ29wWI\nE+gYsS7xLbtQBgEQuQ5pTQcpGkMmj2iVTUTySDtC3h/+BB4TRETn9oVHL0BeLVqW5VRUpd3Lxfnh\na+Gxr/y7/HpWCAuWqdXjV/vkj648ZOLZB8dTtNzN9D8qhEQW8/ndUFn48tE+5kF3M+CFwcRkj2CV\n/SVc5f/220YMsygjH4C58YP4eM4LVI/Ohi8kn2WloE1WIZxtknZRuLmsPL2WYgey+GfZienv3xQm\nGUOSMbfF67IN4yV9J/u83l8BN7+F9YoEbI+OQjG0epYMZ6NwiCFsFbYPFvgqJiMuDClLwyCyIVES\n/1uRnAuBSavcIHFrbBRomP2UQ9KKUfpM+yTCJJHJjMFIoAeYJL+JIQLdcMvSQ+xYXIS5wc1Q8yb6\n3T2JVU+kExafxIT7D7D9vSspq09AsTSSfOdwYrJbrI1MtG/9giwpKWTC5y+yenRgZtQ6xyHFBm9e\nT8Nv/kbJ7G+Ie3MShsguKKN2kqAb7ZMIp8RXKFh19pySSOcmn4WaLUsPceCuTbxa2LJwVcYthWXM\nehb27jiFr3OSOHV+MgkLT8cY3nZnzf2N1ApuxgZQ0uZ1hPPkyNbXY7BHkPhZBmW3r+HA6f8l4YOp\nmAZojIjVEaK7/PVyCnarsseiZfBwPhTIMqABmSMgx2fhfaEVZgagLnPBKnj4YVh0hSqLFHwGmddC\njk85qAcGGZh8Z1uPgB2Li3ihsK2nwV8Ky6j/8/eMu9vANT8ZqNgGP41cQ/nHbfWovo9OhIR72rQl\ncTm/Z0ObthpTV+XF/gX4J/C+53lzF22356GYjdhfnUDkTQMpnvJfGlbKXFh0tNAtLn+hEt1IHMau\nrdyYqN1aJrlPl7nxifRrWYislnYtqSNBXqnKM2ksOAzLCyHPaxKZUwYMAIdAn3YApEFuMRjd4DJA\nVgo4qoH1yMNp/bO4qnh+IAoKYfmnkOe1zJCzGWZdDrMugNzPwGgGVyicN7sZR1wlfK/2i5r0BWsb\nxNdDcv1hZvBfSIWLl8Df88/gk9vWo7zuJvN5J9Y0IBvefb0f+1+8FXd9KBwpYdj+78g+0KpZzuk3\nkM2/mQ8zGsAkkVNE3wSZnd8sSfvq3ooaou4drHM/aqV239qPspMrWpzwdzE8drtAipBJ6DKvmRJB\n2wFJX0lerT2xnlwqc/ritg/gwHmfsPGtkURl+dfdlKW2ENEgkcTqJCX3RKmFtZQrBLmrohZkbo1i\nrVtmWHR5pFeTv7+twQb1dW6x2GiD2u6wE9Azn7+6rcEGyCtRjfWj93g8U45RNbtJYkebLG1vBAdl\nurllfRPfPmnkr2NCmPawiwl3NGPPHoE9u3VFrWbpcCb8oYCIhgZqzGY2X38b1TPO6eCn00IB8JRP\n2zPAPXR1wd6ehjJjIPztIvZe+y5JTw0l5rpglXA78dGNdi9GurDYxRHFslQgxnYGAU1fYOKhwkae\n8Iowv3uQhfQ7U/z6hoTB9EdcjLjSxSe3mfjpDQNJrxwhalKr7hOaPYmvx96v5SO0nyMF4M6nTUIm\npcUfXLbYpi/CASgTUhnwxWR2X7SGutWVJD07DEOortBqRTfavYiCEsjfpQb9OQ1wQGIUXV38PXBK\nFjJdkqur4GvI/xeYmqAuroHpC0xMeyGUB190YqqHUouN9DtTGJ4tKSEOxA2Bqz918svbBpZetJm4\nS2Lp93hfTNFBvKSPFMDB5bStbpMDbjyGW6ZBaHHvO7GxDItk0KrT2TvvJ3Zl/UDf907DGK3/qGmh\nR6VmFWndMo1JqnXXCvwvZZqxzMda1K5V09aii8v2w8tPu6AKlhdDntehu9cANxrgNa+Z9UIjZBlo\n9THWULJMquHK3IQ9nyfFAPNRy+O2cIsCpxppLRHmkRULNsHyDyHvqMzYTM76RmbdCk/eq7Y0TC0D\nyvA99eHhPg0KzLwKhpxzJp8+WMfGEcVkPd/IsEubMaeIr7HPszOE7c0WgaDv6+zwUT40+eT3Jg+s\nuXCKAzZkQ30OuL37LAQuBr9Mf7KTLr7exYg1XKHWLbOLsutDpHWLQyakWndzkv8xLY5PgSgIebcP\ntXevZKtjDdalswhP87+rAm3+0bL0rqJQeK3pMWTrcFrSzzolmrZLoxnWZ9q9hPzStgYb4NlmuD0U\ncpLMKvYAACAASURBVI2qr7VLgawIcHRxAfH9lXAlaqGulji8q9ywQuAskP+Nt8FWySuG3A/AMUn7\ntr9b6uTfi3fgarAQldLAsnuj+fFvScS91EBYv85mzfNBduvg9nwZTQ71h6/R60i4sghkWtQTBcVo\nIHzxFOqf/pnKqR9S98lwwoZpTCx9kqIb7Q5SsBHyvwJTnSoPZI4Hx6Dgbc8kiVuNN8CiZLq1aLip\nWTVLvqbpM4G2LtW/O6AgfLfUyeK7RrK/8I2jbTED52Ox7+eHcWb6PZREn7sSMZgClOtZJtIrXh/K\n5FAfLcgCInVQFIWw35yKISWcTRnfMujNYUTPkrkq6bSgrwJ0gIKNsPx9eGwrLNoDjxWpLm8Fhcd9\na4dxSuyOqwfknpcG7Qjapfp3B2TNdxfHtDHYAOU7XqH2UBXjVw6l9JNKVk/cRNVaWT4AjYzKBKtP\nfm/zQkgIbkKmEx3zvHQG/3MkO67bzME/SyrP6xwl6DNtkSYl04dEWrfMP1KmdZtF30/Zd1ZLLhCv\nW/r8zwS3+BWQ+y04WnRQkX6tVdP22o9MIMcAeV6z17uBqjrILYL6ZrUCl28aCC1ZXGUXg1Vy1xrm\n+TyZbsgxQp7XqV4YCllm4EdPg8ctOTMOcvZDntc5WWiDrIHAl+pr4TkEpk1f1eZ1eM10Yb+o+jrO\nHfwd56yAH980sfycasbMc3LWIw3CMloA3553ul9bnSXGp8UBY4B/5KphpEYXjMmCgZ6ZtU0wsOyH\n/IDEJ7NOlgBGA6IfcpnyoMUCyHy6ZWX0BPJY6QHx4nLk9FOI/zqFfecWUL5TwfbUqSgGRWgrjJKr\nWu4H7S+TBUrT1uJfLkO23zJ0eaQDSG/xg+hq5/Bcd7n1QDOUNKtlaF+Ho6l3H/T09TXcwaZFQ8+t\nAWOIR1uPA0e0oK8ng2puoXq8XFbIGgcOSbwKQMFqyF8Kpkg1NH7mzeDIBHOo2LqHWtQfdEWBsdc6\nST/HxSf3mPnTqAj6vVxJ7CzBjrWXcQ6o0zXqYBAyKJKkb8+m5KKvOXz5d8S9OUm+OH4SoxvtDiC9\nxQ+y2OQwq4+qGni8AZ700bmfBB6g6402qIbbYUGeeA4oKIb8QlUDdxvgrHRwnHnscQtWw/LXIc/L\nQyGnSH2+8+bNFBbNpXBna5GE1EHzuOTOsjbzn8gEN5f9o55ty4z8c/4uoqdEcsrzfQhN0F3NehrG\nWDOJKzI4fN33HJzxBSkfDsQUo5spb/Sj0QEyx0NOedsowIVRkOXr1RVEQhRU/2AfeuIJLaiEN/8L\nIUfgZa+7kZwaIO3Ys+z8pW0NNkDeTsh9FX7/r2rgYx7729k01ocTaqnlkjvLOCPbyOeCsdKzXEze\nMIIdi/bz/agNDP5DH5KututFaXsYisVI3NtTKL/3R9afvYHhHw3DnCLJPnYS0gWatr/+JPfTFvl0\ni7WkUJdESxLpazLNTYvW7ZUqwpEInAG568FY68nhMcgjB7T0E+nUEu26SZKTpEyiuVdx7I/kvZQj\nc8oQqYKyiyFMcpysgvZEn33+2gmfN0KSu02ZAkDVtXM/B4fnmB0NHnrT45FzGpgkx8Z4AJTP4DxT\nNef9/bO2/ywB/NNbqITD2U/DvrkG3r+xgaa33VzwSj3Gfv6a17dZ/jo3QLVFEvQj0rRFbSAv8yX6\nvDIPFJmLsEhSkGnaMq9ILZZB5rkkukgrxBusiPM/UMpTGYQ/+xXrJv1C2gcTCTvN5tk1bbmERZpx\nnSRvS62kXRYXIrJvWvdPKz1xYtYrcAxQH5J87EFnIqoU4l17+z7wq5tyLH4GNqDGXTQBozW+35cf\ngLV1reOdFQKfNcETbrUsr4iWXN4FJbB8E+R5fTdyKqTf8Q55m3iTOq6Z+T/U8vUzobw8Lpy+vyuh\nz21xKEZ91t1TUBSFuIfSCU2PYM+slSS/eipRFyUf/40nOLrR7qW0FBG5F9VI1qMa3DHtfP/PqPVX\nvEvL3ud57ojh/sHzeNo7OrMRDnskHJkXS0su7/xdbQ02QF4l3BQPOdHq30fHTYSsAOR/MobA9IWN\nDL/Eyd9vqWD/m2WMeK0vUaNl2fN0ugPr7BRC+oez96JVNBXVknj3yZ2XO+hGW3SrIAtNFd1qyFz+\nzA0SeUSkpshkEC3tWiUWDWO0yCArUaO+W2aqpyLODdeiwAykteJXyx20rwIjq1C4DvirT9sfgVtQ\nE4l6I7tIvLf1OW0TkgI87obZnr8zgRzaZu34DZCtAJvAJDg2BcCRcjCYYa5JLYgSGQlZY8HRRGuJ\nMMHC8OnT1wj32WUXdB4KcV9MY+3rIayYUc3E25pwLGyURol/P2OisL0yMsm/UWZfZPKNKM2puLKe\n/JoUIfPCkMkmov5arYXo4pNIPUfKxRXWKxI9g4y3Yf8unsNn57O5JpGkHP9INi0h77L0rvK0r+1P\n2SpzSQwUnfJ3WLZsGUOHDiU9PZ2nnvJNSamjhZXAd6iJPLNQpcblntdi89M5jl+kSxuy98Wh1gd3\nALNQQ92vQc1V4jCAwzOp9Q0eKkD9/P/PCX+pgSVOCG+CmWPBIapN2UkUBcbd2MRt62rZv8bIK+PD\nqVytJQ+ITrAx9okg9stZlP+jmH33bsbt7OJ0lj2EDhttl8vFHXfcwbJly9i4cSPvvPMOmzZtCuS+\nnVR8jeqy12KsHgP+juqH/ROBN9yyOImOhgrI3mcBRqH6kOcr6h3EXAM8boIpXldfZpQaoNNCPm1n\n5aBKJCvWdXAH24k11c1V/6lj2m8aWZddyJZf78NZHdyFJZ32Y0wOJ/2ridT/Uk3hrDU4D3c+uKW3\n0WGjvWrVKgYPHkz//v0JCQnh8ssv54MPPgjkvp1UtKyriYxVe2fbG4B3gQ89zxuO0TcdNaLSm7uA\nwe3YjojhqPq6N/cBYz2PB0xwr1F9niK46hxhMMsOuRZYFAN7JAuN7c3R3RkUBU69ysmU9UNpPNTE\ntyM3U/qprNKMTldjig1l4CfjCJ8QzdaJK2nYFqA0Bb2EDmva+/bto0+fPkdfp6Wl8f333/v1e29R\nq+kYnhHH8Iw4qeajRf8OlbkZiYaWTStlY4j6aw3hFVxHdZJrq85reNkJUWjVskWy4A+oGTJf9mq7\nC1UOFRnieM84N6BKG41Amqfd1+tM5qjhvVyX4hlvvtd46aixNvsAp8TYNu1u/XuI55GYAA/XIfRX\ndNWjlmH0RqD1KBINd5JPKHwLDeH+Ao8rwcSZb8L2ZQY+vGEbyrnNzHymCcTyKz86/JeBD5n7ijvL\nXAFF3oTFkr4yrVvLjYFM6xbtn6yvFisi2bfmGvECQm2j/6LwkdAoMELUk+NwD9rB1umrSV56BqFj\nxSdGZENk6VBlIeVaXAG12LcWdn9RxO4viqT/bztOB2lvQMLsRUM7uomTismoLnwybfh4SfB2AK/6\ntL0A3Ih89uy9kBkIhtB+75XjkRkFOU0++UzCIEtiA4PJ4Kxmbl3fQP59Ibw8ysyg/6kidoYsybhO\nV2K9aSCGmFCKZ31N5L+HY3XIfg17Nn0z+tM3o//R19888oW0b4eNdmpqKnv2tJb+3rNnD2lpet23\njjLB8/wh/gUF7kXVhY9FoBcWuxtHGGCH3COeIsSKGnHq8PK6WFoSyeJdQ2nYFIHZWMOC8ZvJTtfi\nUtF+LNFwwf80sX2ZgX/fsBv7WVGc8kwKofF6KHx3Ezk7DYMthO2XrqTPM4OIv+7E9uXusNEeP348\n27Zto6ioiJSUFJYsWcI777wTyH076ZjgefyA6nHRjDrDHsXxfacDvbDYE3CEtXqXAG3c5JaWRHLX\npnMprFtyVM8pLJ8LfBw0ww3qrPv0jcMo/N0Bvh21mWF/SiNxtm82QJ2uJnxGIkO/GMv2S3+h7udq\n+vxhMIrhxAyU6rDRNplM/OlPf2LWrFm4XC7+f3tnHl1Vfe3x77nzzTyQgUwkJISM5IZAIxUw1JeC\nUlTQVqpYl9LXPlvl6Wstz04P21es0Mmpw7PybJfWOlLUBSi1L2prrZKZTIwJSSBiQCAkIQnJeX9c\nijF3b8gvOTf33mR/1nLF/Pjl5HfvPWfnnO/vu/deu3YtsrPHV3yD0p6UPY+UbMRmdjDjlNbNRUXu\nGMTv7GWOMVIVyzv/3wfMnJHzG+GW1tcASIXbE70YwF0A4on5gFrXQs7rTY0bkZZiZbJMo4Y9Tjzc\nnuUO2MM4cPJZPPLmfCwf3M36o+2ML3neYs+t3n7m+aU/xIbLfgq0fx545dZDGHjpIJY+qgNEVdXa\nYvoEORqaRi+EerrnWmUSpU8B0PssKm3nAFq/5oojclo39RDCrYMpGN9/1vMz6LHR+rctNx4pf49C\n23Xvo3H1XiT8vhAmh1nJv81p2lwpaOocMaI12cUYl0/7qquuQlNTE/bv34/77rvPkAUJajTCvdH3\nDICn8LFVcBXc1zoTGgKePp2OvmeHJq5lVeJlwNrKIQTHAY/nm3DsZa5AuzBRmCNtSH7tMkADDpf+\nHYMnAvlZk0Y61wQ4+wD8YsTY4wAiMXb7XiBg12grjsM0sfYvaxBQ+nMd1/5hCHu/2Y6qlQdxtm3y\nBYpAwuQwI/GZIjgvi0Tz5X9FX4sPe/F5AQnaAU4gbEDuB7ANwI7zX+sNOOa68EakW278xFh68Bdw\nV3qjAUdXZ8YVwIKaLIQWOPHu3CYceeoEdJ1p7Cl4Hc2kIW5zLiK+MgN1V1SjZ8/k8XL7pN2YEWgK\nWjKLEYkarEbnOTTAeZUvcYg6uGWQf/qfU+C21wEXt5oPjOLYo1kHx2jevkNw+8uHu2HWwS27ck8C\nFubAzmFa92dwBpvM2/HrofnoDwqGQ+vGXdMasbz3jNv/yDnymPHIcE+FPr+glpzbwxQl6XPY4NoA\nfHCNjh23tKDvTy2Y/atzpMPEnEOfwG0Oojt0BLOh1kEPk/5t7kThPnRKj+a0a66+igH1SwaJjiP9\nQ/QtSb/Jczz0nmw4Yk2o+0wtUp7MQ9jV7t1so+qDnCM08H7mlomvuaQWI+VO28+pg7v08q/g9l3/\nCu5rten8v8+EO4lmOHfCnSjjD7QDeGTE2MMADhNzVbnKcgbbnLtRlvEmdqbvxvJw77lGVIibq2FN\nuQnhaRr+XtCID174SO66fUjUzQlI21aIw1+uw4cPtQT8ZyGlWf2cRnwyyxFwB++v4OMMQsAzs9EL\nNZXGhNHyzV/PuWt0D6/Z7Y+90C0ODVds1qBdm4aGf2tF2xPHkfObFDhT/Em4mjoEL4hA5jvFOLii\nAn17uzH7kaSAtQR6PWirdhoWPgl3iVvw8ZPtTHhW+jw34utIqKdi1QfG0czn7IJnz/8b9e9cc/pX\nu8/X7B429q0+wH744+bCF2gFTTQzTnRpT4k9Rk49Ob2JHO8i8tv7F9oxv1JD5eaz2D2vDgt/ZsPs\nm80Y1OjrwjzT811tDU4mZgJDEYxThrICfkRPVXoy5y5lzudJySOK4eDcgOcPnGOatPbZ6FuEC+VT\nU+1I+dsitK14D43/2oIZj2ePO3BT8U1VBlGVakQe8XM4H0Ig+ROuh7tzzXfhrmJ4J9y1SlR5F58M\n2Dj//S4/32MyWzXM+7YV177mQPkDA9h5Yz8GTkxA5SvBA3OYFcnbi9F3sBcHv1CLwa7A+xwkaPs5\nWQDuGTG2Du4kGn+nBe59vxfhDtr/DeD3cN9dj8U/ziWMmwNEoowpNOHGcgdCkjT8I78OHzwnDhNf\nYAq2YNYOFyxRVjR+6j2cbfTzv/ojEE3bz8k9//UOfKxZJ8JdQc/fOQZ3kB7ObwHcRsxtgtv0YYc7\nfT8H7qzQ4XBGh8EAkiYtDg2LfmaDeVUyGu84jCNbOpH9RCociaJ1TyQmhxkz/icbnU+0o2nRbqQ+\nlYfwpZx25l/4VdCm9CGuhKLOaGMaNZ17larj45xr5eZe4gktFx8Hb4Bu2O1NVFxhw+dyYcg6bF4v\n3D7u4/hklcK74bYt5gwbmwN3TZafDBv7BtydfnpHJCM6aTmab8R8kBiLpaemxjaT4yfNnjnoXYwf\nrm+hDYUVNlQ+0IeKubW4/DEnZt5Av2OWOFoLPeKkRabeEKIWCpeCrlK2WPV6oSRmTv+20E8cFuv4\nU9DpuOIei1ybAmtWKJpXVSHpsWzE3kDUIlD8napp7GZFod+vgrYwuRhtufIWuDv0DOcXcFc7HB60\nC89/vRsfB/5CAAsD9Cw2WTUUfd+B5KUW/N8tPWh5ZQAJjwzCEiab9xNJyOWRSH+tCAeuroB2Og0x\nt/t3lUDRtAWvEQPg6yPGvg53EavhqNgCC+G2N95y/mshMSfQiC22YFVlKCxODe/m1eHYC6J1TzRO\nVxgyyuaj/YfNaLlnH4b6/Lf/pARtwWskwx14b4HbV34r3MlzqSPmTcaysqpYgzUs+nUQcp9Kw8EN\nR1C76gD6O1VzVIXx4MgMRl75PPS3nEXDFZUYOOafZ6BPfNqcTk2lhLI6FbNyCzVdRXPj5nNzuSdZ\nwqNqYdZhZTRtbtmUlqwqN6qEg/GU+U8+/9/wYwx/uQNwJwPdhU9mTv47gPQRcznP92li89/JFdzj\nTOCUBs7o32GH6Is5OcPTHH6S6SvWy6TC9y+2I7c8DJXfO4Pyglpc/kQYkpcxnu4wWiP9wOH5Z/AU\npXMDwBnmxKa0LdXyrtS4g3mCcNDvqdky/jIYVAzpYy7ooKhBpL1YgKP/dRD1n65AxnYXHJnBbMzi\nbjjodRjj0w5QNVCYTMyEex/wn1mdOtx1SaZqozqzXcO8TaFIvMqOv956ChHXNWPmj5NhDhKteyLQ\nNA0JP0iHbYYDTYvLkf7iHERdTv+R9QUijwh+wUy4GzdcBuBzmLoBezjTl9hwTXU0Bo6fw/t5teh8\nhUtrFLzBtLWJSP1dLg6srEbncx/6ejkXkKAtCH6MPdKEnKczkPmbNBy4pwWNtx3AuS7vVM4UPAlf\nGo1Zr89Fy70H0bqhBfqQ7zeIvS6PKOnUxHK4uX12+u+NxUHs+nJ6tMo4N5crV0nMdzLHcDLeOM7S\nSo2rlGC92Ph453KonGiqr4XUujm/IVcIkNLAOUP8B/RwbLLnP0y30wkbHzFaN1m/BDYklAKzq6ah\n4u6TqCqswqefikLCZfQ6zDbPoG5PoDXjrtOevw8A+no9vTtDfdxFoACjUdsIHR6gNW2LATo3BxVv\n7K4IZL33KRxYWYPu+l6kPpkL03mpinI4cTq3UT5tudMWhADBGmJC8W+jULg5HG9d14nm+9sw1O+/\n1rTJhDXOjsy/zIXmMKFp8W70t/uuG44EbUEIMJJXBmFZRTy6dndj95xanCw77eslTQlMDjNSf5eL\nyBvi0HjZ++ip8M37LkFbGBNtAKrhrhlSff57YeIISjAj/5XZmLkpBfU378eB9YflrnsC0DQN8f+Z\niuRfZGLfVVU4s7trwtcwAT5tz1/Rx1SloNr0sHPttL4WbCcUTlU9mipTzLVU4pp/E+NWZm4YU2Ss\nlxFxKQ3XCO2aY6RPuwXuok5PDxu7E26r84xRHuNi4+PxhV+Akz05wZH6DLgbKWY8+Jhn0JyWTAvj\ncaQxnPZ1s+3NYEf0NUDqgiSUf/kYahbU4FNPxyE6a/QaqS2M1rp7Qzx3TvrO0hcS1RKMG+d81/y4\n5xlsNo2/bRe3V0btwVHHjrw+DprFhIar9iD5wfQJTX2XO21BmWMAHh0x9ijo/BTB+9hjzFjwp3ik\nfSUMZQvbcOTRo9AHfe9ymOxEXBuD7DcLcXTzYRxc24ih3olx9UjQFpRRNeMI3kfTNMz8ajhK/pqE\nzueOo2p+DbprAqtOdCDizAlG7ntFGOoeRP2nK9B/RCVHcmz4VRo7NbefCQWcbKIHe4oHmgHShtJc\nxWOEMuMjy41eGCfGVNqKcYy2cuzFLHls5U2FcdWUfFJOUU0epN5AziDAxUJiPBS05hlBtkwHphE+\nQ8oGCAA9xLsdnQU434zH4SdPo+7KPZj9nSjMXBfJ3p5xKdRmk+e4LYiWUrju6BRcqzDOxkdJIard\ny43gom0TQ81IfSYfHRub0bC4EllvuGCfwWmv40futAVlEuGuFTKcsbYQE4xH0zTMuC0cV7ybgrY/\nduHvV7eh/6h/Fj+aLGiahunfSUPMncloWFyB7grvbVBK0BaUSYO7jdhtAL56/ms4xtZCTPAewek2\nLHo7BZHFTlTOrcaHz3woJV+9TNzdKUjelI6mpdU4uumwV/YWJGgLYyINwOUA5p//KgHbPzFZNWTf\nPw3ZL2WhbdMR7CmtR1+b93XXqUz0jXHIfb8IJ1/txN7lNYY3D/a6pk3Z+KgxgLY2hTCaIFfasifY\nc35wMONf5VowUeOc/YtrK0fpnowWamWuoSjmsx5Q2F/iPmDqXeW0aq4kqgoqKfkqcwHmNRrRSo67\n1hS0bmdfDzk1yE6PUxo4p4tHMro4tQ+UuCAWCbvTcODHH6K6qBq5v0xA/PXcBUCnXLPWQxN9PZNl\nKWxqmw0q+rVKiVNv6+L2VCey/uJC89f3oeGKSmRunwNnPLeXpxaGx3yn/fzzzyM3NxdmsxkVFRVj\nPYwgCBOEZtaQ8Z1YFL08A03rO1D75TYMnpHiU95Cs5iQ+utMRK6MQcOnK9C7l/5DrcqYg3Z+fj62\nbt2KxYsXG7IQQRAmhojiIFxemQF9EKgsrMZHr0nJV2+haRoSv5eKhO/MwJ7Lq3FsS8e49xXGLI9k\nZUnFY0EIVCyhZsz53yQ0vzKAA187iLBFYZj5yExYQqXRgjeIWZuA0KJg7L+tCSd3fYT0LZkwO8f2\nXntd0355Q82F/08vSUBGSSLb6ofyXrPaNaO09to95weHM/U4OW2Ykvq4uVypzyhijNv/YaQ4JzMe\nRT3RMjqrireZ0645LVkFLjVdRdMO48Ypnztnk1VoD2fE1WFnGsRymrYTnuOhzEnWxWjdlK+b08WT\nV0Ri+pLZaLqnHdWFlch/OhURxXx7LQpOH6b2rrg0cRW4EqccE+3r5jzdDlc4ct4pwsHbG1F3RQ1m\nbcuHbbodZgziVFkVTpdVj+r4F/1kSktL0dHR4TG+ceNGrFixYlS/YOmG+aOaJwiCb7CEmJH7eAo+\neOkkqq45iJS7YjDtvhBoZs3XS5t0mJxmpP8hB0d+1IL64nLM+lM+wuYGIbzEhfAS14V5bff/jj3G\nRYP2rl27jFutIAh+TdyqCIQXB2HPl1rQ8WoV0h7NQMg8OhtTGDuapiHxu6lwZgehaWk1Un86E9Nu\niYWmje6PpCE+bTHsC8LkwJFoQ9GuDMR9dToal+9B2w9boJ+T69sbRF0fi9m7CtC+qRUH1u7F4CgL\nTo1Ztdu6dSvWrVuHzs5OLF++HIWFhdixY4fHPLqeCFea1VPr5ryhlPYH0HqeM4yeG3yG8W9TejSn\naXM+bUq/VtS0OSht18rUKXEy66Z0Y07TNsKnbYSmHcXo1E5qD4KrCcPVoaG2WThd3ACtm9NZ7fBM\nN7czJw41F6CvjSDmOiJr+5iA1NvCEF+ajfpbD6HhtePIfSoNzlTyEOxroXzTKnWHjIJan4qn25sE\nu0KR824RDn25CXsWVmPWS/mXrFsy5jvtlStXorW1Fb29vejo6CADtiAIgYsjyYbCXZmIWRmB9+c3\n4MOnmeaYwrgwh1iQ/kwOpq2JR31xOU7tOnHR+V53jwiCELhoJg0zvhGPqM+EofbmZhx/9kOkPZYB\ne7L3qthNRTRNQ/w9yQgqDMGBm+ovOtfrQZuy8dkU0thtzKMh97hHWQG77PRmSlA4rSlolKSgKm1Q\n4158InNy0oGCbNLL2AaNKPvKySPUCUha+MDIIABt0aQkLoD3DVK/U7VwuJdkE05+4K8Nz2ugl5ET\nOalypFwRVAg4KnJxePNR1BZVYNYjqYi9kdMGeQaZ18JZAVVTvClUpBBVO6HRhJVEIve9IlQl83Ok\nYJQgCKPC5DAh9XuJmLN9Ng59vw0NXzqAc6f9QxueTNiSvKRpC4IwNQmdF4J5FXkwOU3Y7dqD039j\nHucEryBBWxAEZczBZsz+TRoyfj4De29owKE792OwR4pPTQQTUJrVUwDsZ6xK1DjXboyzAtpULFNR\n9Doi+wiTm2p3b+qpkTuGahlR6i1RbIdGdYAP41LymXUPKDwZW7nXQkmZqu3hKJ1aRf/mxrm53DqI\nz2WQed2cxY0a5zRtTn9V08Xpa4Aap8YSrg1GxBX52LeuBXvmliP7qXSEzuM+QF5D56Be41RIhb8Y\ncqctCMK4sERYkP37dKTen4Saq5vQsrFdusF7EQnagiAYQuyN0Sgqz8NHfz6NqpIGnD3EdYsQxoME\nbUEQDMORbEfBn7Mw7dpI1M6rQMdjR6TMhcH4pN0Y5d0GADPp06Y1N5XUWU7TZo8d63nssHNMN2sj\nHE+cRGeEps3Ji5R+zaXqM3Ke1YjXrpI+zr1GFZ92LDNOWY5VNW1ivM9On+tG6LIc1DXAXy/jv448\ndHgTkPrNWISviMbeNftw8tVOzNqSAdt0LkNDTeP3Jv6kXXPInbYgCF4haLYTc97JQ8j8EFQWVuP4\n1uO+XtKkQIK2IAhew2Q1YcYPUpC9NQuH7m3B/tubJCFnnEjQFgTB64QtCEVhVQE0s4aagnKcfovu\nJC9cmgmoPeIpWqr4TnuZQp1cPQFKd+tS1MUpGc0c20lODQZT3nWUxwXAfwqctmuAT5vUrznPuREl\nZVV82kZo2lyNEa5cBqWBc7o4o3XrxPp6zPT5y+UfUPs9Rui63PXCjVPXoqouPvIYlhAg8/GZOP7K\nCexb3YCYNTGY8cMUmOyT697R27r45Hq3BEHwe6JXRKGw2oWz+8+ian4Numu4HXCBQoK2IAgTjjXG\niqwXZyPxGwmovbIOR37SKtbAUSJBWxAEn6BpGuJujYXr/Tk4/mInGpbtQV8bp8UJ/8Trmjalx6n4\ntMero11srgpmO3OM2I/I4WALoXWr1mFW0am5QmvcuIqmzSW2GWECoPRr7n3itG5vadrcXGa8c11y\nygAACQpJREFUK9zzvO5l676Pvv0Xp2l7twa1iqbN6eWj0+JDUq0oeDsPrQ+0oXZuOdJ+noaYm6aN\nutHtpZjoVmbeRu60BUHwOZpFQ8r3kpG7MwdtG9vQ+Pm9GOhUabMxdZCgLQiC3xAyNwSu8gI40uyo\nnFOFU38Ra+BIfNJujIOSNrjHSBXZxJuPMIN25hE11vNkC7MwqfDcYz+XQEbJI1zKNVdulZJHFNPY\nlVqqqZSfVW3zpfJ+cOOU5MFY/rqj6HudLrNnW7su0K3uuBKl1PVC2WYBY2QQI+DKnFKtxUYjVZgc\nJqRtTkXksgg03dSAmNvikbxhxgVrIHc9c8cOhNR0FeROWxAEvyTiyggU1BThbGMPaudVoLuSuwOZ\nWkjQFgTBb7HG2pD5Ug4S1iejfmktWn/QgqEBhYS2SYgEbUEQ/BpN0xCzJg5zKubizDunUbeoGgPH\nGKlxCuCTdmMcKnp0D5PePtGwJSXNnuP9MV3k3NBg+rHPzr11lC6rYu3jxn1h+aPOQJX0fYDWtFVS\n3plxTrs+aY+kxxHhMcZp2rzWTbXno/VvlXKm/qJ/j0dfDkoyI3dHFlo2tKPaVYG0xzIQvXLaJY89\n0eVdvY3caQuCEDBomobk+1OR+Vw2Dn/rIPbd3IiB41PLGihBWxCEgCNsYTjmVBfBGmtFzZxynNhG\nF3SbjEjQFgQhIDEHmZH683TM+mM2mv/jIA5/+xD0oclfv8QnaeycvsZpdxMNvWa1dkhUijHnWe8L\nosXaUDutgQd1e27CWDitVkXTVtWuKQlR1adNoZrGrlCqliqfCtAp6JTvGqC1a4DWqbm9Fy7/gBpn\nzxtmnLqO+POX/mD8WQcemTYfuSgYrn/ko/66JtRfWYP032bCke4cNn9sfnF/Zcx32vfeey+ys7NR\nUFCAVatW4dQpbidMEATBu1inWZH7ZgEiPxeN2uJKHH2ofdLedY85aH/2s59FXV0dqqurkZmZiQce\neMDIdQmCICihmTUkfCMJee+4cPyFD1G3uBq9+3p9vSzDGXPQLi0thcnk/vHi4mK0tbUZtihBEISx\n4swMQu6bBYj+QgzqFlah653JpQIYomlv2bIFX/ziF8l/q9iw/cL/x5RkIbYky4hfyZaDNAIVvYsr\nj0mNc551rnxnL9OqyhnmefcQFNZDzg3qoe80bIR+ram2FfPWR6DYfu0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| |
} | |
], | |
"prompt_number": 33 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [] | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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{ | |
"metadata": { | |
"name": "shogun_gp_regression" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"Gaussian Process Regression with Shogun" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Import all necessary modules from Shogun" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from modshogun import RealFeatures, RegressionLabels, GaussianKernel, Math\n", | |
"from modshogun import GaussianLikelihood, ZeroMean, ExactInferenceMethod, GaussianProcessRegression" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 2 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Generate some data, a 1d noisy sine wave, evaluated at random points" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"n=15\n", | |
"x_range=4*pi\n", | |
"y_noise_variance=0.1\n", | |
"y_amplitude=1\n", | |
"y_frequency=1\n", | |
"\n", | |
"X=random.rand(1,n)*x_range\n", | |
"Y=sin(X*y_frequency)*y_amplitude+randn(n)*sqrt(y_noise_variance)\n", | |
"X_test=linspace(0,x_range, 200)\n", | |
"Y_true=sin(X_test)\n", | |
"\n", | |
"plot(X_test,Y_true, 'b-')\n", | |
"plot(X,Y, 'ro')\n", | |
"_=legend(['data generating model', 'noisy observations'])\n" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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0SWLSR9euvM84I8Pspybl+PlnPv3Xy0t0JIZxdgY6dOCzjwjRxqjyZKmpqfD3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\n4IcTAAAAAElFTkSuQmCC\n" | |
} | |
], | |
"prompt_number": 3 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Convert data into Shogun representation, print dimensions to be sure data was passed in correct " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"labels=RegressionLabels(Y.ravel())\n", | |
"feats_train=RealFeatures(X)\n", | |
"feats_test=RealFeatures(reshape(X_test, (1, len(X_test))))\n", | |
"\n", | |
"print feats_train.get_num_vectors()\n", | |
"print feats_train.get_num_features()\n", | |
"print feats_test.get_num_vectors()\n", | |
"print feats_test.get_num_features()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"15\n", | |
"1\n", | |
"200\n", | |
"1\n" | |
] | |
} | |
], | |
"prompt_number": 4 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Specify a Shogun GP (exact GP-regression) with fixed hyper-parameters and pass it the data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"kernel_sigma=1\n", | |
"gp_obs_noise=0.5\n", | |
"\n", | |
"kernel=GaussianKernel(10, kernel_sigma)\n", | |
"mean=ZeroMean()\n", | |
"lik=GaussianLikelihood(gp_obs_noise)\n", | |
"inf=ExactInferenceMethod(kernel, feats_train, mean, labels, lik)\n", | |
"gp = GaussianProcessRegression(inf)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 5 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Train GP and plot its predictions" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"_=gp.train()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Perform inference and plot predictions on full range" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"predictions=gp.apply(feats_test)\n", | |
"Y_test=predictions.get_labels()\n", | |
"\n", | |
"plot(X_test,Y_true, 'b')\n", | |
"plot(X_test, Y_test, 'r-')\n", | |
"plot(X,Y, 'ro')\n", | |
"_=legend(['data generating model', 'mean predictions', 'noisy observations'])\n" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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3nj4FGjYEHj8WNoJm6VLgwQNg40bhbDBErK2tkZ6eLrQZHI7asLKyQlpaWpHt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\nGGMNhIs+Y4w1kO+nI5YxXnHGgAAAAABJRU5ErkJggg==\n" | |
} | |
], | |
"prompt_number": 7 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"So far so good. The nice thing is: we have a distribution over the predictions" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"mean = gp.get_mean_vector(feats_test)\n", | |
"variance = gp.get_variance_vector(feats_test)\n", | |
"\n", | |
"# print 95% confidence region\n", | |
"plot(X_test,Y_true, 'b')\n", | |
"plot(X_test, Y_test, 'r-')\n", | |
"plot(X,Y, 'ro')\n", | |
"error=1.96*sqrt(variance)\n", | |
"fill_between(X_test,mean-error,mean+error,color='grey')\n", | |
"\n", | |
"ylim([-y_amplitude,y_amplitude+1])\n", | |
"_=legend(['data generating model', 'mean predictions', 'noisy observations'])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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ZiYmJklnzQMYy3sASin0/xP/4xz9AEARmz54NX19fnDhxAkBvGcWUlBSpEo+/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\n9GloaGhmEbTRp6GhoZlF/H97KsMzWIDPrAAAAABJRU5ErkJggg==\n" | |
} | |
], | |
"prompt_number": 8 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We can even visualise it more fancy" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from scipy.stats import norm\n", | |
"\n", | |
"means = gp.get_mean_vector(feats_test)\n", | |
"variances = gp.get_variance_vector(feats_test)\n", | |
"\n", | |
"y_values=linspace(-y_amplitude-2*y_noise_variance, y_amplitude+2*y_noise_variance)\n", | |
"D=zeros((len(y_values), len(X_test)))\n", | |
"\n", | |
"# evaluate normal distribution at every prediction point (column)\n", | |
"for i in range(shape(D)[1]):\n", | |
" norm.pdf(y_values, means[i], variances[i])\n", | |
" D[:,i]=norm.pdf(y_values, means[i], variances[i])\n", | |
" \n", | |
"pcolor(X_test,y_values,D)\n", | |
"plot(X_test,Y_true, 'b')\n", | |
"plot(X_test, Y_test, 'r-')\n", | |
"_=plot(X,Y, 'ro')\n", | |
" " | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"png": 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d/uPLruPa0/6F3RZtuzLwbjPMjXtlnuuH5W90QXAHwvGIrEnVbVU00ZUY9MpO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\nZOA9TSIKyQAAAABJRU5ErkJggg==\n" | |
} | |
], | |
"prompt_number": 9 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now lets learn the best model parameters with Maximum Likelihood II" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from shogun.ModelSelection import GradientModelSelection, ModelSelectionParameters, R_LINEAR, R_EXP\n", | |
"from shogun.Regression import GradientCriterion, GradientEvaluation\n", | |
"\n", | |
"kernel=GaussianKernel(10, kernel_sigma)\n", | |
"mean=ZeroMean()\n", | |
"lik=GaussianLikelihood(gp_obs_noise)\n", | |
"inf=ExactInferenceMethod(kernel, feats_train, mean, labels, lik)\n", | |
"gp = GaussianProcessRegression(inf)\n", | |
"\n", | |
"# construct model selection parameter tree\n", | |
"root=ModelSelectionParameters();\n", | |
"c1=ModelSelectionParameters(\"inference_method\", inf);\n", | |
"root.append_child(c1);\n", | |
"\n", | |
"c2=ModelSelectionParameters(\"likelihood_model\", lik);\n", | |
"c1.append_child(c2);\n", | |
"\n", | |
"c3=ModelSelectionParameters(\"sigma\");\n", | |
"c2.append_child(c3);\n", | |
"c3.build_values(-1.0, 1.0, R_EXP);\n", | |
"\n", | |
"c4=ModelSelectionParameters(\"scale\");\n", | |
"c1.append_child(c4);\n", | |
"c4.build_values(-1.0, 1.0, R_EXP);\n", | |
"\n", | |
"c5=ModelSelectionParameters(\"kernel\", kernel);\n", | |
"c1.append_child(c5);\n", | |
"\n", | |
"c6=ModelSelectionParameters(\"width\");\n", | |
"c5.append_child(c6);\n", | |
"c6.build_values(-1.0, 1.0, R_EXP);\n", | |
"\n", | |
"\n", | |
"# Criterion for Gradient Search\n", | |
"crit = GradientCriterion()\n", | |
"\n", | |
"# Evaluate our inference method for its derivatives\n", | |
"grad = GradientEvaluation(gp, feats_train, labels, crit)\n", | |
"\n", | |
"grad.set_function(inf) \n", | |
"gp.print_modsel_params() \n", | |
"root.print_tree() \n", | |
"\n", | |
"# gradient descent on marginal likelihood\n", | |
"grad_search = GradientModelSelection(root, grad) \n", | |
"\n", | |
"# Set autolocking to false to get rid of warnings\t\n", | |
"grad.set_autolock(False) \n", | |
"\n", | |
"# Search for best parameters\n", | |
"best_combination = grad_search.select_model(True)\n", | |
"\n", | |
"# apply them to gp\n", | |
"best_combination.apply_to_machine(gp)\n", | |
"\n", | |
"# training and inference with learned parameters\n", | |
"gp.train()\n", | |
"predictions=gp.apply(feats_test)\n", | |
"Y_test=predictions.get_labels()\n", | |
"\n", | |
"# visualise\n", | |
"means = gp.get_mean_vector(feats_test)\n", | |
"variances = gp.get_variance_vector(feats_test)\n", | |
"\n", | |
"y_values=linspace(-y_amplitude-2*y_noise_variance, y_amplitude+2*y_noise_variance)\n", | |
"D=zeros((len(y_values), len(X_test)))\n", | |
"\n", | |
"# evaluate normal distribution at every prediction point (column)\n", | |
"for i in range(shape(D)[1]):\n", | |
" norm.pdf(y_values, means[i], variances[i])\n", | |
" D[:,i]=norm.pdf(y_values, means[i], variances[i])\n", | |
" \n", | |
"pcolor(X_test,y_values,D)\n", | |
"plot(X_test,Y_true, 'b')\n", | |
"plot(X_test, Y_test, 'r-')\n", | |
"_=plot(X,Y, 'ro')\n", | |
"\n", | |
"# print best parameters\n", | |
"print \"kernel width\", kernel.get_width()\n", | |
"print \"kernel scalling\", inf.get_scale()\n", | |
"print \"noise level\", lik.get_sigma()\n" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"kernel width 3.40033132621\n", | |
"kernel scalling 0.560520693722\n", | |
"noise level 0.5\n" | |
] | |
}, | |
{ | |
"output_type": "display_data", | |
"png": 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KJ0eRaWYVFPtdeUsRnlvWdBYcFmi2QmoEvNb4dZ5LuAPHGEwaZ4x8AYzFs1rK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\ngUAg8CP+HwGSUxRh7lYfAAAAAElFTkSuQmCC\n" | |
} | |
], | |
"prompt_number": 11 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 10 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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