{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multiple Outputs Gaussian Processes\n", "\n", "# Gaussian Process Summer School, Melbourne, Australia\n", "### 25th-27th February 2015\n", "### Neil D. Lawrence\n", "\n", "In this lab we are going to build on yestereday's work by looking at multiple output Gaussian processes\n", "\n", "## Getting started: Modelling Multiple Outputs\n", "\n", "Just as in the first lab, we firstly specify to include plots in the notebook and to import relevant libraries." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import GPy\n", "import pods\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first think we will look at is a multiple output model. Our aim is to jointly model all *sprinting* events from olympics since 1896. Data is provided by Rogers & Girolami's \"First Course in Machine Learning\". Firstly, let's load in the data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning. Data from the textbook 'A First Course in Machine Learning'. Available from http://www.dcs.gla.ac.uk/~srogers/firstcourseml/.\n" ] } ], "source": [ "data = pods.datasets.olympic_sprints()\n", "X = data['X']\n", "y = data['Y']\n", "print data['info'], data['details']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using data sets it's good practice to cite the originators of the data, you can get information about the source of the data from `data['citation']`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A First Course in Machine Learning. Simon Rogers and Mark Girolami: Chapman & Hall/CRC, ISBN-13: 978-1439824146\n" ] } ], "source": [ "print data['citation']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data consists of all the male and female sprinting data for 100m, 200m and 400m since 1896 (six outputs in total). The ouput information can be found from: `data['output_info']`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{0: '100m Men', 1: '100m Women', 2: '200m Men', 3: '200m Women', 4: '400m Men', 5: '400m Women'}\n" ] } ], "source": [ "print data['output_info']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In GPy we deal with multiple output data in a particular way. We specify the output we are interested in for modelling as an additional *input*. So whilst for this data, normally, the only input would be the year of the event. We additionally have an input giving the index of the output we are modelling. This can be seen from examining `data['X']`." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First column of X contains the olympic years.\n", "[ 1896. 1900. 1904. 1906. 1908. 1912. 1920. 1924. 1928. 1932.\n", " 1936. 1948. 1952. 1956. 1960. 1964. 1968. 1972. 1976. 1980.\n", " 1984. 1988. 1992. 1996. 2000. 2004. 2008.]\n", "Second column of X contains the event index.\n", "[ 0. 1. 2. 3. 4. 5.]\n" ] } ], "source": [ "print('First column of X contains the olympic years.')\n", "print(np.unique(data['X'][:, 0]))\n", "print('Second column of X contains the event index.')\n", "print(np.unique(data['X'][:, 1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's plot the data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cDjw2MGIDCCgYYQDktVrS2bPSrVvZ7+4duhS3A+/niD8mgICiuXttfrJygIrcvev+53+e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\noK1WB3KxB2iDHsi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "markers = ['bo', 'ro', 'bx', 'rx', 'bs', 'rs']\n", "for i in range(6):\n", " # extract the event \n", " x_event = X[np.nonzero(X[:, 1]==i), 0]\n", " y_event = y[np.nonzero(X[:, 1]==i), 0]\n", " plt.plot(x_event, y_event, markers[i])\n", "plt.title('Olympic Sprint Times')\n", "plt.xlabel('year')\n", "plt.ylabel('time/s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the plot above red is women's events, blue is men's. Squares are 400 m, crosses 200m and circles 100m. Not all events were run in all years, for example the women's 400 m only started in 1964.\n", "\n", "We will look at modelling the data using coregionalization approaches described in this morning's lecture. We introduced these approaches through the Kronecker product. To indicate we want to construct a covariance function of this type in GPy we've overloaded the `**` operator. Stricly speaking this operator means to the power of (like `^` in MATLAB). But for covariance functions we've used it to indicate a tensor product. The linear models of coregionalization we introduced in the lecture were all based on combining a matrix with a standard covariance function. We can think of the matrix as a particular type of covariance function, whose elements are referenced using the event indices. I.e. $k(0, 0)$ references the first row and column of the coregionalization matrix. $k(1, 0)$ references the second row and first column of the coregionalization matrix. Under this set up, we want to build a covariance where the first column from the features (the years) is passed to a covariance function, and the second column from the features (the event number) is passed to the coregionalisation matrix. Let's start by trying a intrinsic coregionalisation model (sometimes known as multitask Gaussian processes). Let's start by checking the help for the `coregionalize` covariance." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "GPy.kern.Coregionalize?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The coregionalize matrix, $\\mathbf{B}$, is itself is constructed from two other matrices, $\\mathbf{B} = \\mathbf{W}\\mathbf{W}^\\top + \\text{diag}(\\boldsymbol{\\kappa})$. This allows us to specify a low rank form for the coregionalization matrix. However, for our first example we want to specify that the matrix $\\mathbf{B}$ is not constrained to have a low rank form. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
mul. valueconstraintspriors
rbf.variance 1.0 +ve
rbf.lengthscale 80.0 +ve
coregion.W (6, 5)
coregion.kappa (6,) +ve
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kern = GPy.kern.RBF(1, lengthscale=80)**GPy.kern.Coregionalize(1,output_dim=6, rank=5)\n", "display(kern)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note here that the rank we specify is that of the $\\mathbf{W}\\mathbf{W}^\\top$ part. When this part is combined with the diagonal matrix from $\\mathbf{\\kappa}$ the matrix $\\mathbf{B}$ is totally general. This covariance function can now be used in a standard Gaussian process regression model. Let's build the model and optimize it." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = GPy.models.GPRegression(X, y, kern)\n", "model.optimize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the results using the ability to 'fix inputs' in the `model.plot()` function. We can specify that column 1 should be fixed to event number 2 by passing `fixed_inputs = [(1, 2)]` to the model. To plot the results for all events on the same figure we also specify `fignum=1` in the loop as below. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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abNieYuOONBt2pNiwI8XmnV2lxKkimiY4pbEYwFHNxFG1PfJ7jgbP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for i in range(6):\n", " model.plot(fignum=1,fixed_inputs=[(1, i)],ax=ax,legend=i==0)\n", "plt.xlabel('years')\n", "plt.ylabel('time/s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a lot we can do with this model. First of all, each of the races is a different length, so the series have a different mean. We can include another coregionalization term to deal with the mean. Below we do this and reduce the rank of the coregionalization matrix to 1." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kern1 = GPy.kern.RBF(1, lengthscale=80)**GPy.kern.Coregionalize(1,output_dim=6, rank=1)\n", "kern2 = GPy.kern.Bias(1)**GPy.kern.Coregionalize(1,output_dim=6, rank=1)\n", "kern = kern1 + kern2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = GPy.models.GPRegression(X, y, kern)\n", "model.optimize()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2Ky+8vptCqwAsQ2Pm1NGc+7bxzJo6CiHAdlwsXVBVFqUyGVVGQDGkkFLS3pmn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\nQDzPE0CUUqKUEkA8z5f6xozsaGiWmpqx4nm+bNi8TdZvrBP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for i in range(6):\n", " model.plot(fignum=1,fixed_inputs=[(1, i)],ax=ax,legend=i==0)\n", "plt.xlabel('years')\n", "plt.ylabel('time/s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a simple form of the linear model of coregionalization. Note how confident the model is about what the women's 400 m performance would have been. You might feel that the model is being over confident in this region. Perhaps we are forcing too much sharing of information between the sprints. We could return to the intrinsic coregionalization model and force the two base covariance functions to share the same coregionalization matrix." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m.plot?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
product. valueconstraintspriors
rbf_plus_bias.rbf.variance 1.0 +ve
rbf_plus_bias.rbf.lengthscale 80.0 +ve
rbf_plus_bias.bias.variance 1.0 +ve
coregion.W (6, 5)
coregion.kappa (6,) +ve
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kern1 = GPy.kern.RBF(1, lengthscale=80) + GPy.kern.Bias(1)\n", "kern1.name = 'rbf_plus_bias'\n", "kern2 = GPy.kern.Coregionalize(1,output_dim=6, rank=5)\n", "kern = kern1**kern2\n", "kern.name = 'product'\n", "display(kern)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = GPy.models.GPRegression(X, y, kern)\n", "model.optimize()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Ewxb+JFBLkMP/TQKhBmiTUmYOZUAld9FMKeV9PY59E3hGSvlcKYA8v+f50jVK\n+Mcg+aIdbEdpeTje4VUTb9ndwYtvBEagJZOvHK9PxTjzxMmceeIxfRaJWbaD67jBKiAVH3A/IYVi\npOK4Hrv2ZslZLpFIENtqnFR19Lde7BEg/g9gUSlo/DDwsJRyTUn4F+8bNBZCyOa2LMlEhJCpsjXG\nIuVq4kzOomj7uJ6PbuhEQgMzBFJK1m1r44U123n5rzvIdFmVc3XJKGeUjMDcqele7qDyKkAXUJMI\nU5uMqe6gilGFZbvs3NtFwfGJRcOVFbTr+SNK+PdKKb9VcvU0EbSBeERKufpAwr9hZwe246IBYVOj\nKhaiOh5WaXtjFN+XdBUsOrqCQLHjS0zTIGwaA2jxIFm3tZU/vb2Tl9/eQVtnsXIuXR3h/cdP5qwT\nj2HejNpe20g6rotlOYQNjdqaKMm4KgxTjFw6c0WaMwUcTxKPhvf7XhxM+IdzjdsKbCo9zwCnlx7T\npWOp0jX78cB376k8P/Psc5h/+lm0dBTRgFDJEFTFwmpFMEbQNEEyHqmIr+f5ZAs2Hdmgv9CBDIGu\nCY6bUcdxM+r47IdPYv32tooR2Jsp8F+vNPFfrzRRHQ+xcN4kTj+ugVNm1xMJGZhG8HXY015k194c\nsbDBhJoY0cjQtrdWKAaClJLWjgJtnQXQdKKRMD0Tll9+6QVefulFgEpBZH8M54x/JnBpycVzC7CR\nwBAslFI+Wjr2rJRyzT73ya0tuQP+bttxKyuCkKERjxok4xHlux2j9DQEA10RSCnZsCPDy2/v4OW3\nd7KnrfszZRoaJ82awOnzGlg4bxK1yWjlnnzRRuATDxtMqFHxAMXw47gee9qyZAsu5gBTpEeMqweg\nlLnTRiD2X+xxrIkDpHOu39FxSF84x/WwbRekj2loRMMGNYkw0SFqRaw4unieT7ZoB64h9+AxAikl\nW/d08up7u3n13V1s2N5Oz6/BrGNqKquBmQ1B2wjP97EsB5QRUAwTHbkiezMFLFcSi4YOKQNuRAn/\n4SCEkKdf8VPqkjGOmZDgmLqq4HFC8JgaQFqe5/vYthPktuoaYUOjOhYiHgupOMEYxPcluULQY6hY\n2p9Y1zVCob7TR9u7iry+djevvbubNRuasR2vci5dHeG0ORM5dU49J8+upyoWqhgBgSQa1qmrVu4g\nxeDguB7N7Tm6Cg66phOJHHoX2q68zep1e/jM4nmjW/jfv/RnlcrNfYmGDSbXlQxBXbdBaKhNHNDn\nbzsutu0s5kspAAAgAElEQVSgAaapEwnpJOPBqkBldowtpJQULIdM1qJgudiOj9A1wqaxX8M5y/F4\na2Mzr767m9fe2017V3dwWBMwa0qKU2fXc+qxEzm2lCpatBzwPSIhnZqqCNXx/YNtCkV/SClp7yrS\n1lnA8SESDh1SoaPj+qzf1sabG1tYvX4PG7e340t47dHPjG7hb9rdxZ62HDv2ZtnR0sXOlizbW7rY\n0dJFtuD0cx/U18SYXDIE5ZXC5LoqUlX7fzE938eyHXzPR9cEIUMjFjGoioaJhA+eTaIYXRQth46s\nRc5ycRwPX4hSC+pu943vS7bs7mD1+mbWrN/De1tacb3u70ssYnLyrAmcMrue046tpz4Vp2g7uK5H\nSBckoiapqqhKOlD0SWeuSFtnkaLtDdh3D6XP5Z5O3trYzJsbWnhn897KdqcAhi6YO62Wf//SRaNb\n+A8U3O3MWfsZhJ17s+xuy/Ub2Y6FDY6ZUMXkCb1XCg218V6uH9f1sB0X3/cxNBHEC0IGVfEQkdDh\nrwy+/OUvc++995LLBf9f8XicW2+9lbvuuuuwfp/iyLEdj668RTZvB3ECX2IYOmGz+9+5YLn8dVML\na9Y3s2Z9Mzv3Znv9jvpUjBNm1nFi4wRObKwjlQhTtB10gjTkmkSERCyk9hEex+QKFq0dRQqWi9A1\nopGBNRLc05bjzY0tvLmhmbeaWujM2b3OT62v4qTSJOTExgmYhjb6ffwHy+rpC8f1S6uELna0lAxD\nyUD0t0rQBNSn4vsZhGPqEiQTwSrBdT0c18XzfHQhMAyNkCGoioWJRcwBxQxM08R13coqQkqJYRg4\nTt/jOhjKkAw+PeMEluNX3EMh06j8Gze351i9rpk3NjTz1saWyvaSZSamYpzQWMcJMydwUmMdVTET\n23Exda1kCMIkomHlWhzDlIsT27osipaLGECFupSSXa1Z3tncyrubW3ln0172tOd7XVObjHLyrAmc\nPGsCJ82aQLo62uv8mAjurt/Rga4JNE074i+JlJLOnM2Olq7ulULpcU9bjv7SX+MRk8kTEkyZUMXk\nuiCG0FAbZ1JtgrCpY7sunuuBlBi6hqkLIiGDRCxEOGT08tnZtk0kEqH8txdCUCwWCYUObzvBwTYk\nir4pWg6deYt80cV2fTwfDFMnbBr4Mmgh8demvbzV1MI7m1vJ72MI6mqizJtWy9xpaeZOSzMxHUWU\nPi9hQyOZCBOPHpp/VzHy8DyfjpxVSTfWDiL2Xsml+M6mvbyzuZX3trSSyVq9rolHTE4qifwpsyfQ\nUJs4oPEYE8Lf2pHH9Txsx8P3A2HzKT36El+WnwNCBMZBgCY0dF1DH6DBcFyP3a25XgYhiCVk9/sS\n96SmKkxDOkFDXWAIGtJxGuoS1FVHMA0N1/PRCL7ghi7Q8JkxZSLSd8v/j0ck/INtSBQDo5xG2lVa\nFTiuj9A0DENH13U27+rgr5taeLtpb5+GIGzqzJmSYu70NHOnppnRUE3E1Hnk4R/y4x89yttvrqY6\nHqYuXdNrBadWeCOP8t4UhVIWWfgAPvtM1mL9tjbWb2tn3fY2NmxrJ2+5va5JxsMcN6OW42fUctyM\nOmY0JHtVmh+MMSH8hzJGz/NxfR/PC76IthM0AXNdHyklXslYeBKkHxgQTQiEJtA1HV0T+2V6SCnp\nyHbHEnbszbK7Ncvu1hy723I4rt/veJLxMA11cRpqE0xKB483LP00rVv/SigaQ/qSYraNaFWa99Y3\nEQubxCIGIdMY8MxPCf/IwXJcsnmbbN7GLsUKNE3D0HV2teVZu7WVtVvbeG9LK7ta93dh1qdibFm7\nCiuzE7trD1bnHuxCFum7rHn9VWIRk8mT6nDtolrhHUUKRYeObDFIDigZ/L52o7Mcj6admYrQr9/e\nTvM+bhuAiel4SeQDsT/YjP5gjDvhP1R8X+J6frCisD0st7yyCIyE55VWFcFg0IQofZGDmZ3vS1o7\nC+xuzZWCyll2tebYtTfLnrYc9gGMQlUsxMR0jNUvP88J8xr5yIUfJF0VIV0dJlUVwdSC1Yuhaxia\nIBLWiYaCzc9NQ6t8MJSrZ+QipaRouXTmLAp22UUk0XSNguWzcWeGtVtaeW9LGxt3ZnrVEJRx8+18\n4PTjmT0lxZQJVdQlw/zdRy/EtS1cx8ZzLXZs2UhVIk7I1NWKYJBxPZ9cwaYzb2GVmggKbf+24h1Z\ni027Oti8K1N67GBHS9d+7uNISGf2MSnmTE0xZ0qKOVPTlWrxwRyzEv5BwvV8XNfDcjwsx6Voe0hf\n4voSz+82EOVYhCY0uvI2u1pzvQzC7rYcu1pzfX7Jy2gC0skoE1Nx6lMxJqbi1CYjpKsj1FWHScbD\nGIaGLgQ/+P53eOSHP6Bp0yaiYZPaVBKQFItBDrr64o8sfF9StB06S72HysZAAi2ZIk0727nn+z8i\nVD0Js2oimr5/cZjvFLCzLTjZFtxsC/fe/RWm1SdJRAzOPPMM7GIez7VxrQK+a6FrAsex9x+Mohe2\n45Ev2GSLNpYTeAp8IUoZXkFad9F22dHSxfaWLNv2dLJ5dyDyPRsCltE0wdT6qorAHzs1xZT66kNy\n2xwqvi+xHZdjp9Qo4R8ufF8GLSNKhqFou8GqoodxkAiEEHTkLFo7LfZ2FGhuz9HcnmdPW46NW/dQ\n9DSE6N/VY+gaE2qiTEzHmVATo7Y6Qqo6QioRZsmnP0HbzrWEInGk9LHznYTjSbZs2U7Y1AmHDExT\nP+wgoppRDj7lIrNswWb2sceD0DFCEXTTIJyYQCQ5iU/903Vs3dPJW+u2oZl9zxBrEmEaauO8+qfn\ncHJtuIUMTq6NXz7xM6ri4WC1qgl0XRANGURCOqGQganrYyK76GCfzeD8fexta6dgucyeM49/vuJK\nrrn2uqBIVAh0XSdk6nTk7CDO19zFjr1dbG/uYntLF3szhT7fOxIymNFQzcyGGmY0JJnZkGTqxGrC\nR1jH4XqB69r3fXwpA5euL9E0Ahd1Ka6pEbh5DUPD1DXq0wkl/COJIP4QGAXLDlYQgVspmPmdftpJ\nZDPNVE+aQziextfDVNfP5GOX/gPN7Xk279yL4x/8w+TkO7BzrXhWF5dfcjG1NXHSVWFSVYErqSYe\nClYNmgh+RFC4FgoZhE0d09B7uZTKKNfS0NJTvGzHpbZ+Mldf+3muvf4GXE/y0MPLeOKpX3P/Qz9m\n594cD/345zRMn0eRaL+rSCGCFMCGdIK9u7ew9u1V3Pz5K0klItx43RV84m8/zDVL/xlNFzz04AP8\n6NHHWL1mFaauMWd2I9dfdw1f/9qd6LrGV++4g/vuOzzDf6SThgPdL6UkFI7geT5CN9CNEEI3CYej\nrHnzTTxfcuaZHwABuq6jGWH0cIJQPM3NX/wqe9ryNLfngsdMvt+/paELGmqDVO8pE6oCkZ+cZGIq\nPiDjKaXcT8yREk0Ewq1r3UKuaYKQoQffx9JkTde1AU3ajnjP3aPJWBT+g2FZFtFoDCmD+IARjvHO\nus1ouoHnSxaccjz5rgzJhjmE4rX4mklV3TQu/uQ/sjdTYG9Hnua2LEI7sHHQNUG6OsLWDX9l+pSJ\nvG/BydQkAjdSMh4iGQ9RkwgTC+touoaGwNAFnucyt3EqnhMsbYXQyOfzlZ1/FEOL70ss2yVbsClY\nbpC84Elc3yfTZfOZf7oKLVKNGUtjxNIYsRRGtOagn4dkPExdTZS317yKZ+Xw7Ryencezc0jP4kcP\nP0hVzOBjH74Qx3GQ0kf6Pr7noWmC1atXIYDvf/97PPboo7zxxptoAo4//jiuvHIpt99+G9OmTsPz\n3EoiAlKiGwa7du0C4K677uLBB3/Ixo0b8aVk7tx5LL3ySm686WaQcOKJJ+JJiSa04P9HCEwzxEsv\nvYQvg9qLiz/xSTQzghaKYITiXH/TbXTkHdo7g7YIb767AT2cQNMPnPyQiJpMqk0wZUKCKfWByB8z\noYqJ6XifwlsWc88PXMC+LxGArgexQV0PxFzXBWHTIGTqhAwdXdf6nGAdKUr4RxkHy9IJzkcrhkE3\nI6zduA3NMPA8n1NPOo5cRwtV9TMJxdP46FRPmMH/XHoDezvy/OGlv6CHqzFjyQGNJxLSSVVFSFVF\nSFdHScZD/OtD3yHXtgMnn0ETkt8/s5zqeBhN13jwB/fz2MM/4I2316JrcNyxM7nu2uv52te+iqFr\n3HnnHXzrvvuUq2iQKft2/9cdd/LQsh+xes2b+D584NwP8ul/+B/83Sf/gZaOAq0dOb59/yMYkSr0\nSBIjmiQcT/XbD2tfhADPLuJ7Nr5bRLoWZ5w+n3g0TDRs8J9P/hzfdZDSA9/D9z0EPl+6/XaEkPzL\n1+5E+h4IgRAa//K1ryE0HSnhzju/ii8lAoHQDdAMdMNkyRVXYbseRcvhyf98CqEZaEYEzYzQOHse\n2YJDrmj3aqlx0L+X5+AVOnjfacczqTbBxHSM+lSciakY9ek48YiJ55f8/NIP3LS+BILZua4FLluj\nNEMvz8xDIR1DD2bnR9N9poR/lHEwV8rBDEN5Odye6cB2fRomTuSq627kxpu+gCclluVw2knzsItZ\nQvE0VfUzuef7j5ItBhuit3dZtHUWSo2jilgHCEL3JGQGje62Nb1HMdsKroVb7KLQsQfT0Pne975L\nIhbiv198IZndGwjHkoCPlesgWpXivbVNiNIXyDR1QobWa2mr+iUdPmWjULRcOrJ5jj/hxJJ4mhim\nyXPP/YG8LWnPFmnvKvDVr92NFoqhh+Lo4TgL3nc2nTmbjpzVb+X7SMD3HHyngO8U8Z0CnpXFt3Nc\nc+U/kaqK8L9uvwkn34Xv5JGejef7CAR/+MNKNBG4VspuT1FyfZqmXnF9lj+PowEl/KOMg/lBj9TH\nvp/h0HR27N6LFDqu4+FBJd7g+ZKi45HpsunI23TmbJ7+/XJeX/02H/745bRnLd786zoiiTQ+h/aF\ncIpduIVOfCfPovPPpaYqSlUsRCxiEAubxKMGiUiIeEQnHjWJhw00TfDgAz/gsUceYM2b7yKE4KTj\nZnPV1dfypS99kVDJD1qebR0NgzHSg999f35Msvk8juNRVz8JKXR0w0TTDITQMMMhXn75FXwJHzzv\n/GAmHopimBGEHkIPRfiXr3+DguWSK1o89PAyEDpC0xGawcc/8Xd4vuTp3/0e0ANRQkIQT+X8888n\nGI5kxfIVgRvJc5G+y6f//pNEwiFCps4D938Pz7WDlYRn49kFhO/w+M9/Sjxq8q8/eoyf/fSnrFix\nAk3AOed8gP/xj//A9ddfjw5893vf4ZGHHmLzpg2Yhk59/QS+cPNNfOPukfFvM5go4R9jHKmwHKrh\nKKex2q5XCka7+J4MDEQ5W8mTFB2XbMGlrTPP0iuW4PkCM1pNNFnPR//uU3TlHTLZYqmcvXjArKW+\n0LSg4+XeXduw8hnwbDwrR7GrFUMXfOlLXyQWMUhETKIRg1jYIBoyghiFFhTpaUJ0B9EAynUSuigt\nzwX/++v/wne/820ymQy6plFVlTikytmRHvweWOZL/+e/9KUvc9+3vk1rayuuL5nU0MB1132eW2+9\nHdf3mTt3XlCrEPyhAYGhG6xes5of/OAH/PjH/8afX/kzEskZZ57BZ//nZ7n22msBWLBgAZ5brmKV\n+L6Prum8/dabIATf/e63WfbII7z33rsYus6M6dP4/PXX8rU770TTBbo2sMDnWEJKWSpWdclbDkXb\nw3F9Zk9JK+FXdDNUM1LflzieR1UigWNb6GYEoWm4Vp5oVZo3392A50sWnnI82UwzibppmJFqXM+n\nesJ0vnjn3eSLLsse/RGe1AgnUuihOJoZwYxUoZmHtwG6JoIWyvGISSwaPMYjZrCKiJjEwoGhiIYM\nohGDG6/+HJ0tWzFDYTwrT659F/GaCbz+5rtoAk494Vjyna2YkQRIiWPlCMeSbN6yDV3X8DyXyRPr\nerTk0MjmckQjA+vTP9JXDAfjSMY/2v/fh4Ke9UNFy8Vyve4Ucc8nyEIVaHpQIW4auirgUgw/A51R\nZjo6cT2fCXW1XPf5m7nl1tuxvWB3qxPmNuIUuwAIx1P8Zc07SKFTKAarhn/8zKdwXA8jnCCWrOdz\nV91AznLpytt05W1yBYdc0SFfdHr1Kj8cpO8Tj4WIhs1K7vsbr/8Zu9CJ5xTRhOSzn/0s8WjQkM/U\n4I7bbyTf0YxnFzDDYf7fr54mEQsTDRk88tCD/OujD/KX199GCFh46vF87opruPGmG9GAecfOpJjN\noIeiICWeU8QMx2jZ29qd6id6rmCCY2WjMpQpk4rBoWdrGdsJVtO24+F7EldSWkX7lY4BAhHEuoyB\n1d8o4VeMOvoLXhuGiev5xOMxXMdG00MITcNzikTiNby7fhO+DJr2+TLIwJASHM+nYHsULJeC5ZK3\nPAqlJXHBcsgVXXKFwEjkig7ZvM2aN98O8rxDsUCABxHftfHsILjouzaulS/5uT9IJGRg6oLH/++/\nY+U78B0LwzD58le+QjQcwjR1nv3db3j6l0/yb//+U0KmxqUXX8Tff/ozXHPVVeiaYOGpx5PLNBOO\nJ5FSYuc7iVanefvdDQgh+N53vs2yhx/g7XfWIwQcP3cWV119HbfffjuaJpjSUI9t5YOsGimRvodh\nhujqyiI0wVfvuINvf/tbdHZ0IoQ4ZFfYkXCkbqrBGFt3c8jSoy8r2T9OKaXT8XxkqXmkJ4PPoVf6\nTAbJU72bSZaDxoMVj1LCrxh1HMxHfqhfXimDOEQQsPZLvZkkjuPhePt3fD3puGPJd+4lHK8JjE6u\ng2T9DH797AtYtsdnPnU5hVyWWLIezQjjei6xZD1LrrqeguWy+o232Ni0mTPO/iAF2+Xdd9dRVVOL\nbkYo2C5D+XE2DY2QqZNpba6kXAokCxYsCFYjhsaK3z+Nle9E1zV8z8XOdxEKh/n8jTcGAiTgX75y\nG8VsG77nEApHeeSxfyMaCRMyBJ/8+EfobNtJOFoF0iPf0UKsuo6/vL4GIXoanhpAYuU6iCXreOPt\ntdx///386JEHeP2NdwDB/JPnsWTpNdxw000AfO8732HZIw/w5ttrAcHJJxzLFVdew8033QxCMHf2\nDIq5DEY4DoBr5QjHk6zbsAWAY2dPx8p19DofidewdsNmkJK5pdWUGUkA4BSzRKtS/HVtE7Ik0ACy\n9FkIHoP/yCAWXSIQ7fJnNCi8KrVq0cSANkb3fBns71FqKOm4Qc8wx/WxXR/b9ig6biWuZtle0OKj\nj+OW45XOu9iOR9H2+O09n1DCrxg9HG1XQ1/v/4VbbuFrX/s6Ukq+cscdfO+732Hrjj34vs/MGdO5\n8prPc/PNX+hlQMozvnLbcFlawucKFovOPwfbKqAbEaI1E3hg2f/B9wWW6/LVr3wF2wpWMcII4fmS\nSLyGD3/sE1hOsEp57bXXS5k3YfRQhHh1CtvxhtSoDAS9FCzP57qCXH7fA3xmzJiOaeisffcdXLsQ\nZJz4Lp5dRDdMLli0CE0TPPNfT2NbOQwjhJQ+jl0gFIrwycv/Hk0TSOnzs//zYxwrj5Q+phlmyZVX\nYxoGmhD4vseD938X2wraKoTCMa6+/gb0UvGa63k89MD3ep2/6trr0XoUt5Vn857vl2bzstejL3se\n8/c/5wWxrkDIu0Xd8bxgVVBaGfS3Q+BgMer33B3pY1QoDoXuFY0GQiB9DzMUprmlHU9K7vrfd/PD\nB+/nnXVNSODE447liiuv5YabbgpcN7bDKSfMwc53AkEM5NU33kXXdRzX57xzziKfzRBNTkTXDaxi\nnqp0A99/6DFcN2gQ95Uv3Ybj2Gi6STiWZOm1N+D5wc51P//5/4frupjhOJpu4kswI3GOP+kUHMfH\ndj127NgBaAjdQNMNzHAE7xCKpxRBwpNZ8tubpepds1S7Yho6kVDQVyvor6VX+myFzdI50+jzeMgM\n3IVnndAwsoRfCHGLlPK+0vNLgAzQKKV8tI9rlfArxhRDmY4rpeRLX/4y3/rWt2hvz+D7ktraWm64\n8Sa+cscd+J5kYn0dtpVHNyMgBJ5dqLhLpJR85zvf5tFHHmT1W2uREhacchyfW3ot111/PRKYf+I8\nch0thOMphIBitp1EahJ/fv0tXF9yzpmnk+toIZqsR2g6dr6LRLqB//vELynaNv/0j5/CymfRdINo\nVZr7vvdDNF3H98F1Xb58+xewizkQGpFYFf/rzrsQmoaU8M27vk6xkCUUiSOEhm0XicaTXHXt5/Gl\n5JGHHsTKZwmFgwww2yoSjsa58uogXfThHz6Ale/CDAdxG8cqEI4luOqa6yt/33IRV+VxPzfOvudK\nAXdNqzwvC/m+oh4Ie9ADS+8RkB9sDubjH9i27oOIEGIRsBi4TwgxH0BKuUII0SiEOE1KuXq4x6RQ\nDCd33XVXL5E/1Pz+W2+9tU/DAYGv+Rt338037r67+/fbvdsFf+HmG4L7sx2V+2/+/DVMmxS08Pj+\nfV/n+/d9vXJ9rmNvr/s/f80S7r33Xjrbdlfuv/6qf+L4mXVIKbn6nz/Ft771LXZvfgcpIZWq4XP/\n/XOce/JkqqoSuI5d6RvUucPjc3+7kOaWNnygoWyUjDAI8ByLz//9C2zZugMfeO/cY3n4hz/gnbUb\nATh+7mw+efV1XH7ebAB2rprCsocf4K131iMllRjB5efNRgI7Vk3j0UceYM1ba5HAaSfN45+XXsMl\n5zZW/v96zjNlj4Oy57Gyv3+fSWnPGAESEOWHUkwAie+5WH6QqRNUsBFcUcrQgsD4CKhkbg16H5/h\nnk0LIS4AbpNSXiiEuAf4vZTyudLx+eWVQI/r1YxfoRgjDEfWzUiiZ6xH9nxNd9ZZudWyL4NCSJ8g\n3VMSvHb9coYQFQNU+ekZS6L7Gs+HEwJDfPRdPeUZvRDimZLwPww8LKVcUxL+xVLK2/e5R67f1kbI\n0KiOhYhFg9JthUKhUPTPgVo2DLerJ93HsYOuYcKldr97sw5OpoCQEtPQCJsa1fEw8Uho1DROUigU\niqPNsAl/aba/Yp/DGbqNQQpo7eve79zbvcw78+xzOPPsc4EgNW53pojr5jCEwDQ0oiGd6kSYSMgc\nEzsKKRQKxUBYuXIlK1euHNC1w+bqKWXvANQCVwBLSq8XSikfFULcAjwrpVyzz31ya0tuwO/jej62\n4+B7ProWtFWNRQyqomEiYUO19lUoFOOCEeHqkVL+ojSYJUAyOCTXCCEWlvz7mX1F/3AIOi323gkq\nZ3m0dWWR0sfQgpVBPGJSFQt6qyhjoFAoxhOjooDrUGb8A6W8Ibr0fQxdI6QLYiVjEAmbg/5+CoVC\nMZyMiBn/kbA3kyddHR1Un32wkXjv7KDOoktrNtunMVArA4VCMVYYFTP+hUt+imloTErHmVQbZ1Jt\ngoZ06bE2Tl1NDH2IArmO6+E4wb6hZTdRNGxQVWrTq4yBQqEYiYz6GX8yHqYjZ7GtuYttzV37nTd0\nQX0qMAoN6QQNdd1GYUJN7Ih25OlrZZC3PTK5XGl3oKA8OxoKtgeMqdRShUIxwhkVM/6tLTnyRYfd\nbTl2t+bY1ZrtfmzL0dZZ7Pd+TRPU18QCo1CbKD3GmZROMDEd20/UDxfP97FtF8/z0URgjMKmRiKi\nis4UCsXwM+r33D1YcLdou+xpy7GrNcfu1mzpMceutiytHYV+W9UKAXXJGA37GoXaBBPTccJHKNZS\nSizbxfM8pB8UnZm6IKrSSxUKxRAz5oX/QNiOR3N7rtsYVAxDlpZMngO1xK5NRitxhfIqITAMcaJH\nkPkTxA3cwFUkBIahETIEVbEw0bCpVgcKheKIGdfCfyAc16cl07dRaG7P4x3AKtQkwt3uo3T3imFS\nbZxENHTIY/F9ieO6OK4HvsQorw5CBolYiEjIULEDhUIxYJTwHwae59PSUWB3OZ7Qlqs8392Ww3H9\nfu9NRM0erqPecYXqeOiQ3Duu62G7Lr4n0ZCYpoahBdXI8UiQZqpaUygUin1Rwj/I+L6krbMQrA7a\nsr1WDHvachRtr997Y2GDSb2MQXcGUqoqMmCjYDsuruvh+z6aEBh6d0VyPGKqugOFYpwz6oV/y65M\npd+075d7V3fvZ1rZsR4QpV1wdF0b0IbHg42UkkzW6h1kLmUf7WoNspP6I2zqgbsoHd9vxVA7wAI2\nq5RZ5PseuhDoepBuGo+axMKmWiEoFOOEUS/8Bxuj5/m4vo/fc5NjN9gbVPoSj+4NlINHqBgLAbqm\nV4zFUM6SpZR05e3SSqHbdVROT+3M2/3eaxoaE1PxShxhYirOxHSM+lSc+lSMSOjAJRnlFYLn++iA\nXtoOLmIG9QfhkDFoqa0KheLoM+aF/3AoGwvP87FtD9vzsB0P3+s2FJ4MdsnxJSWDIND0YDPk8h6c\ng0muYFdWBj0Nwu62HO1d/dcqQFDkNjEdY2IqTn06xsR0YBzqUzHqktF+A8Ou6+G4Hp7ngaTiMjJ0\nQTwSIho2lNtIoRiFKOE/QqSUuF7JSLgelu3heD6O6+OVXE/7Golgr8zASBiDMJMuWEGtwu7WLLva\ncjS35dnTnqO5PU9zex7X6z/YrGmCCclosDqoGIVgtTAxHSfZR8A5yDLycD0P6ftoEGQaaYKQqRON\nmETCBiFDV0ZBoRiBKOEfRqSUgauptIIoG4ryCsL3JV7Z3SQC4yA0UWonfXiuJt+XtHUVaW7Lsac9\nx562PM2lxz3tB65shiC2UJ8KDMKEmhh1NVEm1MSYUHqsSUR6rW4838dxPXzPx/d8NE1gGgJD9DAK\nIYOQqYyCQnG0UMI/QnE9v5SuGRgH2/XwvH0MhB8EtXUtMAqapmEYhxa4th2PlkyePe35fYxDnj1t\nOXIHCDhD4P6pTcaYkIwyoeQ6Ch5j1Kei1CZjlSpnz/dxnCCWUF4p6HrgOjJ0QTRsEg0bffZAUigU\ng4cS/lGO7wdBa9f1sRwP23axXR9fBsbBkxLfkwSLiMDNpGt6SXAPbiByBbtiFFo6CuzN5GnJFGjJ\n5LBoho8AACAASURBVNmbKdCRsw76O6rjodIqIVgp1JWe11ZHqE1GSSYiaIKK+8j3fASBG8rQBboQ\nmKZOJGQQCemETOOImuspFOMdJfzjhF5uJtvDcjysUmaTW45F9ONmOlCw2nI8WjvyNLcX2NuRp6U9\nz96OAi3teVo6CrR25HG9A/8baZogVRUhXR2htjoa/CQjpKuj1Caj1FZHqElEEIIgriAlQko0TaBr\nAkMLsrAiIYNwSCdsBqsGlZqqUPTNqBf+rbszlS97yNAxjMP3hysC9nUz7ResLrmZeq4iysHqfdNe\nfV+SyRZ7rBK6VwxtnUXaOgtksgdfNQBUxUKBcUhGKwYinYyQropQkwhTFQsRj5pBDYfvowsC46UJ\nNBEYifJnJWzqGIauVg7/f3tnHmdHUe79b1V3nzOTfQMBASEBBMWFTREXuGwCV71sgt7rirIpSrZJ\nCJBASIBIFhIBCaBcXr3iBVlE2RRZ1FflVZa4sSiEPQlkksnMZJZzurue94+qPqfPZLJCMmeY+n0+\nZ7q7qpdnenmep56q51ceAxL9X/Gv7CBJUjuyxhhEBETQyv5zgVYoZT9+y3GjKRYCosAbiTeLrBVR\nGdEUJ5Rj2xdhwIaajBvRBOh8qCmXGxEnhpb2bla1dbG6tYtVbV2sautmVWsXq9u6WNXaTUt710Zb\nDgBawbAhRUYOaWDE0AZGDi0yYkgDI4fa39DGiCGDIoYPLlKItH1XgCCwrYYssa0Q9e5MXHDBBVxx\nxRV0dNiM8cGDBzNlyhQuvfTSrXqvPTzeLESEcmLDwcOGNPZvxT/7h/+PQY0Rg4uRXTZElUzUwW67\noWBHkNhOUUPiRpwI1khk8WStFVpB4BRUIdBEUeAok6txcR9C2HwYY4e9ZqGmLDcibyRMriVhw00K\npZ3SRbG2q2wNQt5AtHbR0l5izdpuWtq7aetYf6JbTzQUgopRGOEMxPDBRYYOLjDUvTtDGkOGuPVA\nKz64z+6UutoJQku2lyZlokIDr69cTSEKuGTmxSyYP3eLDYM3LB5birxi7y6ndJeTivOVpPbbUtYj\nZp9dR/VvxX/g6f+z0f20VgwqhgxuLDC4IXIkZlXDMKghZzCydTcWvRhpilGIIqODsB6icmGEQDmD\nYeVx3qGikGtR6BxVhMfGkW9JxGk13JTkOq17y7RWSmHEdki3dcbOGJRY026Nwpq1Jbt02+UNkOn1\nhFYwpLHA0MEFnv3b45Q7W0m62zBxF1OnTmXY4AaGNEac9l+foWPVMjAJIilpuZti4xCeeu5ltFIs\nmH8Fi69eyAsvvUYQaHbZaXsmTJzE7FmzCANNsVggSZJKK1RECMOQON7w6CqPtzfiJK0wDpTihFI5\nrXwHFadJQOusby5YbzQjSQ1jdxjavxX/wtuepLM7pqMrtsvs1xXTWbLLUrx+YrRNxaBiWG1R5IzF\noB4GZFAxpLEY0lAIaSwGNBYCGouhHWIpgnZWQysb+sgbDaUgcnHyKNDooLZz9a3ICH47epRZpnWS\n2OGivWVa9zQUSkFXOaGtI6a9s0xrR5nWjhJtHSXaeqy3dZRY27X5ijctd5GWO9lzj90ZOqjAkMYC\n9911K90dLUhSJi13UO5oIdDwo5tvYXBDgUIgfHT/PUnLNr8iCIv847lXaCjYOZznz/sO1151JS+8\nsowo0Lxzx+2ZMGESs2ZfQqA1M6ZfyNy5629xvB2ff39D1vo1LvpQdu9tnK6HHcBYJzPPDpApdRGh\nu5zQ0WX13tou+z63d5ZZ21WurneWa8ofWPDZ/q34X3i9HZUjYusNSWqqRsHdoPz2OgYjt93ZFdNZ\nSt60rNlE7NlY9UHFkIZiyCC33ZgzGMVCQENkl40FTbEQ0lgIKEYBN15/NTdeu4An/vEiSsF+++zG\nWeeMZ/ykKTY0Qq0RscbDxdS1YuTwoSRJvNU8yjerWLaFYspTcsSJoRQnroXhDESWKyEgLvSUpIaO\nUsqxRx1OKoqGoaMJikPRYQMNw8bw7yd8jta1JVo7Srz0ynKCwmDUFhIBiklISh2k5S5M3M1BBx3A\n4IYCgxpCbr/lR5Q7WhETY+Juyp2tBIHihht/SEMh4LOf+jc6W1faFqpJSeMSxcYh/ONfL6MU7DNu\nZ8rdnQRhwbZg05io0MCrK1YSaM3ls2exaNECXn9jFVrDdqNHMXHSZGbPnoVSaqsalo0du7Xr14e8\n4yBSHeCQOg6wVIQkMXaZGnB9WkLvxJGCdfpAkRhDOTbEqaFUTuksJbV6qCumo7ucK0tyuqpMZ3e8\nwQmj1ofHbvhC/1b8y1e22Rhxamxs2N1gwN58kcr0ihmLp4j9I+4huLPZ8I17ICgBlHtA2IdSTugu\nJe7ml50BSSoPp/JQehqX7hizJU9nPTBJmTTuxsTdpOVOTFrmE5/4OA2FkOeffYp//PVxTvva6RQL\nAYvmzuJjH/8ExxxzLMUoIFDC6V84gaTciYlLiKT89tElDB3cQKAVVy2cz/XXXMmSZ15CK3j/u3fl\nrHMmMGnyVJS96ShlE6+yMJdlO7Xlo0cM69WwlMvlTeocjaJog6GOvvBYM1oOY4QLp89g4ZULePGV\n5ZQTw97jduXMc8YzfuJkjMC+e76LUlc7YaERHTWC0gweuQPf//GddHYntHd0c+nsWaigQFAcQtQ4\njEMOParirb3R3EIQNbwlcqdJCROX2GnHd1SypYuR5tH/+whJuQuTlMCkfPmrp9FYjChGAfMuv5ju\ntWtQSjBJmbirnbBQ5Me33EEYak4+7gi6OloIggCTJsTdaykUG/nL00tBKT7w7t1sH0hUtDLEJYqN\ng3nquVcAWLhgPtddcyV/e+ZFAN63926c+c3xTJrUxF5j31kxSlb+MoXiIP71wmsA7Ln7OymXeq8X\n6P34hkE8s9Qev/fYnSl3dxCExcr9KTYO4W//fAkRuGrRldzwvUX8+a//QsRw8P778qUzzuFLp51p\nW5POUSjH1lPPwo9xbr2cGLrLif2VErpKbt3F3LtLCV1u2V1Otkhp90RDIayEr4c0FmzrclBUaWXW\nLAcVGFQMOejd29eH4ldKne5Wx4nIea7sJGANMFZEbujlmLdkHL9UwgD2JzkLn7pmWWbdE2MwznoI\ntcbFZMYkZ1Qy6dJU6CqndJVjSmX7cpTilO6Sjdl1uZeks+SMSymulHW57W5X/1YakTwKUUBn+xpr\nVJIyJimRxl1gEo459jiKUUAhDChE2mXXatfxXd3WCOee+QWnWGKQlJ/d9xCDGooUQs3HD9ybUmcb\nOggBwaQpUaGBp5YuQwFxHPPecTthjA3PKR3wzxeXU4gKoGDcLtuTxGXrTQuIGMIoYmVzKzpQzJo5\nk4VXzqd5dQsKGD1qJJMmT2bWLOux5ltEWzKaa2OG55BDDuGPf/wj7e3tGCMMHz6MDx18CA8++BBx\nYhgzahgmTVBBiEJh0pggKvLM0mUYgS/+5yk89sff8sCjT9Mdp5xy/HG8d/+P8u1J0+gupXR0l7hi\nzuUoHREUGgkbhnD40Z+iq+zek+6Y5a83E0SNW9zi2BKErmUZhYrVzW9gkhiTWmO99z77VMKUf13y\nOGkSo7DPzqQxWmv+7fAjUQgP/PJeTBIjYtA64FP/cQJaaQSb+X3vL36GGNs3o4OQTx73abRW7rsz\n/PK+e2y90gRhxMcOPRwj9vtL0pS/LFlinRcdoHTIrruNtaEXY2he2QxKo4MIHW7+THlbgijULixs\nW/tDGnsPIw/OhZnzZY3FaLOHJCdJytgdh/W94ldKHQEsFZEXlFK3AtcBq7EK/3ZnFB4TkSd7HFf3\nCVz5ce/G2M5JO6pIiJ0XGaeptfym1pjUzCvgjEl3qcyB7xtX+fCj4mD++9Z7SYyiHKd0lcpcfOF5\noCOCqIGg0Mjnvvg14kTojhMeeuhhe2xUREdFdFAkKDSgnRe0rWANSxmTxuyy8zspRAGFKCAMFI89\n+ntbZxIwKZ854USKLltXK7jxuqsq9UoM502fSbFgR92cP/mbxN2dKDE2ZBKXCLTmlp/da/tOtK7M\nQxAFmjBUhK6sAmcgsMzcFWMBsM/YHYnLJbS2lBLGpESFIv96cQUK2GO3HYjLJZSrF5MSRgVeWdaM\nUoodtx9eUVzVy2la2jp6aTEpZ9gKLFvRTGJg5x1GY9J4HcPx9NJlIPC+zOMuNBCERYwRGoeN4fZ7\nHqKcGL5w6kkIQlQcgo6KCJqoYQjnTj6fUpzypz89xt///jd0WLTvSFhkx53fxXbb70CcGJpXraKt\nfS1Kh+ggQgV2+XaESWMGD2p0zk5AIdQU3Ci/QsUJctvZPpGmsRDRUMwyzbOkwsCFcN0w4YI9Xxgo\n68AAWWjCVEIXbhAJdgnUvJs6t60UlWHT4hxDqegPgxghTl2oCjhg753qQvGfbgWVG5RSc4DngXHA\nAyLyoDMM+4vI3B7H1b3if6vReygkYtWaNkwqdHR1s8tOYyrKRemAZ19YThjZxKaFblTJkqdfQkTY\n7727cfo3JvCNb08kjlPWdpY46rCDUSpAR0XCwiCu+sFPMEZRTqpN3Di2I27KcUo5zkYbpNx/7z2I\nCgjCAjosoIIIHRZ5x0472/3idINsoX0FpSznURjYTvQg17GeLwu04m9LHsekCWJSEMNhhx9BFNrh\nvlrBXbf9LyaNbT3CaV8/iyiy9YiwaO5sV5+AwMzL5hJFIYFWiDFM/tbXSZMYxKAUXHvjzRQLBbSG\nL578KdKk7IYiGySNUUpzz0N/INCKSeecwZLH/sADv3scpeDwg/dlvwM+wvd/eDNKwf5772o9cR2g\nUIhJ0GGB3zzxL8TAJ/bbraKAqjdH8/DjSwE4/ICxiBisOgIQlNLc/+hzJKnhpGM+SprGRA1DUDrE\nJGXChiFcc9MdiBju/vmd3HfnzU55aZQOOfrTp3Lksf/OtHNdo18FKKXtubXmkiuuAmDmtAnE5W6C\nqAAIaVwmjIrMnnc1CJw3/nR7TzJHySSAcM2N/0ug4awvnUJc6iAIQkRS4lInYVjgjvsfQSm4+b9v\n4EffX4QkMcbY1srXvtnE2eeMrxh/MeKUsPv/e6hOpew3WdlFFIH9V1CBthxVgUKjXMuzqrizdVxH\nbqbsJWcYjL3llXB2JbJgI9PVc+Rat9nQ6Gzkm4gwrh48/pqLKvUrYCpwJrBYRJY4xX9UFgLK7Tvg\nFP+bjZFvCCJCodDbcMKI5pY226HlWimp2PXKR4Z9ARfM+w6Lr76SvzzzMgJ8cO93ccY3J/Ct8ZMA\neN9eOxOXSgSFRnQYgQooNg7ljvt+Qykx3HbLT/jFz27nu9f/iNQIk8d/g8OP/hT/duQxGCPMnjkd\nIxCEBZQOQQeEhQZO/a+v2BES5YT77r2n0pTXYcT79zvIhuxycVrLCyS5xL+3+knVJ5TCGS2DiCXL\nGzlyOFpZw7a2vY321jWIpJV9xowZw4477ojWihXLXmPZqy+5uhSMYbex49hjzz1RSvHLe3/unA5x\nyteAghNP/hxKwU9/8kNrOMQZLpdw+eWvn80Tj/+Jvzz+qGvd2n32P+gQPvyRQ1BKcc2Vc5wyt8pL\nTIxSAVMuvASl4HcP/5rfPXQ/uKHXiHDoUcdx1NHHAjB96rk2zKZsaETEoLTmOwu+h1LQ9O0zau5V\npluu/N4PcOq0Jjyo8rpfVcuyfY3TxsbJkq2L8+aFLMxMZf9KmDgfPibbzupq9813PmfcXNVQtakJ\nW2fLq8cfXj+KXym1P/BZEZmmlFoMXCciT25I8V900UWV7cMOO4zDDjtsm8pcb6j3UTX58xsjDB06\nhMmTm5h5yaxKX4uQ63NJpRL6So3h8stmc/WiK/nn0mUIhr3H7cJZ54xn0mT7auzpQi1aB6Co9iE8\n/1q1z6VH0zr7eGJjSBMhFVNJeEkdl5ExhjSFU084liRJCKMCSgcYgULDIBZe+9+kqdA04RukqSEI\nI5QOESAqNDBx6gxSI1y1aL6lkwhcvVKEUZETPvt50lS4++c/cwopdOEihQ5DDj7kE24YYMqSJ5+w\n3rLSKB0wdo+9bBzbWKP8+orXQWf1mobGwa6DeoBYN4910LbsKdqXP13ZXv7EnXWl+JuycI4L+WSh\nnpOB3X2ox2NjeCsMV43Xld9GmDF9Bgvmz6N5dQvGCNuPGcX4CZOYftFFiMCsmTNZtGg+L7/2BsYI\nu+2yA+ecO4GpUy/AAHPmXMq1313IM0vtKJd9nOGaPPk8BJjvWkx//9fLILDvXnZU1fhJUxCBfcbu\nRFzuRgeuj8EZtr8//xoIxOWY97/7nZjUdo7rIGTJM68SRXb6zQ+8e1drGKMCSmnbymsYzIN/WIIx\n8MnDPkQcl12LKkCMIWoYxE/uvB9jhC+eejxpkqADa5hEICwW+e7im6xhSlImf/sMmyWqNDoIuXTe\nVWitEYE4SZh5wRTrTStFEIScN2M2SmuuuPRi0jRF6xBcSCKMCnzz3Ml2SG2Scv33vmvttdZoHfCl\n085AKY0xws3/cxPGGNf/ohAFgQ45/uRTQMAYw113/LTiySut+fTxJ6FdC8CI4e677nCy276X4z5z\nQtXLz6kal1/ea0tRRGoGEFSGWbuRb9lIQZ3bJx/qqRyjqdlG9Tgmq6uwDthQ5Drrbih3Fu4RgS8c\ntXd9KH6l1Bkicr1bPwLbuXugi/s3YY3Akh7HeMXvMaCwNYbDNjU1MXv2pYgIF06/kHlz57KmtQ0R\nGDliGJMmNzFz5iUATJ8xgyud4RMRths9ivETJ5O1vKt5ItVwShhFvN7cioiww3Yj7aismvoCry5f\nxWVzLuV7ixbw3IvLEYQ9d9uJs789gSlTpiECY3d5B0lc23EeFYo899LrAMybezmLr15YGTr6nj16\nbw32PP6fL64AYK98a5Fqx/2zL65gU/TMhnapqcrtKL3tI72V9X5y6blvtq3yq6oyUCFzZN6725j1\nKv5cXGnr/oAjsYr+Obc83JWfDhwBnL6e48TDw6OK888/X8IwlFKpJKVSScIwlPPPP79urv9m5Huz\n/9vWlK3eYIyRNLW/JEklTlIpx4mUyvbndGev+rhfJHDVu4weHh4e9YYN0TJ7RjEPDw+PAYawrwXY\nFDzz0qoKU6Z25GdKVYnPtKaGez909AKeMdPDw8NjXfQLxf+z3y+lWAhtRlzBzqzUULDrdn7WwFEK\n2F8xDFwzJxu7LbUJFLmec13Tc47L8NSVCcIDZzxUxpypNkwW5+Hh4VHv6BeK//ZH/rnZx0ShdinU\ntQajWAhq2DELWYp1Vu84agphQBRpGnLcNYVIV1K6gx7DtPJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for i in range(6):\n", " model.plot(fignum=1,fixed_inputs=[(1, i)],ax=ax,legend=(i==0))\n", "plt.xlabel('years (after first ')\n", "plt.ylabel('time/s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 1\n", "\n", "Can you fix the issue with over confidence in this model? Some things you might try include (a) adding additional covariance functions to handle shorter lengthscale effects. (b) Changing the rank of the coregionalization matrix. (c) Adding a coregionalized noise model using `GPy.kern.white()`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Question 1 answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Predictions in the multioutput case can be very effected by our covariance function *design*. This reflects the themes we saw on the first day where the importance of covariance function choice was emphasized at design time. \n", "\n", "Can you build a covariance matrix that coregionalizes separately over the sex of the athletes and the event identity? Does this matrix perform better?" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# set up the inputs to index sex and event separately.\n", "X2 = np.zeros((X.shape[0], 3))\n", "X2[:, 0] = X[:, 0]\n", "X2[:, 1] = np.remainder(X[:, 1],2) == 1 # Gender\n", "X2[:, 2] = np.floor(X[:, 1]/2) # Event" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X2mean = X2[:,0].mean()\n", "# Zero mean X for the linear trend to be feasible\n", "X2m = X2.copy()\n", "X2m[:,0] -= X2mean" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kern = ((GPy.kern.RBF(1, variance=1, lengthscale=70)+\n", " GPy.kern.Linear(1, 1, active_dims=[0])+\n", " GPy.kern.White(1)+\n", " GPy.kern.Bias(1, 1))\n", " *GPy.kern.Coregionalize(1, output_dim=2, rank=1, active_dims=1, name='gender')\n", " *GPy.kern.Coregionalize(1, output_dim=3, rank=1, active_dims=2, name='event')\n", " )" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#display(kern)\n", "#print \"First coregion:\", [(k.name, k.active_dims) for k in kern.parts[0].parts]\n", "#print \"Second \", [(k.name, k.active_dims) for k in kern.parts[0].parts[1].parts]\n", "\n", "model = GPy.models.GPRegression(X2m, y, kern, normalizer=True) \n", "model.optimize(messages=1,max_iters=5e5)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1962.41269841\n" ] } ], "source": [ "print(X2mean)" ] }, { "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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pU8lyNOLaSr05j3xccC+QOhHOtkkmnIMWlHEC4g/v+yV/fuolZi84iVzo0j/j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\nOK4rjHniCW644UaaNm2K4wpbt2YyY+YsBg4aRNhxef7557n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1+d7UThPxOjlF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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for i in range(2):\n", " for j in range(3):\n", " model.plot(fignum=1,fixed_inputs=[(1, i), (2, j)], \n", " ax=ax, legend=(i==0)*(j==0)*2, \n", " plot_data=(i==0)*(j==0))\n", "ax.set_xlabel('Year (+ {})'.format(X2mean.round(2)))\n", "ax.set_ylabel('Time [s]')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m.plot?" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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S/GTeT5jz2BwASr9fypwrjXaQkcYi22I5tkDmDz0bMXyRf2fX5OHWrolEjrS1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\nq9GHuhYsjJ2mChLH9re2dywJoxzxQr3lB8t7sHyeItXWCayUGgaMEZF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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotlim = (-100, 100)\n", "fig, ax = plt.subplots()\n", "for sex in range(2):\n", " c = 'magenta' if sex == 1 else 'blue'\n", " for event in range(3):\n", " conflabel = label = None\n", " if event == 0:\n", " label = ['Men', 'Women'][sex]\n", " conflabel = label+\" 95\\%\"\n", " model.plot_mean(fixed_inputs=[(1, sex), (2, event)],\n", " color=c, ax=ax, plot_limits=plotlim, label=label)\n", " model.plot_density(fixed_inputs=[(1, sex), (2, event)],\n", " color=c, plot_limits=plotlim,\n", " ax=ax, label=conflabel)\n", " \n", "model.plot_data(visible_dims=[0], ax=ax)\n", "ax.set_xlabel('Year (+ {})'.format(X2mean.round(2)))\n", "ax.set_ylabel('Time [s]')\n", "_ = ax.set_xlim(plotlim)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RDh2d02oxs6VGavsG6IloZXPRjgHH64Cl9AnCIqo+96BMKQjJiwDfC/UoISNINrmkUq4p\nh2kwTAJBGFHIBRQ7Q1ReoXJw3DkNteMEBogjfdrRnf7ain07Gecw0LEQRiG2V+S4OS7NDTWWovkQ\n2plcTlWxe8DxJnRW03I7FZ2+YgKQUlGMAvJWQJSQsShYpBodUknXzEcwGMYRpRSFUkC+MyDqlpAH\n17dJS5ekq+3EkZQ459q1JQBjYSIFoEwQBSSSJeYe51GfqdH4+X1oIfhNvNw/4LiHFoFKUWiauNuL\npKQQBRTsEBmLgl1nkWp0SSVcUyLTYBghUiryBZ9CR4DsUYi8IBk4pC1vyBG3EYBBiKREKolUCoUi\niktNlj9v2fRjKYEQglD6ZDIhJ8xLkanlNAsK2IPOarotXu4c5HXz0aLw+rgtYUJrIISRFoWiEyCT\n2p/g1NmkG12SCac2TG8GwySjlCKX9ym0B8iswsoJUqFL2vZG/OA0rQRAKUUgI3wVEtiS0JZIB5QD\nygZsvbQcC+EKLEsgyk2AiAORVIUgyEihIkAqisU89ZmA+XOSJISj7fMh4IMjLTxp41kOXi1l6eym\nf7rrP9J/Qhro+KsT0WJwatwWM6GiEEQRBRlQcsJeJ3Oi0SFV7xons2HaUCgGZNt8ZLdE5ATp0CXj\neGN+KJpSArDxi4r5s6CpThHIkKIV4jsRkatQHuAJ7JSDm7BxbLsqNmelFL4sMGNGxILXpXsnTvlB\nRBBG+LmIIB+higp8EEWBG9oklUPCcSbf5BGiHcnPxe15YDv9U16XmYcujnMSWhBOQoevTpSTOQwp\nEFDyQkiBVSdIxqGoxp9gmAoEYUS208fvCCELqZJLxvawrfH5/z2lBID4yTyZVMydq5g3D44/3mL+\nfFi0SLemCbJvS6kIVJ6ZrZITj08PO4O2FISUCiGlnhCZU1AEq2SRDB1Sdg1EzJTQIvA8WhReQJuO\nwkFem0RHGpVFYXG83Vz925RSUQgD8k6ATErIQKLJIV1vZjAbjg3KZp38oQDVo3ByNvUkqmY1mFIC\nsGSJYs8eyGaHft2MGX1isGgRLFkCixeDW6WsD1IqQpVn5izJCfOGF4KB7yv6AYWukDAbQQGsgiAZ\nuaSsGhCFEHgZeClu2+PlQCdzmRa0EFS2hVS9nGYpDMmrQKe0yIDTYJNpckkmJjDNh8EwDEEY0dNZ\nImiPED2CdHB0Zp3RMKUEYMsWfT/d3bB3L7z6KuzZA6+8Ajt26FYoHP5ez9NCcNpp8IY36Hb88TCe\n339ZCGa0Sk44PjUmu3U/UeiJRaFokYr0SGG8hoVHRRf9BWEHerSQG+L1s+kvCovR/oYqldUMI0lO\n+RS9ADJgN1jUNXtGEAwTSqEYkD1UQnbFT/mqek/5wzElBWAopIR9+/rE4KWX4LnntEAMpLERzjgD\nli6Fs8+Gk08Gexz+PloICjQ1Ryx4XfKoOx4pFYWijvWVOR3ra5csUpFLyqmRMEqFHhmUBaHcdgH+\nIK8XaF/CwBHDCYz7/PMwkuSkTzGhBcFpsqlrNllQDeOLUoqebInCwQC6IVV0qbMTk/77nFYCMBTd\n3VoI/vjHvtY2INlEXR2ceaYWhGXL4JRT4GgeuJVSlMIiDU0BJxyfoKFu/CaURZGkUNQjBZmVeqRQ\nskjJGhopgHYsv4oeIVQKw58Z3OnsoEWgbD4q+xfmMm7pLoIoIqd8SokQ6iHR4lDXkJh8k5vhmCOM\nJD0dJfz2ENEtqAs90m5thYkbARgEpbQJaetW+O1v4ZlntCmpkqYmePOb4S1v0csZR5GftBQWSWcC\n5s11mNlcnRQTUSQplkIK3QFRTouCKAoSkUNS6dDUmomv99HpsCtNSDvQcxgG+xMn0IJQOVpYwrjk\njC0FIT1WiSgtEfWC9EyXTHpi7LOGY4+SH9J9qITsktg9FvUqQcKp3dGkEYARsm+fFoJnnoGnn9YC\nUckpp2gxOO887UsYy9/cDwMcr0hrq2DenBSuY7N79242bNjA2rVrAVi3bh2XX3458+fPP+rPpJTC\nDyKK+ZBSd4gqKCiBVRJ4kUOSGhOGAtpsVDla2MnQjufZ6LkLb6BvDkP92C+vlCIX+uSdAFWnsJss\n6lsSJiX2NCeX98kd9JFdikTeod46dkaMRgDGgFLab/DEE/Dkk3qUUKqYPFVXp8Xg/PO1INSPstOJ\npCRSBRqaJPd9/3/zj1/+Itdeey0A69ev56tf/So33HDDOH6i/iilKPkhpUKEnw2RhXjOQklghxZe\nZONZNp5dI+LQQ39ReAn4E7oE50BehxaD09BpL05izH6FMJJkVYlSUpuLUjNcMnWmxvJUR0pFtqdE\n4VBszy+51DuJ2vgtjBIjAONAsajNRU88oduf/9x3zLa1E/n883WbM2d05y4FRb7+9c+w8fu3A3Dt\ntddy2223Tdp/tjCS+EGIn9eT2lRJIXyB8hVWIHAjGw8b17Jxx8NrPlYitC/huYr2Ioc7nVP0icEZ\nwOmMOTS1EARkbR9ZJ7EaLOpmeKSSJrpoKuAHET0dJcL2CCsrqAsTpKoVOz6BHI0AmHFvTDKpn/jf\n8hb4zGd0+OkvfgGPPw7btmmT0dNPw623alNRWQxOOunIoaaekwDZ5zg61O5zoK1Aa0tqRBEE421C\ncmwLx/ZIJ+mr0BCjlMIPI3w/opgvEeYlyleIQEAAIgAnsnGlhVvtEYSN9gssBN4T7wvQI4Q/Ar9H\np77YjU6U95v4NQLtXD4TnUb77MM/51CkXJcULuQhykpyu326E0VdUrPFoa7Rm9iiQoajIpf3yR3y\nUd0KN18RqimoStGlYw0zAhgBnZ3wq19pMfjv/+4/D2HuXC0Ey5frCKPBHpjvvvsmbr/9Ri67TJuA\nNm5cz1VX/yMf+ei1NDQojpvt0tQw9PDzpptu4sYbb5xQE9JQKKUIQj2CCPKSsBihfPRTeQCUdM4k\ndyJNTG305ULahjYdDZzhvBAtBGej6yqMYTZ5KQzJ4hOmI0S9IBU7kyc7DNDQR7+0Cz2Q9j3qJmhC\n1mRhTEATSKkEv/kNbN6sRwjtFVWOm5rgHe/QYvCmN0Eijgrdt283P/3pBq64Qj/Bf+c767j44suZ\nM0c/wZcCH9v1aWhQzJnl0ljfP7ZYKcV1113H+vXrgck3IR2JIIxzJuUjgkKELOdMCgTCR4uDsknY\nTnXCWYtoc9FWdF2F33F4crzF9InBWYxaEJTSKStyjo/MKKx6QbrFI50y5TQnEikV2VyJYluo0y7k\nberwajpqZ7wxAjBJSAm//70Wg82bYXdF4ZZUCt76VnjnO+Ftb9NO5ZHgBwGWW6K+XjGjxWFGcwLb\nEseUAAxH2cRUKob4uYioIHWH7YPl65DWxHhHLgVok9EWtCA8S39BEGhHcrni2lkc5kPYvW83G366\ngbWxiK/7zjouv/hy5s+Zz+59u7nnoXv4X5d/hoIT8H++/zVW//VfcdIbFhhBGGek7EufrOL0yZnI\nI+VMv+85iiT5Ukhnl8/r3ltvfAATjWXpmcZnnAF/+7d6ZvLmzfDzn8MLL8CmTbo5Dpxzjh4ZnH8+\nzJw59Dk91wVcCj2wqyNkpyjw79//J77+L+v51Kf+FsexWL9+Pa2trZNiAjpahBAk3LjG8IDIKikV\nfhBSLIR0dRd1ttWiDmlNRA4J4Yztyc5F+wPOBD6G7vwrBeH3aOfyi8C/o30Pp6IFYZl+34afbuDG\n22/kYMdBANZv1GJ8w5U3sOGnG/j8Nz7Poc5DvccyvsffXv73HHRzqIxCZATJZod0eujCHobD8YNy\n+cMAcmDlBenIo9XJ6A7fYsIy1042JT+gOxuSzUmKRQhKFko5KDX2CahmBFAl9u7VPoOf/1w7kaXU\n+4WA00/XYvDOd8JI/Lj79u3moYfu4a8uvw4vEbDx+//M33z4Q5z2+oXTwiFZDmktZEOCnkjPdSjq\n0ngp5ZJ0jrLATBEtAuWKa3+k/yxmB9TpioeDh7npDzfxFE9xzWXXcNt1ehSmlOK6267rFYVrL7u2\n91jlZyiEcSnNZF/VtHSTS9KrkXQfk0wQRhQKAaWuOKNuufyh6it/OF2QUpeC7OoOyeUVpZIgDGxs\n4eAMcDT6YciZV7jGBFSrdHRof8HmzfDUU+BXhDEuWtQnBkuWjDx5XRCGSHySqYi6OpjR4tJYP72e\nLvUEuIBSd9xhFMDzHVLKOboOI4d2JpdrMv+JfrOX8+TZPXs3J19yMuIcgTpFcd2/DC4Aw5mODqua\nltT1lRP1NsmkO2XTXwdhRKkUUspGhPlIC3AR3MAmJcdB0I9BokiSLwZ0dkcUCopiQaCkg2Md+eGg\nJgVACHGLUup6IcSacgF4IcQlQCfDFIUfv1QQSk/GkhKlJKAQlgKhemuCIfSEML0t0N+fQEm9tC0L\ny7LG1VGZz+t5Bps3wy9/CbmKDJvHHddnJjrjjNGlt/ZDHaOZSEZkMtDU6NDUOP0qbZVNBqXOEJWP\na6qGw9dUPSLdcP+t97P3Z3tZ1biKuV1z+x0uuSUeDR4lOjNi++ztfO7hz/GVa77CDVfewE1338SN\nt9/ItXEE2PqN6/nqNV/lhisHN99JqShGASURETgRKqEQSQEeuGkbL2PjOjauY9VsJxmEEWEoCUoR\nfiEiKkoo6SAASuBEljbp2c60emCppF+Hn1cUixZKOrj26MWvVgWgHR2gd5VS6r+EEEuBBUqpB4UQ\na4AtSqmtA94zYgHQ4YgRUoUIO8JxJQlP4HlgO4qEJ0h4FgnPwvMs7LgspCV0i6/Xey6F/vGVhSOM\nFKWSpFiSlEqKMNRP7r6vCAKBjBwsHNyjcFYGAWzZ0udErkxel07DuedqR/Jb3zr6yWd6hBDgeCGZ\nNNTVCZqbXDLTrNKWUnoone8MiLp1plWv6JAW7qj8Cf2e5NvhP/75P1hpr6Tuj3U651EFhUQBtVSR\nPi+NeqPi7x/6e2677zZgcPPQSPHDCF+GBJYksCLwQDkK4QrtzXPA8gSOZ2G5Atvp//9eiPKSQa+v\nlEKpw38PUurfhIwUMlCEgUT6SofaRvQtA90cZenyqeiJhDWTuHASUUqRLwZ0dIXk84piYewd/kBq\nVQAuUUo9WLF9M/BILAYXAEuVUrcOeM+gAhBGEaEMsOyQREKRTEI6Laivc0gnbTy3OqUhh6LsrMwV\nInqy+g9aKECpZIF0cEdR0LnvnPCHP2ifwa9/DTsHFHpfuFCnpDjvPJg9ezePPjp0WOmQ9xwGWHaA\nl5CkUpDJWDQ1OqSTzrTwJZTxg4hct4/fpUv02UWLOuWRdMZoNjqANhWVfQiv9T9cskv8d/TfPMmT\ntL69lSs/fyViRnWe3iMpCaXUnTYSKVRvU4AUir6K2P0pj4SF0rWzLSWw4m1bWVjoGtu2sKbtk/to\nKJZ0h5/NSYoFgYzccenwB1KrArAGneZrqVLqViHEN4FvKqW2xQKwUin1/w54j3r6aak7KicgmZSk\n01BfZ9PU6NS8s6w8rOvoCunpkeTzEPgOFoPPHh1ufsC+fVoInnhCzzvIV+TEcd0SQfAYZ54ZMnv2\nSzz88Oe45pp/5MohzApDIaUiiAL2H3iZRx/5Pldf8xmSKfjOt/43H/nIh1i8cMG0GC2EkSTX41Ps\n0JEmdt4iI72xpwnYgxaDZ6D91+20dA0yDXkuOttpZWul3AvXNEcKiR3q2FTHD0O6ugP92y9op61j\nVT91+9EIQNUMxBV2/5Vxhw8j+O/delyOlmaXulS6pjv7wbBti/pMgvpMX1hWKdBxuh2dIbkcFAsO\nttCC8NOfbuD222+kIw4t3Bg7EXVHvpuurg187WtrCQK4+eZ/x3Hey7ZtDezcmQDezbZt+hqedzXP\nPZfm3nt1nYOFC0dW58CyBAnL47GHH+Abt3+Zzvbu3vtobxN85MrPaNNaEhIJQSopqM/YpJI2nusc\nc3+foXBsi8amJI1NuoRZGEnyWZ+ejpKONc9b1I1GEObF7X2Q25fjngfv4dOnfRp+D6888grz2+Zj\n7bVgL/DzvrflUjnsU2ySpyZ1RbUT4jaDSRGGoTrzI4XEDnXsiCi0A74bnRywJ14vb2fRyQGLFa0w\nYBnQ3yxVXla2cuioPci6DXjoanbllhiwngbqQGYkpUREj4goOIqSayHTHqLexk6BfQy436pyi/HT\nf3tsAmocDYCGAAAgAElEQVRDT8TvpC8jS3O8/zD+7Vtf611fvnw5y5cvr8YtThgJ12H2TIfZcex/\nKQjp6CzR0RnxNx/9FIfaXuvt+C+77Nre0cBAcfjRj9ZzzTVf5Qc/uIH9+xVf+cr3ePJJH/gf+P6C\nXj8C6Elnp58Ob3yjbqedBpnM0Pd4xRVr6eg42O8+1nzsC3qoKiHI69YtFa9GIYoIYZVwXYWXgGRC\n4LqQSdukUxaeZ9dWCupR4tgWDY1JGhr1dhRJcjmfnvZYEAoWddHIBGH+nPlc+0ntAOZ8OOFTJ/TW\nYv7RXT9i+6bt/M9Z/5N5HfPIFDJ69vLW/ucoekXECYLE4gRdDV08se8J/uJ9fwGzYP2m9Vz6/kuZ\nf9zQT9/AmJ7Kh+rM116xloMdB/tFPZXPvfZv1pLbl+O+jffRRBO3L7+dq4+/Gh6kf6deuaxs8ohf\n6dEj4zYwXcgosbBIYencUYMQpSGqh7AeogYIG+LteDlw32h646ef38zTz2/W15Fj/9KqpVE70dZQ\n0M8vj8bby4BNwIJ432F86UtfqtIt1QYJ12FOq8OcVu0YmtXa96guVUAp8El6iUE75bI4PPTQOp58\nspxb6A9s3PgfvPOd/5dk8j1s2wavvaZTXD/5pD6vZcHixbpO8imnwKmn6vDTxCjnj+gRg56opm8Y\nwgJk49xIB8KISEYIK0BYRVxXRzKVHfPJhEUmbZNKWjh2bUeyVGLbFg0NSRoa9HavIPSOEATp0CPt\njnA2qgMshvfe/F6uu+06Tt54MgD/8N5/4Atv/wKPP/g4f37qz5w/43xmdc8i7ad1febt0EgjF3ER\nPK5P9Xf8HcE9AcwB27c5+8DZbP3JVgpeAfWS4sWtL+J7Pi8+/iIbtm4gsiOe+NUTnLj7RP7qvX+l\nRxYR+um7vJR6fe3xa1nwpgX8auOvyJDhZ6f9jHd1vwtugct+exlv5+0008zJPztZj2R6QOQEX43/\nAbA5biMljZ4g2BAvB66n4pYcsCyve+ineDv+nu0BzaJPAOLPedjSR48mShDkQnJtEcUuRZAVULBx\nSzZ2QWDnwcqjlzm97N1X6Nv2hqp1MYAoDWHTEK2xb12m4NxTl3PuqcsBbQK64z//cRRfch9VEQCl\n1CYhxCXxj+GQUmobgBBiWWwO6izvm86sW7eOr3/9X/oleTvppFlcfsXf0dOtiOTgjygXx091ZUFo\nbm7l4ovPALRP4eKL1/L738PGjb+hWDyDHTsSvPgivPhi3zlsW4vAKadAR8cj/PKXv+Uv//IGEok8\nGzeup7m5ddQ+BdexcSvrOkYQRhAW9WaXVIRRhFQRwvKx7AjHoVcoHFdHb6VTNumUjeNYNTmSGCgI\n5fQEB9tzqJxC5HToacb2RmX/7ajrgHfC+e88n+tuu46PbPwIAJ9//+f58kVfRuwWqP2Kpx57is6d\nnRzP8SxyF5EKUvAqzI3/laOSzuM8iB8CLuKi3vUruAJ+jG7DIBBcFv8D4A9xA86N/wF6bB+jUHTS\nicxICl6BFzpeYN7CeZzyxlMO79AHLuuZmNwEw/xJwkiSzQd0qZB8BKWkjX28i3PCKIMkJNg5sHt0\nc7rB7ganRy9718vHevoEI7H3CKf2+gtDqXHsPoZq+gAeHGRfOfZ/U7Wueyxx+eW6Iy+neW5tbY3T\nPNdz00038YP7vs7HPv5JSiXFhu+up76hmTUf+wJz5szv1zmX18tZR8tmo2ef1Waju+++geeeg+ef\nhz/9SbeXX6ZCFN4FvIsf/hBaWhTz5n2aP/95Bnff3c7evQ/xyU9+iKamgU7q4RPcDYZlCTwrjlcs\nM4hIRPLwkYTjghcLRSIhekcSOiZ+ciOYLEtQX5egPq4JXQ497egs9Iaeur5NWvaf0bruO+tYv3F9\nvzkCrc2tveaUMtlkVucnWgoouK/nPtbvjM0vl17LbR+/DdEmUN2KO797J48//jgttHDx6Rdz0ekX\nQR62bNvCjpd3kCDBkrlLOHXeqYhAsHv/bna+tpO5s+aihGLX/l0sft1iFs1fxPP7nuepHU+xePFi\nfMfnl3/6JW9/89s564yz+PWLv+bd73o31MPdm+/monddxNyFc3m151U2PNxnbrrnO/doM9Qow5gn\nCikVuWJAZ1eo4/ELFuDg2tpmmhhrD2nFZp6RFpKSYGfB6QSnK14O0SwfvAO69V5sjNTcTOBaup/J\nZGANgJtuuolLVn2QZHo2Xd2KfM6CyItzB2mUUtx223X9zEbXDRFzns/rzv9Pf9J5i3bsgF27+qe6\nrsTzsvj+HznppDrOP/8N7Nz5Y/7rv27hfe+7gGSym3vv1WIz2lHDWJCVIwk7wrIknhebmxJaKNIp\nm7p40lQtzKj1g4hCvm+CGgU4+Oo+fvjT+/jCx24EDneyDjZ5bLiJZUMdA4Z8z3BpLKZiRI8W55DO\nroBcXlU1PLMqKLCK/QWB9ojj73VqKwrIcHTMnz+/X7K3G2+8sd/xKJJ0Z30OdRTJZaGQt1DRyMMW\n02ldv+DMM/v2SQn79/eJwY4diieffIW2thZ8vx54E9u3w/btAO8F3st//AdAjsbGz/Lss8dx8816\n0trs2Xo5Zw60to6tvvJQDDaSUAH4Afh5/SPfH0ltQrNK2LbE8xSeJ/A8RTJpTXg0k+faeI02NPbt\nazo9xaff8fcc7MqhCoqPrvlf2L7FX164mkhJPn+l/pu3Nrf2OnPLy3KnPNJjw+0fivlz5veL3hlR\nJE+NMTCnTrEgiEIH19bFmBzBsdULCu0D8FPgH6d3+aGCe8d4ulp64jYjgLETRpJ/+Iev8JV//CJ/\nffk1hIHgvnv/lU984it89KM3HvkEg9B/RDGHCy64gfPO+xR79ghefVWxZcsrtLXVof38Q2NZWgRm\nz4bGxjw9Pb9jxYo3M3s2PPXUd3jf+97FkiXzRpwH6Wh57bVX+PFPvsvlH/57LDvie/fcyvvefxmv\nO2EeyaQ2L2XSWhwmesJTFEmKfkgpGxLkdJ4cEQidKyeuo6BLdY6PA320qSpqnSCMKBRDunp0Tp1S\nUc/aH0lOnWOVmpwHYJhYHNvi42uuIJV0WLt2LVIqliyZxV+8+xISmR7yBQhKDhYu3gjzA33nO+vY\nuHF9RSWzv2XJkh4+8YkbuPvudTz8sI5E8v0kDz74MBddtJY3vGEVO3d287vfvUo6fSr798OBA7B/\nv2D/ftBhHm/pncMAV/LAAzpSaNYsLRKVrTyamD0b6usPT5Y3Fl/Ez372Pe684/+jp7s9/lzrESrB\nlVfeQLZTsS+KCGWIZRewHUkyoe+v7Huoy2izUjV8D7ZtkUl5ZFKenhhWgR9E+H5IruD3VWILgBBd\ntjMSWFJgSwubeGn1pYCwRF9KiDLDjRp6U0OgkEo3FL3rUiiUUEjK68QzjuMEWxZ6Wb7cwO5J0Rd1\nJLXT2VbxfSuBIywcyx5ShKVUFP2Qrp6QYkGnSPZ9C6EcXEf7Y465J/wJxowAphF+ENGT0w6vfF5R\nKELoOyAdPPdwG+jwM5WHPnZ4Ccyv86EPrecd7/gk+/YpHnjgIZ59djcwn6ams5ByLt3dR354SaUO\nF4jt2x9i8+Z/4eKL30k63ckDD9zCNdd8lYsvvnzI+xuNr2QgQRgRSZ1/yrYlXgISnhaIZDxRLpnQ\nqTUm44kziuJUEFGcvydQOodP3FSkiHMj9mU4LS8rO2oBwgKEQFhg2bGQOAJh622rnGNoQK6hsX7u\n3pxDUhFGksiXBEVJVJT4hYhiT0C2MyLssVB5GytwcUQC26pSZbljhJpMBTEWjABMPH4QkcsHdPXE\nSaqK4PvaTiqw8caQmne4DnaoY8WiHiHs26f9EIO1ynQYQ+G6BV73uiRh+BJ//vNPWbr0dWQybfzy\nl+tZs+bDXHXVZ49KAIYjkpIwjMCKEFaE66reeRCuqwUik7JJJmxcd+gn2+lKGEmCICKbD8nlI4pF\nRakExaIgCm2EcnDi6bVBGOKXQqJchFVUiBIkAoc0Lt6xMAV3HKmaCUgI0XCkEyilukd7UUPtUHZO\nNlc4J8sFWHSyu1LvD7FUEkShhYxsbMvGGUPB98PNSn1zDk48EU48cfD3KQXZLL0ioc1KsG+fYsuW\n7bF5aT5BkGLHDtA1Hj/NM8+Uz/Ax7roLHnwQXHcP+/cv4w1veJhMpo2NG+/Acf6VT33qU9hDWHVG\nYmqyLQvbs+idKDcgxLVdSsIoQhFhWQEijl6yHYXnChwnHkkk7Hh2tZ4w59jWMW2/Lo9KokhSKkkK\nRUnJl/gBhLHzPvAhDAVK2li4uE6y9/+Ww+FBBAnPJeG5/arKBWFEZyEgyhYQBXBKli4XaU+vYjKj\n4UhS+TJ6Vu9QLOBIHkDDMYcQgmTCJZlwmTGgWHoYSZ0JNR+QzRUplXR6bN/X6a2j0Oaef/ta3Ml/\nGhD9OvnBJ7EdOSJFCO0DqK/Xs5rL3H33Oh56SJublIJ7793AqlW3sGzZlWzc+CO2bXsVWEh9/VKK\nxVm0twvgeOCv+eMfy2e5jA0bYONGaG0NcJzdnHvuAubNgxde+CEXX3wezz77A7797aHyNo0Mu7e2\nREXobtwJhnH4rVLa/CGlRCFB+AhLYlkKx9ET+MrNssCywbb0um0LXEf0rju2XtcmGv13Leu1GCS5\nUDlHaHkQrpRCKlBSEUaxiSZSSAlh1LceRfo9UUTvtpQQhvExKVDKQkkLIWxsyz2sqpWN/kwchVvF\ndWzcehvqdU6nSEqyBZ/O7hIir0gUbOpFAsea/LDgWmFYE1C5qMtYj4/6ZowJ6JimXPB9166Xuee7\n32fN1ddR8uEbX/8af3HxZbTMmIcMLaS0QZWfbo/uxzjUk3k5l1LlSOMTn/gq73nPDezdC3v2HN4O\nHRr+Wp6Xw/efA3Zy6qkzeP/7L2D+fMG8edqBPVio61ic1GN5D/Tl71fEefxjp63eBiHKS72vcvCm\nVH9R0D/DuHYAolc8hBC928faqCQIIwrdJVRPhJsT1MkEySkwOqi6D0AI0YhO5NYOfBy4Xyn18mgv\nNoLrGAGY4kSRJAgj/FCSz0fkC5Ig1CaAINQFd2RkoaSNklZsahp9yONYOtFiEfbsUfzrv36LX/zi\nWWAh8+atIJk8jT17tC16KGxbV3SbN69/++1vv83993+Gyy67AtBCVJ4wNxrxOtJ7jiQO40kt3MPR\nEklJIecTdQW4WUFdeOyKwUSEgd4F3AFcjzYJ3Q+cM9qLGQy2bWHbFskENAyRoVRKXe3NDyLyBZ9i\nKaJU0iamsliEoUBGFih7UJEYKl3GcCSTOpX2vHnPAV8H4B3v0A7iu+9exze+8X9YuXIt2ewMnnxy\nO0uWXEo6/UZeeSWkrc3h1Vfh1VcHnvWjwEfZuLEdeIW5c9ewd++p3HUXvPDCs2ze/At27XJIpbr4\n4Q9vAgbPzjpUlthKU9R4d8xHEqiJuIdqYVsWdfVJqE8ipaI7V6Kjs4jbI2iUSdxp4kge6adsihO8\nXa+UuloIsbqqd2WY1liWIOE5JDyH+hGKRL4Q4fsqLtuJLtspLdQoHdZDOanf/e7LEQKuuOLTva+7\n+OJm5syBu+/+J26//cu8+91fJJudweOPP8sZZ1xOU9Ob2LNHsWuXTxS1AC3s3Us8exrg3cC7+dnP\n9JZtf5Ef/9jl17+GQ4euBF4PHOL551/PT34Czc1wzjlrec97FBs3/l+ghw9+8MjiMFxI7HAd9lDn\nG6tAlSn7C8Kwz08wcBlFfX+TXr+F6GtlLIte53l56bqHzxcZDssSvWIQSUl7TxHZUSCZtWkQySkd\nYjpSE1A5nbMA7kObgBYP/64x3IwxARnGkSCMCMKIXD4iX4golsCPndalErGZye7nixjLE+xwYaXl\nORHve9/nKRSaePjhx3nHO65h0aK/4MABxZYtL7J/v0RXkDli0N0gSDIZQTotSKcVPT27aW/fARSZ\nN+94liw5jd27n2P79t+waNESLCti+/bfcMYZb+G0097MH/7wFL/73a9ZsuRslIIXX9zK6aefx+tf\nfw6geOaZX7N9+3OAywknvJGTTlpKEMD27c+zd++rgMfMmSfQ2noiUSQIAsXBg4fIZnOARyJRj+fV\nEUVi0M69WpSzzFYKQyqlU6CUmy6Levj++npoaIB0JsSTJRqlpFV41LmjzJ8+QUyED2AhcAlwJ7Ca\nQQq6jwdGAAwTiR9EOtw1H5HLS4IASiVFyYco0M5qCz2pa7jRw3ACMLoJc9/mgx+8lTPPfD8///kv\nWbr0L+nqgl//+mlaWk6jVMrw0kt7OXCgQCIxizBMEEVe9b+oKmDb9EY1DbYc+NCti9X3RSiV95Wj\njYJAN9/X2+N/v4r6jKIhA80ZQVOdoDEDjXXQXA8zGqClof8ynRzdSGSsVE0AhBB/qZT64ViPj/pm\njAAYaoQoktq8VIzI5kI9F8KHwNchr0o6IG0cx+a7/3bzkE7b4RiPCKFvf/tmli+/nLq64/ne9+7g\n3nu/zTvf+RGkdHj88Ye48MIP87a3vZ+HHtrIU09tBjzOOmsFb3/7/xOLmuIXv/hPtm79BaA466x3\nsHz5+7AswVNPPcKvfvVjzjnn7VhWxFNPPcTKlZdwzjlvZ9u2/+Kii1bhOPDIIxt429vexezZs/nJ\nT+7k3nu/xnvf+yEsS/Kf//kNrrzyev7mb67Dcfo6+Wp2jEr1CUK56QllejLhcC2X0/NNurqgpwe6\nu/V6sTj6+0i4/UVhoEBU7mtuAG+MbodqCsBLwDcZuiLpVeNpCjICYDgWUErhBxHFUkhPLmLHjlf4\n4QP38uErPoPvC/7t7tu48MK/5rg5J+LYE5cSYixRRYePQo4u4uhYcQKPllKpTxDKonDwYEDnvojs\nQZtcj0NHt6CtG9q7oa0biv7ortFYp8VgZqNezmjUbWYDzGyq2NegU56XqaYAXEpfppBBGazwy1gx\nAmCYCpQnyxWKkmwu7I1gKs94jULROxdirGGuo2GsOZ0MIyMIQ3KHijjd0BQk41TlkC/2iUF7d//1\nyn2HuqGjG+Qour6GdJ8YtDRIHvmNbXIBGQzHAv3mQhQkhUJEECiCoBwZU3aYCijPoEWng7B6ZxMb\nag0pFbmeAupQRH3BI2OP3GkcSejs0cJwqAvauvR6W7x+qGJ/e7d+fX9EbQqAEOKzSqlb4/VL0BVE\nF1aUh6x8rREAgyFGSkUQRVowAknJV/iBJAgUYdg/7UIUEadt0NvlVp7RS7xUAIP0E5U/u6EGI/1m\nD6vhXtc3s7jv+jrFaOUsYtuq7shnMikUSviHfJLdNo0ihTWOn1NK6Mr1CcL+joi1d4ytIlhVBUAI\nsQL4nFLqQiHEUmCBUupBIcQaBokkMgJgMIw/fSki+nL8Q1/unyNRThFR2YcdKSpKL+M0FDKuHxDn\nEtIzviVBqCOvoqhvkl8YlCf52QhsXHtiKrZVi7J5yO2CxjCFV4U8RLVcEKbyf9gHgIfj9Z3ACmDc\nQ0kNBkN/dAd67HSiUqrYhxLRk/MpxMVe+tJCj7yo0WTjOg5Nc+qQsxTtnQVoj2goJEg7tRG+O6Jv\nUQhxFjr9QwfwA2DHkcI/hRBnlWcPx7sa0bmEypgsogaD4TAsqy8bbWWacoBSEJLNhXR0FsnlFYW8\nQIYuju3WtG/EsgQNLWlogVyuSOfBbjI5lwaRnFQz2GhyAS0D7lRK3SqE2AIcKf6/ZZB9x85jiMFg\nqDkSrkOiyelNU66ULvp+sK1Ad7ckl9OC4DlezfoXMpkkZJKUgoB9B7O4XRbNMjUpAjbicZRSqrPi\nC20f7rXlp/8BuzvpE4VmoG2k1zYYDIbBEEKQTnqcMM+DeVoQsnmfA4dydHcr8nkbVySxa7D6WsJ1\nScx1iWZLDnYUEO2SRj85oVlJRyoAvxVCfBNoEkLcjO7Mh2NhnD5iBtASm5DuQ48iNqELyTw62Bu/\n9KUv9a4vX76c5cuXj/AWDQbDdEcIQX0mQX1Gh2CWgpCDbQXa2yW5rIVQSVyntgrC2LZFw8wMaoai\nK1uk41CBupxHvZ0c8j1PP7+Zp5/fDOjU1mNlxFFAQoiPA0uB3w4WwjnEe9YAnwNWKaW2xds7MWGg\nBoNhggkjycG2IgfbIrI9FjbJoy5IVC2KJZ/igRLJHotGNbx5aCKSwZ2FjuIpFwhUSqlPjPZiI7iO\nEQCDwVB1gjBi/6Eihw5J8jkH10rWZLhpGEXk2gpYnWpI89BECMAWYB3a9CPQAjDQxn/UGAEwGAwT\nTckP2bOvSFubIiglSLi1EaJZiVKKbLaIbAvJZF3qrUSvk3siBOARpdSFo7/tUd6MEQCDwTCJdHYX\n2bMvoLvTwiZVk87jUimg2FbE7RI0RimUUlWfCPaoEOIRtP0eqmQCMhgMhsmkqSFJU0OSIIx47UCB\nAwclQdHDc2qnGEwiEUcPzZG0dRaJ2kaZdrSCkY4AXkLXA+6KdxkTkMFgmBZ0ZUu8utenq9PCEZMT\nrz8ckZS86dyxZQMd6QjgmfFM+2wwGAzHCo11CRpPTuAHEXv35zlwUBH5STxn4uL1q8VoagIr4Jl4\nl1JKrR33mzEjAIPBUOMopTjUUWTP3oB8ziPhDB2vPxFMxAjg5tGe2GAwGKYiQghaW1K0tqTIFXxe\nebWHjo7aNA8diWEFoCKX/8pBDo+7D8BgMBiOJTIpj1NP8gjCiD37tHlI+ilc59jIVnqkuyxH/Wzh\nCKUhDQaDYbriOjYnHl/HCfMUB9qK7N2Xp1AD5qEjcaSawD9QSq2esJsxPgCDwTBF6M6W2L3Xp6vD\nxrVSVZtpXE0fwMIx3pPBYDBMaxrqErzh5ASlIGT3nhyHDgFRqqaS0R1pBNAO3MHhefxNFJDBYDCM\ngiiS7DtUYN9+SSk/fiknqjkCaKfPD2AwGAyGMWLbFvNmZ5g3Gzq6iuze001Pt0vCmbyqYEcSgM6R\npn42GAwGw8hobkzS3JikWAp4ZU+WtjaBpVITnp76SAKwZULuwmAwGKYhyYTLyQtdwhMk+w5q81BQ\nTOBNUNH4EReEmQiMD8BgMEx32jt1RtKeLgfPPrJ5aCJmAhsMBoNhAmhpStLSlKTkh7yyN0t7m4Ao\niWOPf3dtRgAGg8FQw0ipONBWYN/+iFzWJeEk+o0KzAjAYDAYpiiWJZjTmmZOK+QKPntey9LeLsZl\nToERAIPBYDhGyKQ8Tl7oEZ0gOdheZN/+iELP2EWgaiYgIcSlQAewSil1dbzvEnRd4YWDhZcaE5DB\nYDCMjnwhIJP2xmQCqkruUiHEBcAFcdWwhUKIs4QQSwHKlcSEEGdV49oGg8EwnUinxl6YpioCoJTa\nVFEzuEUptRX4AHpEAHp28YpqXNtgMBgMI6NqPgAhRCPwcWBdvKsRnVqizIzB3rd/Vw9O2iZRZ+N5\nDp5bO4mTDAaDYSpRNQFQSnUBtwohHhFClEtJHtFG9Y11/4xUilBK3nz2ebz5TW9DJRQkwEoJvDqH\nRMom4TmTlj/DYDAYJpPNmzezefPmoz5PVZzAsb1fKaW2CiFuBtrQT/yPKqU2xQ7iBXG1scr3KbVl\n6PtRSuGHESVCSnaI9LQwiJTAzehRQ8J1sO1jqyybwWAwHA1CiJqaB3ABfQXkm4CngceAZehSkguA\nR0d7UiEECdchUb7tMG458PdFlGRIu11AelILQ1Lg1Fkk6hwSnoNjhMFgMBh6qdYIoBEoVxJbWK4d\nIIRYg3YADx0GOswIYCwEkRaGohUSuRKSQALstEWy3iGRcGqqQIPBYDCMlrGOAGovFcQ4C8BQhJGk\nFGlTUujIfn6GRJ1DIqUd0MbPYDAYah0jAOOElAo/CimJCL/sZ/CAJDo6KWOikwwGQ20xdQTgfyhI\n09dSQCZeDtxfBzTEywkw7wdRhB9FlKx41FAWh4QWBy9j43k2nmNGDgaDYeKYOgLAGO5HAPVxa4yX\nDQNaPdod3RK3JrSwjFM/XRYHX0QEdgQJUK5CJAQiAW7Kxk3ZeK5tfA4Gg2FcmToCcLOCPH2tAOTi\n5cD9WaA7Xo4FD2hGC0LzgPWySLRU7EuM7TJKKS0OMiKwIkJbotx49OCC8AR2wsJNW3ieFggTymow\nGEbKlBGA338vh+cpPA8SCYu6jE3SO8JTc4gWgR6ga4hlNzoNXUfc2tEiMhoy9AlCC3pmQ3O8bBnQ\nRjm6CKKIMJIEIiKwJJEtwaW3KUdheRZu0sJN2Ti2hWNbRigMBsPUEYDnNvTdTyQlkYxQIsR2JAkP\nPA9SSS0MRx3bX6BPDCqXA/eV16NRnDtB38hhMIGo3NfAiHwYUioCGRHIiFBIAkuibKVFwkYvHd3s\nhIVTbrFQmHkQBsPUZEoKwFBEkSSUIcKKcByJl4BkUlCXtkinbDy3CmkiFHoU0Y6e19wRL9sHrJeP\nl0Zxbps+s9Nwo4oZaLPUCKbvhZEkkpJARUSWIhSSSMhegcCm37pwBY5nYScEttMnGrYljEPbYKhx\nppUADEUYhUQqwrYjXE+RSEAyaVFfZ5Oc6JnAeQYXi8pWPtYzynM30ufsHuj0Hs4Rnhz6lFLq/EuR\nlERILRpIpCVRZbGwD2/CFdixeFiuwLEtLMuKl0Y4DIZqIaXSv9dIkkqOrR7AlKkIppTCsmyEslDK\nwS+CX1R0dEheiUIsUcSKRwuJRMVowdGjBSFAICBeioqlJcbwFFwOVz1+BK/1OVwchhKLDrRfo2t0\ntwNop3M9Omw2U7HMgJUReBkbMvagx3tbHf3+10ipiFQsHErhi4hIhIRCoix1uHBUbNueNlGVhaNs\nphqNcOzevZsNGzawdu1aANatW8fll1/O/Pnzx/AFGQyTRxRJwrhFviQo6iUh2vxcuQxBSIEtBUKO\n/UGr5gQgGxaJLNX7FKpsQICyAKtiKSq2BQgLEAJhCYQVD4msuBO3LBAeoAOKhBC8GoVEYYClCiRT\nioQHqYSgscHBc2wcy0JJLSwqUqgIbQaSFcsoXsoB2xFYSmArC0sJLClwhO7YbDGIWcUD5sTtSETo\nzvLxst0AACAASURBVL97iDaUA7wbLTRtcTsaEvQTDqvOxi0LxwibSigipZ9gQikJrIiiCAmtCuGo\nNFXZ+k9oexZOLBy2bfHd797DF77weQ4ePAjA+vXrAbjhhhvG9NGMoBjGEymVDvAIJWFJEhQjZEnp\n33FAXz6zAN1PSAtHWXjCJm15w5tg474wknLM91dzAnDh3yVIp4mbIJOBVIp+y3QaGhqgsVEvK9fT\naRjZw/qAmM4I8llFV1cAVojjRiSTkExCXdqmsWV0CeWiSBJJhSp3cpFEBoqwJJGhRJXVvCwc5fV4\nv5AglMCWFrYSWAhsYhFptLCbRzkqUWi/RBdaBcstO2D7SPtz8XlK9K/uMEqELXDq4mR95ZFFuTWg\nzVhN9Jm7mkA1KMIGSeRJAiXxrYCPr/w0u597rbfj//iHP8mVqz/F1v/+Ew/+6D4++7nP4SQsbvva\nrXz4w/9/e+cdJ0WR/v93dfeEXXKQuICA/oxnBPWCflExnHeeniiyIIqSVKJIlBMQBUl6SlBQVAQE\nVDBHUOG8O707UdFDPQOIJNkVkLBhQnc9vz+6Z3d2WWBZgqtbb15FVVf3TPfMTtenn6eeqrqe5s2b\n7/O65s2bx8iRIw+ZoBh+maRmJi7RsMcEXJCkoFyFJAXLswhpC0dbRJVDdTuMbZXRhiiKreQjSKUT\ngB07FDt2VPz1tl1SGFLiULcu1KtXnKfKtWr5rwGwLEXECgNh0JAs8NOOHzTrdBKsQpyQ9oUhApmZ\nFjVrOETLmBrCtq2i960IIhL4+AStfTFJuh46KXhJ8UUkXTj2ZZFo3xKyQgqrrp+UgIUvLCkXl5WW\n9iouKSFJCUR6Xp6UOjYlRgfgylIoQtiEIjbRQByklnBzTndO4QRyyOHkz0+m4Ys1eOP9fzF/yRMU\nfLWbuJPgoaenEfs+Sf8eg9m8bRPPvraQAX0GgwNTZ91H5y5dOLp1CwYNHkJubm5Rwz9w4MAia8BQ\nNXA9jet6JBIebkzjFuqip/RUUi6EtL3/hj14Sq+sVDoBePNNKCyE/PzivKCgZMrPh927YedO2LXL\nT6lyYSFs3+6n8mBZUKeOLwalxaFePahfH+rXt6hfP0KNGhFUujBs8/sXUAlsx/P7F8JCJKKolumH\nqoZDFZs3SCmFbatARA7+sUBrQQeikhIXLb7AuK5Ge1KUilxeKXdXyuWVStXwn85L12tQ4lsuFsp3\nf4nyt0UVmbO2srBchVWgyhaJXfjCsKNUnirHgRw/KRSnB/8A+MBP3YJ/LIGd7GR0jb9w1L+OQn2l\n2JCbh/Vfj+XvvUpeNI+l777KURvq0qNXbwpCLrEct+h7K9ieJOeb3Xy/dTOLX1rE0GHDDsiiMFQe\n0t0xyUKPZEyjExoSoFzlN/IJsLVFyLMIYZNhhwiV9SRXyRv28lLpooBWHuRkcIlEsSikhOHHH4tF\nYdu24vL27f7+8hIO+4JQLAwly6lUpw4IHq7noSwPy/aCwW0qGOAGmRk2mRn+ALeQYx2QO6cy+6nT\nxUWLICmh8QRxfYHxkoHQuFLSWkm3YNJcYZYEbjCtcMTCjlvYuyyc3Ra53+by3t/f44pTroDt8Mm/\nPuG46scRzYuy47sdVCuoRphwua+/IFLA2vhawg3D7IruYsV3KzjtN6eRn5nP1LemcsGl7dkR3cmU\nFyZyxy2jGNBzCJu2beTZV32LQmxh2qz7ye7ShaNbNcdxLBzHNmMwDiOpJ/akq/HifuepJKToqV2S\nfjndHRNSNiHb/kVEqnla45xl/zLCQA9WAA6UZLJYIFLisG1bcdq61U/btvmWR3lIWRWlhSElFvXq\nCXXqeMST37F8+XxuuHEQoRDMnTOFK6/KplmzLD+ENWKTkWERLtWIjB8/npEjRzJw4EDA91OPGzfu\nF+mnlsBKSYW7eZ7gxTVeUvYZIfHgY1MY//Bd3NqxL9UT1XnphRcYecVIrjvrOmSb8Pbrb7P+i/U0\nohGn1DmFpk5T1HZV7sF+ruViH2Wj6iu+yv+K5euWc/TJR7MruounVj7F5X++guzruvCdt56nlj9F\nv26DIAQPzpnCNVdk07RZFptyNvLsSwu57bYhOGGLB6ZNoUuXLrQ4ugW2dWTGYOzrYeJIP2ikwhq1\nFj8SxtXohOAm/XyPv3XQiWpri5BY2NqPIgtZv4yGvbwcjABUOhfQkSYUggYN/LQ/CgtLikK6OKRv\n//hjsYB8+WVZ76Twv/rWQD8WLcgnI2MXOTnH8o93d9Cu3fHUry/UqaOpU9ejbn2PGjWSKEsTCsHv\n/9SPL77cXOSnvqlHH27sMYAfd8YIhw98EFdFbvQj1TgopXBsP0yUUFBZbf+v61X/JqKNHW4fPJRk\n0iM0McrJV7Yht34eD0yfzL1fjKXXdbeCVvxhwR/4y81jGNplOKFdFs6PNs4OC7bip20gW4U1n6zB\nzXVpRCNq69pFbqj/F/xjtX/ua7gGngeehxM4htvpT+KpAnZFd3Fy7nEk/raTBucez8rVa/lyxWoW\nrZxDfiSf2W/Mwtno8Oc/XcOiNxbQv9sgxA5E40/ZNG2SxabcjTz78kIG3DoYLHhw5hSu6ZBNVrNm\nWLbCsv0oOMtWYPn9Wps2b2TRwgUMGTIMpWDypIl07nIdzZo144k5TzJ61J1syckBYNrUqbieZtjw\n4cX7tuQgwPRpU0m6HtnZnVmwYAG3Dx6CCEyZPIlOHbNp0jgL0X5DLrqUK3F/0XNeEPSQip7zFCFl\nFwlhmf51+MW4YvaGiC+GWmtcT0gkfUFMJAXPA09DPHEgUxSU5GcrAKknBRFBS/DrUoJS/pemLN95\n7bd/vlWRagv32r8pJXM//lT5rxaFchT1GyjqHaU4QSksS2Ep332T/sThuntaD2WLhuB5dQN3VSPg\n/7FqFaxaFZw7LSzAcZLUr69o2NChfn1hw4aO+EH9W1jz9cm8tTRCLL6J999bwE09BqKUZt7cSfzh\nj9k0aZqF7YBtgeOAZftl24ZQSPHwzMe5d/wYNm/eglKK6dOn4mnN9V2v56mn5pfZyFf2aJkWLZpz\n551/8Tcy4J5xo4v23TKwB9XrRYo+V/MTG9Mpuwt2E0Ui7pEfS+LFte86SILyFA/MnMy9y8Zyc6c+\nWKJ44unHmNhlEn0v6APbYMkzS/h85ec0ohG/afobTqp5Emq7QrYKdbw6sAsa7mrIsRwLnwCfwGXB\nP5b71zWcYfAYJJ9McrPbneTCXRSGC/l1bhvCb3o0OLk6367dSfgTxb+Wv0M8FOfbD77i688+5dTL\nj0OHBHEECQd5CHRIWLJ4MffOGUfuZ7mg4KGnp+N+L9zebSj9LhrEls9zmDZ1qv/ddOpLv4sGUfhR\n0t/3WQ7Tpvn7br22LwMvGsz9903m7llj2L56GxYWjy6eReaGCLdl387mnM08/9YS+nfoj+UpZj07\niyvPvZLGtRuXsNCKXH5lWG/l2lfWcaUthNLHpghCyIvK5UkWxaHJ5U3po+1DFE3+6DoaCWlcR0gg\nJJQmoTSuBZ4dXK6A54H2QEQh2kLEwrYsLBUqMQdYwi3uszpQKp0L6L1/5yNK49jFjZVj+41V6YYr\n5Fg4jiIU8jsYraAhTg3sSkWzKMUBm9Iiggi+H7uUP1tr36edSAquKyRdP/c89kiu6+eiFSIWov3Y\nG8uyUCgmThzF88+/CDTm7LO70aZNNtu2KT744AvWrNlK9erHE4vVxHXLPxWpZbmEwwXEYhto1Kgm\nxx7bbI/IqFTIbEamJhrVPPPMvbz+2nRgN9dc250Bt01g7pOTeeThu+iU3QdlwcKnZtC3/yh69h6C\nUsLkiSOY9+QMALrd1Ie/jLoXx1HkbNnE888t4rZBQ3BsxQMPTKZLly40b9a8xN/HtqwK/W3SOVKW\nSOo8g4cMxXU1EyZM4Jo/d6JR/Sb89YHJ3HvfWHp17QMCj8yfwR23jGbIDcOwk4p77ruLV194mUY0\nots53ehyVhfUjwrZKfz3o/+yY+MO6lKX5tHm1HBr+B2Sh4kECZSlcILpUsQS4ok4MTeGRhMJR8jM\nzPT3aSFWEMNNutjYhK0wtrJRXtVxr/xU6BBIKjmltkPBtuOXXUdT7/1fSB9A0vV+kfPP+PMX+X7s\nZFJTGNfcP2UikyeO5YZuffA0zJ87g1v7jqLrDUPQHjz41xE88/R0ADp0GEKnThPYvt3im2+28t57\nH9O6dXu2boVPP12HbTeloCDE9u1JPK/8nZ5loZRH9eoWmZkQi21h5851wC6aNWvEKaecQjSqiEaF\nVauW8tlny4ECzjrrN/zpT9cSjcKKFQt55ZUZXHzJFdh2gtdfm8n1N/Tn+m792bFjA0vfXMD1NwwG\nJcx7cjK/vyybxk2ysCy//0RZYCmKtstKtq0IhxTTHpzM+HFj6NOnHyiYMX0aY8feTdeuXXnqqflF\nFsnh9F+XJUJdulxHoyZNuHf8vdw1ZhS9e/VBPHjksRmMuG0UA3sO8a2KqWPpld0HJTBr0Qzu7HUX\nQ68exuSp9/LiKy9Qhzp0ObcL3S/qjipQSIGw9O2lfP7Z59SgBmc2P5PTsk5DFSo2bdrED7k/0Lhm\nYxzPIZYfo060DhlWBl7Mw9GH1uB3cfHwULYiFAmhLIXYwu74bnbFduHiUq16NerXr4+y1R4D+0qM\nEi9drui+vb1/eiBPemRb+napJEHwgnb9Pic3JrhxkIQgSYVOgiRAXIVyAVehXH9kruX5QQvKAxVY\nIlYQPqqSxclKlrFdAY+OopLNBRQsAA/QWkSGB3Ud8AP59r4ofCUSpMPNvp5eXddj0O2Dikzznr37\ncMdf7iWZVEWWheuB5/plrRXaU0x9YBjPPD0bqMOll/bnyiuHkpdn7REuu2tXcVjthg1b2Lo1n0ik\nAclkFK1D+7jqg0MpjUg+kQg4ToL8/C0cdVQdmjVrQkYGRSkapcztzEw/j0SESFQTiWjmzx/Lyy9N\nBfLp2OkW+g+YyNy5k4qsFxQsWjCDPv1H0av3EHJzN/Lqywu5+dbBWBbMmjmFjh0706JFc3JzNrJ4\n8UKGDR2OZSsmT5rI9V2vp0WLioV7lreTVWth/PjxdOrUmflPzWfsXaO5ubcvGrNmz2DE7aMYeMsQ\nHpgxmXv/WtLaGNFnFANvHIJ4wui/jmDWIt8qu/navtw7cBL3z5nM3bNG06djfxxtM3PxQ9x502hu\n63w7m3M3sXjps/TvNBAFTF8wlQ4XXk2T+k3ZtG0TS955ln5dBiAK7l84hWsv7cSCZU8x5pFR9OnY\nD4AZz0xjVO+7GHz9MARhxNShPPT0NMB3G43vPwlVulNWKJouXQXtlgrKqWlYLL9QvvEp5STV0exH\nDmniCU0yKSSTgcXuCZ6nfMvdBQisduW7e52DGdxT7ossForSAlGiLjhGxzxazHYqTyewUupC4C0R\n+VYp9UywvR1ARN5WSrVSSp0uIh8fjvP/XGjWrFkJn3l6edKkiUybOrVEpM/RzZvs1ceutTBu3Die\neXo6ffr0w9XCrIdH8KtTC+l9yxDcwJ+Yck1pXeye2rJlB2+89jRdrx+CaJc5T0zkvHOv4803X2bB\ngke58MLuuG6Ev/3tHS64oCtt215Obu4OVq/+lBNOOJdYDD799BMaNDgWqEZhofDtt+vYunUnkElG\nRgNCoVrEYopEwgJqEI9DPA5Qjx9+gKAr4QBI7yO5O0iw+BmXN16ziUZHU7PmLSxauA7YRVZWT75b\newr33q34+uutfPhhAf/8++s4Toz331/JN1+2psPVx/Daa2+waOGDfPbZNmxb88yi6Wza7PL7yzrx\n5hsL6d5zMJYNT8yewhV/ziYrK8t3ZwUuy5BjBe5JheMoYrGkv86D6+29IxO/s9a2LaLREJf9/hLe\nf+8fzHjIF/+1333FFddcRoPW1bnlth5Ur1/cf9HihCZ07dqVBs2qIyJEGxbf0qGGFtHTHdonL+Cf\n37zLhCcmIgKfbv+UX3f9HV5boYFuzK1/7I+LBg29z+kLQAKP+MYkeV8XkmjjgQWJd13ckzUdf5WN\nbiwMHjIUgPonHkV2587YzRSTJ03ioaen0bdvfwCmT59KwxMbMHTY8D0+c1GfW9BHJ8FTtxYQ7Q90\nFF1qfIpLUeexl9B4rqATGu1pEjFIFgbRQgkL5VqowHeuxMINXLFa+3PL+K5YJ3BFlmw7bYoHiB7p\n0blYIGE/lWeSh4QrMLtipzpcncCtgvQosDYoXwQsC/avBdoDVVoA9kXXrl0Bim70o446qqiuLCxL\n0a3bDdi2Vdy5mdU4eNqsvs9ziRzHH39/J57WiBZOP933c592xqU0P3o7t/btjucJ06Z+zxVXHEvD\nxnmIdvC8M9A6H61ByzH+zarzeHz2ZD74YCzXduoDwNOLZtD1hjFcf8MwEglh2oOjeP65uUAml/6+\nF1dfPYR43CIeU8RiisJCP+IqFqOonL5dUFBczsnZzq5dcRynDp4XRmunyMKBhkGCjRv95PM74Hd8\n8EFq+3pefBFefBGgH9CPJc8CFBKJjOe1V6rz3OIt/PBDO156YR2hUJx16xrwj3e3c845x6P1j3z9\n9T+4+OI/kJEhLF8xl3bt2pPVrCEvv7SYJ+eM4bPPt6CC72Lj5jg39RjCnMdnM2PaWL74cjNKwbwn\nZ/DDtjhKwbJly+jRq39R+cy2v6XVsafw484YsbhLXkEcpRRJ1yMWTxJPuNxxx3AefPBB+vXvjwKm\nTp1KNBKhVq1avPP229w50v9dLH/nbdpfeAEXtDt3n7+LF2Y8y913j2b37h8B/yEkGnHo2rUrkbBD\n9Uy/XyocssmMhsmMhrmx2w04tsXw4SMQERo2bEDnLl18C6Cogff71zwtfgMuBH1oguv5/WlaU5SK\nHlqk5EOMaNvvVxO/QbfCFlbUP48OpmDxEhqd9JNyQbmC8nyXjeWCo21/0JeycSzbtzqqEIdFAEq5\nd84AngbOpOQ0ZPUOx7l/KezLOjiUr4FSoZZpnHLysZySFj1z36S7yvV+DYf1IKtJhOHDh6O1cNJJ\njcju3IVmWQ4TJtzL889N4dY+/dBamPnwHZx2eoI+/Qb7Mf5e8Y0vQomGoKy0+ftc3nxtEV1vGILo\nOE88dj/nnZfN0jdfZsGCWVx0UQ+SySgrVrzN+effyBln/JG8POFvf1vB//73NVCTxo1PoUGDE8jP\nV+TnC9u2FZBIRIAM4nHYsAGgMdCYTZtSn7INH38MH38M/gIOl/POO6l9N/LC86nyUGAozywqAHZT\nvfpo3l5Wn3+/r8jMHEPz5lcwf+5KYBcnn/w6buISIhFo2/ZEnnjsZaCQ886bSVbTHrz4vPDmG8tZ\nvHg+H33k4jhJXnxhKhs3KLp1H8yWHP95cUuOW7QY3eYtSa68uj/Xri6eL+na7D5ccHE//v1hHrk5\nG3n9tYXccONglII5j0/hsj9k07BRFu0v7cd/PysON+7UuQ8XXtKPeydO4eEZY/nv6s0ALFo4g/Ub\n49xw4xBycgrYsCnJv1cWIMCGTS6r/ptgS24SUP5Tf5GnQqGUFcy6mzYlyX5i+C38PqJyPZnvI3Yi\nFWIZ8zzyEy5ePA5JKXa3eOAkFWFtE1bOL1IgDmsnsFLqDOAaERmhlJoJzBKRjwOX0EWpvoG042X0\n6OIGp127drRr1+6wXZ/hyHM4onY8TxeNPNaen69b9x0LFyykT7/bcT3hwQcmc8UVfsP28IzJPHD/\nWK67vg8i8NS8GdzSZxTXdxvCnMcnM+vhsVxzbV88N8RzSxbQ8do7ueSS3syfN4vly5cBNTnzzCs5\n++w/U1CgyMsTVq5cybp1G4Ea1Kt3LDVqNKegwBeU/HzhSASr27YASTwvBiSJRMLUqlUdx4Fdu7aS\nl/cDkKROnbo0bpyFUoqtWzeRk7Oe+vX9qWi3bt1E48YtaNSoGSBs2rSG3NwNgNCgQTOaNz8GpWD9\n+q/JyVkPQMOGzWne/FiUUmze/C0bN66hYUO/zyQnZz1ZWa1p0qTlfq//cDVFSvnuuZSbrqw8VU6l\n1LZSGmUJCo0lmpASwkoIWRBVkGFbZDgWmSGbaNgi7EA4RFEecorL+1rV9kD5zxcr+M8XKwA/JH7W\ni3dXrk5gAKXUEBGZHJQnAMuCPoCrgZapfWnHV6lOYMNPw75EaP369cydO4/BQ4eiPWHipAlcfXU2\nixYuYPy4MdzUow9aw5zHZzBw0Ch69BqC6/ohsU/N8ztfr83uQ/+BE0AsnpwzmUdmjuGqDreTTEZ5\n+aVn6HD1UNpf2J2XX36W1157gbZtrySZjLJq1UecfvoluG6I//53FS1anI7nhdi4cRNNmpxInTot\niMeFLVu2kpeXADKw7epACM+EZlZ6LBUIQiAQoZRQlBKN9H2RUKl9oaAubdu2PEY9VrFO4MMZBdRL\nRB4JyqlO4DYi8qhSagi+GKwq9RojAIZKyb5Eo6ypOe655x6GDR/Buu++46n587l9sN+vMnnSJK66\nOpujGjRlw4YNPLdkIb16D0YEZj40hT/+KRvtwcsvLeTG7oPRGp54bAqX/j6boxpkFVko13bqgwDP\nLJpBr5vv4rrrhjJ3zhRmzx7PlVf2RbB48YU5dO4ygvPOu4rly1/m8st74nmKF198knPO+T116zRC\nRHj66Wm89dazgOKCCzrSsWNfQLF9ey7//vcyLr64MwBvvrmQtm3b85//vM3LLz/O+ed3AGD58iVc\nfvlNXHppZ0SEJUtmsnz5EgDOP78DHTrcXO7oncPhYUn1I6SCHsrK97Uv1eeQTPopkSg7L1knJBIl\n9+mDWLhl/1SiMFClVHvgGfxGvy5wtYi8E4SGrsWEgRp+QRzJOXM2bNjA3LlzGTpsOCLChAkT6Ny5\nC1lZWaxfv75oigYtcN/kSVzTMZsmTZuhtX9f6WCRo1R56oOTmXjvGLr39MNKH5s9gyHDRtO3/5DA\nJbPn/bh580aWLF7IrX0GA/DQjCn8+apsGjfO4qHpk7lv8lhu7O671+Y8PoNBg0dx861D0qJ+/P9S\n/Twl+nqEYDqJ4oYbUeigoxdRQfSOwrKsfUZWVTZc1xeCWEwoiLnECjwKd3kkC8AthGShQuIKidto\n10Z7FklPEU9AwoV4SmSCciJIhQnNK+/9QgaCVabrMRh+6Rxq8TocYpg+SZyn/YGUsbiQdDWJhBQ9\npSeTBCPzwXMVom0/OkjZwdxYlV8sPB2sRRB30YUeJIrj/h1XEfZsIkGHdMqqSrgup90YMgJgMBgM\nQNGMon6YrKYw5hGL+WKR7roRbSPawrb81f4q8wwEntYkXZdk3AvEwY9Y0nFN++tqV56BYAaDwfBT\nYlmKsGUTDtlUyyj7GBHfiki6Hnn5CQoKPeJxIZlURbn2bBCbkO2UmIDtp8C2LOxwmGgYfx7IgF/U\nmsAGg8FwJFBKEQ7tXSREhETSIxZ32Z1fSEGhJplQJBL+KHbPtVHYONZPLw4VxQiAwWAwlIFSikjY\nIRJ2qFVjz/2JpEcs4ZKXFyOvwCMRh0QgEJ5ro8TBcexK3fdgBMBgMBgqQMp6qFlqgSIRIeF6xGIu\nu/ISFBZqEglFLCYkEgrRDgrfrfRTr1xmBMBgMBgOIUopIiGHSGhPy0FrIZF0KYx57MqLE4sJsTiB\na8mPXLJwCDn2EemQNgJgMBgMRwjLUkQjIaKREHVqldzneZpE0qMg5pKXHysSh0QcXNdCe/YhtxyM\nABgMBkMlwLYtf26haIh6tUvuS7ccducniMV00N8gePGKn7PSjQPYuasQJ+Qvau5Y1s+2d91gMBiO\nFEpVbCqISmcBOKstPISElcRVGm1J2YssO2CHLZxIkOxANIxgGAwGQ7modAKQGS5jPdtgTc3SuJ72\nR8eJR8xycZXGs3QJkUglFQInYmNHFKGQPzQ85FTukX8Gg8FwOKl0AsBGIBykUJA7FK0fmo4TPPFH\nSn+MlGCk+cZEhGRKMEgJhoekC0WIIivDDiwLO6wIObaxLgwGwy+OStcHIGXMPgiUFIT0cnoeBTLS\n0v620+uqAZkUrduRbl14lvjWhdL+efbhjrLDqoQ7ylgYBoPhcFPRPoBKJwCx+h6WG6zZmcRfx/NI\nLnZRbR+p+t7r3ajGy9S4mRqvmsYN+4IhKaFIrV+eJh522MIOKd/asC0sW5mOb4PBcMD8YgTg83ll\nXI8OhCAJkhBIeuiEYLmC5elgcWdwXH8NTycJdkLhJBUhV2EnFFZCYcUUKqagkOIUC/ICIP8QfpgI\nJQUjk5JCkglepkZnSlHyqmvc6hqvukZqgNQSpDolhcPy+zMsxxcPK6SwHQu7aH50f03Vw215HMk5\n8A0Gw775xUQBPf1O8XJnkVDJJdT8OkXYcQhFi+sywv5xpds8fw5xjRYPZWmUEixb/PU+bX8NVcdR\n/vJrYYtIyCKUUNiFNk7M8gUhH8ijuJye8vaxPx6kbXv/rDbW/te1tigpHtVAqgmSEeSZgq7mC4ib\n4RHPEHSGRjJBqoFkip9XK87JpEhQlK18IbGDFPIFxFIKFeSW2lNU5s2bx8iRI/nhhx8AihYOv+OO\nO474AilGiAxVFa39NbArSqUTgLueqNjrbFuIRiAaFTKifh6N4pcjFtGoIqOoLsgjQkYGVMvQZGS4\nZEQ9MjM11at5VMvQhGuDUx8cC2wlOLYiHFKEbYtIOGjAbRsHCyXgr1WksLTCKlRYuxVWvmL+4rks\nem4B2b/rTEYyg3/9+306nN2B8084v1hAUhZI6RQL9ucVf1aFCvrED+IpPwJEQTLEz6NAVNBRkIhA\nGHRE8CIaN+zX6XBwbAT6RgbR+DdZvP7AK8SI8df207i++U1sX1LA8y+9zJy5c9j1SSGuk+TRBQ8T\n2+4xYPDtbM7ZxDPPLmDw4KEoR3HffZPo0rkLzZs3RylfZCzL/3zlsWT2JkRdu3bdqzBURDSM0Bgq\nQqqB9rRGUmVPEFfQnp+8pCCegMZPQnE5PZVRr1B+uYJUOhfQlVfKHmttxuPF2+n7UikWC5aO2eFb\nMAAAFqFJREFUO8RkZkL16sWpWjWoVk2oVk3IrCZUq66pUUOoVk1To6ZQs5amVi1NrVpCjRpCzdqa\njAxwFIy/ZySPzX4YgF7db2XsqAls3rSJZ5csZMAtg0HDgw9P4ZrLs2naIItN32/k2ZcWMqDrYLas\n3cz4CWMY030cjSKNeOnF5zj/xAup79THygeV7wuNioNKublioGIUlYvcXan0E5G0k+R5eVhRi6Tt\n8kN+LrXq16ZBo4a+AEWAsJ9LRPwUBSIgUb8s0WLR0lHNky88zuJli4gR449XXknfgQN59OmZTHp4\nHNfd2A03lGTmIzO48y93MWTYMO67bxJ3jRlF3379UQqmTZ3K3Xffw8iRd7Bx48YyG/qU0KSv+ztu\n3Lj9WjwV2QdUCrGpaqK3r8baSwYNtquRVJShBLney3bQQFtaYaOwRKG0wg6sahXktrIOamoHT2uc\nsyq2JORhtQCUUhNFZFjadgdgB3tZExjgL3+p2LmSSSgshIICXxBS5cLCsrdT5YICyMvbMxUUFKfc\n3BKfiuIn7/06cIhGherVIR4fBXQFdvL60qPYsjXCd99t45NVHsvefZ1wuJD3/vkx32xqQdcbOvHy\nS6/z6KxpbI3v4JNP/sN/Vv2L9U99R5u2bZn+6lQujLXnlVdfQ2th0qQJXNspG3GFhQsXcNuAIWhP\n+OsDk7nmqk40aZRV/NQA4OILRD6oAsUP3+XwztJlZF/SFVUAr736Er/91XnUjdZFxYF4IC5xhUqA\nSig++mgla775mpOanUzIC7Fl8/e0Pqo1R9dviUrA1pytxPJiRIlS06lJhAjKVYS8EHWoUyRC9akH\nW/FTBRlIXwbS1994wU8jGMwIBkNgUU61/orzoIPMhFGhv9C7Zg82TdtEIYXc3PBmWi89lvi7Lrkb\ntlL7f7V5a947JJ0keavz+PSNz+h7/iAa/7YZrz/wMoUUcv8lU7nh2O5sf7WAJc+/wCOPPcKPX+zG\nDblMf+IBYnGXocOG8fgTcxgzehRbtuRAIDZaCyNH3rFX6wU4pO61igrUkbKuyouIFDXSWgTR4q8n\nHNSnnqpTjXWJp+r0lGqs0xpqvKCxFoUtfm5J0HArK0gK+0D616xSeSXksFkASqlewFAROSbYPgNo\nKSJLgsXhV4rIx6VeI/98PxEsWi2pK0QpCfb79YKgFHtN/rEl872R/vF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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotlim = (-100, 100)\n", "fig, ax = plt.subplots()\n", "for sex in range(2):\n", " c = 'magenta' if sex == 1 else 'blue'\n", " for event in range(3):\n", " conflabel = label = None\n", " if event == 0:\n", " label = ['Men', 'Women'][sex]\n", " conflabel = label+\" 95\\%\"\n", " model.plot_mean(fixed_inputs=[(1, sex), (2, event)],\n", " color=c, ax=ax, plot_limits=plotlim, label=label)\n", " model.plot_confidence(fixed_inputs=[(1, sex), (2, event)],\n", " color=c, plot_limits=plotlim,\n", " ax=ax, label=conflabel)\n", " \n", "model.plot_data(visible_dims=[0], ax=ax)\n", "ax.set_xlabel('Year (+ {})'.format(X2mean.round(2)))\n", "ax.set_ylabel('Time [s]')\n", "_ = ax.set_xlim(plotlim)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "

\n", "Model: GP regression
\n", "Objective: 100.923305908
\n", "Number of Parameters: 16
\n", "Number of Optimization Parameters: 16
\n", "Updates: True
\n", "

\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
GP_regression. valueconstraintspriors
mul.sum.rbf.variance 0.0132120051848 +ve
mul.sum.rbf.lengthscale 36.4508323102 +ve
mul.sum.linear.variances1.84224719324e-06 +ve
mul.sum.white.variance 0.000531301474393 +ve
mul.sum.bias.variance 9.98742548332 +ve
mul.gender.W (2, 1)
mul.gender.kappa (2,) +ve
mul.event.W (3, 1)
mul.event.kappa (3,) +ve
Gaussian_noise.variance 0.0117504562955 +ve
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Low Rank Approximations\n", "\n", "In the worst case, inference in a Gaussian process is $\\mathcal{O}(n^3)$ computational complexity and $\\mathcal{O}(n^2)$ storage. For efficient inference in larger data sets we need to consider approximations. One approach is low rank approximation of the covariance matrix (also known as sparse approximations or perhaps more accurately parsimonious approximations). We'll study these approximations by first creating a simple data set by sampling from a GP." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = np.sort(np.random.rand(50,1)*12,0)\n", "k = GPy.kern.RBF(1)\n", "K = k.K(X)\n", "K+= np.eye(50)*0.01 # add some independence (noise) to K\n", "y = np.random.multivariate_normal(np.zeros(50), K).reshape(50,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build a straightforward GP model of our simulation. We’ll also plot the posterior of $f$." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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M9uaBVgBmjCuiOIUbai7GZTNxy+ENAKzbWY+ORDiShKPOspDXH8GchflsyMKgDYkywFZP\nEFWkSYQMFArH2H2sA4DZNYVJO05ssNwuK9cf34IpHmXf8XZ6Q3HaukfmgmQki1vVZmXQhkSapKFN\npEmEzHOy1cuxRg+KIjF/UvpTI6cYDQrOaJBltTvRddiwp4FQVB1xpbSBUAQpi7atnytrR67IMhEN\negIjNy8nZKa3DrSi6XDVaDeji13pHs5Z2rt7mfn1/wfAxr2NGIwGPL0jK9XY3RvOul2QZ8raoA2J\nA4KbuwIjvnRJyBxxVWPn0URqZNb4IsymzEiNnOJ22qipyMVpM9HY0UtTZ4DunpE18QlF1JS3E0im\nrA7aAGazieZO0SdYyBBGEwd2HwNg/uTMSY2cYlBkbCaFRdMSNdsb9jQQ10fODslYXEXN8nRQ1gdt\ng6LQE4yLE6eFjLCnpAa/2U5pgYPJlfnpHs4FOe0mFvTVbG/a14jZZKLdMzIWJD29IcxZnBqBYRC0\nAew2M/ViUVLIABtHzwRg5vjCtPUauRS308aYIgfFeTa6e8IcqusiEBoZkx5/KI4xC3dBnmlYBG1J\nkpAUA12+YLqHIoxggVCETX1B++qJmZcaOcWgyBgViSXTE2dIbtjbgKwo+Ib5or6u68PiVPqkBG1J\nkr6bjOsMhtlkpN0TErXbQtq8fbKT+txROMN+rs6AXZAX43KYWDA1kSLZ+nYziqLQ6R3eVSSBUBQp\ny9qwXsig/waSJD0A3J6EsQya1WamsUMsSgrpsbVvF+TChr3kOq1pHs3FuZ02CpwWxo5yEQzH2HWk\njWhcG9aTnu7eMNYsz2dDEoK2ruurgBNJGMugKbJMOKbhD4kjlYTUiqsae/p2QS5u2JPxJWUGRcYo\nw5IZfSmSPQ2YzUa6eoZvijEcVZGGwak92X+vcA6b1Uxju1/Ubgsp1dbt50i9B0nXuLpxf7qHMyA5\nViMLJ49CkmDn4VaicR2ff3ieEBWLq1l3FmR/hl3QBjCZjbR1iXMlhdTZdrCNmKpRWZqLtdeT7uEM\niNtlw2ZWmDy2gFhc4823m1CHac22zx/BlCE9YAZrWAZto8FAdyA6LH/4hMy0+2g7AFPHFmRMg6hL\nMRkVFFnimr4Uyca9jVgsJjqGYc12TzA6bIJ2Sv4WP/reY6c/XrBoCQsWXTPkr+mwWahv9TG+IjM3\nOAjDRzgSY/+JTgBmTyhK82guj8NqZO5VozAoe9l/ogOfP4JleMS2s0RjKsYMX4PcunkDWzdvBCAa\n7b9lrjTY3K8kSXcAq4BHdF3/9QUe1+s70vPOHYnGcFkNFOZlRj9jYXjaf6KDTz7+KmajwvPfvoVC\nV/b8vEVicU629PDz5/bw1jstfOKmKayYPYaKAhs2a4ZHuQEKhqM0dgaxWrLn7+MPhJhUWYCu6+et\nnCajeuRZXdfdFwrY6WY2Gen0hYdFQb2QubYdTJT6XTXGTYHTlubRXB6z0YAs6ac32mzc24jVYqRz\nGB1F1t0TytoDDy5kWOa0z2S3W8QWd2HIaJrOvuOJUr8Z4wqzsqTMZjYwo6YIq9nA8SYvLV0BgpHh\nc/BvKKplfAnm5Rj2QVuSJHRZod0jqkmE5PMFwrx9sguAq68qTvNorkyBy4aualw9aRQAm/Y2IhsU\neoLZv99BVTViw2zD0LAP2gAWk5HOnsiIPQ9PGDr7jnXQE4iS77Qwtbow3cO5IhazEQnt3RTJvgYs\nJiPdvuzvRdIbGD6lfqeMiKANkGO3UtfaM+KOVhKG1o4jiVK/KWMLsJizN29qNRuYMrYAp91EU4ef\n2hYf4Vj2H0XmDWTvAb79GTFBG8BkNon8tpA0sbjKvuOJUr+ZNdk5yz6l0GUjFldZOKUMSCxImkxG\nvP7snm1HYsMrNQIpqtPOFAaDQjii0e7xU5TnSPdwhCzX1h3gSIMHSYKFfYcKZCurxQi6xpIZFfzj\nrZNs2tfI3ddPprsnhDvDm1/1JxKNwxUsDOu6zrFGD7uPtHOksZveQBRN18l3WRk7ysWM8UWML3en\nbXFzRAVtSOTvPL1hzMYwLocl3cMRstj2g63EVY2xo1yMLnKmeziDZjUp1JTnUZBrpdMb4lBdFxVF\ndlRVQ8nClqaenhCmy0yN7D7Sxp/WHuRIw/mtCI43edn2Tgt/WnuI0gIHN86vYuXcSkzG1B6qMOKC\nNoDNZqG5K4BBkbEP8QYCTdNRNS3rT8sQzrerb+v6tKqCrAxq58p3WWjsDLJkWjnPbzjKxn2N3HvT\nFLp6gll5ZxqIxDGZB3Z6UCAc47cv7WP9rnogsVN08bRyplQVku+yAjod3hAHa7t4651mmjv9/Oal\nffx101E+fv0UFk0rS1m554gM2gAOu5W6tl7GluQkfadUKBzF2nfU1KHaTmRJ4qoxie30Hl+QvCy9\n3RTeFY7E2N9X6pdtW9f7Y7eaQfOzeHoFz284ytb9Tdx38zR8/ihFeeke3eXRdZ1oXMM0gJjd4Q3y\nrd9toaG9F5NB5oPLJ3LTgiosprPD44TRsHhaOZ9871S2H2zhT+sOUdfaw4/+tJ039tTz6Q/MTEkq\nKfunB4OQ47BS29pLT5IWWyLROMeaPKcDNiSqVuy2d9MweS4bR+q7hl0zK13X8QcjtHT20uEJDPuD\nlo82eWnq8GM2Kszvq28eDiwmhTHFOZQX5tATjLLvWDsxLbHomk16ghGUAdzdNnf28pUn3qChvZfy\nwhx+8Nn3cNvSmvMC9pkURWb+lDJ+8Nn38OkPzMRmMbLzcBtf/Nl69vdttBpKI3amfYrDYaWpO0g4\nFr/iW8C4qtHU0UswEsdus9Bqd7NhzGx2l0yg/bebUGSZGXPuZEHjPma0HsZitXC8yUd5gR3nMMir\n+/xhWroCyIqMyWQkFNfo7O3BrEiMLnFhGAapg3Od2ro+cYyb3Jzs/x6eku+y0twdZMn0cv645iAb\n9zUyrbqQLm+QkoKcdA9vwHy9ESyXyGd3+kJ847eb6e4JM6kyn0c/Nh/HZaRLFVli5dxKZk8o5ier\nd7L/RAff/O0mPrRyErddUzNkC5WDbhh1yRdIY8OoyxGOxpA0lYpi54Bba8ZVjdauXsqLcwHosOXy\nwx+9yOvbj6PKF77GtNYj3PX1TzFxTD7+YJiSXGtWp0sa230EIvoFmwtpmk4wGGLsKFdW1zCfS9d1\n/t9P17P1QAufuGES/3TbzHQPKakO1XXRE1b5px++hsVk4L+/eiOaGqemwp3uoQ3YkYZurNb+30xD\nkThfeeIN6tt6qKnI4xv3Lb7o7PpSVE3nT2sP8uz6wwDMnVjCv3xwLlbzlV1zSBtGDRcWkxGT2cyJ\nlh5ONnvoCYQv2HtBVTW8PSGON3kwGJTTAXvt2Ll8+PbvsHZnHToy157cxlc3/Jp/v3cRj3z0aj6y\n72VyQz3sK6nh31Zt4Nn1h7FZzLR6Q0lLz6RafZuPSJx+u8HJsoTDYcNiMV1R6VWm8vrDHOjLZ8/L\n0q3rF2M1KZS47YwvzyMcjbPzcFtWHY4QjV38lBpd1/nlc7uob+uhrMDBv96zcFABGxKz7o+snMS/\n3rMAh9XI9oOt/NuTG+nuSf7v9ohPj5xJkiQcffnnVk+Yps4Aiiyh9N3mxDUdVdMxGQ2Y+1aldWDV\nrNv49ezbAJhZU8zX/vNuRve0AdAwPrFIVfHWH3hg13P8dsat/O+MW/jDa+/Q0N7DP98xG2dO30w7\nmxr0SBKjgYaOAPVtPbyw6ShH6j10eIOU5NuZVFnAB5bWUOB69y5C1/WsbKh0rr3HOugJZvfW9Ys5\nlSJZPL2co40eNu5t4OrJo+j0BikrzPzSRq//4l39Xt56gs37m7CYDHz57vnk2JJXiDB7QgnfeWgp\n3/qfrZxo9vLoE6/ztXsWMro4ef9uYqbdD6vFhMNuxWq1YDKbMZnN2KwWcuzW0z8QGhLfXvxJfj37\nNmRN4wtbfs/X7llwOmDXNnvQYlGikUTjHXsszOe2/4mv3bMAi8nAxr2N/PTZnah9gSxbtgyf2iUX\nkxVW/XUPX/jpWtbtrKexo5dITKWutYe/v3mCz/7wVf6y8Sin/lYNbT3pG3QS1d37T0D2b13vj8Nm\nRlM1Fk0t7zs/so1wJI4/nB29e3qDsX5LbBvbe/n9P94G4LO3z6K8KPl5+rLCHB5/aCk1FXl0ekN8\n9VcbkrpAKYL2FdJ1nR8u/Bh/mfgezPEI33vtv/jwgVeIhMMEgmHQdSpH5TJmVC7VZXloZ3QamzG+\nmH+7d+HpwP2LuXcBZMUW+7iq0dIZwGe289kbH+Ufb51EkiRuuHos3/vMMv7nazfx2IPXsHBKGdG4\nxv/+/W1+sPDjaEgEoyr+UHZ3jovFVd4smwrAzPHDb5Z9itWkkJdjZsrYQuKqxpvvtKAjZ3zTtVOl\nfheiqho/fXYn0bjGtbNGs3Bq2ZCNw+Uw881PLWH+5FKC4Rj/8d+bWb+rLinXFkH7Cj2z7jCrJ1+H\nUY3xw1d/zNL6XQCMr8hP1LueQ5Yl0HVisTjhUJjx5Xl85WPzUWSJ30+/mb+NX0xUBW9vZue3a1t9\nyAYD/3zDI+wqnUhejoXHP72UB26dwbjyPHJsZiaOyefhj8zj4Q/Pw6DIrJ58HT+9+kPYbRaa2v1Z\n3ae5rTvA3pIaJF1j4ZThU+p3rnyXlVA4yuLp5UCiXavVbKLdG0zzyC7uYqV+L245zrFGDwW5Vj55\n87QhH4vZqPDwh+fxvsXjUDWdnz27iz++9s6gf/5F0L4Cbx5o5um1B5F0je+s/RmFOzZT3+odUE7a\naFAYX+FGjUWZVOnmvr4fnu8s/iSdvjAt3QHUDO3/2+kNENd0/mv1Dt4pqqa0p53vfmYZ1WUX3nmx\ncGoZX/34AhQtzlPT3svGvQ1MqCxAkrP3x27bwVZiipGJHScpHwZb1/vjsJlB01gwpRSDIrH/eDu+\nQIRgOLMXI/sr9evwBlm99iAAD71/JnZLatJasizxiZumcv/7piNL8Mz6w/zkmZ2DqnvP3t+eNKlr\n9fHTZ3YA8Lltf2Jp3S7UWIyKy/gFlmWJ6rI8YtEoK+dV8t4jG4gYTPx49Q4MRgMNGZgmiasaHd4w\nL24+yY5DrbjCvfzkH9+nwGUlGIoSCoXQYlG0eIxwKEywLw0yY3wRX9j6fwD84rnd1DsT1RbZOts+\nder6/Kb9w741gdVswGY2MmtCCZoOG/c2ICsy/gw+HCEcVS+42P2bl/YRjia6GM6qSX3Fz43zq3j0\nYwuwmBQ27Gngm7/dTG8wekXXEkH7MvQGIzz++zcJR1WWTC/n7n1/A6ByVO5lV0VIkkR1mZtwKMKX\ntvwvZT1t1Lb4WL3uMBEt8VqZxGBQCC1YxLPrDyFJ8O21P6fS14I/EKIs30pNRT5jRuUypsTF+Ao3\nZQU2goEQqqpy5ztruP7YZqIxle8s+SQ60NaVfScJhcKxd0+padyf5tEMvcJcG6FIlGUzRwPw+u4G\nLGYTXRl6fmQkFudC96h7j7Wz7Z0WLCYD9753asrHdcqcq0p47IFrcDstvFPbxVeeeIOWK/g9EEF7\ngFRN54dPb6fNE6S6LJfP3DaLU2H6SmdcsiwxttSFPRbmP9f/EkmCFzYdQ54+nRy7JWNmo/5QBL/R\nyr9f+2k0HW5bWsO85gMATKhwJ26lz+GwmqkZ7SYeiyEBD2/5PU6biR2lk3lp/BK8gWjG/P0G6mhj\nN40dfmzREFPbj6V7OEPOajEi6RqzJxTjsBqpbfFR29pDKKpm5PfO4wthNp9dvqdqOr97OfEGe8e1\nNX3Nn9JnbGkuj396GZWjXDR3+vnyL19nz9G2y7qGCNoD9PyGI+w71oHTbuLLH70as1HhcF3noGur\nzUYD3b4gll07uOHqKjRN5zuL70VDor07M3aSNnf4+eXcO2lzFFBdlsvty2o40dgNun7RrbqJu4lE\nvjs34j89y/nZ1R9CRaLDmxl/v4HadiiRGpnVchCjll29OK6U3WJAlqXTR5G9vqsOxWigx59Zd4IA\n/nD8vJYJ63bWUtfaQ2GujZsXjkvTyM5W4LLy2ANLmD2hGH8oxn/+bgvPrj884JJfEbQH4FBdF0+v\nSSxi/L8751CQayMciVLitifl+m6nFYOkc9eKq8jLsbC/uIa/XLWMbn+EeLoXJSWJ0NwFPDtpBYoW\n59MfmAlLqRmVAAAgAElEQVRqnLGluQP8cgk1rnKwtpNrZlQwrfUIHquLl7eexNObeb/4/VFVjb19\ntbbVD96dXRuhBqEwz04oHGXZzETQ3rCnEaOi0NmTWSkSVdWInvO7Eomp/GntIQA+dv3klPe9vhir\n2chXPraAu5ZfBcAfXnuH7z715oDy3CJoX0IgFOXHf9qBpuncumQcM08tYmhaUg9RGF3sQtY1Ptk3\nG/35vA8RUXWa2tO3IUXTdFRJ4ruLP4Euydyz9yUKnebLzuEriszYEif+YJjPbXsaSKSBesMq3Rn2\ny98fT++7W9fnDpNWrANhNhpQJBhXnkdZoQNfIMLuo+1E41pGVTl5/eHzdkG+tu0k3T1hxo5yDWlN\n9pWSZYm7lk/kqx9fgN2S2Pr+Lz9Zy64jF0+XiKB9Ebqu88vnd9PhDTKuPI+PrJwMJPplF7ttSX0t\nRZEpybMxq6aQBQ176TXb+f0/3iEY0wiFr2yVebCaO3v464RlHM0fw6jeDu7d/VfKC+xXlMO3mI24\n7SZmtB1hcd0uwtE4L289kbGLWufae7yD3mCUfJeFSZX56R5OSrnsJuKqdsaCZD0mkzGj3nB9gehZ\np65HonGee+MIAHetmJi2o8EGYvaEEr7/2Wu5aowbT2+Yb/1uC7/7+4F+ny+C9kW8tr2WrW83YzUb\n+MJdczAa+v65dA2nPfntOPOcVmQJHtn8O8zxKBv2NFDf5qexM/WVFrG4SpsnxBNz7gDgc9uexqLG\nBtVK9lRrz4d2/hmAV7fV4gvGMn6XHcCOQ4nZz7SqggsuvA5n+a5EOnDpjAokCbYfbCEa1/BmSF5b\n13WisbPXGP7+1km8/gjVZbnMvaokTSMbuBK3nf+8/xruvn4yBkVi3a6Gfp8rgnY/6tt6+O1L+wB4\n8NYZlOQnem1HY3HynUPXP3l0kZPy3g7u2fsiAL9+aR+aLuFLcSfAxvYeXn6rDo/VxYzWw5Stfems\nrfhXKhAMw+7dzKopJhJTWbergbYMWXDtTygcY9+JxKnrs2qKhkXTq8thUGTMBpmCXBtTqwqJxTU2\n72sirp0fLNOhxx9BOWOWHYrE+UvfLPvDKyZmzfdLkSVuW1rD9z5zLRUX6YkigvYFRKJxfvT09tM9\nCq6ZUXH6sVg0NqRHCp1aLPnY3pcozLVR2+Jj475mWrsDKSuzCoai1LX7eXnrcSRd4wtbf09ZgSMp\nt5h2qxmjnCgbhETHta7eSEblR89V3+bjWJMXRZaYN2H4tWIdiAKnlVAkyrWzEimSNTtqsVrNGVEB\n5PGHz9oF+fc3j9MTjFJTkffuGlQWqRzl4hv3Luj3cRG0L+DXL+073Wv3U7dMP/3/46pGjs2Ykndu\nixrjEzdNAeCpVw8Qjukp+wVp7Ohl9fojxFWdm49sZGJnbVIXXcuKchhd5OCqMW4C4RhvHWyjM4N7\nWmw/1Iam6Ywvz6NsGG9dvxhXjgUtrjJ/ShkOq5HjTV5qW3z4Q+lNbem6Tijy7mw/GI7xlw1HAfjQ\niklZM8s+1+lU7AUMOmhLknS7JEnLJUm6f7DXygRv7K5n7Y46TAaZL3543lknT4TDUYryklPmd1G6\njq8nxIxxBUytKsQfivHnN47S5Rv6GWl3T4i3az1sP9iKxWRgxS+/QTTJ5z2ajQZsZoWbFlQD8Mq2\nk3gDmdkoKxZX2XM8kRqZVl2AeZDN8rOZzWLAqMgs65ttv7qtFklW0rqt/dwGUS9tOY4/FGNSZT7T\nxw3PLoyDCtqSJM0C0HV9bd/nWX3uUmN7L7/66x4A7rtlGpWjXGc9bjJIKes34cqxIEvwyZunIssS\nr7x1gjZfmKbO3iF7TV3Xae7y89RriZr025aOp7I4Z0jqW0sLHEypdON2Wmjq8PN2rZdABrZt9fSE\n2N+Xz54zgkr9LqTYbScYjnDd3EoANu5tBEmiM40VQJ6eMFZLYhdkIBTlhU2JnaofyqJc9uUa7Ez7\ng4Cn7+MTwIpBXi9tItE4P3x62+m+IivmVJ71eCgSpSDFZzlWFOaQ7zRz49VVaDr87uW36Q3Fh+yk\n85YuP5vfbqG+rYfiPBvLZ5VT4r6yw44vxWhQcFoNrOwLAOt21tPhzZwSslPeqeumuydMrsPM1KqC\ndA8nrcxGAwYJyoucTKrMJxyNs2FPI6GompYDPHRdJxx9NzXy4ubjBMMxplQVMKVqeM6yYfBBOxfo\nPuPzrCxg1TSdn/15F3WtPZQWOHjo/TPOe5fW4iquFJ+6bTYZsJsVbr+2BqfNxIGTnew70UVDR/Jn\n25FYnIYOP0+vSewg+/DKiUlbfOzPqIIcFva1/txxqIWGDn9mnd4jSTR84jMATKkqIDcnew9gTpZ8\nl5VINMbKeWMBeHX7SUwmA52+1C9IenvDGPqqRnqDEV7cnJhlf3jFxJSPJZWSsRCZ9fcgq9cdYsv+\nJqxmA1/6yNVYzzlCStN0bJb05DLLCnIwoPPR6ycB8L9/f5tQRMWT5I0NDW09/GVjIh84bVwhc8YX\nkO9K7gaic5mMCiW5FhZMKUPT4Y09TXT5MmtBckt5ot/5jHGF5/W1GInycizEY3EWTC4lx2biZLOP\nujY/Xn/qN4B19767C/KvG48RisSZMb6IiZXD+45osJHIC7j7Ps4Dui70pB9977HTHy9YtIQFi64Z\n5Msmz6a9jaxedwhZgi9+aC5jSs6vDghHolQWJ/8suYFQFJl8l5kl08p55a1aTjR7+ftbtdyycCxO\nuxklCYGkuyfEsaYe1u6sw6BI3H3dRMqG4Oy8Cylx27l2VgUb9zayblc9H1hSRWEqFnsHIGgws6dk\nArIEc2pGdj77FEmScFgNxJFYNms0L246xmvbTnLPjZMJhqLYrMk7JPdiVFUjGtcwmcHnj/Dy1uNA\nIpedrbZu3sDWzRsBiEb7r8oZbND+EzAHWAuMBV670JO+8Mi/DvJlhsaRhm5+/uedANxz01RmTbjw\nzikJLa1VA0V5Djy9Xdx3yzT+9Vcb+MvGoyydWUFTRy+jS1yXvsBFxOIqrlw7f7j1m+iFVdy8aBxj\nCh04LnBk2lCwWU1MKHNRVZrLiWYvm99uYVxZXtqrNHRdZ3vpZOKKgXGluYxO05t2Jip2OzjW5OO6\nuZW8uOkYG/c2cvf1k2n3BqlMUdBWDAoTgYaOAH/ZeIRwVGX2hGJqKtyX/NpMtWDRNacntP5AiF/+\n5PsXfN6gpmm6ru8GkCRpOeDVdX3PYK6XSnWtPXzrd1uIxjVWzq3k5oXVF3xePK7iTNEP4sWUFToY\nU+TgmhkVxOIav3v5bYJRDd8gz5Q0Gg38dcIyDhZWke+ycuPVY1J+jFZxnp3lc071tWjMiB2S/mCE\njWMSxVDTqkfe1vWLMRoUzAaJssIcZowvIhJTWbOjjlBUTXlXSk9vmL+/eRLI7ln25Rj0vbWu60/q\nur5W1/Un+3vO53+6lqfXHORkszcjmqc3tvfwzd9uwh+KMeeqEu5/3/R+y4NCkRj5uUOb2x0Ih9WM\nxSDx0esmYrMY2XGole2H22jqClzxL0prZy9dVie/nPtBAD5+/SSKXJaUt7B05Vi4emIxNouRo40e\nDtZ3p/3npMMbYuPoWQBcPal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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = GPy.models.GPRegression(X,y)\n", "model.optimize()\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "model.plot_f(ax=ax)\n", "model._raw_predict?\n", "mu, var = model._raw_predict(X) # this fetches the posterior of f\n", "\n", "plt.vlines(X[:,0], mu[:,0]-2.*np.sqrt(var[:,0]), mu[:,0]+2.*np.sqrt(var[:,0]),color='r',lw=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 2\n", "\n", "One thought that occurs is as follows. Do we need all the data to create this posterior estimate? Are any of the data points redundant? What happens to the model if you remove some data?\n", "\n", "*Hint:* \n", "```python\n", "X2 = np.delete(X,range(8),0)\n", "y2 = np.delete(y,range(8),0)\n", "```" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Exercise 2 answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building the Low Rank Approximation\n", "\n", "Now we’ll consider a GP that uses a low rank approximation to fit the data." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "

\n", "Model: sparse gp
\n", "Objective: 70.0551347892
\n", "Number of Parameters: 6
\n", "Number of Optimization Parameters: 6
\n", "Updates: True
\n", "

\n", "\n", "\n", "\n", "\n", "\n", "\n", "
sparse_gp. valueconstraintspriors
inducing inputs (3, 1)
rbf.variance 1.0 +ve
rbf.lengthscale 1.0 +ve
Gaussian_noise.variance 1.0 +ve
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z = np.random.rand(3,1)*12\n", "model = GPy.models.SparseGPRegression(X,y,Z=Z)\n", "display(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In GPy, the sparse inputs $\\mathbf{Z}$ are abbreviated 'iip' , for inducing input. Plot the posterior\n", "of $u$ in the same manner as for the full GP:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mu, var = model._raw_predict(Z) \n", "plt.vlines(Z[:,0], mu[:,0]-2.*np.sqrt(var[:,0]), mu[:,0]+2.*np.sqrt(var[:,0]),color='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 3\n", "\n", "Optimise and plot the model. The inducing inputs are marked – how\n", "are they placed? You can move them around with e.g. `m['iip_2_0'] = 100` . What\n", "happens to the likelihood? What happens to the fit if you remove an input?" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Exercise 3 answer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 4\n", "\n", "How does the fit of the sparse compare with the full GP? Play around\n", "with the number of inducing inputs, the fit should improve as $M$ increases. How many\n", "inducing points are needed? What do you think happens in higher dimensions?" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Exercise 4 answer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 5\n", "\n", "Can you build a low rank Gaussian process with the intrinsic model of coregionalization? Do you have to treat the 2nd input (which specifies the event number) in a special way?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Exercise 5 answer" ] } ], "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" } }, "nbformat": 4, "nbformat_minor": 0 }