yorkerlin/gist:d991d9c0c7eeb14a62ff Secret

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Variational Inference for Gaussian Process Classification"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "By Wu Lin - <a href=\"mailto:yorker.lin@gmail.com\">yorker.lin@gmail.com</a> - <a href=\"https://github.com/yorkerlin\">https://github.com/yorkerlin</a> Suggested by Heiko Strathmann and Mohammad Emtiyaz Khan\n",
      "\n",
      "Based on the notebook of Gaussian Processes by Heiko Strathmann - <a href=\"mailto:heiko.strathmann@gmail.com\">heiko.strathmann@gmail.com</a> - <a href=\"https://github.com/karlnapf\">github.com/karlnapf</a> - <a href=\"http://herrstrathmann.de\">herrstrathmann.de</a>, and the GP framework of the Google summer of code 2014 project of Wu Lin,  - Google summer of code 2013 projec of Roman Votyakov - <a href=\"mailto:votjakovr@gmail.com\">votjakovr@gmail.com</a> - <a href=\"https://github.com/votjakovr\">github.com/votjakovr</a>, and the Google summer of code 2012 project of Jacob Walker - <a href=\"mailto:walke434@gmail.com\">walke434@gmail.com</a> - <a href=\"https://github.com/puffin444\">github.com/puffin444</a> "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook describes how to do <a href=\"http://en.wikipedia.org/wiki/Variational_Bayesian_methods\">variational inference</a> for <a href=\"http://en.wikipedia.org/wiki/Gaussian_process\">Gaussian Process <a href=\"http://en.wikipedia.org/wiki/Statistical_classification\">classification</a> (GPC) models in Shogun. In a GPC model, a set of GP functions are used to predict the label of a data point given its features. \n",
      "We assume that readers have some background in <a href=\"http://en.wikipedia.org/wiki/Bayesian_statistics\">Bayesian statistics</a>. For the background, please see the <a href=\"http://www.shogun-toolbox.org/static/notebook/current/gaussian_processes.html\">notebook</a> about Gaussian Process. After providing a semi-formal introduction, we illustrate how to do training, make predictions, and automatically learn hyper-parameters for GPC models in Shogun"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "# import all shogun classes\n",
      "from modshogun import *\n",
      "\n",
      "# import all required libraries\n",
      "import scipy\n",
      "import scipy.io\n",
      "import numpy as np\n",
      "from math import exp,sqrt\n",
      "import time\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Brief Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In binary classification, we get binary labels in form of a column vector, $\\mathbf{y}\\in\\mathcal{Y}^n=\\{-1,+1\\}^n$, and features as a matrix, $\\mathbf{X}\\in\\mathcal{R}^{n\\times d}$, where $n$ is the number of data points and $d$ is the number of features.  A likelihood function $p(\\mathbf{y}|\\text{f},\\mathbf{X})=p(\\mathbf{y}|\\mathbf{f})$ is used to model data with labels, where a function $\\text{f}:\\mathcal{R}^{d}\\to \\mathcal{R} $ is drawn from a Gaussian Process prior and $\\text{f}(\\mathbf{X})=\\mathbf{f} \\in \\mathcal{R}^{n}$ is a column vector by applying f to each row of $\\mathbf{X}$. Note that the difference between f and $\\mathbf{f}$ is that f is a function while $\\mathbf{f}$ is an n-dimensional vector. $p(\\text{f})$ and $p(\\mathbf{f})$ are also different because $p(\\text{f})$ is a distribution in an infinite dimensional (function) space while $p(\\mathbf{f})$ is a distribution in an finite dimensional space.\n",
      "\n",
      "Given data with labels, our goal is to train a Gaussian Process classifier to fit the data well.\n",
      "In other words, we want to learn posterior, $p(\\text{f}|\\mathbf{y},\\mathbf{X}) \\propto p(\\mathbf{y}| \\text{f},\\mathbf{X}) \\cdot p(\\text{f}) $, given training data points. Note that $p(\\text{f})$ is the prior over the GP functions. In fact, we will see in the next session that all we need is to learn $p(\\mathbf{f}|\\mathbf{y})$ (Note that we use $\\mathbf{f}$ as $\\text{f}(\\mathbf{X})$ for short).\n",
      "\n",
      "The key intuition of variational inference is to approximate the distribution of interest (here $p(\\mathbf{f}|\\mathbf{y})$, which is a non-Gaussian distribution) with a more tractable distribution (here a Gaussian $q(\\mathbf{f}|\\mathbf{y}))$, via minimizing the <a href=\"http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence\">Kullback\u2013Leibler divergence</a> (KL divergence) \n",
      "\n",
      "$${\\mathrm{KL}}(Q\\|P) = \\int_{-\\infty}^\\infty \\ln\\left(\\frac{q(\\mathbf{f}|\\mathbf{y})}{p(\\mathbf{f}|\\mathbf{y})}\\right) q(\\mathbf{f}|\\mathbf{y}) \\ {\\rm d}\\mathbf{f}$$\n",
      "\n",
      "between the true distribution and the approximation.\n",
      "\n",
      "Please see the next session if you want to know the reason why non-Gaussian $p(\\mathbf{f}|\\mathbf{y})$ cannot be used in inference process of GPC models.\n",
      "\n",
      "Throughout this notebook, we deal with binary classification using <a href=\"http://en.wikipedia.org/wiki/Logit\">inverse-logit</a>, (also known as Bernoulli-logistic function)\n",
      "likelihood to model labels, given by \n",
      "\n",
      "$p(\\mathbf{y}|\\mathbf{f})=\\prod_{i=1}^n p(y_\\text{i}|\\text{f}(\\mathbf{X}_\\text{i}))=\\prod_{i=1}^n \\frac{1}{1-\\exp(-y_\\text{i} \\mathbf{f}_\\text{i})}$,\n",
      "where $\\mathbf{f}_\\text{i}$ is the i-th row of $\\mathbf{X}$ and $\\text{f}(\\mathbf{X}_\\text{i})=\\mathbf{f}_\\text{i}$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "More Detailed Background about GPC Models (Skip if you just want code examples)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In GPC models, our goal is to predict the label ($y_\\text{new}$) of a new data point, a column vector, $\\mathbf{x}_\\text{new}\\in\\mathcal{R}^{d}$ based on $p(y_\\text{new}|\\mathbf{y},\\mathbf{X},\\mathbf{x}_\\text{new})$. \n",
      "According to <a href=\"http://en.wikipedia.org/wiki/Bayes%27_theorem\">Bayes' theorem</a>, we know that <a href=\"http://en.wikipedia.org/wiki/Posterior_predictive_distribution\"> the Posterior predictive distribution</a> is    $$p(y_\\text{new}|\\mathbf{y},\\mathbf{X},\\mathbf{x}_\\text{new})= \\int {p(y_\\text{new}|f,\\mathbf{x}_\\text{new})p(f|\\mathbf{y},\\mathbf{X}) df}$$ \n",
      "Informally, according to <a href=\"http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Marginal_distributions\">the property</a> of GP about marginalization, we have:\n",
      "\n",
      "$$\\int {p(\\mathbf{y}_\\text{new}|\\text{f},\\mathbf{x}_\\text{new})p(\\text{f}|\\mathbf{y},\\mathbf{X}) d\\text{f}}= \\int {p(\\mathbf{y}_\\text{new}|\\mathbf{f}_\\text{new})p(\\mathbf{f}_\\text{new}|\\mathbf{y},\\mathbf{X}) d\\mathbf{f}_\\text{new}}$$.\n",
      "\n",
      "where $\\text{f}(\\mathbf{x}_\\text{new})=\\mathbf{f}_\\text{new}$, $p(\\mathbf{y}_\\text{new}|\\mathbf{f}_\\text{new})=p(\\mathbf{y}_\\text{new}|\\text{f},\\mathbf{x}_\\text{new})$ and $p(\\mathbf{f}_\\text{new}|\\mathbf{y},\\mathbf{X})=p(\\mathbf{f}_\\text{new}|\\mathbf{y},\\mathbf{X}, \\mathbf{x}_\\text{new})$ .\n",
      "\n",
      "The key difference here is that $p(f|\\mathbf{y}, \\mathbf{X})$ is a distribution in infinite dimensional space while $p(\\mathbf{f}_{new}|\\mathbf{y},\\mathbf{X})$ is a distribution in one-dimensional space.\n",
      "\n",
      "Note that $p(\\mathbf{f}_\\text{new}|\\mathbf{y},\\mathbf{X})=\\int {p(\\mathbf{f}_\\text{new}|\\text{f}) p(\\text{f}|\\mathbf{y},\\mathbf{X}) d\\text{f}}$.\n",
      "Similarly, $\\int {p(\\mathbf{f}_\\text{new}|\\text{f}) p(\\text{f}|\\mathbf{y},\\mathbf{X}) d\\text{f}}=\\int {p(\\mathbf{f}_\\text{new}|\\mathbf{f}) p(\\mathbf{f}|\\mathbf{y}) d\\mathbf{f}}$.\n",
      "Again, $p(\\mathbf{f}|\\mathbf{y})=p(\\mathbf{f}|\\mathbf{y}, \\mathbf{X})$ is a distribution in n-dimensional space while $p(\\text{f}|\\mathbf{y},\\mathbf{X})$ is distribution in infinite dimensional space.\n",
      "Informally, according to the definition of GP, the following holds:\n",
      "\n",
      "$p(\\mathbf{f}_\\text{new}|\\mathbf{f})=\\frac{p(\\text{f}(\\mathbf{[\\mathbf{X};\\mathbf{x}_\\text{new}^T]}))}{p(\\text{f}(\\mathbf{\\mathbf{X}}))}$, which follows from the conditional properties of jointly Gaussian variables, where $[\\mathbf{X};\\mathbf{x}_\\text{new}^T]\\in \\mathcal{R}^{(n+1)\\times d}$ and $\\text{f}([\\mathbf{X};\\mathbf{x}_\\text{new}^T]) \\in \\mathcal{R}^{n+1} $\n",
      "\n",
      "Note that If $p(\\mathbf{f}|\\mathbf{y})$ is a Gaussian distribution, $p(\\mathbf{f}_{new}|\\mathbf{y},\\mathbf{X})$ has a close form. However,in classification $p(\\mathbf{f}|\\mathbf{y})$ usually is NOT a Gaussian distribution since $p(\\mathbf{f}|\\mathbf{y}) \\propto p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f})$, where $p(\\mathbf{f})$ is a Gaussian distribution but $p(\\mathbf{y}|\\mathbf{f}))$ (the likelihood) is a non-Gaussian distribution.\n",
      "\n",
      "Variational inference in GPC is to approximate $p(\\mathbf{f}|\\mathbf{y})$ using a Gaussian distribution, $q(\\mathbf{f}|\\mathbf{y})$, via minimizing the KL divergence, ${\\mathrm{KL}}(Q\\|P)$. Reader may note that KL divergence is asymmetric. If we minimize ${\\mathrm{KL}}(P\\|Q)$ instead of ${\\mathrm{KL}}(Q\\|P)$, it is called <a href=\"http://en.wikipedia.org/wiki/Expectation_propagation\">expectation propagation</a> (EP). "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Variational Inference in GPC Models (Skip if you just want code examples)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As mentioned in the first section, variaitonal inference in GPC models is about **minimizing** the KL divergence given as below:\n",
      "\n",
      "${\\mathrm{KL}}(Q\\|P) = \\int_{-\\infty}^\\infty \\ln\\left(\\frac{q(\\mathbf{f}|\\mathbf{y})}{p(\\mathbf{f}|y)}\\right) q(\\mathbf{f}|\\mathbf{y}) \\ {\\rm d}\\mathbf{f} = \\int_{-\\infty}^\\infty \\ln\\left(\\frac{q(\\mathbf{f}|\\mathbf{y})}{  \\frac{p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f})}{p(\\mathbf{y})}  }\\right) q(\\mathbf{f}|\\mathbf{y}) \\ {\\rm d}\\mathbf{f}=\\int_{-\\infty}^\\infty \\ln\\left(\\frac{q(\\mathbf{f}|\\mathbf{y})}{ p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f}|\\mathbf{y})  }\\right) q(\\mathbf{f}|\\mathbf{y}) \\ {\\rm d}\\mathbf{f} - \\text{const}$. \n",
      "\n",
      "\n",
      "Another way to explain variational inference in GPC is to **maximize** a lower bound of the log of marginal likelihood, $\\ln (p(\\mathbf{y}|\\mathbf{X})) $.\n",
      "\n",
      "$\\ln (p(\\mathbf{y}|\\mathbf{X})) = \\ln (\\int_{-\\infty}^\\infty {p(\\mathbf{y}|\\text{f})p(\\text{f}|\\mathbf{X})} d\\text{f}) =  \\ln (\\int_{-\\infty}^\\infty {p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f})} d\\mathbf{f})= \\ln (\\int_{-\\infty}^\\infty { q(\\mathbf{f}) \\frac{ p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f})} {q(\\mathbf{f}|\\mathbf{y})}} d\\mathbf{f}) \\geq  (\\int_{-\\infty}^\\infty { q(\\mathbf{f}|\\mathbf{y}) \\ln ( \\frac{ p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f})} {q(\\mathbf{f}|\\mathbf{y})}} ) d\\mathbf{f})$\n",
      "\n",
      "\n",
      "where the inequality is based on <a href=\"http://en.wikipedia.org/wiki/Jensen%27s_inequality\">Jensen\u2019s inequality</a>.\n",
      "\n",
      "$\\int_{-\\infty}^\\infty { q(\\mathbf{f}|\\mathbf{y}) \\ln ( \\frac{ p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f})} {q(\\mathbf{f}|\\mathbf{y})}} ) d\\mathbf{f}=- \\int_{-\\infty}^\\infty \\ln\\left(\\frac{q(\\mathbf{f}|\\mathbf{y})}{ p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f})  }\\right) q(\\mathbf{f}|\\mathbf{y}) \\ {\\rm d}\\mathbf{f} = -E_q[\\ln(q(\\mathbf{f}|\\mathbf{y}))] + E_q[\\ln(p(\\mathbf{f}))] + E_q[\\ln(p(\\mathbf{y}|\\mathbf{f}))]$, \n",
      "\n",
      "where $E_q(\\cdot)$ is the expectation with respect to $q(\\mathbf{f})$.\n",
      "\n",
      "Note that the last term, $E_q[\\ln(p(\\mathbf{y}|\\mathbf{f}))]$, in GPC usually does NOT have a close form.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Dealing with the Non-closed Form Issue in GPC Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "<tr>\n",
      "<th>Approach to deal with non-closed Form</th>\n",
      "<th>Implemenation in Shogun</th>\n",
      "<th>Supported Inference Method</th>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://en.wikipedia.org/wiki/Gauss%E2%80%93Hermite_quadrature\">Gaussian Numerical Integration for One-dimensional Space</a></td>\n",
      "<td><a href=\"http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CIntegration.html\">Integration</a>, <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLogitVGLikelihood.html\">LogitVGLikelihood (Bernoulli-logistic function)</a> </td>\n",
      "\n",
      "\n",
      "<td>\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CSingleLaplacianInferenceMethodWithLBFGS.html\">Laplace Method With LBFGS</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCovarianceInferenceMethod.html\">Covariance Variational Method</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLApproxDiagonalInferenceMethod.html\">Mean-field Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCholeskyInferenceMethod.html\">Cholesky Variational Method</a>\n",
      "</td>\n",
      "\n",
      "\n",
      "\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://www.icml-2011.org/papers/376_icmlpaper.pdf\">The Piecewise Variational Bound for Bernoulli-logistic function</a></td>\n",
      "<td><a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLogitVGPiecewiseBoundLikelihood.html\">LogitVGPiecewiseBoundLikelihood</a></td>\n",
      "<td>\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CSingleLaplacianInferenceMethodWithLBFGS.html\">Laplace Method With LBFGS</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCovarianceInferenceMethod.html\">Covariance Variational Method</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLApproxDiagonalInferenceMethod.html\">Mean-field Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCholeskyInferenceMethod.html\">Cholesky Variational Method</a>\n",
      "</td>\n",
      "</tr>\n",
      "\n",
      "<tr>\n",
      "<td><a href=\"http://www.cs.cmu.edu/~lafferty/pub/ctm.pdf\">The Bound for Bernoulli-logistic function from Blei's work</a></td>\n",
      "<td><a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLogitDVGLikelihood.html\">LogitDVGLikelihood</a></td>\n",
      "\n",
      "\n",
      "<td>\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLDualInferenceMethod.html\">Dual Variational Method</a>\n",
      "</td>\n",
      "</tr>\n",
      "\n",
      "\n",
      "<tr>\n",
      "<td><a href=\"https://circle.ubc.ca/handle/2429/43640\">The Jaakkola Bound for Bernoulli-logistic function</a></td>\n",
      "<td>NA</td>\n",
      "<td>NA</td>\n",
      "</tr>\n",
      "\n",
      "<tr>\n",
      "<td><a href=\"https://circle.ubc.ca/handle/2429/43640\">The Bohning Bound for Bernoulli-logistic function</a></td>\n",
      "<td>NA</td>\n",
      "<td>NA</td>\n",
      "</tr>\n",
      "\n",
      "\n",
      "<tr>\n",
      "<td><a href=\"http://arxiv.org/abs/1206.6430\">Monte Carlo Method</a></td>\n",
      "<td>NA</td>\n",
      "<td>NA</td>\n",
      "</tr>\n",
      "\n",
      "</table>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can show that $E_q[\\ln(p(\\mathbf{y}|\\mathbf{f}))]$ can be expressed in term of summation of one-dimensional integrations via the similar <a href=\"http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Marginal_distributions\">marginalization property</a> of multivariate Gaussian distribution. Note that according to <a href=\"http://en.wikipedia.org/wiki/De_Finetti%27s_theorem\"> De Finetti's theorem</a> labels ($\\mathbf{y}$) are <a href=\"http://en.wikipedia.org/wiki/Exchangeable_random_variables\"> exchangeable</a> since we assume that these labels are independently generated from latent GP functions.  \n",
      "\n",
      "$$E_q[\\ln(p(\\mathbf{y}|\\mathbf{f}))]=E_q[\\sum_{i=1}^n {\\ln(p(\\mathbf{y}_\\text{i}|\\mathbf{f}_\\text{i})}]=\\sum_{i=1}^n {E_q[\\ln(p(\\mathbf{y}_\\text{i}|\\mathbf{f}_\\text{i})]}=\\sum_{i=1}^n {E_{q_\\text{i}}[\\ln(p(\\mathbf{y}_\\text{i}|\\mathbf{f}_\\text{i})]}$$\n",
      "where $q$ denotes $q(\\mathbf{f}|\\mathbf{y})$ is a multivariable $N(\\mu, \\Sigma)$, $q_i$ denotes $q_i(\\mathbf{f}_\\text{i}|\\mathbf{y}_\\text{i})$ is a univariate $N(\\mu_\\text{i}, \\Sigma_\\text{i,i})$, and $\\mu_\\text{i}$ and $\\Sigma_\\text{i,i}$ are the i-th element of the mean $\\mu$ and the i-th diagonal element of the covariance matrix $\\Sigma$ of $q(\\mathbf{f}|\\mathbf{y})$ respectively.\n",
      "\n",
      "Of course, even $E_{q_\\text{i}}[\\ln(p(\\mathbf{y}_\\text{i}|\\mathbf{f}_\\text{i})]$ usually does not have a close form in GPC. However, it is relatively easy to deal with this one-dimensional problem.  \n",
      "One way to deal with this issue is one dimensional numerical integration.\n",
      "Another way is to use some variational bound. For now, we assume this expectation is given and we will discuss later in the notebook."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Summary of Inference Methods in GPC Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "<tr>\n",
      "<th>Inference Method in GPC</th>\n",
      "<th>Idea of Approximation</th>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CSingleLaplacianInferenceMethod.html\">Laplace Method</a></td>\n",
      "<td>Using the second-order Taylor expansion in the mode of the true posterior</td>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCovarianceInferenceMethod.html\">Covariance Variational Method</a></td>\n",
      "<td>Minimizing KL(approximated distribution || true posterior), \n",
      "where the approximated distribution has a complete covariance matrix\n",
      "</td>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLApproxDiagonalInferenceMethod.html\">Mean-field Variational Method</a></td>\n",
      "<td>Minimizing KL(approximated distribution || true posterior), \n",
      "where the approximated distribution has a diagonal covariance matrix</td>\n",
      "</tr>\n",
      "\n",
      "<tr>\n",
      "<td><a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCholeskyInferenceMethod.html\">Cholesky Variational Method</a></td>\n",
      "<td>Minimizing KL(approximated distribution || true posterior), \n",
      "where the approximated distribution has a complete covariance matrix in \n",
      "Cholesky representation</td>\n",
      "</tr>\n",
      "\n",
      "<tr>\n",
      "<td><a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLDualInferenceMethod.html\">Dual Variational Method</a></td>\n",
      "<td>Minimizing KL(approximated distribution || true posterior) in a dual form\n",
      "</td>\n",
      "</tr>\n",
      "\n",
      "<tr>\n",
      "<td><a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CEPInferenceMethod.html\">Expectation Propagation Method</a></td>\n",
      "<td>Minimizing KL(true posterior || approximated distribution)\n",
      "</td>\n",
      "</tr>\n",
      "\n",
      "</table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Summary of Optimizers used in Inference Methods in GPC Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "<tr>\n",
      "<th>Optimizer</th>\n",
      "<th>Inference Method</th>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://en.wikipedia.org/wiki/Limited-memory_BFGS\">Limited-memory BFGS</a></td>\n",
      "<td>\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CSingleLaplacianInferenceMethodWithLBFGS.html\">Laplace Method With LBFGS</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCovarianceInferenceMethod.html\">Covariance Variational Method</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLApproxDiagonalInferenceMethod.html\">Mean-field Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLDualInferenceMethod.html\">Dual Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCholeskyInferenceMethod.html\">Cholesky Variational Method</a>\n",
      "</td>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://en.wikipedia.org/wiki/Newton%27s_method_in_optimization\">Newton-Raphson</a></td>\n",
      "<td><a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CSingleLaplacianInferenceMethod.html\">Laplace Method</a>\n",
      "</td>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://en.wikipedia.org/wiki/Conjugate_gradient_method\">Conjugate Gradient</a></td>\n",
      "<td>NA</td>\n",
      "</tr>\n",
      "\n",
      "\n",
      "</table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Summary of Supported Line Search Methods used in Inference Methods for GPC models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "<tr>\n",
      "<th>Line Search Method in Limited-memory BFGS Optimizer</th>\n",
      "<th>Inference Method</th>\n",
      "<th>Value in <a href=\"http://www.shogun-toolbox.org/doc/en/latest/lbfgs_8cpp.html\">the ShogunAPI</a></th>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.660\">MoreThuente method</a></td>\n",
      "<td>\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CSingleLaplacianInferenceMethodWithLBFGS.html\">Laplace Method With LBFGS</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCovarianceInferenceMethod.html\">Covariance Variational Method</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLApproxDiagonalInferenceMethod.html\">Mean-field Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCholeskyInferenceMethod.html\">Cholesky Variational Method</a>,\n",
      "(For now, **NOT** support <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLDualInferenceMethod.html\">Dual Variational Method</a>)\n",
      "</td>\n",
      "<td>0</td>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://en.wikipedia.org/wiki/Wolfe_conditions\">Backtracking method with the Armijo condition</a></td>\n",
      "\n",
      "\n",
      "<td>\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CSingleLaplacianInferenceMethodWithLBFGS.html\">Laplace Method With LBFGS</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCovarianceInferenceMethod.html\">Covariance Variational Method</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLApproxDiagonalInferenceMethod.html\">Mean-field Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLDualInferenceMethod.html\">Dual Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCholeskyInferenceMethod.html\">Cholesky Variational Method</a>\n",
      "</td>\n",
      "<td>1</td>\n",
      "</tr>\n",
      "<tr>\n",
      "<td><a href=\"http://en.wikipedia.org/wiki/Wolfe_conditions\">Backtracking method with the (regular or strong) Wolfe condition</a></td>\n",
      "\n",
      "<td>\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CSingleLaplacianInferenceMethodWithLBFGS.html\">Laplace Method With LBFGS</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCovarianceInferenceMethod.html\">Covariance Variational Method</a>,\n",
      "<a href=\"www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLApproxDiagonalInferenceMethod.html\">Mean-field Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLDualInferenceMethod.html\">Dual Variational Method</a>,\n",
      "<a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKLCholeskyInferenceMethod.html\">Cholesky Variational Method</a>\n",
      "</td>\n",
      "<td>2 (regular),3 (strong)</td>\n",
      "\n",
      "</tr>\n",
      "\n",
      "\n",
      "</table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "A Toy Example of Variational Inference"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we present a 2-D example for GP classification using variational inference. The gaussian prior and the likelihood are shown in Fig. (a) and (b). For this simple example, we can numerically compute the unnormalized true posterior, shown in Fig. (c), using the Bayes rule: $p(\\mathbf{f}|\\mathbf{y}) \\propto p(\\mathbf{y}|\\mathbf{f})p(\\mathbf{f})$, where $p(\\mathbf{f})$ is the prior and $p(\\mathbf{y}|\\mathbf{f}))$ is the likelihood. The approximated posterior obtained using various methods are shown in Fig. (g)-(i).\n",
      "These figures can be obtained by using the following code. Note that this example is closely related to the example at page 7-8 of <a href=\"http://www.jmlr.org/papers/volume9/nickisch08a/nickisch08a.pdf\">this paper</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def Gaussian_points(Sigma,mu,xmin,xmax,ymin,ymax,delta=0.01):\n",
      "    \"\"\"\n",
      "    This function is used to evaluate the likelihood of a Gaussian distribution on mesh.\n",
      "    \n",
      "    Parameters:\n",
      "         Sigma - covariance of Gaussian\n",
      "         mu - mean of Gaussian\n",
      "         xmin, xmax, ymin, ymax, delta - defines mesh\n",
      "         \n",
      "    Returns:\n",
      "    X, Y, Z, where Z = log(p(X,Y)) and p is a bivariate gaussian (mu, Sigma) evaluated at a mesh X,Y\n",
      "    \"\"\"\n",
      "\n",
      "    xlist = np.arange(xmin, xmax, delta)\n",
      "    ylist = np.arange(ymin, ymax, delta)\n",
      "    X, Y = np.meshgrid(xlist, ylist)\n",
      "    model = GaussianDistribution(mu, Sigma)\n",
      "    Z = np.zeros(len(X)*len(Y))\n",
      "    idx = 0\n",
      "    for items in zip(X,Y):\n",
      "        for sample in zip(items[0],items[1]):\n",
      "            sample = np.asarray(sample)\n",
      "            Z[idx] = model.log_pdf(sample)\n",
      "            idx += 1\n",
      "    Z = np.asarray(Z).reshape(len(X),len(Y))\n",
      "    return (X,Y,Z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def likelihood_points(X,Y,labels,likelihood):\n",
      "    \"\"\"\n",
      "    This function is used to evaluate a given likelihood function on a given mesh of points\n",
      "    \n",
      "    Parameters:\n",
      "        X, Y - coordiates of the mesh\n",
      "        labels - labels for the likelihood\n",
      "        likelihood - likelihood function\n",
      "        \n",
      "    Returns:\n",
      "        Z - log(p(X,Y,labels)), where p comes from likelihood\n",
      "    \"\"\"\n",
      "    \n",
      "    Z = np.zeros(len(X)*len(Y))    \n",
      "    idx = 0\n",
      "    for items in zip(X,Y):\n",
      "        for sample in zip(items[0],items[1]):\n",
      "            sample = np.asarray(sample)\n",
      "            lpdf = likelihood.get_log_probability_f(labels, sample).sum()\n",
      "            Z[idx] = lpdf\n",
      "            idx += 1\n",
      "    Z = np.asarray(Z).reshape(len(X),len(Y))\n",
      "    return Z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def approx_posterior_plot(methods, kernel_func, features, mean_func, \n",
      "                          labels, likelihoods, kernel_log_scale, \n",
      "                          xmin, xmax, ymin, ymax, delta, plots):\n",
      "    \"\"\"\n",
      "    This function is used to generate points drawn from approximated posterior and plot them\n",
      "        \n",
      "    Parameters:\n",
      "        methods - a list of methods used to approximate the posterior\n",
      "        kernel_func - a covariance function for GP\n",
      "        features - X\n",
      "        mean_func -  mean function for GP\n",
      "        labels - Y\n",
      "        likelihood- a data likelihood to model labels\n",
      "        kernel_log_scale - a hyper-parameter of covariance function\n",
      "        xmin, ymin, xmax, ymax, delta - used in linespace function: maximum and minimum values used to generate points from an approximated posterior\n",
      "        plots\n",
      "    \n",
      "    Returns:\n",
      "        Nothing\n",
      "    \"\"\"\n",
      "    \n",
      "    (rows, cols) = plots.shape\n",
      "    methods = np.asarray(methods).reshape(rows, cols)\n",
      "    likelihoods = np.asarray(likelihoods).reshape(rows, cols)\n",
      "    for r in xrange(rows):\n",
      "        for c in xrange(cols):\n",
      "            inference = methods[r][c]\n",
      "            likelihood = likelihoods[r][c]\n",
      "            inf = inference(kernel_func, features, mean_func, labels, likelihood())\n",
      "            inf.set_scale(exp(kernel_log_scale))\n",
      "            #get the approximated Gaussian distribution\n",
      "            mu = inf.get_posterior_mean()\n",
      "            Sigma = inf.get_posterior_covariance()\n",
      "            #normalized approximated posterior\n",
      "            (X,Y,Z) = Gaussian_points(Sigma, mu, xmin, xmax, ymin, ymax, delta)\n",
      "            plots[r][c].contour(X, Y, np.exp(Z))\n",
      "            plots[r][c].set_title('posterior via %s'%inf.get_name())\n",
      "            plots[r][c].axis('equal')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#a toy 2D example (data)\n",
      "x=np.asarray([sqrt(2),-sqrt(2)]).reshape(1,2)\n",
      "y=np.asarray([1,-1])\n",
      "\n",
      "features = RealFeatures(x)\n",
      "labels = BinaryLabels(y)\n",
      "kernel_log_sigma = 1.0\n",
      "kernel_log_scale = 1.5\n",
      "\n",
      "#a mean function and a covariance function for GP\n",
      "mean_func = ConstMean()\n",
      "#using log_sigma as a hyper-parameter of GP instead of sigma\n",
      "kernel_sigma = 2*exp(2*kernel_log_sigma)\n",
      "kernel_func = GaussianKernel(10, kernel_sigma)\n",
      "kernel_func.init(features, features)\n",
      "#a prior distribution derived from GP via applying the mean function and the covariance function to data\n",
      "Sigma = kernel_func.get_kernel_matrix()\n",
      "Sigma = Sigma * exp(2.0*kernel_log_scale)\n",
      "mu = mean_func.get_mean_vector(features)\n",
      "\n",
      "delta = 0.1\n",
      "xmin = -4\n",
      "xmax = 6\n",
      "ymin = -6\n",
      "ymax = 4\n",
      "\n",
      "#a prior (Gaussian) derived from GP\n",
      "(X,Y,Z1) = Gaussian_points(Sigma, mu, xmin, xmax, ymin, ymax, delta)\n",
      "\n",
      "col_size = 6\n",
      "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(col_size*3,col_size))\n",
      "\n",
      "ax1.contour(X, Y, np.exp(Z1))\n",
      "ax1.set_title('Prior')\n",
      "ax1.axis('equal')\n",
      "\n",
      "#likelihood (inverse logit, A.K.A. Bernoulli-logistic)\n",
      "#likelihoods classes for inference methods  (see the table above)\n",
      "likelihoods=[\n",
      "LogitLikelihood,\n",
      "LogitLikelihood,\n",
      "LogitVGLikelihood,\n",
      "LogitVGLikelihood,\n",
      "LogitVGLikelihood,\n",
      "LogitDVGLikelihood\n",
      "]\n",
      "\n",
      "\n",
      "Z2 = likelihood_points(X,Y,labels,LogitLikelihood())\n",
      "ax2.contour(X, Y, np.exp(Z2))\n",
      "ax2.set_title('Likelihood')\n",
      "ax2.axis('equal')\n",
      "\n",
      "#a unnormalized true posterior (non-Gaussian)\n",
      "Z3 = Z1+Z2\n",
      "ax3.contour(X, Y, np.exp(Z3))\n",
      "ax3.set_title('True posterior')\n",
      "ax3.axis('equal')\n",
      "\n",
      "f, plots =plt.subplots(2, 3, figsize=(col_size*3,col_size*2))\n",
      "\n",
      "#Inference methods used to approximate a posterior (see the table below)\n",
      "methods=[\n",
      "SingleLaplacianInferenceMethod,\n",
      "SingleLaplacianInferenceMethodWithLBFGS,\n",
      "KLApproxDiagonalInferenceMethod,\n",
      "KLCovarianceInferenceMethod,\n",
      "KLCholeskyInferenceMethod,\n",
      "KLDualInferenceMethod\n",
      "]\n",
      "\n",
      "approx_posterior_plot(methods, kernel_func, features, mean_func, labels, likelihoods, kernel_log_scale, \n",
      "                      xmin, xmax, ymin, ymax, delta, plots)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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i2DwXpkeYqg2mHIUhH0jS4EbpdDD4fciMN60HYcZ0WKLHDiMFmsaRQin+WGsa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C8iGKpI7H0Y6Ig6gvOpTLDaobUCepJ7Cwfn9oqnAFWxFP4iWo8cyWdt2tB/7ly8/dRz1FSaojNn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lhE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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3cbb090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rSuoC796WcXFxNsuXL19z9OjRrw4dOjRx48aNS0RbPoguS1pamvGqVatW7N+/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rOMHbubPXv6m8VMA0WBr9arRbjvXoMejbUdTxw6VhPHCuLwwoaX8OV5PHEvAP0K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\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xopzJxVJFXI/Zp1gMHXoZIm58N3DaoLFSLM4PGPvrX5O/dm2HF3EdFgtLFywgKDiY+Z98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\nEEIIIYQQQuRLGgdCCCGEEEIIIYTI1/8Ds8Ffmlequ6oAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4c04210>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remark:\n",
      "The true posterior is non-Gaussian and the normalized constant usually is difficult to compute.\n",
      "All approximated posteriors are Gaussian. A posterior Gaussian distribution is required to perform inference and predictions.\n",
      "The Laplace approximation is not ideal in term of approximating a posterior and making predictions although it is fast.\n",
      "With the price of speed, variational methods such as the KLCovariance and the KLCholesky offer more accurate approximations compared than the Laplace method. The KLDual method also can have a good approximation and in many cases are better than the Laplace method. On the other hand, the KLApproxDiagonal (mean-field) method is as fast as the Laplace method and for certain datasets, the KLApproxDiagonal method is more accurate than Laplace method in term of making predictions. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "A Real-World Example"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We apply variational methods to the sonar data set. The data set can be found at <a href=\"https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/\">here</a>. This is a binary classification problem. The goal of this example is to compare all inference methods implemented in shogun in term of making prediction predictions.  The following is the description of the data set. This example is about reproducing the example at page 29 of <a href=\"http://www.jmlr.org/papers/volume9/nickisch08a/nickisch08a.pdf\">this paper</a>. Please refer the paper about the definition of the information bit measure.\n",
      "\n",
      "The class \"sonar.mines\" contains 111 data points  obtained by bouncing sonar\n",
      "signals off a metal cylinder at various angles and under various\n",
      "conditions.  The class \"sonar.rocks\" contains 97 data points obtained from\n",
      "rocks under similar conditions.  The transmitted sonar signal is a\n",
      "frequency-modulated chirp, rising in frequency.  The data set contains\n",
      "signals obtained from a variety of different aspect angles, spanning 90\n",
      "degrees for the cylinder and 180 degrees for the rock.\n",
      "Each data point is a set of 60 features in the range 0.0 to 1.0.  Each feature\n",
      "represents the energy within a particular frequency band, integrated over\n",
      "a certain period of time.  The integration aperture for higher frequencies\n",
      "occur later in time, since these frequencies are transmitted later during\n",
      "the chirp.\n",
      "The label associated with each record contains the letter \"R\" if the object\n",
      "is a rock and \"M\" if it is a mine (metal cylinder).  The features in the\n",
      "labels are in increasing order of aspect angle, but they do not encode the\n",
      "angle directly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def learning_example1(inference, linesearch, likelihood, x_train, y_train, x_test, y_test, kernel_log_sigma, kernel_log_scale):\n",
      "    \n",
      "    \"\"\"\n",
      "    This function is used to train a GP classifer given an inference method\n",
      "        \n",
      "    Parameters:\n",
      "        inference - an inference methods used to train the classifer\n",
      "        linesearch - a linesearch method used in the inference method (see the table below)\n",
      "        likelihood - likelihood function to model data\n",
      "        x_train, x_test - X for training and testing\n",
      "        y_train, y_test - Y for training and testing\n",
      "        kernel_log_sigma, kernel_log_scale hyper-parameters of covariance function    \n",
      "    Returns:\n",
      "        predictive result of the testing data set\n",
      "    \"\"\"\n",
      "\n",
      "    mean_func = ZeroMean()\n",
      "    kernel_sigma = 2*exp(2*kernel_log_sigma);\n",
      "    kernel_func = GaussianKernel(10, kernel_sigma)\n",
      "\n",
      "    #Y is a sample-by-1 vector\n",
      "    labels_train = BinaryLabels(y_train)\n",
      "    labels_test = BinaryLabels(y_test)\n",
      "    #X is a feature-by-sample matrix\n",
      "    features_train=RealFeatures(x_train)\n",
      "    features_test=RealFeatures(x_test)\n",
      "\n",
      "    inf = inference(kernel_func, features_train, mean_func, labels_train, likelihood)\n",
      "    inf.set_scale(exp(kernel_log_scale))\n",
      "    try:\n",
      "        #setting lbfgs parameters\n",
      "        inf.set_lbfgs_parameters(100,2000,int(linesearch),2000)\n",
      "        #used to make sure the kernel matrix is positive definite\n",
      "        inf.set_noise_factor(1e-6)\n",
      "        inf.set_min_coeff_kernel(1e-5)\n",
      "        inf.set_max_attempt(10)\n",
      "    except:\n",
      "        pass\n",
      "\n",
      "    gp = GaussianProcessClassification(inf)\n",
      "    gp.train()\n",
      "    prob=gp.get_probabilities(features_test)\n",
      "\n",
      "    return prob, inf.get_name()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "labels={\n",
      "    \"m\":1,\n",
      "    \"r\":-1,\n",
      "       }\n",
      "from math import log\n",
      "\n",
      "def extract(inf, train_size):\n",
      "    \"\"\"\n",
      "    This function is used to processing raw data\n",
      "        \n",
      "    Parameters:\n",
      "        inf - the path of the raw data\n",
      "        train_size - number of data points used for testing   \n",
      "        \n",
      "    Returns:\n",
      "        x_train, x_test - X for training and testing\n",
      "        y_train, y_test - Y for training and testing\n",
      "        B - for computing the information bit\n",
      "    \"\"\"\n",
      "\n",
      "    random.seed(1)\n",
      "    x=[]\n",
      "    y=[]\n",
      "    for line in open(inf):\n",
      "        line=line.strip()\n",
      "        info=line.split(',')\n",
      "        label=labels[info[-1].lower()]\n",
      "        x.append(map(float, info[:-1]))\n",
      "        y.append(label)\n",
      "        \n",
      "    #train_size should be less than the size of all available data points\n",
      "    assert train_size < len(x) \n",
      "    idx=range(len(y))\n",
      "    random.shuffle(idx)\n",
      "    train_idx=set(idx[:train_size])\n",
      "    test_idx=set(idx[train_size:])\n",
      "    x_train = np.asarray([value for (idx, value) in enumerate(x) if idx in train_idx]).T\n",
      "    y_train = np.asarray([label for (idx, label) in enumerate(y) if idx in train_idx])\n",
      "    x_test = np.asarray([value for (idx, value) in enumerate(x) if idx in test_idx]).T\n",
      "    y_test = np.asarray([label for (idx, label) in enumerate(y) if idx in test_idx])\n",
      "\n",
      "    y_train_positive=(y_train==1).sum()\n",
      "    y_train_negative=(y_train==-1).sum()\n",
      "    y_test_positive=(y_test==1).sum()\n",
      "    y_test_negative=(y_test==-1).sum()\n",
      "\n",
      "    prb_positive=float(y_train_positive)/len(y_train)\n",
      "    B=float(y_test_positive)*log(prb_positive,2)+float(y_test_negative)*log(1.0-prb_positive,2)\n",
      "    B=-B/len(y_test)\n",
      "\n",
      "    return x_train, y_train, x_test, y_test, B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def approx_bit_plot(methods, linesearchs, likelihoods, x_train, y_train, x_test , y_test, plots, lScale, lSigma, B):\n",
      "    \"\"\"\n",
      "    This function is used to plot information bit figures\n",
      "        \n",
      "    Parameters:\n",
      "        methods - a list of inference methods used to train the classifer\n",
      "        linesearchs - a list of linesearch methods used in the inference method (see the table below)\n",
      "        likelihood - a list of likelihood functions to model data\n",
      "        x_train, x_test - X for training and testing\n",
      "        y_train, y_test - Y for training and testing\n",
      "        plots  - plot objects\n",
      "        lScale, lSigma  - a list of hyper-parameters of covariance function    \n",
      "        B - for computing information bits\n",
      "        \n",
      "    Returns:\n",
      "        predictive result of the testing data set\n",
      "    \"\"\"\n",
      "    #Note that information bit is used to measure effectiveness of an inference method\n",
      "    if len(plots.shape)==1:\n",
      "        rows=1\n",
      "        cols=plots.shape[0]\n",
      "    else:\n",
      "        (rows, cols) = plots.shape\n",
      "\n",
      "    methods=np.asarray(methods).reshape(rows, cols)\n",
      "    linesearchs=np.asarray(linesearchs).reshape(rows, cols)\n",
      "    likelihoods=np.asarray(likelihoods).reshape(rows, cols) \n",
      "\n",
      "    for r in xrange(rows):\n",
      "        for c in xrange(cols):\n",
      "            inference = methods[r][c]\n",
      "            linesearch = linesearchs[r][c]\n",
      "            likelihood = likelihoods[r][c]\n",
      "            try:\n",
      "                likelihood.set_noise_factor(1e-15)\n",
      "                likelihood.set_strict_scale(0.01)\n",
      "            except:\n",
      "                pass\n",
      "            scores=[]\n",
      "            for items in zip(lScale, lSigma):\n",
      "                for parameters in zip(items[0],items[1]):\n",
      "                    lscale=parameters[0]\n",
      "                    lsigma=parameters[1]\n",
      "                    (prob, inf_name)=learning_example1(inference, linesearch, likelihood, x_train, y_train, x_test,  y_test, \n",
      "                                              lscale, lsigma)\n",
      "                    #compute the information bit\n",
      "                    score=0.0\n",
      "                    for (label_idx, prb_idx) in zip(y_test, prob):\n",
      "                        if label_idx==1:\n",
      "                            score+=(1.0+label_idx)*log(prb_idx,2)\n",
      "                        else: #label_idx==-1:\n",
      "                            score+=(1.0-label_idx)*log(1.0-prb_idx,2)\n",
      "                    score=score/(2.0*len(y_test))+B\n",
      "                    scores.append(score)\n",
      "            scores=np.asarray(scores).reshape(len(lScale),len(scores)/len(lScale))\n",
      "\n",
      "            if len(plots.shape)==1:\n",
      "                sub_plot=plots\n",
      "            else:\n",
      "                sub_plot=plots[r]\n",
      "            CS =sub_plot[c].contour(lScale, lSigma, scores)\n",
      "            sub_plot[c].clabel(CS, inline=1, fontsize=10)\n",
      "            sub_plot[c].set_title('information bit for %s'%inf_name)\n",
      "            sub_plot[c].set_xlabel('log_scale')\n",
      "            sub_plot[c].set_ylabel('log_sigma')\n",
      "            sub_plot[c].axis('equal')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#an example for the sonar data set\n",
      "train_size=108\n",
      "(x_train, y_train, x_test, y_test, B)=extract('../../../data/uci/sonar/sonar.all-data', train_size)\n",
      "inference_methods =[\n",
      "                  EPInferenceMethod,\n",
      "                  KLDualInferenceMethod,\n",
      "                  SingleLaplacianInferenceMethod,\n",
      "                  KLApproxDiagonalInferenceMethod,\n",
      "                  KLCholeskyInferenceMethod,\n",
      "                  KLCovarianceInferenceMethod,\n",
      "                  ]\n",
      "\n",
      "likelihoods =[\n",
      "            LogitLikelihood(),\n",
      "            LogitDVGLikelihood(), #KLDual method uses a likelihood class that supports dual variational inference\n",
      "            LogitLikelihood(),\n",
      "            LogitVGLikelihood(), #KL method uses a likelihood class that supports variational inference\n",
      "            LogitVGLikelihood(), #KL method uses a likelihood class that supports variational inference\n",
      "            LogitVGLikelihood(), #KL method uses a likelihood class that supports variational inference\n",
      "            ]\n",
      "\n",
      "linesearchs =[\n",
      "            3,\n",
      "            1, #KLDual method using the type 3 line_search method will cause an error during computing the information bit\n",
      "            3,\n",
      "            3,\n",
      "            3,\n",
      "            3,\n",
      "            ]\n",
      "\n",
      "col_size=8\n",
      "lscale_min=0.0\n",
      "lscale_max=5.0\n",
      "lsigma_min=0.0\n",
      "lsigma_max=5.0\n",
      "delta=0.5\n",
      "scale=5.0\n",
      "\n",
      "lscale_list = np.arange(lscale_min, lscale_max, delta)\n",
      "lsigma_list = np.arange(lsigma_min, lsigma_max, delta*scale)\n",
      "lScale, lSigma = np.meshgrid(lscale_list, lsigma_list)\n",
      "f, plots =plt.subplots(2, 3, figsize=(col_size*3,col_size*2))\n",
      "\n",
      "approx_bit_plot(inference_methods, linesearchs, likelihoods, x_train, y_train,  x_test, y_test, plots, lScale, lSigma, B)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QX3/9tY+Wlpai4G1VDUJLS8t49dN0CltmeddjYctSXelUNZmYmKSoTj0p7Te0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\nyIj6KnhU0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T4jiMIfB7ZQDV9xyLzL0T8gbAX8/An4qWNgqdBcupTI+PEHOHy4N87O4jUi\nKD5sucY64E155pLEBq7QlRDG4IfVyrUIeUSTzDcks51AuhDEu+iwrZn5PMzoMeOFAzp0mJAeOqki\nIbGRZOYTxwiCeR0/VZy5i5K0LGgzBSZ3g5c1njMP8MUXJ0lMzGHKlCZKSxEUEwWKU6hTp87Zs2fP\n1vm3x2rWrHnx4sWLNYtFnC2Pe+b/AOkDwKkxeM0GO3+lFRUpl/iDbXxLW1pTkyeVllMkXMmHTjFQ\n3hHCg8HHtq4Jioz0TKjfBYb2gAGdlFajHGFh+/jrr2S2b+8kcoSsGFsf9xT1VTsksoFbLCSUz/Ck\nvtJyCkxBO4X2ksoUbtKPkvQkUBwy7+F6ItQPg/D3oWVdpdUoS16ekeeeW8IHH9Sjb9+nlZYjeAi2\nXl9BPTU2lz+5xgjcqU1pxmOPW5H93daOhJF0DpLEenK5jB/tCaAjzpRVWtojs5MUtpNCCE6kYGQU\nIZTF+YFfn4aRj7jOTfTMIpQKuFhQrW1gNMFrk6B6GZjXT2k1ynP+/C2aNVvN8eN9qVjRT2k5gofw\nODW2QKGsJpPJ/sSJE3dX2p08efI5s9lsB+Dg4GAszBOrFnMKpPWSDVzvr8FnpaoMXAmJQxxmJ9/x\nNj1UY+CuT4fno+AdH9hcWrsGrskEXcPg5XraNnB/+CGC9esvEh7eVhi4gmJF1Ff1I2HgBlNIYi1V\nWG31Bm4WJjIwEkkeADp0mHmwGZGHmUncYDaxfEUF3qaEMHDvITUL2k2Dke2FgQvyDdLq1QPp06fO\nf3+xQPCYqKXGulKNqmwE4DJvkctlhRUVPwYSiONLfucVEliOH+2pwY+EMNImDdx49HzJLQZSkv6U\n5Alc6chlNpP0r1//K1m8wZ8E48QGqggD919IyoBOn4GdHXzeR2k1ypOdradz5y3MnfuqMHBVToFm\nmMLDw/v27t17eVZWlgeAp6dnZnh4eN/s7Gz3sWPHTi9eiVaOJIGUAqYYMJyGzPFybELARbBTVw6c\nESPb2UE88QykP954KS3psckxwwe34Occ+KEs1NFofMIdxs6B3HyYO0ZpJcqRkJBN797fsnr16wQE\naKfTQaAMor6qGyNpXGMYdjhThXXY28Diz2Fcwwt78jDjgI5PKIf7A8ZVI8ljBNcojwubqYanjY21\nFjd7TkP/L6FLYxjWTmk1yrNjx2V27rzMuXMDxQ1SgUVQU421x51yTCOFnVylD0G8TwCdVHXTTEIi\ni+MksoEsTuBLSyryNa5UVVraY5OHRHVcqYU7Tuh4l1LUx5PxXCcBA+9SCgAjEl9zi40kMZWyNMZb\nYeXWybZjMHgRdHkBpnQHB3H5wdChe3nuuRC6dROZEmqnQHEKd0hPT/cGORC+2BTdg+LjnuZsMMeA\nKRbMsbJRe+ejKfb25+JA5wb2IWBfDjzGg1MD5TQXE9lks5b1eODBm3TASQVbUi/lQ8eb8JQzfF0K\nPDX+5r96B0z8Ek5+A/7qW5ReICRJonXr9dSqVZLp05sqLUdQANQw7gkarK8aIJerRPIePjQlmOE2\nkdu3hHjOk81cypOFiTnE8hPpTKYsL/3jILmdZGYSyweU4i38VWUkPC4ZOTA8HH48D+FDREYfQExM\nBnXrLmbr1k48/3wZpeUICoBa6iuor8bmEUUUw3GmLGWYjIONN9YYSSeF7STxDTocCaAzfrTBXkWL\nMTMx8Sk3qYEbXQm8+3gEeSwnntGUxhN7sjExgxjepxSBKlt8WhQkZcD7i+B0BCwfAg2fUFqRdbBx\n4++MH3+AM2f64+n54IgOgfXwODX2oZ24q1ev7tGjR4/Vn3/++QidTne3EkmSpNPpdNLw4cNnF+ZJ\nFUcygPnWPWbsPwzaOx8l/W1zNhjs7nwsA4715MftguXHdOpu34wnntWs5SmeoilNsCtYCofVIkmw\nIh1GxcOMEtDbR1X75grFiQswfAYcXKldAxdgwYKTJCZmM3nyS0pLEagc1dZXARISqewmhukEMxJ/\n2istqcAE4UguLjigwwcHJlGWF0jjc2LJwcxr+GLGzIfc4AI5LKcSVVD3NdCjsv8c9F0ALZ6GC/PB\nUwx0YDKZ6dFjG++995wwcAUWQe011oVQqrCBWGZymTcIZRbuPKW0rEcmm4sksYF09uPFi5RlCu48\nraqbgvHoKYkTntjTiQBGEMVFcphOOQAq4kIsei6QTUO8cMeeyTYYF2EJ7u2+DR8CbsKrBCAqKo33\n3tvNnj3dhIGrER5q4ubk5LgBZGZmev5bASxucY+MJIE56R9mbOzf3bR3HjOngF3gPUbs7Y9OTe43\nbHXC3bvMZbawnVa05Clsv5UkywzvxsHpPDhYDmqIeCE274XBUyF8CtSopLQa5Th//hZTphzi+PG+\nODpaf8ecwLaxufoqKBDZnOMmM5DQU4GFuNtY3QzFmW2kcJQMnr/d2dUMH4xIRNzOyLXDjqb4MJ7S\nuNlAd7GlyMqFUStg1ylY+j40F5Gvd5kx4whms8TYsY2UliLQCFqosXY4UZrxeFCfSN6jBL0oQW90\nVt5sYyKHNPaQyAZMpBNAR4LZgyPqy/BMxkA/IqiPJ/0oSS3c2UV1BhNJB/6kMwHcIJ9MTDxvA3FL\nSnFv9+2mUaL79l6MRjNdu25h9OiG1K0brLQcgYV4pDgFS3PfKIo5655ogzumbOw/HosDnYdswP7T\noL23c9auJOjEweNhSEgc5RiHOUJXOlMW6+2ckKSCee3n8+T4hBfcYH4QuFn3NU6xk5IG702DX3+D\nldOhQW2lFSlHTo6BunUXM25cI3r0sL1OBi2jpnFPSyLiFIqWfG4Qy2yyOU8wH+BLG6s/SN9LBHlU\nvL00ZRcpfMJN3sCfEYQAcJlcxhPNUirhU7B1Cpri59+g9zx4qSbM7gM+6pkAfmyOH79Ju3YbOH26\nP6VL2/bIt9YQ9bXwWLrG6oklilHY4UY5puOI9S3VziOCJL4hhZ24U4dAOuNJI5uqlY9KHHrCiCIU\nZ1Ix0omAuxm3+0njGvkYMNMMHzHV8gD+mX0rum/v58MPf+LUqRh27+6GnZ14u7YlHqfGFuhdc9So\nUZ9lZGR4GQwGx6ZNm/4YEBCQtHr16h6FecJHJrE63PKC+BKQ0hqyJkLebjAngH1ZcHkTvD4FvwMQ\nlAZByRB4Efy+B59l4DkF3AeBS1tweuZ2h60wcB/GnQVmZzjLAPpZnYGbYYJ0E1zOl3+v04H5IddJ\nkgRfp0CzaPgoEJYGCwP3u5+hZnsI9IVzW7Vt4AIMG7aXunVLCQNXYHEUra+Cx8ZIOjF8xmU64UpV\nnuA7/GhnU4fS7STTn6uEE48eM63xYzPVOEs2b3OFL4ljAtG0wlcYuP8gJx8+WALdPof5/WHZEGHg\n3kt6eh5du25h0aLWwsAVKIJWaqwTwVRmBW48wWXeIJNjSksCwIyeVL7nCr24Qm/scKMaW6jIQrxo\nbFO18lExI1EKJ+riQQ3ceBVftpLCGKKYzA2a4UNvSvAupYSB+y8kZUCXmTB6pdx9+3lfYeD+k4MH\nowgPP8PKle2FgasxCvTOuXfv3le9vLwydu3a1To0NDQqIiKi4syZM8OKWxwAPpuhxHUIyoYSV8D/\nZ/BdB16zwGM4uHYCpxfAoYLqs2ktQQ45rGA12WTTn3fwxfoCUjvehAFxEBYPb9yATBM86H0r3QSd\nYmBRGhwJhW4aX/CZkQXvfAjvTYU1M2DeOHDT+I/N1q1/sH9/JAsXtlJaikCDKFpfBYXGjJ4EVvMH\nrTCRQ3W+JYiB2NngQcwTe8rjQhx6RhPNVXIJxok1VKE7gVTDlT6UpDcllZZqVRy5BE8NgZRMOfu2\n9bNKK7IuJEli4MDvaNGiEu3bV1NajkCjaKnG6nAgmKGUZTrRjCWWeUgYFdGiJ45Y5vM7r5DEegLo\nRA32E8xQnNDGyLfd7VzfV/AhBj1t8aMZ3uwjnQQMZGDEQUXZv0XJ1qNQawgE+8G5eSI+4d9ISsqh\nR49tLF/ejpIlxd1jrVGglgqj0egAsGvXrtZvvvnmZm9v73SL5Qk51rDI0wggkURWsZYaPEFzmlnl\nArMZSeCsgzUhskE7LgEqXZW7a9v8I0roVC50vgktPWBVKLhY37djUQ4chz4ToHlDuLAdPN2VVqQ8\nN26kM2jQd+zY0RkvL3F7V2B5FK2vgkdGQiKdH4llFk6UoxLLcaWy0rIei7p4sIc0GuNNBLnMJY5k\nDHQlkDYqzCh8XHLz4cO1sO4QfDkAXm+gtCLrZOXK81y8GM+pU/2UliLQMFqssV40oCqbiWYsV+hF\nKJ9ZxDiVMJPJEZL4hixO40trKhGOKxpeuIG8MPQ6+Vwll22k8Cb+eGHPp8QwjbKqWuL2uIjs24Ih\nSRJ9++6gc+cnefVVbf98aZUCmbht2rTZWa1atT9dXFzyvvrqq0EJCQklXFxc8opbnMByXOEqm9hC\nC5rzNNa7jaOMI9R0AQcd+DvAomBomQFjEuSlZV285fiEVelyp+5XpeANjU/wZefAmDmwbT8smQQt\nGyutyDowmcx0776NoUPrUa9eaaXlCDSKqK+2QzYXieEzTGRSmg/xoqHSkh4bCQkfHGiMF+kY6U1J\nIrnOabK4QDaN8MJXRCjc5cRl6DUPaoXK3bcBGr++eBCXLycRFraPn356G1dXR6XlCDSMVmusIwFU\nZBEJLOMynSjDRHxoWizPZSSVZLaRxEbscSeAzpRjBvZos1tkMbfIwoQeiTGUJgBHWuPHKKIphSNj\nKU0KBswgDNx72HpUzr7t2hjCh4johIexcOEpYmIy2LTpLaWlCBSiwIvNkpOT/X18fNLs7e1N2dnZ\n7pmZmZ5BQUG3APbt2/fKK6+8sq/IxYnFKxbhGCc4yM90oROhlFNazkM5lQtjE2CUPzS/Z3JgSwZc\nyINJJeTfX8wDDzso76SMTmvh6Fl4eyzUfwrmjwNfjcdJ3MuUKT/z009R7NvXA3t7jbdp2zBqWLwi\n6qt1oyeWWOaSxUlK8T5+tEeH7WfrG5HujnKeJostJNMSX6ZygzBC+JUsyuNCJwIUVqo8+QaYtB6W\n7Zezbzs2UlqR9ZKfb6RBg3D69XuaQYNExoQto4b6CqLGZnOOKEbiTROCCcOOxz8cSUhkc44kNpDB\nQbx5mQC64EZNTRuTi7nFcbJ4hxIsI4ESODKZslwnn92k0psSuGOPGelu3ILWubf7dvkQ0X37X1y4\nEE/Tpqs4erQPlStb3wJDQcF5nBpbYBP3YdSpU+fs2bNni7x905oKoBoxYeI79nCNa/SgG35WPDb5\nZz5Uu31Hbl06DLkFfX1gxu2Ivt/y4O1Y+KGs3KGrdfLy4aMFsHoHLPwIXm+mtCLr4ujRG3To8A2n\nT/cnJES0UtkyajlkPghRX5XDRCbxLCWJjQTSjRL0VkVnUTYmlpHALfRUxIU+t7NuV5PAMhIYSBCd\nCCARA4GILsozEfD2XKhUCr5+F0pa36oAq2LEiB+IjExl69aO6HSqfWvWBGqvr6CdGmsknet8hJ6b\nhPI5LoQW6u8xkU0qu0hkAxK5+NMJf17HwQp3qFiaq+QykihWUBkfHLiFnpnEMJ1yOGGHHvN9HwX3\nd99O6S66b/+LnBwDzzyzmLFjG4ll3CrgcWqssLs0SjbZbGQzdtgxgH644KK0pAeyJg3GJ8JgXxjq\nD129oZEbdIuBF6OgqTvszIROXsLABTj9O/QcA9UqyNm3gdbrzStCenoe3bptZdGi1sLAFQgE/4eE\nkSQ2cYuv8OIFqrEdJxUt9ZrIDdyx41V8+IJbGJHoTxC1cGcQQXS83XmrdQNXb4Bpm+DrPTC7L3R9\nEYQn+XC+//4qmzb9zrlzA4WBKxBYEQ54U565JLGBK3QjhDH40abAfz6XKySxgVR248GzhBCGJ/XR\nCTPyLhLQl5L44IAJiUAcycLEabJogBfXyacSrsLA5f7u282jRfdtQRk2bC916wYLA1cgTFwt8juX\n2MEunqYOzXgZeysfC/W0h6pOcN0A3W7Cx4HwpAscDpVjFOyBKk7QWeNRAQYDTFsMC9fD3DHQpZU4\ncP6TO9uyW7asRLt2Ylu2QCD4GwmJDH4mhlk4UYKKLMKN6krLKlL2k0YcetZQBQB37NlCMgBP4U4t\n3AAwIOGo4VHPC1Fy922IH5ydC8FiYvE/uXUriz59vmX9+jfw83NVWo5AIPgHOnQE0gUP6nCNEWRy\njNKMf+CEiRk9afxAEhvQcxN/3qQa23AiyMLKbYPKuOJ/21rRAXb8j727Do/qagI4/NsIARJIIEaA\nQBLcgxTXUty1uGuwYi20OC0VXEvRIoWWENyluLsXjYcIcc9m935/XLRfaUOEm717Xh4eNrsbmIQk\ns2fuOTMa3MjJE5IoS248eMY8XKioghM9GSF636bP9u33OX78GdevD1U6FCEbEEVcI5JIIvs4gD/+\n9KAbRSmidEhpUj83/B4NLa3grxSYEgYhqeCRD3qL0zsA3Hkk974tYAc3d0BBB6Ujyp7EtGxBEP5J\nAg8IZC6phFGIieSlvur6+klI5MGUIS93FUtIFCMn3iS9bp1wnljqkNdoC7ipOvjRCxbvhZ/6Qd9P\nxcXQtNDrJfr23cWgQVVo0MBF6XAEQfgXuShNKbYRwBwe0hVXFpCLUq8fTyaQcLYRzg5yUQIH+mBN\nIzRGfjrj3+iQMEVD/pefo1dp4zNsOE0MPxJIB2yNuoArdt+mn69vFB4e+9m/vwd584qqt5BJRVxX\nV1fvzPh7hKzziMfsYjdlKMNIPMiRCU3tPwZJgnym0MoKInQwzlbuj3smAS4lQnMrsDfiSxE6Hcxb\nL//+YRwM6CgWnO/z6FE4Eyce5c8/+4hp2YLBEPk1a6UQwnMWE8NZnBiBLZ3QqPT6tgYNNchDBNrX\n99lghhWm+JKMN0nMxJ+dlMYym5/QyQr3/eTdt/nzwLUF4GyvdESGY8GCC8TFpTBtWgOlQxGED2Ks\nOdYUS4ryHRHs5QkDKMBIcuDEC34nntvkpx0l2EhOXJUONVt7dWrF9K0Ln28PLbPDnDWE0JC8zMnm\nw8Ozkth9m36pqXp69tzBxIm1+eSTQkqHI2QTaRps5uXl1Umj0bzzRGtr6+gKFSrccXBwCM2y4LJZ\nU3hDlEwyBznMY57QkXYUo5jSIaVZqgRmL3Pi2QRYEym3TBjxHOY6yveVsoCh+ZSNUymPfOTdt7ly\nwvrvoGhBpSPKvlJSdNSuvZb+/d0ZMaK60uEImcjQB6+I/KoMHfGEso4wsqz77QAAIABJREFUtmBH\nVxwZjClWSoeVpV7tFHpFQkKDht1E4E8y14ijPw7Ux7h6E+l0MH8XzN0Jc3rDoKbiYuiHuHo1iJYt\nf+PKlcEULSqOR6mJoedXEDk2LZLwwZevALDjc/LRAhNES5T/okNiIUEEo6UmVthjToOX+fNVvk1B\nz3cEMAan17t0DZ0kpT1Hvtp9e/0ZrB8NtdXVoeqjmD79JBcu+HPoUC9MTAz6x7HwNxnJsWkq4rZq\n1Wr/hQsXajVq1OgEwMmTJxtWqVLlure3t+u0adNm9enTZ2N6/vH/DM6AEmB29AxvdrATN9xoSfNs\nPbzsbfF6+OmF3AO3rAVMlGessDgc5oXDZDvwyA/BqVBAnRum/pVeD8u2wKwVMGMEeHQHE9Ej/19N\nnHiUR4/C2bXrczFsRWUMfZEp8uvHJaEjnB08Zxl5qEFBviAH6r4CloqE2d/aI7y9U+gacfThMYNw\nZKwKPhcfssB8GAD9FkOuHLBuNLioZ37dRxEbm0yVKquYM+dTunQpp3Q4QiYz9PwKIscKWWckz7DH\nnKpYEkQKwWgpgDmdsSU/5qQiEYsOK0wNukXRXV+ITYRkLTSsIN+n1//32vPt3bff9oJcYvftBzt1\nyodu3by4cWMoBQqoe6OBMcpIjk1TCUyr1Zo/ePCgjKOjYwhASEiIY+/evTddunSpRv369U9nVQIU\n0ieFFI5yjLvcpz1tKPVWnyNDMCQILE2gc16YEQYpEnxjDzVywRT7NztvjbGA6xMI/b+B5BS4sBVK\nGO/JnDQ7cuQpW7feEdOyhWxJ5NePJ4azBDIXU/LixjIsqaB0SFkuHh1rCSGCVKphRQFyUA0rTNC8\n3ilUjJz0wd5gC7gnboOpCSSmQLMqcgH3vxaYer3c93aOJ8zoDsNbiIuh6TFy5EEaNnQRBVwh2xI5\nVsgKyejIgyljcMIGMyJJ5Q4JXCGW3UTQH0f2EUFezPjUgE+3PAuGzj9ArdIQlwSzfoedX4O15fvz\n7Nu7b70mid236RUenkDv3jtZt66tKOAK/ydNZTB/f3/nV8kPwMHBIdTf39/Z1tY2PEeOHClZF57w\nofzwx4sdFKIQo/Ag98tJ04ZiZwz4pcIZF/lta1O5jQJAzdxyIRdAK4G5EdXjJAnWesHkhTBxAIzv\nB6bG17Lwg4WGxtO//242beqAnZ1hfS8IxkHk16yXyCMCmUcK/hRkAtZ8qrqhZe/jwTPKk5uiWOBP\nCleJ4y8S6IAtlpgSQyoAEzDMPmvHb8HQFdCqGviEwLydsGeKvOPnfQvMp8+h/xL59sW5UMzp48as\nFr/9dpvLlwO5elUMChWyL5Fjhcx2jhgWEkQ5cvMVPnxHUewwpy55MAPWEEI98lKHvNgbeAuFn3ZA\np9rwXW95LTpqFVQYBfunQQWX/3/+27tv140Wu2/TS5IkBg3aS5cu5WjRooTS4QjZUJqKuI0aNTrR\nqlWr/V27dt0mSZLGy8urU8OGDU/Gx8db2tjYRGV1kMJ/SyWV45zgOjdoQyvKY3i7Il4NMZts++bt\nshbwMAWCtFDQHI7GQ1Mr4yrgBobA4GkQEg4nN0C54kpHZBgkSaJ//9306VOJTz8VgxmE7Enk16yj\nJYznLCWaP3FkGHZ0xcRAhnpmhnBSscaUiS8LtMGkcJcErhHHPiL5HDv2EklpclHVAPsBa1Nh2X74\nujMMaCLfN2gplBsJB6dDqcLvPl+vh58Pwoyt8E1XGN1a7L5Nr6dPI/jii8McPdobS0vj+Z4SDI/I\nsUJm0SKxlCD2Esn3FKUmeVhAIMt4TjvyUxkrapOXy8Rxhhj6Y/j9eRqUly+QgnzKZdlQKFkQus+D\nLROgoov8mNh9m7lWrryKn180v//eSelQhGwqTT1x9Xq9yY4dOzqeO3euDkCdOnXOderUyevvjeIz\nPTjRTyhNgnjOdrywxZZ2tMHKABdjbwtLBXuzN33tmvvCJDvQAP2D4HYxsDLwhVdaevZJEvy2D8b9\nCCO6w9dDwNywL+h+VIsXX2TLlrucPdsfc3OxbVmtDL1nn8ivmU9PIqH8SigbsaUjjgzBzICPM34o\n7cthK5FoiUGHDWZMx5kcmJCCnjPE8BthfEtRzNEY9E6hZfvkVgrDW765b95O+P0MbBkPJV9uMPYJ\ngYFLISEZfh3z/wVeIe20Wh11666nZ88KjB5dQ+lwhCxk6PkVRI4VMkcAyUzEh7yY8T1FXg8pCyaF\ng0TygEScyEFXbBnIU8biRDMMd/J2wAsoZAsXH8LQ5TCzB3So9ebxGVuhTGH4vJ78tsfPcm950fs2\n4+7cCeHTTzdy7twASpa0VTocIQtleU9cExMTfd26dc9aWFgkA9SoUeNSZiS/AQMGrNu/f38rBweH\n0Dt37qi/OV0m06HjFGe4yCVa0Ax3Khn0EVGdBKYauYD7tp7W8Gc8nE2ApQUMs4B7+qpctE1IhGZ1\n/7tnX2g4DJsJj3zg0CqoUvajhmvwbt0K5ttvz3Dx4kBRwBWytazKr2B8OVZCTwS7ec4SLKlMKbZh\ngbPSYX1UoWgZhzeWmPIjRTFDw0KCWEAQHbGlJLlojA0XieUsMXTFTumQ08U/DJztoURBGLsW3ArI\n/XAlCSZ0gLBo8Al9U8QNioBmlWF8e9GKKKOmTTuJvX1uRo2qrnQogvCfxBpWyKjDRDKbAAbhSB/s\nXw8FBShADrpjzwMS+JVQfiaYNuQz6ALurN/lIu5P/eReuHP6QJ+F8CgIvnq5MTSnOZy4A13rvtmh\nK062ZFxCgpbu3b2YO7eJKOAK/ypN327btm3rWqNGjUuenp5dPD09u1SvXv2yp6dnl4z+4/37919/\n6NCh5hn9e4xRKKH8wmr88GMEw6iMu8EWcFNfvpQyfSt8/Vs7VV1zwLcvoHouaJXn48eXUScvw4Ap\n4HkIfv4dmgyUi7kmJnIh9+92HIVKHaCkC1zbLgq4HyohQUu3bl4sWNCUYsXyKx2OIPyrrMqv8N85\nVksYKTxHj+G3BYzlIg/pTDieuLIQVxYYXQH3ErF04S/qkpefccMGM6wwpTf25MOM1YSwimACSeZP\noilsoK0lhi6H5QcgPkku3M7sDt3mwsLdb143xCfDqbtv3qd2GfiykyjgZtSxY8/YuPEW69e3E4NC\nBYMg1rBCeiWhZwZ+LCCIn3GjHw7vFHBfyYkJlbFiMW5Mw5kRGG6j9SM3YPs5eeetjZV8YbT1J3B9\nIXieg94L4MtfYc1R6NXwTc4VBdzMMX78ESpWdKRv30pKhyJkc2lqp1CxYsXbx44d+8zBwSEUICws\nzL5x48bHb9++XTGjAfj4+Li0adNm7z9dxRRHUf6fHj3nuMBpztCUz6hGVYMt3gLE6eHHFxCaCg0s\nobAZ1LeUH3u1MzdSB7PCYGEBZWNND60Wuk2AZnVgSFf5vkFT4fhFeYdtqbdatUZGw6jv4PId2PA9\n1HJXJmZDN3ToPuLjU9i8uaPSoQgfgaEf98zK/Arvz7EajUYKkdYTygZSicCE3Jhjhxl27/z55rYt\nZthhRj40ZJ9KWBJPCWQ+STyhIOOwoZlB58T00COxhhA2E8aPFKUWef/vOXHouEMCvxKCAzlwJgdD\nMLykuukELN8P5358tyD7JAi6zYNyRSCHGZy9D8dmy8dBhcwRFhaPu/svbNzYnsaN3ZQOR/gIDD2/\ngljDCunzhETG40MJcjEDZ6yy0euerLThOASEy33j/7wNuy+BhTlUcZNbJ+y6CFod2FhC08pKR6su\nO3Y8YMKEI9y4MRRr65xKhyN8BFneTkGSJI29vX3Yq7dtbW3DDT2pG6JwwvFiJxo0DGcI+TH8XYZt\n/aBKLihpAU9T4FQ83EiCATaQxxSidCAB8wy0N7y5OTSuKQ9geWXNbJi3HnpMhC1z5UJuaDhU6Qyd\nmsDNHZA7l3IxG7IdOx5w7NgzbtwYqnQogpAmSuZXB/rhQD8k9OiIRssLtLwg9a0/E3n01tvhpBKD\nGTYvi7v2r4u75n8r/pphhyl5sqygqiWcYJYTxWEcGIQri4xqaNkr0aQyGV+i0LGNUhR4z+fAClNq\nkYda5CEFPTnSdhAr24mMg36N5QLu76fh9D0wM4W6ZeHyPHkXkVYHo1qLAm5mejUotHfviqKAKxgU\nsYYVPoSEhBfhLOQ54ylIB/Ib1YXhfFZw7BYkJsN326BjLchtATtfFm97N1I6QnXy84tm2LB97N3b\nXRRwhTRJUxG3efPmh5o1a3a4R48eWyRJ0vzxxx+ft2jR4mBWBwcwY8aM17cbNmxIw4YNP8Y/m63o\n0XOZKxznBA1pQC1qYGKgC7C3hWjBxvRNgTZAC1cS5d63W2JgaD7YHA3uOaFubmVjTQ//5+DsBCWK\nwhffQzFnaF7vZc++/vAiErwD5CKug628M7d8CaWjNlz+/tEMH76fPXu6kTev6KqvVidPnuTkyZNK\nh5Fpslt+zcW//xCS0JJK5P8VfFMIIJ6br9/W8gKJFMywfVngtf/bLt93i78mpO3KlZ4kwthECOvJ\nTxvKsB8zbDLyaTBY90ngC7xphDWLKJjmwqyhFnABrHPDuQcQGgU/H4RhLeS2RNvOgpkJdKytdITq\ntHTpZcLCEpg9W6zg1Uxt+RWyX44Vsq9YdMzAj6cksZESFMP4immtP4HVR6DJNGjiDiNavdmI5B2i\nbGxqlZqqp1evHYwbV4saNcTUVTXLzBybpnYKkiRpduzY0fHs2bN1NRqNVK9evTMdOnTYmRkBiKMo\n/y6KKHawixRS6ERH7A10CMnf/RYNM0KhtAXYmcLPTpDTBJL1cCgeloTDuoKQQwNOBjg422MWWOWG\n6R5gmVvuhztkBkwbDmP7ys8ZM0fecfv9WEVDVQWdTs+nn26kefNiTJ5cT+lwhI/I0I97ZmV+hX9v\np5DV+VVPElrC39nZ+/afb9/WYPaywGv7TsH37d29STwjiEXkphwFGU9OimZp/NmVhIQn4SzmOVMp\nTHMDHqDyoZJSoO23kJAMXerAmLag08GKg5CslYeaCZnr1q1gPvtsExcvDhR95o2MoedXEGtYIW1u\nE89EfKhLXiZSiJwGfLHzQ931hbLOb/raPo+ArzfBnstw/kcoVRimbJbvXzta2VjVaObMk5w548eR\nI70xMTHoH7fCB8pIjk1TETcriQT4zyQkrnODQxyhLnWoS21MVdCPJ0kPY0PgeDxsLwwu5jA5VC7W\nDrCBCi8veo56Lt8eYoBr09/2wpLNcHaz3E7hlad+8Pl4KFsMcuWEU1fg8GooWlC5WNVi9uxTnDjh\nw9GjvTE1NZ4XXoI6FplZSckiblpJSOiJe6fgKxd4w163cdDyAlMscWIUVlRTOmTFJKJnFv7cI4HF\nuOJqBDuF7vpC+bfq9X5h8mCVU3fhwk/g4giDl0F+K/ixn1JRqlN8fArVqq1mypR69OyZKW26BQMi\n8uu/E2tYw6dH4ldCWUco03GmiZGd7BmzGq49gSHNoH1NyJtbPjEaGQeL9sD289Cyqtyq6OyP8uNC\n5jlzxpcuXTy5fn0oBQsa4PR2IUOyrIhrZWUVp9Fo/vEJGo1GiomJ+f/pGR+ge/fuW0+dOtUgPDzc\n1sHBIXTWrFnT+vfvv/7tf8MYE2AssexkNzHE0JmOFDDA4SP/xDsFOgeAmzmsLQh5X9akHyWDZwzc\nS4byFtDDGur7yM9pYqVoyOmydLPcr8+ju7wD9+QVuWdfnSpyz9uj5yFFC0WcwL2M0tEavvPn/enY\n8Q+uXRtCoUIZ+pEkGCBDXWRmdX6Ff8+xxppfDZkPSXyBN6XIxXScya2CC7v/pd8iecjKwCbQrobc\nm0+vlxeYC/fIQ1calIebz+QFppC5Bg/eS0qKjg0b2isdiqAAQ82vINawwn8LR8vX+BKLnnm4UNDI\n+urfeAo9F0DXuvLpliJ20KHWu/3k7/hAzhyQJxcUMMCNVdlZREQilSv/wooVLWnVqqTS4QgKMOid\nuP/G2BKghMQd7rKPA1SnGg1pgFna2hZne3tiYXAQfGMHo/KD5m9frjE6uJIE815AIXO50Pu1vTKx\nZtTG3XDmmtwmodMYGNwFNMDO49CtBXRupnSE6hEVlUTlyr+waFEz2rUrrXQ4ggIMeZGpJGPLr4bu\nCFHMwp9RONEVW6MYtHL2PgxZDj0byEXbYgXkBebbC8n7fvLiMp8VWImBoJnK0/MeX3/9J9evDyFP\nHtFn3hiJ/Jp+IsdmbxeIZTK+tCc/I3DC3Ahy6t9Fx4P/CyhZEHZdgvMP5PzashpUdIFUnbwJSch8\nkiTRubMnzs55WbSoudLhCAoRRVwViCeePewjhFA605HCFFI6pEyRKsE3obA1Gv4oDLXScAwjRZLb\nKxiqpGRoNxLiE6HjZzCuH6Smwso/5GmfEwcoHaE6SJJE9+5e2NrmZvnylkqHIyhELDLTx5jyqyHT\nIrGAQI4RzUJcKY/xnGWMiIXgSCjuBJ7n4PJjKJQfWn0C5YrIJ1pyGGDPfEPg6xvFJ5+s5sCBnlSr\nJno+GSuRX9NP5NjsKRWJZTxnFxF8TxFqYdwn+JK1YPEyjx69CQevgX1eqF0GZv0Ov44BZwPdVJWd\n/fLLVVauvMbFiwOxsFDHhj3hw2Ukx4rmkdnAfR6wlBXYYMMIhqmmgPtcC4194VYSXHdLWwEXDLOA\ne/fxm9s5LWD1THAuAD+tA59AMDOTnxPyQrkY1WbDhlvcvRvKvHlNlA5FEAQh04WQQn8e400ynpQy\nqgIuQP48UMxJLtT2bAgtqsDzSDhwVV5ofjoFQqKUjlJ9UlP19Oixgy+/rCMKuIIgqEYQKfTlMfdJ\nYDuljLqA++r6goX5m9tN3OXWRSmp0G0uONuJAm5WuHcvlClTTrB1aydRwBXSTezEVVAiieznIL74\n0YkOuKho0vaJeOgZCMPzyS0U1Dxssf834PccBnaE9o0hd66XPftiYOEG2HUcGtWAa/fg/Balo1WH\nR4/CqVNnHX/+2YcKFRyVDkdQkNgplD5qz6+G7iKxfIUPPbBnMI6YGNlRT0l603bp7dv3/WDLaVh5\nEPo1hnniZEumCg2NZ/jw/cTFpXDwYE8xKdvIifyafiLHZi/HiGIm/vTHgX44GF1OfeV5BDjl//8c\nC2/ebjpNbqNwYLoyMarZiRPeDBiwh6lT6zNgQGWlwxEUJtopGKDHPGYneyhNKZrRBAvU0W9ML8EP\nL2BpJGwqCJ8Z4GCyD3HuOgyeDj1aQVQMFCsCHRpDgbeuXN57AnlyQz5ryGOpXKxqERWVxGefbaR/\nf3dGjKiudDiCwsQiM33UnF8NmR6JNYSwmTB+xIVaGNe04pAocLR5s8B8+0v01QKz5gRwdYStE5WJ\nUY0kSWLz5ttMmHCUfv3cmTGjAblyiV4Vxk7k1/QTOTZ7SEbPTwRyhhjm4UJFjHchNnoVPHkO5qbw\nmTs0LA8VXOTHJEn+rdPDHE+Y0lUe0i1kjsjIRL788hiHDz9h+fKWtGlTSumQhGxAFHENSDLJHOIw\nD3lMR9pRnOJKh5RpwlOhTxBE6+T+t4WM4PV/RBQEv4DiRWD7Ebh0Gwo5Qsv6UL4EpKRADuMadpql\ndu36i5EjD9ClSzkWLGiK5u8T8gSjIxaZ6aPG/GrookllEr7EoGMBLjga2aTsUb/A02CwzAkNysmL\nzNKF5cckST7hotPD3J3wTVdlY1UTX98ohg3bz/Pnsaxd25aqVUULBUEm8mv6iRyrvKckMQFvXMnJ\nTIqQB+OtSm4/BzN/h1uL5dMsfmHgEwKf14PGleTnhMeArfF2mMgyXl73GT36EB06lGbOnMbkzauO\njXtCxokiroHwxgcvduKKC61oQU5yKh1SprmcCF0DoHNe+N4BzI3oJV9yCli8XGsfOgOHz4GTPVQs\nCd/9Ap4L392ZK3y4kJA4Ro06yK1bIaxZ04Z69dTTekTIGLHITB+15VdDd48ExuJNY6wZRyGjm5S9\n+STM3wWX58Fvp+SJ2QEvoEcDaFBefk5YNNhbKxqmquj1EitWXGHGjJOMG1eLiRNrY25uvEUO4f+J\n/Jp+IscqR0JiJxHMJ4ixONEJWzRGllP/bv8VOHANlg+T334YIA8ye/wcvmgLuXLA+mMwtLncj17I\nuKCgWEaOPMCDBy9Ys6YNdeoUUTokIZsRg82yOS1a9nOQbXjSmpZ0ooNqCriSBMsioLUfLHSEeY7G\nU8B93RQ+x5vbzevBoM4QGw+9J0G18qKAmxGSJLFx4y0qVlxJ8eL5uXVrmCjgCoKgGhIS23jBUJ4y\ngUJ8RWGjK+AC5M4BjSuCuZnc67ZTLSjrDDsvgH+Y/HvNEYiOVzpSdfjrrxfUr7+e33+/y9mzA/j6\n63qigCsIgsGLQ8dX+PIroWygOJ2xM9oCbqoOlu6De35QrQRcfAjL9smPlSoMLapCfBIcuSEXcdvW\nEAXczKDXS6xefQ1395WUL+/AjRtDRQFXyHRiJ24W8yeA7eygIE60oRW5VTRdOlYHg5/DwxTYXhiK\nGcnJz+AwuTD7bz37avcA5wLwxwJlYlSDV0c8g4PjWLu2LVWqOCkdkpANiZ1C6aOG/GroEtEzEz/+\nIpFFuOKikou7aSVJsOmEvLCc1BnazIYRrWBIM/nxR4Fyb7765aBjLblfbqnCysZs6LRaHXPnnmfB\nggvMnNmQ4cM/EcPLhPcS+TX9RI79+O6SwAS8qUkevqIwuYx4r9qlhzD8Z7C2hFUjoERB+PM2bDkF\n5YrA6NZyz9tz92Hpftj4BeQwgjaIWe3x43AGD95LUlIqq1e3EcO3hX+VkRxrltnBCLJUUvmTk1zl\nGm1oRQXKKx1SprqXBJ0CoH5uOO8CuYwkT476Dp76gWVuaPgJfFoDyhSTH3vVsy81Ve6JO2WYsrEa\nKr1e4uefrzBjxinGjavJhAniiKcgCOriQxJj8KYsudlKKaNbbD7wB4+VEJMAKz2giD381A/+OAvJ\nWhjZCkoWknflrj4CvRuJAm5GXbsWxMCBe3ByysO1a0MoWtRG6ZAEQRAyTEJiI2GsJoQpFKY5+ZQO\nSTERsTB5I+y9AnP7yS2JXm0walhe7iu/6yJ0nweTO8PivfLuW1HAzRitVsf8+ReYN+88U6fWZ+TI\n6piaGtfrOuHjEjtxs8BzgtmOF/nIRzvakEdl06U3RcG4ELl1Ql8jWgNs2Qc/rYMrf8Bv+8A/GAJC\noHtLaFhdfk5YBNjnVzZOQ/bw4QsGDtwDwJo1bSld2k7hiITsTuwUSh9Dza9qcJhIZhPAGJzobGS9\n+hKS4bttsOowTOsGHi3eTMDW6eDITdhzGSLj4OsuMGMrFMwPy4YqG7chS0zUMmPGKX799Sbz5zel\nZ88KYiiokCYiv6afyLEfRwRavsGPSFKZhwuFMc6hUXo9bPhTLuB2qQOze4KN1T8/NzBcvjj6JEje\nqbtcbDrKkGvXghg0aC+OjpasXNkaFxcjKo4IGSIGm2UTOnSc4SznuUhzmlIZd1UtzpL0MCYYTibI\n7RMqGMnJT+8AcC0MO47CuRsw/0v5/gdP4ch5+fHx/eQrnZv2gEd3sFZX3T7LabU65s07z/z5F5gx\noyEeHuKIp5A2YpGZPoaWX9VAi8QCAjlONAtxpZyK2iulxf4rMGoVVC8JCwZAQdt/fp5/mFzk9QmV\nF5iigJt+p075MGjQXqpWdWLJkhY4OFgqHZJgQER+TT+RY7PeZWKZhC+tyccoChplP3mA2z7g8TOk\npMLPw6Fq8bS9nzZV7kMvpE9Cgpbp00+yadMt5s0TF0iFDyeKuApKJZUAAnmGN/e4hxVWdKA9Nqhr\nhPKzFOgcACVywGonyGsEp9vDIuCr+XDsItzfA5Ex0GYEeHSDIV3l5zz2hTmroE5l6NwUQsKhlKuy\ncRuaGzeeM2DAHhwdLfnll9biiKfwQcQiM30MIb+qSQgpjMMHa0z5nqJYG1E3q4AXMGY13PKBFcOg\naeW0vZ9YYKZfdHQSX311jH37HrFiRSvati2ldEiCARL5Nf1Ejs06KehZSTBehDOHotQhr9IhKSI2\nQT6tsumkvPN2UJM3J1uErHX8+DOGDNlHzZqFWbSoGfb24gKp8OFET9yPSIeOQIJ4hjfP8MYff+yx\nwxVXmtOU4hRX1e5bgN2xMDgIptrDyHxveuuolV4Pa7bDlCXQqw3c3Q1WlvLvn8bDHwchKQVG9YQS\nRaFfe/hlG/RtJwq4HyIxUcusWadZt+4Gc+c2oXfviuIKpiAIqnOBWCbhQ0/sGYQjJip7jfA+qTpY\nslceTjayNfw2HnJ+wABUUcBNn717H+LhcYCWLUtw754H1tZGcmxKEATVu0Ec0/CnCDnYTmnsMb5m\nrpIE28/BuHXwWSW4uxQcxP6XjyIiIpEJE45w/Lg3P//cipYtSygdkmCkxEvk/6BHTxDP8X5ZtPXF\nj3zY4IYrtahBd7qSi1xKh5kltBJ8Ewp/xMBeZ6hhBCc/r9+H4TPBzAyOroFKpd99vHFNuW/f3pPQ\nfQJMHgyLN0FBBzA3vtcR6Xb2rB8DB+6hUiVHbt8ehqPjexo3CYIgGCg9EqsJYQth/IQLNVTWH//f\nXPhLnoxtnxfO/yQPKROyVlhYPGPGHOLy5UA2bmxPo0biqrIgCOoQh45FBHGUKCZTmGbYqG7TVFo8\nDoKRv0BQBGwZD/XKKR2RcZAkie3b7zNmzCE6dy7L3bvDyZPHOPsvC9mDaKfwN3r0hBD6cqftM3zw\nJS95cMP15S8XLFH/lvkgLXweCFYa2FwIbFVe7o+OhalL4I9D8P1YeXetyb8MlfR/Dqs8wSdQ7n+7\nbMrHi9WQxcYmM3nycXbu/Itly1rQoUMZpUMSDJw47pk+4qhn1ooilcn4EouOBbjiYCS7hd6ejL1g\nAHxeT/2nd5QmSRJbttxh/Pgj9O5diZkzG5I7t3F8vQlZS+TX9BM5NvOcIJpv8ac2eZlAQaNqR/RK\nYjL84AXL98PkLjC6tTit8rEEBMQwYsQBnjyJYM2aNtSq5ax0SIK8EImLAAAgAElEQVRKiHYKGSAh\nEUYYT/F++cuHXOTCDVcqUZH2tCWPEe2eATgeD70DYUQ+mGwHap4vJUmwdT9MmAutG8L9vWCbhiMp\nzk4wezRotWIHblodPPiYYcP206SJG3fvDidfPnXuYBcEwbidJprp+NOSfHxhJMNWJAk2/glfbZAn\nYz9YLg8lE7KWv380w4btx98/mn37elCtWkGlQxIEQcgUYWj5ngDuk8gcihrVaZa3HbwmDwWt7AY3\nF0NhO6UjMg56vcSqVdeYOvUEI0d+wrZtnbGwMPrSmZBNGN1XooREOOGve9p644M5ZrjiSlnK0IoW\nWKtsKFla6SWY8wJWRMKmQtBY5QuwB09hxLcQGQ07lkDNSh/+d4gC7n8LD09g7NjDnD3rx9q1bfns\nMzelQxIEQch0sej4kQAuE8ePFKW6kSw47/uBx0qIS4J9U6GaaBGX5fR6iZUrrzJ9+knGjKnBzp2f\nkyOHmGgjCILhk5DYSQQLCKITtsyhKDn5l+ORKuUfBl+skYeCLhsCzasqHZHxePjwBYMH70Wr1XPy\nZF/KlXNQOiRBeIdRFHEjiHzd0/YZ3mgAV1wpQXGa0YR85FM6RMWFp0LvIIjVw1VXKKji4mRCIny7\nElZvh6nDwaOb3ANXyFySJOHpKfcP6tatPHfuDMfS8gOm2giCIBiIC8QwBT/qY81OSmOJ+gtqCcnw\n7R+w+ghM7wbDW4jJ2B/Dq8VlaqqeU6f6UbasvdIhCYIgZApfkpmJH3HoWUNxSqt07sy/0abCoj3w\no1f6hoIK6ZeSomPu3HMsWnSJ6dMbMHx4NUxNje8CgpD9qbJ0FUX0O0XbVFJxwxU3XGlEQ2zJb5TN\n0N/nUgJ0DYTP88J3DmCu4k/Nnj9h9ByoUwVu7wInsfbJEkFBsXh47Ofx4wh27vycmjULKx2SIAhC\npotHxzyCOE00sylCbfIqHdJHsf+KPFylZim4vQSc8isdkWGTJAnNfzQP1mp1zJ9/gXnzzjN9egM8\nPD4Ri0tBEFRBi8SvhLKeEIZSgJ7YY2aEa/XTd+WTLYXt4MJcKCE65GRYWvIrwJUrgQwcuAdnZ2uu\nXRtCkSLGeTJbMAyqKOLGEvtWewRvEknCFRfccKMedbHHThRt/4EkwbJImB0Gq5ygvYrXnj6BcvH2\nkQ+s+xY+ral0RIYlrQlQkiTWrr3B118fZ/jwT/jjD9E/SBAEdbpCLN/gxydYsYsy5DGC3bf+YTBm\nDdzxgVUjoYm70hEZpvj4FG7cCMbR0ZISJWzRaDTo9RIm7xlCcP36cwYO3IODgyVXrw7BxSUNzfsF\nQRAMwF0SmIYfdpixjVIUxkLpkD660CiY+Cv8eRsWDoROtcVQ0Iy4eDGAQoXy4Oxs/Z/5NT4+halT\nT7Blyx0WLmxGt27l07TmFQQlGWR1JZ54vPF5XbiNIw4XiuKGK7WogQMOmBhh75wPEa2DIc/hcQpc\ncIViKj2mkZwC83+FBb/C+H7guRAsVPqxZqaUFB1XrgTi5JQHN7d8/5kAAZ49i2Tw4L3ExCRz7Fgf\nKlZ0/IgRC4IgfByJ6FlEEEeIYjrONDSCPvraVFiyD773hFGtYYs43pluUVFJNG++GTu73OTLlwut\nVsfvv3fGxOT/82xiopZZs06zbt0N5s5tQu/eFcXiUhAEVUhAxzKC2UcEEylEa/IZ3aYrnQ5WHYZp\nW6Dvp3B/GeTJrXRUhq15882kpOgoUsSapKTU9+ZXgCNHnjJs2D7q1i3C3bse2NmJT75gGAyiiJtI\n4jtF2yiiKPqyaNuFKjhRQBRtP8D5BOgVCM2s4LwL5FTpp+74BXlwWUkXuOoJLoWUjsgwxMen0KzZ\nZvLkscDePjdarZ6tWzu9NwHqdHqWLLnEd9+dYdKkunzxRU3MzFT6RSUIglG7QRxf40cFcrOT0tgY\nxsuoDDn/AIb/DI424nhnZli69BLlyjmwdm1boqKS6NdvFw0a/MqpU/0wMdG8Pvly5owvgwbtpVIl\nR27fHoajo5XSoQuCIGSKc8QwE38qY8kuSpMfFQ9jeY+rj+XcamEOf34LFVyUjsjwLVt2mZw5zTh0\nqBcREYkMH76fOnXWce7cgHfya3h4AuPGHeH0aV9WrmxFs2bFlQ5dED6IRpIkpWN4L41GIy2TVvCC\ncIrgjBtuuOFKQZwwNYJji5ktVYJvX8DKCPilILRT6eDs52Ew/ie4cBMWT4a2nyodkWH54Yez3LsX\nxqZNHYiKSmLgwD2EhcVz+nR/4N3WCnfvhjJw4B5y5TJjzZq2FC8uGiMKH5dGo0GSJOPaupEJNBqN\nlJ3zf3aTjJ6lPGcPEUzDmc9Q/3H28BiYtBEOXIX5A+DzeuJ4Z2bYseMBt2+HMGNGw9f3tW69BRMT\nDXv2dAfg2LFn9Ou3i2XLWtK+fWmFIhWMnciv6Sdy7D+LJJUfCeAa8UzHmbpG0kf+bVFx8M1m8DoP\nP/SFPo3AROx9yRTHjj3j2LFn/PDDZ6/va9/+d5KTdRw82PP1fZMmHSM5Wcfs2Y2wshLHigRlZCTH\nZvsfGa1oyTdMoj99aUA9nCksCrjp8CwF6vvIu3BvuKmzgJuaCks2Q8X24FoI7u0RBdz0KFvW/nW/\nPRubnHh5dcXaOidt224F5B84Wq2OmTNP0qjRBgYOrMyff/YVBVxBEFTpDvF05iGBpLCL0qov4EoS\nbDgO5UZCTnO4vxy61RcF3IyIi0tBq9Wh10u4ueXD0/M+ly8Hvn58374emJmZsG/fIwAaNnTh7l0P\nUcAVBEEVJCT2EkE7HpAPM3ZR2ugKuJIEm05AmRGgl+Tc2q+xKOBmVGqqHq1WhyRJFC6clwMHHnPm\njO/rx3ft6kbu3Ob88cfd1/d9/31jFi5sJgq4gsHK9ucAXSiqdAgGTZJgczSMC4Gv7WBMfviXtqYG\n6+ItGD4T8tvAmU1Q2k3piAxLYqIWMzMTzMxMcHW14euvj9OqVQlq1iwMwN693enUaRu7d/9Fu3al\n0Wg0xMSkcOPGUAoXNq4XYYIgGIcU9PxMMF6EM5nCtCCf0iFluft+8vHOhGTYPw2qihOGGebnF83g\nwXvJnz8XNWsWYsyYmowZU4OWLX/j6NHeVK7sBEDp0nYkJmoBMDMzwcYmp5JhC4IgZIpAkpmFP6Fo\nWY4bFbBUOqSP7r4feKyE2ETY/Q1UL6l0ROoQHZ1Ev367sbAwpXr1QowbV4vx42vRrt3vHDzYkxo1\n5HVs9eoFSUpKff1+ore8YOiyfRFXSL8oHXg8h5tJcLQouKtwPRAeBZMXwv5TMG8idGspdgt9qKCg\nWAYN2oO1dU5q1CjEF1/U5IsvatKq1RaOHOlF1apyA8Ty5R1ITJQToJmZCfPnN1UybEEQhCzzgAS+\nxpdCWOBFaexV3q8vIRlm/wFrjsCM7jCsOZiKQ08Z9uJFAm3abGX48Go4Olqydu0N+vVzZ8iQqpiZ\nmdCx4zamTauPg4Mlu3c/xN29gNIhC4IgZAodEpsJ4xeC6Y8j/XDA3AgHl83dCfN3wfRuMLyFyK2Z\nJSFBS/Pmv9G2bUnKl3dgxYqr9OxZgb593TEx0dCliyffffcpxYrlx8vrAX37VlI6ZEHINKKIq1Jn\nXw4va2UFV90gt8qOauj18Osu+HoRfN4c7u8FaxW2iMhqkZGJtG69hf793XF2tmb16uv07VuJQYOq\nYG5uQqdO25gxoyGFC+dl584HFC9eW+mQBUEQsowWiTWE8BthTKQQbY1gWvbFh9BrAXxSHG4vASfR\nGSfT3L8fRpEi1gwbVg2AxYsvMWPGKUqXtqV79wq4ueXj5EkfDhx4wqhR1enatZzCEQuCIGTcQxKZ\nhh+5MGErJSmKCncS/Qe/MOi9QN5cdG0hFLFXOiJ1uX8/DBubnEyeXA+AZcuu8NNP5ylTxo4uXcri\n7GzNoUNP2LnzL1q1KsGIEdUVjlgQMk+2H2yWnePLjrQSzAqDNVGw2glaq7CwefshDJ8lX938eTpU\nLqN0RIbr3Dk/5sw5y/79PQBo3HgjlSo5UqqULd26lefGjWCOH/fm/v0w6tUrwhdf1FQ4YkF4lxi8\nkj4iv/6/JyQyGV/yY8ZMilAAdfdKS9XBHE9Yvh9+Hg4dxTW6TBcRkUjduuuoVq0gt2+H4OaWj5Yt\nS/DsWSTx8VrmzWuCubkpqal6zMxUdrVdMHgiv6afsebYpLfaEI2lIB3Jr/oLof9k62kYsxrGt4cJ\n7cXu26wQF5dCvXrrKVXKluDgODQaDd26lePZsyji41OYP78pFhZmpKToyJFD/AcI2U9GcqzYiasi\nT1OgZyDYmMjDywqo7H83Nh6mL4Pf9sG3o2FgJ9EMPqPKlXPA1zeK3r138tdfL3B0tKRMGTuePYti\n6tQTzJvXlIYNXdBqdZibiwQoCIL66JBYTyjrCWUsTnTCVvWLTu8QefdtrhxwfREUslU6IvV49iyS\n0NB49HqJ2rWdOXt2AGfP+hEfr8XLqysAFy8GMG/eeZKSUjE3NxUFXEEQDN5lYpmOP6XJxU4jaEP0\nT6LjYeQvcOUxHJwu+spntqCgWPz8ol/n11On+nHs2DO2bLnD9u1yfr12LYg5c84SF5eChYWZKOAK\nqiReNaqAJMGGKKjpDT3ywoEi6ivg7j4OZVpDdCzc3Q2Du4gCbnr5+kZx8WIA58/7Y2OTkzNn+tO5\ncxmKFLFm374eDB5clQ4dShMUFPt6yIoo4AqCoEbeJNGLR5wnBk9K0Rk71RdwfzsJ1cdDx1pwZKYo\n4Game/dCqVdvPVu33qV//918991pLC3NadasGKGh8axZcx0Ae/vcBATEEBwcp3DEgiAIGRNNKtPw\nYxK+TKAgC3E1ygLu2fvgPgascsntE0QBN3N5e0dSq9ZaNm68xaBBe5g27QQ5cpjStm0pwsMTWbHi\nCgCurvkICIghKChW4YgFIeuIdgoGLlIHw5/D3WTYWggqqKzlUEwcjPkezlyD9d9BvapKR2TYHj58\nQePGG+nQoQxHjjylV68KfPllHTQaDZ99tpFevSoyZEhV/P2j6dzZkw0b2lO6tJ3SYQvCe4njnulj\n7PlVj8QmwlhFCCMpwOfYYaLy4m10vDwd+8Yz2DIe3N2Ujkhd9HqJr746hq1tLiZNqsvjx+GMGHGA\nOnWcGTu2FocOPWHNmus4OeXh0qUApkypT69eFZUOWxDeS+TX9DOGHCshcYQovieQxlgzloJYYXyb\nPrSpMPN3eTDo6pHQRrRezXSSJDF9+kn0eolvv/0UX98ohgzZR40ahRg/vhbHj3uzZs11ChSw4urV\nIAYNqsLo0TWUDlsQ/pVop2CkTsVDnyBolweuFIRcKtuZeuoK9PsamtWBm15gZal0RIZNr5dYv/4m\nQ4dWZerUBjx5EoGHx350Oolx42oxalR11qy5wfnz/ly+HMiECbVFAVcQBNXxI5lv8AVgKyUpgoXC\nEWW9s/fl9gktq8LVBZBb/R/yR2dioqFMGTuuXAkiKiqJEiVsWbmyNR4e+8mX7yajR9egYkVHrl4N\nYsiQKtSpU0TpkAVBENIlhBRmE4AvySzAhSpYKR2SIh4HQc/5YJsHbi6GAvmUjkidNBoNpUvbcfy4\nNxERiRQtasPq1W0YNmwf69bdYOzYWpQpY8eFCwF061aepk2LKR2yIGQpUcQ1QFoJZoTBuihY6wQt\nVTa8LCkZpiyGrQdg9SxoWV/piNTh1QLz/PkAIiMTKV48P7/80hoPjwOsX3+DMWNqUr68A5cvB9K/\nvzsNGrgoHbIgCEKm0SPxBy9YRjBDcKQ39qrffatNhVm/w2qxQ+ijKFXKlgsXAnj0KBx39wK4ueVj\n4cJmtGy5BXf3AtSvX1RcHBUEwWDpkdjGC5YSTA/sWIALOYywO6MkwdqjMHkjTO8GI1qBRt0vJxRX\nokR+Tpzw4dGjcKpUcaJIEWsWLWpO8+abqVjRkcaN3ShTxl7pMAXhoxBFXAPzOFkeXmZvBjfdwFFl\n/4M3H0DvSVDKFW7tBDtxRTNTlSxpy9mz/jx+HIG7ewFcXeUFZosWv+HuXoAGDVxEAhQEQXWCSGEK\nviSgZzMlcEVlvYf+wZMg6LkA8lmJHUJZTZIkNBoNdeoU4ciRZyxdepkxY2pQokR+ypSxp0ePCuh0\neqXDFARBSLenJDEdP/TABopTnFxKh6SI8BgYvByePoeTc6CcOFSRpV7l108+KcThw09ZsuQSY8fW\npGRJW0qWtKVfP3e0WpFfBeNifJfODJQkwbpIqO0DfWxgn7O6Crg6HXy/CpoOhi8HgudCUcDNTK/6\nctWq5UyhQnlYsuQSt2+HEB2dROnSdvTqVZHUVJEABUFQFwmJ7bygCw+pTV42U1L1BVxJgvXHoNaX\n0LMBHJgmCrhZ4fHjcE6f9iUhQYtO96b35cyZDXFxsWHVqmt8//1ZNm++zerV18iZU0Uv2gRBMBop\n6FnOc/rwmFbkYzMljLaAe/QmVBoDbo5web4o4GaVJ08iOHHCm7i4lHfWp1Om1KdECVtWrbrOjz+e\nw8vrPitWXCFHDuPrxSwYNzHYzABE6GBoEDxMgS2FoLzK1p9P/aDPZMhpAeu/hSIFlY5IHR4/Dico\nKJZPPilEjhymmJm9uWYzdeoJQkLiyJ8/F+7uBRgz5hBeXl2pW1e8GhEMixi8kj7GkF+DSWE6foST\nyvcUpYQRLDojYmHYCngQIA8vq+CidETqdPDgY0aOPEipUrbkzm1O7drO9OpVEQeHN837jxx5yu3b\nIdy5E0rPnhVEjz7B4Ij8mn5qybE3iGMa/hQhB1NwxokcSoekiKQUuXXC9vOwfjR85q50ROp15MhT\nhg7dR/nyDpiZmVCnjjM9e1bAyelN/8jDh59w+3YIt26F0LlzWdq3L61gxIKQPhnJsaKIm82djIc+\ngdAxL/zgADlVtHdakmC1J3yzGKYMg1E9wURFH5+SDh16gofHfsqUsSdnTjNq1y5Mr14VcXR8M3jg\nyJGn3LoVzO3boXTvXp6WLUsoGLEgpI9YZKaPmvOrhMQeIplLID2wYzAFMFd571uAk3egz0LoWAt+\n6As5jXOtneV0Oj1Dh+6jU6cytGhRgl27/uLSpUB0Oj0TJ9bG3v7dKaxJSaliF65gkER+TT9Dz7Fx\n6FhEEEeJYjKFaYYNGiPIo//kri/0mAclC8EvHmCbV+mI1EWSJMLDE7Gzy41eLzFixAFatChO27al\n2LPnIZcuBZKUlMqXX9Z+Zx0LkJioJVcuc4UiF4SMyUiOFSWzbCpFgkkhcv/bVQVhUQF1FXCDw6CN\nB/yyDU5tgDG9RQE3s+j1Ejt2PGDJkhbs39+DPn0q8uJFIvPmXSA0NP7185o2LcbEiXVYvbqNKOAK\ngqAKYWgZhTe/EsJqiuGBk+oLuClamLQBesyHVSNg0WBRwM0sycmpbNp0i1q11hIYGPP6/sjIJO7d\nCwOgffvStG5dAhMTDb/9dgeA27dD2LPnIQAWFuKYpyAIhuME0bTjAclI7KEMzclnlAVcvR4W74FG\n38AXbcHzK1HAzUxJSals2HCTatVWM2DAbkAeDhcVlcStW8EAtG1bitatS2BhYfo6v96/H8bu3X8B\niAukgtESZbNs6FEy1PaGe8ny8LLmVv/9PobE6wi4d4LKZeDCFihbXOmIDN+DB2H4+ES9flteYIYC\n0K5dadq0KYmZmQlbtsgJ8O7dULHAFARBNSQkDhBJR/6iBDn5g1KUIbfSYWW5hwFy79t7fnBzETSv\nqnRE6hAcHMeMGSdxcVnMxo23+eabeq+PcpqamjBpUh1On/bl8OEnANSu7UzVqk5cuBBAYqKWBw/C\nKFtWHhKqESPLBUEwAGFoGYc3PxLIdxRlNkWwNtIZ6M8joOUs2HoaLvwEA5rIBUYh44KCYpk69QRF\niy5i69a7zJ7diF27ugFyvpw0qQ5XrgSxf/8jQJ7nUq1aQS5dCiQhQcvdu6GUKmX3+vmCYIxEETcb\nkSRYEwl1fGCgDexxBnsV5c7oWOgzCSYvhF1LYfZoyCF2C6WbXi+xf/8jmjbdRKNGG7hzJwQAExMN\nX31Vh3Pn/Dl0SF5g1qpV+PUCMyFBy717oZQpIxKgIAiGLwIt4/DhZ4JZgRtjKEgOlb+8kSRYfRjq\nToJBTWDPFHCwUToqw3ftWhB9+uykTJnlBAfHcexYb44e7U3r1iUxMXmTK93dC9C6dUk8Pe9z9OhT\nNBoNXbqUIywsnidPIvj88/IUL55fwY9EEAThvyWj5zpx/EIwHfgLZyzYRWlqkue/31mldl2Eyl9A\njZJw5gcoLma1ZJgkSVy8GECPHl6UL7+CyMhETp3qx6FDvWjZssQ7+bVcOQfatSvFzp1/cfDgYwA6\ndizDixcJPH4cTteu5Shd2k6pD0UQsgUVlQgNW3gqDHkOT1PglAuUtVA6osz150Xo/w20bgg3vMBS\n/Rukskx0dBK//nqTpUsvY2OTkzFjatC1azksLN58O1eq5EibNiXZvv0+JiYamjYtRufOZVmx4gpP\nn8oLTEEQBEN3jChm4U9b8vMDRbFQefEW4EUMDF4GPqFw+nso46x0RIYtNVXPjh0PWLz4EgEBMYwY\n8QmLFjUnf/73D8IzNzele3c5jy5YcJGHD8Oxtc1FUFAs1tYqmz4rCIJqRJLKdeK4QTw3iOcvEnHF\ngspYsZpiRnGC5X3ik2DsGjh2C7wmQZ2ySkdk+FJSdGzbdo8lSy4RHp7IyJGfsGJFK2xs3p8nzcxM\n6NatPJIES5de5tGjcAoWzENAQIzIr4LwkijiZgPH46FfIHTNC1sKgYWK1qCJSfD1IvA8DGtmQfN6\nSkdkuB4+fMGyZVf47bfbNGtWnE2bOlCzZuF/3EkrLzArIEmwcOFFHj58gaOjFYGBseTNq7IrBIIg\nGJ0oUplDAHdIYDGuVEZlfYfe4+hN6L8YuteH3yeChZjnkW7h4QmsXn2d5cuv4Opqw9ixNWnfvjRm\nZml7EZYnjwX9+7tTrpw9S5dexsLCjHXr2lGkiHUWRy4IgvDfJCR8SOYG8a8Lt2FoqYQllbFkJE5U\nJDeW/2PvzsNjuv///z8mJLVGInYJse+7ohRBSylKqX1rlaKWblrtW0tVUbXU0qKotVVKLdWW1hL7\nLmjtVOw7ESHrzPn9kU5/+fhagoxzZnK/Xde5LsmMM495zWSec57zmtcRy6rtOCq1Hy09U1zaM07y\nTb297BRx4UKUpkzZqcmTd6l06Rz6+ONaatSoiNKkSV59zZjRR126JNbXceO2KV26tJo+vamCg/nK\nESBJNiufOdPdz+z5IHGGNPCS9P0NaUYeqb6HHYPuPiB1/EAqXUT65hMpgNfdh+ZwGFq58pjGj9+u\n3bvPq3v3SurRo5Ly5k3eyvrx8XZt3372vwPM7t0rqkaNfC5ODTw5nD370bhzfV2nGxqk06ovP72t\nPEqfCmbfxsZLH82W5m+UZvaTnitvdiL39ffflzR+/Db99NMBvfRSMfXtW1UVK+Z+rH3a7Q5JSvYB\nKuAOqK+PzowaGyeH9uv2v03bW9qjW3pKNlVUJlX4t3FbVOmVJhWepOxe7Hbpi5+lr5ZJE9+QWj1r\ndiL3tmPHWY0fv13Llx9Rmzal1bv30ypVKsdj7ZP6Ck/1ODWWJq5JDsVK7c5KQWml6XmkbB40Jzoh\nQRoxTRo/Vxr3odT2RbMTuZ+bN2P/WzIhUyYf9e1bVW3alH7ks3BSAOGpOMh8NO5YX2/KrhE6ox2K\n0ufKp6dTyZp9B05JbUdJhXJLU9/k7NiPwm536Ndfj2rcuG06ePCyevSorDfeqKScOT3s03MgBVFf\nH92TqLERSvh3WYQo7dYtHVS0gvWUKirjf43bXOLkI/dy8pLUcayUxkua/ZYUlN3sRO4pPt6uRYsO\navz4bTp37qZ6966irl0ryN//3ksSAXi8GutBrUP3YBjStxGJM3CH5pC6+3nW2S6Pnkw8eVnmjNLu\nhVJgLrMTuZdjx65p4sTtmjNnn+rVK6DvvntJNWoEPfbJx2jeAnBX5xSnPxShObqkWsqixSqeKr7+\naRjSpN+lQT9IwztJXTk79kO7cSNGM2YkfiAaEJBe/fpV1SuvlJKPj+c/fwB4DkOGTilWu/+dZRum\nKF1MsjTCm8qlssqYKmpjSvhhndRvqtS/ufRuMykNw/bQLl++pSlTdmnSpJ0qWjRA779fQ02aFOWY\nE3gCaOI+QVcSpNfPS6fipQ3BUnEPWprUMKTJ86VPJkif9JTebCd58RqeLIZh6M8//9H48du0fftZ\nvf56Re3Z84aCglhXD0DqdEax+kMRWqkInVas6iqLRipYlVLJ2reXIqTXxksXIqRNX0hF85qdyL0c\nOXJVEyZs1/ff71P9+oU0d+6915AHAKuJk0MHFP3fLNs9uiUf2VTh3xm27ZRNRZReaVka4aFERElv\nTpF2HZNWfipVLGR2IvcTFnZe48dv15Ilh9SyZUn9/nt7lS2b0+xYQKpCE/cJ+TNKevWc1C6LNN/D\nTl527pLU9WPpaoS0YY5UvKDZidxDVFScZs/eqwkTtsvHJ4369auqn356RenTc6YaAKnPyX8bt3/o\nus4rXs8pi/opt55WZnmnogPV33dJXSdIXepKP7eVfCgJyeL8QHTcuG3aseOsunWrpH37eiowkPUn\nAFhbhBK0R7f+OwnZQUUr379LIzSUvz5SoHKzNMJj2bA/cfmERpWk3V9JGTxoMpWrJSQ4tGTJIY0b\nt00nT0bozTef1rFjfRQQwBngADOwJq6LxTqkjy5J8yOlWXmlehnNTpSyFqyQ+nwu9WojfdRd8uZg\n84GOH7+mr7/eoVmz9iokJFj9+lVVzZr5mCEEPALW7Hs0VqmvJxTz34zbK4rXc/JTffmpsjKluhlG\n0bHSB7Okpduk2W9LtUubncg93Lr1/38gmjatl/r1q6p27crwgSjwmKivj+5+NTZxaYQ4hSnqv5OQ\nXVCcyiijKv27PEJZZVQmlkZIEfEJ0uB50nerpKm9pcZPmx95d+EAACAASURBVJ3IfVy5clvTpu3W\nN9/sUHCwn/r2rapmzYorbVoPmo0GmIQ1cS3q4L8nLyvgLe0tKAV40GhfvyH1/lzat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CBQteyZs375mdO3dWcmY5efJkPrvd7lWxYsVdn3322cD4+Pi0//zzT4GC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dTe9+VMOPR8H0LGQpXystXkPXPmHKZNm0cpVswOLooUxAhxCwpNg70rhPkVfQRGroVH68tW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fIi48XS\nIjAJvgyAGKM6X764zs50iqJejo7+AT5vDDf3Q15ZDvpGAgKi6N17P/b2dtSs6cyIEbUYMaIWzZpt\n4fjxblSpUgiAcuXyExublPLzBgyoplXIQmSITN+J66s8xAVnec6ZDlEGGPYUzsXBFmf1KYre/HIa\nhs6AiqVg/jh1i6d4MwkJydjaWhEeHk/btjvo168KHTuWT/nxLl12U7p0PiZNagCAl1cA5csXwM5O\nfm8K7UinUNqkZua8+Hfe8erTzvdsYUUhyKOzp53+Qeo4oht3YcVktYgr3tz160E0bbqJ1q3LsHr1\nFdat+xxbW0v++CMIOzsrJk78QOsQhfhXkl/TTnJs+uyPgn4BMDAvfOMAVjr7LrzpA4OmQWQ0LPsO\nalbUOiLT8/x5HB9+uJFevSrj7JyT1auv8vPPrcmTJyvr1l1lypRTTJ7cEGfnnHz11THGjKlL167y\nL1pkHrruxC1GUa1DMGmecdDZDxplhyslIIfOmq0e+sPIWeB9DxZNgE/lPJQmU6b8zp9/BlOjhjND\nh9bgm2/q07btDuzt7fj4YzcAunatgIeHf8rPqVZNKuVCCPOiKLA0DCYHwxxH6KGz5WUGAyzbBlOW\nwqBOsHkO2MnL3zdy6tRDALJmtaZGDWeuXu3P9etBREYmpBwgrayysG/fHQ2jFEKIzCfWCF8HwZFo\n2F0Y6uhsSltMLExbDmvc4btBMPALsNTZJfC7cvNmME5OORkypAYAixZ5Mm3aad57z4GOHctRvHge\nTpzw5cCBu/TuXVkKuEJXdFbSEy8ZFJgeDC0ew2xHWOWkrwJuQiJMXwHV2kPVcnBjnxRw02rYsF+4\neNGfTp3Kc/z4A7y8AmjcuDjr1n1Ot257cHe/SXh4PBcv+nP9ehBJSQatQxZCiHcuOBlaPoH14Y6s\n/6AAACAASURBVHC+GPS011cB99otqNMZdh6F0xthyhAp4L6pbdu86dTJnePHH9C+/U6WL/ciX75s\nvP9+QR48CGP7dm8AfH3DiYpKJDFR8qkQQgD8EQ/VHkCkEa6W0FcBV1Fg3wko1xKePIXre2FIFyng\npkf58gXw9Q2nW7c9VKu2kuzZbShTxgFf33C+/fYkdesWZtq0Rmzf3o7hw2tpHa4QGSrTd+KKN/fq\n8rLLJfS3vOzoWRg6Hcq6gtdOKOasdUSmKzAwCl/fcHbv7kDWrNb89ttD9u69Q2hoHE2aFGfXrg6s\nXHmZrVu9CQiIYt++L7C2lk8cQgjzciwaegZAt9zgXhhsdFS8jU+A7xbD+r0wcyT0aAVZdHTp+65E\nRyfi7n6LTZta06RJCZo3L8XEib8RG5tE375V6N+/Kj/+eJGtW7159CiCY8e6YmMj+VQIYd6MCvz0\nHGaEwHxH6KqzubD+QTBwKtx9CGu/h8ZST0yzR4/CCQyMxmhUqFOnMGfP9uT06Uds3JiEu3sHADw9\n/Zkz5xxxcclYW1tiZSUfaIT+ZPqZuJk5vsxIz8vLHgfAyNlw7TYsnADNGmgdkT58/vk2YmISqVev\nCAsWXGTkyFoEBkaTnGxk7tymWFtnISnJSNasVtjayr2PyFxkZl/aSH5NnQQjTHgGOyJhgzM01tny\nsj9uQ9exULo4LP0WCuTTOiLTNmnSSezsrBg+vCbZs9tw9Wogw4cfoU+fKnTv/j6PHoXz4EEYlSsX\nwt7eTutwhXgtya9pJzk2dQKToEcARBnhZ2coobPlZdt/URuPBnWC8X1l4XZ63L4dQpMmG2nTpgzH\njt2na9cKjBlTFwsLC5o02Ui3bhXp168qjx9H0L79TjZubEXp0g5ahy3Ev9L1TFyROq8uLztcRF/L\ny4xG+GmTOj5hWFeZ0ZcRPDz8sLTMQrVqTri7d2DduqucO/eE48e7Ub26M7duBTN16mkePgynUqWC\nWocrhBDv3K0EdXlZcWu4VgLy6egTk8EAc9fBvPUwbwx0baGv0RDvmqIoWFhYUKlSQX77zRdf33De\ne8+BypULMWvWh3Tvvodq1ZwoWzY/RYvqrM1MCCHS4GAU9A2EfvbwbX59LS8Li4DB38OVm3BoGVSv\noHVEps1oVFi37hqDBlXjm28+wMfnOYMGHcJoVBg5sjbDhtVg9eqrnD//BE9Pf0aPriMFXKFrOjqS\nmC/POOjiDw2y6W952UN/6DEBkg1wcSu4yZ67dDEYjNSvvw5Hxxw8fRpN5coFmTevKX37VuXMmccc\nOHCX6tWdKVMmP6Ghsfj6hkkRVwhhVhQFVobDxGcwvQD01dnsW18/6D4OrKzg0g4oKjsqM0zr1u9x\n6tQjFi70YPDg6pQsmY86dQrzwQdFdfU9JIQQaRX3YnnZ4WjY6QL1dDT7FuD4eeg1EVo1gSu7IJuO\nGqu0kiWLBWXKOHDxoh9hYXG4ueVl+fLmDBp0CHv7qwwfXovy5Qvg6elPz56VaNCgmNYhC/FWyTgF\nE2ZQYHaIOkdoaSFom0vriDKOosD6PTBmHozuBV/1kOHvGWH9+mv88osP27e3IzIygS5ddlOyZF7G\nj6/HjRvPWLnyMg4O2Xj+PI7ERAO7dnXQOmQhXkuee6aN5Nd/FpIMfQLV2fJbnOE9Hb36UBRYtwfG\nzlOfdY7oLrNv0+POnRAiIhKoWrUQFhYWZMmi/jGkKApff32cuLgkLC2zkC9fVjZs+INLl/ri4KCz\naoXQNcmvaSc59p9dj4dO/vC+rXp2tdfR2S42Dsb9CHtOqLNvP6qjdUT6cv78E9atu0bfvlWoVKkg\nNjaW3L4dwqefbmb9+s+lcCtMTnpyrHx8N1FPkqDxIzgeA14l9FXADQqBVkNhwSY4sRbG9JYCbkYp\nWTIviYkGAgOjyJXLlo0bW/HkSSRLllyicePijB9fD0fH7FSv7iQFXCGEWTkRA5UfgJsNXCimrwLu\ns1BoPRQW/gwn18OoHlLATY/t271p3Xo7o0cfp2/fAyxZ4kl0dCKgfiifN68pHTuWo1SpvERGJnD6\ndA8p4AohzJZRgQWh0OQRjMsHm531VcD18oaq7SE4DP7YLQXcjPTyMqROncI4O+dk4UIPbtwIIiIi\nnvfec6Br14okJxs1jlKId0s6cU3QzkgYEgijdLi8bO8JGDgFeraG7wbLAPiM5uPznNmzz/HFF+Wo\nU6cwWbNa8/RpNA0arGfKlIZ88UV5rUMU4o1Ip1DaSH79S6Kijk7YHAHrneCjHFpHlLH2/wb9J0OP\n1jBZ8mq6xccn88UXu5g48QOqVXNix44/8fDwJ3t2a0aPrkPOnH+v/hsMRiwtpWIuTI/k17STHPuX\np8nQwx8ijGrxVk/Ly5KTYcZKWLwFfhoPnZppHZE+3LsXSkBAFNWrO2NjY4mV1V859NtvTxIUFE3e\nvFl5/31HRow4yu7dHahbt4iGEQvx5mSxmZmIMsDwIDgbCweLQHUdzdiJiILhM+HsFdi1AOpW0Toi\nfTEaFbJkscDNLS+VKjmydKkXdnZWlC2bn4IFczB2bN2ULiIhhDAXd18sL3OyUpeX5dfRp6KoGBg5\nC056ws75UK+q1hGZrps3g7lzJ4TWrcsAEBWVyIMHYVSr5kSbNmXInz8bhw7dY9s2b/r2rcqpUw+J\niUnis89KSgFXCGG2DkWpI4r6vlheZq2jK4G7D9X58rlyqLNvXWSFSIY4csSHQYMOUaZMfrJmtaJ2\nbRe6dq2Io6N6wz5tWiOOHbvP9etB/PKLD+vXfy4FXGF25JOlifCMgyq+6n9hV0roq4B70gPebw1Z\nbeGauxRwM8qjR+EcOeLDs2cxGAx/PTMZPLgGNWs6s2rVFRYv9uTChSf8+OMFeYoihDAbigJrwqDu\nQ+htD/sK66uAe/YyVGqjLmS7tlsKuGl16ZI/bdpsp1GjDTx5EgmAnZ0Vw4bV4Oefr3Pxoh9WVlmo\nV68IFSs6curUIxISkgkKiqFGDWeNoxdCCG3EGdVXo4Oewg5nmFpAPwVcRYFl26BOZ+jSHI6slAJu\nRjEYjLi732LRok85dKgz3bpVJCQkjrlzLxAcHJPydU2buvL113VYubIFn35aUsOIhdCGjFPI5PS8\nvCwuHiYsgJ1HYdUU+PQDrSPSj4sX/Wjffie1a7sQFhZPkybFadeuLG5ueVO+5vDhe1y79pRLlwKo\nWdOZcePqaRixEGkjzz3Txpzz63MD9AuAe4nq8rJydlpHlHESE+G7JbBhLyz/Dlo21joi06MoCr//\n/pAZM85y+3YIo0fXoU+fKmTLZp3yNXFxSaxYcZmbN4P58sv3U7qAGjZcz+rVLf+Wa4UwVZJf086c\nc+yNF8vLytvCcp0tLwt4Br2/hZAw2DQL3iuhdUSm7/nzOG7fDqFOncIYjQodO+6iWrVCjB2rnkvP\nn3/CgQN3cXTMzogRtbhxIwhf33BatiyNoihYWMgfUcI0yWIznXqSpA6A1+PyMi9vqNJOTYZ/7JEC\nbnopisKrHxYPH77H1KkN2bGjPaNH1yEx0cDChR48fBie8jWffVaSCRPqs2FDKyngCiHMwu8xUOk+\nuFiDR3F9FXC970GNL+DWfbX7Vgq4b8ZoVDhw4A516qxlwIBDdOpUnvv3hzFsWM2/FXABsma1plu3\nipQvX4Bp006zZs0VVq26TGhoHLlz62gjnhBCpJKiwMJQdfH26HywVWfLy3YegcptoUYFOL9ZCrjp\n5e39jP79D+LqupDdu28BkCWLBWPH1uXcuSccOeIDQO3aLlStWogLF/yIjU3i5s1gypRxAJACrjBb\n0ombSb1cXjYyn5oI9bK8LClJHQC/ZCssnABffKZ1RKYtLi6Jbdu8WbLkEkuWfEbNmi4ADBlyGKNR\nYelSdcL+lSuB7N17Gzs7KyZMqI+/fySXLwfKLaYwedIplDbmll+TFJgcDOvCYY0TfKqj5WVGI8zf\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       "text": [
        "<matplotlib.figure.Figure at 0x507ad10>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the information bit is used to measure the accuracy of classification. \n",
      "If there is a perfect classification, the information bit should be 1. \n",
      "From these figures, we can observe that the Laplace method is worst among these methods in term of information bit. The EP method, the KLCholesky method and the KLCovariance method are equally good. Surprisingly, for this data set the KLApproxDiagonal method performs as good as the EP method in term of information bit. Note that the KLApproxDiagonal method enforces sparsity in non-diagonal entries of the posterior co-variance matrix.  "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Another Real-world Example"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We use the US Postal Service (USPS) database of handwritten digits as another example. The dataset refers to numeric data obtained from the scanning of handwritten digits from envelopes by the U.S. Postal Service. The original scanned digits are binary and of different sizes and orientations; the images here have been deslanted and size normalized, resulting in 16 x 16 grayscale images.\n",
      "\n",
      "We consider to classify the images of digit 3 from images of digit 5 in this example, which means only images of digit 3 and digit 5 are used. Note that this is example is also used in session 3.7.3 of the textbook, <a href=\"http://www.gaussianprocess.org/gpml/\">Gaussian Processes for Machine Learning</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def binary_extract(idx, features, labels):\n",
      "    \"\"\"\n",
      "    This function is used to extract a trainning set and a test set\n",
      "    \n",
      "    Parameters:\n",
      "        idx - a 2-wide list of digits (eg, [0,3] means to extract a sub set of images and labels for digit 3 and digit 0) \n",
      "        features - the whole image set \n",
      "        labels - the whole label set\n",
      "        \n",
      "    Returns:\n",
      "        binary_features - X (images for binary classification)\n",
      "        binary_labels - y (labels for binary classification)\n",
      "    \"\"\"\n",
      "    #binary classification\n",
      "    assert len(idx) == 2\n",
      "    positive_idx = (labels[idx[0],:] == 1)\n",
      "    negative_idx = (labels[idx[1],:] == 1)\n",
      "    binary_idx = (positive_idx | negative_idx)\n",
      "    ds = binary_idx.shape[-1]\n",
      "\n",
      "    bin_labels = np.zeros(ds) \n",
      "    bin_labels[positive_idx] = 1\n",
      "    bin_labels[negative_idx] = -1\n",
      "\n",
      "    binary_features = (features[:,binary_idx])\n",
      "    binary_labels = (bin_labels[binary_idx])\n",
      " \n",
      "    positive_count = bin_labels[positive_idx].shape[-1]\n",
      "    negative_count = bin_labels[negative_idx].shape[-1]\n",
      "    binary_count = binary_labels.shape[-1]\n",
      " \n",
      "    print \"There are %d positive samples and %d negative samples\" %(positive_count, negative_count)\n",
      "    return binary_features, binary_labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def learning_example2(inference, likelihood, x_train, x_test, y_train, y_test):\n",
      "    \"\"\"\n",
      "    This function is used to train a GP classifer given an inference method\n",
      "        \n",
      "    Parameters:\n",
      "        inference - an inference methods used to train the classifer\n",
      "        likelihood - likelihood function to model data\n",
      "        x_train, x_test - X for training and testing\n",
      "        y_train, y_test - Y for training and testing\n",
      "        kernel_log_sigma, kernel_log_scale hyper-parameters of covariance function    \n",
      "        \n",
      "    Returns:\n",
      "        Nothing\n",
      "    \"\"\"\n",
      "        \n",
      "    #train a GP classifer\n",
      "    error_eval = ErrorRateMeasure()\n",
      "    mean_func = ConstMean()\n",
      "    kernel_log_sigma = 1.0\n",
      "    kernel_sigma = 2*exp(2*kernel_log_sigma);\n",
      "    kernel_func = GaussianKernel(10, kernel_sigma)\n",
      "\n",
      "    #sample by 1\n",
      "    labels_train = BinaryLabels(y_train)\n",
      "    labels_test = BinaryLabels(y_test)\n",
      "    #feature by sample\n",
      "    features_train=RealFeatures(x_train)\n",
      "    features_test=RealFeatures(x_test)\n",
      "\n",
      "    kernel_log_scale = 1.0\n",
      "\n",
      "    inf = inference(kernel_func, features_train, mean_func, labels_train, likelihood)\n",
      "    print \"\\nusing %s\"%inf.get_name()\n",
      "    \n",
      "    inf.set_scale(exp(kernel_log_scale))\n",
      "    try:\n",
      "        inf.set_lbfgs_parameters(100,80,0,80)\n",
      "    except:\n",
      "        pass\n",
      "\n",
      "    start = time.time()\n",
      "    gp = GaussianProcessClassification(inf)\n",
      "    gp.train()\n",
      "    end = time.time()\n",
      "    print \"cost %.2f seconds at training\"%(end-start)\n",
      "    nlz=inf.get_negative_log_marginal_likelihood()\n",
      "    print \"the negative_log_marginal_likelihood is %.4f\"%nlz\n",
      "    start = time.time()\n",
      "    #classification on train_data\n",
      "    pred_labels_train = gp.apply_binary(features_train)\n",
      "    #classification on test_data\n",
      "    pred_labels_test = gp.apply_binary(features_test)\n",
      "    end = time.time()    \n",
      "    print \"cost %.2f seconds at prediction\"%(end-start)\n",
      "    \n",
      "    error_train = error_eval.evaluate(pred_labels_train, labels_train)\n",
      "    error_test = error_eval.evaluate(pred_labels_test, labels_test)\n",
      "    \n",
      "    print \"Train error : %.2f Test error: %.2f\\n\" % (error_train, error_test)  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#an example for the USPS data set\n",
      "inf='../../../data/uci/usps/usps_resampled.mat'\n",
      "data=scipy.io.loadmat(inf)\n",
      "train_labels=data['train_labels']\n",
      "test_labels=data['test_labels']\n",
      "train_features=data['train_patterns']\n",
      "test_features=data['test_patterns']\n",
      "#using images of digit 3 and digit 5 from the dataset\n",
      "idx=[3,5]\n",
      "#Note that \n",
      "#y_train and y_test are followed the definition in the first session \n",
      "#the transpose of x_train and x_test are followed the definition in the first session\n",
      "print \"Training set statistics\"\n",
      "(x_train, y_train)=binary_extract(idx,train_features, train_labels)\n",
      "print \"Test set statistics\"\n",
      "(x_test, y_test)=binary_extract(idx,test_features, test_labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training set statistics\n",
        "There are 406 positive samples and 361 negative samples\n",
        "Test set statistics\n",
        "There are 418 positive samples and 355 negative samples\n"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Laplace Method in GPC Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a href=\"http://en.wikipedia.org/wiki/Laplace%27s_method\">The Laplace method</a> can be viewed as a special case of variational method.\n",
      "The idea of the Laplace method is to use the second-order <a href=\"http://en.wikipedia.org/wiki/Taylor_series\">Taylor  expansion</a> in the <a href=\"http://en.wikipedia.org/wiki/Mode_%28statistics%29\">mode</a>, $\\mathbf{\\hat{f}}$ of $p(\\mathbf{f}|\\mathbf{y})$ to approximate $p(\\mathbf{f}|\\mathbf{y})$, which is given below:\n",
      "\n",
      "$p(\\mathbf{f}|\\mathbf{y}) \\approx \\hat{p}(\\mathbf{f}|\\mathbf{y})$ followed by $N(\\mathbf{\\hat{f}}, H_{\\mathbf{\\hat{f}}}^{-1})$ ,where $H_{\\mathbf{\\hat{f}}}$ is the <a href=\"http://en.wikipedia.org/wiki/Hessian_matrix\">Hessian matrix</a> in $\\mathbf{\\hat{f}}$\n",
      "\n",
      "Therefore, the KL divergence, ${\\mathrm{KL}}(Q\\|P)$, is approximated by $\\int_{-\\infty}^\\infty \\ln\\left(\\frac{q(\\mathbf{f}|\\mathbf{y})}{\\hat{p}(\\mathbf{f}|y)}\\right) q(\\mathbf{f}|\\mathbf{y}) \\ {\\rm d}\\mathbf{f}$.\n",
      "\n",
      "By **minimizing** $\\int_{-\\infty}^\\infty \\ln\\left(\\frac{q(\\mathbf{f}|\\mathbf{y})}{\\hat{p}(\\mathbf{f}|y)}\\right) q(\\mathbf{f}|\\mathbf{y}) \\ {\\rm d}\\mathbf{f}$\n",
      "we can get $q(\\mathbf{f}|\\mathbf{y})= \\hat{p}(\\mathbf{f}|\\mathbf{y})$.\n",
      "In practice, we can use <a href=\"http://en.wikipedia.org/wiki/Newton%27s_method_in_optimization\">Newton-Raphson</a> optimizer or <a href=\"http://en.wikipedia.org/wiki/Quasi-Newton_method\">Quasi-Newton</a> optimizers(ie, <a href=\"http://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm\">Limited-memory Broyden\u2013Fletcher\u2013Goldfarb\u2013Shanno</a> (LBFGS)) to find $\\mathbf{\\hat{f}}$.\n",
      "Since the likelihood is the inverse-logit, we can show that the objective function, $\\ln(p(\\mathbf{f}|\\mathbf{y}))$, is strictly <a href=\"http://en.wikipedia.org/wiki/Concave\">concave</a>, which means in theory these optimizers can find the same global solution $\\mathbf{\\hat{f}}$.\n",
      "We demonstrate how to apply the Laplace method via Newton-Raphson optimizer and LBFGS optimizer in Shogun to the USPS data set as below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inference_methods=[\n",
      "                  SingleLaplacianInferenceMethodWithLBFGS,     #using LBFGS optimizer\n",
      "                  SingleLaplacianInferenceMethod,              #using Newton-Raphson optimizer\n",
      "                  ]\n",
      "likelihood = LogitLikelihood()\n",
      "#using default optimizer and default linesearch method\n",
      "for inference in inference_methods:\n",
      "    learning_example2(inference, likelihood, x_train, x_test, y_train, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "using SingleLaplacianInferenceMethodWithLBFGS\n",
        "cost 1.05 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 328.1212\n",
        "cost 1.91 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n",
        "\n",
        "using SingleLaplacianInferenceMethod\n",
        "cost 1.16 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 328.1212\n",
        "cost 1.94 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remark:  In practice, the Laplace method is the fastest method among all implemented variational methods in Shogun. However, the second-order Taylor approximation in the mode of marginal likelihood is not always a good approximation, which can be observed in the figures in the previous session for the sonar data set."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Covariance Variational Method in GPC Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This method is mentioned in <a href=\"http://jmlr.org/papers/volume9/nickisch08a/nickisch08a.pdf\">the paper</a> of Nickisch and Rasmussen in 2008, which is called the KL method in the paper.\n",
      "The idea of this method is to maximize $E_q[ln(p(\\mathbf{f}))] + E_q[ln(p(\\mathbf{y}|\\mathbf{f}))] - E_q[ln(q(\\mathbf{f}|\\mathbf{y}))]$ with respect to $\\mu$ and $\\Sigma$, by reparametrizing $\\Sigma$ to reduce the dimension of variables to be optimized.\n",
      "However, such parametrization does not preserve convexity (concave in this setting) according to <a href=\"http://arxiv.org/abs/1306.1052\">the paper</a> of Khan et. al. in 2013. \n",
      "Except for this method, convexity holds in other variational methods implemented in Shogun. We demonstrate how to apply this variational method in Shogun to the USPS example."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "likelihood = LogitVGLikelihood()\n",
      "#using the default linesearch method\n",
      "learning_example2(KLCovarianceInferenceMethod, likelihood, x_train, x_test, y_train, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "using KLCovarianceInferenceMethod\n",
        "cost 64.35 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 319.6124"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "cost 2.04 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remark: This method may be the first variational method used in GPC models. However, in practice, it is the slowest method among all implemented variational methods in Shogun."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Mean-field Variational Method in GPC Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This method is also called the factorial variational method in <a href=\"http://jmlr.org/papers/volume9/nickisch08a/nickisch08a.pdf\">the paper</a> of Nickisch and Rasmussen in 2008.\n",
      "The idea of mean-field variatonal method is to enforce artificial structure on the co-variance matrix, $\\Sigma$ of $q(\\mathbf{f})$.\n",
      "In mean-field variatonal method, $\\Sigma$ is a diagonal positive-definite matrix.\n",
      "We demonstrate how to apply this variational method in Shogun to the USPS example."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "likelihood = LogitVGLikelihood()\n",
      "#using the default linesearch method\n",
      "learning_example2(KLApproxDiagonalInferenceMethod, likelihood, x_train, x_test, y_train, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "using KLApproxDiagonalInferenceMethod\n",
        "cost 1.52 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 337.4957\n",
        "cost 2.07 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remark: This method could be as fast as the Laplace method in Shogun in practice but ignores all off-diagonal elements of the covariance matrix. However, in some case (ie, there are a lot of noise in data set), such structure regularization may bring some promising result compared to other variational methods, which can be observed in the previous session for the sonar data set."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Cholesky Variational Method in GPC Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This method is proposed in <a href=\"http://jmlr.org/proceedings/papers/v15/challis11a/challis11a.pdf\">the paper</a> of Challis and Barber in 2011.\n",
      "The idea of this method is to maximize $E_q[\\ln(p(\\mathbf{f}))] + E_q[\\ln(p(\\mathbf{y}|\\mathbf{f}))] - E_q[\\ln(q(\\mathbf{f}|\\mathbf{y}))]$ in terms of the Cholesky representation, C, for the covariance matrix of $q(\\mathbf{f})$, where $\\Sigma=CC^T$ and C is a lower triangular matrix.\n",
      "We demonstrate how to apply this variational method in Shogun to the USPS data set as below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "likelihood = LogitVGLikelihood()\n",
      "#using the default linesearch method\n",
      "learning_example2(KLCholeskyInferenceMethod, likelihood, x_train, x_test, y_train, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "using KLCholeskyInferenceMethod\n",
        "cost 33.17 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 319.5930"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "cost 2.04 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remark: This method may be faster to learn the complete structure of the covariance matrix from data than covariance variational method.\n",
      "The reason is solving linear system related to $\\Sigma$ is required at each optimization step.\n",
      "In Cholesky representation, the time complexity at each optimization step is reduced to O(n^2) from O(n^3) compared to covariance variational method.\n",
      "However, the overall time complexity is still O(n^3)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Dual Variational Method in GPC Models"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This method is proposed in <a href=\"http://arxiv.org/abs/1306.1052\">the paper</a> of Khan et. al. in 2013.\n",
      "The idea of this method is to optimize $E_q[ln(p(\\mathbf{f}))] + E_q[ln(p(\\mathbf{y}|\\mathbf{f}))] - E_q[ln(q(\\mathbf{f}|\\mathbf{y}))]$ in <a href=\"http://en.wikipedia.org/wiki/Duality_%28optimization%29\">Lagrange dual</a> form instead of the primal form used in covariance variational method via explicitly expressing constraint in the covariance matrix, $\\Sigma$, in terms of auxiliary variable.\n",
      "However, the time complexity of each optimization step is still O(n^3) and variational bound usually is required to approximate $E_{q_i}[ln(p(\\mathbf{y_i}|\\mathbf{f_i})]$ for GPC in this method.\n",
      "We demonstrate how to apply this variational method in Shogun to the USPS data set as below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "likelihood = LogitDVGLikelihood()\n",
      "likelihood.set_strict_scale(0.1)\n",
      "#using the default linesearch method\n",
      "learning_example2(KLDualInferenceMethod, likelihood, x_train, x_test, y_train, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "using KLDualInferenceMethod\n",
        "cost 9.26 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 497.7747"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "cost 2.04 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.04\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remark: This method requires O(N) variables to be optimized while the Cholesky method requires O(N^2) variables to be optimized. This fact is important for large-scale cases. A promising observation in practice is that this method is faster than covariance variational method and may not be slower than the Cholesky method. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Variational Bounds"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we discuss how to approximate $E_{q_i}[ln(p(\\mathbf{y_i}|\\mathbf{f_i})]$.\n",
      "Since this term is about one-dimensional integration, for GPC we can use <a href=\"http://en.wikipedia.org/wiki/Gauss%E2%80%93Hermite_quadrature\">Gauss\u2013Hermite quadrature</a> to appromxiate this term. In Shogun, the corresponding implementation of Bernoulli-logistic likelihood for variational inference is called <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLogitVGLikelihood.html\">LogitVGLikelihood</a> . This likelihood can be used for all variational methods except the dual variational method.\n",
      "\n",
      "The piecewise variational bound proposed at <a href=\"http://www.icml-2011.org/papers/376_icmlpaper.pdf\">the paper</a> of Benjamin M. Marlin et. al. in 2011 is also implemented in Shogun, which is called <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLogitVGPiecewiseBoundLikelihood.html\">LogitVGPiecewiseBoundLikelihood</a>. This likelihood can be used for all variational methods except the dual variational method.\n",
      "\n",
      "For dual variational method, we use the variational bound in <a href=\"http://www.cs.cmu.edu/~lafferty/pub/ctm.pdf\">the paper</a> of DM Blei et. al. in 2007. The corresponding implementation in Shogun is called <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLogitVGPiecewiseBoundLikelihood.html\">LogitVGPiecewiseBoundLikelihood</a>. Note that when using LogitDVGLikelihood, the bound is only enabled for the dual variational method. When other variational methods use this class, it uses one-dimensional integration instead of the variational bound. For detailed discussion about variational bounds in GPC models, please refer to <a href=\"https://circle.ubc.ca/handle/2429/43640\">Khan's work</a>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "likelihood = LogitVGPiecewiseBoundLikelihood()\n",
      "likelihood.set_default_variational_bound()\n",
      "likelihood.set_noise_factor(1e-15)\n",
      "inference_methods=[\n",
      "                  KLCholeskyInferenceMethod,\n",
      "                  KLApproxDiagonalInferenceMethod,\n",
      "                  KLCovarianceInferenceMethod,\n",
      "                  ]\n",
      "for inference in inference_methods:\n",
      "    #using the default linesearch method\n",
      "    learning_example2(inference, likelihood, x_train, x_test, y_train, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "using KLCholeskyInferenceMethod\n",
        "cost 36.36 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 319.6664\n",
        "cost 2.13 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n",
        "\n",
        "using KLApproxDiagonalInferenceMethod\n",
        "cost 2.66 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 337.5679\n",
        "cost 2.01 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n",
        "\n",
        "using KLCovarianceInferenceMethod\n",
        "cost 65.77 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 319.6807"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "cost 2.05 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Optimizing Hyper-parameters"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We demonstrate how to optimize hypyer-parameters in Shogun for applying the mean-field variational method to the USPS data set."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def learning_example3(inference, likelihood, x_train, x_test, y_train, y_test):\n",
      "    \"\"\"\n",
      "    This function is used to train a GP classifer given an inference method and hyper-parameters are automatically optimized\n",
      "        \n",
      "    Parameters:\n",
      "        inference - an inference methods used to train the classifer\n",
      "        likelihood - likelihood function to model data\n",
      "        x_train, x_test - X for training and testing\n",
      "        y_train, y_test - Y for training and testing\n",
      "        \n",
      "    Returns:\n",
      "        Nothing\n",
      "    \"\"\"\n",
      "    error_eval = ErrorRateMeasure()\n",
      "    mean_func = ZeroMean()\n",
      "    kernel_log_sigma = 1.0\n",
      "    kernel_sigma = 2*exp(2*kernel_log_sigma);\n",
      "    kernel_func = GaussianKernel(10, kernel_sigma)\n",
      "\n",
      "    #sample by 1\n",
      "    labels_train = BinaryLabels(y_train)\n",
      "    labels_test = BinaryLabels(y_test)\n",
      "    #feature by sample\n",
      "    features_train=RealFeatures(x_train)\n",
      "    features_test=RealFeatures(x_test)\n",
      "\n",
      "    kernel_log_scale = 1.0\n",
      "\n",
      "    inf = inference(kernel_func, features_train, mean_func, labels_train, likelihood)\n",
      "    print \"\\nusing %s\"%inf.get_name()\n",
      "    \n",
      "    inf.set_scale(exp(kernel_log_scale))\n",
      "    try:\n",
      "            inf.set_lbfgs_parameters(100,80,3,80)\n",
      "    except:\n",
      "            pass\n",
      "\n",
      "    gp = GaussianProcessClassification(inf)\n",
      "\n",
      "    # evaluate our inference method for its derivatives\n",
      "    grad = GradientEvaluation(gp, features_train, labels_train, GradientCriterion(), False)\n",
      "    grad.set_function(inf)\n",
      "\n",
      "    # handles all of the above structures in memory\n",
      "    grad_search = GradientModelSelection(grad)\n",
      "\n",
      "    # search for best parameters and store them\n",
      "    best_combination = grad_search.select_model()\n",
      "\n",
      "    # apply best parameters to GP\n",
      "    best_combination.apply_to_machine(gp)\n",
      "\n",
      "    # we have to \"cast\" objects to the specific kernel interface we used (soon to be easier)\n",
      "    best_width=GaussianKernel.obtain_from_generic(inf.get_kernel()).get_width()\n",
      "    best_scale=inf.get_scale()\n",
      "    print \"Selected kernel bandwidth:\", best_width\n",
      "    print \"Selected kernel scale:\", best_scale\n",
      "\n",
      "    start = time.time()\n",
      "    gp.train()\n",
      "    end = time.time()\n",
      "    print \"cost %s seconds at training\"%(end-start)\n",
      "    nlz=inf.get_negative_log_marginal_likelihood()\n",
      "    print \"the negative_log_marginal_likelihood is %.4f\"%nlz\n",
      "    start = time.time()\n",
      "    #classification on train_data\n",
      "    pred_labels_train = gp.apply_binary(features_train)\n",
      "    #classification on test_data\n",
      "    pred_labels_test = gp.apply_binary(features_test)\n",
      "    end = time.time()    \n",
      "    print \"cost %s seconds at prediction\"%(end-start)\n",
      "    \n",
      "    error_train = error_eval.evaluate(pred_labels_train, labels_train)\n",
      "    error_test = error_eval.evaluate(pred_labels_test, labels_test)\n",
      "    \n",
      "    print \"Train error : %.2f Test error: %.2f\\n\" % (error_train, error_test);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "likelihood = LogitVGLikelihood()\n",
      "likelihood.set_noise_factor(1e-15)\n",
      "#using the default linesearch method\n",
      "learning_example3(KLApproxDiagonalInferenceMethod, likelihood, x_train, x_test, y_train, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "using KLApproxDiagonalInferenceMethod\n",
        "Selected kernel bandwidth:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 39.423999518\n",
        "Selected kernel scale: 6.54419494733\n",
        "cost 1.05980706215 seconds at training"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "the negative_log_marginal_likelihood is 232.8626"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "cost 8.6821308136 seconds at prediction"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Train error : 0.00 Test error: 0.02\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Reference"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Rasmussen, Carl Edward. \"Gaussian processes for machine learning.\" (2006).\n",
      "* Bishop, Christopher M. Pattern recognition and machine learning. Vol. 1. New York: springer, 2006.\n",
      "* Nickisch, Hannes, and Carl Edward Rasmussen. \"Approximations for binary Gaussian process classification.\" Journal of Machine Learning Research 9 (2008): 2035-2078.\n",
      "* Challis, Edward, and David Barber. \"Concave Gaussian variational approximations for inference in large-scale Bayesian linear models.\" International conference on Artificial Intelligence and Statistics. 2011.\n",
      "* Emtiyaz Khan, Mohammad, et al. \"Fast dual variational inference for non-conjugate latent gaussian models.\" Proceedings of The 30th International Conference on Machine Learning. 2013.\n",
      "* Marlin, Benjamin M., Mohammad Emtiyaz Khan, and Kevin P. Murphy. \"Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models.\" ICML. 2011.\n",
      "* Khan, Mohammad. \"Variational learning for latent Gaussian model of discrete data.\" (2012).\n",
      "* Wang, Chong, and David M. Blei. \"Variational inference in nonconjugate models.\" The Journal of Machine Learning Research 14.1 (2013): 1005-1031.\n",
      "* Paisley, John, David Blei, and Michael Jordan. \"Variational Bayesian inference with stochastic search.\" ICML. 2012."
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      "Soon to Come"
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     "source": [
      "* Large-scale sparse variational inference in GPC models\n",
      "* Stochastic variational inference in GPC models"
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