add a new LaplacianInferenceMethodWithLBFGS class, where the Newton m…

…ethod is replaced by the L-BFGS method

src/shogun/machine/gp/LaplacianInferenceMethodWithLBFGS.h

@@ -0,0 +1,104 @@
+/*
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * Written (W) 2014 Wu Lin
+ * Copyright (C) 2012 Jacob Walker
+ * Copyright (C) 2013 Roman Votyakov
+ * Copyright (C) 2014 Wu Lin
+ *
+ * Code adapted from Gaussian Process Machine Learning Toolbox
+ * http://www.gaussianprocess.org/gpml/code/matlab/doc/
+ */
+
+#ifndef CLAPLACIANINFERENCEMETHODWITHLBFGS_H_
+#define CLAPLACIANINFERENCEMETHODWITHLBFGS_H_
+
+#include <shogun/lib/config.h>
+
+#ifdef HAVE_EIGEN3
+
+#include <shogun/machine/gp/LaplacianInferenceMethod.h>
+#include <shogun/mathematics/eigen3.h>
+
+
+namespace shogun {
+
+/** @brief The Laplace approximation inference method with LBFGS class.
+ *
+ * This inference method approximates the posterior likelihood function by using
+ * Laplace's method. Here, we compute a Gaussian approximation to the posterior
+ * via a Taylor expansion around the maximum of the posterior likelihood
+ * function.
+ *
+ * For more details, see "Bayesian Classification with Gaussian Processes" by
+ * Christopher K.I Williams and David Barber, published 1998 in the IEEE
+ * Transactions on Pattern Analysis and Machine Intelligence, Volume 20, Number
+ * 12, Pages 1342-1351.
+ *
+ * This specific implementation was adapted from the infLaplace.m file in the
+ * GPML toolbox.
+ */
+
+/** Wrapper class used for the LBFGS minimizer */
+class CSharedInfoForLBFGS {
+public:
+ index_t dim;
+ Eigen::Map<Eigen::MatrixXd>* kernel;
+ Eigen::Map<Eigen::VectorXd>* mean_f;
+ CSharedInfoForLBFGS()
+     :dim(0), kernel(NULL), mean_f(NULL){}
+};
+
+
+class CLaplacianInferenceMethodWithLBFGS: public CLaplacianInferenceMethod {
+public:
+ /** default constructor */
+ CLaplacianInferenceMethodWithLBFGS();
+
+ /** constructor
+  *
+  * @param kernel covariance function
+  * @param features features to use in inference
+  * @param mean mean function
+  * @param labels labels of the features
+  * @param model Likelihood model to use
+  */
+ CLaplacianInferenceMethodWithLBFGS(CKernel* kernel, 
+                                   CFeatures* features,
+                                   CMeanFunction* mean, 
+                                   CLabels* labels, 
+                                   CLikelihoodModel* model);
+
+ virtual ~CLaplacianInferenceMethodWithLBFGS();
+
+ /** returns the name of the inference method
+  *
+  * @return name LaplacianWithLBFGS
+  */
+ virtual const char* get_name() const { return "LaplacianInferenceMethodWithLBFGS"; }
+
+protected:
+ /** update alpha using the LBFGS method*/
+ virtual void update_alpha();
+
+ /** compute the gradient given the current alpha*/
+ virtual void get_gradient_wrt_alpha(Eigen::Map<Eigen::VectorXd>* alpha,
+                                     Eigen::Map<Eigen::VectorXd>* gradient);
+
+ /** compute the function value given the current alpha*/
+ virtual void get_psi_wrt_alpha(Eigen::Map<Eigen::VectorXd>* alpha, 
+                                float64_t* psi);
+
+ /** only uses for dimension check*/
+ void check(int left, int right, int line);
+
+ /** variables needed to compute gradient and function value*/
+ CSharedInfoForLBFGS m_shared;
+};
+
+}
+#endif /* HAVE_EIGEN3 */
+#endif /* CLAPLACIANINFERENCEMETHODWITHLBFGS_H_ */