Merge https://bitbucket.org/mblum/libgp

CMakeLists.txt

@@ -1,17 +1,17 @@
 # Created by Manuel Blum on 2011-05-25.
-# Copyright 2011 University of Freiburg.
+# Copyright 2013 University of Freiburg.
 
 CMAKE_MINIMUM_REQUIRED(VERSION 2.8)
 
+PROJECT(libgp CXX C)
+
 # if no option is given, standard is release
 IF(NOT CMAKE_CONFIGURATION_TYPES AND NOT CMAKE_BUILD_TYPE)
    SET(CMAKE_BUILD_TYPE Release)
 ENDIF(NOT CMAKE_CONFIGURATION_TYPES AND NOT CMAKE_BUILD_TYPE)
 
-set(CMAKE_CXX_FLAGS_RELEASE "-DCLSQUARE -Wall -O2")
-set(CMAKE_CXX_FLAGS_DEBUG "-DCLSQUARE -Wall -g")
-
-PROJECT(libgp CXX C)
+set(CMAKE_CXX_FLAGS_RELEASE "-DCLSQUARE -Wall -O2 -fPIC")
+set(CMAKE_CXX_FLAGS_DEBUG "-DCLSQUARE -Wall -g -fPIC")
 
 OPTION(BUILD_TESTS "Build tests" ON)
 OPTION(BUILD_EXAMPLES "Build examples" ON)

Sources.cmake

@@ -6,16 +6,18 @@ SET(LIBGP_SRC
   src/cov_matern3_iso.cc
   src/cov_matern5_iso.cc
   src/cov_noise.cc
-  src/cov_rbf_cs.cc
   src/cov_rq_iso.cc
+  src/cov_periodic_matern3_iso.cc
   src/cov_se_ard.cc
   src/cov_se_iso.cc
   src/cov_sum.cc
+  src/cov_prod.cc
   src/gp.cc
-  #src/gp_sparse.cc
   src/gp_utils.cc
   src/sampleset.cc
   src/rprop.cc
+  src/input_dim_filter.cc
+  src/cg.cc
 )
 
 SET(LIBGP_INTERFACES 
@@ -26,14 +28,16 @@ SET(LIBGP_INTERFACES
   include/cov_matern3_iso.h
   include/cov_matern5_iso.h
   include/cov_noise.h
-  include/cov_rbf_cs.h
   include/cov_rq_iso.h
+  include/cov_periodic_matern3_iso.h
   include/cov_se_ard.h
   include/cov_se_iso.h
   include/cov_sum.h
+  include/cov_prod.h
   include/gp.h
-  #include/gp_sparse.h
   include/gp_utils.h
   include/sampleset.h
   include/rprop.h
+  include/input_dim_filter.h
+  include/cg.h
 )

examples/CMakeLists.txt

@@ -1,2 +1,6 @@
+# libgp - Gaussian process library for Machine Learning
+# Copyright (c) 2013, Manuel Blum <[email protected]>
+# All rights reserved.
+
 ADD_EXECUTABLE(gpdense gp_example_dense.cc)
 TARGET_LINK_LIBRARIES(gpdense gp)

examples/gp_example_dense.cc

@@ -1,5 +1,5 @@
 // libgp - Gaussian process library for Machine Learning
-// Copyright (c) 2011, Manuel Blum <[email protected]>
+// Copyright (c) 2013, Manuel Blum <[email protected]>
 // All rights reserved.
 
 #include "gp.h"

include/cg.h

@@ -0,0 +1,26 @@
+/*
+ * cg.h
+ *
+ *  Created on: Feb 22, 2013
+ *      Author: Joao Cunha <[email protected]>
+ */
+
+#ifndef CG_H_
+#define CG_H_
+
+#include "gp.h"
+
+namespace libgp
+{
+
+class CG
+{
+public:
+	CG();
+	virtual ~CG();
+	void maximize(GaussianProcess* gp, size_t n=100, bool verbose=1);
+};
+
+}
+
+#endif /* CG_H_ */

include/cov.h

@@ -1,5 +1,5 @@
 // libgp - Gaussian process library for Machine Learning
-// Copyright (c) 2011, Manuel Blum <[email protected]>
+// Copyright (c) 2013, Manuel Blum <[email protected]>
 // All rights reserved.
 
 #ifndef __COV_H__
@@ -13,94 +13,93 @@
 namespace libgp
 {
 
-/** Covariance function base class.
- *  @author Manuel Blum
- *  @ingroup cov_group 
- *  @todo implement more covariance functions */
-class CovarianceFunction
-{
-public:
-	/** Constructor. */
-	CovarianceFunction() {};
-
-	/** Destructor. */
-	virtual ~CovarianceFunction() {};
-
-  /** Initialization method for atomic covariance functions. 
-   *  @param input_dim dimensionality of the input vectors */
-  virtual bool init(int input_dim) 
-  { 
-    return false;
-  };
+  /** Covariance function base class.
+   *  @author Manuel Blum
+   *  @ingroup cov_group 
+   *  @todo implement more covariance functions */
+  class CovarianceFunction
+  {
+    public:
+      /** Constructor. */
+      CovarianceFunction() {};
+
+      /** Destructor. */
+      virtual ~CovarianceFunction() {};
+
+      /** Initialization method for atomic covariance functions. 
+       *  @param input_dim dimensionality of the input vectors */
+      virtual bool init(int input_dim) 
+      { 
+        return false;
+      };
+
+      /** Initialization method for compound covariance functions. 
+       *  @param input_dim dimensionality of the input vectors 
+       *  @param first first covariance function of compound
+       *  @param second second covariance function of compound */
+      virtual bool init(int input_dim, CovarianceFunction * first, CovarianceFunction * second)
+      {
+        return false;
+      };
+
+      virtual bool init(int input_dim, int filter, CovarianceFunction * covf)
+      {
+        return false;
+      };
+
+      /** Computes the covariance of two input vectors.
+       *  @param x1 first input vector
+       *  @param x2 second input vector
+       *  @return covariance of x1 and x2 */
+      virtual double get(const Eigen::VectorXd &x1, const Eigen::VectorXd &x2) = 0;
+
+      /** Covariance gradient of two input vectors with respect to the hyperparameters.
+       *  @param x1 first input vector
+       *  @param x2 second input vector
+       *  @param grad covariance gradient */
+      virtual void grad(const Eigen::VectorXd &x1, const Eigen::VectorXd &x2, Eigen::VectorXd &grad) = 0;
+
+      /** Update parameter vector.
+       *  @param p new parameter vector */
+      virtual void set_loghyper(const Eigen::VectorXd &p);
+
+      /** Update parameter vector.
+       *  @param p new parameter vector */
+      virtual void set_loghyper(const double p[]);
+
+      /** Get number of parameters for this covariance function.
+       *  @return parameter vector dimensionality */
+      size_t get_param_dim();
+
+      /** Get input dimensionality.
+       *  @return input dimensionality */
+      size_t get_input_dim();
+
+      /** Get log-hyperparameter of covariance function.
+       *  @return log-hyperparameter */
+      Eigen::VectorXd get_loghyper();
+
+      /** Returns a string representation of this covariance function.
+       *  @return string containing the name of this covariance function */
+      virtual std::string to_string() = 0;
+
+      /** Draw random target values from this covariance function for input X. */
+      Eigen::VectorXd draw_random_sample(Eigen::MatrixXd &X);
+
+      bool loghyper_changed;
+
+    protected:
+      /** Input dimensionality. */
+      size_t input_dim;
+
+      /** Size of parameter vector. */
+      size_t param_dim;
+
+      /** Parameter vector containing the log hyperparameters of the covariance function.
+       *  The number of necessary parameters is given in param_dim. */
+      Eigen::VectorXd loghyper;
 
-  /** Initialization method for compound covariance functions. 
-   *  @param input_dim dimensionality of the input vectors 
-   *  @param first first covariance function of compound
-   *  @param second second covariance function of compound */
-	virtual bool init(int input_dim, CovarianceFunction * first, CovarianceFunction * second)
-	{
-    return false;
-	};
-
-	/** Computes the covariance of two input vectors.
-	 *  @param x1 first input vector
-	 *  @param x2 second input vector
-   *  @return covariance of x1 and x2 */
-	virtual double get(const Eigen::VectorXd &x1, const Eigen::VectorXd &x2) = 0;
-
-	/** Covariance gradient of two input vectors with respect to the hyperparameters.
-	 *  @param x1 first input vector
-	 *  @param x2 second input vector
-	 *  @param grad covariance gradient */
-	virtual void grad(const Eigen::VectorXd &x1, const Eigen::VectorXd &x2, Eigen::VectorXd &grad) = 0;
-
-	/** Update parameter vector.
-	 *  @param p new parameter vector */
-	virtual void set_loghyper(const Eigen::VectorXd &p);
-
-	/** Update parameter vector.
-	 *  @param p new parameter vector */
-	virtual void set_loghyper(const double p[]);
-
-	/** Get number of parameters for this covariance function.
-	 *  @return parameter vector dimensionality */
-	size_t get_param_dim();
-
-	/** Get input dimensionality.
-	 *  @return input dimensionality */
-	size_t get_input_dim();
-
-	/** Get log-hyperparameter of covariance function.
-	 *  @return log-hyperparameter */
-	Eigen::VectorXd get_loghyper();
-
-  /** Returns a string representation of this covariance function.
-   *  @return string containing the name of this covariance function */
-	virtual std::string to_string() = 0;
-
-	/** Draw random target values from this covariance function for input X. */
-  Eigen::VectorXd draw_random_sample(Eigen::MatrixXd &X);
-
-  /** Get distance threshold of this covariance function. */
-  virtual double get_threshold();
-
-  /** Set distance threshold of this covariance function. */
-  virtual void set_threshold(double threshold);
-
-  bool loghyper_changed;
-
-protected:
-	/** Input dimensionality. */
-	size_t input_dim;
-
-	/** Size of parameter vector. */
-	size_t param_dim;
-
-	/** Parameter vector containing the log hyperparameters of the covariance function.
-	 *  The number of necessary parameters is given in param_dim. */
-	Eigen::VectorXd loghyper;
-
- };
+  };
 
 }
 

include/cov_factory.h

@@ -1,5 +1,5 @@
 // libgp - Gaussian process library for Machine Learning
-// Copyright (c) 2011, Manuel Blum <[email protected]>
+// Copyright (c) 2013, Manuel Blum <[email protected]>
 // All rights reserved.
 
 #ifndef __COV_FACTORY_H__

include/cov_linear_ard.h

@@ -1,5 +1,5 @@
 // libgp - Gaussian process library for Machine Learning
-// Copyright (c) 2011, Manuel Blum <[email protected]>
+// Copyright (c) 2013, Manuel Blum <[email protected]>
 // All rights reserved.
 
 #ifndef __COV_LINEAR_ARD_H__

include/cov_linear_one.h

@@ -1,5 +1,5 @@
 // libgp - Gaussian process library for Machine Learning
-// Copyright (c) 2011, Manuel Blum <[email protected]>
+// Copyright (c) 2013, Manuel Blum <[email protected]>
 // All rights reserved.
 
 #ifndef __COV_LINEAR_ONE__

include/cov_matern3_iso.h

@@ -1,5 +1,5 @@
 // libgp - Gaussian process library for Machine Learning
-// Copyright (c) 2011, Manuel Blum <[email protected]>
+// Copyright (c) 2013, Manuel Blum <[email protected]>
 // All rights reserved.
 
 #ifndef __COV_MATERN3_ISO_H__

include/cov_matern5_iso.h

@@ -1,5 +1,5 @@
 // libgp - Gaussian process library for Machine Learning
-// Copyright (c) 2011, Manuel Blum <[email protected]>
+// Copyright (c) 2013, Manuel Blum <[email protected]>
 // All rights reserved.
 
 #ifndef __COV_MATERN5_ISO_H__

include/cov_noise.h

@@ -1,5 +1,5 @@
 // libgp - Gaussian process library for Machine Learning
-// Copyright (c) 2011, Manuel Blum <[email protected]>
+// Copyright (c) 2013, Manuel Blum <[email protected]>
 // All rights reserved.
 
 #ifndef __COV_NOISE_H__