adding periodic covariance function

include/cov_factory.h

@@ -32,7 +32,7 @@ namespace libgp {
      *  @param input_dim input vector dimensionality
      *  @param key string representation of covariance function
      *  @return instance of CovarianceFunction */
-    CovarianceFunction* create(size_t input_dim, const std::string key);
+    libgp::CovarianceFunction* create(size_t input_dim, const std::string key);
 
     /** Returns a string vector of available covariance functions. */
     std::vector<std::string> list();

include/cov_periodic.h

@@ -0,0 +1,30 @@
+// libgp - Gaussian process library for Machine Learning
+// Copyright (c) 2013, Manuel Blum <[email protected]>
+// All rights reserved.
+
+#ifndef __COV_PERIODIC_H__
+#define __COV_PERIODIC_H__
+
+#include "cov.h"
+
+namespace libgp
+{
+  class CovPeriodic : public CovarianceFunction
+  {
+  public:
+    CovPeriodic ();
+    virtual ~CovPeriodic ();
+    bool init(int n);
+    double get(const Eigen::VectorXd &x1, const Eigen::VectorXd &x2);
+    void grad(const Eigen::VectorXd &x1, const Eigen::VectorXd &x2, Eigen::VectorXd &grad);
+    void set_loghyper(const Eigen::VectorXd &p);
+    virtual std::string to_string();
+  private:
+    double ell;
+    double sf2;
+    double T;
+  };
+
+}
+
+#endif

src/cov_factory.cc

@@ -18,6 +18,7 @@
 #include "cov_sum.h"
 #include "cov_prod.h"
 #include "cov_periodic_matern3_iso.h"
+#include "cov_periodic.h"
 #include "input_dim_filter.h"
 
 namespace libgp {
@@ -34,12 +35,13 @@ namespace libgp {
     registry["CovSum"] = & create_func<CovSum>;
     registry["CovProd"] = & create_func<CovProd>;
     registry["CovPeriodicMatern3iso"] = & create_func<CovPeriodicMatern3iso>;
+    registry["CovPeriodic"] = & create_func<CovPeriodic>;
     registry["InputDimFilter"] = & create_func<InputDimFilter>;
   }
 
   CovFactory::~CovFactory () {};
 
-  CovarianceFunction* CovFactory::create(size_t input_dim, const std::string key) {
+  libgp::CovarianceFunction* CovFactory::create(size_t input_dim, const std::string key) {
 
     CovarianceFunction * covf = NULL;
 

src/cov_periodic.cc

@@ -0,0 +1,50 @@
+// libgp - Gaussian process library for Machine Learning
+// Copyright (c) 2013, Manuel Blum <[email protected]>
+// All rights reserved.
+
+#include "cov_periodic.h"
+#include <cmath>
+
+namespace libgp
+{
+
+  CovPeriodic::CovPeriodic() {}
+
+  CovPeriodic::~CovPeriodic() {}
+
+  bool CovPeriodic::init(int n)
+  {
+    input_dim = n;
+    param_dim = 3;
+    loghyper.resize(param_dim);
+    loghyper.setZero();
+    return true;
+  }
+
+  double CovPeriodic::get(const Eigen::VectorXd &x1, const Eigen::VectorXd &x2)
+  {
+    double s = sin(M_PI * (x1-x2).norm() / T) / ell;
+    return sf2*exp(-2*s*s);
+  }
+
+  void CovPeriodic::grad(const Eigen::VectorXd &x1, const Eigen::VectorXd &x2, Eigen::VectorXd &grad)
+  {
+    double k = M_PI * (x1-x2).norm() / T;
+    double s = sin(k) / ell;
+    grad << 4*sf2*exp(-2*s*s)*s*s, 2*sf2*exp(-2*s*s), 0;// 4*sf2/ell*exp(-2*s*s)*s*cos(k)*k;
+  }
+
+  void CovPeriodic::set_loghyper(const Eigen::VectorXd &p)
+  {
+    CovarianceFunction::set_loghyper(p);
+    ell = exp(loghyper(0));
+    sf2 = exp(2*loghyper(1));
+    T = fabs(loghyper(2));
+  }
+
+  std::string CovPeriodic::to_string()
+  {
+    return "CovPeriodic";
+  }
+
+}