Merge pull request #1214 from votjakovr/feature/gp_refactoring

refactor get_log_probability_f()
shogun-toolbox · Jul 7, 2013 · 370764d · 370764d
2 parents e827211 + 9c4453f
commit 370764d
Show file tree

Hide file tree

Showing 8 changed files with 22 additions and 21 deletions.
diff --git a/src/shogun/machine/gp/GaussianLikelihood.cpp b/src/shogun/machine/gp/GaussianLikelihood.cpp
@@ -70,7 +70,7 @@ SGVector<float64_t> CGaussianLikelihood::evaluate_variances(SGVector<float64_t>
 	return result;
 }
 
-float64_t CGaussianLikelihood::get_log_probability_f(CLabels* lab,
+SGVector<float64_t> CGaussianLikelihood::get_log_probability_f(CLabels* lab,
 		SGVector<float64_t> func)
 {
 	REQUIRE(lab->get_label_type()==LT_REGRESSION,
@@ -89,7 +89,7 @@ float64_t CGaussianLikelihood::get_log_probability_f(CLabels* lab,
 	eigen_result=-eigen_result.cwiseProduct(eigen_result)/(2*CMath::sq(m_sigma))-
 		VectorXd::Ones(result.vlen)*log(2*CMath::PI*CMath::sq(m_sigma))/2.0;
 
-	return eigen_result.sum();
+	return result;
 }
 
 SGVector<float64_t> CGaussianLikelihood::get_log_probability_derivative_f(

diff --git a/src/shogun/machine/gp/GaussianLikelihood.h b/src/shogun/machine/gp/GaussianLikelihood.h
@@ -105,7 +105,7 @@ class CGaussianLikelihood: public CLikelihoodModel
 	 *
 	 * @return log likelihood
 	 */
-	virtual float64_t get_log_probability_f(CLabels* lab,
+	virtual SGVector<float64_t> get_log_probability_f(CLabels* lab,
 			SGVector<float64_t> func);
 
 	/** get derivative of log likelihood \f$log(P(y|f))\f$ with

diff --git a/src/shogun/machine/gp/LaplacianInferenceMethod.cpp b/src/shogun/machine/gp/LaplacianInferenceMethod.cpp
@@ -62,8 +62,8 @@ namespace shogun
 			W->scale(-1.0);
 
 			// compute psi=alpha'*(f-m)/2-lp
-			float64_t result = (*alpha).dot(eigen_f-eigen_m)/2.0-
-				lik->get_log_probability_f(lab, *f);
+			float64_t result = (*alpha).dot(eigen_f-eigen_m)/2.0
+				-SGVector<float64_t>::sum(lik->get_log_probability_f(lab, *f));
 
 			return result;
 		}
@@ -443,7 +443,7 @@ void CLaplacianInferenceMethod::update_alpha()
 		W=m_model->get_log_probability_derivative_f(m_labels, function, 2);
 		W.scale(-1.0);
 
-		Psi_New=-m_model->get_log_probability_f(m_labels, function);
+		Psi_New=-SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels, function));
 	}
 	else
 	{
@@ -457,16 +457,16 @@ void CLaplacianInferenceMethod::update_alpha()
 		W.scale(-1.0);
 
 		Psi_New=eigen_temp_alpha.dot(eigen_function-eigen_m_means)/2.0-
-			m_model->get_log_probability_f(m_labels, function);
+			SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels, function));
 
-		Psi_Def=-m_model->get_log_probability_f(m_labels, m_means);
+		Psi_Def=-SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels, m_means));
 
 		// if default is better, then use it
 		if (Psi_Def < Psi_New)
 		{
 			temp_alpha.zero();
 			eigen_function=eigen_m_means;
-			Psi_New=-m_model->get_log_probability_f(m_labels, function);
+			Psi_New=-SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels, function));
 		}
 	}
 
@@ -542,7 +542,7 @@ void CLaplacianInferenceMethod::update_alpha()
 	eigen_function=eigen_ktrtr*CMath::sq(m_scale)*eigen_temp_alpha+eigen_m_means;
 
 	// get log probability
-	lp=m_model->get_log_probability_f(m_labels, function);
+	lp=SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels, function));
 
 	// get log probability derivatives
 	dlp=m_model->get_log_probability_derivative_f(m_labels, function, 1);

diff --git a/src/shogun/machine/gp/LikelihoodModel.h b/src/shogun/machine/gp/LikelihoodModel.h
@@ -23,7 +23,9 @@ enum ELikelihoodModelType
 {
 	LT_NONE = 0,
 	LT_GAUSSIAN = 10,
-	LT_STUDENTST = 20
+	LT_STUDENTST = 20,
+	LT_LOGIT = 30,
+	LT_PROBIT = 40
 };
 
 /** @brief The Likelihood model base class.
@@ -71,7 +73,7 @@ class CLikelihoodModel : public CSGObject
 	 *
 	 * @return log likelihood
 	 */
-	virtual float64_t get_log_probability_f(CLabels* lab,
+	virtual SGVector<float64_t> get_log_probability_f(CLabels* lab,
 			SGVector<float64_t> func)=0;
 
 	/** get derivative of log likelihood \f$log(P(y|f))\f$ with
@@ -118,8 +120,7 @@ class CLikelihoodModel : public CSGObject
 
 	/** get derivative of the second derivative of log likelihood with
 	 * respect to function location, i.e. \f$\frac{\partial^{2}
-	 * log(P(y|f))}{\partial f^{2}}\f$ with respect to given
-	 * parameter
+	 * log(P(y|f))}{\partial f^{2}}\f$ with respect to given parameter
 	 *
 	 * @param lab labels used
 	 * @param param parameter

diff --git a/src/shogun/machine/gp/StudentsTLikelihood.cpp b/src/shogun/machine/gp/StudentsTLikelihood.cpp
@@ -85,7 +85,7 @@ SGVector<float64_t> CStudentsTLikelihood::evaluate_variances(SGVector<float64_t>
 	return result;
 }
 
-float64_t CStudentsTLikelihood::get_log_probability_f(CLabels* lab,
+SGVector<float64_t> CStudentsTLikelihood::get_log_probability_f(CLabels* lab,
 		SGVector<float64_t> func)
 {
 	REQUIRE(lab->get_label_type()==LT_REGRESSION,
@@ -110,7 +110,7 @@ float64_t CStudentsTLikelihood::get_log_probability_f(CLabels* lab,
 	eigen_r=eigen_lZ-(m_df+1)*
 		(eigen_r+VectorXd::Ones(r.vlen)).array().log().matrix()/2.0;
 
-	return eigen_r.sum();
+	return r;
 }
 
 SGVector<float64_t> CStudentsTLikelihood::get_log_probability_derivative_f(

diff --git a/src/shogun/machine/gp/StudentsTLikelihood.h b/src/shogun/machine/gp/StudentsTLikelihood.h
@@ -125,7 +125,7 @@ class CStudentsTLikelihood: public CLikelihoodModel
 	 *
 	 * @return log likelihood
 	 */
-	virtual float64_t get_log_probability_f(CLabels* lab,
+	virtual SGVector<float64_t> get_log_probability_f(CLabels* lab,
 			SGVector<float64_t> func);
 
 	/** get derivative of log likelihood \f$log(P(y|f))\f$ with

diff --git a/tests/unit/machine/gp/GaussianLikelihood_unittest.cc b/tests/unit/machine/gp/GaussianLikelihood_unittest.cc
@@ -19,7 +19,7 @@
 
 using namespace shogun;
 
-TEST(GaussianLikelihood,get_log_probability_f)
+TEST(GaussianLikelihood,get_log_probability_f_sum)
 {
 	// create some easy data:
 	// f(x) approximately equals to (x^3 + sin(x)^2)/10, y = f(x) + noise
@@ -46,7 +46,7 @@ TEST(GaussianLikelihood,get_log_probability_f)
 	// Gaussian likelihood with sigma = 0.13
 	CGaussianLikelihood* likelihood=new CGaussianLikelihood(0.13);
 
-	float64_t lp=likelihood->get_log_probability_f(labels, func);
+	float64_t lp=SGVector<float64_t>::sum(likelihood->get_log_probability_f(labels, func));
 
 	// comparison of log likelihood with result from GPML package
 	EXPECT_NEAR(lp, 4.2428, 1E-4);

diff --git a/tests/unit/machine/gp/StudentsTLikelihood_unittest.cc b/tests/unit/machine/gp/StudentsTLikelihood_unittest.cc
@@ -19,7 +19,7 @@
 
 using namespace shogun;
 
-TEST(StudentsTLikelihood,get_log_probability_f)
+TEST(StudentsTLikelihood,get_log_probability_f_sum)
 {
 	// create some easy data:
 	// f(x) approximately equals to (x^3 + sin(x)^2)/10, y = f(x) + noise
@@ -46,7 +46,7 @@ TEST(StudentsTLikelihood,get_log_probability_f)
 	// Stundent's-t likelihood with sigma = 0.17, df = 3
 	CStudentsTLikelihood* likelihood=new CStudentsTLikelihood(0.17, 3);
 
-	float64_t lp=likelihood->get_log_probability_f(labels, func);
+	float64_t lp=SGVector<float64_t>::sum(likelihood->get_log_probability_f(labels, func));
 
 	// comparison of log likelihood with result from GPML package
 	EXPECT_NEAR(lp, 2.8647, 1E-4);