Merge pull request #221 from karlnapf/master

changes towards kernel machine model storage

src/interfaces/modular/Classifier.i

@@ -21,7 +21,6 @@
 %rename(GNPPSVM) CGNPPSVM;
 %rename(GPBTSVM) CGPBTSVM;
 %rename(GaussianNaiveBayes) CGaussianNaiveBayes;
-%rename(KernelPerceptron) CKernelPerceptron;
 %rename(KNN) CKNN;
 %rename(LDA) CLDA;
 %rename(LibLinear) CLibLinear;
@@ -79,7 +78,6 @@
 %include <shogun/classifier/svm/GMNPSVM.h>
 %include <shogun/classifier/svm/GNPPSVM.h>
 %include <shogun/classifier/svm/GPBTSVM.h>
-%include <shogun/classifier/KernelPerceptron.h> 
 %include <shogun/classifier/KNN.h>
 %include <shogun/classifier/LDA.h>
 %include <shogun/classifier/svm/LibLinear.h>

src/interfaces/modular/Classifier_includes.i

@@ -3,7 +3,6 @@
  #include <shogun/classifier/svm/GMNPSVM.h>
  #include <shogun/classifier/svm/GNPPSVM.h>
  #include <shogun/classifier/svm/GPBTSVM.h>
- #include <shogun/classifier/KernelPerceptron.h> 
  #include <shogun/machine/DistanceMachine.h>
  #include <shogun/classifier/KNN.h>
  #include <shogun/classifier/LDA.h>

src/shogun/classifier/mkl/MKL.cpp

@@ -190,7 +190,7 @@ bool CMKL::check_lpx_status(LPX *lp)
 }
 #endif // USE_GLPK
 
-bool CMKL::train(CFeatures* data)
+bool CMKL::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(kernel);
 	ASSERT(labels && labels->get_num_labels());

src/shogun/classifier/mkl/MKL.h

@@ -133,17 +133,6 @@ class CMKL : public CSVM
 			return svm;
 		}
 
-
-		/** train MKL classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** set C mkl
 		 *
 		 * @param C new C_mkl
@@ -265,7 +254,20 @@ class CMKL : public CSVM
 		 */
 		virtual void compute_sum_beta(float64_t* sumw);
 
+		/** @return object name */
+		inline virtual const char* get_name() const { return "MKL"; }
+
 	protected:
+		/** train MKL classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 		/** check run before starting training (to e.g. check if labeling is
 		 * two-class labeling in classification case
 		 */
@@ -442,9 +444,6 @@ class CMKL : public CSVM
 		bool check_lpx_status(LPX *lp);
 #endif
 
-		/** @return object name */
-		inline virtual const char* get_name() const { return "MKL"; }
-
 	protected:
 		/** wrapper SVM */
 		CSVM* svm;

src/shogun/classifier/mkl/MKLMultiClass.cpp

@@ -325,7 +325,7 @@ float64_t CMKLMultiClass::getsquarenormofprimalcoefficients(
 }
 
 
-bool CMKLMultiClass::train(CFeatures* data)
+bool CMKLMultiClass::train_kernel_machine(CFeatures* data)
 {
 	int numcl=labels->get_num_classes();
 	ASSERT(kernel);

src/shogun/classifier/mkl/MKLMultiClass.h

@@ -49,16 +49,6 @@ class CMKLMultiClass : public CMultiClassSVM
 	 */
 	virtual ~CMKLMultiClass();
 
-	/** train Multiclass MKL classifier
-	 *
-	 * @param data training data (parameter can be avoided if distance or
-	 * kernel-based classifiers are used and distance/kernels are
-	 * initialized with train data)
-	 *
-	 * @return whether training was successful
-	 */
-	virtual bool train(CFeatures* data=NULL);
-
 	/** get classifier type
 	 *
 	 * @return classifier type GMNPMKL
@@ -158,6 +148,15 @@ class CMKLMultiClass : public CMultiClassSVM
 			const int32_t ind);
 
 
+	/** train Multiclass MKL classifier
+	 *
+	 * @param data training data (parameter can be avoided if distance or
+	 * kernel-based classifiers are used and distance/kernels are
+	 * initialized with train data)
+	 *
+	 * @return whether training was successful
+	 */
+	virtual bool train_kernel_machine(CFeatures* data=NULL);
 
 
 protected:

src/shogun/classifier/svm/CPLEXSVM.cpp

@@ -28,7 +28,7 @@ CCPLEXSVM::~CCPLEXSVM()
 {
 }
 
-bool CCPLEXSVM::train(CFeatures* data)
+bool CCPLEXSVM::train_kernel_machine(CFeatures* data)
 {
 	bool result = false;
 	CCplex cplex;

src/shogun/classifier/svm/CPLEXSVM.h

@@ -26,6 +26,9 @@ IGNORE_IN_CLASSLIST class CCPLEXSVM : public CSVM
 		CCPLEXSVM();
 		virtual ~CCPLEXSVM();
 
+		virtual inline EClassifierType get_classifier_type() { return CT_CPLEXSVM; }
+
+	protected:
 		/** train SVM classifier
 		 *
 		 * @param data training data (parameter can be avoided if distance or
@@ -34,9 +37,7 @@ IGNORE_IN_CLASSLIST class CCPLEXSVM : public CSVM
 		 *
 		 * @return whether training was successful
 		 */
-		virtual bool train(CFeatures* data=NULL);
-
-		virtual inline EClassifierType get_classifier_type() { return CT_CPLEXSVM; }
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
 };
 }
 #endif

src/shogun/classifier/svm/DomainAdaptationSVM.cpp

@@ -78,7 +78,7 @@ bool CDomainAdaptationSVM::is_presvm_sane()
 }
 
 
-bool CDomainAdaptationSVM::train(CFeatures* data)
+bool CDomainAdaptationSVM::train_kernel_machine(CFeatures* data)
 {
 
 	if (data)
@@ -111,7 +111,7 @@ bool CDomainAdaptationSVM::train(CFeatures* data)
 	delete[] lin_term;
 
 	//train SVM
-	bool success = CSVMLight::train();
+	bool success = CSVMLight::train_kernel_machine();
 
 	ASSERT(presvm)
 

src/shogun/classifier/svm/DomainAdaptationSVM.h

@@ -49,16 +49,6 @@ class CDomainAdaptationSVM : public CSVMLight
 		 * */
 		void init(CSVM* presvm, float64_t B);
 
-		/** train SVM classifier
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type
@@ -106,6 +96,16 @@ class CDomainAdaptationSVM : public CSVMLight
 		 */
 		virtual bool is_presvm_sane();
 
+		/** train SVM classifier
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	private:
 		void init();
 

src/shogun/classifier/svm/GMNPSVM.cpp

@@ -48,7 +48,7 @@ CGMNPSVM::init(void)
 	m_basealphas = NULL, m_basealphas_y = 0, m_basealphas_x = 0;
 }
 
-bool CGMNPSVM::train(CFeatures* data)
+bool CGMNPSVM::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(kernel);
 	ASSERT(labels && labels->get_num_labels());

src/shogun/classifier/svm/GMNPSVM.h

@@ -40,16 +40,6 @@ class CGMNPSVM : public CMultiClassSVM
 		/** default destructor */
 		virtual ~CGMNPSVM();
 
-		/** train SVM
-		 *
-		 * @param data training data (parameter can be avoided if distance or
-		 * kernel-based classifiers are used and distance/kernels are
-		 * initialized with train data)
-		 *
-		 * @return whether training was successful
-		 */
-		virtual bool train(CFeatures* data=NULL);
-
 		/** get classifier type
 		 *
 		 * @return classifier type GMNPSVM
@@ -70,6 +60,17 @@ class CGMNPSVM : public CMultiClassSVM
 		/** @return object name */
 		inline virtual const char* get_name() const { return "GMNPSVM"; }
 
+	protected:
+		/** train SVM
+		 *
+		 * @param data training data (parameter can be avoided if distance or
+		 * kernel-based classifiers are used and distance/kernels are
+		 * initialized with train data)
+		 *
+		 * @return whether training was successful
+		 */
+		virtual bool train_kernel_machine(CFeatures* data=NULL);
+
 	protected:
 		/** required for CMKLMulticlass
 		 * stores the untransformed alphas of this algorithm

src/shogun/classifier/svm/GNPPSVM.cpp

@@ -29,7 +29,7 @@ CGNPPSVM::~CGNPPSVM()
 {
 }
 
-bool CGNPPSVM::train(CFeatures* data)
+bool CGNPPSVM::train_kernel_machine(CFeatures* data)
 {
 	ASSERT(kernel);
 	ASSERT(labels && labels->get_num_labels());