Merge pull request #2686 from lisitsyn/bugfix/clang_warnings

Removed some warning causes

examples/undocumented/libshogun/preprocessor_fisherlda.cpp

@@ -45,7 +45,7 @@ void test()
 	// Initiate the FisherLDA class
 	CFisherLDA* fisherlda=new CFisherLDA(AUTO_FLDA);
 	SG_REF(fisherlda)
-	fisherlda->init(features, labels, 1);
+	fisherlda->fit(features, labels, 1);
 	SGMatrix<float64_t> y=fisherlda->apply_to_feature_matrix(features);
 
 	// display output

examples/undocumented/libshogun/streaming_onlineliblinear_sparse.cpp

@@ -79,7 +79,7 @@ int main(int argc, char* argv[])
 
         uint64_t train_time_int = train_time.cur_time_diff();
         fprintf(stderr,
-            "*** total training time: %lum%lus (or %.1f sec), #dim = %d, ||w|| = %f\n",
+            "*** total training time: %llum%llus (or %.1f sec), #dim = %d, ||w|| = %f\n",
             train_time_int / 60, train_time_int % 60, train_time.cur_time_diff(),
             w_now.vlen, w_now_norm
         );
@@ -105,7 +105,7 @@ int main(int argc, char* argv[])
 
         test_time.stop();
         uint64_t test_time_int = test_time.cur_time_diff();
-        fprintf(stderr, "*** testing took %lum%lus (or %.1f sec)\n",
+        fprintf(stderr, "*** testing took %llum%llus (or %.1f sec)\n",
             test_time_int / 60, test_time_int % 60, test_time.cur_time_diff());
 
         SG_UNREF(test_features);

examples/undocumented/python_modular/preprocessor_fisherlda_modular.py

@@ -18,7 +18,7 @@ def preprocessor_fisherlda_modular (data, labels, method):
 	sg_labels = MulticlassLabels(labels)
 
 	preprocessor=FisherLda(method)
-	preprocessor.init(sg_features, sg_labels, 1)
+	preprocessor.fit(sg_features, sg_labels, 1)
 	yn=preprocessor.apply_to_feature_matrix(sg_features)
 
 	return yn

src/shogun/machine/gp/SoftMaxLikelihood.cpp

@@ -301,7 +301,7 @@ SGVector<float64_t> CSoftMaxLikelihood::predictive_helper(SGVector<float64_t> mu
 }
 
 SGVector<float64_t> CSoftMaxLikelihood::get_predictive_log_probabilities(
-	SGVector<float64_t> mu, SGVector<float64_t> s2, const CLabels *lab) const
+	SGVector<float64_t> mu, SGVector<float64_t> s2, const CLabels *lab)
 {
 	return predictive_helper(mu, s2, lab, MC_Probability);
 }

src/shogun/machine/gp/SoftMaxLikelihood.h

@@ -161,7 +161,7 @@ class CSoftMaxLikelihood : public CLikelihoodModel
 	 * @return \f$log(p(y_*|X, y, x*))\f$ for each label \f$y_*\f$ (based on 0 and 1 bernoulli-encoding)
 	 */
 	virtual SGVector<float64_t> get_predictive_log_probabilities(SGVector<float64_t> mu,
-		SGVector<float64_t> s2, const CLabels *lab=NULL) const;
+		SGVector<float64_t> s2, const CLabels *lab=NULL);
 
 	/** returns the logarithm of the point-wise likelihood \f$log(p(y_i|f_i))\f$
 	 * for each label \f$y_i\f$, an integer between 1 and C (ie. number of classes).

src/shogun/preprocessor/BAHSIC.cpp

@@ -35,10 +35,10 @@ using namespace shogun;
 
 CBAHSIC::CBAHSIC() : CKernelDependenceMaximization()
 {
-	init();
+	initialize();
 }
 
-void CBAHSIC::init()
+void CBAHSIC::initialize()
 {
 	m_estimator=new CHSIC();
 	SG_REF(m_estimator);

src/shogun/preprocessor/BAHSIC.h

@@ -84,7 +84,7 @@ class CBAHSIC : public CKernelDependenceMaximization
 
 private:
 	/** Register params and initialize with default values */
-	void init();
+	void initialize();
 
 };
 

src/shogun/preprocessor/DimensionReductionPreprocessor.cpp

@@ -15,7 +15,7 @@ CDimensionReductionPreprocessor::CDimensionReductionPreprocessor()
 	m_kernel = new CLinearKernel();
 	m_converter = NULL;
 
-	init();
+	initialize();
 }
 
 CDimensionReductionPreprocessor::CDimensionReductionPreprocessor(CEmbeddingConverter* converter)
@@ -27,7 +27,7 @@ CDimensionReductionPreprocessor::CDimensionReductionPreprocessor(CEmbeddingConve
 	m_kernel = new CLinearKernel();
 	m_converter = converter;
 
-	init();
+	initialize();
 }
 
 CDimensionReductionPreprocessor::~CDimensionReductionPreprocessor()
@@ -104,7 +104,7 @@ CKernel* CDimensionReductionPreprocessor::get_kernel() const
 	return m_kernel;
 }
 
-void CDimensionReductionPreprocessor::init()
+void CDimensionReductionPreprocessor::initialize()
 {
 	SG_ADD((CSGObject**)&m_converter, "converter",
 					  "embedding converter used to apply to data", MS_AVAILABLE);

src/shogun/preprocessor/DimensionReductionPreprocessor.h

@@ -107,10 +107,10 @@ class CDimensionReductionPreprocessor: public CDensePreprocessor<float64_t>
 	 */
 	CKernel* get_kernel() const;
 
-protected:
+private:
 
 	/** default init */
-	void init();
+	void initialize();
 
 protected:
 

src/shogun/preprocessor/FeatureSelection.cpp

@@ -42,11 +42,11 @@ namespace shogun
 template <class ST>
 CFeatureSelection<ST>::CFeatureSelection() : CPreprocessor()
 {
-	init();
+	initialize();
 }
 
 template <class ST>
-void CFeatureSelection<ST>::init()
+void CFeatureSelection<ST>::initialize()
 {
 	SG_ADD(&m_target_dim, "target_dim", "target dimension",
 			MS_NOT_AVAILABLE);

src/shogun/preprocessor/FeatureSelection.h

@@ -320,7 +320,7 @@ template <class ST> class CFeatureSelection : public CPreprocessor
 
 private:
 	/** Register params and initialize with default values */
-	void init();
+	void initialize();
 
 };
 

src/shogun/preprocessor/FisherLDA.cpp

@@ -52,12 +52,12 @@ using namespace shogun;
 CFisherLDA::CFisherLDA (EFLDAMethod method, float64_t thresh):
 	CDimensionReductionPreprocessor()
 {
-	init();
+	initialize();
 	m_method=method;
 	m_threshold=thresh;
 }
 
-void CFisherLDA::init()
+void CFisherLDA::initialize()
 {
 	m_method=AUTO_FLDA;
 	m_threshold=0.01;
@@ -77,7 +77,7 @@ CFisherLDA::~CFisherLDA()
 {
 }
 
-bool CFisherLDA::init (CFeatures *features, CLabels *labels, int32_t num_dimensions)
+bool CFisherLDA::fit(CFeatures *features, CLabels *labels, int32_t num_dimensions)
 {
 	REQUIRE(features, "Features are not provided!\n")
 

src/shogun/preprocessor/FisherLDA.h

@@ -104,13 +104,13 @@ class CFisherLDA: public CDimensionReductionPreprocessor
 		/** destructor */
 		virtual ~CFisherLDA();
 
-		/** initialize preprocessor from features and corresponding labels
+		/** fits fisher lda transformation using features and corresponding labels
 		 * @param features using which the transformation matrix will be formed
 		 * @param labels of the given features which will be used here to find 
 		 * the transformation matrix unlike PCA where it is not needed.
 		 * @param dimensions number of dimensions to retain
 		 */
-		virtual bool init(CFeatures* features, CLabels* labels, int32_t num_dimensions=0);
+		virtual bool fit(CFeatures* features, CLabels* labels, int32_t num_dimensions=0);
 
 		/** cleanup */
 		virtual void cleanup();
@@ -145,10 +145,13 @@ class CFisherLDA: public CDimensionReductionPreprocessor
 
 		/** @return a type of preprocessor */
 		virtual EPreprocessorType get_type() const {return P_FISHERLDA;}
+
+	private:
+
+		void initialize();
 
 	protected:
 
-		void init();
 
 		/** transformation matrix */
 		SGMatrix<float64_t> m_transformation_matrix;

src/shogun/preprocessor/KernelDependenceMaximization.cpp

@@ -37,10 +37,10 @@ using namespace shogun;
 CKernelDependenceMaximization::CKernelDependenceMaximization()
 	: CDependenceMaximization()
 {
-	init();
+	initialize();
 }
 
-void CKernelDependenceMaximization::init()
+void CKernelDependenceMaximization::initialize()
 {
 	SG_ADD((CSGObject**)&m_kernel_features, "kernel_features",
 			"the kernel to be used for features", MS_NOT_AVAILABLE);

src/shogun/preprocessor/KernelDependenceMaximization.h

@@ -97,7 +97,7 @@ class CKernelDependenceMaximization : public CDependenceMaximization
 
 private:
 	/** Register params and initialize with default values */
-	void init();
+	void initialize();
 
 };
 

src/shogun/regression/LeastAngleRegression.cpp

@@ -58,14 +58,14 @@ static void plane_rot(float64_t x0, float64_t x1,
 	}
 }
 
-static void find_max_abs(const vector<float64_t> &vec, const vector<bool> &ignore,
+static void find_max_abs(const vector<float64_t> &vec, const vector<bool> &ignore_mask,
 	int32_t &imax, float64_t& vmax)
 {
 	imax = -1;
 	vmax = -1;
 	for (uint32_t i=0; i < vec.size(); ++i)
 	{
-		if (ignore[i])
+		if (ignore_mask[i])
 			continue;
 
 		if (CMath::abs(vec[i]) > vmax)

tests/unit/preprocessor/FisherLDA_unittest.cc

@@ -136,7 +136,7 @@ TEST_F(FLDATest, CANVAR_FLDA_Unit_test)
 	// comparing outputs against BRMLtoolbox MATLAB "CannonVar.m" implementation 
 	// http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Software
 	CFisherLDA fisherlda(CANVAR_FLDA);
-	fisherlda.init(dense_feat, labels, 1);
+	fisherlda.fit(dense_feat, labels, 1);
 	SGMatrix<float64_t> y=fisherlda.apply_to_feature_matrix(dense_feat);
 
 	float64_t epsilon=0.00000000001;
@@ -174,7 +174,7 @@ TEST_F(FLDATest, CLASSIC_FLDA_Unit_test)
 	SG_REF(labels);
 
 	CFisherLDA fisherlda(CLASSIC_FLDA);
-	fisherlda.init(dense_feat, labels, 1);
+	fisherlda.fit(dense_feat, labels, 1);
 	SGMatrix<float64_t> y=fisherlda.apply_to_feature_matrix(dense_feat);
 
 	float64_t epsilon=0.00000000001;
@@ -259,7 +259,7 @@ TEST(FLDATesti, CANVAR_FLDA_for_D_greater_than_N )
 	SG_REF(dense_feat);
 
 	CFisherLDA fisherlda(CANVAR_FLDA);
-	fisherlda.init(dense_feat, labels, 1);
+	fisherlda.fit(dense_feat, labels, 1);
 	SGMatrix<float64_t> transformy=fisherlda.get_transformation_matrix();  
 
 	// comparing eigenvectors from the transformation_matrix with that from the