Proper multiclass labels handling in KNN

shogun-toolbox · May 22, 2012 · d204fcd · d204fcd
1 parent de154b3
commit d204fcd
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Showing 2 changed files with 6 additions and 6 deletions.
diff --git a/src/shogun/multiclass/KNN.cpp b/src/shogun/multiclass/KNN.cpp
@@ -108,7 +108,7 @@ bool CKNN::train_machine(CFeatures* data)
 	return true;
 }
 
-CLabels* CKNN::apply(CFeatures* data)
+CMulticlassLabels* CKNN::apply_multiclass(CFeatures* data)
 {
 	if (data)
 		init_distance(data);
@@ -124,7 +124,7 @@ CLabels* CKNN::apply(CFeatures* data)
 	int32_t num_lab=distance->get_num_vec_rhs();
 	ASSERT(m_k<=distance->get_num_vec_lhs());
 
-	CRegressionLabels* output=new CRegressionLabels(num_lab);
+	CMulticlassLabels* output=new CMulticlassLabels(num_lab);
 
 	float64_t* dists   = NULL;
 	int32_t* train_lab = NULL;
@@ -273,15 +273,15 @@ CLabels* CKNN::apply(CFeatures* data)
 	return output;
 }
 
-CLabels* CKNN::classify_NN()
+CMulticlassLabels* CKNN::classify_NN()
 {
 	ASSERT(distance);
 	ASSERT(m_num_classes>0);
 
 	int32_t num_lab = distance->get_num_vec_rhs();
 	ASSERT(num_lab);
 
-	CRegressionLabels* output = new CRegressionLabels(num_lab);
+	CMulticlassLabels* output = new CMulticlassLabels(num_lab);
 	float64_t* distances = SG_MALLOC(float64_t, m_train_labels.vlen);
 
 	SG_INFO("%d test examples\n", num_lab);

diff --git a/src/shogun/multiclass/KNN.h b/src/shogun/multiclass/KNN.h
@@ -81,7 +81,7 @@ class CKNN : public CDistanceMachine
 		 * @param data (test)data to be classified
 		 * @return classified labels
 		 */
-		virtual CLabels* apply(CFeatures* data=NULL);
+		virtual CMulticlassLabels* apply_multiclass(CFeatures* data=NULL);
 
 		/// get output for example "vec_idx"
 		virtual float64_t apply_one(int32_t vec_idx)
@@ -168,7 +168,7 @@ class CKNN : public CDistanceMachine
 		/** classify all examples with nearest neighbor (k=1)
 		 * @return classified labels
 		 */
-		virtual CLabels* classify_NN();
+		virtual CMulticlassLabels* classify_NN();
 
 		/** init distances to test examples
 		 * @param data test examples