Add method in KNN to obtain nearest neighbors.

Internally, the apply now calls the new method to obtain the nearest
neighbors first and then computes the labels.

src/shogun/multiclass/KNN.cpp

@@ -107,12 +107,56 @@ bool CKNN::train_machine(CFeatures* data)
 	return true;
 }
 
+SGMatrix<int32_t> CKNN::nearest_neighbors()
+{
+	//number of examples to which kNN is applied
+	int32_t n=distance->get_num_vec_rhs();
+	//distances to train data
+	float64_t* dists=SG_MALLOC(float64_t, m_train_labels.vlen);
+	//indices to train data
+	int32_t* train_idxs=SG_MALLOC(int32_t, m_train_labels.vlen);
+	//pre-allocation of the nearest neighbors
+	SGMatrix<int32_t> NN(m_k, n);
+
+	//for each test example
+	for (int32_t i=0; i<n && (!CSignal::cancel_computations()); i++)
+	{
+		SG_PROGRESS(i, 0, n)
+
+		//lhs idx 0..n-1 (i.e., all train examples) and rhs idx i
+		distances_lhs(dists,0,m_train_labels.vlen-1,i);
+
+		//fill in an array with 0, 1,2, ..., num train examples
+		for (int32_t j=0; j<m_train_labels.vlen; j++)
+			train_idxs[j]=j;
+
+		//sort the distance vector between test example i and all train examples
+		CMath::qsort_index(dists, train_idxs, m_train_labels.vlen);
+
+#ifdef DEBUG_KNN
+		SG_PRINT("\nQuick sort query %d\n", i)
+		for (int32_t j=0; j<m_k; j++)
+			SG_PRINT("%d ", train_idxs[j])
+		SG_PRINT("\n")
+#endif
+
+		//fill in the output the indices of the nearest neighbors
+		for (int32_t j=0; j<m_k; j++)
+			NN(j,i) = train_idxs[j];
+	}
+
+	SG_FREE(train_idxs);
+	SG_FREE(dists);
+
+	return NN;
+}
+
 CMulticlassLabels* CKNN::apply_multiclass(CFeatures* data)
 {
 	if (data)
 		init_distance(data);
 
-	// redirecting to fast (without sorting) classify if k==1
+	//redirecting to fast (without sorting) classify if k==1
 	if (m_k == 1)
 		return classify_NN();
 
@@ -125,24 +169,13 @@ CMulticlassLabels* CKNN::apply_multiclass(CFeatures* data)
 
 	CMulticlassLabels* output=new CMulticlassLabels(num_lab);
 
-	float64_t* dists   = NULL;
-	int32_t* train_lab = NULL;
-
-	//distances to train data and working buffer of m_train_labels
-	if ( ! m_use_covertree )
-	{
-		dists=SG_MALLOC(float64_t, m_train_labels.vlen);
-		train_lab=SG_MALLOC(int32_t, m_train_labels.vlen);
-	}
-	else
-	{
-		train_lab=SG_MALLOC(int32_t, m_k);
-	}
+	//labels of the k nearest neighbors
+	int32_t* train_lab=SG_MALLOC(int32_t, m_k);
 
 	SG_INFO("%d test examples\n", num_lab)
 	CSignal::clear_cancel();
 
-	///histogram of classes and returned output
+	//histogram of classes and returned output
 	float64_t* classes=SG_MALLOC(float64_t, m_num_classes);
 
 #ifdef BENCHMARK_KNN
@@ -152,36 +185,19 @@ CMulticlassLabels* CKNN::apply_multiclass(CFeatures* data)
 
 	if ( ! m_use_covertree )
 	{
+		//get the k nearest neighbors of each example
+		SGMatrix<int32_t> NN = nearest_neighbors();
+
+		//from the indices to the nearest neighbors, compute the class labels
 		for (int32_t i=0; i<num_lab && (!CSignal::cancel_computations()); i++)
 		{
-			SG_PROGRESS(i, 0, num_lab)
-
-#ifdef DEBUG_KNN
-			distances_lhs(dists,0,m_train_labels.vlen-1,i);
-
-			for (int32_t j=0; j<m_train_labels.vlen; j++)
-				train_lab[j]=j;
-
-			CMath::qsort_index(dists, train_lab, m_train_labels.vlen);
-
-			SG_PRINT("\nQuick sort query %d\n", i)
+			//write the labels of the k nearest neighbors from theirs indices
 			for (int32_t j=0; j<m_k; j++)
-				SG_PRINT("%d ", train_lab[j])
-			SG_PRINT("\n")
-#endif
-
-			//lhs idx 1..n and rhs idx i
-			distances_lhs(dists,0,m_train_labels.vlen-1,i);
-
-			for (int32_t j=0; j<m_train_labels.vlen; j++)
-				train_lab[j]=m_train_labels.vector[j];
-
-			//sort the distance vector for test example j to all 
-			//train examples
-			CMath::qsort_index(dists, train_lab, m_train_labels.vlen);
+				train_lab[j] = m_train_labels[ NN(j,i) ];
 
-			// Get the index of the 'nearest' class
+			//get the index of the 'nearest' class
 			int32_t out_idx = choose_class(classes, train_lab);
+			//write the label of 'nearest' in the output
 			output->set_label(i, out_idx + m_min_label);
 		}
 
@@ -266,8 +282,6 @@ CMulticlassLabels* CKNN::apply_multiclass(CFeatures* data)
 
 	SG_FREE(classes);
 	SG_FREE(train_lab);
-	if ( ! m_use_covertree )
-		SG_FREE(dists);
 
 	return output;
 }

src/shogun/multiclass/KNN.h

@@ -75,6 +75,17 @@ class CKNN : public CDistanceMachine
 		 */
 		virtual EMachineType get_classifier_type() { return CT_KNN; }
 
+		/**
+		 * for each example in the rhs features of the distance member, find the m_k
+		 * nearest neighbors among the vectors in the lhs features
+		 *
+		 * @return matrix with indices to the nearest neighbors, the dimensions of the
+		 * matrix are k rows and n columns, where n is the number of feature vectors in rhs;
+		 * among the nearest neighbors, the closest are in the first row, and the furthest
+		 * in the last one
+		 */
+		SGMatrix<int32_t> nearest_neighbors();
+
 		/** classify objects
 		 *
 		 * @param data (test)data to be classified