Merge branch 'nearest_centroid' of git://github.com/PhilippeTillet/sh…

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shogun-toolbox · Apr 10, 2012 · a690dd7 · a690dd7
2 parents 7b3b7bc + 04677a7
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diff --git a/examples/undocumented/libshogun/classifier_nearest_centroid.cpp b/examples/undocumented/libshogun/classifier_nearest_centroid.cpp
@@ -0,0 +1,54 @@
+#include <shogun/features/Labels.h>
+#include <shogun/features/SimpleFeatures.h>
+#include <shogun/distance/EuclidianDistance.h>
+#include <shogun/classifier/NearestCentroid.h>
+#include <shogun/base/init.h>
+
+using namespace shogun;
+
+void print_message(FILE* target, const char* str)
+{
+	fprintf(target, "%s", str);
+}
+
+int main(){
+	init_shogun(&print_message);
+	index_t num_vec=7;
+	index_t num_feat=2;
+	index_t num_class=2;
+
+	// create some data
+	SGMatrix<float64_t> matrix(num_feat, num_vec);
+	CMath::range_fill_vector(matrix.matrix, num_feat*num_vec);
+
+	// Create features ; shogun will now own the matrix created
+	CSimpleFeatures<float64_t>* features=new CSimpleFeatures<float64_t>(matrix);
+
+	CMath::display_matrix(matrix.matrix,num_feat,num_vec);
+
+	//Create labels
+	CLabels* labels=new CLabels(num_vec);
+	for (index_t i=0; i<num_vec; ++i)
+		labels->set_label(i, i%num_class);
+
+	//Create Euclidian Distance
+	CEuclidianDistance* distance = new CEuclidianDistance(features,features);
+
+	//Create Nearest Centroid
+	CNearestCentroid* nearest_centroid = new CNearestCentroid(distance, labels);
+	nearest_centroid->train();
+
+//  	classify on training examples
+	CLabels* output=nearest_centroid->apply();
+	CMath::display_vector(output->get_labels().vector, output->get_num_labels(),
+			"batch output");
+
+	SG_UNREF(output);
+
+	// free up memory
+	SG_UNREF(nearest_centroid);
+
+	exit_shogun();
+	return 0;
+
+}
diff --git a/src/shogun/classifier/NearestCentroid.cpp b/src/shogun/classifier/NearestCentroid.cpp
@@ -0,0 +1,120 @@
+/*
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * Written (W) 2012 Philippe Tillet
+ */
+
+#include <shogun/classifier/NearestCentroid.h>
+
+namespace shogun{
+
+  	CNearestCentroid::CNearestCentroid() : CDistanceMachine()
+	{
+		init();
+	}
+
+	CNearestCentroid::CNearestCentroid(CDistance* d, CLabels* trainlab) : CDistanceMachine()
+	{
+		init();
+		ASSERT(d);
+		ASSERT(trainlab);
+		set_distance(d);
+		set_labels(trainlab);
+	}
+
+	CNearestCentroid::~CNearestCentroid()
+	{
+		if(m_is_trained)
+			distance->remove_lhs();
+		else
+			delete m_centroids;
+	}
+
+	void CNearestCentroid::init()
+	{
+		m_shrinking=0;
+		m_is_trained=false;
+		set_store_model_features(true);
+		m_centroids = new CSimpleFeatures<float64_t>();
+	}
+
+	void CNearestCentroid::store_model_features()
+	{
+
+	}
+
+	bool CNearestCentroid::train_machine(CFeatures* data)
+	{
+		ASSERT(m_labels);
+		ASSERT(distance);
+
+		if (data)
+		{
+			if (m_labels->get_num_labels() != data->get_num_vectors())
+				SG_ERROR("Number of training vectors does not match number of labels\n");
+			distance->init(data, data);
+		}
+		else
+		{
+			data = distance->get_lhs();
+		}
+		int32_t num_vectors = data->get_num_vectors();
+		int32_t num_classes = m_labels->get_num_classes();
+		int32_t num_feats = ((CSimpleFeatures<float64_t>*)data)->get_num_features();
+		float64_t* centroids = SG_CALLOC(float64_t, num_feats*num_classes);
+		for(int32_t i=0 ; i < num_feats*num_classes ; i++)
+		{
+			centroids[i]=0;
+		}
+		m_centroids->set_num_features(num_feats);
+		m_centroids->set_num_vectors(num_classes);
+
+		int64_t* num_per_class = new int64_t[num_classes];
+		for(int32_t i=0 ; i<num_classes ; i++)
+		{
+			num_per_class[i]=0;
+		}
+
+		for(int32_t idx=0 ; idx<num_vectors ; idx++)
+		{
+			int32_t current_len;
+			bool current_free;
+			int32_t current_class = m_labels->get_label(idx);
+			float64_t* target = centroids + num_feats*current_class;
+			float64_t* current = ((CSimpleFeatures<float64_t>*)data)->get_feature_vector(idx,current_len,current_free);
+			CMath::add(target,1.0,target,1.0,current,current_len);
+			num_per_class[current_class]++;
+			((CSimpleFeatures<float64_t>*)data)->free_feature_vector(current, current_len, current_free);
+		}
+
+
+		for(int32_t i=0 ; i<num_classes ; i++)
+		{
+			float64_t* target = centroids + num_feats*i;
+			int32_t total = num_per_class[i];
+			float64_t scale = 0;
+			if(total>1)
+				scale = 1.0/((float64_t)(total-1));
+			else
+				scale = 1.0/(float64_t)total;
+
+			CMath::scale_vector(scale,target,num_feats);
+		}
+
+		m_centroids->free_feature_matrix();
+		m_centroids->set_feature_matrix(centroids,num_feats,num_classes);
+
+
+		distance->init(m_centroids,distance->get_rhs());
+
+		m_is_trained=true;
+
+		delete [] num_per_class;
+
+		return true;
+	}
+
+}
diff --git a/src/shogun/classifier/NearestCentroid.h b/src/shogun/classifier/NearestCentroid.h
@@ -0,0 +1,97 @@
+/*
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * Written (W) 2012 Philippe Tillet
+ */
+
+#ifndef _NEAREST_CENTROID_H__
+#define _NEAREST_CENTROID_H__
+
+#include <stdio.h>
+#include <shogun/lib/common.h>
+#include <shogun/io/SGIO.h>
+#include <shogun/features/Features.h>
+#include <shogun/features/SimpleFeatures.h>
+#include <shogun/distance/Distance.h>
+#include <shogun/lib/CoverTree.h>
+#include <shogun/machine/DistanceMachine.h>
+
+namespace shogun
+{
+
+class CDistanceMachine;
+
+/** @brief Class NearestCentroid, an implementation of Nearest Shrunk Centroid classifier
+ * 
+ * To define how close examples are
+ * NearestCentroid requires a CDistance object to work with (e.g., CEuclideanDistance ).
+ */
+
+class CNearestCentroid : public CDistanceMachine{
+
+public:
+	/**
+	 * Default constructor
+	 */
+	CNearestCentroid();
+
+	/** constructor
+	 *
+	 * @param d distance
+	 * @param trainlab labels for training
+	 */
+	CNearestCentroid(CDistance* distance, CLabels* trainlab);
+
+	/** Destructor
+	 */
+	virtual ~CNearestCentroid();
+
+	/** Set shrinking constant
+	 * 
+	 * @param shrinking to be set
+	 */
+	void set_shrinking(float64_t shrinking) {
+		m_shrinking = shrinking ;
+	}
+
+	/** Get shrinking constant
+	 * 
+	 * @return value of the shrinking constant
+	 */
+	float64_t get_shrinking() const{
+		return m_shrinking;
+	}
+
+	CSimpleFeatures<float64_t>* get_centroids() const{
+		return m_centroids;
+	}
+
+protected:
+	/** train Nearest Centroid 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_machine(CFeatures* data=NULL);
+
+	virtual void store_model_features();
+
+private:
+	void init();
+
+protected:
+	int32_t m_num_classes;
+	float64_t m_shrinking;
+	CSimpleFeatures<float64_t>* m_centroids;
+	bool m_is_trained;
+};
+
+}
+
+#endif