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KNN_unittest.cc
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KNN_unittest.cc
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#include <shogun/labels/MulticlassLabels.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/multiclass/KNN.h>
#include <shogun/distance/EuclideanDistance.h>
#include <gtest/gtest.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/features/DataGenerator.h>
using namespace shogun;
#ifdef HAVE_LAPACK
void generate_knn_data(SGMatrix<float64_t>& feat, SGVector<float64_t>& lab,
int32_t num, int32_t classes, int32_t feats)
{
CMath::init_random(1);
feat = CDataGenerator::generate_gaussians(num,classes,feats);
for( int i = 0 ; i < classes ; ++i )
for( int j = 0 ; j < num ; ++j )
lab[i*num+j] = double(i);
}
TEST(KNN, brute_solver)
{
int32_t num = 50;
int32_t feats = 2;
int32_t classes = 3;
SGVector< float64_t > lab(classes*num);
SGMatrix< float64_t > feat(feats, classes*num);
generate_knn_data(feat, lab, num, classes, feats);
SGVector<index_t> train (int32_t(num*classes*0.75));
SGVector<index_t> test (int32_t(num*classes*0.25));
//generate random subset for train and test data
train.random(0, classes*num-1);
test.random(0, classes*num-1);
CMulticlassLabels* labels = new CMulticlassLabels(lab);
CDenseFeatures< float64_t >* features = new CDenseFeatures< float64_t >(feat);
CFeatures* features_test = (CFeatures*) features->clone();
CLabels* labels_test = (CLabels*) labels->clone();
int32_t k=4;
CEuclideanDistance* distance = new CEuclideanDistance();
CKNN* knn=new CKNN (k, distance, labels, KNN_BRUTE);
SG_REF(knn);
features->add_subset(train);
labels->add_subset(train);
knn->train(features);
features_test->add_subset(test);
labels_test->add_subset(test);
CMulticlassLabels* output=CLabelsFactory::to_multiclass(knn->apply(features_test));
SG_REF(output);
features_test->remove_subset();
for ( index_t i = 0; i < labels_test->get_num_labels(); ++i )
EXPECT_EQ(output->get_label(i), ((CMulticlassLabels*)labels_test)->get_label(i));
SG_UNREF(output);
SG_UNREF(knn);
SG_UNREF(features_test);
SG_UNREF(labels_test);
}
TEST(KNN, kdtree_solver)
{
int32_t num = 50;
int32_t feats = 2;
int32_t classes = 3;
SGVector< float64_t > lab(classes*num);
SGMatrix< float64_t > feat(feats, classes*num);
generate_knn_data(feat, lab, num, classes, feats);
SGVector<index_t> train (int32_t(num*classes*0.75));
SGVector<index_t> test (int32_t(num*classes*0.25));
train.random(0, classes*num-1);
test.random(0, classes*num-1);
CMulticlassLabels* labels = new CMulticlassLabels(lab);
CDenseFeatures< float64_t >* features = new CDenseFeatures< float64_t >(feat);
CFeatures* features_test = (CFeatures*) features->clone();
CLabels* labels_test = (CLabels*) labels->clone();
int32_t k=4;
CEuclideanDistance* distance = new CEuclideanDistance();
CKNN* knn=new CKNN (k, distance, labels, KNN_BRUTE);
SG_REF(knn);
features->add_subset(train);
labels->add_subset(train);
knn->train(features);
features_test->add_subset(test);
labels_test->add_subset(test);
CMulticlassLabels* output=CLabelsFactory::to_multiclass(knn->apply(features_test));
SG_REF(output);
features_test->remove_subset();
for ( index_t i = 0; i < labels_test->get_num_labels(); ++i )
EXPECT_EQ(output->get_label(i), ((CMulticlassLabels*)labels_test)->get_label(i));
SG_UNREF(output);
SG_UNREF(knn);
SG_UNREF(features_test);
SG_UNREF(labels_test);
}
TEST(KNN, lsh_solver)
{
int32_t num = 50;
int32_t feats = 2;
int32_t classes = 3;
SGVector< float64_t > lab(classes*num);
SGMatrix< float64_t > feat(feats, classes*num);
generate_knn_data(feat, lab, num, classes, feats);
SGVector<index_t> train (int32_t(num*classes*0.75));
SGVector<index_t> test (int32_t(num*classes*0.25));
train.random(0, classes*num-1);
test.random(0, classes*num-1);
CMulticlassLabels* labels = new CMulticlassLabels(lab);
CDenseFeatures< float64_t >* features = new CDenseFeatures< float64_t >(feat);
CFeatures* features_test = (CFeatures*) features->clone();
CLabels* labels_test = (CLabels*) labels->clone();
int32_t k=4;
CEuclideanDistance* distance = new CEuclideanDistance();
CKNN* knn=new CKNN (k, distance, labels, KNN_LSH);
SG_REF(knn);
features->add_subset(train);
labels->add_subset(train);
knn->train(features);
features_test->add_subset(test);
labels_test->add_subset(test);
CMulticlassLabels* output=CLabelsFactory::to_multiclass(knn->apply(features_test));
SG_REF(output);
features_test->remove_subset();
for ( index_t i = 0; i < labels_test->get_num_labels(); ++i )
EXPECT_EQ(output->get_label(i), ((CMulticlassLabels*)labels_test)->get_label(i));
SG_UNREF(features_test);
SG_UNREF(labels_test);
SG_UNREF(output);
SG_UNREF(knn);
}
TEST(KNN, classify_multiple_kdtree)
{
int32_t num = 50;
int32_t feats = 2;
int32_t classes = 3;
SGVector< float64_t > lab(classes*num);
SGMatrix< float64_t > feat(feats, classes*num);
generate_knn_data(feat, lab, num, classes, feats);
SGVector<index_t> train (int32_t(num*classes*0.75));
SGVector<index_t> test (int32_t(num*classes*0.25));
train.random(0, classes*num-1);
test.random(0, classes*num-1);
CMulticlassLabels* labels = new CMulticlassLabels(lab);
CDenseFeatures< float64_t >* features = new CDenseFeatures< float64_t >(feat);
CFeatures* features_test = (CFeatures*) features->clone();
CLabels* labels_test = (CLabels*) labels->clone();
int32_t k=4;
CEuclideanDistance* distance = new CEuclideanDistance();
CKNN* knn=new CKNN (k, distance, labels, KNN_BRUTE);
SG_REF(knn);
features->add_subset(train);
labels->add_subset(train);
knn->train(features);
// classify for multiple k
features_test->add_subset(test);
labels_test->add_subset(test);
CEuclideanDistance* dist = new CEuclideanDistance(features, ((CDotFeatures*)features_test));
knn->set_distance(dist);
SGMatrix<int32_t> out_mat =knn->classify_for_multiple_k();
features_test->remove_subset();
for ( index_t i = 0; i < labels_test->get_num_labels(); ++i )
for ( index_t j = 0; j < k; ++j )
EXPECT_EQ(out_mat(i, j), ((CMulticlassLabels*)labels_test)->get_label(i));
SG_UNREF(knn);
SG_UNREF(features_test);
SG_UNREF(labels_test);
}
#endif /* HAVE_LAPACK */