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GaussianProcessRegression_unittest.cc
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GaussianProcessRegression_unittest.cc
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#include <shogun/lib/config.h>
#ifdef HAVE_EIGEN3
#include <shogun/labels/RegressionLabels.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/regression/GaussianProcessRegression.h>
#include <shogun/regression/gp/ExactInferenceMethod.h>
#include <shogun/regression/gp/ZeroMean.h>
#include <shogun/regression/gp/GaussianLikelihood.h>
#include <gtest/gtest.h>
using namespace shogun;
TEST(GaussianProcessRegression,apply_regression)
{
/* create some easy regression data: 1d noisy sine wave */
index_t n=3;
SGMatrix<float64_t> X(1, n);
SGMatrix<float64_t> X_test(1, n);
SGVector<float64_t> Y(n);
X[0]=0;
X[1]=1.1;
X[2]=2.2;
X_test[0]=0.3;
X_test[1]=1.3;
X_test[2]=2.5;
for (index_t i=0; i<n; ++i)
{
Y[i]=CMath::sin(X(0, i));
}
/* shogun representation */
CDenseFeatures<float64_t>* feat_train=new CDenseFeatures<float64_t>(X);
CDenseFeatures<float64_t>* feat_test=new CDenseFeatures<float64_t>(X_test);
CRegressionLabels* label_train=new CRegressionLabels(Y);
/* specity GPR with exact inference */
float64_t sigma=1;
float64_t shogun_sigma=sigma*sigma*2;
CGaussianKernel* kernel=new CGaussianKernel(10, shogun_sigma);
CZeroMean* mean=new CZeroMean();
CGaussianLikelihood* lik=new CGaussianLikelihood();
lik->set_sigma(1);
CExactInferenceMethod* inf=new CExactInferenceMethod(kernel, feat_train,
mean, label_train, lik);
CGaussianProcessRegression* gpr=new CGaussianProcessRegression(inf,
feat_train, label_train);
/* perform inference */
gpr->set_return_type(CGaussianProcessRegression::GP_RETURN_MEANS);
CRegressionLabels* predictions=gpr->apply_regression(feat_test);
/* do some checks against gpml toolbox*/
/* m =
0.221198406887592
0.537437461176145
0.431605035301329 */
SGVector<float64_t> prediction_vector=predictions->get_labels();
EXPECT_LE(CMath::abs(prediction_vector[0]-0.221198406887592), 10E-15);
EXPECT_LE(CMath::abs(prediction_vector[1]-0.537437461176145), 10E-15);
EXPECT_LE(CMath::abs(prediction_vector[2]-0.431605035301329), 10E-15);
SG_UNREF(predictions);
SG_UNREF(gpr);
}
TEST(GaussianProcessRegression,apply_regression_larger_test)
{
/* create some easy regression data: 1d noisy sine wave */
index_t n=3;
index_t n_test=5;
SGMatrix<float64_t> X(1, n);
SGMatrix<float64_t> X_test(1, n_test);
SGVector<float64_t> Y(n);
X[0]=0;
X[1]=1.1;
X[2]=2.2;
X_test[0]=0.3;
X_test[1]=1.3;
X_test[2]=2.5;
X_test[3]=2.7;
X_test[4]=3.1;
for (index_t i=0; i<n; ++i)
{
Y[i]=CMath::sin(X(0, i));
}
/* shogun representation */
CDenseFeatures<float64_t>* feat_train=new CDenseFeatures<float64_t>(X);
CDenseFeatures<float64_t>* feat_test=new CDenseFeatures<float64_t>(X_test);
CRegressionLabels* label_train=new CRegressionLabels(Y);
/* specity GPR with exact inference */
float64_t sigma=1;
float64_t shogun_sigma=sigma*sigma*2;
CGaussianKernel* kernel=new CGaussianKernel(10, shogun_sigma);
CZeroMean* mean=new CZeroMean();
CGaussianLikelihood* lik=new CGaussianLikelihood();
lik->set_sigma(1);
CExactInferenceMethod* inf=new CExactInferenceMethod(kernel, feat_train,
mean, label_train, lik);
CGaussianProcessRegression* gpr=new CGaussianProcessRegression(inf,
feat_train, label_train);
/* perform inference */
gpr->set_return_type(CGaussianProcessRegression::GP_RETURN_MEANS);
CRegressionLabels* predictions=gpr->apply_regression(feat_test);
/* do some checks against gpml toolbox*/
/* m =
0.221198406887592
0.537437461176145
0.431605035301329
0.373048041692408
0.253688340068952 */
SGVector<float64_t> prediction_vector=predictions->get_labels();
EXPECT_LE(CMath::abs(prediction_vector[0]-0.221198406887592), 10E-15);
EXPECT_LE(CMath::abs(prediction_vector[1]-0.537437461176145), 10E-15);
EXPECT_LE(CMath::abs(prediction_vector[2]-0.431605035301329), 10E-15);
EXPECT_LE(CMath::abs(prediction_vector[3]-0.373048041692408), 10E-15);
EXPECT_LE(CMath::abs(prediction_vector[4]-0.253688340068952), 10E-15);
SG_UNREF(predictions);
SG_UNREF(gpr);
}
TEST(GaussianProcessRegression, get_mean_vector)
{
/* create some easy regression data: 1d noisy sine wave */
index_t n=3;
SGMatrix<float64_t> X(1, n);
SGMatrix<float64_t> X_test(1, n);
SGVector<float64_t> Y(n);
X[0]=0;
X[1]=1.1;
X[2]=2.2;
X_test[0]=0.3;
X_test[1]=1.3;
X_test[2]=2.5;
for (index_t i=0; i<n; ++i)
{
Y[i]=CMath::sin(X(0, i));
}
/* shogun representation */
CDenseFeatures<float64_t>* feat_train=new CDenseFeatures<float64_t>(X);
CDenseFeatures<float64_t>* feat_test=new CDenseFeatures<float64_t>(X_test);
CRegressionLabels* label_train=new CRegressionLabels(Y);
/* specity GPR with exact inference */
float64_t sigma=1;
float64_t shogun_sigma=sigma*sigma*2;
CGaussianKernel* kernel=new CGaussianKernel(10, shogun_sigma);
CZeroMean* mean=new CZeroMean();
CGaussianLikelihood* lik=new CGaussianLikelihood();
lik->set_sigma(1);
CExactInferenceMethod* inf=new CExactInferenceMethod(kernel, feat_train,
mean, label_train, lik);
CGaussianProcessRegression* gpr=new CGaussianProcessRegression(inf,
feat_train, label_train);
/* perform inference */
gpr->set_return_type(CGaussianProcessRegression::GP_RETURN_MEANS);
CRegressionLabels* predictions=gpr->apply_regression(feat_test);
/* do some checks against gpml toolbox*/
/* m =
0.221198406887592
0.537437461176145
0.431605035301329 */
SGVector<float64_t> mean_vector=gpr->get_mean_vector();
EXPECT_LE(CMath::abs(mean_vector[0]-0.221198406887592), 10E-15);
EXPECT_LE(CMath::abs(mean_vector[1]-0.537437461176145), 10E-15);
EXPECT_LE(CMath::abs(mean_vector[2]-0.431605035301329), 10E-15);
SG_UNREF(predictions);
SG_UNREF(gpr);
}
TEST(GaussianProcessRegression, get_covariance_vector)
{
/* create some easy regression data: 1d noisy sine wave */
index_t n=3;
SGMatrix<float64_t> X(1, n);
SGMatrix<float64_t> X_test(1, n);
SGVector<float64_t> Y(n);
X[0]=0;
X[1]=1.1;
X[2]=2.2;
X_test[0]=0.3;
X_test[1]=1.3;
X_test[2]=2.5;
for (index_t i=0; i<n; ++i)
{
Y[i]=CMath::sin(X(0, i));
}
/* shogun representation */
CDenseFeatures<float64_t>* feat_train=new CDenseFeatures<float64_t>(X);
CDenseFeatures<float64_t>* feat_test=new CDenseFeatures<float64_t>(X_test);
CRegressionLabels* label_train=new CRegressionLabels(Y);
/* specity GPR with exact inference */
float64_t sigma=1;
float64_t shogun_sigma=sigma*sigma*2;
CGaussianKernel* kernel=new CGaussianKernel(10, shogun_sigma);
CZeroMean* mean=new CZeroMean();
CGaussianLikelihood* lik=new CGaussianLikelihood();
lik->set_sigma(1);
CExactInferenceMethod* inf=new CExactInferenceMethod(kernel, feat_train,
mean, label_train, lik);
CGaussianProcessRegression* gpr=new CGaussianProcessRegression(inf,
feat_train, label_train);
/* perform inference */
gpr->set_return_type(CGaussianProcessRegression::GP_RETURN_MEANS);
CRegressionLabels* predictions=gpr->apply_regression(feat_test);
/* do some checks against gpml toolbox*/
/* s2 =
1.426104216614624
1.416896787316447
1.535464779087576 */
SGVector<float64_t> covariance_vector=gpr->get_covariance_vector();
EXPECT_LE(CMath::abs(covariance_vector[0]-1.426104216614624), 10E-15);
EXPECT_LE(CMath::abs(covariance_vector[1]-1.416896787316447), 10E-15);
EXPECT_LE(CMath::abs(covariance_vector[2]-1.535464779087576), 10E-15);
SG_UNREF(predictions);
SG_UNREF(gpr);
}
#endif