Merge pull request #406 from puffin444/master

Gaussian Process Regression
shogun-toolbox · Apr 11, 2012 · a27e1ae · a27e1ae
2 parents 73db76e + 6f128a5
commit a27e1ae
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Showing 8 changed files with 607 additions and 2 deletions.
diff --git a/examples/undocumented/libshogun/Makefile b/examples/undocumented/libshogun/Makefile
@@ -33,6 +33,7 @@ TARGETS = basic_minimal \
 		  evaluation_cross_validation_regression \
 		  evaluation_cross_validation_locked_comparison \
 		  modelselection_parameter_combination_test \
+		  regression_gaussian_process \
 		  modelselection_model_selection_parameters_test \
 		  modelselection_parameter_tree \
 		  modelselection_apply_parameter_tree \

diff --git a/examples/undocumented/libshogun/regression_gaussian_process.cpp b/examples/undocumented/libshogun/regression_gaussian_process.cpp
@@ -0,0 +1,78 @@
+#include <shogun/features/SimpleFeatures.h>
+#include <shogun/io/SGIO.h>
+#include <shogun/kernel/GaussianKernel.h>
+#include <shogun/regression/GaussianProcessRegression.h>
+#include <shogun/features/Labels.h>
+
+/* Example mean prediction from a Gaussian Kernel adapted from 
+ * classifier_minimal_svm.cpp
+ * Jacob Walker
+ */
+
+using namespace shogun;
+
+void print_message(FILE* target, const char* str)
+{
+	fprintf(target, "%s", str);
+}
+
+int main(int argc, char** argv)
+{
+	init_shogun(&print_message);
+
+#ifdef HAVE_LAPACK
+
+	// create some data
+	float64_t* matrix = SG_MALLOC(float64_t, 6);
+	for (int32_t i=0; i<6; i++)
+		matrix[i]=i;
+
+	//Labels
+	CLabels* labels = new CLabels(3);
+	SG_REF(labels);
+
+	labels->set_label(0, -1);
+	labels->set_label(1, +1);
+	labels->set_label(2, -1);
+
+	// create three 2-dimensional vectors 
+	// shogun will now own the matrix created
+	CSimpleFeatures<float64_t>* features= new CSimpleFeatures<float64_t>();
+	SG_REF(features);
+	features->set_feature_matrix(matrix, 2, 3);
+
+	// create gaussian kernel with cache 10MB, width 0.5
+	CGaussianKernel* kernel = new CGaussianKernel(10, 0.5);
+	SG_REF(kernel);
+
+	//Gaussian Process Regression with sigma = 1.
+	CGaussianProcessRegression regressor(1.0, kernel, features, labels);
+
+	regressor.train(features);
+	//Get mean predictions
+	CLabels* result = regressor.apply();
+	SG_REF(result);
+
+	SGMatrix<float64_t> cov = regressor.getCovarianceMatrix(features);
+
+	CMath::display_matrix(cov.matrix, cov.num_rows, cov.num_cols, "Covariance Matrix");
+
+	// output predictions
+	for (int32_t i=0; i<3; i++)
+		SG_SPRINT("output[%d]=%f\n", i, result->get_label(i));
+
+	// free up memory
+	SG_UNREF(result);
+	SG_UNREF(features);
+	SG_UNREF(labels);
+	SG_UNREF(kernel);
+	cov.destroy_matrix();
+
+	#endif
+
+	exit_shogun();
+	return 0;
+}
+
+
+
diff --git a/examples/undocumented/python_modular/regression_gaussian_process_modular.py b/examples/undocumented/python_modular/regression_gaussian_process_modular.py
@@ -0,0 +1,30 @@
+###########################################################################
+# Mean prediction from Gaussian Processes based on classifier_libsvm_minimal_modular.py
+###########################################################################
+from numpy import *
+from numpy.random import randn
+from shogun.Features import *
+from shogun.Classifier import *
+from shogun.Kernel import *
+
+num=100
+dist=1
+width=2.1
+C=1
+
+traindata_real=concatenate((randn(2,num)-dist, randn(2,num)+dist), axis=1)
+testdata_real=concatenate((randn(2,num)-dist, randn(2,num)+dist), axis=1);
+
+trainlab=concatenate((-ones(num), ones(num)));
+testlab=concatenate((-ones(num), ones(num)));
+
+feats_train=RealFeatures(traindata_real);
+feats_test=RealFeatures(testdata_real);
+kernel=GaussianKernel(feats_train, feats_train, width);
+
+labels=Labels(trainlab);
+gp=GaussianProcessRegression(1.0, kernel, feats_train, labels);
+gp.train(feats_train);
+out=gp.apply(feats_test).get_labels();
+testerr=mean(sign(out)!=testlab)
+print testerr
diff --git a/src/interfaces/modular/Regression.i b/src/interfaces/modular/Regression.i
@@ -13,6 +13,7 @@
 %rename(KernelRidgeRegression) CKernelRidgeRegression;
 %rename(LinearRidgeRegression) CLinearRidgeRegression;
 %rename(LeastSquaresRegression) CLeastSquaresRegression;
+%rename(GaussianProcessRegression) CGaussianProcessRegression;
 %rename(LibSVR) CLibSVR;
 %rename(MKL) CMKL;
 %rename(MKLRegression) CMKLRegression;
@@ -25,6 +26,7 @@
 %include <shogun/regression/KernelRidgeRegression.h>
 %include <shogun/regression/LinearRidgeRegression.h>
 %include <shogun/regression/LeastSquaresRegression.h>
+%include <shogun/regression/GaussianProcessRegression.h>
 %include <shogun/regression/svr/LibSVR.h>
 %include <shogun/classifier/mkl/MKL.h>
 %include <shogun/regression/svr/MKLRegression.h>

diff --git a/src/interfaces/modular/Regression_includes.i b/src/interfaces/modular/Regression_includes.i
@@ -5,6 +5,7 @@
  #include <shogun/regression/KernelRidgeRegression.h>
  #include <shogun/regression/LinearRidgeRegression.h>
  #include <shogun/regression/LeastSquaresRegression.h>
+ #include <shogun/regression/GaussianProcessRegression.h>
  #include <shogun/classifier/svm/SVM.h>
  #include <shogun/classifier/svm/LibSVM.h>
  #include <shogun/regression/svr/LibSVR.h>

diff --git a/src/shogun/machine/Machine.h b/src/shogun/machine/Machine.h
@@ -72,8 +72,8 @@ enum EClassifierType
 	CT_LINEARRIDGEREGRESSION = 410,
 	CT_LEASTSQUARESREGRESSION = 420,
 	CT_QDA = 430,
-	CT_NEWTONSVM = 440
-
+	CT_NEWTONSVM = 440,
+	CT_GAUSSIANPROCESSREGRESSION = 450
 };
 
 /** solver type */