added example for combined kernel weights optimization

shogun-toolbox · Jul 10, 2012 · b8b8336 · b8b8336
1 parent e1b4a40
commit b8b8336
Show file tree

Hide file tree

Showing 2 changed files with 88 additions and 0 deletions.
diff --git a/examples/undocumented/libshogun/Makefile b/examples/undocumented/libshogun/Makefile
@@ -85,6 +85,7 @@ TARGETS = basic_minimal \
 		  transfer_multitasklogisticregression \
 		  statistics_quadratic_time_mmd \
 		  statistics_linear_time_mmd \
+		  statistics_linear_time_mmd_kernel_choice \
 		  statistics_hsic \
 		  transfer_multitasklsregression \
 		  transfer_multitasklogisticregression \

diff --git a/examples/undocumented/libshogun/statistics_linear_time_mmd_kernel_choice.cpp b/examples/undocumented/libshogun/statistics_linear_time_mmd_kernel_choice.cpp
@@ -0,0 +1,87 @@
+/*
+ * 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 Heiko Strathmann
+ */
+
+#include <shogun/base/init.h>
+#include <shogun/statistics/LinearTimeMMD.h>
+#include <shogun/kernel/GaussianKernel.h>
+#include <shogun/kernel/CombinedKernel.h>
+#include <shogun/features/DenseFeatures.h>
+#include <shogun/features/CombinedFeatures.h>
+#include <shogun/mathematics/Statistics.h>
+
+using namespace shogun;
+
+void create_mean_data(SGMatrix<float64_t> target, float64_t difference)
+{
+	/* create data matrix for P and Q. P is a standard normal, Q is the same but
+	 * has a mean difference in one dimension */
+	for (index_t i=0; i<target.num_rows; ++i)
+	{
+		for (index_t j=0; j<target.num_cols/2; ++j)
+			target(i,j)=CMath::randn_double();
+
+		/* add mean difference in first dimension of second half of data */
+		for (index_t j=target.num_cols/2; j<target.num_cols; ++j)
+				target(i,j)=CMath::randn_double() + (i==0 ? difference : 0);
+	}
+}
+
+SGMatrix<float64_t> create_fixed_data(index_t m, index_t dim)
+{
+	SGMatrix<float64_t> data(dim,2*m);
+	for (index_t i=0; i<2*dim*m; ++i)
+		data.matrix[i]=i*i;
+
+	data.display_matrix("data");
+
+	return data;
+}
+
+void test_linear_mmd_optimize_weights()
+{
+	index_t m=8;
+	index_t dim=2;
+	SGMatrix<float64_t> data=create_fixed_data(m, dim);
+
+	/* create a number of kernels with different widths */
+	SGVector<float64_t> sigmas(3);
+	SGVector<float64_t> shogun_sigmas(sigmas.vlen);
+
+	CCombinedKernel* kernel=new CCombinedKernel();
+	CCombinedFeatures* features=new CCombinedFeatures();
+	for (index_t i=0; i<sigmas.vlen; ++i)
+	{
+		sigmas[i]=CMath::pow(2.0, i-2)*1000;
+		shogun_sigmas[i]=sigmas[i]*sigmas[i]*2;
+		kernel->append_kernel(new CGaussianKernel(10, shogun_sigmas[i]));
+		features->append_feature_obj(new CDenseFeatures<float64_t>(data));
+	}
+
+	sigmas.display_vector("sigmas");
+
+	CLinearTimeMMD* mmd=new CLinearTimeMMD(kernel, features, m);
+	mmd->optimize_kernel_weights();
+
+	kernel->get_subkernel_weights().display_vector("weights");
+
+	SG_UNREF(mmd);
+}
+
+int main(int argc, char** argv)
+{
+	init_shogun_with_defaults();
+
+	sg_io->set_loglevel(MSG_DEBUG);
+
+	test_linear_mmd_optimize_weights();
+
+	exit_shogun();
+	return 0;
+}
+