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KernelSelectionMaxMMD_unittest.cc
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KernelSelectionMaxMMD_unittest.cc
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/*
* Copyright (c) The Shogun Machine Learning Toolbox
* Written (W) 2012-2013 Heiko Strathmann
* Written (w) 2016 Soumyajit De
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* The views and conclusions contained in the software and documentation are those
* of the authors and should not be interpreted as representing official policies,
* either expressed or implied, of the Shogun Development Team.
*/
#include <shogun/base/some.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/kernel/CombinedKernel.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/features/streaming/generators/MeanShiftDataGenerator.h>
#include <shogun/mathematics/Statistics.h>
#include <shogun/mathematics/eigen3.h>
#include <shogun/mathematics/Math.h>
#include <shogun/statistical_testing/LinearTimeMMD.h>
#include <shogun/statistical_testing/kernelselection/KernelSelectionStrategy.h>
#include <gtest/gtest.h>
using namespace shogun;
TEST(KernelSelectionMaxMMD, single_kernel)
{
const index_t m=20;
const index_t n=30;
const index_t dim=2;
const float64_t difference=0.5;
const index_t num_kernels=10;
// use fixed seed
sg_rand->set_seed(12345);
// streaming data generator for mean shift distributions
auto gen_p=new CMeanShiftDataGenerator(0, dim, 0);
auto gen_q=new CMeanShiftDataGenerator(difference, dim, 0);
// create MMD instance, convienience constructor
auto mmd=some<CLinearTimeMMD>(gen_p, gen_q);
mmd->set_statistic_type(ST_BIASED_FULL);
mmd->set_num_samples_p(m);
mmd->set_num_samples_q(n);
mmd->set_num_blocks_per_burst(1000);
for (auto i=0; i<num_kernels; ++i)
{
// shoguns kernel width is different
float64_t sigma=(i+1)*0.5;
float64_t sq_sigma_twice=sigma*sigma*2;
mmd->add_kernel(new CGaussianKernel(10, sq_sigma_twice));
}
mmd->set_kernel_selection_strategy(KSM_MAXIMIZE_MMD);
mmd->set_train_test_mode(true);
mmd->select_kernel();
auto selected_kernel=static_cast<CGaussianKernel*>(mmd->get_kernel());
EXPECT_NEAR(selected_kernel->get_width(), 0.5, 1E-10);
}
TEST(KernelSelectionMaxMMD, weighted_kernel)
{
const index_t m=20;
const index_t n=30;
const index_t dim=2;
const float64_t difference=0.5;
const index_t num_kernels=10;
// use fixed seed
sg_rand->set_seed(12345);
// streaming data generator for mean shift distributions
auto gen_p=new CMeanShiftDataGenerator(0, dim, 0);
auto gen_q=new CMeanShiftDataGenerator(difference, dim, 0);
// create MMD instance, convienience constructor
auto mmd=some<CLinearTimeMMD>(gen_p, gen_q);
mmd->set_statistic_type(ST_BIASED_FULL);
mmd->set_num_samples_p(m);
mmd->set_num_samples_q(n);
mmd->set_num_blocks_per_burst(1000);
for (auto i=0; i<num_kernels; ++i)
{
// shoguns kernel width is different
float64_t sigma=(i+1)*0.5;
float64_t sq_sigma_twice=sigma*sigma*2;
mmd->add_kernel(new CGaussianKernel(10, sq_sigma_twice));
}
mmd->set_kernel_selection_strategy(KSM_MAXIMIZE_MMD, true);
mmd->set_train_test_mode(true);
mmd->select_kernel();
auto weighted_kernel=dynamic_cast<CCombinedKernel*>(mmd->get_kernel());
ASSERT_TRUE(weighted_kernel!=nullptr);
ASSERT_TRUE(weighted_kernel->get_num_subkernels()==num_kernels);
SGVector<float64_t> weights=weighted_kernel->get_subkernel_weights();
weights.display_vector("weights"); // TODO remove
}