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gmm_unittest.cc
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gmm_unittest.cc
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/*
* Copyright (c) The Shogun Machine Learning Toolbox
* 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.
*
* Written (W) 2018 Wuwei Lin
*/
#include <gtest/gtest.h>
#include <shogun/base/some.h>
#include <shogun/clustering/GMM.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/lib/common.h>
using namespace shogun;
TEST(GMM, train_em_full_cov)
{
/*create a rectangle with four points as (0,0) (1,7) (10,6) (4,4) */
SGMatrix<float64_t> rect(2, 4);
rect(0, 0) = 0;
rect(0, 1) = 1;
rect(0, 2) = 10;
rect(0, 3) = 4;
rect(1, 0) = 0;
rect(1, 1) = 7;
rect(1, 2) = 6;
rect(1, 3) = 4;
const int num_components = 2;
auto clustering = some<CGMM>(num_components, FULL);
auto features = some<CDenseFeatures<float64_t> >(rect);
clustering->train(features);
clustering->train_em();
SGVector<float64_t> coef = clustering->get_coef();
EXPECT_NEAR(coef[0], 0.75, 1e-10);
EXPECT_NEAR(coef[1], 0.25, 1e-10);
SGVector<float64_t> mean = clustering->get_nth_mean(0);
EXPECT_NEAR(mean[0], 4.66666666666, 1e-10);
EXPECT_NEAR(mean[1], 3.33333333333, 1e-10);
mean = clustering->get_nth_mean(1);
EXPECT_NEAR(mean[0], 1.0, 1e-10);
EXPECT_NEAR(mean[1], 7.0, 1e-10);
SGVector<float64_t> cov = clustering->get_nth_cov(0);
EXPECT_NEAR(cov[0], 16.88888888888, 1e-10);
EXPECT_NEAR(cov[1], 9.77777777777, 1e-10);
cov = clustering->get_nth_cov(1);
EXPECT_NEAR(cov[0], 0.000000001, 1e-10);
EXPECT_NEAR(cov[1], 0.0, 1e-10);
}