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MaxMeasure.cpp
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MaxMeasure.cpp
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/*
* Copyright (c) The Shogun Machine Learning Toolbox
* Written (W) 2013 Heiko Strathmann
* Written (w) 2014 - 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 <algorithm>
#include <shogun/lib/SGVector.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/statistical_testing/MMD.h>
#include <shogun/statistical_testing/internals/MaxMeasure.h>
#include <shogun/statistical_testing/internals/KernelManager.h>
using namespace shogun;
using namespace internal;
MaxMeasure::MaxMeasure(KernelManager& km, CMMD* est) : KernelSelection(km), estimator(est)
{
}
MaxMeasure::~MaxMeasure()
{
}
SGVector<float64_t> MaxMeasure::compute_measures()
{
REQUIRE(estimator!=nullptr, "Estimator is not set!\n");
const size_t num_kernels=kernel_mgr.num_kernels();
REQUIRE(num_kernels>0, "Number of kernels is %d!\n", kernel_mgr.num_kernels());
SGVector<float64_t> result(num_kernels);
auto existing_kernel=estimator->get_kernel();
for (size_t i=0; i<num_kernels; ++i)
{
auto kernel=kernel_mgr.kernel_at(i);
estimator->set_kernel(kernel);
result[i]=estimator->compute_statistic();
estimator->cleanup();
}
estimator->set_kernel(existing_kernel);
return result;
}
CKernel* MaxMeasure::select_kernel()
{
SGVector<float64_t> measures=compute_measures();
auto max_element=std::max_element(measures.vector, measures.vector+measures.vlen);
auto max_idx=std::distance(measures.vector, max_element);
SG_SDEBUG("Selected kernel at %d position!\n", max_idx);
return kernel_mgr.kernel_at(max_idx);
}