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MMD.h
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MMD.h
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/*
* Copyright (c) The Shogun Machine Learning Toolbox
* 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.
*/
#ifndef MMD_H_
#define MMD_H_
#include <memory>
#include <functional>
#include <shogun/statistical_testing/TwoSampleTest.h>
namespace shogun
{
class CKernel;
template <typename> class SGVector;
template <typename> class SGMatrix;
enum class EStatisticType
{
UNBIASED_FULL,
UNBIASED_INCOMPLETE,
BIASED_FULL
};
enum class EVarianceEstimationMethod
{
DIRECT,
PERMUTATION
};
enum class ENullApproximationMethod
{
PERMUTATION,
MMD1_GAUSSIAN,
MMD2_SPECTRUM,
MMD2_GAMMA
};
enum class EKernelSelectionMethod
{
MEDIAN_HEURISRIC,
MAXIMIZE_MMD,
MAXIMIZE_POWER
};
class CMMD : public CTwoSampleTest
{
using operation=std::function<float32_t(SGMatrix<float32_t>)>;
public:
CMMD();
virtual ~CMMD();
void add_kernel(CKernel *kernel);
/*
void select_kernel(EKernelSelectionMethod kmethod);
CKernel* get_kernel() const;
*/
virtual float64_t compute_statistic() override;
virtual float64_t compute_variance();
void set_statistic_type(EStatisticType stype);
const EStatisticType get_statistic_type() const;
void set_variance_estimation_method(EVarianceEstimationMethod vmethod);
const EVarianceEstimationMethod get_variance_estimation_method() const;
void set_num_null_samples(index_t null_samples);
const index_t get_num_null_samples() const;
void set_null_approximation_method(ENullApproximationMethod nmethod);
const ENullApproximationMethod get_null_approximation_method() const;
virtual SGVector<float64_t> sample_null() override;
void use_gpu(bool gpu);
virtual const char* get_name() const;
protected:
virtual const operation get_direct_estimation_method() const = 0;
virtual const float64_t normalize_statistic(float64_t statistic) const = 0;
virtual const float64_t normalize_variance(float64_t variance) const = 0;
bool use_gpu() const;
private:
struct Self;
std::unique_ptr<Self> self;
};
}
#endif // MMD_H_