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IndependenceTest.h
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IndependenceTest.h
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/*
* Copyright (c) The Shogun Machine Learning Toolbox
* Written (w) 2012 - 2013 Heiko Strathmann
* Written (w) 2014 - 2017 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 INDEPENDENCE_TEST_H_
#define INDEPENDENCE_TEST_H_
#include <memory>
#include <shogun/statistical_testing/TwoDistributionTest.h>
namespace shogun
{
class CKernel;
namespace internal
{
class KernelManager;
}
/**
* @brief Provides an interface for performing the independence test.
* Given samples \f$Z=\{(x_i,y_i)\}_{i=1}^m\f$ from the joint distribution
* \f$\textbf{P}_{xy}\f$, whether the joint distribution factorize as
* \f$\textbf{P}_{xy}=\textbf{P}_x\textbf{P}_y\f$, i.e. product of the marginals.
* The null-hypothesis says yes, i.e. no dependence, the alternative hypothesis
* says no.
*
* Abstract base class. Provides all interfaces and implements approximating
* the null distribution via permutation, i.e. shuffling the samples from
* one distribution repeatedly using subsets while keeping the samples from
* the other distribution in its original order
*
*/
class CIndependenceTest : public CTwoDistributionTest
{
public:
/** Default constructor */
CIndependenceTest();
/** Destructor */
virtual ~CIndependenceTest();
/**
* Method that sets the kernel to be used for performing the test for the
* samples from p.
*
* @param kernel_p The kernel instance to be used for samples from p
*/
void set_kernel_p(CKernel* kernel_p);
/** @return The kernel instance that is used for samples from p */
CKernel* get_kernel_p() const;
/**
* Method that sets the kernel to be used for performing the test for the
* samples from q.
*
* @param kernel_q The kernel instance to be used for samples from q
*/
void set_kernel_q(CKernel* kernel_q);
/** @return The kernel instance that is used for samples from q */
CKernel* get_kernel_q() const;
/**
* Interface for computing the test-statistic for the hypothesis test.
*
* @return test statistic for the given data/parameters/methods
*/
virtual float64_t compute_statistic()=0;
/**
* Interface for computing the samples under the null-hypothesis.
*
* @return vector of all statistics
*/
virtual SGVector<float64_t> sample_null()=0;
/** @return The name of the class */
virtual const char* get_name() const;
protected:
internal::KernelManager& get_kernel_mgr();
const internal::KernelManager& get_kernel_mgr() const;
private:
struct Self;
std::unique_ptr<Self> self;
};
}
#endif // INDEPENDENCE_TEST_H_