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TwoSampleTest.h
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TwoSampleTest.h
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/*
* Copyright (c) The Shogun Machine Learning Toolbox
* Written (w) 2012-2013 Heiko Strathmann
* Written (w) 2014 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 TWO_SAMPLE_TEST_H_
#define TWO_SAMPLE_TEST_H_
#include <shogun/lib/config.h>
#include <shogun/statistics/HypothesisTest.h>
namespace shogun
{
class CFeatures;
/** @brief Provides an interface for performing the classical two-sample test
* i.e. Given samples from two distributions \f$p\f$ and \f$q\f$, the
* null-hypothesis is: \f$H_0: p=q\f$, the alternative hypothesis:
* \f$H_1: p\neq q\f$.
*
* Abstract base class. Provides all interfaces and implements approximating
* the null distribution via permutation, i.e. repeatedly merging both samples
* and them compute the test statistic on them.
*
*/
class CTwoSampleTest : public CHypothesisTest
{
public:
/** default constructor */
CTwoSampleTest();
/** Constructor
*
* @param p_and_q feature data. Is assumed to contain samples from both
* p and q. First all samples from p, then from index m all
* samples from q
*
* @param p_and_q samples from p and q, appended
* @param m index of first sample of q
*/
CTwoSampleTest(CFeatures* p_and_q, index_t m);
/** Constructor.
* This is a convienience constructor which copies both features to one
* element and then calls the other constructor. Needs twice the memory
* for a short time
*
* @param p samples from distribution p, will be copied and NOT
* SG_REF'ed
* @param q samples from distribution q, will be copied and NOT
* SG_REF'ed
*/
CTwoSampleTest(CFeatures* p, CFeatures* q);
/** destructor */
virtual ~CTwoSampleTest();
/** merges both sets of samples and computes the test statistic
* m_num_permutation_iteration times
*
* @return vector of all statistics
*/
virtual SGVector<float64_t> sample_null();
/** computes a p-value based on current method for approximating the
* null-distribution. The p-value is the 1-p quantile of the null-
* distribution where the given statistic lies in.
*
* @param statistic statistic value to compute the p-value for
* @return p-value parameter statistic is the (1-p) percentile of the
* null distribution
*/
virtual float64_t compute_p_value(float64_t statistic);
/** computes a threshold based on current method for approximating the
* null-distribution. The threshold is the argument of the \f$1-\alpha\f$
* quantile of the null. \f$\alpha\f$ is provided.
*
* @param alpha \f$\alpha\f$ quantile to get the threshold for
* @return threshold which is the \f$1-\alpha\f$ quantile of the null
* distribution
*/
virtual float64_t compute_threshold(float64_t alpha);
/** Setter for joint features
* @param p_and_q joint features from p and q to set
*/
virtual void set_p_and_q(CFeatures* p_and_q);
/** Getter for joint features, SG_REF'ed
* @return joint feature object
*/
virtual CFeatures* get_p_and_q();
/** @param m number of samples from first distribution p */
void set_m(index_t m);
/** @return number of to be used samples m */
index_t get_m() { return m_m; }
virtual const char* get_name() const=0;
private:
void init();
protected:
/** concatenated samples of the two distributions (two blocks) */
CFeatures* m_p_and_q;
/** defines the first index of samples of q */
index_t m_m;
};
}
#endif /* TWO_SAMPLE_TEST_H_ */