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131 lines
4.6 KiB
131 lines
4.6 KiB
/** @file */
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/* Copyright (c) 2008, Naotoshi Seo. All rights reserved.
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*
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* The program is free to use for non-commercial academic purposes,
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* but for course works, you must understand what is going inside to
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* use. The program can be used, modified, or re-distributed for any
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* purposes only if you or one of your group understand not only
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* programming codes but also theory and math behind (if any).
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* Please contact the authors if you are interested in using the
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* program without meeting the above conditions.
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*/
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#include "cv.h"
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#include "cvaux.h"
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#include <stdio.h>
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#include <iostream>
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#define _USE_MATH_DEFINES
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#include <math.h>
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#ifndef CV_PCAPROBDIST_INCLUDED
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#define CV_PCAPROBDIST_INCLUDED
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#include "cvpcadist.h"
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#include "cvlogsum.h"
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#ifndef NO_DOXYGEN
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CVAPI(void)
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cvMatPcaProbDist( const CvMat* samples, const CvMat* avg,
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const CvMat* eigenvectors, double sqsigma, CvMat* probs,
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int normalize, bool logprob );
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CV_INLINE double
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cvPcaProbDist( const CvMat* sample, const CvMat* avg,
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const CvMat* eigenvectors, double sqsigma,
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int normalize, bool logprob );
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#endif
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/**
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* Probabilistic model of Distance from PCA subspace with Gaussian
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*
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* @param samples D x N sample vectors
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* @param avg D x 1 mean vector
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* @param eigenvectors M x D or D x M (automatically adjusted) eigen vectors
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* @param sqsigma A scalar representing sigma^2, i.e, variances
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* of distribution of reconstruction errors.
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* Estimate in training data.
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* @param probs 1 x N computed likelihood probabilities
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* @param normalize Compute normalization term or not
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* 0 - nothing
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* 1 - normalization term
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* 2 - normalize so that sum becomes 1.0
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* @param logprob Log probability or not
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*
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* @see CVAPI(void) cvCalcPCA( const CvArr* data, CvArr* avg, CvArr* eigenvalues, CvArr* eigenvectors, int flags );
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* @see CVAPI(void) cvProjectPCA( const CvArr* data, const CvArr* avg, const CvArr* eigenvectors, CvArr* result );
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* @see CVAPI(void) cvBackProjectPCA( const CvArr* proj, const CvArr* avg,const CvArr* eigenvects, CvArr* result );
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* @see cvMatPcaDist
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*/
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CVAPI(void)
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cvMatPcaProbDist( const CvMat* samples, const CvMat* avg,
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const CvMat* eigenvectors, double sqsigma, CvMat* probs,
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int normalize CV_DEFAULT(1), bool logprob CV_DEFAULT(false) )
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{
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int D = samples->rows;
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int N = samples->cols;
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int type = samples->type;
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CvMat *dists = cvCreateMat( 1, N, type );
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CV_FUNCNAME( "cvMatPcaProbDist" );
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__CV_BEGIN__;
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CV_ASSERT( CV_IS_MAT(samples) );
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CV_ASSERT( CV_IS_MAT(avg) );
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CV_ASSERT( CV_IS_MAT(eigenvectors) );
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CV_ASSERT( D == eigenvectors->rows || D == eigenvectors->cols );
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CV_ASSERT( D == avg->rows && 1 == avg->cols );
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CV_ASSERT( 1 == probs->rows && N == probs->cols );
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cvMatPcaDist( samples, avg, eigenvectors, dists );
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cvScale( dists, probs, -1/(2*sqsigma) );
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if( normalize == 1 )
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{
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cvSubS( probs, cvScalar(log(sqrt(2*M_PI*sqsigma))), probs );
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}
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else if( normalize == 2 )
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{
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CvScalar logsum = cvLogSum( probs );
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cvSubS( probs, logsum, probs );
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}
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if( !logprob ) // finally make exp
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{
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cvExp( probs, probs );
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}
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cvReleaseMat( &dists );
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__CV_END__;
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}
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/**
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* Distance between sample and PCA subspace, i.e, reconstruction error
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* Probabiliticlly model with Gaussian
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*
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* @param sample D x 1 feature vector
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* @param avg D x 1 mean vector
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* @param eigenvectors M x D eigen vectors
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* @param sqsigma A scalar representing sigma^2, i.e, variances
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* of distribution of reconstruction errors.
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* Estimate in training data.
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* @param normalize Compute normalization term or not
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* 0 - nothing
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* 1 - normalization term
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* 2 - normalize so that sum becomes 1.0
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* @param logprob Log probability or not
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* @return double
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*/
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CV_INLINE double
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cvPcaProbDist( const CvMat* sample, const CvMat* avg,
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const CvMat* eigenvectors, double sqsigma,
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int normalize CV_DEFAULT(1), bool logprob CV_DEFAULT(false) )
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{
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double prob;
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CvMat *_probs = cvCreateMat( 1, 1, sample->type );
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cvMatPcaProbDist( sample, avg, eigenvectors, sqsigma, _probs, normalize, logprob );
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prob = cvmGet( _probs, 0, 0);
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cvReleaseMat( &_probs );
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return prob;
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}
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#endif
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