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Commit 85fa0e77 authored by Maria Dimashova's avatar Maria Dimashova
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added cv::EM, moved CvEM to legacy, added/updated tests

parent cdc5bbc0
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Showing with 1606 additions and 1329 deletions
modules/contrib/include/opencv2/contrib/hybridtracker.hpp
View file @ 85fa0e77
......@@ -66,7 +66,7 @@ struct CV_EXPORTS CvMotionModel
}
float low_pass_gain; // low pass gain
CvEMParams em_params; // EM parameters
cv::EM::Params em_params; // EM parameters
};
// Mean Shift Tracker parameters for specifying use of HSV channel and CamShift parameters.
......@@ -109,7 +109,7 @@ struct CV_EXPORTS CvHybridTrackerParams
float ms_tracker_weight;
CvFeatureTrackerParams ft_params;
CvMeanShiftTrackerParams ms_params;
CvEMParams em_params;
cv::EM::Params em_params;
int motion_model;
float low_pass_gain;
};
......@@ -182,7 +182,7 @@ private:
CvMat* samples;
CvMat* labels;
CvEM em_model;
cv::EM em_model;
Rect prev_window;
Point2f prev_center;
......
modules/contrib/src/hybridtracker.cpp
View file @ 85fa0e77
......@@ -137,11 +137,11 @@ void CvHybridTracker::newTracker(Mat image, Rect selection) {
params.em_params.probs = NULL;
params.em_params.nclusters = 1;
params.em_params.weights = NULL;
params.em_params.cov_mat_type = CvEM::COV_MAT_SPHERICAL;
params.em_params.start_step = CvEM::START_AUTO_STEP;
params.em_params.term_crit.max_iter = 10000;
params.em_params.term_crit.epsilon = 0.001;
params.em_params.term_crit.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
params.em_params.covMatType = cv::EM::COV_MAT_SPHERICAL;
params.em_params.startStep = cv::EM::START_AUTO_STEP;
params.em_params.termCrit.maxCount = 10000;
params.em_params.termCrit.epsilon = 0.001;
params.em_params.termCrit.type = cv::TermCriteria::COUNT + cv::TermCriteria::EPS;
samples = cvCreateMat(2, 1, CV_32FC1);
labels = cvCreateMat(2, 1, CV_32SC1);
......@@ -221,7 +221,10 @@ void CvHybridTracker::updateTrackerWithEM(Mat image) {
count++;
}
em_model.train(samples, 0, params.em_params, labels);
cv::Mat lbls;
em_model.train(samples, cv::Mat(), params.em_params, &lbls);
if(labels)
*labels = lbls;
curr_center.x = (float)em_model.getMeans().at<double> (0, 0);
curr_center.y = (float)em_model.getMeans().at<double> (0, 1);
......
modules/legacy/CMakeLists.txt
View file @ 85fa0e77
ocv_define_module(legacy opencv_calib3d opencv_highgui opencv_video)
ocv_define_module(legacy opencv_calib3d opencv_highgui opencv_video opencv_ml)
modules/legacy/include/opencv2/legacy/legacy.hpp
View file @ 85fa0e77
......@@ -46,6 +46,7 @@
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/ml/ml.hpp"
#ifdef __cplusplus
extern "C" {
......@@ -1761,10 +1762,106 @@ protected:
IplImage* m_mask;
};
/****************************************************************************************\
* Expectation - Maximization *
\****************************************************************************************/
struct CV_EXPORTS_W_MAP CvEMParams
{
CvEMParams();
CvEMParams( int nclusters, int cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/,
int start_step=0/*CvEM::START_AUTO_STEP*/,
CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 );
CV_PROP_RW int nclusters;
CV_PROP_RW int cov_mat_type;
CV_PROP_RW int start_step;
const CvMat* probs;
const CvMat* weights;
const CvMat* means;
const CvMat** covs;
CV_PROP_RW CvTermCriteria term_crit;
};
class CV_EXPORTS_W CvEM : public CvStatModel
{
public:
// Type of covariation matrices
enum { COV_MAT_SPHERICAL=cv::EM::COV_MAT_SPHERICAL,
COV_MAT_DIAGONAL =cv::EM::COV_MAT_DIAGONAL,
COV_MAT_GENERIC =cv::EM::COV_MAT_GENERIC };
// The initial step
enum { START_E_STEP=cv::EM::START_E_STEP,
START_M_STEP=cv::EM::START_M_STEP,
START_AUTO_STEP=cv::EM::START_AUTO_STEP };
CV_WRAP CvEM();
CvEM( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 );
virtual ~CvEM();
virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 );
virtual float predict( const CvMat* sample, CV_OUT CvMat* probs, bool isNormalize=true ) const;
#ifndef SWIG
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams() );
CV_WRAP virtual bool train( const cv::Mat& samples,
const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams(),
CV_OUT cv::Mat* labels=0 );
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0, bool isNormalize=true ) const;
CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
CV_WRAP int getNClusters() const;
CV_WRAP const cv::Mat& getMeans() const;
CV_WRAP void getCovs(CV_OUT std::vector<cv::Mat>& covs) const;
CV_WRAP const cv::Mat& getWeights() const;
CV_WRAP const cv::Mat& getProbs() const;
CV_WRAP inline double getLikelihood() const { return emObj.isTrained() ? likelihood : DBL_MAX; }
#endif
CV_WRAP virtual void clear();
int get_nclusters() const;
const CvMat* get_means() const;
const CvMat** get_covs() const;
const CvMat* get_weights() const;
const CvMat* get_probs() const;
inline double get_log_likelihood() const { return getLikelihood(); }
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name ) const;
protected:
void set_mat_hdrs();
cv::EM emObj;
cv::Mat probs;
double likelihood;
CvMat meansHdr;
std::vector<CvMat> covsHdrs;
std::vector<CvMat*> covsPtrs;
CvMat weightsHdr;
CvMat probsHdr;
};
namespace cv
{
typedef CvEMParams EMParams;
typedef CvEM ExpectationMaximization;
/*!
The Patch Generator class
*/
......
modules/legacy/src/em.cpp 0 → 100644
View file @ 85fa0e77
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright( C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// 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 Intel Corporation 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 ifadvised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
CvEMParams::CvEMParams() : nclusters(10), cov_mat_type(CvEM::COV_MAT_DIAGONAL),
start_step(CvEM::START_AUTO_STEP), probs(0), weights(0), means(0), covs(0)
{
term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON );
}
CvEMParams::CvEMParams( int _nclusters, int _cov_mat_type, int _start_step,
CvTermCriteria _term_crit, const CvMat* _probs,
const CvMat* _weights, const CvMat* _means, const CvMat** _covs ) :
nclusters(_nclusters), cov_mat_type(_cov_mat_type), start_step(_start_step),
probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
{}
CvEM::CvEM() : likelihood(DBL_MAX)
{
}
CvEM::CvEM( const CvMat* samples, const CvMat* sample_idx,
CvEMParams params, CvMat* labels ) : likelihood(DBL_MAX)
{
train(samples, sample_idx, params, labels);
}
CvEM::~CvEM()
{
clear();
}
void CvEM::clear()
{
emObj.clear();
}
void CvEM::read( CvFileStorage* fs, CvFileNode* node )
{
cv::FileNode fn(fs, node);
emObj.read(fn);
set_mat_hdrs();
}
void CvEM::write( CvFileStorage* _fs, const char* name ) const
{
cv::FileStorage fs = _fs;
if(name)
fs << name << "{";
emObj.write(fs);
if(name)
fs << "}";
}
double CvEM::calcLikelihood( const cv::Mat &input_sample ) const
{
double likelihood;
emObj.predict(input_sample, 0, &likelihood);
return likelihood;
}
float
CvEM::predict( const CvMat* _sample, CvMat* _probs, bool isNormalize ) const
{
cv::Mat prbs;
int cls = emObj.predict(_sample, _probs ? &prbs : 0);
if(_probs)
{
if(isNormalize)
cv::normalize(prbs, prbs, 1, 0, cv::NORM_L1);
*_probs = prbs;
}
return (float)cls;
}
void CvEM::set_mat_hdrs()
{
if(emObj.isTrained())
{
meansHdr = emObj.getMeans();
covsHdrs.resize(emObj.getNClusters());
covsPtrs.resize(emObj.getNClusters());
const std::vector<cv::Mat>& covs = emObj.getCovs();
for(size_t i = 0; i < covsHdrs.size(); i++)
{
covsHdrs[i] = covs[i];
covsPtrs[i] = &covsHdrs[i];
}
weightsHdr = emObj.getWeights();
probsHdr = probs;
}
}
static
void init_params(const CvEMParams& src, cv::EM::Params& dst,
cv::Mat& prbs, cv::Mat& weights,
cv::Mat& means, cv::vector<cv::Mat>& covsHdrs)
{
dst.nclusters = src.nclusters;
dst.covMatType = src.cov_mat_type;
dst.startStep = src.start_step;
dst.termCrit = src.term_crit;
prbs = src.probs;
dst.probs = &prbs;
weights = src.weights;
dst.weights = &weights;
means = src.means;
dst.means = &means;
if(src.covs)
{
covsHdrs.resize(src.nclusters);
for(size_t i = 0; i < covsHdrs.size(); i++)
covsHdrs[i] = src.covs[i];
dst.covs = &covsHdrs;
}
}
bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
CvEMParams _params, CvMat* _labels )
{
cv::EM::Params params;
cv::Mat prbs, weights, means;
std::vector<cv::Mat> covsHdrs;
init_params(_params, params, prbs, weights, means, covsHdrs);
cv::Mat lbls;
cv::Mat likelihoods;
bool isOk = emObj.train(_samples, _sample_idx, params, _labels ? &lbls : 0, &probs, &likelihoods );
if(isOk)
{
if(_labels)
*_labels = lbls;
likelihood = cv::sum(likelihoods)[0];
set_mat_hdrs();
}
return isOk;
}
int CvEM::get_nclusters() const
{
return emObj.getNClusters();
}
const CvMat* CvEM::get_means() const
{
return emObj.isTrained() ? &meansHdr : 0;
}
const CvMat** CvEM::get_covs() const
{
return emObj.isTrained() ? (const CvMat**)&covsPtrs[0] : 0;
}
const CvMat* CvEM::get_weights() const
{
return emObj.isTrained() ? &weightsHdr : 0;
}
const CvMat* CvEM::get_probs() const
{
return emObj.isTrained() ? &probsHdr : 0;
}
using namespace cv;
CvEM::CvEM( const Mat& samples, const Mat& sample_idx, CvEMParams params )
{
train(samples, sample_idx, params, 0);
}
bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
CvEMParams _params, Mat* _labels )
{
cv::EM::Params params;
cv::Mat prbs, weights, means;
std::vector<cv::Mat> covsHdrs;
init_params(_params, params, prbs, weights, means, covsHdrs);
cv::Mat likelihoods;
bool isOk = emObj.train(_samples, _sample_idx, params, _labels, &probs, &likelihoods);
if(isOk)
{
likelihoods = cv::sum(likelihoods).val[0];
set_mat_hdrs();
}
return isOk;
}
float
CvEM::predict( const Mat& _sample, Mat* _probs, bool isNormalize ) const
{
int cls = emObj.predict(_sample, _probs);
if(_probs && isNormalize)
cv::normalize(*_probs, *_probs, 1, 0, cv::NORM_L1);
return (float)cls;
}
int CvEM::getNClusters() const
{
return emObj.getNClusters();
}
const Mat& CvEM::getMeans() const
{
return emObj.getMeans();
}
void CvEM::getCovs(vector<Mat>& _covs) const
{
_covs = emObj.getCovs();
}
const Mat& CvEM::getWeights() const
{
return emObj.getWeights();
}
const Mat& CvEM::getProbs() const
{
return probs;
}
/* End of file. */
modules/legacy/test/test_em.cpp 0 → 100644
View file @ 85fa0e77
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's 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.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// 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 Intel Corporation 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.
//
//M*/
#include "test_precomp.hpp"
using namespace std;
using namespace cv;
static
void defaultDistribs( Mat& means, vector<Mat>& covs )
{
float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
means.create(3, 2, CV_32FC1);
Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
means.resize(3), covs.resize(3);
Mat mr0 = means.row(0);
m0.copyTo(mr0);
c0.copyTo(covs[0]);
Mat mr1 = means.row(1);
m1.copyTo(mr1);
c1.copyTo(covs[1]);
Mat mr2 = means.row(2);
m2.copyTo(mr2);
c2.copyTo(covs[2]);
}
// generate points sets by normal distributions
static
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int labelType )
{
vector<int>::const_iterator sit = sizes.begin();
int total = 0;
for( ; sit != sizes.end(); ++sit )
total += *sit;
assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
assert( !data.empty() && data.rows == total );
assert( data.type() == CV_32FC1 );
labels.create( data.rows, 1, labelType );
randn( data, Scalar::all(0.0), Scalar::all(1.0) );
vector<Mat> means(sizes.size());
for(int i = 0; i < _means.rows; i++)
means[i] = _means.row(i);
vector<Mat>::const_iterator mit = means.begin(), cit = covs.begin();
int bi, ei = 0;
sit = sizes.begin();
for( int p = 0, l = 0; sit != sizes.end(); ++sit, ++mit, ++cit, l++ )
{
bi = ei;
ei = bi + *sit;
assert( mit->rows == 1 && mit->cols == data.cols );
assert( cit->rows == data.cols && cit->cols == data.cols );
for( int i = bi; i < ei; i++, p++ )
{
Mat r(1, data.cols, CV_32FC1, data.ptr<float>(i));
r = r * (*cit) + *mit;
if( labelType == CV_32FC1 )
labels.at<float>(p, 0) = (float)l;
else if( labelType == CV_32SC1 )
labels.at<int>(p, 0) = l;
else
CV_DbgAssert(0);
}
}
}
static
int maxIdx( const vector<int>& count )
{
int idx = -1;
int maxVal = -1;
vector<int>::const_iterator it = count.begin();
for( int i = 0; it != count.end(); ++it, i++ )
{
if( *it > maxVal)
{
maxVal = *it;
idx = i;
}
}
assert( idx >= 0);
return idx;
}
static
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap )
{
size_t total = 0, nclusters = sizes.size();
for(size_t i = 0; i < sizes.size(); i++)
total += sizes[i];
assert( !labels.empty() );
assert( labels.total() == total && (labels.cols == 1 || labels.rows == 1));
assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
bool isFlt = labels.type() == CV_32FC1;
labelsMap.resize(nclusters);
vector<bool> buzy(nclusters, false);
int startIndex = 0;
for( size_t clusterIndex = 0; clusterIndex < sizes.size(); clusterIndex++ )
{
vector<int> count( nclusters, 0 );
for( int i = startIndex; i < startIndex + sizes[clusterIndex]; i++)
{
int lbl = isFlt ? (int)labels.at<float>(i) : labels.at<int>(i);
CV_Assert(lbl < (int)nclusters);
count[lbl]++;
CV_Assert(count[lbl] < (int)total);
}
startIndex += sizes[clusterIndex];
int cls = maxIdx( count );
CV_Assert( !buzy[cls] );
labelsMap[clusterIndex] = cls;
buzy[cls] = true;
}
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
return true;
}
static
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true )
{
err = 0;
CV_Assert( !labels.empty() && !origLabels.empty() );
CV_Assert( labels.rows == 1 || labels.cols == 1 );
CV_Assert( origLabels.rows == 1 || origLabels.cols == 1 );
CV_Assert( labels.total() == origLabels.total() );
CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
CV_Assert( origLabels.type() == labels.type() );
vector<int> labelsMap;
bool isFlt = labels.type() == CV_32FC1;
if( !labelsEquivalent )
{
if( !getLabelsMap( labels, sizes, labelsMap ) )
return false;
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i) != labelsMap[(int)origLabels.at<float>(i)] ? 1.f : 0.f;
else
err += labels.at<int>(i) != labelsMap[origLabels.at<int>(i)] ? 1.f : 0.f;
}
else
{
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i) != origLabels.at<float>(i) ? 1.f : 0.f;
else
err += labels.at<int>(i) != origLabels.at<int>(i) ? 1.f : 0.f;
}
err /= (float)labels.rows;
return true;
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
class CV_CvEMTest : public cvtest::BaseTest
{
public:
CV_CvEMTest() {}
protected:
virtual void run( int start_from );
int runCase( int caseIndex, const CvEMParams& params,
const cv::Mat& trainData, const cv::Mat& trainLabels,
const cv::Mat& testData, const cv::Mat& testLabels,
const vector<int>& sizes);
};
int CV_CvEMTest::runCase( int caseIndex, const CvEMParams& params,
const cv::Mat& trainData, const cv::Mat& trainLabels,
const cv::Mat& testData, const cv::Mat& testLabels,
const vector<int>& sizes )
{
int code = cvtest::TS::OK;
cv::Mat labels;
float err;
CvEM em;
em.train( trainData, Mat(), params, &labels );
// check train error
if( !calcErr( labels, trainLabels, sizes, err , false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.006f )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on train data.\n", caseIndex, err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// check test error
labels.create( testData.rows, 1, CV_32SC1 );
for( int i = 0; i < testData.rows; i++ )
{
Mat sample = testData.row(i);
labels.at<int>(i,0) = (int)em.predict( sample, 0 );
}
if( !calcErr( labels, testLabels, sizes, err, false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.006f )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on test data.\n", caseIndex, err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
return code;
}
void CV_CvEMTest::run( int /*start_from*/ )
{
int sizesArr[] = { 500, 700, 800 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
// Points distribution
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
// train data
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
generateData( trainData, trainLabels, sizes, means, covs, CV_32SC1 );
// test data
Mat testData( pointsCount, 2, CV_32FC1 ), testLabels;
generateData( testData, testLabels, sizes, means, covs, CV_32SC1 );
CvEMParams params;
params.nclusters = 3;
Mat probs(trainData.rows, params.nclusters, CV_32FC1, cv::Scalar(1));
CvMat probsHdr = probs;
params.probs = &probsHdr;
Mat weights(1, params.nclusters, CV_32FC1, cv::Scalar(1));
CvMat weightsHdr = weights;
params.weights = &weightsHdr;
CvMat meansHdr = means;
params.means = &meansHdr;
std::vector<CvMat> covsHdrs(params.nclusters);
std::vector<const CvMat*> covsPtrs(params.nclusters);
for(int i = 0; i < params.nclusters; i++)
{
covsHdrs[i] = covs[i];
covsPtrs[i] = &covsHdrs[i];
}
params.covs = &covsPtrs[0];
int code = cvtest::TS::OK;
int caseIndex = 0;
{
params.start_step = cv::EM::START_AUTO_STEP;
params.cov_mat_type = cv::EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.start_step = cv::EM::START_AUTO_STEP;
params.cov_mat_type = cv::EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.start_step = cv::EM::START_AUTO_STEP;
params.cov_mat_type = cv::EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.start_step = cv::EM::START_M_STEP;
params.cov_mat_type = cv::EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.start_step = cv::EM::START_M_STEP;
params.cov_mat_type = cv::EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.start_step = cv::EM::START_M_STEP;
params.cov_mat_type = cv::EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.start_step = cv::EM::START_E_STEP;
params.cov_mat_type = cv::EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.start_step = cv::EM::START_E_STEP;
params.cov_mat_type = cv::EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.start_step = cv::EM::START_E_STEP;
params.cov_mat_type = cv::EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
ts->set_failed_test_info( code );
}
class CV_CvEMTest_SaveLoad : public cvtest::BaseTest {
public:
CV_CvEMTest_SaveLoad() {}
protected:
virtual void run( int /*start_from*/ )
{
int code = cvtest::TS::OK;
cv::EM em;
Mat samples = Mat(3,1,CV_32F);
samples.at<float>(0,0) = 1;
samples.at<float>(1,0) = 2;
samples.at<float>(2,0) = 3;
cv::EM::Params params;
params.nclusters = 2;
Mat labels;
em.train(samples, Mat(), params, &labels);
Mat firstResult(samples.rows, 1, CV_32FC1);
for( int i = 0; i < samples.rows; i++)
firstResult.at<float>(i) = em.predict( samples.row(i) );
// Write out
string filename = tempfile() + ".xml";
{
FileStorage fs = FileStorage(filename, FileStorage::WRITE);
try
{
fs << "em" << "{";
em.write(fs);
fs << "}";
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "Crash in write method.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_EXCEPTION );
}
}
em.clear();
// Read in
{
FileStorage fs = FileStorage(filename, FileStorage::READ);
CV_Assert(fs.isOpened());
FileNode fn = fs["em"];
try
{
em.read(fn);
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "Crash in read method.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_EXCEPTION );
}
}
remove( filename.c_str() );
int errCaseCount = 0;
for( int i = 0; i < samples.rows; i++)
errCaseCount = std::abs(em.predict(samples.row(i)) - firstResult.at<float>(i)) < FLT_EPSILON ? 0 : 1;
if( errCaseCount > 0 )
{
ts->printf( cvtest::TS::LOG, "Different prediction results before writeing and after reading (errCaseCount=%d).\n", errCaseCount );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info( code );
}
};
TEST(ML_CvEM, accuracy) { CV_CvEMTest test; test.safe_run(); }
TEST(ML_CvEM, save_load) { CV_CvEMTest_SaveLoad test; test.safe_run(); }
modules/ml/include/opencv2/ml/ml.hpp
View file @ 85fa0e77
......@@ -46,6 +46,10 @@
#ifdef __cplusplus
#include <map>
#include <string>
#include <iostream>
// Apple defines a check() macro somewhere in the debug headers
// that interferes with a method definiton in this header
#undef check
......@@ -549,114 +553,93 @@ protected:
/****************************************************************************************\
* Expectation - Maximization *
\****************************************************************************************/
struct CV_EXPORTS_W_MAP CvEMParams
namespace cv
{
CvEMParams();
CvEMParams( int nclusters, int cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/,
int start_step=0/*CvEM::START_AUTO_STEP*/,
CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 );
CV_PROP_RW int nclusters;
CV_PROP_RW int cov_mat_type;
CV_PROP_RW int start_step;
const CvMat* probs;
const CvMat* weights;
const CvMat* means;
const CvMat** covs;
CV_PROP_RW CvTermCriteria term_crit;
};
class CV_EXPORTS_W CvEM : public CvStatModel
class CV_EXPORTS_W EM : public Algorithm
{
public:
// Type of covariation matrices
enum { COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2 };
enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2};
// The initial step
enum { START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0 };
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
CV_WRAP CvEM();
CvEM( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 );
//CvEM (CvEMParams params, CvMat * means, CvMat ** covs, CvMat * weights,
// CvMat * probs, CvMat * log_weight_div_det, CvMat * inv_eigen_values, CvMat** cov_rotate_mats);
class CV_EXPORTS_W Params
{
public:
Params(int nclusters=10, int covMatType=EM::COV_MAT_DIAGONAL, int startStep=EM::START_AUTO_STEP,
const cv::TermCriteria& termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON),
const cv::Mat* probs=0, const cv::Mat* weights=0,
const cv::Mat* means=0, const std::vector<cv::Mat>* covs=0);
int nclusters;
int covMatType;
int startStep;
// all 4 following matrices should have type CV_32FC1
const cv::Mat* probs;
const cv::Mat* weights;
const cv::Mat* means;
const std::vector<cv::Mat>* covs;
cv::TermCriteria termCrit;
};
virtual ~CvEM();
EM();
EM(const cv::Mat& samples, const cv::Mat samplesMask=cv::Mat(),
const EM::Params& params=EM::Params(), cv::Mat* labels=0, cv::Mat* probs=0, cv::Mat* likelihoods=0);
virtual ~EM();
virtual void clear();
virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 );
virtual bool train(const cv::Mat& samples, const cv::Mat& samplesMask=cv::Mat(),
const EM::Params& params=EM::Params(), cv::Mat* labels=0, cv::Mat* probs=0, cv::Mat* likelihoods=0);
int predict(const cv::Mat& sample, cv::Mat* probs=0, double* likelihood=0) const;
virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const;
bool isTrained() const;
int getNClusters() const;
int getCovMatType() const;
#ifndef SWIG
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams() );
CV_WRAP virtual bool train( const cv::Mat& samples,
const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams(),
CV_OUT cv::Mat* labels=0 );
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) const;
CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
CV_WRAP int getNClusters() const;
CV_WRAP cv::Mat getMeans() const;
CV_WRAP void getCovs(CV_OUT std::vector<cv::Mat>& covs) const;
CV_WRAP cv::Mat getWeights() const;
CV_WRAP cv::Mat getProbs() const;
CV_WRAP inline double getLikelihood() const { return log_likelihood; }
CV_WRAP inline double getLikelihoodDelta() const { return log_likelihood_delta; }
#endif
CV_WRAP virtual void clear();
const cv::Mat& getWeights() const;
const cv::Mat& getMeans() const;
const std::vector<cv::Mat>& getCovs() const;
int get_nclusters() const;
const CvMat* get_means() const;
const CvMat** get_covs() const;
const CvMat* get_weights() const;
const CvMat* get_probs() const;
AlgorithmInfo* info() const;
virtual void read(const FileNode& fn);
inline double get_log_likelihood() const { return log_likelihood; }
inline double get_log_likelihood_delta() const { return log_likelihood_delta; }
// inline const CvMat * get_log_weight_div_det () const { return log_weight_div_det; };
// inline const CvMat * get_inv_eigen_values () const { return inv_eigen_values; };
// inline const CvMat ** get_cov_rotate_mats () const { return cov_rotate_mats; };
protected:
virtual void setTrainData(const cv::Mat& samples, const cv::Mat& samplesMask, const EM::Params& params);
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name ) const;
bool doTrain(const cv::TermCriteria& termCrit);
virtual void eStep();
virtual void mStep();
virtual void write_params( CvFileStorage* fs ) const;
virtual void read_params( CvFileStorage* fs, CvFileNode* node );
void clusterTrainSamples();
void decomposeCovs();
void computeLogWeightDivDet();
protected:
void computeProbabilities(const cv::Mat& sample, int& label, cv::Mat* probs, float* likelihood) const;
virtual void set_params( const CvEMParams& params,
const CvVectors& train_data );
virtual void init_em( const CvVectors& train_data );
virtual double run_em( const CvVectors& train_data );
virtual void init_auto( const CvVectors& samples );
virtual void kmeans( const CvVectors& train_data, int nclusters,
CvMat* labels, CvTermCriteria criteria,
const CvMat* means );
CvEMParams params;
double log_likelihood;
double log_likelihood_delta;
CvMat* means;
CvMat** covs;
CvMat* weights;
CvMat* probs;
// all inner matrices have type CV_32FC1
int nclusters;
int covMatType;
int startStep;
CvMat* log_weight_div_det;
CvMat* inv_eigen_values;
CvMat** cov_rotate_mats;
cv::Mat trainSamples;
cv::Mat trainProbs;
cv::Mat trainLikelihoods;
cv::Mat trainLabels;
cv::Mat trainCounts;
cv::Mat weights;
cv::Mat means;
std::vector<cv::Mat> covs;
std::vector<cv::Mat> covsEigenValues;
std::vector<cv::Mat> covsRotateMats;
std::vector<cv::Mat> invCovsEigenValues;
cv::Mat logWeightDivDet;
};
} // namespace cv
/****************************************************************************************\
* Decision Tree *
......@@ -2012,17 +1995,10 @@ CVAPI(void) cvCreateTestSet( int type, CvMat** samples,
CvMat** responses,
int num_classes, ... );
#endif
/****************************************************************************************\
* Data *
\****************************************************************************************/
#include <map>
#include <string>
#include <iostream>
#define CV_COUNT 0
#define CV_PORTION 1
......@@ -2133,8 +2109,6 @@ typedef CvSVMParams SVMParams;
typedef CvSVMKernel SVMKernel;
typedef CvSVMSolver SVMSolver;
typedef CvSVM SVM;
typedef CvEMParams EMParams;
typedef CvEM ExpectationMaximization;
typedef CvDTreeParams DTreeParams;
typedef CvMLData TrainData;
typedef CvDTree DecisionTree;
......@@ -2156,5 +2130,7 @@ template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
}
#endif
#endif // __cplusplus
#endif // __OPENCV_ML_HPP__
/* End of file. */
modules/ml/src/em.cpp
View file @ 85fa0e77
......@@ -41,1266 +41,634 @@
#include "precomp.hpp"
/*
CvEM:
* params.nclusters - number of clusters to cluster samples to.
* means - calculated by the EM algorithm set of gaussians' means.
* log_weight_div_det - auxilary vector that k-th component is equal to
(-2)*ln(weights_k/det(Sigma_k)^0.5),
where <weights_k> is the weight,
<Sigma_k> is the covariation matrice of k-th cluster.
* inv_eigen_values - set of 1*dims matrices, <inv_eigen_values>[k] contains
inversed eigen values of covariation matrice of the k-th cluster.
In the case of <cov_mat_type> == COV_MAT_DIAGONAL,
inv_eigen_values[k] = Sigma_k^(-1).
* covs_rotate_mats - used only if cov_mat_type == COV_MAT_GENERIC, in all the
other cases it is NULL. <covs_rotate_mats>[k] is the orthogonal
matrice, obtained by the SVD-decomposition of Sigma_k.
Both <inv_eigen_values> and <covs_rotate_mats> fields are used for representation of
covariation matrices and simplifying EM calculations.
For fixed k denote
u = covs_rotate_mats[k],
v = inv_eigen_values[k],
w = v^(-1);
if <cov_mat_type> == COV_MAT_GENERIC, then Sigma_k = u w u',
else Sigma_k = w.
Symbol ' means transposition.
*/
CvEMParams::CvEMParams() : nclusters(10), cov_mat_type(1/*CvEM::COV_MAT_DIAGONAL*/),
start_step(0/*CvEM::START_AUTO_STEP*/), probs(0), weights(0), means(0), covs(0)
namespace cv
{
term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON );
}
CvEMParams::CvEMParams( int _nclusters, int _cov_mat_type, int _start_step,
CvTermCriteria _term_crit, const CvMat* _probs,
const CvMat* _weights, const CvMat* _means, const CvMat** _covs ) :
nclusters(_nclusters), cov_mat_type(_cov_mat_type), start_step(_start_step),
probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
const float minEigenValue = 1.e-3;
EM::Params::Params( int nclusters, int covMatType, int startStep, const cv::TermCriteria& termCrit,
const cv::Mat* probs, const cv::Mat* weights,
const cv::Mat* means, const std::vector<cv::Mat>* covs )
: nclusters(nclusters), covMatType(covMatType), startStep(startStep),
probs(probs), weights(weights), means(means), covs(covs), termCrit(termCrit)
{}
CvEM::CvEM()
{
means = weights = probs = inv_eigen_values = log_weight_div_det = 0;
covs = cov_rotate_mats = 0;
}
///////////////////////////////////////////////////////////////////////////////////////////////////////
CvEM::CvEM( const CvMat* samples, const CvMat* sample_idx,
CvEMParams params, CvMat* labels )
{
means = weights = probs = inv_eigen_values = log_weight_div_det = 0;
covs = cov_rotate_mats = 0;
EM::EM()
{}
// just invoke the train() method
train(samples, sample_idx, params, labels);
EM::EM(const cv::Mat& samples, const cv::Mat samplesMask,
const EM::Params& params, cv::Mat* labels, cv::Mat* probs, cv::Mat* likelihoods)
{
train(samples, samplesMask, params, labels, probs, likelihoods);
}
CvEM::~CvEM()
EM::~EM()
{
clear();
}
void CvEM::clear()
void EM::clear()
{
int i;
trainSamples.release();
trainProbs.release();
trainLikelihoods.release();
trainLabels.release();
trainCounts.release();
cvReleaseMat( &means );
cvReleaseMat( &weights );
cvReleaseMat( &probs );
cvReleaseMat( &inv_eigen_values );
cvReleaseMat( &log_weight_div_det );
weights.release();
means.release();
covs.clear();
if( covs || cov_rotate_mats )
{
for( i = 0; i < params.nclusters; i++ )
{
if( covs )
cvReleaseMat( &covs[i] );
if( cov_rotate_mats )
cvReleaseMat( &cov_rotate_mats[i] );
}
cvFree( &covs );
cvFree( &cov_rotate_mats );
}
covsEigenValues.clear();
invCovsEigenValues.clear();
covsRotateMats.clear();
logWeightDivDet.release();
}
void CvEM::read( CvFileStorage* fs, CvFileNode* node )
bool EM::train(const cv::Mat& samples, const cv::Mat& samplesMask,
const EM::Params& params, cv::Mat* labels, cv::Mat* probs, cv::Mat* likelihoods)
{
bool ok = false;
CV_FUNCNAME( "CvEM::read" );
setTrainData(samples, samplesMask, params);
__BEGIN__;
clear();
size_t data_size;
CvSeqReader reader;
CvFileNode* em_node = 0;
CvFileNode* tmp_node = 0;
CvSeq* seq = 0;
read_params( fs, node );
em_node = cvGetFileNodeByName( fs, node, "cvEM" );
if( !em_node )
CV_ERROR( CV_StsBadArg, "cvEM tag not found" );
CV_CALL( weights = (CvMat*)cvReadByName( fs, em_node, "weights" ));
CV_CALL( means = (CvMat*)cvReadByName( fs, em_node, "means" ));
CV_CALL( log_weight_div_det = (CvMat*)cvReadByName( fs, em_node, "log_weight_div_det" ));
CV_CALL( inv_eigen_values = (CvMat*)cvReadByName( fs, em_node, "inv_eigen_values" ));
// Size of all the following data
data_size = params.nclusters*sizeof(CvMat*);
CV_CALL( covs = (CvMat**)cvAlloc( data_size ));
memset( covs, 0, data_size );
CV_CALL( tmp_node = cvGetFileNodeByName( fs, em_node, "covs" ));
seq = tmp_node->data.seq;
if( !CV_NODE_IS_SEQ(tmp_node->tag) || seq->total != params.nclusters)
CV_ERROR( CV_StsParseError, "Missing or invalid sequence of covariance matrices" );
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
for( int i = 0; i < params.nclusters; i++ )
{
CV_CALL( covs[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
}
bool isOk = doTrain(params.termCrit);
CV_CALL( cov_rotate_mats = (CvMat**)cvAlloc( data_size ));
memset( cov_rotate_mats, 0, data_size );
CV_CALL( tmp_node = cvGetFileNodeByName( fs, em_node, "cov_rotate_mats" ));
seq = tmp_node->data.seq;
if( !CV_NODE_IS_SEQ(tmp_node->tag) || seq->total != params.nclusters)
CV_ERROR( CV_StsParseError, "Missing or invalid sequence of covariance matrices" );
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
for( int i = 0; i < params.nclusters; i++ )
if(isOk)
{
CV_CALL( cov_rotate_mats[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
if(labels)
cv::swap(*labels, trainLabels);
if(probs)
cv::swap(*probs, trainProbs);
if(likelihoods)
cv::swap(*likelihoods, trainLikelihoods);
trainSamples.release();
trainProbs.release();
trainLabels.release();
trainLikelihoods.release();
trainCounts.release();
}
ok = true;
__END__;
if (!ok)
else
clear();
return isOk;
}
void CvEM::read_params( CvFileStorage *fs, CvFileNode *node)
int EM::predict(const cv::Mat& sample, cv::Mat* _probs, double* _likelihood) const
{
CV_FUNCNAME( "CvEM::read_params");
__BEGIN__;
size_t data_size;
CvEMParams _params;
CvSeqReader reader;
CvFileNode* param_node = 0;
CvFileNode* tmp_node = 0;
CvSeq* seq = 0;
const char * start_step_name = 0;
const char * cov_mat_type_name = 0;
param_node = cvGetFileNodeByName( fs, node, "params" );
if( !param_node )
CV_ERROR( CV_StsBadArg, "params tag not found" );
CV_Assert(isTrained());
CV_CALL( start_step_name = cvReadStringByName( fs, param_node, "start_step", 0 ) );
CV_CALL( cov_mat_type_name = cvReadStringByName( fs, param_node, "cov_mat_type", 0 ) );
CV_Assert(!sample.empty());
CV_Assert(sample.type() == CV_32FC1);
if( start_step_name )
_params.start_step = strcmp( start_step_name, "START_E_STEP" ) == 0 ? START_E_STEP :
strcmp( start_step_name, "START_M_STEP" ) == 0 ? START_M_STEP :
strcmp( start_step_name, "START_AUTO_STEP" ) == 0 ? START_AUTO_STEP : 0;
else
CV_CALL( _params.start_step = cvReadIntByName( fs, param_node, "start_step", -1 ) );
int label;
float likelihood;
computeProbabilities(sample, label, _probs, _likelihood ? &likelihood : 0);
if(_likelihood)
*_likelihood = static_cast<double>(likelihood);
return label;
}
if( cov_mat_type_name )
_params.cov_mat_type = strcmp( cov_mat_type_name, "COV_MAT_SPHERICAL" ) == 0 ? COV_MAT_SPHERICAL :
strcmp( cov_mat_type_name, "COV_MAT_DIAGONAL" ) == 0 ? COV_MAT_DIAGONAL :
strcmp( cov_mat_type_name, "COV_MAT_GENERIC" ) == 0 ? COV_MAT_GENERIC : 0;
else
CV_CALL( _params.cov_mat_type = cvReadIntByName( fs, param_node, "cov_mat_type", -1) );
CV_CALL( _params.nclusters = cvReadIntByName( fs, param_node, "nclusters", -1 ));
CV_CALL( _params.weights = (CvMat*)cvReadByName( fs, param_node, "weights" ));
CV_CALL( _params.means = (CvMat*)cvReadByName( fs, param_node, "means" ));
data_size = _params.nclusters*sizeof(CvMat*);
CV_CALL( _params.covs = (const CvMat**)cvAlloc( data_size ));
memset( _params.covs, 0, data_size );
CV_CALL( tmp_node = cvGetFileNodeByName( fs, param_node, "covs" ));
seq = tmp_node->data.seq;
if( !CV_NODE_IS_SEQ(tmp_node->tag) || seq->total != _params.nclusters)
CV_ERROR( CV_StsParseError, "Missing or invalid sequence of covariance matrices" );
CV_CALL( cvStartReadSeq( seq, &reader, 0 ));
for( int i = 0; i < _params.nclusters; i++ )
{
CV_CALL( _params.covs[i] = (CvMat*)cvRead( fs, (CvFileNode*)reader.ptr ));
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
}
params = _params;
bool EM::isTrained() const
{
return !means.empty();
}
__END__;
int EM::getNClusters() const
{
return isTrained() ? nclusters : -1;
}
void CvEM::write_params( CvFileStorage* fs ) const
int EM::getCovMatType() const
{
CV_FUNCNAME( "CvEM::write_params" );
return isTrained() ? covMatType : -1;
}
__BEGIN__;
const cv::Mat& EM::getWeights() const
{
CV_Assert((isTrained() && !weights.empty()) || (!isTrained() && weights.empty()));
return weights;
}
const char* cov_mat_type_name =
(params.cov_mat_type == COV_MAT_SPHERICAL) ? "COV_MAT_SPHERICAL" :
(params.cov_mat_type == COV_MAT_DIAGONAL) ? "COV_MAT_DIAGONAL" :
(params.cov_mat_type == COV_MAT_GENERIC) ? "COV_MAT_GENERIC" : 0;
const cv::Mat& EM::getMeans() const
{
CV_Assert((isTrained() && !means.empty()) || (!isTrained() && means.empty()));
return means;
}
const char* start_step_name =
(params.start_step == START_E_STEP) ? "START_E_STEP" :
(params.start_step == START_M_STEP) ? "START_M_STEP" :
(params.start_step == START_AUTO_STEP) ? "START_AUTO_STEP" : 0;
const std::vector<cv::Mat>& EM::getCovs() const
{
CV_Assert((isTrained() && !covs.empty()) || (!isTrained() && covs.empty()));
return covs;
}
CV_CALL( cvStartWriteStruct( fs, "params", CV_NODE_MAP ) );
static
void checkTrainData(const cv::Mat& samples, const cv::Mat& samplesMask, const EM::Params& params)
{
// Check samples.
CV_Assert(!samples.empty());
CV_Assert(samples.type() == CV_32FC1);
int nsamples = samples.rows;
int dim = samples.cols;
// Check samples indices.
CV_Assert(samplesMask.empty() ||
((samplesMask.rows == 1 || samplesMask.cols == 1) &&
static_cast<int>(samplesMask.total()) == nsamples && samplesMask.type() == CV_8UC1));
// Check training params.
CV_Assert(params.nclusters > 0);
CV_Assert(params.nclusters <= nsamples);
CV_Assert(params.startStep == EM::START_AUTO_STEP || params.startStep == EM::START_E_STEP || params.startStep == EM::START_M_STEP);
CV_Assert(!params.probs ||
(!params.probs->empty() &&
params.probs->rows == nsamples && params.probs->cols == params.nclusters &&
params.probs->type() == CV_32FC1));
CV_Assert(!params.weights ||
(!params.weights->empty() &&
(params.weights->cols == 1 || params.weights->rows == 1) && static_cast<int>(params.weights->total()) == params.nclusters &&
params.weights->type() == CV_32FC1));
CV_Assert(!params.means ||
(!params.means->empty() &&
params.means->rows == params.nclusters && params.means->cols == dim &&
params.means->type() == CV_32FC1));
CV_Assert(!params.covs ||
(!params.covs->empty() &&
static_cast<int>(params.covs->size()) == params.nclusters));
if(params.covs)
{
const cv::Size covSize(dim, dim);
for(size_t i = 0; i < params.covs->size(); i++)
{
const cv::Mat& m = (*params.covs)[i];
CV_Assert(!m.empty() && m.size() == covSize && (m.type() == CV_32FC1));
}
}
if( cov_mat_type_name )
if(params.startStep == EM::START_E_STEP)
{
CV_CALL( cvWriteString( fs, "cov_mat_type", cov_mat_type_name) );
CV_Assert(params.means);
}
else
else if(params.startStep == EM::START_M_STEP)
{
CV_CALL( cvWriteInt( fs, "cov_mat_type", params.cov_mat_type ) );
CV_Assert(params.probs);
}
}
if( start_step_name )
static
void preprocessSampleData(const cv::Mat& src, cv::Mat& dst, int dstType, const cv::Mat& samplesMask, bool isAlwaysClone)
{
if(samplesMask.empty() || cv::countNonZero(samplesMask) == src.rows)
{
CV_CALL( cvWriteString( fs, "start_step", start_step_name) );
if(src.type() == dstType && !isAlwaysClone)
dst = src;
else
src.convertTo(dst, dstType);
}
else
{
CV_CALL( cvWriteInt( fs, "cov_mat_type", params.start_step ) );
dst.release();
for(int sampleIndex = 0; sampleIndex < src.rows; sampleIndex++)
{
if(samplesMask.at<uchar>(sampleIndex))
{
cv::Mat sample = src.row(sampleIndex);
cv::Mat sample_dbl;
sample.convertTo(sample_dbl, dstType);
dst.push_back(sample_dbl);
}
}
}
CV_CALL( cvWriteInt( fs, "nclusters", params.nclusters ));
CV_CALL( cvWrite( fs, "weights", weights ));
CV_CALL( cvWrite( fs, "means", means ));
CV_CALL( cvStartWriteStruct( fs, "covs", CV_NODE_SEQ ));
for( int i = 0; i < params.nclusters; i++ )
CV_CALL( cvWrite( fs, NULL, covs[i] ));
CV_CALL( cvEndWriteStruct( fs ) );
// Close params struct
CV_CALL( cvEndWriteStruct( fs ) );
__END__;
}
void CvEM::write( CvFileStorage* fs, const char* name ) const
static
void preprocessProbability(cv::Mat& probs)
{
CV_FUNCNAME( "CvEM::write" );
__BEGIN__;
CV_CALL( cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_EM ) );
cv::max(probs, 0., probs);
write_params(fs);
CV_CALL( cvStartWriteStruct( fs, "cvEM", CV_NODE_MAP ) );
CV_CALL( cvWrite( fs, "means", means ) );
CV_CALL( cvWrite( fs, "weights", weights ) );
CV_CALL( cvWrite( fs, "log_weight_div_det", log_weight_div_det ) );
CV_CALL( cvWrite( fs, "inv_eigen_values", inv_eigen_values ) );
CV_CALL( cvStartWriteStruct( fs, "covs", CV_NODE_SEQ ));
for( int i = 0; i < params.nclusters; i++ )
CV_CALL( cvWrite( fs, NULL, covs[i] ));
CV_CALL( cvEndWriteStruct( fs ));
CV_CALL( cvStartWriteStruct( fs, "cov_rotate_mats", CV_NODE_SEQ ));
for( int i = 0; i < params.nclusters; i++ )
CV_CALL( cvWrite( fs, NULL, cov_rotate_mats[i] ));
CV_CALL( cvEndWriteStruct( fs ) );
// close cvEM
CV_CALL( cvEndWriteStruct( fs ) );
// close top level
CV_CALL( cvEndWriteStruct( fs ) );
const float uniformProbability = 1./probs.cols;
for(int y = 0; y < probs.rows; y++)
{
cv::Mat sampleProbs = probs.row(y);
__END__;
double maxVal = 0;
cv::minMaxLoc(sampleProbs, 0, &maxVal);
if(maxVal < FLT_EPSILON)
sampleProbs.setTo(uniformProbability);
else
cv::normalize(sampleProbs, sampleProbs, 1, 0, cv::NORM_L1);
}
}
void CvEM::set_params( const CvEMParams& _params, const CvVectors& train_data )
void EM::setTrainData(const cv::Mat& samples, const cv::Mat& samplesMask, const EM::Params& params)
{
CV_FUNCNAME( "CvEM::set_params" );
clear();
__BEGIN__;
checkTrainData(samples, samplesMask, params);
int k;
// Set checked data
params = _params;
params.term_crit = cvCheckTermCriteria( params.term_crit, 1e-6, 10000 );
nclusters = params.nclusters;
covMatType = params.covMatType;
startStep = params.startStep;
if( params.cov_mat_type != COV_MAT_SPHERICAL &&
params.cov_mat_type != COV_MAT_DIAGONAL &&
params.cov_mat_type != COV_MAT_GENERIC )
CV_ERROR( CV_StsBadArg, "Unknown covariation matrix type" );
preprocessSampleData(samples, trainSamples, CV_32FC1, samplesMask, false);
switch( params.start_step )
// set probs
if(params.probs && startStep == EM::START_M_STEP)
{
case START_M_STEP:
if( !params.probs )
CV_ERROR( CV_StsNullPtr, "Probabilities must be specified when EM algorithm starts with M-step" );
break;
case START_E_STEP:
if( !params.means )
CV_ERROR( CV_StsNullPtr, "Mean's must be specified when EM algorithm starts with E-step" );
break;
case START_AUTO_STEP:
break;
default:
CV_ERROR( CV_StsBadArg, "Unknown start_step" );
preprocessSampleData(*params.probs, trainProbs, CV_32FC1, samplesMask, true);
preprocessProbability(trainProbs);
}
if( params.nclusters < 1 )
CV_ERROR( CV_StsOutOfRange, "The number of clusters (mixtures) should be > 0" );
if( params.probs )
// set weights
if(params.weights && (startStep == EM::START_E_STEP && params.covs))
{
const CvMat* p = params.probs;
if( !CV_IS_MAT(p) ||
(CV_MAT_TYPE(p->type) != CV_32FC1 &&
CV_MAT_TYPE(p->type) != CV_64FC1) ||
p->rows != train_data.count ||
p->cols != params.nclusters )
CV_ERROR( CV_StsBadArg, "The array of probabilities must be a valid "
"floating-point matrix (CvMat) of 'nsamples' x 'nclusters' size" );
params.weights->convertTo(weights, CV_32FC1);
weights.reshape(1,1);
preprocessProbability(weights);
}
if( params.means )
{
const CvMat* m = params.means;
if( !CV_IS_MAT(m) ||
(CV_MAT_TYPE(m->type) != CV_32FC1 &&
CV_MAT_TYPE(m->type) != CV_64FC1) ||
m->rows != params.nclusters ||
m->cols != train_data.dims )
CV_ERROR( CV_StsBadArg, "The array of mean's must be a valid "
"floating-point matrix (CvMat) of 'nsamples' x 'dims' size" );
}
// set means
if(params.means && (startStep == EM::START_E_STEP || startStep == EM::START_AUTO_STEP))
params.means->convertTo(means, CV_32FC1);
if( params.weights )
// set covs
if(params.covs && (startStep == EM::START_E_STEP && params.weights))
{
const CvMat* w = params.weights;
if( !CV_IS_MAT(w) ||
(CV_MAT_TYPE(w->type) != CV_32FC1 &&
CV_MAT_TYPE(w->type) != CV_64FC1) ||
(w->rows != 1 && w->cols != 1) ||
w->rows + w->cols - 1 != params.nclusters )
CV_ERROR( CV_StsBadArg, "The array of weights must be a valid "
"1d floating-point vector (CvMat) of 'nclusters' elements" );
covs.resize(nclusters);
for(size_t i = 0; i < params.covs->size(); i++)
(*params.covs)[i].convertTo(covs[i], CV_32FC1);
}
if( params.covs )
for( k = 0; k < params.nclusters; k++ )
{
const CvMat* cov = params.covs[k];
if( !CV_IS_MAT(cov) ||
(CV_MAT_TYPE(cov->type) != CV_32FC1 &&
CV_MAT_TYPE(cov->type) != CV_64FC1) ||
cov->rows != cov->cols || cov->cols != train_data.dims )
CV_ERROR( CV_StsBadArg,
"Each of covariation matrices must be a valid square "
"floating-point matrix (CvMat) of 'dims' x 'dims'" );
}
__END__;
}
/****************************************************************************************/
double CvEM::calcLikelihood( const cv::Mat &input_sample ) const
void EM::decomposeCovs()
{
const CvMat _input_sample = input_sample;
const CvMat* _sample = &_input_sample ;
float* sample_data = 0;
int cov_mat_type = params.cov_mat_type;
int i, dims = means->cols;
int nclusters = params.nclusters;
cvPreparePredictData( _sample, dims, 0, params.nclusters, 0, &sample_data );
CV_Assert(!covs.empty());
covsEigenValues.resize(nclusters);
if(covMatType == EM::COV_MAT_GENERIC)
covsRotateMats.resize(nclusters);
invCovsEigenValues.resize(nclusters);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
CV_Assert(!covs[clusterIndex].empty());
// allocate memory and initializing headers for calculating
cv::AutoBuffer<double> buffer(nclusters + dims);
CvMat expo = cvMat(1, nclusters, CV_64F, &buffer[0] );
CvMat diff = cvMat(1, dims, CV_64F, &buffer[nclusters] );
cv::SVD svd(covs[clusterIndex], cv::SVD::MODIFY_A + cv::SVD::FULL_UV);
CV_DbgAssert(svd.w.rows == 1 || svd.w.cols == 1);
CV_DbgAssert(svd.w.type() == CV_32FC1 && svd.u.type() == CV_32FC1);
// calculate the probabilities
for( int k = 0; k < nclusters; k++ )
{
const double* mean_k = (const double*)(means->data.ptr + means->step*k);
const double* w = (const double*)(inv_eigen_values->data.ptr + inv_eigen_values->step*k);
double cur = log_weight_div_det->data.db[k];
CvMat* u = cov_rotate_mats[k];
// cov = u w u' --> cov^(-1) = u w^(-1) u'
if( cov_mat_type == COV_MAT_SPHERICAL )
if(covMatType == EM::COV_MAT_SPHERICAL)
{
double w0 = w[0];
for( i = 0; i < dims; i++ )
{
double val = sample_data[i] - mean_k[i];
cur += val*val*w0;
}
float maxSingularVal = svd.w.at<float>(0);
covsEigenValues[clusterIndex] = cv::Mat(1, 1, CV_32FC1, cv::Scalar(maxSingularVal));
}
else
else if(covMatType == EM::COV_MAT_DIAGONAL)
{
for( i = 0; i < dims; i++ )
diff.data.db[i] = sample_data[i] - mean_k[i];
if( cov_mat_type == COV_MAT_GENERIC )
cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T );
for( i = 0; i < dims; i++ )
{
double val = diff.data.db[i];
cur += val*val*w[i];
}
covsEigenValues[clusterIndex] = svd.w;
}
expo.data.db[k] = cur;
else //EM::COV_MAT_GENERIC
{
covsEigenValues[clusterIndex] = svd.w;
covsRotateMats[clusterIndex] = svd.u;
}
cv::max(covsEigenValues[clusterIndex], minEigenValue, covsEigenValues[clusterIndex]);
invCovsEigenValues[clusterIndex] = 1./covsEigenValues[clusterIndex];
}
// probability = (2*pi)^(-dims/2)*exp( -0.5 * cur )
cvConvertScale( &expo, &expo, -0.5 );
double factor = -double(dims)/2.0 * log(2.0*CV_PI);
cvAndS( &expo, cvScalar(factor), &expo );
// Calculate the log-likelihood of the given sample -
// see Alex Smola's blog http://blog.smola.org/page/2 for
// details on the log-sum-exp trick
double mini,maxi,retval;
cvMinMaxLoc( &expo, &mini, &maxi, 0, 0 );
CvMat *flp = cvCloneMat(&expo);
cvSubS( &expo, cvScalar(maxi), flp);
cvExp( flp, flp );
CvScalar ss = cvSum( flp );
retval = log(ss.val[0]) + maxi;
cvReleaseMat(&flp);
if( sample_data != _sample->data.fl )
cvFree( &sample_data );
return retval;
}
/****************************************************************************************/
float
CvEM::predict( const CvMat* _sample, CvMat* _probs ) const
void EM::clusterTrainSamples()
{
float* sample_data = 0;
int cls = 0;
int cov_mat_type = params.cov_mat_type;
double opt = FLT_MAX;
int i, dims = means->cols;
int nclusters = params.nclusters;
cvPreparePredictData( _sample, dims, 0, params.nclusters, _probs, &sample_data );
// allocate memory and initializing headers for calculating
cv::AutoBuffer<double> buffer(nclusters + dims);
CvMat expo = cvMat(1, nclusters, CV_64F, &buffer[0] );
CvMat diff = cvMat(1, dims, CV_64F, &buffer[nclusters] );
// calculate the probabilities
for( int k = 0; k < nclusters; k++ )
int nsamples = trainSamples.rows;
// Cluster samples, compute/update means
cv::Mat labels;
cv::kmeans(trainSamples, nclusters, labels,
cv::TermCriteria(cv::TermCriteria::COUNT, means.empty() ? 10 : 1, 0.5),
10, cv::KMEANS_PP_CENTERS, means);
CV_Assert(means.type() == CV_32FC1);
// Compute weights and covs
weights = cv::Mat(1, nclusters, CV_32FC1, cv::Scalar(0));
covs.resize(nclusters);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
const double* mean_k = (const double*)(means->data.ptr + means->step*k);
const double* w = (const double*)(inv_eigen_values->data.ptr + inv_eigen_values->step*k);
double cur = log_weight_div_det->data.db[k];
CvMat* u = cov_rotate_mats[k];
// cov = u w u' --> cov^(-1) = u w^(-1) u'
if( cov_mat_type == COV_MAT_SPHERICAL )
{
double w0 = w[0];
for( i = 0; i < dims; i++ )
{
double val = sample_data[i] - mean_k[i];
cur += val*val*w0;
}
}
else
cv::Mat clusterSamples;
for(int sampleIndex = 0; sampleIndex < nsamples; sampleIndex++)
{
for( i = 0; i < dims; i++ )
diff.data.db[i] = sample_data[i] - mean_k[i];
if( cov_mat_type == COV_MAT_GENERIC )
cvGEMM( &diff, u, 1, 0, 0, &diff, CV_GEMM_B_T );
for( i = 0; i < dims; i++ )
if(labels.at<int>(sampleIndex) == clusterIndex)
{
double val = diff.data.db[i];
cur += val*val*w[i];
const cv::Mat sample = trainSamples.row(sampleIndex);
clusterSamples.push_back(sample);
}
}
CV_Assert(!clusterSamples.empty());
expo.data.db[k] = cur;
if( cur < opt )
{
cls = k;
opt = cur;
}
}
// probability = (2*pi)^(-dims/2)*exp( -0.5 * cur )
cvConvertScale( &expo, &expo, -0.5 );
double factor = -double(dims)/2.0 * log(2.0*CV_PI);
cvAndS( &expo, cvScalar(factor), &expo );
// Calculate the posterior probability of each component
// given the sample data.
if( _probs )
{
cvExp( &expo, &expo );
if( _probs->cols == 1 )
cvReshape( &expo, &expo, 0, nclusters );
cvConvertScale( &expo, _probs, 1./cvSum( &expo ).val[0] );
cv::calcCovarMatrix(clusterSamples, covs[clusterIndex], means.row(clusterIndex),
CV_COVAR_NORMAL + CV_COVAR_ROWS + CV_COVAR_USE_AVG + CV_COVAR_SCALE, CV_32FC1);
weights.at<float>(clusterIndex) = static_cast<float>(clusterSamples.rows)/static_cast<float>(nsamples);
}
if( sample_data != _sample->data.fl )
cvFree( &sample_data );
return (float)cls;
decomposeCovs();
}
bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
CvEMParams _params, CvMat* labels )
void EM::computeLogWeightDivDet()
{
bool result = false;
CvVectors train_data;
CvMat* sample_idx = 0;
train_data.data.fl = 0;
train_data.count = 0;
CV_Assert(!covsEigenValues.empty());
CV_FUNCNAME("cvEM");
__BEGIN__;
int i, nsamples, nclusters, dims;
clear();
cv::Mat logWeights;
cv::log(weights, logWeights);
CV_CALL( cvPrepareTrainData( "cvEM",
_samples, CV_ROW_SAMPLE, 0, CV_VAR_CATEGORICAL,
0, _sample_idx, false, (const float***)&train_data.data.fl,
&train_data.count, &train_data.dims, &train_data.dims,
0, 0, 0, &sample_idx ));
logWeightDivDet.create(1, nclusters, CV_32FC1);
// note: logWeightDivDet = log(weight_k) - 0.5 * log(|det(cov_k)|)
CV_CALL( set_params( _params, train_data ));
nsamples = train_data.count;
nclusters = params.nclusters;
dims = train_data.dims;
if( labels && (!CV_IS_MAT(labels) || CV_MAT_TYPE(labels->type) != CV_32SC1 ||
(labels->cols != 1 && labels->rows != 1) || labels->cols + labels->rows - 1 != nsamples ))
CV_ERROR( CV_StsBadArg,
"labels array (when passed) must be a valid 1d integer vector of <sample_count> elements" );
if( nsamples <= nclusters )
CV_ERROR( CV_StsOutOfRange,
"The number of samples should be greater than the number of clusters" );
CV_CALL( log_weight_div_det = cvCreateMat( 1, nclusters, CV_64FC1 ));
CV_CALL( probs = cvCreateMat( nsamples, nclusters, CV_64FC1 ));
CV_CALL( means = cvCreateMat( nclusters, dims, CV_64FC1 ));
CV_CALL( weights = cvCreateMat( 1, nclusters, CV_64FC1 ));
CV_CALL( inv_eigen_values = cvCreateMat( nclusters,
params.cov_mat_type == COV_MAT_SPHERICAL ? 1 : dims, CV_64FC1 ));
CV_CALL( covs = (CvMat**)cvAlloc( nclusters * sizeof(*covs) ));
CV_CALL( cov_rotate_mats = (CvMat**)cvAlloc( nclusters * sizeof(cov_rotate_mats[0]) ));
for( i = 0; i < nclusters; i++ )
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
CV_CALL( covs[i] = cvCreateMat( dims, dims, CV_64FC1 ));
CV_CALL( cov_rotate_mats[i] = cvCreateMat( dims, dims, CV_64FC1 ));
cvZero( cov_rotate_mats[i] );
}
init_em( train_data );
log_likelihood = run_em( train_data );
float logDetCov = 0.;
for(int di = 0; di < covsEigenValues[clusterIndex].cols; di++)
logDetCov += std::log(covsEigenValues[clusterIndex].at<float>(covMatType != EM::COV_MAT_SPHERICAL ? di : 0));
if( log_likelihood <= -DBL_MAX/10000. )
EXIT;
logWeightDivDet.at<float>(clusterIndex) = logWeights.at<float>(clusterIndex) - 0.5 * logDetCov;
}
}
if( labels )
bool EM::doTrain(const cv::TermCriteria& termCrit)
{
int dim = trainSamples.cols;
// Precompute the empty initial train data in the cases of EM::START_E_STEP and START_AUTO_STEP
if(startStep != EM::START_M_STEP)
{
if( nclusters == 1 )
cvZero( labels );
else
if(weights.empty())
{
CvMat sample = cvMat( 1, dims, CV_32F );
CvMat prob = cvMat( 1, nclusters, CV_64F );
int lstep = CV_IS_MAT_CONT(labels->type) ? 1 : labels->step/sizeof(int);
for( i = 0; i < nsamples; i++ )
{
int idx = sample_idx ? sample_idx->data.i[i] : i;
sample.data.ptr = _samples->data.ptr + _samples->step*idx;
prob.data.ptr = probs->data.ptr + probs->step*i;
labels->data.i[i*lstep] = cvRound(predict(&sample, &prob));
}
CV_Assert(covs.empty());
clusterTrainSamples();
}
}
result = true;
__END__;
if( sample_idx != _sample_idx )
cvReleaseMat( &sample_idx );
cvFree( &train_data.data.ptr );
return result;
}
if(!covs.empty() && covsEigenValues.empty() )
{
CV_Assert(invCovsEigenValues.empty());
decomposeCovs();
}
if(startStep == EM::START_M_STEP)
mStep();
void CvEM::init_em( const CvVectors& train_data )
{
CvMat *w = 0, *u = 0, *tcov = 0;
double trainLikelihood, prevTrainLikelihood;
for(int iter = 0; ; iter++)
{
eStep();
trainLikelihood = cv::sum(trainLikelihoods)[0];
CV_FUNCNAME( "CvEM::init_em" );
if(iter >= termCrit.maxCount - 1)
break;
__BEGIN__;
double trainLikelihoodDelta = trainLikelihood - (iter > 0 ? prevTrainLikelihood : 0);
if( iter != 0 &&
(trainLikelihoodDelta < -DBL_EPSILON ||
trainLikelihoodDelta < termCrit.epsilon * std::fabs(trainLikelihood)))
break;
double maxval = 0;
int i, force_symm_plus = 0;
int nclusters = params.nclusters, nsamples = train_data.count, dims = train_data.dims;
mStep();
if( params.start_step == START_AUTO_STEP || nclusters == 1 || nclusters == nsamples )
init_auto( train_data );
else if( params.start_step == START_M_STEP )
{
for( i = 0; i < nsamples; i++ )
{
CvMat prob;
cvGetRow( params.probs, &prob, i );
cvMaxS( &prob, 0., &prob );
cvMinMaxLoc( &prob, 0, &maxval );
if( maxval < FLT_EPSILON )
cvSet( &prob, cvScalar(1./nclusters) );
else
cvNormalize( &prob, &prob, 1., 0, CV_L1 );
}
EXIT; // do not preprocess covariation matrices,
// as in this case they are initialized at the first iteration of EM
}
else
{
CV_ASSERT( params.start_step == START_E_STEP && params.means );
if( params.weights && params.covs )
{
cvConvert( params.means, means );
cvReshape( weights, weights, 1, params.weights->rows );
cvConvert( params.weights, weights );
cvReshape( weights, weights, 1, 1 );
cvMaxS( weights, 0., weights );
cvMinMaxLoc( weights, 0, &maxval );
if( maxval < FLT_EPSILON )
cvSet( weights, cvScalar(1./nclusters) );
cvNormalize( weights, weights, 1., 0, CV_L1 );
for( i = 0; i < nclusters; i++ )
CV_CALL( cvConvert( params.covs[i], covs[i] ));
force_symm_plus = 1;
}
else
init_auto( train_data );
prevTrainLikelihood = trainLikelihood;
}
CV_CALL( tcov = cvCreateMat( dims, dims, CV_64FC1 ));
CV_CALL( w = cvCreateMat( dims, dims, CV_64FC1 ));
if( params.cov_mat_type != COV_MAT_SPHERICAL )
CV_CALL( u = cvCreateMat( dims, dims, CV_64FC1 ));
if( trainLikelihood <= -DBL_MAX/10000. )
return false;
for( i = 0; i < nclusters; i++ )
// postprocess covs
covs.resize(nclusters);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
if( force_symm_plus )
if(covMatType == EM::COV_MAT_SPHERICAL)
{
cvTranspose( covs[i], tcov );
cvAddWeighted( covs[i], 0.5, tcov, 0.5, 0, tcov );
}
else
cvCopy( covs[i], tcov );
cvSVD( tcov, w, u, 0, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
if( params.cov_mat_type == COV_MAT_SPHERICAL )
cvSetIdentity( covs[i], cvScalar(cvTrace(w).val[0]/dims) );
/*else if( params.cov_mat_type == COV_MAT_DIAGONAL )
cvCopy( w, covs[i] );*/
else
{
// generic case: covs[i] = (u')'*max(w,0)*u'
cvGEMM( u, w, 1, 0, 0, tcov, CV_GEMM_A_T );
cvGEMM( tcov, u, 1, 0, 0, covs[i], 0 );
covs[clusterIndex].create(dim, dim, CV_32FC1);
cv::setIdentity(covs[clusterIndex], cv::Scalar(covsEigenValues[clusterIndex].at<float>(0)));
}
else if(covMatType == EM::COV_MAT_DIAGONAL)
covs[clusterIndex] = cv::Mat::diag(covsEigenValues[clusterIndex].t());
}
__END__;
cvReleaseMat( &w );
cvReleaseMat( &u );
cvReleaseMat( &tcov );
return true;
}
void CvEM::init_auto( const CvVectors& train_data )
void EM::computeProbabilities(const cv::Mat& sample, int& label, cv::Mat* probs, float* likelihood) const
{
CvMat* hdr = 0;
const void** vec = 0;
CvMat* class_ranges = 0;
CvMat* labels = 0;
// L_ik = log(weight_k) - 0.5 * log(|det(cov_k)|) - 0.5 *(x_i - mean_k)' cov_k^(-1) (x_i - mean_k)]
// q = arg(max_k(L_ik))
// probs_ik = exp(L_ik - L_iq) / (1 + sum_j!=q (exp(L_jk))
CV_FUNCNAME( "CvEM::init_auto" );
CV_DbgAssert(sample.rows == 1);
__BEGIN__;
int dim = sample.cols;
int nclusters = params.nclusters, nsamples = train_data.count, dims = train_data.dims;
int i, j;
cv::Mat L(1, nclusters, CV_32FC1);
cv::Mat expL(1, nclusters, CV_32FC1);
if( nclusters == nsamples )
label = 0;
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
CvMat src = cvMat( 1, dims, CV_32F );
CvMat dst = cvMat( 1, dims, CV_64F );
for( i = 0; i < nsamples; i++ )
{
src.data.ptr = train_data.data.ptr[i];
dst.data.ptr = means->data.ptr + means->step*i;
cvConvert( &src, &dst );
cvZero( covs[i] );
cvSetIdentity( cov_rotate_mats[i] );
}
cvSetIdentity( probs );
cvSet( weights, cvScalar(1./nclusters) );
}
else
{
int max_count = 0;
const cv::Mat centeredSample = sample - means.row(clusterIndex);
CV_CALL( class_ranges = cvCreateMat( 1, nclusters+1, CV_32SC1 ));
if( nclusters > 1 )
{
CV_CALL( labels = cvCreateMat( 1, nsamples, CV_32SC1 ));
// Not fully executed in case means are already given
kmeans( train_data, nclusters, labels, cvTermCriteria( CV_TERMCRIT_ITER,
params.means ? 1 : 10, 0.5 ), params.means );
cv::Mat rotatedCenteredSample = covMatType != EM::COV_MAT_GENERIC ?
centeredSample : centeredSample * covsRotateMats[clusterIndex];
CV_CALL( cvSortSamplesByClasses( (const float**)train_data.data.fl,
labels, class_ranges->data.i ));
}
else
float Lval = 0;
for(int di = 0; di < dim; di++)
{
class_ranges->data.i[0] = 0;
class_ranges->data.i[1] = nsamples;
float w = invCovsEigenValues[clusterIndex].at<float>(covMatType != EM::COV_MAT_SPHERICAL ? di : 0);
float val = rotatedCenteredSample.at<float>(di);
Lval += w * val * val;
}
CV_DbgAssert(!logWeightDivDet.empty());
Lval = logWeightDivDet.at<float>(clusterIndex) - 0.5 * Lval;
L.at<float>(clusterIndex) = Lval;
for( i = 0; i < nclusters; i++ )
{
int left = class_ranges->data.i[i], right = class_ranges->data.i[i+1];
max_count = MAX( max_count, right - left );
}
CV_CALL( hdr = (CvMat*)cvAlloc( max_count*sizeof(hdr[0]) ));
CV_CALL( vec = (const void**)cvAlloc( max_count*sizeof(vec[0]) ));
hdr[0] = cvMat( 1, dims, CV_32F );
for( i = 0; i < max_count; i++ )
{
vec[i] = hdr + i;
hdr[i] = hdr[0];
}
if(Lval > L.at<float>(label))
label = clusterIndex;
}
for( i = 0; i < nclusters; i++ )
{
int left = class_ranges->data.i[i], right = class_ranges->data.i[i+1];
int cluster_size = right - left;
CvMat avg;
if(!probs && !likelihood)
return;
if( cluster_size <= 0 )
continue;
// TODO maybe without finding max L value
cv::exp(L, expL);
float partExpSum = 0, // sum_j!=q (exp(L_jk)
factor; // 1/(1 + sum_j!=q (exp(L_jk))
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
if(clusterIndex != label)
partExpSum += expL.at<float>(clusterIndex);
}
factor = 1./(1 + partExpSum);
for( j = left; j < right; j++ )
hdr[j - left].data.fl = train_data.data.fl[j];
cv::exp(L - L.at<float>(label), expL);
CV_CALL( cvGetRow( means, &avg, i ));
CV_CALL( cvCalcCovarMatrix( vec, cluster_size, covs[i],
&avg, CV_COVAR_NORMAL | CV_COVAR_SCALE ));
weights->data.db[i] = (double)cluster_size/(double)nsamples;
}
if(probs)
{
probs->create(1, nclusters, CV_32FC1);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
probs->at<float>(clusterIndex) = expL.at<float>(clusterIndex) * factor;
}
__END__;
cvReleaseMat( &class_ranges );
cvReleaseMat( &labels );
cvFree( &hdr );
cvFree( &vec );
if(likelihood)
{
// note likelihood = log (sum_j exp(L_ij)) - 0.5 * dims * ln2Pi
*likelihood = std::log(partExpSum + expL.at<float>(label)) - 0.5 * dim * CV_LOG2PI;
}
}
void CvEM::kmeans( const CvVectors& train_data, int nclusters, CvMat* labels,
CvTermCriteria termcrit, const CvMat* /*centers0*/ )
void EM::eStep()
{
int i, nsamples = train_data.count, dims = train_data.dims;
cv::Ptr<CvMat> temp_mat = cvCreateMat(nsamples, dims, CV_32F);
// Compute probs_ik from means_k, covs_k and weights_k.
trainProbs.create(trainSamples.rows, nclusters, CV_32FC1);
trainLabels.create(trainSamples.rows, 1, CV_32SC1);
trainLikelihoods.create(trainSamples.rows, 1, CV_32FC1);
for( i = 0; i < nsamples; i++ )
memcpy( temp_mat->data.ptr + temp_mat->step*i, train_data.data.fl[i], dims*sizeof(float));
computeLogWeightDivDet();
cvKMeans2(temp_mat, nclusters, labels, termcrit, 10);
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
{
cv::Mat sampleProbs = trainProbs.row(sampleIndex);
computeProbabilities(trainSamples.row(sampleIndex), trainLabels.at<int>(sampleIndex),
&sampleProbs, &trainLikelihoods.at<float>(sampleIndex));
}
}
/****************************************************************************************/
/* log_weight_div_det[k] = -2*log(weights_k) + log(det(Sigma_k)))
covs[k] = cov_rotate_mats[k] * cov_eigen_values[k] * (cov_rotate_mats[k])'
cov_rotate_mats[k] are orthogonal matrices of eigenvectors and
cov_eigen_values[k] are diagonal matrices (represented by 1D vectors) of eigen values.
The <alpha_ik> is the probability of the vector x_i to belong to the k-th cluster:
<alpha_ik> ~ weights_k * exp{ -0.5[ln(det(Sigma_k)) + (x_i - mu_k)' Sigma_k^(-1) (x_i - mu_k)] }
We calculate these probabilities here by the equivalent formulae:
Denote
S_ik = -0.5(log(det(Sigma_k)) + (x_i - mu_k)' Sigma_k^(-1) (x_i - mu_k)) + log(weights_k),
M_i = max_k S_ik = S_qi, so that the q-th class is the one where maximum reaches. Then
alpha_ik = exp{ S_ik - M_i } / ( 1 + sum_j!=q exp{ S_ji - M_i })
*/
double CvEM::run_em( const CvVectors& train_data )
void EM::mStep()
{
CvMat* centered_sample = 0;
CvMat* covs_item = 0;
CvMat* log_det = 0;
CvMat* log_weights = 0;
CvMat* cov_eigen_values = 0;
CvMat* samples = 0;
CvMat* sum_probs = 0;
log_likelihood = -DBL_MAX;
CV_FUNCNAME( "CvEM::run_em" );
__BEGIN__;
int nsamples = train_data.count, dims = train_data.dims, nclusters = params.nclusters;
double min_variation = FLT_EPSILON;
double min_det_value = MAX( DBL_MIN, pow( min_variation, dims ));
double _log_likelihood = -DBL_MAX;
int start_step = params.start_step;
double sum_max_val;
int i, j, k, n;
int is_general = 0, is_diagonal = 0, is_spherical = 0;
double prev_log_likelihood = -DBL_MAX / 1000., det, d;
CvMat whdr, iwhdr, diag, *w, *iw;
double* w_data;
double* sp_data;
if( nclusters == 1 )
{
double log_weight;
CV_CALL( cvSet( probs, cvScalar(1.)) );
if( params.cov_mat_type == COV_MAT_SPHERICAL )
{
d = cvTrace(*covs).val[0]/dims;
d = MAX( d, FLT_EPSILON );
inv_eigen_values->data.db[0] = 1./d;
log_weight = pow( d, dims*0.5 );
}
else
{
w_data = inv_eigen_values->data.db;
if( params.cov_mat_type == COV_MAT_GENERIC )
cvSVD( *covs, inv_eigen_values, *cov_rotate_mats, 0, CV_SVD_U_T );
else
cvTranspose( cvGetDiag(*covs, &diag), inv_eigen_values );
trainCounts.create(1, nclusters, CV_32SC1);
trainCounts = cv::Scalar(0);
cvMaxS( inv_eigen_values, FLT_EPSILON, inv_eigen_values );
for( j = 0, det = 1.; j < dims; j++ )
det *= w_data[j];
log_weight = sqrt(det);
cvDiv( 0, inv_eigen_values, inv_eigen_values );
}
log_weight_div_det->data.db[0] = -2*log(weights->data.db[0]/log_weight);
log_likelihood = DBL_MAX/1000.;
EXIT;
}
for(int sampleIndex = 0; sampleIndex < trainLabels.rows; sampleIndex++)
trainCounts.at<int>(trainLabels.at<int>(sampleIndex))++;
if( params.cov_mat_type == COV_MAT_GENERIC )
is_general = 1;
else if( params.cov_mat_type == COV_MAT_DIAGONAL )
is_diagonal = 1;
else if( params.cov_mat_type == COV_MAT_SPHERICAL )
is_spherical = 1;
/* In the case of <cov_mat_type> == COV_MAT_DIAGONAL, the k-th row of cov_eigen_values
contains the diagonal elements (variations). In the case of
<cov_mat_type> == COV_MAT_SPHERICAL - the 0-ths elements of the vectors cov_eigen_values[k]
are to be equal to the mean of the variations over all the dimensions. */
CV_CALL( log_det = cvCreateMat( 1, nclusters, CV_64FC1 ));
CV_CALL( log_weights = cvCreateMat( 1, nclusters, CV_64FC1 ));
CV_CALL( covs_item = cvCreateMat( dims, dims, CV_64FC1 ));
CV_CALL( centered_sample = cvCreateMat( 1, dims, CV_64FC1 ));
CV_CALL( cov_eigen_values = cvCreateMat( inv_eigen_values->rows, inv_eigen_values->cols, CV_64FC1 ));
CV_CALL( samples = cvCreateMat( nsamples, dims, CV_64FC1 ));
CV_CALL( sum_probs = cvCreateMat( 1, nclusters, CV_64FC1 ));
sp_data = sum_probs->data.db;
// copy the training data into double-precision matrix
for( i = 0; i < nsamples; i++ )
if(cv::countNonZero(trainCounts) != (int)trainCounts.total())
{
const float* src = train_data.data.fl[i];
double* dst = (double*)(samples->data.ptr + samples->step*i);
for( j = 0; j < dims; j++ )
dst[j] = src[j];
clusterTrainSamples();
}
if( start_step != START_M_STEP )
else
{
for( k = 0; k < nclusters; k++ )
{
if( is_general || is_diagonal )
{
w = cvGetRow( cov_eigen_values, &whdr, k );
if( is_general )
cvSVD( covs[k], w, cov_rotate_mats[k], 0, CV_SVD_U_T );
else
cvTranspose( cvGetDiag( covs[k], &diag ), w );
w_data = w->data.db;
for( j = 0, det = 0.; j < dims; j++ )
det += std::log(w_data[j]);
if( det < std::log(min_det_value) )
{
if( start_step == START_AUTO_STEP )
det = std::log(min_det_value);
else
EXIT;
}
log_det->data.db[k] = det;
}
else // spherical
{
d = cvTrace(covs[k]).val[0]/(double)dims;
if( d < min_variation )
{
if( start_step == START_AUTO_STEP )
d = min_variation;
else
EXIT;
}
cov_eigen_values->data.db[k] = d;
log_det->data.db[k] = d;
}
}
// Update means_k, covs_k and weights_k from probs_ik
int dim = trainSamples.cols;
if( is_spherical )
{
cvLog( log_det, log_det );
cvScale( log_det, log_det, dims );
}
}
// Update weights
// not normalized first
cv::reduce(trainProbs, weights, 0, CV_REDUCE_SUM);
for( n = 0; n < params.term_crit.max_iter; n++ )
{
if( n > 0 || start_step != START_M_STEP )
// Update means
means.create(nclusters, dim, CV_32FC1);
means = cv::Scalar(0);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
// e-step: compute probs_ik from means_k, covs_k and weights_k.
CV_CALL(cvLog( weights, log_weights ));
sum_max_val = 0.;
// S_ik = -0.5[log(det(Sigma_k)) + (x_i - mu_k)' Sigma_k^(-1) (x_i - mu_k)] + log(weights_k)
for( k = 0; k < nclusters; k++ )
{
CvMat* u = cov_rotate_mats[k];
const double* mean = (double*)(means->data.ptr + means->step*k);
w = cvGetRow( cov_eigen_values, &whdr, k );
iw = cvGetRow( inv_eigen_values, &iwhdr, k );
cvDiv( 0, w, iw );
w_data = (double*)(inv_eigen_values->data.ptr + inv_eigen_values->step*k);
for( i = 0; i < nsamples; i++ )
{
double *csample = centered_sample->data.db, p = log_det->data.db[k];
const double* sample = (double*)(samples->data.ptr + samples->step*i);
double* pp = (double*)(probs->data.ptr + probs->step*i);
for( j = 0; j < dims; j++ )
csample[j] = sample[j] - mean[j];
if( is_general )
cvGEMM( centered_sample, u, 1, 0, 0, centered_sample, CV_GEMM_B_T );
for( j = 0; j < dims; j++ )
p += csample[j]*csample[j]*w_data[is_spherical ? 0 : j];
//pp[k] = -0.5*p + log_weights->data.db[k];
pp[k] = -0.5*(p+CV_LOG2PI * (double)dims) + log_weights->data.db[k];
// S_ik <- S_ik - max_j S_ij
if( k == nclusters - 1 )
{
double max_val = pp[0];
for( j = 1; j < nclusters; j++ )
max_val = MAX( max_val, pp[j] );
sum_max_val += max_val;
for( j = 0; j < nclusters; j++ )
pp[j] -= max_val;
}
}
}
CV_CALL(cvExp( probs, probs )); // exp( S_ik )
cvZero( sum_probs );
// alpha_ik = exp( S_ik ) / sum_j exp( S_ij ),
// log_likelihood = sum_i log (sum_j exp(S_ij))
for( i = 0, _log_likelihood = 0; i < nsamples; i++ )
{
double* pp = (double*)(probs->data.ptr + probs->step*i), sum = 0;
for( j = 0; j < nclusters; j++ )
sum += pp[j];
sum = 1./MAX( sum, DBL_EPSILON );
for( j = 0; j < nclusters; j++ )
{
double p = pp[j] *= sum;
sp_data[j] += p;
}
_log_likelihood -= log( sum );
}
_log_likelihood+=sum_max_val;
// Check termination criteria. Use the same termination criteria as it is used in MATLAB
log_likelihood_delta = _log_likelihood - prev_log_likelihood;
// if( n>0 )
// fprintf(stderr, "iter=%d, ll=%0.5f (delta=%0.5f, goal=%0.5f)\n",
// n, _log_likelihood, delta, params.term_crit.epsilon * fabs( _log_likelihood));
if ( log_likelihood_delta > 0 && log_likelihood_delta < params.term_crit.epsilon * std::fabs( _log_likelihood) )
break;
prev_log_likelihood = _log_likelihood;
cv::Mat clusterMean = means.row(clusterIndex);
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
clusterMean += trainProbs.at<float>(sampleIndex, clusterIndex) * trainSamples.row(sampleIndex);
clusterMean /= weights.at<float>(clusterIndex);
}
// m-step: update means_k, covs_k and weights_k from probs_ik
cvGEMM( probs, samples, 1, 0, 0, means, CV_GEMM_A_T );
for( k = 0; k < nclusters; k++ )
// Update covsEigenValues and invCovsEigenValues
covs.resize(nclusters);
covsEigenValues.resize(nclusters);
if(covMatType == EM::COV_MAT_GENERIC)
covsRotateMats.resize(nclusters);
invCovsEigenValues.resize(nclusters);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
double sum = sp_data[k], inv_sum = 1./sum;
CvMat* cov = covs[k], _mean, _sample;
w = cvGetRow( cov_eigen_values, &whdr, k );
w_data = w->data.db;
cvGetRow( means, &_mean, k );
cvGetRow( samples, &_sample, k );
if(covMatType != EM::COV_MAT_SPHERICAL)
covsEigenValues[clusterIndex].create(1, dim, CV_32FC1);
else
covsEigenValues[clusterIndex].create(1, 1, CV_32FC1);
// update weights_k
weights->data.db[k] = sum;
if(covMatType == EM::COV_MAT_GENERIC)
covs[clusterIndex].create(dim, dim, CV_32FC1);
// update means_k
cvScale( &_mean, &_mean, inv_sum );
cv::Mat clusterCov = covMatType != EM::COV_MAT_GENERIC ?
covsEigenValues[clusterIndex] : covs[clusterIndex];
// compute covs_k
cvZero( cov );
cvZero( w );
clusterCov = cv::Scalar(0);
for( i = 0; i < nsamples; i++ )
cv::Mat centeredSample;
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
{
double p = probs->data.db[i*nclusters + k]*inv_sum;
_sample.data.db = (double*)(samples->data.ptr + samples->step*i);
centeredSample = trainSamples.row(sampleIndex) - means.row(clusterIndex);
if( is_general )
{
cvMulTransposed( &_sample, covs_item, 1, &_mean );
cvScaleAdd( covs_item, cvRealScalar(p), cov, cov );
}
if(covMatType == EM::COV_MAT_GENERIC)
clusterCov += trainProbs.at<float>(sampleIndex, clusterIndex) * centeredSample.t() * centeredSample;
else
for( j = 0; j < dims; j++ )
{
float p = trainProbs.at<float>(sampleIndex, clusterIndex);
for(int di = 0; di < dim; di++ )
{
double val = _sample.data.db[j] - _mean.data.db[j];
w_data[is_spherical ? 0 : j] += p*val*val;
float val = centeredSample.at<float>(di);
clusterCov.at<float>(covMatType != EM::COV_MAT_SPHERICAL ? di : 0) += p*val*val;
}
}
}
if( is_spherical )
{
d = w_data[0]/(double)dims;
d = MAX( d, min_variation );
w->data.db[0] = d;
log_det->data.db[k] = d;
}
else
if(covMatType == EM::COV_MAT_SPHERICAL)
clusterCov /= dim;
clusterCov /= weights.at<float>(clusterIndex);
// Update covsRotateMats for EM::COV_MAT_GENERIC only
if(covMatType == EM::COV_MAT_GENERIC)
{
// Det. of general NxN cov. matrix is the prod. of the eig. vals
if( is_general )
cvSVD( cov, w, cov_rotate_mats[k], 0, CV_SVD_U_T );
cvMaxS( w, min_variation, w );
for( j = 0, det = 0.; j < dims; j++ )
det += std::log( w_data[j] );
log_det->data.db[k] = det;
cv::SVD svd(covs[clusterIndex], cv::SVD::MODIFY_A + cv::SVD::FULL_UV);
covsEigenValues[clusterIndex] = svd.w;
covsRotateMats[clusterIndex] = svd.u;
}
}
cvConvertScale( weights, weights, 1./(double)nsamples, 0 );
cvMaxS( weights, DBL_MIN, weights );
cv::max(covsEigenValues[clusterIndex], minEigenValue, covsEigenValues[clusterIndex]);
if( is_spherical )
{
cvLog( log_det, log_det );
cvScale( log_det, log_det, dims );
// update invCovsEigenValues
invCovsEigenValues[clusterIndex] = 1./covsEigenValues[clusterIndex];
}
} // end of iteration process
//log_weight_div_det[k] = -2*log(weights_k/det(Sigma_k))^0.5) = -2*log(weights_k) + log(det(Sigma_k)))
if( log_weight_div_det )
{
cvScale( log_weights, log_weight_div_det, -2 );
cvAdd( log_weight_div_det, log_det, log_weight_div_det );
}
/* Now finalize all the covariation matrices:
1) if <cov_mat_type> == COV_MAT_DIAGONAL we used array of <w> as diagonals.
Now w[k] should be copied back to the diagonals of covs[k];
2) if <cov_mat_type> == COV_MAT_SPHERICAL we used the 0-th element of w[k]
as an average variation in each cluster. The value of the 0-th element of w[k]
should be copied to the all of the diagonal elements of covs[k]. */
if( is_spherical )
{
for( k = 0; k < nclusters; k++ )
cvSetIdentity( covs[k], cvScalar(cov_eigen_values->data.db[k]));
// Normalize weights
weights /= trainSamples.rows;
}
else if( is_diagonal )
{
for( k = 0; k < nclusters; k++ )
cvTranspose( cvGetRow( cov_eigen_values, &whdr, k ),
cvGetDiag( covs[k], &diag ));
}
cvDiv( 0, cov_eigen_values, inv_eigen_values );
log_likelihood = _log_likelihood;
__END__;
cvReleaseMat( &log_det );
cvReleaseMat( &log_weights );
cvReleaseMat( &covs_item );
cvReleaseMat( &centered_sample );
cvReleaseMat( &cov_eigen_values );
cvReleaseMat( &samples );
cvReleaseMat( &sum_probs );
return log_likelihood;
}
int CvEM::get_nclusters() const
void EM::read(const FileNode& fn)
{
return params.nclusters;
}
Algorithm::read(fn);
const CvMat* CvEM::get_means() const
{
return means;
}
const CvMat** CvEM::get_covs() const
{
return (const CvMat**)covs;
}
const CvMat* CvEM::get_weights() const
{
return weights;
}
const CvMat* CvEM::get_probs() const
{
return probs;
decomposeCovs();
computeLogWeightDivDet();
}
using namespace cv;
CvEM::CvEM( const Mat& samples, const Mat& sample_idx, CvEMParams params )
static Algorithm* createEM()
{
means = weights = probs = inv_eigen_values = log_weight_div_det = 0;
covs = cov_rotate_mats = 0;
// just invoke the train() method
train(samples, sample_idx, params);
return new EM;
}
static AlgorithmInfo em_info("StatModel.EM", createEM);
bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
CvEMParams _params, Mat* _labels )
AlgorithmInfo* EM::info() const
{
CvMat samples = _samples, sidx = _sample_idx, labels, *plabels = 0;
if( _labels )
static volatile bool initialized = false;
if( !initialized )
{
int nsamples = sidx.data.ptr ? sidx.rows : samples.rows;
EM obj;
em_info.addParam(obj, "nclusters", obj.nclusters);
em_info.addParam(obj, "covMatType", obj.covMatType);
if( !(_labels->data && _labels->type() == CV_32SC1 &&
(_labels->cols == 1 || _labels->rows == 1) &&
_labels->cols + _labels->rows - 1 == nsamples) )
_labels->create(nsamples, 1, CV_32SC1);
plabels = &(labels = *_labels);
}
return train(&samples, sidx.data.ptr ? &sidx : 0, _params, plabels);
}
float
CvEM::predict( const Mat& _sample, Mat* _probs ) const
{
CvMat sample = _sample, probs, *pprobs = 0;
em_info.addParam(obj, "weights", obj.weights);
em_info.addParam(obj, "means", obj.means);
em_info.addParam(obj, "covs", obj.covs);
if( _probs )
{
int nclusters = params.nclusters;
if(!(_probs->data && (_probs->type() == CV_32F || _probs->type()==CV_64F) &&
(_probs->cols == 1 || _probs->rows == 1) &&
_probs->cols + _probs->rows - 1 == nclusters))
_probs->create(nclusters, 1, _sample.type());
pprobs = &(probs = *_probs);
initialized = true;
}
return predict(&sample, pprobs);
return &em_info;
}
int CvEM::getNClusters() const
{
return params.nclusters;
}
Mat CvEM::getMeans() const
{
return Mat(means);
}
void CvEM::getCovs(vector<Mat>& _covs) const
{
int i, n = params.nclusters;
_covs.resize(n);
for( i = 0; i < n; i++ )
Mat(covs[i]).copyTo(_covs[i]);
}
Mat CvEM::getWeights() const
{
return Mat(weights);
}
Mat CvEM::getProbs() const
{
return Mat(probs);
}
} // namespace cv
/* End of file. */
modules/ml/test/test_emknearestkmeans.cpp
View file @ 85fa0e77
......@@ -44,34 +44,49 @@
using namespace std;
using namespace cv;
void defaultDistribs( vector<Mat>& means, vector<Mat>& covs )
static
void defaultDistribs( Mat& means, vector<Mat>& covs )
{
float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
means.create(3, 2, CV_32FC1);
Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
means.resize(3), covs.resize(3);
m0.copyTo(means[0]), c0.copyTo(covs[0]);
m1.copyTo(means[1]), c1.copyTo(covs[1]);
m2.copyTo(means[2]), c2.copyTo(covs[2]);
Mat mr0 = means.row(0);
m0.copyTo(mr0);
c0.copyTo(covs[0]);
Mat mr1 = means.row(1);
m1.copyTo(mr1);
c1.copyTo(covs[1]);
Mat mr2 = means.row(2);
m2.copyTo(mr2);
c2.copyTo(covs[2]);
}
// generate points sets by normal distributions
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const vector<Mat>& means, const vector<Mat>& covs, int labelType )
static
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int labelType )
{
vector<int>::const_iterator sit = sizes.begin();
int total = 0;
for( ; sit != sizes.end(); ++sit )
total += *sit;
assert( means.size() == sizes.size() && covs.size() == sizes.size() );
assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
assert( !data.empty() && data.rows == total );
assert( data.type() == CV_32FC1 );
labels.create( data.rows, 1, labelType );
randn( data, Scalar::all(0.0), Scalar::all(1.0) );
vector<Mat> means(sizes.size());
for(int i = 0; i < _means.rows; i++)
means[i] = _means.row(i);
vector<Mat>::const_iterator mit = means.begin(), cit = covs.begin();
int bi, ei = 0;
sit = sizes.begin();
......@@ -95,6 +110,7 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const vecto
}
}
static
int maxIdx( const vector<int>& count )
{
int idx = -1;
......@@ -112,74 +128,83 @@ int maxIdx( const vector<int>& count )
return idx;
}
static
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap )
{
int total = 0, setCount = (int)sizes.size();
vector<int>::const_iterator sit = sizes.begin();
for( ; sit != sizes.end(); ++sit )
total += *sit;
size_t total = 0, nclusters = sizes.size();
for(size_t i = 0; i < sizes.size(); i++)
total += sizes[i];
assert( !labels.empty() );
assert( labels.rows == total && labels.cols == 1 );
assert( labels.total() == total && (labels.cols == 1 || labels.rows == 1));
assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
bool isFlt = labels.type() == CV_32FC1;
labelsMap.resize(setCount);
vector<int>::iterator lmit = labelsMap.begin();
vector<bool> buzy(setCount, false);
int bi, ei = 0;
for( sit = sizes.begin(); sit != sizes.end(); ++sit, ++lmit )
labelsMap.resize(nclusters);
vector<bool> buzy(nclusters, false);
int startIndex = 0;
for( size_t clusterIndex = 0; clusterIndex < sizes.size(); clusterIndex++ )
{
vector<int> count( setCount, 0 );
bi = ei;
ei = bi + *sit;
if( isFlt )
vector<int> count( nclusters, 0 );
for( int i = startIndex; i < startIndex + sizes[clusterIndex]; i++)
{
for( int i = bi; i < ei; i++ )
count[(int)labels.at<float>(i, 0)]++;
int lbl = isFlt ? (int)labels.at<float>(i) : labels.at<int>(i);
CV_Assert(lbl < (int)nclusters);
count[lbl]++;
CV_Assert(count[lbl] < (int)total);
}
else
{
for( int i = bi; i < ei; i++ )
count[labels.at<int>(i, 0)]++;
}
*lmit = maxIdx( count );
if( buzy[*lmit] )
return false;
buzy[*lmit] = true;
startIndex += sizes[clusterIndex];
int cls = maxIdx( count );
CV_Assert( !buzy[cls] );
labelsMap[clusterIndex] = cls;
buzy[cls] = true;
}
return true;
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
return true;
}
float calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, bool labelsEquivalent = true )
static
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true )
{
int err = 0;
assert( !labels.empty() && !origLabels.empty() );
assert( labels.cols == 1 && origLabels.cols == 1 );
assert( labels.rows == origLabels.rows );
assert( labels.type() == origLabels.type() );
assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
err = 0;
CV_Assert( !labels.empty() && !origLabels.empty() );
CV_Assert( labels.rows == 1 || labels.cols == 1 );
CV_Assert( origLabels.rows == 1 || origLabels.cols == 1 );
CV_Assert( labels.total() == origLabels.total() );
CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
CV_Assert( origLabels.type() == labels.type() );
vector<int> labelsMap;
bool isFlt = labels.type() == CV_32FC1;
if( !labelsEquivalent )
{
getLabelsMap( labels, sizes, labelsMap );
if( !getLabelsMap( labels, sizes, labelsMap ) )
return false;
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i, 0) != labelsMap[(int)origLabels.at<float>(i, 0)];
err += labels.at<float>(i) != labelsMap[(int)origLabels.at<float>(i)] ? 1.f : 0.f;
else
err += labels.at<int>(i, 0) != labelsMap[origLabels.at<int>(i, 0)];
err += labels.at<int>(i) != labelsMap[origLabels.at<int>(i)] ? 1.f : 0.f;
}
else
{
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i, 0) != origLabels.at<float>(i, 0);
err += labels.at<float>(i) != origLabels.at<float>(i) ? 1.f : 0.f;
else
err += labels.at<int>(i, 0) != origLabels.at<int>(i, 0);
err += labels.at<int>(i) != origLabels.at<int>(i) ? 1.f : 0.f;
}
return (float)err / (float)labels.rows;
err /= (float)labels.rows;
return true;
}
//--------------------------------------------------------------------------------------------
......@@ -198,7 +223,8 @@ void CV_KMeansTest::run( int /*start_from*/ )
Mat data( pointsCount, 2, CV_32FC1 ), labels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
vector<Mat> means, covs;
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
generateData( data, labels, sizes, means, covs, CV_32SC1 );
......@@ -207,8 +233,12 @@ void CV_KMeansTest::run( int /*start_from*/ )
Mat bestLabels;
// 1. flag==KMEANS_PP_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_PP_CENTERS, noArray() );
err = calcErr( bestLabels, labels, sizes, false );
if( err > 0.01f )
if( !calcErr( bestLabels, labels, sizes, err , false ) )
{
ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_PP_CENTERS.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_PP_CENTERS.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
......@@ -216,10 +246,14 @@ void CV_KMeansTest::run( int /*start_from*/ )
// 2. flag==KMEANS_RANDOM_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_RANDOM_CENTERS, noArray() );
err = calcErr( bestLabels, labels, sizes, false );
if( err > 0.01f )
if( !calcErr( bestLabels, labels, sizes, err, false ) )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_PP_CENTERS.\n", err );
ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_RANDOM_CENTERS.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_RANDOM_CENTERS.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
......@@ -229,10 +263,14 @@ void CV_KMeansTest::run( int /*start_from*/ )
for( int i = 0; i < 0.5f * pointsCount; i++ )
bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3;
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_USE_INITIAL_LABELS, noArray() );
err = calcErr( bestLabels, labels, sizes, false );
if( err > 0.01f )
if( !calcErr( bestLabels, labels, sizes, err, false ) )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_PP_CENTERS.\n", err );
ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_USE_INITIAL_LABELS.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_USE_INITIAL_LABELS.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
......@@ -255,7 +293,8 @@ void CV_KNearestTest::run( int /*start_from*/ )
// train data
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
vector<Mat> means, covs;
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1 );
......@@ -267,8 +306,13 @@ void CV_KNearestTest::run( int /*start_from*/ )
KNearest knearest;
knearest.train( trainData, trainLabels );
knearest.find_nearest( testData, 4, &bestLabels );
float err = calcErr( bestLabels, testLabels, sizes, true );
if( err > 0.01f )
float err;
if( !calcErr( bestLabels, testLabels, sizes, err, true ) )
{
ts->printf( cvtest::TS::LOG, "Bad output labels.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) on test data.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
......@@ -277,76 +321,167 @@ void CV_KNearestTest::run( int /*start_from*/ )
}
//--------------------------------------------------------------------------------------------
class CV_EMTest : public cvtest::BaseTest {
class CV_EMTest : public cvtest::BaseTest
{
public:
CV_EMTest() {}
protected:
virtual void run( int start_from );
int runCase( int caseIndex, const cv::EM::Params& params,
const cv::Mat& trainData, const cv::Mat& trainLabels,
const cv::Mat& testData, const cv::Mat& testLabels,
const vector<int>& sizes);
};
int CV_EMTest::runCase( int caseIndex, const cv::EM::Params& params,
const cv::Mat& trainData, const cv::Mat& trainLabels,
const cv::Mat& testData, const cv::Mat& testLabels,
const vector<int>& sizes )
{
int code = cvtest::TS::OK;
cv::Mat labels;
float err;
cv::EM em;
em.train( trainData, Mat(), params, &labels );
// check train error
if( !calcErr( labels, trainLabels, sizes, err , false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.006f )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on train data.\n", caseIndex, err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// check test error
labels.create( testData.rows, 1, CV_32SC1 );
for( int i = 0; i < testData.rows; i++ )
{
Mat sample = testData.row(i);
labels.at<int>(i,0) = (int)em.predict( sample, 0 );
}
if( !calcErr( labels, testLabels, sizes, err, false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.006f )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on test data.\n", caseIndex, err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
return code;
}
void CV_EMTest::run( int /*start_from*/ )
{
int sizesArr[] = { 5000, 7000, 8000 };
int sizesArr[] = { 500, 700, 800 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
// Points distribution
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
// train data
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
vector<Mat> means, covs;
defaultDistribs( means, covs );
generateData( trainData, trainLabels, sizes, means, covs, CV_32SC1 );
// test data
Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels;
Mat testData( pointsCount, 2, CV_32FC1 ), testLabels;
generateData( testData, testLabels, sizes, means, covs, CV_32SC1 );
int code = cvtest::TS::OK;
float err;
ExpectationMaximization em;
CvEMParams params;
cv::EM::Params params;
params.nclusters = 3;
em.train( trainData, Mat(), params, &bestLabels );
Mat probs(trainData.rows, params.nclusters, CV_32FC1, cv::Scalar(1));
params.probs = &probs;
Mat weights(1, params.nclusters, CV_32FC1, cv::Scalar(1));
params.weights = &weights;
params.means = &means;
params.covs = &covs;
// check train error
err = calcErr( bestLabels, trainLabels, sizes, false );
if( err > 0.002f )
int code = cvtest::TS::OK;
int caseIndex = 0;
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) on train data.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
params.startStep = cv::EM::START_AUTO_STEP;
params.covMatType = cv::EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
// check test error
bestLabels.create( testData.rows, 1, CV_32SC1 );
for( int i = 0; i < testData.rows; i++ )
{
Mat sample( 1, testData.cols, CV_32FC1, testData.ptr<float>(i));
bestLabels.at<int>(i,0) = (int)em.predict( sample, 0 );
params.startStep = cv::EM::START_AUTO_STEP;
params.covMatType = cv::EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
err = calcErr( bestLabels, testLabels, sizes, false );
if( err > 0.005f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) on test data.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
params.startStep = cv::EM::START_AUTO_STEP;
params.covMatType = cv::EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = cv::EM::START_M_STEP;
params.covMatType = cv::EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = cv::EM::START_M_STEP;
params.covMatType = cv::EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = cv::EM::START_M_STEP;
params.covMatType = cv::EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = cv::EM::START_E_STEP;
params.covMatType = cv::EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = cv::EM::START_E_STEP;
params.covMatType = cv::EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = cv::EM::START_E_STEP;
params.covMatType = cv::EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
ts->set_failed_test_info( code );
}
class CV_EMTest_Smoke : public cvtest::BaseTest {
class CV_EMTest_SaveLoad : public cvtest::BaseTest {
public:
CV_EMTest_Smoke() {}
CV_EMTest_SaveLoad() {}
protected:
virtual void run( int /*start_from*/ )
{
int code = cvtest::TS::OK;
CvEM em;
cv::EM em;
Mat samples = Mat(3,2,CV_32F);
Mat samples = Mat(3,1,CV_32F);
samples.at<float>(0,0) = 1;
samples.at<float>(1,0) = 2;
samples.at<float>(2,0) = 3;
CvEMParams params;
cv::EM::Params params;
params.nclusters = 2;
Mat labels;
......@@ -361,10 +496,11 @@ protected:
string filename = tempfile() + ".xml";
{
FileStorage fs = FileStorage(filename, FileStorage::WRITE);
try
{
em.write(fs.fs, "EM");
fs << "em" << "{";
em.write(fs);
fs << "}";
}
catch(...)
{
......@@ -378,11 +514,11 @@ protected:
// Read in
{
FileStorage fs = FileStorage(filename, FileStorage::READ);
FileNode fileNode = fs["EM"];
CV_Assert(fs.isOpened());
FileNode fn = fs["em"];
try
{
em.read(const_cast<CvFileStorage*>(fileNode.fs), const_cast<CvFileNode*>(fileNode.node));
em.read(fn);
}
catch(...)
{
......@@ -410,4 +546,4 @@ protected:
TEST(ML_KMeans, accuracy) { CV_KMeansTest test; test.safe_run(); }
TEST(ML_KNearest, accuracy) { CV_KNearestTest test; test.safe_run(); }
TEST(ML_EM, accuracy) { CV_EMTest test; test.safe_run(); }
TEST(ML_EM, smoke) { CV_EMTest_Smoke test; test.safe_run(); }
TEST(ML_EM, save_load) { CV_EMTest_SaveLoad test; test.safe_run(); }
modules/ml/test/test_mltests2.cpp
View file @ 85fa0e77
......@@ -451,7 +451,6 @@ CV_MLBaseTest::CV_MLBaseTest(const char* _modelName)
nbayes = 0;
knearest = 0;
svm = 0;
em = 0;
ann = 0;
dtree = 0;
boost = 0;
......@@ -463,8 +462,6 @@ CV_MLBaseTest::CV_MLBaseTest(const char* _modelName)
knearest = new CvKNearest;
else if( !modelName.compare(CV_SVM) )
svm = new CvSVM;
else if( !modelName.compare(CV_EM) )
em = new CvEM;
else if( !modelName.compare(CV_ANN) )
ann = new CvANN_MLP;
else if( !modelName.compare(CV_DTREE) )
......@@ -487,8 +484,6 @@ CV_MLBaseTest::~CV_MLBaseTest()
delete knearest;
if( svm )
delete svm;
if( em )
delete em;
if( ann )
delete ann;
if( dtree )
......@@ -756,8 +751,6 @@ void CV_MLBaseTest::save( const char* filename )
knearest->save( filename );
else if( !modelName.compare(CV_SVM) )
svm->save( filename );
else if( !modelName.compare(CV_EM) )
em->save( filename );
else if( !modelName.compare(CV_ANN) )
ann->save( filename );
else if( !modelName.compare(CV_DTREE) )
......@@ -778,8 +771,6 @@ void CV_MLBaseTest::load( const char* filename )
knearest->load( filename );
else if( !modelName.compare(CV_SVM) )
svm->load( filename );
else if( !modelName.compare(CV_EM) )
em->load( filename );
else if( !modelName.compare(CV_ANN) )
ann->load( filename );
else if( !modelName.compare(CV_DTREE) )
......
modules/ml/test/test_precomp.hpp
View file @ 85fa0e77
......@@ -44,7 +44,6 @@ protected:
CvNormalBayesClassifier* nbayes;
CvKNearest* knearest;
CvSVM* svm;
CvEM* em;
CvANN_MLP* ann;
CvDTree* dtree;
CvBoost* boost;
......
samples/cpp/em.cpp
View file @ 85fa0e77
#include "opencv2/ml/ml.hpp"
#include "opencv2/legacy/legacy.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
......
samples/cpp/points_classifier.cpp
View file @ 85fa0e77
......@@ -11,7 +11,6 @@ const Scalar WHITE_COLOR = CV_RGB(255,255,255);
const string winName = "points";
const int testStep = 5;
Mat img, imgDst;
RNG rng;
......@@ -19,16 +18,16 @@ vector<Point> trainedPoints;
vector<int> trainedPointsMarkers;
vector<Scalar> classColors;
#define NBC 0 // normal Bayessian classifier
#define KNN 0 // k nearest neighbors classifier
#define SVM 0 // support vectors machine
#define DT 1 // decision tree
#define BT 0 // ADA Boost
#define GBT 0 // gradient boosted trees
#define RF 0 // random forest
#define ERT 0 // extremely randomized trees
#define ANN 0 // artificial neural networks
#define EM 0 // expectation-maximization
#define _NBC_ 0 // normal Bayessian classifier
#define _KNN_ 0 // k nearest neighbors classifier
#define _SVM_ 0 // support vectors machine
#define _DT_ 1 // decision tree
#define _BT_ 0 // ADA Boost
#define _GBT_ 0 // gradient boosted trees
#define _RF_ 0 // random forest
#define _ERT_ 0 // extremely randomized trees
#define _ANN_ 0 // artificial neural networks
#define _EM_ 0 // expectation-maximization
void on_mouse( int event, int x, int y, int /*flags*/, void* )
{
......@@ -48,13 +47,13 @@ void on_mouse( int event, int x, int y, int /*flags*/, void* )
}
else if( event == CV_EVENT_RBUTTONUP )
{
#if BT
#if _BT_
if( classColors.size() < 2 )
{
#endif
classColors.push_back( Scalar((uchar)rng(256), (uchar)rng(256), (uchar)rng(256)) );
updateFlag = true;
#if BT
#if _BT_
}
else
cout << "New class can not be added, because CvBoost can only be used for 2-class classification" << endl;
......@@ -98,7 +97,7 @@ void prepare_train_data( Mat& samples, Mat& classes )
samples.convertTo( samples, CV_32FC1 );
}
#if NBC
#if _NBC_
void find_decision_boundary_NBC()
{
img.copyTo( imgDst );
......@@ -125,7 +124,7 @@ void find_decision_boundary_NBC()
#endif
#if KNN
#if _KNN_
void find_decision_boundary_KNN( int K )
{
img.copyTo( imgDst );
......@@ -151,7 +150,7 @@ void find_decision_boundary_KNN( int K )
}
#endif
#if SVM
#if _SVM_
void find_decision_boundary_SVM( CvSVMParams params )
{
img.copyTo( imgDst );
......@@ -185,7 +184,7 @@ void find_decision_boundary_SVM( CvSVMParams params )
}
#endif
#if DT
#if _DT_
void find_decision_boundary_DT()
{
img.copyTo( imgDst );
......@@ -225,7 +224,7 @@ void find_decision_boundary_DT()
}
#endif
#if BT
#if _BT_
void find_decision_boundary_BT()
{
img.copyTo( imgDst );
......@@ -265,7 +264,7 @@ void find_decision_boundary_BT()
#endif
#if GBT
#if _GBT_
void find_decision_boundary_GBT()
{
img.copyTo( imgDst );
......@@ -305,7 +304,7 @@ void find_decision_boundary_GBT()
#endif
#if RF
#if _RF_
void find_decision_boundary_RF()
{
img.copyTo( imgDst );
......@@ -346,7 +345,7 @@ void find_decision_boundary_RF()
#endif
#if ERT
#if _ERT_
void find_decision_boundary_ERT()
{
img.copyTo( imgDst );
......@@ -390,7 +389,7 @@ void find_decision_boundary_ERT()
}
#endif
#if ANN
#if _ANN_
void find_decision_boundary_ANN( const Mat& layer_sizes )
{
img.copyTo( imgDst );
......@@ -435,7 +434,7 @@ void find_decision_boundary_ANN( const Mat& layer_sizes )
}
#endif
#if EM
#if _EM_
void find_decision_boundary_EM()
{
img.copyTo( imgDst );
......@@ -443,19 +442,12 @@ void find_decision_boundary_EM()
Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses );
CvEM em;
CvEMParams params;
params.covs = NULL;
params.means = NULL;
params.weights = NULL;
params.probs = NULL;
cv::EM em;
cv::EM::Params params;
params.nclusters = classColors.size();
params.cov_mat_type = CvEM::COV_MAT_GENERIC;
params.start_step = CvEM::START_AUTO_STEP;
params.term_crit.max_iter = 10;
params.term_crit.epsilon = 0.1;
params.term_crit.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
params.covMatType = cv::EM::COV_MAT_GENERIC;
params.startStep = cv::EM::START_AUTO_STEP;
params.termCrit = cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::COUNT, 10, 0.1);
// learn classifier
em.train( trainSamples, Mat(), params, &trainClasses );
......@@ -509,12 +501,12 @@ int main()
if( key == 'r' ) // run
{
#if NBC
#if _NBC_
find_decision_boundary_NBC();
cvNamedWindow( "NormalBayesClassifier", WINDOW_AUTOSIZE );
imshow( "NormalBayesClassifier", imgDst );
#endif
#if KNN
#if _KNN_
int K = 3;
find_decision_boundary_KNN( K );
namedWindow( "kNN", WINDOW_AUTOSIZE );
......@@ -526,7 +518,7 @@ int main()
imshow( "kNN2", imgDst );
#endif
#if SVM
#if _SVM_
//(1)-(2)separable and not sets
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
......@@ -549,37 +541,37 @@ int main()
imshow( "classificationSVM2", imgDst );
#endif
#if DT
#if _DT_
find_decision_boundary_DT();
namedWindow( "DT", WINDOW_AUTOSIZE );
imshow( "DT", imgDst );
#endif
#if BT
#if _BT_
find_decision_boundary_BT();
namedWindow( "BT", WINDOW_AUTOSIZE );
imshow( "BT", imgDst);
#endif
#if GBT
#if _GBT_
find_decision_boundary_GBT();
namedWindow( "GBT", WINDOW_AUTOSIZE );
imshow( "GBT", imgDst);
#endif
#if RF
#if _RF_
find_decision_boundary_RF();
namedWindow( "RF", WINDOW_AUTOSIZE );
imshow( "RF", imgDst);
#endif
#if ERT
#if _ERT_
find_decision_boundary_ERT();
namedWindow( "ERT", WINDOW_AUTOSIZE );
imshow( "ERT", imgDst);
#endif
#if ANN
#if _ANN_
Mat layer_sizes1( 1, 3, CV_32SC1 );
layer_sizes1.at<int>(0) = 2;
layer_sizes1.at<int>(1) = 5;
......@@ -589,7 +581,7 @@ int main()
imshow( "ANN", imgDst );
#endif
#if EM
#if _EM_
find_decision_boundary_EM();
namedWindow( "EM", WINDOW_AUTOSIZE );
imshow( "EM", imgDst );
......
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