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refactored train and predict methods of em

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This commit is contained in:
Maria Dimashova
2012-04-17 06:29:40 +00:00
parent 8f7e5811b6
commit 3dfa917879
7 changed files with 56 additions and 65 deletions
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27
modules/legacy/src/em.cpp
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@@ -56,12 +56,12 @@ CvEMParams::CvEMParams( int _nclusters, int _cov_mat_type, int _start_step,
probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
{}
CvEM::CvEM() : likelihood(DBL_MAX)
CvEM::CvEM() : logLikelihood(DBL_MAX)
{
}
CvEM::CvEM( const CvMat* samples, const CvMat* sample_idx,
CvEMParams params, CvMat* labels ) : likelihood(DBL_MAX)
CvEMParams params, CvMat* labels ) : logLikelihood(DBL_MAX)
{
train(samples, sample_idx, params, labels);
}
@@ -96,16 +96,14 @@ void CvEM::write( CvFileStorage* _fs, const char* name ) const
double CvEM::calcLikelihood( const Mat &input_sample ) const
{
double likelihood;
emObj.predict(input_sample, noArray(), &likelihood);
return likelihood;
return emObj.predict(input_sample)[0];
}
float
CvEM::predict( const CvMat* _sample, CvMat* _probs ) const
{
Mat prbs0 = cvarrToMat(_probs), prbs = prbs0, sample = cvarrToMat(_sample);
int cls = emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray());
int cls = static_cast<int>(emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray())[1]);
if(_probs)
{
if( prbs.data != prbs0.data )
@@ -203,29 +201,27 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
CvEMParams _params, Mat* _labels )
{
CV_Assert(_sample_idx.empty());
Mat prbs, weights, means, likelihoods;
Mat prbs, weights, means, logLikelihoods;
std::vector<Mat> covsHdrs;
init_params(_params, prbs, weights, means, covsHdrs);
emObj = EM(_params.nclusters, _params.cov_mat_type, _params.term_crit);
bool isOk = false;
if( _params.start_step == EM::START_AUTO_STEP )
isOk = emObj.train(_samples, _labels ? _OutputArray(*_labels) : cv::noArray(),
probs, likelihoods);
isOk = emObj.train(_samples,
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else if( _params.start_step == EM::START_E_STEP )
isOk = emObj.trainE(_samples, means, covsHdrs, weights,
_labels ? _OutputArray(*_labels) : cv::noArray(),
probs, likelihoods);
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else if( _params.start_step == EM::START_M_STEP )
isOk = emObj.trainM(_samples, prbs,
_labels ? _OutputArray(*_labels) : cv::noArray(),
probs, likelihoods);
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else
CV_Error(CV_StsBadArg, "Bad start type of EM algorithm");
if(isOk)
{
likelihoods = sum(likelihoods).val[0];
logLikelihood = sum(logLikelihoods).val[0];
set_mat_hdrs();
}
@@ -235,8 +231,7 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
float
CvEM::predict( const Mat& _sample, Mat* _probs ) const
{
int cls = emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray());
return (float)cls;
return static_cast<float>(emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray())[1]);
}
int CvEM::getNClusters() const