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fixed build with gcc 4.5+ (#1774)
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cf02b2e258
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@ -105,7 +105,7 @@ float
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CvEM::predict( const CvMat* _sample, CvMat* _probs, bool isNormalize ) const
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{
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Mat prbs0 = cvarrToMat(_probs), prbs = prbs0, sample = cvarrToMat(_sample);
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int cls = emObj.predict(sample, _probs ? _OutputArray(prbs) : _OutputArray::_OutputArray());
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int cls = emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray());
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if(_probs)
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{
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if(isNormalize)
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@ -212,15 +212,15 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
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emObj = EM(_params.nclusters, _params.cov_mat_type, _params.term_crit);
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bool isOk = false;
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if( _params.start_step == EM::START_AUTO_STEP )
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isOk = emObj.train(_samples, _labels ? _OutputArray(*_labels) : _OutputArray::_OutputArray(),
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isOk = emObj.train(_samples, _labels ? _OutputArray(*_labels) : cv::noArray(),
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probs, likelihoods);
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else if( _params.start_step == EM::START_E_STEP )
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isOk = emObj.trainE(_samples, means, covsHdrs, weights,
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_labels ? _OutputArray(*_labels) : _OutputArray::_OutputArray(),
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_labels ? _OutputArray(*_labels) : cv::noArray(),
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probs, likelihoods);
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else if( _params.start_step == EM::START_M_STEP )
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isOk = emObj.trainM(_samples, prbs,
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_labels ? _OutputArray(*_labels) : _OutputArray::_OutputArray(),
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_labels ? _OutputArray(*_labels) : cv::noArray(),
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probs, likelihoods);
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else
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CV_Error(CV_StsBadArg, "Bad start type of EM algorithm");
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@ -237,7 +237,7 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
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float
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CvEM::predict( const Mat& _sample, Mat* _probs, bool isNormalize ) const
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{
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int cls = emObj.predict(_sample, _probs ? _OutputArray(*_probs) : _OutputArray::_OutputArray());
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int cls = emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray());
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if(_probs && isNormalize)
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normalize(*_probs, *_probs, 1, 0, NORM_L1);
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