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removed unnecessary param
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d8cec20e1b
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@ -1806,7 +1806,7 @@ public:
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virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0,
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CvEMParams params=CvEMParams(), CvMat* labels=0 );
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virtual float predict( const CvMat* sample, CV_OUT CvMat* probs, bool isNormalize=true ) const;
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virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const;
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#ifndef SWIG
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CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
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@ -1817,7 +1817,7 @@ public:
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CvEMParams params=CvEMParams(),
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CV_OUT cv::Mat* labels=0 );
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CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0, bool isNormalize=true ) const;
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CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) const;
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CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
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CV_WRAP int getNClusters() const;
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@ -102,15 +102,12 @@ double CvEM::calcLikelihood( const Mat &input_sample ) const
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}
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float
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CvEM::predict( const CvMat* _sample, CvMat* _probs, bool isNormalize ) const
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CvEM::predict( const CvMat* _sample, CvMat* _probs ) 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) : cv::noArray());
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if(_probs)
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{
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if(isNormalize)
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normalize(prbs, prbs, 1, 0, NORM_L1);
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if( prbs.data != prbs0.data )
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{
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CV_Assert( prbs.size == prbs0.size );
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@ -236,12 +233,9 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
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}
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float
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CvEM::predict( const Mat& _sample, Mat* _probs, bool isNormalize ) const
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CvEM::predict( const Mat& _sample, Mat* _probs ) const
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{
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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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return (float)cls;
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}
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