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Remove deprecated methods from cv::Algorithm

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This commit is contained in:
Maksim Shabunin
2015-02-05 17:40:15 +03:00
parent fdf31ec14f
commit da383e65e2
37 changed files with 411 additions and 2037 deletions
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97
apps/traincascade/old_ml.hpp
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@@ -122,7 +122,6 @@ CV_INLINE CvParamLattice cvDefaultParamLattice( void )
#define CV_TYPE_NAME_ML_SVM "opencv-ml-svm"
#define CV_TYPE_NAME_ML_KNN "opencv-ml-knn"
#define CV_TYPE_NAME_ML_NBAYES "opencv-ml-bayesian"
#define CV_TYPE_NAME_ML_EM "opencv-ml-em"
#define CV_TYPE_NAME_ML_BOOSTING "opencv-ml-boost-tree"
#define CV_TYPE_NAME_ML_TREE "opencv-ml-tree"
#define CV_TYPE_NAME_ML_ANN_MLP "opencv-ml-ann-mlp"
@@ -562,100 +561,6 @@ private:
CvSVM& operator = (const CvSVM&);
};
/****************************************************************************************\
* Expectation - Maximization *
\****************************************************************************************/
namespace cv
{
class EM : public Algorithm
{
public:
// Type of covariation matrices
enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2, COV_MAT_DEFAULT=COV_MAT_DIAGONAL};
// Default parameters
enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
// The initial step
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
EM::DEFAULT_MAX_ITERS, FLT_EPSILON));
virtual ~EM();
CV_WRAP virtual void clear();
CV_WRAP virtual bool train(InputArray samples,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP virtual bool trainE(InputArray samples,
InputArray means0,
InputArray covs0=noArray(),
InputArray weights0=noArray(),
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP virtual bool trainM(InputArray samples,
InputArray probs0,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray());
CV_WRAP Vec2d predict(InputArray sample,
OutputArray probs=noArray()) const;
CV_WRAP bool isTrained() const;
AlgorithmInfo* info() const;
virtual void read(const FileNode& fn);
protected:
virtual void setTrainData(int startStep, const Mat& samples,
const Mat* probs0,
const Mat* means0,
const std::vector<Mat>* covs0,
const Mat* weights0);
bool doTrain(int startStep,
OutputArray logLikelihoods,
OutputArray labels,
OutputArray probs);
virtual void eStep();
virtual void mStep();
void clusterTrainSamples();
void decomposeCovs();
void computeLogWeightDivDet();
Vec2d computeProbabilities(const Mat& sample, Mat* probs) const;
// all inner matrices have type CV_64FC1
CV_PROP_RW int nclusters;
CV_PROP_RW int covMatType;
CV_PROP_RW int maxIters;
CV_PROP_RW double epsilon;
Mat trainSamples;
Mat trainProbs;
Mat trainLogLikelihoods;
Mat trainLabels;
CV_PROP Mat weights;
CV_PROP Mat means;
CV_PROP std::vector<Mat> covs;
std::vector<Mat> covsEigenValues;
std::vector<Mat> covsRotateMats;
std::vector<Mat> invCovsEigenValues;
Mat logWeightDivDet;
};
} // namespace cv
/****************************************************************************************\
* Decision Tree *
\****************************************************************************************/\
@@ -2155,8 +2060,6 @@ typedef CvGBTreesParams GradientBoostingTreeParams;
typedef CvGBTrees GradientBoostingTrees;
template<> void DefaultDeleter<CvDTreeSplit>::operator ()(CvDTreeSplit* obj) const;
bool initModule_ml(void);
}
#endif // __cplusplus