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merged all the latest changes from 2.4 to trunk

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This commit is contained in:
Vadim Pisarevsky
2012-04-13 21:50:59 +00:00
parent 020f9a6047
commit 2fd1e2ea57
416 changed files with 12852 additions and 6070 deletions
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183
modules/ml/include/opencv2/ml/ml.hpp
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@@ -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
@@ -121,6 +125,7 @@ CV_INLINE CvParamLattice cvDefaultParamLattice( void )
#define CV_TYPE_NAME_ML_ANN_MLP "opencv-ml-ann-mlp"
#define CV_TYPE_NAME_ML_CNN "opencv-ml-cnn"
#define CV_TYPE_NAME_ML_RTREES "opencv-ml-random-trees"
#define CV_TYPE_NAME_ML_ERTREES "opencv-ml-extremely-randomized-trees"
#define CV_TYPE_NAME_ML_GBT "opencv-ml-gradient-boosting-trees"
#define CV_TRAIN_ERROR 0
@@ -549,114 +554,99 @@ 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, COV_MAT_DEFAULT=COV_MAT_DIAGONAL};
// Default parameters
enum {DEFAULT_NCLUSTERS=10, DEFAULT_MAX_ITERS=100};
// 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);
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 ) 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 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();
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 log_likelihood; }
inline double get_log_likelihood_delta() const { return log_likelihood_delta; }
CV_WRAP virtual bool train(InputArray samples,
OutputArray labels=noArray(),
OutputArray probs=noArray(),
OutputArray logLikelihoods=noArray());
// 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; };
CV_WRAP virtual bool trainE(InputArray samples,
InputArray means0,
InputArray covs0=noArray(),
InputArray weights0=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray(),
OutputArray logLikelihoods=noArray());
CV_WRAP virtual bool trainM(InputArray samples,
InputArray probs0,
OutputArray labels=noArray(),
OutputArray probs=noArray(),
OutputArray logLikelihoods=noArray());
CV_WRAP int predict(InputArray sample,
OutputArray probs=noArray(),
CV_OUT double* logLikelihood=0) const;
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name ) const;
CV_WRAP bool isTrained() const;
virtual void write_params( CvFileStorage* fs ) const;
virtual void read_params( CvFileStorage* fs, CvFileNode* node );
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 vector<Mat>* covs0,
const Mat* weights0);
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;
bool doTrain(int startStep,
OutputArray labels,
OutputArray probs,
OutputArray logLikelihoods);
virtual void eStep();
virtual void mStep();
CvMat* means;
CvMat** covs;
CvMat* weights;
CvMat* probs;
void clusterTrainSamples();
void decomposeCovs();
void computeLogWeightDivDet();
CvMat* log_weight_div_det;
CvMat* inv_eigen_values;
CvMat** cov_rotate_mats;
void computeProbabilities(const Mat& sample, int& label, Mat* probs, double* logLikelihood) 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;
Mat trainCounts;
CV_PROP Mat weights;
CV_PROP Mat means;
CV_PROP vector<Mat> covs;
vector<Mat> covsEigenValues;
vector<Mat> covsRotateMats;
vector<Mat> invCovsEigenValues;
Mat logWeightDivDet;
};
} // namespace cv
/****************************************************************************************\
* Decision Tree *
@@ -1052,6 +1042,7 @@ public:
CvForestTree* get_tree(int i) const;
protected:
virtual std::string getName() const;
virtual bool grow_forest( const CvTermCriteria term_crit );
@@ -1125,6 +1116,7 @@ public:
#endif
virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
protected:
virtual std::string getName() const;
virtual bool grow_forest( const CvTermCriteria term_crit );
};
@@ -2012,17 +2004,10 @@ 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 +2118,6 @@ 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 +2139,7 @@ template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
}
#endif
#endif // __cplusplus
#endif // __OPENCV_ML_HPP__
/* End of file. */