generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

extended Python bindings; not merged into cv.cpp yet; and many of the helper functions, like pyopencv_to_*, pyopencv_from_* etc. are still missing

Browse Source
This commit is contained in:
Vadim Pisarevsky
2010-10-29 13:09:49 +00:00
parent 69e329c9fd
commit 893fb90b87
7 changed files with 1432 additions and 277 deletions
Download Patch File Download Diff File Expand all files Collapse all files

426
modules/ml/include/opencv2/ml/ml.hpp
View File

@@ -247,22 +247,22 @@ public:
CV_WRAP CvNormalBayesClassifier();
virtual ~CvNormalBayesClassifier();
CvNormalBayesClassifier( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _var_idx=0, const CvMat* _sample_idx=0 );
CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx=0, const CvMat* sampleIdx=0 );
virtual bool train( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _var_idx = 0, const CvMat* _sample_idx=0, bool update=false );
virtual bool train( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false );
virtual float predict( const CvMat* _samples, CV_OUT CvMat* results=0 ) const;
virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
CV_WRAP virtual void clear();
#ifndef SWIG
CV_WRAP CvNormalBayesClassifier( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _var_idx=cv::Mat(), const cv::Mat& _sample_idx=cv::Mat() );
CV_WRAP virtual bool train( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _var_idx = cv::Mat(), const cv::Mat& _sample_idx=cv::Mat(),
CV_WRAP CvNormalBayesClassifier( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat() );
CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx = cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& _samples, cv::Mat* results=0 ) const;
CV_WRAP virtual float predict( const cv::Mat& samples, cv::Mat* results=0 ) const;
#endif
virtual void write( CvFileStorage* storage, const char* name ) const;
@@ -294,27 +294,26 @@ public:
CV_WRAP CvKNearest();
virtual ~CvKNearest();
CvKNearest( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _sample_idx=0, bool _is_regression=false, int max_k=32 );
CvKNearest( const CvMat* trainData, const CvMat* responses,
const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 );
virtual bool train( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _sample_idx=0, bool is_regression=false,
int _max_k=32, bool _update_base=false );
virtual bool train( const CvMat* trainData, const CvMat* responses,
const CvMat* sampleIdx=0, bool is_regression=false,
int maxK=32, bool updateBase=false );
virtual float find_nearest( const CvMat* _samples, int k, CV_OUT CvMat* results=0,
const float** neighbors=0, CV_OUT CvMat* neighbor_responses=0, CV_OUT CvMat* dist=0 ) const;
virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0,
const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const;
#ifndef SWIG
CV_WRAP CvKNearest( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _sample_idx=cv::Mat(), bool _is_regression=false, int max_k=32 );
CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 );
CV_WRAP virtual bool train( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _sample_idx=cv::Mat(), bool is_regression=false,
int _max_k=32, bool _update_base=false );
CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false,
int maxK=32, bool updateBase=false );
CV_WRAP virtual float find_nearest( const cv::Mat& _samples, int k, cv::Mat* results=0,
const float** neighbors=0,
cv::Mat* neighbor_responses=0,
CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, cv::Mat* results=0,
const float** neighbors=0, cv::Mat* neighborResponses=0,
cv::Mat* dist=0 ) const;
#endif
@@ -372,8 +371,8 @@ struct CV_EXPORTS CvSVMKernel
typedef void (CvSVMKernel::*Calc)( int vec_count, int vec_size, const float** vecs,
const float* another, float* results );
CvSVMKernel();
CvSVMKernel( const CvSVMParams* _params, Calc _calc_func );
virtual bool create( const CvSVMParams* _params, Calc _calc_func );
CvSVMKernel( const CvSVMParams* params, Calc _calc_func );
virtual bool create( const CvSVMParams* params, Calc _calc_func );
virtual ~CvSVMKernel();
virtual void clear();
@@ -523,45 +522,45 @@ public:
CV_WRAP CvSVM();
virtual ~CvSVM();
CvSVM( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _var_idx=0, const CvMat* _sample_idx=0,
CvSVMParams _params=CvSVMParams() );
CvSVM( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx=0, const CvMat* sampleIdx=0,
CvSVMParams params=CvSVMParams() );
virtual bool train( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _var_idx=0, const CvMat* _sample_idx=0,
CvSVMParams _params=CvSVMParams() );
virtual bool train( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx=0, const CvMat* sampleIdx=0,
CvSVMParams params=CvSVMParams() );
virtual bool train_auto( const CvMat* _train_data, const CvMat* _responses,
const CvMat* _var_idx, const CvMat* _sample_idx, CvSVMParams _params,
int k_fold = 10,
CvParamGrid C_grid = get_default_grid(CvSVM::C),
CvParamGrid gamma_grid = get_default_grid(CvSVM::GAMMA),
CvParamGrid p_grid = get_default_grid(CvSVM::P),
CvParamGrid nu_grid = get_default_grid(CvSVM::NU),
CvParamGrid coef_grid = get_default_grid(CvSVM::COEF),
CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) );
virtual bool train_auto( const CvMat* trainData, const CvMat* responses,
const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params,
int kfold = 10,
CvParamGrid Cgrid = get_default_grid(CvSVM::C),
CvParamGrid gammaGrid = get_default_grid(CvSVM::GAMMA),
CvParamGrid pGrid = get_default_grid(CvSVM::P),
CvParamGrid nuGrid = get_default_grid(CvSVM::NU),
CvParamGrid coeffGrid = get_default_grid(CvSVM::COEF),
CvParamGrid degreeGrid = get_default_grid(CvSVM::DEGREE) );
virtual float predict( const CvMat* _sample, bool returnDFVal=false ) const;
virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
#ifndef SWIG
CV_WRAP CvSVM( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _var_idx=cv::Mat(), const cv::Mat& _sample_idx=cv::Mat(),
CvSVMParams _params=CvSVMParams() );
CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
CvSVMParams params=CvSVMParams() );
CV_WRAP virtual bool train( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _var_idx=cv::Mat(), const cv::Mat& _sample_idx=cv::Mat(),
CvSVMParams _params=CvSVMParams() );
CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
CvSVMParams params=CvSVMParams() );
CV_WRAP virtual bool train_auto( const cv::Mat& _train_data, const cv::Mat& _responses,
const cv::Mat& _var_idx, const cv::Mat& _sample_idx, CvSVMParams _params,
CV_WRAP virtual bool train_auto( const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx, const cv::Mat& sampleIdx, CvSVMParams params,
int k_fold = 10,
CvParamGrid C_grid = get_default_grid(CvSVM::C),
CvParamGrid gamma_grid = get_default_grid(CvSVM::GAMMA),
CvParamGrid p_grid = get_default_grid(CvSVM::P),
CvParamGrid nu_grid = get_default_grid(CvSVM::NU),
CvParamGrid coef_grid = get_default_grid(CvSVM::COEF),
CvParamGrid degree_grid = get_default_grid(CvSVM::DEGREE) );
CV_WRAP virtual float predict( const cv::Mat& _sample, bool returnDFVal=false ) const;
CvParamGrid Cgrid = get_default_grid(CvSVM::C),
CvParamGrid gammaGrid = get_default_grid(CvSVM::GAMMA),
CvParamGrid pGrid = get_default_grid(CvSVM::P),
CvParamGrid nuGrid = get_default_grid(CvSVM::NU),
CvParamGrid coeffGrid = get_default_grid(CvSVM::COEF),
CvParamGrid degreeGrid = get_default_grid(CvSVM::DEGREE) );
CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
#endif
CV_WRAP virtual int get_support_vector_count() const;
@@ -577,12 +576,12 @@ public:
protected:
virtual bool set_params( const CvSVMParams& _params );
virtual bool set_params( const CvSVMParams& params );
virtual bool train1( int sample_count, int var_count, const float** samples,
const void* _responses, double Cp, double Cn,
const void* responses, double Cp, double Cn,
CvMemStorage* _storage, double* alpha, double& rho );
virtual bool do_train( int svm_type, int sample_count, int var_count, const float** samples,
const CvMat* _responses, CvMemStorage* _storage, double* alpha );
const CvMat* responses, CvMemStorage* _storage, double* alpha );
virtual void create_kernel();
virtual void create_solver();
@@ -646,23 +645,23 @@ public:
enum { START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0 };
CV_WRAP CvEM();
CvEM( const CvMat* samples, const CvMat* sample_idx=0,
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* sample_idx=0,
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& sample_idx=cv::Mat(),
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams(), cv::Mat* labels=0 );
CV_WRAP virtual bool train( const cv::Mat& samples, const cv::Mat& sample_idx=cv::Mat(),
CV_WRAP virtual bool train( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams(), cv::Mat* labels=0 );
CV_WRAP virtual float predict( const cv::Mat& sample, cv::Mat* probs ) const;
@@ -807,19 +806,19 @@ struct CV_EXPORTS_AS_MAP CvDTreeParams
struct CV_EXPORTS CvDTreeTrainData
{
CvDTreeTrainData();
CvDTreeTrainData( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
const CvDTreeParams& _params=CvDTreeParams(),
CvDTreeTrainData( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
const CvDTreeParams& params=CvDTreeParams(),
bool _shared=false, bool _add_labels=false );
virtual ~CvDTreeTrainData();
virtual void set_data( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
const CvDTreeParams& _params=CvDTreeParams(),
virtual void set_data( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
const CvDTreeParams& params=CvDTreeParams(),
bool _shared=false, bool _add_labels=false,
bool _update_data=false );
virtual void do_responses_copy();
@@ -921,30 +920,31 @@ public:
CV_WRAP CvDTree();
virtual ~CvDTree();
virtual bool train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvDTreeParams params=CvDTreeParams() );
virtual bool train( CvMLData* _data, CvDTreeParams _params=CvDTreeParams() );
virtual bool train( CvMLData* trainData, CvDTreeParams params=CvDTreeParams() );
virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
// type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
virtual float calc_error( CvMLData* trainData, int type, std::vector<float> *resp = 0 );
virtual bool train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx );
virtual bool train( CvDTreeTrainData* trainData, const CvMat* subsampleIdx );
virtual CvDTreeNode* predict( const CvMat* _sample, const CvMat* _missing_data_mask=0,
bool preprocessed_input=false ) const;
virtual CvDTreeNode* predict( const CvMat* sample, const CvMat* missingDataMask=0,
bool preprocessedInput=false ) const;
#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& _train_data, int _tflag,
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
const cv::Mat& _missing_mask=cv::Mat(),
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvDTreeParams params=CvDTreeParams() );
CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& _sample, const cv::Mat& _missing_data_mask=cv::Mat(),
bool preprocessed_input=false ) const;
CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(),
bool preprocessedInput=false ) const;
#endif
CV_WRAP virtual const CvMat* get_var_importance();
@@ -1021,19 +1021,19 @@ public:
CvForestTree();
virtual ~CvForestTree();
virtual bool train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx, CvRTrees* forest );
virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx, CvRTrees* forest );
virtual int get_var_count() const {return data ? data->var_count : 0;}
virtual void read( CvFileStorage* fs, CvFileNode* node, CvRTrees* forest, CvDTreeTrainData* _data );
/* dummy methods to avoid warnings: BEGIN */
virtual bool train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvDTreeParams params=CvDTreeParams() );
virtual bool train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx );
virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx );
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void read( CvFileStorage* fs, CvFileNode* node,
CvDTreeTrainData* data );
@@ -1082,10 +1082,10 @@ class CV_EXPORTS_AS(RTrees) CvRTrees : public CvStatModel
public:
CV_WRAP CvRTrees();
virtual ~CvRTrees();
virtual bool train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvRTParams params=CvRTParams() );
virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
@@ -1093,10 +1093,10 @@ public:
virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const;
#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& _train_data, int _tflag,
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
const cv::Mat& _missing_mask=cv::Mat(),
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvRTParams params=CvRTParams() );
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
@@ -1143,11 +1143,11 @@ protected:
\****************************************************************************************/
struct CV_EXPORTS CvERTreeTrainData : public CvDTreeTrainData
{
virtual void set_data( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
const CvDTreeParams& _params=CvDTreeParams(),
virtual void set_data( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
const CvDTreeParams& params=CvDTreeParams(),
bool _shared=false, bool _add_labels=false,
bool _update_data=false );
virtual void get_ord_var_data( CvDTreeNode* n, int vi, float* ord_values_buf, int* missing_buf,
@@ -1181,16 +1181,16 @@ class CV_EXPORTS_AS(ERTrees) CvERTrees : public CvRTrees
public:
CV_WRAP CvERTrees();
virtual ~CvERTrees();
virtual bool train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvRTParams params=CvRTParams());
#ifndef SWIG
CV_WRAP virtual bool train( const cv::Mat& _train_data, int _tflag,
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
const cv::Mat& _missing_mask=cv::Mat(),
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvRTParams params=CvRTParams());
#endif
virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
@@ -1224,7 +1224,7 @@ public:
CvBoostTree();
virtual ~CvBoostTree();
virtual bool train( CvDTreeTrainData* _train_data,
virtual bool train( CvDTreeTrainData* trainData,
const CvMat* subsample_idx, CvBoost* ensemble );
virtual void scale( double s );
@@ -1233,12 +1233,12 @@ public:
virtual void clear();
/* dummy methods to avoid warnings: BEGIN */
virtual bool train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvDTreeParams params=CvDTreeParams() );
virtual bool train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx );
virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx );
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void read( CvFileStorage* fs, CvFileNode* node,
@@ -1277,16 +1277,16 @@ public:
CV_WRAP CvBoost();
virtual ~CvBoost();
CvBoost( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
CvBoost( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvBoostParams params=CvBoostParams() );
virtual bool train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvBoostParams params=CvBoostParams(),
bool update=false );
@@ -1294,27 +1294,27 @@ public:
CvBoostParams params=CvBoostParams(),
bool update=false );
virtual float predict( const CvMat* _sample, const CvMat* _missing=0,
virtual float predict( const CvMat* sample, const CvMat* missing=0,
CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ,
bool raw_mode=false, bool return_sum=false ) const;
#ifndef SWIG
CV_WRAP CvBoost( const cv::Mat& _train_data, int _tflag,
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
const cv::Mat& _missing_mask=cv::Mat(),
CV_WRAP CvBoost( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvBoostParams params=CvBoostParams() );
CV_WRAP virtual bool train( const cv::Mat& _train_data, int _tflag,
const cv::Mat& _responses, const cv::Mat& _var_idx=cv::Mat(),
const cv::Mat& _sample_idx=cv::Mat(), const cv::Mat& _var_type=cv::Mat(),
const cv::Mat& _missing_mask=cv::Mat(),
CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
const cv::Mat& missingDataMask=cv::Mat(),
CvBoostParams params=CvBoostParams(),
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& _sample, const cv::Mat& _missing=cv::Mat(),
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
cv::Mat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ,
bool raw_mode=false, bool return_sum=false ) const;
bool rawMode=false, bool returnSum=false ) const;
#endif
virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
@@ -1337,7 +1337,7 @@ public:
protected:
virtual bool set_params( const CvBoostParams& _params );
virtual bool set_params( const CvBoostParams& params );
virtual void update_weights( CvBoostTree* tree );
virtual void trim_weights();
virtual void write_params( CvFileStorage* fs ) const;
@@ -1409,7 +1409,7 @@ struct CV_EXPORTS_AS_MAP CvGBTreesParams : public CvDTreeParams
// computed via sum_responses values. When the current
// step is complete sum_response values become equal to
// sum_responses_tmp.
// sample_idx - indices of samples used for training the ensemble.
// sampleIdx - indices of samples used for training the ensemble.
// CvGBTrees training procedure takes a set of samples
// (train_data) and a set of responses (responses).
// Only pairs (train_data[i], responses[i]), where i is
@@ -1491,42 +1491,42 @@ public:
// train.
//
// API
// CvGBTrees( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
// CvGBTrees( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams() );
// INPUT
// _train_data - a set of input feature vectors.
// trainData - a set of input feature vectors.
// size of matrix is
// <count of samples> x <variables count>
// or <variables count> x <count of samples>
// depending on the _tflag parameter.
// depending on the tflag parameter.
// matrix values are float.
// _tflag - a flag showing how do samples stored in the
// _train_data matrix row by row (tflag=CV_ROW_SAMPLE)
// tflag - a flag showing how do samples stored in the
// trainData matrix row by row (tflag=CV_ROW_SAMPLE)
// or column by column (tflag=CV_COL_SAMPLE).
// _responses - a vector of responses corresponding to the samples
// in _train_data.
// _var_idx - indices of used variables. zero value means that all
// responses - a vector of responses corresponding to the samples
// in trainData.
// varIdx - indices of used variables. zero value means that all
// variables are active.
// _sample_idx - indices of used samples. zero value means that all
// samples from _train_data are in the training set.
// _var_type - vector of <variables count> length. gives every
// sampleIdx - indices of used samples. zero value means that all
// samples from trainData are in the training set.
// varType - vector of <variables count> length. gives every
// variable type CV_VAR_CATEGORICAL or CV_VAR_ORDERED.
// _var_type = 0 means all variables are numerical.
// _missing_mask - a mask of misiing values in _train_data.
// _missing_mask = 0 means that there are no missing
// varType = 0 means all variables are numerical.
// missingDataMask - a mask of misiing values in trainData.
// missingDataMask = 0 means that there are no missing
// values.
// params - parameters of GTB algorithm.
// OUTPUT
// RESULT
*/
CV_WRAP CvGBTrees( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
CV_WRAP CvGBTrees( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams() );
@@ -1540,34 +1540,34 @@ public:
// Gradient tree boosting model training
//
// API
// virtual bool train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
// virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
// INPUT
// _train_data - a set of input feature vectors.
// trainData - a set of input feature vectors.
// size of matrix is
// <count of samples> x <variables count>
// or <variables count> x <count of samples>
// depending on the _tflag parameter.
// depending on the tflag parameter.
// matrix values are float.
// _tflag - a flag showing how do samples stored in the
// _train_data matrix row by row (tflag=CV_ROW_SAMPLE)
// tflag - a flag showing how do samples stored in the
// trainData matrix row by row (tflag=CV_ROW_SAMPLE)
// or column by column (tflag=CV_COL_SAMPLE).
// _responses - a vector of responses corresponding to the samples
// in _train_data.
// _var_idx - indices of used variables. zero value means that all
// responses - a vector of responses corresponding to the samples
// in trainData.
// varIdx - indices of used variables. zero value means that all
// variables are active.
// _sample_idx - indices of used samples. zero value means that all
// samples from _train_data are in the training set.
// _var_type - vector of <variables count> length. gives every
// sampleIdx - indices of used samples. zero value means that all
// samples from trainData are in the training set.
// varType - vector of <variables count> length. gives every
// variable type CV_VAR_CATEGORICAL or CV_VAR_ORDERED.
// _var_type = 0 means all variables are numerical.
// _missing_mask - a mask of misiing values in _train_data.
// _missing_mask = 0 means that there are no missing
// varType = 0 means all variables are numerical.
// missingDataMask - a mask of misiing values in trainData.
// missingDataMask = 0 means that there are no missing
// values.
// params - parameters of GTB algorithm.
// update - is not supported now. (!)
@@ -1575,10 +1575,10 @@ public:
// RESULT
// Error state.
*/
CV_WRAP virtual bool train( const CvMat* _train_data, int _tflag,
const CvMat* _responses, const CvMat* _var_idx=0,
const CvMat* _sample_idx=0, const CvMat* _var_type=0,
const CvMat* _missing_mask=0,
CV_WRAP virtual bool train( const CvMat* trainData, int tflag,
const CvMat* responses, const CvMat* varIdx=0,
const CvMat* sampleIdx=0, const CvMat* varType=0,
const CvMat* missingDataMask=0,
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
@@ -1608,14 +1608,14 @@ public:
// Response value prediction
//
// API
// virtual float predict( const CvMat* _sample, const CvMat* _missing=0,
// virtual float predict( const CvMat* sample, const CvMat* missing=0,
CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
int k=-1 ) const;
// INPUT
// _sample - input sample of the same type as in the training set.
// _missing - missing values mask. _missing=0 if there are no
// missing values in _sample vector.
// sample - input sample of the same type as in the training set.
// missing - missing values mask. missing=0 if there are no
// missing values in sample vector.
// weak_responses - predictions of all of the trees.
// not implemented (!)
// slice - part of the ensemble used for prediction.
@@ -1631,8 +1631,8 @@ public:
// RESULT
// Predicted value.
*/
CV_WRAP virtual float predict( const CvMat* _sample, const CvMat* _missing=0,
CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
CV_WRAP virtual float predict( const CvMat* sample, const CvMat* missing=0,
CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
int k=-1 ) const;
/*
@@ -1917,37 +1917,37 @@ class CV_EXPORTS_AS(ANN_MLP) CvANN_MLP : public CvStatModel
{
public:
CV_WRAP CvANN_MLP();
CvANN_MLP( const CvMat* _layer_sizes,
int _activ_func=SIGMOID_SYM,
double _f_param1=0, double _f_param2=0 );
CvANN_MLP( const CvMat* layerSizes,
int activateFunc=SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
virtual ~CvANN_MLP();
virtual void create( const CvMat* _layer_sizes,
int _activ_func=SIGMOID_SYM,
double _f_param1=0, double _f_param2=0 );
virtual void create( const CvMat* layerSizes,
int activateFunc=SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
virtual int train( const CvMat* _inputs, const CvMat* _outputs,
const CvMat* _sample_weights, const CvMat* _sample_idx=0,
CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(),
virtual int train( const CvMat* inputs, const CvMat* outputs,
const CvMat* sampleWeights, const CvMat* sampleIdx=0,
CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(),
int flags=0 );
virtual float predict( const CvMat* _inputs, CV_OUT CvMat* _outputs ) const;
virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const;
#ifndef SWIG
CV_WRAP CvANN_MLP( const cv::Mat& _layer_sizes,
int _activ_func=SIGMOID_SYM,
double _f_param1=0, double _f_param2=0 );
CV_WRAP CvANN_MLP( const cv::Mat& layerSizes,
int activateFunc=SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
CV_WRAP virtual void create( const cv::Mat& _layer_sizes,
int _activ_func=SIGMOID_SYM,
double _f_param1=0, double _f_param2=0 );
CV_WRAP virtual void create( const cv::Mat& layerSizes,
int activateFunc=SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
CV_WRAP virtual int train( const cv::Mat& _inputs, const cv::Mat& _outputs,
const cv::Mat& _sample_weights, const cv::Mat& _sample_idx=cv::Mat(),
CvANN_MLP_TrainParams _params = CvANN_MLP_TrainParams(),
CV_WRAP virtual int train( const cv::Mat& inputs, const cv::Mat& outputs,
const cv::Mat& sampleWeights, const cv::Mat& sampleIdx=cv::Mat(),
CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(),
int flags=0 );
CV_WRAP virtual float predict( const cv::Mat& _inputs, cv::Mat& _outputs ) const;
CV_WRAP virtual float predict( const cv::Mat& inputs, cv::Mat& outputs ) const;
#endif
CV_WRAP virtual void clear();
@@ -1972,7 +1972,7 @@ public:
protected:
virtual bool prepare_to_train( const CvMat* _inputs, const CvMat* _outputs,
const CvMat* _sample_weights, const CvMat* _sample_idx,
const CvMat* _sample_weights, const CvMat* sampleIdx,
CvVectors* _ivecs, CvVectors* _ovecs, double** _sw, int _flags );
// sequential random backpropagation