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

almost finished Python wrappers

Browse Source
This commit is contained in:
Vadim Pisarevsky
2010-11-02 17:58:22 +00:00
parent eda72a3e8b
commit 5b6a755719
27 changed files with 1292 additions and 634 deletions
Download Patch File Download Diff File Expand all files Collapse all files

208
modules/ml/include/opencv2/ml/ml.hpp
View File

@@ -188,7 +188,7 @@ CV_INLINE CvParamLattice cvDefaultParamLattice( void )
#define CV_TRAIN_ERROR 0
#define CV_TEST_ERROR 1
class CV_EXPORTS_AS(StatModel) CvStatModel
class CV_EXPORTS_W CvStatModel
{
public:
CvStatModel();
@@ -217,7 +217,7 @@ protected:
class CvMLData;
struct CV_EXPORTS CvParamGrid
struct CV_EXPORTS_W_MAP CvParamGrid
{
// SVM params type
enum { SVM_C=0, SVM_GAMMA=1, SVM_P=2, SVM_NU=3, SVM_COEF=4, SVM_DEGREE=5 };
@@ -236,12 +236,12 @@ struct CV_EXPORTS CvParamGrid
//CvParamGrid( int param_id );
bool check() const;
double min_val;
double max_val;
double step;
CV_PROP_RW double min_val;
CV_PROP_RW double max_val;
CV_PROP_RW double step;
};
class CV_EXPORTS_AS(NormalBayesClassifier) CvNormalBayesClassifier : public CvStatModel
class CV_EXPORTS_W CvNormalBayesClassifier : public CvStatModel
{
public:
CV_WRAP CvNormalBayesClassifier();
@@ -262,7 +262,7 @@ public:
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_OUT cv::Mat* results=0 ) const;
#endif
virtual void write( CvFileStorage* storage, const char* name ) const;
@@ -287,7 +287,7 @@ protected:
\****************************************************************************************/
// k Nearest Neighbors
class CV_EXPORTS_AS(KNearest) CvKNearest : public CvStatModel
class CV_EXPORTS_W CvKNearest : public CvStatModel
{
public:
@@ -312,9 +312,11 @@ public:
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,
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;
CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results,
CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const;
#endif
virtual void clear();
@@ -344,7 +346,7 @@ protected:
\****************************************************************************************/
// SVM training parameters
struct CV_EXPORTS_AS_MAP CvSVMParams
struct CV_EXPORTS_W_MAP CvSVMParams
{
CvSVMParams();
CvSVMParams( int _svm_type, int _kernel_type,
@@ -352,17 +354,17 @@ struct CV_EXPORTS_AS_MAP CvSVMParams
double Cvalue, double _nu, double _p,
CvMat* _class_weights, CvTermCriteria _term_crit );
int svm_type;
int kernel_type;
double degree; // for poly
double gamma; // for poly/rbf/sigmoid
double coef0; // for poly/sigmoid
CV_PROP_RW int svm_type;
CV_PROP_RW int kernel_type;
CV_PROP_RW double degree; // for poly
CV_PROP_RW double gamma; // for poly/rbf/sigmoid
CV_PROP_RW double coef0; // for poly/sigmoid
double C; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
double nu; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
double p; // for CV_SVM_EPS_SVR
CV_PROP_RW double C; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
CV_PROP_RW double nu; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
CV_PROP_RW double p; // for CV_SVM_EPS_SVR
CvMat* class_weights; // for CV_SVM_C_SVC
CvTermCriteria term_crit; // termination criteria
CV_PROP_RW CvTermCriteria term_crit; // termination criteria
};
@@ -507,7 +509,7 @@ struct CvSVMDecisionFunc
// SVM model
class CV_EXPORTS_AS(SVM) CvSVM : public CvStatModel
class CV_EXPORTS_W CvSVM : public CvStatModel
{
public:
// SVM type
@@ -554,18 +556,18 @@ public:
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 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) );
CvParamGrid Cgrid = CvSVM::get_default_grid(CvSVM::C),
CvParamGrid gammaGrid = CvSVM::get_default_grid(CvSVM::GAMMA),
CvParamGrid pGrid = CvSVM::get_default_grid(CvSVM::P),
CvParamGrid nuGrid = CvSVM::get_default_grid(CvSVM::NU),
CvParamGrid coeffGrid = CvSVM::get_default_grid(CvSVM::COEF),
CvParamGrid degreeGrid = CvSVM::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;
virtual const float* get_support_vector(int i) const;
CV_WRAP virtual CvSVMParams get_params() const { return params; };
virtual CvSVMParams get_params() const { return params; };
CV_WRAP virtual void clear();
static CvParamGrid get_default_grid( int param_id );
@@ -608,7 +610,7 @@ protected:
* Expectation - Maximization *
\****************************************************************************************/
struct CV_EXPORTS_AS_MAP CvEMParams
struct CV_EXPORTS_W_MAP CvEMParams
{
CvEMParams() : nclusters(10), cov_mat_type(1/*CvEM::COV_MAT_DIAGONAL*/),
start_step(0/*CvEM::START_AUTO_STEP*/), probs(0), weights(0), means(0), covs(0)
@@ -624,18 +626,18 @@ struct CV_EXPORTS_AS_MAP CvEMParams
probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
{}
int nclusters;
int cov_mat_type;
int start_step;
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;
CvTermCriteria term_crit;
CV_PROP_RW CvTermCriteria term_crit;
};
class CV_EXPORTS_AS(EM) CvEM : public CvStatModel
class CV_EXPORTS_W CvEM : public CvStatModel
{
public:
// Type of covariation matrices
@@ -659,23 +661,33 @@ public:
#ifndef SWIG
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams(), cv::Mat* labels=0 );
CvEMParams params=CvEMParams() );
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 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::Mat* probs ) const;
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) 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; };
#endif
CV_WRAP virtual void clear();
CV_WRAP int get_nclusters() const;
CV_WRAP const CvMat* get_means() const;
CV_WRAP const CvMat** get_covs() const;
CV_WRAP const CvMat* get_weights() const;
CV_WRAP const CvMat* get_probs() const;
int get_nclusters() const;
const CvMat* get_means() const;
const CvMat** get_covs() const;
const CvMat* get_weights() const;
const CvMat* get_probs() const;
CV_WRAP inline double get_log_likelihood () const { return log_likelihood; };
inline double get_log_likelihood () const { return log_likelihood; };
// 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; };
@@ -771,16 +783,16 @@ struct CvDTreeNode
};
struct CV_EXPORTS_AS_MAP CvDTreeParams
struct CV_EXPORTS_W_MAP CvDTreeParams
{
int max_categories;
int max_depth;
int min_sample_count;
int cv_folds;
bool use_surrogates;
bool use_1se_rule;
bool truncate_pruned_tree;
float regression_accuracy;
CV_PROP_RW int max_categories;
CV_PROP_RW int max_depth;
CV_PROP_RW int min_sample_count;
CV_PROP_RW int cv_folds;
CV_PROP_RW bool use_surrogates;
CV_PROP_RW bool use_1se_rule;
CV_PROP_RW bool truncate_pruned_tree;
CV_PROP_RW float regression_accuracy;
const float* priors;
CvDTreeParams() : max_categories(10), max_depth(INT_MAX), min_sample_count(10),
@@ -914,7 +926,7 @@ namespace cv
struct ForestTreeBestSplitFinder;
}
class CV_EXPORTS_AS(DTree) CvDTree : public CvStatModel
class CV_EXPORTS_W CvDTree : public CvStatModel
{
public:
CV_WRAP CvDTree();
@@ -945,9 +957,10 @@ public:
CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(),
bool preprocessedInput=false ) const;
CV_WRAP virtual cv::Mat getVarImportance();
#endif
CV_WRAP virtual const CvMat* get_var_importance();
virtual const CvMat* get_var_importance();
CV_WRAP virtual void clear();
virtual void read( CvFileStorage* fs, CvFileNode* node );
@@ -1047,12 +1060,12 @@ protected:
};
struct CV_EXPORTS_AS_MAP CvRTParams : public CvDTreeParams
struct CV_EXPORTS_W_MAP CvRTParams : public CvDTreeParams
{
//Parameters for the forest
bool calc_var_importance; // true <=> RF processes variable importance
int nactive_vars;
CvTermCriteria term_crit;
CV_PROP_RW bool calc_var_importance; // true <=> RF processes variable importance
CV_PROP_RW int nactive_vars;
CV_PROP_RW CvTermCriteria term_crit;
CvRTParams() : CvDTreeParams( 5, 10, 0, false, 10, 0, false, false, 0 ),
calc_var_importance(false), nactive_vars(0)
@@ -1077,7 +1090,7 @@ struct CV_EXPORTS_AS_MAP CvRTParams : public CvDTreeParams
};
class CV_EXPORTS_AS(RTrees) CvRTrees : public CvStatModel
class CV_EXPORTS_W CvRTrees : public CvStatModel
{
public:
CV_WRAP CvRTrees();
@@ -1100,11 +1113,12 @@ public:
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;
CV_WRAP virtual cv::Mat getVarImportance();
#endif
CV_WRAP virtual void clear();
CV_WRAP virtual const CvMat* get_var_importance();
virtual const CvMat* get_var_importance();
virtual float get_proximity( const CvMat* sample1, const CvMat* sample2,
const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const;
@@ -1176,7 +1190,7 @@ protected:
virtual void split_node_data( CvDTreeNode* n );
};
class CV_EXPORTS_AS(ERTrees) CvERTrees : public CvRTrees
class CV_EXPORTS_W CvERTrees : public CvRTrees
{
public:
CV_WRAP CvERTrees();
@@ -1203,12 +1217,12 @@ protected:
* Boosted tree classifier *
\****************************************************************************************/
struct CV_EXPORTS_AS_MAP CvBoostParams : public CvDTreeParams
struct CV_EXPORTS_W_MAP CvBoostParams : public CvDTreeParams
{
int boost_type;
int weak_count;
int split_criteria;
double weight_trim_rate;
CV_PROP_RW int boost_type;
CV_PROP_RW int weak_count;
CV_PROP_RW int split_criteria;
CV_PROP_RW double weight_trim_rate;
CvBoostParams();
CvBoostParams( int boost_type, int weak_count, double weight_trim_rate,
@@ -1265,7 +1279,7 @@ protected:
};
class CV_EXPORTS_AS(Boost) CvBoost : public CvStatModel
class CV_EXPORTS_W CvBoost : public CvStatModel
{
public:
// Boosting type
@@ -1313,8 +1327,8 @@ public:
bool update=false );
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 rawMode=false, bool returnSum=false ) const;
const cv::Range& slice=cv::Range::all(), 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}
@@ -1382,12 +1396,12 @@ protected:
// Each tree prediction is multiplied on shrinkage value.
struct CV_EXPORTS_AS_MAP CvGBTreesParams : public CvDTreeParams
struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams
{
int weak_count;
int loss_function_type;
float subsample_portion;
float shrinkage;
CV_PROP_RW int weak_count;
CV_PROP_RW int loss_function_type;
CV_PROP_RW float subsample_portion;
CV_PROP_RW float shrinkage;
CvGBTreesParams();
CvGBTreesParams( int loss_function_type, int weak_count, float shrinkage,
@@ -1442,7 +1456,7 @@ struct CV_EXPORTS_AS_MAP CvGBTreesParams : public CvDTreeParams
class CV_EXPORTS_AS(GBTrees) CvGBTrees : public CvStatModel
class CV_EXPORTS_W CvGBTrees : public CvStatModel
{
public:
@@ -1523,7 +1537,7 @@ public:
// OUTPUT
// RESULT
*/
CV_WRAP CvGBTrees( const CvMat* trainData, int tflag,
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,
@@ -1575,7 +1589,7 @@ public:
// RESULT
// Error state.
*/
CV_WRAP virtual bool train( const CvMat* trainData, int tflag,
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,
@@ -1631,7 +1645,7 @@ public:
// RESULT
// Predicted value.
*/
CV_WRAP virtual float predict( const CvMat* sample, const CvMat* missing=0,
virtual float predict( const CvMat* sample, const CvMat* missing=0,
CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
int k=-1 ) const;
@@ -1702,7 +1716,25 @@ public:
*/
virtual void read( CvFileStorage* fs, CvFileNode* node );
// new-style C++ interface
CV_WRAP CvGBTrees( 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(),
CvGBTreesParams params=CvGBTreesParams() );
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(),
CvGBTreesParams params=CvGBTreesParams(),
bool update=false );
CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
const cv::Range& slice = cv::Range::all(),
int k=-1 ) const;
protected:
/*
@@ -1893,7 +1925,7 @@ protected:
/////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
struct CV_EXPORTS_AS_MAP CvANN_MLP_TrainParams
struct CV_EXPORTS_W_MAP CvANN_MLP_TrainParams
{
CvANN_MLP_TrainParams();
CvANN_MLP_TrainParams( CvTermCriteria term_crit, int train_method,
@@ -1902,29 +1934,29 @@ struct CV_EXPORTS_AS_MAP CvANN_MLP_TrainParams
enum { BACKPROP=0, RPROP=1 };
CvTermCriteria term_crit;
int train_method;
CV_PROP_RW CvTermCriteria term_crit;
CV_PROP_RW int train_method;
// backpropagation parameters
double bp_dw_scale, bp_moment_scale;
CV_PROP_RW double bp_dw_scale, bp_moment_scale;
// rprop parameters
double rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min, rp_dw_max;
CV_PROP_RW double rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min, rp_dw_max;
};
class CV_EXPORTS_AS(ANN_MLP) CvANN_MLP : public CvStatModel
class CV_EXPORTS_W CvANN_MLP : public CvStatModel
{
public:
CV_WRAP CvANN_MLP();
CvANN_MLP( const CvMat* layerSizes,
int activateFunc=SIGMOID_SYM,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
virtual ~CvANN_MLP();
virtual void create( const CvMat* layerSizes,
int activateFunc=SIGMOID_SYM,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
virtual int train( const CvMat* inputs, const CvMat* outputs,
@@ -1935,11 +1967,11 @@ public:
#ifndef SWIG
CV_WRAP CvANN_MLP( const cv::Mat& layerSizes,
int activateFunc=SIGMOID_SYM,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
CV_WRAP virtual void create( const cv::Mat& layerSizes,
int activateFunc=SIGMOID_SYM,
int activateFunc=CvANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0 );
CV_WRAP virtual int train( const cv::Mat& inputs, const cv::Mat& outputs,