OpenCV
3.0.0
Open Source Computer Vision
|
The class implements the random forest predictor. More...
#include "ml.hpp"
Public Member Functions | |
virtual int | getActiveVarCount () const =0 |
virtual bool | getCalculateVarImportance () const =0 |
virtual TermCriteria | getTermCriteria () const =0 |
virtual Mat | getVarImportance () const =0 |
virtual void | setActiveVarCount (int val)=0 |
virtual void | setCalculateVarImportance (bool val)=0 |
virtual void | setTermCriteria (const TermCriteria &val)=0 |
Public Member Functions inherited from cv::ml::DTrees | |
virtual int | getCVFolds () const =0 |
virtual int | getMaxCategories () const =0 |
virtual int | getMaxDepth () const =0 |
virtual int | getMinSampleCount () const =0 |
virtual const std::vector< Node > & | getNodes () const =0 |
Returns all the nodes. More... | |
virtual cv::Mat | getPriors () const =0 |
The array of a priori class probabilities, sorted by the class label value. More... | |
virtual float | getRegressionAccuracy () const =0 |
virtual const std::vector< int > & | getRoots () const =0 |
Returns indices of root nodes. More... | |
virtual const std::vector < Split > & | getSplits () const =0 |
Returns all the splits. More... | |
virtual const std::vector< int > & | getSubsets () const =0 |
Returns all the bitsets for categorical splits. More... | |
virtual bool | getTruncatePrunedTree () const =0 |
virtual bool | getUse1SERule () const =0 |
virtual bool | getUseSurrogates () const =0 |
virtual void | setCVFolds (int val)=0 |
virtual void | setMaxCategories (int val)=0 |
virtual void | setMaxDepth (int val)=0 |
virtual void | setMinSampleCount (int val)=0 |
virtual void | setPriors (const cv::Mat &val)=0 |
The array of a priori class probabilities, sorted by the class label value. More... | |
virtual void | setRegressionAccuracy (float val)=0 |
virtual void | setTruncatePrunedTree (bool val)=0 |
virtual void | setUse1SERule (bool val)=0 |
virtual void | setUseSurrogates (bool val)=0 |
Public Member Functions inherited from cv::ml::StatModel | |
virtual float | calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const |
Computes error on the training or test dataset. More... | |
virtual bool | empty () const |
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More... | |
virtual int | getVarCount () const =0 |
Returns the number of variables in training samples. More... | |
virtual bool | isClassifier () const =0 |
Returns true if the model is classifier. More... | |
virtual bool | isTrained () const =0 |
Returns true if the model is trained. More... | |
virtual float | predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0 |
Predicts response(s) for the provided sample(s) More... | |
virtual bool | train (const Ptr< TrainData > &trainData, int flags=0) |
Trains the statistical model. More... | |
virtual bool | train (InputArray samples, int layout, InputArray responses) |
Trains the statistical model. More... | |
Public Member Functions inherited from cv::Algorithm | |
Algorithm () | |
virtual | ~Algorithm () |
virtual void | clear () |
Clears the algorithm state. More... | |
virtual String | getDefaultName () const |
virtual void | read (const FileNode &fn) |
Reads algorithm parameters from a file storage. More... | |
virtual void | save (const String &filename) const |
virtual void | write (FileStorage &fs) const |
Stores algorithm parameters in a file storage. More... | |
Static Public Member Functions | |
static Ptr< RTrees > | create () |
Static Public Member Functions inherited from cv::ml::DTrees | |
static Ptr< DTrees > | create () |
Creates the empty model. More... | |
Static Public Member Functions inherited from cv::ml::StatModel | |
template<typename _Tp > | |
static Ptr< _Tp > | train (const Ptr< TrainData > &data, int flags=0) |
Create and train model with default parameters. More... | |
Static Public Member Functions inherited from cv::Algorithm | |
template<typename _Tp > | |
static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
Loads algorithm from the file. More... | |
template<typename _Tp > | |
static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
Loads algorithm from a String. More... | |
template<typename _Tp > | |
static Ptr< _Tp > | read (const FileNode &fn) |
Reads algorithm from the file node. More... | |
Additional Inherited Members | |
Public Types inherited from cv::ml::DTrees | |
enum | Flags { PREDICT_AUTO =0, PREDICT_SUM =(1<<8), PREDICT_MAX_VOTE =(2<<8), PREDICT_MASK =(3<<8) } |
Public Types inherited from cv::ml::StatModel | |
enum | Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 } |
The class implements the random forest predictor.
Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.
|
pure virtual |
The size of the randomly selected subset of features at each tree node and that are used to find the best split(s). If you set it to 0 then the size will be set to the square root of the total number of features. Default value is 0.
|
pure virtual |
If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false.
|
pure virtual |
The termination criteria that specifies when the training algorithm stops. Either when the specified number of trees is trained and added to the ensemble or when sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain number of trees. Also to keep in mind, the number of tree increases the prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS + TermCriteria::EPS, 50, 0.1)
|
pure virtual |
Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.
|
pure virtual |
|
pure virtual |
|
pure virtual |