OpenCV
3.3.0
Open Source Computer Vision
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Boosted tree classifier derived from DTrees. More...
#include "ml.hpp"
Public Types | |
enum | Types { DISCRETE =0, REAL =1, LOGIT =2, GENTLE =3 } |
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 } |
Public Member Functions | |
virtual int | getBoostType () const =0 |
virtual int | getWeakCount () const =0 |
virtual double | getWeightTrimRate () const =0 |
virtual void | setBoostType (int val)=0 |
virtual void | setWeakCount (int val)=0 |
virtual void | setWeightTrimRate (double 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< Boost > | create () |
static Ptr< Boost > | load (const String &filepath, const String &nodeName=String()) |
Loads and creates a serialized Boost from a file. More... | |
Static Public Member Functions inherited from cv::ml::DTrees | |
static Ptr< DTrees > | create () |
Creates the empty model. More... | |
static Ptr< DTrees > | load (const String &filepath, const String &nodeName=String()) |
Loads and creates a serialized DTrees from a file. 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 | |
Protected Member Functions inherited from cv::Algorithm | |
void | writeFormat (FileStorage &fs) const |
enum cv::ml::Boost::Types |
Boosting type. Gentle AdaBoost and Real AdaBoost are often the preferable choices.
Creates the empty model. Use StatModel::train to train the model, Algorithm::load<Boost>(filename) to load the pre-trained model.
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Type of the boosting algorithm. See Boost::Types. Default value is Boost::REAL.
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The number of weak classifiers. Default value is 100.
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A threshold between 0 and 1 used to save computational time. Samples with summary weight \(\leq 1 - weight_trim_rate\) do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.
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Loads and creates a serialized Boost from a file.
Use Boost::save to serialize and store an RTree to disk. Load the Boost from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
filepath | path to serialized Boost |
nodeName | name of node containing the classifier |
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pure virtual |
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pure virtual |