OpenCV  4.1.2-dev
Open Source Computer Vision
Public Types | Public Member Functions | Static Public Member Functions | List of all members
cv::ml::Boost Class Referenceabstract

Boosted tree classifier derived from DTrees. More...

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::Boost:
cv::ml::DTrees cv::ml::StatModel cv::Algorithm

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 CV_OVERRIDE
 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...
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
 

Static Public Member Functions

static Ptr< Boostcreate ()
 
static Ptr< Boostload (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< DTreescreate ()
 Creates the empty model. More...
 
static Ptr< DTreesload (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
 

Detailed Description

Boosted tree classifier derived from DTrees.

See also
Boosting

Member Enumeration Documentation

§ Types

Boosting type. Gentle AdaBoost and Real AdaBoost are often the preferable choices.

Enumerator
DISCRETE 

Discrete AdaBoost.

REAL 

Real AdaBoost. It is a technique that utilizes confidence-rated predictions and works well with categorical data.

LOGIT 

LogitBoost. It can produce good regression fits.

GENTLE 

Gentle AdaBoost. It puts less weight on outlier data points and for that reason is often good with regression data.

Member Function Documentation

§ create()

static Ptr<Boost> cv::ml::Boost::create ( )
static
Python:
retval=cv.ml.Boost_create()

Creates the empty model. Use StatModel::train to train the model, Algorithm::load<Boost>(filename) to load the pre-trained model.

§ getBoostType()

virtual int cv::ml::Boost::getBoostType ( ) const
pure virtual
Python:
retval=cv.ml_Boost.getBoostType()

Type of the boosting algorithm. See Boost::Types. Default value is Boost::REAL.

See also
setBoostType

§ getWeakCount()

virtual int cv::ml::Boost::getWeakCount ( ) const
pure virtual
Python:
retval=cv.ml_Boost.getWeakCount()

The number of weak classifiers. Default value is 100.

See also
setWeakCount

§ getWeightTrimRate()

virtual double cv::ml::Boost::getWeightTrimRate ( ) const
pure virtual
Python:
retval=cv.ml_Boost.getWeightTrimRate()

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.

See also
setWeightTrimRate

§ load()

static Ptr<Boost> cv::ml::Boost::load ( const String filepath,
const String nodeName = String() 
)
static
Python:
retval=cv.ml.Boost_load(filepath[, nodeName])

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

Parameters
filepathpath to serialized Boost
nodeNamename of node containing the classifier

§ setBoostType()

virtual void cv::ml::Boost::setBoostType ( int  val)
pure virtual
Python:
None=cv.ml_Boost.setBoostType(val)

See also
getBoostType

§ setWeakCount()

virtual void cv::ml::Boost::setWeakCount ( int  val)
pure virtual
Python:
None=cv.ml_Boost.setWeakCount(val)

See also
getWeakCount

§ setWeightTrimRate()

virtual void cv::ml::Boost::setWeightTrimRate ( double  val)
pure virtual
Python:
None=cv.ml_Boost.setWeightTrimRate(val)

The documentation for this class was generated from the following file: