OpenCV: cv::ml::Boost Class Reference

| OpenCV 3.4.2 Open Source Computer Vision

Public Types | Public Member Functions | Static Public Member Functions | List of all members
cv::ml::Boost Class Referenceabstract
Machine Learning

Boosted tree classifier derived from DTrees. More...

`#include "ml.hpp"`

Inheritance diagram for cv::ml::Boost:

## 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< 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

## Detailed Description

Boosted tree classifier derived from DTrees.

See also
  Boosting

## Member Enumeration Documentation

## ◆ Types

enum cv::ml::Boost::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

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

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

The number of weak classifiers. Default value is 100.

See also
  setWeakCount

## ◆ getWeightTrimRate()

virtual double cv::ml::Boost::getWeightTrimRate | ( |  | ) | const
| pure virtual

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

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

  filepath | path to serialized Boost
  nodeName | name of node containing the classifier

## ◆ setBoostType()

virtual void cv::ml::Boost::setBoostType | ( | int | val | ) |
| pure virtual

See also
  getBoostType

## ◆ setWeakCount()

virtual void cv::ml::Boost::setWeakCount | ( | int | val | ) |
| pure virtual

See also
  getWeakCount

## ◆ setWeightTrimRate()

virtual void cv::ml::Boost::setWeightTrimRate | ( | double | val | ) |
| pure virtual

See also
  getWeightTrimRate

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

- ml/include/opencv2/ml.hpp