Class cv::ml::Boost#

Boosted tree classifier derived from DTrees. View details

#include <opencv2/ml.hpp>

Collaboration diagram for cv::ml::Boost:

Public Types#

enum Types {
    DISCRETE =0,
    REAL =1,
    LOGIT =2,
    GENTLE =3
}

Public Member Functions#

Public Member Functions inherited from cv::ml::DTrees

Return	Name	Description
`int`	`getCVFolds()`
`int`	`getMaxCategories()`
`int`	`getMaxDepth()`
`int`	`getMinSampleCount()`
`const std::vector< Node > &`	`getNodes()`	Returns all the nodes.
`cv::Mat`	`getPriors()`	The array of a priori class probabilities, sorted by the class label value.
`float`	`getRegressionAccuracy()`
`const std::vector< int > &`	`getRoots()`	Returns indices of root nodes.
`const std::vector< Split > &`	`getSplits()`	Returns all the splits.
`const std::vector< int > &`	`getSubsets()`	Returns all the bitsets for categorical splits.
`bool`	`getTruncatePrunedTree()`
`bool`	`getUse1SERule()`
`bool`	`getUseSurrogates()`
`void`	`setCVFolds(int val)`
`void`	`setMaxCategories(int val)`
`void`	`setMaxDepth(int val)`
`void`	`setMinSampleCount(int val)`
`void`	`setPriors(const cv::Mat & val)`	The array of a priori class probabilities, sorted by the class label value.
`void`	`setRegressionAccuracy(float val)`
`void`	`setTruncatePrunedTree(bool val)`
`void`	`setUse1SERule(bool val)`
`void`	`setUseSurrogates(bool val)`

Public Member Functions inherited from cv::ml::StatModel

Return	Name	Description
`float`	`calcError(` `const Ptr< TrainData > & data,` `bool test,` `OutputArray resp )`	Computes error on the training or test dataset.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`int`	`getVarCount()`	Returns the number of variables in training samples.
`bool`	`isClassifier()`	Returns true if the model is classifier.
`bool`	`isTrained()`	Returns true if the model is trained.
`float`	`predict(` `InputArray samples,` `OutputArray results = noArray(),` `int flags = 0 )`	Predicts response(s) for the provided sample(s)
`bool`	`train(` `const Ptr< TrainData > & trainData,` `int flags = 0 )`	Trains the statistical model.
`bool`	`train(` `InputArray samples,` `int layout,` `InputArray responses )`	Trains the statistical model.

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::ml::DTrees

Return	Name	Description
`static Ptr< DTrees >`	`create()`	Creates the empty model.
`static Ptr< DTrees >`	`load(` `const String & filepath,` `const String & nodeName = String() )`	Loads and creates a serialized DTrees from a file.

Static Public Member Functions inherited from cv::ml::StatModel

Return	Name	Description
`static Ptr< _Tp >`	`train(` `const Ptr< TrainData > & data,` `int flags = 0 )`	Create and train model with default parameters.

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

Boosted tree classifier derived from DTrees.

See also

ml_intro_boost

Member Enumeration Documentation#

enum Types

`DISCRETE` Python: `cv.ml.Boost_DISCRETE`	Discrete AdaBoost.
`REAL` Python: `cv.ml.Boost_REAL`	Real AdaBoost. It is a technique that utilizes confidence-rated predictions and works well with categorical data.
`LOGIT` Python: `cv.ml.Boost_LOGIT`	LogitBoost. It can produce good regression fits.
`GENTLE` Python: `cv.ml.Boost_GENTLE`	Gentle AdaBoost. It puts less weight on outlier data points and for that reason is often good with regression data.

Member Function Documentation#

getBoostType()#

int cv::ml::Boost::getBoostType()

Python:

cv.ml.Boost.getBoostType() -> retval

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

See also

setBoostType

getWeakCount()#

int cv::ml::Boost::getWeakCount()

Python:

cv.ml.Boost.getWeakCount() -> retval

The number of weak classifiers. Default value is 100.

See also

setWeakCount

getWeightTrimRate()#

double cv::ml::Boost::getWeightTrimRate()

Python:

cv.ml.Boost.getWeightTrimRate() -> retval

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

setBoostType()#

void cv::ml::Boost::setBoostType(int val)

Python:

cv.ml.Boost.setBoostType(val)

See also

getBoostType

setWeakCount()#

void cv::ml::Boost::setWeakCount(int val)

Python:

cv.ml.Boost.setWeakCount(val)

See also

getWeakCount

setWeightTrimRate()#

void cv::ml::Boost::setWeightTrimRate(double val)

Python:

cv.ml.Boost.setWeightTrimRate(val)

See also

getWeightTrimRate

create()#

static Ptr< Boost > cv::ml::Boost::create()

Python:

cv.ml.Boost.create() -> retval

cv.ml.Boost_create() -> retval

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

load()#

static Ptr< Boost > cv::ml::Boost::load(
const String & filepath,
const String & nodeName = String() )

Python:

cv.ml.Boost.load(filepath[, nodeName]) -> retval

cv.ml.Boost_load(filepath[, nodeName]) -> retval

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

Source file#

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

opencv2/ml.hpp