The class implements the random forest predictor. More...

Public Member Functions
int	getActiveVarCount ()

bool	getCalculateVarImportance ()

double	getOOBError ()

TermCriteria	getTermCriteria ()

double double double epsilon	getTermCriteriaAsValueTuple ()

Vec3d	getTermCriteriaAsVec3d ()

Mat	getVarImportance ()

void	getVotes (Mat samples, Mat results, int flags)

void	setActiveVarCount (int val)

void	setCalculateVarImportance (bool val)

void	setTermCriteria (in Vec3d val)

void	setTermCriteria (in(double type, double maxCount, double epsilon) val)

void	setTermCriteria (TermCriteria val)

Public Member Functions inherited from OpenCVForUnity.MlModule.DTrees
int	getCVFolds ()

int	getMaxCategories ()

int	getMaxDepth ()

int	getMinSampleCount ()

Mat	getPriors ()

float	getRegressionAccuracy ()

bool	getTruncatePrunedTree ()

bool	getUse1SERule ()

bool	getUseSurrogates ()

void	setCVFolds (int val)

void	setMaxCategories (int val)

void	setMaxDepth (int val)

void	setMinSampleCount (int val)

void	setPriors (Mat val)

void	setRegressionAccuracy (float val)

void	setTruncatePrunedTree (bool val)

void	setUse1SERule (bool val)

void	setUseSurrogates (bool val)

Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
float	calcError (TrainData data, bool test, Mat resp)
	Computes error on the training or test dataset.

override 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.

virtual float	predict (Mat samples)
	Predicts response(s) for the provided sample(s)

virtual float	predict (Mat samples, Mat results)
	Predicts response(s) for the provided sample(s)

virtual float	predict (Mat samples, Mat results, int flags)
	Predicts response(s) for the provided sample(s)

bool	train (Mat samples, int layout, Mat responses)
	Trains the statistical model.

bool	train (TrainData trainData)
	Trains the statistical model.

bool	train (TrainData trainData, int flags)
	Trains the statistical model.

Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
virtual void	clear ()
	Clears the algorithm state.

virtual string	getDefaultName ()

IntPtr	getNativeObjAddr ()

void	save (string filename)

Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()

void	ThrowIfDisposed ()

Static Public Member Functions
static new RTrees	__fromPtr__ (IntPtr addr)

static new RTrees	create ()

static new RTrees	load (string filepath)
	Loads and creates a serialized RTree from a file.

static new RTrees	load (string filepath, string nodeName)
	Loads and creates a serialized RTree from a file.

Static Public Member Functions inherited from OpenCVForUnity.MlModule.DTrees
static new DTrees	__fromPtr__ (IntPtr addr)

static DTrees	create ()
	Creates the empty model.

static DTrees	load (string filepath)
	Loads and creates a serialized DTrees from a file.

static DTrees	load (string filepath, string nodeName)
	Loads and creates a serialized DTrees from a file.

Static Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
static new StatModel	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Public Attributes
double double	maxCount

double	type

Protected Member Functions
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.MlModule.DTrees
Protected Member Functions inherited from OpenCVForUnity.MlModule.StatModel
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()

	DisposableOpenCVObject (bool isEnabledDispose)

	DisposableOpenCVObject (IntPtr ptr)

	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)

Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()

	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Static Public Attributes inherited from OpenCVForUnity.MlModule.DTrees
const int	PREDICT_AUTO = 0

const int	PREDICT_MASK = (3 << 8)

const int	PREDICT_MAX_VOTE = (2 << 8)

const int	PREDICT_SUM = (1 << 8)

Static Public Attributes inherited from OpenCVForUnity.MlModule.StatModel
const int	COMPRESSED_INPUT = 2

const int	PREPROCESSED_INPUT = 4

const int	RAW_OUTPUT = 1

const int	UPDATE_MODEL = 1

Package Functions inherited from OpenCVForUnity.MlModule.DTrees
Package Functions inherited from OpenCVForUnity.MlModule.StatModel
Package Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed `[get, protected set]`

bool	IsEnabledDispose `[get, set]`

Detailed Description

The class implements the random forest predictor.

See also: ml_intro_rtrees

Member Function Documentation

◆ fromPtr()

static new RTrees OpenCVForUnity.MlModule.RTrees.__fromPtr__ ( IntPtr addr )

static

◆ create()

static new RTrees OpenCVForUnity.MlModule.RTrees.create ( )

static

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.

◆ Dispose()

override void OpenCVForUnity.MlModule.RTrees.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.MlModule.DTrees.

◆ getActiveVarCount()

int OpenCVForUnity.MlModule.RTrees.getActiveVarCount ( )

See also: setActiveVarCount

◆ getCalculateVarImportance()

bool OpenCVForUnity.MlModule.RTrees.getCalculateVarImportance ( )

See also: setCalculateVarImportance

◆ getOOBError()

double OpenCVForUnity.MlModule.RTrees.getOOBError ( )

Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.

◆ getTermCriteria()

TermCriteria OpenCVForUnity.MlModule.RTrees.getTermCriteria ( )

See also: setTermCriteria

◆ getTermCriteriaAsValueTuple()

double double double epsilon OpenCVForUnity.MlModule.RTrees.getTermCriteriaAsValueTuple ( )

◆ getTermCriteriaAsVec3d()

Vec3d OpenCVForUnity.MlModule.RTrees.getTermCriteriaAsVec3d ( )

See also: setTermCriteria

◆ getVarImportance()

Mat OpenCVForUnity.MlModule.RTrees.getVarImportance ( )

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.

◆ getVotes()

void OpenCVForUnity.MlModule.RTrees.getVotes	(	Mat	samples,
		Mat	results,
		int	flags )

Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.

Parameters

samples	Array containing the samples for which votes will be calculated.
results	Array where the result of the calculation will be written.
flags	Flags for defining the type of RTrees.

◆ load() [1/2]

static new RTrees OpenCVForUnity.MlModule.RTrees.load ( string filepath )

static

Loads and creates a serialized RTree from a file.

Use RTree::save to serialize and store an RTree to disk. Load the RTree 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 RTree
nodeName	name of node containing the classifier

◆ load() [2/2]

static new RTrees OpenCVForUnity.MlModule.RTrees.load	(	string	filepath,
		string	nodeName )

static

Loads and creates a serialized RTree from a file.

Use RTree::save to serialize and store an RTree to disk. Load the RTree 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 RTree
nodeName	name of node containing the classifier

◆ setActiveVarCount()

void OpenCVForUnity.MlModule.RTrees.setActiveVarCount ( int val )

See also: getActiveVarCount

◆ setCalculateVarImportance()

void OpenCVForUnity.MlModule.RTrees.setCalculateVarImportance ( bool val )

See also: getCalculateVarImportance

◆ setTermCriteria() [1/3]

void OpenCVForUnity.MlModule.RTrees.setTermCriteria ( in Vec3d val )

See also: getTermCriteria

◆ setTermCriteria() [2/3]

void OpenCVForUnity.MlModule.RTrees.setTermCriteria ( in(double type, double maxCount, double epsilon) val )

See also: getTermCriteria

◆ setTermCriteria() [3/3]

void OpenCVForUnity.MlModule.RTrees.setTermCriteria ( TermCriteria val )

See also: getTermCriteria

Member Data Documentation

◆ maxCount

double double OpenCVForUnity.MlModule.RTrees.maxCount

◆ type

double OpenCVForUnity.MlModule.RTrees.type

See also: setTermCriteria

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

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/RTrees.cs
OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/RTrees_Struct.cs
OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/RTrees_ValueTuple.cs

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ __fromPtr__()

◆ create()

◆ Dispose()

◆ getActiveVarCount()

◆ getCalculateVarImportance()

◆ getOOBError()

◆ getTermCriteria()

◆ getTermCriteriaAsValueTuple()

◆ getTermCriteriaAsVec3d()

◆ getVarImportance()

◆ getVotes()

◆ load() [1/2]

◆ load() [2/2]

◆ setActiveVarCount()

◆ setCalculateVarImportance()

◆ setTermCriteria() [1/3]

◆ setTermCriteria() [2/3]

◆ setTermCriteria() [3/3]

Member Data Documentation

◆ maxCount

◆ type

◆ fromPtr()