OpenCVForUnity.MlModule.TrainData Class Reference

Class encapsulating training data. More...

Inheritance diagram for OpenCVForUnity.MlModule.TrainData:

Public Member Functions
IntPtr	getNativeObjAddr ()
int	getLayout ()
int	getNTrainSamples ()
int	getNTestSamples ()
int	getNSamples ()
int	getNVars ()
int	getNAllVars ()
void	getSample (Mat varIdx, int sidx, float buf)
Mat	getSamples ()
Mat	getMissing ()
Mat	getTrainSamples (int layout, bool compressSamples, bool compressVars)
	Returns matrix of train samples. More...
Mat	getTrainSamples (int layout, bool compressSamples)
	Returns matrix of train samples. More...
Mat	getTrainSamples (int layout)
	Returns matrix of train samples. More...
Mat	getTrainSamples ()
	Returns matrix of train samples. More...
Mat	getTrainResponses ()
	Returns the vector of responses. More...
Mat	getTrainNormCatResponses ()
	Returns the vector of normalized categorical responses. More...
Mat	getTestResponses ()
Mat	getTestNormCatResponses ()
Mat	getResponses ()
Mat	getNormCatResponses ()
Mat	getSampleWeights ()
Mat	getTrainSampleWeights ()
Mat	getTestSampleWeights ()
Mat	getVarIdx ()
Mat	getVarType ()
Mat	getVarSymbolFlags ()
int	getResponseType ()
Mat	getTrainSampleIdx ()
Mat	getTestSampleIdx ()
void	getValues (int vi, Mat sidx, float values)
Mat	getDefaultSubstValues ()
int	getCatCount (int vi)
Mat	getClassLabels ()
	Returns the vector of class labels. More...
Mat	getCatOfs ()
Mat	getCatMap ()
void	setTrainTestSplit (int count, bool shuffle)
	Splits the training data into the training and test parts. More...
void	setTrainTestSplit (int count)
	Splits the training data into the training and test parts. More...
void	setTrainTestSplitRatio (double ratio, bool shuffle)
	Splits the training data into the training and test parts. More...
void	setTrainTestSplitRatio (double ratio)
	Splits the training data into the training and test parts. More...
void	shuffleTrainTest ()
Mat	getTestSamples ()
	Returns matrix of test samples. More...
void	getNames (List< string > names)
	Returns vector of symbolic names captured in loadFromCSV() More...
Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()
void	ThrowIfDisposed ()

Static Public Member Functions
static TrainData	__fromPtr__ (IntPtr addr)
static Mat	getSubVector (Mat vec, Mat idx)
	Extract from 1D vector elements specified by passed indexes. More...
static Mat	getSubMatrix (Mat matrix, Mat idx, int layout)
	Extract from matrix rows/cols specified by passed indexes. More...
static TrainData	create (Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights, Mat varType)
	Creates training data from in-memory arrays. More...
static TrainData	create (Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights)
	Creates training data from in-memory arrays. More...
static TrainData	create (Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx)
	Creates training data from in-memory arrays. More...
static TrainData	create (Mat samples, int layout, Mat responses, Mat varIdx)
	Creates training data from in-memory arrays. More...
static TrainData	create (Mat samples, int layout, Mat responses)
	Creates training data from in-memory arrays. More...
Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Protected Member Functions
override void	Dispose (bool disposing)
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()
	DisposableOpenCVObject (IntPtr ptr)
	DisposableOpenCVObject (bool isEnabledDispose)
	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)
Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()
	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed [get, protected set]
bool	IsEnabledDispose [get, set]

Detailed Description

Class encapsulating training data.

Please note that the class only specifies the interface of training data, but not implementation. All the statistical model classes in ml module accepts Ptr<TrainData> as parameter. In other words, you can create your own class derived from TrainData and pass smart pointer to the instance of this class into StatModel::train.

See also

ml_intro_data

Member Function Documentation

__fromPtr__()

static TrainData OpenCVForUnity.MlModule.TrainData.__fromPtr__ ( IntPtr  addr )

static

create() [1/5]

static TrainData OpenCVForUnity.MlModule.TrainData.create ( Mat  samples, int  layout, Mat  responses, Mat  varIdx, Mat  sampleIdx, Mat  sampleWeights, Mat  varType  )

static

Creates training data from in-memory arrays.

Parameters

samples	matrix of samples. It should have CV_32F type.
layout	see ml::SampleTypes.
responses	matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical)
varIdx	vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables.
sampleIdx	vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples.
sampleWeights	optional vector with weights for each sample. It should have CV_32F type.
varType	optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes.

create() [2/5]

static TrainData OpenCVForUnity.MlModule.TrainData.create ( Mat  samples, int  layout, Mat  responses, Mat  varIdx, Mat  sampleIdx, Mat  sampleWeights  )

static

Creates training data from in-memory arrays.

Parameters

samples	matrix of samples. It should have CV_32F type.
layout	see ml::SampleTypes.
responses	matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical)
varIdx	vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables.
sampleIdx	vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples.
sampleWeights	optional vector with weights for each sample. It should have CV_32F type.
varType	optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes.

create() [3/5]

static TrainData OpenCVForUnity.MlModule.TrainData.create ( Mat  samples, int  layout, Mat  responses, Mat  varIdx, Mat  sampleIdx  )

static

Creates training data from in-memory arrays.

Parameters

samples	matrix of samples. It should have CV_32F type.
layout	see ml::SampleTypes.
responses	matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical)
varIdx	vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables.
sampleIdx	vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples.
sampleWeights	optional vector with weights for each sample. It should have CV_32F type.
varType	optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes.

create() [4/5]

static TrainData OpenCVForUnity.MlModule.TrainData.create ( Mat  samples, int  layout, Mat  responses, Mat  varIdx  )

static

Creates training data from in-memory arrays.

Parameters

samples	matrix of samples. It should have CV_32F type.
layout	see ml::SampleTypes.
responses	matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical)
varIdx	vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables.
sampleIdx	vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples.
sampleWeights	optional vector with weights for each sample. It should have CV_32F type.
varType	optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes.

create() [5/5]

static TrainData OpenCVForUnity.MlModule.TrainData.create ( Mat  samples, int  layout, Mat  responses  )

static

Creates training data from in-memory arrays.

Parameters

samples	matrix of samples. It should have CV_32F type.
layout	see ml::SampleTypes.
responses	matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical)
varIdx	vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables.
sampleIdx	vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples.
sampleWeights	optional vector with weights for each sample. It should have CV_32F type.
varType	optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes.

Dispose()

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

protectedvirtual

Reimplemented from OpenCVForUnity.DisposableOpenCVObject.

getCatCount()

int OpenCVForUnity.MlModule.TrainData.getCatCount

(

int

vi

)

getCatMap()

Mat OpenCVForUnity.MlModule.TrainData.getCatMap

(

)

getCatOfs()

Mat OpenCVForUnity.MlModule.TrainData.getCatOfs

(

)

getClassLabels()

Mat OpenCVForUnity.MlModule.TrainData.getClassLabels

(

)

Returns the vector of class labels.

The function returns vector of unique labels occurred in the responses.

getDefaultSubstValues()

Mat OpenCVForUnity.MlModule.TrainData.getDefaultSubstValues

(

)

getLayout()

int OpenCVForUnity.MlModule.TrainData.getLayout

(

)

getMissing()

Mat OpenCVForUnity.MlModule.TrainData.getMissing

(

)

getNAllVars()

int OpenCVForUnity.MlModule.TrainData.getNAllVars

(

)

getNames()

void OpenCVForUnity.MlModule.TrainData.getNames

(

List< string >

names

)

Returns vector of symbolic names captured in loadFromCSV()

getNativeObjAddr()

IntPtr OpenCVForUnity.MlModule.TrainData.getNativeObjAddr

(

)

getNormCatResponses()

Mat OpenCVForUnity.MlModule.TrainData.getNormCatResponses

(

)

getNSamples()

int OpenCVForUnity.MlModule.TrainData.getNSamples

(

)

getNTestSamples()

int OpenCVForUnity.MlModule.TrainData.getNTestSamples

(

)

getNTrainSamples()

int OpenCVForUnity.MlModule.TrainData.getNTrainSamples

(

)

getNVars()

int OpenCVForUnity.MlModule.TrainData.getNVars

(

)

getResponses()

Mat OpenCVForUnity.MlModule.TrainData.getResponses

(

)

getResponseType()

int OpenCVForUnity.MlModule.TrainData.getResponseType

(

)

getSample()

void OpenCVForUnity.MlModule.TrainData.getSample	(	Mat	varIdx,
		int	sidx,
		float	buf
	)

getSamples()

Mat OpenCVForUnity.MlModule.TrainData.getSamples

(

)

getSampleWeights()

Mat OpenCVForUnity.MlModule.TrainData.getSampleWeights

(

)

getSubMatrix()

static Mat OpenCVForUnity.MlModule.TrainData.getSubMatrix ( Mat  matrix, Mat  idx, int  layout  )

static

Extract from matrix rows/cols specified by passed indexes.

Parameters

matrix	input matrix (supported types: CV_32S, CV_32F, CV_64F)
idx	1D index vector
layout	specifies to extract rows (cv::ml::ROW_SAMPLES) or to extract columns (cv::ml::COL_SAMPLES)

getSubVector()

static Mat OpenCVForUnity.MlModule.TrainData.getSubVector ( Mat  vec, Mat  idx  )

static

Extract from 1D vector elements specified by passed indexes.

Parameters

vec	input vector (supported types: CV_32S, CV_32F, CV_64F)
idx	1D index vector

getTestNormCatResponses()

Mat OpenCVForUnity.MlModule.TrainData.getTestNormCatResponses

(

)

getTestResponses()

Mat OpenCVForUnity.MlModule.TrainData.getTestResponses

(

)

getTestSampleIdx()

Mat OpenCVForUnity.MlModule.TrainData.getTestSampleIdx

(

)

getTestSamples()

Mat OpenCVForUnity.MlModule.TrainData.getTestSamples

(

)

Returns matrix of test samples.

getTestSampleWeights()

Mat OpenCVForUnity.MlModule.TrainData.getTestSampleWeights

(

)

getTrainNormCatResponses()

Mat OpenCVForUnity.MlModule.TrainData.getTrainNormCatResponses

(

)

Returns the vector of normalized categorical responses.

The function returns vector of responses. Each response is integer from 0 to <number of classes>-1. The actual label value can be retrieved then from the class label vector, see TrainData::getClassLabels.

getTrainResponses()

Mat OpenCVForUnity.MlModule.TrainData.getTrainResponses

(

)

Returns the vector of responses.

The function returns ordered or the original categorical responses. Usually it's used in regression algorithms.

getTrainSampleIdx()

Mat OpenCVForUnity.MlModule.TrainData.getTrainSampleIdx

(

)

getTrainSamples() [1/4]

Mat OpenCVForUnity.MlModule.TrainData.getTrainSamples	(	int	layout,
		bool	compressSamples,
		bool	compressVars
	)

Returns matrix of train samples.

Parameters

layout	The requested layout. If it's different from the initial one, the matrix is transposed. See ml::SampleTypes.
compressSamples	if true, the function returns only the training samples (specified by sampleIdx)
compressVars	if true, the function returns the shorter training samples, containing only the active variables.

In current implementation the function tries to avoid physical data copying and returns the matrix stored inside TrainData (unless the transposition or compression is needed).

getTrainSamples() [2/4]

Mat OpenCVForUnity.MlModule.TrainData.getTrainSamples	(	int	layout,
		bool	compressSamples
	)

Returns matrix of train samples.

Parameters

layout	The requested layout. If it's different from the initial one, the matrix is transposed. See ml::SampleTypes.
compressSamples	if true, the function returns only the training samples (specified by sampleIdx)
compressVars	if true, the function returns the shorter training samples, containing only the active variables.

In current implementation the function tries to avoid physical data copying and returns the matrix stored inside TrainData (unless the transposition or compression is needed).

getTrainSamples() [3/4]

Mat OpenCVForUnity.MlModule.TrainData.getTrainSamples

(

int

layout

)

Returns matrix of train samples.

Parameters

layout	The requested layout. If it's different from the initial one, the matrix is transposed. See ml::SampleTypes.
compressSamples	if true, the function returns only the training samples (specified by sampleIdx)
compressVars	if true, the function returns the shorter training samples, containing only the active variables.

In current implementation the function tries to avoid physical data copying and returns the matrix stored inside TrainData (unless the transposition or compression is needed).

getTrainSamples() [4/4]

Mat OpenCVForUnity.MlModule.TrainData.getTrainSamples

(

)

Returns matrix of train samples.

Parameters

layout	The requested layout. If it's different from the initial one, the matrix is transposed. See ml::SampleTypes.
compressSamples	if true, the function returns only the training samples (specified by sampleIdx)
compressVars	if true, the function returns the shorter training samples, containing only the active variables.

In current implementation the function tries to avoid physical data copying and returns the matrix stored inside TrainData (unless the transposition or compression is needed).

getTrainSampleWeights()

Mat OpenCVForUnity.MlModule.TrainData.getTrainSampleWeights

(

)

getValues()

void OpenCVForUnity.MlModule.TrainData.getValues	(	int	vi,
		Mat	sidx,
		float	values
	)

getVarIdx()

Mat OpenCVForUnity.MlModule.TrainData.getVarIdx

(

)

getVarSymbolFlags()

Mat OpenCVForUnity.MlModule.TrainData.getVarSymbolFlags

(

)

getVarType()

Mat OpenCVForUnity.MlModule.TrainData.getVarType

(

)

setTrainTestSplit() [1/2]

void OpenCVForUnity.MlModule.TrainData.setTrainTestSplit	(	int	count,
		bool	shuffle
	)

Splits the training data into the training and test parts.

See also

TrainData::setTrainTestSplitRatio

setTrainTestSplit() [2/2]

void OpenCVForUnity.MlModule.TrainData.setTrainTestSplit

(

int

count

)

Splits the training data into the training and test parts.

See also

TrainData::setTrainTestSplitRatio

setTrainTestSplitRatio() [1/2]

void OpenCVForUnity.MlModule.TrainData.setTrainTestSplitRatio	(	double	ratio,
		bool	shuffle
	)

Splits the training data into the training and test parts.

The function selects a subset of specified relative size and then returns it as the training set. If the function is not called, all the data is used for training. Please, note that for each of TrainData::getTrain* there is corresponding TrainData::getTest*, so that the test subset can be retrieved and processed as well.

See also

TrainData::setTrainTestSplit

setTrainTestSplitRatio() [2/2]

void OpenCVForUnity.MlModule.TrainData.setTrainTestSplitRatio

(

double

ratio

)

Splits the training data into the training and test parts.

The function selects a subset of specified relative size and then returns it as the training set. If the function is not called, all the data is used for training. Please, note that for each of TrainData::getTrain* there is corresponding TrainData::getTest*, so that the test subset can be retrieved and processed as well.

See also

TrainData::setTrainTestSplit

shuffleTrainTest()

void OpenCVForUnity.MlModule.TrainData.shuffleTrainTest

(

)

---

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

• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/TrainData.cs