Class encapsulating training data. More...

#include <opencv2/ml.hpp>

Public Member Functions
virtual	~TrainData ()

virtual int	getCatCount (int vi) const =0

virtual Mat	getCatMap () const =0

virtual Mat	getCatOfs () const =0

virtual Mat	getClassLabels () const =0
	Returns the vector of class labels.

virtual Mat	getDefaultSubstValues () const =0

virtual int	getLayout () const =0

virtual Mat	getMissing () const =0

virtual int	getNAllVars () const =0

virtual void	getNames (std::vector< String > &names) const =0
	Returns vector of symbolic names captured in loadFromCSV()

virtual Mat	getNormCatResponses () const =0

virtual void	getNormCatValues (int vi, InputArray sidx, int *values) const =0

virtual int	getNSamples () const =0

virtual int	getNTestSamples () const =0

virtual int	getNTrainSamples () const =0

virtual int	getNVars () const =0

virtual Mat	getResponses () const =0

virtual int	getResponseType () const =0

virtual void	getSample (InputArray varIdx, int sidx, float *buf) const =0

virtual Mat	getSamples () const =0

virtual Mat	getSampleWeights () const =0

virtual Mat	getTestNormCatResponses () const =0

virtual Mat	getTestResponses () const =0

virtual Mat	getTestSampleIdx () const =0

virtual Mat	getTestSamples () const =0
	Returns matrix of test samples.

virtual Mat	getTestSampleWeights () const =0

virtual Mat	getTrainNormCatResponses () const =0
	Returns the vector of normalized categorical responses.

virtual Mat	getTrainResponses () const =0
	Returns the vector of responses.

virtual Mat	getTrainSampleIdx () const =0

virtual Mat	getTrainSamples (int layout=ROW_SAMPLE, bool compressSamples=true, bool compressVars=true) const =0
	Returns matrix of train samples.

virtual Mat	getTrainSampleWeights () const =0

virtual void	getValues (int vi, InputArray sidx, float *values) const =0

virtual Mat	getVarIdx () const =0

virtual Mat	getVarSymbolFlags () const =0

virtual Mat	getVarType () const =0

virtual void	setTrainTestSplit (int count, bool shuffle=true)=0
	Splits the training data into the training and test parts.

virtual void	setTrainTestSplitRatio (double ratio, bool shuffle=true)=0
	Splits the training data into the training and test parts.

virtual void	shuffleTrainTest ()=0

Static Public Member Functions
static Ptr< TrainData >	create (InputArray samples, int layout, InputArray responses, InputArray varIdx=noArray(), InputArray sampleIdx=noArray(), InputArray sampleWeights=noArray(), InputArray varType=noArray())
	Creates training data from in-memory arrays.

static Mat	getSubMatrix (const Mat &matrix, const Mat &idx, int layout)
	Extract from matrix rows/cols specified by passed indexes.

static Mat	getSubVector (const Mat &vec, const Mat &idx)
	Extract from 1D vector elements specified by passed indexes.

static Ptr< TrainData >	loadFromCSV (const String &filename, int headerLineCount, int responseStartIdx=-1, int responseEndIdx=-1, const String &varTypeSpec=String(), char delimiter=',', char missch='?')
	Reads the dataset from a .csv file and returns the ready-to-use training data.

static float	missingValue ()

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: Training Data

Constructor & Destructor Documentation

virtual cv::ml::TrainData::~TrainData ( )

virtual

Member Function Documentation

static Ptr<TrainData> cv::ml::TrainData::create	(	InputArray	samples,
		int	layout,
		InputArray	responses,
		InputArray	varIdx = `noArray()`,
		InputArray	sampleIdx = `noArray()`,
		InputArray	sampleWeights = `noArray()`,
		InputArray	varType = `noArray()`
	)

static

Python:
	retval	=	cv.ml.TrainData_create(	samples, layout, responses[, varIdx[, sampleIdx[, sampleWeights[, varType]]]]	)

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.

virtual int cv::ml::TrainData::getCatCount ( int vi ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getCatCount(	vi	)

virtual Mat cv::ml::TrainData::getCatMap ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getCatMap(		)

virtual Mat cv::ml::TrainData::getCatOfs ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getCatOfs(		)

virtual Mat cv::ml::TrainData::getClassLabels ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getClassLabels(		)

Returns the vector of class labels.

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

virtual Mat cv::ml::TrainData::getDefaultSubstValues ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getDefaultSubstValues(		)

virtual int cv::ml::TrainData::getLayout ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getLayout(		)

virtual Mat cv::ml::TrainData::getMissing ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getMissing(		)

virtual int cv::ml::TrainData::getNAllVars ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNAllVars(		)

virtual void cv::ml::TrainData::getNames ( std::vector< String > & names ) const

pure virtual

Python:
	None	=	cv.ml_TrainData.getNames(	names	)

Returns vector of symbolic names captured in loadFromCSV()

virtual Mat cv::ml::TrainData::getNormCatResponses ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNormCatResponses(		)

virtual void cv::ml::TrainData::getNormCatValues	(	int	vi,
		InputArray	sidx,
		int *	values
	)		const

pure virtual

virtual int cv::ml::TrainData::getNSamples ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNSamples(		)

virtual int cv::ml::TrainData::getNTestSamples ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNTestSamples(		)

virtual int cv::ml::TrainData::getNTrainSamples ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNTrainSamples(		)

virtual int cv::ml::TrainData::getNVars ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNVars(		)

virtual Mat cv::ml::TrainData::getResponses ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getResponses(		)

virtual int cv::ml::TrainData::getResponseType ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getResponseType(		)

virtual void cv::ml::TrainData::getSample	(	InputArray	varIdx,
		int	sidx,
		float *	buf
	)		const

pure virtual

Python:
	None	=	cv.ml_TrainData.getSample(	varIdx, sidx, buf	)

virtual Mat cv::ml::TrainData::getSamples ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getSamples(		)

virtual Mat cv::ml::TrainData::getSampleWeights ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getSampleWeights(		)

static Mat cv::ml::TrainData::getSubMatrix	(	const Mat &	matrix,
		const Mat &	idx,
		int	layout
	)

static

Python:
	retval	=	cv.ml.TrainData_getSubMatrix(	matrix, idx, layout	)

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)

static Mat cv::ml::TrainData::getSubVector	(	const Mat &	vec,
		const Mat &	idx
	)

static

Python:
	retval	=	cv.ml.TrainData_getSubVector(	vec, idx	)

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

virtual Mat cv::ml::TrainData::getTestNormCatResponses ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestNormCatResponses(		)

virtual Mat cv::ml::TrainData::getTestResponses ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestResponses(		)

virtual Mat cv::ml::TrainData::getTestSampleIdx ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestSampleIdx(		)

virtual Mat cv::ml::TrainData::getTestSamples ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestSamples(		)

Returns matrix of test samples.

virtual Mat cv::ml::TrainData::getTestSampleWeights ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestSampleWeights(		)

virtual Mat cv::ml::TrainData::getTrainNormCatResponses ( ) const

pure virtual

Python:
	retval	=	cv.ml_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.

virtual Mat cv::ml::TrainData::getTrainResponses ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTrainResponses(		)

Returns the vector of responses.

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

virtual Mat cv::ml::TrainData::getTrainSampleIdx ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTrainSampleIdx(		)

virtual Mat cv::ml::TrainData::getTrainSamples	(	int	layout = `ROW_SAMPLE`,
		bool	compressSamples = `true`,
		bool	compressVars = `true`
	)		const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTrainSamples(	[, layout[, compressSamples[, 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).

virtual Mat cv::ml::TrainData::getTrainSampleWeights ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTrainSampleWeights(		)

virtual void cv::ml::TrainData::getValues	(	int	vi,
		InputArray	sidx,
		float *	values
	)		const

pure virtual

Python:
	None	=	cv.ml_TrainData.getValues(	vi, sidx, values	)

virtual Mat cv::ml::TrainData::getVarIdx ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getVarIdx(		)

virtual Mat cv::ml::TrainData::getVarSymbolFlags ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getVarSymbolFlags(		)

virtual Mat cv::ml::TrainData::getVarType ( ) const

pure virtual

Python:
	retval	=	cv.ml_TrainData.getVarType(		)

static Ptr<TrainData> cv::ml::TrainData::loadFromCSV	(	const String &	filename,
		int	headerLineCount,
		int	responseStartIdx = `-1`,
		int	responseEndIdx = `-1`,
		const String &	varTypeSpec = `String()`,
		char	delimiter = `','`,
		char	missch = `'?'`
	)

static

Reads the dataset from a .csv file and returns the ready-to-use training data.

Parameters

filename	The input file name
headerLineCount	The number of lines in the beginning to skip; besides the header, the function also skips empty lines and lines staring with `#`
responseStartIdx	Index of the first output variable. If -1, the function considers the last variable as the response
responseEndIdx	Index of the last output variable + 1. If -1, then there is single response variable at responseStartIdx.
varTypeSpec	The optional text string that specifies the variables' types. It has the format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2` (inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]` should cover all the variables. If varTypeSpec is not specified, then algorithm uses the following rules: all input variables are considered ordered by default. If some column contains has non- numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding variable is considered categorical. if there are several output variables, they are all considered as ordered. Error is reported when non-numerical values are used. if there is a single output variable, then if its values are non-numerical or are all integers, then it's considered categorical. Otherwise, it's considered ordered.
delimiter	The character used to separate values in each line.
missch	The character used to specify missing measurements. It should not be a digit. Although it's a non-numerical value, it surely does not affect the decision of whether the variable ordered or categorical.

Note: If the dataset only contains input variables and no responses, use responseStartIdx = -2 and responseEndIdx = 0. The output variables vector will just contain zeros.

static float cv::ml::TrainData::missingValue ( )

inlinestatic

virtual void cv::ml::TrainData::setTrainTestSplit	(	int	count,
		bool	shuffle = `true`
	)

pure virtual

Python:
	None	=	cv.ml_TrainData.setTrainTestSplit(	count[, shuffle]	)

Splits the training data into the training and test parts.

See Also: TrainData::setTrainTestSplitRatio

virtual void cv::ml::TrainData::setTrainTestSplitRatio	(	double	ratio,
		bool	shuffle = `true`
	)

pure virtual

Python:
	None	=	cv.ml_TrainData.setTrainTestSplitRatio(	ratio[, 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

virtual void cv::ml::TrainData::shuffleTrainTest ( )

pure virtual

Python:
	None	=	cv.ml_TrainData.shuffleTrainTest(		)

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

ml.hpp

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation