cv::ml::TrainData Class Reference

Class encapsulating training data. More...

#include "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. More...
virtual Mat	getDefaultSubstValues () const =0
virtual int	getLayout () const =0
virtual Mat	getMissing () const =0
virtual int	getNAllVars () const =0
void	getNames (std::vector< String > &names) const
	Returns vector of symbolic names captured in loadFromCSV() More...
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
Mat	getTestSamples () const
	Returns matrix of test samples. More...
virtual Mat	getTestSampleWeights () const =0
virtual Mat	getTrainNormCatResponses () const =0
	Returns the vector of normalized categorical responses. More...
virtual Mat	getTrainResponses () const =0
	Returns the vector of responses. More...
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. More...
virtual Mat	getTrainSampleWeights () const =0
virtual void	getValues (int vi, InputArray sidx, float *values) const =0
virtual Mat	getVarIdx () const =0
Mat	getVarSymbolFlags () const
virtual Mat	getVarType () const =0
virtual void	setTrainTestSplit (int count, bool shuffle=true)=0
	Splits the training data into the training and test parts. More...
virtual void	setTrainTestSplitRatio (double ratio, bool shuffle=true)=0
	Splits the training data into the training and test parts. More...
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. More...
static Mat	getSubVector (const Mat &vec, const Mat &idx)
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. More...
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

~TrainData()

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

virtual

Member Function Documentation

create()

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.

getCatCount()

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

pure virtual

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

getCatMap()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getCatMap(		)

getCatOfs()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getCatOfs(		)

getClassLabels()

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.

getDefaultSubstValues()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getDefaultSubstValues(		)

getLayout()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getLayout(		)

getMissing()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getMissing(		)

getNAllVars()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNAllVars(		)

getNames()

void cv::ml::TrainData::getNames

(

std::vector< String > &

names

)

const

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

Returns vector of symbolic names captured in loadFromCSV()

getNormCatResponses()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNormCatResponses(		)

getNormCatValues()

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

pure virtual

getNSamples()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNSamples(		)

getNTestSamples()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNTestSamples(		)

getNTrainSamples()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNTrainSamples(		)

getNVars()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getNVars(		)

getResponses()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getResponses(		)

getResponseType()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getResponseType(		)

getSample()

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

pure virtual

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

getSamples()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getSamples(		)

getSampleWeights()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getSampleWeights(		)

getSubVector()

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

static

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

getTestNormCatResponses()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestNormCatResponses(		)

getTestResponses()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestResponses(		)

getTestSampleIdx()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestSampleIdx(		)

getTestSamples()

Mat cv::ml::TrainData::getTestSamples

(

)

const

Python:
	retval	=	cv.ml_TrainData.getTestSamples(		)

Returns matrix of test samples.

getTestSampleWeights()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTestSampleWeights(		)

getTrainNormCatResponses()

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.

getTrainResponses()

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.

getTrainSampleIdx()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTrainSampleIdx(		)

getTrainSamples()

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).

getTrainSampleWeights()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getTrainSampleWeights(		)

getValues()

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

pure virtual

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

getVarIdx()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getVarIdx(		)

getVarSymbolFlags()

Mat cv::ml::TrainData::getVarSymbolFlags

(

)

const

Python:
	retval	=	cv.ml_TrainData.getVarSymbolFlags(		)

getVarType()

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

pure virtual

Python:
	retval	=	cv.ml_TrainData.getVarType(		)

loadFromCSV()

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.

missingValue()

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

inlinestatic

setTrainTestSplit()

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

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

shuffleTrainTest()

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/include/opencv2/ml.hpp