org.opencv.ml

Class TrainData

java.lang.Object

org.opencv.ml.TrainData

public class TrainData
extends Object

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: REF: ml_intro_data

Field Summary

Fields

Modifier and Type	Field and Description
protected long	nativeObj

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	TrainData(long addr)

Method Summary

Modifier and Type	Method and Description
static TrainData	__fromPtr__(long addr)
static TrainData	create(Mat samples, int layout, Mat responses) Creates training data from in-memory arrays.
static TrainData	create(Mat samples, int layout, Mat responses, Mat varIdx) Creates training data from in-memory arrays.
static TrainData	create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx) Creates training data from in-memory arrays.
static TrainData	create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights) Creates training data from in-memory arrays.
static TrainData	create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights, Mat varType) Creates training data from in-memory arrays.
protected void	finalize()
int	getCatCount(int vi)
Mat	getCatMap()
Mat	getCatOfs()
Mat	getClassLabels() Returns the vector of class labels The function returns vector of unique labels occurred in the responses.
Mat	getDefaultSubstValues()
int	getLayout()
Mat	getMissing()
int	getNAllVars()
void	getNames(List<String> names) Returns vector of symbolic names captured in loadFromCSV()
long	getNativeObjAddr()
Mat	getNormCatResponses()
int	getNSamples()
int	getNTestSamples()
int	getNTrainSamples()
int	getNVars()
Mat	getResponses()
int	getResponseType()
void	getSample(Mat varIdx, int sidx, float buf)
Mat	getSamples()
Mat	getSampleWeights()
static Mat	getSubMatrix(Mat matrix, Mat idx, int layout) Extract from matrix rows/cols specified by passed indexes.
static Mat	getSubVector(Mat vec, Mat idx) Extract from 1D vector elements specified by passed indexes.
Mat	getTestNormCatResponses()
Mat	getTestResponses()
Mat	getTestSampleIdx()
Mat	getTestSamples() Returns matrix of test samples
Mat	getTestSampleWeights()
Mat	getTrainNormCatResponses() Returns the vector of normalized categorical responses The function returns vector of responses.
Mat	getTrainResponses() Returns the vector of responses The function returns ordered or the original categorical responses.
Mat	getTrainSampleIdx()
Mat	getTrainSamples() Returns matrix of train samples transposed.
Mat	getTrainSamples(int layout) Returns matrix of train samples
Mat	getTrainSamples(int layout, boolean compressSamples) Returns matrix of train samples
Mat	getTrainSamples(int layout, boolean compressSamples, boolean compressVars) Returns matrix of train samples
Mat	getTrainSampleWeights()
void	getValues(int vi, Mat sidx, float values)
Mat	getVarIdx()
Mat	getVarSymbolFlags()
Mat	getVarType()
void	setTrainTestSplit(int count) Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatio
void	setTrainTestSplit(int count, boolean shuffle) Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatio
void	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.
void	setTrainTestSplitRatio(double ratio, boolean 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.
void	shuffleTrainTest()

Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

nativeObj

protected final long nativeObj

Constructor Detail

TrainData

protected TrainData(long addr)

Method Detail

getNativeObjAddr

public long getNativeObjAddr()

__fromPtr__

public static TrainData __fromPtr__(long addr)

getCatMap

public Mat getCatMap()

getCatOfs

public Mat getCatOfs()

getClassLabels

public Mat getClassLabels()

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

Returns:

automatically generated

getDefaultSubstValues

public Mat getDefaultSubstValues()

getMissing

public Mat getMissing()

getNormCatResponses

public Mat getNormCatResponses()

getResponses

public Mat getResponses()

getSampleWeights

public Mat getSampleWeights()

getSamples

public Mat getSamples()

getSubMatrix

public static Mat getSubMatrix(Mat matrix,
                               Mat idx,
                               int 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)

Returns:

automatically generated

getSubVector

public static Mat getSubVector(Mat vec,
                               Mat 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

Returns:

automatically generated

getTestNormCatResponses

public Mat getTestNormCatResponses()

getTestResponses

public Mat getTestResponses()

getTestSampleIdx

public Mat getTestSampleIdx()

getTestSampleWeights

public Mat getTestSampleWeights()

getTestSamples

public Mat getTestSamples()

Returns matrix of test samples

Returns:

automatically generated

getTrainNormCatResponses

public Mat 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.

Returns:

automatically generated

getTrainResponses

public Mat getTrainResponses()

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

Returns:

automatically generated

getTrainSampleIdx

public Mat getTrainSampleIdx()

getTrainSampleWeights

public Mat getTrainSampleWeights()

getTrainSamples

public Mat getTrainSamples(int layout,
                           boolean compressSamples,
                           boolean 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).

Returns:

automatically generated

getTrainSamples

public Mat getTrainSamples(int layout,
                           boolean 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) 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).

Returns:

automatically generated

getTrainSamples

public Mat 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. sampleIdx) 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).

Returns:

automatically generated

getTrainSamples

public Mat getTrainSamples()

Returns matrix of train samples transposed. See ml::SampleTypes. sampleIdx) 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).

Returns:

automatically generated

getVarIdx

public Mat getVarIdx()

getVarSymbolFlags

public Mat getVarSymbolFlags()

getVarType

public Mat getVarType()

create

public static TrainData create(Mat samples,
                               int layout,
                               Mat responses,
                               Mat varIdx,
                               Mat sampleIdx,
                               Mat sampleWeights,
                               Mat 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.

Returns:

automatically generated

create

public static TrainData create(Mat samples,
                               int layout,
                               Mat responses,
                               Mat varIdx,
                               Mat sampleIdx,
                               Mat sampleWeights)

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. <number_of_variables_in_responses>`, containing types of each input and output variable. See ml::VariableTypes.

Returns:

automatically generated

create

public static TrainData create(Mat samples,
                               int layout,
                               Mat responses,
                               Mat varIdx,
                               Mat sampleIdx)

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. <number_of_variables_in_responses>`, containing types of each input and output variable. See ml::VariableTypes.

Returns:

automatically generated

create

public static TrainData create(Mat samples,
                               int layout,
                               Mat responses,
                               Mat varIdx)

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. vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples. <number_of_variables_in_responses>`, containing types of each input and output variable. See ml::VariableTypes.

Returns:

automatically generated

create

public static TrainData create(Mat samples,
                               int layout,
                               Mat responses)

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) (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables. vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples. <number_of_variables_in_responses>`, containing types of each input and output variable. See ml::VariableTypes.

Returns:

automatically generated

getCatCount

public int getCatCount(int vi)

getLayout

public int getLayout()

getNAllVars

public int getNAllVars()

getNSamples

public int getNSamples()

getNTestSamples

public int getNTestSamples()

getNTrainSamples

public int getNTrainSamples()

getNVars

public int getNVars()

getResponseType

public int getResponseType()

getNames

public void getNames(List<String> names)

Returns vector of symbolic names captured in loadFromCSV()

Parameters:

names - automatically generated

getSample

public void getSample(Mat varIdx,
                      int sidx,
                      float buf)

getValues

public void getValues(int vi,
                      Mat sidx,
                      float values)

setTrainTestSplit

public void setTrainTestSplit(int count,
                              boolean shuffle)

Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatio

Parameters:

count - automatically generated

shuffle - automatically generated

setTrainTestSplit

public void setTrainTestSplit(int count)

Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatio

Parameters:

count - automatically generated

setTrainTestSplitRatio

public void setTrainTestSplitRatio(double ratio,
                                   boolean 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: TrainData::setTrainTestSplit

Parameters:

ratio - automatically generated

shuffle - automatically generated

setTrainTestSplitRatio

public void 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: TrainData::setTrainTestSplit

Parameters:

ratio - automatically generated

shuffleTrainTest

public void shuffleTrainTest()

finalize

protected void finalize()
                 throws Throwable

Overrides:

finalize in class Object

Throws:

Throwable