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 Description

protected long nativeObj
Constructor Summary

Constructors

Modifier Constructor Description

protected TrainData(long addr)

Method Summary

Modifier and Type	Method	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 Details
- nativeObj
  
  protected final long nativeObj
Constructor Details
- TrainData
  
  protected TrainData(long addr)
Method Details
- getNativeObjAddr
  
  public long getNativeObjAddr()
- __fromPtr__
  
  public static TrainData __fromPtr__(long addr)
- getLayout
  
  public int getLayout()
- getNTrainSamples
  
  public int getNTrainSamples()
- getNTestSamples
  
  public int getNTestSamples()
- getNSamples
  
  public int getNSamples()
- getNVars
  
  public int getNVars()
- getNAllVars
  
  public int getNAllVars()
- getSample
  
  public void getSample(Mat varIdx, int sidx, float buf)
- getSamples
  
  public Mat getSamples()
- getMissing
  
  public Mat getMissing()
- 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
- 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
- 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
- getTestResponses
  
  public Mat getTestResponses()
- getTestNormCatResponses
  
  public Mat getTestNormCatResponses()
- getResponses
  
  public Mat getResponses()
- getNormCatResponses
  
  public Mat getNormCatResponses()
- getSampleWeights
  
  public Mat getSampleWeights()
- getTrainSampleWeights
  
  public Mat getTrainSampleWeights()
- getTestSampleWeights
  
  public Mat getTestSampleWeights()
- getVarIdx
  
  public Mat getVarIdx()
- getVarType
  
  public Mat getVarType()
- getVarSymbolFlags
  
  public Mat getVarSymbolFlags()
- getResponseType
  
  public int getResponseType()
- getTrainSampleIdx
  
  public Mat getTrainSampleIdx()
- getTestSampleIdx
  
  public Mat getTestSampleIdx()
- getValues
  
  public void getValues(int vi, Mat sidx, float values)
- getDefaultSubstValues
  
  public Mat getDefaultSubstValues()
- getCatCount
  
  public int getCatCount(int vi)
- getClassLabels
  
  public Mat getClassLabels()
  
  Returns the vector of class labels The function returns vector of unique labels occurred in the responses.
  
  Returns:
  
  automatically generated
- getCatOfs
  
  public Mat getCatOfs()
- getCatMap
  
  public Mat getCatMap()
- 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()
- getTestSamples
  
  public Mat getTestSamples()
  
  Returns matrix of test samples
  
  Returns:
  
  automatically generated
- getNames
  
  public void getNames(List<String> names)
  
  Returns vector of symbolic names captured in loadFromCSV()
  
  Parameters:
  
  names - 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
- 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
- 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
- finalize
  
  protected void finalize() throws Throwable
  
  Overrides:
  
  finalize in class Object
  
  Throws:
  
  Throwable

Class TrainData

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Details

Constructor Details

Method Details