Package org.opencv.ml
Class TrainData
- java.lang.Object
-
- org.opencv.ml.TrainData
-
public class TrainData extends java.lang.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
All Methods Static Methods Instance Methods Concrete Methods 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(java.util.List<java.lang.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 samplesMat
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 samplesMat
getTrainSamples(int layout, boolean compressSamples)
Returns matrix of train samplesMat
getTrainSamples(int layout, boolean compressSamples, boolean compressVars)
Returns matrix of train samplesMat
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::setTrainTestSplitRatiovoid
setTrainTestSplit(int count, boolean shuffle)
Splits the training data into the training and test parts SEE: TrainData::setTrainTestSplitRatiovoid
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()
-
-
-
Method Detail
-
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 from0
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 generatedshuffle
- 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 generatedshuffle
- 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(java.util.List<java.lang.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 vectorlayout
- 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 java.lang.Throwable
- Overrides:
finalize
in classjava.lang.Object
- Throws:
java.lang.Throwable
-
-