org.bytedeco.opencv.opencv_ml

Class TrainData

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_ml.TrainData

All Implemented Interfaces:

AutoCloseable

@Namespace(value="cv::ml")
 @Properties(inherit=opencv_ml.class)
public class TrainData
extends Pointer

\brief 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\ 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:

ml_intro_data

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
TrainData(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
static TrainData	create(GpuMat samples, int layout, GpuMat responses)
static TrainData	create(GpuMat samples, int layout, GpuMat responses, GpuMat varIdx, GpuMat sampleIdx, GpuMat sampleWeights, GpuMat varType)
static TrainData	create(Mat samples, int layout, Mat responses)
static TrainData	create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights, Mat varType) \brief Creates training data from in-memory arrays.
static TrainData	create(UMat samples, int layout, UMat responses)
static TrainData	create(UMat samples, int layout, UMat responses, UMat varIdx, UMat sampleIdx, UMat sampleWeights, UMat varType)
int	getCatCount(int vi)
Mat	getCatMap()
Mat	getCatOfs()
Mat	getClassLabels() \brief Returns the vector of class labels
Mat	getDefaultSubstValues()
int	getLayout()
Mat	getMissing()
int	getNAllVars()
void	getNames(StringVector names) \brief Returns vector of symbolic names captured in loadFromCSV()
Mat	getNormCatResponses()
void	getNormCatValues(int vi, GpuMat sidx, int[] values)
void	getNormCatValues(int vi, GpuMat sidx, IntBuffer values)
void	getNormCatValues(int vi, GpuMat sidx, IntPointer values)
void	getNormCatValues(int vi, Mat sidx, int[] values)
void	getNormCatValues(int vi, Mat sidx, IntBuffer values)
void	getNormCatValues(int vi, Mat sidx, IntPointer values)
void	getNormCatValues(int vi, UMat sidx, int[] values)
void	getNormCatValues(int vi, UMat sidx, IntBuffer values)
void	getNormCatValues(int vi, UMat sidx, IntPointer values)
int	getNSamples()
int	getNTestSamples()
int	getNTrainSamples()
int	getNVars()
Mat	getResponses()
int	getResponseType()
void	getSample(GpuMat varIdx, int sidx, float[] buf)
void	getSample(GpuMat varIdx, int sidx, FloatBuffer buf)
void	getSample(GpuMat varIdx, int sidx, FloatPointer buf)
void	getSample(Mat varIdx, int sidx, float[] buf)
void	getSample(Mat varIdx, int sidx, FloatBuffer buf)
void	getSample(Mat varIdx, int sidx, FloatPointer buf)
void	getSample(UMat varIdx, int sidx, float[] buf)
void	getSample(UMat varIdx, int sidx, FloatBuffer buf)
void	getSample(UMat varIdx, int sidx, FloatPointer buf)
Mat	getSamples()
Mat	getSampleWeights()
static Mat	getSubMatrix(Mat matrix, Mat idx, int layout) \brief Extract from matrix rows/cols specified by passed indexes.
static Mat	getSubVector(Mat vec, Mat idx) \brief Extract from 1D vector elements specified by passed indexes.
Mat	getTestNormCatResponses()
Mat	getTestResponses()
Mat	getTestSampleIdx()
Mat	getTestSamples() \brief Returns matrix of test samples
Mat	getTestSampleWeights()
Mat	getTrainNormCatResponses() \brief Returns the vector of normalized categorical responses
Mat	getTrainResponses() \brief Returns the vector of responses
Mat	getTrainSampleIdx()
Mat	getTrainSamples()
Mat	getTrainSamples(int layout, boolean compressSamples, boolean compressVars) \brief Returns matrix of train samples
Mat	getTrainSampleWeights()
void	getValues(int vi, GpuMat sidx, float[] values)
void	getValues(int vi, GpuMat sidx, FloatBuffer values)
void	getValues(int vi, GpuMat sidx, FloatPointer values)
void	getValues(int vi, Mat sidx, float[] values)
void	getValues(int vi, Mat sidx, FloatBuffer values)
void	getValues(int vi, Mat sidx, FloatPointer values)
void	getValues(int vi, UMat sidx, float[] values)
void	getValues(int vi, UMat sidx, FloatBuffer values)
void	getValues(int vi, UMat sidx, FloatPointer values)
Mat	getVarIdx()
Mat	getVarSymbolFlags()
Mat	getVarType()
static TrainData	loadFromCSV(BytePointer filename, int headerLineCount)
static TrainData	loadFromCSV(BytePointer filename, int headerLineCount, int responseStartIdx, int responseEndIdx, BytePointer varTypeSpec, byte delimiter, byte missch) \brief Reads the dataset from a .csv file and returns the ready-to-use training data.
static TrainData	loadFromCSV(String filename, int headerLineCount)
static TrainData	loadFromCSV(String filename, int headerLineCount, int responseStartIdx, int responseEndIdx, String varTypeSpec, byte delimiter, byte missch)
static float	missingValue()
void	setTrainTestSplit(int count)
void	setTrainTestSplit(int count, boolean shuffle) \brief Splits the training data into the training and test parts
void	setTrainTestSplitRatio(double ratio)
void	setTrainTestSplitRatio(double ratio, boolean shuffle) \brief Splits the training data into the training and test parts
void	shuffleTrainTest()

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

TrainData

public TrainData(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

missingValue

public static float missingValue()

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(@ByVal
                      Mat varIdx,
                      int sidx,
                      FloatPointer buf)

getSample

public void getSample(@ByVal
                      Mat varIdx,
                      int sidx,
                      FloatBuffer buf)

getSample

public void getSample(@ByVal
                      Mat varIdx,
                      int sidx,
                      float[] buf)

getSample

public void getSample(@ByVal
                      UMat varIdx,
                      int sidx,
                      FloatPointer buf)

getSample

public void getSample(@ByVal
                      UMat varIdx,
                      int sidx,
                      FloatBuffer buf)

getSample

public void getSample(@ByVal
                      UMat varIdx,
                      int sidx,
                      float[] buf)

getSample

public void getSample(@ByVal
                      GpuMat varIdx,
                      int sidx,
                      FloatPointer buf)

getSample

public void getSample(@ByVal
                      GpuMat varIdx,
                      int sidx,
                      FloatBuffer buf)

getSample

public void getSample(@ByVal
                      GpuMat varIdx,
                      int sidx,
                      float[] buf)

getSamples

@ByVal
public Mat getSamples()

getMissing

@ByVal
public Mat getMissing()

getTrainSamples

@ByVal
public Mat getTrainSamples(int layout,
                                  @Cast(value="bool")
                                  boolean compressSamples,
                                  @Cast(value="bool")
                                  boolean compressVars)

\brief 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).

getTrainSamples

@ByVal
public Mat getTrainSamples()

getTrainResponses

@ByVal
public Mat getTrainResponses()

\brief Returns the vector of responses

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

getTrainNormCatResponses

@ByVal
public Mat getTrainNormCatResponses()

\brief 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.

getTestResponses

@ByVal
public Mat getTestResponses()

getTestNormCatResponses

@ByVal
public Mat getTestNormCatResponses()

getResponses

@ByVal
public Mat getResponses()

getNormCatResponses

@ByVal
public Mat getNormCatResponses()

getSampleWeights

@ByVal
public Mat getSampleWeights()

getTrainSampleWeights

@ByVal
public Mat getTrainSampleWeights()

getTestSampleWeights

@ByVal
public Mat getTestSampleWeights()

getVarIdx

@ByVal
public Mat getVarIdx()

getVarType

@ByVal
public Mat getVarType()

getVarSymbolFlags

@ByVal
public Mat getVarSymbolFlags()

getResponseType

public int getResponseType()

getTrainSampleIdx

@ByVal
public Mat getTrainSampleIdx()

getTestSampleIdx

@ByVal
public Mat getTestSampleIdx()

getValues

public void getValues(int vi,
                      @ByVal
                      Mat sidx,
                      FloatPointer values)

getValues

public void getValues(int vi,
                      @ByVal
                      Mat sidx,
                      FloatBuffer values)

getValues

public void getValues(int vi,
                      @ByVal
                      Mat sidx,
                      float[] values)

getValues

public void getValues(int vi,
                      @ByVal
                      UMat sidx,
                      FloatPointer values)

getValues

public void getValues(int vi,
                      @ByVal
                      UMat sidx,
                      FloatBuffer values)

getValues

public void getValues(int vi,
                      @ByVal
                      UMat sidx,
                      float[] values)

getValues

public void getValues(int vi,
                      @ByVal
                      GpuMat sidx,
                      FloatPointer values)

getValues

public void getValues(int vi,
                      @ByVal
                      GpuMat sidx,
                      FloatBuffer values)

getValues

public void getValues(int vi,
                      @ByVal
                      GpuMat sidx,
                      float[] values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             Mat sidx,
                             IntPointer values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             Mat sidx,
                             IntBuffer values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             Mat sidx,
                             int[] values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             UMat sidx,
                             IntPointer values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             UMat sidx,
                             IntBuffer values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             UMat sidx,
                             int[] values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             GpuMat sidx,
                             IntPointer values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             GpuMat sidx,
                             IntBuffer values)

getNormCatValues

public void getNormCatValues(int vi,
                             @ByVal
                             GpuMat sidx,
                             int[] values)

getDefaultSubstValues

@ByVal
public Mat getDefaultSubstValues()

getCatCount

public int getCatCount(int vi)

getClassLabels

@ByVal
public Mat getClassLabels()

\brief Returns the vector of class labels

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

getCatOfs

@ByVal
public Mat getCatOfs()

getCatMap

@ByVal
public Mat getCatMap()

setTrainTestSplit

public void setTrainTestSplit(int count,
                              @Cast(value="bool")
                              boolean shuffle)

\brief Splits the training data into the training and test parts

See Also:

TrainData::setTrainTestSplitRatio

setTrainTestSplit

public void setTrainTestSplit(int count)

setTrainTestSplitRatio

public void setTrainTestSplitRatio(double ratio,
                                   @Cast(value="bool")
                                   boolean shuffle)

\brief 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

setTrainTestSplitRatio

public void setTrainTestSplitRatio(double ratio)

shuffleTrainTest

public void shuffleTrainTest()

getTestSamples

@ByVal
public Mat getTestSamples()

\brief Returns matrix of test samples

getNames

public void getNames(@ByRef
                     StringVector names)

\brief Returns vector of symbolic names captured in loadFromCSV()

getSubVector

@ByVal
public static Mat getSubVector(@Const @ByRef
                                      Mat vec,
                                      @Const @ByRef
                                      Mat idx)

\brief 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

getSubMatrix

@ByVal
public static Mat getSubMatrix(@Const @ByRef
                                      Mat matrix,
                                      @Const @ByRef
                                      Mat idx,
                                      int layout)

\brief 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)

loadFromCSV

@opencv_core.Ptr
public static TrainData loadFromCSV(@opencv_core.Str
                                                     BytePointer filename,
                                                     int headerLineCount,
                                                     int responseStartIdx,
                                                     int responseEndIdx,
                                                     @opencv_core.Str
                                                     BytePointer varTypeSpec,
                                                     @Cast(value="char")
                                                     byte delimiter,
                                                     @Cast(value="char")
                                                     byte missch)

\brief 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.

loadFromCSV

@opencv_core.Ptr
public static TrainData loadFromCSV(@opencv_core.Str
                                                     BytePointer filename,
                                                     int headerLineCount)

loadFromCSV

@opencv_core.Ptr
public static TrainData loadFromCSV(@opencv_core.Str
                                                     String filename,
                                                     int headerLineCount,
                                                     int responseStartIdx,
                                                     int responseEndIdx,
                                                     @opencv_core.Str
                                                     String varTypeSpec,
                                                     @Cast(value="char")
                                                     byte delimiter,
                                                     @Cast(value="char")
                                                     byte missch)

loadFromCSV

@opencv_core.Ptr
public static TrainData loadFromCSV(@opencv_core.Str
                                                     String filename,
                                                     int headerLineCount)

create

@opencv_core.Ptr
public static TrainData create(@ByVal
                                                Mat samples,
                                                int layout,
                                                @ByVal
                                                Mat responses,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                Mat varIdx,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                Mat sampleIdx,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                Mat sampleWeights,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                Mat varType)

\brief 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.

create

@opencv_core.Ptr
public static TrainData create(@ByVal
                                                Mat samples,
                                                int layout,
                                                @ByVal
                                                Mat responses)

create

@opencv_core.Ptr
public static TrainData create(@ByVal
                                                UMat samples,
                                                int layout,
                                                @ByVal
                                                UMat responses,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                UMat varIdx,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                UMat sampleIdx,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                UMat sampleWeights,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                UMat varType)

create

@opencv_core.Ptr
public static TrainData create(@ByVal
                                                UMat samples,
                                                int layout,
                                                @ByVal
                                                UMat responses)

create

@opencv_core.Ptr
public static TrainData create(@ByVal
                                                GpuMat samples,
                                                int layout,
                                                @ByVal
                                                GpuMat responses,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                GpuMat varIdx,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                GpuMat sampleIdx,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                GpuMat sampleWeights,
                                                @ByVal(nullValue="cv::InputArray(cv::noArray())")
                                                GpuMat varType)

create

@opencv_core.Ptr
public static TrainData create(@ByVal
                                                GpuMat samples,
                                                int layout,
                                                @ByVal
                                                GpuMat responses)