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//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Mat;
import org.opencv.ml.TrainData;
import org.opencv.utils.Converters;
// C++: class TrainData
/**
* 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
*/
public class TrainData {
protected final long nativeObj;
protected TrainData(long addr) { nativeObj = addr; }
public long getNativeObjAddr() { return nativeObj; }
// internal usage only
public static TrainData __fromPtr__(long addr) { return new TrainData(addr); }
//
// C++: Mat cv::ml::TrainData::getCatMap()
//
public Mat getCatMap() {
return new Mat(getCatMap_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getCatOfs()
//
public Mat getCatOfs() {
return new Mat(getCatOfs_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getClassLabels()
//
/**
* Returns the vector of class labels
*
* The function returns vector of unique labels occurred in the responses.
* @return automatically generated
*/
public Mat getClassLabels() {
return new Mat(getClassLabels_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getDefaultSubstValues()
//
public Mat getDefaultSubstValues() {
return new Mat(getDefaultSubstValues_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getMissing()
//
public Mat getMissing() {
return new Mat(getMissing_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getNormCatResponses()
//
public Mat getNormCatResponses() {
return new Mat(getNormCatResponses_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getResponses()
//
public Mat getResponses() {
return new Mat(getResponses_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getSampleWeights()
//
public Mat getSampleWeights() {
return new Mat(getSampleWeights_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getSamples()
//
public Mat getSamples() {
return new Mat(getSamples_0(nativeObj));
}
//
// C++: static Mat cv::ml::TrainData::getSubMatrix(Mat matrix, Mat idx, int layout)
//
/**
* Extract from matrix rows/cols specified by passed indexes.
* @param matrix input matrix (supported types: CV_32S, CV_32F, CV_64F)
* @param idx 1D index vector
* @param layout specifies to extract rows (cv::ml::ROW_SAMPLES) or to extract columns (cv::ml::COL_SAMPLES)
* @return automatically generated
*/
public static Mat getSubMatrix(Mat matrix, Mat idx, int layout) {
return new Mat(getSubMatrix_0(matrix.nativeObj, idx.nativeObj, layout));
}
//
// C++: static Mat cv::ml::TrainData::getSubVector(Mat vec, Mat idx)
//
/**
* Extract from 1D vector elements specified by passed indexes.
* @param vec input vector (supported types: CV_32S, CV_32F, CV_64F)
* @param idx 1D index vector
* @return automatically generated
*/
public static Mat getSubVector(Mat vec, Mat idx) {
return new Mat(getSubVector_0(vec.nativeObj, idx.nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTestNormCatResponses()
//
public Mat getTestNormCatResponses() {
return new Mat(getTestNormCatResponses_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTestResponses()
//
public Mat getTestResponses() {
return new Mat(getTestResponses_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTestSampleIdx()
//
public Mat getTestSampleIdx() {
return new Mat(getTestSampleIdx_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTestSampleWeights()
//
public Mat getTestSampleWeights() {
return new Mat(getTestSampleWeights_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTestSamples()
//
/**
* Returns matrix of test samples
* @return automatically generated
*/
public Mat getTestSamples() {
return new Mat(getTestSamples_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTrainNormCatResponses()
//
/**
* Returns the vector of normalized categorical responses
*
* The function returns vector of responses. Each response is integer from {@code 0} to `<number of
* classes>-1`. The actual label value can be retrieved then from the class label vector, see
* TrainData::getClassLabels.
* @return automatically generated
*/
public Mat getTrainNormCatResponses() {
return new Mat(getTrainNormCatResponses_0(nativeObj));
}
//
// C++: Mat 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.
* @return automatically generated
*/
public Mat getTrainResponses() {
return new Mat(getTrainResponses_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTrainSampleIdx()
//
public Mat getTrainSampleIdx() {
return new Mat(getTrainSampleIdx_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTrainSampleWeights()
//
public Mat getTrainSampleWeights() {
return new Mat(getTrainSampleWeights_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getTrainSamples(int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true)
//
/**
* Returns matrix of train samples
*
* @param layout The requested layout. If it's different from the initial one, the matrix is
* transposed. See ml::SampleTypes.
* @param compressSamples if true, the function returns only the training samples (specified by
* sampleIdx)
* @param 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).
* @return automatically generated
*/
public Mat getTrainSamples(int layout, boolean compressSamples, boolean compressVars) {
return new Mat(getTrainSamples_0(nativeObj, layout, compressSamples, compressVars));
}
/**
* Returns matrix of train samples
*
* @param layout The requested layout. If it's different from the initial one, the matrix is
* transposed. See ml::SampleTypes.
* @param 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).
* @return automatically generated
*/
public Mat getTrainSamples(int layout, boolean compressSamples) {
return new Mat(getTrainSamples_1(nativeObj, layout, compressSamples));
}
/**
* Returns matrix of train samples
*
* @param 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).
* @return automatically generated
*/
public Mat getTrainSamples(int layout) {
return new Mat(getTrainSamples_2(nativeObj, layout));
}
/**
* 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).
* @return automatically generated
*/
public Mat getTrainSamples() {
return new Mat(getTrainSamples_3(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getVarIdx()
//
public Mat getVarIdx() {
return new Mat(getVarIdx_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getVarSymbolFlags()
//
public Mat getVarSymbolFlags() {
return new Mat(getVarSymbolFlags_0(nativeObj));
}
//
// C++: Mat cv::ml::TrainData::getVarType()
//
public Mat getVarType() {
return new Mat(getVarType_0(nativeObj));
}
//
// C++: static Ptr_TrainData cv::ml::TrainData::create(Mat samples, int layout, Mat responses, Mat varIdx = Mat(), Mat sampleIdx = Mat(), Mat sampleWeights = Mat(), Mat varType = Mat())
//
/**
* Creates training data from in-memory arrays.
*
* @param samples matrix of samples. It should have CV_32F type.
* @param layout see ml::SampleTypes.
* @param 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)
* @param 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.
* @param 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.
* @param sampleWeights optional vector with weights for each sample. It should have CV_32F type.
* @param 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.
* @return automatically generated
*/
public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights, Mat varType) {
return TrainData.__fromPtr__(create_0(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, sampleWeights.nativeObj, varType.nativeObj));
}
/**
* Creates training data from in-memory arrays.
*
* @param samples matrix of samples. It should have CV_32F type.
* @param layout see ml::SampleTypes.
* @param 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)
* @param 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.
* @param 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.
* @param 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.
* @return automatically generated
*/
public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights) {
return TrainData.__fromPtr__(create_1(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, sampleWeights.nativeObj));
}
/**
* Creates training data from in-memory arrays.
*
* @param samples matrix of samples. It should have CV_32F type.
* @param layout see ml::SampleTypes.
* @param 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)
* @param 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.
* @param 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.
* @return automatically generated
*/
public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx) {
return TrainData.__fromPtr__(create_2(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj));
}
/**
* Creates training data from in-memory arrays.
*
* @param samples matrix of samples. It should have CV_32F type.
* @param layout see ml::SampleTypes.
* @param 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)
* @param 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.
* @return automatically generated
*/
public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx) {
return TrainData.__fromPtr__(create_3(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj));
}
/**
* Creates training data from in-memory arrays.
*
* @param samples matrix of samples. It should have CV_32F type.
* @param layout see ml::SampleTypes.
* @param 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.
* @return automatically generated
*/
public static TrainData create(Mat samples, int layout, Mat responses) {
return TrainData.__fromPtr__(create_4(samples.nativeObj, layout, responses.nativeObj));
}
//
// C++: int cv::ml::TrainData::getCatCount(int vi)
//
public int getCatCount(int vi) {
return getCatCount_0(nativeObj, vi);
}
//
// C++: int cv::ml::TrainData::getLayout()
//
public int getLayout() {
return getLayout_0(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNAllVars()
//
public int getNAllVars() {
return getNAllVars_0(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNSamples()
//
public int getNSamples() {
return getNSamples_0(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNTestSamples()
//
public int getNTestSamples() {
return getNTestSamples_0(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNTrainSamples()
//
public int getNTrainSamples() {
return getNTrainSamples_0(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNVars()
//
public int getNVars() {
return getNVars_0(nativeObj);
}
//
// C++: int cv::ml::TrainData::getResponseType()
//
public int getResponseType() {
return getResponseType_0(nativeObj);
}
//
// C++: void cv::ml::TrainData::getNames(vector_String names)
//
/**
* Returns vector of symbolic names captured in loadFromCSV()
* @param names automatically generated
*/
public void getNames(List names) {
getNames_0(nativeObj, names);
}
//
// C++: void cv::ml::TrainData::getSample(Mat varIdx, int sidx, float* buf)
//
public void getSample(Mat varIdx, int sidx, float buf) {
getSample_0(nativeObj, varIdx.nativeObj, sidx, buf);
}
//
// C++: void cv::ml::TrainData::getValues(int vi, Mat sidx, float* values)
//
public void getValues(int vi, Mat sidx, float values) {
getValues_0(nativeObj, vi, sidx.nativeObj, values);
}
//
// C++: void cv::ml::TrainData::setTrainTestSplit(int count, bool shuffle = true)
//
/**
* Splits the training data into the training and test parts
* SEE: TrainData::setTrainTestSplitRatio
* @param count automatically generated
* @param shuffle automatically generated
*/
public void setTrainTestSplit(int count, boolean shuffle) {
setTrainTestSplit_0(nativeObj, count, shuffle);
}
/**
* Splits the training data into the training and test parts
* SEE: TrainData::setTrainTestSplitRatio
* @param count automatically generated
*/
public void setTrainTestSplit(int count) {
setTrainTestSplit_1(nativeObj, count);
}
//
// C++: void cv::ml::TrainData::setTrainTestSplitRatio(double ratio, bool shuffle = true)
//
/**
* 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
* @param ratio automatically generated
* @param shuffle automatically generated
*/
public void setTrainTestSplitRatio(double ratio, boolean shuffle) {
setTrainTestSplitRatio_0(nativeObj, 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: TrainData::setTrainTestSplit
* @param ratio automatically generated
*/
public void setTrainTestSplitRatio(double ratio) {
setTrainTestSplitRatio_1(nativeObj, ratio);
}
//
// C++: void cv::ml::TrainData::shuffleTrainTest()
//
public void shuffleTrainTest() {
shuffleTrainTest_0(nativeObj);
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: Mat cv::ml::TrainData::getCatMap()
private static native long getCatMap_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getCatOfs()
private static native long getCatOfs_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getClassLabels()
private static native long getClassLabels_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getDefaultSubstValues()
private static native long getDefaultSubstValues_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getMissing()
private static native long getMissing_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getNormCatResponses()
private static native long getNormCatResponses_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getResponses()
private static native long getResponses_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getSampleWeights()
private static native long getSampleWeights_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getSamples()
private static native long getSamples_0(long nativeObj);
// C++: static Mat cv::ml::TrainData::getSubMatrix(Mat matrix, Mat idx, int layout)
private static native long getSubMatrix_0(long matrix_nativeObj, long idx_nativeObj, int layout);
// C++: static Mat cv::ml::TrainData::getSubVector(Mat vec, Mat idx)
private static native long getSubVector_0(long vec_nativeObj, long idx_nativeObj);
// C++: Mat cv::ml::TrainData::getTestNormCatResponses()
private static native long getTestNormCatResponses_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTestResponses()
private static native long getTestResponses_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTestSampleIdx()
private static native long getTestSampleIdx_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTestSampleWeights()
private static native long getTestSampleWeights_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTestSamples()
private static native long getTestSamples_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTrainNormCatResponses()
private static native long getTrainNormCatResponses_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTrainResponses()
private static native long getTrainResponses_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTrainSampleIdx()
private static native long getTrainSampleIdx_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTrainSampleWeights()
private static native long getTrainSampleWeights_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getTrainSamples(int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true)
private static native long getTrainSamples_0(long nativeObj, int layout, boolean compressSamples, boolean compressVars);
private static native long getTrainSamples_1(long nativeObj, int layout, boolean compressSamples);
private static native long getTrainSamples_2(long nativeObj, int layout);
private static native long getTrainSamples_3(long nativeObj);
// C++: Mat cv::ml::TrainData::getVarIdx()
private static native long getVarIdx_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getVarSymbolFlags()
private static native long getVarSymbolFlags_0(long nativeObj);
// C++: Mat cv::ml::TrainData::getVarType()
private static native long getVarType_0(long nativeObj);
// C++: static Ptr_TrainData cv::ml::TrainData::create(Mat samples, int layout, Mat responses, Mat varIdx = Mat(), Mat sampleIdx = Mat(), Mat sampleWeights = Mat(), Mat varType = Mat())
private static native long create_0(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long sampleWeights_nativeObj, long varType_nativeObj);
private static native long create_1(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long sampleWeights_nativeObj);
private static native long create_2(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj);
private static native long create_3(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj);
private static native long create_4(long samples_nativeObj, int layout, long responses_nativeObj);
// C++: int cv::ml::TrainData::getCatCount(int vi)
private static native int getCatCount_0(long nativeObj, int vi);
// C++: int cv::ml::TrainData::getLayout()
private static native int getLayout_0(long nativeObj);
// C++: int cv::ml::TrainData::getNAllVars()
private static native int getNAllVars_0(long nativeObj);
// C++: int cv::ml::TrainData::getNSamples()
private static native int getNSamples_0(long nativeObj);
// C++: int cv::ml::TrainData::getNTestSamples()
private static native int getNTestSamples_0(long nativeObj);
// C++: int cv::ml::TrainData::getNTrainSamples()
private static native int getNTrainSamples_0(long nativeObj);
// C++: int cv::ml::TrainData::getNVars()
private static native int getNVars_0(long nativeObj);
// C++: int cv::ml::TrainData::getResponseType()
private static native int getResponseType_0(long nativeObj);
// C++: void cv::ml::TrainData::getNames(vector_String names)
private static native void getNames_0(long nativeObj, List names);
// C++: void cv::ml::TrainData::getSample(Mat varIdx, int sidx, float* buf)
private static native void getSample_0(long nativeObj, long varIdx_nativeObj, int sidx, float buf);
// C++: void cv::ml::TrainData::getValues(int vi, Mat sidx, float* values)
private static native void getValues_0(long nativeObj, int vi, long sidx_nativeObj, float values);
// C++: void cv::ml::TrainData::setTrainTestSplit(int count, bool shuffle = true)
private static native void setTrainTestSplit_0(long nativeObj, int count, boolean shuffle);
private static native void setTrainTestSplit_1(long nativeObj, int count);
// C++: void cv::ml::TrainData::setTrainTestSplitRatio(double ratio, bool shuffle = true)
private static native void setTrainTestSplitRatio_0(long nativeObj, double ratio, boolean shuffle);
private static native void setTrainTestSplitRatio_1(long nativeObj, double ratio);
// C++: void cv::ml::TrainData::shuffleTrainTest()
private static native void shuffleTrainTest_0(long nativeObj);
// native support for java finalize()
private static native void delete(long nativeObj);
}
