Health/Assets/OpenCVForUnity/org/opencv/ml/TrainData.cs

1109 lines
38 KiB
C#

using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
namespace OpenCVForUnity.MlModule
{
// 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 : DisposableOpenCVObject
{
protected override void Dispose(bool disposing)
{
try
{
if (disposing)
{
}
if (IsEnabledDispose)
{
if (nativeObj != IntPtr.Zero)
ml_TrainData_delete(nativeObj);
nativeObj = IntPtr.Zero;
}
}
finally
{
base.Dispose(disposing);
}
}
protected internal TrainData(IntPtr addr) : base(addr) { }
public IntPtr getNativeObjAddr() { return nativeObj; }
// internal usage only
public static TrainData __fromPtr__(IntPtr addr) { return new TrainData(addr); }
//
// C++: int cv::ml::TrainData::getLayout()
//
public int getLayout()
{
ThrowIfDisposed();
return ml_TrainData_getLayout_10(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNTrainSamples()
//
public int getNTrainSamples()
{
ThrowIfDisposed();
return ml_TrainData_getNTrainSamples_10(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNTestSamples()
//
public int getNTestSamples()
{
ThrowIfDisposed();
return ml_TrainData_getNTestSamples_10(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNSamples()
//
public int getNSamples()
{
ThrowIfDisposed();
return ml_TrainData_getNSamples_10(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNVars()
//
public int getNVars()
{
ThrowIfDisposed();
return ml_TrainData_getNVars_10(nativeObj);
}
//
// C++: int cv::ml::TrainData::getNAllVars()
//
public int getNAllVars()
{
ThrowIfDisposed();
return ml_TrainData_getNAllVars_10(nativeObj);
}
//
// C++: void cv::ml::TrainData::getSample(Mat varIdx, int sidx, float* buf)
//
public void getSample(Mat varIdx, int sidx, float buf)
{
ThrowIfDisposed();
if (varIdx != null) varIdx.ThrowIfDisposed();
ml_TrainData_getSample_10(nativeObj, varIdx.nativeObj, sidx, buf);
}
//
// C++: Mat cv::ml::TrainData::getSamples()
//
public Mat getSamples()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getSamples_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getMissing()
//
public Mat getMissing()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getMissing_10(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, bool compressSamples, bool compressVars)
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTrainSamples_10(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, bool compressSamples)
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTrainSamples_11(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)
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTrainSamples_12(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()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTrainSamples_13(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()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTrainResponses_10(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()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTrainNormCatResponses_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getTestResponses()
//
public Mat getTestResponses()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTestResponses_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getTestNormCatResponses()
//
public Mat getTestNormCatResponses()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTestNormCatResponses_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getResponses()
//
public Mat getResponses()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getResponses_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getNormCatResponses()
//
public Mat getNormCatResponses()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getNormCatResponses_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getSampleWeights()
//
public Mat getSampleWeights()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getSampleWeights_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getTrainSampleWeights()
//
public Mat getTrainSampleWeights()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTrainSampleWeights_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getTestSampleWeights()
//
public Mat getTestSampleWeights()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTestSampleWeights_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getVarIdx()
//
public Mat getVarIdx()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getVarIdx_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getVarType()
//
public Mat getVarType()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getVarType_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getVarSymbolFlags()
//
public Mat getVarSymbolFlags()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getVarSymbolFlags_10(nativeObj)));
}
//
// C++: int cv::ml::TrainData::getResponseType()
//
public int getResponseType()
{
ThrowIfDisposed();
return ml_TrainData_getResponseType_10(nativeObj);
}
//
// C++: Mat cv::ml::TrainData::getTrainSampleIdx()
//
public Mat getTrainSampleIdx()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTrainSampleIdx_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getTestSampleIdx()
//
public Mat getTestSampleIdx()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTestSampleIdx_10(nativeObj)));
}
//
// C++: void cv::ml::TrainData::getValues(int vi, Mat sidx, float* values)
//
public void getValues(int vi, Mat sidx, float values)
{
ThrowIfDisposed();
if (sidx != null) sidx.ThrowIfDisposed();
ml_TrainData_getValues_10(nativeObj, vi, sidx.nativeObj, values);
}
//
// C++: Mat cv::ml::TrainData::getDefaultSubstValues()
//
public Mat getDefaultSubstValues()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getDefaultSubstValues_10(nativeObj)));
}
//
// C++: int cv::ml::TrainData::getCatCount(int vi)
//
public int getCatCount(int vi)
{
ThrowIfDisposed();
return ml_TrainData_getCatCount_10(nativeObj, vi);
}
//
// 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()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getClassLabels_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getCatOfs()
//
public Mat getCatOfs()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getCatOfs_10(nativeObj)));
}
//
// C++: Mat cv::ml::TrainData::getCatMap()
//
public Mat getCatMap()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getCatMap_10(nativeObj)));
}
//
// 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, bool shuffle)
{
ThrowIfDisposed();
ml_TrainData_setTrainTestSplit_10(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)
{
ThrowIfDisposed();
ml_TrainData_setTrainTestSplit_11(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, bool shuffle)
{
ThrowIfDisposed();
ml_TrainData_setTrainTestSplitRatio_10(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)
{
ThrowIfDisposed();
ml_TrainData_setTrainTestSplitRatio_11(nativeObj, ratio);
}
//
// C++: void cv::ml::TrainData::shuffleTrainTest()
//
public void shuffleTrainTest()
{
ThrowIfDisposed();
ml_TrainData_shuffleTrainTest_10(nativeObj);
}
//
// C++: Mat cv::ml::TrainData::getTestSamples()
//
/**
* Returns matrix of test samples
* return automatically generated
*/
public Mat getTestSamples()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getTestSamples_10(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<string> names)
{
ThrowIfDisposed();
Mat names_mat = Converters.vector_String_to_Mat(names);
ml_TrainData_getNames_10(nativeObj, names_mat.nativeObj);
}
//
// 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)
{
if (vec != null) vec.ThrowIfDisposed();
if (idx != null) idx.ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getSubVector_10(vec.nativeObj, idx.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)
{
if (matrix != null) matrix.ThrowIfDisposed();
if (idx != null) idx.ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_getSubMatrix_10(matrix.nativeObj, idx.nativeObj, layout)));
}
//
// 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 `&lt;number_of_variables_in_samples&gt; +
* &lt;number_of_variables_in_responses&gt;`, 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)
{
if (samples != null) samples.ThrowIfDisposed();
if (responses != null) responses.ThrowIfDisposed();
if (varIdx != null) varIdx.ThrowIfDisposed();
if (sampleIdx != null) sampleIdx.ThrowIfDisposed();
if (sampleWeights != null) sampleWeights.ThrowIfDisposed();
if (varType != null) varType.ThrowIfDisposed();
return TrainData.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_create_10(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.
* &lt;number_of_variables_in_responses&gt;`, 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)
{
if (samples != null) samples.ThrowIfDisposed();
if (responses != null) responses.ThrowIfDisposed();
if (varIdx != null) varIdx.ThrowIfDisposed();
if (sampleIdx != null) sampleIdx.ThrowIfDisposed();
if (sampleWeights != null) sampleWeights.ThrowIfDisposed();
return TrainData.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_create_11(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.
* &lt;number_of_variables_in_responses&gt;`, 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)
{
if (samples != null) samples.ThrowIfDisposed();
if (responses != null) responses.ThrowIfDisposed();
if (varIdx != null) varIdx.ThrowIfDisposed();
if (sampleIdx != null) sampleIdx.ThrowIfDisposed();
return TrainData.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_create_12(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.
* &lt;number_of_variables_in_responses&gt;`, 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)
{
if (samples != null) samples.ThrowIfDisposed();
if (responses != null) responses.ThrowIfDisposed();
if (varIdx != null) varIdx.ThrowIfDisposed();
return TrainData.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_create_13(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.
* &lt;number_of_variables_in_responses&gt;`, containing types of each input and output variable. See
* ml::VariableTypes.
* return automatically generated
*/
public static TrainData create(Mat samples, int layout, Mat responses)
{
if (samples != null) samples.ThrowIfDisposed();
if (responses != null) responses.ThrowIfDisposed();
return TrainData.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(ml_TrainData_create_14(samples.nativeObj, layout, responses.nativeObj)));
}
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
const string LIBNAME = "__Internal";
#else
const string LIBNAME = "opencvforunity";
#endif
// C++: int cv::ml::TrainData::getLayout()
[DllImport(LIBNAME)]
private static extern int ml_TrainData_getLayout_10(IntPtr nativeObj);
// C++: int cv::ml::TrainData::getNTrainSamples()
[DllImport(LIBNAME)]
private static extern int ml_TrainData_getNTrainSamples_10(IntPtr nativeObj);
// C++: int cv::ml::TrainData::getNTestSamples()
[DllImport(LIBNAME)]
private static extern int ml_TrainData_getNTestSamples_10(IntPtr nativeObj);
// C++: int cv::ml::TrainData::getNSamples()
[DllImport(LIBNAME)]
private static extern int ml_TrainData_getNSamples_10(IntPtr nativeObj);
// C++: int cv::ml::TrainData::getNVars()
[DllImport(LIBNAME)]
private static extern int ml_TrainData_getNVars_10(IntPtr nativeObj);
// C++: int cv::ml::TrainData::getNAllVars()
[DllImport(LIBNAME)]
private static extern int ml_TrainData_getNAllVars_10(IntPtr nativeObj);
// C++: void cv::ml::TrainData::getSample(Mat varIdx, int sidx, float* buf)
[DllImport(LIBNAME)]
private static extern void ml_TrainData_getSample_10(IntPtr nativeObj, IntPtr varIdx_nativeObj, int sidx, float buf);
// C++: Mat cv::ml::TrainData::getSamples()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getSamples_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getMissing()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getMissing_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTrainSamples(int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true)
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTrainSamples_10(IntPtr nativeObj, int layout, [MarshalAs(UnmanagedType.U1)] bool compressSamples, [MarshalAs(UnmanagedType.U1)] bool compressVars);
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTrainSamples_11(IntPtr nativeObj, int layout, [MarshalAs(UnmanagedType.U1)] bool compressSamples);
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTrainSamples_12(IntPtr nativeObj, int layout);
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTrainSamples_13(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTrainResponses()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTrainResponses_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTrainNormCatResponses()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTrainNormCatResponses_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTestResponses()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTestResponses_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTestNormCatResponses()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTestNormCatResponses_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getResponses()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getResponses_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getNormCatResponses()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getNormCatResponses_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getSampleWeights()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getSampleWeights_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTrainSampleWeights()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTrainSampleWeights_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTestSampleWeights()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTestSampleWeights_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getVarIdx()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getVarIdx_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getVarType()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getVarType_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getVarSymbolFlags()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getVarSymbolFlags_10(IntPtr nativeObj);
// C++: int cv::ml::TrainData::getResponseType()
[DllImport(LIBNAME)]
private static extern int ml_TrainData_getResponseType_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTrainSampleIdx()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTrainSampleIdx_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTestSampleIdx()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTestSampleIdx_10(IntPtr nativeObj);
// C++: void cv::ml::TrainData::getValues(int vi, Mat sidx, float* values)
[DllImport(LIBNAME)]
private static extern void ml_TrainData_getValues_10(IntPtr nativeObj, int vi, IntPtr sidx_nativeObj, float values);
// C++: Mat cv::ml::TrainData::getDefaultSubstValues()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getDefaultSubstValues_10(IntPtr nativeObj);
// C++: int cv::ml::TrainData::getCatCount(int vi)
[DllImport(LIBNAME)]
private static extern int ml_TrainData_getCatCount_10(IntPtr nativeObj, int vi);
// C++: Mat cv::ml::TrainData::getClassLabels()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getClassLabels_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getCatOfs()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getCatOfs_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getCatMap()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getCatMap_10(IntPtr nativeObj);
// C++: void cv::ml::TrainData::setTrainTestSplit(int count, bool shuffle = true)
[DllImport(LIBNAME)]
private static extern void ml_TrainData_setTrainTestSplit_10(IntPtr nativeObj, int count, [MarshalAs(UnmanagedType.U1)] bool shuffle);
[DllImport(LIBNAME)]
private static extern void ml_TrainData_setTrainTestSplit_11(IntPtr nativeObj, int count);
// C++: void cv::ml::TrainData::setTrainTestSplitRatio(double ratio, bool shuffle = true)
[DllImport(LIBNAME)]
private static extern void ml_TrainData_setTrainTestSplitRatio_10(IntPtr nativeObj, double ratio, [MarshalAs(UnmanagedType.U1)] bool shuffle);
[DllImport(LIBNAME)]
private static extern void ml_TrainData_setTrainTestSplitRatio_11(IntPtr nativeObj, double ratio);
// C++: void cv::ml::TrainData::shuffleTrainTest()
[DllImport(LIBNAME)]
private static extern void ml_TrainData_shuffleTrainTest_10(IntPtr nativeObj);
// C++: Mat cv::ml::TrainData::getTestSamples()
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getTestSamples_10(IntPtr nativeObj);
// C++: void cv::ml::TrainData::getNames(vector_String names)
[DllImport(LIBNAME)]
private static extern void ml_TrainData_getNames_10(IntPtr nativeObj, IntPtr names_mat_nativeObj);
// C++: static Mat cv::ml::TrainData::getSubVector(Mat vec, Mat idx)
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getSubVector_10(IntPtr vec_nativeObj, IntPtr idx_nativeObj);
// C++: static Mat cv::ml::TrainData::getSubMatrix(Mat matrix, Mat idx, int layout)
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_getSubMatrix_10(IntPtr matrix_nativeObj, IntPtr idx_nativeObj, int layout);
// 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())
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_create_10(IntPtr samples_nativeObj, int layout, IntPtr responses_nativeObj, IntPtr varIdx_nativeObj, IntPtr sampleIdx_nativeObj, IntPtr sampleWeights_nativeObj, IntPtr varType_nativeObj);
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_create_11(IntPtr samples_nativeObj, int layout, IntPtr responses_nativeObj, IntPtr varIdx_nativeObj, IntPtr sampleIdx_nativeObj, IntPtr sampleWeights_nativeObj);
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_create_12(IntPtr samples_nativeObj, int layout, IntPtr responses_nativeObj, IntPtr varIdx_nativeObj, IntPtr sampleIdx_nativeObj);
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_create_13(IntPtr samples_nativeObj, int layout, IntPtr responses_nativeObj, IntPtr varIdx_nativeObj);
[DllImport(LIBNAME)]
private static extern IntPtr ml_TrainData_create_14(IntPtr samples_nativeObj, int layout, IntPtr responses_nativeObj);
// native support for java finalize()
[DllImport(LIBNAME)]
private static extern void ml_TrainData_delete(IntPtr nativeObj);
}
}