//javadoc: TrainData::create(samples, layout, responses, varIdx, sampleIdx)
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();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
TrainData retVal = TrainData.__fromPtr__(ml_TrainData_create_12(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj));
return(retVal);
#else
return(null);
#endif
}
/**
* 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)
{
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__(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)
* (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)
{
if (samples != null)
{
samples.ThrowIfDisposed();
}
if (responses != null)
{
responses.ThrowIfDisposed();
}
return(TrainData.__fromPtr__(ml_TrainData_create_14(samples.nativeObj, layout, responses.nativeObj)));
}
