//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))); }