/// <summary>
/// Trains the statistical model.
/// </summary>
/// <param name="model">The stat model.</param>
/// <param name="samples">The training samples.</param>
/// <param name="layoutType">Type of the layout.</param>
/// <param name="responses">Vector of responses associated with the training samples.</param>
/// <returns></returns>
public static bool Train(this IStatModel model, IInputArray samples, DataLayoutType layoutType, IInputArray responses)
{
using (InputArray iaSamples = samples.GetInputArray())
using (InputArray iaResponses = responses.GetInputArray())
{
return(MlInvoke.StatModelTrain(model.StatModelPtr, iaSamples, layoutType, iaResponses));
}
}
/// <summary>
/// Trains the statistical model.
/// </summary>
/// <param name="model">The stat model.</param>
/// <param name="samples">The training samples.</param>
/// <param name="layoutType">Type of the layout.</param>
/// <param name="responses">Vector of responses associated with the training samples.</param>
/// <returns></returns>
public static bool Train(this IStatModel model, IInputArray samples, DataLayoutType layoutType, IInputArray responses)
{
using (InputArray iaSamples = samples.GetInputArray())
using (InputArray iaResponses = responses.GetInputArray())
{
return MlInvoke.StatModelTrain(model.StatModelPtr, iaSamples, layoutType, iaResponses);
}
}
/// <summary>
/// Creates training data from in-memory arrays.
/// </summary>
/// <param name="samples">Matrix of samples. It should have CV_32F type.</param>
/// <param name="layoutType">Type of the layout.</param>
/// <param name="response">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>
/// <param name="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>
/// <param name="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>
/// <param name="sampleWeight">Optional vector with weights for each sample. It should have CV_32F type.</param>
/// <param name="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.</param>
public TrainData(
IInputArray samples, DataLayoutType layoutType, IInputArray response,
IInputArray varIdx = null, IInputArray sampleIdx = null,
IInputArray sampleWeight = null, IInputArray varType = null
)
{
using (InputArray iaSamples = samples.GetInputArray())
using (InputArray iaResponse = response.GetInputArray())
using (InputArray iaVarIdx = varIdx == null ? InputArray.GetEmpty() : varIdx.GetInputArray())
using (InputArray iaSampleIdx = sampleIdx == null ? InputArray.GetEmpty() : sampleIdx.GetInputArray())
using (InputArray iaSampleWeight = sampleWeight == null ? InputArray.GetEmpty() : sampleWeight.GetInputArray())
using (InputArray iaVarType = varType == null ? InputArray.GetEmpty() : varType.GetInputArray())
{
_ptr = MlInvoke.cveTrainDataCreate(iaSamples, layoutType, iaResponse, iaVarIdx, iaSampleIdx, iaSampleWeight,
iaVarType);
}
}
/// <summary>
/// Creates training data from in-memory arrays.
/// </summary>
/// <param name="samples">Matrix of samples. It should have CV_32F type.</param>
/// <param name="layoutType">Type of the layout.</param>
/// <param name="response">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>
/// <param name="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>
/// <param name="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>
/// <param name="sampleWeight">Optional vector with weights for each sample. It should have CV_32F type.</param>
/// <param name="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.</param>
public TrainData(
IInputArray samples, DataLayoutType layoutType, IInputArray response,
IInputArray varIdx = null, IInputArray sampleIdx = null,
IInputArray sampleWeight = null, IInputArray varType = null
)
{
using (InputArray iaSamples = samples.GetInputArray())
using (InputArray iaResponse = response.GetInputArray())
using (InputArray iaVarIdx = varIdx == null ? InputArray.GetEmpty() : varIdx.GetInputArray())
using (InputArray iaSampleIdx = sampleIdx == null ? InputArray.GetEmpty() : sampleIdx.GetInputArray())
using (InputArray iaSampleWeight = sampleWeight == null ? InputArray.GetEmpty() : sampleWeight.GetInputArray())
using (InputArray iaVarType = varType == null ? InputArray.GetEmpty() : varType.GetInputArray())
{
_ptr = MlInvoke.cveTrainDataCreate(iaSamples, layoutType, iaResponse, iaVarIdx, iaSampleIdx, iaSampleWeight,
iaVarType);
}
}
internal static extern IntPtr cveTrainDataCreate(
IntPtr samples, DataLayoutType layout, IntPtr responses,
IntPtr varIdx, IntPtr sampleIdx,
IntPtr sampleWeights, IntPtr varType);
internal static extern bool StatModelTrain(IntPtr model, IntPtr samples, DataLayoutType layout, IntPtr responses);
internal static extern IntPtr cveTrainDataCreate(
IntPtr samples, DataLayoutType layout, IntPtr responses,
IntPtr varIdx, IntPtr sampleIdx,
IntPtr sampleWeights, IntPtr varType);
internal static extern bool StatModelTrain(IntPtr model, IntPtr samples, DataLayoutType layout, IntPtr responses);
