public static float Predict(this IStatModel model, IInputArray samples, IOutputArray results = null, int flags = 0)
{
using (InputArray iaSamples = samples.GetInputArray())
using (OutputArray oaResults = results == null ? OutputArray.GetEmpty() : results.GetOutputArray())
{
return(MlInvoke.StatModelPredict(model.StatModelPtr, iaSamples, oaResults, flags));
}
}
/// <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>
* /// Load the statistic model from file
* /// </summary>
* /// <param name="fileName">The file to load the model from</param>
* public void Load(String fileName)
* {
* using (CvString fs = new CvString(fileName))
* MlInvoke.StatModelLoad(_ptr, fs);
* }*/
/// <summary>
/// Clear the statistic model
/// </summary>
public static void Clear(this IStatModel model)
{
MlInvoke.StatModelClear(model.StatModelPtr);
}
public static bool Train(this IStatModel model, TrainData trainData, int flags = 0)
{
return(MlInvoke.StatModelTrainWithData(model.StatModelPtr, trainData, flags));
}
/// <summary>
/// Save the statistic model to file
/// </summary>
/// <param name="fileName">The file name where this StatModel will be saved</param>
public static void Save(this IStatModel model, String fileName)
{
using (CvString fs = new CvString(fileName))
MlInvoke.StatModelSave(model.StatModelPtr, fs);
}
