/// <summary>
/// Make a prediction using the standard interface
/// </summary>
/// <param name="input">an instance of RamenInput to predict from</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public RamenOutput GetPrediction(RamenInput input, out NSError error)
{
if (input == null)
{
throw new ArgumentNullException(nameof(input));
}
var prediction = model.GetPrediction(input, out error);
if (prediction == null)
{
return(null);
}
var lossValue = prediction.GetFeatureValue("loss").DictionaryValue;
var classLabelValue = prediction.GetFeatureValue("classLabel").StringValue;
return(new RamenOutput(lossValue, classLabelValue));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="data"> as color (kCVPixelFormatType_32BGRA) image buffer, 224 pizels wide by 224 pixels high</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public RamenOutput GetPrediction(CVPixelBuffer data, MLPredictionOptions options, out NSError error)
{
var input = new RamenInput(data);
return(GetPrediction(input, options, out error));
}