/// <summary>
/// Gives decision values on a test vector x given a model, and return the predicted label(classification) or the function value(regression).
/// </summary>
/// <param name="model"><see cref="Model"/>.</param>
/// <param name="x">The test vector.</param>
/// <param name="decisionValues">When this method returns, contains decision values if succeeded, or null if failed.</param>
/// <returns>
/// <para>For a classification model, the predicted class for x and decision values.</para>
/// <para>For a regression model, <code>decisionValues[0]</code> and the returned value are both the function value of x calculated using the model. </para>
/// <para>For a one-class model, <code>decisionValues[0]</code> is the decision value of x, while the returned value is +1/-1.</para>
/// </returns>
public static double PredictValues(Model model, NodeArray x, out double[] decisionValues)
{
decisionValues = null;
if (model == null)
{
throw new ArgumentNullException(nameof(model));
}
if (x == null)
{
throw new ArgumentNullException(nameof(x));
}
unsafe
{
var m = model.NativePtr;
switch (model.Parameter.SvmType)
{
case SvmType.CSVC:
case SvmType.NuSVC:
var length = model.Classes * (model.Classes - 1) / 2;
decisionValues = new double[length];
return(NativeMethods.svm_predict_values(m, (NativeMethods.svm_node *)x.NativePtr, decisionValues));
default:
decisionValues = new double[1];
return(NativeMethods.svm_predict_values(m, (NativeMethods.svm_node *)x.NativePtr, decisionValues));
}
}
}
/// <summary>
/// Does classification or regression on a test vector x given a model.
/// </summary>
/// <param name="model"><see cref="Model"/>.</param>
/// <param name="x">The test vector.</param>
/// <returns>
/// <para>For a classification model, the predicted class for x is returned.</para>
/// <para>For a regression model, the function value of x calculated using the model is returned. For an one-class model, +1 or -1 is returned.</para>
/// </returns>
public static double Predict(Model model, NodeArray x)
{
if (model == null)
{
throw new ArgumentNullException(nameof(model));
}
if (x == null)
{
throw new ArgumentNullException(nameof(x));
}
unsafe
{
var m = model.NativePtr;
return(NativeMethods.svm_predict(m, (NativeMethods.svm_node *)x.NativePtr));
}
}
/// <summary>
/// Does classification or regression on a test vector x given a model.
/// </summary>
/// <param name="model"><see cref="Model"/>.</param>
/// <param name="x">The test vector.</param>
/// <param name="probability">When this method returns, contains probability estimates if succeeded, or null if failed.</param>
/// <returns>
/// <para>For a classification model, the predicted class for x is returned.</para>
/// <para>For a regression model, the function value of x calculated using the model is returned. For an one-class model, +1 or -1 is returned.</para>
/// </returns>
/// <remarks>This methods returns valid probability when <see cref="Model.ProbabilityA"/> and <see cref="Model.ProbabilityB"/> are not null.</remarks>
public static double Predict(Model model, NodeArray x, out double[] probability)
{
probability = null;
if (model == null)
{
throw new ArgumentNullException(nameof(model));
}
if (x == null)
{
throw new ArgumentNullException(nameof(x));
}
unsafe
{
var m = model.NativePtr;
probability = new double[model.Classes];
return(NativeMethods.svm_predict_probability(m, (NativeMethods.svm_node *)x.NativePtr, probability));
}
}
