/// <summary>
/// Create a prediction based on the learned Theta values and the supplied test item.
/// </summary>
/// <param name="y">Training record</param>
/// <returns></returns>
public override double Predict(Vector y)
{
var tempy = PolynomialFeatures > 0 ? PreProcessing.FeatureDimensions.IncreaseDimensions(y, PolynomialFeatures) : y;
tempy = tempy.Insert(0, 1.0);
return(LogisticFunction.Compute((tempy * Theta).ToDouble()) >= 0.5 ? 1d : 0d);
}
/// <summary>
/// Computes the probability of the prediction being True.
/// </summary>
/// <param name="x"></param>
/// <returns></returns>
public double PredictRaw(Vector x)
{
x = IncreaseDimensions(x, this.PolynomialFeatures);
this.Preprocess(x);
return(LogisticFunction.Compute(x.Insert(0, 1.0, false).Dot(Theta)));
}
/// <summary>Projects vector into a logistic kernel space.</summary>
/// <param name="m">Kernel Matrix.</param>
/// <param name="x">Vector in original space.</param>
/// <returns>Vector in logistic kernel space.</returns>
public Vector Project(Matrix m, Vector x)
{
var K = Vector.Zeros(m.Rows);
for (var i = 0; i < K.Length; i++)
{
var xy = m[i].Dot(x);
K[i] = LogisticFunction.Compute(Lambda * xy);
}
return(K);
}