/// <summary>
/// Compute the error gradient of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <param name="X">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="lambda">Regularisation constant</param>
/// <param name="regularizer">Regularization term function.</param>
/// <returns></returns>
public Vector ComputeGradient(Vector theta, Matrix X, Vector y, double lambda, IRegularizer regularizer)
{
int m = X.Rows;
Vector gradient = Vector.Zeros(theta.Length);
Vector s = (X * theta).ToVector();
IFunction function = new Logistic();
s = s.Each(v => function.Compute(v));
for (int i = 0; i < theta.Length; i++)
{
gradient[i] = (1.0 / m) * ((s - y) * X[i, VectorType.Col]).Sum();
}
if (lambda != 0)
{
gradient = regularizer.Regularize(theta, gradient, m, lambda);
}
return gradient;
}
/// <summary>
/// Compute the error cost of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <param name="X">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="lambda">Regularization constant</param>
/// <param name="regularizer">Regularization term function.</param>
/// <returns></returns>
public double ComputeCost(Vector theta, Matrix X, Vector y, double lambda, IRegularizer regularizer)
{
int m = X.Rows;
double j = 0.0;
Vector s = (X * theta).ToVector();
IFunction function = new Logistic();
s = s.Each(v => function.Compute(v));
Vector slog = s.Copy().Each(v => System.Math.Log(System.Math.Abs(1.0 - v)));
j = (-1.0 / m) * ( (y.Dot(s.Log())) + (-1.0 * ((1.0 - y).Dot(slog))) );
if (lambda != 0)
{
j = regularizer.Regularize(j, theta, m, lambda);
}
return j;
}
/// <summary>
/// Compute the error gradient of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <param name="X">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="lambda">Regularisation constant</param>
/// <param name="regularizer">Regularization term function.</param>
/// <returns></returns>
public Vector ComputeGradient(Vector theta, Matrix X, Vector y, double lambda, IRegularizer regularizer)
{
var m = X.Rows;
var gradient = Vector.Zeros(theta.Length);
var s = (X * theta).ToVector();
IFunction function = new Logistic();
s = s.Each(v => function.Compute(v));
for (var i = 0; i < theta.Length; i++)
{
gradient[i] = (1.0 / m) * ((s - y) * X[i, VectorType.Col]).Sum();
}
if (lambda != 0)
{
gradient = regularizer.Regularize(theta, gradient, m, lambda);
}
return(gradient);
}
/// <summary>
/// Compute the error cost of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <param name="X">Training set</param>
/// <param name="y">Training labels</param>
/// <param name="lambda">Regularization constant</param>
/// <param name="regularizer">Regularization term function.</param>
/// <returns></returns>
public double ComputeCost(Vector theta, Matrix X, Vector y, double lambda, IRegularizer regularizer)
{
var m = X.Rows;
var j = 0.0;
var s = (X * theta).ToVector();
IFunction function = new Logistic();
s = s.Each(v => function.Compute(v));
var slog = s.Copy().Each(v => Math.Log(Math.Abs(1.0 - v)));
j = (-1.0 / m) * (y.Dot(s.Log()) + (-1.0 * (1.0 - y).Dot(slog)));
if (lambda != 0)
{
j = regularizer.Regularize(j, theta, m, lambda);
}
return(j);
}