/// <summary>
/// Compute the error cost of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <returns></returns>
public override double ComputeCost(Vector theta)
{
var m = X.Rows;
var s = (X * theta).ToVector();
var j = 1.0 / (2.0 * m) * ((s - Y) ^ 2.0).Sum();
if (Lambda != 0)
{
j = Regularizer.Regularize(j, theta, m, Lambda);
}
return(j);
}
/// <summary>
/// Compute the error cost of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <returns></returns>
public override double ComputeCost(Vector theta)
{
int m = X.Rows;
Vector s = (X * theta).ToVector();
s = LogisticFunction.Compute(s);
Vector slog = s.Calc(v => System.Math.Log(1.0 - v));
double j = (-1.0 / m) * (Y.Dot(s.Log()) + ((1.0 - Y).Dot(slog)));
if (Lambda != 0)
{
j = Regularizer.Regularize(j, theta, m, Lambda);
}
return(j);
}
/// <summary>
/// Compute the error cost of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <returns></returns>
public override Vector ComputeGradient(Vector theta)
{
int m = X.Rows;
Vector gradient = Vector.Zeros(theta.Length);
Vector s = (X * theta).ToVector();
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(gradient, theta, m, Lambda);
}
return(gradient);
}
/// <summary>
/// Compute the error gradient of the given Theta parameter for the training and label sets
/// </summary>
/// <param name="theta">Learning Theta parameters</param>
/// <returns></returns>
public override Vector ComputeGradient(Vector theta)
{
var m = X.Rows;
var gradient = Vector.Zeros(theta.Length);
var s = (X * theta).ToVector();
s = LogisticFunction.Compute(s);
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(gradient, theta, m, Lambda);
}
return(gradient);
}