/// <summary>
/// Computes the total error of the solution with parameters theta.
/// </summary>
/// <param name="X">Training data</param>
/// <param name="y">Target variable</param>
/// <param name="theta">Model parameters</param>
/// <param name="lambda">Regularization weight</param
/// <returns>Solution error</returns>
private double ComputeError(InsightMatrix X, InsightVector y, InsightVector theta, double lambda)
{
var inner = ((X * theta.ToColumnMatrix()) - y.ToColumnMatrix()).Power(2);
var thetaSub = theta.SubVector(1, theta.Count - 1);
var reg = lambda * thetaSub.Multiply(thetaSub).Sum();
return (inner.Column(0).Sum() / (2 * X.RowCount)) + reg;
}
/// <summary>
/// Computes the total error of the solution with parameters theta.
/// </summary>
/// <param name="X">Training data</param>
/// <param name="y">Target variable</param>
/// <param name="theta">Model parameters</param>
/// <param name="lambda">Regularization weight</param
/// <returns>Solution error</returns>
private double ComputeError(InsightMatrix X, InsightVector y, InsightVector theta, double lambda)
{
var first = y.Multiply(Sigmoid((X * theta.ToColumnMatrix()).Column(0)).Log());
var second = (1 - y).Multiply(1 - Sigmoid((X * theta.ToColumnMatrix()).Column(0)).Log());
var thetaSub = theta.SubVector(1, theta.Count - 1);
var reg = (lambda / 2 * X.RowCount) * thetaSub.Power(2).Sum();
return (first - second).Sum() / X.RowCount + reg;
}
