/// <summary>
/// Runs one iteration of the Reweighted Least Squares algorithm.
/// </summary>
/// <param name="inputs">The input data.</param>
/// <param name="outputs">The outputs associated with each input vector.</param>
/// <returns>The maximum relative change in the parameters after the iteration.</returns>
///
public double Run(double[][] inputs, double[] outputs)
{
// Regress using Iteratively Reweighted Least Squares estimation.
// References:
// - Bishop, Christopher M.; Pattern Recognition
// and Machine Learning. Springer; 1st ed. 2006.
// Initial definitions and memory allocations
int N = inputs.Length;
double[][] design = new double[N][];
double[] errors = new double[N];
double[] weights = new double[N];
double[] coefficients = this.regression.Coefficients;
double[] deltas;
// Compute the regression matrix
for (int i = 0; i < inputs.Length; i++)
{
double[] row = design[i] = new double[parameterCount];
row[0] = 1; // for intercept
for (int j = 0; j < inputs[i].Length; j++)
row[j + 1] = inputs[i][j];
}
// Compute errors and weighing matrix
for (int i = 0; i < inputs.Length; i++)
{
double y = regression.Compute(inputs[i]);
// Calculate error vector
errors[i] = y - outputs[i];
// Calculate weighting matrix
weights[i] = y * (1.0 - y);
}
// Reset Hessian matrix and gradient
for (int i = 0; i < gradient.Length; i++)
{
gradient[i] = 0;
for (int j = 0; j < gradient.Length; j++)
hessian[i, j] = 0;
}
// (Re-) Compute error gradient
for (int j = 0; j < design.Length; j++)
for (int i = 0; i < gradient.Length; i++)
gradient[i] += design[j][i] * errors[j];
// (Re-) Compute weighted "Hessian" matrix
for (int k = 0; k < weights.Length; k++)
{
double[] rk = design[k];
for (int j = 0; j < rk.Length; j++)
for (int i = 0; i < rk.Length; i++)
hessian[j, i] += rk[i] * rk[j] * weights[k];
}
// The SVD is very stable, but is quite expensive, being on average
// about 10-15 times more expensive than LU decomposition. There are
// other ways to avoid a singular Hessian. For a very interesting
// reading on the subject, please see:
//
// - Jeff Gill & Gary King, "What to Do When Your Hessian Is Not Invertible",
// Sociological Methods & Research, Vol 33, No. 1, August 2004, 54-87.
// Available in: http://gking.harvard.edu/files/help.pdf
//
// Moreover, the computation of the inverse is optional, as it will
// be used only to compute the standard errors of the regression.
// Hessian Matrix is singular, try pseudo-inverse solution
decomposition = new SingularValueDecomposition(hessian);
deltas = decomposition.Solve(gradient);
previous = (double[])coefficients.Clone();
// Update coefficients using the calculated deltas
for (int i = 0; i < coefficients.Length; i++)
coefficients[i] -= deltas[i];
// Return the relative maximum parameter change
for (int i = 0; i < deltas.Length; i++)
deltas[i] = Math.Abs(deltas[i]) / Math.Abs(previous[i]);
return Matrix.Max(deltas);
}