/// <summary>
/// Creates a new linear regression directly from data points.
/// </summary>
///
/// <param name="x">The input vectors <c>x</c>.</param>
/// <param name="y">The output vectors <c>y</c>.</param>
///
/// <returns>A linear regression f(x) that most approximates y.</returns>
///
public static MultipleLinearRegression FromData(double[][] x, double[] y)
{
var regression = new MultipleLinearRegression(x[0].Length);
regression.Regress(x, y);
return(regression);
}
/// <summary>
/// Performs the regression using the input and output
/// data, returning the sum of squared errors of the fit.
/// </summary>
///
/// <param name="inputs">The input data.</param>
/// <param name="outputs">The output data.</param>
/// <returns>The regression Sum-of-Squares error.</returns>
///
public double Regress(double[] inputs, double[] outputs)
{
if (inputs.Length != outputs.Length)
{
throw new ArgumentException("Number of input and output samples does not match", "outputs");
}
double[][] X = new double[inputs.Length][];
for (int i = 0; i < inputs.Length; i++)
{
// b[0]*1 + b[1]*inputs[i]
X[i] = new double[] { 1.0, inputs[i] };
}
return(regression.Regress(X, outputs));
}
/// <summary>
/// Performs the regression using the input and output
/// data, returning the sum of squared errors of the fit.
/// </summary>
///
/// <param name="inputs">The input data.</param>
/// <param name="outputs">The output data.</param>
///
/// <returns>The regression Sum-of-Squares error.</returns>
///
public double Regress(double[] inputs, double[] outputs)
{
if (inputs.Length != outputs.Length)
{
throw new ArgumentException("Number of input and output samples does not match", "outputs");
}
int N = inputs.Length;
int order = this.Degree + 1;
// Create polynomials to regress over
double[][] X = new double[N][];
for (int i = 0; i < inputs.Length; i++)
{
X[i] = new double[order];
for (int j = 0; j < order; j++)
{
X[i][j] = Math.Pow(inputs[i], order - j - 1);
}
}
return(regression.Regress(X, outputs));
}
