private LinearRegressionModel(LinearRegressionModel original, Cloner cloner)
: base(original, cloner)
{
this.W = original.W;
this.C = original.C;
this.NoiseSigma = original.NoiseSigma;
doubleVariables = (string[])original.doubleVariables.Clone();
this.factorVariables = original.factorVariables.Select(kvp => new KeyValuePair <string, IEnumerable <string> >(kvp.Key, new List <string>(kvp.Value))).ToList();
}
public static IRegressionSolution CreateSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError)
{
IEnumerable <string> doubleVariables;
IEnumerable <KeyValuePair <string, IEnumerable <string> > > factorVariables;
double[,] inputMatrix;
PrepareData(problemData, out inputMatrix, out doubleVariables, out factorVariables);
alglib.linearmodel lm = new alglib.linearmodel();
alglib.lrreport ar = new alglib.lrreport();
int nRows = inputMatrix.GetLength(0);
int nFeatures = inputMatrix.GetLength(1) - 1;
int retVal = 1;
alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);
if (retVal != 1)
{
throw new ArgumentException("Error in calculation of linear regression solution");
}
rmsError = ar.rmserror;
cvRmsError = ar.cvrmserror;
// get parameters of the model
double[] w;
int nVars;
alglib.lrunpack(lm, out w, out nVars);
// ar.c is the covariation matrix, array[0..NVars,0..NVars].
// C[i, j] = Cov(A[i], A[j])
var solution = new LinearRegressionModel(w, ar.c, cvRmsError, problemData.TargetVariable, doubleVariables, factorVariables)
.CreateRegressionSolution((IRegressionProblemData)problemData.Clone());
solution.Name = "Linear Regression Solution";
return(solution);
}