public ModelErrors Calculate(IReadOnlyList <double[]> indices)
{
// TODO: use prepared indexes
var factors = _nodes.Factors();
var resultPosition = _nodes.FindIndex(x => x.IsResult);
var formated = _regressionIndexListFormatter.FormatIndexList(indices);
var given = indices.Skip(indices.Count - formated.Count).Select(x => x[resultPosition]);
var calculated = formated.Select(x => _equationBuilder.Calculate(factors, x));
var yAverage = given.Average();
var count = formated.Count;
var epsilons = given.Zip(calculated,
(givenValue, calculatedValue) =>
new { EpsilonCalculated = givenValue - calculatedValue, EpsilonAverage = givenValue - yAverage });
var eSquared = epsilons.Sum(x => x.EpsilonCalculated * x.EpsilonCalculated);
var eAverage = epsilons.Sum(x => x.EpsilonAverage * x.EpsilonAverage);
var determination = 1 - eSquared / eAverage;
var dw =
epsilons.Skip(1).Zip(epsilons, (e1, e2) => e1.EpsilonCalculated - e2.EpsilonCalculated).Sum(x => x * x) /
eSquared;
var skp = Math.Sqrt(eSquared / count);
var sapp = epsilons.Zip(given, (x, y) => Math.Abs(x.EpsilonCalculated) / Math.Abs(y)).Sum() / count;
var theil = skp / (Math.Sqrt(given.Sum(x => x * x) / count) + Math.Sqrt(calculated.Sum(x => x * x) / count));
return(new ModelErrors(determination, eSquared, dw, skp, sapp, theil));
}
public double[] Solve(IReadOnlyList <double[]> indices)
{
var indexList = _regressionIndexListFormatter.FormatIndexList(indices);
var solver = new LMSolver();
var optimizationResult =
solver.Minimize(
(p, r) =>
{
indexList.AsParallel()
.Select((x, i) => new { Result = _equationBuilder.Calculate(x, p), Row = i })
.ForAll(x => r[x.Row] = x.Result);
},
new double[indexList.First().Length],
indexList.Count);
return(optimizationResult.OptimizedParameters);
}
