private void ComputeErrors(IEnumerable <EstimationResult> result)
{
var weightedMeanError = 0.0;
var trendError = 0.0;
var armaError = 0.0;
var neuronNetworkError = 0.0;
var estimatesCount = 0;
for (int i = 0; i < Data.Count - 1; i++)
{
if (Data[i].WeightedMeanEstimation.HasValue)
{
weightedMeanError += Math.Abs(Data[i].WeightedMeanEstimation.Value - Data[i + 1].Value);
trendError += Math.Abs(Data[i].TrendEstimation.Value - Data[i + 1].Value);
armaError += Math.Abs(Data[i].ArmaEstimation.Value - Data[i + 1].Value);
neuronNetworkError += Math.Abs(Data[i].NeuronNetworkEstimation.Value - Data[i + 1].Value);
estimatesCount++;
}
}
result.First(x => x.Estimator == Estimator.WeightedMean).Error = Math2.DivWithZero(weightedMeanError, estimatesCount);
result.First(x => x.Estimator == Estimator.Trend).Error = Math2.DivWithZero(trendError, estimatesCount);
result.First(x => x.Estimator == Estimator.NeuronNetwork).Error = Math2.DivWithZero(neuronNetworkError, estimatesCount);
result.First(x => x.Estimator == Estimator.ARMA).Error = Math2.DivWithZero(armaError, estimatesCount);
}
public void DivWithZero_is_correct(double a, double b, double expectedResult)
{
Math2.DivWithZero(a, b).ShouldBe(expectedResult);
}
