public static double GetLambda(double[] sample)
{
List <double> arr = new List <double>(sample);
var normal2 = new MathNet.Numerics.Distributions.Normal();
arr.Sort();
double dMax = 0.0;
double dp, dm;
double MX = StatisticData.CalculateMathExpectation(sample);
double D = StatisticData.CalculateDispersoin(sample, MX);
for (int i = 0; i < arr.Count; i++)
{
double v = (arr[i] - MX) / Math.Sqrt(D);
dp = Math.Abs((double)(i + 1) / arr.Count - normal2.CumulativeDistribution(v));
dm = Math.Abs(normal2.CumulativeDistribution(v) - (double)i / arr.Count);
if (dp > dMax)
{
dMax = dp;
}
if (dm > dMax)
{
dMax = dm;
}
}
return(dMax * Math.Sqrt(arr.Count));
}
private void Form1_Load(object sender, EventArgs e)
{
double[] randomValues = WalkModeling.Calculate();
leftXBorder = randomValues.Min();
rightXBorder = randomValues.Max();
double averageDistance = randomValues.Sum() / randomValues.Length;
double mx = StatisticData.CalculateMathExpectation(randomValues);
double dx = StatisticData.CalculateDispersoin(randomValues, mx);
double N = tb * tb * dx / (d * d);
label5.Text = $"Оптимальный объем выборки = {N}";
// GetAverageDistances();
ConstructFrequencyHistogram(randomValues);
ConstructStatisticalDistributionFunction(randomValues);
//вычисление критерия Колмагорова
double kolmogorovCriterionLambdaValue = KolmogorovCriterion.GetLambda(randomValues);
double kolmogorovCriterionValue = KolmogorovCriterion.Calculate(randomValues, randomValues.Length);
label1.Text = "средняя дистанция = " + averageDistance;
//label2.Text = $"Лямбда по критерию Колмогорова равна {Math.Round(kolmogorovCriterionLambdaValue, 3)}; \nОценка генератора " +
// $"случайных чисел равна {kolmogorovCriterionValue}";
}
