public static bool CheckZn(double[] data, Zakon zn, float alpha)
{
double[] ΔLeft, ΔRight;
int[] v;
int k = StatisticsProcessor.ToIntervals(data, out ΔLeft, out ΔRight, out v);
double σв = Math.Sqrt(StatisticsProcessor.Dispersion(data));
double mean = StatisticsProcessor.Srednee(data);
double[] p = new double[k];
for (int i = 0; i < k; i++)
{
double x1 = ΔLeft[i];
double x2 = ΔRight[i];
switch (zn)
{
case Zakon.Ravnom:
p[i] = FxRavnom(x2, mean - 1.732 * σв, mean + 1.732 * σв) -
FxRavnom(x1, mean - 1.732 * σв, mean + 1.732 * σв);
break;
case Zakon.Exponential:
p[i] = FxExponential(x2, 1f / mean) - FxExponential(x1, 1f / mean);
break;
case Zakon.Normal:
p[i] = FxNormal((x2 - mean) / σв) - FxNormal((x1 - mean) / σв);
break;
}
}
double χSqr = 0;
for (int i = 0; i < k; i++)
{
χSqr += (v[i] - data.Length * p[i]) * (v[i] - data.Length * p[i]) / data.Length / p[i];
}
int s = 0;
switch (zn)
{
case Zakon.Ravnom:
s = 2;
break;
case Zakon.Exponential:
s = 1;
break;
case Zakon.Normal:
s = 2;
break;
}
return(χSqr < χSqrTable(alpha, (uint)(k - s - 1)));
}
public static double[] EmpVals(double[] data)
{
double[] lX, rX;
int[] nX;
StatisticsProcessor.ToIntervals(data, out lX, out rX, out nX);
double[] res = new double[nX.Length];
double sum = 0;
for (int i = 0; i < nX.Length; i++)
{
sum += nX[i];
res[i] = sum / data.Length;
}
return(res);
}
