public static ClimatData FDistribution(ClimatData data)
{
int n1 = data.Y1.Count;
int n2 = data.Y2.Count;
float closest = Table2_C.Aggregate((x, y) => Math.Abs(x - data.Asymmetry) < Math.Abs(y - data.Asymmetry) ? x : y);
float g = 0;
for (int i = 0; i < Table2_C.Count; i++)
{
if (Table2_C[i] == closest)
{
g = Table2_G[i];
}
}
data.FDistribution1 = (n1 * g)
/ ((1 + (2 * (float)Math.Pow(data.Asymmetry, 2)) * (1 - (float)Math.Pow(data.Asymmetry, 2)))
* (1 - (1 - (float)Math.Pow(data.Asymmetry, 2 * n1)) / (n1 * (1 - (float)Math.Pow(data.Asymmetry, 2)))));
data.FDistribution2 = (n2 * g)
/ ((1 + (2 * (float)Math.Pow(data.Asymmetry, 2)) * (1 - (float)Math.Pow(data.Asymmetry, 2)))
* (1 - (1 - (float)Math.Pow(data.Asymmetry, 2 * n2)) / (n2 * (1 - (float)Math.Pow(data.Asymmetry, 2)))));
return(data);
}
public static ClimatData TDistribution(ClimatData data)
{
int n = data.Y.Count;
data.TDistribution = data.Autocorrelation * (float)Math.Sqrt(n - 2) / (float)Math.Sqrt(1 - (float)Math.Pow(data.Autocorrelation, 2));
return(data);
}
public static ClimatData Grubbs(ClimatData data)
{
int n = data.Y.Count - 1;
data.GrubbsN = (data.Y[n] - data.Y.Average()) / data.Deviation;
data.Grubbs1 = (data.Y.Average() - data.Y[0]) / data.Deviation;
return(data);
}
public static ClimatData Student(ClimatData data)
{
int n1 = data.Y1.Count;
int n2 = data.Y2.Count;
data.Student = (data.Y1.Average() - data.Y2.Average()) / (float)Math.Sqrt(n1 * data.Variance1 + n2 * data.Variance2)
* (float)Math.Sqrt((n1 * n2 * (n1 + n2 - 2)) / (n1 + n2));
return(data);
}
public static ClimatData Dixon(ClimatData data)
{
int n = data.Y.Count - 1;
data.DixonN.Add((data.Y[n] - data.Y[n - 1]) / (data.Y[n] - data.Y[0]));
data.DixonN.Add((data.Y[n] - data.Y[n - 1]) / (data.Y[n] - data.Y[1]));
data.DixonN.Add((data.Y[n] - data.Y[n - 2]) / (data.Y[n] - data.Y[1]));
data.DixonN.Add((data.Y[n] - data.Y[n - 2]) / (data.Y[n] - data.Y[2]));
data.DixonN.Add((data.Y[n] - data.Y[n - 2]) / (data.Y[n] - data.Y[0]));
data.Dixon1.Add((data.Y[0] - data.Y[1]) / (data.Y[0] - data.Y[n]));
data.Dixon1.Add((data.Y[0] - data.Y[1]) / (data.Y[0] - data.Y[n - 1]));
data.Dixon1.Add((data.Y[0] - data.Y[2]) / (data.Y[0] - data.Y[n - 1]));
data.Dixon1.Add((data.Y[0] - data.Y[2]) / (data.Y[0] - data.Y[n - 2]));
data.Dixon1.Add((data.Y[0] - data.Y[2]) / (data.Y[0] - data.Y[n]));
return(data);
}
public static ClimatData Asymmetry(ClimatData data)
{
int n = data.Y.Count;
data.Asymmetry = 0;
for (int i = 0; i < n; i++)
{
//data.Asymmetry += (float)Math.Pow((data.Y[i] / data.Y.Average()) - 1, 3);
data.Asymmetry += (float)Math.Pow(data.Y[i] - data.Y.Average(), 3);
}
data.Asymmetry /= n * (float)Math.Pow(data.Deviation, 3);
//data.Asymmetry *= n;
//data.Asymmetry /= (float)Math.Pow(data.Deviation / data.Y.Average(), 3) * (n - 1) * (n - 2);
return(data);
}
public static ClimatData Deviation(ClimatData data)
{
int n = data.Y.Count;
data.Variance = 0;
for (int i = 0; i < n; i++)
{
data.Variance += (float)Math.Pow(data.Y[i] - data.Y.Average(), 2);
}
data.Variance /= n - 1;
data.Deviation = (float)Math.Sqrt(data.Variance);
int n1 = data.Y1.Count;
int n2 = data.Y2.Count;
data.Variance1 = 0;
data.Variance2 = 0;
for (int i = 0; i < n1; i++)
{
data.Variance1 += (float)Math.Pow(data.Y1[i] - data.Y1.Average(), 2);
}
data.Variance1 /= n1 - 1;
for (int i = 0; i < n2; i++)
{
data.Variance2 += (float)Math.Pow(data.Y2[i] - data.Y2.Average(), 2);
}
data.Variance2 /= n2 - 1;
data.Deviation1 = (float)Math.Sqrt(data.Variance1);
data.Deviation2 = (float)Math.Sqrt(data.Variance2);
return(data);
}
public static ClimatData Autocorrelation(ClimatData data)
{
int n = data.Y.Count;
float Y_Average1 = 0, Y_Average2 = 0;
for (int i = 0; i < n - 1; i++)
{
Y_Average1 += data.Y[i] / (n - 1);
Y_Average2 += data.Y[i + 1] / (n - 1);
}
float Deviation1 = 0, Deviation2 = 0;
for (int i = 0; i < n - 1; i++)
{
Deviation1 += (float)Math.Pow(data.Y[i] - data.Y.Average(), 2);
Deviation2 += (float)Math.Pow(data.Y[i + 1] - data.Y.Average(), 2);
}
Deviation1 /= n - 2;
Deviation2 /= n - 2;
Deviation1 = (float)Math.Sqrt(Deviation1);
Deviation2 = (float)Math.Sqrt(Deviation2);
data.Autocorrelation = 0;
for (int i = 0; i < n - 1; i++)
{
data.Autocorrelation += (data.Y[i] - Y_Average1) * (data.Y[i + 1] - Y_Average2);
}
data.Autocorrelation /= n * Deviation1 * Deviation2;
return(data);
}
public static ClimatData Fisher(ClimatData data)
{
data.Fisher = data.Variance1 / data.Variance2;
return(data);
}