public static bool Test(Distribution distribution)
{
var dataCount = 10000000;
var dataSampling = new List <double>();
for (var i = 0; i < dataCount; i++)
{
dataSampling.Add(distribution.GetNewRandomValue());
}
dataSampling.Sort();
// Определяем кол-во интервалов для разбиения по формуле Стерджеса
var numberOfIntervals = (int)Math.Ceiling(1 + 3.322 * Math.Log10(dataCount));
numberOfIntervals = 100;
var(pdfValuesExpected, pdfValuesActual) = CalculateExpectedAndActualPDFValues(distribution, dataSampling.ToArray());
var chiSquareTest = new Test(pdfValuesExpected, pdfValuesActual, numberOfIntervals - 1);
var significant = chiSquareTest.Significant;
return(!significant);
}
public void getReport(double alpha)
{
if (gConf == null)
{
buildModel();
}
Forms.RunningProcess.frmRunningProcessDialog rd = new Forms.RunningProcess.frmRunningProcessDialog(false);
rd.Text = "Accuracy Assessment";
rd.TopLevel = true;
rd.pgbProcess.Visible = false;
rd.FormBorderStyle = System.Windows.Forms.FormBorderStyle.Sizable;
string[] hd = new string[labels.Count];
for (int i = 0; i < hd.Length; i++)
{
string s = labels[i];
if (s.Length > 4)
{
hd[i] = s.Substring(0, 4);
}
else
{
hd[i] = s.PadRight(3);
}
}
rd.addMessage(" " + String.Join(" ", hd));
rd.addMessage("------".PadRight((labels.Count + 1) * 7, '-'));
for (int i = 0; i < labels.Count; i++)
{
string[] lnArr = new string[labels.Count + 1];
string vl = labels[i];
if (vl.Length > 4)
{
vl = vl.Substring(0, 4);
}
else
{
vl = vl.PadRight(4);
}
lnArr[0] = vl;
for (int j = 0; j < labels.Count; j++)
{
vl = xtable[i, j].ToString();
if (vl.Length < 4)
{
vl = vl.PadRight(4);
}
lnArr[j + 1] = vl;
}
rd.addMessage(String.Join(" | ", lnArr) + "|");
rd.addMessage("------".PadRight((labels.Count + 1) * 7, '-'));
}
Accord.Statistics.Testing.ChiSquareTest ct = new Accord.Statistics.Testing.ChiSquareTest(ChiSquare, System.Convert.ToInt32(Math.Pow(labels.Count - 1, 2)));
rd.addMessage("Chi-square = " + ChiSquare + " DF = " + ct.DegreesOfFreedom.ToString() + " p-value = " + ct.PValue.ToString());
rd.addMessage("Overall = " + Overall.ToString());
rd.addMessage("Kappa = " + Kappa.ToString());
rd.addMessage("STE = " + STE.ToString());
ci conf = getConfidenceInterval(alpha);
rd.addMessage("Kappa CI = " + conf.LowerBound.ToString() + " - " + conf.UpperBound.ToString());
rd.addMessage("Cohen Kappa Variance = " + CohenKappaVariance.ToString());
rd.Show();
rd.enableClose();
}
public void getReport(double alpha)
{
if(gConf==null)buildModel();
Forms.RunningProcess.frmRunningProcessDialog rd = new Forms.RunningProcess.frmRunningProcessDialog(false);
rd.Text = "Accuracy Assessment";
rd.TopLevel = true;
rd.pgbProcess.Visible = false;
rd.FormBorderStyle = System.Windows.Forms.FormBorderStyle.Sizable;
string[] hd = new string[labels.Count];
for (int i = 0; i < hd.Length; i++)
{
string s = labels[i];
if(s.Length>4)
{
hd[i] = s.Substring(0, 4);
}
else
{
hd[i] = s.PadRight(3);
}
}
rd.addMessage(" "+String.Join(" ", hd));
rd.addMessage("------".PadRight((labels.Count+1)*7,'-'));
for (int i = 0; i < labels.Count; i++)
{
string[] lnArr = new string[labels.Count+1];
string vl = labels[i];
if (vl.Length > 4)
{
vl = vl.Substring(0, 4);
}
else
{
vl = vl.PadRight(4);
}
lnArr[0] = vl;
for (int j = 0; j < labels.Count; j++)
{
vl = xtable[i,j].ToString();
if (vl.Length < 4)
{
vl = vl.PadRight(4);
}
lnArr[j + 1] = vl;
}
rd.addMessage(String.Join(" | ", lnArr)+"|");
rd.addMessage("------".PadRight((labels.Count+1) * 7, '-'));
}
Accord.Statistics.Testing.ChiSquareTest ct = new Accord.Statistics.Testing.ChiSquareTest(ChiSquare, System.Convert.ToInt32(Math.Pow(labels.Count - 1, 2)));
rd.addMessage("Chi-square = " + ChiSquare + " DF = " + ct.DegreesOfFreedom.ToString() + " p-value = " + ct.PValue.ToString());
rd.addMessage("Overall = " + Overall.ToString());
rd.addMessage("Kappa = " + Kappa.ToString());
rd.addMessage("STE = " + STE.ToString());
ci conf = getConfidenceInterval(alpha);
rd.addMessage("Kappa CI = " + conf.LowerBound.ToString() + " - " + conf.UpperBound.ToString());
rd.addMessage("Cohen Kappa Variance = " + CohenKappaVariance.ToString());
rd.Show();
rd.enableClose();
}
private void buildModel()
{
if (independentVls == null)
{
getMatrix();
}
Accord.Statistics.Links.ILinkFunction lFunc = new Accord.Statistics.Links.IdentityLinkFunction();
switch (Link)
{
case LinkFunction.Absolute:
lFunc = new Accord.Statistics.Links.AbsoluteLinkFunction();
break;
case LinkFunction.Cauchit:
lFunc = new Accord.Statistics.Links.CauchitLinkFunction();
break;
case LinkFunction.Inverse:
lFunc = new Accord.Statistics.Links.InverseLinkFunction();
break;
case LinkFunction.InverseSquared:
lFunc = new Accord.Statistics.Links.InverseSquaredLinkFunction();
break;
case LinkFunction.Logit:
lFunc = new Accord.Statistics.Links.LogitLinkFunction();
break;
case LinkFunction.Log:
lFunc = new Accord.Statistics.Links.LogLinkFunction();
break;
case LinkFunction.LogLog:
lFunc = new Accord.Statistics.Links.LogLogLinkFunction();
break;
case LinkFunction.Probit:
lFunc = new Accord.Statistics.Links.ProbitLinkFunction();
break;
case LinkFunction.Sin:
lFunc = new Accord.Statistics.Links.SinLinkFunction();
break;
case LinkFunction.Threshold:
lFunc = new Accord.Statistics.Links.ThresholdLinkFunction();
break;
default:
break;
}
glm = new Accord.Statistics.Models.Regression.GeneralizedLinearRegression(lFunc, NumberOfVariables);
Accord.Statistics.Models.Regression.Fitting.IterativeReweightedLeastSquares isl = new Accord.Statistics.Models.Regression.Fitting.IterativeReweightedLeastSquares(glm);
delta = 0;
do
{
delta = isl.Run(independentVls, dependentVls);
iterations += 1;
}while (delta > converge && iterations < totiterations);
coefficients = glm.Coefficients;
stdError = glm.StandardErrors;
loglikelihood = glm.GetLogLikelihood(independentVls, dependentVls);
if (Double.IsNaN(loglikelihood))
{
loglikelihood = 0;
}
loglikelihoodratio = glm.GetLogLikelihoodRatio(independentVls, dependentVls, glm);
if (Double.IsNaN(loglikelihoodratio))
{
loglikelihoodratio = 0;
}
deviance = glm.GetDeviance(independentVls, dependentVls);
if (Double.IsNaN(deviance))
{
deviance = 0;
}
Accord.Statistics.Testing.ChiSquareTest chiTest = glm.ChiSquare(independentVls, dependentVls);
pv = chiTest.PValue;
if (Double.IsNaN(pv))
{
pv = 0;
}
chisqr = chiTest.Statistic;
if (double.IsNaN(chisqr))
{
chisqr = 0;
}
waldTestValues = new double[IndependentFieldNames.Length];
waldTestPValues = new double[waldTestValues.Length];
for (int i = 0; i < waldTestValues.Length; i++)
{
Accord.Statistics.Testing.WaldTest wTest = glm.GetWaldTest(i);
double wS = wTest.Statistic;
double wP = wTest.PValue;
//if (Double.IsNaN(wS)) wS = 0;
//if (Double.IsNaN(wP)) wP = 0;
waldTestValues[i] = wS;
waldTestPValues[i] = wP;
}
//if (stdError.Length != coefficients.Length) stdError = new double[coefficients.Length];
//for (int i = 0; i < stdError.Length; i++)
//{
// double vl = stdError[i];
// if (Double.IsNaN(vl)) stdError[i] = 0;
//}
}
