public double DiversityCivilState()
{
List <double> listeCount = new List <double>();
foreach (CivilState item in Enum.GetValues(typeof(CivilState)))
{
listeCount.Add(this.employees.Where(x => x.civil_State == item).Count());
}
return(Math.Round((KernelDensity.GaussianKernel(1 / Statistics.StandardDeviation(listeCount))) * 100));
}
public double DiversityDisabilities()
{
List <double> listeCount = new List <double>();
foreach (Disabilities item in Enum.GetValues(typeof(Disabilities)))
{
listeCount.Add(this.employees.Where(x => x.disabilities == item).Count());
}
return(Math.Round((1 - KernelDensity.GaussianKernel(1 / Statistics.StandardDeviation(listeCount))) * 100));
}
public double DiversityAge()
{
List <double> listeCount = new List <double>();
foreach (Age item in Enum.GetValues(typeof(Age)))
{
listeCount.Add(this.employees.Where(x => x.age == item).Count());
}
var y = Statistics.StandardDeviation(listeCount);
return(Math.Round((KernelDensity.GaussianKernel(1 / y)) * 100));
}
public void KDETestGaussianKernelBandwidth1()
{
//Density of standard normal distribution at 0
AssertHelpers.AlmostEqualRelative(0.398942280401433, KernelDensity.GaussianKernel(0), 10);
var estimate = KernelDensity.EstimateGaussian(-3.5d, 1.0d, _testData);
AssertHelpers.AlmostEqualRelative(0.004115405028907, estimate, 10);
estimate = KernelDensity.EstimateGaussian(0.0d, 1.0d, _testData);
AssertHelpers.AlmostEqualRelative(0.310485907659139, estimate, 10);
estimate = KernelDensity.EstimateGaussian(2.0d, 1.0d, _testData);
AssertHelpers.AlmostEqualRelative(0.099698581377801, estimate, 10);
}