public void Statistics_Standard_Deviation_Population_precomputed_mean()
{
double mean = Dispersion.Mean(ta);
double value = Dispersion.StandardDeviationPopulation(ta, mean);
Assert.IsTrue((6.81 <= value && value <= 6.83));
}
/// <summary>
/// Compute the SD of each column
///
/// </summary>
/// <param name="classInputMatrix"></param>
/// <returns>Returns a list of 2D array.
/// Even though there is 1 rowm the 2D array is returned
/// for use in any future matrix computations</returns>
protected List <double[][]> GetClassStandardDeviationMatrix(List <double[][]>
classInputMatrix, List <double[][]>
classMeanMatrix)
{
if (classInputMatrix == null || classMeanMatrix == null)
{
throw new InvalidMatrixException();
}
List <double[][]> sdMatrix = new List <double[][]>();
int noOfAttributes = classInputMatrix[0].Length;
//foreach (double[][] cim in classInputMatrix)
for (int ii = 0; ii < classInputMatrix.Count; ii++)
{
double[][] cim = classInputMatrix[ii];
double[][] sd = new double[noOfAttributes][];
//For each attribute
Parallel.For(0, sd.Length, idx =>
{
sd[idx] = new double[1];
//Do not call SD functionin Statistic routine
//as mean is already known and it will be double computation
sd[idx][0] =
Dispersion.StandardDeviationPopulation(
cim[idx], classMeanMatrix[ii][idx][0]);
});
sdMatrix.Add(sd);
}
return(sdMatrix);
}
public void Statistics_Standard_Deviation_Population()
{
double value = Dispersion.StandardDeviationPopulation(ta);
Assert.IsTrue((6.81 <= value && value <= 6.83));
}
