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>
///
/// </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[][]> GetClassMeanMatrix(List <double[][]>
classInputMatrix)
{
if (classInputMatrix == null)
{
throw new InvalidMatrixException();
}
List <double[][]> meanMatrix = new List <double[][]>();
int noOfAttributes = classInputMatrix[0].Length;
foreach (double[][] cim in classInputMatrix)
{
double[][] mean = new double[noOfAttributes][];
Parallel.For(0, mean.Length, idx =>
{
mean[idx] = new double[1];
mean[idx][0] =
Dispersion.Mean(cim[idx]);
});
meanMatrix.Add(mean);
}
return(meanMatrix);
}
//Return mean matrix of dataset
//Private members are not being used due to unit test purposes
/// <summary>
/// Assume target attributes is last column in Training Data
/// </summary>
/// <param name="trainingData"></param>
/// <param name="indexTargetAttribute"></param>
/// <param name="noOfAttributes"></param>
/// <returns></returns>
protected double[] GetDataSetMeanMatrix()
{
double[] mean = new double[_noOfAttributes];
//int idx = 0;
//Assumption: Target Attributer is last column
Parallel.For(0, _trainingData.Length - 1, colIdx => //For each column
{
//if (colIdx != indexTargetAttribute)
//{
mean[colIdx] = Dispersion.Mean(_trainingData[colIdx]);
//}
});
return(mean);
}
//Training Data Should have only Two Attributes
public override Common.MLCore.ModelBase BuildModel(double[][] trainingData,
string[] attributeHeaders,
int indexTargetAttribute)
{
VerifyData(trainingData, attributeHeaders, indexTargetAttribute);
double[] x, y;
ModelLinearBase model = new ModelLinearBase(_missingValue,
_indexTargetAttribute, _trainingData.Length - 1);
//Additional Checks
if (trainingData.Length != 2)
{
throw new InvalidTrainingDataException();
}
if (indexTargetAttribute == 0)
{
x = trainingData[1];
y = trainingData[0];
}
else
{
x = trainingData[0];
y = trainingData[1];
}
//x,y are arrays
model.B1 = Dispersion.CorrelationPearson(x, y)
* (Dispersion.StandardDeviationSample(y) /
Dispersion.StandardDeviationSample(x));
model.B0 = Dispersion.Mean(y) -
model.B1 * Dispersion.Mean(x);
return(model);
}
public void Statistics_Mean()
{
double value = Dispersion.Mean(ta);
Assert.IsTrue((9.887 <= value && value <= 9.889));
}