//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_Standard_Deviation_Sample()
{
double value = Dispersion.StandardDeviationSample(ta);
Assert.IsTrue((7.236 <= value && value <= 7.237));
}
