//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];
}
model.B1 = _initB1; // Dispersion.CorrelationPearson(x, y)
//* (Dispersion.StandardDeviationSample(y) /
// Dispersion.StandardDeviationSample(x));
model.B0 = _initB0; // Dispersion.Mean(y) -
//_lm.B1 * Dispersion.Mean(x);
int iter = 0;
int row;
double predY, error;
while (iter++ < _maxIterations) //Do for max iterations
{
for (row = 0; row < x.Length; row++)
{
predY = model.B0 + x[row] * model.B1;
error = predY - y[row];
model.B0 = model.B0 - error * _learningRate;
model.B1 = model.B1 - error * _learningRate * x[row];
}
}
//B0 and B1 values are computed
return(model);
}
//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);
}
