private void initCoefficients(ModelLogisticBase model,
int noOfAttributes)
{
model.B0 = 0;
model.B = new List <double>();
//Add Coeff for each attributes
for (int idx = 0; idx < noOfAttributes; idx++)
{
double b = 0.0;
model.B.Add(b);
}
}
/// <summary>
/// Training Data can have any number of attributes
/// </summary>
/// <param name="trainingData"></param>
/// <param name="attributeHeaders"></param>
/// <param name="indexTargetAttribute"></param>
public override Common.MLCore.ModelBase BuildModel(double[][] trainingData,
string[] attributeHeaders,
int indexTargetAttribute)
{
VerifyData(trainingData,
attributeHeaders,
indexTargetAttribute);
ModelLogisticBase model =
new ModelLogisticBase(_missingValue, _indexTargetAttribute,
_trainingData.Length - 1);
//Additional Checks
if (trainingData.Length < 2)
{
throw new InvalidTrainingDataException();
}
//Also create a coeff for Target Attributes,m just dont use it.
initCoefficients(model, trainingData.Length);
int iter = 0;
int row, col;
double predY = 0, pow = 0;
while (iter++ < _maxIterations) //Do for max iterations
{
for (row = 0; row < trainingData[0].Length; row++) //For each row of training data
{
pow = 0;
//For each training attributes
for (col = 0; col < trainingData.Length; col++)
{
if (col != _indexTargetAttribute)
{
pow += model.B[col] * trainingData[col][row];
}
}
pow += model.B0;
predY = 1 / (1 + System.Math.Exp(-pow));
//Now update coeffs
model.B0 = model.B0 + getNewValue(1.0,
trainingData[_indexTargetAttribute][row],
predY);
for (col = 0; col < trainingData.Length; col++)
{
//b = b + alpha (y prediction) prediction (1 prediction) x
if (col != _indexTargetAttribute)
{
model.B[col] = model.B[col] +
getNewValue(trainingData[col][row],
trainingData[_indexTargetAttribute][row],
predY);
}
}
} //For each row
} //For max iterations
return(model);
} //buildModel