public double[] calculateOutput(bool isTraining, bool probValue = false)
{
if (Factory.ProgresReport.BestSolution == null)
{
return(null);
}
//
var ch = Factory.ProgresReport.BestSolution;//.expressionTree;
var par = Factory.Parameters;
if (par.RootFunctionNode == null)
{
setLearningType(par);
}
double[] yc = null;
if (ch != null)
{
yc = Inputs.CalculateOutput(ch, par, isTraining);//gp
//no testing data set
if (yc == null)
{
return(null);
}
//de normalize output
double[] output = yc;
for (int i = 0; i < yc.Length; i++)
{
//calculate de normalization
if (ExpData.GetOutputColumnType() == ColumnType.Numeric)
{
double[] normRow = new double[1] {
yc[i]
};
output[i] = ExpData.GetDecodedOutputRow(normRow)[0];
}
else
{
if (par.RootFunctionNode.Id == 2048) //sigmoid
{
if (probValue) //when model is evaluate we need probability f event in order to optimize threshold value
{
output[i] = yc[i];
}
else
{
output[i] = yc[i] > par.RootFunctionNode.Parameter ? 1 : 0;
}
}
else if (par.RootFunctionNode.Id == 2050)//step
{
output[i] = Math.Truncate(yc[i]);
}
else
{
output[i] = Math.Truncate(yc[i]);
}
}
}
return(output);
}
return(null);//no solution yet
}