public float Evaluate(IChromosome ch, IFunctionSet functionSet)
{
var expTree = ((GPChromosome)ch).expressionTree;
double fitness = 0;
double rowFitness = 0.0;
double y;
//index of output parameter
int indexOutput = Globals.gpterminals.NumConstants + Globals.gpterminals.NumVariables;
for (int i = 0; i < Globals.gpterminals.RowCount; i++)
{
// evalue the function agains eachh rowData
y = functionSet.Evaluate(expTree, i);
// check for correct numeric value
if (double.IsNaN(y) || double.IsInfinity(y))
{
return(float.NaN);
}
//this fitness algoritm is specialized only for Binary and Classification problems
if (m_ProblemType == ColumnDataType.Categorical || m_ProblemType == ColumnDataType.Binary)
{
var valClass = Experiment.getCategoryFromNumeric(y, Globals.classCount);
if (double.IsNaN(valClass))
{
return(float.NaN);
}
//check is the calculated value correct
var val = Globals.gpterminals.TrainingData[i][indexOutput] == valClass ? 1 : 0;
//add the result to the fitness
rowFitness += val;
}
else//thros exception if the problems is not as expected
{
throw new Exception("Problem type is unknown!");
}
}
if (double.IsNaN(rowFitness) || double.IsInfinity(rowFitness))
{
fitness = float.NaN;
}
else
{
fitness = (float)(rowFitness / Globals.gpterminals.RowCount) * 1000.0;
}
return((float)Math.Round(fitness, 2));
}
public float Evaluate(IChromosome ch, IFunctionSet functionSet)
{
var expTree = ((GPChromosome)ch).expressionTree;
double fitness = 0;
double rowFitness = 0.0;
double y, SS_tot = 0;
//index of output parameter
int indexOutput = Globals.gpterminals.NumConstants + Globals.gpterminals.NumVariables;
for (int i = 0; i < Globals.gpterminals.RowCount; i++)
{
// evalue the function agains eachh rowData
y = functionSet.Evaluate(expTree, i);
// check for correct numeric value
if (double.IsNaN(y) || double.IsInfinity(y))
{
return(float.NaN);
}
//Calculate square error
rowFitness += Math.Pow(y - Globals.gpterminals.TrainingData[i][indexOutput], 2);
SS_tot += Math.Pow(Globals.gpterminals.TrainingData[i][indexOutput] - Globals.gpterminals.AverageValue, 2);
}
if (SS_tot != 0)
{
rowFitness = rowFitness / SS_tot;
}
if (double.IsNaN(rowFitness) || double.IsInfinity(rowFitness))
{
fitness = float.NaN;
}
else
{
fitness = (float)((1.0 / (1.0 + rowFitness / Globals.gpterminals.RowCount)) * 1000.0);
}
return((float)Math.Round(fitness, 2));
}
//Calculate model agains specific data
public static double[] CalculateGPModel(GPNode node, bool btrainingData = true)
{
validateFunctionSet();
double[][] data = btrainingData ? gpterminals.TrainingData : gpterminals.TestingData;
if (data == null)
{
return(null);
}
var model = new double[data.Length];
for (int i = 0; i < data.Length; i++)
{
model[i] = functions.Evaluate(node, i, btrainingData);
}
return(model);
}
public float Evaluate(IChromosome ch, IFunctionSet functionSet)
{
var expTree = ((GPChromosome)ch).expressionTree;
double fitness = 0;
double rowFitness = 0.0;
double y;
//index of output parameter
int indexOutput = Globals.gpterminals.NumConstants + Globals.gpterminals.NumVariables;
for (int i = 0; i < Globals.gpterminals.RowCount; i++)
{
// evalue the function agains eachh rowData
y = functionSet.Evaluate(expTree, i);
// check for correct numeric value
if (double.IsNaN(y) || double.IsInfinity(y))
{
return(float.NaN);
}
var temp = y - Globals.gpterminals.TrainingData[i][indexOutput];
rowFitness += temp * temp;
}
if (double.IsNaN(rowFitness) || double.IsInfinity(rowFitness))
{
fitness = float.NaN;
}
else
{
fitness = (float)((1.0 / (1.0 + rowFitness)) * 1000.0);
}
return((float)Math.Round(fitness, 2));
}
/// <summary>
/// Evaluates function agains terminals
/// </summary>
/// <param name="chromosome"></param>
/// <param name="functionSet"></param>
/// <returns></returns>
public float Evaluate(IChromosome chromosome, IFunctionSet functionSet)
{
GANumChromosome ch = chromosome as GANumChromosome;
if (ch == null)
{
return(0);
}
else
{
//prepare terminals
var term = Globals.gpterminals.SingleTrainingData;
for (int i = 0; i < ch.val.Length; i++)
{
term[i] = ch.val[i];
}
var y = functionSet.Evaluate(_funToOptimize, -1);
if (double.IsNaN(y) || double.IsInfinity(y))
{
y = float.NaN;
}
//Save output in to output variable
term[term.Length - 1] = y;
if (IsMinimize)
{
y *= -1;
}
return((float)y);
}
}