public override void Train(IForecastingDataSets datasets)
{
OnStartRunning(new ComponentRunEventArgs(datasets));
NumberOfVariables = datasets.InputVectorLength;
NumberOfSamples = datasets.Length;
EnvolutionStep = 0;
if (functionSet == null)
{
functionSet = new GPFunctionSet();
}
Initialize();
GenerateFunction();
double [] gpConstraints = GenerateConstants(mGPModelParameter.ConstantsIntervalFrom, mGPModelParameter.ConstantsIntervalTo, mGPModelParameter.ConstantsNumber);
GenerateTerminals(datasets, gpConstraints);
if (population == null)
{
EnvolutionStep = 1;
population = new GPPopulation(mGPModelParameter.PopulationSize, terminalSet, functionSet, parameters, mGPModelParameter.MultipleCore);
}
GPBestHromosome = population.bestChromosome;
while (ProveEnvolution(EnvolutionStep, mGPModelParameter.EnvolveConditionValue, mGPModelParameter.EnvolveIndicator))
{
population.StartEvolution();
OnRunningEpoch(new ComponentRunEpochEventArgs(EnvolutionStep));
EnvolutionStep++;
}
int indexOutput = terminalSet.NumConstants + terminalSet.NumVariables - 1;
List <int> lst = new List <int>();
FunctionTree.ToListExpression(lst, GPBestHromosome.Root);
double y = 0;
datasets.ForecastedData = new double[datasets.Length][];
for (int i = 0; i < terminalSet.RowCount; i++)
{
// evalue the function
y = functionSet.Evaluate(lst, terminalSet, i);
// check for correct numeric value
if (double.IsNaN(y) || double.IsInfinity(y))
{
y = 0;
}
datasets.ForecastedData[i] = new double[1];
datasets.ForecastedData[i][0] = y;
}
OnFinishRunning(new ComponentRunEventArgs(datasets)
{
State = functionSet.DecodeExpression(lst, terminalSet)
});
}
public override double Forecast(double[] inputVector)
{
if (GPBestHromosome != null)
{
List <int> lst = new List <int>();
FunctionTree.ToListExpression(lst, GPBestHromosome.Root);
return(functionSet.Evaluate(lst, inputVector));
}
else
{
return(0);
}
}
