public ApproximationWrap(double[,] data, int functionsSet, int populationSize, int geneticMethod, int selectionMethod, float minRange, float lengthRange)
{
_data = data;
// create fitness function
SymbolicRegressionFitness fitness = new SymbolicRegressionFitness(data, constants);
// create gene function
IGPGene gene = (functionsSet == 0) ? (IGPGene) new SimpleGeneFunction(6) : (IGPGene) new ExtendedGeneFunction(6);
// create population
Population = new Population(populationSize,
(geneticMethod == 0) ? (IChromosome) new GPTreeChromosome(gene) : (IChromosome) new GEPChromosome(gene, 15),
fitness,
(selectionMethod == 0) ? (ISelectionMethod) new EliteSelection() : (selectionMethod == 1) ? (ISelectionMethod) new RankSelection() : (ISelectionMethod) new RouletteWheelSelection());
// solution array
solution = new double[50, 2];
// calculate X values to be used with solution function
for (int j = 0; j < 50; j++)
{
solution[j, 0] = minRange + (double)j * lengthRange / 49;
}
}
// Worker thread
void SearchSolution()
{
// create fitness function
var fitness = new SymbolicRegressionFitness(data, new double[] { 1, 2, 3, 5, 7 });
// create gene function
var gene = (functionsSet == 0) ?
new SimpleGeneFunction(6) :
(IGPGene) new ExtendedGeneFunction(6);
// create population
var population = new Population(populationSize,
(geneticMethod == 0) ?
new GPTreeChromosome(gene) :
(IChromosome) new GEPChromosome(gene, 15),
fitness,
(selectionMethod == 0) ? new EliteSelection() :
(selectionMethod == 1) ? new RankSelection() :
(ISelectionMethod) new RouletteWheelSelection()
);
// iterations
var i = 1;
// solution array
var solution = new double[50, 2];
var input = new double[6] {
0, 1, 2, 3, 5, 7
};
// calculate X values to be used with solution function
for (var j = 0; j < 50; j++)
{
solution[j, 0] = chart.RangeX.Min + (double)j * chart.RangeX.Length / 49;
}
// loop
while (!needToStop)
{
// run one epoch of genetic algorithm
population.RunEpoch();
try
{
// get best solution
var bestFunction = population.BestChromosome.ToString();
// calculate best function
for (var j = 0; j < 50; j++)
{
input[0] = solution[j, 0];
solution[j, 1] = PolishExpression.Evaluate(bestFunction, input);
}
chart.UpdateDataSeries("solution", solution);
// calculate error
var error = 0.0;
for (int j = 0, k = data.GetLength(0); j < k; j++)
{
input[0] = data[j, 0];
error += Math.Abs(data[j, 1] - PolishExpression.Evaluate(bestFunction, input));
}
// set current iteration's info
SetText(currentIterationBox, i.ToString());
SetText(currentErrorBox, error.ToString("F3"));
}
catch
{
// remove any solutions from chart in case of any errors
chart.UpdateDataSeries("solution", null);
}
// increase current iteration
i++;
//
if ((iterations != 0) && (i > iterations))
{
break;
}
}
// show solution
SetText(solutionBox, population.BestChromosome.ToString());
// enable settings controls
EnableControls(true);
}
