void GenBasis(int DIM)
{
if (B != null ? B.Length == DIM : false)
{
return;
}
double sp;
int i, j, k;
/* generate orthogonal basis */
B = new double[DIM][];
for (int l = 0; l < B.Length; l++)
{
B[l] = new double[DIM];
}
for (i = 0; i < DIM; ++i)
{
/* sample components gaussian */
for (j = 0; j < DIM; ++j)
{
B[i][j] = CMAEvolutionStrategy.nextGaussian(rand);
}
/* substract projection of previous vectors */
for (j = i - 1; j >= 0; --j)
{
for (sp = 0.0, k = 0; k < DIM; ++k)
{
sp += B[i][k] * B[j][k]; /* scalar product */
}
for (k = 0; k < DIM; ++k)
{
B[i][k] -= sp * B[j][k]; /* substract */
}
}
/* normalize */
for (sp = 0.0, k = 0; k < DIM; ++k)
{
sp += B[i][k] * B[i][k]; /* squared norm */
}
for (k = 0; k < DIM; ++k)
{
B[i][k] /= Math.Sqrt(sp);
}
}
}
public Timing()
{
birth = CMAEvolutionStrategy.CurrentTimeMillis();
start = birth; // on the save side
}
public override double valueOf(double[] x)
{
double res = CMAEvolutionStrategy.nextDouble(rand);
return(res);
}
public void Run()
{
#region cma settings (auto generated)
CMAEvolutionStrategy cma = new CMAEvolutionStrategy(this.taskType);
//number of dimension (only those who have candidate value counted)
int dimension = evalHelper.Dimensions();
//initial X and STD (only those who have candidate value counted)
List <double> initialX = new List <double>();
List <double> initialStd = new List <double>();
foreach (Parameter parameter in evalHelper.Parameters)
{
if (parameter.Source == ParameterSource.CandidateValue)
{
initialX.Add((parameter.MaxCandidateValue() + parameter.MinCandidateValue()) / 2);
initialStd.Add((parameter.MaxCandidateValue() - parameter.MinCandidateValue()) / 3);
}
}
double[] fitness = cma.init(dimension, initialX.ToArray(), initialStd.ToArray());
#endregion
while (cma.getNumber() == 0)
{
double[][] pop = cma.samplePopulation();
for (int i = 0; i < pop.Length; ++i)
{
//break the loop if canceled by user while in process
if (evalHelper.IsCanceled())
{
evalHelper.OnCanceled(EventArgs.Empty);
return;
}
while (!evalHelper.IsFeasible(pop[i]))
{
pop[i] = cma.resampleSingle(i);
}
fitness[i] = evalHelper.ValueOf(pop[i]);
}
cma.updateDistribution(fitness);
//stopping criteria.
cma.options.stopTolFun = 0.000001 * cma.getBestFunctionValue();//default:0.02
cma.options.stopTolFunHist = 0.1 * cma.options.stopTolFun;
}
cma.setFitnessOfMeanX(evalHelper.ValueOf(cma.getMeanX()));
//final output
string simpleTextReport;
simpleTextReport = "Terminated due to" + Environment.NewLine;
foreach (String s in cma.getMessages())
{
simpleTextReport = simpleTextReport + (" " + s + Environment.NewLine);
}
simpleTextReport = simpleTextReport + "Best function value " + cma.getBestFunctionValue();
evalHelper.OnOptimizationComplete(new OptimizationCompleteEventArgs {
SimpleTextReport = simpleTextReport
});
}
