/*
* Reinitialises part of, or the whole, population
* @param topology The population to be reinitialised
*/
private void reinitialisePosition(fj.data.List<ClusterParticle> topology) {
int index = 0;
for (int i = index; i < topology.length(); i += reinitialisationInterval) {
ClusterParticle c = topology.index(i);
c.reinitialise();
c.calculateFitness();
}
}
public void performIteration(final TuningAlgorithm alg) {
final List<Vector> parameterList = alg.getParameterList();
// TODO: deal with maximisation problems
results =
results.snoc(
parameterList.map(
new F<Vector, OptimisationSolution>() {
@Override
public OptimisationSolution f(Vector a) {
return new OptimisationSolution(a, alg.evaluate(a));
}
}));
// (+1 because iterations start at 0)
if (alg.getIterations() + 1 >= minProblems.getParameter() && parameterList.length() != 1) {
List<List<Double>> data =
results.map(
List.<OptimisationSolution, Double>map_().f(getFitness().andThen(getValue())));
P2<Double, Double> friedman = StatsTests.friedman(0.05, data);
if (friedman._1() > friedman._2()) {
final List<Integer> indexes = StatsTests.postHoc(0.05, friedman._1(), data);
alg.setParameterList(indexes.map(flip(Utils.<Vector>index()).f(parameterList)));
results =
results.map(
new F<List<OptimisationSolution>, List<OptimisationSolution>>() {
@Override
public List<OptimisationSolution> f(final List<OptimisationSolution> a) {
return indexes.map(flip(Utils.<OptimisationSolution>index()).f(a));
}
});
}
}
}
