Examples with NoisySolutionEvaluator evodef.NoisySolutionEvaluator used on opensource projects

Example 1 with NoisySolutionEvaluator

use of evodef.NoisySolutionEvaluator in project SimpleAsteroids by ljialin.

the class TestSimpleGA method main.

public static void main(String[] args) {
    NoisySolutionEvaluator evaluator = new EvalMaxM(10, 2, 1.0);
    EvoAlg evoAlg = new SimpleGA().setPopulationSize(100);
    int[] solution = evoAlg.runTrial(evaluator, 10000);
    System.out.println(Arrays.toString(solution));
}

Example 2 with NoisySolutionEvaluator

use of evodef.NoisySolutionEvaluator in project SimpleAsteroids by ljialin.

the class PowerOfDifferencePairsTest method main.

// okay this is interesting: the paired idea does not work well when the
// vectors are far apart
// this might have been expected from the way that having the
// sliding history window too big causes deterioration in performance
public static void main(String[] args) {
    // create the random vectors, score them
    // and put them in a list
    // now run an experiment each way to determine the arg max
    // and then evaluate the quality of that
    ScoredVectorLearner meanLearner = new MeanLearner();
    ScoredVectorLearner diffLearner = new PairedDifferenceLearner();
    ScoredVectorLearner[] learners = new ScoredVectorLearner[] { meanLearner, diffLearner };
    int nTrials = 30;
    int n = 100, m = 2;
    double noise = 1.0;
    NoisySolutionEvaluator evaluator = new EvalMaxM(n, m, noise);
    int k = 500;
    List<StatSummary> stats = new ArrayList<>();
    for (ScoredVectorLearner learner : learners) {
        stats.add(new StatSummary(learner.getClass().getSimpleName()));
    }
    LineChart lineChart = new LineChart();
    for (int i = 0; i < nTrials; i++) {
        // ProblemInstance problem = new ProblemInstance(n, m, k, evaluator).useRandomVecs();
        ProblemInstance problem = new ProblemInstance(n, m, k, evaluator).useVecsAroundRandomPoint();
        int ix = 0;
        for (ScoredVectorLearner learner : learners) {
            System.out.println("Testing: " + learner.getClass().getSimpleName());
            int[] p = learner.learn(problem.scoredVecs, problem.evaluator);
            // System.out.println(Arrays.toString(p));
            System.out.println("True fitness is: " + evaluator.trueFitness(p));
            stats.get(ix).add(evaluator.trueFitness(p));
            System.out.println();
            // now show evolution of fitness
            // for (double x : learner.getFitness()) {
            // System.out.println(x);
            // }
            Color color = ix++ % 2 == 0 ? Color.red : Color.blue;
            LinePlot linePlot = new LinePlot().setData(learner.getFitness()).setColor(color);
            lineChart.addLine(linePlot);
            System.out.println(learner.getFitness().length);
        }
    }
    new JEasyFrame(lineChart, "Fitness v. vectors processes");
    for (StatSummary ss : stats) System.out.println(ss);
}