Examples with LineChart plot.LineChart used on opensource projects

Example 6 with LineChart

use of plot.LineChart in project SimpleAsteroids by ljialin.

the class HyperParamTuneRunner method plotConvergence.

private void plotConvergence(EvolutionLogger logger, int[] solution) {
    LineChart lineChart = new LineChart().setBG(Color.gray);
    lineChart.plotBG = Color.white;
    // now the plots we will add are as follows
    ArrayList<Double> bestMatches = new ArrayList<>();
    ArrayList<Double> bestCumulative = new ArrayList<>();
    ArrayList<Double> sampleMatches = new ArrayList<>();
    ArrayList<Double> sampleCumulative = new ArrayList<>();
    StatSummary cumul = new StatSummary();
    for (int[] best : logger.bestYetSolutions) {
        double x = match(best, solution);
        bestMatches.add(x);
        cumul.add(x);
        bestCumulative.add(cumul.mean());
    }
    StatSummary sample = new StatSummary();
    for (int[] sol : logger.solutions) {
        double x = match(sol, solution);
        sampleMatches.add(x);
        sample.add(x);
        sampleCumulative.add(sample.mean());
    }
    // LinePlot matchPlot = new LinePlot().
    LineGroup lgMatch = new LineGroup().setName("Best Match").setColor(Color.red).add(bestMatches);
    LineGroup lgCumul = new LineGroup().setName("Best Cumul").setColor(Color.black).add(bestCumulative);
    lineChart.addLineGroup(lgMatch);
    lineChart.addLineGroup(lgCumul);
    LineGroup lgSampleMatch = new LineGroup().setName("Sample Match").setColor(Color.green).add(sampleMatches);
    LineGroup lgSampleCumul = new LineGroup().setName("Sample Cumul").setColor(Color.magenta).add(sampleCumulative);
    lineChart.addLineGroup(lgSampleMatch);
    lineChart.addLineGroup(lgSampleCumul);
    lineChart.setXLabel("Iteration");
    lineChart.setYLabel("Candidate == solution");
    lineChart.yAxis = new LineChartAxis(new double[] { 0, 1 });
    lineChart.xAxis = new LineChartAxis(new double[] { 0, bestMatches.size() / 2, bestMatches.size() });
    new JEasyFrame(lineChart, "NTBEA Best Guess Convergence");
}

Example 7 with LineChart

use of plot.LineChart in project SimpleAsteroids by ljialin.

the class EvolveMarioLevelTest method plotData.

private void plotData(ArrayList<Double> data) {
    LineChart lineChart = LineChart.easyPlot(data);
    int mid = (data.size() - 1) / 2;
    int end = data.size() - 2;
    System.out.println("Endpoint: " + end);
    lineChart.xAxis = new LineChartAxis(new double[] { 0, mid, end });
    StatSummary ss = new StatSummary().add(data);
    lineChart.yAxis = new LineChartAxis(new double[] { ss.min(), ss.max() });
    lineChart.title = "Evolution of Fitness";
    lineChart.setXLabel("Iterations").setYLabel("Fitness");
    new JEasyFrame(lineChart, "KL-Based PCG");
}

Example 8 with LineChart

use of plot.LineChart 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);
}

Example 9 with LineChart

use of plot.LineChart in project SimpleAsteroids by ljialin.

the class GameActionSpaceAdapterMulti method evaluate.

@Override
public double evaluate(int[] actions) {
    // take a copy of the current game state and accumulate the score as we go along
    // System.out.println("Checking action length: " + actions.length + " : " + sequenceLength);
    // System.out.println("PLayer id: " + playerID);
    StateObservationMulti obs = stateObservation.copy();
    // note the score now - for normalisation reasons
    // we wish to track the change in score, not the absolute score
    double initScore = obs.getGameScore(playerID);
    double discount = 1.0;
    double denom = 0;
    double discountedTot = 0;
    double total = 0;
    // need to do the visual stuff here ...
    LinePlot linePlot = null;
    if (visual) {
        if (lineChart == null) {
            lineChart = new LineChart().setBG(Color.gray);
            lineChart.xAxis = new LineChartAxis(new double[] { 0, sequenceLength / 2, sequenceLength });
            lineChart.yAxis = new LineChartAxis(new double[] { -50, -25, 0, 25, 50 });
            lineChart.plotBG = Color.white;
            lineChart.setYLabel("Score");
            lineChart.setXLabel("Rollout depth");
            frame = new JEasyFrame(lineChart, "Score versus depth");
        }
        float grey = (nEvals % 100) / 150.0f;
        // add in a zero for the first element of the plot, since there
        // will be zero difference before any action has been taken
        linePlot = new LinePlot().setColor(new Color(grey, grey, grey));
    // linePlot = new LinePlot().setColor(Color.red);
    }
    // deltas.add(0);
    for (int i = 0; i < actions.length; i++) {
        // Note here that we need to look at the advance method which takes multiple players
        // hence an array of actions
        // the idea is that we'll pad out the
        int myAction = actions[i];
        int opAction = random.nextInt(obs.getAvailableActions(opponentID).size());
        // opAction = AsteroidsGameState.doNothing;
        Types.ACTIONS[] acts = new Types.ACTIONS[2];
        acts[playerID] = gvgaiActions[myAction];
        acts[opponentID] = gvgaiActions[opAction];
        for (int k = 0; k < actionRepeat; k++) {
            obs.advance(acts);
        }
        discountedTot += discount * (obs.getGameScore(playerID) - initScore);
        if (useHeuristic && obs instanceof SpaceBattleLinkStateTwoPlayer) {
            SpaceBattleLinkStateTwoPlayer state = (SpaceBattleLinkStateTwoPlayer) obs;
            discountedTot += state.getHeuristicScore();
        }
        denom += discount;
        discount *= discountFactor;
        if (linePlot != null) {
            // linePlot.add(discountedTot);
            double delta = obs.getGameScore((playerID)) - initScore;
            linePlot.add(delta);
            deltas.add(delta);
        }
    }
    if (visual) {
        linePlots.add(linePlot);
    }
    nEvals++;
    double delta;
    if (useDiscountFactor) {
        delta = discountedTot / denom;
    } else {
        delta = obs.getGameScore(playerID) - initScore;
    }
    delta += noiseLevel * random.nextGaussian();
    logger.log(delta, actions, false);
    return delta;
}

Example 10 with LineChart

use of plot.LineChart in project SimpleAsteroids by ljialin.

the class TestEASimple method main.

public static void main(String[] args) {
    for (int w = 30; w <= 30; w = w + 10) {
        outputName = "data/noisefree_w" + w + ".dat";
        File f = new File(outputName);
        if (f.exists() && !f.isDirectory()) {
            f.delete();
        }
        // run configuration for an experiment
        // todo need an easy and general way to log the best solution yet
        // just add a new log method to the logger
        useFirstHit = false;
        // DefaultMutator.flipAtLeastOneValueDefault = true;
        // DefaultMutator.defaultPointProb = 1.0;
        // select which one to use
        // solutionEvaluator = new EvalMaxM(nDims, mValues, noise);
        solutionEvaluator = new EvalNoisyWinRate(nDims, mValues, noise);
        // solutionEvaluator = new Eval2DNonLinear(8, noise);
        System.out.println("Running experiment with following settings:");
        System.out.println("Solution evaluator: " + solutionEvaluator.getClass());
        System.out.format("Use first hitting time    :\t %s\n", useFirstHit);
        System.out.format("RMHC: (flip at least one) :\t %s\n", DefaultMutator.flipAtLeastOneValueDefault);
        System.out.format("Point mutation probability:\t %.4f\n", DefaultMutator.defaultPointProb / nDims);
        ElapsedTimer t = new ElapsedTimer();
        // StatSummary nt = testNTupleBanditEA(nTrialsNTupleBanditEA);
        // System.out.println(t);
        // DefaultMutator.totalRandomChaosMutation = true;
        int defaultK = 2000;
        CompactBinaryGA cga = new CompactBinaryGA(defaultK);
        cga.nParents = 10;
        // each time we run a test, we want to get
        // the way the fitness evolves over time
        // hence we need to get a list of arrays for each experiment
        // have an alpha less than 1 to be able to spot overlapping lines more easily
        float alpha = 0.8f;
        // lineColor = new Color(1f, 1f, 0, alpha);
        // testEvoAlg(new SimpleRMHC());
        // lineColor = new Color(0f, 1f, 1, alpha);
        // testEvoAlg(cga);
        // 
        // lineColor = new Color(1f, 0f, 1, alpha);
        // testEvoAlg(new CompactSlidingGA(2000).setHistoryLength(w));
        // lineColor = Color.getHSBColor(0.7f, 1, 1);
        // lineColor = Color.red;
        // testEvoAlg(new CompactSlidingModelGA().setHistoryLength(w));
        // lineColor = Color.getHSBColor(0.5f, 1, 1);
        lineColor = Color.green;
        // testEvoAlg(new NTupleBanditEA());
        // lineColor = Color.getHSBColor(0.3f, 1, 1);
        lineColor = Color.blue;
        testEvoAlg(new SlidingMeanEDA().setHistoryLength(w));
        // testBanditEA();
        System.out.println(t);
        LineChart lineChart = new LineChart();
        for (LinePlot line : linePlots) {
            lineChart.addLine(line);
        }
        // add a linear plot
        // LinePlot linear = new LinePlot();
        // linear.setColor(Color.white);
        // for (int i = 0; i < nDims; i++)
        // linear.add(solutionEvaluator.optimalIfKnown()  * (0.5 + i / (2.0 * nDims)));
        // lineChart.addLine(linear);
        new JEasyFrame(lineChart, "Evolution Traces");
    }
}