Examples with LinePlot plot.LinePlot used on opensource projects

Example 1 with LinePlot

use of plot.LinePlot in project SimpleAsteroids by ljialin.

the class GameRunner method plotGameScores.

public LineChart plotGameScores() {
    LineChart lineChart = new LineChart();
    for (GameLog gameLog : gameLogs) {
        LinePlot linePlot = new LinePlot().setData(gameLog.scores).setRandomColor();
        lineChart.addLine(linePlot);
    }
    lineChart.xAxis = new LineChartAxis(new double[] { 0, nSteps / 2, nSteps });
    lineChart.setXLabel("Game Tick");
    lineChart.setYLabel("Score");
    String title = String.format("%s (%d) v. %s (%d)", p1.toString(), p1Wins, p2.toString(), p2Wins);
    lineChart.setTitle(title);
    lineChart.bg = Color.gray;
    lineChart.plotBG = Color.white;
    double[] scoreTics = new double[] { scores.min(), 0, scores.max() };
    // double[] scoreTics = new  double[]{-100, 0, 1000, 2000 }; // scores.max()};
    lineChart.yAxis = new LineChartAxis(scoreTics);
    new JEasyFrame(lineChart, "Game Scores");
    return lineChart;
}

Example 2 with LinePlot

use of plot.LinePlot in project SimpleAsteroids by ljialin.

the class SortedBernoulliTest method main.

public static void main(String[] args) {
    LineChart lineChart = new LineChart();
    LinePlot linePlot = new LinePlot();
    LineGroup lineGroup = new LineGroup();
    int nTrials = 100;
    int nGroups = 288;
    double pWin = 0.5;
    ArrayList<StatSummary> stats = new ArrayList<>();
    for (int i = 0; i < nGroups; i++) {
        StatSummary ss = new StatSummary();
        stats.add(ss);
        for (int j = 0; j < nTrials; j++) {
            int result = Math.random() < pWin ? 1 : -1;
            ss.add(result);
        }
    }
    Collections.sort(stats);
    lineGroup.stats = stats;
    lineChart.addLineGroup(lineGroup);
    lineChart.bg = Color.gray;
    lineChart.plotBG = Color.white;
    lineChart.setTitle("Sorted Coin Toss Trials, n=100, p=0.5, error bars 1 std-err");
    lineChart.setYLabel("Fitness");
    lineChart.setXLabel("Trial");
    lineChart.stdErrs = 1.0;
    lineChart.xAxis = new LineChartAxis(new double[] { 0, 144, 288 });
    lineChart.yAxis = new LineChartAxis(new double[] { -1, 0, 0.8 });
    new JEasyFrame(lineChart, "Coin toss");
}

Example 3 with LinePlot

use of plot.LinePlot in project SimpleAsteroids by ljialin.

the class GameActionSpaceAdapter method evaluate.

@Override
public double evaluate(int[] actions) {
    // take a copy of the current game state and accumulate the score as we go along
    StateObservation 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();
    double discount = 1.0;
    double denom = 0;
    double discountedTot = 0;
    // need to do the visual stuff here ...
    LinePlot linePlot = null;
    if (visual) {
        float grey = (nEvals % 100) / 100;
        linePlot = new LinePlot().setColor(new Color(grey, grey, grey));
    }
    for (int i = 0; i < sequenceLength; i++) {
        obs.advance(gvgaiActions[actions[i]]);
        discountedTot += discount * (obs.getGameScore() - initScore);
        if (useHeuristic && obs instanceof SpaceBattleLinkState) {
            SpaceBattleLinkState state = (SpaceBattleLinkState) obs;
            discountedTot += state.getHeuristicScore();
        }
        denom += discount;
        discount *= discountFactor;
        if (linePlot != null) {
            linePlot.add(discountedTot + Math.random() * 0);
        }
    }
    if (visual) {
        linePlots.add(linePlot);
    }
    nEvals++;
    double delta;
    if (useDiscountFactor) {
        delta = discountedTot / denom;
    } else {
        delta = obs.getGameScore() - initScore;
    }
    delta += noiseLevel * random.nextGaussian();
    logger.log(delta, actions, false);
    return delta;
}

Example 4 with LinePlot

use of plot.LinePlot in project SimpleAsteroids by ljialin.

the class TestEASimple method runTrial.

static double runTrial(EvoAlg ea, StatSummary nTrueOpt, StatSummary trueFit) {
    // grab from static var for now
    NoisySolutionEvaluator evaluator = solutionEvaluator;
    evaluator.reset();
    int[] solution = ea.runTrial(evaluator, nFitnessEvals);
    // System.out.println("Solution: " + Arrays.toString(solution) + " : " + solutionEvaluator.trueFitness(solution));
    trueFit.add(solutionEvaluator.trueFitness(solution));
    // linePlots.add(new LinePlot().setData(solutionEvaluator.logger().fa).setColor(lineColor));
    // ok, instead look at the true fitnesses of the evaluated solutions
    ArrayList<Double> noiseFree = new ArrayList<>();
    // System.out.println("Best yet solutions length: " + solutionEvaluator.logger().bestYetSolutions.size());
    for (int[] p : solutionEvaluator.logger().bestYetSolutions) {
        noiseFree.add(solutionEvaluator.trueFitness(p));
    }
    // TODO: 23/06/2017 save data
    try {
        FileWriter fw = new FileWriter(outputName, true);
        BufferedWriter bw = new BufferedWriter(fw);
        PrintWriter out = new PrintWriter(bw);
        for (int i = 0; i < noiseFree.size(); i++) {
            out.print(noiseFree.get(i) + " ");
        }
        out.print("\n");
        out.close();
    } catch (Exception e) {
        e.printStackTrace();
        System.out.println("No such file exists.");
    }
    linePlots.add(new LinePlot().setData(noiseFree).setColor(lineColor));
    if (useFirstHit && evaluator.logger().firstHit != null) {
        // System.out.println("Optimal first hit?: " + evaluator.logger().firstHit);
        nTrueOpt.add(evaluator.logger().firstHit);
    } else if (evaluator.isOptimal(solution)) {
        nTrueOpt.add(1);
    }
    return solutionEvaluator.trueFitness(solution);
}

Example 5 with LinePlot

use of plot.LinePlot 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);
}