Package ch.idsia.tools

Examples of ch.idsia.tools.Evaluator


        options.setMaxFPS(true);
        options.setLevelDifficulty(level);
        options.setPauseWorld(false);
        agent.reset();
        options.setAgent(agent);
        Evaluator evaluator = new Evaluator (options);
        EvaluationInfo result = evaluator.evaluate().get(0);
       // System.out.print(".");
        double score = result.computeDistancePassed();
         System.out.println("score: " +score);
        return score;
    }
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        options.setMaxFPS(false);
        options.setLevelDifficulty(level);
        options.setPauseWorld(false);
        agent.reset();
        options.setAgent(agent);
        Evaluator evaluator = new Evaluator (options);
        EvaluationInfo result = evaluator.evaluate().get(0);
       // System.out.print(".");
        double score = result.computeDistancePassed();
         System.out.println("score: " +score);
        return score;
    }
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        StatisticalSummary ss = new StatisticalSummary ();
        for (int i = 0; i < numberOfTrials; i++) {
            options.setLevelRandSeed(seed + i);
            controller.reset();
            options.setAgent(controller);
            Evaluator evaluator = new Evaluator (options);
            EvaluationInfo result = evaluator.evaluate().get(0);
      System.out.printf("  map seed %d diff %2d -> %f (%f ms/frame)\n", seed+i, level, result.computeDistancePassed(), controller.averageTimeTaken());
            ss.add (result.computeDistancePassed());
        }
        return ss;
    }
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public class CustomRun
{
    public static void main(String[] args) {
        new ForwardAgent(); // default agent to be evalutated. It is registered automatically in system.
        CmdLineOptions options = new CmdLineOptions(args);
        Evaluator evaluator = new Evaluator(options);
        evaluator.evaluate();               
    }
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        StatisticalSummary ss = new StatisticalSummary ();
        for (int i = 0; i < numberOfTrials; i++) {
            options.setLevelRandSeed(seed + i);
            controller.reset();
            options.setAgent(controller);
            Evaluator evaluator = new Evaluator (options);
            EvaluationInfo result = evaluator.evaluate().get(0);
            ss.add (result.computeDistancePassed());
        }
        return ss;
    }
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        for (int i = 0; i < difficulties.length; i++) {
            controller.reset();
            options.setLevelRandSeed(startingSeed);
            options.setLevelDifficulty(difficulties[i]);
            options.setAgent(controller);
            Evaluator evaluator = new Evaluator(options);
            List<EvaluationInfo> results = evaluator.evaluate();
            EvaluationInfo result = results.get(0);
            double thisDistance = result.computeDistancePassed();
            fitnesses[i + 1] = thisDistance;
            distanceTravelled += thisDistance;
        }
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        options.setAgent(controller);
        for (int i = 0; i < numberOfSeeds; i++) {
            controller.reset();
            options.setLevelRandSeed(startingSeed + i);
            Evaluator evaluator = new Evaluator(options);
            List<EvaluationInfo> results = evaluator.evaluate();    
            EvaluationInfo result = results.get(0);
            distanceTravelled += result.computeDistancePassed();
        }
        distanceTravelled = distanceTravelled / numberOfSeeds;
        return new double[]{distanceTravelled};
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        double distanceTravelled = 0;
        controller.reset();
        options.setAgent(controller);
        for (int i = 0; i < numberOfSeeds; i++) {
            options.setLevelRandSeed(startingSeed + i);
            Evaluator evaluator = new Evaluator(options);
            List<EvaluationInfo> results = evaluator.evaluate();
            EvaluationInfo result = results.get(0);
            distanceTravelled += result.computeDistancePassed();
        }
        distanceTravelled = distanceTravelled / numberOfSeeds;
        return new double[]{distanceTravelled};
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    public double[] evaluate(Agent controller) {
        double distanceTravelled = 0;
//        controller.reset();
        options.setAgent(controller);
        Evaluator evaluator = new Evaluator(options);
        List<EvaluationInfo> results = evaluator.evaluate();
        for (EvaluationInfo result : results) {
            //if (result.marioStatus == Mario.STATUS_WIN )
            //    Easy.save(options.getAgent(), options.getAgent().getName() + ".xml");
            distanceTravelled += result.computeDistancePassed();
        }
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{
    public static void main(String[] args) {
        CmdLineOptions cmdLineOptions = new CmdLineOptions(args);
        EvaluationOptions evaluationOptions = cmdLineOptions;  // if none options mentioned, all defalults are used.
        createNativeAgents(cmdLineOptions);
        Evaluator evaluator = new Evaluator(evaluationOptions);
        List<EvaluationInfo> evaluationSummary = evaluator.evaluate();
//        LOGGER.save("log.txt");

        if (cmdLineOptions.isExitProgramWhenFinished())
            System.exit(0);
    }
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