Package com.heatonresearch.aifh.learning.score

Examples of com.heatonresearch.aifh.learning.score.ScoreRegressionData

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            final List<BasicData> trainingData = ds.extractSupervised(0,
                    codec.getInputCount(), codec.getRbfCount(), codec.getOutputCount());

            Population pop = initPopulation(rnd, codec);

            ScoreFunction score = new ScoreRegressionData(trainingData);

            BasicEA genetic = new BasicEA(pop, score);
            genetic.setSpeciation(new ArraySpeciation<DoubleArrayGenome>());
            genetic.setCODEC(codec);
            genetic.addOperation(0.7, new Splice(codec.size() / 5));
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            final List<BasicData> trainingData = ds.extractSupervised(0,
                    codec.getInputCount(), 4, codec.getOutputCount());

            Population pop = initPopulation(rnd, codec);

            ScoreFunction score = new ScoreRegressionData(trainingData);

            BasicEA genetic = new BasicEA(pop, score);
            genetic.setCODEC(codec);
            genetic.addOperation(0.7, new Splice(codec.size() / 3));
            genetic.addOperation(0.3, new MutatePerturb(0.1));
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            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 2);

            final RBFNetwork network = new RBFNetwork(4, 4, 2);
            network.reset(new MersenneTwisterGenerateRandom());
            final ScoreFunction score = new ScoreRegressionData(trainingData);
            final TrainNelderMead train = new TrainNelderMead(network, score);
            performIterations(train, 1000, 0.01, true);
            queryEquilateral(network, trainingData, species, 0, 1);

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     * Run the example.
     */
    public void process() {
        final List<BasicData> trainingData = generateTrainingData();
        final PolynomialFn poly = new PolynomialFn(3);
        final ScoreFunction score = new ScoreRegressionData(trainingData);
        final TrainGreedyRandom train = new TrainGreedyRandom(true, poly, score);
        performIterations(train, 1000000, 0.01, true);
        System.out.println(poly.toString());
    }
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            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 2);

            final RBFNetwork network = new RBFNetwork(4, 4, 2);
            network.reset(new MersenneTwisterGenerateRandom());
            final ScoreFunction score = new ScoreRegressionData(trainingData);
            final TrainHillClimb train = new TrainHillClimb(true, network, score);
            performIterations(train, 100000, 0.01, true);
            queryEquilateral(network, trainingData, species, 0, 1);

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     * Perform the example.
     */
    public void process() {
        final List<BasicData> trainingData = BasicData.convertArrays(XOR_INPUT, XOR_IDEAL);
        final RBFNetwork network = new RBFNetwork(2, 5, 1);
        final ScoreFunction score = new ScoreRegressionData(trainingData);
        final TrainGreedyRandom train = new TrainGreedyRandom(true, network, score);
        performIterations(train, 1000000, 0.01, true);
        query(network, trainingData);
    }
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            istream.close();

            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 2);

            final RBFNetwork network = new RBFNetwork(4, 4, 2);
            final ScoreFunction score = new ScoreRegressionData(trainingData);
            final TrainGreedyRandom train = new TrainGreedyRandom(true, network, score);
            performIterations(train, 100000, 0.01, true);
            queryEquilateral(network, trainingData, species, 0, 1);

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            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 3);

            final RBFNetwork network = new RBFNetwork(4, 4, 3);
            network.reset(new MersenneTwisterGenerateRandom());

            final ScoreFunction score = new ScoreRegressionData(trainingData);
            final TrainAnneal train = new TrainAnneal(network, score);
            performIterations(train, 100000, 0.01, true);
            queryOneOfN(network, trainingData, species);
            System.out.println(Arrays.toString(network.getLongTermMemory()));
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                particles[i].reset(rnd);
            }

            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 3);

            ScoreFunction score = new ScoreRegressionData(trainingData);

            TrainPSO train = new TrainPSO(particles, score);

            performIterations(train, 100000, 0.05, true);
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            RBFNetwork network = new RBFNetwork(4, 4, 3);

            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 3);

            ScoreFunction score = new ScoreRegressionData(trainingData);

            ContinuousACO train = new ContinuousACO(network, score, 30);

            performIterations(train, 100000, 0.05, true);
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Related Classes of com.heatonresearch.aifh.learning.score.ScoreRegressionData

  • com.heatonresearch.aifh.error.ErrorCalculation
  • com.heatonresearch.aifh.examples.aco.ExampleTrainIrisACO
  • com.heatonresearch.aifh.examples.ga.iris.ModelIris
  • com.heatonresearch.aifh.examples.ga.iris.ModelSpeciationIris
  • com.heatonresearch.aifh.examples.learning.LearnIris
  • com.heatonresearch.aifh.examples.learning.LearnPolynomial
  • com.heatonresearch.aifh.examples.learning.LearnXOR
  • com.heatonresearch.aifh.examples.optimization.LearnIrisAnneal
  • com.heatonresearch.aifh.examples.optimization.LearnIrisClimb
  • com.heatonresearch.aifh.examples.optimization.LearnIrisNelderMead
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