Package com.heatonresearch.aifh.learning

Examples of com.heatonresearch.aifh.learning.RBFNetwork.reset()

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        result.getSpecies().add(defaultSpecies);

        // Create a new population of random networks.
        for (int i = 0; i < POPULATION_SIZE; i++) {
            final DoubleArrayGenome genome = new DoubleArrayGenome(size);
            network.reset(rnd);
            System.arraycopy(network.getLongTermMemory(), 0, genome.getData(), 0, size);
            defaultSpecies.add(genome);
        }

        // Set the genome factory to use the double array genome.
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        result.getSpecies().add(defaultSpecies);

        // Create a new population of random networks.
        for (int i = 0; i < POPULATION_SIZE; i++) {
            final DoubleArrayGenome genome = new DoubleArrayGenome(size);
            network.reset(rnd);
            System.arraycopy(network.getLongTermMemory(), 0, genome.getData(), 0, size);
            defaultSpecies.add(genome);
        }

        // Set the genome factory to use the double array genome.
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            istream.close();

            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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            istream.close();

            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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            istream.close();

            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);
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