List of extractSupervised() Examples

Examples of extractSupervised()

com.heatonresearch.aifh.normalize.DataSet.extractSupervised()
Extract a supervised training set. This has both input and expected (ideal) output. @param inputBegin The first input column. @param inputCount The number of columns for input. @param idealBegin The first ideal column. @param idealCount The number of columns for ideal. @return The training set.

Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            final Map<String, Integer> species = ds.encodeOneOfN(4);
            istream.close();


            final RBFNetworkGenomeCODEC codec = new RBFNetworkGenomeCODEC(4, 4, 3);


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


            Population pop = initPopulation(rnd, codec);


            ScoreFunction score = new ScoreRegressionData(trainingData);

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            final Map<String, Integer> species = ds.encodeOneOfN(4);
            istream.close();


            final RBFNetworkGenomeCODEC codec = new RBFNetworkGenomeCODEC(4, RBF_COUNT, 3);


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


            Population pop = initPopulation(rnd, codec);


            ScoreFunction score = new ScoreRegressionData(trainingData);

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

        }


        // Load the file and obtain training data.
        final DataSet ds = DataSet.load(istream);
        // Extract supervised training.
        List<BasicData> training = ds.extractSupervised(0, 1, 1, 1);




        GenerateRandom rnd = new MersenneTwisterGenerateRandom();
        EvaluateExpression eval = new EvaluateExpression(rnd);
        Population pop = initPopulation(rnd, eval);

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            ds.normalizeRange(2, 0, 1);
            ds.normalizeRange(3, 0, 1);
            final Map<String, Integer> species = ds.encodeEquilateral(4);
            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);

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            ds.normalizeRange(2, 0, 1);
            ds.normalizeRange(3, 0, 1);
            final Map<String, Integer> species = ds.encodeEquilateral(4);
            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);

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            ds.normalizeRange(2, 0, 1);
            ds.normalizeRange(3, 0, 1);
            final Map<String, Integer> species = ds.encodeEquilateral(4);
            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);

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            ds.normalizeRange(2, 0, 1);
            ds.normalizeRange(3, 0, 1);
            final Map<String, Integer> species = ds.encodeOneOfN(4);
            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);

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            istream.close();


            ds.deleteUnknowns();
            ds.deleteColumn(0);
            ds.replaceColumn(9, 4, 1, 0);
            final List<BasicData> trainingData = ds.extractSupervised(0, 9, 9, 1);


            final MultipleLinearRegression reg = new MultipleLinearRegression(9);
            reg.setLinkFunction(new LogitLinkFunction());
            final TrainReweightLeastSquares train = new TrainReweightLeastSquares(reg, trainingData);

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            // The following ranges are setup for the Abalone data set.  If you wish to normalize other files you will
            // need to modify the below function calls other files.
            ds.encodeOneOfN(0, 0, 1);
            istream.close();


            final List<BasicData> trainingData = ds.extractSupervised(0, 10, 10, 1);


            final MultipleLinearRegression reg = new MultipleLinearRegression(10);
            final TrainLeastSquares train = new TrainLeastSquares(reg, trainingData);
            train.iteration();
            System.out.println(Arrays.toString(reg.getLongTermMemory()));

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Examples of com.heatonresearch.aifh.normalize.DataSet.extractSupervised()

            for (int i = 0; i < particles.length; i++) {
                particles[i] = new RBFNetwork(4, 4, 3);
                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);

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