Package com.heatonresearch.aifh.normalize

Examples of com.heatonresearch.aifh.normalize.DataSet

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                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            GenerateRandom rnd = new MersenneTwisterGenerateRandom();

            final DataSet ds = DataSet.load(istream);
            // The following ranges are setup for the Iris data set.  If you wish to normalize other files you will
            // need to modify the below function calls other files.
            ds.normalizeRange(0, -1, 1);
            ds.normalizeRange(1, -1, 1);
            ds.normalizeRange(2, -1, 1);
            ds.normalizeRange(3, -1, 1);
            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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                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            GenerateRandom rnd = new MersenneTwisterGenerateRandom();

            final DataSet ds = DataSet.load(istream);
            // The following ranges are setup for the Iris data set.  If you wish to normalize other files you will
            // need to modify the below function calls other files.
            ds.normalizeRange(0, -1, 1);
            ds.normalizeRange(1, -1, 1);
            ds.normalizeRange(2, -1, 1);
            ds.normalizeRange(3, -1, 1);
            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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                System.exit(1);
            }
        }

        // 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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            final InputStream istream = this.getClass().getResourceAsStream("/iris.csv");
            if( istream==null ) {
                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            final DataSet ds = DataSet.load(istream);
            // The following ranges are setup for the Iris data set.  If you wish to normalize other files you will
            // need to modify the below function calls other files.
            ds.normalizeRange(0, 0, 1);
            ds.normalizeRange(1, 0, 1);
            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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            if( istream==null ) {
                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }

            final DataSet ds = DataSet.load(istream);
            istream.close();
            final List<BasicData> observations = ds.extractUnsupervisedLabeled(4);
            final KMeans kmeans = new KMeans(3);
            kmeans.initForgy(observations);
            final int iterations = kmeans.iteration(1000);
            System.out.println("Finished after " + iterations + " iterations.");
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            final InputStream istream = this.getClass().getResourceAsStream("/iris.csv");
            if( istream==null ) {
                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            final DataSet ds = DataSet.load(istream);
            // The following ranges are setup for the Iris data set.  If you wish to normalize other files you will
            // need to modify the below function calls other files.
            ds.normalizeRange(0, 0, 1);
            ds.normalizeRange(1, 0, 1);
            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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            final InputStream istream = this.getClass().getResourceAsStream("/iris.csv");
            if( istream==null ) {
                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            final DataSet ds = DataSet.load(istream);
            // The following ranges are setup for the Iris data set.  If you wish to normalize other files you will
            // need to modify the below function calls other files.
            ds.normalizeRange(0, 0, 1);
            ds.normalizeRange(1, 0, 1);
            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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            final InputStream istream = this.getClass().getResourceAsStream("/iris.csv");
            if( istream==null ) {
                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            final DataSet ds = DataSet.load(istream);
            // The following ranges are setup for the Iris data set.  If you wish to normalize other files you will
            // need to modify the below function calls other files.
            ds.normalizeRange(0, 0, 1);
            ds.normalizeRange(1, 0, 1);
            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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            final InputStream istream = this.getClass().getResourceAsStream("/breast-cancer-wisconsin.csv");
            if( istream==null ) {
                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            final DataSet ds = DataSet.load(istream);
            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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            final InputStream istream = this.getClass().getResourceAsStream("/abalone.csv");
            if( istream==null ) {
                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            final DataSet ds = DataSet.load(istream);
            // 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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Related Classes of com.heatonresearch.aifh.normalize.DataSet

  • au.com.bytecode.opencsv.CSVReader
  • au.com.bytecode.opencsv.CSVWriter
  • com.heatonresearch.aifh.AIFHError
  • com.heatonresearch.aifh.examples.aco.ExampleTrainIrisACO
  • com.heatonresearch.aifh.examples.ga.iris.ModelIris
  • com.heatonresearch.aifh.examples.ga.iris.ModelSpeciationIris
  • com.heatonresearch.aifh.examples.gp.FindEquation
  • com.heatonresearch.aifh.examples.kmeans.PerformCluster
  • com.heatonresearch.aifh.examples.learning.LearnIris
  • com.heatonresearch.aifh.examples.normalize.NormalizeCSVExample
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