Package com.heatonresearch.aifh.normalize

Examples of com.heatonresearch.aifh.normalize.DataSet

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                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();

            RBFNetwork[] particles = new RBFNetwork[PARTICLE_COUNT];
            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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            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, -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();

            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);
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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);
            istream.close();

            // 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);
            ds.encodeEquilateral(4);
            final File outputFile = new File("normalized.csv");
            DataSet.save(outputFile, ds);
            System.out.println("Output written to: " + outputFile.getAbsolutePath());
        } catch (Exception ex) {
            ex.printStackTrace();
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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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