it.poliba.sisinflab.simlib.featureSelection.methods.PCA.java Source code

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Here is the source code for it.poliba.sisinflab.simlib.featureSelection.methods.PCA.java

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/*
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package it.poliba.sisinflab.simlib.featureSelection.methods;

/**
 *
 * @author Corrado
 */
import java.io.File;
import java.io.PrintStream;
import java.util.Arrays;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.attributeSelection.AttributeSelection;
import weka.attributeSelection.PrincipalComponents;
import weka.attributeSelection.Ranker;

/**
 * This class use the featureSelection libary to implement Principal Components Analysis.
 */
public class PCA {

    public void execute(String dataset) {
        try {

            if (dataset.length() == 0)
                throw new IllegalArgumentException();
            // Load input dataset.
            DataSource source = new DataSource(dataset);
            Instances data = source.getDataSet();

            // Performs a principal components analysis.
            PrincipalComponents pcaEvaluator = new PrincipalComponents();

            // Sets the amount of variance to account for when retaining principal
            // components.
            pcaEvaluator.setVarianceCovered(1.0);
            // Sets maximum number of attributes to include in transformed attribute
            // names.
            pcaEvaluator.setMaximumAttributeNames(-1);

            // Scaled X such that the variance of each feature is 1.
            boolean scale = true;
            if (scale) {
                pcaEvaluator.setCenterData(true);
            } else {
                pcaEvaluator.setCenterData(false);
            }

            // Ranking the attributes.
            Ranker ranker = new Ranker();

            ranker.setNumToSelect(-1);

            AttributeSelection selector = new AttributeSelection();
            selector.setSearch(ranker);
            selector.setEvaluator(pcaEvaluator);
            selector.SelectAttributes(data);

            // Transform data into eigenvector basis.
            Instances transformedData = selector.reduceDimensionality(data);
            PrintStream o = new PrintStream(new File("data/" + "PCAResults" + ".txt"));
            System.setOut(o);
            System.out.println(Arrays.toString(selector.rankedAttributes()));
            System.out.println(Arrays.toString(selector.selectedAttributes()));
            //System.out.println(selector.CVResultsString());
            System.out.println(selector.toResultsString());

            System.out.println();

        } catch (IllegalArgumentException e) {
            System.err.println("Error");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}