Example usage for org.apache.commons.math3.linear BlockRealMatrix getData

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear BlockRealMatrix getData.

Prototype

@Override
public double[][] getData() 

Source Link

Usage

From source file:bigdataproject.PCA.java

public double[][] reduceDimensions() {
    BlockRealMatrix matrix = new BlockRealMatrix(dataSet);
    Covariance cov = new Covariance(matrix, false);
    RealMatrix covarianceMatrix = cov.getCovarianceMatrix();
    EigenDecomposition dec = new EigenDecomposition(covarianceMatrix);
    RealVector principalEigenVector = dec.getEigenvector(0);
    RealVector secondEigenVector = dec.getEigenvector(1);
    BlockRealMatrix pca = new BlockRealMatrix(principalEigenVector.getDimension(), 2);
    pca.setColumnVector(0, principalEigenVector);
    pca.setColumnVector(1, secondEigenVector);
    BlockRealMatrix pcaTranspose = pca.transpose();
    BlockRealMatrix columnVectorMatrix = matrix.transpose();
    BlockRealMatrix matrix2D = pcaTranspose.multiply(columnVectorMatrix);
    return matrix2D.getData();
}

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From source file:com.clust4j.algo.preprocess.PCA.java

@Override
public double[][] transform(double[][] data) {
    checkFit();//  w  w  w.  ja  va  2s .c  om
    MatUtils.checkDimsForUniformity(data);
    double[][] x = this.centerer.transform(data);

    // use block because it's faster for multiplication of potentially large matrices
    BlockRealMatrix X = new BlockRealMatrix(x);
    BlockRealMatrix transformed = X.multiply(this.components.transpose());

    return transformed.getData();
}

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From source file:bigdataproject.MainJFrame.java

private void jButton1ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton1ActionPerformed
    this.jLabel8.setText("");
    ReadDataSet read = new ReadDataSet();
    read.readFromFile();/* w  w  w.  j a  va  2 s.c o  m*/
    read.filter();
    matrix = read.getMatrix();
    PCA pca = new PCA(matrix);
    double[][] matrix2DPCA = pca.reduceDimensions();
    BlockRealMatrix pcaMatrix = new BlockRealMatrix(matrix2DPCA);
    BlockRealMatrix pcaMatrixTranspose = pcaMatrix.transpose();
    List<DoublePoint> list = read.getCollection(read.getHashMap(pcaMatrixTranspose.getData()));
    List<Cluster<DoublePoint>> clusterList;
    if (kMeans) {
        int k;
        if (this.jCheckBox1.isSelected()) {
            KMeansKFinder kFinder = new KMeansKFinder(list);
            k = kFinder.find(0.15);
        } else
            k = (int) this.jSpinner1.getValue();
        KMeansPlusPlusClusterer kmeans = new KMeansPlusPlusClusterer(k, 1000, new EuclideanDistance());
        clusterList = kmeans.cluster(list);
    } else {
        int minPts;
        double eps;
        if (this.jCheckBox2.isSelected()) {
            minPts = 6;
            //KDistances dist = new KDistances(pcaMatrixTranspose.getData());
            //dist.calculateDistances();
            //dist.getKSortedNearestNeighbors(minPts);
            //dist.printKdistances();
            eps = 1.0;
        } else {
            minPts = (int) this.jSpinner2.getValue();
            try {
                eps = Double.parseDouble(this.jTextField1.getText());
            } catch (NumberFormatException e) {
                this.jLabel8.setText("Wrong eps Value");
                return;
            }
        }
        DBSCANClusterer dbscan = new DBSCANClusterer(eps, minPts);
        clusterList = dbscan.cluster(list);
    }
    final ScatterPlot demo = new ScatterPlot("Big Data Clustering Project", matrix2DPCA, clusterList);
    demo.pack();
    RefineryUtilities.centerFrameOnScreen(demo);
    demo.setVisible(true);
}

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