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

Introduction

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

Prototype

public BlockRealMatrix(final double[][] rawData)
        throws DimensionMismatchException, NotStrictlyPositiveException 

Source Link

Document

Create a new dense matrix copying entries from raw layout data.

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:bigdataproject.KDistances.java

public void calculateDistances() {
    for (int i = 0; i < samples.length; i++) {
        double[] point = samples[i];
        for (int j = i + 1; j < samples.length; j++) {
            double[] point2 = samples[j];
            EuclideanDistance dist = new EuclideanDistance();
            distanceMatrix[i][j] = dist.compute(point, point2);
        }// w  w w .  j a  v a  2s.  c  o  m
    }
    BlockRealMatrix dist = new BlockRealMatrix(distanceMatrix);
    BlockRealMatrix distTranspose = dist.transpose();
    dist = dist.add(distTranspose);
    distanceMatrix = dist.getData();
}

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From source file:gamlss.utilities.MatrixFunctions.java

/**
 * if the length of the return matrix is zero,
 *  then the diff was not completed properly.
 *  @param data - matrix/* ww w.j  av  a 2 s  .co  m*/
 *  @return - matrix
 */
private static BlockRealMatrix getDiff(final BlockRealMatrix data) {
    double[][] newdata = new double[data.getRowDimension() - 1][data.getColumnDimension()];
    for (int i = 0; i < newdata.length; i++) {
        for (int j = 0; j < newdata[i].length; j++) {
            newdata[i][j] = data.getEntry(i + 1, j) - data.getEntry(i, j);
        }
    }
    return new BlockRealMatrix(newdata);
}

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From source file:edu.macalester.tagrelatedness.KendallsCorrelation.java

/**
 * Computes the Kendall's Tau rank correlation matrix for the columns of
 * the input rectangular array.  The columns of the array represent values
 * of variables to be correlated./*from  ww  w .  j ava 2 s.c o m*/
 *
 * @param matrix matrix with columns representing variables to correlate
 * @return correlation matrix
 */
static public RealMatrix computeCorrelationMatrix(final double[][] matrix) {
    return computeCorrelationMatrix(new BlockRealMatrix(matrix));
}

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From source file:edu.cmu.tetrad.search.EstimateRank.java

public double[] CanCor(double[][] A, double[][] B) {
    this.A = A;//from   w  w w .  j av  a 2 s  . c o  m
    this.B = B;
    RealMatrix Ua = new SingularValueDecomposition(new BlockRealMatrix(A)).getU();
    RealMatrix UTa = Ua.transpose();
    RealMatrix Ub = new SingularValueDecomposition(new BlockRealMatrix(B)).getU();
    return new SingularValueDecomposition(UTa.multiply(Ub)).getSingularValues();
}

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From source file:com.itemanalysis.psychometrics.mixture.MvNormalComponentDistributionTest.java

/**
 * This test uses the iris data from R. It is a three group mixture model.
 * Results confirmed with the following R code:
 *
 * library(mixtools)/*from  w  w w. java2  s. c  om*/
 * mvnormalmixEM(iris[,-5], arbvar=TRUE, k=3)
 *
 */
//    @Test
public void mixModel1Test() {
    System.out.println("Mixture model test with Iris Data");
    MixtureModelTestData testData = new MixtureModelTestData();

    BlockRealMatrix allData = new BlockRealMatrix(testData.getMixtureData3());
    BlockRealMatrix group1Data = allData.getSubMatrix(0, 49, 0, 3);
    BlockRealMatrix group2Data = allData.getSubMatrix(50, 99, 0, 3);
    BlockRealMatrix group3Data = allData.getSubMatrix(100, 149, 0, 3);
    BlockRealMatrix data = allData.getSubMatrix(0, 149, 0, 3);

    //        System.out.println(Arrays.toString(allData.getRow(0)));

    VectorialMean trueMean1 = new VectorialMean(4);
    VectorialMean trueMean2 = new VectorialMean(4);
    VectorialMean trueMean3 = new VectorialMean(4);
    Covariance trueCov1 = new Covariance(group1Data);
    Covariance trueCov2 = new Covariance(group2Data);
    Covariance trueCov3 = new Covariance(group3Data);

    for (int i = 0; i < group1Data.getRowDimension(); i++) {
        trueMean1.increment(group1Data.getRow(i));
    }
    for (int i = 0; i < group2Data.getRowDimension(); i++) {
        trueMean2.increment(group2Data.getRow(i));
    }

    for (int i = 0; i < group3Data.getRowDimension(); i++) {
        trueMean3.increment(group3Data.getRow(i));
    }

    double[] trueMeanGroup1 = trueMean1.getResult();
    double[] trueMeanGroup2 = trueMean2.getResult();
    double[] trueMeanGroup3 = trueMean3.getResult();

    int startGroup = 3;
    int endGroup = 3;
    int maxIter = 1000;
    double converge = 1e-8;
    int starts = 100;

    MvNormalMixtureModel model = null;

    model = new MvNormalMixtureModel(data, endGroup);
    model.setModelConstraints(false, false, false, false);
    model.setEmOptions(maxIter, converge, starts);
    model.call();

    System.out.println("    Testing convergence...");
    assertTrue("Convergence test", model.converged());

    double[] pi = new double[3];
    pi[0] = model.getMixingProportion(0);
    pi[1] = model.getMixingProportion(1);
    pi[2] = model.getMixingProportion(2);

    //check mixing proportions
    //            System.out.println("    Testing proportions...");
    //            assertEquals("Proportion 1 test: ", 0.40, pi[index[0]], 5e-2);
    //            assertEquals("Proportion 2 test: ", 0.60, pi[index[1]], 5e-2);

    double[] estMean1 = model.getMean(0);
    double[] estMean2 = model.getMean(1);
    double[] estMean3 = model.getMean(2);
    double[][] estCov1 = model.getCov(0);
    double[][] estCov2 = model.getCov(1);
    double[][] estCov3 = model.getCov(2);
    double[][] trueCov1Array = trueCov1.getCovarianceMatrix().getData();
    double[][] trueCov2Array = trueCov2.getCovarianceMatrix().getData();
    double[][] trueCov3Array = trueCov3.getCovarianceMatrix().getData();

    System.out.println(model.printResults());

    /**
     * Test means
     */
    //            System.out.println("    Testing means...");
    //            for(int i=0;i<3;i++){
    //                assertEquals("Group 1 mean test", trueMeanGroup1[i], estMean1[i], 5e-2);
    //                assertEquals("Group 2 mean test", trueMeanGroup2[i], estMean2[i], 5e-2);
    //                assertEquals("Group 3 mean test", trueMeanGroup3[i], estMean3[i], 5e-2);
    //            }
    //
    //            /**
    //             * Test Covariance matrix
    //             */
    //            System.out.println("    Testing covariances...");
    //            for(int i=0;i<3;i++){
    //                for(int j=0;j<3;j++){
    //                    assertEquals("Cov[" +i + "," + j+"] test:", trueCov1Array[i][j], estCov1[i][j], 5e-2);
    //                }
    //            }
    //
    //            for(int i=0;i<3;i++){
    //                for(int j=0;j<3;j++){
    //                    assertEquals("Cov[" +i + "," + j+"] test:", trueCov2Array[i][j], estCov2[i][j], 5e-2);
    //                }
    //            }
    //
    //            for(int i=0;i<3;i++){
    //                for(int j=0;j<3;j++){
    //                    assertEquals("Cov[" +i + "," + j+"] test:", trueCov2Array[i][j], estCov2[i][j], 5e-2);
    //                }
    //            }

}

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From source file:gamlss.utilities.MatrixFunctions.java

/**
 * Calculate this: (x - t) ^ p * (x > t).
 * @param v1 vector 1/*from  ww  w .  j  a  v a  2  s  .co  m*/
 * @param v2 vector 2
 * @param p - power
 * @return  matrix C with Cij = v1i * v2j.
 */
//tpower <- function(x, t, p)
public static BlockRealMatrix outertpowerPB(final ArrayRealVector v1, final ArrayRealVector v2, final int p) {

    double[][] newdata = new double[v1.getDimension()][v2.getDimension()];
    for (int j = 0; j < v1.getDimension(); j++) {
        for (int j2 = 0; j2 < v2.getDimension(); j2++) {
            if (v1.getEntry(j) > v2.getEntry(j2)) {
                newdata[j][j2] = FastMath.pow((v1.getEntry(j) - v2.getEntry(j2)), p);
            } else {
                newdata[j][j2] = 0d;
            }
        }
    }
    return new BlockRealMatrix(newdata);
}

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From source file:edu.cmu.tetrad.util.ApacheTetradMatrix.java

private ApacheTetradMatrix(double[][] apacheData) {
    if (apacheData.length == 0) {
        this.apacheData = new Array2DRowRealMatrix();
    } else {/*from   w  w w .  j ava 2 s  .com*/
        this.apacheData = new BlockRealMatrix(apacheData);
    }
}

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From source file:edu.cmu.tetrad.search.EstimateRank.java

public double[] CanCor(int[] iA, int[] iB, double[][] cov) {
    this.iA = iA;
    this.iB = iB;
    this.cov = cov;
    RealMatrix covA = new BlockRealMatrix(cov).getSubMatrix(iA, iA);
    RealMatrix covB = new BlockRealMatrix(cov).getSubMatrix(iB, iB);
    RealMatrix covAB = new BlockRealMatrix(cov).getSubMatrix(iA, iB);
    RealMatrix covBA = new BlockRealMatrix(cov).getSubMatrix(iB, iA);
    RealMatrix S = getInverse(covA).multiply(covAB).multiply(getInverse(covB)).multiply(covBA);
    double[] rtCors = new EigenDecomposition(S).getRealEigenvalues();
    Arrays.sort(rtCors);// w  ww . j a  v a  2 s . c  om
    double[] Cors = new double[rtCors.length];
    for (int i = rtCors.length; i > 0; i--) {
        Cors[rtCors.length - i] = Math.pow(rtCors[i - 1], .5);
    }
    return Cors;
}

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From source file:gamlss.utilities.MatrixFunctions.java

/**
 * Build identity matrix.//from  w ww .j  ava  2  s .c  o m
 * @param size - size of the matrix
 * @return identity matrix
 */
public static BlockRealMatrix buildIdentityMatrix(final int size) {
    double[] tempArr = new double[size];
    for (int i = 0; i < size; i++) {
        tempArr[i] = 1;
    }
    return new BlockRealMatrix(MatrixUtils.createRealDiagonalMatrix(tempArr).getData());
}

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