Example usage for org.apache.commons.math.linear SingularValueDecomposition getV

Introduction

In this page you can find the example usage for org.apache.commons.math.linear SingularValueDecomposition getV.

Prototype

RealMatrix getV();

Source Link

Document

Returns the matrix V of the decomposition.

Usage

From source file:geogebra.common.kernel.statistics.AlgoFitImplicit.java

@Override
public final void compute() {

    int order = (int) orderGeo.evaluateDouble();

    datasize = pointlist.size(); // rows in M and Y

    if (!pointlist.isDefined() || datasize < order * (order + 3) / 2) {
        fitfunction.setUndefined();/*w ww  .  ja v  a  2s . com*/
        return;
    }

    if (!pointlist.getElementType().equals(GeoClass.POINT)) {
        fitfunction.setUndefined();
        return;
    }
    try {

        // Get functions, x and y from lists
        if (!makeMatrixes()) {
            fitfunction.setUndefined();
            return;
        }

        // App.debug("datasize = " + datasize);
        // App.debug("order = " + order);
        // App.debug("M cols = "+M.getColumnDimension());
        // App.debug("M rows = "+M.getRowDimension());
        // App.debug("M = "+M.toString());

        SingularValueDecomposition svd = new SingularValueDecompositionImpl(M);

        V = svd.getV();

        // App.debug("V = "+V.toString());

        // App.debug("size of M = "+M.getColumnDimension()+" "+M.getRowDimension());
        // App.debug("size of V = "+V.getColumnDimension()+" "+V.getRowDimension());

        makeFunction();

    } catch (Throwable t) {
        fitfunction.setUndefined();
        t.printStackTrace();
    }

}

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From source file:dr.math.Procrustes.java

public Procrustes(RealMatrix X, RealMatrix Xstar, boolean allowTranslation, boolean allowDilation) {
    rowDimension = X.getRowDimension();/*from   w ww  . j ava 2 s  . c  om*/
    columnDimension = X.getColumnDimension();

    if (Xstar.getRowDimension() != rowDimension) {
        throw new IllegalArgumentException("X and Xstar do not have the same number of rows");
    }
    if (Xstar.getColumnDimension() != columnDimension) {
        throw new IllegalArgumentException("X and Xstar do not have the same number of columns");
    }

    RealMatrix J = new Array2DRowRealMatrix(rowDimension, rowDimension);

    if (allowTranslation) {
        //           J <- diag(n) - 1/n * matrix(1, n, n)
        //           for n = 3, J = {{1, -2/3, -2/3}, {-2/3, 1, -2/3}, {-2/3, -2/3, 1}}

        for (int i = 0; i < rowDimension; i++) {
            J.setEntry(i, i, 1.0 - (1.0 / rowDimension));

            for (int j = i + 1; j < rowDimension; j++) {
                J.setEntry(i, j, -1.0 / rowDimension);
                J.setEntry(j, i, -1.0 / rowDimension);
            }
        }
    } else {
        //           J <- diag(n)

        for (int i = 0; i < rowDimension; i++) {
            J.setEntry(i, i, 1);
        }

    }

    //       C <- t(Xstar) %*% J %*% X

    RealMatrix C = Xstar.transpose().multiply(J.multiply(X));

    //       svd.out <- svd(C)
    //       R <- svd.out$v %*% t(svd.out$u)
    //      NB: Apache math does a different SVD from R.  TODO Should use Colt library
    SingularValueDecomposition SVD = new SingularValueDecompositionImpl(C);
    R = SVD.getV().multiply(SVD.getUT());

    //       s <- 1
    double s = 1.0; // scale = 1 unless dilation is being used

    if (allowDilation) {
        //           mat1 <- t(Xstar) %*% J %*% X %*% R
        RealMatrix mat1 = Xstar.transpose().multiply(J.multiply(X.multiply(R)));

        //           mat2 <- t(X) %*% J %*% X
        RealMatrix mat2 = X.transpose().multiply(J.multiply(X));

        //           s.numer <- 0
        //           s.denom <- 0
        double numer = 0.0;
        double denom = 0.0;

        //           for (i in 1:m) {
        //               s.numer <- s.numer + mat1[i, i]
        //               s.denom <- s.denom + mat2[i, i]
        //           }
        for (int i = 0; i < columnDimension; i++) {
            numer = numer + mat1.getEntry(i, i);
            denom = denom + mat2.getEntry(i, i);
        }
        //           s <- s.numer/s.denom
        s = numer / denom;
    }
    this.s = s;

    //       tt <- matrix(0, m, 1)
    RealMatrix tmpT = new Array2DRowRealMatrix(columnDimension, 1); // a translation vector of zero unless translation is being used

    if (allowTranslation) {
        //           tt <- 1/n * t(Xstar - s * X %*% R) %*% matrix(1, n, 1)
        RealMatrix tmp = new Array2DRowRealMatrix(rowDimension, 1);
        for (int i = 0; i < rowDimension; i++) {
            tmp.setEntry(i, 0, 1);
        }
        tmpT = Xstar.subtract(X.multiply(R).scalarMultiply(s)).transpose().scalarMultiply(1.0 / rowDimension)
                .multiply(tmp);
    }

    T = tmpT;
}

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From source file:com.opengamma.analytics.math.linearalgebra.SVDecompositionCommonsResult.java

/**
 * @param svd The result of the SV decomposition, not null
 *///  w w w  .j a v a 2s.  c o  m
public SVDecompositionCommonsResult(final SingularValueDecomposition svd) {
    Validate.notNull(svd);
    _condition = svd.getConditionNumber();
    _norm = svd.getNorm();
    _rank = svd.getRank();
    _s = CommonsMathWrapper.unwrap(svd.getS());
    _singularValues = svd.getSingularValues();
    _u = CommonsMathWrapper.unwrap(svd.getU());
    _uTranspose = CommonsMathWrapper.unwrap(svd.getUT());
    _v = CommonsMathWrapper.unwrap(svd.getV());
    _vTranspose = CommonsMathWrapper.unwrap(svd.getVT());
    _solver = svd.getSolver();
}

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