Example usage for org.apache.commons.math.linear RealMatrix preMultiply

Introduction

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

Prototype

RealVector preMultiply(RealVector v) throws IllegalArgumentException;

Source Link

Document

Returns the (row) vector result of premultiplying this by the vector v.

Usage

From source file:eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.java

/**
 * {@inheritDoc}// ww  w . j  a  v  a  2s. c  o m
 */
@Override
public void updateNaturalFromMomentParameters() {

    /*
     * First step: means and convariances
     */
    CompoundVector globalMomentParam = (CompoundVector) this.momentParameters;
    double mean_X = globalMomentParam.getXYbaseMatrix().getEntry(0);
    RealVector mean_Y = globalMomentParam.getTheta_beta0BetaRV();

    double cov_XX = globalMomentParam.getcovbaseMatrix().getEntry(0, 0) - mean_X * mean_X;
    RealMatrix cov_YY = globalMomentParam.getcovbaseMatrix().getSubMatrix(1, nOfParents, 1, nOfParents)
            .subtract(mean_Y.outerProduct(mean_Y));
    RealVector cov_XY = globalMomentParam.getcovbaseMatrix().getSubMatrix(0, 0, 1, nOfParents).getRowVector(0)
            .subtract(mean_Y.mapMultiply(mean_X));
    //RealVector cov_YX = cov_XY; //outerProduct transposes the vector automatically

    /*
     * Second step: betas and variance
     */
    RealMatrix cov_YYInverse = new LUDecompositionImpl(cov_YY).getSolver().getInverse();
    RealVector beta = cov_YYInverse.preMultiply(cov_XY);

    this.betas = new double[beta.getDimension()];
    for (int i = 0; i < beta.getDimension(); i++) {
        this.betas[i] = beta.getEntry(i);
    }
    this.beta0 = mean_X - beta.dotProduct(mean_Y);
    this.variance = cov_XX - beta.dotProduct(cov_XY);

}

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From source file:eu.amidst.core.exponentialfamily.EF_Normal_NormalParents2.java

/**
 * {@inheritDoc}/* w ww.j a v  a  2s  . co  m*/
 */
@Override
public void updateNaturalFromMomentParameters() {

    /*
     * First step: means and convariances
     */
    CompoundVector globalMomentParam = (CompoundVector) this.momentParameters;
    double mean_X = globalMomentParam.getXYbaseMatrix().getEntry(0);
    RealVector mean_Y = globalMomentParam.getTheta_beta0BetaRV();

    double cov_XX = globalMomentParam.getcovbaseMatrix().getEntry(0, 0) - mean_X * mean_X;
    RealMatrix cov_YY = globalMomentParam.getcovbaseMatrix().getSubMatrix(1, nOfParents, 1, nOfParents)
            .subtract(mean_Y.outerProduct(mean_Y));
    RealVector cov_XY = globalMomentParam.getcovbaseMatrix().getSubMatrix(0, 0, 1, nOfParents).getRowVector(0)
            .subtract(mean_Y.mapMultiply(mean_X));
    //RealVector cov_YX = cov_XY; //outerProduct transposes the vector automatically

    /*
     * Second step: betas and variance
     */
    RealMatrix cov_YYInverse = new LUDecompositionImpl(cov_YY).getSolver().getInverse();
    RealVector beta = cov_YYInverse.preMultiply(cov_XY);

    double beta_0 = mean_X - beta.dotProduct(mean_Y);
    double variance = cov_XX - beta.dotProduct(cov_XY);

    /*
     * Third step: natural parameters (5 in total)
     */

    /*
     * 1) theta_0
     */
    double theta_0 = beta_0 / variance;
    double[] theta_0array = { theta_0 };

    /*
     * 2) theta_0Theta
     */
    double variance2Inv = 1.0 / (2 * variance);
    RealVector theta_0Theta = beta.mapMultiply(-beta_0 / variance);
    ((CompoundVector) this.naturalParameters)
            .setXYbaseVector(new ArrayRealVector(theta_0array, theta_0Theta.getData()));

    /*
     * 3) theta_Minus1
     */
    double theta_Minus1 = -variance2Inv;

    /*
     * 4) theta_beta
     */
    RealVector theta_beta = beta.mapMultiply(variance2Inv);

    /*
     * 5) theta_betaBeta
     */
    RealMatrix theta_betaBeta = beta.outerProduct(beta).scalarMultiply(-variance2Inv * 2);

    /*
     * Store natural parameters
     */
    RealMatrix natural_XY = new Array2DRowRealMatrix(nOfParents + 1, nOfParents + 1);
    double[] theta_Minus1array = { theta_Minus1 };
    RealVector covXY = new ArrayRealVector(theta_Minus1array, theta_beta.getData());
    natural_XY.setColumnVector(0, covXY);
    natural_XY.setRowVector(0, covXY);
    natural_XY.setSubMatrix(theta_betaBeta.getData(), 1, 1);
    ((CompoundVector) this.naturalParameters).setcovbaseVector(natural_XY);

}

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