Example usage for org.apache.commons.math.linear ArrayRealVector ArrayRealVector

Introduction

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

Prototype

public ArrayRealVector(double[] v1, double[] v2) 

Source Link

Document

Construct a vector by appending one vector to another vector.

Usage

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

/**
 * {@inheritDoc}//www .j a  v  a 2  s. 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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From source file:eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.java

/**
 * {@inheritDoc}/*from  w w  w  .j a v a  2  s. com*/
 */
@Override
public SufficientStatistics getSufficientStatistics(Assignment data) {
    CompoundVector vectorSS = this.createEmtpyCompoundVector();

    double[] Xarray = { data.getValue(this.var) };

    double[] Yarray = this.parents.stream().mapToDouble(w -> data.getValue(w)).toArray();
    RealVector XYRealVector = new ArrayRealVector(Xarray, Yarray);
    vectorSS.setXYbaseVector(XYRealVector);

    RealMatrix covRealmatrix = XYRealVector.outerProduct(XYRealVector);

    vectorSS.setcovbaseVector(covRealmatrix);

    return vectorSS;
}

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

/**
 * {@inheritDoc}/*from  w  w w  . j a  v  a2s  .  c o  m*/
 */
@Override
public SufficientStatistics createInitSufficientStatistics() {
    CompoundVector vectorSS = this.createEmtpyCompoundVector();

    double[] Xarray = { 0.0 };

    double[] Yarray = this.parents.stream().mapToDouble(w -> 0.0).toArray();
    RealVector XYRealVector = new ArrayRealVector(Xarray, Yarray);
    vectorSS.setXYbaseVector(XYRealVector);

    RealMatrix covRealmatrix = new Array2DRowRealMatrix(Yarray.length + 1, Yarray.length + 1);

    //We perform the "laplace" correction in that way to break symmetric covariance matrixes.
    Random rand = new Random(0);
    for (int i = 0; i < Yarray.length + 1; i++) {
        for (int j = 0; j < Yarray.length + 1; j++) {
            covRealmatrix.addToEntry(i, j, rand.nextDouble() + 0.01);
        }
    }
    //covRealmatrix = covRealmatrix.scalarAdd(1.0);

    vectorSS.setcovbaseVector(covRealmatrix);

    return vectorSS;
}

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From source file:org.deegree.geometry.linearization.CurveLinearizer.java

private double[] solveLinearEquation(double[][] matrixA, double[] vectorb) {

    RealMatrix coefficients = new Array2DRowRealMatrix(matrixA, false);

    // LU-decomposition
    DecompositionSolver solver = new LUDecompositionImpl(coefficients).getSolver();

    RealVector constants = new ArrayRealVector(vectorb, false);
    RealVector solution = null;//  w w  w.j  av a2  s . com
    try {
        solution = solver.solve(constants);
    } catch (SingularMatrixException e) {
        LOG.error(e.getLocalizedMessage());
        e.printStackTrace();
    }
    return solution.getData();
}

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