Example usage for org.apache.commons.math3.linear RealVector toArray

Introduction

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

Prototype

public double[] toArray()

Source Link

Document

Convert the vector to an array of double s.

Usage

From source file:org.lambda3.indra.core.IndraDriver.java

public final Map<String, double[]> getVectorsAsArray(List<String> terms, Params params) {
    Map<String, RealVector> inVectors = getVectors(terms, params);

    Map<String, double[]> outVectors = new HashMap<>();
    for (String term : inVectors.keySet()) {
        RealVector realVector = inVectors.get(term);

        outVectors.put(term, realVector != null ? realVector.toArray() : null);
    }/*from   ww  w .j  a  v a  2  s .  c  om*/

    return outVectors;
}

From source file:org.lenskit.mf.funksvd.FunkSVDModelBuilder.java

private double realVectorSum(RealVector rv) {
    double total = 0;
    for (double i : rv.toArray()) {
        total += i;// ww w .j  av  a  2  s.com
    }
    return total;
}

From source file:org.orekit.propagation.conversion.AbstractPropagatorConverter.java

/** Find the propagator that minimize the mean square error for a sample of {@link SpacecraftState states}.
 * @param initial initial estimation parameters (position, velocity, free parameters)
 * @return fitted parameters// w  ww  .j  av a  2  s.co m
 * @exception OrekitException if propagator cannot be adapted
 * @exception MaxCountExceededException if maximal number of iterations is exceeded
 */
private double[] fit(final double[] initial) throws OrekitException, MaxCountExceededException {

    final MultivariateVectorFunction f = getObjectiveFunction();
    final MultivariateMatrixFunction jac = getObjectiveFunctionJacobian();
    final MultivariateJacobianFunction fJac = new MultivariateJacobianFunction() {
        /** {@inheritDoc} */
        @Override
        public Pair<RealVector, RealMatrix> value(final RealVector point) {
            final double[] p = point.toArray();
            return new Pair<RealVector, RealMatrix>(MatrixUtils.createRealVector(f.value(p)),
                    MatrixUtils.createRealMatrix(jac.value(p)));
        }
    };
    final LeastSquaresProblem problem = new LeastSquaresBuilder().maxIterations(maxIterations)
            .maxEvaluations(Integer.MAX_VALUE).model(fJac).target(target).weight(new DiagonalMatrix(weight))
            .start(initial).checker(checker).build();
    optimum = optimizer.optimize(problem);
    return optimum.getPoint().toArray();
}

From source file:org.rhwlab.variationalbayesian.OldFaithfulDataSource.java

public List<CentroidCluster<Clusterable>> cluster(int K) {
    KMeansPlusPlusClusterer clusterer = new KMeansPlusPlusClusterer(K);
    ArrayList<DoublePoint> points = new ArrayList<>();
    for (RealVector v : data) {
        DoublePoint point = new DoublePoint(v.toArray());
        points.add(point);//www.  ja  v  a  2s  . c o m
    }
    return clusterer.cluster(points);
}

From source file:org.wallerlab.yoink.math.linear.CommonsMatrix.java

/**
 * Element-by-element multiplication of two 3D vectors.
 *//*  w  w  w  .  j a va  2 s  . co m*/
@Override
public Matrix ebeMultiply(Matrix m) {
    double[] v1 = this.internalMatrix.getRow(0);
    RealVector vector1 = new ArrayRealVector(v1);
    double[] v2 = ((RealMatrix) m.getInternalMatrix()).getRow(0);
    RealVector vector2 = new ArrayRealVector(v2);
    RealVector vectorEBE = vector1.ebeMultiply(vector2);
    double[] vEBE = vectorEBE.toArray();
    tempMatrix = MatrixUtils.createRealMatrix(1, 3);
    tempMatrix.setRow(0, vEBE);
    Matrix newMatrix = new CommonsMatrix();
    newMatrix.setInternalMatrix(tempMatrix);
    return newMatrix;
}

From source file:rcdemo.graphics.TrackHelper.java

public Vector toVector(RealVector v) {
    double[] x = v.toArray();
    return va.fromDouble(x);
}

From source file:se.toxbee.sleepfighter.challenge.math.LinearEquationProblem.java

private static RealVector multiply(RealMatrix matrix, RealVector vector) {

    double[] m1 = matrix.getRow(0);
    double[] m2 = matrix.getRow(1);
    double[] m3 = matrix.getRow(2);

    double[] v = vector.toArray();

    return new ArrayRealVector(new double[] { m1[0] * v[0] + m1[1] * v[1] + m1[2] * v[2],
            m2[0] * v[0] + m2[1] * v[1] + m2[2] * v[2], m3[0] * v[0] + m3[1] * v[1] + m3[2] * v[2],

    });/*from  ww  w  .j  av a  2s  . com*/
}

From source file:syncleus.gremlann.Graphs.java

public static double[] doubles(RealVector v) {
    if (v instanceof ArrayRealVector)
        return ((ArrayRealVector) v).getDataRef();
    return v.toArray();
}

From source file:syncleus.gremlann.train.Backprop.java

/** convenience method which extracts the direct array from a RealVector if possible, avoiding an array copy when a copy isnt necessary */
public double[] array(RealVector v) {
    if (v instanceof ArrayRealVector)
        return ((ArrayRealVector) v).getDataRef();
    return v.toArray();
}

From source file:uk.ac.diamond.scisoft.analysis.optimize.ApacheOptimizer.java

public MultivariateJacobianFunction createJacobianFunction() {
    final int size = coords[0].getSize();
    final AFunction afn;
    final CoordinatesIterator it;
    final DoubleDataset vd, pvd;
    final double[][] dm = new double[size][n];
    if (function instanceof AFunction) {
        afn = (AFunction) function;/* w  w w  . j  a  v  a  2 s .  c o m*/
        it = afn.getIterator(coords);
        vd = (DoubleDataset) DatasetFactory.zeros(coords[0].getShapeRef(), Dataset.FLOAT64);
        pvd = vd.clone();
    } else {
        afn = null;
        it = null;
        vd = null;
        pvd = null;
    }

    MultivariateJacobianFunction f = new MultivariateJacobianFunction() {
        @SuppressWarnings("null")
        @Override
        public Pair<RealVector, RealMatrix> value(RealVector point) {
            IMonitor monitor = function.getMonitor();
            if (monitor != null && monitor.isCancelled()) {
                throw new IllegalMonitorStateException("Monitor cancelled");
            }

            if (point instanceof ArrayRealVector) {
                setParameterValues(((ArrayRealVector) point).getDataRef());
            } else {
                setParameterValues(point.toArray());
            }
            final double[] dv;
            if (afn != null) {
                dv = vd.getData();
                AFunction afn = (AFunction) function;
                afn.fillWithValues(vd, it);
                double[] pd = pvd.getData();
                for (int i = 0; i < n; i++) { // assuming number of parameters is less than number of coordinates
                    IParameter p = params.get(i);
                    afn.fillWithPartialDerivativeValues(p, pvd, it);
                    for (int j = 0; j < size; j++) {
                        dm[j][i] = pd[j];
                    }
                }
            } else {
                dv = calculateValues().getData();
                for (int i = 0; i < n; i++) {
                    IParameter p = params.get(i);
                    DoubleDataset dp = (DoubleDataset) DatasetUtils
                            .cast(function.calculatePartialDerivativeValues(p, coords), Dataset.FLOAT64);
                    double[] pd = dp.getData();
                    for (int j = 0; j < size; j++) {
                        dm[j][i] = pd[j];
                    }
                }
            }
            return new Pair<RealVector, RealMatrix>(new ArrayRealVector(dv, false),
                    new Array2DRowRealMatrix(dm, false));
        }
    };

    return f;
}