Example usage for org.apache.commons.math3.linear ArrayRealVector getDataRef

Introduction

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

Prototype

public double[] getDataRef() 

Source Link

Document

Get a reference to the underlying data array.

Usage

From source file:automenta.vivisect.dimensionalize.HyperassociativeMap.java

public static void add(ArrayRealVector target, ArrayRealVector add) {
    double[] a = add.getDataRef();
    double[] t = target.getDataRef();
    int dim = t.length;
    for (int i = 0; i < dim; i++) {
        t[i] += a[i];/*w w  w . jav a 2 s .  c  om*/
    }
}

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From source file:automenta.vivisect.dimensionalize.HyperassociativeMap.java

public static void sub(ArrayRealVector target, ArrayRealVector add) {
    double[] a = add.getDataRef();
    double[] t = target.getDataRef();
    int dim = t.length;
    for (int i = 0; i < dim; i++) {
        t[i] -= a[i];/* ww  w  .ja v a2s.  c  o m*/
    }
}

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From source file:automenta.vivisect.dimensionalize.HyperassociativeMap.java

public static void add(ArrayRealVector target, ArrayRealVector add, double factor) {
    if (factor == 0)
        return;/*from   w ww .jav  a 2 s  .com*/

    double[] a = add.getDataRef();
    double[] t = target.getDataRef();
    int dim = t.length;
    for (int i = 0; i < dim; i++) {
        t[i] += a[i] * factor;
    }
}

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From source file:hivemall.anomaly.ChangeFinder2D.java

@Nonnull
private ArrayRealVector parseX(final Object arg) throws UDFArgumentException {
    ArrayRealVector xVec = xRing.head();
    if (xVec == null) {
        double[] data = HiveUtils.asDoubleArray(arg, listOI, elemOI);
        if (data.length == 0) {
            throw new UDFArgumentException("Dimension of x SHOULD be more than zero");
        }/*  ww  w .  ja  v  a  2  s  .  c  o m*/
        xVec = new ArrayRealVector(data, false);
    } else {
        double[] ref = xVec.getDataRef();
        HiveUtils.toDoubleArray(arg, listOI, elemOI, ref, 0.d);
    }
    return xVec;
}

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From source file:gamlss.distributions.NO.java

/** Calculates initial value of sigma.
 * @param y - vector of values of response variable
 * @return vector of initial values of sigma
 *///from w  ww  .  j  a  v  a  2s.  c  o  m
private ArrayRealVector setSigmaInitial(final ArrayRealVector y) {
    tempV = new ArrayRealVector(y.getDimension());
    final double ySD = new StandardDeviation().evaluate(y.getDataRef());
    tempV.set(ySD);
    return tempV;

}

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From source file:gamlss.distributions.NO.java

/**  Calculates initial value of mu, by assumption these values lie
between observed data and the trend line.
 * @param y - vector of values of response variable
 * @return vector of initial values of mu
 *//*from   w ww.j av a2  s  .  c o  m*/
private ArrayRealVector setMuInitial(final ArrayRealVector y) {
    size = y.getDimension();
    double[] out = new double[size];
    final double yMean = new Mean().evaluate(y.getDataRef());
    for (int i = 0; i < size; i++) {
        out[i] = (y.getEntry(i) + yMean) / 2;
    }
    return new ArrayRealVector(out, false);
}

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From source file:gamlss.distributions.GA.java

/**  Calculates initial value of mu, by assumption these
  * values lie between observed data and the trend line.
 * @param y - vector of values of response variable
 * @return  a vector of initial values of mu
 *//*  w  w  w  .j av a2s.com*/
private ArrayRealVector setMuInitial(final ArrayRealVector y) {
    //mu.initial = expression({mu <- (y+mean(y))/2})
    size = y.getDimension();
    double[] out = new double[size];
    Mean mean = new Mean();
    double yMean = mean.evaluate(y.getDataRef());
    for (int i = 0; i < size; i++) {
        out[i] = (y.getEntry(i) + yMean) / 2;
    }
    return new ArrayRealVector(out, false);
}

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From source file:automenta.vivisect.dimensionalize.HyperassociativeMap.java

private ArrayRealVector getThePosition(V n, ArrayRealVector v) {
    getPosition(n, v.getDataRef());
    return (ArrayRealVector) v.mapMultiply(1.0 / scale);
}

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From source file:automenta.vivisect.dimensionalize.HyperassociativeMap.java

public double magnitude(ArrayRealVector x) {
    return distanceFunction.getDistance(zero, x.getDataRef());
}

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From source file:nars.concept.util.BeliefClusterer.java

/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 *///from w  w w.  j  av  a 2s  .c o m
@NotNull
private ArrayRealVector getPointFromLargestVarianceCluster(@NotNull final Collection<Cluster> clusters)
        throws ConvergenceException {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster selected = null;
    for (final Cluster cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final ArrayRealVector center = cluster.pos;
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(distance(point, center.getDataRef()));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return p(selectedPoints.remove(random.nextInt(selectedPoints.size())));

}

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