/** * {@inheritDoc} */ @Override public void increment(final double d) { variance.increment(d); }
/** {@inheritDoc} */ @Override public double score(final List<? extends Cluster<T>> clusters) { double varianceSum = 0.0; for (final Cluster<T> cluster : clusters) { if (!cluster.getPoints().isEmpty()) { final Clusterable center = centroidOf(cluster); // compute the distance variance of the current cluster final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center)); } varianceSum += stat.getResult(); } } return varianceSum; }
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(point.distanceFrom(center));
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center));
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(point.distanceFrom(center));
/** * {@inheritDoc} */ @Override public void increment(final double d) { variance.increment(d); }
/** * {@inheritDoc} */ @Override public void increment(final double d) { variance.increment(d); }
/** * An incremental update to the scale quality statistics. * * @param estimate a person ability or item difficulty estimate. * @param stdError */ public void increment(double estimate, double stdError){ var.increment(estimate); mean.increment(Math.pow(stdError, 2)); }
public static void main(String[] args) { final double[] values = new java.util.Random().doubles(5000).toArray(); final Variance stat1 = new Variance(true); final org.apache.commons.math3.stat.descriptive.moment.Variance stat2 = new org.apache.commons.math3.stat.descriptive.moment.Variance(true); for (double value : values) { stat1.add(value); stat2.increment(value); } final double result1 = stat1.getValue(); final double result2 = stat2.getResult(); if (result1 != result2) { throw new RuntimeException("Error: " + result1 + " != " + result2); } }
/** {@inheritDoc} */ @Override public double score(final List<? extends Cluster<T>> clusters) { double varianceSum = 0.0; for (final Cluster<T> cluster : clusters) { if (!cluster.getPoints().isEmpty()) { final Clusterable center = centroidOf(cluster); // compute the distance variance of the current cluster final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center)); } varianceSum += stat.getResult(); } } return varianceSum; }
/** {@inheritDoc} */ @Override public double score(final List<? extends Cluster<T>> clusters) { double varianceSum = 0.0; for (final Cluster<T> cluster : clusters) { if (!cluster.getPoints().isEmpty()) { final Clusterable center = centroidOf(cluster); // compute the distance variance of the current cluster final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center)); } varianceSum += stat.getResult(); } } return varianceSum; }
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(point.distanceFrom(center));
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(point.distanceFrom(center));
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center));
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center));
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(distance(point, center));
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(point.distanceFrom(center));
final Variance stat = new Variance(); for (final T point : cluster.getPoints()) { stat.increment(point.distanceFrom(center));
private static void computeNormalConsensusAndVariance(Point3DReadOnly pointOnPlane, Vector3DReadOnly planeNormal, Iterable<NormalOcTreeNode> neighbors, double maxDistanceFromPlane, MutableDouble varianceToPack, MutableInt consensusToPack) { Variance variance = new Variance(); consensusToPack.setValue(0); Vector3D toNeighborHitLocation = new Vector3D(); for (NormalOcTreeNode neighbor : neighbors) { toNeighborHitLocation.set(neighbor.getHitLocationX(), neighbor.getHitLocationY(), neighbor.getHitLocationZ()); toNeighborHitLocation.sub(pointOnPlane); double distanceFromPlane = Math.abs(planeNormal.dot(toNeighborHitLocation)); if (distanceFromPlane <= maxDistanceFromPlane) { variance.increment(distanceFromPlane); consensusToPack.increment(); } } if (consensusToPack.intValue() == 0) varianceToPack.setValue(Double.POSITIVE_INFINITY); else varianceToPack.setValue(variance.getResult()); } }