/** * {@inheritDoc} */ @Override public double getResult() { return FastMath.sqrt(variance.getResult()); }
/** * Returns the <a href="http://en.wikibooks.org/wiki/Statistics/Summary/Variance"> * population variance</a> of the values that have been added. * * <p>Double.NaN is returned if no values have been added.</p> * * @return the population variance */ public double getPopulationVariance() { Variance populationVariance = new Variance(secondMoment); populationVariance.setBiasCorrected(false); return populationVariance.getResult(); }
/** {@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; }
stat.increment(point.distanceFrom(center)); final double variance = stat.getResult();
stat.increment(distance(point, center)); final double variance = stat.getResult();
stat.increment(point.distanceFrom(center)); varianceSum += stat.getResult();
variance.incrementAll(values, begin, length); double mean = variance.moment.m1; double stdDev = FastMath.sqrt(variance.getResult());
/** * Observed variance of the estimate. * * @return observed variance. */ public double observedVariance(){ return var.getResult(); }
/** * {@inheritDoc} */ @Override public double getResult() { return Math.sqrt(variance.getResult()); }
/** * Observed standard deviation of the estimate. * * @return stanmdard deviation. */ public double observedStandardDeviation(){ return Math.sqrt(var.getResult()); }
/** * An adjusted variance for computing scale quality statistics. * * @return adjusted variance. */ public double adjustedVariance(){ return Math.max(0,var.getResult() - meanSquareError());//can be negative. constrain to be nonnegative }
/** * {@inheritDoc} */ @Override public double getResult() { return FastMath.sqrt(variance.getResult()); }
/** * Reliability is the reproduceability of the item or person estimates. * * @return reliability of the estimates. */ public double reliability(){ return adjustedVariance()/var.getResult(); }
/** * Returns the (sample) variance of the available values. * * <p>This method returns the bias-corrected sample variance (using {@code n - 1} in the * denominator). Use {@link #getPopulationVariance()} for the non-bias-corrected population * variance. * * <p>Double.NaN is returned if no values have been added. * * @return the variance */ @Override public double getVariance() { return _getVariance().getResult(); }
/** * Returns the <a href="http://en.wikibooks.org/wiki/Statistics/Summary/Variance"> * population variance</a> of the values that have been added. * * <p>Double.NaN is returned if no values have been added.</p> * * @return the population variance */ public double getPopulationVariance() { Variance populationVariance = new Variance(secondMoment); populationVariance.setBiasCorrected(false); return populationVariance.getResult(); }
/** * Returns the <a href="http://en.wikibooks.org/wiki/Statistics/Summary/Variance"> * population variance</a> of the values that have been added. * * <p>Double.NaN is returned if no values have been added.</p> * * @return the population variance */ public double getPopulationVariance() { Variance populationVariance = new Variance(secondMoment); populationVariance.setBiasCorrected(false); return populationVariance.getResult(); }
/** * Returns the <a href="http://en.wikibooks.org/wiki/Statistics/Summary/Variance">population * variance</a> of the values that have been added. * * <p>Double.NaN is returned if no values have been added. * * @return the population variance */ @Override public double getPopulationVariance() { Variance populationVariance = new Variance(_getSecondMoment()); populationVariance.setBiasCorrected(false); return populationVariance.getResult(); }
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; }