Java Code Examples for org.apache.commons.math3.stat.descriptive.moment.Variance#getResult()
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org.apache.commons.math3.stat.descriptive.moment.Variance#getResult() .
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Example 1
| Source File: SumOfClusterVariances.java From astor with GNU General Public License v2.0 | 6 votes |
|
@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;
}
Example 2
| Source File: SumOfClusterVariances.java From astor with GNU General Public License v2.0 | 6 votes |
|
@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;
}
Example 3
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters)
throws ConvergenceException {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final Cluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final T center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(point.distanceFrom(center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 4
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<CentroidCluster<T>> clusters)
throws ConvergenceException {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final CentroidCluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final Clusterable center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(distance(point, center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 5
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters)
throws ConvergenceException {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final Cluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final T center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(point.distanceFrom(center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 6
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters)
throws ConvergenceException {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final Cluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final T center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(point.distanceFrom(center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 7
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters)
throws ConvergenceException {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final Cluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final T center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(point.distanceFrom(center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 8
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final Cluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final T center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(point.distanceFrom(center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 9
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<CentroidCluster<T>> clusters)
throws ConvergenceException {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final CentroidCluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final Clusterable center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(distance(point, center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 10
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final Cluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final T center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(point.distanceFrom(center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 11
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters)
throws ConvergenceException {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final Cluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final T center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(point.distanceFrom(center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 12
| Source File: KMeansPlusPlusClusterer.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* 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
*/
private T getPointFromLargestVarianceCluster(final Collection<CentroidCluster<T>> clusters)
throws ConvergenceException {
double maxVariance = Double.NEGATIVE_INFINITY;
Cluster<T> selected = null;
for (final CentroidCluster<T> cluster : clusters) {
if (!cluster.getPoints().isEmpty()) {
// compute the distance variance of the current cluster
final Clusterable center = cluster.getCenter();
final Variance stat = new Variance();
for (final T point : cluster.getPoints()) {
stat.increment(distance(point, center));
}
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 selectedPoints.remove(random.nextInt(selectedPoints.size()));
}
Example 13
| Source File: SummaryStatistics.java From astor with GNU General Public License v2.0 | 2 votes |
|
/**
* 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();
}
Example 14
| Source File: SummaryStatistics.java From astor with GNU General Public License v2.0 | 2 votes |
|
/**
* 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();
}
Example 15
| Source File: SummaryStatistics.java From astor with GNU General Public License v2.0 | 2 votes |
|
/**
* 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();
}
Example 16
| Source File: SummaryStatistics.java From astor with GNU General Public License v2.0 | 2 votes |
|
/**
* 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();
}
Example 17
| Source File: SummaryStatistics.java From astor with GNU General Public License v2.0 | 2 votes |
|
/**
* 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();
}
Example 18
| Source File: SummaryStatistics.java From astor with GNU General Public License v2.0 | 2 votes |
|
/**
* 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();
}
Example 19
| Source File: SummaryStatistics.java From astor with GNU General Public License v2.0 | 2 votes |
|
/**
* 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();
}
Example 20
| Source File: SummaryStatistics.java From astor with GNU General Public License v2.0 | 2 votes |
|
/**
* 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();
}
