Java Code Examples for org.apache.commons.math3.stat.descriptive.moment.Variance#increment()
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org.apache.commons.math3.stat.descriptive.moment.Variance#increment() .
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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<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 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<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 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
*/
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 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
*/
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 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<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 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
* @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 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
*/
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 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
* @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 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<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 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<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 13
| 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 14
| 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 15
| Source File: VarianceByBED.java From HMMRATAC with GNU General Public License v3.0 | 4 votes |
|
public static void main(String[] args) throws IOException {
for (int i = 0; i < args.length; i++) {
switch (args[i].charAt((1))) {
case'a':
wig1 = args[i+1];
i++;
break;
case'r':
bed = args[i+1];
i++;
break;
case'h':
printUsage();
System.exit(1);
}
}
if (wig1 == null || bed == null){
printUsage();
System.exit(1);
}
bedFileReader reader = new bedFileReader(bed);
ArrayList<TagNode> bedData = reader.getData();
reader = null;
BBFileReader wigReader1 = new BBFileReader(wig1);
for (int i = 0; i < bedData.size();i++){
String chrom = bedData.get(i).getChrom();
int start = bedData.get(i).getStart();
int stop = bedData.get(i).getStop();
Variance var = new Variance();
BigWigIterator iter1 = wigReader1.getBigWigIterator(chrom, start, chrom, stop, false);
while(iter1.hasNext()){
WigItem item = iter1.next();
int begin = item.getStartBase();
int end = item.getEndBase();
double value = item.getWigValue();
for (int a = begin;a < end;a++){
var.increment(value);
}
}
System.out.println(chrom+"\t"+start+"\t"+stop+"\t"+var.getResult());
}
}
Example 16
| Source File: TestDiseaseGenePerformance.java From systemsgenetics with GNU General Public License v3.0 | 3 votes |
|
private static HashMap<String, MeanSd> calculatePathayMeansOfAnnotatedGenes(DoubleMatrixDataset<String, String> predictionMatrixSignificant, DoubleMatrixDataset<String, String> annotationMatrixSignificant) {
HashMap<String, MeanSd> pathwayMeanSdMap = new HashMap<>(predictionMatrixSignificant.columns());
Mean meanCalculator = new Mean();
Variance varianceCalculator = new Variance();
for (String pathway : predictionMatrixSignificant.getColObjects()) {
meanCalculator.clear();
varianceCalculator.clear();
DoubleMatrix1D pathwayPredictions = predictionMatrixSignificant.getCol(pathway);
DoubleMatrix1D pathwayAnnotations = annotationMatrixSignificant.getCol(pathway);
for (int g = 0; g < pathwayPredictions.size(); ++g) {
if (pathwayAnnotations.get(g) != 0) {
meanCalculator.increment(pathwayPredictions.getQuick(g));
varianceCalculator.increment(pathwayPredictions.getQuick(g));
}
}
double v = varianceCalculator.getResult();
pathwayMeanSdMap.put(pathway, new MeanSd(meanCalculator.getResult(), v * v));
}
return pathwayMeanSdMap;
}
