| Package | Description |
|---|---|
| org.apache.commons.math3.analysis.solvers |
Root finding algorithms, for univariate real functions.
|
| org.apache.commons.math3.fraction |
Fraction number type and fraction number formatting.
|
| org.apache.commons.math3.geometry.euclidean.twod.hull |
This package provides algorithms to generate the convex hull
for a set of points in an two-dimensional euclidean space.
|
| org.apache.commons.math3.geometry.hull |
This package provides interfaces and classes related to the convex hull problem.
|
| org.apache.commons.math3.ml.clustering |
Clustering algorithms.
|
| org.apache.commons.math3.special |
Implementations of special functions such as Beta and Gamma.
|
| org.apache.commons.math3.stat.clustering |
All classes and sub-packages of this package are deprecated.
|
| org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing.
|
| org.apache.commons.math3.util |
Convenience routines and common data structures used throughout the commons-math library.
|
| Modifier and Type | Method and Description |
|---|---|
protected double |
BaseSecantSolver.doSolve()
Method for implementing actual optimization algorithms in derived
classes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FractionConversionException
Error thrown when a double value cannot be converted to a fraction
in the allowed number of iterations.
|
| Modifier and Type | Method and Description |
|---|---|
ConvexHull2D |
ConvexHullGenerator2D.generate(Collection<Vector2D> points)
Builds the convex hull from the set of input points.
|
| Modifier and Type | Method and Description |
|---|---|
ConvexHull<S,P> |
ConvexHullGenerator.generate(Collection<P> points)
Builds the convex hull from the set of input points.
|
| Modifier and Type | Method and Description |
|---|---|
List<CentroidCluster<T>> |
KMeansPlusPlusClusterer.cluster(Collection<T> points)
Runs the K-means++ clustering algorithm.
|
abstract List<? extends Cluster<T>> |
Clusterer.cluster(Collection<T> points)
Perform a cluster analysis on the given set of
Clusterable instances. |
List<CentroidCluster<T>> |
MultiKMeansPlusPlusClusterer.cluster(Collection<T> points)
Runs the K-means++ clustering algorithm.
|
| Modifier and Type | Method and Description |
|---|---|
double |
BesselJ.value(double x)
Returns the value of the constructed Bessel function of the first kind,
for the passed argument.
|
static double |
BesselJ.value(double order,
double x)
Returns the first Bessel function, \(J_{order}(x)\).
|
| Modifier and Type | Method and Description |
|---|---|
List<Cluster<T>> |
KMeansPlusPlusClusterer.cluster(Collection<T> points,
int k,
int maxIterations)
Deprecated.
Runs the K-means++ clustering algorithm.
|
List<Cluster<T>> |
KMeansPlusPlusClusterer.cluster(Collection<T> points,
int k,
int numTrials,
int maxIterationsPerTrial)
Deprecated.
Runs the K-means++ clustering algorithm.
|
| Modifier and Type | Method and Description |
|---|---|
double |
OneWayAnova.anovaPValue(Collection<double[]> categoryData)
Computes the ANOVA P-value for a collection of
double[]
arrays. |
double |
OneWayAnova.anovaPValue(Collection<SummaryStatistics> categoryData,
boolean allowOneElementData)
Computes the ANOVA P-value for a collection of
SummaryStatistics. |
boolean |
OneWayAnova.anovaTest(Collection<double[]> categoryData,
double alpha)
Performs an ANOVA test, evaluating the null hypothesis that there
is no difference among the means of the data categories.
|
double |
MannWhitneyUTest.mannWhitneyUTest(double[] x,
double[] y)
Returns the asymptotic observed significance level, or
p-value, associated with a Mann-Whitney
U statistic comparing mean for two independent samples.
|
static double |
TestUtils.oneWayAnovaPValue(Collection<double[]> categoryData) |
static boolean |
TestUtils.oneWayAnovaTest(Collection<double[]> categoryData,
double alpha) |
double |
WilcoxonSignedRankTest.wilcoxonSignedRankTest(double[] x,
double[] y,
boolean exactPValue)
Returns the observed significance level, or
p-value, associated with a
Wilcoxon signed ranked statistic comparing mean for two related
samples or repeated measurements on a single sample.
|
| Modifier and Type | Method and Description |
|---|---|
double |
ContinuedFraction.evaluate(double x)
Evaluates the continued fraction at the value x.
|
double |
ContinuedFraction.evaluate(double x,
double epsilon)
Evaluates the continued fraction at the value x.
|
double |
ContinuedFraction.evaluate(double x,
double epsilon,
int maxIterations)
Evaluates the continued fraction at the value x.
|
double |
ContinuedFraction.evaluate(double x,
int maxIterations)
Evaluates the continued fraction at the value x.
|
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