| Package | Description |
|---|---|
| org.apache.commons.math3.analysis.differentiation |
This package holds the main interfaces and basic building block classes
dealing with differentiation.
|
| org.apache.commons.math3.analysis.interpolation |
Univariate real functions interpolation algorithms.
|
| org.apache.commons.math3.complex |
Complex number type and implementations of complex transcendental
functions.
|
| org.apache.commons.math3.distribution |
Implementations of common discrete and continuous distributions.
|
| org.apache.commons.math3.genetics |
This package provides Genetic Algorithms components and implementations.
|
| org.apache.commons.math3.linear |
Linear algebra support.
|
| org.apache.commons.math3.ml.clustering |
Clustering algorithms.
|
| org.apache.commons.math3.optim.nonlinear.scalar.noderiv |
This package provides optimization algorithms that do not require derivatives.
|
| org.apache.commons.math3.optimization.direct |
This package provides optimization algorithms that don't require derivatives.
|
| org.apache.commons.math3.random |
Random number and random data generators.
|
| 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.stat.interval |
Classes providing binomial proportion confidence interval construction.
|
| org.apache.commons.math3.util |
Convenience routines and common data structures used throughout the commons-math library.
|
| Constructor and Description |
|---|
FiniteDifferencesDifferentiator(int nbPoints,
double stepSize)
Build a differentiator with number of points and step size when independent variable is unbounded.
|
FiniteDifferencesDifferentiator(int nbPoints,
double stepSize,
double tLower,
double tUpper)
Build a differentiator with number of points and step size when independent variable is bounded.
|
| Constructor and Description |
|---|
LoessInterpolator(double bandwidth,
int robustnessIters,
double accuracy)
Construct a new
LoessInterpolator
with given bandwidth, number of robustness iterations and accuracy. |
MicrosphereInterpolator(int elements,
int exponent)
Deprecated.
Create a microsphere interpolator.
|
MicrosphereProjectionInterpolator(InterpolatingMicrosphere microsphere,
double exponent,
boolean sharedSphere,
double noInterpolationTolerance)
Create a microsphere interpolator.
|
SmoothingPolynomialBicubicSplineInterpolator(int degree)
Deprecated.
|
SmoothingPolynomialBicubicSplineInterpolator(int xDegree,
int yDegree)
Deprecated.
|
| Modifier and Type | Method and Description |
|---|---|
List<Complex> |
Complex.nthRoot(int n)
Computes the n-th roots of this complex number.
|
| Constructor and Description |
|---|
EnumeratedDistribution(List<Pair<T,Double>> pmf)
Create an enumerated distribution using the given probability mass function
enumeration.
|
EnumeratedDistribution(RandomGenerator rng,
List<Pair<T,Double>> pmf)
Create an enumerated distribution using the given random number generator
and probability mass function enumeration.
|
EnumeratedIntegerDistribution(int[] singletons,
double[] probabilities)
Create a discrete distribution using the given probability mass function
definition.
|
EnumeratedIntegerDistribution(RandomGenerator rng,
int[] singletons,
double[] probabilities)
Create a discrete distribution using the given random number generator
and probability mass function definition.
|
EnumeratedRealDistribution(double[] singletons,
double[] probabilities)
Create a discrete real-valued distribution using the given probability mass function
enumeration.
|
EnumeratedRealDistribution(RandomGenerator rng,
double[] singletons,
double[] probabilities)
Create a discrete real-valued distribution using the given random number generator
and probability mass function enumeration.
|
HypergeometricDistribution(int populationSize,
int numberOfSuccesses,
int sampleSize)
Construct a new hypergeometric distribution with the specified population
size, number of successes in the population, and sample size.
|
HypergeometricDistribution(RandomGenerator rng,
int populationSize,
int numberOfSuccesses,
int sampleSize)
Creates a new hypergeometric distribution.
|
MixtureMultivariateNormalDistribution(RandomGenerator rng,
List<Pair<Double,MultivariateNormalDistribution>> components)
Creates a mixture model from a list of distributions and their
associated weights.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ListPopulation.setPopulationLimit(int populationLimit)
Sets the maximal population size.
|
| Constructor and Description |
|---|
ElitisticListPopulation(int populationLimit,
double elitismRate)
Creates a new
ElitisticListPopulation instance and initializes its inner chromosome list. |
ElitisticListPopulation(List<Chromosome> chromosomes,
int populationLimit,
double elitismRate)
Creates a new
ElitisticListPopulation instance. |
ListPopulation(int populationLimit)
Creates a new ListPopulation instance and initializes its inner chromosome list.
|
ListPopulation(List<Chromosome> chromosomes,
int populationLimit)
Creates a new ListPopulation instance.
|
| Modifier and Type | Method and Description |
|---|---|
FieldVector<T> |
FieldVector.getSubVector(int index,
int n)
Get a subvector from consecutive elements.
|
abstract RealVector |
RealVector.getSubVector(int index,
int n)
Get a subvector from consecutive elements.
|
OpenMapRealVector |
OpenMapRealVector.getSubVector(int index,
int n)
Get a subvector from consecutive elements.
|
RealVector |
ArrayRealVector.getSubVector(int index,
int n)
Get a subvector from consecutive elements.
|
FieldVector<T> |
SparseFieldVector.getSubVector(int index,
int n)
Get a subvector from consecutive elements.
|
FieldVector<T> |
ArrayFieldVector.getSubVector(int index,
int n)
Get a subvector from consecutive elements.
|
RealMatrix |
AbstractRealMatrix.power(int p)
Returns the result of multiplying
this with itself p
times. |
FieldMatrix<T> |
AbstractFieldMatrix.power(int p)
Returns the result multiplying this with itself
p times. |
RealMatrix |
RealMatrix.power(int p)
Returns the result of multiplying
this with itself p
times. |
FieldMatrix<T> |
FieldMatrix.power(int p)
Returns the result multiplying this with itself
p times. |
| Constructor and Description |
|---|
DBSCANClusterer(double eps,
int minPts)
Creates a new instance of a DBSCANClusterer.
|
DBSCANClusterer(double eps,
int minPts,
DistanceMeasure measure)
Creates a new instance of a DBSCANClusterer.
|
| Constructor and Description |
|---|
CMAESOptimizer.Sigma(double[] s) |
| Constructor and Description |
|---|
CMAESOptimizer.Sigma(double[] s) |
| Modifier and Type | Method and Description |
|---|---|
int |
RandomDataGenerator.nextHypergeometric(int populationSize,
int numberOfSuccesses,
int sampleSize)
Generates a random value from the
Hypergeometric Distribution. |
int |
RandomDataImpl.nextHypergeometric(int populationSize,
int numberOfSuccesses,
int sampleSize)
Deprecated.
Generates a random value from the
Hypergeometric Distribution. |
double[] |
HaltonSequenceGenerator.skipTo(int index)
Skip to the i-th point in the Halton sequence.
|
double[] |
SobolSequenceGenerator.skipTo(int index)
Skip to the i-th point in the Sobol sequence.
|
| Constructor and Description |
|---|
DBSCANClusterer(double eps,
int minPts)
Deprecated.
Creates a new instance of a DBSCANClusterer.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
TestUtils.chiSquare(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquare(double[] expected,
long[] observed)
|
static double |
TestUtils.chiSquare(long[][] counts) |
double |
ChiSquareTest.chiSquare(long[][] counts)
Computes the Chi-Square statistic associated with a
chi-square test of independence based on the input
counts
array, viewed as a two-way table. |
static double |
TestUtils.chiSquareDataSetsComparison(long[] observed1,
long[] observed2) |
double |
ChiSquareTest.chiSquareDataSetsComparison(long[] observed1,
long[] observed2)
Computes a
Chi-Square two sample test statistic comparing bin frequency counts
in
observed1 and observed2. |
static double |
TestUtils.chiSquareTest(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed)
Returns the observed significance level, or
p-value, associated with a
Chi-square goodness of fit test comparing the
observed
frequency counts to those in the expected array. |
static boolean |
TestUtils.chiSquareTest(double[] expected,
long[] observed,
double alpha) |
boolean |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed,
double alpha)
Performs a
Chi-square goodness of fit test evaluating the null hypothesis that the
observed counts conform to the frequency distribution described by the expected
counts, with significance level
alpha. |
static double |
TestUtils.chiSquareTest(long[][] counts) |
double |
ChiSquareTest.chiSquareTest(long[][] counts)
Returns the observed significance level, or
p-value, associated with a
chi-square test of independence based on the input
counts
array, viewed as a two-way table. |
static boolean |
TestUtils.chiSquareTest(long[][] counts,
double alpha) |
boolean |
ChiSquareTest.chiSquareTest(long[][] counts,
double alpha)
Performs a
chi-square test of independence evaluating the null hypothesis that the
classifications represented by the counts in the columns of the input 2-way table
are independent of the rows, with significance level
alpha. |
static double |
TestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2) |
double |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2)
Returns the observed significance level, or
p-value, associated with a Chi-Square two sample test comparing
bin frequency counts in
observed1 and
observed2. |
static boolean |
TestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
boolean |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha)
Performs a Chi-Square two sample test comparing two binned data
sets.
|
static double |
TestUtils.g(double[] expected,
long[] observed) |
double |
GTest.g(double[] expected,
long[] observed)
|
static double |
TestUtils.gDataSetsComparison(long[] observed1,
long[] observed2) |
double |
GTest.gDataSetsComparison(long[] observed1,
long[] observed2)
Computes a G (Log-Likelihood Ratio) two sample test statistic for
independence comparing frequency counts in
observed1 and observed2. |
static double |
TestUtils.gTest(double[] expected,
long[] observed) |
double |
GTest.gTest(double[] expected,
long[] observed)
Returns the observed significance level, or p-value,
associated with a G-Test for goodness of fit comparing the
observed frequency counts to those in the expected array. |
static boolean |
TestUtils.gTest(double[] expected,
long[] observed,
double alpha) |
boolean |
GTest.gTest(double[] expected,
long[] observed,
double alpha)
Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit
evaluating the null hypothesis that the observed counts conform to the
frequency distribution described by the expected counts, with
significance level
alpha. |
static double |
TestUtils.gTestDataSetsComparison(long[] observed1,
long[] observed2) |
double |
GTest.gTestDataSetsComparison(long[] observed1,
long[] observed2)
Returns the observed significance level, or
p-value, associated with a G-Value (Log-Likelihood Ratio) for two
sample test comparing bin frequency counts in
observed1 and
observed2. |
static boolean |
TestUtils.gTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
boolean |
GTest.gTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha)
Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned
data sets.
|
static double |
TestUtils.gTestIntrinsic(double[] expected,
long[] observed) |
double |
GTest.gTestIntrinsic(double[] expected,
long[] observed)
Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described
in p64-69 of McDonald, J.H.
|
static double |
TestUtils.rootLogLikelihoodRatio(long k11,
long k12,
long k21,
long k22) |
| Modifier and Type | Method and Description |
|---|---|
ConfidenceInterval |
BinomialConfidenceInterval.createInterval(int numberOfTrials,
int numberOfSuccesses,
double confidenceLevel)
Create a confidence interval for the true probability of success
of an unknown binomial distribution with the given observed number
of trials, successes and confidence level.
|
| Modifier and Type | Method and Description |
|---|---|
static long |
CombinatoricsUtils.binomialCoefficient(int n,
int k)
Returns an exact representation of the Binomial
Coefficient, "
n choose k", the number of
k-element subsets that can be selected from an
n-element set. |
static long |
ArithmeticUtils.binomialCoefficient(int n,
int k)
Deprecated.
|
static double |
CombinatoricsUtils.binomialCoefficientDouble(int n,
int k)
Returns a
double representation of the Binomial
Coefficient, "n choose k", the number of
k-element subsets that can be selected from an
n-element set. |
static double |
ArithmeticUtils.binomialCoefficientDouble(int n,
int k)
Deprecated.
|
static double |
CombinatoricsUtils.binomialCoefficientLog(int n,
int k)
Returns the natural
log of the Binomial
Coefficient, "n choose k", the number of
k-element subsets that can be selected from an
n-element set. |
static double |
ArithmeticUtils.binomialCoefficientLog(int n,
int k)
Deprecated.
|
static void |
CombinatoricsUtils.checkBinomial(int n,
int k)
Check binomial preconditions.
|
static void |
MathArrays.checkNonNegative(long[] in)
Check that all entries of the input array are >= 0.
|
static void |
MathArrays.checkNonNegative(long[][] in)
Check all entries of the input array are >= 0.
|
static long |
CombinatoricsUtils.factorial(int n)
Returns n!.
|
static long |
ArithmeticUtils.factorial(int n)
Deprecated.
|
static double |
CombinatoricsUtils.factorialDouble(int n)
|
static double |
ArithmeticUtils.factorialDouble(int n)
Deprecated.
|
static double |
CombinatoricsUtils.factorialLog(int n)
Compute the natural logarithm of the factorial of
n. |
static double |
ArithmeticUtils.factorialLog(int n)
Deprecated.
|
static BigInteger |
ArithmeticUtils.pow(BigInteger k,
BigInteger e)
Raise a BigInteger to a BigInteger power.
|
static BigInteger |
ArithmeticUtils.pow(BigInteger k,
int e)
Raise a BigInteger to an int power.
|
static BigInteger |
ArithmeticUtils.pow(BigInteger k,
long e)
Raise a BigInteger to a long power.
|
static int |
ArithmeticUtils.pow(int k,
int e)
Raise an int to an int power.
|
static int |
ArithmeticUtils.pow(int k,
long e)
Deprecated.
As of 3.3. Please use
ArithmeticUtils.pow(int,int) instead. |
static long |
ArithmeticUtils.pow(long k,
int e)
Raise a long to an int power.
|
static long |
ArithmeticUtils.pow(long k,
long e)
Deprecated.
As of 3.3. Please use
ArithmeticUtils.pow(long,int) instead. |
static long |
CombinatoricsUtils.stirlingS2(int n,
int k)
Returns the
Stirling number of the second kind, "
S(n,k)", the number of
ways of partitioning an n-element set into k non-empty
subsets. |
static long |
ArithmeticUtils.stirlingS2(int n,
int k)
Deprecated.
|
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