Package | Description |
---|---|
org.apache.commons.math3.analysis.integration |
Numerical integration (quadrature) algorithms for univariate real functions.
|
org.apache.commons.math3.analysis.polynomials |
Univariate real polynomials implementations, seen as differentiable
univariate real functions.
|
org.apache.commons.math3.complex |
Complex number type and implementations of complex transcendental
functions.
|
org.apache.commons.math3.fraction |
Fraction number type and fraction number formatting.
|
org.apache.commons.math3.linear |
Linear algebra support.
|
org.apache.commons.math3.random |
Random number and random data generators.
|
org.apache.commons.math3.stat.descriptive |
Generic univariate summary statistic objects.
|
org.apache.commons.math3.stat.descriptive.moment |
Summary statistics based on moments.
|
org.apache.commons.math3.stat.descriptive.rank |
Summary statistics based on ranks.
|
org.apache.commons.math3.stat.descriptive.summary |
Other summary statistics.
|
org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing and confidence interval
construction.
|
org.apache.commons.math3.util |
Convenience routines and common data structures used throughout the commons-math library.
|
Modifier and Type | Method and Description |
---|---|
double |
BaseAbstractUnivariateIntegrator.integrate(int maxEval,
UnivariateFunction f,
double lower,
double upper)
Integrate the function in the given interval.
|
double |
UnivariateIntegrator.integrate(int maxEval,
UnivariateFunction f,
double min,
double max)
Integrate the function in the given interval.
|
protected void |
BaseAbstractUnivariateIntegrator.setup(int maxEval,
UnivariateFunction f,
double lower,
double upper)
Prepare for computation.
|
Modifier and Type | Method and Description |
---|---|
protected static double[] |
PolynomialFunction.differentiate(double[] coefficients)
Returns the coefficients of the derivative of the polynomial with the given coefficients.
|
protected static double |
PolynomialFunction.evaluate(double[] coefficients,
double argument)
Uses Horner's Method to evaluate the polynomial with the given coefficients at
the argument.
|
Constructor and Description |
---|
PolynomialFunction(double[] c)
Construct a polynomial with the given coefficients.
|
Modifier and Type | Method and Description |
---|---|
Complex |
Complex.add(Complex addend)
Returns a
Complex whose value is
(this + addend) . |
Complex |
Complex.divide(Complex divisor)
Returns a
Complex whose value is
(this / divisor) . |
Complex |
Complex.multiply(Complex factor)
Returns a
Complex whose value is this * factor . |
Complex |
Complex.pow(Complex x)
Returns of value of this complex number raised to the power of
x . |
Complex |
Complex.subtract(Complex subtrahend)
Returns a
Complex whose value is
(this - subtrahend) . |
Modifier and Type | Method and Description |
---|---|
BigFraction |
BigFraction.add(BigInteger bg)
Adds the value of this fraction to the passed
BigInteger ,
returning the result in reduced form. |
Modifier and Type | Method and Description |
---|---|
protected static void |
PreconditionedIterativeLinearSolver.checkParameters(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x0)
Performs all dimension checks on the parameters of
solve
and
solveInPlace ,
and throws an exception if one of the checks fails. |
protected static void |
IterativeLinearSolver.checkParameters(RealLinearOperator a,
RealVector b,
RealVector x0)
Performs all dimension checks on the parameters of
solve and
solveInPlace ,
and throws an exception if one of the checks fails. |
void |
AbstractRealMatrix.setSubMatrix(double[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
row, column using data in
the input subMatrix array. |
void |
RealMatrix.setSubMatrix(double[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
row, column using data in
the input subMatrix array. |
void |
BlockRealMatrix.setSubMatrix(double[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
row, column using data in
the input subMatrix array. |
RealVector |
PreconditionedIterativeLinearSolver.solve(RealLinearOperator a,
RealLinearOperator minv,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealLinearOperator minv,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
boolean goodb,
double shift)
Returns an estimate of the solution to the linear system (A - shift
· I) · x = b.
|
RealVector |
PreconditionedIterativeLinearSolver.solve(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
PreconditionedIterativeLinearSolver.solve(RealLinearOperator a,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
IterativeLinearSolver.solve(RealLinearOperator a,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealVector b,
boolean goodb,
double shift)
Returns the solution to the system (A - shift · I) · x = b.
|
RealVector |
PreconditionedIterativeLinearSolver.solve(RealLinearOperator a,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealVector b,
RealVector x)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
IterativeLinearSolver.solve(RealLinearOperator a,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
ConjugateGradient.solveInPlace(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
abstract RealVector |
PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solveInPlace(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solveInPlace(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x,
boolean goodb,
double shift)
Returns an estimate of the solution to the linear system (A - shift
· I) · x = b.
|
RealVector |
PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solveInPlace(RealLinearOperator a,
RealVector b,
RealVector x)
Returns an estimate of the solution to the linear system A · x =
b.
|
abstract RealVector |
IterativeLinearSolver.solveInPlace(RealLinearOperator a,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
Constructor and Description |
---|
Array2DRowFieldMatrix(Field<T> field,
T[][] d,
boolean copyArray)
Create a new
FieldMatrix<T> using the input array as the underlying
data array. |
Array2DRowRealMatrix(double[][] d)
Create a new
RealMatrix using the input array as the underlying
data array. |
IterativeLinearSolver(IterationManager manager)
Creates a new instance of this class, with custom iteration manager.
|
PreconditionedIterativeLinearSolver(IterationManager manager)
Creates a new instance of this class, with custom iteration manager.
|
Modifier and Type | Method and Description |
---|---|
void |
EmpiricalDistribution.load(double[] in)
Computes the empirical distribution from the provided
array of numbers.
|
void |
EmpiricalDistribution.load(File file)
Computes the empirical distribution from the input file.
|
void |
EmpiricalDistribution.load(URL url)
Computes the empirical distribution using data read from a URL.
|
Modifier and Type | Method and Description |
---|---|
static void |
DescriptiveStatistics.copy(DescriptiveStatistics source,
DescriptiveStatistics dest)
Copies source to dest.
|
static void |
SummaryStatistics.copy(SummaryStatistics source,
SummaryStatistics dest)
Copies source to dest.
|
static void |
SynchronizedDescriptiveStatistics.copy(SynchronizedDescriptiveStatistics source,
SynchronizedDescriptiveStatistics dest)
Copies source to dest.
|
static void |
SynchronizedSummaryStatistics.copy(SynchronizedSummaryStatistics source,
SynchronizedSummaryStatistics dest)
Copies source to dest.
|
Modifier and Type | Method and Description |
---|---|
static void |
GeometricMean.copy(GeometricMean source,
GeometricMean dest)
Copies source to dest.
|
static void |
Kurtosis.copy(Kurtosis source,
Kurtosis dest)
Copies source to dest.
|
static void |
Mean.copy(Mean source,
Mean dest)
Copies source to dest.
|
static void |
SecondMoment.copy(SecondMoment source,
SecondMoment dest)
Copies source to dest.
|
static void |
SemiVariance.copy(SemiVariance source,
SemiVariance dest)
Copies source to dest.
|
static void |
Skewness.copy(Skewness source,
Skewness dest)
Copies source to dest.
|
static void |
StandardDeviation.copy(StandardDeviation source,
StandardDeviation dest)
Copies source to dest.
|
static void |
Variance.copy(Variance source,
Variance dest)
Copies source to dest.
|
Modifier and Type | Method and Description |
---|---|
static void |
Max.copy(Max source,
Max dest)
Copies source to dest.
|
static void |
Min.copy(Min source,
Min dest)
Copies source to dest.
|
static void |
Percentile.copy(Percentile source,
Percentile dest)
Copies source to dest.
|
Modifier and Type | Method and Description |
---|---|
static void |
Product.copy(Product source,
Product dest)
Copies source to dest.
|
static void |
SumOfLogs.copy(SumOfLogs source,
SumOfLogs dest)
Copies source to dest.
|
static void |
SumOfSquares.copy(SumOfSquares source,
SumOfSquares dest)
Copies source to dest.
|
static void |
Sum.copy(Sum source,
Sum dest)
Copies source to dest.
|
Modifier and Type | Method and Description |
---|---|
double |
OneWayAnova.anovaFValue(Collection<double[]> categoryData)
Computes the ANOVA F-value for a collection of
double[]
arrays. |
double |
OneWayAnova.anovaPValue(Collection<double[]> categoryData)
Computes the ANOVA P-value for a collection of
double[]
arrays. |
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.
|
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.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 . |
double |
TTest.homoscedasticT(double[] sample1,
double[] sample2)
Computes a 2-sample t statistic, under the hypothesis of equal
subpopulation variances.
|
static double |
TestUtils.homoscedasticT(double[] sample1,
double[] sample2) |
double |
TTest.homoscedasticT(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2)
Computes a 2-sample t statistic, comparing the means of the datasets
described by two
StatisticalSummary instances, under the
assumption of equal subpopulation variances. |
static double |
TestUtils.homoscedasticT(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
double |
TTest.homoscedasticTTest(double[] sample1,
double[] sample2)
Returns the observed significance level, or
p-value, associated with a two-sample, two-tailed t-test
comparing the means of the input arrays, under the assumption that
the two samples are drawn from subpopulations with equal variances.
|
static double |
TestUtils.homoscedasticTTest(double[] sample1,
double[] sample2) |
boolean |
TTest.homoscedasticTTest(double[] sample1,
double[] sample2,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that
sample1
and sample2 are drawn from populations with the same mean,
with significance level alpha , assuming that the
subpopulation variances are equal. |
static boolean |
TestUtils.homoscedasticTTest(double[] sample1,
double[] sample2,
double alpha) |
double |
TTest.homoscedasticTTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2)
Returns the observed significance level, or
p-value, associated with a two-sample, two-tailed t-test
comparing the means of the datasets described by two StatisticalSummary
instances, under the hypothesis of equal subpopulation variances.
|
static double |
TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
double |
MannWhitneyUTest.mannWhitneyU(double[] x,
double[] y)
Computes the Mann-Whitney
U statistic comparing mean for two independent samples possibly of
different length.
|
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.oneWayAnovaFValue(Collection<double[]> categoryData) |
static double |
TestUtils.oneWayAnovaPValue(Collection<double[]> categoryData) |
static boolean |
TestUtils.oneWayAnovaTest(Collection<double[]> categoryData,
double alpha) |
double |
TTest.pairedT(double[] sample1,
double[] sample2)
Computes a paired, 2-sample t-statistic based on the data in the input
arrays.
|
static double |
TestUtils.pairedT(double[] sample1,
double[] sample2) |
double |
TTest.pairedTTest(double[] sample1,
double[] sample2)
Returns the observed significance level, or
p-value, associated with a paired, two-sample, two-tailed t-test
based on the data in the input arrays.
|
static double |
TestUtils.pairedTTest(double[] sample1,
double[] sample2) |
boolean |
TTest.pairedTTest(double[] sample1,
double[] sample2,
double alpha)
Performs a paired t-test evaluating the null hypothesis that the
mean of the paired differences between
sample1 and
sample2 is 0 in favor of the two-sided alternative that the
mean paired difference is not equal to 0, with significance level
alpha . |
static boolean |
TestUtils.pairedTTest(double[] sample1,
double[] sample2,
double alpha) |
double |
TTest.t(double[] sample1,
double[] sample2)
Computes a 2-sample t statistic, without the hypothesis of equal
subpopulation variances.
|
static double |
TestUtils.t(double[] sample1,
double[] sample2) |
double |
TTest.t(double mu,
double[] observed)
Computes a
t statistic given observed values and a comparison constant.
|
static double |
TestUtils.t(double mu,
double[] observed) |
double |
TTest.t(double mu,
StatisticalSummary sampleStats)
|
static double |
TestUtils.t(double mu,
StatisticalSummary sampleStats) |
double |
TTest.t(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2)
Computes a 2-sample t statistic , comparing the means of the datasets
described by two
StatisticalSummary instances, without the
assumption of equal subpopulation variances. |
static double |
TestUtils.t(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
double |
TTest.tTest(double[] sample1,
double[] sample2)
Returns the observed significance level, or
p-value, associated with a two-sample, two-tailed t-test
comparing the means of the input arrays.
|
static double |
TestUtils.tTest(double[] sample1,
double[] sample2) |
boolean |
TTest.tTest(double[] sample1,
double[] sample2,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that
sample1
and sample2 are drawn from populations with the same mean,
with significance level alpha . |
static boolean |
TestUtils.tTest(double[] sample1,
double[] sample2,
double alpha) |
double |
TTest.tTest(double mu,
double[] sample)
Returns the observed significance level, or
p-value, associated with a one-sample, two-tailed t-test
comparing the mean of the input array with the constant
mu . |
static double |
TestUtils.tTest(double mu,
double[] sample) |
boolean |
TTest.tTest(double mu,
double[] sample,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that the mean of the population from
which
sample is drawn equals mu . |
static boolean |
TestUtils.tTest(double mu,
double[] sample,
double alpha) |
double |
TTest.tTest(double mu,
StatisticalSummary sampleStats)
Returns the observed significance level, or
p-value, associated with a one-sample, two-tailed t-test
comparing the mean of the dataset described by
sampleStats
with the constant mu . |
static double |
TestUtils.tTest(double mu,
StatisticalSummary sampleStats) |
boolean |
TTest.tTest(double mu,
StatisticalSummary sampleStats,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that the mean of the
population from which the dataset described by
stats is
drawn equals mu . |
static boolean |
TestUtils.tTest(double mu,
StatisticalSummary sampleStats,
double alpha) |
double |
TTest.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2)
Returns the observed significance level, or
p-value, associated with a two-sample, two-tailed t-test
comparing the means of the datasets described by two StatisticalSummary
instances.
|
static double |
TestUtils.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
boolean |
TTest.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2,
double alpha)
Performs a
two-sided t-test evaluating the null hypothesis that
sampleStats1 and sampleStats2 describe
datasets drawn from populations with the same mean, with significance
level alpha . |
static boolean |
TestUtils.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2,
double alpha) |
double |
WilcoxonSignedRankTest.wilcoxonSignedRank(double[] x,
double[] y)
Computes the
Wilcoxon signed ranked statistic comparing mean for two related
samples or repeated measurements on a single sample.
|
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 |
---|---|
static void |
MathUtils.checkNotNull(Object o)
Checks that an object is not null.
|
static void |
ResizableDoubleArray.copy(ResizableDoubleArray source,
ResizableDoubleArray dest)
Copies source to dest, copying the underlying data, so dest is
a new, independent copy of source.
|
double |
DefaultTransformer.transform(Object o) |
Constructor and Description |
---|
ResizableDoubleArray(ResizableDoubleArray original)
Copy constructor.
|
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