Apache Commons Math can be considered an extension to the Java core and Apache Commons Lang packages, addressing various loopholes regarding mathematical operations and operators.
There are many smaller packages inside the Commons Math component, each targeting one or more niche operation, operator, or algorithm.
Some of the areas the Commons Math packages can be used are:
- arithmetic and geometric means
- variance and standard deviation
- sum, product, log sum, sum of squared values
- minimum, maximum, median, and percentiles
- skewness and kurtosis
- first, second, third and fourth moments
- frequency distributions
- simple regression
- multiple regression
- rank transformations
- covariance and correlation
- statistical tests
- generating random numbers
- generating random vectors
- generating random strings
- generating cryptographically secure sequences of random numbers or strings
- generating random samples and permutations
- analyzing distributions of values in an input file and generating values "like" the values in the file
- generating data for grouped frequency distributions or histograms
- matrix addition, subtraction, multiplication
- scalar addition and multiplication
- transpose
- norm and trace
- operation on a vector
- vector addition, subtraction
- element by element multiplication, division
- scalar addition, subtraction, multiplication, division and power
- mapping of mathematical functions (cos, sin ...)
- dot product, outer product
- distance and norm according to norms L1, L2 and Linf-
- solving linear systems
- eigenvalues/eigenvectors and singular values/singular vectors
- non-real fields (complex, fractions ...)
- root finding
- interpolation
- integration
- numerical analysis
- polynomials
- differentiation
- Erf functions
- Gamma functions
- Beta functions
- double array utilities
- int/double hash map
- continued fractions
- fast mathematical functions
- binomial coefficients, factorials, Stirling numbers and other common math functions
- complex numbers
- complex transcendental functions
- complex formatting and parsing
- probability distributions
- fraction numbers
- fraction formatting and parsing
- transform methods
- 3D geometry
- Euclidean spaces
- n-Sphere
- binary space partitioning
- univariate functions
- ordinary differential equations
- genetic algorithms
- Kalman filter
- curve fitting
- clustering algorithms
- distance measures
Documentation is of course included for each of these packages.
What is new in this release:
- Framework for creating artificial neural nets
- Self organizing feature maps
- Computational geometry algorithms (convex hull, enclosing ball)
- Performance improvements of the linear simplex solver
- Refactoring of curve fitters
- Low-discrepancy random generators (sobol, halton)
- Least-squares fitting
What is new in version 3.5:
- Framework for creating artificial neural nets
- Self organizing feature maps
- Computational geometry algorithms (convex hull, enclosing ball)
- Performance improvements of the linear simplex solver
- Refactoring of curve fitters
- Low-discrepancy random generators (sobol, halton)
- Least-squares fitting
What is new in version 3.4.1:
- Framework for creating artificial neural nets
- Self organizing feature maps
- Computational geometry algorithms (convex hull, enclosing ball)
- Performance improvements of the linear simplex solver
- Refactoring of curve fitters
- Low-discrepancy random generators (sobol, halton)
- Least-squares fitting
What is new in version 3.1:
- All contents of package "o.a.c.m.optimization" refactored into new packages "o.a.c.m.optimization" and "o.a.c.m.fitting".
- DBSCAN clustering algorithm (in package "o.a.c.m.stat.clustering").
- Added element-by-element addition, subtraction, multiplication and division (in class "o.a.c.m.util.MathArrays").
- New constructor in the custom checker classes (package "o.a.c.m.optimization") for passing the number of iterations after which the "convergence test" will return true. This allows an algorithm to return the best solution found (after the user-defined number of iterations) even if it does not meet the other convergence criteria.
- Added a new "SynchronizedRandomGenerator" that wraps another "RandomGenerator" with all methods being synchronized, thus rendering the code thread-safe (at some efficiency cost).
- Added new "NaNStrategy": FAILED, used in "RankingAlgorithm" implementations. Any encountered input value that succeeds a "Double#isNaN" check, results in a "NotANumberException".
What is new in version 2.2:
- This is primarily a maintenance release, but it also includes new features and enhancements. Users of version 2.1 are encouraged to upgrade to 2.2, as this release includes some important bug fixes.
What is new in version 2.0:
- Fixed an error induced by entries set to 0 in simplex solver.
- Removed an unused argument in a private method in simplex solver.
- Changed probability calculations for Binomial, Poisson, and Hypergeometric distributions to use Catherine Loader's saddle point approximations.
- Removed dead code from Complex#divide.
- Added support for weighted descriptive statistics.
Requirements:
- Java 5 or higher
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