Apache Commons Math ™
Last Published: 25 June 2015
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Version: 3.5
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Contents
Overview
Statistics
Data Generation
Linear Algebra
Numerical Analysis
Special Functions
Utilities
Complex Numbers
Distributions
Fractions
Transform Methods
3D Geometry
Optimization
Ordinary Differential Equations
Genetic Algorithms
Filters
Fitting
Machine Learning
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Table of Contents
0. Overview
0.1 About the User Guide
0.2 What's in commons-math
0.3 How commons-math is organized
0.4 How interface contracts are specified in commons-math javadoc
0.5 Dependencies
1. Statistics
1.1 Overview
1.2 Descriptive statistics
1.3 Frequency distributions
1.4 Simple regression
1.5 Multiple Regression
1.6 Rank transformations
1.7 Covariance and correlation
1.8 Statistical Tests
2. Data Generation
2.1 Overview
2.2 Random numbers
2.3 Random Vectors
2.4 Random Strings
2.5 Random permutations, combinations, sampling
2.6 Generating data 'like' an input file
2.7 PRNG Pluggability
3. Linear Algebra
3.1 Overview
3.2 Real matrices
3.3 Real vectors
3.4 Solving linear systems
3.5 Eigenvalues/eigenvectors and singular values/singular vectors
3.6 Non-real fields (complex, fractions ...)
4. Numerical Analysis
4.1 Overview
4.2 Error handling
4.3 Root-finding
4.4 Interpolation
4.5 Integration
4.6 Polynomials
4.7 Differentiation
5. Special Functions
5.1 Overview
5.2 Erf functions
5.3 Gamma functions
5.4 Beta funtions
6. Utilities
6.1 Overview
6.2 Double array utilities
6.3 int/double hash map
6.4 Continued Fractions
6.5 Binomial coefficients, factorials, Stirling numbers and other common math functions
6.6 Fast mathematical functions
6.7 Miscellaneous
7. Complex Numbers
7.1 Overview
7.2 Complex Numbers
7.3 Complex Transcendental Functions
7.4 Complex Formatting and Parsing
8. Probability Distributions
8.1 Overview
8.2 Distribution Framework
8.3 User Defined Distributions
9. Fractions
9.1 Overview
9.2 Fraction Numbers
9.3 Fraction Formatting and Parsing
10. Transform methods
11. Geometry
11.1 Overview
11.2 Euclidean spaces
11.3 n-Sphere
11.4 Binary Space Partitioning
12. Optimization
12.1 Overview
12.2 Univariate Functions
12.3 Linear Programming
12.4 Direct Methods
12.5 General Case
13. Ordinary Differential Equations Integration
13.1 Overview
13.2 Continuous Output
13.3 Discrete Events Handling
13.4 Available Integrators
13.5 Derivatives
14. Genetic Algorithms
14.1 Overview
14.2 GA Framework
14.3 Implementation and Examples
15. Filters
15.1 Overview
15.2 Kalman Filter
16. Exceptions
16.1 Overview
16.2 Unchecked Exceptions
16.3 Hierarchies
16.4 Features
17. Curve Fitting
17.1 Overview
17.2 General Case
17.3 Special Cases
18. Machine Learning
18.1 Overview
18.2 Clustering algorithms and distance measures
18.3 Implementation