vectorz-clj is designed so that you don't have to compromise, offering both:
- An idiomatic high-level Clojure API using core.matrix
- General purpose multi-dimensional arrays
- High performance (about as fast as you can get on the JVM). vectorz-clj is currently the fastest pure-JVM vector/matrix library available for Clojure
The library was originally designed for games, simulations and machine learning applications,
but should be applicable for any situations where you need numerical
- "Pure" functions for an idiomatic functional programming style are provided. These return new vectors without mutating their arguments.
- Primitive-backed special purpose vectors and matrices for performance, e.g.
Vector3for fast 3D maths.
- Flexible DSL-style functions for manipulating vectors and matrices, e.g. the ability to create a "view" into a subspace of a large vector.
- core.matrix fully supported - see: https://github.com/mikera/core.matrix
- Pure JVM code - no native dependencies
- "Impure" functions that mutate vectors are available for performance when you need it: i.e. you can use a nice functional style most of the time, but switch to mutation when you hit a bottleneck.
For more information see the vectorz-clj Wiki.
vectorz-clj requires Clojure 1.4 or above, and an up to date version of
vectorz-clj is reasonably stable, and implements all of the
core.matrix API feature set. The
is still under development, so users may expect some minor changes to the API in future releases.
vectorz-clj is licensed under the LGPL license:
Follow the instructions to install with Leiningen / Maven from Clojars:
You can then use
Vectorz as a standard
core.matrix implementation. Example:
(use 'clojure.core.matrix) (use 'clojure.core.matrix.operators) ;; overrides *, + etc. for matrices (set-current-implementation :vectorz) ;; use Vectorz as default matrix implementation ;; define a 2x2 Matrix (def M (matrix [[1 2] [3 4]])) M => #<Matrix22 [[1.0,2.0][3.0,4.0]]> ;; define a length 2 vector (a 1D matrix is considered equivalent to a vector in core.matrix) (def v (matrix [1 2])) v => #<Vector2 [1.0,2.0]> ;; Matrix x Vector elementwise multiply (mul M v) => #<Matrix22 [[1.0,4.0],[3.0,8.0]]> ;; Matrix x Vector matrix multiply (inner product) (inner-product M v) => #<Vector2 [5.0,11.0]>
For more examples see Wiki Examples