Mir

Generic Numerical Library for Science and Machine Learning.

Documentation

Documentation API can be found here. Read also the Mir blog.

mir.glas - Generic Linear Algebra Subroutines
mir.ndslice Multidimensional Random Access Ranges and Arrays
mir.sparse Sparse Tensors
Sparse - DOK format
Different ranges for COO format
CompressedTensor - CSR/CSC formats
mir.sparse.blas - Sparse BLAS for CompressedTensor
mir.model.lda.hoffman - Online variational Bayes for latent Dirichlet allocation (Online VB LDA) for sparse documents. LDA is used for topic modeling.
mir.combinatorics Combinations, combinations with repeats, cartesian power, permutations.
mir.random.flex - Tinfex non-uniform RNG implementation.
mir.las.sum Functions and Output Ranges for Summation Algorithms. Works with user-defined types.
Precise algorithm: improved analog of Python's fsum
Pairwise algorithm: fast version for Input Ranges
Kahan, KBN, and KB2 algorithms

Mir can be compiled with LDC (LLVM D Compiler) >= 1.1.0-beta2 only. DMD is not supported anymore.

	Linux	Mac OS X	Windows
64-bit
32-bit		N/A	N/A

Dub is the D's package manager. You can create a new project with:

dub init <project-name>

Now you need to edit the dub.json add mir as dependency.

{
	...
	"dependencies": {
		"mir": "~><current-version>"
	},
	"dflags-ldc": ["-mcpu=native"]
}

Now you can create an app.d file in the source folder and run your code with

dub --compiler=ldmd2

Flag --build=release and can be added for a performance boost:

dub --compiler=ldmd2 --build=release

ldmd2 is a shell on top of LDC (LLVM D Compiler).

"dflags-ldc": ["-mcpu=native"] allows LDC to optimize Mir for your CPU.

See our TODO List. Mir is very young and we are open for contributing to source code, documentation, examples and benchmarks.

mir.ndslice is a development version of the std.experimental.ndslice package.