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Showing content from https://github.com/libmir/mir below:

libmir/mir: Mir (backports): Sparse tensors, Hoffman

The last Mir version with old ndslice is v0.22.1.

Generic Numerical Library for Science and Machine Learning.

• Mir Algorithm - Multidimensional arrays (ndslice), iterators, algorithms.
• Mir Random - Professional Random Number Generators
• Mir GLAS - Linear Algebra Library (Experimental, not supported for now)
• Mir BLAS - Bindings to libraries with CBLAS API like OpenBLAS and Intel MKL.
• Mir LAPACK - Bindings to libraries with LAPACK API like OpenBLAS and Intel MKL.
• Mir Optim - Nonlinear Solvers.
• Mir CPUID - CPU Identification routines (less buggy then Phobos).

Documentation API can be found here.

• mir.glas - Generic Linear Algebra Subroutines
• 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.

/+dub.sdl:
dependency "mir" version="~>3.1.0"
+/
import std.stdio;
import mir.combinatorics;
void main(string[] args)
{
    writeln([1, 2].combinations);
}

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

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

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.

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