NMatrix¶ ↑

Fast Numerical Linear Algebra Library for Ruby

• sciruby.com

• Google+

• Google Group - Mailing List

• NMatrix Installation wiki

• SciRuby Installation guide

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Description¶ ↑

NMatrix is a fast numerical linear algebra library for Ruby, with dense and sparse matrices, written mostly in C and C++. It is part of the SciRuby project.

NMatrix was inspired by NArray, by Masahiro Tanaka.

Installation¶ ↑

To install the latest stable version:

gem install nmatrix --pre

However, you will need to install ATLAS with CBLAS (C interface to BLAS) first. Detailed directions can be found here. The requirements for NMatrix are:

• ATLAS, preferably with CLAPACK (see here for details)

• a version of GCC or clang which supports C++0x or C++11

• Ruby 1.9.3+

• packable 1.3.5 (used for I/O)

If you want to obtain the latest (development) code, you should generally do:

git clone https://github.com/SciRuby/nmatrix.git
cd nmatrix/
bundle install
bundle exec rake compile
bundle exec rake repackage
gem install pkg/nmatrix-0.1.0.rc5.gem

Detailed instructions are available for Mac and Linux. We are currently working on Mavericks (Mac OS X) installation instructions, but in general, you’ll need Homebrew and should probably use +brew install gcc48+ instead of using the install script.

Documentation¶ ↑

If you have a suggestion or want to add documentation for any class or method in NMatrix, please open an issue or send a pull request with the changes.

You can find the complete API documentation on our website.

Examples¶ ↑

Create a new NMatrix from a ruby Array:

>> require 'nmatrix'
>> NMatrix.new([2, 3], [0, 1, 2, 3, 4, 5], dtype: :int64)
=> [
    [0, 1, 2],
    [3, 4, 5]
   ]

Create a new NMatrix using the N shortcut:

>> m = N[ [2, 3, 4], [7, 8, 9] ]
=> [
    [2, 3, 4],
    [7, 8, 9]
   ]
>> m.inspect
=> #<NMatrix:0x007f8e121b6cf8shape:[2,3] dtype:int32 stype:dense>

The above output requires that you have a pretty-print-enabled console such as Pry; otherwise, you’ll see the output given by inspect.

If you want to learn more about how to create a matrix, read the guide in our wiki.

Again, you can find the complete API documentation on our website.

Developers¶ ↑

Read the instructions in CONTRIBUTING.md if you want to help NMatrix.

Features¶ ↑

The following features exist in the current version of NMatrix (0.1.0.rc1):

• Matrix and vector storage containers: dense, yale, list (more to come)

• Data types: byte (uint8), int8, int16, int32, int64, float32, float64, complex64, complex128, rational64, rational128, Ruby object

• Interconversion between storage and data types

• Element-wise and right-hand-scalar operations and comparisons for all matrix types

• Matrix-matrix multiplication for dense (with and without ATLAS) and yale

• Matrix-vector multiplication for dense (with and without ATLAS)

• Lots of enumerators (each, each_with_indices, each_row, each_column, each_rank, map, etc.)

• Matrix slicing by copy and reference (for dense, yale, and list)

• Native reading and writing of dense and yale matrices

  • Optional compression for dense matrices with symmetry or triangularity: symmetric, skew, hermitian, upper, lower

• Input/output:

  • Matlab .MAT v5 file input

  • MatrixMarket file input/output

  • Harwell-Boeing and Fortran file input

  • Point Cloud Library PCD file input

• C and C++ API

• BLAS internal implementations (no library) and ATLAS (with library) access:

  • Level 1: xROT, xROTG (BLAS dtypes only), xASUM, xNRM2, IxAMAX

  • Level 2: xGEMV

  • Level 3: xGEMM, xTRSM

• LAPACK ATLAS access:

  • xGETRF, xGETRI, xGETRS, xGESV (Gaussian elimination)

  • xPOTRF, xPOTRI, xPOTRS, xPOSV (Cholesky factorization)

  • xLASWP, xSCAL, xLAUUM

• LAPACK-less internal implementations (no LAPACK needed and working on non-BLAS dtypes):

  • xGETRF

  • xLASWP, xSCAL

  • xLAUUM (no LAPACK needed, but BLAS dtypes only)

• LAPACK (non-ATLAS) access:

  • xGESVD, xGESDD (singular value decomposition)

  • xGEEV (eigenvalue decomposition of a asymmetric square matrices)

• LU decomposition

• Matrix inversions (requires LAPACK; BLAS dtypes only)

• Determinant calculation for BLAS dtypes

• Vector 2-norms

• Traces

• Ruby/GSL interoperability (requires SciRuby’s fork of rb-gsl)

• slice assignments, e.g.,

  x[1..3,0..4] = some_other_matrix
  

Planned Features (Short-to-Medium Term)¶ ↑

We are nearing the release of NMatrix 0.1.0, our first beta.

These are features planned for NMatrix 0.2.0:

• LAPACK-free calculation of determinant, trace, and eigenvalues (characteristic polynomial)

• LAPACK-free matrix inversions

• tensor products

• improved file I/O

  • compression of yale symmetries in I/O

• optimization of non-BLAS data types on BLAS-like operations (e.g., matrix multiplication for rational numbers)

License¶ ↑

Copyright © 2012–14, John Woods and the Ruby Science Foundation.

All rights reserved.

NMatrix, along with SciRuby, is licensed under the BSD 2-clause license. See LICENSE.txt for details.

Donations¶ ↑

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