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Swift Matrix Library

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최종 버전 다운로드 (.zip)

- - image.csv
  - ndarray.csv
  - x3.npy
  - x4.npy
  - x_test.csv
  - x_train.csv
  - y_test.csv
  - y_train.csv
- - speed.py
- readme.md
- testing.py
- - .name
  - encodings.xml
  - misc.xml
  - modules.xml
  - swix.iml
  - vcs.xml
  - workspace.xml
  - xcode.xml
- - speed.jl
  - speed.m
  - speed.py
  - speed.swift
- - main.swift
- - .LSOverride
  - project.pbxproj
- - AppDelegate.swift
  - Info.plist
  - space_shuttle.png
  - ViewController.swift
- - project.pbxproj
- - Info.plist
  - swix_ios_appTests.swift
- - Info.plist
  - swix_ios_appUITests.swift
- README.md
.gitignore
LICENSE
README.md
swix.podspec

// repository documentation

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## Swift Matrix and Machine Learning Library

*Note: [tensorflow/swift][t4sw] and [apple/swift-numerics/issues/6][asn6] have
or will have more complete support for NumPy-like ndarrays and autodiff. Fast AI
has a good overview: https://www.fast.ai/2019/01/10/swift-numerics/*

*An alternate and much mature library is https://github.com/AlexanderTar/LASwift*

[t4sw]:https://github.com/tensorflow/swift
[asn6]:https://github.com/apple/swift-numerics/issues/6

Apple's Swift is a high level language that's *asking* for some numerical
library to perform computation *fast* or at the very least *easily*. This is a
bare-bones wrapper for that library.

A way to have iOS run high-level code similar to Python or Matlab is something
I've been waiting for, and am incredibly excited to see the results. This will
make porting complex signal processing algorithms to C *much* easier. Porting
from Python/MATLAB to C was (and is) a pain in the butt, and this library aims
to make the conversion between a Python/Matlab algorithm and a mobile app
*simple.*

In most cases, this library calls [Accelerate][accel] or [OpenCV][opencv]. If
you want to speed up some function or add add another feature in those
libraries, feel free to file an issue or submit a pull request (preferred!).

Currently, this library gives you

* operators and various functions (sin, etc) that operate on entire arrays
* helper function (reshape, reverse, delete, repeat, etc)
* easy initializers for 1D and 2D arrays
* complex math (dot product, matrix inversion, eigenvalues, etc)
* machine learning algorithms (SVM, kNN, SVD/PCA, more to come)
* one dimensional Fourier transforms
* speed optimization using Accelerate and OpenCV

When I was crafting this library, I primarily followed the footsteps and
example set by [NumPy][numpy]. For the more complex mathematical functions
(e.g., SVD) I tested it against NumPy. Matlab, at least for the SVD, returns
slightly different output.

Additionally, I followed NumPy's syntax whenever possible. For example, NumPy
and Matlab differ in their initializer called `ones` by `ones((M,N))` and
`ones(M, N)` respectively. If in doubt or getting weird compiler bugs, look at
[NumPy for Matlab users][nfm] or the section on possible swix bugs that may pop
up during the [*Install*][install] or other [*Bugs*][bugs] you may find.

[bugs]:http://scottsievert.com/swix/bugs.html
[nfm]:http://wiki.scipy.org/NumPy_for_Matlab_Users

## [Documentation][swix-doc]
Details on how to install can be found in [*Install*][install]. The
[swix documentation][swix-doc] includes details on each individual function and
possible bugs.

[install]:http://scottsievert.com/swix/installation.html
[swix-doc]:http://scottsievert.com/swix/

## Third Party Frameworks/Libraries
* [Accelerate][accel]
* [OpenCV][opencv]
* ...and I used some of [SwiftAccelerate][ais] to avoid some BLAS/LAPACK agony.

### To be integrated
* [swix-complex][complex]
* [ScalarArithmetic][scalar]

## Uses
* [EERegression](https://github.com/erkekin/EERegression/) -- General purpose
  multivaritate and quadratic Regression library for Swift 2.1. This can be
  used to fit a polynomial of different degrees to points you draw with your
  finger! (and slick gif on readme!)
* [Click - The Artificial Intelligence
  Game](https://itunes.apple.com/ai/app/click-artificial-intelligence/id1032660757?mt=8)

## FAQ
> **Why does this library exist?**

Not only should you be able to do simple math in arrays like in [Surge], Swift
makes it possible to call high level mathematical functions just like in
Python/Matlab.

> **How does this library compare to Python/Matlab?**

Complete speed results can be found in *[Speed]*

[Speed]:http://scottsievert.com/swix/speed.html
[Surge]:https://github.com/mattt/Surge

[ais]:https://github.com/haginile/SwiftAccelerate
[so]:http://stackoverflow.com/q/24727674/1141256
[opencv]:http://opencv.org
[scalar]:https://github.com/seivan/ScalarArithmetic
[complex]:https://github.com/dankogai/swift-complex
[numpy]:http://www.numpy.org
[accel]:https://developer.apple.com/library/prerelease/mac/documentation/Accelerate/Reference/AccelerateFWRef/_index.html#//apple_ref/doc/uid/TP40009465
[@]:https://developer.apple.com/library/prerelease/ios/documentation/swift/conceptual/swift_programming_language/AdvancedOperators.html#//apple_ref/doc/uid/TP40014097-CH27-XID_48
[ones]:http://docs.scipy.org/doc/numpy/reference/generated/numpy.ones.html
[zeros]:http://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html#numpy.zeros
[pep]:http://legacy.python.org/dev/peps/pep-0465/#implementation-details
[st]:https://twitter.com/stsievert

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