https://github.com/JuliaStats/SVM.jl

SVM's for Julia
https://github.com/JuliaStats/SVM.jl

Last synced: 17 days ago
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SVM's for Julia

• Host: GitHub
• URL: https://github.com/JuliaStats/SVM.jl
• Owner: JuliaStats
• License: other
• Archived: true
• Created: 2013-01-16T02:26:00.000Z (almost 12 years ago)
• Default Branch: master
• Last Pushed: 2016-07-26T03:46:18.000Z (over 8 years ago)
• Last Synced: 2024-08-01T00:57:38.856Z (3 months ago)
• Language: Julia
• Size: 99.6 KB
• Stars: 41
• Watchers: 21
• Forks: 25
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-julia-datasciences - SVM - SVM's for Julia. (APL / General-Purpose Machine Learning)

README

        
SVM.jl

======

# THIS PACKAGE IS UNMAINTAINED AND WILL BE REMOVED FROM METADATA

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[![Build Status](https://travis-ci.org/JuliaStats/SVM.jl.svg?branch=master)](https://travis-ci.org/JuliaStats/SVM.jl)

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[![SVM](http://pkg.julialang.org/badges/SVM_0.4.svg)](http://pkg.julialang.org/?pkg=SVM&ver=0.4)

# SVMs in Julia

Native Julia implementations of standard SVM algorithms.

Currently, there are textbook style implementations of

two popular linear SVM algorithms:

* Pegasos (Shalev-Schwartz et al., 2007)

* Dual Coordinate Descent (Hsieh et al., 2008)

The `svm` function is a wrapper for `pegasos`, but it is

possible to call `cddual` explicitly. See the source code

for the hyperparameters of the `cddual` function.

# Usage

The demo below shows how SVMs work:

```julia

# To show how SVMs work, we'll use Fisher's iris data set

using SVM

using RDatasets

# We'll learn to separate setosa from other species

iris = dataset("datasets", "iris")

# SVM format expects observations in columns and features in rows

X = array(iris[:, 1:4])'

p, n = size(X)

# SVM format expects positive and negative examples to +1/-1

Y = [species == "setosa" ? 1.0 : -1.0 for species in iris[:Species]]

# Select a subset of the data for training, test on the rest.

train = randbool(n)

# We'll fit a model with all of the default parameters

model = svm(X[:,train], Y[train])

# And now evaluate that model on the testset

accuracy = countnz(predict(model, X[:,~train]) .== Y[~train])/countnz(~train)

```

You may specify non-default values for the various parameters:

```julia

# The algorithm processes minibatches of data of size k

model = svm(X, Y, k = 150)

# Weight regularization is controlled by lambda

model = svm(X, Y, lambda = 0.1)

# The algorithm performs T iterations

model = svm(X, Y, T = 1000)

```