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https://github.com/JuliaStats/SVM.jl
SVM's for Julia
https://github.com/JuliaStats/SVM.jl
Last synced: 14 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
[![Project Status: Unsupported - The project has reached a stable, usable state but the author(s) have ceased all work on it. A new maintainer may be desired.](http://www.repostatus.org/badges/latest/unsupported.svg)](http://www.repostatus.org/#unsupported)
[![Build Status](https://travis-ci.org/JuliaStats/SVM.jl.svg?branch=master)](https://travis-ci.org/JuliaStats/SVM.jl)
[![Coverage Status](https://coveralls.io/repos/JuliaStats/SVM.jl/badge.svg)](https://coveralls.io/r/JuliaStats/SVM.jl)
[![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)
```