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

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

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SVM's for Julia

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SVM.jl

======



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# 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)

```