# LIBSVM.jl

This is a Julia interface for LIBSVM.

**Features:**

- Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM
- Model objects are represented by Julia type SVM which gives you easy access to model features and can be saved e.g. as JLD file
- Supports ScikitLearn.jl API

## Usage

### LIBSVM API

This provides a lower level API similar to LIBSVM C-interface. See `?svmtrain`

for options.

```
using LIBSVM
using RDatasets
using Printf
using Statistics
# Load Fisher's classic iris data
iris = dataset("datasets", "iris")
# First four dimension of input data is features
X = Matrix(iris[:, 1:4])'
# LIBSVM handles multi-class data automatically using a one-against-one strategy
y = iris.Species
# Split the dataset into training set and testing set
Xtrain = X[:, 1:2:end]
Xtest = X[:, 2:2:end]
ytrain = y[1:2:end]
ytest = y[2:2:end]
# Train SVM on half of the data using default parameters. See documentation
# of svmtrain for options
model = svmtrain(Xtrain, ytrain)
# Test model on the other half of the data.
ŷ, decision_values = svmpredict(model, Xtest);
# Compute accuracy
@printf "Accuracy: %.2f%%\n" mean(ŷ .== ytest) * 100
```

### ScikitLearn API

You can alternatively use `ScikitLearn.jl`

API with same options as `svmtrain`

:

```
using LIBSVM
using RDatasets
# Classification C-SVM
iris = dataset("datasets", "iris")
X = Matrix(iris[:, 1:4])
y = iris.Species
Xtrain = X[1:2:end, :]
Xtest = X[2:2:end, :]
ytrain = y[1:2:end]
ytest = y[2:2:end]
model = fit!(SVC(), Xtrain, ytrain)
ŷ = predict(model, Xtest)
```

```
# Epsilon-Regression
whiteside = RDatasets.dataset("MASS", "whiteside")
X = Matrix(whiteside[:, 3:3]) # the `Gas` column
y = whiteside.Temp
model = fit!(EpsilonSVR(cost = 10., gamma = 1.), X, y)
ŷ = predict(model, X)
```

## Credits

The library is currently developed and maintained by Matti Pastell. It was originally developed by Simon Kornblith.

LIBSVM by Chih-Chung Chang and Chih-Jen Lin