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Update the usage in README. #58

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Update the usage in README. #58

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10 changes: 6 additions & 4 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -21,13 +21,15 @@ This provides a lower level API similar to LIBSVM C-interface. See `?svmtrain`
for options.

```julia
using RDatasets, LIBSVM
using LIBSVM
using RDatasets
using Printf, Statistics

# Load Fisher's classic iris data
iris = dataset("datasets", "iris")

# LIBSVM handles multi-class data automatically using a one-against-one strategy
labels = convert(Vector, iris[:Species])
labels = levelcode.(iris[:Species])

# First dimension of input data is features; second is instances
instances = convert(Array, iris[:, 1:4])'
Expand All @@ -49,11 +51,11 @@ You can alternatively use `ScikitLearn.jl` API with same options as `svmtrain`:

```julia
using LIBSVM
using RDatasets: dataset
using RDatasets

#Classification C-SVM
iris = dataset("datasets", "iris")
labels = convert(Vector, iris[:, :Species])
labels = levelcode.(iris[:, :Species])
instances = convert(Array, iris[:, 1:4])
model = fit!(SVC(), instances[1:2:end, :], labels[1:2:end])
yp = predict(model, instances[2:2:end, :])
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