using Base.Test for tests.

Author: innerlee

test/runtests.jl

@@ -1,23 +1,21 @@
 using LIBLINEAR
+using Base.Test
 
 # computation validation
 iris = readcsv(joinpath(dirname(@__FILE__), "iris.csv"))
 labels = iris[:, 5]
 instances = convert(Matrix{Float64}, iris[:, 1:4]')
+
 model = linear_train(labels[1:2:end], instances[:, 1:2:end]; verbose=true, solver_type=Cint(0))
 gc()
 (class, decvalues) = linear_predict(model, instances[:, 2:2:end], verbose=true)
 correct = Bool[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-@assert (class .== labels[2:2:end]) == correct
+@test (class .== labels[2:2:end]) == correct
 println("pass 1.")
 
 model = linear_train(labels[1:2:end], sparse(instances[:, 1:2:end]); verbose=true, solver_type=Cint(0))
 gc()
 (class, decvalues) = linear_predict(model, sparse(instances[:, 2:2:end]), verbose=true)
 correct = Bool[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
-@assert (class .== labels[2:2:end]) == correct
+@test (class .== labels[2:2:end]) == correct
 println("pass 2.")
-
-# # test bias
-# model = linear_train(labels[1:2:end], instances[:, 1:2:end]; verbose=true, solver_type=Cint(0))
-# gc()