Merge pull request #11 from JuliaMath/sjk/fix-tests

Add missing test files

Author: andreasnoack

src/VML.jl

@@ -25,7 +25,7 @@ function vml_check_error()
     vml_error = ccall((:_vmlClearErrStatus, lib), Cint, ())
     if vml_error != 0
         if vml_error == 1
-            error(DomainError())
+            throw(DomainError())
         elseif vml_error == 2 || vml_error == 3 || vml_error == 4
             # Singularity, overflow, or underflow
             # I don't think Base throws on these

test/common.jl

@@ -67,8 +67,8 @@ const base_binary_complex = (
 )
 
 function randindomain{T<:Real}(t::Type{T}, n, domain)
-    d1 = oftype(t, domain[1])
-    d2 = oftype(t, domain[2])
+    d1 = convert(t, domain[1])
+    d2 = convert(t, domain[2])
     ddiff = d2 - d1
     @assert isfinite(ddiff)
     v = rand(t, n)
@@ -79,8 +79,8 @@ function randindomain{T<:Real}(t::Type{T}, n, domain)
 end
 
 function randindomain{T<:Complex}(t::Type{T}, n, domain)
-    d1 = oftype(t, domain[1])
-    d2 = oftype(t, domain[2])
+    d1 = convert(t, domain[1])
+    d2 = convert(t, domain[2])
     ddiff = d2 - d1
     @assert isfinite(ddiff)
     v = rand(t, 2*n)

test/complex.jl

@@ -0,0 +1,16 @@
+# First generate some random data and test functions in Base on it
+const NVALS = 1000
+input = Dict(t=>[[(randindomain(t, NVALS, domain),) for (fn, domain) in base_unary_complex];
+             [(randindomain(t, NVALS, domain1), randindomain(t, NVALS, domain2))
+              for (fn, domain1, domain2) in base_binary_complex];
+             (randindomain(t, NVALS, (0, 100)), randindomain(t, 1, (-2, 10))[1])]
+            for t in (Complex64, Complex128))
+fns = [[x[1] for x in base_unary_complex]; [x[1] for x in base_binary_complex]; .^]
+output = Dict(t=>[fns[i](input[t][i]...) for i = 1:length(fns)] for t in (Complex64, Complex128))
+
+# Now test the same data with VML
+using VML
+for t in (Complex64, Complex128), i = 1:length(fns)
+    fn = fns[i]
+    Base.Test.test_approx_eq(output[t][i], fn(input[t][i]...), "Base $t $fn", "VML $t $fn")
+end

test/real.jl

@@ -0,0 +1,25 @@
+# First generate some random data and test functions in Base on it
+const NVALS = 1000
+input = Dict(t=>[[(randindomain(t, NVALS, domain),) for (fn, domain) in base_unary_real];
+             [(randindomain(t, NVALS, domain1), randindomain(t, NVALS, domain2))
+              for (fn, domain1, domain2) in base_binary_real];
+             (randindomain(t, NVALS, (0, 100)), randindomain(t, 1, (-5, 20))[1])]
+            for t in (Float32, Float64))
+fns = [[x[1] for x in base_unary_real]; [x[1] for x in base_binary_real]; .^]
+output = Dict(t=>[fns[i](input[t][i]...) for i = 1:length(fns)] for t in (Float32, Float64))
+
+# Now test the same data with VML
+using VML
+for t in (Float32, Float64), i = 1:length(fns)
+    fn = fns[i]
+    Base.Test.test_approx_eq(output[t][i], fn(input[t][i]...), "Base $t $fn", "VML $t $fn")
+end
+
+# Verify that we still throw DomainErrors
+Base.Test.@test_throws DomainError sqrt([-1.0])
+
+# Setting accuracy
+vml_set_accuracy(VML_LA)
+@assert vml_get_accuracy() == VML_LA
+vml_set_accuracy(VML_EP)
+@assert vml_get_accuracy() == VML_EP

test/runtests.jl

@@ -1 +1,3 @@
-include("common.jl")
+include("common.jl")
+include("real.jl")
+include("complex.jl")