increase coverage and change authors

Author: mohdibntarek

Project.toml

@@ -1,6 +1,6 @@
 name = "ReliabilityOptimization"
 uuid = "55eddd50-7f45-4398-96cf-6a37e2b16f80"
-authors = ["LucasMSpereira"]
+authors = ["Lucas Pereira and Mohamed Tarek"]
 version = "0.2.0"
 
 [deps]
@@ -34,8 +34,7 @@ Zygote = "0.6"
 julia = "1"
 
 [extras]
-FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["FiniteDifferences", "Test"]
+test = ["Test"]

src/ReliabilityOptimization.jl

@@ -85,12 +85,6 @@ end
 _vec(x::Real) = [x]
 _vec(x) = x
 
-function get_identity_vecs(M)
-    return [Vector(sparsevec([i], [1.0], M)) for i in 1:M]
-end
-function ChainRulesCore.rrule(::typeof(get_identity_vecs), M::Int)
-    get_identity_vecs(M), _ -> (NoTangent(), NoTangent())
-end
 reduce_hcat(vs) = reduce(hcat, vs)
 
 const fdm = FiniteDifferences.central_fdm(5, 1)

test/runtests.jl

@@ -2,20 +2,25 @@ using ReliabilityOptimization, Test, FiniteDifferences, Zygote
 
 @testset "ReliabilityOptimization.jl" begin
     # test function - y is random
-    pol(x, y) = [norm(x + y)^2]
-    # input value to be used in example
-    x = [2.0, 3.0, 6.0]
-    # wrap original function in RandomFunction struct
-    y = MvNormal(zeros(3), Diagonal(ones(3)))
-    rf = RandomFunction(pol, y, FORM(RIA()))
-    # call wrapper with example input
-    d = rf(x)
-    function obj(x)
-        dist = rf(x)
-        mean(dist)[1] + 2 * sqrt(cov(dist)[1, 1])
+    for pol in [
+        (x, y) -> [norm(x + y)^2],
+        (x, y) -> norm(x + y)^2
+    ]
+        # input value to be used in example
+        x = [2.0, 3.0, 6.0]
+        # wrap original function in RandomFunction struct
+        y = MvNormal(zeros(3), Diagonal(ones(3)))
+        rf = RandomFunction(pol, y)
+        rf = RandomFunction(pol, y, FORM(RIA()))
+        # call wrapper with example input
+        d = rf(x)
+        function obj(x)
+            dist = rf(x)
+            mean(dist)[1] + 2 * sqrt(cov(dist)[1, 1])
+        end
+        obj(x)
+        g1 = FiniteDifferences.grad(central_fdm(5, 1), obj, x)[1]
+        g2 = Zygote.gradient(obj, x)[1]
+        @test norm(g1 - g2) < 1e-7
     end
-    obj(x)
-    g1 = FiniteDifferences.grad(central_fdm(5, 1), obj, x)[1]
-    g2 = Zygote.gradient(obj, x)[1]
-    @test norm(g1 - g2) < 1e-7
 end