clearer name for list of auto-defined unary functions

docs/source/contributing.rst

@@ -16,13 +16,13 @@ Manually Optimizing Unary Functions
 1. Pick a function to optimize
 ++++++++++++++++++++++++++++++
 
-To see a list of functions to pick from, look at ``ForwardDiff.supported_unary_funcs``:
+To see a list of functions to pick from, look at ``ForwardDiff.auto_defined_unary_funcs``:
 
 .. code-block:: julia
 
     julia> using ForwardDiff
 
-    julia> ForwardDiff.supported_unary_funcs
+    julia> ForwardDiff.auto_defined_unary_funcs
     57-element Array{Symbol,1}:
      :sqrt
      :cbrt
@@ -133,7 +133,7 @@ In ``src/TensorNumber.jl``, under the ``Special Cases``/``Manually Optimized`` s
 4. Run Tests
 ++++++++++++
 
-The functions in ``ForwardDiff.supported_unary_funcs`` are automatically tested as part of ForwardDiff.jl's test suite, so you don't need to write tests yourself. Go ahead and test your changes by running ``Pkg.test("ForwardDiff")``.
+The functions in ``ForwardDiff.auto_defined_unary_funcs`` are automatically tested as part of ForwardDiff.jl's test suite, so you don't need to write tests yourself. Go ahead and test your changes by running ``Pkg.test("ForwardDiff")``.
 
 If everything passes, you can submit a PR to the ForwardDiff.jl repository to share your work!
 

src/ForwardDiff.jl

@@ -14,9 +14,9 @@ module ForwardDiff
                  ctranspose, eltype, abs, abs2, start, 
                  next, done, atan2
 
-    const supported_unary_funcs = map(first, Calculus.symbolic_derivatives_1arg())
+    const auto_defined_unary_funcs = map(first, Calculus.symbolic_derivatives_1arg())
 
-    for fsym in supported_unary_funcs
+    for fsym in auto_defined_unary_funcs
         @eval import Base.$(fsym);
     end
 

src/GradientNumber.jl

@@ -160,7 +160,7 @@ end
 
 # Unary functions on GradientNumbers #
 #------------------------------------#
-for fsym in supported_unary_funcs
+for fsym in auto_defined_unary_funcs
     a = :a
     new_a = :($(fsym)($a))
     deriv = Calculus.differentiate(new_a, a)

src/HessianNumber.jl

@@ -241,13 +241,13 @@ end
 # Unary functions on HessianNumbers #
 #-----------------------------------#
 # the second derivatives of functions in 
-# unsupported_unary_hess_funcs involves 
+# unsupported_unary_hess_funcs involve 
 # differentiating elementary functions 
 # that are unsupported by Calculus.jl
 const unsupported_unary_hess_funcs = [:asec, :acsc, :asecd, :acscd, :acsch, :trigamma]
-const unary_hess_funcs = filter!(sym -> !in(sym, unsupported_unary_hess_funcs), supported_unary_funcs)
+const auto_defined_unary_hess_funcs = filter!(sym -> !in(sym, unsupported_unary_hess_funcs), auto_defined_unary_funcs)
 
-for fsym in unary_hess_funcs
+for fsym in auto_defined_unary_hess_funcs
     a = :a
     new_a = :($(fsym)($a))
     deriv1 = Calculus.differentiate(new_a, a)

src/TensorNumber.jl

@@ -300,13 +300,13 @@ end
 
 # Unary functions on TensorNumbers #
 #----------------------------------#
-
-# the third derivatives of functions in unsupported_unary_tens_funcs involves differentiating 
-# elementary functions that are unsupported by Calculus.jl
+# the third derivatives of functions in unsupported_unary_tens_funcs 
+# involve differentiating elementary functions that are unsupported 
+# by Calculus.jl
 const unsupported_unary_tens_funcs = [:digamma]
-const unary_tens_funcs = filter!(sym -> !in(sym, unsupported_unary_tens_funcs), ForwardDiff.unary_hess_funcs)
+const auto_defined_unary_tens_funcs = filter!(sym -> !in(sym, unsupported_unary_tens_funcs), ForwardDiff.auto_defined_unary_hess_funcs)
 
-for fsym in unary_tens_funcs
+for fsym in auto_defined_unary_tens_funcs
     a = :a
     new_a = :($(fsym)($a))
     deriv1 = Calculus.differentiate(new_a, a)

test/test_derivatives.jl

@@ -29,7 +29,7 @@ function test_approx_deriv(a::Array, b::Array)
     end
 end
 
-for fsym in ForwardDiff.supported_unary_funcs
+for fsym in ForwardDiff.auto_defined_unary_funcs
     func_expr = :($(fsym)(x) + 4^$(fsym)(x) - x * $(fsym)(x))
     deriv = Calculus.differentiate(func_expr)
     try 

test/test_hessians.jl

@@ -236,7 +236,7 @@ end
 
 chunk_sizes = (ForwardDiff.default_chunk_size, 2, Int(N/2), N)
 
-for fsym in ForwardDiff.unary_hess_funcs
+for fsym in ForwardDiff.auto_defined_unary_hess_funcs
     testexpr = :($(fsym)(a) + $(fsym)(b) - $(fsym)(c) * $(fsym)(l) - $(fsym)(m) + $(fsym)(r)) 
 
     @eval function testf(x::Vector)

test/test_jacobians.jl

@@ -33,7 +33,7 @@ end
 
 chunk_sizes = (ForwardDiff.default_chunk_size, 1, Int(N/2), N)
 
-for fsym in ForwardDiff.supported_unary_funcs
+for fsym in ForwardDiff.auto_defined_unary_funcs
     testexprs = [:($(fsym)(a) + $(fsym)(b)),
                 :(- $(fsym)(c)),
                 :(4 * $(fsym)(d)),

test/test_tensors.jl

@@ -253,7 +253,7 @@ function tens_test_x(fsym, N)
     return rand(randrange, N)
 end
 
-for fsym in ForwardDiff.unary_tens_funcs
+for fsym in ForwardDiff.auto_defined_unary_tens_funcs
     testexpr = :($(fsym)(a) + $(fsym)(b) - $(fsym)(c) * $(fsym)(d))
 
     @eval function testf(x::Vector)