Merge pull request #75 from yuyichao/0.4-binding

0.4 binding

Author: johnmyleswhite

.travis.yml

@@ -1,17 +1,10 @@
-language: cpp 
-compiler:
-  - clang
+language: julia
+os:
+    - linux
+    - osx
+julia:
+    - 0.3
+    - 0.4
+    - nightly
 notifications:
   email: false
-env:
-  matrix:
-    - JULIAVERSION="juliareleases"
-    - JULIAVERSION="julianightlies"
-before_install:
-  - sudo add-apt-repository ppa:staticfloat/julia-deps -y
-  - sudo add-apt-repository ppa:staticfloat/${JULIAVERSION} -y
-  - sudo apt-get update -qq -y
-  - sudo apt-get install libpcre3-dev julia -y
-  - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
-script:
-  - julia -e 'Pkg.init(); Pkg.clone(pwd()); Pkg.test("Calculus")'

src/derivative.jl

@@ -8,10 +8,10 @@ function derivative(f::Function, ftype::Symbol, dtype::Symbol)
   end
   return g
 end
-derivative{T <: Number}(f::Function, x::Union(T, Vector{T}), dtype::Symbol = :central) = finite_difference(f, float(x), dtype)
+Compat.@compat derivative{T <: Number}(f::Function, x::Union{T, Vector{T}}, dtype::Symbol = :central) = finite_difference(f, float(x), dtype)
 derivative(f::Function, dtype::Symbol = :central) = derivative(f, :scalar, dtype)
 
-gradient{T <: Number}(f::Function, x::Union(T, Vector{T}), dtype::Symbol = :central) = finite_difference(f, float(x), dtype)
+Compat.@compat gradient{T <: Number}(f::Function, x::Union{T, Vector{T}}, dtype::Symbol = :central) = finite_difference(f, float(x), dtype)
 gradient(f::Function, dtype::Symbol = :central) = derivative(f, :vector, dtype)
 
 ctranspose(f::Function) = derivative(f)
@@ -35,16 +35,16 @@ function second_derivative(f::Function, g::Function, ftype::Symbol, dtype::Symbo
   end
   return h
 end
-function second_derivative{T <: Number}(f::Function, g::Function, x::Union(T, Vector{T}), dtype::Symbol)
+Compat.@compat function second_derivative{T <: Number}(f::Function, g::Function, x::Union{T, Vector{T}}, dtype::Symbol)
   finite_difference_hessian(f, g, x, dtype)
 end
-function hessian{T <: Number}(f::Function, g::Function, x::Union(T, Vector{T}), dtype::Symbol)
+Compat.@compat function hessian{T <: Number}(f::Function, g::Function, x::Union{T, Vector{T}}, dtype::Symbol)
   finite_difference_hessian(f, g, x, dtype)
 end
-function second_derivative{T <: Number}(f::Function, g::Function, x::Union(T, Vector{T}))
+Compat.@compat function second_derivative{T <: Number}(f::Function, g::Function, x::Union{T, Vector{T}})
   finite_difference_hessian(f, g, x, :central)
 end
-function hessian{T <: Number}(f::Function, g::Function, x::Union(T, Vector{T}))
+Compat.@compat function hessian{T <: Number}(f::Function, g::Function, x::Union{T, Vector{T}})
   finite_difference_hessian(f, g, x, :central)
 end
 function second_derivative(f::Function, x::Number, dtype::Symbol)

src/differentiate.jl

@@ -270,6 +270,6 @@ function differentiate(ex::Expr, targets::Vector{Symbol})
 end
 
 differentiate(ex::Expr) = differentiate(ex, :x)
-differentiate(s::String, target...) = differentiate(parse(s), target...)
-differentiate(s::String, target::String) = differentiate(parse(s), symbol(target))
-differentiate{T <: String}(s::String, targets::Vector{T}) = differentiate(parse(s), map(symbol, targets))
+differentiate(s::Compat.AbstractString, target...) = differentiate(parse(s), target...)
+differentiate(s::Compat.AbstractString, target::Compat.AbstractString) = differentiate(parse(s), symbol(target))
+differentiate{T <: Compat.AbstractString}(s::Compat.AbstractString, targets::Vector{T}) = differentiate(parse(s), map(symbol, targets))

src/symbolic.jl

@@ -1,6 +1,5 @@
-
 export Symbolic, AbstractVariable, SymbolicVariable, BasicVariable, processExpr, @sexpr
-export SymbolParameter, simplify 
+export SymbolParameter, simplify
 import Base.show, Base.(==)
 
 #################################################################
@@ -15,7 +14,7 @@ import Base.show, Base.(==)
 
 abstract Symbolic
 abstract AbstractVariable <: Symbolic
-typealias SymbolicVariable Union(Symbol, AbstractVariable)
+Compat.@compat typealias SymbolicVariable Union{Symbol, AbstractVariable}
 
 
 #################################################################