Fix deprecation warnings for Julia 0.6

JuliaStats · Jan 17, 2017 · 7b3d7f7 · 7b3d7f7
1 parent 5de325f
commit 7b3d7f7
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Showing 3 changed files with 38 additions and 37 deletions.
diff --git a/src/basicfuns.jl b/src/basicfuns.jl
@@ -148,4 +148,4 @@ function softmax!{R<:AbstractFloat,T<:Real}(r::AbstractArray{R}, x::AbstractArra
 end
 
 softmax!{T<:AbstractFloat}(x::AbstractArray{T}) = softmax!(x, x)
-softmax{T<:Real}(x::AbstractArray{T}) = softmax!(Array(Float64, size(x)), x)
+softmax{T<:Real}(x::AbstractArray{T}) = softmax!(Array{Float64}(size(x)), x)
diff --git a/src/tvpack.jl b/src/tvpack.jl
@@ -124,10 +124,10 @@ end
 # # One Dimensional Globally Adaptive Integration Function
 
 function adonet(f::Function, a::Float64, b::Float64, tol::Float64)
-	ai = Array(Float64, 100)
-	bi = Array(Float64, 100)
-	ei = Array(Float64, 100)
-	fi = Array(Float64, 100)
+	ai = Vector{Float64}(100)
+	bi = Vector{Float64}(100)
+	ei = Vector{Float64}(100)
+	fi = Vector{Float64}(100)
 	ai[1] = a
 	bi[1] = b
 	err = 1.0

diff --git a/test/basicfuns.jl b/test/basicfuns.jl
@@ -7,82 +7,83 @@ using Compat
 println("\ttesting xlogx & xlogy ...")
 
 @test xlogx(0) === 0.0
-@test_approx_eq xlogx(2) 2.0 * log(2.0)
+@test xlogx(2) ≈ 2.0 * log(2.0)
 
 @test xlogy(0, 1) === 0.0
-@test_approx_eq xlogy(2, 3) 2.0 * log(3.0)
+@test xlogy(2, 3) ≈ 2.0 * log(3.0)
 
 # logistic & logit
 
 println("\ttesting logistic & logit ...")
 
-@test_approx_eq logistic(2) 1.0 / (1.0 + exp(-2.0))
-@test_approx_eq logit(0.5) 0.0
-@test_approx_eq logit(logistic(2)) 2.0
+@test logistic(2)        ≈ 1.0 / (1.0 + exp(-2.0))
+@test logit(0.5)         ≈ 0.0
+@test logit(logistic(2)) ≈ 2.0
 
 # log1psq
 
 println("\ttesting log1psq ...")
 
-@test_approx_eq log1psq(0.0) 0.0
-@test_approx_eq log1psq(1.0) log1p(1.0)
-@test_approx_eq log1psq(2.0) log1p(4.0)
+@test log1psq(0.0) ≈ 0.0
+@test log1psq(1.0) ≈ log1p(1.0)
+@test log1psq(2.0) ≈ log1p(4.0)
 
 # log1pexp, log1mexp, log2mexp & logexpm1
 
 println("\ttesting log1pexp ...")
 
-@test_approx_eq log1pexp(2.0) log(1.0 + exp(2.0))
-@test_approx_eq log1pexp(-2.0) log(1.0 + exp(-2.0))
-@test_approx_eq log1pexp(10000) 10000.0
-@test_approx_eq log1pexp(-10000) 0.0
+@test log1pexp(2.0)    ≈ log(1.0 + exp(2.0))
+@test log1pexp(-2.0)   ≈ log(1.0 + exp(-2.0))
+@test log1pexp(10000)  ≈ 10000.0
+@test log1pexp(-10000) ≈ 0.0
 
 println("\ttesting log1mexp ...")
 
-@test_approx_eq log1mexp(-1.0) log1p(- exp(-1.0))
-@test_approx_eq log1mexp(-10.0) log1p(- exp(-10.0))
+@test log1mexp(-1.0)  ≈ log1p(- exp(-1.0))
+@test log1mexp(-10.0) ≈ log1p(- exp(-10.0))
 
 println("\ttesting log2mexp ...")
 
-@test_approx_eq log2mexp(0.0) 0.0
-@test_approx_eq log2mexp(-1.0) log(2.0 - exp(-1.0))
+@test log2mexp(0.0)  ≈ 0.0
+@test log2mexp(-1.0) ≈ log(2.0 - exp(-1.0))
 
 println("\ttesting logexpm1 ...")
 
-@test_approx_eq logexpm1(2.0) log(exp(2.0) - 1.0)
-@test_approx_eq logexpm1(log1pexp(2.0)) 2.0
-@test_approx_eq logexpm1(log1pexp(-2.0)) -2.0
+@test logexpm1(2.0)            ≈  log(exp(2.0) - 1.0)
+@test logexpm1(log1pexp(2.0))  ≈  2.0
+@test logexpm1(log1pexp(-2.0)) ≈ -2.0
 
 # log1pmx
 
 println("\ttesting log1pmx ...")
 
-@test_approx_eq log1pmx(0.0) 0.0
-@test_approx_eq log1pmx(1.0) log(2.0) - 1.0
-@test_approx_eq log1pmx(2.0) log(3.0) - 2.0
+@test log1pmx(0.0) ≈ 0.0
+@test log1pmx(1.0) ≈ log(2.0) - 1.0
+@test log1pmx(2.0) ≈ log(3.0) - 2.0
 
 println("\ttesting logmxp1 ...")
 
-@test_approx_eq logmxp1(1.0) 0.0
-@test_approx_eq logmxp1(2.0) log(2.0) - 1.0
-@test_approx_eq logmxp1(3.0) log(3.0) - 2.0
+@test logmxp1(1.0) ≈ 0.0
+@test logmxp1(2.0) ≈ log(2.0) - 1.0
+@test logmxp1(3.0) ≈ log(3.0) - 2.0
 
 # logsumexp
 
 println("\ttesting logsumexp ...")
 
-@test_approx_eq logsumexp(2.0, 3.0) log(exp(2.0) + exp(3.0))
-@test_approx_eq logsumexp(10002, 10003) 10000 + logsumexp(2.0, 3.0)
+@test logsumexp(2.0, 3.0)     ≈ log(exp(2.0) + exp(3.0))
+@test logsumexp(10002, 10003) ≈ 10000 + logsumexp(2.0, 3.0)
 
-@test_approx_eq logsumexp([1.0, 2.0, 3.0]) 3.40760596444438
-@test_approx_eq logsumexp([1.0, 2.0, 3.0] .+ 1000.) 1003.40760596444438
+@test logsumexp([1.0, 2.0, 3.0])          ≈ 3.40760596444438
+@test logsumexp([1.0, 2.0, 3.0] .+ 1000.) ≈ 1003.40760596444438
 
 # softmax
 
 println("\ttesting softmax ...")
 
 x = [1.0, 2.0, 3.0]
-r = @compat exp.(x) ./ sum(exp.(x))
-@test_approx_eq softmax([1.0, 2.0, 3.0]) r
+# Explicit conversion to Vector{Float64} can be romved when we drop 0.4 support
+r = Vector{Float64}(@compat exp.(x) ./ sum(exp.(x)))
+@test softmax([1.0, 2.0, 3.0]) ≈ r
 softmax!(x)
-@test_approx_eq x r
+@test x ≈ r