Updates for Julia 0.6 (#44)

* Update package for Julia 0.6

* Fix inner constructor

* Deal with inference thinking Distribution is UnionAll

* Use scale from Interpolations

Author: ararslan

README.md

@@ -2,8 +2,9 @@
 
 [![Build Status](https://travis-ci.org/JuliaStats/KernelDensity.jl.svg?branch=master)](https://travis-ci.org/JuliaStats/KernelDensity.jl)
 [![Coverage Status](https://coveralls.io/repos/github/JuliaStats/KernelDensity.jl/badge.svg)](https://coveralls.io/github/JuliaStats/KernelDensity.jl)
-[![KernelDensity](http://pkg.julialang.org/badges/KernelDensity_0.4.svg)](http://pkg.julialang.org/?pkg=KernelDensity&ver=0.4)
+[![KernelDensity](http://pkg.julialang.org/badges/KernelDensity_0.4.svg)](http://pkg.julialang.org/?pkg=KernelDensity)
 [![KernelDensity](http://pkg.julialang.org/badges/KernelDensity_0.5.svg)](http://pkg.julialang.org/?pkg=KernelDensity)
+[![KernelDensity](http://pkg.julialang.org/badges/KernelDensity_0.6.svg)](http://pkg.julialang.org/?pkg=KernelDensity)
 
 Kernel density estimators for Julia.
 

REQUIRE

@@ -1,4 +1,5 @@
 julia 0.4
+Compat 0.19.0
 StatsBase
 Distributions
 Optim

src/KernelDensity.jl

@@ -2,6 +2,7 @@ __precompile__()
 
 module KernelDensity
 
+using Compat
 using StatsBase
 using Distributions
 using Optim
@@ -13,7 +14,7 @@ import Distributions: twoπ, pdf
 
 export kde, kde_lscv, UnivariateKDE, BivariateKDE, InterpKDE, pdf
 
-abstract AbstractKDE
+@compat abstract type AbstractKDE end
 
 include("univariate.jl")
 include("bivariate.jl")

src/bivariate.jl

@@ -12,9 +12,15 @@ function kernel_dist{Dx<:UnivariateDistribution,Dy<:UnivariateDistribution}(::Ty
     kernel_dist(Dx,w[1]), kernel_dist(Dy,w[2])
 end
 
+if VERSION >= v"0.6.0-dev.2123"
+    const DataTypeOrUnionAll = Union{DataType, UnionAll}
+else
+    const DataTypeOrUnionAll = DataType
+end
+
 # this function provided for backwards compatibility, though it doesn't have the type restrictions
 # to ensure that the given tuple only contains univariate distributions
-function kernel_dist(d::Tuple{DataType, DataType}, w::Tuple{Real,Real})
+function kernel_dist(d::Tuple{DataTypeOrUnionAll, DataTypeOrUnionAll}, w::Tuple{Real,Real})
     kernel_dist(d[1],w[1]), kernel_dist(d[2],w[2])
 end
 
@@ -79,7 +85,7 @@ function conv(k::BivariateKDE, dist::Tuple{UnivariateDistribution,UnivariateDist
     BivariateKDE(k.x, k.y, dens)
 end
 
-typealias BivariateDistribution Union{MultivariateDistribution,Tuple{UnivariateDistribution,UnivariateDistribution}}
+@compat const BivariateDistribution = Union{MultivariateDistribution,Tuple{UnivariateDistribution,UnivariateDistribution}}
 
 function kde(data::Tuple{RealVector, RealVector}, midpoints::Tuple{Range, Range}, dist::BivariateDistribution)
     k = tabulate(data,midpoints)

src/interp.jl

@@ -1,9 +1,9 @@
-import Interpolations: interpolate
+import Interpolations: interpolate, scale
 
 type InterpKDE{K,I} <: AbstractKDE
     kde::K
     itp::I
-    InterpKDE(kde,itp) = new(kde,itp)
+    @compat (::Type{InterpKDE{K,I}}){K,I}(kde::K, itp::I) = new{K,I}(kde, itp)
 end
 
 

src/univariate.jl

@@ -8,7 +8,7 @@ end
 kernel_dist(::Type{Normal},w::Real) = Normal(0.0,w)
 kernel_dist(::Type{Uniform},w::Real) = (s = 1.7320508075688772*w; Uniform(-s,s))
 
-typealias LocationScale Union{Laplace,Logistic,SymTriangularDist}
+@compat const LocationScale = Union{Laplace,Logistic,SymTriangularDist}
 kernel_dist{D}(::Type{D},w::Real) = (s = w/std(D(0.0,1.0)); D(0.0,s))
 
 
@@ -161,7 +161,7 @@ function kde_lscv(data::RealVector, midpoints::Range;
     K = length(k.density)
     ft = rfft(k.density)
 
-    ft2 = abs2(ft)
+    ft2 = @compat(abs2.(ft))
     c = -twoπ/(step(k.x)*K)
     hlb, hub = bandwidth_range
 

test/bivariate.jl

@@ -10,8 +10,8 @@ for D in [Tuple{Normal,Normal}, Tuple{Uniform,Uniform}, Tuple{Logistic,Logistic}
     dx,dy = d
     @test mean(dx) == 0.0
     @test mean(dy) == 0.0
-    @test_approx_eq std(dx) 0.5
-    @test_approx_eq std(dy) 0.5
+    @test std(dx) ≈ 0.5
+    @test std(dy) ≈ 0.5
 end
 
 r = kde_range((-2.0,2.0), 128)
@@ -30,31 +30,31 @@ for X in ([0.0], [0.0,0.0], [0.0,0.5], [-0.5:0.1:0.5;])
         @test isa(k1,BivariateKDE)
         @test size(k1.density) == (length(k1.x), length(k1.y))
         @test all(k1.density .>= 0.0)
-        @test_approx_eq sum(k1.density)*step(k1.x)*step(k1.y) 1.0
+        @test sum(k1.density)*step(k1.x)*step(k1.y) ≈ 1.0
 
         k2 = conv(k1,kernel_dist(Tuple{D,D}, (0.1,0.1)))
         @test isa(k2,BivariateKDE)
         @test size(k2.density) == (length(k2.x), length(k2.y))
         @test all(k2.density .>= 0.0)
-        @test_approx_eq sum(k2.density)*step(k2.x)*step(k2.y) 1.0
+        @test sum(k2.density)*step(k2.x)*step(k2.y) ≈ 1.0
 
         k3 = kde((X,X);kernel=D)
         @test isa(k3,BivariateKDE)
         @test size(k3.density) == (length(k3.x), length(k3.y))
         @test all(k3.density .>= 0.0)
-        @test_approx_eq sum(k3.density)*step(k3.x)*step(k3.y) 1.0
+        @test sum(k3.density)*step(k3.x)*step(k3.y) ≈ 1.0
 
         k4 = kde((X,X),(r,r);kernel=D)
         @test isa(k4,BivariateKDE)
         @test size(k4.density) == (length(k4.x), length(k4.y))
         @test all(k4.density .>= 0.0)
-        @test_approx_eq sum(k4.density)*step(k4.x)*step(k4.y) 1.0
+        @test sum(k4.density)*step(k4.x)*step(k4.y) ≈ 1.0
 
         k5 = kde([X X];kernel=D)
         @test isa(k5,BivariateKDE)
         @test size(k5.density) == (length(k5.x), length(k5.y))
         @test all(k5.density .>= 0.0)
-        @test_approx_eq sum(k5.density)*step(k5.x)*step(k5.y) 1.0
+        @test sum(k5.density)*step(k5.x)*step(k5.y) ≈ 1.0
 
     end
 end

test/interp.jl

@@ -5,7 +5,7 @@ X = randn(100)
 Y = randn(100)
 
 k = kde(X)
-@test_approx_eq pdf(k, k.x) k.density
+@test pdf(k, k.x) ≈ k.density
 
 k = kde((X,Y))
-@test_approx_eq pdf(k, k.x, k.y) k.density
+@test pdf(k, k.x, k.y) ≈ k.density

test/univariate.jl

@@ -8,7 +8,7 @@ for D in [Normal,Uniform,Logistic]
     d = kernel_dist(D,0.5)
     @test isa(d,D)
     @test mean(d) == 0.0
-    @test_approx_eq std(d) 0.5
+    @test std(d) ≈ 0.5
 end
 
 r = kde_range((-2.0,2.0), 128)
@@ -27,31 +27,31 @@ for X in ([0.0], [0.0,0.0], [0.0,0.5], [-0.5:0.1:0.5;])
         @test isa(k1,UnivariateKDE)
         @test length(k1.density) == length(k1.x)
         @test all(k1.density .>= 0.0)
-        @test_approx_eq sum(k1.density)*step(k1.x) 1.0
+        @test sum(k1.density)*step(k1.x) ≈ 1.0
 
         k2 = conv(k1,kernel_dist(D,0.1))
         @test isa(k2,UnivariateKDE)
         @test length(k2.density) == length(k2.x)
         @test all(k2.density .>= 0.0)
-        @test_approx_eq sum(k2.density)*step(k2.x) 1.0
+        @test sum(k2.density)*step(k2.x) ≈ 1.0
 
         k3 = kde(X;kernel=D)
         @test isa(k3,UnivariateKDE)
         @test length(k3.density) == length(k3.x)
         @test all(k3.density .>= 0.0)
-        @test_approx_eq sum(k3.density)*step(k3.x) 1.0
+        @test sum(k3.density)*step(k3.x) ≈ 1.0
 
         k4 = kde(X,r;kernel=D)
         @test isa(k4,UnivariateKDE)
         @test length(k4.density) == length(k4.x)
         @test all(k4.density .>= 0.0)
-        @test_approx_eq sum(k4.density)*step(k4.x) 1.0
+        @test sum(k4.density)*step(k4.x) ≈ 1.0
 
         k5 = kde_lscv(X)
         @test isa(k5,UnivariateKDE)
         @test length(k5.density) == length(k5.x)
         @test all(k5.density .>= 0.0)
-        @test_approx_eq sum(k5.density)*step(k5.x) 1.0
+        @test sum(k5.density)*step(k5.x) ≈ 1.0
 
     end
 end