Merge pull request #13 from Tchanders/deprecation-warnings

Fix deprecated Array syntax

src/categorical_discretizer.jl

@@ -32,7 +32,7 @@ supports_decoding{N,D}(cd::CategoricalDiscretizer{N,D}, d::D) = 1 ≤ d ≤ nlab
 encode{N,D}(cd::CategoricalDiscretizer{N,D}, x::N) = cd.n2d[x]::D
 encode{N,D}(cd::CategoricalDiscretizer{N,D}, x) = cd.n2d[convert(N,x)]::D
 function encode{N,D}(cd::CategoricalDiscretizer{N,D}, data::AbstractArray)
-    arr = Array(D, length(data))
+    arr = Array{D}(length(data))
     for (i,x) in enumerate(data)
         arr[i] = encode(cd, x)
     end
@@ -42,7 +42,7 @@ end
 decode{N,D}(cd::CategoricalDiscretizer{N,D}, x::D) = cd.d2n[x]::N
 decode{N,D}(cd::CategoricalDiscretizer{N,D}, x) = cd.d2n[convert(D,x)]::N
 function decode{N,D}(cd::CategoricalDiscretizer{N,D}, data::AbstractArray{D})
-    arr = Array(N, length(data))
+    arr = Array{N}(length(data))
     for (i,d) in enumerate(data)
         arr[i] = decode(cd, d)
     end

src/common.jl

@@ -26,11 +26,11 @@ function get_histogram_plot_arrays{R<:Real, I<:Integer}(binedges::Vector{R}, cou
     n = length(binedges)
     n == length(counts)+1 || error("binedges must have exactly one more entry than counts!")
 
-    arr_x = Array(R, n+1)
+    arr_x = Array{R}(n+1)
     arr_x[1] = binedges[1]
     arr_x[2:n+1] = binedges
 
-    arr_y = Array(I, n+1)
+    arr_y = Array{I}(n+1)
     arr_y[1] = zero(I)
     arr_y[2:n] = counts
     arr_y[end] = zero(I)
@@ -41,11 +41,11 @@ function get_histogram_plot_arrays{R<:Real, F<:Real}(binedges::Vector{R}, pdfs::
     n = length(binedges)
     n == length(pdfs)+1 || error("binedges must have exactly one more entry than counts!")
 
-    arr_x = Array(R, n+1)
+    arr_x = Array{R}(n+1)
     arr_x[1] = binedges[1]
     arr_x[2:n+1] = binedges
 
-    arr_y = Array(F, n+1)
+    arr_y = Array{F}(n+1)
     arr_y[1] = zero(F)
     arr_y[2:n] = pdfs
     arr_y[end] = zero(F)

src/disc_uniformcount.jl

@@ -12,7 +12,7 @@ function binedges{N<:AbstractFloat}(alg::DiscretizeUniformCount, data::AbstractA
 
     p = sortperm(data)
     counts_per_bin, remainder = div(n,nbins), rem(n,nbins)
-    retval = Array(N, nbins+1)
+    retval = Array{N}(nbins+1)
     retval[1] = data[p[1]]
     retval[end] = data[p[end]]
 
@@ -36,7 +36,7 @@ function binedges{N<:Integer}(alg::DiscretizeUniformCount, data::AbstractArray{N
 
     p = sortperm(data)
     counts_per_bin, remainder = div(n,nbins), rem(n,nbins)
-    retval = Array(N, nbins+1)
+    retval = Array{N}(nbins+1)
     retval[1] = data[p[1]]
     retval[end] = data[p[end]]
 

src/hybrid_discretizer.jl

@@ -41,7 +41,7 @@ function encode{N,D<:Integer}(disc::HybridDiscretizer{N,D}, x::N)
 end
 encode{N,D}(disc::HybridDiscretizer{N,D}, x) = encode(disc, convert(N, x))::D
 function encode{N,D<:Integer}(disc::HybridDiscretizer{N,D}, data::AbstractArray)
-    arr = Array(D, length(data))
+    arr = Array{D}(length(data))
     for (i,x) in enumerate(data)
         arr[i] = encode(disc, x)
     end
@@ -60,7 +60,7 @@ decode{N<:Real,D<:Integer,E<:Integer}(disc::HybridDiscretizer{N,D}, d::E, method
     decode(disc, convert(D, d), method)
 
 function decode{N,D<:Integer}(disc::HybridDiscretizer{N,D}, data::AbstractArray{D}, ::AbstractSampleMethod=SAMPLE_UNIFORM)
-    arr = Array(N, length(data))
+    arr = Array{N}(length(data))
     for (i,d) in enumerate(data)
         arr[i] = decode(disc, d)
     end

src/linear_discretizer.jl

@@ -113,7 +113,7 @@ function encode{N,D<:Integer}(ld::LinearDiscretizer{N,D}, x::N)
 end
 encode{N,D}(ld::LinearDiscretizer{N,D}, x) = encode(ld, convert(N, x))::D
 function encode{N,D<:Integer}(ld::LinearDiscretizer{N,D}, data::AbstractArray)
-    arr = Array(D, length(data))
+    arr = Array{D}(length(data))
     for (i,x) in enumerate(data)
         arr[i] = encode(ld, x)
     end
@@ -182,7 +182,7 @@ decode{N<:Real,D<:Integer,I<:Integer}(ld::LinearDiscretizer{N,D}, d::I, method::
     decode(ld, convert(D,d), method)
 
 function decode{N,D<:Integer}(ld::LinearDiscretizer{N,D}, data::AbstractArray{D}, ::AbstractSampleMethod=SAMPLE_UNIFORM)
-    arr = Array(N, length(data))
+    arr = Array{N}(length(data))
     for (i,d) in enumerate(data)
         arr[i] = decode(ld, d)
     end
@@ -221,7 +221,7 @@ nlabels(ld::LinearDiscretizer) = ld.nbins
 binedges(ld::LinearDiscretizer) = ld.binedges
 bincenters{N<:AbstractFloat,D}(ld::LinearDiscretizer{N,D}) = (0.5*(ld.binedges[1:ld.nbins] + ld.binedges[2:end]))::Vector{Float64}
 function bincenters{N<:Integer,D}(ld::LinearDiscretizer{N,D})
-    retval = Array(Float64, ld.nbins)
+    retval = Array{Float64}(ld.nbins)
     for i = 1 : length(retval)-1
         retval[i] = 0.5(ld.binedges[i+1]-1 + ld.binedges[i])
     end

test/test_disc_MODL.jl

@@ -37,7 +37,7 @@ class_value = [1,1,1,2,2,2,2,2,2,2,3,3,3,3,3,2,2,2,2,2];
 
 #Case6
 continuous = collect(1.0:1.0:100.0)
-class_value = Array(Int64,100)
+class_value = Array{Int64}(100)
 for i in 1 : 100
     class_value[i] = div((i-1),20)
 end