Julia 1.0 update

.travis.yml

@@ -3,8 +3,7 @@ os:
   - linux
   - osx
 julia:
-  - 0.5
-  - 0.6
+  - 1.0
   - nightly
 notifications:
   email:

REQUIRE

@@ -1,5 +1,4 @@
-julia 0.5
+julia 1.0
 Distances
-FunctionalData
 NearestNeighbors
 ProgressMeter

src/QuickShiftClustering.jl

@@ -1,13 +1,13 @@
-__precompile__()
-
 module QuickShiftClustering
 
-isinstalled(a) = isa(Pkg.installed(a), VersionNumber)
-isinstalled("PyPlot") && begin
-    using PyPlot
+using Pkg
+
+if "PyPlot" in keys(Pkg.installed())
+    import PyPlot
 end
 
-using NearestNeighbors, ProgressMeter, Distances, FunctionalData
+using Statistics
+using NearestNeighbors, ProgressMeter, Distances
 
 export quickshift, quickshiftlabels, quickshiftplot
 
@@ -22,7 +22,8 @@ function dist(a::Array{Float32,2}, i::Int, b::Array{Float32,2}, j::Int)
     for d = 1:size(a,1)
         sum += (a[d,i]-b[d,j])^2
     end
-    sum
+
+    return sum
 end
 
 function link(i, inds, G, data)
@@ -37,42 +38,45 @@ function link(i, inds, G, data)
             end
         end
     end
-    minind, mindist
+
+    return minind, mindist
 end
 
 function gauss(data, n, ind, factor1, factor2)
     s = 0f0
     for m = ind
         s += exp(-dist(data,n,data,m)*factor1)
     end
-    factor2 * s
+
+    return factor2 * s
 end
 
 quickshift(data, a...) = quickshift(convert(Array{Float32,2},data), a...)
 quickshift(data::Array{Float32,2}, sigma) = quickshift(data, convert(Float32,sigma))
-function quickshift(data::Array{Float32,2}, sigma::Float32=convert(Float32,median(pairwise(Euclidean(), randsample(data,1000)))/100))
+
+function quickshift(data::Array{Float32,2}, sigma::Float32=convert(Float32, median(pairwise(Euclidean(), data[:,rand(1:size(data, 2), 1000)] / 100, dims=2))))
     tree = KDTree(data)
     # @show sigma
-    N = len(data)
+    N = size(data, 2)
     factor1 = 1f0 / (2*sigma^2) ::Float32
     factor2 = 1/(2*pi*sigma^2*N)
     G = zeros(Float32, 1, N)
-    nninds = [Array(Int,0) for n in 1:N]
+    nninds = [Array{Int, 1}() for n in 1:N]
     @showprogress 1 "Computing kernel distances ... " for n = 1:N
-        knnind = @p knn tree vec(at(data,n)) 100 true | fst
+        knnind = knn(tree, vec(data[:,n]), 100, true)[1]
         ind = length(knnind) > 10 ? knnind : 1:N
         nninds[n] = ind
         G[n] = gauss(data, n, ind, factor1, factor2)
     end
-    # println("median lenghts:",(@p map nninds length | flatten | median))
+
     links = [Any[] for i in 1:N]
     rootind = -1
     inflength = typemax(eltype(G))
-    Nrange = 1:N
+
     minind = 0
     mindist = inflength
     @showprogress 1 "Linking ... " for i in 1:N
-        for inds = [nninds[i]; Nrange]
+        for inds = [nninds[i]; 1:N]
             minind, mindist = link(i, inds, G, data)
             if minind != 0
                 break
@@ -88,44 +92,49 @@ function quickshift(data::Array{Float32,2}, sigma::Float32=convert(Float32,media
             push!(links[minind], (sqrt(mindist), i))
         end
     end
-    QuickShift(rootind, links, sigma)
+
+    return QuickShift(rootind, links, sigma)
 end
 
 function quickshiftlabels(a::QuickShift, maxlength = 10*a.sigma)
-    labels = zeros(Int32,length(a.links))
-    cut_internal(a.rootind, a.links, labels, maxlength, 1, 2) 
-    labels
+    labels = zeros(Int,length(a.links))
+    cut_internal!(labels, a.rootind, a.links, maxlength, 1, 2) 
+
+    return labels
 end
 
-function cut_internal(ind, links, labels, maxlength, label, maxlabel) 
+function cut_internal!(labels, ind, links, maxlength, label, maxlabel) 
     labels[ind] = label
     for x in links[ind]
         if x[1] > maxlength
             maxlabel += 1
         end
-        maxlabel = max(label, cut_internal(x[2], links, labels, maxlength, x[1] > maxlength ? maxlabel : label, maxlabel)) 
+        maxlabel = max(label, cut_internal!(labels, x[2], links, maxlength, x[1] > maxlength ? maxlabel : label, maxlabel)) 
     end
-    maxlabel
+
+    return maxlabel
 end
 
-function quickshiftplot(a::QuickShift, data, labels)
-    if !isdefined(:PyPlot)
+function quickshiftplot(a::QuickShift, data::Array{T, 2} where T, labels::Array{Int, 1})
+    if !isdefined(Main, :PyPlot)
         error("quickshiftplot needs PyPlot installed and loaded using 'using PyPlot'")
     end
-    if size(data,1) != 2
+
+    if size(data, 1) != 2
         error("quickshiftplot only works on 2D data, i.e. size(data,1)==2")
     end
 
-    for i = 1:len(a.links)
+    for i = 1:length(a.links)
         from = data[:,i]
         for x = a.links[i]
             to = data[:,x[2]]
             p = hcat(from,to)'
-            plot(fst(p),snd(p),"b-")
+            PyPlot.plot(p[:,1],p[:,2],"b-")
         end
 
     end
-    scatter(data[1,:],data[2,:], c = Array{Int, 1}(labels), edgecolor = "none")
+
+    PyPlot.scatter(data[1,:],data[2,:], c = labels, edgecolor = "none")
 end
 
 end

test/REQUIRE

@@ -1 +0,0 @@
-FactCheck

test/runtests.jl

@@ -1,8 +1,7 @@
 println("\n\n\nRunning tests ...")
 
-using QuickShiftClustering, FactCheck
+using QuickShiftClustering
 
 quickshift(rand(2,1000))
 
 println("  done running tests!")
-