fix some lint stuff

src/NearestNeighbors.jl

@@ -28,14 +28,14 @@ abstract NNTree{V <: AbstractVector, P <: Metric}
 typealias DistanceType Float64
 typealias MinkowskiMetric Union{Euclidean, Chebyshev, Cityblock, Minkowski}
 
-function check_input{V1, V2 <: AbstractVector}(tree::NNTree{V1}, points::Vector{V2})
+function check_input{V1, V2 <: AbstractVector}(::NNTree{V1}, ::Vector{V2})
     if length(V1) != length(V2)
         throw(ArgumentError(
             "dimension of input points:$(length(V2)) and tree data:$(length(V1)) must agree"))
     end
 end
 
-function check_input{V1, V2 <: Number}(tree::NNTree{V1}, point::Vector{V2})
+function check_input{V1, V2 <: Number}(::NNTree{V1}, point::Vector{V2})
     if length(V1) != length(point)
         throw(ArgumentError(
             "dimension of input points:$(length(point)) and tree data:$(length(V1)) must agree"))

src/ball_tree.jl

@@ -143,7 +143,6 @@ end
 
 function _knn(tree::BallTree,
               point::AbstractVector,
-              k::Int,
               best_idxs::Vector{Int},
               best_dists::Vector,
               skip::Function)

src/brute_tree.jl

@@ -24,7 +24,6 @@ end
 
 function _knn{V}(tree::BruteTree{V},
                  point::AbstractVector,
-                 k::Int,
                  best_idxs::Vector{Int},
                  best_dists::Vector,
                  skip::Function)

src/evaluation.jl

@@ -3,14 +3,14 @@
 @inline eval_pow(d::Minkowski, s) = abs(s)^d.p
 
 @inline eval_diff(::MinkowskiMetric, a, b) = a - b
-@inline eval_diff(::Chebyshev, a, b) = b
+@inline eval_diff(::Chebyshev, ::Any, b) = b
 
 function Distances.evaluate(d::Distances.UnionMetrics, a::AbstractVector,
                             b::AbstractVector, do_end::Bool=true)
     s = eval_start(d, a, b)
-    @simd for I in eachindex(b)
-        @inbounds ai = a[I]
-        @inbounds bi = b[I]
+    @simd for i in eachindex(b)
+        @inbounds ai = a[i]
+        @inbounds bi = b[i]
         s = eval_reduce(d, s, eval_op(d, ai, bi))
     end
     if do_end
@@ -21,6 +21,6 @@ function Distances.evaluate(d::Distances.UnionMetrics, a::AbstractVector,
 end
 
 function Distances.evaluate(d::Distances.PreMetric, a::AbstractVector,
-                            b::AbstractArray, do_end::Bool=true)
+                            b::AbstractArray, ::Bool=true)
     evaluate(d, a, b)
 end

src/hyperspheres.jl

@@ -17,7 +17,7 @@ end
     evaluate(m, s1.center, s2.center) + s1.r <= s2.r
 end
 
-@inline function interpolate{V <: AbstractVector, M <: NormMetric}(m::M,
+@inline function interpolate{V <: AbstractVector, M <: NormMetric}(::M,
                                                                   c1::V,
                                                                   c2::V,
                                                                   x,
@@ -31,12 +31,12 @@ end
     return ab.center, true
 end
 
-@inline function interpolate{V <: AbstractVector, M <: Metric}(m::M,
+@inline function interpolate{V <: AbstractVector, M <: Metric}(::M,
                                                                c1::V,
-                                                               c2::V,
-                                                               x,
-                                                               d,
-                                                               ab)
+                                                               ::V,
+                                                               ::Any,
+                                                               ::Any,
+                                                               ::Any)
     return c1, false
 end
 
@@ -46,7 +46,7 @@ function create_bsphere{V}(data::Vector{V}, metric::Metric, indices::Vector{Int}
     # First find center of all points
     fill!(ab.center, 0.0)
     for i in low:high
-        for j in length(ab.center)
+        for j in 1:length(ab.center)
             ab.center[j] += data[indices[i]][j]
         end
     end

src/kd_tree.jl

@@ -144,7 +144,6 @@ end
 
 function _knn(tree::KDTree,
               point::AbstractVector,
-              k::Int,
               best_idxs::Vector{Int},
               best_dists::Vector,
               skip::Function)

src/knn.jl

@@ -16,18 +16,18 @@ function knn{V, T <: AbstractVector}(tree::NNTree{V}, points::Vector{T}, k::Int,
     dists = [Vector{DistanceType}(k) for _ in 1:n_points]
     idxs = [Vector{Int}(k) for _ in 1:n_points]
     for i in 1:n_points
-        knn_point!(tree, points[i], k, sortres, dists[i], idxs[i], skip)
+        knn_point!(tree, points[i], sortres, dists[i], idxs[i], skip)
     end
     return idxs, dists
 end
 
-function knn_point!{V, T <: Number}(tree::NNTree{V}, point::AbstractVector{T}, k::Int, sortres, dist, idx, skip)
+function knn_point!{V, T <: Number}(tree::NNTree{V}, point::AbstractVector{T}, sortres, dist, idx, skip)
     fill!(idx, -1)
     fill!(dist, typemax(DistanceType))
-    _knn(tree, point, k, idx, dist, skip)
+    _knn(tree, point, idx, dist, skip)
     sortres && heap_sort_inplace!(dist, idx)
     if tree.reordered
-        for j in 1:k
+        for j in eachindex(idx)
             @inbounds idx[j] = tree.indices[idx[j]]
         end
     end

src/tree_ops.jl

@@ -90,9 +90,9 @@ end
 
 # Checks the distance function and add those points that are among the k best.
 # Uses a heap for fast insertion.
-@inline function add_points_knn!(best_dists::Vector, best_idxs::Vector{Int},
+@inline function add_points_knn!{F}(best_dists::Vector, best_idxs::Vector{Int},
                                  tree::NNTree, index::Int, point::AbstractVector,
-                                 do_end::Bool, skip::Function)
+                                 do_end::Bool, skip::F)
     for z in get_leaf_range(tree.tree_data, index)
         @POINT 1
         idx = tree.reordered ? z : tree.indices[z]

src/utilities.jl

@@ -88,6 +88,6 @@ end
 end
 
 # Default skip function, always false
-@inline function always_false(i::Int)
+@inline function always_false(::Int)
     false
 end