Merge pull request #68 from JuliaStats/an/optimize

Various optimizations

Author: andreasnoack

src/Loess.jl

@@ -10,9 +10,9 @@ export loess, predict
 include("kd.jl")
 
 
-mutable struct LoessModel{T <: AbstractFloat}
-    xs::AbstractMatrix{T} # An n by m predictor matrix containing n observations from m predictors
-    ys::AbstractVector{T} # A length n response vector
+struct LoessModel{T <: AbstractFloat}
+    xs::Matrix{T} # An n by m predictor matrix containing n observations from m predictors
+    ys::Vector{T} # A length n response vector
     predictions_and_gradients::Dict{Vector{T}, Vector{T}} # kd-tree vertexes mapped to prediction and gradient at each vertex
     kdtree::KDTree{T}
 end
@@ -44,6 +44,10 @@ function loess(
     degree::Integer = 2,
     cell::AbstractFloat = 0.2
 ) where T<:AbstractFloat
+
+    Base.require_one_based_indexing(xs)
+    Base.require_one_based_indexing(ys)
+
     if size(xs, 1) != size(ys, 1)
         throw(DimensionMismatch("Predictor and response arrays must of the same length"))
     end
@@ -80,8 +84,12 @@ function loess(
         end
 
         # distance to each point
-        for i in 1:n
-            ds[i] = euclidean(vec(vert), vec(xs[i,:]))
+        @inbounds for i in 1:n
+            s = zero(T)
+            for j in 1:m
+                s += (xs[i, j] - vert[j])^2
+            end
+            ds[i] = sqrt(s)
         end
 
         # find the q closest points
@@ -128,7 +136,7 @@ function loess(
         ]
     end
 
-    LoessModel{T}(xs, ys, predictions_and_gradients, kdtree)
+    LoessModel(xs, ys, predictions_and_gradients, kdtree)
 end
 
 loess(xs::AbstractVector{T}, ys::AbstractVector{T}; kwargs...) where {T<:AbstractFloat} =
@@ -153,50 +161,44 @@ end
 # Returns:
 #   A length n' vector of predicted response values.
 #
-function predict(model::LoessModel, z::Real)
-    predict(model, [z])
-end
-
-function predict(model::LoessModel, zs::AbstractVector)
-
-    Base.require_one_based_indexing(zs)
+function predict(model::LoessModel{T}, z::Number) where T
+    adjacent_verts = traverse(model.kdtree, (T(z),))
 
-    m = size(model.xs, 2)
+    @assert(length(adjacent_verts) == 2)
+    v₁, v₂ = adjacent_verts[1][1], adjacent_verts[2][1]
 
-    # in the univariate case, interpret a non-singleton zs as vector of
-    # ponits, not one point
-    if m == 1 && length(zs) > 1
-        return predict(model, reshape(zs, (length(zs), 1)))
+    if z == v₁ || z == v₂
+        return first(model.predictions_and_gradients[[z]])
     end
 
-    if length(zs) != m
-        error("$(m)-dimensional model applied to length $(length(zs)) vector")
-    end
+    y₁, dy₁ = model.predictions_and_gradients[[v₁]]
+    y₂, dy₂ = model.predictions_and_gradients[[v₂]]
 
-    adjacent_verts = traverse(model.kdtree, zs)
+    b_int = cubic_interpolation(v₁, y₁, dy₁, v₂, y₂, dy₂)
 
-    if m == 1
-        @assert(length(adjacent_verts) == 2)
-        z = zs[1]
-        v₁, v₂ = adjacent_verts[1][1], adjacent_verts[2][1]
+    return evalpoly(z, b_int)
+end
 
-        if z == v₁ || z == v₂
-            return first(model.predictions_and_gradients[[z]])
-        end
+function predict(model::LoessModel, zs::AbstractVector)
+    if size(model.xs, 2) > 1
+        throw(ArgumentError("multivariate blending not yet implemented"))
+    end
 
-        y₁, dy₁ = model.predictions_and_gradients[[v₁]]
-        y₂, dy₂ = model.predictions_and_gradients[[v₂]]
+    return [predict(model, z) for z in zs]
+end
 
-        b_int = cubic_interpolation(v₁, y₁, dy₁, v₂, y₂, dy₂)
+function predict(model::LoessModel, zs::AbstractMatrix)
+    if size(model.xs, 2) != size(zs, 2)
+        throw(DimensionMismatch("number of columns in input matrix must match the number of columns in the model matrix"))
+    end
 
-        return evalpoly(z, b_int)
+    if size(zs, 2) == 1
+        return predict(model, vec(zs))
     else
-        error("Multivariate blending not yet implemented")
+        return [predict(model, row) for row in eachrow(zs)]
     end
 end
 
-predict(model::LoessModel, zs::AbstractMatrix) = map(Base.Fix1(predict, model), eachrow(zs))
-
 """
     tricubic(u)
 

src/kd.jl

@@ -1,22 +1,17 @@
 # Simple static kd-trees.
 
-abstract type KDNode end
-
-struct KDLeafNode <: KDNode
-end
-
-struct KDInternalNode{T <: AbstractFloat} <: KDNode
+struct KDNode{T <: AbstractFloat}
     j::Int             # dimension on which the data is split
     med::T             # median value where the split occours
-    leftnode::KDNode
-    rightnode::KDNode
+    leftnode::Union{Nothing, KDNode{T}}
+    rightnode::Union{Nothing, KDNode{T}}
 end
 
 
 struct KDTree{T <: AbstractFloat}
-    xs::AbstractMatrix{T} # A matrix of n, m-dimensional observations
+    xs::Matrix{T}         # A matrix of n, m-dimensional observations
     perm::Vector{Int}     # permutation of data to avoid modifying xs
-    root::KDNode          # root node
+    root::KDNode{T}       # root node
     verts::Set{Vector{T}}
     bounds::Matrix{T}     # Top-level bounding box
 end
@@ -114,7 +109,7 @@ Modifies:
   `perm`, `verts`
 
 Returns:
-  Either a `KDLeafNode` or a `KDInternalNode`
+  Either a `nothing` or a `KDNode`
 """
 function build_kdtree(xs::AbstractMatrix{T},
                       perm::AbstractVector,
@@ -130,7 +125,7 @@ function build_kdtree(xs::AbstractMatrix{T},
 
     if length(perm) <= leaf_size_cutoff || diameter(bounds) <= leaf_diameter_cutoff
         @debug "Creating leaf node" length(perm) leaf_size_cutoff diameter(bounds) leaf_diameter_cutoff
-        return KDLeafNode()
+        return nothing
     end
 
     # split on the dimension with the largest spread
@@ -226,7 +221,7 @@ function build_kdtree(xs::AbstractMatrix{T},
         push!(verts, T[vert...])
     end
 
-    KDInternalNode{T}(j, med, leftnode, rightnode)
+    KDNode(j, med, leftnode, rightnode)
 end
 
 
@@ -246,14 +241,15 @@ end
 Traverse the tree `kdtree` to the bottom and return the verticies of
 the bounding hypercube of the leaf node containing the point `x`.
 """
-function traverse(kdtree::KDTree, x::AbstractVector)
+function traverse(kdtree::KDTree{T}, x::NTuple{N,T}) where {N,T}
+
     m = size(kdtree.bounds, 2)
 
-    if length(x) != m
+    if N != m
         throw(DimensionMismatch("$(m)-dimensional kd-tree searched with a length $(length(x)) vector."))
     end
 
-    for j in 1:m
+    for j in 1:N
         if x[j] < kdtree.bounds[1, j] || x[j] > kdtree.bounds[2, j]
             error(
                   """
@@ -266,15 +262,18 @@ function traverse(kdtree::KDTree, x::AbstractVector)
 
     bounds = copy(kdtree.bounds)
     node = kdtree.root
-    while !isa(node, KDLeafNode)
-        if x[node.j] <= node.med
-            bounds[2, node.j] = node.med
-            node = node.leftnode
-        else
-            bounds[1, node.j] = node.med
-            node = node.rightnode
-        end
-    end
 
-    bounds_verts(bounds)
+    return _traverse!(bounds, node, x)
+end
+
+_traverse!(bounds, node::Nothing, x) = bounds
+function _traverse!(bounds, node::KDNode, x)
+    if x[node.j] <= node.med
+        bounds[2, node.j] = node.med
+        return _traverse!(bounds, node.leftnode, x)
+    else
+        bounds[1, node.j] = node.med
+        return _traverse!(bounds, node.rightnode, x)
+    end
 end
+