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Fix some NaN problems and a few other fixes. #42

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Merged
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Dec 15, 2020
Merged

Fix some NaN problems and a few other fixes. #42

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3 changes: 2 additions & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,12 @@ version = "0.5.2"

[deps]
Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[compat]
julia = "0.7, 1"
Distances = "0.7, 0.8, 0.9, 0.10"
julia = "0.7, 1"

[extras]
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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34 changes: 22 additions & 12 deletions src/Loess.jl
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Expand Up @@ -2,7 +2,7 @@ module Loess

import Distances.euclidean

using Statistics
using Statistics, LinearAlgebra

export loess, predict

Expand Down Expand Up @@ -44,6 +44,9 @@ function loess(xs::AbstractMatrix{T}, ys::AbstractVector{T};

n, m = size(xs)
q = ceil(Int, (span * n))
if q < degree + 1
throw(ArgumentError("neighborhood size must be larger than degree+1=$(degree + 1) but was $q. Try increasing the value of span."))
end

# TODO: We need to keep track of how we are normalizing so we can
# correctly apply predict to unnormalized data. We should have a normalize
Expand All @@ -53,7 +56,6 @@ function loess(xs::AbstractMatrix{T}, ys::AbstractVector{T};
end

kdtree = KDTree(xs, 0.05 * span)
verts = Array{T}(undef, length(kdtree.verts), m)

# map verticies to their index in the bs coefficient matrix
verts = Dict{Vector{T}, Int}()
Expand All @@ -69,6 +71,7 @@ function loess(xs::AbstractMatrix{T}, ys::AbstractVector{T};
# TODO: higher degree fitting
us = Array{T}(undef, q, 1 + degree * m)
vs = Array{T}(undef, q)

for (vert, k) in verts
# reset perm
for i in 1:n
Expand All @@ -85,20 +88,22 @@ function loess(xs::AbstractMatrix{T}, ys::AbstractVector{T};
dmax = maximum([ds[perm[i]] for i = 1:q])

for i in 1:q
pi = perm[i]
w = tricubic(ds[pi] / dmax)
pᵢ = perm[i]
w = tricubic(ds[pᵢ] / dmax)
us[i,1] = w
for j in 1:m
x = xs[pi, j]
x = xs[pᵢ, j]
wxl = w
for l in 1:degree
wxl *= x
us[i, 1 + (j-1)*degree + l] = wxl # w*x^l
us[i, 1 + (j - 1)*degree + l] = wxl # w*x^l
end
end
vs[i] = ys[pi] * w
vs[i] = ys[pᵢ] * w
end
bs[k,:] = us \ vs

F = qr(us, Val(true))
bs[k,:] = F\vs
end

LoessModel{T}(xs, ys, bs, verts, kdtree)
Expand Down Expand Up @@ -149,11 +154,16 @@ function predict(model::LoessModel{T}, zs::AbstractVector{T}) where T <: Abstrac
if m == 1
@assert(length(adjacent_verts) == 2)
z = zs[1]
u = (z - adjacent_verts[1][1]) /
(adjacent_verts[2][1] - adjacent_verts[1][1])
v₁, v₂ = adjacent_verts[1][1], adjacent_verts[2][1]

if z == v₁ || z == v₂
return evalpoly(zs, model.bs[model.verts[[z]],:])
end

u = (z - v₁)/(v₂ - v₁)

y1 = evalpoly(zs, model.bs[model.verts[[adjacent_verts[1][1]]],:])
y2 = evalpoly(zs, model.bs[model.verts[[adjacent_verts[2][1]]],:])
y1 = evalpoly(zs, model.bs[model.verts[[v₁]],:])
y2 = evalpoly(zs, model.bs[model.verts[[v₂]],:])
return (1.0 - u) * y1 + u * y2
else
error("Multivariate blending not yet implemented")
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2 changes: 1 addition & 1 deletion src/kd.jl
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Expand Up @@ -143,7 +143,7 @@ function build_kdtree(xs::AbstractMatrix{T},

# find the median and partition
if isodd(length(perm))
mid = length(perm) ÷ 2
mid = (length(perm) + 1) ÷ 2
partialsort!(perm, mid, by=i -> xs[i, j])
med = xs[perm[mid], j]
mid1 = mid
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21 changes: 21 additions & 0 deletions test/runtests.jl
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Expand Up @@ -32,3 +32,24 @@ let x = 1:10, y = sin.(1:10)
end

@test_throws DimensionMismatch loess([1.0 2.0; 3.0 4.0], [1.0])

@testset "Issue 28" begin
@testset "Example 1" begin
x = [1.0, 2.0, 3.0, 4.0]
y = [1.0, 2.0, 3.0, 4.0]
@test_throws ArgumentError("neighborhood size must be larger than degree+1=3 but was 1. Try increasing the value of span.") loess(x, y, span = 0.25)
@test_throws ArgumentError("neighborhood size must be larger than degree+1=3 but was 2. Try increasing the value of span.") loess(x, y, span = 0.33)
@test predict(loess(x, y), x) ≈ x
end

@testset "Example 2" begin
x = [1.0, 1.0, 2.0, 3.0, 4.0, 4.0]
y = [1.0, 1.0, 2.0, 3.0, 4.0, 4.0]
@test_throws ArgumentError("neighborhood size must be larger than degree+1=3 but was 2. Try increasing the value of span.") loess(x, y, span = 0.33)
# For 0.4 and 0.5 these current don't hit the middle values. I suspect
# the issue is related to the ties in x.
@test_broken predict(loess(x, y, span = 0.4), x) ≈ x
@test_broken predict(loess(x, y, span = 0.5), x) ≈ x
@test predict(loess(x, y, span = 0.6), x) ≈ x
end
end