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relaxed types for use with Array{TrackedReal} #78

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5b30281
relaxed types for use with TrackedReal
Oct 20, 2019
665f85a
wip : array-like rewrites
Nov 5, 2019
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13 changes: 7 additions & 6 deletions src/bivariate.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@ mutable struct BivariateKDE{Rx<:AbstractRange,Ry<:AbstractRange} <: AbstractKDE
"Second coordinate of gridpoints for evaluating the density."
y::Ry
"Kernel density at corresponding gridpoints `Tuple.(x, permutedims(y))`."
density::Matrix{Float64}
density::AbstractMatrix{}
end

function kernel_dist(::Type{D},w::Tuple{Real,Real}) where D<:UnivariateDistribution
Expand Down Expand Up @@ -54,7 +54,7 @@ function tabulate(data::Tuple{RealVector, RealVector}, midpoints::Tuple{Rx, Ry},
sx, sy = step(xmid), step(ymid)

# Set up a grid for discretized data
grid = zeros(Float64, nx, ny)
grid = zeros(eltype(xdata),nx,ny)
ainc = 1.0 / (sum(weights)*(sx*sy)^2)

# weighted discretization (cf. Jones and Lotwick)
Expand Down Expand Up @@ -91,11 +91,12 @@ function conv(k::BivariateKDE, dist::Tuple{UnivariateDistribution,UnivariateDist
ft[i+1,j+1] *= cf(distx,i*cx)*cf(disty,min(j,Ky-j)*cy)
end
end
dens = irfft(ft, Kx)
# i = 0:size(ft,1)-1
# j = 0:size(ft,2)-1
# ft = ft .* ( cf.(distx,i*cx) * cf.(disty,min.(j,Ky-j)*cy)' )

for i = 1:length(dens)
dens[i] = max(0.0,dens[i])
end
dens = irfft(ft, Kx)
dens = max.(0.0,dens)

# Invert the Fourier transform to get the KDE
BivariateKDE(k.x, k.y, dens)
Expand Down
20 changes: 5 additions & 15 deletions src/univariate.jl
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Expand Up @@ -17,7 +17,7 @@ mutable struct UnivariateKDE{R<:AbstractRange} <: AbstractKDE
"Gridpoints for evaluating the density."
x::R
"Kernel density at corresponding gridpoints `x`."
density::Vector{Float64}
density::AbstractVector{}
end

# construct kernel from bandwidth
Expand Down Expand Up @@ -101,7 +101,7 @@ function tabulate(data::RealVector, midpoints::R, weights::Weights=default_weigh
s = step(midpoints)

# Set up a grid for discretized data
grid = zeros(Float64, npoints)
grid = zeros(eltype(data),npoints)
ainc = 1.0 / (sum(weights)*s*s)

# weighted discretization (cf. Jones and Lotwick)
Expand All @@ -125,22 +125,12 @@ function conv(k::UnivariateKDE, dist::UnivariateDistribution)
K = length(k.density)
ft = rfft(k.density)

# Convolve fft with characteristic function of kernel
# empirical cf
# = \sum_{n=1}^N e^{i*t*X_n} / N
# = \sum_{k=0}^K e^{i*t*(a+k*s)} N_k / N
# = e^{i*t*a} \sum_{k=0}^K e^{-2pi*i*k*(-t*s*K/2pi)/K} N_k / N
# = A * fft(N_k/N)[-t*s*K/2pi + 1]
c = -twoπ/(step(k.x)*K)
for j = 0:length(ft)-1
ft[j+1] *= cf(dist,j*c)
end
j = 0:length(ft)-1
ft = ft .* cf.(dist,j*c)

dens = irfft(ft, K)
# fix rounding error.
for i = 1:K
dens[i] = max(0.0,dens[i])
end
dens = max.(0.0,dens)

# Invert the Fourier transform to get the KDE
UnivariateKDE(k.x, dens)
Expand Down