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name: TagBot | ||
on: | ||
issue_comment: | ||
types: | ||
- created | ||
workflow_dispatch: | ||
inputs: | ||
lookback: | ||
default: 3 | ||
permissions: | ||
actions: read | ||
checks: read | ||
contents: write | ||
deployments: read | ||
issues: read | ||
discussions: read | ||
packages: read | ||
pages: read | ||
pull-requests: read | ||
repository-projects: read | ||
security-events: read | ||
statuses: read | ||
jobs: | ||
TagBot: | ||
if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot' | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: JuliaRegistries/TagBot@v1 | ||
with: | ||
token: ${{ secrets.GITHUB_TOKEN }} | ||
ssh: ${{ secrets.DOCUMENTER_KEY }} |
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name = "Statistics" | ||
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" | ||
keywords = ["statistics"] | ||
license = "MIT" | ||
desc = "Basic statistics for Julia." | ||
version = "1.11.1" | ||
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[compat] | ||
julia = "1.9.4" | ||
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[deps] | ||
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" | ||
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" | ||
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[weakdeps] | ||
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" | ||
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[extensions] | ||
SparseArraysExt = ["SparseArrays"] | ||
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[extras] | ||
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" | ||
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" | ||
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" | ||
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" | ||
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[targets] | ||
test = ["Random", "Test"] | ||
test = ["Dates", "Random", "SparseArrays", "Test"] |
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module SparseArraysExt | ||
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##### SparseArrays optimizations ##### | ||
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using Base: require_one_based_indexing | ||
using LinearAlgebra | ||
using SparseArrays | ||
using Statistics | ||
using Statistics: centralize_sumabs2, unscaled_covzm | ||
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# extended functions | ||
import Statistics: cov, centralize_sumabs2! | ||
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function cov(X::SparseMatrixCSC; dims::Int=1, corrected::Bool=true) | ||
vardim = dims | ||
a, b = size(X) | ||
n, p = vardim == 1 ? (a, b) : (b, a) | ||
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# The covariance can be decomposed into two terms | ||
# 1/(n - 1) ∑ (x_i - x̄)*(x_i - x̄)' = 1/(n - 1) (∑ x_i*x_i' - n*x̄*x̄') | ||
# which can be evaluated via a sparse matrix-matrix product | ||
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# Compute ∑ x_i*x_i' = X'X using sparse matrix-matrix product | ||
out = Matrix(unscaled_covzm(X, vardim)) | ||
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# Compute x̄ | ||
x̄ᵀ = mean(X, dims=vardim) | ||
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# Subtract n*x̄*x̄' from X'X | ||
@inbounds for j in 1:p, i in 1:p | ||
out[i,j] -= x̄ᵀ[i] * x̄ᵀ[j]' * n | ||
end | ||
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# scale with the sample size n or the corrected sample size n - 1 | ||
return rmul!(out, inv(n - corrected)) | ||
end | ||
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# This is the function that does the reduction underlying var/std | ||
function centralize_sumabs2!(R::AbstractArray{S}, A::SparseMatrixCSC{Tv,Ti}, means::AbstractArray) where {S,Tv,Ti} | ||
require_one_based_indexing(R, A, means) | ||
lsiz = Base.check_reducedims(R,A) | ||
for i in 1:max(ndims(R), ndims(means)) | ||
if axes(means, i) != axes(R, i) | ||
throw(DimensionMismatch("dimension $i of `mean` should have indices $(axes(R, i)), but got $(axes(means, i))")) | ||
end | ||
end | ||
isempty(R) || fill!(R, zero(S)) | ||
isempty(A) && return R | ||
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rowval = rowvals(A) | ||
nzval = nonzeros(A) | ||
m = size(A, 1) | ||
n = size(A, 2) | ||
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if size(R, 1) == size(R, 2) == 1 | ||
# Reduction along both columns and rows | ||
R[1, 1] = centralize_sumabs2(A, means[1]) | ||
elseif size(R, 1) == 1 | ||
# Reduction along rows | ||
@inbounds for col = 1:n | ||
mu = means[col] | ||
r = convert(S, (m - length(nzrange(A, col)))*abs2(mu)) | ||
@simd for j = nzrange(A, col) | ||
r += abs2(nzval[j] - mu) | ||
end | ||
R[1, col] = r | ||
end | ||
elseif size(R, 2) == 1 | ||
# Reduction along columns | ||
rownz = fill(convert(Ti, n), m) | ||
@inbounds for col = 1:n | ||
@simd for j = nzrange(A, col) | ||
row = rowval[j] | ||
R[row, 1] += abs2(nzval[j] - means[row]) | ||
rownz[row] -= 1 | ||
end | ||
end | ||
for i = 1:m | ||
R[i, 1] += rownz[i]*abs2(means[i]) | ||
end | ||
else | ||
# Reduction along a dimension > 2 | ||
@inbounds for col = 1:n | ||
lastrow = 0 | ||
@simd for j = nzrange(A, col) | ||
row = rowval[j] | ||
for i = lastrow+1:row-1 | ||
R[i, col] = abs2(means[i, col]) | ||
end | ||
R[row, col] = abs2(nzval[j] - means[row, col]) | ||
lastrow = row | ||
end | ||
for i = lastrow+1:m | ||
R[i, col] = abs2(means[i, col]) | ||
end | ||
end | ||
end | ||
return R | ||
end | ||
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end # module |
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