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| Worst case accuracy is about 2e-16. | ||
| """ | ||
| function owent(h::T, a::T) where {T <: Real} |
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It seems like this implementation is specific to Float64.
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Indeed. I adapted the integration.
src/owent.jl
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| towen = zero(a) | ||
| @inbounds for i in eachindex(w) | ||
| towen += w[i] * t2(h,a,x[i]) | ||
| end |
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Since these are tuples, you should be able to just do:
| towen = zero(a) | |
| @inbounds for i in eachindex(w) | |
| towen += w[i] * t2(h,a,x[i]) | |
| end | |
| towen = sum(w .* t2.(h, a, x)) |
and it will be allocation-free and unrolled, no?
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In general, I think we're hoping to move in the direction of breaking different special functions into their own packages, rather than increasing the size of this package. Maybe this should just be its own package? |
A single package just for this function? Or should it be a package devoted to Owen's table? |
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Following up on #242 and JuliaStats/StatsFuns.jl#99.
This implementation should not have any license issues, because it is based on JuliaStats/StatsFuns.jl#99 (comment).
I compared the implementation to scipy with the following code. The Julia version is usually 1.5-3 times faster than scipy; outliers are due to shortcut implementations in the Julia version.