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Quick fix to use aweights instead of fweights in WeightedHistograms #33

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Quick fix to use aweights instead of fweights in WeightedHistograms #33

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d25331d
replace frequency weights by analytic weights
martin-gutwin Aug 26, 2019
4a56eb9
Merge pull request #1 from twinGu/twinGu-patch-1
martin-gutwin Aug 26, 2019
d5ad632
loading aweights in interface.jl
martin-gutwin Aug 26, 2019
fcd9346
fixing wrong import of aweights
Aug 27, 2019
fa5a62f
Merge branch 'master' into master
gdkrmr Dec 16, 2019
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2 changes: 1 addition & 1 deletion src/WeightedOnlineStats.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ import Statistics
import Statistics: mean, var, std, cov, cor, median, quantile
import LinearAlgebra
import LinearAlgebra: Hermitian, lmul!, rmul!, Diagonal, diag
import StatsBase: midpoints
import StatsBase: midpoints, aweights

include("interface.jl")
include("sum.jl")
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11 changes: 7 additions & 4 deletions src/histogram.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,9 @@ import LinearAlgebra
abstract type WeightedHistogramStat{T} <: WeightedOnlineStat{T} end
abstract type WeightedHist{T} <: WeightedHistogramStat{T} end
split_candidates(o::WeightedHistogramStat) = midpoints(o)
Statistics.mean(o::WeightedHistogramStat) = mean(midpoints(o), fweights(counts(o)))
Statistics.var(o::WeightedHistogramStat) = var(midpoints(o), fweights(counts(o)); corrected=true)

Statistics.mean(o::WeightedHistogramStat) = mean(midpoints(o), aweights(counts(o)))
Statistics.var(o::WeightedHistogramStat) = var(midpoints(o), aweights(counts(o)); corrected=true)
Statistics.std(o::WeightedHistogramStat) = sqrt.(var(o))
Statistics.median(o::WeightedHistogramStat) = quantile(o, .5)

Expand Down Expand Up @@ -128,19 +129,21 @@ end

function Statistics.quantile(o::WeightedHist, p = [0, .25, .5, .75, 1])
x, y = midpoints(o), counts(o)

N = ndims(y)
inds = findall(!iszero, y)
yweights = fweights(y[inds])
subset = collect(x)[inds]
r = ntuple(N) do idim
data = map(i->i[idim],subset)
quantile(data, fweights(y[inds]), p)
quantile(data, aweights(y[inds]), p)
end
if N==1
return r[1]
else
return r
end

end

function area(o::WeightedHist)
Expand Down Expand Up @@ -253,7 +256,7 @@ end
function Statistics.quantile(o::WeightedAdaptiveHist, p = [0, .25, .5, .75, 1])
mids, counts = value(o)
inds = findall(x->x!=0, counts) # filter out zero weights
quantile(mids[inds], fweights(counts[inds]), p)
quantile(mids[inds], aweights(counts[inds]), p)
end

function weightsum(o::WeightedAdaptiveHist)
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