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""" | |
unzip(iter) | |
unzip an iterable object of indexable objects | |
""" | |
function unzip(iter) | |
ntuple(length(first(iter))) do i | |
map(iter) do indexable | |
getindex(indexable, i) | |
end |
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using StatsFuns.RFunctions: betarand, gammarand | |
using LogProbs | |
function categorical_sample(tokens, weights) | |
x = rand() * sum(weights) | |
cum_weights = zero(eltype(weights)) | |
for (t, w) in zip(tokens, weights) | |
cum_weights += w | |
if cum_weights > x | |
return t |
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import Seaborn | |
import Pandas | |
# convert julia dataframe into a pandas dataframe | |
panda(df) = Pandas.DataFrame(Dict(n => df[n] for n in names(df))) |
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using Base.Iterators: take | |
import Base: iterate, IteratorSize, eltype | |
struct Fibs end | |
iterate(::Fibs, (i, j) = (1, 1)) = i, (j, i+j) | |
IteratorSize(::Type{Fibs}) = Base.IsInfinite() | |
eltype(::Type{Fibs}) = Int | |
# collect the first ten fibs in an array |
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partial(f, args...) = let args = args; (more...) -> f(args..., more...) end |
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df = DataFrame(x=randn(500), y= randn(500)) | |
df |> vlplot( | |
mark = :point, | |
encoding = ( | |
x = (field=:x, typ=:quantitative), | |
y = (field=:y, typ=:quantitative) | |
) | |
) |
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module CompoundDistributions | |
using StatsFuns.RFunctions: betarand, gammarand | |
using SpecialFunctions: lbeta | |
using LogProbs | |
# distribution types | |
export BetaBern, DirMul, DirCat, UniCat, CatDist, ChineseRest | |
# conditional distribution type |
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<!doctype html> | |
<html lang="en"> | |
<head> | |
<meta charset="utf-8"> | |
<title>Vega Lite Barplot</title> | |
<!-- Import Vega 4 & Vega-Lite 3 (does not have to be from CDN) --> | |
<script src="https://cdn.jsdelivr.net/npm/vega@4"></script> | |
<script src="https://cdn.jsdelivr.net/npm/vega-lite@3.0.0-rc12"></script> |
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def bootstrap_test(higher, lower, n=100000): | |
xs = higher | |
ys = lower | |
m = np.mean(xs) - np.mean(ys) | |
zs = np.concatenate((xs,ys)) | |
boots = np.random.choice(ys, size=(n,len(zs))) | |
return np.sum(np.mean(boots[:,0:len(xs)], 1) - np.mean(boots[:,len(xs)+1:], 1) > m) | |
# test whether the mean of the array `higher` is higher than the mean of the array `lower` | |
def bootstrap_pvalue(higher, lower, n=100000): |
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module TreeFromString | |
using PEG | |
import Base: show | |
struct Tree{T} | |
val ::T | |
children ::Vector{Tree{T}} | |
end |
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