View typemacros.jl
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using MacroTools: namify | |
using Base.Meta: isexpr | |
## Utilities | |
# Split struct definition (neglecting constructors) | |
function splitstruct(structdef) | |
# Split type definition | |
isexpr(structdef, :struct, 3) || error("Not a type definition:", structdef) |
View benchmark.jl
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using MuladdMacro, EllipsisNotation, BenchmarkTools | |
# In place | |
@generated function f_loop!(out, dt, y, k, b, idxs::Nothing, ::Type{Val{N}}) where N | |
sumexpr = :(+($((:(k[$i][idx]*b[$i]) for i in 1:N)...))) | |
quote | |
@muladd @inbounds for idx in eachindex(out) | |
out[idx] = y[idx] + dt*$sumexpr | |
end |
View blackboxoptim.jl
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using DelayDiffEq, DiffEqParamEstim, BlackBoxOptim, DataFrames, LsqFit | |
using Plots | |
gr() | |
include("importData.jl") | |
include("plot.jl") | |
# import data from the path, in which: | |
# pop: population data | |
# g1, g2: g1 and g2 data |
View demo.jl
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using Turing | |
using StatsPlots | |
using Random | |
using Statistics | |
function demo(N::Int; n::Int = 10) | |
# observation noise | |
σ² = 0.3 |
View examples.jl
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using Turing | |
using StatsPlots | |
using Random | |
using Statistics | |
function demo(N::Int; n::Int = 10) | |
# observation noise | |
σ² = 0.3 |
View processes.jl
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using Turing | |
using Turing.RandomMeasures | |
using Random | |
function stickbreaking(rpm = DirichletProcess(0.25)) | |
# Data | |
data = [-2,2,-1.5,1.5] | |
# Base distribution |
View emcee.ipynb
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View ot.jl
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using Distributions | |
using SparseArrays | |
using LinearAlgebra | |
using StatsBase | |
function _ot_cost_plan(c, μ::DiscreteNonParametric, ν::DiscreteNonParametric; get=:plan) | |
len_μ = length(μ.p) | |
len_ν = length(ν.p) | |
wi = μ.p[1] | |
wj = ν.p[1] |
View discrete1d_benchmarks.jl
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using BenchmarkTools | |
using Distances | |
using Distributions | |
using StatsBase | |
using LinearAlgebra | |
using SparseArrays | |
function f( | |
c, μ::DiscreteNonParametric, ν::DiscreteNonParametric, plan::SparseMatrixCSC |
View debug.txt
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julia> using DynamicPPL | |
julia> @model function demo4(n, ::Type{TV}=Vector{Float64}) where {TV} | |
m ~ Normal() | |
x = TV(undef, n) | |
@show __varinfo__ | |
for i in eachindex(x) | |
x[i] ~ Normal(m, 1.0) | |
end | |
end |
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