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using Statistics: median | |
function ratio_fit(A,B, pignoleria = 1.0) | |
ac = A | |
cb = B | |
#cb * a = ac | |
# 1) divido brutalmente le due quantità | |
Q = ac./cb | |
# 2) ricavo una stima preliminare del coefficiente | |
# tramite l'uso dello stimatore mediana, che è più robusto della media aritmentica |
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macro 𝔼(v,N,code) | |
quote | |
M=$(esc(N)) | |
t = (2-1)/M-1 | |
z = t/sqrt(1.0-t*t) | |
J = exp(-z*z/2.0)/sqrt(2.0π) * (1.0 + z*z)^(3.0/2.0)*2/M | |
$(esc(v)) = z | |
R=$(esc(code))*J | |
for i = 2:M | |
t = (2i-1)/M-1 |
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γ=4/3 | |
N=6000 | |
y=fill(1.0,N) | |
z=fill(0.0,N) | |
x=LinRange(0,10,N)|>collect | |
dx=x[2]-x[1] | |
y[2]=1-(dx^2)/6+dx^4/120/(γ-1) | |
z[2]=-(dx^3)/3 + dx^2/30/(γ-1) | |
for i =2:(N-1) |
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t=0:0.25:10 | |
n=length(t) | |
alpha=1.0 | |
mu=zeros(n) | |
Sigma=zeros(n,n) | |
for i=1:n | |
for j=1:n |
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using PkgTemplates | |
t=Template(;julia=v"1.5",plugins=[ | |
GitHubActions(; x86=true), | |
Documenter{GitHubActions}(), | |
]) | |
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using ApproxBayes | |
using Distributions | |
function normaldist(params, constants, targetdata) | |
simdata = rand(Normal(params...), 1000) | |
ApproxBayes.ksdist(simdata, targetdata), 1 | |
end | |
using Random | |
Random.seed!(1) |
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struct Dual{N,T <: Number} <: Number | |
x::T | |
ϵ::NTuple{N,T} | |
end | |
Dual(x,ϵ...)=Dual(x,ϵ) | |
Dual(x,ϵ::Tuple)=(T=promote(x,ϵ...); Dual(T[1],T[2:end])) | |
Dual{N,T}(a::Number) where {N,T} = Dual{N,T}(convert(T,a),ntuple(i->zero(T),Val(N))) | |
Dual{N,T}(a::Dual{N}) where {N,T} = Dual{N,T}(convert(T,a.x),ntuple(i->convert(T,a.ϵ[i]),Val(N))) |
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using ForwardDiff: Dual, value, partials | |
using PyPlot | |
using DifferentialEquations | |
f(β,m)=tanh(β*m)-m | |
g(β,m,β0,m0)=f(β,m)-f(β0,m0) | |
function mdot(β,m,β0,m0) | |
dm=Dual{1}(m,oftype(m,1)) | |
dβ=Dual{1}(β,oftype(β,1)) |
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using ForwardDiff: Dual, value, partials, npartials | |
function test(μ,σ,iters) | |
x=(μ+tanh(randn())*σ)^2 | |
N=1 | |
xsq=x*x | |
for i in 1:iters | |
v=(μ+tanh(randn())*σ)^2 | |
x+=v | |
xsq+=v*v |
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using PyCall | |
using PyPlot | |
function matplotlibstyle() | |
py""" | |
from matplotlib import rcParams | |
import numpy as np | |
def mplfigsize(hscale, publicationcols=1,columnsinches=3.1): | |
bonuscol=-0.12 | |
W=columnsinches*(publicationcols+bonuscol) |