bicycle1885

# Public Domain

using LinearAlgebra: cross, dot, norm

"""
    dihedral(r1, r2, r3, r4)

Compute the dihedral angle of two planes defined by (`r1`, `r2`, `r3`) and
(`r2`, `r3`, `r4`).

bicycle1885

function f1()
    function g()
        x = 1
        return () -> x
    end
    x = 0
    h = g()
    return h()
end

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# Fattahi, Salar, and Somayeh Sojoudi. 2019. “Graphical Lasso and Thresholding:
# Equivalence and Closed-Form Solutions.” Journal of Machine Learning Research:
# JMLR. https://www.jmlr.org/papers/volume20/17-501/17-501.pdf.
function approxsol(Σ, λ)
    # Residue of Σ relative to λ (see def. 11).
    Σres = zeros(size(Σ))
    for j in axes(Σ, 2), i in axes(Σ, 1)
        if i ≠ j && abs(Σ[i,j]) > λ
            Σres[i,j] = Σ[i,j] - λ*sign(Σ[i,j])
        end

bicycle1885

using LinearAlgebra
using Distributions

# Liu, Huan, Yongqiang Tang, and Hao Helen Zhang. 2009. “A New Chi-Square
# Approximation to the Distribution of Non-Negative Definite Quadratic Forms in
# Non-Central Normal Variables.” Computational Statistics & Data Analysis 53
# (4): 853–56.
function genx2cdf(A, μ, Σ, t)
    # k = 1
    AΣᵏ = A

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# Static version of lowrankdowndate.
# The source code is derived from stdlib/LinearAlgebra/src/cholesky.jl.
# License is MIT (https://julialang.org/license)

using StaticArrays: SMatrix
using LinearAlgebra: LowerTriangular, PosDefException

"""
    lowrankdowndate(L, v)

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using Base.Threads: @spawn, nthreads, threadid

N = 100
chan = Channel(nthreads()) do chan
    for val in 1:N
        put!(chan, val)
        println("T$(threadid()) => put $(val)")
    end
end

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using Random: default_rng, seed!, rand!
using RandomNumbers
using StaticArrays
#using Plots
#using BenchmarkTools

β_crit(q) = log(1 + √q)
rand_potts2d(q, m, n=m, rng=default_rng()) = rand(rng, Base.OneTo(Int8(q)), m, n)

function potts2d_ifelse!(q, s, β, niters, rng)

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using LinearAlgebra

function main()
    # input data 
    train_data = read_data("./ml-100k/u.data")
    n_user = length(unique([t[1] for t in train_data]))
    n_item = length(unique([t[2] for t in train_data]))
    
    # parameters
    P, Q = fit(n_user, n_item, train_data)