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{
  "LanguageServerManager": {
    "language_servers": {
      "julia-lsp": {
        "version": 2,
        "argv": ["/home/lbo/bin/julia", "-e", "using LanguageServer; runserver()"],
        "languages": ["julia"],
        "mime_types": ["text/julia", "text/x-julia"],
        "display_name": "JuliaLSP"
      }

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function testsquare1()::Matrix{Int}
    [1 0 4 0;
     0 2 0 3;
     0 0 3 0;
     0 0 0 4]
end
function testsquare2()::Matrix{Int}
    [1 0 0 0;
     0 2 0 0;
     0 0 3 0;

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function longest_common_subsequence(x::Vector{T}, y::Vector{T})::Vector{T} where {T}
    m, n = length(x), length(y)
    C = zeros(Int, m+1, n+1)
    # Not necessary:
    for i = 1:m
        C[i,1] = 0
    end
    for j = 1:n
        C[1,j] = 0
    end

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function longest_increasing_subsequence(v::Vector{Int})::Vector{Int}
    M = zeros(Int, length(v)) # M[L] stores last index of sequence with length L.
    P = zeros(Int, length(v)) # P[i] stores predecessor of element i in longest sequence.
    Longest = 0
    for i = eachindex(v)
        # Binary search for position in longest-sequence-so-far
        lo, hi = 1, Longest+1
        while lo < hi
            mid = round(Int, floor((lo+hi)/2))
            if v[i] < v[M[mid]]

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# > X = generate_sorted_matrix(10, 10)
# 10×10 Matrix{Int64}:
#   2  139  194  248  337  428  544  625  736  873
#  11  141  195  251  340  454  564  644  743  892
#  39  153  200  272  350  454  582  649  786  900
#  58  163  203  284  357  462  586  652  799  912
#  66  164  219  295  359  473  599  654  806  930
#  73  170  220  302  361  482  604  677  813  947
#  74  177  243  303  364  488  609  681  815  957
#  78  187  245  323  409  504  611  696  845  961

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#include <iostream>
#include <fstream>
#include <unordered_set>
#include <string>
#include <vector>
#include <memory>
#include <cctype>

using namespace std;

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import numpy as np

def knapsack(items=[4,3,1,2], profits=[60, 50, 20, 20], capacity=7):
    table = np.zeros((len(items)+1, capacity+1))
    table[:, 0] = 0
    table[0, :] = 0

    for item in range(1, len(items)+1):
        for cap in range(1, capacity+1):
            y = 0 if items[item-1] > cap else profits[item-1]+table[item-1, cap-items[item-1]]

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using MKL

using DataFrames
using Flux
import ChainRulesCore: ignore_derivatives
import Distributions: Bernoulli
import CSV
import Random: Sampler
import BSON
import Flux.MLUtils: DataLoader

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using Distributions


function mysampler(previous::Union{NamedTuple, Nothing})::NamedTuple
    µ = rand(Normal(3, 2))
    σ = rand(TruncatedNormal(1, 3, 0, Inf))
    (µ=µ, σ=σ)
end

function myloglikelihood(θ::NamedTuple)::Float64

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using Plots
using Random
using Distributions, DistributionsAD
using LinearAlgebra
import Zygote: gradient

struct HMC{T,F}
    sup::AbstractArray{Tuple{T,T}}
    invM::Matrix{T}
    pdist::AbstractMvNormal