swairshah

using Distributions, Plots, Random
using StatsBase, StatsPlots
using Distances

function init(size::Int, I₀=0.1)
    S₀ = 1 - I₀
    x = rand(Uniform(0, 10), size, 2)
    v = zeros(size, 2)
    status = sample([:S, :I, :R], Weights([S₀, I₀, 0]), size)
    x, v, status

swairshah

import random
import itertools
import numpy as np

# counter example:
# a = np.array([25,  1, 46, 25, 45,  6, 25, 43,  9,  3, 21, 34, 41, 32, 36, 12, 33,
#         7, 42, 50, 22, 37, 16, 31,  3, 44, 28,  8,  4, 41, 15, 19, 29, 10,
#                31, 14,  2, 17, 31, 16,  9, 15, 15, 40, 37, 14, 10,  2, 19,  9])
# b = np.array([ 4, 37, 43, 11, 26, 17, 35, 42, 47, 31, 49, 39, 47, 39, 37, 15, 35,
#        26, 25, 43, 47, 49, 19, 42, 28, 49, 18, 19, 48, 35,  1, 15, 40, 15,

swairshah

import numpy as np
from numpy.random import uniform as unif

def euclidean_dist(x,y):
        return np.sqrt(sum((x-y)**2))

def method1(n, N = 10000):
    
    dist = 0
    for i in range(N):

swairshah

from random import randint
import timeit
import sys


def qsort2(list):
    if list == []: 
        return []
    else:
        pivot = list[0]