Juraj Szitás JSzitas

## ou_testing.R
# discretised ornstein-uhlenbeck
ou <- function(steps, theta = 1.0, mu = 1.2, sigma = 0.3, dt = 0.01) {
  x <- rep(NA, steps+1)
  x[1] = 0
  for(i in 2:(steps+1)) {
    x[i] = x[i-1] + theta * (mu - x[i-1]) * dt + sigma * sqrt(dt) * rnorm(1);
  }
  return(x)
}
# ~~2 years of simulated data if you account for weekends and holidays

## beatdown.cpp
#include <array>
#include <chrono>
#include <iostream>
#include <random>
#include <vector>

void generate_array(std::vector<int>& arr, std::mt19937& gen) {
  std::uniform_int_distribution<> dis(0, 99);
  for (int & i : arr) {
    i = dis(gen);

## .cpp
#include <chrono>
#include <iostream>
#include <vector>

constexpr size_t kPeople = 10000;
constexpr size_t kLowBoundAge = 1;
constexpr size_t kHighBoundAge = 100;
constexpr size_t kAgeLen = kHighBoundAge - kLowBoundAge;

int main()
	# discretised ornstein-uhlenbeck
	ou <- function(steps, theta = 1.0, mu = 1.2, sigma = 0.3, dt = 0.01) {
	x <- rep(NA, steps+1)
	x[1] = 0
	for(i in 2:(steps+1)) {
	x[i] = x[i-1] + theta * (mu - x[i-1]) * dt + sigma * sqrt(dt) * rnorm(1);
	}
	return(x)
	}
	# ~~2 years of simulated data if you account for weekends and holidays
	#include <array>
	#include <chrono>
	#include <iostream>
	#include <random>
	#include <vector>

	void generate_array(std::vector<int>& arr, std::mt19937& gen) {
	std::uniform_int_distribution<> dis(0, 99);
	for (int & i : arr) {
	i = dis(gen);