The program does not crash neither executes completely - loops

Question

it has been a few hours since I am dealing with this issue. I was wondering if someone could point out what am I doing wrong, and if possible - how to fix it. Essentially, I am simply trying to generate n number of object pairs and store them into a vector<pair<Foo, Foo>>. The algorithm involves random number generator. I use STL <random> and its components like m19937, uniform_real_distribution and uniform_int_distribution. Below is the simplified version of what I am trying to do representing the case I got at hand. The second loop always cuts short. However, I fail to see the reason why. Essentially, I never get to see the program execute completely. The last two messages never show.
program
#include <iostream>
#include <vector>
#include <random>
#include <utility>
// utility
using std::pair;
// random
using std::mt19937;
using std::uniform_int_distribution;
using std::uniform_real_distribution;
// iostream
using std::cout;
using std::endl;
// vector
using std::vector;
class Event{
private:
double x, y;
public:
Event(const double X, const double Y);
};
Event::Event(const double X, const double Y): x(X), y(Y){}
int main(){
cout << "Initializing storage..." << endl;
vector<Event> population;
vector<pair<Event,Event>> selection;
cout << "Initializing necessary member variables..." << endl;
const unsigned int SEED = 14112017;
const unsigned int MAX_ITERATIONS = 10000;
const double MIN = 1;
const double MAX = 10000;
mt19937 engine(SEED);
cout << "Generating the initial population..." << endl;
uniform_real_distribution<> real_distribution(MIN, MAX);
for(unsigned int i = 0; i < MAX_ITERATIONS; ++i){
double x = real_distribution(engine);
double y = real_distribution(engine);
Event event(x, y);
population.push_back(event);
}
cout << "Success! The initial population has been generated successfully" << endl;
population.shrink_to_fit();
cout << "Starting the selection process..." << endl;
unsigned int random = 0;
uniform_int_distribution<> int_distribution(MIN, MAX);
for(unsigned int i = 0; i < MAX_ITERATIONS; ++i){
random = int_distribution(engine);
Event event_x = population.at(random);
random = int_distribution(engine);
Event event_y = population.at(random);
pair<Event, Event> bound(event_x, event_y);
selection.push_back(bound);
}
cout << "Success! The selection process has been completed successfully" << endl;
selection.shrink_to_fit();
cout << "population size: " << population.size() << endl;
cout << "selection size: " << selection.size() << endl;
return 0;
}
I compile the above using cygwins C++ compiler, and I execute the code in command-prompt. The OS is Windows 10 x64. The box has 32 GB memory.

Answer 1

uniform_int_distributions constructor is as follows:
explicit uniform_int_distribution( IntType a = 0,
IntType b = std::numeric_limits<IntType>::max() );
By default, it returns an integer which covers all positive values of that type. The range includes the value of the second parameter. If it wouldn't, it would be cumbersome to specify we want all positive integers.
cppreference.com does not document it, but the C++ standard does: Thanks #Cubbi
This is documented on cppreference.com, or in the C++ standard:
26.5.8.2.1 Class template uniform_int_distribution [rand.dist.uni.int]
1 A uniform_int_distribution random number
distribution produces random integers i, a ≤ i ≤ b, distributed
according to the constant discrete probability function
[...]
// constructors and reset functions
explicit uniform_int_distribution(IntType a = 0, IntType b = numeric_limits<IntType>::max());
Here:
uniform_int_distribution<> int_distribution(MIN, MAX);
for(unsigned int i = 0; i < MAX_ITERATIONS; ++i){
random = int_distribution(engine);
Event event_x = population.at(random);
random = int_distribution(engine);
Event event_y = population.at(random);
random can take the value MAX, which is out of the bounds of the population vector.