# Fisher-Yates modern shuffle algorithm

I have implemented the shuffling algorithm of Fisher-Yates in C++, but I've stumbled across the modulo bias. Is this random number generation by rand() correct for Fisher-Yates?

srand(time(NULL));
vector<int> elements;
// push numbers from 1 to 9 into the vector
for (int i = 1; i < 9; ++i)
{
elements.push_back(i);
}

// the counter to be used to generate the random number
int currentIndexCounter = elements.size();
for (auto currentIndex = elements.rbegin(); currentIndex != elements.rend() - 1;
currentIndex++ , --currentIndexCounter)
{
int randomIndex = rand() % currentIndexCounter;
// if its not pointing to the same index
if (*currentIndex != elements.at(randomIndex))
{
//then swap the elements
swap(elements.at(randomIndex), *currentIndex);
}
}

• rand() is well know for its bad distribution. But you are using it correctly. Note: rand() % currentIndexCounter does not give you a perfect distribution unless currentIndexCounter is an exact divisor of RAND_MAX. You may want to look at C++11 random header and the generators provided inside. Jan 12, 2014 at 18:37

@Loki Astari is correct in his comment:

rand() is well know for its bad distribution. But you are using it correctly. Note: rand() % currentIndexCounter does not give you a perfect distribution unless currentIndexCounter is an exact divisor of RAND_MAX. You may want to look at C++11 random header and the generators provided inside.

Since you're using C++11, you should instead utilize the <random> library. It includes things such as pseudo-random number generators and random number distributions.

Beyond that, I'll just point out some additional things:

• With C++11, you should now be using nullptr instead of NULL.

This should be changed in srand():

std::srand(std::time(nullptr));


However, as mentioned above, you should not be using this with C++11. But in any situation where you must stay with rand, the above would be recommended.

• With C++11, you should also have access to initializer lists. This will allow you to initialize the vector instead of calling push_back() multiple times:

std::vector<int> elements { 1, 2, 3, 4, 5, 6, 7, 8, 9 };


Also note that your push_back() was only pushing numbers 1-8 into the vector, despite what your comment said. You've used < instead of <= in the loop statement, so the 9 was excluded.

• currentIndexCounter should be of type std::vector<int>::size_type, which is the return type of size() (or you can just use auto). This should've given you compiler warnings as it's a type-mismatch and a possible loss of data. Make sure your compiler warnings are turned up high.

• currentIndex is a misleading name because it's being used with iterators, not indices.

Here, auto gives currentIndex the type std::reverse_iterator from std::rbegin(). You could simply name this something like iter. It's okay for iterator names to be short.

• In the second for loop, you just need elements.rend(), without the - 1. The iterator will already go through the entire vector and stop at the last reverse element.

• The iterator increment should be prefix instead in order to improve performance a bit by avoiding a copy. It's best to do this with all nontrivial types such as iterators.

• I've tested this by giving randomIndex a value greater than elements.size(), causing an exception to be thrown. Even if std::rand() or another RNG is guaranteed to give you an intended value, it may be best to handle this properly.

Here's the final version with my own given changes, which I've also ran here. I've also included a concise way of displaying the vector before and after the shuffling.

#include <algorithm>
#include <iostream>
#include <iterator>
#include <random>
#include <vector>

int main()
{
// seed the RNG
std::random_device rd;
std::mt19937 mt(rd());

std::vector<int> elements { 1, 2, 3, 4, 5, 6, 7, 8, 9 };

std::cout << "Before: ";
std::copy(elements.cbegin(), elements.cend(),
std::ostream_iterator<int>(std::cout, " "));

auto currentIndexCounter = elements.size();

for (auto iter = elements.rbegin(); iter != elements.rend();
++iter, --currentIndexCounter)
{
// get int distribution with new range
std::uniform_int_distribution<> dis(0, currentIndexCounter);
const int randomIndex = dis(mt);

if (*iter != elements.at(randomIndex))
{
std::swap(elements.at(randomIndex), *iter);
}
}

std::cout << "\nAfter: ";
std::copy(elements.cbegin(), elements.cend(),
std::ostream_iterator<int>(std::cout, " "));
}


Output:

Before: 1 2 3 4 5 6 7 8 9
After: 7 9 3 2 4 5 6 1 8

• is it not possible that randomIndex = elements.size() on the first iteration of the loop thus resulting in undefined behavior? Unless the prefix decrement on currentIndexCounter executes on entry of the for loop, possibly? Dec 5, 2019 at 5:57
• @z.karl: That is possible, though that's not one aspect that I caught from the OP's code previously (I only added auto).
– Jamal
Dec 7, 2019 at 1:48

Here is my take on it with the modern <random>:

static const void fisher_yates_shuffle(std::deque<card>& cards)
{
std::random_device                          random                    ;
std::mt19937                                mersenne_twister(random());
std::uniform_int_distribution<std::uint8_t> distribution              ;
for (auto i = cards.size() - 1; i > 0; --i)
{
distribution.param(std::uniform_int_distribution<std::uint8_t>::param_type(0, i));
std::swap(cards[i], cards[distribution(mersenne_twister)]);
}
};

• You have presented an alternative solution, but haven't reviewed the code. Please edit to show what aspects of the question code prompted you to write this version, and in what ways it's an improvement over the original. It may be worth (re-)reading How to Answer. Nov 5, 2019 at 17:18
• If you only have read the flow of the answers, you would have noticed that they advise using the modern <random> to tackle the problem regarding modulo bias. This solution utilizes <random> and furthermore does not unnecessarily reinstantiate the uniform int distribution which is rather expensive. Despite being human, you are worse than a bot at grasping context. Nov 6, 2019 at 19:20
• I just used this to implement a fisher_yates_shuffle on a Poker program and it works wonderfully, better than other answers. Nov 29, 2019 at 20:36