I am an experienced C programmer that is occasionally forced to use a little bit of C++.
I need to generate random numbers from a normal distribution with a variety of means and variances. If I had a C function that did this called normal(float mean, float var) then I could write the following code:
int i;
float sample;
for(i = 0;i < 1000;i++)
{
sample = normal(mean[i],variance[i]);
do_something_with_this_value(sample);
}
Note that there is a different mean and variance for each value of i.
C does not contain a function called normal, but C++ does, well actually its called std::normal_distribution. Unfortunately my C++ is not good enough to understand the syntax in the documentation. Can anyone tell me how to achieve the functionality of my C code but using std::normal_distribution.
std::normal_distribution isn't function but templated class
you can use it like this:
#include <random>
int main(int, char**)
{
// random device class instance, source of 'true' randomness for initializing random seed
std::random_device rd;
// Mersenne twister PRNG, initialized with seed from previous random device instance
std::mt19937 gen(rd());
int i;
float sample;
for(i = 0; i < 1000; ++i)
{
// instance of class std::normal_distribution with specific mean and stddev
std::normal_distribution<float> d(mean[i], stddev[i]);
// get random number with normal distribution using gen as random source
sample = d(gen);
// profit
do_something_with_this_value(sample);
}
return 0;
}
Related
I have a function that calls srand and rand like this:
void foo() {
int seed = some_operation();
std::srand(seed);
int value = std::rand();
// Do something with random value
}
However, I don't want to change the global state of rand. Whats the easiest way to get a random number then?
Requirements:
random number must be deterministic based on seed
C++11 is fine
foo should be thread safe
the global state of rand should not be modified
Edit:
There is a stackoverflow question asking how to generate random numbers. The accepted answer however shows how to generate truly unique random numbers, using a slow std::random_device. I just needed a simple generator using a fixed seed instead.
C++11 is fine
Then use the new pseudorandom number library:
#include <random>
int foo() {
int seed = some_operation();
std::minstd_rand rand(seed);
int value = rand();
// Do something with random value
}
minstd_rand is a simple linear congruential engine, similar to that typically used by std::rand(), but with its state encapsulated in a class. Other engines are available, if you need a higher quality pseudorandom sequence. The Mersenne Twister, std::mt19937, is usually a good choice if you don't have specific requirements.
<random> defines a number of PRNG classes that you can use, which do not use global states.
For example, using a default Mersenne Twister, std::mt19937:
#include <iostream>
#include <random>
int main() {
int seed = 1234;
std::mt19937 rng(seed);
std::cout << "Random number: " << rng() << std::endl;
}
One approach is to provide your own implementation, with its own seed data. A template for doing that is provided in the manpage for rand().
static unsigned long next = 1;
/* RAND_MAX assumed to be 32767 */
int myrand(void) {
next = next * 1103515245 + 12345;
return((unsigned)(next/65536) % 32768);
}
void mysrand(unsigned seed) {
next = seed;
}
I've read some similar questions to the one I'm asking but the answers don't seem complete or completely clear to me.
I'm trying to parallelize a parameter scan that requires the repeated generation of a set of random numbers. With only one thread I currently do something like this:
int main() {
//Get random number generators
typedef std::mt19937 MyRNG;
std::random_device rd;
//seed generator
MyRNG rng;
rng.seed(rd());
//make my uniform distributions for each parameter
std::uniform_real_distribution<> param1(-1,1);
std::uniform_real_distribution<> param2(-1,1);
double x,y;
//Do my scan
for (int i = 0; i < N; i++) {
x = param1(rng)
y = param2(rng)
//Do things with x and y*
}
In this way I get a new x and y for every scan. Now I want to utilize multiple cores to do this in parallel. So I turn define a function void scan() which essentially has the same contents as my main function. I then create multiple threads and each have them run scan(). But I'm not sure if this is thread safe using std::thread. Will my random number generation in each thread as it currently is be independent? Can I save myself time by creating my RNGs outside of my void function? Thanks.
I would probably generate the seeds in main, and pass a seed to each thread function. I wouldn't use the output of std::random_device directly either--I'd put numbers into something like an std::set or std::unordered_set until I got as many seeds as I wanted, to assure that I didn't give two threads the same seed (which would obviously be a waste of time).
Something along this general line:
int do_work(unsigned long long seed) {
//Get random number generators
typedef std::mt19937 MyRNG;
//seed generator
MyRNG rng(seed);
//make my uniform distributions for each parameter
std::uniform_real_distribution<> param1(-1,1);
std::uniform_real_distribution<> param2(-1,1);
double x,y;
//Do my scan
for (int i = 0; i < N; i++) {
x = param1(rng);
y = param2(rng);
//Do things with x and y*
}
}
static const int num_threads = 4;
int main() {
std::set<unsigned long long> seeds;
while (seeds.size() < num_threads)
seeds.insert(std::random_device()());
std::vector<std::thread> threads;
for (auto const seed: seeds)
threads.emplace_back(std::thread(do_work, seed));
for (auto &t : threads)
t.join();
}
As an aside, using a single result from random_device to seed an std::mt19937 restricts the generator quite a bit--you're giving it only 32 (or possibly 64) bits of seed, but it actually has 19937 bits of seed material. std::seed_seq attempts to ameliorate this to at least some degree (among other things, you can use a number of outputs from std::random_device to create the seed.
Oh, and given that your two instances of uniform_real_distribution use the same parameters, there's probably not a whole lot of need for two separate distribution objects either.
Those three lines of generating random number looks a bit tricky. It is hard to always remember those lines. Could someone please shed some light on it to make it easier to understand?
#include <random>
#include <iostream>
int main()
{
std::random_device rd; //1st line: Will be used to obtain a seed for the random number engine
std::mt19937 gen(rd()); //2nd line: Standard mersenne_twister_engine seeded with rd()
std::uniform_int_distribution<> dis(1, 6);
for (int n=0; n<10; ++n)
std::cout << dis(gen) << ' '; //3rd line: Use dis to transform the random unsigned int generated by gen into an int in [1, 6]
std::cout << '\n';
}
Here are some questions I can think of:
1st line of code:
random_device is a class as described by the documentation random_device, so this line means declaring a object rd? If yes, why in 2nd line we pass rd() to construct mt19937 instead of using the object rd (without parentheses)?
3rd line of code:
Why do call class uniform_int_distribution<> object dis()? Is dis() a function? Why shall we pass in gen object into dis()?
random_device is slow but genuinely random, it's used to generate the 'seed' for the random number sequence.
mt19937 is fast but only 'pseudo random'. It needs a 'seed' to start generating a sequence of numbers. That seed can be random (as in your example) so you get a different sequence of random numbers each time. But it could be a constant, so you get the same sequence of numbers each time.
uniform_int_distribution is a way of mapping random numbers (which could have any values) to the numbers you're actually interested in, in this case a uniform distribution of integers from 1 to 6.
As is often the case with OO programming, this code is about division of responsibilities. Each class contributes a small piece to the overall requirement (the generation of dice rolls). If you wanted to do something different it's easy because you've got all the pieces in front of you.
If this is too much then all you need to do is write a function to capture the overall effect, for instance
int dice_roll()
{
static std::random_device rd;
static std::mt19937 gen(rd());
static std::uniform_int_distribution<> dis(1, 6);
return dis(gen);
}
dis is an example of a function object or functor. It's an object which overloads operator() so it can be called as if it was a function.
std::random_device rd; // create access to truly random numbers
std::mt19937 gen{rd()}; // create pseudo random generator.
// initialize its seed to truly random number.
std::uniform_int_distribution<> dis{1, 6}; // define distribution
...
auto x = dis(gen); // generate pseudo random number form `gen`
// and transform its result to desired distribution `dis`.
I'm using the following function to generate gaussian random numbers:
double r_norm(double mean, double sigma){
random_device rd;
mt19937 gen(rd());
normal_distribution<double> d(mean, sigma);
return d(gen);
}
However, when I call this in main() with cout:
for (int k = 0; k < 10; k++){
cout << r_norm(2,0.5) <<endl;
}
It outputs the same number 10 times. Ideally I need to be able to call this function wherever, in order to receive a newly generated number each time.
Update: I managed to fix this by declaring the random device and mersenne twister outside of scope as global variables, but is there a neater way to do this?
The problem is that you need to recycle the random_device.
If you really want to keep the exact same function signature, the easiest way would be to use a global variable:
random_device rd;
mt19937 gen(rd());
double r_norm(double mean, double sigma){
normal_distribution<double> d(mean, sigma);
return d(gen);
}
That being said: stl random distributions are statefull, so you need to also recycle d if you want an actual valid distribution.
At this point, maintaining your interface would require a static std::map<pair<double, double>, normal_distribution> so that you recycle them properly as well.
I created a function that is suppose to generate a set of normal random numbers from 0 to 1. Although, it seems that each time I run the function the output is the same. I am not sure what is wrong.
Here is the code:
MatrixXd generateGaussianNoise(int n, int m){
MatrixXd M(n,m);
normal_distribution<double> nd(0.0, 1.0);
random_device rd;
mt19937 gen(rd());
for(int i = 0; i < n; i++){
for(int j = 0; j < m; j++){
M(i,j) = nd(gen);
}
}
return M;
}
The output when n = 4 and m = 1 is
0.414089
0.225568
0.413464
2.53933
I used the Eigen library for this, I am just wondering why each time I run it produces the same numbers.
From:
http://en.cppreference.com/w/cpp/numeric/random/random_device
std::random_device may be implemented in terms of an implementation-defined pseudo-random number engine if a non-deterministic source (e.g. a hardware device) is not available to the implementation. In this case each std::random_device object may generate the same number sequence.
Thus, I think you should look into what library stack you are actually using here, and what's known about random_device in your specific implementation.
I realize that this then might in fact be a duplicate of "Why do I get the same sequence for every run with std::random_device with mingw gcc4.8.1?".
Furthermore, it at least used to be that initializating a new mt19937 instance would be kind of expensive. Thus, you have performance reasons in addition to quality of randomness to not re-initalize both your random_device and mt19937 instance for every function call. I would go for some kind of singleton here, unless you have very clear constraints (building in a library, unclear concurrency) that would make that an unuistable choice.