When I run my function my x and y values stay the same every time it is run. I'm not sure if I am using the default_random_engine incorrectly or not. How can I get my x and y values using the dist(engine) to randomize and not be the same output every time the program executes?
A 'seed' is something that provides a pseudonumber generator with its 'randomness'. If you initialise it with the same seed, you will get the same 'random' pattern.
Here, you are probably passing the same seed in all the time.
Maybe you should define your function like this instead:
long double fn( long reps, default_random_engine & engine )
Declare a single instance of your random number generator when you run your program, and seed it once with a value that will always be different when your program starts (eg the system time). Then, pass the generator into any functions that require random number generation.
Related
I have a member function of a class that is supposed to generate a random number in a range. To do so, I am using the rand() function. The function generates a random number like this:
unsigned seed;
seed = time(0);
srand(seed);
std::cout << "Random Number: "<< rand() << std::endl;
The function is called on two different objects. The result is:
Random Number: 1321638448
Random Number: 1321638448
This is consistent every-time I call it. What am i doing wrong?
(Converting my comment to an answer).
For most applications, you'll only really want to seed rand once in the course of running a program. Seeding it multiple times requires you to get different random seeds, and it's easy to mess that up.
In your case, the time function usually returns something with resolution on the level of seconds (though this isn't actually required by the standard). As a result, if you call time twice within the same second, you might get back the same value. That would explain why you're getting duplicate values: you're seeding the randomizer with the same value twice and then immediately querying it for a random number.
The best solution to this is to just seed the randomizer once. Typically, you'd do that in main.
If you really do want to seed the randomizer multiple times, make sure that you're doing so using a seed that is going to be pretty much random. Otherwise, you risk something like this happening.
Pseudorandom number generators basically have to pass a set of statistical tests to make sure they're "random enough" as a set of numbers. But of course, it's not actually random. Calling srand(seed) with some seed basically generates a set of numbers which, if passed through those tests, will seem "random enough".
By calling srand(seed) with the same seed multiple times, you're effectively generating the same set over and over again and getting the first value in it.
You call srand(seed) ONCE, and then you call rand() to get the next values in the random number set. Or you need to call srand(seed) with a different (random) seed each time.
If you're on linux, you can also use /dev/urandom to get a random number- the kernel has been taking signal/noise from the environment to generate "entropy" for it, supposedly making it even better than an algorithm psuedorandom number generator.
srand function should be called only once in program(most cases, not all cases). If you want reseed, you should use different seed number. Because rand() function is pseudo-random number generator. In other words, rand() gives you a calculated number.
You can use much for powerful random number generating library after C++11. See: http://en.cppreference.com/w/cpp/numeric/random
I am currently stuck at generating random numbers during runtime. In Java, I just call Math.random() and I'm pretty much done (I just need a simple RNG). In C++, I have tried several ways to generate random numbers and always end up getting the same.
Currently, I am using the following method to get a random number between MIN and MAX:
unsigned int getRandomNumber(int min, int max){
std::mt19937 mt(1729);
std::uniform_int_distribution<int> dist(min, max);
return dist(mt);
}
I have an object that calls this function in its constructor and assigns the value returned to an attribute. I currently create five instances of this object and the random number is always the same. Setting a big range (1 - 1000) does not change this. The number is always the same. Security is not a concern, it is an extremely simple application.
A random number generator works with a seed. Basically it's a number that's set only once for the random number generator to work with. If you re-seed your random number generator each time you try to generate an number you will get the same number every time. You should create the std::mt19937 object only once.
unsigned int getRandomNumber(int min, int max){
static std::mt19937 mt(1729);
std::uniform_int_distribution<int> dist(min, max);
return dist(mt);
}
Making mt static will cause it to only be instantiated once, which means it will only be constructed once, which means it will only be seeded once. Even with this fix, you'll still get the same series of numbers each time you run the program, but they'll be different each time you call getRandomNumber in one single execution.
A much better solution would be to instantiate the mt variable elsewhere, and pass it in to this function as a parameter, that way you could manage how it is seeded with more code than just a constructor call. Typically you would seed with a value based on time. Lots of insight here.
In java random number can get like
protected final static Random RANDOM = new Random(System.currentTimeMillis());
In c++ using GMP Library how it possible to generate random number?
I used code like
gmp_randstate_t s;
unsigned long seed;
seed = time(NULL);
gmp_randinit_default(s);
gmp_randseed_ui(s, seed);
mpz_class ran;
gmp_randclass rr(s);
ran =rr.get_z_bits(125);
long int random=ran.get_ui();
But i dont get random number.
Please help me.
First, there is no gmp_randclass constructor that takes a gmp_randstate instance, so your code didn't compile for me. The recommended way to construct a gmp_randclass instance is using gmp_randinit_default, like this:
gmp_randclass rr(gmp_randinit_default);
The first part of your code is seeding the gmp_randstate_t s, but that random state variable s is not used in the second part of your code (after the above change). Unless seeded otherwise, the default GMP random number generator always starts with the same seed, which means the same sequence of random numbers will be generated each time you run the program. You can seed an instance of gmp_randclass using the gmp_randclass::seed function.
The following code is similar to yours but seeds the random number generator based on the current time.
mpz_class ran;
gmp_randclass rr(gmp_randinit_default);
rr.seed(time(NULL));
ran =rr.get_z_bits(125);
long int random=ran.get_ui();
Note that as discussed in Random State Seeding, using a low-resolution current time is usually a poor choice for a random number generator seed.
I have written a RNG class which holds different algorithms, however it does not work as expected. Besides the fact that i want use normal (rather than uniform) distribution my code always returns either the same number (max) or just 2 numbers out of the interval [min,max]:
std::function<int(int, int)> mt19937 =
[](int min, int max) -> int {
std::uniform_int_distribution<int> distribution(min, max);
std::mt19937 engine;
engine.seed(time(null));
auto generator = std::bind(distribution, engine);
return generator();
};
Can anyone explain me what is missing to solve this puzzle? Furthermore, how can i implement normal distribution? Last time i tried out std::normal_distribution i was not able to enter bounds!
EDIT: When i speak of a normal distribution i mean that the results of the RNG near the two bounds should not be generated as often as the mean of both. E.g. look at the graphical representation of the standard Gauss distribution. I am referring to it because it visualizes the probabilities of the resulting values which i want to implement/use this way, if you understand.
The normal distribution is just this (x is a random uniform number):
But I see something that could be problematic:
std::uniform_int_distribution<int> distribution(min, max);
Isn't this giving your number generator an int type?
To fix the seeding problem, create your engine outside of the lambda and seed it when you create it.
A RNG uses an algorithm that produces numbers that appear random, but have a a very large period of repetition (a highlight of the Mersenne Twister). When you seed, you give the RNG an initial value to start the process with. Each time you ask for another number, it spits out another iteration of the algorithm.
When you seed every iteration:
time(NULL)
this code changes only every second, so when you request a new random number, it will only change every second.
I was creating a simple program that simulates a coin toss for my class. (Actually, class is over this term and i'm just working through the rest of the projects that weren't required). It involves the creating and calling a function that generates a random number between 1 and 2. Originally, I tried to seed the random number generator within the function that would be using it (coinToss); however, it did not produce a random number. Each time the program was run it was the same number as though I had only used
rand()
instead of
unsigned seed = time(0);
srand(seed);
rand();
Yet, when i moved the above within
int main()
it worked fine.
My question is 1)why did it not work when setup within the function that called it and (2) how does rand()
have access to what was done by srand() if they do not both occur in the same function?
Obviously, i'm a beginner so please forgive me if i didn't formulate the question correctly. Also, my book has only briefly touched on rand() and srand() so that's all i really know.
thanks for any help!
Pertinent code:
First attempt that didn't work:
int main()
{
//...........
coinToss();
//...........
}
int coinToss()
{
unsigned seed = time(0);
srand(seed);
return 1 + rand() % 2;
}
Second attempt which did work:
int main()
{
unsigned seed = time(0);
srand(seed);
coinToss();
}
int coinToss()
{
return 1 + rand() % 2;
}
You probably only want to seed the random number generator once. rand() returns the next pseudo-random number from it's internal generator. Every time you call rand() you will get the next number from the internal generator.
srand() however sets the initial conditions of the random number generator. You can think of it as setting the 'starting-out point' for the internal random number generator (in reality it's a lot more complicated than that, but it's a useful cognitive model to follow).
So, you should be calling srand(time(0)) exactly once in your application - somewhere near the beginning. After that, you can call rand() as many times as you want!
However
To answer your actual question - the first version doesn't work because time() returns the number of seconds since the epoch. So If you call coinToss() several times in a second (say, if you wanted to simulate 100 coin tosses), then you'd be constantly seeding the random number generator with the same number, thereby resetting it's internal state (and thus the next number you get) every time.
Anyway - using time() as a seed to srand() is somewhat crappy for this very reason - time() doesn't chage very often, and worse, it's predictable. If you know the current time, you can work out what rand() will return. The internet has many, many examples of better srand() seeds.
Pseudo-random number generators (like rand) work by taking a single starting number (the seed) and performing a numeric transformation on it each time you request a new number. You want to seed the generator just once, or it will continually get reset, which is not what you want.
As you discovered, you should just call srand just once in main. Also note that a number of rand implementations have pretty short cycles on the low 4 bits or so. In practice this means you might get an easily predictable repeating cycle of numbers You might want to shift the return from rand right by 4-8 bits before you take the % 2.
EDIT: The call would look something like:
return 1 + (rand() >> 6) % 2;
Seed only once per program, not every time you call coinToss()
To expand on Mark B's answer: It is not so much that the random number generator is reset as it sets a new variable to be used in calculating random numbers. However your program doesn't do that much work between calls to srand. Therefore every time you call srand(time(0)) it is using the same seed, so you are resetting the internal state of the random number generator . If you put a sleep in there so that time(0) changed you would not get the same number every time.
As for how data passes from srand to rand, it is fairly simple, a global variable is used. All names that start with an underscore and a capital letter or two underscores are reserved for variables used by your compiler. More than likely this variable has been declared static so it isn't visible outside of the translation unit(aka the library file that contains your compiler's standard library.) This is done so that #define STUFF 5 doesn't break your standard library.
for simple simulations, you must not change the seed at all during the simulation. Your simulation will be "worse" in that case.
To understand this, you should see pseudo random sequences as a big wheel of fortune. When you change the seed, it is like you change the position, and then, each call to rand will give you a different number. If you roll again, it will be more probable finding yourself repeating numbers.