This is the first time I'm trying random numbers with C (I miss C#). Here is my code:
int i, j = 0;
for(i = 0; i <= 10; i++) {
j = rand();
printf("j = %d\n", j);
}
with this code, I get the same sequence every time I run the code. But it generates different random sequences if I add srand(/*somevalue/*)
before the for
loop. Can anyone explain why?
There's a lot of answers here, but no-one seems to have really explained why it is that rand() always generates the same sequence given the same seed - or even what the seed is really doing. So here goes.
The rand() function maintains an internal state. Conceptually, you could think of this as a global variable of some type called rand_state. Each time you call rand(), it does two things. It uses the existing state to calculate a new state, and it uses the new state to calculate a number to return to you:
Now you can see that each time you call rand(), it's going to make rand_state move one step along a pre-determined path. The random values you see are just based on where you are along that path, so they're going to follow a pre-determined sequence too.
Now here's where srand() comes in. It lets you jump to a different point on the path:
The exact details of state_t, calculate_next_state(), calculate_return_value() and generate_random_state() can vary from platform to platform, but they're usually quite simple.
You can see from this that every time your program starts, rand_state is going to start off at INITIAL_STATE (which is equivalent to generate_random_state(1)) - which is why you always get the same sequence if you don't use srand().
Random number generators are not actually random, they like most software is completely predictable. What rand does is create a different pseudo-random number each time it is called One which appears to be random. In order to use it properly you need to give it a different starting point.
If I remember the quote from Knuth's seminal work "The Art of Computer Programming" at the beginning of the chapter on Random Number Generation, it goes like this:
"Anyone who attempts to generate random numbers by mathematical means is, technically speaking, in a state of sin".
Simply put, the stock random number generators are algorithms, mathematical and 100% predictable. This is actually a good thing in a lot of situations, where a repeatable sequence of "random" numbers is desirable - for example for certain statistical exercises, where you don't want the "wobble" in results that truly random data introduces thanks to clustering effects.
Although grabbing bits of "random" data from the computer's hardware is a popular second alternative, it's not truly random either - although the more complex the operating environment, the more possibilities for randomness - or at least unpredictability.
Truly random data generators tend to look to outside sources. Radioactive decay is a favorite, as is the behavior of quasars. Anything whose roots are in quantum effects is effectively random - much to Einstein's annoyance.
call
srand(sameSeed)
before callingrand()
. More details here.Seeding the
rand()
You have to seed it. Seeding it with the time is a good idea:
srand()
You get the same sequence because
rand()
is automatically seeded with the a value of 1 if you do not callsrand()
.Edit
Due to comments
rand()
will return a number between 0 andRAND_MAX
(defined in the standard library). Using the modulo operator (%
) gives the remainder of the divisionrand() / 100
. This will force the random number to be within the range 0-99. For example, to get a random number in the range of 0-999 we would applyrand() % 1000
.