my rand number is rand()%6+1 aka dice rolling, when its based on "time", is it possible to make a console app that foresees the future numbers in the time I want to? for example predict a number on time 14:40:32 on a certain day in future?
Yes provided that you use the same implementation of rand i.e. link with the same version of the standard library. All you need is to get the time_t value for the time you are interested in pass it to srand and call rand to get the value.
For example, if time_t holds the number of seconds since the epoch (which is the case for most implementations), then you can do the following to get the value returned by rand with a 10-second-in-the-future seed:
std::srand(std::time(nullptr) + 10);
std::cout << std::rand();
(Leaving aside the questions of whether it's a good idea to use rand at all.)
... for example predict a number on time 14:40:32 on a certain day in future?
It's possible when knowing how exactly rand() generates the pseudo random number on a certain seed (which is available for most compilers open source code implementation).
You have a certain seed number given from your date and time, thus you can just inspect the sequence of random numbers generated consecutively.
Yes and no. If you have a value of time_t, then just run the same library version of srand() on that value, and rand() will definitely yield the same sequence.
But you need to be sure that
the random libraries in the two applications use the same implementation (I think it's Mersenne Twister, but I'd need to check)
the clock of the two applications is synchronised. If you think that the master application's clock is 14:30:17, but it's really 14:30:18, then entering 14:30:17 in the monitor application will (of course) get different values.
the sequence of calls to rand() in both applications is the same, i.e., the number of calls between the srand() and the rand() you are interested in is known by you.
The last point might be a showstopper.
Say that you know that the app was initialised with srand(T) and you know T. Now yes, you know all the future extractions of its rand(). But you still need to know at which point in the sequence you are.
The number extracted at 19:30:17 GMT will not depend on the '19:30:17 GMT', but on how many numbers have been extracted before since the call to srand().
TL;DR if you know the value that time(0) passed to srand(), you cannot predict the output of the rand() call at a given time. You can predict the output of the n-th call to rand() for any given n.
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 have been learning recently how to program games in c++ from a beginner book, and i reached a lesson where i have to make a game in where i have to guess the computer's random picked number, and i have to use this line of code:
srand(static_cast<unsigned int>(time(0)));
variable=rand();
I obviously use iostream cstdlib and ctime.I don't really understand how this works.How is it picking the time and date, and by what rules is it converting into an unsigned int. Basically, how those functions work.
Thank you!
1. About time()
time (or better std::time in C++) is a function that returns some integer or floating point number that represents the current time in some way.
Which arithmetic type it actually returns and how it represents the current time is unspecified, however, most commonly you will get some integer type that holds the seconds since begin of the Unix epoch.
2. About srand()
srand is a function that uses its argument (which is of type unsigned int), the so called seed, to set the internal state of the pseudo number generator rand. When I write random in the rest of this answer, read pseudo random.
Using a different seed will in general result in a different sequence of random numbers produced by subsequent calls to rand, while using the same seed again will result in the exactly same sequence of random numbers.
3. Using time() to seed rand()
If we do not want to get the same random numbers every time we run the program, we need some seed that is different on each run. The current time is a widely used source for such a seed as it changes constantly.
This integer (or whatever else time returned) representing the current time is now converted to unsigned int with a static_cast. This explicit cast is not actually needed as all arithmetic types convert to unsigned int implicitly, but the cast may silence some warnings. As time goes by, we can expect the resulting unsigned int and thus the sequence of random numbers produced by rand to change.
4. Pitfalls
If, as is common, time returns the number of seconds since the beginning of the Unix epoch, there are three important things to note:
The sequence you produce will be different only if at least a second has passed between two invocations.
Depending on the actual implementation, the resulting sequences may start of kind of similar if the time points used to seed rand are close to each other (compared to time since Epoch). Afaik, this is the case in MSVC's implementation. If that is problematic, just discard the first couple of hundred or thousand values of the sequence. (As I have learned by now, this does not really help much for poor RNGs as commonly used for rand. So if that is problematic, use <random> as described below.)
Your numbers are not very random in the end: If someone knows when your call to srand occurred, they can derive the entire sequence of random numbers from that. This has actually led to a decryption tool for a ransom ware that used srand(time(0)) to generate its "random" encryption key.
Also, the sequence generated by rand tends to have poor statistical properties even if the seed was good. For a toy program like yours, that is probably fine, however, for real world use, one should be aware of that.
5. The new <random>
C++11 introduced new random number facilities that are in many ways superior to the old rand based stuff. They provided in the standard header <random>. It includes std::random_device which provides a way to get actually random seeds, powerful pseudo random number generators like std::mt19937 and facilities to map the resulting random sequences to integer or float ranges without introducing unnecessary bias.
Here is an example how to randomly roll a die in C++11:
#include <random>
#include <iostream>
int main()
{
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<> dis(1, 6);
for (int n=0; n<10; ++n)
std::cout << dis(gen) << ' ';
std::cout << '\n';
}
(Code from cppr) Note: std::random_device does not work properly with MinGW, at least in the version (Nuwen MinGW5.3) I tested!
It should also be noted that the state space of a mt19937 is much larger than the 32 bit we (commonly) get out of a single call to random_device. Again, this will most likely not matter for toy programs and homework, but for reference: Here is my attempt to properly seed the entire state space, plus some helpful suggestions in the answers.
If you are interested in more details about rand vs <random>, this is an interesting watch.
First line:
srand() is a pseudo-random number generator. In your case it is initialized with the current time (execution time) on your system.
Second line:
After the pseudo-random number generator is configured, you can retrieve random numbers by calling rand().
my rand number is rand()%6+1 aka dice rolling, when its based on "time", is it possible to make a console app that foresees the future numbers in the time I want to? for example predict a number on time 14:40:32 on a certain day in future?
Yes provided that you use the same implementation of rand i.e. link with the same version of the standard library. All you need is to get the time_t value for the time you are interested in pass it to srand and call rand to get the value.
For example, if time_t holds the number of seconds since the epoch (which is the case for most implementations), then you can do the following to get the value returned by rand with a 10-second-in-the-future seed:
std::srand(std::time(nullptr) + 10);
std::cout << std::rand();
(Leaving aside the questions of whether it's a good idea to use rand at all.)
... for example predict a number on time 14:40:32 on a certain day in future?
It's possible when knowing how exactly rand() generates the pseudo random number on a certain seed (which is available for most compilers open source code implementation).
You have a certain seed number given from your date and time, thus you can just inspect the sequence of random numbers generated consecutively.
Yes and no. If you have a value of time_t, then just run the same library version of srand() on that value, and rand() will definitely yield the same sequence.
But you need to be sure that
the random libraries in the two applications use the same implementation (I think it's Mersenne Twister, but I'd need to check)
the clock of the two applications is synchronised. If you think that the master application's clock is 14:30:17, but it's really 14:30:18, then entering 14:30:17 in the monitor application will (of course) get different values.
the sequence of calls to rand() in both applications is the same, i.e., the number of calls between the srand() and the rand() you are interested in is known by you.
The last point might be a showstopper.
Say that you know that the app was initialised with srand(T) and you know T. Now yes, you know all the future extractions of its rand(). But you still need to know at which point in the sequence you are.
The number extracted at 19:30:17 GMT will not depend on the '19:30:17 GMT', but on how many numbers have been extracted before since the call to srand().
TL;DR if you know the value that time(0) passed to srand(), you cannot predict the output of the rand() call at a given time. You can predict the output of the n-th call to rand() for any given n.
Okay I'm starting to lose my mind. All I want to do is random a number between 0 and 410, and according to this page, my code should do that. And since I want a random number and not a pseudo-random number, I'm using srand() as well, in a way that e.g. this thread told me to do. But this isn't working. All I get is a number that is depending on how long it was since my last execution. If I e.g. execute it again as fast as I can, the number is usually 6 numbers higher than the last number, and if I wait longer, it's higher, etc. When it reaches 410 it goes back to 0 and begins all over again. What am I missing?
Edit: And oh, if I remove the srand(time(NULL)); line I just get the same number (41) every time I run the program. That's not even pseudo random, that's just a static number. Just copying the first line of code from the article I linked to above still gives me number 41 all the time. Am I the star in a sequel to "The Number 23", or have I missed something?
int main(void) {
srand(time(NULL));
int number = rand() % 410;
std::cout << number << std::endl;
system("pause");
}
That is what you get for using deprecated random number generation.
rand produces a fixed sequence of numbers (which by itself is fine), and does that very, very badly.
You tell rand via srand where in the sequence to start. Since your "starting point" (called seed btw) depends on the number of seconds since 1.1.1970 0:00:00 UTC, your output is obviously time depended.
The correct way to do what you want to do is using the C++11 <random> library. In your concrete example, this would look somewhat like this:
std::mt19937 rng (std::random_device{}());
std::uniform_int_distribution<> dist (0, 409);
auto random_number = dist(rng);
For more information on the evils of rand and the advantages of <random> have a look at this.
As a last remark, seeding std::mt19937 like I did above is not quite optimal because the MT's state space is much larger than the 32 bit you get out of a single call to std::random_device{}(). This is not a problem for toy programs and your standard school assignments, but for reference: Here is my take at seeding the MT's entire state space, plus some helpful suggestions in the answers.
From manual:
time() returns the time as the number of seconds since the Epoch,
1970-01-01 00:00:00 +0000 (UTC).
Which means that if you start your program twice both times at the same second you will initialize srand with same value and will get same state of PRNG.
And if you remove initialization via call to srand you will always get exactly same sequence of numbers from rand.
I'm afraid you can't get trully random numbers there. Built in functions are meant to provide just pseudo random numbers. Moreover using srand and rand, because the first uses the same approach as the second one. If you want to cook true random numbers, you must find a correct source of entrophy, working for example with atmospheric noise, as the approach of www.random.org.
The problem here consists in the seed used by the randomness algorithm: if it's a number provided by a machine, it can't be unpredictable. A normal solution for this is using external hardware.
Unfortunately you can't get a real random number from a computer without specific hardware (which is often too slow to be practical).
Therefore you need to make do with a pseudo generator. But you need to use them carefully.
The function rand is designed to return a number between 0 and RAND_MAX in a way that, broadly speaking, satisfies the statistical properties of a uniform distribution. At best you can expect the mean of the drawn numbers to be 0.5 * RAND_MAX and the variance to be RAND_MAX * RAND_MAX / 12.
Typically the implementation of rand is a linear congruential generator which basically means that the returned number is a function of the previous number. That can give surprisingly good results and allows you to seed the generator with a function srand.
But repeated use of srand ruins the statistical properties of the generator, which is what is happening to you: your use of srand is correlated with your system clock time. The behaviour you're observing is completely expected.
What you should do is to only make one call to srand and then draw a sequence of numbers using rand. You cannot easily do this in the way you've set things up. But there are alternatives; you could switch to a random number generator (say mersenne twister) which allows you to draw the (n)th term and you could pass the value of n as a command line argument.
As a final remark, I'd avoid using a modulus when drawing a number. This will create a statistical bias if your modulo is not a multiple of RAND_MAX.
Try by change the NULL in time(NULL) by time(0) (that will give you the current système time). If it doesn't work, you could try to convert time(0) into ms by doing time(0)*1000.
This comment, which states:
srand(time(0)); I would put this line as the first line in main()
instead if calling it multiple times (which will actually lead to less
random numbers).
...and I've bolded the line which I'm having an issue with... repeats common advice to call srand once in a program. Questions like srand() — why call only once? re-iterate that because time(0) returns the current time in seconds, that multiple calls to srand within the same second will produce the same seed. A common workaround is to use milliseconds or nanoseconds instead.
However, I don't understand why this means that srand should or can only be called once, or how it leads to less random numbers.
cppreference:
Generally speaking, the pseudo-random number generator should only be
seeded once, before any calls to rand(), and the start of the program.
It should not be repeatedly seeded, or reseeded every time you wish to generate a new batch of pseudo-random numbers.
phoxis's answer to srand() — why call only once?:
Initializing once the initial state with the seed value will generate
enough random numbers as you do not set the internal state with srand,
thus making the numbers more probable to be random.
Perhaps they're simply using imprecise language, none of the explanations seem to explain why calling srand multiple times is bad (aside from producing the same sequence of random numbers) or how it affects the "randomness" of the numbers. Can somebody clear this up for me?
Look at the source of srand() from this question: Rand Implementation
Also, example implementation from this thread:
static unsigned long int next = 1;
int rand(void) // RAND_MAX assumed to be 32767
{
next = next * 1103515245 + 12345;
return (unsigned int)(next/65536) % 32768;
}
void srand(unsigned int seed)
{
next = seed;
}
As you can see, when you calling srand(time(0)) you will got new numbers on rand() depends on seed. Numbers will repeat after some milions, but calling srand again will make it other. Anyway, it must repeat after some cycles - but order depends on argument for srand. This is why C rand isn't good for cryptography - you can predict next number when you know seed.
If you have fast loop, calling srand every iteration is without sense - you can got same number while your time() (1 second is very big time for modern CPUs) give another seed.
There is no reason in simple app to call srand multiple times - this generator are weak by design and if you want real random numbers, you must use other (the best I know is Blum Blum Shub)
For me, there is no more or less random numbers - it always depends on seed, and they repeat if you use same seed. Using time is good solution because it's easy to implement, but you must use only one (at beginning of main()) or when you sure that you calling srand(time(0)) in another second.
The numbers rand() returns are not actually random but "pseudo-random." What this means is that rand() generates a stream of numbers that look random for given values of "look" and "random" from an internal state that changes with each call.
As a rule, rand() is what is called a linear congruental generator, which means that uses a mechanism roughly like this:
int state; // persistent state
int rand() {
state = (a * state + b) % c;
return state;
}
with carefully chosen constants a, b and c. c tends to be a power of two in practice because that makes it faster to calculate.
The "randomness" of this sequence depends in part on the persistence of the state. If the sequence is constantly reseeded with predictable values, the return values of rand() become predictable in turn. How critical this is depends on the application, but it is not a purely academical consideration. Consider, for example, the case
a = 69069
b = 1
c = 2^32
which was used, for example, by old versions of glibc. Granted that I picked this example for the obviousness of the pattern, but the point remains in less obvious cases. Imagine this RNG were seeded with a sequence of incrementing numbers n, n+1, n+2 and so forth -- you will get from rand() a sequence of numbers, each 69069 larger than the last (modulo 2^32). The pattern will be plainly visible. Starting with 0, we would get
1
69070
138139
207208
...
rising until a bit over 4 billion in steady increments. And to make matters worse, some implementation actually returned the seed value in the first call of rand after a call to srand, in which case you'd just get your seeds back.
A pseudo random generator is an engine which produce numbers that look almost random. However, they are completely deterministic. In other words, given a seed x0, they are produced by repeated application of some injective function on x0, call it f(x0), so that f^m(x0) is quite different from f^{m-1}(x0) or f^{m+1}(x0), where the notation f^m denotes the function composition m times. In other words, f(x) has huge jumps, almost uncorrelated with the previous ones.
If you use sradnd(time) multiple times in a second, you may get the same seed, as the clock is not as fast as you may imagine. So the resulting sequence of random numbers will be the same. And this may be a (huge) problem, especially in cryptography applications (anyway, in the latter case, people buy good number generators based on real-time physical processes such as temperature difference in atmospheric data etc, or, recently, on measuring quantum bits, e.g. superposition of polarized photons, the latter being truly random, as long as quantum mechanics is correct.)
There are also other serious issues with rand. One of it is that the distribution is biased. See e.g. http://eternallyconfuzzled.com/arts/jsw_art_rand.aspx for some discussion, although I remember I've seen something similar on SO, although cannot find it now.
If you plan to use it in crypto applications, just don't do it. Use <random> and a serious random engine like Mersene's twister std::mt19937 combined with std::random_device
If you seed your random number generator twice using srand, and get different seeds, then
you will get two sequences that will be quite different. This may be satisfactory for you. However, each sequence per se will not be a good random distribution due to the issues I mentioned above. On the other hand, if you seed your rng too many times, you will get the same seed, and THIS IS BAD, as you'll generate the same numbers over and over again.
PS: seen in the comments that pseudo-numbers depend on a seed, and this is bad. This is the definition of pseudo-numbers, and it is not a bad thing as it allows you to repeat numerical experiments with the same sequence. The idea is that each different seed should produce a sequence of (almost) random numbers, different from a previous sequence (technically, you shouldn't be able to distinguish them from a perfect random sequence).
The seed determines what random numbers will be generated, in order, i.e. srand(1), will always generate the same number on the first call to rand(), the same on the second call to rand() and so on.
In other words, if you re-seeded with the same seed before each rand() invocation, you'd generate the same random number every single time.
So successive seeding with time(0), during a single second, will mean all your random numbers after re-seeding are actually the same number.
Most of the other answers are saying exactly what the question already stated: multiple calls to srand with the same second will produce the same seed. I believe the actual question is the same one that I had, which is: why would it be bad to call srand multiple times, even if it was with a different seed every time?
I can think of three reasons:
People are not clear in their language and they actually mean srand should not be called multiple times with time() if you want different sequences of random numbers.
It's cryptographically bad because every seed passed to srand is not itself a random number (well, it's probably not). Meaning, every srand is injecting a chance for someone to guess that seed and therefore predict your stream of pseudo-random numbers.
It can mess up the distribution of pseudo-random numbers. #vsoftco's answer gave me a clue. If you call srand once, rand can be designed to give you a uniform distribution of pseudo-random numbers over its lifetime. If you call srand in the middle, however, you'll throw off that uniform distribution because it would "start over" with a new seed.
So, if you don't care about any of that, I would think it's okay to call srand more than once. In my case, I want to call it at the start of my program, but call it again after a fork() because the seed is apparently shared across child processes, and I want each child process to have its own sequence of pseudo-random numbers.
Going back to why it's cryptographically bad, it's easier to guess a seed if it's something like time() because a bad actor can try to guess the time it was seeded. That is why calling srand at the start of a program might be better, because it could be less likely that someone would guess that time as well as, say, when a server request was initiated.
But I would surmise that even passing nanoseconds would be cryptographically dangerous if there's a chance the underlying clock doesn't have that kind of precision. Imagine, for example, that you call srand(get_time_in_ns()) and the underlying clock only returns time to the nearest millisecond.
Now, I'm no crypto expert in any way, but this leads me to wonder if it would be safer than current-time to pass the output of a different pseudo-random generator as seeds to multiple srand calls? For example, can you call each srand with a number from Linux's /dev/random? (I imagine you might want to do that if you want a safer seed than the current time but still want to use rand() so you don't have the overhead of reading from the kernel every time.)