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I am running a bunch of physical simulations in which I need random numbers. I'm using the standard rand() function in C++.
So it works like this: first I precalculate a bunch of probabilities that are of the form 1/(1+exp(a)), for a set of different a. They're of type double as returned by the exp function in the math library, and then things must happen with those probabilities, there are only two of them, so I generate a random number uniformly distributed between 0 and 1 and compared with those precalculated probabilities. To do that, I used:
double p = double(rand()%101)/100.0;
so I'm given random values between 0 and 1 both included. This didn't yield to correct physical results. I tried this:
double p = double(rand()%1000001)/1000000.0;
And this worked. I don't really understand why so I would like some criteria about how to do it. My intuition tells that if I do
double p = double(rand()%(N+1))/double(N);
with N big enough such that the smallest division (1/N) is much smaller than the smallest probability 1/1+exp(a) then I will be getting realistic random numbers.
I would like to understand why, though.
rand() returns a random number between 0 and RAND_MAX.
Therefore you need this:
double p = double(rand() % RAND_MAX) / double(RAND_MAX);
Also run this snippet and you will understand:
int i;
for (i = 1; i < 30; i++)
{
int rnd = rand();
double p0 = double(rnd % 101) / 100.0;
double p1 = double(rnd % 1000001) / 1000000.0;
printf ("%d\t%f\t%f\n", rnd, p0, p1);
}
for (i = 1; i < 30; i++)
{
int rnd = rand();
double p0 = double(rnd) / double(RAND_MAX);
printf ("%d\t%f\n", rnd, p0);
}
You have multiple problems.
rand() isn't very random at all. On almost all operating systems it returns badly distributed, horribly biased numbers. It's actually quite hard to find a good random number generator, but I can guarantee you that rand() will be among the worst you can find.
rand() % N gives a biased distribution. Think about the pigeonhole principle. Let's simplify it, assume that rand returns numbers [0,7) and your N is 6. 0 to 5 map to 0 to 5, 6 maps to 0 and 7 maps to 1, meaning that 0 and 1 are twice as likely to come out.
Converting the numbers to double before division does not remove the bias from 2, it just makes it less visible. The pigeonhole principle applies regardless of the conversions you do.
Converting a well-distributed random number from integer to float/double is harder than it looks. Simple division ignores the problems of how floating point math works.
I can't help you much with 1, you need to do research. Look around the net for random number libraries. If you want something very random and unpredictable you need to look for cryptographic random libraries. If you want a repeatable but good random number Mersenne Twister should probably be good enough. But you need to do the research here.
For 2 and 3 there are standard solutions. You are mapping a set from M elements to N elements and rand % N will only work iff N < M and N and M share prime factors. Since on most systems M will be a power of two it means that N also has to be a power of two. So assuming that M is a power of two the algorithm is: find the nearest power of 2 higher or equal to N, let's call it P. Generate randomness_source() % P. If the number is higher than N, throw it away and try again. This is the only safe way to do this. Cleverer people than you and me have spent years on this problem, there's no better way to remove the bias.
For 4, you can probably ignore the problem and just divide, in an absolute majority of cases this should be good enough. If you really want to study the problem, I've done some work on it and published the code on github. There I go through some basic principles of how floating point numbers work and how it relates to generating random numbers.
// produces pseudorandom bits. These are NOT crypto quality bits. Has the same underlying unpredictability as uncooked
// rand() output. It buffers rand() bits to produce a more convenient zero-to-the-argument range including negative
// arguments, corrects for the toward-zero bias of the modular construction I'd be using otherwise, eliminates the
// RAND_MAX range limitation, (use INT64_MAX instead) and effectively obscures biases and sequence telltales due to
// annoyingly bad rand libraries. It does not correct these biases; anyone tracking the arguments and outputs has
// enough information to reconstruct the rand() output and detect them. But it makes the relationships drastically more complicated.
// needs stdint, stdlib.
int64_t privaterandom(int64_t range, int reset){
static uint64_t state = 0;
int64_t retval;
if (reset != 0){
srand((unsigned int)range);
state = (uint64_t)range;
}
if (range == 0) return (0);
if (range < 0) return -privaterandom(-range, 0);
if (range > UINT64_MAX/0xFFFFFFFF){
retval = (privaterandom(range/0xFFFFFFFF, 0) * 0xFFFFFFFF); // order of operations matters
return (retval + privaterandom(0xFFFFFFFF, 0));
}
while (state < UINT64_MAX / 0xFF){
state *= RAND_MAX;
state += rand();
}
retval = (state % range);
// makes "pigeonhole" bias alternate unpredictably between toward-even and toward-odd
if ((state/range > (state - (retval) )/ range) && state % 2 == 0) retval++;
state /= range;
return retval;
}
int64_t Random(int64_t range){ return (privaterandom(range, 0));}
int64_t Random_Init(int64_t seed){return (privaterandom(seed, 1));}
I am in a coding environment where I only have access to some most basic c functions. #include'ing other lib is not feasible.
In this environment, I can call rand() which gives me a random number between 0 to 32767 inclusively(I think) in my environment. Is the following code the right logic to get a larger random number that is evenly distributed like/as rand()?
rnum = rand() * (32767 + 1) + rand();
rnum = (rand() << 15) | rand() might be faster, but if you need good quality random numbers you should look for an external library. Built-in random functions are generally adequate only for the simplest applications.
static unsigned long next = 1;
int my_rand(void) {
next = next * 1103515245 + 12345;
return((unsigned)(next/65536) % (RAND_MAX+1));
}
void my_srand(unsigned int seed) {
next = seed;
}
on linux
#define RAND_MAX 2147483647
your environment RAND_MAX is probably 32767
reference: http://en.wikipedia.org/wiki/Linear_congruential_generator
if you are not memory constrained you can look also at http://en.wikipedia.org/wiki/Mersenne_twister the code is embeddable as like as the example above
Before using ANY random number generator for a non-trivial use, it should be tested extensively. Here is a link to one article on this subject.
A fun background on the weaknesses of even true random numbers is available in the various histories on the code-breakers at Bletchley Park in WWII. The Germans and Hitler for most of the war assumed their codes were unbreakable because of being encrypted with random numbers, whereas the British were breaking all of these codes completely within months because of various weaknesses in the German implementation of "randomness". Many codes were sufficiently "twisted", if not outright broken, within a few days or months, sufficiently to be of use even though not completely broken.
Besides the other excellent solutions proposed here, you can also do a power expansion in RAND_MAX, truncate at a user defined MY_RAND_MAX, and discard solutions which would cause uniformity to be destroyed.
int myrand(int MY_RAND_MAX)
{
int j = 0, num = 0;
// Generate digits for expansion in powers of RAND_MAX, up to acceptable range.
while (pow(RAND_MAX + 1, j) <= MY_RAND_MAX){
num = num + rand() * (int)pow(RAND_MAX + 1, j);
j++;
}
//compute remainder to determine truncation
int remainder = ((int)pow(RAND_MAX + 1, j)) % (MY_RAND_MAX + 1);
// If number is within accepted bounds, mod and return
if (num <= ( (int)pow(RAND_MAX + 1, j) - remainder ) ){
return (num % (MY_RAND_MAX + 1));
// Else, if number generated exceeds allowed bounds, rerun method.
}else if (num > ( (int)pow(RAND_MAX + 1, j) - remainder ) ){
return myrand(MY_RAND_MAX);
}else{
exit(-1);
}
}
You can check empirically that this method gives you a statistically uniform output in the given range.
I've done so for several trials with different ranges, each with a sample size of 100000, and got an agreement between the variance of the sample and the expected variance to at least 3 sig. fig.s each time.
p.s. I am not a coder, but a mathematician/physicist who has recently learnt to code, so any and all feedback on the quality of my code would be appreciated.
I want to generate 100 nodes with random x and y co ordinates. But i do not want to specify any range. Like rand(100) will generate numbers only between 1 to 100. But i want the numbers distributed over a large region and i want them to be random. How can i implement it using C?
i have tried:
int gen_rand_position(void)
{
int i,j,a[100],b[100];
for(i=0,j=0;i<100,j<100;i++,j++)
{
x=rand();
y=rand();
a[i]=x;
b[j]=y;
}
}
This not choosing randomly. Can i have more efficient random function?
You need to have a range otherwise what are you going to do with an infinite number?
With no arguments - rand() will return an integer between 0 and RAND_MAX ( normally 32765).
If you need a number larger than this you could combine two rand() numbers. There are complicated statistical arguments about the best way to combine random numbers so you don't change the randomness but I don't think you need to worry about that.
Edit: since RAND_MAX is (in this case) a 15bit number, to get a 30bit range multiply two rand() together, to get a 32bit range multiply again - it may wrap around but that doesn't change the randomness (significantly).
To obtain a random number distributed over the entire int range, combine the random bits from multiple calls to rand():
#include <stdlib.h>
int large_rand()
{
const int RAND_BITS = 15; /* covers stdc minimum for RAND_MAX */
const int INT_BITS = 8 * sizeof(int);
const int ITERS = (INT_BITS + RAND_BITS - 1) / RAND_BITS;
int i, result = 0;
for (i = 0; i < ITERS; i++) {
result <<= RAND_BITS;
result |= rand() & ~(~0U << RAND_BITS);
}
return result;
}
To get a random number in the desired range, use large_rand() % (MAX + 1), where MAX is the largest number you want to get.
I've written a C function that I think selects integers from a uniform distribution with range [rangeLow, rangeHigh], inclusive. This isn't homework--I'm just using this in some embedded systems tinkering that I'm doing for fun.
In my test cases, this code appears to produce an appropriate distribution. I'm not feeling fully confident that the implementation is correct, though.
Could someone do a sanity check and let me know if I've done anything wrong here?
//uniform_distribution returns an INTEGER in [rangeLow, rangeHigh], inclusive.
int uniform_distribution(int rangeLow, int rangeHigh)
{
int myRand = (int)rand();
int range = rangeHigh - rangeLow + 1; //+1 makes it [rangeLow, rangeHigh], inclusive.
int myRand_scaled = (myRand % range) + rangeLow;
return myRand_scaled;
}
//note: make sure rand() was already initialized using srand()
P.S. I searched for other questions like this. However, it was hard to filter out the small subset of questions that discuss random integers instead of random floating-point numbers.
Let's assume that rand() generates a uniformly-distributed value I in the range [0..RAND_MAX],
and you want to generate a uniformly-distributed value O in the range [L,H].
Suppose I in is the range [0..32767] and O is in the range [0..2].
According to your suggested method, O= I%3. Note that in the given range, there are 10923 numbers for which I%3=0, 10923 number for which I%3=1, but only 10922 number for which I%3=2. Hence your method will not map a value from I into O uniformly.
As another example, suppose O is in the range [0..32766].
According to your suggested method, O=I%32767. Now you'll get O=0 for both I=0 and I=32767. Hence 0 is twice as likely than any other value - your method is again nonuniform.
The suggest way to generate a uniform mapping is as follow:
Calculate the number of bits that are needed to store a random value in the range [L,H]:
unsigned int nRange = (unsigned int)H - (unsigned int)L + 1;
unsigned int nRangeBits= (unsigned int)ceil(log((double(nRange) / log(2.));
Generate nRangeBits random bits
this can be easily implemented by shifting-right the result of rand()
Ensure that the generated number is not greater than H-L.
If it is - repeat step 2.
Now you can map the generated number into O just by adding a L.
On some implementations, rand() did not provide good randomness on its lower order bits, so the modulus operator would not provide very random results. If you find that to be the case, you could try this instead:
int uniform_distribution(int rangeLow, int rangeHigh) {
double myRand = rand()/(1.0 + RAND_MAX);
int range = rangeHigh - rangeLow + 1;
int myRand_scaled = (myRand * range) + rangeLow;
return myRand_scaled;
}
Using rand() this way will produce a bias as noted by Lior. But, the technique is fine if you can find a uniform number generator to calculate myRand. One possible candidate would be drand48(). This will greatly reduce the amount of bias to something that would be very difficult to detect.
However, if you need something cryptographically secure, you should use an algorithm outlined in Lior's answer, assuming your rand() is itself cryptographically secure (the default one is probably not, so you would need to find one). Below is a simplified implementation of what Lior described. Instead of counting bits, we assume the range falls within RAND_MAX, and compute a suitable multiple. Worst case, the algorithm ends up calling the random number generator twice on average per request for a number in the range.
int uniform_distribution_secure(int rangeLow, int rangeHigh) {
int range = rangeHigh - rangeLow + 1;
int secureMax = RAND_MAX - RAND_MAX % range;
int x;
do x = secure_rand(); while (x >= secureMax);
return rangeLow + x % range;
}
I think it is known that rand() is not very good. It just depends on how good of "random" data you need.
http://www.azillionmonkeys.com/qed/random.html
http://www.linuxquestions.org/questions/programming-9/generating-random-numbers-in-c-378358/
http://forums.indiegamer.com/showthread.php?9460-Using-C-rand%28%29-isn-t-as-bad-as-previously-thought
I suppose you could write a test then calculate the chi-squared value to see how good your uniform generator is:
http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test
Depending on your use (don't use this for your online poker shuffler), you might consider a LFSR
http://en.wikipedia.org/wiki/Linear_feedback_shift_register
It may be faster, if you just want some psuedo-random output. Also, supposedly they can be uniform, although I haven't studied the math enough to back up that claim.
A version which corrects the distribution errors (noted by Lior),
involves the high-bits returned by rand() and
only uses integer math (if that's desirable):
int uniform_distribution(int rangeLow, int rangeHigh)
{
int range = rangeHigh - rangeLow + 1; //+1 makes it [rangeLow, rangeHigh], inclusive.
int copies=RAND_MAX/range; // we can fit n-copies of [0...range-1] into RAND_MAX
// Use rejection sampling to avoid distribution errors
int limit=range*copies;
int myRand=-1;
while( myRand<0 || myRand>=limit){
myRand=rand();
}
return myRand/copies+rangeLow; // note that this involves the high-bits
}
//note: make sure rand() was already initialized using srand()
This should work well provided that range is much smaller than RAND_MAX, otherwise
you'll be back to the problem that rand() isn't a good random number generator in terms of its low-bits.
srand(time(null));
printf("%d", rand());
Gives a high-range random number (0-32000ish), but I only need about 0-63 or 0-127, though I'm not sure how to go about it. Any help?
rand() % (max_number + 1 - minimum_number) + minimum_number
So, for 0-65:
rand() % (65 + 1 - 0) + 0
(obviously you can leave the 0 off, but it's there for completeness).
Note that this will bias the randomness slightly, but probably not anything to be concerned about if you're not doing something particularly sensitive.
You can use this:
int random(int min, int max){
return min + rand() / (RAND_MAX / (max - min + 1) + 1);
}
From the:
comp.lang.c FAQ list · Question 13.16
Q: How can I get random integers in a certain range?
A: The obvious way,
rand() % N /* POOR */
(which tries to return numbers from 0 to N-1) is poor, because the
low-order bits of many random number generators are distressingly
non-random. (See question 13.18.) A better method is something like
(int)((double)rand() / ((double)RAND_MAX + 1) * N)
If you'd rather not use floating point, another method is
rand() / (RAND_MAX / N + 1)
If you just need to do something with probability 1/N, you could use
if(rand() < (RAND_MAX+1u) / N)
All these methods obviously require knowing RAND_MAX (which ANSI #defines in <stdlib.h>), and assume that N is much less than RAND_MAX. When N is close to RAND_MAX, and if the range of the random number
generator is not a multiple of N (i.e. if (RAND_MAX+1) % N != 0), all
of these methods break down: some outputs occur more often than
others. (Using floating point does not help; the problem is that rand
returns RAND_MAX+1 distinct values, which cannot always be evenly
divvied up into N buckets.) If this is a problem, about the only thing
you can do is to call rand multiple times, discarding certain values:
unsigned int x = (RAND_MAX + 1u) / N;
unsigned int y = x * N;
unsigned int r;
do {
r = rand();
} while(r >= y);
return r / x;
For any of these techniques, it's straightforward to shift the range,
if necessary; numbers in the range [M, N] could be generated with
something like
M + rand() / (RAND_MAX / (N - M + 1) + 1)
(Note, by the way, that RAND_MAX is a constant telling you what the
fixed range of the C library rand function is. You cannot set RAND_MAX
to some other value, and there is no way of requesting that rand
return numbers in some other range.)
If you're starting with a random number generator which returns
floating-point values between 0 and 1 (such as the last version of
PMrand alluded to in question 13.15, or drand48 in question
13.21), all you have to do to get integers from 0 to N-1 is
multiply the output of that generator by N:
(int)(drand48() * N)
Additional links
References: K&R2 Sec. 7.8.7 p. 168
PCS Sec. 11 p. 172
Quote from: http://c-faq.com/lib/randrange.html
check here
http://c-faq.com/lib/randrange.html
For any of these techniques, it's straightforward to shift the range, if necessary; numbers in the range [M, N] could be generated with something like
M + rand() / (RAND_MAX / (N - M + 1) + 1)
Taking the modulo of the result, as the other posters have asserted will give you something that's nearly random, but not perfectly so.
Consider this extreme example, suppose you wanted to simulate a coin toss, returning either 0 or 1. You might do this:
isHeads = ( rand() % 2 ) == 1;
Looks harmless enough, right? Suppose that RAND_MAX is only 3. It's much higher of course, but the point here is that there's a bias when you use a modulus that doesn't evenly divide RAND_MAX. If you want high quality random numbers, you're going to have a problem.
Consider my example. The possible outcomes are:
rand()
freq.
rand() % 2
0
1/3
0
1
1/3
1
2
1/3
0
Hence, "tails" will happen twice as often as "heads"!
Mr. Atwood discusses this matter in this Coding Horror Article
The naive way to do it is:
int myRand = rand() % 66; // for 0-65
This will likely be a very slightly non-uniform distribution (depending on your maximum value), but it's pretty close.
To explain why it's not quite uniform, consider this very simplified example:
Suppose RAND_MAX is 4 and you want a number from 0-2. The possible values you can get are shown in this table:
rand() | rand() % 3
---------+------------
0 | 0
1 | 1
2 | 2
3 | 0
See the problem? If your maximum value is not an even divisor of RAND_MAX, you'll be more likely to choose small values. However, since RAND_MAX is generally 32767, the bias is likely to be small enough to get away with for most purposes.
There are various ways to get around this problem; see here for an explanation of how Java's Random handles it.
rand() will return numbers between 0 and RAND_MAX, which is at least 32767.
If you want to get a number within a range, you can just use modulo.
int value = rand() % 66; // 0-65
For more accuracy, check out this article. It discusses why modulo is not necessarily good (bad distributions, particularly on the high end), and provides various options.
As others have noted, simply using a modulus will skew the probabilities for individual numbers so that smaller numbers are preferred.
A very ingenious and good solution to that problem is used in Java's java.util.Random class:
public int nextInt(int n) {
if (n <= 0)
throw new IllegalArgumentException("n must be positive");
if ((n & -n) == n) // i.e., n is a power of 2
return (int)((n * (long)next(31)) >> 31);
int bits, val;
do {
bits = next(31);
val = bits % n;
} while (bits - val + (n-1) < 0);
return val;
}
It took me a while to understand why it works and I leave that as an exercise for the reader but it's a pretty concise solution which will ensure that numbers have equal probabilities.
The important part in that piece of code is the condition for the while loop, which rejects numbers that fall in the range of numbers which otherwise would result in an uneven distribution.
double scale = 1.0 / ((double) RAND_MAX + 1.0);
int min, max;
...
rval = (int)(rand() * scale * (max - min + 1) + min);
Updated to not use a #define
double RAND(double min, double max)
{
return (double)rand()/(double)RAND_MAX * (max - min) + min;
}
If you don't overly care about the 'randomness' of the low-order bits, just rand() % HI_VAL.
Also:
(double)rand() / (double)RAND_MAX; // lazy way to get [0.0, 1.0)
This answer does not focus on the randomness but on the arithmetic order.
To get a number within a range, usually we can do it like this:
// the range is between [aMin, aMax]
double f = (double)rand() / RAND_MAX;
double result = aMin + f * (aMax - aMin);
However, there is a possibility that (aMax - aMin) overflows. E.g. aMax = 1, aMin = -DBL_MAX. A safer way is to write like this:
// the range is between [aMin, aMax]
double f = (double)rand() / RAND_MAX;
double result = aMin - f * aMin + f * aMax;
Based on this concept, something like this may cause a problem.
rand() % (max_number + 1 - minimum_number) + minimum_number
// 1. max_number + 1 might overflow
// 2. max_number + 1 - min_number might overflow
if you care about the quality of your random numbers don't use rand()
use some other prng like http://en.wikipedia.org/wiki/Mersenne_twister or one of the other high quality prng's out there
then just go with the modulus.
Just to add some extra detail to the existing answers.
The mod % operation will always perform a complete division and therefore yield a remainder less than the divisor.
x % y = x - (y * floor((x/y)))
An example of a random range finding function with comments:
uint32_t rand_range(uint32_t n, uint32_t m) {
// size of range, inclusive
const uint32_t length_of_range = m - n + 1;
// add n so that we don't return a number below our range
return (uint32_t)(rand() % length_of_range + n);
}
Another interesting property as per the above:
x % y = x, if x < y
const uint32_t value = rand_range(1, RAND_MAX); // results in rand() % RAND_MAX + 1
// TRUE for all x = RAND_MAX, where x is the result of rand()
assert(value == RAND_MAX);
result of rand()
2 cents (ok 4 cents):
n = rand()
x = result
l = limit
n/RAND_MAX = x/l
Refactor:
(l/1)*(n/RAND_MAX) = (x/l)*(l/1)
Gives:
x = l*n/RAND_MAX
int randn(int limit)
{
return limit*rand()/RAND_MAX;
}
int i;
for (i = 0; i < 100; i++) {
printf("%d ", randn(10));
if (!(i % 16)) printf("\n");
}
> test
0
5 1 8 5 4 3 8 8 7 1 8 7 5 3 0 0
3 1 1 9 4 1 0 0 3 5 5 6 6 1 6 4
3 0 6 7 8 5 3 8 7 9 9 5 1 4 2 8
2 7 8 9 9 6 3 2 2 8 0 3 0 6 0 0
9 2 2 5 6 8 7 4 2 7 4 4 9 7 1 5
3 7 6 5 3 1 2 4 8 5 9 7 3 1 6 4
0 6 5
Just using rand() will give you same random numbers when running program multiple times. i.e. when you run your program first time it would produce random number x,y and z. If you run the program again then it will produce same x,y and z numbers as observed by me.
The solution I found to keep it unique every time is using srand()
Here is the additional code,
#include<stdlib.h>
#include<time.h>
time_t t;
srand((unsigned) time(&t));
int rand_number = rand() % (65 + 1 - 0) + 0 //i.e Random numbers in range 0-65.
To set range you can use formula : rand() % (max_number + 1 - minimum_number) + minimum_number
Hope it helps!
You can change it by adding a % in front of the rand function in order to change to code
For example:
rand() % 50
will give you a random number in a range of 50. For you, replace 50 with 63 or 127
I think the following does it semi right. It's been awhile since I've touched C. The idea is to use division since modulus doesn't always give random results. I added 1 to RAND_MAX since there are that many possible values coming from rand including 0. And since the range is also 0 inclusive, I added 1 there too. I think the math is arranged correctly avoid integer math problems.
#define MK_DIVISOR(max) ((int)((unsigned int)RAND_MAX+1/(max+1)))
num = rand()/MK_DIVISOR(65);
Simpler alternative to #Joey's answer. If you decide to go with the % method, you need to do a reroll to get the correct distribution. However, you can skip rerolls most of the time because you only need to avoid numbers that fall in the last bucket:
int rand_less_than(int max) {
int last_bucket_min = RAND_MAX - RAND_MAX % max;
int value;
do {
value = rand();
} while (last_bucket_min <= value);
return value % max;
}
See #JarosrawPawlak's article for explanation with diagrams: Random number generator using modulo
In case of RAND_MAX < max, you need to expand the generator: Expand a random range from 1–5 to 1–7
#include <stdio.h>
#include <stdlib.h>
#include <time.h> // this line is necessary
int main() {
srand(time(NULL)); // this line is necessary
int random_number = rand() % 65; // [0-64]
return 0;
}
Foy any range between min_num and max_num:
int random_number = rand() % (max_num + 1 - min_num) + min_num;