how to get pseudorandom sequence of numbers in c

Daniel Parker

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17-03-2025

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Generating sequences of pseudorandom numbers is a crucial task in many C programming applications, from simulations and games to cryptography and statistical analysis. This article explores different methods to achieve this, focusing on the standard C library functions and their limitations. We'll also touch upon more advanced techniques for improved randomness.

Understanding Pseudorandom Number Generators (PRNGs)

Before diving into the code, it's important to understand that computers cannot generate truly random numbers. Instead, they use algorithms called Pseudorandom Number Generators (PRNGs) to produce sequences that appear random but are actually deterministic. This means that given the same initial conditions (the seed), a PRNG will always produce the same sequence.

The quality of a PRNG is judged by how well its output mimics truly random numbers. Good PRNGs have long periods (before the sequence repeats), uniform distribution (all numbers have roughly equal probability), and lack of discernible patterns.

Using rand() and srand() in C

The C standard library provides the rand() function for generating pseudorandom numbers and srand() for seeding the generator.

Seeding the Generator with srand()

The srand() function initializes the PRNG's internal state. It's crucial to seed the generator only once at the beginning of your program. Using a different seed will result in a different sequence. A common practice is to use the current time as the seed:

#include <stdio.h>
#include <stdlib.h>
#include <time.h>

int main() {
  // Seed the random number generator using the current time
  srand(time(NULL));

  // ... (Generate random numbers using rand()) ...

  return 0;
}

time(NULL) returns the current time as a time_t value, providing a reasonably unique seed for each program run.

Generating Random Numbers with rand()

The rand() function returns a pseudorandom integer between 0 and RAND_MAX (inclusive), a constant defined in stdlib.h. To generate numbers within a specific range, you'll need to perform a modulo operation:

// Generate a random integer between 0 and 99 (inclusive)
int random_number = rand() % 100; 

// Generate a random integer between min and max (inclusive)
int min = 10;
int max = 20;
int random_number_in_range = min + rand() % (max - min + 1);

Remember that the modulo operation might introduce slight biases, especially if RAND_MAX is not a multiple of the range size.

Limitations of rand()

While convenient, rand() has limitations:

• Limited Period: The sequence generated by rand() might be relatively short, leading to repetitions in simulations or games requiring a large number of random numbers.
• Implementation-Dependent: The quality of the PRNG implemented in rand() varies across different C standard library implementations. Some may be better than others.
• Potential for Bias: As mentioned before, the modulo operation can introduce bias.

Improving Randomness: Better PRNGs

For applications demanding higher-quality random numbers, consider using more advanced PRNGs. These often come from external libraries. One example is the Mersenne Twister, known for its long period and good statistical properties. Many C++ libraries offer implementations of the Mersenne Twister, but it's not readily available in standard C.

Example: Generating a Sequence of Random Numbers

Let's put it all together with an example generating a sequence of 10 random numbers between 1 and 100:

#include <stdio.h>
#include <stdlib.h>
#include <time.h>

int main() {
  srand(time(NULL));

  for (int i = 0; i < 10; i++) {
    int random_number = 1 + rand() % 100; // Generates numbers from 1 to 100
    printf("%d ", random_number);
  }
  printf("\n");

  return 0;
}

This code seeds the generator, then iterates to print 10 random integers in the desired range.

Conclusion

Generating pseudorandom sequences in C involves choosing an appropriate PRNG and seeding it correctly. While rand() is readily available, understanding its limitations is vital. For demanding applications, explore external libraries offering more robust and statistically sound PRNGs like the Mersenne Twister. Remember that even the best PRNGs produce pseudorandom numbers—not truly random ones. Always consider the requirements of your application when selecting a method for random number generation.