Random number generation in C++

Random number generation is an important part of computer programming. In C programming, random numbers are also very common and can be used for simulating data, generating test data, game development, etc. This article will introduce several random number generation methods in C language.

Pseudo-random number generation

The pseudo-random number generation algorithm is a random number generation method used in most programs. It is not a true random number, but a pseudo-random number generated through a certain mathematical algorithm. random number. In C, you can use the rand function to generate pseudo-random numbers.

rand function is defined in the stdlib.h header file. It returns a random number of type int, ranging from 0 to RAND_MAX. RAND_MAX is defined in the stdlib.h header file, and its value is usually 32767.

Sample code:

#include <iostream>
#include <ctime>
#include <cstdlib>
using namespace std;

int main()
{
    // 设置随机数种子
    srand(time(0));
    
    // 生成10个随机数
    for(int i = 0; i < 10; i++)
    {
        cout << rand() << endl;
    }
    
    return 0;
}

In the above code, the srand function is used to set the random number seed, and time(0) can obtain the number of seconds of the current time, so that the results of running the program are different each time. . Then use a for loop to generate 10 random numbers and output them to the screen.

It should be noted that each time the rand function is called, it will return a unique random number, but if it is called multiple times, there will be a certain degree of correlation between the random numbers. Therefore, when using the rand function to generate a large number of random numbers, other methods need to be used for obfuscation.

True random number generation

True random numbers refer to random numbers generated through physical phenomena. For example, seismic wave data or photoelectric characteristics can be collected through hardware devices, but these hardware devices are very Expensive, for most applications, using pseudo-random number generation algorithms is sufficient.

However, in some application scenarios that require high security random numbers, in order to protect the security and confidentiality of data, true random numbers need to be used. In this case, an external device can be used to provide truly random numbers. For example, true random numbers can be generated by rolling dice, tossing coins, drawing lots, etc., but this method is not only inefficient, but also generates limited types of random numbers.

The more common method is to generate truly random numbers through a physical device called a random number generator (RNG). It generates random numbers by collecting unpredictable physical processes or mathematical procedures, while also providing some additional security features. However, the cost of using a true random number generator is much higher than using a pseudo-random number generation algorithm.

Random number generation in C 11

C 11 provides a new random number library, including two parts: one is a pseudo-random number generator, including multiple algorithms that can be better The second is the true random number generator, which can access the specially generated hardware facilities of the system and return true random numbers.

Random number generation libraries in C 11 include:

• minstd_rand0: Linear Congruential Generator
• minstd_rand: Improved Linear Congruential Generator
• mt19937: Mersenne Twister 19937 generator, better performance, longer running time
• mt19937_64: Mersenne Twister 19937 generator, returns 64-bit integer
• ranlux24_base: LUX (level, uniform, eXcellent) generator, fast running speed, high quality
• ranlux48_base: LUX(level, uniform, eXcellent) generator, returns 48-bit integer, fast running speed, high quality
• knuth_b : Knuth-B (T, P) generator, which generates a random sequence that is different from the standard and has higher quality

When using the random number generation library in C 11, you need to include the header file random, And you can use the uniform_int_distribution and uniform_real_distribution functions to control the range and type of random numbers.

Sample code:

#include <iostream>
#include <random>

using namespace std;

int main()
{
    // 以当前时间作为种子
    default_random_engine engine(time(nullptr));
    
    // uniform_int_distribution：以等概率生成min到max范围内的整数
    uniform_int_distribution<int> distribution(0, 100);
    cout << distribution(engine) << endl;
    
    // uniform_real_distribution：以等概率生成min到max范围内的浮点数
    uniform_real_distribution<double> r_distribution(0, 100);
    cout << r_distribution(engine) << endl;

    return 0;
}

In the above code, default_random_engine is used to generate random number seeds, and the ranges of generated integer and floating-point random numbers are specified in uniform_int_distribution and uniform_real_distribution respectively. Finally, random numbers are generated by calling the engine function.

Conclusion

The above are several common random number generation methods in C. Different application scenarios require different random number generation methods, and you need to choose the appropriate method according to the actual situation. In actual programming, you can combine the advantages of pseudo-random numbers and true random numbers, and use some advanced random number generation methods to improve the efficiency and security of the program.

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