How to generate random number between 1 to 10 in C++

Generating random numbers is a common requirement in many programming applications, and C++ provides several ways to generate random numbers within a given range. In this article, we will explore different methods to generate random numbers between 1 and 10 in C++.

Method 1:

Using rand() function:

One of the simplest methods to generate a random number between 1 and 10 in C++ is the rand() function. This function is defined in the <cstdlib> header file and generates a random integer number within a range of 0 to RAND_MAX. The value of RAND_MAX is implementation-dependent and can vary from compiler to compiler.

Example:

Let's take an example to generate a random number between 1 and 10 using rand() function, we can use the following code:

Output

Random number between 1 and 10 is: 
4 5 7 10 7 5 1 7 10 2 

In this code, we have included the <cstdlib> and <ctime> header files. The srand() function is used to initialize the random number generator with the current time as the seed. It ensures that every time the program is run, a new sequence of random numbers is generated.

The rand() function is used to generate a random integer between 0 and RAND_MAX. To limit the range between 1 and 10, we take the remainder of this number when divided by 10 and add 1 to it.

Method 2:

Using C++11 random library

The C++11 standard introduced a new library called <random> that provides a better way to generate random numbers. This library provides several random number generation engines and distributions that can generate random numbers with a uniform distribution.

Example:

Let's take an example to generate a random number between 1 and 10 using the <random> library, we can use the following code:

In this code, we have included the <random> header file. The random_device class is used to obtain a seed value for the random number generator. The mt19937 class is a random number generation engine that produces random numbers with a uniform distribution. The uniform_int_distribution class is used to generate random integers within a given range.

By default, the mt19937 engine uses a seed value of 5489, which can be changed using the seed() method. However, it is recommended to use a random_device to obtain a seed value for better randomness.

The uniform_int_distribution class generates random integers with a uniform distribution within a given range. In this code, we have specified the range as 1 to 10 using the constructor.

This method provides better randomness and a uniform distribution of generated numbers compared to the rand() function. However, it is slower and more complex to implement.

Method 3:

Using modulo operator with time():

Another method to generate a random number between 1 and 10 is the modulo operator with the current time as a seed value. This method is similar to the first method using rand() function, but it uses a more random seed value and provides better randomness.

Example:

Let's take an example to generate a random number between 1 and 10 using the modulo operator with time(), we can use the following code:

Output

Random number between 1 and 10 is: 
6 6 3 6 10 10 1 7 6 4

In this code, we have used the time() function to obtain the current time as a seed value for the srand() function. The srand() function is used to initialize the random number generator. The rand() function generates a random integer between 0 and RAND_MAX, which is then limited to a range between 1 and 10 using the modulo operator and adding 1 to it.

Conclusion:

In conclusion, there are several methods to generate random numbers between 1 and 10 in C++. The choice of method depends on the requirements of the application, such as speed, randomness, and uniformity of generated numbers. While the rand() function is the simplest and easiest to implement, it may not provide good randomness and uniformity. The <random> library provides a better way to generate random numbers with a uniform distribution, but it is slower and more complex to implement. The XORShift algorithm provides good randomness and uniformity, but it is more complex to implement and may not be as fast as the rand() function.