For more information on random number generation, particularly on subtle things that can go wrong, see the CodeProject article Pitfalls in Random Number Generation. If you are using C, see Random number generation using C TR1. 11 th April, 2008: Initial post.
Create a main function that will be able to call the number generator & all algorithms. A) by using function from task 1, generate 300 unique integers. Output The list in a 100data.txt. Get the last integer in the list as k. B)Read the integers from the file in an array or linked list. C) Repeat the process with 200 and 300 integers.
To generate random numbers in C programming, use the function rand to generate and print random numbers.
First we use the srand function to seed the randomizer. Basically, the computer can generate random numbers based on the number that is fed to srand. If you gave the same seed value, then the same random numbers would be generated every time. Therefore, we have to seed the randomizer with a value that is always changing.

One mathematical function in C programming that’s relatively easy to grasp is the rand() function. It generates random numbers. Though that may seem silly, it’s the basis for just about every computer game ever invented. Random numbers are a big deal in programming.

A computer cannot generate truly random numbers. Instead, it produces what are known as pseudo–random numbers. That’s because conditions inside the computer can be replicated. Therefore, serious mathematicians scoff that any value a computer calls random isn’t a truly random number. Can you hear them scoffing?

How to generate random numbers

C and C programming languages provide rand and srand functions in order to create random numbers. Random numbers can be used for security, lottery, etc. In this tutorial, we will learn how to use a random number generating functions rand and srand with their attributes and specialties.

The rand() function is the simplest of C’s random-number functions. It requires the stdlib.h header file, and it coughs up an int value that’s supposedly random. Now, That’s Random demonstrates sample code.

NOW, THAT’S RANDOM

Now, That’s Random uses a nested for loop to display 100 random values. The rand() function in Line 13 generates the values. The printf() function in Line 14 displays the values by using the %d conversion character, which displays int values.

Exercise 1: Create a new project by using the source code shown in Now, That’s Random. Build and run to behold 100 random values.

Exercise 2: Modify the code so that all the values displayed are in the range 0 through 20.

Here’s a hint for Now, That’s Random: Use the modulus assignment operator to limit the range of the random numbers. The format looks like this:

How to generate random number in dev c programming

Generate Number

r is the number returned from the rand() function. %= is the modulus assignment operator. n is the range limit, plus 1. After the preceding statement, values returned are in the range 0 through n-1. So if you want to generate values between 1 and 100, you would use this formula:

How to increase the randomness of numbersin C programming

Just to give some credit to the snooty mathematicians who claim that computers generate pseudo-random numbers, run the program you generated from Exercise 2. Observe the output. Run the program again. See anything familiar?

The rand() function is good at generating a slew of random values, but they’re predictable values. To make the output less predictable, you need to seed the random-number generator. That’s done by using the srand() function.

Like the rand() function, the srand() function requires the stdlib.h header, shown at Line 2 in Even More Randomness. The function requires an unsigned int value, seed, which is declared at Line 6. The scanf() function at Line 10 reads in the unsigned value by using the %u placeholder. Then the srand() function uses the seed value in Line 11.

EVEN MORE RANDOMNESS

The rand() function is used at Line 16, although the results are now based on the seed, which is set when the program runs.

Exercise 3: Create a new project using the source code shown in Even More Randomness. Build it. Run the program a few times, trying different seed values. The output is different every time.

Alas, the random values that are generated are still predictable when you type the same seed number. In fact, when the value 1 is used as the seed, you see the same “random” values you saw in Exercise 1, when you didn’t even use srand()!

There has to be a better way.

The best way to write a random-number generator is not to ask the user to type a seed, but rather to fetch a seed from elsewhere. In More Truly Random Than Ever, the seed value is pulled from the system clock by using the time() function.

MORE TRULY RANDOM THAN EVER

The time() function returns information about the current time of day, a value that’s constantly changing. The NULL argument helps solve some problems, but time() returns an ever-changing value.

The (unsigned) part of the statement ensures that the value returned by the time() function is an unsigned integer. That’s a technique known as typecasting.

The bottom line is that the srand() function is passed a seed value, courtesy of the time() function, and the result is that the rand() function generates values that are more random than you’d get otherwise.

Exercise 4: Type the source code from More Truly Random Than Ever and build the project. Run it a few times to ensure that the numbers are as random as the computer can get them.

How To Generate Random Number In Dev C++ Free

Pseudo-Random Number Generator otherwise called PRNG is a concept that involves producing a series of random numbers using mathematical formulas. Generating a random number is not as easy as it looks. When we have an algorithm and set of inputs, we get output. And if we again use the same algorithm with the same set of inputs, we get the same output and not a different one. We cannot produce a truly random number using any algorithm. But we can produce pseudo-random numbers for which the numbers are almost unpredictable that it seems like a random number.

There are several algorithms available to produce PRNG. We will be seeing a couple of basic algorithms to understand the concept and implement them using javascript.

Middle Square Method in JavaScript

This is one of the simplest algorithms to produce a Pseudo-random number. It has the following step.

Take a seed value (s), of fixed length/size (n), example, n = 4 and s = 1242
Square the value of s, the resultant value will atmost be of length 2 times of n, if not padd 0 to the left of the resultant value, and let’s call it as S. Example sqr(s) = 1542564, S = 01542564
Take the middle 4 digits from S. It is the random value obtained using the seed value s. Example, random number = 5425
Now use this random number as the new seed value and generate the next random number by following step number 2
If you want the number between 0 to 1, divide the resultant number by the maximum number that can be formed using n digits. For example, 5425/9999 = 0.54255425542

Output

Linear Congruential Generator in JavaScript

Linear congruential generator is a popular PRNG algorithm and it is used in lots of programming languages and systems. It is simple to implement and faster to execute. It is also a way lot better algorithm than Middle Square Method.

How To Generate Random Number In Dev C Compiler

Formula :

where,
seed, 0 < seed < m is the initial value that is provided to the algorithm,
a, 0 < a < m – is the multiplier,
c, 0<