The ability to generate random numbers can be useful in certain kinds of programs, particularly in games, statistics modeling programs, and scientific simulations that need to model random events. Take games for example — Without random events, monsters would always attack you the same way, you’d always find the same treasure, the dungeon layout would never change, etc… and that would not make for a very good game.
So how do we generate random numbers? In real life, we often generate random results by doing things like flipping a coin, rolling a dice, or shuffling a deck of cards. These events involve so many physical variables (eg. gravity, friction, air resistance, momentum, etc…) that they become almost impossible to predict or control, and produce results that are for all intents and purposes random.
However, computers aren’t designed to take advantage of physical variables — your computer can’t toss a coin, throw a dice, or shuffle real cards. Computers live in a very controlled electrical world where everything is binary (false or true) and there is no in-between. By their very nature, computers are designed to produce results that are as predictable as possible. When you tell the computer to calculate 2 + 2, you ALWAYS want the answer to be 4. Not 3 or 5 on occasion.
Consequently, computers are generally incapable of generating random numbers. Instead, they must simulate randomness, which is most often done using pseudo-random number generators.
A pseudo-random number generator (PRNG) is a program that takes a starting number (called a seed), and performs mathematical operations on it to transform it into some other number that appears to be unrelated to the seed. It then takes that generated number and performs the same mathematical operation on it to transform it into a new number that appears unrelated to the number it was generated from. By continually applying the algorithm to the last generated number, it can generate a series of new numbers that will appear to be random if the algorithm is complex enough.
It’s actually fairly easy to write a PRNG. Here’s a short program that generates 100 pseudo-random numbers:
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| #include <stdafx.h>#include <iostream>using namespace std;unsigned int PRNG(){ // our initial starting seed is 5323 static unsigned int nSeed = 5323; // Take the current seed and generate a new value from it // Due to our use of large constants and overflow, it would be // very hard for someone to predict what the next number is // going to be from the previous one. nSeed = (8253729 * nSeed + 2396403); // Take the seed and return a value between 0 and 32767 return nSeed % 32767;}int main(){ // Print 100 random numbers for (int nCount=0; nCount < 100; ++nCount) { cout << PRNG() << "\t"; // If we've printed 5 numbers, start a new column if ((nCount+1) % 5 == 0) cout << endl; }} |
The result of this program is:
Each number appears to be pretty random with respect to the previous one. As it turns out, our algorithm actually isn’t very good, for reasons we will discuss later. But it does effectively illustrate the principle of PRNG number generation.
Generating random numbers in C++
C (and by extension C++) comes with a built-in pseudo-random number generator. It is implemented as two separate functions that live in the cstdlib header:
srand() sets the initial seed value. srand() should only be called once.
rand() generates the next random number in the sequence (starting from the seed set by srand()).
Here’s a sample program using these functions:
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| #include <stdafx.h>#include <iostream>#include <cstdlib> // for rand() and srand()using namespace std;int main(){ srand(5323); // set initial seed value to 5323 // Print 100 random numbers for (int nCount=0; nCount < 100; ++nCount) { cout << rand() << "\t"; // If we've printed 5 numbers, start a new column if ((nCount+1) % 5 == 0) cout << endl; }} |
Here’s the output of this program:
The range of rand()
rand() generates pseudo-random integers between 0 and RAND_MAX, a constant in cstdlib that is typically set to 32767.
Generally, we do not want random numbers between 0 and RAND_MAX — we want numbers between two other values, which we’ll call nLow and nHigh. For example, if we’re trying to simulate the user rolling a dice, we want random numbers between 1 and 6.
It turns out it’s quite easy to take the result of rand() can convert it into whatever range we want:
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| // Generate a random number between nLow and nHigh (inclusive)unsigned int GetRandomNumber(int nLow, int nHigh){ return (rand() % (nHigh - nLow + 1)) + nLow;} |
PRNG sequences
If you run the rand() sample program above multiple times, you will note that it prints the same result every time! This means that while each number in the sequence is seemingly random with regards to the previous ones, the entire sequence is not random at all! And that means our program ends up totally predictable (the same inputs lead to the same outputs every time). There are cases where this can be useful or even desired (eg. you want a scientific simulation to be repeatable, or you’re trying to debug why your random dungeon generator crashes).
But often, this is not what is desired. If you’re writing a game of hi-lo (where the user has 10 tries to guess a number, and the computer tells them whether their guess is too high or to low), you don’t want the program picking the same numbers each time. So let’s take a deeper look at why this is happening, and how we can fix it.
Remember that each number in a PRNG sequence is generated from the previous number, in a deterministic way. Thus, given any starting seed number, PRNGs will always generate the same sequence of numbers from that seed as a result! We are getting the same sequence because our starting seed number is always 5323.
In order to make our entire sequence randomized, we need some way to pick a seed that’s not a fixed number. The first answer that probably comes to mind is that we need a random number! That’s a good thought, but if we need a random number to generate random numbers, then we’re in a catch-22. It turns out, we really don’t need our seed to be a random number — we just need to pick something that changes each time the program is run. Then we can use our PRNG to generate a unique sequence of pseudo-random numbers from that seed.
The commonly accepted method for doing this is to enlist the system clock. Each time the user runs the program, the time will be different. If we use this time value as our seed, then our program will generate a different sequence of numbers each time it is run!
C comes with a function called time() that returns the number of seconds since midnight on Jan 1, 1970. To use it, we merely need to include the ctime header, and then initialize srand() with a call to time(0):
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| #include <stdafx.h>#include <iostream>#include <cstdlib> // for rand() and srand()#include <ctime> // for time()using namespace std;int main(){ srand(time(0)); // set initial seed value to system clock for (int nCount=0; nCount < 100; ++nCount) { cout << rand() << "\t"; if ((nCount+1) % 5 == 0) cout << endl; }} |
Now our program will generate a different sequence of random numbers every time!
What is a good PRNG?
As I mentioned above, the PRNG we wrote isn’t a very good one. This section will discuss the reasons why. It is optional reading because it’s not strictly related to C or C++, but if you like programming you will probably find it interesting anyway.
In order to be a good PRNG, the PRNG needs to exhibit a number of properties:
First, the PRNG should generate each number with approximately the same probability. This is called distribution uniformity. If some numbers are generated more often than others, the result of the program that uses the PRNG will be biased!
For example, let’s say you’re trying to write a random item generator for a game. You’ll pick a random number between 1 and 10, and if the result is a 10, the monster will drop a powerful item instead of a common one. You would expect a 1 in 10 chance of this happening. But if the underlying PRNG is not uniform, and generates a lot more 10s than it should, your players will end up getting more rare items than you’d intended, possibly trivializing the difficulty of your game.
Generating PRNGs that produce uniform results is difficult, and it’s one of the main reasons the PRNG we wrote at the top of this lesson isn’t a very good PRNG.
Second, the method by which the next number in the sequence shouldn’t be obvious or predictable. For example, consider the following PRNG algorithm:
nNum = nNum + 1. This PRNG is perfectly uniform, but it’s not very useful as a sequence of random numbers!
Third, the PRNG should have a good dimensional distribution of numbers. This means it should return low numbers, middle numbers, and high numbers seemingly at random. A PRNG that returned all low numbers, then all high numbers may be uniform and non-predictable, but it’s still going to lead to biased results, particularly if the number of random numbers you actually use is small.
Fourth, all PRNGs are periodic, which means that at some point the sequence of numbers generated will eventually begin to repeat itself. As mentioned before, PRNGs are deterministic, and given an input number, a PRNG will produce the same output number every time. Consider what happens when a PRNG generates a number it has previously generated. From that point forward, it will begin to duplicate the sequence between the first occurrence of that number and the next occurrence of that number over and over. The length of this sequence is known as the period
For example, here are the first 100 numbers generated from a PRNG with poor periodicity:
You will note that it generated 9 as the second number, and 9 again as the 16th number. The PRNG gets stuck generating the sequence in-between these two 9′s repeatedly: 9-130-97-64-31-152-119-86-53-20-141-108-75-42-(repeat).
A good PRNG should have a long period for all seed numbers. Designing an algorithm that meets this property can be extremely difficult — most PRNGs will have long periods for some seeds and short periods for others. If the user happens to pick that seed, then the PRNG won’t be doing a good job.
Despite the difficulty in designing algorithms that meet all of these criteria, a lot of research has been done in this area because of it’s importance to scientific computing.
rand() is a mediocre PRNG
The algorithm used to implement rand() can vary from compiler to compiler, leading to results that may not be consistent across compilers. Most implementations of rand() use a method called a Linear Congruential Generator (LCG). If you have a look at the first example in this lesson, you’ll note that it’s actually a LCG, though one with intentionally poorly picked bad constants. LCGs tend to have shortcomings that make them not good choices for certain kinds of problems.
One of the main shortcomings of rand() is that RAND_MAX is usually set to 32767 (essentially 16-bits). This means if you want to generate numbers over a larger range (eg. 32-bit integers), the algorithm is not suitable. Also, rand() isn’t good if you want to generate random floating point numbers (eg. between 0.0 and 1.0), which is often useful when doing statistical modelling. Finally, rand() tends to have a relatively short period compared to other algorithms.
That said, rand() is entirely suitable for learning how to program, and for programs in which a high-quality PRNG is not a necessity. For such applications, I would highly recommend Mersenne Twister, which produces great results and is relatively easy to use.
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