Favorite techniques for random number generation?

I just ran this test (Godot 3.5.1). The numbers are distributed relatively evenly.

	# Compute 1000000 random integers in the range 0..99 and count the number of
	# ocurrences of each value.
	var counts: Array = []
	counts.resize(100)
	counts.fill(0)
	randomize()
	for _i in range(1000000):
		counts[randi() % 100] += 1
	print_debug("counts=", counts)

Here's one method:

	match randi() % 10:
		0,1,2,3: print("A selected")
		4,5,6: print("B selected")
		7,8: print("C selected")
		9: print("D selected")

A has a 40% probability of being selected, B 30%, C 20% and D 10%.

(Pseudo-)random number generation (prng) is a wide field.

In Godot, the prng is 'seeded' (initialized) once with "randomize()", usually when the program starts. The seed determines the sequence of numbers, meaning that when initialized with the same seed, subsequence calls to the (integer part of the) generator will produce the same sequence of numbers. These are evenly distributed over many calls (1000s). When only using few calls (like 10s or even 100s) there may be a local "unevenness" in the sequence of numbers. Should that happen, try a different seed. Just have in mind that the generated sequence based on a seed is reproducible.

The rng-class cybereality mentioned offers, besides the equal distribution of generated numbers, a normal distribution through the "rand_fn()" method, in case you don't want to use float version of the rand_range(), or the module operator, and you need a float number.

Note that you can replicate the behaviour of a normal distribution with multiple rolls of a random number and adding them up. In terms of good old table top role playing games, rolling 3 dice

(randi() % 6) + 1

or

rand_range(1,6)

the integer version of the latter! 3 times and summing up will produce numbers between 3 and 18 with higher probabilities for 9,10,11,12 and lower for the ends of the range. That was called 3D6 and there are a lot of other combinations, that's why there are so many dice. An even distribution for instance with a 20sided die (D20) can be used to determine a hit or a miss, a normal distribution like 3D4 or some such for the amount of damage dealt. Just additionally to @DaveTheCoder 's method (a D10 in that case). You can of course vary the ranges and the number of faces of a die.

For a game that rolls dice you will want to use the prng methods that use integers. For a game where you let's say pick random directions, angles, ... you will want to use the float-methods.

You can play with it if you need other distributions (or look them up somewhere), but even and normal distributions should be sufficient for most cases.

So in Godot randomize() is not the right method for providing a seed for reproducibility, but seed(x) is.

C++ (since 11), besides the C library, also offers various engines/generators and options to combine them with various distributions. So there's C++ typical almost everything for any case. That's ofc too much for a game engine.

But I'm currently getting away from C++ and towards Rust ...

Values 0 - 5 occur 429,496,730 times.
Values 6 - 9 occur 429,496,729 times.

Looks like one of those had to be first ;-)