/gdstats

gdstats provides common statistical distributions in Godot 3.0

Primary LanguageGDScriptMIT LicenseMIT

gdstats

gdstats currently provides a small set of common distributions all implemented in fast forms usually via inversion techniques:

Image of Example Project

Discrete (integers)

  • randi_bernoulli(p), return 1 or 0 based on probability p

    example: Whether a specific die value was rolled or not

  • randi_binomial(p, n), return the number of 1s on n many Bernoulli Trials probability p.

    example: Number of times a specific value is rolled on a die in n many trials

  • randi_geometric(p), return the number of Bernoulli Trials with probability p until a result of 1.

    example: The number of rolls on a die until a specific value shows.

  • randi_poisson(lambda), a binomial where p->0 and n->inf but n*p = lambda.

    example: Number of phone calls in a given amount of time. p is very low and n is very large.

  • randv_histogram(values, probabilities), provide a list of return values and probabilities and return a value fitting the distribution.

    example: Use for distributions of own choosing such as marbles in a bag.

  • randi_pseudo(c), mimics the Warcraft3/Dota style of "fair" number generators. Gives the number of actions until an event occurs where upon a failure, the probability increases by c.

    example: Use to determine the number of attacks until the next critical occurs.

Table of Nominal Probabilities to c Values

The following table gives values of c to approximate a given event's probability of occuring.

Nominal Probability c Nominal Probability c
5% 0.00380 45% 0.20155
10% 0.01475 50% 0.24931
15% 0.03222 55% 0.36040
20% 0.05570 60% 0.42265
25% 0.08474 65% 0.48113
30% 0.11895 70% 0.57143
40% 0.15798

Continuous (floats)

  • randf_uniform(a, b), return a uniformly random value in range a to b

    example: Same as rand_range

  • randf_exponential(lambda), return a value fitting the exponential distribution

    example: Time between independant events which occur at a constant average rate. Memoryless

  • randf_erlang(k, lambda), Sum of k many exponentials of 1/lambda.

    example: The distribution of time between k many incoming telephone calls modelled by a poisson process. k = 1 is the exponential.

  • randf_gaussian()/randf_normal(), return a value from the normal distribution.

    example: Useful in modeling realistic scenarious which use the distribution, i.e. Harmonic Oscilator ground state, position of a diffused particle.