zprob

A Zig Module for Random Number Distributions

The zprob module implements functionality for working with probability distributions in pure Zig, including generating random samples and calculating probabilities using mass/density functions. The instructions below will get you started with integrating zprob into your project, as well as introducing some basic use cases. For more detailed information on the different APIs that zprob implements, please refer to the docs site.

Installation

Note: The current version of zprob was developed and tested using v0.13.0 of Zig and is still a work in progress. Using a version of Zig other than 0.13.0 may lead to the code not compiling.

To include zprob in your Zig project, you can add it to your build.zig.zon file in the dependencies section:

.{
    .name = "my_project",
    .version = "0.1.0",
    .paths = .{
        "build.zig",
        "build.zig.zon",
        "README.md",
        "LICENSE",
        "src",
    },
    .dependencies = .{
        .zprob = {
            .url = "https://github.com/pblischak/zprob/archive/refs/tags/v0.2.0.tar.gz",
            .hash = "12200e840921f2199753be06fa57616ae9458bb467c5fde233a851b5556bca539ed0",
        }
    },
}

Then, in the build.zig file, add the following lines within the build function to include zprob as a module:

pub fn build(b: *std.Build) void {
    // exe setup...

    const zprob_dep = b.dependency("zprob", .{
            .target = target,
            .optimize = optimize,
    });

    const zprob_module = zprob_dep.module("zprob");
    exe.root_module.addImport("zprob", zprob_module);

    // additional build steps...
}

Check out the build files in the examples/ folder for some demos of complete sample code projects.

Getting Started

Below we show a brief "Hello, World!" program that introduces the RandomEnvironment struct, which provides a high-level interface for sampling from distributions and calculating probabilities. It automatically generates and stores everything needed to begin generating random numbers (seed + random generator), and follows the standard Zig convention of initialization with an Allocator that handles memory allocation.

const std = @import("std");
const zprob = @import("zprob");

pub fn main() !void {
    // Set up main memory allocator and defer deinitilization
    var gpa = std.heap.GeneralPurposeAllocator(.{}){};
    const allocator = gpa.allocator();
    defer {
        const status = gpa.deinit();
        std.testing.expect(status == .ok) catch {
            @panic("Memory leak!");
        };
    }

    // Set up random environment and defer deinitialization
    var env = try zprob.RandomEnvironment.init(allocator);
    env.deinit()

    // Generate random samples
    const binomial_sample = env.rBinomial(10, 0.8);
    const geometric_sample = env.rGeometric(3.0);


    // Generate slices of random samples. The caller is responsible for cleaning up
    // the allocated memory for the slice.
    const binomial_slice = try env.rBinomialSlice(100, 20, 0.4);
    defer allocator.free(binomial_samples);
}

Example Projects

As mentioned briefly above, there are several projects in the examples/ folder that demonstrate the usage of zprob for different applications:

• approximate_bayes: Uses approximate Bayesian computation to estimate the posterior mean and standard deviation of a normal distribution using a small sample of observations.
• compound_distributions: Illustrates how to generate samples from compound probability distributions such as the Beta-Binomial.
• distribution_sampling: Shows the basics of the "Distributions API" through the construction of distribution structs with different underlying types.
• enemy_spawner: Shows a gamedev motivated use case where distinct enemy types are sampled with different frequencies, are given different stats based on their type, and are placed randomly on the level map.

Low-Level Distributions API

While the easiest way to get started using zprob is with the RandomEnvironment struct, for users wanting more fine-grained control over the construction and usage of different probability distributions, zprob provides a lower-level "Distributions API".

Available Distributions

Discrete Probability Distributions

Bernoulli :: Binomial :: Geometric :: Multinomial :: Negative Binomial :: Poisson :: Uniform

Continuous Probability Distributions

Beta :: Cauchy :: Chi-squared :: Dirichlet :: Exponential :: Gamma :: Normal :: Uniform

Issues

If you run into any problems while using zprob, please consider filing an issue describing the problem, as well as any steps that may be required to reproduce the problem.

Contributing

We are open for contributions! Please see our contributing guide for more information on how you can help build new features for zprob.

Other Useful Links

• https://ziglang.org/documentation/master/std/#std.Random
• https://zig.guide/standard-library/random-numbers