markovr

Higher-order Markov Chains can have longer memories than your typical Markov Chain, which looks back only 1 element. They are the basic building block for the WaveFunctionCollapse algorithm. A zeroth-order Markov Chain is the equivalent of a weighted die.

Usage

Add this to your Cargo.toml:

toml [dependencies] markovr = {version = "0.2"}

Alternatively, if you don't want to bring in the rand crate into your dependency tree:

toml [dependencies] markovr = {version = "0.2", features = []}

Then it's as simple as this:

```rust // Create a new, first-order Markov Chain. let mut m = MarkovChain::new(1);

// Create a two-way mapping between your input data and u64s. // Each of your inputs needs a unique u64 value. // std::Hash can be your friend here, but let's do it ourselves.

// alpha will be both our encoding mapping and training data. let alpha: Vec = "abcdefghijklmnopqrstuvwxyz".chars().collect(); // encoded is a parallel Vec to alpha that contains the u64 unique ids // for each character. let encoded: Vec = (0..alpha.len()).map(|x| x as u64).collect();

// Train the model. for i in m.order..encoded.len() { m.train(&[encoded[i - 1]], encoded[i], 1); }

// Generate values from the model. for i in 0..(encoded.len() - 1) { let next = m.generate(&[encoded[i]].clone()); // Since every input has exactly one output, the results // are deterministic. match next { Some(v) => assert_eq!(v, encoded[i + 1]), None => panic!( "can't predict next letter after {} (encoded as {})", alpha[i], encoded[i] ), }; } ```