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 = []}

And then, in your program:

```rust extern crate markovr;

pub fn main() { // Create a new, first-order Markov Chain. let mut m = markovr::MarkovChain::new(1);

// alpha will be our training data.
let alpha: Vec<char> = "abcdefghijklmnopqrstuvwxyz".chars().collect();

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

// Generate values from the model.
let mut last: Option<char> = Some('a');
while last.is_some() {
    print!("{} ", last.unwrap());
    last = m.generate(&[last.unwrap()]);
}
// Prints: a b c d e f g h i j k l m n o p q r s t u v w x y z

// What's the probability that 'z' follows 'y'?
print!("\n{}", m.probability(&['y'], 'z'));
// Prints: 1
// What's the probability that 'z' follows 'a'?
print!("\n{}\n", m.probability(&['a'], 'z'));
// Prints: 0

} ```