markovr

Higher-order Markov Chains can have longer memories than your typical Markov Chain, which looks back only 1 element.

Cool features:

• Arbitrary-dimension Markov Chains. Nth-Order chains are possible.
• Partial-view element generation. Missing an input during generation? No problem.
• Fast generation. Generating a value from a trained model is done in O(lg N) time, where N is the number of possible outputs for that position.
• Optionally Deterministic. Need more control in your life? Deterministic generation functions are available.

Usage

Add this to your Cargo.toml:

toml [dependencies] markovr = {version = "0.4"}

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

toml [dependencies] markovr = {version = "0.4", 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(&[Some('y')], 'z'));
// Prints: 1
// What's the probability that 'z' follows 'a'?
print!("\n{}\n", m.probability(&[Some('a')], 'z'));
// Prints: 0

} ```

If you're looking for a more complex example that uses unknown elements (similar to WaveFunctionCollapse):

```rust extern crate markovr;

pub fn main() { // Create a new, fourth-order Markov Chain. // We'll keep track of each orthogonal neighbor, // and allow for any one of them to be unknown. let mut m = markovr::MarkovChain::::new(4, &[0, 1, 2, 3]);

let train: Vec<Vec<char>> = "

┏━━━┓
┃ ┃
┃ ┣━━━┓ ┃ ┃ ┃ ┃ ┣━━━┛ ┃ ┃
┗━━━┛

" .lines() .map(|c| c.chars().take(12).collect()) .collect();

// Train the model.
for r in 1..(train.len() - 1) {
    let ref row = train[r];
    for c in 1..(row.len() - 1) {
        // Build up a view of the neighbors.
        let neighbors = &[
            train[r - 1][c],
            train[r][c - 1],
            train[r][c + 1],
            train[r + 1][c],
        ];
        m.train(neighbors, train[r][c], 1);
    }
}

// Generate values from the model.
const DIM: usize = 16;
let mut map: [[Option<char>; DIM]; DIM];
'gen: loop {
    map = [[None; DIM]; DIM];
    // Fill in spaces around the border. This isn't necessary,
    // but should prevent dangling lines in the output.
    for i in 0..DIM {
        map[i][0] = Some(' ');
        map[i][DIM - 1] = Some(' ');
        map[0][i] = Some(' ');
        map[DIM - 1][i] = Some(' ');
    }
    // Iterate on all non-None spaces and fill them in.
    for r in 1..(DIM - 1) {
        for c in 1..(DIM - 1) {
            let neighbors = &[map[r - 1][c], map[r][c - 1], map[r][c + 1], map[r + 1][c]];

            map[r][c] = m.generate_from_partial(neighbors);
            match map[r][c] {
                Some(_) => {}
                // We saw a case that wasn't in our training data,
                // so throw it away and try again.
                None => {
                    continue 'gen;
                }
            }
        }
    }
    break 'gen;
}

for r in 1..(DIM - 1) {
    for c in 1..(DIM - 1) {
        match map[r][c] {
            Some(v) => print!("{}", v),
            None => print!("?"),
        }
    }
    print!("\n");
}
// Prints:
/*
       ┏━━━━┓
       ┃    ┃
       ┃    ┃
  ┏━━━━┛    ┃
  ┃         ┃
┏━┛         ┃
┃           ┃
┃           ┃
┃           ┃
┣━━━━━━━━━━━┛
┃
┣━━━┓
┃   ┃
┗━━━┛
   */

} ```