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
}