markovian 0.2.1

Simulation of Markov Processes as stochastic processes.
Documentation 
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// Traits
use rand_distr::Distribution;
use crate::traits::{State, StateIterator, Transition};
use core::fmt::Debug;
use rand::Rng;

// Structs
use crate::errors::InvalidState;

// Functions
use core::mem;

/// Markov Chain in discrete time, with arbitrary space.
///
/// # Remarks
/// 
/// If your transition function `transition` could reuse of structs that implement
/// the `Distribution<T>` trait in order to sample the next state, then, 
/// for the best performance possible, create your own struct that implements
/// the `Transition<T, T>` trait.
#[derive(Debug, Clone)]
pub struct MarkovChain<T, F, R> {
    state: T,
    transition: F,
    rng: R,
}

impl<T, F, R> MarkovChain<T, F, R>
where
    R: Rng,
    F: Transition<T, T>,
{
    #[inline]
    pub fn new(state: T, transition: F, rng: R) -> Self {
        MarkovChain {
            state,
            transition,
            rng,
        }
    }
}

impl<T, F, R> State for MarkovChain<T, F, R>
where
    T: Debug + Clone,
{
    type Item = T;

    #[inline]
    fn state(&self) -> Option<&Self::Item> {
        Some(&self.state)
    }

    #[inline]
    fn state_mut(&mut self) -> Option<&mut Self::Item> {
        Some(&mut self.state)
    }

    #[inline]
    fn set_state(
        &mut self,
        mut new_state: Self::Item,
    ) -> Result<Option<Self::Item>, InvalidState<Self::Item>> {
        mem::swap(&mut self.state, &mut new_state);
        Ok(Some(new_state))
    }
}

impl<T, F, R> Iterator for MarkovChain<T, F, R>
where
    T: Debug + Clone,
    F: Transition<T, T>,
    R: Rng,
{
    type Item = T;

    #[inline]
    fn next(&mut self) -> Option<Self::Item> {
        self.state = self.transition.sample_from(&self.state, &mut self.rng);
        self.state().cloned()
    }
}

impl<T, F, R> StateIterator for MarkovChain<T, F, R>
where
    T: Debug + Clone,
    F: Transition<T, T>,
    R: Rng,
{
    #[inline]
    fn state_as_item(&self) -> Option<<Self as std::iter::Iterator>::Item> {
        self.state().cloned()
    }
}

impl<T, F, R> Distribution<T> for MarkovChain<T, F, R>
where
    T: Debug + Clone,
    F: Transition<T, T>,
    R: Rng,
{
    /// Sample a possible next state. 
    #[inline]
    fn sample<R2>(&self, rng: &mut R2) -> T
    where
        R2: Rng + ?Sized,
    { 
        self.transition.sample_from(&self.state, rng)
    }
}


#[cfg(test)]
mod tests {
    use super::*;
    use crate::distributions::Raw;
    use pretty_assertions::assert_eq;

    // use approx::abs_diff_eq;

    #[test]
    fn sampling_stability() {
        let rng = crate::tests::rng(1);
        let expected = 1;
        let transition = |_: &u64| Raw::new(vec![(1.0, expected)]);
        let mc = MarkovChain::new(0, transition, rng);
        for x in mc.take(100) {
            assert_eq!(x, expected);
        }

        let rng = crate::tests::rng(2);
        let transition = |_: &u64| Raw::new(vec![(0.5, 1), (0.5, 2)]);
        let mc = MarkovChain::new(0, transition, rng);
        for x in mc.take(100) {
            assert!(x == 1 || x == 2);
        }
    }

    #[test]
    fn value_stability() {
        let rng = crate::tests::rng(3);
        let expected = vec![1, 2, 1, 1];
        let transition = |_: &u64| Raw::new(vec![(0.5, 1), (0.5, 2)]);
        let mc = MarkovChain::new(0, transition, rng);
        let sample: Vec<u64> = mc.take(4).collect();

        assert_eq!(sample, expected);
    }

    #[test]
    fn construction() {
        let rng = crate::tests::rng(4);
        let expected = 0.39515292318166956;
        let transition = |_: &f64| rand_distr::StandardNormal;
        let mut mc = MarkovChain::new(0., transition, rng);
        let sample: f64 = mc.next().unwrap();

        assert_eq!(sample, expected);
    }
}