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use rand_distr::Distribution;
use crate::traits::{State, StateIterator, Transition};
use core::fmt::Debug;
use rand::Rng;
use crate::errors::InvalidState;
use core::mem;
#[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,
{
#[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;
#[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);
}
}