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use crate::{DependentJoint, Distribution, IndependentJoint, RandomVariable};
use rand::prelude::StdRng;
use std::iter::Iterator;
use std::{error::Error, ops::BitAnd, ops::Mul};
pub struct IndependentArrayJoint<D, T, U>
where
D: Distribution<T = T, U = U>,
T: RandomVariable,
U: RandomVariable,
{
distributions: Vec<D>,
}
impl<D, T, U> Distribution for IndependentArrayJoint<D, T, U>
where
D: Distribution<T = T, U = U>,
T: RandomVariable,
U: RandomVariable,
{
type T = Vec<T>;
type U = U;
fn p(&self, x: &Self::T, theta: &Self::U) -> Result<f64, Box<dyn Error>> {
x.iter()
.enumerate()
.map(|(i, xi)| self.distributions[i].p(xi, theta))
.product()
}
fn sample(&self, theta: &Self::U, rng: &mut StdRng) -> Result<Self::T, Box<dyn Error>> {
self.distributions
.iter()
.map(|di| di.sample(theta, rng))
.collect()
}
}
impl<D, T, U, Rhs, TRhs> Mul<Rhs> for IndependentArrayJoint<D, T, U>
where
D: Distribution<T = T, U = U>,
T: RandomVariable,
U: RandomVariable,
Rhs: Distribution<T = TRhs, U = U>,
TRhs: RandomVariable,
{
type Output = IndependentJoint<Self, Rhs, Vec<T>, TRhs, U>;
fn mul(self, rhs: Rhs) -> Self::Output {
IndependentJoint::new(self, rhs)
}
}
impl<D, T, U, Rhs, URhs> BitAnd<Rhs> for IndependentArrayJoint<D, T, U>
where
D: Distribution<T = T, U = U>,
T: RandomVariable,
U: RandomVariable,
Rhs: Distribution<T = U, U = URhs>,
URhs: RandomVariable,
{
type Output = DependentJoint<Self, Rhs, Vec<T>, U, URhs>;
fn bitand(self, rhs: Rhs) -> Self::Output {
DependentJoint::new(self, rhs)
}
}
pub trait DistributionProduct<D, T, U>
where
D: Distribution<T = T, U = U>,
T: RandomVariable,
U: RandomVariable,
{
fn product(self) -> IndependentArrayJoint<D, T, U>;
}
impl<I, D, T, U> DistributionProduct<D, T, U> for I
where
I: Iterator<Item = D>,
D: Distribution<T = T, U = U>,
T: RandomVariable,
U: RandomVariable,
{
fn product(self) -> IndependentArrayJoint<D, T, U> {
let distributions = self.collect::<Vec<_>>();
IndependentArrayJoint::<D, T, U> { distributions }
}
}