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use crate::{
ConditionDifferentiableDistribution, Distribution, IndependentJoint, RandomVariable,
SampleableDistribution, ValueDifferentiableDistribution,
};
use crate::{DistributionError, Event};
use opensrdk_linear_algebra::Vector;
use rand::prelude::*;
use std::{ops::BitAnd, ops::Mul};
#[derive(Clone, Debug)]
pub struct DependentJoint<L, R, T, UL, UR>
where
L: Distribution<Value = T, Condition = UL>,
R: Distribution<Value = UL, Condition = UR>,
T: RandomVariable,
UL: Event,
UR: Event,
{
pub(crate) lhs: L,
pub(crate) rhs: R,
}
impl<L, R, T, UL, UR> DependentJoint<L, R, T, UL, UR>
where
L: Distribution<Value = T, Condition = UL>,
R: Distribution<Value = UL, Condition = UR>,
T: RandomVariable,
UL: Event,
UR: Event,
{
pub fn new(lhs: L, rhs: R) -> Self {
Self { lhs, rhs }
}
}
impl<L, R, T, UL, UR> Distribution for DependentJoint<L, R, T, UL, UR>
where
L: Distribution<Value = T, Condition = UL>,
R: Distribution<Value = UL, Condition = UR>,
T: RandomVariable,
UL: RandomVariable,
UR: Event,
{
type Value = (T, UL);
type Condition = UR;
fn p_kernel(&self, x: &(T, UL), theta: &UR) -> Result<f64, DistributionError> {
Ok(self.lhs.p_kernel(&x.0, &x.1)? * self.rhs.p_kernel(&x.1, theta)?)
}
}
impl<L, R, T, UL, UR, Rhs, TRhs> Mul<Rhs> for DependentJoint<L, R, T, UL, UR>
where
L: Distribution<Value = T, Condition = UL>,
R: Distribution<Value = UL, Condition = UR>,
T: RandomVariable,
UL: RandomVariable,
UR: RandomVariable,
Rhs: Distribution<Value = TRhs, Condition = UR>,
TRhs: RandomVariable,
{
type Output = IndependentJoint<Self, Rhs, (T, UL), TRhs, UR>;
fn mul(self, rhs: Rhs) -> Self::Output {
IndependentJoint::new(self, rhs)
}
}
impl<L, R, T, UL, UR, Rhs, URhs> BitAnd<Rhs> for DependentJoint<L, R, T, UL, UR>
where
L: Distribution<Value = T, Condition = UL>,
R: Distribution<Value = UL, Condition = UR>,
T: RandomVariable,
UL: RandomVariable,
UR: RandomVariable,
Rhs: Distribution<Value = UR, Condition = URhs>,
URhs: RandomVariable,
{
type Output = DependentJoint<Self, Rhs, (T, UL), UR, URhs>;
fn bitand(self, rhs: Rhs) -> Self::Output {
DependentJoint::new(self, rhs)
}
}
impl<L, R, T, UL, UR> ValueDifferentiableDistribution for DependentJoint<L, R, T, UL, UR>
where
L: Distribution<Value = T, Condition = UL>
+ ValueDifferentiableDistribution
+ ConditionDifferentiableDistribution,
R: Distribution<Value = UL, Condition = UR>
+ ValueDifferentiableDistribution
+ ConditionDifferentiableDistribution,
T: RandomVariable,
UL: RandomVariable,
UR: RandomVariable,
{
fn ln_diff_value(
&self,
x: &Self::Value,
theta: &Self::Condition,
) -> Result<Vec<f64>, crate::DistributionError> {
let diff_a = self.lhs.ln_diff_value(&x.0, &x.1).unwrap();
let diff_b = self.lhs.ln_diff_condition(&x.0, &x.1)?.col_mat()
+ self.rhs.ln_diff_value(&x.1, &theta)?.col_mat();
let diff = [diff_a, diff_b.vec()].concat();
Ok(diff)
}
}
impl<L, R, T, UL, UR> ConditionDifferentiableDistribution for DependentJoint<L, R, T, UL, UR>
where
L: Distribution<Value = T, Condition = UL>,
R: Distribution<Value = UL, Condition = UR> + ConditionDifferentiableDistribution,
T: RandomVariable,
UL: RandomVariable,
UR: RandomVariable,
{
fn ln_diff_condition(
&self,
x: &Self::Value,
theta: &Self::Condition,
) -> Result<Vec<f64>, crate::DistributionError> {
let diff = self.rhs.ln_diff_condition(&x.1, &theta).unwrap();
Ok(diff)
}
}
impl<L, R, T, UL, UR> SampleableDistribution for DependentJoint<L, R, T, UL, UR>
where
L: SampleableDistribution<Value = T, Condition = UL>,
R: SampleableDistribution<Value = UL, Condition = UR>,
T: RandomVariable,
UL: RandomVariable,
UR: Event,
{
fn sample(&self, theta: &UR, rng: &mut dyn RngCore) -> Result<(T, UL), DistributionError> {
let rhs = self.rhs.sample(theta, rng)?;
Ok((self.lhs.sample(&rhs, rng)?, rhs))
}
}
#[cfg(test)]
mod tests {
use crate::*;
use crate::{ConditionDifferentiableDistribution, Distribution};
use opensrdk_linear_algebra::mat;
use rand::prelude::*;
#[test]
fn it_works() {
let model = Normal.condition(|x: &f64| NormalParams::new(1.0, x.powi(2) + 1.0)) & Normal;
let mut rng = StdRng::from_seed([1; 32]);
let x = model
.sample(&NormalParams::new(0.0, 1.0).unwrap(), &mut rng)
.unwrap();
println!("{:#?}", x);
}
#[test]
fn it_works3() {
let model_prior = Normal.condition(|x: &f64| NormalParams::new(1.0, x.powi(2) + 1.0));
let g = |theta: &f64| mat!(0.0, 2.0 * theta);
let model = ConditionDifferentiableConditionedDistribution::new(model_prior, g) & Normal;
let f = model
.ln_diff_condition(&(1.0, 2.0), &NormalParams::new(0.0, 1.0).unwrap())
.unwrap();
println!("{:#?}", f);
}
}