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use rand::Rng;
use special::Beta as SBeta;
use crate::data::{BernoulliSuffStat, DataOrSuffStat};
use crate::dist::{Bernoulli, Beta};
use crate::traits::*;
impl Rv<Bernoulli> for Beta {
fn ln_f(&self, x: &Bernoulli) -> f64 {
self.ln_f(&x.p)
}
fn draw<R: Rng>(&self, mut rng: &mut R) -> Bernoulli {
let p: f64 = self.draw(&mut rng);
Bernoulli::new(p).expect("Failed to draw valid weight")
}
}
impl Support<Bernoulli> for Beta {
fn supports(&self, x: &Bernoulli) -> bool {
0.0 < x.p && x.p < 1.0
}
}
impl ContinuousDistr<Bernoulli> for Beta {}
impl ConjugatePrior<bool, Bernoulli> for Beta {
type Posterior = Self;
fn posterior(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> Self {
let (n, k) = match x {
DataOrSuffStat::Data(ref xs) => {
let mut stat = BernoulliSuffStat::new();
xs.iter().for_each(|x| stat.observe(x));
(stat.n, stat.k)
}
DataOrSuffStat::SuffStat(ref stat) => (stat.n, stat.k),
DataOrSuffStat::None => (0, 0),
};
let a = self.alpha + k as f64;
let b = self.beta + (n - k) as f64;
Beta::new(a, b).expect("Invalid posterior parameters")
}
fn ln_m(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> f64 {
let post = self.posterior(x);
post.alpha.ln_beta(post.beta) - self.alpha.ln_beta(self.beta)
}
fn ln_pp(&self, y: &bool, x: &DataOrSuffStat<bool, Bernoulli>) -> f64 {
let post = self.posterior(x);
let p: f64 = post.mean().expect("Mean undefined");
if *y {
p.ln()
} else {
(1.0 - p).ln()
}
}
}
macro_rules! impl_int_traits {
($kind:ty) => {
impl ConjugatePrior<$kind, Bernoulli> for Beta {
type Posterior = Self;
fn posterior(&self, x: &DataOrSuffStat<$kind, Bernoulli>) -> Self {
let (n, k) = match x {
DataOrSuffStat::Data(ref xs) => {
let mut stat = BernoulliSuffStat::new();
xs.iter().for_each(|x| stat.observe(x));
(stat.n, stat.k)
}
DataOrSuffStat::SuffStat(ref stat) => (stat.n, stat.k),
DataOrSuffStat::None => (0, 0),
};
let a = self.alpha + k as f64;
let b = self.beta + (n - k) as f64;
Beta::new(a, b).expect("Invalid posterior parameters")
}
fn ln_m(&self, x: &DataOrSuffStat<$kind, Bernoulli>) -> f64 {
let post = self.posterior(x);
post.alpha.ln_beta(post.beta) - self.alpha.ln_beta(self.beta)
}
fn ln_pp(
&self,
y: &$kind,
x: &DataOrSuffStat<$kind, Bernoulli>,
) -> f64 {
let post = self.posterior(x);
let p: f64 = post.mean().expect("Mean undefined");
if *y == 1 {
p.ln()
} else {
(1.0 - p).ln()
}
}
}
};
}
impl_int_traits!(u8);
impl_int_traits!(u16);
impl_int_traits!(u32);
impl_int_traits!(u64);
impl_int_traits!(usize);
impl_int_traits!(i8);
impl_int_traits!(i16);
impl_int_traits!(i32);
impl_int_traits!(i64);
impl_int_traits!(isize);
#[cfg(test)]
mod tests {
use super::*;
const TOL: f64 = 1E-12;
#[test]
fn posterior_from_data_bool() {
let data = vec![false, true, false, true, true];
let xs = DataOrSuffStat::Data::<bool, Bernoulli>(&data);
let posterior = Beta::new(1.0, 1.0).unwrap().posterior(&xs);
assert::close(posterior.alpha, 4.0, TOL);
assert::close(posterior.beta, 3.0, TOL);
}
#[test]
fn posterior_from_data_u16() {
let data: Vec<u16> = vec![0, 1, 0, 1, 1];
let xs = DataOrSuffStat::Data::<u16, Bernoulli>(&data);
let posterior = Beta::new(1.0, 1.0).unwrap().posterior(&xs);
assert::close(posterior.alpha, 4.0, TOL);
assert::close(posterior.beta, 3.0, TOL);
}
}