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use crate::rand::SeedableRng;
use crate::{
ContinuousSamplesDistribution, Distribution, DistributionError, RandomVariable,
SampleableDistribution,
};
use rand::rngs::StdRng;
pub struct ParticleFilter<Y, X, DY, DX, PD>
where
Y: RandomVariable,
X: RandomVariable + PartialEq,
DY: Distribution<Value = Y, Condition = X>,
DX: Distribution<Value = X, Condition = X>,
PD: SampleableDistribution<Value = X, Condition = (Vec<X>, Vec<Y>)>,
{
observable: Vec<Y>,
distr_x: DX,
distr_y: DY,
proposal: PD,
}
impl<Y, X, DY, DX, PD> ParticleFilter<Y, X, DY, DX, PD>
where
Y: RandomVariable,
X: RandomVariable + PartialEq,
DY: Distribution<Value = Y, Condition = X>,
DX: Distribution<Value = X, Condition = X>,
PD: SampleableDistribution<Value = X, Condition = (Vec<X>, Vec<Y>)>,
{
pub fn new(
observable: Vec<Y>,
distr_x: DX,
distr_y: DY,
proposal: PD,
) -> Result<Self, DistributionError> {
Ok(Self {
observable,
distr_y,
distr_x,
proposal,
})
}
pub fn filtering(
&self,
particles_initial: Vec<X>,
thr: f64,
) -> Result<Vec<ContinuousSamplesDistribution<X>>, DistributionError> {
let mut rng = StdRng::from_seed([13; 32]);
let mut distr_vec = vec![];
let particles_len = particles_initial.len();
let mut p_previous = particles_initial.clone();
let w_initial = vec![1.0 / particles_len as f64; particles_len];
let mut w_previous = w_initial;
let mut vecvec_p = (0..particles_len)
.into_iter()
.map(|i| -> Result<_, DistributionError> {
let vec_pi = vec![particles_initial[i].clone()];
Ok(vec_pi)
})
.collect::<Result<Vec<_>, _>>()?;
for t in 0..self.observable.len() {
let mut p = (0..particles_len)
.into_iter()
.map(|i| -> Result<_, DistributionError> {
let pi = self.proposal.sample(
&(
(&vecvec_p[i][0..t + 1]).to_vec(),
(self.observable[0..t + 1]).to_vec(),
),
&mut rng,
)?;
Ok(pi)
})
.collect::<Result<Vec<_>, _>>()?;
loop {
let w_orig = (0..particles_len)
.into_iter()
.map(|i| -> Result<_, DistributionError> {
let wi_orig = w_previous[i]
* self.distr_y.p_kernel(&self.observable[t], &p[i])?
* self.distr_x.p_kernel(&p[i], &p_previous[i])?
/ self.proposal.p_kernel(
&p[i],
&(
(vecvec_p[i][0..t + 1]).to_vec(),
(self.observable[0..t + 1]).to_vec(),
),
)?;
Ok(wi_orig)
})
.collect::<Result<Vec<_>, _>>()?;
let w = (0..particles_len)
.into_iter()
.map(|i| -> Result<_, DistributionError> {
let wi = w_orig[i] / (w_orig.iter().map(|wi_orig| wi_orig).sum::<f64>());
Ok(wi)
})
.collect::<Result<Vec<_>, _>>()?;
let eff = 1.0 / (w.iter().map(|wi| wi.powi(2)).sum::<f64>());
println!("{:#?}", eff);
let mut p_sample = vec![];
for i in 0..w.len() {
let num_w = (particles_len as f64 * 100.0 * w[i]).round() as usize;
let mut pi_sample = vec![p[i].clone(); num_w];
p_sample.append(&mut pi_sample);
}
let weighted_distr = ContinuousSamplesDistribution::new(p_sample);
let mut weighted_distr_vec = vec![weighted_distr.clone()];
if eff > thr {
distr_vec.append(&mut weighted_distr_vec);
vecvec_p = (0..particles_len)
.into_iter()
.map(|i| -> Result<_, DistributionError> {
let mut vec_pi = vec![p[i].clone()];
vecvec_p[i].append(&mut vec_pi);
let vecvec_pi = vecvec_p[i].clone();
Ok(vecvec_pi)
})
.collect::<Result<Vec<_>, _>>()?;
p_previous = p;
w_previous = w;
break;
}
p = (0..particles_len)
.into_iter()
.map(|_i| -> Result<_, DistributionError> {
let pi = weighted_distr.sample(&(), &mut rng)?;
Ok(pi)
})
.collect::<Result<Vec<_>, _>>()?;
}
}
Ok(distr_vec)
}
}
#[cfg(test)]
mod tests {
use crate::distribution::Distribution;
use crate::*;
use rand::prelude::*;
use sir::ParticleFilter;
#[test]
fn it_works() {
let x_sigma = 2.0;
let y_sigma = 4.0;
let time = 30;
let mut rng = StdRng::from_seed([5; 32]);
let mut x_series = Vec::new();
let mut x_pre = 0.0;
let mut y_series = Vec::new();
for _i in 0..time {
let x_params = NormalParams::new(x_pre, x_sigma).unwrap();
let x = Normal.sample(&x_params, &mut rng).unwrap();
x_series.append(&mut vec![x]);
x_pre = x;
let y_params = NormalParams::new(x.powi(3) * 0.3, y_sigma).unwrap();
let y = Normal.sample(&y_params, &mut rng).unwrap();
y_series.append(&mut vec![y]);
}
let p_num = 100;
let fn_x = |x: &f64| NormalParams::new(*x, x_sigma);
let fn_y = |y: &f64| NormalParams::new(y.powi(3) * 0.3, y_sigma);
let fn_p = |xy: &(Vec<f64>, Vec<f64>)| NormalParams::new(xy.0[xy.0.len() - 1], x_sigma);
let distr_x = Normal.condition(&fn_x);
let distr_y = Normal.condition(&fn_y);
let proposal = Normal.condition(&fn_p);
let test = ParticleFilter::new(y_series.clone(), distr_x, distr_y, proposal).unwrap();
let p_initial = (0..p_num)
.into_iter()
.map(|_i| -> Result<_, DistributionError> {
let p = Normal
.sample(&NormalParams::new(x_pre, x_sigma).unwrap(), &mut rng)
.unwrap();
Ok(p)
})
.collect::<Result<Vec<_>, _>>()
.unwrap();
let thr = p_num as f64 * 0.99;
let result = &test.filtering(p_initial, thr).unwrap();
let est_x = (0..time)
.into_iter()
.map(|i| -> Result<_, DistributionError> {
let a: f64 = result[i].samples().iter().sum();
let b: f64 = result[i].samples().len() as f64;
let x = a / b;
Ok(x)
})
.collect::<Result<Vec<_>, _>>()
.unwrap();
println!("{:#?}", x_series);
println!("{:#?}", x_series.len());
println!("{:#?}", y_series);
println!("{:#?}", y_series.len());
println!("{:#?}", est_x);
println!("{:#?}", est_x.len());
}
}