use ndarray::Array1;
use ndarray::s;
use stochastic_rs_core::simd_rng::Deterministic;
use stochastic_rs_core::simd_rng::SeedExt;
use stochastic_rs_core::simd_rng::Unseeded;
use stochastic_rs_distributions::normal::SimdNormal;
use crate::traits::FloatExt;
use crate::traits::ProcessExt;
#[derive(Clone, Copy)]
pub struct LinearSDE<T: FloatExt, S: SeedExt = Unseeded> {
pub a: T,
pub b: T,
pub c: T,
pub n: usize,
pub x0: Option<T>,
pub t: Option<T>,
pub seed: S,
}
impl<T: FloatExt> LinearSDE<T> {
pub fn new(a: T, b: T, c: T, n: usize, x0: Option<T>, t: Option<T>) -> Self {
Self {
a,
b,
c,
n,
x0,
t,
seed: Unseeded,
}
}
}
impl<T: FloatExt> LinearSDE<T, Deterministic> {
pub fn seeded(a: T, b: T, c: T, n: usize, x0: Option<T>, t: Option<T>, seed: u64) -> Self {
Self {
a,
b,
c,
n,
x0,
t,
seed: Deterministic::new(seed),
}
}
}
impl<T: FloatExt, S: SeedExt> ProcessExt<T> for LinearSDE<T, S> {
type Output = Array1<T>;
fn sample(&self) -> Self::Output {
let mut x = Array1::<T>::zeros(self.n);
if self.n == 0 {
return x;
}
x[0] = self.x0.unwrap_or(T::zero());
if self.n == 1 {
return x;
}
let n_increments = self.n - 1;
let dt = self.t.unwrap_or(T::one()) / T::from_usize_(n_increments);
let sqrt_dt = dt.sqrt();
let mut prev = x[0];
let mut tail_view = x.slice_mut(s![1..]);
let tail = tail_view
.as_slice_mut()
.expect("LinearSDE output tail must be contiguous");
let normal = SimdNormal::<T>::from_seed_source(T::zero(), sqrt_dt, &self.seed);
normal.fill_slice_fast(tail);
for z in tail.iter_mut() {
let next = prev + (self.a + self.b * prev) * dt + self.c * prev * *z;
*z = next;
prev = next;
}
x
}
}
py_process_1d!(PyLinearSDE, LinearSDE,
sig: (a, b, c, n, x0=None, t=None, seed=None, dtype=None),
params: (a: f64, b: f64, c: f64, n: usize, x0: Option<f64>, t: Option<f64>)
);