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use crate::{
error::{RegressionError, RegressionResult},
glm::{DispersionType, Glm},
link::Link,
math::prod_log,
num::Float,
response::Response,
};
use num_traits::{ToPrimitive, Unsigned};
use std::marker::PhantomData;
pub struct Poisson<L = link::Log>
where
L: Link<Poisson<L>>,
{
_link: PhantomData<L>,
}
impl<U, L> Response<Poisson<L>> for U
where
U: Unsigned + ToPrimitive + ToString + Copy,
L: Link<Poisson<L>>,
{
fn into_float<F: Float>(self) -> RegressionResult<F> {
F::from(self).ok_or_else(|| RegressionError::InvalidY(self.to_string()))
}
}
impl<L> Glm for Poisson<L>
where
L: Link<Poisson<L>>,
{
type Link = L;
const DISPERSED: DispersionType = DispersionType::NoDispersion;
fn log_partition<F: Float>(nat_par: F) -> F {
num_traits::Float::exp(nat_par)
}
fn variance<F: Float>(mean: F) -> F {
mean
}
fn log_like_sat<F: Float>(y: F) -> F {
prod_log(y) - y
}
}
pub mod link {
use super::Poisson;
use crate::{
link::{Canonical, Link},
num::Float,
};
pub struct Log {}
impl Canonical for Log {}
impl Link<Poisson<Log>> for Log {
fn func<F: Float>(y: F) -> F {
num_traits::Float::ln(y)
}
fn func_inv<F: Float>(lin_pred: F) -> F {
num_traits::Float::exp(lin_pred)
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::{error::RegressionResult, model::ModelBuilder};
use approx::assert_abs_diff_eq;
use ndarray::{array, Array1};
#[test]
fn poisson_reg() -> RegressionResult<()> {
let ln2 = f64::ln(2.);
let beta = array![0., ln2, -ln2];
let data_x = array![[1., 0.], [1., 1.], [0., 1.], [0., 1.]];
let data_y: Array1<u32> = array![2, 1, 0, 1];
let model = ModelBuilder::<Poisson>::data(&data_y, &data_x).build()?;
let fit = model.fit_options().max_iter(10).fit()?;
dbg!(fit.n_iter);
assert_abs_diff_eq!(beta, fit.result, epsilon = f32::EPSILON as f64);
Ok(())
}
}