use std::marker::PhantomData;
use num_traits::AsPrimitive;
pub trait MeasureAccumulator<T> {
fn new() -> Self;
fn update_one(&mut self, known: &T, pred: &T);
fn result(&self) -> f64;
fn update<I: Iterator<Item = T>>(&mut self, known: I, predicted: I) {
for (k, p) in known.zip(predicted) {
self.update_one(&k, &p)
}
}
}
#[derive(Debug)]
pub struct PredictiveAccuracy<T> {
n_correct: usize,
n_wrong: usize,
_t: PhantomData<T>,
}
impl<T> MeasureAccumulator<T> for PredictiveAccuracy<T>
where
T: PartialEq,
{
fn new() -> Self {
PredictiveAccuracy {
n_correct: 0,
n_wrong: 0,
_t: PhantomData,
}
}
fn update_one(&mut self, known: &T, pred: &T) {
if known == pred {
self.n_correct += 1;
} else {
self.n_wrong += 1;
}
}
fn result(&self) -> f64 {
self.n_correct as f64 / (self.n_correct + self.n_wrong) as f64
}
}
#[derive(Debug)]
pub struct RootMeanSquaredError<T> {
sum_of_squares: f64,
n: usize,
_t: PhantomData<T>,
}
impl<T> MeasureAccumulator<T> for RootMeanSquaredError<T>
where
T: AsPrimitive<f64>,
{
fn new() -> Self {
RootMeanSquaredError {
sum_of_squares: 0.0,
n: 0,
_t: PhantomData,
}
}
fn update_one(&mut self, known: &T, pred: &T) {
let diff = known.as_() - pred.as_();
self.sum_of_squares += diff * diff;
self.n += 1;
}
fn result(&self) -> f64 {
(self.sum_of_squares / self.n as f64).sqrt()
}
}