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use std::ops::*;
use ndarray::*;
use super::types::*;
pub trait Norm {
type Output;
fn norm(&self) -> Self::Output {
self.norm_l2()
}
fn norm_l1(&self) -> Self::Output;
fn norm_l2(&self) -> Self::Output;
fn norm_max(&self) -> Self::Output;
}
impl<A, S, D, T> Norm for ArrayBase<S, D>
where A: Field + Absolute<Output = T>,
T: RealField,
S: Data<Elem = A>,
D: Dimension
{
type Output = T;
fn norm_l1(&self) -> Self::Output {
self.iter().map(|x| x.abs()).sum()
}
fn norm_l2(&self) -> Self::Output {
self.iter().map(|x| x.squared()).sum::<T>().sqrt()
}
fn norm_max(&self) -> Self::Output {
self.iter().fold(T::zero(), |f, &val| {
let v = val.abs();
if f > v { f } else { v }
})
}
}
pub enum NormalizeAxis {
Row = 0,
Column = 1,
}
pub fn normalize<A, S, T>(mut m: ArrayBase<S, Ix2>, axis: NormalizeAxis) -> (ArrayBase<S, Ix2>, Vec<T>)
where A: Field + Absolute<Output = T> + Div<T, Output = A>,
S: DataMut<Elem = A>,
T: RealField
{
let mut ms = Vec::new();
for mut v in m.axis_iter_mut(Axis(axis as usize)) {
let n = v.norm();
ms.push(n);
v.map_inplace(|x| *x = *x / n)
}
(m, ms)
}