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chore: fix clippy (#283)

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* chore: fix clippy


Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
This commit is contained in:
morenol
2024-11-25 10:34:29 -05:00
committed by GitHub
parent 239c00428f
commit ba75f9ffad
29 changed files with 194 additions and 236 deletions
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src/linear/lasso_optimizer.rs
+4 -10
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@@ -16,7 +16,7 @@ use crate::linalg::basic::arrays::{Array1, Array2, ArrayView1, MutArray, MutArra
use crate::linear::bg_solver::BiconjugateGradientSolver;
use crate::numbers::floatnum::FloatNumber;
///
/// Interior Point Optimizer
pub struct InteriorPointOptimizer<T: FloatNumber, X: Array2<T>> {
ata: X,
d1: Vec<T>,
@@ -25,9 +25,8 @@ pub struct InteriorPointOptimizer<T: FloatNumber, X: Array2<T>> {
prs: Vec<T>,
}
///
impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
///
/// Initialize a new Interior Point Optimizer
pub fn new(a: &X, n: usize) -> InteriorPointOptimizer<T, X> {
InteriorPointOptimizer {
ata: a.ab(true, a, false),
@@ -38,7 +37,7 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
}
}
///
/// Run the optimization
pub fn optimize(
&mut self,
x: &X,
@@ -101,7 +100,7 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
// CALCULATE DUALITY GAP
let xnu = nu.xa(false, x);
let max_xnu = xnu.norm(std::f64::INFINITY);
let max_xnu = xnu.norm(f64::INFINITY);
if max_xnu > lambda_f64 {
let lnu = T::from_f64(lambda_f64 / max_xnu).unwrap();
nu.mul_scalar_mut(lnu);
@@ -208,7 +207,6 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
Ok(w)
}
///
fn sumlogneg(f: &X) -> T {
let (n, _) = f.shape();
let mut sum = T::zero();
@@ -220,11 +218,9 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
}
}
///
impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
for InteriorPointOptimizer<T, X>
{
///
fn solve_preconditioner(&self, a: &'a X, b: &[T], x: &mut [T]) {
let (_, p) = a.shape();
@@ -234,7 +230,6 @@ impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
}
}
///
fn mat_vec_mul(&self, _: &X, x: &Vec<T>, y: &mut Vec<T>) {
let (_, p) = self.ata.shape();
let x_slice = Vec::from_slice(x.slice(0..p).as_ref());
@@ -246,7 +241,6 @@ impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
}
}
///
fn mat_t_vec_mul(&self, a: &X, x: &Vec<T>, y: &mut Vec<T>) {
self.mat_vec_mul(a, x, y);
}