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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/logistic_regression.rs
+9 -12
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@@ -183,14 +183,11 @@ pub struct LogisticRegression<
}
trait ObjectiveFunction<T: Number + FloatNumber, X: Array2<T>> {
///
fn f(&self, w_bias: &[T]) -> T;
///
#[allow(clippy::ptr_arg)]
fn df(&self, g: &mut Vec<T>, w_bias: &Vec<T>);
///
#[allow(clippy::ptr_arg)]
fn partial_dot(w: &[T], x: &X, v_col: usize, m_row: usize) -> T {
let mut sum = T::zero();
@@ -629,11 +626,11 @@ mod tests {
objective.df(&mut g, &vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
objective.df(&mut g, &vec![1., 2., 3., 4., 5., 6., 7., 8., 9.]);
assert!((g[0] + 33.000068218163484).abs() < std::f64::EPSILON);
assert!((g[0] + 33.000068218163484).abs() < f64::EPSILON);
let f = objective.f(&[1., 2., 3., 4., 5., 6., 7., 8., 9.]);
assert!((f - 408.0052230582765).abs() < std::f64::EPSILON);
assert!((f - 408.0052230582765).abs() < f64::EPSILON);
let objective_reg = MultiClassObjectiveFunction {
x: &x,
@@ -689,13 +686,13 @@ mod tests {
objective.df(&mut g, &vec![1., 2., 3.]);
objective.df(&mut g, &vec![1., 2., 3.]);
assert!((g[0] - 26.051064349381285).abs() < std::f64::EPSILON);
assert!((g[1] - 10.239000702928523).abs() < std::f64::EPSILON);
assert!((g[2] - 3.869294270156324).abs() < std::f64::EPSILON);
assert!((g[0] - 26.051064349381285).abs() < f64::EPSILON);
assert!((g[1] - 10.239000702928523).abs() < f64::EPSILON);
assert!((g[2] - 3.869294270156324).abs() < f64::EPSILON);
let f = objective.f(&[1., 2., 3.]);
assert!((f - 59.76994756647412).abs() < std::f64::EPSILON);
assert!((f - 59.76994756647412).abs() < f64::EPSILON);
let objective_reg = BinaryObjectiveFunction {
x: &x,
@@ -916,7 +913,7 @@ mod tests {
let x: DenseMatrix<f32> = DenseMatrix::rand(52181, 94);
let y1: Vec<i32> = vec![1; 2181];
let y2: Vec<i32> = vec![0; 50000];
let y: Vec<i32> = y1.into_iter().chain(y2.into_iter()).collect();
let y: Vec<i32> = y1.into_iter().chain(y2).collect();
let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();
let lr_reg = LogisticRegression::fit(
@@ -938,12 +935,12 @@ mod tests {
let x: &DenseMatrix<f64> = &DenseMatrix::rand(52181, 94);
let y1: Vec<u32> = vec![1; 2181];
let y2: Vec<u32> = vec![0; 50000];
let y: &Vec<u32> = &(y1.into_iter().chain(y2.into_iter()).collect());
let y: &Vec<u32> = &(y1.into_iter().chain(y2).collect());
println!("y vec height: {:?}", y.len());
println!("x matrix shape: {:?}", x.shape());
let lr = LogisticRegression::fit(x, y, Default::default()).unwrap();
let y_hat = lr.predict(&x).unwrap();
let y_hat = lr.predict(x).unwrap();
println!("y_hat shape: {:?}", y_hat.shape());