From a990de90fe41456a23e58bd087d2f107d321f3a1 Mon Sep 17 00:00:00 2001
From: Valentin Popov <valentin@popov.link>
Date: Fri, 19 Jul 2024 16:37:58 +0400
Subject: Deleted vendor folder
---
vendor/smawk/tests/complexity.rs | 83 ----------------------------------------
1 file changed, 83 deletions(-)
delete mode 100644 vendor/smawk/tests/complexity.rs
(limited to 'vendor/smawk/tests/complexity.rs')
diff --git a/vendor/smawk/tests/complexity.rs b/vendor/smawk/tests/complexity.rs
deleted file mode 100644
index c9881ea..0000000
--- a/vendor/smawk/tests/complexity.rs
+++ /dev/null
@@ -1,83 +0,0 @@
-#![cfg(feature = "ndarray")]
-
-use ndarray::{Array1, Array2};
-use rand::SeedableRng;
-use rand_chacha::ChaCha20Rng;
-use smawk::online_column_minima;
-
-mod random_monge;
-use random_monge::random_monge_matrix;
-
-#[derive(Debug)]
-struct LinRegression {
- alpha: f64,
- beta: f64,
- r_squared: f64,
-}
-
-/// Square an expression. Works equally well for floats and matrices.
-macro_rules! squared {
- ($x:expr) => {
- $x * $x
- };
-}
-
-/// Compute the mean of a 1-dimensional array.
-macro_rules! mean {
- ($a:expr) => {
- $a.mean().expect("Mean of empty array")
- };
-}
-
-/// Compute a simple linear regression from the list of values.
-///
-/// See <https://en.wikipedia.org/wiki/Simple_linear_regression>.
-fn linear_regression(values: &[(usize, i32)]) -> LinRegression {
- let xs = values.iter().map(|&(x, _)| x as f64).collect::<Array1<_>>();
- let ys = values.iter().map(|&(_, y)| y as f64).collect::<Array1<_>>();
-
- let xs_mean = mean!(&xs);
- let ys_mean = mean!(&ys);
- let xs_ys_mean = mean!(&xs * &ys);
-
- let cov_xs_ys = ((&xs - xs_mean) * (&ys - ys_mean)).sum();
- let var_xs = squared!(&xs - xs_mean).sum();
-
- let beta = cov_xs_ys / var_xs;
- let alpha = ys_mean - beta * xs_mean;
- let r_squared = squared!(xs_ys_mean - xs_mean * ys_mean)
- / ((mean!(&xs * &xs) - squared!(xs_mean)) * (mean!(&ys * &ys) - squared!(ys_mean)));
-
- LinRegression {
- alpha: alpha,
- beta: beta,
- r_squared: r_squared,
- }
-}
-
-/// Check that the number of matrix accesses in `online_column_minima`
-/// grows as O(*n*) for *n* ✕ *n* matrix.
-#[test]
-fn online_linear_complexity() {
- let mut rng = ChaCha20Rng::seed_from_u64(0);
- let mut data = vec![];
-
- for &size in &[1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100] {
- let matrix: Array2<i32> = random_monge_matrix(size, size, &mut rng);
- let count = std::cell::RefCell::new(0);
- online_column_minima(0, size, |_, i, j| {
- *count.borrow_mut() += 1;
- matrix[[i, j]]
- });
- data.push((size, count.into_inner()));
- }
-
- let lin_reg = linear_regression(&data);
- assert!(
- lin_reg.r_squared > 0.95,
- "r² = {:.4} is lower than expected for a linear fit\nData points: {:?}\n{:?}",
- lin_reg.r_squared,
- data,
- lin_reg
- );
-}
--
cgit v1.2.3