From 1b6a04ca5504955c571d1c97504fb45ea0befee4 Mon Sep 17 00:00:00 2001
From: Valentin Popov <valentin@popov.link>
Date: Mon, 8 Jan 2024 01:21:28 +0400
Subject: Initial vendor packages

Signed-off-by: Valentin Popov <valentin@popov.link>
---
 vendor/smawk/tests/complexity.rs | 83 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create 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
new file mode 100644
index 0000000..c9881ea
--- /dev/null
+++ b/vendor/smawk/tests/complexity.rs
@@ -0,0 +1,83 @@
+#![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
+    );
+}
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