1 files changed, 0 insertions, 83 deletions
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
-    );
-}