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//! Recursive algorithm for finding column minima.
//!
//! The functions here are mostly meant to be used for testing
//! correctness of the SMAWK implementation.
//!
//! **Note: this module is only available if you enable the `ndarray`
//! Cargo feature.**
use ndarray::{s, Array2, ArrayView2, Axis};
/// Compute row minima in O(*m* + *n* log *m*) time.
///
/// This function computes row minima in a totally monotone matrix
/// using a recursive algorithm.
///
/// Running time on an *m* ✕ *n* matrix: O(*m* + *n* log *m*).
///
/// # Examples
///
/// ```
/// let matrix = ndarray::arr2(&[[4, 2, 4, 3],
/// [5, 3, 5, 3],
/// [5, 3, 3, 1]]);
/// assert_eq!(smawk::recursive::row_minima(&matrix),
/// vec![1, 1, 3]);
/// ```
///
/// # Panics
///
/// It is an error to call this on a matrix with zero columns.
pub fn row_minima<T: Ord>(matrix: &Array2<T>) -> Vec<usize> {
let mut minima = vec![0; matrix.nrows()];
recursive_inner(matrix.view(), &|| Direction::Row, 0, &mut minima);
minima
}
/// Compute column minima in O(*n* + *m* log *n*) time.
///
/// This function computes column minima in a totally monotone matrix
/// using a recursive algorithm.
///
/// Running time on an *m* ✕ *n* matrix: O(*n* + *m* log *n*).
///
/// # Examples
///
/// ```
/// let matrix = ndarray::arr2(&[[4, 2, 4, 3],
/// [5, 3, 5, 3],
/// [5, 3, 3, 1]]);
/// assert_eq!(smawk::recursive::column_minima(&matrix),
/// vec![0, 0, 2, 2]);
/// ```
///
/// # Panics
///
/// It is an error to call this on a matrix with zero rows.
pub fn column_minima<T: Ord>(matrix: &Array2<T>) -> Vec<usize> {
let mut minima = vec![0; matrix.ncols()];
recursive_inner(matrix.view(), &|| Direction::Column, 0, &mut minima);
minima
}
/// The type of minima (row or column) we compute.
enum Direction {
Row,
Column,
}
/// Compute the minima along the given direction (`Direction::Row` for
/// row minima and `Direction::Column` for column minima).
///
/// The direction is given as a generic function argument to allow
/// monomorphization to kick in. The function calls will be inlined
/// and optimized away and the result is that the compiler generates
/// differnet code for finding row and column minima.
fn recursive_inner<T: Ord, F: Fn() -> Direction>(
matrix: ArrayView2<'_, T>,
dir: &F,
offset: usize,
minima: &mut [usize],
) {
if matrix.is_empty() {
return;
}
let axis = match dir() {
Direction::Row => Axis(0),
Direction::Column => Axis(1),
};
let mid = matrix.len_of(axis) / 2;
let min_idx = crate::brute_force::lane_minimum(matrix.index_axis(axis, mid));
minima[mid] = offset + min_idx;
if mid == 0 {
return; // Matrix has a single row or column, so we're done.
}
let top_left = match dir() {
Direction::Row => matrix.slice(s![..mid, ..(min_idx + 1)]),
Direction::Column => matrix.slice(s![..(min_idx + 1), ..mid]),
};
let bot_right = match dir() {
Direction::Row => matrix.slice(s![(mid + 1).., min_idx..]),
Direction::Column => matrix.slice(s![min_idx.., (mid + 1)..]),
};
recursive_inner(top_left, dir, offset, &mut minima[..mid]);
recursive_inner(bot_right, dir, offset + min_idx, &mut minima[mid + 1..]);
}
#[cfg(test)]
mod tests {
use super::*;
use ndarray::arr2;
#[test]
fn recursive_1x1() {
let matrix = arr2(&[[2]]);
let minima = vec![0];
assert_eq!(row_minima(&matrix), minima);
assert_eq!(column_minima(&matrix.reversed_axes()), minima);
}
#[test]
fn recursive_2x1() {
let matrix = arr2(&[
[3], //
[2],
]);
let minima = vec![0, 0];
assert_eq!(row_minima(&matrix), minima);
assert_eq!(column_minima(&matrix.reversed_axes()), minima);
}
#[test]
fn recursive_1x2() {
let matrix = arr2(&[[2, 1]]);
let minima = vec![1];
assert_eq!(row_minima(&matrix), minima);
assert_eq!(column_minima(&matrix.reversed_axes()), minima);
}
#[test]
fn recursive_2x2() {
let matrix = arr2(&[
[3, 2], //
[2, 1],
]);
let minima = vec![1, 1];
assert_eq!(row_minima(&matrix), minima);
assert_eq!(column_minima(&matrix.reversed_axes()), minima);
}
#[test]
fn recursive_3x3() {
let matrix = arr2(&[
[3, 4, 4], //
[3, 4, 4],
[2, 3, 3],
]);
let minima = vec![0, 0, 0];
assert_eq!(row_minima(&matrix), minima);
assert_eq!(column_minima(&matrix.reversed_axes()), minima);
}
#[test]
fn recursive_4x4() {
let matrix = arr2(&[
[4, 5, 5, 5], //
[2, 3, 3, 3],
[2, 3, 3, 3],
[2, 2, 2, 2],
]);
let minima = vec![0, 0, 0, 0];
assert_eq!(row_minima(&matrix), minima);
assert_eq!(column_minima(&matrix.reversed_axes()), minima);
}
#[test]
fn recursive_5x5() {
let matrix = arr2(&[
[3, 2, 4, 5, 6],
[2, 1, 3, 3, 4],
[2, 1, 3, 3, 4],
[3, 2, 4, 3, 4],
[4, 3, 2, 1, 1],
]);
let minima = vec![1, 1, 1, 1, 3];
assert_eq!(row_minima(&matrix), minima);
assert_eq!(column_minima(&matrix.reversed_axes()), minima);
}
}
|
