andrey/munkres-rs
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Use a simple SquareMatrix implementation instead of DMat

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This commit is contained in:
Michael Neumann 2015-10-19 22:48:31 +02:00
parent 6ffaa8f0db
commit f487672864
2 changed files with 100 additions and 58 deletions
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93
src/lib.rs
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@ -22,6 +22,9 @@ use na::{DMat, BaseNum};
use std::ops::{Add, Neg, Sub};
use std::num::Zero;
use std::cmp;
use square_matrix::SquareMatrix;
mod square_matrix;
#[derive(Debug)]
struct Coverage {
@ -86,12 +89,16 @@ impl Coverage {
#[derive(Debug)]
struct WeightMatrix<T: Copy> {
n: usize,
c: DMat<T>
c: SquareMatrix<T>
}
impl<T> WeightMatrix<T> where T: BaseNum + Ord + Eq + Sub<Output=T> + Copy {
fn n(&self) -> usize { self.n }
fn from_row_vec(n: usize, data: Vec<T>) -> WeightMatrix<T> {
WeightMatrix{c: SquareMatrix::from_row_vec(n, data)}
}
#[inline(always)]
fn n(&self) -> usize { self.c.n() }
fn is_element_zero(&self, pos: (usize, usize)) -> bool {
self.c[pos] == T::zero()
@ -100,7 +107,7 @@ impl<T> WeightMatrix<T> where T: BaseNum + Ord + Eq + Sub<Output=T> + Copy {
/// Return the minimum element of row `row`.
fn min_of_row(&self, row: usize) -> T {
let mut min = self.c[(row, 0)];
for col in 1 .. self.n {
for col in 1 .. self.n() {
min = cmp::min(min, self.c[(row, col)]);
}
min
@ -109,7 +116,7 @@ impl<T> WeightMatrix<T> where T: BaseNum + Ord + Eq + Sub<Output=T> + Copy {
/// Return the minimum element of column `col`.
fn min_of_col(&self, col: usize) -> T {
let mut min = self.c[(0, col)];
for row in 1 .. self.n {
for row in 1 .. self.n() {
min = cmp::min(min, self.c[(row, col)]);
}
min
@ -117,21 +124,21 @@ impl<T> WeightMatrix<T> where T: BaseNum + Ord + Eq + Sub<Output=T> + Copy {
// Subtract `val` from every element in row `row`.
fn sub_row(&mut self, row: usize, val: T) {
for col in 0 .. self.n {
for col in 0 .. self.n() {
self.c[(row, col)] = self.c[(row, col)] - val;
}
}
// Subtract `val` from every element in column `col`.
fn sub_col(&mut self, col: usize, val: T) {
for row in 0 .. self.n {
for row in 0 .. self.n() {
self.c[(row, col)] = self.c[(row, col)] - val;
}
}
// Add `val` to every element in row `row`.
fn add_row(&mut self, row: usize, val: T) {
for col in 0 .. self.n {
for col in 0 .. self.n() {
self.c[(row, col)] = self.c[(row, col)] - val;
}
}
@ -513,33 +520,30 @@ where T: BaseNum + Ord + Neg<Output=T> + Eq + Copy {
#[test]
fn test_step1() {
let m = DMat::from_row_vec(3, 3,
&[250, 400, 350,
let c = vec![250, 400, 350,
400, 600, 350,
200, 400, 250]);
200, 400, 250];
let mut weights: WeightMatrix<i32> = WeightMatrix{n: 3, c: m};
let mut weights: WeightMatrix<i32> = WeightMatrix::from_row_vec(3, c);
let next_step = step1(&mut weights);
assert_eq!(Step::Step2, next_step);
let exp = DMat::from_row_vec(3, 3,
&[0, 150, 100,
let exp = vec![0, 150, 100,
50, 250, 0,
0, 200, 50]);
0, 200, 50];
assert_eq!(exp, weights.c);
assert_eq!(exp, weights.c.into_vec());
}
#[test]
fn test_step2() {
let m = DMat::from_row_vec(3, 3,
&[0, 150, 100,
let c = vec![0, 150, 100,
50, 250, 0,
0, 200, 50]);
0, 200, 50];
let weights: WeightMatrix<i32> = WeightMatrix{n: 3, c: m};
let weights: WeightMatrix<i32> = WeightMatrix::from_row_vec(3, c);
let mut marks = MarkMatrix::new(weights.n());
let mut coverage = Coverage::new(weights.n());
@ -579,12 +583,11 @@ fn test_step2() {
#[test]
fn test_step3() {
let m = DMat::from_row_vec(3, 3,
&[0, 150, 100,
let c = vec![0, 150, 100,
50, 250, 0,
0, 200, 50]);
0, 200, 50];
let weights: WeightMatrix<i32> = WeightMatrix{n: 3, c: m};
let weights: WeightMatrix<i32> = WeightMatrix::from_row_vec(3, c);
let mut marks = MarkMatrix::new(weights.n());
let mut coverage = Coverage::new(weights.n());
@ -605,12 +608,11 @@ fn test_step3() {
#[test]
fn test_step4_case1() {
let m = DMat::from_row_vec(3, 3,
&[0, 150, 100,
let c = vec![0, 150, 100,
50, 250, 0,
0, 200, 50]);
0, 200, 50];
let weights: WeightMatrix<i32> = WeightMatrix{n: 3, c: m};
let weights: WeightMatrix<i32> = WeightMatrix::from_row_vec(3, c);
let mut marks = MarkMatrix::new(weights.n());
let mut coverage = Coverage::new(weights.n());
@ -645,12 +647,11 @@ fn test_step4_case1() {
#[test]
fn test_step6() {
let m = DMat::from_row_vec(3, 3,
&[0, 150, 100,
let c = vec![0, 150, 100,
50, 250, 0,
0, 200, 50]);
0, 200, 50];
let mut weights: WeightMatrix<i32> = WeightMatrix{n: 3, c: m};
let mut weights: WeightMatrix<i32> = WeightMatrix::from_row_vec(3, c);
let mut marks = MarkMatrix::new(weights.n());
let mut coverage = Coverage::new(weights.n());
@ -663,22 +664,20 @@ fn test_step6() {
assert_eq!(Step::Step4(None), next_step);
let exp = DMat::from_row_vec(3, 3,
&[0, 0, 100,
let exp = vec![0, 0, 100,
50, 100, 0,
0, 50, 50]);
0, 50, 50];
assert_eq!(exp, weights.c);
assert_eq!(exp, weights.c.into_vec());
}
#[test]
fn test_step4_case2() {
let m = DMat::from_row_vec(3, 3,
&[0, 0, 100,
let c = vec![0, 0, 100,
50, 100, 0,
0, 50, 50]);
0, 50, 50];
let weights: WeightMatrix<i32> = WeightMatrix{n: 3, c: m};
let weights: WeightMatrix<i32> = WeightMatrix::from_row_vec(3, c);
let mut marks = MarkMatrix::new(weights.n());
let mut coverage = Coverage::new(weights.n());
@ -713,12 +712,11 @@ fn test_step4_case2() {
#[test]
fn test_step5() {
let m = DMat::from_row_vec(3, 3,
&[0, 0, 100,
let c = vec![0, 0, 100,
50, 100, 0,
0, 50, 50]);
0, 50, 50];
let weights: WeightMatrix<i32> = WeightMatrix{n: 3, c: m};
let weights: WeightMatrix<i32> = WeightMatrix::from_row_vec(3, c);
let mut marks = MarkMatrix::new(weights.n());
let mut coverage = Coverage::new(weights.n());
@ -758,12 +756,11 @@ fn test_step5() {
#[test]
fn test_1() {
let m = DMat::from_row_vec(3, 3,
&[250, 400, 350,
let c = vec![250, 400, 350,
400, 600, 350,
200, 400, 250]);
200, 400, 250];
let mut weights: WeightMatrix<i32> = WeightMatrix{n: 3, c: m};
let mut weights: WeightMatrix<i32> = WeightMatrix::from_row_vec(3, c);
let matching = compute(&mut weights);
assert_eq!(vec![(0,1), (1,2), (2,0)], matching);

45
src/square_matrix.rs Normal file
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@ -0,0 +1,45 @@
use std::ops::{Index, IndexMut};
#[derive(Debug)]
pub struct SquareMatrix<T> {
n: usize,
data: Vec<T>
}
impl<T> Index<(usize, usize)> for SquareMatrix<T> {
type Output = T;
/// (row, col)
#[inline(always)]
fn index<'a>(&'a self, pos: (usize, usize)) -> &'a T {
match pos {
(row, col) => {
let idx = row * self.n + col;
&self.data[idx]
}
}
}
}
impl<T> IndexMut<(usize, usize)> for SquareMatrix<T> {
/// (row, col)
#[inline(always)]
fn index_mut<'a>(&'a mut self, pos: (usize, usize)) -> &'a mut T {
match pos {
(row, col) => {
let idx = row * self.n + col;
&mut self.data[idx]
}
}
}
}
impl<T> SquareMatrix<T> {
pub fn from_row_vec(n: usize, data: Vec<T>) -> SquareMatrix<T> {
assert!(data.len() == n*n);
SquareMatrix {n: n, data: data}
}
#[inline(always)]
pub fn n(&self) -> usize { self.n }
pub fn into_vec(self) -> Vec<T> { self.data }
}