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Split det tests
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2 files changed

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‎tests/det.rs

Lines changed: 141 additions & 0 deletions
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extern crate ndarray;
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#[macro_use]
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extern crate ndarray_linalg;
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extern crate num_traits;
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use ndarray::*;
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use ndarray_linalg::*;
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use num_traits::{One, Zero};
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/// Returns the matrix with the specified `row` and `col` removed.
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fn matrix_minor<A, S>(a: ArrayBase<S, Ix2>, (row, col): (usize, usize)) -> Array2<A>
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where
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A: Scalar,
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S: Data<Elem = A>,
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{
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let mut select_rows = (0..a.rows()).collect::<Vec<_>>();
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select_rows.remove(row);
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let mut select_cols = (0..a.cols()).collect::<Vec<_>>();
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select_cols.remove(col);
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a.select(Axis(0), &select_rows).select(
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Axis(1),
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&select_cols,
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)
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}
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/// Computes the determinant of matrix `a`.
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///
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/// Note: This implementation is written to be clearly correct so that it's
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/// useful for verification, but it's very inefficient.
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fn det_naive<A, S>(a: ArrayBase<S, Ix2>) -> A
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where
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A: Scalar,
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S: Data<Elem = A>,
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{
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assert_eq!(a.rows(), a.cols());
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match a.cols() {
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0 => A::one(),
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1 => a[(0, 0)],
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cols => {
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(0..cols)
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.map(|col| {
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let sign = if col % 2 == 0 { A::one() } else { -A::one() };
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sign * a[(0, col)] * det_naive(matrix_minor(a.view(), (0, col)))
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})
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.fold(A::zero(), |sum, subdet| sum + subdet)
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}
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}
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}
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#[test]
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fn det_empty() {
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macro_rules! det_empty {
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($elem:ty) => {
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let a: Array2<$elem> = Array2::zeros((0, 0));
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assert_eq!(a.factorize().unwrap().det().unwrap(), One::one());
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assert_eq!(a.factorize().unwrap().det_into().unwrap(), One::one());
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assert_eq!(a.det().unwrap(), One::one());
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assert_eq!(a.det_into().unwrap(), One::one());
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}
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}
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det_empty!(f64);
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det_empty!(f32);
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det_empty!(c64);
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det_empty!(c32);
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}
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#[test]
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fn det_zero() {
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macro_rules! det_zero {
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($elem:ty) => {
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let a: Array2<$elem> = Array2::zeros((1, 1));
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assert_eq!(a.det().unwrap(), Zero::zero());
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assert_eq!(a.det_into().unwrap(), Zero::zero());
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}
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}
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det_zero!(f64);
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det_zero!(f32);
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det_zero!(c64);
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det_zero!(c32);
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}
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#[test]
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fn det_zero_nonsquare() {
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macro_rules! det_zero_nonsquare {
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($elem:ty, $shape:expr) => {
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let a: Array2<$elem> = Array2::zeros($shape);
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assert!(a.det().is_err());
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assert!(a.det_into().is_err());
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}
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}
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for &shape in &[(1, 2).into_shape(), (1, 2).f()] {
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det_zero_nonsquare!(f64, shape);
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det_zero_nonsquare!(f32, shape);
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det_zero_nonsquare!(c64, shape);
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det_zero_nonsquare!(c32, shape);
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}
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}
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#[test]
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fn det() {
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macro_rules! det {
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($elem:ty, $shape:expr, $rtol:expr) => {
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let a: Array2<$elem> = random($shape);
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println!("a = \n{:?}", a);
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let det = det_naive(a.view());
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assert_rclose!(a.factorize().unwrap().det().unwrap(), det, $rtol);
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assert_rclose!(a.factorize().unwrap().det_into().unwrap(), det, $rtol);
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assert_rclose!(a.det().unwrap(), det, $rtol);
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assert_rclose!(a.det_into().unwrap(), det, $rtol);
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}
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}
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for rows in 1..5 {
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for &shape in &[(rows, rows).into_shape(), (rows, rows).f()] {
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det!(f64, shape, 1e-9);
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det!(f32, shape, 1e-4);
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det!(c64, shape, 1e-9);
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det!(c32, shape, 1e-4);
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}
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}
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}
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#[test]
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fn det_nonsquare() {
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macro_rules! det_nonsquare {
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($elem:ty, $shape:expr) => {
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let a: Array2<$elem> = random($shape);
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assert!(a.factorize().unwrap().det().is_err());
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assert!(a.factorize().unwrap().det_into().is_err());
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assert!(a.det().is_err());
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assert!(a.det_into().is_err());
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}
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}
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for &dims in &[(1, 0), (1, 2), (2, 1), (2, 3)] {
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for &shape in &[dims.clone().into_shape(), dims.clone().f()] {
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det_nonsquare!(f64, shape);
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det_nonsquare!(f32, shape);
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det_nonsquare!(c64, shape);
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det_nonsquare!(c32, shape);
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}
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}
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}

‎tests/solve.rs

Lines changed: 0 additions & 134 deletions
Original file line numberDiff line numberDiff line change
@@ -5,140 +5,6 @@ extern crate num_traits;
55

66
use ndarray::*;
77
use ndarray_linalg::*;
8-
use num_traits::{One, Zero};
9-
10-
/// Returns the matrix with the specified `row` and `col` removed.
11-
fn matrix_minor<A, S>(a: ArrayBase<S, Ix2>, (row, col): (usize, usize)) -> Array2<A>
12-
where
13-
A: Scalar,
14-
S: Data<Elem = A>,
15-
{
16-
let mut select_rows = (0..a.rows()).collect::<Vec<_>>();
17-
select_rows.remove(row);
18-
let mut select_cols = (0..a.cols()).collect::<Vec<_>>();
19-
select_cols.remove(col);
20-
a.select(Axis(0), &select_rows).select(
21-
Axis(1),
22-
&select_cols,
23-
)
24-
}
25-
26-
/// Computes the determinant of matrix `a`.
27-
///
28-
/// Note: This implementation is written to be clearly correct so that it's
29-
/// useful for verification, but it's very inefficient.
30-
fn det_naive<A, S>(a: ArrayBase<S, Ix2>) -> A
31-
where
32-
A: Scalar,
33-
S: Data<Elem = A>,
34-
{
35-
assert_eq!(a.rows(), a.cols());
36-
match a.cols() {
37-
0 => A::one(),
38-
1 => a[(0, 0)],
39-
cols => {
40-
(0..cols)
41-
.map(|col| {
42-
let sign = if col % 2 == 0 { A::one() } else { -A::one() };
43-
sign * a[(0, col)] * det_naive(matrix_minor(a.view(), (0, col)))
44-
})
45-
.fold(A::zero(), |sum, subdet| sum + subdet)
46-
}
47-
}
48-
}
49-
50-
#[test]
51-
fn det_empty() {
52-
macro_rules! det_empty {
53-
($elem:ty) => {
54-
let a: Array2<$elem> = Array2::zeros((0, 0));
55-
assert_eq!(a.factorize().unwrap().det().unwrap(), One::one());
56-
assert_eq!(a.factorize().unwrap().det_into().unwrap(), One::one());
57-
assert_eq!(a.det().unwrap(), One::one());
58-
assert_eq!(a.det_into().unwrap(), One::one());
59-
}
60-
}
61-
det_empty!(f64);
62-
det_empty!(f32);
63-
det_empty!(c64);
64-
det_empty!(c32);
65-
}
66-
67-
#[test]
68-
fn det_zero() {
69-
macro_rules! det_zero {
70-
($elem:ty) => {
71-
let a: Array2<$elem> = Array2::zeros((1, 1));
72-
assert_eq!(a.det().unwrap(), Zero::zero());
73-
assert_eq!(a.det_into().unwrap(), Zero::zero());
74-
}
75-
}
76-
det_zero!(f64);
77-
det_zero!(f32);
78-
det_zero!(c64);
79-
det_zero!(c32);
80-
}
81-
82-
#[test]
83-
fn det_zero_nonsquare() {
84-
macro_rules! det_zero_nonsquare {
85-
($elem:ty, $shape:expr) => {
86-
let a: Array2<$elem> = Array2::zeros($shape);
87-
assert!(a.det().is_err());
88-
assert!(a.det_into().is_err());
89-
}
90-
}
91-
for &shape in &[(1, 2).into_shape(), (1, 2).f()] {
92-
det_zero_nonsquare!(f64, shape);
93-
det_zero_nonsquare!(f32, shape);
94-
det_zero_nonsquare!(c64, shape);
95-
det_zero_nonsquare!(c32, shape);
96-
}
97-
}
98-
99-
#[test]
100-
fn det() {
101-
macro_rules! det {
102-
($elem:ty, $shape:expr, $rtol:expr) => {
103-
let a: Array2<$elem> = random($shape);
104-
println!("a = \n{:?}", a);
105-
let det = det_naive(a.view());
106-
assert_rclose!(a.factorize().unwrap().det().unwrap(), det, $rtol);
107-
assert_rclose!(a.factorize().unwrap().det_into().unwrap(), det, $rtol);
108-
assert_rclose!(a.det().unwrap(), det, $rtol);
109-
assert_rclose!(a.det_into().unwrap(), det, $rtol);
110-
}
111-
}
112-
for rows in 1..5 {
113-
for &shape in &[(rows, rows).into_shape(), (rows, rows).f()] {
114-
det!(f64, shape, 1e-9);
115-
det!(f32, shape, 1e-4);
116-
det!(c64, shape, 1e-9);
117-
det!(c32, shape, 1e-4);
118-
}
119-
}
120-
}
121-
122-
#[test]
123-
fn det_nonsquare() {
124-
macro_rules! det_nonsquare {
125-
($elem:ty, $shape:expr) => {
126-
let a: Array2<$elem> = random($shape);
127-
assert!(a.factorize().unwrap().det().is_err());
128-
assert!(a.factorize().unwrap().det_into().is_err());
129-
assert!(a.det().is_err());
130-
assert!(a.det_into().is_err());
131-
}
132-
}
133-
for &dims in &[(1, 0), (1, 2), (2, 1), (2, 3)] {
134-
for &shape in &[dims.clone().into_shape(), dims.clone().f()] {
135-
det_nonsquare!(f64, shape);
136-
det_nonsquare!(f32, shape);
137-
det_nonsquare!(c64, shape);
138-
det_nonsquare!(c32, shape);
139-
}
140-
}
141-
}
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1439
#[test]
14410
fn solve_random() {

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