M-Labs
/
nalgebra
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
nalgebra/tests/linalg/svd.rs

366 lines
22 KiB
Rust
Raw Normal View History

Restructure test modules to avoid warnings These warnings occurred only when running the test suite with no features. Lots of uses had to be rescoped into newly created modules to make it easier to separate these issues.
2018-01-17 23:48:47 +08:00
use na::{DMatrix, Matrix6};
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
#[cfg(feature = "arbitrary")]
Restructure test modules to avoid warnings These warnings occurred only when running the test suite with no features. Lots of uses had to be rescoped into newly created modules to make it easier to separate these issues.
2018-01-17 23:48:47 +08:00
mod quickcheck_tests {
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
macro_rules! gen_tests(
($module: ident, $scalar: ty) => {
mod $module {
use na::{
DMatrix, DVector, Matrix2, Matrix2x5, Matrix3, Matrix3x5, Matrix4, Matrix5x2, Matrix5x3,
Rename Complex to ComplexField.
2019-03-25 18:19:36 +08:00
ComplexField
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
};
use std::cmp;
#[allow(unused_imports)]
2018 edition.
2019-03-23 21:29:07 +08:00
use crate::core::helper::{RandScalar, RandComplex};
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
quickcheck! {
fn svd(m: DMatrix<$scalar>) -> bool {
let m = m.map(|e| e.0);
if m.len() > 0 {
let svd = m.clone().svd(true, true);
let recomp_m = svd.clone().recompose().unwrap();
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
Rename Complex to ComplexField.
2019-03-25 18:19:36 +08:00
let ds = DMatrix::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
s.iter().all(|e| *e >= 0.0) &&
relative_eq!(&u * ds * &v_t, recomp_m, epsilon = 1.0e-5) &&
relative_eq!(m, recomp_m, epsilon = 1.0e-5)
}
else {
true
}
}
fn svd_static_5_3(m: Matrix5x3<$scalar>) -> bool {
let m = m.map(|e| e.0);
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
Rename Complex to ComplexField.
2019-03-25 18:19:36 +08:00
let ds = Matrix3::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
s.iter().all(|e| *e >= 0.0) &&
relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5) &&
u.is_orthogonal(1.0e-5) &&
v_t.is_orthogonal(1.0e-5)
}
fn svd_static_5_2(m: Matrix5x2<$scalar>) -> bool {
let m = m.map(|e| e.0);
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
Rename Complex to ComplexField.
2019-03-25 18:19:36 +08:00
let ds = Matrix2::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
s.iter().all(|e| *e >= 0.0) &&
relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5) &&
u.is_orthogonal(1.0e-5) &&
v_t.is_orthogonal(1.0e-5)
}
fn svd_static_3_5(m: Matrix3x5<$scalar>) -> bool {
let m = m.map(|e| e.0);
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
Rename Complex to ComplexField.
2019-03-25 18:19:36 +08:00
let ds = Matrix3::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
s.iter().all(|e| *e >= 0.0) &&
relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5)
}
fn svd_static_2_5(m: Matrix2x5<$scalar>) -> bool {
let m = m.map(|e| e.0);
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
Rename Complex to ComplexField.
2019-03-25 18:19:36 +08:00
let ds = Matrix2::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
s.iter().all(|e| *e >= 0.0) &&
relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5)
}
fn svd_static_square(m: Matrix4<$scalar>) -> bool {
let m = m.map(|e| e.0);
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
Rename Complex to ComplexField.
2019-03-25 18:19:36 +08:00
let ds = Matrix4::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
s.iter().all(|e| *e >= 0.0) &&
relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5) &&
u.is_orthogonal(1.0e-5) &&
v_t.is_orthogonal(1.0e-5)
}
fn svd_static_square_2x2(m: Matrix2<$scalar>) -> bool {
let m = m.map(|e| e.0);
let svd = m.svd(true, true);
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
Rename Complex to ComplexField.
2019-03-25 18:19:36 +08:00
let ds = Matrix2::from_diagonal(&s.map(|e| ComplexField::from_real(e)));
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
s.iter().all(|e| *e >= 0.0) &&
relative_eq!(m, u * ds * v_t, epsilon = 1.0e-5) &&
u.is_orthogonal(1.0e-5) &&
v_t.is_orthogonal(1.0e-5)
}
fn svd_pseudo_inverse(m: DMatrix<$scalar>) -> bool {
let m = m.map(|e| e.0);
if m.len() > 0 {
let svd = m.clone().svd(true, true);
let pinv = svd.pseudo_inverse(1.0e-10).unwrap();
if m.nrows() > m.ncols() {
(pinv * m).is_identity(1.0e-5)
}
else {
(m * pinv).is_identity(1.0e-5)
}
}
else {
true
}
}
fn svd_solve(n: usize, nb: usize) -> bool {
let n = cmp::max(1, cmp::min(n, 10));
let nb = cmp::min(nb, 10);
let m = DMatrix::<$scalar>::new_random(n, n).map(|e| e.0);
let svd = m.clone().svd(true, true);
if svd.rank(1.0e-7) == n {
let b1 = DVector::<$scalar>::new_random(n).map(|e| e.0);
let b2 = DMatrix::<$scalar>::new_random(n, nb).map(|e| e.0);
let sol1 = svd.solve(&b1, 1.0e-7).unwrap();
let sol2 = svd.solve(&b2, 1.0e-7).unwrap();
let recomp = svd.recompose().unwrap();
if !relative_eq!(m, recomp, epsilon = 1.0e-6) {
println!("{}{}", m, recomp);
}
if !relative_eq!(&m * &sol1, b1, epsilon = 1.0e-6) {
println!("Problem 1: {:.6}{:.6}", b1, &m * sol1);
return false;
}
if !relative_eq!(&m * &sol2, b2, epsilon = 1.0e-6) {
println!("Problem 2: {:.6}{:.6}", b2, &m * sol2);
return false;
}
}
true
}
Restructure test modules to avoid warnings These warnings occurred only when running the test suite with no features. Lots of uses had to be rescoped into newly created modules to make it easier to separate these issues.
2018-01-17 23:48:47 +08:00
}
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
}
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
Cleanup warnings and rename Schur -> RealSchur
2019-03-23 18:46:56 +08:00
gen_tests!(complex, RandComplex<f64>);
gen_tests!(f64, RandScalar<f64>);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
// Test proposed on the issue #176 of rulinalg.
#[test]
Use the #[rustfmt::skip] attribute instead of rustfmt_skip.
2020-06-07 15:28:39 +08:00
#[rustfmt::skip]
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
fn svd_singular() {
let m = DMatrix::from_row_slice(24, 24, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 1.0, 1.0,
0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0]);
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = DMatrix::from_diagonal(&s);
assert!(s.iter().all(|e| *e >= 0.0));
assert!(u.is_orthogonal(1.0e-5));
assert!(v_t.is_orthogonal(1.0e-5));
Fix mint tests.
2019-03-20 05:53:21 +08:00
assert_relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
// Same as the previous test but with one additional row.
#[test]
Use the #[rustfmt::skip] attribute instead of rustfmt_skip.
2020-06-07 15:28:39 +08:00
#[rustfmt::skip]
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
fn svd_singular_vertical() {
let m = DMatrix::from_row_slice(25, 24, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 1.0, 1.0,
0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0]);
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = DMatrix::from_diagonal(&s);
assert!(s.iter().all(|e| *e >= 0.0));
Fix mint tests.
2019-03-20 05:53:21 +08:00
assert_relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
// Same as the previous test but with one additional column.
#[test]
Use the #[rustfmt::skip] attribute instead of rustfmt_skip.
2020-06-07 15:28:39 +08:00
#[rustfmt::skip]
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
fn svd_singular_horizontal() {
let m = DMatrix::from_row_slice(24, 25, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, 1.0, 1.0, 1.0,
0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, -4.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]);
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
let ds = DMatrix::from_diagonal(&s);
assert!(s.iter().all(|e| *e >= 0.0));
Fix mint tests.
2019-03-20 05:53:21 +08:00
assert_relative_eq!(m, &u * ds * &v_t, epsilon = 1.0e-5);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
#[test]
fn svd_zeros() {
let m = DMatrix::from_element(10, 10, 0.0);
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Change the SVD methods to return a Result instead of panicking
2018-10-09 05:13:56 +08:00
assert_eq!(Ok(m), svd.recompose());
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
#[test]
fn svd_identity() {
let m = DMatrix::<f64>::identity(10, 10);
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Change the SVD methods to return a Result instead of panicking
2018-10-09 05:13:56 +08:00
assert_eq!(Ok(m), svd.recompose());
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
let m = DMatrix::<f64>::identity(10, 15);
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Change the SVD methods to return a Result instead of panicking
2018-10-09 05:13:56 +08:00
assert_eq!(Ok(m), svd.recompose());
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
let m = DMatrix::<f64>::identity(15, 10);
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Change the SVD methods to return a Result instead of panicking
2018-10-09 05:13:56 +08:00
assert_eq!(Ok(m), svd.recompose());
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
#[test]
Use the #[rustfmt::skip] attribute instead of rustfmt_skip.
2020-06-07 15:28:39 +08:00
#[rustfmt::skip]
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
fn svd_with_delimited_subproblem() {
let mut m = DMatrix::<f64>::from_element(10, 10, 0.0);
m[(0,0)] = 1.0; m[(0,1)] = 2.0;
m[(1,1)] = 0.0; m[(1,2)] = 3.0;
m[(2,2)] = 4.0; m[(2,3)] = 5.0;
m[(3,3)] = 6.0; m[(3,4)] = 0.0;
m[(4,4)] = 8.0; m[(3,5)] = 9.0;
m[(5,5)] = 10.0; m[(3,6)] = 11.0;
m[(6,6)] = 12.0; m[(3,7)] = 12.0;
m[(7,7)] = 14.0; m[(3,8)] = 13.0;
m[(8,8)] = 16.0; m[(3,9)] = 17.0;
m[(9,9)] = 18.0;
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Fix mint tests.
2019-03-20 05:53:21 +08:00
assert_relative_eq!(m, svd.recompose().unwrap(), epsilon = 1.0e-7);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
// Rectangular versions.
let mut m = DMatrix::<f64>::from_element(15, 10, 0.0);
m[(0,0)] = 1.0; m[(0,1)] = 2.0;
m[(1,1)] = 0.0; m[(1,2)] = 3.0;
m[(2,2)] = 4.0; m[(2,3)] = 5.0;
m[(3,3)] = 6.0; m[(3,4)] = 0.0;
m[(4,4)] = 8.0; m[(3,5)] = 9.0;
m[(5,5)] = 10.0; m[(3,6)] = 11.0;
m[(6,6)] = 12.0; m[(3,7)] = 12.0;
m[(7,7)] = 14.0; m[(3,8)] = 13.0;
m[(8,8)] = 16.0; m[(3,9)] = 17.0;
m[(9,9)] = 18.0;
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Fix mint tests.
2019-03-20 05:53:21 +08:00
assert_relative_eq!(m, svd.recompose().unwrap(), epsilon = 1.0e-7);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.transpose().svd(true, true);
Fix mint tests.
2019-03-20 05:53:21 +08:00
assert_relative_eq!(m.transpose(), svd.recompose().unwrap(), epsilon = 1.0e-7);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
#[test]
Use the #[rustfmt::skip] attribute instead of rustfmt_skip.
2020-06-07 15:28:39 +08:00
#[rustfmt::skip]
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
fn svd_fail() {
let m = Matrix6::new(
Add rustfmt.toml and run it.
2018-10-22 13:00:10 +08:00
0.9299319121545955, 0.9955870335651049, 0.8824725266413644, 0.28966880207132295, 0.06102723649846409, 0.9311880746048009,
0.5938395242304351, 0.8398522876024204, 0.06672831951963198, 0.9941213119963099, 0.9431846038057834, 0.8159885168706427,
0.9121962883152357, 0.6471119669367571, 0.4823309702814407, 0.6420516076705516, 0.7731203925207113, 0.7424069470756647,
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
0.07311092531259344, 0.5579247949052946, 0.14518764691585773, 0.03502980663114896, 0.7991329455957719, 0.4929930019965745,
0.12293810556077789, 0.6617084679545999, 0.9002240700227326, 0.027153062135304884, 0.3630189466989524, 0.18207502727558866,
Add rustfmt.toml and run it.
2018-10-22 13:00:10 +08:00
0.843196731466686, 0.08951878746549924, 0.7533450877576973, 0.009558876499740077, 0.9429679490873482, 0.9355764454129878);
Add methods for computing decompositions.
2017-08-14 01:52:46 +08:00
let svd = m.clone().svd(true, true);
Change the SVD methods to return a Result instead of panicking
2018-10-09 05:13:56 +08:00
let recomp = svd.recompose().unwrap();
Fix mint tests.
2019-03-20 05:53:21 +08:00
assert_relative_eq!(m, recomp, epsilon = 1.0e-5);
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
}
Change the SVD methods to return a Result instead of panicking
2018-10-09 05:13:56 +08:00
#[test]
fn svd_err() {
let m = DMatrix::from_element(10, 10, 0.0);
let svd = m.clone().svd(false, false);
Use the #[rustfmt::skip] attribute instead of rustfmt_skip.
2020-06-07 15:28:39 +08:00
assert_eq!(
Err("SVD recomposition: U and V^t have not been computed."),
svd.clone().recompose()
);
assert_eq!(
Err("SVD pseudo inverse: the epsilon must be non-negative."),
svd.clone().pseudo_inverse(-1.0)
);
}