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nalgebra/tests/linalg/eigen.rs

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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;
Add the most common matrix decompositions.
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#[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.
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mod quickcheck_tests {
use std::cmp;
use na::{DMatrix, Matrix2, Matrix3, Matrix4};
Add the most common matrix decompositions.
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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
quickcheck! {
fn symmetric_eigen(n: usize) -> bool {
let n = cmp::max(1, cmp::min(n, 10));
let m = DMatrix::<f64>::new_random(n, n);
let eig = m.clone().symmetric_eigen();
let recomp = eig.recompose();
Add the most common matrix decompositions.
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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
println!("{}{}", m.lower_triangle(), recomp.lower_triangle());
Add the most common matrix decompositions.
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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
relative_eq!(m.lower_triangle(), recomp.lower_triangle(), epsilon = 1.0e-5)
}
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
fn symmetric_eigen_singular(n: usize) -> bool {
let n = cmp::max(1, cmp::min(n, 10));
let mut m = DMatrix::<f64>::new_random(n, n);
m.row_mut(n / 2).fill(0.0);
m.column_mut(n / 2).fill(0.0);
let eig = m.clone().symmetric_eigen();
let recomp = eig.recompose();
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
println!("{}{}", m.lower_triangle(), recomp.lower_triangle());
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
relative_eq!(m.lower_triangle(), recomp.lower_triangle(), epsilon = 1.0e-5)
}
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
fn symmetric_eigen_static_square_4x4(m: Matrix4<f64>) -> bool {
let eig = m.symmetric_eigen();
let recomp = eig.recompose();
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
println!("{}{}", m.lower_triangle(), recomp.lower_triangle());
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
relative_eq!(m.lower_triangle(), recomp.lower_triangle(), epsilon = 1.0e-5)
}
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
fn symmetric_eigen_static_square_3x3(m: Matrix3<f64>) -> bool {
let eig = m.symmetric_eigen();
let recomp = eig.recompose();
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
println!("{}{}", m.lower_triangle(), recomp.lower_triangle());
relative_eq!(m.lower_triangle(), recomp.lower_triangle(), epsilon = 1.0e-5)
}
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
fn symmetric_eigen_static_square_2x2(m: Matrix2<f64>) -> bool {
let eig = m.symmetric_eigen();
let recomp = eig.recompose();
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
println!("{}{}", m.lower_triangle(), recomp.lower_triangle());
Add the most common matrix decompositions.
2017-08-03 01:37:44 +08:00
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
relative_eq!(m.lower_triangle(), recomp.lower_triangle(), epsilon = 1.0e-5)
}
Add the most common matrix decompositions.
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}
}
// Test proposed on the issue #176 of rulinalg.
#[test]
fn symmetric_eigen_singular_24x24() {
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.
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let eig = m.clone().symmetric_eigen();
Add the most common matrix decompositions.
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let recomp = eig.recompose();
assert!(relative_eq!(m.lower_triangle(), recomp.lower_triangle(), epsilon = 1.0e-5));
}
// #[cfg(feature = "arbitrary")]
// quickcheck! {
// FIXME: full eigendecomposition is not implemented yet because of its complexity when some
// eigenvalues have multiplicity > 1.
//
// /*
// * NOTE: for the following tests, we use only upper-triangular matrices.
// * Thes ensures the schur decomposition will work, and allows use to test the eigenvector
// * computation.
// */
// fn eigen(n: usize) -> bool {
// let n = cmp::max(1, cmp::min(n, 10));
// let m = DMatrix::<f64>::new_random(n, n).upper_triangle();
//
// let eig = RealEigen::new(m.clone()).unwrap();
// verify_eigenvectors(m, eig)
// }
//
// fn eigen_with_adjascent_duplicate_diagonals(n: usize) -> bool {
// let n = cmp::max(1, cmp::min(n, 10));
// let mut m = DMatrix::<f64>::new_random(n, n).upper_triangle();
//
// // Suplicate some adjascent diagonal elements.
// for i in 0 .. n / 2 {
// m[(i * 2 + 1, i * 2 + 1)] = m[(i * 2, i * 2)];
// }
//
// let eig = RealEigen::new(m.clone()).unwrap();
// verify_eigenvectors(m, eig)
// }
//
// fn eigen_with_nonadjascent_duplicate_diagonals(n: usize) -> bool {
// let n = cmp::max(3, cmp::min(n, 10));
// let mut m = DMatrix::<f64>::new_random(n, n).upper_triangle();
//
// // Suplicate some diagonal elements.
// for i in n / 2 .. n {
// m[(i, i)] = m[(i - n / 2, i - n / 2)];
// }
//
// let eig = RealEigen::new(m.clone()).unwrap();
// verify_eigenvectors(m, eig)
// }
//
// fn eigen_static_square_4x4(m: Matrix4<f64>) -> bool {
// let m = m.upper_triangle();
// let eig = RealEigen::new(m.clone()).unwrap();
// verify_eigenvectors(m, eig)
// }
//
// fn eigen_static_square_3x3(m: Matrix3<f64>) -> bool {
// let m = m.upper_triangle();
// let eig = RealEigen::new(m.clone()).unwrap();
// verify_eigenvectors(m, eig)
// }
//
// fn eigen_static_square_2x2(m: Matrix2<f64>) -> bool {
// let m = m.upper_triangle();
// println!("{}", m);
// let eig = RealEigen::new(m.clone()).unwrap();
// verify_eigenvectors(m, eig)
// }
// }
//
// fn verify_eigenvectors<D: Dim>(m: MatrixN<f64, D>, mut eig: RealEigen<f64, D>) -> bool
// where DefaultAllocator: Allocator<f64, D, D> +
// Allocator<f64, D> +
// Allocator<usize, D, D> +
// Allocator<usize, D>,
// MatrixN<f64, D>: Display,
// VectorN<f64, D>: Display {
// let mv = &m * &eig.eigenvectors;
//
// println!("eigenvalues: {}eigenvectors: {}", eig.eigenvalues, eig.eigenvectors);
//
// let dim = m.nrows();
// for i in 0 .. dim {
// let mut col = eig.eigenvectors.column_mut(i);
// col *= eig.eigenvalues[i];
// }
//
// println!("{}{:.5}{:.5}", m, mv, eig.eigenvectors);
//
// relative_eq!(eig.eigenvectors, mv, epsilon = 1.0e-5)
// }