52 lines
1.6 KiB
Rust
52 lines
1.6 KiB
Rust
#![cfg(feature = "arbitrary")]
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use na::Matrix2;
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#[test]
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fn hessenberg_simple() {
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let m = Matrix2::new(1.0, 0.0, 1.0, 3.0);
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let hess = m.hessenberg();
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let (p, h) = hess.unpack();
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assert!(relative_eq!(m, p * h * p.transpose(), epsilon = 1.0e-7))
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}
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macro_rules! gen_tests(
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($module: ident, $scalar: ty) => {
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mod $module {
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use na::{DMatrix, Matrix2, Matrix4};
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use std::cmp;
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#[allow(unused_imports)]
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use core::helper::{RandScalar, RandComplex};
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quickcheck! {
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fn hessenberg(n: usize) -> bool {
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let n = cmp::max(1, cmp::min(n, 50));
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let m = DMatrix::<$scalar>::new_random(n, n).map(|e| e.0);
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let hess = m.clone().hessenberg();
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let (p, h) = hess.unpack();
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relative_eq!(m, &p * h * p.adjoint(), epsilon = 1.0e-7)
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}
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fn hessenberg_static_mat2(m: Matrix2<$scalar>) -> bool {
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let m = m.map(|e| e.0);
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let hess = m.hessenberg();
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let (p, h) = hess.unpack();
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relative_eq!(m, p * h * p.adjoint(), epsilon = 1.0e-7)
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}
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fn hessenberg_static(m: Matrix4<$scalar>) -> bool {
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let m = m.map(|e| e.0);
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let hess = m.hessenberg();
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let (p, h) = hess.unpack();
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relative_eq!(m, p * h * p.adjoint(), epsilon = 1.0e-7)
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}
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}
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}
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}
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);
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gen_tests!(complex, RandComplex<f64>);
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gen_tests!(f64, RandScalar<f64>); |