Merge pull request #164 from dshizzle/master
Fix QR algorithm for diagonal matrices
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commit
aa1a4ceb58
@ -145,9 +145,23 @@ pub fn eigen_qr<N, V, VS, M>(m: &M, eps: &N, niter: usize) -> (M, V)
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for k in 0 .. curdim {
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let x_i = unsafe { eigenvalues.unsafe_at((k, k)) };
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let x_j = unsafe { eigenvalues.unsafe_at((k + 1, k)) };
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let r = (x_i * x_i + x_j * x_j).sqrt();
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let ctmp = x_i / r;
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let stmp = -x_j / r;
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let ctmp;
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let stmp;
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if x_j.abs() < *eps {
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ctmp = N::one();
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stmp = N::zero();
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}
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else if x_i.abs() < *eps {
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ctmp = N::zero();
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stmp = -N::one();
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}
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else {
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let r = x_i.hypot(x_j);
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ctmp = x_i / r;
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stmp = -x_j / r;
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}
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c[k] = ctmp;
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s[k] = stmp;
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15
tests/mat.rs
15
tests/mat.rs
@ -104,6 +104,21 @@ macro_rules! test_eigen_qr_impl(
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assert!(na::approx_eq_eps(&randmat, &recomp, &1.0e-2));
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}
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for _ in (0usize .. 10000) {
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let randmat : $t = random();
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// Take only diagonal part
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let randmat: $t = Diag::from_diag(&randmat.diag());
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let (eigenvectors, eigenvalues) = na::eigen_qr(&randmat, &1e-13, 100);
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let diag: $t = Diag::from_diag(&eigenvalues);
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let recomp = eigenvectors * diag * na::transpose(&eigenvectors);
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println!("eigenvalues: {:?}", eigenvalues);
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println!(" mat: {:?}", randmat);
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println!("recomp: {:?}", recomp);
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assert!(na::approx_eq_eps(&randmat, &recomp, &1.0e-2));
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}
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}
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);
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