121 lines
3.5 KiB
Rust
121 lines
3.5 KiB
Rust
#[cfg(feature = "serde-serialize")]
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use serde::{Serialize, Deserialize};
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use alga::general::ComplexField;
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use num_complex::Complex;
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use std::cmp;
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use std::fmt::Display;
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use std::ops::MulAssign;
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use crate::allocator::Allocator;
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use crate::base::dimension::{Dim, DimDiff, DimSub, Dynamic, U1, U2, U3};
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use crate::base::storage::Storage;
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use crate::base::{DefaultAllocator, Hessenberg, MatrixN, SquareMatrix, Unit, Vector2, Vector3, VectorN};
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use crate::constraint::{DimEq, ShapeConstraint};
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use crate::geometry::{Reflection, UnitComplex};
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use crate::linalg::householder;
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use crate::linalg::Schur;
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/// Eigendecomposition of a real matrix with real eigenvalues (or complex eigen values for complex matrices).
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#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
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#[cfg_attr(
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feature = "serde-serialize",
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serde(
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bound(
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serialize = "DefaultAllocator: Allocator<N, D>,
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VectorN<N, D>: Serialize,
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MatrixN<N, D>: Serialize"
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)
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)
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)]
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#[cfg_attr(
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feature = "serde-serialize",
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serde(
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bound(
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deserialize = "DefaultAllocator: Allocator<N, D>,
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VectorN<N, D>: Serialize,
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MatrixN<N, D>: Deserialize<'de>"
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)
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)
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)]
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#[derive(Clone, Debug)]
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pub struct Eigen<N: ComplexField, D: Dim>
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where
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DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
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{
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pub eigenvectors: MatrixN<N, D>,
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pub eigenvalues: VectorN<N, D>,
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}
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impl<N: ComplexField, D: Dim> Copy for Eigen<N, D>
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where
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DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
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MatrixN<N, D>: Copy,
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VectorN<N, D>: Copy,
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{
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}
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impl<N: ComplexField, D: Dim> Eigen<N, D>
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where
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D: DimSub<U1>, // For Hessenberg.
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ShapeConstraint: DimEq<Dynamic, DimDiff<D, U1>>, // For Hessenberg.
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DefaultAllocator: Allocator<N, D, DimDiff<D, U1>>
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+ Allocator<N, DimDiff<D, U1>>
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+ Allocator<N, D, D>
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+ Allocator<N, D>,
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// XXX: for debug
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DefaultAllocator: Allocator<usize, D, D>,
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MatrixN<N, D>: Display,
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{
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/// Computes the eigendecomposition of a diagonalizable matrix with Complex eigenvalues.
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pub fn new(m: MatrixN<N, D>) -> Option<Eigen<N, D>> {
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assert!(
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m.is_square(),
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"Unable to compute the eigendecomposition of a non-square matrix."
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);
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let dim = m.nrows();
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let (mut eigenvectors, mut eigenvalues) = Schur::new(m, 0).unwrap().unpack();
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println!("Schur eigenvalues: {}", eigenvalues);
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// Check that the eigenvalues are all Complex.
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for i in 0..dim - 1 {
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if !eigenvalues[(i + 1, i)].is_zero() {
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return None;
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}
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}
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for j in 1..dim {
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for i in 0..j {
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let diff = eigenvalues[(i, i)] - eigenvalues[(j, j)];
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if diff.is_zero() && !eigenvalues[(i, j)].is_zero() {
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return None;
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}
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let z = -eigenvalues[(i, j)] / diff;
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for k in j + 1..dim {
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eigenvalues[(i, k)] -= z * eigenvalues[(j, k)];
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}
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for k in 0..dim {
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eigenvectors[(k, j)] += z * eigenvectors[(k, i)];
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}
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}
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}
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// Normalize the eigenvector basis.
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for i in 0..dim {
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let _ = eigenvectors.column_mut(i).normalize_mut();
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}
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Some(Eigen {
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eigenvectors,
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eigenvalues: eigenvalues.diagonal(),
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})
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}
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}
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