nalgebra/nalgebra-lapack/src/eigen.rs

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#[cfg(feature = "serde-serialize")]
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use serde::{Deserialize, Serialize};
use num::Zero;
use num_complex::Complex;
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use simba::scalar::RealField;
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use crate::ComplexHelper;
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use na::dimension::{Const, Dim, DimName};
use na::{DefaultAllocator, Matrix, OMatrix, OVector, Scalar, allocator::Allocator};
use lapack;
/// Eigendecomposition of a real square matrix with real or complex eigenvalues.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(
feature = "serde-serialize",
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serde(
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bound(serialize = "DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
OVector<T, D>: Serialize,
OMatrix<T, D, D>: Serialize")
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)
)]
#[cfg_attr(
feature = "serde-serialize",
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serde(
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bound(deserialize = "DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
OVector<T, D>: Serialize,
OMatrix<T, D, D>: Deserialize<'de>")
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)
)]
#[derive(Clone, Debug)]
pub struct Eigen<T: Scalar, D: Dim>
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where
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DefaultAllocator: Allocator<T, D> + Allocator<T, D, D>,
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{
/// The real parts of eigenvalues of the decomposed matrix.
pub eigenvalues_re: OVector<T, D>,
/// The imaginary parts of the eigenvalues of the decomposed matrix.
pub eigenvalues_im: OVector<T, D>,
/// The (right) eigenvectors of the decomposed matrix.
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pub eigenvectors: Option<OMatrix<T, D, D>>,
/// The left eigenvectors of the decomposed matrix.
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pub left_eigenvectors: Option<OMatrix<T, D, D>>,
}
impl<T: Scalar + Copy, D: Dim> Copy for Eigen<T, D>
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where
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DefaultAllocator: Allocator<T, D> + Allocator<T, D, D>,
OVector<T, D>: Copy,
OMatrix<T, D, D>: Copy,
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{
}
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impl<T: EigenScalar + RealField, D: Dim> Eigen<T, D>
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where
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DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
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{
/// Computes the eigenvalues and eigenvectors of the square matrix `m`.
///
/// If `eigenvectors` is `false` then, the eigenvectors are not computed explicitly.
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pub fn new(
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mut m: OMatrix<T, D, D>,
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left_eigenvectors: bool,
eigenvectors: bool,
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) -> Option<Eigen<T, D>> {
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assert!(
m.is_square(),
"Unable to compute the eigenvalue decomposition of a non-square matrix."
);
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let ljob = if left_eigenvectors { b'V' } else { b'N' };
let rjob = if eigenvectors { b'V' } else { b'N' };
let (nrows, ncols) = m.shape_generic();
let n = nrows.value();
let lda = n as i32;
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// TODO: avoid the initialization?
let mut wr = Matrix::zeros_generic(nrows, Const::<1>);
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// TODO: Tap into the workspace.
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let mut wi = Matrix::zeros_generic(nrows, Const::<1>);
let mut info = 0;
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let mut placeholder1 = [T::zero()];
let mut placeholder2 = [T::zero()];
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let lwork = T::xgeev_work_size(
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ljob,
rjob,
n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
&mut placeholder1,
n as i32,
&mut placeholder2,
n as i32,
&mut info,
);
lapack_check!(info);
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let mut work = vec![T::zero(); lwork as usize];
let mut vl = if left_eigenvectors {
Some(Matrix::zeros_generic(nrows, ncols))
} else {
None
};
let mut vr = if eigenvectors {
Some(Matrix::zeros_generic(nrows, ncols))
} else {
None
};
let vl_ref = vl
.as_mut()
.map(|m| m.as_mut_slice())
.unwrap_or(&mut placeholder1);
let vr_ref = vr
.as_mut()
.map(|m| m.as_mut_slice())
.unwrap_or(&mut placeholder2);
T::xgeev(
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ljob,
rjob,
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n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
vl_ref,
if left_eigenvectors { n as i32 } else { 1 },
vr_ref,
if eigenvectors { n as i32 } else { 1 },
&mut work,
lwork,
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&mut info,
);
lapack_check!(info);
Some(Self {
eigenvalues_re: wr,
eigenvalues_im: wi,
left_eigenvectors: vl,
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eigenvectors: vr
})
}
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/// Returns `true` if all the eigenvalues are real.
pub fn eigenvalues_are_real(&self) -> bool {
self.eigenvalues_im.iter().all(|e| e.is_zero())
}
/// The determinant of the decomposed matrix.
#[inline]
#[must_use]
pub fn determinant(&self) -> Complex<T> {
let mut det: Complex<T> = na::one();
for (re, im) in self.eigenvalues_re.iter().zip(self.eigenvalues_im.iter()) {
det *= Complex::new(re.clone(), im.clone());
}
det
}
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/// Returns a tuple of vectors. The elements of the tuple are the complex eigenvalues, complex left eigenvectors and complex right eigenvectors respectively.
/// The elements appear as conjugate pairs within each vector, with the positive of the pair always being first.
pub fn get_complex_elements(&self) -> (Option<Vec<Complex<T>>>, Option<Vec<OVector<Complex<T>, D>>>, Option<Vec<OVector<Complex<T>, D>>>) where DefaultAllocator: Allocator<Complex<T>, D> {
match !self.eigenvalues_are_real() {
true => (None, None, None),
false => {
let number_of_elements = self.eigenvalues_re.nrows();
let number_of_complex_entries = self.eigenvalues_im.iter().fold(0, |acc, e| if !e.is_zero() {acc + 1} else {acc});
let mut eigenvalues = Vec::<Complex<T>>::with_capacity(2*number_of_complex_entries);
let mut eigenvectors = match self.eigenvectors.is_some() {
true => Some(Vec::<OVector<Complex<T>, D>>::with_capacity(2*number_of_complex_entries)),
false => None
};
let mut left_eigenvectors = match self.left_eigenvectors.is_some() {
true => Some(Vec::<OVector<Complex<T>, D>>::with_capacity(2*number_of_complex_entries)),
false => None
};
let eigenvectors_raw = self.eigenvectors;
let left_eigenvectors_raw = self.left_eigenvectors;
for mut i in 0..number_of_elements {
if self.eigenvalues_im[i] != T::zero() {
//Complex conjugate pairs of eigenvalues appear consecutively with the eigenvalue having the positive imaginary part first.
eigenvalues.push(Complex::<T>::new(self.eigenvalues_re[i].clone(), self.eigenvalues_im[i].clone()));
eigenvalues.push(Complex::<T>::new(self.eigenvalues_re[i].clone(), -self.eigenvalues_im[i].clone()));
if eigenvectors.is_some() {
let mut r1_vec = OVector::<Complex<T>, D>::zeros(number_of_elements);
let mut r1_vec_conj = OVector::<Complex<T>, D>::zeros(number_of_elements);
for j in 0..number_of_elements {
r1_vec[j] = Complex::<T>::new(self.eigenvectors.unwrap()[(i,j)].clone(),self.eigenvectors.unwrap()[(i,j+1)].clone());
r1_vec_conj[j] = Complex::<T>::new(self.eigenvectors.unwrap()[(i,j)].clone(),-self.eigenvectors.unwrap()[(i,j+1)].clone());
}
eigenvectors.unwrap().push(r1_vec);
eigenvectors.unwrap().push(r1_vec_conj);
}
if left_eigenvectors.is_some() {
//TODO: Do the same for left
}
i += 1;
}
}
(Some(eigenvalues), left_eigenvectors, eigenvectors)
}
}
}
}
/*
*
* Lapack functions dispatch.
*
*/
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/// Trait implemented by scalar type for which Lapack function exist to compute the
/// eigendecomposition.
pub trait EigenScalar: Scalar {
#[allow(missing_docs)]
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fn xgeev(
jobvl: u8,
jobvr: u8,
n: i32,
a: &mut [Self],
lda: i32,
wr: &mut [Self],
wi: &mut [Self],
vl: &mut [Self],
ldvl: i32,
vr: &mut [Self],
ldvr: i32,
work: &mut [Self],
lwork: i32,
info: &mut i32,
);
#[allow(missing_docs)]
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fn xgeev_work_size(
jobvl: u8,
jobvr: u8,
n: i32,
a: &mut [Self],
lda: i32,
wr: &mut [Self],
wi: &mut [Self],
vl: &mut [Self],
ldvl: i32,
vr: &mut [Self],
ldvr: i32,
info: &mut i32,
) -> i32;
}
macro_rules! real_eigensystem_scalar_impl (
($N: ty, $xgeev: path) => (
impl EigenScalar for $N {
#[inline]
fn xgeev(jobvl: u8, jobvr: u8, n: i32, a: &mut [Self], lda: i32,
wr: &mut [Self], wi: &mut [Self],
vl: &mut [Self], ldvl: i32, vr: &mut [Self], ldvr: i32,
work: &mut [Self], lwork: i32, info: &mut i32) {
unsafe { $xgeev(jobvl, jobvr, n, a, lda, wr, wi, vl, ldvl, vr, ldvr, work, lwork, info) }
}
#[inline]
fn xgeev_work_size(jobvl: u8, jobvr: u8, n: i32, a: &mut [Self], lda: i32,
wr: &mut [Self], wi: &mut [Self], vl: &mut [Self], ldvl: i32,
vr: &mut [Self], ldvr: i32, info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
unsafe { $xgeev(jobvl, jobvr, n, a, lda, wr, wi, vl, ldvl, vr, ldvr, &mut work, lwork, info) };
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
real_eigensystem_scalar_impl!(f32, lapack::sgeev);
real_eigensystem_scalar_impl!(f64, lapack::dgeev);
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//// TODO: decomposition of complex matrix and matrices with complex eigenvalues.
// eigensystem_complex_impl!(f32, lapack::cgeev);
// eigensystem_complex_impl!(f64, lapack::zgeev);