nalgebra/nalgebra-lapack/src/qr.rs

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#[cfg(feature = "serde-serialize")]
use serde;
use num_complex::Complex;
use num::Zero;
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use ComplexHelper;
use na::{DefaultAllocator, Matrix, MatrixMN, Scalar, VectorN};
use na::dimension::{Dim, DimMin, DimMinimum, U1};
use na::storage::Storage;
use na::allocator::Allocator;
use lapack::fortran as interface;
/// The QR decomposition of a general matrix.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(feature = "serde-serialize",
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serde(bound(serialize = "DefaultAllocator: Allocator<N, R, C> +
Allocator<N, DimMinimum<R, C>>,
MatrixMN<N, R, C>: serde::Serialize,
VectorN<N, DimMinimum<R, C>>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
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serde(bound(deserialize = "DefaultAllocator: Allocator<N, R, C> +
Allocator<N, DimMinimum<R, C>>,
MatrixMN<N, R, C>: serde::Deserialize<'de>,
VectorN<N, DimMinimum<R, C>>: serde::Deserialize<'de>")))]
#[derive(Clone, Debug)]
pub struct QR<N: Scalar, R: DimMin<C>, C: Dim>
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where
DefaultAllocator: Allocator<N, R, C> + Allocator<N, DimMinimum<R, C>>,
{
qr: MatrixMN<N, R, C>,
tau: VectorN<N, DimMinimum<R, C>>,
}
impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for QR<N, R, C>
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where
DefaultAllocator: Allocator<N, R, C> + Allocator<N, DimMinimum<R, C>>,
MatrixMN<N, R, C>: Copy,
VectorN<N, DimMinimum<R, C>>: Copy,
{
}
impl<N: QRScalar + Zero, R: DimMin<C>, C: Dim> QR<N, R, C>
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where
DefaultAllocator: Allocator<N, R, C>
+ Allocator<N, R, DimMinimum<R, C>>
+ Allocator<N, DimMinimum<R, C>, C>
+ Allocator<N, DimMinimum<R, C>>,
{
/// Computes the QR decomposition of the matrix `m`.
pub fn new(mut m: MatrixMN<N, R, C>) -> QR<N, R, C> {
let (nrows, ncols) = m.data.shape();
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let mut info = 0;
let mut tau = unsafe { Matrix::new_uninitialized_generic(nrows.min(ncols), U1) };
if nrows.value() == 0 || ncols.value() == 0 {
return QR { qr: m, tau: tau };
}
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let lwork = N::xgeqrf_work_size(
nrows.value() as i32,
ncols.value() as i32,
m.as_mut_slice(),
nrows.value() as i32,
tau.as_mut_slice(),
&mut info,
);
let mut work = unsafe { ::uninitialized_vec(lwork as usize) };
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N::xgeqrf(
nrows.value() as i32,
ncols.value() as i32,
m.as_mut_slice(),
nrows.value() as i32,
tau.as_mut_slice(),
&mut work,
lwork,
&mut info,
);
QR { qr: m, tau: tau }
}
/// Retrieves the upper trapezoidal submatrix `R` of this decomposition.
#[inline]
pub fn r(&self) -> MatrixMN<N, DimMinimum<R, C>, C> {
let (nrows, ncols) = self.qr.data.shape();
self.qr.rows_generic(0, nrows.min(ncols)).upper_triangle()
}
}
impl<N: QRReal + Zero, R: DimMin<C>, C: Dim> QR<N, R, C>
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where
DefaultAllocator: Allocator<N, R, C>
+ Allocator<N, R, DimMinimum<R, C>>
+ Allocator<N, DimMinimum<R, C>, C>
+ Allocator<N, DimMinimum<R, C>>,
{
/// Retrieves the matrices `(Q, R)` of this decompositions.
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pub fn unpack(
self,
) -> (
MatrixMN<N, R, DimMinimum<R, C>>,
MatrixMN<N, DimMinimum<R, C>, C>,
) {
(self.q(), self.r())
}
/// Computes the orthogonal matrix `Q` of this decomposition.
#[inline]
pub fn q(&self) -> MatrixMN<N, R, DimMinimum<R, C>> {
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let (nrows, ncols) = self.qr.data.shape();
let min_nrows_ncols = nrows.min(ncols);
if min_nrows_ncols.value() == 0 {
return MatrixMN::from_element_generic(nrows, min_nrows_ncols, N::zero());
}
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let mut q = self.qr
.generic_slice((0, 0), (nrows, min_nrows_ncols))
.into_owned();
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let mut info = 0;
let nrows = nrows.value() as i32;
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let lwork = N::xorgqr_work_size(
nrows,
min_nrows_ncols.value() as i32,
self.tau.len() as i32,
q.as_mut_slice(),
nrows,
self.tau.as_slice(),
&mut info,
);
let mut work = vec![N::zero(); lwork as usize];
N::xorgqr(
nrows,
min_nrows_ncols.value() as i32,
self.tau.len() as i32,
q.as_mut_slice(),
nrows,
self.tau.as_slice(),
&mut work,
lwork,
&mut info,
);
q
}
}
/*
*
* Lapack functions dispatch.
*
*/
/// Trait implemented by scalar types for which Lapack funtion exist to compute the
/// QR decomposition.
pub trait QRScalar: Scalar {
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fn xgeqrf(
m: i32,
n: i32,
a: &mut [Self],
lda: i32,
tau: &mut [Self],
work: &mut [Self],
lwork: i32,
info: &mut i32,
);
fn xgeqrf_work_size(
m: i32,
n: i32,
a: &mut [Self],
lda: i32,
tau: &mut [Self],
info: &mut i32,
) -> i32;
}
/// Trait implemented by reals for which Lapack funtion exist to compute the
/// QR decomposition.
pub trait QRReal: QRScalar {
#[allow(missing_docs)]
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fn xorgqr(
m: i32,
n: i32,
k: i32,
a: &mut [Self],
lda: i32,
tau: &[Self],
work: &mut [Self],
lwork: i32,
info: &mut i32,
);
#[allow(missing_docs)]
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fn xorgqr_work_size(
m: i32,
n: i32,
k: i32,
a: &mut [Self],
lda: i32,
tau: &[Self],
info: &mut i32,
) -> i32;
}
macro_rules! qr_scalar_impl(
($N: ty, $xgeqrf: path) => (
impl QRScalar for $N {
#[inline]
fn xgeqrf(m: i32, n: i32, a: &mut [Self], lda: i32, tau: &mut [Self],
work: &mut [Self], lwork: i32, info: &mut i32) {
$xgeqrf(m, n, a, lda, tau, work, lwork, info)
}
#[inline]
fn xgeqrf_work_size(m: i32, n: i32, a: &mut [Self], lda: i32, tau: &mut [Self],
info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
$xgeqrf(m, n, a, lda, tau, &mut work, lwork, info);
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
macro_rules! qr_real_impl(
($N: ty, $xorgqr: path) => (
impl QRReal for $N {
#[inline]
fn xorgqr(m: i32, n: i32, k: i32, a: &mut [Self], lda: i32, tau: &[Self],
work: &mut [Self], lwork: i32, info: &mut i32) {
$xorgqr(m, n, k, a, lda, tau, work, lwork, info)
}
#[inline]
fn xorgqr_work_size(m: i32, n: i32, k: i32, a: &mut [Self], lda: i32, tau: &[Self],
info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
$xorgqr(m, n, k, a, lda, tau, &mut work, lwork, info);
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
qr_scalar_impl!(f32, interface::sgeqrf);
qr_scalar_impl!(f64, interface::dgeqrf);
qr_scalar_impl!(Complex<f32>, interface::cgeqrf);
qr_scalar_impl!(Complex<f64>, interface::zgeqrf);
qr_real_impl!(f32, interface::sorgqr);
qr_real_impl!(f64, interface::dorgqr);