Fix all tests and the ColPivQR::solve.
This commit is contained in:
Crozet Sébastien
parent
63a34528e0
commit
308d95386e
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@ -1,98 +1,94 @@
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use num::Zero;
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#[cfg(feature = "serde-serialize")]
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use serde::{Deserialize, Serialize};
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use num::Zero;
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use alga::general::ComplexField;
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use crate::allocator::{Allocator, Reallocator};
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use crate::base::{DefaultAllocator, Matrix, MatrixMN, MatrixN, Unit, VectorN};
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use crate::constraint::{SameNumberOfRows, ShapeConstraint};
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use crate::dimension::{Dim, DimMin, DimMinimum, U1};
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use crate::storage::{Storage, StorageMut};
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use crate::ComplexField;
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use crate::geometry::Reflection;
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use crate::linalg::householder;
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use crate::linalg::PermutationSequence;
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use crate::linalg::{householder, PermutationSequence};
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/// The QRP decomposition of a general matrix.
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/// The QR decomposition (with column pivoting) of a general matrix.
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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(bound(
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serialize = "DefaultAllocator: Allocator<N, R, C> +
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serde(bound(serialize = "DefaultAllocator: Allocator<N, R, C> +
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Allocator<N, DimMinimum<R, C>>,
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MatrixMN<N, R, C>: Serialize,
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PermutationSequence<DimMinimum<R, C>>: Serialize,
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VectorN<N, DimMinimum<R, C>>: Serialize"
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))
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VectorN<N, DimMinimum<R, C>>: Serialize"))
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)]
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#[cfg_attr(
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feature = "serde-serialize",
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serde(bound(
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deserialize = "DefaultAllocator: Allocator<N, R, C> +
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serde(bound(deserialize = "DefaultAllocator: Allocator<N, R, C> +
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Allocator<N, DimMinimum<R, C>>,
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MatrixMN<N, R, C>: Deserialize<'de>,
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PermutationSequence<DimMinimum<R, C>>: Deserialize<'de>,
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VectorN<N, DimMinimum<R, C>>: Deserialize<'de>"
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))
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VectorN<N, DimMinimum<R, C>>: Deserialize<'de>"))
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)]
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#[derive(Clone, Debug)]
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pub struct QRP<N: ComplexField, R: DimMin<C>, C: Dim>
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where DefaultAllocator: Allocator<N, R, C> + Allocator<N, DimMinimum<R, C>> +
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Allocator<(usize, usize), DimMinimum<R, C>>,
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pub struct ColPivQR<N: ComplexField, R: DimMin<C>, C: Dim>
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where
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DefaultAllocator: Allocator<N, R, C>
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+ Allocator<N, DimMinimum<R, C>>
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+ Allocator<(usize, usize), DimMinimum<R, C>>,
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{
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qrp: MatrixMN<N, R, C>,
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col_piv_qr: MatrixMN<N, R, C>,
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p: PermutationSequence<DimMinimum<R, C>>,
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diag: VectorN<N, DimMinimum<R, C>>,
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}
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impl<N: ComplexField, R: DimMin<C>, C: Dim> Copy for QRP<N, R, C>
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impl<N: ComplexField, R: DimMin<C>, C: Dim> Copy for ColPivQR<N, R, C>
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where
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DefaultAllocator: Allocator<N, R, C> + Allocator<N, DimMinimum<R, C>> +
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Allocator<(usize, usize), DimMinimum<R, C>>,
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DefaultAllocator: Allocator<N, R, C>
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+ Allocator<N, DimMinimum<R, C>>
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+ Allocator<(usize, usize), DimMinimum<R, C>>,
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MatrixMN<N, R, C>: Copy,
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PermutationSequence<DimMinimum<R, C>>: Copy,
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VectorN<N, DimMinimum<R, C>>: Copy,
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{}
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impl<N: ComplexField, R: DimMin<C>, C: Dim> QRP<N, R, C>
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where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimMinimum<R, C>> + Allocator<(usize, usize), DimMinimum<R, C>>
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{
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/// Computes the QRP decomposition using householder reflections.
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}
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impl<N: ComplexField, R: DimMin<C>, C: Dim> ColPivQR<N, R, C>
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where
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DefaultAllocator: Allocator<N, R, C>
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+ Allocator<N, R>
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+ Allocator<N, DimMinimum<R, C>>
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+ Allocator<(usize, usize), DimMinimum<R, C>>,
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{
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/// Computes the ColPivQR decomposition using householder reflections.
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pub fn new(mut matrix: MatrixMN<N, R, C>) -> Self {
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let (nrows, ncols) = matrix.data.shape();
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let min_nrows_ncols = nrows.min(ncols);
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let mut p = PermutationSequence::identity_generic(min_nrows_ncols);
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let mut diag = unsafe { MatrixMN::new_uninitialized_generic(min_nrows_ncols, U1) };
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println!("diag: {:?}", &diag);
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if min_nrows_ncols.value() == 0 {
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return QRP {
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qrp: matrix,
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p: p,
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diag: diag,
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return ColPivQR {
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col_piv_qr: matrix,
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p,
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diag,
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};
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}
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for ite in 0..min_nrows_ncols.value() {
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let mut col_norm = Vec::new();
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for column in matrix.column_iter() {
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col_norm.push(column.norm_squared());
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for i in 0..min_nrows_ncols.value() {
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let piv = matrix.slice_range(i.., i..).icamax_full();
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let col_piv = piv.1 + i;
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matrix.swap_columns(i, col_piv);
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p.append_permutation(i, col_piv);
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householder::clear_column_unchecked(&mut matrix, &mut diag[i], i, 0, None);
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}
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let piv = matrix.slice_range(ite.., ite..).icamax_full();
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let col_piv = piv.1 + ite;
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matrix.swap_columns(ite, col_piv);
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p.append_permutation(ite, col_piv);
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householder::clear_column_unchecked(&mut matrix, &mut diag[ite], ite, 0, None);
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println!("matrix: {:?}", &matrix.data);
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}
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QRP {
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qrp: matrix,
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p: p,
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diag: diag,
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ColPivQR {
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col_piv_qr: matrix,
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p,
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diag,
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}
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}
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@ -102,8 +98,11 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
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where
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DefaultAllocator: Allocator<N, DimMinimum<R, C>, C>,
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{
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let (nrows, ncols) = self.qrp.data.shape();
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let mut res = self.qrp.rows_generic(0, nrows.min(ncols)).upper_triangle();
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let (nrows, ncols) = self.col_piv_qr.data.shape();
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let mut res = self
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.col_piv_qr
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.rows_generic(0, nrows.min(ncols))
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.upper_triangle();
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res.set_partial_diagonal(self.diag.iter().map(|e| N::from_real(e.modulus())));
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res
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}
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@ -116,8 +115,10 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
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where
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DefaultAllocator: Reallocator<N, R, C, DimMinimum<R, C>, C>,
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{
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let (nrows, ncols) = self.qrp.data.shape();
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let mut res = self.qrp.resize_generic(nrows.min(ncols), ncols, N::zero());
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let (nrows, ncols) = self.col_piv_qr.data.shape();
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let mut res = self
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.col_piv_qr
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.resize_generic(nrows.min(ncols), ncols, N::zero());
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res.fill_lower_triangle(N::zero(), 1);
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res.set_partial_diagonal(self.diag.iter().map(|e| N::from_real(e.modulus())));
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res
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@ -125,8 +126,10 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
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/// Computes the orthogonal matrix `Q` of this decomposition.
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pub fn q(&self) -> MatrixMN<N, R, DimMinimum<R, C>>
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where DefaultAllocator: Allocator<N, R, DimMinimum<R, C>> {
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let (nrows, ncols) = self.qrp.data.shape();
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where
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DefaultAllocator: Allocator<N, R, DimMinimum<R, C>>,
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{
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let (nrows, ncols) = self.col_piv_qr.data.shape();
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// NOTE: we could build the identity matrix and call q_mul on it.
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// Instead we don't so that we take in account the matrix sparseness.
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@ -134,8 +137,8 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
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let dim = self.diag.len();
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for i in (0..dim).rev() {
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let axis = self.qrp.slice_range(i.., i);
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// FIXME: sometimes, the axis might have a zero magnitude.
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let axis = self.col_piv_qr.slice_range(i.., i);
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// TODO: sometimes, the axis might have a zero magnitude.
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let refl = Reflection::new(Unit::new_unchecked(axis), N::zero());
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let mut res_rows = res.slice_range_mut(i.., i..);
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@ -160,25 +163,27 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
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)
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where
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DimMinimum<R, C>: DimMin<C, Output = DimMinimum<R, C>>,
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DefaultAllocator:
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Allocator<N, R, DimMinimum<R, C>> + Reallocator<N, R, C, DimMinimum<R, C>, C> + Allocator<(usize, usize), DimMinimum<R, C>>,
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DefaultAllocator: Allocator<N, R, DimMinimum<R, C>>
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+ Reallocator<N, R, C, DimMinimum<R, C>, C>
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+ Allocator<(usize, usize), DimMinimum<R, C>>,
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{
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(self.q(), self.r(), self.p)
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}
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#[doc(hidden)]
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pub fn qrp_internal(&self) -> &MatrixMN<N, R, C> {
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&self.qrp
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pub fn col_piv_qr_internal(&self) -> &MatrixMN<N, R, C> {
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&self.col_piv_qr
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}
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/// Multiplies the provided matrix by the transpose of the `Q` matrix of this decomposition.
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pub fn q_tr_mul<R2: Dim, C2: Dim, S2>(&self, rhs: &mut Matrix<N, R2, C2, S2>)
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// FIXME: do we need a static constraint on the number of rows of rhs?
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where S2: StorageMut<N, R2, C2> {
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where
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S2: StorageMut<N, R2, C2>,
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{
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let dim = self.diag.len();
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for i in 0..dim {
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let axis = self.qrp.slice_range(i.., i);
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let axis = self.col_piv_qr.slice_range(i.., i);
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let refl = Reflection::new(Unit::new_unchecked(axis), N::zero());
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let mut rhs_rows = rhs.rows_range_mut(i..);
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@ -187,9 +192,10 @@ where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimM
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}
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}
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impl<N: ComplexField, D: DimMin<D, Output = D>> QRP<N, D, D>
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where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
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Allocator<(usize, usize), DimMinimum<D, D>>
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impl<N: ComplexField, D: DimMin<D, Output = D>> ColPivQR<N, D, D>
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where
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DefaultAllocator:
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Allocator<N, D, D> + Allocator<N, D> + Allocator<(usize, usize), DimMinimum<D, D>>,
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{
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/// Solves the linear system `self * x = b`, where `x` is the unknown to be determined.
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///
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@ -222,20 +228,23 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
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ShapeConstraint: SameNumberOfRows<R2, D>,
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{
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assert_eq!(
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self.qrp.nrows(),
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self.col_piv_qr.nrows(),
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b.nrows(),
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"QRP solve matrix dimension mismatch."
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"ColPivQR solve matrix dimension mismatch."
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);
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assert!(
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self.qrp.is_square(),
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"QRP solve: unable to solve a non-square system."
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self.col_piv_qr.is_square(),
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"ColPivQR solve: unable to solve a non-square system."
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);
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self.q_tr_mul(b);
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self.solve_upper_triangular_mut(b)
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let solved = self.solve_upper_triangular_mut(b);
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self.p.inv_permute_rows(b);
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solved
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}
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// FIXME: duplicate code from the `solve` module.
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// TODO: duplicate code from the `solve` module.
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fn solve_upper_triangular_mut<R2: Dim, C2: Dim, S2>(
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&self,
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b: &mut Matrix<N, R2, C2, S2>,
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@ -244,7 +253,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
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S2: StorageMut<N, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R2, D>,
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{
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let dim = self.qrp.nrows();
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let dim = self.col_piv_qr.nrows();
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for k in 0..b.ncols() {
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let mut b = b.column_mut(k);
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@ -263,7 +272,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
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}
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b.rows_range_mut(..i)
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.axpy(-coeff, &self.qrp.slice_range(..i, i), N::one());
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.axpy(-coeff, &self.col_piv_qr.slice_range(..i, i), N::one());
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}
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}
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@ -275,12 +284,12 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
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/// Returns `None` if the decomposed matrix is not invertible.
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pub fn try_inverse(&self) -> Option<MatrixN<N, D>> {
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assert!(
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self.qrp.is_square(),
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"QRP inverse: unable to compute the inverse of a non-square matrix."
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self.col_piv_qr.is_square(),
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"ColPivQR inverse: unable to compute the inverse of a non-square matrix."
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);
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// FIXME: is there a less naive method ?
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let (nrows, ncols) = self.qrp.data.shape();
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// TODO: is there a less naive method ?
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let (nrows, ncols) = self.col_piv_qr.data.shape();
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let mut res = MatrixN::identity_generic(nrows, ncols);
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if self.solve_mut(&mut res) {
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@ -293,8 +302,8 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
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/// Indicates if the decomposed matrix is invertible.
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pub fn is_invertible(&self) -> bool {
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assert!(
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self.qrp.is_square(),
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"QRP: unable to test the invertibility of a non-square matrix."
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self.col_piv_qr.is_square(),
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"ColPivQR: unable to test the invertibility of a non-square matrix."
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);
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for i in 0..self.diag.len() {
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@ -308,11 +317,14 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
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/// Computes the determinant of the decomposed matrix.
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pub fn determinant(&self) -> N {
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let dim = self.qrp.nrows();
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assert!(self.qrp.is_square(), "QRP determinant: unable to compute the determinant of a non-square matrix.");
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let dim = self.col_piv_qr.nrows();
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assert!(
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self.col_piv_qr.is_square(),
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"ColPivQR determinant: unable to compute the determinant of a non-square matrix."
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);
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let mut res = N::one();
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for i in 0 .. dim {
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for i in 0..dim {
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res *= unsafe { *self.diag.vget_unchecked(i) };
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}
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@ -321,10 +333,14 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D> +
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}
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impl<N: ComplexField, R: DimMin<C>, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
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where DefaultAllocator: Allocator<N, R, C> + Allocator<N, R> + Allocator<N, DimMinimum<R, C>> + Allocator<(usize, usize), DimMinimum<R, C>>
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where
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DefaultAllocator: Allocator<N, R, C>
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+ Allocator<N, R>
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+ Allocator<N, DimMinimum<R, C>>
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+ Allocator<(usize, usize), DimMinimum<R, C>>,
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{
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/// Computes the QRP decomposition of this matrix.
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pub fn qrp(self) -> QRP<N, R, C> {
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QRP::new(self.into_owned())
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/// Computes the QR decomposition (with column pivoting) of this matrix.
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pub fn col_piv_qr(self) -> ColPivQR<N, R, C> {
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ColPivQR::new(self.into_owned())
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}
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}
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@ -34,7 +34,7 @@ pub fn reflection_axis_mut<N: ComplexField, D: Dim, S: StorageMut<N, D>>(
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if !factor.is_zero() {
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column.unscale_mut(factor.sqrt());
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(signed_norm, true)
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(-signed_norm, true)
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} else {
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// TODO: not sure why we don't have a - sign here.
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(signed_norm, false)
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@ -19,6 +19,7 @@ mod inverse;
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mod lu;
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mod permutation_sequence;
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mod qr;
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mod col_piv_qr;
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mod schur;
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mod solve;
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mod svd;
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@ -39,6 +40,7 @@ pub use self::hessenberg::*;
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pub use self::lu::*;
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pub use self::permutation_sequence::*;
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pub use self::qr::*;
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pub use self::col_piv_qr::*;
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pub use self::schur::*;
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pub use self::svd::*;
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pub use self::symmetric_eigen::*;
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@ -60,8 +60,8 @@ where
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return QR { qr: matrix, diag };
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}
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for ite in 0..min_nrows_ncols.value() {
|
||||
householder::clear_column_unchecked(&mut matrix, &mut diag[ite], ite, 0, None);
|
||||
for i in 0..min_nrows_ncols.value() {
|
||||
householder::clear_column_unchecked(&mut matrix, &mut diag[i], i, 0, None);
|
||||
}
|
||||
|
||||
QR { qr: matrix, diag }
|
||||
|
|
|
|||
|
|
@ -3,19 +3,21 @@
|
|||
use na::Matrix4;
|
||||
|
||||
#[test]
|
||||
fn qrp() {
|
||||
let m = Matrix4::new (
|
||||
1.0, -1.0, 2.0, 1.0,
|
||||
-1.0, 3.0, -1.0, -1.0,
|
||||
3.0, -5.0, 5.0, 3.0,
|
||||
1.0, 2.0, 1.0, -2.0);
|
||||
let qrp = m.qrp();
|
||||
assert!(relative_eq!(qrp.determinant(), 0.0, epsilon = 1.0e-7));
|
||||
fn col_piv_qr() {
|
||||
let m = Matrix4::new(
|
||||
1.0, -1.0, 2.0, 1.0, -1.0, 3.0, -1.0, -1.0, 3.0, -5.0, 5.0, 3.0, 1.0, 2.0, 1.0, -2.0,
|
||||
);
|
||||
let col_piv_qr = m.col_piv_qr();
|
||||
assert!(relative_eq!(
|
||||
col_piv_qr.determinant(),
|
||||
0.0,
|
||||
epsilon = 1.0e-7
|
||||
));
|
||||
|
||||
let (q, r, p) = qrp.unpack();
|
||||
let (q, r, p) = col_piv_qr.unpack();
|
||||
|
||||
let mut qr = q * r;
|
||||
p.inv_permute_columns(& mut qr);
|
||||
p.inv_permute_columns(&mut qr);
|
||||
|
||||
assert!(relative_eq!(m, qr, epsilon = 1.0e-7));
|
||||
}
|
||||
|
|
@ -31,64 +33,68 @@ mod quickcheck_tests {
|
|||
use crate::core::helper::{RandScalar, RandComplex};
|
||||
|
||||
quickcheck! {
|
||||
fn qrp(m: DMatrix<$scalar>) -> bool {
|
||||
fn col_piv_qr(m: DMatrix<$scalar>) -> bool {
|
||||
let m = m.map(|e| e.0);
|
||||
let qrp = m.clone().qrp();
|
||||
let q = qrp.q();
|
||||
let r = qrp.r();
|
||||
let col_piv_qr = m.clone().col_piv_qr();
|
||||
let (q, r, p) = col_piv_qr.unpack();
|
||||
let mut qr = &q * &r;
|
||||
p.inv_permute_columns(&mut qr);
|
||||
|
||||
println!("m: {}", m);
|
||||
println!("qrp: {}", &q * &r);
|
||||
println!("col_piv_qr: {}", &q * &r);
|
||||
|
||||
relative_eq!(m, &q * r, epsilon = 1.0e-7) &&
|
||||
relative_eq!(m, &qr, epsilon = 1.0e-7) &&
|
||||
q.is_orthogonal(1.0e-7)
|
||||
}
|
||||
|
||||
fn qrp_static_5_3(m: Matrix5x3<$scalar>) -> bool {
|
||||
fn col_piv_qr_static_5_3(m: Matrix5x3<$scalar>) -> bool {
|
||||
let m = m.map(|e| e.0);
|
||||
let qrp = m.qrp();
|
||||
let q = qrp.q();
|
||||
let r = qrp.r();
|
||||
let col_piv_qr = m.col_piv_qr();
|
||||
let (q, r, p) = col_piv_qr.unpack();
|
||||
let mut qr = q * r;
|
||||
p.inv_permute_columns(&mut qr);
|
||||
|
||||
relative_eq!(m, q * r, epsilon = 1.0e-7) &&
|
||||
relative_eq!(m, qr, epsilon = 1.0e-7) &&
|
||||
q.is_orthogonal(1.0e-7)
|
||||
}
|
||||
|
||||
fn qrp_static_3_5(m: Matrix3x5<$scalar>) -> bool {
|
||||
fn col_piv_qr_static_3_5(m: Matrix3x5<$scalar>) -> bool {
|
||||
let m = m.map(|e| e.0);
|
||||
let qrp = m.qrp();
|
||||
let q = qrp.q();
|
||||
let r = qrp.r();
|
||||
let col_piv_qr = m.col_piv_qr();
|
||||
let (q, r, p) = col_piv_qr.unpack();
|
||||
let mut qr = q * r;
|
||||
p.inv_permute_columns(&mut qr);
|
||||
|
||||
relative_eq!(m, q * r, epsilon = 1.0e-7) &&
|
||||
relative_eq!(m, qr, epsilon = 1.0e-7) &&
|
||||
q.is_orthogonal(1.0e-7)
|
||||
}
|
||||
|
||||
fn qrp_static_square(m: Matrix4<$scalar>) -> bool {
|
||||
fn col_piv_qr_static_square(m: Matrix4<$scalar>) -> bool {
|
||||
let m = m.map(|e| e.0);
|
||||
let qrp = m.qrp();
|
||||
let q = qrp.q();
|
||||
let r = qrp.r();
|
||||
let col_piv_qr = m.col_piv_qr();
|
||||
let (q, r, p) = col_piv_qr.unpack();
|
||||
let mut qr = q * r;
|
||||
p.inv_permute_columns(&mut qr);
|
||||
|
||||
println!("{}{}{}{}", q, r, q * r, m);
|
||||
println!("{}{}{}{}", q, r, qr, m);
|
||||
|
||||
relative_eq!(m, q * r, epsilon = 1.0e-7) &&
|
||||
relative_eq!(m, qr, epsilon = 1.0e-7) &&
|
||||
q.is_orthogonal(1.0e-7)
|
||||
}
|
||||
|
||||
fn qrp_solve(n: usize, nb: usize) -> bool {
|
||||
fn col_piv_qr_solve(n: usize, nb: usize) -> bool {
|
||||
if n != 0 && nb != 0 {
|
||||
let n = cmp::min(n, 50); // To avoid slowing down the test too much.
|
||||
let nb = cmp::min(nb, 50); // To avoid slowing down the test too much.
|
||||
let m = DMatrix::<$scalar>::new_random(n, n).map(|e| e.0);
|
||||
|
||||
let qrp = m.clone().qrp();
|
||||
let col_piv_qr = m.clone().col_piv_qr();
|
||||
let b1 = DVector::<$scalar>::new_random(n).map(|e| e.0);
|
||||
let b2 = DMatrix::<$scalar>::new_random(n, nb).map(|e| e.0);
|
||||
|
||||
if qrp.is_invertible() {
|
||||
let sol1 = qrp.solve(&b1).unwrap();
|
||||
let sol2 = qrp.solve(&b2).unwrap();
|
||||
if col_piv_qr.is_invertible() {
|
||||
let sol1 = col_piv_qr.solve(&b1).unwrap();
|
||||
let sol2 = col_piv_qr.solve(&b2).unwrap();
|
||||
|
||||
return relative_eq!(&m * sol1, b1, epsilon = 1.0e-6) &&
|
||||
relative_eq!(&m * sol2, b2, epsilon = 1.0e-6)
|
||||
|
|
@ -98,15 +104,15 @@ mod quickcheck_tests {
|
|||
return true;
|
||||
}
|
||||
|
||||
fn qrp_solve_static(m: Matrix4<$scalar>) -> bool {
|
||||
fn col_piv_qr_solve_static(m: Matrix4<$scalar>) -> bool {
|
||||
let m = m.map(|e| e.0);
|
||||
let qrp = m.qrp();
|
||||
let col_piv_qr = m.col_piv_qr();
|
||||
let b1 = Vector4::<$scalar>::new_random().map(|e| e.0);
|
||||
let b2 = Matrix4x3::<$scalar>::new_random().map(|e| e.0);
|
||||
|
||||
if qrp.is_invertible() {
|
||||
let sol1 = qrp.solve(&b1).unwrap();
|
||||
let sol2 = qrp.solve(&b2).unwrap();
|
||||
if col_piv_qr.is_invertible() {
|
||||
let sol1 = col_piv_qr.solve(&b1).unwrap();
|
||||
let sol2 = col_piv_qr.solve(&b2).unwrap();
|
||||
|
||||
relative_eq!(m * sol1, b1, epsilon = 1.0e-6) &&
|
||||
relative_eq!(m * sol2, b2, epsilon = 1.0e-6)
|
||||
|
|
@ -116,11 +122,11 @@ mod quickcheck_tests {
|
|||
}
|
||||
}
|
||||
|
||||
fn qrp_inverse(n: usize) -> bool {
|
||||
fn col_piv_qr_inverse(n: usize) -> bool {
|
||||
let n = cmp::max(1, cmp::min(n, 15)); // To avoid slowing down the test too much.
|
||||
let m = DMatrix::<$scalar>::new_random(n, n).map(|e| e.0);
|
||||
|
||||
if let Some(m1) = m.clone().qrp().try_inverse() {
|
||||
if let Some(m1) = m.clone().col_piv_qr().try_inverse() {
|
||||
let id1 = &m * &m1;
|
||||
let id2 = &m1 * &m;
|
||||
|
||||
|
|
@ -131,11 +137,11 @@ mod quickcheck_tests {
|
|||
}
|
||||
}
|
||||
|
||||
fn qrp_inverse_static(m: Matrix4<$scalar>) -> bool {
|
||||
fn col_piv_qr_inverse_static(m: Matrix4<$scalar>) -> bool {
|
||||
let m = m.map(|e| e.0);
|
||||
let qrp = m.qrp();
|
||||
let col_piv_qr = m.col_piv_qr();
|
||||
|
||||
if let Some(m1) = qrp.try_inverse() {
|
||||
if let Some(m1) = col_piv_qr.try_inverse() {
|
||||
let id1 = &m * &m1;
|
||||
let id2 = &m1 * &m;
|
||||
|
||||
|
|
@ -153,4 +159,3 @@ mod quickcheck_tests {
|
|||
gen_tests!(complex, RandComplex<f64>);
|
||||
gen_tests!(f64, RandScalar<f64>);
|
||||
}
|
||||
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
mod balancing;
|
||||
mod bidiagonal;
|
||||
mod cholesky;
|
||||
mod col_piv_qr;
|
||||
mod convolution;
|
||||
mod eigen;
|
||||
mod exp;
|
||||
|
|
|
|||
Loading…
Reference in New Issue