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Merge pull request #736 from rustsim/cholesky_aosoa

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Sébastien Crozet 2020-06-07 10:02:31 +02:00 committed by GitHub
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15 changed files with 537 additions and 126 deletions
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24
nalgebra-glm/src/constructors.rs
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@ -1,9 +1,8 @@
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::{Scalar, RealField, U2, U3, U4};
use crate::aliases::{TMat, Qua, TVec1, TVec2, TVec3, TVec4, TMat2, TMat2x3, TMat2x4, TMat3, TMat3x2, TMat3x4,
TMat4, TMat4x2, TMat4x3};
use crate::aliases::{
Qua, TMat, TMat2, TMat2x3, TMat2x4, TMat3, TMat3x2, TMat3x4, TMat4, TMat4x2, TMat4x3, TVec1,
TVec2, TVec3, TVec4,
};
use na::{RealField, Scalar, U2, U3, U4};
/// Creates a new 1D vector.
///
@ -34,8 +33,8 @@ pub fn vec4<N: Scalar>(x: N, y: N, z: N, w: N) -> TVec4<N> {
TVec4::new(x, y, z, w)
}
/// Create a new 2x2 matrix.
#[rustfmt::skip]
pub fn mat2<N: Scalar>(m11: N, m12: N,
m21: N, m22: N) -> TMat2<N> {
TMat::<N, U2, U2>::new(
@ -45,6 +44,7 @@ pub fn mat2<N: Scalar>(m11: N, m12: N,
}
/// Create a new 2x2 matrix.
#[rustfmt::skip]
pub fn mat2x2<N: Scalar>(m11: N, m12: N,
m21: N, m22: N) -> TMat2<N> {
TMat::<N, U2, U2>::new(
@ -54,6 +54,7 @@ pub fn mat2x2<N: Scalar>(m11: N, m12: N,
}
/// Create a new 2x3 matrix.
#[rustfmt::skip]
pub fn mat2x3<N: Scalar>(m11: N, m12: N, m13: N,
m21: N, m22: N, m23: N) -> TMat2x3<N> {
TMat::<N, U2, U3>::new(
@ -63,6 +64,7 @@ pub fn mat2x3<N: Scalar>(m11: N, m12: N, m13: N,
}
/// Create a new 2x4 matrix.
#[rustfmt::skip]
pub fn mat2x4<N: Scalar>(m11: N, m12: N, m13: N, m14: N,
m21: N, m22: N, m23: N, m24: N) -> TMat2x4<N> {
TMat::<N, U2, U4>::new(
@ -72,6 +74,7 @@ pub fn mat2x4<N: Scalar>(m11: N, m12: N, m13: N, m14: N,
}
/// Create a new 3x3 matrix.
#[rustfmt::skip]
pub fn mat3<N: Scalar>(m11: N, m12: N, m13: N,
m21: N, m22: N, m23: N,
m31: N, m32: N, m33: N) -> TMat3<N> {
@ -83,6 +86,7 @@ pub fn mat3<N: Scalar>(m11: N, m12: N, m13: N,
}
/// Create a new 3x2 matrix.
#[rustfmt::skip]
pub fn mat3x2<N: Scalar>(m11: N, m12: N,
m21: N, m22: N,
m31: N, m32: N) -> TMat3x2<N> {
@ -94,6 +98,7 @@ pub fn mat3x2<N: Scalar>(m11: N, m12: N,
}
/// Create a new 3x3 matrix.
#[rustfmt::skip]
pub fn mat3x3<N: Scalar>(m11: N, m12: N, m13: N,
m21: N, m22: N, m23: N,
m31: N, m32: N, m33: N) -> TMat3<N> {
@ -105,6 +110,7 @@ pub fn mat3x3<N: Scalar>(m11: N, m12: N, m13: N,
}
/// Create a new 3x4 matrix.
#[rustfmt::skip]
pub fn mat3x4<N: Scalar>(m11: N, m12: N, m13: N, m14: N,
m21: N, m22: N, m23: N, m24: N,
m31: N, m32: N, m33: N, m34: N) -> TMat3x4<N> {
@ -116,6 +122,7 @@ pub fn mat3x4<N: Scalar>(m11: N, m12: N, m13: N, m14: N,
}
/// Create a new 4x2 matrix.
#[rustfmt::skip]
pub fn mat4x2<N: Scalar>(m11: N, m12: N,
m21: N, m22: N,
m31: N, m32: N,
@ -129,6 +136,7 @@ pub fn mat4x2<N: Scalar>(m11: N, m12: N,
}
/// Create a new 4x3 matrix.
#[rustfmt::skip]
pub fn mat4x3<N: Scalar>(m11: N, m12: N, m13: N,
m21: N, m22: N, m23: N,
m31: N, m32: N, m33: N,
@ -142,6 +150,7 @@ pub fn mat4x3<N: Scalar>(m11: N, m12: N, m13: N,
}
/// Create a new 4x4 matrix.
#[rustfmt::skip]
pub fn mat4x4<N: Scalar>(m11: N, m12: N, m13: N, m14: N,
m21: N, m22: N, m23: N, m24: N,
m31: N, m32: N, m33: N, m34: N,
@ -155,6 +164,7 @@ pub fn mat4x4<N: Scalar>(m11: N, m12: N, m13: N, m14: N,
}
/// Create a new 4x4 matrix.
#[rustfmt::skip]
pub fn mat4<N: Scalar>(m11: N, m12: N, m13: N, m14: N,
m21: N, m22: N, m23: N, m24: N,
m31: N, m32: N, m33: N, m34: N,

4
src/base/array_storage.rs
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@ -57,7 +57,9 @@ where
N: Default,
{
fn default() -> Self {
ArrayStorage { data: Default::default() }
ArrayStorage {
data: Default::default(),
}
}
}

12
src/base/construction_slice.rs
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@ -220,9 +220,9 @@ macro_rules! impl_constructors(
// FIXME: this is not very pretty. We could find a better call syntax.
impl_constructors!(R, C; // Arguments for Matrix<N, ..., S>
=> R: DimName, => C: DimName; // Type parameters for impl<N, ..., S>
R::name(), C::name(); // Arguments for `_generic` constructors.
); // Arguments for non-generic constructors.
=> R: DimName, => C: DimName; // Type parameters for impl<N, ..., S>
R::name(), C::name(); // Arguments for `_generic` constructors.
); // Arguments for non-generic constructors.
impl_constructors!(R, Dynamic;
=> R: DimName;
@ -279,9 +279,9 @@ macro_rules! impl_constructors_mut(
// FIXME: this is not very pretty. We could find a better call syntax.
impl_constructors_mut!(R, C; // Arguments for Matrix<N, ..., S>
=> R: DimName, => C: DimName; // Type parameters for impl<N, ..., S>
R::name(), C::name(); // Arguments for `_generic` constructors.
); // Arguments for non-generic constructors.
=> R: DimName, => C: DimName; // Type parameters for impl<N, ..., S>
R::name(), C::name(); // Arguments for `_generic` constructors.
); // Arguments for non-generic constructors.
impl_constructors_mut!(R, Dynamic;
=> R: DimName;

2
src/geometry/quaternion.rs
View File

@ -36,7 +36,7 @@ pub struct Quaternion<N: Scalar + SimdValue> {
impl<N: RealField> Default for Quaternion<N> {
fn default() -> Self {
Quaternion {
coords: Vector4::zeros()
coords: Vector4::zeros(),
}
}
}

174
src/linalg/cholesky.rs
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@ -3,6 +3,7 @@ use serde::{Deserialize, Serialize};
use num::One;
use simba::scalar::ComplexField;
use simba::simd::SimdComplexField;
use crate::allocator::Allocator;
use crate::base::{DefaultAllocator, Matrix, MatrixMN, MatrixN, SquareMatrix, Vector};
@ -23,21 +24,123 @@ use crate::storage::{Storage, StorageMut};
MatrixN<N, D>: Deserialize<'de>"))
)]
#[derive(Clone, Debug)]
pub struct Cholesky<N: ComplexField, D: Dim>
pub struct Cholesky<N: SimdComplexField, D: Dim>
where
DefaultAllocator: Allocator<N, D, D>,
{
chol: MatrixN<N, D>,
}
impl<N: ComplexField, D: Dim> Copy for Cholesky<N, D>
impl<N: SimdComplexField, D: Dim> Copy for Cholesky<N, D>
where
DefaultAllocator: Allocator<N, D, D>,
MatrixN<N, D>: Copy,
{
}
impl<N: ComplexField, D: DimSub<Dynamic>> Cholesky<N, D>
impl<N: SimdComplexField, D: Dim> Cholesky<N, D>
where
DefaultAllocator: Allocator<N, D, D>,
{
/// Computes the Cholesky decomposition of `matrix` without checking that the matrix is definite-positive.
///
/// If the input matrix is not definite-positive, the decomposition may contain trash values (Inf, NaN, etc.)
pub fn new_unchecked(mut matrix: MatrixN<N, D>) -> Self {
assert!(matrix.is_square(), "The input matrix must be square.");
let n = matrix.nrows();
for j in 0..n {
for k in 0..j {
let factor = unsafe { -*matrix.get_unchecked((j, k)) };
let (mut col_j, col_k) = matrix.columns_range_pair_mut(j, k);
let mut col_j = col_j.rows_range_mut(j..);
let col_k = col_k.rows_range(j..);
col_j.axpy(factor.simd_conjugate(), &col_k, N::one());
}
let diag = unsafe { *matrix.get_unchecked((j, j)) };
let denom = diag.simd_sqrt();
unsafe {
*matrix.get_unchecked_mut((j, j)) = denom;
}
let mut col = matrix.slice_range_mut(j + 1.., j);
col /= denom;
}
Cholesky { chol: matrix }
}
/// Retrieves the lower-triangular factor of the Cholesky decomposition with its strictly
/// upper-triangular part filled with zeros.
pub fn unpack(mut self) -> MatrixN<N, D> {
self.chol.fill_upper_triangle(N::zero(), 1);
self.chol
}
/// Retrieves the lower-triangular factor of the Cholesky decomposition, without zeroing-out
/// its strict upper-triangular part.
///
/// The values of the strict upper-triangular part are garbage and should be ignored by further
/// computations.
pub fn unpack_dirty(self) -> MatrixN<N, D> {
self.chol
}
/// Retrieves the lower-triangular factor of the Cholesky decomposition with its strictly
/// uppen-triangular part filled with zeros.
pub fn l(&self) -> MatrixN<N, D> {
self.chol.lower_triangle()
}
/// Retrieves the lower-triangular factor of the Cholesky decomposition, without zeroing-out
/// its strict upper-triangular part.
///
/// This is an allocation-less version of `self.l()`. The values of the strict upper-triangular
/// part are garbage and should be ignored by further computations.
pub fn l_dirty(&self) -> &MatrixN<N, D> {
&self.chol
}
/// Solves the system `self * x = b` where `self` is the decomposed matrix and `x` the unknown.
///
/// The result is stored on `b`.
pub fn solve_mut<R2: Dim, C2: Dim, S2>(&self, b: &mut Matrix<N, R2, C2, S2>)
where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
self.chol.solve_lower_triangular_unchecked_mut(b);
self.chol.ad_solve_lower_triangular_unchecked_mut(b);
}
/// Returns the solution of the system `self * x = b` where `self` is the decomposed matrix and
/// `x` the unknown.
pub fn solve<R2: Dim, C2: Dim, S2>(&self, b: &Matrix<N, R2, C2, S2>) -> MatrixMN<N, R2, C2>
where
S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let mut res = b.clone_owned();
self.solve_mut(&mut res);
res
}
/// Computes the inverse of the decomposed matrix.
pub fn inverse(&self) -> MatrixN<N, D> {
let shape = self.chol.data.shape();
let mut res = MatrixN::identity_generic(shape.0, shape.1);
self.solve_mut(&mut res);
res
}
}
impl<N: ComplexField, D: Dim> Cholesky<N, D>
where
DefaultAllocator: Allocator<N, D, D>,
{
@ -82,71 +185,6 @@ where
Some(Cholesky { chol: matrix })
}
/// Retrieves the lower-triangular factor of the Cholesky decomposition with its strictly
/// upper-triangular part filled with zeros.
pub fn unpack(mut self) -> MatrixN<N, D> {
self.chol.fill_upper_triangle(N::zero(), 1);
self.chol
}
/// Retrieves the lower-triangular factor of the Cholesky decomposition, without zeroing-out
/// its strict upper-triangular part.
///
/// The values of the strict upper-triangular part are garbage and should be ignored by further
/// computations.
pub fn unpack_dirty(self) -> MatrixN<N, D> {
self.chol
}
/// Retrieves the lower-triangular factor of the Cholesky decomposition with its strictly
/// uppen-triangular part filled with zeros.
pub fn l(&self) -> MatrixN<N, D> {
self.chol.lower_triangle()
}
/// Retrieves the lower-triangular factor of the Cholesky decomposition, without zeroing-out
/// its strict upper-triangular part.
///
/// This is an allocation-less version of `self.l()`. The values of the strict upper-triangular
/// part are garbage and should be ignored by further computations.
pub fn l_dirty(&self) -> &MatrixN<N, D> {
&self.chol
}
/// Solves the system `self * x = b` where `self` is the decomposed matrix and `x` the unknown.
///
/// The result is stored on `b`.
pub fn solve_mut<R2: Dim, C2: Dim, S2>(&self, b: &mut Matrix<N, R2, C2, S2>)
where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let _ = self.chol.solve_lower_triangular_mut(b);
let _ = self.chol.ad_solve_lower_triangular_mut(b);
}
/// Returns the solution of the system `self * x = b` where `self` is the decomposed matrix and
/// `x` the unknown.
pub fn solve<R2: Dim, C2: Dim, S2>(&self, b: &Matrix<N, R2, C2, S2>) -> MatrixMN<N, R2, C2>
where
S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let mut res = b.clone_owned();
self.solve_mut(&mut res);
res
}
/// Computes the inverse of the decomposed matrix.
pub fn inverse(&self) -> MatrixN<N, D> {
let shape = self.chol.data.shape();
let mut res = MatrixN::identity_generic(shape.0, shape.1);
self.solve_mut(&mut res);
res
}
/// Given the Cholesky decomposition of a matrix `M`, a scalar `sigma` and a vector `v`,
/// performs a rank one update such that we end up with the decomposition of `M + sigma * (v * v.adjoint())`.
#[inline]

334
src/linalg/solve.rs
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@ -1,4 +1,5 @@
use simba::scalar::ComplexField;
use simba::simd::SimdComplexField;
use crate::base::allocator::Allocator;
use crate::base::constraint::{SameNumberOfRows, ShapeConstraint};
@ -432,3 +433,336 @@ impl<N: ComplexField, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
true
}
}
/*
*
* SIMD-compatible unchecked versions.
*
*/
impl<N: SimdComplexField, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
/// Computes the solution of the linear system `self . x = b` where `x` is the unknown and only
/// the lower-triangular part of `self` (including the diagonal) is considered not-zero.
#[inline]
pub fn solve_lower_triangular_unchecked<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<N, R2, C2, S2>,
) -> MatrixMN<N, R2, C2>
where
S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let mut res = b.clone_owned();
self.solve_lower_triangular_unchecked_mut(&mut res);
res
}
/// Computes the solution of the linear system `self . x = b` where `x` is the unknown and only
/// the upper-triangular part of `self` (including the diagonal) is considered not-zero.
#[inline]
pub fn solve_upper_triangular_unchecked<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<N, R2, C2, S2>,
) -> MatrixMN<N, R2, C2>
where
S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let mut res = b.clone_owned();
self.solve_upper_triangular_unchecked_mut(&mut res);
res
}
/// Solves the linear system `self . x = b` where `x` is the unknown and only the
/// lower-triangular part of `self` (including the diagonal) is considered not-zero.
pub fn solve_lower_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<N, R2, C2, S2>,
) where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
for i in 0..b.ncols() {
self.solve_lower_triangular_vector_unchecked_mut(&mut b.column_mut(i));
}
}
fn solve_lower_triangular_vector_unchecked_mut<R2: Dim, S2>(&self, b: &mut Vector<N, R2, S2>)
where
S2: StorageMut<N, R2, U1>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let dim = self.nrows();
for i in 0..dim {
let coeff;
unsafe {
let diag = *self.get_unchecked((i, i));
coeff = *b.vget_unchecked(i) / diag;
*b.vget_unchecked_mut(i) = coeff;
}
b.rows_range_mut(i + 1..)
.axpy(-coeff, &self.slice_range(i + 1.., i), N::one());
}
}
// FIXME: add the same but for solving upper-triangular.
/// Solves the linear system `self . x = b` where `x` is the unknown and only the
/// lower-triangular part of `self` is considered not-zero. The diagonal is never read as it is
/// assumed to be equal to `diag`. Returns `false` and does not modify its inputs if `diag` is zero.
pub fn solve_lower_triangular_with_diag_unchecked_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<N, R2, C2, S2>,
diag: N,
) where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let dim = self.nrows();
let cols = b.ncols();
for k in 0..cols {
let mut bcol = b.column_mut(k);
for i in 0..dim - 1 {
let coeff = unsafe { *bcol.vget_unchecked(i) } / diag;
bcol.rows_range_mut(i + 1..)
.axpy(-coeff, &self.slice_range(i + 1.., i), N::one());
}
}
}
/// Solves the linear system `self . x = b` where `x` is the unknown and only the
/// upper-triangular part of `self` (including the diagonal) is considered not-zero.
pub fn solve_upper_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<N, R2, C2, S2>,
) where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
for i in 0..b.ncols() {
self.solve_upper_triangular_vector_unchecked_mut(&mut b.column_mut(i))
}
}
fn solve_upper_triangular_vector_unchecked_mut<R2: Dim, S2>(&self, b: &mut Vector<N, R2, S2>)
where
S2: StorageMut<N, R2, U1>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let dim = self.nrows();
for i in (0..dim).rev() {
let coeff;
unsafe {
let diag = *self.get_unchecked((i, i));
coeff = *b.vget_unchecked(i) / diag;
*b.vget_unchecked_mut(i) = coeff;
}
b.rows_range_mut(..i)
.axpy(-coeff, &self.slice_range(..i, i), N::one());
}
}
/*
*
* Transpose and adjoint versions
*
*/
/// Computes the solution of the linear system `self.transpose() . x = b` where `x` is the unknown and only
/// the lower-triangular part of `self` (including the diagonal) is considered not-zero.
#[inline]
pub fn tr_solve_lower_triangular_unchecked<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<N, R2, C2, S2>,
) -> MatrixMN<N, R2, C2>
where
S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let mut res = b.clone_owned();
self.tr_solve_lower_triangular_unchecked_mut(&mut res);
res
}
/// Computes the solution of the linear system `self.transpose() . x = b` where `x` is the unknown and only
/// the upper-triangular part of `self` (including the diagonal) is considered not-zero.
#[inline]
pub fn tr_solve_upper_triangular_unchecked<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<N, R2, C2, S2>,
) -> MatrixMN<N, R2, C2>
where
S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let mut res = b.clone_owned();
self.tr_solve_upper_triangular_unchecked_mut(&mut res);
res
}
/// Solves the linear system `self.transpose() . x = b` where `x` is the unknown and only the
/// lower-triangular part of `self` (including the diagonal) is considered not-zero.
pub fn tr_solve_lower_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<N, R2, C2, S2>,
) where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
for i in 0..b.ncols() {
self.xx_solve_lower_triangular_vector_unchecked_mut(
&mut b.column_mut(i),
|e| e,
|a, b| a.dot(b),
)
}
}
/// Solves the linear system `self.transpose() . x = b` where `x` is the unknown and only the
/// upper-triangular part of `self` (including the diagonal) is considered not-zero.
pub fn tr_solve_upper_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<N, R2, C2, S2>,
) where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
for i in 0..b.ncols() {
self.xx_solve_upper_triangular_vector_unchecked_mut(
&mut b.column_mut(i),
|e| e,
|a, b| a.dot(b),
)
}
}
/// Computes the solution of the linear system `self.adjoint() . x = b` where `x` is the unknown and only
/// the lower-triangular part of `self` (including the diagonal) is considered not-zero.
#[inline]
pub fn ad_solve_lower_triangular_unchecked<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<N, R2, C2, S2>,
) -> MatrixMN<N, R2, C2>
where
S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let mut res = b.clone_owned();
self.ad_solve_lower_triangular_unchecked_mut(&mut res);
res
}
/// Computes the solution of the linear system `self.adjoint() . x = b` where `x` is the unknown and only
/// the upper-triangular part of `self` (including the diagonal) is considered not-zero.
#[inline]
pub fn ad_solve_upper_triangular_unchecked<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<N, R2, C2, S2>,
) -> MatrixMN<N, R2, C2>
where
S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let mut res = b.clone_owned();
self.ad_solve_upper_triangular_unchecked_mut(&mut res);
res
}
/// Solves the linear system `self.adjoint() . x = b` where `x` is the unknown and only the
/// lower-triangular part of `self` (including the diagonal) is considered not-zero.
pub fn ad_solve_lower_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<N, R2, C2, S2>,
) where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
for i in 0..b.ncols() {
self.xx_solve_lower_triangular_vector_unchecked_mut(
&mut b.column_mut(i),
|e| e.simd_conjugate(),
|a, b| a.dotc(b),
)
}
}
/// Solves the linear system `self.adjoint() . x = b` where `x` is the unknown and only the
/// upper-triangular part of `self` (including the diagonal) is considered not-zero.
pub fn ad_solve_upper_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<N, R2, C2, S2>,
) where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
for i in 0..b.ncols() {
self.xx_solve_upper_triangular_vector_unchecked_mut(
&mut b.column_mut(i),
|e| e.simd_conjugate(),
|a, b| a.dotc(b),
)
}
}
#[inline(always)]
fn xx_solve_lower_triangular_vector_unchecked_mut<R2: Dim, S2>(
&self,
b: &mut Vector<N, R2, S2>,
conjugate: impl Fn(N) -> N,
dot: impl Fn(
&DVectorSlice<N, S::RStride, S::CStride>,
&DVectorSlice<N, S2::RStride, S2::CStride>,
) -> N,
) where
S2: StorageMut<N, R2, U1>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
let dim = self.nrows();
for i in (0..dim).rev() {
let dot = dot(&self.slice_range(i + 1.., i), &b.slice_range(i + 1.., 0));
unsafe {
let b_i = b.vget_unchecked_mut(i);
let diag = conjugate(*self.get_unchecked((i, i)));
*b_i = (*b_i - dot) / diag;
}
}
}
#[inline(always)]
fn xx_solve_upper_triangular_vector_unchecked_mut<R2: Dim, S2>(
&self,
b: &mut Vector<N, R2, S2>,
conjugate: impl Fn(N) -> N,
dot: impl Fn(
&DVectorSlice<N, S::RStride, S::CStride>,
&DVectorSlice<N, S2::RStride, S2::CStride>,
) -> N,
) where
S2: StorageMut<N, R2, U1>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
for i in 0..self.nrows() {
let dot = dot(&self.slice_range(..i, i), &b.slice_range(..i, 0));
unsafe {
let b_i = b.vget_unchecked_mut(i);
let diag = conjugate(*self.get_unchecked((i, i)));
*b_i = (*b_i - dot) / diag;
}
}
}
}

24
tests/core/edition.rs
View File

@ -1,13 +1,11 @@
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::{Matrix,
DMatrix,
Matrix3, Matrix4, Matrix5,
Matrix4x3, Matrix3x4, Matrix5x3, Matrix3x5, Matrix4x5, Matrix5x4};
use na::{
DMatrix, Matrix, Matrix3, Matrix3x4, Matrix3x5, Matrix4, Matrix4x3, Matrix4x5, Matrix5,
Matrix5x3, Matrix5x4,
};
use na::{Dynamic, U2, U3, U5};
#[test]
#[rustfmt::skip]
fn upper_lower_triangular() {
let m = Matrix4::new(
11.0, 12.0, 13.0, 14.0,
@ -173,6 +171,7 @@ fn upper_lower_triangular() {
}
#[test]
#[rustfmt::skip]
fn swap_rows() {
let mut m = Matrix5x3::new(
11.0, 12.0, 13.0,
@ -194,6 +193,7 @@ fn swap_rows() {
}
#[test]
#[rustfmt::skip]
fn swap_columns() {
let mut m = Matrix3x5::new(
11.0, 12.0, 13.0, 14.0, 15.0,
@ -211,6 +211,7 @@ fn swap_columns() {
}
#[test]
#[rustfmt::skip]
fn remove_columns() {
let m = Matrix3x5::new(
11, 12, 13, 14, 15,
@ -261,6 +262,7 @@ fn remove_columns() {
}
#[test]
#[rustfmt::skip]
fn remove_columns_at() {
let m = DMatrix::from_row_slice(5, 5, &[
11, 12, 13, 14, 15,
@ -317,8 +319,8 @@ fn remove_columns_at() {
assert_eq!(m.remove_columns_at(&[0,3,4]), expected3);
}
#[test]
#[rustfmt::skip]
fn remove_rows() {
let m = Matrix5x3::new(
11, 12, 13,
@ -374,6 +376,7 @@ fn remove_rows() {
}
#[test]
#[rustfmt::skip]
fn remove_rows_at() {
let m = DMatrix::from_row_slice(5, 5, &[
11, 12, 13, 14, 15,
@ -424,8 +427,8 @@ fn remove_rows_at() {
assert_eq!(m.remove_rows_at(&[0,3,4]), expected3);
}
#[test]
#[rustfmt::skip]
fn insert_columns() {
let m = Matrix5x3::new(
11, 12, 13,
@ -490,6 +493,7 @@ fn insert_columns() {
}
#[test]
#[rustfmt::skip]
fn insert_columns_to_empty_matrix() {
let m1 = DMatrix::repeat(0, 0, 0);
let m2 = DMatrix::repeat(3, 0, 0);
@ -502,6 +506,7 @@ fn insert_columns_to_empty_matrix() {
}
#[test]
#[rustfmt::skip]
fn insert_rows() {
let m = Matrix3x5::new(
11, 12, 13, 14, 15,
@ -573,6 +578,7 @@ fn insert_rows_to_empty_matrix() {
}
#[test]
#[rustfmt::skip]
fn resize() {
let m = Matrix3x5::new(
11, 12, 13, 14, 15,

27
tests/core/matrix_slice.rs
View File

@ -1,18 +1,15 @@
#![allow(non_snake_case)]
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::{
DMatrix, DMatrixSlice, DMatrixSliceMut, Matrix2, Matrix2x3, Matrix2x4, Matrix2x6, Matrix3,
Matrix3x2, Matrix3x4, Matrix4x2, Matrix6x2, MatrixSlice2, MatrixSlice2x3, MatrixSlice2xX,
MatrixSlice3, MatrixSlice3x2, MatrixSliceMut2, MatrixSliceMut2x3, MatrixSliceMut2xX,
MatrixSliceMut3, MatrixSliceMut3x2, MatrixSliceMutXx3, MatrixSliceXx3, RowVector4, Vector3,
};
use na::{U2, U3, U4};
use na::{DMatrix,
RowVector4,
Vector3,
Matrix2, Matrix3,
Matrix2x3, Matrix3x2, Matrix3x4, Matrix4x2, Matrix2x4, Matrix6x2, Matrix2x6,
MatrixSlice2, MatrixSlice3, MatrixSlice2x3, MatrixSlice3x2,
MatrixSliceXx3, MatrixSlice2xX, DMatrixSlice,
MatrixSliceMut2, MatrixSliceMut3, MatrixSliceMut2x3, MatrixSliceMut3x2,
MatrixSliceMutXx3, MatrixSliceMut2xX, DMatrixSliceMut};
#[test]
#[rustfmt::skip]
fn nested_fixed_slices() {
let a = Matrix3x4::new(11.0, 12.0, 13.0, 14.0,
21.0, 22.0, 23.0, 24.0,
@ -38,6 +35,7 @@ fn nested_fixed_slices() {
}
#[test]
#[rustfmt::skip]
fn nested_slices() {
let a = Matrix3x4::new(11.0, 12.0, 13.0, 14.0,
21.0, 22.0, 23.0, 24.0,
@ -63,6 +61,7 @@ fn nested_slices() {
}
#[test]
#[rustfmt::skip]
fn slice_mut() {
let mut a = Matrix3x4::new(11.0, 12.0, 13.0, 14.0,
21.0, 22.0, 23.0, 24.0,
@ -82,6 +81,7 @@ fn slice_mut() {
}
#[test]
#[rustfmt::skip]
fn nested_row_slices() {
let a = Matrix6x2::new(11.0, 12.0,
21.0, 22.0,
@ -105,6 +105,7 @@ fn nested_row_slices() {
}
#[test]
#[rustfmt::skip]
fn row_slice_mut() {
let mut a = Matrix6x2::new(11.0, 12.0,
21.0, 22.0,
@ -129,6 +130,7 @@ fn row_slice_mut() {
}
#[test]
#[rustfmt::skip]
fn nested_col_slices() {
let a = Matrix2x6::new(11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
21.0, 22.0, 23.0, 24.0, 25.0, 26.0);
@ -146,6 +148,7 @@ fn nested_col_slices() {
}
#[test]
#[rustfmt::skip]
fn col_slice_mut() {
let mut a = Matrix2x6::new(11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
21.0, 22.0, 23.0, 24.0, 25.0, 26.0);
@ -163,6 +166,7 @@ fn col_slice_mut() {
}
#[test]
#[rustfmt::skip]
fn rows_range_pair() {
let a = Matrix3x4::new(11.0, 12.0, 13.0, 14.0,
21.0, 22.0, 23.0, 24.0,
@ -180,6 +184,7 @@ fn rows_range_pair() {
}
#[test]
#[rustfmt::skip]
fn columns_range_pair() {
let a = Matrix3x4::new(11.0, 12.0, 13.0, 14.0,
21.0, 22.0, 23.0, 24.0,
@ -198,6 +203,7 @@ fn columns_range_pair() {
}
#[test]
#[rustfmt::skip]
fn new_slice() {
let data = [ 1.0, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0,
@ -228,6 +234,7 @@ fn new_slice() {
}
#[test]
#[rustfmt::skip]
fn new_slice_mut() {
let data = [ 1.0, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0,

2
tests/core/mod.rs
View File

@ -3,9 +3,9 @@ mod abomonation;
mod blas;
mod conversion;
mod edition;
mod empty;
mod matrix;
mod matrix_slice;
mod empty;
#[cfg(feature = "mint")]
mod mint;
mod serde;

3
tests/linalg/eigen.rs
View File

@ -1,5 +1,3 @@
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::DMatrix;
#[cfg(feature = "arbitrary")]
@ -67,6 +65,7 @@ mod quickcheck_tests {
// Test proposed on the issue #176 of rulinalg.
#[test]
#[rustfmt::skip]
fn symmetric_eigen_singular_24x24() {
let m = DMatrix::from_row_slice(
24,

12
tests/linalg/full_piv_lu.rs
View File

@ -1,8 +1,7 @@
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::Matrix3;
#[test]
#[rustfmt::skip]
fn full_piv_lu_simple() {
let m = Matrix3::new(
2.0, -1.0, 0.0,
@ -22,11 +21,11 @@ fn full_piv_lu_simple() {
}
#[test]
#[rustfmt::skip]
fn full_piv_lu_simple_with_pivot() {
let m = Matrix3::new(
0.0, -1.0, 2.0,
-1.0, 2.0, -1.0,
2.0, -1.0, 0.0);
let m = Matrix3::new(0.0, -1.0, 2.0,
-1.0, 2.0, -1.0,
2.0, -1.0, 0.0);
let lu = m.full_piv_lu();
assert_eq!(lu.determinant(), -4.0);
@ -175,7 +174,6 @@ mod quickcheck_tests {
gen_tests!(f64, RandScalar<f64>);
}
/*
#[test]
fn swap_rows() {

9
tests/linalg/inverse.rs
View File

@ -1,5 +1,3 @@
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::{Matrix1, Matrix2, Matrix3, Matrix4, Matrix5};
#[test]
@ -11,6 +9,7 @@ fn matrix1_try_inverse() {
}
#[test]
#[rustfmt::skip]
fn matrix2_try_inverse() {
let a = Matrix2::new( 5.0, -2.0,
-10.0, 1.0);
@ -23,6 +22,7 @@ fn matrix2_try_inverse() {
}
#[test]
#[rustfmt::skip]
fn matrix3_try_inverse() {
let a = Matrix3::new(-3.0, 2.0, 0.0,
-6.0, 9.0, -2.0,
@ -37,6 +37,7 @@ fn matrix3_try_inverse() {
}
#[test]
#[rustfmt::skip]
fn matrix4_try_inverse_issue_214() {
let m1 = Matrix4::new(
-0.34727043, 0.00000005397217, -0.000000000000003822135, -0.000000000000003821371,
@ -58,6 +59,7 @@ fn matrix4_try_inverse_issue_214() {
}
#[test]
#[rustfmt::skip]
fn matrix5_try_inverse() {
// Dimension 5 is chosen so that the inversion happens by Gaussian elimination.
// (at the time of writing dimensions <= 3 are implemented as analytic formulas, but we choose
@ -90,6 +92,7 @@ fn matrix1_try_inverse_scaled_identity() {
}
#[test]
#[rustfmt::skip]
fn matrix2_try_inverse_scaled_identity() {
// A perfectly invertible matrix with
// very small coefficients
@ -103,6 +106,7 @@ fn matrix2_try_inverse_scaled_identity() {
}
#[test]
#[rustfmt::skip]
fn matrix3_try_inverse_scaled_identity() {
// A perfectly invertible matrix with
// very small coefficients
@ -118,6 +122,7 @@ fn matrix3_try_inverse_scaled_identity() {
}
#[test]
#[rustfmt::skip]
fn matrix5_try_inverse_scaled_identity() {
// A perfectly invertible matrix with
// very small coefficients

6
tests/linalg/lu.rs
View File

@ -1,8 +1,7 @@
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::Matrix3;
#[test]
#[rustfmt::skip]
fn lu_simple() {
let m = Matrix3::new(
2.0, -1.0, 0.0,
@ -21,6 +20,7 @@ fn lu_simple() {
}
#[test]
#[rustfmt::skip]
fn lu_simple_with_pivot() {
let m = Matrix3::new(
0.0, -1.0, 2.0,
@ -41,7 +41,7 @@ fn lu_simple_with_pivot() {
#[cfg(feature = "arbitrary")]
mod quickcheck_tests {
#[allow(unused_imports)]
use crate::core::helper::{RandScalar, RandComplex};
use crate::core::helper::{RandComplex, RandScalar};
macro_rules! gen_tests(
($module: ident, $scalar: ty) => {

11
tests/linalg/schur.rs
View File

@ -1,12 +1,11 @@
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::{DMatrix, Matrix3, Matrix4};
#[test]
#[rustfmt::skip]
fn schur_simpl_mat3() {
let m = Matrix3::new(-2.0, -4.0, 2.0,
-2.0, 1.0, 2.0,
4.0, 2.0, 5.0);
-2.0, 1.0, 2.0,
4.0, 2.0, 5.0);
let schur = m.schur();
let (vecs, vals) = schur.unpack();
@ -83,6 +82,7 @@ mod quickcheck_tests {
}
#[test]
#[rustfmt::skip]
fn schur_static_mat4_fail() {
let m = Matrix4::new(
33.32699857679677, 46.794945978960044, -20.792148817005838, 84.73945485997737,
@ -95,6 +95,7 @@ fn schur_static_mat4_fail() {
}
#[test]
#[rustfmt::skip]
fn schur_static_mat4_fail2() {
let m = Matrix4::new(
14.623586538485966, 7.646156622760756, -52.11923331576265, -97.50030223503413,
@ -107,6 +108,7 @@ fn schur_static_mat4_fail2() {
}
#[test]
#[rustfmt::skip]
fn schur_static_mat3_fail() {
let m = Matrix3::new(
-21.58457553143394, -67.3881542667948, -14.619829849784338,
@ -119,6 +121,7 @@ fn schur_static_mat3_fail() {
// Test proposed on the issue #176 of rulinalg.
#[test]
#[rustfmt::skip]
fn schur_singular() {
let m = DMatrix::from_row_slice(24, 24, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,

19
tests/linalg/svd.rs
View File

@ -1,4 +1,3 @@
#![cfg_attr(rustfmt, rustfmt_skip)]
use na::{DMatrix, Matrix6};
#[cfg(feature = "arbitrary")]
@ -160,9 +159,9 @@ mod quickcheck_tests {
gen_tests!(f64, RandScalar<f64>);
}
// Test proposed on the issue #176 of rulinalg.
#[test]
#[rustfmt::skip]
fn svd_singular() {
let m = DMatrix::from_row_slice(24, 24, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
@ -202,6 +201,7 @@ fn svd_singular() {
// Same as the previous test but with one additional row.
#[test]
#[rustfmt::skip]
fn svd_singular_vertical() {
let m = DMatrix::from_row_slice(25, 24, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
@ -241,6 +241,7 @@ fn svd_singular_vertical() {
// Same as the previous test but with one additional column.
#[test]
#[rustfmt::skip]
fn svd_singular_horizontal() {
let m = DMatrix::from_row_slice(24, 25, &[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
@ -299,6 +300,7 @@ fn svd_identity() {
}
#[test]
#[rustfmt::skip]
fn svd_with_delimited_subproblem() {
let mut m = DMatrix::<f64>::from_element(10, 10, 0.0);
m[(0,0)] = 1.0; m[(0,1)] = 2.0;
@ -334,6 +336,7 @@ fn svd_with_delimited_subproblem() {
}
#[test]
#[rustfmt::skip]
fn svd_fail() {
let m = Matrix6::new(
0.9299319121545955, 0.9955870335651049, 0.8824725266413644, 0.28966880207132295, 0.06102723649846409, 0.9311880746048009,
@ -351,6 +354,12 @@ fn svd_fail() {
fn svd_err() {
let m = DMatrix::from_element(10, 10, 0.0);
let svd = m.clone().svd(false, false);
assert_eq!(Err("SVD recomposition: U and V^t have not been computed."), svd.clone().recompose());
assert_eq!(Err("SVD pseudo inverse: the epsilon must be non-negative."), svd.clone().pseudo_inverse(-1.0));
}
assert_eq!(
Err("SVD recomposition: U and V^t have not been computed."),
svd.clone().recompose()
);
assert_eq!(
Err("SVD pseudo inverse: the epsilon must be non-negative."),
svd.clone().pseudo_inverse(-1.0)
);
}