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Merge pull request #1057 from dimforge/dev 

Release v0.30.0
This commit is contained in:
Sébastien Crozet 2022-01-02 15:40:03 +01:00 committed by GitHub
parent e48169e234 e8b9c40123
commit 7b6f4c6547
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104 changed files with 4706 additions and 600 deletions
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35
.github/workflows/nalgebra-ci-build.yml vendored
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@ -36,14 +36,20 @@ jobs:
run: cargo build;
- name: Build --features serde-serialize
run: cargo build --features serde-serialize
- name: Build --all-features
run: cargo build --all-features;
- name: Build nalgebra-glm
run: cargo build -p nalgebra-glm --all-features;
- name: Build nalgebra-lapack
run: cd nalgebra-lapack; cargo build;
- name: Build nalgebra-sparse
run: cd nalgebra-sparse; cargo build;
# Run this on it’s own job because it alone takes a lot of time.
# So it’s best to let it run in parallel to the other jobs.
build-nalgebra-all-features:
runs-on: ubuntu-latest
steps:
# Needed because the --all-features build which enables cuda support.
- uses: Jimver/cuda-toolkit@v0.2.4
- uses: actions/checkout@v2
- run: cargo build --all-features;
- run: cargo build -p nalgebra-glm --all-features;
test-nalgebra:
runs-on: ubuntu-latest
# env:
@ -65,10 +71,10 @@ jobs:
- name: test nalgebra-sparse
# Manifest-path is necessary because cargo otherwise won't correctly forward features
# We increase number of proptest cases to hopefully catch more potential bugs
run: PROPTEST_CASES=10000 cargo test --manifest-path=nalgebra-sparse/Cargo.toml --features compare,proptest-support
run: PROPTEST_CASES=10000 cargo test --manifest-path=nalgebra-sparse/Cargo.toml --features compare,proptest-support,io
- name: test nalgebra-sparse (slow tests)
# Unfortunately, the "slow-tests" take so much time that we need to run them with --release
run: PROPTEST_CASES=10000 cargo test --release --manifest-path=nalgebra-sparse/Cargo.toml --features compare,proptest-support,slow-tests slow
run: PROPTEST_CASES=10000 cargo test --release --manifest-path=nalgebra-sparse/Cargo.toml --features compare,proptest-support,io,slow-tests slow
test-nalgebra-macros:
runs-on: ubuntu-latest
steps:
@ -106,3 +112,20 @@ jobs:
run: xargo build --verbose --no-default-features --features alloc --target=x86_64-unknown-linux-gnu;
- name: build thumbv7em-none-eabihf
run: xargo build --verbose --no-default-features --target=thumbv7em-none-eabihf;
- name: build x86_64-unknown-linux-gnu nalgebra-glm
run: xargo build --verbose --no-default-features -p nalgebra-glm --target=x86_64-unknown-linux-gnu;
- name: build thumbv7em-none-eabihf nalgebra-glm
run: xargo build --verbose --no-default-features -p nalgebra-glm --target=thumbv7em-none-eabihf;
build-cuda:
runs-on: ubuntu-latest
steps:
- uses: Jimver/cuda-toolkit@v0.2.4
- name: Install nightly-2021-12-04
uses: actions-rs/toolchain@v1
with:
toolchain: nightly-2021-12-04
override: true
- uses: actions/checkout@v2
- run: rustup target add nvptx64-nvidia-cuda
- run: cargo build --no-default-features --features cuda
- run: cargo build --no-default-features --features cuda --target=nvptx64-nvidia-cuda

46
CHANGELOG.md
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@ -4,6 +4,52 @@ documented here.
This project adheres to [Semantic Versioning](https://semver.org/).
## [0.30.0] (02 Jan. 2022)
### Breaking changes
- The `Dim` trait is now marked as unsafe.
- The `Matrix::pow` and `Matrix::pow_mut` methods only allow positive integer exponents now. To compute negative
exponents, the user is free to invert the matrix before calling `pow` with the exponent’s absolute value.
### Modified
- Use more concise debug impls for matrices and geometric transformation types.
- The singular values computed by the SVD are now sorted in increasing order by default. Use `SVD::new_unordered`
instead to reproduce the older behavior without the sorting overhead.
- The `UnitDualQuaternion::sclerp` method will no longer panic when given two equal rotations.
- The `Matrix::select_rows` and `Matrix::select_columns` methods no longer require the matrix components to implement
the trait `Zero`.
- The `Matrix::pow` and `Matrix::pow_mut` methods will now also work with integer matrices.
### Added
- Added the conversion trait `From<Vec<T>>` and method `from_vec_storage` for `RowDVector`.
- Added implementation of `From` and `Into` for converting between `nalgebra` types and types from
`glam 0.18`. These can be enabled by enabling the `convert-glam018` cargo features.
- Added the methods `Matrix::product`, `::row_product`, `::row_product_tr`, and `::column_product` to compute the
product of the components, rows, or columns, of a single matrix or vector.
- The `Default` trait is now implemented for most geometric types: `Point`, `Isometry`, `Rotation`, `Similarity`,
`Transform`, `UnitComplex`, and `UnitQuaternion`.
- Added the `Scale` geometric type for representing non-uniform scaling.
- Added `Cholesky::new_with_substitute` that will replace diagonal elements by a given constant whenever `Cholesky`
meets a non-definite-positiveness.
- Re-added the conversion from a vector/matrix slice to a static array.
- Added the `cuda` feature that enables the support of [rust-cuda](https://github.com/Rust-GPU/Rust-CUDA) for using
`nalgebra` features with CUDA kernels written in Rust.
- Added special-cases implementations for the 2x2 and 3x3 SVDs for better accuracy and performances.
- Added the methods `Matrix::polar`, `Matrix::try_polar`, and `SVD::to_polar` to compute the polar decomposition of
a matrix, based on its SVD.
- `nalgebra-sparse`: provide constructors for unsorted but otherwise valid data using the CSR format.
- `nalgebra-sparse`: added reading MatrixMarked data files to a sparse `CooMatrix`.
### Fixed
- Fixed a potential unsoundness with `matrix.get(i)` and `matrix.get_mut(i)` where `i` is an `usize`, and `matrix`
is a matrix slice with non-default strides.
- Fixed potential unsoundness with `vector.perp` where `vector` isn’t actually a 2D vector as expected.
- Fixed linkage issue with `nalgebra-lapack`: the user of `nalgebra-lapack` no longer have to add
`extern crate lapack-src` to their `main.rs`.
- Fixed the `no-std` build of `nalgebra-glm`.
- Fix the `pow` and `pow_mut` functions (the result was incorrect for some exponent values).
## [0.29.0]
### Breaking changes
- We updated to the version 0.6 of `simba`. This means that the trait bounds `T: na::RealField`, `na::ComplexField`,

11
Cargo.toml
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@ -1,6 +1,6 @@
[package]
name = "nalgebra"
version = "0.29.0"
version = "0.30.0"
authors = [ "Sébastien Crozet <developer@crozet.re>" ]
description = "General-purpose linear algebra library with transformations and statically-sized or dynamically-sized matrices."
@ -32,6 +32,7 @@ compare = [ "matrixcompare-core" ]
libm = [ "simba/libm" ]
libm-force = [ "simba/libm_force" ]
macros = [ "nalgebra-macros" ]
cuda = [ "cust", "simba/cuda" ]
# Conversion
convert-mint = [ "mint" ]
@ -41,6 +42,7 @@ convert-glam014 = [ "glam014" ]
convert-glam015 = [ "glam015" ]
convert-glam016 = [ "glam016" ]
convert-glam017 = [ "glam017" ]
convert-glam018 = [ "glam018" ]
# Serialization
## To use serde in a #[no-std] environment, enable the
@ -72,7 +74,7 @@ num-traits = { version = "0.2", default-features = false }
num-complex = { version = "0.4", default-features = false }
num-rational = { version = "0.4", default-features = false }
approx = { version = "0.5", default-features = false }
simba = { version = "0.6", default-features = false }
simba = { version = "0.7", default-features = false }
alga = { version = "0.9", default-features = false, optional = true }
rand_distr = { version = "0.4", default-features = false, optional = true }
matrixmultiply = { version = "0.3", optional = true }
@ -91,6 +93,10 @@ glam014 = { package = "glam", version = "0.14", optional = true }
glam015 = { package = "glam", version = "0.15", optional = true }
glam016 = { package = "glam", version = "0.16", optional = true }
glam017 = { package = "glam", version = "0.17", optional = true }
glam018 = { package = "glam", version = "0.18", optional = true }
[target.'cfg(not(target_os = "cuda"))'.dependencies]
cust = { version = "0.2", optional = true }
[dev-dependencies]
@ -127,3 +133,4 @@ lto = true
[package.metadata.docs.rs]
# Enable certain features when building docs for docs.rs
features = [ "proptest-support", "compare", "macros", "rand" ]

12
Makefile
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@ -1,12 +0,0 @@
all:
cargo test --features "debug arbitrary serde-serialize abomonation-serialize compare"
# cargo check --features "debug arbitrary serde-serialize"
doc:
cargo doc --no-deps --features "debug arbitrary serde-serialize abomonation"
bench:
cargo bench
test:
cargo test --features "debug arbitrary serde-serialize abomonation-serialize compare"

16
README.md
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@ -9,7 +9,7 @@
<img src="https://circleci.com/gh/dimforge/nalgebra.svg?style=svg" alt="Build status">
</a>
<a href="https://crates.io/crates/nalgebra">
<img src="https://meritbadge.herokuapp.com/nalgebra?style=flat-square" alt="crates.io">
<img src="https://img.shields.io/crates/v/nalgebra.svg?style=flat-square" alt="crates.io">
</a>
<a href="https://opensource.org/licenses/Apache-2.0">
<img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg">
@ -30,10 +30,18 @@
-----
## Platinum sponsors
Rapier is supported by:
## Acknowledgements
nalgebra is supported by our **platinum** sponsors:
<p>
<a href="https://embark-studios.com">
<img src="https://www.embark.dev/img/logo_black.png" width="401px">
<img src="https://www.embark.dev/img/logo_black.png" width="301px">
</a>
</p>
And our gold sponsors:
<p>
<a href="https://fragcolor.com">
<img src="https://dimforge.com/img/fragcolor_logo1_color_black.svg" width="151px">
</a>
</p>

18
benches/linalg/svd.rs
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@ -1,4 +1,18 @@
use na::{Matrix4, SVD};
use na::{Matrix2, Matrix3, Matrix4, SVD};
fn svd_decompose_2x2_f32(bh: &mut criterion::Criterion) {
let m = Matrix2::<f32>::new_random();
bh.bench_function("svd_decompose_2x2", move |bh| {
bh.iter(|| std::hint::black_box(SVD::new_unordered(m.clone(), true, true)))
});
}
fn svd_decompose_3x3_f32(bh: &mut criterion::Criterion) {
let m = Matrix3::<f32>::new_random();
bh.bench_function("svd_decompose_3x3", move |bh| {
bh.iter(|| std::hint::black_box(SVD::new_unordered(m.clone(), true, true)))
});
}
fn svd_decompose_4x4(bh: &mut criterion::Criterion) {
let m = Matrix4::<f64>::new_random();
@ -114,6 +128,8 @@ fn pseudo_inverse_200x200(bh: &mut criterion::Criterion) {
criterion_group!(
svd,
svd_decompose_2x2_f32,
svd_decompose_3x3_f32,
svd_decompose_4x4,
svd_decompose_10x10,
svd_decompose_100x100,

2
examples/cargo/Cargo.toml
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@ -4,7 +4,7 @@ version = "0.0.0"
authors = [ "You" ]
[dependencies]
nalgebra = "0.29.0"
nalgebra = "0.30.0"
[[bin]]
name = "example"

3
examples/mvp.rs
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@ -3,6 +3,7 @@
extern crate nalgebra as na;
use na::{Isometry3, Perspective3, Point3, Vector3};
use std::f32::consts;
fn main() {
// Our object is translated along the x axis.
@ -15,7 +16,7 @@ fn main() {
let view = Isometry3::look_at_rh(&eye, &target, &Vector3::y());
// A perspective projection.
let projection = Perspective3::new(16.0 / 9.0, 3.14 / 2.0, 1.0, 1000.0);
let projection = Perspective3::new(16.0 / 9.0, consts::PI / 2.0, 1.0, 1000.0);
// The combination of the model with the view is still an isometry.
let model_view = view * model;

1
examples/raw_pointer.rs
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@ -19,6 +19,7 @@ fn main() {
/* Then pass the raw pointers to some graphics API. */
#[allow(clippy::float_cmp)]
unsafe {
assert_eq!(*v_pointer, 1.0);
assert_eq!(*v_pointer.offset(1), 0.0);

3
examples/screen_to_view_coords.rs
View File

@ -3,9 +3,10 @@
extern crate nalgebra as na;
use na::{Perspective3, Point2, Point3, Unit};
use std::f32::consts;
fn main() {
let projection = Perspective3::new(800.0 / 600.0, 3.14 / 2.0, 1.0, 1000.0);
let projection = Perspective3::new(800.0 / 600.0, consts::PI / 2.0, 1.0, 1000.0);
let screen_point = Point2::new(10.0f32, 20.0);
// Compute two points in clip-space.

5
examples/transform_conversion.rs
View File

@ -1,6 +1,7 @@
extern crate nalgebra as na;
use na::{Isometry2, Similarity2, Vector2};
use std::f32::consts;
fn main() {
// Isometry -> Similarity conversion always succeeds.
@ -8,8 +9,8 @@ fn main() {
let _: Similarity2<f32> = na::convert(iso);
// Similarity -> Isometry conversion fails if the scaling factor is not 1.0.
let sim_without_scaling = Similarity2::new(Vector2::new(1.0f32, 2.0), 3.14, 1.0);
let sim_with_scaling = Similarity2::new(Vector2::new(1.0f32, 2.0), 3.14, 2.0);
let sim_without_scaling = Similarity2::new(Vector2::new(1.0f32, 2.0), consts::PI, 1.0);
let sim_with_scaling = Similarity2::new(Vector2::new(1.0f32, 2.0), consts::PI, 2.0);
let iso_success: Option<Isometry2<f32>> = na::try_convert(sim_without_scaling);
let iso_fail: Option<Isometry2<f32>> = na::try_convert(sim_with_scaling);

3
examples/transform_matrix4.rs
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@ -3,6 +3,7 @@ extern crate approx;
extern crate nalgebra as na;
use na::{Matrix4, Point3, Vector3};
use std::f32::consts;
fn main() {
// Create a uniform scaling matrix with scaling factor 2.
@ -28,7 +29,7 @@ fn main() {
);
// Create rotation.
let rot = Matrix4::from_scaled_axis(&Vector3::x() * 3.14);
let rot = Matrix4::from_scaled_axis(Vector3::x() * consts::PI);
let rot_then_m = m * rot; // Right-multiplication is equivalent to prepending `rot` to `m`.
let m_then_rot = rot * m; // Left-multiplication is equivalent to appending `rot` to `m`.

1
examples/transformation_pointer.rs
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@ -12,6 +12,7 @@ fn main() {
/* Then pass the raw pointer to some graphics API. */
#[allow(clippy::float_cmp)]
unsafe {
assert_eq!(*iso_pointer, 1.0);
assert_eq!(*iso_pointer.offset(5), 1.0);

1
examples/unit_wrapper.rs
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@ -1,3 +1,4 @@
#![allow(clippy::float_cmp)]
extern crate nalgebra as na;
use na::{Unit, Vector3};

17
nalgebra-glm/Cargo.toml
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@ -1,6 +1,6 @@
[package]
name = "nalgebra-glm"
version = "0.15.0"
version = "0.16.0"
authors = ["sebcrozet <developer@crozet.re>"]
description = "A computer-graphics oriented API for nalgebra, inspired by the C++ GLM library."
@ -22,9 +22,20 @@ std = [ "nalgebra/std", "simba/std" ]
arbitrary = [ "nalgebra/arbitrary" ]
serde-serialize = [ "nalgebra/serde-serialize-no-std" ]
abomonation-serialize = [ "nalgebra/abomonation-serialize" ]
cuda = [ "nalgebra/cuda" ]
# Conversion
convert-mint = [ "nalgebra/mint" ]
convert-bytemuck = [ "nalgebra/bytemuck" ]
convert-glam013 = [ "nalgebra/glam013" ]
convert-glam014 = [ "nalgebra/glam014" ]
convert-glam015 = [ "nalgebra/glam015" ]
convert-glam016 = [ "nalgebra/glam016" ]
convert-glam017 = [ "nalgebra/glam017" ]
convert-glam018 = [ "nalgebra/glam018" ]
[dependencies]
num-traits = { version = "0.2", default-features = false }
approx = { version = "0.5", default-features = false }
simba = { version = "0.6", default-features = false }
nalgebra = { path = "..", version = "0.29", default-features = false }
simba = { version = "0.7", default-features = false }
nalgebra = { path = "..", version = "0.30", default-features = false }

17
nalgebra-glm/src/constructors.rs
View File

@ -75,6 +75,21 @@ pub fn mat2x4<T: Scalar>(m11: T, m12: T, m13: T, m14: T,
}
/// Create a new 3x3 matrix.
///
/// # Example
/// ```
/// # use nalgebra_glm::mat3;
/// let m = mat3(
/// 1.0, 2.0, 3.0,
/// 4.0, 5.0, 6.0,
/// 7.0, 8.0, 9.0
/// );
/// assert!(
/// m.m11 == 1.0 && m.m12 == 2.0 && m.m13 == 3.0 &&
/// m.m21 == 4.0 && m.m22 == 5.0 && m.m23 == 6.0 &&
/// m.m31 == 7.0 && m.m32 == 8.0 && m.m33 == 9.0
/// );
/// ```
#[rustfmt::skip]
pub fn mat3<T: Scalar>(m11: T, m12: T, m13: T,
m21: T, m22: T, m23: T,
@ -105,8 +120,8 @@ pub fn mat3x3<T: Scalar>(m11: T, m12: T, m13: T,
m31: T, m32: T, m33: T) -> TMat3<T> {
TMat::<T, 3, 3>::new(
m11, m12, m13,
m31, m32, m33,
m21, m22, m23,
m31, m32, m33,
)
}

2
nalgebra-glm/src/traits.rs
View File

@ -1,9 +1,9 @@
use approx::AbsDiffEq;
use num::{Bounded, Signed};
use core::cmp::PartialOrd;
use na::Scalar;
use simba::scalar::{ClosedAdd, ClosedMul, ClosedSub, RealField};
use std::cmp::PartialOrd;
/// A number that can either be an integer or a float.
pub trait Number:

2
nalgebra-glm/src/trigonometric.rs
View File

@ -53,7 +53,7 @@ pub fn degrees<T: RealNumber, const D: usize>(radians: &TVec<T, D>) -> TVec<T, D
radians.map(|e| e * na::convert(180.0) / T::pi())
}
/// Component-wise conversion fro degrees to radians.
/// Component-wise conversion from degrees to radians.
pub fn radians<T: RealNumber, const D: usize>(degrees: &TVec<T, D>) -> TVec<T, D> {
degrees.map(|e| e * T::pi() / na::convert(180.0))
}

8
nalgebra-lapack/Cargo.toml
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@ -1,6 +1,6 @@
[package]
name = "nalgebra-lapack"
version = "0.20.0"
version = "0.21.0"
authors = [ "Sébastien Crozet <developer@crozet.re>", "Andrew Straw <strawman@astraw.com>" ]
description = "Matrix decompositions using nalgebra matrices and Lapack bindings."
@ -29,17 +29,17 @@ accelerate = ["lapack-src/accelerate"]
intel-mkl = ["lapack-src/intel-mkl"]
[dependencies]
nalgebra = { version = "0.29", path = ".." }
nalgebra = { version = "0.30", path = ".." }
num-traits = "0.2"
num-complex = { version = "0.4", default-features = false }
simba = "0.5"
simba = "0.7"
serde = { version = "1.0", features = [ "derive" ], optional = true }
lapack = { version = "0.19", default-features = false }
lapack-src = { version = "0.8", default-features = false }
# clippy = "*"
[dev-dependencies]
nalgebra = { version = "0.29", features = [ "arbitrary", "rand" ], path = ".." }
nalgebra = { version = "0.30", features = [ "arbitrary", "rand" ], path = ".." }
proptest = { version = "1", default-features = false, features = ["std"] }
quickcheck = "1"
approx = "0.5"

4
nalgebra-lapack/src/eigen.rs
View File

@ -294,7 +294,7 @@ where
let mut res = Matrix::zeros_generic(nrows, Const::<1>);
for i in 0..res.len() {
res[i] = Complex::new(wr[i], wi[i]);
res[i] = Complex::new(wr[i].clone(), wi[i].clone());
}
res
@ -306,7 +306,7 @@ where
pub fn determinant(&self) -> T {
let mut det = T::one();
for e in self.eigenvalues.iter() {
det *= *e;
det *= e.clone();
}
det

3
nalgebra-lapack/src/lib.rs
View File

@ -73,6 +73,9 @@
html_root_url = "https://nalgebra.org/rustdoc"
)]
extern crate lapack;
extern crate lapack_src;
extern crate nalgebra as na;
extern crate num_traits as num;

2
nalgebra-lapack/src/schur.rs
View File

@ -155,7 +155,7 @@ where
let mut out = Matrix::zeros_generic(self.t.shape_generic().0, Const::<1>);
for i in 0..out.len() {
out[i] = Complex::new(self.re[i], self.im[i])
out[i] = Complex::new(self.re[i].clone(), self.im[i].clone())
}
out

4
nalgebra-lapack/src/symmetric_eigen.rs
View File

@ -140,7 +140,7 @@ where
pub fn determinant(&self) -> T {
let mut det = T::one();
for e in self.eigenvalues.iter() {
det *= *e;
det *= e.clone();
}
det
@ -153,7 +153,7 @@ where
pub fn recompose(&self) -> OMatrix<T, D, D> {
let mut u_t = self.eigenvectors.clone();
for i in 0..self.eigenvalues.len() {
let val = self.eigenvalues[i];
let val = self.eigenvalues[i].clone();
u_t.column_mut(i).mul_assign(val);
}
u_t.transpose_mut();

3
nalgebra-lapack/tests/lib.rs
View File

@ -6,9 +6,6 @@ compile_error!("Tests must be run with `proptest-support`");
extern crate nalgebra as na;
extern crate nalgebra_lapack as nl;
extern crate lapack;
extern crate lapack_src;
mod linalg;
#[path = "../../tests/proptest/mod.rs"]
mod proptest;

2
nalgebra-macros/Cargo.toml
View File

@ -21,5 +21,5 @@ quote = "1.0"
proc-macro2 = "1.0"
[dev-dependencies]
nalgebra = { version = "0.29.0", path = ".." }
nalgebra = { version = "0.30.0", path = ".." }
trybuild = "1.0.42"

11
nalgebra-sparse/Cargo.toml
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@ -1,6 +1,6 @@
[package]
name = "nalgebra-sparse"
version = "0.5.0"
version = "0.6.0"
authors = [ "Andreas Longva", "Sébastien Crozet <developer@crozet.re>" ]
edition = "2018"
description = "Sparse matrix computation based on nalgebra."
@ -16,19 +16,24 @@ license = "Apache-2.0"
proptest-support = ["proptest", "nalgebra/proptest-support"]
compare = [ "matrixcompare-core" ]
# Enable matrix market I/O
io = [ "pest", "pest_derive" ]
# Enable to enable running some tests that take a lot of time to run
slow-tests = []
[dependencies]
nalgebra = { version="0.29", path = "../" }
nalgebra = { version="0.30", path = "../" }
num-traits = { version = "0.2", default-features = false }
proptest = { version = "1.0", optional = true }
matrixcompare-core = { version = "0.1.0", optional = true }
pest = { version = "2", optional = true }
pest_derive = { version = "2", optional = true }
[dev-dependencies]
itertools = "0.10"
matrixcompare = { version = "0.3.0", features = [ "proptest-support" ] }
nalgebra = { version="0.29", path = "../", features = ["compare"] }
nalgebra = { version="0.30", path = "../", features = ["compare"] }
[package.metadata.docs.rs]
# Enable certain features when building docs for docs.rs

72
nalgebra-sparse/src/csr.rs
View File

@ -10,6 +10,7 @@ use crate::{SparseEntry, SparseEntryMut, SparseFormatError, SparseFormatErrorKin
use nalgebra::Scalar;
use num_traits::One;
use std::iter::FromIterator;
use std::slice::{Iter, IterMut};
/// A CSR representation of a sparse matrix.
@ -170,6 +171,77 @@ impl<T> CsrMatrix<T> {
Self::try_from_pattern_and_values(pattern, values)
}
/// Try to construct a CSR matrix from raw CSR data with unsorted column indices.
///
/// It is assumed that each row contains unique column indices that are in
/// bounds with respect to the number of columns in the matrix. If this is not the case,
/// an error is returned to indicate the failure.
///
/// An error is returned if the data given does not conform to the CSR storage format
/// with the exception of having unsorted column indices and values.
/// See the documentation for [CsrMatrix](struct.CsrMatrix.html) for more information.
pub fn try_from_unsorted_csr_data(
num_rows: usize,
num_cols: usize,
row_offsets: Vec<usize>,
col_indices: Vec<usize>,
values: Vec<T>,
) -> Result<Self, SparseFormatError>
where
T: Scalar,
{
use SparsityPatternFormatError::*;
let count = col_indices.len();
let mut p: Vec<usize> = (0..count).collect();
if col_indices.len() != values.len() {
return Err(SparseFormatError::from_kind_and_msg(
SparseFormatErrorKind::InvalidStructure,
"Number of values and column indices must be the same",
));
}
if row_offsets.len() == 0 {
return Err(SparseFormatError::from_kind_and_msg(
SparseFormatErrorKind::InvalidStructure,
"Number of offsets should be greater than 0",
));
}
for (index, &offset) in row_offsets[0..row_offsets.len() - 1].iter().enumerate() {
let next_offset = row_offsets[index + 1];
if next_offset > count {
return Err(SparseFormatError::from_kind_and_msg(
SparseFormatErrorKind::InvalidStructure,
"No row offset should be greater than the number of column indices",
));
}
if offset > next_offset {
return Err(NonmonotonicOffsets).map_err(pattern_format_error_to_csr_error);
}
p[offset..next_offset].sort_by(|a, b| {
let x = &col_indices[*a];
let y = &col_indices[*b];
x.partial_cmp(y).unwrap()
});
}
// permute indices
let sorted_col_indices: Vec<usize> =
Vec::from_iter((p.iter().map(|i| &col_indices[*i])).cloned());
// permute values
let sorted_values: Vec<T> = Vec::from_iter((p.iter().map(|i| &values[*i])).cloned());
return Self::try_from_csr_data(
num_rows,
num_cols,
row_offsets,
sorted_col_indices,
sorted_values,
);
}
/// Try to construct a CSR matrix from a sparsity pattern and associated non-zero values.
///
/// Returns an error if the number of values does not match the number of minor indices

53
nalgebra-sparse/src/io/matrix_market.pest Normal file
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@ -0,0 +1,53 @@
WHITESPACE = _{ " "|"\t" }
//
Sparsity = {^"coordinate" | ^"array"}
DataType = {^"real" | ^"complex" | ^"pattern" | ^"integer" }
StorageScheme = {^"symmetric" | ^"general" | ^"skew-symmetric" | ^"hermitian"}
// Only consider matrices here.
Header = { ^"%%matrixmarket matrix" ~ Sparsity ~ DataType ~ StorageScheme }
//
Comments = _{ "%" ~ (!NEWLINE ~ ANY)* }
//
Dimension = @{ ASCII_DIGIT+ }
SparseShape = { Dimension ~ Dimension ~ Dimension}
DenseShape = { Dimension ~ Dimension}
Shape = {SparseShape | DenseShape }
//
// grammar from https://doc.rust-lang.org/std/primitive.f64.html#grammar
Sign = {("+" | "-")}
Exp = @{ ^"e" ~ Sign? ~ ASCII_DIGIT+}
Number = @{ ((ASCII_DIGIT+ ~ "." ~ ASCII_DIGIT*) | (ASCII_DIGIT* ~ "." ~ASCII_DIGIT+) | ASCII_DIGIT+ ) ~ Exp? }
Real = @{ Sign? ~ ("inf" | "NaN" | Number) }
SparseReal = {Dimension~ Dimension~ Real }
SparseComplex = {Dimension ~ Dimension ~ Real ~ Real}
SparsePattern = {Dimension ~ Dimension}
DenseReal = {Real}
DenseComplex = {Real ~ Real}
Entry = { SparseComplex | SparseReal | SparsePattern | DenseComplex | DenseReal }
// end of file, a silent way, see https://github.com/pest-parser/pest/issues/304#issuecomment-427198507
eoi = _{ !ANY }
Document = {
SOI ~
NEWLINE* ~
Header ~
(NEWLINE ~ Comments)* ~
(NEWLINE ~ Shape) ~
(NEWLINE ~ Entry?)* ~
eoi
}

1331
nalgebra-sparse/src/io/matrix_market.rs Normal file
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File diff suppressed because it is too large Load Diff

38
nalgebra-sparse/src/io/mod.rs Normal file
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@ -0,0 +1,38 @@
//! Functionality for importing and exporting sparse matrices to and from files.
//!
//! **Available only when the `io` feature is enabled.**
//!
//! The following formats are currently supported:
//!
//! | Format | Import | Export |
//! | ------------------------------------------------|------------|------------|
//! | [Matrix market](#matrix-market-format) | Yes | No |
//!
//! [Matrix market]: https://math.nist.gov/MatrixMarket/formats.html
//!
//! ## Matrix Market format
//!
//! The Matrix Market format is a simple ASCII-based file format for sparse matrices, and was initially developed for
//! the [NIST Matrix Market](https://math.nist.gov/MatrixMarket/), a repository of example sparse matrices.
//! In later years it has largely been superseded by the
//! [SuiteSparse Matrix Collection](https://sparse.tamu.edu/) (formerly University of Florida Sparse Matrix Collection),
//! which also uses the Matrix Market file format.
//!
//! We currently offer functionality for importing a Matrix market file to an instance of a
//! [CooMatrix](crate::CooMatrix) through the function [load_coo_from_matrix_market_file]. It is also possible to load
//! a matrix stored in the matrix market format with the function [load_coo_from_matrix_market_str].
//!
//! Export is currently not implemented, but [planned](https://github.com/dimforge/nalgebra/issues/1037).
//!
//! Our implementation is based on the [format description](https://math.nist.gov/MatrixMarket/formats.html)
//! on the Matrix Market website and the
//! [following NIST whitepaper](https://math.nist.gov/MatrixMarket/reports/MMformat.ps):
//!
//! > Boisvert, Ronald F., Roldan Pozo, and Karin A. Remington.<br/>
//! > "*The Matrix Market Exchange Formats: Initial Design.*" (1996).
pub use self::matrix_market::{
load_coo_from_matrix_market_file, load_coo_from_matrix_market_str, MatrixMarketError,
MatrixMarketErrorKind, MatrixMarketScalar,
};
mod matrix_market;

9
nalgebra-sparse/src/lib.rs
View File

@ -19,6 +19,7 @@
//! - Sparsity patterns in CSR and CSC matrices are explicitly represented by the
//! [SparsityPattern](pattern::SparsityPattern) type, which encodes the invariants of the
//! associated index data structures.
//! - [Matrix market format support](`io`) when the `io` feature is enabled.
//! - [proptest strategies](`proptest`) for sparse matrices when the feature
//! `proptest-support` is enabled.
//! - [matrixcompare support](https://crates.io/crates/matrixcompare) for effortless
@ -142,11 +143,19 @@
)]
pub extern crate nalgebra as na;
#[cfg(feature = "io")]
extern crate pest;
#[macro_use]
#[cfg(feature = "io")]
extern crate pest_derive;
pub mod convert;
pub mod coo;
pub mod csc;
pub mod csr;
pub mod factorization;
#[cfg(feature = "io")]
pub mod io;
pub mod ops;
pub mod pattern;

2
nalgebra-sparse/src/ops/mod.rs
View File

@ -67,7 +67,7 @@
//! As can be seen from the table, only `CSR * Dense` and `CSC * Dense` are supported.
//! The other way around, i.e. `Dense * CSR` and `Dense * CSC` are not implemented.
//!
//! Additionally, [CsrMatrix](`crate::csr::CsrMatrix`) and [CooMatrix](`crate::coo::CooMatrix`)
//! Additionally, [CsrMatrix](`crate::csr::CsrMatrix`) and [CscMatrix](`crate::csc::CscMatrix`)
//! support multiplication with scalars, in addition to division by a scalar.
//! Note that only `Matrix * Scalar` works in a generic context, although `Scalar * Matrix`
//! has been implemented for many of the built-in arithmetic types. This is due to a fundamental

19
nalgebra-sparse/src/proptest.rs
View File

@ -5,11 +5,6 @@
//! The strategies provided here are generally expected to be able to generate the entire range
//! of possible outputs given the constraints on dimensions and values. However, there are no
//! particular guarantees on the distribution of possible values.
// Contains some patched code from proptest that we can remove in the (hopefully near) future.
// See docs in file for more details.
mod proptest_patched;
use crate::coo::CooMatrix;
use crate::csc::CscMatrix;
use crate::csr::CsrMatrix;
@ -31,16 +26,10 @@ fn dense_row_major_coord_strategy(
let mut booleans = vec![true; nnz];
booleans.append(&mut vec![false; (nrows * ncols) - nnz]);
// Make sure that exactly `nnz` of the booleans are true
// TODO: We cannot use the below code because of a bug in proptest, see
// https://github.com/AltSysrq/proptest/pull/217
// so for now we're using a patched version of the Shuffle adapter
// (see also docs in `proptest_patched`
// Just(booleans)
// // Need to shuffle to make sure they are randomly distributed
// .prop_shuffle()
proptest_patched::Shuffle(Just(booleans)).prop_map(move |booleans| {
Just(booleans)
// Need to shuffle to make sure they are randomly distributed
.prop_shuffle()
.prop_map(move |booleans| {
booleans
.into_iter()
.enumerate()

146
nalgebra-sparse/src/proptest/proptest_patched.rs
View File

@ -1,146 +0,0 @@
//! Contains a modified implementation of `proptest::strategy::Shuffle`.
//!
//! The current implementation in `proptest` does not generate all permutations, which is
//! problematic for our proptest generators. The issue has been fixed in
//! https://github.com/AltSysrq/proptest/pull/217
//! but it has yet to be merged and released. As soon as this fix makes it into a new release,
//! the modified code here can be removed.
//!
/*!
This code has been copied and adapted from
https://github.com/AltSysrq/proptest/blob/master/proptest/src/strategy/shuffle.rs
The original licensing text is:
//-
// Copyright 2017 Jason Lingle
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.
*/
use proptest::num;
use proptest::prelude::Rng;
use proptest::strategy::{NewTree, Shuffleable, Strategy, ValueTree};
use proptest::test_runner::{TestRng, TestRunner};
use std::cell::Cell;
#[derive(Clone, Debug)]
#[must_use = "strategies do nothing unless used"]
pub struct Shuffle<S>(pub(super) S);
impl<S: Strategy> Strategy for Shuffle<S>
where
S::Value: Shuffleable,
{
type Tree = ShuffleValueTree<S::Tree>;
type Value = S::Value;
fn new_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
let rng = runner.new_rng();
self.0.new_tree(runner).map(|inner| ShuffleValueTree {
inner,
rng,
dist: Cell::new(None),
simplifying_inner: false,
})
}
}
#[derive(Clone, Debug)]
pub struct ShuffleValueTree<V> {
inner: V,
rng: TestRng,
dist: Cell<Option<num::usize::BinarySearch>>,
simplifying_inner: bool,
}
impl<V: ValueTree> ShuffleValueTree<V>
where
V::Value: Shuffleable,
{
fn init_dist(&self, dflt: usize) -> usize {
if self.dist.get().is_none() {
self.dist.set(Some(num::usize::BinarySearch::new(dflt)));
}
self.dist.get().unwrap().current()
}
fn force_init_dist(&self) {
if self.dist.get().is_none() {
let _ = self.init_dist(self.current().shuffle_len());
}
}
}
impl<V: ValueTree> ValueTree for ShuffleValueTree<V>
where
V::Value: Shuffleable,
{
type Value = V::Value;
fn current(&self) -> V::Value {
let mut value = self.inner.current();
let len = value.shuffle_len();
// The maximum distance to swap elements. This could be larger than
// `value` if `value` has reduced size during shrinking; that's OK,
// since we only use this to filter swaps.
let max_swap = self.init_dist(len);
// If empty collection or all swaps will be filtered out, there's
// nothing to shuffle.
if 0 == len || 0 == max_swap {
return value;
}
let mut rng = self.rng.clone();
for start_index in 0..len - 1 {
// Determine the other index to be swapped, then skip the swap if
// it is too far. This ordering is critical, as it ensures that we
// generate the same sequence of random numbers every time.
// NOTE: The below line is the whole reason for the existence of this adapted code
// We need to be able to swap with the same element, so that some elements remain in
// place rather being swapped
// let end_index = rng.gen_range(start_index + 1..len);
let end_index = rng.gen_range(start_index..len);
if end_index - start_index <= max_swap {
value.shuffle_swap(start_index, end_index);
}
}
value
}
fn simplify(&mut self) -> bool {
if self.simplifying_inner {
self.inner.simplify()
} else {
// Ensure that we've initialised `dist` to *something* to give
// consistent non-panicking behaviour even if called in an
// unexpected sequence.
self.force_init_dist();
if self.dist.get_mut().as_mut().unwrap().simplify() {
true
} else {
self.simplifying_inner = true;
self.inner.simplify()
}
}
}
fn complicate(&mut self) -> bool {
if self.simplifying_inner {
self.inner.complicate()
} else {
self.force_init_dist();
self.dist.get_mut().as_mut().unwrap().complicate()
}
}
}

7
nalgebra-sparse/tests/unit.rs
View File

@ -1,6 +1,9 @@
//! Unit tests
#[cfg(any(not(feature = "proptest-support"), not(feature = "compare")))]
compile_error!("Tests must be run with features `proptest-support` and `compare`");
#[cfg(not(all(feature = "proptest-support", feature = "compare", feature = "io",)))]
compile_error!(
"Please enable the `proptest-support`, `compare` and `io` features in order to compile and run the tests.
Example: `cargo test -p nalgebra-sparse --features proptest-support,compare,io`"
);
mod unit_tests;

148
nalgebra-sparse/tests/unit_tests/csr.rs
View File

@ -5,6 +5,8 @@ use nalgebra_sparse::{SparseEntry, SparseEntryMut, SparseFormatErrorKind};
use proptest::prelude::*;
use proptest::sample::subsequence;
use super::test_data_examples::InvalidCsrDataExamples;
use crate::assert_panics;
use crate::common::csr_strategy;
@ -171,11 +173,36 @@ fn csr_matrix_valid_data() {
}
}
#[test]
fn csr_matrix_valid_data_unsorted_column_indices() {
let csr = CsrMatrix::try_from_unsorted_csr_data(
4,
5,
vec![0, 3, 5, 8, 11],
vec![4, 1, 3, 3, 1, 2, 3, 0, 3, 4, 1],
vec![5, 1, 4, 7, 4, 2, 3, 1, 8, 9, 6],
)
.unwrap();
let expected_csr = CsrMatrix::try_from_csr_data(
4,
5,
vec![0, 3, 5, 8, 11],
vec![1, 3, 4, 1, 3, 0, 2, 3, 1, 3, 4],
vec![1, 4, 5, 4, 7, 1, 2, 3, 6, 8, 9],
)
.unwrap();
assert_eq!(csr, expected_csr);
}
#[test]
fn csr_matrix_try_from_invalid_csr_data() {
let invalid_data: InvalidCsrDataExamples = InvalidCsrDataExamples::new();
{
// Empty offset array (invalid length)
let matrix = CsrMatrix::try_from_csr_data(0, 0, Vec::new(), Vec::new(), Vec::<u32>::new());
let (offsets, indices, values) = invalid_data.empty_offset_array;
let matrix = CsrMatrix::try_from_csr_data(0, 0, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::InvalidStructure
@ -184,10 +211,8 @@ fn csr_matrix_try_from_invalid_csr_data() {
{
// Offset array invalid length for arbitrary data
let offsets = vec![0, 3, 5];
let indices = vec![0, 1, 2, 3, 5];
let values = vec![0, 1, 2, 3, 4];
let (offsets, indices, values) =
invalid_data.offset_array_invalid_length_for_arbitrary_data;
let matrix = CsrMatrix::try_from_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
@ -197,9 +222,7 @@ fn csr_matrix_try_from_invalid_csr_data() {
{
// Invalid first entry in offsets array
let offsets = vec![1, 2, 2, 5];
let indices = vec![0, 5, 1, 2, 3];
let values = vec![0, 1, 2, 3, 4];
let (offsets, indices, values) = invalid_data.invalid_first_entry_in_offsets_array;
let matrix = CsrMatrix::try_from_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
@ -209,9 +232,7 @@ fn csr_matrix_try_from_invalid_csr_data() {
{
// Invalid last entry in offsets array
let offsets = vec![0, 2, 2, 4];
let indices = vec![0, 5, 1, 2, 3];
let values = vec![0, 1, 2, 3, 4];
let (offsets, indices, values) = invalid_data.invalid_last_entry_in_offsets_array;
let matrix = CsrMatrix::try_from_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
@ -221,9 +242,7 @@ fn csr_matrix_try_from_invalid_csr_data() {
{
// Invalid length of offsets array
let offsets = vec![0, 2, 2];
let indices = vec![0, 5, 1, 2, 3];
let values = vec![0, 1, 2, 3, 4];
let (offsets, indices, values) = invalid_data.invalid_length_of_offsets_array;
let matrix = CsrMatrix::try_from_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
@ -233,9 +252,7 @@ fn csr_matrix_try_from_invalid_csr_data() {
{
// Nonmonotonic offsets
let offsets = vec![0, 3, 2, 5];
let indices = vec![0, 1, 2, 3, 4];
let values = vec![0, 1, 2, 3, 4];
let (offsets, indices, values) = invalid_data.nonmonotonic_offsets;
let matrix = CsrMatrix::try_from_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
@ -245,9 +262,7 @@ fn csr_matrix_try_from_invalid_csr_data() {
{
// Nonmonotonic minor indices
let offsets = vec![0, 2, 2, 5];
let indices = vec![0, 2, 3, 1, 4];
let values = vec![0, 1, 2, 3, 4];
let (offsets, indices, values) = invalid_data.nonmonotonic_minor_indices;
let matrix = CsrMatrix::try_from_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
@ -257,9 +272,7 @@ fn csr_matrix_try_from_invalid_csr_data() {
{
// Minor index out of bounds
let offsets = vec![0, 2, 2, 5];
let indices = vec![0, 6, 1, 2, 3];
let values = vec![0, 1, 2, 3, 4];
let (offsets, indices, values) = invalid_data.minor_index_out_of_bounds;
let matrix = CsrMatrix::try_from_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
@ -269,9 +282,7 @@ fn csr_matrix_try_from_invalid_csr_data() {
{
// Duplicate entry
let offsets = vec![0, 2, 2, 5];
let indices = vec![0, 5, 2, 2, 3];
let values = vec![0, 1, 2, 3, 4];
let (offsets, indices, values) = invalid_data.duplicate_entry;
let matrix = CsrMatrix::try_from_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
@ -280,6 +291,91 @@ fn csr_matrix_try_from_invalid_csr_data() {
}
}
#[test]
fn csr_matrix_try_from_unsorted_invalid_csr_data() {
let invalid_data: InvalidCsrDataExamples = InvalidCsrDataExamples::new();
{
// Empty offset array (invalid length)
let (offsets, indices, values) = invalid_data.empty_offset_array;
let matrix = CsrMatrix::try_from_unsorted_csr_data(0, 0, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::InvalidStructure
);
}
{
// Offset array invalid length for arbitrary data
let (offsets, indices, values) =
invalid_data.offset_array_invalid_length_for_arbitrary_data;
let matrix = CsrMatrix::try_from_unsorted_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::InvalidStructure
);
}
{
// Invalid first entry in offsets array
let (offsets, indices, values) = invalid_data.invalid_first_entry_in_offsets_array;
let matrix = CsrMatrix::try_from_unsorted_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::InvalidStructure
);
}
{
// Invalid last entry in offsets array
let (offsets, indices, values) = invalid_data.invalid_last_entry_in_offsets_array;
let matrix = CsrMatrix::try_from_unsorted_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::InvalidStructure
);
}
{
// Invalid length of offsets array
let (offsets, indices, values) = invalid_data.invalid_length_of_offsets_array;
let matrix = CsrMatrix::try_from_unsorted_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::InvalidStructure
);
}
{
// Nonmonotonic offsets
let (offsets, indices, values) = invalid_data.nonmonotonic_offsets;
let matrix = CsrMatrix::try_from_unsorted_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::InvalidStructure
);
}
{
// Minor index out of bounds
let (offsets, indices, values) = invalid_data.minor_index_out_of_bounds;
let matrix = CsrMatrix::try_from_unsorted_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::IndexOutOfBounds
);
}
{
// Duplicate entry
let (offsets, indices, values) = invalid_data.duplicate_entry;
let matrix = CsrMatrix::try_from_unsorted_csr_data(3, 6, offsets, indices, values);
assert_eq!(
matrix.unwrap_err().kind(),
&SparseFormatErrorKind::DuplicateEntry
);
}
}
#[test]
fn csr_disassemble_avoids_clone_when_owned() {
// Test that disassemble avoids cloning the sparsity pattern when it holds the sole reference

345
nalgebra-sparse/tests/unit_tests/matrix_market.rs Normal file
View File

@ -0,0 +1,345 @@
use matrixcompare::assert_matrix_eq;
use nalgebra::dmatrix;
use nalgebra::Complex;
use nalgebra_sparse::io::load_coo_from_matrix_market_str;
use nalgebra_sparse::CooMatrix;
#[test]
#[rustfmt::skip]
fn test_matrixmarket_sparse_real_general_empty() {
// Test several valid zero-shapes of a sparse matrix
let shapes = vec![ (0, 0), (1, 0), (0, 1) ];
let strings: Vec<String> = shapes
.iter()
.map(|(m, n)| format!("%%MatrixMarket matrix coordinate real general\n {} {} 0", m, n))
.collect();
for (shape,string) in shapes.iter().zip(strings.iter()) {
let sparse_mat = load_coo_from_matrix_market_str::<f32>(string).unwrap();
assert_eq!(sparse_mat.nrows(), shape.0);
assert_eq!(sparse_mat.ncols(), shape.1);
assert_eq!(sparse_mat.nnz(), 0);
}
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_dense_real_general_empty() {
// Test several valid zero-shapes of a dense matrix
let shapes = vec![ (0, 0), (1, 0), (0, 1) ];
let strings: Vec<String> = shapes
.iter()
.map(|(m, n)| format!("%%MatrixMarket matrix array real general\n {} {}", m, n))
.collect();
for (shape,string) in shapes.iter().zip(strings.iter()) {
let sparse_mat = load_coo_from_matrix_market_str::<f32>(string).unwrap();
assert_eq!(sparse_mat.nrows(), shape.0);
assert_eq!(sparse_mat.ncols(), shape.1);
assert_eq!(sparse_mat.nnz(), 0);
}
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_sparse_real_general() {
let file_str = r#"
%%MatrixMarket matrix CoOrdinate real general
% This is also an example of free-format features.
%=================================================================================
%
% This ASCII file represents a sparse MxN matrix with L
% nonzeros in the following Matrix Market format:
%
% +----------------------------------------------+
% |%%MatrixMarket matrix coordinate real general | <--- header line
% |% | <--+
% |% comments | |-- 0 or more comment lines
% |% | <--+
% | M T L | <--- rows, columns, entries
% | I1 J1 A(I1, J1) | <--+
% | I2 J2 A(I2, J2) | |
% | I3 J3 A(I3, J3) | |-- L lines
% | . . . | |
% | IL JL A(IL, JL) | <--+
% +----------------------------------------------+
%
% Indices are 1-based, i.e. A(1,1) is the first element.
%
%=================================================================================
5 5 8
1 1 1
2 2 1.050e+01
3 3 1.500e-02
1 4 6.000e+00
4 2 2.505e+02
4 4 -2.800e+02
4 5 3.332e+01
5 5 1.200e+01
"#;
let sparse_mat = load_coo_from_matrix_market_str::<f32>(file_str).unwrap();
let expected = dmatrix![
1.0, 0.0, 0.0, 6.0, 0.0;
0.0, 10.5, 0.0, 0.0, 0.0;
0.0, 0.0, 0.015, 0.0, 0.0;
0.0, 250.5, 0.0, -280.0, 33.32;
0.0, 0.0, 0.0, 0.0, 12.0;
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_sparse_int_symmetric() {
let file_str = r#"
%%MatrixMarket matrix coordinate integer symmetric
%
5 5 9
1 1 11
2 2 22
3 2 23
3 3 33
4 2 24
4 4 44
5 1 -15
5 3 35
5 5 55
"#;
let sparse_mat = load_coo_from_matrix_market_str::<i128>(file_str).unwrap();
let expected = dmatrix![
11, 0, 0, 0, -15;
0, 22, 23, 24, 0;
0, 23, 33, 0, 35;
0, 24, 0, 44, 0;
-15, 0, 35, 0, 55;
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_sparse_complex_hermitian() {
let file_str = r#"
%%MatrixMarket matrix coordinate complex hermitian
%
5 5 7
1 1 1.0 0.0
2 2 10.5 0.0
4 2 250.5 22.22
3 3 0.015 0.0
4 4 -2.8e2 0.0
5 5 12.0 0.0
5 4 0.0 33.32
"#;
let sparse_mat = load_coo_from_matrix_market_str::<Complex<f64>>(file_str).unwrap();
let expected = dmatrix![
Complex::<f64>{re:1.0,im:0.0}, Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:0.0,im:0.0},Complex::<f64>{re:0.0,im:0.0};
Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:10.5,im:0.0}, Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:250.5,im:-22.22},Complex::<f64>{re:0.0,im:0.0};
Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:0.015,im:0.0}, Complex::<f64>{re:0.0,im:0.0},Complex::<f64>{re:0.0,im:0.0};
Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:250.5,im:22.22}, Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:-280.0,im:0.0},Complex::<f64>{re:0.0,im:-33.32};
Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:0.0,im:0.0}, Complex::<f64>{re:0.0,im:33.32},Complex::<f64>{re:12.0,im:0.0};
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_sparse_real_skew() {
let file_str = r#"
%%MatrixMarket matrix coordinate real skew-symmetric
%
5 5 4
3 2 -23.0
4 2 -24.0
5 1 -15.0
5 3 -35.0
"#;
let sparse_mat = load_coo_from_matrix_market_str::<f64>(file_str).unwrap();
let expected = dmatrix![
0.0, 0.0, 0.0, 0.0, 15.0;
0.0, 0.0, 23.0, 24.0, 0.0;
0.0, -23.0, 0.0, 0.0, 35.0;
0.0, -24.0, 0.0, 0.0, 0.0;
-15.0, 0.0, -35.0, 0.0, 0.0;
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_sparse_pattern_general() {
let file_str = r#"
%%MatrixMarket matrix coordinate pattern general
%
5 5 10
1 1
1 5
2 3
2 4
3 2
3 5
4 1
5 2
5 4
5 5
"#;
let pattern_matrix = load_coo_from_matrix_market_str::<()>(file_str).unwrap();
let nrows = pattern_matrix.nrows();
let ncols = pattern_matrix.ncols();
let (row_idx, col_idx, val) = pattern_matrix.disassemble();
let values = vec![1; val.len()];
let sparse_mat = CooMatrix::try_from_triplets(nrows, ncols, row_idx, col_idx, values).unwrap();
let expected = dmatrix![
1, 0, 0, 0, 1;
0, 0, 1, 1, 0;
0, 1, 0, 0, 1;
1, 0, 0, 0, 0;
0, 1, 0, 1, 1;
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_dense_real_general() {
let file_str = r#"
%%MatrixMarket matrix array real general
%
4 3
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
"#;
let sparse_mat = load_coo_from_matrix_market_str::<f32>(file_str).unwrap();
let expected = dmatrix![
1.0, 5.0, 9.0;
2.0, 6.0, 10.0;
3.0, 7.0, 11.0;
4.0, 8.0, 12.0;
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_dense_real_symmetric() {
let file_str = r#"
%%MatrixMarket matrix array real symmetric
%
4 4
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
"#;
let sparse_mat = load_coo_from_matrix_market_str::<f32>(file_str).unwrap();
let expected = dmatrix![
1.0, 2.0, 3.0, 4.0;
2.0, 5.0, 6.0, 7.0;
3.0, 6.0, 8.0, 9.0;
4.0, 7.0, 9.0, 10.0;
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_dense_complex_hermitian() {
let file_str = r#"
%%MatrixMarket matrix array complex hermitian
%
4 4
1.0 0.0
2.0 2.0
3.0 3.0
4.0 4.0
5.0 0.0
6.0 6.0
7.0 7.0
8.0 0.0
9.0 9.0
10.0 0.0
"#;
let sparse_mat = load_coo_from_matrix_market_str::<Complex<f64>>(file_str).unwrap();
let expected = dmatrix![
Complex::<f64>{re:1.0,im:0.0}, Complex::<f64>{re:2.0,im:-2.0} ,Complex::<f64>{re:3.0,im:-3.0} ,Complex::<f64>{re:4.0,im:-4.0};
Complex::<f64>{re:2.0,im:2.0}, Complex::<f64>{re:5.0,im:0.0} ,Complex::<f64>{re:6.0,im:-6.0} ,Complex::<f64>{re:7.0,im:-7.0};
Complex::<f64>{re:3.0,im:3.0}, Complex::<f64>{re:6.0,im:6.0} ,Complex::<f64>{re:8.0,im:0.0} ,Complex::<f64>{re:9.0,im:-9.0};
Complex::<f64>{re:4.0,im:4.0}, Complex::<f64>{re:7.0,im:7.0} ,Complex::<f64>{re:9.0,im:9.0} ,Complex::<f64>{re:10.0,im:0.0};
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_dense_int_skew() {
let file_str = r#"
%%MatrixMarket matrix array integer skew-symmetric
%
4 4
1
2
3
4
5
6
"#;
let sparse_mat = load_coo_from_matrix_market_str::<i32>(file_str).unwrap();
let expected = dmatrix![
0,-1,-2,-3;
1, 0,-4,-5;
2, 4, 0,-6;
3, 5, 6, 0;
];
assert_matrix_eq!(sparse_mat, expected);
}
#[test]
#[rustfmt::skip]
fn test_matrixmarket_dense_complex_general() {
let file_str = r#"
%%MatrixMarket matrix array complex general
%
2 2
1 0
1 0
1 0
1 0
"#;
let sparse_mat = load_coo_from_matrix_market_str::<Complex<f32>>(file_str).unwrap();
let expected = dmatrix![
Complex::<f32>{re:1.0,im:0.0},Complex::<f32>{re:1.0,im:0.0};
Complex::<f32>{re:1.0,im:0.0},Complex::<f32>{re:1.0,im:0.0};
];
assert_matrix_eq!(sparse_mat, expected);
}

2
nalgebra-sparse/tests/unit_tests/mod.rs
View File

@ -3,6 +3,8 @@ mod convert_serial;
mod coo;
mod csc;
mod csr;
mod matrix_market;
mod ops;
mod pattern;
mod proptest;
mod test_data_examples;

44
nalgebra-sparse/tests/unit_tests/test_data_examples.rs Normal file
View File

@ -0,0 +1,44 @@
/// Examples of *invalid* raw CSR data `(offsets, indices, values)`.
pub struct InvalidCsrDataExamples {
pub empty_offset_array: (Vec<usize>, Vec<usize>, Vec<i32>),
pub offset_array_invalid_length_for_arbitrary_data: (Vec<usize>, Vec<usize>, Vec<i32>),
pub invalid_first_entry_in_offsets_array: (Vec<usize>, Vec<usize>, Vec<i32>),
pub invalid_last_entry_in_offsets_array: (Vec<usize>, Vec<usize>, Vec<i32>),
pub invalid_length_of_offsets_array: (Vec<usize>, Vec<usize>, Vec<i32>),
pub nonmonotonic_offsets: (Vec<usize>, Vec<usize>, Vec<i32>),
pub nonmonotonic_minor_indices: (Vec<usize>, Vec<usize>, Vec<i32>),
pub minor_index_out_of_bounds: (Vec<usize>, Vec<usize>, Vec<i32>),
pub duplicate_entry: (Vec<usize>, Vec<usize>, Vec<i32>),
}
impl InvalidCsrDataExamples {
pub fn new() -> Self {
let empty_offset_array = (Vec::<usize>::new(), Vec::<usize>::new(), Vec::<i32>::new());
let offset_array_invalid_length_for_arbitrary_data =
(vec![0, 3, 5], vec![0, 1, 2, 3, 5], vec![0, 1, 2, 3, 4]);
let invalid_first_entry_in_offsets_array =
(vec![1, 2, 2, 5], vec![0, 5, 1, 2, 3], vec![0, 1, 2, 3, 4]);
let invalid_last_entry_in_offsets_array =
(vec![0, 2, 2, 4], vec![0, 5, 1, 2, 3], vec![0, 1, 2, 3, 4]);
let invalid_length_of_offsets_array =
(vec![0, 2, 2], vec![0, 5, 1, 2, 3], vec![0, 1, 2, 3, 4]);
let nonmonotonic_offsets = (vec![0, 3, 2, 5], vec![0, 1, 2, 3, 4], vec![0, 1, 2, 3, 4]);
let nonmonotonic_minor_indices =
(vec![0, 2, 2, 5], vec![0, 2, 3, 1, 4], vec![0, 1, 2, 3, 4]);
let minor_index_out_of_bounds =
(vec![0, 2, 2, 5], vec![0, 6, 1, 2, 3], vec![0, 1, 2, 3, 4]);
let duplicate_entry = (vec![0, 2, 2, 5], vec![0, 5, 2, 2, 3], vec![0, 1, 2, 3, 4]);
return Self {
empty_offset_array,
offset_array_invalid_length_for_arbitrary_data,
invalid_first_entry_in_offsets_array,
invalid_last_entry_in_offsets_array,
invalid_length_of_offsets_array,
nonmonotonic_minor_indices,
nonmonotonic_offsets,
minor_index_out_of_bounds,
duplicate_entry,
};
}
}

4
src/base/array_storage.rs
View File

@ -32,6 +32,10 @@ use std::mem;
/// A array-based statically sized matrix data storage.
#[repr(transparent)]
#[derive(Copy, Clone, PartialEq, Eq, Hash)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub struct ArrayStorage<T, const R: usize, const C: usize>(pub [[T; R]; C]);
impl<T, const R: usize, const C: usize> ArrayStorage<T, R, C> {

52
src/base/conversion.rs
View File

@ -20,10 +20,10 @@ use crate::base::{
MatrixSliceMut, OMatrix, Scalar,
};
#[cfg(any(feature = "std", feature = "alloc"))]
use crate::base::{DVector, VecStorage};
use crate::base::{DVector, RowDVector, VecStorage};
use crate::base::{SliceStorage, SliceStorageMut};
use crate::constraint::DimEq;
use crate::{IsNotStaticOne, RowSVector, SMatrix, SVector};
use crate::{IsNotStaticOne, RowSVector, SMatrix, SVector, VectorSlice, VectorSliceMut};
use std::mem::MaybeUninit;
// TODO: too bad this won't work for slice conversions.
@ -125,6 +125,24 @@ impl<T: Scalar, const D: usize> From<SVector<T, D>> for [T; D] {
}
}
impl<'a, T: Scalar, RStride: Dim, CStride: Dim, const D: usize>
From<VectorSlice<'a, T, Const<D>, RStride, CStride>> for [T; D]
{
#[inline]
fn from(vec: VectorSlice<'a, T, Const<D>, RStride, CStride>) -> Self {
vec.into_owned().into()
}
}
impl<'a, T: Scalar, RStride: Dim, CStride: Dim, const D: usize>
From<VectorSliceMut<'a, T, Const<D>, RStride, CStride>> for [T; D]
{
#[inline]
fn from(vec: VectorSliceMut<'a, T, Const<D>, RStride, CStride>) -> Self {
vec.into_owned().into()
}
}
impl<T: Scalar, const D: usize> From<[T; D]> for RowSVector<T, D>
where
Const<D>: IsNotStaticOne,
@ -197,8 +215,26 @@ impl<T: Scalar, const R: usize, const C: usize> From<[[T; R]; C]> for SMatrix<T,
impl<T: Scalar, const R: usize, const C: usize> From<SMatrix<T, R, C>> for [[T; R]; C] {
#[inline]
fn from(vec: SMatrix<T, R, C>) -> Self {
vec.data.0
fn from(mat: SMatrix<T, R, C>) -> Self {
mat.data.0
}
}
impl<'a, T: Scalar, RStride: Dim, CStride: Dim, const R: usize, const C: usize>
From<MatrixSlice<'a, T, Const<R>, Const<C>, RStride, CStride>> for [[T; R]; C]
{
#[inline]
fn from(mat: MatrixSlice<'a, T, Const<R>, Const<C>, RStride, CStride>) -> Self {
mat.into_owned().into()
}
}
impl<'a, T: Scalar, RStride: Dim, CStride: Dim, const R: usize, const C: usize>
From<MatrixSliceMut<'a, T, Const<R>, Const<C>, RStride, CStride>> for [[T; R]; C]
{
#[inline]
fn from(mat: MatrixSliceMut<'a, T, Const<R>, Const<C>, RStride, CStride>) -> Self {
mat.into_owned().into()
}
}
@ -453,6 +489,14 @@ impl<'a, T: Scalar> From<Vec<T>> for DVector<T> {
}
}
#[cfg(any(feature = "std", feature = "alloc"))]
impl<'a, T: Scalar> From<Vec<T>> for RowDVector<T> {
#[inline]
fn from(vec: Vec<T>) -> Self {
Self::from_vec(vec)
}
}
impl<'a, T: Scalar + Copy, R: Dim, C: Dim, S: RawStorage<T, R, C> + IsContiguous>
From<&'a Matrix<T, R, C, S>> for &'a [T]
{

6
src/base/default_allocator.rs
View File

@ -195,7 +195,7 @@ where
unsafe fn reallocate_copy(
rto: Const<RTO>,
cto: Const<CTO>,
mut buf: <Self as Allocator<T, RFrom, CFrom>>::Buffer,
buf: <Self as Allocator<T, RFrom, CFrom>>::Buffer,
) -> ArrayStorage<MaybeUninit<T>, RTO, CTO> {
let mut res = <Self as Allocator<T, Const<RTO>, Const<CTO>>>::allocate_uninit(rto, cto);
@ -226,7 +226,7 @@ where
unsafe fn reallocate_copy(
rto: Dynamic,
cto: CTo,
mut buf: ArrayStorage<T, RFROM, CFROM>,
buf: ArrayStorage<T, RFROM, CFROM>,
) -> VecStorage<MaybeUninit<T>, Dynamic, CTo> {
let mut res = <Self as Allocator<T, Dynamic, CTo>>::allocate_uninit(rto, cto);
@ -257,7 +257,7 @@ where
unsafe fn reallocate_copy(
rto: RTo,
cto: Dynamic,
mut buf: ArrayStorage<T, RFROM, CFROM>,
buf: ArrayStorage<T, RFROM, CFROM>,
) -> VecStorage<MaybeUninit<T>, RTo, Dynamic> {
let mut res = <Self as Allocator<T, RTo, Dynamic>>::allocate_uninit(rto, cto);

14
src/base/dimension.rs
View File

@ -13,6 +13,10 @@ use serde::{Deserialize, Deserializer, Serialize, Serializer};
/// Dim of dynamically-sized algebraic entities.
#[derive(Clone, Copy, Eq, PartialEq, Debug)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub struct Dynamic {
value: usize,
}
@ -55,7 +59,7 @@ impl IsNotStaticOne for Dynamic {}
/// Trait implemented by any type that can be used as a dimension. This includes type-level
/// integers and `Dynamic` (for dimensions not known at compile-time).
pub trait Dim: Any + Debug + Copy + PartialEq + Send + Sync {
pub unsafe trait Dim: Any + Debug + Copy + PartialEq + Send + Sync {
#[inline(always)]
fn is<D: Dim>() -> bool {
TypeId::of::<Self>() == TypeId::of::<D>()
@ -74,7 +78,7 @@ pub trait Dim: Any + Debug + Copy + PartialEq + Send + Sync {
fn from_usize(dim: usize) -> Self;
}
impl Dim for Dynamic {
unsafe impl Dim for Dynamic {
#[inline]
fn try_to_usize() -> Option<usize> {
None
@ -197,6 +201,10 @@ dim_ops!(
);
#[derive(Debug, Copy, Clone, PartialEq, Eq, Hash)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub struct Const<const R: usize>;
/// Trait implemented exclusively by type-level integers.
@ -270,7 +278,7 @@ pub trait ToTypenum {
type Typenum: Unsigned;
}
impl<const T: usize> Dim for Const<T> {
unsafe impl<const T: usize> Dim for Const<T> {
fn try_to_usize() -> Option<usize> {
Some(T)
}

11
src/base/edition.rs
View File

@ -14,7 +14,7 @@ use crate::base::{DefaultAllocator, Matrix, OMatrix, RowVector, Scalar, Vector};
use crate::{Storage, UninitMatrix};
use std::mem::MaybeUninit;
/// # Rows and columns extraction
/// # Triangular matrix extraction
impl<T: Scalar + Zero, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
/// Extracts the upper triangular part of this matrix (including the diagonal).
#[inline]
@ -41,7 +41,10 @@ impl<T: Scalar + Zero, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
res
}
}
/// # Rows and columns extraction
impl<T: Scalar, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
/// Creates a new matrix by extracting the given set of rows from `self`.
#[cfg(any(feature = "std", feature = "alloc"))]
#[must_use]
@ -95,9 +98,7 @@ impl<T: Scalar + Zero, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
for (destination, source) in icols.enumerate() {
// NOTE: this is basically a copy_frow but wrapping the values insnide of MaybeUninit.
res.column_mut(destination)
.zip_apply(&self.column(*source), |out, e| {
*out = MaybeUninit::new(e.clone())
});
.zip_apply(&self.column(*source), |out, e| *out = MaybeUninit::new(e));
}
// Safety: res is now fully initialized.
@ -1094,7 +1095,7 @@ unsafe fn compress_rows<T: Scalar>(
if new_nrows == 0 || ncols == 0 {
// The output matrix is empty, drop everything.
ptr::drop_in_place(data.as_mut());
ptr::drop_in_place(data);
return;
}

53
src/base/indexing.rs
View File

@ -1,4 +1,5 @@
//! Indexing
#![allow(clippy::reversed_empty_ranges)]
use crate::base::storage::{RawStorage, RawStorageMut};
use crate::base::{
@ -43,7 +44,7 @@ impl<D: Dim> DimRange<D> for usize {
#[test]
fn dimrange_usize() {
assert_eq!(DimRange::contained_by(&0, Const::<0>), false);
assert!(!DimRange::contained_by(&0, Const::<0>));
assert!(DimRange::contained_by(&0, Const::<1>));
}
@ -68,8 +69,8 @@ impl<D: Dim> DimRange<D> for ops::Range<usize> {
#[test]
fn dimrange_range_usize() {
assert_eq!(DimRange::contained_by(&(0..0), Const::<0>), false);
assert_eq!(DimRange::contained_by(&(0..1), Const::<0>), false);
assert!(!DimRange::contained_by(&(0..0), Const::<0>));
assert!(!DimRange::contained_by(&(0..1), Const::<0>));
assert!(DimRange::contained_by(&(0..1), Const::<1>));
assert!(DimRange::contained_by(
&((usize::MAX - 1)..usize::MAX),
@ -110,8 +111,8 @@ impl<D: Dim> DimRange<D> for ops::RangeFrom<usize> {
#[test]
fn dimrange_rangefrom_usize() {
assert_eq!(DimRange::contained_by(&(0..), Const::<0>), false);
assert_eq!(DimRange::contained_by(&(0..), Const::<0>), false);
assert!(!DimRange::contained_by(&(0..), Const::<0>));
assert!(!DimRange::contained_by(&(0..), Const::<0>));
assert!(DimRange::contained_by(&(0..), Const::<1>));
assert!(DimRange::contained_by(
&((usize::MAX - 1)..),
@ -204,16 +205,16 @@ impl<D: Dim> DimRange<D> for ops::RangeInclusive<usize> {
#[test]
fn dimrange_rangeinclusive_usize() {
assert_eq!(DimRange::contained_by(&(0..=0), Const::<0>), false);
assert!(!DimRange::contained_by(&(0..=0), Const::<0>));
assert!(DimRange::contained_by(&(0..=0), Const::<1>));
assert_eq!(
DimRange::contained_by(&(usize::MAX..=usize::MAX), Dynamic::new(usize::MAX)),
false
);
assert_eq!(
DimRange::contained_by(&((usize::MAX - 1)..=usize::MAX), Dynamic::new(usize::MAX)),
false
);
assert!(!DimRange::contained_by(
&(usize::MAX..=usize::MAX),
Dynamic::new(usize::MAX)
));
assert!(!DimRange::contained_by(
&((usize::MAX - 1)..=usize::MAX),
Dynamic::new(usize::MAX)
));
assert!(DimRange::contained_by(
&((usize::MAX - 1)..=(usize::MAX - 1)),
Dynamic::new(usize::MAX)
@ -255,7 +256,7 @@ impl<D: Dim> DimRange<D> for ops::RangeTo<usize> {
#[test]
fn dimrange_rangeto_usize() {
assert!(DimRange::contained_by(&(..0), Const::<0>));
assert_eq!(DimRange::contained_by(&(..1), Const::<0>), false);
assert!(!DimRange::contained_by(&(..1), Const::<0>));
assert!(DimRange::contained_by(&(..0), Const::<1>));
assert!(DimRange::contained_by(
&(..(usize::MAX - 1)),
@ -292,13 +293,13 @@ impl<D: Dim> DimRange<D> for ops::RangeToInclusive<usize> {
#[test]
fn dimrange_rangetoinclusive_usize() {
assert_eq!(DimRange::contained_by(&(..=0), Const::<0>), false);
assert_eq!(DimRange::contained_by(&(..=1), Const::<0>), false);
assert!(!DimRange::contained_by(&(..=0), Const::<0>));
assert!(!DimRange::contained_by(&(..=1), Const::<0>));
assert!(DimRange::contained_by(&(..=0), Const::<1>));
assert_eq!(
DimRange::contained_by(&(..=(usize::MAX)), Dynamic::new(usize::MAX)),
false
);
assert!(!DimRange::contained_by(
&(..=(usize::MAX)),
Dynamic::new(usize::MAX)
));
assert!(DimRange::contained_by(
&(..=(usize::MAX - 1)),
Dynamic::new(usize::MAX)
@ -566,7 +567,10 @@ where
#[doc(hidden)]
#[inline(always)]
unsafe fn get_unchecked(self, matrix: &'a Matrix<T, R, C, S>) -> Self::Output {
matrix.data.get_unchecked_linear(self)
let nrows = matrix.shape().0;
let row = self % nrows;
let col = self / nrows;
matrix.data.get_unchecked(row, col)
}
}
@ -585,7 +589,10 @@ where
where
S: RawStorageMut<T, R, C>,
{
matrix.data.get_unchecked_linear_mut(self)
let nrows = matrix.shape().0;
let row = self % nrows;
let col = self / nrows;
matrix.data.get_unchecked_mut(row, col)
}
}

73
src/base/matrix.rs
View File

@ -32,7 +32,7 @@ use crate::{ArrayStorage, SMatrix, SimdComplexField, Storage, UninitMatrix};
use crate::storage::IsContiguous;
use crate::uninit::{Init, InitStatus, Uninit};
#[cfg(any(feature = "std", feature = "alloc"))]
use crate::{DMatrix, DVector, Dynamic, VecStorage};
use crate::{DMatrix, DVector, Dynamic, RowDVector, VecStorage};
use std::mem::MaybeUninit;
/// A square matrix.
@ -92,6 +92,7 @@ pub type MatrixCross<T, R1, C1, R2, C2> =
/// - [Interpolation <span style="float:right;">`lerp`, `slerp`…</span>](#interpolation)
/// - [BLAS functions <span style="float:right;">`gemv`, `gemm`, `syger`…</span>](#blas-functions)
/// - [Swizzling <span style="float:right;">`xx`, `yxz`…</span>](#swizzling)
/// - [Triangular matrix extraction <span style="float:right;">`upper_triangle`, `lower_triangle`</span>](#triangular-matrix-extraction)
///
/// #### Statistics
/// - [Common operations <span style="float:right;">`row_sum`, `column_mean`, `variance`…</span>](#common-statistics-operations)
@ -154,6 +155,10 @@ pub type MatrixCross<T, R1, C1, R2, C2> =
/// some concrete types for `T` and a compatible data storage type `S`).
#[repr(C)]
#[derive(Clone, Copy)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub struct Matrix<T, R, C, S> {
/// The data storage that contains all the matrix components. Disappointed?
///
@ -188,17 +193,14 @@ pub struct Matrix<T, R, C, S> {
// Note that it would probably make sense to just have
// the type `Matrix<S>`, and have `T, R, C` be associated-types
// of the `RawStorage` trait. However, because we don't have
// specialization, this is not bossible because these `T, R, C`
// specialization, this is not possible because these `T, R, C`
// allows us to desambiguate a lot of configurations.
_phantoms: PhantomData<(T, R, C)>,
}
impl<T, R: Dim, C: Dim, S: fmt::Debug> fmt::Debug for Matrix<T, R, C, S> {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
formatter
.debug_struct("Matrix")
.field("data", &self.data)
.finish()
self.data.fmt(formatter)
}
}
@ -411,6 +413,21 @@ impl<T> DVector<T> {
}
}
// TODO: Consider removing/deprecating `from_vec_storage` once we are able to make
// `from_data` const fn compatible
#[cfg(any(feature = "std", feature = "alloc"))]
impl<T> RowDVector<T> {
/// Creates a new heap-allocated matrix from the given [`VecStorage`].
///
/// This method exists primarily as a workaround for the fact that `from_data` can not
/// work in `const fn` contexts.
pub const fn from_vec_storage(storage: VecStorage<T, U1, Dynamic>) -> Self {
// This is sound because the dimensions of the matrix and the storage are guaranteed
// to be the same
unsafe { Self::from_data_statically_unchecked(storage) }
}
}
impl<T, R: Dim, C: Dim> UninitMatrix<T, R, C>
where
DefaultAllocator: Allocator<T, R, C>,
@ -681,7 +698,7 @@ impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
#[inline]
fn transpose_to_uninit<Status, R2, C2, SB>(
&self,
status: Status,
_status: Status,
out: &mut Matrix<Status::Value, R2, C2, SB>,
) where
Status: InitStatus<T>,
@ -1377,7 +1394,7 @@ impl<T: SimdComplexField, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C
#[inline]
fn adjoint_to_uninit<Status, R2, C2, SB>(
&self,
status: Status,
_status: Status,
out: &mut Matrix<Status::Value, R2, C2, SB>,
) where
Status: InitStatus<T>,
@ -1777,7 +1794,7 @@ where
assert!(self.shape() == other.shape());
self.iter()
.zip(other.iter())
.all(|(a, b)| a.ulps_eq(b, epsilon.clone(), max_ulps.clone()))
.all(|(a, b)| a.ulps_eq(b, epsilon.clone(), max_ulps))
}
}
@ -2021,16 +2038,26 @@ impl<T: Scalar + ClosedAdd + ClosedSub + ClosedMul, R: Dim, C: Dim, S: RawStorag
+ SameNumberOfRows<R2, U2>
+ SameNumberOfColumns<C2, U1>,
{
assert!(
self.shape() == (2, 1),
"2D perpendicular product requires (2, 1) vector but found {:?}",
self.shape()
let shape = self.shape();
assert_eq!(
shape,
b.shape(),
"2D vector perpendicular product dimension mismatch."
);
assert_eq!(
shape,
(2, 1),
"2D perpendicular product requires (2, 1) vectors {:?}",
shape
);
unsafe {
self.get_unchecked((0, 0)).clone() * b.get_unchecked((1, 0)).clone()
- self.get_unchecked((1, 0)).clone() * b.get_unchecked((0, 0)).clone()
}
// SAFETY: assertion above ensures correct shape
let ax = unsafe { self.get_unchecked((0, 0)).clone() };
let ay = unsafe { self.get_unchecked((1, 0)).clone() };
let bx = unsafe { b.get_unchecked((0, 0)).clone() };
let by = unsafe { b.get_unchecked((1, 0)).clone() };
ax * by - ay * bx
}
// TODO: use specialization instead of an assertion.
@ -2051,17 +2078,14 @@ impl<T: Scalar + ClosedAdd + ClosedSub + ClosedMul, R: Dim, C: Dim, S: RawStorag
let shape = self.shape();
assert_eq!(shape, b.shape(), "Vector cross product dimension mismatch.");
assert!(
(shape.0 == 3 && shape.1 == 1) || (shape.0 == 1 && shape.1 == 3),
shape == (3, 1) || shape == (1, 3),
"Vector cross product dimension mismatch: must be (3, 1) or (1, 3) but found {:?}.",
shape
);
if shape.0 == 3 {
unsafe {
// TODO: soooo ugly!
let nrows = SameShapeR::<R, R2>::from_usize(3);
let ncols = SameShapeC::<C, C2>::from_usize(1);
let mut res = Matrix::uninit(nrows, ncols);
let mut res = Matrix::uninit(Dim::from_usize(3), Dim::from_usize(1));
let ax = self.get_unchecked((0, 0));
let ay = self.get_unchecked((1, 0));
@ -2083,10 +2107,7 @@ impl<T: Scalar + ClosedAdd + ClosedSub + ClosedMul, R: Dim, C: Dim, S: RawStorag
}
} else {
unsafe {
// TODO: ugly!
let nrows = SameShapeR::<R, R2>::from_usize(1);
let ncols = SameShapeC::<C, C2>::from_usize(3);
let mut res = Matrix::uninit(nrows, ncols);
let mut res = Matrix::uninit(Dim::from_usize(1), Dim::from_usize(3));
let ax = self.get_unchecked((0, 0));
let ay = self.get_unchecked((0, 1));

4
src/base/matrix_simba.rs
View File

@ -42,14 +42,14 @@ where
#[inline]
fn replace(&mut self, i: usize, val: Self::Element) {
self.zip_apply(&val, |mut a, b| {
self.zip_apply(&val, |a, b| {
a.replace(i, b);
})
}
#[inline]
unsafe fn replace_unchecked(&mut self, i: usize, val: Self::Element) {
self.zip_apply(&val, |mut a, b| {
self.zip_apply(&val, |a, b| {
a.replace_unchecked(i, b);
})
}

1
src/base/matrix_slice.rs
View File

@ -73,7 +73,6 @@ macro_rules! slice_storage_impl(
S: $Storage<T, RStor, CStor>,
RStride: Dim,
CStride: Dim {
$T::from_raw_parts(storage.$get_addr(start.0, start.1), shape, strides)
}
}

4
src/base/min_max.rs
View File

@ -60,7 +60,7 @@ impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
T: SimdPartialOrd + Zero,
{
self.fold_with(
|e| e.map(|e| e.clone()).unwrap_or_else(T::zero),
|e| e.cloned().unwrap_or_else(T::zero),
|a, b| a.simd_max(b.clone()),
)
}
@ -123,7 +123,7 @@ impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
T: SimdPartialOrd + Zero,
{
self.fold_with(
|e| e.map(|e| e.clone()).unwrap_or_else(T::zero),
|e| e.cloned().unwrap_or_else(T::zero),
|a, b| a.simd_min(b.clone()),
)
}

7
src/base/norm.rs
View File

@ -434,12 +434,7 @@ impl<T: Scalar, R: Dim, C: Dim, S: StorageMut<T, R, C>> Matrix<T, R, C, S> {
{
let n = self.norm();
let le = n.clone().simd_le(min_norm);
self.apply(|e| {
*e = e
.clone()
.simd_unscale(n.clone())
.select(le.clone(), e.clone())
});
self.apply(|e| *e = e.clone().simd_unscale(n.clone()).select(le, e.clone()));
SimdOption::new(n, le)
}

4
src/base/ops.rs
View File

@ -146,7 +146,7 @@ macro_rules! componentwise_binop_impl(
#[inline]
fn $method_to_statically_unchecked_uninit<Status, R2: Dim, C2: Dim, SB,
R3: Dim, C3: Dim, SC>(&self,
status: Status,
_status: Status,
rhs: &Matrix<T, R2, C2, SB>,
out: &mut Matrix<Status::Value, R3, C3, SC>)
where Status: InitStatus<T>,
@ -699,7 +699,7 @@ where
#[inline(always)]
fn xx_mul_to_uninit<Status, R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>(
&self,
status: Status,
_status: Status,
rhs: &Matrix<T, R2, C2, SB>,
out: &mut Matrix<Status::Value, R3, C3, SC>,
dot: impl Fn(

42
src/base/properties.rs
View File

@ -7,10 +7,10 @@ use simba::scalar::{ClosedAdd, ClosedMul, ComplexField, RealField};
use crate::base::allocator::Allocator;
use crate::base::dimension::{Dim, DimMin};
use crate::base::storage::Storage;
use crate::base::{DefaultAllocator, Matrix, Scalar, SquareMatrix};
use crate::base::{DefaultAllocator, Matrix, SquareMatrix};
use crate::RawStorage;
impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
/// The total number of elements of this matrix.
///
/// # Examples:
@ -63,50 +63,18 @@ impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
T::Epsilon: Clone,
{
let (nrows, ncols) = self.shape();
let d;
if nrows > ncols {
d = ncols;
for i in d..nrows {
for j in 0..ncols {
if !relative_eq!(self[(i, j)], T::zero(), epsilon = eps.clone()) {
return false;
}
}
}
} else {
// nrows <= ncols
d = nrows;
for i in 0..nrows {
for j in d..ncols {
if !relative_eq!(self[(i, j)], T::zero(), epsilon = eps.clone()) {
return false;
}
}
}
}
// Off-diagonal elements of the sub-square matrix.
for i in 1..d {
for j in 0..i {
// TODO: use unsafe indexing.
if !relative_eq!(self[(i, j)], T::zero(), epsilon = eps.clone())
|| !relative_eq!(self[(j, i)], T::zero(), epsilon = eps.clone())
let el = unsafe { self.get_unchecked((i, j)) };
if (i == j && !relative_eq!(*el, T::one(), epsilon = eps.clone()))
|| (i != j && !relative_eq!(*el, T::zero(), epsilon = eps.clone()))
{
return false;
}
}
}
// Diagonal elements of the sub-square matrix.
for i in 0..d {
if !relative_eq!(self[(i, i)], T::one(), epsilon = eps.clone()) {
return false;
}
}
true
}
}

134
src/base/statistics.rs
View File

@ -1,8 +1,8 @@
use crate::allocator::Allocator;
use crate::storage::RawStorage;
use crate::{Const, DefaultAllocator, Dim, Matrix, OVector, RowOVector, Scalar, VectorSlice, U1};
use num::Zero;
use simba::scalar::{ClosedAdd, Field, SupersetOf};
use num::{One, Zero};
use simba::scalar::{ClosedAdd, ClosedMul, Field, SupersetOf};
use std::mem::MaybeUninit;
/// # Folding on columns and rows
@ -123,7 +123,9 @@ impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
/// 4.0, 5.0, 6.0);
/// assert_eq!(m.row_sum(), RowVector3::new(5.0, 7.0, 9.0));
///
/// let mint = Matrix3x2::new(1,2,3,4,5,6);
/// let mint = Matrix3x2::new(1, 2,
/// 3, 4,
/// 5, 6);
/// assert_eq!(mint.row_sum(), RowVector2::new(9,12));
/// ```
#[inline]
@ -148,8 +150,10 @@ impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
/// 4.0, 5.0, 6.0);
/// assert_eq!(m.row_sum_tr(), Vector3::new(5.0, 7.0, 9.0));
///
/// let mint = Matrix3x2::new(1,2,3,4,5,6);
/// assert_eq!(mint.row_sum_tr(), Vector2::new(9,12));
/// let mint = Matrix3x2::new(1, 2,
/// 3, 4,
/// 5, 6);
/// assert_eq!(mint.row_sum_tr(), Vector2::new(9, 12));
/// ```
#[inline]
#[must_use]
@ -173,8 +177,10 @@ impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
/// 4.0, 5.0, 6.0);
/// assert_eq!(m.column_sum(), Vector2::new(6.0, 15.0));
///
/// let mint = Matrix3x2::new(1,2,3,4,5,6);
/// assert_eq!(mint.column_sum(), Vector3::new(3,7,11));
/// let mint = Matrix3x2::new(1, 2,
/// 3, 4,
/// 5, 6);
/// assert_eq!(mint.column_sum(), Vector3::new(3, 7, 11));
/// ```
#[inline]
#[must_use]
@ -189,6 +195,120 @@ impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
})
}
/*
*
* Product computation.
*
*/
/// The product of all the elements of this matrix.
///
/// # Example
///
/// ```
/// # use nalgebra::Matrix2x3;
///
/// let m = Matrix2x3::new(1.0, 2.0, 3.0,
/// 4.0, 5.0, 6.0);
/// assert_eq!(m.product(), 720.0);
/// ```
#[inline]
#[must_use]
pub fn product(&self) -> T
where
T: ClosedMul + One,
{
self.iter().cloned().fold(T::one(), |a, b| a * b)
}
/// The product of all the rows of this matrix.
///
/// Use `.row_sum_tr` if you need the result in a column vector instead.
///
/// # Example
///
/// ```
/// # use nalgebra::{Matrix2x3, Matrix3x2};
/// # use nalgebra::{RowVector2, RowVector3};
///
/// let m = Matrix2x3::new(1.0, 2.0, 3.0,
/// 4.0, 5.0, 6.0);
/// assert_eq!(m.row_product(), RowVector3::new(4.0, 10.0, 18.0));
///
/// let mint = Matrix3x2::new(1, 2,
/// 3, 4,
/// 5, 6);
/// assert_eq!(mint.row_product(), RowVector2::new(15, 48));
/// ```
#[inline]
#[must_use]
pub fn row_product(&self) -> RowOVector<T, C>
where
T: ClosedMul + One,
DefaultAllocator: Allocator<T, U1, C>,
{
self.compress_rows(|col| col.product())
}
/// The product of all the rows of this matrix. The result is transposed and returned as a column vector.
///
/// # Example
///
/// ```
/// # use nalgebra::{Matrix2x3, Matrix3x2};
/// # use nalgebra::{Vector2, Vector3};
///
/// let m = Matrix2x3::new(1.0, 2.0, 3.0,
/// 4.0, 5.0, 6.0);
/// assert_eq!(m.row_product_tr(), Vector3::new(4.0, 10.0, 18.0));
///
/// let mint = Matrix3x2::new(1, 2,
/// 3, 4,
/// 5, 6);
/// assert_eq!(mint.row_product_tr(), Vector2::new(15, 48));
/// ```
#[inline]
#[must_use]
pub fn row_product_tr(&self) -> OVector<T, C>
where
T: ClosedMul + One,
DefaultAllocator: Allocator<T, C>,
{
self.compress_rows_tr(|col| col.product())
}
/// The product of all the columns of this matrix.
///
/// # Example
///
/// ```
/// # use nalgebra::{Matrix2x3, Matrix3x2};
/// # use nalgebra::{Vector2, Vector3};
///
/// let m = Matrix2x3::new(1.0, 2.0, 3.0,
/// 4.0, 5.0, 6.0);
/// assert_eq!(m.column_product(), Vector2::new(6.0, 120.0));
///
/// let mint = Matrix3x2::new(1, 2,
/// 3, 4,
/// 5, 6);
/// assert_eq!(mint.column_product(), Vector3::new(2, 12, 30));
/// ```
#[inline]
#[must_use]
pub fn column_product(&self) -> OVector<T, R>
where
T: ClosedMul + One,
DefaultAllocator: Allocator<T, R>,
{
let nrows = self.shape_generic().0;
self.compress_columns(
OVector::repeat_generic(nrows, Const::<1>, T::one()),
|out, col| {
out.component_mul_assign(&col);
},
)
}
/*
*
* Variance computation.

4
src/base/uninit.rs
View File

@ -66,11 +66,11 @@ unsafe impl<T> InitStatus<T> for Uninit {
#[inline(always)]
unsafe fn assume_init_ref(t: &MaybeUninit<T>) -> &T {
std::mem::transmute(t.as_ptr()) // TODO: use t.assume_init_ref()
&*t.as_ptr() // TODO: use t.assume_init_ref()
}
#[inline(always)]
unsafe fn assume_init_mut(t: &mut MaybeUninit<T>) -> &mut T {
std::mem::transmute(t.as_mut_ptr()) // TODO: use t.assume_init_mut()
&mut *t.as_mut_ptr() // TODO: use t.assume_init_mut()
}
}

24
src/base/unit.rs
View File

@ -1,3 +1,4 @@
use std::fmt;
#[cfg(feature = "abomonation-serialize")]
use std::io::{Result as IOResult, Write};
use std::ops::Deref;
@ -24,11 +25,21 @@ use crate::{Dim, Matrix, OMatrix, RealField, Scalar, SimdComplexField, SimdRealF
/// and [`UnitQuaternion`](crate::UnitQuaternion); both built on top of `Unit`. If you are interested
/// in their documentation, read their dedicated pages directly.
#[repr(transparent)]
#[derive(Clone, Hash, Debug, Copy)]
#[derive(Clone, Hash, Copy)]
// #[cfg_attr(
// all(not(target_os = "cuda"), feature = "cuda"),
// derive(cust::DeviceCopy)
// )]
pub struct Unit<T> {
pub(crate) value: T,
}
impl<T: fmt::Debug> fmt::Debug for Unit<T> {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.value.fmt(formatter)
}
}
#[cfg(feature = "bytemuck")]
unsafe impl<T> bytemuck::Zeroable for Unit<T> where T: bytemuck::Zeroable {}
@ -111,6 +122,17 @@ mod rkyv_impl {
}
}
#[cfg(all(not(target_os = "cuda"), feature = "cuda"))]
unsafe impl<T: cust::memory::DeviceCopy, R, C, S> cust::memory::DeviceCopy
for Unit<Matrix<T, R, C, S>>
where
T: Scalar,
R: Dim,
C: Dim,
S: RawStorage<T, R, C> + Copy,
{
}
impl<T, R, C, S> PartialEq for Unit<Matrix<T, R, C, S>>
where
T: Scalar + PartialEq,

44
src/geometry/dual_quaternion.rs
View File

@ -39,6 +39,10 @@ use simba::scalar::{ClosedNeg, RealField};
/// See <https://github.com/dimforge/nalgebra/issues/487>
#[repr(C)]
#[derive(Debug, Copy, Clone)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub struct DualQuaternion<T> {
/// The real component of the quaternion
pub real: Quaternion<T>,
@ -351,13 +355,14 @@ impl<T: RealField + UlpsEq<Epsilon = T>> UlpsEq for DualQuaternion<T> {
#[inline]
fn ulps_eq(&self, other: &Self, epsilon: Self::Epsilon, max_ulps: u32) -> bool {
self.clone().to_vector().ulps_eq(&other.clone().to_vector(), epsilon.clone(), max_ulps.clone()) ||
self.clone().to_vector().ulps_eq(&other.clone().to_vector(), epsilon.clone(), max_ulps) ||
// Account for the double-covering of S², i.e. q = -q.
self.clone().to_vector().iter().zip(other.clone().to_vector().iter()).all(|(a, b)| a.ulps_eq(&-b.clone(), epsilon.clone(), max_ulps.clone()))
self.clone().to_vector().iter().zip(other.clone().to_vector().iter()).all(|(a, b)| a.ulps_eq(&-b.clone(), epsilon.clone(), max_ulps))
}
}
/// A unit quaternions. May be used to represent a rotation followed by a translation.
/// A unit dual quaternion. May be used to represent a rotation followed by a
/// translation.
pub type UnitDualQuaternion<T> = Unit<DualQuaternion<T>>;
impl<T: Scalar + ClosedNeg + PartialEq + SimdRealField> PartialEq for UnitDualQuaternion<T> {
@ -593,8 +598,9 @@ where
/// Screw linear interpolation between two unit quaternions. This creates a
/// smooth arc from one dual-quaternion to another.
///
/// Panics if the angle between both quaternion is 180 degrees (in which case the interpolation
/// is not well-defined). Use `.try_sclerp` instead to avoid the panic.
/// Panics if the angle between both quaternion is 180 degrees (in which
/// case the interpolation is not well-defined). Use `.try_sclerp`
/// instead to avoid the panic.
///
/// # Example
/// ```
@ -627,15 +633,16 @@ where
.expect("DualQuaternion sclerp: ambiguous configuration.")
}
/// Computes the screw-linear interpolation between two unit quaternions or returns `None`
/// if both quaternions are approximately 180 degrees apart (in which case the interpolation is
/// not well-defined).
/// Computes the screw-linear interpolation between two unit quaternions or
/// returns `None` if both quaternions are approximately 180 degrees
/// apart (in which case the interpolation is not well-defined).
///
/// # Arguments
/// * `self`: the first quaternion to interpolate from.
/// * `other`: the second quaternion to interpolate toward.
/// * `t`: the interpolation parameter. Should be between 0 and 1.
/// * `epsilon`: the value below which the sinus of the angle separating both quaternion
/// * `epsilon`: the value below which the sinus of the angle separating
/// both quaternion
/// must be to return `None`.
#[inline]
#[must_use]
@ -650,6 +657,10 @@ where
// interpolation.
let other = {
let dot_product = self.as_ref().real.coords.dot(&other.as_ref().real.coords);
if relative_eq!(dot_product, T::zero(), epsilon = epsilon.clone()) {
return None;
}
if dot_product < T::zero() {
-other.clone()
} else {
@ -660,13 +671,21 @@ where
let difference = self.as_ref().conjugate() * other.as_ref();
let norm_squared = difference.real.vector().norm_squared();
if relative_eq!(norm_squared, T::zero(), epsilon = epsilon) {
return None;
return Some(Self::from_parts(
self.translation()
.vector
.lerp(&other.translation().vector, t)
.into(),
self.rotation(),
));
}
let inverse_norm_squared = T::one() / norm_squared;
let scalar: T = difference.real.scalar();
let mut angle = two.clone() * scalar.acos();
let inverse_norm_squared: T = T::one() / norm_squared;
let inverse_norm = inverse_norm_squared.sqrt();
let mut angle = two.clone() * difference.real.scalar().acos();
let mut pitch = -two * difference.dual.scalar() * inverse_norm.clone();
let direction = difference.real.vector() * inverse_norm.clone();
let moment = (difference.dual.vector()
@ -678,6 +697,7 @@ where
let sin = (half.clone() * angle.clone()).sin();
let cos = (half.clone() * angle).cos();
let real = Quaternion::from_parts(cos.clone(), direction.clone() * sin.clone());
let dual = Quaternion::from_parts(
-pitch.clone() * half.clone() * sin.clone(),

22
src/geometry/isometry.rs
View File

@ -54,7 +54,11 @@ use crate::geometry::{AbstractRotation, Point, Translation};
/// * [Conversion to a matrix <span style="float:right;">`to_matrix`…</span>](#conversion-to-a-matrix)
///
#[repr(C)]
#[derive(Debug)]
#[derive(Debug, Copy, Clone)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
#[cfg_attr(feature = "serde-serialize-no-std", derive(Serialize, Deserialize))]
#[cfg_attr(
feature = "serde-serialize-no-std",
@ -170,20 +174,6 @@ where
}
}
impl<T: Scalar + Copy, R: Copy, const D: usize> Copy for Isometry<T, R, D> where
Owned<T, Const<D>>: Copy
{
}
impl<T: Scalar, R: Clone, const D: usize> Clone for Isometry<T, R, D> {
#[inline]
fn clone(&self) -> Self {
Self {
rotation: self.rotation.clone(),
translation: self.translation.clone(),
}
}
}
/// # From the translation and rotation parts
impl<T: Scalar, R: AbstractRotation<T, D>, const D: usize> Isometry<T, R, D> {
/// Creates a new isometry from its rotational and translational parts.
@ -629,7 +619,7 @@ where
#[inline]
fn ulps_eq(&self, other: &Self, epsilon: Self::Epsilon, max_ulps: u32) -> bool {
self.translation
.ulps_eq(&other.translation, epsilon.clone(), max_ulps.clone())
.ulps_eq(&other.translation, epsilon.clone(), max_ulps)
&& self.rotation.ulps_eq(&other.rotation, epsilon, max_ulps)
}
}

9
src/geometry/isometry_construction.rs
View File

@ -21,6 +21,15 @@ use crate::{
UnitQuaternion,
};
impl<T: SimdRealField, R: AbstractRotation<T, D>, const D: usize> Default for Isometry<T, R, D>
where
T::Element: SimdRealField,
{
fn default() -> Self {
Self::identity()
}
}
impl<T: SimdRealField, R: AbstractRotation<T, D>, const D: usize> Isometry<T, R, D>
where
T::Element: SimdRealField,

11
src/geometry/mod.rs
View File

@ -48,6 +48,14 @@ mod translation_coordinates;
mod translation_ops;
mod translation_simba;
mod scale;
mod scale_alias;
mod scale_construction;
mod scale_conversion;
mod scale_coordinates;
mod scale_ops;
mod scale_simba;
mod isometry;
mod isometry_alias;
mod isometry_construction;
@ -95,6 +103,9 @@ pub use self::unit_complex::*;
pub use self::translation::*;
pub use self::translation_alias::*;
pub use self::scale::*;
pub use self::scale_alias::*;
pub use self::isometry::*;
pub use self::isometry_alias::*;

16
src/geometry/orthographic.rs
View File

@ -19,19 +19,15 @@ use crate::geometry::{Point3, Projective3};
/// A 3D orthographic projection stored as a homogeneous 4x4 matrix.
#[repr(C)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
#[derive(Copy, Clone)]
pub struct Orthographic3<T> {
matrix: Matrix4<T>,
}
impl<T: RealField + Copy> Copy for Orthographic3<T> {}
impl<T: RealField> Clone for Orthographic3<T> {
#[inline]
fn clone(&self) -> Self {
Self::from_matrix_unchecked(self.matrix.clone())
}
}
impl<T: RealField> fmt::Debug for Orthographic3<T> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.matrix.fmt(f)
@ -175,7 +171,7 @@ impl<T: RealField> Orthographic3<T> {
);
let half: T = crate::convert(0.5);
let width = zfar.clone() * (vfov.clone() * half.clone()).tan();
let width = zfar.clone() * (vfov * half.clone()).tan();
let height = width.clone() / aspect;
Self::new(

14
src/geometry/perspective.rs
View File

@ -20,19 +20,15 @@ use crate::geometry::{Point3, Projective3};
/// A 3D perspective projection stored as a homogeneous 4x4 matrix.
#[repr(C)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
#[derive(Copy, Clone)]
pub struct Perspective3<T> {
matrix: Matrix4<T>,
}
impl<T: RealField + Copy> Copy for Perspective3<T> {}
impl<T: RealField> Clone for Perspective3<T> {
#[inline]
fn clone(&self) -> Self {
Self::from_matrix_unchecked(self.matrix.clone())
}
}
impl<T: RealField> fmt::Debug for Perspective3<T> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.matrix.fmt(f)

20
src/geometry/point.rs
View File

@ -40,7 +40,7 @@ use std::mem::MaybeUninit;
/// may have some other methods, e.g., `isometry.inverse_transform_point(&point)`. See the documentation
/// of said transformations for details.
#[repr(C)]
#[derive(Debug, Clone)]
#[derive(Clone)]
pub struct OPoint<T: Scalar, D: DimName>
where
DefaultAllocator: Allocator<T, D>,
@ -49,6 +49,15 @@ where
pub coords: OVector<T, D>,
}
impl<T: Scalar + fmt::Debug, D: DimName> fmt::Debug for OPoint<T, D>
where
DefaultAllocator: Allocator<T, D>,
{
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.coords.as_slice().fmt(formatter)
}
}
impl<T: Scalar + hash::Hash, D: DimName> hash::Hash for OPoint<T, D>
where
DefaultAllocator: Allocator<T, D>,
@ -65,6 +74,15 @@ where
{
}
#[cfg(all(not(target_os = "cuda"), feature = "cuda"))]
unsafe impl<T: Scalar + cust::memory::DeviceCopy, D: DimName> cust::memory::DeviceCopy
for OPoint<T, D>
where
DefaultAllocator: Allocator<T, D>,
OVector<T, D>: cust::memory::DeviceCopy,
{
}
#[cfg(feature = "bytemuck")]
unsafe impl<T: Scalar, D: DimName> bytemuck::Zeroable for OPoint<T, D>
where

9
src/geometry/point_construction.rs
View File

@ -19,6 +19,15 @@ use simba::scalar::{ClosedDiv, SupersetOf};
use crate::geometry::Point;
impl<T: Scalar + Zero, D: DimName> Default for OPoint<T, D>
where
DefaultAllocator: Allocator<T, D>,
{
fn default() -> Self {
Self::origin()
}
}
/// # Other construction methods
impl<T: Scalar, D: DimName> OPoint<T, D>
where

29
src/geometry/quaternion.rs
View File

@ -27,12 +27,22 @@ use crate::geometry::{Point3, Rotation};
/// A quaternion. See the type alias `UnitQuaternion = Unit<Quaternion>` for a quaternion
/// that may be used as a rotation.
#[repr(C)]
#[derive(Debug, Copy, Clone)]
#[derive(Copy, Clone)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub struct Quaternion<T> {
/// This quaternion as a 4D vector of coordinates in the `[ x, y, z, w ]` storage order.
pub coords: Vector4<T>,
}
impl<T: fmt::Debug> fmt::Debug for Quaternion<T> {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.coords.as_slice().fmt(formatter)
}
}
impl<T: Scalar + Hash> Hash for Quaternion<T> {
fn hash<H: Hasher>(&self, state: &mut H) {
self.coords.hash(state)
@ -1039,9 +1049,9 @@ impl<T: RealField + UlpsEq<Epsilon = T>> UlpsEq for Quaternion<T> {
#[inline]
fn ulps_eq(&self, other: &Self, epsilon: Self::Epsilon, max_ulps: u32) -> bool {
self.as_vector().ulps_eq(other.as_vector(), epsilon.clone(), max_ulps.clone()) ||
self.as_vector().ulps_eq(other.as_vector(), epsilon.clone(), max_ulps) ||
// Account for the double-covering of S², i.e. q = -q.
self.as_vector().iter().zip(other.as_vector().iter()).all(|(a, b)| a.ulps_eq(&-b.clone(), epsilon.clone(), max_ulps.clone()))
self.as_vector().iter().zip(other.as_vector().iter()).all(|(a, b)| a.ulps_eq(&-b.clone(), epsilon.clone(), max_ulps))
}
}
@ -1058,6 +1068,9 @@ impl<T: RealField + fmt::Display> fmt::Display for Quaternion<T> {
/// A unit quaternions. May be used to represent a rotation.
pub type UnitQuaternion<T> = Unit<Quaternion<T>>;
#[cfg(all(not(target_os = "cuda"), feature = "cuda"))]
unsafe impl<T: cust::memory::DeviceCopy> cust::memory::DeviceCopy for UnitQuaternion<T> {}
impl<T: Scalar + ClosedNeg + PartialEq> PartialEq for UnitQuaternion<T> {
#[inline]
fn eq(&self, rhs: &Self) -> bool {
@ -1492,18 +1505,18 @@ where
let wk = w.clone() * k.clone() * crate::convert(2.0f64);
let wj = w.clone() * j.clone() * crate::convert(2.0f64);
let ik = i.clone() * k.clone() * crate::convert(2.0f64);
let jk = j.clone() * k.clone() * crate::convert(2.0f64);
let wi = w.clone() * i.clone() * crate::convert(2.0f64);
let jk = j * k * crate::convert(2.0f64);
let wi = w * i * crate::convert(2.0f64);
Rotation::from_matrix_unchecked(Matrix3::new(
ww.clone() + ii.clone() - jj.clone() - kk.clone(),
ij.clone() - wk.clone(),
wj.clone() + ik.clone(),
wk.clone() + ij.clone(),
wk + ij,
ww.clone() - ii.clone() + jj.clone() - kk.clone(),
jk.clone() - wi.clone(),
ik.clone() - wj.clone(),
wi.clone() + jk.clone(),
ik - wj,
wi + jk,
ww - ii - jj + kk,
))
}

27
src/geometry/rotation.rs
View File

@ -54,11 +54,21 @@ use crate::geometry::Point;
/// * [Conversion to a matrix <span style="float:right;">`matrix`, `to_homogeneous`…</span>](#conversion-to-a-matrix)
///
#[repr(C)]
#[derive(Debug)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
#[derive(Copy, Clone)]
pub struct Rotation<T, const D: usize> {
matrix: SMatrix<T, D, D>,
}
impl<T: fmt::Debug, const D: usize> fmt::Debug for Rotation<T, D> {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.matrix.fmt(formatter)
}
}
impl<T: Scalar + hash::Hash, const D: usize> hash::Hash for Rotation<T, D>
where
<DefaultAllocator as Allocator<T, Const<D>, Const<D>>>::Buffer: hash::Hash,
@ -68,21 +78,6 @@ where
}
}
impl<T: Scalar + Copy, const D: usize> Copy for Rotation<T, D> where
<DefaultAllocator as Allocator<T, Const<D>, Const<D>>>::Buffer: Copy
{
}
impl<T: Scalar, const D: usize> Clone for Rotation<T, D>
where
<DefaultAllocator as Allocator<T, Const<D>, Const<D>>>::Buffer: Clone,
{
#[inline]
fn clone(&self) -> Self {
Self::from_matrix_unchecked(self.matrix.clone())
}
}
#[cfg(feature = "bytemuck")]
unsafe impl<T, const D: usize> bytemuck::Zeroable for Rotation<T, D>
where

22
src/geometry/rotation_construction.rs
View File

@ -6,6 +6,15 @@ use crate::base::{SMatrix, Scalar};
use crate::geometry::Rotation;
impl<T, const D: usize> Default for Rotation<T, D>
where
T: Scalar + Zero + One,
{
fn default() -> Self {
Self::identity()
}
}
/// # Identity
impl<T, const D: usize> Rotation<T, D>
where
@ -15,9 +24,16 @@ where
///
/// # Example
/// ```
/// # use nalgebra::Quaternion;
/// let rot1 = Quaternion::identity();
/// let rot2 = Quaternion::new(1.0, 2.0, 3.0, 4.0);
/// # use nalgebra::{Rotation2, Rotation3};
/// # use nalgebra::Vector3;
/// let rot1 = Rotation2::identity();
/// let rot2 = Rotation2::new(std::f32::consts::FRAC_PI_2);
///
/// assert_eq!(rot1 * rot2, rot2);
/// assert_eq!(rot2 * rot1, rot2);
///
/// let rot1 = Rotation3::identity();
/// let rot2 = Rotation3::from_axis_angle(&Vector3::z_axis(), std::f32::consts::FRAC_PI_2);
///
/// assert_eq!(rot1 * rot2, rot2);
/// assert_eq!(rot2 * rot1, rot2);

10
src/geometry/rotation_specialization.rs
View File

@ -60,7 +60,7 @@ impl<T: SimdRealField> Rotation2<T> {
impl<T: SimdRealField> Rotation2<T> {
/// Builds a rotation from a basis assumed to be orthonormal.
///
/// In order to get a valid unit-quaternion, the input must be an
/// In order to get a valid rotation matrix, the input must be an
/// orthonormal basis, i.e., all vectors are normalized, and the are
/// all orthogonal to each other. These invariants are not checked
/// by this method.
@ -204,7 +204,7 @@ impl<T: SimdRealField> Rotation2<T> {
*self = Self::from_matrix_eps(self.matrix(), T::default_epsilon(), 0, c.into())
}
/// Raise the quaternion to a given floating power, i.e., returns the rotation with the angle
/// Raise the rotation to a given floating power, i.e., returns the rotation with the angle
/// of `self` multiplied by `n`.
///
/// # Example
@ -660,7 +660,7 @@ where
other * self.inverse()
}
/// Raise the quaternion to a given floating power, i.e., returns the rotation with the same
/// Raise the rotation to a given floating power, i.e., returns the rotation with the same
/// axis as `self` and an angle equal to `self.angle()` multiplied by `n`.
///
/// # Example
@ -692,7 +692,7 @@ where
/// Builds a rotation from a basis assumed to be orthonormal.
///
/// In order to get a valid unit-quaternion, the input must be an
/// In order to get a valid rotation matrix, the input must be an
/// orthonormal basis, i.e., all vectors are normalized, and the are
/// all orthogonal to each other. These invariants are not checked
/// by this method.
@ -846,7 +846,7 @@ impl<T: SimdRealField> Rotation3<T> {
}
}
/// The rotation axis and angle in ]0, pi] of this unit quaternion.
/// The rotation axis and angle in ]0, pi] of this rotation matrix.
///
/// Returns `None` if the angle is zero.
///

443
src/geometry/scale.rs Executable file
View File

@ -0,0 +1,443 @@
use approx::{AbsDiffEq, RelativeEq, UlpsEq};
use num::{One, Zero};
use std::fmt;
use std::hash;
#[cfg(feature = "abomonation-serialize")]
use std::io::{Result as IOResult, Write};
#[cfg(feature = "serde-serialize-no-std")]
use serde::{Deserialize, Deserializer, Serialize, Serializer};
#[cfg(feature = "abomonation-serialize")]
use abomonation::Abomonation;
use crate::base::allocator::Allocator;
use crate::base::dimension::{DimNameAdd, DimNameSum, U1};
use crate::base::storage::Owned;
use crate::base::{Const, DefaultAllocator, OMatrix, OVector, SVector, Scalar};
use crate::ClosedDiv;
use crate::ClosedMul;
use crate::geometry::Point;
/// A scale which supports non-uniform scaling.
#[repr(C)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
#[derive(Copy, Clone)]
pub struct Scale<T, const D: usize> {
/// The scale coordinates, i.e., how much is multiplied to a point's coordinates when it is
/// scaled.
pub vector: SVector<T, D>,
}
impl<T: fmt::Debug, const D: usize> fmt::Debug for Scale<T, D> {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.vector.as_slice().fmt(formatter)
}
}
impl<T: Scalar + hash::Hash, const D: usize> hash::Hash for Scale<T, D>
where
Owned<T, Const<D>>: hash::Hash,
{
fn hash<H: hash::Hasher>(&self, state: &mut H) {
self.vector.hash(state)
}
}
#[cfg(feature = "bytemuck")]
unsafe impl<T, const D: usize> bytemuck::Zeroable for Scale<T, D>
where
T: Scalar + bytemuck::Zeroable,
SVector<T, D>: bytemuck::Zeroable,
{
}
#[cfg(feature = "bytemuck")]
unsafe impl<T, const D: usize> bytemuck::Pod for Scale<T, D>
where
T: Scalar + bytemuck::Pod,
SVector<T, D>: bytemuck::Pod,
{
}
#[cfg(feature = "abomonation-serialize")]
impl<T, const D: usize> Abomonation for Scale<T, D>
where
T: Scalar,
SVector<T, D>: Abomonation,
{
unsafe fn entomb<W: Write>(&self, writer: &mut W) -> IOResult<()> {
self.vector.entomb(writer)
}
fn extent(&self) -> usize {
self.vector.extent()
}
unsafe fn exhume<'a, 'b>(&'a mut self, bytes: &'b mut [u8]) -> Option<&'b mut [u8]> {
self.vector.exhume(bytes)
}
}
#[cfg(feature = "serde-serialize-no-std")]
impl<T: Scalar, const D: usize> Serialize for Scale<T, D>
where
Owned<T, Const<D>>: Serialize,
{
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
{
self.vector.serialize(serializer)
}
}
#[cfg(feature = "serde-serialize-no-std")]
impl<'a, T: Scalar, const D: usize> Deserialize<'a> for Scale<T, D>
where
Owned<T, Const<D>>: Deserialize<'a>,
{
fn deserialize<Des>(deserializer: Des) -> Result<Self, Des::Error>
where
Des: Deserializer<'a>,
{
let matrix = SVector::<T, D>::deserialize(deserializer)?;
Ok(Scale::from(matrix))
}
}
#[cfg(feature = "rkyv-serialize-no-std")]
mod rkyv_impl {
use super::Scale;
use crate::base::SVector;
use rkyv::{offset_of, project_struct, Archive, Deserialize, Fallible, Serialize};
impl<T: Archive, const D: usize> Archive for Scale<T, D> {
type Archived = Scale<T::Archived, D>;
type Resolver = <SVector<T, D> as Archive>::Resolver;
fn resolve(
&self,
pos: usize,
resolver: Self::Resolver,
out: &mut core::mem::MaybeUninit<Self::Archived>,
) {
self.vector.resolve(
pos + offset_of!(Self::Archived, vector),
resolver,
project_struct!(out: Self::Archived => vector),
);
}
}
impl<T: Serialize<S>, S: Fallible + ?Sized, const D: usize> Serialize<S> for Scale<T, D> {
fn serialize(&self, serializer: &mut S) -> Result<Self::Resolver, S::Error> {
self.vector.serialize(serializer)
}
}
impl<T: Archive, _D: Fallible + ?Sized, const D: usize> Deserialize<Scale<T, D>, _D>
for Scale<T::Archived, D>
where
T::Archived: Deserialize<T, _D>,
{
fn deserialize(&self, deserializer: &mut _D) -> Result<Scale<T, D>, _D::Error> {
Ok(Scale {
vector: self.vector.deserialize(deserializer)?,
})
}
}
}
impl<T: Scalar, const D: usize> Scale<T, D> {
/// Inverts `self`.
///
/// # Example
/// ```
/// # use nalgebra::{Scale2, Scale3};
/// let t = Scale3::new(1.0, 2.0, 3.0);
/// assert_eq!(t * t.try_inverse().unwrap(), Scale3::identity());
/// assert_eq!(t.try_inverse().unwrap() * t, Scale3::identity());
///
/// // Work in all dimensions.
/// let t = Scale2::new(1.0, 2.0);
/// assert_eq!(t * t.try_inverse().unwrap(), Scale2::identity());
/// assert_eq!(t.try_inverse().unwrap() * t, Scale2::identity());
///
/// // Returns None if any coordinate is 0.
/// let t = Scale2::new(0.0, 2.0);
/// assert_eq!(t.try_inverse(), None);
/// ```
#[inline]
#[must_use = "Did you mean to use try_inverse_mut()?"]
pub fn try_inverse(&self) -> Option<Scale<T, D>>
where
T: ClosedDiv + One + Zero,
{
for i in 0..D {
if self.vector[i] == T::zero() {
return None;
}
}
return Some(self.vector.map(|e| T::one() / e).into());
}
/// Inverts `self`.
///
/// # Example
/// ```
/// # use nalgebra::{Scale2, Scale3};
///
/// unsafe {
/// let t = Scale3::new(1.0, 2.0, 3.0);
/// assert_eq!(t * t.inverse_unchecked(), Scale3::identity());
/// assert_eq!(t.inverse_unchecked() * t, Scale3::identity());
///
/// // Work in all dimensions.
/// let t = Scale2::new(1.0, 2.0);
/// assert_eq!(t * t.inverse_unchecked(), Scale2::identity());
/// assert_eq!(t.inverse_unchecked() * t, Scale2::identity());
/// }
/// ```
#[inline]
#[must_use]
pub unsafe fn inverse_unchecked(&self) -> Scale<T, D>
where
T: ClosedDiv + One,
{
return self.vector.map(|e| T::one() / e).into();
}
/// Inverts `self`.
///
/// # Example
/// ```
/// # use nalgebra::{Scale2, Scale3};
/// let t = Scale3::new(1.0, 2.0, 3.0);
/// assert_eq!(t * t.pseudo_inverse(), Scale3::identity());
/// assert_eq!(t.pseudo_inverse() * t, Scale3::identity());
///
/// // Work in all dimensions.
/// let t = Scale2::new(1.0, 2.0);
/// assert_eq!(t * t.pseudo_inverse(), Scale2::identity());
/// assert_eq!(t.pseudo_inverse() * t, Scale2::identity());
///
/// // Inverts only non-zero coordinates.
/// let t = Scale2::new(0.0, 2.0);
/// assert_eq!(t * t.pseudo_inverse(), Scale2::new(0.0, 1.0));
/// assert_eq!(t.pseudo_inverse() * t, Scale2::new(0.0, 1.0));
/// ```
#[inline]
#[must_use]
pub fn pseudo_inverse(&self) -> Scale<T, D>
where
T: ClosedDiv + One + Zero,
{
return self
.vector
.map(|e| {
if e != T::zero() {
T::one() / e
} else {
T::zero()
}
})
.into();
}
/// Converts this Scale into its equivalent homogeneous transformation matrix.
///
/// # Example
/// ```
/// # use nalgebra::{Scale2, Scale3, Matrix3, Matrix4};
/// let t = Scale3::new(10.0, 20.0, 30.0);
/// let expected = Matrix4::new(10.0, 0.0, 0.0, 0.0,
/// 0.0, 20.0, 0.0, 0.0,
/// 0.0, 0.0, 30.0, 0.0,
/// 0.0, 0.0, 0.0, 1.0);
/// assert_eq!(t.to_homogeneous(), expected);
///
/// let t = Scale2::new(10.0, 20.0);
/// let expected = Matrix3::new(10.0, 0.0, 0.0,
/// 0.0, 20.0, 0.0,
/// 0.0, 0.0, 1.0);
/// assert_eq!(t.to_homogeneous(), expected);
/// ```
#[inline]
#[must_use]
pub fn to_homogeneous(&self) -> OMatrix<T, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>
where
T: Zero + One + Clone,
Const<D>: DimNameAdd<U1>,
DefaultAllocator: Allocator<T, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>
+ Allocator<T, DimNameSum<Const<D>, U1>, U1>,
{
// TODO: use self.vector.push() instead. We can’t right now because
// that would require the DimAdd bound (but here we use DimNameAdd).
// This should be fixable once Rust gets a more complete support of
// const-generics.
let mut v = OVector::from_element(T::one());
for i in 0..D {
v[i] = self.vector[i].clone();
}
return OMatrix::from_diagonal(&v);
}
/// Inverts `self` in-place.
///
/// # Example
/// ```
/// # use nalgebra::{Scale2, Scale3};
/// let t = Scale3::new(1.0, 2.0, 3.0);
/// let mut inv_t = Scale3::new(1.0, 2.0, 3.0);
/// assert!(inv_t.try_inverse_mut());
/// assert_eq!(t * inv_t, Scale3::identity());
/// assert_eq!(inv_t * t, Scale3::identity());
///
/// // Work in all dimensions.
/// let t = Scale2::new(1.0, 2.0);
/// let mut inv_t = Scale2::new(1.0, 2.0);
/// assert!(inv_t.try_inverse_mut());
/// assert_eq!(t * inv_t, Scale2::identity());
/// assert_eq!(inv_t * t, Scale2::identity());
///
/// // Does not perform any operation if a coordinate is 0.
/// let mut t = Scale2::new(0.0, 2.0);
/// assert!(!t.try_inverse_mut());
/// ```
#[inline]
pub fn try_inverse_mut(&mut self) -> bool
where
T: ClosedDiv + One + Zero,
{
if let Some(v) = self.try_inverse() {
self.vector = v.vector;
true
} else {
false
}
}
}
impl<T: Scalar + ClosedMul, const D: usize> Scale<T, D> {
/// Translate the given point.
///
/// This is the same as the multiplication `self * pt`.
///
/// # Example
/// ```
/// # use nalgebra::{Scale3, Point3};
/// let t = Scale3::new(1.0, 2.0, 3.0);
/// let transformed_point = t.transform_point(&Point3::new(4.0, 5.0, 6.0));
/// assert_eq!(transformed_point, Point3::new(4.0, 10.0, 18.0));
/// ```
#[inline]
#[must_use]
pub fn transform_point(&self, pt: &Point<T, D>) -> Point<T, D> {
self * pt
}
}
impl<T: Scalar + ClosedDiv + ClosedMul + One + Zero, const D: usize> Scale<T, D> {
/// Translate the given point by the inverse of this Scale.
///
/// # Example
/// ```
/// # use nalgebra::{Scale3, Point3};
/// let t = Scale3::new(1.0, 2.0, 3.0);
/// let transformed_point = t.try_inverse_transform_point(&Point3::new(4.0, 6.0, 6.0)).unwrap();
/// assert_eq!(transformed_point, Point3::new(4.0, 3.0, 2.0));
///
/// // Returns None if the inverse doesn't exist.
/// let t = Scale3::new(1.0, 0.0, 3.0);
/// let transformed_point = t.try_inverse_transform_point(&Point3::new(4.0, 6.0, 6.0));
/// assert_eq!(transformed_point, None);
/// ```
#[inline]
#[must_use]
pub fn try_inverse_transform_point(&self, pt: &Point<T, D>) -> Option<Point<T, D>> {
self.try_inverse().map(|s| s * pt)
}
}
impl<T: Scalar + Eq, const D: usize> Eq for Scale<T, D> {}
impl<T: Scalar + PartialEq, const D: usize> PartialEq for Scale<T, D> {
#[inline]
fn eq(&self, right: &Scale<T, D>) -> bool {
self.vector == right.vector
}
}
impl<T: Scalar + AbsDiffEq, const D: usize> AbsDiffEq for Scale<T, D>
where
T::Epsilon: Clone,
{
type Epsilon = T::Epsilon;
#[inline]
fn default_epsilon() -> Self::Epsilon {
T::default_epsilon()
}
#[inline]
fn abs_diff_eq(&self, other: &Self, epsilon: Self::Epsilon) -> bool {
self.vector.abs_diff_eq(&other.vector, epsilon)
}
}
impl<T: Scalar + RelativeEq, const D: usize> RelativeEq for Scale<T, D>
where
T::Epsilon: Clone,
{
#[inline]
fn default_max_relative() -> Self::Epsilon {
T::default_max_relative()
}
#[inline]
fn relative_eq(
&self,
other: &Self,
epsilon: Self::Epsilon,
max_relative: Self::Epsilon,
) -> bool {
self.vector
.relative_eq(&other.vector, epsilon, max_relative)
}
}
impl<T: Scalar + UlpsEq, const D: usize> UlpsEq for Scale<T, D>
where
T::Epsilon: Clone,
{
#[inline]
fn default_max_ulps() -> u32 {
T::default_max_ulps()
}
#[inline]
fn ulps_eq(&self, other: &Self, epsilon: Self::Epsilon, max_ulps: u32) -> bool {
self.vector.ulps_eq(&other.vector, epsilon, max_ulps)
}
}
/*
*
* Display
*
*/
impl<T: Scalar + fmt::Display, const D: usize> fmt::Display for Scale<T, D> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
let precision = f.precision().unwrap_or(3);
writeln!(f, "Scale {{")?;
write!(f, "{:.*}", precision, self.vector)?;
writeln!(f, "}}")
}
}

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use crate::geometry::Scale;
/// A 1-dimensional scale.
pub type Scale1<T> = Scale<T, 1>;
/// A 2-dimensional scale.
pub type Scale2<T> = Scale<T, 2>;
/// A 3-dimensional scale.
pub type Scale3<T> = Scale<T, 3>;
/// A 4-dimensional scale.
pub type Scale4<T> = Scale<T, 4>;
/// A 5-dimensional scale.
pub type Scale5<T> = Scale<T, 5>;
/// A 6-dimensional scale.
pub type Scale6<T> = Scale<T, 6>;

123
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#[cfg(feature = "arbitrary")]
use crate::base::storage::Owned;
#[cfg(feature = "arbitrary")]
use quickcheck::{Arbitrary, Gen};
use num::One;
#[cfg(feature = "rand-no-std")]
use rand::{
distributions::{Distribution, Standard},
Rng,
};
use simba::scalar::{ClosedMul, SupersetOf};
use crate::base::{SVector, Scalar};
use crate::geometry::Scale;
impl<T: Scalar, const D: usize> Scale<T, D> {
/// Creates a new identity scale.
///
/// # Example
/// ```
/// # use nalgebra::{Point2, Point3, Scale2, Scale3};
/// let t = Scale2::identity();
/// let p = Point2::new(1.0, 2.0);
/// assert_eq!(t * p, p);
///
/// // Works in all dimensions.
/// let t = Scale3::identity();
/// let p = Point3::new(1.0, 2.0, 3.0);
/// assert_eq!(t * p, p);
/// ```
#[inline]
pub fn identity() -> Scale<T, D>
where
T: One,
{
Scale::from(SVector::from_element(T::one()))
}
/// Cast the components of `self` to another type.
///
/// # Example
/// ```
/// # use nalgebra::Scale2;
/// let tra = Scale2::new(1.0f64, 2.0);
/// let tra2 = tra.cast::<f32>();
/// assert_eq!(tra2, Scale2::new(1.0f32, 2.0));
/// ```
pub fn cast<To: Scalar>(self) -> Scale<To, D>
where
Scale<To, D>: SupersetOf<Self>,
{
crate::convert(self)
}
}
impl<T: Scalar + One + ClosedMul, const D: usize> One for Scale<T, D> {
#[inline]
fn one() -> Self {
Self::identity()
}
}
#[cfg(feature = "rand-no-std")]
impl<T: Scalar, const D: usize> Distribution<Scale<T, D>> for Standard
where
Standard: Distribution<T>,
{
/// Generate an arbitrary random variate for testing purposes.
#[inline]
fn sample<G: Rng + ?Sized>(&self, rng: &mut G) -> Scale<T, D> {
Scale::from(rng.gen::<SVector<T, D>>())
}
}
#[cfg(feature = "arbitrary")]
impl<T: Scalar + Arbitrary + Send, const D: usize> Arbitrary for Scale<T, D>
where
Owned<T, crate::Const<D>>: Send,
{
#[inline]
fn arbitrary(rng: &mut Gen) -> Self {
let v: SVector<T, D> = Arbitrary::arbitrary(rng);
Self::from(v)
}
}
/*
*
* Small Scale construction from components.
*
*/
macro_rules! componentwise_constructors_impl(
($($doc: expr; $D: expr, $($args: ident:$irow: expr),*);* $(;)*) => {$(
impl<T> Scale<T, $D>
{
#[doc = "Initializes this Scale from its components."]
#[doc = "# Example\n```"]
#[doc = $doc]
#[doc = "```"]
#[inline]
pub const fn new($($args: T),*) -> Self {
Self { vector: SVector::<T, $D>::new($($args),*) }
}
}
)*}
);
componentwise_constructors_impl!(
"# use nalgebra::Scale1;\nlet t = Scale1::new(1.0);\nassert!(t.vector.x == 1.0);";
1, x:0;
"# use nalgebra::Scale2;\nlet t = Scale2::new(1.0, 2.0);\nassert!(t.vector.x == 1.0 && t.vector.y == 2.0);";
2, x:0, y:1;
"# use nalgebra::Scale3;\nlet t = Scale3::new(1.0, 2.0, 3.0);\nassert!(t.vector.x == 1.0 && t.vector.y == 2.0 && t.vector.z == 3.0);";
3, x:0, y:1, z:2;
"# use nalgebra::Scale4;\nlet t = Scale4::new(1.0, 2.0, 3.0, 4.0);\nassert!(t.vector.x == 1.0 && t.vector.y == 2.0 && t.vector.z == 3.0 && t.vector.w == 4.0);";
4, x:0, y:1, z:2, w:3;
"# use nalgebra::Scale5;\nlet t = Scale5::new(1.0, 2.0, 3.0, 4.0, 5.0);\nassert!(t.vector.x == 1.0 && t.vector.y == 2.0 && t.vector.z == 3.0 && t.vector.w == 4.0 && t.vector.a == 5.0);";
5, x:0, y:1, z:2, w:3, a:4;
"# use nalgebra::Scale6;\nlet t = Scale6::new(1.0, 2.0, 3.0, 4.0, 5.0, 6.0);\nassert!(t.vector.x == 1.0 && t.vector.y == 2.0 && t.vector.z == 3.0 && t.vector.w == 4.0 && t.vector.a == 5.0 && t.vector.b == 6.0);";
6, x:0, y:1, z:2, w:3, a:4, b:5;
);

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use num::{One, Zero};
use simba::scalar::{RealField, SubsetOf, SupersetOf};
use simba::simd::PrimitiveSimdValue;
use crate::base::allocator::Allocator;
use crate::base::dimension::{DimNameAdd, DimNameSum, U1};
use crate::base::{Const, DefaultAllocator, OMatrix, OVector, SVector, Scalar};
use crate::geometry::{Scale, SuperTCategoryOf, TAffine, Transform};
use crate::Point;
/*
* This file provides the following conversions:
* =============================================
*
* Scale -> Scale
* Scale -> Transform
* Scale -> Matrix (homogeneous)
*/
impl<T1, T2, const D: usize> SubsetOf<Scale<T2, D>> for Scale<T1, D>
where
T1: Scalar,
T2: Scalar + SupersetOf<T1>,
{
#[inline]
fn to_superset(&self) -> Scale<T2, D> {
Scale::from(self.vector.to_superset())
}
#[inline]
fn is_in_subset(rot: &Scale<T2, D>) -> bool {
crate::is_convertible::<_, SVector<T1, D>>(&rot.vector)
}
#[inline]
fn from_superset_unchecked(rot: &Scale<T2, D>) -> Self {
Scale {
vector: rot.vector.to_subset_unchecked(),
}
}
}
impl<T1, T2, C, const D: usize> SubsetOf<Transform<T2, C, D>> for Scale<T1, D>
where
T1: RealField,
T2: RealField + SupersetOf<T1>,
C: SuperTCategoryOf<TAffine>,
Const<D>: DimNameAdd<U1>,
DefaultAllocator: Allocator<T1, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>
+ Allocator<T1, DimNameSum<Const<D>, U1>, U1>
+ Allocator<T2, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>,
{
#[inline]
fn to_superset(&self) -> Transform<T2, C, D> {
Transform::from_matrix_unchecked(self.to_homogeneous().to_superset())
}
#[inline]
fn is_in_subset(t: &Transform<T2, C, D>) -> bool {
<Self as SubsetOf<_>>::is_in_subset(t.matrix())
}
#[inline]
fn from_superset_unchecked(t: &Transform<T2, C, D>) -> Self {
Self::from_superset_unchecked(t.matrix())
}
}
impl<T1, T2, const D: usize>
SubsetOf<OMatrix<T2, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>> for Scale<T1, D>
where
T1: RealField,
T2: RealField + SupersetOf<T1>,
Const<D>: DimNameAdd<U1>,
DefaultAllocator: Allocator<T1, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>
+ Allocator<T1, DimNameSum<Const<D>, U1>, U1>
+ Allocator<T2, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>,
{
#[inline]
fn to_superset(&self) -> OMatrix<T2, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>> {
self.to_homogeneous().to_superset()
}
#[inline]
fn is_in_subset(m: &OMatrix<T2, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>) -> bool {
if m[(D, D)] != T2::one() {
return false;
}
for i in 0..D + 1 {
for j in 0..D + 1 {
if i != j && m[(i, j)] != T2::zero() {
return false;
}
}
}
true
}
#[inline]
fn from_superset_unchecked(
m: &OMatrix<T2, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>,
) -> Self {
let v = m.fixed_slice::<D, D>(0, 0).diagonal();
Self {
vector: crate::convert_unchecked(v),
}
}
}
impl<T: Scalar + Zero + One, const D: usize> From<Scale<T, D>>
for OMatrix<T, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>
where
Const<D>: DimNameAdd<U1>,
DefaultAllocator: Allocator<T, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>
+ Allocator<T, DimNameSum<Const<D>, U1>, U1>
+ Allocator<T, Const<D>>,
{
#[inline]
fn from(t: Scale<T, D>) -> Self {
t.to_homogeneous()
}
}
impl<T: Scalar, const D: usize> From<OVector<T, Const<D>>> for Scale<T, D> {
#[inline]
fn from(vector: OVector<T, Const<D>>) -> Self {
Scale { vector }
}
}
impl<T: Scalar, const D: usize> From<[T; D]> for Scale<T, D> {
#[inline]
fn from(coords: [T; D]) -> Self {
Scale {
vector: coords.into(),
}
}
}
impl<T: Scalar, const D: usize> From<Point<T, D>> for Scale<T, D> {
#[inline]
fn from(pt: Point<T, D>) -> Self {
Scale { vector: pt.coords }
}
}
impl<T: Scalar, const D: usize> From<Scale<T, D>> for [T; D] {
#[inline]
fn from(t: Scale<T, D>) -> Self {
t.vector.into()
}
}
impl<T: Scalar + PrimitiveSimdValue, const D: usize> From<[Scale<T::Element, D>; 2]> for Scale<T, D>
where
T: From<[<T as simba::simd::SimdValue>::Element; 2]>,
T::Element: Scalar,
{
#[inline]
fn from(arr: [Scale<T::Element, D>; 2]) -> Self {
Self::from(OVector::from([
arr[0].vector.clone(),
arr[1].vector.clone(),
]))
}
}
impl<T: Scalar + PrimitiveSimdValue, const D: usize> From<[Scale<T::Element, D>; 4]> for Scale<T, D>
where
T: From<[<T as simba::simd::SimdValue>::Element; 4]>,
T::Element: Scalar,
{
#[inline]
fn from(arr: [Scale<T::Element, D>; 4]) -> Self {
Self::from(OVector::from([
arr[0].vector.clone(),
arr[1].vector.clone(),
arr[2].vector.clone(),
arr[3].vector.clone(),
]))
}
}
impl<T: Scalar + PrimitiveSimdValue, const D: usize> From<[Scale<T::Element, D>; 8]> for Scale<T, D>
where
T: From<[<T as simba::simd::SimdValue>::Element; 8]>,
T::Element: Scalar,
{
#[inline]
fn from(arr: [Scale<T::Element, D>; 8]) -> Self {
Self::from(OVector::from([
arr[0].vector.clone(),
arr[1].vector.clone(),
arr[2].vector.clone(),
arr[3].vector.clone(),
arr[4].vector.clone(),
arr[5].vector.clone(),
arr[6].vector.clone(),
arr[7].vector.clone(),
]))
}
}
impl<T: Scalar + PrimitiveSimdValue, const D: usize> From<[Scale<T::Element, D>; 16]>
for Scale<T, D>
where
T: From<[<T as simba::simd::SimdValue>::Element; 16]>,
T::Element: Scalar,
{
#[inline]
fn from(arr: [Scale<T::Element, D>; 16]) -> Self {
Self::from(OVector::from([
arr[0].vector.clone(),
arr[1].vector.clone(),
arr[2].vector.clone(),
arr[3].vector.clone(),
arr[4].vector.clone(),
arr[5].vector.clone(),
arr[6].vector.clone(),
arr[7].vector.clone(),
arr[8].vector.clone(),
arr[9].vector.clone(),
arr[10].vector.clone(),
arr[11].vector.clone(),
arr[12].vector.clone(),
arr[13].vector.clone(),
arr[14].vector.clone(),
arr[15].vector.clone(),
]))
}
}

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use std::ops::{Deref, DerefMut};
use crate::base::coordinates::{X, XY, XYZ, XYZW, XYZWA, XYZWAB};
use crate::base::Scalar;
use crate::geometry::Scale;
/*
*
* Give coordinates to Scale{1 .. 6}
*
*/
macro_rules! deref_impl(
($D: expr, $Target: ident $(, $comps: ident)*) => {
impl<T: Scalar> Deref for Scale<T, $D> {
type Target = $Target<T>;
#[inline]
fn deref(&self) -> &Self::Target {
self.vector.deref()
}
}
impl<T: Scalar> DerefMut for Scale<T, $D> {
#[inline]
fn deref_mut(&mut self) -> &mut Self::Target {
self.vector.deref_mut()
}
}
}
);
deref_impl!(1, X, x);
deref_impl!(2, XY, x, y);
deref_impl!(3, XYZ, x, y, z);
deref_impl!(4, XYZW, x, y, z, w);
deref_impl!(5, XYZWA, x, y, z, w, a);
deref_impl!(6, XYZWAB, x, y, z, w, a, b);

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use std::ops::{Mul, MulAssign};
use simba::scalar::ClosedMul;
use crate::base::constraint::{SameNumberOfColumns, SameNumberOfRows, ShapeConstraint};
use crate::base::dimension::U1;
use crate::base::{Const, SVector, Scalar};
use crate::geometry::{Point, Scale};
// Scale × Scale
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: &'a Scale<T, D>, right: &'b Scale<T, D>, Output = Scale<T, D>;
Scale::from(self.vector.component_mul(&right.vector));
'a, 'b);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: &'a Scale<T, D>, right: Scale<T, D>, Output = Scale<T, D>;
Scale::from(self.vector.component_mul(&right.vector));
'a);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: Scale<T, D>, right: &'b Scale<T, D>, Output = Scale<T, D>;
Scale::from(self.vector.component_mul(&right.vector));
'b);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: Scale<T, D>, right: Scale<T, D>, Output = Scale<T, D>;
Scale::from(self.vector.component_mul(&right.vector)); );
// Scale × scalar
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: &'a Scale<T, D>, right: T, Output = Scale<T, D>;
Scale::from(&self.vector * right);
'a);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: Scale<T, D>, right: T, Output = Scale<T, D>;
Scale::from(self.vector * right); );
// Scale × Point
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: &'a Scale<T, D>, right: &'b Point<T, D>, Output = Point<T, D>;
Point::from(self.vector.component_mul(&right.coords));
'a, 'b);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: &'a Scale<T, D>, right: Point<T, D>, Output = Point<T, D>;
Point::from(self.vector.component_mul(&right.coords));
'a);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: Scale<T, D>, right: &'b Point<T, D>, Output = Point<T, D>;
Point::from(self.vector.component_mul(&right.coords));
'b);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: Scale<T, D>, right: Point<T, D>, Output = Point<T, D>;
Point::from(self.vector.component_mul(&right.coords)); );
// Scale * Vector
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: &'a Scale<T, D>, right: &'b SVector<T, D>, Output = SVector<T, D>;
SVector::from(self.vector.component_mul(&right));
'a, 'b);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: &'a Scale<T, D>, right: SVector<T, D>, Output = SVector<T, D>;
SVector::from(self.vector.component_mul(&right));
'a);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: Scale<T, D>, right: &'b SVector<T, D>, Output = SVector<T, D>;
SVector::from(self.vector.component_mul(&right));
'b);
add_sub_impl!(Mul, mul, ClosedMul;
(Const<D>, U1), (Const<D>, U1) -> (Const<D>, U1)
const D; for; where;
self: Scale<T, D>, right: SVector<T, D>, Output = SVector<T, D>;
SVector::from(self.vector.component_mul(&right)); );
// Scale *= Scale
add_sub_assign_impl!(MulAssign, mul_assign, ClosedMul;
const D;
self: Scale<T, D>, right: &'b Scale<T, D>;
self.vector.component_mul_assign(&right.vector);
'b);
add_sub_assign_impl!(MulAssign, mul_assign, ClosedMul;
const D;
self: Scale<T, D>, right: Scale<T, D>;
self.vector.component_mul_assign(&right.vector); );
// Scale ×= scalar
add_sub_assign_impl!(MulAssign, mul_assign, ClosedMul;
const D;
self: Scale<T, D>, right: T;
self.vector *= right; );

49
src/geometry/scale_simba.rs Executable file
View File

@ -0,0 +1,49 @@
use simba::simd::SimdValue;
use crate::base::OVector;
use crate::Scalar;
use crate::geometry::Scale;
impl<T: Scalar + SimdValue, const D: usize> SimdValue for Scale<T, D>
where
T::Element: Scalar,
{
type Element = Scale<T::Element, D>;
type SimdBool = T::SimdBool;
#[inline]
fn lanes() -> usize {
T::lanes()
}
#[inline]
fn splat(val: Self::Element) -> Self {
OVector::splat(val.vector).into()
}
#[inline]
fn extract(&self, i: usize) -> Self::Element {
self.vector.extract(i).into()
}
#[inline]
unsafe fn extract_unchecked(&self, i: usize) -> Self::Element {
self.vector.extract_unchecked(i).into()
}
#[inline]
fn replace(&mut self, i: usize, val: Self::Element) {
self.vector.replace(i, val.vector)
}
#[inline]
unsafe fn replace_unchecked(&mut self, i: usize, val: Self::Element) {
self.vector.replace_unchecked(i, val.vector)
}
#[inline]
fn select(self, cond: Self::SimdBool, other: Self) -> Self {
self.vector.select(cond, other.vector).into()
}
}

24
src/geometry/similarity.rs
View File

@ -23,7 +23,11 @@ use crate::geometry::{AbstractRotation, Isometry, Point, Translation};
/// A similarity, i.e., an uniform scaling, followed by a rotation, followed by a translation.
#[repr(C)]
#[derive(Debug)]
#[derive(Debug, Copy, Clone)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
#[cfg_attr(feature = "serde-serialize-no-std", derive(Serialize, Deserialize))]
#[cfg_attr(
feature = "serde-serialize-no-std",
@ -73,22 +77,6 @@ where
}
}
impl<T: Scalar + Copy + Zero, R: AbstractRotation<T, D> + Copy, const D: usize> Copy
for Similarity<T, R, D>
where
Owned<T, Const<D>>: Copy,
{
}
impl<T: Scalar + Zero, R: AbstractRotation<T, D> + Clone, const D: usize> Clone
for Similarity<T, R, D>
{
#[inline]
fn clone(&self) -> Self {
Similarity::from_isometry(self.isometry.clone(), self.scaling.clone())
}
}
impl<T: Scalar + Zero, R, const D: usize> Similarity<T, R, D>
where
R: AbstractRotation<T, D>,
@ -415,7 +403,7 @@ where
#[inline]
fn ulps_eq(&self, other: &Self, epsilon: Self::Epsilon, max_ulps: u32) -> bool {
self.isometry
.ulps_eq(&other.isometry, epsilon.clone(), max_ulps.clone())
.ulps_eq(&other.isometry, epsilon.clone(), max_ulps)
&& self.scaling.ulps_eq(&other.scaling, epsilon, max_ulps)
}
}

10
src/geometry/similarity_construction.rs
View File

@ -20,6 +20,16 @@ use crate::{
Translation, UnitComplex, UnitQuaternion,
};
impl<T: SimdRealField, R, const D: usize> Default for Similarity<T, R, D>
where
T::Element: SimdRealField,
R: AbstractRotation<T, D>,
{
fn default() -> Self {
Self::identity()
}
}
impl<T: SimdRealField, R, const D: usize> Similarity<T, R, D>
where
T::Element: SimdRealField,

35
src/geometry/transform.rs
View File

@ -1,6 +1,6 @@
use approx::{AbsDiffEq, RelativeEq, UlpsEq};
use std::any::Any;
use std::fmt::Debug;
use std::fmt::{self, Debug};
use std::hash;
use std::marker::PhantomData;
@ -60,14 +60,26 @@ where
/// Tag representing the most general (not necessarily inversible) `Transform` type.
#[derive(Debug, Copy, Clone, Hash, PartialEq, Eq)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub enum TGeneral {}
/// Tag representing the most general inversible `Transform` type.
#[derive(Debug, Copy, Clone, Hash, PartialEq, Eq)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub enum TProjective {}
/// Tag representing an affine `Transform`. Its bottom-row is equal to `(0, 0 ... 0, 1)`.
#[derive(Debug, Copy, Clone, Hash, PartialEq, Eq)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
pub enum TAffine {}
impl TCategory for TGeneral {
@ -157,7 +169,6 @@ super_tcategory_impl!(
/// It is stored as a matrix with dimensions `(D + 1, D + 1)`, e.g., it stores a 4x4 matrix for a
/// 3D transformation.
#[repr(C)]
#[derive(Debug)]
pub struct Transform<T: RealField, C: TCategory, const D: usize>
where
Const<D>: DimNameAdd<U1>,
@ -167,6 +178,16 @@ where
_phantom: PhantomData<C>,
}
impl<T: RealField + Debug, C: TCategory, const D: usize> Debug for Transform<T, C, D>
where
Const<D>: DimNameAdd<U1>,
DefaultAllocator: Allocator<T, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>,
{
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.matrix.fmt(formatter)
}
}
impl<T: RealField + hash::Hash, C: TCategory, const D: usize> hash::Hash for Transform<T, C, D>
where
Const<D>: DimNameAdd<U1>,
@ -186,6 +207,16 @@ where
{
}
#[cfg(all(not(target_os = "cuda"), feature = "cuda"))]
unsafe impl<T: RealField + cust::memory::DeviceCopy, C: TCategory, const D: usize>
cust::memory::DeviceCopy for Transform<T, C, D>
where
Const<D>: DimNameAdd<U1>,
DefaultAllocator: Allocator<T, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>,
Owned<T, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>: cust::memory::DeviceCopy,
{
}
impl<T: RealField, C: TCategory, const D: usize> Clone for Transform<T, C, D>
where
Const<D>: DimNameAdd<U1>,

10
src/geometry/transform_construction.rs
View File

@ -8,6 +8,16 @@ use crate::base::{Const, DefaultAllocator, OMatrix};
use crate::geometry::{TCategory, Transform};
impl<T: RealField, C: TCategory, const D: usize> Default for Transform<T, C, D>
where
Const<D>: DimNameAdd<U1>,
DefaultAllocator: Allocator<T, DimNameSum<Const<D>, U1>, DimNameSum<Const<D>, U1>>,
{
fn default() -> Self {
Self::identity()
}
}
impl<T: RealField, C: TCategory, const D: usize> Transform<T, C, D>
where
Const<D>: DimNameAdd<U1>,

24
src/geometry/translation.rs
View File

@ -22,13 +22,23 @@ use crate::geometry::Point;
/// A translation.
#[repr(C)]
#[derive(Debug)]
#[cfg_attr(
all(not(target_os = "cuda"), feature = "cuda"),
derive(cust::DeviceCopy)
)]
#[derive(Copy, Clone)]
pub struct Translation<T, const D: usize> {
/// The translation coordinates, i.e., how much is added to a point's coordinates when it is
/// translated.
pub vector: SVector<T, D>,
}
impl<T: fmt::Debug, const D: usize> fmt::Debug for Translation<T, D> {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.vector.as_slice().fmt(formatter)
}
}
impl<T: Scalar + hash::Hash, const D: usize> hash::Hash for Translation<T, D>
where
Owned<T, Const<D>>: hash::Hash,
@ -38,18 +48,6 @@ where
}
}
impl<T: Scalar + Copy, const D: usize> Copy for Translation<T, D> {}
impl<T: Scalar, const D: usize> Clone for Translation<T, D>
where
Owned<T, Const<D>>: Clone,
{
#[inline]
fn clone(&self) -> Self {
Translation::from(self.vector.clone())
}
}
#[cfg(feature = "bytemuck")]
unsafe impl<T, const D: usize> bytemuck::Zeroable for Translation<T, D>
where

6
src/geometry/translation_construction.rs
View File

@ -15,6 +15,12 @@ use simba::scalar::{ClosedAdd, SupersetOf};
use crate::base::{SVector, Scalar};
use crate::geometry::Translation;
impl<T: Scalar + Zero, const D: usize> Default for Translation<T, D> {
fn default() -> Self {
Self::identity()
}
}
impl<T: Scalar, const D: usize> Translation<T, D> {
/// Creates a new identity translation.
///

6
src/geometry/unit_complex.rs
View File

@ -31,6 +31,9 @@ use std::cmp::{Eq, PartialEq};
/// * [Conversion to a matrix <span style="float:right;">`to_rotation_matrix`, `to_homogeneous`…</span>](#conversion-to-a-matrix)
pub type UnitComplex<T> = Unit<Complex<T>>;
#[cfg(all(not(target_os = "cuda"), feature = "cuda"))]
unsafe impl<T: cust::memory::DeviceCopy> cust::memory::DeviceCopy for UnitComplex<T> {}
impl<T: Scalar + PartialEq> PartialEq for UnitComplex<T> {
#[inline]
fn eq(&self, rhs: &Self) -> bool {
@ -458,8 +461,7 @@ impl<T: RealField> UlpsEq for UnitComplex<T> {
#[inline]
fn ulps_eq(&self, other: &Self, epsilon: Self::Epsilon, max_ulps: u32) -> bool {
self.re
.ulps_eq(&other.re, epsilon.clone(), max_ulps.clone())
self.re.ulps_eq(&other.re, epsilon.clone(), max_ulps)
&& self.im.ulps_eq(&other.im, epsilon, max_ulps)
}
}

9
src/geometry/unit_complex_construction.rs
View File

@ -17,6 +17,15 @@ use crate::geometry::{Rotation2, UnitComplex};
use simba::scalar::{RealField, SupersetOf};
use simba::simd::SimdRealField;
impl<T: SimdRealField> Default for UnitComplex<T>
where
T::Element: SimdRealField,
{
fn default() -> Self {
Self::identity()
}
}
/// # Identity
impl<T: SimdRealField> UnitComplex<T>
where

3
src/lib.rs
View File

@ -71,10 +71,11 @@ an optimized set of tools for computer graphics and physics. Those features incl
* Insertion and removal of rows of columns of a matrix.
*/
#![allow(unused_variables, unused_mut)]
#![deny(
missing_docs,
nonstandard_style,
unused_variables,
unused_mut,
unused_parens,
unused_qualifications,
unused_results,

49
src/linalg/cholesky.rs
View File

@ -74,6 +74,14 @@ where
Cholesky { chol: matrix }
}
/// Uses the given matrix as-is without any checks or modifications as the
/// Cholesky decomposition.
///
/// It is up to the user to ensure all invariants hold.
pub fn pack_dirty(matrix: OMatrix<T, D, D>) -> Self {
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) -> OMatrix<T, D, D> {
@ -163,7 +171,32 @@ where
///
/// Returns `None` if the input matrix is not definite-positive. The input matrix is assumed
/// to be symmetric and only the lower-triangular part is read.
pub fn new(mut matrix: OMatrix<T, D, D>) -> Option<Self> {
pub fn new(matrix: OMatrix<T, D, D>) -> Option<Self> {
Self::new_internal(matrix, None)
}
/// Attempts to approximate the Cholesky decomposition of `matrix` by
/// replacing non-positive values on the diagonals during the decomposition
/// with the given `substitute`.
///
/// [`try_sqrt`](ComplexField::try_sqrt) will be applied to the `substitute`
/// when it has to be used.
///
/// If your input matrix results only in positive values on the diagonals
/// during the decomposition, `substitute` is unused and the result is just
/// the same as if you used [`new`](Cholesky::new).
///
/// This method allows to compensate for matrices with very small or even
/// negative values due to numerical errors but necessarily results in only
/// an approximation: it is basically a hack. If you don't specifically need
/// Cholesky, it may be better to consider alternatives like the
/// [`LU`](crate::linalg::LU) decomposition/factorization.
pub fn new_with_substitute(matrix: OMatrix<T, D, D>, substitute: T) -> Option<Self> {
Self::new_internal(matrix, Some(substitute))
}
/// Common implementation for `new` and `new_with_substitute`.
fn new_internal(mut matrix: OMatrix<T, D, D>, substitute: Option<T>) -> Option<Self> {
assert!(matrix.is_square(), "The input matrix must be square.");
let n = matrix.nrows();
@ -179,9 +212,18 @@ where
col_j.axpy(factor.conjugate(), &col_k, T::one());
}
let sqrt_denom = |v: T| {
if v.is_zero() {
return None;
}
v.try_sqrt()
};
let diag = unsafe { matrix.get_unchecked((j, j)).clone() };
if !diag.is_zero() {
if let Some(denom) = diag.try_sqrt() {
if let Some(denom) =
sqrt_denom(diag).or_else(|| substitute.clone().and_then(sqrt_denom))
{
unsafe {
*matrix.get_unchecked_mut((j, j)) = denom.clone();
}
@ -190,7 +232,6 @@ where
col /= denom;
continue;
}
}
// The diagonal element is either zero or its square root could not
// be taken (e.g. for negative real numbers).

128
src/linalg/decomposition.rs
View File

@ -1,8 +1,8 @@
use crate::storage::Storage;
use crate::{
Allocator, Bidiagonal, Cholesky, ColPivQR, ComplexField, DefaultAllocator, Dim, DimDiff,
DimMin, DimMinimum, DimSub, FullPivLU, Hessenberg, Matrix, RealField, Schur, SymmetricEigen,
SymmetricTridiagonal, LU, QR, SVD, U1, UDU,
DimMin, DimMinimum, DimSub, FullPivLU, Hessenberg, Matrix, OMatrix, RealField, Schur,
SymmetricEigen, SymmetricTridiagonal, LU, QR, SVD, U1, UDU,
};
/// # Rectangular matrix decomposition
@ -17,6 +17,7 @@ use crate::{
/// | LU with partial pivoting | `P⁻¹ * L * U` | `L` is lower-triangular with a diagonal filled with `1` and `U` is upper-triangular. `P` is a permutation matrix. |
/// | LU with full pivoting | `P⁻¹ * L * U * Q⁻¹` | `L` is lower-triangular with a diagonal filled with `1` and `U` is upper-triangular. `P` and `Q` are permutation matrices. |
/// | SVD | `U * Σ * Vᵀ` | `U` and `V` are two orthogonal matrices and `Σ` is a diagonal matrix containing the singular values. |
/// | Polar (Left Polar) | `P' * U` | `U` is semi-unitary/unitary and `P'` is a positive semi-definite Hermitian Matrix
impl<T: ComplexField, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
/// Computes the bidiagonalization using householder reflections.
pub fn bidiagonalize(self) -> Bidiagonal<T, R, C>
@ -74,7 +75,31 @@ impl<T: ComplexField, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
}
/// Computes the Singular Value Decomposition using implicit shift.
/// The singular values are guaranteed to be sorted in descending order.
/// If this order is not required consider using `svd_unordered`.
pub fn svd(self, compute_u: bool, compute_v: bool) -> SVD<T, R, C>
where
R: DimMin<C>,
DimMinimum<R, C>: DimSub<U1>, // for Bidiagonal.
DefaultAllocator: Allocator<T, R, C>
+ Allocator<T, C>
+ Allocator<T, R>
+ Allocator<T, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<T, DimMinimum<R, C>, C>
+ Allocator<T, R, DimMinimum<R, C>>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<(usize, usize), DimMinimum<R, C>>
+ Allocator<(T::RealField, usize), DimMinimum<R, C>>,
{
SVD::new(self.into_owned(), compute_u, compute_v)
}
/// Computes the Singular Value Decomposition using implicit shift.
/// The singular values are not guaranteed to be sorted in any particular order.
/// If a descending order is required, consider using `svd` instead.
pub fn svd_unordered(self, compute_u: bool, compute_v: bool) -> SVD<T, R, C>
where
R: DimMin<C>,
DimMinimum<R, C>: DimSub<U1>, // for Bidiagonal.
@ -88,10 +113,12 @@ impl<T: ComplexField, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>,
{
SVD::new(self.into_owned(), compute_u, compute_v)
SVD::new_unordered(self.into_owned(), compute_u, compute_v)
}
/// Attempts to compute the Singular Value Decomposition of `matrix` using implicit shift.
/// The singular values are guaranteed to be sorted in descending order.
/// If this order is not required consider using `try_svd_unordered`.
///
/// # Arguments
///
@ -119,10 +146,103 @@ impl<T: ComplexField, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
+ Allocator<T, R, DimMinimum<R, C>>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>,
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<(usize, usize), DimMinimum<R, C>>
+ Allocator<(T::RealField, usize), DimMinimum<R, C>>,
{
SVD::try_new(self.into_owned(), compute_u, compute_v, eps, max_niter)
}
/// Attempts to compute the Singular Value Decomposition of `matrix` using implicit shift.
/// The singular values are not guaranteed to be sorted in any particular order.
/// If a descending order is required, consider using `try_svd` instead.
///
/// # Arguments
///
/// * `compute_u` − set this to `true` to enable the computation of left-singular vectors.
/// * `compute_v` − set this to `true` to enable the computation of right-singular vectors.
/// * `eps` − tolerance used to determine when a value converged to 0.
/// * `max_niter` − maximum total number of iterations performed by the algorithm. If this
/// number of iteration is exceeded, `None` is returned. If `niter == 0`, then the algorithm
/// continues indefinitely until convergence.
pub fn try_svd_unordered(
self,
compute_u: bool,
compute_v: bool,
eps: T::RealField,
max_niter: usize,
) -> Option<SVD<T, R, C>>
where
R: DimMin<C>,
DimMinimum<R, C>: DimSub<U1>, // for Bidiagonal.
DefaultAllocator: Allocator<T, R, C>
+ Allocator<T, C>
+ Allocator<T, R>
+ Allocator<T, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<T, DimMinimum<R, C>, C>
+ Allocator<T, R, DimMinimum<R, C>>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>,
{
SVD::try_new_unordered(self.into_owned(), compute_u, compute_v, eps, max_niter)
}
/// Computes the Polar Decomposition of a `matrix` (indirectly uses SVD).
pub fn polar(self) -> (OMatrix<T, R, R>, OMatrix<T, R, C>)
where
R: DimMin<C>,
DimMinimum<R, C>: DimSub<U1>, // for Bidiagonal.
DefaultAllocator: Allocator<T, R, C>
+ Allocator<T, DimMinimum<R, C>, R>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T, R, R>
+ Allocator<T, DimMinimum<R, C>, DimMinimum<R, C>>
+ Allocator<T, C>
+ Allocator<T, R>
+ Allocator<T, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<T, DimMinimum<R, C>, C>
+ Allocator<T, R, DimMinimum<R, C>>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>,
{
SVD::new_unordered(self.into_owned(), true, true)
.to_polar()
.unwrap()
}
/// Attempts to compute the Polar Decomposition of a `matrix` (indirectly uses SVD).
///
/// # Arguments
///
/// * `eps` − tolerance used to determine when a value converged to 0 when computing the SVD.
/// * `max_niter` − maximum total number of iterations performed by the SVD computation algorithm.
pub fn try_polar(
self,
eps: T::RealField,
max_niter: usize,
) -> Option<(OMatrix<T, R, R>, OMatrix<T, R, C>)>
where
R: DimMin<C>,
DimMinimum<R, C>: DimSub<U1>, // for Bidiagonal.
DefaultAllocator: Allocator<T, R, C>
+ Allocator<T, DimMinimum<R, C>, R>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T, R, R>
+ Allocator<T, DimMinimum<R, C>, DimMinimum<R, C>>
+ Allocator<T, C>
+ Allocator<T, R>
+ Allocator<T, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<T, DimMinimum<R, C>, C>
+ Allocator<T, R, DimMinimum<R, C>>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>,
{
SVD::try_new_unordered(self.into_owned(), true, true, eps, max_niter)
.and_then(|svd| svd.to_polar())
}
}
/// # Square matrix decomposition

2
src/linalg/determinant.rs
View File

@ -49,7 +49,7 @@ impl<T: ComplexField, D: DimMin<D, Output = D>, S: Storage<T, D, D>> SquareMatri
let m33 = self.get_unchecked((2, 2)).clone();
let minor_m12_m23 = m22.clone() * m33.clone() - m32.clone() * m23.clone();
let minor_m11_m23 = m21.clone() * m33.clone() - m31.clone() * m23.clone();
let minor_m11_m23 = m21.clone() * m33 - m31.clone() * m23;
let minor_m11_m22 = m21 * m32 - m31 * m22;
m11 * minor_m12_m23 - m12 * minor_m11_m23 + m13 * minor_m11_m22

70
src/linalg/exp.rs
View File

@ -11,6 +11,47 @@ use crate::{
use crate::num::Zero;
/// Precomputed factorials for integers in range `0..=34`.
/// Note: `35!` does not fit into 128 bits.
// TODO: find a better place for this array?
const FACTORIAL: [u128; 35] = [
1,
1,
2,
6,
24,
120,
720,
5040,
40320,
362880,
3628800,
39916800,
479001600,
6227020800,
87178291200,
1307674368000,
20922789888000,
355687428096000,
6402373705728000,
121645100408832000,
2432902008176640000,
51090942171709440000,
1124000727777607680000,
25852016738884976640000,
620448401733239439360000,
15511210043330985984000000,
403291461126605635584000000,
10888869450418352160768000000,
304888344611713860501504000000,
8841761993739701954543616000000,
265252859812191058636308480000000,
8222838654177922817725562880000000,
263130836933693530167218012160000000,
8683317618811886495518194401280000000,
295232799039604140847618609643520000000,
];
// https://github.com/scipy/scipy/blob/c1372d8aa90a73d8a52f135529293ff4edb98fc8/scipy/sparse/linalg/matfuncs.py
struct ExpmPadeHelper<T, D>
where
@ -321,8 +362,8 @@ where
self.calc_a2();
self.calc_a4();
self.calc_a6();
let mb2 = self.a2.as_ref().unwrap() * convert::<f64, T>(2.0_f64.powf(-2.0 * s.clone()));
let mb4 = self.a4.as_ref().unwrap() * convert::<f64, T>(2.0.powf(-4.0 * s.clone()));
let mb2 = self.a2.as_ref().unwrap() * convert::<f64, T>(2.0_f64.powf(-2.0 * s));
let mb4 = self.a4.as_ref().unwrap() * convert::<f64, T>(2.0.powf(-4.0 * s));
let mb6 = self.a6.as_ref().unwrap() * convert::<f64, T>(2.0.powf(-6.0 * s));
let u2 = &mb6 * (&mb6 * b[13].clone() + &mb4 * b[11].clone() + &mb2 * b[9].clone());
@ -342,15 +383,17 @@ where
}
}
fn factorial(n: u128) -> u128 {
if n == 1 {
return 1;
/// Compute `n!`
#[inline(always)]
fn factorial(n: usize) -> u128 {
match FACTORIAL.get(n) {
Some(f) => *f,
None => panic!("{}! is greater than u128::MAX", n),
}
n * factorial(n - 1)
}
/// Compute the 1-norm of a non-negative integer power of a non-negative matrix.
fn onenorm_matrix_power_nonm<T, D>(a: &OMatrix<T, D, D>, p: u64) -> T
fn onenorm_matrix_power_nonm<T, D>(a: &OMatrix<T, D, D>, p: usize) -> T
where
T: RealField,
D: Dim,
@ -367,7 +410,7 @@ where
v.max()
}
fn ell<T, D>(a: &OMatrix<T, D, D>, m: u64) -> u64
fn ell<T, D>(a: &OMatrix<T, D, D>, m: usize) -> u64
where
T: ComplexField,
D: Dim,
@ -376,8 +419,6 @@ where
+ Allocator<T::RealField, D>
+ Allocator<T::RealField, D, D>,
{
// 2m choose m = (2m)!/(m! * (2m-m)!)
let a_abs = a.map(|x| x.abs());
let a_abs_onenorm = onenorm_matrix_power_nonm(&a_abs, 2 * m + 1);
@ -386,9 +427,11 @@ where
return 0;
}
let choose_2m_m =
factorial(2 * m as u128) / (factorial(m as u128) * factorial(2 * m as u128 - m as u128));
let abs_c_recip = choose_2m_m * factorial(2 * m as u128 + 1);
// 2m choose m = (2m)!/(m! * (2m-m)!) = (2m)!/((m!)^2)
let m_factorial = factorial(m);
let choose_2m_m = factorial(2 * m) / (m_factorial * m_factorial);
let abs_c_recip = choose_2m_m * factorial(2 * m + 1);
let alpha = a_abs_onenorm / one_norm(a);
let alpha: f64 = try_convert(alpha).unwrap() / abs_c_recip as f64;
@ -510,6 +553,7 @@ where
#[cfg(test)]
mod tests {
#[test]
#[allow(clippy::float_cmp)]
fn one_norm() {
use crate::Matrix3;
let m = Matrix3::new(-3.0, 5.0, 7.0, 2.0, 6.0, 4.0, 0.0, 2.0, 8.0);

2
src/linalg/givens.rs
View File

@ -47,7 +47,7 @@ impl<T: ComplexField> GivensRotation<T> {
if denom > eps {
let norm = sign0.scale(denom.clone());
let c = mod0 / denom;
let s = s.clone() / norm.clone();
let s = s / norm.clone();
Some((Self { c, s }, norm))
} else {
None

2
src/linalg/lu.rs
View File

@ -317,7 +317,7 @@ where
pub fn is_invertible(&self) -> bool {
assert!(
self.lu.is_square(),
"QR: unable to test the invertibility of a non-square matrix."
"LU: unable to test the invertibility of a non-square matrix."
);
for i in 0..self.lu.nrows() {

2
src/linalg/mod.rs
View File

@ -24,6 +24,8 @@ mod qr;
mod schur;
mod solve;
mod svd;
mod svd2;
mod svd3;
mod symmetric_eigen;
mod symmetric_tridiagonal;
mod udu;

86
src/linalg/pow.rs
View File

@ -1,83 +1,71 @@
//! This module provides the matrix exponential (pow) function to square matrices.
use std::ops::DivAssign;
use crate::{
allocator::Allocator,
storage::{Storage, StorageMut},
DefaultAllocator, DimMin, Matrix, OMatrix,
DefaultAllocator, DimMin, Matrix, OMatrix, Scalar,
};
use num::PrimInt;
use simba::scalar::ComplexField;
use num::{One, Zero};
use simba::scalar::{ClosedAdd, ClosedMul};
impl<T: ComplexField, D, S> Matrix<T, D, D, S>
impl<T, D, S> Matrix<T, D, D, S>
where
T: Scalar + Zero + One + ClosedAdd + ClosedMul,
D: DimMin<D, Output = D>,
S: StorageMut<T, D, D>,
DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
{
/// Attempts to raise this matrix to an integral power `e` in-place. If this
/// matrix is non-invertible and `e` is negative, it leaves this matrix
/// untouched and returns `false`. Otherwise, it returns `true` and
/// overwrites this matrix with the result.
pub fn pow_mut<I: PrimInt + DivAssign>(&mut self, mut e: I) -> bool {
let zero = I::zero();
/// Raises this matrix to an integral power `exp` in-place.
pub fn pow_mut(&mut self, mut exp: u32) {
// A matrix raised to the zeroth power is just the identity.
if e == zero {
if exp == 0 {
self.fill_with_identity();
return true;
}
// If e is negative, we compute the inverse matrix, then raise it to the
// power of -e.
if e < zero && !self.try_inverse_mut() {
return false;
}
let one = I::one();
let two = I::from(2u8).unwrap();
// We use the buffer to hold the result of multiplier ^ 2, thus avoiding
} else if exp > 1 {
// We use the buffer to hold the result of multiplier^2, thus avoiding
// extra allocations.
let mut multiplier = self.clone_owned();
let mut buf = self.clone_owned();
let mut x = self.clone_owned();
let mut workspace = self.clone_owned();
if exp % 2 == 0 {
self.fill_with_identity();
} else {
// Avoid an useless multiplication by the identity
// if the exponent is odd.
exp -= 1;
}
// Exponentiation by squares.
loop {
if e % two == one {
self.mul_to(&multiplier, &mut buf);
self.copy_from(&buf);
if exp % 2 == 1 {
self.mul_to(&x, &mut workspace);
self.copy_from(&workspace);
}
e /= two;
multiplier.mul_to(&multiplier, &mut buf);
multiplier.copy_from(&buf);
exp /= 2;
if e == zero {
return true;
if exp == 0 {
break;
}
x.mul_to(&x, &mut workspace);
x.copy_from(&workspace);
}
}
}
}
impl<T: ComplexField, D, S: Storage<T, D, D>> Matrix<T, D, D, S>
impl<T, D, S: Storage<T, D, D>> Matrix<T, D, D, S>
where
T: Scalar + Zero + One + ClosedAdd + ClosedMul,
D: DimMin<D, Output = D>,
S: StorageMut<T, D, D>,
DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
{
/// Attempts to raise this matrix to an integral power `e`. If this matrix
/// is non-invertible and `e` is negative, it returns `None`. Otherwise, it
/// returns the result as a new matrix. Uses exponentiation by squares.
/// Raise this matrix to an integral power `exp`.
#[must_use]
pub fn pow<I: PrimInt + DivAssign>(&self, e: I) -> Option<OMatrix<T, D, D>> {
let mut clone = self.clone_owned();
if clone.pow_mut(e) {
Some(clone)
} else {
None
}
pub fn pow(&self, exp: u32) -> OMatrix<T, D, D> {
let mut result = self.clone_owned();
result.pow_mut(exp);
result
}
}

5
src/linalg/qr.rs
View File

@ -147,6 +147,11 @@ where
&self.qr
}
#[must_use]
pub(crate) fn diag_internal(&self) -> &OVector<T, DimMinimum<R, C>> {
&self.diag
}
/// Multiplies the provided matrix by the transpose of the `Q` matrix of this decomposition.
pub fn q_tr_mul<R2: Dim, C2: Dim, S2>(&self, rhs: &mut Matrix<T, R2, C2, S2>)
// TODO: do we need a static constraint on the number of rows of rhs?

209
src/linalg/svd.rs
View File

@ -1,5 +1,6 @@
#[cfg(feature = "serde-serialize-no-std")]
use serde::{Deserialize, Serialize};
use std::any::TypeId;
use approx::AbsDiffEq;
use num::{One, Zero};
@ -9,6 +10,7 @@ use crate::base::{DefaultAllocator, Matrix, Matrix2x3, OMatrix, OVector, Vector2
use crate::constraint::{SameNumberOfRows, ShapeConstraint};
use crate::dimension::{Dim, DimDiff, DimMin, DimMinimum, DimSub, U1};
use crate::storage::Storage;
use crate::{Matrix2, Matrix3, RawStorage, U2, U3};
use simba::scalar::{ComplexField, RealField};
use crate::linalg::givens::GivensRotation;
@ -78,9 +80,21 @@ where
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>,
{
fn use_special_always_ordered_svd2() -> bool {
TypeId::of::<OMatrix<T, R, C>>() == TypeId::of::<Matrix2<T::RealField>>()
&& TypeId::of::<Self>() == TypeId::of::<SVD<T::RealField, U2, U2>>()
}
fn use_special_always_ordered_svd3() -> bool {
TypeId::of::<OMatrix<T, R, C>>() == TypeId::of::<Matrix3<T::RealField>>()
&& TypeId::of::<Self>() == TypeId::of::<SVD<T::RealField, U3, U3>>()
}
/// Computes the Singular Value Decomposition of `matrix` using implicit shift.
pub fn new(matrix: OMatrix<T, R, C>, compute_u: bool, compute_v: bool) -> Self {
Self::try_new(
/// The singular values are not guaranteed to be sorted in any particular order.
/// If a descending order is required, consider using `new` instead.
pub fn new_unordered(matrix: OMatrix<T, R, C>, compute_u: bool, compute_v: bool) -> Self {
Self::try_new_unordered(
matrix,
compute_u,
compute_v,
@ -91,6 +105,8 @@ where
}
/// Attempts to compute the Singular Value Decomposition of `matrix` using implicit shift.
/// The singular values are not guaranteed to be sorted in any particular order.
/// If a descending order is required, consider using `try_new` instead.
///
/// # Arguments
///
@ -100,7 +116,7 @@ where
/// * `max_niter` − maximum total number of iterations performed by the algorithm. If this
/// number of iteration is exceeded, `None` is returned. If `niter == 0`, then the algorithm
/// continues indefinitely until convergence.
pub fn try_new(
pub fn try_new_unordered(
mut matrix: OMatrix<T, R, C>,
compute_u: bool,
compute_v: bool,
@ -113,6 +129,21 @@ where
);
let (nrows, ncols) = matrix.shape_generic();
let min_nrows_ncols = nrows.min(ncols);
if Self::use_special_always_ordered_svd2() {
// SAFETY: the reference transmutes are OK since we checked that the types match exactly.
let matrix: &Matrix2<T::RealField> = unsafe { std::mem::transmute(&matrix) };
let result = super::svd2::svd_ordered2(matrix, compute_u, compute_v);
let typed_result: &Self = unsafe { std::mem::transmute(&result) };
return Some(typed_result.clone());
} else if Self::use_special_always_ordered_svd3() {
// SAFETY: the reference transmutes are OK since we checked that the types match exactly.
let matrix: &Matrix3<T::RealField> = unsafe { std::mem::transmute(&matrix) };
let result = super::svd3::svd_ordered3(matrix, compute_u, compute_v, eps, max_niter);
let typed_result: &Self = unsafe { std::mem::transmute(&result) };
return Some(typed_result.clone());
}
let dim = min_nrows_ncols.value();
let m_amax = matrix.camax();
@ -610,6 +641,144 @@ where
}
}
}
/// converts SVD results to Polar decomposition form of the original Matrix: `A = P' * U`.
///
/// The polar decomposition used here is Left Polar Decomposition (or Reverse Polar Decomposition)
/// Returns None if the singular vectors of the SVD haven't been calculated
pub fn to_polar(&self) -> Option<(OMatrix<T, R, R>, OMatrix<T, R, C>)>
where
DefaultAllocator: Allocator<T, R, C> //result
+ Allocator<T, DimMinimum<R, C>, R> // adjoint
+ Allocator<T, DimMinimum<R, C>> // mapped vals
+ Allocator<T, R, R> // result
+ Allocator<T, DimMinimum<R, C>, DimMinimum<R, C>>, // square matrix
{
match (&self.u, &self.v_t) {
(Some(u), Some(v_t)) => Some((
u * OMatrix::from_diagonal(&self.singular_values.map(|e| T::from_real(e)))
* u.adjoint(),
u * v_t,
)),
_ => None,
}
}
}
impl<T: ComplexField, R: DimMin<C>, C: Dim> SVD<T, R, C>
where
DimMinimum<R, C>: DimSub<U1>, // for Bidiagonal.
DefaultAllocator: Allocator<T, R, C>
+ Allocator<T, C>
+ Allocator<T, R>
+ Allocator<T, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<T, DimMinimum<R, C>, C>
+ Allocator<T, R, DimMinimum<R, C>>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<(usize, usize), DimMinimum<R, C>> // for sorted singular values
+ Allocator<(T::RealField, usize), DimMinimum<R, C>>, // for sorted singular values
{
/// Computes the Singular Value Decomposition of `matrix` using implicit shift.
/// The singular values are guaranteed to be sorted in descending order.
/// If this order is not required consider using `new_unordered`.
pub fn new(matrix: OMatrix<T, R, C>, compute_u: bool, compute_v: bool) -> Self {
let mut svd = Self::new_unordered(matrix, compute_u, compute_v);
if !Self::use_special_always_ordered_svd3() && !Self::use_special_always_ordered_svd2() {
svd.sort_by_singular_values();
}
svd
}
/// Attempts to compute the Singular Value Decomposition of `matrix` using implicit shift.
/// The singular values are guaranteed to be sorted in descending order.
/// If this order is not required consider using `try_new_unordered`.
///
/// # Arguments
///
/// * `compute_u` − set this to `true` to enable the computation of left-singular vectors.
/// * `compute_v` − set this to `true` to enable the computation of right-singular vectors.
/// * `eps` − tolerance used to determine when a value converged to 0.
/// * `max_niter` − maximum total number of iterations performed by the algorithm. If this
/// number of iteration is exceeded, `None` is returned. If `niter == 0`, then the algorithm
/// continues indefinitely until convergence.
pub fn try_new(
matrix: OMatrix<T, R, C>,
compute_u: bool,
compute_v: bool,
eps: T::RealField,
max_niter: usize,
) -> Option<Self> {
Self::try_new_unordered(matrix, compute_u, compute_v, eps, max_niter).map(|mut svd| {
if !Self::use_special_always_ordered_svd3() && !Self::use_special_always_ordered_svd2()
{
svd.sort_by_singular_values();
}
svd
})
}
/// Sort the estimated components of the SVD by its singular values in descending order.
/// Such an ordering is often implicitly required when the decompositions are used for estimation or fitting purposes.
/// Using this function is only required if `new_unordered` or `try_new_unorderd` were used and the specific sorting is required afterward.
pub fn sort_by_singular_values(&mut self) {
const VALUE_PROCESSED: usize = usize::MAX;
// Collect the singular values with their original index, ...
let mut singular_values = self.singular_values.map_with_location(|r, _, e| (e, r));
assert_ne!(
singular_values.data.shape().0.value(),
VALUE_PROCESSED,
"Too many singular values"
);
// ... sort the singular values, ...
singular_values
.as_mut_slice()
.sort_unstable_by(|(a, _), (b, _)| b.partial_cmp(a).expect("Singular value was NaN"));
// ... and store them.
self.singular_values
.zip_apply(&singular_values, |value, (new_value, _)| {
value.clone_from(&new_value)
});
// Calculate required permutations given the sorted indices.
// We need to identify all circles to calculate the required swaps.
let mut permutations =
crate::PermutationSequence::identity_generic(singular_values.data.shape().0);
for i in 0..singular_values.len() {
let mut index_1 = i;
let mut index_2 = singular_values[i].1;
// Check whether the value was already visited ...
while index_2 != VALUE_PROCESSED // ... or a "double swap" must be avoided.
&& singular_values[index_2].1 != VALUE_PROCESSED
{
// Add the permutation ...
permutations.append_permutation(index_1, index_2);
// ... and mark the value as visited.
singular_values[index_1].1 = VALUE_PROCESSED;
index_1 = index_2;
index_2 = singular_values[index_1].1;
}
}
// Permute the optional components
if let Some(u) = self.u.as_mut() {
permutations.permute_columns(u);
}
if let Some(v_t) = self.v_t.as_mut() {
permutations.permute_rows(v_t);
}
}
}
impl<T: ComplexField, R: DimMin<C>, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>
@ -626,9 +795,11 @@ where
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>,
{
/// Computes the singular values of this matrix.
/// The singular values are not guaranteed to be sorted in any particular order.
/// If a descending order is required, consider using `singular_values` instead.
#[must_use]
pub fn singular_values(&self) -> OVector<T::RealField, DimMinimum<R, C>> {
SVD::new(self.clone_owned(), false, false).singular_values
pub fn singular_values_unordered(&self) -> OVector<T::RealField, DimMinimum<R, C>> {
SVD::new_unordered(self.clone_owned(), false, false).singular_values
}
/// Computes the rank of this matrix.
@ -636,7 +807,7 @@ where
/// All singular values below `eps` are considered equal to 0.
#[must_use]
pub fn rank(&self, eps: T::RealField) -> usize {
let svd = SVD::new(self.clone_owned(), false, false);
let svd = SVD::new_unordered(self.clone_owned(), false, false);
svd.rank(eps)
}
@ -647,7 +818,31 @@ where
where
DefaultAllocator: Allocator<T, C, R>,
{
SVD::new(self.clone_owned(), true, true).pseudo_inverse(eps)
SVD::new_unordered(self.clone_owned(), true, true).pseudo_inverse(eps)
}
}
impl<T: ComplexField, R: DimMin<C>, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>
where
DimMinimum<R, C>: DimSub<U1>,
DefaultAllocator: Allocator<T, R, C>
+ Allocator<T, C>
+ Allocator<T, R>
+ Allocator<T, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<T, DimMinimum<R, C>, C>
+ Allocator<T, R, DimMinimum<R, C>>
+ Allocator<T, DimMinimum<R, C>>
+ Allocator<T::RealField, DimMinimum<R, C>>
+ Allocator<T::RealField, DimDiff<DimMinimum<R, C>, U1>>
+ Allocator<(usize, usize), DimMinimum<R, C>>
+ Allocator<(T::RealField, usize), DimMinimum<R, C>>,
{
/// Computes the singular values of this matrix.
/// The singular values are guaranteed to be sorted in descending order.
/// If this order is not required consider using `singular_values_unordered`.
#[must_use]
pub fn singular_values(&self) -> OVector<T::RealField, DimMinimum<R, C>> {
SVD::new(self.clone_owned(), false, false).singular_values
}
}

50
src/linalg/svd2.rs Normal file
View File

@ -0,0 +1,50 @@
use crate::{Matrix2, RealField, Vector2, SVD, U2};
// Implementation of the 2D SVD from https://ieeexplore.ieee.org/document/486688
// See also https://scicomp.stackexchange.com/questions/8899/robust-algorithm-for-2-times-2-svd
pub fn svd_ordered2<T: RealField>(
m: &Matrix2<T>,
compute_u: bool,
compute_v: bool,
) -> SVD<T, U2, U2> {
let half: T = crate::convert(0.5);
let one: T = crate::convert(1.0);
let e = (m.m11.clone() + m.m22.clone()) * half.clone();
let f = (m.m11.clone() - m.m22.clone()) * half.clone();
let g = (m.m21.clone() + m.m12.clone()) * half.clone();
let h = (m.m21.clone() - m.m12.clone()) * half.clone();
let q = (e.clone() * e.clone() + h.clone() * h.clone()).sqrt();
let r = (f.clone() * f.clone() + g.clone() * g.clone()).sqrt();
// Note that the singular values are always sorted because sx >= sy
// because q >= 0 and r >= 0.
let sx = q.clone() + r.clone();
let sy = q - r;
let sy_sign = if sy < T::zero() { -one.clone() } else { one };
let singular_values = Vector2::new(sx, sy * sy_sign.clone());
if compute_u || compute_v {
let a1 = g.atan2(f);
let a2 = h.atan2(e);
let theta = (a2.clone() - a1.clone()) * half.clone();
let phi = (a2 + a1) * half;
let (st, ct) = theta.sin_cos();
let (sp, cp) = phi.sin_cos();
let u = Matrix2::new(cp.clone(), -sp.clone(), sp, cp);
let v_t = Matrix2::new(ct.clone(), -st.clone(), st * sy_sign.clone(), ct * sy_sign);
SVD {
u: if compute_u { Some(u) } else { None },
singular_values,
v_t: if compute_v { Some(v_t) } else { None },
}
} else {
SVD {
u: None,
singular_values,
v_t: None,
}
}
}

55
src/linalg/svd3.rs Normal file
View File

@ -0,0 +1,55 @@
use crate::{Matrix3, SVD, U3};
use simba::scalar::RealField;
// For the 3x3 case, on the GPU, it is much more efficient to compute the SVD
// using an eigendecomposition followed by a QR decomposition.
//
// This is based on the paper "Computing the Singular Value Decomposition of 3 x 3 matrices with
// minimal branching and elementary floating point operations" from McAdams, et al.
pub fn svd_ordered3<T: RealField>(
m: &Matrix3<T>,
compute_u: bool,
compute_v: bool,
eps: T,
niter: usize,
) -> Option<SVD<T, U3, U3>> {
let s = m.tr_mul(&m);
let mut v = s.try_symmetric_eigen(eps, niter)?.eigenvectors;
let mut b = m * &v;
// Sort singular values. This is a necessary step to ensure that
// the QR decompositions R matrix ends up diagonal.
let mut rho0 = b.column(0).norm_squared();
let mut rho1 = b.column(1).norm_squared();
let mut rho2 = b.column(2).norm_squared();
if rho0 < rho1 {
b.swap_columns(0, 1);
b.column_mut(1).neg_mut();
v.swap_columns(0, 1);
v.column_mut(1).neg_mut();
std::mem::swap(&mut rho0, &mut rho1);
}
if rho0 < rho2 {
b.swap_columns(0, 2);
b.column_mut(2).neg_mut();
v.swap_columns(0, 2);
v.column_mut(2).neg_mut();
std::mem::swap(&mut rho0, &mut rho2);
}
if rho1 < rho2 {
b.swap_columns(1, 2);
b.column_mut(2).neg_mut();
v.swap_columns(1, 2);
v.column_mut(2).neg_mut();
std::mem::swap(&mut rho0, &mut rho2);
}
let qr = b.qr();
Some(SVD {
u: if compute_u { Some(qr.q()) } else { None },
singular_values: qr.diag_internal().map(|e| e.abs()),
v_t: if compute_v { Some(v.transpose()) } else { None },
})
}

2
src/third_party/glam/mod.rs vendored
View File

@ -8,3 +8,5 @@ mod v015;
mod v016;
#[cfg(feature = "glam017")]
mod v017;
#[cfg(feature = "glam018")]
mod v018;

18
src/third_party/glam/v018/mod.rs vendored Normal file
View File

@ -0,0 +1,18 @@
#[path = "../common/glam_isometry.rs"]
mod glam_isometry;
#[path = "../common/glam_matrix.rs"]
mod glam_matrix;
#[path = "../common/glam_point.rs"]
mod glam_point;
#[path = "../common/glam_quaternion.rs"]
mod glam_quaternion;
#[path = "../common/glam_rotation.rs"]
mod glam_rotation;
#[path = "../common/glam_similarity.rs"]
mod glam_similarity;
#[path = "../common/glam_translation.rs"]
mod glam_translation;
#[path = "../common/glam_unit_complex.rs"]
mod glam_unit_complex;
pub(self) use glam018 as glam;

4
tests/core/matrix.rs
View File

@ -80,8 +80,8 @@ fn iter() {
#[test]
fn debug_output_corresponds_to_data_container() {
let m = Matrix2::new(1.0, 2.0, 3.0, 4.0);
let output_stable = "Matrix { data: [[1, 3], [2, 4]] }"; // Current output on the stable channel.
let output_nightly = "Matrix { data: [[1.0, 3.0], [2.0, 4.0]] }"; // Current output on the nightly channel.
let output_stable = "[[1, 3], [2, 4]]"; // Current output on the stable channel.
let output_nightly = "[[1.0, 3.0], [2.0, 4.0]]"; // Current output on the nightly channel.
let current_output = format!("{:?}", m);
dbg!(output_stable);
dbg!(output_nightly);

94
tests/geometry/dual_quaternion.rs
View File

@ -1,7 +1,7 @@
#![cfg(feature = "proptest-support")]
#![allow(non_snake_case)]
use na::{DualQuaternion, Point3, UnitDualQuaternion, Vector3};
use na::{DualQuaternion, Point3, Unit, UnitDualQuaternion, UnitQuaternion, Vector3};
use crate::proptest::*;
use proptest::{prop_assert, proptest};
@ -74,6 +74,98 @@ proptest!(
prop_assert!(relative_eq!((dq * t) * p, dq * (t * p), epsilon = 1.0e-7));
}
#[cfg_attr(rustfmt, rustfmt_skip)]
#[test]
fn sclerp_is_defined_for_identical_orientations(
dq in unit_dual_quaternion(),
s in -1.0f64..2.0f64,
t in translation3(),
) {
// Should not panic.
prop_assert!(relative_eq!(dq.sclerp(&dq, 0.0), dq, epsilon = 1.0e-7));
prop_assert!(relative_eq!(dq.sclerp(&dq, 0.5), dq, epsilon = 1.0e-7));
prop_assert!(relative_eq!(dq.sclerp(&dq, 1.0), dq, epsilon = 1.0e-7));
prop_assert!(relative_eq!(dq.sclerp(&dq, s), dq, epsilon = 1.0e-7));
let unit = UnitDualQuaternion::identity();
prop_assert!(relative_eq!(unit.sclerp(&unit, 0.0), unit, epsilon = 1.0e-7));
prop_assert!(relative_eq!(unit.sclerp(&unit, 0.5), unit, epsilon = 1.0e-7));
prop_assert!(relative_eq!(unit.sclerp(&unit, 1.0), unit, epsilon = 1.0e-7));
prop_assert!(relative_eq!(unit.sclerp(&unit, s), unit, epsilon = 1.0e-7));
// ScLERPing two unit dual quaternions with nearly equal rotation
// components should result in a unit dual quaternion with a rotation
// component nearly equal to either input.
let dq2 = t * dq;
prop_assert!(relative_eq!(dq.sclerp(&dq2, 0.0).real, dq.real, epsilon = 1.0e-7));
prop_assert!(relative_eq!(dq.sclerp(&dq2, 0.5).real, dq.real, epsilon = 1.0e-7));
prop_assert!(relative_eq!(dq.sclerp(&dq2, 1.0).real, dq.real, epsilon = 1.0e-7));
prop_assert!(relative_eq!(dq.sclerp(&dq2, s).real, dq.real, epsilon = 1.0e-7));
// ScLERPing two unit dual quaternions with nearly equal rotation
// components should result in a unit dual quaternion with a translation
// component which is nearly equal to linearly interpolating the
// translation components of the inputs.
prop_assert!(relative_eq!(
dq.sclerp(&dq2, s).translation().vector,
dq.translation().vector.lerp(&dq2.translation().vector, s),
epsilon = 1.0e-7
));
let unit2 = t * unit;
prop_assert!(relative_eq!(unit.sclerp(&unit2, 0.0).real, unit.real, epsilon = 1.0e-7));
prop_assert!(relative_eq!(unit.sclerp(&unit2, 0.5).real, unit.real, epsilon = 1.0e-7));
prop_assert!(relative_eq!(unit.sclerp(&unit2, 1.0).real, unit.real, epsilon = 1.0e-7));
prop_assert!(relative_eq!(unit.sclerp(&unit2, s).real, unit.real, epsilon = 1.0e-7));
prop_assert!(relative_eq!(
unit.sclerp(&unit2, s).translation().vector,
unit.translation().vector.lerp(&unit2.translation().vector, s),
epsilon = 1.0e-7
));
}
#[cfg_attr(rustfmt, rustfmt_skip)]
#[test]
fn sclerp_is_not_defined_for_opposite_orientations(
dq in unit_dual_quaternion(),
s in 0.1f64..0.9f64,
t in translation3(),
t2 in translation3(),
v in vector3(),
) {
let iso = dq.to_isometry();
let rot = iso.rotation;
if let Some((axis, angle)) = rot.axis_angle() {
let flipped = UnitQuaternion::from_axis_angle(&axis, angle + std::f64::consts::PI);
let dqf = flipped * rot.inverse() * dq.clone();
prop_assert!(dq.try_sclerp(&dqf, 0.5, 1.0e-7).is_none());
prop_assert!(dq.try_sclerp(&dqf, s, 1.0e-7).is_none());
}
let dq2 = t * dq;
let iso2 = dq2.to_isometry();
let rot2 = iso2.rotation;
if let Some((axis, angle)) = rot2.axis_angle() {
let flipped = UnitQuaternion::from_axis_angle(&axis, angle + std::f64::consts::PI);
let dq3f = t2 * flipped * rot.inverse() * dq.clone();
prop_assert!(dq2.try_sclerp(&dq3f, 0.5, 1.0e-7).is_none());
prop_assert!(dq2.try_sclerp(&dq3f, s, 1.0e-7).is_none());
}
if let Some(axis) = Unit::try_new(v, 1.0e-7) {
let unit = UnitDualQuaternion::identity();
let flip = UnitQuaternion::from_axis_angle(&axis, std::f64::consts::PI);
let unitf = flip * unit;
prop_assert!(unit.try_sclerp(&unitf, 0.5, 1.0e-7).is_none());
prop_assert!(unit.try_sclerp(&unitf, s, 1.0e-7).is_none());
let unit2f = t * unit * flip;
prop_assert!(unit.try_sclerp(&unit2f, 0.5, 1.0e-7).is_none());
prop_assert!(unit.try_sclerp(&unit2f, s, 1.0e-7).is_none());
}
}
#[cfg_attr(rustfmt, rustfmt_skip)]
#[test]
fn all_op_exist(

7
tests/lib.rs
View File

@ -33,3 +33,10 @@ mod proptest;
//#[cfg(all(feature = "debug", feature = "compare", feature = "rand"))]
//#[cfg(feature = "sparse")]
//mod sparse;
mod utils {
/// Checks if a slice is sorted in descending order.
pub fn is_sorted_descending<T: PartialOrd>(slice: &[T]) -> bool {
slice.windows(2).all(|elts| elts[0] >= elts[1])
}
}

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