Merge branch 'master' into abomonation

This commit is contained in:
Sébastien Crozet 2017-08-15 19:18:39 +02:00 committed by GitHub
commit afef66227e
193 changed files with 16454 additions and 6422 deletions

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@ -11,6 +11,12 @@ matrix:
allow_failures:
- rust: nightly
- rust: beta
addons:
apt:
packages:
- gfortran
- libblas3gf
- liblapack3gf
script:
- rustc --version
- cargo --version
@ -19,3 +25,8 @@ script:
- cargo build --verbose --features serde-serialize
- cargo build --verbose --features abomonation-serialize
- cargo test --verbose --features "arbitrary serde-serialize abomonation-serialize"
- cd nalgebra-lapack; cargo test --verbose
env:
matrix:
- CARGO_FEATURE_SYSTEM_NETLIB=1 CARGO_FEATURE_EXCLUDE_LAPACKE=1 CARGO_FEATURE_EXCLUDE_CBLAS=1

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@ -4,20 +4,117 @@ documented here.
This project adheres to [Semantic Versioning](http://semver.org/).
## [0.13.0] - WIP
## [0.13.0]
The **nalgebra-lapack** crate has been updated. This now includes a broad range
matrix decompositions using LAPACK bindings.
### Breaking semantic change
* The implementation of slicing with steps now matches the documentation.
Before, step identified the number to add to pass from one column/row index
to the next one. This made 0 step invalid. Now (and on the documentation so
far), the step is the number of ignored row/columns between each
row/column. Thus, a step of 0 means that no row/column is ignored. For
example, a step of, say, 3 on previous versions should now bet set to 2.
### Modified
* The trait `Axpy` has been replaced by a metod `.axpy`.
* The alias `MatrixNM` is now deprecated. Use `MatrixMN` instead (we
reordered M and N to be in alphabetical order).
* In-place componentwise multiplication and division
`.component_mul_mut(...)` and `.component_div_mut(...)` have bee deprecated
for a future renaming. Use `.component_mul_assign(...)` and
`.component_div_assign(...)` instead.
### Added
* `alga::general::Real` is now re-exported by nalgebra.
elements.)
* `::zeros(...)` that creates a matrix filled with zeroes.
* `::from_partial_diagonal(...)` that creates a matrix from diagonal elements.
The matrix can be rectangular. If not enough elements are provided, the rest
of the diagonal is set to 0.
* `.conjugate_transpose()` computes the transposed conjugate of a
complex matrix.
* `.conjugate_transpose_to(...)` computes the transposed conjugate of a
complex matrix. The result written into a user-provided matrix.
* `.transpose_to(...)` is the same as `.transpose()` but stores the result in
the provided matrix.
* `.conjugate_transpose_to(...)` is the same as `.conjugate_transpose()` but
stores the result in the provided matrix.
* Implements `IntoIterator` for `&Matrix`, `&mut Matrix` and `Matrix`.
* `.mul_to(...)` multiplies two matrices and stores the result to the given buffer.
* `.tr_mul_to(...)` left-multiplies `self.transpose()` to another matrix and stores the result to the given buffer.
* `.add_scalar(...)` that adds a scalar to each component of a matrix.
* `.add_scalar_mut(...)` that adds in-place a scalar to each component of a matrix.
* `.kronecker(a, b)` computes the kronecker product (i.e. matrix tensor
product) of two matrices.
* `.set_row(i, row)` sets the i-th row of the matrix.
* `.set_column(j, column)` sets the i-th column of the matrix.
* `.apply(f)` replaces each component of a matrix with the results of the
closure `f` called on each of them.
Pure Rust implementation of some Blas operations:
* `.iamax()` retuns the index of the maximum value of a vector.
* `.axpy(...)` computes `self = a * x + b * self`.
* `.gemv(...)` computes `self = alpha * a * x + beta * self` with a matrix and vector `a` and `x`.
* `.ger(...)` computes `self = alpha * x^t * y + beta * self` where `x` and `y` are vectors.
* `.gemm(...)` computes `self = alpha * a * b + beta * self` where `a` and `b` are matrices.
* `.gemv_symm(...)` is the same as `.gemv` except that `self` is assumed symmetric.
* `.ger_symm(...)` is the same as `.ger` except that `self` is assumed symmetric.
New slicing methods:
* `.rows_range(...)` that retrieves a reference to a range of rows.
* `.rows_range_mut(...)` that retrieves a mutable reference to a range of rows.
* `.columns_range(...)` that retrieves a reference to a range of columns.
* `.columns_range_mut(...)` that retrieves a mutable reference to a range of columns.
Matrix decompositions implemented in pure Rust:
* Cholesky, SVD, LU, QR, Hessenberg, Schur, Symmetric eigendecompositions,
Bidiagonal, Symmetric tridiagonal
* Computation of householder reflectors and givens rotations.
Matrix edition:
* `.upper_triangle()` extracts the upper triangle of a matrix, including the diagonal.
* `.lower_triangle()` extracts the lower triangle of a matrix, including the diagonal.
* `.fill(...)` fills the matrix with a single value.
* `.fill_with_identity(...)` fills the matrix with the identity.
* `.fill_diagonal(...)` fills the matrix diagonal with a single value.
* `.fill_row(...)` fills a selected matrix row with a single value.
* `.fill_column(...)` fills a selected matrix column with a single value.
* `.set_diagonal(...)` sets the matrix diagonal.
* `.set_row(...)` sets a selected row.
* `.set_column(...)` sets a selected column.
* `.fill_lower_triangle(...)` fills some sub-diagonals bellow the main diagonal with a value.
* `.fill_upper_triangle(...)` fills some sub-diagonals above the main diagonal with a value.
* `.swap_rows(...)` swaps two rows.
* `.swap_columns(...)` swaps two columns.
Column removal:
* `.remove_column(...)` removes one column.
* `.remove_fixed_columns<D>(...)` removes `D` columns.
* `.remove_columns(...)` removes a number of columns known at run-time.
Row removal:
* `.remove_row(...)` removes one row.
* `.remove_fixed_rows<D>(...)` removes `D` rows.
* `.remove_rows(...)` removes a number of rows known at run-time.
Column insertion:
* `.insert_column(...)` adds one column at the given position.
* `.insert_fixed_columns<D>(...)` adds `D` columns at the given position.
* `.insert_columns(...)` adds at the given position a number of columns known at run-time.
Row insertion:
* `.insert_row(...)` adds one row at the given position.
* `.insert_fixed_rows<D>(...)` adds `D` rows at the given position.
* `.insert_rows(...)` adds at the given position a number of rows known at run-time.
## [0.12.0]
The main change of this release is the update of the dependency serde to 1.0.
### Added
* `.trace()` that computes the trace of a matrix (i.e., the sum of its
diagonal elements.)
* `.trace()` that computes the trace of a matrix (the sum of its diagonal
elements.)
## [0.11.0]
The [website](http://nalgebra.org) has been fully rewritten and gives a good

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@ -16,22 +16,23 @@ name = "nalgebra"
path = "src/lib.rs"
[features]
arbitrary = [ "quickcheck" ]
arbitrary = [ "quickcheck" ]
serde-serialize = [ "serde", "serde_derive", "num-complex/serde" ]
abomonation-serialize = [ "abomonation" ]
debug = [ ]
[dependencies]
typenum = "1.4"
generic-array = "0.8"
rand = "0.3"
num-traits = "0.1"
num-complex = "0.1"
approx = "0.1"
alga = "0.5"
serde = { version = "1.0", optional = true }
serde_derive = { version = "1.0", optional = true }
typenum = "1.7"
generic-array = "0.8"
rand = "0.3"
num-traits = "0.1"
num-complex = "0.1"
approx = "0.1"
alga = "0.5"
matrixmultiply = "0.1"
serde = { version = "1.0", optional = true }
serde_derive = { version = "1.0", optional = true }
abomonation = { version = "0.4", optional = true }
# clippy = "*"
[dependencies.quickcheck]
optional = true
@ -39,3 +40,6 @@ version = "0.4"
[dev-dependencies]
serde_json = "1.0"
[workspace]
members = [ "nalgebra-lapack" ]

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@ -1,11 +1,11 @@
all:
CARGO_INCREMENTAL=1 cargo build --features "arbitrary serde-serialize"
cargo check --features "debug arbitrary serde-serialize"
doc:
CARGO_INCREMENTAL=1 cargo doc --no-deps --features "arbitrary serde-serialize"
cargo doc --no-deps --features "debug arbitrary serde-serialize"
bench:
cargo bench
test:
cargo test --features "arbitrary serde-serialize"
cargo test --features "debug arbitrary serde-serialize"

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@ -24,3 +24,13 @@
<b>Linear algebra library</b>
<i>for the Rust programming language.</i>
</p>
-----
<p align = "center">
 <i>Click this button if you which to donate to support the development of</i> <b>nalgebra</b>:
</p>
<p align = "center">
<a href="https://www.patreon.com/bePatron?u=7111380" ><img src="https://c5.patreon.com/external/logo/become_a_patron_button.png" alt="Become a Patron!" /></a>
</p>

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@ -4,20 +4,12 @@ macro_rules! bench_binop(
($name: ident, $t1: ty, $t2: ty, $binop: ident) => {
#[bench]
fn $name(bh: &mut Bencher) {
const LEN: usize = 1 << 13;
let mut rng = IsaacRng::new_unseeded();
let elems1: Vec<$t1> = (0usize .. LEN).map(|_| rng.gen::<$t1>()).collect();
let elems2: Vec<$t2> = (0usize .. LEN).map(|_| rng.gen::<$t2>()).collect();
let mut i = 0;
let a = rng.gen::<$t1>();
let b = rng.gen::<$t2>();
bh.iter(|| {
i = (i + 1) & (LEN - 1);
unsafe {
test::black_box(elems1.get_unchecked(i).$binop(*elems2.get_unchecked(i)))
}
a.$binop(b)
})
}
}
@ -27,43 +19,27 @@ macro_rules! bench_binop_ref(
($name: ident, $t1: ty, $t2: ty, $binop: ident) => {
#[bench]
fn $name(bh: &mut Bencher) {
const LEN: usize = 1 << 13;
let mut rng = IsaacRng::new_unseeded();
let elems1: Vec<$t1> = (0usize .. LEN).map(|_| rng.gen::<$t1>()).collect();
let elems2: Vec<$t2> = (0usize .. LEN).map(|_| rng.gen::<$t2>()).collect();
let mut i = 0;
let a = rng.gen::<$t1>();
let b = rng.gen::<$t2>();
bh.iter(|| {
i = (i + 1) & (LEN - 1);
unsafe {
test::black_box(elems1.get_unchecked(i).$binop(elems2.get_unchecked(i)))
}
a.$binop(&b)
})
}
}
);
macro_rules! bench_binop_na(
($name: ident, $t1: ty, $t2: ty, $binop: ident) => {
macro_rules! bench_binop_fn(
($name: ident, $t1: ty, $t2: ty, $binop: path) => {
#[bench]
fn $name(bh: &mut Bencher) {
const LEN: usize = 1 << 13;
let mut rng = IsaacRng::new_unseeded();
let elems1: Vec<$t1> = (0usize .. LEN).map(|_| rng.gen::<$t1>()).collect();
let elems2: Vec<$t2> = (0usize .. LEN).map(|_| rng.gen::<$t2>()).collect();
let mut i = 0;
let a = rng.gen::<$t1>();
let b = rng.gen::<$t2>();
bh.iter(|| {
i = (i + 1) & (LEN - 1);
unsafe {
test::black_box(na::$binop(elems1.get_unchecked(i), elems2.get_unchecked(i)))
}
$binop(&a, &b)
})
}
}

192
benches/core/matrix.rs Normal file
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@ -0,0 +1,192 @@
use rand::{IsaacRng, Rng};
use test::{self, Bencher};
use na::{Vector2, Vector3, Vector4, Matrix2, Matrix3, Matrix4,
MatrixN, U10,
DMatrix, DVector};
use std::ops::{Add, Sub, Mul, Div};
#[path="../common/macros.rs"]
mod macros;
bench_binop!(mat2_mul_m, Matrix2<f32>, Matrix2<f32>, mul);
bench_binop!(mat3_mul_m, Matrix3<f32>, Matrix3<f32>, mul);
bench_binop!(mat4_mul_m, Matrix4<f32>, Matrix4<f32>, mul);
bench_binop_ref!(mat2_tr_mul_m, Matrix2<f32>, Matrix2<f32>, tr_mul);
bench_binop_ref!(mat3_tr_mul_m, Matrix3<f32>, Matrix3<f32>, tr_mul);
bench_binop_ref!(mat4_tr_mul_m, Matrix4<f32>, Matrix4<f32>, tr_mul);
bench_binop!(mat2_add_m, Matrix2<f32>, Matrix2<f32>, add);
bench_binop!(mat3_add_m, Matrix3<f32>, Matrix3<f32>, add);
bench_binop!(mat4_add_m, Matrix4<f32>, Matrix4<f32>, add);
bench_binop!(mat2_sub_m, Matrix2<f32>, Matrix2<f32>, sub);
bench_binop!(mat3_sub_m, Matrix3<f32>, Matrix3<f32>, sub);
bench_binop!(mat4_sub_m, Matrix4<f32>, Matrix4<f32>, sub);
bench_binop!(mat2_mul_v, Matrix2<f32>, Vector2<f32>, mul);
bench_binop!(mat3_mul_v, Matrix3<f32>, Vector3<f32>, mul);
bench_binop!(mat4_mul_v, Matrix4<f32>, Vector4<f32>, mul);
bench_binop_ref!(mat2_tr_mul_v, Matrix2<f32>, Vector2<f32>, tr_mul);
bench_binop_ref!(mat3_tr_mul_v, Matrix3<f32>, Vector3<f32>, tr_mul);
bench_binop_ref!(mat4_tr_mul_v, Matrix4<f32>, Vector4<f32>, tr_mul);
bench_binop!(mat2_mul_s, Matrix2<f32>, f32, mul);
bench_binop!(mat3_mul_s, Matrix3<f32>, f32, mul);
bench_binop!(mat4_mul_s, Matrix4<f32>, f32, mul);
bench_binop!(mat2_div_s, Matrix2<f32>, f32, div);
bench_binop!(mat3_div_s, Matrix3<f32>, f32, div);
bench_binop!(mat4_div_s, Matrix4<f32>, f32, div);
bench_unop!(mat2_inv, Matrix2<f32>, try_inverse);
bench_unop!(mat3_inv, Matrix3<f32>, try_inverse);
bench_unop!(mat4_inv, Matrix4<f32>, try_inverse);
bench_unop!(mat2_transpose, Matrix2<f32>, transpose);
bench_unop!(mat3_transpose, Matrix3<f32>, transpose);
bench_unop!(mat4_transpose, Matrix4<f32>, transpose);
#[bench]
fn mat_div_scalar(b: &mut Bencher) {
let a = DMatrix::from_row_slice(1000, 1000, &vec![2.0;1000000]);
let n = 42.0;
b.iter(|| {
let mut aa = a.clone();
let mut b = aa.slice_mut((0, 0), (1000, 1000));
b /= n
})
}
#[bench]
fn mat100_add_mat100(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(100, 100);
let b = DMatrix::<f64>::new_random(100, 100);
bench.iter(|| { &a + &b })
}
#[bench]
fn mat4_mul_mat4(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(4, 4);
let b = DMatrix::<f64>::new_random(4, 4);
bench.iter(|| { &a * &b })
}
#[bench]
fn mat5_mul_mat5(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(5, 5);
let b = DMatrix::<f64>::new_random(5, 5);
bench.iter(|| { &a * &b })
}
#[bench]
fn mat6_mul_mat6(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(6, 6);
let b = DMatrix::<f64>::new_random(6, 6);
bench.iter(|| { &a * &b })
}
#[bench]
fn mat7_mul_mat7(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(7, 7);
let b = DMatrix::<f64>::new_random(7, 7);
bench.iter(|| { &a * &b })
}
#[bench]
fn mat8_mul_mat8(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(8, 8);
let b = DMatrix::<f64>::new_random(8, 8);
bench.iter(|| { &a * &b })
}
#[bench]
fn mat9_mul_mat9(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(9, 9);
let b = DMatrix::<f64>::new_random(9, 9);
bench.iter(|| { &a * &b })
}
#[bench]
fn mat10_mul_mat10(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(10, 10);
let b = DMatrix::<f64>::new_random(10, 10);
bench.iter(|| { &a * &b })
}
#[bench]
fn mat10_mul_mat10_static(bench: &mut Bencher) {
let a = MatrixN::<f64, U10>::new_random();
let b = MatrixN::<f64, U10>::new_random();
bench.iter(|| { &a * &b })
}
#[bench]
fn mat100_mul_mat100(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(100, 100);
let b = DMatrix::<f64>::new_random(100, 100);
bench.iter(|| { &a * &b })
}
#[bench]
fn mat500_mul_mat500(bench: &mut Bencher) {
let a = DMatrix::<f64>::from_element(500, 500, 5f64);
let b = DMatrix::<f64>::from_element(500, 500, 6f64);
bench.iter(|| { &a * &b })
}
#[bench]
fn copy_from(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(1000, 1000);
let mut b = DMatrix::<f64>::new_random(1000, 1000);
bench.iter(|| {
b.copy_from(&a);
})
}
#[bench]
fn axpy(bench: &mut Bencher) {
let x = DVector::<f64>::from_element(100000, 2.0);
let mut y = DVector::<f64>::from_element(100000, 3.0);
let a = 42.0;
bench.iter(|| {
y.axpy(a, &x, 1.0);
})
}
#[bench]
fn tr_mul_to(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(1000, 1000);
let b = DVector::<f64>::new_random(1000);
let mut c = DVector::from_element(1000, 0.0);
bench.iter(|| {
a.tr_mul_to(&b, &mut c)
})
}
#[bench]
fn mat_mul_mat(bench: &mut Bencher) {
let a = DMatrix::<f64>::new_random(100, 100);
let b = DMatrix::<f64>::new_random(100, 100);
let mut ab = DMatrix::<f64>::from_element(100, 100, 0.0);
bench.iter(|| {
test::black_box(a.mul_to(&b, &mut ab));
})
}

2
benches/core/mod.rs Normal file
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@ -0,0 +1,2 @@
mod matrix;
mod vector;

128
benches/core/vector.rs Normal file
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@ -0,0 +1,128 @@
use rand::{IsaacRng, Rng};
use test::{self, Bencher};
use typenum::U10000;
use na::{Vector2, Vector3, Vector4, VectorN, DVector};
use std::ops::{Add, Sub, Mul, Div};
#[path="../common/macros.rs"]
mod macros;
bench_binop!(vec2_add_v_f32, Vector2<f32>, Vector2<f32>, add);
bench_binop!(vec3_add_v_f32, Vector3<f32>, Vector3<f32>, add);
bench_binop!(vec4_add_v_f32, Vector4<f32>, Vector4<f32>, add);
bench_binop!(vec2_add_v_f64, Vector2<f64>, Vector2<f64>, add);
bench_binop!(vec3_add_v_f64, Vector3<f64>, Vector3<f64>, add);
bench_binop!(vec4_add_v_f64, Vector4<f64>, Vector4<f64>, add);
bench_binop!(vec2_sub_v, Vector2<f32>, Vector2<f32>, sub);
bench_binop!(vec3_sub_v, Vector3<f32>, Vector3<f32>, sub);
bench_binop!(vec4_sub_v, Vector4<f32>, Vector4<f32>, sub);
bench_binop!(vec2_mul_s, Vector2<f32>, f32, mul);
bench_binop!(vec3_mul_s, Vector3<f32>, f32, mul);
bench_binop!(vec4_mul_s, Vector4<f32>, f32, mul);
bench_binop!(vec2_div_s, Vector2<f32>, f32, div);
bench_binop!(vec3_div_s, Vector3<f32>, f32, div);
bench_binop!(vec4_div_s, Vector4<f32>, f32, div);
bench_binop_ref!(vec2_dot_f32, Vector2<f32>, Vector2<f32>, dot);
bench_binop_ref!(vec3_dot_f32, Vector3<f32>, Vector3<f32>, dot);
bench_binop_ref!(vec4_dot_f32, Vector4<f32>, Vector4<f32>, dot);
bench_binop_ref!(vec2_dot_f64, Vector2<f64>, Vector2<f64>, dot);
bench_binop_ref!(vec3_dot_f64, Vector3<f64>, Vector3<f64>, dot);
bench_binop_ref!(vec4_dot_f64, Vector4<f64>, Vector4<f64>, dot);
bench_binop_ref!(vec3_cross, Vector3<f32>, Vector3<f32>, cross);
bench_unop!(vec2_norm, Vector2<f32>, norm);
bench_unop!(vec3_norm, Vector3<f32>, norm);
bench_unop!(vec4_norm, Vector4<f32>, norm);
bench_unop!(vec2_normalize, Vector2<f32>, normalize);
bench_unop!(vec3_normalize, Vector3<f32>, normalize);
bench_unop!(vec4_normalize, Vector4<f32>, normalize);
bench_binop_ref!(vec10000_dot_f64, VectorN<f64, U10000>, VectorN<f64, U10000>, dot);
bench_binop_ref!(vec10000_dot_f32, VectorN<f32, U10000>, VectorN<f32, U10000>, dot);
#[bench]
fn vec10000_axpy_f64(bh: &mut Bencher) {
let mut rng = IsaacRng::new_unseeded();
let mut a = DVector::new_random(10000);
let b = DVector::new_random(10000);
let n = rng.gen::<f64>();
bh.iter(|| {
a.axpy(n, &b, 1.0)
})
}
#[bench]
fn vec10000_axpy_beta_f64(bh: &mut Bencher) {
let mut rng = IsaacRng::new_unseeded();
let mut a = DVector::new_random(10000);
let b = DVector::new_random(10000);
let n = rng.gen::<f64>();
let beta = rng.gen::<f64>();
bh.iter(|| {
a.axpy(n, &b, beta)
})
}
#[bench]
fn vec10000_axpy_f64_slice(bh: &mut Bencher) {
let mut rng = IsaacRng::new_unseeded();
let mut a = DVector::new_random(10000);
let b = DVector::new_random(10000);
let n = rng.gen::<f64>();
bh.iter(|| {
let mut a = a.fixed_rows_mut::<U10000>(0);
let b = b.fixed_rows::<U10000>(0);
a.axpy(n, &b, 1.0)
})
}
#[bench]
fn vec10000_axpy_f64_static(bh: &mut Bencher) {
let mut rng = IsaacRng::new_unseeded();
let mut a = VectorN::<f64, U10000>::new_random();
let b = VectorN::<f64, U10000>::new_random();
let n = rng.gen::<f64>();
// NOTE: for some reasons, it is much faster if the arument are boxed (Box::new(VectorN...)).
bh.iter(|| {
a.axpy(n, &b, 1.0)
})
}
#[bench]
fn vec10000_axpy_f32(bh: &mut Bencher) {
let mut rng = IsaacRng::new_unseeded();
let mut a = DVector::new_random(10000);
let b = DVector::new_random(10000);
let n = rng.gen::<f32>();
bh.iter(|| {
a.axpy(n, &b, 1.0)
})
}
#[bench]
fn vec10000_axpy_beta_f32(bh: &mut Bencher) {
let mut rng = IsaacRng::new_unseeded();
let mut a = DVector::new_random(10000);
let b = DVector::new_random(10000);
let n = rng.gen::<f32>();
let beta = rng.gen::<f32>();
bh.iter(|| {
a.axpy(n, &b, beta)
})
}

1
benches/geometry/mod.rs Normal file
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@ -0,0 +1 @@
mod quaternion;

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@ -0,0 +1,22 @@
use rand::{IsaacRng, Rng};
use test::{self, Bencher};
use na::{Quaternion, UnitQuaternion, Vector3};
use std::ops::{Add, Sub, Mul, Div};
#[path="../common/macros.rs"]
mod macros;
bench_binop!(quaternion_add_q, Quaternion<f32>, Quaternion<f32>, add);
bench_binop!(quaternion_sub_q, Quaternion<f32>, Quaternion<f32>, sub);
bench_binop!(quaternion_mul_q, Quaternion<f32>, Quaternion<f32>, mul);
bench_binop!(unit_quaternion_mul_v, UnitQuaternion<f32>, Vector3<f32>, mul);
bench_binop!(quaternion_mul_s, Quaternion<f32>, f32, mul);
bench_binop!(quaternion_div_s, Quaternion<f32>, f32, div);
bench_unop!(quaternion_inv, Quaternion<f32>, try_inverse);
bench_unop!(unit_quaternion_inv, UnitQuaternion<f32>, inverse);
// bench_unop_self!(quaternion_conjugate, Quaternion<f32>, conjugate);
// bench_unop!(quaternion_normalize, Quaternion<f32>, normalize);

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#![feature(test)]
#![allow(unused_macros)]
extern crate test;
extern crate rand;
extern crate typenum;
extern crate nalgebra as na;
use rand::{Rng, IsaacRng};
use na::DMatrix;
mod core;
mod linalg;
mod geometry;
fn reproductible_dmatrix(nrows: usize, ncols: usize) -> DMatrix<f64> {
let mut rng = IsaacRng::new_unseeded();
DMatrix::<f64>::from_fn(nrows, ncols, |_, _| rng.gen())
}

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use test::{self, Bencher};
use na::{Matrix4, DMatrix, Bidiagonal};
#[path="../common/macros.rs"]
mod macros;
// Without unpack.
#[bench]
fn bidiagonalize_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| test::black_box(Bidiagonal::new(m.clone())))
}
#[bench]
fn bidiagonalize_100x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 500);
bh.iter(|| test::black_box(Bidiagonal::new(m.clone())))
}
#[bench]
fn bidiagonalize_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(Bidiagonal::new(m.clone())))
}
#[bench]
fn bidiagonalize_500x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 100);
bh.iter(|| test::black_box(Bidiagonal::new(m.clone())))
}
#[bench]
fn bidiagonalize_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| test::black_box(Bidiagonal::new(m.clone())))
}
// With unpack.
#[bench]
fn bidiagonalize_unpack_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| {
let bidiag = Bidiagonal::new(m.clone());
let _ = bidiag.unpack();
})
}
#[bench]
fn bidiagonalize_unpack_100x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 500);
bh.iter(|| {
let bidiag = Bidiagonal::new(m.clone());
let _ = bidiag.unpack();
})
}
#[bench]
fn bidiagonalize_unpack_500x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 100);
bh.iter(|| {
let bidiag = Bidiagonal::new(m.clone());
let _ = bidiag.unpack();
})
}
#[bench]
fn bidiagonalize_unpack_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| {
let bidiag = Bidiagonal::new(m.clone());
let _ = bidiag.unpack();
})
}

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use test::{self, Bencher};
use na::{DMatrix, DVector, Cholesky};
#[bench]
fn cholesky_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let m = &m * m.transpose();
bh.iter(|| test::black_box(Cholesky::new(m.clone())))
}
#[bench]
fn cholesky_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let m = &m * m.transpose();
bh.iter(|| test::black_box(Cholesky::new(m.clone())))
}
// With unpack.
#[bench]
fn cholesky_decompose_unpack_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let m = &m * m.transpose();
bh.iter(|| {
let chol = Cholesky::new(m.clone()).unwrap();
let _ = chol.unpack();
})
}
#[bench]
fn cholesky_decompose_unpack_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let m = &m * m.transpose();
bh.iter(|| {
let chol = Cholesky::new(m.clone()).unwrap();
let _ = chol.unpack();
})
}
#[bench]
fn cholesky_solve_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let m = &m * m.transpose();
let v = DVector::<f64>::new_random(10);
let chol = Cholesky::new(m.clone()).unwrap();
bh.iter(|| {
let _ = chol.solve(&v);
})
}
#[bench]
fn cholesky_solve_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let m = &m * m.transpose();
let v = DVector::<f64>::new_random(100);
let chol = Cholesky::new(m.clone()).unwrap();
bh.iter(|| {
let _ = chol.solve(&v);
})
}
#[bench]
fn cholesky_solve_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let m = &m * m.transpose();
let v = DVector::<f64>::new_random(500);
let chol = Cholesky::new(m.clone()).unwrap();
bh.iter(|| {
let _ = chol.solve(&v);
})
}
#[bench]
fn cholesky_inverse_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let m = &m * m.transpose();
let chol = Cholesky::new(m.clone()).unwrap();
bh.iter(|| {
let _ = chol.inverse();
})
}
#[bench]
fn cholesky_inverse_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let m = &m * m.transpose();
let chol = Cholesky::new(m.clone()).unwrap();
bh.iter(|| {
let _ = chol.inverse();
})
}
#[bench]
fn cholesky_inverse_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let m = &m * m.transpose();
let chol = Cholesky::new(m.clone()).unwrap();
bh.iter(|| {
let _ = chol.inverse();
})
}

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use test::Bencher;
use na::{DMatrix, Eigen};
#[bench]
fn eigen_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| Eigen::new(m.clone(), 1.0e-7, 0))
}
#[bench]
fn eigen_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| Eigen::new(m.clone(), 1.0e-7, 0))
}
#[bench]
fn eigenvalues_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| m.clone().eigenvalues(1.0e-7, 0))
}
#[bench]
fn eigenvalues_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| m.clone().eigenvalues(1.0e-7, 0))
}

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use test::{self, Bencher};
use na::{DMatrix, DVector, FullPivLU};
// Without unpack.
#[bench]
fn full_piv_lu_decompose_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
bh.iter(|| test::black_box(FullPivLU::new(m.clone())))
}
#[bench]
fn full_piv_lu_decompose_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| test::black_box(FullPivLU::new(m.clone())))
}
#[bench]
fn full_piv_lu_decompose_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| test::black_box(FullPivLU::new(m.clone())))
}
#[bench]
fn full_piv_lu_solve_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(10, 1.0);
lu.solve(&mut b);
})
}
#[bench]
fn full_piv_lu_solve_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(100, 1.0);
lu.solve(&mut b);
})
}
#[bench]
fn full_piv_lu_solve_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(500, 1.0);
lu.solve(&mut b);
})
}
#[bench]
fn full_piv_lu_inverse_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
test::black_box(lu.try_inverse())
})
}
#[bench]
fn full_piv_lu_inverse_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
test::black_box(lu.try_inverse())
})
}
#[bench]
fn full_piv_lu_inverse_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
test::black_box(lu.try_inverse())
})
}
#[bench]
fn full_piv_lu_determinant_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
test::black_box(lu.determinant())
})
}
#[bench]
fn full_piv_lu_determinant_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
test::black_box(lu.determinant())
})
}
#[bench]
fn full_piv_lu_determinant_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let lu = FullPivLU::new(m.clone());
bh.iter(|| {
test::black_box(lu.determinant())
})
}

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use test::{self, Bencher};
use na::{Matrix4, DMatrix, Hessenberg};
#[path="../common/macros.rs"]
mod macros;
// Without unpack.
#[bench]
fn hessenberg_decompose_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(Hessenberg::new(m.clone())))
}
#[bench]
fn hessenberg_decompose_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| test::black_box(Hessenberg::new(m.clone())))
}
#[bench]
fn hessenberg_decompose_200x200(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(200, 200);
bh.iter(|| test::black_box(Hessenberg::new(m.clone())))
}
#[bench]
fn hessenberg_decompose_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| test::black_box(Hessenberg::new(m.clone())))
}
// With unpack.
#[bench]
fn hessenberg_decompose_unpack_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| {
let hess = Hessenberg::new(m.clone());
let _ = hess.unpack();
})
}
#[bench]
fn hessenberg_decompose_unpack_200x200(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(200, 200);
bh.iter(|| {
let hess = Hessenberg::new(m.clone());
let _ = hess.unpack();
})
}
#[bench]
fn hessenberg_decompose_unpack_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| {
let hess = Hessenberg::new(m.clone());
let _ = hess.unpack();
})
}

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use test::{self, Bencher};
use na::{DMatrix, DVector, LU};
// Without unpack.
#[bench]
fn lu_decompose_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
bh.iter(|| test::black_box(LU::new(m.clone())))
}
#[bench]
fn lu_decompose_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| test::black_box(LU::new(m.clone())))
}
#[bench]
fn lu_decompose_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| test::black_box(LU::new(m.clone())))
}
#[bench]
fn lu_solve_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let lu = LU::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(10, 1.0);
lu.solve(&mut b);
})
}
#[bench]
fn lu_solve_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let lu = LU::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(100, 1.0);
lu.solve(&mut b);
})
}
#[bench]
fn lu_solve_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let lu = LU::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(500, 1.0);
lu.solve(&mut b);
})
}
#[bench]
fn lu_inverse_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let lu = LU::new(m.clone());
bh.iter(|| {
test::black_box(lu.try_inverse())
})
}
#[bench]
fn lu_inverse_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let lu = LU::new(m.clone());
bh.iter(|| {
test::black_box(lu.try_inverse())
})
}
#[bench]
fn lu_inverse_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let lu = LU::new(m.clone());
bh.iter(|| {
test::black_box(lu.try_inverse())
})
}
#[bench]
fn lu_determinant_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let lu = LU::new(m.clone());
bh.iter(|| {
test::black_box(lu.determinant())
})
}
#[bench]
fn lu_determinant_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let lu = LU::new(m.clone());
bh.iter(|| {
test::black_box(lu.determinant())
})
}
#[bench]
fn lu_determinant_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let lu = LU::new(m.clone());
bh.iter(|| {
test::black_box(lu.determinant())
})
}

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mod solve;
mod cholesky;
mod qr;
mod hessenberg;
mod bidiagonal;
mod lu;
mod full_piv_lu;
mod svd;
mod schur;
mod symmetric_eigen;
// mod eigen;

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use test::{self, Bencher};
use na::{Matrix4, DMatrix, DVector, QR};
#[path="../common/macros.rs"]
mod macros;
// Without unpack.
#[bench]
fn qr_decompose_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| test::black_box(QR::new(m.clone())))
}
#[bench]
fn qr_decompose_100x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 500);
bh.iter(|| test::black_box(QR::new(m.clone())))
}
#[bench]
fn qr_decompose_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(QR::new(m.clone())))
}
#[bench]
fn qr_decompose_500x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 100);
bh.iter(|| test::black_box(QR::new(m.clone())))
}
#[bench]
fn qr_decompose_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| test::black_box(QR::new(m.clone())))
}
// With unpack.
#[bench]
fn qr_decompose_unpack_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| {
let qr = QR::new(m.clone());
let _ = qr.unpack();
})
}
#[bench]
fn qr_decompose_unpack_100x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 500);
bh.iter(|| {
let qr = QR::new(m.clone());
let _ = qr.unpack();
})
}
#[bench]
fn qr_decompose_unpack_500x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 100);
bh.iter(|| {
let qr = QR::new(m.clone());
let _ = qr.unpack();
})
}
#[bench]
fn qr_decompose_unpack_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| {
let qr = QR::new(m.clone());
let _ = qr.unpack();
})
}
#[bench]
fn qr_solve_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let qr = QR::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(10, 1.0);
qr.solve(&mut b);
})
}
#[bench]
fn qr_solve_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let qr = QR::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(100, 1.0);
qr.solve(&mut b);
})
}
#[bench]
fn qr_solve_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let qr = QR::new(m.clone());
bh.iter(|| {
let mut b = DVector::<f64>::from_element(500, 1.0);
qr.solve(&mut b);
})
}
#[bench]
fn qr_inverse_10x10(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(10, 10);
let qr = QR::new(m.clone());
bh.iter(|| {
test::black_box(qr.try_inverse())
})
}
#[bench]
fn qr_inverse_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let qr = QR::new(m.clone());
bh.iter(|| {
test::black_box(qr.try_inverse())
})
}
#[bench]
fn qr_inverse_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
let qr = QR::new(m.clone());
bh.iter(|| {
test::black_box(qr.try_inverse())
})
}

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use test::{self, Bencher};
use na::{Matrix4, RealSchur};
#[bench]
fn schur_decompose_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(RealSchur::new(m.clone())))
}
#[bench]
fn schur_decompose_10x10(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(10, 10);
bh.iter(|| test::black_box(RealSchur::new(m.clone())))
}
#[bench]
fn schur_decompose_100x100(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(100, 100);
bh.iter(|| test::black_box(RealSchur::new(m.clone())))
}
#[bench]
fn schur_decompose_200x200(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(200, 200);
bh.iter(|| test::black_box(RealSchur::new(m.clone())))
}
#[bench]
fn eigenvalues_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(m.complex_eigenvalues()))
}
#[bench]
fn eigenvalues_10x10(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(10, 10);
bh.iter(|| test::black_box(m.complex_eigenvalues()))
}
#[bench]
fn eigenvalues_100x100(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(100, 100);
bh.iter(|| test::black_box(m.complex_eigenvalues()))
}
#[bench]
fn eigenvalues_200x200(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(200, 200);
bh.iter(|| test::black_box(m.complex_eigenvalues()))
}

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use test::Bencher;
use na::{DMatrix, DVector};
#[bench]
fn solve_l_triangular_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let v = DVector::<f64>::new_random(100);
bh.iter(|| {
let _ = m.solve_lower_triangular(&v);
})
}
#[bench]
fn solve_l_triangular_1000x1000(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(1000, 1000);
let v = DVector::<f64>::new_random(1000);
bh.iter(|| {
let _ = m.solve_lower_triangular(&v);
})
}
#[bench]
fn tr_solve_l_triangular_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let v = DVector::<f64>::new_random(100);
bh.iter(|| {
let _ = m.tr_solve_lower_triangular(&v);
})
}
#[bench]
fn tr_solve_l_triangular_1000x1000(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(1000, 1000);
let v = DVector::<f64>::new_random(1000);
bh.iter(|| {
let _ = m.tr_solve_lower_triangular(&v);
})
}
#[bench]
fn solve_u_triangular_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let v = DVector::<f64>::new_random(100);
bh.iter(|| {
let _ = m.solve_upper_triangular(&v);
})
}
#[bench]
fn solve_u_triangular_1000x1000(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(1000, 1000);
let v = DVector::<f64>::new_random(1000);
bh.iter(|| {
let _ = m.solve_upper_triangular(&v);
})
}
#[bench]
fn tr_solve_u_triangular_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
let v = DVector::<f64>::new_random(100);
bh.iter(|| {
let _ = m.tr_solve_upper_triangular(&v);
})
}
#[bench]
fn tr_solve_u_triangular_1000x1000(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(1000, 1000);
let v = DVector::<f64>::new_random(1000);
bh.iter(|| {
let _ = m.tr_solve_upper_triangular(&v);
})
}

99
benches/linalg/svd.rs Normal file
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@ -0,0 +1,99 @@
use test::{self, Bencher};
use na::{Matrix4, SVD};
#[bench]
fn svd_decompose_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(SVD::new(m.clone(), true, true)))
}
#[bench]
fn svd_decompose_10x10(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(10, 10);
bh.iter(|| test::black_box(SVD::new(m.clone(), true, true)))
}
#[bench]
fn svd_decompose_100x100(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(100, 100);
bh.iter(|| test::black_box(SVD::new(m.clone(), true, true)))
}
#[bench]
fn svd_decompose_200x200(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(200, 200);
bh.iter(|| test::black_box(SVD::new(m.clone(), true, true)))
}
#[bench]
fn rank_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(m.rank(1.0e-10)))
}
#[bench]
fn rank_10x10(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(10, 10);
bh.iter(|| test::black_box(m.rank(1.0e-10)))
}
#[bench]
fn rank_100x100(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(100, 100);
bh.iter(|| test::black_box(m.rank(1.0e-10)))
}
#[bench]
fn rank_200x200(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(200, 200);
bh.iter(|| test::black_box(m.rank(1.0e-10)))
}
#[bench]
fn singular_values_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(m.singular_values()))
}
#[bench]
fn singular_values_10x10(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(10, 10);
bh.iter(|| test::black_box(m.singular_values()))
}
#[bench]
fn singular_values_100x100(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(100, 100);
bh.iter(|| test::black_box(m.singular_values()))
}
#[bench]
fn singular_values_200x200(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(200, 200);
bh.iter(|| test::black_box(m.singular_values()))
}
#[bench]
fn pseudo_inverse_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(m.clone().pseudo_inverse(1.0e-10)))
}
#[bench]
fn pseudo_inverse_10x10(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(10, 10);
bh.iter(|| test::black_box(m.clone().pseudo_inverse(1.0e-10)))
}
#[bench]
fn pseudo_inverse_100x100(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(100, 100);
bh.iter(|| test::black_box(m.clone().pseudo_inverse(1.0e-10)))
}
#[bench]
fn pseudo_inverse_200x200(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(200, 200);
bh.iter(|| test::black_box(m.clone().pseudo_inverse(1.0e-10)))
}

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@ -0,0 +1,27 @@
use test::{self, Bencher};
use na::{Matrix4, SymmetricEigen};
#[bench]
fn symmetric_eigen_decompose_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(SymmetricEigen::new(m.clone())))
}
#[bench]
fn symmetric_eigen_decompose_10x10(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(10, 10);
bh.iter(|| test::black_box(SymmetricEigen::new(m.clone())))
}
#[bench]
fn symmetric_eigen_decompose_100x100(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(100, 100);
bh.iter(|| test::black_box(SymmetricEigen::new(m.clone())))
}
#[bench]
fn symmetric_eigen_decompose_200x200(bh: &mut Bencher) {
let m = ::reproductible_dmatrix(200, 200);
bh.iter(|| test::black_box(SymmetricEigen::new(m.clone())))
}

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@ -1,53 +0,0 @@
#![feature(test)]
extern crate test;
extern crate rand;
extern crate nalgebra as na;
use rand::{IsaacRng, Rng};
use test::Bencher;
use na::{Vector2, Vector3, Vector4, Matrix2, Matrix3, Matrix4};
use std::ops::{Add, Sub, Mul, Div};
#[path="common/macros.rs"]
mod macros;
bench_binop!(_bench_mat2_mul_m, Matrix2<f32>, Matrix2<f32>, mul);
bench_binop!(_bench_mat3_mul_m, Matrix3<f32>, Matrix3<f32>, mul);
bench_binop!(_bench_mat4_mul_m, Matrix4<f32>, Matrix4<f32>, mul);
bench_binop_ref!(_bench_mat2_tr_mul_m, Matrix2<f32>, Matrix2<f32>, tr_mul);
bench_binop_ref!(_bench_mat3_tr_mul_m, Matrix3<f32>, Matrix3<f32>, tr_mul);
bench_binop_ref!(_bench_mat4_tr_mul_m, Matrix4<f32>, Matrix4<f32>, tr_mul);
bench_binop!(_bench_mat2_add_m, Matrix2<f32>, Matrix2<f32>, add);
bench_binop!(_bench_mat3_add_m, Matrix3<f32>, Matrix3<f32>, add);
bench_binop!(_bench_mat4_add_m, Matrix4<f32>, Matrix4<f32>, add);
bench_binop!(_bench_mat2_sub_m, Matrix2<f32>, Matrix2<f32>, sub);
bench_binop!(_bench_mat3_sub_m, Matrix3<f32>, Matrix3<f32>, sub);
bench_binop!(_bench_mat4_sub_m, Matrix4<f32>, Matrix4<f32>, sub);
bench_binop!(_bench_mat2_mul_v, Matrix2<f32>, Vector2<f32>, mul);
bench_binop!(_bench_mat3_mul_v, Matrix3<f32>, Vector3<f32>, mul);
bench_binop!(_bench_mat4_mul_v, Matrix4<f32>, Vector4<f32>, mul);
bench_binop_ref!(_bench_mat2_tr_mul_v, Matrix2<f32>, Vector2<f32>, tr_mul);
bench_binop_ref!(_bench_mat3_tr_mul_v, Matrix3<f32>, Vector3<f32>, tr_mul);
bench_binop_ref!(_bench_mat4_tr_mul_v, Matrix4<f32>, Vector4<f32>, tr_mul);
bench_binop!(_bench_mat2_mul_s, Matrix2<f32>, f32, mul);
bench_binop!(_bench_mat3_mul_s, Matrix3<f32>, f32, mul);
bench_binop!(_bench_mat4_mul_s, Matrix4<f32>, f32, mul);
bench_binop!(_bench_mat2_div_s, Matrix2<f32>, f32, div);
bench_binop!(_bench_mat3_div_s, Matrix3<f32>, f32, div);
bench_binop!(_bench_mat4_div_s, Matrix4<f32>, f32, div);
bench_unop!(_bench_mat2_inv, Matrix2<f32>, try_inverse);
bench_unop!(_bench_mat3_inv, Matrix3<f32>, try_inverse);
bench_unop!(_bench_mat4_inv, Matrix4<f32>, try_inverse);
bench_unop!(_bench_mat2_transpose, Matrix2<f32>, transpose);
bench_unop!(_bench_mat3_transpose, Matrix3<f32>, transpose);
bench_unop!(_bench_mat4_transpose, Matrix4<f32>, transpose);

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@ -1,28 +0,0 @@
#![feature(test)]
extern crate test;
extern crate rand;
extern crate nalgebra as na;
use rand::{IsaacRng, Rng};
use test::Bencher;
use na::{Quaternion, UnitQuaternion, Vector3};
use std::ops::{Add, Sub, Mul, Div};
#[path="common/macros.rs"]
mod macros;
bench_binop!(_bench_quaternion_add_q, Quaternion<f32>, Quaternion<f32>, add);
bench_binop!(_bench_quaternion_sub_q, Quaternion<f32>, Quaternion<f32>, sub);
bench_binop!(_bench_quaternion_mul_q, Quaternion<f32>, Quaternion<f32>, mul);
bench_binop!(_bench_unit_quaternion_mul_v, UnitQuaternion<f32>, Vector3<f32>, mul);
bench_binop!(_bench_quaternion_mul_s, Quaternion<f32>, f32, mul);
bench_binop!(_bench_quaternion_div_s, Quaternion<f32>, f32, div);
bench_unop!(_bench_quaternion_inv, Quaternion<f32>, try_inverse);
bench_unop!(_bench_unit_quaternion_inv, UnitQuaternion<f32>, inverse);
// bench_unop_self!(_bench_quaternion_conjugate, Quaternion<f32>, conjugate);
// bench_unop!(_bench_quaternion_normalize, Quaternion<f32>, normalize);

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@ -1,43 +0,0 @@
#![feature(test)]
extern crate test;
extern crate rand;
extern crate nalgebra as na;
use rand::{IsaacRng, Rng};
use test::Bencher;
use na::{Vector2, Vector3, Vector4};
use std::ops::{Add, Sub, Mul, Div};
#[path="common/macros.rs"]
mod macros;
bench_binop!(_bench_vec2_add_v, Vector2<f32>, Vector2<f32>, add);
bench_binop!(_bench_vec3_add_v, Vector3<f32>, Vector3<f32>, add);
bench_binop!(_bench_vec4_add_v, Vector4<f32>, Vector4<f32>, add);
bench_binop!(_bench_vec2_sub_v, Vector2<f32>, Vector2<f32>, sub);
bench_binop!(_bench_vec3_sub_v, Vector3<f32>, Vector3<f32>, sub);
bench_binop!(_bench_vec4_sub_v, Vector4<f32>, Vector4<f32>, sub);
bench_binop!(_bench_vec2_mul_s, Vector2<f32>, f32, mul);
bench_binop!(_bench_vec3_mul_s, Vector3<f32>, f32, mul);
bench_binop!(_bench_vec4_mul_s, Vector4<f32>, f32, mul);
bench_binop!(_bench_vec2_div_s, Vector2<f32>, f32, div);
bench_binop!(_bench_vec3_div_s, Vector3<f32>, f32, div);
bench_binop!(_bench_vec4_div_s, Vector4<f32>, f32, div);
bench_binop_ref!(_bench_vec2_dot, Vector2<f32>, Vector2<f32>, dot);
bench_binop_ref!(_bench_vec3_dot, Vector3<f32>, Vector3<f32>, dot);
bench_binop_ref!(_bench_vec4_dot, Vector4<f32>, Vector4<f32>, dot);
bench_binop_ref!(_bench_vec3_cross, Vector3<f32>, Vector3<f32>, cross);
bench_unop!(_bench_vec2_norm, Vector2<f32>, norm);
bench_unop!(_bench_vec3_norm, Vector3<f32>, norm);
bench_unop!(_bench_vec4_norm, Vector4<f32>, norm);
bench_unop!(_bench_vec2_normalize, Vector2<f32>, normalize);
bench_unop!(_bench_vec3_normalize, Vector3<f32>, normalize);
bench_unop!(_bench_vec4_normalize, Vector4<f32>, normalize);

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@ -1,11 +1,10 @@
extern crate alga;
extern crate nalgebra as na;
use alga::general::Real;
use alga::linear::FiniteDimInnerSpace;
use na::{Unit, ColumnVector, OwnedColumnVector, Vector2, Vector3};
use na::storage::Storage;
use na::dimension::{DimName, U1};
use na::{Real, DefaultAllocator, Unit, VectorN, Vector2, Vector3};
use na::allocator::Allocator;
use na::dimension::Dim;
/// Reflects a vector wrt. the hyperplane with normal `plane_normal`.
fn reflect_wrt_hyperplane_with_algebraic_genericity<V>(plane_normal: &Unit<V>, vector: &V) -> V
@ -16,12 +15,12 @@ fn reflect_wrt_hyperplane_with_algebraic_genericity<V>(plane_normal: &Unit<V>, v
/// Reflects a vector wrt. the hyperplane with normal `plane_normal`.
fn reflect_wrt_hyperplane_with_structural_genericity<N, D, S>(plane_normal: &Unit<ColumnVector<N, D, S>>,
vector: &ColumnVector<N, D, S>)
-> OwnedColumnVector<N, D, S::Alloc>
fn reflect_wrt_hyperplane_with_dimensional_genericity<N: Real, D: Dim>(plane_normal: &Unit<VectorN<N, D>>,
vector: &VectorN<N, D>)
-> VectorN<N, D>
where N: Real,
D: DimName,
S: Storage<N, D, U1> {
D: Dim,
DefaultAllocator: Allocator<N, D> {
let n = plane_normal.as_ref(); // Get the underlying V.
vector - n * (n.dot(vector) * na::convert(2.0))
}
@ -57,8 +56,8 @@ fn main() {
assert_eq!(reflect_wrt_hyperplane_with_algebraic_genericity(&plane2, &v2).y, -2.0);
assert_eq!(reflect_wrt_hyperplane_with_algebraic_genericity(&plane3, &v3).y, -2.0);
assert_eq!(reflect_wrt_hyperplane_with_structural_genericity(&plane2, &v2).y, -2.0);
assert_eq!(reflect_wrt_hyperplane_with_structural_genericity(&plane3, &v3).y, -2.0);
assert_eq!(reflect_wrt_hyperplane_with_dimensional_genericity(&plane2, &v2).y, -2.0);
assert_eq!(reflect_wrt_hyperplane_with_dimensional_genericity(&plane3, &v3).y, -2.0);
// Call each specific implementation depending on the dimension.
assert_eq!(reflect_wrt_hyperplane2(&plane2, &v2).y, -2.0);

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@ -0,0 +1,22 @@
# Change Log
## [0.4.0] - 2016-09-07
* Made all traits use associated types for their output type parameters. This
simplifies usage of the traits and is consistent with the concept of
associated types used as output type parameters (not input type parameters) as
described in [the associated type
RFC](https://github.com/rust-lang/rfcs/blob/master/text/0195-associated-items.md).
* Implemented `check_info!` macro to check all LAPACK calls.
* Implemented error handling with [error_chain](https://crates.io/crates/error-chain).
## [0.3.0] - 2016-09-06
* Documentation is hosted at https://docs.rs/nalgebra-lapack/
* Updated `nalgebra` to 0.10.
* Rename traits `HasSVD` to `SVD` and `HasEigensystem` to `Eigensystem`.
* Added `Solve` trait for solving a linear matrix equation.
* Added `Inverse` for computing the multiplicative inverse of a matrix.
* Added `Cholesky` for decomposing a positive-definite matrix.
* The `Eigensystem` and `SVD` traits are now generic over types. The
associated types have been removed.

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@ -0,0 +1,40 @@
[package]
name = "nalgebra-lapack"
version = "0.11.2"
authors = [ "Sébastien Crozet <developer@crozet.re>", "Andrew Straw <strawman@astraw.com>" ]
description = "Linear algebra library with transformations and satically-sized or dynamically-sized matrices."
documentation = "http://nalgebra.org/doc/nalgebra/index.html"
homepage = "http://nalgebra.org"
repository = "https://github.com/sebcrozet/nalgebra"
readme = "README.md"
keywords = [ "linear", "algebra", "matrix", "vector" ]
license = "BSD-3-Clause"
[features]
serde-serialize = [ "serde", "serde_derive" ]
# For BLAS/LAPACK
default = ["openblas"]
openblas = ["lapack/openblas"]
netlib = ["lapack/netlib"]
accelerate = ["lapack/accelerate"]
[dependencies]
nalgebra = { version = "0.12", path = ".." }
num-traits = "0.1"
num-complex = "0.1"
alga = "0.5"
serde = { version = "0.9", optional = true }
serde_derive = { version = "0.9", optional = true }
# clippy = "*"
[dependencies.lapack]
version = "0.11"
default-features = false
[dev-dependencies]
nalgebra = { version = "0.12", path = "..", features = [ "arbitrary" ] }
quickcheck = "0.4"
approx = "0.1"
rand = "0.3"

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@ -0,0 +1,21 @@
The MIT License (MIT)
Copyright (c) 2015 Andrew D. Straw
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.

11
nalgebra-lapack/Makefile Normal file
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@ -0,0 +1,11 @@
all:
cargo build
test:
cargo test
doc:
cargo doc --all --no-deps
bench:
cargo bench

59
nalgebra-lapack/README.md Normal file
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@ -0,0 +1,59 @@
# nalgebra-lapack [![Version][version-img]][version-url] [![Status][status-img]][status-url] [![Doc][doc-img]][doc-url]
Rust library for linear algebra using nalgebra and LAPACK.
## Documentation
Documentation is available [here](https://docs.rs/nalgebra-lapack/).
## License
MIT
## Cargo features to select lapack provider
Like the [lapack crate](https://crates.io/crates/lapack) from which this
behavior is inherited, nalgebra-lapack uses [cargo
features](http://doc.crates.io/manifest.html#the-[features]-section) to select
which lapack provider (or implementation) is used. Command line arguments to
cargo are the easiest way to do this, and the best provider depends on your
particular system. In some cases, the providers can be further tuned with
environment variables.
Below are given examples of how to invoke `cargo build` on two different systems
using two different providers. The `--no-default-features --features "provider"`
arguments will be consistent for other `cargo` commands.
### Ubuntu
As tested on Ubuntu 12.04, do this to build the lapack package against
the system installation of netlib without LAPACKE (note the E) or
CBLAS:
sudo apt-get install gfortran libblas3gf liblapack3gf
export CARGO_FEATURE_SYSTEM_NETLIB=1
export CARGO_FEATURE_EXCLUDE_LAPACKE=1
export CARGO_FEATURE_EXCLUDE_CBLAS=1
export CARGO_FEATURES='--no-default-features --features netlib'
cargo build ${CARGO_FEATURES}
### Mac OS X
On Mac OS X, do this to use Apple's Accelerate framework:
export CARGO_FEATURES='--no-default-features --features accelerate'
cargo build ${CARGO_FEATURES}
[version-img]: https://img.shields.io/crates/v/nalgebra-lapack.svg
[version-url]: https://crates.io/crates/nalgebra-lapack
[status-img]: https://travis-ci.org/strawlab/nalgebra-lapack.svg?branch=master
[status-url]: https://travis-ci.org/strawlab/nalgebra-lapack
[doc-img]: https://docs.rs/nalgebra-lapack/badge.svg
[doc-url]: https://docs.rs/nalgebra-lapack/
## Contributors
This integration of LAPACK on nalgebra was
[initiated](https://github.com/strawlab/nalgebra-lapack) by Andrew Straw. It
then became officially supported and integrated to the main nalgebra
repository.

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@ -0,0 +1,8 @@
#![feature(test)]
extern crate test;
extern crate rand;
extern crate nalgebra as na;
extern crate nalgebra_lapack as nl;
mod linalg;

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@ -0,0 +1,21 @@
use test::{self, Bencher};
use na::{DMatrix, Matrix4};
use nl::Hessenberg;
#[bench]
fn hessenberg_decompose_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| test::black_box(Hessenberg::new(m.clone())))
}
#[bench]
fn hessenberg_decompose_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(Hessenberg::new(m.clone())))
}
#[bench]
fn hessenberg_decompose_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| test::black_box(Hessenberg::new(m.clone())))
}

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@ -0,0 +1,34 @@
use test::{self, Bencher};
use na::{DMatrix, Matrix4};
use nl::LU;
#[bench]
fn lu_decompose_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| test::black_box(LU::new(m.clone())))
}
#[bench]
fn lu_decompose_100x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 500);
bh.iter(|| test::black_box(LU::new(m.clone())))
}
#[bench]
fn lu_decompose_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(LU::new(m.clone())))
}
#[bench]
fn lu_decompose_500x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 100);
bh.iter(|| test::black_box(LU::new(m.clone())))
}
#[bench]
fn lu_decompose_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| test::black_box(LU::new(m.clone())))
}

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@ -0,0 +1,3 @@
mod qr;
mod lu;
mod hessenberg;

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@ -0,0 +1,33 @@
use test::{self, Bencher};
use na::{DMatrix, Matrix4};
use nl::QR;
#[bench]
fn qr_decompose_100x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 100);
bh.iter(|| test::black_box(QR::new(m.clone())))
}
#[bench]
fn qr_decompose_100x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(100, 500);
bh.iter(|| test::black_box(QR::new(m.clone())))
}
#[bench]
fn qr_decompose_4x4(bh: &mut Bencher) {
let m = Matrix4::<f64>::new_random();
bh.iter(|| test::black_box(QR::new(m.clone())))
}
#[bench]
fn qr_decompose_500x100(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 100);
bh.iter(|| test::black_box(QR::new(m.clone())))
}
#[bench]
fn qr_decompose_500x500(bh: &mut Bencher) {
let m = DMatrix::<f64>::new_random(500, 500);
bh.iter(|| test::black_box(QR::new(m.clone())))
}

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#[cfg(feature = "serde-serialize")]
use serde;
use num::Zero;
use num_complex::Complex;
use na::{Scalar, DefaultAllocator, Matrix, MatrixN, MatrixMN};
use na::dimension::Dim;
use na::storage::Storage;
use na::allocator::Allocator;
use lapack::fortran as interface;
/// The cholesky decomposion of a symmetric-definite-positive matrix.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(serialize =
"DefaultAllocator: Allocator<N, D>,
MatrixN<N, D>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(deserialize =
"DefaultAllocator: Allocator<N, D>,
MatrixN<N, D>: serde::Deserialize<'de>")))]
#[derive(Clone, Debug)]
pub struct Cholesky<N: Scalar, D: Dim>
where DefaultAllocator: Allocator<N, D, D> {
l: MatrixN<N, D>
}
impl<N: Scalar, D: Dim> Copy for Cholesky<N, D>
where DefaultAllocator: Allocator<N, D, D>,
MatrixN<N, D>: Copy { }
impl<N: CholeskyScalar + Zero, D: Dim> Cholesky<N, D>
where DefaultAllocator: Allocator<N, D, D> {
/// Complutes the cholesky decomposition of the given symmetric-definite-positive square
/// matrix.
///
/// Only the lower-triangular part of the input matrix is considered.
#[inline]
pub fn new(mut m: MatrixN<N, D>) -> Option<Self> {
// FIXME: check symmetry as well?
assert!(m.is_square(), "Unable to compute the cholesky decomposition of a non-square matrix.");
let uplo = b'L';
let dim = m.nrows() as i32;
let mut info = 0;
N::xpotrf(uplo, dim, m.as_mut_slice(), dim, &mut info);
lapack_check!(info);
Some(Cholesky { l: m })
}
/// Retrieves the lower-triangular factor of the cholesky decomposition.
pub fn unpack(mut self) -> MatrixN<N, D> {
self.l.fill_upper_triangle(Zero::zero(), 1);
self.l
}
/// Retrieves the lower-triangular factor of che cholesky decomposition, without zeroing-out
/// its strict upper-triangular part.
///
/// This is an allocation-less version of `self.l()`. The values of the strict upper-triangular
/// part are garbage and should be ignored by further computations.
pub fn unpack_dirty(self) -> MatrixN<N, D> {
self.l
}
/// Retrieves the lower-triangular factor of the cholesky decomposition.
pub fn l(&self) -> MatrixN<N, D> {
let mut res = self.l.clone();
res.fill_upper_triangle(Zero::zero(), 1);
res
}
/// Retrieves the lower-triangular factor of the cholesky decomposition, without zeroing-out
/// its strict upper-triangular part.
///
/// This is an allocation-less version of `self.l()`. The values of the strict upper-triangular
/// part are garbage and should be ignored by further computations.
pub fn l_dirty(&self) -> &MatrixN<N, D> {
&self.l
}
/// Solves the symmetric-definite-positive linear system `self * x = b`, where `x` is the
/// unknown to be determined.
pub fn solve<R2: Dim, C2: Dim, S2>(&self, b: &Matrix<N, R2, C2, S2>) -> Option<MatrixMN<N, R2, C2>>
where S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2> {
let mut res = b.clone_owned();
if self.solve_mut(&mut res) {
Some(res)
}
else {
None
}
}
/// Solves in-place the symmetric-definite-positive linear system `self * x = b`, where `x` is
/// the unknown to be determined.
pub fn solve_mut<R2: Dim, C2: Dim>(&self, b: &mut MatrixMN<N, R2, C2>) -> bool
where DefaultAllocator: Allocator<N, R2, C2> {
let dim = self.l.nrows();
assert!(b.nrows() == dim, "The number of rows of `b` must be equal to the dimension of the matrix `a`.");
let nrhs = b.ncols() as i32;
let lda = dim as i32;
let ldb = dim as i32;
let mut info = 0;
N::xpotrs(b'L', dim as i32, nrhs, self.l.as_slice(), lda, b.as_mut_slice(), ldb, &mut info);
lapack_test!(info)
}
/// Computes the inverse of the decomposed matrix.
pub fn inverse(mut self) -> Option<MatrixN<N, D>> {
let dim = self.l.nrows();
let mut info = 0;
N::xpotri(b'L', dim as i32, self.l.as_mut_slice(), dim as i32, &mut info);
lapack_check!(info);
// Copy lower triangle to upper triangle.
for i in 0 .. dim {
for j in i + 1 .. dim {
unsafe { *self.l.get_unchecked_mut(i, j) = *self.l.get_unchecked(j, i) };
}
}
Some(self.l)
}
}
/*
*
* Lapack functions dispatch.
*
*/
/// Trait implemented by floats (`f32`, `f64`) and complex floats (`Complex<f32>`, `Complex<f64>`)
/// supported by the cholesky decompotition.
pub trait CholeskyScalar: Scalar {
#[allow(missing_docs)]
fn xpotrf(uplo: u8, n: i32, a: &mut [Self], lda: i32, info: &mut i32);
#[allow(missing_docs)]
fn xpotrs(uplo: u8, n: i32, nrhs: i32, a: &[Self], lda: i32, b: &mut [Self], ldb: i32, info: &mut i32);
#[allow(missing_docs)]
fn xpotri(uplo: u8, n: i32, a: &mut [Self], lda: i32, info: &mut i32);
}
macro_rules! cholesky_scalar_impl(
($N: ty, $xpotrf: path, $xpotrs: path, $xpotri: path) => (
impl CholeskyScalar for $N {
#[inline]
fn xpotrf(uplo: u8, n: i32, a: &mut [Self], lda: i32, info: &mut i32) {
$xpotrf(uplo, n, a, lda, info)
}
#[inline]
fn xpotrs(uplo: u8, n: i32, nrhs: i32, a: &[Self], lda: i32,
b: &mut [Self], ldb: i32, info: &mut i32) {
$xpotrs(uplo, n, nrhs, a, lda, b, ldb, info)
}
#[inline]
fn xpotri(uplo: u8, n: i32, a: &mut [Self], lda: i32, info: &mut i32) {
$xpotri(uplo, n, a, lda, info)
}
}
)
);
cholesky_scalar_impl!(f32, interface::spotrf, interface::spotrs, interface::spotri);
cholesky_scalar_impl!(f64, interface::dpotrf, interface::dpotrs, interface::dpotri);
cholesky_scalar_impl!(Complex<f32>, interface::cpotrf, interface::cpotrs, interface::cpotri);
cholesky_scalar_impl!(Complex<f64>, interface::zpotrf, interface::zpotrs, interface::zpotri);

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@ -0,0 +1,253 @@
#[cfg(feature = "serde-serialize")]
use serde;
use num::Zero;
use num_complex::Complex;
use alga::general::Real;
use ::ComplexHelper;
use na::{Scalar, DefaultAllocator, Matrix, VectorN, MatrixN};
use na::dimension::{Dim, U1};
use na::storage::Storage;
use na::allocator::Allocator;
use lapack::fortran as interface;
/// Eigendecomposition of a real square matrix with real eigenvalues.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(serialize =
"DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
VectorN<N, D>: serde::Serialize,
MatrixN<N, D>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(deserialize =
"DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
VectorN<N, D>: serde::Serialize,
MatrixN<N, D>: serde::Deserialize<'de>")))]
#[derive(Clone, Debug)]
pub struct Eigen<N: Scalar, D: Dim>
where DefaultAllocator: Allocator<N, D> +
Allocator<N, D, D> {
/// The eigenvalues of the decomposed matrix.
pub eigenvalues: VectorN<N, D>,
/// The (right) eigenvectors of the decomposed matrix.
pub eigenvectors: Option<MatrixN<N, D>>,
/// The left eigenvectors of the decomposed matrix.
pub left_eigenvectors: Option<MatrixN<N, D>>
}
impl<N: Scalar, D: Dim> Copy for Eigen<N, D>
where DefaultAllocator: Allocator<N, D> +
Allocator<N, D, D>,
VectorN<N, D>: Copy,
MatrixN<N, D>: Copy { }
impl<N: EigenScalar + Real, D: Dim> Eigen<N, D>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, D> {
/// Computes the eigenvalues and eigenvectors of the square matrix `m`.
///
/// If `eigenvectors` is `false` then, the eigenvectors are not computed explicitly.
pub fn new(mut m: MatrixN<N, D>, left_eigenvectors: bool, eigenvectors: bool)
-> Option<Eigen<N, D>> {
assert!(m.is_square(), "Unable to compute the eigenvalue decomposition of a non-square matrix.");
let ljob = if left_eigenvectors { b'V' } else { b'N' };
let rjob = if eigenvectors { b'V' } else { b'N' };
let (nrows, ncols) = m.data.shape();
let n = nrows.value();
let lda = n as i32;
let mut wr = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
// FIXME: Tap into the workspace.
let mut wi = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut info = 0;
let mut placeholder1 = [ N::zero() ];
let mut placeholder2 = [ N::zero() ];
let lwork = N::xgeev_work_size(ljob, rjob, n as i32, m.as_mut_slice(), lda,
wr.as_mut_slice(), wi.as_mut_slice(), &mut placeholder1,
n as i32, &mut placeholder2, n as i32, &mut info);
lapack_check!(info);
let mut work = unsafe { ::uninitialized_vec(lwork as usize) };
match (left_eigenvectors, eigenvectors) {
(true, true) => {
let mut vl = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
let mut vr = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
N::xgeev(ljob, rjob, n as i32, m.as_mut_slice(), lda, wr.as_mut_slice(),
wi.as_mut_slice(), &mut vl.as_mut_slice(), n as i32, &mut vr.as_mut_slice(),
n as i32, &mut work, lwork, &mut info);
lapack_check!(info);
if wi.iter().all(|e| e.is_zero()) {
return Some(Eigen {
eigenvalues: wr, left_eigenvectors: Some(vl), eigenvectors: Some(vr)
})
}
},
(true, false) => {
let mut vl = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
N::xgeev(ljob, rjob, n as i32, m.as_mut_slice(), lda, wr.as_mut_slice(),
wi.as_mut_slice(), &mut vl.as_mut_slice(), n as i32, &mut placeholder2,
1 as i32, &mut work, lwork, &mut info);
lapack_check!(info);
if wi.iter().all(|e| e.is_zero()) {
return Some(Eigen {
eigenvalues: wr, left_eigenvectors: Some(vl), eigenvectors: None
});
}
},
(false, true) => {
let mut vr = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
N::xgeev(ljob, rjob, n as i32, m.as_mut_slice(), lda, wr.as_mut_slice(),
wi.as_mut_slice(), &mut placeholder1, 1 as i32, &mut vr.as_mut_slice(),
n as i32, &mut work, lwork, &mut info);
lapack_check!(info);
if wi.iter().all(|e| e.is_zero()) {
return Some(Eigen {
eigenvalues: wr, left_eigenvectors: None, eigenvectors: Some(vr)
});
}
},
(false, false) => {
N::xgeev(ljob, rjob, n as i32, m.as_mut_slice(), lda, wr.as_mut_slice(),
wi.as_mut_slice(), &mut placeholder1, 1 as i32, &mut placeholder2,
1 as i32, &mut work, lwork, &mut info);
lapack_check!(info);
if wi.iter().all(|e| e.is_zero()) {
return Some(Eigen {
eigenvalues: wr, left_eigenvectors: None, eigenvectors: None
});
}
}
}
None
}
/// The complex eigenvalues of the given matrix.
///
/// Panics if the eigenvalue computation does not converge.
pub fn complex_eigenvalues(mut m: MatrixN<N, D>) -> VectorN<Complex<N>, D>
where DefaultAllocator: Allocator<Complex<N>, D> {
assert!(m.is_square(), "Unable to compute the eigenvalue decomposition of a non-square matrix.");
let nrows = m.data.shape().0;
let n = nrows.value();
let lda = n as i32;
let mut wr = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut wi = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut info = 0;
let mut placeholder1 = [ N::zero() ];
let mut placeholder2 = [ N::zero() ];
let lwork = N::xgeev_work_size(b'N', b'N', n as i32, m.as_mut_slice(), lda,
wr.as_mut_slice(), wi.as_mut_slice(), &mut placeholder1,
n as i32, &mut placeholder2, n as i32, &mut info);
lapack_panic!(info);
let mut work = unsafe { ::uninitialized_vec(lwork as usize) };
N::xgeev(b'N', b'N', n as i32, m.as_mut_slice(), lda, wr.as_mut_slice(),
wi.as_mut_slice(), &mut placeholder1, 1 as i32, &mut placeholder2,
1 as i32, &mut work, lwork, &mut info);
lapack_panic!(info);
let mut res = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
for i in 0 .. res.len() {
res[i] = Complex::new(wr[i], wi[i]);
}
res
}
/// The determinant of the decomposed matrix.
#[inline]
pub fn determinant(&self) -> N {
let mut det = N::one();
for e in self.eigenvalues.iter() {
det *= *e;
}
det
}
}
/*
*
* Lapack functions dispatch.
*
*/
/// Trait implemented by scalar type for which Lapack funtion exist to compute the
/// eigendecomposition.
pub trait EigenScalar: Scalar {
#[allow(missing_docs)]
fn xgeev(jobvl: u8, jobvr: u8, n: i32, a: &mut [Self], lda: i32,
wr: &mut [Self], wi: &mut [Self],
vl: &mut [Self], ldvl: i32, vr: &mut [Self], ldvr: i32,
work: &mut [Self], lwork: i32, info: &mut i32);
#[allow(missing_docs)]
fn xgeev_work_size(jobvl: u8, jobvr: u8, n: i32, a: &mut [Self], lda: i32,
wr: &mut [Self], wi: &mut [Self], vl: &mut [Self], ldvl: i32,
vr: &mut [Self], ldvr: i32, info: &mut i32) -> i32;
}
macro_rules! real_eigensystem_scalar_impl (
($N: ty, $xgeev: path) => (
impl EigenScalar for $N {
#[inline]
fn xgeev(jobvl: u8, jobvr: u8, n: i32, a: &mut [Self], lda: i32,
wr: &mut [Self], wi: &mut [Self],
vl: &mut [Self], ldvl: i32, vr: &mut [Self], ldvr: i32,
work: &mut [Self], lwork: i32, info: &mut i32) {
$xgeev(jobvl, jobvr, n, a, lda, wr, wi, vl, ldvl, vr, ldvr, work, lwork, info)
}
#[inline]
fn xgeev_work_size(jobvl: u8, jobvr: u8, n: i32, a: &mut [Self], lda: i32,
wr: &mut [Self], wi: &mut [Self], vl: &mut [Self], ldvl: i32,
vr: &mut [Self], ldvr: i32, info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
$xgeev(jobvl, jobvr, n, a, lda, wr, wi, vl, ldvl, vr, ldvr, &mut work, lwork, info);
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
real_eigensystem_scalar_impl!(f32, interface::sgeev);
real_eigensystem_scalar_impl!(f64, interface::dgeev);
//// FIXME: decomposition of complex matrix and matrices with complex eigenvalues.
// eigensystem_complex_impl!(f32, interface::cgeev);
// eigensystem_complex_impl!(f64, interface::zgeev);

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use num::Zero;
use num_complex::Complex;
use ::ComplexHelper;
use na::{Scalar, Matrix, DefaultAllocator, VectorN, MatrixN};
use na::dimension::{DimSub, DimDiff, U1};
use na::storage::Storage;
use na::allocator::Allocator;
use lapack::fortran as interface;
/// The Hessenberg decomposition of a general matrix.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(serialize =
"DefaultAllocator: Allocator<N, D, D> +
Allocator<N, DimDiff<D, U1>>,
MatrixN<N, D>: serde::Serialize,
VectorN<N, DimDiff<D, U1>>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(deserialize =
"DefaultAllocator: Allocator<N, D, D> +
Allocator<N, DimDiff<D, U1>>,
MatrixN<N, D>: serde::Deserialize<'de>,
VectorN<N, DimDiff<D, U1>>: serde::Deserialize<'de>")))]
#[derive(Clone, Debug)]
pub struct Hessenberg<N: Scalar, D: DimSub<U1>>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, DimDiff<D, U1>> {
h: MatrixN<N, D>,
tau: VectorN<N, DimDiff<D, U1>>
}
impl<N: Scalar, D: DimSub<U1>> Copy for Hessenberg<N, D>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, DimDiff<D, U1>>,
MatrixN<N, D>: Copy,
VectorN<N, DimDiff<D, U1>>: Copy { }
impl<N: HessenbergScalar + Zero, D: DimSub<U1>> Hessenberg<N, D>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, DimDiff<D, U1>> {
/// Computes the hessenberg decomposition of the matrix `m`.
pub fn new(mut m: MatrixN<N, D>) -> Hessenberg<N, D> {
let nrows = m.data.shape().0;
let n = nrows.value() as i32;
assert!(m.is_square(), "Unable to compute the hessenberg decomposition of a non-square matrix.");
assert!(!m.is_empty(), "Unable to compute the hessenberg decomposition of an empty matrix.");
let mut tau = unsafe { Matrix::new_uninitialized_generic(nrows.sub(U1), U1) };
let mut info = 0;
let lwork = N::xgehrd_work_size(n, 1, n, m.as_mut_slice(), n, tau.as_mut_slice(), &mut info);
let mut work = unsafe { ::uninitialized_vec(lwork as usize) };
lapack_panic!(info);
N::xgehrd(n, 1, n, m.as_mut_slice(), n, tau.as_mut_slice(), &mut work, lwork, &mut info);
lapack_panic!(info);
Hessenberg { h: m, tau: tau }
}
/// Computes the hessenberg matrix of this decomposition.
#[inline]
pub fn h(&self) -> MatrixN<N, D> {
let mut h = self.h.clone_owned();
h.fill_lower_triangle(N::zero(), 2);
h
}
}
impl<N: HessenbergReal + Zero, D: DimSub<U1>> Hessenberg<N, D>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, DimDiff<D, U1>> {
/// Computes the matrices `(Q, H)` of this decomposition.
#[inline]
pub fn unpack(self) -> (MatrixN<N, D>, MatrixN<N, D>) {
(self.q(), self.h())
}
/// Computes the unitary matrix `Q` of this decomposition.
#[inline]
pub fn q(&self) -> MatrixN<N, D> {
let n = self.h.nrows() as i32;
let mut q = self.h.clone_owned();
let mut info = 0;
let lwork = N::xorghr_work_size(n, 1, n, q.as_mut_slice(), n, self.tau.as_slice(), &mut info);
let mut work = vec![ N::zero(); lwork as usize ];
N::xorghr(n, 1, n, q.as_mut_slice(), n, self.tau.as_slice(), &mut work, lwork, &mut info);
q
}
}
/*
*
* Lapack functions dispatch.
*
*/
pub trait HessenbergScalar: Scalar {
fn xgehrd(n: i32, ilo: i32, ihi: i32, a: &mut [Self], lda: i32,
tau: &mut [Self], work: &mut [Self], lwork: i32, info: &mut i32);
fn xgehrd_work_size(n: i32, ilo: i32, ihi: i32, a: &mut [Self], lda: i32,
tau: &mut [Self], info: &mut i32) -> i32;
}
/// Trait implemented by scalars for which Lapack implements the hessenberg decomposition.
pub trait HessenbergReal: HessenbergScalar {
#[allow(missing_docs)]
fn xorghr(n: i32, ilo: i32, ihi: i32, a: &mut [Self], lda: i32, tau: &[Self],
work: &mut [Self], lwork: i32, info: &mut i32);
#[allow(missing_docs)]
fn xorghr_work_size(n: i32, ilo: i32, ihi: i32, a: &mut [Self], lda: i32,
tau: &[Self], info: &mut i32) -> i32;
}
macro_rules! hessenberg_scalar_impl(
($N: ty, $xgehrd: path) => (
impl HessenbergScalar for $N {
#[inline]
fn xgehrd(n: i32, ilo: i32, ihi: i32, a: &mut [Self], lda: i32,
tau: &mut [Self], work: &mut [Self], lwork: i32, info: &mut i32) {
$xgehrd(n, ilo, ihi, a, lda, tau, work, lwork, info)
}
#[inline]
fn xgehrd_work_size(n: i32, ilo: i32, ihi: i32, a: &mut [Self], lda: i32,
tau: &mut [Self], info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
$xgehrd(n, ilo, ihi, a, lda, tau, &mut work, lwork, info);
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
macro_rules! hessenberg_real_impl(
($N: ty, $xorghr: path) => (
impl HessenbergReal for $N {
#[inline]
fn xorghr(n: i32, ilo: i32, ihi: i32, a: &mut [Self], lda: i32, tau: &[Self],
work: &mut [Self], lwork: i32, info: &mut i32) {
$xorghr(n, ilo, ihi, a, lda, tau, work, lwork, info)
}
#[inline]
fn xorghr_work_size(n: i32, ilo: i32, ihi: i32, a: &mut [Self], lda: i32,
tau: &[Self], info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
$xorghr(n, ilo, ihi, a, lda, tau, &mut work, lwork, info);
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
hessenberg_scalar_impl!(f32, interface::sgehrd);
hessenberg_scalar_impl!(f64, interface::dgehrd);
hessenberg_scalar_impl!(Complex<f32>, interface::cgehrd);
hessenberg_scalar_impl!(Complex<f64>, interface::zgehrd);
hessenberg_real_impl!(f32, interface::sorghr);
hessenberg_real_impl!(f64, interface::dorghr);

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#![macro_use]
macro_rules! lapack_check(
($info: expr) => (
// FIXME: return a richer error.
if $info != 0 {
return None;
}
// if $info < 0 {
// return Err(Error::from(ErrorKind::LapackIllegalArgument(-$info)));
// } else if $info > 0 {
// return Err(Error::from(ErrorKind::LapackFailure($info)));
// }
);
);
macro_rules! lapack_panic(
($info: expr) => (
assert!($info == 0, "Lapack error.");
);
);
macro_rules! lapack_test(
($info: expr) => (
$info == 0
);
);

148
nalgebra-lapack/src/lib.rs Normal file
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//! # nalgebra-lapack
//!
//! Rust library for linear algebra using nalgebra and LAPACK.
//!
//! ## Documentation
//!
//! Documentation is available [here](https://docs.rs/nalgebra-lapack/).
//!
//! ## License
//!
//! MIT
//!
//! ## Cargo features to select lapack provider
//!
//! Like the [lapack crate](https://crates.io/crates/lapack) from which this
//! behavior is inherited, nalgebra-lapack uses [cargo
//! features](http://doc.crates.io/manifest.html#the-[features]-section) to select
//! which lapack provider (or implementation) is used. Command line arguments to
//! cargo are the easiest way to do this, and the best provider depends on your
//! particular system. In some cases, the providers can be further tuned with
//! environment variables.
//!
//! Below are given examples of how to invoke `cargo build` on two different systems
//! using two different providers. The `--no-default-features --features "provider"`
//! arguments will be consistent for other `cargo` commands.
//!
//! ### Ubuntu
//!
//! As tested on Ubuntu 12.04, do this to build the lapack package against
//! the system installation of netlib without LAPACKE (note the E) or
//! CBLAS:
//!
//! ```.ignore
//! sudo apt-get install gfortran libblas3gf liblapack3gf
//! export CARGO_FEATURE_SYSTEM_NETLIB=1
//! export CARGO_FEATURE_EXCLUDE_LAPACKE=1
//! export CARGO_FEATURE_EXCLUDE_CBLAS=1
//!
//! export CARGO_FEATURES='--no-default-features --features netlib'
//! cargo build ${CARGO_FEATURES}
//! ```
//!
//! ### Mac OS X
//!
//! On Mac OS X, do this to use Apple's Accelerate framework:
//!
//! ```.ignore
//! export CARGO_FEATURES='--no-default-features --features accelerate'
//! cargo build ${CARGO_FEATURES}
//! ```
//!
//! [version-img]: https://img.shields.io/crates/v/nalgebra-lapack.svg
//! [version-url]: https://crates.io/crates/nalgebra-lapack
//! [status-img]: https://travis-ci.org/strawlab/nalgebra-lapack.svg?branch=master
//! [status-url]: https://travis-ci.org/strawlab/nalgebra-lapack
//! [doc-img]: https://docs.rs/nalgebra-lapack/badge.svg
//! [doc-url]: https://docs.rs/nalgebra-lapack/
//!
//! ## Contributors
//! This integration of LAPACK on nalgebra was
//! [initiated](https://github.com/strawlab/nalgebra-lapack) by Andrew Straw. It
//! then became officially supported and integrated to the main nalgebra
//! repository.
#![deny(non_camel_case_types)]
#![deny(unused_parens)]
#![deny(non_upper_case_globals)]
#![deny(unused_qualifications)]
#![deny(unused_results)]
#![deny(missing_docs)]
#![doc(html_root_url = "http://nalgebra.org/rustdoc")]
extern crate num_traits as num;
extern crate num_complex;
extern crate lapack;
extern crate alga;
extern crate nalgebra as na;
mod lapack_check;
mod svd;
mod eigen;
mod symmetric_eigen;
mod cholesky;
mod lu;
mod qr;
mod hessenberg;
mod schur;
use num_complex::Complex;
pub use self::svd::SVD;
pub use self::cholesky::{Cholesky, CholeskyScalar};
pub use self::lu::{LU, LUScalar};
pub use self::eigen::Eigen;
pub use self::symmetric_eigen::SymmetricEigen;
pub use self::qr::QR;
pub use self::hessenberg::Hessenberg;
pub use self::schur::RealSchur;
trait ComplexHelper {
type RealPart;
fn real_part(self) -> Self::RealPart;
}
impl ComplexHelper for f32 {
type RealPart = f32;
#[inline]
fn real_part(self) -> Self::RealPart {
self
}
}
impl ComplexHelper for f64 {
type RealPart = f64;
#[inline]
fn real_part(self) -> Self::RealPart {
self
}
}
impl ComplexHelper for Complex<f32> {
type RealPart = f32;
#[inline]
fn real_part(self) -> Self::RealPart {
self.re
}
}
impl ComplexHelper for Complex<f64> {
type RealPart = f64;
#[inline]
fn real_part(self) -> Self::RealPart {
self.re
}
}
unsafe fn uninitialized_vec<T: Copy>(n: usize) -> Vec<T> {
let mut res = Vec::new();
res.reserve_exact(n);
res.set_len(n);
res
}

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use num::{Zero, One};
use num_complex::Complex;
use ::ComplexHelper;
use na::{Scalar, DefaultAllocator, Matrix, MatrixMN, MatrixN, VectorN};
use na::dimension::{Dim, DimMin, DimMinimum, U1};
use na::storage::Storage;
use na::allocator::Allocator;
use lapack::fortran as interface;
/// LU decomposition with partial pivoting.
///
/// This decomposes a matrix `M` with m rows and n columns into three parts:
/// * `L` which is a `m × min(m, n)` lower-triangular matrix.
/// * `U` which is a `min(m, n) × n` upper-triangular matrix.
/// * `P` which is a `m * m` permutation matrix.
///
/// Those are such that `M == P * L * U`.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(serialize =
"DefaultAllocator: Allocator<N, R, C> +
Allocator<i32, DimMinimum<R, C>>,
MatrixMN<N, R, C>: serde::Serialize,
PermutationSequence<DimMinimum<R, C>>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(deserialize =
"DefaultAllocator: Allocator<N, R, C> +
Allocator<i32, DimMinimum<R, C>>,
MatrixMN<N, R, C>: serde::Deserialize<'de>,
PermutationSequence<DimMinimum<R, C>>: serde::Deserialize<'de>")))]
#[derive(Clone, Debug)]
pub struct LU<N: Scalar, R: DimMin<C>, C: Dim>
where DefaultAllocator: Allocator<i32, DimMinimum<R, C>> +
Allocator<N, R, C> {
lu: MatrixMN<N, R, C>,
p: VectorN<i32, DimMinimum<R, C>>
}
impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for LU<N, R, C>
where DefaultAllocator: Allocator<N, R, C> +
Allocator<i32, DimMinimum<R, C>>,
MatrixMN<N, R, C>: Copy,
VectorN<i32, DimMinimum<R, C>>: Copy { }
impl<N: LUScalar, R: Dim, C: Dim> LU<N, R, C>
where N: Zero + One,
R: DimMin<C>,
DefaultAllocator: Allocator<N, R, C> +
Allocator<N, R, R> +
Allocator<N, R, DimMinimum<R, C>> +
Allocator<N, DimMinimum<R, C>, C> +
Allocator<i32, DimMinimum<R, C>> {
/// Computes the LU decomposition with partial (row) pivoting of `matrix`.
pub fn new(mut m: MatrixMN<N, R, C>) -> Self {
let (nrows, ncols) = m.data.shape();
let min_nrows_ncols = nrows.min(ncols);
let nrows = nrows.value() as i32;
let ncols = ncols.value() as i32;
let mut ipiv: VectorN<i32, _> = Matrix::zeros_generic(min_nrows_ncols, U1);
let mut info = 0;
N::xgetrf(nrows, ncols, m.as_mut_slice(), nrows, ipiv.as_mut_slice(), &mut info);
lapack_panic!(info);
LU { lu: m, p: ipiv }
}
/// Gets the lower-triangular matrix part of the decomposition.
#[inline]
pub fn l(&self) -> MatrixMN<N, R, DimMinimum<R, C>> {
let (nrows, ncols) = self.lu.data.shape();
let mut res = self.lu.columns_generic(0, nrows.min(ncols)).into_owned();
res.fill_upper_triangle(Zero::zero(), 1);
res.fill_diagonal(One::one());
res
}
/// Gets the upper-triangular matrix part of the decomposition.
#[inline]
pub fn u(&self) -> MatrixMN<N, DimMinimum<R, C>, C> {
let (nrows, ncols) = self.lu.data.shape();
let mut res = self.lu.rows_generic(0, nrows.min(ncols)).into_owned();
res.fill_lower_triangle(Zero::zero(), 1);
res
}
/// Gets the row permutation matrix of this decomposition.
///
/// Computing the permutation matrix explicitly is costly and usually not necessary.
/// To permute rows of a matrix or vector, use the method `self.permute(...)` instead.
#[inline]
pub fn p(&self) -> MatrixN<N, R> {
let (dim, _) = self.lu.data.shape();
let mut id = Matrix::identity_generic(dim, dim);
self.permute(&mut id);
id
}
// FIXME: when we support resizing a matrix, we could add unwrap_u/unwrap_l that would
// re-use the memory from the internal matrix!
/// Gets the LAPACK permutation indices.
#[inline]
pub fn permutation_indices(&self) -> &VectorN<i32, DimMinimum<R, C>> {
&self.p
}
/// Applies the permutation matrix to a given matrix or vector in-place.
#[inline]
pub fn permute<C2: Dim>(&self, rhs: &mut MatrixMN<N, R, C2>)
where DefaultAllocator: Allocator<N, R, C2> {
let (nrows, ncols) = rhs.shape();
N::xlaswp(ncols as i32, rhs.as_mut_slice(), nrows as i32,
1, self.p.len() as i32, self.p.as_slice(), -1);
}
fn generic_solve_mut<R2: Dim, C2: Dim>(&self, trans: u8, b: &mut MatrixMN<N, R2, C2>) -> bool
where DefaultAllocator: Allocator<N, R2, C2> +
Allocator<i32, R2> {
let dim = self.lu.nrows();
assert!(self.lu.is_square(), "Unable to solve a set of under/over-determined equations.");
assert!(b.nrows() == dim, "The number of rows of `b` must be equal to the dimension of the matrix `a`.");
let nrhs = b.ncols() as i32;
let lda = dim as i32;
let ldb = dim as i32;
let mut info = 0;
N::xgetrs(trans, dim as i32, nrhs, self.lu.as_slice(), lda, self.p.as_slice(),
b.as_mut_slice(), ldb, &mut info);
lapack_test!(info)
}
/// Solves the linear system `self * x = b`, where `x` is the unknown to be determined.
pub fn solve<R2: Dim, C2: Dim, S2>(&self, b: &Matrix<N, R2, C2, S2>) -> Option<MatrixMN<N, R2, C2>>
where S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2> +
Allocator<i32, R2> {
let mut res = b.clone_owned();
if self.generic_solve_mut(b'N', &mut res) {
Some(res)
}
else {
None
}
}
/// Solves the linear system `self.transpose() * x = b`, where `x` is the unknown to be
/// determined.
pub fn solve_transpose<R2: Dim, C2: Dim, S2>(&self, b: &Matrix<N, R2, C2, S2>)
-> Option<MatrixMN<N, R2, C2>>
where S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2> +
Allocator<i32, R2> {
let mut res = b.clone_owned();
if self.generic_solve_mut(b'T', &mut res) {
Some(res)
}
else {
None
}
}
/// Solves the linear system `self.conjugate_transpose() * x = b`, where `x` is the unknown to
/// be determined.
pub fn solve_conjugate_transpose<R2: Dim, C2: Dim, S2>(&self, b: &Matrix<N, R2, C2, S2>)
-> Option<MatrixMN<N, R2, C2>>
where S2: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R2, C2> +
Allocator<i32, R2> {
let mut res = b.clone_owned();
if self.generic_solve_mut(b'T', &mut res) {
Some(res)
}
else {
None
}
}
/// Solves in-place the linear system `self * x = b`, where `x` is the unknown to be determined.
///
/// Retuns `false` if no solution was found (the decomposed matrix is singular).
pub fn solve_mut<R2: Dim, C2: Dim>(&self, b: &mut MatrixMN<N, R2, C2>) -> bool
where DefaultAllocator: Allocator<N, R2, C2> +
Allocator<i32, R2> {
self.generic_solve_mut(b'N', b)
}
/// Solves in-place the linear system `self.transpose() * x = b`, where `x` is the unknown to be
/// determined.
///
/// Retuns `false` if no solution was found (the decomposed matrix is singular).
pub fn solve_transpose_mut<R2: Dim, C2: Dim>(&self, b: &mut MatrixMN<N, R2, C2>) -> bool
where DefaultAllocator: Allocator<N, R2, C2> +
Allocator<i32, R2> {
self.generic_solve_mut(b'T', b)
}
/// Solves in-place the linear system `self.conjugate_transpose() * x = b`, where `x` is the unknown to
/// be determined.
///
/// Retuns `false` if no solution was found (the decomposed matrix is singular).
pub fn solve_conjugate_transpose_mut<R2: Dim, C2: Dim>(&self, b: &mut MatrixMN<N, R2, C2>) -> bool
where DefaultAllocator: Allocator<N, R2, C2> +
Allocator<i32, R2> {
self.generic_solve_mut(b'T', b)
}
}
impl<N: LUScalar, D: Dim> LU<N, D, D>
where N: Zero + One,
D: DimMin<D, Output = D>,
DefaultAllocator: Allocator<N, D, D> +
Allocator<i32, D> {
/// Computes the inverse of the decomposed matrix.
pub fn inverse(mut self) -> Option<MatrixN<N, D>> {
let dim = self.lu.nrows() as i32;
let mut info = 0;
let lwork = N::xgetri_work_size(dim, self.lu.as_mut_slice(),
dim, self.p.as_mut_slice(),
&mut info);
lapack_check!(info);
let mut work = unsafe { ::uninitialized_vec(lwork as usize) };
N::xgetri(dim, self.lu.as_mut_slice(), dim, self.p.as_mut_slice(),
&mut work, lwork, &mut info);
lapack_check!(info);
Some(self.lu)
}
}
/*
*
* Lapack functions dispatch.
*
*/
/// Trait implemented by scalars for which Lapack implements the LU decomposition.
pub trait LUScalar: Scalar {
#[allow(missing_docs)]
fn xgetrf(m: i32, n: i32, a: &mut [Self], lda: i32, ipiv: &mut [i32], info: &mut i32);
#[allow(missing_docs)]
fn xlaswp(n: i32, a: &mut [Self], lda: i32, k1: i32, k2: i32, ipiv: &[i32], incx: i32);
#[allow(missing_docs)]
fn xgetrs(trans: u8, n: i32, nrhs: i32, a: &[Self], lda: i32, ipiv: &[i32],
b: &mut [Self], ldb: i32, info: &mut i32);
#[allow(missing_docs)]
fn xgetri(n: i32, a: &mut [Self], lda: i32, ipiv: &[i32],
work: &mut [Self], lwork: i32, info: &mut i32);
#[allow(missing_docs)]
fn xgetri_work_size(n: i32, a: &mut [Self], lda: i32, ipiv: &[i32], info: &mut i32) -> i32;
}
macro_rules! lup_scalar_impl(
($N: ty, $xgetrf: path, $xlaswp: path, $xgetrs: path, $xgetri: path) => (
impl LUScalar for $N {
#[inline]
fn xgetrf(m: i32, n: i32, a: &mut [Self], lda: i32, ipiv: &mut [i32], info: &mut i32) {
$xgetrf(m, n, a, lda, ipiv, info)
}
#[inline]
fn xlaswp(n: i32, a: &mut [Self], lda: i32, k1: i32, k2: i32, ipiv: &[i32], incx: i32) {
$xlaswp(n, a, lda, k1, k2, ipiv, incx)
}
#[inline]
fn xgetrs(trans: u8, n: i32, nrhs: i32, a: &[Self], lda: i32, ipiv: &[i32],
b: &mut [Self], ldb: i32, info: &mut i32) {
$xgetrs(trans, n, nrhs, a, lda, ipiv, b, ldb, info)
}
#[inline]
fn xgetri(n: i32, a: &mut [Self], lda: i32, ipiv: &[i32],
work: &mut [Self], lwork: i32, info: &mut i32) {
$xgetri(n, a, lda, ipiv, work, lwork, info)
}
#[inline]
fn xgetri_work_size(n: i32, a: &mut [Self], lda: i32, ipiv: &[i32], info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
$xgetri(n, a, lda, ipiv, &mut work, lwork, info);
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
lup_scalar_impl!(f32, interface::sgetrf, interface::slaswp, interface::sgetrs, interface::sgetri);
lup_scalar_impl!(f64, interface::dgetrf, interface::dlaswp, interface::dgetrs, interface::dgetri);
lup_scalar_impl!(Complex<f32>, interface::cgetrf, interface::claswp, interface::cgetrs, interface::cgetri);
lup_scalar_impl!(Complex<f64>, interface::zgetrf, interface::zlaswp, interface::zgetrs, interface::zgetri);

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

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#[cfg(feature = "serde-serialize")]
use serde;
use num::Zero;
use num_complex::Complex;
use alga::general::Real;
use ::ComplexHelper;
use na::{Scalar, DefaultAllocator, Matrix, VectorN, MatrixN};
use na::dimension::{Dim, U1};
use na::storage::Storage;
use na::allocator::Allocator;
use lapack::fortran as interface;
/// Eigendecomposition of a real square matrix with real eigenvalues.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(serialize =
"DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
VectorN<N, D>: serde::Serialize,
MatrixN<N, D>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(deserialize =
"DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
VectorN<N, D>: serde::Serialize,
MatrixN<N, D>: serde::Deserialize<'de>")))]
#[derive(Clone, Debug)]
pub struct RealSchur<N: Scalar, D: Dim>
where DefaultAllocator: Allocator<N, D> +
Allocator<N, D, D> {
re: VectorN<N, D>,
im: VectorN<N, D>,
t: MatrixN<N, D>,
q: MatrixN<N, D>
}
impl<N: Scalar, D: Dim> Copy for RealSchur<N, D>
where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
MatrixN<N, D>: Copy,
VectorN<N, D>: Copy { }
impl<N: RealSchurScalar + Real, D: Dim> RealSchur<N, D>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, D> {
/// Computes the eigenvalues and real Schur foorm of the matrix `m`.
///
/// Panics if the method did not converge.
pub fn new(m: MatrixN<N, D>) -> Self {
Self::try_new(m).expect("RealSchur decomposition: convergence failed.")
}
/// Computes the eigenvalues and real Schur foorm of the matrix `m`.
///
/// Returns `None` if the method did not converge.
pub fn try_new(mut m: MatrixN<N, D>) -> Option<Self> {
assert!(m.is_square(), "Unable to compute the eigenvalue decomposition of a non-square matrix.");
let (nrows, ncols) = m.data.shape();
let n = nrows.value();
let lda = n as i32;
let mut info = 0;
let mut wr = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut wi = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut q = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
// Placeholders:
let mut bwork = [ 0i32 ];
let mut unused = 0;
let lwork = N::xgees_work_size(b'V', b'N', n as i32, m.as_mut_slice(), lda, &mut unused,
wr.as_mut_slice(), wi.as_mut_slice(), q.as_mut_slice(), n as i32,
&mut bwork, &mut info);
lapack_check!(info);
let mut work = unsafe { ::uninitialized_vec(lwork as usize) };
N::xgees(b'V', b'N', n as i32, m.as_mut_slice(), lda, &mut unused,
wr.as_mut_slice(), wi.as_mut_slice(), q.as_mut_slice(),
n as i32, &mut work, lwork, &mut bwork, &mut info);
lapack_check!(info);
Some(RealSchur { re: wr, im: wi, t: m, q: q })
}
/// Retrieves the unitary matrix `Q` and the upper-quasitriangular matrix `T` such that the
/// decomposed matrix equals `Q * T * Q.transpose()`.
pub fn unpack(self) -> (MatrixN<N, D>, MatrixN<N, D>) {
(self.q, self.t)
}
/// Computes the real eigenvalues of the decomposed matrix.
///
/// Return `None` if some eigenvalues are complex.
pub fn eigenvalues(&self) -> Option<VectorN<N, D>> {
if self.im.iter().all(|e| e.is_zero()) {
Some(self.re.clone())
}
else {
None
}
}
/// Computes the complex eigenvalues of the decomposed matrix.
pub fn complex_eigenvalues(&self) -> VectorN<Complex<N>, D>
where DefaultAllocator: Allocator<Complex<N>, D> {
let mut out = unsafe { VectorN::new_uninitialized_generic(self.t.data.shape().0, U1) };
for i in 0 .. out.len() {
out[i] = Complex::new(self.re[i], self.im[i])
}
out
}
}
/*
*
* Lapack functions dispatch.
*
*/
/// Trait implemented by scalars for which Lapack implements the Real Schur decomposition.
pub trait RealSchurScalar: Scalar {
#[allow(missing_docs)]
fn xgees(jobvs: u8,
sort: u8,
// select: ???
n: i32,
a: &mut [Self],
lda: i32,
sdim: &mut i32,
wr: &mut [Self],
wi: &mut [Self],
vs: &mut [Self],
ldvs: i32,
work: &mut [Self],
lwork: i32,
bwork: &mut [i32],
info: &mut i32);
#[allow(missing_docs)]
fn xgees_work_size(jobvs: u8,
sort: u8,
// select: ???
n: i32,
a: &mut [Self],
lda: i32,
sdim: &mut i32,
wr: &mut [Self],
wi: &mut [Self],
vs: &mut [Self],
ldvs: i32,
bwork: &mut [i32],
info: &mut i32)
-> i32;
}
macro_rules! real_eigensystem_scalar_impl (
($N: ty, $xgees: path) => (
impl RealSchurScalar for $N {
#[inline]
fn xgees(jobvs: u8,
sort: u8,
// select: ???
n: i32,
a: &mut [$N],
lda: i32,
sdim: &mut i32,
wr: &mut [$N],
wi: &mut [$N],
vs: &mut [$N],
ldvs: i32,
work: &mut [$N],
lwork: i32,
bwork: &mut [i32],
info: &mut i32) {
$xgees(jobvs, sort, None, n, a, lda, sdim, wr, wi, vs, ldvs, work, lwork, bwork, info);
}
#[inline]
fn xgees_work_size(jobvs: u8,
sort: u8,
// select: ???
n: i32,
a: &mut [$N],
lda: i32,
sdim: &mut i32,
wr: &mut [$N],
wi: &mut [$N],
vs: &mut [$N],
ldvs: i32,
bwork: &mut [i32],
info: &mut i32)
-> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
$xgees(jobvs, sort, None, n, a, lda, sdim, wr, wi, vs, ldvs, &mut work, lwork, bwork, info);
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
real_eigensystem_scalar_impl!(f32, interface::sgees);
real_eigensystem_scalar_impl!(f64, interface::dgees);

279
nalgebra-lapack/src/svd.rs Normal file
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@ -0,0 +1,279 @@
#[cfg(feature = "serde-serialize")]
use serde;
use std::cmp;
use num::Signed;
use na::{Scalar, Matrix, VectorN, MatrixN, MatrixMN,
DefaultAllocator};
use na::dimension::{Dim, DimMin, DimMinimum, U1};
use na::storage::Storage;
use na::allocator::Allocator;
use lapack::fortran as interface;
/// The SVD decomposition of a general matrix.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(serialize =
"DefaultAllocator: Allocator<N, DimMinimum<R, C>> +
Allocator<N, R, R> +
Allocator<N, C, C>,
MatrixN<N, R>: serde::Serialize,
MatrixN<N, C>: serde::Serialize,
VectorN<N, DimMinimum<R, C>>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(serialize =
"DefaultAllocator: Allocator<N, DimMinimum<R, C>> +
Allocator<N, R, R> +
Allocator<N, C, C>,
MatrixN<N, R>: serde::Deserialize<'de>,
MatrixN<N, C>: serde::Deserialize<'de>,
VectorN<N, DimMinimum<R, C>>: serde::Deserialize<'de>")))]
#[derive(Clone, Debug)]
pub struct SVD<N: Scalar, R: DimMin<C>, C: Dim>
where DefaultAllocator: Allocator<N, R, R> +
Allocator<N, DimMinimum<R, C>> +
Allocator<N, C, C> {
/// The left-singular vectors `U` of this SVD.
pub u: MatrixN<N, R>, // FIXME: should be MatrixMN<N, R, DimMinimum<R, C>>
/// The right-singular vectors `V^t` of this SVD.
pub vt: MatrixN<N, C>, // FIXME: should be MatrixMN<N, DimMinimum<R, C>, C>
/// The singular values of this SVD.
pub singular_values: VectorN<N, DimMinimum<R, C>>
}
impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for SVD<N, R, C>
where DefaultAllocator: Allocator<N, C, C> +
Allocator<N, R, R> +
Allocator<N, DimMinimum<R, C>>,
MatrixMN<N, R, R>: Copy,
MatrixMN<N, C, C>: Copy,
VectorN<N, DimMinimum<R, C>>: Copy { }
/// Trait implemented by floats (`f32`, `f64`) and complex floats (`Complex<f32>`, `Complex<f64>`)
/// supported by the Singular Value Decompotition.
pub trait SVDScalar<R: DimMin<C>, C: Dim>: Scalar
where DefaultAllocator: Allocator<Self, R, R> +
Allocator<Self, R, C> +
Allocator<Self, DimMinimum<R, C>> +
Allocator<Self, C, C> {
/// Computes the SVD decomposition of `m`.
fn compute(m: MatrixMN<Self, R, C>) -> Option<SVD<Self, R, C>>;
}
impl<N: SVDScalar<R, C>, R: DimMin<C>, C: Dim> SVD<N, R, C>
where DefaultAllocator: Allocator<N, R, R> +
Allocator<N, R, C> +
Allocator<N, DimMinimum<R, C>> +
Allocator<N, C, C> {
/// Computes the Singular Value Decomposition of `matrix`.
pub fn new(m: MatrixMN<N, R, C>) -> Option<Self> {
N::compute(m)
}
}
macro_rules! svd_impl(
($t: ty, $lapack_func: path) => (
impl<R: Dim, C: Dim> SVDScalar<R, C> for $t
where R: DimMin<C>,
DefaultAllocator: Allocator<$t, R, C> +
Allocator<$t, R, R> +
Allocator<$t, C, C> +
Allocator<$t, DimMinimum<R, C>> {
fn compute(mut m: MatrixMN<$t, R, C>) -> Option<SVD<$t, R, C>> {
let (nrows, ncols) = m.data.shape();
if nrows.value() == 0 || ncols.value() == 0 {
return None;
}
let job = b'A';
let lda = nrows.value() as i32;
let mut u = unsafe { Matrix::new_uninitialized_generic(nrows, nrows) };
let mut s = unsafe { Matrix::new_uninitialized_generic(nrows.min(ncols), U1) };
let mut vt = unsafe { Matrix::new_uninitialized_generic(ncols, ncols) };
let ldu = nrows.value();
let ldvt = ncols.value();
let mut work = [ 0.0 ];
let mut lwork = -1 as i32;
let mut info = 0;
let mut iwork = unsafe { ::uninitialized_vec(8 * cmp::min(nrows.value(), ncols.value())) };
$lapack_func(job, nrows.value() as i32, ncols.value() as i32, m.as_mut_slice(),
lda, &mut s.as_mut_slice(), u.as_mut_slice(), ldu as i32, vt.as_mut_slice(),
ldvt as i32, &mut work, lwork, &mut iwork, &mut info);
lapack_check!(info);
lwork = work[0] as i32;
let mut work = unsafe { ::uninitialized_vec(lwork as usize) };
$lapack_func(job, nrows.value() as i32, ncols.value() as i32, m.as_mut_slice(),
lda, &mut s.as_mut_slice(), u.as_mut_slice(), ldu as i32, vt.as_mut_slice(),
ldvt as i32, &mut work, lwork, &mut iwork, &mut info);
lapack_check!(info);
Some(SVD { u: u, singular_values: s, vt: vt })
}
}
impl<R: DimMin<C>, C: Dim> SVD<$t, R, C>
// FIXME: All those bounds…
where DefaultAllocator: Allocator<$t, R, C> +
Allocator<$t, C, R> +
Allocator<$t, U1, R> +
Allocator<$t, U1, C> +
Allocator<$t, R, R> +
Allocator<$t, DimMinimum<R, C>> +
Allocator<$t, DimMinimum<R, C>, R> +
Allocator<$t, DimMinimum<R, C>, C> +
Allocator<$t, R, DimMinimum<R, C>> +
Allocator<$t, C, C> {
/// Reconstructs the matrix from its decomposition.
///
/// Useful if some components (e.g. some singular values) of this decomposition have
/// been manually changed by the user.
#[inline]
pub fn recompose(self) -> MatrixMN<$t, R, C> {
let nrows = self.u.data.shape().0;
let ncols = self.vt.data.shape().1;
let min_nrows_ncols = nrows.min(ncols);
let mut res: MatrixMN<_, R, C> = Matrix::zeros_generic(nrows, ncols);
{
let mut sres = res.generic_slice_mut((0, 0), (min_nrows_ncols, ncols));
sres.copy_from(&self.vt.rows_generic(0, min_nrows_ncols));
for i in 0 .. min_nrows_ncols.value() {
let eigval = self.singular_values[i];
let mut row = sres.row_mut(i);
row *= eigval;
}
}
self.u * res
}
/// Computes the pseudo-inverse of the decomposed matrix.
///
/// All singular value bellow epsilon will be set to zero instead of being inverted.
#[inline]
pub fn pseudo_inverse(&self, epsilon: $t) -> MatrixMN<$t, C, R> {
let nrows = self.u.data.shape().0;
let ncols = self.vt.data.shape().1;
let min_nrows_ncols = nrows.min(ncols);
let mut res: MatrixMN<_, C, R> = Matrix::zeros_generic(ncols, nrows);
{
let mut sres = res.generic_slice_mut((0, 0), (min_nrows_ncols, nrows));
self.u.columns_generic(0, min_nrows_ncols).transpose_to(&mut sres);
for i in 0 .. min_nrows_ncols.value() {
let eigval = self.singular_values[i];
let mut row = sres.row_mut(i);
if eigval.abs() > epsilon {
row /= eigval
}
else {
row.fill(0.0);
}
}
}
self.vt.tr_mul(&res)
}
/// The rank of the decomposed matrix.
///
/// This is the number of singular values that are not too small (i.e. greater than
/// the given `epsilon`).
#[inline]
pub fn rank(&self, epsilon: $t) -> usize {
let mut i = 0;
for e in self.singular_values.as_slice().iter() {
if e.abs() > epsilon {
i += 1;
}
}
i
}
// FIXME: add methods to retrieve the null-space and column-space? (Respectively
// corresponding to the zero and non-zero singular values).
}
);
);
/*
macro_rules! svd_complex_impl(
($name: ident, $t: ty, $lapack_func: path) => (
impl SVDScalar for Complex<$t> {
fn compute<R: Dim, C: Dim, S>(mut m: Matrix<$t, R, C, S>) -> Option<SVD<$t, R, C, S::Alloc>>
Option<(MatrixN<Complex<$t>, R, S::Alloc>,
VectorN<$t, DimMinimum<R, C>, S::Alloc>,
MatrixN<Complex<$t>, C, S::Alloc>)>
where R: DimMin<C>,
S: ContiguousStorage<Complex<$t>, R, C>,
S::Alloc: OwnedAllocator<Complex<$t>, R, C, S> +
Allocator<Complex<$t>, R, R> +
Allocator<Complex<$t>, C, C> +
Allocator<$t, DimMinimum<R, C>> {
let (nrows, ncols) = m.data.shape();
if nrows.value() == 0 || ncols.value() == 0 {
return None;
}
let jobu = b'A';
let jobvt = b'A';
let lda = nrows.value() as i32;
let min_nrows_ncols = nrows.min(ncols);
let mut u = unsafe { Matrix::new_uninitialized_generic(nrows, nrows) };
let mut s = unsafe { Matrix::new_uninitialized_generic(min_nrows_ncols, U1) };
let mut vt = unsafe { Matrix::new_uninitialized_generic(ncols, ncols) };
let ldu = nrows.value();
let ldvt = ncols.value();
let mut work = [ Complex::new(0.0, 0.0) ];
let mut lwork = -1 as i32;
let mut rwork = vec![ 0.0; (5 * min_nrows_ncols.value()) ];
let mut info = 0;
$lapack_func(jobu, jobvt, nrows.value() as i32, ncols.value() as i32, m.as_mut_slice(),
lda, s.as_mut_slice(), u.as_mut_slice(), ldu as i32, vt.as_mut_slice(),
ldvt as i32, &mut work, lwork, &mut rwork, &mut info);
lapack_check!(info);
lwork = work[0].re as i32;
let mut work = vec![Complex::new(0.0, 0.0); lwork as usize];
$lapack_func(jobu, jobvt, nrows.value() as i32, ncols.value() as i32, m.as_mut_slice(),
lda, s.as_mut_slice(), u.as_mut_slice(), ldu as i32, vt.as_mut_slice(),
ldvt as i32, &mut work, lwork, &mut rwork, &mut info);
lapack_check!(info);
Some((u, s, vt))
}
);
);
*/
svd_impl!(f32, interface::sgesdd);
svd_impl!(f64, interface::dgesdd);
// svd_complex_impl!(lapack_svd_complex_f32, f32, interface::cgesvd);
// svd_complex_impl!(lapack_svd_complex_f64, f64, interface::zgesvd);

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@ -0,0 +1,176 @@
#[cfg(feature = "serde-serialize")]
use serde;
use num::Zero;
use std::ops::MulAssign;
use alga::general::Real;
use ::ComplexHelper;
use na::{Scalar, DefaultAllocator, Matrix, VectorN, MatrixN};
use na::dimension::{Dim, U1};
use na::storage::Storage;
use na::allocator::Allocator;
use lapack::fortran as interface;
/// Eigendecomposition of a real square symmetric matrix with real eigenvalues.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(serialize =
"DefaultAllocator: Allocator<N, D, D> +
Allocator<N, D>,
VectorN<N, D>: serde::Serialize,
MatrixN<N, D>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(deserialize =
"DefaultAllocator: Allocator<N, D, D> +
Allocator<N, D>,
VectorN<N, D>: serde::Deserialize<'de>,
MatrixN<N, D>: serde::Deserialize<'de>")))]
#[derive(Clone, Debug)]
pub struct SymmetricEigen<N: Scalar, D: Dim>
where DefaultAllocator: Allocator<N, D> +
Allocator<N, D, D> {
/// The eigenvectors of the decomposed matrix.
pub eigenvectors: MatrixN<N, D>,
/// The unsorted eigenvalues of the decomposed matrix.
pub eigenvalues: VectorN<N, D>,
}
impl<N: Scalar, D: Dim> Copy for SymmetricEigen<N, D>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, D>,
MatrixN<N, D>: Copy,
VectorN<N, D>: Copy { }
impl<N: SymmetricEigenScalar + Real, D: Dim> SymmetricEigen<N, D>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, D> {
/// Computes the eigenvalues and eigenvectors of the symmetric matrix `m`.
///
/// Only the lower-triangular part of `m` is read. If `eigenvectors` is `false` then, the
/// eigenvectors are not computed explicitly. Panics if the method did not converge.
pub fn new(m: MatrixN<N, D>) -> Self {
let (vals, vecs) = Self::do_decompose(m, true).expect("SymmetricEigen: convergence failure.");
SymmetricEigen { eigenvalues: vals, eigenvectors: vecs.unwrap() }
}
/// Computes the eigenvalues and eigenvectors of the symmetric matrix `m`.
///
/// Only the lower-triangular part of `m` is read. If `eigenvectors` is `false` then, the
/// eigenvectors are not computed explicitly. Returns `None` if the method did not converge.
pub fn try_new(m: MatrixN<N, D>) -> Option<Self> {
Self::do_decompose(m, true).map(|(vals, vecs)| {
SymmetricEigen { eigenvalues: vals, eigenvectors: vecs.unwrap() }
})
}
fn do_decompose(mut m: MatrixN<N, D>, eigenvectors: bool) -> Option<(VectorN<N, D>, Option<MatrixN<N, D>>)> {
assert!(m.is_square(), "Unable to compute the eigenvalue decomposition of a non-square matrix.");
let jobz = if eigenvectors { b'V' } else { b'N' };
let nrows = m.data.shape().0;
let n = nrows.value();
let lda = n as i32;
let mut values = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut info = 0;
let lwork = N::xsyev_work_size(jobz, b'L', n as i32, m.as_mut_slice(), lda, &mut info);
lapack_check!(info);
let mut work = unsafe { ::uninitialized_vec(lwork as usize) };
N::xsyev(jobz, b'L', n as i32, m.as_mut_slice(), lda, values.as_mut_slice(), &mut work, lwork, &mut info);
lapack_check!(info);
let vectors = if eigenvectors { Some(m) } else { None };
Some((values, vectors))
}
/// Computes only the eigenvalues of the input matrix.
///
/// Panics if the method does not converge.
pub fn eigenvalues(m: MatrixN<N, D>) -> VectorN<N, D> {
Self::do_decompose(m, false).expect("SymmetricEigen eigenvalues: convergence failure.").0
}
/// Computes only the eigenvalues of the input matrix.
///
/// Returns `None` if the method does not converge.
pub fn try_eigenvalues(m: MatrixN<N, D>) -> Option<VectorN<N, D>> {
Self::do_decompose(m, false).map(|res| res.0)
}
/// The determinant of the decomposed matrix.
#[inline]
pub fn determinant(&self) -> N {
let mut det = N::one();
for e in self.eigenvalues.iter() {
det *= *e;
}
det
}
/// Rebuild the original matrix.
///
/// This is useful if some of the eigenvalues have been manually modified.
pub fn recompose(&self) -> MatrixN<N, D> {
let mut u_t = self.eigenvectors.clone();
for i in 0 .. self.eigenvalues.len() {
let val = self.eigenvalues[i];
u_t.column_mut(i).mul_assign(val);
}
u_t.transpose_mut();
&self.eigenvectors * u_t
}
}
/*
*
* Lapack functions dispatch.
*
*/
/// Trait implemented by scalars for which Lapack implements the eigendecomposition of symmetric
/// real matrices.
pub trait SymmetricEigenScalar: Scalar {
#[allow(missing_docs)]
fn xsyev(jobz: u8, uplo: u8, n: i32, a: &mut [Self], lda: i32, w: &mut [Self], work: &mut [Self],
lwork: i32, info: &mut i32);
#[allow(missing_docs)]
fn xsyev_work_size(jobz: u8, uplo: u8, n: i32, a: &mut [Self], lda: i32, info: &mut i32) -> i32;
}
macro_rules! real_eigensystem_scalar_impl (
($N: ty, $xsyev: path) => (
impl SymmetricEigenScalar for $N {
#[inline]
fn xsyev(jobz: u8, uplo: u8, n: i32, a: &mut [Self], lda: i32, w: &mut [Self], work: &mut [Self],
lwork: i32, info: &mut i32) {
$xsyev(jobz, uplo, n, a, lda, w, work, lwork, info)
}
#[inline]
fn xsyev_work_size(jobz: u8, uplo: u8, n: i32, a: &mut [Self], lda: i32, info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let mut w = [ Zero::zero() ];
let lwork = -1 as i32;
$xsyev(jobz, uplo, n, a, lda, &mut w, &mut work, lwork, info);
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
real_eigensystem_scalar_impl!(f32, interface::ssyev);
real_eigensystem_scalar_impl!(f64, interface::dsyev);

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@ -0,0 +1,9 @@
#[macro_use]
extern crate quickcheck;
#[macro_use]
extern crate approx;
extern crate nalgebra as na;
extern crate nalgebra_lapack as nl;
mod linalg;

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@ -0,0 +1,101 @@
use std::cmp;
use nl::Cholesky;
use na::{DMatrix, DVector, Vector4, Matrix3, Matrix4x3, Matrix4};
quickcheck!{
fn cholesky(m: DMatrix<f64>) -> bool {
if m.len() != 0 {
let m = &m * m.transpose();
if let Some(chol) = Cholesky::new(m.clone()) {
let l = chol.unpack();
let reconstructed_m = &l * l.transpose();
return relative_eq!(reconstructed_m, m, epsilon = 1.0e-7)
}
}
return true
}
fn cholesky_static(m: Matrix3<f64>) -> bool {
let m = &m * m.transpose();
if let Some(chol) = Cholesky::new(m) {
let l = chol.unpack();
let reconstructed_m = &l * l.transpose();
relative_eq!(reconstructed_m, m, epsilon = 1.0e-7)
}
else {
false
}
}
fn cholesky_solve(n: usize, nb: usize) -> bool {
if n != 0 {
let n = cmp::min(n, 15); // To avoid slowing down the test too much.
let nb = cmp::min(nb, 15); // To avoid slowing down the test too much.
let m = DMatrix::<f64>::new_random(n, n);
let m = &m * m.transpose();
if let Some(chol) = Cholesky::new(m.clone()) {
let b1 = DVector::new_random(n);
let b2 = DMatrix::new_random(n, nb);
let sol1 = chol.solve(&b1).unwrap();
let sol2 = chol.solve(&b2).unwrap();
return relative_eq!(&m * sol1, b1, epsilon = 1.0e-6) &&
relative_eq!(&m * sol2, b2, epsilon = 1.0e-6)
}
}
return true;
}
fn cholesky_solve_static(m: Matrix4<f64>) -> bool {
let m = &m * m.transpose();
match Cholesky::new(m) {
Some(chol) => {
let b1 = Vector4::new_random();
let b2 = Matrix4x3::new_random();
let sol1 = chol.solve(&b1).unwrap();
let sol2 = chol.solve(&b2).unwrap();
relative_eq!(m * sol1, b1, epsilon = 1.0e-7) &&
relative_eq!(m * sol2, b2, epsilon = 1.0e-7)
},
None => true
}
}
fn cholesky_inverse(n: usize) -> bool {
if n != 0 {
let n = cmp::min(n, 15); // To avoid slowing down the test too much.
let m = DMatrix::<f64>::new_random(n, n);
let m = &m * m.transpose();
if let Some(m1) = Cholesky::new(m.clone()).unwrap().inverse() {
let id1 = &m * &m1;
let id2 = &m1 * &m;
return id1.is_identity(1.0e-6) && id2.is_identity(1.0e-6);
}
}
return true;
}
fn cholesky_inverse_static(m: Matrix4<f64>) -> bool {
let m = m * m.transpose();
match Cholesky::new(m.clone()).unwrap().inverse() {
Some(m1) => {
let id1 = &m * &m1;
let id2 = &m1 * &m;
id1.is_identity(1.0e-5) && id2.is_identity(1.0e-5)
},
None => true
}
}
}

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@ -0,0 +1,38 @@
use std::cmp;
use nl::Hessenberg;
use na::{DMatrix, Matrix4};
quickcheck!{
fn hessenberg(n: usize) -> bool {
if n != 0 {
let n = cmp::min(n, 25);
let m = DMatrix::<f64>::new_random(n, n);
match Hessenberg::new(m.clone()) {
Some(hess) => {
let h = hess.h();
let p = hess.p();
relative_eq!(m, &p * h * p.transpose(), epsilon = 1.0e-7)
},
None => true
}
}
else {
true
}
}
fn hessenberg_static(m: Matrix4<f64>) -> bool {
match Hessenberg::new(m) {
Some(hess) => {
let h = hess.h();
let p = hess.p();
relative_eq!(m, p * h * p.transpose(), epsilon = 1.0e-7)
},
None => true
}
}
}

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@ -0,0 +1,107 @@
use std::cmp;
use nl::LU;
use na::{DMatrix, DVector, Matrix4, Matrix4x3, Matrix3x4, Vector4};
quickcheck!{
fn lup(m: DMatrix<f64>) -> bool {
if m.len() != 0 {
let lup = LU::new(m.clone());
let l = lup.l();
let u = lup.u();
let mut computed1 = &l * &u;
lup.permute(&mut computed1);
let computed2 = lup.p() * l * u;
relative_eq!(computed1, m, epsilon = 1.0e-7) &&
relative_eq!(computed2, m, epsilon = 1.0e-7)
}
else {
true
}
}
fn lu_static(m: Matrix3x4<f64>) -> bool {
let lup = LU::new(m);
let l = lup.l();
let u = lup.u();
let mut computed1 = l * u;
lup.permute(&mut computed1);
let computed2 = lup.p() * l * u;
relative_eq!(computed1, m, epsilon = 1.0e-7) &&
relative_eq!(computed2, m, epsilon = 1.0e-7)
}
fn lu_solve(n: usize, nb: usize) -> bool {
if n != 0 {
let n = cmp::min(n, 25); // To avoid slowing down the test too much.
let nb = cmp::min(nb, 25); // To avoid slowing down the test too much.
let m = DMatrix::<f64>::new_random(n, n);
let lup = LU::new(m.clone());
let b1 = DVector::new_random(n);
let b2 = DMatrix::new_random(n, nb);
let sol1 = lup.solve(&b1).unwrap();
let sol2 = lup.solve(&b2).unwrap();
let tr_sol1 = lup.solve_transpose(&b1).unwrap();
let tr_sol2 = lup.solve_transpose(&b2).unwrap();
relative_eq!(&m * sol1, b1, epsilon = 1.0e-7) &&
relative_eq!(&m * sol2, b2, epsilon = 1.0e-7) &&
relative_eq!(m.transpose() * tr_sol1, b1, epsilon = 1.0e-7) &&
relative_eq!(m.transpose() * tr_sol2, b2, epsilon = 1.0e-7)
}
else {
true
}
}
fn lu_solve_static(m: Matrix4<f64>) -> bool {
let lup = LU::new(m);
let b1 = Vector4::new_random();
let b2 = Matrix4x3::new_random();
let sol1 = lup.solve(&b1).unwrap();
let sol2 = lup.solve(&b2).unwrap();
let tr_sol1 = lup.solve_transpose(&b1).unwrap();
let tr_sol2 = lup.solve_transpose(&b2).unwrap();
relative_eq!(m * sol1, b1, epsilon = 1.0e-7) &&
relative_eq!(m * sol2, b2, epsilon = 1.0e-7) &&
relative_eq!(m.transpose() * tr_sol1, b1, epsilon = 1.0e-7) &&
relative_eq!(m.transpose() * tr_sol2, b2, epsilon = 1.0e-7)
}
fn lu_inverse(n: usize) -> bool {
if n != 0 {
let n = cmp::min(n, 25); // To avoid slowing down the test too much.
let m = DMatrix::<f64>::new_random(n, n);
if let Some(m1) = LU::new(m.clone()).inverse() {
let id1 = &m * &m1;
let id2 = &m1 * &m;
return id1.is_identity(1.0e-7) && id2.is_identity(1.0e-7);
}
}
return true;
}
fn lu_inverse_static(m: Matrix4<f64>) -> bool {
match LU::new(m.clone()).inverse() {
Some(m1) => {
let id1 = &m * &m1;
let id2 = &m1 * &m;
id1.is_identity(1.0e-5) && id2.is_identity(1.0e-5)
},
None => true
}
}
}

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@ -0,0 +1,7 @@
mod real_eigensystem;
mod symmetric_eigen;
mod cholesky;
mod lu;
mod qr;
mod svd;
mod real_schur;

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@ -0,0 +1,20 @@
use nl::QR;
use na::{DMatrix, Matrix4x3};
quickcheck!{
fn qr(m: DMatrix<f64>) -> bool {
let qr = QR::new(m.clone());
let q = qr.q();
let r = qr.r();
relative_eq!(m, q * r, epsilon = 1.0e-7)
}
fn qr_static(m: Matrix4x3<f64>) -> bool {
let qr = QR::new(m);
let q = qr.q();
let r = qr.r();
relative_eq!(m, q * r, epsilon = 1.0e-7)
}
}

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@ -0,0 +1,48 @@
use std::cmp;
use nl::Eigen;
use na::{DMatrix, Matrix4};
quickcheck!{
fn eigensystem(n: usize) -> bool {
if n != 0 {
let n = cmp::min(n, 25);
let m = DMatrix::<f64>::new_random(n, n);
match Eigen::new(m.clone(), true, true) {
Some(eig) => {
let eigvals = DMatrix::from_diagonal(&eig.eigenvalues);
let transformed_eigvectors = &m * eig.eigenvectors.as_ref().unwrap();
let scaled_eigvectors = eig.eigenvectors.as_ref().unwrap() * &eigvals;
let transformed_left_eigvectors = m.transpose() * eig.left_eigenvectors.as_ref().unwrap();
let scaled_left_eigvectors = eig.left_eigenvectors.as_ref().unwrap() * &eigvals;
relative_eq!(transformed_eigvectors, scaled_eigvectors, epsilon = 1.0e-7) &&
relative_eq!(transformed_left_eigvectors, scaled_left_eigvectors, epsilon = 1.0e-7)
},
None => true
}
}
else {
true
}
}
fn eigensystem_static(m: Matrix4<f64>) -> bool {
match Eigen::new(m, true, true) {
Some(eig) => {
let eigvals = Matrix4::from_diagonal(&eig.eigenvalues);
let transformed_eigvectors = m * eig.eigenvectors.unwrap();
let scaled_eigvectors = eig.eigenvectors.unwrap() * eigvals;
let transformed_left_eigvectors = m.transpose() * eig.left_eigenvectors.unwrap();
let scaled_left_eigvectors = eig.left_eigenvectors.unwrap() * eigvals;
relative_eq!(transformed_eigvectors, scaled_eigvectors, epsilon = 1.0e-7) &&
relative_eq!(transformed_left_eigvectors, scaled_left_eigvectors, epsilon = 1.0e-7)
},
None => true
}
}
}

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@ -0,0 +1,21 @@
use std::cmp;
use nl::RealSchur;
use na::{DMatrix, Matrix4};
quickcheck! {
fn schur(n: usize) -> bool {
let n = cmp::max(1, cmp::min(n, 10));
let m = DMatrix::<f64>::new_random(n, n);
let (vecs, vals) = RealSchur::new(m.clone()).unpack();
relative_eq!(&vecs * vals * vecs.transpose(), m, epsilon = 1.0e-7)
}
fn schur_static(m: Matrix4<f64>) -> bool {
let (vecs, vals) = RealSchur::new(m.clone()).unpack();
relative_eq!(vecs * vals * vecs.transpose(), m, epsilon = 1.0e-7)
}
}

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@ -0,0 +1,57 @@
use nl::SVD;
use na::{DMatrix, Matrix3x4};
quickcheck!{
fn svd(m: DMatrix<f64>) -> bool {
if m.nrows() != 0 && m.ncols() != 0 {
let svd = SVD::new(m.clone()).unwrap();
let sm = DMatrix::from_partial_diagonal(m.nrows(), m.ncols(), svd.singular_values.as_slice());
let reconstructed_m = &svd.u * sm * &svd.vt;
let reconstructed_m2 = svd.recompose();
relative_eq!(reconstructed_m, m, epsilon = 1.0e-7) &&
relative_eq!(reconstructed_m2, reconstructed_m, epsilon = 1.0e-7)
}
else {
true
}
}
fn svd_static(m: Matrix3x4<f64>) -> bool {
let svd = SVD::new(m).unwrap();
let sm = Matrix3x4::from_partial_diagonal(svd.singular_values.as_slice());
let reconstructed_m = &svd.u * &sm * &svd.vt;
let reconstructed_m2 = svd.recompose();
relative_eq!(reconstructed_m, m, epsilon = 1.0e-7) &&
relative_eq!(reconstructed_m2, m, epsilon = 1.0e-7)
}
fn pseudo_inverse(m: DMatrix<f64>) -> bool {
if m.nrows() == 0 || m.ncols() == 0 {
return true;
}
let svd = SVD::new(m.clone()).unwrap();
let im = svd.pseudo_inverse(1.0e-7);
if m.nrows() <= m.ncols() {
return (&m * &im).is_identity(1.0e-7)
}
if m.nrows() >= m.ncols() {
return (im * m).is_identity(1.0e-7)
}
return true;
}
fn pseudo_inverse_static(m: Matrix3x4<f64>) -> bool {
let svd = SVD::new(m).unwrap();
let im = svd.pseudo_inverse(1.0e-7);
(m * im).is_identity(1.0e-7)
}
}

View File

@ -0,0 +1,20 @@
use std::cmp;
use nl::SymmetricEigen;
use na::{DMatrix, Matrix4};
quickcheck!{
fn symmetric_eigen(n: usize) -> bool {
let n = cmp::max(1, cmp::min(n, 10));
let m = DMatrix::<f64>::new_random(n, n);
let eig = SymmetricEigen::new(m.clone());
let recomp = eig.recompose();
relative_eq!(m.lower_triangle(), recomp.lower_triangle(), epsilon = 1.0e-5)
}
fn symmetric_eigen_static(m: Matrix4<f64>) -> bool {
let eig = SymmetricEigen::new(m);
let recomp = eig.recompose();
relative_eq!(m.lower_triangle(), recomp.lower_triangle(), epsilon = 1.0e-5)
}
}

View File

@ -1,7 +1,7 @@
use core::Matrix;
use core::dimension::{Dynamic, U1, U2, U3, U4, U5, U6};
use core::matrix_array::MatrixArray;
use core::matrix_vec::MatrixVec;
use core::storage::Owned;
/*
*
@ -10,14 +10,18 @@ use core::matrix_vec::MatrixVec;
*
*
*/
/// A dynamically sized column-major matrix.
pub type DMatrix<N> = Matrix<N, Dynamic, Dynamic, MatrixVec<N, Dynamic, Dynamic>>;
/// A staticaly sized column-major matrix with `R` rows and `C` columns.
#[deprecated(note = "This matrix name contains a typo. Use MatrixMN instead.")]
pub type MatrixNM<N, R, C> = Matrix<N, R, C, Owned<N, R, C>>;
/// A staticaly sized column-major matrix with `R` rows and `C` columns.
pub type MatrixNM<N, R, C> = Matrix<N, R, C, MatrixArray<N, R, C>>;
pub type MatrixMN<N, R, C> = Matrix<N, R, C, Owned<N, R, C>>;
/// A staticaly sized column-major square matrix with `D` rows and columns.
pub type MatrixN<N, D> = MatrixNM<N, D, D>;
pub type MatrixN<N, D> = MatrixMN<N, D, D>;
/// A dynamically sized column-major matrix.
pub type DMatrix<N> = MatrixN<N, Dynamic>;
/// A stack-allocated, column-major, 1x1 square matrix.
pub type Matrix1<N> = MatrixN<N, U1>;
@ -33,75 +37,75 @@ pub type Matrix5<N> = MatrixN<N, U5>;
pub type Matrix6<N> = MatrixN<N, U6>;
/// A stack-allocated, column-major, 1x2 square matrix.
pub type Matrix1x2<N> = MatrixNM<N, U1, U2>;
pub type Matrix1x2<N> = MatrixMN<N, U1, U2>;
/// A stack-allocated, column-major, 1x3 square matrix.
pub type Matrix1x3<N> = MatrixNM<N, U1, U3>;
pub type Matrix1x3<N> = MatrixMN<N, U1, U3>;
/// A stack-allocated, column-major, 1x4 square matrix.
pub type Matrix1x4<N> = MatrixNM<N, U1, U4>;
pub type Matrix1x4<N> = MatrixMN<N, U1, U4>;
/// A stack-allocated, column-major, 1x5 square matrix.
pub type Matrix1x5<N> = MatrixNM<N, U1, U5>;
pub type Matrix1x5<N> = MatrixMN<N, U1, U5>;
/// A stack-allocated, column-major, 1x6 square matrix.
pub type Matrix1x6<N> = MatrixNM<N, U1, U6>;
pub type Matrix1x6<N> = MatrixMN<N, U1, U6>;
/// A stack-allocated, column-major, 2x3 square matrix.
pub type Matrix2x3<N> = MatrixNM<N, U2, U3>;
pub type Matrix2x3<N> = MatrixMN<N, U2, U3>;
/// A stack-allocated, column-major, 2x4 square matrix.
pub type Matrix2x4<N> = MatrixNM<N, U2, U4>;
pub type Matrix2x4<N> = MatrixMN<N, U2, U4>;
/// A stack-allocated, column-major, 2x5 square matrix.
pub type Matrix2x5<N> = MatrixNM<N, U2, U5>;
pub type Matrix2x5<N> = MatrixMN<N, U2, U5>;
/// A stack-allocated, column-major, 2x6 square matrix.
pub type Matrix2x6<N> = MatrixNM<N, U2, U6>;
pub type Matrix2x6<N> = MatrixMN<N, U2, U6>;
/// A stack-allocated, column-major, 3x4 square matrix.
pub type Matrix3x4<N> = MatrixNM<N, U3, U4>;
pub type Matrix3x4<N> = MatrixMN<N, U3, U4>;
/// A stack-allocated, column-major, 3x5 square matrix.
pub type Matrix3x5<N> = MatrixNM<N, U3, U5>;
pub type Matrix3x5<N> = MatrixMN<N, U3, U5>;
/// A stack-allocated, column-major, 3x6 square matrix.
pub type Matrix3x6<N> = MatrixNM<N, U3, U6>;
pub type Matrix3x6<N> = MatrixMN<N, U3, U6>;
/// A stack-allocated, column-major, 4x5 square matrix.
pub type Matrix4x5<N> = MatrixNM<N, U4, U5>;
pub type Matrix4x5<N> = MatrixMN<N, U4, U5>;
/// A stack-allocated, column-major, 4x6 square matrix.
pub type Matrix4x6<N> = MatrixNM<N, U4, U6>;
pub type Matrix4x6<N> = MatrixMN<N, U4, U6>;
/// A stack-allocated, column-major, 5x6 square matrix.
pub type Matrix5x6<N> = MatrixNM<N, U5, U6>;
pub type Matrix5x6<N> = MatrixMN<N, U5, U6>;
/// A stack-allocated, column-major, 2x1 square matrix.
pub type Matrix2x1<N> = MatrixNM<N, U2, U1>;
pub type Matrix2x1<N> = MatrixMN<N, U2, U1>;
/// A stack-allocated, column-major, 3x1 square matrix.
pub type Matrix3x1<N> = MatrixNM<N, U3, U1>;
pub type Matrix3x1<N> = MatrixMN<N, U3, U1>;
/// A stack-allocated, column-major, 4x1 square matrix.
pub type Matrix4x1<N> = MatrixNM<N, U4, U1>;
pub type Matrix4x1<N> = MatrixMN<N, U4, U1>;
/// A stack-allocated, column-major, 5x1 square matrix.
pub type Matrix5x1<N> = MatrixNM<N, U5, U1>;
pub type Matrix5x1<N> = MatrixMN<N, U5, U1>;
/// A stack-allocated, column-major, 6x1 square matrix.
pub type Matrix6x1<N> = MatrixNM<N, U6, U1>;
pub type Matrix6x1<N> = MatrixMN<N, U6, U1>;
/// A stack-allocated, column-major, 3x2 square matrix.
pub type Matrix3x2<N> = MatrixNM<N, U3, U2>;
pub type Matrix3x2<N> = MatrixMN<N, U3, U2>;
/// A stack-allocated, column-major, 4x2 square matrix.
pub type Matrix4x2<N> = MatrixNM<N, U4, U2>;
pub type Matrix4x2<N> = MatrixMN<N, U4, U2>;
/// A stack-allocated, column-major, 5x2 square matrix.
pub type Matrix5x2<N> = MatrixNM<N, U5, U2>;
pub type Matrix5x2<N> = MatrixMN<N, U5, U2>;
/// A stack-allocated, column-major, 6x2 square matrix.
pub type Matrix6x2<N> = MatrixNM<N, U6, U2>;
pub type Matrix6x2<N> = MatrixMN<N, U6, U2>;
/// A stack-allocated, column-major, 4x3 square matrix.
pub type Matrix4x3<N> = MatrixNM<N, U4, U3>;
pub type Matrix4x3<N> = MatrixMN<N, U4, U3>;
/// A stack-allocated, column-major, 5x3 square matrix.
pub type Matrix5x3<N> = MatrixNM<N, U5, U3>;
pub type Matrix5x3<N> = MatrixMN<N, U5, U3>;
/// A stack-allocated, column-major, 6x3 square matrix.
pub type Matrix6x3<N> = MatrixNM<N, U6, U3>;
pub type Matrix6x3<N> = MatrixMN<N, U6, U3>;
/// A stack-allocated, column-major, 5x4 square matrix.
pub type Matrix5x4<N> = MatrixNM<N, U5, U4>;
pub type Matrix5x4<N> = MatrixMN<N, U5, U4>;
/// A stack-allocated, column-major, 6x4 square matrix.
pub type Matrix6x4<N> = MatrixNM<N, U6, U4>;
pub type Matrix6x4<N> = MatrixMN<N, U6, U4>;
/// A stack-allocated, column-major, 6x5 square matrix.
pub type Matrix6x5<N> = MatrixNM<N, U6, U5>;
pub type Matrix6x5<N> = MatrixMN<N, U6, U5>;
/*
@ -115,7 +119,7 @@ pub type Matrix6x5<N> = MatrixNM<N, U6, U5>;
pub type DVector<N> = Matrix<N, Dynamic, U1, MatrixVec<N, Dynamic, U1>>;
/// A statically sized D-dimensional column vector.
pub type VectorN<N, D> = MatrixNM<N, D, U1>;
pub type VectorN<N, D> = MatrixMN<N, D, U1>;
/// A stack-allocated, 1-dimensional column vector.
pub type Vector1<N> = VectorN<N, U1>;
@ -142,7 +146,7 @@ pub type Vector6<N> = VectorN<N, U6>;
pub type RowDVector<N> = Matrix<N, U1, Dynamic, MatrixVec<N, U1, Dynamic>>;
/// A statically sized D-dimensional row vector.
pub type RowVectorN<N, D> = MatrixNM<N, U1, D>;
pub type RowVectorN<N, D> = MatrixMN<N, U1, D>;
/// A stack-allocated, 1-dimensional row vector.
pub type RowVector1<N> = RowVectorN<N, U1>;

View File

@ -2,10 +2,10 @@
use std::any::Any;
use core::Scalar;
use core::{DefaultAllocator, Scalar};
use core::constraint::{SameNumberOfRows, SameNumberOfColumns, ShapeConstraint};
use core::dimension::{Dim, U1};
use core::storage::{Storage, OwnedStorage};
use core::storage::ContiguousStorageMut;
/// A matrix allocator of a memory buffer that may contain `R::to_usize() * C::to_usize()`
/// elements of type `N`.
@ -16,9 +16,9 @@ use core::storage::{Storage, OwnedStorage};
///
/// Every allocator must be both static and dynamic. Though not all implementations may share the
/// same `Buffer` type.
pub trait Allocator<N: Scalar, R: Dim, C: Dim>: Any + Sized {
pub trait Allocator<N: Scalar, R: Dim, C: Dim = U1>: Any + Sized {
/// The type of buffer this allocator can instanciate.
type Buffer: OwnedStorage<N, R, C, Alloc = Self>;
type Buffer: ContiguousStorageMut<N, R, C> + Clone;
/// Allocates a buffer with the given number of rows and columns without initializing its content.
unsafe fn allocate_uninitialized(nrows: R, ncols: C) -> Self::Buffer;
@ -27,15 +27,20 @@ pub trait Allocator<N: Scalar, R: Dim, C: Dim>: Any + Sized {
fn allocate_from_iterator<I: IntoIterator<Item = N>>(nrows: R, ncols: C, iter: I) -> Self::Buffer;
}
/// A matrix data allocator dedicated to the given owned matrix storage.
pub trait OwnedAllocator<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C, Alloc = Self>>:
Allocator<N, R, C, Buffer = S> {
}
impl<N, R, C, T, S> OwnedAllocator<N, R, C, S> for T
where N: Scalar, R: Dim, C: Dim,
T: Allocator<N, R, C, Buffer = S>,
S: OwnedStorage<N, R, C, Alloc = T> {
/// A matrix reallocator. Changes the size of the memory buffer that initially contains (RFrom ×
/// CFrom) elements to a smaller or larger size (RTo, CTo).
pub trait Reallocator<N: Scalar, RFrom: Dim, CFrom: Dim, RTo: Dim, CTo: Dim>:
Allocator<N, RFrom, CFrom> + Allocator<N, RTo, CTo> {
/// Reallocates a buffer of shape `(RTo, CTo)`, possibly reusing a previously allocated buffer
/// `buf`. Data stored by `buf` are linearly copied to the output:
///
/// * The copy is performed as if both were just arrays (without a matrix structure).
/// * If `buf` is larger than the output size, then extra elements of `buf` are truncated.
/// * If `buf` is smaller than the output size, then extra elements of the output are left
/// uninitialized.
unsafe fn reallocate_copy(nrows: RTo, ncols: CTo,
buf: <Self as Allocator<N, RFrom, CFrom>>::Buffer)
-> <Self as Allocator<N, RTo, CTo>>::Buffer;
}
/// The number of rows of the result of a componentwise operation on two matrices.
@ -45,45 +50,36 @@ pub type SameShapeR<R1, R2> = <ShapeConstraint as SameNumberOfRows<R1, R2>>::Rep
pub type SameShapeC<C1, C2> = <ShapeConstraint as SameNumberOfColumns<C1, C2>>::Representative;
// FIXME: Bad name.
/// Restricts the given number of rows and columns to be respectively the same. Can only be used
/// when `Self = SA::Alloc`.
pub trait SameShapeAllocator<N, R1, C1, R2, C2, SA>:
/// Restricts the given number of rows and columns to be respectively the same.
pub trait SameShapeAllocator<N, R1, C1, R2, C2>:
Allocator<N, R1, C1> +
Allocator<N, SameShapeR<R1, R2>, SameShapeC<C1, C2>>
where R1: Dim, R2: Dim, C1: Dim, C2: Dim,
N: Scalar,
SA: Storage<N, R1, C1, Alloc = Self>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
}
impl<N, R1, R2, C1, C2, SA> SameShapeAllocator<N, R1, C1, R2, C2, SA> for SA::Alloc
impl<N, R1, R2, C1, C2> SameShapeAllocator<N, R1, C1, R2, C2> for DefaultAllocator
where R1: Dim, R2: Dim, C1: Dim, C2: Dim,
N: Scalar,
SA: Storage<N, R1, C1>,
SA::Alloc:
Allocator<N, R1, C1> +
Allocator<N, SameShapeR<R1, R2>, SameShapeC<C1, C2>>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
DefaultAllocator: Allocator<N, R1, C1> + Allocator<N, SameShapeR<R1, R2>, SameShapeC<C1, C2>>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
}
// XXX: Bad name.
/// Restricts the given number of rows to be equal. Can only be used when `Self = SA::Alloc`.
pub trait SameShapeColumnVectorAllocator<N, R1, R2, SA>:
Allocator<N, R1, U1> +
Allocator<N, SameShapeR<R1, R2>, U1> +
SameShapeAllocator<N, R1, U1, R2, U1, SA>
/// Restricts the given number of rows to be equal.
pub trait SameShapeVectorAllocator<N, R1, R2>:
Allocator<N, R1> +
Allocator<N, SameShapeR<R1, R2>> +
SameShapeAllocator<N, R1, U1, R2, U1>
where R1: Dim, R2: Dim,
N: Scalar,
SA: Storage<N, R1, U1, Alloc = Self>,
ShapeConstraint: SameNumberOfRows<R1, R2> {
}
impl<N, R1, R2, SA> SameShapeColumnVectorAllocator<N, R1, R2, SA> for SA::Alloc
impl<N, R1, R2> SameShapeVectorAllocator<N, R1, R2> for DefaultAllocator
where R1: Dim, R2: Dim,
N: Scalar,
SA: Storage<N, R1, U1>,
SA::Alloc:
Allocator<N, R1, U1> +
Allocator<N, SameShapeR<R1, R2>, U1>,
ShapeConstraint: SameNumberOfRows<R1, R2> {
DefaultAllocator: Allocator<N, R1, U1> + Allocator<N, SameShapeR<R1, R2>>,
ShapeConstraint: SameNumberOfRows<R1, R2> {
}

458
src/core/blas.rs Normal file
View File

@ -0,0 +1,458 @@
use std::mem;
use num::{Zero, One, Signed};
use matrixmultiply;
use alga::general::{ClosedMul, ClosedAdd};
use core::{Scalar, Matrix, Vector};
use core::dimension::{Dim, U1, U2, U3, U4, Dynamic};
use core::constraint::{ShapeConstraint, SameNumberOfRows, SameNumberOfColumns, AreMultipliable, DimEq};
use core::storage::{Storage, StorageMut};
impl<N: Scalar + PartialOrd + Signed, D: Dim, S: Storage<N, D>> Vector<N, D, S> {
/// Computes the index of the vector component with the largest absolute value.
#[inline]
pub fn iamax(&self) -> usize {
assert!(!self.is_empty(), "The input vector must not be empty.");
let mut the_max = unsafe { self.vget_unchecked(0).abs() };
let mut the_i = 0;
for i in 1 .. self.nrows() {
let val = unsafe { self.vget_unchecked(i).abs() };
if val > the_max {
the_max = val;
the_i = i;
}
}
the_i
}
}
impl<N: Scalar + PartialOrd + Signed, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
/// Computes the index of the matrix component with the largest absolute value.
#[inline]
pub fn iamax_full(&self) -> (usize, usize) {
assert!(!self.is_empty(), "The input matrix must not be empty.");
let mut the_max = unsafe { self.get_unchecked(0, 0).abs() };
let mut the_ij = (0, 0);
for j in 0 .. self.ncols() {
for i in 0 .. self.nrows() {
let val = unsafe { self.get_unchecked(i, j).abs() };
if val > the_max {
the_max = val;
the_ij = (i, j);
}
}
}
the_ij
}
}
impl<N, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
where N: Scalar + Zero + ClosedAdd + ClosedMul {
/// The dot product between two matrices (seen as vectors).
///
/// Note that this is **not** the matrix multiplication as in, e.g., numpy. For matrix
/// multiplication, use one of: `.gemm`, `mul_to`, `.mul`, `*`.
#[inline]
pub fn dot<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> N
where SB: Storage<N, R2, C2>,
ShapeConstraint: DimEq<R, R2> + DimEq<C, C2> {
assert!(self.nrows() == rhs.nrows(), "Dot product dimensions mismatch.");
// So we do some special cases for common fixed-size vectors of dimension lower than 8
// because the `for` loop bellow won't be very efficient on those.
if (R::is::<U2>() || R2::is::<U2>()) &&
(C::is::<U1>() || C2::is::<U1>()) {
unsafe {
let a = *self.get_unchecked(0, 0) * *rhs.get_unchecked(0, 0);
let b = *self.get_unchecked(1, 0) * *rhs.get_unchecked(1, 0);
return a + b;
}
}
if (R::is::<U3>() || R2::is::<U3>()) &&
(C::is::<U1>() || C2::is::<U1>()) {
unsafe {
let a = *self.get_unchecked(0, 0) * *rhs.get_unchecked(0, 0);
let b = *self.get_unchecked(1, 0) * *rhs.get_unchecked(1, 0);
let c = *self.get_unchecked(2, 0) * *rhs.get_unchecked(2, 0);
return a + b + c;
}
}
if (R::is::<U4>() || R2::is::<U4>()) &&
(C::is::<U1>() || C2::is::<U1>()) {
unsafe {
let mut a = *self.get_unchecked(0, 0) * *rhs.get_unchecked(0, 0);
let mut b = *self.get_unchecked(1, 0) * *rhs.get_unchecked(1, 0);
let c = *self.get_unchecked(2, 0) * *rhs.get_unchecked(2, 0);
let d = *self.get_unchecked(3, 0) * *rhs.get_unchecked(3, 0);
a += c;
b += d;
return a + b;
}
}
// All this is inspired from the "unrolled version" discussed in:
// http://blog.theincredibleholk.org/blog/2012/12/10/optimizing-dot-product/
//
// And this comment from bluss:
// https://users.rust-lang.org/t/how-to-zip-two-slices-efficiently/2048/12
let mut res = N::zero();
// We have to define them outside of the loop (and not inside at first assignment)
// otherwize vectorization won't kick in for some reason.
let mut acc0;
let mut acc1;
let mut acc2;
let mut acc3;
let mut acc4;
let mut acc5;
let mut acc6;
let mut acc7;
for j in 0 .. self.ncols() {
let mut i = 0;
acc0 = N::zero();
acc1 = N::zero();
acc2 = N::zero();
acc3 = N::zero();
acc4 = N::zero();
acc5 = N::zero();
acc6 = N::zero();
acc7 = N::zero();
while self.nrows() - i >= 8 {
acc0 += unsafe { *self.get_unchecked(i + 0, j) * *rhs.get_unchecked(i + 0, j) };
acc1 += unsafe { *self.get_unchecked(i + 1, j) * *rhs.get_unchecked(i + 1, j) };
acc2 += unsafe { *self.get_unchecked(i + 2, j) * *rhs.get_unchecked(i + 2, j) };
acc3 += unsafe { *self.get_unchecked(i + 3, j) * *rhs.get_unchecked(i + 3, j) };
acc4 += unsafe { *self.get_unchecked(i + 4, j) * *rhs.get_unchecked(i + 4, j) };
acc5 += unsafe { *self.get_unchecked(i + 5, j) * *rhs.get_unchecked(i + 5, j) };
acc6 += unsafe { *self.get_unchecked(i + 6, j) * *rhs.get_unchecked(i + 6, j) };
acc7 += unsafe { *self.get_unchecked(i + 7, j) * *rhs.get_unchecked(i + 7, j) };
i += 8;
}
res += acc0 + acc4;
res += acc1 + acc5;
res += acc2 + acc6;
res += acc3 + acc7;
for k in i .. self.nrows() {
res += unsafe { *self.get_unchecked(k, j) * *rhs.get_unchecked(k, j) }
}
}
res
}
/// The dot product between the transpose of `self` and `rhs`.
#[inline]
pub fn tr_dot<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> N
where SB: Storage<N, R2, C2>,
ShapeConstraint: DimEq<C, R2> + DimEq<R, C2> {
let (nrows, ncols) = self.shape();
assert!((ncols, nrows) == rhs.shape(), "Transposed dot product dimension mismatch.");
let mut res = N::zero();
for j in 0 .. self.nrows() {
for i in 0 .. self.ncols() {
res += unsafe { *self.get_unchecked(j, i) * *rhs.get_unchecked(i, j) }
}
}
res
}
}
fn array_axpy<N>(y: &mut [N], a: N, x: &[N], beta: N, stride1: usize, stride2: usize, len: usize)
where N: Scalar + Zero + ClosedAdd + ClosedMul {
for i in 0 .. len {
unsafe {
let y = y.get_unchecked_mut(i * stride1);
*y = a * *x.get_unchecked(i * stride2) + beta * *y;
}
}
}
fn array_ax<N>(y: &mut [N], a: N, x: &[N], stride1: usize, stride2: usize, len: usize)
where N: Scalar + Zero + ClosedAdd + ClosedMul {
for i in 0 .. len {
unsafe {
*y.get_unchecked_mut(i * stride1) = a * *x.get_unchecked(i * stride2);
}
}
}
impl<N, D: Dim, S> Vector<N, D, S>
where N: Scalar + Zero + ClosedAdd + ClosedMul,
S: StorageMut<N, D> {
/// Computes `self = a * x + b * self`.
///
/// If be is zero, `self` is never read from.
#[inline]
pub fn axpy<D2: Dim, SB>(&mut self, a: N, x: &Vector<N, D2, SB>, b: N)
where SB: Storage<N, D2>,
ShapeConstraint: DimEq<D, D2> {
assert_eq!(self.nrows(), x.nrows(), "Axpy: mismatched vector shapes.");
let rstride1 = self.strides().0;
let rstride2 = x.strides().0;
let y = self.data.as_mut_slice();
let x = x.data.as_slice();
if !b.is_zero() {
array_axpy(y, a, x, b, rstride1, rstride2, x.len());
}
else {
array_ax(y, a, x, rstride1, rstride2, x.len());
}
}
/// Computes `self = alpha * a * x + beta * self`, where `a` is a matrix, `x` a vector, and
/// `alpha, beta` two scalars.
///
/// If `beta` is zero, `self` is never read.
#[inline]
pub fn gemv<R2: Dim, C2: Dim, D3: Dim, SB, SC>(&mut self,
alpha: N,
a: &Matrix<N, R2, C2, SB>,
x: &Vector<N, D3, SC>,
beta: N)
where N: One,
SB: Storage<N, R2, C2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<D, R2> +
AreMultipliable<R2, C2, D3, U1> {
let dim1 = self.nrows();
let (nrows2, ncols2) = a.shape();
let dim3 = x.nrows();
assert!(ncols2 == dim3 && dim1 == nrows2, "Gemv: dimensions mismatch.");
if ncols2 == 0 {
return;
}
// FIXME: avoid bound checks.
let col2 = a.column(0);
let val = unsafe { *x.vget_unchecked(0) };
self.axpy(alpha * val, &col2, beta);
for j in 1 .. ncols2 {
let col2 = a.column(j);
let val = unsafe { *x.vget_unchecked(j) };
self.axpy(alpha * val, &col2, N::one());
}
}
/// Computes `self = alpha * a * x + beta * self`, where `a` is a **symmetric** matrix, `x` a
/// vector, and `alpha, beta` two scalars.
///
/// If `beta` is zero, `self` is never read. If `self` is read, only its lower-triangular part
/// (including the diagonal) is actually read.
#[inline]
pub fn gemv_symm<D2: Dim, D3: Dim, SB, SC>(&mut self,
alpha: N,
a: &Matrix<N, D2, D2, SB>,
x: &Vector<N, D3, SC>,
beta: N)
where N: One,
SB: Storage<N, D2, D2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<D, D2> +
AreMultipliable<D2, D2, D3, U1> {
let dim1 = self.nrows();
let dim2 = a.nrows();
let dim3 = x.nrows();
assert!(a.is_square(), "Syetric gemv: the input matrix must be square.");
assert!(dim2 == dim3 && dim1 == dim2, "Symmetric gemv: dimensions mismatch.");
if dim2 == 0 {
return;
}
// FIXME: avoid bound checks.
let col2 = a.column(0);
let val = unsafe { *x.vget_unchecked(0) };
self.axpy(alpha * val, &col2, beta);
self[0] += alpha * x.rows_range(1 ..).dot(&a.slice_range(1 .., 0));
for j in 1 .. dim2 {
let col2 = a.column(j);
let dot = x.rows_range(j ..).dot(&col2.rows_range(j ..));
let val;
unsafe {
val = *x.vget_unchecked(j);
*self.vget_unchecked_mut(j) += alpha * dot;
}
self.rows_range_mut(j + 1 ..).axpy(alpha * val, &col2.rows_range(j + 1 ..), N::one());
}
}
}
impl<N, R1: Dim, C1: Dim, S: StorageMut<N, R1, C1>> Matrix<N, R1, C1, S>
where N: Scalar + Zero + ClosedAdd + ClosedMul {
/// Computes `self = alpha * x * y.transpose() + beta * self`.
///
/// If `beta` is zero, `self` is never read.
#[inline]
pub fn ger<D2: Dim, D3: Dim, SB, SC>(&mut self, alpha: N, x: &Vector<N, D2, SB>, y: &Vector<N, D3, SC>, beta: N)
where N: One,
SB: Storage<N, D2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3> {
let (nrows1, ncols1) = self.shape();
let dim2 = x.nrows();
let dim3 = y.nrows();
assert!(nrows1 == dim2 && ncols1 == dim3, "ger: dimensions mismatch.");
for j in 0 .. ncols1 {
// FIXME: avoid bound checks.
let val = unsafe { *y.vget_unchecked(j) };
self.column_mut(j).axpy(alpha * val, x, beta);
}
}
/// Computes `self = alpha * a * b + beta * self`, where `a, b, self` are matrices.
/// `alpha` and `beta` are scalar.
///
/// If `beta` is zero, `self` is never read.
#[inline]
pub fn gemm<R2: Dim, C2: Dim, R3: Dim, C3: Dim, SB, SC>(&mut self,
alpha: N,
a: &Matrix<N, R2, C2, SB>,
b: &Matrix<N, R3, C3, SC>,
beta: N)
where N: One,
SB: Storage<N, R2, C2>,
SC: Storage<N, R3, C3>,
ShapeConstraint: SameNumberOfRows<R1, R2> +
SameNumberOfColumns<C1, C3> +
AreMultipliable<R2, C2, R3, C3> {
let (nrows1, ncols1) = self.shape();
let (nrows2, ncols2) = a.shape();
let (nrows3, ncols3) = b.shape();
assert_eq!(ncols2, nrows3, "gemm: dimensions mismatch for multiplication.");
assert_eq!((nrows1, ncols1), (nrows2, ncols3), "gemm: dimensions mismatch for addition.");
// We assume large matrices will be Dynamic but small matrices static.
// We could use matrixmultiply for large statically-sized matrices but the performance
// threshold to activate it would be different from SMALL_DIM because our code optimizes
// better for statically-sized matrices.
let is_dynamic = R1::is::<Dynamic>() || C1::is::<Dynamic>() ||
R2::is::<Dynamic>() || C2::is::<Dynamic>() ||
R3::is::<Dynamic>() || C3::is::<Dynamic>();
// Thershold determined ampirically.
const SMALL_DIM: usize = 5;
if is_dynamic &&
nrows1 > SMALL_DIM && ncols1 > SMALL_DIM &&
nrows2 > SMALL_DIM && ncols2 > SMALL_DIM {
if N::is::<f32>() {
let (rsa, csa) = a.strides();
let (rsb, csb) = b.strides();
let (rsc, csc) = self.strides();
unsafe {
matrixmultiply::sgemm(
nrows2,
ncols2,
ncols3,
mem::transmute_copy(&alpha),
a.data.ptr() as *const f32,
rsa as isize, csa as isize,
b.data.ptr() as *const f32,
rsb as isize, csb as isize,
mem::transmute_copy(&beta),
self.data.ptr_mut() as *mut f32,
rsc as isize, csc as isize);
}
}
else if N::is::<f64>() {
let (rsa, csa) = a.strides();
let (rsb, csb) = b.strides();
let (rsc, csc) = self.strides();
unsafe {
matrixmultiply::dgemm(
nrows2,
ncols2,
ncols3,
mem::transmute_copy(&alpha),
a.data.ptr() as *const f64,
rsa as isize, csa as isize,
b.data.ptr() as *const f64,
rsb as isize, csb as isize,
mem::transmute_copy(&beta),
self.data.ptr_mut() as *mut f64,
rsc as isize, csc as isize);
}
}
}
else {
for j1 in 0 .. ncols1 {
// FIXME: avoid bound checks.
self.column_mut(j1).gemv(alpha, a, &b.column(j1), beta);
}
}
}
}
impl<N, R1: Dim, C1: Dim, S: StorageMut<N, R1, C1>> Matrix<N, R1, C1, S>
where N: Scalar + Zero + ClosedAdd + ClosedMul {
/// Computes `self = alpha * x * y.transpose() + beta * self`, where `self` is a **symmetric**
/// matrix.
///
/// If `beta` is zero, `self` is never read. The result is symmetric. Only the lower-triangular
/// (including the diagonal) part of `self` is read/written.
#[inline]
pub fn ger_symm<D2: Dim, D3: Dim, SB, SC>(&mut self,
alpha: N,
x: &Vector<N, D2, SB>,
y: &Vector<N, D3, SC>,
beta: N)
where N: One,
SB: Storage<N, D2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3> {
let dim1 = self.nrows();
let dim2 = x.nrows();
let dim3 = y.nrows();
assert!(self.is_square(), "Symmetric ger: the input matrix must be square.");
assert!(dim1 == dim2 && dim1 == dim3, "ger: dimensions mismatch.");
for j in 0 .. dim1 {
// FIXME: avoid bound checks.
let val = unsafe { *y.vget_unchecked(j) };
let subdim = Dynamic::new(dim1 - j);
self.generic_slice_mut((j, j), (subdim, U1)).axpy(alpha * val, &x.rows_range(j ..), beta);
}
}
}

View File

@ -7,20 +7,20 @@
use num::One;
use core::{Scalar, SquareMatrix, OwnedSquareMatrix, ColumnVector, Unit};
use core::dimension::{DimName, DimNameSub, DimNameDiff, U1, U2, U3, U4};
use core::storage::{Storage, StorageMut, OwnedStorage};
use core::allocator::{Allocator, OwnedAllocator};
use geometry::{PointBase, OrthographicBase, PerspectiveBase, IsometryBase, OwnedRotation, OwnedPoint};
use core::{DefaultAllocator, Scalar, SquareMatrix, Vector, Unit,
VectorN, MatrixN, Vector3, Matrix3, Matrix4};
use core::dimension::{DimName, DimNameSub, DimNameDiff, U1};
use core::storage::{Storage, StorageMut};
use core::allocator::Allocator;
use geometry::{Point, Isometry, Point3, Rotation2, Rotation3, Orthographic3, Perspective3, IsometryMatrix3};
use alga::general::{Real, Field};
use alga::linear::Transformation;
impl<N, D: DimName, S> SquareMatrix<N, D, S>
impl<N, D: DimName> MatrixN<N, D>
where N: Scalar + Field,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S> {
DefaultAllocator: Allocator<N, D, D> {
/// Creates a new homogeneous matrix that applies the same scaling factor on each dimension.
#[inline]
pub fn new_scaling(scaling: N) -> Self {
@ -32,9 +32,9 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Creates a new homogeneous matrix that applies a distinct scaling factor for each dimension.
#[inline]
pub fn new_nonuniform_scaling<SB>(scaling: &ColumnVector<N, DimNameDiff<D, U1>, SB>) -> Self
pub fn new_nonuniform_scaling<SB>(scaling: &Vector<N, DimNameDiff<D, U1>, SB>) -> Self
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1> {
SB: Storage<N, DimNameDiff<D, U1>> {
let mut res = Self::one();
for i in 0 .. scaling.len() {
res[(i, i)] = scaling[i];
@ -45,10 +45,9 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Creates a new homogeneous matrix that applies a pure translation.
#[inline]
pub fn new_translation<SB>(translation: &ColumnVector<N, DimNameDiff<D, U1>, SB>) -> Self
pub fn new_translation<SB>(translation: &Vector<N, DimNameDiff<D, U1>, SB>) -> Self
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, DimNameDiff<D, U1>, U1> {
SB: Storage<N, DimNameDiff<D, U1>> {
let mut res = Self::one();
res.fixed_slice_mut::<DimNameDiff<D, U1>, U1>(0, D::dim() - 1).copy_from(translation);
@ -56,44 +55,30 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
}
}
impl<N, S> SquareMatrix<N, U3, S>
where N: Real,
S: OwnedStorage<N, U3, U3>,
S::Alloc: OwnedAllocator<N, U3, U3, S> {
impl<N: Real> Matrix3<N> {
/// Builds a 2 dimensional homogeneous rotation matrix from an angle in radian.
#[inline]
pub fn new_rotation(angle: N) -> Self
where S::Alloc: Allocator<N, U2, U2> {
OwnedRotation::<N, U2, S::Alloc>::new(angle).to_homogeneous()
pub fn new_rotation(angle: N) -> Self {
Rotation2::new(angle).to_homogeneous()
}
}
impl<N, S> SquareMatrix<N, U4, S>
where N: Real,
S: OwnedStorage<N, U4, U4>,
S::Alloc: OwnedAllocator<N, U4, U4, S> {
impl<N: Real> Matrix4<N> {
/// Builds a 3D homogeneous rotation matrix from an axis and an angle (multiplied together).
///
/// Returns the identity matrix if the given argument is zero.
#[inline]
pub fn new_rotation<SB>(axisangle: ColumnVector<N, U3, SB>) -> Self
where SB: Storage<N, U3, U1>,
S::Alloc: Allocator<N, U3, U3> {
OwnedRotation::<N, U3, S::Alloc>::new(axisangle).to_homogeneous()
pub fn new_rotation(axisangle: Vector3<N>) -> Self {
Rotation3::new(axisangle).to_homogeneous()
}
/// Builds a 3D homogeneous rotation matrix from an axis and an angle (multiplied together).
///
/// Returns the identity matrix if the given argument is zero.
#[inline]
pub fn new_rotation_wrt_point<SB>(axisangle: ColumnVector<N, U3, SB>, pt: OwnedPoint<N, U3, S::Alloc>) -> Self
where SB: Storage<N, U3, U1>,
S::Alloc: Allocator<N, U3, U3> +
Allocator<N, U3, U1> +
Allocator<N, U1, U3> {
let rot = OwnedRotation::<N, U3, S::Alloc>::from_scaled_axis(axisangle);
IsometryBase::rotation_wrt_point(rot, pt).to_homogeneous()
pub fn new_rotation_wrt_point(axisangle: Vector3<N>, pt: Point3<N>) -> Self {
let rot = Rotation3::from_scaled_axis(axisangle);
Isometry::rotation_wrt_point(rot, pt).to_homogeneous()
}
/// Builds a 3D homogeneous rotation matrix from an axis and an angle (multiplied together).
@ -101,37 +86,32 @@ impl<N, S> SquareMatrix<N, U4, S>
/// Returns the identity matrix if the given argument is zero.
/// This is identical to `Self::new_rotation`.
#[inline]
pub fn from_scaled_axis<SB>(axisangle: ColumnVector<N, U3, SB>) -> Self
where SB: Storage<N, U3, U1>,
S::Alloc: Allocator<N, U3, U3> {
OwnedRotation::<N, U3, S::Alloc>::from_scaled_axis(axisangle).to_homogeneous()
pub fn from_scaled_axis(axisangle: Vector3<N>) -> Self {
Rotation3::from_scaled_axis(axisangle).to_homogeneous()
}
/// Creates a new rotation from Euler angles.
///
/// The primitive rotations are applied in order: 1 roll 2 pitch 3 yaw.
pub fn from_euler_angles(roll: N, pitch: N, yaw: N) -> Self
where S::Alloc: Allocator<N, U3, U3> {
OwnedRotation::<N, U3, S::Alloc>::from_euler_angles(roll, pitch, yaw).to_homogeneous()
pub fn from_euler_angles(roll: N, pitch: N, yaw: N) -> Self {
Rotation3::from_euler_angles(roll, pitch, yaw).to_homogeneous()
}
/// Builds a 3D homogeneous rotation matrix from an axis and a rotation angle.
pub fn from_axis_angle<SB>(axis: &Unit<ColumnVector<N, U3, SB>>, angle: N) -> Self
where SB: Storage<N, U3, U1>,
S::Alloc: Allocator<N, U3, U3> {
OwnedRotation::<N, U3, S::Alloc>::from_axis_angle(axis, angle).to_homogeneous()
pub fn from_axis_angle(axis: &Unit<Vector3<N>>, angle: N) -> Self {
Rotation3::from_axis_angle(axis, angle).to_homogeneous()
}
/// Creates a new homogeneous matrix for an orthographic projection.
#[inline]
pub fn new_orthographic(left: N, right: N, bottom: N, top: N, znear: N, zfar: N) -> Self {
OrthographicBase::new(left, right, bottom, top, znear, zfar).unwrap()
Orthographic3::new(left, right, bottom, top, znear, zfar).unwrap()
}
/// Creates a new homogeneous matrix for a perspective projection.
#[inline]
pub fn new_perspective(aspect: N, fovy: N, znear: N, zfar: N) -> Self {
PerspectiveBase::new(aspect, fovy, znear, zfar).unwrap()
Perspective3::new(aspect, fovy, znear, zfar).unwrap()
}
/// Creates an isometry that corresponds to the local frame of an observer standing at the
@ -140,57 +120,30 @@ impl<N, S> SquareMatrix<N, U4, S>
/// It maps the view direction `target - eye` to the positive `z` axis and the origin to the
/// `eye`.
#[inline]
pub fn new_observer_frame<SB>(eye: &PointBase<N, U3, SB>,
target: &PointBase<N, U3, SB>,
up: &ColumnVector<N, U3, SB>)
-> Self
where SB: OwnedStorage<N, U3, U1, Alloc = S::Alloc>,
SB::Alloc: OwnedAllocator<N, U3, U1, SB> +
Allocator<N, U1, U3> +
Allocator<N, U3, U3> {
IsometryBase::<N, U3, SB, OwnedRotation<N, U3, SB::Alloc>>
::new_observer_frame(eye, target, up).to_homogeneous()
pub fn new_observer_frame(eye: &Point3<N>, target: &Point3<N>, up: &Vector3<N>) -> Self {
IsometryMatrix3::new_observer_frame(eye, target, up).to_homogeneous()
}
/// Builds a right-handed look-at view matrix.
#[inline]
pub fn look_at_rh<SB>(eye: &PointBase<N, U3, SB>,
target: &PointBase<N, U3, SB>,
up: &ColumnVector<N, U3, SB>)
-> Self
where SB: OwnedStorage<N, U3, U1, Alloc = S::Alloc>,
SB::Alloc: OwnedAllocator<N, U3, U1, SB> +
Allocator<N, U1, U3> +
Allocator<N, U3, U3> {
IsometryBase::<N, U3, SB, OwnedRotation<N, U3, SB::Alloc>>
::look_at_rh(eye, target, up).to_homogeneous()
pub fn look_at_rh(eye: &Point3<N>, target: &Point3<N>, up: &Vector3<N>) -> Self {
IsometryMatrix3::look_at_rh(eye, target, up).to_homogeneous()
}
/// Builds a left-handed look-at view matrix.
#[inline]
pub fn look_at_lh<SB>(eye: &PointBase<N, U3, SB>,
target: &PointBase<N, U3, SB>,
up: &ColumnVector<N, U3, SB>)
-> Self
where SB: OwnedStorage<N, U3, U1, Alloc = S::Alloc>,
SB::Alloc: OwnedAllocator<N, U3, U1, SB> +
Allocator<N, U1, U3> +
Allocator<N, U3, U3> {
IsometryBase::<N, U3, SB, OwnedRotation<N, U3, SB::Alloc>>
::look_at_lh(eye, target, up).to_homogeneous()
pub fn look_at_lh(eye: &Point3<N>, target: &Point3<N>, up: &Vector3<N>) -> Self {
IsometryMatrix3::look_at_lh(eye, target, up).to_homogeneous()
}
}
impl<N, D: DimName, S> SquareMatrix<N, D, S>
where N: Scalar + Field,
S: Storage<N, D, D> {
impl<N: Scalar + Field, D: DimName, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
/// Computes the transformation equal to `self` followed by an uniform scaling factor.
#[inline]
pub fn append_scaling(&self, scaling: N) -> OwnedSquareMatrix<N, D, S::Alloc>
pub fn append_scaling(&self, scaling: N) -> MatrixN<N, D>
where D: DimNameSub<U1>,
S::Alloc: Allocator<N, DimNameDiff<D, U1>, D> {
DefaultAllocator: Allocator<N, D, D> {
let mut res = self.clone_owned();
res.append_scaling_mut(scaling);
res
@ -198,9 +151,9 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes the transformation equal to an uniform scaling factor followed by `self`.
#[inline]
pub fn prepend_scaling(&self, scaling: N) -> OwnedSquareMatrix<N, D, S::Alloc>
pub fn prepend_scaling(&self, scaling: N) -> MatrixN<N, D>
where D: DimNameSub<U1>,
S::Alloc: Allocator<N, D, DimNameDiff<D, U1>> {
DefaultAllocator: Allocator<N, D, D> {
let mut res = self.clone_owned();
res.prepend_scaling_mut(scaling);
res
@ -208,11 +161,10 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes the transformation equal to `self` followed by a non-uniform scaling factor.
#[inline]
pub fn append_nonuniform_scaling<SB>(&self, scaling: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
-> OwnedSquareMatrix<N, D, S::Alloc>
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, U1, D> {
pub fn append_nonuniform_scaling<SB>(&self, scaling: &Vector<N, DimNameDiff<D, U1>, SB>) -> MatrixN<N, D>
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>>,
DefaultAllocator: Allocator<N, D, D> {
let mut res = self.clone_owned();
res.append_nonuniform_scaling_mut(scaling);
res
@ -220,11 +172,10 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes the transformation equal to a non-uniform scaling factor followed by `self`.
#[inline]
pub fn prepend_nonuniform_scaling<SB>(&self, scaling: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
-> OwnedSquareMatrix<N, D, S::Alloc>
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, D, U1> {
pub fn prepend_nonuniform_scaling<SB>(&self, scaling: &Vector<N, DimNameDiff<D, U1>, SB>) -> MatrixN<N, D>
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>>,
DefaultAllocator: Allocator<N, D, D> {
let mut res = self.clone_owned();
res.prepend_nonuniform_scaling_mut(scaling);
res
@ -232,11 +183,10 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes the transformation equal to `self` followed by a translation.
#[inline]
pub fn append_translation<SB>(&self, shift: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
-> OwnedSquareMatrix<N, D, S::Alloc>
pub fn append_translation<SB>(&self, shift: &Vector<N, DimNameDiff<D, U1>, SB>) -> MatrixN<N, D>
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, DimNameDiff<D, U1>, U1> {
SB: Storage<N, DimNameDiff<D, U1>>,
DefaultAllocator: Allocator<N, D, D> {
let mut res = self.clone_owned();
res.append_translation_mut(shift);
res
@ -244,28 +194,23 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes the transformation equal to a translation followed by `self`.
#[inline]
pub fn prepend_translation<SB>(&self, shift: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
-> OwnedSquareMatrix<N, D, S::Alloc>
pub fn prepend_translation<SB>(&self, shift: &Vector<N, DimNameDiff<D, U1>, SB>) -> MatrixN<N, D>
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, DimNameDiff<D, U1>, U1> +
Allocator<N, DimNameDiff<D, U1>, DimNameDiff<D, U1>> +
Allocator<N, U1, DimNameDiff<D, U1>> {
SB: Storage<N, DimNameDiff<D, U1>>,
DefaultAllocator: Allocator<N, D, D> +
Allocator<N, DimNameDiff<D, U1>> {
let mut res = self.clone_owned();
res.prepend_translation_mut(shift);
res
}
}
impl<N, D: DimName, S> SquareMatrix<N, D, S>
where N: Scalar + Field,
S: StorageMut<N, D, D> {
impl<N: Scalar + Field, D: DimName, S: StorageMut<N, D, D>> SquareMatrix<N, D, S> {
/// Computes in-place the transformation equal to `self` followed by an uniform scaling factor.
#[inline]
pub fn append_scaling_mut(&mut self, scaling: N)
where D: DimNameSub<U1>,
S::Alloc: Allocator<N, DimNameDiff<D, U1>, D> {
where D: DimNameSub<U1> {
let mut to_scale = self.fixed_rows_mut::<DimNameDiff<D, U1>>(0);
to_scale *= scaling;
}
@ -273,18 +218,16 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes in-place the transformation equal to an uniform scaling factor followed by `self`.
#[inline]
pub fn prepend_scaling_mut(&mut self, scaling: N)
where D: DimNameSub<U1>,
S::Alloc: Allocator<N, D, DimNameDiff<D, U1>> {
where D: DimNameSub<U1> {
let mut to_scale = self.fixed_columns_mut::<DimNameDiff<D, U1>>(0);
to_scale *= scaling;
}
/// Computes in-place the transformation equal to `self` followed by a non-uniform scaling factor.
#[inline]
pub fn append_nonuniform_scaling_mut<SB>(&mut self, scaling: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
pub fn append_nonuniform_scaling_mut<SB>(&mut self, scaling: &Vector<N, DimNameDiff<D, U1>, SB>)
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, U1, D> {
SB: Storage<N, DimNameDiff<D, U1>> {
for i in 0 .. scaling.len() {
let mut to_scale = self.fixed_rows_mut::<U1>(i);
to_scale *= scaling[i];
@ -293,10 +236,9 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes in-place the transformation equal to a non-uniform scaling factor followed by `self`.
#[inline]
pub fn prepend_nonuniform_scaling_mut<SB>(&mut self, scaling: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
pub fn prepend_nonuniform_scaling_mut<SB>(&mut self, scaling: &Vector<N, DimNameDiff<D, U1>, SB>)
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, D, U1> {
SB: Storage<N, DimNameDiff<D, U1>> {
for i in 0 .. scaling.len() {
let mut to_scale = self.fixed_columns_mut::<U1>(i);
to_scale *= scaling[i];
@ -305,10 +247,9 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes the transformation equal to `self` followed by a translation.
#[inline]
pub fn append_translation_mut<SB>(&mut self, shift: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
pub fn append_translation_mut<SB>(&mut self, shift: &Vector<N, DimNameDiff<D, U1>, SB>)
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, DimNameDiff<D, U1>, U1> {
SB: Storage<N, DimNameDiff<D, U1>> {
for i in 0 .. D::dim() {
for j in 0 .. D::dim() - 1 {
self[(j, i)] += shift[j] * self[(D::dim() - 1, i)];
@ -318,12 +259,10 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
/// Computes the transformation equal to a translation followed by `self`.
#[inline]
pub fn prepend_translation_mut<SB>(&mut self, shift: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
pub fn prepend_translation_mut<SB>(&mut self, shift: &Vector<N, DimNameDiff<D, U1>, SB>)
where D: DimNameSub<U1>,
SB: Storage<N, DimNameDiff<D, U1>, U1>,
S::Alloc: Allocator<N, DimNameDiff<D, U1>, U1> +
Allocator<N, DimNameDiff<D, U1>, DimNameDiff<D, U1>> +
Allocator<N, U1, DimNameDiff<D, U1>> {
SB: Storage<N, DimNameDiff<D, U1>>,
DefaultAllocator: Allocator<N, DimNameDiff<D, U1>> {
let scale = self.fixed_slice::<U1, DimNameDiff<D, U1>>(D::dim() - 1, 0).tr_dot(&shift);
let post_translation = self.fixed_slice::<DimNameDiff<D, U1>, DimNameDiff<D, U1>>(0, 0) * shift;
@ -335,19 +274,12 @@ impl<N, D: DimName, S> SquareMatrix<N, D, S>
}
impl<N, D, SA, SB> Transformation<PointBase<N, DimNameDiff<D, U1>, SB>> for SquareMatrix<N, D, SA>
where N: Real,
D: DimNameSub<U1>,
SA: OwnedStorage<N, D, D>,
SB: OwnedStorage<N, DimNameDiff<D, U1>, U1, Alloc = SA::Alloc>,
SA::Alloc: OwnedAllocator<N, D, D, SA> +
Allocator<N, DimNameDiff<D, U1>, DimNameDiff<D, U1>> +
Allocator<N, DimNameDiff<D, U1>, U1> +
Allocator<N, U1, DimNameDiff<D, U1>>,
SB::Alloc: OwnedAllocator<N, DimNameDiff<D, U1>, U1, SB> {
impl<N: Real, D: DimNameSub<U1>> Transformation<Point<N, DimNameDiff<D, U1>>> for MatrixN<N, D>
where DefaultAllocator: Allocator<N, D, D> +
Allocator<N, DimNameDiff<D, U1>> +
Allocator<N, DimNameDiff<D, U1>, DimNameDiff<D, U1>> {
#[inline]
fn transform_vector(&self, v: &ColumnVector<N, DimNameDiff<D, U1>, SB>)
-> ColumnVector<N, DimNameDiff<D, U1>, SB> {
fn transform_vector(&self, v: &VectorN<N, DimNameDiff<D, U1>>) -> VectorN<N, DimNameDiff<D, U1>> {
let transform = self.fixed_slice::<DimNameDiff<D, U1>, DimNameDiff<D, U1>>(0, 0);
let normalizer = self.fixed_slice::<U1, DimNameDiff<D, U1>>(D::dim() - 1, 0);
let n = normalizer.tr_dot(&v);
@ -360,8 +292,7 @@ impl<N, D, SA, SB> Transformation<PointBase<N, DimNameDiff<D, U1>, SB>> for Squa
}
#[inline]
fn transform_point(&self, pt: &PointBase<N, DimNameDiff<D, U1>, SB>)
-> PointBase<N, DimNameDiff<D, U1>, SB> {
fn transform_point(&self, pt: &Point<N, DimNameDiff<D, U1>>) -> Point<N, DimNameDiff<D, U1>> {
let transform = self.fixed_slice::<DimNameDiff<D, U1>, DimNameDiff<D, U1>>(0, 0);
let translation = self.fixed_slice::<DimNameDiff<D, U1>, U1>(0, D::dim() - 1);
let normalizer = self.fixed_slice::<U1, DimNameDiff<D, U1>>(D::dim() - 1, 0);

View File

@ -4,21 +4,22 @@ use num::Signed;
use alga::general::{ClosedMul, ClosedDiv};
use core::{Scalar, Matrix, OwnedMatrix, MatrixSum};
use core::{DefaultAllocator, Scalar, Matrix, MatrixMN, MatrixSum};
use core::dimension::Dim;
use core::storage::{Storage, StorageMut};
use core::allocator::SameShapeAllocator;
use core::allocator::{Allocator, SameShapeAllocator};
use core::constraint::{ShapeConstraint, SameNumberOfRows, SameNumberOfColumns};
/// The type of the result of a matrix componentwise operation.
pub type MatrixComponentOp<N, R1, C1, R2, C2, SA> = MatrixSum<N, R1, C1, R2, C2, SA>;
pub type MatrixComponentOp<N, R1, C1, R2, C2> = MatrixSum<N, R1, C1, R2, C2>;
impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
/// Computes the componentwise absolute value.
#[inline]
pub fn abs(&self) -> OwnedMatrix<N, R, C, S::Alloc>
where N: Signed {
pub fn abs(&self) -> MatrixMN<N, R, C>
where N: Signed,
DefaultAllocator: Allocator<N, R, C> {
let mut res = self.clone_owned();
for e in res.iter_mut() {
@ -32,48 +33,72 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
}
macro_rules! component_binop_impl(
($($binop: ident, $binop_mut: ident, $Trait: ident . $binop_assign: ident, $desc:expr, $desc_mut:expr);* $(;)*) => {$(
impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
($($binop: ident, $binop_mut: ident, $binop_assign: ident, $Trait: ident . $op_assign: ident, $desc:expr, $desc_mut:expr);* $(;)*) => {$(
impl<N: Scalar, R1: Dim, C1: Dim, SA: Storage<N, R1, C1>> Matrix<N, R1, C1, SA> {
#[doc = $desc]
#[inline]
pub fn $binop<R2, C2, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> MatrixComponentOp<N, R, C, R2, C2, S>
pub fn $binop<R2, C2, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> MatrixComponentOp<N, R1, C1, R2, C2>
where N: $Trait,
R2: Dim, C2: Dim,
SB: Storage<N, R2, C2>,
S::Alloc: SameShapeAllocator<N, R, C, R2, C2, S>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2> {
DefaultAllocator: SameShapeAllocator<N, R1, C1, R2, C2>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
assert_eq!(self.shape(), rhs.shape(), "Componentwise mul/div: mismatched matrix dimensions.");
let mut res = self.clone_owned_sum();
for (res, rhs) in res.iter_mut().zip(rhs.iter()) {
res.$binop_assign(*rhs);
for j in 0 .. res.ncols() {
for i in 0 .. res.nrows() {
unsafe {
res.get_unchecked_mut(i, j).$op_assign(*rhs.get_unchecked(i, j));
}
}
}
res
}
}
impl<N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> Matrix<N, R, C, S> {
impl<N: Scalar, R1: Dim, C1: Dim, SA: StorageMut<N, R1, C1>> Matrix<N, R1, C1, SA> {
#[doc = $desc_mut]
#[inline]
pub fn $binop_assign<R2, C2, SB>(&mut self, rhs: &Matrix<N, R2, C2, SB>)
where N: $Trait,
R2: Dim,
C2: Dim,
SB: Storage<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
assert_eq!(self.shape(), rhs.shape(), "Componentwise mul/div: mismatched matrix dimensions.");
for j in 0 .. self.ncols() {
for i in 0 .. self.nrows() {
unsafe {
self.get_unchecked_mut(i, j).$op_assign(*rhs.get_unchecked(i, j));
}
}
}
}
#[doc = $desc_mut]
#[inline]
#[deprecated(note = "This is renamed using the `_assign` sufix instead of the `_mut` suffix.")]
pub fn $binop_mut<R2, C2, SB>(&mut self, rhs: &Matrix<N, R2, C2, SB>)
where N: $Trait,
R2: Dim,
C2: Dim,
SB: Storage<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2> {
for (me, rhs) in self.iter_mut().zip(rhs.iter()) {
me.$binop_assign(*rhs);
}
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
self.$binop_assign(rhs)
}
}
)*}
);
component_binop_impl!(
component_mul, component_mul_mut, ClosedMul.mul_assign,
component_mul, component_mul_mut, component_mul_assign, ClosedMul.mul_assign,
"Componentwise matrix multiplication.", "Mutable, componentwise matrix multiplication.";
component_div, component_div_mut, ClosedDiv.div_assign,
component_div, component_div_mut, component_div_assign, ClosedDiv.div_assign,
"Componentwise matrix division.", "Mutable, componentwise matrix division.";
// FIXME: add other operators like bitshift, etc. ?
);

View File

@ -6,8 +6,7 @@ use core::dimension::{Dim, DimName, Dynamic};
pub struct ShapeConstraint;
/// Constraints `C1` and `R2` to be equivalent.
pub trait AreMultipliable<R1: Dim, C1: Dim,
R2: Dim, C2: Dim> {
pub trait AreMultipliable<R1: Dim, C1: Dim, R2: Dim, C2: Dim>: DimEq<C1, R2> {
}
@ -15,11 +14,30 @@ impl<R1: Dim, C1: Dim, R2: Dim, C2: Dim> AreMultipliable<R1, C1, R2, C2> for Sha
where ShapeConstraint: DimEq<C1, R2> {
}
/// Constraints `D1` and `D2` to be equivalent.
pub trait DimEq<D1: Dim, D2: Dim> {
/// This is either equal to `D1` or `D2`, always choosing the one (if any) which is a type-level
/// constant.
type Representative: Dim;
}
impl<D: Dim> DimEq<D, D> for ShapeConstraint {
type Representative = D;
}
impl<D: DimName> DimEq<D, Dynamic> for ShapeConstraint {
type Representative = D;
}
impl<D: DimName> DimEq<Dynamic, D> for ShapeConstraint {
type Representative = D;
}
macro_rules! equality_trait_decl(
($($doc: expr, $Trait: ident),* $(,)*) => {$(
// XXX: we can't do something like `DimEq<D1> for D2` because we would require a blancket impl…
#[doc = $doc]
pub trait $Trait<D1: Dim, D2: Dim> {
pub trait $Trait<D1: Dim, D2: Dim>: DimEq<D1, D2> + DimEq<D2, D1> {
/// This is either equal to `D1` or `D2`, always choosing the one (if any) which is a type-level
/// constant.
type Representative: Dim;
@ -40,9 +58,6 @@ macro_rules! equality_trait_decl(
);
equality_trait_decl!(
"Constraints `D1` and `D2` to be equivalent.",
DimEq,
"Constraints `D1` and `D2` to be equivalent. \
They are both assumed to be the number of \
rows of a matrix.",

View File

@ -1,5 +1,7 @@
#[cfg(feature = "arbitrary")]
use quickcheck::{Arbitrary, Gen};
#[cfg(feature = "arbitrary")]
use core::storage::Owned;
use std::iter;
use num::{Zero, One, Bounded};
@ -8,38 +10,44 @@ use typenum::{self, Cmp, Greater};
use alga::general::{ClosedAdd, ClosedMul};
use core::{Scalar, Matrix, SquareMatrix, ColumnVector, Unit};
use core::{DefaultAllocator, Scalar, Matrix, Vector, Unit, MatrixMN, MatrixN, VectorN};
use core::dimension::{Dim, DimName, Dynamic, U1, U2, U3, U4, U5, U6};
use core::allocator::{Allocator, OwnedAllocator};
use core::storage::{Storage, OwnedStorage};
use core::allocator::Allocator;
use core::storage::Storage;
/*
*
* Generic constructors.
*
*/
impl<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C>> Matrix<N, R, C, S>
// XXX: needed because of a compiler bug. See the rust compiler issue #26026.
where S::Alloc: OwnedAllocator<N, R, C, S> {
impl<N: Scalar, R: Dim, C: Dim> MatrixMN<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
/// Creates a new uninitialized matrix. If the matrix has a compile-time dimension, this panics
/// if `nrows != R::to_usize()` or `ncols != C::to_usize()`.
#[inline]
pub unsafe fn new_uninitialized_generic(nrows: R, ncols: C) -> Matrix<N, R, C, S> {
Matrix::from_data(S::Alloc::allocate_uninitialized(nrows, ncols))
pub unsafe fn new_uninitialized_generic(nrows: R, ncols: C) -> Self {
Self::from_data(DefaultAllocator::allocate_uninitialized(nrows, ncols))
}
/// Creates a matrix with all its elements set to `elem`.
#[inline]
pub fn from_element_generic(nrows: R, ncols: C, elem: N) -> Matrix<N, R, C, S> {
pub fn from_element_generic(nrows: R, ncols: C, elem: N) -> Self {
let len = nrows.value() * ncols.value();
Matrix::from_iterator_generic(nrows, ncols, iter::repeat(elem).take(len))
Self::from_iterator_generic(nrows, ncols, iter::repeat(elem).take(len))
}
/// Creates a matrix with all its elements set to 0.
#[inline]
pub fn zeros_generic(nrows: R, ncols: C) -> Self
where N: Zero {
Self::from_element_generic(nrows, ncols, N::zero())
}
/// Creates a matrix with all its elements filled by an iterator.
#[inline]
pub fn from_iterator_generic<I>(nrows: R, ncols: C, iter: I) -> Matrix<N, R, C, S>
pub fn from_iterator_generic<I>(nrows: R, ncols: C, iter: I) -> Self
where I: IntoIterator<Item = N> {
Matrix::from_data(S::Alloc::allocate_from_iterator(nrows, ncols, iter))
Self::from_data(DefaultAllocator::allocate_from_iterator(nrows, ncols, iter))
}
/// Creates a matrix with its elements filled with the components provided by a slice in
@ -48,7 +56,7 @@ impl<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C>> Matrix<N, R, C, S>
/// The order of elements in the slice must follow the usual mathematic writing, i.e.,
/// row-by-row.
#[inline]
pub fn from_row_slice_generic(nrows: R, ncols: C, slice: &[N]) -> Matrix<N, R, C, S> {
pub fn from_row_slice_generic(nrows: R, ncols: C, slice: &[N]) -> Self {
assert!(slice.len() == nrows.value() * ncols.value(),
"Matrix init. error: the slice did not contain the right number of elements.");
@ -69,14 +77,14 @@ impl<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C>> Matrix<N, R, C, S>
/// Creates a matrix with its elements filled with the components provided by a slice. The
/// components must have the same layout as the matrix data storage (i.e. row-major or column-major).
#[inline]
pub fn from_column_slice_generic(nrows: R, ncols: C, slice: &[N]) -> Matrix<N, R, C, S> {
Matrix::from_iterator_generic(nrows, ncols, slice.iter().cloned())
pub fn from_column_slice_generic(nrows: R, ncols: C, slice: &[N]) -> Self {
Self::from_iterator_generic(nrows, ncols, slice.iter().cloned())
}
/// Creates a matrix filled with the results of a function applied to each of its component
/// coordinates.
#[inline]
pub fn from_fn_generic<F>(nrows: R, ncols: C, mut f: F) -> Matrix<N, R, C, S>
pub fn from_fn_generic<F>(nrows: R, ncols: C, mut f: F) -> Self
where F: FnMut(usize, usize) -> N {
let mut res = unsafe { Self::new_uninitialized_generic(nrows, ncols) };
@ -94,7 +102,7 @@ impl<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C>> Matrix<N, R, C, S>
/// If the matrix is not square, the largest square submatrix starting at index `(0, 0)` is set
/// to the identity matrix. All other entries are set to zero.
#[inline]
pub fn identity_generic(nrows: R, ncols: C) -> Matrix<N, R, C, S>
pub fn identity_generic(nrows: R, ncols: C) -> Self
where N: Zero + One {
Self::from_diagonal_element_generic(nrows, ncols, N::one())
}
@ -104,10 +112,9 @@ impl<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C>> Matrix<N, R, C, S>
/// If the matrix is not square, the largest square submatrix starting at index `(0, 0)` is set
/// to the identity matrix. All other entries are set to zero.
#[inline]
pub fn from_diagonal_element_generic(nrows: R, ncols: C, elt: N) -> Matrix<N, R, C, S>
pub fn from_diagonal_element_generic(nrows: R, ncols: C, elt: N) -> Self
where N: Zero + One {
let mut res = unsafe { Self::new_uninitialized_generic(nrows, ncols) };
res.fill(N::zero());
let mut res = Self::zeros_generic(nrows, ncols);
for i in 0 .. ::min(nrows.value(), ncols.value()) {
unsafe { *res.get_unchecked_mut(i, i) = elt }
@ -116,12 +123,29 @@ impl<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C>> Matrix<N, R, C, S>
res
}
/// Creates a new matrix that may be rectangular. The first `elts.len()` diagonal elements are
/// filled with the content of `elts`. Others are set to 0.
///
/// Panics if `elts.len()` is larger than the minimum among `nrows` and `ncols`.
#[inline]
pub fn from_partial_diagonal_generic(nrows: R, ncols: C, elts: &[N]) -> Self
where N: Zero {
let mut res = Self::zeros_generic(nrows, ncols);
assert!(elts.len() <= ::min(nrows.value(), ncols.value()), "Too many diagonal elements provided.");
for (i, elt) in elts.iter().enumerate() {
unsafe { *res.get_unchecked_mut(i, i) = *elt }
}
res
}
/// Builds a new matrix from its rows.
///
/// Panics if not enough rows are provided (for statically-sized matrices), or if all rows do
/// not have the same dimensions.
#[inline]
pub fn from_rows<SB>(rows: &[Matrix<N, U1, C, SB>]) -> Matrix<N, R, C, S>
pub fn from_rows<SB>(rows: &[Matrix<N, U1, C, SB>]) -> Self
where SB: Storage<N, U1, C> {
assert!(rows.len() > 0, "At least one row must be given.");
@ -144,8 +168,8 @@ impl<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C>> Matrix<N, R, C, S>
/// Panics if not enough columns are provided (for statically-sized matrices), or if all
/// columns do not have the same dimensions.
#[inline]
pub fn from_columns<SB>(columns: &[ColumnVector<N, R, SB>]) -> Matrix<N, R, C, S>
where SB: Storage<N, R, U1> {
pub fn from_columns<SB>(columns: &[Vector<N, R, SB>]) -> Self
where SB: Storage<N, R> {
assert!(columns.len() > 0, "At least one column must be given.");
let ncols = C::try_to_usize().unwrap_or(columns.len());
@ -160,31 +184,27 @@ impl<N: Scalar, R: Dim, C: Dim, S: OwnedStorage<N, R, C>> Matrix<N, R, C, S>
// FIXME: optimize that.
Self::from_fn_generic(R::from_usize(nrows), C::from_usize(ncols), |i, j| columns[j][i])
}
}
impl<N, R: Dim, C: Dim, S> Matrix<N, R, C, S>
where N: Scalar + Rand,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
/// Creates a matrix filled with random values.
#[inline]
pub fn new_random_generic(nrows: R, ncols: C) -> Matrix<N, R, C, S> {
Matrix::from_fn_generic(nrows, ncols, |_, _| rand::random())
pub fn new_random_generic(nrows: R, ncols: C) -> Self
where N: Rand {
Self::from_fn_generic(nrows, ncols, |_, _| rand::random())
}
}
impl<N, D: Dim, S> SquareMatrix<N, D, S>
where N: Scalar + Zero,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S> {
impl<N, D: Dim> MatrixN<N, D>
where N: Scalar,
DefaultAllocator: Allocator<N, D, D> {
/// Creates a square matrix with its diagonal set to `diag` and all other entries set to 0.
#[inline]
pub fn from_diagonal<SB: Storage<N, D, U1>>(diag: &ColumnVector<N, D, SB>) -> Self {
pub fn from_diagonal<SB: Storage<N, D>>(diag: &Vector<N, D, SB>) -> Self
where N: Zero {
let (dim, _) = diag.data.shape();
let mut res = Self::from_element_generic(dim, dim, N::zero());
let mut res = Self::zeros_generic(dim, dim);
for i in 0 .. diag.len() {
unsafe { *res.get_unchecked_mut(i, i) = *diag.get_unchecked(i, 0); }
unsafe { *res.get_unchecked_mut(i, i) = *diag.vget_unchecked(i); }
}
res
@ -199,25 +219,31 @@ impl<N, D: Dim, S> SquareMatrix<N, D, S>
*/
macro_rules! impl_constructors(
($($Dims: ty),*; $(=> $DimIdent: ident: $DimBound: ident),*; $($gargs: expr),*; $($args: ident),*) => {
impl<N: Scalar, $($DimIdent: $DimBound, )* S> Matrix<N $(, $Dims)*, S>
where S: OwnedStorage<N $(, $Dims)*>,
S::Alloc: OwnedAllocator<N $(, $Dims)*, S> {
impl<N: Scalar, $($DimIdent: $DimBound, )*> MatrixMN<N $(, $Dims)*>
where DefaultAllocator: Allocator<N $(, $Dims)*> {
/// Creates a new uninitialized matrix.
#[inline]
pub unsafe fn new_uninitialized($($args: usize),*) -> Matrix<N $(, $Dims)*, S> {
pub unsafe fn new_uninitialized($($args: usize),*) -> Self {
Self::new_uninitialized_generic($($gargs),*)
}
/// Creates a matrix with all its elements set to `elem`.
#[inline]
pub fn from_element($($args: usize,)* elem: N) -> Matrix<N $(, $Dims)*, S> {
pub fn from_element($($args: usize,)* elem: N) -> Self {
Self::from_element_generic($($gargs, )* elem)
}
/// Creates a matrix with all its elements set to `0`.
#[inline]
pub fn zeros($($args: usize),*) -> Self
where N: Zero {
Self::zeros_generic($($gargs),*)
}
/// Creates a matrix with all its elements filled by an iterator.
#[inline]
pub fn from_iterator<I>($($args: usize,)* iter: I) -> Matrix<N $(, $Dims)*, S>
pub fn from_iterator<I>($($args: usize,)* iter: I) -> Self
where I: IntoIterator<Item = N> {
Self::from_iterator_generic($($gargs, )* iter)
}
@ -228,14 +254,14 @@ macro_rules! impl_constructors(
/// The order of elements in the slice must follow the usual mathematic writing, i.e.,
/// row-by-row.
#[inline]
pub fn from_row_slice($($args: usize,)* slice: &[N]) -> Matrix<N $(, $Dims)*, S> {
pub fn from_row_slice($($args: usize,)* slice: &[N]) -> Self {
Self::from_row_slice_generic($($gargs, )* slice)
}
/// Creates a matrix with its elements filled with the components provided by a slice
/// in column-major order.
#[inline]
pub fn from_column_slice($($args: usize,)* slice: &[N]) -> Matrix<N $(, $Dims)*, S> {
pub fn from_column_slice($($args: usize,)* slice: &[N]) -> Self {
Self::from_column_slice_generic($($gargs, )* slice)
}
@ -243,7 +269,7 @@ macro_rules! impl_constructors(
/// component coordinates.
// FIXME: don't take a dimension of the matrix is statically sized.
#[inline]
pub fn from_fn<F>($($args: usize,)* f: F) -> Matrix<N $(, $Dims)*, S>
pub fn from_fn<F>($($args: usize,)* f: F) -> Self
where F: FnMut(usize, usize) -> N {
Self::from_fn_generic($($gargs, )* f)
}
@ -252,7 +278,7 @@ macro_rules! impl_constructors(
/// submatrix (starting at the first row and column) is set to the identity while all
/// other entries are set to zero.
#[inline]
pub fn identity($($args: usize,)*) -> Matrix<N $(, $Dims)*, S>
pub fn identity($($args: usize,)*) -> Self
where N: Zero + One {
Self::identity_generic($($gargs),* )
}
@ -260,19 +286,28 @@ macro_rules! impl_constructors(
/// Creates a matrix filled with its diagonal filled with `elt` and all other
/// components set to zero.
#[inline]
pub fn from_diagonal_element($($args: usize,)* elt: N) -> Matrix<N $(, $Dims)*, S>
pub fn from_diagonal_element($($args: usize,)* elt: N) -> Self
where N: Zero + One {
Self::from_diagonal_element_generic($($gargs, )* elt)
}
/// Creates a new matrix that may be rectangular. The first `elts.len()` diagonal
/// elements are filled with the content of `elts`. Others are set to 0.
///
/// Panics if `elts.len()` is larger than the minimum among `nrows` and `ncols`.
#[inline]
pub fn from_partial_diagonal($($args: usize,)* elts: &[N]) -> Self
where N: Zero {
Self::from_partial_diagonal_generic($($gargs, )* elts)
}
}
impl<N: Scalar + Rand, $($DimIdent: $DimBound, )* S> Matrix<N $(, $Dims)*, S>
where S: OwnedStorage<N $(, $Dims)*>,
S::Alloc: OwnedAllocator<N $(, $Dims)*, S> {
impl<N: Scalar + Rand, $($DimIdent: $DimBound, )*> MatrixMN<N $(, $Dims)*>
where DefaultAllocator: Allocator<N $(, $Dims)*> {
/// Creates a matrix filled with random values.
#[inline]
pub fn new_random($($args: usize),*) -> Matrix<N $(, $Dims)*, S> {
pub fn new_random($($args: usize),*) -> Self {
Self::new_random_generic($($gargs),*)
}
}
@ -305,10 +340,9 @@ impl_constructors!(Dynamic, Dynamic;
* Zero, One, Rand traits.
*
*/
impl<N, R: DimName, C: DimName, S> Zero for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> Zero for MatrixMN<N, R, C>
where N: Scalar + Zero + ClosedAdd,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn zero() -> Self {
Self::from_element(N::zero())
@ -320,20 +354,18 @@ impl<N, R: DimName, C: DimName, S> Zero for Matrix<N, R, C, S>
}
}
impl<N, D: DimName, S> One for Matrix<N, D, D, S>
impl<N, D: DimName> One for MatrixN<N, D>
where N: Scalar + Zero + One + ClosedMul + ClosedAdd,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S> {
DefaultAllocator: Allocator<N, D, D> {
#[inline]
fn one() -> Self {
Self::identity()
}
}
impl<N, R: DimName, C: DimName, S> Bounded for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> Bounded for MatrixMN<N, R, C>
where N: Scalar + Bounded,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn max_value() -> Self {
Self::from_element(N::max_value())
@ -345,9 +377,8 @@ impl<N, R: DimName, C: DimName, S> Bounded for Matrix<N, R, C, S>
}
}
impl<N: Scalar + Rand, R: Dim, C: Dim, S> Rand for Matrix<N, R, C, S>
where S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
impl<N: Scalar + Rand, R: Dim, C: Dim> Rand for MatrixMN<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn rand<G: Rng>(rng: &mut G) -> Self {
let nrows = R::try_to_usize().unwrap_or(rng.gen_range(0, 10));
@ -359,11 +390,11 @@ impl<N: Scalar + Rand, R: Dim, C: Dim, S> Rand for Matrix<N, R, C, S>
#[cfg(feature = "arbitrary")]
impl<N, R, C, S> Arbitrary for Matrix<N, R, C, S>
impl<N, R, C> Arbitrary for MatrixMN<N, R, C>
where R: Dim, C: Dim,
N: Scalar + Arbitrary + Send,
S: OwnedStorage<N, R, C> + Send,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C>,
Owned<N, R, C>: Clone + Send {
#[inline]
fn arbitrary<G: Gen>(g: &mut G) -> Self {
let nrows = R::try_to_usize().unwrap_or(g.gen_range(0, 10));
@ -381,13 +412,12 @@ impl<N, R, C, S> Arbitrary for Matrix<N, R, C, S>
*/
macro_rules! componentwise_constructors_impl(
($($R: ty, $C: ty, $($args: ident:($irow: expr,$icol: expr)),*);* $(;)*) => {$(
impl<N, S> Matrix<N, $R, $C, S>
impl<N> MatrixMN<N, $R, $C>
where N: Scalar,
S: OwnedStorage<N, $R, $C>,
S::Alloc: OwnedAllocator<N, $R, $C, S> {
DefaultAllocator: Allocator<N, $R, $C> {
/// Initializes this matrix from its components.
#[inline]
pub fn new($($args: N),*) -> Matrix<N, $R, $C, S> {
pub fn new($($args: N),*) -> Self {
unsafe {
let mut res = Self::new_uninitialized();
$( *res.get_unchecked_mut($irow, $icol) = $args; )*
@ -549,16 +579,15 @@ componentwise_constructors_impl!(
* Axis constructors.
*
*/
impl<N, R: DimName, S> ColumnVector<N, R, S>
impl<N, R: DimName> VectorN<N, R>
where N: Scalar + Zero + One,
S: OwnedStorage<N, R, U1>,
S::Alloc: OwnedAllocator<N, R, U1, S> {
DefaultAllocator: Allocator<N, R> {
/// The column vector with a 1 as its first component, and zero elsewhere.
#[inline]
pub fn x() -> Self
where R::Value: Cmp<typenum::U0, Output = Greater> {
let mut res = Self::from_element(N::zero());
unsafe { *res.get_unchecked_mut(0, 0) = N::one(); }
let mut res = Self::zeros();
unsafe { *res.vget_unchecked_mut(0) = N::one(); }
res
}
@ -567,8 +596,8 @@ where N: Scalar + Zero + One,
#[inline]
pub fn y() -> Self
where R::Value: Cmp<typenum::U1, Output = Greater> {
let mut res = Self::from_element(N::zero());
unsafe { *res.get_unchecked_mut(1, 0) = N::one(); }
let mut res = Self::zeros();
unsafe { *res.vget_unchecked_mut(1) = N::one(); }
res
}
@ -577,8 +606,8 @@ where N: Scalar + Zero + One,
#[inline]
pub fn z() -> Self
where R::Value: Cmp<typenum::U2, Output = Greater> {
let mut res = Self::from_element(N::zero());
unsafe { *res.get_unchecked_mut(2, 0) = N::one(); }
let mut res = Self::zeros();
unsafe { *res.vget_unchecked_mut(2) = N::one(); }
res
}
@ -587,8 +616,8 @@ where N: Scalar + Zero + One,
#[inline]
pub fn w() -> Self
where R::Value: Cmp<typenum::U3, Output = Greater> {
let mut res = Self::from_element(N::zero());
unsafe { *res.get_unchecked_mut(3, 0) = N::one(); }
let mut res = Self::zeros();
unsafe { *res.vget_unchecked_mut(3) = N::one(); }
res
}
@ -597,8 +626,8 @@ where N: Scalar + Zero + One,
#[inline]
pub fn a() -> Self
where R::Value: Cmp<typenum::U4, Output = Greater> {
let mut res = Self::from_element(N::zero());
unsafe { *res.get_unchecked_mut(4, 0) = N::one(); }
let mut res = Self::zeros();
unsafe { *res.vget_unchecked_mut(4) = N::one(); }
res
}
@ -607,8 +636,8 @@ where N: Scalar + Zero + One,
#[inline]
pub fn b() -> Self
where R::Value: Cmp<typenum::U5, Output = Greater> {
let mut res = Self::from_element(N::zero());
unsafe { *res.get_unchecked_mut(5, 0) = N::one(); }
let mut res = Self::zeros();
unsafe { *res.vget_unchecked_mut(5) = N::one(); }
res
}

View File

@ -3,37 +3,35 @@ use std::mem;
use std::convert::{From, Into, AsRef, AsMut};
use alga::general::{SubsetOf, SupersetOf};
use core::{Scalar, Matrix};
use core::{DefaultAllocator, Scalar, Matrix, MatrixMN};
use core::dimension::{Dim,
U1, U2, U3, U4,
U5, U6, U7, U8,
U9, U10, U11, U12,
U13, U14, U15, U16
};
use core::constraint::{ShapeConstraint, SameNumberOfRows, SameNumberOfColumns};
use core::storage::{Storage, StorageMut, OwnedStorage};
use core::iter::{MatrixIter, MatrixIterMut};
use core::allocator::{OwnedAllocator, SameShapeAllocator};
use core::constraint::{ShapeConstraint, SameNumberOfRows, SameNumberOfColumns};
use core::storage::{ContiguousStorage, ContiguousStorageMut, Storage, StorageMut};
use core::allocator::{Allocator, SameShapeAllocator};
// FIXME: too bad this won't work allo slice conversions.
impl<N1, N2, R1, C1, R2, C2, SA, SB> SubsetOf<Matrix<N2, R2, C2, SB>> for Matrix<N1, R1, C1, SA>
impl<N1, N2, R1, C1, R2, C2> SubsetOf<MatrixMN<N2, R2, C2>> for MatrixMN<N1, R1, C1>
where R1: Dim, C1: Dim, R2: Dim, C2: Dim,
N1: Scalar,
N2: Scalar + SupersetOf<N1>,
SA: OwnedStorage<N1, R1, C1>,
SB: OwnedStorage<N2, R2, C2>,
SB::Alloc: OwnedAllocator<N2, R2, C2, SB>,
SA::Alloc: OwnedAllocator<N1, R1, C1, SA> +
SameShapeAllocator<N1, R1, C1, R2, C2, SA>,
DefaultAllocator: Allocator<N2, R2, C2> +
Allocator<N1, R1, C1> +
SameShapeAllocator<N1, R1, C1, R2, C2>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
#[inline]
fn to_superset(&self) -> Matrix<N2, R2, C2, SB> {
fn to_superset(&self) -> MatrixMN<N2, R2, C2> {
let (nrows, ncols) = self.shape();
let nrows2 = R2::from_usize(nrows);
let ncols2 = C2::from_usize(ncols);
let mut res = unsafe { Matrix::<N2, R2, C2, SB>::new_uninitialized_generic(nrows2, ncols2) };
let mut res = unsafe { MatrixMN::<N2, R2, C2>::new_uninitialized_generic(nrows2, ncols2) };
for i in 0 .. nrows {
for j in 0 .. ncols {
unsafe {
@ -46,12 +44,12 @@ impl<N1, N2, R1, C1, R2, C2, SA, SB> SubsetOf<Matrix<N2, R2, C2, SB>> for Matrix
}
#[inline]
fn is_in_subset(m: &Matrix<N2, R2, C2, SB>) -> bool {
fn is_in_subset(m: &MatrixMN<N2, R2, C2>) -> bool {
m.iter().all(|e| e.is_in_subset())
}
#[inline]
unsafe fn from_superset_unchecked(m: &Matrix<N2, R2, C2, SB>) -> Self {
unsafe fn from_superset_unchecked(m: &MatrixMN<N2, R2, C2>) -> Self {
let (nrows2, ncols2) = m.shape();
let nrows = R1::from_usize(nrows2);
let ncols = C1::from_usize(ncols2);
@ -90,10 +88,9 @@ impl<'a, N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> IntoIterator for &'a
macro_rules! impl_from_into_asref_1D(
($(($NRows: ident, $NCols: ident) => $SZ: expr);* $(;)*) => {$(
impl<N, S> From<[N; $SZ]> for Matrix<N, $NRows, $NCols, S>
impl<N> From<[N; $SZ]> for MatrixMN<N, $NRows, $NCols>
where N: Scalar,
S: OwnedStorage<N, $NRows, $NCols>,
S::Alloc: OwnedAllocator<N, $NRows, $NCols, S> {
DefaultAllocator: Allocator<N, $NRows, $NCols> {
#[inline]
fn from(arr: [N; $SZ]) -> Self {
unsafe {
@ -107,8 +104,7 @@ macro_rules! impl_from_into_asref_1D(
impl<N, S> Into<[N; $SZ]> for Matrix<N, $NRows, $NCols, S>
where N: Scalar,
S: OwnedStorage<N, $NRows, $NCols>,
S::Alloc: OwnedAllocator<N, $NRows, $NCols, S> {
S: ContiguousStorage<N, $NRows, $NCols> {
#[inline]
fn into(self) -> [N; $SZ] {
unsafe {
@ -122,8 +118,7 @@ macro_rules! impl_from_into_asref_1D(
impl<N, S> AsRef<[N; $SZ]> for Matrix<N, $NRows, $NCols, S>
where N: Scalar,
S: OwnedStorage<N, $NRows, $NCols>,
S::Alloc: OwnedAllocator<N, $NRows, $NCols, S> {
S: ContiguousStorage<N, $NRows, $NCols> {
#[inline]
fn as_ref(&self) -> &[N; $SZ] {
unsafe {
@ -134,8 +129,7 @@ macro_rules! impl_from_into_asref_1D(
impl<N, S> AsMut<[N; $SZ]> for Matrix<N, $NRows, $NCols, S>
where N: Scalar,
S: OwnedStorage<N, $NRows, $NCols>,
S::Alloc: OwnedAllocator<N, $NRows, $NCols, S> {
S: ContiguousStorageMut<N, $NRows, $NCols> {
#[inline]
fn as_mut(&mut self) -> &mut [N; $SZ] {
unsafe {
@ -165,10 +159,8 @@ impl_from_into_asref_1D!(
macro_rules! impl_from_into_asref_2D(
($(($NRows: ty, $NCols: ty) => ($SZRows: expr, $SZCols: expr));* $(;)*) => {$(
impl<N, S> From<[[N; $SZRows]; $SZCols]> for Matrix<N, $NRows, $NCols, S>
where N: Scalar,
S: OwnedStorage<N, $NRows, $NCols>,
S::Alloc: OwnedAllocator<N, $NRows, $NCols, S> {
impl<N: Scalar> From<[[N; $SZRows]; $SZCols]> for MatrixMN<N, $NRows, $NCols>
where DefaultAllocator: Allocator<N, $NRows, $NCols> {
#[inline]
fn from(arr: [[N; $SZRows]; $SZCols]) -> Self {
unsafe {
@ -180,10 +172,8 @@ macro_rules! impl_from_into_asref_2D(
}
}
impl<N, S> Into<[[N; $SZRows]; $SZCols]> for Matrix<N, $NRows, $NCols, S>
where N: Scalar,
S: OwnedStorage<N, $NRows, $NCols>,
S::Alloc: OwnedAllocator<N, $NRows, $NCols, S> {
impl<N: Scalar, S> Into<[[N; $SZRows]; $SZCols]> for Matrix<N, $NRows, $NCols, S>
where S: ContiguousStorage<N, $NRows, $NCols> {
#[inline]
fn into(self) -> [[N; $SZRows]; $SZCols] {
unsafe {
@ -195,10 +185,8 @@ macro_rules! impl_from_into_asref_2D(
}
}
impl<N, S> AsRef<[[N; $SZRows]; $SZCols]> for Matrix<N, $NRows, $NCols, S>
where N: Scalar,
S: OwnedStorage<N, $NRows, $NCols>,
S::Alloc: OwnedAllocator<N, $NRows, $NCols, S> {
impl<N: Scalar, S> AsRef<[[N; $SZRows]; $SZCols]> for Matrix<N, $NRows, $NCols, S>
where S: ContiguousStorage<N, $NRows, $NCols> {
#[inline]
fn as_ref(&self) -> &[[N; $SZRows]; $SZCols] {
unsafe {
@ -207,10 +195,8 @@ macro_rules! impl_from_into_asref_2D(
}
}
impl<N, S> AsMut<[[N; $SZRows]; $SZCols]> for Matrix<N, $NRows, $NCols, S>
where N: Scalar,
S: OwnedStorage<N, $NRows, $NCols>,
S::Alloc: OwnedAllocator<N, $NRows, $NCols, S> {
impl<N: Scalar, S> AsMut<[[N; $SZRows]; $SZCols]> for Matrix<N, $NRows, $NCols, S>
where S: ContiguousStorageMut<N, $NRows, $NCols> {
#[inline]
fn as_mut(&mut self) -> &mut [[N; $SZRows]; $SZCols] {
unsafe {
@ -222,7 +208,7 @@ macro_rules! impl_from_into_asref_2D(
);
// Implement for matrices with shape 2x2 .. 4x4.
// Implement for matrices with shape 2x2 .. 6x6.
impl_from_into_asref_2D!(
(U2, U2) => (2, 2); (U2, U3) => (2, 3); (U2, U4) => (2, 4); (U2, U5) => (2, 5); (U2, U6) => (2, 6);
(U3, U2) => (3, 2); (U3, U3) => (3, 3); (U3, U4) => (3, 4); (U3, U5) => (3, 5); (U3, U6) => (3, 6);

View File

@ -9,8 +9,7 @@ use std::ops::{Deref, DerefMut};
use core::{Scalar, Matrix};
use core::dimension::{U1, U2, U3, U4, U5, U6};
use core::storage::OwnedStorage;
use core::allocator::OwnedAllocator;
use core::storage::{ContiguousStorage, ContiguousStorageMut};
/*
*
@ -35,22 +34,20 @@ macro_rules! coords_impl(
macro_rules! deref_impl(
($R: ty, $C: ty; $Target: ident) => {
impl<N: Scalar, S> Deref for Matrix<N, $R, $C, S>
where S: OwnedStorage<N, $R, $C>,
S::Alloc: OwnedAllocator<N, $R, $C, S> {
where S: ContiguousStorage<N, $R, $C> {
type Target = $Target<N>;
#[inline]
fn deref(&self) -> &Self::Target {
unsafe { mem::transmute(self) }
unsafe { mem::transmute(self.data.ptr()) }
}
}
impl<N: Scalar, S> DerefMut for Matrix<N, $R, $C, S>
where S: OwnedStorage<N, $R, $C>,
S::Alloc: OwnedAllocator<N, $R, $C, S> {
where S: ContiguousStorageMut<N, $R, $C> {
#[inline]
fn deref_mut(&mut self) -> &mut Self::Target {
unsafe { mem::transmute(self) }
unsafe { mem::transmute(self.data.ptr_mut()) }
}
}
}

View File

@ -1,373 +0,0 @@
use std::cmp;
use alga::general::Real;
use core::{SquareMatrix, OwnedSquareMatrix, ColumnVector, OwnedColumnVector};
use dimension::{Dim, Dynamic, U1};
use storage::{Storage, OwnedStorage};
use allocator::{Allocator, OwnedAllocator};
impl<N, D: Dim, S> SquareMatrix<N, D, S>
where N: Real,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S> {
/// Get the householder matrix corresponding to a reflexion to the hyperplane
/// defined by `vector`. It can be a reflexion contained in a subspace.
///
/// # Arguments
/// * `dimension` - the dimension of the space the resulting matrix operates in
/// * `start` - the starting dimension of the subspace of the reflexion
/// * `vector` - the vector defining the reflection.
pub fn new_householder_generic<SB, D2>(dimension: D, start: usize, vector: &ColumnVector<N, D2, SB>)
-> OwnedSquareMatrix<N, D, S::Alloc>
where D2: Dim,
SB: Storage<N, D2, U1> {
let mut qk = Self::identity_generic(dimension, dimension);
let subdim = vector.shape().0;
let stop = subdim + start;
assert!(dimension.value() >= stop, "Householder matrix creation: subspace dimension index out of bounds.");
for j in start .. stop {
for i in start .. stop {
unsafe {
let vv = *vector.get_unchecked(i - start, 0) * *vector.get_unchecked(j - start, 0);
let qkij = *qk.get_unchecked(i, j);
*qk.get_unchecked_mut(i, j) = qkij - vv - vv;
}
}
}
qk
}
}
impl<N: Real, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S> {
/// QR decomposition using Householder reflections.
pub fn qr(self) -> (OwnedSquareMatrix<N, D, S::Alloc>, OwnedSquareMatrix<N, D, S::Alloc>)
where S::Alloc: Allocator<N, Dynamic, U1> +
Allocator<N, D, U1> {
let (nrows, ncols) = self.data.shape();
// XXX: too restrictive.
assert!(nrows.value() >= ncols.value(), "");
let mut q = OwnedSquareMatrix::<N, D, S::Alloc>::identity_generic(nrows, ncols);
let mut r = self.into_owned();
// Temporary buffer that contains a column.
let mut col = unsafe {
OwnedColumnVector::<N, D, S::Alloc>::new_uninitialized_generic(nrows, U1)
};
for ite in 0 .. cmp::min(nrows.value() - 1, ncols.value()) {
let subdim = Dynamic::new(nrows.value() - ite);
let mut v = col.rows_mut(0, subdim.value());
v.copy_from(&r.generic_slice((ite, ite), (subdim, U1)));
let alpha =
if unsafe { *v.get_unchecked(ite, 0) } >= ::zero() {
-v.norm()
}
else {
v.norm()
};
unsafe {
let x = *v.get_unchecked(0, 0);
*v.get_unchecked_mut(0, 0) = x - alpha;
}
if !v.normalize_mut().is_zero() {
let mut qk = OwnedSquareMatrix::<N, D, S::Alloc>::new_householder_generic(nrows, ite, &v);
r = &qk * r;
// FIXME: add a method `q.mul_tr(qk) := q * qk.transpose` ?
qk.transpose_mut();
q = q * qk;
}
}
(q, r)
}
/// Eigendecomposition of a square symmetric matrix.
pub fn eig(&self, eps: N, niter: usize)
-> (OwnedSquareMatrix<N, D, S::Alloc>, OwnedColumnVector<N, D, S::Alloc>)
where S::Alloc: Allocator<N, D, U1> +
Allocator<N, Dynamic, U1> {
assert!(self.is_square(),
"Unable to compute the eigenvectors and eigenvalues of a non-square matrix.");
let dim = self.data.shape().0;
let (mut eigenvectors, mut eigenvalues) = self.hessenberg();
if dim.value() == 1 {
return (eigenvectors, eigenvalues.diagonal());
}
// Allocate arrays for Givens rotation components
let mut c = unsafe { OwnedColumnVector::<N, D, S::Alloc>::new_uninitialized_generic(dim, U1) };
let mut s = unsafe { OwnedColumnVector::<N, D, S::Alloc>::new_uninitialized_generic(dim, U1) };
let mut iter = 0;
let mut curdim = dim.value() - 1;
for _ in 0 .. dim.value() {
let mut stop = false;
while !stop && iter < niter {
let lambda;
unsafe {
let a = *eigenvalues.get_unchecked(curdim - 1, curdim - 1);
let b = *eigenvalues.get_unchecked(curdim - 1, curdim);
let c = *eigenvalues.get_unchecked(curdim, curdim - 1);
let d = *eigenvalues.get_unchecked(curdim, curdim);
let trace = a + d;
let determinant = a * d - b * c;
let constquarter: N = ::convert(0.25f64);
let consthalf: N = ::convert(0.5f64);
let e = (constquarter * trace * trace - determinant).sqrt();
let lambda1 = consthalf * trace + e;
let lambda2 = consthalf * trace - e;
if (lambda1 - d).abs() < (lambda2 - d).abs() {
lambda = lambda1;
}
else {
lambda = lambda2;
}
}
// Shift matrix
for k in 0 .. curdim + 1 {
unsafe {
let a = *eigenvalues.get_unchecked(k, k);
*eigenvalues.get_unchecked_mut(k, k) = a - lambda;
}
}
// Givens rotation from left
for k in 0 .. curdim {
let x_i = unsafe { *eigenvalues.get_unchecked(k, k) };
let x_j = unsafe { *eigenvalues.get_unchecked(k + 1, k) };
let ctmp;
let stmp;
if x_j.abs() < eps {
ctmp = N::one();
stmp = N::zero();
}
else if x_i.abs() < eps {
ctmp = N::zero();
stmp = -N::one();
}
else {
let r = x_i.hypot(x_j);
ctmp = x_i / r;
stmp = -x_j / r;
}
c[k] = ctmp;
s[k] = stmp;
for j in k .. (curdim + 1) {
unsafe {
let a = *eigenvalues.get_unchecked(k, j);
let b = *eigenvalues.get_unchecked(k + 1, j);
*eigenvalues.get_unchecked_mut(k, j) = ctmp * a - stmp * b;
*eigenvalues.get_unchecked_mut(k + 1, j) = stmp * a + ctmp * b;
}
}
}
// Givens rotation from right applied to eigenvalues
for k in 0 .. curdim {
for i in 0 .. (k + 2) {
unsafe {
let a = *eigenvalues.get_unchecked(i, k);
let b = *eigenvalues.get_unchecked(i, k + 1);
*eigenvalues.get_unchecked_mut(i, k) = c[k] * a - s[k] * b;
*eigenvalues.get_unchecked_mut(i, k + 1) = s[k] * a + c[k] * b;
}
}
}
// Shift back
for k in 0 .. curdim + 1 {
unsafe {
let a = *eigenvalues.get_unchecked(k, k);
*eigenvalues.get_unchecked_mut(k, k) = a + lambda;
}
}
// Givens rotation from right applied to eigenvectors
for k in 0 .. curdim {
for i in 0 .. dim.value() {
unsafe {
let a = *eigenvectors.get_unchecked(i, k);
let b = *eigenvectors.get_unchecked(i, k + 1);
*eigenvectors.get_unchecked_mut(i, k) = c[k] * a - s[k] * b;
*eigenvectors.get_unchecked_mut(i, k + 1) = s[k] * a + c[k] * b;
}
}
}
iter = iter + 1;
stop = true;
for j in 0 .. curdim {
// Check last row.
if unsafe { *eigenvalues.get_unchecked(curdim, j) }.abs() >= eps {
stop = false;
break;
}
// Check last column.
if unsafe { *eigenvalues.get_unchecked(j, curdim) }.abs() >= eps {
stop = false;
break;
}
}
}
if stop {
if curdim > 1 {
curdim = curdim - 1;
}
else {
break;
}
}
}
(eigenvectors, eigenvalues.diagonal())
}
/// Cholesky decomposition G of a square symmetric positive definite matrix A, such that A = G * G^T
///
/// Matrix symmetricness is not checked. Returns `None` if `self` is not definite positive.
#[inline]
pub fn cholesky(&self) -> Result<OwnedSquareMatrix<N, D, S::Alloc>, &'static str> {
let out = self.transpose();
if !out.relative_eq(self, N::default_epsilon(), N::default_max_relative()) {
return Err("Cholesky: Input matrix is not symmetric");
}
self.do_cholesky(out)
}
/// Cholesky decomposition G of a square symmetric positive definite matrix A, such that A = G * G^T
#[inline]
pub fn cholesky_unchecked(&self) -> Result<OwnedSquareMatrix<N, D, S::Alloc>, &'static str> {
let out = self.transpose();
self.do_cholesky(out)
}
#[inline(always)]
fn do_cholesky(&self, mut out: OwnedSquareMatrix<N, D, S::Alloc>)
-> Result<OwnedSquareMatrix<N, D, S::Alloc>, &'static str> {
assert!(self.is_square(), "The input matrix must be square.");
for i in 0 .. out.nrows() {
for j in 0 .. (i + 1) {
let mut sum = out[(i, j)];
for k in 0 .. j {
sum = sum - out[(i, k)] * out[(j, k)];
}
if i > j {
out[(i, j)] = sum / out[(j, j)];
}
else if sum > N::zero() {
out[(i, i)] = sum.sqrt();
}
else {
return Err("Cholesky: Input matrix is not positive definite to machine precision.");
}
}
}
for i in 0 .. out.nrows() {
for j in i + 1 .. out.ncols() {
out[(i, j)] = N::zero();
}
}
Ok(out)
}
/// Hessenberg
/// Returns the matrix `self` in Hessenberg form and the corresponding similarity transformation
///
/// # Returns
/// The tuple (`q`, `h`) that `q * h * q^T = self`
pub fn hessenberg(&self) -> (OwnedSquareMatrix<N, D, S::Alloc>, OwnedSquareMatrix<N, D, S::Alloc>)
where S::Alloc: Allocator<N, D, U1> + Allocator<N, Dynamic, U1> {
let (nrows, ncols) = self.data.shape();
let mut h = self.clone_owned();
let mut q = OwnedSquareMatrix::<N, D, S::Alloc>::identity_generic(nrows, ncols);
if ncols.value() <= 2 {
return (q, h);
}
// Temporary buffer that contains a column.
let mut col = unsafe {
OwnedColumnVector::<N, D, S::Alloc>::new_uninitialized_generic(nrows, U1)
};
for ite in 0 .. (ncols.value() - 2) {
let subdim = Dynamic::new(nrows.value() - (ite + 1));
let mut v = col.rows_mut(0, subdim.value());
v.copy_from(&h.generic_slice((ite + 1, ite), (subdim, U1)));
let alpha = v.norm();
unsafe {
let x = *v.get_unchecked(0, 0);
*v.get_unchecked_mut(0, 0) = x - alpha;
}
if !v.normalize_mut().is_zero() {
// XXX: we output the householder matrix to a pre-allocated matrix instead of
// return a value to `p`. This would avoid allocation at each iteration.
let p = OwnedSquareMatrix::<N, D, S::Alloc>::new_householder_generic(nrows, ite + 1, &v);
q = q * &p;
h = &p * h * p;
}
}
(q, h)
}
}

View File

@ -4,6 +4,8 @@
//! heap-allocated buffers for matrices with at least one dimension unknown at compile-time.
use std::mem;
use std::ptr;
use std::cmp;
use std::ops::Mul;
use typenum::Prod;
@ -11,7 +13,8 @@ use generic_array::ArrayLength;
use core::Scalar;
use core::dimension::{Dim, DimName, Dynamic};
use core::allocator::Allocator;
use core::allocator::{Allocator, Reallocator};
use core::storage::{Storage, StorageMut};
use core::matrix_array::MatrixArray;
use core::matrix_vec::MatrixVec;
@ -107,3 +110,110 @@ impl<N: Scalar, R: DimName> Allocator<N, R, Dynamic> for DefaultAllocator {
MatrixVec::new(nrows, ncols, res)
}
}
/*
*
* Reallocator.
*
*/
// Anything -> Static × Static
impl<N: Scalar, RFrom, CFrom, RTo, CTo> Reallocator<N, RFrom, CFrom, RTo, CTo> for DefaultAllocator
where RFrom: Dim,
CFrom: Dim,
RTo: DimName,
CTo: DimName,
Self: Allocator<N, RFrom, CFrom>,
RTo::Value: Mul<CTo::Value>,
Prod<RTo::Value, CTo::Value>: ArrayLength<N> {
#[inline]
unsafe fn reallocate_copy(rto: RTo, cto: CTo, buf: <Self as Allocator<N, RFrom, CFrom>>::Buffer) -> MatrixArray<N, RTo, CTo> {
let mut res = <Self as Allocator<N, RTo, CTo>>::allocate_uninitialized(rto, cto);
let (rfrom, cfrom) = buf.shape();
let len_from = rfrom.value() * cfrom.value();
let len_to = rto.value() * cto.value();
ptr::copy_nonoverlapping(buf.ptr(), res.ptr_mut(), cmp::min(len_from, len_to));
res
}
}
// Static × Static -> Dynamic × Any
impl<N: Scalar, RFrom, CFrom, CTo> Reallocator<N, RFrom, CFrom, Dynamic, CTo> for DefaultAllocator
where RFrom: DimName,
CFrom: DimName,
CTo: Dim,
RFrom::Value: Mul<CFrom::Value>,
Prod<RFrom::Value, CFrom::Value>: ArrayLength<N> {
#[inline]
unsafe fn reallocate_copy(rto: Dynamic, cto: CTo, buf: MatrixArray<N, RFrom, CFrom>) -> MatrixVec<N, Dynamic, CTo> {
let mut res = <Self as Allocator<N, Dynamic, CTo>>::allocate_uninitialized(rto, cto);
let (rfrom, cfrom) = buf.shape();
let len_from = rfrom.value() * cfrom.value();
let len_to = rto.value() * cto.value();
ptr::copy_nonoverlapping(buf.ptr(), res.ptr_mut(), cmp::min(len_from, len_to));
res
}
}
// Static × Static -> Static × Dynamic
impl<N: Scalar, RFrom, CFrom, RTo> Reallocator<N, RFrom, CFrom, RTo, Dynamic> for DefaultAllocator
where RFrom: DimName,
CFrom: DimName,
RTo: DimName,
RFrom::Value: Mul<CFrom::Value>,
Prod<RFrom::Value, CFrom::Value>: ArrayLength<N> {
#[inline]
unsafe fn reallocate_copy(rto: RTo, cto: Dynamic, buf: MatrixArray<N, RFrom, CFrom>) -> MatrixVec<N, RTo, Dynamic> {
let mut res = <Self as Allocator<N, RTo, Dynamic>>::allocate_uninitialized(rto, cto);
let (rfrom, cfrom) = buf.shape();
let len_from = rfrom.value() * cfrom.value();
let len_to = rto.value() * cto.value();
ptr::copy_nonoverlapping(buf.ptr(), res.ptr_mut(), cmp::min(len_from, len_to));
res
}
}
// All conversion from a dynamic buffer to a dynamic buffer.
impl<N: Scalar, CFrom: Dim, CTo: Dim> Reallocator<N, Dynamic, CFrom, Dynamic, CTo> for DefaultAllocator {
#[inline]
unsafe fn reallocate_copy(rto: Dynamic, cto: CTo, buf: MatrixVec<N, Dynamic, CFrom>) -> MatrixVec<N, Dynamic, CTo> {
let new_buf = buf.resize(rto.value() * cto.value());
MatrixVec::new(rto, cto, new_buf)
}
}
impl<N: Scalar, CFrom: Dim, RTo: DimName> Reallocator<N, Dynamic, CFrom, RTo, Dynamic> for DefaultAllocator {
#[inline]
unsafe fn reallocate_copy(rto: RTo, cto: Dynamic, buf: MatrixVec<N, Dynamic, CFrom>) -> MatrixVec<N, RTo, Dynamic> {
let new_buf = buf.resize(rto.value() * cto.value());
MatrixVec::new(rto, cto, new_buf)
}
}
impl<N: Scalar, RFrom: DimName, CTo: Dim> Reallocator<N, RFrom, Dynamic, Dynamic, CTo> for DefaultAllocator {
#[inline]
unsafe fn reallocate_copy(rto: Dynamic, cto: CTo, buf: MatrixVec<N, RFrom, Dynamic>) -> MatrixVec<N, Dynamic, CTo> {
let new_buf = buf.resize(rto.value() * cto.value());
MatrixVec::new(rto, cto, new_buf)
}
}
impl<N: Scalar, RFrom: DimName, RTo: DimName> Reallocator<N, RFrom, Dynamic, RTo, Dynamic> for DefaultAllocator {
#[inline]
unsafe fn reallocate_copy(rto: RTo, cto: Dynamic, buf: MatrixVec<N, RFrom, Dynamic>) -> MatrixVec<N, RTo, Dynamic> {
let new_buf = buf.resize(rto.value() * cto.value());
MatrixVec::new(rto, cto, new_buf)
}
}

View File

@ -3,9 +3,11 @@
//! Traits and tags for identifying the dimension of all algebraic entities.
use std::fmt::Debug;
use std::any::Any;
use std::any::{TypeId, Any};
use std::cmp;
use std::ops::{Add, Sub, Mul, Div};
use typenum::{self, Unsigned, UInt, B1, Bit, UTerm, Sum, Prod, Diff, Quot};
use typenum::{self, Unsigned, UInt, B1, Bit, UTerm, Sum, Prod, Diff, Quot,
Min, Minimum, Max, Maximum};
#[cfg(feature = "serde-serialize")]
use serde::{Serialize, Serializer, Deserialize, Deserializer};
@ -55,6 +57,11 @@ 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 {
#[inline(always)]
fn is<D: Dim>() -> bool {
TypeId::of::<Self>() == TypeId::of::<D>()
}
/// Gets the compile-time value of `Self`. Returns `None` if it is not known, i.e., if `Self =
/// Dynamic`.
fn try_to_usize() -> Option<usize>;
@ -85,6 +92,24 @@ impl Dim for Dynamic {
}
}
impl Add<usize> for Dynamic {
type Output = Dynamic;
#[inline]
fn add(self, rhs: usize) -> Dynamic {
Dynamic::new(self.value + rhs)
}
}
impl Sub<usize> for Dynamic {
type Output = Dynamic;
#[inline]
fn sub(self, rhs: usize) -> Dynamic {
Dynamic::new(self.value - rhs)
}
}
/*
*
* Operations.
@ -93,7 +118,7 @@ impl Dim for Dynamic {
macro_rules! dim_ops(
($($DimOp: ident, $DimNameOp: ident,
$Op: ident, $op: ident,
$Op: ident, $op: ident, $op_path: path,
$DimResOp: ident, $DimNameResOp: ident,
$ResOp: ident);* $(;)*) => {$(
pub type $DimResOp<D1, D2> = <D1 as $DimOp<D2>>::Output;
@ -120,7 +145,7 @@ macro_rules! dim_ops(
#[inline]
fn $op(self, other: D) -> Dynamic {
Dynamic::new(self.value.$op(other.value()))
Dynamic::new($op_path(self.value, other.value()))
}
}
@ -129,7 +154,7 @@ macro_rules! dim_ops(
#[inline]
fn $op(self, other: Dynamic) -> Dynamic {
Dynamic::new(self.value().$op(other.value))
Dynamic::new($op_path(self.value(), other.value))
}
}
@ -155,10 +180,12 @@ macro_rules! dim_ops(
);
dim_ops!(
DimAdd, DimNameAdd, Add, add, DimSum, DimNameSum, Sum;
DimMul, DimNameMul, Mul, mul, DimProd, DimNameProd, Prod;
DimSub, DimNameSub, Sub, sub, DimDiff, DimNameDiff, Diff;
DimDiv, DimNameDiv, Div, div, DimQuot, DimNameQuot, Quot;
DimAdd, DimNameAdd, Add, add, Add::add, DimSum, DimNameSum, Sum;
DimMul, DimNameMul, Mul, mul, Mul::mul, DimProd, DimNameProd, Prod;
DimSub, DimNameSub, Sub, sub, Sub::sub, DimDiff, DimNameDiff, Diff;
DimDiv, DimNameDiv, Div, div, Div::div, DimQuot, DimNameQuot, Quot;
DimMin, DimNameMin, Min, min, cmp::min, DimMinimum, DimNameNimimum, Minimum;
DimMax, DimNameMax, Max, max, cmp::max, DimMaximum, DimNameMaximum, Maximum;
);

565
src/core/edition.rs Normal file
View File

@ -0,0 +1,565 @@
use num::{Zero, One};
use std::cmp;
use std::ptr;
use core::{DefaultAllocator, Scalar, Matrix, DMatrix, MatrixMN, Vector, RowVector};
use core::dimension::{Dim, DimName, DimSub, DimDiff, DimAdd, DimSum, DimMin, DimMinimum, U1, Dynamic};
use core::constraint::{ShapeConstraint, DimEq, SameNumberOfColumns, SameNumberOfRows};
use core::allocator::{Allocator, Reallocator};
use core::storage::{Storage, StorageMut};
impl<N: Scalar + Zero, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
/// Extracts the upper triangular part of this matrix (including the diagonal).
#[inline]
pub fn upper_triangle(&self) -> MatrixMN<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
let mut res = self.clone_owned();
res.fill_lower_triangle(N::zero(), 1);
res
}
/// Extracts the upper triangular part of this matrix (including the diagonal).
#[inline]
pub fn lower_triangle(&self) -> MatrixMN<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
let mut res = self.clone_owned();
res.fill_upper_triangle(N::zero(), 1);
res
}
}
impl<N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> Matrix<N, R, C, S> {
/// Sets all the elements of this matrix to `val`.
#[inline]
pub fn fill(&mut self, val: N) {
for e in self.iter_mut() {
*e = val
}
}
/// Fills `self` with the identity matrix.
#[inline]
pub fn fill_with_identity(&mut self)
where N: Zero + One {
self.fill(N::zero());
self.fill_diagonal(N::one());
}
/// Sets all the diagonal elements of this matrix to `val`.
#[inline]
pub fn fill_diagonal(&mut self, val: N) {
let (nrows, ncols) = self.shape();
let n = cmp::min(nrows, ncols);
for i in 0 .. n {
unsafe { *self.get_unchecked_mut(i, i) = val }
}
}
/// Sets all the elements of the selected row to `val`.
#[inline]
pub fn fill_row(&mut self, i: usize, val: N) {
assert!(i < self.nrows(), "Row index out of bounds.");
for j in 0 .. self.ncols() {
unsafe { *self.get_unchecked_mut(i, j) = val }
}
}
/// Sets all the elements of the selected column to `val`.
#[inline]
pub fn fill_column(&mut self, j: usize, val: N) {
assert!(j < self.ncols(), "Row index out of bounds.");
for i in 0 .. self.nrows() {
unsafe { *self.get_unchecked_mut(i, j) = val }
}
}
/// Fills the diagonal of this matrix with the content of the given vector.
#[inline]
pub fn set_diagonal<R2: Dim, S2>(&mut self, diag: &Vector<N, R2, S2>)
where R: DimMin<C>,
S2: Storage<N, R2>,
ShapeConstraint: DimEq<DimMinimum<R, C>, R2> {
let (nrows, ncols) = self.shape();
let min_nrows_ncols = cmp::min(nrows, ncols);
assert_eq!(diag.len(), min_nrows_ncols, "Mismatched dimensions.");
for i in 0 .. min_nrows_ncols {
unsafe { *self.get_unchecked_mut(i, i) = *diag.vget_unchecked(i) }
}
}
/// Fills the selected row of this matrix with the content of the given vector.
#[inline]
pub fn set_row<C2: Dim, S2>(&mut self, i: usize, row: &RowVector<N, C2, S2>)
where S2: Storage<N, U1, C2>,
ShapeConstraint: SameNumberOfColumns<C, C2> {
self.row_mut(i).copy_from(row);
}
/// Fills the selected column of this matrix with the content of the given vector.
#[inline]
pub fn set_column<R2: Dim, S2>(&mut self, i: usize, column: &Vector<N, R2, S2>)
where S2: Storage<N, R2, U1>,
ShapeConstraint: SameNumberOfRows<R, R2> {
self.column_mut(i).copy_from(column);
}
/// Sets all the elements of the lower-triangular part of this matrix to `val`.
///
/// The parameter `shift` allows some subdiagonals to be left untouched:
/// * If `shift = 0` then the diagonal is overwritten as well.
/// * If `shift = 1` then the diagonal is left untouched.
/// * If `shift > 1`, then the diagonal and the first `shift - 1` subdiagonals are left
/// untouched.
#[inline]
pub fn fill_lower_triangle(&mut self, val: N, shift: usize) {
for j in 0 .. self.ncols() {
for i in (j + shift) .. self.nrows() {
unsafe { *self.get_unchecked_mut(i, j) = val }
}
}
}
/// Sets all the elements of the lower-triangular part of this matrix to `val`.
///
/// The parameter `shift` allows some superdiagonals to be left untouched:
/// * If `shift = 0` then the diagonal is overwritten as well.
/// * If `shift = 1` then the diagonal is left untouched.
/// * If `shift > 1`, then the diagonal and the first `shift - 1` superdiagonals are left
/// untouched.
#[inline]
pub fn fill_upper_triangle(&mut self, val: N, shift: usize) {
for j in shift .. self.ncols() {
// FIXME: is there a more efficient way to avoid the min ?
// (necessary for rectangular matrices)
for i in 0 .. cmp::min(j + 1 - shift, self.nrows()) {
unsafe { *self.get_unchecked_mut(i, j) = val }
}
}
}
/// Swaps two rows in-place.
#[inline]
pub fn swap_rows(&mut self, irow1: usize, irow2: usize) {
assert!(irow1 < self.nrows() && irow2 < self.nrows());
if irow1 != irow2 {
// FIXME: optimize that.
for i in 0 .. self.ncols() {
unsafe { self.swap_unchecked((irow1, i), (irow2, i)) }
}
}
// Otherwise do nothing.
}
/// Swaps two columns in-place.
#[inline]
pub fn swap_columns(&mut self, icol1: usize, icol2: usize) {
assert!(icol1 < self.ncols() && icol2 < self.ncols());
if icol1 != icol2 {
// FIXME: optimize that.
for i in 0 .. self.nrows() {
unsafe { self.swap_unchecked((i, icol1), (i, icol2)) }
}
}
// Otherwise do nothing.
}
}
impl<N: Scalar, D: Dim, S: StorageMut<N, D, D>> Matrix<N, D, D, S> {
/// Copies the upper-triangle of this matrix to its lower-triangular part.
///
/// This makes the matrix symmetric. Panics if the matrix is not square.
pub fn fill_lower_triangle_with_upper_triangle(&mut self) {
assert!(self.is_square(), "The input matrix should be square.");
let dim = self.nrows();
for j in 0 .. dim {
for i in j + 1 .. dim {
unsafe {
*self.get_unchecked_mut(i, j) = *self.get_unchecked(j, i);
}
}
}
}
/// Copies the upper-triangle of this matrix to its upper-triangular part.
///
/// This makes the matrix symmetric. Panics if the matrix is not square.
pub fn fill_upper_triangle_with_lower_triangle(&mut self) {
assert!(self.is_square(), "The input matrix should be square.");
for j in 1 .. self.ncols() {
for i in 0 .. j {
unsafe {
*self.get_unchecked_mut(i, j) = *self.get_unchecked(j, i);
}
}
}
}
}
/*
*
* FIXME: specialize all the following for slices.
*
*/
impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
/*
*
* Column removal.
*
*/
/// Removes the `i`-th column from this matrix.
#[inline]
pub fn remove_column(self, i: usize) -> MatrixMN<N, R, DimDiff<C, U1>>
where C: DimSub<U1>,
DefaultAllocator: Reallocator<N, R, C, R, DimDiff<C, U1>> {
self.remove_fixed_columns::<U1>(i)
}
/// Removes `D::dim()` consecutive columns from this matrix, starting with the `i`-th
/// (included).
#[inline]
pub fn remove_fixed_columns<D>(self, i: usize) -> MatrixMN<N, R, DimDiff<C, D>>
where D: DimName,
C: DimSub<D>,
DefaultAllocator: Reallocator<N, R, C, R, DimDiff<C, D>> {
self.remove_columns_generic(i, D::name())
}
/// Removes `n` consecutive columns from this matrix, starting with the `i`-th (included).
#[inline]
pub fn remove_columns(self, i: usize, n: usize) -> MatrixMN<N, R, Dynamic>
where C: DimSub<Dynamic, Output = Dynamic>,
DefaultAllocator: Reallocator<N, R, C, R, Dynamic> {
self.remove_columns_generic(i, Dynamic::new(n))
}
/// Removes `nremove.value()` columns from this matrix, starting with the `i`-th (included).
///
/// This is the generic implementation of `.remove_columns(...)` and
/// `.remove_fixed_columns(...)` which have nicer API interfaces.
#[inline]
pub fn remove_columns_generic<D>(self, i: usize, nremove: D) -> MatrixMN<N, R, DimDiff<C, D>>
where D: Dim,
C: DimSub<D>,
DefaultAllocator: Reallocator<N, R, C, R, DimDiff<C, D>> {
let mut m = self.into_owned();
let (nrows, ncols) = m.data.shape();
assert!(i + nremove.value() <= ncols.value(), "Column index out of range.");
if nremove.value() != 0 && i + nremove.value() < ncols.value() {
// The first `deleted_i * nrows` are left untouched.
let copied_value_start = i + nremove.value();
unsafe {
let ptr_in = m.data.ptr().offset((copied_value_start * nrows.value()) as isize);
let ptr_out = m.data.ptr_mut().offset((i * nrows.value()) as isize);
ptr::copy(ptr_in, ptr_out, (ncols.value() - copied_value_start) * nrows.value());
}
}
unsafe {
Matrix::from_data(DefaultAllocator::reallocate_copy(nrows, ncols.sub(nremove), m.data))
}
}
/*
*
* Row removal.
*
*/
/// Removes the `i`-th row from this matrix.
#[inline]
pub fn remove_row(self, i: usize) -> MatrixMN<N, DimDiff<R, U1>, C>
where R: DimSub<U1>,
DefaultAllocator: Reallocator<N, R, C, DimDiff<R, U1>, C> {
self.remove_fixed_rows::<U1>(i)
}
/// Removes `D::dim()` consecutive rows from this matrix, starting with the `i`-th (included).
#[inline]
pub fn remove_fixed_rows<D>(self, i: usize) -> MatrixMN<N, DimDiff<R, D>, C>
where D: DimName,
R: DimSub<D>,
DefaultAllocator: Reallocator<N, R, C, DimDiff<R, D>, C> {
self.remove_rows_generic(i, D::name())
}
/// Removes `n` consecutive rows from this matrix, starting with the `i`-th (included).
#[inline]
pub fn remove_rows(self, i: usize, n: usize) -> MatrixMN<N, Dynamic, C>
where R: DimSub<Dynamic, Output = Dynamic>,
DefaultAllocator: Reallocator<N, R, C, Dynamic, C> {
self.remove_rows_generic(i, Dynamic::new(n))
}
/// Removes `nremove.value()` rows from this matrix, starting with the `i`-th (included).
///
/// This is the generic implementation of `.remove_rows(...)` and `.remove_fixed_rows(...)`
/// which have nicer API interfaces.
#[inline]
pub fn remove_rows_generic<D>(self, i: usize, nremove: D) -> MatrixMN<N, DimDiff<R, D>, C>
where D: Dim,
R: DimSub<D>,
DefaultAllocator: Reallocator<N, R, C, DimDiff<R, D>, C> {
let mut m = self.into_owned();
let (nrows, ncols) = m.data.shape();
assert!(i + nremove.value() <= nrows.value(), "Row index out of range.");
if nremove.value() != 0 {
unsafe {
compress_rows(&mut m.data.as_mut_slice(), nrows.value(), ncols.value(), i, nremove.value());
}
}
unsafe {
Matrix::from_data(DefaultAllocator::reallocate_copy(nrows.sub(nremove), ncols, m.data))
}
}
/*
*
* Columns insertion.
*
*/
/// Inserts a column filled with `val` at the `i-th` position.
#[inline]
pub fn insert_column(self, i: usize, val: N) -> MatrixMN<N, R, DimSum<C, U1>>
where C: DimAdd<U1>,
DefaultAllocator: Reallocator<N, R, C, R, DimSum<C, U1>> {
self.insert_fixed_columns::<U1>(i, val)
}
/// Inserts `D::dim()` columns filled with `val` starting at the `i-th` position.
#[inline]
pub fn insert_fixed_columns<D>(self, i: usize, val: N) -> MatrixMN<N, R, DimSum<C, D>>
where D: DimName,
C: DimAdd<D>,
DefaultAllocator: Reallocator<N, R, C, R, DimSum<C, D>> {
let mut res = unsafe { self.insert_columns_generic_uninitialized(i, D::name()) };
res.fixed_columns_mut::<D>(i).fill(val);
res
}
/// Inserts `n` columns filled with `val` starting at the `i-th` position.
#[inline]
pub fn insert_columns(self, i: usize, n: usize, val: N) -> MatrixMN<N, R, Dynamic>
where C: DimAdd<Dynamic, Output = Dynamic>,
DefaultAllocator: Reallocator<N, R, C, R, Dynamic> {
let mut res = unsafe { self.insert_columns_generic_uninitialized(i, Dynamic::new(n)) };
res.columns_mut(i, n).fill(val);
res
}
/// Inserts `ninsert.value()` columns starting at the `i-th` place of this matrix.
///
/// The added column values are not initialized.
#[inline]
pub unsafe fn insert_columns_generic_uninitialized<D>(self, i: usize, ninsert: D)
-> MatrixMN<N, R, DimSum<C, D>>
where D: Dim,
C: DimAdd<D>,
DefaultAllocator: Reallocator<N, R, C, R, DimSum<C, D>> {
let m = self.into_owned();
let (nrows, ncols) = m.data.shape();
let mut res = Matrix::from_data(DefaultAllocator::reallocate_copy(nrows, ncols.add(ninsert), m.data));
assert!(i <= ncols.value(), "Column insertion index out of range.");
if ninsert.value() != 0 && i != ncols.value() {
let ptr_in = res.data.ptr().offset((i * nrows.value()) as isize);
let ptr_out = res.data.ptr_mut().offset(((i + ninsert.value()) * nrows.value()) as isize);
ptr::copy(ptr_in, ptr_out, (ncols.value() - i) * nrows.value())
}
res
}
/*
*
* Rows insertion.
*
*/
/// Inserts a row filled with `val` at the `i-th` position.
#[inline]
pub fn insert_row(self, i: usize, val: N) -> MatrixMN<N, DimSum<R, U1>, C>
where R: DimAdd<U1>,
DefaultAllocator: Reallocator<N, R, C, DimSum<R, U1>, C> {
self.insert_fixed_rows::<U1>(i, val)
}
/// Inserts `D::dim()` rows filled with `val` starting at the `i-th` position.
#[inline]
pub fn insert_fixed_rows<D>(self, i: usize, val: N) -> MatrixMN<N, DimSum<R, D>, C>
where D: DimName,
R: DimAdd<D>,
DefaultAllocator: Reallocator<N, R, C, DimSum<R, D>, C> {
let mut res = unsafe { self.insert_rows_generic_uninitialized(i, D::name()) };
res.fixed_rows_mut::<D>(i).fill(val);
res
}
/// Inserts `n` rows filled with `val` starting at the `i-th` position.
#[inline]
pub fn insert_rows(self, i: usize, n: usize, val: N) -> MatrixMN<N, Dynamic, C>
where R: DimAdd<Dynamic, Output = Dynamic>,
DefaultAllocator: Reallocator<N, R, C, Dynamic, C> {
let mut res = unsafe { self.insert_rows_generic_uninitialized(i, Dynamic::new(n)) };
res.rows_mut(i, n).fill(val);
res
}
/// Inserts `ninsert.value()` rows at the `i-th` place of this matrix.
///
/// The added rows values are not initialized.
/// This is the generic implementation of `.insert_rows(...)` and
/// `.insert_fixed_rows(...)` which have nicer API interfaces.
#[inline]
pub unsafe fn insert_rows_generic_uninitialized<D>(self, i: usize, ninsert: D)
-> MatrixMN<N, DimSum<R, D>, C>
where D: Dim,
R: DimAdd<D>,
DefaultAllocator: Reallocator<N, R, C, DimSum<R, D>, C> {
let m = self.into_owned();
let (nrows, ncols) = m.data.shape();
let mut res = Matrix::from_data(DefaultAllocator::reallocate_copy(nrows.add(ninsert), ncols, m.data));
assert!(i <= nrows.value(), "Row insertion index out of range.");
if ninsert.value() != 0 {
extend_rows(&mut res.data.as_mut_slice(), nrows.value(), ncols.value(), i, ninsert.value());
}
res
}
/*
*
* Resizing.
*
*/
/// Resizes this matrix so that it contains `new_nrows` rows and `new_ncols` columns.
///
/// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
/// rows and/or columns than `self`, then the extra rows or columns are filled with `val`.
pub fn resize(self, new_nrows: usize, new_ncols: usize, val: N) -> DMatrix<N>
where DefaultAllocator: Reallocator<N, R, C, Dynamic, Dynamic> {
self.resize_generic(Dynamic::new(new_nrows), Dynamic::new(new_ncols), val)
}
/// Resizes this matrix so that it contains `R2::value()` rows and `C2::value()` columns.
///
/// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
/// rows and/or columns than `self`, then the extra rows or columns are filled with `val`.
pub fn fixed_resize<R2: DimName, C2: DimName>(self, val: N) -> MatrixMN<N, R2, C2>
where DefaultAllocator: Reallocator<N, R, C, R2, C2> {
self.resize_generic(R2::name(), C2::name(), val)
}
/// Resizes `self` such that it has dimensions `new_nrows × now_ncols`.
///
/// The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more
/// rows and/or columns than `self`, then the extra rows or columns are filled with `val`.
#[inline]
pub fn resize_generic<R2: Dim, C2: Dim>(self, new_nrows: R2, new_ncols: C2, val: N) -> MatrixMN<N, R2, C2>
where DefaultAllocator: Reallocator<N, R, C, R2, C2> {
let (nrows, ncols) = self.shape();
let mut data = self.data.into_owned();
if new_nrows.value() == nrows {
let res = unsafe { DefaultAllocator::reallocate_copy(new_nrows, new_ncols, data) };
Matrix::from_data(res)
}
else {
let mut res;
unsafe {
if new_nrows.value() < nrows {
compress_rows(&mut data.as_mut_slice(), nrows, ncols, new_nrows.value(), nrows - new_nrows.value());
res = Matrix::from_data(DefaultAllocator::reallocate_copy(new_nrows, new_ncols, data));
}
else {
res = Matrix::from_data(DefaultAllocator::reallocate_copy(new_nrows, new_ncols, data));
extend_rows(&mut res.data.as_mut_slice(), nrows, ncols, nrows, new_nrows.value() - nrows);
}
}
if new_ncols.value() > ncols {
res.columns_range_mut(ncols ..).fill(val);
}
if new_nrows.value() > nrows {
res.slice_range_mut(nrows .., .. cmp::min(ncols, new_ncols.value())).fill(val);
}
res
}
}
}
unsafe fn compress_rows<N: Scalar>(data: &mut [N], nrows: usize, ncols: usize, i: usize, nremove: usize) {
let new_nrows = nrows - nremove;
let ptr_in = data.as_ptr();
let ptr_out = data.as_mut_ptr();
let mut curr_i = i;
for k in 0 .. ncols - 1 {
ptr::copy(ptr_in.offset((curr_i + (k + 1) * nremove) as isize),
ptr_out.offset(curr_i as isize),
new_nrows);
curr_i += new_nrows;
}
// Deal with the last column from which less values have to be copied.
let remaining_len = nrows - i - nremove;
ptr::copy(ptr_in.offset((nrows * ncols - remaining_len) as isize),
ptr_out.offset(curr_i as isize),
remaining_len);
}
unsafe fn extend_rows<N: Scalar>(data: &mut [N], nrows: usize, ncols: usize, i: usize, ninsert: usize) {
let new_nrows = nrows + ninsert;
let ptr_in = data.as_ptr();
let ptr_out = data.as_mut_ptr();
let remaining_len = nrows - i;
let mut curr_i = new_nrows * ncols - remaining_len;
// Deal with the last column from which less values have to be copied.
ptr::copy(ptr_in.offset((nrows * ncols - remaining_len) as isize),
ptr_out.offset(curr_i as isize),
remaining_len);
for k in (0 .. ncols - 1).rev() {
curr_i -= new_nrows;
ptr::copy(ptr_in.offset((k * nrows + i) as isize),
ptr_out.offset(curr_i as isize),
nrows);
}
}

View File

@ -1,203 +0,0 @@
use approx::ApproxEq;
use alga::general::Field;
use core::{Scalar, Matrix, SquareMatrix, OwnedSquareMatrix};
use core::dimension::Dim;
use core::storage::{Storage, StorageMut};
impl<N, D: Dim, S> SquareMatrix<N, D, S>
where N: Scalar + Field + ApproxEq,
S: Storage<N, D, D> {
/// Attempts to invert this matrix.
#[inline]
pub fn try_inverse(self) -> Option<OwnedSquareMatrix<N, D, S::Alloc>> {
let mut res = self.into_owned();
if res.shape().0 <= 3 {
if res.try_inverse_mut() {
Some(res)
}
else {
None
}
}
else {
gauss_jordan_inverse(res)
}
}
}
impl<N, D: Dim, S> SquareMatrix<N, D, S>
where N: Scalar + Field + ApproxEq,
S: StorageMut<N, D, D> {
/// Attempts to invert this matrix in-place. Returns `false` and leaves `self` untouched if
/// inversion fails.
#[inline]
pub fn try_inverse_mut(&mut self) -> bool {
assert!(self.is_square(), "Unable to invert a non-square matrix.");
let dim = self.shape().0;
unsafe {
match dim {
0 => true,
1 => {
let determinant = self.get_unchecked(0, 0).clone();
if determinant == N::zero() {
false
}
else {
*self.get_unchecked_mut(0, 0) = N::one() / determinant;
true
}
},
2 => {
let determinant = self.determinant();
if determinant == N::zero() {
false
}
else {
let m11 = *self.get_unchecked(0, 0); let m12 = *self.get_unchecked(0, 1);
let m21 = *self.get_unchecked(1, 0); let m22 = *self.get_unchecked(1, 1);
*self.get_unchecked_mut(0, 0) = m22 / determinant;
*self.get_unchecked_mut(0, 1) = -m12 / determinant;
*self.get_unchecked_mut(1, 0) = -m21 / determinant;
*self.get_unchecked_mut(1, 1) = m11 / determinant;
true
}
},
3 => {
let m11 = *self.get_unchecked(0, 0);
let m12 = *self.get_unchecked(0, 1);
let m13 = *self.get_unchecked(0, 2);
let m21 = *self.get_unchecked(1, 0);
let m22 = *self.get_unchecked(1, 1);
let m23 = *self.get_unchecked(1, 2);
let m31 = *self.get_unchecked(2, 0);
let m32 = *self.get_unchecked(2, 1);
let m33 = *self.get_unchecked(2, 2);
let minor_m12_m23 = m22 * m33 - m32 * m23;
let minor_m11_m23 = m21 * m33 - m31 * m23;
let minor_m11_m22 = m21 * m32 - m31 * m22;
let determinant = m11 * minor_m12_m23 -
m12 * minor_m11_m23 +
m13 * minor_m11_m22;
if determinant == N::zero() {
false
}
else {
*self.get_unchecked_mut(0, 0) = minor_m12_m23 / determinant;
*self.get_unchecked_mut(0, 1) = (m13 * m32 - m33 * m12) / determinant;
*self.get_unchecked_mut(0, 2) = (m12 * m23 - m22 * m13) / determinant;
*self.get_unchecked_mut(1, 0) = -minor_m11_m23 / determinant;
*self.get_unchecked_mut(1, 1) = (m11 * m33 - m31 * m13) / determinant;
*self.get_unchecked_mut(1, 2) = (m13 * m21 - m23 * m11) / determinant;
*self.get_unchecked_mut(2, 0) = minor_m11_m22 / determinant;
*self.get_unchecked_mut(2, 1) = (m12 * m31 - m32 * m11) / determinant;
*self.get_unchecked_mut(2, 2) = (m11 * m22 - m21 * m12) / determinant;
true
}
},
_ => {
let oself = self.clone_owned();
if let Some(res) = gauss_jordan_inverse(oself) {
self.copy_from(&res);
true
}
else {
false
}
}
}
}
}
}
/// Inverts the given matrix using Gauss-Jordan Ellimitation.
fn gauss_jordan_inverse<N, D, S>(mut matrix: SquareMatrix<N, D, S>) -> Option<OwnedSquareMatrix<N, D, S::Alloc>>
where D: Dim,
N: Scalar + Field + ApproxEq,
S: StorageMut<N, D, D> {
assert!(matrix.is_square(), "Unable to invert a non-square matrix.");
let dim = matrix.data.shape().0;
let mut res: OwnedSquareMatrix<N, D, S::Alloc> = Matrix::identity_generic(dim, dim);
let dim = dim.value();
unsafe {
for k in 0 .. dim {
// Search a non-zero value on the k-th column.
// FIXME: would it be worth it to spend some more time searching for the
// max instead?
let mut n0 = k; // index of a non-zero entry.
while n0 != dim {
if !matrix.get_unchecked(n0, k).is_zero() {
break;
}
n0 += 1;
}
if n0 == dim {
return None
}
// Swap pivot line.
if n0 != k {
for j in 0 .. dim {
matrix.swap_unchecked((n0, j), (k, j));
res.swap_unchecked((n0, j), (k, j));
}
}
let pivot = *matrix.get_unchecked(k, k);
for j in k .. dim {
let selfval = *matrix.get_unchecked(k, j) / pivot;
*matrix.get_unchecked_mut(k, j) = selfval;
}
for j in 0 .. dim {
let resval = *res.get_unchecked(k, j) / pivot;
*res.get_unchecked_mut(k, j) = resval;
}
for l in 0 .. dim {
if l != k {
let normalizer = *matrix.get_unchecked(l, k);
for j in k .. dim {
let selfval = *matrix.get_unchecked(l, j) - *matrix.get_unchecked(k, j) * normalizer;
*matrix.get_unchecked_mut(l, j) = selfval;
}
for j in 0 .. dim {
let resval = *res.get_unchecked(l, j) - *res.get_unchecked(k, j) * normalizer;
*res.get_unchecked_mut(l, j) = resval;
}
}
}
}
Some(res)
}
}

View File

@ -81,6 +81,13 @@ macro_rules! iterator {
self.size_hint().0
}
}
impl<'a, N: Scalar, R: Dim, C: Dim, S: 'a + $Storage<N, R, C>> ExactSizeIterator for $Name<'a, N, R, C, S> {
#[inline]
fn len(&self) -> usize {
self.size
}
}
}
}

File diff suppressed because it is too large Load Diff

View File

@ -7,42 +7,39 @@ use alga::general::{AbstractMagma, AbstractGroupAbelian, AbstractGroup, Abstract
ClosedAdd, ClosedNeg, ClosedMul};
use alga::linear::{VectorSpace, NormedSpace, InnerSpace, FiniteDimVectorSpace, FiniteDimInnerSpace};
use core::{Scalar, Matrix, SquareMatrix};
use core::{DefaultAllocator, Scalar, MatrixMN, MatrixN};
use core::dimension::{Dim, DimName};
use core::storage::OwnedStorage;
use core::allocator::OwnedAllocator;
use core::storage::{Storage, StorageMut};
use core::allocator::Allocator;
/*
*
* Additive structures.
*
*/
impl<N, R: DimName, C: DimName, S> Identity<Additive> for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> Identity<Additive> for MatrixMN<N, R, C>
where N: Scalar + Zero,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn identity() -> Self {
Self::from_element(N::zero())
}
}
impl<N, R: DimName, C: DimName, S> AbstractMagma<Additive> for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> AbstractMagma<Additive> for MatrixMN<N, R, C>
where N: Scalar + ClosedAdd,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn operate(&self, other: &Self) -> Self {
self + other
}
}
impl<N, R: DimName, C: DimName, S> Inverse<Additive> for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> Inverse<Additive> for MatrixMN<N, R, C>
where N: Scalar + ClosedNeg,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn inverse(&self) -> Matrix<N, R, C, S> {
fn inverse(&self) -> MatrixMN<N, R, C> {
-self
}
@ -54,10 +51,9 @@ impl<N, R: DimName, C: DimName, S> Inverse<Additive> for Matrix<N, R, C, S>
macro_rules! inherit_additive_structure(
($($marker: ident<$operator: ident> $(+ $bounds: ident)*),* $(,)*) => {$(
impl<N, R: DimName, C: DimName, S> $marker<$operator> for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> $marker<$operator> for MatrixMN<N, R, C>
where N: Scalar + $marker<$operator> $(+ $bounds)*,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> { }
DefaultAllocator: Allocator<N, R, C> { }
)*}
);
@ -70,10 +66,9 @@ inherit_additive_structure!(
AbstractGroupAbelian<Additive> + Zero + ClosedAdd + ClosedNeg
);
impl<N, R: DimName, C: DimName, S> AbstractModule for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> AbstractModule for MatrixMN<N, R, C>
where N: Scalar + RingCommutative,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
type AbstractRing = N;
#[inline]
@ -82,24 +77,21 @@ impl<N, R: DimName, C: DimName, S> AbstractModule for Matrix<N, R, C, S>
}
}
impl<N, R: DimName, C: DimName, S> Module for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> Module for MatrixMN<N, R, C>
where N: Scalar + RingCommutative,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
type Ring = N;
}
impl<N, R: DimName, C: DimName, S> VectorSpace for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> VectorSpace for MatrixMN<N, R, C>
where N: Scalar + Field,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
type Field = N;
}
impl<N, R: DimName, C: DimName, S> FiniteDimVectorSpace for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> FiniteDimVectorSpace for MatrixMN<N, R, C>
where N: Scalar + Field,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn dimension() -> usize {
R::dim() * C::dim()
@ -131,10 +123,8 @@ impl<N, R: DimName, C: DimName, S> FiniteDimVectorSpace for Matrix<N, R, C, S>
}
}
impl<N, R: DimName, C: DimName, S> NormedSpace for Matrix<N, R, C, S>
where N: Real,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
impl<N: Real, R: DimName, C: DimName> NormedSpace for MatrixMN<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn norm_squared(&self) -> N {
self.norm_squared()
@ -166,10 +156,8 @@ impl<N, R: DimName, C: DimName, S> NormedSpace for Matrix<N, R, C, S>
}
}
impl<N, R: DimName, C: DimName, S> InnerSpace for Matrix<N, R, C, S>
where N: Real,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
impl<N: Real, R: DimName, C: DimName> InnerSpace for MatrixMN<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
type Real = N;
#[inline]
@ -187,12 +175,10 @@ impl<N, R: DimName, C: DimName, S> InnerSpace for Matrix<N, R, C, S>
// In particular:
// use `x()` instead of `::canonical_basis_element`
// use `::new(x, y, z)` instead of `::from_slice`
impl<N, R: DimName, C: DimName, S> FiniteDimInnerSpace for Matrix<N, R, C, S>
where N: Real,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
impl<N: Real, R: DimName, C: DimName> FiniteDimInnerSpace for MatrixMN<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn orthonormalize(vs: &mut [Matrix<N, R, C, S>]) -> usize {
fn orthonormalize(vs: &mut [MatrixMN<N, R, C>]) -> usize {
let mut nbasis_elements = 0;
for i in 0 .. vs.len() {
@ -229,7 +215,7 @@ impl<N, R: DimName, C: DimName, S> FiniteDimInnerSpace for Matrix<N, R, C, S>
match Self::dimension() {
1 => {
if vs.len() == 0 {
f(&Self::canonical_basis_element(0));
let _ = f(&Self::canonical_basis_element(0));
}
},
2 => {
@ -241,7 +227,7 @@ impl<N, R: DimName, C: DimName, S> FiniteDimInnerSpace for Matrix<N, R, C, S>
let v = &vs[0];
let res = Self::from_column_slice(&[-v[1], v[0]]);
f(&res.normalize());
let _ = f(&res.normalize());
}
// Otherwise, nothing.
@ -266,11 +252,11 @@ impl<N, R: DimName, C: DimName, S> FiniteDimInnerSpace for Matrix<N, R, C, S>
let _ = a.normalize_mut();
if f(&a.cross(v)) {
f(&a);
let _ = f(&a);
}
}
else if vs.len() == 2 {
f(&vs[0].cross(&vs[1]).normalize());
let _ = f(&vs[0].cross(&vs[1]).normalize());
}
},
_ => {
@ -307,20 +293,18 @@ impl<N, R: DimName, C: DimName, S> FiniteDimInnerSpace for Matrix<N, R, C, S>
*
*
*/
impl<N, D: DimName, S> Identity<Multiplicative> for SquareMatrix<N, D, S>
impl<N, D: DimName> Identity<Multiplicative> for MatrixN<N, D>
where N: Scalar + Zero + One,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S> {
DefaultAllocator: Allocator<N, D, D> {
#[inline]
fn identity() -> Self {
Self::identity()
}
}
impl<N, D: DimName, S> AbstractMagma<Multiplicative> for SquareMatrix<N, D, S>
where N: Scalar + Zero + ClosedAdd + ClosedMul,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S> {
impl<N, D: DimName> AbstractMagma<Multiplicative> for MatrixN<N, D>
where N: Scalar + Zero + One + ClosedAdd + ClosedMul,
DefaultAllocator: Allocator<N, D, D> {
#[inline]
fn operate(&self, other: &Self) -> Self {
self * other
@ -329,10 +313,9 @@ impl<N, D: DimName, S> AbstractMagma<Multiplicative> for SquareMatrix<N, D, S>
macro_rules! impl_multiplicative_structure(
($($marker: ident<$operator: ident> $(+ $bounds: ident)*),* $(,)*) => {$(
impl<N, D: DimName, S> $marker<$operator> for SquareMatrix<N, D, S>
where N: Scalar + Zero + ClosedAdd + ClosedMul + $marker<$operator> $(+ $bounds)*,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S> { }
impl<N, D: DimName> $marker<$operator> for MatrixN<N, D>
where N: Scalar + Zero + One + ClosedAdd + ClosedMul + $marker<$operator> $(+ $bounds)*,
DefaultAllocator: Allocator<N, D, D> { }
)*}
);
@ -341,421 +324,24 @@ impl_multiplicative_structure!(
AbstractMonoid<Multiplicative> + One
);
// // FIXME: Field too strong?
// impl<N, S> Matrix for Matrix<N, S>
// where N: Scalar + Field,
// S: Storage<N> {
// type Field = N;
// type Row = OwnedMatrix<N, Static<U1>, S::C, S::Alloc>;
// type Column = OwnedMatrix<N, S::R, Static<U1>, S::Alloc>;
// type Transpose = OwnedMatrix<N, S::C, S::R, S::Alloc>;
// #[inline]
// fn nrows(&self) -> usize {
// self.shape().0
// }
// #[inline]
// fn ncolumns(&self) -> usize {
// self.shape().1
// }
// #[inline]
// fn row(&self, row: usize) -> Self::Row {
// let mut res: Self::Row = ::zero();
// for (column, e) in res.iter_mut().enumerate() {
// *e = self[(row, column)];
// }
// res
// }
// #[inline]
// fn column(&self, column: usize) -> Self::Column {
// let mut res: Self::Column = ::zero();
// for (row, e) in res.iter_mut().enumerate() {
// *e = self[(row, column)];
// }
// res
// }
// #[inline]
// unsafe fn get_unchecked(&self, i: usize, j: usize) -> Self::Field {
// self.get_unchecked(i, j)
// }
// #[inline]
// fn transpose(&self) -> Self::Transpose {
// self.transpose()
// }
// }
// impl<N, S> MatrixMut for Matrix<N, S>
// where N: Scalar + Field,
// S: StorageMut<N> {
// #[inline]
// fn set_row_mut(&mut self, irow: usize, row: &Self::Row) {
// assert!(irow < self.shape().0, "Row index out of bounds.");
// for (icol, e) in row.iter().enumerate() {
// unsafe { self.set_unchecked(irow, icol, *e) }
// }
// }
// #[inline]
// fn set_column_mut(&mut self, icol: usize, col: &Self::Column) {
// assert!(icol < self.shape().1, "Column index out of bounds.");
// for (irow, e) in col.iter().enumerate() {
// unsafe { self.set_unchecked(irow, icol, *e) }
// }
// }
// #[inline]
// unsafe fn set_unchecked(&mut self, i: usize, j: usize, val: Self::Field) {
// *self.get_unchecked_mut(i, j) = val
// }
// }
// // FIXME: Real is needed here only for invertibility...
// impl<N: Real> SquareMatrixMut for $t<N> {
// #[inline]
// fn from_diagonal(diag: &Self::Coordinates) -> Self {
// let mut res: $t<N> = ::zero();
// res.set_diagonal_mut(diag);
// res
// }
// #[inline]
// fn set_diagonal_mut(&mut self, diag: &Self::Coordinates) {
// for (i, e) in diag.iter().enumerate() {
// unsafe { self.set_unchecked(i, i, *e) }
// }
// }
// }
// Specializations depending on the dimension.
// matrix_group_approx_impl!(common: $t, 1, $vector, $($compN),+);
// // FIXME: Real is needed here only for invertibility...
// impl<N: Real> SquareMatrix for $t<N> {
// type Vector = $vector<N>;
// #[inline]
// fn diagonal(&self) -> Self::Coordinates {
// $vector::new(self.m11)
// }
// #[inline]
// fn determinant(&self) -> Self::Field {
// self.m11
// }
// #[inline]
// fn try_inverse(&self) -> Option<Self> {
// let mut res = *self;
// if res.try_inverse_mut() {
// Some(res)
// }
// else {
// None
// }
// }
// #[inline]
// fn try_inverse_mut(&mut self) -> bool {
// if relative_eq!(&self.m11, &::zero()) {
// false
// }
// else {
// self.m11 = ::one::<N>() / ::determinant(self);
// true
// }
// }
// #[inline]
// fn transpose_mut(&mut self) {
// // no-op
// }
// }
// ident, 2, $vector: ident, $($compN: ident),+) => {
// matrix_group_approx_impl!(common: $t, 2, $vector, $($compN),+);
// // FIXME: Real is needed only for inversion here.
// impl<N: Real> SquareMatrix for $t<N> {
// type Vector = $vector<N>;
// #[inline]
// fn diagonal(&self) -> Self::Coordinates {
// $vector::new(self.m11, self.m22)
// }
// #[inline]
// fn determinant(&self) -> Self::Field {
// self.m11 * self.m22 - self.m21 * self.m12
// }
// #[inline]
// fn try_inverse(&self) -> Option<Self> {
// let mut res = *self;
// if res.try_inverse_mut() {
// Some(res)
// }
// else {
// None
// }
// }
// #[inline]
// fn try_inverse_mut(&mut self) -> bool {
// let determinant = ::determinant(self);
// if relative_eq!(&determinant, &::zero()) {
// false
// }
// else {
// *self = Matrix2::new(
// self.m22 / determinant , -self.m12 / determinant,
// -self.m21 / determinant, self.m11 / determinant);
// true
// }
// }
// #[inline]
// fn transpose_mut(&mut self) {
// mem::swap(&mut self.m12, &mut self.m21)
// }
// }
// ident, 3, $vector: ident, $($compN: ident),+) => {
// matrix_group_approx_impl!(common: $t, 3, $vector, $($compN),+);
// // FIXME: Real is needed only for inversion here.
// impl<N: Real> SquareMatrix for $t<N> {
// type Vector = $vector<N>;
// #[inline]
// fn diagonal(&self) -> Self::Coordinates {
// $vector::new(self.m11, self.m22, self.m33)
// }
// #[inline]
// fn determinant(&self) -> Self::Field {
// let minor_m12_m23 = self.m22 * self.m33 - self.m32 * self.m23;
// let minor_m11_m23 = self.m21 * self.m33 - self.m31 * self.m23;
// let minor_m11_m22 = self.m21 * self.m32 - self.m31 * self.m22;
// self.m11 * minor_m12_m23 - self.m12 * minor_m11_m23 + self.m13 * minor_m11_m22
// }
// #[inline]
// fn try_inverse(&self) -> Option<Self> {
// let mut res = *self;
// if res.try_inverse_mut() {
// Some(res)
// }
// else {
// None
// }
// }
// #[inline]
// fn try_inverse_mut(&mut self) -> bool {
// let minor_m12_m23 = self.m22 * self.m33 - self.m32 * self.m23;
// let minor_m11_m23 = self.m21 * self.m33 - self.m31 * self.m23;
// let minor_m11_m22 = self.m21 * self.m32 - self.m31 * self.m22;
// let determinant = self.m11 * minor_m12_m23 -
// self.m12 * minor_m11_m23 +
// self.m13 * minor_m11_m22;
// if relative_eq!(&determinant, &::zero()) {
// false
// }
// else {
// *self = Matrix3::new(
// (minor_m12_m23 / determinant),
// ((self.m13 * self.m32 - self.m33 * self.m12) / determinant),
// ((self.m12 * self.m23 - self.m22 * self.m13) / determinant),
// (-minor_m11_m23 / determinant),
// ((self.m11 * self.m33 - self.m31 * self.m13) / determinant),
// ((self.m13 * self.m21 - self.m23 * self.m11) / determinant),
// (minor_m11_m22 / determinant),
// ((self.m12 * self.m31 - self.m32 * self.m11) / determinant),
// ((self.m11 * self.m22 - self.m21 * self.m12) / determinant)
// );
// true
// }
// }
// #[inline]
// fn transpose_mut(&mut self) {
// mem::swap(&mut self.m12, &mut self.m21);
// mem::swap(&mut self.m13, &mut self.m31);
// mem::swap(&mut self.m23, &mut self.m32);
// }
// }
// ident, $dimension: expr, $vector: ident, $($compN: ident),+) => {
// matrix_group_approx_impl!(common: $t, $dimension, $vector, $($compN),+);
// // FIXME: Real is needed only for inversion here.
// impl<N: Real> SquareMatrix for $t<N> {
// type Vector = $vector<N>;
// #[inline]
// fn diagonal(&self) -> Self::Coordinates {
// let mut diagonal: $vector<N> = ::zero();
// for i in 0 .. $dimension {
// unsafe { diagonal.unsafe_set(i, self.get_unchecked(i, i)) }
// }
// diagonal
// }
// #[inline]
// fn determinant(&self) -> Self::Field {
// // FIXME: extremely naive implementation.
// let mut det = ::zero();
// for icol in 0 .. $dimension {
// let e = unsafe { self.unsafe_at((0, icol)) };
// if e != ::zero() {
// let minor_mat = self.delete_row_column(0, icol);
// let minor = minor_mat.determinant();
// if icol % 2 == 0 {
// det += minor;
// }
// else {
// det -= minor;
// }
// }
// }
// det
// }
// #[inline]
// fn try_inverse(&self) -> Option<Self> {
// let mut res = *self;
// if res.try_inverse_mut() {
// Some(res)
// }
// else {
// None
// }
// }
// #[inline]
// fn try_inverse_mut(&mut self) -> bool {
// let mut res: $t<N> = ::one();
// // Inversion using Gauss-Jordan elimination
// for k in 0 .. $dimension {
// // search a non-zero value on the k-th column
// // FIXME: would it be worth it to spend some more time searching for the
// // max instead?
// let mut n0 = k; // index of a non-zero entry
// while n0 != $dimension {
// if self[(n0, k)] != ::zero() {
// break;
// }
// n0 = n0 + 1;
// }
// if n0 == $dimension {
// return false
// }
// // swap pivot line
// if n0 != k {
// for j in 0 .. $dimension {
// self.swap((n0, j), (k, j));
// res.swap((n0, j), (k, j));
// }
// }
// let pivot = self[(k, k)];
// for j in k .. $dimension {
// let selfval = self[(k, j)] / pivot;
// self[(k, j)] = selfval;
// }
// for j in 0 .. $dimension {
// let resval = res[(k, j)] / pivot;
// res[(k, j)] = resval;
// }
// for l in 0 .. $dimension {
// if l != k {
// let normalizer = self[(l, k)];
// for j in k .. $dimension {
// let selfval = self[(l, j)] - self[(k, j)] * normalizer;
// self[(l, j)] = selfval;
// }
// for j in 0 .. $dimension {
// let resval = res[(l, j)] - res[(k, j)] * normalizer;
// res[(l, j)] = resval;
// }
// }
// }
// }
// *self = res;
// true
// }
// #[inline]
// fn transpose_mut(&mut self) {
// for i in 1 .. $dimension {
// for j in 0 .. i {
// self.swap((i, j), (j, i))
// }
// }
// }
/*
*
* Ordering
*
*/
impl<N, R: Dim, C: Dim, S> MeetSemilattice for Matrix<N, R, C, S>
impl<N, R: Dim, C: Dim> MeetSemilattice for MatrixMN<N, R, C>
where N: Scalar + MeetSemilattice,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn meet(&self, other: &Self) -> Self {
self.zip_map(other, |a, b| a.meet(&b))
}
}
impl<N, R: Dim, C: Dim, S> JoinSemilattice for Matrix<N, R, C, S>
impl<N, R: Dim, C: Dim> JoinSemilattice for MatrixMN<N, R, C>
where N: Scalar + JoinSemilattice,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn join(&self, other: &Self) -> Self {
self.zip_map(other, |a, b| a.join(&b))
@ -763,10 +349,9 @@ impl<N, R: Dim, C: Dim, S> JoinSemilattice for Matrix<N, R, C, S>
}
impl<N, R: Dim, C: Dim, S> Lattice for Matrix<N, R, C, S>
impl<N, R: Dim, C: Dim> Lattice for MatrixMN<N, R, C>
where N: Scalar + Lattice,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S> {
DefaultAllocator: Allocator<N, R, C> {
#[inline]
fn meet_join(&self, other: &Self) -> (Self, Self) {
let shape = self.data.shape();

View File

@ -21,7 +21,7 @@ use generic_array::{ArrayLength, GenericArray};
use core::Scalar;
use core::dimension::{DimName, U1};
use core::storage::{Storage, StorageMut, Owned, OwnedStorage};
use core::storage::{Storage, StorageMut, Owned, ContiguousStorage, ContiguousStorageMut};
use core::allocator::Allocator;
use core::default_allocator::DefaultAllocator;
@ -139,22 +139,10 @@ unsafe impl<N, R, C> Storage<N, R, C> for MatrixArray<N, R, C>
R: DimName,
C: DimName,
R::Value: Mul<C::Value>,
Prod<R::Value, C::Value>: ArrayLength<N> {
Prod<R::Value, C::Value>: ArrayLength<N>,
DefaultAllocator: Allocator<N, R, C, Buffer = Self> {
type RStride = U1;
type CStride = R;
type Alloc = DefaultAllocator;
#[inline]
fn into_owned(self) -> Owned<N, R, C, Self::Alloc> {
self
}
#[inline]
fn clone_owned(&self) -> Owned<N, R, C, Self::Alloc> {
let it = self.iter().cloned();
Self::Alloc::allocate_from_iterator(self.shape().0, self.shape().1, it)
}
#[inline]
fn ptr(&self) -> *const N {
@ -170,30 +158,44 @@ unsafe impl<N, R, C> Storage<N, R, C> for MatrixArray<N, R, C>
fn strides(&self) -> (Self::RStride, Self::CStride) {
(Self::RStride::name(), Self::CStride::name())
}
#[inline]
fn is_contiguous(&self) -> bool {
true
}
#[inline]
fn into_owned(self) -> Owned<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
self
}
#[inline]
fn clone_owned(&self) -> Owned<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
let it = self.iter().cloned();
DefaultAllocator::allocate_from_iterator(self.shape().0, self.shape().1, it)
}
#[inline]
fn as_slice(&self) -> &[N] {
&self[..]
}
}
unsafe impl<N, R, C> StorageMut<N, R, C> for MatrixArray<N, R, C>
where N: Scalar,
R: DimName,
C: DimName,
R::Value: Mul<C::Value>,
Prod<R::Value, C::Value>: ArrayLength<N> {
Prod<R::Value, C::Value>: ArrayLength<N>,
DefaultAllocator: Allocator<N, R, C, Buffer = Self> {
#[inline]
fn ptr_mut(&mut self) -> *mut N {
self[..].as_mut_ptr()
}
}
unsafe impl<N, R, C> OwnedStorage<N, R, C> for MatrixArray<N, R, C>
where N: Scalar,
R: DimName,
C: DimName,
R::Value: Mul<C::Value>,
Prod<R::Value, C::Value>: ArrayLength<N> {
#[inline]
fn as_slice(&self) -> &[N] {
&self[..]
}
#[inline]
fn as_mut_slice(&mut self) -> &mut [N] {
@ -201,6 +203,24 @@ unsafe impl<N, R, C> OwnedStorage<N, R, C> for MatrixArray<N, R, C>
}
}
unsafe impl<N, R, C> ContiguousStorage<N, R, C> for MatrixArray<N, R, C>
where N: Scalar,
R: DimName,
C: DimName,
R::Value: Mul<C::Value>,
Prod<R::Value, C::Value>: ArrayLength<N>,
DefaultAllocator: Allocator<N, R, C, Buffer = Self> {
}
unsafe impl<N, R, C> ContiguousStorageMut<N, R, C> for MatrixArray<N, R, C>
where N: Scalar,
R: DimName,
C: DimName,
R::Value: Mul<C::Value>,
Prod<R::Value, C::Value>: ArrayLength<N>,
DefaultAllocator: Allocator<N, R, C, Buffer = Self> {
}
/*
*

View File

@ -1,28 +1,31 @@
use std::marker::PhantomData;
use std::ops::{Range, RangeFrom, RangeTo, RangeFull};
use std::slice;
use core::{Scalar, Matrix};
use core::dimension::{Dim, DimName, Dynamic, DimMul, DimProd, U1};
use core::dimension::{Dim, DimName, Dynamic, U1};
use core::iter::MatrixIter;
use core::storage::{Storage, StorageMut, Owned};
use core::allocator::Allocator;
use core::default_allocator::DefaultAllocator;
macro_rules! slice_storage_impl(
($doc: expr; $Storage: ident as $SRef: ty; $T: ident.$get_addr: ident ($Ptr: ty as $Ref: ty)) => {
#[doc = $doc]
pub struct $T<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim, Alloc> {
#[derive(Debug)]
pub struct $T<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> {
ptr: $Ptr,
shape: (R, C),
strides: (RStride, CStride),
_phantoms: PhantomData<($Ref, Alloc)>,
_phantoms: PhantomData<$Ref>,
}
// Dynamic and () are arbitrary. It's just to be able to call the constructors with
// `Slice::`
impl<'a, N: Scalar, R: Dim, C: Dim> $T<'a, N, R, C, Dynamic, Dynamic, ()> {
// Dynamic is arbitrary. It's just to be able to call the constructors with `Slice::`
impl<'a, N: Scalar, R: Dim, C: Dim> $T<'a, N, R, C, Dynamic, Dynamic> {
/// Create a new matrix slice without bound checking.
#[inline]
pub unsafe fn new_unchecked<RStor, CStor, S>(storage: $SRef, start: (usize, usize), shape: (R, C))
-> $T<'a, N, R, C, S::RStride, S::CStride, S::Alloc>
-> $T<'a, N, R, C, S::RStride, S::CStride>
where RStor: Dim,
CStor: Dim,
S: $Storage<N, RStor, CStor> {
@ -37,17 +40,29 @@ macro_rules! slice_storage_impl(
start: (usize, usize),
shape: (R, C),
strides: (RStride, CStride))
-> $T<'a, N, R, C, RStride, CStride, S::Alloc>
-> $T<'a, N, R, C, RStride, CStride>
where RStor: Dim,
CStor: Dim,
S: $Storage<N, RStor, CStor>,
RStride: Dim,
CStride: Dim {
$T::from_raw_parts(storage.$get_addr(start.0, start.1), shape, strides)
}
/// Create a new matrix slice without bound checking and from a raw pointer.
#[inline]
pub unsafe fn from_raw_parts<RStride, CStride>(ptr: $Ptr,
shape: (R, C),
strides: (RStride, CStride))
-> $T<'a, N, R, C, RStride, CStride>
where RStride: Dim,
CStride: Dim {
$T {
ptr: storage.$get_addr(start.0, start.1),
ptr: ptr,
shape: shape,
strides: (strides.0, strides.1),
strides: strides,
_phantoms: PhantomData
}
}
@ -65,11 +80,11 @@ slice_storage_impl!("A mutable matrix data storage for mutable matrix slice. Onl
);
impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim, Alloc> Copy
for SliceStorage<'a, N, R, C, RStride, CStride, Alloc> { }
impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> Copy
for SliceStorage<'a, N, R, C, RStride, CStride> { }
impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim, Alloc> Clone
for SliceStorage<'a, N, R, C, RStride, CStride, Alloc> {
impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> Clone
for SliceStorage<'a, N, R, C, RStride, CStride> {
#[inline]
fn clone(&self) -> Self {
SliceStorage {
@ -83,26 +98,11 @@ for SliceStorage<'a, N, R, C, RStride, CStride, Alloc> {
macro_rules! storage_impl(
($($T: ident),* $(,)*) => {$(
unsafe impl<'a, N, R: Dim, C: Dim, RStride: Dim, CStride: Dim, Alloc> Storage<N, R, C>
for $T<'a, N, R, C, RStride, CStride, Alloc>
where N: Scalar,
Alloc: Allocator<N, R, C> {
unsafe impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> Storage<N, R, C>
for $T<'a, N, R, C, RStride, CStride> {
type RStride = RStride;
type CStride = CStride;
type Alloc = Alloc;
#[inline]
fn into_owned(self) -> Owned<N, R, C, Self::Alloc> {
self.clone_owned()
}
#[inline]
fn clone_owned(&self) -> Owned<N, R, C, Self::Alloc> {
let (nrows, ncols) = self.shape();
let it = MatrixIter::new(self).cloned();
Alloc::allocate_from_iterator(nrows, ncols, it)
}
#[inline]
fn ptr(&self) -> *const N {
@ -118,20 +118,74 @@ macro_rules! storage_impl(
fn strides(&self) -> (Self::RStride, Self::CStride) {
self.strides
}
#[inline]
fn is_contiguous(&self) -> bool {
// Common cases that can be deduced at compile-time even if one of the dimensions
// is Dynamic.
if (RStride::is::<U1>() && C::is::<U1>()) || // Column vector.
(CStride::is::<U1>() && R::is::<U1>()) { // Row vector.
true
}
else {
let (nrows, _) = self.shape();
let (srows, scols) = self.strides();
srows.value() == 1 && scols.value() == nrows.value()
}
}
#[inline]
fn into_owned(self) -> Owned<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
self.clone_owned()
}
#[inline]
fn clone_owned(&self) -> Owned<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
let (nrows, ncols) = self.shape();
let it = MatrixIter::new(self).cloned();
DefaultAllocator::allocate_from_iterator(nrows, ncols, it)
}
#[inline]
fn as_slice(&self) -> &[N] {
let (nrows, ncols) = self.shape();
if nrows.value() != 0 && ncols.value() != 0 {
let sz = self.linear_index(nrows.value() - 1, ncols.value() - 1);
unsafe { slice::from_raw_parts(self.ptr, sz + 1) }
}
else {
unsafe { slice::from_raw_parts(self.ptr, 0) }
}
}
}
)*}
);
storage_impl!(SliceStorage, SliceStorageMut);
unsafe impl<'a, N, R: Dim, C: Dim, RStride: Dim, CStride: Dim, Alloc> StorageMut<N, R, C>
for SliceStorageMut<'a, N, R, C, RStride, CStride, Alloc>
where N: Scalar,
Alloc: Allocator<N, R, C> {
unsafe impl<'a, N: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> StorageMut<N, R, C>
for SliceStorageMut<'a, N, R, C, RStride, CStride> {
#[inline]
fn ptr_mut(&mut self) -> *mut N {
self.ptr
}
#[inline]
fn as_mut_slice(&mut self) -> &mut [N] {
let (nrows, ncols) = self.shape();
if nrows.value() != 0 && ncols.value() != 0 {
let sz = self.linear_index(nrows.value() - 1, ncols.value() - 1);
unsafe { slice::from_raw_parts_mut(self.ptr, sz + 1) }
}
else {
unsafe { slice::from_raw_parts_mut(self.ptr, 0) }
}
}
}
@ -139,35 +193,45 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
#[inline]
fn assert_slice_index(&self, start: (usize, usize), shape: (usize, usize), steps: (usize, usize)) {
let my_shape = self.shape();
assert!(start.0 + (shape.0 - 1) * steps.0 <= my_shape.0, "Matrix slicing out of bounds.");
assert!(start.1 + (shape.1 - 1) * steps.1 <= my_shape.1, "Matrix slicing out of bounds.");
// NOTE: we don't do any subtraction to avoid underflow for zero-sized matrices.
//
// Terms that would have been negative are moved to the other side of the inequality
// instead.
assert!(start.0 + (steps.0 + 1) * shape.0 <= my_shape.0 + steps.0, "Matrix slicing out of bounds.");
assert!(start.1 + (steps.1 + 1) * shape.1 <= my_shape.1 + steps.1, "Matrix slicing out of bounds.");
}
}
macro_rules! matrix_slice_impl(
($me: ident: $Me: ty, $MatrixSlice: ident, $SliceStorage: ident, $Storage: ident, $data: expr;
($me: ident: $Me: ty, $MatrixSlice: ident, $SliceStorage: ident, $Storage: ident.$get_addr: ident (), $data: expr;
$row: ident,
$row_part: ident,
$rows: ident,
$rows_with_step: ident,
$fixed_rows: ident,
$fixed_rows_with_step: ident,
$rows_generic: ident,
$rows_generic_with_step: ident,
$column: ident,
$column_part: ident,
$columns: ident,
$columns_with_step: ident,
$fixed_columns: ident,
$fixed_columns_with_step: ident,
$columns_generic: ident,
$columns_generic_with_step: ident,
$slice: ident,
$slice_with_steps: ident,
$fixed_slice: ident,
$fixed_slice_with_steps: ident,
$generic_slice: ident,
$generic_slice_with_steps: ident) => {
$generic_slice_with_steps: ident,
$rows_range_pair: ident,
$columns_range_pair: ident) => {
/// A matrix slice.
pub type $MatrixSlice<'a, N, R, C, RStride, CStride, Alloc>
= Matrix<N, R, C, $SliceStorage<'a, N, R, C, RStride, CStride, Alloc>>;
pub type $MatrixSlice<'a, N, R, C, RStride, CStride>
= Matrix<N, R, C, $SliceStorage<'a, N, R, C, RStride, CStride>>;
impl<N: Scalar, R: Dim, C: Dim, S: $Storage<N, R, C>> Matrix<N, R, C, S> {
/*
@ -175,73 +239,80 @@ macro_rules! matrix_slice_impl(
* Row slicing.
*
*/
/// Returns a slice containing the i-th column of this matrix.
/// Returns a slice containing the i-th row of this matrix.
#[inline]
pub fn $row($me: $Me, i: usize) -> $MatrixSlice<N, U1, C, S::RStride, S::CStride, S::Alloc> {
pub fn $row($me: $Me, i: usize) -> $MatrixSlice<N, U1, C, S::RStride, S::CStride> {
$me.$fixed_rows::<U1>(i)
}
/// Returns a slice containing the `n` first elements of the i-th row of this matrix.
#[inline]
pub fn $row_part($me: $Me, i: usize, n: usize) -> $MatrixSlice<N, U1, Dynamic, S::RStride, S::CStride> {
$me.$generic_slice((i, 0), (U1, Dynamic::new(n)))
}
/// Extracts from this matrix a set of consecutive rows.
#[inline]
pub fn $rows($me: $Me, first_row: usize, nrows: usize)
-> $MatrixSlice<N, Dynamic, C, S::RStride, S::CStride, S::Alloc> {
-> $MatrixSlice<N, Dynamic, C, S::RStride, S::CStride> {
let my_shape = $me.data.shape();
$me.assert_slice_index((first_row, 0), (nrows, my_shape.1.value()), (1, 1));
let shape = (Dynamic::new(nrows), my_shape.1);
unsafe {
let data = $SliceStorage::new_unchecked($data, (first_row, 0), shape);
Matrix::from_data_statically_unchecked(data)
}
$me.$rows_generic(first_row, Dynamic::new(nrows))
}
/// Extracts from this matrix a set of consecutive rows regularly spaced by `step` rows.
/// Extracts from this matrix a set of consecutive rows regularly skipping `step` rows.
#[inline]
pub fn $rows_with_step($me: $Me, first_row: usize, nrows: usize, step: usize)
-> $MatrixSlice<N, Dynamic, C, Dynamic, S::CStride, S::Alloc> {
-> $MatrixSlice<N, Dynamic, C, Dynamic, S::CStride> {
$me.$rows_generic(first_row, Dynamic::new(nrows), Dynamic::new(step))
$me.$rows_generic_with_step(first_row, Dynamic::new(nrows), step)
}
/// Extracts a compile-time number of consecutive rows from this matrix.
#[inline]
pub fn $fixed_rows<RSlice>($me: $Me, first_row: usize)
-> $MatrixSlice<N, RSlice, C, S::RStride, S::CStride, S::Alloc>
where RSlice: DimName {
pub fn $fixed_rows<RSlice: DimName>($me: $Me, first_row: usize)
-> $MatrixSlice<N, RSlice, C, S::RStride, S::CStride> {
let my_shape = $me.data.shape();
$me.assert_slice_index((first_row, 0), (RSlice::dim(), my_shape.1.value()), (1, 1));
let shape = (RSlice::name(), my_shape.1);
$me.$rows_generic(first_row, RSlice::name())
}
/// Extracts from this matrix a compile-time number of rows regularly skipping `step`
/// rows.
#[inline]
pub fn $fixed_rows_with_step<RSlice: DimName>($me: $Me, first_row: usize, step: usize)
-> $MatrixSlice<N, RSlice, C, Dynamic, S::CStride> {
$me.$rows_generic_with_step(first_row, RSlice::name(), step)
}
/// Extracts from this matrix `nrows` rows regularly skipping `step` rows. Both
/// argument may or may not be values known at compile-time.
#[inline]
pub fn $rows_generic<RSlice: Dim>($me: $Me, row_start: usize, nrows: RSlice)
-> $MatrixSlice<N, RSlice, C, S::RStride, S::CStride> {
let my_shape = $me.data.shape();
$me.assert_slice_index((row_start, 0), (nrows.value(), my_shape.1.value()), (0, 0));
let shape = (nrows, my_shape.1);
unsafe {
let data = $SliceStorage::new_unchecked($data, (first_row, 0), shape);
let data = $SliceStorage::new_unchecked($data, (row_start, 0), shape);
Matrix::from_data_statically_unchecked(data)
}
}
/// Extracts from this matrix a compile-time number of rows regularly spaced by `step` rows.
/// Extracts from this matrix `nrows` rows regularly skipping `step` rows. Both
/// argument may or may not be values known at compile-time.
#[inline]
pub fn $fixed_rows_with_step<RSlice>($me: $Me, first_row: usize, step: usize)
-> $MatrixSlice<N, RSlice, C, Dynamic, S::CStride, S::Alloc>
where RSlice: DimName {
$me.$rows_generic(first_row, RSlice::name(), Dynamic::new(step))
}
/// Extracts from this matrix `nrows` rows regularly spaced by `step` rows. Both argument may
/// or may not be values known at compile-time.
#[inline]
pub fn $rows_generic<RSlice, RStep>($me: $Me, row_start: usize, nrows: RSlice, step: RStep)
-> $MatrixSlice<N, RSlice, C, DimProd<RStep, S::RStride>, S::CStride, S::Alloc>
where RSlice: Dim,
RStep: DimMul<S::RStride> {
pub fn $rows_generic_with_step<RSlice>($me: $Me, row_start: usize, nrows: RSlice, step: usize)
-> $MatrixSlice<N, RSlice, C, Dynamic, S::CStride>
where RSlice: Dim {
let my_shape = $me.data.shape();
let my_strides = $me.data.strides();
$me.assert_slice_index((row_start, 0), (nrows.value(), my_shape.1.value()), (step.value(), 1));
$me.assert_slice_index((row_start, 0), (nrows.value(), my_shape.1.value()), (step, 0));
let strides = (step.mul(my_strides.0), my_strides.1);
let strides = (Dynamic::new((step + 1) * my_strides.0.value()), my_strides.1);
let shape = (nrows, my_shape.1);
unsafe {
@ -257,42 +328,59 @@ macro_rules! matrix_slice_impl(
*/
/// Returns a slice containing the i-th column of this matrix.
#[inline]
pub fn $column($me: $Me, i: usize) -> $MatrixSlice<N, R, U1, S::RStride, S::CStride, S::Alloc> {
pub fn $column($me: $Me, i: usize) -> $MatrixSlice<N, R, U1, S::RStride, S::CStride> {
$me.$fixed_columns::<U1>(i)
}
/// Returns a slice containing the `n` first elements of the i-th column of this matrix.
#[inline]
pub fn $column_part($me: $Me, i: usize, n: usize) -> $MatrixSlice<N, Dynamic, U1, S::RStride, S::CStride> {
$me.$generic_slice((0, i), (Dynamic::new(n), U1))
}
/// Extracts from this matrix a set of consecutive columns.
#[inline]
pub fn $columns($me: $Me, first_col: usize, ncols: usize)
-> $MatrixSlice<N, R, Dynamic, S::RStride, S::CStride, S::Alloc> {
-> $MatrixSlice<N, R, Dynamic, S::RStride, S::CStride> {
let my_shape = $me.data.shape();
$me.assert_slice_index((0, first_col), (my_shape.0.value(), ncols), (1, 1));
let shape = (my_shape.0, Dynamic::new(ncols));
unsafe {
let data = $SliceStorage::new_unchecked($data, (0, first_col), shape);
Matrix::from_data_statically_unchecked(data)
}
$me.$columns_generic(first_col, Dynamic::new(ncols))
}
/// Extracts from this matrix a set of consecutive columns regularly spaced by `step` columns.
/// Extracts from this matrix a set of consecutive columns regularly skipping `step`
/// columns.
#[inline]
pub fn $columns_with_step($me: $Me, first_col: usize, ncols: usize, step: usize)
-> $MatrixSlice<N, R, Dynamic, S::RStride, Dynamic, S::Alloc> {
-> $MatrixSlice<N, R, Dynamic, S::RStride, Dynamic> {
$me.$columns_generic(first_col, Dynamic::new(ncols), Dynamic::new(step))
$me.$columns_generic_with_step(first_col, Dynamic::new(ncols), step)
}
/// Extracts a compile-time number of consecutive columns from this matrix.
#[inline]
pub fn $fixed_columns<CSlice>($me: $Me, first_col: usize)
-> $MatrixSlice<N, R, CSlice, S::RStride, S::CStride, S::Alloc>
where CSlice: DimName {
pub fn $fixed_columns<CSlice: DimName>($me: $Me, first_col: usize)
-> $MatrixSlice<N, R, CSlice, S::RStride, S::CStride> {
$me.$columns_generic(first_col, CSlice::name())
}
/// Extracts from this matrix a compile-time number of columns regularly skipping
/// `step` columns.
#[inline]
pub fn $fixed_columns_with_step<CSlice: DimName>($me: $Me, first_col: usize, step: usize)
-> $MatrixSlice<N, R, CSlice, S::RStride, Dynamic> {
$me.$columns_generic_with_step(first_col, CSlice::name(), step)
}
/// Extracts from this matrix `ncols` columns. The number of columns may or may not be
/// known at compile-time.
#[inline]
pub fn $columns_generic<CSlice: Dim>($me: $Me, first_col: usize, ncols: CSlice)
-> $MatrixSlice<N, R, CSlice, S::RStride, S::CStride> {
let my_shape = $me.data.shape();
$me.assert_slice_index((0, first_col), (my_shape.0.value(), CSlice::dim()), (1, 1));
let shape = (my_shape.0, CSlice::name());
$me.assert_slice_index((0, first_col), (my_shape.0.value(), ncols.value()), (0, 0));
let shape = (my_shape.0, ncols);
unsafe {
let data = $SliceStorage::new_unchecked($data, (0, first_col), shape);
@ -300,30 +388,19 @@ macro_rules! matrix_slice_impl(
}
}
/// Extracts from this matrix a compile-time number of columns regularly spaced by `step`
/// columns.
#[inline]
pub fn $fixed_columns_with_step<CSlice>($me: $Me, first_col: usize, step: usize)
-> $MatrixSlice<N, R, CSlice, S::RStride, Dynamic, S::Alloc>
where CSlice: DimName {
$me.$columns_generic(first_col, CSlice::name(), Dynamic::new(step))
}
/// Extracts from this matrix `ncols` columns regularly spaced by `step` columns. Both argument may
/// Extracts from this matrix `ncols` columns skipping `step` columns. Both argument may
/// or may not be values known at compile-time.
#[inline]
pub fn $columns_generic<CSlice, CStep>($me: $Me, first_col: usize, ncols: CSlice, step: CStep)
-> $MatrixSlice<N, R, CSlice, S::RStride, DimProd<CStep, S::CStride>, S::Alloc>
where CSlice: Dim,
CStep: DimMul<S::CStride> {
pub fn $columns_generic_with_step<CSlice: Dim>($me: $Me, first_col: usize, ncols: CSlice, step: usize)
-> $MatrixSlice<N, R, CSlice, S::RStride, Dynamic> {
let my_shape = $me.data.shape();
let my_strides = $me.data.strides();
$me.assert_slice_index((0, first_col), (my_shape.0.value(), ncols.value()), (1, step.value()));
$me.assert_slice_index((0, first_col), (my_shape.0.value(), ncols.value()), (0, step));
let strides = (my_strides.0, step.mul(my_strides.1));
let strides = (my_strides.0, Dynamic::new((step + 1) * my_strides.1.value()));
let shape = (my_shape.0, ncols);
unsafe {
@ -341,9 +418,9 @@ macro_rules! matrix_slice_impl(
/// consecutive elements.
#[inline]
pub fn $slice($me: $Me, start: (usize, usize), shape: (usize, usize))
-> $MatrixSlice<N, Dynamic, Dynamic, S::RStride, S::CStride, S::Alloc> {
-> $MatrixSlice<N, Dynamic, Dynamic, S::RStride, S::CStride> {
$me.assert_slice_index(start, shape, (1, 1));
$me.assert_slice_index(start, shape, (0, 0));
let shape = (Dynamic::new(shape.0), Dynamic::new(shape.1));
unsafe {
@ -359,9 +436,8 @@ macro_rules! matrix_slice_impl(
/// original matrix.
#[inline]
pub fn $slice_with_steps($me: $Me, start: (usize, usize), shape: (usize, usize), steps: (usize, usize))
-> $MatrixSlice<N, Dynamic, Dynamic, Dynamic, Dynamic, S::Alloc> {
-> $MatrixSlice<N, Dynamic, Dynamic, Dynamic, Dynamic> {
let shape = (Dynamic::new(shape.0), Dynamic::new(shape.1));
let steps = (Dynamic::new(steps.0), Dynamic::new(steps.1));
$me.$generic_slice_with_steps(start, shape, steps)
}
@ -370,11 +446,11 @@ macro_rules! matrix_slice_impl(
/// CSlice::dim())` consecutive components.
#[inline]
pub fn $fixed_slice<RSlice, CSlice>($me: $Me, irow: usize, icol: usize)
-> $MatrixSlice<N, RSlice, CSlice, S::RStride, S::CStride, S::Alloc>
-> $MatrixSlice<N, RSlice, CSlice, S::RStride, S::CStride>
where RSlice: DimName,
CSlice: DimName {
$me.assert_slice_index((irow, icol), (RSlice::dim(), CSlice::dim()), (1, 1));
$me.assert_slice_index((irow, icol), (RSlice::dim(), CSlice::dim()), (0, 0));
let shape = (RSlice::name(), CSlice::name());
unsafe {
@ -389,22 +465,21 @@ macro_rules! matrix_slice_impl(
/// the original matrix.
#[inline]
pub fn $fixed_slice_with_steps<RSlice, CSlice>($me: $Me, start: (usize, usize), steps: (usize, usize))
-> $MatrixSlice<N, RSlice, CSlice, Dynamic, Dynamic, S::Alloc>
-> $MatrixSlice<N, RSlice, CSlice, Dynamic, Dynamic>
where RSlice: DimName,
CSlice: DimName {
let shape = (RSlice::name(), CSlice::name());
let steps = (Dynamic::new(steps.0), Dynamic::new(steps.1));
$me.$generic_slice_with_steps(start, shape, steps)
}
/// Creates a slice that may or may not have a fixed size and stride.
#[inline]
pub fn $generic_slice<RSlice, CSlice>($me: $Me, start: (usize, usize), shape: (RSlice, CSlice))
-> $MatrixSlice<N, RSlice, CSlice, S::RStride, S::CStride, S::Alloc>
-> $MatrixSlice<N, RSlice, CSlice, S::RStride, S::CStride>
where RSlice: Dim,
CSlice: Dim {
$me.assert_slice_index(start, (shape.0.value(), shape.1.value()), (1, 1));
$me.assert_slice_index(start, (shape.0.value(), shape.1.value()), (0, 0));
unsafe {
let data = $SliceStorage::new_unchecked($data, start, shape);
@ -414,69 +489,335 @@ macro_rules! matrix_slice_impl(
/// Creates a slice that may or may not have a fixed size and stride.
#[inline]
pub fn $generic_slice_with_steps<RSlice, CSlice, RStep, CStep>($me: $Me,
start: (usize, usize),
shape: (RSlice, CSlice),
steps: (RStep, CStep))
-> $MatrixSlice<N, RSlice, CSlice, DimProd<RStep, S::RStride>, DimProd<CStep, S::CStride>, S::Alloc>
pub fn $generic_slice_with_steps<RSlice, CSlice>($me: $Me,
start: (usize, usize),
shape: (RSlice, CSlice),
steps: (usize, usize))
-> $MatrixSlice<N, RSlice, CSlice, Dynamic, Dynamic>
where RSlice: Dim,
CSlice: Dim,
RStep: DimMul<S::RStride>,
CStep: DimMul<S::CStride> {
CSlice: Dim {
$me.assert_slice_index(start, (shape.0.value(), shape.1.value()), (steps.0.value(), steps.1.value()));
$me.assert_slice_index(start, (shape.0.value(), shape.1.value()), steps);
let my_strides = $me.data.strides();
let strides = (steps.0.mul(my_strides.0), steps.1.mul(my_strides.1));
let strides = (Dynamic::new((steps.0 + 1) * my_strides.0.value()),
Dynamic::new((steps.1 + 1) * my_strides.1.value()));
unsafe {
let data = $SliceStorage::new_with_strides_unchecked($data, start, shape, strides);
Matrix::from_data_statically_unchecked(data)
}
}
/*
*
* Splitting.
*
*/
/// Splits this NxM matrix into two parts delimited by two ranges.
///
/// Panics if the ranges overlap or if the first range is empty.
#[inline]
pub fn $rows_range_pair<Range1: SliceRange<R>, Range2: SliceRange<R>>($me: $Me, r1: Range1, r2: Range2)
-> ($MatrixSlice<N, Range1::Size, C, S::RStride, S::CStride>,
$MatrixSlice<N, Range2::Size, C, S::RStride, S::CStride>) {
let (nrows, ncols) = $me.data.shape();
let strides = $me.data.strides();
let start1 = r1.begin(nrows);
let start2 = r2.begin(nrows);
let end1 = r1.end(nrows);
let end2 = r2.end(nrows);
let nrows1 = r1.size(nrows);
let nrows2 = r2.size(nrows);
assert!(start2 >= end1 || start1 >= end2, "Rows range pair: the slice ranges must not overlap.");
assert!(end2 <= nrows.value(), "Rows range pair: index out of range.");
unsafe {
let ptr1 = $data.$get_addr(start1, 0);
let ptr2 = $data.$get_addr(start2, 0);
let data1 = $SliceStorage::from_raw_parts(ptr1, (nrows1, ncols), strides);
let data2 = $SliceStorage::from_raw_parts(ptr2, (nrows2, ncols), strides);
let slice1 = Matrix::from_data_statically_unchecked(data1);
let slice2 = Matrix::from_data_statically_unchecked(data2);
(slice1, slice2)
}
}
/// Splits this NxM matrix into two parts delimited by two ranges.
///
/// Panics if the ranges overlap or if the first range is empty.
#[inline]
pub fn $columns_range_pair<Range1: SliceRange<C>, Range2: SliceRange<C>>($me: $Me, r1: Range1, r2: Range2)
-> ($MatrixSlice<N, R, Range1::Size, S::RStride, S::CStride>,
$MatrixSlice<N, R, Range2::Size, S::RStride, S::CStride>) {
let (nrows, ncols) = $me.data.shape();
let strides = $me.data.strides();
let start1 = r1.begin(ncols);
let start2 = r2.begin(ncols);
let end1 = r1.end(ncols);
let end2 = r2.end(ncols);
let ncols1 = r1.size(ncols);
let ncols2 = r2.size(ncols);
assert!(start2 >= end1 || start1 >= end2, "Columns range pair: the slice ranges must not overlap.");
assert!(end2 <= ncols.value(), "Columns range pair: index out of range.");
unsafe {
let ptr1 = $data.$get_addr(0, start1);
let ptr2 = $data.$get_addr(0, start2);
let data1 = $SliceStorage::from_raw_parts(ptr1, (nrows, ncols1), strides);
let data2 = $SliceStorage::from_raw_parts(ptr2, (nrows, ncols2), strides);
let slice1 = Matrix::from_data_statically_unchecked(data1);
let slice2 = Matrix::from_data_statically_unchecked(data2);
(slice1, slice2)
}
}
}
}
);
matrix_slice_impl!(
self: &Self, MatrixSlice, SliceStorage, Storage, &self.data;
self: &Self, MatrixSlice, SliceStorage, Storage.get_address_unchecked(), &self.data;
row,
row_part,
rows,
rows_with_step,
fixed_rows,
fixed_rows_with_step,
rows_generic,
rows_generic_with_step,
column,
column_part,
columns,
columns_with_step,
fixed_columns,
fixed_columns_with_step,
columns_generic,
columns_generic_with_step,
slice,
slice_with_steps,
fixed_slice,
fixed_slice_with_steps,
generic_slice,
generic_slice_with_steps);
generic_slice_with_steps,
rows_range_pair,
columns_range_pair);
matrix_slice_impl!(
self: &mut Self, MatrixSliceMut, SliceStorageMut, StorageMut, &mut self.data;
self: &mut Self, MatrixSliceMut, SliceStorageMut, StorageMut.get_address_unchecked_mut(), &mut self.data;
row_mut,
row_part_mut,
rows_mut,
rows_with_step_mut,
fixed_rows_mut,
fixed_rows_with_step_mut,
rows_generic_mut,
rows_generic_with_step_mut,
column_mut,
column_part_mut,
columns_mut,
columns_with_step_mut,
fixed_columns_mut,
fixed_columns_with_step_mut,
columns_generic_mut,
columns_generic_with_step_mut,
slice_mut,
slice_with_steps_mut,
fixed_slice_mut,
fixed_slice_with_steps_mut,
generic_slice_mut,
generic_slice_with_steps_mut);
generic_slice_with_steps_mut,
rows_range_pair_mut,
columns_range_pair_mut);
/// A range with a size that may be known at compile-time.
///
/// This may be:
/// * A single `usize` index, e.g., `4`
/// * A left-open range `std::ops::RangeTo`, e.g., `.. 4`
/// * A right-open range `std::ops::RangeFrom`, e.g., `4 ..`
/// * A full range `std::ops::RangeFull`, e.g., `..`
pub trait SliceRange<D: Dim> {
/// Type of the range size. May be a type-level integer.
type Size: Dim;
/// The start index of the range.
fn begin(&self, shape: D) -> usize;
// NOTE: this is the index immediatly after the last index.
/// The index immediatly after the last index inside of the range.
fn end(&self, shape: D) -> usize;
/// The number of elements of the range, i.e., `self.end - self.begin`.
fn size(&self, shape: D) -> Self::Size;
}
impl<D: Dim> SliceRange<D> for usize {
type Size = U1;
#[inline(always)]
fn begin(&self, _: D) -> usize {
*self
}
#[inline(always)]
fn end(&self, _: D) -> usize {
*self + 1
}
#[inline(always)]
fn size(&self, _: D) -> Self::Size {
U1
}
}
impl<D: Dim> SliceRange<D> for Range<usize> {
type Size = Dynamic;
#[inline(always)]
fn begin(&self, _: D) -> usize {
self.start
}
#[inline(always)]
fn end(&self, _: D) -> usize {
self.end
}
#[inline(always)]
fn size(&self, _: D) -> Self::Size {
Dynamic::new(self.end - self.start)
}
}
impl<D: Dim> SliceRange<D> for RangeFrom<usize> {
type Size = Dynamic;
#[inline(always)]
fn begin(&self, _: D) -> usize {
self.start
}
#[inline(always)]
fn end(&self, dim: D) -> usize {
dim.value()
}
#[inline(always)]
fn size(&self, dim: D) -> Self::Size {
Dynamic::new(dim.value() - self.start)
}
}
impl<D: Dim> SliceRange<D> for RangeTo<usize> {
type Size = Dynamic;
#[inline(always)]
fn begin(&self, _: D) -> usize {
0
}
#[inline(always)]
fn end(&self, _: D) -> usize {
self.end
}
#[inline(always)]
fn size(&self, _: D) -> Self::Size {
Dynamic::new(self.end)
}
}
impl<D: Dim> SliceRange<D> for RangeFull {
type Size = D;
#[inline(always)]
fn begin(&self, _: D) -> usize {
0
}
#[inline(always)]
fn end(&self, dim: D) -> usize {
dim.value()
}
#[inline(always)]
fn size(&self, dim: D) -> Self::Size {
dim
}
}
impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
/// Slices a sub-matrix containing the rows indexed by the range `rows` and the columns indexed
/// by the range `cols`.
#[inline]
pub fn slice_range<RowRange, ColRange>(&self, rows: RowRange, cols: ColRange)
-> MatrixSlice<N, RowRange::Size, ColRange::Size, S::RStride, S::CStride>
where RowRange: SliceRange<R>,
ColRange: SliceRange<C> {
let (nrows, ncols) = self.data.shape();
self.generic_slice((rows.begin(nrows), cols.begin(ncols)),
(rows.size(nrows), cols.size(ncols)))
}
/// Slice containing all the rows indexed by the range `rows`.
#[inline]
pub fn rows_range<RowRange: SliceRange<R>>(&self, rows: RowRange)
-> MatrixSlice<N, RowRange::Size, C, S::RStride, S::CStride> {
self.slice_range(rows, ..)
}
/// Slice containing all the columns indexed by the range `rows`.
#[inline]
pub fn columns_range<ColRange: SliceRange<C>>(&self, cols: ColRange)
-> MatrixSlice<N, R, ColRange::Size, S::RStride, S::CStride> {
self.slice_range(.., cols)
}
}
impl<N: Scalar, R: Dim, C: Dim, S: StorageMut<N, R, C>> Matrix<N, R, C, S> {
/// Slices a mutable sub-matrix containing the rows indexed by the range `rows` and the columns
/// indexed by the range `cols`.
pub fn slice_range_mut<RowRange, ColRange>(&mut self, rows: RowRange, cols: ColRange)
-> MatrixSliceMut<N, RowRange::Size, ColRange::Size, S::RStride, S::CStride>
where RowRange: SliceRange<R>,
ColRange: SliceRange<C> {
let (nrows, ncols) = self.data.shape();
self.generic_slice_mut((rows.begin(nrows), cols.begin(ncols)),
(rows.size(nrows), cols.size(ncols)))
}
/// Slice containing all the rows indexed by the range `rows`.
#[inline]
pub fn rows_range_mut<RowRange: SliceRange<R>>(&mut self, rows: RowRange)
-> MatrixSliceMut<N, RowRange::Size, C, S::RStride, S::CStride> {
self.slice_range_mut(rows, ..)
}
/// Slice containing all the columns indexed by the range `cols`.
#[inline]
pub fn columns_range_mut<ColRange: SliceRange<C>>(&mut self, cols: ColRange)
-> MatrixSliceMut<N, R, ColRange::Size, S::RStride, S::CStride> {
self.slice_range_mut(.., cols)
}
}

View File

@ -2,7 +2,8 @@ use std::ops::Deref;
use core::Scalar;
use core::dimension::{Dim, DimName, Dynamic, U1};
use core::storage::{Storage, StorageMut, Owned, OwnedStorage};
use core::storage::{Storage, StorageMut, Owned, ContiguousStorage, ContiguousStorageMut};
use core::allocator::Allocator;
use core::default_allocator::DefaultAllocator;
#[cfg(feature = "abomonation-serialize")]
@ -48,6 +49,26 @@ impl<N, R: Dim, C: Dim> MatrixVec<N, R, C> {
pub unsafe fn data_mut(&mut self) -> &mut Vec<N> {
&mut self.data
}
/// Resizes the undelying mutable data storage and unrwaps it.
///
/// If `sz` is larger than the current size, additional elements are uninitialized.
/// If `sz` is smaller than the current size, additional elements are trucated.
#[inline]
pub unsafe fn resize(mut self, sz: usize) -> Vec<N>{
let len = self.len();
if sz < len {
self.data.set_len(sz);
self.data.shrink_to_fit();
}
else {
self.data.reserve_exact(sz - len);
self.data.set_len(sz);
}
self.data
}
}
impl<N, R: Dim, C: Dim> Deref for MatrixVec<N, R, C> {
@ -65,24 +86,14 @@ impl<N, R: Dim, C: Dim> Deref for MatrixVec<N, R, C> {
* Dynamic Dynamic
*
*/
unsafe impl<N: Scalar, C: Dim> Storage<N, Dynamic, C> for MatrixVec<N, Dynamic, C> {
unsafe impl<N: Scalar, C: Dim> Storage<N, Dynamic, C> for MatrixVec<N, Dynamic, C>
where DefaultAllocator: Allocator<N, Dynamic, C, Buffer = Self> {
type RStride = U1;
type CStride = Dynamic;
type Alloc = DefaultAllocator;
#[inline]
fn into_owned(self) -> Owned<N, Dynamic, C, Self::Alloc> {
self
}
#[inline]
fn clone_owned(&self) -> Owned<N, Dynamic, C, Self::Alloc> {
self.clone()
}
#[inline]
fn ptr(&self) -> *const N {
self[..].as_ptr()
self.data.as_ptr()
}
#[inline]
@ -94,27 +105,39 @@ unsafe impl<N: Scalar, C: Dim> Storage<N, Dynamic, C> for MatrixVec<N, Dynamic,
fn strides(&self) -> (Self::RStride, Self::CStride) {
(Self::RStride::name(), self.nrows)
}
}
unsafe impl<N: Scalar, R: DimName> Storage<N, R, Dynamic> for MatrixVec<N, R, Dynamic> {
type RStride = U1;
type CStride = R;
type Alloc = DefaultAllocator;
#[inline]
fn into_owned(self) -> Owned<N, R, Dynamic, Self::Alloc> {
fn is_contiguous(&self) -> bool {
true
}
#[inline]
fn into_owned(self) -> Owned<N, Dynamic, C>
where DefaultAllocator: Allocator<N, Dynamic, C> {
self
}
#[inline]
fn clone_owned(&self) -> Owned<N, R, Dynamic, Self::Alloc> {
fn clone_owned(&self) -> Owned<N, Dynamic, C>
where DefaultAllocator: Allocator<N, Dynamic, C> {
self.clone()
}
#[inline]
fn as_slice(&self) -> &[N] {
&self[..]
}
}
unsafe impl<N: Scalar, R: DimName> Storage<N, R, Dynamic> for MatrixVec<N, R, Dynamic>
where DefaultAllocator: Allocator<N, R, Dynamic, Buffer = Self> {
type RStride = U1;
type CStride = R;
#[inline]
fn ptr(&self) -> *const N {
self[..].as_ptr()
self.data.as_ptr()
}
#[inline]
@ -126,6 +149,28 @@ unsafe impl<N: Scalar, R: DimName> Storage<N, R, Dynamic> for MatrixVec<N, R, Dy
fn strides(&self) -> (Self::RStride, Self::CStride) {
(Self::RStride::name(), self.nrows)
}
#[inline]
fn is_contiguous(&self) -> bool {
true
}
#[inline]
fn into_owned(self) -> Owned<N, R, Dynamic>
where DefaultAllocator: Allocator<N, R, Dynamic> {
self
}
#[inline]
fn clone_owned(&self) -> Owned<N, R, Dynamic>
where DefaultAllocator: Allocator<N, R, Dynamic> {
self.clone()
}
#[inline]
fn as_slice(&self) -> &[N] {
&self[..]
}
}
@ -133,20 +178,14 @@ unsafe impl<N: Scalar, R: DimName> Storage<N, R, Dynamic> for MatrixVec<N, R, Dy
/*
*
* StorageMut, OwnedStorage.
* StorageMut, ContiguousStorage.
*
*/
unsafe impl<N: Scalar, C: Dim> StorageMut<N, Dynamic, C> for MatrixVec<N, Dynamic, C> {
unsafe impl<N: Scalar, C: Dim> StorageMut<N, Dynamic, C> for MatrixVec<N, Dynamic, C>
where DefaultAllocator: Allocator<N, Dynamic, C, Buffer = Self> {
#[inline]
fn ptr_mut(&mut self) -> *mut N {
self.as_mut_slice().as_mut_ptr()
}
}
unsafe impl<N: Scalar, C: Dim> OwnedStorage<N, Dynamic, C> for MatrixVec<N, Dynamic, C> {
#[inline]
fn as_slice(&self) -> &[N] {
&self[..]
self.data.as_mut_ptr()
}
#[inline]
@ -155,18 +194,20 @@ unsafe impl<N: Scalar, C: Dim> OwnedStorage<N, Dynamic, C> for MatrixVec<N, Dyna
}
}
unsafe impl<N: Scalar, R: DimName> StorageMut<N, R, Dynamic> for MatrixVec<N, R, Dynamic> {
#[inline]
fn ptr_mut(&mut self) -> *mut N {
self.as_mut_slice().as_mut_ptr()
}
unsafe impl<N: Scalar, C: Dim> ContiguousStorage<N, Dynamic, C> for MatrixVec<N, Dynamic, C>
where DefaultAllocator: Allocator<N, Dynamic, C, Buffer = Self> {
}
unsafe impl<N: Scalar, R: DimName> OwnedStorage<N, R, Dynamic> for MatrixVec<N, R, Dynamic> {
unsafe impl<N: Scalar, C: Dim> ContiguousStorageMut<N, Dynamic, C> for MatrixVec<N, Dynamic, C>
where DefaultAllocator: Allocator<N, Dynamic, C, Buffer = Self> {
}
unsafe impl<N: Scalar, R: DimName> StorageMut<N, R, Dynamic> for MatrixVec<N, R, Dynamic>
where DefaultAllocator: Allocator<N, R, Dynamic, Buffer = Self> {
#[inline]
fn as_slice(&self) -> &[N] {
&self[..]
fn ptr_mut(&mut self) -> *mut N {
self.data.as_mut_ptr()
}
#[inline]
@ -189,3 +230,11 @@ impl<N: Abomonation, R: Dim, C: Dim> Abomonation for MatrixVec<N, R, C> {
self.data.exhume(bytes)
}
}
unsafe impl<N: Scalar, R: DimName> ContiguousStorage<N, R, Dynamic> for MatrixVec<N, R, Dynamic>
where DefaultAllocator: Allocator<N, R, Dynamic, Buffer = Self> {
}
unsafe impl<N: Scalar, R: DimName> ContiguousStorageMut<N, R, Dynamic> for MatrixVec<N, R, Dynamic>
where DefaultAllocator: Allocator<N, R, Dynamic, Buffer = Self> {
}

View File

@ -6,6 +6,7 @@ pub mod allocator;
pub mod storage;
pub mod coordinates;
mod ops;
mod blas;
pub mod iter;
pub mod default_allocator;
@ -15,8 +16,6 @@ mod construction;
mod properties;
mod alias;
mod matrix_alga;
mod determinant;
mod inverse;
mod conversion;
mod matrix_slice;
mod matrix_array;
@ -24,8 +23,7 @@ mod matrix_vec;
mod cg;
mod unit;
mod componentwise;
mod decompositions;
mod edition;
#[doc(hidden)]
pub mod helper;

View File

@ -1,15 +1,16 @@
use std::iter;
use std::ops::{Add, AddAssign, Sub, SubAssign, Mul, MulAssign, Div, DivAssign, Neg,
Index, IndexMut};
use num::{Zero, One};
use std::cmp::PartialOrd;
use num::{Zero, One, Signed};
use alga::general::{ClosedMul, ClosedDiv, ClosedAdd, ClosedSub, ClosedNeg};
use core::{Scalar, Matrix, OwnedMatrix, SquareMatrix, MatrixSum, MatrixMul, MatrixTrMul};
use core::dimension::{Dim, DimMul, DimName, DimProd};
use core::constraint::{ShapeConstraint, SameNumberOfRows, SameNumberOfColumns, AreMultipliable};
use core::storage::{Storage, StorageMut, OwnedStorage};
use core::allocator::{SameShapeAllocator, Allocator, OwnedAllocator};
use core::{DefaultAllocator, Scalar, Matrix, MatrixN, MatrixMN, MatrixSum};
use core::dimension::{Dim, DimName, DimProd, DimMul};
use core::constraint::{ShapeConstraint, SameNumberOfRows, SameNumberOfColumns, AreMultipliable, DimEq};
use core::storage::{Storage, StorageMut, ContiguousStorageMut};
use core::allocator::{SameShapeAllocator, Allocator, SameShapeR, SameShapeC};
/*
*
@ -70,8 +71,9 @@ impl<N, R: Dim, C: Dim, S> IndexMut<(usize, usize)> for Matrix<N, R, C, S>
*/
impl<N, R: Dim, C: Dim, S> Neg for Matrix<N, R, C, S>
where N: Scalar + ClosedNeg,
S: Storage<N, R, C> {
type Output = OwnedMatrix<N, R, C, S::Alloc>;
S: Storage<N, R, C>,
DefaultAllocator: Allocator<N, R, C> {
type Output = MatrixMN<N, R, C>;
#[inline]
fn neg(self) -> Self::Output {
@ -83,8 +85,9 @@ impl<N, R: Dim, C: Dim, S> Neg for Matrix<N, R, C, S>
impl<'a, N, R: Dim, C: Dim, S> Neg for &'a Matrix<N, R, C, S>
where N: Scalar + ClosedNeg,
S: Storage<N, R, C> {
type Output = OwnedMatrix<N, R, C, S::Alloc>;
S: Storage<N, R, C>,
DefaultAllocator: Allocator<N, R, C> {
type Output = MatrixMN<N, R, C>;
#[inline]
fn neg(self) -> Self::Output {
@ -109,33 +112,156 @@ impl<N, R: Dim, C: Dim, S> Matrix<N, R, C, S>
* Addition & Substraction
*
*/
macro_rules! componentwise_binop_impl(
($Trait: ident, $method: ident, $bound: ident;
$TraitAssign: ident, $method_assign: ident) => {
$TraitAssign: ident, $method_assign: ident, $method_assign_statically_unchecked: ident,
$method_assign_statically_unchecked_rhs: ident;
$method_to: ident, $method_to_statically_unchecked: ident) => {
impl<N, R1: Dim, C1: Dim, SA: Storage<N, R1, C1>> Matrix<N, R1, C1, SA>
where N: Scalar + $bound {
/*
*
* Methods without dimension checking at compile-time.
* This is useful for code reuse because the sum representative system does not plays
* easily with static checks.
*
*/
#[inline]
fn $method_to_statically_unchecked<R2: Dim, C2: Dim, SB,
R3: Dim, C3: Dim, SC>(&self,
rhs: &Matrix<N, R2, C2, SB>,
out: &mut Matrix<N, R3, C3, SC>)
where SB: Storage<N, R2, C2>,
SC: StorageMut<N, R3, C3> {
assert!(self.shape() == rhs.shape(), "Matrix addition/subtraction dimensions mismatch.");
assert!(self.shape() == out.shape(), "Matrix addition/subtraction output dimensions mismatch.");
// This is the most common case and should be deduced at compile-time.
// FIXME: use specialization instead?
if self.data.is_contiguous() && rhs.data.is_contiguous() && out.data.is_contiguous() {
let arr1 = self.data.as_slice();
let arr2 = rhs.data.as_slice();
let out = out.data.as_mut_slice();
for i in 0 .. arr1.len() {
unsafe {
*out.get_unchecked_mut(i) = arr1.get_unchecked(i).$method(*arr2.get_unchecked(i));
}
}
}
else {
for j in 0 .. self.ncols() {
for i in 0 .. self.nrows() {
unsafe {
let val = self.get_unchecked(i, j).$method(*rhs.get_unchecked(i, j));
*out.get_unchecked_mut(i, j) = val;
}
}
}
}
}
#[inline]
fn $method_assign_statically_unchecked<R2, C2, SB>(&mut self, rhs: &Matrix<N, R2, C2, SB>)
where R2: Dim,
C2: Dim,
SA: StorageMut<N, R1, C1>,
SB: Storage<N, R2, C2> {
assert!(self.shape() == rhs.shape(), "Matrix addition/subtraction dimensions mismatch.");
// This is the most common case and should be deduced at compile-time.
// FIXME: use specialization instead?
if self.data.is_contiguous() && rhs.data.is_contiguous() {
let arr1 = self.data.as_mut_slice();
let arr2 = rhs.data.as_slice();
for i in 0 .. arr2.len() {
unsafe {
arr1.get_unchecked_mut(i).$method_assign(*arr2.get_unchecked(i));
}
}
}
else {
for j in 0 .. rhs.ncols() {
for i in 0 .. rhs.nrows() {
unsafe {
self.get_unchecked_mut(i, j).$method_assign(*rhs.get_unchecked(i, j))
}
}
}
}
}
#[inline]
fn $method_assign_statically_unchecked_rhs<R2, C2, SB>(&self, rhs: &mut Matrix<N, R2, C2, SB>)
where R2: Dim,
C2: Dim,
SB: StorageMut<N, R2, C2> {
assert!(self.shape() == rhs.shape(), "Matrix addition/subtraction dimensions mismatch.");
// This is the most common case and should be deduced at compile-time.
// FIXME: use specialization instead?
if self.data.is_contiguous() && rhs.data.is_contiguous() {
let arr1 = self.data.as_slice();
let arr2 = rhs.data.as_mut_slice();
for i in 0 .. arr1.len() {
unsafe {
let res = arr1.get_unchecked(i).$method(*arr2.get_unchecked(i));
*arr2.get_unchecked_mut(i) = res;
}
}
}
else {
for j in 0 .. self.ncols() {
for i in 0 .. self.nrows() {
unsafe {
let r = rhs.get_unchecked_mut(i, j);
*r = self.get_unchecked(i, j).$method(*r)
}
}
}
}
}
/*
*
* Methods without dimension checking at compile-time.
* This is useful for code reuse because the sum representative system does not plays
* easily with static checks.
*
*/
/// Equivalent to `self + rhs` but stores the result into `out` to avoid allocations.
#[inline]
pub fn $method_to<R2: Dim, C2: Dim, SB,
R3: Dim, C3: Dim, SC>(&self,
rhs: &Matrix<N, R2, C2, SB>,
out: &mut Matrix<N, R3, C3, SC>)
where SB: Storage<N, R2, C2>,
SC: StorageMut<N, R3, C3>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> +
SameNumberOfRows<R1, R3> + SameNumberOfColumns<C1, C3> {
self.$method_to_statically_unchecked(rhs, out)
}
}
impl<'b, N, R1, C1, R2, C2, SA, SB> $Trait<&'b Matrix<N, R2, C2, SB>> for Matrix<N, R1, C1, SA>
where R1: Dim, C1: Dim, R2: Dim, C2: Dim,
N: Scalar + $bound,
SA: Storage<N, R1, C1>,
SB: Storage<N, R2, C2>,
SA::Alloc: SameShapeAllocator<N, R1, C1, R2, C2, SA>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
type Output = MatrixSum<N, R1, C1, R2, C2, SA>;
DefaultAllocator: SameShapeAllocator<N, R1, C1, R2, C2>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
type Output = MatrixSum<N, R1, C1, R2, C2>;
#[inline]
fn $method(self, right: &'b Matrix<N, R2, C2, SB>) -> Self::Output {
assert!(self.shape() == right.shape(), "Matrix addition/subtraction dimensions mismatch.");
fn $method(self, rhs: &'b Matrix<N, R2, C2, SB>) -> Self::Output {
assert!(self.shape() == rhs.shape(), "Matrix addition/subtraction dimensions mismatch.");
let mut res = self.into_owned_sum::<R2, C2>();
// XXX: optimize our iterator!
//
// Using our own iterator prevents loop unrolling, wich breaks some optimization
// (like SIMD). On the other hand, using the slice iterator is 4x faster.
// for (left, right) in res.iter_mut().zip(right.iter()) {
for (left, right) in res.as_mut_slice().iter_mut().zip(right.iter()) {
*left = left.$method(*right)
}
res.$method_assign_statically_unchecked(rhs);
res
}
}
@ -145,26 +271,16 @@ macro_rules! componentwise_binop_impl(
N: Scalar + $bound,
SA: Storage<N, R1, C1>,
SB: Storage<N, R2, C2>,
SB::Alloc: SameShapeAllocator<N, R2, C2, R1, C1, SB>,
ShapeConstraint: SameNumberOfRows<R2, R1> + SameNumberOfColumns<C2, C1> {
type Output = MatrixSum<N, R2, C2, R1, C1, SB>;
DefaultAllocator: SameShapeAllocator<N, R2, C2, R1, C1>,
ShapeConstraint: SameNumberOfRows<R2, R1> + SameNumberOfColumns<C2, C1> {
type Output = MatrixSum<N, R2, C2, R1, C1>;
#[inline]
fn $method(self, right: Matrix<N, R2, C2, SB>) -> Self::Output {
assert!(self.shape() == right.shape(), "Matrix addition/subtraction dimensions mismatch.");
let mut res = right.into_owned_sum::<R1, C1>();
// XXX: optimize our iterator!
//
// Using our own iterator prevents loop unrolling, wich breaks some optimization
// (like SIMD). On the other hand, using the slice iterator is 4x faster.
// for (left, right) in self.iter().zip(res.iter_mut()) {
for (left, right) in self.iter().zip(res.as_mut_slice().iter_mut()) {
*right = left.$method(*right)
}
res
fn $method(self, rhs: Matrix<N, R2, C2, SB>) -> Self::Output {
let mut rhs = rhs.into_owned_sum::<R1, C1>();
assert!(self.shape() == rhs.shape(), "Matrix addition/subtraction dimensions mismatch.");
self.$method_assign_statically_unchecked_rhs(&mut rhs);
rhs
}
}
@ -173,13 +289,13 @@ macro_rules! componentwise_binop_impl(
N: Scalar + $bound,
SA: Storage<N, R1, C1>,
SB: Storage<N, R2, C2>,
SA::Alloc: SameShapeAllocator<N, R1, C1, R2, C2, SA>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
type Output = MatrixSum<N, R1, C1, R2, C2, SA>;
DefaultAllocator: SameShapeAllocator<N, R1, C1, R2, C2>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
type Output = MatrixSum<N, R1, C1, R2, C2>;
#[inline]
fn $method(self, right: Matrix<N, R2, C2, SB>) -> Self::Output {
self.$method(&right)
fn $method(self, rhs: Matrix<N, R2, C2, SB>) -> Self::Output {
self.$method(&rhs)
}
}
@ -188,13 +304,21 @@ macro_rules! componentwise_binop_impl(
N: Scalar + $bound,
SA: Storage<N, R1, C1>,
SB: Storage<N, R2, C2>,
SA::Alloc: SameShapeAllocator<N, R1, C1, R2, C2, SA>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
type Output = MatrixSum<N, R1, C1, R2, C2, SA>;
DefaultAllocator: SameShapeAllocator<N, R1, C1, R2, C2>,
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
type Output = MatrixSum<N, R1, C1, R2, C2>;
#[inline]
fn $method(self, right: &'b Matrix<N, R2, C2, SB>) -> Self::Output {
self.clone_owned().$method(right)
fn $method(self, rhs: &'b Matrix<N, R2, C2, SB>) -> Self::Output {
let mut res = unsafe {
let (nrows, ncols) = self.shape();
let nrows: SameShapeR<R1, R2> = Dim::from_usize(nrows);
let ncols: SameShapeC<C1, C2> = Dim::from_usize(ncols);
Matrix::new_uninitialized_generic(nrows, ncols)
};
self.$method_to_statically_unchecked(rhs, &mut res);
res
}
}
@ -206,11 +330,8 @@ macro_rules! componentwise_binop_impl(
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
#[inline]
fn $method_assign(&mut self, right: &'b Matrix<N, R2, C2, SB>) {
assert!(self.shape() == right.shape(), "Matrix addition/subtraction dimensions mismatch.");
for (left, right) in self.iter_mut().zip(right.iter()) {
left.$method_assign(*right)
}
fn $method_assign(&mut self, rhs: &'b Matrix<N, R2, C2, SB>) {
self.$method_assign_statically_unchecked(rhs)
}
}
@ -222,32 +343,34 @@ macro_rules! componentwise_binop_impl(
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
#[inline]
fn $method_assign(&mut self, right: Matrix<N, R2, C2, SB>) {
self.$method_assign(&right)
fn $method_assign(&mut self, rhs: Matrix<N, R2, C2, SB>) {
self.$method_assign(&rhs)
}
}
}
);
componentwise_binop_impl!(Add, add, ClosedAdd; AddAssign, add_assign);
componentwise_binop_impl!(Sub, sub, ClosedSub; SubAssign, sub_assign);
componentwise_binop_impl!(Add, add, ClosedAdd;
AddAssign, add_assign, add_assign_statically_unchecked, add_assign_statically_unchecked_mut;
add_to, add_to_statically_unchecked);
componentwise_binop_impl!(Sub, sub, ClosedSub;
SubAssign, sub_assign, sub_assign_statically_unchecked, sub_assign_statically_unchecked_mut;
sub_to, sub_to_statically_unchecked);
impl<N, R: DimName, C: DimName, S> iter::Sum for Matrix<N, R, C, S>
impl<N, R: DimName, C: DimName> iter::Sum for MatrixMN<N, R, C>
where N: Scalar + ClosedAdd + Zero,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S>
DefaultAllocator: Allocator<N, R, C>
{
fn sum<I: Iterator<Item = Matrix<N, R, C, S>>>(iter: I) -> Matrix<N, R, C, S> {
fn sum<I: Iterator<Item = MatrixMN<N, R, C>>>(iter: I) -> MatrixMN<N, R, C> {
iter.fold(Matrix::zero(), |acc, x| acc + x)
}
}
impl<'a, N, R: DimName, C: DimName, S> iter::Sum<&'a Matrix<N, R, C, S>> for Matrix<N, R, C, S>
impl<'a, N, R: DimName, C: DimName> iter::Sum<&'a MatrixMN<N, R, C>> for MatrixMN<N, R, C>
where N: Scalar + ClosedAdd + Zero,
S: OwnedStorage<N, R, C>,
S::Alloc: OwnedAllocator<N, R, C, S>
DefaultAllocator: Allocator<N, R, C>
{
fn sum<I: Iterator<Item = &'a Matrix<N, R, C, S>>>(iter: I) -> Matrix<N, R, C, S> {
fn sum<I: Iterator<Item = &'a MatrixMN<N, R, C>>>(iter: I) -> MatrixMN<N, R, C> {
iter.fold(Matrix::zero(), |acc, x| acc + x)
}
}
@ -266,8 +389,9 @@ macro_rules! componentwise_scalarop_impl(
$TraitAssign: ident, $method_assign: ident) => {
impl<N, R: Dim, C: Dim, S> $Trait<N> for Matrix<N, R, C, S>
where N: Scalar + $bound,
S: Storage<N, R, C> {
type Output = OwnedMatrix<N, R, C, S::Alloc>;
S: Storage<N, R, C>,
DefaultAllocator: Allocator<N, R, C> {
type Output = MatrixMN<N, R, C>;
#[inline]
fn $method(self, rhs: N) -> Self::Output {
@ -289,8 +413,9 @@ macro_rules! componentwise_scalarop_impl(
impl<'a, N, R: Dim, C: Dim, S> $Trait<N> for &'a Matrix<N, R, C, S>
where N: Scalar + $bound,
S: Storage<N, R, C> {
type Output = OwnedMatrix<N, R, C, S::Alloc>;
S: Storage<N, R, C>,
DefaultAllocator: Allocator<N, R, C> {
type Output = MatrixMN<N, R, C>;
#[inline]
fn $method(self, rhs: N) -> Self::Output {
@ -302,9 +427,11 @@ macro_rules! componentwise_scalarop_impl(
where N: Scalar + $bound,
S: StorageMut<N, R, C> {
#[inline]
fn $method_assign(&mut self, right: N) {
for left in self.iter_mut() {
left.$method_assign(right)
fn $method_assign(&mut self, rhs: N) {
for j in 0 .. self.ncols() {
for i in 0 .. self.nrows() {
unsafe { self.get_unchecked_mut(i, j).$method_assign(rhs) };
}
}
}
}
@ -316,35 +443,35 @@ componentwise_scalarop_impl!(Div, div, ClosedDiv; DivAssign, div_assign);
macro_rules! left_scalar_mul_impl(
($($T: ty),* $(,)*) => {$(
impl<R: Dim, C: Dim, S> Mul<Matrix<$T, R, C, S>> for $T
where S: Storage<$T, R, C> {
type Output = OwnedMatrix<$T, R, C, S::Alloc>;
impl<R: Dim, C: Dim, S: Storage<$T, R, C>> Mul<Matrix<$T, R, C, S>> for $T
where DefaultAllocator: Allocator<$T, R, C> {
type Output = MatrixMN<$T, R, C>;
#[inline]
fn mul(self, right: Matrix<$T, R, C, S>) -> Self::Output {
let mut res = right.into_owned();
fn mul(self, rhs: Matrix<$T, R, C, S>) -> Self::Output {
let mut res = rhs.into_owned();
// XXX: optimize our iterator!
//
// Using our own iterator prevents loop unrolling, wich breaks some optimization
// (like SIMD). On the other hand, using the slice iterator is 4x faster.
// for right in res.iter_mut() {
for right in res.as_mut_slice().iter_mut() {
*right = self * *right
// for rhs in res.iter_mut() {
for rhs in res.as_mut_slice().iter_mut() {
*rhs = self * *rhs
}
res
}
}
impl<'b, R: Dim, C: Dim, S> Mul<&'b Matrix<$T, R, C, S>> for $T
where S: Storage<$T, R, C> {
type Output = OwnedMatrix<$T, R, C, S::Alloc>;
impl<'b, R: Dim, C: Dim, S: Storage<$T, R, C>> Mul<&'b Matrix<$T, R, C, S>> for $T
where DefaultAllocator: Allocator<$T, R, C> {
type Output = MatrixMN<$T, R, C>;
#[inline]
fn mul(self, right: &'b Matrix<$T, R, C, S>) -> Self::Output {
self * right.clone_owned()
fn mul(self, rhs: &'b Matrix<$T, R, C, S>) -> Self::Output {
self * rhs.clone_owned()
}
}
)*}
@ -361,84 +488,66 @@ left_scalar_mul_impl!(
// Matrix × Matrix
impl<'a, 'b, N, R1: Dim, C1: Dim, R2: Dim, C2: Dim, SA, SB> Mul<&'b Matrix<N, R2, C2, SB>>
for &'a Matrix<N, R1, C1, SA>
where N: Scalar + Zero + ClosedAdd + ClosedMul,
SB: Storage<N, R2, C2>,
where N: Scalar + Zero + One + ClosedAdd + ClosedMul,
SA: Storage<N, R1, C1>,
SA::Alloc: Allocator<N, R1, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2> {
type Output = MatrixMul<N, R1, C1, C2, SA>;
SB: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, R1, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2> {
type Output = MatrixMN<N, R1, C2>;
#[inline]
fn mul(self, right: &'b Matrix<N, R2, C2, SB>) -> Self::Output {
let (nrows1, ncols1) = self.shape();
let (nrows2, ncols2) = right.shape();
assert!(ncols1 == nrows2, "Matrix multiplication dimensions mismatch.");
let mut res: MatrixMul<N, R1, C1, C2, SA> = unsafe {
Matrix::new_uninitialized_generic(self.data.shape().0, right.data.shape().1)
fn mul(self, rhs: &'b Matrix<N, R2, C2, SB>) -> Self::Output {
let mut res = unsafe {
Matrix::new_uninitialized_generic(self.data.shape().0, rhs.data.shape().1)
};
for i in 0 .. nrows1 {
for j in 0 .. ncols2 {
let mut acc = N::zero();
unsafe {
for k in 0 .. ncols1 {
acc = acc + *self.get_unchecked(i, k) * *right.get_unchecked(k, j);
}
*res.get_unchecked_mut(i, j) = acc;
}
}
}
self.mul_to(rhs, &mut res);
res
}
}
impl<'a, N, R1: Dim, C1: Dim, R2: Dim, C2: Dim, SA, SB> Mul<Matrix<N, R2, C2, SB>>
for &'a Matrix<N, R1, C1, SA>
where N: Scalar + Zero + ClosedAdd + ClosedMul,
where N: Scalar + Zero + One + ClosedAdd + ClosedMul,
SB: Storage<N, R2, C2>,
SA: Storage<N, R1, C1>,
SA::Alloc: Allocator<N, R1, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2> {
type Output = MatrixMul<N, R1, C1, C2, SA>;
DefaultAllocator: Allocator<N, R1, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2> {
type Output = MatrixMN<N, R1, C2>;
#[inline]
fn mul(self, right: Matrix<N, R2, C2, SB>) -> Self::Output {
self * &right
fn mul(self, rhs: Matrix<N, R2, C2, SB>) -> Self::Output {
self * &rhs
}
}
impl<'b, N, R1: Dim, C1: Dim, R2: Dim, C2: Dim, SA, SB> Mul<&'b Matrix<N, R2, C2, SB>>
for Matrix<N, R1, C1, SA>
where N: Scalar + Zero + ClosedAdd + ClosedMul,
where N: Scalar + Zero + One + ClosedAdd + ClosedMul,
SB: Storage<N, R2, C2>,
SA: Storage<N, R1, C1>,
SA::Alloc: Allocator<N, R1, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2> {
type Output = MatrixMul<N, R1, C1, C2, SA>;
DefaultAllocator: Allocator<N, R1, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2> {
type Output = MatrixMN<N, R1, C2>;
#[inline]
fn mul(self, right: &'b Matrix<N, R2, C2, SB>) -> Self::Output {
&self * right
fn mul(self, rhs: &'b Matrix<N, R2, C2, SB>) -> Self::Output {
&self * rhs
}
}
impl<N, R1: Dim, C1: Dim, R2: Dim, C2: Dim, SA, SB> Mul<Matrix<N, R2, C2, SB>>
for Matrix<N, R1, C1, SA>
where N: Scalar + Zero + ClosedAdd + ClosedMul,
where N: Scalar + Zero + One + ClosedAdd + ClosedMul,
SB: Storage<N, R2, C2>,
SA: Storage<N, R1, C1>,
SA::Alloc: Allocator<N, R1, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2> {
type Output = MatrixMul<N, R1, C1, C2, SA>;
DefaultAllocator: Allocator<N, R1, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2> {
type Output = MatrixMN<N, R1, C2>;
#[inline]
fn mul(self, right: Matrix<N, R2, C2, SB>) -> Self::Output {
&self * &right
fn mul(self, rhs: Matrix<N, R2, C2, SB>) -> Self::Output {
&self * &rhs
}
}
@ -447,84 +556,106 @@ for Matrix<N, R1, C1, SA>
// we can't use `a *= b` when C2 is not equal to C1.
impl<N, R1, C1, R2, SA, SB> MulAssign<Matrix<N, R2, C1, SB>> for Matrix<N, R1, C1, SA>
where R1: Dim, C1: Dim, R2: Dim,
N: Scalar + Zero + ClosedAdd + ClosedMul,
N: Scalar + Zero + One + ClosedAdd + ClosedMul,
SB: Storage<N, R2, C1>,
SA: OwnedStorage<N, R1, C1>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C1>,
SA::Alloc: OwnedAllocator<N, R1, C1, SA> {
SA: ContiguousStorageMut<N, R1, C1> + Clone,
ShapeConstraint: AreMultipliable<R1, C1, R2, C1>,
DefaultAllocator: Allocator<N, R1, C1, Buffer = SA> {
#[inline]
fn mul_assign(&mut self, right: Matrix<N, R2, C1, SB>) {
*self = &*self * right
fn mul_assign(&mut self, rhs: Matrix<N, R2, C1, SB>) {
*self = &*self * rhs
}
}
impl<'b, N, R1, C1, R2, SA, SB> MulAssign<&'b Matrix<N, R2, C1, SB>> for Matrix<N, R1, C1, SA>
where R1: Dim, C1: Dim, R2: Dim,
N: Scalar + Zero + ClosedAdd + ClosedMul,
N: Scalar + Zero + One + ClosedAdd + ClosedMul,
SB: Storage<N, R2, C1>,
SA: OwnedStorage<N, R1, C1>,
SA: ContiguousStorageMut<N, R1, C1> + Clone,
ShapeConstraint: AreMultipliable<R1, C1, R2, C1>,
// FIXME: this is too restrictive. See comments for the non-ref version.
SA::Alloc: OwnedAllocator<N, R1, C1, SA> {
DefaultAllocator: Allocator<N, R1, C1, Buffer = SA> {
#[inline]
fn mul_assign(&mut self, right: &'b Matrix<N, R2, C1, SB>) {
*self = &*self * right
fn mul_assign(&mut self, rhs: &'b Matrix<N, R2, C1, SB>) {
*self = &*self * rhs
}
}
// Transpose-multiplication.
impl<N, R1: Dim, C1: Dim, SA> Matrix<N, R1, C1, SA>
where N: Scalar,
where N: Scalar + Zero + One + ClosedAdd + ClosedMul,
SA: Storage<N, R1, C1> {
/// Equivalent to `self.transpose() * right`.
/// Equivalent to `self.transpose() * rhs`.
#[inline]
pub fn tr_mul<R2: Dim, C2: Dim, SB>(&self, right: &Matrix<N, R2, C2, SB>) -> MatrixTrMul<N, R1, C1, C2, SA>
where N: Zero + ClosedAdd + ClosedMul,
SB: Storage<N, R2, C2>,
SA::Alloc: Allocator<N, C1, C2>,
ShapeConstraint: AreMultipliable<C1, R1, R2, C2> {
pub fn tr_mul<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> MatrixMN<N, C1, C2>
where SB: Storage<N, R2, C2>,
DefaultAllocator: Allocator<N, C1, C2>,
ShapeConstraint: SameNumberOfRows<R1, R2> {
let mut res = unsafe {
Matrix::new_uninitialized_generic(self.data.shape().1, rhs.data.shape().1)
};
self.tr_mul_to(rhs, &mut res);
res
}
/// Equivalent to `self.transpose() * rhs` but stores the result into `out` to avoid
/// allocations.
#[inline]
pub fn tr_mul_to<R2: Dim, C2: Dim, SB,
R3: Dim, C3: Dim, SC>(&self,
rhs: &Matrix<N, R2, C2, SB>,
out: &mut Matrix<N, R3, C3, SC>)
where SB: Storage<N, R2, C2>,
SC: StorageMut<N, R3, C3>,
ShapeConstraint: SameNumberOfRows<R1, R2> +
DimEq<C1, R3> +
DimEq<C2, C3> {
let (nrows1, ncols1) = self.shape();
let (nrows2, ncols2) = right.shape();
let (nrows2, ncols2) = rhs.shape();
let (nrows3, ncols3) = out.shape();
assert!(nrows1 == nrows2, "Matrix multiplication dimensions mismatch.");
let mut res: MatrixTrMul<N, R1, C1, C2, SA> = unsafe {
Matrix::new_uninitialized_generic(self.data.shape().1, right.data.shape().1)
};
assert!(nrows3 == ncols1 && ncols3 == ncols2, "Matrix multiplication output dimensions mismatch.");
for i in 0 .. ncols1 {
for j in 0 .. ncols2 {
let mut acc = N::zero();
unsafe {
for k in 0 .. nrows1 {
acc += *self.get_unchecked(k, i) * *right.get_unchecked(k, j);
}
*res.get_unchecked_mut(i, j) = acc;
}
let dot = self.column(i).dot(&rhs.column(j));
unsafe { *out.get_unchecked_mut(i, j) = dot };
}
}
}
res
/// Equivalent to `self * rhs` but stores the result into `out` to avoid allocations.
#[inline]
pub fn mul_to<R2: Dim, C2: Dim, SB,
R3: Dim, C3: Dim, SC>(&self,
rhs: &Matrix<N, R2, C2, SB>,
out: &mut Matrix<N, R3, C3, SC>)
where SB: Storage<N, R2, C2>,
SC: StorageMut<N, R3, C3>,
ShapeConstraint: SameNumberOfRows<R3, R1> +
SameNumberOfColumns<C3, C2> +
AreMultipliable<R1, C1, R2, C2> {
out.gemm(N::one(), self, rhs, N::zero());
}
/// The kronecker product of two matrices (aka. tensor product of the corresponding linear
/// maps).
pub fn kronecker<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<N, R2, C2, SB>)
-> OwnedMatrix<N, DimProd<R1, R2>, DimProd<C1, C2>, SA::Alloc>
-> MatrixMN<N, DimProd<R1, R2>, DimProd<C1, C2>>
where N: ClosedMul,
R1: DimMul<R2>,
C1: DimMul<C2>,
SB: Storage<N, R2, C2>,
SA::Alloc: Allocator<N, DimProd<R1, R2>, DimProd<C1, C2>> {
DefaultAllocator: Allocator<N, DimProd<R1, R2>, DimProd<C1, C2>> {
let (nrows1, ncols1) = self.data.shape();
let (nrows2, ncols2) = rhs.data.shape();
let mut res: OwnedMatrix<_, _, _, SA::Alloc> =
unsafe { Matrix::new_uninitialized_generic(nrows1.mul(nrows2), ncols1.mul(ncols2)) };
let mut res = unsafe { Matrix::new_uninitialized_generic(nrows1.mul(nrows2), ncols1.mul(ncols2)) };
{
let mut data_res = res.data.ptr_mut();
@ -549,22 +680,76 @@ impl<N, R1: Dim, C1: Dim, SA> Matrix<N, R1, C1, SA>
}
}
impl<N, D: DimName, S> iter::Product for SquareMatrix<N, D, S>
impl<N: Scalar + ClosedAdd, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
/// Adds a scalar to `self`.
#[inline]
pub fn add_scalar(&self, rhs: N) -> MatrixMN<N, R, C>
where DefaultAllocator: Allocator<N, R, C> {
let mut res = self.clone_owned();
res.add_scalar_mut(rhs);
res
}
/// Adds a scalar to `self` in-place.
#[inline]
pub fn add_scalar_mut(&mut self, rhs: N)
where S: StorageMut<N, R, C> {
for e in self.iter_mut() {
*e += rhs
}
}
}
impl<N, D: DimName> iter::Product for MatrixN<N, D>
where N: Scalar + Zero + One + ClosedMul + ClosedAdd,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S>
DefaultAllocator: Allocator<N, D, D>
{
fn product<I: Iterator<Item = SquareMatrix<N, D, S>>>(iter: I) -> SquareMatrix<N, D, S> {
fn product<I: Iterator<Item = MatrixN<N, D>>>(iter: I) -> MatrixN<N, D> {
iter.fold(Matrix::one(), |acc, x| acc * x)
}
}
impl<'a, N, D: DimName, S> iter::Product<&'a SquareMatrix<N, D, S>> for SquareMatrix<N, D, S>
impl<'a, N, D: DimName> iter::Product<&'a MatrixN<N, D>> for MatrixN<N, D>
where N: Scalar + Zero + One + ClosedMul + ClosedAdd,
S: OwnedStorage<N, D, D>,
S::Alloc: OwnedAllocator<N, D, D, S>
DefaultAllocator: Allocator<N, D, D>
{
fn product<I: Iterator<Item = &'a SquareMatrix<N, D, S>>>(iter: I) -> SquareMatrix<N, D, S> {
fn product<I: Iterator<Item = &'a MatrixN<N, D>>>(iter: I) -> MatrixN<N, D> {
iter.fold(Matrix::one(), |acc, x| acc * x)
}
}
impl<N: Scalar + PartialOrd + Signed, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
/// Returns the absolute value of the coefficient with the largest absolute value.
#[inline]
pub fn amax(&self) -> N {
let mut max = N::zero();
for e in self.iter() {
let ae = e.abs();
if ae > max {
max = ae;
}
}
max
}
/// Returns the absolute value of the coefficient with the smallest absolute value.
#[inline]
pub fn amin(&self) -> N {
let mut it = self.iter();
let mut min = it.next().expect("amin: empty matrices not supported.").abs();
for e in it {
let ae = e.abs();
if ae < min {
min = ae;
}
}
min
}
}

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@ -2,33 +2,38 @@
use num::{Zero, One};
use approx::ApproxEq;
use alga::general::{ClosedAdd, ClosedMul, ClosedSub, Field};
use alga::general::{ClosedAdd, ClosedMul, Real};
use core::{Scalar, Matrix, SquareMatrix};
use core::dimension::Dim;
use core::{DefaultAllocator, Scalar, Matrix, SquareMatrix};
use core::dimension::{Dim, DimMin};
use core::storage::Storage;
use core::allocator::Allocator;
impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
/// Indicates if this is a square matrix.
#[inline]
pub fn is_square(&self) -> bool {
let shape = self.shape();
shape.0 == shape.1
pub fn is_empty(&self) -> bool {
let (nrows, ncols) = self.shape();
nrows == 0 || ncols == 0
}
/// Indicates if this is a square matrix.
#[inline]
pub fn is_square(&self) -> bool {
let (nrows, ncols) = self.shape();
nrows == ncols
}
}
impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
// FIXME: ApproxEq prevents us from using those methods on integer matrices…
where N: ApproxEq,
N::Epsilon: Copy {
/// Indicated if this is the identity matrix within a relative error of `eps`.
///
/// If the matrix is diagonal, this checks that diagonal elements (i.e. at coordinates `(i, i)`
/// for i from `0` to `min(R, C)`) are equal one; and that all other elements are zero.
#[inline]
pub fn is_identity(&self, eps: N::Epsilon) -> bool
where N: Zero + One {
where N: Zero + One + ApproxEq,
N::Epsilon: Copy {
let (nrows, ncols) = self.shape();
let d;
@ -75,32 +80,35 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
true
}
}
impl<N: Scalar + ApproxEq, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S>
where N: Zero + One + ClosedAdd + ClosedMul,
N::Epsilon: Copy {
/// Checks that this matrix is orthogonal, i.e., that it is square and `M × Mᵀ = Id`.
/// Checks that `Mᵀ × M = Id`.
///
/// In this definition `Id` is approximately equal to the identity matrix with a relative error
/// equal to `eps`.
#[inline]
pub fn is_orthogonal(&self, eps: N::Epsilon) -> bool {
self.is_square() && (self.tr_mul(self)).is_identity(eps)
pub fn is_orthogonal(&self, eps: N::Epsilon) -> bool
where N: Zero + One + ClosedAdd + ClosedMul + ApproxEq,
S: Storage<N, R, C>,
N::Epsilon: Copy,
DefaultAllocator: Allocator<N, C, C> {
(self.tr_mul(self)).is_identity(eps)
}
}
impl<N: Real, D: Dim, S: Storage<N, D, D>> SquareMatrix<N, D, S>
where DefaultAllocator: Allocator<N, D, D> {
/// Checks that this matrix is orthogonal and has a determinant equal to 1.
#[inline]
pub fn is_special_orthogonal(&self, eps: N::Epsilon) -> bool
where N: ClosedSub + PartialOrd {
self.is_orthogonal(eps) && self.determinant() > N::zero()
pub fn is_special_orthogonal(&self, eps: N) -> bool
where D: DimMin<D, Output = D>,
DefaultAllocator: Allocator<(usize, usize), D> {
self.is_square() && self.is_orthogonal(eps) && self.determinant() > N::zero()
}
/// Returns `true` if this matrix is invertible.
#[inline]
pub fn is_invertible(&self) -> bool
where N: Field {
pub fn is_invertible(&self) -> bool {
// FIXME: improve this?
self.clone_owned().try_inverse().is_some()
}

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@ -1,3 +1,4 @@
use std::any::TypeId;
use std::fmt::Debug;
use std::any::Any;
@ -5,5 +6,12 @@ use std::any::Any;
///
/// This does not make any assumption on the algebraic properties of `Self`.
pub trait Scalar: Copy + PartialEq + Debug + Any {
#[inline]
/// Tests if `Self` the the same as the type `T`
///
/// Typically used to test of `Self` is a f32 or a f64 with `N::is::<f32>()`.
fn is<T: Scalar>() -> bool {
TypeId::of::<Self>() == TypeId::of::<T>()
}
}
impl<T: Copy + PartialEq + Debug + Any> Scalar for T { }

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@ -1,39 +1,28 @@
//! Abstract definition of a matrix data storage.
use std::fmt::Debug;
use std::mem;
use std::any::Any;
use core::Scalar;
use dimension::Dim;
use allocator::{Allocator, SameShapeR, SameShapeC};
use core::default_allocator::DefaultAllocator;
use core::dimension::{Dim, U1};
use core::allocator::{Allocator, SameShapeR, SameShapeC};
/*
* Aliases for sum storage.
* Aliases for allocation results.
*/
/// The data storage for the sum of two matrices with dimensions `(R1, C1)` and `(R2, C2)`.
pub type SumStorage<N, R1, C1, R2, C2, SA> =
<<SA as Storage<N, R1, C1>>::Alloc as Allocator<N, SameShapeR<R1, R2>, SameShapeC<C1, C2>>>::Buffer;
pub type SameShapeStorage<N, R1, C1, R2, C2> = <DefaultAllocator as Allocator<N, SameShapeR<R1, R2>, SameShapeC<C1, C2>>>::Buffer;
/*
* Aliases for multiplication storage.
*/
/// The data storage for the multiplication of two matrices with dimensions `(R1, C1)` on the left
/// hand side, and with `C2` columns on the right hand side.
pub type MulStorage<N, R1, C1, C2, SA> =
<<SA as Storage<N, R1, C1>>::Alloc as Allocator<N, R1, C2>>::Buffer;
/// The data storage for the multiplication of two matrices with dimensions `(R1, C1)` on the left
/// hand side, and with `C2` columns on the right hand side. The first matrix is implicitly
/// transposed.
pub type TrMulStorage<N, R1, C1, C2, SA> =
<<SA as Storage<N, R1, C1>>::Alloc as Allocator<N, C1, C2>>::Buffer;
/*
* Alias for allocation result.
*/
// FIXME: better name than Owned ?
/// The owned data storage that can be allocated from `S`.
pub type Owned<N, R, C, A> =
<A as Allocator<N, R, C>>::Buffer;
pub type Owned<N, R, C = U1> = <DefaultAllocator as Allocator<N, R, C>>::Buffer;
/// The row-stride of the owned data storage for a buffer of dimension `(R, C)`.
pub type RStride<N, R, C = U1> = <<DefaultAllocator as Allocator<N, R, C>>::Buffer as Storage<N, R, C>>::RStride;
/// The column-stride of the owned data storage for a buffer of dimension `(R, C)`.
pub type CStride<N, R, C = U1> = <<DefaultAllocator as Allocator<N, R, C>>::Buffer as Storage<N, R, C>>::CStride;
/// The trait shared by all matrix data storage.
@ -45,22 +34,13 @@ pub type Owned<N, R, C, A> =
/// should **not** allow the user to modify the size of the underlying buffer with safe methods
/// (for example the `MatrixVec::data_mut` method is unsafe because the user could change the
/// vector's size so that it no longer contains enough elements: this will lead to UB.
pub unsafe trait Storage<N: Scalar, R: Dim, C: Dim>: Sized {
pub unsafe trait Storage<N: Scalar, R: Dim, C: Dim = U1>: Debug + Sized {
/// The static stride of this storage's rows.
type RStride: Dim;
/// The static stride of this storage's columns.
type CStride: Dim;
/// The allocator for this family of storage.
type Alloc: Allocator<N, R, C>;
/// Builds a matrix data storage that does not contain any reference.
fn into_owned(self) -> Owned<N, R, C, Self::Alloc>;
/// Clones this data storage into one that does not contain any reference.
fn clone_owned(&self) -> Owned<N, R, C, Self::Alloc>;
/// The matrix data pointer.
fn ptr(&self) -> *const N;
@ -110,6 +90,24 @@ pub unsafe trait Storage<N: Scalar, R: Dim, C: Dim>: Sized {
unsafe fn get_unchecked(&self, irow: usize, icol: usize) -> &N {
self.get_unchecked_linear(self.linear_index(irow, icol))
}
/// Indicates whether this data buffer stores its elements contiguously.
#[inline]
fn is_contiguous(&self) -> bool;
/// Retrieves the data buffer as a contiguous slice.
///
/// The matrix components may not be stored in a contiguous way, depending on the strides.
#[inline]
fn as_slice(&self) -> &[N];
/// Builds a matrix data storage that does not contain any reference.
fn into_owned(self) -> Owned<N, R, C>
where DefaultAllocator: Allocator<N, R, C>;
/// Clones this data storage to one that does not contain any reference.
fn clone_owned(&self) -> Owned<N, R, C>
where DefaultAllocator: Allocator<N, R, C>;
}
@ -118,7 +116,7 @@ pub unsafe trait Storage<N: Scalar, R: Dim, C: Dim>: Sized {
/// Note that a mutable access does not mean that the matrix owns its data. For example, a mutable
/// matrix slice can provide mutable access to its elements even if it does not own its data (it
/// contains only an internal reference to them).
pub unsafe trait StorageMut<N: Scalar, R: Dim, C: Dim>: Storage<N, R, C> {
pub unsafe trait StorageMut<N: Scalar, R: Dim, C: Dim = U1>: Storage<N, R, C> {
/// The matrix mutable data pointer.
fn ptr_mut(&mut self) -> *mut N;
@ -163,22 +161,24 @@ pub unsafe trait StorageMut<N: Scalar, R: Dim, C: Dim>: Storage<N, R, C> {
self.swap_unchecked_linear(lid1, lid2)
}
}
/// A matrix storage that does not contain any reference and that is stored contiguously in memory.
///
/// The storage requirement means that for any value of `i` in `[0, nrows * ncols[`, the value
/// `.get_unchecked_linear` succeeds. This trait is unsafe because failing to comply to this may
/// cause Undefined Behaviors.
pub unsafe trait OwnedStorage<N: Scalar, R: Dim, C: Dim>: StorageMut<N, R, C> + Clone + Any
where Self::Alloc: Allocator<N, R, C, Buffer = Self> {
// NOTE: We could auto-impl those two methods but we don't to make sure the user is aware that
// data must be contiguous.
/// Converts this data storage to a slice.
#[inline]
fn as_slice(&self) -> &[N];
/// Converts this data storage to a mutable slice.
/// Retrieves the mutable data buffer as a contiguous slice.
///
/// Matrix components may not be contiguous, depending on its strides.
#[inline]
fn as_mut_slice(&mut self) -> &mut [N];
}
/// A matrix storage that is stored contiguously in memory.
///
/// The storage requirement means that for any value of `i` in `[0, nrows * ncols[`, the value
/// `.get_unchecked_linear` returns one of the matrix component. This trait is unsafe because
/// failing to comply to this may cause Undefined Behaviors.
pub unsafe trait ContiguousStorage<N: Scalar, R: Dim, C: Dim = U1>: Storage<N, R, C> { }
/// A mutable matrix storage that is stored contiguously in memory.
///
/// The storage requirement means that for any value of `i` in `[0, nrows * ncols[`, the value
/// `.get_unchecked_linear` returns one of the matrix component. This trait is unsafe because
/// failing to comply to this may cause Undefined Behaviors.
pub unsafe trait ContiguousStorageMut<N: Scalar, R: Dim, C: Dim = U1>: ContiguousStorage<N, R, C> + StorageMut<N, R, C> { }

8
src/debug/mod.rs Normal file
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@ -0,0 +1,8 @@
//! Various tools useful for testing/debugging/benchmarking.
mod random_orthogonal;
mod random_sdp;
pub use self::random_orthogonal::*;
pub use self::random_sdp::*;

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@ -0,0 +1,51 @@
#[cfg(feature = "arbitrary")]
use quickcheck::{Arbitrary, Gen};
#[cfg(feature = "arbitrary")]
use core::storage::Owned;
use num_complex::Complex;
use alga::general::Real;
use core::{DefaultAllocator, MatrixN};
use core::dimension::{Dim, Dynamic, U2};
use core::allocator::Allocator;
use geometry::UnitComplex;
/// A random orthogonal matrix.
#[derive(Clone, Debug)]
pub struct RandomOrthogonal<N: Real, D: Dim = Dynamic>
where DefaultAllocator: Allocator<N, D, D> {
m: MatrixN<N, D>
}
impl<N: Real, D: Dim> RandomOrthogonal<N, D>
where DefaultAllocator: Allocator<N, D, D> {
/// Retrieve the generated matrix.
pub fn unwrap(self) -> MatrixN<N, D> {
self.m
}
/// Creates a new random orthogonal matrix from its dimension and a random reals generators.
pub fn new<Rand: FnMut() -> N>(dim: D, mut rand: Rand) -> Self {
let mut res = MatrixN::identity_generic(dim, dim);
// Create an orthogonal matrix by compositing planar 2D rotations.
for i in 0 .. dim.value() - 1 {
let c = Complex::new(rand(), rand());
let rot: UnitComplex<N> = UnitComplex::from_complex(c);
rot.rotate(&mut res.fixed_rows_mut::<U2>(i));
}
RandomOrthogonal { m: res }
}
}
#[cfg(feature = "arbitrary")]
impl<N: Real + Arbitrary + Send, D: Dim> Arbitrary for RandomOrthogonal<N, D>
where DefaultAllocator: Allocator<N, D, D>,
Owned<N, D, D>: Clone + Send {
fn arbitrary<G: Gen>(g: &mut G) -> Self {
let dim = D::try_to_usize().unwrap_or(g.gen_range(1, 50));
Self::new(D::from_usize(dim), || N::arbitrary(g))
}
}

54
src/debug/random_sdp.rs Normal file
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@ -0,0 +1,54 @@
#[cfg(feature = "arbitrary")]
use quickcheck::{Arbitrary, Gen};
#[cfg(feature = "arbitrary")]
use core::storage::Owned;
use alga::general::Real;
use core::{DefaultAllocator, MatrixN};
use core::dimension::{Dim, Dynamic};
use core::allocator::Allocator;
use debug::RandomOrthogonal;
/// A random, well-conditioned, symmetric definite-positive matrix.
#[derive(Clone, Debug)]
pub struct RandomSDP<N: Real, D: Dim = Dynamic>
where DefaultAllocator: Allocator<N, D, D> {
m: MatrixN<N, D>
}
impl<N: Real, D: Dim> RandomSDP<N, D>
where DefaultAllocator: Allocator<N, D, D> {
/// Retrieve the generated matrix.
pub fn unwrap(self) -> MatrixN<N, D> {
self.m
}
/// Creates a new well conditioned symmetric definite-positive matrix from its dimension and a
/// random reals generators.
pub fn new<Rand: FnMut() -> N>(dim: D, mut rand: Rand) -> Self {
let mut m = RandomOrthogonal::new(dim, || rand()).unwrap();
let mt = m.transpose();
for i in 0 .. dim.value() {
let mut col = m.column_mut(i);
let eigenval = N::one() + rand().abs();
col *= eigenval;
}
RandomSDP { m: m * mt }
}
}
#[cfg(feature = "arbitrary")]
impl<N: Real + Arbitrary + Send, D: Dim> Arbitrary for RandomSDP<N, D>
where DefaultAllocator: Allocator<N, D, D>,
Owned<N, D, D>: Clone + Send {
fn arbitrary<G: Gen>(g: &mut G) -> Self {
let dim = D::try_to_usize().unwrap_or(g.gen_range(1, 50));
Self::new(D::from_usize(dim), || N::arbitrary(g))
}
}

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@ -1,33 +1,43 @@
use std::fmt;
use std::hash;
use std::marker::PhantomData;
use approx::ApproxEq;
use alga::general::{Real, SubsetOf};
use alga::linear::Rotation;
use core::{Scalar, OwnedSquareMatrix};
use core::dimension::{DimName, DimNameSum, DimNameAdd, U1};
use core::storage::{Storage, OwnedStorage};
use core::allocator::{Allocator, OwnedAllocator};
use geometry::{TranslationBase, PointBase};
#[cfg(feature = "serde-serialize")]
use serde;
#[cfg(feature = "abomonation-serialize")]
use abomonation::Abomonation;
use alga::general::{Real, SubsetOf};
use alga::linear::Rotation;
/// An isometry that uses a data storage deduced from the allocator `A`.
pub type OwnedIsometryBase<N, D, A, R> =
IsometryBase<N, D, <A as Allocator<N, D, U1>>::Buffer, R>;
use core::{DefaultAllocator, MatrixN};
use core::dimension::{DimName, DimNameSum, DimNameAdd, U1};
use core::storage::Owned;
use core::allocator::Allocator;
use geometry::{Translation, Point};
/// A direct isometry, i.e., a rotation followed by a translation.
#[repr(C)]
#[derive(Hash, Debug, Clone, Copy)]
#[derive(Debug)]
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
pub struct IsometryBase<N: Scalar, D: DimName, S, R> {
#[cfg_attr(feature = "serde-serialize",
serde(bound(
serialize = "R: serde::Serialize,
DefaultAllocator: Allocator<N, D>,
Owned<N, D>: serde::Serialize")))]
#[cfg_attr(feature = "serde-serialize",
serde(bound(
deserialize = "R: serde::Deserialize<'de>,
DefaultAllocator: Allocator<N, D>,
Owned<N, D>: serde::Deserialize<'de>")))]
pub struct Isometry<N: Real, D: DimName, R>
where DefaultAllocator: Allocator<N, D> {
/// The pure rotational part of this isometry.
pub rotation: R,
/// The pure translational part of this isometry.
pub translation: TranslationBase<N, D, S>,
pub translation: Translation<N, D>,
// One dummy private field just to prevent explicit construction.
@ -36,11 +46,12 @@ pub struct IsometryBase<N: Scalar, D: DimName, S, R> {
}
#[cfg(feature = "abomonation-serialize")]
impl<N, D, S, R> Abomonation for IsometryBase<N, D, S, R>
impl<N, D, R> Abomonation for IsometryBase<N, D, R>
where N: Scalar,
D: DimName,
R: Abomonation,
TranslationBase<N, D, S>: Abomonation
TranslationBase<N, D>: Abomonation,
DefaultAllocator: Allocator<N, D>
{
unsafe fn entomb(&self, writer: &mut Vec<u8>) {
self.rotation.entomb(writer);
@ -58,15 +69,35 @@ impl<N, D, S, R> Abomonation for IsometryBase<N, D, S, R>
}
}
impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real + hash::Hash, D: DimName + hash::Hash, R: hash::Hash> hash::Hash for Isometry<N, D, R>
where DefaultAllocator: Allocator<N, D>,
Owned<N, D>: hash::Hash {
fn hash<H: hash::Hasher>(&self, state: &mut H) {
self.translation.hash(state);
self.rotation.hash(state);
}
}
impl<N: Real, D: DimName + Copy, R: Rotation<Point<N, D>> + Copy> Copy for Isometry<N, D, R>
where DefaultAllocator: Allocator<N, D>,
Owned<N, D>: Copy {
}
impl<N: Real, D: DimName, R: Rotation<Point<N, D>> + Clone> Clone for Isometry<N, D, R>
where DefaultAllocator: Allocator<N, D> {
#[inline]
fn clone(&self) -> Self {
Isometry::from_parts(self.translation.clone(), self.rotation.clone())
}
}
impl<N: Real, D: DimName, R: Rotation<Point<N, D>>> Isometry<N, D, R>
where DefaultAllocator: Allocator<N, D> {
/// Creates a new isometry from its rotational and translational parts.
#[inline]
pub fn from_parts(translation: TranslationBase<N, D, S>, rotation: R) -> IsometryBase<N, D, S, R> {
IsometryBase {
pub fn from_parts(translation: Translation<N, D>, rotation: R) -> Isometry<N, D, R> {
Isometry {
rotation: rotation,
translation: translation,
_noconstruct: PhantomData
@ -75,7 +106,7 @@ impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
/// Inverts `self`.
#[inline]
pub fn inverse(&self) -> IsometryBase<N, D, S, R> {
pub fn inverse(&self) -> Isometry<N, D, R> {
let mut res = self.clone();
res.inverse_mut();
res
@ -91,7 +122,7 @@ impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
/// Appends to `self` the given translation in-place.
#[inline]
pub fn append_translation_mut(&mut self, t: &TranslationBase<N, D, S>) {
pub fn append_translation_mut(&mut self, t: &Translation<N, D>) {
self.translation.vector += &t.vector
}
@ -105,7 +136,7 @@ impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
/// Appends in-place to `self` a rotation centered at the point `p`, i.e., the rotation that
/// lets `p` invariant.
#[inline]
pub fn append_rotation_wrt_point_mut(&mut self, r: &R, p: &PointBase<N, D, S>) {
pub fn append_rotation_wrt_point_mut(&mut self, r: &R, p: &Point<N, D>) {
self.translation.vector -= &p.coords;
self.append_rotation_mut(r);
self.translation.vector += &p.coords;
@ -115,7 +146,7 @@ impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
/// `self.translation`.
#[inline]
pub fn append_rotation_wrt_center_mut(&mut self, r: &R) {
let center = PointBase::from_coordinates(self.translation.vector.clone());
let center = Point::from_coordinates(self.translation.vector.clone());
self.append_rotation_wrt_point_mut(r, &center)
}
}
@ -124,16 +155,15 @@ impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
// and makes it hard to use it, e.g., for Transform × Isometry implementation.
// This is OK since all constructors of the isometry enforce the Rotation bound already (and
// explicit struct construction is prevented by the dummy ZST field).
impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
where N: Scalar,
S: Storage<N, D, U1> {
impl<N: Real, D: DimName, R> Isometry<N, D, R>
where DefaultAllocator: Allocator<N, D> {
/// Converts this isometry into its equivalent homogeneous transformation matrix.
#[inline]
pub fn to_homogeneous(&self) -> OwnedSquareMatrix<N, DimNameSum<D, U1>, S::Alloc>
pub fn to_homogeneous(&self) -> MatrixN<N, DimNameSum<D, U1>>
where D: DimNameAdd<U1>,
R: SubsetOf<OwnedSquareMatrix<N, DimNameSum<D, U1>, S::Alloc>>,
S::Alloc: Allocator<N, DimNameSum<D, U1>, DimNameSum<D, U1>> {
let mut res: OwnedSquareMatrix<N, _, S::Alloc> = ::convert_ref(&self.rotation);
R: SubsetOf<MatrixN<N, DimNameSum<D, U1>>>,
DefaultAllocator: Allocator<N, DimNameSum<D, U1>, DimNameSum<D, U1>> {
let mut res: MatrixN<N, _> = ::convert_ref(&self.rotation);
res.fixed_slice_mut::<D, U1>(0, D::dim()).copy_from(&self.translation.vector);
res
@ -141,30 +171,24 @@ impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
}
impl<N, D: DimName, S, R> Eq for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>> + Eq,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> Eq for Isometry<N, D, R>
where R: Rotation<Point<N, D>> + Eq,
DefaultAllocator: Allocator<N, D> {
}
impl<N, D: DimName, S, R> PartialEq for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>> + PartialEq,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> PartialEq for Isometry<N, D, R>
where R: Rotation<Point<N, D>> + PartialEq,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn eq(&self, right: &IsometryBase<N, D, S, R>) -> bool {
fn eq(&self, right: &Isometry<N, D, R>) -> bool {
self.translation == right.translation &&
self.rotation == right.rotation
}
}
impl<N, D: DimName, S, R> ApproxEq for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>> + ApproxEq<Epsilon = N::Epsilon>,
S::Alloc: OwnedAllocator<N, D, U1, S>,
impl<N: Real, D: DimName, R> ApproxEq for Isometry<N, D, R>
where R: Rotation<Point<N, D>> + ApproxEq<Epsilon = N::Epsilon>,
DefaultAllocator: Allocator<N, D>,
N::Epsilon: Copy {
type Epsilon = N::Epsilon;
@ -201,32 +225,16 @@ impl<N, D: DimName, S, R> ApproxEq for IsometryBase<N, D, S, R>
* Display
*
*/
impl<N, D: DimName, S, R> fmt::Display for IsometryBase<N, D, S, R>
where N: Real + fmt::Display,
S: OwnedStorage<N, D, U1>,
R: fmt::Display,
S::Alloc: OwnedAllocator<N, D, U1, S> + Allocator<usize, D, U1> {
impl<N: Real + fmt::Display, D: DimName, R> fmt::Display for Isometry<N, D, R>
where R: fmt::Display,
DefaultAllocator: Allocator<N, D> +
Allocator<usize, D> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
let precision = f.precision().unwrap_or(3);
try!(writeln!(f, "IsometryBase {{"));
try!(writeln!(f, "Isometry {{"));
try!(write!(f, "{:.*}", precision, self.translation));
try!(write!(f, "{:.*}", precision, self.rotation));
writeln!(f, "}}")
}
}
// /*
// *
// * Absolute
// *
// */
// impl<N: Absolute> Absolute for $t<N> {
// type AbsoluteValue = $submatrix<N::AbsoluteValue>;
//
// #[inline]
// fn abs(m: &$t<N>) -> $submatrix<N::AbsoluteValue> {
// Absolute::abs(&m.submatrix)
// }
// }

View File

@ -1,14 +1,14 @@
use alga::general::{AbstractMagma, AbstractGroup, AbstractLoop, AbstractMonoid, AbstractQuasigroup,
AbstractSemigroup, Real, Inverse, Multiplicative, Identity, Id};
use alga::linear::{Transformation, Similarity, AffineTransformation, DirectIsometry, Isometry,
use alga::linear::{Transformation, Similarity, AffineTransformation, DirectIsometry,
Rotation, ProjectiveTransformation};
use alga::linear::Isometry as AlgaIsometry;
use core::ColumnVector;
use core::dimension::{DimName, U1};
use core::storage::OwnedStorage;
use core::allocator::OwnedAllocator;
use core::{DefaultAllocator, VectorN};
use core::dimension::DimName;
use core::allocator::Allocator;
use geometry::{IsometryBase, TranslationBase, PointBase};
use geometry::{Isometry, Translation, Point};
/*
@ -16,22 +16,18 @@ use geometry::{IsometryBase, TranslationBase, PointBase};
* Algebraic structures.
*
*/
impl<N, D: DimName, S, R> Identity<Multiplicative> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> Identity<Multiplicative> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn identity() -> Self {
Self::identity()
}
}
impl<N, D: DimName, S, R> Inverse<Multiplicative> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> Inverse<Multiplicative> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn inverse(&self) -> Self {
self.inverse()
@ -43,11 +39,9 @@ impl<N, D: DimName, S, R> Inverse<Multiplicative> for IsometryBase<N, D, S, R>
}
}
impl<N, D: DimName, S, R> AbstractMagma<Multiplicative> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> AbstractMagma<Multiplicative> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn operate(&self, rhs: &Self) -> Self {
self * rhs
@ -56,11 +50,9 @@ impl<N, D: DimName, S, R> AbstractMagma<Multiplicative> for IsometryBase<N, D, S
macro_rules! impl_multiplicative_structures(
($($marker: ident<$operator: ident>),* $(,)*) => {$(
impl<N, D: DimName, S, R> $marker<$operator> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> { }
impl<N: Real, D: DimName, R> $marker<$operator> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> { }
)*}
);
@ -77,49 +69,43 @@ impl_multiplicative_structures!(
* Transformation groups.
*
*/
impl<N, D: DimName, S, R> Transformation<PointBase<N, D, S>> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> Transformation<Point<N, D>> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn transform_point(&self, pt: &PointBase<N, D, S>) -> PointBase<N, D, S> {
fn transform_point(&self, pt: &Point<N, D>) -> Point<N, D> {
self * pt
}
#[inline]
fn transform_vector(&self, v: &ColumnVector<N, D, S>) -> ColumnVector<N, D, S> {
fn transform_vector(&self, v: &VectorN<N, D>) -> VectorN<N, D> {
self * v
}
}
impl<N, D: DimName, S, R> ProjectiveTransformation<PointBase<N, D, S>> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> ProjectiveTransformation<Point<N, D>> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn inverse_transform_point(&self, pt: &PointBase<N, D, S>) -> PointBase<N, D, S> {
fn inverse_transform_point(&self, pt: &Point<N, D>) -> Point<N, D> {
self.rotation.inverse_transform_point(&(pt - &self.translation.vector))
}
#[inline]
fn inverse_transform_vector(&self, v: &ColumnVector<N, D, S>) -> ColumnVector<N, D, S> {
fn inverse_transform_vector(&self, v: &VectorN<N, D>) -> VectorN<N, D> {
self.rotation.inverse_transform_vector(v)
}
}
impl<N, D: DimName, S, R> AffineTransformation<PointBase<N, D, S>> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> AffineTransformation<Point<N, D>> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
type Rotation = R;
type NonUniformScaling = Id;
type Translation = TranslationBase<N, D, S>;
type Translation = Translation<N, D>;
#[inline]
fn decompose(&self) -> (TranslationBase<N, D, S>, R, Id, R) {
fn decompose(&self) -> (Translation<N, D>, R, Id, R) {
(self.translation.clone(), self.rotation.clone(), Id::new(), R::identity())
}
@ -136,7 +122,7 @@ impl<N, D: DimName, S, R> AffineTransformation<PointBase<N, D, S>> for IsometryB
#[inline]
fn append_rotation(&self, r: &Self::Rotation) -> Self {
let shift = r.transform_vector(&self.translation.vector);
IsometryBase::from_parts(TranslationBase::from_vector(shift), r.clone() * self.rotation.clone())
Isometry::from_parts(Translation::from_vector(shift), r.clone() * self.rotation.clone())
}
#[inline]
@ -155,22 +141,20 @@ impl<N, D: DimName, S, R> AffineTransformation<PointBase<N, D, S>> for IsometryB
}
#[inline]
fn append_rotation_wrt_point(&self, r: &Self::Rotation, p: &PointBase<N, D, S>) -> Option<Self> {
fn append_rotation_wrt_point(&self, r: &Self::Rotation, p: &Point<N, D>) -> Option<Self> {
let mut res = self.clone();
res.append_rotation_wrt_point_mut(r, p);
Some(res)
}
}
impl<N, D: DimName, S, R> Similarity<PointBase<N, D, S>> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> Similarity<Point<N, D>> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
type Scaling = Id;
#[inline]
fn translation(&self) -> TranslationBase<N, D, S> {
fn translation(&self) -> Translation<N, D> {
self.translation.clone()
}
@ -187,12 +171,10 @@ impl<N, D: DimName, S, R> Similarity<PointBase<N, D, S>> for IsometryBase<N, D,
macro_rules! marker_impl(
($($Trait: ident),*) => {$(
impl<N, D: DimName, S, R> $Trait<PointBase<N, D, S>> for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> { }
impl<N: Real, D: DimName, R> $Trait<Point<N, D>> for Isometry<N, D, R>
where R: Rotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> { }
)*}
);
marker_impl!(Isometry, DirectIsometry);
marker_impl!(AlgaIsometry, DirectIsometry);

View File

@ -1,19 +1,16 @@
use core::MatrixArray;
use core::dimension::{U1, U2, U3};
use core::dimension::{U2, U3};
use geometry::{Rotation, IsometryBase, UnitQuaternion, UnitComplex};
use geometry::{Isometry, Rotation2, Rotation3, UnitQuaternion, UnitComplex};
/// A D-dimensional isometry.
pub type Isometry<N, D> = IsometryBase<N, D, MatrixArray<N, D, U1>, Rotation<N, D>>;
/// A 2-dimensional isometry using a unit complex number for its rotational part.
pub type Isometry2<N> = IsometryBase<N, U2, MatrixArray<N, U2, U1>, UnitComplex<N>>;
pub type Isometry2<N> = Isometry<N, U2, UnitComplex<N>>;
/// A 3-dimensional isometry using a unit quaternion for its rotational part.
pub type Isometry3<N> = IsometryBase<N, U3, MatrixArray<N, U3, U1>, UnitQuaternion<N>>;
pub type Isometry3<N> = Isometry<N, U3, UnitQuaternion<N>>;
/// A 2-dimensional isometry using a rotation matrix for its rotation part.
pub type IsometryMatrix2<N> = Isometry<N, U2>;
/// A 2-dimensional isometry using a rotation matrix for its rotational part.
pub type IsometryMatrix2<N> = Isometry<N, U2, Rotation2<N>>;
/// A 3-dimensional isometry using a rotation matrix for its rotation part.
pub type IsometryMatrix3<N> = Isometry<N, U3>;
/// A 3-dimensional isometry using a rotation matrix for its rotational part.
pub type IsometryMatrix3<N> = Isometry<N, U3, Rotation3<N>>;

View File

@ -1,5 +1,7 @@
#[cfg(feature = "arbitrary")]
use quickcheck::{Arbitrary, Gen};
#[cfg(feature = "arbitrary")]
use core::storage::Owned;
use num::One;
use rand::{Rng, Rand};
@ -7,31 +9,33 @@ use rand::{Rng, Rand};
use alga::general::Real;
use alga::linear::Rotation as AlgaRotation;
use core::ColumnVector;
use core::dimension::{DimName, U1, U2, U3, U4};
use core::allocator::{OwnedAllocator, Allocator};
use core::storage::OwnedStorage;
use core::{DefaultAllocator, Vector2, Vector3};
use core::dimension::{DimName, U2, U3};
use core::allocator::Allocator;
use geometry::{PointBase, TranslationBase, RotationBase, IsometryBase, UnitQuaternionBase, UnitComplex};
use geometry::{Point, Translation, Rotation, Isometry, UnitQuaternion, UnitComplex,
Point3, Rotation2, Rotation3};
impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: AlgaRotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R: AlgaRotation<Point<N, D>>> Isometry<N, D, R>
where DefaultAllocator: Allocator<N, D> {
/// Creates a new identity isometry.
#[inline]
pub fn identity() -> Self {
Self::from_parts(TranslationBase::identity(), R::identity())
Self::from_parts(Translation::identity(), R::identity())
}
/// The isometry that applies the rotation `r` with its axis passing through the point `p`.
/// This effectively lets `p` invariant.
#[inline]
pub fn rotation_wrt_point(r: R, p: Point<N, D>) -> Self {
let shift = r.transform_vector(&-&p.coords);
Self::from_parts(Translation::from_vector(shift + p.coords), r)
}
}
impl<N, D: DimName, S, R> One for IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: AlgaRotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R: AlgaRotation<Point<N, D>>> One for Isometry<N, D, R>
where DefaultAllocator: Allocator<N, D> {
/// Creates a new identity isometry.
#[inline]
fn one() -> Self {
@ -39,37 +43,21 @@ impl<N, D: DimName, S, R> One for IsometryBase<N, D, S, R>
}
}
impl<N, D: DimName, S, R> Rand for IsometryBase<N, D, S, R>
where N: Real + Rand,
S: OwnedStorage<N, D, U1>,
R: AlgaRotation<PointBase<N, D, S>> + Rand,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real + Rand, D: DimName, R> Rand for Isometry<N, D, R>
where R: AlgaRotation<Point<N, D>> + Rand,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn rand<G: Rng>(rng: &mut G) -> Self {
Self::from_parts(rng.gen(), rng.gen())
}
}
impl<N, D: DimName, S, R> IsometryBase<N, D, S, R>
where N: Real,
S: OwnedStorage<N, D, U1>,
R: AlgaRotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
/// The isometry that applies the rotation `r` with its axis passing through the point `p`.
/// This effectively lets `p` invariant.
#[inline]
pub fn rotation_wrt_point(r: R, p: PointBase<N, D, S>) -> Self {
let shift = r.transform_vector(&-&p.coords);
Self::from_parts(TranslationBase::from_vector(shift + p.coords), r)
}
}
#[cfg(feature = "arbitrary")]
impl<N, D: DimName, S, R> Arbitrary for IsometryBase<N, D, S, R>
impl<N, D: DimName, R> Arbitrary for Isometry<N, D, R>
where N: Real + Arbitrary + Send,
S: OwnedStorage<N, D, U1> + Send,
R: AlgaRotation<PointBase<N, D, S>> + Arbitrary + Send,
S::Alloc: OwnedAllocator<N, D, U1, S> {
R: AlgaRotation<Point<N, D>> + Arbitrary + Send,
Owned<N, D>: Send,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn arbitrary<G: Gen>(rng: &mut G) -> Self {
Self::from_parts(Arbitrary::arbitrary(rng), Arbitrary::arbitrary(rng))
@ -83,45 +71,31 @@ impl<N, D: DimName, S, R> Arbitrary for IsometryBase<N, D, S, R>
*/
// 2D rotation.
impl<N, S, SR> IsometryBase<N, U2, S, RotationBase<N, U2, SR>>
where N: Real,
S: OwnedStorage<N, U2, U1, Alloc = SR::Alloc>,
SR: OwnedStorage<N, U2, U2>,
S::Alloc: OwnedAllocator<N, U2, U1, S>,
SR::Alloc: OwnedAllocator<N, U2, U2, SR> {
impl<N: Real> Isometry<N, U2, Rotation2<N>> {
/// Creates a new isometry from a translation and a rotation angle.
#[inline]
pub fn new(translation: ColumnVector<N, U2, S>, angle: N) -> Self {
Self::from_parts(TranslationBase::from_vector(translation), RotationBase::<N, U2, SR>::new(angle))
pub fn new(translation: Vector2<N>, angle: N) -> Self {
Self::from_parts(Translation::from_vector(translation), Rotation::<N, U2>::new(angle))
}
}
impl<N, S> IsometryBase<N, U2, S, UnitComplex<N>>
where N: Real,
S: OwnedStorage<N, U2, U1>,
S::Alloc: OwnedAllocator<N, U2, U1, S> {
impl<N: Real> Isometry<N, U2, UnitComplex<N>> {
/// Creates a new isometry from a translation and a rotation angle.
#[inline]
pub fn new(translation: ColumnVector<N, U2, S>, angle: N) -> Self {
Self::from_parts(TranslationBase::from_vector(translation), UnitComplex::from_angle(angle))
pub fn new(translation: Vector2<N>, angle: N) -> Self {
Self::from_parts(Translation::from_vector(translation), UnitComplex::from_angle(angle))
}
}
// 3D rotation.
macro_rules! isometry_construction_impl(
($RotId: ident < $($RotParams: ident),*>, $RRDim: ty, $RCDim: ty) => {
impl<N, S, SR> IsometryBase<N, U3, S, $RotId<$($RotParams),*>>
where N: Real,
S: OwnedStorage<N, U3, U1, Alloc = SR::Alloc>,
SR: OwnedStorage<N, $RRDim, $RCDim>,
S::Alloc: OwnedAllocator<N, U3, U1, S>,
SR::Alloc: OwnedAllocator<N, $RRDim, $RCDim, SR> +
Allocator<N, U3, U3> {
impl<N: Real> Isometry<N, U3, $RotId<$($RotParams),*>> {
/// Creates a new isometry from a translation and a rotation axis-angle.
#[inline]
pub fn new(translation: ColumnVector<N, U3, S>, axisangle: ColumnVector<N, U3, S>) -> Self {
pub fn new(translation: Vector3<N>, axisangle: Vector3<N>) -> Self {
Self::from_parts(
TranslationBase::from_vector(translation),
Translation::from_vector(translation),
$RotId::<$($RotParams),*>::from_scaled_axis(axisangle))
}
@ -137,12 +111,12 @@ macro_rules! isometry_construction_impl(
/// * up - Vertical direction. The only requirement of this parameter is to not be collinear
/// to `eye - at`. Non-collinearity is not checked.
#[inline]
pub fn new_observer_frame(eye: &PointBase<N, U3, S>,
target: &PointBase<N, U3, S>,
up: &ColumnVector<N, U3, S>)
pub fn new_observer_frame(eye: &Point3<N>,
target: &Point3<N>,
up: &Vector3<N>)
-> Self {
Self::from_parts(
TranslationBase::from_vector(eye.coords.clone()),
Translation::from_vector(eye.coords.clone()),
$RotId::new_observer_frame(&(target - eye), up))
}
@ -157,14 +131,14 @@ macro_rules! isometry_construction_impl(
/// * up - A vector approximately aligned with required the vertical axis. The only
/// requirement of this parameter is to not be collinear to `target - eye`.
#[inline]
pub fn look_at_rh(eye: &PointBase<N, U3, S>,
target: &PointBase<N, U3, S>,
up: &ColumnVector<N, U3, S>)
pub fn look_at_rh(eye: &Point3<N>,
target: &Point3<N>,
up: &Vector3<N>)
-> Self {
let rotation = $RotId::look_at_rh(&(target - eye), up);
let trans = &rotation * (-eye);
Self::from_parts(TranslationBase::from_vector(trans.coords), rotation)
Self::from_parts(Translation::from_vector(trans.coords), rotation)
}
/// Builds a left-handed look-at view matrix.
@ -178,18 +152,18 @@ macro_rules! isometry_construction_impl(
/// * up - A vector approximately aligned with required the vertical axis. The only
/// requirement of this parameter is to not be collinear to `target - eye`.
#[inline]
pub fn look_at_lh(eye: &PointBase<N, U3, S>,
target: &PointBase<N, U3, S>,
up: &ColumnVector<N, U3, S>)
pub fn look_at_lh(eye: &Point3<N>,
target: &Point3<N>,
up: &Vector3<N>)
-> Self {
let rotation = $RotId::look_at_lh(&(target - eye), up);
let trans = &rotation * (-eye);
Self::from_parts(TranslationBase::from_vector(trans.coords), rotation)
Self::from_parts(Translation::from_vector(trans.coords), rotation)
}
}
}
);
isometry_construction_impl!(RotationBase<N, U3, SR>, U3, U3);
isometry_construction_impl!(UnitQuaternionBase<N, SR>, U4, U1);
isometry_construction_impl!(Rotation3<N>, U3, U3);
isometry_construction_impl!(UnitQuaternion<N>, U4, U1);

View File

@ -1,50 +1,47 @@
use alga::general::{Real, SubsetOf, SupersetOf};
use alga::linear::Rotation;
use core::{SquareMatrix, OwnedSquareMatrix};
use core::dimension::{DimName, DimNameAdd, DimNameSum, U1};
use core::storage::OwnedStorage;
use core::allocator::{Allocator, OwnedAllocator};
use core::{DefaultAllocator, MatrixN};
use core::dimension::{DimName, DimNameAdd, DimNameSum, DimMin, U1};
use core::allocator::Allocator;
use geometry::{PointBase, TranslationBase, IsometryBase, SimilarityBase, TransformBase, SuperTCategoryOf, TAffine};
use geometry::{Point, Translation, Isometry, Similarity, Transform, SuperTCategoryOf, TAffine};
/*
* This file provides the following conversions:
* =============================================
*
* IsometryBase -> IsometryBase
* IsometryBase -> SimilarityBase
* IsometryBase -> TransformBase
* IsometryBase -> Matrix (homogeneous)
* Isometry -> Isometry
* Isometry -> Similarity
* Isometry -> Transform
* Isometry -> Matrix (homogeneous)
*/
impl<N1, N2, D: DimName, SA, SB, R1, R2> SubsetOf<IsometryBase<N2, D, SB, R2>> for IsometryBase<N1, D, SA, R1>
impl<N1, N2, D: DimName, R1, R2> SubsetOf<Isometry<N2, D, R2>> for Isometry<N1, D, R1>
where N1: Real,
N2: Real + SupersetOf<N1>,
R1: Rotation<PointBase<N1, D, SA>> + SubsetOf<R2>,
R2: Rotation<PointBase<N2, D, SB>>,
SA: OwnedStorage<N1, D, U1>,
SB: OwnedStorage<N2, D, U1>,
SA::Alloc: OwnedAllocator<N1, D, U1, SA>,
SB::Alloc: OwnedAllocator<N2, D, U1, SB> {
R1: Rotation<Point<N1, D>> + SubsetOf<R2>,
R2: Rotation<Point<N2, D>>,
DefaultAllocator: Allocator<N1, D> +
Allocator<N2, D> {
#[inline]
fn to_superset(&self) -> IsometryBase<N2, D, SB, R2> {
IsometryBase::from_parts(
fn to_superset(&self) -> Isometry<N2, D, R2> {
Isometry::from_parts(
self.translation.to_superset(),
self.rotation.to_superset()
)
}
#[inline]
fn is_in_subset(iso: &IsometryBase<N2, D, SB, R2>) -> bool {
::is_convertible::<_, TranslationBase<N1, D, SA>>(&iso.translation) &&
fn is_in_subset(iso: &Isometry<N2, D, R2>) -> bool {
::is_convertible::<_, Translation<N1, D>>(&iso.translation) &&
::is_convertible::<_, R1>(&iso.rotation)
}
#[inline]
unsafe fn from_superset_unchecked(iso: &IsometryBase<N2, D, SB, R2>) -> Self {
IsometryBase::from_parts(
unsafe fn from_superset_unchecked(iso: &Isometry<N2, D, R2>) -> Self {
Isometry::from_parts(
iso.translation.to_subset_unchecked(),
iso.rotation.to_subset_unchecked()
)
@ -52,95 +49,91 @@ impl<N1, N2, D: DimName, SA, SB, R1, R2> SubsetOf<IsometryBase<N2, D, SB, R2>> f
}
impl<N1, N2, D: DimName, SA, SB, R1, R2> SubsetOf<SimilarityBase<N2, D, SB, R2>> for IsometryBase<N1, D, SA, R1>
impl<N1, N2, D: DimName, R1, R2> SubsetOf<Similarity<N2, D, R2>> for Isometry<N1, D, R1>
where N1: Real,
N2: Real + SupersetOf<N1>,
R1: Rotation<PointBase<N1, D, SA>> + SubsetOf<R2>,
R2: Rotation<PointBase<N2, D, SB>>,
SA: OwnedStorage<N1, D, U1>,
SB: OwnedStorage<N2, D, U1>,
SA::Alloc: OwnedAllocator<N1, D, U1, SA>,
SB::Alloc: OwnedAllocator<N2, D, U1, SB> {
R1: Rotation<Point<N1, D>> + SubsetOf<R2>,
R2: Rotation<Point<N2, D>>,
DefaultAllocator: Allocator<N1, D> +
Allocator<N2, D> {
#[inline]
fn to_superset(&self) -> SimilarityBase<N2, D, SB, R2> {
SimilarityBase::from_isometry(
fn to_superset(&self) -> Similarity<N2, D, R2> {
Similarity::from_isometry(
self.to_superset(),
N2::one()
)
}
#[inline]
fn is_in_subset(sim: &SimilarityBase<N2, D, SB, R2>) -> bool {
::is_convertible::<_, IsometryBase<N1, D, SA, R1>>(&sim.isometry) &&
fn is_in_subset(sim: &Similarity<N2, D, R2>) -> bool {
::is_convertible::<_, Isometry<N1, D, R1>>(&sim.isometry) &&
sim.scaling() == N2::one()
}
#[inline]
unsafe fn from_superset_unchecked(sim: &SimilarityBase<N2, D, SB, R2>) -> Self {
unsafe fn from_superset_unchecked(sim: &Similarity<N2, D, R2>) -> Self {
::convert_ref_unchecked(&sim.isometry)
}
}
impl<N1, N2, D, SA, SB, R, C> SubsetOf<TransformBase<N2, D, SB, C>> for IsometryBase<N1, D, SA, R>
impl<N1, N2, D, R, C> SubsetOf<Transform<N2, D, C>> for Isometry<N1, D, R>
where N1: Real,
N2: Real + SupersetOf<N1>,
SA: OwnedStorage<N1, D, U1>,
SB: OwnedStorage<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>,
C: SuperTCategoryOf<TAffine>,
R: Rotation<PointBase<N1, D, SA>> +
SubsetOf<OwnedSquareMatrix<N1, DimNameSum<D, U1>, SA::Alloc>> + // needed by: .to_homogeneous()
SubsetOf<SquareMatrix<N2, DimNameSum<D, U1>, SB>>, // needed by: ::convert_unchecked(mm)
D: DimNameAdd<U1>,
SA::Alloc: OwnedAllocator<N1, D, U1, SA> +
Allocator<N1, D, D> + // needed by R
Allocator<N1, DimNameSum<D, U1>, DimNameSum<D, U1>> + // needed by: .to_homogeneous()
Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>, // needed by R
SB::Alloc: OwnedAllocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>, SB> +
Allocator<N2, D, D> + // needed by: mm.fixed_slice_mut
Allocator<N2, D, U1> + // needed by: m.fixed_slice
Allocator<N2, U1, D> { // needed by: m.fixed_slice
R: Rotation<Point<N1, D>> +
SubsetOf<MatrixN<N1, DimNameSum<D, U1>>> +
SubsetOf<MatrixN<N2, DimNameSum<D, U1>>>,
D: DimNameAdd<U1> +
DimMin<D, Output = D>, // needed by .is_special_orthogonal()
DefaultAllocator: Allocator<N1, D> +
Allocator<N1, D, D> + // needed by R
Allocator<N1, DimNameSum<D, U1>, DimNameSum<D, U1>> + // needed by: .to_homogeneous()
Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>> + // needed by R
Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>> +
Allocator<(usize, usize), D> + // needed by .is_special_orthogonal()
Allocator<N2, D, D> +
Allocator<N2, D> {
#[inline]
fn to_superset(&self) -> TransformBase<N2, D, SB, C> {
TransformBase::from_matrix_unchecked(self.to_homogeneous().to_superset())
fn to_superset(&self) -> Transform<N2, D, C> {
Transform::from_matrix_unchecked(self.to_homogeneous().to_superset())
}
#[inline]
fn is_in_subset(t: &TransformBase<N2, D, SB, C>) -> bool {
fn is_in_subset(t: &Transform<N2, D, C>) -> bool {
<Self as SubsetOf<_>>::is_in_subset(t.matrix())
}
#[inline]
unsafe fn from_superset_unchecked(t: &TransformBase<N2, D, SB, C>) -> Self {
unsafe fn from_superset_unchecked(t: &Transform<N2, D, C>) -> Self {
Self::from_superset_unchecked(t.matrix())
}
}
impl<N1, N2, D, SA, SB, R> SubsetOf<SquareMatrix<N2, DimNameSum<D, U1>, SB>> for IsometryBase<N1, D, SA, R>
impl<N1, N2, D, R> SubsetOf<MatrixN<N2, DimNameSum<D, U1>>> for Isometry<N1, D, R>
where N1: Real,
N2: Real + SupersetOf<N1>,
SA: OwnedStorage<N1, D, U1>,
SB: OwnedStorage<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>,
R: Rotation<PointBase<N1, D, SA>> +
SubsetOf<OwnedSquareMatrix<N1, DimNameSum<D, U1>, SA::Alloc>> + // needed by: .to_homogeneous()
SubsetOf<SquareMatrix<N2, DimNameSum<D, U1>, SB>>, // needed by: ::convert_unchecked(mm)
D: DimNameAdd<U1>,
SA::Alloc: OwnedAllocator<N1, D, U1, SA> +
Allocator<N1, D, D> + // needed by R
Allocator<N1, DimNameSum<D, U1>, DimNameSum<D, U1>> + // needed by: .to_homogeneous()
Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>, // needed by R
SB::Alloc: OwnedAllocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>, SB> +
Allocator<N2, D, D> + // needed by: mm.fixed_slice_mut
Allocator<N2, D, U1> + // needed by: m.fixed_slice
Allocator<N2, U1, D> { // needed by: m.fixed_slice
R: Rotation<Point<N1, D>> +
SubsetOf<MatrixN<N1, DimNameSum<D, U1>>> +
SubsetOf<MatrixN<N2, DimNameSum<D, U1>>>,
D: DimNameAdd<U1> +
DimMin<D, Output = D>, // needed by .is_special_orthogonal()
DefaultAllocator: Allocator<N1, D> +
Allocator<N1, D, D> + // needed by R
Allocator<N1, DimNameSum<D, U1>, DimNameSum<D, U1>> + // needed by: .to_homogeneous()
Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>> + // needed by R
Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>> +
Allocator<(usize, usize), D> + // needed by .is_special_orthogonal()
Allocator<N2, D, D> +
Allocator<N2, D> {
#[inline]
fn to_superset(&self) -> SquareMatrix<N2, DimNameSum<D, U1>, SB> {
fn to_superset(&self) -> MatrixN<N2, DimNameSum<D, U1>> {
self.to_homogeneous().to_superset()
}
#[inline]
fn is_in_subset(m: &SquareMatrix<N2, DimNameSum<D, U1>, SB>) -> bool {
fn is_in_subset(m: &MatrixN<N2, DimNameSum<D, U1>>) -> bool {
let rot = m.fixed_slice::<D, D>(0, 0);
let bottom = m.fixed_slice::<U1, D>(D::dim(), 0);
@ -154,9 +147,9 @@ impl<N1, N2, D, SA, SB, R> SubsetOf<SquareMatrix<N2, DimNameSum<D, U1>, SB>> for
}
#[inline]
unsafe fn from_superset_unchecked(m: &SquareMatrix<N2, DimNameSum<D, U1>, SB>) -> Self {
unsafe fn from_superset_unchecked(m: &MatrixN<N2, DimNameSum<D, U1>>) -> Self {
let t = m.fixed_slice::<D, U1>(0, D::dim()).into_owned();
let t = TranslationBase::from_vector(::convert_unchecked(t));
let t = Translation::from_vector(::convert_unchecked(t));
Self::from_parts(t, ::convert_unchecked(m.clone_owned()))
}

View File

@ -1,14 +1,13 @@
use std::ops::{Mul, MulAssign, Div, DivAssign};
use alga::general::Real;
use alga::linear::Rotation;
use alga::linear::Rotation as AlgaRotation;
use core::ColumnVector;
use core::{DefaultAllocator, VectorN};
use core::dimension::{DimName, U1, U3, U4};
use core::storage::OwnedStorage;
use core::allocator::OwnedAllocator;
use core::allocator::Allocator;
use geometry::{PointBase, RotationBase, IsometryBase, TranslationBase, UnitQuaternionBase};
use geometry::{Point, Rotation, Isometry, Translation, UnitQuaternion};
// FIXME: there are several cloning of rotations that we could probably get rid of (but we didn't
// yet because that would require to add a bound like `where for<'a, 'b> &'a R: Mul<&'b R, Output = R>`
@ -22,41 +21,41 @@ use geometry::{PointBase, RotationBase, IsometryBase, TranslationBase, UnitQuate
*
* (Operators)
*
* IsometryBase × IsometryBase
* IsometryBase × R
* Isometry × Isometry
* Isometry × R
*
*
* IsometryBase ÷ IsometryBase
* IsometryBase ÷ R
* Isometry ÷ Isometry
* Isometry ÷ R
*
* IsometryBase × PointBase
* IsometryBase × ColumnVector
* Isometry × Point
* Isometry × Vector
*
*
* IsometryBase × TranslationBase
* TranslationBase × IsometryBase
* TranslationBase × R -> IsometryBase<R>
* Isometry × Translation
* Translation × Isometry
* Translation × R -> Isometry<R>
*
* NOTE: The following are provided explicitly because we can't have R × IsometryBase.
* RotationBase × IsometryBase<RotationBase>
* UnitQuaternion × IsometryBase<UnitQuaternion>
* NOTE: The following are provided explicitly because we can't have R × Isometry.
* Rotation × Isometry<Rotation>
* UnitQuaternion × Isometry<UnitQuaternion>
*
* RotationBase ÷ IsometryBase<RotationBase>
* UnitQuaternion ÷ IsometryBase<UnitQuaternion>
* Rotation ÷ Isometry<Rotation>
* UnitQuaternion ÷ Isometry<UnitQuaternion>
*
* RotationBase × TranslationBase -> IsometryBase<RotationBase>
* UnitQuaternion × TranslationBase -> IsometryBase<UnitQuaternion>
* Rotation × Translation -> Isometry<Rotation>
* UnitQuaternion × Translation -> Isometry<UnitQuaternion>
*
*
* (Assignment Operators)
*
* IsometryBase ×= TranslationBase
* Isometry ×= Translation
*
* IsometryBase ×= IsometryBase
* IsometryBase ×= R
* Isometry ×= Isometry
* Isometry ×= R
*
* IsometryBase ÷= IsometryBase
* IsometryBase ÷= R
* Isometry ÷= Isometry
* Isometry ÷= R
*
*/
@ -65,11 +64,9 @@ macro_rules! isometry_binop_impl(
($Op: ident, $op: ident;
$lhs: ident: $Lhs: ty, $rhs: ident: $Rhs: ty, Output = $Output: ty;
$action: expr; $($lives: tt),*) => {
impl<$($lives ,)* N, D: DimName, S, R> $Op<$Rhs> for $Lhs
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<$($lives ,)* N: Real, D: DimName, R> $Op<$Rhs> for $Lhs
where R: AlgaRotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
type Output = $Output;
#[inline]
@ -114,22 +111,18 @@ macro_rules! isometry_binop_assign_impl_all(
$lhs: ident: $Lhs: ty, $rhs: ident: $Rhs: ty;
[val] => $action_val: expr;
[ref] => $action_ref: expr;) => {
impl<N, D: DimName, S, R> $OpAssign<$Rhs> for $Lhs
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Real, D: DimName, R> $OpAssign<$Rhs> for $Lhs
where R: AlgaRotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn $op_assign(&mut $lhs, $rhs: $Rhs) {
$action_val
}
}
impl<'b, N, D: DimName, S, R> $OpAssign<&'b $Rhs> for $Lhs
where N: Real,
S: OwnedStorage<N, D, U1>,
R: Rotation<PointBase<N, D, S>>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<'b, N: Real, D: DimName, R> $OpAssign<&'b $Rhs> for $Lhs
where R: AlgaRotation<Point<N, D>>,
DefaultAllocator: Allocator<N, D> {
#[inline]
fn $op_assign(&mut $lhs, $rhs: &'b $Rhs) {
$action_ref
@ -138,18 +131,18 @@ macro_rules! isometry_binop_assign_impl_all(
}
);
// IsometryBase × IsometryBase
// IsometryBase ÷ IsometryBase
// Isometry × Isometry
// Isometry ÷ Isometry
isometry_binop_impl_all!(
Mul, mul;
self: IsometryBase<N, D, S, R>, rhs: IsometryBase<N, D, S, R>, Output = IsometryBase<N, D, S, R>;
self: Isometry<N, D, R>, rhs: Isometry<N, D, R>, Output = Isometry<N, D, R>;
[val val] => &self * &rhs;
[ref val] => self * &rhs;
[val ref] => &self * rhs;
[ref ref] => {
let shift = self.rotation.transform_vector(&rhs.translation.vector);
IsometryBase::from_parts(TranslationBase::from_vector(&self.translation.vector + shift),
Isometry::from_parts(Translation::from_vector(&self.translation.vector + shift),
self.rotation.clone() * rhs.rotation.clone()) // FIXME: too bad we have to clone.
};
);
@ -157,7 +150,7 @@ isometry_binop_impl_all!(
isometry_binop_impl_all!(
Div, div;
self: IsometryBase<N, D, S, R>, rhs: IsometryBase<N, D, S, R>, Output = IsometryBase<N, D, S, R>;
self: Isometry<N, D, R>, rhs: Isometry<N, D, R>, Output = Isometry<N, D, R>;
[val val] => self * rhs.inverse();
[ref val] => self * rhs.inverse();
[val ref] => self * rhs.inverse();
@ -165,10 +158,10 @@ isometry_binop_impl_all!(
);
// IsometryBase ×= TranslationBase
// Isometry ×= Translation
isometry_binop_assign_impl_all!(
MulAssign, mul_assign;
self: IsometryBase<N, D, S, R>, rhs: TranslationBase<N, D, S>;
self: Isometry<N, D, R>, rhs: Translation<N, D>;
[val] => *self *= &rhs;
[ref] => {
let shift = self.rotation.transform_vector(&rhs.vector);
@ -176,11 +169,11 @@ isometry_binop_assign_impl_all!(
};
);
// IsometryBase ×= IsometryBase
// IsometryBase ÷= IsometryBase
// Isometry ×= Isometry
// Isometry ÷= Isometry
isometry_binop_assign_impl_all!(
MulAssign, mul_assign;
self: IsometryBase<N, D, S, R>, rhs: IsometryBase<N, D, S, R>;
self: Isometry<N, D, R>, rhs: Isometry<N, D, R>;
[val] => *self *= &rhs;
[ref] => {
let shift = self.rotation.transform_vector(&rhs.translation.vector);
@ -191,66 +184,68 @@ isometry_binop_assign_impl_all!(
isometry_binop_assign_impl_all!(
DivAssign, div_assign;
self: IsometryBase<N, D, S, R>, rhs: IsometryBase<N, D, S, R>;
self: Isometry<N, D, R>, rhs: Isometry<N, D, R>;
[val] => *self /= &rhs;
[ref] => *self *= rhs.inverse();
);
// IsometryBase ×= R
// IsometryBase ÷= R
// Isometry ×= R
// Isometry ÷= R
isometry_binop_assign_impl_all!(
MulAssign, mul_assign;
self: IsometryBase<N, D, S, R>, rhs: R;
self: Isometry<N, D, R>, rhs: R;
[val] => self.rotation *= rhs;
[ref] => self.rotation *= rhs.clone();
);
isometry_binop_assign_impl_all!(
DivAssign, div_assign;
self: IsometryBase<N, D, S, R>, rhs: R;
self: Isometry<N, D, R>, rhs: R;
// FIXME: don't invert explicitly?
[val] => *self *= rhs.inverse();
[ref] => *self *= rhs.inverse();
);
// IsometryBase × R
// IsometryBase ÷ R
// Isometry × R
// Isometry ÷ R
isometry_binop_impl_all!(
Mul, mul;
self: IsometryBase<N, D, S, R>, rhs: R, Output = IsometryBase<N, D, S, R>;
[val val] => IsometryBase::from_parts(self.translation, self.rotation * rhs);
[ref val] => IsometryBase::from_parts(self.translation.clone(), self.rotation.clone() * rhs); // FIXME: do not clone.
[val ref] => IsometryBase::from_parts(self.translation, self.rotation * rhs.clone());
[ref ref] => IsometryBase::from_parts(self.translation.clone(), self.rotation.clone() * rhs.clone());
self: Isometry<N, D, R>, rhs: R, Output = Isometry<N, D, R>;
[val val] => Isometry::from_parts(self.translation, self.rotation * rhs);
[ref val] => Isometry::from_parts(self.translation.clone(), self.rotation.clone() * rhs); // FIXME: do not clone.
[val ref] => Isometry::from_parts(self.translation, self.rotation * rhs.clone());
[ref ref] => Isometry::from_parts(self.translation.clone(), self.rotation.clone() * rhs.clone());
);
isometry_binop_impl_all!(
Div, div;
self: IsometryBase<N, D, S, R>, rhs: R, Output = IsometryBase<N, D, S, R>;
[val val] => IsometryBase::from_parts(self.translation, self.rotation / rhs);
[ref val] => IsometryBase::from_parts(self.translation.clone(), self.rotation.clone() / rhs);
[val ref] => IsometryBase::from_parts(self.translation, self.rotation / rhs.clone());
[ref ref] => IsometryBase::from_parts(self.translation.clone(), self.rotation.clone() / rhs.clone());
self: Isometry<N, D, R>, rhs: R, Output = Isometry<N, D, R>;
[val val] => Isometry::from_parts(self.translation, self.rotation / rhs);
[ref val] => Isometry::from_parts(self.translation.clone(), self.rotation.clone() / rhs);
[val ref] => Isometry::from_parts(self.translation, self.rotation / rhs.clone());
[ref ref] => Isometry::from_parts(self.translation.clone(), self.rotation.clone() / rhs.clone());
);
// IsometryBase × PointBase
// Isometry × Point
isometry_binop_impl_all!(
Mul, mul;
self: IsometryBase<N, D, S, R>, right: PointBase<N, D, S>, Output = PointBase<N, D, S>;
[val val] => self.translation * self.rotation.transform_point(&right);
self: Isometry<N, D, R>, right: Point<N, D>, Output = Point<N, D>;
[val val] => self.translation * self.rotation.transform_point(&right);
[ref val] => &self.translation * self.rotation.transform_point(&right);
[val ref] => self.translation * self.rotation.transform_point(right);
[val ref] => self.translation * self.rotation.transform_point(right);
[ref ref] => &self.translation * self.rotation.transform_point(right);
);
// IsometryBase × Vector
// Isometry × Vector
isometry_binop_impl_all!(
Mul, mul;
self: IsometryBase<N, D, S, R>, right: ColumnVector<N, D, S>, Output = ColumnVector<N, D, S>;
// FIXME: because of `transform_vector`, we cant use a generic storage type for the rhs vector,
// i.e., right: Vector<N, D, S> where S: Storage<N, D>.
self: Isometry<N, D, R>, right: VectorN<N, D>, Output = VectorN<N, D>;
[val val] => self.rotation.transform_vector(&right);
[ref val] => self.rotation.transform_vector(&right);
[val ref] => self.rotation.transform_vector(right);
@ -258,38 +253,38 @@ isometry_binop_impl_all!(
);
// IsometryBase × TranslationBase
// Isometry × Translation
isometry_binop_impl_all!(
Mul, mul;
self: IsometryBase<N, D, S, R>, right: TranslationBase<N, D, S>, Output = IsometryBase<N, D, S, R>;
self: Isometry<N, D, R>, right: Translation<N, D>, Output = Isometry<N, D, R>;
[val val] => &self * &right;
[ref val] => self * &right;
[val ref] => &self * right;
[ref ref] => {
let new_tr = &self.translation.vector + self.rotation.transform_vector(&right.vector);
IsometryBase::from_parts(TranslationBase::from_vector(new_tr), self.rotation.clone())
Isometry::from_parts(Translation::from_vector(new_tr), self.rotation.clone())
};
);
// TranslationBase × IsometryBase
// Translation × Isometry
isometry_binop_impl_all!(
Mul, mul;
self: TranslationBase<N, D, S>, right: IsometryBase<N, D, S, R>, Output = IsometryBase<N, D, S, R>;
[val val] => IsometryBase::from_parts(self * right.translation, right.rotation);
[ref val] => IsometryBase::from_parts(self * &right.translation, right.rotation);
[val ref] => IsometryBase::from_parts(self * &right.translation, right.rotation.clone());
[ref ref] => IsometryBase::from_parts(self * &right.translation, right.rotation.clone());
self: Translation<N, D>, right: Isometry<N, D, R>, Output = Isometry<N, D, R>;
[val val] => Isometry::from_parts(self * right.translation, right.rotation);
[ref val] => Isometry::from_parts(self * &right.translation, right.rotation);
[val ref] => Isometry::from_parts(self * &right.translation, right.rotation.clone());
[ref ref] => Isometry::from_parts(self * &right.translation, right.rotation.clone());
);
// TranslationBase × R
// Translation × R
isometry_binop_impl_all!(
Mul, mul;
self: TranslationBase<N, D, S>, right: R, Output = IsometryBase<N, D, S, R>;
[val val] => IsometryBase::from_parts(self, right);
[ref val] => IsometryBase::from_parts(self.clone(), right);
[val ref] => IsometryBase::from_parts(self, right.clone());
[ref ref] => IsometryBase::from_parts(self.clone(), right.clone());
self: Translation<N, D>, right: R, Output = Isometry<N, D, R>;
[val val] => Isometry::from_parts(self, right);
[ref val] => Isometry::from_parts(self.clone(), right);
[val ref] => Isometry::from_parts(self, right.clone());
[ref ref] => Isometry::from_parts(self.clone(), right.clone());
);
@ -300,12 +295,9 @@ macro_rules! isometry_from_composition_impl(
($R1: ty, $C1: ty),($R2: ty, $C2: ty) $(for $Dims: ident: $DimsBound: ident),*;
$lhs: ident: $Lhs: ty, $rhs: ident: $Rhs: ty, Output = $Output: ty;
$action: expr; $($lives: tt),*) => {
impl<$($lives ,)* N $(, $Dims: $DimsBound)*, SA, SB> $Op<$Rhs> for $Lhs
where N: Real,
SA: OwnedStorage<N, $R1, $C1>,
SB: OwnedStorage<N, $R2, $C2, Alloc = SA::Alloc>,
SA::Alloc: OwnedAllocator<N, $R1, $C1, SA>,
SB::Alloc: OwnedAllocator<N, $R2, $C2, SB> {
impl<$($lives ,)* N: Real $(, $Dims: $DimsBound)*> $Op<$Rhs> for $Lhs
where DefaultAllocator: Allocator<N, $R1, $C1> +
Allocator<N, $R2, $C2> {
type Output = $Output;
#[inline]
@ -352,51 +344,51 @@ macro_rules! isometry_from_composition_impl_all(
);
// RotationBase × TranslationBase
// Rotation × Translation
isometry_from_composition_impl_all!(
Mul, mul;
(D, D), (D, U1) for D: DimName;
self: RotationBase<N, D, SA>, right: TranslationBase<N, D, SB>, Output = IsometryBase<N, D, SB, RotationBase<N, D, SA>>;
[val val] => IsometryBase::from_parts(TranslationBase::from_vector(&self * right.vector), self);
[ref val] => IsometryBase::from_parts(TranslationBase::from_vector(self * right.vector), self.clone());
[val ref] => IsometryBase::from_parts(TranslationBase::from_vector(&self * &right.vector), self);
[ref ref] => IsometryBase::from_parts(TranslationBase::from_vector(self * &right.vector), self.clone());
self: Rotation<N, D>, right: Translation<N, D>, Output = Isometry<N, D, Rotation<N, D>>;
[val val] => Isometry::from_parts(Translation::from_vector(&self * right.vector), self);
[ref val] => Isometry::from_parts(Translation::from_vector(self * right.vector), self.clone());
[val ref] => Isometry::from_parts(Translation::from_vector(&self * &right.vector), self);
[ref ref] => Isometry::from_parts(Translation::from_vector(self * &right.vector), self.clone());
);
// UnitQuaternionBase × TranslationBase
// UnitQuaternion × Translation
isometry_from_composition_impl_all!(
Mul, mul;
(U4, U1), (U3, U1);
self: UnitQuaternionBase<N, SA>, right: TranslationBase<N, U3, SB>,
Output = IsometryBase<N, U3, SB, UnitQuaternionBase<N, SA>>;
[val val] => IsometryBase::from_parts(TranslationBase::from_vector(&self * right.vector), self);
[ref val] => IsometryBase::from_parts(TranslationBase::from_vector( self * right.vector), self.clone());
[val ref] => IsometryBase::from_parts(TranslationBase::from_vector(&self * &right.vector), self);
[ref ref] => IsometryBase::from_parts(TranslationBase::from_vector( self * &right.vector), self.clone());
self: UnitQuaternion<N>, right: Translation<N, U3>,
Output = Isometry<N, U3, UnitQuaternion<N>>;
[val val] => Isometry::from_parts(Translation::from_vector(&self * right.vector), self);
[ref val] => Isometry::from_parts(Translation::from_vector( self * right.vector), self.clone());
[val ref] => Isometry::from_parts(Translation::from_vector(&self * &right.vector), self);
[ref ref] => Isometry::from_parts(Translation::from_vector( self * &right.vector), self.clone());
);
// RotationBase × IsometryBase
// Rotation × Isometry
isometry_from_composition_impl_all!(
Mul, mul;
(D, D), (D, U1) for D: DimName;
self: RotationBase<N, D, SA>, right: IsometryBase<N, D, SB, RotationBase<N, D, SA>>,
Output = IsometryBase<N, D, SB, RotationBase<N, D, SA>>;
self: Rotation<N, D>, right: Isometry<N, D, Rotation<N, D>>,
Output = Isometry<N, D, Rotation<N, D>>;
[val val] => &self * &right;
[ref val] => self * &right;
[val ref] => &self * right;
[ref ref] => {
let shift = self * &right.translation.vector;
IsometryBase::from_parts(TranslationBase::from_vector(shift), self * &right.rotation)
Isometry::from_parts(Translation::from_vector(shift), self * &right.rotation)
};
);
// RotationBase ÷ IsometryBase
// Rotation ÷ Isometry
isometry_from_composition_impl_all!(
Div, div;
(D, D), (D, U1) for D: DimName;
self: RotationBase<N, D, SA>, right: IsometryBase<N, D, SB, RotationBase<N, D, SA>>,
Output = IsometryBase<N, D, SB, RotationBase<N, D, SA>>;
self: Rotation<N, D>, right: Isometry<N, D, Rotation<N, D>>,
Output = Isometry<N, D, Rotation<N, D>>;
// FIXME: don't call iverse explicitly?
[val val] => self * right.inverse();
[ref val] => self * right.inverse();
@ -405,28 +397,28 @@ isometry_from_composition_impl_all!(
);
// UnitQuaternion × IsometryBase
// UnitQuaternion × Isometry
isometry_from_composition_impl_all!(
Mul, mul;
(U4, U1), (U3, U1);
self: UnitQuaternionBase<N, SA>, right: IsometryBase<N, U3, SB, UnitQuaternionBase<N, SA>>,
Output = IsometryBase<N, U3, SB, UnitQuaternionBase<N, SA>>;
self: UnitQuaternion<N>, right: Isometry<N, U3, UnitQuaternion<N>>,
Output = Isometry<N, U3, UnitQuaternion<N>>;
[val val] => &self * &right;
[ref val] => self * &right;
[val ref] => &self * right;
[ref ref] => {
let shift = self * &right.translation.vector;
IsometryBase::from_parts(TranslationBase::from_vector(shift), self * &right.rotation)
Isometry::from_parts(Translation::from_vector(shift), self * &right.rotation)
};
);
// UnitQuaternion ÷ IsometryBase
// UnitQuaternion ÷ Isometry
isometry_from_composition_impl_all!(
Div, div;
(U4, U1), (U3, U1);
self: UnitQuaternionBase<N, SA>, right: IsometryBase<N, U3, SB, UnitQuaternionBase<N, SA>>,
Output = IsometryBase<N, U3, SB, UnitQuaternionBase<N, SA>>;
self: UnitQuaternion<N>, right: Isometry<N, U3, UnitQuaternion<N>>,
Output = Isometry<N, U3, UnitQuaternion<N>>;
// FIXME: don't call inverse explicitly?
[val val] => self * right.inverse();
[ref val] => self * right.inverse();

View File

@ -14,7 +14,7 @@ mod point_coordinates;
mod rotation;
mod rotation_construction;
mod rotation_ops;
mod rotation_alga; // FIXME: implement RotationBase methods.
mod rotation_alga; // FIXME: implement Rotation methods.
mod rotation_conversion;
mod rotation_alias;
mod rotation_specialization;
@ -23,9 +23,8 @@ mod quaternion;
mod quaternion_construction;
mod quaternion_ops;
mod quaternion_alga;
mod quaternion_alias;
mod quaternion_coordinates;
mod quaternion_conversion;
mod quaternion_coordinates;
mod unit_complex;
mod unit_complex_construction;
@ -61,6 +60,8 @@ mod transform_alga;
mod transform_conversion;
mod transform_alias;
mod reflection;
mod orthographic;
mod perspective;
@ -71,7 +72,6 @@ pub use self::rotation::*;
pub use self::rotation_alias::*;
pub use self::quaternion::*;
pub use self::quaternion_alias::*;
pub use self::unit_complex::*;
@ -87,5 +87,7 @@ pub use self::similarity_alias::*;
pub use self::transform::*;
pub use self::transform_alias::*;
pub use self::orthographic::{OrthographicBase, Orthographic3};
pub use self::perspective::{PerspectiveBase, Perspective3};
pub use self::reflection::*;
pub use self::orthographic::Orthographic3;
pub use self::perspective::Perspective3;

View File

@ -8,7 +8,7 @@ macro_rules! md_impl(
// Operator, operator method, and calar bounds.
$Op: ident, $op: ident $(where N: $($ScalarBounds: ident),*)*;
// Storage dimensions, and dimension bounds.
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident $(<$BoundParam: ty>)*),+
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident $(<$($BoundParam: ty),*>)*),+
// [Optional] Extra allocator bounds.
$(where $ConstraintType: ty: $ConstraintBound: ident<$($ConstraintBoundParams: ty $( = $EqBound: ty )*),*> )*;
// Argument identifiers and types + output.
@ -17,10 +17,11 @@ macro_rules! md_impl(
$action: expr;
// Lifetime.
$($lives: tt),*) => {
impl<$($lives ,)* N $(, $Dims: $DimsBound $(<$BoundParam>)*)*, SA, SB> $Op<$Rhs> for $Lhs
where N: Scalar + Zero + ClosedAdd + ClosedMul $($(+ $ScalarBounds)*)*,
SA: Storage<N, $R1, $C1>,
SB: Storage<N, $R2, $C2>,
impl<$($lives ,)* N $(, $Dims: $DimsBound $(<$($BoundParam),*>)*)*> $Op<$Rhs> for $Lhs
where N: Scalar + Zero + One + ClosedAdd + ClosedMul $($(+ $ScalarBounds)*)*,
DefaultAllocator: Allocator<N, $R1, $C1> +
Allocator<N, $R2, $C2> +
Allocator<N, $R1, $C2>,
$( $ConstraintType: $ConstraintBound<$( $ConstraintBoundParams $( = $EqBound )*),*> ),*
{
type Output = $Result;
@ -41,7 +42,7 @@ macro_rules! md_impl_all(
// Operator, operator method, and calar bounds.
$Op: ident, $op: ident $(where N: $($ScalarBounds: ident),*)*;
// Storage dimensions, and dimension bounds.
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident $(<$BoundParam: ty>)*),+
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident $(<$($BoundParam: ty),*>)*),+
// [Optional] Extra allocator bounds.
$(where $ConstraintType: ty: $ConstraintBound: ident<$($ConstraintBoundParams: ty $( = $EqBound: ty )*),*> )*;
// Argument identifiers and types + output.
@ -54,28 +55,28 @@ macro_rules! md_impl_all(
md_impl!(
$Op, $op $(where N: $($ScalarBounds),*)*;
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$BoundParam>)*),+
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$($BoundParam),*>)*),+
$(where $ConstraintType: $ConstraintBound<$($ConstraintBoundParams $( = $EqBound )*),*>)*;
$lhs: $Lhs, $rhs: $Rhs, Output = $Result;
$action_val_val; );
md_impl!(
$Op, $op $(where N: $($ScalarBounds),*)*;
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$BoundParam>)*),+
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$($BoundParam),*>)*),+
$(where $ConstraintType: $ConstraintBound<$($ConstraintBoundParams $( = $EqBound )*),*>)*;
$lhs: &'a $Lhs, $rhs: $Rhs, Output = $Result;
$action_ref_val; 'a);
md_impl!(
$Op, $op $(where N: $($ScalarBounds),*)*;
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$BoundParam>)*),+
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$($BoundParam),*>)*),+
$(where $ConstraintType: $ConstraintBound<$($ConstraintBoundParams $( = $EqBound )*),*>)*;
$lhs: $Lhs, $rhs: &'b $Rhs, Output = $Result;
$action_val_ref; 'b);
md_impl!(
$Op, $op $(where N: $($ScalarBounds),*)*;
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$BoundParam>)*),+
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$($BoundParam),*>)*),+
$(where $ConstraintType: $ConstraintBound<$($ConstraintBoundParams $( = $EqBound )*),*>)*;
$lhs: &'a $Lhs, $rhs: &'b $Rhs, Output = $Result;
$action_ref_ref; 'a, 'b);
@ -89,19 +90,18 @@ macro_rules! md_assign_impl(
// Operator, operator method, and scalar bounds.
$Op: ident, $op: ident $(where N: $($ScalarBounds: ident),*)*;
// Storage dimensions, and dimension bounds.
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident $(<$BoundParam: ty>)*),+
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident $(<$($BoundParam: ty),*>)*),+
// [Optional] Extra allocator bounds.
$(where $ConstraintType: ty: $ConstraintBound: ident<$($ConstraintBoundParams: ty $( = $EqBound: ty )*),*> )*;
$(where $ConstraintType: ty: $ConstraintBound: ident $(<$($ConstraintBoundParams: ty $( = $EqBound: ty )*),*>)* )*;
// Argument identifiers and types.
$lhs: ident: $Lhs: ty, $rhs: ident: $Rhs: ty;
// Actual implementation and lifetimes.
$action: expr; $($lives: tt),*) => {
impl<$($lives ,)* N $(, $Dims: $DimsBound $(<$BoundParam>)*)*, SA, SB> $Op<$Rhs> for $Lhs
where N: Scalar + Zero + ClosedAdd + ClosedMul $($(+ $ScalarBounds)*)*,
SA: OwnedStorage<N, $R1, $C1>, // FIXME: this is too restrictive.
SB: Storage<N, $R2, $C2>,
SA::Alloc: OwnedAllocator<N, $R1, $C1, SA>,
$( $ConstraintType: $ConstraintBound<$( $ConstraintBoundParams $( = $EqBound )*),*> ),*
impl<$($lives ,)* N $(, $Dims: $DimsBound $(<$($BoundParam),*>)*)*> $Op<$Rhs> for $Lhs
where N: Scalar + Zero + One + ClosedAdd + ClosedMul $($(+ $ScalarBounds)*)*,
DefaultAllocator: Allocator<N, $R1, $C1> +
Allocator<N, $R2, $C2>,
$( $ConstraintType: $ConstraintBound $(<$( $ConstraintBoundParams $( = $EqBound )*),*>)* ),*
{
#[inline]
fn $op(&mut $lhs, $rhs: $Rhs) {
@ -118,9 +118,9 @@ macro_rules! md_assign_impl_all(
// Operator, operator method, and scalar bounds.
$Op: ident, $op: ident $(where N: $($ScalarBounds: ident),*)*;
// Storage dimensions, and dimension bounds.
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident $(<$BoundParam: ty>)*),+
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident $(<$($BoundParam: ty),*>)*),+
// [Optional] Extra allocator bounds.
$(where $ConstraintType: ty: $ConstraintBound: ident<$($ConstraintBoundParams: ty $( = $EqBound: ty )*),*> )*;
$(where $ConstraintType: ty: $ConstraintBound: ident$(<$($ConstraintBoundParams: ty $( = $EqBound: ty )*),*>)* )*;
// Argument identifiers and types.
$lhs: ident: $Lhs: ty, $rhs: ident: $Rhs: ty;
// Actual implementation and lifetimes.
@ -128,15 +128,15 @@ macro_rules! md_assign_impl_all(
[ref] => $action_ref: expr;) => {
md_assign_impl!(
$Op, $op $(where N: $($ScalarBounds),*)*;
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$BoundParam>)*),+
$(where $ConstraintType: $ConstraintBound<$($ConstraintBoundParams $( = $EqBound )*),*>)*;
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$($BoundParam),*>)*),+
$(where $ConstraintType: $ConstraintBound $(<$($ConstraintBoundParams $( = $EqBound )*),*>)*)*;
$lhs: $Lhs, $rhs: $Rhs;
$action_val; );
md_assign_impl!(
$Op, $op $(where N: $($ScalarBounds),*)*;
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$BoundParam>)*),+
$(where $ConstraintType: $ConstraintBound<$($ConstraintBoundParams $( = $EqBound )*),*>)*;
($R1, $C1),($R2, $C2) for $($Dims: $DimsBound $(<$($BoundParam),*>)*),+
$(where $ConstraintType: $ConstraintBound $(<$($ConstraintBoundParams $( = $EqBound )*),*>)*)*;
$lhs: $Lhs, $rhs: &'b $Rhs;
$action_ref; 'b);
}
@ -146,14 +146,14 @@ macro_rules! md_assign_impl_all(
/// Macro for the implementation of addition and subtraction.
macro_rules! add_sub_impl(
($Op: ident, $op: ident, $bound: ident;
($R1: ty, $C1: ty),($R2: ty, $C2: ty) $(-> ($RRes: ty))* for $($Dims: ident: $DimsBound: ident),+;
($R1: ty, $C1: ty),($R2: ty, $C2: ty) $(-> ($RRes: ty))* for $($Dims: ident: $DimsBound: ident $(<$($BoundParam: ty),*>)*),+;
$lhs: ident: $Lhs: ty, $rhs: ident: $Rhs: ty, Output = $Result: ty;
$action: expr; $($lives: tt),*) => {
impl<$($lives ,)* N $(, $Dims: $DimsBound)*, SA, SB> $Op<$Rhs> for $Lhs
where N: Scalar + $bound,
SA: Storage<N, $R1, $C1>,
SB: Storage<N, $R2, $C2>,
SA::Alloc: SameShapeAllocator<N, $R1, $C1, $R2, $C2, SA>,
impl<$($lives ,)* N $(, $Dims: $DimsBound $(<$($BoundParam),*>)*)*> $Op<$Rhs> for $Lhs
where N: Scalar + $bound,
DefaultAllocator: Allocator<N, $R1, $C1> +
Allocator<N, $R2, $C2> +
SameShapeAllocator<N, $R1, $C1, $R2, $C2>,
ShapeConstraint: SameNumberOfRows<$R1, $R2 $(, Representative = $RRes)*> +
SameNumberOfColumns<$C1, $C2> {
type Output = $Result;
@ -173,11 +173,10 @@ macro_rules! add_sub_assign_impl(
($R1: ty, $C1: ty),($R2: ty, $C2: ty) for $($Dims: ident: $DimsBound: ident),+;
$lhs: ident: $Lhs: ty, $rhs: ident: $Rhs: ty;
$action: expr; $($lives: tt),*) => {
impl<$($lives ,)* N $(, $Dims: $DimsBound)*, SA, SB> $Op<$Rhs> for $Lhs
where N: Scalar + $bound,
SA: OwnedStorage<N, $R1, $C1>, // FIXME: this is too restrictive.
SB: Storage<N, $R2, $C2>,
SA::Alloc: OwnedAllocator<N, $R1, $C1, SA>,
impl<$($lives ,)* N $(, $Dims: $DimsBound)*> $Op<$Rhs> for $Lhs
where N: Scalar + $bound,
DefaultAllocator: Allocator<N, $R1, $C1> +
Allocator<N, $R2, $C2>,
ShapeConstraint: SameNumberOfRows<$R1, $R2> + SameNumberOfColumns<$C1, $C2> {
#[inline]
fn $op(&mut $lhs, $rhs: $Rhs) {

View File

@ -1,70 +1,65 @@
#[cfg(feature="arbitrary")]
use quickcheck::{Arbitrary, Gen};
use rand::{Rand, Rng};
#[cfg(feature = "serde-serialize")]
use serde::{Serialize, Serializer, Deserialize, Deserializer};
use serde;
use std::fmt;
use alga::general::Real;
use core::{Scalar, SquareMatrix, OwnedSquareMatrix, ColumnVector, OwnedColumnVector, MatrixArray};
use core::dimension::{U1, U3, U4};
use core::storage::{OwnedStorage, Storage, StorageMut};
use core::allocator::OwnedAllocator;
use core::{Matrix4, Vector, Vector3};
use core::dimension::U3;
use core::storage::Storage;
use core::helper;
use geometry::{PointBase, OwnedPoint};
use geometry::Point3;
/// A 3D orthographic projection stored as an homogeneous 4x4 matrix.
#[derive(Debug, Clone, Copy)] // FIXME: Hash
pub struct OrthographicBase<N: Scalar, S: Storage<N, U4, U4>> {
matrix: SquareMatrix<N, U4, S>
pub struct Orthographic3<N: Real> {
matrix: Matrix4<N>
}
#[cfg(feature = "serde-serialize")]
impl<N, S> Serialize for OrthographicBase<N, S>
where N: Scalar,
S: Storage<N, U4, U4>,
SquareMatrix<N, U4, S>: Serialize,
{
fn serialize<T>(&self, serializer: T) -> Result<T::Ok, T::Error>
where T: Serializer
{
self.matrix.serialize(serializer)
impl<N: Real> Copy for Orthographic3<N> { }
impl<N: Real> Clone for Orthographic3<N> {
#[inline]
fn clone(&self) -> Self {
Orthographic3::from_matrix_unchecked(self.matrix.clone())
}
}
#[cfg(feature = "serde-serialize")]
impl<'de, N, S> Deserialize<'de> for OrthographicBase<N, S>
where N: Scalar,
S: Storage<N, U4, U4>,
SquareMatrix<N, U4, S>: Deserialize<'de>,
{
fn deserialize<T>(deserializer: T) -> Result<Self, T::Error>
where T: Deserializer<'de>
{
SquareMatrix::deserialize(deserializer).map(|x| OrthographicBase { matrix: x })
impl<N: Real> fmt::Debug for Orthographic3<N> {
fn fmt(&self, f: &mut fmt::Formatter) -> Result<(), fmt::Error> {
self.matrix.fmt(f)
}
}
/// A 3D orthographic projection stored as a static homogeneous 4x4 matrix.
pub type Orthographic3<N> = OrthographicBase<N, MatrixArray<N, U4, U4>>;
impl<N, S> Eq for OrthographicBase<N, S>
where N: Scalar + Eq,
S: Storage<N, U4, U4> { }
impl<N: Scalar, S: Storage<N, U4, U4>> PartialEq for OrthographicBase<N, S> {
impl<N: Real> PartialEq for Orthographic3<N> {
#[inline]
fn eq(&self, right: &Self) -> bool {
self.matrix == right.matrix
}
}
impl<N, S> OrthographicBase<N, S>
where N: Real,
S: OwnedStorage<N, U4, U4>,
S::Alloc: OwnedAllocator<N, U4, U4, S> {
#[cfg(feature = "serde-serialize")]
impl<N: Real + serde::Serialize> serde::Serialize for Orthographic3<N> {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
where S: serde::Serializer {
self.matrix.serialize(serializer)
}
}
#[cfg(feature = "serde-serialize")]
impl<'a, N: Real + serde::Deserialize<'a>> serde::Deserialize<'a> for Orthographic3<N> {
fn deserialize<Des>(deserializer: Des) -> Result<Self, Des::Error>
where Des: serde::Deserializer<'a> {
let matrix = Matrix4::<N>::deserialize(deserializer)?;
Ok(Orthographic3::from_matrix_unchecked(matrix))
}
}
impl<N: Real> Orthographic3<N> {
/// Creates a new orthographic projection matrix.
#[inline]
pub fn new(left: N, right: N, bottom: N, top: N, znear: N, zfar: N) -> Self {
@ -72,7 +67,7 @@ impl<N, S> OrthographicBase<N, S>
assert!(bottom < top, "The top corner must be higher than the bottom corner.");
assert!(znear < zfar, "The far plane must be farther than the near plane.");
let matrix = SquareMatrix::<N, U4, S>::identity();
let matrix = Matrix4::<N>::identity();
let mut res = Self::from_matrix_unchecked(matrix);
res.set_left_and_right(left, right);
@ -87,8 +82,8 @@ impl<N, S> OrthographicBase<N, S>
/// It is not checked whether or not the given matrix actually represents an orthographic
/// projection.
#[inline]
pub fn from_matrix_unchecked(matrix: SquareMatrix<N, U4, S>) -> Self {
OrthographicBase {
pub fn from_matrix_unchecked(matrix: Matrix4<N>) -> Self {
Orthographic3 {
matrix: matrix
}
}
@ -105,24 +100,10 @@ impl<N, S> OrthographicBase<N, S>
Self::new(-width * half, width * half, -height * half, height * half, znear, zfar)
}
}
impl<N: Real, S: Storage<N, U4, U4>> OrthographicBase<N, S> {
/// A reference to the underlying homogeneous transformation matrix.
#[inline]
pub fn as_matrix(&self) -> &SquareMatrix<N, U4, S> {
&self.matrix
}
/// Retrieves the underlying homogeneous matrix.
#[inline]
pub fn unwrap(self) -> SquareMatrix<N, U4, S> {
self.matrix
}
/// Retrieves the inverse of the underlying homogeneous matrix.
#[inline]
pub fn inverse(&self) -> OwnedSquareMatrix<N, U4, S::Alloc> {
pub fn inverse(&self) -> Matrix4<N> {
let mut res = self.to_homogeneous();
let inv_m11 = N::one() / self.matrix[(0, 0)];
@ -142,10 +123,22 @@ impl<N: Real, S: Storage<N, U4, U4>> OrthographicBase<N, S> {
/// Computes the corresponding homogeneous matrix.
#[inline]
pub fn to_homogeneous(&self) -> OwnedSquareMatrix<N, U4, S::Alloc> {
pub fn to_homogeneous(&self) -> Matrix4<N> {
self.matrix.clone_owned()
}
/// A reference to the underlying homogeneous transformation matrix.
#[inline]
pub fn as_matrix(&self) -> &Matrix4<N> {
&self.matrix
}
/// Retrieves the underlying homogeneous matrix.
#[inline]
pub fn unwrap(self) -> Matrix4<N> {
self.matrix
}
/// The smallest x-coordinate of the view cuboid.
#[inline]
pub fn left(&self) -> N {
@ -185,10 +178,8 @@ impl<N: Real, S: Storage<N, U4, U4>> OrthographicBase<N, S> {
// FIXME: when we get specialization, specialize the Mul impl instead.
/// Projects a point. Faster than matrix multiplication.
#[inline]
pub fn project_point<SB>(&self, p: &PointBase<N, U3, SB>) -> OwnedPoint<N, U3, SB::Alloc>
where SB: Storage<N, U3, U1> {
OwnedPoint::<N, U3, SB::Alloc>::new(
pub fn project_point(&self, p: &Point3<N>) -> Point3<N> {
Point3::new(
self.matrix[(0, 0)] * p[0] + self.matrix[(0, 3)],
self.matrix[(1, 1)] * p[1] + self.matrix[(1, 3)],
self.matrix[(2, 2)] * p[2] + self.matrix[(2, 3)]
@ -197,10 +188,9 @@ impl<N: Real, S: Storage<N, U4, U4>> OrthographicBase<N, S> {
/// Un-projects a point. Faster than multiplication by the underlying matrix inverse.
#[inline]
pub fn unproject_point<SB>(&self, p: &PointBase<N, U3, SB>) -> OwnedPoint<N, U3, SB::Alloc>
where SB: Storage<N, U3, U1> {
pub fn unproject_point(&self, p: &Point3<N>) -> Point3<N> {
OwnedPoint::<N, U3, SB::Alloc>::new(
Point3::new(
(p[0] - self.matrix[(0, 3)]) / self.matrix[(0, 0)],
(p[1] - self.matrix[(1, 3)]) / self.matrix[(1, 1)],
(p[2] - self.matrix[(2, 3)]) / self.matrix[(2, 2)]
@ -210,18 +200,16 @@ impl<N: Real, S: Storage<N, U4, U4>> OrthographicBase<N, S> {
// FIXME: when we get specialization, specialize the Mul impl instead.
/// Projects a vector. Faster than matrix multiplication.
#[inline]
pub fn project_vector<SB>(&self, p: &ColumnVector<N, U3, SB>) -> OwnedColumnVector<N, U3, SB::Alloc>
where SB: Storage<N, U3, U1> {
pub fn project_vector<SB>(&self, p: &Vector<N, U3, SB>) -> Vector3<N>
where SB: Storage<N, U3> {
OwnedColumnVector::<N, U3, SB::Alloc>::new(
Vector3::new(
self.matrix[(0, 0)] * p[0],
self.matrix[(1, 1)] * p[1],
self.matrix[(2, 2)] * p[2]
)
}
}
impl<N: Real, S: StorageMut<N, U4, U4>> OrthographicBase<N, S> {
/// Sets the smallest x-coordinate of the view cuboid.
#[inline]
pub fn set_left(&mut self, left: N) {
@ -289,10 +277,7 @@ impl<N: Real, S: StorageMut<N, U4, U4>> OrthographicBase<N, S> {
}
}
impl<N, S> Rand for OrthographicBase<N, S>
where N: Real + Rand,
S: OwnedStorage<N, U4, U4>,
S::Alloc: OwnedAllocator<N, U4, U4, S> {
impl<N: Real + Rand> Rand for Orthographic3<N> {
fn rand<R: Rng>(r: &mut R) -> Self {
let left = Rand::rand(r);
let right = helper::reject_rand(r, |x: &N| *x > left);
@ -306,10 +291,8 @@ impl<N, S> Rand for OrthographicBase<N, S>
}
#[cfg(feature="arbitrary")]
impl<N, S> Arbitrary for OrthographicBase<N, S>
where N: Real + Arbitrary,
S: OwnedStorage<N, U4, U4> + Send,
S::Alloc: OwnedAllocator<N, U4, U4, S> {
impl<N: Real + Arbitrary> Arbitrary for Orthographic3<N>
where Matrix4<N>: Send {
fn arbitrary<G: Gen>(g: &mut G) -> Self {
let left = Arbitrary::arbitrary(g);
let right = helper::reject(g, |x: &N| *x > left);

View File

@ -3,77 +3,71 @@ use quickcheck::{Arbitrary, Gen};
use rand::{Rand, Rng};
#[cfg(feature = "serde-serialize")]
use serde::{Serialize, Serializer, Deserialize, Deserializer};
use serde;
use std::fmt;
use alga::general::Real;
use core::{Scalar, SquareMatrix, OwnedSquareMatrix, ColumnVector, OwnedColumnVector, MatrixArray};
use core::dimension::{U1, U3, U4};
use core::storage::{OwnedStorage, Storage, StorageMut};
use core::allocator::OwnedAllocator;
use core::{Scalar, Matrix4, Vector, Vector3};
use core::dimension::U3;
use core::storage::Storage;
use core::helper;
use geometry::{PointBase, OwnedPoint};
use geometry::Point3;
/// A 3D perspective projection stored as an homogeneous 4x4 matrix.
#[derive(Debug, Clone, Copy)] // FIXME: Hash
pub struct PerspectiveBase<N: Scalar, S: Storage<N, U4, U4>> {
matrix: SquareMatrix<N, U4, S>
pub struct Perspective3<N: Scalar> {
matrix: Matrix4<N>
}
#[cfg(feature = "serde-serialize")]
impl<N, S> Serialize for PerspectiveBase<N, S>
where N: Scalar,
S: Storage<N, U4, U4>,
SquareMatrix<N, U4, S>: Serialize,
{
fn serialize<T>(&self, serializer: T) -> Result<T::Ok, T::Error>
where T: Serializer
{
self.matrix.serialize(serializer)
impl<N: Real> Copy for Perspective3<N> { }
impl<N: Real> Clone for Perspective3<N> {
#[inline]
fn clone(&self) -> Self {
Perspective3::from_matrix_unchecked(self.matrix.clone())
}
}
#[cfg(feature = "serde-serialize")]
impl<'de, N, S> Deserialize<'de> for PerspectiveBase<N, S>
where N: Scalar,
S: Storage<N, U4, U4>,
SquareMatrix<N, U4, S>: Deserialize<'de>,
{
fn deserialize<T>(deserializer: T) -> Result<Self, T::Error>
where T: Deserializer<'de>
{
SquareMatrix::deserialize(deserializer).map(|x| PerspectiveBase { matrix: x })
impl<N: Real> fmt::Debug for Perspective3<N> {
fn fmt(&self, f: &mut fmt::Formatter) -> Result<(), fmt::Error> {
self.matrix.fmt(f)
}
}
/// A 3D perspective projection stored as a static homogeneous 4x4 matrix.
pub type Perspective3<N> = PerspectiveBase<N, MatrixArray<N, U4, U4>>;
impl<N, S> Eq for PerspectiveBase<N, S>
where N: Scalar + Eq,
S: Storage<N, U4, U4> { }
impl<N, S> PartialEq for PerspectiveBase<N, S>
where N: Scalar,
S: Storage<N, U4, U4> {
impl<N: Real> PartialEq for Perspective3<N> {
#[inline]
fn eq(&self, right: &Self) -> bool {
self.matrix == right.matrix
}
}
impl<N, S> PerspectiveBase<N, S>
where N: Real,
S: OwnedStorage<N, U4, U4>,
S::Alloc: OwnedAllocator<N, U4, U4, S> {
#[cfg(feature = "serde-serialize")]
impl<N: Real + serde::Serialize> serde::Serialize for Perspective3<N> {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
where S: serde::Serializer {
self.matrix.serialize(serializer)
}
}
#[cfg(feature = "serde-serialize")]
impl<'a, N: Real + serde::Deserialize<'a>> serde::Deserialize<'a> for Perspective3<N> {
fn deserialize<Des>(deserializer: Des) -> Result<Self, Des::Error>
where Des: serde::Deserializer<'a> {
let matrix = Matrix4::<N>::deserialize(deserializer)?;
Ok(Perspective3::from_matrix_unchecked(matrix))
}
}
impl<N: Real> Perspective3<N> {
/// Creates a new perspective matrix from the aspect ratio, y field of view, and near/far planes.
pub fn new(aspect: N, fovy: N, znear: N, zfar: N) -> Self {
assert!(!relative_eq!(zfar - znear, N::zero()), "The near-plane and far-plane must not be superimposed.");
assert!(!relative_eq!(aspect, N::zero()), "The apsect ratio must not be zero.");
let matrix = SquareMatrix::<N, U4, S>::identity();
let mut res = PerspectiveBase::from_matrix_unchecked(matrix);
let matrix = Matrix4::identity();
let mut res = Perspective3::from_matrix_unchecked(matrix);
res.set_fovy(fovy);
res.set_aspect(aspect);
@ -91,32 +85,15 @@ impl<N, S> PerspectiveBase<N, S>
/// It is not checked whether or not the given matrix actually represents an orthographic
/// projection.
#[inline]
pub fn from_matrix_unchecked(matrix: SquareMatrix<N, U4, S>) -> Self {
PerspectiveBase {
pub fn from_matrix_unchecked(matrix: Matrix4<N>) -> Self {
Perspective3 {
matrix: matrix
}
}
}
impl<N, S> PerspectiveBase<N, S>
where N: Real,
S: Storage<N, U4, U4> {
/// A reference to the underlying homogeneous transformation matrix.
#[inline]
pub fn as_matrix(&self) -> &SquareMatrix<N, U4, S> {
&self.matrix
}
/// Retrieves the underlying homogeneous matrix.
#[inline]
pub fn unwrap(self) -> SquareMatrix<N, U4, S> {
self.matrix
}
/// Retrieves the inverse of the underlying homogeneous matrix.
#[inline]
pub fn inverse(&self) -> OwnedSquareMatrix<N, U4, S::Alloc> {
pub fn inverse(&self) -> Matrix4<N> {
let mut res = self.to_homogeneous();
res[(0, 0)] = N::one() / self.matrix[(0, 0)];
@ -135,10 +112,22 @@ impl<N, S> PerspectiveBase<N, S>
/// Computes the corresponding homogeneous matrix.
#[inline]
pub fn to_homogeneous(&self) -> OwnedSquareMatrix<N, U4, S::Alloc> {
pub fn to_homogeneous(&self) -> Matrix4<N> {
self.matrix.clone_owned()
}
/// A reference to the underlying homogeneous transformation matrix.
#[inline]
pub fn as_matrix(&self) -> &Matrix4<N> {
&self.matrix
}
/// Retrieves the underlying homogeneous matrix.
#[inline]
pub fn unwrap(self) -> Matrix4<N> {
self.matrix
}
/// Gets the `width / height` aspect ratio of the view frustrum.
#[inline]
pub fn aspect(&self) -> N {
@ -174,11 +163,9 @@ impl<N, S> PerspectiveBase<N, S>
// FIXME: when we get specialization, specialize the Mul impl instead.
/// Projects a point. Faster than matrix multiplication.
#[inline]
pub fn project_point<SB>(&self, p: &PointBase<N, U3, SB>) -> OwnedPoint<N, U3, SB::Alloc>
where SB: Storage<N, U3, U1> {
pub fn project_point(&self, p: &Point3<N>) -> Point3<N> {
let inverse_denom = -N::one() / p[2];
OwnedPoint::<N, U3, SB::Alloc>::new(
Point3::new(
self.matrix[(0, 0)] * p[0] * inverse_denom,
self.matrix[(1, 1)] * p[1] * inverse_denom,
(self.matrix[(2, 2)] * p[2] + self.matrix[(2, 3)]) * inverse_denom
@ -187,12 +174,10 @@ impl<N, S> PerspectiveBase<N, S>
/// Un-projects a point. Faster than multiplication by the matrix inverse.
#[inline]
pub fn unproject_point<SB>(&self, p: &PointBase<N, U3, SB>) -> OwnedPoint<N, U3, SB::Alloc>
where SB: Storage<N, U3, U1> {
pub fn unproject_point(&self, p: &Point3<N>) -> Point3<N> {
let inverse_denom = self.matrix[(2, 3)] / (p[2] + self.matrix[(2, 2)]);
OwnedPoint::<N, U3, SB::Alloc>::new(
Point3::new(
p[0] * inverse_denom / self.matrix[(0, 0)],
p[1] * inverse_denom / self.matrix[(1, 1)],
-inverse_denom
@ -202,22 +187,17 @@ impl<N, S> PerspectiveBase<N, S>
// FIXME: when we get specialization, specialize the Mul impl instead.
/// Projects a vector. Faster than matrix multiplication.
#[inline]
pub fn project_vector<SB>(&self, p: &ColumnVector<N, U3, SB>) -> OwnedColumnVector<N, U3, SB::Alloc>
where SB: Storage<N, U3, U1> {
pub fn project_vector<SB>(&self, p: &Vector<N, U3, SB>) -> Vector3<N>
where SB: Storage<N, U3> {
let inverse_denom = -N::one() / p[2];
OwnedColumnVector::<N, U3, SB::Alloc>::new(
Vector3::new(
self.matrix[(0, 0)] * p[0] * inverse_denom,
self.matrix[(1, 1)] * p[1] * inverse_denom,
self.matrix[(2, 2)]
)
}
}
impl<N, S> PerspectiveBase<N, S>
where N: Real,
S: StorageMut<N, U4, U4> {
/// Updates this perspective matrix with a new `width / height` aspect ratio of the view
/// frustrum.
#[inline]
@ -256,10 +236,7 @@ impl<N, S> PerspectiveBase<N, S>
}
}
impl<N, S> Rand for PerspectiveBase<N, S>
where N: Real + Rand,
S: OwnedStorage<N, U4, U4>,
S::Alloc: OwnedAllocator<N, U4, U4, S> {
impl<N: Real + Rand> Rand for Perspective3<N> {
fn rand<R: Rng>(r: &mut R) -> Self {
let znear = Rand::rand(r);
let zfar = helper::reject_rand(r, |&x: &N| !(x - znear).is_zero());
@ -270,10 +247,7 @@ impl<N, S> Rand for PerspectiveBase<N, S>
}
#[cfg(feature="arbitrary")]
impl<N, S> Arbitrary for PerspectiveBase<N, S>
where N: Real + Arbitrary,
S: OwnedStorage<N, U4, U4> + Send,
S::Alloc: OwnedAllocator<N, U4, U4, S> {
impl<N: Real + Arbitrary> Arbitrary for Perspective3<N> {
fn arbitrary<G: Gen>(g: &mut G) -> Self {
let znear = Arbitrary::arbitrary(g);
let zfar = helper::reject(g, |&x: &N| !(x - znear).is_zero());

View File

@ -1,89 +1,81 @@
use num::One;
use std::hash;
use std::fmt;
use std::cmp::Ordering;
use approx::ApproxEq;
#[cfg(feature = "serde-serialize")]
use serde::{Serialize, Serializer, Deserialize, Deserializer};
use serde;
#[cfg(feature = "abomonation-serialize")]
use abomonation::Abomonation;
use core::{Scalar, ColumnVector, OwnedColumnVector};
use core::{DefaultAllocator, Scalar, VectorN};
use core::iter::{MatrixIter, MatrixIterMut};
use core::dimension::{DimName, DimNameSum, DimNameAdd, U1};
use core::storage::{Storage, StorageMut, MulStorage};
use core::allocator::{Allocator, SameShapeR};
// XXX Bad name: we can't even add points…
/// The type of the result of the sum of a point with a vector.
pub type PointSum<N, D1, D2, SA> =
PointBase<N, SameShapeR<D1, D2>,
<<SA as Storage<N, D1, U1>>::Alloc as Allocator<N, SameShapeR<D1, D2>, U1>>::Buffer>;
/// The type of the result of the multiplication of a point by a matrix.
pub type PointMul<N, R1, C1, SA> = PointBase<N, R1, MulStorage<N, R1, C1, U1, SA>>;
/// A point with an owned storage.
pub type OwnedPoint<N, D, A> = PointBase<N, D, <A as Allocator<N, D, U1>>::Buffer>;
use core::allocator::Allocator;
/// A point in a n-dimensional euclidean space.
#[repr(C)]
#[derive(Hash, Debug)]
pub struct PointBase<N: Scalar, D: DimName, S: Storage<N, D, U1>> {
#[derive(Debug)]
pub struct Point<N: Scalar, D: DimName>
where DefaultAllocator: Allocator<N, D> {
/// The coordinates of this point, i.e., the shift from the origin.
pub coords: ColumnVector<N, D, S>
pub coords: VectorN<N, D>
}
impl<N, D, S> Copy for PointBase<N, D, S>
where N: Scalar,
D: DimName,
S: Storage<N, D, U1> + Copy { }
impl<N: Scalar + hash::Hash, D: DimName + hash::Hash> hash::Hash for Point<N, D>
where DefaultAllocator: Allocator<N, D>,
<DefaultAllocator as Allocator<N, D>>::Buffer: hash::Hash {
fn hash<H: hash::Hasher>(&self, state: &mut H) {
self.coords.hash(state)
}
}
impl<N, D, S> Clone for PointBase<N, D, S>
where N: Scalar,
D: DimName,
S: Storage<N, D, U1> + Clone {
impl<N: Scalar, D: DimName> Copy for Point<N, D>
where DefaultAllocator: Allocator<N, D>,
<DefaultAllocator as Allocator<N, D>>::Buffer: Copy { }
impl<N: Scalar, D: DimName> Clone for Point<N, D>
where DefaultAllocator: Allocator<N, D>,
<DefaultAllocator as Allocator<N, D>>::Buffer: Clone {
#[inline]
fn clone(&self) -> Self {
PointBase::from_coordinates(self.coords.clone())
Point::from_coordinates(self.coords.clone())
}
}
#[cfg(feature = "serde-serialize")]
impl<N, D, S> Serialize for PointBase<N, D, S>
where N: Scalar,
D: DimName,
S: Storage<N, D, U1>,
ColumnVector<N, D, S>: Serialize,
{
fn serialize<T>(&self, serializer: T) -> Result<T::Ok, T::Error>
where T: Serializer
{
self.coords.serialize(serializer)
}
impl<N: Scalar, D: DimName> serde::Serialize for Point<N, D>
where DefaultAllocator: Allocator<N, D>,
<DefaultAllocator as Allocator<N, D>>::Buffer: serde::Serialize {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
where S: serde::Serializer {
self.coords.serialize(serializer)
}
}
#[cfg(feature = "serde-serialize")]
impl<'de, N, D, S> Deserialize<'de> for PointBase<N, D, S>
where N: Scalar,
D: DimName,
S: Storage<N, D, U1>,
ColumnVector<N, D, S>: Deserialize<'de>,
{
fn deserialize<T>(deserializer: T) -> Result<Self, T::Error>
where T: Deserializer<'de>
{
ColumnVector::deserialize(deserializer).map(|x| PointBase { coords: x })
}
impl<'a, N: Scalar, D: DimName> serde::Deserialize<'a> for Point<N, D>
where DefaultAllocator: Allocator<N, D>,
<DefaultAllocator as Allocator<N, D>>::Buffer: serde::Deserialize<'a> {
fn deserialize<Des>(deserializer: Des) -> Result<Self, Des::Error>
where Des: serde::Deserializer<'a> {
let coords = VectorN::<N, D>::deserialize(deserializer)?;
Ok(Point::from_coordinates(coords))
}
}
#[cfg(feature = "abomonation-serialize")]
impl<N, D, S> Abomonation for PointBase<N, D, S>
impl<N, D> Abomonation for PointBase<N, D>
where N: Scalar,
D: DimName,
S: Storage<N, D, U1>,
ColumnVector<N, D, S>: Abomonation
ColumnVector<N, D>: Abomonation,
DefaultAllocator: Allocator<N, D>
{
unsafe fn entomb(&self, writer: &mut Vec<u8>) {
self.coords.entomb(writer)
@ -98,27 +90,38 @@ impl<N, D, S> Abomonation for PointBase<N, D, S>
}
}
impl<N: Scalar, D: DimName, S: Storage<N, D, U1>> PointBase<N, D, S> {
/// Creates a new point with the given coordinates.
#[inline]
pub fn from_coordinates(coords: ColumnVector<N, D, S>) -> PointBase<N, D, S> {
PointBase {
coords: coords
}
}
}
impl<N: Scalar, D: DimName, S: Storage<N, D, U1>> PointBase<N, D, S> {
/// Moves this point into one that owns its data.
#[inline]
pub fn into_owned(self) -> OwnedPoint<N, D, S::Alloc> {
PointBase::from_coordinates(self.coords.into_owned())
}
impl<N: Scalar, D: DimName> Point<N, D>
where DefaultAllocator: Allocator<N, D> {
/// Clones this point into one that owns its data.
#[inline]
pub fn clone_owned(&self) -> OwnedPoint<N, D, S::Alloc> {
PointBase::from_coordinates(self.coords.clone_owned())
pub fn clone(&self) -> Point<N, D> {
Point::from_coordinates(self.coords.clone_owned())
}
/// Converts this point into a vector in homogeneous coordinates, i.e., appends a `1` at the
/// end of it.
#[inline]
pub fn to_homogeneous(&self) -> VectorN<N, DimNameSum<D, U1>>
where N: One,
D: DimNameAdd<U1>,
DefaultAllocator: Allocator<N, DimNameSum<D, U1>> {
let mut res = unsafe {
VectorN::<_, DimNameSum<D, U1>>::new_uninitialized()
};
res.fixed_slice_mut::<D, U1>(0, 0).copy_from(&self.coords);
res[(D::dim(), 0)] = N::one();
res
}
/// Creates a new point with the given coordinates.
#[inline]
pub fn from_coordinates(coords: VectorN<N, D>) -> Point<N, D> {
Point {
coords: coords
}
}
/// The dimension of this point.
@ -136,44 +139,26 @@ impl<N: Scalar, D: DimName, S: Storage<N, D, U1>> PointBase<N, D, S> {
/// Iterates through this point coordinates.
#[inline]
pub fn iter(&self) -> MatrixIter<N, D, U1, S> {
pub fn iter(&self) -> MatrixIter<N, D, U1, <DefaultAllocator as Allocator<N, D>>::Buffer> {
self.coords.iter()
}
/// Gets a reference to i-th element of this point without bound-checking.
#[inline]
pub unsafe fn get_unchecked(&self, i: usize) -> &N {
self.coords.get_unchecked(i, 0)
self.coords.vget_unchecked(i)
}
/// Converts this point into a vector in homogeneous coordinates, i.e., appends a `1` at the
/// end of it.
#[inline]
pub fn to_homogeneous(&self) -> OwnedColumnVector<N, DimNameSum<D, U1>, S::Alloc>
where N: One,
D: DimNameAdd<U1>,
S::Alloc: Allocator<N, DimNameSum<D, U1>, U1> {
let mut res = unsafe { OwnedColumnVector::<N, _, S::Alloc>::new_uninitialized() };
res.fixed_slice_mut::<D, U1>(0, 0).copy_from(&self.coords);
res[(D::dim(), 0)] = N::one();
res
}
}
impl<N: Scalar, D: DimName, S: StorageMut<N, D, U1>> PointBase<N, D, S> {
/// Mutably iterates through this point coordinates.
#[inline]
pub fn iter_mut(&mut self) -> MatrixIterMut<N, D, U1, S> {
pub fn iter_mut(&mut self) -> MatrixIterMut<N, D, U1, <DefaultAllocator as Allocator<N, D>>::Buffer> {
self.coords.iter_mut()
}
/// Gets a mutable reference to i-th element of this point without bound-checking.
#[inline]
pub unsafe fn get_unchecked_mut(&mut self, i: usize) -> &mut N {
self.coords.get_unchecked_mut(i, 0)
self.coords.vget_unchecked_mut(i)
}
/// Swaps two entries without bound-checking.
@ -183,9 +168,8 @@ impl<N: Scalar, D: DimName, S: StorageMut<N, D, U1>> PointBase<N, D, S> {
}
}
impl<N, D: DimName, S> ApproxEq for PointBase<N, D, S>
where N: Scalar + ApproxEq,
S: Storage<N, D, U1>,
impl<N: Scalar + ApproxEq, D: DimName> ApproxEq for Point<N, D>
where DefaultAllocator: Allocator<N, D>,
N::Epsilon: Copy {
type Epsilon = N::Epsilon;
@ -215,22 +199,19 @@ impl<N, D: DimName, S> ApproxEq for PointBase<N, D, S>
}
}
impl<N, D: DimName, S> Eq for PointBase<N, D, S>
where N: Scalar + Eq,
S: Storage<N, D, U1> { }
impl<N: Scalar + Eq, D: DimName> Eq for Point<N, D>
where DefaultAllocator: Allocator<N, D> { }
impl<N, D: DimName, S> PartialEq for PointBase<N, D, S>
where N: Scalar,
S: Storage<N, D, U1> {
impl<N: Scalar, D: DimName> PartialEq for Point<N, D>
where DefaultAllocator: Allocator<N, D> {
#[inline]
fn eq(&self, right: &Self) -> bool {
self.coords == right.coords
}
}
impl<N, D: DimName, S> PartialOrd for PointBase<N, D, S>
where N: Scalar + PartialOrd,
S: Storage<N, D, U1> {
impl<N: Scalar + PartialOrd, D: DimName> PartialOrd for Point<N, D>
where DefaultAllocator: Allocator<N, D> {
#[inline]
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
self.coords.partial_cmp(&other.coords)
@ -262,9 +243,8 @@ impl<N, D: DimName, S> PartialOrd for PointBase<N, D, S>
* Display
*
*/
impl<N, D: DimName, S> fmt::Display for PointBase<N, D, S>
where N: Scalar + fmt::Display,
S: Storage<N, D, U1> {
impl<N: Scalar + fmt::Display, D: DimName> fmt::Display for Point<N, D>
where DefaultAllocator: Allocator<N, D> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
try!(write!(f, "{{"));

View File

@ -1,26 +1,22 @@
use alga::general::{Field, Real, MeetSemilattice, JoinSemilattice, Lattice};
use alga::linear::{AffineSpace, EuclideanSpace};
use core::{ColumnVector, Scalar};
use core::dimension::{DimName, U1};
use core::storage::OwnedStorage;
use core::allocator::OwnedAllocator;
use core::{DefaultAllocator, Scalar, VectorN};
use core::dimension::DimName;
use core::allocator::Allocator;
use geometry::PointBase;
use geometry::Point;
impl<N, D: DimName, S> AffineSpace for PointBase<N, D, S>
impl<N: Scalar + Field, D: DimName> AffineSpace for Point<N, D>
where N: Scalar + Field,
S: OwnedStorage<N, D, U1>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
type Translation = ColumnVector<N, D, S>;
DefaultAllocator: Allocator<N, D> {
type Translation = VectorN<N, D>;
}
impl<N, D: DimName, S> EuclideanSpace for PointBase<N, D, S>
where N: Real,
S: OwnedStorage<N, D, U1>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
type Coordinates = ColumnVector<N, D, S>;
impl<N: Real, D: DimName> EuclideanSpace for Point<N, D>
where DefaultAllocator: Allocator<N, D> {
type Coordinates = VectorN<N, D>;
type Real = N;
#[inline]
@ -49,35 +45,32 @@ impl<N, D: DimName, S> EuclideanSpace for PointBase<N, D, S>
* Ordering
*
*/
impl<N, D: DimName, S> MeetSemilattice for PointBase<N, D, S>
impl<N, D: DimName> MeetSemilattice for Point<N, D>
where N: Scalar + MeetSemilattice,
S: OwnedStorage<N, D, U1>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
DefaultAllocator: Allocator<N, D> {
#[inline]
fn meet(&self, other: &Self) -> Self {
PointBase::from_coordinates(self.coords.meet(&other.coords))
Point::from_coordinates(self.coords.meet(&other.coords))
}
}
impl<N, D: DimName, S> JoinSemilattice for PointBase<N, D, S>
impl<N, D: DimName> JoinSemilattice for Point<N, D>
where N: Scalar + JoinSemilattice,
S: OwnedStorage<N, D, U1>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
DefaultAllocator: Allocator<N, D> {
#[inline]
fn join(&self, other: &Self) -> Self {
PointBase::from_coordinates(self.coords.join(&other.coords))
Point::from_coordinates(self.coords.join(&other.coords))
}
}
impl<N, D: DimName, S> Lattice for PointBase<N, D, S>
impl<N, D: DimName> Lattice for Point<N, D>
where N: Scalar + Lattice,
S: OwnedStorage<N, D, U1>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
DefaultAllocator: Allocator<N, D> {
#[inline]
fn meet_join(&self, other: &Self) -> (Self, Self) {
let (meet, join) = self.coords.meet_join(&other.coords);
(PointBase::from_coordinates(meet), PointBase::from_coordinates(join))
(Point::from_coordinates(meet), Point::from_coordinates(join))
}
}

View File

@ -1,10 +1,6 @@
use core::MatrixArray;
use core::dimension::{U1, U2, U3, U4, U5, U6};
use geometry::PointBase;
/// A statically sized D-dimensional column point.
pub type Point<N, D> = PointBase<N, D, MatrixArray<N, D, U1>>;
use geometry::Point;
/// A statically sized 1-dimensional column point.
pub type Point1<N> = Point<N, U1>;

View File

@ -5,28 +5,25 @@ use rand::{Rand, Rng};
use num::{Zero, One, Bounded};
use alga::general::ClosedDiv;
use core::{Scalar, ColumnVector};
use core::storage::{Storage, OwnedStorage};
use core::allocator::{Allocator, OwnedAllocator};
use core::{DefaultAllocator, Scalar, VectorN};
use core::allocator::Allocator;
use core::dimension::{DimName, DimNameAdd, DimNameSum, U1, U2, U3, U4, U5, U6};
use geometry::PointBase;
use geometry::Point;
impl<N, D: DimName, S> PointBase<N, D, S>
where N: Scalar,
S: OwnedStorage<N, D, U1>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Scalar, D: DimName> Point<N, D>
where DefaultAllocator: Allocator<N, D> {
/// Creates a new point with uninitialized coordinates.
#[inline]
pub unsafe fn new_uninitialized() -> Self {
Self::from_coordinates(ColumnVector::<_, D, _>::new_uninitialized())
Self::from_coordinates(VectorN::new_uninitialized())
}
/// Creates a new point with all coordinates equal to zero.
#[inline]
pub fn origin() -> Self
where N: Zero {
Self::from_coordinates(ColumnVector::<_, D, _>::from_element(N::zero()))
Self::from_coordinates(VectorN::from_element(N::zero()))
}
/// Creates a new point from its homogeneous vector representation.
@ -34,11 +31,10 @@ impl<N, D: DimName, S> PointBase<N, D, S>
/// In practice, this builds a D-dimensional points with the same first D component as `v`
/// divided by the last component of `v`. Returns `None` if this divisor is zero.
#[inline]
pub fn from_homogeneous<SB>(v: ColumnVector<N, DimNameSum<D, U1>, SB>) -> Option<Self>
where N: Scalar + Zero + One + ClosedDiv,
D: DimNameAdd<U1>,
SB: Storage<N, DimNameSum<D, U1>, U1, Alloc = S::Alloc>,
S::Alloc: Allocator<N, DimNameSum<D, U1>, U1> {
pub fn from_homogeneous(v: VectorN<N, DimNameSum<D, U1>>) -> Option<Self>
where N: Scalar + Zero + One + ClosedDiv,
D: DimNameAdd<U1>,
DefaultAllocator: Allocator<N, DimNameSum<D, U1>> {
if !v[D::dim()].is_zero() {
let coords = v.fixed_slice::<D, U1>(0, 0) / v[D::dim()];
@ -56,39 +52,34 @@ impl<N, D: DimName, S> PointBase<N, D, S>
* Traits that buid points.
*
*/
impl<N, D: DimName, S> Bounded for PointBase<N, D, S>
where N: Scalar + Bounded,
S: OwnedStorage<N, D, U1>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Scalar + Bounded, D: DimName> Bounded for Point<N, D>
where DefaultAllocator: Allocator<N, D> {
#[inline]
fn max_value() -> Self {
Self::from_coordinates(ColumnVector::max_value())
Self::from_coordinates(VectorN::max_value())
}
#[inline]
fn min_value() -> Self {
Self::from_coordinates(ColumnVector::min_value())
Self::from_coordinates(VectorN::min_value())
}
}
impl<N, D: DimName, S> Rand for PointBase<N, D, S>
where N: Scalar + Rand,
S: OwnedStorage<N, D, U1>,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Scalar + Rand, D: DimName> Rand for Point<N, D>
where DefaultAllocator: Allocator<N, D> {
#[inline]
fn rand<G: Rng>(rng: &mut G) -> Self {
PointBase::from_coordinates(rng.gen())
Point::from_coordinates(rng.gen())
}
}
#[cfg(feature="arbitrary")]
impl<N, D: DimName, S> Arbitrary for PointBase<N, D, S>
where N: Scalar + Arbitrary + Send,
S: OwnedStorage<N, D, U1> + Send,
S::Alloc: OwnedAllocator<N, D, U1, S> {
impl<N: Scalar + Arbitrary + Send, D: DimName> Arbitrary for Point<N, D>
where DefaultAllocator: Allocator<N, D>,
<DefaultAllocator as Allocator<N, D>>::Buffer: Send {
#[inline]
fn arbitrary<G: Gen>(g: &mut G) -> Self {
PointBase::from_coordinates(ColumnVector::arbitrary(g))
Point::from_coordinates(VectorN::arbitrary(g))
}
}
@ -99,13 +90,11 @@ impl<N, D: DimName, S> Arbitrary for PointBase<N, D, S>
*/
macro_rules! componentwise_constructors_impl(
($($D: ty, $($args: ident:$irow: expr),*);* $(;)*) => {$(
impl<N, S> PointBase<N, $D, S>
where N: Scalar,
S: OwnedStorage<N, $D, U1>,
S::Alloc: OwnedAllocator<N, $D, U1, S> {
impl<N: Scalar> Point<N, $D>
where DefaultAllocator: Allocator<N, $D> {
/// Initializes this matrix from its components.
#[inline]
pub fn new($($args: N),*) -> PointBase<N, $D, S> {
pub fn new($($args: N),*) -> Point<N, $D> {
unsafe {
let mut res = Self::new_uninitialized();
$( *res.get_unchecked_mut($irow) = $args; )*

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