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use num::{One, Zero};
use approx::{AbsDiffEq, RelativeEq, UlpsEq};
use std::any::TypeId;
use std::cmp::Ordering;
use std::fmt;
use std::hash::{Hash, Hasher};
use std::marker::PhantomData;
use std::mem;
#[cfg(feature = "serde-serialize-no-std")]
use serde::{Deserialize, Deserializer, Serialize, Serializer};
#[cfg(feature = "rkyv-serialize-no-std")]
use super::rkyv_wrappers::CustomPhantom;
#[cfg(feature = "rkyv-serialize")]
use rkyv::bytecheck;
#[cfg(feature = "rkyv-serialize-no-std")]
use rkyv::{with::With, Archive, Archived};
use simba::scalar::{ClosedAddAssign, ClosedMulAssign, ClosedSubAssign, Field, SupersetOf};
use simba::simd::SimdPartialOrd;
use crate::base::allocator::{Allocator, SameShapeAllocator, SameShapeC, SameShapeR};
use crate::base::constraint::{DimEq, SameNumberOfColumns, SameNumberOfRows, ShapeConstraint};
use crate::base::dimension::{Dim, DimAdd, DimSum, IsNotStaticOne, U1, U2, U3};
use crate::base::iter::{
ColumnIter, ColumnIterMut, MatrixIter, MatrixIterMut, RowIter, RowIterMut,
};
use crate::base::storage::{Owned, RawStorage, RawStorageMut, SameShapeStorage};
use crate::base::{Const, DefaultAllocator, OMatrix, OVector, Scalar, Unit};
use crate::{ArrayStorage, SMatrix, SimdComplexField, Storage, UninitMatrix};
use crate::storage::IsContiguous;
use crate::uninit::{Init, InitStatus, Uninit};
#[cfg(any(feature = "std", feature = "alloc"))]
use crate::{DMatrix, DVector, Dyn, RowDVector, VecStorage};
use std::mem::MaybeUninit;
/// A square matrix.
pub type SquareMatrix<T, D, S> = Matrix<T, D, D, S>;
/// A matrix with one column and `D` rows.
pub type Vector<T, D, S> = Matrix<T, D, U1, S>;
/// A matrix with one row and `D` columns .
pub type RowVector<T, D, S> = Matrix<T, U1, D, S>;
/// The type of the result of a matrix sum.
pub type MatrixSum<T, R1, C1, R2, C2> =
Matrix<T, SameShapeR<R1, R2>, SameShapeC<C1, C2>, SameShapeStorage<T, R1, C1, R2, C2>>;
/// The type of the result of a matrix sum.
pub type VectorSum<T, R1, R2> =
Matrix<T, SameShapeR<R1, R2>, U1, SameShapeStorage<T, R1, U1, R2, U1>>;
/// The type of the result of a matrix cross product.
pub type MatrixCross<T, R1, C1, R2, C2> =
Matrix<T, SameShapeR<R1, R2>, SameShapeC<C1, C2>, SameShapeStorage<T, R1, C1, R2, C2>>;
/// The most generic column-major matrix (and vector) type.
///
/// # Methods summary
/// Because `Matrix` is the most generic types used as a common representation of all matrices and
/// vectors of **nalgebra** this documentation page contains every single matrix/vector-related
/// method. In order to make browsing this page simpler, the next subsections contain direct links
/// to groups of methods related to a specific topic.
///
/// #### Vector and matrix construction
/// - [Constructors of statically-sized vectors or statically-sized matrices](#constructors-of-statically-sized-vectors-or-statically-sized-matrices)
/// (`Vector3`, `Matrix3x6`…)
/// - [Constructors of fully dynamic matrices](#constructors-of-fully-dynamic-matrices) (`DMatrix`)
/// - [Constructors of dynamic vectors and matrices with a dynamic number of rows](#constructors-of-dynamic-vectors-and-matrices-with-a-dynamic-number-of-rows)
/// (`DVector`, `MatrixXx3`…)
/// - [Constructors of matrices with a dynamic number of columns](#constructors-of-matrices-with-a-dynamic-number-of-columns)
/// (`Matrix2xX`…)
/// - [Generic constructors](#generic-constructors)
/// (For code generic wrt. the vectors or matrices dimensions.)
///
/// #### Computer graphics utilities for transformations
/// - [2D transformations as a Matrix3 <span style="float:right;">`new_rotation`…</span>](#2d-transformations-as-a-matrix3)
/// - [3D transformations as a Matrix4 <span style="float:right;">`new_rotation`, `new_perspective`, `look_at_rh`…</span>](#3d-transformations-as-a-matrix4)
/// - [Translation and scaling in any dimension <span style="float:right;">`new_scaling`, `new_translation`…</span>](#translation-and-scaling-in-any-dimension)
/// - [Append/prepend translation and scaling <span style="float:right;">`append_scaling`, `prepend_translation_mut`…</span>](#appendprepend-translation-and-scaling)
/// - [Transformation of vectors and points <span style="float:right;">`transform_vector`, `transform_point`…</span>](#transformation-of-vectors-and-points)
///
/// #### Common math operations
/// - [Componentwise operations <span style="float:right;">`component_mul`, `component_div`, `inf`…</span>](#componentwise-operations)
/// - [Special multiplications <span style="float:right;">`tr_mul`, `ad_mul`, `kronecker`…</span>](#special-multiplications)
/// - [Dot/scalar product <span style="float:right;">`dot`, `dotc`, `tr_dot`…</span>](#dotscalar-product)
/// - [Cross product <span style="float:right;">`cross`, `perp`…</span>](#cross-product)
/// - [Magnitude and norms <span style="float:right;">`norm`, `normalize`, `metric_distance`…</span>](#magnitude-and-norms)
/// - [In-place normalization <span style="float:right;">`normalize_mut`, `try_normalize_mut`…</span>](#in-place-normalization)
/// - [Interpolation <span style="float:right;">`lerp`, `slerp`…</span>](#interpolation)
/// - [BLAS functions <span style="float:right;">`gemv`, `gemm`, `syger`…</span>](#blas-functions)
/// - [Swizzling <span style="float:right;">`xx`, `yxz`…</span>](#swizzling)
/// - [Triangular matrix extraction <span style="float:right;">`upper_triangle`, `lower_triangle`</span>](#triangular-matrix-extraction)
///
/// #### Statistics
/// - [Common operations <span style="float:right;">`row_sum`, `column_mean`, `variance`…</span>](#common-statistics-operations)
/// - [Find the min and max components <span style="float:right;">`min`, `max`, `amin`, `amax`, `camin`, `cmax`…</span>](#find-the-min-and-max-components)
/// - [Find the min and max components (vector-specific methods) <span style="float:right;">`argmin`, `argmax`, `icamin`, `icamax`…</span>](#find-the-min-and-max-components-vector-specific-methods)
///
/// #### Iteration, map, and fold
/// - [Iteration on components, rows, and columns <span style="float:right;">`iter`, `column_iter`…</span>](#iteration-on-components-rows-and-columns)
/// - [Parallel iterators using rayon <span style="float:right;">`par_column_iter`, `par_column_iter_mut`…</span>](#parallel-iterators-using-rayon)
/// - [Elementwise mapping and folding <span style="float:right;">`map`, `fold`, `zip_map`…</span>](#elementwise-mapping-and-folding)
/// - [Folding or columns and rows <span style="float:right;">`compress_rows`, `compress_columns`…</span>](#folding-on-columns-and-rows)
///
/// #### Vector and matrix views
/// - [Creating matrix views from `&[T]` <span style="float:right;">`from_slice`, `from_slice_with_strides`…</span>](#creating-matrix-views-from-t)
/// - [Creating mutable matrix views from `&mut [T]` <span style="float:right;">`from_slice_mut`, `from_slice_with_strides_mut`…</span>](#creating-mutable-matrix-views-from-mut-t)
/// - [Views based on index and length <span style="float:right;">`row`, `columns`, `view`…</span>](#views-based-on-index-and-length)
/// - [Mutable views based on index and length <span style="float:right;">`row_mut`, `columns_mut`, `view_mut`…</span>](#mutable-views-based-on-index-and-length)
/// - [Views based on ranges <span style="float:right;">`rows_range`, `columns_range`…</span>](#views-based-on-ranges)
/// - [Mutable views based on ranges <span style="float:right;">`rows_range_mut`, `columns_range_mut`…</span>](#mutable-views-based-on-ranges)
///
/// #### In-place modification of a single matrix or vector
/// - [In-place filling <span style="float:right;">`fill`, `fill_diagonal`, `fill_with_identity`…</span>](#in-place-filling)
/// - [In-place swapping <span style="float:right;">`swap`, `swap_columns`…</span>](#in-place-swapping)
/// - [Set rows, columns, and diagonal <span style="float:right;">`set_column`, `set_diagonal`…</span>](#set-rows-columns-and-diagonal)
///
/// #### Vector and matrix size modification
/// - [Rows and columns insertion <span style="float:right;">`insert_row`, `insert_column`…</span>](#rows-and-columns-insertion)
/// - [Rows and columns removal <span style="float:right;">`remove_row`, `remove column`…</span>](#rows-and-columns-removal)
/// - [Rows and columns extraction <span style="float:right;">`select_rows`, `select_columns`…</span>](#rows-and-columns-extraction)
/// - [Resizing and reshaping <span style="float:right;">`resize`, `reshape_generic`…</span>](#resizing-and-reshaping)
/// - [In-place resizing <span style="float:right;">`resize_mut`, `resize_vertically_mut`…</span>](#in-place-resizing)
///
/// #### Matrix decomposition
/// - [Rectangular matrix decomposition <span style="float:right;">`qr`, `lu`, `svd`…</span>](#rectangular-matrix-decomposition)
/// - [Square matrix decomposition <span style="float:right;">`cholesky`, `symmetric_eigen`…</span>](#square-matrix-decomposition)
///
/// #### Vector basis computation
/// - [Basis and orthogonalization <span style="float:right;">`orthonormal_subspace_basis`, `orthonormalize`…</span>](#basis-and-orthogonalization)
///
/// # Type parameters
/// The generic `Matrix` type has four type parameters:
/// - `T`: for the matrix components scalar type.
/// - `R`: for the matrix number of rows.
/// - `C`: for the matrix number of columns.
/// - `S`: for the matrix data storage, i.e., the buffer that actually contains the matrix
/// components.
///
/// The matrix dimensions parameters `R` and `C` can either be:
/// - type-level unsigned integer constants (e.g. `U1`, `U124`) from the `nalgebra::` root module.
/// All numbers from 0 to 127 are defined that way.
/// - type-level unsigned integer constants (e.g. `U1024`, `U10000`) from the `typenum::` crate.
/// Using those, you will not get error messages as nice as for numbers smaller than 128 defined on
/// the `nalgebra::` module.
/// - the special value `Dyn` from the `nalgebra::` root module. This indicates that the
/// specified dimension is not known at compile-time. Note that this will generally imply that the
/// matrix data storage `S` performs a dynamic allocation and contains extra metadata for the
/// matrix shape.
///
/// Note that mixing `Dyn` with type-level unsigned integers is allowed. Actually, a
/// dynamically-sized column vector should be represented as a `Matrix<T, Dyn, U1, S>` (given
/// some concrete types for `T` and a compatible data storage type `S`).
#[repr(C)]
#[derive(Clone, Copy)]
#[cfg_attr(
feature = "rkyv-serialize-no-std",
derive(Archive, rkyv::Serialize, rkyv::Deserialize),
archive(
as = "Matrix<T::Archived, R, C, S::Archived>",
bound(archive = "
T: Archive,
S: Archive,
With<PhantomData<(T, R, C)>, CustomPhantom<(Archived<T>, R, C)>>: Archive<Archived = PhantomData<(Archived<T>, R, C)>>
")
)
)]
#[cfg_attr(feature = "rkyv-serialize", derive(bytecheck::CheckBytes))]
pub struct Matrix<T, R, C, S> {
/// The data storage that contains all the matrix components. Disappointed?
///
/// Well, if you came here to see how you can access the matrix components,
/// you may be in luck: you can access the individual components of all vectors with compile-time
/// dimensions <= 6 using field notation like this:
/// `vec.x`, `vec.y`, `vec.z`, `vec.w`, `vec.a`, `vec.b`. Reference and assignation work too:
/// ```
/// # use nalgebra::Vector3;
/// let mut vec = Vector3::new(1.0, 2.0, 3.0);
/// vec.x = 10.0;
/// vec.y += 30.0;
/// assert_eq!(vec.x, 10.0);
/// assert_eq!(vec.y + 100.0, 132.0);
/// ```
/// Similarly, for matrices with compile-time dimensions <= 6, you can use field notation
/// like this: `mat.m11`, `mat.m42`, etc. The first digit identifies the row to address
/// and the second digit identifies the column to address. So `mat.m13` identifies the component
/// at the first row and third column (note that the count of rows and columns start at 1 instead
/// of 0 here. This is so we match the mathematical notation).
///
/// For all matrices and vectors, independently from their size, individual components can
/// be accessed and modified using indexing: `vec[20]`, `mat[(20, 19)]`. Here the indexing
/// starts at 0 as you would expect.
pub data: S,
// NOTE: the fact that this field is private is important because
// this prevents the user from constructing a matrix with
// dimensions R, C that don't match the dimension of the
// storage S. Instead they have to use the unsafe function
// from_data_statically_unchecked.
// Note that it would probably make sense to just have
// the type `Matrix<S>`, and have `T, R, C` be associated-types
// of the `RawStorage` trait. However, because we don't have
// specialization, this is not possible because these `T, R, C`
// allows us to desambiguate a lot of configurations.
#[cfg_attr(feature = "rkyv-serialize-no-std", with(CustomPhantom<(T::Archived, R, C)>))]
_phantoms: PhantomData<(T, R, C)>,
}
impl<T, R: Dim, C: Dim, S: fmt::Debug> fmt::Debug for Matrix<T, R, C, S> {
fn fmt(&self, formatter: &mut fmt::Formatter<'_>) -> Result<(), fmt::Error> {
self.data.fmt(formatter)
}
}
impl<T, R, C, S> Default for Matrix<T, R, C, S>
where
T: Scalar,
R: Dim,
C: Dim,
S: Default,
{
fn default() -> Self {
Matrix {
data: Default::default(),
_phantoms: PhantomData,
}
}
}
#[cfg(feature = "serde-serialize-no-std")]
impl<T, R, C, S> Serialize for Matrix<T, R, C, S>
where
T: Scalar,
R: Dim,
C: Dim,
S: Serialize,
{
fn serialize<Ser>(&self, serializer: Ser) -> Result<Ser::Ok, Ser::Error>
where
Ser: Serializer,
{
self.data.serialize(serializer)
}
}
#[cfg(feature = "serde-serialize-no-std")]
impl<'de, T, R, C, S> Deserialize<'de> for Matrix<T, R, C, S>
where
T: Scalar,
R: Dim,
C: Dim,
S: Deserialize<'de>,
{
fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
where
D: Deserializer<'de>,
{
S::deserialize(deserializer).map(|x| Matrix {
data: x,
_phantoms: PhantomData,
})
}
}
#[cfg(feature = "compare")]
impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> matrixcompare_core::Matrix<T>
for Matrix<T, R, C, S>
{
fn rows(&self) -> usize {
self.nrows()
}
fn cols(&self) -> usize {
self.ncols()
}
fn access(&self) -> matrixcompare_core::Access<'_, T> {
matrixcompare_core::Access::Dense(self)
}
}
#[cfg(feature = "compare")]
impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> matrixcompare_core::DenseAccess<T>
for Matrix<T, R, C, S>
{
fn fetch_single(&self, row: usize, col: usize) -> T {
self.index((row, col)).clone()
}
}
#[cfg(feature = "bytemuck")]
unsafe impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> bytemuck::Zeroable
for Matrix<T, R, C, S>
where
S: bytemuck::Zeroable,
{
}
#[cfg(feature = "bytemuck")]
unsafe impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> bytemuck::Pod for Matrix<T, R, C, S>
where
S: bytemuck::Pod,
Self: Copy,
{
}
impl<T, R, C, S> Matrix<T, R, C, S> {
/// Creates a new matrix with the given data without statically checking that the matrix
/// dimension matches the storage dimension.
///
/// # Safety
///
/// The storage dimension must match the given dimensions.
#[inline(always)]
pub const unsafe fn from_data_statically_unchecked(data: S) -> Matrix<T, R, C, S> {
Matrix {
data,
_phantoms: PhantomData,
}
}
}
impl<T, const R: usize, const C: usize> SMatrix<T, R, C> {
/// Creates a new statically-allocated matrix from the given [`ArrayStorage`].
///
/// This method exists primarily as a workaround for the fact that `from_data` can not
/// work in `const fn` contexts.
#[inline(always)]
pub const fn from_array_storage(storage: ArrayStorage<T, R, C>) -> Self {
// This is sound because the row and column types are exactly the same as that of the
// storage, so there can be no mismatch
unsafe { Self::from_data_statically_unchecked(storage) }
}
}
// TODO: Consider removing/deprecating `from_vec_storage` once we are able to make
// `from_data` const fn compatible
#[cfg(any(feature = "std", feature = "alloc"))]
impl<T> DMatrix<T> {
/// Creates a new heap-allocated matrix from the given [`VecStorage`].
///
/// This method exists primarily as a workaround for the fact that `from_data` can not
/// work in `const fn` contexts.
pub const fn from_vec_storage(storage: VecStorage<T, Dyn, Dyn>) -> Self {
// This is sound because the dimensions of the matrix and the storage are guaranteed
// to be the same
unsafe { Self::from_data_statically_unchecked(storage) }
}
}
// TODO: Consider removing/deprecating `from_vec_storage` once we are able to make
// `from_data` const fn compatible
#[cfg(any(feature = "std", feature = "alloc"))]
impl<T> DVector<T> {
/// Creates a new heap-allocated matrix from the given [`VecStorage`].
///
/// This method exists primarily as a workaround for the fact that `from_data` can not
/// work in `const fn` contexts.
pub const fn from_vec_storage(storage: VecStorage<T, Dyn, U1>) -> Self {
// This is sound because the dimensions of the matrix and the storage are guaranteed
// to be the same
unsafe { Self::from_data_statically_unchecked(storage) }
}
}
// TODO: Consider removing/deprecating `from_vec_storage` once we are able to make
// `from_data` const fn compatible
#[cfg(any(feature = "std", feature = "alloc"))]
impl<T> RowDVector<T> {
/// Creates a new heap-allocated matrix from the given [`VecStorage`].
///
/// This method exists primarily as a workaround for the fact that `from_data` can not
/// work in `const fn` contexts.
pub const fn from_vec_storage(storage: VecStorage<T, U1, Dyn>) -> Self {
// This is sound because the dimensions of the matrix and the storage are guaranteed
// to be the same
unsafe { Self::from_data_statically_unchecked(storage) }
}
}
impl<T: Scalar, R: Dim, C: Dim> UninitMatrix<T, R, C>
where
DefaultAllocator: Allocator<R, C>,
{
/// Assumes a matrix's entries to be initialized. This operation should be near zero-cost.
///
/// # Safety
/// The user must make sure that every single entry of the buffer has been initialized,
/// or Undefined Behavior will immediately occur.
#[inline(always)]
pub unsafe fn assume_init(self) -> OMatrix<T, R, C> {
OMatrix::from_data(<DefaultAllocator as Allocator<R, C>>::assume_init(
self.data,
))
}
}
impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
/// Creates a new matrix with the given data.
#[inline(always)]
pub fn from_data(data: S) -> Self {
unsafe { Self::from_data_statically_unchecked(data) }
}
/// The shape of this matrix returned as the tuple (number of rows, number of columns).
///
/// # Example
/// ```
/// # use nalgebra::Matrix3x4;
/// let mat = Matrix3x4::<f32>::zeros();
/// assert_eq!(mat.shape(), (3, 4));
/// ```
#[inline]
#[must_use]
pub fn shape(&self) -> (usize, usize) {
let (nrows, ncols) = self.shape_generic();
(nrows.value(), ncols.value())
}
/// The shape of this matrix wrapped into their representative types (`Const` or `Dyn`).
#[inline]
#[must_use]
pub fn shape_generic(&self) -> (R, C) {
self.data.shape()
}
/// The number of rows of this matrix.
///
/// # Example
/// ```
/// # use nalgebra::Matrix3x4;
/// let mat = Matrix3x4::<f32>::zeros();
/// assert_eq!(mat.nrows(), 3);
/// ```
#[inline]
#[must_use]
pub fn nrows(&self) -> usize {
self.shape().0
}
/// The number of columns of this matrix.
///
/// # Example
/// ```
/// # use nalgebra::Matrix3x4;
/// let mat = Matrix3x4::<f32>::zeros();
/// assert_eq!(mat.ncols(), 4);
/// ```
#[inline]
#[must_use]
pub fn ncols(&self) -> usize {
self.shape().1
}
/// The strides (row stride, column stride) of this matrix.
///
/// # Example
/// ```
/// # use nalgebra::DMatrix;
/// let mat = DMatrix::<f32>::zeros(10, 10);
/// let view = mat.view_with_steps((0, 0), (5, 3), (1, 2));
/// // The column strides is the number of steps (here 2) multiplied by the corresponding dimension.
/// assert_eq!(mat.strides(), (1, 10));
/// ```
#[inline]
#[must_use]
pub fn strides(&self) -> (usize, usize) {
let (srows, scols) = self.data.strides();
(srows.value(), scols.value())
}
/// Computes the row and column coordinates of the i-th element of this matrix seen as a
/// vector.
///
/// # Example
/// ```
/// # use nalgebra::Matrix2;
/// let m = Matrix2::new(1, 2,
/// 3, 4);
/// let i = m.vector_to_matrix_index(3);
/// assert_eq!(i, (1, 1));
/// assert_eq!(m[i], m[3]);
/// ```
#[inline]
#[must_use]
pub fn vector_to_matrix_index(&self, i: usize) -> (usize, usize) {
let (nrows, ncols) = self.shape();
// Two most common uses that should be optimized by the compiler for statically-sized
// matrices.
if nrows == 1 {
(0, i)
} else if ncols == 1 {
(i, 0)
} else {
(i % nrows, i / nrows)
}
}
/// Returns a pointer to the start of the matrix.
///
/// If the matrix is not empty, this pointer is guaranteed to be aligned
/// and non-null.
///
/// # Example
/// ```
/// # use nalgebra::Matrix2;
/// let m = Matrix2::new(1, 2,
/// 3, 4);
/// let ptr = m.as_ptr();
/// assert_eq!(unsafe { *ptr }, m[0]);
/// ```
#[inline]
#[must_use]
pub fn as_ptr(&self) -> *const T {
self.data.ptr()
}
/// Tests whether `self` and `rhs` are equal up to a given epsilon.
///
/// See `relative_eq` from the `RelativeEq` trait for more details.
#[inline]
#[must_use]
pub fn relative_eq<R2, C2, SB>(
&self,
other: &Matrix<T, R2, C2, SB>,
eps: T::Epsilon,
max_relative: T::Epsilon,
) -> bool
where
T: RelativeEq + Scalar,
R2: Dim,
C2: Dim,
SB: Storage<T, R2, C2>,
T::Epsilon: Clone,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
assert!(self.shape() == other.shape());
self.iter()
.zip(other.iter())
.all(|(a, b)| a.relative_eq(b, eps.clone(), max_relative.clone()))
}
/// Tests whether `self` and `rhs` are exactly equal.
#[inline]
#[must_use]
#[allow(clippy::should_implement_trait)]
pub fn eq<R2, C2, SB>(&self, other: &Matrix<T, R2, C2, SB>) -> bool
where
T: PartialEq,
R2: Dim,
C2: Dim,
SB: RawStorage<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
assert!(self.shape() == other.shape());
self.iter().zip(other.iter()).all(|(a, b)| *a == *b)
}
/// Moves this matrix into one that owns its data.
#[inline]
pub fn into_owned(self) -> OMatrix<T, R, C>
where
T: Scalar,
S: Storage<T, R, C>,
DefaultAllocator: Allocator<R, C>,
{
Matrix::from_data(self.data.into_owned())
}
// TODO: this could probably benefit from specialization.
// XXX: bad name.
/// Moves this matrix into one that owns its data. The actual type of the result depends on
/// matrix storage combination rules for addition.
#[inline]
pub fn into_owned_sum<R2, C2>(self) -> MatrixSum<T, R, C, R2, C2>
where
T: Scalar,
S: Storage<T, R, C>,
R2: Dim,
C2: Dim,
DefaultAllocator: SameShapeAllocator<R, C, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
if TypeId::of::<SameShapeStorage<T, R, C, R2, C2>>() == TypeId::of::<Owned<T, R, C>>() {
// We can just return `self.into_owned()`.
unsafe {
// TODO: check that those copies are optimized away by the compiler.
let owned = self.into_owned();
let res = mem::transmute_copy(&owned);
mem::forget(owned);
res
}
} else {
self.clone_owned_sum()
}
}
/// Clones this matrix to one that owns its data.
#[inline]
#[must_use]
pub fn clone_owned(&self) -> OMatrix<T, R, C>
where
T: Scalar,
S: Storage<T, R, C>,
DefaultAllocator: Allocator<R, C>,
{
Matrix::from_data(self.data.clone_owned())
}
/// Clones this matrix into one that owns its data. The actual type of the result depends on
/// matrix storage combination rules for addition.
#[inline]
#[must_use]
pub fn clone_owned_sum<R2, C2>(&self) -> MatrixSum<T, R, C, R2, C2>
where
T: Scalar,
S: Storage<T, R, C>,
R2: Dim,
C2: Dim,
DefaultAllocator: SameShapeAllocator<R, C, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
let (nrows, ncols) = self.shape();
let nrows: SameShapeR<R, R2> = Dim::from_usize(nrows);
let ncols: SameShapeC<C, C2> = Dim::from_usize(ncols);
let mut res = Matrix::uninit(nrows, ncols);
unsafe {
// TODO: use copy_from?
for j in 0..res.ncols() {
for i in 0..res.nrows() {
*res.get_unchecked_mut((i, j)) =
MaybeUninit::new(self.get_unchecked((i, j)).clone());
}
}
// SAFETY: the output has been initialized above.
res.assume_init()
}
}
/// Transposes `self` and store the result into `out`.
#[inline]
fn transpose_to_uninit<Status, R2, C2, SB>(
&self,
_status: Status,
out: &mut Matrix<Status::Value, R2, C2, SB>,
) where
Status: InitStatus<T>,
T: Scalar,
R2: Dim,
C2: Dim,
SB: RawStorageMut<Status::Value, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, C2> + SameNumberOfColumns<C, R2>,
{
let (nrows, ncols) = self.shape();
assert!(
(ncols, nrows) == out.shape(),
"Incompatible shape for transposition."
);
// TODO: optimize that.
for i in 0..nrows {
for j in 0..ncols {
// Safety: the indices are in range.
unsafe {
Status::init(
out.get_unchecked_mut((j, i)),
self.get_unchecked((i, j)).clone(),
);
}
}
}
}
/// Transposes `self` and store the result into `out`.
#[inline]
pub fn transpose_to<R2, C2, SB>(&self, out: &mut Matrix<T, R2, C2, SB>)
where
T: Scalar,
R2: Dim,
C2: Dim,
SB: RawStorageMut<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, C2> + SameNumberOfColumns<C, R2>,
{
self.transpose_to_uninit(Init, out)
}
/// Transposes `self`.
#[inline]
#[must_use = "Did you mean to use transpose_mut()?"]
pub fn transpose(&self) -> OMatrix<T, C, R>
where
T: Scalar,
DefaultAllocator: Allocator<C, R>,
{
let (nrows, ncols) = self.shape_generic();
let mut res = Matrix::uninit(ncols, nrows);
self.transpose_to_uninit(Uninit, &mut res);
// Safety: res is now fully initialized.
unsafe { res.assume_init() }
}
}
/// # Elementwise mapping and folding
impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
/// Returns a matrix containing the result of `f` applied to each of its entries.
#[inline]
#[must_use]
pub fn map<T2: Scalar, F: FnMut(T) -> T2>(&self, mut f: F) -> OMatrix<T2, R, C>
where
T: Scalar,
DefaultAllocator: Allocator<R, C>,
{
let (nrows, ncols) = self.shape_generic();
let mut res = Matrix::uninit(nrows, ncols);
for j in 0..ncols.value() {
for i in 0..nrows.value() {
// Safety: all indices are in range.
unsafe {
let a = self.data.get_unchecked(i, j).clone();
*res.data.get_unchecked_mut(i, j) = MaybeUninit::new(f(a));
}
}
}
// Safety: res is now fully initialized.
unsafe { res.assume_init() }
}
/// Cast the components of `self` to another type.
///
/// # Example
/// ```
/// # use nalgebra::Vector3;
/// let q = Vector3::new(1.0f64, 2.0, 3.0);
/// let q2 = q.cast::<f32>();
/// assert_eq!(q2, Vector3::new(1.0f32, 2.0, 3.0));
/// ```
pub fn cast<T2: Scalar>(self) -> OMatrix<T2, R, C>
where
T: Scalar,
OMatrix<T2, R, C>: SupersetOf<Self>,
DefaultAllocator: Allocator<R, C>,
{
crate::convert(self)
}
/// Attempts to cast the components of `self` to another type.
///
/// # Example
/// ```
/// # use nalgebra::Vector3;
/// let q = Vector3::new(1.0f64, 2.0, 3.0);
/// let q2 = q.try_cast::<i32>();
/// assert_eq!(q2, Some(Vector3::new(1, 2, 3)));
/// ```
pub fn try_cast<T2: Scalar>(self) -> Option<OMatrix<T2, R, C>>
where
T: Scalar,
Self: SupersetOf<OMatrix<T2, R, C>>,
DefaultAllocator: Allocator<R, C>,
{
crate::try_convert(self)
}
/// Similar to `self.iter().fold(init, f)` except that `init` is replaced by a closure.
///
/// The initialization closure is given the first component of this matrix:
/// - If the matrix has no component (0 rows or 0 columns) then `init_f` is called with `None`
/// and its return value is the value returned by this method.
/// - If the matrix has has least one component, then `init_f` is called with the first component
/// to compute the initial value. Folding then continues on all the remaining components of the matrix.
#[inline]
#[must_use]
pub fn fold_with<T2>(
&self,
init_f: impl FnOnce(Option<&T>) -> T2,
f: impl FnMut(T2, &T) -> T2,
) -> T2
where
T: Scalar,
{
let mut it = self.iter();
let init = init_f(it.next());
it.fold(init, f)
}
/// Returns a matrix containing the result of `f` applied to each of its entries. Unlike `map`,
/// `f` also gets passed the row and column index, i.e. `f(row, col, value)`.
#[inline]
#[must_use]
pub fn map_with_location<T2: Scalar, F: FnMut(usize, usize, T) -> T2>(
&self,
mut f: F,
) -> OMatrix<T2, R, C>
where
T: Scalar,
DefaultAllocator: Allocator<R, C>,
{
let (nrows, ncols) = self.shape_generic();
let mut res = Matrix::uninit(nrows, ncols);
for j in 0..ncols.value() {
for i in 0..nrows.value() {
// Safety: all indices are in range.
unsafe {
let a = self.data.get_unchecked(i, j).clone();
*res.data.get_unchecked_mut(i, j) = MaybeUninit::new(f(i, j, a));
}
}
}
// Safety: res is now fully initialized.
unsafe { res.assume_init() }
}
/// Returns a matrix containing the result of `f` applied to each entries of `self` and
/// `rhs`.
#[inline]
#[must_use]
pub fn zip_map<T2, N3, S2, F>(&self, rhs: &Matrix<T2, R, C, S2>, mut f: F) -> OMatrix<N3, R, C>
where
T: Scalar,
T2: Scalar,
N3: Scalar,
S2: RawStorage<T2, R, C>,
F: FnMut(T, T2) -> N3,
DefaultAllocator: Allocator<R, C>,
{
let (nrows, ncols) = self.shape_generic();
let mut res = Matrix::uninit(nrows, ncols);
assert_eq!(
(nrows.value(), ncols.value()),
rhs.shape(),
"Matrix simultaneous traversal error: dimension mismatch."
);
for j in 0..ncols.value() {
for i in 0..nrows.value() {
// Safety: all indices are in range.
unsafe {
let a = self.data.get_unchecked(i, j).clone();
let b = rhs.data.get_unchecked(i, j).clone();
*res.data.get_unchecked_mut(i, j) = MaybeUninit::new(f(a, b))
}
}
}
// Safety: res is now fully initialized.
unsafe { res.assume_init() }
}
/// Returns a matrix containing the result of `f` applied to each entries of `self` and
/// `b`, and `c`.
#[inline]
#[must_use]
pub fn zip_zip_map<T2, N3, N4, S2, S3, F>(
&self,
b: &Matrix<T2, R, C, S2>,
c: &Matrix<N3, R, C, S3>,
mut f: F,
) -> OMatrix<N4, R, C>
where
T: Scalar,
T2: Scalar,
N3: Scalar,
N4: Scalar,
S2: RawStorage<T2, R, C>,
S3: RawStorage<N3, R, C>,
F: FnMut(T, T2, N3) -> N4,
DefaultAllocator: Allocator<R, C>,
{
let (nrows, ncols) = self.shape_generic();
let mut res = Matrix::uninit(nrows, ncols);
assert_eq!(
(nrows.value(), ncols.value()),
b.shape(),
"Matrix simultaneous traversal error: dimension mismatch."
);
assert_eq!(
(nrows.value(), ncols.value()),
c.shape(),
"Matrix simultaneous traversal error: dimension mismatch."
);
for j in 0..ncols.value() {
for i in 0..nrows.value() {
// Safety: all indices are in range.
unsafe {
let a = self.data.get_unchecked(i, j).clone();
let b = b.data.get_unchecked(i, j).clone();
let c = c.data.get_unchecked(i, j).clone();
*res.data.get_unchecked_mut(i, j) = MaybeUninit::new(f(a, b, c))
}
}
}
// Safety: res is now fully initialized.
unsafe { res.assume_init() }
}
/// Folds a function `f` on each entry of `self`.
#[inline]
#[must_use]
pub fn fold<Acc>(&self, init: Acc, mut f: impl FnMut(Acc, T) -> Acc) -> Acc
where
T: Scalar,
{
let (nrows, ncols) = self.shape_generic();
let mut res = init;
for j in 0..ncols.value() {
for i in 0..nrows.value() {
// Safety: all indices are in range.
unsafe {
let a = self.data.get_unchecked(i, j).clone();
res = f(res, a)
}
}
}
res
}
/// Folds a function `f` on each pairs of entries from `self` and `rhs`.
#[inline]
#[must_use]
pub fn zip_fold<T2, R2, C2, S2, Acc>(
&self,
rhs: &Matrix<T2, R2, C2, S2>,
init: Acc,
mut f: impl FnMut(Acc, T, T2) -> Acc,
) -> Acc
where
T: Scalar,
T2: Scalar,
R2: Dim,
C2: Dim,
S2: RawStorage<T2, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
let (nrows, ncols) = self.shape_generic();
let mut res = init;
assert_eq!(
(nrows.value(), ncols.value()),
rhs.shape(),
"Matrix simultaneous traversal error: dimension mismatch."
);
for j in 0..ncols.value() {
for i in 0..nrows.value() {
unsafe {
let a = self.data.get_unchecked(i, j).clone();
let b = rhs.data.get_unchecked(i, j).clone();
res = f(res, a, b)
}
}
}
res
}
/// Applies a closure `f` to modify each component of `self`.
#[inline]
pub fn apply<F: FnMut(&mut T)>(&mut self, mut f: F)
where
S: RawStorageMut<T, R, C>,
{
let (nrows, ncols) = self.shape();
for j in 0..ncols {
for i in 0..nrows {
unsafe {
let e = self.data.get_unchecked_mut(i, j);
f(e)
}
}
}
}
/// Replaces each component of `self` by the result of a closure `f` applied on its components
/// joined with the components from `rhs`.
#[inline]
pub fn zip_apply<T2, R2, C2, S2>(
&mut self,
rhs: &Matrix<T2, R2, C2, S2>,
mut f: impl FnMut(&mut T, T2),
) where
S: RawStorageMut<T, R, C>,
T2: Scalar,
R2: Dim,
C2: Dim,
S2: RawStorage<T2, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
let (nrows, ncols) = self.shape();
assert_eq!(
(nrows, ncols),
rhs.shape(),
"Matrix simultaneous traversal error: dimension mismatch."
);
for j in 0..ncols {
for i in 0..nrows {
unsafe {
let e = self.data.get_unchecked_mut(i, j);
let rhs = rhs.get_unchecked((i, j)).clone();
f(e, rhs)
}
}
}
}
/// Replaces each component of `self` by the result of a closure `f` applied on its components
/// joined with the components from `b` and `c`.
#[inline]
pub fn zip_zip_apply<T2, R2, C2, S2, N3, R3, C3, S3>(
&mut self,
b: &Matrix<T2, R2, C2, S2>,
c: &Matrix<N3, R3, C3, S3>,
mut f: impl FnMut(&mut T, T2, N3),
) where
S: RawStorageMut<T, R, C>,
T2: Scalar,
R2: Dim,
C2: Dim,
S2: RawStorage<T2, R2, C2>,
N3: Scalar,
R3: Dim,
C3: Dim,
S3: RawStorage<N3, R3, C3>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
let (nrows, ncols) = self.shape();
assert_eq!(
(nrows, ncols),
b.shape(),
"Matrix simultaneous traversal error: dimension mismatch."
);
assert_eq!(
(nrows, ncols),
c.shape(),
"Matrix simultaneous traversal error: dimension mismatch."
);
for j in 0..ncols {
for i in 0..nrows {
unsafe {
let e = self.data.get_unchecked_mut(i, j);
let b = b.get_unchecked((i, j)).clone();
let c = c.get_unchecked((i, j)).clone();
f(e, b, c)
}
}
}
}
}
/// # Iteration on components, rows, and columns
impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
/// Iterates through this matrix coordinates in column-major order.
///
/// # Example
/// ```
/// # use nalgebra::Matrix2x3;
/// let mat = Matrix2x3::new(11, 12, 13,
/// 21, 22, 23);
/// let mut it = mat.iter();
/// assert_eq!(*it.next().unwrap(), 11);
/// assert_eq!(*it.next().unwrap(), 21);
/// assert_eq!(*it.next().unwrap(), 12);
/// assert_eq!(*it.next().unwrap(), 22);
/// assert_eq!(*it.next().unwrap(), 13);
/// assert_eq!(*it.next().unwrap(), 23);
/// assert!(it.next().is_none());
/// ```
#[inline]
pub fn iter(&self) -> MatrixIter<'_, T, R, C, S> {
MatrixIter::new(&self.data)
}
/// Iterate through the rows of this matrix.
///
/// # Example
/// ```
/// # use nalgebra::Matrix2x3;
/// let mut a = Matrix2x3::new(1, 2, 3,
/// 4, 5, 6);
/// for (i, row) in a.row_iter().enumerate() {
/// assert_eq!(row, a.row(i))
/// }
/// ```
#[inline]
pub fn row_iter(&self) -> RowIter<'_, T, R, C, S> {
RowIter::new(self)
}
/// Iterate through the columns of this matrix.
///
/// # Example
/// ```
/// # use nalgebra::Matrix2x3;
/// let mut a = Matrix2x3::new(1, 2, 3,
/// 4, 5, 6);
/// for (i, column) in a.column_iter().enumerate() {
/// assert_eq!(column, a.column(i))
/// }
/// ```
#[inline]
pub fn column_iter(&self) -> ColumnIter<'_, T, R, C, S> {
ColumnIter::new(self)
}
/// Mutably iterates through this matrix coordinates.
#[inline]
pub fn iter_mut(&mut self) -> MatrixIterMut<'_, T, R, C, S>
where
S: RawStorageMut<T, R, C>,
{
MatrixIterMut::new(&mut self.data)
}
/// Mutably iterates through this matrix rows.
///
/// # Example
/// ```
/// # use nalgebra::Matrix2x3;
/// let mut a = Matrix2x3::new(1, 2, 3,
/// 4, 5, 6);
/// for (i, mut row) in a.row_iter_mut().enumerate() {
/// row *= (i + 1) * 10;
/// }
///
/// let expected = Matrix2x3::new(10, 20, 30,
/// 80, 100, 120);
/// assert_eq!(a, expected);
/// ```
#[inline]
pub fn row_iter_mut(&mut self) -> RowIterMut<'_, T, R, C, S>
where
S: RawStorageMut<T, R, C>,
{
RowIterMut::new(self)
}
/// Mutably iterates through this matrix columns.
///
/// # Example
/// ```
/// # use nalgebra::Matrix2x3;
/// let mut a = Matrix2x3::new(1, 2, 3,
/// 4, 5, 6);
/// for (i, mut col) in a.column_iter_mut().enumerate() {
/// col *= (i + 1) * 10;
/// }
///
/// let expected = Matrix2x3::new(10, 40, 90,
/// 40, 100, 180);
/// assert_eq!(a, expected);
/// ```
#[inline]
pub fn column_iter_mut(&mut self) -> ColumnIterMut<'_, T, R, C, S>
where
S: RawStorageMut<T, R, C>,
{
ColumnIterMut::new(self)
}
}
impl<T, R: Dim, C: Dim, S: RawStorageMut<T, R, C>> Matrix<T, R, C, S> {
/// Returns a mutable pointer to the start of the matrix.
///
/// If the matrix is not empty, this pointer is guaranteed to be aligned
/// and non-null.
#[inline]
pub fn as_mut_ptr(&mut self) -> *mut T {
self.data.ptr_mut()
}
/// Swaps two entries without bound-checking.
///
/// # Safety
///
/// Both `(r, c)` must have `r < nrows(), c < ncols()`.
#[inline]
pub unsafe fn swap_unchecked(&mut self, row_cols1: (usize, usize), row_cols2: (usize, usize)) {
debug_assert!(row_cols1.0 < self.nrows() && row_cols1.1 < self.ncols());
debug_assert!(row_cols2.0 < self.nrows() && row_cols2.1 < self.ncols());
self.data.swap_unchecked(row_cols1, row_cols2)
}
/// Swaps two entries.
#[inline]
pub fn swap(&mut self, row_cols1: (usize, usize), row_cols2: (usize, usize)) {
let (nrows, ncols) = self.shape();
assert!(
row_cols1.0 < nrows && row_cols1.1 < ncols,
"Matrix elements swap index out of bounds."
);
assert!(
row_cols2.0 < nrows && row_cols2.1 < ncols,
"Matrix elements swap index out of bounds."
);
unsafe { self.swap_unchecked(row_cols1, row_cols2) }
}
/// Fills this matrix with the content of a slice. Both must hold the same number of elements.
///
/// The components of the slice are assumed to be ordered in column-major order.
#[inline]
pub fn copy_from_slice(&mut self, slice: &[T])
where
T: Scalar,
{
let (nrows, ncols) = self.shape();
assert!(
nrows * ncols == slice.len(),
"The slice must contain the same number of elements as the matrix."
);
for j in 0..ncols {
for i in 0..nrows {
unsafe {
*self.get_unchecked_mut((i, j)) = slice.get_unchecked(i + j * nrows).clone();
}
}
}
}
/// Fills this matrix with the content of another one. Both must have the same shape.
#[inline]
pub fn copy_from<R2, C2, SB>(&mut self, other: &Matrix<T, R2, C2, SB>)
where
T: Scalar,
R2: Dim,
C2: Dim,
SB: RawStorage<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
assert!(
self.shape() == other.shape(),
"Unable to copy from a matrix with a different shape."
);
for j in 0..self.ncols() {
for i in 0..self.nrows() {
unsafe {
*self.get_unchecked_mut((i, j)) = other.get_unchecked((i, j)).clone();
}
}
}
}
/// Fills this matrix with the content of the transpose another one.
#[inline]
pub fn tr_copy_from<R2, C2, SB>(&mut self, other: &Matrix<T, R2, C2, SB>)
where
T: Scalar,
R2: Dim,
C2: Dim,
SB: RawStorage<T, R2, C2>,
ShapeConstraint: DimEq<R, C2> + SameNumberOfColumns<C, R2>,
{
let (nrows, ncols) = self.shape();
assert!(
(ncols, nrows) == other.shape(),
"Unable to copy from a matrix with incompatible shape."
);
for j in 0..ncols {
for i in 0..nrows {
unsafe {
*self.get_unchecked_mut((i, j)) = other.get_unchecked((j, i)).clone();
}
}
}
}
// TODO: rename `apply` to `apply_mut` and `apply_into` to `apply`?
/// Returns `self` with each of its components replaced by the result of a closure `f` applied on it.
#[inline]
pub fn apply_into<F: FnMut(&mut T)>(mut self, f: F) -> Self {
self.apply(f);
self
}
}
impl<T, D: Dim, S: RawStorage<T, D>> Vector<T, D, S> {
/// Gets a reference to the i-th element of this column vector without bound checking.
/// # Safety
/// `i` must be less than `D`.
#[inline]
#[must_use]
pub unsafe fn vget_unchecked(&self, i: usize) -> &T {
debug_assert!(i < self.nrows(), "Vector index out of bounds.");
let i = i * self.strides().0;
self.data.get_unchecked_linear(i)
}
}
impl<T, D: Dim, S: RawStorageMut<T, D>> Vector<T, D, S> {
/// Gets a mutable reference to the i-th element of this column vector without bound checking.
/// # Safety
/// `i` must be less than `D`.
#[inline]
#[must_use]
pub unsafe fn vget_unchecked_mut(&mut self, i: usize) -> &mut T {
debug_assert!(i < self.nrows(), "Vector index out of bounds.");
let i = i * self.strides().0;
self.data.get_unchecked_linear_mut(i)
}
}
impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C> + IsContiguous> Matrix<T, R, C, S> {
/// Extracts a slice containing the entire matrix entries ordered column-by-columns.
#[inline]
#[must_use]
pub fn as_slice(&self) -> &[T] {
// Safety: this is OK thanks to the IsContiguous trait.
unsafe { self.data.as_slice_unchecked() }
}
}
impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C> + IsContiguous> AsRef<[T]> for Matrix<T, R, C, S> {
/// Extracts a slice containing the entire matrix entries ordered column-by-columns.
#[inline]
fn as_ref(&self) -> &[T] {
self.as_slice()
}
}
impl<T, R: Dim, C: Dim, S: RawStorageMut<T, R, C> + IsContiguous> Matrix<T, R, C, S> {
/// Extracts a mutable slice containing the entire matrix entries ordered column-by-columns.
#[inline]
#[must_use]
pub fn as_mut_slice(&mut self) -> &mut [T] {
// Safety: this is OK thanks to the IsContiguous trait.
unsafe { self.data.as_mut_slice_unchecked() }
}
}
impl<T: Scalar, D: Dim, S: RawStorageMut<T, D, D>> Matrix<T, D, D, S> {
/// Transposes the square matrix `self` in-place.
pub fn transpose_mut(&mut self) {
assert!(
self.is_square(),
"Unable to transpose a non-square matrix in-place."
);
let dim = self.shape().0;
for i in 1..dim {
for j in 0..i {
unsafe { self.swap_unchecked((i, j), (j, i)) }
}
}
}
}
impl<T: SimdComplexField, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
/// Takes the adjoint (aka. conjugate-transpose) of `self` and store the result into `out`.
#[inline]
fn adjoint_to_uninit<Status, R2, C2, SB>(
&self,
_status: Status,
out: &mut Matrix<Status::Value, R2, C2, SB>,
) where
Status: InitStatus<T>,
R2: Dim,
C2: Dim,
SB: RawStorageMut<Status::Value, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, C2> + SameNumberOfColumns<C, R2>,
{
let (nrows, ncols) = self.shape();
assert!(
(ncols, nrows) == out.shape(),
"Incompatible shape for transpose-copy."
);
// TODO: optimize that.
for i in 0..nrows {
for j in 0..ncols {
// Safety: all indices are in range.
unsafe {
Status::init(
out.get_unchecked_mut((j, i)),
self.get_unchecked((i, j)).clone().simd_conjugate(),
);
}
}
}
}
/// Takes the adjoint (aka. conjugate-transpose) of `self` and store the result into `out`.
#[inline]
pub fn adjoint_to<R2, C2, SB>(&self, out: &mut Matrix<T, R2, C2, SB>)
where
R2: Dim,
C2: Dim,
SB: RawStorageMut<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, C2> + SameNumberOfColumns<C, R2>,
{
self.adjoint_to_uninit(Init, out)
}
/// The adjoint (aka. conjugate-transpose) of `self`.
#[inline]
#[must_use = "Did you mean to use adjoint_mut()?"]
pub fn adjoint(&self) -> OMatrix<T, C, R>
where
DefaultAllocator: Allocator<C, R>,
{
let (nrows, ncols) = self.shape_generic();
let mut res = Matrix::uninit(ncols, nrows);
self.adjoint_to_uninit(Uninit, &mut res);
// Safety: res is now fully initialized.
unsafe { res.assume_init() }
}
/// Takes the conjugate and transposes `self` and store the result into `out`.
#[deprecated(note = "Renamed `self.adjoint_to(out)`.")]
#[inline]
pub fn conjugate_transpose_to<R2, C2, SB>(&self, out: &mut Matrix<T, R2, C2, SB>)
where
R2: Dim,
C2: Dim,
SB: RawStorageMut<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, C2> + SameNumberOfColumns<C, R2>,
{
self.adjoint_to(out)
}
/// The conjugate transposition of `self`.
#[deprecated(note = "Renamed `self.adjoint()`.")]
#[inline]
pub fn conjugate_transpose(&self) -> OMatrix<T, C, R>
where
DefaultAllocator: Allocator<C, R>,
{
self.adjoint()
}
/// The conjugate of `self`.
#[inline]
#[must_use = "Did you mean to use conjugate_mut()?"]
pub fn conjugate(&self) -> OMatrix<T, R, C>
where
DefaultAllocator: Allocator<R, C>,
{
self.map(|e| e.simd_conjugate())
}
/// Divides each component of the complex matrix `self` by the given real.
#[inline]
#[must_use = "Did you mean to use unscale_mut()?"]
pub fn unscale(&self, real: T::SimdRealField) -> OMatrix<T, R, C>
where
DefaultAllocator: Allocator<R, C>,
{
self.map(|e| e.simd_unscale(real.clone()))
}
/// Multiplies each component of the complex matrix `self` by the given real.
#[inline]
#[must_use = "Did you mean to use scale_mut()?"]
pub fn scale(&self, real: T::SimdRealField) -> OMatrix<T, R, C>
where
DefaultAllocator: Allocator<R, C>,
{
self.map(|e| e.simd_scale(real.clone()))
}
}
impl<T: SimdComplexField, R: Dim, C: Dim, S: RawStorageMut<T, R, C>> Matrix<T, R, C, S> {
/// The conjugate of the complex matrix `self` computed in-place.
#[inline]
pub fn conjugate_mut(&mut self) {
self.apply(|e| *e = e.clone().simd_conjugate())
}
/// Divides each component of the complex matrix `self` by the given real.
#[inline]
pub fn unscale_mut(&mut self, real: T::SimdRealField) {
self.apply(|e| *e = e.clone().simd_unscale(real.clone()))
}
/// Multiplies each component of the complex matrix `self` by the given real.
#[inline]
pub fn scale_mut(&mut self, real: T::SimdRealField) {
self.apply(|e| *e = e.clone().simd_scale(real.clone()))
}
}
impl<T: SimdComplexField, D: Dim, S: RawStorageMut<T, D, D>> Matrix<T, D, D, S> {
/// Sets `self` to its adjoint.
#[deprecated(note = "Renamed to `self.adjoint_mut()`.")]
pub fn conjugate_transform_mut(&mut self) {
self.adjoint_mut()
}
/// Sets `self` to its adjoint (aka. conjugate-transpose).
pub fn adjoint_mut(&mut self) {
assert!(
self.is_square(),
"Unable to transpose a non-square matrix in-place."
);
let dim = self.shape().0;
for i in 0..dim {
for j in 0..i {
unsafe {
let ref_ij = self.get_unchecked((i, j)).clone();
let ref_ji = self.get_unchecked((j, i)).clone();
let conj_ij = ref_ij.simd_conjugate();
let conj_ji = ref_ji.simd_conjugate();
*self.get_unchecked_mut((i, j)) = conj_ji;
*self.get_unchecked_mut((j, i)) = conj_ij;
}
}
{
let diag = unsafe { self.get_unchecked_mut((i, i)) };
*diag = diag.clone().simd_conjugate();
}
}
}
}
impl<T: Scalar, D: Dim, S: RawStorage<T, D, D>> SquareMatrix<T, D, S> {
/// The diagonal of this matrix.
#[inline]
#[must_use]
pub fn diagonal(&self) -> OVector<T, D>
where
DefaultAllocator: Allocator<D>,
{
self.map_diagonal(|e| e)
}
/// Apply the given function to this matrix's diagonal and returns it.
///
/// This is a more efficient version of `self.diagonal().map(f)` since this
/// allocates only once.
#[must_use]
pub fn map_diagonal<T2: Scalar>(&self, mut f: impl FnMut(T) -> T2) -> OVector<T2, D>
where
DefaultAllocator: Allocator<D>,
{
assert!(
self.is_square(),
"Unable to get the diagonal of a non-square matrix."
);
let dim = self.shape_generic().0;
let mut res = Matrix::uninit(dim, Const::<1>);
for i in 0..dim.value() {
// Safety: all indices are in range.
unsafe {
*res.vget_unchecked_mut(i) =
MaybeUninit::new(f(self.get_unchecked((i, i)).clone()));
}
}
// Safety: res is now fully initialized.
unsafe { res.assume_init() }
}
/// Computes a trace of a square matrix, i.e., the sum of its diagonal elements.
#[inline]
#[must_use]
pub fn trace(&self) -> T
where
T: Scalar + Zero + ClosedAddAssign,
{
assert!(
self.is_square(),
"Cannot compute the trace of non-square matrix."
);
let dim = self.shape_generic().0;
let mut res = T::zero();
for i in 0..dim.value() {
res += unsafe { self.get_unchecked((i, i)).clone() };
}
res
}
}
impl<T: SimdComplexField, D: Dim, S: Storage<T, D, D>> SquareMatrix<T, D, S> {
/// The symmetric part of `self`, i.e., `0.5 * (self + self.transpose())`.
#[inline]
#[must_use]
pub fn symmetric_part(&self) -> OMatrix<T, D, D>
where
DefaultAllocator: Allocator<D, D>,
{
assert!(
self.is_square(),
"Cannot compute the symmetric part of a non-square matrix."
);
let mut tr = self.transpose();
tr += self;
tr *= crate::convert::<_, T>(0.5);
tr
}
/// The hermitian part of `self`, i.e., `0.5 * (self + self.adjoint())`.
#[inline]
#[must_use]
pub fn hermitian_part(&self) -> OMatrix<T, D, D>
where
DefaultAllocator: Allocator<D, D>,
{
assert!(
self.is_square(),
"Cannot compute the hermitian part of a non-square matrix."
);
let mut tr = self.adjoint();
tr += self;
tr *= crate::convert::<_, T>(0.5);
tr
}
}
impl<T: Scalar + Zero + One, D: DimAdd<U1> + IsNotStaticOne, S: RawStorage<T, D, D>>
Matrix<T, D, D, S>
{
/// Yields the homogeneous matrix for this matrix, i.e., appending an additional dimension and
/// and setting the diagonal element to `1`.
#[inline]
#[must_use]
pub fn to_homogeneous(&self) -> OMatrix<T, DimSum<D, U1>, DimSum<D, U1>>
where
DefaultAllocator: Allocator<DimSum<D, U1>, DimSum<D, U1>>,
{
assert!(
self.is_square(),
"Only square matrices can currently be transformed to homogeneous coordinates."
);
let dim = DimSum::<D, U1>::from_usize(self.nrows() + 1);
let mut res = OMatrix::identity_generic(dim, dim);
res.generic_view_mut::<D, D>((0, 0), self.shape_generic())
.copy_from(self);
res
}
}
impl<T: Scalar + Zero, D: DimAdd<U1>, S: RawStorage<T, D>> Vector<T, D, S> {
/// Computes the coordinates in projective space of this vector, i.e., appends a `0` to its
/// coordinates.
#[inline]
#[must_use]
pub fn to_homogeneous(&self) -> OVector<T, DimSum<D, U1>>
where
DefaultAllocator: Allocator<DimSum<D, U1>>,
{
self.push(T::zero())
}
/// Constructs a vector from coordinates in projective space, i.e., removes a `0` at the end of
/// `self`. Returns `None` if this last component is not zero.
#[inline]
pub fn from_homogeneous<SB>(v: Vector<T, DimSum<D, U1>, SB>) -> Option<OVector<T, D>>
where
SB: RawStorage<T, DimSum<D, U1>>,
DefaultAllocator: Allocator<D>,
{
if v[v.len() - 1].is_zero() {
let nrows = D::from_usize(v.len() - 1);
Some(v.generic_view((0, 0), (nrows, Const::<1>)).into_owned())
} else {
None
}
}
}
impl<T: Scalar, D: DimAdd<U1>, S: RawStorage<T, D>> Vector<T, D, S> {
/// Constructs a new vector of higher dimension by appending `element` to the end of `self`.
#[inline]
#[must_use]
pub fn push(&self, element: T) -> OVector<T, DimSum<D, U1>>
where
DefaultAllocator: Allocator<DimSum<D, U1>>,
{
let len = self.len();
let hnrows = DimSum::<D, U1>::from_usize(len + 1);
let mut res = Matrix::uninit(hnrows, Const::<1>);
// This is basically a copy_from except that we warp the copied
// values into MaybeUninit.
res.generic_view_mut((0, 0), self.shape_generic())
.zip_apply(self, |out, e| *out = MaybeUninit::new(e));
res[(len, 0)] = MaybeUninit::new(element);
// Safety: res has been fully initialized.
unsafe { res.assume_init() }
}
}
impl<T, R: Dim, C: Dim, S> AbsDiffEq for Matrix<T, R, C, S>
where
T: Scalar + AbsDiffEq,
S: RawStorage<T, R, C>,
T::Epsilon: Clone,
{
type Epsilon = T::Epsilon;
#[inline]
fn default_epsilon() -> Self::Epsilon {
T::default_epsilon()
}
#[inline]
fn abs_diff_eq(&self, other: &Self, epsilon: Self::Epsilon) -> bool {
self.iter()
.zip(other.iter())
.all(|(a, b)| a.abs_diff_eq(b, epsilon.clone()))
}
}
impl<T, R: Dim, C: Dim, S> RelativeEq for Matrix<T, R, C, S>
where
T: Scalar + RelativeEq,
S: Storage<T, R, C>,
T::Epsilon: Clone,
{
#[inline]
fn default_max_relative() -> Self::Epsilon {
T::default_max_relative()
}
#[inline]
fn relative_eq(
&self,
other: &Self,
epsilon: Self::Epsilon,
max_relative: Self::Epsilon,
) -> bool {
self.relative_eq(other, epsilon, max_relative)
}
}
impl<T, R: Dim, C: Dim, S> UlpsEq for Matrix<T, R, C, S>
where
T: Scalar + UlpsEq,
S: RawStorage<T, R, C>,
T::Epsilon: Clone,
{
#[inline]
fn default_max_ulps() -> u32 {
T::default_max_ulps()
}
#[inline]
fn ulps_eq(&self, other: &Self, epsilon: Self::Epsilon, max_ulps: u32) -> bool {
assert!(self.shape() == other.shape());
self.iter()
.zip(other.iter())
.all(|(a, b)| a.ulps_eq(b, epsilon.clone(), max_ulps))
}
}
impl<T, R: Dim, C: Dim, S> PartialOrd for Matrix<T, R, C, S>
where
T: Scalar + PartialOrd,
S: RawStorage<T, R, C>,
{
#[inline]
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
if self.shape() != other.shape() {
return None;
}
if self.nrows() == 0 || self.ncols() == 0 {
return Some(Ordering::Equal);
}
let mut first_ord = unsafe {
self.data
.get_unchecked_linear(0)
.partial_cmp(other.data.get_unchecked_linear(0))
};
if let Some(first_ord) = first_ord.as_mut() {
let mut it = self.iter().zip(other.iter());
let _ = it.next(); // Drop the first elements (we already tested it).
for (left, right) in it {
if let Some(ord) = left.partial_cmp(right) {
match ord {
Ordering::Equal => { /* Does not change anything. */ }
Ordering::Less => {
if *first_ord == Ordering::Greater {
return None;
}
*first_ord = ord
}
Ordering::Greater => {
if *first_ord == Ordering::Less {
return None;
}
*first_ord = ord
}
}
} else {
return None;
}
}
}
first_ord
}
#[inline]
fn lt(&self, right: &Self) -> bool {
assert_eq!(
self.shape(),
right.shape(),
"Matrix comparison error: dimensions mismatch."
);
self.iter().zip(right.iter()).all(|(a, b)| a.lt(b))
}
#[inline]
fn le(&self, right: &Self) -> bool {
assert_eq!(
self.shape(),
right.shape(),
"Matrix comparison error: dimensions mismatch."
);
self.iter().zip(right.iter()).all(|(a, b)| a.le(b))
}
#[inline]
fn gt(&self, right: &Self) -> bool {
assert_eq!(
self.shape(),
right.shape(),
"Matrix comparison error: dimensions mismatch."
);
self.iter().zip(right.iter()).all(|(a, b)| a.gt(b))
}
#[inline]
fn ge(&self, right: &Self) -> bool {
assert_eq!(
self.shape(),
right.shape(),
"Matrix comparison error: dimensions mismatch."
);
self.iter().zip(right.iter()).all(|(a, b)| a.ge(b))
}
}
impl<T, R: Dim, C: Dim, S> Eq for Matrix<T, R, C, S>
where
T: Eq,
S: RawStorage<T, R, C>,
{
}
impl<T, R, R2, C, C2, S, S2> PartialEq<Matrix<T, R2, C2, S2>> for Matrix<T, R, C, S>
where
T: PartialEq,
C: Dim,
C2: Dim,
R: Dim,
R2: Dim,
S: RawStorage<T, R, C>,
S2: RawStorage<T, R2, C2>,
{
#[inline]
fn eq(&self, right: &Matrix<T, R2, C2, S2>) -> bool {
self.shape() == right.shape() && self.iter().zip(right.iter()).all(|(l, r)| l == r)
}
}
macro_rules! impl_fmt {
($trait: path, $fmt_str_without_precision: expr, $fmt_str_with_precision: expr) => {
impl<T, R: Dim, C: Dim, S> $trait for Matrix<T, R, C, S>
where
T: Scalar + $trait,
S: RawStorage<T, R, C>,
{
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
#[cfg(feature = "std")]
fn val_width<T: Scalar + $trait>(val: &T, f: &mut fmt::Formatter<'_>) -> usize {
match f.precision() {
Some(precision) => format!($fmt_str_with_precision, val, precision)
.chars()
.count(),
None => format!($fmt_str_without_precision, val).chars().count(),
}
}
#[cfg(not(feature = "std"))]
fn val_width<T: Scalar + $trait>(_: &T, _: &mut fmt::Formatter<'_>) -> usize {
4
}
let (nrows, ncols) = self.shape();
if nrows == 0 || ncols == 0 {
return write!(f, "[ ]");
}
let mut max_length = 0;
for i in 0..nrows {
for j in 0..ncols {
max_length = crate::max(max_length, val_width(&self[(i, j)], f));
}
}
let max_length_with_space = max_length + 1;
writeln!(f)?;
writeln!(
f,
" ┌ {:>width$} ┐",
"",
width = max_length_with_space * ncols - 1
)?;
for i in 0..nrows {
write!(f, " │")?;
for j in 0..ncols {
let number_length = val_width(&self[(i, j)], f) + 1;
let pad = max_length_with_space - number_length;
write!(f, " {:>thepad$}", "", thepad = pad)?;
match f.precision() {
Some(precision) => {
write!(f, $fmt_str_with_precision, (*self)[(i, j)], precision)?
}
None => write!(f, $fmt_str_without_precision, (*self)[(i, j)])?,
}
}
writeln!(f, " │")?;
}
writeln!(
f,
" └ {:>width$} ┘",
"",
width = max_length_with_space * ncols - 1
)?;
writeln!(f)
}
}
};
}
impl_fmt!(fmt::Display, "{}", "{:.1$}");
impl_fmt!(fmt::LowerExp, "{:e}", "{:.1$e}");
impl_fmt!(fmt::UpperExp, "{:E}", "{:.1$E}");
impl_fmt!(fmt::Octal, "{:o}", "{:1$o}");
impl_fmt!(fmt::LowerHex, "{:x}", "{:1$x}");
impl_fmt!(fmt::UpperHex, "{:X}", "{:1$X}");
impl_fmt!(fmt::Binary, "{:b}", "{:.1$b}");
impl_fmt!(fmt::Pointer, "{:p}", "{:.1$p}");
#[cfg(test)]
mod tests {
#[test]
fn empty_display() {
let vec: Vec<f64> = Vec::new();
let dvector = crate::DVector::from_vec(vec);
assert_eq!(format!("{}", dvector), "[ ]")
}
#[test]
fn lower_exp() {
let test = crate::Matrix2::new(1e6, 2e5, 2e-5, 1.);
assert_eq!(
format!("{:e}", test),
r"
┌ ┐
│ 1e6 2e5 │
│ 2e-5 1e0 │
└ ┘
"
)
}
}
/// # Cross product
impl<
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
> Matrix<T, R, C, S>
{
/// The perpendicular product between two 2D column vectors, i.e. `a.x * b.y - a.y * b.x`.
#[inline]
#[must_use]
pub fn perp<R2, C2, SB>(&self, b: &Matrix<T, R2, C2, SB>) -> T
where
R2: Dim,
C2: Dim,
SB: RawStorage<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, U2>
+ SameNumberOfColumns<C, U1>
+ SameNumberOfRows<R2, U2>
+ SameNumberOfColumns<C2, U1>,
{
let shape = self.shape();
assert_eq!(
shape,
b.shape(),
"2D vector perpendicular product dimension mismatch."
);
assert_eq!(
shape,
(2, 1),
"2D perpendicular product requires (2, 1) vectors {:?}",
shape
);
// SAFETY: assertion above ensures correct shape
let ax = unsafe { self.get_unchecked((0, 0)).clone() };
let ay = unsafe { self.get_unchecked((1, 0)).clone() };
let bx = unsafe { b.get_unchecked((0, 0)).clone() };
let by = unsafe { b.get_unchecked((1, 0)).clone() };
ax * by - ay * bx
}
// TODO: use specialization instead of an assertion.
/// The 3D cross product between two vectors.
///
/// Panics if the shape is not 3D vector. In the future, this will be implemented only for
/// dynamically-sized matrices and statically-sized 3D matrices.
#[inline]
#[must_use]
pub fn cross<R2, C2, SB>(&self, b: &Matrix<T, R2, C2, SB>) -> MatrixCross<T, R, C, R2, C2>
where
R2: Dim,
C2: Dim,
SB: RawStorage<T, R2, C2>,
DefaultAllocator: SameShapeAllocator<R, C, R2, C2>,
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,
{
let shape = self.shape();
assert_eq!(shape, b.shape(), "Vector cross product dimension mismatch.");
assert!(
shape == (3, 1) || shape == (1, 3),
"Vector cross product dimension mismatch: must be (3, 1) or (1, 3) but found {:?}.",
shape
);
if shape.0 == 3 {
unsafe {
let mut res = Matrix::uninit(Dim::from_usize(3), Dim::from_usize(1));
let ax = self.get_unchecked((0, 0));
let ay = self.get_unchecked((1, 0));
let az = self.get_unchecked((2, 0));
let bx = b.get_unchecked((0, 0));
let by = b.get_unchecked((1, 0));
let bz = b.get_unchecked((2, 0));
*res.get_unchecked_mut((0, 0)) =
MaybeUninit::new(ay.clone() * bz.clone() - az.clone() * by.clone());
*res.get_unchecked_mut((1, 0)) =
MaybeUninit::new(az.clone() * bx.clone() - ax.clone() * bz.clone());
*res.get_unchecked_mut((2, 0)) =
MaybeUninit::new(ax.clone() * by.clone() - ay.clone() * bx.clone());
// Safety: res is now fully initialized.
res.assume_init()
}
} else {
unsafe {
let mut res = Matrix::uninit(Dim::from_usize(1), Dim::from_usize(3));
let ax = self.get_unchecked((0, 0));
let ay = self.get_unchecked((0, 1));
let az = self.get_unchecked((0, 2));
let bx = b.get_unchecked((0, 0));
let by = b.get_unchecked((0, 1));
let bz = b.get_unchecked((0, 2));
*res.get_unchecked_mut((0, 0)) =
MaybeUninit::new(ay.clone() * bz.clone() - az.clone() * by.clone());
*res.get_unchecked_mut((0, 1)) =
MaybeUninit::new(az.clone() * bx.clone() - ax.clone() * bz.clone());
*res.get_unchecked_mut((0, 2)) =
MaybeUninit::new(ax.clone() * by.clone() - ay.clone() * bx.clone());
// Safety: res is now fully initialized.
res.assume_init()
}
}
}
}
impl<T: Scalar + Field, S: RawStorage<T, U3>> Vector<T, U3, S> {
/// Computes the matrix `M` such that for all vector `v` we have `M * v == self.cross(&v)`.
#[inline]
#[must_use]
pub fn cross_matrix(&self) -> OMatrix<T, U3, U3> {
OMatrix::<T, U3, U3>::new(
T::zero(),
-self[2].clone(),
self[1].clone(),
self[2].clone(),
T::zero(),
-self[0].clone(),
-self[1].clone(),
self[0].clone(),
T::zero(),
)
}
}
impl<T: SimdComplexField, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S> {
/// The smallest angle between two vectors.
#[inline]
#[must_use]
pub fn angle<R2: Dim, C2: Dim, SB>(&self, other: &Matrix<T, R2, C2, SB>) -> T::SimdRealField
where
SB: Storage<T, R2, C2>,
ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,
{
let prod = self.dotc(other);
let n1 = self.norm();
let n2 = other.norm();
if n1.is_zero() || n2.is_zero() {
T::SimdRealField::zero()
} else {
let cang = prod.simd_real() / (n1 * n2);
cang.simd_clamp(-T::SimdRealField::one(), T::SimdRealField::one())
.simd_acos()
}
}
}
impl<T, R: Dim, C: Dim, S> AbsDiffEq for Unit<Matrix<T, R, C, S>>
where
T: Scalar + AbsDiffEq,
S: RawStorage<T, R, C>,
T::Epsilon: Clone,
{
type Epsilon = T::Epsilon;
#[inline]
fn default_epsilon() -> Self::Epsilon {
T::default_epsilon()
}
#[inline]
fn abs_diff_eq(&self, other: &Self, epsilon: Self::Epsilon) -> bool {
self.as_ref().abs_diff_eq(other.as_ref(), epsilon)
}
}
impl<T, R: Dim, C: Dim, S> RelativeEq for Unit<Matrix<T, R, C, S>>
where
T: Scalar + RelativeEq,
S: Storage<T, R, C>,
T::Epsilon: Clone,
{
#[inline]
fn default_max_relative() -> Self::Epsilon {
T::default_max_relative()
}
#[inline]
fn relative_eq(
&self,
other: &Self,
epsilon: Self::Epsilon,
max_relative: Self::Epsilon,
) -> bool {
self.as_ref()
.relative_eq(other.as_ref(), epsilon, max_relative)
}
}
impl<T, R: Dim, C: Dim, S> UlpsEq for Unit<Matrix<T, R, C, S>>
where
T: Scalar + UlpsEq,
S: RawStorage<T, R, C>,
T::Epsilon: Clone,
{
#[inline]
fn default_max_ulps() -> u32 {
T::default_max_ulps()
}
#[inline]
fn ulps_eq(&self, other: &Self, epsilon: Self::Epsilon, max_ulps: u32) -> bool {
self.as_ref().ulps_eq(other.as_ref(), epsilon, max_ulps)
}
}
impl<T, R, C, S> Hash for Matrix<T, R, C, S>
where
T: Scalar + Hash,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
{
fn hash<H: Hasher>(&self, state: &mut H) {
let (nrows, ncols) = self.shape();
(nrows, ncols).hash(state);
for j in 0..ncols {
for i in 0..nrows {
unsafe {
self.get_unchecked((i, j)).hash(state);
}
}
}
}
}
impl<T, D, S> Unit<Vector<T, D, S>>
where
T: Scalar,
D: Dim,
S: RawStorage<T, D, U1>,
{
/// Cast the components of `self` to another type.
///
/// # Example
/// ```
/// # use nalgebra::Vector3;
/// let v = Vector3::<f64>::y_axis();
/// let v2 = v.cast::<f32>();
/// assert_eq!(v2, Vector3::<f32>::y_axis());
/// ```
pub fn cast<T2: Scalar>(self) -> Unit<OVector<T2, D>>
where
T: Scalar,
OVector<T2, D>: SupersetOf<Vector<T, D, S>>,
DefaultAllocator: Allocator<D, U1>,
{
Unit::new_unchecked(crate::convert_ref(self.as_ref()))
}
}
impl<T, S> Matrix<T, U1, U1, S>
where
S: RawStorage<T, U1, U1>,
{
/// Returns a reference to the single element in this matrix.
///
/// As opposed to indexing, using this provides type-safety
/// when flattening dimensions.
///
/// # Example
/// ```
/// # use nalgebra::Vector3;
/// let v = Vector3::new(0., 0., 1.);
/// let inner_product: f32 = *(v.transpose() * v).as_scalar();
/// ```
///
///```compile_fail
/// # use nalgebra::Vector3;
/// let v = Vector3::new(0., 0., 1.);
/// let inner_product = (v * v.transpose()).item(); // Typo, does not compile.
///```
pub fn as_scalar(&self) -> &T {
&self[(0, 0)]
}
/// Get a mutable reference to the single element in this matrix
///
/// As opposed to indexing, using this provides type-safety
/// when flattening dimensions.
///
/// # Example
/// ```
/// # use nalgebra::Vector3;
/// let v = Vector3::new(0., 0., 1.);
/// let mut inner_product = (v.transpose() * v);
/// *inner_product.as_scalar_mut() = 3.;
/// ```
///
///```compile_fail
/// # use nalgebra::Vector3;
/// let v = Vector3::new(0., 0., 1.);
/// let mut inner_product = (v * v.transpose());
/// *inner_product.as_scalar_mut() = 3.;
///```
pub fn as_scalar_mut(&mut self) -> &mut T
where
S: RawStorageMut<T, U1>,
{
&mut self[(0, 0)]
}
/// Convert this 1x1 matrix by reference into a scalar.
///
/// As opposed to indexing, using this provides type-safety
/// when flattening dimensions.
///
/// # Example
/// ```
/// # use nalgebra::Vector3;
/// let v = Vector3::new(0., 0., 1.);
/// let mut inner_product: f32 = (v.transpose() * v).to_scalar();
/// ```
///
///```compile_fail
/// # use nalgebra::Vector3;
/// let v = Vector3::new(0., 0., 1.);
/// let mut inner_product: f32 = (v * v.transpose()).to_scalar();
///```
pub fn to_scalar(&self) -> T
where
T: Clone,
{
self.as_scalar().clone()
}
}
impl<T> super::alias::Matrix1<T> {
/// Convert this 1x1 matrix into a scalar.
///
/// As opposed to indexing, using this provides type-safety
/// when flattening dimensions.
///
/// # Example
/// ```
/// # use nalgebra::{Vector3, Matrix2, U1};
/// let v = Vector3::new(0., 0., 1.);
/// let inner_product: f32 = (v.transpose() * v).into_scalar();
/// assert_eq!(inner_product, 1.);
/// ```
///
///```compile_fail
/// # use nalgebra::Vector3;
/// let v = Vector3::new(0., 0., 1.);
/// let mut inner_product: f32 = (v * v.transpose()).into_scalar();
///```
pub fn into_scalar(self) -> T {
let [[scalar]] = self.data.0;
scalar
}
}