# Struct nalgebra::base::Matrix

source ยท
``````#[repr(C)]pub struct Matrix<T, R, C, S> {
pub data: S,
/* private fields */
}``````
Expand description

The most generic column-major matrix (and vector) type.

## ยงMethods 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.

## ยงType 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 127ย 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`).

## Fieldsยง

ยง`data: 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:

``````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.

## Implementationsยง

sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>where T: Scalar + Zero + ClosedAdd + ClosedMul,

source

#### pub fn dot<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<T, R2, C2, SB>) -> Twhere SB: RawStorage<T, R2, C2>, ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,

The dot product between two vectors or matrices (seen as vectors).

This is equal to `self.transpose() * rhs`. For the sesquilinear complex dot product, use `self.dotc(rhs)`.

Note that this is not the matrix multiplication as in, e.g., numpy. For matrix multiplication, use one of: `.gemm`, `.mul_to`, `.mul`, the `*` operator.

##### ยงExample
``````let vec1 = Vector3::new(1.0, 2.0, 3.0);
let vec2 = Vector3::new(0.1, 0.2, 0.3);
assert_eq!(vec1.dot(&vec2), 1.4);

let mat1 = Matrix2x3::new(1.0, 2.0, 3.0,
4.0, 5.0, 6.0);
let mat2 = Matrix2x3::new(0.1, 0.2, 0.3,
0.4, 0.5, 0.6);
assert_eq!(mat1.dot(&mat2), 9.1);``````
source

#### pub fn dotc<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<T, R2, C2, SB>) -> Twhere T: SimdComplexField, SB: RawStorage<T, R2, C2>, ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,

The conjugate-linear dot product between two vectors or matrices (seen as vectors).

This is equal to `self.adjoint() * rhs`. For real vectors, this is identical to `self.dot(&rhs)`. Note that this is not the matrix multiplication as in, e.g., numpy. For matrix multiplication, use one of: `.gemm`, `.mul_to`, `.mul`, the `*` operator.

##### ยงExample
``````let vec1 = Vector2::new(Complex::new(1.0, 2.0), Complex::new(3.0, 4.0));
let vec2 = Vector2::new(Complex::new(0.4, 0.3), Complex::new(0.2, 0.1));
assert_eq!(vec1.dotc(&vec2), Complex::new(2.0, -1.0));

// Note that for complex vectors, we generally have:
// vec1.dotc(&vec2) != vec2.dot(&vec2)
assert_ne!(vec1.dotc(&vec2), vec1.dot(&vec2));``````
source

#### pub fn tr_dot<R2: Dim, C2: Dim, SB>(&self, rhs: &Matrix<T, R2, C2, SB>) -> Twhere SB: RawStorage<T, R2, C2>, ShapeConstraint: DimEq<C, R2> + DimEq<R, C2>,

The dot product between the transpose of `self` and `rhs`.

##### ยงExample
``````let vec1 = Vector3::new(1.0, 2.0, 3.0);
let vec2 = RowVector3::new(0.1, 0.2, 0.3);
assert_eq!(vec1.tr_dot(&vec2), 1.4);

let mat1 = Matrix2x3::new(1.0, 2.0, 3.0,
4.0, 5.0, 6.0);
let mat2 = Matrix3x2::new(0.1, 0.4,
0.2, 0.5,
0.3, 0.6);
assert_eq!(mat1.tr_dot(&mat2), 9.1);``````
sourceยง

### impl<T, D: Dim, S> Matrix<T, D, Const<1>, S>where T: Scalar + Zero + ClosedAdd + ClosedMul, S: StorageMut<T, D>,

source

#### pub fn axcpy<D2: Dim, SB>(&mut self, a: T, x: &Vector<T, D2, SB>, c: T, b: T)where SB: Storage<T, D2>, ShapeConstraint: DimEq<D, D2>,

Computes `self = a * x * c + b * self`.

If `b` is zero, `self` is never read from.

##### ยงExample
``````let mut vec1 = Vector3::new(1.0, 2.0, 3.0);
let vec2 = Vector3::new(0.1, 0.2, 0.3);
vec1.axcpy(5.0, &vec2, 2.0, 5.0);
assert_eq!(vec1, Vector3::new(6.0, 12.0, 18.0));``````
source

#### pub fn axpy<D2: Dim, SB>(&mut self, a: T, x: &Vector<T, D2, SB>, b: T)where T: One, SB: Storage<T, D2>, ShapeConstraint: DimEq<D, D2>,

Computes `self = a * x + b * self`.

If `b` is zero, `self` is never read from.

##### ยงExample
``````let mut vec1 = Vector3::new(1.0, 2.0, 3.0);
let vec2 = Vector3::new(0.1, 0.2, 0.3);
vec1.axpy(10.0, &vec2, 5.0);
assert_eq!(vec1, Vector3::new(6.0, 12.0, 18.0));``````
source

#### pub fn gemv<R2: Dim, C2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, x: &Vector<T, D3, SC>, beta: T )where T: One, SB: Storage<T, R2, C2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, R2> + AreMultipliable<R2, C2, D3, U1>,

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.

##### ยงExample
``````let mut vec1 = Vector2::new(1.0, 2.0);
let vec2 = Vector2::new(0.1, 0.2);
let mat = Matrix2::new(1.0, 2.0,
3.0, 4.0);
vec1.gemv(10.0, &mat, &vec2, 5.0);
assert_eq!(vec1, Vector2::new(10.0, 21.0));``````
source

#### pub fn sygemv<D2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, a: &SquareMatrix<T, D2, SB>, x: &Vector<T, D3, SC>, beta: T )where T: One, SB: Storage<T, D2, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, U1>,

Computes `self = alpha * a * x + beta * self`, where `a` is a symmetric matrix, `x` a vector, and `alpha, beta` two scalars.

For hermitian matrices, use `.hegemv` instead. If `beta` is zero, `self` is never read. If `self` is read, only its lower-triangular part (including the diagonal) is actually read.

##### ยงExamples
``````let mat = Matrix2::new(1.0, 2.0,
2.0, 4.0);
let mut vec1 = Vector2::new(1.0, 2.0);
let vec2 = Vector2::new(0.1, 0.2);
vec1.sygemv(10.0, &mat, &vec2, 5.0);
assert_eq!(vec1, Vector2::new(10.0, 20.0));

// The matrix upper-triangular elements can be garbage because it is never
// read by this method. Therefore, it is not necessary for the caller to
// fill the matrix struct upper-triangle.
let mat = Matrix2::new(1.0, 9999999.9999999,
2.0, 4.0);
let mut vec1 = Vector2::new(1.0, 2.0);
vec1.sygemv(10.0, &mat, &vec2, 5.0);
assert_eq!(vec1, Vector2::new(10.0, 20.0));``````
source

#### pub fn hegemv<D2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, a: &SquareMatrix<T, D2, SB>, x: &Vector<T, D3, SC>, beta: T )where T: SimdComplexField, SB: Storage<T, D2, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, U1>,

Computes `self = alpha * a * x + beta * self`, where `a` is an hermitian 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.

##### ยงExamples
``````let mat = Matrix2::new(Complex::new(1.0, 0.0), Complex::new(2.0, -0.1),
Complex::new(2.0, 1.0), Complex::new(4.0, 0.0));
let mut vec1 = Vector2::new(Complex::new(1.0, 2.0), Complex::new(3.0, 4.0));
let vec2 = Vector2::new(Complex::new(0.1, 0.2), Complex::new(0.3, 0.4));
vec1.sygemv(Complex::new(10.0, 20.0), &mat, &vec2, Complex::new(5.0, 15.0));
assert_eq!(vec1, Vector2::new(Complex::new(-48.0, 44.0), Complex::new(-75.0, 110.0)));

// The matrix upper-triangular elements can be garbage because it is never
// read by this method. Therefore, it is not necessary for the caller to
// fill the matrix struct upper-triangle.

let mat = Matrix2::new(Complex::new(1.0, 0.0), Complex::new(99999999.9, 999999999.9),
Complex::new(2.0, 1.0), Complex::new(4.0, 0.0));
let mut vec1 = Vector2::new(Complex::new(1.0, 2.0), Complex::new(3.0, 4.0));
let vec2 = Vector2::new(Complex::new(0.1, 0.2), Complex::new(0.3, 0.4));
vec1.sygemv(Complex::new(10.0, 20.0), &mat, &vec2, Complex::new(5.0, 15.0));
assert_eq!(vec1, Vector2::new(Complex::new(-48.0, 44.0), Complex::new(-75.0, 110.0)));``````
source

#### pub fn gemv_tr<R2: Dim, C2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, x: &Vector<T, D3, SC>, beta: T )where T: One, SB: Storage<T, R2, C2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, C2> + AreMultipliable<C2, R2, D3, U1>,

Computes `self = alpha * a.transpose() * x + beta * self`, where `a` is a matrix, `x` a vector, and `alpha, beta` two scalars.

If `beta` is zero, `self` is never read.

##### ยงExample
``````let mat = Matrix2::new(1.0, 3.0,
2.0, 4.0);
let mut vec1 = Vector2::new(1.0, 2.0);
let vec2 = Vector2::new(0.1, 0.2);
let expected = mat.transpose() * vec2 * 10.0 + vec1 * 5.0;

vec1.gemv_tr(10.0, &mat, &vec2, 5.0);
assert_eq!(vec1, expected);``````
source

#### pub fn gemv_ad<R2: Dim, C2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, x: &Vector<T, D3, SC>, beta: T )where T: SimdComplexField, SB: Storage<T, R2, C2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, C2> + AreMultipliable<C2, R2, D3, U1>,

Computes `self = alpha * a.adjoint() * x + beta * self`, where `a` is a matrix, `x` a vector, and `alpha, beta` two scalars.

For real matrices, this is the same as `.gemv_tr`. If `beta` is zero, `self` is never read.

##### ยงExample
``````let mat = Matrix2::new(Complex::new(1.0, 2.0), Complex::new(3.0, 4.0),
Complex::new(5.0, 6.0), Complex::new(7.0, 8.0));
let mut vec1 = Vector2::new(Complex::new(1.0, 2.0), Complex::new(3.0, 4.0));
let vec2 = Vector2::new(Complex::new(0.1, 0.2), Complex::new(0.3, 0.4));
let expected = mat.adjoint() * vec2 * Complex::new(10.0, 20.0) + vec1 * Complex::new(5.0, 15.0);

vec1.gemv_ad(Complex::new(10.0, 20.0), &mat, &vec2, Complex::new(5.0, 15.0));
assert_eq!(vec1, expected);``````
sourceยง

### impl<T, R1: Dim, C1: Dim, S: StorageMut<T, R1, C1>> Matrix<T, R1, C1, S>where T: Scalar + Zero + ClosedAdd + ClosedMul,

source

#### pub fn ger<D2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, x: &Vector<T, D2, SB>, y: &Vector<T, D3, SC>, beta: T )where T: One, SB: Storage<T, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,

Computes `self = alpha * x * y.transpose() + beta * self`.

If `beta` is zero, `self` is never read.

##### ยงExample
``````let mut mat = Matrix2x3::repeat(4.0);
let vec1 = Vector2::new(1.0, 2.0);
let vec2 = Vector3::new(0.1, 0.2, 0.3);
let expected = vec1 * vec2.transpose() * 10.0 + mat * 5.0;

mat.ger(10.0, &vec1, &vec2, 5.0);
assert_eq!(mat, expected);``````
source

#### pub fn gerc<D2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, x: &Vector<T, D2, SB>, y: &Vector<T, D3, SC>, beta: T )where T: SimdComplexField, SB: Storage<T, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,

Computes `self = alpha * x * y.adjoint() + beta * self`.

If `beta` is zero, `self` is never read.

##### ยงExample
``````let mut mat = Matrix2x3::repeat(Complex::new(4.0, 5.0));
let vec1 = Vector2::new(Complex::new(1.0, 2.0), Complex::new(3.0, 4.0));
let vec2 = Vector3::new(Complex::new(0.6, 0.5), Complex::new(0.4, 0.5), Complex::new(0.2, 0.1));
let expected = vec1 * vec2.adjoint() * Complex::new(10.0, 20.0) + mat * Complex::new(5.0, 15.0);

mat.gerc(Complex::new(10.0, 20.0), &vec1, &vec2, Complex::new(5.0, 15.0));
assert_eq!(mat, expected);``````
source

#### pub fn gemm<R2: Dim, C2: Dim, R3: Dim, C3: Dim, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, b: &Matrix<T, R3, C3, SC>, beta: T )where T: One, SB: Storage<T, R2, C2>, SC: Storage<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C3> + AreMultipliable<R2, C2, R3, C3>,

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.

##### ยงExample
``````let mut mat1 = Matrix2x4::identity();
let mat2 = Matrix2x3::new(1.0, 2.0, 3.0,
4.0, 5.0, 6.0);
let mat3 = Matrix3x4::new(0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8,
0.9, 1.0, 1.1, 1.2);
let expected = mat2 * mat3 * 10.0 + mat1 * 5.0;

mat1.gemm(10.0, &mat2, &mat3, 5.0);
assert_relative_eq!(mat1, expected);``````
source

#### pub fn gemm_tr<R2: Dim, C2: Dim, R3: Dim, C3: Dim, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, b: &Matrix<T, R3, C3, SC>, beta: T )where T: One, SB: Storage<T, R2, C2>, SC: Storage<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, C2> + SameNumberOfColumns<C1, C3> + AreMultipliable<C2, R2, R3, C3>,

Computes `self = alpha * a.transpose() * b + beta * self`, where `a, b, self` are matrices. `alpha` and `beta` are scalar.

If `beta` is zero, `self` is never read.

##### ยงExample
``````let mut mat1 = Matrix2x4::identity();
let mat2 = Matrix3x2::new(1.0, 4.0,
2.0, 5.0,
3.0, 6.0);
let mat3 = Matrix3x4::new(0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8,
0.9, 1.0, 1.1, 1.2);
let expected = mat2.transpose() * mat3 * 10.0 + mat1 * 5.0;

mat1.gemm_tr(10.0, &mat2, &mat3, 5.0);
assert_eq!(mat1, expected);``````
source

#### pub fn gemm_ad<R2: Dim, C2: Dim, R3: Dim, C3: Dim, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, b: &Matrix<T, R3, C3, SC>, beta: T )where T: SimdComplexField, SB: Storage<T, R2, C2>, SC: Storage<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, C2> + SameNumberOfColumns<C1, C3> + AreMultipliable<C2, R2, R3, C3>,

Computes `self = alpha * a.adjoint() * b + beta * self`, where `a, b, self` are matrices. `alpha` and `beta` are scalar.

If `beta` is zero, `self` is never read.

##### ยงExample
``````let mut mat1 = Matrix2x4::identity();
let mat2 = Matrix3x2::new(Complex::new(1.0, 4.0), Complex::new(7.0, 8.0),
Complex::new(2.0, 5.0), Complex::new(9.0, 10.0),
Complex::new(3.0, 6.0), Complex::new(11.0, 12.0));
let mat3 = Matrix3x4::new(Complex::new(0.1, 1.3), Complex::new(0.2, 1.4), Complex::new(0.3, 1.5), Complex::new(0.4, 1.6),
Complex::new(0.5, 1.7), Complex::new(0.6, 1.8), Complex::new(0.7, 1.9), Complex::new(0.8, 2.0),
Complex::new(0.9, 2.1), Complex::new(1.0, 2.2), Complex::new(1.1, 2.3), Complex::new(1.2, 2.4));
let expected = mat2.adjoint() * mat3 * Complex::new(10.0, 20.0) + mat1 * Complex::new(5.0, 15.0);

mat1.gemm_ad(Complex::new(10.0, 20.0), &mat2, &mat3, Complex::new(5.0, 15.0));
assert_eq!(mat1, expected);``````
sourceยง

### impl<T, R1: Dim, C1: Dim, S: StorageMut<T, R1, C1>> Matrix<T, R1, C1, S>where T: Scalar + Zero + ClosedAdd + ClosedMul,

source

#### pub fn ger_symm<D2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, x: &Vector<T, D2, SB>, y: &Vector<T, D3, SC>, beta: T )where T: One, SB: Storage<T, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,

๐Deprecated: This is renamed `syger` to match the original BLAS terminology.

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.

##### ยงExample
``````let mut mat = Matrix2::identity();
let vec1 = Vector2::new(1.0, 2.0);
let vec2 = Vector2::new(0.1, 0.2);
let expected = vec1 * vec2.transpose() * 10.0 + mat * 5.0;
mat.m12 = 99999.99999; // This component is on the upper-triangular part and will not be read/written.

mat.ger_symm(10.0, &vec1, &vec2, 5.0);
assert_eq!(mat.lower_triangle(), expected.lower_triangle());
assert_eq!(mat.m12, 99999.99999); // This was untouched.``````
source

#### pub fn syger<D2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, x: &Vector<T, D2, SB>, y: &Vector<T, D3, SC>, beta: T )where T: One, SB: Storage<T, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,

Computes `self = alpha * x * y.transpose() + beta * self`, where `self` is a symmetric matrix.

For hermitian complex matrices, use `.hegerc` instead. 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.

##### ยงExample
``````let mut mat = Matrix2::identity();
let vec1 = Vector2::new(1.0, 2.0);
let vec2 = Vector2::new(0.1, 0.2);
let expected = vec1 * vec2.transpose() * 10.0 + mat * 5.0;
mat.m12 = 99999.99999; // This component is on the upper-triangular part and will not be read/written.

mat.syger(10.0, &vec1, &vec2, 5.0);
assert_eq!(mat.lower_triangle(), expected.lower_triangle());
assert_eq!(mat.m12, 99999.99999); // This was untouched.``````
source

#### pub fn hegerc<D2: Dim, D3: Dim, SB, SC>( &mut self, alpha: T, x: &Vector<T, D2, SB>, y: &Vector<T, D3, SC>, beta: T )where T: SimdComplexField, SB: Storage<T, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,

Computes `self = alpha * x * y.adjoint() + beta * self`, where `self` is an hermitian 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.

##### ยงExample
``````let mut mat = Matrix2::identity();
let vec1 = Vector2::new(Complex::new(1.0, 3.0), Complex::new(2.0, 4.0));
let vec2 = Vector2::new(Complex::new(0.2, 0.4), Complex::new(0.1, 0.3));
let expected = vec1 * vec2.adjoint() * Complex::new(10.0, 20.0) + mat * Complex::new(5.0, 15.0);
mat.m12 = Complex::new(99999.99999, 88888.88888); // This component is on the upper-triangular part and will not be read/written.

mat.hegerc(Complex::new(10.0, 20.0), &vec1, &vec2, Complex::new(5.0, 15.0));
assert_eq!(mat.lower_triangle(), expected.lower_triangle());
assert_eq!(mat.m12, Complex::new(99999.99999, 88888.88888)); // This was untouched.``````
sourceยง

### impl<T, D1: Dim, S: StorageMut<T, D1, D1>> Matrix<T, D1, D1, S>where T: Scalar + Zero + One + ClosedAdd + ClosedMul,

source

#### pub fn quadform_tr_with_workspace<D2, S2, R3, C3, S3, D4, S4>( &mut self, work: &mut Vector<T, D2, S2>, alpha: T, lhs: &Matrix<T, R3, C3, S3>, mid: &SquareMatrix<T, D4, S4>, beta: T )where D2: Dim, R3: Dim, C3: Dim, D4: Dim, S2: StorageMut<T, D2>, S3: Storage<T, R3, C3>, S4: Storage<T, D4, D4>, ShapeConstraint: DimEq<D1, D2> + DimEq<D1, R3> + DimEq<D2, R3> + DimEq<C3, D4>,

Computes the quadratic form `self = alpha * lhs * mid * lhs.transpose() + beta * self`.

This uses the provided workspace `work` to avoid allocations for intermediate results.

##### ยงExample
``````// Note that all those would also work with statically-sized matrices.
// We use DMatrix/DVector since that's the only case where pre-allocating the
// workspace is actually useful (assuming the same workspace is re-used for
// several computations) because it avoids repeated dynamic allocations.
let mut mat = DMatrix::identity(2, 2);
let lhs = DMatrix::from_row_slice(2, 3, &[1.0, 2.0, 3.0,
4.0, 5.0, 6.0]);
let mid = DMatrix::from_row_slice(3, 3, &[0.1, 0.2, 0.3,
0.5, 0.6, 0.7,
0.9, 1.0, 1.1]);
// The random shows that values on the workspace do not
// matter as they will be overwritten.
let mut workspace = DVector::new_random(2);
let expected = &lhs * &mid * lhs.transpose() * 10.0 + &mat * 5.0;

mat.quadform_tr_with_workspace(&mut workspace, 10.0, &lhs, &mid, 5.0);
assert_relative_eq!(mat, expected);``````
source

#### pub fn quadform_tr<R3, C3, S3, D4, S4>( &mut self, alpha: T, lhs: &Matrix<T, R3, C3, S3>, mid: &SquareMatrix<T, D4, S4>, beta: T )where R3: Dim, C3: Dim, D4: Dim, S3: Storage<T, R3, C3>, S4: Storage<T, D4, D4>, ShapeConstraint: DimEq<D1, D1> + DimEq<D1, R3> + DimEq<C3, D4>, DefaultAllocator: Allocator<T, D1>,

Computes the quadratic form `self = alpha * lhs * mid * lhs.transpose() + beta * self`.

This allocates a workspace vector of dimension D1 for intermediate results. If `D1` is a type-level integer, then the allocation is performed on the stack. Use `.quadform_tr_with_workspace(...)` instead to avoid allocations.

##### ยงExample
``````let mut mat = Matrix2::identity();
let lhs = Matrix2x3::new(1.0, 2.0, 3.0,
4.0, 5.0, 6.0);
let mid = Matrix3::new(0.1, 0.2, 0.3,
0.5, 0.6, 0.7,
0.9, 1.0, 1.1);
let expected = lhs * mid * lhs.transpose() * 10.0 + mat * 5.0;

assert_relative_eq!(mat, expected);``````
source

#### pub fn quadform_with_workspace<D2, S2, D3, S3, R4, C4, S4>( &mut self, work: &mut Vector<T, D2, S2>, alpha: T, mid: &SquareMatrix<T, D3, S3>, rhs: &Matrix<T, R4, C4, S4>, beta: T )where D2: Dim, D3: Dim, R4: Dim, C4: Dim, S2: StorageMut<T, D2>, S3: Storage<T, D3, D3>, S4: Storage<T, R4, C4>, ShapeConstraint: DimEq<D3, R4> + DimEq<D1, C4> + DimEq<D2, D3> + AreMultipliable<C4, R4, D2, U1>,

Computes the quadratic form `self = alpha * rhs.transpose() * mid * rhs + beta * self`.

This uses the provided workspace `work` to avoid allocations for intermediate results.

##### ยงExample
``````// Note that all those would also work with statically-sized matrices.
// We use DMatrix/DVector since that's the only case where pre-allocating the
// workspace is actually useful (assuming the same workspace is re-used for
// several computations) because it avoids repeated dynamic allocations.
let mut mat = DMatrix::identity(2, 2);
let rhs = DMatrix::from_row_slice(3, 2, &[1.0, 2.0,
3.0, 4.0,
5.0, 6.0]);
let mid = DMatrix::from_row_slice(3, 3, &[0.1, 0.2, 0.3,
0.5, 0.6, 0.7,
0.9, 1.0, 1.1]);
// The random shows that values on the workspace do not
// matter as they will be overwritten.
let mut workspace = DVector::new_random(3);
let expected = rhs.transpose() * &mid * &rhs * 10.0 + &mat * 5.0;

mat.quadform_with_workspace(&mut workspace, 10.0, &mid, &rhs, 5.0);
assert_relative_eq!(mat, expected);``````
source

#### pub fn quadform<D2, S2, R3, C3, S3>( &mut self, alpha: T, mid: &SquareMatrix<T, D2, S2>, rhs: &Matrix<T, R3, C3, S3>, beta: T )where D2: Dim, R3: Dim, C3: Dim, S2: Storage<T, D2, D2>, S3: Storage<T, R3, C3>, ShapeConstraint: DimEq<D2, R3> + DimEq<D1, C3> + AreMultipliable<C3, R3, D2, U1>, DefaultAllocator: Allocator<T, D2>,

Computes the quadratic form `self = alpha * rhs.transpose() * mid * rhs + beta * self`.

This allocates a workspace vector of dimension D2 for intermediate results. If `D2` is a type-level integer, then the allocation is performed on the stack. Use `.quadform_with_workspace(...)` instead to avoid allocations.

##### ยงExample
``````let mut mat = Matrix2::identity();
let rhs = Matrix3x2::new(1.0, 2.0,
3.0, 4.0,
5.0, 6.0);
let mid = Matrix3::new(0.1, 0.2, 0.3,
0.5, 0.6, 0.7,
0.9, 1.0, 1.1);
let expected = rhs.transpose() * mid * rhs * 10.0 + mat * 5.0;

assert_relative_eq!(mat, expected);``````
sourceยง

### impl<T, R: Dim, C: Dim, S> Matrix<T, R, C, S>where T: Scalar + ClosedNeg, S: StorageMut<T, R, C>,

source

#### pub fn neg_mut(&mut self)

Negates `self` in-place.

sourceยง

### impl<T, R1: Dim, C1: Dim, SA: Storage<T, R1, C1>> Matrix<T, R1, C1, SA>where T: Scalar + ClosedAdd,

source

#### pub fn add_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC> )where SB: Storage<T, R2, C2>, SC: StorageMut<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> + SameNumberOfRows<R1, R3> + SameNumberOfColumns<C1, C3>,

Equivalent to `self + rhs` but stores the result into `out` to avoid allocations.

sourceยง

### impl<T, R1: Dim, C1: Dim, SA: Storage<T, R1, C1>> Matrix<T, R1, C1, SA>where T: Scalar + ClosedSub,

source

#### pub fn sub_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC> )where SB: Storage<T, R2, C2>, SC: StorageMut<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> + SameNumberOfRows<R1, R3> + SameNumberOfColumns<C1, C3>,

Equivalent to `self + rhs` but stores the result into `out` to avoid allocations.

sourceยง

### impl<T, R1: Dim, C1: Dim, SA> Matrix<T, R1, C1, SA>where T: Scalar + Zero + One + ClosedAdd + ClosedMul, SA: Storage<T, R1, C1>,

source

#### pub fn tr_mul<R2: Dim, C2: Dim, SB>( &self, rhs: &Matrix<T, R2, C2, SB> ) -> OMatrix<T, C1, C2>where SB: Storage<T, R2, C2>, DefaultAllocator: Allocator<T, C1, C2>, ShapeConstraint: SameNumberOfRows<R1, R2>,

Equivalent to `self.transpose() * rhs`.

source

#### pub fn ad_mul<R2: Dim, C2: Dim, SB>( &self, rhs: &Matrix<T, R2, C2, SB> ) -> OMatrix<T, C1, C2>where T: SimdComplexField, SB: Storage<T, R2, C2>, DefaultAllocator: Allocator<T, C1, C2>, ShapeConstraint: SameNumberOfRows<R1, R2>,

Equivalent to `self.adjoint() * rhs`.

source

#### pub fn tr_mul_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC> )where SB: Storage<T, R2, C2>, SC: StorageMut<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, R2> + DimEq<C1, R3> + DimEq<C2, C3>,

Equivalent to `self.transpose() * rhs` but stores the result into `out` to avoid allocations.

source

#### pub fn ad_mul_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC> )where T: SimdComplexField, SB: Storage<T, R2, C2>, SC: StorageMut<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, R2> + DimEq<C1, R3> + DimEq<C2, C3>,

Equivalent to `self.adjoint() * rhs` but stores the result into `out` to avoid allocations.

source

#### pub fn mul_to<R2: Dim, C2: Dim, SB, R3: Dim, C3: Dim, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC> )where SB: Storage<T, R2, C2>, SC: StorageMut<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R3, R1> + SameNumberOfColumns<C3, C2> + AreMultipliable<R1, C1, R2, C2>,

Equivalent to `self * rhs` but stores the result into `out` to avoid allocations.

source

#### pub fn kronecker<R2: Dim, C2: Dim, SB>( &self, rhs: &Matrix<T, R2, C2, SB> ) -> OMatrix<T, DimProd<R1, R2>, DimProd<C1, C2>>where T: ClosedMul, R1: DimMul<R2>, C1: DimMul<C2>, SB: Storage<T, R2, C2>, DefaultAllocator: Allocator<T, DimProd<R1, R2>, DimProd<C1, C2>>,

The kronecker product of two matrices (aka. tensor product of the corresponding linear maps).

sourceยง

### impl<T, D: DimName> Matrix<T, D, D, <DefaultAllocator as Allocator<T, D, D>>::Buffer>where T: Scalar + Zero + One, DefaultAllocator: Allocator<T, D, D>,

source

#### pub fn new_scaling(scaling: T) -> Self

Creates a new homogeneous matrix that applies the same scaling factor on each dimension.

source

#### pub fn new_nonuniform_scaling<SB>( scaling: &Vector<T, DimNameDiff<D, U1>, SB> ) -> Selfwhere D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>,

Creates a new homogeneous matrix that applies a distinct scaling factor for each dimension.

source

#### pub fn new_translation<SB>( translation: &Vector<T, DimNameDiff<D, U1>, SB> ) -> Selfwhere D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>,

Creates a new homogeneous matrix that applies a pure translation.

sourceยง

### impl<T: RealField> Matrix<T, Const<3>, Const<3>, ArrayStorage<T, 3, 3>>

source

#### pub fn new_rotation(angle: T) -> Self

Builds a 2 dimensional homogeneous rotation matrix from an angle in radian.

source

#### pub fn new_nonuniform_scaling_wrt_point( scaling: &Vector2<T>, pt: &Point2<T> ) -> Self

Creates a new homogeneous matrix that applies a scaling factor for each dimension with respect to point.

Can be used to implement `zoom_to` functionality.

sourceยง

### impl<T: RealField> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

source

#### pub fn new_rotation(axisangle: Vector3<T>) -> Self

Builds a 3D homogeneous rotation matrix from an axis and an angle (multiplied together).

Returns the identity matrix if the given argument is zero.

source

#### pub fn new_rotation_wrt_point(axisangle: Vector3<T>, pt: Point3<T>) -> Self

Builds a 3D homogeneous rotation matrix from an axis and an angle (multiplied together).

Returns the identity matrix if the given argument is zero.

source

#### pub fn new_nonuniform_scaling_wrt_point( scaling: &Vector3<T>, pt: &Point3<T> ) -> Self

Creates a new homogeneous matrix that applies a scaling factor for each dimension with respect to point.

Can be used to implement `zoom_to` functionality.

source

#### pub fn from_scaled_axis(axisangle: Vector3<T>) -> Self

Builds a 3D homogeneous rotation matrix from an axis and an angle (multiplied together).

Returns the identity matrix if the given argument is zero. This is identical to `Self::new_rotation`.

source

#### pub fn from_euler_angles(roll: T, pitch: T, yaw: T) -> Self

Creates a new rotation from Euler angles.

The primitive rotations are applied in order: 1 roll โ 2 pitch โ 3 yaw.

source

#### pub fn from_axis_angle(axis: &Unit<Vector3<T>>, angle: T) -> Self

Builds a 3D homogeneous rotation matrix from an axis and a rotation angle.

source

#### pub fn new_orthographic( left: T, right: T, bottom: T, top: T, znear: T, zfar: T ) -> Self

Creates a new homogeneous matrix for an orthographic projection.

source

#### pub fn new_perspective(aspect: T, fovy: T, znear: T, zfar: T) -> Self

Creates a new homogeneous matrix for a perspective projection.

source

#### pub fn face_towards( eye: &Point3<T>, target: &Point3<T>, up: &Vector3<T> ) -> Self

Creates an isometry that corresponds to the local frame of an observer standing at the point `eye` and looking toward `target`.

It maps the view direction `target - eye` to the positive `z` axis and the origin to the `eye`.

source

#### pub fn new_observer_frame( eye: &Point3<T>, target: &Point3<T>, up: &Vector3<T> ) -> Self

๐Deprecated: renamed to `face_towards`

Deprecated: Use `Matrix4::face_towards` instead.

source

#### pub fn look_at_rh(eye: &Point3<T>, target: &Point3<T>, up: &Vector3<T>) -> Self

Builds a right-handed look-at view matrix.

source

#### pub fn look_at_lh(eye: &Point3<T>, target: &Point3<T>, up: &Vector3<T>) -> Self

Builds a left-handed look-at view matrix.

sourceยง

### impl<T: Scalar + Zero + One + ClosedMul + ClosedAdd, D: DimName, S: Storage<T, D, D>> Matrix<T, D, D, S>

source

#### pub fn append_scaling(&self, scaling: T) -> OMatrix<T, D, D>where D: DimNameSub<U1>, DefaultAllocator: Allocator<T, D, D>,

Computes the transformation equal to `self` followed by an uniform scaling factor.

source

#### pub fn prepend_scaling(&self, scaling: T) -> OMatrix<T, D, D>where D: DimNameSub<U1>, DefaultAllocator: Allocator<T, D, D>,

Computes the transformation equal to an uniform scaling factor followed by `self`.

source

#### pub fn append_nonuniform_scaling<SB>( &self, scaling: &Vector<T, DimNameDiff<D, U1>, SB> ) -> OMatrix<T, D, D>where D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<T, D, D>,

Computes the transformation equal to `self` followed by a non-uniform scaling factor.

source

#### pub fn prepend_nonuniform_scaling<SB>( &self, scaling: &Vector<T, DimNameDiff<D, U1>, SB> ) -> OMatrix<T, D, D>where D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<T, D, D>,

Computes the transformation equal to a non-uniform scaling factor followed by `self`.

source

#### pub fn append_translation<SB>( &self, shift: &Vector<T, DimNameDiff<D, U1>, SB> ) -> OMatrix<T, D, D>where D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<T, D, D>,

Computes the transformation equal to `self` followed by a translation.

source

#### pub fn prepend_translation<SB>( &self, shift: &Vector<T, DimNameDiff<D, U1>, SB> ) -> OMatrix<T, D, D>where D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<T, D, D> + Allocator<T, DimNameDiff<D, U1>>,

Computes the transformation equal to a translation followed by `self`.

source

#### pub fn append_scaling_mut(&mut self, scaling: T)where S: StorageMut<T, D, D>, D: DimNameSub<U1>,

Computes in-place the transformation equal to `self` followed by an uniform scaling factor.

source

#### pub fn prepend_scaling_mut(&mut self, scaling: T)where S: StorageMut<T, D, D>, D: DimNameSub<U1>,

Computes in-place the transformation equal to an uniform scaling factor followed by `self`.

source

#### pub fn append_nonuniform_scaling_mut<SB>( &mut self, scaling: &Vector<T, DimNameDiff<D, U1>, SB> )where S: StorageMut<T, D, D>, D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>,

Computes in-place the transformation equal to `self` followed by a non-uniform scaling factor.

source

#### pub fn prepend_nonuniform_scaling_mut<SB>( &mut self, scaling: &Vector<T, DimNameDiff<D, U1>, SB> )where S: StorageMut<T, D, D>, D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>,

Computes in-place the transformation equal to a non-uniform scaling factor followed by `self`.

source

#### pub fn append_translation_mut<SB>( &mut self, shift: &Vector<T, DimNameDiff<D, U1>, SB> )where S: StorageMut<T, D, D>, D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>,

Computes the transformation equal to `self` followed by a translation.

source

#### pub fn prepend_translation_mut<SB>( &mut self, shift: &Vector<T, DimNameDiff<D, U1>, SB> )where D: DimNameSub<U1>, S: StorageMut<T, D, D>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<T, DimNameDiff<D, U1>>,

Computes the transformation equal to a translation followed by `self`.

sourceยง

### impl<T: RealField, D: DimNameSub<U1>, S: Storage<T, D, D>> Matrix<T, D, D, S>where DefaultAllocator: Allocator<T, D, D> + Allocator<T, DimNameDiff<D, U1>> + Allocator<T, DimNameDiff<D, U1>, DimNameDiff<D, U1>>,

source

#### pub fn transform_vector( &self, v: &OVector<T, DimNameDiff<D, U1>> ) -> OVector<T, DimNameDiff<D, U1>>

Transforms the given vector, assuming the matrix `self` uses homogeneous coordinates.

sourceยง

### impl<T: RealField, S: Storage<T, Const<3>, Const<3>>> Matrix<T, Const<3>, Const<3>, S>

source

#### pub fn transform_point(&self, pt: &Point<T, 2>) -> Point<T, 2>

Transforms the given point, assuming the matrix `self` uses homogeneous coordinates.

sourceยง

### impl<T: RealField, S: Storage<T, Const<4>, Const<4>>> Matrix<T, Const<4>, Const<4>, S>

source

#### pub fn transform_point(&self, pt: &Point<T, 3>) -> Point<T, 3>

Transforms the given point, assuming the matrix `self` uses homogeneous coordinates.

sourceยง

### impl<T: Scalar, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn abs(&self) -> OMatrix<T, R, C>where T: Signed, DefaultAllocator: Allocator<T, R, C>,

Computes the component-wise absolute value.

##### ยงExample
``````let a = Matrix2::new(0.0, 1.0,
-2.0, -3.0);
assert_eq!(a.abs(), Matrix2::new(0.0, 1.0, 2.0, 3.0))``````
sourceยง

### impl<T: Scalar, R1: Dim, C1: Dim, SA: Storage<T, R1, C1>> Matrix<T, R1, C1, SA>

source

#### pub fn component_mul<R2, C2, SB>( &self, rhs: &Matrix<T, R2, C2, SB> ) -> MatrixSum<T, R1, C1, R2, C2>where T: ClosedMul, R2: Dim, C2: Dim, SB: Storage<T, R2, C2>, DefaultAllocator: SameShapeAllocator<T, R1, C1, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

Componentwise matrix or vector multiplication.

##### ยงExample
``````let a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let expected = Matrix2::new(0.0, 5.0, 12.0, 21.0);

assert_eq!(a.component_mul(&b), expected);``````
source

#### pub fn cmpy<R2, C2, SB, R3, C3, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, b: &Matrix<T, R3, C3, SC>, beta: T )where T: ClosedMul + Zero + Mul<T, Output = T> + Add<T, Output = T>, R2: Dim, C2: Dim, R3: Dim, C3: Dim, SA: StorageMut<T, R1, C1>, SB: Storage<T, R2, C2>, SC: Storage<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> + SameNumberOfRows<R1, R3> + SameNumberOfColumns<C1, C3>,

Computes componentwise `self[i] = alpha * a[i] * b[i] + beta * self[i]`.

##### ยงExample
``````let mut m = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let expected = (a.component_mul(&b) * 5.0) + m * 10.0;

m.cmpy(5.0, &a, &b, 10.0);
assert_eq!(m, expected);``````
source

#### pub fn component_mul_assign<R2, C2, SB>(&mut self, rhs: &Matrix<T, R2, C2, SB>)where T: ClosedMul, R2: Dim, C2: Dim, SA: StorageMut<T, R1, C1>, SB: Storage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

Inplace componentwise matrix or vector multiplication.

##### ยงExample
``````let mut a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let expected = Matrix2::new(0.0, 5.0, 12.0, 21.0);

a.component_mul_assign(&b);

assert_eq!(a, expected);``````
source

#### pub fn component_mul_mut<R2, C2, SB>(&mut self, rhs: &Matrix<T, R2, C2, SB>)where T: ClosedMul, R2: Dim, C2: Dim, SA: StorageMut<T, R1, C1>, SB: Storage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

๐Deprecated: This is renamed using the `_assign` suffix instead of the `_mut` suffix.

Inplace componentwise matrix or vector multiplication.

##### ยงExample
``````let mut a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let expected = Matrix2::new(0.0, 5.0, 12.0, 21.0);

a.component_mul_assign(&b);

assert_eq!(a, expected);``````
source

#### pub fn component_div<R2, C2, SB>( &self, rhs: &Matrix<T, R2, C2, SB> ) -> MatrixSum<T, R1, C1, R2, C2>where T: ClosedDiv, R2: Dim, C2: Dim, SB: Storage<T, R2, C2>, DefaultAllocator: SameShapeAllocator<T, R1, C1, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

Componentwise matrix or vector division.

##### ยงExample
``````let a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let expected = Matrix2::new(0.0, 1.0 / 5.0, 2.0 / 6.0, 3.0 / 7.0);

assert_eq!(a.component_div(&b), expected);``````
source

#### pub fn cdpy<R2, C2, SB, R3, C3, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, b: &Matrix<T, R3, C3, SC>, beta: T )where T: ClosedDiv + Zero + Mul<T, Output = T> + Add<T, Output = T>, R2: Dim, C2: Dim, R3: Dim, C3: Dim, SA: StorageMut<T, R1, C1>, SB: Storage<T, R2, C2>, SC: Storage<T, R3, C3>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> + SameNumberOfRows<R1, R3> + SameNumberOfColumns<C1, C3>,

Computes componentwise `self[i] = alpha * a[i] / b[i] + beta * self[i]`.

##### ยงExample
``````let mut m = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let a = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let expected = (a.component_div(&b) * 5.0) + m * 10.0;

m.cdpy(5.0, &a, &b, 10.0);
assert_eq!(m, expected);``````
source

#### pub fn component_div_assign<R2, C2, SB>(&mut self, rhs: &Matrix<T, R2, C2, SB>)where T: ClosedDiv, R2: Dim, C2: Dim, SA: StorageMut<T, R1, C1>, SB: Storage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

Inplace componentwise matrix or vector division.

##### ยงExample
``````let mut a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let expected = Matrix2::new(0.0, 1.0 / 5.0, 2.0 / 6.0, 3.0 / 7.0);

a.component_div_assign(&b);

assert_eq!(a, expected);``````
source

#### pub fn component_div_mut<R2, C2, SB>(&mut self, rhs: &Matrix<T, R2, C2, SB>)where T: ClosedDiv, R2: Dim, C2: Dim, SA: StorageMut<T, R1, C1>, SB: Storage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

๐Deprecated: This is renamed using the `_assign` suffix instead of the `_mut` suffix.

Inplace componentwise matrix or vector division.

##### ยงExample
``````let mut a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
let expected = Matrix2::new(0.0, 1.0 / 5.0, 2.0 / 6.0, 3.0 / 7.0);

a.component_div_assign(&b);

assert_eq!(a, expected);``````
source

#### pub fn inf(&self, other: &Self) -> OMatrix<T, R1, C1>where T: SimdPartialOrd, DefaultAllocator: Allocator<T, R1, C1>,

Computes the infimum (aka. componentwise min) of two matrices/vectors.

##### ยงExample
``````let u = Matrix2::new(4.0, 2.0, 1.0, -2.0);
let v = Matrix2::new(2.0, 4.0, -2.0, 1.0);
let expected = Matrix2::new(2.0, 2.0, -2.0, -2.0);
assert_eq!(u.inf(&v), expected)``````
source

#### pub fn sup(&self, other: &Self) -> OMatrix<T, R1, C1>where T: SimdPartialOrd, DefaultAllocator: Allocator<T, R1, C1>,

Computes the supremum (aka. componentwise max) of two matrices/vectors.

##### ยงExample
``````let u = Matrix2::new(4.0, 2.0, 1.0, -2.0);
let v = Matrix2::new(2.0, 4.0, -2.0, 1.0);
let expected = Matrix2::new(4.0, 4.0, 1.0, 1.0);
assert_eq!(u.sup(&v), expected)``````
source

#### pub fn inf_sup(&self, other: &Self) -> (OMatrix<T, R1, C1>, OMatrix<T, R1, C1>)where T: SimdPartialOrd, DefaultAllocator: Allocator<T, R1, C1>,

Computes the (infimum, supremum) of two matrices/vectors.

##### ยงExample
``````let u = Matrix2::new(4.0, 2.0, 1.0, -2.0);
let v = Matrix2::new(2.0, 4.0, -2.0, 1.0);
let expected = (Matrix2::new(2.0, 2.0, -2.0, -2.0), Matrix2::new(4.0, 4.0, 1.0, 1.0));
assert_eq!(u.inf_sup(&v), expected)``````
source

#### pub fn add_scalar(&self, rhs: T) -> OMatrix<T, R1, C1>where T: ClosedAdd, DefaultAllocator: Allocator<T, R1, C1>,

Adds a scalar to `self`.

##### ยงExample
``````let u = Matrix2::new(1.0, 2.0, 3.0, 4.0);
let s = 10.0;
let expected = Matrix2::new(11.0, 12.0, 13.0, 14.0);
source

#### pub fn add_scalar_mut(&mut self, rhs: T)where T: ClosedAdd, SA: StorageMut<T, R1, C1>,

Adds a scalar to `self` in-place.

##### ยงExample
``````let mut u = Matrix2::new(1.0, 2.0, 3.0, 4.0);
let s = 10.0;
let expected = Matrix2::new(11.0, 12.0, 13.0, 14.0);
assert_eq!(u, expected)``````
sourceยง

### impl<T: Scalar, R: Dim, C: Dim> Matrix<MaybeUninit<T>, R, C, <DefaultAllocator as Allocator<T, R, C>>::BufferUninit>where DefaultAllocator: Allocator<T, R, C>,

source

#### pub fn uninit(nrows: R, ncols: C) -> Self

Builds a matrix with uninitialized elements of type `MaybeUninit<T>`.

sourceยง

### impl<T: Scalar, R: Dim, C: Dim> Matrix<T, R, C, <DefaultAllocator as Allocator<T, R, C>>::Buffer>where DefaultAllocator: Allocator<T, R, C>,

#### ยงGeneric constructors

This set of matrix and vector construction functions are all generic with-regard to the matrix dimensions. They all expect to be given the dimension as inputs.

These functions should only be used when working on dimension-generic code.

source

#### pub fn from_element_generic(nrows: R, ncols: C, elem: T) -> Self

Creates a matrix with all its elements set to `elem`.

source

#### pub fn repeat_generic(nrows: R, ncols: C, elem: T) -> Self

Creates a matrix with all its elements set to `elem`.

Same as `from_element_generic`.

source

#### pub fn zeros_generic(nrows: R, ncols: C) -> Selfwhere T: Zero,

Creates a matrix with all its elements set to 0.

source

#### pub fn from_iterator_generic<I>(nrows: R, ncols: C, iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix with all its elements filled by an iterator.

source

#### pub fn from_row_iterator_generic<I>(nrows: R, ncols: C, iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix with all its elements filled by an row-major order iterator.

source

#### pub fn from_row_slice_generic(nrows: R, ncols: C, slice: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in row-major order.

The order of elements in the slice must follow the usual mathematic writing, i.e., row-by-row.

source

#### pub fn from_column_slice_generic(nrows: R, ncols: C, slice: &[T]) -> Self

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. column-major).

source

#### pub fn from_fn_generic<F>(nrows: R, ncols: C, f: F) -> Selfwhere F: FnMut(usize, usize) -> T,

Creates a matrix filled with the results of a function applied to each of its component coordinates.

source

#### pub fn identity_generic(nrows: R, ncols: C) -> Selfwhere T: Zero + One,

Creates a new identity matrix.

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.

source

#### pub fn from_diagonal_element_generic(nrows: R, ncols: C, elt: T) -> Selfwhere T: Zero + One,

Creates a new matrix with its diagonal filled with copies of `elt`.

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.

source

#### pub fn from_partial_diagonal_generic(nrows: R, ncols: C, elts: &[T]) -> Selfwhere T: Zero,

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`.

source

#### pub fn from_rows<SB>(rows: &[Matrix<T, Const<1>, C, SB>]) -> Selfwhere SB: RawStorage<T, Const<1>, C>,

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.

##### ยงExample
``````
let m = Matrix3::from_rows(&[ RowVector3::new(1.0, 2.0, 3.0),  RowVector3::new(4.0, 5.0, 6.0),  RowVector3::new(7.0, 8.0, 9.0) ]);

assert!(m.m11 == 1.0 && m.m12 == 2.0 && m.m13 == 3.0 &&
m.m21 == 4.0 && m.m22 == 5.0 && m.m23 == 6.0 &&
m.m31 == 7.0 && m.m32 == 8.0 && m.m33 == 9.0);``````
source

#### pub fn from_columns<SB>(columns: &[Vector<T, R, SB>]) -> Selfwhere SB: RawStorage<T, R>,

Builds a new matrix from its columns.

Panics if not enough columns are provided (for statically-sized matrices), or if all columns do not have the same dimensions.

##### ยงExample
``````
let m = Matrix3::from_columns(&[ Vector3::new(1.0, 2.0, 3.0),  Vector3::new(4.0, 5.0, 6.0),  Vector3::new(7.0, 8.0, 9.0) ]);

assert!(m.m11 == 1.0 && m.m12 == 4.0 && m.m13 == 7.0 &&
m.m21 == 2.0 && m.m22 == 5.0 && m.m23 == 8.0 &&
m.m31 == 3.0 && m.m32 == 6.0 && m.m33 == 9.0);``````
source

#### pub fn new_random_generic(nrows: R, ncols: C) -> Selfwhere Standard: Distribution<T>,

Creates a matrix filled with random values.

source

#### pub fn from_distribution_generic<Distr: Distribution<T> + ?Sized, G: Rng + ?Sized>( nrows: R, ncols: C, distribution: &Distr, rng: &mut G ) -> Self

Creates a matrix filled with random values from the given distribution.

source

#### pub fn from_vec_generic(nrows: R, ncols: C, data: Vec<T>) -> Self

Creates a matrix backed by a given `Vec`.

The output matrix is filled column-by-column.

##### ยงExample
``````
let vec = vec![0, 1, 2, 3, 4, 5];
let vec_ptr = vec.as_ptr();

let matrix = Matrix::from_vec_generic(Dyn(vec.len()), Const::<1>, vec);
let matrix_storage_ptr = matrix.data.as_vec().as_ptr();

// `matrix` is backed by exactly the same `Vec` as it was constructed from.
assert_eq!(matrix_storage_ptr, vec_ptr);``````
sourceยง

### impl<T, D: Dim> Matrix<T, D, D, <DefaultAllocator as Allocator<T, D, D>>::Buffer>where T: Scalar, DefaultAllocator: Allocator<T, D, D>,

source

#### pub fn from_diagonal<SB: RawStorage<T, D>>(diag: &Vector<T, D, SB>) -> Selfwhere T: Zero,

Creates a square matrix with its diagonal set to `diag` and all other entries set to 0.

##### ยงExample
``````
let m = Matrix3::from_diagonal(&Vector3::new(1.0, 2.0, 3.0));
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_diagonal(&DVector::from_row_slice(&[1.0, 2.0, 3.0]));

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 2.0 && m.m23 == 0.0 &&
m.m31 == 0.0 && m.m32 == 0.0 && m.m33 == 3.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 0.0 &&
dm[(2, 0)] == 0.0 && dm[(2, 1)] == 0.0 && dm[(2, 2)] == 3.0);``````
sourceยง

### impl<T: Scalar, R: DimName, C: DimName> Matrix<T, R, C, <DefaultAllocator as Allocator<T, R, C>>::Buffer>where DefaultAllocator: Allocator<T, R, C>,

source

#### pub fn from_element(elem: T) -> Self

Creates a matrix or vector with all its elements set to `elem`.

##### ยงExample
``````
let v = Vector3::from_element(2.0);
// The additional argument represents the vector dimension.
let dv = DVector::from_element(3, 2.0);
let m = Matrix2x3::from_element(2.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_element(2, 3, 2.0);

assert!(v.x == 2.0 && v.y == 2.0 && v.z == 2.0);
assert!(dv[0] == 2.0 && dv[1] == 2.0 && dv[2] == 2.0);
assert!(m.m11 == 2.0 && m.m12 == 2.0 && m.m13 == 2.0 &&
m.m21 == 2.0 && m.m22 == 2.0 && m.m23 == 2.0);
assert!(dm[(0, 0)] == 2.0 && dm[(0, 1)] == 2.0 && dm[(0, 2)] == 2.0 &&
dm[(1, 0)] == 2.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 2.0);``````
source

#### pub fn repeat(elem: T) -> Self

Creates a matrix or vector with all its elements set to `elem`.

Same as `.from_element`.

##### ยงExample
``````
let v = Vector3::repeat(2.0);
// The additional argument represents the vector dimension.
let dv = DVector::repeat(3, 2.0);
let m = Matrix2x3::repeat(2.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::repeat(2, 3, 2.0);

assert!(v.x == 2.0 && v.y == 2.0 && v.z == 2.0);
assert!(dv[0] == 2.0 && dv[1] == 2.0 && dv[2] == 2.0);
assert!(m.m11 == 2.0 && m.m12 == 2.0 && m.m13 == 2.0 &&
m.m21 == 2.0 && m.m22 == 2.0 && m.m23 == 2.0);
assert!(dm[(0, 0)] == 2.0 && dm[(0, 1)] == 2.0 && dm[(0, 2)] == 2.0 &&
dm[(1, 0)] == 2.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 2.0);``````
source

#### pub fn zeros() -> Selfwhere T: Zero,

Creates a matrix or vector with all its elements set to `0`.

##### ยงExample
``````
let v = Vector3::<f32>::zeros();
// The argument represents the vector dimension.
let dv = DVector::<f32>::zeros(3);
let m = Matrix2x3::<f32>::zeros();
// The two arguments represent the matrix dimensions.
let dm = DMatrix::<f32>::zeros(2, 3);

assert!(v.x == 0.0 && v.y == 0.0 && v.z == 0.0);
assert!(dv[0] == 0.0 && dv[1] == 0.0 && dv[2] == 0.0);
assert!(m.m11 == 0.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 0.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 0.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 0.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_iterator<I>(iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix or vector with all its elements filled by an iterator.

The output matrix is filled column-by-column.

##### ยงExample
``````
let v = Vector3::from_iterator((0..3).into_iter());
// The additional argument represents the vector dimension.
let dv = DVector::from_iterator(3, (0..3).into_iter());
let m = Matrix2x3::from_iterator((0..6).into_iter());
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_iterator(2, 3, (0..6).into_iter());

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
source

#### pub fn from_row_iterator<I>(iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix or vector with all its elements filled by a row-major iterator.

The output matrix is filled row-by-row.

###### ยงExample
``````
let v = Vector3::from_row_iterator((0..3).into_iter());
// The additional argument represents the vector dimension.
let dv = DVector::from_row_iterator(3, (0..3).into_iter());
let m = Matrix2x3::from_row_iterator((0..6).into_iter());
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_row_iterator(2, 3, (0..6).into_iter());

// For Vectors from_row_iterator is identical to from_iterator
assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn from_fn<F>(f: F) -> Selfwhere F: FnMut(usize, usize) -> T,

Creates a matrix or vector filled with the results of a function applied to each of its component coordinates.

##### ยงExample
``````
let v = Vector3::from_fn(|i, _| i);
// The additional argument represents the vector dimension.
let dv = DVector::from_fn(3, |i, _| i);
let m = Matrix2x3::from_fn(|i, j| i * 3 + j);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_fn(2, 3, |i, j| i * 3 + j);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn identity() -> Selfwhere T: Zero + One,

Creates an identity matrix. If the matrix is not square, the largest square submatrix (starting at the first row and column) is set to the identity while all other entries are set to zero.

##### ยงExample
``````
let m = Matrix2x3::<f32>::identity();
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::<f32>::identity(2, 3);

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 1.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 1.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_diagonal_element(elt: T) -> Selfwhere T: Zero + One,

Creates a matrix filled with its diagonal filled with `elt` and all other components set to zero.

##### ยงExample
``````
let m = Matrix2x3::from_diagonal_element(5.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_diagonal_element(2, 3, 5.0);

assert!(m.m11 == 5.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 5.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 5.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 5.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_partial_diagonal(elts: &[T]) -> Selfwhere T: Zero,

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`.

##### ยงExample
``````
let m = Matrix3::from_partial_diagonal(&[1.0, 2.0]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_partial_diagonal(3, 3, &[1.0, 2.0]);

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 2.0 && m.m23 == 0.0 &&
m.m31 == 0.0 && m.m32 == 0.0 && m.m33 == 0.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 0.0 &&
dm[(2, 0)] == 0.0 && dm[(2, 1)] == 0.0 && dm[(2, 2)] == 0.0);``````
source

#### pub fn from_distribution<Distr: Distribution<T> + ?Sized, G: Rng + ?Sized>( distribution: &Distr, rng: &mut G ) -> Self

Creates a matrix or vector filled with random values from the given distribution.

source

#### pub fn new_random() -> Selfwhere Standard: Distribution<T>,

Creates a matrix filled with random values.

sourceยง

### impl<T: Scalar, R: DimName> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<T, R, Dyn>>::Buffer>where DefaultAllocator: Allocator<T, R, Dyn>,

source

#### pub fn from_element(ncols: usize, elem: T) -> Self

Creates a matrix or vector with all its elements set to `elem`.

##### ยงExample
``````
let v = Vector3::from_element(2.0);
// The additional argument represents the vector dimension.
let dv = DVector::from_element(3, 2.0);
let m = Matrix2x3::from_element(2.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_element(2, 3, 2.0);

assert!(v.x == 2.0 && v.y == 2.0 && v.z == 2.0);
assert!(dv[0] == 2.0 && dv[1] == 2.0 && dv[2] == 2.0);
assert!(m.m11 == 2.0 && m.m12 == 2.0 && m.m13 == 2.0 &&
m.m21 == 2.0 && m.m22 == 2.0 && m.m23 == 2.0);
assert!(dm[(0, 0)] == 2.0 && dm[(0, 1)] == 2.0 && dm[(0, 2)] == 2.0 &&
dm[(1, 0)] == 2.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 2.0);``````
source

#### pub fn repeat(ncols: usize, elem: T) -> Self

Creates a matrix or vector with all its elements set to `elem`.

Same as `.from_element`.

##### ยงExample
``````
let v = Vector3::repeat(2.0);
// The additional argument represents the vector dimension.
let dv = DVector::repeat(3, 2.0);
let m = Matrix2x3::repeat(2.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::repeat(2, 3, 2.0);

assert!(v.x == 2.0 && v.y == 2.0 && v.z == 2.0);
assert!(dv[0] == 2.0 && dv[1] == 2.0 && dv[2] == 2.0);
assert!(m.m11 == 2.0 && m.m12 == 2.0 && m.m13 == 2.0 &&
m.m21 == 2.0 && m.m22 == 2.0 && m.m23 == 2.0);
assert!(dm[(0, 0)] == 2.0 && dm[(0, 1)] == 2.0 && dm[(0, 2)] == 2.0 &&
dm[(1, 0)] == 2.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 2.0);``````
source

#### pub fn zeros(ncols: usize) -> Selfwhere T: Zero,

Creates a matrix or vector with all its elements set to `0`.

##### ยงExample
``````
let v = Vector3::<f32>::zeros();
// The argument represents the vector dimension.
let dv = DVector::<f32>::zeros(3);
let m = Matrix2x3::<f32>::zeros();
// The two arguments represent the matrix dimensions.
let dm = DMatrix::<f32>::zeros(2, 3);

assert!(v.x == 0.0 && v.y == 0.0 && v.z == 0.0);
assert!(dv[0] == 0.0 && dv[1] == 0.0 && dv[2] == 0.0);
assert!(m.m11 == 0.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 0.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 0.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 0.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_iterator<I>(ncols: usize, iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix or vector with all its elements filled by an iterator.

The output matrix is filled column-by-column.

##### ยงExample
``````
let v = Vector3::from_iterator((0..3).into_iter());
// The additional argument represents the vector dimension.
let dv = DVector::from_iterator(3, (0..3).into_iter());
let m = Matrix2x3::from_iterator((0..6).into_iter());
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_iterator(2, 3, (0..6).into_iter());

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
source

#### pub fn from_row_iterator<I>(ncols: usize, iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix or vector with all its elements filled by a row-major iterator.

The output matrix is filled row-by-row.

###### ยงExample
``````
let v = Vector3::from_row_iterator((0..3).into_iter());
// The additional argument represents the vector dimension.
let dv = DVector::from_row_iterator(3, (0..3).into_iter());
let m = Matrix2x3::from_row_iterator((0..6).into_iter());
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_row_iterator(2, 3, (0..6).into_iter());

// For Vectors from_row_iterator is identical to from_iterator
assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn from_fn<F>(ncols: usize, f: F) -> Selfwhere F: FnMut(usize, usize) -> T,

Creates a matrix or vector filled with the results of a function applied to each of its component coordinates.

##### ยงExample
``````
let v = Vector3::from_fn(|i, _| i);
// The additional argument represents the vector dimension.
let dv = DVector::from_fn(3, |i, _| i);
let m = Matrix2x3::from_fn(|i, j| i * 3 + j);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_fn(2, 3, |i, j| i * 3 + j);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn identity(ncols: usize) -> Selfwhere T: Zero + One,

Creates an identity matrix. If the matrix is not square, the largest square submatrix (starting at the first row and column) is set to the identity while all other entries are set to zero.

##### ยงExample
``````
let m = Matrix2x3::<f32>::identity();
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::<f32>::identity(2, 3);

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 1.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 1.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_diagonal_element(ncols: usize, elt: T) -> Selfwhere T: Zero + One,

Creates a matrix filled with its diagonal filled with `elt` and all other components set to zero.

##### ยงExample
``````
let m = Matrix2x3::from_diagonal_element(5.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_diagonal_element(2, 3, 5.0);

assert!(m.m11 == 5.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 5.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 5.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 5.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_partial_diagonal(ncols: usize, elts: &[T]) -> Selfwhere T: Zero,

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`.

##### ยงExample
``````
let m = Matrix3::from_partial_diagonal(&[1.0, 2.0]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_partial_diagonal(3, 3, &[1.0, 2.0]);

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 2.0 && m.m23 == 0.0 &&
m.m31 == 0.0 && m.m32 == 0.0 && m.m33 == 0.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 0.0 &&
dm[(2, 0)] == 0.0 && dm[(2, 1)] == 0.0 && dm[(2, 2)] == 0.0);``````
source

#### pub fn from_distribution<Distr: Distribution<T> + ?Sized, G: Rng + ?Sized>( ncols: usize, distribution: &Distr, rng: &mut G ) -> Self

Creates a matrix or vector filled with random values from the given distribution.

source

#### pub fn new_random(ncols: usize) -> Selfwhere Standard: Distribution<T>,

Creates a matrix filled with random values.

sourceยง

### impl<T: Scalar, C: DimName> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<T, Dyn, C>>::Buffer>where DefaultAllocator: Allocator<T, Dyn, C>,

source

#### pub fn from_element(nrows: usize, elem: T) -> Self

Creates a matrix or vector with all its elements set to `elem`.

##### ยงExample
``````
let v = Vector3::from_element(2.0);
// The additional argument represents the vector dimension.
let dv = DVector::from_element(3, 2.0);
let m = Matrix2x3::from_element(2.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_element(2, 3, 2.0);

assert!(v.x == 2.0 && v.y == 2.0 && v.z == 2.0);
assert!(dv[0] == 2.0 && dv[1] == 2.0 && dv[2] == 2.0);
assert!(m.m11 == 2.0 && m.m12 == 2.0 && m.m13 == 2.0 &&
m.m21 == 2.0 && m.m22 == 2.0 && m.m23 == 2.0);
assert!(dm[(0, 0)] == 2.0 && dm[(0, 1)] == 2.0 && dm[(0, 2)] == 2.0 &&
dm[(1, 0)] == 2.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 2.0);``````
source

#### pub fn repeat(nrows: usize, elem: T) -> Self

Creates a matrix or vector with all its elements set to `elem`.

Same as `.from_element`.

##### ยงExample
``````
let v = Vector3::repeat(2.0);
// The additional argument represents the vector dimension.
let dv = DVector::repeat(3, 2.0);
let m = Matrix2x3::repeat(2.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::repeat(2, 3, 2.0);

assert!(v.x == 2.0 && v.y == 2.0 && v.z == 2.0);
assert!(dv[0] == 2.0 && dv[1] == 2.0 && dv[2] == 2.0);
assert!(m.m11 == 2.0 && m.m12 == 2.0 && m.m13 == 2.0 &&
m.m21 == 2.0 && m.m22 == 2.0 && m.m23 == 2.0);
assert!(dm[(0, 0)] == 2.0 && dm[(0, 1)] == 2.0 && dm[(0, 2)] == 2.0 &&
dm[(1, 0)] == 2.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 2.0);``````
source

#### pub fn zeros(nrows: usize) -> Selfwhere T: Zero,

Creates a matrix or vector with all its elements set to `0`.

##### ยงExample
``````
let v = Vector3::<f32>::zeros();
// The argument represents the vector dimension.
let dv = DVector::<f32>::zeros(3);
let m = Matrix2x3::<f32>::zeros();
// The two arguments represent the matrix dimensions.
let dm = DMatrix::<f32>::zeros(2, 3);

assert!(v.x == 0.0 && v.y == 0.0 && v.z == 0.0);
assert!(dv[0] == 0.0 && dv[1] == 0.0 && dv[2] == 0.0);
assert!(m.m11 == 0.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 0.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 0.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 0.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_iterator<I>(nrows: usize, iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix or vector with all its elements filled by an iterator.

The output matrix is filled column-by-column.

##### ยงExample
``````
let v = Vector3::from_iterator((0..3).into_iter());
// The additional argument represents the vector dimension.
let dv = DVector::from_iterator(3, (0..3).into_iter());
let m = Matrix2x3::from_iterator((0..6).into_iter());
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_iterator(2, 3, (0..6).into_iter());

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
source

#### pub fn from_row_iterator<I>(nrows: usize, iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix or vector with all its elements filled by a row-major iterator.

The output matrix is filled row-by-row.

###### ยงExample
``````
let v = Vector3::from_row_iterator((0..3).into_iter());
// The additional argument represents the vector dimension.
let dv = DVector::from_row_iterator(3, (0..3).into_iter());
let m = Matrix2x3::from_row_iterator((0..6).into_iter());
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_row_iterator(2, 3, (0..6).into_iter());

// For Vectors from_row_iterator is identical to from_iterator
assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn from_fn<F>(nrows: usize, f: F) -> Selfwhere F: FnMut(usize, usize) -> T,

Creates a matrix or vector filled with the results of a function applied to each of its component coordinates.

##### ยงExample
``````
let v = Vector3::from_fn(|i, _| i);
// The additional argument represents the vector dimension.
let dv = DVector::from_fn(3, |i, _| i);
let m = Matrix2x3::from_fn(|i, j| i * 3 + j);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_fn(2, 3, |i, j| i * 3 + j);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn identity(nrows: usize) -> Selfwhere T: Zero + One,

Creates an identity matrix. If the matrix is not square, the largest square submatrix (starting at the first row and column) is set to the identity while all other entries are set to zero.

##### ยงExample
``````
let m = Matrix2x3::<f32>::identity();
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::<f32>::identity(2, 3);

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 1.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 1.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_diagonal_element(nrows: usize, elt: T) -> Selfwhere T: Zero + One,

Creates a matrix filled with its diagonal filled with `elt` and all other components set to zero.

##### ยงExample
``````
let m = Matrix2x3::from_diagonal_element(5.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_diagonal_element(2, 3, 5.0);

assert!(m.m11 == 5.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 5.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 5.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 5.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_partial_diagonal(nrows: usize, elts: &[T]) -> Selfwhere T: Zero,

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`.

##### ยงExample
``````
let m = Matrix3::from_partial_diagonal(&[1.0, 2.0]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_partial_diagonal(3, 3, &[1.0, 2.0]);

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 2.0 && m.m23 == 0.0 &&
m.m31 == 0.0 && m.m32 == 0.0 && m.m33 == 0.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 0.0 &&
dm[(2, 0)] == 0.0 && dm[(2, 1)] == 0.0 && dm[(2, 2)] == 0.0);``````
source

#### pub fn from_distribution<Distr: Distribution<T> + ?Sized, G: Rng + ?Sized>( nrows: usize, distribution: &Distr, rng: &mut G ) -> Self

Creates a matrix or vector filled with random values from the given distribution.

source

#### pub fn new_random(nrows: usize) -> Selfwhere Standard: Distribution<T>,

Creates a matrix filled with random values.

sourceยง

### impl<T: Scalar> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<T, Dyn, Dyn>>::Buffer>where DefaultAllocator: Allocator<T, Dyn, Dyn>,

source

#### pub fn from_element(nrows: usize, ncols: usize, elem: T) -> Self

Creates a matrix or vector with all its elements set to `elem`.

##### ยงExample
``````
let v = Vector3::from_element(2.0);
// The additional argument represents the vector dimension.
let dv = DVector::from_element(3, 2.0);
let m = Matrix2x3::from_element(2.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_element(2, 3, 2.0);

assert!(v.x == 2.0 && v.y == 2.0 && v.z == 2.0);
assert!(dv[0] == 2.0 && dv[1] == 2.0 && dv[2] == 2.0);
assert!(m.m11 == 2.0 && m.m12 == 2.0 && m.m13 == 2.0 &&
m.m21 == 2.0 && m.m22 == 2.0 && m.m23 == 2.0);
assert!(dm[(0, 0)] == 2.0 && dm[(0, 1)] == 2.0 && dm[(0, 2)] == 2.0 &&
dm[(1, 0)] == 2.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 2.0);``````
source

#### pub fn repeat(nrows: usize, ncols: usize, elem: T) -> Self

Creates a matrix or vector with all its elements set to `elem`.

Same as `.from_element`.

##### ยงExample
``````
let v = Vector3::repeat(2.0);
// The additional argument represents the vector dimension.
let dv = DVector::repeat(3, 2.0);
let m = Matrix2x3::repeat(2.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::repeat(2, 3, 2.0);

assert!(v.x == 2.0 && v.y == 2.0 && v.z == 2.0);
assert!(dv[0] == 2.0 && dv[1] == 2.0 && dv[2] == 2.0);
assert!(m.m11 == 2.0 && m.m12 == 2.0 && m.m13 == 2.0 &&
m.m21 == 2.0 && m.m22 == 2.0 && m.m23 == 2.0);
assert!(dm[(0, 0)] == 2.0 && dm[(0, 1)] == 2.0 && dm[(0, 2)] == 2.0 &&
dm[(1, 0)] == 2.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 2.0);``````
source

#### pub fn zeros(nrows: usize, ncols: usize) -> Selfwhere T: Zero,

Creates a matrix or vector with all its elements set to `0`.

##### ยงExample
``````
let v = Vector3::<f32>::zeros();
// The argument represents the vector dimension.
let dv = DVector::<f32>::zeros(3);
let m = Matrix2x3::<f32>::zeros();
// The two arguments represent the matrix dimensions.
let dm = DMatrix::<f32>::zeros(2, 3);

assert!(v.x == 0.0 && v.y == 0.0 && v.z == 0.0);
assert!(dv[0] == 0.0 && dv[1] == 0.0 && dv[2] == 0.0);
assert!(m.m11 == 0.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 0.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 0.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 0.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_iterator<I>(nrows: usize, ncols: usize, iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix or vector with all its elements filled by an iterator.

The output matrix is filled column-by-column.

##### ยงExample
``````
let v = Vector3::from_iterator((0..3).into_iter());
// The additional argument represents the vector dimension.
let dv = DVector::from_iterator(3, (0..3).into_iter());
let m = Matrix2x3::from_iterator((0..6).into_iter());
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_iterator(2, 3, (0..6).into_iter());

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
source

#### pub fn from_row_iterator<I>(nrows: usize, ncols: usize, iter: I) -> Selfwhere I: IntoIterator<Item = T>,

Creates a matrix or vector with all its elements filled by a row-major iterator.

The output matrix is filled row-by-row.

###### ยงExample
``````
let v = Vector3::from_row_iterator((0..3).into_iter());
// The additional argument represents the vector dimension.
let dv = DVector::from_row_iterator(3, (0..3).into_iter());
let m = Matrix2x3::from_row_iterator((0..6).into_iter());
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_row_iterator(2, 3, (0..6).into_iter());

// For Vectors from_row_iterator is identical to from_iterator
assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn from_fn<F>(nrows: usize, ncols: usize, f: F) -> Selfwhere F: FnMut(usize, usize) -> T,

Creates a matrix or vector filled with the results of a function applied to each of its component coordinates.

##### ยงExample
``````
let v = Vector3::from_fn(|i, _| i);
// The additional argument represents the vector dimension.
let dv = DVector::from_fn(3, |i, _| i);
let m = Matrix2x3::from_fn(|i, j| i * 3 + j);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_fn(2, 3, |i, j| i * 3 + j);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn identity(nrows: usize, ncols: usize) -> Selfwhere T: Zero + One,

Creates an identity matrix. If the matrix is not square, the largest square submatrix (starting at the first row and column) is set to the identity while all other entries are set to zero.

##### ยงExample
``````
let m = Matrix2x3::<f32>::identity();
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::<f32>::identity(2, 3);

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 1.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 1.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_diagonal_element(nrows: usize, ncols: usize, elt: T) -> Selfwhere T: Zero + One,

Creates a matrix filled with its diagonal filled with `elt` and all other components set to zero.

##### ยงExample
``````
let m = Matrix2x3::from_diagonal_element(5.0);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_diagonal_element(2, 3, 5.0);

assert!(m.m11 == 5.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 5.0 && m.m23 == 0.0);
assert!(dm[(0, 0)] == 5.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 5.0 && dm[(1, 2)] == 0.0);``````
source

#### pub fn from_partial_diagonal(nrows: usize, ncols: usize, elts: &[T]) -> Selfwhere T: Zero,

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`.

##### ยงExample
``````
let m = Matrix3::from_partial_diagonal(&[1.0, 2.0]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_partial_diagonal(3, 3, &[1.0, 2.0]);

assert!(m.m11 == 1.0 && m.m12 == 0.0 && m.m13 == 0.0 &&
m.m21 == 0.0 && m.m22 == 2.0 && m.m23 == 0.0 &&
m.m31 == 0.0 && m.m32 == 0.0 && m.m33 == 0.0);
assert!(dm[(0, 0)] == 1.0 && dm[(0, 1)] == 0.0 && dm[(0, 2)] == 0.0 &&
dm[(1, 0)] == 0.0 && dm[(1, 1)] == 2.0 && dm[(1, 2)] == 0.0 &&
dm[(2, 0)] == 0.0 && dm[(2, 1)] == 0.0 && dm[(2, 2)] == 0.0);``````
source

#### pub fn from_distribution<Distr: Distribution<T> + ?Sized, G: Rng + ?Sized>( nrows: usize, ncols: usize, distribution: &Distr, rng: &mut G ) -> Self

Creates a matrix or vector filled with random values from the given distribution.

source

#### pub fn new_random(nrows: usize, ncols: usize) -> Selfwhere Standard: Distribution<T>,

Creates a matrix filled with random values.

sourceยง

### impl<T: Scalar, R: DimName, C: DimName> Matrix<T, R, C, <DefaultAllocator as Allocator<T, R, C>>::Buffer>where DefaultAllocator: Allocator<T, R, C>,

source

#### pub fn from_row_slice(data: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in row-major order.

The order of elements in the slice must follow the usual mathematic writing, i.e., row-by-row.

##### ยงExample
``````
let v = Vector3::from_row_slice(&[0, 1, 2]);
// The additional argument represents the vector dimension.
let dv = DVector::from_row_slice(&[0, 1, 2]);
let m = Matrix2x3::from_row_slice(&[0, 1, 2, 3, 4, 5]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_row_slice(2, 3, &[0, 1, 2, 3, 4, 5]);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn from_column_slice(data: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in column-major order.

##### ยงExample
``````
let v = Vector3::from_column_slice(&[0, 1, 2]);
// The additional argument represents the vector dimension.
let dv = DVector::from_column_slice(&[0, 1, 2]);
let m = Matrix2x3::from_column_slice(&[0, 1, 2, 3, 4, 5]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_column_slice(2, 3, &[0, 1, 2, 3, 4, 5]);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
source

#### pub fn from_vec(data: Vec<T>) -> Self

Creates a matrix backed by a given `Vec`.

The output matrix is filled column-by-column.

##### ยงExample
``````
let m = Matrix2x3::from_vec(vec![0, 1, 2, 3, 4, 5]);

assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);

// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_vec(2, 3, vec![0, 1, 2, 3, 4, 5]);

assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
sourceยง

### impl<T: Scalar, R: DimName> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<T, R, Dyn>>::Buffer>where DefaultAllocator: Allocator<T, R, Dyn>,

source

#### pub fn from_row_slice(data: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in row-major order.

The order of elements in the slice must follow the usual mathematic writing, i.e., row-by-row.

##### ยงExample
``````
let v = Vector3::from_row_slice(&[0, 1, 2]);
// The additional argument represents the vector dimension.
let dv = DVector::from_row_slice(&[0, 1, 2]);
let m = Matrix2x3::from_row_slice(&[0, 1, 2, 3, 4, 5]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_row_slice(2, 3, &[0, 1, 2, 3, 4, 5]);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn from_column_slice(data: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in column-major order.

##### ยงExample
``````
let v = Vector3::from_column_slice(&[0, 1, 2]);
// The additional argument represents the vector dimension.
let dv = DVector::from_column_slice(&[0, 1, 2]);
let m = Matrix2x3::from_column_slice(&[0, 1, 2, 3, 4, 5]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_column_slice(2, 3, &[0, 1, 2, 3, 4, 5]);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
source

#### pub fn from_vec(data: Vec<T>) -> Self

Creates a matrix backed by a given `Vec`.

The output matrix is filled column-by-column.

##### ยงExample
``````
let m = Matrix2x3::from_vec(vec![0, 1, 2, 3, 4, 5]);

assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);

// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_vec(2, 3, vec![0, 1, 2, 3, 4, 5]);

assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
sourceยง

### impl<T: Scalar, C: DimName> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<T, Dyn, C>>::Buffer>where DefaultAllocator: Allocator<T, Dyn, C>,

source

#### pub fn from_row_slice(data: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in row-major order.

The order of elements in the slice must follow the usual mathematic writing, i.e., row-by-row.

##### ยงExample
``````
let v = Vector3::from_row_slice(&[0, 1, 2]);
// The additional argument represents the vector dimension.
let dv = DVector::from_row_slice(&[0, 1, 2]);
let m = Matrix2x3::from_row_slice(&[0, 1, 2, 3, 4, 5]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_row_slice(2, 3, &[0, 1, 2, 3, 4, 5]);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn from_column_slice(data: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in column-major order.

##### ยงExample
``````
let v = Vector3::from_column_slice(&[0, 1, 2]);
// The additional argument represents the vector dimension.
let dv = DVector::from_column_slice(&[0, 1, 2]);
let m = Matrix2x3::from_column_slice(&[0, 1, 2, 3, 4, 5]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_column_slice(2, 3, &[0, 1, 2, 3, 4, 5]);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
source

#### pub fn from_vec(data: Vec<T>) -> Self

Creates a matrix backed by a given `Vec`.

The output matrix is filled column-by-column.

##### ยงExample
``````
let m = Matrix2x3::from_vec(vec![0, 1, 2, 3, 4, 5]);

assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);

// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_vec(2, 3, vec![0, 1, 2, 3, 4, 5]);

assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
sourceยง

### impl<T: Scalar> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<T, Dyn, Dyn>>::Buffer>where DefaultAllocator: Allocator<T, Dyn, Dyn>,

source

#### pub fn from_row_slice(nrows: usize, ncols: usize, data: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in row-major order.

The order of elements in the slice must follow the usual mathematic writing, i.e., row-by-row.

##### ยงExample
``````
let v = Vector3::from_row_slice(&[0, 1, 2]);
// The additional argument represents the vector dimension.
let dv = DVector::from_row_slice(&[0, 1, 2]);
let m = Matrix2x3::from_row_slice(&[0, 1, 2, 3, 4, 5]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_row_slice(2, 3, &[0, 1, 2, 3, 4, 5]);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 1 && m.m13 == 2 &&
m.m21 == 3 && m.m22 == 4 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 1 && dm[(0, 2)] == 2 &&
dm[(1, 0)] == 3 && dm[(1, 1)] == 4 && dm[(1, 2)] == 5);``````
source

#### pub fn from_column_slice(nrows: usize, ncols: usize, data: &[T]) -> Self

Creates a matrix with its elements filled with the components provided by a slice in column-major order.

##### ยงExample
``````
let v = Vector3::from_column_slice(&[0, 1, 2]);
// The additional argument represents the vector dimension.
let dv = DVector::from_column_slice(&[0, 1, 2]);
let m = Matrix2x3::from_column_slice(&[0, 1, 2, 3, 4, 5]);
// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_column_slice(2, 3, &[0, 1, 2, 3, 4, 5]);

assert!(v.x == 0 && v.y == 1 && v.z == 2);
assert!(dv[0] == 0 && dv[1] == 1 && dv[2] == 2);
assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);
assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
source

#### pub fn from_vec(nrows: usize, ncols: usize, data: Vec<T>) -> Self

Creates a matrix backed by a given `Vec`.

The output matrix is filled column-by-column.

##### ยงExample
``````
let m = Matrix2x3::from_vec(vec![0, 1, 2, 3, 4, 5]);

assert!(m.m11 == 0 && m.m12 == 2 && m.m13 == 4 &&
m.m21 == 1 && m.m22 == 3 && m.m23 == 5);

// The two additional arguments represent the matrix dimensions.
let dm = DMatrix::from_vec(2, 3, vec![0, 1, 2, 3, 4, 5]);

assert!(dm[(0, 0)] == 0 && dm[(0, 1)] == 2 && dm[(0, 2)] == 4 &&
dm[(1, 0)] == 1 && dm[(1, 1)] == 3 && dm[(1, 2)] == 5);``````
sourceยง

### impl<T> Matrix<T, Const<2>, Const<2>, ArrayStorage<T, 2, 2>>

source

#### pub const fn new(m11: T, m12: T, m21: T, m22: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<3>, Const<3>, ArrayStorage<T, 3, 3>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m21: T, m22: T, m23: T, m31: T, m32: T, m33: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m21: T, m22: T, m23: T, m24: T, m31: T, m32: T, m33: T, m34: T, m41: T, m42: T, m43: T, m44: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<5>, Const<5>, ArrayStorage<T, 5, 5>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m21: T, m22: T, m23: T, m24: T, m25: T, m31: T, m32: T, m33: T, m34: T, m35: T, m41: T, m42: T, m43: T, m44: T, m45: T, m51: T, m52: T, m53: T, m54: T, m55: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<6>, Const<6>, ArrayStorage<T, 6, 6>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m16: T, m21: T, m22: T, m23: T, m24: T, m25: T, m26: T, m31: T, m32: T, m33: T, m34: T, m35: T, m36: T, m41: T, m42: T, m43: T, m44: T, m45: T, m46: T, m51: T, m52: T, m53: T, m54: T, m55: T, m56: T, m61: T, m62: T, m63: T, m64: T, m65: T, m66: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<2>, Const<3>, ArrayStorage<T, 2, 3>>

source

#### pub const fn new(m11: T, m12: T, m13: T, m21: T, m22: T, m23: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<2>, Const<4>, ArrayStorage<T, 2, 4>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m21: T, m22: T, m23: T, m24: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<2>, Const<5>, ArrayStorage<T, 2, 5>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m21: T, m22: T, m23: T, m24: T, m25: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<2>, Const<6>, ArrayStorage<T, 2, 6>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m16: T, m21: T, m22: T, m23: T, m24: T, m25: T, m26: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<3>, Const<2>, ArrayStorage<T, 3, 2>>

source

#### pub const fn new(m11: T, m12: T, m21: T, m22: T, m31: T, m32: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<3>, Const<4>, ArrayStorage<T, 3, 4>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m21: T, m22: T, m23: T, m24: T, m31: T, m32: T, m33: T, m34: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<3>, Const<5>, ArrayStorage<T, 3, 5>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m21: T, m22: T, m23: T, m24: T, m25: T, m31: T, m32: T, m33: T, m34: T, m35: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<3>, Const<6>, ArrayStorage<T, 3, 6>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m16: T, m21: T, m22: T, m23: T, m24: T, m25: T, m26: T, m31: T, m32: T, m33: T, m34: T, m35: T, m36: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<4>, Const<2>, ArrayStorage<T, 4, 2>>

source

#### pub const fn new( m11: T, m12: T, m21: T, m22: T, m31: T, m32: T, m41: T, m42: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<4>, Const<3>, ArrayStorage<T, 4, 3>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m21: T, m22: T, m23: T, m31: T, m32: T, m33: T, m41: T, m42: T, m43: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<4>, Const<5>, ArrayStorage<T, 4, 5>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m21: T, m22: T, m23: T, m24: T, m25: T, m31: T, m32: T, m33: T, m34: T, m35: T, m41: T, m42: T, m43: T, m44: T, m45: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<4>, Const<6>, ArrayStorage<T, 4, 6>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m16: T, m21: T, m22: T, m23: T, m24: T, m25: T, m26: T, m31: T, m32: T, m33: T, m34: T, m35: T, m36: T, m41: T, m42: T, m43: T, m44: T, m45: T, m46: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<5>, Const<2>, ArrayStorage<T, 5, 2>>

source

#### pub const fn new( m11: T, m12: T, m21: T, m22: T, m31: T, m32: T, m41: T, m42: T, m51: T, m52: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<5>, Const<3>, ArrayStorage<T, 5, 3>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m21: T, m22: T, m23: T, m31: T, m32: T, m33: T, m41: T, m42: T, m43: T, m51: T, m52: T, m53: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<5>, Const<4>, ArrayStorage<T, 5, 4>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m21: T, m22: T, m23: T, m24: T, m31: T, m32: T, m33: T, m34: T, m41: T, m42: T, m43: T, m44: T, m51: T, m52: T, m53: T, m54: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<5>, Const<6>, ArrayStorage<T, 5, 6>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m16: T, m21: T, m22: T, m23: T, m24: T, m25: T, m26: T, m31: T, m32: T, m33: T, m34: T, m35: T, m36: T, m41: T, m42: T, m43: T, m44: T, m45: T, m46: T, m51: T, m52: T, m53: T, m54: T, m55: T, m56: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<6>, Const<2>, ArrayStorage<T, 6, 2>>

source

#### pub const fn new( m11: T, m12: T, m21: T, m22: T, m31: T, m32: T, m41: T, m42: T, m51: T, m52: T, m61: T, m62: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<6>, Const<3>, ArrayStorage<T, 6, 3>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m21: T, m22: T, m23: T, m31: T, m32: T, m33: T, m41: T, m42: T, m43: T, m51: T, m52: T, m53: T, m61: T, m62: T, m63: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<6>, Const<4>, ArrayStorage<T, 6, 4>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m21: T, m22: T, m23: T, m24: T, m31: T, m32: T, m33: T, m34: T, m41: T, m42: T, m43: T, m44: T, m51: T, m52: T, m53: T, m54: T, m61: T, m62: T, m63: T, m64: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<6>, Const<5>, ArrayStorage<T, 6, 5>>

source

#### pub const fn new( m11: T, m12: T, m13: T, m14: T, m15: T, m21: T, m22: T, m23: T, m24: T, m25: T, m31: T, m32: T, m33: T, m34: T, m35: T, m41: T, m42: T, m43: T, m44: T, m45: T, m51: T, m52: T, m53: T, m54: T, m55: T, m61: T, m62: T, m63: T, m64: T, m65: T ) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<1>, Const<1>, ArrayStorage<T, 1, 1>>

source

#### pub const fn new(x: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<1>, Const<2>, ArrayStorage<T, 1, 2>>

source

#### pub const fn new(x: T, y: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<1>, Const<3>, ArrayStorage<T, 1, 3>>

source

#### pub const fn new(x: T, y: T, z: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<1>, Const<4>, ArrayStorage<T, 1, 4>>

source

#### pub const fn new(x: T, y: T, z: T, w: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<1>, Const<5>, ArrayStorage<T, 1, 5>>

source

#### pub const fn new(x: T, y: T, z: T, w: T, a: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<1>, Const<6>, ArrayStorage<T, 1, 6>>

source

#### pub const fn new(x: T, y: T, z: T, w: T, a: T, b: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<2>, Const<1>, ArrayStorage<T, 2, 1>>

source

#### pub const fn new(x: T, y: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>

source

#### pub const fn new(x: T, y: T, z: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<4>, Const<1>, ArrayStorage<T, 4, 1>>

source

#### pub const fn new(x: T, y: T, z: T, w: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<5>, Const<1>, ArrayStorage<T, 5, 1>>

source

#### pub const fn new(x: T, y: T, z: T, w: T, a: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T> Matrix<T, Const<6>, Const<1>, ArrayStorage<T, 6, 1>>

source

#### pub const fn new(x: T, y: T, z: T, w: T, a: T, b: T) -> Self

Initializes this matrix from its components.

sourceยง

### impl<T, R> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<T, R>>::Buffer>where R: ToTypenum + DimName, T: Scalar + Zero + One, DefaultAllocator: Allocator<T, R>,

source

#### pub fn ith(i: usize, val: T) -> Self

The column vector with `val` as its i-th component.

source

#### pub fn ith_axis(i: usize) -> Unit<Self>

The column unit vector with `T::one()` as its i-th component.

source

#### pub fn x() -> Selfwhere R::Typenum: Cmp<U0, Output = Greater>,

The column vector with a 1 as its first component, and zero elsewhere.

source

#### pub fn y() -> Selfwhere R::Typenum: Cmp<U1, Output = Greater>,

The column vector with a 1 as its second component, and zero elsewhere.

source

#### pub fn z() -> Selfwhere R::Typenum: Cmp<U2, Output = Greater>,

The column vector with a 1 as its third component, and zero elsewhere.

source

#### pub fn w() -> Selfwhere R::Typenum: Cmp<U3, Output = Greater>,

The column vector with a 1 as its fourth component, and zero elsewhere.

source

#### pub fn a() -> Selfwhere R::Typenum: Cmp<U4, Output = Greater>,

The column vector with a 1 as its fifth component, and zero elsewhere.

source

#### pub fn b() -> Selfwhere R::Typenum: Cmp<U5, Output = Greater>,

The column vector with a 1 as its sixth component, and zero elsewhere.

source

#### pub fn x_axis() -> Unit<Self>where R::Typenum: Cmp<U0, Output = Greater>,

The unit column vector with a 1 as its first component, and zero elsewhere.

source

#### pub fn y_axis() -> Unit<Self>where R::Typenum: Cmp<U1, Output = Greater>,

The unit column vector with a 1 as its second component, and zero elsewhere.

source

#### pub fn z_axis() -> Unit<Self>where R::Typenum: Cmp<U2, Output = Greater>,

The unit column vector with a 1 as its third component, and zero elsewhere.

source

#### pub fn w_axis() -> Unit<Self>where R::Typenum: Cmp<U3, Output = Greater>,

The unit column vector with a 1 as its fourth component, and zero elsewhere.

source

#### pub fn a_axis() -> Unit<Self>where R::Typenum: Cmp<U4, Output = Greater>,

The unit column vector with a 1 as its fifth component, and zero elsewhere.

source

#### pub fn b_axis() -> Unit<Self>where R::Typenum: Cmp<U5, Output = Greater>,

The unit column vector with a 1 as its sixth component, and zero elsewhere.

sourceยง

### impl<'a, T: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> Matrix<T, R, C, ViewStorage<'a, T, R, C, RStride, CStride>>

source

#### pub unsafe fn from_slice_with_strides_generic_unchecked( data: &'a [T], start: usize, nrows: R, ncols: C, rstride: RStride, cstride: CStride ) -> Self

Creates, without bounds checking, a matrix view from an array and with dimensions and strides specified by generic types instances.

##### ยงSafety

This method is unsafe because the input data array is not checked to contain enough elements. The generic types `R`, `C`, `RStride`, `CStride` can either be type-level integers or integers wrapped with `Dyn()`.

source

#### pub fn from_slice_with_strides_generic( data: &'a [T], nrows: R, ncols: C, rstride: RStride, cstride: CStride ) -> Self

Creates a matrix view from an array and with dimensions and strides specified by generic types instances.

Panics if the input data array dose not contain enough elements. The generic types `R`, `C`, `RStride`, `CStride` can either be type-level integers or integers wrapped with `Dyn()`.

sourceยง

### impl<'a, T: Scalar, R: Dim, C: Dim> Matrix<T, R, C, ViewStorage<'a, T, R, C, Const<1>, R>>

source

#### pub unsafe fn from_slice_generic_unchecked( data: &'a [T], start: usize, nrows: R, ncols: C ) -> Self

Creates, without bound-checking, a matrix view from an array and with dimensions specified by generic types instances.

##### ยงSafety

This method is unsafe because the input data array is not checked to contain enough elements. The generic types `R` and `C` can either be type-level integers or integers wrapped with `Dyn()`.

source

#### pub fn from_slice_generic(data: &'a [T], nrows: R, ncols: C) -> Self

Creates a matrix view from an array and with dimensions and strides specified by generic types instances.

Panics if the input data array dose not contain enough elements. The generic types `R` and `C` can either be type-level integers or integers wrapped with `Dyn()`.

sourceยง

### impl<'a, T: Scalar, R: DimName, C: DimName> Matrix<T, R, C, ViewStorage<'a, T, R, C, Const<1>, R>>

source

#### pub fn from_slice(data: &'a [T]) -> Self

Creates a new matrix view from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_unchecked(data: &'a [T], start: usize) -> Self

Creates, without bound checking, a new matrix view from the given data array.

sourceยง

### impl<'a, T: Scalar, R: DimName, C: DimName> Matrix<T, R, C, ViewStorage<'a, T, R, C, Dyn, Dyn>>

source

#### pub fn from_slice_with_strides( data: &'a [T], rstride: usize, cstride: usize ) -> Self

Creates a new matrix view with the specified strides from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_with_strides_unchecked( data: &'a [T], start: usize, rstride: usize, cstride: usize ) -> Self

Creates, without bound checking, a new matrix view with the specified strides from the given data array.

sourceยง

### impl<'a, T: Scalar, R: DimName> Matrix<T, R, Dyn, ViewStorage<'a, T, R, Dyn, Const<1>, R>>

source

#### pub fn from_slice(data: &'a [T], ncols: usize) -> Self

Creates a new matrix view from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_unchecked( data: &'a [T], start: usize, ncols: usize ) -> Self

Creates, without bound checking, a new matrix view from the given data array.

sourceยง

### impl<'a, T: Scalar, R: DimName> Matrix<T, R, Dyn, ViewStorage<'a, T, R, Dyn, Dyn, Dyn>>

source

#### pub fn from_slice_with_strides( data: &'a [T], ncols: usize, rstride: usize, cstride: usize ) -> Self

Creates a new matrix view with the specified strides from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_with_strides_unchecked( data: &'a [T], start: usize, ncols: usize, rstride: usize, cstride: usize ) -> Self

Creates, without bound checking, a new matrix view with the specified strides from the given data array.

sourceยง

### impl<'a, T: Scalar, C: DimName> Matrix<T, Dyn, C, ViewStorage<'a, T, Dyn, C, Const<1>, Dyn>>

source

#### pub fn from_slice(data: &'a [T], nrows: usize) -> Self

Creates a new matrix view from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_unchecked( data: &'a [T], start: usize, nrows: usize ) -> Self

Creates, without bound checking, a new matrix view from the given data array.

sourceยง

### impl<'a, T: Scalar, C: DimName> Matrix<T, Dyn, C, ViewStorage<'a, T, Dyn, C, Dyn, Dyn>>

source

#### pub fn from_slice_with_strides( data: &'a [T], nrows: usize, rstride: usize, cstride: usize ) -> Self

Creates a new matrix view with the specified strides from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_with_strides_unchecked( data: &'a [T], start: usize, nrows: usize, rstride: usize, cstride: usize ) -> Self

Creates, without bound checking, a new matrix view with the specified strides from the given data array.

sourceยง

### impl<'a, T: Scalar> Matrix<T, Dyn, Dyn, ViewStorage<'a, T, Dyn, Dyn, Const<1>, Dyn>>

source

#### pub fn from_slice(data: &'a [T], nrows: usize, ncols: usize) -> Self

Creates a new matrix view from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_unchecked( data: &'a [T], start: usize, nrows: usize, ncols: usize ) -> Self

Creates, without bound checking, a new matrix view from the given data array.

sourceยง

### impl<'a, T: Scalar> Matrix<T, Dyn, Dyn, ViewStorage<'a, T, Dyn, Dyn, Dyn, Dyn>>

source

#### pub fn from_slice_with_strides( data: &'a [T], nrows: usize, ncols: usize, rstride: usize, cstride: usize ) -> Self

Creates a new matrix view with the specified strides from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_with_strides_unchecked( data: &'a [T], start: usize, nrows: usize, ncols: usize, rstride: usize, cstride: usize ) -> Self

Creates, without bound checking, a new matrix view with the specified strides from the given data array.

sourceยง

### impl<'a, T: Scalar, R: Dim, C: Dim, RStride: Dim, CStride: Dim> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, RStride, CStride>>

source

#### pub unsafe fn from_slice_with_strides_generic_unchecked( data: &'a mut [T], start: usize, nrows: R, ncols: C, rstride: RStride, cstride: CStride ) -> Self

Creates, without bound-checking, a mutable matrix view from an array and with dimensions and strides specified by generic types instances.

##### ยงSafety

This method is unsafe because the input data array is not checked to contain enough elements. The generic types `R`, `C`, `RStride`, `CStride` can either be type-level integers or integers wrapped with `Dyn()`.

source

#### pub fn from_slice_with_strides_generic( data: &'a mut [T], nrows: R, ncols: C, rstride: RStride, cstride: CStride ) -> Self

Creates a mutable matrix view from an array and with dimensions and strides specified by generic types instances.

Panics if the input data array dose not contain enough elements. The generic types `R`, `C`, `RStride`, `CStride` can either be type-level integers or integers wrapped with `Dyn()`.

sourceยง

### impl<'a, T: Scalar, R: Dim, C: Dim> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Const<1>, R>>

source

#### pub unsafe fn from_slice_generic_unchecked( data: &'a mut [T], start: usize, nrows: R, ncols: C ) -> Self

Creates, without bound-checking, a mutable matrix view from an array and with dimensions specified by generic types instances.

##### ยงSafety

This method is unsafe because the input data array is not checked to contain enough elements. The generic types `R` and `C` can either be type-level integers or integers wrapped with `Dyn()`.

source

#### pub fn from_slice_generic(data: &'a mut [T], nrows: R, ncols: C) -> Self

Creates a mutable matrix view from an array and with dimensions and strides specified by generic types instances.

Panics if the input data array dose not contain enough elements. The generic types `R` and `C` can either be type-level integers or integers wrapped with `Dyn()`.

sourceยง

### impl<'a, T: Scalar, R: DimName, C: DimName> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Const<1>, R>>

source

#### pub fn from_slice(data: &'a mut [T]) -> Self

Creates a new mutable matrix view from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_unchecked(data: &'a mut [T], start: usize) -> Self

Creates, without bound checking, a new mutable matrix view from the given data array.

sourceยง

### impl<'a, T: Scalar, R: DimName, C: DimName> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Dyn, Dyn>>

source

#### pub fn from_slice_with_strides_mut( data: &'a mut [T], rstride: usize, cstride: usize ) -> Self

Creates a new mutable matrix view with the specified strides from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_with_strides_unchecked( data: &'a mut [T], start: usize, rstride: usize, cstride: usize ) -> Self

Creates, without bound checking, a new mutable matrix view with the specified strides from the given data array.

sourceยง

### impl<'a, T: Scalar, R: DimName> Matrix<T, R, Dyn, ViewStorageMut<'a, T, R, Dyn, Const<1>, R>>

source

#### pub fn from_slice(data: &'a mut [T], ncols: usize) -> Self

Creates a new mutable matrix view from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_unchecked( data: &'a mut [T], start: usize, ncols: usize ) -> Self

Creates, without bound checking, a new mutable matrix view from the given data array.

sourceยง

### impl<'a, T: Scalar, R: DimName> Matrix<T, R, Dyn, ViewStorageMut<'a, T, R, Dyn, Dyn, Dyn>>

source

#### pub fn from_slice_with_strides_mut( data: &'a mut [T], ncols: usize, rstride: usize, cstride: usize ) -> Self

Creates a new mutable matrix view with the specified strides from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_with_strides_unchecked( data: &'a mut [T], start: usize, ncols: usize, rstride: usize, cstride: usize ) -> Self

Creates, without bound checking, a new mutable matrix view with the specified strides from the given data array.

sourceยง

### impl<'a, T: Scalar, C: DimName> Matrix<T, Dyn, C, ViewStorageMut<'a, T, Dyn, C, Const<1>, Dyn>>

source

#### pub fn from_slice(data: &'a mut [T], nrows: usize) -> Self

Creates a new mutable matrix view from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_unchecked( data: &'a mut [T], start: usize, nrows: usize ) -> Self

Creates, without bound checking, a new mutable matrix view from the given data array.

sourceยง

### impl<'a, T: Scalar, C: DimName> Matrix<T, Dyn, C, ViewStorageMut<'a, T, Dyn, C, Dyn, Dyn>>

source

#### pub fn from_slice_with_strides_mut( data: &'a mut [T], nrows: usize, rstride: usize, cstride: usize ) -> Self

Creates a new mutable matrix view with the specified strides from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_with_strides_unchecked( data: &'a mut [T], start: usize, nrows: usize, rstride: usize, cstride: usize ) -> Self

Creates, without bound checking, a new mutable matrix view with the specified strides from the given data array.

sourceยง

### impl<'a, T: Scalar> Matrix<T, Dyn, Dyn, ViewStorageMut<'a, T, Dyn, Dyn, Const<1>, Dyn>>

source

#### pub fn from_slice(data: &'a mut [T], nrows: usize, ncols: usize) -> Self

Creates a new mutable matrix view from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_unchecked( data: &'a mut [T], start: usize, nrows: usize, ncols: usize ) -> Self

Creates, without bound checking, a new mutable matrix view from the given data array.

sourceยง

### impl<'a, T: Scalar> Matrix<T, Dyn, Dyn, ViewStorageMut<'a, T, Dyn, Dyn, Dyn, Dyn>>

source

#### pub fn from_slice_with_strides_mut( data: &'a mut [T], nrows: usize, ncols: usize, rstride: usize, cstride: usize ) -> Self

Creates a new mutable matrix view with the specified strides from the given data array.

Panics if `data` does not contain enough elements.

source

#### pub unsafe fn from_slice_with_strides_unchecked( data: &'a mut [T], start: usize, nrows: usize, ncols: usize, rstride: usize, cstride: usize ) -> Self

Creates, without bound checking, a new mutable matrix view with the specified strides from the given data array.

sourceยง

### impl<T: Scalar + Zero, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn upper_triangle(&self) -> OMatrix<T, R, C>where DefaultAllocator: Allocator<T, R, C>,

Extracts the upper triangular part of this matrix (including the diagonal).

source

#### pub fn lower_triangle(&self) -> OMatrix<T, R, C>where DefaultAllocator: Allocator<T, R, C>,

Extracts the lower triangular part of this matrix (including the diagonal).

sourceยง

### impl<T: Scalar, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn select_rows<'a, I>(&self, irows: I) -> OMatrix<T, Dyn, C>where I: IntoIterator<Item = &'a usize>, I::IntoIter: ExactSizeIterator + Clone, DefaultAllocator: Allocator<T, Dyn, C>,

Creates a new matrix by extracting the given set of rows from `self`.

source

#### pub fn select_columns<'a, I>(&self, icols: I) -> OMatrix<T, R, Dyn>where I: IntoIterator<Item = &'a usize>, I::IntoIter: ExactSizeIterator, DefaultAllocator: Allocator<T, R, Dyn>,

Creates a new matrix by extracting the given set of columns from `self`.

sourceยง

### impl<T: Scalar, R: Dim, C: Dim, S: RawStorageMut<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn set_diagonal<R2: Dim, S2>(&mut self, diag: &Vector<T, R2, S2>)where R: DimMin<C>, S2: RawStorage<T, R2>, ShapeConstraint: DimEq<DimMinimum<R, C>, R2>,

Fills the diagonal of this matrix with the content of the given vector.

source

#### pub fn set_partial_diagonal(&mut self, diag: impl Iterator<Item = T>)

Fills the diagonal of this matrix with the content of the given iterator.

This will fill as many diagonal elements as the iterator yields, up to the minimum of the number of rows and columns of `self`, and starting with the diagonal element at index (0, 0).

source

#### pub fn set_row<C2: Dim, S2>(&mut self, i: usize, row: &RowVector<T, C2, S2>)where S2: RawStorage<T, U1, C2>, ShapeConstraint: SameNumberOfColumns<C, C2>,

Fills the selected row of this matrix with the content of the given vector.

source

#### pub fn set_column<R2: Dim, S2>(&mut self, i: usize, column: &Vector<T, R2, S2>)where S2: RawStorage<T, R2, U1>, ShapeConstraint: SameNumberOfRows<R, R2>,

Fills the selected column of this matrix with the content of the given vector.

sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorageMut<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn fill_with(&mut self, val: impl Fn() -> T)

Sets all the elements of this matrix to the value returned by the closure.

source

#### pub fn fill(&mut self, val: T)where T: Scalar,

Sets all the elements of this matrix to `val`.

source

#### pub fn fill_with_identity(&mut self)where T: Scalar + Zero + One,

Fills `self` with the identity matrix.

source

#### pub fn fill_diagonal(&mut self, val: T)where T: Scalar,

Sets all the diagonal elements of this matrix to `val`.

source

#### pub fn fill_row(&mut self, i: usize, val: T)where T: Scalar,

Sets all the elements of the selected row to `val`.

source

#### pub fn fill_column(&mut self, j: usize, val: T)where T: Scalar,

Sets all the elements of the selected column to `val`.

source

#### pub fn fill_lower_triangle(&mut self, val: T, shift: usize)where T: Scalar,

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.
source

#### pub fn fill_upper_triangle(&mut self, val: T, shift: usize)where T: Scalar,

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.
sourceยง

### impl<T: Scalar, D: Dim, S: RawStorageMut<T, D, D>> Matrix<T, D, D, S>

source

#### pub fn fill_lower_triangle_with_upper_triangle(&mut self)

Copies the upper-triangle of this matrix to its lower-triangular part.

This makes the matrix symmetric. Panics if the matrix is not square.

source

#### pub fn fill_upper_triangle_with_lower_triangle(&mut self)

Copies the upper-triangle of this matrix to its upper-triangular part.

This makes the matrix symmetric. Panics if the matrix is not square.

sourceยง

### impl<T: Scalar, R: Dim, C: Dim, S: RawStorageMut<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn swap_rows(&mut self, irow1: usize, irow2: usize)

Swaps two rows in-place.

source

#### pub fn swap_columns(&mut self, icol1: usize, icol2: usize)

Swaps two columns in-place.

sourceยง

### impl<T: Scalar, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn remove_column(self, i: usize) -> OMatrix<T, R, DimDiff<C, U1>>where C: DimSub<U1>, DefaultAllocator: Reallocator<T, R, C, R, DimDiff<C, U1>>,

Removes the `i`-th column from this matrix.

source

#### pub fn remove_columns_at(self, indices: &[usize]) -> OMatrix<T, R, Dyn>where C: DimSub<Dyn, Output = Dyn>, DefaultAllocator: Reallocator<T, R, C, R, Dyn>,

Removes all columns in `indices`

source

#### pub fn remove_rows_at(self, indices: &[usize]) -> OMatrix<T, Dyn, C>where R: DimSub<Dyn, Output = Dyn>, DefaultAllocator: Reallocator<T, R, C, Dyn, C>,

Removes all rows in `indices`

source

#### pub fn remove_fixed_columns<const D: usize>( self, i: usize ) -> OMatrix<T, R, DimDiff<C, Const<D>>>where C: DimSub<Const<D>>, DefaultAllocator: Reallocator<T, R, C, R, DimDiff<C, Const<D>>>,

Removes `D::dim()` consecutive columns from this matrix, starting with the `i`-th (included).

source

#### pub fn remove_columns(self, i: usize, n: usize) -> OMatrix<T, R, Dyn>where C: DimSub<Dyn, Output = Dyn>, DefaultAllocator: Reallocator<T, R, C, R, Dyn>,

Removes `n` consecutive columns from this matrix, starting with the `i`-th (included).

source

#### pub fn remove_columns_generic<D>( self, i: usize, nremove: D ) -> OMatrix<T, R, DimDiff<C, D>>where D: Dim, C: DimSub<D>, DefaultAllocator: Reallocator<T, R, C, R, DimDiff<C, D>>,

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.

source

#### pub fn remove_row(self, i: usize) -> OMatrix<T, DimDiff<R, U1>, C>where R: DimSub<U1>, DefaultAllocator: Reallocator<T, R, C, DimDiff<R, U1>, C>,

Removes the `i`-th row from this matrix.

source

#### pub fn remove_fixed_rows<const D: usize>( self, i: usize ) -> OMatrix<T, DimDiff<R, Const<D>>, C>where R: DimSub<Const<D>>, DefaultAllocator: Reallocator<T, R, C, DimDiff<R, Const<D>>, C>,

Removes `D::dim()` consecutive rows from this matrix, starting with the `i`-th (included).

source

#### pub fn remove_rows(self, i: usize, n: usize) -> OMatrix<T, Dyn, C>where R: DimSub<Dyn, Output = Dyn>, DefaultAllocator: Reallocator<T, R, C, Dyn, C>,

Removes `n` consecutive rows from this matrix, starting with the `i`-th (included).

source

#### pub fn remove_rows_generic<D>( self, i: usize, nremove: D ) -> OMatrix<T, DimDiff<R, D>, C>where D: Dim, R: DimSub<D>, DefaultAllocator: Reallocator<T, R, C, DimDiff<R, D>, C>,

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.

sourceยง

### impl<T: Scalar, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn insert_column(self, i: usize, val: T) -> OMatrix<T, R, DimSum<C, U1>>where C: DimAdd<U1>, DefaultAllocator: Reallocator<T, R, C, R, DimSum<C, U1>>,

Inserts a column filled with `val` at the `i-th` position.

source

#### pub fn insert_fixed_columns<const D: usize>( self, i: usize, val: T ) -> OMatrix<T, R, DimSum<C, Const<D>>>where C: DimAdd<Const<D>>, DefaultAllocator: Reallocator<T, R, C, R, DimSum<C, Const<D>>>,

Inserts `D` columns filled with `val` starting at the `i-th` position.

source

#### pub fn insert_columns(self, i: usize, n: usize, val: T) -> OMatrix<T, R, Dyn>where C: DimAdd<Dyn, Output = Dyn>, DefaultAllocator: Reallocator<T, R, C, R, Dyn>,

Inserts `n` columns filled with `val` starting at the `i-th` position.

source

#### pub unsafe fn insert_columns_generic_uninitialized<D>( self, i: usize, ninsert: D ) -> UninitMatrix<T, R, DimSum<C, D>>where D: Dim, C: DimAdd<D>, DefaultAllocator: Reallocator<T, R, C, R, DimSum<C, D>>,

Inserts `ninsert.value()` columns starting at the `i-th` place of this matrix.

##### ยงSafety

The output matrix has all its elements initialized except for the the components of the added columns.

source

#### pub fn insert_row(self, i: usize, val: T) -> OMatrix<T, DimSum<R, U1>, C>where R: DimAdd<U1>, DefaultAllocator: Reallocator<T, R, C, DimSum<R, U1>, C>,

Inserts a row filled with `val` at the `i-th` position.

source

#### pub fn insert_fixed_rows<const D: usize>( self, i: usize, val: T ) -> OMatrix<T, DimSum<R, Const<D>>, C>where R: DimAdd<Const<D>>, DefaultAllocator: Reallocator<T, R, C, DimSum<R, Const<D>>, C>,

Inserts `D::dim()` rows filled with `val` starting at the `i-th` position.

source

#### pub fn insert_rows(self, i: usize, n: usize, val: T) -> OMatrix<T, Dyn, C>where R: DimAdd<Dyn, Output = Dyn>, DefaultAllocator: Reallocator<T, R, C, Dyn, C>,

Inserts `n` rows filled with `val` starting at the `i-th` position.

source

#### pub unsafe fn insert_rows_generic_uninitialized<D>( self, i: usize, ninsert: D ) -> UninitMatrix<T, DimSum<R, D>, C>where D: Dim, R: DimAdd<D>, DefaultAllocator: Reallocator<T, R, C, DimSum<R, D>, C>,

Inserts `ninsert.value()` rows at the `i-th` place of this matrix.

##### ยงSafety

The added rows values are not initialized. This is the generic implementation of `.insert_rows(...)` and `.insert_fixed_rows(...)` which have nicer API interfaces.

sourceยง

### impl<T: Scalar, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn resize( self, new_nrows: usize, new_ncols: usize, val: T ) -> OMatrix<T, Dyn, Dyn>where DefaultAllocator: Reallocator<T, R, C, Dyn, Dyn>,

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`.

source

#### pub fn resize_vertically(self, new_nrows: usize, val: T) -> OMatrix<T, Dyn, C>where DefaultAllocator: Reallocator<T, R, C, Dyn, C>,

Resizes this matrix vertically, i.e., so that it contains `new_nrows` rows while keeping the same number of columns.

The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more rows than `self`, then the extra rows are filled with `val`.

source

#### pub fn resize_horizontally(self, new_ncols: usize, val: T) -> OMatrix<T, R, Dyn>where DefaultAllocator: Reallocator<T, R, C, R, Dyn>,

Resizes this matrix horizontally, i.e., so that it contains `new_ncolumns` columns while keeping the same number of columns.

The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more columns than `self`, then the extra columns are filled with `val`.

source

#### pub fn fixed_resize<const R2: usize, const C2: usize>( self, val: T ) -> OMatrix<T, Const<R2>, Const<C2>>where DefaultAllocator: Reallocator<T, R, C, Const<R2>, Const<C2>>,

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`.

source

#### pub fn resize_generic<R2: Dim, C2: Dim>( self, new_nrows: R2, new_ncols: C2, val: T ) -> OMatrix<T, R2, C2>where DefaultAllocator: Reallocator<T, R, C, R2, C2>,

Resizes `self` such that it has dimensions `new_nrows ร new_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`.

source

#### pub fn reshape_generic<R2, C2>( self, new_nrows: R2, new_ncols: C2 ) -> Matrix<T, R2, C2, S::Output>where R2: Dim, C2: Dim, S: ReshapableStorage<T, R, C, R2, C2>,

Reshapes `self` such that it has dimensions `new_nrows ร new_ncols`.

This will reinterpret `self` as if it is a matrix with `new_nrows` rows and `new_ncols` columns. The arrangements of the component in the output matrix are the same as what would be obtained by `Matrix::from_slice_generic(self.as_slice(), new_nrows, new_ncols)`.

If `self` is a dynamically-sized matrix, then its components are neither copied nor moved. If `self` is staticyll-sized, then a copy may happen in some situations. This function will panic if the given dimensions are such that the number of elements of the input matrix are not equal to the number of elements of the output matrix.

##### ยงExamples
``````
let m1 = Matrix2x3::new(
1.1, 1.2, 1.3,
2.1, 2.2, 2.3
);
let m2 = Matrix3x2::new(
1.1, 2.2,
2.1, 1.3,
1.2, 2.3
);
let reshaped = m1.reshape_generic(Const::<3>, Const::<2>);
assert_eq!(reshaped, m2);

let dm1 = DMatrix::from_row_slice(
4,
3,
&[
1.0, 0.0, 0.0,
0.0, 0.0, 1.0,
0.0, 0.0, 0.0,
0.0, 1.0, 0.0
],
);
let dm2 = DMatrix::from_row_slice(
6,
2,
&[
1.0, 0.0,
0.0, 1.0,
0.0, 0.0,
0.0, 1.0,
0.0, 0.0,
0.0, 0.0,
],
);
let reshaped = dm1.reshape_generic(Dyn(6), Dyn(2));
assert_eq!(reshaped, dm2);``````
sourceยง

### impl<T: Scalar> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<T, Dyn, Dyn>>::Buffer>

source

#### pub fn resize_mut(&mut self, new_nrows: usize, new_ncols: usize, val: T)where DefaultAllocator: Reallocator<T, Dyn, Dyn, Dyn, Dyn>,

Resizes this matrix in-place.

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`.

Defined only for owned fully-dynamic matrices, i.e., `DMatrix`.

sourceยง

### impl<T: Scalar, C: Dim> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<T, Dyn, C>>::Buffer>where DefaultAllocator: Allocator<T, Dyn, C>,

source

#### pub fn resize_vertically_mut(&mut self, new_nrows: usize, val: T)where DefaultAllocator: Reallocator<T, Dyn, C, Dyn, C>,

Changes the number of rows of this matrix in-place.

The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more rows than `self`, then the extra rows are filled with `val`.

Defined only for owned matrices with a dynamic number of rows (for example, `DVector`).

sourceยง

### impl<T: Scalar, R: Dim> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<T, R, Dyn>>::Buffer>where DefaultAllocator: Allocator<T, R, Dyn>,

source

#### pub fn resize_horizontally_mut(&mut self, new_ncols: usize, val: T)where DefaultAllocator: Reallocator<T, R, Dyn, R, Dyn>,

Changes the number of column of this matrix in-place.

The values are copied such that `self[(i, j)] == result[(i, j)]`. If the result has more columns than `self`, then the extra columns are filled with `val`.

Defined only for owned matrices with a dynamic number of columns (for example, `DVector`).

sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>

#### ยงViews based on ranges

##### ยงIndices to Individual Elements
###### ยงTwo-Dimensional Indices
``````let matrix = Matrix2::new(0, 2,
1, 3);

assert_eq!(matrix.index((0, 0)), &0);
assert_eq!(matrix.index((1, 0)), &1);
assert_eq!(matrix.index((0, 1)), &2);
assert_eq!(matrix.index((1, 1)), &3);``````
``````let matrix = Matrix2::new(0, 2,
1, 3);

assert_eq!(matrix.get(0), Some(&0));
assert_eq!(matrix.get(1), Some(&1));
assert_eq!(matrix.get(2), Some(&2));
assert_eq!(matrix.get(3), Some(&3));``````
##### ยงIndices to Individual Rows and Columns
###### ยงIndex to a Row
``````let matrix = Matrix2::new(0, 2,
1, 3);

assert!(matrix.index((0, ..))
.eq(&Matrix1x2::new(0, 2)));``````
###### ยงIndex to a Column
``````let matrix = Matrix2::new(0, 2,
1, 3);

assert!(matrix.index((.., 0))
.eq(&Matrix2x1::new(0,
1)));``````
##### ยงIndices to Parts of Individual Rows and Columns
###### ยงIndex to a Partial Row
``````let matrix = Matrix3::new(0, 3, 6,
1, 4, 7,
2, 5, 8);

assert!(matrix.index((0, ..2))
.eq(&Matrix1x2::new(0, 3)));``````
###### ยงIndex to a Partial Column
``````let matrix = Matrix3::new(0, 3, 6,
1, 4, 7,
2, 5, 8);

assert!(matrix.index((..2, 0))
.eq(&Matrix2x1::new(0,
1)));

assert!(matrix.index((Const::<1>.., 0))
.eq(&Matrix2x1::new(1,
2)));``````
##### ยงIndices to Ranges of Rows and Columns
###### ยงIndex to a Range of Rows
``````let matrix = Matrix3::new(0, 3, 6,
1, 4, 7,
2, 5, 8);

assert!(matrix.index((1..3, ..))
.eq(&Matrix2x3::new(1, 4, 7,
2, 5, 8)));``````
###### ยงIndex to a Range of Columns
``````let matrix = Matrix3::new(0, 3, 6,
1, 4, 7,
2, 5, 8);

assert!(matrix.index((.., 1..3))
.eq(&Matrix3x2::new(3, 6,
4, 7,
5, 8)));``````
source

#### pub fn get<'a, I>(&'a self, index: I) -> Option<I::Output>where I: MatrixIndex<'a, T, R, C, S>,

Produces a view of the data at the given index, or `None` if the index is out of bounds.

source

#### pub fn get_mut<'a, I>(&'a mut self, index: I) -> Option<I::OutputMut>where S: RawStorageMut<T, R, C>, I: MatrixIndexMut<'a, T, R, C, S>,

Produces a mutable view of the data at the given index, or `None` if the index is out of bounds.

source

#### pub fn index<'a, I>(&'a self, index: I) -> I::Outputwhere I: MatrixIndex<'a, T, R, C, S>,

Produces a view of the data at the given index, or panics if the index is out of bounds.

source

#### pub fn index_mut<'a, I>(&'a mut self, index: I) -> I::OutputMutwhere S: RawStorageMut<T, R, C>, I: MatrixIndexMut<'a, T, R, C, S>,

Produces a mutable view of the data at the given index, or panics if the index is out of bounds.

source

#### pub unsafe fn get_unchecked<'a, I>(&'a self, index: I) -> I::Outputwhere I: MatrixIndex<'a, T, R, C, S>,

Produces a view of the data at the given index, without doing any bounds checking.

source

#### pub unsafe fn get_unchecked_mut<'a, I>(&'a mut self, index: I) -> I::OutputMutwhere S: RawStorageMut<T, R, C>, I: MatrixIndexMut<'a, T, R, C, S>,

Returns a mutable view of the data at the given index, without doing any bounds checking.

sourceยง

### impl<T, R, C, S> Matrix<T, R, C, S>

source

#### pub const unsafe fn from_data_statically_unchecked( data: 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.

sourceยง

### impl<T, const R: usize, const C: usize> Matrix<T, Const<R>, Const<C>, ArrayStorage<T, R, C>>

source

#### pub const fn from_array_storage(storage: ArrayStorage<T, R, C>) -> Self

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.

sourceยง

### impl<T> Matrix<T, Dyn, Dyn, VecStorage<T, Dyn, Dyn>>

source

#### pub const fn from_vec_storage(storage: VecStorage<T, Dyn, Dyn>) -> Self

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.

sourceยง

### impl<T> Matrix<T, Dyn, Const<1>, VecStorage<T, Dyn, Const<1>>>

source

#### pub const fn from_vec_storage(storage: VecStorage<T, Dyn, U1>) -> Self

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.

sourceยง

### impl<T> Matrix<T, Const<1>, Dyn, VecStorage<T, Const<1>, Dyn>>

source

#### pub const fn from_vec_storage(storage: VecStorage<T, U1, Dyn>) -> Self

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.

sourceยง

### impl<T, R: Dim, C: Dim> Matrix<MaybeUninit<T>, R, C, <DefaultAllocator as Allocator<T, R, C>>::BufferUninit>where DefaultAllocator: Allocator<T, R, C>,

source

#### pub unsafe fn assume_init(self) -> OMatrix<T, R, C>

Assumes a matrixโs entries to be initialized. This operation should be near zero-cost.

##### ยงSafety

The user must make sure that every single entry of the buffer has been initialized, or Undefined Behavior will immediately occur.

sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn from_data(data: S) -> Self

Creates a new matrix with the given data.

source

#### pub fn shape(&self) -> (usize, usize)

The shape of this matrix returned as the tuple (number of rows, number of columns).

##### ยงExample
``````let mat = Matrix3x4::<f32>::zeros();
assert_eq!(mat.shape(), (3, 4));``````
source

#### pub fn shape_generic(&self) -> (R, C)

The shape of this matrix wrapped into their representative types (`Const` or `Dyn`).

source

#### pub fn nrows(&self) -> usize

The number of rows of this matrix.

##### ยงExample
``````let mat = Matrix3x4::<f32>::zeros();
assert_eq!(mat.nrows(), 3);``````
source

#### pub fn ncols(&self) -> usize

The number of columns of this matrix.

##### ยงExample
``````let mat = Matrix3x4::<f32>::zeros();
assert_eq!(mat.ncols(), 4);``````
source

#### pub fn strides(&self) -> (usize, usize)

The strides (row stride, column stride) of this matrix.

##### ยงExample
``````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));``````
source

#### pub fn vector_to_matrix_index(&self, i: usize) -> (usize, usize)

Computes the row and column coordinates of the i-th element of this matrix seen as a vector.

##### ยงExample
``````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]);``````
source

#### pub fn as_ptr(&self) -> *const T

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.

##### ยงExample
``````let m = Matrix2::new(1, 2,
3, 4);
let ptr = m.as_ptr();
assert_eq!(unsafe { *ptr }, m[0]);``````
source

#### pub fn relative_eq<R2, C2, SB>( &self, other: &Matrix<T, R2, C2, SB>, eps: T::Epsilon, max_relative: T::Epsilon ) -> boolwhere T: RelativeEq, R2: Dim, C2: Dim, SB: Storage<T, R2, C2>, T::Epsilon: Clone, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

Tests whether `self` and `rhs` are equal up to a given epsilon.

See `relative_eq` from the `RelativeEq` trait for more details.

source

#### pub fn eq<R2, C2, SB>(&self, other: &Matrix<T, R2, C2, SB>) -> boolwhere T: PartialEq, R2: Dim, C2: Dim, SB: RawStorage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

Tests whether `self` and `rhs` are exactly equal.

source

#### pub fn into_owned(self) -> OMatrix<T, R, C>where T: Scalar, S: Storage<T, R, C>, DefaultAllocator: Allocator<T, R, C>,

Moves this matrix into one that owns its data.

source

#### 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<T, R, C, R2, C2>, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

Moves this matrix into one that owns its data. The actual type of the result depends on matrix storage combination rules for addition.

source

#### pub fn clone_owned(&self) -> OMatrix<T, R, C>where T: Scalar, S: Storage<T, R, C>, DefaultAllocator: Allocator<T, R, C>,

Clones this matrix to one that owns its data.

source

#### 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<T, R, C, R2, C2>, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

Clones this matrix into one that owns its data. The actual type of the result depends on matrix storage combination rules for addition.

source

#### 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>,

Transposes `self` and store the result into `out`.

source

#### pub fn transpose(&self) -> OMatrix<T, C, R>where T: Scalar, DefaultAllocator: Allocator<T, C, R>,

Transposes `self`.

sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn map<T2: Scalar, F: FnMut(T) -> T2>(&self, f: F) -> OMatrix<T2, R, C>where T: Scalar, DefaultAllocator: Allocator<T2, R, C>,

Returns a matrix containing the result of `f` applied to each of its entries.

source

#### pub fn cast<T2: Scalar>(self) -> OMatrix<T2, R, C>where T: Scalar, OMatrix<T2, R, C>: SupersetOf<Self>, DefaultAllocator: Allocator<T2, R, C>,

Cast the components of `self` to another type.

##### ยงExample
``````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));``````
source

#### pub fn try_cast<T2: Scalar>(self) -> Option<OMatrix<T2, R, C>>where T: Scalar, Self: SupersetOf<OMatrix<T2, R, C>>, DefaultAllocator: Allocator<T2, R, C>,

Attempts to cast the components of `self` to another type.

##### ยงExample
``````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)));``````
source

#### pub fn fold_with<T2>( &self, init_f: impl FnOnce(Option<&T>) -> T2, f: impl FnMut(T2, &T) -> T2 ) -> T2where T: Scalar,

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.
source

#### pub fn map_with_location<T2: Scalar, F: FnMut(usize, usize, T) -> T2>( &self, f: F ) -> OMatrix<T2, R, C>where T: Scalar, DefaultAllocator: Allocator<T2, R, C>,

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)`.

source

#### pub fn zip_map<T2, N3, S2, F>( &self, rhs: &Matrix<T2, R, C, S2>, 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<N3, R, C>,

Returns a matrix containing the result of `f` applied to each entries of `self` and `rhs`.

source

#### pub fn zip_zip_map<T2, N3, N4, S2, S3, F>( &self, b: &Matrix<T2, R, C, S2>, c: &Matrix<N3, R, C, S3>, 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<N4, R, C>,

Returns a matrix containing the result of `f` applied to each entries of `self` and `b`, and `c`.

source

#### pub fn fold<Acc>(&self, init: Acc, f: impl FnMut(Acc, T) -> Acc) -> Accwhere T: Scalar,

Folds a function `f` on each entry of `self`.

source

#### pub fn zip_fold<T2, R2, C2, S2, Acc>( &self, rhs: &Matrix<T2, R2, C2, S2>, init: Acc, f: impl FnMut(Acc, T, T2) -> Acc ) -> Accwhere T: Scalar, T2: Scalar, R2: Dim, C2: Dim, S2: RawStorage<T2, R2, C2>, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

Folds a function `f` on each pairs of entries from `self` and `rhs`.

source

#### pub fn apply<F: FnMut(&mut T)>(&mut self, f: F)where S: RawStorageMut<T, R, C>,

Applies a closure `f` to modify each component of `self`.

source

#### pub fn zip_apply<T2, R2, C2, S2>( &mut self, rhs: &Matrix<T2, R2, C2, S2>, 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>,

Replaces each component of `self` by the result of a closure `f` applied on its components joined with the components from `rhs`.

source

#### 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>, 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>,

Replaces each component of `self` by the result of a closure `f` applied on its components joined with the components from `b` and `c`.

sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn iter(&self) -> MatrixIter<'_, T, R, C, S> โ

Iterates through this matrix coordinates in column-major order.

##### ยงExample
``````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());``````
source

#### pub fn row_iter(&self) -> RowIter<'_, T, R, C, S> โ

Iterate through the rows of this matrix.

##### ยงExample
``````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))
}``````
source

#### pub fn column_iter(&self) -> ColumnIter<'_, T, R, C, S> โ

Iterate through the columns of this matrix.

##### ยงExample
``````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))
}``````
source

#### pub fn iter_mut(&mut self) -> MatrixIterMut<'_, T, R, C, S> โwhere S: RawStorageMut<T, R, C>,

Mutably iterates through this matrix coordinates.

source

#### pub fn row_iter_mut(&mut self) -> RowIterMut<'_, T, R, C, S> โwhere S: RawStorageMut<T, R, C>,

Mutably iterates through this matrix rows.

##### ยงExample
``````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);``````
source

#### pub fn column_iter_mut(&mut self) -> ColumnIterMut<'_, T, R, C, S> โwhere S: RawStorageMut<T, R, C>,

Mutably iterates through this matrix columns.

##### ยงExample
``````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);``````
sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorageMut<T, R, C>> Matrix<T, R, C, S>

source

#### pub fn as_mut_ptr(&mut self) -> *mut T

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.

source

#### pub unsafe fn swap_unchecked( &mut self, row_cols1: (usize, usize), row_cols2: (usize, usize) )

Swaps two entries without bound-checking.

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#### pub fn swap(&mut self, row_cols1: (usize, usize), row_cols2: (usize, usize))

Swaps two entries.

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#### pub fn copy_from_slice(&mut self, slice: &[T])where T: Scalar,

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.

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#### 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>,

Fills this matrix with the content of another one. Both must have the same shape.

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#### 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>,

Fills this matrix with the content of the transpose another one.

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#### pub fn apply_into<F: FnMut(&mut T)>(self, f: F) -> Self

Returns `self` with each of its components replaced by the result of a closure `f` applied on it.

sourceยง

### impl<T, D: Dim, S: RawStorage<T, D>> Matrix<T, D, Const<1>, S>

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#### pub unsafe fn vget_unchecked(&self, i: usize) -> &T

Gets a reference to the i-th element of this column vector without bound checking.

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### impl<T, D: Dim, S: RawStorageMut<T, D>> Matrix<T, D, Const<1>, S>

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#### pub unsafe fn vget_unchecked_mut(&mut self, i: usize) -> &mut T

Gets a mutable reference to the i-th element of this column vector without bound checking.

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### impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C> + IsContiguous> Matrix<T, R, C, S>

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#### pub fn as_slice(&self) -> &[T]

Extracts a slice containing the entire matrix entries ordered column-by-columns.

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### impl<T, R: Dim, C: Dim, S: RawStorageMut<T, R, C> + IsContiguous> Matrix<T, R, C, S>

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#### pub fn as_mut_slice(&mut self) -> &mut [T]

Extracts a mutable slice containing the entire matrix entries ordered column-by-columns.

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### impl<T: Scalar, D: Dim, S: RawStorageMut<T, D, D>> Matrix<T, D, D, S>

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#### pub fn transpose_mut(&mut self)

Transposes the square matrix `self` in-place.

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### impl<T: SimdComplexField, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>

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#### 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>,

Takes the adjoint (aka. conjugate-transpose) of `self` and store the result into `out`.

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#### pub fn adjoint(&self) -> OMatrix<T, C, R>where DefaultAllocator: Allocator<T, C, R>,

The adjoint (aka. conjugate-transpose) of `self`.

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#### 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>,

๐Deprecated: Renamed `self.adjoint_to(out)`.

Takes the conjugate and transposes `self` and store the result into `out`.

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#### pub fn conjugate_transpose(&self) -> OMatrix<T, C, R>where DefaultAllocator: Allocator<T, C, R>,

๐Deprecated: Renamed `self.adjoint()`.

The conjugate transposition of `self`.

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#### pub fn conjugate(&self) -> OMatrix<T, R, C>where DefaultAllocator: Allocator<T, R, C>,

The conjugate of `self`.

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#### pub fn unscale(&self, real: T::SimdRealField) -> OMatrix<T, R, C>where DefaultAllocator: Allocator<T, R, C>,

Divides each component of the complex matrix `self` by the given real.

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#### pub fn scale(&self, real: T::SimdRealField) -> OMatrix<T, R, C>where DefaultAllocator: Allocator<T, R, C>,

Multiplies each component of the complex matrix `self` by the given real.

sourceยง

### impl<T: SimdComplexField, R: Dim, C: Dim, S: RawStorageMut<T, R, C>> Matrix<T, R, C, S>

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#### pub fn conjugate_mut(&mut self)

The conjugate of the complex matrix `self` computed in-place.

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#### pub fn unscale_mut(&mut self, real: T::SimdRealField)

Divides each component of the complex matrix `self` by the given real.

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#### pub fn scale_mut(&mut self, real: T::SimdRealField)

Multiplies each component of the complex matrix `self` by the given real.

sourceยง

### impl<T: SimdComplexField, D: Dim, S: RawStorageMut<T, D, D>> Matrix<T, D, D, S>

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#### pub fn conjugate_transform_mut(&mut self)

๐Deprecated: Renamed to `self.adjoint_mut()`.

Sets `self` to its adjoint.

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Sets `self` to its adjoint (aka. conjugate-transpose).

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### impl<T: Scalar, D: Dim, S: RawStorage<T, D, D>> Matrix<T, D, D, S>

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#### pub fn diagonal(&self) -> OVector<T, D>where DefaultAllocator: Allocator<T, D>,

The diagonal of this matrix.

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#### pub fn map_diagonal<T2: Scalar>(&self, f: impl FnMut(T) -> T2) -> OVector<T2, D>where DefaultAllocator: Allocator<T2, D>,

Apply the given function to this matrixโs diagonal and returns it.

This is a more efficient version of `self.diagonal().map(f)` since this allocates only once.

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#### pub fn trace(&self) -> Twhere T: Scalar + Zero + ClosedAdd,

Computes a trace of a square matrix, i.e., the sum of its diagonal elements.

sourceยง

### impl<T: SimdComplexField, D: Dim, S: Storage<T, D, D>> Matrix<T, D, D, S>

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#### pub fn symmetric_part(&self) -> OMatrix<T, D, D>where DefaultAllocator: Allocator<T, D, D>,

The symmetric part of `self`, i.e., `0.5 * (self + self.transpose())`.

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#### pub fn hermitian_part(&self) -> OMatrix<T, D, D>where DefaultAllocator: Allocator<T, D, D>,

The hermitian part of `self`, i.e., `0.5 * (self + self.adjoint())`.

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### impl<T: Scalar + Zero + One, D: DimAdd<U1> + IsNotStaticOne, S: RawStorage<T, D, D>> Matrix<T, D, D, S>

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#### pub fn to_homogeneous(&self) -> OMatrix<T, DimSum<D, U1>, DimSum<D, U1>>where DefaultAllocator: Allocator<T, DimSum<D, U1>, DimSum<D, U1>>,

Yields the homogeneous matrix for this matrix, i.e., appending an additional dimension and and setting the diagonal element to `1`.

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### impl<T: Scalar + Zero, D: DimAdd<U1>, S: RawStorage<T, D>> Matrix<T, D, Const<1>, S>

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#### pub fn to_homogeneous(&self) -> OVector<T, DimSum<D, U1>>where DefaultAllocator: Allocator<T, DimSum<D, U1>>,

Computes the coordinates in projective space of this vector, i.e., appends a `0` to its coordinates.

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#### 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<T, D>,

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.

sourceยง

### impl<T: Scalar, D: DimAdd<U1>, S: RawStorage<T, D>> Matrix<T, D, Const<1>, S>

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#### pub fn push(&self, element: T) -> OVector<T, DimSum<D, U1>>where DefaultAllocator: Allocator<T, DimSum<D, U1>>,

Constructs a new vector of higher dimension by appending `element` to the end of `self`.

sourceยง

### impl<T: Scalar + ClosedAdd + ClosedSub + ClosedMul, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>

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#### pub fn perp<R2, C2, SB>(&self, b: &Matrix<T, R2, C2, SB>) -> Twhere R2: Dim, C2: Dim, SB: RawStorage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R, U2> + SameNumberOfColumns<C, U1> + SameNumberOfRows<R2, U2> + SameNumberOfColumns<C2, U1>,

The perpendicular product between two 2D column vectors, i.e. `a.x * b.y - a.y * b.x`.

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#### 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<T, R, C, R2, C2>, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

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.

sourceยง

### impl<T: Scalar + Field, S: RawStorage<T, U3>> Matrix<T, Const<3>, Const<1>, S>

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#### pub fn cross_matrix(&self) -> OMatrix<T, U3, U3>

Computes the matrix `M` such that for all vector `v` we have `M * v == self.cross(&v)`.

sourceยง

### impl<T: SimdComplexField, R: Dim, C: Dim, S: Storage<T, R, C>> Matrix<T, R, C, S>

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#### pub fn angle<R2: Dim, C2: Dim, SB>( &self, other: &Matrix<T, R2, C2, SB> ) -> T::SimdRealFieldwhere SB: Storage<T, R2, C2>, ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,

The smallest angle between two vectors.

sourceยง

### impl<T, S> Matrix<T, U1, U1, S>where S: RawStorage<T, U1, U1>,

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#### pub fn as_scalar(&self) -> &T

Returns a reference to the single element in this matrix.

As opposed to indexing, using this provides type-safety when flattening dimensions.

##### ยงExample
``````let v = Vector3::new(0., 0., 1.);
let inner_product: f32 = *(v.transpose() * v).as_scalar();``````
`````` let v = Vector3::new(0., 0., 1.);
let inner_product = (v * v.transpose()).item(); // Typo, does not compile.``````
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#### pub fn as_scalar_mut(&mut self) -> &mut Twhere S: RawStorageMut<T, U1>,

Get a mutable reference to the single element in this matrix

As opposed to indexing, using this provides type-safety when flattening dimensions.

##### ยงExample
``````let v = Vector3::new(0., 0., 1.);
let mut inner_product = (v.transpose() * v);
*inner_product.as_scalar_mut() = 3.;``````
`````` let v = Vector3::new(0., 0., 1.);
let mut inner_product = (v * v.transpose());
*inner_product.as_scalar_mut() = 3.;``````
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#### pub fn to_scalar(&self) -> Twhere T: Clone,

Convert this 1x1 matrix by reference into a scalar.

As opposed to indexing, using this provides type-safety when flattening dimensions.

##### ยงExample
``````let v = Vector3::new(0., 0., 1.);
let mut inner_product: f32 = (v.transpose() * v).to_scalar();``````
`````` let v = Vector3::new(0., 0., 1.);
let mut inner_product: f32 = (v * v.transpose()).to_scalar();``````
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### impl<T> Matrix<T, Const<1>, Const<1>, ArrayStorage<T, 1, 1>>

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#### pub fn into_scalar(self) -> T

Convert this 1x1 matrix into a scalar.

As opposed to indexing, using this provides type-safety when flattening dimensions.

##### ยงExample
``````let v = Vector3::new(0., 0., 1.);
let inner_product: f32 = (v.transpose() * v).into_scalar();
assert_eq!(inner_product, 1.);``````
`````` let v = Vector3::new(0., 0., 1.);
let mut inner_product: f32 = (v * v.transpose()).into_scalar();``````
sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>

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#### pub fn row(&self, i: usize) -> MatrixView<'_, T, U1, C, S::RStride, S::CStride>

Returns a view containing the i-th row of this matrix.

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#### pub fn row_part( &self, i: usize, n: usize ) -> MatrixView<'_, T, U1, Dyn, S::RStride, S::CStride>

Returns a view containing the `n` first elements of the i-th row of this matrix.

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#### pub fn rows( &self, first_row: usize, nrows: usize ) -> MatrixView<'_, T, Dyn, C, S::RStride, S::CStride>

Extracts from this matrix a set of consecutive rows.

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#### pub fn rows_with_step( &self, first_row: usize, nrows: usize, step: usize ) -> MatrixView<'_, T, Dyn, C, Dyn, S::CStride>

Extracts from this matrix a set of consecutive rows regularly skipping `step` rows.

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#### pub fn fixed_rows<const RVIEW: usize>( &self, first_row: usize ) -> MatrixView<'_, T, Const<RVIEW>, C, S::RStride, S::CStride>

Extracts a compile-time number of consecutive rows from this matrix.

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#### pub fn fixed_rows_with_step<const RVIEW: usize>( &self, first_row: usize, step: usize ) -> MatrixView<'_, T, Const<RVIEW>, C, Dyn, S::CStride>

Extracts from this matrix a compile-time number of rows regularly skipping `step` rows.

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#### pub fn rows_generic<RView: Dim>( &self, row_start: usize, nrows: RView ) -> MatrixView<'_, T, RView, C, S::RStride, S::CStride>

Extracts from this matrix `nrows` rows regularly skipping `step` rows. Both argument may or may not be values known at compile-time.

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#### pub fn rows_generic_with_step<RView>( &self, row_start: usize, nrows: RView, step: usize ) -> MatrixView<'_, T, RView, C, Dyn, S::CStride>where RView: Dim,

Extracts from this matrix `nrows` rows regularly skipping `step` rows. Both argument may or may not be values known at compile-time.

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#### pub fn column( &self, i: usize ) -> MatrixView<'_, T, R, U1, S::RStride, S::CStride>

Returns a view containing the i-th column of this matrix.

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#### pub fn column_part( &self, i: usize, n: usize ) -> MatrixView<'_, T, Dyn, U1, S::RStride, S::CStride>

Returns a view containing the `n` first elements of the i-th column of this matrix.

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#### pub fn columns( &self, first_col: usize, ncols: usize ) -> MatrixView<'_, T, R, Dyn, S::RStride, S::CStride>

Extracts from this matrix a set of consecutive columns.

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#### pub fn columns_with_step( &self, first_col: usize, ncols: usize, step: usize ) -> MatrixView<'_, T, R, Dyn, S::RStride, Dyn>

Extracts from this matrix a set of consecutive columns regularly skipping `step` columns.

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#### pub fn fixed_columns<const CVIEW: usize>( &self, first_col: usize ) -> MatrixView<'_, T, R, Const<CVIEW>, S::RStride, S::CStride>

Extracts a compile-time number of consecutive columns from this matrix.

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#### pub fn fixed_columns_with_step<const CVIEW: usize>( &self, first_col: usize, step: usize ) -> MatrixView<'_, T, R, Const<CVIEW>, S::RStride, Dyn>

Extracts from this matrix a compile-time number of columns regularly skipping `step` columns.

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#### pub fn columns_generic<CView: Dim>( &self, first_col: usize, ncols: CView ) -> MatrixView<'_, T, R, CView, S::RStride, S::CStride>

Extracts from this matrix `ncols` columns. The number of columns may or may not be known at compile-time.

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#### pub fn columns_generic_with_step<CView: Dim>( &self, first_col: usize, ncols: CView, step: usize ) -> MatrixView<'_, T, R, CView, S::RStride, Dyn>

Extracts from this matrix `ncols` columns skipping `step` columns. Both argument may or may not be values known at compile-time.

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#### pub fn slice( &self, start: (usize, usize), shape: (usize, usize) ) -> MatrixView<'_, T, Dyn, Dyn, S::RStride, S::CStride>

Slices this matrix starting at its component `(irow, icol)` and with `(nrows, ncols)` consecutive elements.

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#### pub fn view( &self, start: (usize, usize), shape: (usize, usize) ) -> MatrixView<'_, T, Dyn, Dyn, S::RStride, S::CStride>

Return a view of this matrix starting at its component `(irow, icol)` and with `(nrows, ncols)` consecutive elements.

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#### pub fn slice_with_steps( &self, start: (usize, usize), shape: (usize, usize), steps: (usize, usize) ) -> MatrixView<'_, T, Dyn, Dyn, Dyn, Dyn>

Slices this matrix starting at its component `(start.0, start.1)` and with `(shape.0, shape.1)` components. Each row (resp. column) of the sliced matrix is separated by `steps.0` (resp. `steps.1`) ignored rows (resp. columns) of the original matrix.

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#### pub fn view_with_steps( &self, start: (usize, usize), shape: (usize, usize), steps: (usize, usize) ) -> MatrixView<'_, T, Dyn, Dyn, Dyn, Dyn>

Return a view of this matrix starting at its component `(start.0, start.1)` and with `(shape.0, shape.1)` components. Each row (resp. column) of the matrix view is separated by `steps.0` (resp. `steps.1`) ignored rows (resp. columns) of the original matrix.

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#### pub fn fixed_slice<const RVIEW: usize, const CVIEW: usize>( &self, irow: usize, icol: usize ) -> MatrixView<'_, T, Const<RVIEW>, Const<CVIEW>, S::RStride, S::CStride>

Slices this matrix starting at its component `(irow, icol)` and with `(R::dim(), CView::dim())` consecutive components.

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#### pub fn fixed_view<const RVIEW: usize, const CVIEW: usize>( &self, irow: usize, icol: usize ) -> MatrixView<'_, T, Const<RVIEW>, Const<CVIEW>, S::RStride, S::CStride>

Return a view of this matrix starting at its component `(irow, icol)` and with `(R::dim(), CView::dim())` consecutive components.

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#### pub fn fixed_slice_with_steps<const RVIEW: usize, const CVIEW: usize>( &self, start: (usize, usize), steps: (usize, usize) ) -> MatrixView<'_, T, Const<RVIEW>, Const<CVIEW>, Dyn, Dyn>

Slices this matrix starting at its component `(start.0, start.1)` and with `(RVIEW, CVIEW)` components. Each row (resp. column) of the sliced matrix is separated by `steps.0` (resp. `steps.1`) ignored rows (resp. columns) of the original matrix.

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#### pub fn fixed_view_with_steps<const RVIEW: usize, const CVIEW: usize>( &self, start: (usize, usize), steps: (usize, usize) ) -> MatrixView<'_, T, Const<RVIEW>, Const<CVIEW>, Dyn, Dyn>

Returns a view of this matrix starting at its component `(start.0, start.1)` and with `(RVIEW, CVIEW)` components. Each row (resp. column) of the matrix view is separated by `steps.0` (resp. `steps.1`) ignored rows (resp. columns) of the original matrix.

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#### pub fn generic_slice<RView, CView>( &self, start: (usize, usize), shape: (RView, CView) ) -> MatrixView<'_, T, RView, CView, S::RStride, S::CStride>where RView: Dim, CView: Dim,

Creates a slice that may or may not have a fixed size and stride.

source

#### pub fn generic_view<RView, CView>( &self, start: (usize, usize), shape: (RView, CView) ) -> MatrixView<'_, T, RView, CView, S::RStride, S::CStride>where RView: Dim, CView: Dim,

Creates a matrix view that may or may not have a fixed size and stride.

source

#### pub fn generic_slice_with_steps<RView, CView>( &self, start: (usize, usize), shape: (RView, CView), steps: (usize, usize) ) -> MatrixView<'_, T, RView, CView, Dyn, Dyn>where RView: Dim, CView: Dim,

Creates a slice that may or may not have a fixed size and stride.

source

#### pub fn generic_view_with_steps<RView, CView>( &self, start: (usize, usize), shape: (RView, CView), steps: (usize, usize) ) -> MatrixView<'_, T, RView, CView, Dyn, Dyn>where RView: Dim, CView: Dim,

Creates a matrix view that may or may not have a fixed size and stride.

source

#### pub fn rows_range_pair<Range1: DimRange<R>, Range2: DimRange<R>>( &self, r1: Range1, r2: Range2 ) -> (MatrixView<'_, T, Range1::Size, C, S::RStride, S::CStride>, MatrixView<'_, T, Range2::Size, C, S::RStride, S::CStride>)

Splits this `NxM` matrix into two parts delimited by two ranges.

Panics if the ranges overlap or if the first range is empty.

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#### pub fn columns_range_pair<Range1: DimRange<C>, Range2: DimRange<C>>( &self, r1: Range1, r2: Range2 ) -> (MatrixView<'_, T, R, Range1::Size, S::RStride, S::CStride>, MatrixView<'_, T, R, Range2::Size, S::RStride, S::CStride>)

Splits this `NxM` matrix into two parts delimited by two ranges.

Panics if the ranges overlap or if the first range is empty.

sourceยง

### impl<T, R: Dim, C: Dim, S: RawStorageMut<T, R, C>> Matrix<T, R, C, S>

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#### pub fn row_mut( &mut self, i: usize ) -> MatrixViewMut<'_, T, U1, C, S::RStride, S::CStride>

Returns a view containing the i-th row of this matrix.

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#### pub fn row_part_mut( &mut self, i: usize, n: usize ) -> MatrixViewMut<'_, T, U1, Dyn, S::RStride, S::CStride>

Returns a view containing the `n` first elements of the i-th row of this matrix.

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#### pub fn rows_mut( &mut self, first_row: usize, nrows: usize ) -> MatrixViewMut<'_, T, Dyn, C, S::RStride, S::CStride>

Extracts from this matrix a set of consecutive rows.

source

#### pub fn rows_with_step_mut( &mut self, first_row: usize, nrows: usize, step: usize ) -> MatrixViewMut<'_, T, Dyn, C, Dyn, S::CStride>

Extracts from this matrix a set of consecutive rows regularly skipping `step` rows.

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#### pub fn fixed_rows_mut<const RVIEW: usize>( &mut self, first_row: usize ) -> MatrixViewMut<'_, T, Const<RVIEW>, C, S::RStride, S::CStride>

Extracts a compile-time number of consecutive rows from this matrix.

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#### pub fn fixed_rows_with_step_mut<const RVIEW: usize>( &mut self, first_row: usize, step: usize ) -> MatrixViewMut<'_, T, Const<RVIEW>, C, Dyn, S::CStride>

Extracts from this matrix a compile-time number of rows regularly skipping `step` rows.

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#### pub fn rows_generic_mut<RView: Dim>( &mut self, row_start: usize, nrows: RView ) -> MatrixViewMut<'_, T, RView, C, S::RStride, S::CStride>

Extracts from this matrix `nrows` rows regularly skipping `step` rows. Both argument may or may not be values known at compile-time.

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#### pub fn rows_generic_with_step_mut<RView>( &mut self, row_start: usize, nrows: RView, step: usize ) -> MatrixViewMut<'_, T, RView, C, Dyn, S::CStride>where RView: Dim,

Extracts from this matrix `nrows` rows regularly skipping `step` rows. Both argument may or may not be values known at compile-time.

source

#### pub fn column_mut( &mut self, i: usize ) -> MatrixViewMut<'_, T, R, U1, S::RStride, S::CStride>

Returns a view containing the i-th column of this matrix.

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#### pub fn column_part_mut( &mut self, i: usize, n: usize ) -> MatrixViewMut<'_, T, Dyn, U1, S::RStride, S::CStride>

Returns a view containing the `n` first elements of the i-th column of this matrix.

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#### pub fn columns_mut( &mut self, first_col: usize, ncols: usize ) -> MatrixViewMut<'_, T, R, Dyn, S::RStride, S::CStride>

Extracts from this matrix a set of consecutive columns.

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#### pub fn columns_with_step_mut( &mut self, first_col: usize, ncols: usize, step: usize ) -> MatrixViewMut<'_, T, R, Dyn, S::RStride, Dyn>

Extracts from this matrix a set of consecutive columns regularly skipping `step` columns.

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#### pub fn fixed_columns_mut<const CVIEW: usize>( &mut self, first_col: usize ) -> MatrixViewMut<'_, T, R, Const<CVIEW>, S::RStride, S::CStride>

Extracts a compile-time number of consecutive columns from this matrix.

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#### pub fn fixed_columns_with_step_mut<const CVIEW: usize>( &mut self, first_col: usize, step: usize ) -> MatrixViewMut<'_, T, R, Const<CVIEW>, S::RStride, Dyn>

Extracts from this matrix a compile-time number of columns regularly skipping `step` columns.

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#### pub fn columns_generic_mut<CView: Dim>( &mut self, first_col: usize, ncols: CView ) -> MatrixViewMut<'_, T, R, CView, S::RStride, S::CStride>

Extracts from this matrix `ncols` columns. The number of columns may or may not be known at compile-time.

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#### pub fn columns_generic_with_step_mut<CView: Dim>( &mut self, first_col: usize, ncols: CView, step: usize ) -> MatrixViewMut<'_, T, R, CView, S::RStride, Dyn>

Extracts from this matrix `ncols` columns skipping `step` columns. Both argument may or may not be values known at compile-time.

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#### pub fn slice_mut( &mut self, start: (usize, usize), shape: (usize, usize) ) -> MatrixViewMut<'_, T, Dyn, Dyn, S::RStride, S::CStride>

Slices this matrix starting at its component `(irow, icol)` and with `(nrows, ncols)` consecutive elements.

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#### pub fn view_mut( &mut self, start: (usize, usize), shape: (usize, usize) ) -> MatrixViewMut<'_, T, Dyn, Dyn, S::RStride, S::CStride>

Return a view of this matrix starting at its component `(irow, icol)` and with `(nrows, ncols)` consecutive elements.

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#### pub fn slice_with_steps_mut( &mut self, start: (usize, usize), shape: (usize, usize), steps: (usize, usize) ) -> MatrixViewMut<'_, T, Dyn, Dyn, Dyn, Dyn>

Slices this matrix starting at its component `(start.0, start.1)` and with `(shape.0, shape.1)` components. Each row (resp. column) of the sliced matrix is separated by `steps.0` (resp. `steps.1`) ignored rows (resp. columns) of the original matrix.

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#### pub fn view_with_steps_mut( &mut self, start: (usize, usize), shape: (usize, usize), steps: (usize, usize) ) -> MatrixViewMut<'_, T, Dyn, Dyn, Dyn, Dyn>

Return a view of this matrix starting at its component `(start.0, start.1)` and with `(shape.0, shape.1)` components. Each row (resp. column) of the matrix view is separated by `steps.0` (resp. `steps.1`) ignored rows (resp. columns) of the original matrix.

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#### pub fn fixed_slice_mut<const RVIEW: usize, const CVIEW: usize>( &mut self, irow: usize, icol: usize ) -> MatrixViewMut<'_, T, Const<RVIEW>, Const<CVIEW>, S::RStride, S::CStride>

Slices this matrix starting at its component `(irow, icol)` and with `(R::dim(), CView::dim())` consecutive components.

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#### pub fn fixed_view_mut<const RVIEW: usize, const CVIEW: usize>( &mut self, irow: usize, icol: usize ) -> MatrixViewMut<'_, T, Const<RVIEW>, Const<CVIEW>, S::RStride, S::CStride>

Return a view of this matrix starting at its component `(irow, icol)` and with `(R::dim(), CView::dim())` consecutive components.

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#### pub fn fixed_slice_with_steps_mut<const RVIEW: usize, const CVIEW: usize>( &mut self, start: (usize, usize), steps: (usize, usize) ) -> MatrixViewMut<'_, T, Const<RVIEW>, Const<CVIEW>, Dyn, Dyn>

Slices this matrix starting at its component `(start.0, start.1)` and with `(RVIEW, CVIEW)` components. Each row (resp. column) of the sliced matrix is separated by `steps.0` (resp. `steps.1`) ignored rows (resp. columns) of the original matrix.

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#### pub fn fixed_view_with_steps_mut<const RVIEW: usize, const CVIEW: usize>( &mut self, start: (usize, usize), steps: (usize, usize) ) -> MatrixViewMut<'_, T, Const<RVIEW>, Const<CVIEW>, Dyn, Dyn>

Returns a view of this matrix starting at its component `(start.0, start.1)` and with `(RVIEW, CVIEW)` components. Each row (resp. column) of the matrix view is separated by `steps.0` (resp. `steps.1`) ignored rows (resp. columns) of the original matrix.

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#### pub fn generic_slice_mut<RView, CView>( &mut self, start: (usize, usize), shape: (RView, CView) ) -> MatrixViewMut<'_, T, RView, CView, S::RStride, S::CStride>where RView: Dim, CView: Dim,

Creates a slice that may or may not have a fixed size and stride.

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#### pub fn generic_view_mut<RView, CView>( &mut self, start: (usize, usize), shape: (RView, CView) ) -> MatrixViewMut<'_, T, RView, CView, S::RStride, S::CStride>where RView: Dim, CView: Dim,

Creates a matrix view that may or may not have a fixed size and stride.

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#### pub fn generic_slice_with_steps_mut<RView, CView>( &mut self, start: (usize, usize), shape: (RView, CView), steps: (usize, usize) ) -> MatrixViewMut<'_, T, RView, CView, Dyn, Dyn>where RView: Dim, CView: Dim,

Creates a slice that may or may not have a fixed size and stride.

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#### pub fn generic_view_with_steps_mut<RView, CView>( &mut self, start: (usize, usize), shape: (RView, CView), steps: (usize, usize) ) -> MatrixViewMut<'_, T, RView, CView, Dyn, Dyn>where RView: Dim, CView: Dim,

Creates a matrix view that may or may not have a fixed size and stride.

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#### pub fn rows_range_pair_mut<Range1: DimRange<R>, Range2: DimRange<R>>( &mut self, r1: Range1, r2: Range2 ) -> (MatrixViewMut<'_, T, Range1::Size, C, S::RStride, S::CStride>, MatrixViewMut<'_, T, Range2::Size, C, S::RStride, S::CStride>)

Splits this `NxM` matrix into two parts delimited by two ranges.

Panics if the ranges overlap or if the first range is empty.

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#### pub fn columns_range_pair_mut<Range1: DimRange<C>, Range2: DimRange<C>>( &mut self, r1: Range1, r2: Range2 ) -> (MatrixViewMut<'_, T, R, Range1::Size, S::RStride, S::CStride>, MatrixViewMut<'_, T, R, Range2::Size, S::RStride, S::CStride>)

Splits this `NxM` matrix into two parts delimited by two ranges.

Panics if the ranges overlap or if the first range is empty.

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### impl<T, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S>

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#### pub fn slice_range<RowRange, ColRange>( &self, rows: RowRange, cols: ColRange ) -> MatrixView<'_, T, RowRange::Size, ColRange::Size, S::RStride, S::CStride>where RowRange: DimRange<R>, ColRange: DimRange<C>,

Slices a sub-matrix containing the rows indexed by the range `rows` and the columns indexed by the range `cols`.