nyx_space::linalg

Struct Matrix

source
#[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.

§Methods summary

Because Matrix is the most generic types used as a common representation of all matrices and vectors of nalgebra this documentation page contains every single matrix/vector-related method. In order to make browsing this page simpler, the next subsections contain direct links to groups of methods related to a specific topic.

§Vector and matrix construction
§Computer graphics utilities for transformations
§Common math operations
§Statistics
§Iteration, map, and fold
§Vector and matrix views
§In-place modification of a single matrix or vector
§Vector and matrix size modification
§Matrix decomposition
§Vector basis computation

§Type parameters

The generic Matrix type has four type parameters:

  • T: for the matrix components scalar type.
  • R: for the matrix number of rows.
  • C: for the matrix number of columns.
  • S: for the matrix data storage, i.e., the buffer that actually contains the matrix components.

The matrix dimensions parameters R and C can either be:

  • type-level unsigned integer constants (e.g. U1, U124) from the nalgebra:: root module. All numbers from 0 to 127 are defined that way.
  • type-level unsigned integer constants (e.g. U1024, U10000) from the typenum:: crate. Using those, you will not get error messages as nice as for numbers smaller than 128 defined on the nalgebra:: module.
  • the special value Dyn from the nalgebra:: root module. This indicates that the specified dimension is not known at compile-time. Note that this will generally imply that the matrix data storage S performs a dynamic allocation and contains extra metadata for the matrix shape.

Note that mixing Dyn with type-level unsigned integers is allowed. Actually, a dynamically-sized column vector should be represented as a Matrix<T, Dyn, U1, S> (given some concrete types for T and a compatible data storage type S).

Fields§

§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.

Implementations§

source§

impl<T, R, C, S> Matrix<T, R, C, S>
where R: Dim, C: Dim, S: RawStorage<T, R, C>, T: Scalar + Zero + ClosedAddAssign + ClosedMulAssign,

§Dot/scalar product

source

pub fn dot<R2, C2, SB>(&self, rhs: &Matrix<T, R2, C2, SB>) -> T
where R2: Dim, C2: Dim, 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.

§Example
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, C2, SB>(&self, rhs: &Matrix<T, R2, C2, SB>) -> T
where R2: Dim, C2: Dim, 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.

§Example
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, C2, SB>(&self, rhs: &Matrix<T, R2, C2, SB>) -> T
where R2: Dim, C2: Dim, SB: RawStorage<T, R2, C2>, ShapeConstraint: DimEq<C, R2> + DimEq<R, C2>,

The dot product between the transpose of self and rhs.

§Example
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);
source§

impl<T, D, S> Matrix<T, D, Const<1>, S>
where D: Dim, T: Scalar + Zero + ClosedAddAssign + ClosedMulAssign, S: StorageMut<T, D>,

§BLAS functions

source

pub fn axcpy<D2, SB>( &mut self, a: T, x: &Matrix<T, D2, Const<1>, SB>, c: T, b: T, )
where D2: Dim, SB: Storage<T, D2>, ShapeConstraint: DimEq<D, D2>,

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

If b is zero, self is never read from.

§Example
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, SB>(&mut self, a: T, x: &Matrix<T, D2, Const<1>, SB>, b: T)
where D2: Dim, 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.

§Example
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, C2, D3, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, x: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where R2: Dim, C2: Dim, D3: Dim, T: One, SB: Storage<T, R2, C2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, R2> + AreMultipliable<R2, C2, D3, Const<1>>,

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.

§Example
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, D3, SB, SC>( &mut self, alpha: T, a: &Matrix<T, D2, D2, SB>, x: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where D2: Dim, D3: Dim, T: One, SB: Storage<T, D2, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, Const<1>>,

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.

§Examples
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, D3, SB, SC>( &mut self, alpha: T, a: &Matrix<T, D2, D2, SB>, x: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where D2: Dim, D3: Dim, T: SimdComplexField, SB: Storage<T, D2, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, Const<1>>,

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.

§Examples
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, C2, D3, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, x: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where R2: Dim, C2: Dim, D3: Dim, T: One, SB: Storage<T, R2, C2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, C2> + AreMultipliable<C2, R2, D3, Const<1>>,

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.

§Example
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, C2, D3, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, x: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where R2: Dim, C2: Dim, D3: Dim, T: SimdComplexField, SB: Storage<T, R2, C2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<D, C2> + AreMultipliable<C2, R2, D3, Const<1>>,

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.

§Example
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);
source§

impl<T, R1, C1, S> Matrix<T, R1, C1, S>
where R1: Dim, C1: Dim, S: StorageMut<T, R1, C1>, T: Scalar + Zero + ClosedAddAssign + ClosedMulAssign,

source

pub fn ger<D2, D3, SB, SC>( &mut self, alpha: T, x: &Matrix<T, D2, Const<1>, SB>, y: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where D2: Dim, D3: Dim, 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.

§Example
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, D3, SB, SC>( &mut self, alpha: T, x: &Matrix<T, D2, Const<1>, SB>, y: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where D2: Dim, D3: Dim, 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.

§Example
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, C2, R3, C3, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, b: &Matrix<T, R3, C3, SC>, beta: T, )
where R2: Dim, C2: Dim, R3: Dim, C3: Dim, 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.

§Example
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, C2, R3, C3, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, b: &Matrix<T, R3, C3, SC>, beta: T, )
where R2: Dim, C2: Dim, R3: Dim, C3: Dim, 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.

§Example
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, C2, R3, C3, SB, SC>( &mut self, alpha: T, a: &Matrix<T, R2, C2, SB>, b: &Matrix<T, R3, C3, SC>, beta: T, )
where R2: Dim, C2: Dim, R3: Dim, C3: Dim, 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.

§Example
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);
source§

impl<T, R1, C1, S> Matrix<T, R1, C1, S>
where R1: Dim, C1: Dim, S: StorageMut<T, R1, C1>, T: Scalar + Zero + ClosedAddAssign + ClosedMulAssign,

source

pub fn ger_symm<D2, D3, SB, SC>( &mut self, alpha: T, x: &Matrix<T, D2, Const<1>, SB>, y: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where D2: Dim, D3: Dim, T: One, SB: Storage<T, D2>, SC: Storage<T, D3>, ShapeConstraint: DimEq<R1, D2> + DimEq<C1, D3>,

👎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.

§Example
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, D3, SB, SC>( &mut self, alpha: T, x: &Matrix<T, D2, Const<1>, SB>, y: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where D2: Dim, D3: Dim, 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.

§Example
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, D3, SB, SC>( &mut self, alpha: T, x: &Matrix<T, D2, Const<1>, SB>, y: &Matrix<T, D3, Const<1>, SC>, beta: T, )
where D2: Dim, D3: Dim, 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.

§Example
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.
source§

impl<T, D1, S> Matrix<T, D1, D1, S>
where D1: Dim, S: StorageMut<T, D1, D1>, T: Scalar + Zero + One + ClosedAddAssign + ClosedMulAssign,

source

pub fn quadform_tr_with_workspace<D2, S2, R3, C3, S3, D4, S4>( &mut self, work: &mut Matrix<T, D2, Const<1>, S2>, alpha: T, lhs: &Matrix<T, R3, C3, S3>, mid: &Matrix<T, D4, 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.

§Example
// 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);
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pub fn quadform_tr<R3, C3, S3, D4, S4>( &mut self, alpha: T, lhs: &Matrix<T, R3, C3, S3>, mid: &Matrix<T, D4, 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<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.

§Example
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;

mat.quadform_tr(10.0, &lhs, &mid, 5.0);
assert_relative_eq!(mat, expected);
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pub fn quadform_with_workspace<D2, S2, D3, S3, R4, C4, S4>( &mut self, work: &mut Matrix<T, D2, Const<1>, S2>, alpha: T, mid: &Matrix<T, D3, 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, Const<1>>,

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

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

§Example
// 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);
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pub fn quadform<D2, S2, R3, C3, S3>( &mut self, alpha: T, mid: &Matrix<T, D2, 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, Const<1>>, DefaultAllocator: Allocator<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.

§Example
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;

mat.quadform(10.0, &mid, &rhs, 5.0);
assert_relative_eq!(mat, expected);
source§

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

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pub fn neg_mut(&mut self)

Negates self in-place.

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

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pub fn add_to<R2, C2, SB, R3, C3, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC>, )
where R2: Dim, C2: Dim, R3: Dim, C3: Dim, 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.

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

source

pub fn sub_to<R2, C2, SB, R3, C3, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC>, )
where R2: Dim, C2: Dim, R3: Dim, C3: Dim, 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.

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impl<T, R1, C1, SA> Matrix<T, R1, C1, SA>
where R1: Dim, C1: Dim, T: Scalar + Zero + One + ClosedAddAssign + ClosedMulAssign, SA: Storage<T, R1, C1>,

§Special multiplications.

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

Equivalent to self.transpose() * rhs.

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

Equivalent to self.adjoint() * rhs.

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pub fn tr_mul_to<R2, C2, SB, R3, C3, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC>, )
where R2: Dim, C2: Dim, R3: Dim, C3: Dim, 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.

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pub fn ad_mul_to<R2, C2, SB, R3, C3, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC>, )
where R2: Dim, C2: Dim, R3: Dim, C3: Dim, 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.

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pub fn mul_to<R2, C2, SB, R3, C3, SC>( &self, rhs: &Matrix<T, R2, C2, SB>, out: &mut Matrix<T, R3, C3, SC>, )
where R2: Dim, C2: Dim, R3: Dim, C3: Dim, 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.

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pub fn kronecker<R2, C2, SB>( &self, rhs: &Matrix<T, R2, C2, SB>, ) -> Matrix<T, <R1 as DimMul<R2>>::Output, <C1 as DimMul<C2>>::Output, <DefaultAllocator as Allocator<<R1 as DimMul<R2>>::Output, <C1 as DimMul<C2>>::Output>>::Buffer<T>>
where R2: Dim, C2: Dim, T: ClosedMulAssign, R1: DimMul<R2>, C1: DimMul<C2>, SB: Storage<T, R2, C2>, DefaultAllocator: Allocator<<R1 as DimMul<R2>>::Output, <C1 as DimMul<C2>>::Output>,

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

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

§Translation and scaling in any dimension

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pub fn new_scaling( scaling: T, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>

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

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pub fn new_nonuniform_scaling<SB>( scaling: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>
where D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>,

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

source

pub fn new_translation<SB>( translation: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>
where D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>,

Creates a new homogeneous matrix that applies a pure translation.

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

§2D transformations as a Matrix3

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pub fn new_rotation( angle: T, ) -> Matrix<T, Const<3>, Const<3>, ArrayStorage<T, 3, 3>>

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

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pub fn new_nonuniform_scaling_wrt_point( scaling: &Matrix<T, Const<2>, Const<1>, ArrayStorage<T, 2, 1>>, pt: &OPoint<T, Const<2>>, ) -> Matrix<T, Const<3>, Const<3>, ArrayStorage<T, 3, 3>>

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.

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

§3D transformations as a Matrix4

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pub fn new_rotation( axisangle: Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

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

Returns the identity matrix if the given argument is zero.

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pub fn new_rotation_wrt_point( axisangle: Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>, pt: OPoint<T, Const<3>>, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

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

Returns the identity matrix if the given argument is zero.

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pub fn new_nonuniform_scaling_wrt_point( scaling: &Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>, pt: &OPoint<T, Const<3>>, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

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.

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pub fn from_scaled_axis( axisangle: Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

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.

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pub fn from_euler_angles( roll: T, pitch: T, yaw: T, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

Creates a new rotation from Euler angles.

The primitive rotations are applied in order: 1 roll − 2 pitch − 3 yaw.

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pub fn from_axis_angle( axis: &Unit<Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>>, angle: T, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

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

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pub fn new_orthographic( left: T, right: T, bottom: T, top: T, znear: T, zfar: T, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

Creates a new homogeneous matrix for an orthographic projection.

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pub fn new_perspective( aspect: T, fovy: T, znear: T, zfar: T, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

Creates a new homogeneous matrix for a perspective projection.

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pub fn face_towards( eye: &OPoint<T, Const<3>>, target: &OPoint<T, Const<3>>, up: &Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

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.

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pub fn new_observer_frame( eye: &OPoint<T, Const<3>>, target: &OPoint<T, Const<3>>, up: &Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

👎Deprecated: renamed to face_towards

Deprecated: Use Matrix4::face_towards instead.

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pub fn look_at_rh( eye: &OPoint<T, Const<3>>, target: &OPoint<T, Const<3>>, up: &Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

Builds a right-handed look-at view matrix.

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pub fn look_at_lh( eye: &OPoint<T, Const<3>>, target: &OPoint<T, Const<3>>, up: &Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

Builds a left-handed look-at view matrix.

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impl<T, D, S> Matrix<T, D, D, S>
where T: Scalar + Zero + One + ClosedMulAssign + ClosedAddAssign, D: DimName, S: Storage<T, D, D>,

§Append/prepend translation and scaling

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pub fn append_scaling( &self, scaling: T, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>

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

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pub fn prepend_scaling( &self, scaling: T, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>

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

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pub fn append_nonuniform_scaling<SB>( &self, scaling: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>
where D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>, DefaultAllocator: Allocator<D, D>,

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

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pub fn prepend_nonuniform_scaling<SB>( &self, scaling: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>
where D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>, DefaultAllocator: Allocator<D, D>,

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

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pub fn append_translation<SB>( &self, shift: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>
where D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>, DefaultAllocator: Allocator<D, D>,

Computes the transformation equal to self followed by a translation.

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pub fn prepend_translation<SB>( &self, shift: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>
where D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>, DefaultAllocator: Allocator<D, D> + Allocator<<D as DimNameSub<Const<1>>>::Output>,

Computes the transformation equal to a translation followed by self.

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pub fn append_scaling_mut(&mut self, scaling: T)
where S: StorageMut<T, D, D>, D: DimNameSub<Const<1>>,

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

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pub fn prepend_scaling_mut(&mut self, scaling: T)
where S: StorageMut<T, D, D>, D: DimNameSub<Const<1>>,

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

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pub fn append_nonuniform_scaling_mut<SB>( &mut self, scaling: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, )
where S: StorageMut<T, D, D>, D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>,

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

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pub fn prepend_nonuniform_scaling_mut<SB>( &mut self, scaling: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, )
where S: StorageMut<T, D, D>, D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>,

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

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pub fn append_translation_mut<SB>( &mut self, shift: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, )
where S: StorageMut<T, D, D>, D: DimNameSub<Const<1>>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>,

Computes the transformation equal to self followed by a translation.

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pub fn prepend_translation_mut<SB>( &mut self, shift: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, SB>, )
where D: DimNameSub<Const<1>>, S: StorageMut<T, D, D>, SB: Storage<T, <D as DimNameSub<Const<1>>>::Output>, DefaultAllocator: Allocator<<D as DimNameSub<Const<1>>>::Output>,

Computes the transformation equal to a translation followed by self.

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impl<T, D, S> Matrix<T, D, D, S>
where T: RealField, D: DimNameSub<Const<1>>, S: Storage<T, D, D>, DefaultAllocator: Allocator<D, D> + Allocator<<D as DimNameSub<Const<1>>>::Output> + Allocator<<D as DimNameSub<Const<1>>>::Output, <D as DimNameSub<Const<1>>>::Output>,

§Transformation of vectors and points

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pub fn transform_vector( &self, v: &Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, <DefaultAllocator as Allocator<<D as DimNameSub<Const<1>>>::Output>>::Buffer<T>>, ) -> Matrix<T, <D as DimNameSub<Const<1>>>::Output, Const<1>, <DefaultAllocator as Allocator<<D as DimNameSub<Const<1>>>::Output>>::Buffer<T>>

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

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

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pub fn transform_point(&self, pt: &OPoint<T, Const<2>>) -> OPoint<T, Const<2>>

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

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

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pub fn transform_point(&self, pt: &OPoint<T, Const<3>>) -> OPoint<T, Const<3>>

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

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

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pub fn abs( &self, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

Computes the component-wise absolute value.

§Example
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))
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impl<T, R1, C1, SA> Matrix<T, R1, C1, SA>
where T: Scalar, R1: Dim, C1: Dim, SA: Storage<T, R1, C1>,

§Componentwise operations

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pub fn component_mul<R2, C2, SB>( &self, rhs: &Matrix<T, R2, C2, SB>, ) -> Matrix<T, <ShapeConstraint as SameNumberOfRows<R1, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C1, C2>>::Representative, <DefaultAllocator as Allocator<<ShapeConstraint as SameNumberOfRows<R1, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C1, C2>>::Representative>>::Buffer<T>>
where T: ClosedMulAssign, R2: Dim, C2: Dim, SB: Storage<T, R2, C2>, DefaultAllocator: SameShapeAllocator<R1, C1, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

Componentwise matrix or vector multiplication.

§Example
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);
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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: ClosedMulAssign<Output = T> + Zero<Output = T> + Mul + Add, 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].

§Example
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: ClosedMulAssign, 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.

§Example
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: ClosedMulAssign, R2: Dim, C2: Dim, SA: StorageMut<T, R1, C1>, SB: Storage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

👎Deprecated: This is renamed using the _assign suffix instead of the _mut suffix.

Inplace componentwise matrix or vector multiplication.

§Example
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>, ) -> Matrix<T, <ShapeConstraint as SameNumberOfRows<R1, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C1, C2>>::Representative, <DefaultAllocator as Allocator<<ShapeConstraint as SameNumberOfRows<R1, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C1, C2>>::Representative>>::Buffer<T>>
where T: ClosedDivAssign, R2: Dim, C2: Dim, SB: Storage<T, R2, C2>, DefaultAllocator: SameShapeAllocator<R1, C1, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

Componentwise matrix or vector division.

§Example
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: ClosedDivAssign + Zero<Output = T> + Mul<Output = T> + Add, 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].

§Example
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: ClosedDivAssign, 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.

§Example
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: ClosedDivAssign, R2: Dim, C2: Dim, SA: StorageMut<T, R1, C1>, SB: Storage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>,

👎Deprecated: This is renamed using the _assign suffix instead of the _mut suffix.

Inplace componentwise matrix or vector division.

§Example
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: &Matrix<T, R1, C1, SA>, ) -> Matrix<T, R1, C1, <DefaultAllocator as Allocator<R1, C1>>::Buffer<T>>
where T: SimdPartialOrd, DefaultAllocator: Allocator<R1, C1>,

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

§Example
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: &Matrix<T, R1, C1, SA>, ) -> Matrix<T, R1, C1, <DefaultAllocator as Allocator<R1, C1>>::Buffer<T>>
where T: SimdPartialOrd, DefaultAllocator: Allocator<R1, C1>,

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

§Example
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: &Matrix<T, R1, C1, SA>, ) -> (Matrix<T, R1, C1, <DefaultAllocator as Allocator<R1, C1>>::Buffer<T>>, Matrix<T, R1, C1, <DefaultAllocator as Allocator<R1, C1>>::Buffer<T>>)
where T: SimdPartialOrd, DefaultAllocator: Allocator<R1, C1>,

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

§Example
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, ) -> Matrix<T, R1, C1, <DefaultAllocator as Allocator<R1, C1>>::Buffer<T>>
where T: ClosedAddAssign, DefaultAllocator: Allocator<R1, C1>,

Adds a scalar to self.

§Example
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);
assert_eq!(u.add_scalar(s), expected)
source

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

Adds a scalar to self in-place.

§Example
let mut u = Matrix2::new(1.0, 2.0, 3.0, 4.0);
let s = 10.0;
u.add_scalar_mut(s);
let expected = Matrix2::new(11.0, 12.0, 13.0, 14.0);
assert_eq!(u, expected)
source§

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

source

pub fn uninit( nrows: R, ncols: C, ) -> Matrix<MaybeUninit<T>, R, C, <DefaultAllocator as Allocator<R, C>>::BufferUninit<T>>

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

source§

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

§Generic 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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

Creates a matrix with all its elements set to elem.

source

pub fn repeat_generic( nrows: R, ncols: C, elem: T, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

Creates a matrix with all its elements set to elem.

Same as from_element_generic.

source

pub fn zeros_generic( nrows: R, ncols: C, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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], ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

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], ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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], ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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>], ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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.

§Example

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: &[Matrix<T, R, Const<1>, SB>], ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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.

§Example

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 from_vec_generic( nrows: R, ncols: C, data: Vec<T>, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

Creates a matrix backed by a given Vec.

The output matrix is filled column-by-column.

§Example

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);
source§

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

source

pub fn from_diagonal<SB>( diag: &Matrix<T, D, Const<1>, SB>, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>
where SB: RawStorage<T, D>, T: Zero,

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

§Example

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);
source§

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

§Constructors of statically-sized vectors or statically-sized matrices

source

pub fn from_element( elem: T, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

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

§Example

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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

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

Same as .from_element.

§Example

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() -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where T: Zero,

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

§Example

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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where F: FnMut(usize, usize) -> T,

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

§Example

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() -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where T: Zero + One,

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

§Example

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], ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where 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.

§Example

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§

impl<T, R> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

§Constructors of matrices with a dynamic number of columns

source

pub fn from_element( ncols: usize, elem: T, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

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

§Example

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, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

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

Same as .from_element.

§Example

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, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
where T: Zero,

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

§Example

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, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
where F: FnMut(usize, usize) -> T,

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

§Example

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, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
where T: Zero + One,

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

§Example

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], ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
where 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.

§Example

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§

impl<T, C> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

§Constructors of dynamic vectors and matrices with a dynamic number of rows

source

pub fn from_element( nrows: usize, elem: T, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

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

§Example

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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

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

Same as .from_element.

§Example

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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
where T: Zero,

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

§Example

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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
where F: FnMut(usize, usize) -> T,

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

§Example

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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
where T: Zero + One,

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

§Example

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], ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
where 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.

§Example

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§

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

§Constructors of fully dynamic matrices

source

pub fn from_element( nrows: usize, ncols: usize, elem: T, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>

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

§Example

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, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>

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

Same as .from_element.

§Example

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, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>
where T: Zero,

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

§Example

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, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>
where F: FnMut(usize, usize) -> T,

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

§Example

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, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>
where 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.

§Example

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, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>
where T: Zero + One,

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

§Example

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], ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>
where 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.

§Example

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§

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

source

pub fn from_row_slice( data: &[T], ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

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.

§Example

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], ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

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

§Example

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>, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

Creates a matrix backed by a given Vec.

The output matrix is filled column-by-column.

§Example

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);
source§

impl<T, R> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

source

pub fn from_row_slice( data: &[T], ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

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.

§Example

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], ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

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

§Example

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>, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

Creates a matrix backed by a given Vec.

The output matrix is filled column-by-column.

§Example

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);
source§

impl<T, C> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

source

pub fn from_row_slice( data: &[T], ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

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.

§Example

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], ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

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

§Example

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>, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

Creates a matrix backed by a given Vec.

The output matrix is filled column-by-column.

§Example

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);
source§

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

source

pub fn from_row_slice( nrows: usize, ncols: usize, data: &[T], ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>

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.

§Example

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], ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>

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

§Example

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>, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>

Creates a matrix backed by a given Vec.

The output matrix is filled column-by-column.

§Example

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);
source§

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, ) -> Matrix<T, Const<2>, Const<2>, ArrayStorage<T, 2, 2>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<3>, Const<3>, ArrayStorage<T, 3, 3>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<4>, Const<4>, ArrayStorage<T, 4, 4>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<5>, Const<5>, ArrayStorage<T, 5, 5>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<6>, Const<6>, ArrayStorage<T, 6, 6>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<2>, Const<3>, ArrayStorage<T, 2, 3>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<2>, Const<4>, ArrayStorage<T, 2, 4>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<2>, Const<5>, ArrayStorage<T, 2, 5>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<2>, Const<6>, ArrayStorage<T, 2, 6>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<3>, Const<2>, ArrayStorage<T, 3, 2>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<3>, Const<4>, ArrayStorage<T, 3, 4>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<3>, Const<5>, ArrayStorage<T, 3, 5>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<3>, Const<6>, ArrayStorage<T, 3, 6>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<4>, Const<2>, ArrayStorage<T, 4, 2>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<4>, Const<3>, ArrayStorage<T, 4, 3>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<4>, Const<5>, ArrayStorage<T, 4, 5>>

Initializes this matrix from its components.

source§

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, ) -> Matrix<T, Const<4>, Const<6>, ArrayStorage<T, 4, 6>>

Initializes this matrix from its components.

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

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pub const fn new( m11: T, m12: T, m21: T, m22: T, m31: T, m32: T, m41: T, m42: T, m51: T, m52: T, ) -> Matrix<T, Const<5>, Const<2>, ArrayStorage<T, 5, 2>>

Initializes this matrix from its components.

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

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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, ) -> Matrix<T, Const<5>, Const<3>, ArrayStorage<T, 5, 3>>

Initializes this matrix from its components.

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

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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, ) -> Matrix<T, Const<5>, Const<4>, ArrayStorage<T, 5, 4>>

Initializes this matrix from its components.

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

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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, ) -> Matrix<T, Const<5>, Const<6>, ArrayStorage<T, 5, 6>>

Initializes this matrix from its components.

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

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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, ) -> Matrix<T, Const<6>, Const<2>, ArrayStorage<T, 6, 2>>

Initializes this matrix from its components.

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

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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, ) -> Matrix<T, Const<6>, Const<3>, ArrayStorage<T, 6, 3>>

Initializes this matrix from its components.

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

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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, ) -> Matrix<T, Const<6>, Const<4>, ArrayStorage<T, 6, 4>>

Initializes this matrix from its components.

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

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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, ) -> Matrix<T, Const<6>, Const<5>, ArrayStorage<T, 6, 5>>

Initializes this matrix from its components.

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

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pub const fn new(x: T) -> Matrix<T, Const<1>, Const<1>, ArrayStorage<T, 1, 1>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, ) -> Matrix<T, Const<1>, Const<2>, ArrayStorage<T, 1, 2>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, z: T, ) -> Matrix<T, Const<1>, Const<3>, ArrayStorage<T, 1, 3>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, z: T, w: T, ) -> Matrix<T, Const<1>, Const<4>, ArrayStorage<T, 1, 4>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, z: T, w: T, a: T, ) -> Matrix<T, Const<1>, Const<5>, ArrayStorage<T, 1, 5>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, z: T, w: T, a: T, b: T, ) -> Matrix<T, Const<1>, Const<6>, ArrayStorage<T, 1, 6>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, ) -> Matrix<T, Const<2>, Const<1>, ArrayStorage<T, 2, 1>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, z: T, ) -> Matrix<T, Const<3>, Const<1>, ArrayStorage<T, 3, 1>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, z: T, w: T, ) -> Matrix<T, Const<4>, Const<1>, ArrayStorage<T, 4, 1>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, z: T, w: T, a: T, ) -> Matrix<T, Const<5>, Const<1>, ArrayStorage<T, 5, 1>>

Initializes this matrix from its components.

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

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pub const fn new( x: T, y: T, z: T, w: T, a: T, b: T, ) -> Matrix<T, Const<6>, Const<1>, ArrayStorage<T, 6, 1>>

Initializes this matrix from its components.

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impl<T, R> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>

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pub fn ith( i: usize, val: T, ) -> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>

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

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pub fn ith_axis( i: usize, ) -> Unit<Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>>

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

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pub fn x() -> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>
where <R as ToTypenum>::Typenum: Cmp<UTerm, Output = Greater>,

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

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pub fn y() -> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UTerm, B1>, Output = Greater>,

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

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pub fn z() -> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UInt<UTerm, B1>, B0>, Output = Greater>,

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

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pub fn w() -> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UInt<UTerm, B1>, B1>, Output = Greater>,

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

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pub fn a() -> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UInt<UInt<UTerm, B1>, B0>, B0>, Output = Greater>,

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

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pub fn b() -> Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UInt<UInt<UTerm, B1>, B0>, B1>, Output = Greater>,

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

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pub fn x_axis() -> Unit<Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>>
where <R as ToTypenum>::Typenum: Cmp<UTerm, Output = Greater>,

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

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pub fn y_axis() -> Unit<Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UTerm, B1>, Output = Greater>,

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

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pub fn z_axis() -> Unit<Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UInt<UTerm, B1>, B0>, Output = Greater>,

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

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pub fn w_axis() -> Unit<Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UInt<UTerm, B1>, B1>, Output = Greater>,

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

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pub fn a_axis() -> Unit<Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UInt<UInt<UTerm, B1>, B0>, B0>, Output = Greater>,

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

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pub fn b_axis() -> Unit<Matrix<T, R, Const<1>, <DefaultAllocator as Allocator<R>>::Buffer<T>>>
where <R as ToTypenum>::Typenum: Cmp<UInt<UInt<UInt<UTerm, B1>, B0>, B1>, Output = Greater>,

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

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

§Creating matrix views from &[T]

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

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

§Safety

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().

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

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().

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

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pub unsafe fn from_slice_generic_unchecked( data: &'a [T], start: usize, nrows: R, ncols: C, ) -> Matrix<T, R, C, ViewStorage<'a, T, R, C, Const<1>, R>>

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

§Safety

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().

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pub fn from_slice_generic( data: &'a [T], nrows: R, ncols: C, ) -> Matrix<T, R, C, ViewStorage<'a, T, R, C, Const<1>, R>>

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().

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

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pub fn from_slice( data: &'a [T], ) -> Matrix<T, R, C, ViewStorage<'a, T, R, C, Const<1>, R>>

Creates a new matrix view from the given data array.

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_unchecked( data: &'a [T], start: usize, ) -> Matrix<T, R, C, ViewStorage<'a, T, R, C, Const<1>, R>>

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

§Safety

data[start..start+rstride * cstride] must be within bounds.

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

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pub fn from_slice_with_strides( data: &'a [T], rstride: usize, cstride: usize, ) -> Matrix<T, R, C, ViewStorage<'a, T, R, C, Dyn, Dyn>>

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

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_with_strides_unchecked( data: &'a [T], start: usize, rstride: usize, cstride: usize, ) -> Matrix<T, R, C, ViewStorage<'a, T, R, C, Dyn, Dyn>>

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

§Safety

start, rstride, and cstride, with the given matrix size will not index outside of data.

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

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pub fn from_slice( data: &'a [T], ncols: usize, ) -> Matrix<T, R, Dyn, ViewStorage<'a, T, R, Dyn, Const<1>, R>>

Creates a new matrix view from the given data array.

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_unchecked( data: &'a [T], start: usize, ncols: usize, ) -> Matrix<T, R, Dyn, ViewStorage<'a, T, R, Dyn, Const<1>, R>>

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

§Safety

data[start..start+rstride * cstride] must be within bounds.

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

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pub fn from_slice_with_strides( data: &'a [T], ncols: usize, rstride: usize, cstride: usize, ) -> Matrix<T, R, Dyn, ViewStorage<'a, T, R, Dyn, Dyn, Dyn>>

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

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_with_strides_unchecked( data: &'a [T], start: usize, ncols: usize, rstride: usize, cstride: usize, ) -> Matrix<T, R, Dyn, ViewStorage<'a, T, R, Dyn, Dyn, Dyn>>

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

§Safety

start, rstride, and cstride, with the given matrix size will not index outside of data.

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

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pub fn from_slice( data: &'a [T], nrows: usize, ) -> Matrix<T, Dyn, C, ViewStorage<'a, T, Dyn, C, Const<1>, Dyn>>

Creates a new matrix view from the given data array.

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_unchecked( data: &'a [T], start: usize, nrows: usize, ) -> Matrix<T, Dyn, C, ViewStorage<'a, T, Dyn, C, Const<1>, Dyn>>

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

§Safety

data[start..start+rstride * cstride] must be within bounds.

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

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pub fn from_slice_with_strides( data: &'a [T], nrows: usize, rstride: usize, cstride: usize, ) -> Matrix<T, Dyn, C, ViewStorage<'a, T, Dyn, C, Dyn, Dyn>>

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

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_with_strides_unchecked( data: &'a [T], start: usize, nrows: usize, rstride: usize, cstride: usize, ) -> Matrix<T, Dyn, C, ViewStorage<'a, T, Dyn, C, Dyn, Dyn>>

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

§Safety

start, rstride, and cstride, with the given matrix size will not index outside of data.

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

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pub fn from_slice( data: &'a [T], nrows: usize, ncols: usize, ) -> Matrix<T, Dyn, Dyn, ViewStorage<'a, T, Dyn, Dyn, Const<1>, Dyn>>

Creates a new matrix view from the given data array.

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_unchecked( data: &'a [T], start: usize, nrows: usize, ncols: usize, ) -> Matrix<T, Dyn, Dyn, ViewStorage<'a, T, Dyn, Dyn, Const<1>, Dyn>>

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

§Safety

data[start..start+rstride * cstride] must be within bounds.

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

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pub fn from_slice_with_strides( data: &'a [T], nrows: usize, ncols: usize, rstride: usize, cstride: usize, ) -> Matrix<T, Dyn, Dyn, ViewStorage<'a, T, Dyn, Dyn, Dyn, Dyn>>

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

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_with_strides_unchecked( data: &'a [T], start: usize, nrows: usize, ncols: usize, rstride: usize, cstride: usize, ) -> Matrix<T, Dyn, Dyn, ViewStorage<'a, T, Dyn, Dyn, Dyn, Dyn>>

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

§Safety

start, rstride, and cstride, with the given matrix size will not index outside of data.

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

§Creating mutable matrix views from &mut [T]

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

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

§Safety

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().

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

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().

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

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pub unsafe fn from_slice_generic_unchecked( data: &'a mut [T], start: usize, nrows: R, ncols: C, ) -> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Const<1>, R>>

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

§Safety

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().

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pub fn from_slice_generic( data: &'a mut [T], nrows: R, ncols: C, ) -> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Const<1>, R>>

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().

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

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pub fn from_slice( data: &'a mut [T], ) -> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Const<1>, R>>

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

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_unchecked( data: &'a mut [T], start: usize, ) -> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Const<1>, R>>

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

§Safety

data[start..start+(R * C)] must be within bounds.

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

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pub fn from_slice_with_strides_mut( data: &'a mut [T], rstride: usize, cstride: usize, ) -> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Dyn, Dyn>>

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

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_with_strides_unchecked( data: &'a mut [T], start: usize, rstride: usize, cstride: usize, ) -> Matrix<T, R, C, ViewStorageMut<'a, T, R, C, Dyn, Dyn>>

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

§Safety

data[start..start+rstride * cstride] must be within bounds.

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

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pub fn from_slice( data: &'a mut [T], ncols: usize, ) -> Matrix<T, R, Dyn, ViewStorageMut<'a, T, R, Dyn, Const<1>, R>>

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

Panics if data does not contain enough elements.

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pub unsafe fn from_slice_unchecked( data: &'a mut [T], start: usize, ncols: usize, ) -> Matrix<T, R, Dyn, ViewStorageMut<'a, T, R, Dyn, Const<1>, R>>

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

§Safety

data[start..start+(R * C)] must be within bounds.

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

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pub fn from_slice_with_strides_mut( data: &'a mut [T], ncols: usize, rstride: usize, cstride: usize, ) -> Matrix<T, R, Dyn, ViewStorageMut<'a, T, R, Dyn, Dyn, Dyn>>

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, ) -> Matrix<T, R, Dyn, ViewStorageMut<'a, T, R, Dyn, Dyn, Dyn>>

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

§Safety

data[start..start+rstride * cstride] must be within bounds.

source§

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

source

pub fn from_slice( data: &'a mut [T], nrows: usize, ) -> Matrix<T, Dyn, C, ViewStorageMut<'a, T, Dyn, C, Const<1>, Dyn>>

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, ) -> Matrix<T, Dyn, C, ViewStorageMut<'a, T, Dyn, C, Const<1>, Dyn>>

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

§Safety

data[start..start+(R * C)] must be within bounds.

source§

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

source

pub fn from_slice_with_strides_mut( data: &'a mut [T], nrows: usize, rstride: usize, cstride: usize, ) -> Matrix<T, Dyn, C, ViewStorageMut<'a, T, Dyn, C, Dyn, Dyn>>

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, ) -> Matrix<T, Dyn, C, ViewStorageMut<'a, T, Dyn, C, Dyn, Dyn>>

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

§Safety

data[start..start+rstride * cstride] must be within bounds.

source§

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

source

pub fn from_slice( data: &'a mut [T], nrows: usize, ncols: usize, ) -> Matrix<T, Dyn, Dyn, ViewStorageMut<'a, T, Dyn, Dyn, Const<1>, Dyn>>

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, ) -> Matrix<T, Dyn, Dyn, ViewStorageMut<'a, T, Dyn, Dyn, Const<1>, Dyn>>

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

§Safety

data[start..start+(R * C)] must be within bounds.

source§

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

source

pub fn from_slice_with_strides_mut( data: &'a mut [T], nrows: usize, ncols: usize, rstride: usize, cstride: usize, ) -> Matrix<T, Dyn, Dyn, ViewStorageMut<'a, T, Dyn, Dyn, Dyn, Dyn>>

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, ) -> Matrix<T, Dyn, Dyn, ViewStorageMut<'a, T, Dyn, Dyn, Dyn, Dyn>>

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

§Safety

data[start..start+rstride * cstride] must be within bounds.

source§

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

§Triangular matrix extraction

source

pub fn upper_triangle( &self, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

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

source

pub fn lower_triangle( &self, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

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

source§

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

§Rows and columns extraction

source

pub fn select_rows<'a, I>( &self, irows: I, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

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

source

pub fn select_columns<'a, I>( &self, icols: I, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

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

source§

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

§Set rows, columns, and diagonal

source

pub fn set_diagonal<R2, S2>(&mut self, diag: &Matrix<T, R2, Const<1>, S2>)
where R2: Dim, R: DimMin<C>, S2: RawStorage<T, R2>, ShapeConstraint: DimEq<<R as DimMin<C>>::Output, 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, S2>(&mut self, i: usize, row: &Matrix<T, Const<1>, C2, S2>)
where C2: Dim, S2: RawStorage<T, Const<1>, 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, S2>( &mut self, i: usize, column: &Matrix<T, R2, Const<1>, S2>, )
where R2: Dim, S2: RawStorage<T, R2>, ShapeConstraint: SameNumberOfRows<R, R2>,

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

source§

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

§In-place filling

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.
source§

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

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.

source§

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

§In-place swapping

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.

source§

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

§Rows and columns removal

source

pub fn remove_column( self, i: usize, ) -> Matrix<T, R, <C as DimSub<Const<1>>>::Output, <DefaultAllocator as Allocator<R, <C as DimSub<Const<1>>>::Output>>::Buffer<T>>
where C: DimSub<Const<1>>, DefaultAllocator: Reallocator<T, R, C, R, <C as DimSub<Const<1>>>::Output>,

Removes the i-th column from this matrix.

source

pub fn remove_columns_at( self, indices: &[usize], ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
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], ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
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, ) -> Matrix<T, R, <C as DimSub<Const<D>>>::Output, <DefaultAllocator as Allocator<R, <C as DimSub<Const<D>>>::Output>>::Buffer<T>>
where C: DimSub<Const<D>>, DefaultAllocator: Reallocator<T, R, C, R, <C as DimSub<Const<D>>>::Output>,

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

source

pub fn remove_columns( self, i: usize, n: usize, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
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, ) -> Matrix<T, R, <C as DimSub<D>>::Output, <DefaultAllocator as Allocator<R, <C as DimSub<D>>::Output>>::Buffer<T>>
where D: Dim, C: DimSub<D>, DefaultAllocator: Reallocator<T, R, C, R, <C as DimSub<D>>::Output>,

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, ) -> Matrix<T, <R as DimSub<Const<1>>>::Output, C, <DefaultAllocator as Allocator<<R as DimSub<Const<1>>>::Output, C>>::Buffer<T>>
where R: DimSub<Const<1>>, DefaultAllocator: Reallocator<T, R, C, <R as DimSub<Const<1>>>::Output, C>,

Removes the i-th row from this matrix.

source

pub fn remove_fixed_rows<const D: usize>( self, i: usize, ) -> Matrix<T, <R as DimSub<Const<D>>>::Output, C, <DefaultAllocator as Allocator<<R as DimSub<Const<D>>>::Output, C>>::Buffer<T>>
where R: DimSub<Const<D>>, DefaultAllocator: Reallocator<T, R, C, <R as DimSub<Const<D>>>::Output, 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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
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, ) -> Matrix<T, <R as DimSub<D>>::Output, C, <DefaultAllocator as Allocator<<R as DimSub<D>>::Output, C>>::Buffer<T>>
where D: Dim, R: DimSub<D>, DefaultAllocator: Reallocator<T, R, C, <R as DimSub<D>>::Output, 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.

source§

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

§Rows and columns insertion

source

pub fn insert_column( self, i: usize, val: T, ) -> Matrix<T, R, <C as DimAdd<Const<1>>>::Output, <DefaultAllocator as Allocator<R, <C as DimAdd<Const<1>>>::Output>>::Buffer<T>>
where C: DimAdd<Const<1>>, DefaultAllocator: Reallocator<T, R, C, R, <C as DimAdd<Const<1>>>::Output>,

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, ) -> Matrix<T, R, <C as DimAdd<Const<D>>>::Output, <DefaultAllocator as Allocator<R, <C as DimAdd<Const<D>>>::Output>>::Buffer<T>>
where C: DimAdd<Const<D>>, DefaultAllocator: Reallocator<T, R, C, R, <C as DimAdd<Const<D>>>::Output>,

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

source

pub fn insert_columns( self, i: usize, n: usize, val: T, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
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, ) -> Matrix<MaybeUninit<T>, R, <C as DimAdd<D>>::Output, <DefaultAllocator as Allocator<R, <C as DimAdd<D>>::Output>>::BufferUninit<T>>
where D: Dim, C: DimAdd<D>, DefaultAllocator: Reallocator<T, R, C, R, <C as DimAdd<D>>::Output>,

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

§Safety

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, ) -> Matrix<T, <R as DimAdd<Const<1>>>::Output, C, <DefaultAllocator as Allocator<<R as DimAdd<Const<1>>>::Output, C>>::Buffer<T>>
where R: DimAdd<Const<1>>, DefaultAllocator: Reallocator<T, R, C, <R as DimAdd<Const<1>>>::Output, 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, ) -> Matrix<T, <R as DimAdd<Const<D>>>::Output, C, <DefaultAllocator as Allocator<<R as DimAdd<Const<D>>>::Output, C>>::Buffer<T>>
where R: DimAdd<Const<D>>, DefaultAllocator: Reallocator<T, R, C, <R as DimAdd<Const<D>>>::Output, 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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
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, ) -> Matrix<MaybeUninit<T>, <R as DimAdd<D>>::Output, C, <DefaultAllocator as Allocator<<R as DimAdd<D>>::Output, C>>::BufferUninit<T>>
where D: Dim, R: DimAdd<D>, DefaultAllocator: Reallocator<T, R, C, <R as DimAdd<D>>::Output, C>,

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

§Safety

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

source§

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

§Resizing and reshaping

source

pub fn resize( self, new_nrows: usize, new_ncols: usize, val: T, ) -> Matrix<T, Dyn, Dyn, <DefaultAllocator as Allocator<Dyn, Dyn>>::Buffer<T>>
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, ) -> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>
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, ) -> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>
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, ) -> Matrix<T, Const<R2>, Const<C2>, <DefaultAllocator as Allocator<Const<R2>, Const<C2>>>::Buffer<T>>
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, C2>( self, new_nrows: R2, new_ncols: C2, val: T, ) -> Matrix<T, R2, C2, <DefaultAllocator as Allocator<R2, C2>>::Buffer<T>>
where R2: Dim, C2: Dim, DefaultAllocator: Reallocator<T, R, C, R2, C2>,

Resizes self such that it has dimensions new_nrows × 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 as ReshapableStorage<T, R, C, R2, C2>>::Output>
where R2: Dim, C2: Dim, S: ReshapableStorage<T, R, C, R2, C2>,

Reshapes self such that it has dimensions new_nrows × 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.

§Examples

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);
source§

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

§In-place resizing

source

pub fn resize_mut(&mut self, new_nrows: usize, new_ncols: usize, val: T)

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.

source§

impl<T, C> Matrix<T, Dyn, C, <DefaultAllocator as Allocator<Dyn, C>>::Buffer<T>>

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).

source§

impl<T, R> Matrix<T, R, Dyn, <DefaultAllocator as Allocator<R, Dyn>>::Buffer<T>>

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).

source§

impl<T, R, C, S> Matrix<T, R, C, S>
where R: Dim, C: Dim, S: RawStorage<T, R, C>,

§Views based on ranges

§Indices to Individual Elements
§Two-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);
§Linear Address Indexing
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));
§Indices to Individual Rows and Columns
§Index to a Row
let matrix = Matrix2::new(0, 2,
                          1, 3);

assert!(matrix.index((0, ..))
    .eq(&Matrix1x2::new(0, 2)));
§Index to a Column
let matrix = Matrix2::new(0, 2,
                          1, 3);

assert!(matrix.index((.., 0))
    .eq(&Matrix2x1::new(0,
                        1)));
§Indices to Parts of Individual Rows and Columns
§Index 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)));
§Index 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)));
§Indices to Ranges of Rows and Columns
§Index 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)));
§Index 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 as MatrixIndex<'a, T, R, C, S>>::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 as MatrixIndexMut<'a, T, R, C, S>>::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.

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pub fn index<'a, I>( &'a self, index: I, ) -> <I as MatrixIndex<'a, T, R, C, S>>::Output
where 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 as MatrixIndexMut<'a, T, R, C, S>>::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 panics if the index is out of bounds.

source

pub unsafe fn get_unchecked<'a, I>( &'a self, index: I, ) -> <I as MatrixIndex<'a, T, R, C, S>>::Output
where I: MatrixIndex<'a, T, R, C, S>,

Produces a view of the data at the given index, without doing any bounds checking.

§Safety

index must within bounds of the array.

source

pub unsafe fn get_unchecked_mut<'a, I>( &'a mut self, index: I, ) -> <I as MatrixIndexMut<'a, T, R, C, S>>::OutputMut
where 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.

§Safety

index must within bounds of the array.

source§

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.

§Safety

The storage dimension must match the given dimensions.

source§

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>, ) -> Matrix<T, Const<R>, Const<C>, ArrayStorage<T, R, C>>

Creates a new statically-allocated matrix from the given ArrayStorage.

This method exists primarily as a workaround for the fact that from_data can not work in const fn contexts.

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impl<T> Matrix<T, Dyn, Dyn, VecStorage<T, Dyn, Dyn>>

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pub const fn from_vec_storage( storage: VecStorage<T, Dyn, Dyn>, ) -> Matrix<T, Dyn, Dyn, VecStorage<T, Dyn, Dyn>>

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.

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impl<T> Matrix<T, Dyn, Const<1>, VecStorage<T, Dyn, Const<1>>>

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pub const fn from_vec_storage( storage: VecStorage<T, Dyn, Const<1>>, ) -> Matrix<T, Dyn, Const<1>, VecStorage<T, Dyn, Const<1>>>

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.

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impl<T> Matrix<T, Const<1>, Dyn, VecStorage<T, Const<1>, Dyn>>

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pub const fn from_vec_storage( storage: VecStorage<T, Const<1>, Dyn>, ) -> Matrix<T, Const<1>, Dyn, VecStorage<T, Const<1>, Dyn>>

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.

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

source

pub unsafe fn assume_init( self, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

Assumes a matrix’s entries to be initialized. This operation should be near zero-cost.

§Safety

The user must make sure that every single entry of the buffer has been initialized, or Undefined Behavior will immediately occur.

source§

impl<T, R, C, S> Matrix<T, R, C, S>
where R: Dim, C: Dim, S: RawStorage<T, R, C>,

source

pub fn from_data(data: S) -> Matrix<T, R, C, S>

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).

§Example
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.

§Example
let mat = Matrix3x4::<f32>::zeros();
assert_eq!(mat.nrows(), 3);
source

pub fn ncols(&self) -> usize

The number of columns of this matrix.

§Example
let mat = Matrix3x4::<f32>::zeros();
assert_eq!(mat.ncols(), 4);
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pub fn strides(&self) -> (usize, usize)

The strides (row stride, column stride) of this matrix.

§Example
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));
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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.

§Example
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]);
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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.

§Example
let m = Matrix2::new(1, 2,
                     3, 4);
let ptr = m.as_ptr();
assert_eq!(unsafe { *ptr }, m[0]);
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pub fn relative_eq<R2, C2, SB>( &self, other: &Matrix<T, R2, C2, SB>, eps: <T as AbsDiffEq>::Epsilon, max_relative: <T as AbsDiffEq>::Epsilon, ) -> bool
where T: RelativeEq + Scalar, R2: Dim, C2: Dim, SB: Storage<T, R2, C2>, <T as AbsDiffEq>::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.

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pub fn eq<R2, C2, SB>(&self, other: &Matrix<T, R2, C2, SB>) -> bool
where T: PartialEq, R2: Dim, C2: Dim, SB: RawStorage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

Tests whether self and rhs are exactly equal.

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pub fn into_owned( self, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where T: Scalar, S: Storage<T, R, C>, DefaultAllocator: Allocator<R, C>,

Moves this matrix into one that owns its data.

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pub fn into_owned_sum<R2, C2>( self, ) -> Matrix<T, <ShapeConstraint as SameNumberOfRows<R, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C, C2>>::Representative, <DefaultAllocator as Allocator<<ShapeConstraint as SameNumberOfRows<R, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C, C2>>::Representative>>::Buffer<T>>
where T: Scalar, S: Storage<T, R, C>, R2: Dim, C2: Dim, DefaultAllocator: SameShapeAllocator<R, C, R2, C2>, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

Moves this matrix into one that owns its data. The actual type of the result depends on matrix storage combination rules for addition.

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pub fn clone_owned( &self, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>
where T: Scalar, S: Storage<T, R, C>, DefaultAllocator: Allocator<R, C>,

Clones this matrix to one that owns its data.

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pub fn clone_owned_sum<R2, C2>( &self, ) -> Matrix<T, <ShapeConstraint as SameNumberOfRows<R, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C, C2>>::Representative, <DefaultAllocator as Allocator<<ShapeConstraint as SameNumberOfRows<R, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C, C2>>::Representative>>::Buffer<T>>
where T: Scalar, S: Storage<T, R, C>, R2: Dim, C2: Dim, DefaultAllocator: SameShapeAllocator<R, C, R2, C2>, ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>,

Clones this matrix into one that owns its data. The actual type of the result depends on matrix storage combination rules for addition.

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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.

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pub fn transpose( &self, ) -> Matrix<T, C, R, <DefaultAllocator as Allocator<C, R>>::Buffer<T>>

Transposes self.

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impl<T, R, C, S> Matrix<T, R, C, S>
where R: Dim, C: Dim, S: RawStorage<T, R, C>,

§Elementwise mapping and folding

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pub fn map<T2, F>( &self, f: F, ) -> Matrix<T2, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T2>>
where T2: Scalar, F: FnMut(T) -> T2, T: Scalar, DefaultAllocator: Allocator<R, C>,

Returns a matrix containing the result of f applied to each of its entries.

source

pub fn cast<T2>( self, ) -> Matrix<T2, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T2>>
where T2: Scalar, T: Scalar, Matrix<T2, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T2>>: SupersetOf<Matrix<T, R, C, S>>, DefaultAllocator: Allocator<R, C>,

Cast the components of self to another type.

§Example
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>( self, ) -> Option<Matrix<T2, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T2>>>
where T2: Scalar, T: Scalar, Matrix<T, R, C, S>: SupersetOf<Matrix<T2, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T2>>>, DefaultAllocator: Allocator<R, C>,

Attempts to cast the components of self to another type.

§Example
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, ) -> T2
where 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.
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pub fn map_with_location<T2, F>( &self, f: F, ) -> Matrix<T2, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T2>>
where T2: Scalar, F: FnMut(usize, usize, T) -> T2, T: Scalar, DefaultAllocator: Allocator<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).

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pub fn zip_map<T2, N3, S2, F>( &self, rhs: &Matrix<T2, R, C, S2>, f: F, ) -> Matrix<N3, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<N3>>
where T: Scalar, T2: Scalar, N3: Scalar, S2: RawStorage<T2, R, C>, F: FnMut(T, T2) -> N3, DefaultAllocator: Allocator<R, C>,

Returns a matrix containing the result of f applied to each entries of self and rhs.

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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, ) -> Matrix<N4, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<N4>>
where T: Scalar, T2: Scalar, N3: Scalar, N4: Scalar, S2: RawStorage<T2, R, C>, S3: RawStorage<N3, R, C>, F: FnMut(T, T2, N3) -> N4, DefaultAllocator: Allocator<R, C>,

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) -> Acc
where T: Scalar,

Folds a function f on each entry of self.

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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, ) -> Acc
where 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.

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pub fn apply<F>(&mut self, f: F)
where F: FnMut(&mut T), S: RawStorageMut<T, R, C>,

Applies a closure f to modify each component of self.

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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.

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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.

source§

impl<T, R, C, S> Matrix<T, R, C, S>
where R: Dim, C: Dim, S: RawStorage<T, R, C>,

§Iteration on components, rows, and columns

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pub fn iter(&self) -> MatrixIter<'_, T, R, C, S>

Iterates through this matrix coordinates in column-major order.

§Example
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());
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pub fn row_iter(&self) -> RowIter<'_, T, R, C, S>

Iterate through the rows of this matrix.

§Example
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))
}
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pub fn column_iter(&self) -> ColumnIter<'_, T, R, C, S>

Iterate through the columns of this matrix.

§Example
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))
}
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pub fn iter_mut(&mut self) -> MatrixIterMut<'_, T, R, C, S>
where S: RawStorageMut<T, R, C>,

Mutably iterates through this matrix coordinates.

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pub fn row_iter_mut(&mut self) -> RowIterMut<'_, T, R, C, S>
where S: RawStorageMut<T, R, C>,

Mutably iterates through this matrix rows.

§Example
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);
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pub fn column_iter_mut(&mut self) -> ColumnIterMut<'_, T, R, C, S>
where S: RawStorageMut<T, R, C>,

Mutably iterates through this matrix columns.

§Example
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);
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impl<T, R, C, S> Matrix<T, R, C, S>
where R: Dim, C: Dim, S: RawStorageMut<T, R, C>,

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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.

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pub unsafe fn swap_unchecked( &mut self, row_cols1: (usize, usize), row_cols2: (usize, usize), )

Swaps two entries without bound-checking.

§Safety

Both (r, c) must have r < nrows(), c < ncols().

source

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>(self, f: F) -> Matrix<T, R, C, S>
where F: FnMut(&mut T),

Returns self with each of its components replaced by the result of a closure f applied on it.

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impl<T, D, S> Matrix<T, D, Const<1>, S>
where D: Dim, S: RawStorage<T, D>,

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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.

§Safety

i must be less than D.

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impl<T, D, S> Matrix<T, D, Const<1>, S>
where D: Dim, S: RawStorageMut<T, D>,

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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.

§Safety

i must be less than D.

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impl<T, R, C, S> Matrix<T, R, C, S>
where R: Dim, C: Dim, S: RawStorage<T, R, C> + IsContiguous,

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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, C, S> Matrix<T, R, C, S>
where R: Dim, C: Dim, S: RawStorageMut<T, R, C> + IsContiguous,

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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, D, S> Matrix<T, D, D, S>
where T: Scalar, D: Dim, S: RawStorageMut<T, D, D>,

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pub fn transpose_mut(&mut self)

Transposes the square matrix self in-place.

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impl<T, R, C, S> Matrix<T, R, C, S>
where T: SimdComplexField, R: Dim, C: Dim, S: RawStorage<T, R, C>,

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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, ) -> Matrix<T, C, R, <DefaultAllocator as Allocator<C, R>>::Buffer<T>>

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>,

👎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, ) -> Matrix<T, C, R, <DefaultAllocator as Allocator<C, R>>::Buffer<T>>

👎Deprecated: Renamed self.adjoint().

The conjugate transposition of self.

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pub fn conjugate( &self, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

The conjugate of self.

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pub fn unscale( &self, real: <T as SimdComplexField>::SimdRealField, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

Divides each component of the complex matrix self by the given real.

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pub fn scale( &self, real: <T as SimdComplexField>::SimdRealField, ) -> Matrix<T, R, C, <DefaultAllocator as Allocator<R, C>>::Buffer<T>>

Multiplies each component of the complex matrix self by the given real.

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

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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 as SimdComplexField>::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 as SimdComplexField>::SimdRealField)

Multiplies each component of the complex matrix self by the given real.

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impl<T, D, S> Matrix<T, D, D, S>
where T: SimdComplexField, D: Dim, S: RawStorageMut<T, D, D>,

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pub fn conjugate_transform_mut(&mut self)

👎Deprecated: Renamed to self.adjoint_mut().

Sets self to its adjoint.

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pub fn adjoint_mut(&mut self)

Sets self to its adjoint (aka. conjugate-transpose).

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impl<T, D, S> Matrix<T, D, D, S>
where T: Scalar, D: Dim, S: RawStorage<T, D, D>,

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pub fn diagonal( &self, ) -> Matrix<T, D, Const<1>, <DefaultAllocator as Allocator<D>>::Buffer<T>>

The diagonal of this matrix.

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pub fn map_diagonal<T2>( &self, f: impl FnMut(T) -> T2, ) -> Matrix<T2, D, Const<1>, <DefaultAllocator as Allocator<D>>::Buffer<T2>>

Apply the given function to this matrix’s diagonal and returns it.

This is a more efficient version of self.diagonal().map(f) since this allocates only once.

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pub fn trace(&self) -> T
where T: Scalar + Zero + ClosedAddAssign,

Computes a trace of a square matrix, i.e., the sum of its diagonal elements.

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impl<T, D, S> Matrix<T, D, D, S>
where T: SimdComplexField, D: Dim, S: Storage<T, D, D>,

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pub fn symmetric_part( &self, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>

The symmetric part of self, i.e., 0.5 * (self + self.transpose()).

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pub fn hermitian_part( &self, ) -> Matrix<T, D, D, <DefaultAllocator as Allocator<D, D>>::Buffer<T>>

The hermitian part of self, i.e., 0.5 * (self + self.adjoint()).

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impl<T, D, S> Matrix<T, D, D, S>
where T: Scalar + Zero + One, D: DimAdd<Const<1>> + IsNotStaticOne, S: RawStorage<T, D, D>,

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pub fn to_homogeneous( &self, ) -> Matrix<T, <D as DimAdd<Const<1>>>::Output, <D as DimAdd<Const<1>>>::Output, <DefaultAllocator as Allocator<<D as DimAdd<Const<1>>>::Output, <D as DimAdd<Const<1>>>::Output>>::Buffer<T>>
where DefaultAllocator: Allocator<<D as DimAdd<Const<1>>>::Output, <D as DimAdd<Const<1>>>::Output>,

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, D, S> Matrix<T, D, Const<1>, S>
where T: Scalar + Zero, D: DimAdd<Const<1>>, S: RawStorage<T, D>,

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pub fn to_homogeneous( &self, ) -> Matrix<T, <D as DimAdd<Const<1>>>::Output, Const<1>, <DefaultAllocator as Allocator<<D as DimAdd<Const<1>>>::Output>>::Buffer<T>>

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: Matrix<T, <D as DimAdd<Const<1>>>::Output, Const<1>, SB>, ) -> Option<Matrix<T, D, Const<1>, <DefaultAllocator as Allocator<D>>::Buffer<T>>>
where SB: RawStorage<T, <D as DimAdd<Const<1>>>::Output>, DefaultAllocator: Allocator<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.

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impl<T, D, S> Matrix<T, D, Const<1>, S>
where T: Scalar, D: DimAdd<Const<1>>, S: RawStorage<T, D>,

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pub fn push( &self, element: T, ) -> Matrix<T, <D as DimAdd<Const<1>>>::Output, Const<1>, <DefaultAllocator as Allocator<<D as DimAdd<Const<1>>>::Output>>::Buffer<T>>

Constructs a new vector of higher dimension by appending element to the end of self.

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impl<T, R, C, S> Matrix<T, R, C, S>
where T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign, R: Dim, C: Dim, S: RawStorage<T, R, C>,

§Cross product

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pub fn perp<R2, C2, SB>(&self, b: &Matrix<T, R2, C2, SB>) -> T
where R2: Dim, C2: Dim, SB: RawStorage<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R, Const<2>> + SameNumberOfColumns<C, Const<1>> + SameNumberOfRows<R2, Const<2>> + SameNumberOfColumns<C2, Const<1>>,

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>, ) -> Matrix<T, <ShapeConstraint as SameNumberOfRows<R, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C, C2>>::Representative, <DefaultAllocator as Allocator<<ShapeConstraint as SameNumberOfRows<R, R2>>::Representative, <ShapeConstraint as SameNumberOfColumns<C, C2>>::Representative>>::Buffer<T>>
where R2: Dim, C2: Dim, SB: RawStorage<T, R2, C2>, DefaultAllocator: SameShapeAllocator<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.

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impl<T, S> Matrix<T, Const<3>, Const<1>, S>
where T: Scalar + Field, S: RawStorage<T, Const<3>>,

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pub fn cross_matrix( &self, ) -> Matrix<T, Const<3>, Const<3>, <DefaultAllocator as Allocator<Const<3>, Const<3>>>::Buffer<T>>

Computes the matrix M such that for all vector v we have M * v == self.cross(&v).

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impl<T, R, C, S> Matrix<T, R, C, S>
where T: SimdComplexField, R: Dim, C: Dim, S: Storage<T, R, C>,

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pub fn angle<R2, C2, SB>( &self, other: &Matrix<T, R2, C2, SB>, ) -> <T as SimdComplexField>::SimdRealField
where R2: Dim, C2: Dim, SB: Storage<T, R2, C2>, ShapeConstraint: DimEq<R, R2> + DimEq<C, C2>,

The smallest angle between two vectors.

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impl<T, S> Matrix<T, Const<1>, Const<1>, S>
where S: RawStorage<T, Const<1>>,

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pub fn as_scalar(&self) -> &T

Returns a reference to the single