Expand description
A matrix with one column and D
rows.
Implementations§
source§impl<N: Scalar + PartialOrd + Signed, D: Dim, S: Storage<N, D>> Vector<N, D, S>
impl<N: Scalar + PartialOrd + Signed, D: Dim, S: Storage<N, D>> Vector<N, D, S>
sourcepub fn imax(&self) -> usize
pub fn imax(&self) -> usize
Computes the index of the vector component with the largest value.
Examples:
let vec = Vector3::new(11, -15, 13);
assert_eq!(vec.imax(), 2);
sourcepub fn iamax(&self) -> usize
pub fn iamax(&self) -> usize
Computes the index of the vector component with the largest absolute value.
Examples:
let vec = Vector3::new(11, -15, 13);
assert_eq!(vec.iamax(), 1);
source§impl<N, D: Dim, S> Vector<N, D, S>where
N: Scalar + Zero + ClosedAdd + ClosedMul,
S: StorageMut<N, D>,
impl<N, D: Dim, S> Vector<N, D, S>where
N: Scalar + Zero + ClosedAdd + ClosedMul,
S: StorageMut<N, D>,
sourcepub fn axpy<D2: Dim, SB>(&mut self, a: N, x: &Vector<N, D2, SB>, b: N)where
SB: Storage<N, D2>,
ShapeConstraint: DimEq<D, D2>,
pub fn axpy<D2: Dim, SB>(&mut self, a: N, x: &Vector<N, D2, SB>, b: N)where
SB: Storage<N, D2>,
ShapeConstraint: DimEq<D, D2>,
Computes self = a * x + b * self
.
If be is zero, self
is never read from.
Examples:
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));
sourcepub fn gemv<R2: Dim, C2: Dim, D3: Dim, SB, SC>(
&mut self,
alpha: N,
a: &Matrix<N, R2, C2, SB>,
x: &Vector<N, D3, SC>,
beta: N
)where
N: One,
SB: Storage<N, R2, C2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<D, R2> + AreMultipliable<R2, C2, D3, U1>,
pub fn gemv<R2: Dim, C2: Dim, D3: Dim, SB, SC>(
&mut self,
alpha: N,
a: &Matrix<N, R2, C2, SB>,
x: &Vector<N, D3, SC>,
beta: N
)where
N: One,
SB: Storage<N, R2, C2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<D, R2> + AreMultipliable<R2, C2, D3, U1>,
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.
Examples:
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));
sourcepub fn gemv_symm<D2: Dim, D3: Dim, SB, SC>(
&mut self,
alpha: N,
a: &SquareMatrix<N, D2, SB>,
x: &Vector<N, D3, SC>,
beta: N
)where
N: One,
SB: Storage<N, D2, D2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, U1>,
pub fn gemv_symm<D2: Dim, D3: Dim, SB, SC>(
&mut self,
alpha: N,
a: &SquareMatrix<N, D2, SB>,
x: &Vector<N, D3, SC>,
beta: N
)where
N: One,
SB: Storage<N, D2, D2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<D, D2> + AreMultipliable<D2, D2, D3, U1>,
Computes self = alpha * a * x + beta * self
, where a
is a symmetric matrix, x
a
vector, and alpha, beta
two scalars.
If beta
is zero, self
is never read. If self
is read, only its lower-triangular part
(including the diagonal) is actually read.
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.gemv_symm(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.gemv_symm(10.0, &mat, &vec2, 5.0);
assert_eq!(vec1, Vector2::new(10.0, 20.0));
sourcepub fn gemv_tr<R2: Dim, C2: Dim, D3: Dim, SB, SC>(
&mut self,
alpha: N,
a: &Matrix<N, R2, C2, SB>,
x: &Vector<N, D3, SC>,
beta: N
)where
N: One,
SB: Storage<N, R2, C2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<D, C2> + AreMultipliable<C2, R2, D3, U1>,
pub fn gemv_tr<R2: Dim, C2: Dim, D3: Dim, SB, SC>(
&mut self,
alpha: N,
a: &Matrix<N, R2, C2, SB>,
x: &Vector<N, D3, SC>,
beta: N
)where
N: One,
SB: Storage<N, R2, C2>,
SC: Storage<N, D3>,
ShapeConstraint: DimEq<D, C2> + AreMultipliable<C2, R2, D3, U1>,
Computes self = alpha * a.transpose() * x + beta * self
, where a
is a matrix, x
a vector, and
alpha, beta
two scalars.
If beta
is zero, self
is never read.
Examples:
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§impl<N: Scalar, D: Dim, S: Storage<N, D>> Vector<N, D, S>
impl<N: Scalar, D: Dim, S: Storage<N, D>> Vector<N, D, S>
sourcepub unsafe fn vget_unchecked(&self, i: usize) -> &N
pub unsafe fn vget_unchecked(&self, i: usize) -> &N
Gets a reference to the i-th element of this column vector without bound checking.
source§impl<N: Scalar, D: Dim, S: StorageMut<N, D>> Vector<N, D, S>
impl<N: Scalar, D: Dim, S: StorageMut<N, D>> Vector<N, D, S>
sourcepub unsafe fn vget_unchecked_mut(&mut self, i: usize) -> &mut N
pub unsafe fn vget_unchecked_mut(&mut self, i: usize) -> &mut N
Gets a mutable reference to the i-th element of this column vector without bound checking.
source§impl<N: Scalar + Zero, D: DimAdd<U1>, S: Storage<N, D>> Vector<N, D, S>
impl<N: Scalar + Zero, D: DimAdd<U1>, S: Storage<N, D>> Vector<N, D, S>
sourcepub fn to_homogeneous(&self) -> VectorN<N, DimSum<D, U1>>where
DefaultAllocator: Allocator<N, DimSum<D, U1>>,
pub fn to_homogeneous(&self) -> VectorN<N, DimSum<D, U1>>where
DefaultAllocator: Allocator<N, DimSum<D, U1>>,
Computes the coordinates in projective space of this vector, i.e., appends a 0
to its
coordinates.
sourcepub fn from_homogeneous<SB>(
v: Vector<N, DimSum<D, U1>, SB>
) -> Option<VectorN<N, D>>where
SB: Storage<N, DimSum<D, U1>>,
DefaultAllocator: Allocator<N, D>,
pub fn from_homogeneous<SB>(
v: Vector<N, DimSum<D, U1>, SB>
) -> Option<VectorN<N, D>>where
SB: Storage<N, DimSum<D, U1>>,
DefaultAllocator: Allocator<N, 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.
source§impl<N: Real, S: Storage<N, U3>> Vector<N, U3, S>where
DefaultAllocator: Allocator<N, U3>,
impl<N: Real, S: Storage<N, U3>> Vector<N, U3, S>where
DefaultAllocator: Allocator<N, U3>,
sourcepub fn cross_matrix(&self) -> MatrixN<N, U3>
pub fn cross_matrix(&self) -> MatrixN<N, U3>
Computes the matrix M
such that for all vector v
we have M * v == self.cross(&v)
.
source§impl<N: Scalar + Zero + One + ClosedAdd + ClosedSub + ClosedMul, D: Dim, S: Storage<N, D>> Vector<N, D, S>
impl<N: Scalar + Zero + One + ClosedAdd + ClosedSub + ClosedMul, D: Dim, S: Storage<N, D>> Vector<N, D, S>
sourcepub fn lerp<S2: Storage<N, D>>(
&self,
rhs: &Vector<N, D, S2>,
t: N
) -> VectorN<N, D>where
DefaultAllocator: Allocator<N, D>,
pub fn lerp<S2: Storage<N, D>>(
&self,
rhs: &Vector<N, D, S2>,
t: N
) -> VectorN<N, D>where
DefaultAllocator: Allocator<N, D>,
Returns self * (1.0 - t) + rhs * t
, i.e., the linear blend of the vectors x and y using the scalar value a.
The value for a is not restricted to the range [0, 1]
.
Examples:
let x = Vector3::new(1.0, 2.0, 3.0);
let y = Vector3::new(10.0, 20.0, 30.0);
assert_eq!(x.lerp(&y, 0.1), Vector3::new(1.9, 3.8, 5.7));