2018-10-21 04:26:44 +08:00
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// Non-conventional component-wise operators.
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2016-12-05 05:44:42 +08:00
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2018-02-02 19:26:35 +08:00
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use num::{Signed, Zero};
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2018-05-19 23:15:15 +08:00
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use std::ops::{Add, Mul};
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2016-12-05 05:44:42 +08:00
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2018-02-02 19:26:35 +08:00
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use alga::general::{ClosedDiv, ClosedMul};
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2016-12-05 05:44:42 +08:00
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2019-03-23 21:29:07 +08:00
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use crate::base::allocator::{Allocator, SameShapeAllocator};
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use crate::base::constraint::{SameNumberOfColumns, SameNumberOfRows, ShapeConstraint};
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use crate::base::dimension::Dim;
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use crate::base::storage::{Storage, StorageMut};
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use crate::base::{DefaultAllocator, Matrix, MatrixMN, MatrixSum, Scalar};
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2016-12-05 05:44:42 +08:00
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2018-10-20 17:36:35 +08:00
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/// The type of the result of a matrix component-wise operation.
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2017-08-03 01:37:44 +08:00
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pub type MatrixComponentOp<N, R1, C1, R2, C2> = MatrixSum<N, R1, C1, R2, C2>;
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2016-12-05 05:44:42 +08:00
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impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
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2018-10-20 17:36:35 +08:00
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/// Computes the component-wise absolute value.
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///
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/// # Example
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///
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/// ```
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/// # use nalgebra::Matrix2;
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/// let a = Matrix2::new(0.0, 1.0,
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/// -2.0, -3.0);
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/// assert_eq!(a.abs(), Matrix2::new(0.0, 1.0, 2.0, 3.0))
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/// ```
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2016-12-05 05:44:42 +08:00
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#[inline]
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2017-08-03 01:37:44 +08:00
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pub fn abs(&self) -> MatrixMN<N, R, C>
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2018-02-02 19:26:35 +08:00
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where
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N: Signed,
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DefaultAllocator: Allocator<N, R, C>,
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{
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2016-12-05 05:44:42 +08:00
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let mut res = self.clone_owned();
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for e in res.iter_mut() {
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*e = e.abs();
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}
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res
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}
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// FIXME: add other operators like component_ln, component_pow, etc. ?
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}
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macro_rules! component_binop_impl(
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2018-02-02 19:26:40 +08:00
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($($binop: ident, $binop_mut: ident, $binop_assign: ident, $cmpy: ident, $Trait: ident . $op: ident . $op_assign: ident, $desc:expr, $desc_cmpy:expr, $desc_mut:expr);* $(;)*) => {$(
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2017-08-03 01:37:44 +08:00
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impl<N: Scalar, R1: Dim, C1: Dim, SA: Storage<N, R1, C1>> Matrix<N, R1, C1, SA> {
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2017-04-07 05:58:44 +08:00
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#[doc = $desc]
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2016-12-05 05:44:42 +08:00
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#[inline]
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2017-08-03 01:37:44 +08:00
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pub fn $binop<R2, C2, SB>(&self, rhs: &Matrix<N, R2, C2, SB>) -> MatrixComponentOp<N, R1, C1, R2, C2>
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2016-12-05 05:44:42 +08:00
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where N: $Trait,
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R2: Dim, C2: Dim,
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SB: Storage<N, R2, C2>,
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2017-08-03 01:37:44 +08:00
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DefaultAllocator: SameShapeAllocator<N, R1, C1, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
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2018-02-02 19:26:35 +08:00
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2017-08-03 01:37:44 +08:00
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assert_eq!(self.shape(), rhs.shape(), "Componentwise mul/div: mismatched matrix dimensions.");
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2016-12-05 05:44:42 +08:00
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let mut res = self.clone_owned_sum();
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2017-08-03 01:37:44 +08:00
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for j in 0 .. res.ncols() {
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for i in 0 .. res.nrows() {
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unsafe {
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2018-12-03 04:00:08 +08:00
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res.get_unchecked_mut((i, j)).$op_assign(*rhs.get_unchecked((i, j)));
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2017-08-03 01:37:44 +08:00
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}
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}
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2016-12-05 05:44:42 +08:00
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}
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2017-08-03 01:37:44 +08:00
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2016-12-05 05:44:42 +08:00
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res
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}
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}
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2017-08-03 01:37:44 +08:00
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impl<N: Scalar, R1: Dim, C1: Dim, SA: StorageMut<N, R1, C1>> Matrix<N, R1, C1, SA> {
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2018-02-02 19:26:27 +08:00
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// componentwise binop plus Y.
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2018-02-02 19:26:40 +08:00
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#[doc = $desc_cmpy]
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2018-02-02 19:26:27 +08:00
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#[inline]
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2018-02-02 19:26:40 +08:00
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pub fn $cmpy<R2, C2, SB, R3, C3, SC>(&mut self, alpha: N, a: &Matrix<N, R2, C2, SB>, b: &Matrix<N, R3, C3, SC>, beta: N)
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2018-02-02 19:26:27 +08:00
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where N: $Trait + Zero + Mul<N, Output = N> + Add<N, Output = N>,
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R2: Dim, C2: Dim,
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R3: Dim, C3: Dim,
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SB: Storage<N, R2, C2>,
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SC: Storage<N, R3, C3>,
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ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> +
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SameNumberOfRows<R1, R3> + SameNumberOfColumns<C1, C3> {
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assert_eq!(self.shape(), a.shape(), "Componentwise mul/div: mismatched matrix dimensions.");
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assert_eq!(self.shape(), b.shape(), "Componentwise mul/div: mismatched matrix dimensions.");
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if beta.is_zero() {
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for j in 0 .. self.ncols() {
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for i in 0 .. self.nrows() {
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unsafe {
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2018-12-03 04:00:08 +08:00
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let res = alpha * a.get_unchecked((i, j)).$op(*b.get_unchecked((i, j)));
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*self.get_unchecked_mut((i, j)) = res;
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2018-02-02 19:26:27 +08:00
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}
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}
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}
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}
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else {
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for j in 0 .. self.ncols() {
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for i in 0 .. self.nrows() {
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unsafe {
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2018-12-03 04:00:08 +08:00
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let res = alpha * a.get_unchecked((i, j)).$op(*b.get_unchecked((i, j)));
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*self.get_unchecked_mut((i, j)) = beta * *self.get_unchecked((i, j)) + res;
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2018-02-02 19:26:27 +08:00
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}
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}
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}
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}
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}
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2017-04-07 05:58:44 +08:00
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#[doc = $desc_mut]
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2016-12-05 05:44:42 +08:00
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#[inline]
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2017-08-14 01:53:04 +08:00
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pub fn $binop_assign<R2, C2, SB>(&mut self, rhs: &Matrix<N, R2, C2, SB>)
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2016-12-05 05:44:42 +08:00
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where N: $Trait,
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R2: Dim,
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C2: Dim,
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SB: Storage<N, R2, C2>,
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2017-08-03 01:37:44 +08:00
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ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
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assert_eq!(self.shape(), rhs.shape(), "Componentwise mul/div: mismatched matrix dimensions.");
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for j in 0 .. self.ncols() {
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for i in 0 .. self.nrows() {
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unsafe {
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2018-12-03 04:00:08 +08:00
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self.get_unchecked_mut((i, j)).$op_assign(*rhs.get_unchecked((i, j)));
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2017-08-03 01:37:44 +08:00
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}
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}
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2016-12-05 05:44:42 +08:00
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}
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}
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2017-08-14 01:53:04 +08:00
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#[doc = $desc_mut]
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#[inline]
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2018-10-16 15:47:04 +08:00
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#[deprecated(note = "This is renamed using the `_assign` suffix instead of the `_mut` suffix.")]
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2017-08-14 01:53:04 +08:00
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pub fn $binop_mut<R2, C2, SB>(&mut self, rhs: &Matrix<N, R2, C2, SB>)
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where N: $Trait,
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R2: Dim,
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C2: Dim,
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SB: Storage<N, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
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self.$binop_assign(rhs)
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}
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2016-12-05 05:44:42 +08:00
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}
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)*}
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);
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component_binop_impl!(
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2018-02-02 19:26:27 +08:00
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component_mul, component_mul_mut, component_mul_assign, cmpy, ClosedMul.mul.mul_assign,
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2018-10-20 17:36:35 +08:00
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r"
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Componentwise matrix or vector multiplication.
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# Example
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```
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# use nalgebra::Matrix2;
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let a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
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let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
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let expected = Matrix2::new(0.0, 5.0, 12.0, 21.0);
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assert_eq!(a.component_mul(&b), expected);
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```
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",
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r"
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Computes componentwise `self[i] = alpha * a[i] * b[i] + beta * self[i]`.
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# Example
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```
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# use nalgebra::Matrix2;
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let mut m = Matrix2::new(0.0, 1.0, 2.0, 3.0);
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let a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
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let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
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let expected = (a.component_mul(&b) * 5.0) + m * 10.0;
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m.cmpy(5.0, &a, &b, 10.0);
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assert_eq!(m, expected);
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```
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",
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r"
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Inplace componentwise matrix or vector multiplication.
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# Example
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```
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# use nalgebra::Matrix2;
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let mut a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
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let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
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let expected = Matrix2::new(0.0, 5.0, 12.0, 21.0);
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a.component_mul_assign(&b);
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assert_eq!(a, expected);
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```
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";
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2018-02-02 19:26:27 +08:00
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component_div, component_div_mut, component_div_assign, cdpy, ClosedDiv.div.div_assign,
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2018-10-20 17:36:35 +08:00
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r"
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Componentwise matrix or vector division.
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# Example
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```
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# use nalgebra::Matrix2;
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let a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
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let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
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let expected = Matrix2::new(0.0, 1.0 / 5.0, 2.0 / 6.0, 3.0 / 7.0);
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assert_eq!(a.component_div(&b), expected);
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```
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",
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r"
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Computes componentwise `self[i] = alpha * a[i] / b[i] + beta * self[i]`.
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# Example
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```
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# use nalgebra::Matrix2;
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let mut m = Matrix2::new(0.0, 1.0, 2.0, 3.0);
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let a = Matrix2::new(4.0, 5.0, 6.0, 7.0);
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let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
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let expected = (a.component_div(&b) * 5.0) + m * 10.0;
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m.cdpy(5.0, &a, &b, 10.0);
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assert_eq!(m, expected);
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```
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",
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r"
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Inplace componentwise matrix or vector division.
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# Example
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```
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# use nalgebra::Matrix2;
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let mut a = Matrix2::new(0.0, 1.0, 2.0, 3.0);
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let b = Matrix2::new(4.0, 5.0, 6.0, 7.0);
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let expected = Matrix2::new(0.0, 1.0 / 5.0, 2.0 / 6.0, 3.0 / 7.0);
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a.component_div_assign(&b);
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assert_eq!(a, expected);
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```
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";
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2016-12-05 05:44:42 +08:00
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// FIXME: add other operators like bitshift, etc. ?
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);
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