nalgebra/nalgebra-sparse/src/ops/impl_std_ops.rs

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use crate::csr::CsrMatrix;
use crate::csc::CscMatrix;
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use std::ops::{Add, Mul, MulAssign, Sub, Neg};
use crate::ops::serial::{spadd_csr_prealloc, spadd_csc_prealloc, spadd_pattern,
spmm_pattern, spmm_csr_prealloc, spmm_csc_prealloc};
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use nalgebra::{ClosedAdd, ClosedMul, ClosedSub, Scalar};
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use num_traits::{Zero, One};
use std::sync::Arc;
use crate::ops::{Op};
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/// Helper macro for implementing binary operators for different matrix types
/// See below for usage.
macro_rules! impl_bin_op {
($trait:ident, $method:ident,
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<$($life:lifetime),* $(,)? $($scalar_type:ident)?>($a:ident : $a_type:ty, $b:ident : $b_type:ty) -> $ret:ty $body:block)
=>
{
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impl<$($life,)* $($scalar_type)?> $trait<$b_type> for $a_type
where
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// Note: The Signed bound is currently required because we delegate e.g.
// Sub to SpAdd with negative coefficients. This is not well-defined for
// unsigned data types.
$($scalar_type: Scalar + ClosedAdd + ClosedSub + ClosedMul + Zero + One + Neg<Output=T>)?
{
type Output = $ret;
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fn $method(self, $b: $b_type) -> Self::Output {
let $a = self;
$body
}
}
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}
}
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/// Implements a +/- b for all combinations of reference and owned matrices, for
/// CsrMatrix or CscMatrix.
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macro_rules! impl_sp_plus_minus {
// We first match on some special-case syntax, and forward to the actual implementation
($matrix_type:ident, $spadd_fn:ident, +) => {
impl_sp_plus_minus!(Add, add, $matrix_type, $spadd_fn, +, T::one());
};
($matrix_type:ident, $spadd_fn:ident, -) => {
impl_sp_plus_minus!(Sub, sub, $matrix_type, $spadd_fn, -, -T::one());
};
($trait:ident, $method:ident, $matrix_type:ident, $spadd_fn:ident, $sign:tt, $factor:expr) => {
impl_bin_op!($trait, $method,
<'a, T>(a: &'a $matrix_type<T>, b: &'a $matrix_type<T>) -> $matrix_type<T> {
// If both matrices have the same pattern, then we can immediately re-use it
let pattern = if Arc::ptr_eq(a.pattern(), b.pattern()) {
Arc::clone(a.pattern())
} else {
Arc::new(spadd_pattern(a.pattern(), b.pattern()))
};
let values = vec![T::zero(); pattern.nnz()];
// We are giving data that is valid by definition, so it is safe to unwrap below
let mut result = $matrix_type::try_from_pattern_and_values(pattern, values)
.unwrap();
$spadd_fn(T::zero(), &mut result, T::one(), Op::NoOp(&a)).unwrap();
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$spadd_fn(T::one(), &mut result, $factor * T::one(), Op::NoOp(&b)).unwrap();
result
});
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impl_bin_op!($trait, $method,
<'a, T>(a: $matrix_type<T>, b: &'a $matrix_type<T>) -> $matrix_type<T> {
let mut a = a;
if Arc::ptr_eq(a.pattern(), b.pattern()) {
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$spadd_fn(T::one(), &mut a, $factor * T::one(), Op::NoOp(b)).unwrap();
a
} else {
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&a $sign b
}
});
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impl_bin_op!($trait, $method,
<'a, T>(a: &'a $matrix_type<T>, b: $matrix_type<T>) -> $matrix_type<T> {
let mut b = b;
if Arc::ptr_eq(a.pattern(), b.pattern()) {
$spadd_fn($factor * T::one(), &mut b, T::one(), Op::NoOp(a)).unwrap();
b
} else {
a $sign &b
}
});
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impl_bin_op!($trait, $method, <T>(a: $matrix_type<T>, b: $matrix_type<T>) -> $matrix_type<T> {
a $sign &b
});
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}
}
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impl_sp_plus_minus!(CsrMatrix, spadd_csr_prealloc, +);
impl_sp_plus_minus!(CsrMatrix, spadd_csr_prealloc, -);
impl_sp_plus_minus!(CscMatrix, spadd_csc_prealloc, +);
impl_sp_plus_minus!(CscMatrix, spadd_csc_prealloc, -);
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macro_rules! impl_mul {
($($args:tt)*) => {
impl_bin_op!(Mul, mul, $($args)*);
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}
}
/// Implements a + b for all combinations of reference and owned matrices, for
/// CsrMatrix or CscMatrix.
macro_rules! impl_spmm {
($matrix_type:ident, $pattern_fn:expr, $spmm_fn:expr) => {
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impl_mul!(<'a, T>(a: &'a $matrix_type<T>, b: &'a $matrix_type<T>) -> $matrix_type<T> {
let pattern = $pattern_fn(a.pattern(), b.pattern());
let values = vec![T::zero(); pattern.nnz()];
let mut result = $matrix_type::try_from_pattern_and_values(Arc::new(pattern), values)
.unwrap();
$spmm_fn(T::zero(),
&mut result,
T::one(),
Op::NoOp(a),
Op::NoOp(b))
.expect("Internal error: spmm failed (please debug).");
result
});
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impl_mul!(<'a, T>(a: &'a $matrix_type<T>, b: $matrix_type<T>) -> $matrix_type<T> { a * &b});
impl_mul!(<'a, T>(a: $matrix_type<T>, b: &'a $matrix_type<T>) -> $matrix_type<T> { &a * b});
impl_mul!(<T>(a: $matrix_type<T>, b: $matrix_type<T>) -> $matrix_type<T> { &a * &b});
}
}
impl_spmm!(CsrMatrix, spmm_pattern, spmm_csr_prealloc);
// Need to switch order of operations for CSC pattern
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impl_spmm!(CscMatrix, |a, b| spmm_pattern(b, a), spmm_csc_prealloc);
/// Implements Scalar * Matrix operations for *concrete* scalar types. The reason this is necessary
/// is that we are not able to implement Mul<Matrix<T>> for all T generically due to orphan rules.
macro_rules! impl_concrete_scalar_matrix_mul {
($matrix_type:ident, $($scalar_type:ty),*) => {
// For each concrete scalar type, forward the implementation of scalar * matrix
// to matrix * scalar, which we have already implemented through generics
$(
impl_mul!(<>(a: $scalar_type, b: $matrix_type<$scalar_type>)
-> $matrix_type<$scalar_type> { b * a });
impl_mul!(<'a>(a: $scalar_type, b: &'a $matrix_type<$scalar_type>)
-> $matrix_type<$scalar_type> { b * a });
impl_mul!(<'a>(a: &'a $scalar_type, b: $matrix_type<$scalar_type>)
-> $matrix_type<$scalar_type> { b * (*a) });
impl_mul!(<'a>(a: &'a $scalar_type, b: &'a $matrix_type<$scalar_type>)
-> $matrix_type<$scalar_type> { b * *a });
)*
}
}
/// Implements multiplication between matrix and scalar for various matrix types
macro_rules! impl_scalar_mul {
($matrix_type: ident) => {
impl_mul!(<'a, T>(a: &'a $matrix_type<T>, b: &'a T) -> $matrix_type<T> {
let values: Vec<_> = a.values()
.iter()
.map(|v_i| v_i.inlined_clone() * b.inlined_clone())
.collect();
$matrix_type::try_from_pattern_and_values(Arc::clone(a.pattern()), values).unwrap()
});
impl_mul!(<'a, T>(a: &'a $matrix_type<T>, b: T) -> $matrix_type<T> {
a * &b
});
impl_mul!(<'a, T>(a: $matrix_type<T>, b: &'a T) -> $matrix_type<T> {
let mut a = a;
for value in a.values_mut() {
*value = b.inlined_clone() * value.inlined_clone();
}
a
});
impl_mul!(<T>(a: $matrix_type<T>, b: T) -> $matrix_type<T> {
a * &b
});
impl_concrete_scalar_matrix_mul!(
$matrix_type,
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i8, i16, i32, i64, isize, f32, f64);
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impl<T> MulAssign<T> for $matrix_type<T>
where
T: Scalar + ClosedAdd + ClosedMul + Zero + One
{
fn mul_assign(&mut self, scalar: T) {
for val in self.values_mut() {
*val *= scalar.inlined_clone();
}
}
}
impl<'a, T> MulAssign<&'a T> for $matrix_type<T>
where
T: Scalar + ClosedAdd + ClosedMul + Zero + One
{
fn mul_assign(&mut self, scalar: &'a T) {
for val in self.values_mut() {
*val *= scalar.inlined_clone();
}
}
}
}
}
impl_scalar_mul!(CsrMatrix);
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impl_scalar_mul!(CscMatrix);
// TODO: Neg, Div