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use num::{One, Zero};
use simba::scalar::{ClosedAdd, ClosedMul};
use std::ops::{Add, Mul};
use crate::allocator::Allocator;
use crate::constraint::{AreMultipliable, DimEq, ShapeConstraint};
use crate::sparse::{CsMatrix, CsStorage, CsStorageMut, CsVector};
use crate::storage::StorageMut;
use crate::{Const, DefaultAllocator, Dim, Matrix, OVector, Scalar, Vector};
impl<T: Scalar, R: Dim, C: Dim, S: CsStorage<T, R, C>> CsMatrix<T, R, C, S> {
fn scatter<R2: Dim, C2: Dim>(
&self,
j: usize,
beta: T,
timestamps: &mut [usize],
timestamp: usize,
workspace: &mut [T],
mut nz: usize,
res: &mut CsMatrix<T, R2, C2>,
) -> usize
where
T: ClosedAdd + ClosedMul,
DefaultAllocator: Allocator<usize, C2>,
{
for (i, val) in self.data.column_entries(j) {
if timestamps[i] < timestamp {
timestamps[i] = timestamp;
res.data.i[nz] = i;
nz += 1;
workspace[i] = val * beta.clone();
} else {
workspace[i] += val * beta.clone();
}
}
nz
}
}
/*
impl<T: Scalar, R, S> CsVector<T, R, S> {
pub fn axpy(&mut self, alpha: T, x: CsVector<T, R, S>, beta: T) {
// First, compute the number of non-zero entries.
let mut nnzero = 0;
// Allocate a size large enough.
self.data.set_column_len(0, nnzero);
// Fill with the axpy.
let mut i = self.len();
let mut j = x.len();
let mut k = nnzero - 1;
let mut rid1 = self.data.row_index(0, i - 1);
let mut rid2 = x.data.row_index(0, j - 1);
while k > 0 {
if rid1 == rid2 {
self.data.set_row_index(0, k, rid1);
self[k] = alpha * x[j] + beta * self[k];
i -= 1;
j -= 1;
} else if rid1 < rid2 {
self.data.set_row_index(0, k, rid1);
self[k] = beta * self[i];
i -= 1;
} else {
self.data.set_row_index(0, k, rid2);
self[k] = alpha * x[j];
j -= 1;
}
k -= 1;
}
}
}
*/
impl<T: Scalar + Zero + ClosedAdd + ClosedMul, D: Dim, S: StorageMut<T, D>> Vector<T, D, S> {
/// Perform a sparse axpy operation: `self = alpha * x + beta * self` operation.
pub fn axpy_cs<D2: Dim, S2>(&mut self, alpha: T, x: &CsVector<T, D2, S2>, beta: T)
where
S2: CsStorage<T, D2>,
ShapeConstraint: DimEq<D, D2>,
{
if beta.is_zero() {
for i in 0..x.len() {
unsafe {
let k = x.data.row_index_unchecked(i);
let y = self.vget_unchecked_mut(k);
*y = alpha.clone() * x.data.get_value_unchecked(i).clone();
}
}
} else {
// Needed to be sure even components not present on `x` are multiplied.
*self *= beta.clone();
for i in 0..x.len() {
unsafe {
let k = x.data.row_index_unchecked(i);
let y = self.vget_unchecked_mut(k);
*y += alpha.clone() * x.data.get_value_unchecked(i).clone();
}
}
}
}
/*
pub fn gemv_sparse<R2: Dim, C2: Dim, S2>(&mut self, alpha: T, a: &CsMatrix<T, R2, C2, S2>, x: &DVector<T>, beta: T)
where
S2: CsStorage<T, R2, C2> {
let col2 = a.column(0);
let val = unsafe { *x.vget_unchecked(0) };
self.axpy_sparse(alpha * val, &col2, beta);
for j in 1..ncols2 {
let col2 = a.column(j);
let val = unsafe { *x.vget_unchecked(j) };
self.axpy_sparse(alpha * val, &col2, T::one());
}
}
*/
}
impl<'a, 'b, T, R1, R2, C1, C2, S1, S2> Mul<&'b CsMatrix<T, R2, C2, S2>>
for &'a CsMatrix<T, R1, C1, S1>
where
T: Scalar + ClosedAdd + ClosedMul + Zero,
R1: Dim,
C1: Dim,
R2: Dim,
C2: Dim,
S1: CsStorage<T, R1, C1>,
S2: CsStorage<T, R2, C2>,
ShapeConstraint: AreMultipliable<R1, C1, R2, C2>,
DefaultAllocator: Allocator<usize, C2> + Allocator<usize, R1> + Allocator<T, R1>,
{
type Output = CsMatrix<T, R1, C2>;
fn mul(self, rhs: &'b CsMatrix<T, R2, C2, S2>) -> Self::Output {
let (nrows1, ncols1) = self.data.shape();
let (nrows2, ncols2) = rhs.data.shape();
assert_eq!(
ncols1.value(),
nrows2.value(),
"Mismatched dimensions for matrix multiplication."
);
let mut res = CsMatrix::new_uninitialized_generic(nrows1, ncols2, self.len() + rhs.len());
let mut workspace = OVector::<T, R1>::zeros_generic(nrows1, Const::<1>);
let mut nz = 0;
for j in 0..ncols2.value() {
res.data.p[j] = nz;
let new_size_bound = nz + nrows1.value();
res.data.i.resize(new_size_bound, 0);
res.data.vals.resize(new_size_bound, T::zero());
for (i, beta) in rhs.data.column_entries(j) {
for (k, val) in self.data.column_entries(i) {
workspace[k] += val.clone() * beta.clone();
}
}
for (i, val) in workspace.as_mut_slice().iter_mut().enumerate() {
if !val.is_zero() {
res.data.i[nz] = i;
res.data.vals[nz] = val.clone();
*val = T::zero();
nz += 1;
}
}
}
// NOTE: the following has a lower complexity, but is slower in many cases, likely because
// of branching inside of the inner loop.
//
// let mut res = CsMatrix::new_uninitialized_generic(nrows1, ncols2, self.len() + rhs.len());
// let mut timestamps = OVector::zeros_generic(nrows1, Const::<)>;
// let mut workspace = unsafe { OVector::new_uninitialized_generic(nrows1, Const::<)> };
// let mut nz = 0;
//
// for j in 0..ncols2.value() {
// res.data.p[j] = nz;
// let new_size_bound = nz + nrows1.value();
// res.data.i.resize(new_size_bound, 0);
// res.data.vals.resize(new_size_bound, T::zero());
//
// for (i, val) in rhs.data.column_entries(j) {
// nz = self.scatter(
// i,
// val,
// timestamps.as_mut_slice(),
// j + 1,
// workspace.as_mut_slice(),
// nz,
// &mut res,
// );
// }
//
// // Keep the output sorted.
// let range = res.data.p[j]..nz;
// res.data.i[range.clone()].sort();
//
// for p in range {
// res.data.vals[p] = workspace[res.data.i[p]]
// }
// }
res.data.i.truncate(nz);
res.data.i.shrink_to_fit();
res.data.vals.truncate(nz);
res.data.vals.shrink_to_fit();
res
}
}
impl<'a, 'b, T, R1, R2, C1, C2, S1, S2> Add<&'b CsMatrix<T, R2, C2, S2>>
for &'a CsMatrix<T, R1, C1, S1>
where
T: Scalar + ClosedAdd + ClosedMul + Zero + One,
R1: Dim,
C1: Dim,
R2: Dim,
C2: Dim,
S1: CsStorage<T, R1, C1>,
S2: CsStorage<T, R2, C2>,
ShapeConstraint: DimEq<R1, R2> + DimEq<C1, C2>,
DefaultAllocator: Allocator<usize, C2> + Allocator<usize, R1> + Allocator<T, R1>,
{
type Output = CsMatrix<T, R1, C2>;
fn add(self, rhs: &'b CsMatrix<T, R2, C2, S2>) -> Self::Output {
let (nrows1, ncols1) = self.data.shape();
let (nrows2, ncols2) = rhs.data.shape();
assert_eq!(
(nrows1.value(), ncols1.value()),
(nrows2.value(), ncols2.value()),
"Mismatched dimensions for matrix sum."
);
let mut res = CsMatrix::new_uninitialized_generic(nrows1, ncols2, self.len() + rhs.len());
let mut timestamps = OVector::zeros_generic(nrows1, Const::<1>);
let mut workspace = Matrix::zeros_generic(nrows1, Const::<1>);
let mut nz = 0;
for j in 0..ncols2.value() {
res.data.p[j] = nz;
nz = self.scatter(
j,
T::one(),
timestamps.as_mut_slice(),
j + 1,
workspace.as_mut_slice(),
nz,
&mut res,
);
nz = rhs.scatter(
j,
T::one(),
timestamps.as_mut_slice(),
j + 1,
workspace.as_mut_slice(),
nz,
&mut res,
);
// Keep the output sorted.
let range = res.data.p[j]..nz;
res.data.i[range.clone()].sort_unstable();
for p in range {
res.data.vals[p] = workspace[res.data.i[p]].clone()
}
}
res.data.i.truncate(nz);
res.data.i.shrink_to_fit();
res.data.vals.truncate(nz);
res.data.vals.shrink_to_fit();
res
}
}
impl<'a, 'b, T, R, C, S> Mul<T> for CsMatrix<T, R, C, S>
where
T: Scalar + ClosedAdd + ClosedMul + Zero,
R: Dim,
C: Dim,
S: CsStorageMut<T, R, C>,
{
type Output = Self;
fn mul(mut self, rhs: T) -> Self::Output {
for e in self.values_mut() {
*e *= rhs.clone()
}
self
}
}