nalgebra/src/sparse/cs_matrix.rs

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use alga::general::{ClosedAdd, ClosedMul};
use num::{One, Zero};
use std::iter;
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use std::marker::PhantomData;
use std::ops::{Add, Mul, Range};
use std::slice;
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use allocator::Allocator;
use constraint::{AreMultipliable, DimEq, SameNumberOfRows, ShapeConstraint};
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use sparse::cs_utils;
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use storage::{Storage, StorageMut};
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use {
DVector, DefaultAllocator, Dim, Dynamic, Matrix, MatrixMN, MatrixVec, Real, Scalar, Vector,
VectorN, U1,
};
pub struct ColumnEntries<'a, N> {
curr: usize,
i: &'a [usize],
v: &'a [N],
}
impl<'a, N> ColumnEntries<'a, N> {
#[inline]
pub fn new(i: &'a [usize], v: &'a [N]) -> Self {
assert_eq!(i.len(), v.len());
ColumnEntries { curr: 0, i, v }
}
}
impl<'a, N: Copy> Iterator for ColumnEntries<'a, N> {
type Item = (usize, N);
#[inline]
fn next(&mut self) -> Option<(usize, N)> {
if self.curr >= self.i.len() {
None
} else {
let res = Some((unsafe { *self.i.get_unchecked(self.curr) }, unsafe {
*self.v.get_unchecked(self.curr)
}));
self.curr += 1;
res
}
}
}
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// FIXME: this structure exists for now only because impl trait
// cannot be used for trait method return types.
pub trait CsStorageIter<'a, N, R, C = U1> {
type ColumnEntries: Iterator<Item = (usize, N)>;
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type ColumnRowIndices: Iterator<Item = usize>;
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fn column_row_indices(&'a self, j: usize) -> Self::ColumnRowIndices;
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#[inline(always)]
fn column_entries(&'a self, j: usize) -> Self::ColumnEntries;
}
pub trait CsStorageIterMut<'a, N: 'a, R, C = U1> {
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type ValuesMut: Iterator<Item = &'a mut N>;
type ColumnEntriesMut: Iterator<Item = (usize, &'a mut N)>;
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fn values_mut(&'a mut self) -> Self::ValuesMut;
fn column_entries_mut(&'a mut self, j: usize) -> Self::ColumnEntriesMut;
}
pub trait CsStorage<N, R, C = U1>: for<'a> CsStorageIter<'a, N, R, C> {
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fn shape(&self) -> (R, C);
unsafe fn row_index_unchecked(&self, i: usize) -> usize;
unsafe fn get_value_unchecked(&self, i: usize) -> &N;
fn get_value(&self, i: usize) -> &N;
fn row_index(&self, i: usize) -> usize;
fn column_range(&self, i: usize) -> Range<usize>;
fn len(&self) -> usize;
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}
pub trait CsStorageMut<N, R, C = U1>:
CsStorage<N, R, C> + for<'a> CsStorageIterMut<'a, N, R, C>
{
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}
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#[derive(Clone, Debug, PartialEq)]
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pub struct CsVecStorage<N: Scalar, R: Dim, C: Dim>
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where DefaultAllocator: Allocator<usize, C>
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{
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pub(crate) shape: (R, C),
pub(crate) p: VectorN<usize, C>,
pub(crate) i: Vec<usize>,
pub(crate) vals: Vec<N>,
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}
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impl<N: Scalar, R: Dim, C: Dim> CsVecStorage<N, R, C>
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where DefaultAllocator: Allocator<usize, C>
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{
pub fn values(&self) -> &[N] {
&self.vals
}
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pub fn p(&self) -> &[usize] {
self.p.as_slice()
}
pub fn i(&self) -> &[usize] {
&self.i
}
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}
impl<N: Scalar, R: Dim, C: Dim> CsVecStorage<N, R, C> where DefaultAllocator: Allocator<usize, C> {}
impl<'a, N: Scalar, R: Dim, C: Dim> CsStorageIter<'a, N, R, C> for CsVecStorage<N, R, C>
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where DefaultAllocator: Allocator<usize, C>
{
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type ColumnEntries = ColumnEntries<'a, N>;
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type ColumnRowIndices = iter::Cloned<slice::Iter<'a, usize>>;
#[inline]
fn column_entries(&'a self, j: usize) -> Self::ColumnEntries {
let rng = self.column_range(j);
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ColumnEntries::new(&self.i[rng.clone()], &self.vals[rng])
}
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#[inline]
fn column_row_indices(&'a self, j: usize) -> Self::ColumnRowIndices {
let rng = self.column_range(j);
self.i[rng.clone()].iter().cloned()
}
}
impl<N: Scalar, R: Dim, C: Dim> CsStorage<N, R, C> for CsVecStorage<N, R, C>
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where DefaultAllocator: Allocator<usize, C>
{
#[inline]
fn shape(&self) -> (R, C) {
self.shape
}
#[inline]
fn len(&self) -> usize {
self.vals.len()
}
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#[inline]
fn row_index(&self, i: usize) -> usize {
self.i[i]
}
#[inline]
unsafe fn row_index_unchecked(&self, i: usize) -> usize {
*self.i.get_unchecked(i)
}
#[inline]
unsafe fn get_value_unchecked(&self, i: usize) -> &N {
self.vals.get_unchecked(i)
}
#[inline]
fn get_value(&self, i: usize) -> &N {
&self.vals[i]
}
#[inline]
fn column_range(&self, j: usize) -> Range<usize> {
let end = if j + 1 == self.p.len() {
self.len()
} else {
self.p[j + 1]
};
self.p[j]..end
}
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}
impl<'a, N: Scalar, R: Dim, C: Dim> CsStorageIterMut<'a, N, R, C> for CsVecStorage<N, R, C>
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where DefaultAllocator: Allocator<usize, C>
{
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type ValuesMut = slice::IterMut<'a, N>;
type ColumnEntriesMut = iter::Zip<iter::Cloned<slice::Iter<'a, usize>>, slice::IterMut<'a, N>>;
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#[inline]
fn values_mut(&'a mut self) -> Self::ValuesMut {
self.vals.iter_mut()
}
#[inline]
fn column_entries_mut(&'a mut self, j: usize) -> Self::ColumnEntriesMut {
let rng = self.column_range(j);
self.i[rng.clone()]
.iter()
.cloned()
.zip(self.vals[rng].iter_mut())
}
}
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impl<N: Scalar, R: Dim, C: Dim> CsStorageMut<N, R, C> for CsVecStorage<N, R, C> where DefaultAllocator: Allocator<usize, C>
{}
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/*
pub struct CsSliceStorage<'a, N: Scalar, R: Dim, C: DimAdd<U1>> {
shape: (R, C),
p: VectorSlice<usize, DimSum<C, U1>>,
i: VectorSlice<usize, Dynamic>,
vals: VectorSlice<N, Dynamic>,
}*/
/// A compressed sparse column matrix.
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#[derive(Clone, Debug, PartialEq)]
pub struct CsMatrix<
N: Scalar,
R: Dim = Dynamic,
C: Dim = Dynamic,
S: CsStorage<N, R, C> = CsVecStorage<N, R, C>,
> {
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pub data: S,
_phantoms: PhantomData<(N, R, C)>,
}
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pub type CsVector<N, R = Dynamic, S = CsVecStorage<N, R, U1>> = CsMatrix<N, R, U1, S>;
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impl<N: Scalar, R: Dim, C: Dim> CsMatrix<N, R, C>
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where DefaultAllocator: Allocator<usize, C>
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{
pub fn new_uninitialized_generic(nrows: R, ncols: C, nvals: usize) -> Self {
let mut i = Vec::with_capacity(nvals);
unsafe {
i.set_len(nvals);
}
i.shrink_to_fit();
let mut vals = Vec::with_capacity(nvals);
unsafe {
vals.set_len(nvals);
}
vals.shrink_to_fit();
CsMatrix {
data: CsVecStorage {
shape: (nrows, ncols),
p: VectorN::zeros_generic(ncols, U1),
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i,
vals,
},
_phantoms: PhantomData,
}
}
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pub fn from_parts_generic(
nrows: R,
ncols: C,
p: VectorN<usize, C>,
i: Vec<usize>,
vals: Vec<N>,
) -> Self
where
N: Zero + ClosedAdd,
DefaultAllocator: Allocator<N, R>,
{
assert_eq!(ncols.value(), p.len(), "Invalid inptr size.");
assert_eq!(i.len(), vals.len(), "Invalid value size.");
// Check p.
for ptr in &p {
assert!(*ptr < i.len(), "Invalid inptr value.");
}
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for ptr in p.as_slice().windows(2) {
assert!(ptr[0] <= ptr[1], "Invalid inptr ordering.");
}
// Check i.
for i in &i {
assert!(*i < nrows.value(), "Invalid row ptr value.")
}
let mut res = CsMatrix {
data: CsVecStorage {
shape: (nrows, ncols),
p,
i,
vals,
},
_phantoms: PhantomData,
};
// Sort and remove duplicates.
res.sort();
res.dedup();
res
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}
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}
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impl<N: Scalar + Zero + ClosedAdd> CsMatrix<N> {
pub fn from_parts(
nrows: usize,
ncols: usize,
p: Vec<usize>,
i: Vec<usize>,
vals: Vec<N>,
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) -> Self
{
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let nrows = Dynamic::new(nrows);
let ncols = Dynamic::new(ncols);
let p = DVector::from_data(MatrixVec::new(ncols, U1, p));
Self::from_parts_generic(nrows, ncols, p, i, vals)
}
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}
impl<N: Scalar, R: Dim, C: Dim, S: CsStorage<N, R, C>> CsMatrix<N, R, C, S> {
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pub fn from_data(data: S) -> Self {
CsMatrix {
data,
_phantoms: PhantomData,
}
}
pub fn len(&self) -> usize {
self.data.len()
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}
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pub fn nrows(&self) -> usize {
self.data.shape().0.value()
}
pub fn ncols(&self) -> usize {
self.data.shape().1.value()
}
pub fn shape(&self) -> (usize, usize) {
let (nrows, ncols) = self.data.shape();
(nrows.value(), ncols.value())
}
pub fn is_square(&self) -> bool {
let (nrows, ncols) = self.data.shape();
nrows.value() == ncols.value()
}
/// Should always return `true`.
///
/// This method is generally used for debugging and should typically not be called in user code.
/// This checks that the row inner indices of this matrix are sorted. It takes `O(n)` time,
/// where n` is `self.len()`.
/// All operations of CSC matrices on nalgebra assume, and will return, sorted indices.
/// If at any time this `is_sorted` method returns `false`, then, something went wrong
/// and an issue should be open on the nalgebra repository with details on how to reproduce
/// this.
pub fn is_sorted(&self) -> bool {
for j in 0..self.ncols() {
let mut curr = None;
for idx in self.data.column_row_indices(j) {
if let Some(curr) = curr {
if idx <= curr {
return false;
}
}
curr = Some(idx);
}
}
true
}
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pub fn transpose(&self) -> CsMatrix<N, C, R>
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where DefaultAllocator: Allocator<usize, R> {
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let (nrows, ncols) = self.data.shape();
let nvals = self.len();
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let mut res = CsMatrix::new_uninitialized_generic(ncols, nrows, nvals);
let mut workspace = Vector::zeros_generic(nrows, U1);
// Compute p.
for i in 0..nvals {
let row_id = self.data.row_index(i);
workspace[row_id] += 1;
}
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let _ = cs_utils::cumsum(&mut workspace, &mut res.data.p);
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// Fill the result.
for j in 0..ncols.value() {
for (row_id, value) in self.data.column_entries(j) {
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let shift = workspace[row_id];
res.data.vals[shift] = value;
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res.data.i[shift] = j;
workspace[row_id] += 1;
}
}
res
}
}
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impl<N: Scalar, R: Dim, C: Dim, S: CsStorageMut<N, R, C>> CsMatrix<N, R, C, S> {
#[inline]
pub fn values_mut(&mut self) -> impl Iterator<Item = &mut N> {
self.data.values_mut()
}
}
impl<N: Scalar, R: Dim, C: Dim> CsMatrix<N, R, C>
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where DefaultAllocator: Allocator<usize, C>
{
pub(crate) fn sort(&mut self)
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where DefaultAllocator: Allocator<N, R> {
// Size = R
let nrows = self.data.shape().0;
let mut workspace = unsafe { VectorN::new_uninitialized_generic(nrows, U1) };
self.sort_with_workspace(workspace.as_mut_slice());
}
pub(crate) fn sort_with_workspace(&mut self, workspace: &mut [N]) {
assert!(
workspace.len() >= self.nrows(),
"Workspace must be able to hold at least self.nrows() elements."
);
for j in 0..self.ncols() {
// Scatter the row in the workspace.
for (irow, val) in self.data.column_entries(j) {
workspace[irow] = val;
}
// Sort the index vector.
let range = self.data.column_range(j);
self.data.i[range.clone()].sort();
// Permute the values too.
for (i, irow) in range.clone().zip(self.data.i[range].iter().cloned()) {
self.data.vals[i] = workspace[irow];
}
}
}
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// Remove dupliate entries on a sorted CsMatrix.
pub(crate) fn dedup(&mut self)
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where N: Zero + ClosedAdd {
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let mut curr_i = 0;
for j in 0..self.ncols() {
let range = self.data.column_range(j);
self.data.p[j] = curr_i;
if range.start != range.end {
let mut value = N::zero();
let mut irow = self.data.i[range.start];
for idx in range {
let curr_irow = self.data.i[idx];
if curr_irow == irow {
value += self.data.vals[idx];
} else {
self.data.i[curr_i] = irow;
self.data.vals[curr_i] = value;
value = self.data.vals[idx];
irow = curr_irow;
curr_i += 1;
}
}
// Handle the last entry.
self.data.i[curr_i] = irow;
self.data.vals[curr_i] = value;
curr_i += 1;
}
}
self.data.i.truncate(curr_i);
self.data.i.shrink_to_fit();
self.data.vals.truncate(curr_i);
self.data.vals.shrink_to_fit();
}
}