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vec.rs
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vec.rs
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use ndarray::{self, ArrayBase};
use std::cmp;
use std::collections::HashSet;
use std::convert::AsRef;
use std::hash::Hash;
/// A sparse vector, which can be extracted from a sparse matrix
///
/// # Example
/// ```rust
/// use sprs::CsVec;
/// let vec1 = CsVec::new(8, vec![0, 2, 5, 6], vec![1.; 4]);
/// let vec2 = CsVec::new(8, vec![1, 3, 5], vec![2.; 3]);
/// let res = &vec1 + &vec2;
/// let mut iter = res.iter();
/// assert_eq!(iter.next(), Some((0, &1.)));
/// assert_eq!(iter.next(), Some((1, &2.)));
/// assert_eq!(iter.next(), Some((2, &1.)));
/// assert_eq!(iter.next(), Some((3, &2.)));
/// assert_eq!(iter.next(), Some((5, &3.)));
/// assert_eq!(iter.next(), Some((6, &1.)));
/// assert_eq!(iter.next(), None);
/// ```
use std::iter::{Enumerate, FilterMap, IntoIterator, Peekable, Sum, Zip};
use std::marker::PhantomData;
use std::ops::{Add, Deref, DerefMut, Index, IndexMut, Mul, Neg, Sub};
use std::slice::{self, Iter, IterMut};
use Ix1;
use num_traits::{Num, Zero};
use array_backend::Array2;
use errors::SprsError;
use indexing::SpIndex;
use sparse::csmat::CompressedStorage::{CSC, CSR};
use sparse::permutation::PermViewI;
use sparse::prelude::*;
use sparse::utils;
use sparse::{binop, prod};
#[derive(Clone, Copy, PartialEq, Eq, Debug)]
/// Hold the index of a non-zero element in the compressed storage
///
/// An NnzIndex can be used to later access the non-zero element in constant
/// time.
pub struct NnzIndex(pub usize);
/// A trait to represent types which can be interpreted as vectors
/// of a given dimension.
pub trait VecDim<N> {
/// The dimension of the vector
fn dim(&self) -> usize;
}
impl<N, IS, DS: Deref<Target = [N]>> VecDim<N> for CsVecBase<IS, DS> {
fn dim(&self) -> usize {
self.dim
}
}
impl<N, T: ?Sized> VecDim<N> for T
where
T: AsRef<[N]>,
{
fn dim(&self) -> usize {
self.as_ref().len()
}
}
/// An iterator over the non-zero elements of a sparse vector
pub struct VectorIterator<'a, N: 'a, I: 'a> {
ind_data: Zip<Iter<'a, I>, Iter<'a, N>>,
}
pub struct VectorIteratorPerm<'a, N: 'a, I: 'a> {
ind_data: Zip<Iter<'a, I>, Iter<'a, N>>,
perm: PermViewI<'a, I>,
}
/// An iterator over the mutable non-zero elements of a sparse vector
pub struct VectorIteratorMut<'a, N: 'a, I: 'a> {
ind_data: Zip<Iter<'a, I>, IterMut<'a, N>>,
}
impl<'a, N: 'a, I: 'a + SpIndex> Iterator for VectorIterator<'a, N, I> {
type Item = (usize, &'a N);
fn next(&mut self) -> Option<<Self as Iterator>::Item> {
match self.ind_data.next() {
None => None,
Some((inner_ind, data)) => Some((inner_ind.index(), data)),
}
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.ind_data.size_hint()
}
}
impl<'a, N: 'a, I: 'a + SpIndex> Iterator for VectorIteratorPerm<'a, N, I> {
type Item = (usize, &'a N);
fn next(&mut self) -> Option<<Self as Iterator>::Item> {
match self.ind_data.next() {
None => None,
Some((inner_ind, data)) => {
Some((self.perm.at(inner_ind.index()), data))
}
}
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.ind_data.size_hint()
}
}
impl<'a, N: 'a, I: 'a + SpIndex> Iterator for VectorIteratorMut<'a, N, I> {
type Item = (usize, &'a mut N);
fn next(&mut self) -> Option<<Self as Iterator>::Item> {
match self.ind_data.next() {
None => None,
Some((inner_ind, data)) => Some((inner_ind.index(), data)),
}
}
fn size_hint(&self) -> (usize, Option<usize>) {
self.ind_data.size_hint()
}
}
pub trait SparseIterTools: Iterator {
/// Iterate over non-zero elements of either of two vectors.
/// This is useful for implementing eg addition of vectors.
///
/// # Example
///
/// ```rust
/// use sprs::CsVec;
/// use sprs::vec::NnzEither;
/// use sprs::vec::SparseIterTools;
/// let v0 = CsVec::new(5, vec![0, 2, 4], vec![1., 2., 3.]);
/// let v1 = CsVec::new(5, vec![1, 2, 3], vec![-1., -2., -3.]);
/// let mut nnz_or_iter = v0.iter().nnz_or_zip(v1.iter());
/// assert_eq!(nnz_or_iter.next(), Some(NnzEither::Left((0, &1.))));
/// assert_eq!(nnz_or_iter.next(), Some(NnzEither::Right((1, &-1.))));
/// assert_eq!(nnz_or_iter.next(), Some(NnzEither::Both((2, &2., &-2.))));
/// assert_eq!(nnz_or_iter.next(), Some(NnzEither::Right((3, &-3.))));
/// assert_eq!(nnz_or_iter.next(), Some(NnzEither::Left((4, &3.))));
/// assert_eq!(nnz_or_iter.next(), None);
/// ```
fn nnz_or_zip<'a, I, N1, N2>(
self,
other: I,
) -> NnzOrZip<'a, Self, I::IntoIter, N1, N2>
where
Self: Iterator<Item = (usize, &'a N1)> + Sized,
I: IntoIterator<Item = (usize, &'a N2)>,
{
NnzOrZip {
left: self.peekable(),
right: other.into_iter().peekable(),
life: PhantomData,
}
}
/// Iterate over the matching non-zero elements of both vectors
/// Useful for vector dot product.
///
/// # Example
///
/// ```rust
/// use sprs::CsVec;
/// use sprs::vec::SparseIterTools;
/// let v0 = CsVec::new(5, vec![0, 2, 4], vec![1., 2., 3.]);
/// let v1 = CsVec::new(5, vec![1, 2, 3], vec![-1., -2., -3.]);
/// let mut nnz_zip = v0.iter().nnz_zip(v1.iter());
/// assert_eq!(nnz_zip.next(), Some((2, &2., &-2.)));
/// assert_eq!(nnz_zip.next(), None);
/// ```
fn nnz_zip<'a, I, N1, N2>(
self,
other: I,
) -> FilterMap<
NnzOrZip<'a, Self, I::IntoIter, N1, N2>,
fn(NnzEither<'a, N1, N2>) -> Option<(usize, &'a N1, &'a N2)>,
>
where
Self: Iterator<Item = (usize, &'a N1)> + Sized,
I: IntoIterator<Item = (usize, &'a N2)>,
{
let nnz_or_iter = NnzOrZip {
left: self.peekable(),
right: other.into_iter().peekable(),
life: PhantomData,
};
nnz_or_iter.filter_map(filter_both_nnz)
}
}
impl<T: Iterator> SparseIterTools for Enumerate<T> {}
impl<'a, N: 'a, I: 'a + SpIndex> SparseIterTools for VectorIterator<'a, N, I> {}
/// Trait for types that can be iterated as sparse vectors
pub trait IntoSparseVecIter<'a, N: 'a> {
type IterType;
/// Transform self into an iterator that yields (usize, &N) tuples
/// where the usize is the index of the value in the sparse vector.
/// The indices should be sorted.
fn into_sparse_vec_iter(
self,
) -> <Self as IntoSparseVecIter<'a, N>>::IterType
where
<Self as IntoSparseVecIter<'a, N>>::IterType:
Iterator<Item = (usize, &'a N)>;
/// The dimension of the vector
fn dim(&self) -> usize;
/// Indicator to check whether the vector is actually dense
fn is_dense(&self) -> bool {
false
}
/// Random access to an element in the vector.
///
/// # Panics
///
/// - if the vector is not dense
/// - if the index is out of bounds
#[allow(unused_variables)]
fn index(self, idx: usize) -> &'a N
where
Self: Sized,
{
panic!("cannot be called on a vector that is not dense");
}
}
impl<'a, N: 'a, I: 'a> IntoSparseVecIter<'a, N> for CsVecViewI<'a, N, I>
where
I: SpIndex,
{
type IterType = VectorIterator<'a, N, I>;
fn dim(&self) -> usize {
self.dim()
}
fn into_sparse_vec_iter(self) -> VectorIterator<'a, N, I> {
self.iter_rbr()
}
}
impl<'a, N: 'a, I: 'a, IS, DS> IntoSparseVecIter<'a, N>
for &'a CsVecBase<IS, DS>
where
I: SpIndex,
IS: Deref<Target = [I]>,
DS: Deref<Target = [N]>,
{
type IterType = VectorIterator<'a, N, I>;
fn dim(&self) -> usize {
(*self).dim()
}
fn into_sparse_vec_iter(self) -> VectorIterator<'a, N, I> {
self.iter()
}
}
impl<'a, N: 'a> IntoSparseVecIter<'a, N> for &'a [N] {
type IterType = Enumerate<Iter<'a, N>>;
fn dim(&self) -> usize {
self.len()
}
fn into_sparse_vec_iter(self) -> Enumerate<Iter<'a, N>> {
self.into_iter().enumerate()
}
fn is_dense(&self) -> bool {
true
}
fn index(self, idx: usize) -> &'a N {
&self[idx]
}
}
impl<'a, N: 'a> IntoSparseVecIter<'a, N> for &'a Vec<N> {
type IterType = Enumerate<Iter<'a, N>>;
fn dim(&self) -> usize {
self.len()
}
fn into_sparse_vec_iter(self) -> Enumerate<Iter<'a, N>> {
self.into_iter().enumerate()
}
fn is_dense(&self) -> bool {
true
}
fn index(self, idx: usize) -> &'a N {
&self[idx]
}
}
impl<'a, N: 'a, S> IntoSparseVecIter<'a, N> for &'a ArrayBase<S, Ix1>
where
S: ndarray::Data<Elem = N>,
{
type IterType = Enumerate<ndarray::iter::Iter<'a, N, Ix1>>;
fn dim(&self) -> usize {
self.shape()[0]
}
fn into_sparse_vec_iter(
self,
) -> Enumerate<ndarray::iter::Iter<'a, N, Ix1>> {
self.iter().enumerate()
}
fn is_dense(&self) -> bool {
true
}
fn index(self, idx: usize) -> &'a N {
&self[[idx]]
}
}
/// A trait for types representing dense vectors, useful for
/// defining a fast sparse-dense dot product.
pub trait DenseVector<N> {
/// The dimension of the vector
fn dim(&self) -> usize;
/// Random access to an element in the vector.
///
/// # Panics
///
/// If the index is out of bounds
fn index(&self, idx: usize) -> &N;
}
impl<'a, N: 'a> DenseVector<N> for &'a [N] {
fn dim(&self) -> usize {
self.len()
}
fn index(&self, idx: usize) -> &N {
&self[idx]
}
}
impl<N> DenseVector<N> for Vec<N> {
fn dim(&self) -> usize {
self.len()
}
fn index(&self, idx: usize) -> &N {
&self[idx]
}
}
impl<'a, N: 'a> DenseVector<N> for &'a Vec<N> {
fn dim(&self) -> usize {
self.len()
}
fn index(&self, idx: usize) -> &N {
&self[idx]
}
}
impl<N, S> DenseVector<N> for ArrayBase<S, Ix1>
where
S: ndarray::Data<Elem = N>,
{
fn dim(&self) -> usize {
self.shape()[0]
}
fn index(&self, idx: usize) -> &N {
&self[[idx]]
}
}
/// An iterator over the non zeros of either of two vector iterators, ordered,
/// such that the sum of the vectors may be computed
pub struct NnzOrZip<'a, Ite1, Ite2, N1: 'a, N2: 'a>
where
Ite1: Iterator<Item = (usize, &'a N1)>,
Ite2: Iterator<Item = (usize, &'a N2)>,
{
left: Peekable<Ite1>,
right: Peekable<Ite2>,
life: PhantomData<(&'a N1, &'a N2)>,
}
#[derive(PartialEq, Debug)]
pub enum NnzEither<'a, N1: 'a, N2: 'a> {
Both((usize, &'a N1, &'a N2)),
Left((usize, &'a N1)),
Right((usize, &'a N2)),
}
fn filter_both_nnz<'a, N: 'a, M: 'a>(
elem: NnzEither<'a, N, M>,
) -> Option<(usize, &'a N, &'a M)> {
match elem {
NnzEither::Both((ind, lval, rval)) => Some((ind, lval, rval)),
_ => None,
}
}
impl<'a, Ite1, Ite2, N1: 'a, N2: 'a> Iterator
for NnzOrZip<'a, Ite1, Ite2, N1, N2>
where
Ite1: Iterator<Item = (usize, &'a N1)>,
Ite2: Iterator<Item = (usize, &'a N2)>,
{
type Item = NnzEither<'a, N1, N2>;
fn next(&mut self) -> Option<(NnzEither<'a, N1, N2>)> {
match (self.left.peek(), self.right.peek()) {
(None, Some(&(_, _))) => {
let (rind, rval) = self.right.next().unwrap();
Some(NnzEither::Right((rind, rval)))
}
(Some(&(_, _)), None) => {
let (lind, lval) = self.left.next().unwrap();
Some(NnzEither::Left((lind, lval)))
}
(None, None) => None,
(Some(&(lind, _)), Some(&(rind, _))) => {
if lind < rind {
let (lind, lval) = self.left.next().unwrap();
Some(NnzEither::Left((lind, lval)))
} else if rind < lind {
let (rind, rval) = self.right.next().unwrap();
Some(NnzEither::Right((rind, rval)))
} else {
let (lind, lval) = self.left.next().unwrap();
let (_, rval) = self.right.next().unwrap();
Some(NnzEither::Both((lind, lval, rval)))
}
}
}
}
#[inline]
fn size_hint(&self) -> (usize, Option<usize>) {
let (left_lower, left_upper) = self.left.size_hint();
let (right_lower, right_upper) = self.right.size_hint();
let upper = match (left_upper, right_upper) {
(Some(x), Some(y)) => Some(x + y),
(Some(x), None) => Some(x),
(None, Some(y)) => Some(y),
(None, None) => None,
};
(cmp::max(left_lower, right_lower), upper)
}
}
/// # Methods operating on owning sparse vectors
impl<N, I: SpIndex> CsVecBase<Vec<I>, Vec<N>> {
/// Create an owning CsVec from vector data.
///
/// # Panics
///
/// - if `indices` and `data` lengths differ
/// - if the vector contains out of bounds indices
pub fn new(n: usize, mut indices: Vec<I>, mut data: Vec<N>) -> CsVecI<N, I>
where
N: Copy,
{
let mut buf = Vec::with_capacity(indices.len());
utils::sort_indices_data_slices(
&mut indices[..],
&mut data[..],
&mut buf,
);
let v = CsVecI {
dim: n,
indices: indices,
data: data,
};
v.check_structure().and(Ok(v)).unwrap()
}
/// Create an empty CsVec, which can be used for incremental construction
pub fn empty(dim: usize) -> CsVecI<N, I> {
CsVecI {
dim: dim,
indices: Vec::new(),
data: Vec::new(),
}
}
/// Append an element to the sparse vector. Used for incremental
/// building of the CsVec. The append should preserve the structure
/// of the vector, ie the newly added index should be strictly greater
/// than the last element of indices.
///
/// # Panics
///
/// - Panics if `ind` is lower or equal to the last
/// element of `self.indices()`
/// - Panics if `ind` is greater than `self.dim()`
pub fn append(&mut self, ind: usize, val: N) {
match self.indices.last() {
None => (),
Some(&last_ind) => {
assert!(ind > last_ind.index(), "unsorted append")
}
}
assert!(ind <= self.dim, "out of bounds index");
self.indices.push(I::from_usize(ind));
self.data.push(val);
}
/// Reserve `size` additional non-zero values.
pub fn reserve(&mut self, size: usize) {
self.indices.reserve(size);
self.data.reserve(size);
}
/// Reserve exactly `exact_size` non-zero values.
pub fn reserve_exact(&mut self, exact_size: usize) {
self.indices.reserve_exact(exact_size);
self.data.reserve_exact(exact_size);
}
/// Clear the underlying storage
pub fn clear(&mut self) {
self.indices.clear();
self.data.clear();
}
}
/// # Common methods of sparse vectors
impl<N, I, IStorage, DStorage> CsVecBase<IStorage, DStorage>
where
I: SpIndex,
IStorage: Deref<Target = [I]>,
DStorage: Deref<Target = [N]>,
{
/// Get a view of this vector.
pub fn view(&self) -> CsVecViewI<N, I> {
CsVecViewI {
dim: self.dim,
indices: &self.indices[..],
data: &self.data[..],
}
}
/// Iterate over the non zero values.
///
/// # Example
///
/// ```rust
/// use sprs::CsVec;
/// let v = CsVec::new(5, vec![0, 2, 4], vec![1., 2., 3.]);
/// let mut iter = v.iter();
/// assert_eq!(iter.next(), Some((0, &1.)));
/// assert_eq!(iter.next(), Some((2, &2.)));
/// assert_eq!(iter.next(), Some((4, &3.)));
/// assert_eq!(iter.next(), None);
/// ```
pub fn iter(&self) -> VectorIterator<N, I> {
VectorIterator {
ind_data: self.indices.iter().zip(self.data.iter()),
}
}
/// Permuted iteration. Not finished
#[doc(hidden)]
pub fn iter_perm<'a, 'perm: 'a>(
&'a self,
perm: PermViewI<'perm, I>,
) -> VectorIteratorPerm<'a, N, I>
where
N: 'a,
{
VectorIteratorPerm {
ind_data: self.indices.iter().zip(self.data.iter()),
perm: perm,
}
}
/// The underlying indices.
pub fn indices(&self) -> &[I] {
&self.indices
}
/// The underlying non zero values.
pub fn data(&self) -> &[N] {
&self.data
}
/// The dimension of this vector.
pub fn dim(&self) -> usize {
self.dim
}
/// The non zero count of this vector.
pub fn nnz(&self) -> usize {
self.data.len()
}
/// Check the sparse structure, namely that:
/// - indices is sorted
/// - indices are lower than dims()
pub fn check_structure(&self) -> Result<(), SprsError> {
if !self.indices.windows(2).all(|x| x[0] < x[1]) {
return Err(SprsError::NonSortedIndices);
}
if self.dim == 0 && self.indices.len() == 0 && self.data.len() == 0 {
return Ok(());
}
let max_ind = self.indices.iter().max().unwrap_or(&I::zero()).index();
if max_ind >= self.dim {
panic!("Out of bounds index");
}
Ok(())
}
/// Allocate a new vector equal to this one.
pub fn to_owned(&self) -> CsVecI<N, I>
where
N: Clone,
{
CsVecI {
dim: self.dim,
indices: self.indices.to_vec(),
data: self.data.to_vec(),
}
}
/// Clone the vector with another integer type for its indices
///
/// # Panics
///
/// If the indices cannot be represented by the requested integer type.
pub fn to_other_types<I2>(&self) -> CsVecI<N, I2>
where
N: Clone,
I2: SpIndex,
{
let indices = self
.indices
.iter()
.map(|i| I2::from_usize(i.index()))
.collect();
let data = self.data.iter().map(|x| x.clone().into()).collect();
CsVecI {
dim: self.dim,
indices: indices,
data: data,
}
}
/// View this vector as a matrix with only one row.
pub fn row_view(&self) -> CsMatVecView_<N, I> {
// Safe because we're taking a view into a vector that has
// necessarily been checked
let indptr = Array2 {
data: [I::zero(), I::from_usize(self.indices.len())],
};
CsMatBase {
storage: CSR,
nrows: 1,
ncols: self.dim,
indptr: indptr,
indices: &self.indices[..],
data: &self.data[..],
}
}
/// View this vector as a matrix with only one column.
pub fn col_view(&self) -> CsMatVecView_<N, I> {
// Safe because we're taking a view into a vector that has
// necessarily been checked
let indptr = Array2 {
data: [I::zero(), I::from_usize(self.indices.len())],
};
CsMatBase {
storage: CSC,
nrows: self.dim,
ncols: 1,
indptr: indptr,
indices: &self.indices[..],
data: &self.data[..],
}
}
/// Access element at given index, with logarithmic complexity
pub fn get<'a>(&'a self, index: usize) -> Option<&'a N>
where
I: 'a,
{
self.view().get_rbr(index)
}
/// Find the non-zero index of the requested dimension index,
/// returning None if no non-zero is present at the requested location.
///
/// Looking for the NnzIndex is done with logarithmic complexity, but
/// once it is available, the NnzIndex enables retrieving the data with
/// O(1) complexity.
pub fn nnz_index(&self, index: usize) -> Option<NnzIndex> {
self.indices
.binary_search(&I::from_usize(index))
.map(|i| NnzIndex(i.index()))
.ok()
}
/// Sparse vector dot product. The right-hand-side can be any type
/// that can be interpreted as a sparse vector (hence sparse vectors, std
/// vectors and slices, and ndarray's dense vectors work).
///
/// However, even if dense vectors work, it is more performant to use
/// the [`dot_dense`](struct.CsVecBase.html#method.dot_dense).
///
/// # Panics
///
/// If the dimension of the vectors do not match.
///
/// # Example
///
/// ```rust
/// use sprs::CsVec;
/// let v1 = CsVec::new(8, vec![1, 2, 4, 6], vec![1.; 4]);
/// let v2 = CsVec::new(8, vec![1, 3, 5, 7], vec![2.; 4]);
/// assert_eq!(2., v1.dot(&v2));
/// assert_eq!(4., v1.dot(&v1));
/// assert_eq!(16., v2.dot(&v2));
/// ```
pub fn dot<'b, T: IntoSparseVecIter<'b, N>>(&'b self, rhs: T) -> N
where
N: 'b + Num + Copy + Sum,
I: 'b,
<T as IntoSparseVecIter<'b, N>>::IterType:
Iterator<Item = (usize, &'b N)>,
T: Copy, // T is supposed to be a reference type
{
assert_eq!(self.dim(), rhs.dim());
if rhs.is_dense() {
self.iter()
.map(|(idx, val)| *val * *rhs.index(idx.index()))
.sum()
} else {
self.iter()
.nnz_zip(rhs.into_sparse_vec_iter())
.map(|(_, &lval, &rval)| lval * rval)
.fold(N::zero(), |x, y| x + y)
}
}
/// Sparse-dense vector dot product. The right-hand-side can be any type
/// that can be interpreted as a dense vector (hence std vectors and
/// slices, and ndarray's dense vectors work).
///
/// Since the `dot` method can work with the same performance on
/// dot vectors, the main interest of this method is to enforce at
/// compile time that the rhs is dense.
///
/// # Panics
///
/// If the dimension of the vectors do not match.
pub fn dot_dense<T>(&self, rhs: T) -> N
where
T: DenseVector<N>,
N: Num + Copy + Sum,
{
assert_eq!(self.dim(), rhs.dim());
self.iter()
.map(|(idx, val)| *val * *rhs.index(idx.index()))
.sum()
}
/// Fill a dense vector with our values
pub fn scatter(&self, out: &mut [N])
where
N: Clone,
{
for (ind, val) in self.iter() {
out[ind] = val.clone();
}
}
/// Transform this vector into a set of (index, value) tuples
pub fn to_set(self) -> HashSet<(usize, N)>
where
N: Hash + Eq + Clone,
{
self.indices()
.iter()
.map(|i| i.index())
.zip(self.data.iter().cloned())
.collect()
}
/// Apply a function to each non-zero element, yielding a new matrix
/// with the same sparsity structure.
pub fn map<F>(&self, f: F) -> CsVecI<N, I>
where
F: FnMut(&N) -> N,
N: Clone,
{
let mut res = self.to_owned();
res.map_inplace(f);
res
}
}
/// # Methods on sparse vectors with mutable access to their data
impl<'a, N, I, IStorage, DStorage> CsVecBase<IStorage, DStorage>
where
N: 'a,
I: 'a + SpIndex,
IStorage: 'a + Deref<Target = [I]>,
DStorage: DerefMut<Target = [N]>,
{
/// The underlying non zero values as a mutable slice.
fn data_mut(&mut self) -> &mut [N] {
&mut self.data[..]
}
pub fn view_mut(&mut self) -> CsVecViewMutI<N, I> {
CsVecBase {
dim: self.dim,
indices: &self.indices[..],
data: &mut self.data[..],
}
}
/// Access element at given index, with logarithmic complexity
pub fn get_mut(&mut self, index: usize) -> Option<&mut N> {
if let Some(NnzIndex(position)) = self.nnz_index(index) {
Some(&mut self.data[position])
} else {
None
}
}
/// Apply a function to each non-zero element, mutating it
pub fn map_inplace<F>(&mut self, mut f: F)
where
F: FnMut(&N) -> N,
{
for val in &mut self.data[..] {
*val = f(val);
}
}
/// Mutable iteration over the non-zero values of a sparse vector
///
/// Only the values can be changed, the sparse structure is kept.
pub fn iter_mut(&mut self) -> VectorIteratorMut<N, I> {
VectorIteratorMut {
ind_data: self.indices.iter().zip(self.data.iter_mut()),
}
}
}
/// # Methods propagating the lifetime of a `CsVecViewI`.
impl<'a, N: 'a, I: 'a + SpIndex> CsVecBase<&'a [I], &'a [N]> {
/// Create a borrowed CsVec over slice data.
pub fn new_view(
n: usize,
indices: &'a [I],
data: &'a [N],
) -> Result<CsVecViewI<'a, N, I>, SprsError> {
let v = CsVecViewI {
dim: n,
indices: indices,
data: data,
};
v.check_structure().and(Ok(v))
}
/// Access element at given index, with logarithmic complexity
///
/// Re-borrowing version of `at()`.
pub fn get_rbr(&self, index: usize) -> Option<&'a N> {
self.nnz_index(index)
.map(|NnzIndex(position)| &self.data[position])
}
/// Re-borrowing version of `iter()`. Namely, the iterator's lifetime
/// will be bound to the lifetime of the underlying slices instead
/// of being bound to the lifetime of the borrow.
fn iter_rbr(&self) -> VectorIterator<'a, N, I> {
VectorIterator {
ind_data: self.indices.iter().zip(self.data.iter()),
}
}
/// Create a borrowed CsVec over slice data without checking the structure
/// This is unsafe because algorithms are free to assume
/// that properties guaranteed by check_structure are enforced.
/// For instance, non out-of-bounds indices can be relied upon to
/// perform unchecked slice access.
pub unsafe fn new_view_raw(
n: usize,
nnz: usize,
indices: *const I,
data: *const N,
) -> CsVecViewI<'a, N, I> {
CsVecViewI {
dim: n,
indices: slice::from_raw_parts(indices, nnz),
data: slice::from_raw_parts(data, nnz),
}
}
}
/// # Methods propagating the lifetome of a `CsVecViewMutI`.
impl<'a, N, I> CsVecBase<&'a [I], &'a mut [N]>
where
N: 'a,
I: 'a + SpIndex,
{
/// Create a borrowed CsVec over slice data without checking the structure
/// This is unsafe because algorithms are free to assume
/// that properties guaranteed by check_structure are enforced, and
/// because the lifetime of the pointers is unconstrained.
/// For instance, non out-of-bounds indices can be relied upon to
/// perform unchecked slice access.
/// For safety, lifetime of the resulting vector should match the lifetime
/// of the input pointers.
pub unsafe fn new_view_mut_raw(
n: usize,
nnz: usize,
indices: *const I,
data: *mut N,
) -> CsVecViewMutI<'a, N, I> {
CsVecBase {
dim: n,
indices: slice::from_raw_parts(indices, nnz),
data: slice::from_raw_parts_mut(data, nnz),
}
}
}
impl<'a, 'b, N, I, IS1, DS1, IpS2, IS2, DS2>
Mul<&'b CsMatBase<N, I, IpS2, IS2, DS2>> for &'a CsVecBase<IS1, DS1>
where
N: 'a + Copy + Num + Default,
I: 'a + SpIndex,
IS1: 'a + Deref<Target = [I]>,
DS1: 'a + Deref<Target = [N]>,
IpS2: 'b + Deref<Target = [I]>,
IS2: 'b + Deref<Target = [I]>,
DS2: 'b + Deref<Target = [N]>,
{
type Output = CsVecI<N, I>;
fn mul(self, rhs: &CsMatBase<N, I, IpS2, IS2, DS2>) -> CsVecI<N, I> {
(&self.row_view() * rhs).outer_view(0).unwrap().to_owned()
}
}
impl<'a, 'b, N, I, IpS1, IS1, DS1, IS2, DS2> Mul<&'b CsVecBase<IS2, DS2>>
for &'a CsMatBase<N, I, IpS1, IS1, DS1>
where
N: Copy + Num + Default + Sum,
I: SpIndex,
IpS1: Deref<Target = [I]>,
IS1: Deref<Target = [I]>,
DS1: Deref<Target = [N]>,
IS2: Deref<Target = [I]>,
DS2: Deref<Target = [N]>,
{
type Output = CsVecI<N, I>;
fn mul(self, rhs: &CsVecBase<IS2, DS2>) -> CsVecI<N, I> {
if self.is_csr() {
prod::csr_mul_csvec(self.view(), rhs.view())
} else {
(self * &rhs.col_view()).outer_view(0).unwrap().to_owned()
}
}
}
impl<N, I, IS1, DS1, IS2, DS2> Add<CsVecBase<IS2, DS2>> for CsVecBase<IS1, DS1>
where
N: Copy + Num,