nalgebra/nalgebra-sparse/src/csc.rs

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//! An implementation of the CSC sparse matrix format.
use crate::{SparseFormatError, SparseFormatErrorKind};
use crate::pattern::{SparsityPattern, SparsityPatternFormatError, SparsityPatternIter};
use std::sync::Arc;
use std::slice::{IterMut, Iter};
use std::ops::Range;
use num_traits::Zero;
use std::ptr::slice_from_raw_parts_mut;
/// A CSC representation of a sparse matrix.
///
/// The Compressed Sparse Column (CSC) format is well-suited as a general-purpose storage format
/// for many sparse matrix applications.
///
/// TODO: Storage explanation and examples
///
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct CscMatrix<T> {
// Cols are major, rows are minor in the sparsity pattern
sparsity_pattern: Arc<SparsityPattern>,
values: Vec<T>,
}
impl<T> CscMatrix<T> {
/// Create a zero CSC matrix with no explicitly stored entries.
pub fn new(nrows: usize, ncols: usize) -> Self {
Self {
sparsity_pattern: Arc::new(SparsityPattern::new(ncols, nrows)),
values: vec![],
}
}
/// The number of rows in the matrix.
#[inline]
pub fn nrows(&self) -> usize {
self.sparsity_pattern.minor_dim()
}
/// The number of columns in the matrix.
#[inline]
pub fn ncols(&self) -> usize {
self.sparsity_pattern.major_dim()
}
/// The number of non-zeros in the matrix.
///
/// Note that this corresponds to the number of explicitly stored entries, *not* the actual
/// number of algebraically zero entries in the matrix. Explicitly stored entries can still
/// be zero. Corresponds to the number of entries in the sparsity pattern.
#[inline]
pub fn nnz(&self) -> usize {
self.sparsity_pattern.nnz()
}
/// The column offsets defining part of the CSC format.
#[inline]
pub fn col_offsets(&self) -> &[usize] {
self.sparsity_pattern.major_offsets()
}
/// The row indices defining part of the CSC format.
#[inline]
pub fn row_indices(&self) -> &[usize] {
self.sparsity_pattern.minor_indices()
}
/// The non-zero values defining part of the CSC format.
#[inline]
pub fn values(&self) -> &[T] {
&self.values
}
/// Mutable access to the non-zero values.
#[inline]
pub fn values_mut(&mut self) -> &mut [T] {
&mut self.values
}
/// Try to construct a CSC matrix from raw CSC data.
///
/// It is assumed that each column contains unique and sorted row indices that are in
/// bounds with respect to the number of rows in the matrix. If this is not the case,
/// an error is returned to indicate the failure.
///
/// An error is returned if the data given does not conform to the CSC storage format.
/// See the documentation for [CscMatrix](struct.CscMatrix.html) for more information.
pub fn try_from_csc_data(
num_rows: usize,
num_cols: usize,
col_offsets: Vec<usize>,
row_indices: Vec<usize>,
values: Vec<T>,
) -> Result<Self, SparseFormatError> {
let pattern = SparsityPattern::try_from_offsets_and_indices(
num_cols, num_rows, col_offsets, row_indices)
.map_err(pattern_format_error_to_csc_error)?;
Self::try_from_pattern_and_values(Arc::new(pattern), values)
}
/// Try to construct a CSC matrix from a sparsity pattern and associated non-zero values.
///
/// Returns an error if the number of values does not match the number of minor indices
/// in the pattern.
pub fn try_from_pattern_and_values(pattern: Arc<SparsityPattern>, values: Vec<T>)
-> Result<Self, SparseFormatError> {
if pattern.nnz() == values.len() {
Ok(Self {
sparsity_pattern: pattern,
values,
})
} else {
Err(SparseFormatError::from_kind_and_msg(
SparseFormatErrorKind::InvalidStructure,
"Number of values and row indices must be the same"))
}
}
/// An iterator over non-zero triplets (i, j, v).
///
/// The iteration happens in column-major fashion, meaning that j increases monotonically,
/// and i increases monotonically within each row.
///
/// Examples
/// --------
/// ```
/// # use nalgebra_sparse::csc::CscMatrix;
/// let col_offsets = vec![0, 2, 3, 4];
/// let row_indices = vec![0, 2, 1, 0];
/// let values = vec![1, 3, 2, 4];
/// let mut csc = CscMatrix::try_from_csc_data(4, 3, col_offsets, row_indices, values)
/// .unwrap();
///
/// let triplets: Vec<_> = csc.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect();
/// assert_eq!(triplets, vec![(0, 0, 1), (2, 0, 3), (1, 1, 2), (0, 2, 4)]);
/// ```
pub fn triplet_iter(&self) -> CscTripletIter<T> {
CscTripletIter {
pattern_iter: self.sparsity_pattern.entries(),
values_iter: self.values.iter()
}
}
/// A mutable iterator over non-zero triplets (i, j, v).
///
/// Iteration happens in the same order as for [triplet_iter](#method.triplet_iter).
///
/// Examples
/// --------
/// ```
/// # use nalgebra_sparse::csc::CscMatrix;
/// let col_offsets = vec![0, 2, 3, 4];
/// let row_indices = vec![0, 2, 1, 0];
/// let values = vec![1, 3, 2, 4];
/// // Using the same data as in the `triplet_iter` example
/// let mut csc = CscMatrix::try_from_csc_data(4, 3, col_offsets, row_indices, values)
/// .unwrap();
///
/// // Zero out lower-triangular terms
/// csc.triplet_iter_mut()
/// .filter(|(i, j, _)| j < i)
/// .for_each(|(_, _, v)| *v = 0);
///
/// let triplets: Vec<_> = csc.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect();
/// assert_eq!(triplets, vec![(0, 0, 1), (2, 0, 0), (1, 1, 2), (0, 2, 4)]);
/// ```
pub fn triplet_iter_mut(&mut self) -> CscTripletIterMut<T> {
CscTripletIterMut {
pattern_iter: self.sparsity_pattern.entries(),
values_mut_iter: self.values.iter_mut()
}
}
/// Return the column at the given column index.
///
/// Panics
/// ------
/// Panics if column index is out of bounds.
#[inline]
pub fn col(&self, index: usize) -> CscCol<T> {
self.get_col(index)
.expect("Row index must be in bounds")
}
/// Mutable column access for the given column index.
///
/// Panics
/// ------
/// Panics if column index is out of bounds.
#[inline]
pub fn col_mut(&mut self, index: usize) -> CscColMut<T> {
self.get_col_mut(index)
.expect("Row index must be in bounds")
}
/// Return the column at the given column index, or `None` if out of bounds.
#[inline]
pub fn get_col(&self, index: usize) -> Option<CscCol<T>> {
let range = self.get_index_range(index)?;
Some(CscCol {
row_indices: &self.sparsity_pattern.minor_indices()[range.clone()],
values: &self.values[range],
nrows: self.nrows()
})
}
/// Mutable column access for the given column index, or `None` if out of bounds.
#[inline]
pub fn get_col_mut(&mut self, index: usize) -> Option<CscColMut<T>> {
let range = self.get_index_range(index)?;
Some(CscColMut {
nrows: self.nrows(),
row_indices: &self.sparsity_pattern.minor_indices()[range.clone()],
values: &mut self.values[range]
})
}
/// Internal method for simplifying access to a column's data.
fn get_index_range(&self, col_index: usize) -> Option<Range<usize>> {
let col_begin = *self.sparsity_pattern.major_offsets().get(col_index)?;
let col_end = *self.sparsity_pattern.major_offsets().get(col_index + 1)?;
Some(col_begin .. col_end)
}
/// An iterator over columns in the matrix.
pub fn col_iter(&self) -> CscColIter<T> {
CscColIter {
current_col_idx: 0,
matrix: self
}
}
/// A mutable iterator over columns in the matrix.
pub fn col_iter_mut(&mut self) -> CscColIterMut<T> {
CscColIterMut {
current_col_idx: 0,
pattern: &self.sparsity_pattern,
remaining_values: self.values.as_mut_ptr()
}
}
/// Returns the underlying vector containing the values for the explicitly stored entries.
pub fn take_values(self) -> Vec<T> {
self.values
}
/// Disassembles the CSC matrix into its underlying offset, index and value arrays.
///
/// If the matrix contains the sole reference to the sparsity pattern,
/// then the data is returned as-is. Otherwise, the sparsity pattern is cloned.
///
/// Examples
/// --------
///
/// ```
/// # use nalgebra_sparse::csc::CscMatrix;
/// let col_offsets = vec![0, 2, 3, 4];
/// let row_indices = vec![0, 2, 1, 0];
/// let values = vec![1, 3, 2, 4];
/// let mut csc = CscMatrix::try_from_csc_data(
/// 4,
/// 3,
/// col_offsets.clone(),
/// row_indices.clone(),
/// values.clone())
/// .unwrap();
/// let (col_offsets2, row_indices2, values2) = csc.disassemble();
/// assert_eq!(col_offsets2, col_offsets);
/// assert_eq!(row_indices2, row_indices);
/// assert_eq!(values2, values);
/// ```
pub fn disassemble(self) -> (Vec<usize>, Vec<usize>, Vec<T>) {
// Take an Arc to the pattern, which might be the sole reference to the data after
// taking the values. This is important, because it might let us avoid cloning the data
// further below.
let pattern = self.pattern();
let values = self.take_values();
// Try to take the pattern out of the `Arc` if possible,
// otherwise clone the pattern.
let owned_pattern = Arc::try_unwrap(pattern)
.unwrap_or_else(|arc| SparsityPattern::clone(&*arc));
let (offsets, indices) = owned_pattern.disassemble();
(offsets, indices, values)
}
/// Returns the underlying sparsity pattern.
///
/// The sparsity pattern is stored internally inside an `Arc`. This allows users to re-use
/// the same sparsity pattern for multiple matrices without storing the same pattern multiple
/// times in memory.
pub fn pattern(&self) -> Arc<SparsityPattern> {
Arc::clone(&self.sparsity_pattern)
}
}
impl<T: Clone + Zero> CscMatrix<T> {
/// Return the value in the matrix at the given global row/col indices, or `None` if out of
/// bounds.
///
/// If the indices are in bounds, but no explicitly stored entry is associated with it,
/// `T::zero()` is returned. Note that this method offers no way of distinguishing
/// explicitly stored zero entries from zero values that are only implicitly represented.
///
/// Each call to this function incurs the cost of a binary search among the explicitly
/// stored column entries for the given row.
#[inline]
pub fn get(&self, row_index: usize, col_index: usize) -> Option<T> {
self.get_col(row_index)?.get(col_index)
}
/// Same as `get`, but panics if indices are out of bounds.
///
/// Panics
/// ------
/// Panics if either index is out of bounds.
#[inline]
pub fn index(&self, row_index: usize, col_index: usize) -> T {
self.get(row_index, col_index).unwrap()
}
}
/// Convert pattern format errors into more meaningful CSC-specific errors.
///
/// This ensures that the terminology is consistent: we are talking about rows and columns,
/// not lanes, major and minor dimensions.
fn pattern_format_error_to_csc_error(err: SparsityPatternFormatError) -> SparseFormatError {
use SparsityPatternFormatError::*;
use SparsityPatternFormatError::DuplicateEntry as PatternDuplicateEntry;
use SparseFormatError as E;
use SparseFormatErrorKind as K;
match err {
InvalidOffsetArrayLength => E::from_kind_and_msg(
K::InvalidStructure,
"Length of col offset array is not equal to ncols + 1."),
InvalidOffsetFirstLast => E::from_kind_and_msg(
K::InvalidStructure,
"First or last col offset is inconsistent with format specification."),
NonmonotonicOffsets => E::from_kind_and_msg(
K::InvalidStructure,
"Col offsets are not monotonically increasing."),
NonmonotonicMinorIndices => E::from_kind_and_msg(
K::InvalidStructure,
"Row indices are not monotonically increasing (sorted) within each column."),
MinorIndexOutOfBounds => E::from_kind_and_msg(
K::IndexOutOfBounds,
"Row indices are out of bounds."),
PatternDuplicateEntry => E::from_kind_and_msg(
K::DuplicateEntry,
"Matrix data contains duplicate entries."),
}
}
/// Iterator type for iterating over triplets in a CSC matrix.
#[derive(Debug)]
pub struct CscTripletIter<'a, T> {
pattern_iter: SparsityPatternIter<'a>,
values_iter: Iter<'a, T>
}
impl<'a, T> Iterator for CscTripletIter<'a, T> {
type Item = (usize, usize, &'a T);
fn next(&mut self) -> Option<Self::Item> {
let next_entry = self.pattern_iter.next();
let next_value = self.values_iter.next();
match (next_entry, next_value) {
(Some((i, j)), Some(v)) => Some((j, i, v)),
_ => None
}
}
}
/// Iterator type for mutably iterating over triplets in a CSC matrix.
#[derive(Debug)]
pub struct CscTripletIterMut<'a, T> {
pattern_iter: SparsityPatternIter<'a>,
values_mut_iter: IterMut<'a, T>
}
impl<'a, T> Iterator for CscTripletIterMut<'a, T> {
type Item = (usize, usize, &'a mut T);
#[inline]
fn next(&mut self) -> Option<Self::Item> {
let next_entry = self.pattern_iter.next();
let next_value = self.values_mut_iter.next();
match (next_entry, next_value) {
(Some((i, j)), Some(v)) => Some((j, i, v)),
_ => None
}
}
}
/// An immutable representation of a column in a CSC matrix.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct CscCol<'a, T> {
nrows: usize,
row_indices: &'a [usize],
values: &'a [T],
}
/// A mutable representation of a column in a CSC matrix.
///
/// Note that only explicitly stored entries can be mutated. The sparsity pattern belonging
/// to the column cannot be modified.
#[derive(Debug, PartialEq, Eq)]
pub struct CscColMut<'a, T> {
nrows: usize,
row_indices: &'a [usize],
values: &'a mut [T]
}
/// Implement the methods common to both CscCol and CscColMut
macro_rules! impl_csc_col_common_methods {
($name:ty) => {
impl<'a, T> $name {
/// The number of global rows in the column.
#[inline]
pub fn nrows(&self) -> usize {
self.nrows
}
/// The number of non-zeros in this column.
#[inline]
pub fn nnz(&self) -> usize {
self.row_indices.len()
}
/// The row indices corresponding to explicitly stored entries in this column.
#[inline]
pub fn row_indices(&self) -> &[usize] {
self.row_indices
}
/// The values corresponding to explicitly stored entries in this column.
#[inline]
pub fn values(&self) -> &[T] {
self.values
}
}
impl<'a, T: Clone + Zero> $name {
/// Return the value in the matrix at the given global row index, or `None` if out of
/// bounds.
///
/// If the index is in bounds, but no explicitly stored entry is associated with it,
/// `T::zero()` is returned. Note that this method offers no way of distinguishing
/// explicitly stored zero entries from zero values that are only implicitly represented.
///
/// Each call to this function incurs the cost of a binary search among the explicitly
/// stored row entries for the current column.
pub fn get(&self, global_row_index: usize) -> Option<T> {
let local_index = self.row_indices().binary_search(&global_row_index);
if let Ok(local_index) = local_index {
Some(self.values[local_index].clone())
} else if global_row_index < self.nrows {
Some(T::zero())
} else {
None
}
}
}
}
}
impl_csc_col_common_methods!(CscCol<'a, T>);
impl_csc_col_common_methods!(CscColMut<'a, T>);
impl<'a, T> CscColMut<'a, T> {
/// Mutable access to the values corresponding to explicitly stored entries in this column.
pub fn values_mut(&mut self) -> &mut [T] {
self.values
}
/// Provides simultaneous access to row indices and mutable values corresponding to the
/// explicitly stored entries in this column.
///
/// This method primarily facilitates low-level access for methods that process data stored
/// in CSC format directly.
pub fn rows_and_values_mut(&mut self) -> (&[usize], &mut [T]) {
(self.row_indices, self.values)
}
}
/// Column iterator for [CscMatrix](struct.CscMatrix.html).
pub struct CscColIter<'a, T> {
// The index of the row that will be returned on the next
current_col_idx: usize,
matrix: &'a CscMatrix<T>
}
impl<'a, T> Iterator for CscColIter<'a, T> {
type Item = CscCol<'a, T>;
fn next(&mut self) -> Option<Self::Item> {
let col = self.matrix.get_col(self.current_col_idx);
self.current_col_idx += 1;
col
}
}
/// Mutable column iterator for [CscMatrix](struct.CscMatrix.html).
pub struct CscColIterMut<'a, T> {
current_col_idx: usize,
pattern: &'a SparsityPattern,
remaining_values: *mut T,
}
impl<'a, T> Iterator for CscColIterMut<'a, T>
where
T: 'a
{
type Item = CscColMut<'a, T>;
fn next(&mut self) -> Option<Self::Item> {
let lane = self.pattern.get_lane(self.current_col_idx);
let nrows = self.pattern.minor_dim();
if let Some(row_indices) = lane {
let count = row_indices.len();
// Note: I can't think of any way to construct this iterator without unsafe.
let values_in_row;
unsafe {
values_in_row = &mut *slice_from_raw_parts_mut(self.remaining_values, count);
self.remaining_values = self.remaining_values.add(count);
}
self.current_col_idx += 1;
Some(CscColMut {
nrows,
row_indices,
values: values_in_row
})
} else {
None
}
}
}