698 lines
24 KiB
Rust
698 lines
24 KiB
Rust
//! An implementation of the CSC sparse matrix format.
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//!
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//! This is the module-level documentation. See [`CscMatrix`] for the main documentation of the
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//! CSC implementation.
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use crate::{SparseFormatError, SparseFormatErrorKind, SparseEntry, SparseEntryMut};
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use crate::pattern::{SparsityPattern, SparsityPatternFormatError, SparsityPatternIter};
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use crate::csr::CsrMatrix;
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use crate::cs::{CsMatrix, CsLane, CsLaneMut, CsLaneIter, CsLaneIterMut};
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use std::slice::{IterMut, Iter};
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use num_traits::{One};
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use nalgebra::Scalar;
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/// A CSC representation of a sparse matrix.
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///
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/// The Compressed Sparse Column (CSC) format is well-suited as a general-purpose storage format
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/// for many sparse matrix applications.
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///
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/// # Usage
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///
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/// ```rust
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/// use nalgebra_sparse::csc::CscMatrix;
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/// use nalgebra::{DMatrix, Matrix3x4};
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/// use matrixcompare::assert_matrix_eq;
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///
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/// // The sparsity patterns of CSC matrices are immutable. This means that you cannot dynamically
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/// // change the sparsity pattern of the matrix after it has been constructed. The easiest
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/// // way to construct a CSC matrix is to first incrementally construct a COO matrix,
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/// // and then convert it to CSC.
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/// # use nalgebra_sparse::coo::CooMatrix;
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/// # let coo = CooMatrix::<f64>::new(3, 3);
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/// let csc = CscMatrix::from(&coo);
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///
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/// // Alternatively, a CSC matrix can be constructed directly from raw CSC data.
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/// // Here, we construct a 3x4 matrix
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/// let col_offsets = vec![0, 1, 3, 4, 5];
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/// let row_indices = vec![0, 0, 2, 2, 0];
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/// let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
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///
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/// // The dense representation of the CSC data, for comparison
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/// let dense = Matrix3x4::new(1.0, 2.0, 0.0, 5.0,
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/// 0.0, 0.0, 0.0, 0.0,
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/// 0.0, 3.0, 4.0, 0.0);
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///
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/// // The constructor validates the raw CSC data and returns an error if it is invalid.
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/// let csc = CscMatrix::try_from_csc_data(3, 4, col_offsets, row_indices, values)
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/// .expect("CSC data must conform to format specifications");
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/// assert_matrix_eq!(csc, dense);
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///
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/// // A third approach is to construct a CSC matrix from a pattern and values. Sometimes this is
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/// // useful if the sparsity pattern is constructed separately from the values of the matrix.
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/// let (pattern, values) = csc.into_pattern_and_values();
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/// let csc = CscMatrix::try_from_pattern_and_values(pattern, values)
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/// .expect("The pattern and values must be compatible");
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///
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/// // Once we have constructed our matrix, we can use it for arithmetic operations together with
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/// // other CSC matrices and dense matrices/vectors.
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/// let x = csc;
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/// # #[allow(non_snake_case)]
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/// let xTx = x.transpose() * &x;
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/// let z = DMatrix::from_fn(4, 8, |i, j| (i as f64) * (j as f64));
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/// let w = 3.0 * xTx * z;
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///
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/// // Although the sparsity pattern of a CSC matrix cannot be changed, its values can.
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/// // Here are two different ways to scale all values by a constant:
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/// let mut x = x;
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/// x *= 5.0;
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/// x.values_mut().iter_mut().for_each(|x_i| *x_i *= 5.0);
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/// ```
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///
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/// # Format
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///
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/// An `m x n` sparse matrix with `nnz` non-zeros in CSC format is represented by the
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/// following three arrays:
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///
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/// - `col_offsets`, an array of integers with length `n + 1`.
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/// - `row_indices`, an array of integers with length `nnz`.
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/// - `values`, an array of values with length `nnz`.
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///
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/// The relationship between the arrays is described below.
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///
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/// - Each consecutive pair of entries `col_offsets[j] .. col_offsets[j + 1]` corresponds to an
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/// offset range in `row_indices` that holds the row indices in column `j`.
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/// - For an entry represented by the index `idx`, `row_indices[idx]` stores its column index and
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/// `values[idx]` stores its value.
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///
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/// The following invariants must be upheld and are enforced by the data structure:
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///
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/// - `col_offsets[0] == 0`
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/// - `col_offsets[m] == nnz`
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/// - `col_offsets` is monotonically increasing.
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/// - `0 <= row_indices[idx] < m` for all `idx < nnz`.
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/// - The row indices associated with each column are monotonically increasing (see below).
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///
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/// The CSC format is a standard sparse matrix format (see [Wikipedia article]). The format
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/// represents the matrix in a column-by-column fashion. The entries associated with column `j` are
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/// determined as follows:
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///
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/// ```rust
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/// # let col_offsets: Vec<usize> = vec![0, 0];
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/// # let row_indices: Vec<usize> = vec![];
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/// # let values: Vec<i32> = vec![];
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/// # let j = 0;
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/// let range = col_offsets[j] .. col_offsets[j + 1];
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/// let col_j_rows = &row_indices[range.clone()];
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/// let col_j_vals = &values[range];
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///
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/// // For each pair (i, v) in (col_j_rows, col_j_vals), we obtain a corresponding entry
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/// // (i, j, v) in the matrix.
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/// assert_eq!(col_j_rows.len(), col_j_vals.len());
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/// ```
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///
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/// In the above example, for each column `j`, the row indices `col_j_cols` must appear in
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/// monotonically increasing order. In other words, they must be *sorted*. This criterion is not
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/// standard among all sparse matrix libraries, but we enforce this property as it is a crucial
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/// assumption for both correctness and performance for many algorithms.
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///
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/// Note that the CSR and CSC formats are essentially identical, except that CSC stores the matrix
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/// column-by-column instead of row-by-row like CSR.
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///
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/// [Wikipedia article]: https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_(CSC_or_CCS)
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#[derive(Debug, Clone, PartialEq, Eq)]
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pub struct CscMatrix<T> {
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// Cols are major, rows are minor in the sparsity pattern
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pub(crate) cs: CsMatrix<T>,
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}
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impl<T> CscMatrix<T> {
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/// Create a zero CSC matrix with no explicitly stored entries.
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pub fn zeros(nrows: usize, ncols: usize) -> Self {
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Self {
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cs: CsMatrix::new(ncols, nrows)
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}
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}
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/// The number of rows in the matrix.
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#[inline]
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pub fn nrows(&self) -> usize {
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self.cs.pattern().minor_dim()
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}
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/// The number of columns in the matrix.
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#[inline]
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pub fn ncols(&self) -> usize {
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self.cs.pattern().major_dim()
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}
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/// The number of non-zeros in the matrix.
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///
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/// Note that this corresponds to the number of explicitly stored entries, *not* the actual
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/// number of algebraically zero entries in the matrix. Explicitly stored entries can still
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/// be zero. Corresponds to the number of entries in the sparsity pattern.
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#[inline]
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pub fn nnz(&self) -> usize {
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self.pattern().nnz()
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}
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/// The column offsets defining part of the CSC format.
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#[inline]
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pub fn col_offsets(&self) -> &[usize] {
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self.pattern().major_offsets()
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}
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/// The row indices defining part of the CSC format.
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#[inline]
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pub fn row_indices(&self) -> &[usize] {
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self.pattern().minor_indices()
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}
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/// The non-zero values defining part of the CSC format.
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#[inline]
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pub fn values(&self) -> &[T] {
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self.cs.values()
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}
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/// Mutable access to the non-zero values.
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#[inline]
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pub fn values_mut(&mut self) -> &mut [T] {
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self.cs.values_mut()
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}
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/// Try to construct a CSC matrix from raw CSC data.
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///
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/// It is assumed that each column contains unique and sorted row indices that are in
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/// bounds with respect to the number of rows in the matrix. If this is not the case,
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/// an error is returned to indicate the failure.
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///
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/// An error is returned if the data given does not conform to the CSC storage format.
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/// See the documentation for [CscMatrix](struct.CscMatrix.html) for more information.
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pub fn try_from_csc_data(
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num_rows: usize,
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num_cols: usize,
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col_offsets: Vec<usize>,
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row_indices: Vec<usize>,
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values: Vec<T>,
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) -> Result<Self, SparseFormatError> {
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let pattern = SparsityPattern::try_from_offsets_and_indices(
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num_cols, num_rows, col_offsets, row_indices)
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.map_err(pattern_format_error_to_csc_error)?;
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Self::try_from_pattern_and_values(pattern, values)
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}
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/// Try to construct a CSC matrix from a sparsity pattern and associated non-zero values.
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///
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/// Returns an error if the number of values does not match the number of minor indices
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/// in the pattern.
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pub fn try_from_pattern_and_values(pattern: SparsityPattern, values: Vec<T>)
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-> Result<Self, SparseFormatError> {
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if pattern.nnz() == values.len() {
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Ok(Self {
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cs: CsMatrix::from_pattern_and_values(pattern, values)
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})
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} else {
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Err(SparseFormatError::from_kind_and_msg(
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SparseFormatErrorKind::InvalidStructure,
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"Number of values and row indices must be the same"))
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}
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}
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/// An iterator over non-zero triplets (i, j, v).
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///
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/// The iteration happens in column-major fashion, meaning that j increases monotonically,
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/// and i increases monotonically within each row.
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///
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/// Examples
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/// --------
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/// ```
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/// # use nalgebra_sparse::csc::CscMatrix;
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/// let col_offsets = vec![0, 2, 3, 4];
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/// let row_indices = vec![0, 2, 1, 0];
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/// let values = vec![1, 3, 2, 4];
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/// let mut csc = CscMatrix::try_from_csc_data(4, 3, col_offsets, row_indices, values)
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/// .unwrap();
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///
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/// let triplets: Vec<_> = csc.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect();
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/// assert_eq!(triplets, vec![(0, 0, 1), (2, 0, 3), (1, 1, 2), (0, 2, 4)]);
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/// ```
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pub fn triplet_iter(&self) -> CscTripletIter<T> {
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CscTripletIter {
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pattern_iter: self.pattern().entries(),
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values_iter: self.values().iter()
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}
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}
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/// A mutable iterator over non-zero triplets (i, j, v).
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///
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/// Iteration happens in the same order as for [triplet_iter](#method.triplet_iter).
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///
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/// Examples
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/// --------
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/// ```
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/// # use nalgebra_sparse::csc::CscMatrix;
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/// let col_offsets = vec![0, 2, 3, 4];
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/// let row_indices = vec![0, 2, 1, 0];
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/// let values = vec![1, 3, 2, 4];
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/// // Using the same data as in the `triplet_iter` example
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/// let mut csc = CscMatrix::try_from_csc_data(4, 3, col_offsets, row_indices, values)
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/// .unwrap();
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///
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/// // Zero out lower-triangular terms
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/// csc.triplet_iter_mut()
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/// .filter(|(i, j, _)| j < i)
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/// .for_each(|(_, _, v)| *v = 0);
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///
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/// let triplets: Vec<_> = csc.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect();
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/// assert_eq!(triplets, vec![(0, 0, 1), (2, 0, 0), (1, 1, 2), (0, 2, 4)]);
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/// ```
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pub fn triplet_iter_mut(&mut self) -> CscTripletIterMut<T> {
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let (pattern, values) = self.cs.pattern_and_values_mut();
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CscTripletIterMut {
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pattern_iter: pattern.entries(),
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values_mut_iter: values.iter_mut()
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}
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}
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/// Return the column at the given column index.
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///
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/// Panics
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/// ------
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/// Panics if column index is out of bounds.
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#[inline]
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pub fn col(&self, index: usize) -> CscCol<T> {
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self.get_col(index)
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.expect("Row index must be in bounds")
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}
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/// Mutable column access for the given column index.
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///
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/// Panics
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/// ------
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/// Panics if column index is out of bounds.
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#[inline]
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pub fn col_mut(&mut self, index: usize) -> CscColMut<T> {
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self.get_col_mut(index)
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.expect("Row index must be in bounds")
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}
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/// Return the column at the given column index, or `None` if out of bounds.
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#[inline]
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pub fn get_col(&self, index: usize) -> Option<CscCol<T>> {
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self.cs
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.get_lane(index)
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.map(|lane| CscCol { lane })
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}
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/// Mutable column access for the given column index, or `None` if out of bounds.
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#[inline]
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pub fn get_col_mut(&mut self, index: usize) -> Option<CscColMut<T>> {
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self.cs
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.get_lane_mut(index)
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.map(|lane| CscColMut { lane })
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}
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/// An iterator over columns in the matrix.
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pub fn col_iter(&self) -> CscColIter<T> {
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CscColIter {
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lane_iter: CsLaneIter::new(self.pattern(), self.values())
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}
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}
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/// A mutable iterator over columns in the matrix.
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pub fn col_iter_mut(&mut self) -> CscColIterMut<T> {
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let (pattern, values) = self.cs.pattern_and_values_mut();
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CscColIterMut {
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lane_iter: CsLaneIterMut::new(pattern, values)
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}
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}
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/// Disassembles the CSC matrix into its underlying offset, index and value arrays.
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///
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/// If the matrix contains the sole reference to the sparsity pattern,
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/// then the data is returned as-is. Otherwise, the sparsity pattern is cloned.
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///
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/// Examples
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/// --------
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///
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/// ```
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/// # use nalgebra_sparse::csc::CscMatrix;
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/// let col_offsets = vec![0, 2, 3, 4];
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/// let row_indices = vec![0, 2, 1, 0];
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/// let values = vec![1, 3, 2, 4];
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/// let mut csc = CscMatrix::try_from_csc_data(
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/// 4,
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/// 3,
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/// col_offsets.clone(),
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/// row_indices.clone(),
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/// values.clone())
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/// .unwrap();
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/// let (col_offsets2, row_indices2, values2) = csc.disassemble();
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/// assert_eq!(col_offsets2, col_offsets);
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/// assert_eq!(row_indices2, row_indices);
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/// assert_eq!(values2, values);
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/// ```
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pub fn disassemble(self) -> (Vec<usize>, Vec<usize>, Vec<T>) {
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self.cs.disassemble()
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}
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/// Returns the sparsity pattern and values associated with this matrix.
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pub fn into_pattern_and_values(self) -> (SparsityPattern, Vec<T>) {
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self.cs.into_pattern_and_values()
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}
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/// Returns a reference to the sparsity pattern and a mutable reference to the values.
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#[inline]
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pub fn pattern_and_values_mut(&mut self) -> (&SparsityPattern, &mut [T]) {
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self.cs.pattern_and_values_mut()
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}
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/// Returns the underlying sparsity pattern.
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///
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/// The sparsity pattern is stored internally inside an `Arc`. This allows users to re-use
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/// the same sparsity pattern for multiple matrices without storing the same pattern multiple
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/// times in memory.
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pub fn pattern(&self) -> &SparsityPattern {
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self.cs.pattern()
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}
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/// Reinterprets the CSC matrix as its transpose represented by a CSR matrix.
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///
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/// This operation does not touch the CSC data, and is effectively a no-op.
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pub fn transpose_as_csr(self) -> CsrMatrix<T> {
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let (pattern, values) = self.cs.take_pattern_and_values();
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CsrMatrix::try_from_pattern_and_values(pattern, values).unwrap()
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}
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/// Returns an entry for the given row/col indices, or `None` if the indices are out of bounds.
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///
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/// Each call to this function incurs the cost of a binary search among the explicitly
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/// stored row entries for the given column.
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pub fn get_entry(&self, row_index: usize, col_index: usize) -> Option<SparseEntry<T>> {
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self.cs.get_entry(col_index, row_index)
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}
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/// Returns a mutable entry for the given row/col indices, or `None` if the indices are out
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/// of bounds.
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///
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/// Each call to this function incurs the cost of a binary search among the explicitly
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/// stored row entries for the given column.
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pub fn get_entry_mut(&mut self, row_index: usize, col_index: usize)
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-> Option<SparseEntryMut<T>> {
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self.cs.get_entry_mut(col_index, row_index)
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}
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/// Returns an entry for the given row/col indices.
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///
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/// Same as `get_entry`, except that it directly panics upon encountering row/col indices
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/// out of bounds.
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///
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/// Panics
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/// ------
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/// Panics if `row_index` or `col_index` is out of bounds.
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pub fn index_entry(&self, row_index: usize, col_index: usize) -> SparseEntry<T> {
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self.get_entry(row_index, col_index)
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.expect("Out of bounds matrix indices encountered")
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}
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/// Returns a mutable entry for the given row/col indices.
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///
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/// Same as `get_entry_mut`, except that it directly panics upon encountering row/col indices
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/// out of bounds.
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///
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/// Panics
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/// ------
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/// Panics if `row_index` or `col_index` is out of bounds.
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pub fn index_entry_mut(&mut self, row_index: usize, col_index: usize) -> SparseEntryMut<T> {
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self.get_entry_mut(row_index, col_index)
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.expect("Out of bounds matrix indices encountered")
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}
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/// Returns a triplet of slices `(row_offsets, col_indices, values)` that make up the CSC data.
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pub fn csc_data(&self) -> (&[usize], &[usize], &[T]) {
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self.cs.cs_data()
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}
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/// Returns a triplet of slices `(row_offsets, col_indices, values)` that make up the CSC data,
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/// where the `values` array is mutable.
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pub fn csc_data_mut(&mut self) -> (&[usize], &[usize], &mut [T]) {
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self.cs.cs_data_mut()
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}
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/// Creates a sparse matrix that contains only the explicit entries decided by the
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/// given predicate.
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pub fn filter<P>(&self, predicate: P) -> Self
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where
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T: Clone,
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P: Fn(usize, usize, &T) -> bool
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{
|
|
// Note: Predicate uses (row, col, value), so we have to switch around since
|
|
// cs uses (major, minor, value)
|
|
Self { cs: self.cs.filter(|col_idx, row_idx, v| predicate(row_idx, col_idx, v)) }
|
|
}
|
|
|
|
/// Returns a new matrix representing the upper triangular part of this matrix.
|
|
///
|
|
/// The result includes the diagonal of the matrix.
|
|
pub fn upper_triangle(&self) -> Self
|
|
where
|
|
T: Clone
|
|
{
|
|
self.filter(|i, j, _| i <= j)
|
|
}
|
|
|
|
/// Returns a new matrix representing the lower triangular part of this matrix.
|
|
///
|
|
/// The result includes the diagonal of the matrix.
|
|
pub fn lower_triangle(&self) -> Self
|
|
where
|
|
T: Clone
|
|
{
|
|
self.filter(|i, j, _| i >= j)
|
|
}
|
|
|
|
/// Returns the diagonal of the matrix as a sparse matrix.
|
|
pub fn diagonal_as_csc(&self) -> Self
|
|
where
|
|
T: Clone
|
|
{
|
|
Self { cs: self.cs.diagonal_as_matrix() }
|
|
}
|
|
}
|
|
|
|
impl<T> CscMatrix<T>
|
|
where
|
|
T: Scalar
|
|
{
|
|
/// Compute the transpose of the matrix.
|
|
pub fn transpose(&self) -> CscMatrix<T> {
|
|
CsrMatrix::from(self).transpose_as_csc()
|
|
}
|
|
}
|
|
|
|
impl<T: Scalar + One> CscMatrix<T> {
|
|
/// Constructs a CSC representation of the (square) `n x n` identity matrix.
|
|
#[inline]
|
|
pub fn identity(n: usize) -> Self {
|
|
Self {
|
|
cs: CsMatrix::identity(n)
|
|
}
|
|
}
|
|
}
|
|
|
|
/// 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: Clone> CscTripletIter<'a, T> {
|
|
/// Adapts the triplet iterator to return owned values.
|
|
///
|
|
/// The triplet iterator returns references to the values. This method adapts the iterator
|
|
/// so that the values are cloned.
|
|
#[inline]
|
|
pub fn cloned_values(self) -> impl 'a + Iterator<Item=(usize, usize, T)> {
|
|
self.map(|(i, j, v)| (i, j, v.clone()))
|
|
}
|
|
}
|
|
|
|
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> {
|
|
lane: CsLane<'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> {
|
|
lane: CsLaneMut<'a, 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.lane.minor_dim()
|
|
}
|
|
|
|
/// The number of non-zeros in this column.
|
|
#[inline]
|
|
pub fn nnz(&self) -> usize {
|
|
self.lane.nnz()
|
|
}
|
|
|
|
/// The row indices corresponding to explicitly stored entries in this column.
|
|
#[inline]
|
|
pub fn row_indices(&self) -> &[usize] {
|
|
self.lane.minor_indices()
|
|
}
|
|
|
|
/// The values corresponding to explicitly stored entries in this column.
|
|
#[inline]
|
|
pub fn values(&self) -> &[T] {
|
|
self.lane.values()
|
|
}
|
|
|
|
/// Returns an entry for the given global row index.
|
|
///
|
|
/// Each call to this function incurs the cost of a binary search among the explicitly
|
|
/// stored row entries.
|
|
pub fn get_entry(&self, global_row_index: usize) -> Option<SparseEntry<T>> {
|
|
self.lane.get_entry(global_row_index)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
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.lane.values_mut()
|
|
}
|
|
|
|
/// 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.lane.indices_and_values_mut()
|
|
}
|
|
|
|
/// Returns a mutable entry for the given global row index.
|
|
pub fn get_entry_mut(&mut self, global_row_index: usize) -> Option<SparseEntryMut<T>> {
|
|
self.lane.get_entry_mut(global_row_index)
|
|
}
|
|
}
|
|
|
|
/// Column iterator for [CscMatrix](struct.CscMatrix.html).
|
|
pub struct CscColIter<'a, T> {
|
|
lane_iter: CsLaneIter<'a, T>
|
|
}
|
|
|
|
impl<'a, T> Iterator for CscColIter<'a, T> {
|
|
type Item = CscCol<'a, T>;
|
|
|
|
fn next(&mut self) -> Option<Self::Item> {
|
|
self.lane_iter
|
|
.next()
|
|
.map(|lane| CscCol { lane })
|
|
}
|
|
}
|
|
|
|
/// Mutable column iterator for [CscMatrix](struct.CscMatrix.html).
|
|
pub struct CscColIterMut<'a, T> {
|
|
lane_iter: CsLaneIterMut<'a, T>
|
|
}
|
|
|
|
impl<'a, T> Iterator for CscColIterMut<'a, T>
|
|
where
|
|
T: 'a
|
|
{
|
|
type Item = CscColMut<'a, T>;
|
|
|
|
fn next(&mut self) -> Option<Self::Item> {
|
|
self.lane_iter
|
|
.next()
|
|
.map(|lane| CscColMut { lane })
|
|
}
|
|
} |