Struct sprs::CsMatBase[−][src]

pub struct CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr = I> where
    I: SpIndex,
    Iptr: SpIndex,
    IptrStorage: Deref<Target = [Iptr]>,
    IndStorage: Deref<Target = [I]>,
    DataStorage: Deref<Target = [N]>,  { /* fields omitted */ }

Compressed matrix in the CSR or CSC format, with sorted indices.

This sparse matrix format is the preferred format for performing arithmetic operations. Constructing a sparse matrix directly in this format requires a deep knowledge of its internals. For easier matrix construction, the triplet format is preferred.

The CsMatBase type is parameterized by the scalar type N, the indexing type I, the indexing storage backend types IptrStorage and IndStorage, and the value storage backend type DataStorage. Convenient aliases are available to specify frequent variants: CsMat refers to a sparse matrix that owns its data, similar to Vec<T>; CsMatView refers to a sparse matrix that borrows its data, similar to & [T]; and CsMatViewMut refers to a sparse matrix borrowing its data, with a mutable borrow for its values. No mutable borrow is allowed for the structure of the matrix, allowing the invariants to be preserved.

Additionaly, the type aliases CsMatI, CsMatViewI and CsMatViewMutI can be used to choose an index type different from the default usize.

Storage format

In the compressed storage format, the non-zero values of a sparse matrix are stored as the row and column location of the non-zero values, with a compression along the rows (CSR) or columns (CSC) indices. The dimension along which the storage is compressed is referred to as the outer dimension, the other dimension is called the inner dimension. For clarity, the remaining explanation will assume a CSR matrix, but the information stands for CSC matrices as well.

Indptr

An index pointer array indptr of size corresponding to the number of rows stores the cumulative sum of non-zero elements for each row. For instance, the number of non-zero elements of the i-th row can be obtained by computing indptr[i + 1] - indptr[i]. The total number of non-zero elements is thus nnz = indptr[nb_rows + 1]. This index pointer array can then be used to efficiently index the indices and data array, which respectively contain the column indices and the values of the non-zero elements.

Indices and data

The non-zero locations and values are stored in arrays of size nnz, indices and data. For row i, the non-zeros are located in the slices indices[indptr[i]..indptr[i+1]] and data[indptr[i]..indptr[i+1]]. We require and enforce sorted indices for each row.

Construction

A sparse matrix can be directly constructed by providing its index pointer, indices and data arrays. The coherence of the provided structure is then verified.

For situations where the compressed structure is hard to figure out up front, the triplet format can be used. A matrix in the triplet format can then be efficiently converted to a CsMat.

Alternately, a sparse matrix can be constructed from other sparse matrices using vstack, hstack or bmat.

Implementations

`impl<N, I: SpIndex, Iptr: SpIndex, IptrStorage, IStorage, DStorage> CsMatBase<N, I, IptrStorage, IStorage, DStorage, Iptr> where IptrStorage: Deref<Target = [Iptr]>, IStorage: Deref<Target = [I]>, DStorage: Deref<Target = [N]>,` [src]

`pub fn new( shape: (usize, usize), indptr: IptrStorage, indices: IStorage, data: DStorage ) -> Self`[src]

Create a new CSR sparse matrix

See new_csc for the CSC equivalent

This constructor can be used to construct all sparse matrix types. By using the type aliases one helps constrain the resulting type, as shown below

Example

// This creates an owned matrix
let owned_matrix = CsMat::new((2, 2), vec![0, 1, 1], vec![1], vec![4_u8]);
// This creates a matrix which only borrows the elements
let borrow_matrix = CsMatView::new((2, 2), &[0, 1, 1], &[1], &[4_u8]);
// A combination of storage types may also be used for a
// general sparse matrix
let mixed_matrix = CsMatBase::new((2, 2), &[0, 1, 1] as &[_], vec![1_i64].into_boxed_slice(), vec![4_u8]);

`pub fn new_csc( shape: (usize, usize), indptr: IptrStorage, indices: IStorage, data: DStorage ) -> Self`[src]

Create a new CSC sparse matrix

See new for the CSR equivalent

`pub fn try_new( shape: (usize, usize), indptr: IptrStorage, indices: IStorage, data: DStorage ) -> Result<Self, (IptrStorage, IStorage, DStorage, StructureError)>`[src]

Try to create a new CSR sparse matrix

See try_new_csc for the CSC equivalent

`pub fn try_new_csc( shape: (usize, usize), indptr: IptrStorage, indices: IStorage, data: DStorage ) -> Result<Self, (IptrStorage, IStorage, DStorage, StructureError)>`[src]

Try to create a new CSC sparse matrix

See new for the CSR equivalent

`pub unsafe fn new_unchecked( storage: CompressedStorage, shape: Shape, indptr: IptrStorage, indices: IStorage, data: DStorage ) -> Self`[src]

Create a CsMat matrix from raw data, without checking their validity

Safety

This is unsafe because algorithms are free to assume that properties guaranteed by check_compressed_structure are enforced. For instance, non out-of-bounds indices can be relied upon to perform unchecked slice access.

`impl<N, I: SpIndex, Iptr: SpIndex, IptrStorage, IStorage, DStorage> CsMatBase<N, I, IptrStorage, IStorage, DStorage, Iptr> where IptrStorage: Deref<Target = [Iptr]>, IStorage: DerefMut<Target = [I]>, DStorage: DerefMut<Target = [N]>,` [src]

`pub fn new_from_unsorted( shape: Shape, indptr: IptrStorage, indices: IStorage, data: DStorage ) -> Result<Self, (IptrStorage, IStorage, DStorage, StructureError)> where N: Clone,` [src]

Try create a CSR matrix which acts as an owner of its data.

A CSC matrix can be created with new_from_unsorted_csc().

If necessary, the indices will be sorted in place.

`pub fn new_from_unsorted_csc( shape: Shape, indptr: IptrStorage, indices: IStorage, data: DStorage ) -> Result<Self, (IptrStorage, IStorage, DStorage, StructureError)> where N: Clone,` [src]

Try create a CSC matrix which acts as an owner of its data.

A CSR matrix can be created with new_from_unsorted_csr().

If necessary, the indices will be sorted in place.

`impl<N, I: SpIndex, Iptr: SpIndex> CsMatBase<N, I, Vec<Iptr, Global>, Vec<I, Global>, Vec<N, Global>, Iptr>`[src]

Constructor methods for owned sparse matrices

`pub fn eye(dim: usize) -> Self where N: Num + Clone,` [src]

Identity matrix, stored as a CSR matrix.

use sprs::{CsMat, CsVec};
let eye = CsMat::eye(5);
assert!(eye.is_csr());
let x = CsVec::new(5, vec![0, 2, 4], vec![1., 2., 3.]);
let y = &eye * &x;
assert_eq!(x, y);

`pub fn eye_csc(dim: usize) -> Self where N: Num + Clone,` [src]

Identity matrix, stored as a CSC matrix.

use sprs::{CsMat, CsVec};
let eye = CsMat::eye_csc(5);
assert!(eye.is_csc());
let x = CsVec::new(5, vec![0, 2, 4], vec![1., 2., 3.]);
let y = &eye * &x;
assert_eq!(x, y);

`pub fn empty(storage: CompressedStorage, inner_size: usize) -> Self`[src]

Create an empty CsMat for building purposes

`pub fn zero(shape: Shape) -> Self`[src]

Create a new CsMat representing the zero matrix. Hence it has no non-zero elements.

`pub fn reserve_outer_dim(&mut self, outer_dim_additional: usize)`[src]

Reserve the storage for the given additional number of nonzero data

`pub fn reserve_nnz(&mut self, nnz_additional: usize)`[src]

Reserve the storage for the given additional number of nonzero data

`pub fn reserve_outer_dim_exact(&mut self, outer_dim_lim: usize)`[src]

Reserve the storage for the given number of nonzero data

`pub fn reserve_nnz_exact(&mut self, nnz_lim: usize)`[src]

Reserve the storage for the given number of nonzero data

`pub fn csr_from_dense(m: ArrayView<'_, N, Ix2>, epsilon: N) -> Self where N: Num + Clone + PartialOrd + Signed,` [src]

Create a CSR matrix from a dense matrix, ignoring elements lower than epsilon.

If epsilon is negative, it will be clamped to zero.

`pub fn csc_from_dense(m: ArrayView<'_, N, Ix2>, epsilon: N) -> Self where N: Num + Clone + PartialOrd + Signed,` [src]

Create a CSC matrix from a dense matrix, ignoring elements lower than epsilon.

If epsilon is negative, it will be clamped to zero.

`pub fn append_outer(self, data: &[N]) -> Self where N: Clone + Num,` [src]

Append an outer dim to an existing matrix, compressing it in the process

`pub fn append_outer_csvec(self, vec: CsVecViewI<'_, N, I>) -> Self where N: Clone,` [src]

Append an outer dim to an existing matrix, provided by a sparse vector

`pub fn insert(&mut self, row: usize, col: usize, val: N)`[src]

Insert an element in the matrix. If the element is already present, its value is overwritten.

Warning: this is not an efficient operation, as it requires a non-constant lookup followed by two Vec insertions.

The insertion will be efficient, however, if the elements are inserted according to the matrix’s order, eg following the row order for a CSR matrix.

`impl<'a, N: 'a, I: 'a + SpIndex, Iptr: 'a + SpIndex> CsMatBase<N, I, &'a [Iptr], &'a [I], &'a [N], Iptr>`[src]

Constructor methods for sparse matrix views

These constructors can be used to create views over non-matrix data such as slices.

`pub fn middle_outer_views( &self, i: usize, count: usize ) -> CsMatViewI<'a, N, I, Iptr>`[src]

👎 Deprecated since 0.10.0:

Please use the slice_outer method instead

Get a view into count contiguous outer dimensions, starting from i.

eg this gets the rows from i to i + count in a CSR matrix

This function is now deprecated, as using an index and a count is not ergonomic. The replacement, slice_outer, leverages the std::ops::Range family of types, which is better integrated into the ecosystem.

`pub fn iter_rbr(&self) -> CsIter<'a, N, I, Iptr>ⓘNotable traits for CsIter<'a, N, I, Iptr> impl<'a, N, I, Iptr> Iterator for CsIter<'a, N, I, Iptr> where I: SpIndex, Iptr: SpIndex, N: 'a, type Item = (&'a N, (I, I));`[src]

Get an iterator that yields the non-zero locations and values stored in this matrix, in the fastest iteration order.

This method will yield the correct lifetime for iterating over a sparse matrix view.

`impl<N, I, Iptr, IptrStorage, IndStorage, DataStorage> CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: Deref<Target = [N]>,` [src]

Common methods for all variants of compressed sparse matrices.

`pub fn storage(&self) -> CompressedStorage`[src]

The underlying storage of this matrix

`pub fn rows(&self) -> usize`[src]

The number of rows of this matrix

`pub fn cols(&self) -> usize`[src]

The number of cols of this matrix

`pub fn shape(&self) -> Shape`[src]

The shape of the matrix. Equivalent to let shape = (a.rows(), a.cols()).

`pub fn nnz(&self) -> usize`[src]

The number of non-zero elements this matrix stores. This is often relevant for the complexity of most sparse matrix algorithms, which are often linear in the number of non-zeros.

`pub fn density(&self) -> f64`[src]

The density of the sparse matrix, defined as the number of non-zero elements divided by the maximum number of elements

`pub fn outer_dims(&self) -> usize`[src]

Number of outer dimensions, that ie equal to self.rows() for a CSR matrix, and equal to self.cols() for a CSC matrix

`pub fn inner_dims(&self) -> usize`[src]

Number of inner dimensions, that ie equal to self.cols() for a CSR matrix, and equal to self.rows() for a CSC matrix

`pub fn get(&self, i: usize, j: usize) -> Option<&N>`[src]

Access the element located at row i and column j. Will return None if there is no non-zero element at this location.

This access is logarithmic in the number of non-zeros in the corresponding outer slice. It is therefore advisable not to rely on this for algorithms, and prefer outer_iterator which accesses elements in storage order.

`pub fn indptr(&self) -> IndPtrView<'_, Iptr>`[src]

The array of offsets in the indices() and data() slices. The elements of the slice at outer dimension i are available between the elements indptr[i] and indptr[i+1] in the indices() and data() slices.

Example

use sprs::{CsMat};
let eye : CsMat<f64> = CsMat::eye(5);
// get the element of row 3
// there is only one element in this row, with a column index of 3
// and a value of 1.
let range = eye.indptr().outer_inds_sz(3);
assert_eq!(range.start, 3);
assert_eq!(range.end, 4);
assert_eq!(eye.indices()[range.start], 3);
assert_eq!(eye.data()[range.start], 1.);

`pub fn proper_indptr(&self) -> Cow<'_, [Iptr]>`[src]

Get an indptr representation suitable for ffi, cloning if necessary to get a compatible representation.

Warning

For ffi usage, one needs to call Cow::as_ptr, but it’s important to keep the Cow alive during the lifetime of the pointer. Example of a correct and incorrect ffi usage:

let mat: sprs::CsMat<f64> = sprs::CsMat::eye(5);
let mid = mat.view().middle_outer_views(1, 2);
let ptr = {
    let indptr_proper = mid.proper_indptr();
    println!(
        "ptr {:?} is valid as long as _indptr_proper_owned is in scope",
        indptr_proper.as_ptr()
    );
    indptr_proper.as_ptr()
};
// This line is UB.
// println!("ptr deref: {}", *ptr);

`pub fn indices(&self) -> &[I]ⓘNotable traits for &'_ mut [u8] impl<'_> Write for &'_ mut [u8]impl<'_> Read for &'_ [u8]`[src]

The inner dimension location for each non-zero value. See the documentation of indptr() for more explanations.

`pub fn data(&self) -> &[N]ⓘNotable traits for &'_ mut [u8] impl<'_> Write for &'_ mut [u8]impl<'_> Read for &'_ [u8]`[src]

The non-zero values. See the documentation of indptr() for more explanations.

`pub fn into_raw_storage(self) -> (IptrStorage, IndStorage, DataStorage)`[src]

Destruct the matrix object and recycle its storage containers.

Example

use sprs::{CsMat};
let (indptr, indices, data) = CsMat::<i32>::eye(3).into_raw_storage();
assert_eq!(indptr, vec![0, 1, 2, 3]);
assert_eq!(indices, vec![0, 1, 2]);
assert_eq!(data, vec![1, 1, 1]);

`pub fn is_csc(&self) -> bool`[src]

Test whether the matrix is in CSC storage

`pub fn is_csr(&self) -> bool`[src]

Test whether the matrix is in CSR storage

`pub fn transpose_mut(&mut self)`[src]

Transpose a matrix in place No allocation required (this is simply a storage order change)

`pub fn transpose_into(self) -> Self`[src]

Transpose a matrix in place No allocation required (this is simply a storage order change)

`pub fn transpose_view(&self) -> CsMatViewI<'_, N, I, Iptr>`[src]

Transposed view of this matrix No allocation required (this is simply a storage order change)

`pub fn to_owned(&self) -> CsMatI<N, I, Iptr> where N: Clone,` [src]

Get an owned version of this matrix. If the matrix was already owned, this will make a deep copy.

`pub fn to_inner_onehot(&self) -> CsMatI<N, I, Iptr> where N: Clone + Float + PartialOrd,` [src]

Generate a one-hot matrix, compressing the inner dimension.

Returns a matrix with the same size, the same CSR/CSC type, and a single value of 1.0 within each populated inner vector.

See into_csc and into_csr if you need to prepare a matrix for one-hot compression.

`pub fn to_other_types<I2, N2, Iptr2>(&self) -> CsMatI<N2, I2, Iptr2> where N: Clone + Into<N2>, I2: SpIndex, Iptr2: SpIndex,` [src]

Clone the matrix with another integer type for indptr and indices

Panics

If the indices or indptr values cannot be represented by the requested integer type.

`pub fn view(&self) -> CsMatViewI<'_, N, I, Iptr>`[src]

Return a view into the current matrix

`pub fn structure_view(&self) -> CsStructureViewI<'_, I, Iptr>`[src]

`pub fn to_dense(&self) -> Array<N, Ix2> where N: Clone + Zero,` [src]

`pub fn outer_iterator( &self ) -> impl DoubleEndedIterator<Item = CsVecViewI<'_, N, I>> + ExactSizeIterator<Item = CsVecViewI<'_, N, I>> + '_`[src]

Return an outer iterator for the matrix

This can be used for iterating over the rows (resp. cols) of a CSR (resp. CSC) matrix.

use sprs::{CsMat};
let eye = CsMat::eye(5);
for (row_ind, row_vec) in eye.outer_iterator().enumerate() {
    let (col_ind, &val): (_, &f64) = row_vec.iter().next().unwrap();
    assert_eq!(row_ind, col_ind);
    assert_eq!(val, 1.);
}

`pub fn max_outer_nnz(&self) -> usize`[src]

Get the max number of nnz for each outer dim

`pub fn degrees(&self) -> Vec<usize>`[src]

Get the degrees of each vertex on a symmetric matrix

The nonzero pattern of a symmetric matrix can be interpreted as an undirected graph. In such a graph, a vertex i is connected to another vertex j if there is a corresponding nonzero entry in the matrix at location (i, j).

This function returns a vector containing the degree of each vertex, that is to say the number of neighbor of each vertex. We do not count diagonal entries as a neighbor.

`pub fn outer_view(&self, i: usize) -> Option<CsVecViewI<'_, N, I>>`[src]

Get a view into the i-th outer dimension (eg i-th row for a CSR matrix)

`pub fn diag(&self) -> CsVecI<N, I> where N: Clone,` [src]

Get the diagonal of a sparse matrix

`pub fn diag_iter( &self ) -> impl ExactSizeIterator<Item = Option<&N>> + DoubleEndedIterator<Item = Option<&N>>`[src]

Iteration over all entries on the diagonal

`pub fn outer_block_iter( &self, block_size: usize ) -> impl DoubleEndedIterator<Item = CsMatViewI<'_, N, I, Iptr>> + ExactSizeIterator<Item = CsMatViewI<'_, N, I, Iptr>> + '_`[src]

Iteration on outer blocks of size block_size

Panics

If the block size is 0.

`pub fn map<F, N2>(&self, f: F) -> CsMatI<N2, I, Iptr> where F: FnMut(&N) -> N2,` [src]

Return a new sparse matrix with the same sparsity pattern, with all non-zero values mapped by the function f.

`pub fn get_outer_inner(&self, outer_ind: usize, inner_ind: usize) -> Option<&N>`[src]

Access an element given its outer_ind and inner_ind. Will return None if there is no non-zero element at this location.

This access is logarithmic in the number of non-zeros in the corresponding outer slice. It is therefore advisable not to rely on this for algorithms, and prefer outer_iterator which accesses elements in storage order.

`pub fn nnz_index(&self, row: usize, col: usize) -> Option<NnzIndex>`[src]

Find the non-zero index of the element specified by row and col

Searching this index is logarithmic in the number of non-zeros in the corresponding outer slice. Once it is available, the NnzIndex enables retrieving the data with O(1) complexity.

`pub fn nnz_index_outer_inner( &self, outer_ind: usize, inner_ind: usize ) -> Option<NnzIndex>`[src]

Find the non-zero index of the element specified by outer_ind and inner_ind.

Searching this index is logarithmic in the number of non-zeros in the corresponding outer slice.

`pub fn check_compressed_structure(&self) -> Result<(), StructureError>`[src]

Check the structure of CsMat components This will ensure that:

indptr is of length outer_dim() + 1
indices and data have the same length, nnz == indptr[outer_dims()]
indptr is sorted
indptr values do not exceed usize::MAX/ 2, as that would mean indices and indptr would take more space than the addressable memory
indices is sorted for each outer slice
indices are lower than inner_dims()

`pub fn iter(&self) -> CsIter<'_, N, I, Iptr>ⓘNotable traits for CsIter<'a, N, I, Iptr> impl<'a, N, I, Iptr> Iterator for CsIter<'a, N, I, Iptr> where I: SpIndex, Iptr: SpIndex, N: 'a, type Item = (&'a N, (I, I));`[src]

Get an iterator that yields the non-zero locations and values stored in this matrix, in the fastest iteration order.

`impl<N, I, Iptr, IptrStorage, IndStorage, DataStorage> CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where N: Default, I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: Deref<Target = [N]>,` [src]

Methods to convert between storage orders

`pub fn to_other_storage(&self) -> CsMatI<N, I, Iptr> where N: Clone,` [src]

Create a matrix mathematically equal to this one, but with the opposed storage (a CSC matrix will be converted to CSR, and vice versa)

`pub fn to_csc(&self) -> CsMatI<N, I, Iptr> where N: Clone,` [src]

Create a new CSC matrix equivalent to this one. A new matrix will be created even if this matrix was already CSC.

`pub fn to_csr(&self) -> CsMatI<N, I, Iptr> where N: Clone,` [src]

Create a new CSR matrix equivalent to this one. A new matrix will be created even if this matrix was already CSR.

`impl<N, I, Iptr> CsMatBase<N, I, Vec<Iptr, Global>, Vec<I, Global>, Vec<N, Global>, Iptr> where N: Default, I: SpIndex, Iptr: SpIndex,` [src]

`pub fn into_csc(self) -> Self where N: Clone,` [src]

Create a new CSC matrix equivalent to this one. If this matrix is CSR, it is converted to CSC If this matrix is CSC, it is returned by value

`pub fn into_csr(self) -> Self where N: Clone,` [src]

Create a new CSR matrix equivalent to this one. If this matrix is CSC, it is converted to CSR If this matrix is CSR, it is returned by value

`impl<N, I, Iptr, IptrStorage, IndStorage, DataStorage> CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: DerefMut<Target = [N]>,` [src]

Methods for sparse matrices holding mutable access to their values.

`pub fn data_mut(&mut self) -> &mut [N]ⓘNotable traits for &'_ mut [u8] impl<'_> Write for &'_ mut [u8]impl<'_> Read for &'_ [u8]`[src]

Mutable access to the non zero values

This enables changing the values without changing the matrix’s structure. To also change the matrix’s structure, see modify

`pub fn scale(&mut self, val: N) where N: MulAssign<&'r N>,` [src]

Sparse matrix self-multiplication by a scalar

`pub fn outer_view_mut(&mut self, i: usize) -> Option<CsVecViewMutI<'_, N, I>>`[src]

Get a mutable view into the i-th outer dimension (eg i-th row for a CSR matrix)

`pub fn get_mut(&mut self, i: usize, j: usize) -> Option<&mut N>`[src]

Get a mutable reference to the element located at row i and column j. Will return None if there is no non-zero element at this location.

This access is logarithmic in the number of non-zeros in the corresponding outer slice. It is therefore advisable not to rely on this for algorithms, and prefer outer_iterator_mut which accesses elements in storage order. TODO: outer_iterator_mut is not yet implemented

`pub fn get_outer_inner_mut( &mut self, outer_ind: usize, inner_ind: usize ) -> Option<&mut N>`[src]

Get a mutable reference to an element given its outer_ind and inner_ind. Will return None if there is no non-zero element at this location.

This access is logarithmic in the number of non-zeros in the corresponding outer slice. It is therefore advisable not to rely on this for algorithms, and prefer outer_iterator_mut which accesses elements in storage order.

`pub fn set(&mut self, row: usize, col: usize, val: N)`[src]

Set the value of the non-zero element located at (row, col)

Panics

on out-of-bounds access
if no non-zero element exists at the given location

`pub fn map_inplace<F>(&mut self, f: F) where F: FnMut(&N) -> N,` [src]

Apply a function to every non-zero element

`pub fn outer_iterator_mut( &mut self ) -> impl DoubleEndedIterator<Item = CsVecViewMutI<'_, N, I>> + ExactSizeIterator<Item = CsVecViewMutI<'_, N, I>> + '_`[src]

Return a mutable outer iterator for the matrix

This iterator yields mutable sparse vector views for each outer dimension. Only the non-zero values can be modified, the structure is kept immutable.

`pub fn view_mut(&mut self) -> CsMatViewMutI<'_, N, I, Iptr>`[src]

Return a mutable view into the current matrix

`pub fn diag_iter_mut( &mut self ) -> impl ExactSizeIterator<Item = Option<&mut N>> + DoubleEndedIterator<Item = Option<&mut N>> + '_`[src]

Iteration over all entries on the diagonal

`impl<N, I, Iptr, IptrStorage, IndStorage, DataStorage> CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: DerefMut<Target = [Iptr]>, IndStorage: DerefMut<Target = [I]>, DataStorage: DerefMut<Target = [N]>,` [src]

`pub fn modify<F>(&mut self, f: F) where F: FnMut(&mut [Iptr], &mut [I], &mut [N]),` [src]

Modify the matrix’s structure without changing its nonzero count.

The coherence of the structure will be checked afterwards.

Panics

If the resulting matrix breaks the CsMat invariants (sorted indices, no out of bounds indices).

Example

use sprs::CsMat;
// |   1   |
// | 1     |
// |   1 1 |
let mut mat = CsMat::new_csc((3, 3),
                                  vec![0, 1, 3, 4],
                                  vec![1, 0, 2, 2],
                                  vec![1.; 4]);

// | 1 2   |
// | 1     |
// |   1   |
mat.modify(|indptr, indices, data| {
    indptr[1] = 2;
    indptr[2] = 4;
    indices[0] = 0;
    indices[1] = 1;
    indices[2] = 0;
    data[2] = 2.;
});

`impl<N, I: SpIndex, Iptr: SpIndex, IptrStorage, IStorage, DStorage> CsMatBase<N, I, IptrStorage, IStorage, DStorage, Iptr> where IptrStorage: Deref<Target = [Iptr]>, IStorage: Deref<Target = [I]>, DStorage: Deref<Target = [N]>,` [src]

`pub fn slice_outer<S: Range>(&self, range: S) -> CsMatViewI<'_, N, I, Iptr>`[src]

Slice the outer dimension of the matrix according to the specified range.

`impl<N, I: SpIndex, Iptr: SpIndex, IptrStorage, IStorage, DStorage> CsMatBase<N, I, IptrStorage, IStorage, DStorage, Iptr> where IptrStorage: Deref<Target = [Iptr]>, IStorage: Deref<Target = [I]>, DStorage: DerefMut<Target = [N]>,` [src]

`pub fn slice_outer_mut<S: Range>( &mut self, range: S ) -> CsMatViewMutI<'_, N, I, Iptr>`[src]

Slice the outer dimension of the matrix according to the specified range.

`impl<'a, N, I, Iptr> CsMatBase<N, I, &'a [Iptr], &'a [I], &'a [N], Iptr> where I: SpIndex, Iptr: SpIndex,` [src]

`pub fn slice_outer_rbr<S>(&self, range: S) -> CsMatViewI<'a, N, I, Iptr> where S: Range,` [src]

Slice the outer dimension of the matrix according to the specified range.

Trait Implementations

impl<N, I, Iptr, IS1, DS1, ISptr1, IS2, ISptr2, DS2> AbsDiffEq<CsMatBase<N, I, ISptr2, IS2, DS2, Iptr>> for CsMatBase<N, I, ISptr1, IS1, DS1, Iptr> where I: SpIndex, Iptr: SpIndex, CsMatBase<N, I, ISptr1, IS1, DS1, Iptr>: PartialEq<CsMatBase<N, I, ISptr2, IS2, DS2, Iptr>>, IS1: Deref<Target = [I]>, IS2: Deref<Target = [I]>, ISptr1: Deref<Target = [Iptr]>, ISptr2: Deref<Target = [Iptr]>, DS1: Deref<Target = [N]>, DS2: Deref<Target = [N]>, N: AbsDiffEq, N::Epsilon: Clone, N: Zero, [src]

`type Epsilon = N::Epsilon`

Used for specifying relative comparisons.

`fn default_epsilon() -> N::Epsilon`[src]

`fn abs_diff_eq( &self, other: &CsMatBase<N, I, ISptr2, IS2, DS2, Iptr>, epsilon: N::Epsilon ) -> bool`[src]

`pub fn abs_diff_ne(&self, other: &Rhs, epsilon: Self::Epsilon) -> bool`[src]

`impl<'a, 'b, N, I, Iptr, IpS, IS, DS, DS2> Add<&'b ArrayBase<DS2, Dim<[usize; 2]>>> for &'a CsMatBase<N, I, IpS, IS, DS, Iptr> where N: 'a + Copy + Num + Default, I: 'a + SpIndex, Iptr: 'a + SpIndex, IpS: 'a + Deref<Target = [Iptr]>, IS: 'a + Deref<Target = [I]>, DS: 'a + Deref<Target = [N]>, DS2: 'b + Data<Elem = N>,` [src]

`type Output = Array<N, Ix2>`

The resulting type after applying the + operator.

`fn add(self, rhs: &'b ArrayBase<DS2, Ix2>) -> Array<N, Ix2>`[src]

impl<'a, 'b, N, I, Iptr, IpStorage, IStorage, DStorage, IpS2, IS2, DS2> Add<&'b CsMatBase<N, I, IpS2, IS2, DS2, Iptr>> for &'a CsMatBase<N, I, IpStorage, IStorage, DStorage, Iptr> where N: Zero + PartialEq + Clone + Default, &'r N: Add<&'r N, Output = N>, I: 'a + SpIndex, Iptr: 'a + SpIndex, IpStorage: 'a + Deref<Target = [Iptr]>, IStorage: 'a + Deref<Target = [I]>, DStorage: 'a + Deref<Target = [N]>, IpS2: 'a + Deref<Target = [Iptr]>, IS2: 'a + Deref<Target = [I]>, DS2: 'a + Deref<Target = [N]>, [src]

`type Output = CsMatI<N, I, Iptr>`

The resulting type after applying the + operator.

`fn add( self, rhs: &'b CsMatBase<N, I, IpS2, IS2, DS2, Iptr> ) -> CsMatI<N, I, Iptr>`[src]

`impl<N: Clone, I: Clone, IptrStorage: Clone, IndStorage: Clone, DataStorage: Clone, Iptr: Clone> Clone for CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: Deref<Target = [N]>,` [src]

`fn clone(&self) -> CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr>`[src]

`pub fn clone_from(&mut self, source: &Self)`1.0.0[src]

`impl<N: Copy, I: Copy, IptrStorage: Copy, IndStorage: Copy, DataStorage: Copy, Iptr: Copy> Copy for CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: Deref<Target = [N]>,` [src]

`impl<N: Debug, I: Debug, IptrStorage: Debug, IndStorage: Debug, DataStorage: Debug, Iptr: Debug> Debug for CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: Deref<Target = [N]>,` [src]

`fn fmt(&self, f: &mut Formatter<'_>) -> Result`[src]

`impl<'de, N, I, IptrStorage, IndStorage, DataStorage, Iptr> Deserialize<'de> for CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: Deref<Target = [N]>, IptrStorage: Deserialize<'de>, IndStorage: Deserialize<'de>, DataStorage: Deserialize<'de>, Iptr: Deserialize<'de>,` [src]

`fn deserialize<D>(deserializer: D) -> Result<Self, D::Error> where __D: Deserializer<'de>,` [src]

`impl<'a, I, Iptr, IpStorage, IStorage, DStorage, T> DivAssign<T> for CsMatBase<T, I, IpStorage, IStorage, DStorage, Iptr> where I: 'a + SpIndex, Iptr: 'a + SpIndex, IpStorage: 'a + Deref<Target = [Iptr]>, IStorage: 'a + Deref<Target = [I]>, DStorage: 'a + DerefMut<Target = [T]>, T: DivAssign<T> + Clone,` [src]

`fn div_assign(&mut self, rhs: T)`[src]

`impl<'a, 'b, N, I, Iptr, IpS, IS, DS, DS2> Dot<ArrayBase<DS2, Dim<[usize; 1]>>> for CsMatBase<N, I, IpS, IS, DS, Iptr> where N: 'a + Clone + MulAcc + Zero, I: 'a + SpIndex, Iptr: 'a + SpIndex, IpS: 'a + Deref<Target = [Iptr]>, IS: 'a + Deref<Target = [I]>, DS: 'a + Deref<Target = [N]>, DS2: 'b + Data<Elem = N>,` [src]

`type Output = Array<N, Ix1>`

The result of the operation. Read more

`fn dot(&self, rhs: &ArrayBase<DS2, Ix1>) -> Array<N, Ix1>`[src]

`impl<'a, 'b, N, I, Iptr, IpS, IS, DS, DS2> Dot<ArrayBase<DS2, Dim<[usize; 2]>>> for CsMatBase<N, I, IpS, IS, DS, Iptr> where N: 'a + Clone + MulAcc + Zero, I: 'a + SpIndex, Iptr: 'a + SpIndex, IpS: 'a + Deref<Target = [Iptr]>, IS: 'a + Deref<Target = [I]>, DS: 'a + Deref<Target = [N]>, DS2: 'b + Data<Elem = N>,` [src]

`type Output = Array<N, Ix2>`

The result of the operation. Read more

`fn dot(&self, rhs: &ArrayBase<DS2, Ix2>) -> Array<N, Ix2>`[src]

`impl<N: Eq, I: Eq, IptrStorage: Eq, IndStorage: Eq, DataStorage: Eq, Iptr: Eq> Eq for CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: Deref<Target = [N]>,` [src]

`impl<N: Hash, I: Hash, IptrStorage: Hash, IndStorage: Hash, DataStorage: Hash, Iptr: Hash> Hash for CsMatBase<N, I, IptrStorage, IndStorage, DataStorage, Iptr> where I: SpIndex, Iptr: SpIndex, IptrStorage: Deref<Target = [Iptr]>, IndStorage: Deref<Target = [I]>, DataStorage: Deref<Target = [N]>,` [src]

`fn hash<H: Hasher>(&self, state: &mut H)`[src]

`pub fn hash_slice<H>(data: &[Self], state: &mut H) where H: Hasher,` 1.3.0[src]

`impl<N, I, Iptr, IpS, IS, DS> Index<[usize; 2]> for CsMatBase<N, I, IpS, IS, DS, Iptr> where I: SpIndex, Iptr: SpIndex, IpS: Deref<Target = [Iptr]>, IS: Deref<Target = [I]>, DS: Deref<Target = [N]>,` [src]

`type Output = N`

The returned type after indexing.

`fn index(&self, index: [usize; 2]) -> &N`[src]

`impl<N, I, Iptr, IpS, IS, DS> IndexMut<[usize; 2]> for CsMatBase<N, I, IpS, IS, DS, Iptr> where I: SpIndex, Iptr: SpIndex, IpS: Deref<Target = [Iptr]>, IS: Deref<Target = [I]>, DS: DerefMut<Target = [N]>,` [src]

`fn index_mut(&mut self, index: [usize; 2]) -> &mut N`[src]

`impl<'a, N, I, IpS, IS, DS, Iptr> IntoIterator for &'a CsMatBase<N, I, IpS, IS, DS, Iptr> where I: 'a + SpIndex, Iptr: 'a + SpIndex, N: 'a, IpS: Deref<Target = [Iptr]>, IS: Deref<Target = [I]>, DS: Deref<Target = [N]>,` [src]

`type Item = (&'a N, (I, I))`

The type of the elements being iterated over.

`type IntoIter = CsIter<'a, N, I, Iptr>`

Which kind of iterator are we turning this into?