LInked List sparse matrix class
||Convert index and data arrays to form suitable for passing to the|
||Is x of lil_matrix type?|
lil_matrix(arg1, shape=None, dtype=None, copy=False)¶
Row-based linked list sparse matrix
This is a structure for constructing sparse matrices incrementally. Note that inserting a single item can take linear time in the worst case; to construct a matrix efficiently, make sure the items are pre-sorted by index, per row.
- This can be instantiated in several ways:
- with a dense matrix or rank-2 ndarray D
- with another sparse matrix S (equivalent to S.tolil())
- lil_matrix((M, N), [dtype])
- to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
- dtype : dtype
- Data type of the matrix
- shape : 2-tuple
- Shape of the matrix
- ndim : int
- Number of dimensions (this is always 2)
- Number of nonzero elements
- LIL format data array of the matrix
- LIL format row index array of the matrix
Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
- Advantages of the LIL format
- supports flexible slicing
- changes to the matrix sparsity structure are efficient
- Disadvantages of the LIL format
- arithmetic operations LIL + LIL are slow (consider CSR or CSC)
- slow column slicing (consider CSC)
- slow matrix vector products (consider CSR or CSC)
- Intended Usage
- LIL is a convenient format for constructing sparse matrices
- once a matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations
- consider using the COO format when constructing large matrices
- Data Structure
- An array (
self.rows) of rows, each of which is a sorted list of column indices of non-zero elements.
- The corresponding nonzero values are stored in similar
- An array (
__init__(arg1, shape=None, dtype=None, copy=False)¶
Returns a view of the ‘i’th row (without copying).
Returns a copy of the ‘i’th row.
Return the element(s) index=(i, j), where j may be a slice. This always returns a copy for consistency, since slices into Python lists return copies.
Fast path for indexing in the case where column index is slice.
This gains performance improvement over brute force by more efficient skipping of zeros, by accessing the elements column-wise in order.
- rows : sequence or xrange
- Rows indexed. If xrange, must be within valid bounds.
- col_slice : slice
- Columns indexed
_prepare_index_for_memoryview(i, j, x=None)¶
Convert index and data arrays to form suitable for passing to the Cython fancy getset routines.
The conversions are necessary since to (i) ensure the integer index arrays are in one of the accepted types, and (ii) to ensure the arrays are writable so that Cython memoryview support doesn’t choke on them.
- i, j
- Index arrays
- x : optional
- Data arrays
- i, j, x
- Re-formatted arrays (x is omitted, if input was None)
Is x of lil_matrix type?
- object to check for being a lil matrix
- True if x is a lil matrix, False otherwise
>>> from scipy.sparse import lil_matrix, isspmatrix_lil >>> isspmatrix_lil(lil_matrix([])) True
>>> from scipy.sparse import lil_matrix, csr_matrix, isspmatrix_lil >>> isspmatrix_lil(csr_matrix([])) False