sparse.lil

LInked List sparse matrix class

Module Contents

Classes

lil_matrix(self,arg1,shape=None,dtype=None,copy=False) Row-based linked list sparse matrix

Functions

_prepare_index_for_memoryview(i,j,x=None) Convert index and data arrays to form suitable for passing to the
isspmatrix_lil(x) Is x of lil_matrix type?
class 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:
lil_matrix(D)
with a dense matrix or rank-2 ndarray D
lil_matrix(S)
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)
nnz
Number of nonzero elements
data
LIL format data array of the matrix
rows
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 fashion in self.data.
__init__(arg1, shape=None, dtype=None, copy=False)
set_shape(shape)
__iadd__(other)
__isub__(other)
__imul__(other)
__itruediv__(other)
getnnz(axis=None)
count_nonzero()
__str__()
getrowview(i)

Returns a view of the ‘i’th row (without copying).

getrow(i)

Returns a copy of the ‘i’th row.

_check_row_bounds(i)
_check_col_bounds(j)
__getitem__(index)

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.

_get_row_ranges(rows, col_slice)

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
__setitem__(index, x)
_mul_scalar(other)
__truediv__(other)
copy()
reshape(shape, order="C")
toarray(order=None, out=None)
transpose(axes=None, copy=False)
tolil(copy=False)
tocsr(copy=False)
_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)
isspmatrix_lil(x)

Is x of lil_matrix type?

x
object to check for being a lil matrix
bool
True if x is a lil matrix, False otherwise
>>> from scipy.sparse import lil_matrix, isspmatrix_lil
>>> isspmatrix_lil(lil_matrix([[5]]))
True
>>> from scipy.sparse import lil_matrix, csr_matrix, isspmatrix_lil
>>> isspmatrix_lil(csr_matrix([[5]]))
False