Data type of the matrix
Shape of the matrix
Number of dimensions (this is always 2)
Number of stored values, including explicit zeros
LIL format data array of the matrix
LIL format row index array of the 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'.
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
.
Row-based list of lists sparse matrix
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.sparse._lil.isspmatrix_lil
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