scipy.sparse.lil_matrix¶
- class scipy.sparse.lil_matrix(arg1, shape=None, dtype=None, copy=False)¶
Row-based linked list sparse matrix
This is an efficient structure for constructing sparse matrices incrementally.
- 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.tocsc())
- lil_matrix((M, N), [dtype])
- to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
Notes
- 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.
Methods
asformat asfptype astype conj conjugate copy Generic (shallow and deep) copying operations. diagonal dot getH get_shape getcol getdata getformat getmaxprint getnnz getrow getrowview listprint matmat matvec mean multiply nonzero reshape rmatvec rowcol save set_shape setdiag sum toarray tobsr tocoo tocsc tocsr todense todia todok tolil transpose
