scipy tutorial - Baustatik-Info-Server

Notes
lil_matrix(D)
with a dense matrix or rank-2 ndarray D
lil_matrix(S)
with another sparse matrix S (equivalent to S.tocsc())
SciPy Reference Guide, Release 0.8.dev
lil_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
Advantages of the LIL format
• supports flexible slicing
• changes to the matrix sparsity structure are efficient
Disadvantages of the LIL format
Intended Usage
Data Structure
Methods
• arithmetic operations LIL + LIL are slow (consider CSR or CSC)
• slow column slicing (consider CSC)
• slow matrix vector products (consider CSR or CSC)
• 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
• 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.
asformat(format) Return this matrix in a given sparse format
asfptype() Upcast matrix to a floating point format (if necessary)
astype(t)
conj()
conjugate()
copy()
diagonal() Returns the main diagonal of the matrix
dot(*args, **kwds) dot is deprecated!
getH()
get_shape()
getcol(j) Returns a copy of column j of the matrix, as an (m x 1) sparse
getdata(*args, **kwds) getdata is deprecated!
getformat()
getmaxprint()
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3.14. Sparse matrices (scipy.sparse) 367