Sparse matrix with DIAgonal storage
Notes
Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Examples
>>> from scipy.sparse import *
>>> from scipy import *
>>> dia_matrix( (3,4), dtype=int8).todense()
matrix([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> data = array([[1,2,3,4]]).repeat(3,axis=0)
>>> offsets = array([0,-1,2])
>>> dia_matrix( (data,offsets), shape=(4,4)).todense()
matrix([[1, 0, 3, 0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]])
Attributes
dtype | |
shape | |
ndim | int(x[, base]) -> integer |
nnz | number of nonzero values |
data | DIA format data array of the matrix |
offsets | DIA format offset array of the matrix |
Methods
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(other) | |
getH() | |
get_shape() | |
getcol(j) | Returns a copy of column j of the matrix, as an (m x 1) sparse |
getformat() | |
getmaxprint() | |
getnnz() | number of nonzero values |
getrow(i) | Returns a copy of row i of the matrix, as a (1 x n) sparse |
mean([axis]) | Average the matrix over the given axis. |
multiply(other) | Point-wise multiplication by another matrix |
nonzero() | nonzero indices |
reshape(shape) | |
set_shape(shape) | |
setdiag(values[, k]) | Fills the diagonal elements {a_ii} with the values from the given sequence. |
sum([axis]) | Sum the matrix over the given axis. |
toarray() | |
tobsr([blocksize]) | |
tocoo() | |
tocsc() | |
tocsr() | |
todense() | |
todia([copy]) | |
todok() | |
tolil() | |
transpose() |