mvpa2.algorithms.group_clusterthr.dok_matrix

class mvpa2.algorithms.group_clusterthr.dok_matrix(arg1, shape=None, dtype=None, copy=False)

Dictionary Of Keys based sparse matrix.

This is an efficient structure for constructing sparse matrices incrementally.

This can be instantiated in several ways:

dok_matrix(D)

with a dense matrix, D

dok_matrix(S)

with a sparse matrix, S

dok_matrix((M,N), [dtype])

create the matrix with initial shape (M,N) dtype is optional, defaulting to dtype=’d’

Notes

Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.

Allows for efficient O(1) access of individual elements. Duplicates are not allowed. Can be efficiently converted to a coo_matrix once constructed.

Examples

>>> import numpy as np
>>> from scipy.sparse import dok_matrix
>>> S = dok_matrix((5, 5), dtype=np.float32)
>>> for i in range(5):
...     for j in range(5):
...         S[i, j] = i + j    # Update element

Attributes

shape
ndim
nnz

dtype

dtype

Data type of the matrix

Methods

clear(() -> None.  Remove all items from D.)
fromkeys(...)	v defaults to None.
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
itervalues(...)
keys(() -> list of D’s keys)
pop((k[,d]) -> v, ...)	If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...)	2-tuple; but raise KeyError if D is empty.
setdefault((k[,d]) -> D.get(k,d), ...)
update(([E, ...)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)
viewitems(...)
viewkeys(...)
viewvalues(...)

conjtransp()

Return the conjugate transpose

copy()

get(key, default=0.0)

This overrides the dict.get method, providing type checking but otherwise equivalent functionality.

getcol(j)

Returns a copy of column j of the matrix as a (m x 1) DOK matrix.

getnnz()

getrow(i)

Returns a copy of row i of the matrix as a (1 x n) DOK matrix.

nnz

resize(shape)

Resize the matrix in-place to dimensions given by ‘shape’.

Any non-zero elements that lie outside the new shape are removed.

tocoo()

Return a copy of this matrix in COOrdinate format

tocsc()

Return a copy of this matrix in Compressed Sparse Column format

tocsr()

Return a copy of this matrix in Compressed Sparse Row format

todok(copy=False)

transpose()

Return the transpose