mvpa2.algorithms.searchlight_hyperalignment.coo_matrix

class mvpa2.algorithms.searchlight_hyperalignment.coo_matrix(arg1, shape=None, dtype=None, copy=False)

A sparse matrix in COOrdinate format.

Also known as the ‘ijv’ or ‘triplet’ format.

This can be instantiated in several ways:

coo_matrix(D)

with a dense matrix D

coo_matrix(S)

with another sparse matrix S (equivalent to S.tocoo())

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

to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.

coo_matrix((data, (i, j)), [shape=(M, N)])

to construct from three arrays:

1. data[:] the entries of the matrix, in any order
2. i[:] the row indices of the matrix entries
3. j[:] the column indices of the matrix entries

Where A[i[k], j[k]] = data[k]. When shape is not specified, it is inferred from the index arrays

Notes

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

Advantages of the COO format

• facilitates fast conversion among sparse formats
• permits duplicate entries (see example)
• very fast conversion to and from CSR/CSC formats

Disadvantages of the COO format

• does not directly support:

  • arithmetic operations
  • slicing

Intended Usage

• COO is a fast format for constructing sparse matrices
• Once a matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations
• By default when converting to CSR or CSC format, duplicate (i,j) entries will be summed together. This facilitates efficient construction of finite element matrices and the like. (see example)

Examples

>>> from scipy.sparse import coo_matrix
>>> coo_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]], dtype=int8)

>>> row  = np.array([0, 3, 1, 0])
>>> col  = np.array([0, 3, 1, 2])
>>> data = np.array([4, 5, 7, 9])
>>> coo_matrix((data, (row, col)), shape=(4, 4)).toarray()
array([[4, 0, 9, 0],
       [0, 7, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 5]])

>>> # example with duplicates
>>> row  = np.array([0, 0, 1, 3, 1, 0, 0])
>>> col  = np.array([0, 2, 1, 3, 1, 0, 0])
>>> data = np.array([1, 1, 1, 1, 1, 1, 1])
>>> coo_matrix((data, (row, col)), shape=(4, 4)).toarray()
array([[3, 0, 1, 0],
       [0, 2, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 1]])

Attributes

nnz	Number of stored values, including explicit zeros.

dtype	(dtype) Data type of the matrix
shape	(2-tuple) Shape of the matrix
ndim	(int) Number of dimensions (this is always 2)
data	COO format data array of the matrix
row	COO format row index array of the matrix
col	COO format column index array of the matrix

Methods

arcsin()	Element-wise arcsin.
arcsinh()	Element-wise arcsinh.
arctan()	Element-wise arctan.
arctanh()	Element-wise arctanh.
asformat(format)	Return this matrix in a given sparse format
asfptype()	Upcast matrix to a floating point format (if necessary)
astype(t)
ceil()	Element-wise ceil.
conj()
conjugate()
copy()
count_nonzero()	Number of non-zero entries, equivalent to
deg2rad()	Element-wise deg2rad.
diagonal()	Returns the main diagonal of the matrix
dot(other)	Ordinary dot product
eliminate_zeros()	Remove zero entries from the matrix
expm1()	Element-wise expm1.
floor()	Element-wise floor.
getH()
get_shape()
getcol(j)	Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector).
getformat()
getmaxprint()
getnnz([axis])	Number of stored values, including explicit zeros.
getrow(i)	Returns a copy of row i of the matrix, as a (1 x n) sparse matrix (row vector).
log1p()	Element-wise log1p.
max([axis, out])	Return the maximum of the matrix or maximum along an axis.
maximum(other)
mean([axis, dtype, out])	Compute the arithmetic mean along the specified axis.
min([axis, out])	Return the minimum of the matrix or maximum along an axis.
minimum(other)
multiply(other)	Point-wise multiplication by another matrix
nonzero()	nonzero indices
power(n[, dtype])	This function performs element-wise power.
rad2deg()	Element-wise rad2deg.
reshape(shape[, order])	Gives a new shape to a sparse matrix without changing its data.
rint()	Element-wise rint.
set_shape(shape)
setdiag(values[, k])	Set diagonal or off-diagonal elements of the array.
sign()	Element-wise sign.
sin()	Element-wise sin.
sinh()	Element-wise sinh.
sqrt()	Element-wise sqrt.
sum([axis, dtype, out])	Sum the matrix elements over a given axis.
sum_duplicates()	Eliminate duplicate matrix entries by adding them together
tan()	Element-wise tan.
tanh()	Element-wise tanh.
toarray([order, out])	See the docstring for spmatrix.toarray.
tobsr([blocksize, copy])	Convert this matrix to Block Sparse Row format.
tocoo([copy])	Convert this matrix to COOrdinate format.
tocsc([copy])	Convert this matrix to Compressed Sparse Column format
tocsr([copy])	Convert this matrix to Compressed Sparse Row format
todense([order, out])	Return a dense matrix representation of this matrix.
todia([copy])	Convert this matrix to sparse DIAgonal format.
todok([copy])	Convert this matrix to Dictionary Of Keys format.
tolil([copy])	Convert this matrix to LInked List format.
transpose([axes, copy])	Reverses the dimensions of the sparse matrix.
trunc()	Element-wise trunc.

diagonal()

Returns the main diagonal of the matrix

eliminate_zeros()

Remove zero entries from the matrix

This is an in place operation

format = 'coo'

getnnz(axis=None)

Number of stored values, including explicit zeros.

Parameters:

axis : None, 0, or 1

Select between the number of values across the whole matrix, in each column, or in each row.

See also

count_nonzero

Number of non-zero entries

sum_duplicates()

Eliminate duplicate matrix entries by adding them together

This is an in place operation

toarray(order=None, out=None)

See the docstring for spmatrix.toarray.

tocoo(copy=False)

Convert this matrix to COOrdinate format.

With copy=False, the data/indices may be shared between this matrix and the resultant coo_matrix.

tocsc(copy=False)

Convert this matrix to Compressed Sparse Column format

Duplicate entries will be summed together.

Examples

>>> from numpy import array
>>> from scipy.sparse import coo_matrix
>>> row  = array([0, 0, 1, 3, 1, 0, 0])
>>> col  = array([0, 2, 1, 3, 1, 0, 0])
>>> data = array([1, 1, 1, 1, 1, 1, 1])
>>> A = coo_matrix((data, (row, col)), shape=(4, 4)).tocsc()
>>> A.toarray()
array([[3, 0, 1, 0],
       [0, 2, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 1]])

tocsr(copy=False)

Convert this matrix to Compressed Sparse Row format

Duplicate entries will be summed together.

Examples

>>> from numpy import array
>>> from scipy.sparse import coo_matrix
>>> row  = array([0, 0, 1, 3, 1, 0, 0])
>>> col  = array([0, 2, 1, 3, 1, 0, 0])
>>> data = array([1, 1, 1, 1, 1, 1, 1])
>>> A = coo_matrix((data, (row, col)), shape=(4, 4)).tocsr()
>>> A.toarray()
array([[3, 0, 1, 0],
       [0, 2, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 1]])

todia(copy=False)

Convert this matrix to sparse DIAgonal format.

With copy=False, the data/indices may be shared between this matrix and the resultant dia_matrix.

todok(copy=False)

Convert this matrix to Dictionary Of Keys format.

With copy=False, the data/indices may be shared between this matrix and the resultant dok_matrix.

transpose(axes=None, copy=False)

Reverses the dimensions of the sparse matrix.

Parameters:

axes : None, optional

This argument is in the signature solely for NumPy compatibility reasons. Do not pass in anything except for the default value.

copy : bool, optional

Indicates whether or not attributes of self should be copied whenever possible. The degree to which attributes are copied varies depending on the type of sparse matrix being used.

Returns:

p : self with the dimensions reversed.

See also

np.matrix.transpose

NumPy’s implementation of ‘transpose’ for matrices