cupyx.scipy.sparse.dia_matrix#

class cupyx.scipy.sparse.dia_matrix(arg1, shape=None, dtype=None, copy=False)[source]#

Sparse matrix with DIAgonal storage.

Now it has only one initializer format below:

dia_matrix((data, offsets))

Parameters

arg1 – Arguments for the initializer.
shape (tuple) – Shape of a matrix. Its length must be two.
dtype – Data type. It must be an argument of numpy.dtype.
copy (bool) – If True, copies of given arrays are always used.

See also

scipy.sparse.dia_matrix

Methods

__len__()[source]#

__iter__()[source]#

arcsin()[source]#: Elementwise arcsin.

arcsinh()[source]#: Elementwise arcsinh.

arctan()[source]#: Elementwise arctan.

arctanh()[source]#: Elementwise arctanh.

asformat(format)[source]#

Return this matrix in a given sparse format.

Parameters: format (str or None) – Format you need.

asfptype()[source]#

Upcasts matrix to a floating point format.

When the matrix has floating point type, the method returns itself. Otherwise it makes a copy with floating point type and the same format.

Returns: A matrix with float type.
Return type: cupyx.scipy.sparse.spmatrix

astype(t)[source]#

Casts the array to given data type.

Parameters: dtype – Type specifier.
Returns: A copy of the array with a given type.

ceil()[source]#: Elementwise ceil.

conj(copy=True)[source]#

Element-wise complex conjugation.

If the matrix is of non-complex data type and copy is False, this method does nothing and the data is not copied.

Parameters: copy (bool) – If True, the result is guaranteed to not share data with self.
Returns: The element-wise complex conjugate.
Return type: cupyx.scipy.sparse.spmatrix

conjugate(copy=True)[source]#

Element-wise complex conjugation.

If the matrix is of non-complex data type and copy is False, this method does nothing and the data is not copied.

Parameters: copy (bool) – If True, the result is guaranteed to not share data with self.
Returns: The element-wise complex conjugate.
Return type: cupyx.scipy.sparse.spmatrix

copy()[source]#

Returns a copy of this matrix.

No data/indices will be shared between the returned value and current matrix.

count_nonzero()[source]#

Returns number of non-zero entries.

Note

This method counts the actual number of non-zero entories, which does not include explicit zero entries. Instead nnz returns the number of entries including explicit zeros.

Returns: Number of non-zero entries.

deg2rad()[source]#: Elementwise deg2rad.

diagonal(k=0)[source]#

Returns the k-th diagonal of the matrix.

Parameters

k (int, optional) – Which diagonal to get, corresponding to elements
a[i – 0 (the main diagonal).
Default (i+k].) – 0 (the main diagonal).

Returns

The k-th diagonal.

Return type

dot(other)[source]#: Ordinary dot product

expm1()[source]#: Elementwise expm1.

floor()[source]#: Elementwise floor.

get(stream=None)[source]#

Returns a copy of the array on host memory.

Parameters: stream (cupy.cuda.Stream) – CUDA stream object. If it is given, the copy runs asynchronously. Otherwise, the copy is synchronous.
Returns: Copy of the array on host memory.
Return type: scipy.sparse.dia_matrix

getH()[source]#

get_shape()[source]#

Returns the shape of the matrix.

Returns: Shape of the matrix.
Return type: tuple

getformat()[source]#

getmaxprint()[source]#

getnnz(axis=None)[source]#

Returns the number of stored values, including explicit zeros.

Parameters: axis – Not supported yet.
Returns: The number of stored values.
Return type: int

log1p()[source]#: Elementwise log1p.

maximum(other)[source]#

mean(axis=None, dtype=None, out=None)[source]#

Compute the arithmetic mean along the specified axis.

Parameters: axis (int or None) – Axis along which the sum is computed. If it is None, it computes the average of all the elements. Select from {None, 0, 1, -2, -1}.
Returns: Summed array.
Return type: cupy.ndarray

See also

scipy.sparse.spmatrix.mean()

minimum(other)[source]#

multiply(other)[source]#: Point-wise multiplication by another matrix

power(n, dtype=None)[source]#

Elementwise power function.

Parameters

n – Exponent.
dtype – Type specifier.

rad2deg()[source]#: Elementwise rad2deg.

reshape(*shape, order='C')[source]#

Gives a new shape to a sparse matrix without changing its data.

Parameters

shape (tuple) – The new shape should be compatible with the original shape.
order – {‘C’, ‘F’} (optional) Read the elements using this index order. ‘C’ means to read and write the elements using C-like index order. ‘F’ means to read and write the elements using Fortran-like index order. Default: C.

Returns

sparse matrix

Return type

cupyx.scipy.sparse.coo_matrix

rint()[source]#: Elementwise rint.

set_shape(shape)[source]#

setdiag(values, k=0)[source]#

Set diagonal or off-diagonal elements of the array.

Parameters

values (cupy.ndarray) – New values of the diagonal elements. Values may have any length. If the diagonal is longer than values, then the remaining diagonal entries will not be set. If values is longer than the diagonal, then the remaining values are ignored. If a scalar value is given, all of the diagonal is set to it.
k (int, optional) – Which diagonal to set, corresponding to elements a[i, i+k]. Default: 0 (the main diagonal).

sign()[source]#: Elementwise sign.

sin()[source]#: Elementwise sin.

sinh()[source]#: Elementwise sinh.

sqrt()[source]#: Elementwise sqrt.

sum(axis=None, dtype=None, out=None)[source]#

Sums the matrix elements over a given axis.

Parameters

axis (int or None) – Axis along which the sum is comuted. If it is None, it computes the sum of all the elements. Select from {None, 0, 1, -2, -1}.
dtype – The type of returned matrix. If it is not specified, type of the array is used.
out (cupy.ndarray) – Output matrix.

Returns

Summed array.

Return type

See also

scipy.sparse.spmatrix.sum()

tan()[source]#: Elementwise tan.

tanh()[source]#: Elementwise tanh.

toarray(order=None, out=None)[source]#: Returns a dense matrix representing the same value.

tobsr(blocksize=None, copy=False)[source]#: Convert this matrix to Block Sparse Row format.

tocoo(copy=False)[source]#: Convert this matrix to COOrdinate format.

tocsc(copy=False)[source]#

Converts the matrix to Compressed Sparse Column format.

Parameters: copy (bool) – If False, it shares data arrays as much as possible. Actually this option is ignored because all arrays in a matrix cannot be shared in dia to csc conversion.
Returns: Converted matrix.
Return type: cupyx.scipy.sparse.csc_matrix

tocsr(copy=False)[source]#

Converts the matrix to Compressed Sparse Row format.

Parameters: copy (bool) – If False, it shares data arrays as much as possible. Actually this option is ignored because all arrays in a matrix cannot be shared in dia to csr conversion.
Returns: Converted matrix.
Return type: cupyx.scipy.sparse.csc_matrix

todense(order=None, out=None)[source]#: Return a dense matrix representation of this matrix.

todia(copy=False)[source]#: Convert this matrix to sparse DIAgonal format.

todok(copy=False)[source]#: Convert this matrix to Dictionary Of Keys format.

tolil(copy=False)[source]#: Convert this matrix to LInked List format.

transpose(axes=None, copy=False)[source]#: Reverses the dimensions of the sparse matrix.

trunc()[source]#: Elementwise trunc.

__eq__(other)[source]#: Return self==value.

__ne__(other)[source]#: Return self!=value.

__lt__(other)[source]#: Return self<value.

__le__(other)[source]#: Return self<=value.

__gt__(other)[source]#: Return self>value.

__ge__(other)[source]#: Return self>=value.

__nonzero__()[source]#

__bool__()[source]#

Attributes

A#

Dense ndarray representation of this matrix.

This property is equivalent to toarray() method.

H#

T#

device#: CUDA device on which this array resides.

dtype#: Data type of the matrix.

format = 'dia'#

ndim#

nnz#

shape#

size#