# 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.

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]

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

cupy.ndarray

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

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]

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

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

rint()[source]

Elementwise rint.

set_shape(shape)[source]
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

cupy.ndarray

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