cupyx.scipy.sparse.spmatrix#

class cupyx.scipy.sparse.spmatrix(maxprint=50)[source]#

Base class of all sparse matrixes.

See scipy.sparse.spmatrix

Methods

__len__()[source]#

__iter__()[source]#

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: t – Type specifier.
Returns: A copy of the array with the given type and the same format.
Return type: cupyx.scipy.sparse.spmatrix

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]#: Number of non-zero entries, equivalent to

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

get(stream=None)[source]#

Return 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: An array on host memory.
Return type: scipy.sparse.spmatrix

getH()[source]#

get_shape()[source]#

getformat()[source]#

getmaxprint()[source]#

getnnz(axis=None)[source]#: Number of stored values, including explicit zeros.

maximum(other)[source]#

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

Compute the arithmetic mean along the specified axis.

Returns the average of the matrix elements. The average is taken over all elements in the matrix by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.

Parameters

{-2 (axis) – optional Axis along which the mean is computed. The default is to compute the mean of all elements in the matrix (i.e., axis = None).
-1 – optional Axis along which the mean is computed. The default is to compute the mean of all elements in the matrix (i.e., axis = None).
0 – optional Axis along which the mean is computed. The default is to compute the mean of all elements in the matrix (i.e., axis = None).
1 – optional Axis along which the mean is computed. The default is to compute the mean of all elements in the matrix (i.e., axis = None).
None} – optional Axis along which the mean is computed. The default is to compute the mean of all elements in the matrix (i.e., axis = None).
dtype (dtype) – optional Type to use in computing the mean. For integer inputs, the default is float64; for floating point inputs, it is the same as the input dtype.
out (cupy.ndarray) – optional Alternative output matrix in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary.

Returns

Output array of means

Return type

m (cupy.ndarray)