cupyx.scipy.sparse.spmatrix¶

class cupyx.scipy.sparse.spmatrix(maxprint=50)¶

Base class of all sparse matrixes.

See scipy.sparse.spmatrix

Methods

__len__()¶

__iter__()¶

asformat(format)¶

Return this matrix in a given sparse format.

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

asfptype()¶

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)¶

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)¶

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)¶

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()¶

Returns a copy of this matrix.

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

count_nonzero()¶: Number of non-zero entries, equivalent to

diagonal(k=0)¶

Returns the k-th diagonal of the matrix.

Parameters:	k (int, optional) – Which diagonal to get, corresponding to elements i+k] Default (a[i,) – 0 (the main diagonal).
Returns:	The k-th diagonal.
Return type:	cupy.ndarray

dot(other)¶: Ordinary dot product

get(stream=None)¶

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()¶

get_shape()¶

getformat()¶

getmaxprint()¶

getnnz(axis=None)¶: Number of stored values, including explicit zeros.

maximum(other)¶

minimum(other)¶

multiply(other)¶: Point-wise multiplication by another matrix

power(n, dtype=None)¶

reshape(shape, order='C')¶: Gives a new shape to a sparse matrix without changing its data.

set_shape(shape)¶

sum(axis=None, dtype=None, out=None)¶

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