cupy.sparse.csr_matrix

class cupy.sparse.csr_matrix(arg1, shape=None, dtype=None, copy=False)

Compressed Sparse Row matrix.

Now it has only part of initializer formats:

csr_matrix(D)

D is a rank-2 cupy.ndarray.

csr_matrix(S)

S is another sparse matrix. It is equivalent to S.tocsr().

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

It constructs an empty matrix whose shape is (M, N). Default dtype is float64.

csr_matrix((data, indices, indptr))

All data, indices and indptr are one-dimenaional cupy.ndarray.

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.csr_matrix

Methods

__getitem__(slices)

__len__()

__iter__()

arcsin()

Elementwise arcsin.

arcsinh()

Elementwise arcsinh.

arctan()

Elementwise arctan.

arctanh()

Elementwise arctanh.

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:cupy.sparse.spmatrix

astype(t)

Casts the array to given data type.

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

ceil()

Elementwise ceil.

conj()

conjugate()

copy()

Returns a copy of this matrix.

count_nonzero()

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()

Elementwise deg2rad.

diagonal()

Returns the main diagonal of the matrix

dot(other)

Ordinary dot product

eliminate_zeros()

Removes zero entories in place.

expm1()

Elementwise expm1.

floor()

Elementwise floor.

get(stream=None)

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.csr_matrix

getH()

get_shape()

Returns the shape of the matrix.

Returns:Shape of the matrix. Return type:tuple

getformat()

getmaxprint()

getnnz(axis=None)

Returns the number of stored values, including explicit zeros.

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

log1p()

Elementwise log1p.

maximum(other)

minimum(other)

multiply(other)

Point-wise multiplication by another matrix

power(n, dtype=None)

Elementwise power function.

Parameters:

• n – Exponent.
• dtype – Type specifier.

rad2deg()

Elementwise rad2deg.

reshape(shape, order='C')

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

rint()

Elementwise rint.

set_shape(shape)

sign()

Elementwise sign.

sin()

Elementwise sin.

sinh()

Elementwise sinh.

sort_indices()

Sorts the indices of the matrix in place.

sqrt()

Elementwise sqrt.

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

See also

scipy.sparse.spmatrix.sum()

sum_duplicates()

tan()

Elementwise tan.

tanh()

Elementwise tanh.

toarray(order=None, out=None)

Returns a dense matrix representing the same value.

Parameters:

• order ({'C', 'F', None}) – Whether to store data in C (row-major) order or F (column-major) order. Default is C-order.
• out – Not supported.

Returns:

Dense array representing the same matrix.

Return type:

cupy.ndarray

See also

scipy.sparse.csr_matrix.toarray()

tobsr(blocksize=None, copy=False)

Convert this matrix to Block Sparse Row format.

tocoo(copy=False)

Converts the matrix to COOdinate format.

Parameters:copy (bool) – If False, it shares data arrays as much as possible. Returns:Converted matrix. Return type:cupy.sparse.coo_matrix

tocsc(copy=False)

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 csr to csc conversion. Returns:Converted matrix. Return type:cupy.sparse.csc_matrix

tocsr(copy=False)

Converts the matrix to Compressed Sparse Row format.

Parameters:copy (bool) – If False, the method returns itself. Otherwise it makes a copy of the matrix. Returns:Converted matrix. Return type:cupy.sparse.csr_matrix

todense(order=None, out=None)

Return a dense matrix representation of this matrix.

todia(copy=False)

Convert this matrix to sparse DIAgonal format.

todok(copy=False)

Convert this matrix to Dictionary Of Keys format.

tolil(copy=False)

Convert this matrix to LInked List format.

transpose(axes=None, copy=False)

Returns a transpose matrix.

Parameters:

• axes – This option is not supported.
• copy (bool) – If True, a returned matrix shares no data. Otherwise, it shared data arrays as much as possible.

Returns:

Transpose matrix.

Return type:

cupy.sparse.spmatrix

trunc()

Elementwise trunc.

__eq__(other)

Return self==value.

__ne__(other)

Return self!=value.

__lt__(other)

Return self<value.

__le__(other)

Return self<=value.

__gt__(other)

Return self>value.

__ge__(other)

Return self>=value.

__nonzero__()

__bool__()

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 = 'csr'

has_canonical_format

ndim

nnz

shape

size