cupyx.scipy.sparse.csc_matrix#

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

Compressed Sparse Column matrix.

This can be instantiated in several ways.

csc_matrix(D): D is a rank-2 cupy.ndarray.
csc_matrix(S): S is another sparse matrix. It is equivalent to S.tocsc().
csc_matrix((M, N), [dtype]): It constructs an empty matrix whose shape is (M, N). Default dtype is float64.
csc_matrix((data, (row, col))): All data, row and col are one-dimenaional cupy.ndarray.
csc_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.csc_matrix

Methods

__getitem__(key)[source]#

__setitem__(key, x)[source]#

__len__()[source]#

__iter__()[source]#

arcsin()[source]#: Elementwise arcsin.

arcsinh()[source]#: Elementwise arcsinh.

arctan()[source]#: Elementwise arctan.

arctanh()[source]#: Elementwise arctanh.

argmax(axis=None, out=None)[source]#

Returns indices of maximum elements along an axis.

Implicit zero elements are taken into account. If there are several maximum values, the index of the first occurrence is returned. If NaN values occur in the matrix, the output defaults to a zero entry for the row/column in which the NaN occurs.

Parameters

axis (int) – {-2, -1, 0, 1, None} (optional) Axis along which the argmax is computed. If None (default), index of the maximum element in the flatten data is returned.
out (None) – (optional) This argument is in the signature solely for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.

Returns

Indices of maximum elements. If array, its size along axis is 1.

Return type

(cupy.narray or int)

argmin(axis=None, out=None)[source]#

Returns indices of minimum elements along an axis.

Implicit zero elements are taken into account. If there are several minimum values, the index of the first occurrence is returned. If NaN values occur in the matrix, the output defaults to a zero entry for the row/column in which the NaN occurs.

Parameters

axis (int) – {-2, -1, 0, 1, None} (optional) Axis along which the argmin is computed. If None (default), index of the minimum element in the flatten data is returned.
out (None) – (optional) This argument is in the signature solely for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.

Returns

Indices of minimum elements. If matrix, its size along axis is 1.

Return type

(cupy.narray or int)

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

cupy.ndarray

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

eliminate_zeros()[source]#: Removes zero entories in place.

expm1()[source]#: Elementwise expm1.

floor()[source]#: Elementwise floor.

get(stream=None)[source]#

Returns a copy of the array on host memory.

Warning

You need to install SciPy to use this method.

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.csc_matrix

getH()[source]#

get_shape()[source]#

Returns the shape of the matrix.

Returns: Shape of the matrix.
Return type: tuple

getcol(i)[source]#

Returns a copy of column i of the matrix, as a (m x 1) CSC matrix (column vector).

Parameters: i (integer) – Column
Returns: Sparse matrix with single column
Return type: cupyx.scipy.sparse.csc_matrix

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

getrow(i)[source]#

Returns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector).

Parameters: i (integer) – Row
Returns: Sparse matrix with single row
Return type: cupyx.scipy.sparse.csc_matrix

log1p()[source]#: Elementwise log1p.

max(axis=None, out=None, *, explicit=False)[source]#

Returns the maximum of the matrix or maximum along an axis.

Parameters

axis (int) – {-2, -1, 0, 1, None} (optional) Axis along which the sum is computed. The default is to compute the maximum over all the matrix elements, returning a scalar (i.e. axis = None).
out (None) – (optional) This argument is in the signature solely for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.
explicit (bool) – Return the maximum value explicitly specified and ignore all implicit zero entries. If the dimension has no explicit values, a zero is then returned to indicate that it is the only implicit value. This parameter is experimental and may change in the future.

Returns

Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1. This differs from numpy for computational efficiency.

Return type

(cupy.ndarray or float)

See also

min : The minimum value of a sparse matrix along a given axis.

See also

numpy.matrix.max : NumPy’s implementation of max for matrices

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()

min(axis=None, out=None, *, explicit=False)[source]#

Returns the minimum of the matrix or maximum along an axis.

Parameters

axis (int) – {-2, -1, 0, 1, None} (optional) Axis along which the sum is computed. The default is to compute the minimum over all the matrix elements, returning a scalar (i.e. axis = None).
out (None) – (optional) This argument is in the signature solely for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used.
explicit (bool) – Return the minimum value explicitly specified and ignore all implicit zero entries. If the dimension has no explicit values, a zero is then returned to indicate that it is the only implicit value. This parameter is experimental and may change in the future.

Returns

Minimum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1. This differs from numpy for computational efficiency.

Return type

(cupy.ndarray or float)

See also

max : The maximum value of a sparse matrix along a given axis.

See also

numpy.matrix.min : NumPy’s implementation of ‘min’ for matrices

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.

sort_indices()[source]#: Sorts the indices of this matrix in place.

Warning

Calling this function might synchronize the device.

sorted_indices()[source]#: Return a copy of this matrix with sorted indices

Warning

Calling this function might synchronize the device.

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

See also

scipy.sparse.spmatrix.sum()

sum_duplicates()[source]#: Eliminate duplicate matrix entries by adding them together.

Note

This is an in place operation.

Warning

Calling this function might synchronize the device.

See also

scipy.sparse.csr_matrix.sum_duplicates(), scipy.sparse.csc_matrix.sum_duplicates()

tan()[source]#: Elementwise tan.

tanh()[source]#: Elementwise tanh.

toarray(order=None, out=None)[source]#

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.csc_matrix.toarray()

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

tocoo(copy=False)[source]#

Converts the matrix to COOdinate format.

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

tocsc(copy=None)[source]#

Converts the matrix to Compressed Sparse Column format.

Parameters: copy (bool) – If False, the method returns itself. Otherwise it makes a copy of the matrix.
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 csr to csc conversion.
Returns: Converted matrix.
Return type: cupyx.scipy.sparse.csr_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]#

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

cupyx.scipy.sparse.spmatrix

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 = 'csc'#

has_canonical_format#

Determine whether the matrix has sorted indices and no duplicates.

Returns: bool: True if the above applies, otherwise False.

Note

has_canonical_format implies has_sorted_indices, so if the latter flag is False, so will the former be; if the former is found True, the latter flag is also set.

Warning

Getting this property might synchronize the device.

has_sorted_indices#

Determine whether the matrix has sorted indices.

Returns

bool:: True if the indices of the matrix are in sorted order, otherwise False.

Warning

Getting this property might synchronize the device.

ndim#

nnz#

shape#

size#