backends: NoneType
cacheing_manager: cacheing_manager
initializer_list: initializer_list

get_backends(): 

Provides the set of backends to try by default

• :returns: _

from_data(data, shape=None, dtype=None, target_backend=None, constructor=None, **kwargs): 

A wrapper so that we can dispatch to the best sparse backend we’ve got defined. Can be monkey patched.

• data: Any

• kwargs: Any

• :returns: SparseArray

from_diag(data, shape=None, dtype=None, **kwargs): 

A wrapper so that we can dispatch to the best sparse backend we’ve got defined. Can be monkey patched.

• data: Any

• kwargs: Any

• :returns: _

from_diagonal_data(diags, **kw): 

Constructs a sparse tensor from diagonal elements

• diags: Any

• kw: Any

• :returns: _

@property
shape(self): 

Provides the shape of the sparse array

• :returns: tuple[int]

@property
ndim(self): 

Provides the number of dimensions in the array

• :returns: _

to_state(self, serializer=None): 

Provides just the state that is needed to serialize the object

• serializer: Any

• :returns: _

from_state(state, serializer=None): 

Loads from the stored state

• serializer: Any

• :returns: _

empty(shape, dtype=None, **kw): 

initialize_empty(shp, shape=None, **kw): 

Returns an empty SparseArray with the appropriate shape and dtype

• shape: Any

• dtype: Any

• kw: Any

• :returns: _

@property
block_data(self): 

Returns the vector of values and corresponding indices

• :returns: _

@property
block_inds(self): 

Returns indices for the stored values

• :returns: _

transpose(self, axes): 

Returns a transposed version of the tensor

• axes: Any

• :returns: _

ascoo(self): 

Converts the tensor into a scipy COO matrix…

• :returns: sp.coo_matrix

ascsr(self): 

Converts the tensor into a scipy CSR matrix…

• :returns: sp.csr_matrix

asarray(self): 

Converts the tensor into a dense np.ndarray

• :returns: np.ndarray

reshape(self, newshape): 

Returns a reshaped version of the tensor

• axes: Any

• :returns: _

resize(self, newsize): 

Returns a resized version of the tensor

• axes: Any

• :returns: _

pad_right(self, newshape): 

Returns a right-padded version of the tensor

• axes: Any

• :returns: _

broadcast_to(self, shape) -> 'SparseArray': 

Returns a broadcasted version of the tensor

• axes: Any

• :returns: _

expand_and_broadcast_to(self, expansion, new_shape) -> 'SparseArray': 

Expands, then broadcasts (memory efficient)

• axes: Any

• :returns: _

expand_and_pad(self, expansion, padding) -> 'SparseArray': 

Expands, then pads (memory efficient)

• axes: Any

• :returns: _

get_expanded_shape(shape, axis): 

adapted from np.expand_dims

• axis: Any

• :returns: _

expand_dims(self, axis): 

adapted from np.expand_dims

• axis: Any

• :returns: _

moveaxis(self, start, end): 

Adapted from np.moveaxis

• start: Any

• end: Any

• :returns: _

concatenate(self, *others, axis=0): 

Concatenates multiple SparseArrays along the specified axis

• :returns: SparseArray

__truediv__(self, other): 

__rtruediv__(self, other): 

__rmul__(self, other): 

__mul__(self, other): 

true_multiply(self, other): 

Multiplies self and other

• other: Any

• :returns: SparseArray

multiply(self, other): 

Multiplies self and other but allows for broadcasting

• other: SparseArray | np.ndarray | int | float

• :returns: _

dot(self, other): 

Takes a regular dot product of self and other

• other: Any

• axes: Any

• :returns: _

outer(self, other): 

Takes a tensor outer product of self and other

• other: Any

• axes: Any

• :returns: _

tensordot(self, other, axes=2): 

Takes the dot product of self and other along the specified axes

• other: Any

• axes: Iterable[int] | Iterable[Iterable[int]]

  the axes to contract along

• :returns: _

cache_options(enabled=True, clear=False): 

get_caching_status(): 

A method to be overloaded. Subclasses may want to cache things for performance, so we provide a way for them to specify if caching is on or not

• :returns: _

enable_caches(): 

A method to be overloaded. Subclasses may want to cache things for performance, so we provide a way for them to turn this on

• :returns: _

disable_caches(): 

A method to be overloaded. Subclasses may want to cache things for performance, so we provide a way for them to turn this off

• :returns: _

clear_cache(): 

A method to be overloaded. Subclasses may want to cache things for performance, so we provide a way for them to clear this out.

• :returns: _