COO
Bases: SparseArray
, NDArrayOperatorsMixin
A sparse multidimensional array.
This is stored in COO format. It depends on NumPy and Scipy.sparse for computation, but supports arrays of arbitrary dimension.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
coords
|
ndarray(ndim, nnz)
|
An array holding the index locations of every value Should have shape (number of dimensions, number of non-zeros). |
required |
data
|
ndarray(nnz)
|
An array of Values. A scalar can also be supplied if the data is the same across
all coordinates. If not given, defers to |
None
|
shape
|
tuple[int](ndim)
|
The shape of the array. |
None
|
has_duplicates
|
bool_
|
A value indicating whether the supplied value for |
True
|
sorted
|
bool_
|
A value indicating whether the values in |
False
|
prune
|
bool_
|
A flag indicating whether or not we should prune any fill-values present in
|
False
|
cache
|
bool_
|
Whether to enable cacheing for various operations. See
|
False
|
fill_value
|
The fill value for this array. |
None
|
Attributes:
Name | Type | Description |
---|---|---|
coords |
ndarray(ndim, nnz)
|
An array holding the coordinates of every nonzero element. |
data |
ndarray(nnz)
|
An array holding the values corresponding to |
shape |
tuple[int](ndim)
|
The dimensions of this array. |
See Also
sparse.DOK
: A mostly write-only sparse array.sparse.as_coo
: Convert any given format tosparse.COO
.
Examples:
You can create sparse.COO
objects from Numpy arrays.
>>> x = np.eye(4, dtype=np.uint8)
>>> x[2, 3] = 5
>>> s = COO.from_numpy(x)
>>> s
<COO: shape=(4, 4), dtype=uint8, nnz=5, fill_value=0>
>>> s.data
array([1, 1, 1, 5, 1], dtype=uint8)
>>> s.coords
array([[0, 1, 2, 2, 3],
[0, 1, 2, 3, 3]])
sparse.COO
objects support basic arithmetic and binary operations.
>>> x2 = np.eye(4, dtype=np.uint8)
>>> x2[3, 2] = 5
>>> s2 = COO.from_numpy(x2)
>>> (s + s2).todense()
array([[2, 0, 0, 0],
[0, 2, 0, 0],
[0, 0, 2, 5],
[0, 0, 5, 2]], dtype=uint8)
>>> (s * s2).todense()
array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], dtype=uint8)
Binary operations support broadcasting.
>>> x3 = np.zeros((4, 1), dtype=np.uint8)
>>> x3[2, 0] = 1
>>> s3 = COO.from_numpy(x3)
>>> (s * s3).todense()
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 1, 5],
[0, 0, 0, 0]], dtype=uint8)
sparse.COO
objects also support dot products and reductions.
>>> s.dot(s.T).sum(axis=0).todense()
array([ 1, 1, 31, 6], dtype=uint64)
You can use Numpy ufunc
operations on sparse.COO
arrays as well.
>>> np.sum(s, axis=1).todense()
array([1, 1, 6, 1], dtype=uint64)
>>> np.round(np.sqrt(s, dtype=np.float64), decimals=1).todense()
array([[ 1. , 0. , 0. , 0. ],
[ 0. , 1. , 0. , 0. ],
[ 0. , 0. , 1. , 2.2],
[ 0. , 0. , 0. , 1. ]])
Operations that will result in a dense array will usually result in a different fill value, such as the following.
>>> np.exp(s)
<COO: shape=(4, 4), dtype=float16, nnz=5, fill_value=1.0>
You can also create sparse.COO
arrays from coordinates and data.
>>> coords = [[0, 0, 0, 1, 1], [0, 1, 2, 0, 3], [0, 3, 2, 0, 1]]
>>> data = [1, 2, 3, 4, 5]
>>> s4 = COO(coords, data, shape=(3, 4, 5))
>>> s4
<COO: shape=(3, 4, 5), dtype=int64, nnz=5, fill_value=0>
If the data is same across all coordinates, you can also specify a scalar.
>>> coords = [[0, 0, 0, 1, 1], [0, 1, 2, 0, 3], [0, 3, 2, 0, 1]]
>>> data = 1
>>> s5 = COO(coords, data, shape=(3, 4, 5))
>>> s5
<COO: shape=(3, 4, 5), dtype=int64, nnz=5, fill_value=0>
Following scipy.sparse conventions you can also pass these as a tuple with rows and columns
>>> rows = [0, 1, 2, 3, 4]
>>> cols = [0, 0, 0, 1, 1]
>>> data = [10, 20, 30, 40, 50]
>>> z = COO((data, (rows, cols)), shape=(5, 2))
>>> z.todense()
array([[10, 0],
[20, 0],
[30, 0],
[ 0, 40],
[ 0, 50]])
You can also pass a dictionary or iterable of index/value pairs. Repeated indices imply summation:
>>> d = {(0, 0, 0): 1, (1, 2, 3): 2, (1, 1, 0): 3}
>>> COO(d, shape=(2, 3, 4))
<COO: shape=(2, 3, 4), dtype=int64, nnz=3, fill_value=0>
>>> L = [((0, 0), 1), ((1, 1), 2), ((0, 0), 3)]
>>> COO(L, shape=(2, 2)).todense()
array([[4, 0],
[0, 2]])
You can convert sparse.DOK
arrays to sparse.COO
arrays.
>>> from sparse import DOK
>>> s6 = DOK((5, 5), dtype=np.int64)
>>> s6[1:3, 1:3] = [[4, 5], [6, 7]]
>>> s6
<DOK: shape=(5, 5), dtype=int64, nnz=4, fill_value=0>
>>> s7 = s6.asformat("coo")
>>> s7
<COO: shape=(5, 5), dtype=int64, nnz=4, fill_value=0>
>>> s7.todense()
array([[0, 0, 0, 0, 0],
[0, 4, 5, 0, 0],
[0, 6, 7, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
Source code in sparse/numba_backend/_coo/core.py
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|
Attributes
shape = tuple(int(sh) for sh in shape)
instance-attribute
device
property
ndim
property
The number of dimensions of this array.
Returns:
Type | Description |
---|---|
int
|
The number of dimensions of this array. |
See Also
sparse.DOK.ndim
: Equivalent property forsparse.DOK
arrays.numpy.ndarray.ndim
: Numpy equivalent property.
Examples:
>>> from sparse import COO
>>> import numpy as np
>>> x = np.random.rand(1, 2, 3, 1, 2)
>>> s = COO.from_numpy(x)
>>> s.ndim
5
>>> s.ndim == x.ndim
True
size
property
The number of all elements (including zeros) in this array.
Returns:
Type | Description |
---|---|
int
|
The number of elements. |
See Also
numpy.ndarray.size
: Numpy equivalent property.
Examples:
>>> from sparse import COO
>>> import numpy as np
>>> x = np.zeros((10, 10))
>>> s = COO.from_numpy(x)
>>> s.size
100
density
property
The ratio of nonzero to all elements in this array.
Returns:
Type | Description |
---|---|
float
|
The ratio of nonzero to all elements. |
See Also
sparse.COO.size
: Number of elements.sparse.COO.nnz
: Number of nonzero elements.
Examples:
>>> import numpy as np
>>> from sparse import COO
>>> x = np.zeros((8, 8))
>>> x[0, :] = 1
>>> s = COO.from_numpy(x)
>>> s.density
0.125
amax = max
class-attribute
instance-attribute
amin = min
class-attribute
instance-attribute
round_ = round
class-attribute
instance-attribute
real
property
The real part of the array.
Examples:
>>> from sparse import COO
>>> x = COO.from_numpy([1 + 0j, 0 + 1j])
>>> x.real.todense()
array([1., 0.])
>>> x.real.dtype
dtype('float64')
Returns:
Name | Type | Description |
---|---|---|
out |
SparseArray
|
The real component of the array elements. If the array dtype is real, the dtype of the array is used for the output. If the array is complex, the output dtype is float. |
See Also
numpy.ndarray.real
: NumPy equivalent attribute.numpy.real
: NumPy equivalent function.
imag
property
The imaginary part of the array.
Examples:
>>> from sparse import COO
>>> x = COO.from_numpy([1 + 0j, 0 + 1j])
>>> x.imag.todense()
array([0., 1.])
>>> x.imag.dtype
dtype('float64')
Returns:
Name | Type | Description |
---|---|---|
out |
SparseArray
|
The imaginary component of the array elements. If the array dtype is real, the dtype of the array is used for the output. If the array is complex, the output dtype is float. |
See Also
numpy.ndarray.imag
: NumPy equivalent attribute.numpy.imag
: NumPy equivalent function.
fill_value = self.data.dtype.type(fill_value)
instance-attribute
data = np.asarray(data)
instance-attribute
coords = np.asarray(coords)
instance-attribute
dtype
property
The datatype of this array.
Returns:
Type | Description |
---|---|
dtype
|
The datatype of this array. |
See Also
numpy.ndarray.dtype
: Numpy equivalent property.scipy.sparse.coo_matrix.dtype
: Scipy equivalent property.
Examples:
>>> x = (200 * np.random.rand(5, 4)).astype(np.int32)
>>> s = COO.from_numpy(x)
>>> s.dtype
dtype('int32')
>>> x.dtype == s.dtype
True
nnz
property
The number of nonzero elements in this array. Note that any duplicates in
coords
are counted multiple times.
Returns:
Type | Description |
---|---|
int
|
The number of nonzero elements in this array. |
See Also
sparse.DOK.nnz
: Equivalentsparse.DOK
array property.numpy.count_nonzero
: A similar Numpy function.scipy.sparse.coo_matrix.nnz
: The Scipy equivalent property.
Examples:
>>> x = np.array([0, 0, 1, 0, 1, 2, 0, 1, 2, 3, 0, 0])
>>> np.count_nonzero(x)
6
>>> s = COO.from_numpy(x)
>>> s.nnz
6
>>> np.count_nonzero(x) == s.nnz
True
format
property
The storage format of this array.
Returns:
Type | Description |
---|---|
str
|
The storage format of this array. |
See Also
scipy.sparse.dok_matrix.format
: The Scipy equivalent property.
Examples:
>>> import sparse
>>> s = sparse.random((5, 5), density=0.2, format="dok")
>>> s.format
'dok'
>>> t = sparse.random((5, 5), density=0.2, format="coo")
>>> t.format
'coo'
nbytes
property
The number of bytes taken up by this object. Note that for small arrays, this may undercount the number of bytes due to the large constant overhead.
Returns:
Type | Description |
---|---|
int
|
The approximate bytes of memory taken by this object. |
See Also
numpy.ndarray.nbytes
: The equivalent Numpy property.
Examples:
>>> data = np.arange(6, dtype=np.uint8)
>>> coords = np.random.randint(1000, size=(3, 6), dtype=np.uint16)
>>> s = COO(coords, data, shape=(1000, 1000, 1000))
>>> s.nbytes
42
T
property
Returns a new array which has the order of the axes reversed.
Returns:
Type | Description |
---|---|
COO
|
The new array with the axes in the desired order. |
See Also
sparse.COO.transpose
: A method where you can specify the order of the axes.numpy.ndarray.T
: Numpy equivalent property.
Examples:
We can change the order of the dimensions of any sparse.COO
array with this
function.
>>> x = np.add.outer(np.arange(5), np.arange(5)[::-1])
>>> x
array([[4, 3, 2, 1, 0],
[5, 4, 3, 2, 1],
[6, 5, 4, 3, 2],
[7, 6, 5, 4, 3],
[8, 7, 6, 5, 4]])
>>> s = COO.from_numpy(x)
>>> s.T.todense()
array([[4, 5, 6, 7, 8],
[3, 4, 5, 6, 7],
[2, 3, 4, 5, 6],
[1, 2, 3, 4, 5],
[0, 1, 2, 3, 4]])
Note that by default, this reverses the order of the axes rather than switching the last and second-to-last axes as required by some linear algebra operations.
>>> x = np.random.rand(2, 3, 4)
>>> s = COO.from_numpy(x)
>>> s.T.shape
(4, 3, 2)
mT
property
Transpose of a matrix (or a stack of matrices). If an array instance has fewer than two dimensions, an error should be raised.
Returns:
Type | Description |
---|---|
COO
|
array whose last two dimensions (axes) are permuted in reverse order relative to original array (i.e., for an array instance having shape (..., M, N), the returned array must have shape (..., N, M)). The returned array must have the same data type as the original array. |
See Also
sparse.COO.transpose
: A method where you can specify the order of the axes.numpy.ndarray.mT
: Numpy equivalent property.
Examples:
>>> x = np.arange(8).reshape((2, 2, 2))
>>> x
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> s = COO.from_numpy(x)
>>> s.mT.todense()
array([[[0, 2],
[1, 3]],
[[4, 6],
[5, 7]]])
Functions
to_device(device, /, *, stream=None)
Source code in sparse/numba_backend/_sparse_array.py
55 56 57 58 59 |
|
reduce(method, axis=(0,), keepdims=False, **kwargs)
Performs a reduction operation on this array.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
method
|
ufunc
|
The method to use for performing the reduction. |
required |
axis
|
Union[int, Iterable[int]]
|
The axes along which to perform the reduction. Uses all axes by default. |
(0,)
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
**kwargs
|
dict
|
Any extra arguments to pass to the reduction operation. |
{}
|
See Also
numpy.ufunc.reduce
: A similar Numpy method.sparse.COO.reduce
: This method implemented on COO arrays.sparse.GCXS.reduce
: This method implemented on GCXS arrays.
Source code in sparse/numba_backend/_sparse_array.py
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|
sum(axis=None, keepdims=False, dtype=None, out=None)
Performs a sum operation along the given axes. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to sum. Uses all axes by default. |
None
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
dtype
|
dtype
|
The data type of the output array. |
None
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.sum
: Equivalent numpy function.scipy.sparse.coo_matrix.sum
: Equivalent Scipy function.
Source code in sparse/numba_backend/_sparse_array.py
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|
max(axis=None, keepdims=False, out=None)
Maximize along the given axes. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to maximize. Uses all axes by default. |
None
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
out
|
dtype
|
The data type of the output array. |
None
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.max
: Equivalent numpy function.scipy.sparse.coo_matrix.max
: Equivalent Scipy function.
Source code in sparse/numba_backend/_sparse_array.py
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|
any(axis=None, keepdims=False, out=None)
See if any values along array are True
. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to minimize. Uses all axes by default. |
None
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.any
: Equivalent numpy function.
Source code in sparse/numba_backend/_sparse_array.py
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|
all(axis=None, keepdims=False, out=None)
See if all values in an array are True
. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to minimize. Uses all axes by default. |
None
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.all
: Equivalent numpy function.
Source code in sparse/numba_backend/_sparse_array.py
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|
min(axis=None, keepdims=False, out=None)
Minimize along the given axes. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to minimize. Uses all axes by default. |
None
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
out
|
dtype
|
The data type of the output array. |
None
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.min
: Equivalent numpy function.scipy.sparse.coo_matrix.min
: Equivalent Scipy function.
Source code in sparse/numba_backend/_sparse_array.py
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|
prod(axis=None, keepdims=False, dtype=None, out=None)
Performs a product operation along the given axes. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to multiply. Uses all axes by default. |
None
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
dtype
|
dtype
|
The data type of the output array. |
None
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.prod
: Equivalent numpy function.
Source code in sparse/numba_backend/_sparse_array.py
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|
round(decimals=0, out=None)
Evenly round to the given number of decimals.
See Also
numpy.round
: NumPy equivalent ufunc.sparse.elemwise
: Apply an arbitrary element-wise function to one or two arguments.
Source code in sparse/numba_backend/_sparse_array.py
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|
clip(min=None, max=None, out=None)
Clip (limit) the values in the array.
Return an array whose values are limited to [min, max]
. One of min
or max must be given.
See Also
- sparse.clip : For full documentation and more details.
numpy.clip
: Equivalent NumPy function.
Source code in sparse/numba_backend/_sparse_array.py
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|
astype(dtype, casting='unsafe', copy=True)
Copy of the array, cast to a specified type.
See Also
scipy.sparse.coo_matrix.astype
: SciPy sparse equivalent functionnumpy.ndarray.astype
: NumPy equivalent ufunc.sparse.elemwise
: Apply an arbitrary element-wise function to one or two arguments.
Source code in sparse/numba_backend/_sparse_array.py
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|
mean(axis=None, keepdims=False, dtype=None, out=None)
Compute the mean along the given axes. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to compute the mean. Uses all axes by default. |
None
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
dtype
|
dtype
|
The data type of the output array. |
None
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.ndarray.mean
: Equivalent numpy method.scipy.sparse.coo_matrix.mean
: Equivalent Scipy method.
Notes
- The
out
parameter is provided just for compatibility with Numpy and isn't actually supported.
Examples:
You can use sparse.COO.mean
to compute the mean of an array across any
dimension.
>>> from sparse import COO
>>> x = np.array([[1, 2, 0, 0], [0, 1, 0, 0]], dtype="i8")
>>> s = COO.from_numpy(x)
>>> s2 = s.mean(axis=1)
>>> s2.todense()
array([0.5, 1.5, 0., 0.])
You can also use the keepdims
argument to keep the dimensions
after the mean.
>>> s3 = s.mean(axis=0, keepdims=True)
>>> s3.shape
(1, 4)
You can pass in an output datatype, if needed.
>>> s4 = s.mean(axis=0, dtype=np.float16)
>>> s4.dtype
dtype('float16')
By default, this reduces the array down to one number, computing the mean along all axes.
>>> s.mean()
np.float64(0.5)
Source code in sparse/numba_backend/_sparse_array.py
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|
var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)
Compute the variance along the given axes. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to compute the variance. Uses all axes by default. |
None
|
dtype
|
dtype
|
The output datatype. |
None
|
out
|
SparseArray
|
The array to write the output to. |
None
|
ddof
|
int
|
The degrees of freedom. |
0
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.ndarray.var
: Equivalent numpy method.
Examples:
You can use sparse.COO.var
to compute the variance of an array across any
dimension.
>>> from sparse import COO
>>> x = np.array([[1, 2, 0, 0], [0, 1, 0, 0]], dtype="i8")
>>> s = COO.from_numpy(x)
>>> s2 = s.var(axis=1)
>>> s2.todense()
array([0.6875, 0.1875])
You can also use the keepdims
argument to keep the dimensions
after the variance.
>>> s3 = s.var(axis=0, keepdims=True)
>>> s3.shape
(1, 4)
You can pass in an output datatype, if needed.
>>> s4 = s.var(axis=0, dtype=np.float16)
>>> s4.dtype
dtype('float16')
By default, this reduces the array down to one number, computing the variance along all axes.
>>> s.var()
np.float64(0.5)
Source code in sparse/numba_backend/_sparse_array.py
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|
std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)
Compute the standard deviation along the given axes. Uses all axes by default.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[int, Iterable[int]]
|
The axes along which to compute the standard deviation. Uses all axes by default. |
None
|
dtype
|
dtype
|
The output datatype. |
None
|
out
|
SparseArray
|
The array to write the output to. |
None
|
ddof
|
int
|
The degrees of freedom. |
0
|
keepdims
|
bool_
|
Whether or not to keep the dimensions of the original array. |
False
|
Returns:
Type | Description |
---|---|
SparseArray
|
The reduced output sparse array. |
See Also
numpy.ndarray.std
: Equivalent numpy method.
Examples:
You can use sparse.COO.std
to compute the standard deviation of an array
across any dimension.
>>> from sparse import COO
>>> x = np.array([[1, 2, 0, 0], [0, 1, 0, 0]], dtype="i8")
>>> s = COO.from_numpy(x)
>>> s2 = s.std(axis=1)
>>> s2.todense()
array([0.8291562, 0.4330127])
You can also use the keepdims
argument to keep the dimensions
after the standard deviation.
>>> s3 = s.std(axis=0, keepdims=True)
>>> s3.shape
(1, 4)
You can pass in an output datatype, if needed.
>>> s4 = s.std(axis=0, dtype=np.float16)
>>> s4.dtype
dtype('float16')
By default, this reduces the array down to one number, computing the standard deviation along all axes.
>>> s.std()
0.7071067811865476
Source code in sparse/numba_backend/_sparse_array.py
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|
conj()
Return the complex conjugate, element-wise.
The complex conjugate of a complex number is obtained by changing the sign of its imaginary part.
Examples:
>>> from sparse import COO
>>> x = COO.from_numpy([1 + 2j, 2 - 1j])
>>> res = x.conj()
>>> res.todense()
array([1.-2.j, 2.+1.j])
>>> res.dtype
dtype('complex128')
Returns:
Name | Type | Description |
---|---|---|
out |
SparseArray
|
The complex conjugate, with same dtype as the input. |
See Also
numpy.ndarray.conj
: NumPy equivalent method.numpy.conj
: NumPy equivalent function.
Source code in sparse/numba_backend/_sparse_array.py
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|
copy(deep=True)
Return a copy of the array.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
deep
|
boolean
|
If True (default), the internal coords and data arrays are also
copied. Set to |
True
|
Source code in sparse/numba_backend/_coo/core.py
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|
enable_caching()
Enable caching of reshape, transpose, and tocsr/csc operations
This enables efficient iterative workflows that make heavy use of csr/csc operations, such as tensordot. This maintains a cache of recent results of reshape and transpose so that operations like tensordot (which uses both internally) store efficiently stored representations for repeated use. This can significantly cut down on computational costs in common numeric algorithms.
However, this also assumes that neither this object, nor the downstream objects will have their data mutated.
Examples:
>>> s.enable_caching()
>>> csr1 = s.transpose((2, 0, 1)).reshape((100, 120)).tocsr()
>>> csr2 = s.transpose((2, 0, 1)).reshape((100, 120)).tocsr()
>>> csr1 is csr2
True
Source code in sparse/numba_backend/_coo/core.py
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|
from_numpy(x, fill_value=None, idx_dtype=None)
classmethod
Convert the given sparse.COO
object.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x
|
ndarray
|
The dense array to convert. |
required |
fill_value
|
scalar
|
The fill value of the constructed |
None
|
Returns:
Type | Description |
---|---|
COO
|
The converted COO array. |
Examples:
>>> x = np.eye(5)
>>> s = COO.from_numpy(x)
>>> s
<COO: shape=(5, 5), dtype=float64, nnz=5, fill_value=0.0>
>>> x[x == 0] = np.nan
>>> COO.from_numpy(x, fill_value=np.nan)
<COO: shape=(5, 5), dtype=float64, nnz=5, fill_value=nan>
Source code in sparse/numba_backend/_coo/core.py
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|
todense()
Convert this sparse.COO
array to a dense numpy.ndarray
. Note that
this may take a large amount of memory if the COO
object's shape
is large.
Returns:
Type | Description |
---|---|
ndarray
|
The converted dense array. |
See Also
sparse.DOK.todense
: EquivalentDOK
array method.scipy.sparse.coo_matrix.todense
: Equivalent Scipy method.
Examples:
>>> x = np.random.randint(100, size=(7, 3))
>>> s = COO.from_numpy(x)
>>> x2 = s.todense()
>>> np.array_equal(x, x2)
True
Source code in sparse/numba_backend/_coo/core.py
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|
from_scipy_sparse(x, /, *, fill_value=None)
classmethod
Construct a sparse.COO
array from a scipy.sparse.spmatrix
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x
|
spmatrix
|
The sparse matrix to construct the array from. |
required |
fill_value
|
scalar
|
The fill-value to use when converting. |
None
|
Returns:
Type | Description |
---|---|
COO
|
The converted |
Examples:
>>> import scipy.sparse
>>> x = scipy.sparse.rand(6, 3, density=0.2)
>>> s = COO.from_scipy_sparse(x)
>>> np.array_equal(x.todense(), s.todense())
True
Source code in sparse/numba_backend/_coo/core.py
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|
from_iter(x, shape, fill_value=None, dtype=None)
classmethod
Converts an iterable in certain formats to a sparse.COO
array. See examples
for details.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
x
|
Iterable or Iterator
|
The iterable to convert to |
required |
shape
|
tuple[int]
|
The shape of the array. |
required |
fill_value
|
scalar
|
The fill value for this array. |
None
|
dtype
|
dtype
|
The dtype of the input array. Inferred from the input if not given. |
None
|
Returns:
Name | Type | Description |
---|---|---|
out |
COO
|
The output |
Examples:
You can convert items of the format sparse.COO
.
Here, the first part represents the coordinate and the second part represents the value.
>>> x = [((0, 0), 1), ((1, 1), 1)]
>>> s = COO.from_iter(x, shape=(2, 2))
>>> s.todense()
array([[1, 0],
[0, 1]])
You can also have a similar format with a dictionary.
>>> x = {(0, 0): 1, (1, 1): 1}
>>> s = COO.from_iter(x, shape=(2, 2))
>>> s.todense()
array([[1, 0],
[0, 1]])
The third supported format is (data, (..., row, col))
.
>>> x = ([1, 1], ([0, 1], [0, 1]))
>>> s = COO.from_iter(x, shape=(2, 2))
>>> s.todense()
array([[1, 0],
[0, 1]])
You can also pass in a collections.abc.Iterator
object.
>>> x = [((0, 0), 1), ((1, 1), 1)].__iter__()
>>> s = COO.from_iter(x, shape=(2, 2))
>>> s.todense()
array([[1, 0],
[0, 1]])
Source code in sparse/numba_backend/_coo/core.py
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|
transpose(axes=None)
Returns a new array which has the order of the axes switched.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axes
|
Iterable[int]
|
The new order of the axes compared to the previous one. Reverses the axes by default. |
None
|
Returns:
Type | Description |
---|---|
COO
|
The new array with the axes in the desired order. |
See Also
sparse.COO.T
: A quick property to reverse the order of the axes.numpy.ndarray.transpose
: Numpy equivalent function.
Examples:
We can change the order of the dimensions of any sparse.COO
array with this
function.
>>> x = np.add.outer(np.arange(5), np.arange(5)[::-1])
>>> x
array([[4, 3, 2, 1, 0],
[5, 4, 3, 2, 1],
[6, 5, 4, 3, 2],
[7, 6, 5, 4, 3],
[8, 7, 6, 5, 4]])
>>> s = COO.from_numpy(x)
>>> s.transpose((1, 0)).todense()
array([[4, 5, 6, 7, 8],
[3, 4, 5, 6, 7],
[2, 3, 4, 5, 6],
[1, 2, 3, 4, 5],
[0, 1, 2, 3, 4]])
Note that by default, this reverses the order of the axes rather than switching the last and second-to-last axes as required by some linear algebra operations.
>>> x = np.random.rand(2, 3, 4)
>>> s = COO.from_numpy(x)
>>> s.transpose().shape
(4, 3, 2)
Source code in sparse/numba_backend/_coo/core.py
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|
swapaxes(axis1, axis2)
Returns array that has axes axis1 and axis2 swapped.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis1
|
int
|
first axis to swap |
required |
axis2
|
int
|
second axis to swap |
required |
Returns:
Type | Description |
---|---|
COO
|
The new array with the axes axis1 and axis2 swapped. |
Examples:
>>> x = COO.from_numpy(np.ones((2, 3, 4)))
>>> x.swapaxes(0, 2)
<COO: shape=(4, 3, 2), dtype=float64, nnz=24, fill_value=0.0>
Source code in sparse/numba_backend/_coo/core.py
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|
dot(other)
Performs the equivalent of x.dot(y)
for sparse.COO
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
other
|
Union[COO, ndarray, spmatrix]
|
The second operand of the dot product operation. |
required |
Returns:
Type | Description |
---|---|
{COO, ndarray}
|
The result of the dot product. If the result turns out to be dense, then a dense array is returned, otherwise, a sparse array. |
Raises:
Type | Description |
---|---|
ValueError
|
If all arguments don't have zero fill-values. |
See Also
sparse.dot
: Equivalent function for two arguments.numpy.dot
: Numpy equivalent function.scipy.sparse.coo_matrix.dot
: Scipy equivalent function.
Examples:
>>> x = np.arange(4).reshape((2, 2))
>>> s = COO.from_numpy(x)
>>> s.dot(s)
array([[ 2, 3],
[ 6, 11]], dtype=int64)
Source code in sparse/numba_backend/_coo/core.py
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|
linear_loc()
The nonzero coordinates of a flattened version of this array. Note that the coordinates may be out of order.
Returns:
Type | Description |
---|---|
ndarray
|
The flattened coordinates. |
See Also
numpy.flatnonzero
: Equivalent Numpy function.
Examples:
>>> x = np.eye(5)
>>> s = COO.from_numpy(x)
>>> s.linear_loc()
array([ 0, 6, 12, 18, 24])
>>> np.array_equal(np.flatnonzero(x), s.linear_loc())
True
Source code in sparse/numba_backend/_coo/core.py
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|
flatten(order='C')
Returns a new sparse.COO
array that is a flattened version of this array.
Returns:
Type | Description |
---|---|
COO
|
The flattened output array. |
Notes
The order
parameter is provided just for compatibility with
Numpy and isn't actually supported.
Examples:
>>> s = COO.from_numpy(np.arange(10))
>>> s2 = s.reshape((2, 5)).flatten()
>>> s2.todense()
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Source code in sparse/numba_backend/_coo/core.py
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|
reshape(shape, order='C')
Returns a new sparse.COO
array that is a reshaped version of this array.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
shape
|
tuple[int]
|
The desired shape of the output array. |
required |
Returns:
Type | Description |
---|---|
COO
|
The reshaped output array. |
See Also
numpy.ndarray.reshape
: The equivalent Numpy function.
Notes
The order
parameter is provided just for compatibility with
Numpy and isn't actually supported.
Examples:
>>> s = COO.from_numpy(np.arange(25))
>>> s2 = s.reshape((5, 5))
>>> s2.todense()
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
Source code in sparse/numba_backend/_coo/core.py
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|
squeeze(axis=None)
Removes singleton dimensions (axes) from x
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
axis
|
Union[None, int, Tuple[int, ...]]
|
The axis (or axes) to squeeze. If a specified axis has a size greater than one,
a |
None
|
Returns:
Type | Description |
---|---|
COO
|
The output array without |
Examples:
>>> s = COO.from_numpy(np.eye(2)).reshape((2, 1, 2, 1))
>>> s.squeeze().shape
(2, 2)
>>> s.squeeze(axis=1).shape
(2, 2, 1)
Source code in sparse/numba_backend/_coo/core.py
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|
to_scipy_sparse(*, accept_fv=None)
Converts this sparse.COO
object into a scipy.sparse.coo_matrix
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
accept_fv
|
scalar or list of scalar
|
The list of accepted fill-values. The default accepts only zero. |
None
|
Returns:
Type | Description |
---|---|
coo_matrix
|
The converted Scipy sparse matrix. |
Raises:
Type | Description |
---|---|
ValueError
|
If the array is not two-dimensional. |
ValueError
|
If all the array doesn't zero fill-values. |
See Also
sparse.COO.tocsr
: Convert to ascipy.sparse.csr_matrix
.sparse.COO.tocsc
: Convert to ascipy.sparse.csc_matrix
.
Source code in sparse/numba_backend/_coo/core.py
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|
tocsr()
Converts this array to a scipy.sparse.csr_matrix
.
Returns:
Type | Description |
---|---|
csr_matrix
|
The result of the conversion. |
Raises:
Type | Description |
---|---|
ValueError
|
If the array is not two-dimensional. |
ValueError
|
If all the array doesn't have zero fill-values. |
See Also
sparse.COO.tocsc
: Convert to ascipy.sparse.csc_matrix
.sparse.COO.to_scipy_sparse
: Convert to ascipy.sparse.coo_matrix
.scipy.sparse.coo_matrix.tocsr
: Equivalent Scipy function.
Source code in sparse/numba_backend/_coo/core.py
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|
tocsc()
Converts this array to a scipy.sparse.csc_matrix
.
Returns:
Type | Description |
---|---|
csc_matrix
|
The result of the conversion. |
Raises:
Type | Description |
---|---|
ValueError
|
If the array is not two-dimensional. |
ValueError
|
If the array doesn't have zero fill-values. |
See Also
sparse.COO.tocsr
: Convert to ascipy.sparse.csr_matrix
.sparse.COO.to_scipy_sparse
: Convert to ascipy.sparse.coo_matrix
.scipy.sparse.coo_matrix.tocsc
: Equivalent Scipy function.
Source code in sparse/numba_backend/_coo/core.py
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|
broadcast_to(shape)
Performs the equivalent of sparse.COO
. Note that
this function returns a new array instead of a view.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
shape
|
tuple[int]
|
The shape to broadcast the data to. |
required |
Returns:
Type | Description |
---|---|
COO
|
The broadcasted sparse array. |
Raises:
Type | Description |
---|---|
ValueError
|
If the operand cannot be broadcast to the given shape. |
See Also
numpy.broadcast_to
: NumPy equivalent function
Source code in sparse/numba_backend/_coo/core.py
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|
maybe_densify(max_size=1000, min_density=0.25)
Converts this sparse.COO
array to a numpy.ndarray
if not too
costly.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
max_size
|
int
|
Maximum number of elements in output |
1000
|
min_density
|
float
|
Minimum density of output |
0.25
|
Returns:
Type | Description |
---|---|
ndarray
|
The dense array. |
Raises:
Type | Description |
---|---|
ValueError
|
If the returned array would be too large. |
Examples:
Convert a small sparse array to a dense array.
>>> s = COO.from_numpy(np.random.rand(2, 3, 4))
>>> x = s.maybe_densify()
>>> np.allclose(x, s.todense())
True
You can also specify the minimum allowed density or the maximum number of output elements. If both conditions are unmet, this method will throw an error.
>>> x = np.zeros((5, 5), dtype=np.uint8)
>>> x[2, 2] = 1
>>> s = COO.from_numpy(x)
>>> s.maybe_densify(max_size=5, min_density=0.25)
Traceback (most recent call last):
...
ValueError: Operation would require converting large sparse array to dense
Source code in sparse/numba_backend/_coo/core.py
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|
nonzero()
Get the indices where this array is nonzero.
Returns:
Name | Type | Description |
---|---|---|
idx |
tuple[`numpy.ndarray`]
|
The indices where this array is nonzero. |
See Also
numpy.ndarray.nonzero
: NumPy equivalent function
Raises:
Type | Description |
---|---|
ValueError
|
If the array doesn't have zero fill-values. |
Examples:
>>> s = COO.from_numpy(np.eye(5))
>>> s.nonzero()
(array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4]))
Source code in sparse/numba_backend/_coo/core.py
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|
asformat(format, **kwargs)
Convert this sparse array to a given format.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
format
|
str
|
A format string. |
required |
Returns:
Name | Type | Description |
---|---|---|
out |
SparseArray
|
The converted array. |
Raises:
Type | Description |
---|---|
NotImplementedError
|
If the format isn't supported. |
Source code in sparse/numba_backend/_coo/core.py
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|
isinf()
Tests each element x_i
of the array to determine if equal to positive or negative infinity.
Source code in sparse/numba_backend/_coo/core.py
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|
isnan()
Tests each element x_i
of the array to determine whether the element is NaN
.
Source code in sparse/numba_backend/_coo/core.py
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|