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724,701 | scipy.sparse._compressed | maximum | Element-wise maximum between this and another array/matrix. | def maximum(self, other):
return self._maximum_minimum(other, np.maximum,
'_maximum_', lambda x: np.asarray(x) > 0)
| (self, other) | [
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] |
724,702 | scipy.sparse._base | mean |
Compute the arithmetic mean along the specified axis.
Returns the average of the array/matrix elements. The average is taken
over all elements in the array/matrix by default, otherwise over the
specified axis. `float64` intermediate and return values are used
for integer inputs.
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the mean is computed. The default is to compute
the mean of all elements in the array/matrix (i.e., `axis` = `None`).
dtype : data-type, optional
Type to use in computing the mean. For integer inputs, the default
is `float64`; for floating point inputs, it is the same as the
input dtype.
.. versionadded:: 0.18.0
out : np.matrix, optional
Alternative output matrix in which to place the result. It must
have the same shape as the expected output, but the type of the
output values will be cast if necessary.
.. versionadded:: 0.18.0
Returns
-------
m : np.matrix
See Also
--------
numpy.matrix.mean : NumPy's implementation of 'mean' for matrices
| def mean(self, axis=None, dtype=None, out=None):
"""
Compute the arithmetic mean along the specified axis.
Returns the average of the array/matrix elements. The average is taken
over all elements in the array/matrix by default, otherwise over the
specified axis. `float64` intermediate and return values are used
for integer inputs.
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the mean is computed. The default is to compute
the mean of all elements in the array/matrix (i.e., `axis` = `None`).
dtype : data-type, optional
Type to use in computing the mean. For integer inputs, the default
is `float64`; for floating point inputs, it is the same as the
input dtype.
.. versionadded:: 0.18.0
out : np.matrix, optional
Alternative output matrix in which to place the result. It must
have the same shape as the expected output, but the type of the
output values will be cast if necessary.
.. versionadded:: 0.18.0
Returns
-------
m : np.matrix
See Also
--------
numpy.matrix.mean : NumPy's implementation of 'mean' for matrices
"""
validateaxis(axis)
res_dtype = self.dtype.type
integral = (np.issubdtype(self.dtype, np.integer) or
np.issubdtype(self.dtype, np.bool_))
# output dtype
if dtype is None:
if integral:
res_dtype = np.float64
else:
res_dtype = np.dtype(dtype).type
# intermediate dtype for summation
inter_dtype = np.float64 if integral else res_dtype
inter_self = self.astype(inter_dtype)
if self.ndim == 1:
if axis not in (None, -1, 0):
raise ValueError("axis must be None, -1 or 0")
res = inter_self / self.shape[-1]
return res.sum(dtype=res_dtype, out=out)
if axis is None:
return (inter_self / (self.shape[0] * self.shape[1]))\
.sum(dtype=res_dtype, out=out)
if axis < 0:
axis += 2
# axis = 0 or 1 now
if axis == 0:
return (inter_self * (1.0 / self.shape[0])).sum(
axis=0, dtype=res_dtype, out=out)
else:
return (inter_self * (1.0 / self.shape[1])).sum(
axis=1, dtype=res_dtype, out=out)
| (self, axis=None, dtype=None, out=None) | [
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724,703 | scipy.sparse._data | min |
Return the minimum of the array/matrix or maximum along an axis.
This takes all elements into account, not just the non-zero ones.
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the sum is computed. The default is to
compute the minimum over all 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.
Returns
-------
amin : coo_matrix or scalar
Minimum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is a sparse.coo_matrix of dimension
``a.ndim - 1``.
See Also
--------
max : The maximum value of a sparse array/matrix along a given axis.
numpy.matrix.min : NumPy's implementation of 'min' for matrices
| def min(self, axis=None, out=None):
"""
Return the minimum of the array/matrix or maximum along an axis.
This takes all elements into account, not just the non-zero ones.
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the sum is computed. The default is to
compute the minimum over all 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.
Returns
-------
amin : coo_matrix or scalar
Minimum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is a sparse.coo_matrix of dimension
``a.ndim - 1``.
See Also
--------
max : The maximum value of a sparse array/matrix along a given axis.
numpy.matrix.min : NumPy's implementation of 'min' for matrices
"""
return self._min_or_max(axis, out, np.minimum)
| (self, axis=None, out=None) | [
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724,704 | scipy.sparse._compressed | minimum | Element-wise minimum between this and another array/matrix. | def minimum(self, other):
return self._maximum_minimum(other, np.minimum,
'_minimum_', lambda x: np.asarray(x) < 0)
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] |
724,705 | scipy.sparse._compressed | multiply | Point-wise multiplication by another array/matrix, vector, or
scalar.
| def multiply(self, other):
"""Point-wise multiplication by another array/matrix, vector, or
scalar.
"""
# Scalar multiplication.
if isscalarlike(other):
return self._mul_scalar(other)
# Sparse matrix or vector.
if issparse(other):
if self.shape == other.shape:
other = self.__class__(other)
return self._binopt(other, '_elmul_')
if other.ndim == 1:
raise TypeError("broadcast from a 1d array not yet supported")
# Single element.
elif other.shape == (1, 1):
return self._mul_scalar(other.toarray()[0, 0])
elif self.shape == (1, 1):
return other._mul_scalar(self.toarray()[0, 0])
# A row times a column.
elif self.shape[1] == 1 and other.shape[0] == 1:
return self._matmul_sparse(other.tocsc())
elif self.shape[0] == 1 and other.shape[1] == 1:
return other._matmul_sparse(self.tocsc())
# Row vector times matrix. other is a row.
elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
other = self._dia_container(
(other.toarray().ravel(), [0]),
shape=(other.shape[1], other.shape[1])
)
return self._matmul_sparse(other)
# self is a row.
elif self.shape[0] == 1 and self.shape[1] == other.shape[1]:
copy = self._dia_container(
(self.toarray().ravel(), [0]),
shape=(self.shape[1], self.shape[1])
)
return other._matmul_sparse(copy)
# Column vector times matrix. other is a column.
elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
other = self._dia_container(
(other.toarray().ravel(), [0]),
shape=(other.shape[0], other.shape[0])
)
return other._matmul_sparse(self)
# self is a column.
elif self.shape[1] == 1 and self.shape[0] == other.shape[0]:
copy = self._dia_container(
(self.toarray().ravel(), [0]),
shape=(self.shape[0], self.shape[0])
)
return copy._matmul_sparse(other)
else:
raise ValueError("inconsistent shapes")
# Assume other is a dense matrix/array, which produces a single-item
# object array if other isn't convertible to ndarray.
other = np.atleast_2d(other)
if other.ndim != 2:
return np.multiply(self.toarray(), other)
# Single element / wrapped object.
if other.size == 1:
if other.dtype == np.object_:
# 'other' not convertible to ndarray.
return NotImplemented
return self._mul_scalar(other.flat[0])
# Fast case for trivial sparse matrix.
elif self.shape == (1, 1):
return np.multiply(self.toarray()[0, 0], other)
ret = self.tocoo()
# Matching shapes.
if self.shape == other.shape:
data = np.multiply(ret.data, other[ret.row, ret.col])
# Sparse row vector times...
elif self.shape[0] == 1:
if other.shape[1] == 1: # Dense column vector.
data = np.multiply(ret.data, other)
elif other.shape[1] == self.shape[1]: # Dense matrix.
data = np.multiply(ret.data, other[:, ret.col])
else:
raise ValueError("inconsistent shapes")
row = np.repeat(np.arange(other.shape[0]), len(ret.row))
col = np.tile(ret.col, other.shape[0])
return self._coo_container(
(data.view(np.ndarray).ravel(), (row, col)),
shape=(other.shape[0], self.shape[1]),
copy=False
)
# Sparse column vector times...
elif self.shape[1] == 1:
if other.shape[0] == 1: # Dense row vector.
data = np.multiply(ret.data[:, None], other)
elif other.shape[0] == self.shape[0]: # Dense matrix.
data = np.multiply(ret.data[:, None], other[ret.row])
else:
raise ValueError("inconsistent shapes")
row = np.repeat(ret.row, other.shape[1])
col = np.tile(np.arange(other.shape[1]), len(ret.col))
return self._coo_container(
(data.view(np.ndarray).ravel(), (row, col)),
shape=(self.shape[0], other.shape[1]),
copy=False
)
# Sparse matrix times dense row vector.
elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
data = np.multiply(ret.data, other[:, ret.col].ravel())
# Sparse matrix times dense column vector.
elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
data = np.multiply(ret.data, other[ret.row].ravel())
else:
raise ValueError("inconsistent shapes")
ret.data = data.view(np.ndarray).ravel()
return ret
| (self, other) | [
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724,706 | scipy.sparse._data | nanmax |
Return the maximum of the array/matrix or maximum along an axis, ignoring any
NaNs. This takes all elements into account, not just the non-zero
ones.
.. versionadded:: 1.11.0
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the maximum is computed. The default is to
compute the maximum over all 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.
Returns
-------
amax : coo_matrix or scalar
Maximum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is a sparse.coo_matrix of dimension
``a.ndim - 1``.
See Also
--------
nanmin : The minimum value of a sparse array/matrix along a given axis,
ignoring NaNs.
max : The maximum value of a sparse array/matrix along a given axis,
propagating NaNs.
numpy.nanmax : NumPy's implementation of 'nanmax'.
| def nanmax(self, axis=None, out=None):
"""
Return the maximum of the array/matrix or maximum along an axis, ignoring any
NaNs. This takes all elements into account, not just the non-zero
ones.
.. versionadded:: 1.11.0
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the maximum is computed. The default is to
compute the maximum over all 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.
Returns
-------
amax : coo_matrix or scalar
Maximum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is a sparse.coo_matrix of dimension
``a.ndim - 1``.
See Also
--------
nanmin : The minimum value of a sparse array/matrix along a given axis,
ignoring NaNs.
max : The maximum value of a sparse array/matrix along a given axis,
propagating NaNs.
numpy.nanmax : NumPy's implementation of 'nanmax'.
"""
return self._min_or_max(axis, out, np.fmax)
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724,707 | scipy.sparse._data | nanmin |
Return the minimum of the array/matrix or minimum along an axis, ignoring any
NaNs. This takes all elements into account, not just the non-zero
ones.
.. versionadded:: 1.11.0
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the minimum is computed. The default is to
compute the minimum over all 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.
Returns
-------
amin : coo_matrix or scalar
Minimum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is a sparse.coo_matrix of dimension
``a.ndim - 1``.
See Also
--------
nanmax : The maximum value of a sparse array/matrix along a given axis,
ignoring NaNs.
min : The minimum value of a sparse array/matrix along a given axis,
propagating NaNs.
numpy.nanmin : NumPy's implementation of 'nanmin'.
| def nanmin(self, axis=None, out=None):
"""
Return the minimum of the array/matrix or minimum along an axis, ignoring any
NaNs. This takes all elements into account, not just the non-zero
ones.
.. versionadded:: 1.11.0
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the minimum is computed. The default is to
compute the minimum over all 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.
Returns
-------
amin : coo_matrix or scalar
Minimum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is a sparse.coo_matrix of dimension
``a.ndim - 1``.
See Also
--------
nanmax : The maximum value of a sparse array/matrix along a given axis,
ignoring NaNs.
min : The minimum value of a sparse array/matrix along a given axis,
propagating NaNs.
numpy.nanmin : NumPy's implementation of 'nanmin'.
"""
return self._min_or_max(axis, out, np.fmin)
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] |
724,708 | scipy.sparse._csc | nonzero | Nonzero indices of the array/matrix.
Returns a tuple of arrays (row,col) containing the indices
of the non-zero elements of the array.
Examples
--------
>>> from scipy.sparse import csr_array
>>> A = csr_array([[1,2,0],[0,0,3],[4,0,5]])
>>> A.nonzero()
(array([0, 0, 1, 2, 2]), array([0, 1, 2, 0, 2]))
| def nonzero(self):
# CSC can't use _cs_matrix's .nonzero method because it
# returns the indices sorted for self transposed.
# Get row and col indices, from _cs_matrix.tocoo
major_dim, minor_dim = self._swap(self.shape)
minor_indices = self.indices
major_indices = np.empty(len(minor_indices), dtype=self.indices.dtype)
expandptr(major_dim, self.indptr, major_indices)
row, col = self._swap((major_indices, minor_indices))
# Remove explicit zeros
nz_mask = self.data != 0
row = row[nz_mask]
col = col[nz_mask]
# Sort them to be in C-style order
ind = np.argsort(row, kind='mergesort')
row = row[ind]
col = col[ind]
return row, col
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] |
724,709 | scipy.sparse._data | power |
This function performs element-wise power.
Parameters
----------
n : scalar
n is a non-zero scalar (nonzero avoids dense ones creation)
If zero power is desired, special case it to use `np.ones`
dtype : If dtype is not specified, the current dtype will be preserved.
Raises
------
NotImplementedError : if n is a zero scalar
If zero power is desired, special case it to use
`np.ones(A.shape, dtype=A.dtype)`
| def power(self, n, dtype=None):
"""
This function performs element-wise power.
Parameters
----------
n : scalar
n is a non-zero scalar (nonzero avoids dense ones creation)
If zero power is desired, special case it to use `np.ones`
dtype : If dtype is not specified, the current dtype will be preserved.
Raises
------
NotImplementedError : if n is a zero scalar
If zero power is desired, special case it to use
`np.ones(A.shape, dtype=A.dtype)`
"""
if not isscalarlike(n):
raise NotImplementedError("input is not scalar")
if not n:
raise NotImplementedError(
"zero power is not supported as it would densify the matrix.\n"
"Use `np.ones(A.shape, dtype=A.dtype)` for this case."
)
data = self._deduped_data()
if dtype is not None:
data = data.astype(dtype)
return self._with_data(data ** n)
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724,710 | scipy.sparse._compressed | prune | Remove empty space after all non-zero elements.
| def prune(self):
"""Remove empty space after all non-zero elements.
"""
major_dim = self._swap(self.shape)[0]
if len(self.indptr) != major_dim + 1:
raise ValueError('index pointer has invalid length')
if len(self.indices) < self.nnz:
raise ValueError('indices array has fewer than nnz elements')
if len(self.data) < self.nnz:
raise ValueError('data array has fewer than nnz elements')
self.indices = _prune_array(self.indices[:self.nnz])
self.data = _prune_array(self.data[:self.nnz])
| (self) | [
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724,711 | scipy.sparse._data | rad2deg | Element-wise rad2deg.
See `numpy.rad2deg` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
| (self) | [
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724,712 | scipy.sparse._base | reshape | reshape(self, shape, order='C', copy=False)
Gives a new shape to a sparse array/matrix without changing its data.
Parameters
----------
shape : length-2 tuple of ints
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; e.g., read entire first
row, then second row, etc. 'F' means to read and write the elements
using Fortran-like index order; e.g., read entire first column, then
second column, etc.
copy : bool, optional
Indicates whether or not attributes of self should be copied
whenever possible. The degree to which attributes are copied varies
depending on the type of sparse array being used.
Returns
-------
reshaped : sparse array/matrix
A sparse array/matrix with the given `shape`, not necessarily of the same
format as the current object.
See Also
--------
numpy.reshape : NumPy's implementation of 'reshape' for ndarrays
| def reshape(self, *args, **kwargs):
"""reshape(self, shape, order='C', copy=False)
Gives a new shape to a sparse array/matrix without changing its data.
Parameters
----------
shape : length-2 tuple of ints
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; e.g., read entire first
row, then second row, etc. 'F' means to read and write the elements
using Fortran-like index order; e.g., read entire first column, then
second column, etc.
copy : bool, optional
Indicates whether or not attributes of self should be copied
whenever possible. The degree to which attributes are copied varies
depending on the type of sparse array being used.
Returns
-------
reshaped : sparse array/matrix
A sparse array/matrix with the given `shape`, not necessarily of the same
format as the current object.
See Also
--------
numpy.reshape : NumPy's implementation of 'reshape' for ndarrays
"""
# If the shape already matches, don't bother doing an actual reshape
# Otherwise, the default is to convert to COO and use its reshape
is_array = isinstance(self, sparray)
shape = check_shape(args, self.shape, allow_1d=is_array)
order, copy = check_reshape_kwargs(kwargs)
if shape == self.shape:
if copy:
return self.copy()
else:
return self
return self.tocoo(copy=copy).reshape(shape, order=order, copy=False)
| (self, *args, **kwargs) | [
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724,713 | scipy.sparse._compressed | resize | Resize the array/matrix in-place to dimensions given by ``shape``
Any elements that lie within the new shape will remain at the same
indices, while non-zero elements lying outside the new shape are
removed.
Parameters
----------
shape : (int, int)
number of rows and columns in the new array/matrix
Notes
-----
The semantics are not identical to `numpy.ndarray.resize` or
`numpy.resize`. Here, the same data will be maintained at each index
before and after reshape, if that index is within the new bounds. In
numpy, resizing maintains contiguity of the array, moving elements
around in the logical array but not within a flattened representation.
We give no guarantees about whether the underlying data attributes
(arrays, etc.) will be modified in place or replaced with new objects.
| def resize(self, *shape):
shape = check_shape(shape)
if hasattr(self, 'blocksize'):
bm, bn = self.blocksize
new_M, rm = divmod(shape[0], bm)
new_N, rn = divmod(shape[1], bn)
if rm or rn:
raise ValueError("shape must be divisible into {} blocks. "
"Got {}".format(self.blocksize, shape))
M, N = self.shape[0] // bm, self.shape[1] // bn
else:
new_M, new_N = self._swap(shape)
M, N = self._swap(self.shape)
if new_M < M:
self.indices = self.indices[:self.indptr[new_M]]
self.data = self.data[:self.indptr[new_M]]
self.indptr = self.indptr[:new_M + 1]
elif new_M > M:
self.indptr = np.resize(self.indptr, new_M + 1)
self.indptr[M + 1:].fill(self.indptr[M])
if new_N < N:
mask = self.indices < new_N
if not np.all(mask):
self.indices = self.indices[mask]
self.data = self.data[mask]
major_index, val = self._minor_reduce(np.add, mask)
self.indptr.fill(0)
self.indptr[1:][major_index] = val
np.cumsum(self.indptr, out=self.indptr)
self._shape = shape
| (self, *shape) | [
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724,714 | scipy.sparse._data | rint | Element-wise rint.
See `numpy.rint` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
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724,715 | scipy.sparse._matrix | set_shape | Set the shape of the matrix in-place | def set_shape(self, shape):
"""Set the shape of the matrix in-place"""
# Make sure copy is False since this is in place
# Make sure format is unchanged because we are doing a __dict__ swap
new_self = self.reshape(shape, copy=False).asformat(self.format)
self.__dict__ = new_self.__dict__
| (self, shape) | [
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724,716 | scipy.sparse._base | setdiag |
Set diagonal or off-diagonal elements of the array/matrix.
Parameters
----------
values : array_like
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 are
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 off-diagonal to set, corresponding to elements a[i,i+k].
Default: 0 (the main diagonal).
| def setdiag(self, values, k=0):
"""
Set diagonal or off-diagonal elements of the array/matrix.
Parameters
----------
values : array_like
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 are
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 off-diagonal to set, corresponding to elements a[i,i+k].
Default: 0 (the main diagonal).
"""
M, N = self.shape
if (k > 0 and k >= N) or (k < 0 and -k >= M):
raise ValueError("k exceeds array dimensions")
self._setdiag(np.asarray(values), k)
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724,717 | scipy.sparse._data | sign | Element-wise sign.
See `numpy.sign` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
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724,718 | scipy.sparse._data | sin | Element-wise sin.
See `numpy.sin` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
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724,719 | scipy.sparse._data | sinh | Element-wise sinh.
See `numpy.sinh` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
| (self) | [
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] |
724,720 | scipy.sparse._compressed | sort_indices | Sort the indices of this array/matrix *in place*
| def sort_indices(self):
"""Sort the indices of this array/matrix *in place*
"""
if not self.has_sorted_indices:
_sparsetools.csr_sort_indices(len(self.indptr) - 1, self.indptr,
self.indices, self.data)
self.has_sorted_indices = True
| (self) | [
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724,721 | scipy.sparse._compressed | sorted_indices | Return a copy of this array/matrix with sorted indices
| def sorted_indices(self):
"""Return a copy of this array/matrix with sorted indices
"""
A = self.copy()
A.sort_indices()
return A
# an alternative that has linear complexity is the following
# although the previous option is typically faster
# return self.toother().toother()
| (self) | [
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724,722 | scipy.sparse._data | sqrt | Element-wise sqrt.
See `numpy.sqrt` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
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724,723 | scipy.sparse._compressed | sum |
Sum the array/matrix elements over a given axis.
Parameters
----------
axis : {-2, -1, 0, 1, None} optional
Axis along which the sum is computed. The default is to
compute the sum of all the array/matrix elements, returning a scalar
(i.e., `axis` = `None`).
dtype : dtype, optional
The type of the returned array/matrix and of the accumulator in which
the elements are summed. The dtype of `a` is used by default
unless `a` has an integer dtype of less precision than the default
platform integer. In that case, if `a` is signed then the platform
integer is used while if `a` is unsigned then an unsigned integer
of the same precision as the platform integer is used.
.. versionadded:: 0.18.0
out : np.matrix, optional
Alternative output matrix in which to place the result. It must
have the same shape as the expected output, but the type of the
output values will be cast if necessary.
.. versionadded:: 0.18.0
Returns
-------
sum_along_axis : np.matrix
A matrix with the same shape as `self`, with the specified
axis removed.
See Also
--------
numpy.matrix.sum : NumPy's implementation of 'sum' for matrices
| def sum(self, axis=None, dtype=None, out=None):
"""Sum the array/matrix over the given axis. If the axis is None, sum
over both rows and columns, returning a scalar.
"""
# The _spbase base class already does axis=0 and axis=1 efficiently
# so we only do the case axis=None here
if (not hasattr(self, 'blocksize') and
axis in self._swap(((1, -1), (0, 2)))[0]):
# faster than multiplication for large minor axis in CSC/CSR
res_dtype = get_sum_dtype(self.dtype)
ret = np.zeros(len(self.indptr) - 1, dtype=res_dtype)
major_index, value = self._minor_reduce(np.add)
ret[major_index] = value
ret = self._ascontainer(ret)
if axis % 2 == 1:
ret = ret.T
if out is not None and out.shape != ret.shape:
raise ValueError('dimensions do not match')
return ret.sum(axis=(), dtype=dtype, out=out)
# _spbase will handle the remaining situations when axis
# is in {None, -1, 0, 1}
else:
return _spbase.sum(self, axis=axis, dtype=dtype, out=out)
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724,724 | scipy.sparse._compressed | sum_duplicates | Eliminate duplicate entries by adding them together
This is an *in place* operation.
| def sum_duplicates(self):
"""Eliminate duplicate entries by adding them together
This is an *in place* operation.
"""
if self.has_canonical_format:
return
self.sort_indices()
M, N = self._swap(self.shape)
_sparsetools.csr_sum_duplicates(M, N, self.indptr, self.indices,
self.data)
self.prune() # nnz may have changed
self.has_canonical_format = True
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724,725 | scipy.sparse._data | tan | Element-wise tan.
See `numpy.tan` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
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724,726 | scipy.sparse._data | tanh | Element-wise tanh.
See `numpy.tanh` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
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] |
724,727 | scipy.sparse._compressed | toarray |
Return a dense ndarray representation of this sparse array/matrix.
Parameters
----------
order : {'C', 'F'}, optional
Whether to store multidimensional data in C (row-major)
or Fortran (column-major) order in memory. The default
is 'None', which provides no ordering guarantees.
Cannot be specified in conjunction with the `out`
argument.
out : ndarray, 2-D, optional
If specified, uses this array as the output buffer
instead of allocating a new array to return. The provided
array must have the same shape and dtype as the sparse
array/matrix on which you are calling the method. For most
sparse types, `out` is required to be memory contiguous
(either C or Fortran ordered).
Returns
-------
arr : ndarray, 2-D
An array with the same shape and containing the same
data represented by the sparse array/matrix, with the requested
memory order. If `out` was passed, the same object is
returned after being modified in-place to contain the
appropriate values.
| def toarray(self, order=None, out=None):
if out is None and order is None:
order = self._swap('cf')[0]
out = self._process_toarray_args(order, out)
if not (out.flags.c_contiguous or out.flags.f_contiguous):
raise ValueError('Output array must be C or F contiguous')
# align ideal order with output array order
if out.flags.c_contiguous:
x = self.tocsr()
y = out
else:
x = self.tocsc()
y = out.T
M, N = x._swap(x.shape)
csr_todense(M, N, x.indptr, x.indices, x.data, y)
return out
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724,728 | scipy.sparse._base | tobsr | Convert this array/matrix to Block Sparse Row format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant bsr_array/matrix.
When blocksize=(R, C) is provided, it will be used for construction of
the bsr_array/matrix.
| def tobsr(self, blocksize=None, copy=False):
"""Convert this array/matrix to Block Sparse Row format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant bsr_array/matrix.
When blocksize=(R, C) is provided, it will be used for construction of
the bsr_array/matrix.
"""
return self.tocsr(copy=False).tobsr(blocksize=blocksize, copy=copy)
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] |
724,729 | scipy.sparse._compressed | tocoo | Convert this array/matrix to COOrdinate format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant coo_array/matrix.
| def tocoo(self, copy=True):
major_dim, minor_dim = self._swap(self.shape)
minor_indices = self.indices
major_indices = np.empty(len(minor_indices), dtype=self.indices.dtype)
_sparsetools.expandptr(major_dim, self.indptr, major_indices)
coords = self._swap((major_indices, minor_indices))
return self._coo_container(
(self.data, coords), self.shape, copy=copy, dtype=self.dtype
)
| (self, copy=True) | [
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724,730 | scipy.sparse._csc | tocsc | Convert this array/matrix to Compressed Sparse Column format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant csc_array/matrix.
| def tocsc(self, copy=False):
if copy:
return self.copy()
else:
return self
| (self, copy=False) | [
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] |
724,731 | scipy.sparse._csc | tocsr | Convert this array/matrix to Compressed Sparse Row format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant csr_array/matrix.
| def tocsr(self, copy=False):
M,N = self.shape
idx_dtype = self._get_index_dtype((self.indptr, self.indices),
maxval=max(self.nnz, N))
indptr = np.empty(M + 1, dtype=idx_dtype)
indices = np.empty(self.nnz, dtype=idx_dtype)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csc_tocsr(M, N,
self.indptr.astype(idx_dtype),
self.indices.astype(idx_dtype),
self.data,
indptr,
indices,
data)
A = self._csr_container(
(data, indices, indptr),
shape=self.shape, copy=False
)
A.has_sorted_indices = True
return A
| (self, copy=False) | [
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724,732 | scipy.sparse._base | todense |
Return a dense representation of this sparse array/matrix.
Parameters
----------
order : {'C', 'F'}, optional
Whether to store multi-dimensional data in C (row-major)
or Fortran (column-major) order in memory. The default
is 'None', which provides no ordering guarantees.
Cannot be specified in conjunction with the `out`
argument.
out : ndarray, 2-D, optional
If specified, uses this array (or `numpy.matrix`) as the
output buffer instead of allocating a new array to
return. The provided array must have the same shape and
dtype as the sparse array/matrix on which you are calling the
method.
Returns
-------
arr : numpy.matrix, 2-D
A NumPy matrix object with the same shape and containing
the same data represented by the sparse array/matrix, with the
requested memory order. If `out` was passed and was an
array (rather than a `numpy.matrix`), it will be filled
with the appropriate values and returned wrapped in a
`numpy.matrix` object that shares the same memory.
| def todense(self, order=None, out=None):
"""
Return a dense representation of this sparse array/matrix.
Parameters
----------
order : {'C', 'F'}, optional
Whether to store multi-dimensional data in C (row-major)
or Fortran (column-major) order in memory. The default
is 'None', which provides no ordering guarantees.
Cannot be specified in conjunction with the `out`
argument.
out : ndarray, 2-D, optional
If specified, uses this array (or `numpy.matrix`) as the
output buffer instead of allocating a new array to
return. The provided array must have the same shape and
dtype as the sparse array/matrix on which you are calling the
method.
Returns
-------
arr : numpy.matrix, 2-D
A NumPy matrix object with the same shape and containing
the same data represented by the sparse array/matrix, with the
requested memory order. If `out` was passed and was an
array (rather than a `numpy.matrix`), it will be filled
with the appropriate values and returned wrapped in a
`numpy.matrix` object that shares the same memory.
"""
return self._ascontainer(self.toarray(order=order, out=out))
| (self, order=None, out=None) | [
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] |
724,733 | scipy.sparse._base | todia | Convert this array/matrix to sparse DIAgonal format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant dia_array/matrix.
| def todia(self, copy=False):
"""Convert this array/matrix to sparse DIAgonal format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant dia_array/matrix.
"""
return self.tocoo(copy=copy).todia(copy=False)
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] |
724,734 | scipy.sparse._base | todok | Convert this array/matrix to Dictionary Of Keys format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant dok_array/matrix.
| def todok(self, copy=False):
"""Convert this array/matrix to Dictionary Of Keys format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant dok_array/matrix.
"""
return self.tocoo(copy=copy).todok(copy=False)
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] |
724,735 | scipy.sparse._base | tolil | Convert this array/matrix to List of Lists format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant lil_array/matrix.
| def tolil(self, copy=False):
"""Convert this array/matrix to List of Lists format.
With copy=False, the data/indices may be shared between this array/matrix and
the resultant lil_array/matrix.
"""
return self.tocsr(copy=False).tolil(copy=copy)
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724,736 | scipy.sparse._base | trace | Returns the sum along diagonals of the sparse array/matrix.
Parameters
----------
offset : int, optional
Which diagonal to get, corresponding to elements a[i, i+offset].
Default: 0 (the main diagonal).
| def trace(self, offset=0):
"""Returns the sum along diagonals of the sparse array/matrix.
Parameters
----------
offset : int, optional
Which diagonal to get, corresponding to elements a[i, i+offset].
Default: 0 (the main diagonal).
"""
return self.diagonal(k=offset).sum()
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] |
724,737 | scipy.sparse._csc | transpose |
Reverses the dimensions of the sparse array/matrix.
Parameters
----------
axes : None, optional
This argument is in the signature *solely* for NumPy
compatibility reasons. Do not pass in anything except
for the default value.
copy : bool, optional
Indicates whether or not attributes of `self` should be
copied whenever possible. The degree to which attributes
are copied varies depending on the type of sparse array/matrix
being used.
Returns
-------
p : `self` with the dimensions reversed.
Notes
-----
If `self` is a `csr_array` or a `csc_array`, then this will return a
`csc_array` or a `csr_array`, respectively.
See Also
--------
numpy.transpose : NumPy's implementation of 'transpose' for ndarrays
| def transpose(self, axes=None, copy=False):
if axes is not None and axes != (1, 0):
raise ValueError("Sparse arrays/matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation.")
M, N = self.shape
return self._csr_container((self.data, self.indices,
self.indptr), (N, M), copy=copy)
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724,738 | scipy.sparse._data | trunc | Element-wise trunc.
See `numpy.trunc` for more information. | def _create_method(op):
def method(self):
result = op(self._deduped_data())
return self._with_data(result, copy=True)
method.__doc__ = (f"Element-wise {name}.\n\n"
f"See `numpy.{name}` for more information.")
method.__name__ = name
return method
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] |
724,739 | markov_clustering.modularity | delta_matrix |
Compute delta matrix where delta[i,j]=1 if i and j belong
to same cluster and i!=j
:param matrix: The adjacency matrix
:param clusters: The clusters returned by get_clusters
:returns: delta matrix
| def delta_matrix(matrix, clusters):
"""
Compute delta matrix where delta[i,j]=1 if i and j belong
to same cluster and i!=j
:param matrix: The adjacency matrix
:param clusters: The clusters returned by get_clusters
:returns: delta matrix
"""
if isspmatrix(matrix):
delta = dok_matrix(matrix.shape)
else:
delta = np.zeros(matrix.shape)
for i in clusters :
for j in permutations(i, 2):
delta[j] = 1
return delta
| (matrix, clusters) | [
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0.0043686931021511555,
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724,740 | scipy.sparse._dok | dok_matrix |
Dictionary Of Keys based sparse matrix.
This is an efficient structure for constructing sparse
matrices incrementally.
This can be instantiated in several ways:
dok_matrix(D)
where D is a 2-D ndarray
dok_matrix(S)
with another sparse array or matrix S (equivalent to S.todok())
dok_matrix((M,N), [dtype])
create the matrix with initial shape (M,N)
dtype is optional, defaulting to dtype='d'
Attributes
----------
dtype : dtype
Data type of the matrix
shape : 2-tuple
Shape of the matrix
ndim : int
Number of dimensions (this is always 2)
nnz
Number of nonzero elements
size
T
Notes
-----
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
- Allows for efficient O(1) access of individual elements.
- Duplicates are not allowed.
- Can be efficiently converted to a coo_matrix once constructed.
Examples
--------
>>> import numpy as np
>>> from scipy.sparse import dok_matrix
>>> S = dok_matrix((5, 5), dtype=np.float32)
>>> for i in range(5):
... for j in range(5):
... S[i, j] = i + j # Update element
| class dok_matrix(spmatrix, _dok_base):
"""
Dictionary Of Keys based sparse matrix.
This is an efficient structure for constructing sparse
matrices incrementally.
This can be instantiated in several ways:
dok_matrix(D)
where D is a 2-D ndarray
dok_matrix(S)
with another sparse array or matrix S (equivalent to S.todok())
dok_matrix((M,N), [dtype])
create the matrix with initial shape (M,N)
dtype is optional, defaulting to dtype='d'
Attributes
----------
dtype : dtype
Data type of the matrix
shape : 2-tuple
Shape of the matrix
ndim : int
Number of dimensions (this is always 2)
nnz
Number of nonzero elements
size
T
Notes
-----
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
- Allows for efficient O(1) access of individual elements.
- Duplicates are not allowed.
- Can be efficiently converted to a coo_matrix once constructed.
Examples
--------
>>> import numpy as np
>>> from scipy.sparse import dok_matrix
>>> S = dok_matrix((5, 5), dtype=np.float32)
>>> for i in range(5):
... for j in range(5):
... S[i, j] = i + j # Update element
"""
def set_shape(self, shape):
new_matrix = self.reshape(shape, copy=False).asformat(self.format)
self.__dict__ = new_matrix.__dict__
def get_shape(self):
"""Get shape of a sparse matrix."""
return self._shape
shape = property(fget=get_shape, fset=set_shape)
def __reversed__(self):
return self._dict.__reversed__()
def __or__(self, other):
if isinstance(other, _dok_base):
return self._dict | other._dict
return self._dict | other
def __ror__(self, other):
if isinstance(other, _dok_base):
return self._dict | other._dict
return self._dict | other
def __ior__(self, other):
if isinstance(other, _dok_base):
self._dict |= other._dict
else:
self._dict |= other
return self
| (arg1, shape=None, dtype=None, copy=False) | [
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724,741 | scipy.sparse._base | __abs__ | null | def __abs__(self):
return abs(self.tocsr())
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] |
724,742 | scipy.sparse._dok | __add__ | null | def __add__(self, other):
if isscalarlike(other):
res_dtype = upcast_scalar(self.dtype, other)
new = self._dok_container(self.shape, dtype=res_dtype)
# Add this scalar to each element.
for key in itertools.product(*[range(d) for d in self.shape]):
aij = self._dict.get(key, 0) + other
if aij:
new[key] = aij
elif issparse(other):
if other.shape != self.shape:
raise ValueError("Matrix dimensions are not equal.")
res_dtype = upcast(self.dtype, other.dtype)
new = self._dok_container(self.shape, dtype=res_dtype)
new._dict = self._dict.copy()
if other.format == "dok":
o_items = other.items()
else:
other = other.tocoo()
if self.ndim == 1:
o_items = zip(other.coords[0], other.data)
else:
o_items = zip(zip(*other.coords), other.data)
with np.errstate(over='ignore'):
new._dict.update((k, new[k] + v) for k, v in o_items)
elif isdense(other):
new = self.todense() + other
else:
return NotImplemented
return new
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724,744 | scipy.sparse._dok | __contains__ | null | def __contains__(self, key):
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724,745 | scipy.sparse._dok | __delitem__ | null | def __delitem__(self, key, /):
del self._dict[key]
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724,747 | scipy.sparse._base | __eq__ | null | def __eq__(self, other):
return self.tocsr().__eq__(other)
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724,748 | scipy.sparse._base | __ge__ | null | def __ge__(self, other):
return self.tocsr().__ge__(other)
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724,749 | scipy.sparse._dok | __getitem__ | null | def __getitem__(self, key):
if self.ndim == 2:
return super().__getitem__(key)
if isinstance(key, tuple) and len(key) == 1:
key = key[0]
INT_TYPES = (int, np.integer)
if isinstance(key, INT_TYPES):
if key < 0:
key += self.shape[-1]
if key < 0 or key >= self.shape[-1]:
raise IndexError('index value out of bounds')
return self._get_int(key)
else:
raise IndexError('array/slice index for 1d dok_array not yet supported')
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724,750 | scipy.sparse._base | __gt__ | null | def __gt__(self, other):
return self.tocsr().__gt__(other)
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724,753 | scipy.sparse._dok | __imul__ | null | def __imul__(self, other):
if isscalarlike(other):
self._dict.update((k, v * other) for k, v in self.items())
return self
return NotImplemented
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724,754 | scipy.sparse._dok | __init__ | null | def __init__(self, arg1, shape=None, dtype=None, copy=False):
_spbase.__init__(self)
is_array = isinstance(self, sparray)
if isinstance(arg1, tuple) and isshape(arg1, allow_1d=is_array):
self._shape = check_shape(arg1, allow_1d=is_array)
self._dict = {}
self.dtype = getdtype(dtype, default=float)
elif issparse(arg1): # Sparse ctor
if arg1.format == self.format:
arg1 = arg1.copy() if copy else arg1
else:
arg1 = arg1.todok()
if dtype is not None:
arg1 = arg1.astype(dtype, copy=False)
self._dict = arg1._dict
self._shape = check_shape(arg1.shape, allow_1d=is_array)
self.dtype = arg1.dtype
else: # Dense ctor
try:
arg1 = np.asarray(arg1)
except Exception as e:
raise TypeError('Invalid input format.') from e
if arg1.ndim > 2:
raise TypeError('Expected rank <=2 dense array or matrix.')
if arg1.ndim == 1:
if dtype is not None:
arg1 = arg1.astype(dtype)
self._dict = {i: v for i, v in enumerate(arg1) if v != 0}
self.dtype = arg1.dtype
else:
d = self._coo_container(arg1, dtype=dtype).todok()
self._dict = d._dict
self.dtype = d.dtype
self._shape = check_shape(arg1.shape, allow_1d=is_array)
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724,755 | scipy.sparse._dok | __ior__ | null | def __ior__(self, other):
if isinstance(other, _dok_base):
self._dict |= other._dict
else:
self._dict |= other
return self
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724,757 | scipy.sparse._base | __iter__ | null | def __iter__(self):
for r in range(self.shape[0]):
yield self[r]
| (self) | [
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724,758 | scipy.sparse._dok | __itruediv__ | null | def __itruediv__(self, other):
if isscalarlike(other):
self._dict.update((k, v / other) for k, v in self.items())
return self
return NotImplemented
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724,759 | scipy.sparse._base | __le__ | null | def __le__(self, other):
return self.tocsr().__le__(other)
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724,761 | scipy.sparse._base | __lt__ | null | def __lt__(self, other):
return self.tocsr().__lt__(other)
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724,764 | scipy.sparse._base | __ne__ | null | def __ne__(self, other):
return self.tocsr().__ne__(other)
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] |
724,765 | scipy.sparse._dok | __neg__ | null | def __neg__(self):
if self.dtype.kind == 'b':
raise NotImplementedError(
'Negating a sparse boolean matrix is not supported.'
)
new = self._dok_container(self.shape, dtype=self.dtype)
new._dict.update((k, -v) for k, v in self.items())
return new
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724,767 | scipy.sparse._dok | __or__ | null | def __or__(self, other):
if isinstance(other, _dok_base):
return self._dict | other._dict
return self._dict | other
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724,769 | scipy.sparse._dok | __radd__ | null | def __radd__(self, other):
return self + other # addition is comutative
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724,771 | scipy.sparse._dok | __reduce__ | null | def __reduce__(self):
# this approach is necessary because __setstate__ is called after
# __setitem__ upon unpickling and since __init__ is not called there
# is no shape attribute hence it is not possible to unpickle it.
return dict.__reduce__(self)
| (self) | [
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724,773 | scipy.sparse._dok | __reversed__ | null | def __reversed__(self):
return self._dict.__reversed__()
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724,776 | scipy.sparse._dok | __ror__ | null | def __ror__(self, other):
if isinstance(other, _dok_base):
return self._dict | other._dict
return self._dict | other
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724,777 | scipy.sparse._base | __round__ | null | def __round__(self, ndigits=0):
return round(self.tocsr(), ndigits=ndigits)
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724,780 | scipy.sparse._dok | __setitem__ | null | def __setitem__(self, key, value):
if self.ndim == 2:
return super().__setitem__(key, value)
if isinstance(key, tuple) and len(key) == 1:
key = key[0]
INT_TYPES = (int, np.integer)
if isinstance(key, INT_TYPES):
if key < 0:
key += self.shape[-1]
if key < 0 or key >= self.shape[-1]:
raise IndexError('index value out of bounds')
return self._set_int(key, value)
else:
raise IndexError('array index for 1d dok_array not yet provided')
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724,783 | scipy.sparse._dok | __truediv__ | null | def __truediv__(self, other):
if isscalarlike(other):
res_dtype = upcast_scalar(self.dtype, other)
new = self._dok_container(self.shape, dtype=res_dtype)
new._dict.update(((k, v / other) for k, v in self.items()))
return new
return self.tocsr() / other
| (self, other) | [
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724,784 | scipy.sparse._base | _add_dense | null | def _add_dense(self, other):
return self.tocoo()._add_dense(other)
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724,785 | scipy.sparse._base | _add_sparse | null | def _add_sparse(self, other):
return self.tocsr()._add_sparse(other)
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724,789 | scipy.sparse._dok | _get_arrayXarray | null | def _get_arrayXarray(self, row, col):
# inner indexing
i, j = map(np.atleast_2d, np.broadcast_arrays(row, col))
newdok = self._dok_container(i.shape, dtype=self.dtype)
for key in itertools.product(range(i.shape[0]), range(i.shape[1])):
v = self._dict.get((i[key], j[key]), 0)
if v:
newdok._dict[key] = v
return newdok
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724,790 | scipy.sparse._dok | _get_arrayXint | null | def _get_arrayXint(self, row, col):
row = row.squeeze()
return self._get_columnXarray(row, [col])
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724,791 | scipy.sparse._dok | _get_arrayXslice | null | def _get_arrayXslice(self, row, col):
col = list(range(*col.indices(self.shape[1])))
return self._get_columnXarray(row, col)
| (self, row, col) | [
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724,792 | scipy.sparse._dok | _get_columnXarray | null | def _get_columnXarray(self, row, col):
# outer indexing
newdok = self._dok_container((len(row), len(col)), dtype=self.dtype)
for i, r in enumerate(row):
for j, c in enumerate(col):
v = self._dict.get((r, c), 0)
if v:
newdok._dict[i, j] = v
return newdok
| (self, row, col) | [
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724,794 | scipy.sparse._dok | _get_int | null | def _get_int(self, idx):
return self._dict.get(idx, self.dtype.type(0))
| (self, idx) | [
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724,795 | scipy.sparse._dok | _get_intXarray | null | def _get_intXarray(self, row, col):
col = col.squeeze()
return self._get_columnXarray([row], col)
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724,796 | scipy.sparse._dok | _get_intXint | null | def _get_intXint(self, row, col):
return self._dict.get((row, col), self.dtype.type(0))
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724,797 | scipy.sparse._dok | _get_intXslice | null | def _get_intXslice(self, row, col):
return self._get_sliceXslice(slice(row, row + 1), col)
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724,798 | scipy.sparse._dok | _get_sliceXarray | null | def _get_sliceXarray(self, row, col):
row = list(range(*row.indices(self.shape[0])))
return self._get_columnXarray(row, col)
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724,799 | scipy.sparse._dok | _get_sliceXint | null | def _get_sliceXint(self, row, col):
return self._get_sliceXslice(row, slice(col, col + 1))
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724,800 | scipy.sparse._dok | _get_sliceXslice | null | def _get_sliceXslice(self, row, col):
row_start, row_stop, row_step = row.indices(self.shape[0])
col_start, col_stop, col_step = col.indices(self.shape[1])
row_range = range(row_start, row_stop, row_step)
col_range = range(col_start, col_stop, col_step)
shape = (len(row_range), len(col_range))
# Switch paths only when advantageous
# (count the iterations in the loops, adjust for complexity)
if len(self) >= 2 * shape[0] * shape[1]:
# O(nr*nc) path: loop over <row x col>
return self._get_columnXarray(row_range, col_range)
# O(nnz) path: loop over entries of self
newdok = self._dok_container(shape, dtype=self.dtype)
for key in self.keys():
i, ri = divmod(int(key[0]) - row_start, row_step)
if ri != 0 or i < 0 or i >= shape[0]:
continue
j, rj = divmod(int(key[1]) - col_start, col_step)
if rj != 0 or j < 0 or j >= shape[1]:
continue
newdok._dict[i, j] = self._dict[key]
return newdok
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] |
724,801 | scipy.sparse._base | _getcol | Returns a copy of column j of the array, as an (m x 1) sparse
array (column vector).
| def _getcol(self, j):
"""Returns a copy of column j of the array, as an (m x 1) sparse
array (column vector).
"""
if self.ndim == 1:
raise ValueError("getcol not provided for 1d arrays. Use indexing A[j]")
# Subclasses should override this method for efficiency.
# Post-multiply by a (n x 1) column vector 'a' containing all zeros
# except for a_j = 1
N = self.shape[-1]
if j < 0:
j += N
if j < 0 or j >= N:
raise IndexError("index out of bounds")
col_selector = self._csc_container(([1], [[j], [0]]),
shape=(N, 1), dtype=self.dtype)
result = self @ col_selector
return result
| (self, j) | [
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] |
724,803 | scipy.sparse._dok | _getnnz | Number of stored values, including explicit zeros.
Parameters
----------
axis : None, 0, or 1
Select between the number of values across the whole array, in
each column, or in each row.
See also
--------
count_nonzero : Number of non-zero entries
| def _getnnz(self, axis=None):
if axis is not None:
raise NotImplementedError(
"_getnnz over an axis is not implemented for DOK format."
)
return len(self._dict)
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724,804 | scipy.sparse._base | _getrow | Returns a copy of row i of the array, as a (1 x n) sparse
array (row vector).
| def _getrow(self, i):
"""Returns a copy of row i of the array, as a (1 x n) sparse
array (row vector).
"""
if self.ndim == 1:
raise ValueError("getrow not meaningful for a 1d array")
# Subclasses should override this method for efficiency.
# Pre-multiply by a (1 x m) row vector 'a' containing all zeros
# except for a_i = 1
M = self.shape[0]
if i < 0:
i += M
if i < 0 or i >= M:
raise IndexError("index out of bounds")
row_selector = self._csr_container(([1], [[0], [i]]),
shape=(1, M), dtype=self.dtype)
return row_selector @ self
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724,805 | scipy.sparse._base | _imag | null | def _imag(self):
return self.tocsr()._imag()
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724,807 | scipy.sparse._dok | _matmul_multivector | null | def _matmul_multivector(self, other):
result_dtype = upcast(self.dtype, other.dtype)
# vector @ multivector
if self.ndim == 1:
# works for other 1d or 2d
return sum(v * other[j] for j, v in self._dict.items())
# matrix @ multivector
M = self.shape[0]
new_shape = (M,) if other.ndim == 1 else (M, other.shape[1])
result = np.zeros(new_shape, dtype=result_dtype)
for (i, j), v in self.items():
result[i] += v * other[j]
return result
| (self, other) | [
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724,808 | scipy.sparse._base | _matmul_sparse | null | def _matmul_sparse(self, other):
return self.tocsr()._matmul_sparse(other)
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724,809 | scipy.sparse._dok | _matmul_vector | null | def _matmul_vector(self, other):
res_dtype = upcast(self.dtype, other.dtype)
# vector @ vector
if self.ndim == 1:
if issparse(other):
if other.format == "dok":
keys = self.keys() & other.keys()
else:
keys = self.keys() & other.tocoo().coords[0]
return res_dtype(sum(self._dict[k] * other._dict[k] for k in keys))
elif isdense(other):
return res_dtype(sum(other[k] * v for k, v in self.items()))
else:
return NotImplemented
# matrix @ vector
result = np.zeros(self.shape[0], dtype=res_dtype)
for (i, j), v in self.items():
result[i] += v * other[j]
return result
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724,810 | scipy.sparse._dok | _mul_scalar | null | def _mul_scalar(self, other):
res_dtype = upcast_scalar(self.dtype, other)
# Multiply this scalar by every element.
new = self._dok_container(self.shape, dtype=res_dtype)
new._dict.update(((k, v * other) for k, v in self.items()))
return new
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724,813 | scipy.sparse._base | _real | null | def _real(self):
return self.tocsr()._real()
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724,816 | scipy.sparse._dok | _set_arrayXarray | null | def _set_arrayXarray(self, row, col, x):
row = list(map(int, row.ravel()))
col = list(map(int, col.ravel()))
x = x.ravel()
self._dict.update(zip(zip(row, col), x))
for i in np.nonzero(x == 0)[0]:
key = (row[i], col[i])
if self._dict[key] == 0:
# may have been superseded by later update
del self._dict[key]
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724,817 | scipy.sparse._index | _set_arrayXarray_sparse | null | def _set_arrayXarray_sparse(self, row, col, x):
# Fall back to densifying x
x = np.asarray(x.toarray(), dtype=self.dtype)
x, _ = _broadcast_arrays(x, row)
self._set_arrayXarray(row, col, x)
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724,818 | scipy.sparse._dok | _set_int | null | def _set_int(self, idx, x):
if x:
self._dict[idx] = x
elif idx in self._dict:
del self._dict[idx]
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724,819 | scipy.sparse._dok | _set_intXint | null | def _set_intXint(self, row, col, x):
key = (row, col)
if x:
self._dict[key] = x
elif key in self._dict:
del self._dict[key]
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] |
724,820 | scipy.sparse._base | _setdiag | This part of the implementation gets overridden by the
different formats.
| def _setdiag(self, values, k):
"""This part of the implementation gets overridden by the
different formats.
"""
M, N = self.shape
if k < 0:
if values.ndim == 0:
# broadcast
max_index = min(M+k, N)
for i in range(max_index):
self[i - k, i] = values
else:
max_index = min(M+k, N, len(values))
if max_index <= 0:
return
for i, v in enumerate(values[:max_index]):
self[i - k, i] = v
else:
if values.ndim == 0:
# broadcast
max_index = min(M, N-k)
for i in range(max_index):
self[i, i + k] = values
else:
max_index = min(M, N-k, len(values))
if max_index <= 0:
return
for i, v in enumerate(values[:max_index]):
self[i, i + k] = v
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724,822 | scipy.sparse._base | _sub_sparse | null | def _sub_sparse(self, other):
return self.tocsr()._sub_sparse(other)
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724,826 | scipy.sparse._dok | astype | null | def astype(self, dtype, casting='unsafe', copy=True):
dtype = np.dtype(dtype)
if self.dtype != dtype:
result = self._dok_container(self.shape, dtype=dtype)
data = np.array(list(self._dict.values()), dtype=dtype)
result._dict = dict(zip(self._dict, data))
return result
elif copy:
return self.copy()
return self
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724,827 | scipy.sparse._dok | clear | null | def clear(self):
return self._dict.clear()
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724,829 | scipy.sparse._dok | conjtransp | Return the conjugate transpose. | def conjtransp(self):
"""Return the conjugate transpose."""
if self.ndim == 1:
new = self.tocoo()
new.data = new.data.conjugate()
return new
M, N = self.shape
new = self._dok_container((N, M), dtype=self.dtype)
new._dict = {(right, left): np.conj(val) for (left, right), val in self.items()}
return new
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] |
724,830 | scipy.sparse._base | conjugate | Element-wise complex conjugation.
If the array/matrix is of non-complex data type and `copy` is False,
this method does nothing and the data is not copied.
Parameters
----------
copy : bool, optional
If True, the result is guaranteed to not share data with self.
Returns
-------
A : The element-wise complex conjugate.
| def conjugate(self, copy=True):
"""Element-wise complex conjugation.
If the array/matrix is of non-complex data type and `copy` is False,
this method does nothing and the data is not copied.
Parameters
----------
copy : bool, optional
If True, the result is guaranteed to not share data with self.
Returns
-------
A : The element-wise complex conjugate.
"""
if np.issubdtype(self.dtype, np.complexfloating):
return self.tocsr(copy=copy).conjugate(copy=False)
elif copy:
return self.copy()
else:
return self
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724,831 | scipy.sparse._dok | copy | Returns a copy of this array/matrix.
No data/indices will be shared between the returned value and current
array/matrix.
| def copy(self):
new = self._dok_container(self.shape, dtype=self.dtype)
new._dict.update(self._dict)
return new
| (self) | [
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] |
724,832 | scipy.sparse._dok | count_nonzero | Number of non-zero entries, equivalent to
np.count_nonzero(a.toarray())
Unlike the nnz property, which return the number of stored
entries (the length of the data attribute), this method counts the
actual number of non-zero entries in data.
| def count_nonzero(self):
return sum(x != 0 for x in self.values())
| (self) | [
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724,833 | scipy.sparse._dok | diagonal | null | def diagonal(self, k=0):
if self.ndim == 2:
return super().diagonal(k)
raise ValueError("diagonal requires two dimensions")
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0.020847072824835777,
0.10207396000623703,
0.021962635219097137
] |
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