本文整理匯總了Python中numpy.core.numeric.empty方法的典型用法代碼示例。如果您正苦於以下問題：Python numeric.empty方法的具體用法？Python numeric.empty怎麽用？Python numeric.empty使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy.core.numeric的用法示例。
在下文中一共展示了numeric.empty方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _replace_zero_by_x_arrays
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import empty [as 別名]
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if _nx.ndim(sub_arys[i]) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys 示例2: _replace_zero_by_x_arrays
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import empty [as 別名]
def _replace_zero_by_x_arrays(sub_arys):
for i in range(len(sub_arys)):
if len(_nx.shape(sub_arys[i])) == 0:
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
elif _nx.sometrue(_nx.equal(_nx.shape(sub_arys[i]), 0)):
sub_arys[i] = _nx.empty(0, dtype=sub_arys[i].dtype)
return sub_arys 示例3: isposinf
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import empty [as 別名]
def isposinf(x, y=None):
"""
Test element-wise for positive infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
y : array_like, optional
A boolean array with the same shape as `x` to store the result.
Returns
-------
y : ndarray
A boolean array with the same dimensions as the input.
If second argument is not supplied then a boolean array is returned
with values True where the corresponding element of the input is
positive infinity and values False where the element of the input is
not positive infinity.
If a second argument is supplied the result is stored there. If the
type of that array is a numeric type the result is represented as zeros
and ones, if the type is boolean then as False and True.
The return value `y` is then a reference to that array.
See Also
--------
isinf, isneginf, isfinite, isnan
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when `x` is a
scalar input, or if first and second arguments have different shapes.
Examples
--------
>>> np.isposinf(np.PINF)
array(True, dtype=bool)
>>> np.isposinf(np.inf)
array(True, dtype=bool)
>>> np.isposinf(np.NINF)
array(False, dtype=bool)
>>> np.isposinf([-np.inf, 0., np.inf])
array([False, False, True], dtype=bool)
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isposinf(x, y)
array([0, 0, 1])
>>> y
array([0, 0, 1])
"""
if y is None:
x = nx.asarray(x)
y = nx.empty(x.shape, dtype=nx.bool_)
nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
return y 示例4: isposinf
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import empty [as 別名]
def isposinf(x, y=None):
"""
Test element-wise for positive infinity, return result as bool array.
Parameters
----------
x : array_like
The input array.
y : array_like, optional
A boolean array with the same shape as `x` to store the result.
Returns
-------
y : ndarray
A boolean array with the same dimensions as the input.
If second argument is not supplied then a boolean array is returned
with values True where the corresponding element of the input is
positive infinity and values False where the element of the input is
not positive infinity.
If a second argument is supplied the result is stored there. If the
type of that array is a numeric type the result is represented as zeros
and ones, if the type is boolean then as False and True.
The return value `y` is then a reference to that array.
See Also
--------
isinf, isneginf, isfinite, isnan
Notes
-----
Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754).
Errors result if the second argument is also supplied when `x` is a
scalar input, or if first and second arguments have different shapes.
Examples
--------
>>> np.isposinf(np.PINF)
array(True, dtype=bool)
>>> np.isposinf(np.inf)
array(True, dtype=bool)
>>> np.isposinf(np.NINF)
array(False, dtype=bool)
>>> np.isposinf([-np.inf, 0., np.inf])
array([False, False, True], dtype=bool)
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isposinf(x, y)
array([0, 0, 1])
>>> y
array([0, 0, 1])
"""
if y is None:
x = nx.asarray(x)
y = nx.empty(x.shape, dtype=nx.bool_)
nx.logical_and(nx.isinf(x), ~nx.signbit(x), y)
return y