#
# * The source code in this file is developed independently by NEC Corporation.
#
# # NLCPy License #
#
# Copyright (c) 2020 NEC Corporation
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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import nlcpy
[ドキュメント]def all(a, axis=None, out=None, keepdims=nlcpy._NoValue):
"""Tests whether all array elements along a given axis evaluate to True.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical AND reduction is performed. The default
(*axis* = *None*) is to perform a logical AND over all the dimensions of the
input array. *axis* may be negative, in which case it counts from the last to the
first axis.
If this is a tuple of ints, a reduction is performed on multiple axes.
out : ndarray, optional
Alternate output array in which to place the result. It must have the same shape
as the expected output and its type is preserved (e.g., if ``dtype(out)`` is
float, the result will consist of 0.0's and 1.0's).
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will broadcast correctly
against the input array.
Returns
-------
all : ndarray
A new array is returned unless *out* is specified, in which case a reference to
*out* is returned.
Note
----
Not a Number (NaN), positive infinity and negative infinity evaluate to `True`
because these are not equal to zero.
>>> import nlcpy as vp
>>> vp.all([[True, False], [True, True]])
array(False)
>>> vp.all([[True,False],[True,True]], axis=0)
array([ True, False])
>>> vp.all([-1, 4, 5])
array(True)
>>> vp.all([1.0, vp.nan])
array(True)
>>> o=vp.array(False)
>>> z=vp.all([-1, 4, 5], out=o)
>>> id(z), id(o), z # doctest: +SKIP
(140052379774144, 140052379774144, array(True)) # may vary
See Also
--------
any : Tests whether any array element along a given axis
evaluates to True.
"""
args = dict()
if keepdims is not nlcpy._NoValue:
args["keepdims"] = keepdims
ret = nlcpy.logical_and.reduce(a, axis=axis, out=out, **args)
return ret
[ドキュメント]def any(a, axis=None, out=None, keepdims=nlcpy._NoValue):
"""Tests whether any array elements along a given axis evaluate to True.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical OR reduction is performed. The default (axis =
None) is to perform a logical OR over all the dimensions of the input array. axis
may be negative, in which case it counts from the last to the first axis.
If this is a tuple of ints, a reduction is performed on multiple axes.
out : ndarray, optional
Alternate output array in which to place the result. It must have the same shape
as the expected output and its type is preserved (e.g., if ``dtype(out)`` is
float, the result will consist of 0.0's and 1.0's).
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will broadcast correctly
against the input array.
Returns
-------
any : ndarray
A new array is returned unless out is specified, in which case a reference to out
is returned.
Note
----
Not a Number (NaN), positive infinity and negative infinity evaluate to True because
these are not equal to zero.
>>> import nlcpy as vp
>>> vp.any([[True, False], [True, True]])
array(True)
>>> vp.any([[True,False],[False,False]], axis=0)
array([ True, False])
>>> vp.any([-1, 0, 5])
array(True)
>>> vp.any(vp.nan)
array(True)
>>> o=vp.array(False)
>>> z=vp.any([-1, 4, 5], out=o)
>>> z, o
(array(True), array(True))
>>> z is o
True
>>> id(z), id(o) # doctest: +SKIP
(140196865556152, 140196865556152) # may vary
See Also
--------
all : Tests whether all array element along a given axis
evaluates to True.
"""
args = dict()
if keepdims is not nlcpy._NoValue:
args["keepdims"] = keepdims
ret = nlcpy.logical_or.reduce(a, axis=axis, out=out, **args)
return ret