Python numpy.setxor1d() Examples
The following are 30 code examples for showing how to use numpy.setxor1d(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
| Project: recruit Author: Frank-qlu File: arraysetops.py License: Apache License 2.0 | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 2
| Project: lambda-packs Author: ryfeus File: arraysetops.py License: MIT License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 3
| Project: vnpy_crypto Author: birforce File: arraysetops.py License: MIT License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 4
| Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: arraysetops.py License: MIT License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 5
| Project: GraphicDesignPatternByPython Author: Relph1119 File: arraysetops.py License: MIT License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 6
| Project: predictive-maintenance-using-machine-learning Author: awslabs File: arraysetops.py License: Apache License 2.0 | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 7
| Project: pySINDy Author: luckystarufo File: arraysetops.py License: MIT License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 8
| Project: sisl Author: zerothi File: base.py License: GNU Lesser General Public License v3.0 | 5 votes |
|
def within_index(self, *args, **kwargs):
A = self.A.within_index(*args, **kwargs)
B = self.B.within_index(*args, **kwargs)
return setxor1d(A, B, assume_unique=True)
Example 9
| Project: e3fp Author: keiserlab File: fprint.py License: GNU Lesser General Public License v3.0 | 5 votes |
|
def __xor__(self, other):
if not isinstance(other, Fingerprint):
raise E3FPInvalidFingerprintError(
"variable is %s not Fingerprint" % (other.__class__.__name__)
)
if self.bits != other.bits:
raise E3FPBitsValueError(
"cannot compare fingerprints of different sizes"
)
return Fingerprint(
np.setxor1d(self.indices, other.indices, assume_unique=True),
bits=self.bits,
)
Example 10
| Project: coffeegrindsize Author: jgagneastro File: arraysetops.py License: MIT License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 11
| Project: landlab Author: landlab File: hand_calculator.py License: MIT License | 5 votes |
|
def run_one_step(self):
is_drainage_node = self._channel_mask
is_drainage_node[self._grid.open_boundary_nodes] = 1
# check for pits
self_draining_nodes = np.where(
self._receivers == np.arange(self._grid.number_of_nodes)
)
pits = np.setxor1d(self_draining_nodes, self._grid.boundary_nodes)
if pits.any():
warn(
"Pits detected in the flow directions supplied. "
"Pits will be treated as drainage nodes."
)
is_drainage_node[pits] = 1
# iterate downstream through stack to find nearest drainage elevation
nearest_drainage_elev = np.empty(self._elev.shape)
for n in self._node_order:
r = self._receivers[n]
# if not drainage node set drainage elevation to downstream.
if not is_drainage_node[n]:
nearest_drainage_elev[n] = nearest_drainage_elev[r]
else: # set elevation of drainage to self.
nearest_drainage_elev[n] = self._elev[n]
self._hand[:] = self._elev - nearest_drainage_elev
Example 12
| Project: am3 Author: ElementAI File: AM3_TADAM.py License: Apache License 2.0 | 5 votes |
|
def get_train_datasets(flags):
mini_imagenet = _load_mini_imagenet(data_dir=flags.data_dir, split='sources')
few_shot_data_train = Dataset(mini_imagenet)
pretrain_data_train, pretrain_data_test = None, None
if flags.feat_extract_pretrain:
train_idx = np.random.choice(range(len(mini_imagenet[0])), size=int(0.9 * len(mini_imagenet[0])),
replace=False)
test_idx = np.setxor1d(range(len(mini_imagenet[0])), train_idx)
new_labels = mini_imagenet[1]
for i, old_class in enumerate(set(mini_imagenet[1])):
new_labels[mini_imagenet[1] == old_class] = i
pretrain_data_train = Dataset((mini_imagenet[0][train_idx], new_labels[train_idx]))
pretrain_data_test = Dataset((mini_imagenet[0][test_idx], new_labels[test_idx]))
return few_shot_data_train, pretrain_data_train, pretrain_data_test
Example 13
| Project: am3 Author: ElementAI File: AM3_TADAM.py License: Apache License 2.0 | 5 votes |
|
def get_few_shot_idxs(labels, classes, num_shots):
train_idxs, test_idxs = [], []
idxs = np.arange(len(labels))
for cl in classes:
class_idxs = idxs[labels == cl]
class_idxs_train = np.random.choice(class_idxs, size=num_shots, replace=False)
class_idxs_test = np.setxor1d(class_idxs, class_idxs_train)
train_idxs.extend(class_idxs_train)
test_idxs.extend(class_idxs_test)
assert set(class_idxs_train).isdisjoint(test_idxs)
return np.array(train_idxs), np.array(test_idxs)
Example 14
| Project: am3 Author: ElementAI File: protonet++.py License: Apache License 2.0 | 5 votes |
|
def get_few_shot_idxs(labels, classes, num_shots):
train_idxs, test_idxs = [], []
idxs = np.arange(len(labels))
for cl in classes:
class_idxs = idxs[labels == cl]
class_idxs_train = np.random.choice(class_idxs, size=num_shots, replace=False)
class_idxs_test = np.setxor1d(class_idxs, class_idxs_train)
train_idxs.extend(class_idxs_train)
test_idxs.extend(class_idxs_test)
assert set(class_idxs_train).isdisjoint(test_idxs)
return np.array(train_idxs), np.array(test_idxs)
Example 15
| Project: am3 Author: ElementAI File: util.py License: Apache License 2.0 | 5 votes |
|
def get_few_shot_idxs(self, labels, classes, num_shots):
train_idxs, test_idxs = [], []
idxs = np.arange(len(labels))
for cl in classes:
class_idxs = idxs[labels == cl]
class_idxs_train = np.random.choice(class_idxs, size=num_shots, replace=False)
class_idxs_test = np.setxor1d(class_idxs, class_idxs_train)
train_idxs.extend(class_idxs_train)
test_idxs.extend(class_idxs_test)
assert set(class_idxs_train).isdisjoint(test_idxs)
return np.array(train_idxs), np.array(test_idxs)
Example 16
| Project: am3 Author: ElementAI File: AM3_protonet++.py License: Apache License 2.0 | 5 votes |
|
def get_few_shot_idxs(labels, classes, num_shots):
train_idxs, test_idxs = [], []
idxs = np.arange(len(labels))
for cl in classes:
class_idxs = idxs[labels == cl]
class_idxs_train = np.random.choice(class_idxs, size=num_shots, replace=False)
class_idxs_test = np.setxor1d(class_idxs, class_idxs_train)
train_idxs.extend(class_idxs_train)
test_idxs.extend(class_idxs_test)
assert set(class_idxs_train).isdisjoint(test_idxs)
return np.array(train_idxs), np.array(test_idxs)
Example 17
| Project: am3 Author: ElementAI File: tadam.py License: Apache License 2.0 | 5 votes |
|
def get_few_shot_idxs(labels, classes, num_shots):
train_idxs, test_idxs = [], []
idxs = np.arange(len(labels))
for cl in classes:
class_idxs = idxs[labels == cl]
class_idxs_train = np.random.choice(class_idxs, size=num_shots, replace=False)
class_idxs_test = np.setxor1d(class_idxs, class_idxs_train)
train_idxs.extend(class_idxs_train)
test_idxs.extend(class_idxs_test)
assert set(class_idxs_train).isdisjoint(test_idxs)
return np.array(train_idxs), np.array(test_idxs)
Example 18
| Project: Carnets Author: holzschu File: test_quantity_non_ufuncs.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_setxor1d(self):
self.check2(np.setxor1d)
Example 19
| Project: Carnets Author: holzschu File: arraysetops.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 20
| Project: Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda Author: PacktPublishing File: arraysetops.py License: MIT License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 21
| Project: twitter-stock-recommendation Author: alvarobartt File: arraysetops.py License: MIT License | 5 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
return aux[flag[1:] & flag[:-1]]
Example 22
| Project: lambda-packs Author: ryfeus File: arraysetops.py License: MIT License | 4 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
Example 23
| Project: auto-alt-text-lambda-api Author: abhisuri97 File: arraysetops.py License: MIT License | 4 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
Example 24
| Project: Computable Author: ktraunmueller File: arraysetops.py License: MIT License | 4 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate( (ar1, ar2) )
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate( ([True], aux[1:] != aux[:-1], [True] ) )
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
Example 25
| Project: Fluid-Designer Author: Microvellum File: arraysetops.py License: GNU General Public License v3.0 | 4 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
Example 26
| Project: mxnet-lambda Author: awslabs File: arraysetops.py License: Apache License 2.0 | 4 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
Example 27
| Project: ImageFusion Author: pfchai File: arraysetops.py License: MIT License | 4 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
Example 28
| Project: Splunking-Crime Author: nccgroup File: arraysetops.py License: GNU Affero General Public License v3.0 | 4 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
Example 29
| Project: elasticintel Author: securityclippy File: arraysetops.py License: GNU General Public License v3.0 | 4 votes |
|
def setxor1d(ar1, ar2, assume_unique=False):
"""
Find the set exclusive-or of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
ar1, ar2 : array_like
Input arrays.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
setxor1d : ndarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = np.array([1, 2, 3, 2, 4])
>>> b = np.array([2, 3, 5, 7, 5])
>>> np.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if not assume_unique:
ar1 = unique(ar1)
ar2 = unique(ar2)
aux = np.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
Example 30
| Project: metaheuristics Author: thieunguyen5991 File: PFA.py License: Apache License 2.0 | 4 votes |
|
def _train__(self):
# Init pop and calculate fitness
pop = [self._create_solution__(minmax=0) for _ in range(self.pop_size)]
# Find the pathfinder
pop = sorted(pop, key=lambda temp: temp[self.ID_FIT])
g_best = deepcopy(pop[0])
gbest_present = deepcopy(g_best)
for i in range(self.epoch):
alpha, beta = np.random.uniform(1, 2, 2)
A = np.random.uniform(self.domain_range[0], self.domain_range[1]) * np.exp(-2 * (i + 1) / self.epoch)
## Update the position of pathfinder and check the bound
temp = gbest_present[self.ID_POS] + 2 * np.random.uniform() * (gbest_present[self.ID_POS] - g_best[self.ID_POS]) + A
temp = self._amend_solution_and_return__(temp)
fit = self._fitness_model__(temp)
g_best = deepcopy(gbest_present)
if fit < gbest_present[self.ID_FIT]:
gbest_present = [temp, fit]
pop[0] = deepcopy(gbest_present)
## Update positions of members, check the bound and calculate new fitness
for j in range(1, self.pop_size):
temp1 = deepcopy(pop[j][self.ID_POS])
t1 = beta * np.random.uniform() * (gbest_present[self.ID_POS] - temp1)
my_list_idx = np.setxor1d( np.array(range(1, self.pop_size)) , np.array([j]) )
idx = np.random.choice(my_list_idx)
dist = np.linalg.norm(pop[idx][self.ID_POS] - temp1)
t2 = alpha * np.random.uniform() * (pop[idx][self.ID_POS] - temp1)
t3 = np.random.uniform(self.domain_range[0], self.domain_range[1], self.problem_size) * (1 - (i + 1) * 1.0 / self.epoch) * dist
temp1 += t1 + t2 + t3
## Update members
temp1 = self._amend_solution_and_return__(temp1)
fit = self._fitness_model__(temp1)
if fit < pop[j][self.ID_FIT]:
pop[j] = [temp1, fit]
## Update the best solution found so far (current pathfinder)
pop = sorted(pop, key=lambda temp: temp[self.ID_FIT])
current_best = deepcopy(pop[self.ID_MIN_PROBLEM])
if current_best[self.ID_FIT] < gbest_present[self.ID_FIT]:
gbest_present = deepcopy(current_best)
self.loss_train.append(gbest_present[self.ID_FIT])
if self.print_train:
print("Generation : {0}, best result so far: {1}".format(i + 1, gbest_present[self.ID_FIT]))
return gbest_present[self.ID_FIT], self.loss_train
