def __init__(self, reindices, auto=True):
"""Perturbations by permuting elements.
Parameters
----------
reindices: np.ndarray
the reindices to apply permutation perturbations.
"""
self._initialization()
self.basecoreperturbation = BaseCorePermutationArray(reindices, auto)
class BasePermutation(BasePerturbation):
"""Base permutation. Main structure to manage the permutation perturbation.
"""
_perturbtype = "element_permutation"
def __init__(self, reindices, auto=True):
"""Perturbations by permuting elements.
Parameters
----------
reindices: np.ndarray
the reindices to apply permutation perturbations.
"""
self._initialization()
self.basecoreperturbation = BaseCorePermutationArray(reindices, auto)
#self._format_reindices(reindices, auto)
###################### Administrative class functions #####################
def _check_features(self, features):
"""Check the proper format of features.
Parameters
----------
features: np.ndarray, shape (n, m)
the features to apply perturbation.
"""
assert(len(features) == len(self.basecoreperturbation.reindices))
assert(len(features.shape) == 2)
#
# def _format_reindices(self, reindices, auto=True):
# """Format reindices.
#
# Parameters
# ----------
# reindices: np.ndarray or tuple
# the reindices to apply permutation perturbations.
#
# """
# if type(reindices) == np.ndarray:
# self.k_perturb = reindices.shape[1]
# self.reindices = reindices
# elif type(reindices) == tuple:
# n, k_perturb = reindices
# if check_int(n) and check_int(k_perturb):
# auto_indices = [np.arange(n)] if auto else []
# self.k_perturb = k_perturb
# self.reindices = np.vstack(auto_indices +
# [np.random.permutation(n)
# for i in xrange(k_perturb)]).T
def _filter_indices(self, i, k):
"""Filter indices to get the transformed data.
Parameters
----------
i: int, list, np.ndarray
the indices of elements.
k: int, list, np.ndarray
the indices of perturbations.
Returns
-------
i: int, list, np.ndarray
the indices of elements.
k: int, list, np.ndarray
the indices of perturbations.
info_input: list
the boolean information about the if the input is sequencial or
only a unique index.
"""
info_input = [True, True]
## Check i
if type(i) == np.ndarray:
i = list(i)
elif check_int(i):
info_input[0] = False
i = [i]
assert(type(i) == list)
## Check k
k = self._format_k_perturb(k)
if type(k) == np.ndarray:
k = list(k)
elif check_int(k):
info_input[1] = False
k = [k]
assert(type(k) == list)
return i, k, info_input
def _filter_output(self, result, info_input):
"""Filter output for 2d array results (locations and features array).
Parameters
----------
result: np.ndarray, shape: (ni, ndim, nk)
the result we want to format to be output.
Returns
-------
result: np.ndarray
properly formatted adapted to the format of the indices input.
"""
if not info_input[1]:
result = result[:, :, 0]
if not info_input[0]:
result = result[0]
return result
########################### Main class functions ##########################
def apply2indices(self, i, k=None):
"""Apply the transformation to the indices.
Parameters
----------
i: int, list or np.ndarray
the indices of the elements `i`.
k: int, list
the perturbation indices.
Returns
-------
i: int, list or np.ndarray
the indices of the elements `i`.
"""
k = self._format_k_perturb(k)
if check_int(i):
return self.basecoreperturbation.reindices[i, k]
elif type(i) == list:
return list(self.basecoreperturbation.reindices[i][:, k])
else:
return self.basecoreperturbation.reindices[list(i)][:, k]
def _apply2someelements(self, array, i, k):
"""Apply perturbation to array for individual elements.
Parameters
----------
array: np.ndarray
the spatial information to be perturbed.
k: int (default=None)
the perturbation indices.
Returns
-------
array_p: np.ndarray
the spatial information perturbated.
"""
## Apply permutation to array
rei = self.basecoreperturbation.reindices[i][:, k]
array_p = array[rei].swapaxes(1, 2)
return array_p
def _apply2allelements(self, array, k):
"""Apply perturbation to array of complete elements.
"""
## Apply permutation to array
array_p = self.basecoreperturbation.apply(array, k)
# array_p = array[self.reindices[:, k]].swapaxes(1, 2)
return array_p
