def _get_output_features_constant(self, neighs_info, k, typefeats=(0, 0)):
"""Get 'output' features. Get the features of the elements in the
neighbourhood of the elements we want to study. That case is under
assumption of the constant selection of features.
Parameters
----------
neighs_info: pst.Neighs_Info
the container of all the information of the neighbourhood.
k: list or np.ndarray
perturbations indices we want to get.
typefeats: tuple or list
the selectors for the features of the neighs of elements `i`.
Returns
-------
feats_neighs: np.ndarray or list
the features of the neighs of the elements `i`.
"""
## Neighs info as an object
neighs_info = ensuring_neighs_info(neighs_info, k)
## Input mapping
neighs_info = self._maps_input[typefeats[0]](neighs_info)
## Features retrieve
feats_neighs = self.features[typefeats[1]].compute((neighs_info, k))
## Outformat
feats_neighs = self._maps_output(self, feats_neighs)
return feats_neighs
def compute_descriptors(self, i, neighs_info, k=None, feat_selectors=None):
"""General compute descriptors for descriptormodel class.
Parameters
----------
i: np.ndarray or list
the indices of the elements `i`.
neighs_info: pst.Neighs_Info
the container of all the information of the neighbourhood.
k: int or list (default=None)
the perturbations indices we wantto get.
feat_selectors: list, tuple or pst.BaseSelector (default=None)
the selection information.
Returns
-------
descriptors: list
the descriptors for each perturbation and element.
vals_i: list or np.ndarray
the store information index of each element `i`.
"""
## 0. Prepare list of k
ks = list(range(self.k_perturb + 1)) if k is None else k
ks = [ks] if type(ks) == int else ks
i_input = [i] if type(i) == int else i
# if type(feat_selectors) == tuple:
# feat_selectors = [feat_selectors]
neighs_info = ensuring_neighs_info(neighs_info, k)
# sh = neighs_info.shape
# print sh, len(i_input), len(ks), ks, i_input
# assert(len(i_input) == sh[1])
# assert(len(ks) == sh[0])
## 1. Prepare selectors
t_feat_in, t_feat_out, t_feat_des = self.get_type_feats(i, feat_selectors)
# print t_feat_in, t_feat_out, t_feat_des
## 2. Get pfeats (pfeats 2dim array (krein, jvars))
desc_i = self._get_input_features(i_input, ks, t_feat_in)
print i_input, ks, self._get_input_features, type(t_feat_in)
print "." * 20, desc_i
desc_neigh = self._get_output_features(neighs_info, ks, t_feat_out)
# print i, ks, i_input, neighs_info, neighs_info.ks, neighs_info.idxs
## 3. Map vals_i
vals_i = self._get_vals_i(i, ks)
# print '+'*10, vals_i, desc_neigh, desc_i
## 4. Complete descriptors (TODO)
descriptors = self._complete_desc_i(i, neighs_info, desc_i, desc_neigh, vals_i, t_feat_des)
# descriptors =\
# self.descriptormodel.complete_desc_i(i, neighs_info, desc_i,
# desc_neigh, vals_i)
## 5. Join descriptors
descriptors = self._join_descriptors(descriptors)
return descriptors, vals_i
def _get_output_features_variable(self, neighs_info, k, typefeats=(0, 0)):
"""Get 'output' features. Get the features of the elements in the
neighbourhood of the elements we want to study. That case is under
assumption of the variable selection of features.
Parameters
----------
neighs_info: pst.Neighs_Info
the container of all the information of the neighbourhood.
k: list or np.ndarray
perturbations indices we want to get.
typefeats: tuple or list
the selectors for the features of the neighs of elements `i`.
Returns
-------
feats_neighs: np.ndarray or list
the features of the neighs of the elements `i`.
"""
## Neighs info as an object
neighs_info = ensuring_neighs_info(neighs_info, k)
## Loop for all typefeats
i_l = len(neighs_info.iss)
# print i_l, neighs_info.iss, neighs_info.idxs
k_l = 1 if type(k) == int else len(k)
typefeats = [typefeats] * i_l if type(typefeats) == tuple else typefeats
feats_neighs = [[] for kl in range(k_l)]
for j in range(i_l):
## Input mapping
neighs_info_j = neighs_info.get_copy_iss_by_ind(j)
neighs_info_j = self._maps_input[typefeats[j][0]](neighs_info_j)
## Features retrieve
feats_neighs_j = self.features[typefeats[j][1]].compute((neighs_info_j, k))
## Outformat
feats_neighs_j = self._maps_output(self, feats_neighs_j)
## Store
for k_j in range(len(feats_neighs_j)):
feats_neighs[k_j].append(feats_neighs_j[k_j][0])
assert len(feats_neighs) == k_l
assert len(feats_neighs[0]) == i_l
return feats_neighs
