def relabel_variables(self, mapping, inplace=True):
"""Relabel variables of a binary polynomial as specified by mapping.
Args:
mapping (dict):
Dict mapping current variable labels to new ones. If an
incomplete mapping is provided, unmapped variables retain their
current labels.
inplace (bool, optional, default=True):
If True, the binary polynomial is updated in-place; otherwise, a
new binary polynomial is returned.
Returns:
:class:`.BinaryPolynomial`: A binary polynomial with the variables
relabeled. If `inplace` is set to True, returns itself.
"""
if not inplace:
return self.copy().relabel_variables(mapping, inplace=True)
for submap in iter_safe_relabels(mapping, self.variables):
for oldterm, bias in list(self.items()):
newterm = frozenset((submap.get(v, v) for v in oldterm))
if newterm != oldterm:
self[newterm] = bias
del self[oldterm]
return self
def _relabel(self, mapping):
for submap in iter_safe_relabels(mapping, self):
for old, new in submap.items():
if old == new:
continue
idx = self._label_to_idx.pop(old, old)
if new != idx:
self._label_to_idx[new] = idx
self._idx_to_label[idx] = new # overwrites old idx
else:
self._idx_to_label.pop(idx, None)
def relabel(self, mapping):
for submap in iter_safe_relabels(mapping, self):
label = self._label
index = self.index
for old, new in submap.items():
if old not in self:
continue
label[index[old]] = new
index[new] = index[old]
del index[old]
def relabel_variables(self, mapping: Mapping[Variable, Variable]):
adj = self._adj
for submap in iter_safe_relabels(mapping, self.variables):
for old, new in submap.items():
if old == new:
continue
# replace the linear bias
adj[new] = {new: adj[old].pop(old)}
# copy the quadratic biases
for v in adj[old]:
adj[new][v] = adj[v][new] = adj[v].pop(old)
# remove the old adj for old
del adj[old]
def _relabel(self, mapping):
"""Relabel the variables in-place.
This method is semi-public. it is intended to be used by
classes that have :class:`.Variables` as an attribute, not by the
the user.
"""
for submap in iter_safe_relabels(mapping, self):
for old, new in submap.items():
if old == new:
continue
idx = self._label_to_idx.pop(old, old)
if new != idx:
self._label_to_idx[new] = idx
self._idx_to_label[idx] = new # overwrites old idx
else:
self._idx_to_label.pop(idx, None)
def relabel_variables(self, mapping, inplace=True):
"""Relabel variables of a binary quadratic model as specified by mapping.
Args:
mapping (dict):
Dict mapping current variable labels to new ones. If an
incomplete mapping is provided, unmapped variables retain their
current labels.
inplace (bool, optional, default=True):
If True, the binary quadratic model is updated in-place;
otherwise, a new binary quadratic model is returned.
Returns:
A binary quadratic model with the variables relabeled. If `inplace`
is set to True, returns itself.
"""
if not inplace:
return self.copy().relabel_variables(mapping, inplace=True)
adj = self._adj
for submap in iter_safe_relabels(mapping, self.linear):
for old, new in submap.items():
if old == new:
continue
# replace the linear bias
adj[new] = {new: adj[old].pop(old)}
# copy the quadratic biases
for v in adj[old]:
adj[new][v] = adj[v][new] = adj[v].pop(old)
# remove the old adj for old
del adj[old]
return self
