def schelling_step_avg(self):
adopted = [x for x in self.G.nodes if self.G.node[x]['apt_markakis'] > -1]
candidates = [x for x in adopted if self.unhappy(x)]
add_ctr = 0
rem_ctr = 0
if candidates:
tar = np.random.choice(candidates)
probs = [val for (_, val) in self.G.degree()]
tot = sum([np.e**x for x in probs])
probs = [np.e**x / tot for x in probs]
friends = list(set([n for i in self.G[tar] for n in self.G[i]]))
friend_probs = [1 / (len(friends)) if x in friends else 0 for x in range(len(probs))]
probs = [0.5 * probs[i] + 0.5 * friend_probs[i] for i in range(len(probs))]
test_pts = np.random.choice(len(probs), p=probs, replace=False, size=len(probs))
test_pts = [test for test in test_pts if self.G.node[test]['apt_markakis'] > -1]
bs = list(bridges(self.G))
for test in test_pts:
neighbors = [x for x in self.G[tar] if self.G.node[x]['apt_markakis'] > -1]
neighbor_dist = self.euclidian(tar, neighbors)
if self.G.has_edge(test, tar) and (test, tar) not in bs and (tar, test) not in bs:
neighbor_test = copy.deepcopy(neighbors)
neighbor_test.remove(test)
if self.euclidian(tar, neighbor_test) / neighbor_dist < self.thresh:
self.G.remove_edge(test, tar)
bs = list(bridges(self.G))
rem_ctr += 1
elif self.euclidian(tar, neighbors + [test]) / neighbor_dist < self.thresh:
self.G.add_edge(test, tar)
add_ctr += 1
if not self.unhappy(tar):
break
def improved_schelling(self):
adopted = [x for x in self.G.nodes if self.G.node[x]['apt_markakis'] > -1]
candidates = [x for x in adopted if self.unhappy(x)]
add_ctr = 0
rem_ctr = 0
if not candidates:
return
tar = np.random.choice(candidates)
similars = self.similar(tar)
sim_vals = [True for _ in similars]
difs = self.dif_nodes(tar)
dif_vals = [False for _ in difs]
test_pts = similars + difs
test_vals = sim_vals + dif_vals
degrees = [self.G.degree[x] for x in test_pts]
degrees = [degrees[i] / 4 if test_vals[i] else degrees[i] for i in range(len(degrees))]
degrees = [x/sum(degrees) for x in degrees]
indices = np.random.choice(len(degrees), p=degrees, replace=False, size=len(degrees))
bs = list(bridges(self.G))
for idx in indices:
if test_vals[idx]:
self.G.add_edge(tar, test_pts[idx])
else:
if not (test_pts[idx], tar) in bs and not (tar, test_pts[idx]) in bs:
self.G.remove_edge(test_pts[idx], tar)
bs = list(bridges(self.G))
if not self.unhappy(tar):
break
def bridge_components(G):
"""Finds all bridge-connected components G.
Parameters
----------
G : NetworkX undirected graph
Returns
-------
bridge_components : a generator of 2-edge-connected components
See Also
--------
:func:`k_edge_subgraphs` : this function is a special case for an
undirected graph where k=2.
:func:`biconnected_components` : similar to this function, but is defined
using 2-node-connectivity instead of 2-edge-connectivity.
Raises
------
NetworkXNotImplemented:
If the input graph is directed or a multigraph.
Notes
-----
Bridge-connected components are also known as 2-edge-connected components.
Example
-------
>>> # The barbell graph with parameter zero has a single bridge
>>> G = nx.barbell_graph(5, 0)
>>> from networkx.algorithms.connectivity.edge_kcomponents import bridge_components
>>> sorted(map(sorted, bridge_components(G)))
[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
"""
H = G.copy()
H.remove_edges_from(bridges(G))
for cc in nx.connected_components(H):
yield cc
def getBridges(self, G):
return nalgos.bridges(G)
