def dotest_mod(g, known_mod=None):
from pcd.old.models import Graph
G = Graph.fromNetworkX(g)
G.enableModularity(None)
G.verbosity = -1
if known_mod:
#G.anneal(1.0, Escale=100, betafactor=1.00001, p_binary=0.05, p_collective=0)
G.trials(1.0, 10, minimizer='anneal',
Escale=100, betafactor=1.001, p_binary=0.05, p_collective=0)
else:
G.anneal(1.0)
cmtys = pcd.cmty.Communities.from_pcd(G)
#print cmtys.q
#print repr(cmtys.Q(g))
print repr(cmtys._Q_cmty(g))
#print repr(cmtys._Q_pcd(g))
#print repr(G.modularity())
print repr(cmtys._Q_pcd_energy(g))
assert approxeq(cmtys.Q(g), cmtys._Q_cmty(g))
assert approxeq(cmtys.Q(g), cmtys._Q_pcd(g))
assert approxeq(cmtys.Q(g), G.modularity())
#assert approxeq(cmtys.Q(g), cmtys._Q_pcd_energy(g))
if known_mod:
assert approxeq(cmtys.Q(g), known_mod, .001), (cmtys.Q(g), known_mod)
def run(self):
g = self.g
true_q = self.true_q
if true_q is None:
spec = pcd.support.spectral.Flow(g, num_ev=self.q_hint+1)
true_q = spec.q()
self.time_ev = spec.time_ev
for i in range(self.max_louvain_rounds):
lv = self.parent_method(g, verbosity=-1)
print "%s result sizes:"%self.parent_method.name(), \
[ r.q for r in lv.results ], ", wanted size %s"%true_q
# lv.results should have greatest q first.
results = [ r for r in lv.results if r.q >= true_q ]
if len(results) > 0:
break
self.num_parent_method_trials = i+1
if len(results) < 1:
raise ValueError("All %s results return fewer than q communities."%self.parent_method.name())
cmtys = results[-1]
G = Graph.fromNetworkX(g)
cmtys.load_pcd(G)
#from fitz import interactnow
if self.Emode == 'mod':
G.enableModularity(mode=None)
elif self.Emode == 'APM':
pass
else: raise ValueError("self.Emode=%s not recognized"%self.Emode)
self.fixq(G, gamma=self.gamma, q=true_q)
self.cmtys = cmtys.from_pcd(G)
self.results = [ self.cmtys ]
def dotest_mod(g, known_mod=None):
from pcd.old.models import Graph
G = Graph.fromNetworkX(g)
G.enableModularity(None)
G.verbosity = -1
if known_mod:
#G.anneal(1.0, Escale=100, betafactor=1.00001, p_binary=0.05, p_collective=0)
G.trials(1.0,
10,
minimizer='anneal',
Escale=100,
betafactor=1.001,
p_binary=0.05,
p_collective=0)
else:
G.anneal(1.0)
cmtys = pcd.cmty.Communities.from_pcd(G)
#print cmtys.q
#print repr(cmtys.Q(g))
print repr(cmtys._Q_cmty(g))
#print repr(cmtys._Q_pcd(g))
#print repr(G.modularity())
print repr(cmtys._Q_pcd_energy(g))
assert approxeq(cmtys.Q(g), cmtys._Q_cmty(g))
assert approxeq(cmtys.Q(g), cmtys._Q_pcd(g))
assert approxeq(cmtys.Q(g), G.modularity())
#assert approxeq(cmtys.Q(g), cmtys._Q_pcd_energy(g))
if known_mod:
assert approxeq(cmtys.Q(g), known_mod, .001), (cmtys.Q(g), known_mod)
