def simple_loops(g, u):
"""
iterator over the list of simple loops of graph g at the undersample rate u
"""
gx = graph2nx(num2CG(g2num(undersample(g, u)), len(g)))
for l in networkx.simple_cycles(gx):
yield l
def compat(G):
n = len(G)
num = g2num(G)
# sample all the graph for gStar
star_l = all_undersamples(G)
hits = {}
# brute force all graphs
for i in range(0, 2 ** (n**2)):
tmpG = num2CG(i, n)
tmp_l = all_undersamples(tmpG)
c = compatible(tmp_l, star_l)
if len(sum(c)) > 0:
hits[i] = c
return hits
def compatibleAtU(uGstar):
compat = []
n = len(uGstar)
numG = 2 ** (n ** 2)
# pbar = Percentage()
for i in range(1, numG):
G = num2CG(i, n)
# pbar.update(i+1)
if len(ecj.scc(G)) > 1:
continue
l = searchMatch(uGstar, G, iter=5)
if l:
compat.append((l, G))
# pbar.finish()
return compat
def cc_all(i, nodes, steps):
# print i
g = num2CG(i, nodes)
return cc_undersamples(g, steps=steps)
def iall(i, nodes):
print i
g = num2CG(i, nodes)
return compact_call_undersamples(g)
def ilength(i, nodes):
print i
g = num2CG(i, nodes)
return len(call_undersamples(g))
def icompat(i, nodes):
print i
g = num2CG(i, nodes)
return compat(g)
def randSCC(n):
G = num2CG(scipy.random.randint(2 ** (n ** 2)), n)
while (len(ecj.scc(G)) > 1) or gcd4scc(G) > 1:
G = num2CG(scipy.random.randint(2 ** (n ** 2)), n)
return G
def wrapper(fold, n=10, dens=0.1):
scipy.random.seed()
rate = 2
r = None
s = set()
counter = 0
while not s:
scipy.random.seed()
sst = 0.9
r = None
while not r:
r = lm.getAring(n, dens, sst, False, dist=DIST)
print sst,
sys.stdout.flush()
if sst < 0.03:
sst -= 0.001
else:
sst -= 0.01
if sst < 0:
sst = 0.02
# pprint.pprint(r['transition'].round(2),width=200)
# d = zkl.load('leibnitz_nodes_'+str(n)+'_OCE_model_.zkl')
# r = d[dens][fold]
g = r['graph']
true_g2 = bfu.undersample(g, rate - 1)
data = lm.drawsamplesLG(r['transition'], samples=BURNIN + SAMPLESIZE * 2,
nstd=np.double(NOISE_STD))
data = data[:, BURNIN:]
if np.max(data) > 1000.:
pprint.pprint(r['transition'].round(2), width=200)
# raise ValueError
startTime = int(round(time.time() * 1000))
if EST == 'pc':
g2 = pc.dpc(data[:, ::2], pval=0.0001)
elif EST == 'svar':
g2 = lm.data2graph(data[:, ::2])
if trv.density(g2) < 0.7:
print gk.OCE(g2, true_g2)
# s = examine_bidirected_flips(g2, depth=DEPTH)
s = find_nearest_reachable(g2, max_depth=1)
# s = trv.v2g22g1(g2, capsize=CAPSIZE, verbose=False)
# s = trv.edge_backtrack2g1_directed(g2, capsize=CAPSIZE)
# s = timeout(trv.v2g22g1,
# s = timeout(trv.edge_backtrack2g1_directed,
# args=(g2,CAPSIZE),
# timeout_duration=1000, default=set())
print 'o',
sys.stdout.flush()
if -1 in s:
s = set()
endTime = int(round(time.time() * 1000))
# if counter > 3:
# print 'not found'
# return None
counter += 1
print ''
oce = [gk.OCE(num2CG(x, n), g) for x in s]
cum_oce = [sum(x['directed']) + sum(x['bidirected']) for x in oce]
idx = np.argmin(cum_oce)
print "{:2}: {:8} : {:4} {:10} seconds".\
format(fold, round(dens, 3), cum_oce[idx],
round((endTime - startTime) / 1000., 3))
# np.set_printoptions(formatter={'float': lambda x: format(x, '6.3f')+", "})
# pprint.pprint(r['transition'].round(2))
# np.set_printoptions()
return {'gt': r,
'eq': s,
'OCE': oce[idx],
'tries_till_found': counter,
'estimate': g2,
'graphs_tried': counter,
'strength': sst + 0.01,
'ms': endTime - startTime}
