def add2set(gset, elist, H):
n = len(H)
s = set()
ss = set()
eremove = {e: True for e in elist}
for gnum in gset:
g = bfu.num2CG(gnum, n)
for e in elist:
if not e[1] in g[e[0]]:
gk.addanedge(g,e)
num = bfu.g2num(g)
if not num in s:
au = bfu.call_undersamples(g)
if not gk.checkconflict(H, g, au=au):
eremove[e] = False
s.add(num)
if gk.checkequality(H, g, au=au): ss.add(num)
gk.delanedge(g,e)
for e in eremove:
if eremove[e]: elist.remove(e)
return s, ss, elist
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 edgemask(gl, H, cds):
nl= len(gl)
ss = set()
mask = np.zeros((nl,nl),'int')
np.fill_diagonal(mask,-1)
idx = np.triu_indices(nl,1)
for i,j in zip(idx[0], idx[1]):
if gl[i] & gl[j]:
mask[i,j] = -1
mask[j,i] = -1
continue
if skip_conflict(gl[i], gl[j], cds):
gnum = gl[i] | gl[j]
cache[gnum] = False
continue
gnum = gl[i] | gl[j]
if gnum in cache:
if cache[gnum]:
mask[i,j] = gnum
mask[j,i] = gnum
else:
cache[gnum] = False
g = bfu.num2CG(gnum,n)
if not bfu.call_u_conflicts(g, H):
if bfu.call_u_equals(g, H): ss.add(gnum)
mask[i,j] = gnum
mask[j,i] = gnum
cache[gnum] = True
return mask, ss
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 ledgemask(gl,H, cds):
"""given a list of encoded graphs and observed undersampled graph
H returns a matrix with -1 on diagonal, 0 at the conflicting graph
combination and encoded graph at non-conflicted
positions. Furthermore, returns a set of graphs that are in the
equivalence class of H
Arguments:
- `gl`: list of integer encoded graphs
- `H`: the observed undersampled graph
"""
n = len(H)
nl= len(gl)
s = set()
mask = np.zeros((nl,nl),'int')
np.fill_diagonal(mask,-1)
for i in xrange(nl):
for j in xrange(i+1,nl):
if gl[i] & gl[j]: continue
gnum = gl[i] | gl[j]
if skip_conflictors(gnum, cds): continue
g = bfu.num2CG(gnum,n)
if not bfu.call_u_conflicts(g, H):
if bfu.call_u_equals(g, H): s.add(gnum)
mask[i,j] = gnum
mask[j,i] = gnum
return mask, s
def del_loop(ds, H, iter=0, verbose=True, capsize=1000):
n = len(H)
dsr = {}
s = set()
ss = set()
print iter,
for gnum in ds:
gset = []
s = set()
for sloop in ds[gnum]:
rset = ds[gnum] - set([sloop])
num = reduce(operator.or_, rset)
if not num in s:
g = bfu.num2CG(num, n)
if bfu.overshoot(g, H):
s.add(num)
gset.append((num,rset))
if gset == []:
print '.',
ss.add(gnum)
for gn in gset: dsr[gn[0]] = gn[1]
print ''
return dsr, ss
def edgemask(gl, H, cds):
nl = len(gl)
ss = set()
mask = np.zeros((nl, nl), 'int')
np.fill_diagonal(mask, -1)
idx = np.triu_indices(nl, 1)
for i, j in zip(idx[0], idx[1]):
if gl[i] & gl[j]:
mask[i, j] = -1
mask[j, i] = -1
continue
if skip_conflict(gl[i], gl[j], cds):
gnum = gl[i] | gl[j]
cache[gnum] = False
continue
gnum = gl[i] | gl[j]
if gnum in cache:
if cache[gnum]:
mask[i, j] = gnum
mask[j, i] = gnum
else:
cache[gnum] = False
g = bfu.num2CG(gnum, n)
if not bfu.call_u_conflicts(g, H):
if bfu.call_u_equals(g, H): ss.add(gnum)
mask[i, j] = gnum
mask[j, i] = gnum
cache[gnum] = True
return mask, ss
def delAloop(g, loop):
n = len(g)
l = []
l = [bfu.g2num(ur.loop2graph(s,n)) for s in sls.simple_loops(g,0)]
l = [num for num in l if not num == loop ]
print loop, ': ', l
return bfu.num2CG(reduce(operator.or_, l),n)
def build_loop_step(ds, loop, n, iter=1):
n2 = n*n +n
dsr = {}
s = set()
ss = set()
pbar = start_progress_bar(iter, len(ds))
c = 0
for gnum in ds:
c += 1
pbar.update(c)
gset = set()
eset = set()
for sloop in ds[gnum]:
num = sloop | gnum
if not num in s:
g = bfu.num2CG(num, n)
s.add(num)
if bfu.forms_loop(g, loop):
ss.add(num)
else:
gset.add((num,sloop))
eset.add(sloop)
for gn,e in gset:
dsr[gn] = eset - set([e])
pbar.finish()
return dsr, ss
def add2set(gset, elist, H):
n = len(H)
s = set()
ss = set()
eremove = {e: True for e in elist}
for gnum in gset:
g = bfu.num2CG(gnum, n)
for e in elist:
if not e[1] in g[e[0]]:
gk.addanedge(g, e)
num = bfu.g2num(g)
if not num in s:
au = bfu.call_undersamples(g)
if not gk.checkconflict(H, g, au=au):
eremove[e] = False
s.add(num)
if gk.checkequality(H, g, au=au): ss.add(num)
gk.delanedge(g, e)
for e in eremove:
if eremove[e]: elist.remove(e)
return s, ss, elist
def edgemask(gl, H, cds):
"""given a list of encoded graphs and observed undersampled graph
H returns a matrix with -1 on diagonal, 0 at the conflicting graph
combination and encoded graph at non-conflicted
positions. Furthermore, returns a set of graphs that are in the
equivalence class of H
Arguments:
- `gl`: list of integer encoded graphs
- `H`: the observed undersampled graph
"""
n = len(H)
nl = len(gl)
s = set()
mask = np.zeros((nl, nl), 'int')
np.fill_diagonal(mask, -1)
for i in xrange(nl):
for j in xrange(i + 1, nl):
if gl[i] & gl[j]: continue
if skip_conflict(gl[i], gl[j], cds): continue
gnum = gl[i] | gl[j]
g = bfu.num2CG(gnum, n)
if not bfu.call_u_conflicts(g, H):
if bfu.call_u_equals(g, H): s.add(gnum)
mask[i, j] = gnum
mask[j, i] = gnum
return mask, s
def prune_loops(loops, H):
l = []
n = len(H)
for loop in loops:
g = bfu.num2CG(loop, n)
x = [k for k in g if g[k]]
if len(x) == 1:
s = reduce(lambda x, s: s.union(x),
[H[x[0]][w] for w in H[x[0]]])
if not (2,0) in s: continue
if not bfu.call_u_conflicts_d(g, H): l.append(loop)
return l
def lconfpairs(H, cap=10, sloops=None):
n = len(H)
d = {}
if not sloops: sloops = prune_loops(allsloops(len(H)),H)
c = 0
for p in combinations(sloops,2):
g = bfu.num2CG(p[0]|p[1], n)
if bfu.call_u_conflicts(g, H):
d.setdefault(p[0],set()).add(p[1])
d.setdefault(p[1],set()).add(p[0])
if c >= cap: break
c +=1
return d
def prune(gl, H):
l = []
ss = set()
for gnum in gl:
if gnum in cache:
if cache[gnum]: l.append(gnum)
else:
cache[gnum] = False
g = bfu.num2CG(gnum, n)
if not bfu.call_u_conflicts(g, H):
if bfu.call_u_equals(g, H): ss.add(gnum)
l.append(gnum)
cache[gnum] = True
return l
def matchAloop(loop, n):
"""returns a set of minimal graphs that generate this loop
Arguments:
- `loop`: binary encoding of the loop
- `n`: number of nodes in the graph
"""
s = set()
l = forward_loop_match(loop,n)
print len(l)
for g in l:
s = s | reverse_edge_match(bfu.num2CG(g,n),loop)
return s
def prune(gl, H):
l = []
ss = set()
for gnum in gl:
if gnum in cache:
if cache[gnum]: l.append(gnum)
else:
cache[gnum] = False
g = bfu.num2CG(gnum,n)
if not bfu.call_u_conflicts(g, H):
if bfu.call_u_equals(g, H): ss.add(gnum)
l.append(gnum)
cache[gnum] = True
return l
def add2set_(ds, H, cp, ccf, iter=1, verbose=True, capsize=100):
n = len(H)
n2 = n*n +n
dsr = {}
s = set()
ss = set()
pbar = start_progress_bar(iter, len(ds), verbose = verbose)
c = 0
for gnum in ds:
g = bfu.num2CG(gnum, n)
c += 1
pbar.update(c)
glist = []
elist = []
eset = set()
for e in ds[gnum]:
if not e[1] in g[e[0]]:
gk.addanedge(g,e)
num = bfu.g2num(g)
ekey = (1<<(n2 - int(e[0],10)*n - int(e[1],10)))
if ekey in ccf and skip_conflictors(num,ccf[ekey]):
gk.delanedge(g,e)
continue
if not num in s:
s.add(num)
if not bfu.call_u_conflicts(g, H):
#cf, gl2 = bfu.call_u_conflicts2(g, H)
#if not cf:
glist.append((num,ekey))
elist.append(e)
eset.add(ekey)
if bfu.call_u_equals(g, H):
ss.add(num)
#if bfu.call_u_equals2(g, gl2, H): ss.add(num)
if capsize <= len(ss): break
gk.delanedge(g,e)
for gn,e in glist:
if e in cp:
dsr[gn] = [ekey2e(k,n) for k in eset - cp[e]]
else:
dsr[gn] = elist
if capsize <= len(ss): return dsr, ss
pbar.finish()
return dsr, ss
def add2set_(ds, H, cp, ccf, iter=1, verbose=True, capsize=100):
n = len(H)
n2 = n * n + n
dsr = {}
s = set()
ss = set()
pbar = start_progress_bar(iter, len(ds), verbose=verbose)
c = 0
for gnum in ds:
g = bfu.num2CG(gnum, n)
gnum = bfu.g2num(g)
c += 1
pbar.update(c)
glist = []
elist = []
eset = set()
for e in ds[gnum]:
if not e[1] in g[e[0]]:
gk.addanedge(g, e)
num = bfu.g2num(g)
ekey = (1 << (n2 - int(e[0], 10) * n - int(e[1], 10)))
if ekey in ccf and skip_conflictors(num, ccf[ekey]):
gk.delanedge(g, e)
continue
if not num in s:
if not bfu.call_u_conflicts(g, H):
#cf, gl2 = bfu.call_u_conflicts2(g, H)
#if not cf:
glist.append((num, ekey))
elist.append(e)
eset.add(ekey)
s.add(num)
if bfu.call_u_equals(g, H): ss.add(num)
if capsize <= len(ss): break
#if bfu.call_u_equals2(g, gl2, H): ss.add(num)
gk.delanedge(g, e)
for gn, e in glist:
if e in cp:
dsr[gn] = [ekey2e(k, n) for k in eset - cp[e]]
else:
dsr[gn] = elist
if capsize <= len(ss): return dsr, ss
pbar.finish()
return dsr, ss
def patchmerge(ds, H, cds):
n = len(H)
l = set()
s = set()
o = set()
for gkey in ds:
for num in ds[gkey]:
if gkey & num: continue
gnum = gkey | num
if gnum is s: continue
if skip_conflictors(gnum, cds): continue
g = bfu.num2CG(gnum,n)
if not bfu.call_u_conflicts(g, H):
l.add(gnum)
if bfu.call_u_equals(g, H): s.add(gnum)
elif not gnum in o and bfu.overshoot(g, H): o.add(gnum)
return l, s, o
def patchmerge(ds, H, cds):
n = len(H)
l = set()
s = set()
o = set()
for gkey in ds:
for num in ds[gkey]:
if gkey & num: continue
gnum = gkey | num
if gnum is s: continue
if skip_conflictors(gnum, cds): continue
g = bfu.num2CG(gnum, n)
if not bfu.call_u_conflicts(g, H):
l.add(gnum)
if bfu.call_u_equals(g, H): s.add(gnum)
elif not gnum in o and bfu.overshoot(g, H): o.add(gnum)
return l, s, o
def dceqclass2(H):
"""Find all graphs in the same equivalence class with respect to H
Arguments:
- `H`: an undersampled graph
"""
if cmp.isSclique(H):
print 'not running on superclique'
return set()
n = len(H)
s = set()
cp = confpairs(H)
confs = conflictor_set(H)
ccf = conflictors(H)
def prune_loops(gl, H):
l = []
for e in gl:
if e[0] == e[1] and not (e[1] in H[e[0]] and
(1, 0) in H[e[0]][e[1]]):
continue
l.append(e)
return l
edges = gk.edgelist(gk.complement(bfu.num2CG(0, n)))
edges = prune_loops(edges, H)
glist = map(lambda x: e2num(x, n), edges)
#glist = list(2**np.arange(n**2))
i = 0
while glist != []:
print 2**i, len(glist)
glist_prev = glist
glist, ss = quadmerge21(glist, H, confs)
s = s | ss
i += 1
ds = {x: edges for x in glist_prev}
for j in range(i, len(H)**2):
ds, ss = add2set_(ds, H, cp, ccf, iter=j, verbose=True)
s = s | ss
if not ds: break
return s
def edgemask2(gl,H, cds):
n = len(H)
nl= len(gl)
s = set()
o = set()
mask = np.zeros((nl,nl),'int')
np.fill_diagonal(mask,-1)
for i in xrange(nl):
for j in xrange(i+1,nl):
if gl[i] & gl[j]: continue
gnum = gl[i] | gl[j]
if skip_conflictors(gnum, cds): continue
g = bfu.num2CG(gnum,n)
if not bfu.call_u_conflicts(g, H):
if bfu.call_u_equals(g, H): s.add(gnum)
mask[i,j] = gnum
mask[j,i] = gnum
elif bfu.overshoot(g, H): o.add(gnum)
return mask, s, o # mask, found eqc members, overshoots
def dceqclass2(H):
"""Find all graphs in the same equivalence class with respect to H
Arguments:
- `H`: an undersampled graph
"""
if cmp.isSclique(H):
print 'not running on superclique'
return set()
n = len(H)
s = set()
cp = confpairs(H)
confs = conflictor_set(H)
ccf = conflictors(H)
def prune_loops(gl, H):
l = []
for e in gl:
if e[0] == e[1] and not (e[1] in H[e[0]] and (1,0) in H[e[0]][e[1]]): continue
l.append(e)
return l
edges = gk.edgelist(gk.complement(bfu.num2CG(0,n)))
edges = prune_loops(edges, H)
glist = map(lambda x: e2num(x,n),edges)
#glist = list(2**np.arange(n**2))
i = 0
while glist != []:
print 2**i, len(glist)
glist_prev = glist
glist, ss = quadmerge21(glist, H, confs)
s = s|ss
i += 1
ds = {x: edges for x in glist_prev}
for j in range(i, len(H)**2):
ds, ss = add2set_(ds, H, cp, ccf, iter=j, verbose=True)
s = s | ss
if not ds: break
return s
def edgemask2(gl, H, cds):
n = len(H)
nl = len(gl)
s = set()
o = set()
mask = np.zeros((nl, nl), 'int')
np.fill_diagonal(mask, -1)
for i in xrange(nl):
for j in xrange(i + 1, nl):
if gl[i] & gl[j]: continue
gnum = gl[i] | gl[j]
if skip_conflictors(gnum, cds): continue
g = bfu.num2CG(gnum, n)
if not bfu.call_u_conflicts(g, H):
if bfu.call_u_equals(g, H): s.add(gnum)
mask[i, j] = gnum
mask[j, i] = gnum
elif bfu.overshoot(g, H):
o.add(gnum)
return mask, s, o # mask, found eqc members, overshoots
def quadmerge_(glist, H, ds):
n = len(H)
gl = set()
ss = set()
conflicts = set()
for gi in combinations(glist, 2):
if gi[0] & gi[1]: continue
#if skip_conflict(gi[0], gi[1], ds): continue
gnum = gi[0] | gi[1]
if gnum in conflicts: continue
if skip_conflictors(gnum, ds):
conflicts.add(gnum)
continue
if gnum in gl: continue
g = bfu.num2CG(gnum,n)
if not bfu.call_u_conflicts(g, H):
gl.add(gnum)
if bfu.call_u_equals(g, H): ss.add(gnum)
else:
conflicts.add(gnum)
return gl, ss
def quadmerge_(glist, H, ds):
n = len(H)
gl = set()
ss = set()
conflicts = set()
for gi in combinations(glist, 2):
if gi[0] & gi[1]: continue
#if skip_conflict(gi[0], gi[1], ds): continue
gnum = gi[0] | gi[1]
if gnum in conflicts: continue
if skip_conflictors(gnum, ds):
conflicts.add(gnum)
continue
if gnum in gl: continue
g = bfu.num2CG(gnum, n)
if not bfu.call_u_conflicts(g, H):
gl.add(gnum)
if bfu.call_u_equals(g, H): ss.add(gnum)
else:
conflicts.add(gnum)
return gl, ss
def add2set_loop(ds, H, cp, ccf, iter=1, verbose=True,
capsize=100, currsize=0):
n = len(H)
n2 = n*n +n
dsr = {}
s = set()
ss = set()
pbar = start_progress_bar(iter, len(ds), verbose = verbose)
c = 0
for gnum in ds:
c += 1
pbar.update(c)
gset = set()
eset = set()
for sloop in ds[gnum]:
if sloop & gnum == sloop: continue
num = sloop | gnum
if sloop in ccf and skip_conflictors(num,ccf[sloop]):continue
if not num in s:
g = bfu.num2CG(num, n)
if not bfu.call_u_conflicts(g, H):
s.add(num)
gset.add((num,sloop))
eset.add(sloop)
if bfu.call_u_equals(g, H):
ss.add(num)
if capsize <= len(ss)+currsize: return dsr, ss
#for gn,e in gset:
# if e in cp:
# dsr[gn] = eset - cp[e] - set([e])
# else:
# dsr[gn] = eset - set([e])
for gn in gset: dsr[gn[0]] = eset - set([gn[1]])
pbar.finish()
return dsr, ss
def lquadmerge(gl, H, cds):
n = len(H)
l = set()
s = set()
mask, ss = ledgemask(gl, H, cds)
s = s | ss
ds = edgeds(mask)
#pp = pprint.PrettyPrinter(indent=1)
#pp.pprint(ds)
for idx in ds:
for gn in ds[idx]:
if mask[idx]&gn: continue
if skip_conflictors(mask[idx], gn, cds): continue
gnum = mask[idx] | gn
if gnum in l or gnum in ss: continue
g = bfu.num2CG(gnum,n)
if not bfu.call_u_conflicts(g, H):
l.add(gnum)
if bfu.call_u_equals(g, H): s.add(gnum)
return list(l), s
def quadmerge(gl, H, cds):
n = len(H)
l = set()
s = set()
mask, ss = edgemask(gl, H, cds)
s = s | ss
ds = edgeds(mask)
#pp = pprint.PrettyPrinter(indent=1)
#pp.pprint(ds)
for idx in ds:
for gn in ds[idx]:
if mask[idx] & gn: continue
if skip_conflict(mask[idx], gn, cds): continue
gnum = mask[idx] | gn
if gnum in l or gnum in ss: continue
g = bfu.num2CG(gnum, n)
if not bfu.call_u_conflicts(g, H):
l.add(gnum)
if bfu.call_u_equals(g, H): s.add(gnum)
return list(l), s
def withrates(s, H):
n = len(H)
d = {g: set() for g in s}
for g in s:
d[g] = getrates(bfu.num2CG(g, n), H)
return d
def permuteAset(s, perm):
n = len(perm)
ns = set()
for e in s:
ns.add(bfu.g2num(permute(bfu.num2CG(e,n),perm)))
return ns
def wrapper(fold,n=10,dens=0.1, urate=URATE):
scipy.random.seed()
rate = urate
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[:,::rate], pval=0.0001)
elif EST=='svar':
g2 = lm.data2graph(data[:,::rate])
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(bfu.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}
def wrapper(fold, n=10, dens=0.1, urate=URATE):
scipy.random.seed()
rate = urate
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[:, ::rate], pval=0.0001)
elif EST == 'svar':
g2 = lm.data2graph(data[:, ::rate])
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(bfu.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
}
def withrates(s,H):
n = len(H)
d = {g:set() for g in s}
for g in s:
d[g] = getrates(bfu.num2CG(g,n),H)
return d
