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 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 getAring(n,
density=0.1,
st=0.5,
verbose=True,
dist='flatsigned',
permute=False):
keeptrying = True
plusedges = bfutils.dens2edgenum(density, n)
while keeptrying:
G = gk.ringmore(n, plusedges, permute=permute)
try:
A = transitionMatrix4(G, minstrength=st, distribution=dist)
try:
s = A.shape
keeptrying = False
except AttributeError:
keeptrying = True
except ValueError:
if verbose:
print "!!! Unable to find strong links for a stable matrix !!!"
print "*** trying a different graph"
return {
'graph': G,
'transition': A,
'converges': len(bfutils.call_undersamples(G))
}
def checkconflict(H,G_test, au = None):
if not au:
allundersamples = bfu.call_undersamples(G_test)
else:
allundersamples = au
for graph in allundersamples:
if isedgesubset(graph,H): return False
return True
def checkconflict(H, G_test, au=None):
if not au:
allundersamples = bfu.call_undersamples(G_test)
else:
allundersamples = au
for graph in allundersamples:
if isedgesubset(graph, H): return False
return True
def checkequality(H, G_test, au=None):
if not au:
allundersamples = bfu.call_undersamples(G_test)
else:
allundersamples = au
for graph in allundersamples:
if graph == H: return True
return False
def checkequality(H,G_test, au = None):
if not au:
allundersamples = bfu.call_undersamples(G_test)
else:
allundersamples = au
for graph in allundersamples:
if graph == H: return True
return False
def determine_reachable_graphs(n):
reachableList = []
gts = determine_domain(n)
for graph in gts:
reachable_graphs = bfutils.call_undersamples(graph)[1:] #because 0 returns itself and that's boring and we want undersampling rates higher than 1
for rg in reachable_graphs:
if rg not in reachableList:
reachableList.append(rg)
return reachableList
def determine_SCC_reachable_graphs(n):
reachableList = []
gts = determine_SCC_domain(n)
for graph in gts:
reachable_graphs = bfutils.call_undersamples(graph)[1:] #because 0 returns itself
for rg in reachable_graphs:
if rg not in reachableList:
reachableList.append(rg)
return reachableList
def generate_H(num_nodes): numextraedge = np.random.randint(low = 1,high = 3) g = bfutils.ringmore(num_nodes,numextraedge) #ground truth gs= bfutils.call_undersamples(g) # all possible undersamples for g randomu = np.random.randint(low = 1,high = len(gs)) #now we can make the H H = bfutils.undersample(g,randomu) #print H return H
def checkconflict_(Hnum, G_test, au = None):
if not au:
allundersamples = bfu.call_undersamples(G_test)
else:
allundersamples = au
#Hnum = bfu.ug2num(H)
for graph in allundersamples:
gnum = bfu.ug2num(graph)
if gnum[0]&Hnum[0] == gnum[0] and gnum[1]&Hnum[1] == gnum[1]:
return False
return True
def checkconflict_(Hnum, G_test, au=None):
if not au:
allundersamples = bfu.call_undersamples(G_test)
else:
allundersamples = au
#Hnum = bfu.ug2num(H)
for graph in allundersamples:
gnum = bfu.ug2num(graph)
if gnum[0] & Hnum[0] == gnum[0] and gnum[1] & Hnum[1] == gnum[1]:
return False
return True
def fastest_g(L):
x = []
y = []
for l in L:
d = zkl.load(l)
for i in range(0,100):
gs = bfutils.call_undersamples(d[i]['gt']) #this helps us determine how far u will go
for u in range(1,len(d[i]['solutions'])+1):
g2 = bfutils.undersample(d[i]['gt'],u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
return x,map(lambda x: x/1000./60., y)
def getAgraph(n, mp=2, st=0.5, verbose=True):
keeptrying = True
while keeptrying:
G = gk.rnd_cg(n, maxindegree=mp, force_connected=True)
try:
A = transitionMarix2(G, minstrength=st)
keeptrying = False
except ValueError as e:
if verbose:
print "!!! Unable to find strong links for a stable matrix !!!"
print "*** trying a different graph"
return {'graph': G,
'transition': A,
'converges': len(bfutils.call_undersamples(G))}
def fastest_g(L):
x = []
y = []
for l in L:
d = zkl.load(l)
for i in range(0, 100):
gs = bfutils.call_undersamples(
d[i]['gt']) #this helps us determine how far u will go
for u in range(1, len(d[i]['solutions']) + 1):
g2 = bfutils.undersample(d[i]['gt'], u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
return x, map(lambda x: x / 1000. / 60., y)
def gen_x_y(L):
x = []
y = []
for l in L:
d = zkl.load(l)
for i in range(0,100):
gs = bfutils.call_undersamples(d[i]['gt']) #this helps us determine how far u will go
for u in range(1,len(d[i]['solutions'])+1):
#for u in range(1,min([len(gs),4])):
g2 = bfutils.undersample(d[i]['gt'],u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
return x,y
def gen_x_y(L):
x = []
y = []
for l in L:
d = zkl.load(l)
for i in range(0, 100):
gs = bfutils.call_undersamples(
d[i]['gt']) #this helps us determine how far u will go
for u in range(1, len(d[i]['solutions']) + 1):
#for u in range(1,min([len(gs),4])):
g2 = bfutils.undersample(d[i]['gt'], u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
return x, y
def getAgraph(n, mp=2, st=0.5, verbose=True):
keeptrying = True
while keeptrying:
G = gk.rnd_cg(n, maxindegree=mp, force_connected=True)
try:
A = transitionMarix2(G, minstrength=st)
keeptrying = False
except ValueError as e:
if verbose:
print "!!! Unable to find strong links for a stable matrix !!!"
print "*** trying a different graph"
return {
'graph': G,
'transition': A,
'converges': len(bfutils.call_undersamples(G))
}
def getAring(n, density=0.1, st=0.5, verbose=True, dist='flatsigned', permute=False):
keeptrying = True
plusedges = bfutils.dens2edgenum(density,n)
while keeptrying:
G = gk.ringmore(n, plusedges, permute=permute)
try:
A = transitionMatrix4(G, minstrength=st, distribution=dist)
try:
s = A.shape
keeptrying = False
except AttributeError:
keeptrying = True
except ValueError:
if verbose:
print "!!! Unable to find strong links for a stable matrix !!!"
print "*** trying a different graph"
return {'graph': G,
'transition': A,
'converges': len(bfutils.call_undersamples(G))}
def wrapper_rate_agnostic(fold, n=10, k=10):
scipy.random.seed()
l = {}
while True:
try:
g = bfutils.ringmore(n, k) # random ring of given density
gs = bfutils.call_undersamples(g)
for u in range(1, min([len(gs), UMAX])):
g2 = bfutils.undersample(g, u)
print fold, ': ', traversal.density(g), ':',
startTime = int(round(time.time() * 1000))
s = ur.iteqclass(g2, verbose=False)
endTime = int(round(time.time() * 1000))
print len(s)
l[u] = {'eq': s, 'ms': endTime - startTime}
except MemoryError:
print 'memory error... retrying'
continue
break
return {'gt': g, 'solutions': l}
def ra_wrapper_preset(fold, glist=[]):
scipy.random.seed()
l = {}
while True:
try:
g = glist[fold]
gs= bfutils.call_undersamples(g)
for u in range(1,min([len(gs),UMAX])):
g2 = bfutils.undersample(g,u)
print fold,': ',traversal.density(g),':',
startTime = int(round(time.time() * 1000))
s = ur.liteqclass(g2, verbose=False, capsize=CAPSIZE)
endTime = int(round(time.time() * 1000))
print len(s), u
l[u] = {'eq':s,'ms':endTime-startTime}
except MemoryError:
print 'memory error... retrying'
continue
break
return {'gt':g,'solutions':l}
def wrapper_rate_agnostic(fold, n=10, k=10):
scipy.random.seed()
l = {}
while True:
try:
g = bfutils.ringmore(n,k) # random ring of given density
gs= bfutils.call_undersamples(g)
for u in range(1,min([len(gs),UMAX])):
g2 = bfutils.undersample(g,u)
print fold,': ',traversal.density(g),':',
startTime = int(round(time.time() * 1000))
s = ur.iteqclass(g2, verbose=False)
endTime = int(round(time.time() * 1000))
print len(s)
l[u] = {'eq':s,'ms':endTime-startTime}
except MemoryError:
print 'memory error... retrying'
continue
break
return {'gt':g,'solutions':l}
import bfutils as bfu
import matplotlib.pyplot as plt
NODES = 6
DENSITY = 0.2
UMAX = 6
REPEATS = 100
d = zkl.load('leibnitz_nodes_6_density_0.2_ra_.zkl')
x = []
y = []
for i in range(0, REPEATS):
gs = bfutils.call_undersamples(
d[i]['gt']) #this helps us determine how far u will go
for u in range(1, min([len(gs), UMAX])):
g2 = bfutils.undersample(d[i]['gt'], u) #this is H
x.append(traversal.density(g2)) #add the density of H
y.append(d[i]['solutions'][u]['ms']) #add the time
print len(x)
print len(y)
fig = plt.figure()
ax = plt.gca()
ax.scatter(x, y)
ax.set_yscale('log')
plt.xlabel('edge density of H')
plt.ylabel('log scale time')
plt.title('Number of Nodes: %s , Density: %s ,UMAX: %s' %
(NODES, DENSITY, UMAX))
def determine_reachable_combination(X,Y):
print "tbd"
reachableList = []
gts = determine_domain(n)
for graph in gts:
reachable_graphs = bfutils.call_undersamples(graph)[1:]
def getrates(g,H):
n = len(H)
au = bfu.call_undersamples(g)
return list(np.where(map(lambda x: x == H, au))[0])
def getrates(g, H):
n = len(H)
au = bfu.call_undersamples(g)
return list(np.where(map(lambda x: x == H, au))[0])
