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####
##
##
## This is the vertical participation model. For each node i, we use
## exactly the same value of B_i as in the original network, but we
## choose at random the layers in which node i will be active. This
## breaks down intra-layer correlations.
##
## We get as input a file which reports, for each value of B_i, the
## number of nodes in the original network which have that value, i the format:
##
## B_i N(B_i)
##
##
##
## The output is the obtained distribution of bit-strings.
##
##
import sys
import random
def to_binary(l):
s = 0
e = 0
for v in l:
s += v * pow(2,e)
e +=1
return s
if len(sys.argv) < 3:
print "Usage: %s <Bi_file> <M>" % sys.argv[0]
sys.exit(1)
M = int(sys.argv[2])
layers = range(M)
distr = {}
with open(sys.argv[1], "r") as f:
for l in f:
if l[0] == "#":
continue
val, num = [int(x) for x in l.strip(" \n").split(" ")]
for j in range(num):
node_layers = random.sample(layers, val)
node_bitstring = [0 for x in range(M)]
#print node_bitstring, node_layers
for i in node_layers:
#print i,
node_bitstring[i] = 1
#print node_bitstring
bs = to_binary(node_bitstring)
if bs in distr:
distr[bs] += 1
else:
distr[bs] = 1
for k in distr:
bin_list = bin(k)
bin_num = sum([int(x) if x=='1' else 0 for x in bin_list[2:]])
sys.stderr.write("%d %0175s %d \n" % (bin_num, bin_list[2:], distr[k]))
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