def tick(print_nets=False, fitness='eigvals'):
global stats, populations, fitnesses
for i in xrange(_npops):
populations[i], fitnesses[i] = ga.tick(populations[i], fitnesses[i], fitness=fitness)
sccs = [net.sccs_in_array(m) for m in populations[i]]
sccs_flat = [x for s in sccs for x in s] # sccs across population
sccs_hist = util.histogram(sccs_flat)
sccs_hist = util.truncate_or_pad(sccs_hist, _n + 1, 0)[1:]
hamm = util.mean_hamming(populations[i])
stats[i] = {
'mean_fit': sum(fitnesses[i]) / _popsize,
'mean_sccs': sum([len(s) for s in sccs]) / _popsize,
'mean_hamm': hamm,
'sccs_hist': sccs_hist
}
if print_nets:
rand_net = populations[i][np.random.randint(_popsize)]
stats[i]['net'] = net.array_to_dot(rand_net)
from __future__ import division
import sys
import numpy as np
fp = np.load(sys.stdin)
N = fp[fp.keys()[0]].shape[0]
from modmat import net
scc_count = [len(net.sccs_in_array(arr)) for k, arr in fp.iteritems()]
mean_sccs = sum(scc_count) / len(scc_count)
serr_sccs = np.std(scc_count, ddof=1) / np.sqrt(len(scc_count))
tot = 0
nonz = 0
for k, arr in fp.iteritems():
for i in xrange(N):
for j in xrange(N):
tot += 1
if arr[i, j] != 0:
nonz += 1
print "Mean number of SCCS: %g +- %g" % (mean_sccs, serr_sccs)
print "Mean entry density: %g" % (nonz/tot)
from __future__ import division
import sys
import numpy as np
fp = np.load(sys.stdin)
N = fp[fp.keys()[0]].shape[0]
from modmat import net
scc_count = [len(net.sccs_in_array(arr)) for k, arr in fp.iteritems()]
mean_sccs = sum(scc_count) / len(scc_count)
serr_sccs = np.std(scc_count, ddof=1) / np.sqrt(len(scc_count))
tot = 0
nonz = 0
for k, arr in fp.iteritems():
for i in xrange(N):
for j in xrange(N):
tot += 1
if arr[i, j] != 0:
nonz += 1
print "Mean number of SCCS: %g +- %g" % (mean_sccs, serr_sccs)
print "Mean entry density: %g" % (nonz / tot)
