def testAccept(self):
"Generator should accept better ProbMeasures."
generator = mfng.Generator(T0=.2, steps=10, Tlimit=.04, m=3)
generator.append_property(mfng.DistributionFunction("k**-2", maxdeg=10, mindeg=1))
for i in range(3):
divs = known_values[i]["divs"]
probs = known_values[i]["probs"]
energy = known_values[i]["energy"]
self.assertTrue(generator.has_accepted(mfng.ProbMeasure(divs, probs)))
def properties(self, n=100):
"""
Prints the statistical properties (maximal and average degree) of the results.
It goes through the labels we have set with `self.set_labels`.
Parameters:
`n`: optional
the number of the generated networks to create statistics.
Example::
>>> r.set_labels("200_020")
['200_020']
>>> r.properties(n=100)
====================
label = 200_020
number of generated networks = 100
divs = [0.81104014013270886, 1.0],
probs=[[ 0.27609856 0.23149258]
[ 0.23149258 0.26091629]]
avg max deg = 9.87+-1.0115993937, avg average deg=3.5651+-0.175231419857
"""
out = Output(os.path.join(self.project_dir, "properties.txt"))
assert isinstance(n, int) and n > 0
if not isinstance(self.labels, list) or not self.labels:
raise NoLabelsError
for label in self.labels:
run = self.runs[label]
doubleline = "=" * 20
out.write(doubleline)
out.write("label = %s" % label)
divs, probs = run["divs"], run["probs"]
pm = mfng.ProbMeasure(divs, probs)
K = run.get("K") or run.get(
"k") # Older files have k instead of K.
ipm = pm.iterate(K=K)
out.write(
"number of generated networks = %s\ndivs = %s,\nprobs=%s" %
(n, divs, probs))
maxdegs = []
avgdegs = []
for i in range(n):
nw = ipm.generate(n=run["n"])
deg = nw.degree()
maxdegs.append(max(deg))
avgdegs.append(pylab.average(deg))
avgmaxdeg, avgmaxdegsigma = avg_sigma(maxdegs)
avgavgdeg, avgavgdegsigma = avg_sigma(avgdegs)
out.write("\navg max deg = %5s+-%5s, avg average deg=%5s+-%5s\n" %
(avgmaxdeg, avgmaxdegsigma, avgavgdeg, avgavgdegsigma))
del out
def testDegdistValues(self):
"The calculated degree distribution should be correct."
for i in range(3):
divs = known_values[i]["divs"]
probs = known_values[i]["probs"]
result = known_values[i]["result"]
pm = mfng.ProbMeasure(divs, probs)
lpm = pm.iterate(4)
ddnumpy = lpm.degdist_numpy(n=2000, maxdeg=20)
dditerated = pm.degdist_iterated(n=2000, maxdeg=20)
for i in range(20):
self.assertAlmostEqual(ddnumpy[i]/result[i], 1, places=5)
for i in range(20):
self.assertAlmostEqual(dditerated[i]/result[i], 1, places=5)
def testDistributionFunctionValues(self):
"The energy of the DistributionFunction should be correct."
for i in range(2):
divs = known_values[i]["divs"]
probs = known_values[i]["probs"]
energy = known_values[i]["energy"]
pm = mfng.ProbMeasure(divs, probs)
lpm = pm.iterate(4)
prop = mfng.DistributionFunction('k**-2',
maxdeg=2000 - 1,
mindeg=1)
self.assertAlmostEqual(prop.energy(lpm, 2000)[0], energy)
prop = mfng.DistributionFunctionC('k**-2',
maxdeg=2000 - 1,
mindeg=1)
self.assertAlmostEqual(prop.energy(pm, 2000)[0], energy, places=4)
def setUp(self):
self.pm = mfng.ProbMeasure(numpy.array([.3, 1]),
numpy.array([[0.1, .25], [0.25, 0.4]]))
self.iterated_pm = self.pm.iterate(2)