def _plot(eprobs): print 'plotting' probs = numpy.array([0.5, 0.5]) lst = [oneclasstree.bayesian(eprobs[:x,:])[1] for x in range(1, eprobs.shape[0]+1)] plt.figure() plt.plot(lst) plt.show()
def _plot(eprobs):
print 'plotting'
probs = numpy.array([0.5, 0.5])
lst = [
oneclasstree.bayesian(eprobs[:x, :])[1]
for x in range(1, eprobs.shape[0] + 1)
]
plt.figure()
plt.plot(lst)
plt.show()
try:
with open(classifier, 'rb') as f:
classifier = pickle.load(f)
assert hasattr(classifier, 'classify')
except:
print "could not load classifier"
print_help()
exit()
def _plot(eprobs):
print 'plotting'
probs = numpy.array([0.5, 0.5])
lst = [
oneclasstree.bayesian(eprobs[:x, :])[1]
for x in range(1, eprobs.shape[0] + 1)
]
plt.figure()
plt.plot(lst)
plt.show()
for pcap in remainder:
X = pacumen.make_feature_vectors_from_pcap(pcap)
eprobs = classifier.classify(X)
if visualize:
_plot(eprobs)
eprobs = oneclasstree.bayesian(eprobs)
print '%f %s' % (eprobs[1], pcap)
if classifier == None or len(remainder) < 1:
print_help()
exit()
try:
with open(classifier, 'rb') as f:
classifier = pickle.load(f)
assert hasattr(classifier, 'classify')
except:
print "could not load classifier"
print_help()
exit()
def _plot(eprobs):
print 'plotting'
probs = numpy.array([0.5, 0.5])
lst = [oneclasstree.bayesian(eprobs[:x,:])[1] for x in range(1, eprobs.shape[0]+1)]
plt.figure()
plt.plot(lst)
plt.show()
for pcap in remainder:
X = pacumen.make_feature_vectors_from_pcap(pcap)
eprobs = classifier.classify(X)
if visualize:
_plot(eprobs)
eprobs = oneclasstree.bayesian(eprobs)
print '%f %s' % (eprobs[1], pcap)
def classify_pcap(classifier, pcap): fv = make_feature_vectors_from_pcap(pcap) result = classifier.classify(fv) result = oneclasstree.bayesian(result)[1] #print pcap, result, result > 0.5 return result
