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