if mean > maxMean: maxMean = mean if std > maxStd: maxStd = std l = 2*(minMean - maxStd) r = 2*(maxMean + maxStd) for m, s in toPlot: normDist(m,s,l,r) formt = FormatJson(sys.argv[1]) naive = NaiveBayes(formt.data()) print "Total Accuracy (cross validate) = " + str(naive.crossValidate(10)) labels = formt.channels() counts = [[0]*len(labels) for i in range(len(labels))] for label, data in formt.data(): expectedChn, _ = naive.predict(data) # Confusion matrix correct = labels.index(label) actual = labels.index(expectedChn) counts[correct][actual] += 1 print counts labels = [''] + labels confusion(labels, counts)