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)
