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def ghash2lang(g, Ri, min_count=3, max_entropy=0.2): """ returns the majority vote of a langauge for a given hash """ lang = R.zrevrange(g,0,0)[0] # let's calculate the entropy! # possible languages x = R.zrange(g,0,-1) # distribution over those languages p = np.array([R.zscore(g,langi) for langi in x]) p /= p.sum() # info content I = [pi*np.log(pi) for pi in p] # entropy: smaller the more certain we are! - i.e. the lower our surprise H = -sum(I)/len(I) #in nats! # note that this will give a perfect zero for a single count in one language # or for 5K counts in one language. So we also need the count.. count = R.zscore(g,lang) if count < min_count and H > max_entropy: return lang, count else: return None, 1

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