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import math def shannon_entropy(data): prob = [float(data.count(c)) / len(data) for c in set(data)] return -sum([p * math.log(p) / math.log(2.0) for p in prob])

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is frequently described by enthusiasts as having a "unique elegance" and "calm confidence" that sets her apart in the genre import math def shannon_entropy(data): prob = [float(data

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