import mathp = [0.25, 0.75] # 符号集中“0”和“1”的概率I = sum([-p[i]*math.log2(p[i]) for i in range(2)]) # 计算信息熵print("每信源符号平均携带的信息量为：{:.3f}比特".format(I))m = 30 # “0”的个数I_m = m * math.log2(4) + (100-m) * math.log2(4/3) # 计算信息量print("包含{}个“0”的序列信息量为：{:.3f}比特".format(m, I_m))p = [m/100, (100-m)/100] # “0”和“1”的概率H = -sum([p[i]*math.log2(p[i]) for i in range(2)]) # 计算信息熵print("产生形式如同（2）中的序列所对应的信源的熵为：{:.3f}比特".format(H))