22 lines
663 B
Python
22 lines
663 B
Python
import numpy as np
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from sklearn.naive_bayes import BernoulliNB
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x = np.array([[0, 1, 0, 1], [1, 1, 1, 1], [1, 1, 1, 0],
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[0, 1, 1, 0], [0, 1, 0, 0], [0, 1, 0, 1],
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[1, 1, 0, 1], [1, 0, 0, 1], [1, 1, 0, 1],
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[0, 0, 0, 0]])
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# 有风-潮湿-多云-闷热
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y = np.array([1, 1, 1, 1, 0, 1, 0, 1, 1, 0])
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bnb = BernoulliNB()
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bnb.fit(x, y)
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day_pre = [[1, 0, 1, 0]]
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pre = bnb.predict(day_pre)
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print("预测结果如下:\n", "*" * 50)
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print("结果为: ", pre)
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print("*" * 50)
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# 进一步查看概率分析
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pre_pro = bnb.predict_proba(day_pre)
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print("不下雨的概率为: ", pre_pro[0][0], "\n下雨的概率为: ", pre_pro[0][1])
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