关于朴素贝叶斯中将概率归到[1.10001]问题

[LeonardNiu]

您好，请问在naive bayes 中train函数
`for i in range(class_num):
for j in range(feature_len):

        pix_0 = conditional_probability[i][j][0]
        pix_1 = conditional_probability[i][j][1]

      
        probalility_0 = (float(pix_0)/float(pix_0+pix_1))*1000000 + 1
        probalility_1 = (float(pix_1)/float(pix_0+pix_1))*1000000 + 1

        conditional_probability[i][j][0] = probalility_0
        conditional_probability[i][j][1] = probalility_1`

为什么要*1000000+1呢？