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04 机器学习 贝叶斯代码实现没有P(Y=C)
probabilities = {}
for label, value in self.model.items():
probabilities[label] = 1
for i in range(len(value)):
mean, stdev = value[i]
probabilities[label] *= self.gaussian_probability(
input_data[i], mean, stdev)
return probabilities
没有实现P(Y=C)
公式中不是要计算p(y)p(x|y)吗?
是不是少乘一个p(y)
The text was updated successfully, but these errors were encountered:
04 机器学习 贝叶斯代码实现没有P(Y=C)
probabilities = {}
for label, value in self.model.items():
probabilities[label] = 1
for i in range(len(value)):
mean, stdev = value[i]
probabilities[label] *= self.gaussian_probability(
input_data[i], mean, stdev)
return probabilities
没有实现P(Y=C)
公式中不是要计算p(y)p(x|y)吗?
是不是少乘一个p(y)
The text was updated successfully, but these errors were encountered: