mean, std = value[1][i]
prob[label] *= self.gaus_prob(input_data[i], mean, std)
# 分类器概率公式
return prob
def predict(self, X_test):
label = sorted(self.cal_prob(X_test).items(),
key=lambda x: x[-1])[-1][0]
# {label : prob},按照概率排序，取最后（最大）的【0】标签
return label
def predict_prob(self, X_test):
prob = sorted(self.cal_prob(X_test).items(),
key=lambda x: x[-1])[-1][1]
s = sum(i for i in self.cal_prob(X_test).values())
return prob / s # 预测概率
def score(self, X_test, y_test):
right = 0
for X, y in zip(X_test, y_test):
label = self.predict(X)
if label == y:
right += 1
return right / float(len(X_test))
clf = GausNB()
clf.fit(X_train, y_train)
x = [2, ord('S')]
print(x, "自编程高斯贝叶斯预测", clf.predict(x), clf.predict_prob(x))
