李航《统计学习方法》第2版 第4章 编程实现朴素贝叶斯、调用sklearn模块实现（实现书本63页例题4.1）

自编程实现：

"""
	朴素贝叶斯：实现课本63页例题4.1
	参数估计：贝叶斯估计

"""
import numpy as np
import pandas as pd

class NaiveBayes():
	def __init__(self, lambda_):
		self.lambda_=lambda_
		self.y_types = None
		self.y_counts = None
		self.y_prob = None
		# (xi第几维度,xi,y)
		self.x_under_y_prob = dict()

	def fit(self, x_train, y_train):
		self.y_types = np.unique(y_train)
		x = pd.DataFrame(x_train)
		y = pd.DataFrame(y_train)


		self.y_counts = y[0].value_counts()

		self.y_prob = (self.y_counts + self.lambda_) / (y.shape[0] + len(self.y_types)*self.lambda_)

		for idx in x.columns:
			for j in self.y_types:
				p_x_y = x[(y==j).values][idx].value_counts()

				for i in p_x_y.index:
					self.x_under_y_prob[(idx, i, j)] = (p_x_y[i] + self.lambda_) / (self.y_counts[j] + p_x_y.shape[0]*self.lambda_)


	def predict(self, x_test):
		res = []
		for y in self.y_types:  # 遍历y的可能取值
			p_y = self.y_prob[y]  # 计算y的先验概率P(Y=ck)
			p_xy = 1
			for idx, x in enumerate(x_test):
				p_xy *= self.x_under_y_prob[(idx, x, y)]  # 计算P(X=(x1,x2...xd)/Y=ck)
			res.append(p_y * p_xy)
		for i in range(len(self.y_types)):
			print("[{}]对应概率：{:.2%}".format(self.y_types[i], res[i]))
		# 返回最大后验概率对应的y值
		return self.y_types[np.argmax(res)]

def main():
	x_train = np.array([
					  [1,"S"],
					  [1,"M"],
					  [1,"M"],
					  [1,"S"],
					  [1,"S"],
					  [2,"S"],
					  [2,"M"],
					  [2,"M"],
					  [2,"L"],
					  [2,"L"],
					  [3,"L"],
					  [3,"M"],
					  [3,"M"],
					  [3,"L"],
					  [3,"L"]
					  ])
	y_train=np.array([-1,-1,1,1,-1,-1,-1,1,1,1,1,1,1,1,-1])
	x_test = np.array([2,'S'])

	clf = NaiveBayes(lambda_=0.2)
	clf.fit(x_train, y_train)
	y_predict = clf.predict(x_test)
	print('类别为: ', y_predict)

if __name__ == '__main__':
	main()

---

调用sklearn模块实现：

---

"""
	朴素贝叶斯：利用sklearn实现课本63页例题4.1
	参数估计：贝叶斯估计
"""

import numpy as np
from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB
from sklearn import preprocessing  #预处理

def main():
    X_train=np.array([
                      [1,"S"],
                      [1,"M"],
                      [1,"M"],
                      [1,"S"],
                      [1,"S"],
                      [2,"S"],
                      [2,"M"],
                      [2,"M"],
                      [2,"L"],
                      [2,"L"],
                      [3,"L"],
                      [3,"M"],
                      [3,"M"],
                      [3,"L"],
                      [3,"L"]
                      ])
    y_train=np.array([-1,-1,1,1,-1,-1,-1,1,1,1,1,1,1,1,-1])
    #预处理onehot编码
    enc = preprocessing.OneHotEncoder(categories='auto')
    enc.fit(X_train)
    X_train = enc.transform(X_train).toarray()
    print(X_train)
    clf=MultinomialNB(alpha=0.0000001)
    clf.fit(X_train,y_train)
    X_new=np.array([[2,"S"]])
    X_new=enc.transform(X_new).toarray()
    print(X_new)
    y_predict=clf.predict(X_new)
    print("{}被分类为:{}".format(X_new,y_predict))
    print(clf.predict_proba(X_new))

if __name__=="__main__":
    main()

函数用法：