樸素貝葉斯程式碼（原理很簡單） 直接上程式碼

import numpy as np 
from collections import Counter,defaultdict

class Nbayes:
		def __init__(self):
			self.p_prior={}
			self.p_condiction={}
		def fit(self,x_train,y_train):
			#首先求出y_train的長度
			N=len(y_train)#N=y_train.shape[0]
			c_y=Counter(y_train)
			for key,var in c_y.items():
				self.p_prior[key]=var/N#求出各個y所佔的概率
			#接下來求條件概率
			for d in range(x.train.shape[0]):
				xd_y=default(int)
				vector=x_train[:,d]
				for x,y in zip(vextor,y_train):
					xd_y[(x,y)]+=1
					for key,var in xd_y.items():
						self.p_condiction[(d,key[0],key[1])]=var/c_y(key[1])
		return 
		#寫測試
		def predict(self,x_test):
			p=defaultdict()
			for y,y1 in self.p_prior.items():
				temp=y1
				for d,xd in enumerate(x):#列舉型別
						temp*=slef.p_condiction[(d,xd,y)] # 分母P(X)相同，故直接儲存聯合概率分佈即可
				p[y]=temp
		return max(p,key=p.get)
		

if __name__ == '__main__':
    data = np.array([[1, 0, -1], [1, 1, -1], [1, 1, 1], [1, 0, 1],
                     [1, 0, -1], [2, 0, -1], [2, 1, -1], [2, 1, 1],
                     [2, 2, 1], [2, 2, 1], [3, 2, 1], [3, 1, 1],
                     [3, 1, 1], [3, 2, 1], [3, 2, -1]])
    X_data = data[:, :-1]
    y_data = data[:, -1]
    clf = NBayes()
    clf.fit(X_data, y_data)
    print(clf.p_prior, '\n')##每個特徵的概率
    print(clf.p_condition,'\n')
    print(clf.predict(np.array([2, 0])))	
				

 

寫完了，這就是朴樹貝葉斯