import numpy as np
import pandas as pd
data_list = [
        ['青绿', '蜷缩', '浊响', '清晰', '凹陷', '碍滑', 'YES'],
        ['乌黑', '蜷缩', '沉闷', '清晰', '凹陷', '碍滑', 'YES'],
        ['乌黑', '蜷缩', '浊响', '清晰', '凹陷', '碍滑', 'YES'],
        ['青绿', '蜷缩', '沉闷', '清晰', '凹陷', '碍滑', 'YES'],
        ['浅白', '蜷缩', '浊响', '清晰', '凹陷', '碍滑', 'YES'],
        ['青绿', '稍缩', '浊响', '清晰', '稍凹', '软粘', 'YES'],
        ['乌黑', '稍缩', '浊响', '清晰', '稍凹', '软粘', 'YES'],
        ['乌黑', '稍缩', '浊响', '清晰', '稍凹', '硬滑', 'YES'],
        ['乌黑', '稍缩', '沉闷', '稍糊', '稍凹', '硬滑', 'NO'],
        ['青绿', '硬挺', '清脆', '清晰', '平坦', '软粘', 'NO'],
        ['浅白', '硬挺', '清脆', '模糊', '平坦', '硬滑', 'NO'],
        ['浅白', '蜷缩', '浊响', '模糊', '平坦', '软粘', 'NO'],
        ['青绿', '稍缩', '浊响', '稍糊', '凹陷', '硬滑', 'NO'],
        ['浅白', '稍缩', '沉闷', '稍糊', '凹陷', '硬滑', 'NO'],
        ['乌黑', '稍缩', '浊响', '清晰', '稍凹', '软粘', 'NO'],
        ['浅白', '蜷缩', '浊响', '模糊', '稍凹', '硬滑', 'NO'],
        ['青绿', '蜷缩', '沉闷', '稍糊', '稍凹', '硬滑', 'NO']
        ]

classes_list = ['色泽','根蒂','敲声','纹理','脐部','触感','好瓜']
property_list = [
        '青绿','乌黑','浅白',
        '蜷缩','稍蜷','硬挺',
        '浊响','沉闷','清脆',
        '清晰','稍糊','模糊',
        '凹陷','平坦','稍凹',
        '硬滑','软粘',]

import pandas as pd
import numpy  as np

class NaiveBayes:
    def __init__(self):
        self.model = {}

    # def calEntropy(self, y): 
    #     valRate = y.value_counts().apply(lambda x : x / y.size) 
    #     valEntropy = np.inner(valRate, np.log2(valRate)) * -1  
    #     return valEntropy

    def fit(self, xTrain, yTrain = pd.Series()):
        if not yTrain.empty:#如果不传，自动选择最后一列作为分类标签
            xTrain = pd.concat([xTrain, yTrain], axis=1)   #按列合并
        self.model = self.buildNaiveBayes(xTrain) 
        return self.model

    def buildNaiveBayes(self, xTrain):
        yTrain = xTrain.iloc[:,-1]   #获取特征

        yTrainCounts = yTrain.value_counts()# 得到各个特征对应的概率

        yTrainCounts = yTrainCounts.apply(lambda x : (x + 1) / (yTrain.size + yTrainCounts.size)) #使用了拉普拉斯平滑 分别计算YES和NO的概率
        # print("1111:",yTrainCounts)
        # print("@@@")
        retModel = {} #使用拉普拉斯的模型
        for nameClass, val in yTrainCounts.items():
            retModel[nameClass] = {'PClass': val, 'PFeature':{}}
        # print("@@")
        # print(retModel) 
        # print("@@")   
        propNamesAll = xTrain.columns[:-1]  #训练数据
        # print("@@")
        # print(xTrain[propNamesAll]) 
        # print("@@")   
        allPropByFeature = {}
        for nameFeature in propNamesAll:
            allPropByFeature[nameFeature] = list(xTrain[nameFeature].value_counts().index)#获取每列的特征
        # print("@@")
        # print(allPropByFeature) 
        # print("@@")   
        for nameClass, group in xTrain.groupby(xTrain.columns[-1]): #根据最后一列分组
            for nameFeature in propNamesAll:
                eachClassPFeature = {}
                propDatas = group[nameFeature]
                propClassSummary = propDatas.value_counts()# 频次汇总 得到各个特征对应的概率
                for propName in allPropByFeature[nameFeature]:
                    if not propClassSummary.get(propName):
                        propClassSummary[propName] = 0#如果有属性没有，那么自动补0
                Ni = len(allPropByFeature[nameFeature])
                propClassSummary = propClassSummary.apply(lambda x : (x + 1) / (propDatas.size + Ni))#使用了拉普拉斯平滑   计算条件概率
                for nameFeatureProp, valP in propClassSummary.items():
                    eachClassPFeature[nameFeatureProp] = valP
                retModel[nameClass]['PFeature'][nameFeature] = eachClassPFeature
        # print("@@")
        # print(propClassSummary) 
        # print("@@")
        return retModel

    def predictBySeries(self, data):
        curMaxRate = None
        curClassSelect = None
        for nameClass, infoModel in self.model.items():
            rate = 0
            rate += np.log(infoModel['PClass'])
            PFeature = infoModel['PFeature']     #每个特征的概率
     

            for nameFeature, val in data.items():
                propsRate = PFeature.get(nameFeature)
                if not propsRate:
                    continue
                rate += np.log(propsRate.get(val, 0))#使用log加法避免很小的小数连续乘，接近零
                #print(nameFeature, val, propsRate.get(val, 0))
            #print(nameClass, rate)
            if curMaxRate == None or rate > curMaxRate:
                curMaxRate = rate
                curClassSelect = nameClass
        # print("@@")
        # print(PFeature)
        # print("@@")
        return curClassSelect
    def predict(self, data):
        if isinstance(data, pd.Series):     #对比类型
            return self.predictBySeries(data)
        return data.apply(lambda d: self.predictBySeries(d), axis=1)

dataTrain = data_df

naiveBayes = NaiveBayes()
treeData = naiveBayes.fit(dataTrain)

import json
print(json.dumps(treeData, ensure_ascii=False))

pd = pd.DataFrame({'预测值':naiveBayes.predict(dataTrain), '正取值':dataTrain.iloc[:,-1]})
print(pd)
print('正确率:%f%%'%(pd[pd['预测值'] == pd['正取值']].shape[0] * 100.0 / pd.shape[0]))

{"NO": {"PClass": 0.5263157894736842, "PFeature": {"0": {"浅白": 0.4166666666666667, "青绿": 0.3333333333333333, "乌黑": 0.25}, "1": {"稍缩": 0.4166666666666667, "蜷缩": 0.3333333333333333, "硬挺": 0.25}, "2": {"浊响": 0.4166666666666667, "沉闷": 0.3333333333333333, "清脆": 0.25}, "3": {"稍糊": 0.4166666666666667, "模糊": 0.3333333333333333, "清晰": 0.25}, "4": {"稍凹": 0.4166666666666667, "平坦": 0.3333333333333333, "凹陷": 0.25}, "5": {"硬滑": 0.5833333333333334, "软粘": 0.3333333333333333, "碍滑": 0.08333333333333333}}}, "YES": {"PClass": 0.47368421052631576, "PFeature": {"0": {"乌黑": 0.45454545454545453, "青绿": 0.36363636363636365, "浅白": 0.18181818181818182}, "1": {"蜷缩": 0.5454545454545454, "稍缩": 0.36363636363636365, "硬挺": 0.09090909090909091}, "2": {"浊响": 0.6363636363636364, "沉闷": 0.2727272727272727, "清脆": 0.09090909090909091}, "3": {"清晰": 0.8181818181818182, "稍糊": 0.09090909090909091, "模糊": 0.09090909090909091}, "4": {"凹陷": 0.5454545454545454, "稍凹": 0.36363636363636365, "平坦": 0.09090909090909091}, "5": {"碍滑": 0.5454545454545454, "软粘": 0.2727272727272727, "硬滑": 0.18181818181818182}}}}
    预测值  正取值
0   YES  YES
1   YES  YES
2   YES  YES
3   YES  YES
4   YES  YES
5   YES  YES
6   YES  YES
7   YES  YES
8    NO   NO
9    NO   NO
10   NO   NO
11   NO   NO
12   NO   NO
13   NO   NO
14  YES   NO
15   NO   NO
16   NO   NO

 sklearn包中的朴素贝叶斯

from sklearn.model_selection import train_test_split
import numpy as np
data_list = [#青绿 0 乌黑 1 浅白 2  蜷缩 0 稍缩 1  硬挺 2 浊响 0 沉闷 1 清脆 2 清晰 0 稍糊 1 模糊 2 平坦 0 稍凹 1 凹陷 2 碍滑 0 软粘 1 硬滑 2
        [0, 0, 0, 0, 2, 0, 1],
        [1, 0, 1, 0, 2, 0, 1],
        [1, 0, 0, 0, 2, 0, 1],
        [0, 0, 1, 0, 2, 0, 1],
        [2, 0, 0, 0, 2, 0, 1],
        [0, 1, 0, 0, 1, 1, 1],
        [1, 1, 0, 0, 1, 1, 1],
        [1, 1, 0, 0, 1, 2, 1],
        [1, 1, 1, 1, 1, 2, 0],
        [0, 2, 2, 0, 0, 1, 0],
        [2, 2, 2, 2, 0, 2, 0],
        [2, 0, 0, 2, 0, 1, 0],
        [0, 1, 0, 1, 2, 2, 0],
        [2, 1, 1, 1, 2, 2, 0],
        [1, 1, 0, 0, 1, 1, 0],
        [2, 0, 0, 2, 1, 2, 0],
        [0, 0, 1, 1, 1, 2, 0]
        ]
target = np.array([0,1,2,3,4,5,6],dtype='float32')
data = np.array(data_list,dtype='float32')

from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(data.T,target,random_state=1)   #按比例分割数据
nb_clf = GaussianNB() #实例化模型
nb_clf.fit(x_train,y_train)  #模型训练
a=nb_clf.predict(x_test)    #预测
acc_score = nb_clf.score(x_test,y_test)   #查看模型分数