python实现朴素贝叶斯分类器

Python  /  管理员 发布于 7年前   237

本文用的是sciki-learn库的iris数据集进行测试。用的模型也是最简单的，就是用贝叶斯定理P(A|B) = P(B|A)*P(A)/P(B)，计算每个类别在样本中概率（代码中是pLabel变量）

以及每个类下每个特征的概率（代码中是pNum变量）。

写得比较粗糙，对于某个类下没有此特征的情况采用p=1/样本数量。

有什么错误有人发现麻烦提出，谢谢。

[python] view plain copy# -*- coding:utf-8 -*- from numpy import * from sklearn import datasets import numpy as np  class NaiveBayesClassifier(object):    def __init__(self):     self.dataMat = list()     self.labelMat = list()     self.pLabel = {}     self.pNum = {}    def loadDataSet(self):     iris = datasets.load_iris()     self.dataMat = iris.data     self.labelMat = iris.target     labelSet = set(iris.target)     labelList = [i for i in labelSet]     labelNum = len(labelList)     for i in range(labelNum):       self.pLabel.setdefault(labelList[i])       self.pLabel[labelList[i]] = np.sum(self.labelMat==labelList[i])/float(len(self.labelMat))    def seperateByClass(self):     seperated = {}     for i in range(len(self.dataMat)):       vector = self.dataMat[i]       if self.labelMat[i] not in seperated:         seperated[self.labelMat[i]] = []       seperated[self.labelMat[i]].append(vector)     return seperated    # 通过numpy array二维数组来获取每一维每种数的概率   def getProbByArray(self, data):     prob = {}     for i in range(len(data[0])):       if i not in prob:         prob[i] = {}       dataSetList = list(set(data[:, i]))       for j in dataSetList:         if j not in prob[i]:           prob[i][j] = 0         prob[i][j] = np.sum(data[:, i] == j) / float(len(data[:, i]))     prob[0] = [1 / float(len(data[:,0]))] # 防止feature不存在的情况     return prob    def train(self):     featureNum = len(self.dataMat[0])     seperated = self.seperateByClass()     t_pNum = {} # 存储每个类别下每个特征每种情况出现的概率     for label, data in seperated.iteritems():       if label not in t_pNum:         t_pNum[label] = {}       t_pNum[label] = self.getProbByArray(np.array(data))     self.pNum = t_pNum    def classify(self, data):     label = 0     pTest = np.ones(3)     for i in self.pLabel:       for j in self.pNum[i]:         if data[j] not in self.pNum[i][j]:           pTest[i] *= self.pNum[i][0][0]         else:           pTest[i] *= self.pNum[i][j][data[j]]     pMax = np.max(pTest)     ind = np.where(pTest == pMax)     return ind[0][0]    def test(self):     self.loadDataSet()     self.train()     pred = []     right = 0     for d in self.dataMat:       pred.append(self.classify(d))     for i in range(len(self.labelMat)):       if pred[i] == self.labelMat[i]:         right += 1     print right / float(len(self.labelMat))  if __name__ == '__main__':   NB = NaiveBayesClassifier()   NB.test() 

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