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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