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。
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# -*- 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()
