def classifyNavieBayesianTest():
wordsList, classTypes = bayes.loadDataSet()
inputTestWords = ['my', 'dog', 'has', 'flea', 'problems', 'help', 'please']
result = bayes.classifyNavieBayesian(wordsList, classTypes, inputTestWords)
print inputTestWords, ':', result
inputTestWords2 = ['love', 'stupid']
result2 = bayes.classifyNavieBayesian(wordsList, classTypes, inputTestWords2)
print inputTestWords2, ':', result2
def filterSpamEmail():
"""
过滤垃圾邮件
:return:
"""
initialDocList, classTypes = loadEmailText()
# 从initialDocList中随机创建10个待测试的文档
testDocList = []
# 待测试邮件的类型
testDocClassList = []
"""
注意此处随机选择10封email，添加到测试集合，同时将原有的数据集删除，
这种随机选择数据的一部分作为训练集合，而剩余部分作为测试集合的过程称为
留存交叉验证：hold-out cross validation
"""
for i in range(10):
randomIndex = int(random.uniform(0, len(initialDocList)))
testDocClassList.append(classTypes[randomIndex])
testDocList.append(initialDocList[randomIndex])
del (initialDocList[randomIndex])
del (classTypes[randomIndex])
errorCount = 0
for i in range(len(testDocList)):
# 对给定的待测试的邮件进行分类
classType = bayes.classifyNavieBayesian(
initialDocList, classTypes, testDocList[i])
if classType != testDocClassList[i]: # 预测的结果和实际的结果进行比较
print '分类错误的邮件：', testDocList[i], '\n属于', testDocClassList[i], \
'错误分类成了：', classType
errorCount += 1
# 计算分类的误差
print 'the error rate is :', float(errorCount) / len(testDocList)
