关于贝叶斯的一个问题 有图有代码

没有土豆的洋芋

import csv
#导入数据
from numpy import genfromtxt, zeros
data = genfromtxt('F:\wc1.csv',delimiter=',',usecols=(0,1,2,3))
target = genfromtxt('F:\wc1.csv',delimiter=',',usecols=(4),dtype=str)

filename='F://wc1.csv'
with open(filename) as f:
    reader=csv.reader(f)
    header_row=next(reader)
    print(header_row)
    for index,column_header in enumerate(header_row):
        print(index,column_header)
#检验是否导入成功
#print data.shape
#print target.shape
#print target.shape
#print set(target)
from pylab import plot,show
plot(data[target=='1',1],data[target=='1',0],'bo')
plot(data[target=='2',1],data[target=='2',0],'ro')
plot(data[target=='3',1],data[target=='3',0],'go')
#show()


#分类朴素贝叶斯
#设置label值
t = zeros(len(target))
t[target == '1'] = 1
t[target == '2'] = 2
t[target == '3'] = 3
#导入贝叶斯函数
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(data,t)
#预测检验一下
#print classifier.predict(data[0])
#print t[0]



#validation,测试集被指定为源数据的40%
from sklearn import cross_validation
train, test, t_train, t_test = cross_validation.train_test_split(data, t, test_size=0.4, random_state=0)
classifier.fit(train,t_train)
print classifier.score(test,t_test)


#混淆矩阵
from sklearn.metrics import confusion_matrix
print confusion_matrix(classifier.predict(test),t_test)

#分类器性能完整报告
from sklearn.metrics import classification_report
print classification_report(classifier.predict(test), t_test, target_names=['1', '2', '3'])


请教各位大神 给点指点

那就举个栗子吧

老哥，我也在学贝叶斯分类器，这个分类器你想实现什么功能？

小锟

你的数据里有Nan的值吧，先把Nan的值删除了或者填充好，才能进行训练