朴素贝叶斯算法(Naive Bayes)算法的python实现 含源代码

算法原理不在赘述，请参考：

朴素贝叶斯分类算法

将代码保存为.py格式，默认使用的数据是代码文件所在目录下data目录下的 bayes_train.txt 和bayes_test.txt 两个文件分别作为训练样例和测试样例。以上参数可以在源代码中修改，也可以使用命令行参数传入，参考以下启动方式：

python bayes.py bayes_train.txt bayes_test.txt

命令中后两个参数为别为训练集和测试集合的途径。

python源代码如下：

__author__ = 'Administrator'
import re
import sys

DataLength = 100
Attr_num = 10
Val_num = 5
tr_data = []
test_data = []
tr_lg = ts_lg = 0
attrs = [0 for i in range(DataLength)]
wd = 0 ### the number of attributes, included category{yes,no}
values = [set() for i in range(2)]

val_ls =[]
pro_p = [[0 for i in range(Val_num)] for j in range(Attr_num)]
pro_n = [[0 for i in range(Val_num)] for j in range(Attr_num)]
pos = neg = 0

def naive_bayes(ls):
res = [0 for i in range(len(val_ls[wd-1]))]
# print wd
# print res
p = q = 1.0
for i in range(wd-1):
k=val_ls[i].index(ls[i])
p *= float(pro_p[i][k])/(pro_p[i][k] + pro_n[i][k])
q *= float(pro_n[i][k])/(pro_p[i][k] + pro_n[i][k])
res[0] = p
res[1] = q
# print res
return val_ls[wd-1][res.index(max(res))]

if __name__ == '__main__':
#for a in sys.argv:
# print a
train = "data/bayes_train.txt"
test = "data/bayes_test.txt"
if len(sys.argv)>1:
train = sys.argv[1]
test = sys.argv[2]
fp1=open(train,"r")
fp2=open(test,"r")
i=0
for line in fp1:
line = re.sub(r"\n\r","",line)
if i==0:
attrs = line.split()[1:]
wd = len(attrs)
i+=1
values = [set() for j in range(wd)]
val_ls = [0 for j in range(wd)]
continue
tr_data.append(line.split()[1:])
for j in range(wd):
values[j].add(tr_data[tr_lg][j])
tr_lg += 1

for i in range(wd):
val_ls[i] = list(values[i])
# print val_ls#,'here'

for i in range(tr_lg):
for j in range(wd-1):
k = val_ls[j].index(tr_data[i][j])
if tr_data[i][wd-1]=='Yes':
pro_p[j][k]+=1
pos+=1
else:
pro_n[j][k]+=1
neg+=1
pos = pos/(wd-1)
neg = neg/(wd-1)
# print pos,neg
for line in fp2:
line = re.sub(r"\n\r","",line)
test_data.append( line.split())
ts_lg += 1
fp1.close()
fp2.close()
# print tr_lg,ts_lg,attrs,wd,tr_data[0:2]
# for i in range(wd-1):
# print 'pos:',pro_p[i][0:len(val_ls[i])],"neg:",pro_n[i][0:len(values[i])]
# print test_data
for ls in test_data:
print ls,'\t\ttypes:',naive_bayes(ls)