# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
 
'''
樸素貝葉斯分類器的原理
 
事件H的先驗概率P(H)，即無條件概率
事件H在X發生時的後驗概率P(H|X)
 
這裡面H通常是指某一個分類
X是指樣本事件
P(H|X)就表示在樣本X發生的情況下，分類H的概率
假設有N個分類H1到HN，那麼P(Hi|X)最大的分類就是我們要找的，假設是Hk
因為在X發生的情況下，Hk發生的概率最大
 
P(H|X) = [P(X|H)*P(H)]/P(X)
 
分類器的構造
條件如下
1.每個元組有N個屬性 X = ｛x1 , x2 , ... , xn｝，分別對應屬性｛A1 ... An｝，並且屬性之間相互獨立
2.當前有M個訓練元組，並且知道它們的分類標號
3.一共有K個分類C1 ... Ck，並且知道每個分類的先驗概率，如果不知道則擬定其為1/K
 
問題是
對於任意給定的新的元組Xgive，確定其分類Cget，使得P(Cget|Xgive)最大（相對於其它分類）
 
解析
P(Ci|X) = [P(X|Ci)*P(Ci)]/P(X)
對於所有的分類｛C｝，P(X)是不變的（其實是未知的），為了使P(Ci|X)最大，
只需要使P(X|Ci)*P(Ci)最大
如果P(Ci)取1/K，則只要使P(X|Ci)最大
由於X = ｛x1 , x2 , ... , xn｝，分別對應屬性｛A1 ... An｝，並且各個屬性之間沒有關聯
則P(X|Ci)*P(Ci) = P(x1|Ci)*P(x2|Ci)*...*P(xn|Ci) * P(Ci)
 
P(xi|Ci)由訓練元組得到
分為兩種情況
如果Ai為離散值，則P(xi|Ci)等於【屬於Ci的訓練元組中，Ai=xi的元組個數】/【屬於Ci的元組總數】
如果Ai為連續值，則假定Ci中的Ai服從高斯分佈，計算出高斯分佈的引數，分別為平均值和標準差
P(xi|Ci)等於xi處的概率密度
 
'''
 
 
'''
問題一：訓練元組從何而來？
 
'''
 
#載入訓練資料
#檔案格式：屬性標號，是否連續【yes|no】，屬性說明
attribute_file_dest = 'F:\\bayes_categorize\\attribute.dat'
attribute_file = open(attribute_file_dest)
 
#檔案格式：rec_id,attr1_value,attr2_value,...，attrn_value,class_id
trainning_data_file_dest = 'F:\\bayes_categorize\\trainning_data.dat'
trainning_data_file = open(trainning_data_file_dest)
 
#檔案格式：class_id,class_desc
class_desc_file_dest = 'F:\\bayes_categorize\\class_desc.dat'
class_desc_file = open(class_desc_file_dest)
 
 
attr_dict = {}
for line in attribute_file :
    line = line.strip()
    fld_list = line.split(',')
    attr_dict[int(fld_list[0])] = tuple(fld_list[1:])
 
class_dict = {}
for line in class_desc_file :
    line = line.strip()
    fld_list = line.split(',')
    class_dict[int(fld_list[0])] = fld_list[1]
    
trainning_data_dict = {}
class_member_set_dict = {}
for line in trainning_data_file :
    line = line.strip()
    fld_list = line.split(',')
    rec_id = int(fld_list[0])
    a1 = int(fld_list[1])
    a2 = int(fld_list[2])
    a3 = float(fld_list[3])
    c_id = int(fld_list[4])
    
    if c_id not in class_member_set_dict :
        class_member_set_dict[c_id] = set()
    class_member_set_dict[c_id].add(rec_id)
    trainning_data_dict[rec_id] = (a1 , a2 , a3)
    
attribute_file.close()
class_desc_file.close()
trainning_data_file.close()
 
class_possibility_dict = {}
for c_id in class_member_set_dict :
    class_possibility_dict[c_id] = (len(class_member_set_dict[c_id]) + 0.0)/len(trainning_data_dict)    
 
#等待分類的資料
data_to_classify_file_dest = 'F:\\bayes_categorize\\trainning_data_new.dat'
data_to_classify_file = open(data_to_classify_file_dest)
data_to_classify_dict = {}
for line in data_to_classify_file :
    line = line.strip()
    fld_list = line.split(',')
    rec_id = int(fld_list[0])
    a1 = int(fld_list[1])
    a2 = int(fld_list[2])
    a3 = float(fld_list[3])
    c_id = int(fld_list[4])
    data_to_classify_dict[rec_id] = (a1 , a2 , a3 , c_id)
data_to_classify_file.close()
 
 
diff_cnt = 0
#對於每一個待分類元組，對於每一個分類計算P(X|Ci)*P(Ci)，尋找取得最大值的分類
for rec_id in data_to_classify_dict :
    
    res_class_id = 0
    max_P_X_Ci = 0.0
    a1_x1 = data_to_classify_dict[rec_id][0]
    a2_x2 = data_to_classify_dict[rec_id][1]
    a3_x3 = data_to_classify_dict[rec_id][2]
    for c_id in class_possibility_dict :
        P_Ci = class_possibility_dict[c_id]
        #求P_x1_Ci
        cnt_Ci = len(class_member_set_dict[c_id])
        cnt_x1_Ci = len([tmp_rec_id for tmp_rec_id in trainning_data_dict \
        if trainning_data_dict[tmp_rec_id][0] == a1_x1 and tmp_rec_id in class_member_set_dict[c_id]])
        P_x1_Ci = (cnt_x1_Ci + 0.0) / cnt_Ci
        #求P_x2_Ci
        cnt_Ci = len(class_member_set_dict[c_id])
        cnt_x2_Ci = len([tmp_rec_id for tmp_rec_id in trainning_data_dict \
        if trainning_data_dict[tmp_rec_id][1] == a2_x2 and tmp_rec_id in class_member_set_dict[c_id]])
        P_x2_Ci = (cnt_x2_Ci + 0.0) / cnt_Ci
        #求P_x3_Ci
        #按正態分佈處理，取標準差和平均值
        a3_data = [ trainning_data_dict[tmp_rec_id][2] for tmp_rec_id in trainning_data_dict \
        if tmp_rec_id in class_member_set_dict[c_id] ]
        a3_std_err = np.sqrt(np.var(a3_data))
        a3_mean = np.mean(a3_data)
        P_x3_Ci = mlab.normpdf(a3_x3 , a3_mean , a3_std_err )
        
        res = P_x1_Ci * P_x2_Ci * P_x3_Ci * P_Ci
        if res > max_P_X_Ci :
            max_P_X_Ci = res
            res_class_id = c_id
        
    if res_class_id == 0 :
        print 'error 2'
    
    if res_class_id != data_to_classify_dict[rec_id][3] :
        print 'different'
        print res_class_id
        print data_to_classify_dict[rec_id]
        diff_cnt += 1
 
print diff_cnt        
        
        
產生測試資料的指令碼：
# -*- coding: utf-8 -*-
import numpy as np
from random import random as rdn
 
#attr : a1 離【1 -- 10 】, a2 離【1 -- 10 】, a3 連【1 -- 100】正態分佈
 
#class : c1 , c2 , c3 , c4 , c5 , c6 , c7 , c8
 
#data : 1 - 1000
 
'''
c1 : a1[1 - 3] a2[4 - 10] a3[<= 50]
c2 : a1[1 - 3] a2[4 - 10] a3[> 50]
c3 : a1[1 - 3] a2[1 - 3] a3[> 30]
c4 : a1[1 - 3] a2[1 - 3] a3[<= 30]
c5 : a1[4 - 10] a2[4 - 10] a3[<= 50]
c6 : a1[4 - 10] a2[4 - 10] a3[> 50]
c7 : a1[4 - 10] a2[1 - 3] a3[> 30]
c8 : a1[4 - 10] a2[1 - 3] a3[<= 30]
'''
 
 
data_file = open('F:\\bayes_categorize\\trainning_data_new.dat' , 'w')
a3_data = np.random.randn(1000 ) * 30 + 50
 
for i in range(1 , 1001 ) :
    rec_id = i
    a1 = int(rdn()*10) + 1
    if a1 > 10 :
        a1 = 10
        
    a2 = int(rdn()*10) + 1
    if a2 > 10 :
        a2 = 10
       
    a3 = a3_data[i-1]
    
    c_id = 0
    if a1 <= 3 and a2 >= 4 and a3 <= 50 :
        c_id = 1
    elif a1 <= 3 and a2 >= 4 and a3 > 50 :
        c_id = 2
    elif a1 <= 3 and a2 < 4 and a3 > 30 :
        c_id = 3
    elif a1 <= 3 and a2 < 4 and a3 <= 30 :
        c_id = 4
    elif a1 > 3 and a2 >= 4 and a3 <= 50 :
        c_id = 5
    elif a1 > 3 and a2 >= 4 and a3 > 50 :
        c_id = 6
    elif a1 > 3 and a2 < 4 and a3 > 30 :
        c_id = 7
    elif a1 > 3 and a2 < 4 and a3 <= 30 :
        c_id = 8
    else :
        print 'error'
        
    str_line = str(rec_id) + ',' + str(a1) + ',' + str(a2) + ','  + str(a3) + ',' + str(c_id) + '\n'
    data_file.write(str_line)
data_file.close()
 
配置檔案：
1,no,
2,no,
3,yes,