-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmax_entroy.py
184 lines (133 loc) · 4.79 KB
/
max_entroy.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
# -*- coding: utf-8 -*-
"""
# Author : Ming
# File : {NAME}.py
# Time : 2019/5/7 0007 上午 10:22
"""
import pandas as pd
import numpy as np
import time
import math
import random
from collections import defaultdict
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
class MaxEnt(object):
def init_params(self, X, Y):
self.X_ = X
self.Y_ = set()
self.cal_Pxy_Px(X, Y)
self.N = len(X) # 训练集大小
self.n = len(self.Pxy) # 书中(x,y)对数
self.M = 10000.0 # 书91页那个M，但实际操作中并没有用那个值
# 可认为是学习速率
self.build_dict()
self.cal_EPxy()
def build_dict(self):
self.id2xy = {}
self.xy2id = {}
for i, (x, y) in enumerate(self.Pxy):
self.id2xy[i] = (x, y)
self.xy2id[(x, y)] = i
def cal_Pxy_Px(self, X, Y):
self.Pxy = defaultdict(int)
self.Px = defaultdict(int)
for i in range(len(X)):
x_, y = X[i], Y[i]
self.Y_.add(y)
for x in x_:
self.Pxy[(x, y)] += 1
self.Px[x] += 1
def cal_EPxy(self):
'''
计算书中82页最下面那个期望
'''
self.EPxy = defaultdict(float)
for id in range(self.n):
(x, y) = self.id2xy[id]
self.EPxy[id] = float(self.Pxy[(x, y)]) / float(self.N)
def cal_pyx(self, X, y):
result = 0.0
for x in X:
if self.fxy(x, y):
id = self.xy2id[(x, y)]
result += self.w[id]
return (math.exp(result), y)
def cal_probality(self, X):
'''
计算书85页公式6.22
'''
Pyxs = [(self.cal_pyx(X, y)) for y in self.Y_]
Z = sum([prob for prob, y in Pyxs])
return [(prob / Z, y) for prob, y in Pyxs]
def cal_EPx(self):
'''
计算书83页最上面那个期望
'''
self.EPx = [0.0 for i in range(self.n)]
for i, X in enumerate(self.X_):
Pyxs = self.cal_probality(X)
for x in X:
for Pyx, y in Pyxs:
if self.fxy(x, y):
id = self.xy2id[(x, y)]
self.EPx[id] += Pyx * (1.0 / self.N)
def fxy(self, x, y):
return (x, y) in self.xy2id
def train(self, X, Y):
self.init_params(X, Y)
self.w = [0.0 for i in range(self.n)]
max_iteration = 1000
for times in range(max_iteration):
print('iterater times %d' % times)
sigmas = []
self.cal_EPx()
for i in range(self.n):
sigma = 1 / self.M * math.log(self.EPxy[i] / self.EPx[i])
sigmas.append(sigma)
# if len(filter(lambda x: abs(x) >= 0.01, sigmas)) == 0:
# break
self.w = [self.w[i] + sigmas[i] for i in range(self.n)]
def predict(self, testset):
results = []
for test in testset:
result = self.cal_probality(test)
results.append(max(result, key=lambda x: x[0])[1])
return results
def rebuild_features(features):
'''
将原feature的（a0,a1,a2,a3,a4,...）
变成 (0_a0,1_a1,2_a2,3_a3,4_a4,...)形式
'''
new_features = []
for feature in features:
new_feature = []
for i, f in enumerate(feature):
new_feature.append(str(i) + '_' + str(f))
new_features.append(new_feature)
return new_features
if __name__ == "__main__":
print('Start read data')
time_1 = time.time()
raw_data = pd.read_csv('entroy_data.txt', header=0)
data = raw_data.values
imgs = data[0::, 1::]
labels = data[::, 0]
# 选取 2/3 数据作为训练集， 1/3 数据作为测试集
train_features, test_features, train_labels, test_labels = train_test_split(
imgs, labels, test_size=0.33, random_state=23323)
train_features = rebuild_features(train_features)
test_features = rebuild_features(test_features)
time_2 = time.time()
print('read data cost ', time_2 - time_1, ' second', '\n')
print('Start training')
met = MaxEnt()
met.train(train_features, train_labels)
time_3 = time.time()
print('training cost ', time_3 - time_2, ' second', '\n')
print('Start predicting')
test_predict = met.predict(test_features)
time_4 = time.time()
print('predicting cost ', time_4 - time_3, ' second', '\n')
score = accuracy_score(test_labels, test_predict)
print("The accruacy socre is ", score)