/
alex_rox.py
143 lines (127 loc) · 3.83 KB
/
alex_rox.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
import sys
import neural_network_matrix as nn_matrix
import train
import numpy as np
try:
import cPickle as pickle
except:
import pickle
files_location = "AUTHORS/"
train_rounds = 200
train_range = 10
min_verify_range = 10
max_verify_range = 20
min_test_range = 10
max_test_range = 20
learn_rate = .1
dummy_var = 1
nlayers = [100, 50, 25]
class authors:
def __init__(self, load):
self.lastPercent = 0.0
self.bestPercent = 0.0
if (load != '0'):
print("\ncreating train data...")
sys.stdout.flush()
self.td = train.train_data(files_location)
pickle.dump(self.td, open("_train_data.p", "wb"))
print("created sucessfully\n")
sys.stdout.flush()
else:
print("\nloading train data...")
sys.stdout.flush()
self.td = pickle.load(open("_train_data.p", "rb"))
print("loaded sucessfully\n")
sys.stdout.flush()
self.nn = nn_matrix.neural_network(len(self.td.inputs[0][0]),len(self.td.authors),nlayers,dummy_var, learn_rate)
def verify(self):
self.nn.save("lastNeuralNetwork.p")
total_wrong = 0.0
total_tests = 0.0
lp = self.lastPercent
nn_output = self.nn.output
inp = self.td.inputs
for i in range(min_verify_range, max_verify_range):
for j in range(0, len(inp)):
if (i < len(self.td.inputs[j])):
total_tests += 1
total_wrong += self.td.test_np(nn_output(inp[j][i]), j, i)
currentPercent = 1 - (total_wrong/total_tests)
if (self.bestPercent < currentPercent):
self.nn.save("bestNeuralNetwork.p")
self.bestPercent = currentPercent
self.lastPercent = currentPercent
if (lp > currentPercent):
if (lp > 1):
return True
return False
else:
return False
def best(self):
total_wrong = 0.0
total_tests = 0.0
nn_output = self.nn.output
inp = self.td.inputs
for i in range(min_verify_range, max_verify_range):
for j in range(0, len(inp)):
if (i < len(self.td.inputs[j])):
total_tests += 1
total_wrong += self.td.test_np(nn_output(inp[j][i]), j, i)
currentPercent = 1 - (total_wrong/total_tests)
if (self.bestPercent < currentPercent):
self.nn.save("bestNeuralNetwork.p")
self.bestPercent = currentPercent
def progress(self, x, train_rounds):
sys.stdout.write('\r')
sys.stdout.write("[%-50s] %d%% %f%%" % ('='*(((x+1)*50)//train_rounds), ((x+1)*100)/train_rounds, 100*self.lastPercent))
if(self.verify()):
# self.nn = nn_matrix.neural_network.load("lastNeuralNetwork.p")
sys.stdout.write("\nNot improving anymore at test: %d" % (x))
sys.stdout.flush()
return True
else:
sys.stdout.flush()
return False
def run(self):
print("\ntraining...")
sys.stdout.flush()
inp = self.td.inputs
des = self.td.desired
nn_out_prop = self.nn.out_prop
for x in range(0, train_rounds):
if ((x+1)%(train_rounds//50) == 0):
if (self.progress(x, train_rounds)):
break
else:
self.verify()
for i in range (0, train_range):
for j in range(0,len(inp)):
if (i < len(inp[j])):
nn_out_prop(inp[j][i], des[j])
# self.nn = nn_matrix.neural_network.load("bestNeuralNetwork.p")
print("\nfinished training")
sys.stdout.flush()
print("\ntesting...")
sys.stdout.flush()
total_wrong = 0
total_tests = 0
nn_output = self.nn.output
for i in range(min_test_range, max_test_range):
for j in range(0, len(inp)):
if (i < len(inp[j])):
total_tests += 1
total_wrong += self.td.test(nn_output(inp[j][i]), j, i)
nlayers.insert(0, len(inp[0][0]))
nlayers.append(len(self.td.authors))
print("\ncorrect percentage: ", (1 - total_wrong/total_tests) * 100)
print("number wrong: ", total_wrong)
print("number of tests: ", total_tests)
print("traing rounds: ", train_rounds)
print("train range: ", train_range)
print("test range: ", min_test_range, "to",max_test_range)
print("learning rate: ", learn_rate)
print("neural network: ", nlayers)
def main():
auth = authors(sys.argv[1])
auth.run()
main()