-
Notifications
You must be signed in to change notification settings - Fork 0
/
experiment.py
144 lines (130 loc) · 4.81 KB
/
experiment.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
import sys
import numpy as np
import mlp
import math
def getParameter(array,parameter):
info = np.loadtxt('normalizeOutput.txt',delimiter=',')
numDict={}
foldDict={}
for num in array:
numDict[str(num)]=[0,10000,0]
foldDict[str(num)]=[]
for i in range(5):
np.random.shuffle(info)
###START Split between test and valid sample###
trainInputs=[]
trainTargets=[]
validInputs=[]
validTargets=[]
testInputs=[]
testTargets=[]
for sample in info[:len(info)//5]:
new=[0,0] #1-N
new[int(sample[-1])]=1
validInputs.append(sample[:-1])
validTargets.append(new)
np.array(np)
for sample in info[len(info)//5:2*(len(info)//5)]:
new=[0,0]
new[int(sample[-1])]=1
np.array(np)
testInputs.append(sample[:-1])
testTargets.append(new)
for sample in info[2*(len(info)//5):]:
new=[0,0]
new[int(sample[-1])]=1
np.array(np)
trainInputs.append(sample[:-1])
trainTargets.append(new)
trainInputs=np.array(trainInputs)
trainTargets=np.array(trainTargets)
trainInputs=np.array(trainInputs)
trainTargets=np.array(trainTargets)
testInputs=np.array(testInputs)
testTargets=np.array(testTargets)
###END Split between test and valid sample###
print("-------------- Loading fold",i+1,"---------------")
for num in array:
sectAvg=0
for i in range(20):
if(parameter=="nHidden"):
net = mlp.mlp(trainInputs,trainTargets,int(num))
err=net.earlystopping(trainInputs,trainTargets,validInputs,validTargets,0.3)
elif(parameter=="momentum"):
net = mlp.mlp(trainInputs,trainTargets,10,momentum=float(num))
err=net.earlystopping(trainInputs,trainTargets,validInputs,validTargets,0.3)
numDict[str(num)][0]=numDict[str(num)][0]+err
numDict[str(num)].append(err)
sectAvg=sectAvg+err
if(err<numDict[str(num)][1]):
numDict[str(num)][1]=err
if (err>numDict[str(num)][2]):
numDict[str(num)][2]=err
foldDict[str(num)].append(sectAvg)
print("----- Average Means of 5-fold------")
for case in foldDict:
print("----"+case+" --------")
acc=0
for i in foldDict[case]:
acc=acc+i
print(i/20)
print("Overall Mean Error ",acc/(20*5))
print("---- Stats of each n tested----")
for num in numDict:
if (parameter=="nHidden"):
print("---- Size "+ num +" hidden layer----")
elif(parameter=="momentum"):
print("----- Momentum of "+num+" ----------")
mAvg=numDict[num][0]/(len(numDict[num])-3)
print("Mean Error: ",mAvg)
sd=0.0
for i in range(3,len(numDict[num])):
sd=sd+((numDict[num][i]-mAvg)**2)
sd=sd/(len(numDict[num])-4)
sd=(sd**(1/2))
print("Standard Deviation: ",sd)
print("Max Error: ",numDict[num][2])
print("Min Error: ",numDict[num][1])
def getMatrix(nHidden,momentum):
info = np.loadtxt('normalizeOutput.txt',delimiter=',')
np.random.shuffle(info)
###START Split between test and valid sample###
trainInputs=[]
trainTargets=[]
validInputs=[]
validTargets=[]
testInputs=[]
testTargets=[]
for sample in info[:len(info)//5]:
new=[0,0]
new[int(sample[-1])]=1
np.array(new)
validInputs.append(sample[:-1])
validTargets.append(new)
for sample in info[len(info)//5:2*(len(info)//5)]:
new=[0,0]
new[int(sample[-1])]=1
np.array(new)
testInputs.append(sample[:-1])
testTargets.append(new)
for sample in info[2*(len(info)//5):]:
new=[0,0]
new[int(sample[-1])]=1
np.array(new)
trainInputs.append(sample[:-1])
trainTargets.append(new)
###END Split between test and valid sample###
trainInputs=np.array(trainInputs)
trainTargets=np.array(trainTargets)
trainInputs=np.array(trainInputs)
trainTargets=np.array(trainTargets)
testInputs=np.array(testInputs)
testTargets=np.array(testTargets)
net = mlp.mlp(trainInputs,trainTargets,nHidden,momentum=momentum)
net.earlystopping(trainInputs,trainTargets,validInputs,validTargets,0.3)
net.confmat(testInputs,testTargets)
def main():
#getParameter([1,2,3,5,10,25,50],"nHidden")
#getParameter([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],"momentum")
getMatrix(10,0.7)
main()