Newer
Older
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
#!/usr/bin/env python3
from keras.models import Sequential
from keras.layers import Dense
import numpy
import numpy.random as rand
#from matplotlib import pyplot
from keras.models import model_from_json
import math
import sys
#python3.6 -m pip install keras
#python3.6 -m pip install theano
data_set = None
# estes vetores terao seus valores definidos a partir do treinamento do modelo.
INV1 = [1, 2]
INV2 = [0, 2]
INV3 = [0, 1]
MAXS = [4000, 4000, 4000]
MINS = [0, 0, 0]
alpha = 2.5
betha = 1.5
#MAES = [0.0040331535722, 0.00447660606592, 0.00339290297573]
MAES = [0.00912140117378,0.0104236871072,0.00361583732514]
def load_normalize(csvName, separator):
ds = numpy.loadtxt(csvName, delimiter=separator)
for i in range(0, len(ds)) :
lin = ds[i,:]
for j in range(0, len(lin)):
ds[i,j] = (ds[i,j] - MINS[j])/(MAXS[j]-MINS[j])
return numpy.array(ds)
def make_data(data, cols, res) :
X = numpy.array(data[:, cols])
Y = numpy.array(data[:, res])
return X, Y
def make_data_set(data, dtype):
if (dtype == 'inv1'):
return make_data(data, INV1, 0)
if (dtype == 'inv2') :
return make_data(data, INV2, 1)
if (dtype == 'inv3') :
return make_data(data, INV3, 2)
def load_model(model_file, weights_file):
jf = open(model_file, "r")
desc = jf.read()
model = model_from_json(desc)
model.load_weights(weights_file)
model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['accuracy'])
return model
def rescale(data, index):
resp = numpy.zeros(len(data))
for i in range(0, len(data)) :
resp[i] = (data[i] * (MAXS[index]-MINS[index])) + MINS[index]
return resp
def predict(model, inputs, expected_value, MAE):
aux = model.predict( inputs )
pv = aux[0,0]
delta = math.fabs(expected_value - pv)
if delta <= (betha * MAE):
c = 1
else :
c = 1 - (( math.fabs(delta - (betha * MAE)) ) / ( alpha * MAE ))
if c < 0:
c = 0
return pv, c
def simulate_all( arquivo, saida ):
ds = load_normalize( arquivo, "," )
model_inv1 = load_model('inv1.json', 'inv1.h5')
model_inv2 = load_model('inv2.json', 'inv2.h5')
model_inv3 = load_model('inv3.json', 'inv3.h5')
lin = numpy.array(ds[0]);
vs1 = []
vs2 = []
vs3 = []
ps1 = []
ps2 = []
ps3 = []
c1 = []
c2 = []
c3 = []
cta = 1
for i in range(len(ds)):
y1 = ds[i, 0]
y2 = ds[i, 1]
y3 = ds[i, 2]
lin_inv1 = numpy.array(lin[INV1])
lin_inv1 = numpy.reshape(lin_inv1, (1, len(lin_inv1)))
vi1, cv1 = predict( model_inv1, lin_inv1, y1, MAES[0] )
lin_inv2 = numpy.array(lin[INV2])
lin_inv2 = numpy.reshape(lin_inv2, (1, len(lin_inv2)))
vi2, cv2 = predict( model_inv2, lin_inv2, y2, MAES[1] )
lin_inv3 = numpy.array(lin[INV3])
lin_inv3 = numpy.reshape(lin_inv3, (1, len(lin_inv3)))
vi3, cv3 = predict( model_inv3, lin_inv3, y3, MAES[2] )
vs1.append(y1)
ps1.append(vi1)
vs2.append(y2)
ps2.append(vi2)
vs3.append(y3)
ps3.append(vi3)
c1.append(cv1 * 100)
c2.append(cv2 * 100)
c3.append(cv3 * 100)
lin = numpy.array(ds[i])
if (cv1 <= 0.5):
lin[0] = vi1 # ds[i-1, 10]
if (cv2 <= 0.5):
lin[1] = vi2 # ds[i-1, 5]
if (cv3 <= 0.5):
lin[2] = vi3 # ds[i-1, 7]
ps1 = rescale(ps1,0)
vs1 = rescale(vs1,0);
ps2 = rescale(ps2,1)
vs2 = rescale(vs2,1);
ps3 = rescale(ps3,2)
vs3 = rescale(vs3,2);
fout = open(saida,"w")
for i in range(len(c1)):
fout.write(str(int(c1[i]))+","+str(int(c2[i]))+","+str(int(c3[i]))+"\n")
fout.close()
if __name__ == '__main__':
numpy.random.seed( 147)
infile = sys.argv[1]
outfile = sys.argv[2]
if (len(sys.argv) >= 4):
alpha = float(sys.argv[3])
if (len(sys.argv) >= 5):
betha = float(sys.argv[4])
data_set = load_normalize(infile, ",")
simulate_all( infile, outfile )