SIMAP-layer/Iris_data.py at main · Cimagroup/SIMAP-layer · GitHub

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
from main_SMNN import *
from sklearn import datasets
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

iris = datasets.load_iris()

X = iris.data
y = iris.target
# X_centered = X-X.mean()
# X_scaled = X_centered/(np.abs(X_centered).max())/2+1/2

X_centered = X-X.mean()
X_scaled = (X_centered-X_centered.min())/(X_centered.max()-X_centered.min())

X_train,X_test,y_train,y_test = train_test_split(X_scaled,y,test_size=0.2)


# plt.scatter(X[:,0],X[:,1],c=y)
# plt.show()

# plt.plot(V(2)[:,0],V(2)[:,1],'.')
# t1 = plt.Polygon(V(2),edgecolor="blue",facecolor="none")
# plt.gca().add_patch(t1)
# plt.scatter(X_train[:,0],X_train[:,1],c=y_train)
# plt.show()

# plt.plot(V(2)[:,0],V(2)[:,1],'.')
# t1 = plt.Polygon(V(2),edgecolor="blue",facecolor="none")
# plt.gca().add_patch(t1)
# plt.scatter(X_test[:,0],X_test[:,1],c=y_test)
# plt.show()


bar_iterations=2
dim=4

#dic_sups = dic_supports(supports(bar_iterations,dim-1))

sups = supports(bar_iterations,dim)

data = X_train

d=itek_barycentrics(data,bar_iterations)

bis = [bis_cons(d,ite,dim) for ite in range(bar_iterations+1)]

n_samples = len(X_train)

n_classes = len(set(y_train))
y_hot=tf.one_hot(y_train,depth=n_classes)
y_hot=np.array(y_hot)


verbose = False
epochs = 1000
model0,history0=SMNN(bis[0],y_train,epochs,verbose =verbose)
print(model0.evaluate(bis[0],y_hot))
plt.plot(history0.history['loss'])
#plt.show()
vsi = [bis_cons(itek_barycentrics(sups[i],i),i,dim) for i in range(bar_iterations)]
vs0 = np.matmul(vsi[0],model0.get_weights())
weights0=vs0 #np.matmul(P(2),model0.get_weights())#vs0


model1,history1=SMNN(bis[1],y_train,epochs,weights0,verbose =verbose)
print(model1.evaluate(bis[1],y_hot))
plt.plot(history1.history['loss'])
#plt.show()
vs1=np.matmul(vsi[1],model1.get_weights())
weights1=vs1

model2,history2=SMNN(bis[2],y_train,epochs,weights1,verbose =verbose)
print(model2.evaluate(bis[2],y_hot))
plt.plot(history2.history['loss'])
plt.legend(["model0","model1","model2"])
plt.title("Loss")
plt.show()
plt.plot(history0.history['accuracy'])
plt.plot(history1.history['accuracy'])
plt.plot(history2.history['accuracy'])
plt.legend(["model0","model1","model2"])
plt.title("Accuracy")
plt.show()
n_classes = len(set(y_train))
y_hot=tf.one_hot(y_train,depth=n_classes)
y_hot=np.array(y_hot)


# def plot_model_out(model):
#   """
#   x,y: 2D MeshGrid input
#   model: Keras Model API Object
#   """
#   a = np.linspace(-1, 1, 100)
#   xx, yy = np.meshgrid(a,a)
#   z =
#   plt.contourf(xx, yy, z,)
#   plt.show()

#   plt.show()


data = X_test

d_test=itek_barycentrics(data,bar_iterations)

bis_test = [bis_cons(d_test,ite,dim) for ite in range(bar_iterations+1)]


# data_test = X_test
# bar_iterations=2
# dim=2
# d_test=itek_barycentrics(data,bar_iterations)
# bis_test, v_ords_test, matchings_test = general_matching(d_test,bar_iterations)
# n_samples_test = len(X_test)
# bis_ordered_test = reorder_matchings(n_samples_test, matchings_test, bis_test, bar_iterations)
# n_classes = len(set(y_test))
yt_hot=tf.one_hot(y_test,depth=n_classes)
yt_hot=np.array(yt_hot)
print("Evaluation on test")
print("Model 0")
model0.evaluate(bis_test[0],yt_hot)
print("Model 1")
model1.evaluate(bis_test[1],yt_hot)
print("Model 2")
model2.evaluate(bis_test[2],yt_hot)