import numpy as np
import neurolab as nl
import matplotlib.pyplot as plt
# 0 1 2-----------16*8
target = np.array([[0,0,0,0,0,0,0,0,
0,0,0,1,1,0,0,0,
0,0,1,0,0,1,0,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,0,1,0,0,1,0,0,
0,0,0,1,1,0,0,0,
0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,1,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,1,1,1,0,0,
0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,
0,0,1,1,1,1,0,0,
0,1,1,0,0,1,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,0,0,0,0,1,1,0,
0,0,0,0,1,1,0,0,
0,0,0,1,1,0,0,0,
0,0,1,1,0,0,0,0,
0,1,1,0,0,0,0,0,
0,1,0,0,0,0,0,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,1,1,1,1,1,0,
0,0,0,0,0,0,0,0]])
#画图函数
def visualized (data, title):
fig, ax = plt.subplots()
ax.imshow(data, cmap=plt.cm.gray,interpolation='nearest')
ax.set_title(title)
plt.show()
#显示012
for i in range(len(target)):
visualized(np.reshape(target[i], (16,8)), i)
#hopfield网络的值是1和-1
target[target == 0] = -1
#创建一个hopfield神经网络，吸引子为target(012)
net = nl.net.newhop(target)
#定义3个测试数据
test_data1 =np.asfarray([0,0,0,0,0,0,0,0,
0,0,0,1,1,0,1,0,
0,0,1,0,0,1,0,0,
0,1,0,0,0,0,1,0,
0,1,0,0,1,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,1,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,1,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,0,0,0,1,0,
0,1,0,1,0,0,1,0,
0,0,1,0,0,1,0,0,
0,0,1,1,1,0,0,0,
0,0,0,0,0,0,0,0])
test_data2 =np.asfarray([0,0,0,1,0,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,1,1,0,0,0,
0,0,0,0,0,0,1,0,
0,1,0,0,1,0,0,0,
0,0,0,0,1,0,0,1,
0,0,0,1,1,0,1,0,
0,1,0,0,1,0,1,0,
0,0,0,0,1,0,0,0,
0,0,1,0,1,0,1,0,
0,0,0,1,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,1,0,0,1,
0,0,1,0,1,0,0,0,
0,0,0,1,1,1,0,0,
0,1,0,0,0,0,0,0])
test_data3 =np.asfarray([0,0,0,1,0,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,1,1,0,0,0,
0,0,0,1,0,0,1,0,
0,1,0,0,0,0,0,0,
0,0,0,0,1,0,0,1,
0,0,0,1,0,0,1,0,
0,1,0,0,1,0,1,0,
0,0,0,0,1,0,0,0,
0,0,1,0,0,0,1,0,
0,0,0,1,1,0,0,0,
0,0,0,0,1,0,0,0,
0,0,0,0,0,0,0,1,
0,0,1,0,0,0,0,0,
0,0,0,0,1,1,0,0,
0,1,0,0,0,0,0,0])
#显示测试数据
visualized(np.reshape(test_data1, (16,8)), "test_data1")
visualized(np.reshape(test_data2, (16,8)), "test_data2")
visualized(np.reshape(test_data3, (16,8)), "test_data3")
test_data1[test_data1==0] = -1
#把测试数据输入hopfield网络，得到输出
out1 = net.sim([test_data1])
#判断测试数据的数字是多少
for i in range(len(target)):
if((out1 == target[i]).all()):
print("test_data is :",i)
#显示输出
visualized(np.reshape(out1, (16,8)), "output1")
test_data2[test_data2==0] = -1
#把测试数据输入hopfield网络，得到输出
out2 = net.sim([test_data2])
#判断测试数据的数字是多少
for i in range(len(target)):
if((out2 == target[i]).all()):
print("test_data is :",i)
#显示输出
visualized(np.reshape(out2, (16,8)), "output2")
test_data3[test_data3==0] = -1
#把测试数据输入hopfield网络，得到输出
out3 = net.sim([test_data3])
#判断测试数据的数字是多少
for i in range(len(target)):
if((out3 == target[i]).all()):
print("test_data is :",i)
#显示输出
visualized(np.reshape(out3, (16,8)), "output3")
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