思路：
定义三个训练测试图片0 1 2（16*8），即三个吸引子。然后创建一个Hopfield神经网络，把训练数据输入。然后在用测试数据输入测试结果。
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) # In[2]: #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") # In[3]: 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")