# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import neurolab as nldef create_waveform(num_points):
# 创建训练样本
data1 = 1 * np.cos(np.arange(0, num_points))
data2 = 2 * np.cos(np.arange(0, num_points))
data3 = 3 * np.cos(np.arange(0, num_points))
data4 = 4 * np.cos(np.arange(0, num_points)) # 创建不同的振幅
amp1 = np.ones(num_points)
amp2 = 4 + np.zeros(num_points)
amp3 = 2 * np.ones(num_points)
amp4 = 0.5 + np.zeros(num_points)
# 数组合并
data = np.array([data1, data2, data3, data4]).reshape(num_points * 4, 1)
amplitude = np.array([[amp1, amp2, amp3, amp4]]).reshape(num_points * 4, 1) return data, amplitude
# 使用网格画出输出结果
def draw_output(net, num_points_test):
data_test, amplitude_test = create_waveform(num_points_test)
output_test = net.sim(data_test)
plt.plot(amplitude_test.reshape(num_points_test * 4))
plt.plot(output_test.reshape(num_points_test * 4))if __name__=='__main__':
# 获取数据
num_points = 30
data, amplitude = create_waveform(num_points) # 创建2层的神经网络
net = nl.net.newelm([[-2, 2]], [10, 1], [nl.trans.TanSig(), nl.trans.PureLin()]) # 设置初始化函数
net.layers[0].initf = nl.init.InitRand([-0.1, 0.1], 'wb')
net.layers[1].initf= nl.init.InitRand([-0.1, 0.1], 'wb')
net.init() # 训练递归神经网络
error = net.train(data, amplitude, epochs=1000, show=100, goal=0.01) # 计算输出结果
output = net.sim(data) # 画出训练误差
plt.subplot(211)
plt.plot(error)
plt.xlabel('Number of epochs')
plt.ylabel('Error (MSE)') plt.subplot(212)
plt.plot(amplitude.reshape(num_points * 4))
plt.plot(output.reshape(num_points * 4))
plt.legend(['Ground truth', 'Predicted output']) # 在多个尺度上进行测试
plt.figure() plt.subplot(211)
draw_output(net, 74)
plt.xlim([0, 300]) plt.subplot(212)
draw_output(net, 54)
plt.xlim([0, 300]) plt.show()
