import numpy as np
import nnet as net
import matplotlib.pyplot as plt
x = np.array([[-1.0, -0.9, -0.8, -0.7, -0.6, -0.5]])

y_t = np.array([[-0.9602, -0.5770, -0.0729, 0.3771, 0.6405, 0.6600]])
L = x.shape[0]
K1 = y_t.shape[0]
w1, b1 = net.nwlog(K1, L)
max_epoch = 20000
err_goal = 0.01
disp_freq = 1000
lr = 0.1
SSE_vec = []
for epoch in range(1, max_epoch+1):
    y1 = net.logsig(np.dot(w1, x), b1)
    e = y_t - y1
    SSE = net.sumsqr(e)
    if np.isnan(SSE):
        break
    SSE_vec.append(SSE)
    if SSE < err_goal:
        break
    d1 = net.deltalog(y1, e)
    dw1, db1 = net.learnbp(x, d1, lr)
    w1 += dw1
    b1 += db1
    if (epoch % disp_freq) == 0:
        plt.clf()
        print("Epoch: ] | SSE: %5.5f " % (epoch, SSE))
        plt.plot(x[0], y_t[0], 'r', x[0], y1[0], 'g')
        plt.grid()
        plt.show()
        plt.pause(1e-2)

plt.figure()
plt.plot(SSE_vec)
plt.ylabel('SSE')
plt.yscale('linear')
plt.title('epoch')
plt.grid(True)
plt.show()