import numpy as np
import sys
sys.path.append('./src')
sys.path.append('./src/lib')
from simple_net import SimpleNet
from gradient import numerical_gradient
net = SimpleNet()
print(net.W)
# >>> [[-0.44439281 0.30789016 -1.50579685]
# [-0.93170709 0.08170439 -0.12740328]]
x = np.array([0.6, 0.9])
p = net.predict(x)
print(p)
# >>> [ 1.00824761 -1.47819523 0.03650346]
print(np.argmax(p))
# >> 1
t = np.array([0, 0, 1])
print(net.loss(x, t))
# >>> 1.704819611629646
f = lambda w: net.loss(x, t)
dW = numerical_gradient(f, net.W)
print(dW)
# >>> [[ 0.09999078 0.39092591 -0.49091668]
# [ 0.14998616 0.58638886 -0.73637502]]
# load model
# network = SimpleNet(input_size=1 , hidden_size=10, output_size=1 )
network = SimpleNet(input_size=3, hidden_size=10, output_size=1)
network.load_params("params.pkl")
#print( network.params["W1"] )
#pred
train_acc = network.accuracy(x_train, y_train)
test_acc = network.accuracy(x_test, y_test)
#
print("train acc, test acc | " + str(train_acc) + ", " + str(test_acc))
#
# x_test_dt= conv_num_date(x_test_pred )
# x_train_dt= conv_num_date(x_train )
#print(x_test_dt.shape )
print(x_test[:10])
y_val = network.predict(x_test[:10])
y_val = y_val * num_max_y
print(y_val)
# quit()
y_train = y_train * num_max_y
y_val = y_val * num_max_y
print('time : ', time.time() - global_start_time)
#print(y_val[:10] )
#print(x_test_dt[:10] )
quit()
#plt
plt.plot(x_train_dt, y_train, label="temp")
plt.plot(x_test_dt, y_val, label="predict")
plt.legend()
plt.grid(True)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)
# load
network = SimpleNet(input_size=1, hidden_size=10, output_size=1)
network.load_params("params.pkl")
#print( network.params["W1"] )
#pred
train_acc = network.accuracy(x_train, y_train)
test_acc = network.accuracy(x_test, y_test)
#
print("train acc, test acc | " + str(train_acc) + ", " + str(test_acc))
#
x_test_dt = conv_num_date(x_test_pred)
x_train_dt = conv_num_date(x_train)
#print(x_test_dt.shape )
y_val = network.predict(x_test_pred)
y_train = y_train * num_max_y
y_val = y_val * num_max_y
print('time : ', time.time() - global_start_time)
#print(y_val[:10] )
#print(x_test_dt[:10] )
#quit()
#plt
plt.plot(x_train_dt, y_train, label="temp")
plt.plot(x_test_dt, y_val, label="predict")
plt.legend()
plt.grid(True)
plt.title("IoT data")
plt.xlabel("x_test")
plt.ylabel("temperature")
plt.show()