# the three different parameters has any significant impact on predicting the
# value of any of the 3 parameters
xDatas = [yArray, yArray, (yArray - np.mean(yArray)) / np.std(yArray) - uArray]
yDatas = [taus, kps, thetas]
m = model(nstep)
modelList = []
uSum = np.sum(uArray, axis=0)
plt.figure()
plt.plot(np.linspace(0, 100, 100), uSum)
for j in range(0, len(xDatas)):
xData = xDatas[j]
yData = yDatas[j]
x_train, x_val, y_train, y_val, numDim = sig.preprocess(xData, yData)
model = m.model_1()
print("Fit model on training data")
history = model.fit(
x_train,
y_train,
batch_size=16,
epochs=100,
# We pass some validation for
# monitoring validation loss and metrics
# at the end of each epoch
validation_data=(x_val, y_val),
)
modelList.append(model)
