inDims = range(1, 2)
outDims = range(1, 2)
for (inDimension, outDimension) in zip(inDims, outDims):
name = 'MIMO ' + '1' + 'x' + '1'
path = valPath + name + '/Checkpoints/'
start_time = time.time()
numTrials = int(NUMTRIALS / (inDimension * outDimension))
sig = Signal(inDimension,
outDimension,
numTrials,
numPlots=plots,
stdev='variable')
# In this case, since we are only loading the model, not trying to train it,
# we can use function simulate and preprocess
xData, yData = sig.closed_loop_validation(b_possible_values=[.01, .99],
a_possible_values=[.01, .99],
k_possible_values=[1, 10],
order=False)
print("--- %s seconds ---" % (time.time() - start_time))
# Initialize the models that are saved using the parameters declared above
predictor = Model()
predictor.load_model(sig, path)
# Function to make predictions based off the simulation
predDict, errDict = predictor.predict_system(sig,
savePredict=True,
stepResponse=False)
