outDims = range(1,2) for (inDimension,outDimension) in zip(inDims,outDims): name ='MIMO ' + str(inDimension) + 'x' + str(outDimension) path = valPath + name + '/Checkpoints/' start_time = time.time() numTrials=int(NUMTRIALS/(inDimension*outDimension)) sig = Signal(inDimension,outDimension,numTrials,numPlots=plots) # 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.system_validation_multi(disturbance=False,a_possible_values=[0.01,0.99],b_possible_values=[0.01,0.99],k_possible_values=[0,1],not_prbs=False) print("--- %s seconds ---" % (time.time() - start_time)) xData=sig.PRBS() xData = sig.random_process() # Initialize the models that are saved using the parameters declared above predictor = Model() predictor.load_model(sig,path) #xData=np.sin(np.linspace(0,300)) x=(xData) + 1 huh = (scipy.stats.entropy(x)) # Function to make predictions based off the simulation kp_yhat = predictor.predict_multinomial(sig,stepResponse=False) #tau_yhat = self.modelDict['tau'](sig.xData['tau']) #theta_yhat = self.modelDict['theta'](sig.xData['theta'])