model_paths = [f.path for f in os.scandir(valPath) if f.is_dir()]
inDims = range(1, 2)
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
sig.system_validation([1, 10], [1, 10], [1, 10])
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)
#start_time = time.time()
kps = np.zeros(1000)
taus = np.zeros(1000)
thetas = np.zeros(1000)
# Function to make predictions based off the simulation
a, b = yData.shape
inlet = yData - uData
inlet = inlet.reshape((a, b, 1))
inDims = range(1, 2)
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(KpRange=[1, 10],
tauRange=[1, 10],
thetaRange=[1, 10])
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
#kp_yhat = self.modelDict['kp'](sig.xData['kp'])
#tau_yhat = self.modelDict['tau'](sig.xData['tau'])
#theta_yhat = self.modelDict['theta'](sig.xData['theta'])
print("--- %s seconds ---" % (time.time() - start_time))
inDims = range(1, 2)
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
sig.system_validation(KpRange=[1, 10],
tauRange=[1, 10],
thetaRange=[1, 10])
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)
start_time = time.time()
kps = np.zeros(1000)
taus = np.zeros(1000)
thetas = np.zeros(1000)
# Function to make predictions based off the simulation
for i, (u, y) in enumerate(zip(uData, yData)):
in1 = (u * y).reshape(1, 100, 1)
