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_multi()
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)
# Function to make predictions based off the simulation
a, b = yData.shape
inlet = yData - uData
inlet = inlet.reshape((a, b, 1))
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
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)
ellipse.set_transform(transf + ax.transData)
return ax.add_patch(ellipse)
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,
b_possible_values=[.299, .3005],
a_possible_values=[.899, .9005],
k_possible_values=[0, 1])
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)
sns.set_style('dark')
# 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'])
fig, ax_nstd = plt.subplots(figsize=(6, 6), dpi=200)
#ax_nstd.set_xlim([0.475,0.535])
#ax_nstd.set_ylim([0.475,0.535])
