def test_fhmm_correctness(self):
elec = self.dataset.buildings[1].elec
fhmm = FHMM()
fhmm.train(elec)
mains = elec.mains()
output = HDFDataStore('output.h5', 'w')
fhmm.disaggregate(mains, output, sample_period=1)
for meter in range(2, 4):
df1 = output.store.get('/building1/elec/meter{}'.format(meter))
df2 = self.dataset.store.store.get(
'/building1/elec/meter{}'.format(meter))
self.assertEqual((df1 == df2).sum().values[0], len(df1.index))
self.assertEqual(len(df1.index), len(df2.index))
output.close()
remove("output.h5")
return gt_overall, pred_overall
# Since the methods use randomized initialization, let's fix a seed here
# to make this notebook reproducible
import numpy.random
numpy.random.seed(42)
sample_period = 120
predictions = []
gts = []
fhmm = FHMM()
for building in range(5):
print('*' * 20 )
print('Casa : {}'.format(building))
print('Usando o metodo FHHM...')
print('*' * 20 )
fhmm.train(top_5_train_elec[building], sample_period=sample_period)
gt, prediction = predict(fhmm, test_elec[building], sample_period, train.metadata['timezone'])
gts.append(gt)
predictions.append(prediction)
for i in range(5):
appliance_labels = [m.label() for m in gts[i].columns.values]
gts[i].columns = appliance_labels
predictions[i].columns = appliance_labels
list_geladeira = predictions[0]['Fridge'].tolist()
list_luzes = predictions[0]['Light'].tolist()
list_tomadas = predictions[0]['Sockets'].tolist()
list_microondas = predictions[0]['Microwave'].tolist()
list_lavadora = predictions[0]['Dish washer'].tolist()
thread_processamento.start()
for b in buffer:
response.write(b.encode())
self.wfile.write(response.getvalue())
print("[OK] Carregando base de dados da casa: " + str(HOUSE))
buffer = []
train = DataSet('banco_unifei_casa_2.h5')
#Enumera todas as casas
buildings = [i for i in range(3)]
#Vetor que guarda os dados de todas as casas
train_elec = [None for i in range(3)]
for building in buildings:
train_elec[building] = train.buildings[building + 1].elec
print("[OK] Treinamento inicializado...")
numpy.random.seed(42)
sample_period = 120
predictions = []
gts = []
fhmm = FHMM()
fhmm.train(train_elec[0], sample_period=sample_period)
print("[OK] Processo de desagregacao iniciado...")
httpd = HTTPServer((SERVER_IP, PORT), SimpleHTTPRequestHandler)
print("[OK] Servidor HTTP Estabelecido com sucesso")
httpd.serve_forever()