def get_model_error_from_trace0(test_instances,model,plot=False):
error=[]
for obs_house in test_instances:
obs_power=utils.trace_series_to_numpy_array(obs_house.traces[0].series)
est_states=model.predict(obs_power)
est_power=[]
for val in est_states:
est_power.append(float(np.random.normal(model._means_[val],model._covars_[val],1)[0]))
if(plot):
plt.figure()
plt.plot(obs_power,'k')
plt.plot(est_power,'b',alpha=.5)
error.append(metric.sum_error(obs_power,np.array(est_power)))
return np.mean(error)
def get_model_error_from_trace0(test_instances, model, plot=False):
error = []
for obs_house in test_instances:
obs_power = utils.trace_series_to_numpy_array(
obs_house.traces[0].series)
est_states = model.predict(obs_power)
est_power = []
for val in est_states:
est_power.append(
float(
np.random.normal(model._means_[val], model._covars_[val],
1)[0]))
if (plot):
plt.figure()
plt.plot(obs_power, 'k')
plt.plot(est_power, 'b', alpha=.5)
error.append(metric.sum_error(obs_power, np.array(est_power)))
return np.mean(error)
def test_sum_error(self):
self.assertEqual(evm.sum_error(self.truth,self.prediction), 7,
'incorrect sum of error')
def test_sum_error(self):
self.assertEqual(evm.sum_error(self.truth, self.prediction), 7,
'incorrect sum of error')
