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')