b523b464d8cafe29e352981c1c6df941f205592a,nilmtk/metrics.py,,mean_normalized_error_power,#Any#Any#,109

Before Change

    numerator = {}
    denominator = {}

    for appliance in predicted_power:
        numerator[appliance] = np.sum(np.abs(predicted_power[appliance] -
                                             df_appliances_ground_truth[appliance].values))
        denominator[appliance] = np.sum(
            df_appliances_ground_truth[appliance].values)
        mne[appliance] = numerator[appliance] * 1.0 / denominator[appliance]
    return mne


def rms_error_power(predicted_power, df_appliances_ground_truth):

After Change

    // TODO: need to resample to keep things in step
    mne = {}
    for meter in predictions.submeters():
        ground_truth_meter_identifier = meter.identifier._replace(
            dataset=ground_truth.dataset())
        ground_truth_meter = ground_truth[ground_truth_meter_identifier]
        sections = meter.good_sections()
        sample_period = meter.sample_period()
        period_alias = "{:d}S".format(sample_period)

Instances