nilmtk.feature_detectors package¶

Submodules¶

nilmtk.feature_detectors.cluster module¶

nilmtk.feature_detectors.cluster.cluster(X, max_num_clusters=3)[source]¶

Applies clustering on reduced data, i.e. data where power is greater than threshold.

Parameters:	X : pd.Series or single-column pd.DataFrame max_num_clusters : int
Returns:	centroids : ndarray of int32s Power in different states of an appliance, sorted

nilmtk.feature_detectors.cluster.hart85_means_shift_cluster(pair_buffer_df, cols)[source]¶

nilmtk.feature_detectors.steady_states module¶

nilmtk.feature_detectors.steady_states.cluster(x, max_num_clusters=3)[source]¶

Applies clustering on reduced data, i.e. data where power is greater than threshold.

Parameters:	X : pd.Series or single-column pd.DataFrame max_num_clusters : int
Returns:	centroids : ndarray of int32s Power in different states of an appliance, sorted

nilmtk.feature_detectors.steady_states.find_steady_states(dataframe, min_n_samples=2, stateThreshold=15, noise_level=70)[source]¶

Finds steady states given a DataFrame of power

Parameters:	dataframe: pd.DataFrame with DateTimeIndex min_n_samples(int): number of samples to consider constituting a steady state. stateThreshold: maximum difference between highest and lowest value in steady state. noise_level: the level used to define significant appliances, transitions below this level will be ignored. See Hart 1985. p27.

nilmtk.feature_detectors.steady_states.find_steady_states_transients(metergroup, cols, noise_level, state_threshold, **load_kwargs)[source]¶

Module contents¶