Method list¶

`set_sequence`	Sets the sequence of contexts that will be used for testing the energy-efficiency of settings.
`set_path`	Sets the path to a folder from which the data is loaded and to which data is saved.
`set_settings`	Defines the possible settings and their performance/energy for the optimization.
`set_dca_costs`	Sets the base energy costs for duty-cycling.
`sca_real`	Tests the SCA configurations using a simulation.
`sca_simple`	Tests the SCA configurations using a simple mathematical model.
`sca_model`	Tests the SCA configurations using the SCA mathematical model.
`find_sca_tradeoffs`	Attempts to find best SCA trade-offs for the current dataset.
`find_sca_static`	Returns all Pareto-optimal configurations where the same setting is used for all contexts.
`find_sca_random`	Returns `n_samples` random SCA configurations.
`dca_real`	Tests the DCA configurations using a simulation.
`dca_model`	Tests the DCA configurations using the DCA mathematical model.
`find_dca_static`	Returns all configurations where the same duty-cycle length is used for all contexts.
`find_dca_tradeoffs`	Attempts to find the best DCA trade-offs for the current dataset.
`find_dca_random`	Returns `n_samples` random DCA configurations.
`sca_dca_real`	Tests the SCA-DCA configurations using a simulation.
`sca_dca_model`	Tests the SCA-DCA configurations using a simulation.
`find_sca_dca_tradeoffs`	Attempts to find the best SCA-DCA trade-offs for the current dataset.
`load_data_config`	Loads the sequence and settings information from files.
`load_data`	Loads the base sequence from a file (instead of calling `set_sequence()`).
`load_config`	Loads the settings information from a file (instead of calling `set_settings()`)
`load_solution`	Loads a set of energy-efficient solutions from a file.
`save_data`	Saves the base sequence set with `set_settings()` to a file.
`save_solution`	Saves a set of energy-efficient solutions from a file.
`save_config`	Save the settings information (set with `set_settings()`) to a file.
`add_subsets`	Automatically generates settings by taking different attribute subsets from a database
`add_csdt_weighted`	Generates different CS-DTs with different ratios between energy and classification quality.
`add_csdt_borders`	Generates different CS-DTs from data around different contexts.