nimsos.visualization package

nimsos.visualization.plot_history module

nimsos.visualization.plot_history.best(input_file, num_cycles)

Creating the figure of best datapoints depending on the cycles

This function do not depend on robot.

Parameters

input_file (list[float]) – the file for history results
num_cycles (int) – the number of cycles

nimsos.visualization.plot_history.cycle(input_file, num_cycles)

Creating the figure of datapoints depending on the cycles

This function do not depend on robot.

Parameters

input_file (list[float]) – the file for history results
num_cycles (int) – the number of cycles

nimsos.visualization.plot_distribution module

nimsos.visualization.plot_distribution.load_data(input_file, num_objectives)

Loading candidates

This function do not depend on robot.

Parameters

input_file (str) – the file for candidates for MI algorithm
num_objectives (int) – the number of objectives

Returns

the list where observed objectives are stored X_all (list[float]): the list where all descriptors are stored train_actions (list[float]): the list where observed actions are stored test_actions (list[float]): the list where test actions are stored

Return type

t_train (list[float])

nimsos.visualization.plot_distribution.plot(input_file, num_objectives)

Loading candidates

This function do not depend on robot.

Parameters

input_file (str) – the file for candidates for MI algorithm
num_objectives (int) – the number of objectives

nimsos.visualization.plot_phase_diagram module

nimsos.visualization.plot_phase_diagram.load_data(input_file)

Loading candidates

This function do not depend on robot.

Parameters: input_file (str) – the file for candidates for MI algorithm
Returns: the list where observed objectives are stored X_all (list[float]): the list where all descriptors are stored train_actions (list[float]): the list where observed actions are stored test_actions (list[float]): the list where test actions are stored
Return type: t_train (list[float])

nimsos.visualization.plot_phase_diagram.plot(input_file)

Loading candidates

This function do not depend on robot.

Parameters: input_file (str) – the file for candidates for MI algorithm