nimsos.ai_tools package

nimsos.ai_tools.ai_tool_blox module

class nimsos.ai_tools.ai_tool_blox.BLOX(input_file, output_file, num_objectives, num_proposals)

Bases: object

Class of BLOX

This class can select the next candidates by random exploration.

calc_ai(t_train, X_all, train_actions, test_actions)

Calculating the proposals by AI algorithm

This function is for BLOX. This function do not depend on robot. If the new AI alborithm is developed, this function is only changed.

Parameters

• t_train (list[float]) – 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

Returns

the list where the selected actions are stored

Return type

actions (list[int])

load_data()

Loading candidates

This function do not depend on robot.

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])

select()

Selecting the proposals by MI algorithm

This function do not depend on robot.

Returns

True (str) for success.

nimsos.ai_tools.ai_tool_pdc module

class nimsos.ai_tools.ai_tool_pdc.PDC(input_file, output_file, num_objectives, num_proposals)

Bases: object

Class of PDC

This class can select the next candidates by phase diagram construction.

calc_ai(t_train, X_all, train_actions, test_actions)

Calculating the proposals by AI algorithm

This function is for PDC. This function do not depend on robot. If the new AI alborithm is developed, this function is only changed.

Parameters

• t_train (list[float]) – 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

Returns

the list where the selected actions are stored

Return type

actions (list[int])

load_data()

Loading candidates

This function do not depend on robot.

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])

select()

Selecting the proposals by MI algorithm

This function do not depend on robot.

Returns

True (str) for success.

nimsos.ai_tools.ai_tool_physbo module

class nimsos.ai_tools.ai_tool_physbo.PHYSBO(input_file, output_file, num_objectives, num_proposals)

Bases: object

Class of PHYSBO

This class can select the next candidates by Bayesian optimization based on PHYSBO package.

calc_ai(t_train, X_all, train_actions, test_actions)

Calculating the proposals by AI algorithm

This function is for PHYSBO. This function do not depend on robot. If the new AI alborithm is developed, this function is only changed.

Parameters

• t_train (list[float]) – 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

Returns

the list where the selected actions are stored

Return type

actions (list[int])

load_data()

Loading candidates

This function do not depend on robot.

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])

select()

Main function to select the proposals by AI algorithm

This function do not depend on robot.

Returns

True (str) for success.

nimsos.ai_tools.ai_tool_re module

class nimsos.ai_tools.ai_tool_re.RE(input_file, output_file, num_objectives, num_proposals)

Bases: object

Class of RE

This class can select the next candidates by random exploration.

calc_ai(t_train, X_all, train_actions, test_actions)

Calculating the proposals by AI algorithm

This function is for RE. This function do not depend on robot. If the new AI alborithm is developed, this function is only changed.

Parameters

• t_train (list[float]) – 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

Returns

the list where the selected actions are stored

Return type

actions (list[int])

load_data()

Loading candidates

This function do not depend on robot.

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])

select()

Selecting the proposals by MI algorithm

This function do not depend on robot.

Returns

True (str) for success.