soft.fuzzy.information package

Submodules

soft.fuzzy.information.granulation module

Implements the methods required to work with fuzzy theory.

class soft.fuzzy.information.granulation.FuzzyGraph

Bases: Core

This class implements several methods that are relevant to KnowledgeBase objects working with fuzzy logic.

add_fuzzy_granules(granules: List[Type[ContinuousFuzzySet]], is_input: None | bool = None, variable_offset: int = 0)

Given a list of granules, ‘anchor’ them into the KnowledgeBase’s graph by adding the necessary vertices for easy lookup later.

Parameters:

  • granules – A list of granules, where each granule is a ContinuousFuzzySet object.

  • is_input – Whether this vertex should be considered ‘input’ (e.g., input granules or

  • None (output granules). If)

  • True (then the 'input' attribute is not set. If)

  • the (then)

  • False ('input' attribute is set to True. If)

  • False. (then the 'input' attribute is set to)

  • None. (Default is)

  • variable_offset – Begin counting from a different variable index other than zero.

  • zero. (Default is)

Returns:

None

add_fuzzy_logic_rules(rules: Set[Rule], rough_compatibility: bool = True) → None

Add fuzzy logic rules to the KnowledgeBase’s graph.

Parameters:

  • rules – A set of Rule objects, where the premise and consequence members are frozensets, and each item within the frozenset is a 2-tuple in the form (input variable index, input linguistic term index)

  • rough_compatibility – Whether to create the necessary data for rough set compatibility within PySoft.

  • which (Disabling this feature with False may improve the speed in) – rules are added to the KnowledgeBase.

Returns:

None

get_fuzzy_logic_rules() → set

Get a list of fuzzy logic rules, where each element in the list is a Rule object.

Returns:

A set of frozensets, where each frozenset contains the ‘type’ of vertices that are neighbors which interact with .

Module contents