soft.fuzzy.relation.continuous package

Submodules

soft.fuzzy.relation.continuous.aggregation module

Implements aggregation operators in fuzzy theory.

class soft.fuzzy.relation.continuous.aggregation.OrderedWeightedAveraging(in_features, weights)

Bases: Module

Yager’s On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decisionmaking (1988)

An operator that lies between the ‘anding’ or the ‘oring’ of multiple criteria. The weight vector allows us to easily adjust the degree of ‘anding’ and ‘oring’ implicit in the aggregation.

dispersion()

The measure of dispersion; essentially, it is a measure of entropy that is related to the Shannon information concept. The more disperse the weight vector, the more information is being used in the aggregation of the aggregate value.

Returns:

The amount of dispersion in the weight vector.

forward(input_observation)

Applies the Ordered Weighted Averaging operator. First, it will sort the argument in descending order, then multiply by the weight vector, and finally sum over the entries.

Parameters:

input_observation – Argument vector, unordered.

Returns:

The aggregation of the ordered argument vector with the weight vector.

orness()

A degree of 1 means the OWA operator is the ‘or’ operator, and this occurs when the first element of the weight vector is equal to 1 and all other elements in the weight vector are zero.

Returns:

The degree to which the Ordered Weighted Averaging operator is an ‘or’ operator.

soft.fuzzy.relation.continuous.tnorm module

Implements the t-norm fuzzy relations.

class soft.fuzzy.relation.continuous.tnorm.AlgebraicProduct(in_features=None, importance=None)

Bases: Module

Implementation of the Algebraic Product t-norm (Fuzzy AND).

forward(elements)

Forward pass of the function. Applies the function to the input elementwise.

class soft.fuzzy.relation.continuous.tnorm.Minimum

Bases: object

A placeholder class for operations expecting the minimum t-norm.

Module contents