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lfl (version 1.2)

Linguistic Fuzzy Logic

Description

Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association rules, performing perception-based logical deduction (PbLD), and forecasting time-series using fuzzy rule-based ensemble (FRBE).

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Version

Install

install.packages('lfl')

Monthly Downloads

435

Version

1.2

License

GPL (>= 3.0)

Maintainer

Michal Burda

Last Published

June 18th, 2015

Functions in lfl (1.2)

Print an instance of the farules class

as.data.frame.farules

Convert the 'farules' object into a data frame

Evaluate the performance of the FRBE forecast

Determine whether the first set of predicates is more specific (or equal) than the other.

Implicational aggregation of rules' consequents into a fuzzy set

Return the first part of an instance of the farules class

Transform data into a set of fuzzy attributes using triangular or raised cosine shapes of the fuzzy sets

Triangular norm

Return the last part of an instance of the farules class

Select several rows and columns from a data object

Compute membership degrees of values to the fuzzy set

as.matrix.fsets

Convert a 'fsets' object into matrix

From a set of rules, remove each rule for which another rule exists that is more specific.

Compute rule base coverage of data

Return the first part of an instance of the fsets class

Plot a 'fsets' object

Test whether x is a valid object of the frbe class

Test whether x is a valid object of the fsets class

Return the last part of an instance of the fsets class

Fuzzy Rule-Based Ensemble (FRBE) of time-series forecasts

Compute forecast errors

A class of rules with statistical characteristics.

Compute truth-degrees of rules on data

Print an instance of the frbe class

Searching for fuzzy association rules

Linguistic Fuzzy Logic

Transform data into a set of linguistic fuzzy attributes

Combine several 'fsets' objects into a single one

Test whether x is a valid object of the farules class

Return vector of values from given interval

Reduce the size of rule base

Extract consequent-part (RHS) of the rules in a list

Extract antecedent-part (LHS) of the rules in a list

Convert fuzzy set into a crisp numeric value

Print an instance of the fsets class

A class of a table with several fuzzy sets.

Perform a Perception-based Logical Deduction (PbLD) with given rule-base on given dataset