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lfl

The lfl package provides various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), and forecasting time-series using fuzzy rule-based ensemble (FRBE).

Installation

To install the stable version from CRAN, simply issue the following command within your R session:

install.packages("lfl")

If you want to install the development version instead, type:

install.packages("devtools")
devtools::install_github("beerda/lfl")

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Version

Install

install.packages('lfl')

Monthly Downloads

671

Version

2.0

License

GPL-3

Maintainer

Michal Burda

Last Published

February 11th, 2020

Functions in lfl (2.0)

Context for linguistic expressions

Plot membership degrees stored in the instance of the S3 class fsets() as a line diagram.

Creator of functions representing linguistic expressions

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

Compute Symmetric Mean Absolute Percentage Error (SMAPE)

aggregateConsequents

Aggregation of fired consequents into a resulting fuzzy set

Take a sequence of instances of S3 class farules() and combine them into a single object. An error is thrown if some argument does not inherit from the farules() class.

Evaluate the performance of the FRBE forecast

Modify algebra's way of computing with NA values.

Algebra for Fuzzy Sets

as.data.frame.fsets

Convert an object of fsets class into a matrix or data frame This function converts an instance of S3 class fsets into a matrix or a data frame. The vars() and specs() attributes of the original object are deleted.

Evaluate rules and obtain truth-degrees

Create an instance of S3 class farules which represents a set of fuzzy association rules and their statistical characteristics.

Test whether x inherits from the S3 farules class.

Transform data into a fsets S3 class using shapes derived from triangles or raised cosines

Compute Mean Absolute Scaled Error (MASE)

Compute Root Mean Squared Error (RMSE)

Creating linguistic context directly from values

Reduce the size of rule base

Searching for fuzzy association rules

Return vector of values from given interval

Combine several 'fsets' objects into a single one

Print an instance of the fsets() class

Extract antecedent-part (left-hand side) of rules in a list

Composition of Fuzzy Relations

as.data.frame.farules

Convert the instance of the farules() S3 class into a data frame. Empty farules() object is converted into an empty data.frame().

S3 class representing a set of fuzzy sets on the fixed universe

Test whether x is a valid object of the S3 frbe class

Linguistic hedges

Create a function that computes linguistic horizons

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

Test whether x is a valid object of the S3 fsets class

Compute rule base coverage of data

Print an instance of the farules() S3 class in a human readable form.

Deprecated functions to compute membership degrees of numeric fuzzy sets

Determine whether the first set x of predicates is more specific (or equal) than y with respect to vars and specs.

Print an instance of the frbe() class

Transform data into a fsets S3 class of linguistic fuzzy attributes

Factories for functions that convert numeric data into membership degrees of fuzzy sets

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

Callback-based Multiplication of Matrices

Deprecated functions for transformation of data into a fsets S3 class of linguistic fuzzy attributes

Extract consequent-part (right-hand side) of rules in a list

defaultHedgeParams

A list of the parameters that define the shape of the hedges.

Convert fuzzy set into a crisp numeric value