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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

695

Version

2.1.0

License

GPL-3

Maintainer

Michal Burda

Last Published

October 14th, 2020

Functions in lfl (2.1.0)

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

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

Composition of Fuzzy Relations

Algebra for Fuzzy Sets

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.

aggregateConsequents

Aggregation of fired consequents into a resulting fuzzy set

Combine several 'fsets' objects into a single one

defaultHedgeParams

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

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

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

Context for linguistic expressions

lfl - Linguistic Fuzzy Logic

Creator of functions representing linguistic expressions

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

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

Print an instance of the frbe() class

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

Convert fuzzy set into a crisp numeric value

Linguistic hedges

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

Evaluate the performance of the FRBE forecast

Create a function that computes linguistic horizons

Transform data into a fsets S3 class of linguistic fuzzy attributes

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

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

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

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

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().

Compute Symmetric Mean Absolute Percentage Error (SMAPE)

Evaluate rules and obtain truth-degrees

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.

Return vector of values from given interval

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

Print the linguistic context

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

Test whether x inherits from the S3 farules class.

A factory function for creation of sugeno-integrals.

Deprecated functions to compute membership degrees of numeric fuzzy sets

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

Reduce the size of rule base

Compute rule base coverage of data

Callback-based Multiplication of Matrices

Compute Root Mean Squared Error (RMSE)

Creating linguistic context directly from values

Compute Mean Absolute Scaled Error (MASE)

A quantifier is a function that computes a fuzzy truth value of a claim about the quantity. This function creates the <1>-type quantifier. (See the examples below on how to use it as a quantifier of the <1,1> type.)

Print an instance of the fsets() class

Searching for fuzzy association rules

Modify algebra's way of computing with NA values.