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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

2.0.1

License

GPL-3

Maintainer

Michal Burda

Last Published

May 19th, 2020

Functions in lfl (2.0.1)

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

Compute Mean Absolute Scaled Error (MASE)

Creating linguistic context directly from values

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

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.

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().

Print an instance of the frbe() class

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.

Create a function that computes linguistic horizons

Linguistic hedges

Evaluate the performance of the FRBE forecast

aggregateConsequents

Aggregation of fired consequents into a resulting fuzzy set

Algebra for Fuzzy Sets

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

Context for linguistic expressions

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

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

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

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

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

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

Transform data into a fsets S3 class of linguistic fuzzy attributes

Convert fuzzy set into a crisp numeric value

defaultHedgeParams

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

Evaluate rules and obtain truth-degrees

Deprecated functions to compute membership degrees of numeric fuzzy sets

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

Combine several 'fsets' objects into a single one

Callback-based Multiplication of Matrices

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

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

Test whether x inherits from the S3 farules class.

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

Composition of Fuzzy Relations

lfl - Linguistic Fuzzy Logic

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

Compute Symmetric Mean Absolute Percentage Error (SMAPE)

Compute Root Mean Squared Error (RMSE)

Print an instance of the fsets() class

Creator of functions representing linguistic expressions

Modify algebra's way of computing with NA values.

Reduce the size of rule base

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

Compute rule base coverage of data

Searching for fuzzy association rules

Return vector of values from given interval