rLDCP (version 1.0.2)

cp: Define the CP

Description

In general, CP corresponds with specific parts of the analyzed phenomenon at a certain degree of granularity. To create a computational model of the analyzed phenomenon, the designer analyzes the everyday use of natural language about the monitored phenomenon with the aim of identifying different parts (units of information or granules) based on his/her subjective perceptions. According with Zadeh (1996), a granule is a clump of elements which are drawn together by indistinguishability, similarity, proximity or functionality. The GLMP handles granules by using CPs.

Usage

cp(name, a, b = NULL, r = NULL)

Arguments

name

is the identifier of the CP.

a

is a vector A = (a1, a2,... , an) of linguistic expressions that represents the whole linguistic domain of CP, e.g. we have the linguistic domain "statistical data" that is represented with three linguistic variables (bad, good, very good).

b

is a vector B = (b1, b2,... , bn) of linguistic expressions (words or sentences in natural language) that represents the reliability of the CP, e.g., the reliability of the "statistical data" are (low, moderate, high). By default (b = NULL), the CP does not manage information about reliability.

r

is a vector R = (r1, r2,... , rn) of relevance degrees 0 <= ri <=1 assigned to each ai in the specific context, e.g., the relevance of the linguistic expressions (bad, good, very good) is (0.5, 0.5, 1) means the perception of "very good" is more relevant than the other two choices. By default (r = NULL), the function create a r vector with the maximum degree of relevance for all linguistic expression, e.g., (1,1,1).

Value

The generated CP = list(a, w, r, b, wb) where w and wb are vectors with the validity degrees (wi and wbi in [0,1]) of the linguistic expressions in a and b respectively. These vectors are initialized with 0.

Examples

# NOT RUN {
myCP <-cp("myCP", c("bad", "good", "very good"))
myCP <- cp("myCP", c("bad", "good", "very good"), c("low", "moderate", "high"))
myCP <- cp("myCP", c("bad", "good", "very good"), r=c(1,0.8,0.9))
myCP <- cp("myCP", c("bad", "good", "very good"), c("low", "moderate", "high"), c(1,0.8,0.9))

# }