fsr (version 1.0.0)

fsi_eval: Evaluating an FSI model for a given point location

Description

This function executes the reasoning process of a fuzzy spatial inference (FSI) model for a given point location (i.e., sfg object of the type POINT).

Usage

fsi_eval(fsi, point, ...)

Arguments

fsi

An FSI model built with the function fsi_create and populated by the functions fsi_add_fsa, fsi_add_cs, and fsi_add_rules.

point

An sfg object of geometry type point, which is created through the function st_point of the sf package.

...

<dynamic-dots> Informs the fsi_eval how the elements of the resulting fuzzy set should be discretized if the user does not want the default configuration (see below). Default values: discret_by is 0.5 and discret_length is NULL.

Value

A numeric value that belongs to the domain of the consequent (i.e., as specified by fsi_add_cs) and represents the result of the reasoning process in a particular point location.

Details

This function evaluates an FSI model populated with its fuzzy spatial antecedent, consequent, and fuzzy rules set on a specific point location. This evaluation is based on the algorithm specified by FIFUS.

The default behavior of the function fsi_eval in the parameter ... is to consider a discrete interval of values with an increment of 0.5 between lower and upper values for the consequent domain (i.e., defined at fsi_add_cs function with the parameter bounds).

The user can modify the default behavior by using one of the following two ways:

• define a value for the parameter discret_by by changing the incremental value.

• define a desired length for the sequence of values domain of the consequent discret_length.

References

Carniel, A. C.; Schneider, M. Fuzzy inference on fuzzy spatial objects (FIFUS) for spatial decision support systems. In Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), pp. 1-6, 2017.

Examples

# NOT RUN {
library(sf)
# Creating the FSI model from an example implemented with the visitation function:
fsi <- visitation()

# Creating a vector of fuzzy rules; 
## note that we make use of the linguistic variables and linguistic values previously defined:
rules <- c(
  "IF accommodation review is reasonable AND food safety is low 
  THEN visiting experience is awful",
 "IF accommodation price is expensive AND accommodation review is reasonable 
   THEN visiting experience is awful",
 "IF accommodation price is affordable AND accommodation review is good AND food safety is medium 
   THEN visiting experience is average",
 "IF accommodation price is affordable AND accommodation review is excellent 
                                                                AND food safety is high 
   THEN visiting experience is great",
 "IF accommodation price is cut-rate AND accommodation review is excellent AND food safety is high 
   THEN visiting experience is great")

# Adding these rules to the FSI model previously instantiated:
fsi <- fsi_add_rules(fsi, rules)

# Using the default configuration:
res <- fsi_eval(fsi, st_point(c(-74.0, 40.7)))

# Change the default discretization by modifying the default step value:
res <- fsi_eval(fsi, st_point(c(-74.0, 40.7)), discret_by=0.8)

# Change the default discretization by choosing the quantity of values 
## between the lower and upper values for the consequent domain:
res <- fsi_eval(fsi, st_point(c(-74.0, 40.7)), discret_length=200)

# }