fugeR (version 0.1.2)

fugeR.predict: Compute the prediction of a fuzzy system for the given input data.

Description

Compute the prediction of a fuzzy system for the given input data.

Usage

fugeR.predict(fuzzySystem, dataset)

Arguments

fuzzySystem

[NULL] The fuzzy system to use for computing the prediction.

dataset

[NULL] The data to use.

Value

, A data.frame containing the predictions.

See Also

fugeR.run

Examples

##
## Not run: 
# #We use the iris dataset for this example
# #We need to convert the output in a numeric format.
# data(iris)
# OUT <- data.matrix(iris[5])[,1]
# fIris <- cbind(iris[1:4], OUT)
# In <- fIris[1:4]
# Out <- fIris[5]
# 
# #Launch the evolution, fugeR.run will return
# #the best fuzzy system found during the evolution
# fuzzySystem  <- fugeR.run( In,
#                    Out,
#                    generation=100, # Increase the number of generation for a better accuracy
#                    population=100,
#                    elitism=20,
#                    verbose=TRUE,
#                    threshold=NA,
#                    sensiW=0.0,
#                    speciW=0.0,
#                    accuW=0.0,
#                    rmseW=1.0,
#                    maxRules=5,
#                    maxVarPerRule=2,
#                    labelsMf=3
# )
# 
# #Plot the predicton given by the best fuzzy system found during the evolution
# prediction <- fugeR.predict(fuzzySystem, In)
# plot(prediction[[1]], ylim=c(1,max(unlist(Out))), col='blue', pch=21, axes=FALSE, ann=FALSE)
# points(Out[[1]], col="red", pch=21)
# axis(1)
# axis(2, at=1:3, lab=c('setosa', 'versicolor', 'virginica'))
# title(main='Fuzzy system prediction on Iris problem')
# title(xlab="Cases")
# title(ylab="Specie")
# box()
# legend(0.0, 3.0, c("Predicted","Actual"), cex=0.8, 
#        col=c("blue","red"), pch=c(21,21))
# 
# #Display the fuzzy system
# fugeR.summary(fuzzySystem)
# ## End(Not run)