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#Code automatically generated with fuzzylite 6.0.
library(ggplot2);
engine.name = "AllTerms"
engine.fll = "Engine: AllTerms
InputVariable: AllInputTerms
enabled: true
range: 0.000 6.500
lock-range: false
term: A Sigmoid 0.500 -20.000
term: B ZShape 0.000 1.000
term: C Ramp 1.000 0.000
term: D Triangle 0.500 1.000 1.500
term: E Trapezoid 1.000 1.250 1.750 2.000
term: F Concave 0.850 0.250
term: G Rectangle 1.750 2.250
term: H Discrete 2.000 0.000 2.250 1.000 2.500 0.500 2.750 1.000 3.000 0.000
term: I Gaussian 3.000 0.200
term: J Cosine 3.250 0.650
term: K GaussianProduct 3.500 0.100 3.300 0.300
term: L Spike 3.640 1.040
term: M Bell 4.000 0.250 3.000
term: N PiShape 4.000 4.500 4.500 5.000
term: O Concave 5.650 6.250
term: P SigmoidDifference 4.750 10.000 30.000 5.250
term: Q SigmoidProduct 5.250 20.000 -10.000 5.750
term: R Ramp 5.500 6.500
term: S SShape 5.500 6.500
term: T Sigmoid 6.000 20.000
OutputVariable: AllOutputTerms
enabled: true
range: 0.000 6.500
lock-range: false
aggregation: Maximum
defuzzifier: Centroid 200
default: nan
lock-previous: false
term: A Sigmoid 0.500 -20.000
term: B ZShape 0.000 1.000
term: C Ramp 1.000 0.000
term: D Triangle 0.500 1.000 1.500
term: E Trapezoid 1.000 1.250 1.750 2.000
term: F Concave 0.850 0.250
term: G Rectangle 1.750 2.250
term: H Discrete 2.000 0.000 2.250 1.000 2.500 0.500 2.750 1.000 3.000 0.000
term: I Gaussian 3.000 0.200
term: J Cosine 3.250 0.650
term: K GaussianProduct 3.500 0.100 3.300 0.300
term: L Spike 3.640 1.040
term: M Bell 4.000 0.250 3.000
term: N PiShape 4.000 4.500 4.500 5.000
term: O Concave 5.650 6.250
term: P SigmoidDifference 4.750 10.000 30.000 5.250
term: Q SigmoidProduct 5.250 20.000 -10.000 5.750
term: R Ramp 5.500 6.500
term: S SShape 5.500 6.500
term: T Sigmoid 6.000 20.000
RuleBlock:
enabled: true
conjunction: Minimum
disjunction: Maximum
implication: Minimum
activation: General
rule: if AllInputTerms is A then AllOutputTerms is T
rule: if AllInputTerms is B then AllOutputTerms is S
rule: if AllInputTerms is C then AllOutputTerms is R
rule: if AllInputTerms is D then AllOutputTerms is Q
rule: if AllInputTerms is E then AllOutputTerms is P
rule: if AllInputTerms is F then AllOutputTerms is O
rule: if AllInputTerms is G then AllOutputTerms is N
rule: if AllInputTerms is H then AllOutputTerms is M
rule: if AllInputTerms is I then AllOutputTerms is L
rule: if AllInputTerms is J then AllOutputTerms is K
rule: if AllInputTerms is K then AllOutputTerms is J
rule: if AllInputTerms is L then AllOutputTerms is I
rule: if AllInputTerms is M then AllOutputTerms is H
rule: if AllInputTerms is N then AllOutputTerms is G
rule: if AllInputTerms is O then AllOutputTerms is F
rule: if AllInputTerms is P then AllOutputTerms is E
rule: if AllInputTerms is Q then AllOutputTerms is D
rule: if AllInputTerms is R then AllOutputTerms is C
rule: if AllInputTerms is S then AllOutputTerms is B
rule: if AllInputTerms is T then AllOutputTerms is A"
engine.fldFile = "AllTerms.fld"
if (require(data.table)) {
engine.df = data.table::fread(engine.fldFile, sep="auto", header="auto")
} else {
engine.df = read.table(engine.fldFile, header=TRUE)
}
engine.plot.i1_o1 = ggplot(engine.df, aes(AllInputTerms, AllOutputTerms)) +
geom_line(aes(color=AllOutputTerms), size=3, lineend="round", linejoin="mitre") +
scale_color_gradient(low="yellow", high="red") +
ggtitle("AllInputTerms vs AllOutputTerms")
engine.plot.o1_i1 = ggplot(engine.df, aes(AllInputTerms, AllOutputTerms)) +
geom_line(aes(color=AllOutputTerms), size=3, lineend="round", linejoin="mitre") +
scale_color_gradient(low="yellow", high="red") +
coord_flip() +
ggtitle("AllOutputTerms vs AllInputTerms")
if (require(gridExtra)) {
engine.plots = arrangeGrob(engine.plot.i1_o1, engine.plot.o1_i1, ncol=2, top=engine.name)
ggsave(paste0(engine.name, ".pdf"), engine.plots)
if (require(grid)) {
grid.newpage()
grid.draw(engine.plots)
}
}
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