Rのセットライブラリを使用してファジーサーフェスを効率的にプロットする方法

Question

Rでは、ファジーロジックのsetsパッケージを使用してファジーサーフェスを3Dプロットに最適にプロットできますか？サービス変数と食品変数はx軸とy軸で、z軸はファジィ推論変数を非ファジィ化して求めた重心でなければなりません。

私はexpand.gridを使って4,000ポイントのファジー表面をプロットしてみましたが、コンピュータディスクは100％になり、2回再起動してもそのままになりました。私はあなたのコンピュータの安全のために、私が書いたコードを意図的に表示しません。

文書のこの例のシステムは、良い出発点になります。結果をプロットするのを助けてくれますか？ありがとう。

library(sets) 
# set universe 
sets_options("universe", seq(from = 0, to = 25, by = 1)) 

# set up fuzzy variables 
variables <- 
    set(service = fuzzy_partition(varnames = c(poor = 0, good = 5, excellent = 10), sd = 1.5), 
     food = fuzzy_variable(rancid = fuzzy_trapezoid(corners = c(-2, 0, 2, 4)), 
          delicious = fuzzy_trapezoid(corners = c(7, 9, 11, 13))), 
     tip = fuzzy_partition(varnames = c(cheap = 5, average = 12.5, generous = 20), 
          FUN = fuzzy_cone, radius = 5) 
) 

# set up rules 
rules <- 
    set(
    fuzzy_rule(service %is% poor || food %is% rancid, tip %is% cheap), 
    fuzzy_rule(service %is% good, tip %is% average), 
    fuzzy_rule(service %is% excellent || food %is% delicious, tip %is% generous) 
) 

# combine to a system 
system <- fuzzy_system(variables, rules) 
print(system) 
plot(system) ## plots variables 

# do inference 
fi <- fuzzy_inference(system, list(service = 3, food = 8.123)) 

# plot resulting fuzzy set 
plot(fi) 

# defuzzify 
print(gset_defuzzify(fi, "centroid")) 

# reset universe 
sets_options("universe", NULL) 

Answer 1

私はコンピュータを傷つけたにもかかわらず、実験を続けることに決めました。このコードは動作しますが、私はまだ経験豊富なプログラマーからの回答に感謝します。

# Please help me take this example from the docs and make a nice 3d plot 
options(show.error.locations = TRUE) 

library(sets) 
## set universe 
sets_options("universe", seq(from = 0, to = 25, by = 1)) 

## set up fuzzy variables 
variables <- 
    set(service = fuzzy_partition(varnames = c(poor = 0, good = 5, excellent = 10), sd = 1.5), 
     food = fuzzy_variable(rancid = fuzzy_trapezoid(corners = c(-2, 0, 2, 4)), 
          delicious = fuzzy_trapezoid(corners = c(7, 9, 11, 13))), 
     tip = fuzzy_partition(varnames = c(cheap = 5, average = 12.5, generous = 20), 
          FUN = fuzzy_cone, radius = 5) 
) 

## set up rules 
rules <- 
    set(
    fuzzy_rule(service %is% poor || food %is% rancid, tip %is% cheap), 
    fuzzy_rule(service %is% good, tip %is% average), 
    fuzzy_rule(service %is% excellent || food %is% delicious, tip %is% generous) 
) 

## combine to a system 
system <- fuzzy_system(variables, rules) 
print(system) 
plot(system) ## plots variables 

## do inference 
fi <- fuzzy_inference(system, list(service = 3, food = 8)) 

## plot resulting fuzzy set 
#plot(fi) 

# define a function to compute a tip given a row 
# that has a service column and food column 
defuzzify <- function(row){ 
    fi <- fuzzy_inference(system, list(service = row$service, food = row$food)) 
    gset_defuzzify(fi, "centroid") 
} 

# create a dataframe with food and service combinations to plot 
food.sequence = seq(from = 0, to = 10, by = 1) 
service.sequence = seq(from = 0, to = 10, by = 1) 
df.to.plot <- expand.grid(food = food.sequence, service = service.sequence) 

# for each food and service combination, compute a tip 
# "by" is supposed to be better than a for loop 
df.to.plot$tip <- by(df.to.plot, 1:nrow(df.to.plot), function(row) defuzzify(row))[] 

# the plotting function that comes later requires a numeric matrix, 
# so pivot by food and service and convert to a matrix 
library(reshape2) 
df <- dcast(df.to.plot, food ~ service, value.var = "tip") 
df <- df[,-1] # get rid of the food column 
row.names(df) <- food.sequence # name the rows 
tip.matrix <- data.matrix(df) 

# make a 3D interactive plot 
library(plotly) 
p <- plot_ly(z = tip.matrix) %>% add_surface() %>% 
    layout(title = "Tipping Plan", 
     scene = list(
      xaxis = list(title = "Service (x)"), 
      yaxis = list(title = "Food (y)"), 
      zaxis = list(title = "Tip (z)") 
     )) 

print(p) 

## reset universe 
sets_options("universe", NULL)