Last updated on 2020-02-19 10:48:46 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.99.1 | 6.67 | 35.14 | 41.81 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.99.1 | 6.28 | 28.14 | 34.42 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.99.1 | 51.21 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.99.1 | 51.19 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 0.99.1 | 13.00 | 51.00 | 64.00 | NOTE | |
| r-devel-windows-ix86+x86_64-gcc8 | 0.99.1 | 17.00 | 68.00 | 85.00 | NOTE | |
| r-patched-linux-x86_64 | 0.99.1 | 5.70 | 30.88 | 36.58 | NOTE | |
| r-patched-solaris-x86 | 0.99.1 | 69.40 | NOTE | |||
| r-release-linux-x86_64 | 0.99.1 | 5.69 | 30.51 | 36.20 | NOTE | |
| r-release-windows-ix86+x86_64 | 0.99.1 | 11.00 | 70.00 | 81.00 | NOTE | |
| r-release-osx-x86_64 | 0.99.1 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 0.99.1 | 9.00 | 45.00 | 54.00 | NOTE | |
| r-oldrel-osx-x86_64 | 0.99.1 | NOTE |
Version: 0.99.1
Check: R code for possible problems
Result: NOTE
plot,ANFIS: no visible binding for global variable 'X'
plotMF,ANFIS : <anonymous>: no visible global function definition for
'lines'
plotMFs,ANFIS: no visible global function definition for 'par'
Undefined global functions or variables:
X lines par
Consider adding
importFrom("graphics", "lines", "par")
to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-devel-windows-ix86+x86_64-gcc8, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Version: 0.99.1
Check: examples
Result: ERROR
Running examples in 'anfis-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ANFIS-class
> ### Title: ANFIS S4 class implementation in R
> ### Aliases: ANFIS-class
>
> ### ** Examples
>
> ##Set 2 cores using global options for parallel library
> require("parallel")
> if(.Platform$OS.type == "windows"){
+ options(mc.cores=1)
+ }else{
+ options(mc.cores=2) ##You could use all calling detectCores()
+ }
>
> ##Example domain for bidimentional sinc(x,y) function
> x <- seq(-10, 10, length= 11)
> trainingSet <- trainSet(x,x)
> Z <- matrix(trainingSet[,"z"],ncol=length(x),nrow=length(x))
> persp(x,x,Z,theta = 45, phi = 15, expand = 0.8, col = "lightblue",
+ ticktype="detailed",main="sinc(x)*sinc(y)")
>
> ##Training domain patterns
> X <- trainingSet[,1:2]
> Y <- trainingSet[,3,drop=FALSE]
>
> ##Defining the required MembershipFunctions for the ANFIS
> membershipFunction<-list(
+ x=c(new(Class="NormalizedGaussianMF",parameters=c(mu=-10,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=-5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=0,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=10,sigma=2))),
+ y=c(new(Class="NormalizedGaussianMF",parameters=c(mu=-10,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=-5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=0,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=10,sigma=2))))
>
> ##Creating the ANFIS network with 2 inputs and 4 MembershipFunctions in
> ##each input
> anfis3 <- new(Class="ANFIS",X,Y,membershipFunction)
> anfis3
ANFIS network
Trainning Set:
dim(x)= 121x2
dim(y)= 121x1
Arquitecture: 2 ( 5x5 ) - 25 - 75 ( 75x1 ) - 1
Network not trained yet
>
> ##Check for epsilon-completeness in each input
> plotMFs(anfis3)
>
> ##Training the ANFIS network.
> ##We will use instead an already trained object to reduce example time.
> data(anfis3)
>
> ##How the training went. You can keep on training as the training error
> ##is still descending.
> plot(anfis3)
>
> ##Test the fit, i. e., how the MembershipFunctions partition the input space
> plotMFs(anfis3)
>
> ##Just to see if premises, consequents and errors were updated
> getPremises(anfis3)[[input=1]][[mf=1]]
MembershipFunction: Normalized Gaussian Membership Function
Number of parameters: 2
mu sigma
-9.984987 2.038630
Expression: expression(exp(-1/2 * ((x - mu)/sigma)^2))
> getConsequents(anfis3)[1:2,]
[1] 0.005011691 0.005011691
> getErrors(anfis3) #Training errors
[1] 0.03741022 0.03238312 0.02992609 0.02866609 0.02759035 0.02661405
[7] 0.02569998 0.02482807 0.02398628 0.02316705 0.02271256 0.02224352
[13] 0.02176043 0.02126397 0.02075495 0.02023433 0.01970327 0.01916307
> getTrainingType(anfis3)
[1] "trainHybridJangOffLine"
> names(coef(anfis3))
[1] "premises" "consequents"
> ##An alternative to get premises and/or consequents ...
> coef(anfis3)$premises[[input=1]][[mf=1]]
MembershipFunction: Normalized Gaussian Membership Function
Number of parameters: 2
mu sigma
-9.984987 2.038630
Expression: expression(exp(-1/2 * ((x - mu)/sigma)^2))
> coef(anfis3)$consequents[1:2,]
[1] 0.005011691 0.005011691
>
> ##First five train pattern associated values for the training process
> fitted(anfis3)[1:5,]
[1] -0.008394804 0.007514315 0.012824644 -0.003044622 -0.030125619
> resid(anfis3)[1:5,]
[1] 0.011354394 -0.014242212 -0.010291178 0.013337535 0.005391769
> summary(anfis3)
ANFIS network
Trainning Set:
dim(x)= 121x2
dim(y)= 121x1
Arquitecture: 2 ( 5x5 ) - 25 - 75 ( 75x1 ) - 1
Last training error: 0.01916307
Call: trainHybridJangOffLine(object = anfis3, epochs = 10)
Statistics for Off-line training
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.01916 0.02139 0.02358 0.02489 0.02735 0.03741
>
> ##Surface comparison between the original training set and the predicted
> ##ANFIS network
> y <- predict(anfis3,X)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
anfis
--- call from context ---
FUN(X[[i]], ...)
--- call from argument ---
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)), ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
--- R stacktrace ---
where 1: FUN(X[[i]], ...)
where 2: lapply(1:nrow(x), function(pattern) {
tita_1 <- sapply(seq(along = object@premises), function(input,
pattern) {
unlist(lapply(object@premises[[input]], function(MF) {
evaluateMF(MF, x[pattern, input])
}))
}, pattern)
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)),
ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
if (!is.matrix(tita_1)) {
tita_1 <- t(tita_1)
}
w <<- cbind(w, apply(object@rules, 1, function(rule, tita_1) {
prod(sapply(seq(along = rule), function(input, tita_1) {
tita_1[rule[input], input]
}, tita_1))
}, tita_1))
wSum <<- c(wSum, sum(w[, pattern]))
wNormalized <<- cbind(wNormalized, w[, pattern]/wSum[pattern])
A <<- rbind(A, matrix(matrix(c(x[pattern, ], 1), ncol = 1) %*%
wNormalized[, pattern], nrow = 1, byrow = FALSE))
return(NULL)
})
where 3: .local(object, ...)
where 4: predict(anfis3, X)
where 5: predict(anfis3, X)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (pattern)
{
tita_1 <- sapply(seq(along = object@premises), function(input,
pattern) {
unlist(lapply(object@premises[[input]], function(MF) {
evaluateMF(MF, x[pattern, input])
}))
}, pattern)
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)),
ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
if (!is.matrix(tita_1)) {
tita_1 <- t(tita_1)
}
w <<- cbind(w, apply(object@rules, 1, function(rule, tita_1) {
prod(sapply(seq(along = rule), function(input, tita_1) {
tita_1[rule[input], input]
}, tita_1))
}, tita_1))
wSum <<- c(wSum, sum(w[, pattern]))
wNormalized <<- cbind(wNormalized, w[, pattern]/wSum[pattern])
A <<- rbind(A, matrix(matrix(c(x[pattern, ], 1), ncol = 1) %*%
wNormalized[, pattern], nrow = 1, byrow = FALSE))
return(NULL)
}
<environment: 0x33a1de0>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Error in if (class(tita_1) == "list") { : the condition has length > 1
Calls: predict -> predict -> .local -> lapply -> FUN
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.99.1
Check: examples
Result: ERROR
Running examples in ‘anfis-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ANFIS-class
> ### Title: ANFIS S4 class implementation in R
> ### Aliases: ANFIS-class
>
> ### ** Examples
>
> ##Set 2 cores using global options for parallel library
> require("parallel")
> if(.Platform$OS.type == "windows"){
+ options(mc.cores=1)
+ }else{
+ options(mc.cores=2) ##You could use all calling detectCores()
+ }
>
> ##Example domain for bidimentional sinc(x,y) function
> x <- seq(-10, 10, length= 11)
> trainingSet <- trainSet(x,x)
> Z <- matrix(trainingSet[,"z"],ncol=length(x),nrow=length(x))
> persp(x,x,Z,theta = 45, phi = 15, expand = 0.8, col = "lightblue",
+ ticktype="detailed",main="sinc(x)*sinc(y)")
>
> ##Training domain patterns
> X <- trainingSet[,1:2]
> Y <- trainingSet[,3,drop=FALSE]
>
> ##Defining the required MembershipFunctions for the ANFIS
> membershipFunction<-list(
+ x=c(new(Class="NormalizedGaussianMF",parameters=c(mu=-10,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=-5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=0,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=10,sigma=2))),
+ y=c(new(Class="NormalizedGaussianMF",parameters=c(mu=-10,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=-5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=0,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=10,sigma=2))))
>
> ##Creating the ANFIS network with 2 inputs and 4 MembershipFunctions in
> ##each input
> anfis3 <- new(Class="ANFIS",X,Y,membershipFunction)
> anfis3
ANFIS network
Trainning Set:
dim(x)= 121x2
dim(y)= 121x1
Arquitecture: 2 ( 5x5 ) - 25 - 75 ( 75x1 ) - 1
Network not trained yet
>
> ##Check for epsilon-completeness in each input
> plotMFs(anfis3)
>
> ##Training the ANFIS network.
> ##We will use instead an already trained object to reduce example time.
> data(anfis3)
>
> ##How the training went. You can keep on training as the training error
> ##is still descending.
> plot(anfis3)
>
> ##Test the fit, i. e., how the MembershipFunctions partition the input space
> plotMFs(anfis3)
>
> ##Just to see if premises, consequents and errors were updated
> getPremises(anfis3)[[input=1]][[mf=1]]
MembershipFunction: Normalized Gaussian Membership Function
Number of parameters: 2
mu sigma
-9.984987 2.038630
Expression: expression(exp(-1/2 * ((x - mu)/sigma)^2))
> getConsequents(anfis3)[1:2,]
[1] 0.005011691 0.005011691
> getErrors(anfis3) #Training errors
[1] 0.03741022 0.03238312 0.02992609 0.02866609 0.02759035 0.02661405
[7] 0.02569998 0.02482807 0.02398628 0.02316705 0.02271256 0.02224352
[13] 0.02176043 0.02126397 0.02075495 0.02023433 0.01970327 0.01916307
> getTrainingType(anfis3)
[1] "trainHybridJangOffLine"
> names(coef(anfis3))
[1] "premises" "consequents"
> ##An alternative to get premises and/or consequents ...
> coef(anfis3)$premises[[input=1]][[mf=1]]
MembershipFunction: Normalized Gaussian Membership Function
Number of parameters: 2
mu sigma
-9.984987 2.038630
Expression: expression(exp(-1/2 * ((x - mu)/sigma)^2))
> coef(anfis3)$consequents[1:2,]
[1] 0.005011691 0.005011691
>
> ##First five train pattern associated values for the training process
> fitted(anfis3)[1:5,]
[1] -0.008394804 0.007514315 0.012824644 -0.003044622 -0.030125619
> resid(anfis3)[1:5,]
[1] 0.011354394 -0.014242212 -0.010291178 0.013337535 0.005391769
> summary(anfis3)
ANFIS network
Trainning Set:
dim(x)= 121x2
dim(y)= 121x1
Arquitecture: 2 ( 5x5 ) - 25 - 75 ( 75x1 ) - 1
Last training error: 0.01916307
Call: trainHybridJangOffLine(object = anfis3, epochs = 10)
Statistics for Off-line training
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.01916 0.02139 0.02358 0.02489 0.02735 0.03741
>
> ##Surface comparison between the original training set and the predicted
> ##ANFIS network
> y <- predict(anfis3,X)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
anfis
--- call from context ---
FUN(X[[i]], ...)
--- call from argument ---
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)), ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
--- R stacktrace ---
where 1: FUN(X[[i]], ...)
where 2: lapply(1:nrow(x), function(pattern) {
tita_1 <- sapply(seq(along = object@premises), function(input,
pattern) {
unlist(lapply(object@premises[[input]], function(MF) {
evaluateMF(MF, x[pattern, input])
}))
}, pattern)
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)),
ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
if (!is.matrix(tita_1)) {
tita_1 <- t(tita_1)
}
w <<- cbind(w, apply(object@rules, 1, function(rule, tita_1) {
prod(sapply(seq(along = rule), function(input, tita_1) {
tita_1[rule[input], input]
}, tita_1))
}, tita_1))
wSum <<- c(wSum, sum(w[, pattern]))
wNormalized <<- cbind(wNormalized, w[, pattern]/wSum[pattern])
A <<- rbind(A, matrix(matrix(c(x[pattern, ], 1), ncol = 1) %*%
wNormalized[, pattern], nrow = 1, byrow = FALSE))
return(NULL)
})
where 3: .local(object, ...)
where 4: predict(anfis3, X)
where 5: predict(anfis3, X)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (pattern)
{
tita_1 <- sapply(seq(along = object@premises), function(input,
pattern) {
unlist(lapply(object@premises[[input]], function(MF) {
evaluateMF(MF, x[pattern, input])
}))
}, pattern)
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)),
ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
if (!is.matrix(tita_1)) {
tita_1 <- t(tita_1)
}
w <<- cbind(w, apply(object@rules, 1, function(rule, tita_1) {
prod(sapply(seq(along = rule), function(input, tita_1) {
tita_1[rule[input], input]
}, tita_1))
}, tita_1))
wSum <<- c(wSum, sum(w[, pattern]))
wNormalized <<- cbind(wNormalized, w[, pattern]/wSum[pattern])
A <<- rbind(A, matrix(matrix(c(x[pattern, ], 1), ncol = 1) %*%
wNormalized[, pattern], nrow = 1, byrow = FALSE))
return(NULL)
}
<environment: 0x558a73431470>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Error in if (class(tita_1) == "list") { : the condition has length > 1
Calls: predict -> predict -> .local -> lapply -> FUN
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.99.1
Check: examples
Result: ERROR
Running examples in ‘anfis-Ex.R’ failed
The error most likely occurred in:
> ### Name: ANFIS-class
> ### Title: ANFIS S4 class implementation in R
> ### Aliases: ANFIS-class
>
> ### ** Examples
>
> ##Set 2 cores using global options for parallel library
> require("parallel")
> if(.Platform$OS.type == "windows"){
+ options(mc.cores=1)
+ }else{
+ options(mc.cores=2) ##You could use all calling detectCores()
+ }
>
> ##Example domain for bidimentional sinc(x,y) function
> x <- seq(-10, 10, length= 11)
> trainingSet <- trainSet(x,x)
> Z <- matrix(trainingSet[,"z"],ncol=length(x),nrow=length(x))
> persp(x,x,Z,theta = 45, phi = 15, expand = 0.8, col = "lightblue",
+ ticktype="detailed",main="sinc(x)*sinc(y)")
>
> ##Training domain patterns
> X <- trainingSet[,1:2]
> Y <- trainingSet[,3,drop=FALSE]
>
> ##Defining the required MembershipFunctions for the ANFIS
> membershipFunction<-list(
+ x=c(new(Class="NormalizedGaussianMF",parameters=c(mu=-10,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=-5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=0,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=10,sigma=2))),
+ y=c(new(Class="NormalizedGaussianMF",parameters=c(mu=-10,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=-5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=0,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=10,sigma=2))))
>
> ##Creating the ANFIS network with 2 inputs and 4 MembershipFunctions in
> ##each input
> anfis3 <- new(Class="ANFIS",X,Y,membershipFunction)
> anfis3
ANFIS network
Trainning Set:
dim(x)= 121x2
dim(y)= 121x1
Arquitecture: 2 ( 5x5 ) - 25 - 75 ( 75x1 ) - 1
Network not trained yet
>
> ##Check for epsilon-completeness in each input
> plotMFs(anfis3)
>
> ##Training the ANFIS network.
> ##We will use instead an already trained object to reduce example time.
> data(anfis3)
>
> ##How the training went. You can keep on training as the training error
> ##is still descending.
> plot(anfis3)
>
> ##Test the fit, i. e., how the MembershipFunctions partition the input space
> plotMFs(anfis3)
>
> ##Just to see if premises, consequents and errors were updated
> getPremises(anfis3)[[input=1]][[mf=1]]
MembershipFunction: Normalized Gaussian Membership Function
Number of parameters: 2
mu sigma
-9.984987 2.038630
Expression: expression(exp(-1/2 * ((x - mu)/sigma)^2))
> getConsequents(anfis3)[1:2,]
[1] 0.005011691 0.005011691
> getErrors(anfis3) #Training errors
[1] 0.03741022 0.03238312 0.02992609 0.02866609 0.02759035 0.02661405
[7] 0.02569998 0.02482807 0.02398628 0.02316705 0.02271256 0.02224352
[13] 0.02176043 0.02126397 0.02075495 0.02023433 0.01970327 0.01916307
> getTrainingType(anfis3)
[1] "trainHybridJangOffLine"
> names(coef(anfis3))
[1] "premises" "consequents"
> ##An alternative to get premises and/or consequents ...
> coef(anfis3)$premises[[input=1]][[mf=1]]
MembershipFunction: Normalized Gaussian Membership Function
Number of parameters: 2
mu sigma
-9.984987 2.038630
Expression: expression(exp(-1/2 * ((x - mu)/sigma)^2))
> coef(anfis3)$consequents[1:2,]
[1] 0.005011691 0.005011691
>
> ##First five train pattern associated values for the training process
> fitted(anfis3)[1:5,]
[1] -0.008394804 0.007514315 0.012824644 -0.003044622 -0.030125619
> resid(anfis3)[1:5,]
[1] 0.011354394 -0.014242212 -0.010291178 0.013337535 0.005391769
> summary(anfis3)
ANFIS network
Trainning Set:
dim(x)= 121x2
dim(y)= 121x1
Arquitecture: 2 ( 5x5 ) - 25 - 75 ( 75x1 ) - 1
Last training error: 0.01916307
Call: trainHybridJangOffLine(object = anfis3, epochs = 10)
Statistics for Off-line training
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.01916 0.02139 0.02358 0.02489 0.02735 0.03741
>
> ##Surface comparison between the original training set and the predicted
> ##ANFIS network
> y <- predict(anfis3,X)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
anfis
--- call from context ---
FUN(X[[i]], ...)
--- call from argument ---
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)), ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
--- R stacktrace ---
where 1: FUN(X[[i]], ...)
where 2: lapply(1:nrow(x), function(pattern) {
tita_1 <- sapply(seq(along = object@premises), function(input,
pattern) {
unlist(lapply(object@premises[[input]], function(MF) {
evaluateMF(MF, x[pattern, input])
}))
}, pattern)
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)),
ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
if (!is.matrix(tita_1)) {
tita_1 <- t(tita_1)
}
w <<- cbind(w, apply(object@rules, 1, function(rule, tita_1) {
prod(sapply(seq(along = rule), function(input, tita_1) {
tita_1[rule[input], input]
}, tita_1))
}, tita_1))
wSum <<- c(wSum, sum(w[, pattern]))
wNormalized <<- cbind(wNormalized, w[, pattern]/wSum[pattern])
A <<- rbind(A, matrix(matrix(c(x[pattern, ], 1), ncol = 1) %*%
wNormalized[, pattern], nrow = 1, byrow = FALSE))
return(NULL)
})
where 3: .local(object, ...)
where 4: predict(anfis3, X)
where 5: predict(anfis3, X)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (pattern)
{
tita_1 <- sapply(seq(along = object@premises), function(input,
pattern) {
unlist(lapply(object@premises[[input]], function(MF) {
evaluateMF(MF, x[pattern, input])
}))
}, pattern)
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)),
ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
if (!is.matrix(tita_1)) {
tita_1 <- t(tita_1)
}
w <<- cbind(w, apply(object@rules, 1, function(rule, tita_1) {
prod(sapply(seq(along = rule), function(input, tita_1) {
tita_1[rule[input], input]
}, tita_1))
}, tita_1))
wSum <<- c(wSum, sum(w[, pattern]))
wNormalized <<- cbind(wNormalized, w[, pattern]/wSum[pattern])
A <<- rbind(A, matrix(matrix(c(x[pattern, ], 1), ncol = 1) %*%
wNormalized[, pattern], nrow = 1, byrow = FALSE))
return(NULL)
}
<environment: 0x3be5720>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Error in if (class(tita_1) == "list") { : the condition has length > 1
Calls: predict -> predict -> .local -> lapply -> FUN
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.99.1
Check: examples
Result: ERROR
Running examples in ‘anfis-Ex.R’ failed
The error most likely occurred in:
> ### Name: ANFIS-class
> ### Title: ANFIS S4 class implementation in R
> ### Aliases: ANFIS-class
>
> ### ** Examples
>
> ##Set 2 cores using global options for parallel library
> require("parallel")
> if(.Platform$OS.type == "windows"){
+ options(mc.cores=1)
+ }else{
+ options(mc.cores=2) ##You could use all calling detectCores()
+ }
>
> ##Example domain for bidimentional sinc(x,y) function
> x <- seq(-10, 10, length= 11)
> trainingSet <- trainSet(x,x)
> Z <- matrix(trainingSet[,"z"],ncol=length(x),nrow=length(x))
> persp(x,x,Z,theta = 45, phi = 15, expand = 0.8, col = "lightblue",
+ ticktype="detailed",main="sinc(x)*sinc(y)")
>
> ##Training domain patterns
> X <- trainingSet[,1:2]
> Y <- trainingSet[,3,drop=FALSE]
>
> ##Defining the required MembershipFunctions for the ANFIS
> membershipFunction<-list(
+ x=c(new(Class="NormalizedGaussianMF",parameters=c(mu=-10,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=-5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=0,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=10,sigma=2))),
+ y=c(new(Class="NormalizedGaussianMF",parameters=c(mu=-10,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=-5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=0,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=5,sigma=2)),
+ new(Class="NormalizedGaussianMF",parameters=c(mu=10,sigma=2))))
>
> ##Creating the ANFIS network with 2 inputs and 4 MembershipFunctions in
> ##each input
> anfis3 <- new(Class="ANFIS",X,Y,membershipFunction)
> anfis3
ANFIS network
Trainning Set:
dim(x)= 121x2
dim(y)= 121x1
Arquitecture: 2 ( 5x5 ) - 25 - 75 ( 75x1 ) - 1
Network not trained yet
>
> ##Check for epsilon-completeness in each input
> plotMFs(anfis3)
>
> ##Training the ANFIS network.
> ##We will use instead an already trained object to reduce example time.
> data(anfis3)
>
> ##How the training went. You can keep on training as the training error
> ##is still descending.
> plot(anfis3)
>
> ##Test the fit, i. e., how the MembershipFunctions partition the input space
> plotMFs(anfis3)
>
> ##Just to see if premises, consequents and errors were updated
> getPremises(anfis3)[[input=1]][[mf=1]]
MembershipFunction: Normalized Gaussian Membership Function
Number of parameters: 2
mu sigma
-9.984987 2.038630
Expression: expression(exp(-1/2 * ((x - mu)/sigma)^2))
> getConsequents(anfis3)[1:2,]
[1] 0.005011691 0.005011691
> getErrors(anfis3) #Training errors
[1] 0.03741022 0.03238312 0.02992609 0.02866609 0.02759035 0.02661405
[7] 0.02569998 0.02482807 0.02398628 0.02316705 0.02271256 0.02224352
[13] 0.02176043 0.02126397 0.02075495 0.02023433 0.01970327 0.01916307
> getTrainingType(anfis3)
[1] "trainHybridJangOffLine"
> names(coef(anfis3))
[1] "premises" "consequents"
> ##An alternative to get premises and/or consequents ...
> coef(anfis3)$premises[[input=1]][[mf=1]]
MembershipFunction: Normalized Gaussian Membership Function
Number of parameters: 2
mu sigma
-9.984987 2.038630
Expression: expression(exp(-1/2 * ((x - mu)/sigma)^2))
> coef(anfis3)$consequents[1:2,]
[1] 0.005011691 0.005011691
>
> ##First five train pattern associated values for the training process
> fitted(anfis3)[1:5,]
[1] -0.008394804 0.007514315 0.012824644 -0.003044622 -0.030125619
> resid(anfis3)[1:5,]
[1] 0.011354394 -0.014242212 -0.010291178 0.013337535 0.005391769
> summary(anfis3)
ANFIS network
Trainning Set:
dim(x)= 121x2
dim(y)= 121x1
Arquitecture: 2 ( 5x5 ) - 25 - 75 ( 75x1 ) - 1
Last training error: 0.01916307
Call: trainHybridJangOffLine(object = anfis3, epochs = 10)
Statistics for Off-line training
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.01916 0.02139 0.02358 0.02489 0.02735 0.03741
>
> ##Surface comparison between the original training set and the predicted
> ##ANFIS network
> y <- predict(anfis3,X)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
anfis
--- call from context ---
FUN(X[[i]], ...)
--- call from argument ---
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)), ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
--- R stacktrace ---
where 1: FUN(X[[i]], ...)
where 2: lapply(1:nrow(x), function(pattern) {
tita_1 <- sapply(seq(along = object@premises), function(input,
pattern) {
unlist(lapply(object@premises[[input]], function(MF) {
evaluateMF(MF, x[pattern, input])
}))
}, pattern)
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)),
ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
if (!is.matrix(tita_1)) {
tita_1 <- t(tita_1)
}
w <<- cbind(w, apply(object@rules, 1, function(rule, tita_1) {
prod(sapply(seq(along = rule), function(input, tita_1) {
tita_1[rule[input], input]
}, tita_1))
}, tita_1))
wSum <<- c(wSum, sum(w[, pattern]))
wNormalized <<- cbind(wNormalized, w[, pattern]/wSum[pattern])
A <<- rbind(A, matrix(matrix(c(x[pattern, ], 1), ncol = 1) %*%
wNormalized[, pattern], nrow = 1, byrow = FALSE))
return(NULL)
})
where 3: .local(object, ...)
where 4: predict(anfis3, X)
where 5: predict(anfis3, X)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (pattern)
{
tita_1 <- sapply(seq(along = object@premises), function(input,
pattern) {
unlist(lapply(object@premises[[input]], function(MF) {
evaluateMF(MF, x[pattern, input])
}))
}, pattern)
if (class(tita_1) == "list") {
aux <- matrix(NA, nrow = max(sapply(tita_1, length)),
ncol = ncol(x))
invisible(sapply(seq(along = tita_1), function(input) {
aux[1:length(tita_1[[input]]), input] <<- tita_1[[input]]
return(NULL)
}))
tita_1 <- aux
}
if (!is.matrix(tita_1)) {
tita_1 <- t(tita_1)
}
w <<- cbind(w, apply(object@rules, 1, function(rule, tita_1) {
prod(sapply(seq(along = rule), function(input, tita_1) {
tita_1[rule[input], input]
}, tita_1))
}, tita_1))
wSum <<- c(wSum, sum(w[, pattern]))
wNormalized <<- cbind(wNormalized, w[, pattern]/wSum[pattern])
A <<- rbind(A, matrix(matrix(c(x[pattern, ], 1), ncol = 1) %*%
wNormalized[, pattern], nrow = 1, byrow = FALSE))
return(NULL)
}
<environment: 0x4622f80>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Error in if (class(tita_1) == "list") { : the condition has length > 1
Calls: predict -> predict -> .local -> lapply -> FUN
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc