CRAN Package Check Results for Package frbs

Last updated on 2015-05-26 02:46:46.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 3.1-0 1.16 32.14 33.30 OK
r-devel-linux-x86_64-debian-gcc 3.1-0 1.22 30.22 31.45 OK
r-devel-linux-x86_64-fedora-clang 3.1-0 61.25 OK
r-devel-linux-x86_64-fedora-gcc 3.1-0 43.48 OK
r-devel-osx-x86_64-clang 3.1-0 57.40 OK
r-devel-windows-ix86+x86_64 3.1-0 5.00 43.00 48.00 OK
r-patched-linux-x86_64 3.1-0 1.19 30.51 31.70 OK
r-patched-solaris-sparc 3.1-0 376.70 OK
r-patched-solaris-x86 3.1-0 72.30 OK
r-release-linux-x86_64 3.1-0 1.16 30.57 31.72 OK
r-release-osx-x86_64-mavericks 3.1-0 OK
r-release-osx-x86_64-snowleopard 3.0-0 ERROR
r-release-windows-ix86+x86_64 3.1-0 6.00 47.00 53.00 OK
r-oldrel-windows-ix86+x86_64 3.1-0 4.00 45.00 49.00 OK

Check Details

Version: 3.0-0
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘XML’
Flavor: r-release-osx-x86_64-snowleopard

Version: 3.0-0
Check: examples
Result: ERROR
    Running examples in ‘frbs-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: frbs-package
    > ### Title: Getting started with the frbs package
    > ### Aliases: frbs frbs-package
    >
    > ### ** Examples
    >
    > ##################################
    > ## I. Regression Problem
    > ## In this example, we are using the gas furnace dataset that
    > ## contains two input and one output variables.
    > ##################################
    >
    > ## Input data: Using the Gas Furnace dataset
    > ## then split the data to be training and testing datasets
    > data(frbsData)
    > data.train <- frbsData$GasFurnance.dt[1 : 204, ]
    > data.tst <- frbsData$GasFurnance.dt[205 : 292, 1 : 2]
    > real.val <- matrix(frbsData$GasFurnance.dt[205 : 292, 3], ncol = 1)
    >
    > ## Define interval of data
    > range.data <-apply(data.train, 2, range)
    >
    > ## Set the method and its parameters,
    > ## for example, we use Wang and Mendel's algorithm
    > method.type <- "WM"
    > control <- list(num.labels = 15, type.mf = "GAUSSIAN", type.defuz = "WAM",
    + type.tnorm = "MIN", type.snorm = "MAX", type.implication.func = "ZADEH",
    + name = "sim-0")
    >
    > ## Learning step: Generate an FRBS model
    > object.reg <- frbs.learn(data.train, range.data, method.type, control)
    >
    > ## Predicting step: Predict for newdata
    > res.test <- predict(object.reg, data.tst)
    >
    > ## Display the FRBS model
    > summary(object.reg)
    The name of model: sim-0
    Model was trained using: WM
    The names of attributes: var.1 var.2 var.3
    The interval of training data:
     var.1 var.2 var.3
    min -2.716 45.6 45.6
    max 2.834 60.5 60.5
    Type of FRBS model:
    [1] "MAMDANI"
    Type of membership functions:
    [1] "GAUSSIAN"
    Type of t-norm method:
    [1] "Standard t-norm (min)"
    Type of s-norm method:
    [1] "Standard s-norm"
    Type of defuzzification technique:
    [1] "Weighted average method"
    Type of implication function:
    [1] "ZADEH"
    The names of linguistic terms on the input variables:
     [1] "v.1_a.1" "v.1_a.2" "v.1_a.3" "v.1_a.4" "v.1_a.5" "v.1_a.6"
     [7] "v.1_a.7" "v.1_a.8" "v.1_a.9" "v.1_a.10" "v.1_a.11" "v.1_a.12"
    [13] "v.1_a.13" "v.1_a.14" "v.1_a.15" "v.2_a.1" "v.2_a.2" "v.2_a.3"
    [19] "v.2_a.4" "v.2_a.5" "v.2_a.6" "v.2_a.7" "v.2_a.8" "v.2_a.9"
    [25] "v.2_a.10" "v.2_a.11" "v.2_a.12" "v.2_a.13" "v.2_a.14" "v.2_a.15"
    The parameter values of membership function on the input variable (normalized):
     v.1_a.1 v.1_a.2 v.1_a.3 v.1_a.4 v.1_a.5 v.1_a.6 v.1_a.7
    [1,] 5.000 5.00000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000
    [2,] 0.000 0.07142857 0.1428571 0.2142857 0.2857143 0.3571429 0.4285714
    [3,] 0.025 0.02500000 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000
    [4,] NA NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA NA
     v.1_a.8 v.1_a.9 v.1_a.10 v.1_a.11 v.1_a.12 v.1_a.13 v.1_a.14
    [1,] 5.000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000
    [2,] 0.500 0.5714286 0.6428571 0.7142857 0.7857143 0.8571429 0.9285714
    [3,] 0.025 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000
    [4,] NA NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA NA
     v.1_a.15 v.2_a.1 v.2_a.2 v.2_a.3 v.2_a.4 v.2_a.5 v.2_a.6
    [1,] 5.000 5.000 5.00000000 5.0000000 5.0000000 5.0000000 5.0000000
    [2,] 1.000 0.000 0.07142857 0.1428571 0.2142857 0.2857143 0.3571429
    [3,] 0.025 0.025 0.02500000 0.0250000 0.0250000 0.0250000 0.0250000
    [4,] NA NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA NA
     v.2_a.7 v.2_a.8 v.2_a.9 v.2_a.10 v.2_a.11 v.2_a.12 v.2_a.13
    [1,] 5.0000000 5.000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000
    [2,] 0.4285714 0.500 0.5714286 0.6428571 0.7142857 0.7857143 0.8571429
    [3,] 0.0250000 0.025 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000
    [4,] NA NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA NA
     v.2_a.14 v.2_a.15
    [1,] 5.0000000 5.000
    [2,] 0.9285714 1.000
    [3,] 0.0250000 0.025
    [4,] NA NA
    [5,] NA NA
    The names of linguistic terms on the output variable:
     [1] "c.1" "c.2" "c.3" "c.4" "c.5" "c.6" "c.7" "c.8" "c.9" "c.10"
    [11] "c.11" "c.12" "c.13" "c.14" "c.15"
    The parameter values of membership function on the output variable (normalized):
     c.1 c.2 c.3 c.4 c.5 c.6 c.7 c.8
    [1,] 5.000 5.00000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.000
    [2,] 0.000 0.07142857 0.1428571 0.2142857 0.2857143 0.3571429 0.4285714 0.500
    [3,] 0.025 0.02500000 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.025
    [4,] NA NA NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA NA NA
     c.9 c.10 c.11 c.12 c.13 c.14 c.15
    [1,] 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.000
    [2,] 0.5714286 0.6428571 0.7142857 0.7857143 0.8571429 0.9285714 1.000
    [3,] 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.025
    [4,] NA NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA NA
    The number of linguistic terms on each variables
     var.1 var.2 var.3
    [1,] 15 15 15
    The fuzzy IF-THEN rules:
     V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
    1 IF var.1 is v.1_a.9 and var.2 is v.2_a.7 THEN var.3 is c.7
    2 IF var.1 is v.1_a.10 and var.2 is v.2_a.6 THEN var.3 is c.6
    3 IF var.1 is v.1_a.2 and var.2 is v.2_a.15 THEN var.3 is c.15
    4 IF var.1 is v.1_a.5 and var.2 is v.2_a.8 THEN var.3 is c.9
    5 IF var.1 is v.1_a.8 and var.2 is v.2_a.9 THEN var.3 is c.8
    6 IF var.1 is v.1_a.8 and var.2 is v.2_a.6 THEN var.3 is c.7
    7 IF var.1 is v.1_a.8 and var.2 is v.2_a.7 THEN var.3 is c.7
    8 IF var.1 is v.1_a.6 and var.2 is v.2_a.7 THEN var.3 is c.8
    9 IF var.1 is v.1_a.4 and var.2 is v.2_a.12 THEN var.3 is c.12
    10 IF var.1 is v.1_a.15 and var.2 is v.2_a.1 THEN var.3 is c.1
    11 IF var.1 is v.1_a.10 and var.2 is v.2_a.4 THEN var.3 is c.5
    12 IF var.1 is v.1_a.7 and var.2 is v.2_a.10 THEN var.3 is c.10
    13 IF var.1 is v.1_a.7 and var.2 is v.2_a.9 THEN var.3 is c.9
    14 IF var.1 is v.1_a.11 and var.2 is v.2_a.6 THEN var.3 is c.5
    15 IF var.1 is v.1_a.4 and var.2 is v.2_a.11 THEN var.3 is c.12
    16 IF var.1 is v.1_a.10 and var.2 is v.2_a.8 THEN var.3 is c.7
    17 IF var.1 is v.1_a.11 and var.2 is v.2_a.5 THEN var.3 is c.5
    18 IF var.1 is v.1_a.7 and var.2 is v.2_a.11 THEN var.3 is c.10
    19 IF var.1 is v.1_a.9 and var.2 is v.2_a.5 THEN var.3 is c.6
    20 IF var.1 is v.1_a.4 and var.2 is v.2_a.13 THEN var.3 is c.13
    21 IF var.1 is v.1_a.8 and var.2 is v.2_a.8 THEN var.3 is c.8
    22 IF var.1 is v.1_a.5 and var.2 is v.2_a.10 THEN var.3 is c.11
    23 IF var.1 is v.1_a.4 and var.2 is v.2_a.9 THEN var.3 is c.11
    24 IF var.1 is v.1_a.7 and var.2 is v.2_a.10 THEN var.3 is c.9
    25 IF var.1 is v.1_a.8 and var.2 is v.2_a.10 THEN var.3 is c.9
    26 IF var.1 is v.1_a.13 and var.2 is v.2_a.3 THEN var.3 is c.3
    27 IF var.1 is v.1_a.10 and var.2 is v.2_a.6 THEN var.3 is c.5
    28 IF var.1 is v.1_a.5 and var.2 is v.2_a.13 THEN var.3 is c.12
    29 IF var.1 is v.1_a.12 and var.2 is v.2_a.4 THEN var.3 is c.4
    30 IF var.1 is v.1_a.1 and var.2 is v.2_a.14 THEN var.3 is c.15
    31 IF var.1 is v.1_a.9 and var.2 is v.2_a.8 THEN var.3 is c.7
    32 IF var.1 is v.1_a.7 and var.2 is v.2_a.7 THEN var.3 is c.8
    33 IF var.1 is v.1_a.6 and var.2 is v.2_a.8 THEN var.3 is c.9
    34 IF var.1 is v.1_a.7 and var.2 is v.2_a.6 THEN var.3 is c.7
    35 IF var.1 is v.1_a.12 and var.2 is v.2_a.3 THEN var.3 is c.3
    36 IF var.1 is v.1_a.15 and var.2 is v.2_a.2 THEN var.3 is c.1
    37 IF var.1 is v.1_a.9 and var.2 is v.2_a.8 THEN var.3 is c.8
    38 IF var.1 is v.1_a.6 and var.2 is v.2_a.11 THEN var.3 is c.11
    39 IF var.1 is v.1_a.3 and var.2 is v.2_a.15 THEN var.3 is c.15
    40 IF var.1 is v.1_a.7 and var.2 is v.2_a.8 THEN var.3 is c.8
    41 IF var.1 is v.1_a.11 and var.2 is v.2_a.6 THEN var.3 is c.6
    42 IF var.1 is v.1_a.7 and var.2 is v.2_a.8 THEN var.3 is c.9
    43 IF var.1 is v.1_a.8 and var.2 is v.2_a.9 THEN var.3 is c.9
    44 IF var.1 is v.1_a.3 and var.2 is v.2_a.12 THEN var.3 is c.13
    45 IF var.1 is v.1_a.3 and var.2 is v.2_a.13 THEN var.3 is c.13
    46 IF var.1 is v.1_a.2 and var.2 is v.2_a.11 THEN var.3 is c.13
    47 IF var.1 is v.1_a.1 and var.2 is v.2_a.13 THEN var.3 is c.14
    48 IF var.1 is v.1_a.2 and var.2 is v.2_a.13 THEN var.3 is c.14
    49 IF var.1 is v.1_a.11 and var.2 is v.2_a.3 THEN var.3 is c.4
    50 IF var.1 is v.1_a.5 and var.2 is v.2_a.9 THEN var.3 is c.10
    51 IF var.1 is v.1_a.11 and var.2 is v.2_a.5 THEN var.3 is c.4
    52 IF var.1 is v.1_a.11 and var.2 is v.2_a.4 THEN var.3 is c.4
    53 IF var.1 is v.1_a.9 and var.2 is v.2_a.6 THEN var.3 is c.7
    54 IF var.1 is v.1_a.7 and var.2 is v.2_a.6 THEN var.3 is c.8
    55 IF var.1 is v.1_a.2 and var.2 is v.2_a.14 THEN var.3 is c.15
    56 IF var.1 is v.1_a.10 and var.2 is v.2_a.7 THEN var.3 is c.7
    57 IF var.1 is v.1_a.10 and var.2 is v.2_a.7 THEN var.3 is c.6
    58 IF var.1 is v.1_a.11 and var.2 is v.2_a.7 THEN var.3 is c.6
    59 IF var.1 is v.1_a.6 and var.2 is v.2_a.9 THEN var.3 is c.10
    60 IF var.1 is v.1_a.13 and var.2 is v.2_a.4 THEN var.3 is c.3
    61 IF var.1 is v.1_a.14 and var.2 is v.2_a.1 THEN var.3 is c.1
    62 IF var.1 is v.1_a.13 and var.2 is v.2_a.1 THEN var.3 is c.2
    63 IF var.1 is v.1_a.3 and var.2 is v.2_a.11 THEN var.3 is c.12
    64 IF var.1 is v.1_a.3 and var.2 is v.2_a.12 THEN var.3 is c.12
    65 IF var.1 is v.1_a.6 and var.2 is v.2_a.12 THEN var.3 is c.11
    66 IF var.1 is v.1_a.10 and var.2 is v.2_a.4 THEN var.3 is c.4
    67 IF var.1 is v.1_a.8 and var.2 is v.2_a.7 THEN var.3 is c.8
    68 IF var.1 is v.1_a.9 and var.2 is v.2_a.9 THEN var.3 is c.8
    69 IF var.1 is v.1_a.12 and var.2 is v.2_a.5 THEN var.3 is c.5
    70 IF var.1 is v.1_a.6 and var.2 is v.2_a.10 THEN var.3 is c.10
    71 IF var.1 is v.1_a.3 and var.2 is v.2_a.10 THEN var.3 is c.13
    72 IF var.1 is v.1_a.8 and var.2 is v.2_a.5 THEN var.3 is c.6
    73 IF var.1 is v.1_a.12 and var.2 is v.2_a.2 THEN var.3 is c.3
    74 IF var.1 is v.1_a.9 and var.2 is v.2_a.5 THEN var.3 is c.5
    75 IF var.1 is v.1_a.5 and var.2 is v.2_a.8 THEN var.3 is c.10
    76 IF var.1 is v.1_a.9 and var.2 is v.2_a.3 THEN var.3 is c.5
    77 IF var.1 is v.1_a.12 and var.2 is v.2_a.6 THEN var.3 is c.5
    78 IF var.1 is v.1_a.6 and var.2 is v.2_a.12 THEN var.3 is c.12
    79 IF var.1 is v.1_a.11 and var.2 is v.2_a.3 THEN var.3 is c.3
    80 IF var.1 is v.1_a.8 and var.2 is v.2_a.11 THEN var.3 is c.10
    81 IF var.1 is v.1_a.9 and var.2 is v.2_a.7 THEN var.3 is c.6
    82 IF var.1 is v.1_a.8 and var.2 is v.2_a.11 THEN var.3 is c.9
    83 IF var.1 is v.1_a.5 and var.2 is v.2_a.9 THEN var.3 is c.9
    84 IF var.1 is v.1_a.10 and var.2 is v.2_a.5 THEN var.3 is c.6
    85 IF var.1 is v.1_a.6 and var.2 is v.2_a.11 THEN var.3 is c.10
    86 IF var.1 is v.1_a.5 and var.2 is v.2_a.12 THEN var.3 is c.11
    87 IF var.1 is v.1_a.15 and var.2 is v.2_a.4 THEN var.3 is c.2
    88 IF var.1 is v.1_a.9 and var.2 is v.2_a.6 THEN var.3 is c.6
    89 IF var.1 is v.1_a.3 and var.2 is v.2_a.15 THEN var.3 is c.14
    90 IF var.1 is v.1_a.7 and var.2 is v.2_a.12 THEN var.3 is c.11
    91 IF var.1 is v.1_a.12 and var.2 is v.2_a.4 THEN var.3 is c.3
    92 IF var.1 is v.1_a.5 and var.2 is v.2_a.9 THEN var.3 is c.11
    93 IF var.1 is v.1_a.10 and var.2 is v.2_a.10 THEN var.3 is c.8
    94 IF var.1 is v.1_a.9 and var.2 is v.2_a.4 THEN var.3 is c.5
    95 IF var.1 is v.1_a.4 and var.2 is v.2_a.14 THEN var.3 is c.13
    96 IF var.1 is v.1_a.12 and var.2 is v.2_a.5 THEN var.3 is c.4
    >
    > ## Plot the membership functions
    > plotMF(object.reg)
    >
    > ##################################
    > ## II. Classification Problem
    > ## In this example, we are using the iris dataset that
    > ## contains four input and one output variables.
    > ##################################
    >
    > ## Input data: Using the Iris dataset
    > data(iris)
    > set.seed(2)
    >
    > ## Shuffle the data
    > ## then split the data to be training and testing datasets
    > irisShuffled <- iris[sample(nrow(iris)), ]
    > irisShuffled[, 5] <- unclass(irisShuffled[, 5])
    > tra.iris <- irisShuffled[1 : 105, ]
    > tst.iris <- irisShuffled[106 : nrow(irisShuffled), 1 : 4]
    > real.iris <- matrix(irisShuffled[106 : nrow(irisShuffled), 5], ncol = 1)
    >
    > ## Define range of input data. Note that it is only for the input variables.
    > range.data.input <- apply(iris[, -ncol(iris)], 2, range)
    >
    > ## Set the method and its parameters. In this case we use FRBCS.W algorithm
    > method.type <- "FRBCS.W"
    > control <- list(num.labels = 7, type.mf = "GAUSSIAN", type.tnorm = "MIN",
    + type.snorm = "MAX", type.implication.func = "ZADEH")
    >
    > ## Learning step: Generate fuzzy model
    > object.cls <- frbs.learn(tra.iris, range.data.input, method.type, control)
    >
    > ## Predicting step: Predict newdata
    > res.test <- predict(object.cls, tst.iris)
    >
    > ## Display the FRBS model
    > summary(object.cls)
    The name of model: sim-0
    Model was trained using: FRBCS.W
    The names of attributes: Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    The interval of input data:
     Sepal.Length Sepal.Width Petal.Length Petal.Width
    min 4.3 2.0 1.0 0.1
    max 7.9 4.4 6.9 2.5
    Type of FRBS model:
    [1] "FRBCS"
    Type of membership functions:
    [1] "GAUSSIAN"
    Type of t-norm method:
    [1] "Standard t-norm (min)"
    Type of s-norm method:
    [1] "Standard s-norm"
    Type of implication function:
    [1] "ZADEH"
    The names of linguistic terms on the input variables:
     [1] "vv.small" "v.small" "small" "medium" "large" "v.large"
     [7] "vv.large" "vv.small" "v.small" "small" "medium" "large"
    [13] "v.large" "vv.large" "vv.small" "v.small" "small" "medium"
    [19] "large" "v.large" "vv.large" "vv.small" "v.small" "small"
    [25] "medium" "large" "v.large" "vv.large"
    The parameter values of membership function on the input variable (normalized):
     vv.small v.small small medium large v.large
    [1,] 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000
    [2,] 0.00000000 0.16666667 0.33333333 0.50000000 0.66666667 0.83333333
    [3,] 0.05833333 0.05833333 0.05833333 0.05833333 0.05833333 0.05833333
    [4,] NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA
     vv.large vv.small v.small small medium large
    [1,] 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000
    [2,] 1.00000000 0.00000000 0.16666667 0.33333333 0.50000000 0.66666667
    [3,] 0.05833333 0.05833333 0.05833333 0.05833333 0.05833333 0.05833333
    [4,] NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA
     v.large vv.large vv.small v.small small medium
    [1,] 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000
    [2,] 0.83333333 1.00000000 0.00000000 0.16666667 0.33333333 0.50000000
    [3,] 0.05833333 0.05833333 0.05833333 0.05833333 0.05833333 0.05833333
    [4,] NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA
     large v.large vv.large vv.small v.small small
    [1,] 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000 5.00000000
    [2,] 0.66666667 0.83333333 1.00000000 0.00000000 0.16666667 0.33333333
    [3,] 0.05833333 0.05833333 0.05833333 0.05833333 0.05833333 0.05833333
    [4,] NA NA NA NA NA NA
    [5,] NA NA NA NA NA NA
     medium large v.large vv.large
    [1,] 5.00000000 5.00000000 5.00000000 5.00000000
    [2,] 0.50000000 0.66666667 0.83333333 1.00000000
    [3,] 0.05833333 0.05833333 0.05833333 0.05833333
    [4,] NA NA NA NA
    [5,] NA NA NA NA
    The number of linguistic terms on each variables
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species
    [1,] 7 7 7 7 3
    The fuzzy IF-THEN rules:
     V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11
    1 IF Sepal.Length is large and Sepal.Width is medium and Petal.Length is
    2 IF Sepal.Length is medium and Sepal.Width is small and Petal.Length is
    3 IF Sepal.Length is medium and Sepal.Width is small and Petal.Length is
    4 IF Sepal.Length is small and Sepal.Width is small and Petal.Length is
    5 IF Sepal.Length is v.small and Sepal.Width is medium and Petal.Length is
    6 IF Sepal.Length is v.large and Sepal.Width is medium and Petal.Length is
    7 IF Sepal.Length is medium and Sepal.Width is small and Petal.Length is
    8 IF Sepal.Length is large and Sepal.Width is v.small and Petal.Length is
    9 IF Sepal.Length is small and Sepal.Width is v.small and Petal.Length is
    10 IF Sepal.Length is small and Sepal.Width is v.large and Petal.Length is
    11 IF Sepal.Length is small and Sepal.Width is v.large and Petal.Length is
    12 IF Sepal.Length is medium and Sepal.Width is medium and Petal.Length is
    13 IF Sepal.Length is medium and Sepal.Width is medium and Petal.Length is
    14 IF Sepal.Length is v.small and Sepal.Width is medium and Petal.Length is
    15 IF Sepal.Length is small and Sepal.Width is small and Petal.Length is
    16 IF Sepal.Length is v.small and Sepal.Width is v.small and Petal.Length is
    17 IF Sepal.Length is large and Sepal.Width is small and Petal.Length is
    18 IF Sepal.Length is v.small and Sepal.Width is medium and Petal.Length is
    19 IF Sepal.Length is v.small and Sepal.Width is large and Petal.Length is
    20 IF Sepal.Length is v.large and Sepal.Width is small and Petal.Length is
    21 IF Sepal.Length is v.small and Sepal.Width is large and Petal.Length is
    22 IF Sepal.Length is small and Sepal.Width is large and Petal.Length is
    23 IF Sepal.Length is large and Sepal.Width is small and Petal.Length is
    24 IF Sepal.Length is small and Sepal.Width is small and Petal.Length is
    25 IF Sepal.Length is large and Sepal.Width is medium and Petal.Length is
    26 IF Sepal.Length is medium and Sepal.Width is v.small and Petal.Length is
    27 IF Sepal.Length is large and Sepal.Width is small and Petal.Length is
    28 IF Sepal.Length is small and Sepal.Width is v.large and Petal.Length is
    29 IF Sepal.Length is medium and Sepal.Width is medium and Petal.Length is
    30 IF Sepal.Length is medium and Sepal.Width is v.small and Petal.Length is
    31 IF Sepal.Length is small and Sepal.Width is small and Petal.Length is
    32 IF Sepal.Length is large and Sepal.Width is medium and Petal.Length is
    33 IF Sepal.Length is v.large and Sepal.Width is small and Petal.Length is
    34 IF Sepal.Length is large and Sepal.Width is medium and Petal.Length is
    35 IF Sepal.Length is v.small and Sepal.Width is large and Petal.Length is
    36 IF Sepal.Length is medium and Sepal.Width is small and Petal.Length is
    37 IF Sepal.Length is large and Sepal.Width is medium and Petal.Length is
    38 IF Sepal.Length is vv.large and Sepal.Width is large and Petal.Length is
    39 IF Sepal.Length is v.small and Sepal.Width is small and Petal.Length is
    40 IF Sepal.Length is medium and Sepal.Width is v.small and Petal.Length is
    41 IF Sepal.Length is small and Sepal.Width is v.large and Petal.Length is
    42 IF Sepal.Length is large and Sepal.Width is small and Petal.Length is
    43 IF Sepal.Length is large and Sepal.Width is small and Petal.Length is
    44 IF Sepal.Length is small and Sepal.Width is v.small and Petal.Length is
    45 IF Sepal.Length is vv.large and Sepal.Width is small and Petal.Length is
    46 IF Sepal.Length is vv.small and Sepal.Width is large and Petal.Length is
    47 IF Sepal.Length is vv.small and Sepal.Width is small and Petal.Length is
    48 IF Sepal.Length is v.small and Sepal.Width is v.small and Petal.Length is
    49 IF Sepal.Length is vv.small and Sepal.Width is medium and Petal.Length is
    50 IF Sepal.Length is small and Sepal.Width is medium and Petal.Length is
    51 IF Sepal.Length is v.small and Sepal.Width is v.small and Petal.Length is
    52 IF Sepal.Length is medium and Sepal.Width is v.small and Petal.Length is
    53 IF Sepal.Length is medium and Sepal.Width is v.small and Petal.Length is
    54 IF Sepal.Length is small and Sepal.Width is medium and Petal.Length is
    55 IF Sepal.Length is small and Sepal.Width is small and Petal.Length is
    56 IF Sepal.Length is vv.large and Sepal.Width is large and Petal.Length is
    57 IF Sepal.Length is small and Sepal.Width is small and Petal.Length is
    58 IF Sepal.Length is small and Sepal.Width is medium and Petal.Length is
    59 IF Sepal.Length is v.small and Sepal.Width is small and Petal.Length is
    60 IF Sepal.Length is medium and Sepal.Width is small and Petal.Length is
    61 IF Sepal.Length is v.small and Sepal.Width is vv.small and Petal.Length is
    62 IF Sepal.Length is vv.small and Sepal.Width is medium and Petal.Length is
    63 IF Sepal.Length is large and Sepal.Width is medium and Petal.Length is
     V12 V13 V14 V15 V16 V17 V18 V19 V20
    1 v.large and Petal.Width is vv.large THEN Species is 3
    2 large and Petal.Width is large THEN Species is 2
    3 large and Petal.Width is medium THEN Species is 2
    4 large and Petal.Width is v.large THEN Species is 3
    5 vv.small and Petal.Width is vv.small THEN Species is 1
    6 v.large and Petal.Width is large THEN Species is 3
    7 large and Petal.Width is large THEN Species is 3
    8 v.large and Petal.Width is large THEN Species is 3
    9 medium and Petal.Width is small THEN Species is 2
    10 v.small and Petal.Width is v.small THEN Species is 1
    11 vv.small and Petal.Width is v.small THEN Species is 1
    12 v.large and Petal.Width is vv.large THEN Species is 3
    13 large and Petal.Width is large THEN Species is 2
    14 v.small and Petal.Width is v.small THEN Species is 1
    15 medium and Petal.Width is medium THEN Species is 2
    16 small and Petal.Width is small THEN Species is 2
    17 large and Petal.Width is medium THEN Species is 2
    18 v.small and Petal.Width is vv.small THEN Species is 1
    19 v.small and Petal.Width is v.small THEN Species is 1
    20 v.large and Petal.Width is large THEN Species is 3
    21 vv.small and Petal.Width is vv.small THEN Species is 1
    22 v.small and Petal.Width is vv.small THEN Species is 1
    23 v.large and Petal.Width is v.large THEN Species is 3
    24 large and Petal.Width is large THEN Species is 2
    25 v.large and Petal.Width is large THEN Species is 3
    26 large and Petal.Width is large THEN Species is 3
    27 large and Petal.Width is large THEN Species is 2
    28 vv.small and Petal.Width is vv.small THEN Species is 1
    29 large and Petal.Width is v.large THEN Species is 3
    30 large and Petal.Width is v.large THEN Species is 3
    31 large and Petal.Width is medium THEN Species is 2
    32 large and Petal.Width is v.large THEN Species is 3
    33 v.large and Petal.Width is v.large THEN Species is 3
    34 large and Petal.Width is large THEN Species is 2
    35 v.small and Petal.Width is vv.small THEN Species is 1
    36 medium and Petal.Width is large THEN Species is 2
    37 v.large and Petal.Width is v.large THEN Species is 3
    38 vv.large and Petal.Width is v.large THEN Species is 3
    39 vv.small and Petal.Width is vv.small THEN Species is 1
    40 large and Petal.Width is large THEN Species is 2
    41 v.small and Petal.Width is vv.small THEN Species is 1
    42 medium and Petal.Width is medium THEN Species is 2
    43 v.large and Petal.Width is large THEN Species is 3
    44 medium and Petal.Width is medium THEN Species is 2
    45 v.large and Petal.Width is v.large THEN Species is 3
    46 vv.small and Petal.Width is vv.small THEN Species is 1
    47 vv.small and Petal.Width is vv.small THEN Species is 1
    48 large and Petal.Width is large THEN Species is 3
    49 v.small and Petal.Width is vv.small THEN Species is 1
    50 vv.small and Petal.Width is vv.small THEN Species is 1
    51 small and Petal.Width is medium THEN Species is 2
    52 medium and Petal.Width is medium THEN Species is 2
    53 medium and Petal.Width is small THEN Species is 2
    54 v.small and Petal.Width is vv.small THEN Species is 1
    55 large and Petal.Width is vv.large THEN Species is 3
    56 v.large and Petal.Width is v.large THEN Species is 3
    57 medium and Petal.Width is small THEN Species is 2
    58 v.small and Petal.Width is v.small THEN Species is 1
    59 v.small and Petal.Width is vv.small THEN Species is 1
    60 v.large and Petal.Width is medium THEN Species is 3
    61 medium and Petal.Width is small THEN Species is 2
    62 vv.small and Petal.Width is vv.small THEN Species is 1
    63 medium and Petal.Width is medium THEN Species is 2
    The certainty factor:
    
     [1,] 0.5309169
     [2,] 0.5121273
     [3,] 0.5121273
     [4,] 0.5309169
     [5,] 0.4569558
     [6,] 0.5309169
     [7,] 0.5309169
     [8,] 0.5309169
     [9,] 0.5121273
    [10,] 0.4569558
    [11,] 0.4569558
    [12,] 0.5309169
    [13,] 0.5121273
    [14,] 0.4569558
    [15,] 0.5121273
    [16,] 0.5121273
    [17,] 0.5121273
    [18,] 0.4569558
    [19,] 0.4569558
    [20,] 0.5309169
    [21,] 0.4569558
    [22,] 0.4569558
    [23,] 0.5309169
    [24,] 0.5121273
    [25,] 0.5309169
    [26,] 0.5309169
    [27,] 0.5121273
    [28,] 0.4569558
    [29,] 0.5309169
    [30,] 0.5309169
    [31,] 0.5121273
    [32,] 0.5309169
    [33,] 0.5309169
    [34,] 0.5121273
    [35,] 0.4569558
    [36,] 0.5121273
    [37,] 0.5309169
    [38,] 0.5309169
    [39,] 0.4569558
    [40,] 0.5121273
    [41,] 0.4569558
    [42,] 0.5121273
    [43,] 0.5309169
    [44,] 0.5121273
    [45,] 0.5309169
    [46,] 0.4569558
    [47,] 0.4569558
    [48,] 0.5309169
    [49,] 0.4569558
    [50,] 0.4569558
    [51,] 0.5121273
    [52,] 0.5121273
    [53,] 0.5121273
    [54,] 0.4569558
    [55,] 0.5309169
    [56,] 0.5309169
    [57,] 0.5121273
    [58,] 0.4569558
    [59,] 0.4569558
    [60,] 0.5309169
    [61,] 0.5121273
    [62,] 0.4569558
    [63,] 0.5121273
    >
    > ## Plot the membership functions
    > plotMF(object.cls)
    >
    > #################################################
    > ## III. Constructing an FRBS model from human expert.
    > ## In this example, we only consider the Mamdani model for regression. However,
    > ## other models can be done in the same way.
    > ## Note:
    > ## In the examples, let us consider four input and one output variables.
    > #################################################
    >
    > ## Define a matrix representing shape and parameters of membership functions of input variables.
    > ## The matrix has 5 rows where the first row represent the type of the membership function whereas
    > ## others are values of its parameters.
    > ## Detailed explanation can be seen in the fuzzifier function to construct the matrix.
    > varinp.mf <- matrix(c(2, 0, 20, 40, NA, 4, 20, 40, 60, 80, 3, 60, 80, 100, NA,
    + 2, 0, 35, 75, NA, 3, 35, 75, 100, NA,
    + 2, 0, 20, 40, NA, 1, 20, 50, 80, NA, 3, 60, 80, 100, NA,
    + 2, 0, 20, 40, NA, 4, 20, 40, 60, 80, 3, 60, 80, 100, NA),
    + nrow = 5, byrow = FALSE)
    >
    > ## Define number of linguistic terms of input variables.
    > ## Suppose, we have 3, 2, 3, and 3 numbers of linguistic terms
    > ## for the first, second, third and fourth variables, respectively.
    > num.fvalinput <- matrix(c(3, 2, 3, 3), nrow=1)
    >
    > ## Give the names of the linguistic terms of each input variables.
    > varinput.1 <- c("low", "medium", "high")
    > varinput.2 <- c("yes", "no")
    > varinput.3 <- c("bad", "neutral", "good")
    > varinput.4 <- c("low", "medium", "high")
    > names.varinput <- c(varinput.1, varinput.2, varinput.3, varinput.4)
    >
    > ## Set interval of data.
    > range.data <- matrix(c(0, 100, 0, 100, 0, 100, 0, 100, 0, 100), nrow = 2)
    >
    > ## Define inference parameters.
    > ## Detailed information about values can be seen in the inference function.
    > type.defuz <- "WAM"
    > type.tnorm <- "MIN"
    > type.snorm <- "MAX"
    > type.implication.func <- "ZADEH"
    >
    > ## Give the name of simulation.
    > name <- "Sim-0"
    >
    > ## Provide new data for testing.
    > newdata<- matrix(c(25, 40, 35, 15, 45, 75, 78, 70), nrow = 2, byrow = TRUE)
    > ## the names of variables
    > colnames.var <- c("input1", "input2", "input3", "input4", "output1")
    >
    > ## Define number of linguistic terms of output variable.
    > ## In this case, we set the number of linguistic terms to 3.
    > num.fvaloutput <- matrix(c(3), nrow = 1)
    >
    > ## Give the names of the linguistic terms of the output variable.
    > varoutput.1 <- c("bad", "neutral", "good")
    > names.varoutput <- c(varoutput.1)
    >
    > ## Define the shapes and parameters of the membership functions of the output variables.
    > varout.mf <- matrix(c(2, 0, 20, 40, NA, 4, 20, 40, 60, 80, 3, 60, 80, 100, NA),
    + nrow = 5, byrow = FALSE)
    >
    > ## Set type of model which is "MAMDANI".
    > type.model <- "MAMDANI"
    >
    > ## Define the fuzzy IF-THEN rules;
    > ## In this example we are using the Mamdani model
    > ## Note: e.g.,
    > ## "a1", "and", "b1, "->", "e1" means that
    > ## "IF inputvar.1 is a1 and inputvar.2 is b1 THEN outputvar.1 is e1"
    > ## Make sure that each rule has a "->" sign.
    > rule <- matrix(
    + c("low", "and", "yes", "and", "bad", "and", "low", "->", "bad",
    + "medium", "and", "no", "and", "neutral", "and", "medium", "->", "neutral",
    + "high", "and", "no", "and", "neutral", "and", "low", "->", "good"),
    + nrow = 3, byrow = TRUE)
    >
    > ## Generate a fuzzy model with frbs.gen.
    > object <- frbs.gen(range.data, num.fvalinput, names.varinput,
    + num.fvaloutput, varout.mf, names.varoutput, rule,
    + varinp.mf, type.model, type.defuz, type.tnorm,
    + type.snorm, func.tsk = NULL, colnames.var, type.implication.func, name)
    >
    > ## Plot the membership function.
    > plotMF(object)
    >
    > ## Predicting using new data.
    > res <- predict(object, newdata)$predicted.val
    >
    > #################################################
    > ## IV. Specifying an FRBS model in the frbsPMML format.
    > ## other examples can be seen in the frbsPMML function.
    > #################################################
    > ## Input data
    > data(frbsData)
    > data.train <- frbsData$GasFurnance.dt[1 : 204, ]
    > data.fit <- data.train[, 1 : 2]
    > data.tst <- frbsData$GasFurnance.dt[205 : 292, 1 : 2]
    > real.val <- matrix(frbsData$GasFurnance.dt[205 : 292, 3], ncol = 1)
    > range.data<-matrix(c(-2.716, 2.834, 45.6, 60.5, 45.6, 60.5), nrow = 2)
    >
    > ## Set the method and its parameters
    > method.type <- "WM"
    > control <- list(num.labels = 3, type.mf = "GAUSSIAN", type.defuz = "WAM",
    + type.tnorm = "MIN", type.snorm = "MAX",
    + type.implication.func = "ZADEH", name="sim-0")
    >
    > ## Generate fuzzy model
    > object <- frbs.learn(data.train, range.data, method.type, control)
    >
    > ## 2. Constructing the frbsPMML format
    > frbsPMML(object)
    Error in loadNamespace(name) : there is no package called ‘XML’
    Calls: frbsPMML ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
    Execution halted
Flavor: r-release-osx-x86_64-snowleopard