FuzzyNumberList {FuzzyStatTraEOO} | R Documentation |
'FuzzyNumberList' must contain valid 'FuzzyNumbers'. This class implements a version of the empty 'StatList' methods.
FuzzyStatTraEOO::StatList
-> FuzzyNumberList
new()
This method creates a 'FuzzyNumberList' object with the columns and dimensions attributes set where the 'FuzzyNumbers' must be valid.
FuzzyNumberList$new(numbers = NA)
numbers
is a list of dimension nl x 3 x n which contains n
fuzzy numbers. nl is the number of considered \alpha
-levels and 3 is the number of
columns of the list. The first column represents the number of considered
\alpha
-levels, the second one represents their infimum values and the
third and last column represents their supremum values.
See examples.
The FuzzyNumberList object created with the columns and dimensions attributes set where the 'FuzzyNumbers' must be valid.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1.5,-1.0,-1.0, 2.0, 1.5, 1.0), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1.5,-1.25,-1.0, 3.0, 2.0, 1.0), dim = c(3, 3))))) # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2.0, 1.5), dim = c(2, 3)))))
dthetaphi()
This method calculates the mid/spr distance between the FuzzyNumbers contained in the current object and the one passed as parameter. See Blanco-Fernandez et al. (2013) [1].
FuzzyNumberList$dthetaphi(s = NA, a = 1, b = 1, theta = 1)
s
FuzzyNumberList containing FuzzyNumbers characterized by means of
nl \alpha
-levels each. The \alpha
-levels of the FuzzyNumberList
s should coincide with the ones of the current FuzzyNumberList (the method
checks this condition).
a
real number > 0, by default a=1. It is the first parameter of a beta distribution which corresponds to a weighting measure on [0,1].
b
real number > 0, by default b=1. It is the second parameter of a beta distribution which corresponds to a weighting measure on [0,1].
theta
real number > 0, by default theta=1. It is the weight of the spread in the mid/spr distance.
See examples.
a matrix containing the mid/spr distances between the two previous mentioned FuzzyNumberLists. If the body's method inner conditions are not met, NA will be returned.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3))) ))$dthetaphi( FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.5, 1.5), dim = c(2, 3))))), 1,5,1) # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.0, -1.0, 2.0, 1.5, 1.0), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.25, -1.0, 3.0, 2.0, 1.0), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0, 1.0, 1.0, 2.5, 2.0, 1.5), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0.5 , 1, 1.5, 3, 2.0, 2), dim = c(3, 3)))))$dthetaphi(FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0,1,1.25,1.5, 2, 1.75, 1.5), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1,-0.5,0, 1.5, 1.25, 1), dim = c(3, 3)))) ), 1, 1, 1/3) # Example 3: F=Simulation$new()$simulCase1(10L) S=Simulation$new()$simulCase1(20L) F=F$transfTra() S=S$transfTra() F$dthetaphi(S,1,5,1) # Example 4: F=Simulation$new()$simulCase1(10L) S=Simulation$new()$simulCase1(10L) F$dthetaphi(S,2,1,1/3) # Example 5: F=Simulation$new()$simulCase1(10L) S=Simulation$new()$simulCase1(10L) F=F$transfTra() S=S$transfTra(50L) F$dthetaphi(S,2,1,1)
dwablphi()
This method calculates the (\phi
,\theta
)-wabl/ldev/rdev distance between
the 'FuzzyNumbers' contained in two 'FuzzyNumberLists'. The method checks
if the \alpha
-levels of all 'FuzzyNumbers' coincide.
See Sinova et al. (2013) [3] and Sinova et al. (2016) [4].
FuzzyNumberList$dwablphi(s = NA, a = 1, b = 1, theta = 1)
s
FuzzyNumberList containing FuzzyNumbers characterized by means of nl
\alpha
-levels each. The \alpha
-levels should coincide with
ones of the other FuzzyNumberList (the method checks this condition).
a
real number > 0, by default a=1. It is the first parameter of a beta distribution which corresponds to a weighting measure on [0,1].
b
real number > 0, by default b=1. It is the second parameter of a beta distribution which corresponds to a weighting measure on [0,1].
theta
real number > 0, by default theta=1. It is the weight of the
ldev and rdev in the (\phi
,\theta
)-wabl/ldev/rdev distance.
See examples.
a matrix containing the (\phi
,\theta
)-wabl/ldev/rdev distances
between the two previous mentioned FuzzyNumberLists. If the body's
method inner conditions are not met, NA will be returned.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3))) ))$dwablphi( FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.5, 1.5), dim = c(2, 3))))), 1,5,1) # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.0, -1.0, 2.0, 1.5, 1.0), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.25, -1.0, 3.0, 2.0, 1.0), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0, 1.0, 1.0, 2.5, 2.0, 1.5), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0.5 , 1, 1.5, 3, 2.0, 2), dim = c(3, 3)))))$dwablphi(FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0,1,1.25,1.5, 2, 1.75, 1.5), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1,-0.5,0, 1.5, 1.25, 1), dim = c(3, 3)))) ), 1, 1, 1/3) # Example 3: F=Simulation$new()$simulCase1(3L) S=Simulation$new()$simulCase1(4L) F=F$transfTra() S=S$transfTra() F$dwablphi(S,2,1,1) # Example 4: F=Simulation$new()$simulCase1(10L) S=Simulation$new()$simulCase1(10L) F$dwablphi(S) # Example 5: F=Simulation$new()$simulCase1(10L) S=Simulation$new()$simulCase1(10L) F=F$transfTra() S=S$transfTra(50L) F$dwablphi(S,2,1,1)
rho1()
This method calculates the 1-norm distance between the 'FuzzyNumbers' contained
in two 'FuzzyNumberLists'. The method checks if the \alpha
-levels of
all 'FuzzyNumbers' coincide.
See Diamond and Kloeden. (1990) [2].
FuzzyNumberList$rho1(s = NA)
s
FuzzyNumberList containing FuzzyNumbers characterized by means of nl
\alpha
-levels each. The method checks that the \alpha
-levels
should coincide with ones of the other FuzzyNumberList.
See examples.
a matrix containing the 1-norm distances between the two previous mentioned FuzzyNumberLists. If the body's method inner conditions are not met, NA will be returned.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3))) ))$rho1( FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.5, 1.5), dim = c(2, 3)))))) # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.0, -1.0, 2.0, 1.5, 1.0), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.25, -1.0, 3.0, 2.0, 1.0), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0, 1.0, 1.0, 2.5, 2.0, 1.5), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0.5 , 1, 1.5, 3, 2.0, 2), dim = c(3, 3)))))$rho1(FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0,1,1.25,1.5, 2, 1.75, 1.5), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1,-0.5,0, 1.5, 1.25, 1), dim = c(3, 3)))))) # Example 3: F=Simulation$new()$simulCase1(4L) S=Simulation$new()$simulCase1(5L) F=F$transfTra() S=S$transfTra() F$rho1(S) S$rho1(F) # Example 4: F=Simulation$new()$simulCase1(4L) S=Simulation$new()$simulCase1(5L) F=F$transfTra() S=S$transfTra(10L) F$rho1(S) S$rho1(F)
addFuzzyNumber()
This method adds a 'FuzzyNumber' to the current collection of fuzzy numbers. Therefore, the dimensions' field is increased in a unit.
FuzzyNumberList$addFuzzyNumber(n = NA, verbose = TRUE)
n
is the FuzzyNumber to be added to the current collection of fuzzy numbers.
verbose
if TRUE the messages are written to the console unless the user actively decides to set verbose=FALSE.
See examples.
NULL.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3)))))$addFuzzyNumber( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3)))) # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))) ))$addFuzzyNumber( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3))))
removeFuzzyNumber()
This method removes a 'FuzzyNumber' to the current collection of fuzzy numbers. Therefore, the dimensions' field is decreased in a unit.
FuzzyNumberList$removeFuzzyNumber(i = NA, verbose = TRUE)
i
is the position of the FuzzyNumber to be removed in the current collection of fuzzy numbers.
verbose
if TRUE the messages are written to the console unless the user actively decides to set verbose=FALSE.
See examples.
NULL.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -1, -0.5, 1.5, 1.25), dim = c(2, 3))) ))$removeFuzzyNumber(1L) # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3))) ))$removeFuzzyNumber(2L)
getDimension()
This method gives the number contained in the dimension passed as parameter when the dimension is greater than 0 and not greater than the dimensions of the 'FuzzyNumberList's' numbers array.
FuzzyNumberList$getDimension(i = NA)
i
is the dimension of the FuzzyNumber wanted to be retrieved.
See examples.
The FuzzyNumber contained in the dimension passed as parameter or an error if the dimension is not valid.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3))) ))$getDimension(1L) # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3))) ))$getDimension(2L)
plot()
This method shows in a graph the values of the attribute numbers of the corresponding 'FuzzyNumberList'.
FuzzyNumberList$plot(color = "grey")
color
is the color of the lines representing the numbers to be shown in the graph. The default value is grey, other colors can be specified, the option palette() too.
See examples.
a graph with the values of the attribute numbers of the corresponding 'FuzzyNumberList'.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3))) ))$plot() # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.85, 1.7), dim = c(2, 3)))) )$plot("blue") # Example 3: Simulation$new()$simulCase1(8L)$transfTra()$plot(palette()) # Example 4: Simulation$new()$simulCase1(5L)$transfTra()$plot(palette()[2:6])
getLength()
This method returns the number of dimensions that are equivalent to the number of 'FuzzyNumbers' in the corresponding 'FuzzyNumberList'.
FuzzyNumberList$getLength()
See examples.
the number of dimensions that are equivalent to the number of 'FuzzyNumbers' in the corresponding 'FuzzyNumberList'.
# Example 1: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3))) ))$getLength() # Example 2: FuzzyNumberList$new(c( FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))), FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.5, 1.5), dim = c(2, 3)))) )$getLength()
clone()
The objects of this class are cloneable with this method.
FuzzyNumberList$clone(deep = FALSE)
deep
Whether to make a deep clone.
In case you find (almost surely existing) bugs or have recommendations for improving the method, comments are welcome to the above mentioned mail addresses.
(s) Andrea Garcia Cernuda <uo270115@uniovi.es>, Asun Lubiano <lubiano@uniovi.es>, Sara de la Rosa de Saa
[1] Blanco-Fernandez, A.; Casals, R.M.; Colubi, A.; Corral, N.; Garcia-Barzana, M.; Gil, M.A.; Gonzalez-Rodriguez, G.; Lopez, M.T.; Lubiano, M.A.; Montenegro, M.; Ramos-Guajardo, A.B.; de la Rosa de Saa, S.; Sinova, B.: Random fuzzy sets: A mathematical tool to develop statistical fuzzy data analysis, Iranian Journal on Fuzzy Systems 10(2), 1-28 (2013)
[2] Diamond, P.; Kloeden, P.: Metric spaces of fuzzy sets, Fuzzy Sets and Systems 35, 241-249 (1990)
[3] Sinova, B.; de la Rosa de Saa, S.; Gil, M.A.: A generalized L1-type metric between fuzzy numbers for an approach to central tendency of fuzzy data, Information Sciences 242, 22-34 (2013)
[4] Sinova, B.; Gil, M.A.; Van Aelst, S.: M-estimates of location for the robust central tendency of fuzzy data, IEEE Transactions on Fuzzy Systems 24(4), 945-956 (2016)
## ------------------------------------------------
## Method `FuzzyNumberList$new`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1.5,-1.0,-1.0, 2.0, 1.5, 1.0), dim =
c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1.5,-1.25,-1.0, 3.0, 2.0,
1.0), dim = c(3, 3)))))
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2.0, 1.5), dim = c(2, 3)))))
## ------------------------------------------------
## Method `FuzzyNumberList$dthetaphi`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3)))
))$dthetaphi(
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.5, 1.5), dim = c(2, 3))))),
1,5,1)
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.0, -1.0, 2.0, 1.5, 1.0), dim =
c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.25, -1.0, 3.0, 2.0,
1.0), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0, 1.0, 1.0, 2.5,
2.0, 1.5), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0.5 , 1, 1.5,
3, 2.0, 2), dim = c(3, 3)))))$dthetaphi(FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0,1,1.25,1.5, 2, 1.75, 1.5), dim = c(3, 3))),
FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1,-0.5,0, 1.5, 1.25, 1), dim = c(3, 3))))
), 1, 1, 1/3)
# Example 3:
F=Simulation$new()$simulCase1(10L)
S=Simulation$new()$simulCase1(20L)
F=F$transfTra()
S=S$transfTra()
F$dthetaphi(S,1,5,1)
# Example 4:
F=Simulation$new()$simulCase1(10L)
S=Simulation$new()$simulCase1(10L)
F$dthetaphi(S,2,1,1/3)
# Example 5:
F=Simulation$new()$simulCase1(10L)
S=Simulation$new()$simulCase1(10L)
F=F$transfTra()
S=S$transfTra(50L)
F$dthetaphi(S,2,1,1)
## ------------------------------------------------
## Method `FuzzyNumberList$dwablphi`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3)))
))$dwablphi(
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.5, 1.5), dim = c(2, 3))))),
1,5,1)
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.0, -1.0, 2.0, 1.5, 1.0), dim =
c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.25, -1.0, 3.0, 2.0,
1.0), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0, 1.0, 1.0, 2.5,
2.0, 1.5), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0.5 , 1, 1.5,
3, 2.0, 2), dim = c(3, 3)))))$dwablphi(FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0,1,1.25,1.5, 2, 1.75, 1.5), dim = c(3, 3))),
FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1,-0.5,0, 1.5, 1.25, 1), dim = c(3, 3))))
), 1, 1, 1/3)
# Example 3:
F=Simulation$new()$simulCase1(3L)
S=Simulation$new()$simulCase1(4L)
F=F$transfTra()
S=S$transfTra()
F$dwablphi(S,2,1,1)
# Example 4:
F=Simulation$new()$simulCase1(10L)
S=Simulation$new()$simulCase1(10L)
F$dwablphi(S)
# Example 5:
F=Simulation$new()$simulCase1(10L)
S=Simulation$new()$simulCase1(10L)
F=F$transfTra()
S=S$transfTra(50L)
F$dwablphi(S,2,1,1)
## ------------------------------------------------
## Method `FuzzyNumberList$rho1`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3)))
))$rho1(
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.5, 1.5), dim = c(2, 3))))))
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.0, -1.0, 2.0, 1.5, 1.0), dim =
c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1.5, -1.25, -1.0, 3.0, 2.0,
1.0), dim = c(3, 3))), FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0, 1.0, 1.0, 2.5,
2.0, 1.5), dim = c(3, 3))),FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 0.5 , 1, 1.5,
3, 2.0, 2), dim = c(3, 3)))))$rho1(FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0,1,1.25,1.5, 2, 1.75, 1.5), dim = c(3, 3))),
FuzzyNumber$new(array(c(0.0, 0.5, 1.0,-1,-0.5,0, 1.5, 1.25, 1), dim = c(3, 3))))))
# Example 3:
F=Simulation$new()$simulCase1(4L)
S=Simulation$new()$simulCase1(5L)
F=F$transfTra()
S=S$transfTra()
F$rho1(S)
S$rho1(F)
# Example 4:
F=Simulation$new()$simulCase1(4L)
S=Simulation$new()$simulCase1(5L)
F=F$transfTra()
S=S$transfTra(10L)
F$rho1(S)
S$rho1(F)
## ------------------------------------------------
## Method `FuzzyNumberList$addFuzzyNumber`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3)))))$addFuzzyNumber(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))))
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3)))
))$addFuzzyNumber( FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75,
1.5), dim = c(3, 3))))
## ------------------------------------------------
## Method `FuzzyNumberList$removeFuzzyNumber`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -1, -0.5, 1.5, 1.25), dim = c(2, 3)))
))$removeFuzzyNumber(1L)
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))),
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3)))
))$removeFuzzyNumber(2L)
## ------------------------------------------------
## Method `FuzzyNumberList$getDimension`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))),
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3)))
))$getDimension(1L)
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))),
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3)))
))$getDimension(2L)
## ------------------------------------------------
## Method `FuzzyNumberList$plot`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))),
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3)))
))$plot()
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.85, 1.7), dim = c(2, 3))))
)$plot("blue")
# Example 3:
Simulation$new()$simulCase1(8L)$transfTra()$plot(palette())
# Example 4:
Simulation$new()$simulCase1(5L)$transfTra()$plot(palette()[2:6])
## ------------------------------------------------
## Method `FuzzyNumberList$getLength`
## ------------------------------------------------
# Example 1:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, -1, -0.5, 0, 1.5, 1.25, 1), dim = c(3, 3))),
FuzzyNumber$new(array(c(0.0, 0.5, 1.0, 1, 1.25, 1.5, 2, 1.75, 1.5), dim = c(3, 3)))
))$getLength()
# Example 2:
FuzzyNumberList$new(c(
FuzzyNumber$new(array(c(0.0, 1.0, -1.5, -1.0, 2, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -1.0, -1.0, 1.5, 1.0), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, -0.5, 0, 1.5, 1), dim = c(2, 3))),
FuzzyNumber$new(array(c(0.0, 1.0, 1, 1.5, 1.5, 1.5), dim = c(2, 3))))
)$getLength()