Package 'FisPro'

Class "Fis"

Description

Class to manage a Fis "Fuzzy Inference System"

Fields

name

character vector, The name of the Fis

conjunction

character vector, The conjunction operator of rules in the Fis
Allowed values are: "min" (the default), "prod" or "luka"

Constructors

Fis()

The default constructor to build an empty Fis
The Fis is initialized with "min" conjunction and empty name
The design must be completed using the available functions to add inputs, outputs and rules before it can be used for inference

return:

Fis object

Fis(fis_file)

The constructor to build a Fis from a configuration file
The configuration file can be designed using the FisPro open source software

argument: fis_file

character vector, The filename of the Fis configuration file

return:

Fis object

Methods

input_size()

return:

integer value, The number of inputs in the Fis

add_input(input)

argument: input

FisIn object, The input to add in the Fis

get_input(input_index)

argument: input_index

integer value, The index (1-based index) of the input in the Fis

return:

FisIn object

get_inputs()

Get all inputs in the Fis

return:

list of FisIn objects

output_size()

return:

integer value, The number of outputs in the Fis

add_output(output)

argument: output

FisOut object, The output to add in the Fis

get_output(output_index)

argument: output_index

integer value, The index (1-based index) of the output in the Fis

return:

FisOut object

get_outputs()

Get all outputs in the Fis

return:

list of FisOut objects

rule_size()

return:

integer value, The number of rules in the Fis

add_rule(rule)

argument: rule

Rule object, The rule to add in the Fis

get_rule(rule_index)

argument: rule_index

integer value, The index (1-based index) of the rule in the Fis

return:

Rule object

get_rules()

Get all rules in the Fis

return:

list of Rule objects

infer(data)

Infers all outputs

argument: data

numeric vector, matrix or data.frame, The input data or dataset to infer (the vector length or the number of columns must be equal to the number of inputs)

return:

numeric vector or matrix (in case of 2D input data)

infer_output(data, output_index)

Infers a single output

argument: data

numeric vector, matrix or data.frame, The input data or dataset to infer (the vector length or the number of columns must be equal to the number of inputs)

argument: output_index

integer value, The index (1-based index) of the output to infer

return:

numeric value or vector (in case of 2D input data)

See Also

NewFis

Fuzzy Logic Elementary Glossary

Examples

# build a Fis from a configuration file
fis_file <- system.file("extdata", "test.fis", package = "FisPro")
fis <- NewFis(fis_file)

# infers all outputs
inferred <- fis$infer(c(0.25, 0.75))

# infers first output
inferred_output1 <- fis$infer_output(c(0.25, 0.75), 1)

# infers second output
inferred_output2 <- fis$infer_output(c(0.25, 0.75), 2)

# infers test_data dataset
test_file <- system.file("extdata", "test_data.csv", package = "FisPro")
dataset <- read.csv(test_file)
inferred_dataset <- fis$infer(dataset)

########################################################################

# or build a Fis from scratch
fis <- NewFis()
fis$name <- "foo"

# build the first input
fisin1 <- NewFisIn(0, 1)
fisin1$name <- "input1"
fisin1$add_mf(NewMfTrapezoidalInf(0, 1))
fisin1$add_mf(NewMfTrapezoidalSup(0, 1))
fis$add_input(fisin1)

# build the second input
fisin2 <- NewFisIn(0, 1)
fisin2$name <- "input2"
fisin2$add_mf(NewMfTrapezoidalInf(0, 0.5))
fisin2$add_mf(NewMfTriangular(0, 0.5, 1))
fisin2$add_mf(NewMfTrapezoidalSup(0.5, 1))
fis$add_input(fisin2)

# build an output
fisout <- NewFisOutCrisp(0, 1)
fisout$name <- "output"
fis$add_output(fisout)

# add rules to the Fis
fis$add_rule(NewRule(c(1, 2), 0))
fis$add_rule(NewRule(c(2, 0), 1))

---

Class "Fisin"

Description

Class to manage a Fis input

Fields

name

character vector, The name of the input

Constructors

FisIn()

The default constructor to build an empty input with the default range [0, 1]

return:

FisIn object

FisIn(minimum, maximum)

The constructor to build an empty input

argument: minimum

numeric value, The minimum range value of the input

argument: maximum

numeric value, The maximum range value of the input

return:

FisIn object

FisIn(number_of_mfs, minimum, maximum)

The constructor to build an input with a regular standardized fuzzy partition

argument: number_of_mfs

integer value, The number of Mfs in the fuzzy partition

argument: minimum

numeric value, The minimum range value of the input

argument: maximum

numeric value, The maximum range value of the input

return:

FisIn object

FisIn(breakpoints, minimum, maximum)

The constructor to build an input with an irregular standardized fuzzy partition

argument: breakpoints

numeric vector, The breakpoint values (sorted in ascending order) of the Mfs in the fuzzy partition

argument: minimum

numeric value, The minimum range value of the input

argument: maximum

numeric value, The maximum range value of the input

return:

FisIn object

Methods

range()

return:

numeric vector, The range of the input (min max values)

mf_size()

return:

integer value, The number of Mfs in the input partition

add_mf(mf)

Add an Mf in the input partition

argument: mf

Mf object, The Mf to add

get_mf(mf_index)

argument: mf_index

integer value, The index (1-based index) of the mf to return

return:

Mf object

get_mfs()

Get all mfs in the input

return:

list of Mf objects

is_standardized()

return:

logical value, TRUE if the input is a standardized fuzzy partition, FALSE otherwise

See Also

NewFisIn

Fuzzy Logic Elementary Glossary

Examples

input <- NewFisIn(0, 2)
input$name <- "foo"
input$add_mf(NewMfTrapezoidalInf(0, 1))
input$add_mf(NewMfTriangular(0, 1, 2))
input$add_mf(NewMfTrapezoidalSup(1, 2))

---

Class "FisOut"

Description

The base class of Fis output (cannot be instantiate)
Use derived classes FisOutCrisp or FisOutFuzzy

Fields

name

character vector, The name of the output

Methods

range()

return:

numeric vector, The range of the output (min max values)

---

Class "FisOutCrisp"

Description

Class to manage a Fis crisp output

Fields

defuzzification

character vector, The defuzzification operator of the crisp output
Allowed values are: "sugeno" (the default) or "MaxCrisp"

disjunction

character vector, The disjunction operator of the crisp output
Allowed values are: "max" (the default) or "sum"

Inherits

FisOutCrisp class inherits all fields and methods of FisOut class

Constructors

FisOutCrisp()

The default constructor to build a crisp output with the default range [0, 1]

return:

FisOutCrisp object

FisOutCrisp(minimum, maximum)

The constructor to build a crisp output

argument: minimum

numeric value, The minimum range value of the output

argument: maximum

numeric value, The maximum range value of the output

return:

FisOutCrisp object

See Also

NewFisOutCrisp

Fuzzy Logic Elementary Glossary

Examples

output <- NewFisOutCrisp(0, 1)
output$name <- "foo"
output$defuzzification <- "sugeno"
output$disjunction <- "max"

---

Class "FisOutFuzzy"

Description

Class to manage a Fis fuzzy output

Fields

defuzzification

character vector, The defuzzification operator of the fuzzy output
Allowed values are: "sugeno" (the default) "MeanMax", or "area"

disjunction

character vector, The disjunction operator of the fuzzy output
Allowed values are: "max" (the default) or "sum"

Inherits

FisOutFuzzy class inherits all fields and methods of FisOut class

Constructors

FisOutFuzzy()

The default constructor to build a fuzzy output with the default range [0, 1]

return:

FisOutFuzzy object

FisOutFuzzy(minimum, maximum)

The constructor to build a fuzzy output

argument: minimum

numeric value, The minimum range value of the output

argument: maximum

numeric value, The maximum range value of the output

return:

FisOutFuzzy object

FisOutFuzzy(number_of_mfs, minimum, maximum)

The constructor to build a fuzzy with a regular standardized fuzzy partition

argument: number_of_mfs

integer value, The number of Mfs in the fuzzy partition

argument: minimum

numeric value, The minimum range value of the output

argument: maximum

numeric value, The maximum range value of the output

return:

FisOutFuzzy object

FisOutFuzzy(breakpoints, minimum, maximum)

The constructor to build a fuzzy with an irregular standardized fuzzy partition

argument: breakpoints

numeric vector, The breakpoint values (sorted in ascending order) of the Mfs in the fuzzy partition

argument: minimum

numeric value, The minimum range value of the output

argument: maximum

numeric value, The maximum range value of the output

return:

FisOutFuzzy object

Methods

mf_size()

return:

integer value, The number of Mfs in the output partition

add_mf(mf)

Add an Mf in the output partition

argument: mf

Mf object, The Mf to add

get_mf(mf_index)

argument: mf_index

integer value, The index (1-based index) of the mf to return

return:

Mf object

get_mfs()

Get all mfs in the output

return:

list of Mf objects

is_standardized()

return:

logical value, TRUE if the output is a standardized fuzzy partition, FALSE otherwise

See Also

NewFisOutFuzzy

Fuzzy Logic Elementary Glossary

Examples

output <- NewFisOutFuzzy(0, 2)
output$name <- "foo"
output$defuzzification <- "sugeno"
output$disjunction <- "max"
output$add_mf(NewMfTrapezoidalInf(0, 1))
output$add_mf(NewMfTriangular(0, 1, 2))
output$add_mf(NewMfTrapezoidalSup(1, 2))

---

FisPro package

Description

This package is a basic implementation of the main functions to use a "Fuzzy Inference System" that can be used for reasoning purposes, especially for simulating a physical or biological system. It is derived from the FisPro open source software. Fuzzy inference systems are briefly described in the Fuzzy Logic Elementary Glossary. They are based on fuzzy rules, which have a good capability for managing progressive phenomenons. Fuzzy logic, since the pioneer work by Zadeh, has proven to be a powerful interface between symbolic and numerical spaces. One of the reasons for this success is the ability of fuzzy systems to incorporate human expert knowledge with its nuances, as well as to express the behaviour of the system in an interpretable way for humans. Another reason is the possibility of designing data-driven FIS to make the most of available data.
To design a fuzzy system that can be handled by this package the user can use the FisPro software. If needed, the package can be extended to other functions.
All the mentioned publications are available from the FisPro web site.
Enjoy FisPro!

Author(s)

FisPro Team [email protected]

References

Guillaume S, Charnomordic B (2011). “Learning interpretable Fuzzy Inference Systems with FisPro.” International Journal of Information Sciences, 181(20), 4409-4427. doi:10.1016/j.ins.2011.03.025, Special Issue on Interpretable Fuzzy Systems.

Guillaume S, Charnomordic B (2012). “Fuzzy Inference Systems: an integrated modelling environment for collaboration between expert knowledge and data using FisPro.” Expert Systems with Applications, 39(10), 8744-8755. doi:10.1016/j.eswa.2012.01.206.

See Also

https://www.fispro.org

---

Class "Mf"

Description

The base class of all "membership function" classes (cannot be instantiate)
Use derived classes MfTriangular, MfTrapezoidal, MfTrapezoidalInf or MfTrapezoidalSup

Fields

label

character vector, The label of the membership function

Methods

degree(value)

Get the membership degree

argument: value

numeric value to compute the membership degree

return:

numeric value

See Also

Fuzzy Logic Elementary Glossary

---

Class "MfTrapezoidal"

Description

Class to manage a trapezoidal membership function

Inherits

MfTrapezoidal class inherits all fields and methods of Mf class

Constructors

MfTrapezoidal(lower_support, lower_kernel, upper_kernel, upper_support)

argument: lower_support

numeric lower value of support

argument: lower_kernel

numeric lower value of kernel

argument: upper_kernel

numeric upper value of kernel

argument: upper_support

numeric upper value of support

return:

MfTrapezoidal object

See Also

NewMfTrapezoidal

Examples

mf <- NewMfTrapezoidal(0, 1, 2, 3)
mf$degree(0.5)

---

Class "MfTrapezoidalInf"

Description

Class to manage a trapezoidal inf membership function

Inherits

MfTrapezoidalInf class inherits all fields and methods of Mf class

Constructors

MfTrapezoidalInf(upper_kernel, upper_support)

argument: upper_kernel

numeric upper value of kernel

argument: upper_support

numeric upper value of support

return:

MfTrapezoidalInf object

See Also

NewMfTrapezoidalInf

Examples

mf <- NewMfTrapezoidalInf(0, 1)
mf$degree(0.5)

---

Class "MfTrapezoidalSup"

Description

Class to manage a trapezoidal sup membership function

Inherits

MfTrapezoidalSup class inherits all fields and methods of Mf class

Constructors

MfTrapezoidalSup(lower_support, lower_kernel)

argument: lower_support

numeric lower value of support

argument: lower_kernel

numeric lower value of kernel

return:

MfTrapezoidalSup object

See Also

NewMfTrapezoidalSup

Examples

mf <- NewMfTrapezoidalSup(0, 1)
mf$degree(0.5)

---

Class "MfTriangular"

Description

Class to manage a triangular membership function

Inherits

MfTriangular class inherits all fields and methods of Mf class

Constructors

MfTriangular(lower_support, kernel, upper_support)

argument: lower_support

numeric lower value of support

argument: kernel

numeric value of kernel

argument: upper_support

numeric upper value of support

return:

MfTriangular object

See Also

NewMfTriangular

Examples

mf <- NewMfTriangular(0, 1, 2)
mf$degree(0.5)

---

Create object of class "Fis"

Description

Function to create object of class Fis

Usage

NewFis(...)

Arguments

...	arguments of Fis constructor

Value

Fis object

---

Create object of class "FisIn"

Description

Function to create object of class FisIn

Usage

NewFisIn(...)

Arguments

...	arguments of FisIn constructor

Value

FisIn object

---

Create object of class "FisOutCrisp"

Description

Function to create object of class FisOutCrisp

Usage

NewFisOutCrisp(...)

Arguments

...	arguments of FisOutCrisp constructor

Value

FisOutCrisp object

---

Create object of class "FisOutFuzzy"

Description

Function to create object of class FisOutFuzzy

Usage

NewFisOutFuzzy(...)

Arguments

...	arguments of FisOutFuzzy constructor

Value

FisOutFuzzy object

---

Create object of class "MfTrapezoidal"

Description

Function to create object of class MfTrapezoidal

Usage

NewMfTrapezoidal(...)

Arguments

...	arguments of MfTrapezoidal constructor

Value

MfTrapezoidal object

---

Create object of class "MfTrapezoidalInf"

Description

Function to create object of class MfTrapezoidalInf

Usage

NewMfTrapezoidalInf(...)

Arguments

...	arguments of MfTrapezoidalInf constructor

Value

MfTrapezoidalInf object

---

Create object of class "MfTrapezoidalSup"

Description

Function to create object of class MfTrapezoidalSup

Usage

NewMfTrapezoidalSup(...)

Arguments

...	arguments of MfTrapezoidalSup constructor

Value

MfTrapezoidalSup object

---

Create object of class "MfTriangular"

Description

Function to create object of class MfTriangular

Usage

NewMfTriangular(...)

Arguments

...	arguments of MfTriangular constructor

Value

MfTriangular object

---

Create object of class "Rule"

Description

Function to create object of class Rule

Usage

NewRule(...)

Arguments

...	arguments of Rule constructor

Value

Rule object

---

Class "Rule"

Description

Class to manage a Fis rule

Fields

premises

integer vector, The premises of the rule
A premise is the 1-based index of MF in the FisIn
0 means the input is not taken into account for this rule, i.e. the rule is incomplete
The vector length must be equal to the number of inputs in the Fis

conclusions

numeric vector, The conclusions of the rule
A conclusion is a numeric value for crisp output FisOutCrisp, or the 1-based index of MF in the fuzzy output FisOutFuzzy
The vector length must be equal to the number of outputs in the Fis

Constructors

Rule()

The default constructor to build an empty rule
The rule is initialized with empty premises and conclusions

return:

Rule object

Rule(premises, conclusions)

The constructor to build a rule

argument: premises

integer vector, The premises of the rule (the vector length must be equal to the number of inputs in the Fis)

argument: conclusions

numeric vector, The conclusions of the rule (the vector length must be equal to the number of outputs in the Fis)

return:

Rule object

See Also

NewRule

Fuzzy Logic Elementary Glossary

Examples

rule1 <- NewRule()
rule1$premises <- c(1, 2, 0)
rule1$conclusions <- c(1, 2)

rule2 <- NewRule(c(2, 1, 1), c(2, 1))

---