Module `fcmpy.expert_fcm.membership`

Expand source code

import skfuzzy as fuzz
from abc import ABC, abstractclassmethod  
from fcmpy.expert_fcm.input_validator import type_check


class FuzzyMembership(ABC):
    """
        Fuzzy membership functions.
    """
    @abstractclassmethod
    def membershipFunction() -> dict:
        raise NotImplementedError('membershipFunction method is not defined')


class TriangularMembership(FuzzyMembership):
    """
        Triangular Fuzzy Membership Functions. 
    """
    @staticmethod
    @type_check
    def membershipFunction(**kwargs) -> dict:
        """
            Generate triangular membership functions.

            Other Parameters
            ----------------
            **linguisticTerms: dict,
                                terms and the associated parameters 
                                'abc' --> e.g., {'+VL': [0.25, 0.5, 0.75]}
            
            **universe: numpy.ndarray,
                        the universe of discourse                 

            Return
            -------
            y: dict,
                Generated membership functions. The keys are the 
                linguistic terms and the values are 1d arrays.
        """
        linguisticTerms = kwargs['linguistic_terms'] 
        universe = kwargs['universe'] 
        mfs = {}

        for term in linguisticTerms.keys():
            mfs[term] = fuzz.trimf(x = universe, abc=linguisticTerms[term])
        
        return mfs


class GaussianMembership(FuzzyMembership):
    """
        Gaussian Fuzzy Membership Functions. 
    """
    @staticmethod
    @type_check
    def membershipFunction(**kwargs) -> dict:
        """
            Generate Gaussian membership functions.

            Other Parameters
            ----------------
            **linguisticTerms: dict,
                                terms and the associated parameters 
                                'mean' and 'sigma' -> e.g., {'+VL': [0.25, 0.1]}}
            **universe: numpy.ndarray,
                        universe of discourse
                            
            Return
            -------
            y: dict,
                Generated membership functions. The keys are the linguistic terms 
                and the values are 1d arrays.
        """
        universe = kwargs['universe']
        linguisticTerms = kwargs['linguistic_terms'] 
        mfs = {}

        for term in linguisticTerms.keys():
            mfs[term] = fuzz.gaussmf(x = universe, mean=linguisticTerms[term][0],
                                        sigma = linguisticTerms[term][1])
        
        return mfs


class TrapezoidalMembership(FuzzyMembership):
    """
        Trapezoidal membership function.
    """
    @staticmethod
    @type_check
    def membershipFunction(**kwargs) -> dict:
        """
            Generate Trapezoidal membership functions.

            Other Parameters
            ----------------
            **linguisticTerms: dict,
                                terms and the associated parameters 'abcd' --> e.g., {'+VL': [0, 0.25, 0.5, 0.75]}
            **universe: numpy.ndarray,
                        universe of discourse

            Return
            -------
            y: dict,
                Generated membership functions. The keys are the linguistic terms and the values are 1d arrays.
        """
        linguisticTerms = kwargs['linguistic_terms'] 
        universe = kwargs['universe'] 

        for term in linguisticTerms.keys():
            assert linguisticTerms[term][0] <= linguisticTerms[term][1] <= linguisticTerms[term][2] <= linguisticTerms[term][3]
            mfs[term] = fuzz.trapmf(x = universe, abcd=linguisticTerms[term])
        
        return mfs

Classes

class FuzzyMembership

Fuzzy membership functions.

Expand source code

class FuzzyMembership(ABC):
    """
        Fuzzy membership functions.
    """
    @abstractclassmethod
    def membershipFunction() -> dict:
        raise NotImplementedError('membershipFunction method is not defined')

Ancestors

abc.ABC

Subclasses

GaussianMembership
TrapezoidalMembership
TriangularMembership

Static methods

def membershipFunction(...) ‑> dict

Expand source code

@abstractclassmethod
def membershipFunction() -> dict:
    raise NotImplementedError('membershipFunction method is not defined')

class GaussianMembership

Gaussian Fuzzy Membership Functions.

Expand source code

class GaussianMembership(FuzzyMembership):
    """
        Gaussian Fuzzy Membership Functions. 
    """
    @staticmethod
    @type_check
    def membershipFunction(**kwargs) -> dict:
        """
            Generate Gaussian membership functions.

            Other Parameters
            ----------------
            **linguisticTerms: dict,
                                terms and the associated parameters 
                                'mean' and 'sigma' -> e.g., {'+VL': [0.25, 0.1]}}
            **universe: numpy.ndarray,
                        universe of discourse
                            
            Return
            -------
            y: dict,
                Generated membership functions. The keys are the linguistic terms 
                and the values are 1d arrays.
        """
        universe = kwargs['universe']
        linguisticTerms = kwargs['linguistic_terms'] 
        mfs = {}

        for term in linguisticTerms.keys():
            mfs[term] = fuzz.gaussmf(x = universe, mean=linguisticTerms[term][0],
                                        sigma = linguisticTerms[term][1])
        
        return mfs

Ancestors

FuzzyMembership
abc.ABC

Static methods

def membershipFunction(**kwargs) ‑> dict

Generate Gaussian membership functions.

Other Parameters

**linguisticTerms : dict,: terms and the associated parameters 'mean' and 'sigma' -> e.g., {'+VL': [0.25, 0.1]}}
**universe : numpy.ndarray,: universe of discourse

Return

y: dict, Generated membership functions. The keys are the linguistic terms and the values are 1d arrays.

Expand source code

@staticmethod
@type_check
def membershipFunction(**kwargs) -> dict:
    """
        Generate Gaussian membership functions.

        Other Parameters
        ----------------
        **linguisticTerms: dict,
                            terms and the associated parameters 
                            'mean' and 'sigma' -> e.g., {'+VL': [0.25, 0.1]}}
        **universe: numpy.ndarray,
                    universe of discourse
                        
        Return
        -------
        y: dict,
            Generated membership functions. The keys are the linguistic terms 
            and the values are 1d arrays.
    """
    universe = kwargs['universe']
    linguisticTerms = kwargs['linguistic_terms'] 
    mfs = {}

    for term in linguisticTerms.keys():
        mfs[term] = fuzz.gaussmf(x = universe, mean=linguisticTerms[term][0],
                                    sigma = linguisticTerms[term][1])
    
    return mfs

class TrapezoidalMembership

Trapezoidal membership function.

Expand source code

class TrapezoidalMembership(FuzzyMembership):
    """
        Trapezoidal membership function.
    """
    @staticmethod
    @type_check
    def membershipFunction(**kwargs) -> dict:
        """
            Generate Trapezoidal membership functions.

            Other Parameters
            ----------------
            **linguisticTerms: dict,
                                terms and the associated parameters 'abcd' --> e.g., {'+VL': [0, 0.25, 0.5, 0.75]}
            **universe: numpy.ndarray,
                        universe of discourse

            Return
            -------
            y: dict,
                Generated membership functions. The keys are the linguistic terms and the values are 1d arrays.
        """
        linguisticTerms = kwargs['linguistic_terms'] 
        universe = kwargs['universe'] 

        for term in linguisticTerms.keys():
            assert linguisticTerms[term][0] <= linguisticTerms[term][1] <= linguisticTerms[term][2] <= linguisticTerms[term][3]
            mfs[term] = fuzz.trapmf(x = universe, abcd=linguisticTerms[term])
        
        return mfs

Ancestors

FuzzyMembership
abc.ABC

Static methods

def membershipFunction(**kwargs) ‑> dict

Generate Trapezoidal membership functions.

Other Parameters

**linguisticTerms : dict,: terms and the associated parameters 'abcd' –> e.g., {'+VL': [0, 0.25, 0.5, 0.75]}
**universe : numpy.ndarray,: universe of discourse

Return

y: dict, Generated membership functions. The keys are the linguistic terms and the values are 1d arrays.

Expand source code

@staticmethod
@type_check
def membershipFunction(**kwargs) -> dict:
    """
        Generate Trapezoidal membership functions.

        Other Parameters
        ----------------
        **linguisticTerms: dict,
                            terms and the associated parameters 'abcd' --> e.g., {'+VL': [0, 0.25, 0.5, 0.75]}
        **universe: numpy.ndarray,
                    universe of discourse

        Return
        -------
        y: dict,
            Generated membership functions. The keys are the linguistic terms and the values are 1d arrays.
    """
    linguisticTerms = kwargs['linguistic_terms'] 
    universe = kwargs['universe'] 

    for term in linguisticTerms.keys():
        assert linguisticTerms[term][0] <= linguisticTerms[term][1] <= linguisticTerms[term][2] <= linguisticTerms[term][3]
        mfs[term] = fuzz.trapmf(x = universe, abcd=linguisticTerms[term])
    
    return mfs

class TriangularMembership

Triangular Fuzzy Membership Functions.

Expand source code

class TriangularMembership(FuzzyMembership):
    """
        Triangular Fuzzy Membership Functions. 
    """
    @staticmethod
    @type_check
    def membershipFunction(**kwargs) -> dict:
        """
            Generate triangular membership functions.

            Other Parameters
            ----------------
            **linguisticTerms: dict,
                                terms and the associated parameters 
                                'abc' --> e.g., {'+VL': [0.25, 0.5, 0.75]}
            
            **universe: numpy.ndarray,
                        the universe of discourse                 

            Return
            -------
            y: dict,
                Generated membership functions. The keys are the 
                linguistic terms and the values are 1d arrays.
        """
        linguisticTerms = kwargs['linguistic_terms'] 
        universe = kwargs['universe'] 
        mfs = {}

        for term in linguisticTerms.keys():
            mfs[term] = fuzz.trimf(x = universe, abc=linguisticTerms[term])
        
        return mfs

Ancestors

FuzzyMembership
abc.ABC

Static methods

def membershipFunction(**kwargs) ‑> dict

Generate triangular membership functions.

Other Parameters

**linguisticTerms : dict,: terms and the associated parameters 'abc' –> e.g., {'+VL': [0.25, 0.5, 0.75]}
**universe : numpy.ndarray,: the universe of discourse

Return

y: dict, Generated membership functions. The keys are the linguistic terms and the values are 1d arrays.

Expand source code

@staticmethod
@type_check
def membershipFunction(**kwargs) -> dict:
    """
        Generate triangular membership functions.

        Other Parameters
        ----------------
        **linguisticTerms: dict,
                            terms and the associated parameters 
                            'abc' --> e.g., {'+VL': [0.25, 0.5, 0.75]}
        
        **universe: numpy.ndarray,
                    the universe of discourse                 

        Return
        -------
        y: dict,
            Generated membership functions. The keys are the 
            linguistic terms and the values are 1d arrays.
    """
    linguisticTerms = kwargs['linguistic_terms'] 
    universe = kwargs['universe'] 
    mfs = {}

    for term in linguisticTerms.keys():
        mfs[term] = fuzz.trimf(x = universe, abc=linguisticTerms[term])
    
    return mfs