Package: pyifdm
pyifdmPython 3 package to perform Multi-Criteria Decision Analysis in the Intuitionistic Fuzzy environmentInstallationThe package can be download using pip:pipinstallpyifdmTestingThe modules performance can be verified with pytest librarypipinstallpytest
pytesttestsModules and functionalitiesMCDA methods based on Intuitionistic Fuzzy Sets (IFS):AbbreviationFull nameReferenceARASAdditive Ratio ASsessment[1]CODASCOmbinative Distance-based ASsessment[2]COPRASCOmplex PRoportional ASsessment[3]EDASEvaluation based on Distance from Average Solution[4]MABACMulti-Attributive Border Approximation area Comparison[5]MAIRCAMultiAttributive Ideal-Real Comparative Analysis[6]MARCOSMeasurement of Alternatives and Ranking according to the Compromise Solution[31]MOORAMulti-Objective Optimization Method by Ratio Analysis[7]OCRAOperational Competitiveness Rating[30]TOPSISTechnique for the Order of Prioritisation by Similarity to Ideal Solution[8]VIKORVIseKriterijumska Optimizacija I Kompromisno Resenje[9]WASPASWeighted Aggregated Sum Product Assessment[38]WSMWeighted Sum Method[37]WPMWeighted Product Method[37]Weighting methods:NameReferenceBurillo entropy weights[25]Equal weights[10]Entropy weights[9]Liu entropy weights[27]Szmidt entropy weights[26]Thakur entropy weights[3]Ye entropy weights[24]Normalization methods:NameReferenceEcer normalization[10]Max normalization[37]Min-Max normalization[6]Supriya normalization[11]Swap normalization[2]Score functions:NameReferenceChen score 1[29]Chen score 2[29]Kharal score 1[15]Kharal score 2[15]Liu Wang score[28]Supriya score[11]Thakur score[3]Wan Dong score 1[13]Wan Dong score 2[13]Wei score[12]Zhang Xu score 1[14]Zhang Xu score 2[14]Distance measures:NameReferenceEuclidean distance[16]Grzegorzewski distance[17]Hamming distance[16]Hausdorf Euclidean distance[32]Luo Distance[9]Normalized Euclidean distance[16]Normalized Hamming distance[16]Wang Xin distance 1[18]Wang Xin distance 2[18]Yang Chiclana distance[19]IFS-similarity measures:NameReferenceChen similarity[38]Fan-Zhang similarity[38]Hong-Kim similarity[38]Li similarity[38]Li-Xu similarity[38]Ye similarity[38]Correlation coefficients:NameReferencePearson correlation coefficient[21]Spearman correlation coefficient[20]Weighted Spearman correlation coefficient[22]WS Rank Similarity coefficient[23]Intuitionistic Fuzzy Set[33],[34],[35],[36]:Functionality nameAdditionSubtractionMultiplicationDivisionPowerEqualityAND (Intersection)OR (Union)InvertOWA aggregationDominanceJaccard similarityFuzzy relationGraphs:Functionality nameIFS criteria bar plotIFS heatmap plotIFS radar plotSingle IFS bar plotSingle IFS pie plotHelpers methodsrankgenerate ifs matrixUsage exampleBelow the sample example of the Intuitionistic Fuzzy EDAS method application is presented.
More examples of package functionalities can be found inJupyter examples.frompyifdm.methodsimportifEDASfrompyifdm.helpersimportrankimportnumpyasnpif__name__=='__main__':matrix=np.array([[[0.4745,0.5255],[0.4752,0.5248],[0.2981,0.7019],[0.4374,0.5627]],[[0.5346,0.4654],[0.5532,0.4468],[0.6300,0.3700],[0.5901,0.4099]],[[0.4324,0.5676],[0.4030,0.5970],[0.4298,0.5702],[0.4361,0.5639]],[[0.5235,0.4765],[0.4808,0.5192],[0.5667,0.4333],[0.2913,0.7087]],[[0.4168,0.5832],[0.4923,0.5077],[0.4732,0.5268],[0.4477,0.5523]]])weights=np.array([0.2,0.3,0.15,0.35])types=np.array([1,-1,1,1])if_edas=ifEDAS()pref=if_edas(matrix,weights,types)print(f'IF-EDAS preferences:{pref}')print(f'IF-EDAS ranking:{rank(pref)}')Output:IF-EDASpreferences:0.2760.2590.5230.9950.322
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