from simpful import * FS = FuzzySystem() # Define fuzzy sets for the variable # Define fuzzy sets for the variable # RELEASE_YEAR # release_year_newer = FuzzySet(points=[[-68.0, 0.0], [68.0, 1.0]], term="newer") release_year_newer = TriangleFuzzySet(-68, -68, 0, term="newer") # release_year_similar = FuzzySet(points=[[-68.0, 0.0], [0.0, 1.0], [68.0, 0.0]], term="similar") release_year_similar = TriangleFuzzySet(-68, 0, 68, term="similar") # release_year_older = FuzzySet(points=[[-68.0, 1.0], [68.0, 0.0]], term="older") release_year_older = TriangleFuzzySet(0, 68, 68, term="older") FS.add_linguistic_variable("RELEASE_YEAR", LinguisticVariable( [release_year_newer, release_year_similar, release_year_older], universe_of_discourse=[-136, 136] )) # RUNTIME # runtime_shorter = FuzzySet(points=[[-238.0, 0.0], [238.0, 1.0]], term="shorter") runtime_shorter = TriangleFuzzySet(-238, -238, 0, term="shorter") # runtime_similar = FuzzySet(points=[[-238.0, 0.0], [0.0, 1.0], [238.0, 0.0]], term="similar") runtime_similar = TriangleFuzzySet(-238, 0, 238, term="similar") # runtime_longer = FuzzySet(points=[[-238.0, 1.0], [238.0, 0]], term="longer") runtime_longer = TriangleFuzzySet(0, 238, 238, term="longer") FS.add_linguistic_variable("RUNTIME", LinguisticVariable( [runtime_shorter, runtime_similar, runtime_longer], universe_of_discourse=[-476, 476] )) # SEASONS # seasons_less = FuzzySet(points=[[-42.0, 0.0], [42.0, 1.0]], term="less") seasons_less = TriangleFuzzySet(-42, -42, 0, term="less") # seasons_similar = FuzzySet(points=[[-42.0, 0.0], [0.0, 1.0], [42.0, 0.0]], term="similar") seasons_similar = TriangleFuzzySet(-42, 0, 42, term="similar") # seasons_more = FuzzySet(points=[[-42.0, 1.0], [42.0, 0.0]], term="more") seasons_more = TriangleFuzzySet(0, 42, 42, term="more") FS.add_linguistic_variable("SEASONS", LinguisticVariable( [seasons_less, seasons_similar, seasons_more], universe_of_discourse=[-84, 84] )) # GENRES # genres_different = FuzzySet(points=[[-100.0, 0.0], [100.0, 1.0]], term="different") genres_different = TriangleFuzzySet(-100, -100, 0, term="different") # genres_similar = FuzzySet(points=[[-100.0, 0.0], [0.0, 1.0], [100.0, 0.0]], term="similar") genres_similar = TriangleFuzzySet(-100, 0, 100, term="similar") # genres_same = FuzzySet(points=[[-100.0, 1.0], [100.0, 0.0]], term="same") genres_same = TriangleFuzzySet(0, 100, 100, term="same") FS.add_linguistic_variable("GENRES", LinguisticVariable( [genres_different, genres_similar, genres_same], universe_of_discourse=[-200, 200] )) # EMOTIONS # emotions_different = FuzzySet(points=[[-4.0, 0.0], [4.0, 1.0]], term="different") emotions_different = TriangleFuzzySet(-4, -4, 0, term="different") # emotions_similar = FuzzySet(points=[[-4.0, 0.0], [0.0, 1.0], [4.0, 0.0]], term="similar") emotions_similar = TriangleFuzzySet(-4, 0, 4, term="similar") # emotions_same = FuzzySet(points=[[-4.0, 1.0], [4.0, 0.0]], term="same") emotions_same = TriangleFuzzySet(0, 4, 4, term="same") FS.add_linguistic_variable("EMOTIONS", LinguisticVariable( [emotions_different, emotions_similar, emotions_same], universe_of_discourse=[-8, 8] )) FS.set_crisp_output_value("low_recomendation", 0) FS.set_crisp_output_value("medium_recomendation", 50) FS.set_crisp_output_value("high_recomendation", 100) RULE1 = "IF (RELEASE_YEAR IS older) AND (RUNTIME IS longer) AND (SEASONS IS more) THEN (RECOMENDATION IS low_recomendation)" RULE2 = "IF (EMOTIONS IS different) AND (GENRES IS different) THEN (RECOMENDATION IS low_recomendation)" RULE3 = "IF (RELEASE_YEAR IS newer) AND (RUNTIME IS similar) AND (SEASONS IS less) THEN (RECOMENDATION IS medium_recomendation)" RULE4 = "IF (EMOTIONS IS similar) AND (GENRES IS similar) THEN (RECOMENDATION IS medium_recomendation)" RULE5 = "IF (RELEASE_YEAR IS similar) AND (RUNTIME IS similar) AND (SEASONS IS similar) AND (EMOTIONS IS same) AND (GENRES IS same) THEN (RECOMENDATION IS high_recomendation)" FS.add_rules([RULE1, RULE2, RULE3, RULE4, RULE5]) FS.set_variable("RELEASE_YEAR", -12.0) FS.set_variable("RUNTIME", -10.0) FS.set_variable("SEASONS", -2.0) FS.set_variable("GENRES", 50.0) FS.set_variable("EMOTIONS", 1.0) print(FS.inference(["RECOMENDATION"]))