from simpful import * FS = FuzzySystem() # Define fuzzy sets for the variable # RELEASE_YEAR release_year_newer = TriangleFuzzySet(-68, -68, 0, term="newer") release_year_similar = TriangleFuzzySet(-68, 0, 68, term="similar") 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 = TriangleFuzzySet(-238, -238, 0, term="shorter") runtime_similar = TriangleFuzzySet(-238, 0, 238, term="similar") 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 = TriangleFuzzySet(-42, -42, 0, term="less") seasons_similar = TriangleFuzzySet(-42, 0, 42, term="similar") 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 = TriangleFuzzySet(-100, -100, 0, term="different") genres_similar = TriangleFuzzySet(-100, 0, 100, term="similar") 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 = TriangleFuzzySet(-4, -4, 0, term="different") emotions_similar = TriangleFuzzySet(-4, 0, 4, term="similar") 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])) # RECOMMENDATION low_recommendation = TriangleFuzzySet(0, 0, 50, term="low_recommendation") medium_recommendation = TriangleFuzzySet(0, 50, 100, term="medium_recommendation") high_recommendation = TriangleFuzzySet(0, 100, 100, term="high_recommendation") FS.add_linguistic_variable("RECOMMENDATION", LinguisticVariable([low_recommendation, medium_recommendation, high_recommendation], universe_of_discourse=[0, 200])) # RULES RULE1 = "IF (RELEASE_YEAR IS older) AND (RUNTIME IS longer) AND (SEASONS IS more) THEN (RECOMMENDATION IS low_recommendation)" RULE2 = "IF (EMOTIONS IS different) AND (GENRES IS different) THEN (RECOMMENDATION IS low_recommendation)" RULE3 = "IF (RELEASE_YEAR IS newer) AND (RUNTIME IS similar) AND (SEASONS IS less) THEN (RECOMMENDATION IS medium_recommendation)" RULE4 = "IF (EMOTIONS IS similar) AND (GENRES IS similar) THEN (RECOMMENDATION IS medium_recommendation)" RULE5 = "IF (RELEASE_YEAR IS similar) AND (RUNTIME IS similar) AND (SEASONS IS similar) AND (EMOTIONS IS same) AND (GENRES IS same) THEN (RECOMMENDATION IS high_recommendation)" # Z regułami trzeba eksperymentować, można porównywać ze scorem dla sprawdzania skuteczności 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(["RECOMMENDATION"])) # FS.produce_figure(outputfile='visualize_terms.pdf')