fuzzy-logic-movies/engine.py

from simpful import *

FS = FuzzySystem()

# Define fuzzy sets for the variable # 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] ))

# RECOMENDATION
low_recomendation = TriangleFuzzySet(0, 0, 50, term="low_recomendation")
medium_recomendation = TriangleFuzzySet(0, 50, 100, term="medium_recomendation")
high_recomendation = TriangleFuzzySet(0, 100, 100, term="high_recomendation")
FS.add_linguistic_variable("RECOMENDATION", LinguisticVariable( [low_recomendation, medium_recomendation, high_recomendation], universe_of_discourse=[0, 200] ))

# RULES
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)"
# 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(["RECOMENDATION"]))

FS.produce_figure(outputfile='visualize_terms.pdf')