137 lines
6.3 KiB
Python
137 lines
6.3 KiB
Python
import simpful as sf
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import numpy as np
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import matplotlib.pylab as plt
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# Fix for the "detected Sugeno model type" spam - overwrite the annoying method from simpful
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class FuzzySystem(sf.FuzzySystem):
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def _set_model_type(self, model_type):
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if self._detected_type == "inconsistent": return
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if self._detected_type is None:
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self._detected_type = model_type
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# Just commenting the line below!
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#print(" * Detected %s model type" % model_type)
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elif self._detected_type != model_type:
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print("WARNING: model type is unclear (simpful detected %s, but a %s output was specified)" % (
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self._detected_type, model_type))
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self._detected_type = 'inconsistent'
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def fuzzy_controler_popularity(price: float, game_length: int, rating: float, number_of_owners: int) -> float:
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FS = FuzzySystem(show_banner=False)
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S_1 = sf.FuzzySet(points=[[0., 1.], [30., 0.]], term="negative")
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S_2 = sf.FuzzySet(points=[[35., 0.], [40., 1.], [60., 1.], [70., 0.]], term="average")
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S_3 = sf.FuzzySet(points=[[80., 0.], [90., 1.]], term="positive")
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FS.add_linguistic_variable("Rating", sf.LinguisticVariable([S_1, S_2, S_3], concept="Rating"))
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S_1 = sf.FuzzySet(points=[[0., 1.], [50000., 0.]], term="small")
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S_2 = sf.FuzzySet(points=[[100000., 0.], [300000., 1.], [3000000., 1.], [5000000., 0.]], term="average")
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S_3 = sf.FuzzySet(points=[[10000000., 0.], [30000000., 1.]], term="big")
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FS.add_linguistic_variable("Number_of_owners",
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sf.LinguisticVariable([S_1, S_2, S_3], concept="Number_of_owners"))
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S_1 = sf.FuzzySet(points=[[0., 1.], [10., 0.]], term="cheap")
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S_2 = sf.FuzzySet(points=[[10., 0.], [15., 1.], [20., 1.], [25., 0.]], term="average")
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S_3 = sf.FuzzySet(points=[[25., 0.], [35., 1.]], term="expensive")
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FS.add_linguistic_variable("Price", sf.LinguisticVariable([S_1, S_2, S_3], concept="Price"))
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F_1 = sf.FuzzySet(points=[[200., 1.], [300., 0.]], term="short")
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F_2 = sf.FuzzySet(points=[[300., 0.], [360., 1.], [420., 1.], [500., 0.]], term="average")
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F_3 = sf.FuzzySet(points=[[500., 0.], [550., 1.]], term="long")
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FS.add_linguistic_variable("Game_length", sf.LinguisticVariable([F_1, F_2, F_3], concept="Game_length"))
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FS.set_crisp_output_value("small", 0)
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FS.set_crisp_output_value("average", 0.5)
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FS.set_crisp_output_value("big", 1)
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R1 = "IF (Price IS average) OR (Game_length IS average) OR (Rating IS average) OR (Number_of_owners IS average) " \
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"THEN (Popularity IS average)"
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R2 = "IF (Price IS expensive) AND (Game_length IS long) AND (Rating IS positive) THEN (Popularity IS big)"
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R3 = "IF (Price IS expensive) AND (Game_length IS short) THEN (Popularity IS small)"
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R4 = "IF (Price IS cheap) THEN (Popularity IS big)"
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R5 = "IF (Rating IS negative) THEN (Popularity IS small)"
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R6 = "IF (Rating IS positive) AND (Number_of_owners IS small) THEN (Popularity IS average)"
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R7 = "IF (Rating IS average) AND (Price IS cheap) THEN (Popularity IS big)"
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FS.add_rules([R1, R2, R3, R4, R5, R6, R7])
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FS.set_variable("Price", price)
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FS.set_variable("Game_length", game_length)
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FS.set_variable("Rating", rating)
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FS.set_variable("Number_of_owners", number_of_owners)
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popularity = FS.Sugeno_inference(["Popularity"], ignore_warnings=True)
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return round(popularity["Popularity"], 2)
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def fuzzy_controler_similiarity(categorical_data: str, numerical_data: str, vector_distance: float, show_graph: bool)\
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-> float:
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FSS = FuzzySystem(show_banner=False)
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S_1 = sf.FuzzySet(points=[[.20, 1.], [.30, 0.]], term="small")
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S_2 = sf.FuzzySet(points=[[.30, 0.], [.55, 1.], [.65, 1.], [.85, 0.]], term="average")
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S_3 = sf.FuzzySet(points=[[.85, 0.], [.90, 1.]], term="big")
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FSS.add_linguistic_variable("Categorical_similarity",
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sf.LinguisticVariable([S_1, S_2, S_3], concept="Categorical similarity"))
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S_1 = sf.FuzzySet(points=[[.30, 1.], [.40, 0.]], term="small")
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S_2 = sf.FuzzySet(points=[[.40, 0.], [.50, 1.], [.60, 1.], [.70, 0.]], term="average")
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S_3 = sf.FuzzySet(points=[[.70, 0.], [.90, 1.]], term="big")
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FSS.add_linguistic_variable("Numerical_difference",
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sf.LinguisticVariable([S_1, S_2, S_3], concept="Numerical difference"))
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S_1 = sf.FuzzySet(points=[[.20, 1.], [.30, 0.]], term="small")
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S_2 = sf.FuzzySet(points=[[.30, 0.], [.55, 1.], [.65, 1.], [.85, 0.]], term="average")
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S_3 = sf.FuzzySet(points=[[.85, 0.], [.90, 1.]], term="big")
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FSS.add_linguistic_variable("Word_vector_distance",
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sf.LinguisticVariable([S_1, S_2, S_3], concept="Word vector distance"))
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FSS.set_crisp_output_value("small", 0)
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FSS.set_crisp_output_value("average", 0.5)
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FSS.set_crisp_output_value("big", 1)
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# TODO: add Word_vector_distance to rules
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R1 = "IF (Categorical_similarity IS average) AND (Numerical_difference IS average) THEN (Similarity IS average)"
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R2 = "IF (Categorical_similarity IS small) AND (Numerical_difference IS big) THEN (Similarity IS small)"
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R3 = "IF (Categorical_similarity IS big) AND (Numerical_difference IS small) THEN (Similarity IS big)"
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FSS.add_rules([R1, R2, R3])
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# show graph for two variables
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if show_graph:
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plot_graphs(FS=FSS)
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FSS.set_variable("Categorical_similarity", vector_distance)
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FSS.set_variable("Numerical_difference", numerical_data)
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popularity = FSS.Sugeno_inference(["Similarity"], ignore_warnings=True)
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return round(popularity["Similarity"], 2)
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def plot_graphs(FS: sf.FuzzySystem):
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xs = []
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ys = []
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zs = []
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for x in np.linspace(0, 1):
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for y in np.linspace(0, 1):
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FS.set_variable("Categorical_similarity", x)
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FS.set_variable("Numerical_difference", y)
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tip = FS.inference()['Similarity']
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xs.append(x)
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ys.append(y)
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zs.append(tip)
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xs = np.array(xs)
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ys = np.array(ys)
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zs = np.array(zs)
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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xx, yy = plt.meshgrid(xs, ys)
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ax.plot_trisurf(xs, ys, zs, vmin=0, vmax=100)
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ax.set_xlabel("Categorical_similarity")
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ax.set_ylabel("Numerical_difference")
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ax.set_zlabel("Similarity")
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ax.set_zlim(0, 1)
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plt.tight_layout()
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plt.show()
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