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main.py
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main.py
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import pandas as pd
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import sys
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import numpy as np
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import skfuzzy as fuzz
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import matplotlib.pyplot as plt
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def fuzzy(goalsV, xGPerMatchV, shotsPerMatchV, onTargetPerMatchV):
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goals = np.arange(0, 35, 1)
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xGPerMatch = np.arange(0, 1, 0.01)
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shotsPerMatch = np.arange(0, 5, 0.01)
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onTargetPerMatch = np.arange(0, 5, 0.01)
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ocena = np.arange(0, 101, 1)
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goals_low = fuzz.trapmf(goals, [0, 0, 10, 15])
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goals_md = fuzz.trimf(goals, [10, 15, 20])
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goals_hi = fuzz.trapmf(goals, [15, 20, 35, 100])
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xGPerMatch_lo = fuzz.trapmf(xGPerMatch, [0, 0, 0.2, 0.5])
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xGPerMatch_md = fuzz.trimf(xGPerMatch, [0.2, 0.5, 0.7])
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xGPerMatch_hi = fuzz.trapmf(xGPerMatch, [0.5, 0.7, 2, 10])
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shotsPerMatch_lo = fuzz.trapmf(shotsPerMatch, [0, 0, 1, 2])
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shotsPerMatch_md = fuzz.trapmf(shotsPerMatch, [1, 2, 3, 4])
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shotsPerMatch_hi = fuzz.trapmf(shotsPerMatch, [3, 4, 4.5, 10])
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onTargetPerMatch_lo = fuzz.trapmf(onTargetPerMatch, [0, 0, 0.5, 1])
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onTargetPerMatch_md = fuzz.trimf(onTargetPerMatch, [0.5, 1, 1.5])
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onTargetPerMatch_hi = fuzz.trimf(onTargetPerMatch, [1, 1.5, 5])
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ocena_lo = fuzz.trapmf(ocena, [0, 0, 40, 50])
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ocena_md = fuzz.trapmf(ocena, [40, 50, 60, 80])
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ocena_hi = fuzz.trapmf(ocena, [70, 80, 100, 100])
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#
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# fig, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(nrows=5, figsize=(8, 9))
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#
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# ax0.plot(goals, goals_low, 'b', linewidth=1.5, label='Mało')
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# ax0.plot(goals, goals_md, 'g', linewidth=1.5, label='Średnio')
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# ax0.plot(goals, goals_hi, 'r', linewidth=1.5, label='Dużo')
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# ax0.set_title('Liczba bramek')
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# ax0.legend()
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#
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# ax1.plot(xGPerMatch, xGPerMatch_lo, 'b', linewidth=1.5, label='Mało')
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# ax1.plot(xGPerMatch, xGPerMatch_md, 'g', linewidth=1.5, label='Średno')
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# ax1.plot(xGPerMatch, xGPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
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# ax1.set_title('Service quality')
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# ax1.legend()
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#
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# ax2.plot(shotsPerMatch, shotsPerMatch_lo, 'b', linewidth=1.5, label='Mało')
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# ax2.plot(shotsPerMatch, shotsPerMatch_md, 'g', linewidth=1.5, label='Średnio')
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# ax2.plot(shotsPerMatch, shotsPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
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# ax2.set_title('Strzały na mecz')
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# ax2.legend()
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#
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# ax3.plot(onTargetPerMatch, onTargetPerMatch_lo, 'b', linewidth=1.5, label='Mało')
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# ax3.plot(onTargetPerMatch, onTargetPerMatch_md, 'g', linewidth=1.5, label='Średnio')
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# ax3.plot(onTargetPerMatch, onTargetPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
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# ax3.set_title('Strzały w światlo bramki na mecz')
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# ax3.legend()
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#
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# ax4.plot(ocena, ocena_lo, 'b', linewidth=1.5, label='Niska')
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# ax4.plot(ocena, ocena_md, 'g', linewidth=1.5, label='Średnia')
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# ax4.plot(ocena, ocena_hi, 'r', linewidth=1.5, label='Wysoka')
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# ax4.set_title('Ocena')
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# ax4.legend()
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#
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# # Turn off top/right axes
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# for ax in (ax0, ax1, ax2, ax3, ax4):
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# ax.spines['top'].set_visible(False)
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# ax.spines['right'].set_visible(False)
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# ax.get_xaxis().tick_bottom()
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# ax.get_yaxis().tick_left()
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#
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# plt.tight_layout()
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goals_value = goalsV
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xGPerMatch_value = xGPerMatchV
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shotsPerMatch_value = shotsPerMatchV
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onTargetPerMatch_value = onTargetPerMatchV
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goals_level_lo = fuzz.interp_membership(goals, goals_low, goals_value)
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goals_level_md = fuzz.interp_membership(goals, goals_md, goals_value)
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goals_level_hi = fuzz.interp_membership(goals, goals_hi, goals_value)
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xGPerMatch_level_lo = fuzz.interp_membership(xGPerMatch, xGPerMatch_lo, xGPerMatch_value)
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xGPerMatch_level_md = fuzz.interp_membership(xGPerMatch, xGPerMatch_md, xGPerMatch_value)
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xGPerMatch_level_hi = fuzz.interp_membership(xGPerMatch, xGPerMatch_hi, xGPerMatch_value)
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shotsPerMatch_level_lo = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_lo, shotsPerMatch_value)
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shotsPerMatch_level_md = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_md, shotsPerMatch_value)
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shotsPerMatch_level_hi = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_hi, shotsPerMatch_value)
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onTargetPerMatch_level_lo = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_lo, onTargetPerMatch_value)
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onTargetPerMatch_level_md = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_md, onTargetPerMatch_value)
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onTargetPerMatch_level_hi = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_hi, onTargetPerMatch_value)
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active_rule1 = np.fmax(goals_level_hi, xGPerMatch_level_hi)
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active_rule2 = np.fmax(active_rule1, onTargetPerMatch_level_hi)
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ocena_activation_hi = np.fmin(active_rule2, ocena_hi) # removed entirely to 0
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active_rule3 = np.fmax(np.fmin(goals_level_lo, xGPerMatch_level_hi),
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np.fmin(goals_level_lo, shotsPerMatch_level_hi))
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active_rule4=np.fmax(np.fmin(goals_level_md, xGPerMatch_level_md), np.fmin(goals_level_md, shotsPerMatch_level_md))
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active_rule5=np.fmax(active_rule3,active_rule4)
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ocena_activation_md = np.fmin(active_rule5, ocena_md)
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ocena_activation_lo = np.fmin(goals_level_lo, ocena_lo)
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ocena0 = np.zeros_like(ocena)
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#
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# fig, ax0 = plt.subplots(figsize=(8, 3))
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#
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# ax0.fill_between(ocena, ocena0, ocena_activation_lo, facecolor='b', alpha=0.7)
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# ax0.plot(ocena, ocena_lo, 'b', linewidth=0.5, linestyle='--', )
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# ax0.fill_between(ocena, ocena0, ocena_activation_md, facecolor='g', alpha=0.7)
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# ax0.plot(ocena, ocena_md, 'g', linewidth=0.5, linestyle='--')
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# ax0.fill_between(ocena, ocena0, ocena_activation_hi, facecolor='r', alpha=0.7)
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# ax0.plot(ocena, ocena_hi, 'r', linewidth=0.5, linestyle='--')
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# ax0.set_title('Output membership activity')
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#
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# # Turn off top/right axes
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# for ax in (ax0,):
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# ax.spines['top'].set_visible(False)
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# ax.spines['right'].set_visible(False)
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# ax.get_xaxis().tick_bottom()
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# ax.get_yaxis().tick_left()
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#
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# plt.tight_layout()
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aggregated = np.fmax(ocena_activation_lo,
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np.fmax(ocena_activation_md, ocena_activation_hi))
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# Calculate defuzzified result
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ocena_output = fuzz.defuzz(ocena, aggregated, 'centroid')
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ocena_activation = fuzz.interp_membership(ocena, aggregated, ocena_output) # for plot
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# # Visualize this
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# fig, ax0 = plt.subplots(figsize=(8, 3))
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#
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# ax0.plot(ocena, ocena_lo, 'b', linewidth=0.5, linestyle='--', )
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# ax0.plot(ocena, ocena_md, 'g', linewidth=0.5, linestyle='--')
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# ax0.plot(ocena, ocena_hi, 'r', linewidth=0.5, linestyle='--')
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# ax0.fill_between(ocena, ocena0, aggregated, facecolor='Orange', alpha=0.7)
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# ax0.plot([ocena_output, ocena_output], [0, ocena_activation], 'k', linewidth=1.5, alpha=0.9)
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# ax0.set_title('Aggregated membership and result (line)')
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#
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# # Turn off top/right axes
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# for ax in (ax0,):
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# ax.spines['top'].set_visible(False)
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# ax.spines['right'].set_visible(False)
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# ax.get_xaxis().tick_bottom()
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# ax.get_yaxis().tick_left()
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#
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# plt.tight_layout()
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return ocena_output
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def main():
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data = pd.read_csv("resources/Data.csv")
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# league = sys.argv[1]
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# year = sys.argv[2]
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# filtered_df = data.loc[data['Year'] == year]
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filtered_df = data.loc[data['League'] == "La Liga"]
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#print(filtered_df)
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results = []
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for item in filtered_df.iterrows():
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print(item)
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goals=item[1]["Goals"]
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xGPerMatch=item[1]["xG Per Avg Match"]
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shotsPerMatch=item[1]["Shots Per Avg Match"]
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onTargetPerMatch=item[1]["On Target Per Avg Match"]
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rate = fuzzy(goals,xGPerMatch,shotsPerMatch,onTargetPerMatch)
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results.append(rate)
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filtered_df['Rate'] = results
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filtered_df.to_csv('resources/out.csv')
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if __name__ == '__main__':
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main()
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#goals srednio
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#xG malo/srednio
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#shotspM srednio
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#onTarget malo/srednio
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161
old.py
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old.py
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import numpy as np
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import skfuzzy as fuzz
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import matplotlib.pyplot as plt
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# Generate universe variables
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# * Quality and service on subjective ranges [0, 10]
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# * Tip has a range of [0, 25] in units of percentage points
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goals = np.arange(0, 35, 1)
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xGPerMatch = np.arange(0, 1, 0.01)
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shotsPerMatch=np.arange(0, 5, 0.01)
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onTargetPerMatch = np.arange(0, 5, 0.01)
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ocena=np.arange(0, 101, 1)
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# Generate fuzzy membership functions
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goals_low = fuzz.trapmf(goals, [0, 0, 10,15])
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goals_md = fuzz.trimf(goals, [10, 15, 20])
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goals_hi = fuzz.trapmf(goals, [15, 20, 35, 100])
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xGPerMatch_lo = fuzz.trapmf(xGPerMatch, [0,0, 0.2, 0.5])
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xGPerMatch_md = fuzz.trimf(xGPerMatch, [0.2, 0.5, 0.7])
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xGPerMatch_hi = fuzz.trapmf(xGPerMatch, [0.5, 0.7, 2, 10])
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shotsPerMatch_lo = fuzz.trapmf(shotsPerMatch, [0,0, 1, 2])
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shotsPerMatch_md = fuzz.trapmf(shotsPerMatch, [1, 2, 3, 4])
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shotsPerMatch_hi = fuzz.trapmf(shotsPerMatch, [3, 4, 4.5, 10])
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onTargetPerMatch_lo = fuzz.trapmf(onTargetPerMatch, [0, 0, 0.5, 1])
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onTargetPerMatch_md = fuzz.trimf(onTargetPerMatch, [0.5, 1, 1.5])
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onTargetPerMatch_hi = fuzz.trimf(onTargetPerMatch, [1, 1.5, 5])
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ocena_lo = fuzz.trapmf(ocena, [0, 0, 40,50])
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ocena_md = fuzz.trapmf(ocena, [40, 50, 60, 80])
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ocena_hi = fuzz.trapmf(ocena, [70, 80, 100, 100])
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# Visualize these universes and membership functions
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fig, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(nrows=5, figsize=(8, 9))
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ax0.plot(goals, goals_low, 'b', linewidth=1.5, label='Mało')
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ax0.plot(goals, goals_md, 'g', linewidth=1.5, label='Średnio')
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ax0.plot(goals, goals_hi, 'r', linewidth=1.5, label='Dużo')
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ax0.set_title('Liczba bramek')
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ax0.legend()
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ax1.plot(xGPerMatch, xGPerMatch_lo, 'b', linewidth=1.5, label='Mało')
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ax1.plot(xGPerMatch, xGPerMatch_md, 'g', linewidth=1.5, label='Średno')
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ax1.plot(xGPerMatch, xGPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
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ax1.set_title('Service quality')
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ax1.legend()
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ax2.plot(shotsPerMatch, shotsPerMatch_lo, 'b', linewidth=1.5, label='Mało')
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ax2.plot(shotsPerMatch, shotsPerMatch_md, 'g', linewidth=1.5, label='Średnio')
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ax2.plot(shotsPerMatch, shotsPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
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ax2.set_title('Strzały na mecz')
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ax2.legend()
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ax3.plot(onTargetPerMatch, onTargetPerMatch_lo, 'b', linewidth=1.5, label='Mało')
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ax3.plot(onTargetPerMatch, onTargetPerMatch_md, 'g', linewidth=1.5, label='Średnio')
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ax3.plot(onTargetPerMatch, onTargetPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
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ax3.set_title('Strzały w światlo bramki na mecz')
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ax3.legend()
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ax4.plot(ocena, ocena_lo, 'b', linewidth=1.5, label='Niska')
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ax4.plot(ocena, ocena_md, 'g', linewidth=1.5, label='Średnia')
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ax4.plot(ocena, ocena_hi, 'r', linewidth=1.5, label='Wysoka')
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ax4.set_title('Ocena')
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ax4.legend()
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# Turn off top/right axes
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for ax in (ax0, ax1, ax2, ax3, ax4):
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.get_xaxis().tick_bottom()
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ax.get_yaxis().tick_left()
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plt.tight_layout()
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plt.show()
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goals_value=22
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xGPerMatch_value=0.9
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shotsPerMatch_value=4.43
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onTargetPerMatch_value=1.57
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goals_level_lo = fuzz.interp_membership(goals, goals_low, goals_value)
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goals_level_md = fuzz.interp_membership(goals, goals_md, goals_value)
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goals_level_hi = fuzz.interp_membership(goals, goals_hi, goals_value)
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xGPerMatch_level_lo = fuzz.interp_membership(xGPerMatch, xGPerMatch_lo, xGPerMatch_value)
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xGPerMatch_level_md = fuzz.interp_membership(xGPerMatch, xGPerMatch_md, xGPerMatch_value)
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xGPerMatch_level_hi = fuzz.interp_membership(xGPerMatch, xGPerMatch_hi, xGPerMatch_value)
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shotsPerMatch_level_lo = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_lo, shotsPerMatch_value)
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shotsPerMatch_level_md = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_md, shotsPerMatch_value)
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shotsPerMatch_level_hi = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_hi, shotsPerMatch_value)
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onTargetPerMatch_level_lo = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_lo, onTargetPerMatch_value)
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onTargetPerMatch_level_md = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_md, onTargetPerMatch_value)
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onTargetPerMatch_level_hi = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_hi, onTargetPerMatch_value)
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# Now we take our rules and apply them. Rule 1 concerns bad food OR service.
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# The OR operator means we take the maximum of these two.
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active_rule1 = np.fmax(goals_level_hi, xGPerMatch_level_hi)
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active_rule2=np.fmax(active_rule1, onTargetPerMatch_level_hi)
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# Now we apply this by clipping the top off the corresponding output
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# membership function with `np.fmin`
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ocena_activation_hi = np.fmin(active_rule2, ocena_hi) # removed entirely to 0
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# For rule 2 we connect acceptable service to medium tipping
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active_rule3=np.fmax(np.fmin(goals_level_lo,xGPerMatch_level_hi),np.fmin(goals_level_lo, shotsPerMatch_level_hi))
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ocena_activation_md = np.fmin(active_rule2, ocena_md)
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# For rule 3 we connect high service OR high food with high tipping
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ocena_activation_lo = np.fmin(goals_level_lo, ocena_lo)
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ocena0 = np.zeros_like(ocena)
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# Visualize this
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fig, ax0 = plt.subplots(figsize=(8, 3))
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ax0.fill_between(ocena, ocena0, ocena_activation_lo, facecolor='b', alpha=0.7)
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ax0.plot(ocena, ocena_lo, 'b', linewidth=0.5, linestyle='--', )
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ax0.fill_between(ocena, ocena0, ocena_activation_md, facecolor='g', alpha=0.7)
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ax0.plot(ocena, ocena_md, 'g', linewidth=0.5, linestyle='--')
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ax0.fill_between(ocena, ocena0, ocena_activation_hi, facecolor='r', alpha=0.7)
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ax0.plot(ocena, ocena_hi, 'r', linewidth=0.5, linestyle='--')
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ax0.set_title('Output membership activity')
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# Turn off top/right axes
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for ax in (ax0,):
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.get_xaxis().tick_bottom()
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ax.get_yaxis().tick_left()
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plt.tight_layout()
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plt.show()
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aggregated = np.fmax(ocena_activation_lo,
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np.fmax(ocena_activation_md, ocena_activation_hi))
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# Calculate defuzzified result
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ocena_output = fuzz.defuzz(ocena, aggregated, 'centroid')
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ocena_activation = fuzz.interp_membership(ocena, aggregated, ocena_output) # for plot
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# Visualize this
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fig, ax0 = plt.subplots(figsize=(8, 3))
|
||||
|
||||
ax0.plot(ocena, ocena_lo, 'b', linewidth=0.5, linestyle='--', )
|
||||
ax0.plot(ocena, ocena_md, 'g', linewidth=0.5, linestyle='--')
|
||||
ax0.plot(ocena, ocena_hi, 'r', linewidth=0.5, linestyle='--')
|
||||
ax0.fill_between(ocena, ocena0, aggregated, facecolor='Orange', alpha=0.7)
|
||||
ax0.plot([ocena_output, ocena_output], [0, ocena_activation], 'k', linewidth=1.5, alpha=0.9)
|
||||
ax0.set_title('Aggregated membership and result (line)')
|
||||
|
||||
# Turn off top/right axes
|
||||
for ax in (ax0,):
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.get_xaxis().tick_bottom()
|
||||
ax.get_yaxis().tick_left()
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
print(ocena_output)
|
||||
Loading…
Reference in New Issue
Block a user