Q-learning-PID/fu.py

import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt
#from  mpl_toolkit.mplot3d import Axes3D
from skfuzzy import control as ctrl
import math

x_qual = np.arange(0,11,1)
x_serv = np.arange(0,11,1)
x_tip = np.arange(0,26,1)

quality = ctrl.Antecedent(x_qual,'quality')
service = ctrl.Antecedent(x_serv,'service')
tip = ctrl.Consequent(x_tip,'tip')

quality['L']=fuzz.trimf(x_qual,[0,0,5])
quality['M']=fuzz.trimf(x_qual,[0,5,10])
quality['H']=fuzz.trimf(x_qual,[5,10,10])

service['L']=fuzz.trimf(x_serv,[0,0,5])
service['M']=fuzz.trimf(x_serv,[0,5,10])
service['H']=fuzz.trimf(x_serv,[5,10,10])

tip['L']=fuzz.trimf(x_tip,[0,0,13])
tip['M']=fuzz.trimf(x_tip,[0,13,25])
tip['H']=fuzz.trimf(x_tip,[13,25,25])

quality.view()
service.view()
tip.view()

tip.defuzzify_method='centroid'

rule1=ctrl.Rule(antecedent=((quality['L']&service['L'])|(quality['L']&service['M'])|(quality['M']&service['L'])),consequent=tip['L'],label='Low')
rule2=ctrl.Rule(antecedent=((quality['M']&service['M'])|(quality['L']&service['H'])|(quality['H']&service['L'])),consequent=tip['M'],label='Medium')
rule3=ctrl.Rule(antecedent=((quality['M']&service['H'])|(quality['H']&service['M'])|(quality['H']&service['H'])),consequent=tip['H'],label='High')
#rule2.view()
tipping_ctrl = ctrl.ControlSystem([rule1,rule2,rule3])
tipping = ctrl.ControlSystemSimulation(tipping_ctrl)


tipping.input['quality']=2
tipping.input['service']=2
tipping.compute()
p=tipping.output['tip']
tip.view(sim=tipping)
print(p)


#upsampled = np.linspace(0,11,21)
#x,y=np.meshgrid(upsampled,upsampled)
#z=np.zeros_like(x)
#pp=[]
#for i in range(0,21):
#    for j in range(0,21):
#        tipping.input['quality']=x[i,j]
#        tipping.input['service']=y[i,j]
#        tipping.compute()
#        z[i,j]=tipping.output['tip']
#        pp.append(z[i,j])
#print('max:',max(pp))
#print('min:',min(pp))

#fig = plt.figure(figsize=(8,8))
#ax = fig.add_subplot(111,projection='3d')
#surf = ax.plot_surface(x,y,z,rstride=1,cstride=1,cmap='viridis',linewidth=0.4,antialiased=True)
#ax.view_init(30,200)
plt.show()