def membership_f(mf, x, abcd):
"""
Returns y values corresponding to type of type of Membership fn.
arguments:
mf - string containing type of Membership function
x - x axis values
"""
if mf == "zmf":
return fuzz.zmf(x, abcd[0], abcd[1]) # zmf(x, a, b)
elif mf == "trimf":
return fuzz.trimf(x, abcd[0:3]) # trimf(x, abc)
elif mf == "dsigmf":
return fuzz.dsigmf(x, abcd[0], abcd[1], abcd[2], abcd[3]) # dsigmf(x, b1, c1, b2, c2)
elif mf == "gauss2mf":
return fuzz.gauss2mf(x, abcd[0], abcd[1], abcd[2], abcd[3]) # gauss2mf(x, mean1, sigma1, mean2, sigma2)
elif mf == "gaussmf":
return fuzz.gaussmf(x, abcd[0], abcd[1]) # gaussmf(x, mean, sigma)
elif mf == "gbellmf":
return fuzz.gbellmf(x, abcd[0], abcd[1], abcd[2]) # gbellmf(x, a, b, c)
elif mf == "piecemf":
return fuzz.piecemf(x, abcd[0:3]) # piecemf(x, abc)
elif mf == "pimf":
return fuzz.pimf(x, abcd[0], abcd[1], abcd[2], abcd[3]) # pimf(x, a, b, c, d)
elif mf == "psigmf":
return fuzz.psigmf(x, abcd[0], abcd[1], abcd[2], abcd[3]) # psigmf(x, b1, c1, b2, c2)
elif mf == "sigmf":
return fuzz.sigmf(x, abcd[0], abcd[1]) # sigmf(x, b, c)
elif mf == "smf":
return fuzz.smf(x, abcd[0], abcd[1]) # smf(x, a, b)
elif mf == "trapmf":
return fuzz.trapmf(x, abcd) # trapmf(x, abcd)
def membership_f(mf, x, abcd):
"""
Returns y values corresponding to type of type of Membership fn.
arguments:
mf - string containing type of Membership function
x - x axis values
"""
if mf == 'zmf': return fuzz.zmf(x, abcd[0], abcd[1]) # zmf(x, a, b)
elif mf == 'trimf': return fuzz.trimf(x, abcd[0:3]) # trimf(x, abc)
elif mf == 'dsigmf':
return fuzz.dsigmf(x, abcd[0], abcd[1], abcd[2],
abcd[3]) # dsigmf(x, b1, c1, b2, c2)
elif mf == 'gauss2mf':
return fuzz.gauss2mf(
x, abcd[0], abcd[1], abcd[2],
abcd[3]) # gauss2mf(x, mean1, sigma1, mean2, sigma2)
elif mf == 'gaussmf':
return fuzz.gaussmf(x, abcd[0], abcd[1]) # gaussmf(x, mean, sigma)
elif mf == 'gbellmf':
return fuzz.gbellmf(x, abcd[0], abcd[1],
abcd[2]) # gbellmf(x, a, b, c)
elif mf == 'piecemf':
return fuzz.piecemf(x, abcd[0:3]) # piecemf(x, abc)
elif mf == 'pimf':
return fuzz.pimf(x, abcd[0], abcd[1], abcd[2],
abcd[3]) # pimf(x, a, b, c, d)
elif mf == 'psigmf':
return fuzz.psigmf(x, abcd[0], abcd[1], abcd[2],
abcd[3]) # psigmf(x, b1, c1, b2, c2)
elif mf == 'sigmf':
return fuzz.sigmf(x, abcd[0], abcd[1]) # sigmf(x, b, c)
elif mf == 'smf':
return fuzz.smf(x, abcd[0], abcd[1]) # smf(x, a, b)
elif mf == 'trapmf':
return fuzz.trapmf(x, abcd) # trapmf(x, abcd)
def membership_f(mf, x, abc = [0,0,0], a = 1, b = 2, c = 3, d = 4, abcd = [0,0,0,0]):
return {
'trimf' : fuzz.trimf(x, abc), # trimf(x, abc)
'dsigmf' : fuzz.dsigmf(x, a, b, c, d), # dsigmf(x, b1, c1, b2, c2)
'gauss2mf': fuzz.gauss2mf(x, a, b, c, d), # gauss2mf(x, mean1, sigma1, mean2, sigma2)
'gaussmf' : fuzz.gaussmf(x, a, b), # gaussmf(x, mean, sigma)
'gbellmf' : fuzz.gbellmf(x, a, b, c), # gbellmf(x, a, b, c)
'piecemf' : fuzz.piecemf(x, abc), # piecemf(x, abc)
'pimf' : fuzz.pimf(x, a, b, c, d), # pimf(x, a, b, c, d)
'psigmf' : fuzz.psigmf(x, a, b, c, d), # psigmf(x, b1, c1, b2, c2)
'sigmf' : fuzz.sigmf(x, a, b), # sigmf(x, b, c)
'smf' : fuzz.smf(x, a, b), # smf(x, a, b)
'trapmf' : fuzz.trapmf(x, abcd), # trapmf(x, abcd)
'zmf' : fuzz.zmf(x, a, b), # zmf(x, a, b)
}[mf]
def membership_f(mf, x, abc=[0, 0, 0], a=1, b=2, c=3, d=4, abcd=[0, 0, 0, 0]):
"""
Returns y values corresponding to type of type of Membership fn.
arguments:
mf - string containing type of Membership function
x - x axis values
abc - list containing triangular edge point x-values
"""
return {
"trimf": fuzz.trimf(x, abc), # trimf(x, abc)
"dsigmf": fuzz.dsigmf(x, a, b, c, d), # dsigmf(x, b1, c1, b2, c2)
"gauss2mf": fuzz.gauss2mf(x, a, b, c, d), # gauss2mf(x, mean1, sigma1, mean2, sigma2)
"gaussmf": fuzz.gaussmf(x, a, b), # gaussmf(x, mean, sigma)
"gbellmf": fuzz.gbellmf(x, a, b, c), # gbellmf(x, a, b, c)
"piecemf": fuzz.piecemf(x, abc), # piecemf(x, abc)
"pimf": fuzz.pimf(x, a, b, c, d), # pimf(x, a, b, c, d)
"psigmf": fuzz.psigmf(x, a, b, c, d), # psigmf(x, b1, c1, b2, c2)
"sigmf": fuzz.sigmf(x, a, b), # sigmf(x, b, c)
"smf": fuzz.smf(x, a, b), # smf(x, a, b)
"trapmf": fuzz.trapmf(x, abcd), # trapmf(x, abcd)
"zmf": fuzz.zmf(x, a, b), # zmf(x, a, b)
}[mf]
def membership_f(mf, x, abc = [0,0,0], a = 1, b = 2, c = 3, d = 4, abcd = [0,0,0,0]):
"""
Returns y values corresponding to type of type of Membership fn.
arguments:
mf - string containing type of Membership function
x - x axis values
abc - list containing triangular edge point x-values
"""
return {
'trimf' : fuzz.trimf(x, abc), # trimf(x, abc)
'dsigmf' : fuzz.dsigmf(x, a, b, c, d), # dsigmf(x, b1, c1, b2, c2)
'gauss2mf': fuzz.gauss2mf(x, a, b, c, d), # gauss2mf(x, mean1, sigma1, mean2, sigma2)
'gaussmf' : fuzz.gaussmf(x, a, b), # gaussmf(x, mean, sigma)
'gbellmf' : fuzz.gbellmf(x, a, b, c), # gbellmf(x, a, b, c)
'piecemf' : fuzz.piecemf(x, abc), # piecemf(x, abc)
'pimf' : fuzz.pimf(x, a, b, c, d), # pimf(x, a, b, c, d)
'psigmf' : fuzz.psigmf(x, a, b, c, d), # psigmf(x, b1, c1, b2, c2)
'sigmf' : fuzz.sigmf(x, a, b), # sigmf(x, b, c)
'smf' : fuzz.smf(x, a, b), # smf(x, a, b)
'trapmf' : fuzz.trapmf(x, abcd), # trapmf(x, abcd)
'zmf' : fuzz.zmf(x, a, b), # zmf(x, a, b)
}[mf]
import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt
# Generate universe variables
# * Quality and service on subjective ranges [0, 10]
# * Tip has a range of [0, 25] in units of percentage points
x_qual = np.arange(0, 11, 1)
x_serv = np.arange(0, 11, 1)
x_tip = np.arange(0, 26, 1)
# Generate fuzzy membership functions
qual_lo = fuzz.gaussmf(x_qual, 3, 0.5)
qual_md = fuzz.gbellmf(x_qual, 1, 1, 5)
qual_hi = fuzz.piecemf(x_qual, [6, 7, 10])
serv_lo = fuzz.pimf(x_serv, 0, 2.9, 3, 8)
serv_md = fuzz.sigmoid(x_serv, 0.5)
serv_hi = fuzz.smf(x_serv, 6, 9)
tip_lo = fuzz.trapmf(x_tip, [0, 0, 3, 6])
tip_md = fuzz.trimf(x_tip, [0, 13, 25])
tip_hi = fuzz.zmf(x_tip, 14, 20)
# Visualize these universes and membership functions
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(8, 9))
ax0.plot(x_qual, qual_lo, 'b', linewidth=1.5, label='Bad')
ax0.plot(x_qual, qual_md, 'g', linewidth=1.5, label='Decent')
ax0.plot(x_qual, qual_hi, 'r', linewidth=1.5, label='Great')
ax0.set_title('Food quality')
ax0.legend()
