tipping_controller

import os
import ipdb; # ipdb.set_trace()
import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl
import matplotlib.pyplot as plt

# example from https://pythonhosted.org/scikit-fuzzy/auto_examples/plot_tipping_problem_newapi.html

# New Antecedent/Consequent objects hold universe variables and membership
# functions
quality = ctrl.Antecedent(np.arange(0, 11, 1), 'quality')
service = ctrl.Antecedent(np.arange(0, 11, 1), 'service')
tip = ctrl.Consequent(np.arange(0, 26, 1), 'tip')

# Auto-membership function population is possible with .automf(3, 5, or 7)
quality.automf(3)
service.automf(3)

# Custom membership functions can be built interactively with a familiar,
# Pythonic API
tip['low'] = fuzz.trimf(tip.universe, [0, 0, 13])
tip['medium'] = fuzz.trimf(tip.universe, [0, 13, 25])
tip['high'] = fuzz.trimf(tip.universe, [13, 25, 25])


quality['average'].view()
#ipdb.set_trace()
quality['good'].view()
#ipdb.set_trace()
#fig, ax = tip.view()
#ipdb.set_trace()
#ipdb.set_trace()
rule1 = ctrl.Rule(quality['poor'] | service['poor'], tip['low'])
rule2 = ctrl.Rule(service['average'], tip['medium'])
rule3 = ctrl.Rule(service['good'] | quality['good'], tip['high'])

#fig, ax = rule1.view()
#fig.show()
#ipdb.set_trace()

# Control System Creation and Simulation

tipping_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])
tipping = ctrl.ControlSystemSimulation(tipping_ctrl)

# Pass inputs to the ControlSystem using Antecedent labels with Pythonic API
# Note: if you like passing many inputs all at once, use .inputs(dict_of_data)
tipping.input['quality'] = 5
tipping.input['service'] = 4

# Crunch the numbers
tipping.compute()

print(tipping.output['tip'])
tip.view(sim=tipping)