def setup_rule_order():
global a, b, c, d
a = Antecedent(np.linspace(0, 10, 11), 'a')
b = Antecedent(np.linspace(0, 10, 11), 'b')
c = Antecedent(np.linspace(0, 10, 11), 'c')
d = Antecedent(np.linspace(0, 10, 11), 'd')
for v in (a, b, c, d):
v.automf(3)
def test_instantiate_antecedent():
# Correctly create a minimal Antecedent
universe = np.linspace(0, 5, 7)
label = 'test-Antecedent Labeling 130948@!!"'
ant = Antecedent(universe, label)
# Assure expected behavior
tst.assert_equal(universe, ant.universe)
assert ant.label == label
assert ant._id == id(ant)
assert_empty_ordereddict(ant.terms)
assert ant.__name__ == 'Antecedent'
assert ant.__repr__() == 'Antecedent: {0}'.format(label)
def test_instantiate_antecedent():
# Correctly create a minimal Antecedent
universe = np.linspace(0, 5, 7)
label = 'test-Antecedent Labeling 130948@!!"'
ant = Antecedent(universe, label)
# Assure expected behavior
tst.assert_equal(universe, ant.universe)
assert ant.label == label
assert ant._id == id(ant)
assert_empty_ordereddict(ant.terms)
assert ant.__name__ == 'Antecedent'
assert ant.__repr__() == 'Antecedent: {0}'.format(label)
def test_add_mf():
universe = np.linspace(0, 7, 21)
ant_label = 'service'
con_label = 'TIP'
ant = Antecedent(universe, ant_label)
con = Consequent(universe, con_label)
mf0 = np.sin(universe / 7. * 4 * np.pi) / 2. + 0.5
mf1 = np.arange(ant.universe.size) / ant.universe.size
# Assign mfs to Antecedent
ant['wavy'] = mf0
ant['high'] = mf1
# Ensure they were added correctly
assert list(ant.terms.keys()) == ['wavy', 'high']
tst.assert_equal(ant.terms['wavy'].mf, mf0)
tst.assert_equal(ant.terms['high'].mf, mf1)
# Assign mfs to Consequent
con['wavy'] = mf0
con['high'] = mf1
# Ensure they were added correctly
assert list(con.terms.keys()) == ['wavy', 'high']
tst.assert_equal(con.terms['wavy'].mf, mf0)
tst.assert_equal(con.terms['high'].mf, mf1)
def test_term_aggregate_1():
x1 = Antecedent(np.linspace(0, 10, 11), "x1")
x1.automf(3) # term labels: poor, average, good
x2 = Antecedent(np.linspace(0, 10, 11), "x2")
x2.automf(3)
ta = (x1["poor"] & x2["good"])
# print("- ta:", ta)
assert isinstance(ta, TermAggregate)
assert str(ta) == "x1[poor] AND x2[good]"
def setup():
global ant
global con
global universe
universe0 = np.linspace(0, 5, 6)
universe1 = np.linspace(0, 9, 10)
ant_label = 'service'
con_label = 'TIP'
ant = Antecedent(universe0, ant_label)
con = Consequent(universe1, con_label)
def test_tipping_problem():
# The full tipping problem uses many of these methods
food = Antecedent(np.linspace(0, 10, 11), 'quality')
service = Antecedent(np.linspace(0, 10, 11), 'service')
tip = Consequent(np.linspace(0, 25, 26), 'tip')
food.automf(3)
service.automf(3)
# Manual membership function definition
tip['bad'] = trimf(tip.universe, [0, 0, 13])
tip['middling'] = trimf(tip.universe, [0, 13, 25])
tip['lots'] = trimf(tip.universe, [13, 25, 25])
# Define fuzzy rules
rule1 = Rule(food['poor'] | service['poor'], tip['bad'])
rule2 = Rule(service['average'], tip['middling'])
rule3 = Rule(service['good'] | food['good'], tip['lots'])
# The control system - defined both possible ways
tipping = ControlSystem([rule1, rule2, rule3])
tipping2 = ControlSystem()
tipping2.addrule(rule2)
tipping2.addrule(rule3)
tipping2.addrule(rule1)
tip_sim = ControlSystemSimulation(tipping)
tip_sim2 = ControlSystemSimulation(tipping2)
# Inputs added both possible ways
inputs = {'quality': 6.5, 'service': 9.8}
for key, value in inputs.items():
tip_sim.input[key] = value
tip_sim2.inputs(inputs)
# Compute the system
tip_sim.compute()
tip_sim2.compute()
assert tip_sim.output == tip_sim2.output
tst.assert_allclose(tip_sim.output['tip'], 19.8578, atol=1e-2, rtol=1e-2)
def test_tipping_problem():
# The full tipping problem uses many of these methods
food = Antecedent(np.linspace(0, 10, 11), 'quality')
service = Antecedent(np.linspace(0, 10, 11), 'service')
tip = Consequent(np.linspace(0, 25, 26), 'tip')
food.automf(3)
service.automf(3)
# Manual membership function definition
tip['bad'] = trimf(tip.universe, [0, 0, 13])
tip['middling'] = trimf(tip.universe, [0, 13, 25])
tip['lots'] = trimf(tip.universe, [13, 25, 25])
# Define fuzzy rules
rule1 = Rule(food['poor'] | service['poor'], tip['bad'])
rule2 = Rule(service['average'], tip['middling'])
rule3 = Rule(service['good'] | food['good'], tip['lots'])
# The control system - defined both possible ways
tipping = ControlSystem([rule1, rule2, rule3])
tipping2 = ControlSystem()
tipping2.addrule(rule2)
tipping2.addrule(rule3)
tipping2.addrule(rule1)
tip_sim = ControlSystemSimulation(tipping)
tip_sim2 = ControlSystemSimulation(tipping2)
# Inputs added both possible ways
inputs = {'quality': 6.5, 'service': 9.8}
for key, value in inputs.items():
tip_sim.input[key] = value
tip_sim2.inputs(inputs)
# Compute the system
tip_sim.compute()
tip_sim2.compute()
assert tip_sim.output == tip_sim2.output
tst.assert_allclose(tip_sim.output['tip'], 19.8578, atol=1e-2, rtol=1e-2)
