def least_revision(rule, state_1, state_2):
"""
Compute the least revision of rule w.r.t. the transition (state_1, state_2)
Agrs:
rule: ContinuumRule
state_1: list of float
state_2: list of float
Returns: list of ContinuumRule
The least generalisation of the rule over its conclusion and
the least specializations of the rule over each condition
"""
# 0) Check Consistence
#----------------------
#Consistent rule
if not rule.matches(state_1):
raise ValueError(
"Attempting to revise a consistent rule, revision would be itself, this call is useless in ACEDIA and must be an error"
)
# Consistent rule
if rule.get_head_value().includes(state_2[rule.get_head_variable()]):
raise ValueError(
"Attempting to revise a consistent rule, revision would be itself, this call is useless in ACEDIA and must be an error"
)
# 1) Revise conclusion
#----------------------
head_var = rule.get_head_variable()
next_value = state_2[head_var]
revisions = []
head_revision = rule.copy()
head_value = head_revision.get_head_value()
# Empty set head case
if head_value.is_empty():
head_value = Continuum(next_value, next_value, True, True)
elif next_value <= head_value.get_min_value():
head_value.set_lower_bound(next_value, True)
else:
head_value.set_upper_bound(next_value, True)
head_revision.set_head_value(head_value)
revisions.append(head_revision)
# 2) Revise each condition
#---------------------------
for var, val in rule.get_body():
state_value = state_1[var]
# min revision
min_revision = rule.copy()
new_val = val.copy()
new_val.set_lower_bound(state_value, False)
if not new_val.is_empty():
min_revision.set_condition(var, new_val)
revisions.append(min_revision)
# max revision
max_revision = rule.copy()
new_val = val.copy()
new_val.set_upper_bound(state_value, False)
if not new_val.is_empty():
max_revision.set_condition(var, new_val)
revisions.append(max_revision)
return revisions
def test_set_lower_bound(self):
eprint(">> Continuum.set_lower_bound(self, value, included)")
for i in range(self.__nb_unit_test):
# Empty set
c = Continuum()
self.assertRaises(TypeError, c.set_lower_bound, "string", True)
self.assertRaises(TypeError, c.set_lower_bound, "string", False)
self.assertRaises(TypeError, c.set_lower_bound, 0.5, 10)
value = random.uniform(self.__min_value, self.__max_value)
# extend with exclusion gives empty set, mistake expected from user
# or both min and max will be changed and constructor must be used
self.assertRaises(ValueError, c.set_lower_bound, value, False)
c.set_lower_bound(value, True)
# Empty set to one value interval
self.assertEqual(c, Continuum(value, value, True, True))
# Regular continuum
# over max value
c = Continuum.random(self.__min_value, self.__max_value)
value = random.uniform(c.get_max_value(), self.__max_value)
while value == c.get_max_value():
value = random.uniform(c.get_max_value(), self.__max_value)
self.assertRaises(ValueError, c.set_lower_bound, value, True)
self.assertRaises(ValueError, c.set_lower_bound, value, False)
# on max value
c = Continuum.random(self.__min_value, self.__max_value)
c_old = c.copy()
value = c.get_max_value()
if not c.max_included() or not c.min_included():
c.set_lower_bound(value, False)
self.assertEqual(c,
Continuum()) # continuum reduced to empty set
else:
c.set_lower_bound(value, True)
self.assertEqual(c.get_min_value(), value)
self.assertEqual(c.min_included(), True)
self.assertEqual(c.get_min_value(), c.get_max_value())
self.assertEqual(c.get_max_value(), c_old.get_max_value())
self.assertEqual(c.max_included(), c_old.max_included())
# other valid value
c = Continuum.random(self.__min_value, self.__max_value)
c_old = c.copy()
value = random.uniform(self.__min_value, c.get_max_value())
while value == c.get_max_value():
value = random.uniform(self.__min_value, c.get_max_value())
c.set_lower_bound(value, True)
self.assertEqual(c.get_min_value(), value)
self.assertEqual(c.min_included(), True)
c = Continuum.random(self.__min_value, self.__max_value)
c_old = c.copy()
value = random.uniform(self.__min_value, c.get_max_value())
while value == c.get_max_value():
value = random.uniform(self.__min_value, c.get_max_value())
c.set_lower_bound(value, False)
self.assertEqual(c.get_min_value(), value)
self.assertEqual(c.min_included(), False)
def test_includes(self):
eprint(">> Continuum.includes(self, element)")
for i in range(self.__nb_unit_test):
# bad argument type
c = Continuum.random(self.__min_value, self.__max_value)
self.assertRaises(TypeError, c.includes, "test")
# float argument
#----------------
# empty set includes nothing
c = Continuum()
value = random.uniform(self.__min_value, self.__max_value)
self.assertFalse(c.includes(value))
c = Continuum.random(self.__min_value, self.__max_value)
# Before min
value = c.get_min_value()
while value == c.get_min_value():
value = random.uniform(c.get_min_value() - 100.0,
c.get_min_value())
self.assertFalse(c.includes(value))
# on min bound
self.assertEqual(c.includes(c.get_min_value()), c.min_included())
# Inside
value = c.get_min_value()
while value == c.get_min_value() or value == c.get_max_value():
value = random.uniform(c.get_min_value(), c.get_max_value())
self.assertTrue(c.includes(value))
# on max bound
self.assertEqual(c.includes(c.get_max_value()), c.max_included())
# after max bound
value = c.get_max_value()
while value == c.get_max_value():
value = random.uniform(c.get_max_value(),
c.get_max_value() + 100.0)
self.assertFalse(c.includes(value))
# int argument
#--------------
# empty set includes nothing
c = Continuum()
value = random.randint(int(self.__min_value),
int(self.__max_value))
self.assertFalse(c.includes(value))
c = Continuum.random(self.__min_value, self.__max_value)
while int(c.get_max_value()) - int(c.get_min_value()) <= 1:
min = random.uniform(self.__min_value, self.__max_value)
max = random.uniform(min, self.__max_value)
c = Continuum.random(min, max)
#eprint(c.to_string())
# Before min
value = random.randint(int(c.get_min_value() - 100),
int(c.get_min_value()) - 1)
self.assertFalse(c.includes(value))
# on min bound
self.assertEqual(c.includes(c.get_min_value()), c.min_included())
# Inside
value = random.randint(
int(c.get_min_value()) + 1,
int(c.get_max_value()) - 1)
#eprint(value)
self.assertTrue(c.includes(value))
# on max bound
self.assertEqual(c.includes(c.get_max_value()), c.max_included())
# after max bound
value = random.randint(
int(c.get_max_value()) + 1, int(c.get_max_value() + 100))
self.assertFalse(c.includes(value))
# continuum argument
#--------------------
# 0) c is empty set
c = Continuum()
c_ = Continuum()
self.assertTrue(c.includes(c_)) # empty set VS empty set
c_ = Continuum.random(self.__min_value, self.__max_value)
while c_.is_empty():
c_ = Continuum.random(self.__min_value, self.__max_value)
self.assertFalse(c.includes(c_)) # empty set VS non empty
# 1) c is non empty
c = Continuum.random(self.__min_value, self.__max_value)
self.assertTrue(c.includes(Continuum())) # non empty VS empty set
self.assertTrue(c.includes(c)) # includes itself
# 1.1) Lower bound over
c_ = Continuum.random(c.get_min_value(), self.__max_value)
while c_.is_empty():
c_ = Continuum.random(c.get_min_value(), self.__max_value)
value = c.get_min_value()
while value == c.get_min_value():
value = random.uniform(c.get_min_value() - 100,
c.get_min_value())
c_.set_lower_bound(value, random.choice([True, False]))
self.assertFalse(c.includes(c_))
# 1.2) on min bound
c_ = Continuum.random(c.get_min_value(), self.__max_value)
while c_.is_empty():
c_ = Continuum.random(c.get_min_value(), self.__max_value)
c_.set_lower_bound(c.get_min_value(), random.choice([True, False]))
if not c.min_included() and c_.min_included(): # one value over
self.assertFalse(c.includes(c_))
# 1.3) upper bound over
c_ = Continuum.random(self.__min_value, c.get_max_value())
while c_.is_empty():
c_ = Continuum.random(self.__min_value, c.get_max_value())
value = c.get_max_value()
while value == c.get_max_value():
value = random.uniform(c.get_max_value(),
c.get_max_value() + 100)
c_.set_upper_bound(value, random.choice([True, False]))
self.assertFalse(c.includes(c_))
# 1.4) on upper bound
c_ = Continuum.random(self.__min_value, c.get_max_value())
while c_.is_empty():
c_ = Continuum.random(self.__min_value, c.get_max_value())
c_.set_upper_bound(c.get_max_value(), random.choice([True, False]))
if not c.max_included() and c_.max_included(): # one value over
self.assertFalse(c.includes(c_))
# 1.5) inside
min = c.get_min_value()
while min == c.get_min_value():
min = random.uniform(c.get_min_value(), c.get_max_value())
max = c.get_max_value()
while max == c.get_max_value():
max = random.uniform(min, c.get_max_value())
c_ = Continuum(min, max, random.choice([True, False]),
random.choice([True, False]))
self.assertTrue(c.includes(c_))
self.assertFalse(c_.includes(c))
def test_intersects(self):
eprint(">> Continuum.intersects(self, continuum)")
for i in range(self.__nb_unit_test):
c = Continuum.random(self.__min_value, self.__max_value)
c_ = Continuum()
# emptyset
self.assertFalse(c.intersects(c_))
self.assertFalse(c_.intersects(c))
self.assertFalse(c_.intersects(c_))
# stricly before
c = Continuum.random(self.__min_value, self.__max_value)
c_ = Continuum.random(c.get_min_value() - 100, c.get_min_value())
self.assertFalse(c.intersects(c_))
self.assertFalse(c_.intersects(c))
# touching on lower bound
c = Continuum.random(self.__min_value, self.__max_value)
c_ = Continuum.random(c.get_min_value() - 100, c.get_min_value())
c_.set_upper_bound(c.get_min_value(), True)
self.assertEqual(c.intersects(c_), c.min_included())
self.assertEqual(c_.intersects(c), c.min_included())
c_.set_upper_bound(c.get_min_value(), False)
self.assertFalse(c.intersects(c_))
self.assertFalse(c_.intersects(c))
# strictly after
c = Continuum.random(self.__min_value, self.__max_value)
c_ = Continuum.random(c.get_max_value(), c.get_max_value() + 100)
self.assertFalse(c.intersects(c_))
self.assertFalse(c_.intersects(c))
# touching on lower bound
c = Continuum.random(self.__min_value, self.__max_value)
c_ = Continuum.random(c.get_max_value(), c.get_max_value() + 100)
c_.set_lower_bound(c.get_max_value(), True)
self.assertEqual(c.intersects(c_), c.max_included())
self.assertEqual(c_.intersects(c), c.max_included())
c_.set_lower_bound(c.get_max_value(), False)
self.assertFalse(c.intersects(c_))
self.assertFalse(c_.intersects(c))
# same (not empty)
c = Continuum.random(self.__min_value, self.__max_value)
while c.is_empty():
c = Continuum.random(self.__min_value, self.__max_value)
self.assertTrue(c.includes(c))
# smaller
c_ = Continuum.random(c.get_min_value(), c.get_max_value())
while c_.get_min_value() == c.get_min_value() and c_.get_max_value(
) == c.get_max_value():
c_ = Continuum.random(c.get_min_value(), c.get_max_value())
self.assertTrue(c.intersects(c_))
self.assertTrue(c_.intersects(c))
# bigger
c_ = Continuum.random(c.get_min_value() - 100,
c.get_max_value() + 100)
while c_.get_min_value() >= c.get_min_value() or c_.get_max_value(
) <= c.get_max_value():
c_ = Continuum.random(c.get_min_value() - 100,
c.get_max_value() + 100)
#eprint(c.to_string())
#eprint(c_.to_string())
self.assertTrue(c.intersects(c_))
self.assertTrue(c_.intersects(c))
def test_least_revision(self):
eprint(">> ACEDIA.least_revision(rule, state_1, state_2)")
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
state_1 = self.random_state(variables, domains)
state_2 = self.random_state(variables, domains)
# not matching
#--------------
rule = self.random_rule(variables, domains)
while rule.matches(state_1):
rule = self.random_rule(variables, domains)
self.assertRaises(ValueError, ACEDIA.least_revision, rule, state_1,
state_2)
# matching
#--------------
rule = self.random_rule(variables, domains)
while not rule.matches(state_1):
rule = self.random_rule(variables, domains)
head_var = rule.get_head_variable()
target_val = state_2[rule.get_head_variable()]
# Consistent
head_value = Continuum()
while not head_value.includes(target_val):
head_value = Continuum.random(
domains[head_var].get_min_value(),
domains[head_var].get_max_value())
rule.set_head_value(head_value)
self.assertRaises(ValueError, ACEDIA.least_revision, rule, state_1,
state_2)
# Empty set head
rule.set_head_value(Continuum())
LR = ACEDIA.least_revision(rule, state_1, state_2)
lg = rule.copy()
lg.set_head_value(Continuum(target_val, target_val, True, True))
self.assertTrue(lg in LR)
nb_valid_revision = 1
for var, val in rule.get_body():
state_value = state_1[var]
# min rev
ls = rule.copy()
new_val = val.copy()
new_val.set_lower_bound(state_value, False)
if not new_val.is_empty():
ls.set_condition(var, new_val)
self.assertTrue(ls in LR)
nb_valid_revision += 1
# max rev
ls = rule.copy()
new_val = val.copy()
new_val.set_upper_bound(state_value, False)
if not new_val.is_empty():
ls.set_condition(var, new_val)
self.assertTrue(ls in LR)
nb_valid_revision += 1
self.assertEqual(len(LR), nb_valid_revision)
#eprint(nb_valid_revision)
# usual head
head_value = Continuum.random(domains[head_var].get_min_value(),
domains[head_var].get_max_value())
while head_value.includes(target_val):
head_value = Continuum.random(
domains[head_var].get_min_value(),
domains[head_var].get_max_value())
rule.set_head_value(head_value)
LR = ACEDIA.least_revision(rule, state_1, state_2)
lg = rule.copy()
head_value = lg.get_head_value()
if target_val <= head_value.get_min_value():
head_value.set_lower_bound(target_val, True)
else:
head_value.set_upper_bound(target_val, True)
lg.set_head_value(head_value)
self.assertTrue(lg in LR)
nb_valid_revision = 1
for var, val in rule.get_body():
state_value = state_1[var]
# min rev
ls = rule.copy()
new_val = val.copy()
new_val.set_lower_bound(state_value, False)
if not new_val.is_empty():
ls.set_condition(var, new_val)
self.assertTrue(ls in LR)
nb_valid_revision += 1
# max rev
ls = rule.copy()
new_val = val.copy()
new_val.set_upper_bound(state_value, False)
if not new_val.is_empty():
ls.set_condition(var, new_val)
self.assertTrue(ls in LR)
nb_valid_revision += 1
self.assertEqual(len(LR), nb_valid_revision)
