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)
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 random(variables, domains, rule_min_size, rule_max_size, epsilon, delay=1):
"""
Generate a epsilon-complete ContinuumLogicProgram with a random dynamics.
For each variable of the system, each possible epsilon state of the system is matched by at least one rule.
Args:
variables: list of String
variables of the represented system
domains: list of Continuum
domain of values that each variable can take
rule_min_size: int
minimal number of conditions in each rule
rule_max_size: int
maximal number of conditions in each rule
epsilon: float in ]0,1]
precision of the completness of the program
delay: int
maximal delay of the conditions of each rule
Returns:
ContinuumLogicProgram
an epsilon-complete CLP with a random dynamics
"""
#eprint("Start random CLP generation: var ", len(variables), " delay: ", delay)
extended_variables = variables.copy()
extended_domains = domains.copy()
# Delayed logic program: extend local herbrand base
if delay > 1:
for d in range(1,delay):
extended_variables += [var+"_"+str(d) for var in variables]
extended_domains += domains
rules = []
states = ContinuumLogicProgram.states(extended_domains, epsilon) # aggregated reversed time serie of size delay
for s in states:
#eprint(s)
for var in range(len(variables)):
matching = False
for r in rules: # check if matched
if r.get_head_variable() == var and r.matches(s):
matching = True
break
if not matching: # need new rule
val = Continuum()
while val.is_empty():
# Enumerate each possible value
min = domains[var].get_min_value()
max = domains[var].get_max_value()
step = epsilon * (max - min)
values = [min+(step*i) for i in range( int(1.0 / epsilon) )]
if values[-1] != max:
values.append(max)
min = random.choice(values)
max = random.choice(values)
while min > max:
min = random.choice(values)
max = random.choice(values)
val = Continuum(min, max, random.choice([True,False]), random.choice([True,False]))
body_size = random.randint(rule_min_size, rule_max_size)
new_rule = ContinuumRule(var, val, [])
# Prevent cross-match
# not necessarry, since next(state) assume determinism
# Complete the rule body if needed
while (new_rule.size() < body_size): # create body
cond_var = random.randint(0, len(s)-1)
if new_rule.has_condition(cond_var):
continue
# Enumerate each possible value
min = extended_domains[cond_var].get_min_value()
max = extended_domains[cond_var].get_max_value()
step = epsilon * (max - min)
values = [min+(step*i) for i in range( int(1.0 / epsilon) )]
if values[-1] != max:
values.append(max)
cond_val = Continuum()
while not cond_val.includes(s[cond_var]):
min = random.choice(values)
max = random.choice(values)
while min > max:
min = random.choice(values)
max = random.choice(values)
cond_val = Continuum(min, max, random.choice([True,False]), random.choice([True,False]))
new_rule.set_condition(cond_var, cond_val)
rules.append(new_rule)
return ContinuumLogicProgram(variables, domains, rules)
