def test_constructor_full(self):
eprint(
">> Continuum.__init__(self, min_value=None, max_value=None, min_included=None, max_included=None)"
)
for i in range(self.__nb_unit_test):
# Valid continuum
#-----------------
min = random.uniform(self.__min_value, self.__max_value)
max = random.uniform(min, self.__max_value)
min_included = random.choice([True, False])
max_included = random.choice([True, False])
c = Continuum(min, max, min_included, max_included)
self.assertFalse(c.is_empty())
self.assertEqual(c.get_min_value(), min)
self.assertEqual(c.get_max_value(), max)
self.assertEqual(c.min_included(), min_included)
self.assertEqual(c.max_included(), max_included)
# Implicit emptyset
#-------------------
min = random.uniform(self.__min_value, self.__max_value)
c = Continuum(min, min, False, False)
self.assertTrue(c.is_empty())
self.assertEqual(c.get_min_value(), None)
self.assertEqual(c.get_max_value(), None)
self.assertEqual(c.min_included(), None)
self.assertEqual(c.max_included(), None)
# Invalid Continuum
#--------------------
max = random.uniform(self.__min_value, min - 0.001)
self.assertRaises(ValueError, Continuum, min, max, min_included,
max_included)
# Invalid number of arguments
#-------------------------------
self.assertRaises(ValueError, Continuum, min)
self.assertRaises(ValueError, Continuum, min, max)
self.assertRaises(ValueError, Continuum, min, max, min_included)
self.assertRaises(ValueError, Continuum, min, max, min_included,
max_included)
def test_to_string(self):
eprint(">> Continuum.to_string(self)")
for i in range(self.__nb_unit_test):
c = Continuum()
self.assertEqual(c.to_string(), u"\u2205")
c = Continuum.random(self.__min_value, self.__max_value)
if c.is_empty():
self.assertEqual(c.to_string(), u"\u2205")
out = ""
if c.min_included():
out += "["
else:
out += "]"
out += str(c.get_min_value()) + "," + str(c.get_max_value())
if c.max_included():
out += "]"
else:
out += "["
self.assertEqual(c.to_string(), out)
self.assertEqual(c.__str__(), out)
self.assertEqual(c.__repr__(), out)
def test_constructor_empty(self):
eprint(">> Continuum.__init__(self)")
for i in range(self.__nb_unit_test):
c = Continuum()
self.assertTrue(c.is_empty())
self.assertEqual(c.get_min_value(), None)
self.assertEqual(c.get_max_value(), None)
self.assertEqual(c.min_included(), None)
self.assertEqual(c.max_included(), None)
def test_size(self):
eprint(">> Continuum.size(self)")
for i in range(self.__nb_unit_test):
# empty set
c = Continuum()
self.assertEqual(c.size(), 0.0)
# regular
c = Continuum.random(self.__min_value, self.__max_value)
if not c.is_empty():
self.assertEqual(c.size(),
c.get_max_value() - c.get_min_value())
def test__eq__(self):
eprint(">> Continuum.__eq__(self, continuum)")
for i in range(self.__nb_unit_test):
# emptyset
c = Continuum()
self.assertTrue(Continuum() == Continuum())
self.assertTrue(c == Continuum())
self.assertTrue(c == c)
self.assertFalse(Continuum() != Continuum())
self.assertFalse(c != Continuum())
self.assertFalse(c != c)
c = Continuum.random(self.__min_value, self.__max_value)
self.assertTrue(c == c)
self.assertFalse(c != c)
self.assertEqual(c == Continuum(), c.is_empty())
c_ = Continuum.random(self.__min_value, self.__max_value)
if c.is_empty() and c_.is_empty():
self.assertTrue(c == c_)
self.assertTrue(c != c_)
if c.is_empty() != c_.is_empty():
self.assertFalse(c == c_)
self.assertTrue(c != c_)
if c.get_min_value() != c_.get_min_value():
self.assertFalse(c == c_)
self.assertTrue(c != c_)
if c.get_max_value() != c_.get_max_value():
self.assertFalse(c == c_)
self.assertTrue(c != c_)
if c.min_included() != c_.min_included():
self.assertFalse(c == c_)
self.assertTrue(c != c_)
if c.max_included() != c_.max_included():
self.assertFalse(c == c_)
self.assertTrue(c != c_)
# exaustive modifications
if not c.is_empty():
c_ = c.copy()
value = random.uniform(1, 100)
c_.set_lower_bound(c.get_min_value() - value, True)
self.assertFalse(c == c_)
self.assertTrue(c != c_)
c_.set_lower_bound(c.get_min_value() - value, False)
self.assertFalse(c == c_)
self.assertTrue(c != c_)
c_ = c.copy()
c_.set_lower_bound(c.get_min_value(), not c.min_included())
self.assertFalse(c == c_)
self.assertTrue(c != c_)
c_ = c.copy()
value = random.uniform(1, 100)
c_.set_upper_bound(c.get_min_value() + value, True)
self.assertFalse(c == c_)
self.assertTrue(c != c_)
c_.set_upper_bound(c.get_min_value() + value, False)
self.assertFalse(c == c_)
self.assertTrue(c != c_)
c_ = c.copy()
c_.set_upper_bound(c.get_max_value(), not c.max_included())
self.assertFalse(c == c_)
self.assertTrue(c != c_)
# different type
self.assertFalse(c == "test")
self.assertFalse(c == 0)
self.assertFalse(c == True)
self.assertFalse(c == [])
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)