def test_copy(self):
eprint(">> ContinuumRule.copy(self)")
for i in range(self.__nb_unit_test):
head_var_id = random.randint(0, self.__max_variables)
head_domain = Continuum.random(self.__min_value, self.__max_value)
size = random.randint(1, self.__max_variables)
body = []
locked = []
for j in range(size):
var_id = random.randint(0, self.__max_variables)
domain = Continuum.random(self.__min_value, self.__max_value)
if var_id not in locked:
body.append((var_id, domain))
locked.append(var_id)
r_ = ContinuumRule(head_var_id, head_domain, body)
r = r_.copy()
self.assertEqual(r.get_head_variable(), head_var_id)
self.assertEqual(r.get_head_value(), head_domain)
for e in body:
self.assertTrue(e in r.get_body())
for e in r.get_body():
self.assertTrue(e in body)
self.assertEqual(r.get_head_variable(), r_.get_head_variable())
self.assertEqual(r.get_head_value(), r_.get_head_value())
for e in r_.get_body():
self.assertTrue(e in r.get_body())
for e in r.get_body():
self.assertTrue(e in r_.get_body())
def test_remove_condition(self):
print(">> ContinuumRule.remove_condition(self, variable)")
# Empty rule
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
var = random.randint(0, len(variables) - 1)
val = Continuum.random(domains[var].get_min_value(),
domains[var].get_max_value())
r = ContinuumRule(var, val)
var = random.randint(0, len(variables) - 1)
self.assertFalse(r.has_condition(var))
r.remove_condition(var)
self.assertFalse(r.has_condition(var))
while r.size() == 0:
r = self.random_rule(variables, domains)
var, val = random.choice(r.get_body())
self.assertTrue(r.has_condition(var))
r.remove_condition(var)
self.assertFalse(r.has_condition(var))
def random_system(self):
# generates variables/domains
nb_variables = random.randint(1, self.__max_variables)
variables = ["x"+str(var) for var in range(nb_variables)]
domains = [ Continuum.random(self.__min_value, self.__max_value, self.__min_domain_size) for var in variables ]
return variables, domains
def test_has_condition(self):
print(">> ContinuumRule.has_condition(self, variable)")
for i in range(self.__nb_unit_test):
# Empty rule
variables, domains = self.random_system()
var = random.randint(0, len(variables))
val = Continuum.random(self.__min_value, self.__max_value)
r = ContinuumRule(var, val)
for var in range(len(variables)):
self.assertEqual(r.has_condition(var), False)
# Regular rule
variables, domains = self.random_system()
while r.size() == 0:
r = self.random_rule(variables, domains)
body = r.get_body()
appears = []
for var, val in body:
appears.append(var)
self.assertEqual(r.has_condition(var), True)
for var in range(len(variables)):
if var not in appears:
self.assertEqual(r.has_condition(var), False)
def random_rule(self, variables, domains):
var = random.randint(0,len(variables)-1)
var_domain = domains[var]
val = Continuum.random(var_domain.get_min_value(), var_domain.get_max_value(), self.__min_continuum_size)
min_size = random.randint(0, len(variables))
max_size = random.randint(min_size, len(variables))
return ContinuumRule.random(var, val, variables, domains, min_size, max_size)
def test_constructor_empty(self):
eprint(">> ContinuumRule.__init__(self, head_variable, head_value)")
for i in range(self.__nb_unit_test):
var_id = random.randint(0, self.__max_variables)
domain = Continuum.random(self.__min_value, self.__max_value)
c = ContinuumRule(var_id, domain)
self.assertEqual(c.get_head_variable(), var_id)
self.assertEqual(c.get_head_value(), domain)
self.assertEqual(c.get_body(), [])
def test_static_random(self):
eprint(">> Continuum.random(min_value, max_value)")
for i in range(self.__nb_unit_test):
# Valid values
min = random.uniform(self.__min_value, self.__max_value)
max = random.uniform(min, self.__max_value)
c = Continuum.random(min, max)
self.assertTrue(c.get_min_value() >= min
and c.get_min_value() <= max)
self.assertTrue(isinstance(c.min_included(), bool))
self.assertFalse(c.is_empty())
# with min size
min_size = 0
while min_size == 0:
min_size = random.uniform(-100, 0)
self.assertRaises(ValueError, Continuum.random, min, max, min_size)
min_size = random.uniform(0, self.__max_value)
if min_size >= (max - min):
self.assertRaises(ValueError, Continuum.random, min, max,
min_size)
c = Continuum.random(min, max)
self.assertTrue(c.get_min_value() >= min
and c.get_min_value() <= max)
self.assertTrue(isinstance(c.min_included(), bool))
self.assertFalse(c.is_empty())
# Invalid values
min = random.uniform(self.__min_value, self.__max_value)
max = random.uniform(self.__min_value, min - 0.001)
self.assertRaises(ValueError, Continuum.random, min, max)
def test_copy(self):
eprint(">> Continuum.copy(self)")
for i in range(self.__nb_unit_test):
# Emptyset
c_ = Continuum()
c = c_.copy()
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)
# Non empty
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)
c = c_.copy()
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)
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_static_random(self):
eprint(">> ContinuumRule.random(min_value, max_value)")
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
# rule characteristics
var = random.randint(0, len(variables) - 1)
var_domain = domains[var]
val = Continuum.random(var_domain.get_min_value(),
var_domain.get_max_value())
min_size = random.randint(0, len(variables))
max_size = random.randint(min_size, len(variables))
r = ContinuumRule.random(var, val, variables, domains, min_size,
max_size)
# Check head
self.assertEqual(r.get_head_variable(), var)
self.assertEqual(r.get_head_value(), val)
# Check body
self.assertTrue(r.size() >= min_size)
self.assertTrue(r.size() <= max_size)
appears = []
for var, val in r.get_body():
self.assertTrue(var >= 0 and var < len(variables))
self.assertTrue(domains[var].includes(val))
self.assertFalse(var in appears)
appears.append(var)
# min > max
min_size = random.randint(0, len(variables))
max_size = random.randint(-100, min_size - 1)
self.assertRaises(ValueError, ContinuumRule.random, var, val,
variables, domains, min_size, max_size)
# min > nb variables
min_size = random.randint(len(variables) + 1, len(variables) + 100)
max_size = random.randint(min_size, len(variables) + 100)
self.assertRaises(ValueError, ContinuumRule.random, var, val,
variables, domains, min_size, max_size)
# max > nb variables
min_size = random.randint(0, len(variables))
max_size = random.randint(len(variables) + 1, len(variables) + 100)
self.assertRaises(ValueError, ContinuumRule.random, var, val,
variables, domains, min_size, max_size)
def test___eq__(self):
print(">> ContinuumRule.__eq__(self, other)")
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
r = self.random_rule(variables, domains)
self.assertTrue(r == r)
self.assertFalse(r != r)
r_ = self.random_rule(variables, domains)
if r.get_head_variable() != r_.get_head_variable():
self.assertFalse(r == r_)
if r.get_head_value() != r_.get_head_value():
self.assertFalse(r == r_)
if r.get_body() != r_.get_body():
self.assertFalse(r == r_)
# different type
self.assertFalse(r == "")
self.assertFalse(r == 0)
self.assertFalse(r == [])
self.assertFalse(r == [1, 2, 3])
# different size
r_ = self.random_rule(variables, domains)
while r_.size() == r.size():
r_ = self.random_rule(variables, domains)
r_.set_head_variable(r.get_head_variable())
r_.set_head_value(r.get_head_value())
self.assertFalse(r == r_)
# Same size, same head
while r.size() == 0:
r = self.random_rule(variables, domains)
r_ = r.copy()
var, val = random.choice(r.get_body())
new_val = val
while new_val == val:
new_val = Continuum.random(domains[var].get_min_value(),
domains[var].get_max_value())
r_.set_condition(var, new_val)
self.assertFalse(r == r_)
def test_precision(self):
print(">> ContinuumLogicProgram.precision(expected, predicted)")
self.assertEqual(ContinuumLogicProgram.precision([],[]), 1.0)
# Equal programs
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
nb_states = random.randint(1,100)
expected = []
predicted = []
for j in range(nb_states):
s1 = [ random.uniform(d.get_min_value(),d.get_max_value()) for d in domains ]
s2 = [ random.uniform(d.get_min_value(),d.get_max_value()) for d in domains ]
s2_ = [ Continuum.random(d.get_min_value(), d.get_max_value()) for d in domains ]
expected.append( (s1,s2) )
predicted.append( (s1,s2_) )
precision = ContinuumLogicProgram.precision(expected, predicted)
error = 0
for j in range(len(expected)):
s1, s2 = expected[j]
s1_, s2_ = predicted[j]
for k in range(len(s2)):
if not s2_[k].includes(s2[k]):
error += 1
total = nb_states * len(variables)
self.assertEqual( precision, 1.0 - (error / total) )
#eprint("precision: ", precision)
# error of size
state_id = random.randint(0, len(expected)-1)
modif = random.randint(1,len(expected[state_id]))
expected[state_id] = ( expected[state_id][0][:-modif], expected[state_id][1] )
self.assertRaises(ValueError, ContinuumLogicProgram.precision, expected, predicted)
def test___init__(self):
print(">> ContinuumLogicProgram.__init__(self, variables, domains, rules)")
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
rules = []
for j in range(random.randint(0,self.__nb_rules)):
r = self.random_rule(variables, domains)
rules.append(r)
p = ContinuumLogicProgram(variables, domains, rules)
self.assertEqual(p.get_variables(), variables)
self.assertEqual(p.get_domains(), domains)
self.assertEqual(p.get_rules(), rules)
modif = random.randint(1,len(variables))
self.assertRaises(ValueError, ContinuumLogicProgram, variables, domains[:-modif], rules)
for var in range(0, modif):
domains.append(Continuum.random(self.__min_value, self.__max_value, self.__min_domain_size))
self.assertRaises(ValueError, ContinuumLogicProgram, variables, domains, rules)
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__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_set_condition(self):
print(">> ContinuumRule.set_condition(self, variable, value)")
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
var = random.randint(0, len(variables) - 1)
# empty rule
r = self.random_rule(variables, domains)
r = ContinuumRule(r.get_head_variable(), r.get_head_value(), [])
for j in range(len(variables)):
var = random.randint(0, len(variables) - 1)
val = Continuum.random(domains[var].get_min_value(),
domains[var].get_max_value())
size = r.size()
exist = r.has_condition(var)
r.set_condition(var, val)
# condition updated
self.assertEqual(r.get_condition(var), val)
# change rather than add when variable exist in a condition
if exist:
self.assertEqual(r.size(), size)
else:
self.assertEqual(r.size(), size + 1)
# Ordering ensured
prev = -1
for v, val in r.get_body():
v > prev
prev = v
# regular rule
r = self.random_rule(variables, domains)
for j in range(len(variables)):
var = random.randint(0, len(variables) - 1)
val = Continuum.random(domains[var].get_min_value(),
domains[var].get_max_value())
size = r.size()
exist = r.has_condition(var)
#eprint("r: ", r)
#eprint("var: ", var)
#eprint("val: ", val)
r.set_condition(var, val)
# condition updated
self.assertEqual(r.get_condition(var), val)
# change rather than add when variable exist in a condition
if exist:
self.assertEqual(r.size(), size)
else:
self.assertEqual(r.size(), size + 1)
# Ordering ensured
prev = -1
for v, val in r.get_body():
v > prev
prev = v
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_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_set_upper_bound(self):
eprint(">> Continuum.set_upper_bound(self, value, included)")
for i in range(self.__nb_unit_test):
# Empty set
c = Continuum()
self.assertRaises(TypeError, c.set_upper_bound, "string", True)
self.assertRaises(TypeError, c.set_upper_bound, "string", False)
self.assertRaises(TypeError, c.set_upper_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_upper_bound, value, False)
c.set_upper_bound(value, True)
# Empty set to one value interval
self.assertEqual(c, Continuum(value, value, True, True))
# Regular continuum
# over min value
c = Continuum.random(self.__min_value, self.__max_value)
value = random.uniform(self.__min_value, c.get_min_value())
while value == c.get_min_value():
value = random.uniform(self.__min_value, c.get_min_value())
self.assertRaises(ValueError, c.set_upper_bound, value, True)
self.assertRaises(ValueError, c.set_upper_bound, value, False)
# on min value
c = Continuum.random(self.__min_value, self.__max_value)
c_old = c.copy()
value = c.get_min_value()
if not c.max_included() or not c.min_included():
c.set_upper_bound(value, False)
self.assertEqual(c,
Continuum()) # continuum reduced to empty set
else:
c.set_upper_bound(value, True)
self.assertEqual(c.get_max_value(), value)
self.assertEqual(c.max_included(), True)
self.assertEqual(c.get_max_value(), c.get_min_value())
self.assertEqual(c.get_min_value(), c_old.get_min_value())
self.assertEqual(c.min_included(), c_old.min_included())
# other valid value
c = Continuum.random(self.__min_value, self.__max_value)
c_old = c.copy()
value = random.uniform(c.get_min_value(), self.__max_value)
while value == c.get_min_value():
value = random.uniform(c.get_min_value(), self.__max_value)
c.set_upper_bound(value, True)
self.assertEqual(c.get_max_value(), value)
self.assertEqual(c.max_included(), True)
c = Continuum.random(self.__min_value, self.__max_value)
c_old = c.copy()
value = random.uniform(c.get_min_value(), self.__max_value)
while value == c.get_min_value():
value = random.uniform(c.get_min_value(), self.__max_value)
c.set_upper_bound(value, False)
self.assertEqual(c.get_max_value(), value)
self.assertEqual(c.max_included(), False)
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_fit(self):
eprint(">> ACEDIA.fit(variables, values, transitions)")
for i in range(self.__nb_unit_test):
eprint("\rTest ", i + 1, "/", self.__nb_unit_test, end='')
# Generate transitions
epsilon = random.choice([0.1, 0.25, 0.3, 0.5])
variables, domains = self.random_system()
p = ContinuumLogicProgram.random(variables, domains, 1,
len(variables), epsilon)
#eprint("Progam: ", p)
# Valid and realistic epsilon
#epsilon = round(random.uniform(0.1,1.0), 2)
#while epsilon == 1.0:
# epsilon = round(random.uniform(0.1,1.0), 2)
t = p.generate_all_transitions(epsilon)
#sys.exit()
#eprint("Transitions: ")
#for s1, s2 in t:
# eprint(s1, s2)
#eprint("Transitions: ", t)
p_ = ACEDIA.fit(p.get_variables(), p.get_domains(), t)
rules = p_.get_rules()
#eprint("learned: ", p_)
# All transitions are realized
#------------------------------
for head_var in range(len(p.get_variables())):
for s1, s2 in t:
for idx, val in enumerate(s2):
realized = 0
for r in rules:
if r.get_head_variable(
) == idx and r.get_head_value().includes(
val) and r.matches(s1):
realized += 1
break
if realized <= 0:
eprint("head_var: ", head_var)
eprint("s1: ", s1)
eprint("s2: ", s2)
eprint("learned: ", p_)
self.assertTrue(
realized >= 1) # One rule realize the example
# All rules are minimals
#------------------------
for r in rules:
#eprint("r: ", r)
# Try reducing head min
#-----------------------
r_ = r.copy()
h = r_.get_head_value()
if h.get_min_value() + epsilon <= h.get_max_value():
r_.set_head_value(
Continuum(h.get_min_value() + epsilon,
h.get_max_value(), h.min_included(),
h.max_included()))
#eprint("spec: ", r_)
conflict = False
for s1, s2 in t:
if not r_.get_head_value().includes(
s2[r_.get_head_variable()]) and r_.matches(
s1): # Cover a negative example
conflict = True
#eprint("conflict")
break
if not conflict:
eprint("Non minimal rule: ", r)
eprint("head can be specialized into: ",
r_.get_head_variable(), "=",
r_.get_head_value())
self.assertTrue(conflict)
# Try reducing head max
#-----------------------
r_ = r.copy()
h = r_.get_head_value()
if h.get_max_value() - epsilon >= h.get_min_value():
r_.set_head_value(
Continuum(h.get_min_value(),
h.get_max_value() - epsilon,
h.min_included(), h.max_included()))
#eprint("spec: ", r_)
conflict = False
for s1, s2 in t:
if not r_.get_head_value().includes(
s2[r_.get_head_variable()]) and r_.matches(
s1): # Cover a negative example
conflict = True
#eprint("conflict")
break
if not conflict:
eprint("Non minimal rule: ", r)
eprint("head can be generalized to: ",
r_.get_head_variable(), "=",
r_.get_head_value())
self.assertTrue(conflict)
# Try extending condition
#-------------------------
for (var, val) in r.get_body():
# Try extend min
r_ = r.copy()
if val.get_min_value(
) - epsilon >= domains[var].get_min_value():
val_ = val.copy()
if not val_.min_included():
val_.set_lower_bound(val_.get_min_value(), True)
else:
val_.set_lower_bound(
val_.get_min_value() - epsilon, False)
r_.set_condition(var, val_)
#eprint("gen: ", r_)
conflict = False
for s1, s2 in t:
if not r_.get_head_value().includes(
s2[r_.get_head_variable()]) and r_.matches(
s1): # Cover a negative example
conflict = True
#eprint("conflict")
break
if not conflict:
eprint("Non minimal rule: ", r)
eprint("condition can be generalized: ", var, "=",
val_)
self.assertTrue(conflict)
# Try extend max
r_ = r.copy()
if val.get_max_value(
) + epsilon <= domains[var].get_max_value():
val_ = val.copy()
if not val_.max_included():
val_.set_upper_bound(val_.get_max_value(), True)
else:
val_.set_upper_bound(
val_.get_max_value() + epsilon, False)
r_.set_condition(var, val_)
#eprint("gen: ", r_)
conflict = False
for s1, s2 in t:
if not r_.get_head_value().includes(
s2[r_.get_head_variable()]) and r_.matches(
s1): # Cover a negative example
conflict = True
#eprint("conflict")
break
if not conflict:
eprint("Non minimal rule: ", r)
eprint("condition can be generalized: ", var, "=",
val_)
self.assertTrue(conflict)
eprint()
def test_dominates(self):
print(">> ContinuumRule.dominates(self, rule)")
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
var = random.randint(0, len(variables) - 1)
r = self.random_rule(variables, domains)
# Undominated rule: var = emptyset if anything
r0 = ContinuumRule(r.get_head_variable(), Continuum())
self.assertTrue(r0.dominates(r0))
# r1 is a regular rule
r1 = self.random_rule(variables, domains)
while r1.size() == 0:
r1 = self.random_rule(variables, domains)
var = r.get_head_variable()
val = Continuum.random(domains[var].get_min_value(),
domains[var].get_max_value())
r1 = ContinuumRule(var, val, r1.get_body()) # set head
self.assertTrue(r0.dominates(r1))
self.assertFalse(r1.dominates(r0))
# r2 is precision of r1 (head specification)
r2 = r1.copy()
var = r2.get_head_variable()
val = r2.get_head_value()
while r2.get_head_value(
) == val or not r2.get_head_value().includes(val):
val = Continuum.random(val.get_min_value(),
val.get_max_value())
r2.set_head_value(val)
self.assertEqual(r0.dominates(r2), True)
self.assertEqual(r2.dominates(r0), False)
self.assertEqual(r2.dominates(r1), True)
self.assertEqual(r1.dominates(r2), False)
# r3 is a generalization of r1 (body generalization)
r3 = r1.copy()
var, val = random.choice(r3.get_body())
val_ = val
modif_min = random.uniform(0, self.__max_value)
modif_max = random.uniform(0, self.__max_value)
while val_ == val or not val_.includes(val):
val_ = Continuum.random(val.get_min_value() - modif_min,
val.get_max_value() + modif_max)
r3.set_condition(var, val_)
self.assertEqual(r0.dominates(r3), True)
self.assertEqual(r3.dominates(r0), False)
self.assertEqual(r3.dominates(r1), True)
self.assertEqual(r1.dominates(r3), False)
# r4 is unprecision of r1 (head generalization)
r4 = r1.copy()
var = r4.get_head_variable()
val = r4.get_head_value()
modif_min = random.uniform(0, self.__max_value)
modif_max = random.uniform(0, self.__max_value)
while r4.get_head_value() == val or not val.includes(
r4.get_head_value()):
val = Continuum(val.get_min_value() - modif_min,
val.get_max_value() + modif_max,
random.choice([True, False]),
random.choice([True, False]))
r4.set_head_value(val)
self.assertEqual(r0.dominates(r4), True)
self.assertEqual(r4.dominates(r0), False)
self.assertEqual(r4.dominates(r1), False)
self.assertEqual(r1.dominates(r4), True)
# r5 is specialization of r1 (body specialization)
r5 = r1.copy()
var, val = random.choice(r5.get_body())
val_ = val
while val_ == val or not val.includes(val_):
val_ = Continuum.random(val.get_min_value(),
val.get_max_value())
r5.set_condition(var, val_)
self.assertEqual(r0.dominates(r5), True)
self.assertEqual(r5.dominates(r0), False)
self.assertEqual(r5.dominates(r1), False)
self.assertEqual(r1.dominates(r5), True)
# r6 is a random rule
r6 = self.random_rule(variables, domains)
# head var difference
if r6.get_head_variable() != r1.get_head_variable():
self.assertFalse(r6.dominates(r1))
self.assertFalse(r1.dominates(r6))
r6 = ContinuumRule(r1.get_head_variable(), r6.get_head_value(),
r6.get_body()) # same head var
#eprint("r1: ", r1)
#eprint("r6: ", r6)
# head val inclusion
if not r1.get_head_value().includes(r6.get_head_value()):
self.assertFalse(r6.dominates(r1))
r6 = ContinuumRule(r1.get_head_variable(), r1.get_head_value(),
r6.get_body()) # same head var, same head val
# body subsumption
if r1.dominates(r6):
for var, val in r1.get_body():
self.assertTrue(val.includes(r6.get_condition(var)))
# body subsumption
if r6.dominates(r1):
for var, val in r6.get_body():
self.assertTrue(val.includes(r1.get_condition(var)))
# incomparable
if not r1.dominates(r6) and not r6.dominates(r1):
#eprint("r1: ", r1)
#eprint("r6: ", r6)
conflicts = False
dominant_r1 = False
dominant_r6 = False
for var, val in r1.get_body():
# condition not appearing
if not r6.has_condition(var):
conflicts = True
break
# value not included
if not r6.get_condition(var).includes(
val) and not val.includes(r6.get_condition(var)):
conflicts = True
break
# local dominates
if val.includes(r6.get_condition(var)):
dominant_r1 = True
# local dominated
if r6.get_condition(var).includes(val):
if dominant_r1:
conflicts = True
break
for var, val in r6.get_body():
# condition not appearing
if not r1.has_condition(var):
conflicts = True
break
# value not included
if not r1.get_condition(var).includes(
val) and not val.includes(r1.get_condition(var)):
conflicts = True
break
# local dominates
if val.includes(r1.get_condition(var)):
dominant_r6 = True
if dominant_r1:
conflicts = True
break
# local dominated
if r1.get_condition(var).includes(val):
if dominant_r6:
conflicts = True
break
self.assertTrue(conflicts)