def action_sw(bg_all):
#num = 70
#inx=random.sample(range(0,100),num)
#neg_inx = list(set(range(100))-set(inx))
B_0, P_up, P_sw, P_stay, N_up, N_sw, N_stay = get_B_N_P(bg_all)
B = [Atom([Term(False, str(x[1]))], x[0]) for x in B_0]
P = [Atom([Term(False, str(id))], 'sw') for id in P_sw]
N = [Atom([Term(False, str(id))], 'sw') for id in N_sw]
term_x_0 = Term(True, 'X')
term_x_1 = Term(True, 'X1')
p_e = [
Atom([term_x_0], 'busy'),
Atom([term_x_0], 'NObusy'),
Atom([term_x_0], 'side'),
Atom([term_x_0], 'front'),
Atom([term_x_0], 'back')
]
p_a = []
target = Atom([term_x_0], 'sw')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, False), None)
rules = {target: target_rule}
constants = [str(i) for i in range(0, 400)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
loss, cl_set = dilp.train(steps=51)
return loss, cl_set
def generate_ground_atoms_test():
term1 = Term(True, 'X_0')
term2 = Term(True, 'X_1')
target = Atom([term1, term2], 'r')
p_e = [Atom([term1, term2], 'p')]
p_a = [Atom([term1, term2], 'q')]
rule_template_1 = Rule_Template(0, False)
rule_template_2 = Rule_Template(1, True)
language_frame = Language_Frame(target, p_e, set(['a', 'b']))
program_template = Program_Template(p_a,
(rule_template_1, rule_template_2),
300)
term_a = Term(False, 'a')
term_b = Term(False, 'b')
# Background facts: p(a,a) p(a,b)
background = [Atom([term_a, term_a], 'p'), Atom([term_a, term_b], 'p')]
# Positive: p(b,a)
positive = [Atom([term_b, term_a], 'p')]
# Negative: p(b,b)
negative = [Atom([term_b, term_b], 'p')]
ilp = ILP(language_frame, background, positive, negative, program_template)
initial_valuation = ilp.convert()
for valuation in initial_valuation:
if valuation[0] in background:
assert valuation[1] == 1
else:
assert valuation[1] == 0
def generate_ground_atoms(self):
'''Generates the ground atoms from p_i,p_a,target and constants
'''
p = list(
set(self.language_frame.p_e + self.program_template.p_a +
[self.language_frame.target]))
constants = self.language_frame.constants
# Build constant matrix
constant_matrix = []
for const1 in constants:
for const2 in constants:
term1 = Term(False, const1)
term2 = Term(False, const2)
constant_matrix.append([term1, term2])
# Build ground atoms
ground_atoms = []
ground_atoms.append(Atom([], '⊥'))
added_atoms = {}
for pred in p:
for term in constant_matrix:
atom = Atom([term[i] for i in range(0, pred.arity)],
pred.predicate)
if atom not in added_atoms:
ground_atoms.append(atom)
added_atoms[atom] = 1
return ground_atoms
def generate_ground_literals(self):
'''Generates the ground atoms from p_i,p_a,target and constants
'''
p = list(set(self.language_frame.p_e +
self.program_template.p_a + [self.language_frame.target]))
constants = self.language_frame.constants
# Build constant matrix
constant_matrix = []
for const1 in constants:
for const2 in constants:
term1 = Term(False, const1)
term2 = Term(False, const2)
constant_matrix.append([term1, term2])
# Build ground, non negated, atoms
ground_positive_literals = []
ground_positive_literals.append(Literal(Atom([], '⊥'), False))
added_literals = {}
for pred in p:
for term in constant_matrix:
literal = Literal(Atom([term[i]
for i in range(0, pred.arity)], pred.predicate), False)
if literal not in added_literals:
ground_positive_literals.append(literal)
added_literals[literal] = 1
ground_literals = []
ground_literals += ground_positive_literals
if self.uses_negation:
for literal in ground_positive_literals:
#if atom == Atom([],'⊥'):
# continue
negative_literal = literal.__copy__()
negative_literal.negate()
ground_literals.append(negative_literal)
return ground_literals
def action_up_2():
B_0, P_up, P_sw, P_stay, N_up, N_sw, N_stay = get_B_N_P()
B = [Atom([Term(False, str(x[1]))], x[0]) for x in B_0]
N = [Atom([Term(False, str(id))], 'up') for id in P_up]
P = [Atom([Term(False, str(id))], 'up') for id in N_up]
term_x_0 = Term(True, 'X')
term_x_1 = Term(True, 'X1')
p_e = [
Atom([term_x_0], 'busy'),
Atom([term_x_0], 'NObusy'),
Atom([term_x_0], 'side'),
Atom([term_x_0], 'front'),
Atom([term_x_0], 'back')
]
p_a = []
target = Atom([term_x_0], 'up')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, False), None)
rules = {target: target_rule}
constants = [str(i) for i in range(0, 400)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train(steps=250)
def generate_clauses(self):
'''Generate the clauses based on brute force approach with no optmization
'''
rule_matrix = []
for rule in self.rules:
clauses = []
if (rule.allow_intensional):
p = self.p_e + self.p_a + [self.target]
p_i = self.p_a + [self.target]
intensional_predicates = [atom.predicate for atom in p_i]
else:
p = self.p_e
variables = [
'X_%d' % i for i in range(0, self.target.arity + rule.v)
]
target_variables = [
'X_%d' % i for i in range(0, self.target.arity)
]
# NOTE: Only allows 2 predicates and 2 arity maximum
# TODO: Need for optimizing
for i1 in range(0, len(p)):
for v1 in variables:
for v2 in variables:
for i2 in range(i1, len(p)):
for v3 in variables:
for v4 in variables:
# unsafe
if not set(target_variables).issubset(
[v1, v2, v3, v4]):
continue
head = Atom([
Term(True, var)
for var in target_variables
], self.target.predicate)
body1 = Atom(
[Term(True, v1),
Term(True, v2)], p[i1].predicate)
body2 = Atom(
[Term(True, v3),
Term(True, v4)], p[i2].predicate)
clause = Clause(head, [body1, body2])
if head == body1 or head == body2:
continue
# NOTE: Based on appendix requires to have a intensional predicate
if rule.allow_intensional and not (
body1.predicate
in intensional_predicates
or body2.predicate
in intensional_predicates):
continue
# duplicate
if clause not in clauses:
clauses.append(clause)
print(clauses)
rule_matrix.append(clauses)
return rule_matrix
def optimized_rule_test():
rule_template_1 = Rule_Template(0, False)
rule_template_2 = Rule_Template(1, True)
target = Atom([Term(True, 'X_0'), Term(True, 'X_1')], 'q')
p_e = [Atom([Term(True, 'X_0'), Term(True, 'X_1')], 'p')]
rule_manager = Optimized_Combinatorial_Generator(
[target], (rule_template_1, rule_template_2), target, p_e)
rule_matrix = rule_manager.generate_clauses()
assert len(rule_matrix[0]) == 8
assert len(rule_matrix[1]) == 58
def less_than():
B = [Atom([Term(False, '0')], 'zero')] + \
[Atom([Term(False, str(i)), Term(False, str(i + 1))], 'succ')
for i in range(0, 9)]
P = []
N = []
for i in range(0, 10):
for j in range(0, 10):
if j >= i:
N.append(
Atom([Term(False, str(j)), Term(False, str(i))], 'target'))
else:
P.append(
Atom([Term(False, str(j)), Term(False, str(i))], 'target'))
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [Atom([term_x_0], 'zero'), Atom([term_x_0, term_x_1], 'succ')]
p_a = []
target = Atom([term_x_0, term_x_1], 'target')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, False), Rule_Template(1, True))
rules = {target: target_rule}
constants = [str(i) for i in range(0, 10)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train()
def equality_test():
term1 = Term(True, 'X_0')
term2 = Term(True, 'X_1')
atom1 = Atom([term1, term1], 'p')
atom2 = Atom([term1, term2], 'p')
head = Atom([term1, term2], 'q')
clause1 = Clause(head, [atom1, atom2])
clause2 = Clause(head, [atom2, atom1])
pred_dict = {}
pred_dict[clause1] = 1
pred_dict[clause2] = 1
assert clause1 == clause2
assert len(pred_dict) == 1
def process_file(filename):
atoms = []
predicates = set()
with open(filename) as f:
data = f.read().replace('\n', '')
result = prolog_sentences.parseString(data)
for fact in result['facts']:
predicate = fact['relationship']
terms = [Term(False, term) for term in fact['arguments']]
constants = set([term for term in fact['arguments']])
term_var = [Term(True, f'X_{i}') for i in range(len(terms))]
predicates.add(Atom(term_var, predicate))
atoms.append(Atom(terms, predicate))
return atoms, predicates, constants
def action_switch_2():
num = 30
inx = random.sample(range(0, 100), num)
neg_inx = list(set(range(100)) - set(inx))
B = [Atom([Term(False, str(id))], 'busy') for id in inx
] + [Atom([Term(False, str(id))], 'NObusy') for id in neg_inx] + [
Atom([Term(False, str(id))], 'front')
for id in neg_inx if id % 2 == 0
] + [
Atom([Term(False, str(id))], 'back')
for id in neg_inx if id % 2 != 0
]
P = [Atom([Term(False, str(id))], 'switch') for id in neg_inx]
N = [Atom([Term(False, str(id))], 'switch') for id in inx]
term_x_0 = Term(True, 'X')
term_x_1 = Term(True, 'X1')
p_e = [
Atom([term_x_0], 'busy'),
Atom([term_x_0], 'NObusy'),
Atom([term_x_0], 'side'),
Atom([term_x_0], 'front'),
Atom([term_x_0], 'back')
]
p_a = []
target = Atom([term_x_0], 'switch')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, False), None)
rules = {target: target_rule}
constants = [str(i) for i in range(0, 100)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train(steps=252)
def even_numbers_negation_test():
B_atom = [Atom([Term(False, '0')], 'zero')] + \
[Atom([Term(False, str(i)), Term(False, str(i + 1))], 'succ')
for i in range(0, 20)]
B = [Literal(atom, False) for atom in B_atom]
P_atom = [Atom([Term(False, str(i))], 'target') for i in range(0, 21, 2)]
P = [Literal(atom, False) for atom in P_atom]
N_atom = [Atom([Term(False, str(i))], 'target') for i in range(1, 21, 2)]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [
Literal(Atom([term_x_0], 'zero'), False),
Literal(Atom([term_x_0, term_x_1], 'succ'), False)
]
p_a = [Literal(Atom([term_x_0, term_x_1], 'pred'), False)]
target = Literal(Atom([term_x_0], 'target'), False)
constants = [str(i) for i in range(0, 21)]
# Define rules for intensional predicates
p_a_rule = (Rule_Template_Negation(1, False, False), None)
target_rule = (Rule_Template_Negation(0, False, True),
Rule_Template_Negation(1, True, False))
rules = {p_a[0]: p_a_rule, target: target_rule}
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
#program_template = Program_Template(p_a, rules, 300)
snafdilp = SNAFDILP(langage_frame, B, P, N, program_template)
return snafdilp.train(steps=300)
def even_numbers_test():
B = [Atom([Term(False, '0')], 'zero')] + \
[Atom([Term(False, str(i)), Term(False, str(i + 1))], 'succ')
for i in range(0, 20)]
P = [Atom([Term(False, str(i))], 'target') for i in range(0, 21, 2)]
N = [Atom([Term(False, str(i))], 'target') for i in range(1, 21, 2)]
print(P)
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [Atom([term_x_0], 'zero'), Atom([term_x_0, term_x_1], 'succ')]
p_a = [Atom([term_x_0, term_x_1], 'pred')]
target = Atom([term_x_0], 'target')
constants = [str(i) for i in range(0, 21)]
# Define rules for intensional predicates
p_a_rule = (Rule_Template(1, False), None)
target_rule = (Rule_Template(0, False), Rule_Template(1, True))
rules = {p_a[0]: p_a_rule, target: target_rule}
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
#program_template = Program_Template(p_a, rules, 300)
dilp = DILP(langage_frame, B, P, N, program_template)
def greater_than():
'''
Learn the target predicate greater_than(X,Y) which is true
if X is greater than Y. The aim is to mirror the less_than
predicate use of negation as failure.
'''
constants = [str(i) for i in range(0, 10)]
B_atom = [Atom([Term(False, '0')], 'zero')] + \
[Atom([Term(False, str(i)), Term(False, str(i + 1))], 'succ')
for i in range(0, 9)]
B = [Literal(atom, False) for atom in B_atom]
P_atom = []
N_atom = []
for i in range(0, 10):
for j in range(0, 10):
if j > i:
P_atom.append(
Atom([Term(False, str(j)),
Term(False, str(i))], 'target'))
else:
N_atom.append(
Atom([Term(False, str(j)),
Term(False, str(i))], 'target'))
P = [Literal(atom, False) for atom in P_atom]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [
Literal(Atom([term_x_0], 'zero'), False),
Literal(Atom([term_x_0, term_x_1], 'succ'), False)
]
p_a = [Literal(Atom([term_x_0, term_x_1], 'pred'), False)]
target = Atom([term_x_0, term_x_1], 'target')
# Define rules for intensional predicates
p_a_rule = [(Rule_Template_Negation(0, False, False),
Rule_Template_Negation(1, True, False))]
target_rule = (Rule_Template_Negation(0, True, True), None)
rules = {p_a[0]: p_a_rule[0], target: target_rule}
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
snafdilp = SNAFDILP(langage_frame, B, P, N, program_template)
return snafdilp.train(steps=500)
def x_c(clause: Clause, dict_valuation: dict, constants: list):
'''
Arguments:
clause {Clause} -- clause to generate f_c
valuations {list} -- valuation list
constants {list} -- constants
'''
if clause == None:
return np.zeros((len(dict_valuation), 1, 2), dtype=int)
constant_terms = [Term(False, constant) for constant in constants]
head_variables = clause.head.variables
variables = [Term(True, variable) for variable in clause.variables]
existential_variable = len(variables) - len(head_variables)
comb = []
for var in variables:
temp = []
for c in constant_terms:
temp.append((var, c))
comb.append(temp)
w = np.power(
len(constants), existential_variable)
x_c = np.zeros((len(dict_valuation), w, 2), dtype=int)
derived_valuation = defaultdict(list)
for elm in product(*comb):
subs = {a[0]: a[1] for a in elm}
substituted_head = Atom(
[subs[term] for term in clause.head.terms], clause.head.predicate)
derived = []
for literal in clause.body:
substituted_body_atom = Atom([subs[term]
for term in literal.terms], literal.predicate)
substituted_body = Literal(substituted_body_atom, literal.negated)
derived.append(int(dict_valuation[substituted_body]))
if derived not in derived_valuation[substituted_head]: # BIG MAYBE
derived_valuation[substituted_head].append(derived)
for literal in derived_valuation:
for i in range(0, len(derived_valuation[literal])):
x_c[dict_valuation[literal], i, 0] = 0
x_c[dict_valuation[literal], i, 0] = derived_valuation[literal][i][0]
x_c[dict_valuation[literal], i, 1] = derived_valuation[literal][i][1]
return x_c
def f_c(clause: Clause, valuations, dict_valuation: dict, constants: list):
'''
Arguments:
clause {Clause} -- clause to generate f_c
valuations {list} -- valuation list
constants {list} -- constants
'''
constant_terms = [Term(False, constant) for constant in constants]
variables = [Term(True, variable) for variable in clause.variables]
comb = []
for var in variables:
temp = []
for c in constant_terms:
temp.append((var, c))
comb.append(temp)
# dict_valuation = {valuations[i][0]:
# valuations[i][1] for i in range(0, len(valuations))}
derived_valuation = defaultdict(list)
derived_valuation2 = defaultdict(list)
for elm in product(*comb):
subs = {a[0]: a[1] for a in elm}
substituted_head = Atom(
[subs[term] for term in clause.head.terms], clause.head.predicate)
derived = []
for atom in clause.body:
substituted_body = Atom([subs[term]
for term in atom.terms], atom.predicate)
derived.append(valuations[dict_valuation[substituted_body]])
derived_valuation[substituted_head].append(
reduce(lambda a, b: a * b, derived, 1))
derived_valuation2[substituted_head].append(derived)
z_c = np.zeros(valuations.shape[0], dtype=np.float32)
for atom in dict_valuation:
if(atom in derived_valuation):
z_c[dict_valuation[atom]] = max(derived_valuation[atom])
else:
z_c[dict_valuation[atom]] = 0
return z_c
def action_switch():
#num = 70
#inx=random.sample(range(0,100),num)
#neg_inx = list(set(range(100))-set(inx))
B_0, P_up, P_sw, P_stay, N_up, N_sw, N_stay = get_B_N_P()
B = [Atom([Term(False, str(x[1]))], x[0]) for x in B_0]
# B = [Atom([Term(False, str(id))], 'busy')
# for id in inx]+ [Atom([Term(False, str(id))], 'NObusy')
# for id in neg_inx]
# print("B..........")
# print(B)
P = [Atom([Term(False, str(id))], 'sw') for id in P_sw]
N = [] #[Atom([Term(False, str(id))], 'sw')
#for id in N_sw]
# print("P..........")
# print(P)
# print("N..........")
# print(N)
term_x_0 = Term(True, 'X')
term_x_1 = Term(True, 'X1')
p_e = [
Atom([term_x_0], 'busy'),
Atom([term_x_0], 'NObusy'),
Atom([term_x_0], 'side'),
Atom([term_x_0], 'front'),
Atom([term_x_0], 'back')
]
#p_a = []
p_a = [Atom([term_x_0], 'pred')]
target = Atom([term_x_0], 'sw')
# target_rule = (Rule_Template(0, False), Rule_Template(1, True))
target_rule = (Rule_Template(0, True), Rule_Template(0, False))
p_a_rule = (Rule_Template(0, False), None)
rules = {p_a[0]: p_a_rule, target: target_rule}
constants = [str(i) for i in range(0, 400)]
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train(steps=252)
def x_c_test():
'''Testing f_c function
'''
term_x = Term(True, 'X_0')
term_y = Term(True, 'X_1')
term_z = Term(True, 'X_2')
r = Atom([term_x, term_y], 'r')
p = Atom([term_x, term_z], 'p')
q = Atom([term_z, term_y], 'q')
clause = Clause(r, [p, q])
# Example from the book
example_val = {
Atom([term_a, term_a], 'p'): 1.0,
Atom([term_a, term_b], 'p'): 0.9,
Atom([term_a, term_a], 'q'): 0.1,
Atom([term_b, term_a], 'q'): 0.2,
Atom([term_b, term_b], 'q'): 0.8,
}
for key in example_val:
initial_valuation[valuation_mapping[key]] = example_val[key]
x_c = Inference.x_c(clause, valuation_mapping, ['a', 'b'])
print(valuation_mapping)
print(x_c)
def f_c_test2():
'''Testing f_c function
'''
term1 = Term(True, 'X_0')
term2 = Term(True, 'X_1')
target = Atom([term1], 'r')
p_e = [Atom([term1, term2], 'p')]
p_a = [Atom([term1, term2], 'q')]
rule_template_1 = Rule_Template(0, False)
rule_template_2 = Rule_Template(1, True)
language_frame = Language_Frame(target, p_e, set(['a', 'b']))
program_template = Program_Template(p_a,
(rule_template_1, rule_template_2),
300)
term_a = Term(False, 'a')
term_b = Term(False, 'b')
# Background facts: p(a,a) p(a,b)
background = [Atom([term_a, term_a], 'p'), Atom([term_a, term_b], 'p')]
# Positive: p(b,a)
positive = [Atom([term_b, term_a], 'p')]
# Negative: p(b,b)
negative = [Atom([term_b, term_b], 'p')]
ilp = ILP(language_frame, background, positive, negative, program_template)
initial_valuation, valuation_mapping = ilp.convert()
term_x = Term(True, 'X_0')
term_y = Term(True, 'X_1')
term_z = Term(True, 'X_2')
r = Atom([term_x], 'r')
p = Atom([term_x, term_z], 'p')
q = Atom([term_z, term_y], 'q')
clause = Clause(r, [p, q])
# Example from the book
example_val = {
Atom([term_a, term_a], 'p'): 1.0,
Atom([term_a, term_b], 'p'): 0.9,
Atom([term_a, term_a], 'q'): 0.1,
Atom([term_b, term_a], 'q'): 0.2,
Atom([term_b, term_b], 'q'): 0.8,
}
for key in example_val:
initial_valuation[valuation_mapping[key]] = example_val[key]
x_c = Inference.x_c(clause, valuation_mapping, ['a', 'b'])
print(valuation_mapping)
print(x_c)
assert False
def odd():
'''
Learn the target predicate of odd(X)
which is true if X is an odd number
'''
B_atom = [Atom([Term(False, '0')], 'zero')] + \
[Atom([Term(False, str(i)), Term(False, str(i + 1))], 'succ')
for i in range(0, 20)]
B = [Literal(atom, False) for atom in B_atom]
# Odd numbers
P_atom = [Atom([Term(False, str(i))], 'target') for i in range(1, 21, 2)]
P = [Literal(atom, False) for atom in P_atom]
# Even numbers
N_atom = [Atom([Term(False, str(i))], 'target') for i in range(0, 21, 2)]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [
Literal(Atom([term_x_0], 'zero'), False),
Literal(Atom([term_x_0, term_x_1], 'succ'), False)
]
p_a = [
Literal(Atom([term_x_0, term_x_1], 'succ2'), False),
Literal(Atom([term_x_0], 'even'), False)
]
target = Literal(Atom([term_x_0], 'target'), False)
constants = [str(i) for i in range(0, 21)]
# Define rules for intensional predicates
succ2_rule = (Rule_Template_Negation(1, False, False), None)
even_rule = (Rule_Template_Negation(0, False, False),
Rule_Template_Negation(1, True, False))
target_rule = (Rule_Template_Negation(0, True, True), None)
rules = {p_a[0]: succ2_rule, p_a[1]: even_rule, target: target_rule}
language_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
# program_template = Program_Template(p_a, rules, 300)
snafdilp = SNAFDILP(language_frame, B, P, N, program_template)
return snafdilp.train(steps=400)
def has_roommate():
'''
Learn the target predicate of has_roommate(X, Y)
which is true if there exists a person Y that X is married to
and Y is not a researcher.
'''
constants = [
'Paul', 'Randy', 'Rachel', 'Bob', 'Alice', 'Steve', 'Frank', 'Julia',
'Stan', 'Kyle', 'Peter', 'Tony', 'Monica', 'Carl', 'Dolores', 'Tommy',
'Pedro', 'Will', 'Sophie', 'Eric', 'Jon', 'Robert', 'Sansa', 'Arya',
'Tormund', 'Mance'
]
# married(X,Y) with symmetric closure
B_atom = [
Atom([Term(False, 'Bob'), Term(False, 'Rachel')], 'married'),
Atom([Term(False, 'Rachel'), Term(False, 'Bob')], 'married'),
Atom([Term(False, 'Randy'), Term(False, 'Alice')], 'married'),
Atom([Term(False, 'Alice'), Term(False, 'Randy')], 'married'),
Atom([Term(False, 'Steve'), Term(False, 'Julia')], 'married'),
Atom([Term(False, 'Julia'), Term(False, 'Steve')], 'married'),
Atom(
[Term(False, 'Frank'), Term(False, 'Monica')], 'married'),
Atom([Term(False, 'Monica'),
Term(False, 'Frank')], 'married'),
Atom([Term(False, 'Stan'), Term(False, 'Dolores')], 'married'),
Atom([Term(False, 'Dolores'),
Term(False, 'Frank')], 'married'),
Atom([Term(False, 'Will'), Term(False, 'Sophie')], 'married'),
Atom(
[Term(False, 'Sophie'), Term(False, 'Will')], 'married'),
Atom([Term(False, 'Eric'), Term(False, 'Arya')], 'married'),
Atom([Term(False, 'Arya'), Term(False, 'Eric')], 'married'),
# researcher(X)
Atom([Term(False, 'Robert')], 'researcher'),
Atom([Term(False, 'Eric')], 'researcher'),
Atom([Term(False, 'Bob')], 'researcher'),
Atom([Term(False, 'Frank')], 'researcher'),
Atom([Term(False, 'Monica')], 'researcher'),
Atom([Term(False, 'Mance')], 'researcher'),
Atom([Term(False, 'Jon')], 'researcher'),
Atom([Term(False, 'Tormund')], 'researcher'),
Atom([Term(False, 'Sansa')], 'researcher')
]
B = [Literal(atom, False) for atom in B_atom]
P_atom = [
Atom([Term(False, 'Randy')], 'target'),
Atom([Term(False, 'Alice')], 'target'),
Atom([Term(False, 'Steve')], 'target'),
Atom([Term(False, 'Julia')], 'target'),
Atom([Term(False, 'Dolores')], 'target'),
Atom([Term(False, 'Stan')], 'target'),
Atom([Term(False, 'Will')], 'target'),
Atom([Term(False, 'Sophie')], 'target')
]
P = [Literal(atom, False) for atom in P_atom]
N_atom = [
Atom([Term(False, 'Bob')], 'target'),
Atom([Term(False, 'Rachel')], 'target'),
Atom([Term(False, 'Frank')], 'target'),
Atom([Term(False, 'Monica')], 'target'),
Atom([Term(False, 'Kyle')], 'target'),
Atom([Term(False, 'Peter')], 'target'),
Atom([Term(False, 'Tony')], 'target'),
Atom([Term(False, 'Carl')], 'target'),
Atom([Term(False, 'Tommy')], 'target'),
Atom([Term(False, 'Pedro')], 'target'),
Atom([Term(False, 'Tommy')], 'target'),
Atom([Term(False, 'Eric')], 'target'),
Atom([Term(False, 'Arya')], 'target'),
Atom([Term(False, 'Jon')], 'target'),
Atom([Term(False, 'Robert')], 'target'),
Atom([Term(False, 'Mance')], 'target'),
Atom([Term(False, 'Tormund')], 'target')
]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [
Literal(Atom([term_x_0, term_x_1], 'married'), False),
Literal(Atom([term_x_0], 'researcher'), False)
]
p_a = [Literal(Atom([term_x_0, term_x_1], 'pred'), False)]
target = Literal(Atom([term_x_0], 'target'), False)
# Define rules for intensional predicates
p_a_rule = (Rule_Template_Negation(0, False, True), None)
target_rule = (Rule_Template_Negation(1, True, False), None)
rules = {p_a[0]: p_a_rule, target: target_rule}
language_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
# program_template = Program_Template(p_a, rules, 300)
snafdilp = SNAFDILP(language_frame, B, P, N, program_template)
return snafdilp.train(steps=200)
def two_children():
'''
Learn the target predicate of has_at_least_two_children(X).
In a directed graph we want to check if a node has at least two children
(that are not equal).
'''
constants = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
edge_relation = [
Atom([Term(False, 'a'), Term(False, 'b')], 'edge'),
Atom([Term(False, 'a'), Term(False, 'c')], 'edge'),
Atom([Term(False, 'b'), Term(False, 'd')], 'edge'),
Atom([Term(False, 'c'), Term(False, 'd')], 'edge'),
Atom([Term(False, 'c'), Term(False, 'e')], 'edge'),
Atom([Term(False, 'd'), Term(False, 'e')], 'edge'),
Atom([Term(False, 'f'), Term(False, 'a')], 'edge'),
Atom([Term(False, 'f'), Term(False, 'b')], 'edge'),
Atom([Term(False, 'g'), Term(False, 'e')], 'edge'),
Atom([Term(False, 'g'), Term(False, 'c')], 'edge')
]
equals_relation = [
Atom([Term(False, x), Term(False, y)], 'equals')
for x, y in zip(constants, constants) if x == y
]
B_atom = edge_relation + equals_relation
B = [Literal(atom, False) for atom in B_atom]
P_atom = [
Atom([Term(False, 'a')], 'target'),
Atom([Term(False, 'c')], 'target'),
Atom([Term(False, 'f')], 'target'),
Atom([Term(False, 'g')], 'target')
]
P = [Literal(atom, False) for atom in P_atom]
N_atom = [
Atom([Term(False, 'b')], 'target'),
Atom([Term(False, 'd')], 'target'),
Atom([Term(False, 'e')], 'target'),
Atom([Term(False, 'h')], 'target')
]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
term_x_2 = Term(True, 'X_2')
p_e = [
Literal(Atom([term_x_0, term_x_1], 'edge'), False),
Literal(Atom([term_x_0, term_x_1], 'equals'), False)
]
p_a = [Literal(Atom([term_x_0, term_x_2], 'pred'), False)]
target = Literal(Atom([term_x_0], 'target'), False)
# Define rules for intensional predicates
p_a_rule = (Rule_Template_Negation(1, False, True), None)
target_rule = (Rule_Template_Negation(1, True, False), None)
rules = {p_a[0]: p_a_rule, target: target_rule}
language_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
# program_template = Program_Template(p_a, rules, 300)
snafdilp = SNAFDILP(language_frame, B, P, N, program_template)
return snafdilp.train(steps=300)
'''File for testing inference functions (Inference.py)
'''
from src.core import Term, Atom, Clause
from src.ilp import Language_Frame, Program_Template, Rule_Template, Inference, ILP
epsilon = 0.001
term1 = Term(True, 'X_0')
term2 = Term(True, 'X_1')
target = Atom([term1, term2], 'r')
p_e = [Atom([term1, term2], 'p')]
p_a = [Atom([term1, term2], 'q')]
rule_template_1 = Rule_Template(0, False)
rule_template_2 = Rule_Template(1, True)
language_frame = Language_Frame(target, p_e, set(['a', 'b']))
program_template = Program_Template(p_a, (rule_template_1, rule_template_2),
300)
term_a = Term(False, 'a')
term_b = Term(False, 'b')
# Background facts: p(a,a) p(a,b)
background = [Atom([term_a, term_a], 'p'), Atom([term_a, term_b], 'p')]
# Positive: p(b,a)
positive = [Atom([term_b, term_a], 'p')]
# Negative: p(b,b)
negative = [Atom([term_b, term_b], 'p')]
ilp = ILP(language_frame, background, positive, negative, program_template)
(initial_valuation, valuation_mapping) = ilp.convert()
B, pred_f, constants_f = process_file('%s/facts.dilp' % sys.argv[1])
P, target_p, constants_p = process_file('%s/positive.dilp' % sys.argv[1])
N, target_n, constants_n = process_file('%s/negative.dilp' % sys.argv[1])
if not (target_p == target_n):
raise Exception('Positive and Negative files have different targets')
elif not len(target_p) == 1:
raise Exception('Can learn only one predicate at a time')
elif not constants_n.issubset(constants_f) or not constants_p.issubset(
constants_f):
raise Exception(
'Constants not in fact file exists in positive/negative file')
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = list(pred_f)
p_a = [Atom([term_x_0, term_x_1], 'pred')]
target = list(target_p)[0]
constants = constants_f
p_a_rule = (Rule_Template(1, False), None)
target_rule = (Rule_Template(0, False), Rule_Template(1, True))
rules = {p_a[0]: p_a_rule, target: target_rule}
langage_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
dilp = DILP(langage_frame, B, P, N, program_template)
dilp.train()
def generate_clauses(self):
'''Generate all clauses with some level of optimization
'''
rule_matrix = []
for rule in self.rules:
# logger.info('Generating clauses')
if rule is None:
rule_matrix.append([None])
continue
clauses = []
if rule.allow_intensional:
p = list(set(self.p_e + self.p_i + [self.target]))
p_i = list(set(self.p_i))
intensional_predicates = [atom.predicate for atom in p_i]
else:
p = list(set(self.p_e))
variables = ['X_%d' %
i for i in range(0, self.target.arity + rule.v)]
target_variables = ['X_%d' %
i for i in range(0, self.target.arity)]
# Generate the body list
body_list = []
head = Atom(
[Term(True, var) for var in target_variables], self.target.predicate)
for var1 in variables:
for var2 in variables:
term1 = Term(True, var1)
term2 = Term(True, var2)
body_list.append([term1, term2])
# Generate the list
added_pred = {}
for ind1 in range(0, len(p)):
pred1 = p[ind1]
for b1 in body_list:
for ind2 in range(ind1, len(p)):
pred2 = p[ind2]
for b2 in body_list:
for negations in range(4):
if not rule.neg and negations > 0:
continue
body1_atom = Atom([b1[index]
for index in range(0, pred1.arity)], pred1.predicate)
body2_atom = Atom([b2[index]
for index in range(0, pred2.arity)], pred2.predicate)
if negations == 0: # No negation
body1 = Literal(body1_atom, False)
body2 = Literal(body2_atom, False)
if negations == 1: # First body is negated
body1 = Literal(body1_atom, True)
body2 = Literal(body2_atom, False)
if negations == 2: # Second body is negated
body1 = Literal(body1_atom, False)
body2 = Literal(body2_atom, True)
if negations == 3: # Both bodies are negated
body1 = Literal(body1_atom, True)
body2 = Literal(body2_atom, True)
clause = Clause(head, [body1, body2])
# logger.info(clause)
# All variables in head should be in the body
if not set(target_variables).issubset([v.name for v in b1] + [v.name for v in b2]):
continue
elif head == body1 or head == body2: # No Circular
continue
# NOTE: Based on appendix requires to have a intensional predicate
elif rule.allow_intensional and not (body1.predicate in intensional_predicates or body2.predicate in intensional_predicates):
continue
elif clause in added_pred:
continue
# Disallow a clause with the head negated in the body
elif head.predicate == body1.predicate and body1.negated:
continue
elif head.predicate == body2.predicate and body2.negated:
continue
# Disallow positive and negative versions of the same literal in a clause
elif body1_atom == body2_atom and body1.negated != body2.negated:
# If the variables of the clause are different then allow this clause
variables1 = body1_atom.terms
variables2 = body2_atom.terms
add_clause = True
for i in range(len(variables1)):
if variables1[i] != variables2[i]:
add_clause = False
break
if not add_clause:
continue
else:
added_pred[clause] = 1
clauses.append(clause)
rule_matrix.append(clauses)
# logger.info('Clauses Generated')
return rule_matrix
def no_negative_cycle():
'''
Learn the target predicate no_negative_cycle(X). The predicate checks if
a given node in a graph is part of a negative cycle (cycle with at least one
negative edge).
'''
constants = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
edge_relations = [
Atom([Term(False, 'a'), Term(False, 'b')], 'edge'),
Atom([Term(False, 'a'), Term(False, 'c')], 'edge'),
Atom([Term(False, 'b'), Term(False, 'c')], 'edge'),
Atom([Term(False, 'c'), Term(False, 'd')], 'edge'),
Atom([Term(False, 'c'), Term(False, 'e')], 'edge'),
Atom([Term(False, 'd'), Term(False, 'h')], 'edge'),
Atom([Term(False, 'h'), Term(False, 'd')], 'edge'),
Atom([Term(False, 'f'), Term(False, 'a')], 'edge'),
Atom([Term(False, 'f'), Term(False, 'b')], 'edge'),
Atom([Term(False, 'g'), Term(False, 'e')], 'edge'),
Atom([Term(False, 'g'), Term(False, 'c')], 'edge'),
Atom([Term(False, 'e'), Term(False, 'a')], 'edge')
]
edge_type = [
Atom([Term(False, 'a'), Term(False, 'b')], 'positive'),
Atom([Term(False, 'a'), Term(False, 'c')], 'negative'),
Atom([Term(False, 'b'), Term(False, 'c')], 'positive'),
Atom([Term(False, 'c'), Term(False, 'd')], 'positive'),
Atom([Term(False, 'c'), Term(False, 'e')], 'negative'),
Atom([Term(False, 'd'), Term(False, 'e')], 'positive'),
Atom([Term(False, 'h'), Term(False, 'd')], 'positive'),
Atom([Term(False, 'f'), Term(False, 'a')], 'positive'),
Atom([Term(False, 'f'), Term(False, 'b')], 'positive'),
Atom([Term(False, 'g'), Term(False, 'e')], 'positive'),
Atom([Term(False, 'g'), Term(False, 'c')], 'positive'),
Atom([Term(False, 'e'), Term(False, 'a')], 'positive')
]
B_atom = edge_relations + edge_type
B = [Literal(atom, False) for atom in B_atom]
P_atom = [
Atom([Term(False, 'f')], 'target'),
Atom([Term(False, 'g')], 'target'),
Atom([Term(False, 'd')], 'target'),
Atom([Term(False, 'h')], 'target')
]
P = [Literal(atom, False) for atom in P_atom]
N_atom = [
Atom([Term(False, 'a')], 'target'),
Atom([Term(False, 'b')], 'target'),
Atom([Term(False, 'c')], 'target'),
Atom([Term(False, 'e')], 'target')
]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
term_x_2 = Term(True, 'X_2')
p_e = [
Literal(Atom([term_x_0, term_x_1], 'edge'), False),
Literal(Atom([term_x_0, term_x_1], 'positive'), False),
Literal(Atom([term_x_0, term_x_1], 'negative'), False)
]
p_a = [Literal(Atom([term_x_0, term_x_2], 'pred'), False)]
target = Literal(Atom([term_x_0], 'target'), False)
# Define rules for intensional predicates
p_a_rule = [(Rule_Template_Negation(0, False, True),
Rule_Template_Negation(1, True, False))]
target_rule = (Rule_Template_Negation(0, True, True), None)
rules = {p_a[0]: p_a_rule[0], target: target_rule}
language_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
# program_template = Program_Template(p_a, rules, 300)
snafdilp = SNAFDILP(language_frame, B, P, N, program_template)
return snafdilp.train(steps=300)
def innocent():
'''
Learn the target predicate of innocent(X)
which is true if X is not guilty
'''
constants = [
'Paul', 'Randy', 'Rachel', 'Bob', 'Alice', 'Stan', 'Kyle', 'Peter',
'Tony', 'Monica'
]
B_atom = [
Atom([Term(False, 'Bob')], 'guilty'),
Atom([Term(False, 'Randy')], 'guilty'),
Atom([Term(False, 'Peter')], 'guilty'),
Atom([Term(False, 'Alice')], 'guilty'),
Atom([Term(False, 'Monica')], 'guilty')
]
B = [Literal(atom, False) for atom in B_atom]
P_atom = [
Atom([Term(False, 'Paul')], 'target'),
Atom([Term(False, 'Rachel')], 'target'),
Atom([Term(False, 'Kyle')], 'target'),
Atom([Term(False, 'Tony')], 'target'),
Atom([Term(False, 'Stan')], 'target')
]
P = [Literal(atom, False) for atom in P_atom]
N_atom = [
Atom([Term(False, 'Bob')], 'target'),
Atom([Term(False, 'Randy')], 'target'),
Atom([Term(False, 'Peter')], 'target'),
Atom([Term(False, 'Alice')], 'target'),
Atom([Term(False, 'Monica')], 'target')
]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
p_e = [Literal(Atom([term_x_0], 'guilty'), False)]
p_a = []
target = Literal(Atom([term_x_0], 'target'), False)
# Define rules for intensional predicates
p_a_rule = (None, None)
target_rule = (Rule_Template_Negation(0, False, True), None)
rules = {target: target_rule}
language_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
# program_template = Program_Template(p_a, rules, 300)
snafdilp = SNAFDILP(language_frame, B, P, N, program_template)
return snafdilp.train(steps=200)
def can_fly():
'''
Learn the target predicate of can_fly(X)
which is true if X is a bird, but not an abnormal bird
'''
constants = [
'bluebird', 'penguin', 'ostrich', 'blackbird', 'robin', 'sparrow',
'starling', 'chicken', 'kiwi', 'steamerduck', 'kakapo', 'takahe',
'weka', 'pigeon', 'swan', 'duck', 'goldfinch', 'woodpecker', 'bluetit',
'greattit', 'puddle', 'forrestcat', 'pitbull', 'goldenretriever',
'perser', 'bengal', 'siamese', 'sphynx', 'ragdoll', 'savannah',
'sibiriancat', 'greyhound', 'malteser', 'dobermann', 'rottweiler',
'bostonterrier', 'scottishfold', 'exotic', 'russianblue'
]
B_atom = [ # birds
Atom([Term(False, 'bluebird')], 'is_bird'),
Atom([Term(False, 'woodpecker')], 'is_bird'),
Atom([Term(False, 'penguin')], 'is_bird'),
Atom([Term(False, 'bluetit')], 'is_bird'),
Atom([Term(False, 'greattit')], 'is_bird'),
Atom([Term(False, 'ostrich')], 'is_bird'),
Atom([Term(False, 'blackbird')], 'is_bird'),
Atom([Term(False, 'robin')], 'is_bird'),
Atom([Term(False, 'sparrow')], 'is_bird'),
Atom([Term(False, 'starling')], 'is_bird'),
Atom([Term(False, 'chicken')], 'is_bird'),
Atom([Term(False, 'kiwi')], 'is_bird'),
Atom([Term(False, 'steamerduck')], 'is_bird'),
Atom([Term(False, 'kakapo')], 'is_bird'),
Atom([Term(False, 'takahe')], 'is_bird'),
Atom([Term(False, 'weka')], 'is_bird'),
Atom([Term(False, 'pigeon')], 'is_bird'),
Atom([Term(False, 'swan')], 'is_bird'),
Atom([Term(False, 'duck')], 'is_bird'),
Atom([Term(False, 'goldfinch')], 'is_bird'),
# abnormal birds
Atom([Term(False, 'penguin')], 'abnormal_bird'),
Atom([Term(False, 'ostrich')], 'abnormal_bird'),
Atom([Term(False, 'chicken')], 'abnormal_bird'),
Atom([Term(False, 'kiwi')], 'abnormal_bird'),
Atom([Term(False, 'steamerduck')], 'abnormal_bird'),
Atom([Term(False, 'kakapo')], 'abnormal_bird'),
Atom([Term(False, 'takahe')], 'abnormal_bird'),
Atom([Term(False, 'weka')], 'abnormal_bird')
]
B = [Literal(atom, False) for atom in B_atom]
P_atom = [
Atom([Term(False, 'bluebird')], 'target'),
Atom([Term(False, 'blackbird')], 'target'),
Atom([Term(False, 'robin')], 'target'),
Atom([Term(False, 'sparrow')], 'target'),
Atom([Term(False, 'starling')], 'target'),
Atom([Term(False, 'pigeon')], 'target'),
Atom([Term(False, 'swan')], 'target'),
Atom([Term(False, 'duck')], 'target'),
Atom([Term(False, 'goldfinch')], 'target'),
Atom([Term(False, 'woodpecker')], 'target'),
Atom([Term(False, 'bluetit')], 'target'),
Atom([Term(False, 'greattit')], 'target')
]
P = [Literal(atom, False) for atom in P_atom]
N_atom = [
Atom([Term(False, 'penguin')], 'target'),
Atom([Term(False, 'chicken')], 'target'),
Atom([Term(False, 'kiwi')], 'target'),
Atom([Term(False, 'steamerduck')], 'target'),
Atom([Term(False, 'takahe')], 'target'),
Atom([Term(False, 'puddle')], 'target'),
Atom([Term(False, 'siamese')], 'target'),
Atom([Term(False, 'forrestcat')], 'target'),
Atom([Term(False, 'pitbull')], 'target'),
Atom([Term(False, 'sphynx')], 'target'),
Atom([Term(False, 'sibiriancat')], 'target'),
Atom([Term(False, 'greyhound')], 'target'),
Atom([Term(False, 'russianblue')], 'target')
]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
p_e = [
Literal(Atom([term_x_0], 'is_bird'), False),
Literal(Atom([term_x_0], 'abnormal_bird'), False)
]
p_a = []
target = Literal(Atom([term_x_0], 'target'), False)
# Define rules for intensional predicates
p_a_rule = (None, None)
target_rule = (Rule_Template_Negation(0, False, True), None)
rules = {target: target_rule}
language_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
# program_template = Program_Template(p_a, rules, 300)
snafdilp = SNAFDILP(language_frame, B, P, N, program_template)
return snafdilp.train(steps=200)
def generate_clauses(self):
'''Generate all clauses with some level of optimization
'''
rule_matrix = []
for rule in self.rules:
# logger.info('Generating clauses')
if rule == None:
rule_matrix.append([None])
continue
clauses = []
if (rule.allow_intensional):
p = list(set(self.p_e + self.p_i + [self.target]))
p_i = list(set(self.p_i))
intensional_predicates = [atom.predicate for atom in p_i]
else:
p = list(set(self.p_e))
variables = [
'X_%d' % i for i in range(0, self.target.arity + rule.v)
]
target_variables = [
'X_%d' % i for i in range(0, self.target.arity)
]
# Generate the body list
body_list = []
head = Atom([Term(True, var) for var in target_variables],
self.target.predicate)
for var1 in variables:
for var2 in variables:
term1 = Term(True, var1)
term2 = Term(True, var2)
body_list.append([term1, term2])
# Generate the list
added_pred = {}
for ind1 in range(0, len(p)):
pred1 = p[ind1]
for b1 in body_list:
for ind2 in range(ind1, len(p)):
pred2 = p[ind2]
for b2 in body_list:
body1 = Atom(
[b1[index] for index in range(0, pred1.arity)],
pred1.predicate)
body2 = Atom(
[b2[index] for index in range(0, pred2.arity)],
pred2.predicate)
clause = Clause(head, [body1, body2])
# logger.info(clause)
# All variables in head should be in the body
if not set(target_variables).issubset(
[v.name for v in b1] + [v.name for v in b2]):
continue
elif head == body1 or head == body2: # No Circular
continue
# NOTE: Based on appendix requires to have a intensional predicate
elif rule.allow_intensional and not (
body1.predicate in intensional_predicates
or body2.predicate
in intensional_predicates):
continue
elif clause in added_pred:
continue
else:
added_pred[clause] = 1
clauses.append(clause)
rule_matrix.append(clauses)
# logger.info('Clauses Generated')
return rule_matrix
def orphan():
'''
Learn the target predicate of orphan(X)
which is true if X has no parent
'''
constants = [
'Paul', 'Randy', 'Rachel', 'Bob', 'Alice', 'Steve', 'Frank', 'Julia',
'Stan', 'Kyle', 'Peter', 'Tony', 'Monica', 'Carl', 'Dolores', 'Tommy',
'Pedro', 'Will', 'Sophie', 'Eric', 'Jon', 'Robert', 'Sansa', 'Arya',
'Tormund', 'Mance'
]
# parent(X, Y) is true if X is the father of Y
B_atom = [
Atom([Term(False, 'Bob'), Term(False, 'Paul')], 'parent'),
Atom([Term(False, 'Bob'), Term(False, 'Alice')], 'parent'),
Atom([Term(False, 'Randy'), Term(False, 'Tony')], 'parent'),
Atom([Term(False, 'Randy'), Term(False, 'Stan')], 'parent'),
Atom([Term(False, 'Peter'), Term(False, 'Kyle')], 'parent'),
Atom([Term(False, 'Alice'), Term(False, 'Kyle')], 'parent'),
Atom([Term(False, 'Tony'), Term(False, 'Carl')], 'parent'),
Atom([Term(False, 'Dolores'),
Term(False, 'Tony')], 'parent'),
Atom([Term(False, 'Stan'), Term(False, 'Julia')], 'parent'),
Atom([Term(False, 'Julia'), Term(False, 'Tommy')], 'parent'),
Atom([Term(False, 'Kyle'), Term(False, 'Tommy')], 'parent'),
Atom([Term(False, 'Carl'), Term(False, 'Sophie')], 'parent'),
Atom([Term(False, 'Carl'), Term(False, 'Eric')], 'parent'),
Atom([Term(False, 'Tommy'), Term(False, 'Arya')], 'parent'),
Atom([Term(False, 'Tommy'),
Term(False, 'Tormund')], 'parent'),
Atom([Term(False, 'Tommy'), Term(False, 'Mance')], 'parent')
]
B = [Literal(atom, False) for atom in B_atom]
P_atom = [
Atom([Term(False, 'Randy')], 'target'),
Atom([Term(False, 'Bob')], 'target'),
Atom([Term(False, 'Peter')], 'target'),
Atom([Term(False, 'Rachel')], 'target'),
Atom([Term(False, 'Monica')], 'target'),
Atom([Term(False, 'Dolores')], 'target'),
Atom([Term(False, 'Steve')], 'target'),
Atom([Term(False, 'Frank')], 'target'),
Atom([Term(False, 'Pedro')], 'target'),
Atom([Term(False, 'Will')], 'target'),
Atom([Term(False, 'Jon')], 'target'),
Atom([Term(False, 'Robert')], 'target'),
Atom([Term(False, 'Sansa')], 'target'),
]
P = [Literal(atom, False) for atom in P_atom]
N_atom = [
Atom([Term(False, 'Paul')], 'target'),
Atom([Term(False, 'Alice')], 'target'),
Atom([Term(False, 'Kyle')], 'target'),
Atom([Term(False, 'Tony')], 'target'),
Atom([Term(False, 'Stan')], 'target'),
Atom([Term(False, 'Carl')], 'target'),
Atom([Term(False, 'Julia')], 'target'),
Atom([Term(False, 'Tommy')], 'target'),
Atom([Term(False, 'Sophie')], 'target'),
Atom([Term(False, 'Eric')], 'target'),
Atom([Term(False, 'Arya')], 'target'),
Atom([Term(False, 'Tormund')], 'target'),
Atom([Term(False, 'Mance')], 'target')
]
N = [Literal(atom, False) for atom in N_atom]
term_x_0 = Term(True, 'X_0')
term_x_1 = Term(True, 'X_1')
p_e = [Literal(Atom([term_x_0, term_x_1], 'parent'), False)]
p_a = []
target = Literal(Atom([term_x_0], 'target'), False)
# Define rules for intensional predicates
p_a_rule = (None, None)
target_rule = (Rule_Template_Negation(1, False, True), None)
rules = {target: target_rule}
language_frame = Language_Frame(target, p_e, constants)
program_template = Program_Template(p_a, rules, 10)
# program_template = Program_Template(p_a, rules, 300)
snafdilp = SNAFDILP(language_frame, B, P, N, program_template)
return snafdilp.train(steps=200)