def answerQuestion(peer_name, net, user_password):
policy_filename = 'entities/' + peer_name + '/datalog_policy.data'
data_filename = 'entities/' + peer_name + '/data.data'
queryString = net.recv();
queryList = queryString.split(network_protocol.SEPARATOR)
print(queryList)
trusted_peer_dict = getTrustedPeers(peer_name)
for other_peer, trusted_with in trusted_peer_dict.items():
data = getDataFromTrustedPeer(peer_name, other_peer, user_password)
pruned_data = prune(data, trusted_with)
add_facts(pruned_data)
# Add our own data
if(fileIO.fileExists(data_filename)):
add_facts(fileIO.readFile(data_filename))
pyDatalog.load(fileIO.readFile(policy_filename))
print("Asking question: " + queryList[0])
query_result = pyDatalog.ask(queryList[0]);
print('Query result:',query_result)
if(query_result != None):
net.send(queryList[1])
else:
net.send(queryList[2])
def assertDefaultPolicy(self,direction):
if not self.assertedDefaultPolicy:
last_clause = self.clauses[-1]
if len(last_clause.matchers) == 0:
for a in last_clause.actions:
pyDatalog.load("+(default%sPolicy(%s,%s,%s))" % (direction,self.name,a.getActionTuple()[0],a.getActionTuple()[1]))
self.assertedDefaultPolicy = True
def load(self, console):
"""Loads the clauses from the file"""
start = time.time()
pyDatalog.load(self.data)
if console:
console.print(self.included_files,
"Finished ", time.time() - start, " s")
def fillComplexRules(parent_child, child_parent, all_relations, relations):
rules = ""
#Originated
for relation in parent_child:
if relations[relation]:
rules += 'originated(X, Xi, Y, Yi) <= ' + relation + '(X, Xi, Y, Yi )\n'
for relation in child_parent:
if relations[relation]:
if relation == "etymology":
rules += 'originated(X, Xi, Y, Yi) <= etymological_origin_of(Y, Yi, X, Xi)\n'
elif relation == "is_derived_from":
rules += 'originated(X, Xi, Y, Yi) <= has_derived_form(Y, Yi, X, Xi)\n'
else:
rules += 'originated(X, Xi, Y, Yi) <= ' + relation + '(Y, Yi, X, Xi)\n'
#Listing
for relation in all_relations:
if relations[relation]:
if relation == "etymology":
rules += 'listing(Xi, Y, Yi) <= ~listing(Xi, Y, Yi) & etymological_origin_of(X, Xi, Y, Yi)\n'
rules += 'listing(Xi, Y, Yi) <= ~listing(Xi, Y, Yi) & etymological_origin_of(Y, Yi, X, Xi)\n'
elif relation == "is_derived_from":
rules += 'listing(Xi, Y, Yi) <= ~listing(Xi, Y, Yi) & has_derived_form(X, Xi, Y, Yi)\n'
rules += 'listing(Xi, Y, Yi) <= ~listing(Xi, Y, Yi) & has_derived_form(Y, Yi, X, Xi)\n'
else:
rules += 'listing(Xi, Y, Yi) <= ~listing(Xi, Y, Yi) & ' + relation + '(X, Xi, Y, Yi)\n'
rules += 'listing(Xi, Y, Yi) <= ~listing(Xi, Y, Yi) & ' + relation + '(Y, Yi, X, Xi)\n'
pyDatalog.load(rules)
def load_logic():
pyDatalog.load("""
mother(X, Y) <= (parent(X, Y) & gender(X, 'Female'))
father(X, Y) <= (parent(X, Y) & gender(X, 'Male'))
married(X,Y) <= married(Y, X)
husband(X,Y) <= married(X, Y) & gender(X, 'Male')
wife(X,Y) <= husband(Y, X)
child(Y, X) <= parent(X, Y)
parent(X,Y) <= child(Y,X)
decendent(X, Y) <= ancestor(Y, X)
sibling(X,Y) <= (father(Z, X) & father(Z, Y) & ~(X==Y)) & (mother(W, X) & mother(W, Y) & ~(X==Y))
cousin(X, Y) <= parent(Z, X) & parent(W,Y) & sibling(Z, W)
aunt(X,Y) <= parent(Z, Y) & sibling(Z, X) & gender(X, 'Female')
aunt(X,Y) <= uncle(Z, Y) & married(Z, X)
uncle(X,Y) <= parent(Z, Y) & sibling(Z, X) & gender(X, 'Male')
uncle(X,Y) <= aunt(Z, Y) & married(Z, X)
ancestor(X, Y) <= (parent(W,Y) & (X==W))
ancestor(X, Y) <= (parent(W,Y) & ancestor(X, W))
brother(X,Y) <= sibling(X,Y) & gender(X, 'Male')
sister(X, Y) <= sibling(X,Y) & gender(X, 'Female')
inbred(X) <= (father(Y, X) & buendia_blood(Y, True)) &(mother(W, X) & buendia_blood(W, True))
bastard(X) <= father(W,X) & mother(Y, X) & ~married(W, Y)
buendia_blood(X, True) <= parent(Y, X) & buendia_blood(Y, True)
buendia(X, True) <= buendia_blood(X, True)
buendia(X, True) <= married(X, Y) & buendia_blood(Y, True)
son(X, Y) <= child(X, Y) & gender(X, 'Male')
daughter(X, Y) <= child(X, Y) & gender(X, 'Female')
""")
def __init__(self):
print('Base de conocimientos')
self.conocimiento=Logic(True)
pyDatalog.load("""
+ usar_computadora('experto','si')
+ usar_computadora('inexperto','no')
es_experto(Y) <= usar_computadora(Y,Z) & resp_experto(Z)
# Es médico
+ terminologia('medico','si')
+ pacientes('medico','si')
+ recetar('medico','si')
# No es médico
+ terminologia('persona','no')
+ pacientes('persona','no')
+ recetar('persona','no')
es_medico(X) <= terminologia(X,A) & pacientes(X,A) & recetar(X,A) & resp_medico(A)
tipo_usuario(X,Y) <= es_medico(X) & es_experto(Y)
""")
def materialize(facts, clauses, parser):
logger.info('Asserting facts ..')
for f in facts:
# Each fact is asserted using the index of the subject, predicate and object for avoiding syntax issues
s_idx = parser.entity_to_index[f.argument_names[0]]
p_idx = parser.predicate_to_index[f.predicate_name]
o_idx = parser.entity_to_index[f.argument_names[1]]
# Asserting p(S, P, O)
pyDatalog.assert_fact('p', s_idx, p_idx, o_idx)
rules_str = '\n'.join(
[clause_to_str(clause, parser) for clause in clauses])
pyDatalog.load(rules_str)
# Asking for all P(s, p, o) triples which hold true in the Knowledge Graph
logger.info('Querying triples ..')
_ans = pyDatalog.ask('p(S, P, O)')
index_to_predicate = {
idx: p
for p, idx in parser.predicate_to_index.items()
}
index_to_entity = {idx: e for e, idx in parser.entity_to_index.items()}
# Generating a list of inferred facts by replacing each entity and predicate index with their corresponding symbols
inferred_facts = [
Fact(index_to_predicate[p], [index_to_entity[s], index_to_entity[o]])
for (s, p, o) in sorted(_ans.answers)
]
return inferred_facts
def loadFacts(fileName): """ Convenience function to open a fact file and load it into the LogKB. """ facts = open(fileName) pyDatalog.load(facts.read()) facts.close()
def loadFacts(fileName):
"""
Convenience function to open a fact file and load it into the LogKB.
"""
facts = open(fileName)
pyDatalog.load(facts.read())
facts.close()
def _(): # the function name is ignored
pyDatalog.load(mc)
#pyDatalog.load("""
#+ (factorial[1]==1)
#(factorial[N] == F) <= (N > 1) & (F == N*factorial[N-1])
#""")
print(pyDatalog.ask('factorial[4]==F'))
def __init__(self, rulesPath='../user.rules'):
Logic()
with open(rulesPath, 'r') as f:
rules = "\n".join(f.readlines())
load(rules)
print "Rules for the user loaded"
print rules
def __init__(self, rulesPath='../user.rules'):
Logic()
with open(rulesPath, 'r') as f:
rules = "\n".join(f.readlines())
load(rules)
print "Rules for the user loaded"
print rules
def setup(instanceFile):
pyDatalog.load(open(instanceFile,"r").read())
#Rules for the Architectural Style
pyDatalog.load(open("architectureStyle.py","r").read())
#Rules for Vulnerabilities (Architectural Style)
pyDatalog.load(open("vulnerabilityStyle.py","r").read())
#Rules for Functionalities
pyDatalog.load(open("functionalStyle.py","r").read())
#Make sure every component has every plausible vulnerability
pyDatalog.load(open("applyAllVulnerabilities.py","r").read())
def assert_error(code, message='^$'):
_error = False
try:
pyDatalog.load(code)
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0] # python 2 and 3
if not re.match(message, e_message):
print(e_message)
_error = True
assert _error
def assert_error(code, message='^$'):
_error = False
try:
pyDatalog.load(code)
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0] # python 2 and 3
if not re.match(message, e_message):
print(e_message)
_error = True
assert _error
def load(self, console):
"""Loads the clauses from the file"""
start = time.time()
try:
pyDatalog.load(self.data)
if console:
console.print(self.included_files,
"Finished ", time.time() - start, " s")
except BaseException as exception:
print(exception)
def test_sudoku(self):
from pyDatalog import pyDatalog
from examples.RLP import sudoku_example
from reloop.languages.rlp.logkb import PyDatalogLogKb
for u in range(1, 10):
pyDatalog.assert_fact('num', u)
for u in range(1, 4):
pyDatalog.assert_fact('boxind', u)
pyDatalog.assert_fact('initial', 1, 1, 5)
pyDatalog.assert_fact('initial', 2, 1, 6)
pyDatalog.assert_fact('initial', 4, 1, 8)
pyDatalog.assert_fact('initial', 5, 1, 4)
pyDatalog.assert_fact('initial', 6, 1, 7)
pyDatalog.assert_fact('initial', 1, 2, 3)
pyDatalog.assert_fact('initial', 3, 2, 9)
pyDatalog.assert_fact('initial', 7, 2, 6)
pyDatalog.assert_fact('initial', 3, 3, 8)
pyDatalog.assert_fact('initial', 2, 4, 1)
pyDatalog.assert_fact('initial', 5, 4, 8)
pyDatalog.assert_fact('initial', 8, 4, 4)
pyDatalog.assert_fact('initial', 1, 5, 7)
pyDatalog.assert_fact('initial', 2, 5, 9)
pyDatalog.assert_fact('initial', 4, 5, 6)
pyDatalog.assert_fact('initial', 6, 5, 2)
pyDatalog.assert_fact('initial', 8, 5, 1)
pyDatalog.assert_fact('initial', 9, 5, 8)
pyDatalog.assert_fact('initial', 2, 6, 5)
pyDatalog.assert_fact('initial', 5, 6, 3)
pyDatalog.assert_fact('initial', 8, 6, 9)
pyDatalog.assert_fact('initial', 7, 7, 2)
pyDatalog.assert_fact('initial', 3, 8, 6)
pyDatalog.assert_fact('initial', 7, 8, 8)
pyDatalog.assert_fact('initial', 9, 8, 7)
pyDatalog.assert_fact('initial', 4, 9, 3)
pyDatalog.assert_fact('initial', 5, 9, 1)
pyDatalog.assert_fact('initial', 6, 9, 6)
pyDatalog.assert_fact('initial', 8, 9, 5)
pyDatalog.load("""
box(I, J, U, V) <= boxind(U) & boxind(V) & num(I) & num(J) & (I > (U-1)*3) & (I <= U*3) & (J > (V-1)*3) & (J <= V*3)
""")
logkb = PyDatalogLogKb()
grounder = BlockGrounder(logkb)
# Note: CVXOPT needs to be compiled with glpk support. See the CVXOPT documentation.
solver = CvxoptSolver(solver_solver='glpk')
model = sudoku_example.sudoku(grounder, solver)
self.assertEqual(model, 0, "ERROR : Sudoku couldn't be solved")
def test_sudoku(self):
from pyDatalog import pyDatalog
from examples.RLP import sudoku_example
from reloop.languages.rlp.logkb import PyDatalogLogKb
for u in range(1, 10):
pyDatalog.assert_fact('num', u)
for u in range(1, 4):
pyDatalog.assert_fact('boxind', u)
pyDatalog.assert_fact('initial', 1, 1, 5)
pyDatalog.assert_fact('initial', 2, 1, 6)
pyDatalog.assert_fact('initial', 4, 1, 8)
pyDatalog.assert_fact('initial', 5, 1, 4)
pyDatalog.assert_fact('initial', 6, 1, 7)
pyDatalog.assert_fact('initial', 1, 2, 3)
pyDatalog.assert_fact('initial', 3, 2, 9)
pyDatalog.assert_fact('initial', 7, 2, 6)
pyDatalog.assert_fact('initial', 3, 3, 8)
pyDatalog.assert_fact('initial', 2, 4, 1)
pyDatalog.assert_fact('initial', 5, 4, 8)
pyDatalog.assert_fact('initial', 8, 4, 4)
pyDatalog.assert_fact('initial', 1, 5, 7)
pyDatalog.assert_fact('initial', 2, 5, 9)
pyDatalog.assert_fact('initial', 4, 5, 6)
pyDatalog.assert_fact('initial', 6, 5, 2)
pyDatalog.assert_fact('initial', 8, 5, 1)
pyDatalog.assert_fact('initial', 9, 5, 8)
pyDatalog.assert_fact('initial', 2, 6, 5)
pyDatalog.assert_fact('initial', 5, 6, 3)
pyDatalog.assert_fact('initial', 8, 6, 9)
pyDatalog.assert_fact('initial', 7, 7, 2)
pyDatalog.assert_fact('initial', 3, 8, 6)
pyDatalog.assert_fact('initial', 7, 8, 8)
pyDatalog.assert_fact('initial', 9, 8, 7)
pyDatalog.assert_fact('initial', 4, 9, 3)
pyDatalog.assert_fact('initial', 5, 9, 1)
pyDatalog.assert_fact('initial', 6, 9, 6)
pyDatalog.assert_fact('initial', 8, 9, 5)
pyDatalog.load("""
box(I, J, U, V) <= boxind(U) & boxind(V) & num(I) & num(J) & (I > (U-1)*3) & (I <= U*3) & (J > (V-1)*3) & (J <= V*3)
""")
logkb = PyDatalogLogKb()
grounder = BlockGrounder(logkb)
# Note: CVXOPT needs to be compiled with glpk support. See the CVXOPT documentation.
solver = CvxoptSolver(solver_solver='glpk')
model = sudoku_example.sudoku(grounder, solver)
self.assertEqual(model, 0, "ERROR : Sudoku couldn't be solved")
def determineResidualUtilityOnceTest(rD,query="",debug=True):
rMax = addRisksToLogic(rD)
maxUtility = sumAllUtilities()
#rMax = 4
for c,p in rD.items():
s = "probCapability[" + str(c) + "] = " + str(p)
pyDatalog.load(s)
#This needs to change
if query == "":
query = "attackPaths(SourceService,TargetService,P,E,AttackerMoves,TotalC)"
#query = "worstCasePath[\"3\"] == Y"
#query = "worstCasePath[TotalC] == Y"
#query = "worstCasePathCombo[X,Combo] == Y"
#query = "worstCasePathCombo[" + str(3) + ",[businessWorkstations]" + "] == Y"
print("Query: " + query)
#query = "worstCasePathSpecific[X,SourceService]==Y"
utilitiesByAttackerCapabilityAnswers = pyDatalog.ask(query)
if utilitiesByAttackerCapabilityAnswers != None:
utilitiesByAttackerCapability = utilitiesByAttackerCapabilityAnswers.answers
estimatedValue = 0
if debug:
print("Query calculated.")
if utilitiesByAttackerCapabilityAnswers != None:
if debug:
print("Query Answers:")
#print("Utilities compromised by attacker capability:")
#pprint.pprint(utilitiesByAttackerCapability)
if len(utilitiesByAttackerCapability) > 0:
#print(sorted(utilitiesByAttackerCapability,key=itemgetter(0)))
pprint.pprint(sorted(utilitiesByAttackerCapability,key=itemgetter(0)))
else:
print("No answers")
#print("Number of items: " + str(len(utilitiesByAttackerCapability)))
#Undo highlight later
#estimatedValue = sum(map(itemgetter(1),utilitiesByAttackerCapability))
#for capability in range(rMax+1):
#query2 = "worstCasePath[" + str(capability) + "] == X"
#wCPsAnswers = pyDatalog.ask(query2)
#if wCPsAnswers != None:
#wCPs = wCPsAnswers.answers
#print "************"
#print("Worst case paths by attacker capability " + str(capability) + ": ")
#pprint.pprint(str(wCPs))
#Undo highlight later
#estimatedValue = maxUtility - estimatedValue
#if debug:
# print("Single Scenario Expected Value: " + str(estimatedValue))
return estimatedValue
def alimentarBD():
load("""
hijo2(P,H,R) <= padre(X,I,X2,H) & (I==P)
hermano(X, Y, H) <= padre(Z, X) & padre (Z,Y) & (X!=Y)
hijo(X, Y, H) <= padre(Z,Y) & (X==Z)
tio(T, S, H) <= padre(P, S) & hermano(P, T, A) & (P!=T)
primo(X,Y,R) <= padre(Q,X) & padre(Z,Y) & (Q!=Y) & hermano(Q,Z,R)
primoGrado(X,Y,H) <= padre(P,X) & padre(A,Y) & hermano(A,P,H)
primoGrado(X,Y,R) <= padre(P,X) & padre(A,Y) & hermano(P, A, Q) &primoGrado(A,P,H)
""")
def runsource(self, source, filename='console', symbol='single'):
pySource = """
pyDatalog.load('''
%s
''')
""" % source
try:
code.InteractiveConsole.runsource(self, pySource, filename, symbol)
except Exception as e:
print(e)
def runsource(self, source, filename='console', symbol='single'):
pySource = """
pyDatalog.load('''
%s
''')
""" % source
try:
code.InteractiveConsole.runsource(self, pySource, filename, symbol)
except Exception as e:
print(e)
def ask(self, query_symbols, logical_query, coeff_expr=None):
"""
Builds a pyDataLog program from the logical_query and loads it. Then executes the query for the query_symbols.
:param query_symbols: The symbols to be queried.
:type query_symbols: list(SubSymbol)
:param logical_query:
:type:
:return:
"""
helper_len = 0
tmp = None
if not query_symbols:
return None
if coeff_expr is None:
helper_len = len(query_symbols)
helper_predicate = 'helper(' + ','.join(
[str(v) for v in query_symbols]) + ')'
tmp = helper_predicate + " <= " + self.transform_query(
logical_query)
else:
helper_len = len(query_symbols) + 1
syms = OrderedSet(query_symbols)
syms.add('COEFF_EXPR')
helper_predicate = 'helper(' + ','.join([str(v)
for v in syms]) + ')'
index_query = self.transform_query(logical_query)
coeff_query = "(COEFF_EXPR == " + str(coeff_expr) + ")"
if index_query is None:
tmp = helper_predicate + " <= " + coeff_query
else:
tmp = helper_predicate + " <= " + " & ".join(
[index_query, coeff_query])
log.debug("pyDatalog query: " + tmp)
pyDatalog.load(tmp)
answer = pyDatalog.ask(helper_predicate)
pyEngine.Pred.reset_clauses(pyEngine.Pred("helper", helper_len))
if answer is None:
return []
return self.transform_answer(answer.answers)
def addRisksToLogic(rD):
riskStr = ""
maxR = 0
totalProb = float(0)
#print(rD.items())
for cap, prob in list(rD.items()):
#pyDatalog.assert_fact(pC(cap,prob))
#MaxR?
#probCapability[cap] = prob
#print("probCapability[" + str(cap) + "] = " + str(prob) + "\n")
riskStr += "probCapability[" + str(cap) + "] = " + str(prob) + "\n"
if cap > maxR:
maxR = cap
totalProb += prob
#print("MaxR=" + str(maxR) + "\n")
riskStr += "MaxR=" + str(maxR) + "\n"
#if totalProb != 1.00:
# print("FAIL: Probabilities add to " + str(totalProb))
#else:
#print("OK: Probabilities add to 1.00")
pyDatalog.load(riskStr)
return maxR
def eval_datalog(data, rule):
"""
Evaluation using pyDatalog.
:param data: a list of tuple string
:param rule: a rule node
:return: a list of resulting tuple string
"""
assert isinstance(data, list)
assert isinstance(rule, RuleNode)
def extract_query_predicate(rule):
# print(rule)
return QUERY_PRED.match(rule).group(1)
# def db2str(tuples):
# return "\n".join(["+%s(%s,%s)" % (p, s, o) for (s, p, o) in tuples])
def result2tuplestring(result):
# print(rule)
# query_pred = extract_query_predicate(rule.left)
for (s, o) in result:
yield (s, rule.left, o)
pyDatalog.clear()
# loguru.logger.debug('Size of loaded data: %d' % len(data))
for (s, p, o) in data:
pyDatalog.assert_fact(p, s, o)
pyDatalog.load(str(rule))
result = pyDatalog.ask(rule.left + '(X, Y)')
if not result:
loguru.logger.debug("Empty evaluation")
return []
# if rule.left == config.query_relation_name:
# pyDatalog.ask(config.query_relation_name + "(" + config.subject + ", Y)")
return list(result2tuplestring(result.answers))
def ask(self, query_symbols, logical_query, coeff_expr=None):
"""
Builds a pyDataLog program from the logical_query and loads it. Then executes the query for the query_symbols.
:param query_symbols: The symbols to be queried.
:type query_symbols: list(SubSymbol)
:param logical_query:
:type:
:return:
"""
helper_len = 0
tmp = None
if not query_symbols:
return None
if coeff_expr is None:
helper_len = len(query_symbols)
helper_predicate = 'helper(' + ','.join([str(v) for v in query_symbols]) + ')'
tmp = helper_predicate + " <= " + self.transform_query(logical_query)
else:
helper_len = len(query_symbols) + 1
syms = OrderedSet(query_symbols)
syms.add('COEFF_EXPR')
helper_predicate = 'helper(' + ','.join([str(v) for v in syms]) + ')'
index_query = self.transform_query(logical_query)
coeff_query = "(COEFF_EXPR == " + str(coeff_expr) + ")"
if index_query is None:
tmp = helper_predicate + " <= " + coeff_query
else:
tmp = helper_predicate + " <= " + " & ".join([index_query, coeff_query])
log.debug("pyDatalog query: " + tmp)
pyDatalog.load(tmp)
answer = pyDatalog.ask(helper_predicate)
pyEngine.Pred.reset_clauses(pyEngine.Pred("helper", helper_len))
if answer is None:
return []
return self.transform_answer(answer.answers)
def fillComplexRules(parent_child, child_parent, all_relations, relations):
rules = ""
for relation in all_relations:
if relations[relation]:
rules += 'palabras(X,I) <= ~palabras(X,I) & ' + relation + '(X,I,A,B)\n'
rules += 'palabras(X,I) <= ~palabras(X,I) & ' + relation + '(A,B,X,I)\n'
rules += 'igualesEntreIdiomas(I1,I2,X) <= palabras(X,I1) & palabras(X,I2)\n'
rules += 'porcentaje(X, Y, Z) <= (Z == X / Y*100)\n'
for relation in parent_child:
if relations[relation]:
rules += 'aporte(I1,I2,Y) <= ' + relation + '(_,I1,Y,I2) & ~(I2 == I1)\n'
for relation in child_parent:
if relations[relation]:
if relation == "etymology":
rules += 'aporte(I1,I2,Y) <= etymological_origin_of(Y,I2, _,I1) & ~(I2 == I1)\n'
elif relation == "is_derived_from":
rules += 'aporte(I1,I2,Y) <= has_derived_form(Y,I2, _,I1) & ~(I2 == I1)\n'
else:
rules += 'aporte(I1,I2,Y) <= ' + relation + '(Y,I2, _,I1) & ~(I2 == I1)\n'
pyDatalog.load(rules)
return
def determineResidualUtilityHelper(rD,maxUtility,debug=True):
rMax = addRisksToLogic(rD)
#rMax = 4
for c,p in rD.items():
s = "probCapability[" + str(c) + "] = " + str(p)
pyDatalog.load(s)
query = "weightedWorstCasePath[X]==Y"
utilitiesByAttackerCapabilityAnswers = pyDatalog.ask(query)
if utilitiesByAttackerCapabilityAnswers != None:
utilitiesByAttackerCapability = utilitiesByAttackerCapabilityAnswers.answers
estimatedValue = 0
if debug:
print("Attacks calculated.")
if utilitiesByAttackerCapabilityAnswers != None:
if debug:
print("Utilities compromised by attacker capability:")
#pprint.pprint(utilitiesByAttackerCapability)
if len(utilitiesByAttackerCapability) > 0:
pprint.pprint(sorted(utilitiesByAttackerCapability,key=itemgetter(0)))
else:
print("None")
#print("Number of items: " + str(len(utilitiesByAttackerCapability)))
estimatedValue = sum(map(itemgetter(1),utilitiesByAttackerCapability))
#for capability in range(rMax+1):
#query2 = "worstCasePath[" + str(capability) + "] == X"
#wCPsAnswers = pyDatalog.ask(query2)
#if wCPsAnswers != None:
#wCPs = wCPsAnswers.answers
#print "************"
#print("Worst case paths by attacker capability " + str(capability) + ": ")
#pprint.pprint(str(wCPs))
estimatedValue = maxUtility - estimatedValue
if debug:
print("Single Scenario Expected Value: " + str(estimatedValue))
return estimatedValue
def runDatalog(facts, rules, predicates):
"""виконує логічне виведення в pyDatalog. Повертає список тріплетів.
facts - список Datalog-фактів,
rules - список Datalog-правил,
predicates - список предикатів, для яких будуть шукатись факти"""
#if not predicates:
# predicates={p for s,p,o in facts}|{r.split('(')[0] for r in rules}
from pyDatalog.pyDatalog import assert_fact, load, ask, clear
code = '\n'.join(facts) + '\n' + '\n'.join(rules) # факти і правила
load(code)
allFacts = set()
for pred in predicates:
# try:
res = ask('%s(X,Y)' % pred).answers
# except AttributeError: #Predicate without definition
# continue
for subj, obj in res:
allFacts.add((subj.encode('utf-8'), pred.encode('utf-8'),
obj.encode('utf-8')))
print subj, pred, obj
clear()
return allFacts
def test():
# test of expressions
pyDatalog.load("""
+ p(a) # p is a proposition
""")
assert pyDatalog.ask('p(a)') == set([('a',)])
pyDatalog.assert_fact('p', 'a', 'b')
assert pyDatalog.ask('p(a, "b")') == set([('a', 'b')])
pyDatalog.retract_fact('p', 'a', 'b')
assert pyDatalog.ask('p(a, "b")') == None
"""unary facts """
@pyDatalog.program()
def unary():
+z()
assert ask(z()) == set([()])
+ p(a)
# check that unary queries work
assert ask(p(a)) == set([('a',)])
assert ask(p(X)) == set([('a',)])
assert ask(p(Y)) == set([('a',)])
assert ask(p(_X)) == set([('a',)])
assert ask(p(b)) == None
assert ask(p(a) & p(b)) == None
+ p(b)
assert ask(p(X), _fast=True) == set([('a',), ('b',)])
+ p(b) # facts are unique
assert ask(p(X)) == set([('a',), ('b',)])
- p(b) # retract a unary fact
assert ask(p(X)) == set([('a',)])
- p(a)
assert ask(p(X)) == None
+ p(a)
# strings and integers
+ p('c')
assert ask(p(c)) == set([('c',)])
+ p(1)
assert ask(p(1)) == set([(1,)])
+ n(None)
assert ask(n(X)) == set([(None,)])
assert ask(n(None)) == set([(None,)])
# spaces and uppercase in strings
+ farmer('Moshe dayan')
+ farmer('omar')
assert ask(farmer(X)) == set([('Moshe dayan',), ('omar',)])
# execute queries in a python program
moshe_is_a_farmer = pyDatalog.ask("farmer('Moshe dayan')")
assert moshe_is_a_farmer == set([('Moshe dayan',)])
""" binary facts """
@pyDatalog.program()
def binary():
+ q(a, b)
assert ask(q(a, b)) == set([('a', 'b')])
assert ask(q(X, b)) == set([('a', 'b')])
assert ask(q(a, Y)) == set([('a', 'b')])
assert ask(q(a, c)) == None
assert ask(q(X, Y)) == set([('a', 'b')])
+ q(a,c)
assert ask(q(a, Y)) == set([('a', 'b'), ('a', 'c')])
- q(a,c)
assert ask(q(a, Y)) == set([('a', 'b')])
assert ask(q(X, X)) == None
+q(a, a)
assert ask(q(X, X)) == set([('a', 'a')])
-q(a, a)
""" (in)equality """
@pyDatalog.program()
def equality():
assert ask(X==1) == set([(1,)])
assert ask(X==Y) == None
assert ask(X==Y+1) == None
assert ask((X==1) & (Y==1) & (X==Y)) == set([(1,1)])
assert ask((X==1) & (Y==2) & (X==Y-1)) == set([(1,2)])
#assert ask((X==1) & (Y==2) & (X+2==Y+1)) == set([(1,2)])
assert ask((X==2) & (Y==X/2)) == set([(2,1)])
assert ask((X==2) & (Y==X//2)) == set([(2,1)])
assert ask((X==1) & (Y==1+X)) == set([(1,2)])
assert ask((X==1) & (Y==1-X)) == set([(1,0)])
assert ask((X==1) & (Y==2*X)) == set([(1,2)])
assert ask((X==2) & (Y==2/X)) == set([(2,1)])
assert ask((X==2) & (Y==2//X)) == set([(2,1)])
""" Conjunctive queries """
@pyDatalog.program()
def conjuctive():
assert ask(q(X, Y) & p(X)) == set([('a', 'b')])
assert ask(p(X) & p(a)) == set([('a',),('c',),(1,)])
assert ask(p(X) & p(Y) & (X==Y)) == set([('a', 'a'), ('c', 'c'), (1, 1)])
assert ask(p(X) & p(Y) & (X==Y) & (Y==a)) == set([('a', 'a')])
assert ask(q(X, Y)) == set([('a', 'b')])
assert ask(q(X, Y) & p(X)) == set([('a', 'b')])
@pyDatalog.program()
def equality2():
assert ask((X==1) & (X<X+1)) == set([(1,)])
assert ask((X==1) & (Y==X)) == set([(1,1)])
assert ask((X==1) & (Y==X+1)) == set([(1,2)])
assert ask((X==1) & (Y==X+1) & (X<Y)) == set([(1,2)])
assert ask((X==1) & (X<1)) == None
assert ask((X==1) & (X<=1)) == set([(1,)])
assert ask((X==1) & (X>1)) == None
assert ask((X==1) & (X>=1)) == set([(1,)])
# assert ask(X==(1,2)) == set([((1,2), (1,2))])
assert ask(X in (1,)) == set([(1,)])
assert ask((X==1) & (X not in (2,))) == set([(1,)])
assert ask((X==1) & ~(X in (2,))) == set([(1,)])
assert ask((X==1) & (X not in (1,))) == None
assert ask((X==1) & ~(X in (1,))) == None
@pyDatalog.program()
def equality3():
# equality (must be between parenthesis):
s(X) <= (X == a)
assert ask(s(X)) == set([('a',)])
s(X) <= (X == 1)
assert ask(s(X)) == set([(1,), ('a',)])
s(X, Y) <= p(X) & (X == Y)
assert ask(s(a, a)) == set([('a', 'a')])
assert ask(s(a, b)) == None
assert ask(s(X,a)) == set([('a', 'a')])
assert ask(s(X, Y)) == set([('a', 'a'),('c', 'c'),(1, 1)])
assert pyDatalog.ask('p(a)') == set([('a',)])
""" clauses """
@pyDatalog.program()
def clauses():
p2(X) <= p(X)
assert ask(p2(a)) == set([('a',)])
p2(X) <= p(X)
r(X, Y) <= p(X) & p(Y)
assert ask(r(a, a)) == set([('a', 'a')])
assert ask(r(a, c)) == set([('a', 'c')])
r(X, b) <= p(X)
assert ask(r(a, b)) == set([('a', 'b')])
- (r(X, b) <= p(X))
assert ask(r(a, b)) == None
# TODO more tests
# integer variable
for i in range(10):
+ successor(i+1, i)
assert ask(successor(2, 1)) == set([(2, 1)])
# built-in
assert abs(-3)==3
assert math.sin(3)==math.sin(3)
""" in """
pyDatalog.assert_fact('is_list', (1,2))
@pyDatalog.program()
def _in():
assert ((X==1) & (X in (1,2))) == [(1,)]
_in(X) <= (X in [1,2])
assert ask(_in(1)) == set([(1,)])
assert ask(_in(9)) == None
assert ask(_in(X)) == set([(1,), (2,)])
_in2(X) <= is_list(Y) & (X in Y)
assert ask(_in2(X)) == set([(1,), (2,)])
assert ask((Y==(1,2)) & (X==1) & (X in Y)) == set([((1, 2), 1)])
assert ask((Y==(1,2)) & (X==1) & (X in Y+(3,))) == set([((1, 2), 1)])
""" recursion """
@pyDatalog.program()
def recursion():
+ even(0)
even(N) <= successor(N, N1) & odd(N1)
odd(N) <= ~ even(N)
assert ask(even(0)) == set([(0,)])
assert ask(even(X)) == set([(4,), (10,), (6,), (0,), (2,), (8,)])
assert ask(even(10)) == set([(10,)])
assert ask(odd(1)) == set([(1,)])
assert ask(odd(5)) == set([(5,)])
assert ask(even(5)) == None
""" recursion with expressions """
# reset the engine
pyDatalog.clear()
@pyDatalog.program()
def recursive_expression():
predecessor(X,Y) <= (X==Y-1)
assert ask(predecessor(X,11)) == set([(10, 11)])
p(X,Z) <= (Y==Z-1) & (X==Y-1)
assert ask(p(X,11)) == set([(9, 11)])
# odd and even
+ even(0)
even(N) <= (N > 0) & odd(N-1)
assert ask(even(0)) == set([(0,)])
odd(N) <= (N > 0) & ~ even(N)
assert ask(even(0)) == set([(0,)])
assert ask(odd(1)) == set([(1,)])
assert ask(odd(5)) == set([(5,)])
assert ask(even(5)) == None
assert ask((X==3) & odd(X+2)) == set([(3,)])
# Factorial
pyDatalog.clear()
@pyDatalog.program()
def factorial():
# (factorial[N] == F) <= (N < 1) & (F == -factorial[-N])
# + (factorial[1]==1)
# (factorial[N] == F) <= (N > 1) & (F == N*factorial[N-1])
# assert ask(factorial[1] == F) == set([(1, 1)])
# assert ask(factorial[4] == F) == set([(4, 24)])
# assert ask(factorial[-4] == F) == set([(-4, -24)])
pass
# Fibonacci
pyDatalog.clear()
@pyDatalog.program()
def fibonacci():
(fibonacci[N] == F) <= (N == 0) & (F==0)
(fibonacci[N] == F) <= (N == 1) & (F==1)
(fibonacci[N] == F) <= (N > 1) & (F == fibonacci[N-1]+fibonacci[N-2])
assert ask(fibonacci[1] == F) == set([(1, 1)])
assert ask(fibonacci[4] == F) == set([(4, 3)])
assert ask(fibonacci[18] == F) == set([(18, 2584)])
# string manipulation
@pyDatalog.program()
def _lambda():
split(X, Y,Z) <= (X == Y+'-'+Z)
assert ask(split(X, 'a', 'b')) == set([('a-b', 'a', 'b')])
split(X, Y,Z) <= (Y == (lambda X: X.split('-')[0])) & (Z == (lambda X: X.split('-')[1]))
assert ask(split('a-b', Y, Z)) == set([('a-b', 'a', 'b')])
assert ask(split(X, 'a', 'b')) == set([('a-b', 'a', 'b')])
(two[X]==Z) <= (Z==X+(lambda X: X))
assert ask(two['A']==Y) == set([('A','AA')])
""" negation """
@pyDatalog.program()
def _negation():
+p(a, b)
assert ask(~p(X, b)) == None
assert ask(~p(X, c)) == set([('X', 'c')])
pyDatalog.load("""
+ even(0)
even(N) <= (N > 0) & (N1==N-1) & odd(N1)
odd(N) <= (N2==N+2) & ~ even(N) & (N2>0)
""")
assert pyDatalog.ask('~ odd(7)', _fast=True) == None
assert pyDatalog.ask('~ odd(2)', _fast=True) == set([(2,)])
assert pyDatalog.ask('odd(3)', _fast=True) == set([(3,)])
assert pyDatalog.ask('odd(3)' ) == set([(3,)])
assert pyDatalog.ask('odd(5)', _fast=True) == set([(5,)])
assert pyDatalog.ask('odd(5)' ) == set([(5,)])
assert pyDatalog.ask('even(5)', _fast=True) == None
assert pyDatalog.ask('even(5)' ) == None
""" functions """
pyDatalog.clear()
@pyDatalog.program()
def function():
+ (f[a]==b)
assert ask(f[X]==Y) == set([('a', 'b')])
assert ask(f[X]==b) == set([('a', 'b')]) #TODO remove 'b' from result
assert ask(f[a]==X) == set([('a', 'b')])
assert ask(f[a]==b) == set([('a', 'b')])
+ (f[a]==c)
assert ask(f[a]==X) == set([('a', 'c')])
+ (f[a]==a)
assert ask(f[f[a]]==X) == set([('a',)])
assert ask(f[X]==f[a]) == set([('a',)])
assert ask(f[X]==f[a]+'') == set([('a',)])
- (f[a]==a)
assert ask(f[f[a]]==X) == None
+ (f[a]==None)
assert (ask(f[a]==X)) == set([('a',None)])
+ (f[a]==(1,2))
assert (ask(f[a]==X)) == set([('a',(1,2))])
assert (ask(f[X]==(1,2))) == set([('a',(1,2))])
+ (f[a]==c)
+ (f2[a,x]==b)
assert ask(f2[a,x]==b) == set([('a', 'x', 'b')])
+ (f2[a,x]==c)
assert ask(f2[a,x]==X) == set([('a', 'x', 'c')])
g[X] = f[X]+f[X]
assert(ask(g[a]==X)) == set([('a', 'cc')])
h(X,Y) <= (f[X]==Y)
assert (ask(h(X,'c'))) == set([('a', 'c')])
assert (ask(h(X,Y))) == set([('a', 'c')])
@pyDatalog.program()
def function_comparison():
assert ask(f[X]==Y) == set([('a', 'c')])
assert ask(f[a]<'d') == set([('c',)])
assert ask(f[a]>'a') == set([('c',)])
assert ask(f[a]>='c') == set([('c',)])
assert ask(f[a]>'c') == None
assert ask(f[a]<='c') == set([('c',)])
assert ask(f[a]>'c') == None
assert ask(f[a] in ['c',]) == set([('c',)])
assert ask((f[X]=='c') & (f[Y]==f[X])) == set([('a', 'a')])
assert ask((f[X]=='c') & (f[Y]==f[X]+'')) == set([('a', 'a')])
assert ask((f[X]=='c') & (f[Y]==(lambda X : 'c'))) == set([('a', 'a')])
assert ask(f[X]==Y+'') == None
assert ask((Y=='c') &(f[X]==Y+'')) == set([('c', 'a')])
assert ask((Y=='c') &(f[X]<=Y+'')) == set([('c', 'a')])
assert ask((Y=='c') &(f[X]<Y+'')) == None
assert ask((Y=='c') &(f[X]<'d'+Y+'')) == set([('c', 'a')])
assert ask((Y==('a','c')) & (f[X] in Y)) == set([(('a', 'c'), 'a')])
assert ask((Y==('a','c')) & (f[X] in (Y+('z',)))) == set([(('a', 'c'), 'a')])
assert ask(f[X]==f[X]+'') == set([('a',)])
@pyDatalog.program()
def function_negation():
assert not(ask(~(f[a]<'d')))
assert not(ask(~(f[X]<'d')))
assert ask(~(f[a] in('d',)))
""" aggregates """
pyDatalog.clear()
@pyDatalog.program()
def sum():
+ p(a, c, 1)
+ p(b, b, 4)
+ p(a, b, 1)
assert(sum(1,2)) == 3
(a_sum[X] == sum(Y, key=Z)) <= p(X, Z, Y)
assert ask(a_sum[X]==Y) == set([('a', 2), ('b', 4)])
assert ask(a_sum[a]==X) == set([('a', 2)])
assert ask(a_sum[a]==2) == set([('a', 2)])
assert ask(a_sum[X]==4) == set([('b', 4)])
assert ask(a_sum[c]==X) == None
assert ask((a_sum[X]==2) & (p(X, Z, Y))) == set([('a', 'c', 1), ('a', 'b', 1)])
(a_sum2[X] == sum(Y, for_each=X)) <= p(X, Z, Y)
assert ask(a_sum2[a]==X) == set([('a', 1)])
(a_sum3[X] == sum(Y, key=(X,Z))) <= p(X, Z, Y)
assert ask(a_sum3[X]==Y) == set([('a', 2), ('b', 4)])
assert ask(a_sum3[a]==X) == set([('a', 2)])
@pyDatalog.program()
def len():
assert(len((1,2))) == 2
(a_len[X] == len(Z)) <= p(X, Z, Y)
assert ask(a_len[X]==Y) == set([('a', 2), ('b', 1)])
assert ask(a_len[a]==X) == set([('a', 2)])
assert ask(a_len[X]==1) == set([('b', 1)])
assert ask(a_len[X]==5) == None
(a_lenY[X] == len(Y)) <= p(X, Z, Y)
assert ask(a_lenY[a]==X) == set([('a', 1)])
assert ask(a_lenY[c]==X) == None
(a_len2[X,Y] == len(Z)) <= p(X, Y, Z)
assert ask(a_len2[a,b]==X) == set([('a', 'b', 1)])
assert ask(a_len2[a,X]==Y) == set([('a', 'b', 1), ('a', 'c', 1)])
+ q(a, c, 1)
+ q(a, b, 2)
+ q(b, b, 4)
@pyDatalog.program()
def concat():
(a_concat[X] == concat(Y, key=Z, sep='+')) <= q(X, Y, Z)
assert ask(a_concat[X]==Y) == set([('b', 'b'), ('a', 'c+b')])
assert ask(a_concat[a]=='c+b') == set([('a', 'c+b')])
assert ask(a_concat[a]==X) == set([('a', 'c+b')])
assert ask(a_concat[X]==b) == set([('b', 'b')])
(a_concat2[X] == concat(Y, order_by=(Z,), sep='+')) <= q(X, Y, Z)
assert ask(a_concat2[a]==X) == set([('a', 'c+b')])
(a_concat3[X] == concat(Y, key=(-Z,), sep='-')) <= q(X, Y, Z)
assert ask(a_concat3[a]==X) == set([('a', 'b-c')])
@pyDatalog.program()
def min():
assert min(1,2) == 1
(a_min[X] == min(Y, key=Z)) <= q(X, Y, Z)
assert ask(a_min[X]==Y) == set([('b', 'b'), ('a', 'c')])
assert ask(a_min[a]=='c') == set([('a', 'c')])
assert ask(a_min[a]==X) == set([('a', 'c')])
assert ask(a_min[X]=='b') == set([('b', 'b')])
(a_minD[X] == min(Y, order_by=-Z)) <= q(X, Y, Z)
assert ask(a_minD[a]==X) == set([('a', 'b')])
(a_min2[X, Y] == min(Z, key=(X,Y))) <= q(X, Y, Z)
assert ask(a_min2[Y, b]==X) == set([('a', 'b', 2),('b', 'b', 4)])
assert ask(a_min2[Y, Y]==X) == set([('b', 'b', 4)]), "a_min2"
(a_min3[Y] == min(Z, key=(-X,Z))) <= q(X, Y, Z)
assert ask(a_min3[b]==Y) == set([('b', 4)]), "a_min3"
@pyDatalog.program()
def max():
assert max(1,2) == 2
(a_max[X] == max(Y, key=-Z)) <= q(X, Y, Z)
assert ask(a_max[a]==X) == set([('a', 'c')])
(a_maxD[X] == max(Y, order_by=Z)) <= q(X, Y, Z)
assert ask(a_maxD[a]==X) == set([('a', 'b')])
@pyDatalog.program()
def rank():
(a_rank1[Z] == rank(for_each=Z, order_by=Z)) <= q(X, Y, Z)
assert ask(a_rank1[X]==Y) == set([(1, 0), (2, 0), (4, 0)])
assert ask(a_rank1[X]==0) == set([(1, 0), (2, 0), (4, 0)])
assert ask(a_rank1[1]==X) == set([(1, 0)])
assert ask(a_rank1[1]==0) == set([(1, 0)])
assert ask(a_rank1[1]==1) == None
# rank
(a_rank[X,Y] == rank(for_each=(X,Y2), order_by=Z2)) <= q(X, Y, Z) & q(X,Y2,Z2)
assert ask(a_rank[X,Y]==Z) == set([('a', 'b', 1), ('a', 'c', 0), ('b', 'b', 0)])
assert ask(a_rank[a,b]==1) == set([('a', 'b', 1)])
assert ask(a_rank[a,b]==Y) == set([('a', 'b', 1)])
assert ask(a_rank[a,X]==0) == set([('a', 'c', 0)])
assert ask(a_rank[a,X]==Y) == set([('a', 'b', 1), ('a', 'c', 0)])
assert ask(a_rank[X,Y]==1) == set([('a', 'b', 1)])
assert ask(a_rank[a,y]==Y) == None
# reversed
(b_rank[X,Y] == rank(for_each=(X,Y2), order_by=-Z2)) <= q(X, Y, Z) & q(X,Y2,Z2)
assert ask(b_rank[X,Y]==Z) == set([('a', 'b', 0), ('a', 'c', 1), ('b', 'b', 0)])
assert ask(b_rank[a,b]==0) == set([('a', 'b', 0)])
assert ask(b_rank[a,b]==Y) == set([('a', 'b', 0)])
assert ask(b_rank[a,X]==1) == set([('a', 'c', 1)])
assert ask(b_rank[a,X]==Y) == set([('a', 'b', 0), ('a', 'c', 1)])
assert ask(b_rank[X,Y]==0) == set([('a', 'b', 0), ('b', 'b', 0)])
assert ask(b_rank[a,y]==Y) == None
@pyDatalog.program()
def running_sum():
# running_sum
(a_run_sum[X,Y] == running_sum(Z2, for_each=(X,Y2), order_by=Z2)) <= q(X, Y, Z) & q(X,Y2,Z2)
assert ask(a_run_sum[X,Y]==Z) == set([('a', 'b', 3), ('a', 'c', 1), ('b', 'b', 4)])
assert ask(a_run_sum[a,b]==3) == set([('a', 'b', 3)])
assert ask(a_run_sum[a,b]==Y) == set([('a', 'b', 3)])
assert ask(a_run_sum[a,X]==1) == set([('a', 'c', 1)])
assert ask(a_run_sum[a,X]==Y) == set([('a', 'b', 3), ('a', 'c', 1)])
assert ask(a_run_sum[X,Y]==4) == set([('b', 'b', 4)])
assert ask(a_run_sum[a,y]==Y) == None
(b_run_sum[X,Y] == running_sum(Z2, for_each=(X,Y2), order_by=-Z2)) <= q(X, Y, Z) & q(X,Y2,Z2)
assert ask(b_run_sum[X,Y]==Z) == set([('a', 'b', 2), ('a', 'c', 3), ('b', 'b', 4)])
assert ask(b_run_sum[a,b]==2) == set([('a', 'b', 2)])
assert ask(b_run_sum[a,b]==Y) == set([('a', 'b', 2)])
assert ask(b_run_sum[a,X]==3) == set([('a', 'c', 3)])
assert ask(b_run_sum[a,X]==Y) == set([('a', 'b', 2), ('a', 'c', 3)])
assert ask(b_run_sum[X,Y]==4) == set([('b', 'b', 4)])
assert ask(b_run_sum[a,y]==Y) == None
""" simple in-line queries """
X = pyDatalog.Variable()
assert ((X==1) >= X) == 1
assert ((X==1) & (X!=2) >= X) == 1
assert set(X._in((1,2))) == set([(1,),(2,)])
assert ((X==1) & (X._in ((1,2)))) == [(1,)]
""" interface with python classes """
class A(pyDatalog.Mixin):
def __init__(self, b):
super(A, self).__init__()
self.b = b
def __repr__(self):
return self.b
@pyDatalog.program() # indicates that the following method contains pyDatalog clauses
def _():
(A.c[X]==N) <= (A.b[X]==N)
(A.len[X]==len(N)) <= (A.b[X]==N)
@classmethod
def _pyD_x1(cls, X):
if X.is_const() and X.id.b == 'za':
yield (X.id,)
else:
for X in pyDatalog.metaMixin.__refs__[cls]:
if X.b == 'za':
yield (X,)
a = A('a')
b = A('b')
assert a.c == 'a'
X, Y = pyDatalog.variables(2)
assert (A.c[X]=='a') == [(a,)]
assert (A.c[X]=='a')[0] == (a,)
assert list(X.data) == [a]
assert X.v() == a
assert ((A.c[a]==X) >= X) == 'a'
assert ((A.c[a]==X) & (A.c[a]==X) >= X) == 'a'
assert ((A.c[a]==X) & (A.c[b]==X) >= X) == None
(A.c[X]=='b') & (A.b[X]=='a')
assert list(X.data) == []
(A.c[X]=='a') & (A.b[X]=='a')
assert list(X.data) == [a]
result = (A.c[X]=='a') & (A.b[X]=='a')
assert result == [(a,)]
assert (A.c[a] == 'a') == [()]
assert (A.b[a] == 'a') == [()]
assert (A.c[a]=='a') & (A.b[a]=='a') == [()]
assert (A.b[a]=='f') == []
assert ((A.c[a]=='a') & (A.b[a]=='f')) == []
""" filters on python classes """
assert (A.b[X]!=Y) == [(a, None), (b, None)]
assert (A.b[X]!='a') == [(b,)]
assert (A.b[X]!='z') == [(a,), (b,)]
assert (A.b[a]!='a') == []
assert list(A.b[b]!='a') == [()]
assert ((A.b[b]!='a') & (A.b[b]!='z')) == [()]
assert (A.b[X]<Y) == [(a, None), (b, None)]
assert (A.b[X]<'a') == []
assert (A.b[X]<'z') == [(a,), (b,)]
assert (A.b[a]<'b') == [()]
assert (A.b[b]<'a') == []
assert ((A.b[b]<'z') & (A.b[b]!='z')) == [()]
assert (A.b[X]<='a') == [(a,)]
assert (A.b[X]<='z') == [(a,), (b,)]
assert (A.b[a]<='b') == [()]
assert (A.b[b]<='a') == []
assert ((A.b[b]<='z') & (A.b[b]!='z')) == [()]
assert (A.b[X]>'a') == [(b,)]
assert (A.b[X]>='a') == [(a,), (b,)]
assert (A.c[X]<='a') == [(a,)]
assert (A.c[X]<='a'+'') == [(a,)]
assert (A.c[X]._in(('a',))) == [(a,)]
assert (A.c[X]._in(('a',)+('z',))) == [(a,)]
assert ((Y==('a',)) & (A.c[X]._in(Y))) == [(('a',), a)] # TODO make ' in ' work
assert ((Y==('a',)) & (A.c[X]._in(Y+('z',)))) == [(('a',), a)] # TODO make ' in ' work
assert (A.c[X]._in(('z',))) == []
# more complex queries
assert ((Y=='a') & (A.b[X]!=Y)) == [('a', b)] # order of appearance of the variables !
assert (A.len[X]==Y) == [(b, 1), (a, 1)]
assert (A.len[a]==Y) == [(1,)]
""" subclass """
class Z(A):
def __init__(self, z):
super(Z, self).__init__(z+'a')
self.z = z
def __repr__(self):
return self.z
@pyDatalog.program() # indicates that the following method contains pyDatalog clauses
def _():
(Z.w[X]==N) <= (Z.z[X]!=N)
@classmethod
def _pyD_query(cls, pred_name, args):
if pred_name == 'Z.pred':
if args[0].is_const() and args[0].id.b != 'za':
yield (args[0].id,)
else:
for X in pyDatalog.metaMixin.__refs__[cls]:
if X.b != 'za':
yield (X,)
else:
raise AttributeError
z = Z('z')
assert z.z == 'z'
assert (Z.z[X]=='z') == [(z,)]
assert ((Z.z[X]=='z') & (Z.z[X]>'a')) == [(z,)]
assert list(X.data) == [z]
try:
a.z == 'z'
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if e_message != "Predicate without definition (or error in resolver): A.z[1]==/2":
print(e_message)
else:
assert False
try:
(Z.z[a] == 'z') == None
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if e_message != "Object is incompatible with the class that is queried.":
print(e_message)
else:
assert False
assert (Z.b[X]==Y) == [(z, 'za')]
assert (Z.c[X]==Y) == [(z, 'za')]
assert ((Z.c[X]==Y) & (Z.c[X]>'a')) == [(z, 'za')]
assert (Z.c[X]>'a') == [(z,)]
assert ((Z.c[X]>'a') & (A.c[X]=='za')) == [(z,)]
assert (A.c[X]=='za') == [(z,)]
assert (A.c[z]=='za') == [()]
assert (z.b) == 'za'
assert (z.c) == 'za'
w = Z('w')
w = Z('w') # duplicated to test __refs__[cls]
assert(Z.x(X)) == [(z,)]
assert not (~Z.x(z))
assert ~Z.x(w)
assert ~ (Z.z[w]=='z')
assert(Z.pred(X)) == [(w,)] # not duplicated !
assert(Z.pred(X) & ~ (Z.z[X]>='z')) == [(w,)]
assert(Z.x(X) & ~(Z.pred(X))) == [(z,)]
assert (Z.len[X]==Y) == [(w, 1), (z, 1)]
assert (Z.len[z]==Y) == [(1,)]
# TODO print (A.b[w]==Y)
""" python resolvers """
@pyDatalog.predicate()
def p(X,Y):
yield (1,2)
yield (2,3)
assert pyDatalog.ask('p(X,Y)') == set([(1, 2), (2, 3)])
assert pyDatalog.ask('p(1,Y)') == set([(1, 2)])
assert pyDatalog.ask('p(1,2)') == set([(1, 2)])
""" error detection """
@pyDatalog.program()
def _():
pass
error = False
try:
_()
except: error = True
assert error
def assert_error(code, message='^$'):
_error = False
try:
pyDatalog.load(code)
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0] # python 2 and 3
if not re.match(message, e_message):
print(e_message)
_error = True
assert _error
def assert_ask(code, message='^$'):
_error = False
try:
pyDatalog.ask(code)
except Exception as e:
e_message = e.message if hasattr(e, 'message') else e.args[0]
if not re.match(message, e_message):
print(e_message)
_error = True
assert _error
assert_error('ask(z(a),True)', 'Too many arguments for ask \!')
assert_error('ask(z(a))', 'Predicate without definition \(or error in resolver\): z/1')
assert_error("+ farmer(farmer(moshe))", "Syntax error: Literals cannot have a literal as argument : farmer\[\]")
assert_error("+ manager[Mary]==John", "Left-hand side of equality must be a symbol or function, not an expression.")
assert_error("manager[X]==Y <= (X==Y)", "Syntax error: please verify parenthesis around \(in\)equalities")
assert_error("p(X) <= (Y==2)", "Can't create clause")
assert_error("p(X) <= X==1 & X==2", "Syntax error: please verify parenthesis around \(in\)equalities")
assert_error("p(X) <= (manager[X]== min(X))", "Error: argument missing in aggregate")
assert_error("p(X) <= (manager[X]== max(X, order_by=X))", "Aggregation cannot appear in the body of a clause")
assert_error("q(min(X, order_by=X)) <= p(X)", "Syntax error: Incorrect use of aggregation\.")
assert_error("manager[X]== min(X, order_by=X) <= manager(X)", "Syntax error: please verify parenthesis around \(in\)equalities")
assert_error("(manager[X]== min(X, order_by=X+2)) <= manager(X)", "order_by argument of aggregate must be variable\(s\), not expression\(s\).")
assert_error("ask(X<1)", 'Error: left hand side of comparison must be bound: =X<1/1')
assert_error("ask(X<Y)", 'Error: left hand side of comparison must be bound: =X<Y/2')
assert_error("ask(1<Y)", 'Error: left hand side of comparison must be bound: =Y>1/1')
assert_error("ask( (A.c[X]==Y) & (Z.c[X]==Y))", "TypeError: First argument of Z.c\[1\]==\('.','.'\) must be a Z, not a A ")
assert_ask("A.u[X]==Y", "Predicate without definition \(or error in resolver\): A.u\[1\]==/2")
assert_ask("A.u[X,Y]==Z", "Predicate without definition \(or error in resolver\): A.u\[2\]==/3")
assert_error('(a_sum[X] == sum(Y, key=Y)) <= p(X, Z, Y)', "Error: Duplicate definition of aggregate function.")
assert_error('(two(X)==Z) <= (Z==X+(lambda X: X))', 'Syntax error near equality: consider using brackets. two\(X\)')
assert_error('p(X) <= sum(X, key=X)', 'Invalid body for clause')
assert_error('ask(- manager[X]==1)', "Left-hand side of equality must be a symbol or function, not an expression.")
assert_error("p(X) <= (X=={})", "unhashable type: 'dict'")
""" SQL Alchemy """
from sqlalchemy import create_engine
from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, relationship
engine = create_engine('sqlite:///:memory:', echo=False) # create database in memory
Session = sessionmaker(bind=engine)
session = Session()
Base = declarative_base(cls=pyDatalog.Mixin, metaclass=pyDatalog.sqlMetaMixin)
Base.session = session
class Employee(Base): # --> Employee inherits from the Base class
__tablename__ = 'employee'
name = Column(String, primary_key=True)
manager_name = Column(String, ForeignKey('employee.name'))
salary = Column(Integer)
def __init__(self, name, manager_name, salary):
super(Employee, self).__init__()
self.name = name
self.manager_name = manager_name # direct manager of the employee, or None
self.salary = salary # monthly salary of the employee
def __repr__(self): # specifies how to display the employee
return "Employee: %s" % self.name
@pyDatalog.program() # --> the following function contains pyDatalog clauses
def Employee():
(Employee.manager[X]==Y) <= (Employee.manager_name[X]==Z) & (Z==Employee.name[Y])
# the salary class of employee X is computed as a function of his/her salary
# this statement is a logic equality, not an assignment !
Employee.salary_class[X] = Employee.salary[X]//1000
# all the indirect managers of employee X are derived from his manager, recursively
Employee.indirect_manager(X,Y) <= (Employee.manager[X]==Y) & (Y != None)
Employee.indirect_manager(X,Y) <= (Employee.manager[X]==Z) & Employee.indirect_manager(Z,Y) & (Y != None)
# count the number of reports of X
(Employee.report_count[X] == len(Y)) <= Employee.indirect_manager(Y,X)
Employee.p(X,Y) <= (Y <= Employee.salary[X] + 1)
Base.metadata.create_all(engine)
John = Employee('John', None, 6800)
Mary = Employee('Mary', 'John', 6300)
Sam = Employee('Sam', 'Mary', 5900)
session.add(John)
session.add(Mary)
session.add(Sam)
session.commit()
assert (John.salary_class ==6)
X = pyDatalog.Variable()
result = (Employee.salary[X] == 6300) # notice the similarity to a pyDatalog query
assert result == [(Mary,), ]
assert (X._value() == [Mary,]) # prints [Employee: Mary]
assert (X.v() == Mary) # prints Employee:Mary
result = (Employee.indirect_manager(Mary, X))
assert result == [(John,), ]
assert (X.v() == John) # prints [Employee: John]
Mary.salary_class = ((Employee.salary_class[Mary]==X) >= X)
Mary.salary = 10000
assert Mary.salary_class != ((Employee.salary_class[Mary]==X) >= X)
X, Y, N = pyDatalog.variables(3)
result = (Employee.salary[X]==6800) & (Employee.name[X]==N)
assert result == [(John,'John'), ]
assert N.v() == 'John'
result = (Employee.salary[X]==Employee.salary[X])
assert result == [(John,), (Mary,), (Sam,)]
result = (Employee.p(X,1))
assert result == [(John,), (Mary,), (Sam,)]
result = (Employee.salary[X]<Employee.salary[X]+1)
assert result == [(John,), (Mary,), (Sam,)]
result = (Employee.salary[John]==N) & Employee.p(John, N)
assert result == [(6800,)]
result = (Employee.salary[X]==6800) & (Employee.salary[X]==N) & Employee.p(X, N)
assert result == [(John, 6800)]
"""
else:
new_source = source
try:
code.InteractiveConsole.runsource(self, new_source, filename, symbol)
except Exception as e:
print(e)
pyEngine.Auto_print = True
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='FoundationDB Query Console')
parser.add_argument('-p', '--python', help='''Python module to be imported.
pyDatalog.create_atoms must be called for any Datalog included.''')
parser.add_argument('-d', '--datalog', help='''File with Datalog statements
(only) to be loaded. Atoms will be automatically created.''')
args = parser.parse_args()
if args.python:
import_all_from(args.python)
if args.datalog:
with open(args.datalog, 'r') as f:
dl_defs = f.read()
f.closed
pyDatalog.load(dl_defs)
globalize_atoms(compile(dl_defs, '<string>', 'exec'))
console = fdbqueryConsole(locals=locals())
console.set_mode('query')
console.interact('')
from pyDatalog import pyDatalog
pyDatalog.create_terms('croakes, eatflies, frog, chrips, sings, canary, green, yellow, P, X')
pyDatalog.load("""
frog(X) <= croakes(X) & eatflies(X)
canary(X) <= sings(X) & chrips(X)
green(X) <= frog(X)
yellow(X) <= canary(X)
""")
pyDatalog.assert_fact('croakes', 'Fritz')
pyDatalog.assert_fact('eatflies', 'Fritz')
pyDatalog.assert_fact('sings', 'Paul')
pyDatalog.assert_fact('chrips', 'Paul')
query = 'yellow(X)'
answers = pyDatalog.ask(query).answers
print(answers)
print(pyDatalog.ask('green(X)'))
Employee.indirect_manager(Mary, X)
print(X) # prints [John]
# Who are the employees of John with a salary below 6000 ?
result = (Employee.salary[X] < 6000) & Employee.indirect_manager(X, John)
print(result) # prints [(Sam,)]
print(X) # prints [Sam]
print((Employee.salary_class[X] == 5) & Employee.indirect_manager(X, John) >= X) # Sam
# verify that the manager of Mary is John
assert Employee.manager[Mary]==John
# who is his own indirect manager ?
Employee.indirect_manager(X, X)
print(X) # prints []
# who has 2 reports ?
Employee.report_count[X] == 2
print(X) # prints [John]
# what is the total salary of the employees of John ?
# note : it is better to place aggregation clauses in the class definition
pyDatalog.load("(Employee.budget[X] == sum(N, for_each=Y)) <= (Employee.indirect_manager(Y, X)) & (Employee.salary[Y]==N)")
Employee.budget[John]==X
print(X) # prints [12200]
# who has the lowest salary ?
pyDatalog.load("(lowest[1] == min(X, order_by=N)) <= (Employee.salary[X]==N)")
# must use ask() because inline queries cannot use unprefixed literals
print(pyDatalog.ask("lowest[1]==X")) # prints set([(1, 'Sam')])
pdl.load('''
+ male(daniel)
+ male(nicolas)
+ male(alain)
+ male(luigi)
+ male(johnny)
+ male(sacha)
+ male(ernest)
+ male(robert)
+ male(gilbert)
+ female(suzanne)
+ female(christine)
+ female(keiko)
+ female(rose_marie)
+ female(sylvie)
+ female(muriel)
+ female(magali)
+ female(maya)
+ female(nana)
+ female(suzanne_madeleine)
+ female(violette)
+ female(jacqueline)
+ husband(nicolas, keiko)
+ husband(johnny, luigi)
+ husband(maurice, jacqueline)
+ husband(gilbert, rose_marie)
+ father(gilbert, muriel)
+ father(gilbert, sylvie)
+ mother(rose_marie, muriel)
+ mother(rose_marie, sylvie)
+ father(ernest, suzanne)
+ father(ernest, jacqueline)
+ mother(suzanne_madeleine, suzanne)
+ mother(suzanne_madeleine, jacqueline)
+ father(robert, daniel)
+ father(robert, gilbert)
+ mother(violette, daniel)
+ mother(violette, gilbert)
+ father(daniel, nicolas)
+ father(daniel, alain)
+ father(daniel, christine)
+ mother(suzanne, nicolas)
+ mother(suzanne, alain)
+ mother(suzanne, christine)
+ father(johnny, luigi)
+ father(johnny, magali)
+ mother(christine, luigi)
+ mother(christine, magali)
+ father(nicolas, sacha)
+ father(nicolas, maya)
+ father(nicolas, nana)
+ mother(keiko, sacha)
+ mother(keiko, maya)
+ mother(keiko, nana)
parent(X, Y) <= (father(X, Y))
parent(X, Y) <= (mother(X, Y))
print(parent(Parent, Child) & (Child == nicolas))
print()
grand_parent(X, Y) <= (parent(X, Z) & parent(Z, Y))
grand_father(X, Y) <= (grand_parent(X, Y) & male(X))
grand_mother(X, Y) <= (grand_parent(X, Y) & female(X))
print(grand_parent(GrandParent, GrandChild) & (GrandChild == nicolas))
print()
print(grand_father(GrandFather, GrandChild) & (GrandChild == nicolas))
print()
print(grand_mother(GrandMother, GrandChild) & (GrandChild == nicolas))
print()
sibling(X, Y) <= (parent(Z, X) & parent(Z, Y) & (X != Y))
brother(X, Y) <= (male(X) & sibling(X, Y))
sister(X, Y) <= (female(X) & sibling(X, Y))
print(sibling(Sibling, X) & (X == nicolas))
print()
print(brother(Brother, X) & (X == nicolas))
print()
print(sister(Sister, X) & (X == nicolas))
print()
uncle(X, Y) <= (parent(Z, Y) & brother(X, Z))
uncle(X, Y) <= (parent(Z, Y) & sibling(A, Z) & husband(X, A))
aunt(X, Y) <= (parent(Z, Y) & sister(X, Z))
aunt(X, Y) <= (parent(Z, Y) & sibling(A, Z) & husband(A, X))
print(uncle(Uncle, X) & (X == nicolas))
print()
print(aunt(Aunt, X) & (X == nicolas))
print()
ancestor(X, Y) <= (parent(X, Y))
ancestor(X, Y) <= (parent(X, Z) & ancestor(Z, Y))
print(ancestor(MaleAncestor, X) & (X == nicolas) & male(MaleAncestor))
print()
descendant(X, Y) <= (ancestor(Y, X))
print(descendant(FemaleDescendant, X) & (X == daniel) & female(FemaleDescendant))
print()
''')
return self.statements
def asDatacode(self):
datacode = "\n".join(self.statements)
print datacode
return datacode
def asTerms(self):
allterms = ",".join(set(self.terms))
#print allterms
return allterms
csvToDatalog = CsvToDatalog()
csvToDatalog.load()
pyDatalog.create_terms(csvToDatalog.asTerms())
pyDatalog.load(csvToDatalog.asDatacode())
pyDatalog.create_terms('SC', 'NAME', 'FRONT', 'HORIZ', 'VERT', 'VP')
#print shapes(SC,NAME,FRONT,HORIZ,VERT,VP)
pyDatalog.create_terms('nextShape','startShape')
shape_brothers = defaultdict(list)
for shape in shapes(SC,NAME,FRONT,HORIZ,VERT,VP).data:
new_conf = (shape[2],shape[3],shape[4],shape[5])
shape_brothers[new_conf].append(shape[1])
for shape in shape_brothers.items():
items = shape[1]
items.sort()
+ startShape(items[0])
from pyDatalog.pyDatalog import create_terms, ask, load, assert_fact, clear
if __name__ == "__main__":
clear()
create_terms('X, frog, canary, green, yellow, chirps, sings, croakes, eatFlies')
load("""
frog(X) <= croakes(X) & eatFlies(X)
canary(X) <= chirps(X) & sings(X)
green(X) <= frog(X)
yellow(X) <= canary(X)
""")
assert_fact('croakes', 'fritz')
assert_fact('eatFlies', 'fritz')
print("frog: ", ask('frog(X)'))
print("green: ", ask('green(X)'))
print("green: ", ask("green('cuitcuit')"))
#
print(indirect_manager('Sam', Y))
#print(indirect_manager(X,'John'))
indirect_manager('Sam', Y)
resultado = Y.data[1]
print("resultado", resultado)
# + parent(bill, 'John Adams')
pyDatalog.assert_fact('parent', 'bill', 'John Adams')
print(pyDatalog.ask('parent(bill,X)'))
# specify what an ancestor is
pyDatalog.load("""
ancestor(X,Y) <= parent(X,Y)
ancestor(X,Y) <= parent(X,Z) & ancestor(Z,Y)
""")
# prints a set with one element : the ('bill', 'John Adams') tuple
W = pyDatalog.ask('parent(bill,X)')
print(W.answers[0][0])
#
W = pyDatalog.ask('ancestor(bill,X)')
print('Bill es ancestro de:', W.answers[0][0])
# - parent(bill, 'John Adams')
pyDatalog.retract_fact('parent', 'bill', 'John Adams')
W = pyDatalog.ask('parent(bill,X)')
if (W.__str__() != 'None'):
print(W.__str__())
# Who are the employees of John with a salary below 6000 ?
result = (Employee.salary[X] < 6000) & Employee.indirect_manager(X, John)
print(result) # prints [(Sam,)]
print(X) # prints [Sam]
print((Employee.salary_class[X] == 5) & Employee.indirect_manager(X, John) >=
X) # Sam
# verify that the manager of Mary is John
assert Employee.manager[Mary] == John
# who is his own indirect manager ?
Employee.indirect_manager(X, X)
print(X) # prints []
# who has 2 reports ?
Employee.report_count[X] == 2
print(X) # prints [John]
# what is the total salary of the employees of John ?
# note : it is better to place aggregation clauses in the class definition
pyDatalog.load(
"(Employee.budget[X] == sum(N, for_each=Y)) <= (Employee.indirect_manager(Y, X)) & (Employee.salary[Y]==N)"
)
Employee.budget[John] == X
print(X) # prints [12200]
# who has the lowest salary ?
pyDatalog.load("(lowest[1] == min(X, order_by=N)) <= (Employee.salary[X]==N)")
# must use ask() because inline queries cannot use unprefixed literals
print(pyDatalog.ask("lowest[1]==X")) # prints set([(1, 'Sam')])
def loadBigString(string, cnt):
try:
load(string)
except Exception:
import traceback
print(traceback.format_exc())
#>> Terms of Dimension
#Person
pd.create_terms("person, specialty, contract, division, all_persons")
pd.create_terms("personSpec, personContract, specDiv")
#Drug
pd.create_terms("drug, drtype, all_drugs")
pd.create_terms("drugType")
#>> Terms for Facts
pd.create_terms("admDrug, bills")
#>> Reading Dimension and Facts
dim = open("Dimension.txt", 'r').read()
pd.load(dim)
facts = open("Facts.txt", 'r').read()
pd.load(facts)
print(facts)
print("==========================================================================")
#>>Rule
pd.create_terms("Date, Specialist, DrType, Patient, Amount, Age, Drug, PrescribedBy, Gen, billsT, admDrug1, admDrugT")
admDrug1(Date, PrescribedBy, Drug, Patient) <= bills(Date, Specialist, DrType, Patient, Amount) & personSpec(PrescribedBy, Specialist) & drugType(Drug, DrType)
admDrugT(Date, PrescribedBy, Drug, Patient, Age) <= admDrug(Date, PrescribedBy, Drug, Patient, Age) & ~(admDrug1(Date, PrescribedBy, Drug, Patient))
#>> Generation (Rule ID: G_01)
billsT(Date, Specialist, DrType, Patient, "G_01", Gen) <= (admDrugT(Date, PrescribedBy, Drug, Patient, Age) & personSpec(PrescribedBy, Specialist) & drugType(Drug, DrType) & (Gen=="G_01"))
from pyDatalog.pyDatalog import create_terms, load, ask, assert_fact
create_terms(
'X, croakes, eatFlies, green, frog, sings, chirps, yellow, canary')
load("""
""")
frog(X) <= croakes(X) & eatFlies(X)
canary(X) <= sings(X) & chirps(X)
green(X) <= frog(X)
yellow(X) <= canary(X)
assert_fact('croakes', 'fritz')
assert_fact('eatFlies', 'fritz')
+sings('titi')
+chirps('titi')
+frog('Toto')
# assert_fact('sings','Jean' )
# assert_fact('chirps','Jean' )
query = 'yellow(X)'
answers = ask(query).answers
print(answers)
print(ask('frog(X)'))
print(ask("frog('fritz')"))
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 29 14:34:10 2019
@author: Margot
"""
from pyDatalog import pyDatalog
pyDatalog.clear()
pyDatalog.create_terms(
'X, P, rectangle, angleDroit, quelconque, equilateral, deuxcote, troiscote, isocele, rectangleIsocele, triangle'
)
pyDatalog.load("""
""")
rectangle(X) <= angleDroit(X)
isocele(X) <= deuxcote(X)
rectangleIsocele(X) <= angleDroit(X) & deuxcote(X)
equilateral(X) <= troiscote(X)
pyDatalog.assert_fact('deuxcote', 'triangle')
pyDatalog.assert_fact('angleDroit', 'triangle')
query = 'isocele(X)'
answers = pyDatalog.ask(query).answers
print(answers)