def main():
path = os.path.join('Vessel Traffic Data sub-areas April 2020',
'cts_sub-areas_04_2020_pt',
'cts_bass_st_04_2020_pt.shp')
df = gpd.read_file(path)
mln = MLN()
populatePredicates(mln)
populateFormulas(mln)
mln.write()
dbs = []
for i in range(100):
collection = df.sample(n=3)
lons = collection['LON'].tolist()
lats = collection['LAT'].tolist()
points = list(zip(lons, lats))
evidence = ThreePointEvidence(*points)
db = Database(mln)
evidence.generateEvidence(db)
dbs.append(db)
mln.learn(dbs)
def test_GSMLN():
# mln = MLN(grammar='GSMLNGrammar')
# mln << 'residue(id, profile)'
# mln << 'partners(id, id)'
# f = "residue(a, $pa) v residue(b, $pb) => partners(a,b)"
# # f = "((a(x) ^ b(x)) v (c(x) ^ !(d(x) ^ e(x) ^ g(x)))) => f(x)"
# # f = "(a(x) v (b(x) ^ c(x))) => f(x)"
# f = mln.logic.grammar.parse_formula(f)
# f.print_structure()
# print(list(f.literals()))
# g = "partners(id, id)"
# g = mln.logic.grammar.parse_predicate(g)
# print(g)
# print(mln.predicates)
mln = MLN(mlnfile='smokers.mln', grammar='GSMLNGrammar')
# mln.write()
# print(mln.predicates)
dbs = Database.load(mln, dbfiles='smokers.db')
# dbs[0].write()
# print(mln.formulas[0].neural)
# print(mln.formulas[0].cnf())
# print(mln.nnformulas[0].idx)
# print(mln.domains)
# print(dbs[0].domains)
# mln.formulas[1].print_structure()
# mrf = mln.ground(dbs[0])
# grounder = DefaultGroundingFactory(mrf, simplify=False, unsatfailure=True, verbose=False, cache=0)
# for f in grounder.itergroundings():
# print(f)
# print((mrf.gndatoms))
# mln = MLN(grammar='GSMLNGrammar')
# mln << 'Cancer(&person)'
# mln << 'Friends(&person,&person)'
# mln << 'Smokes(&person)'
# f = 'Smokes($x) => Cancer($x)'
# g = 'Friends($x,$y) => (Smokes($x) <=> Smokes($y))'
# print(mln.logic.grammar.parse_formula(f))
# mln.formula(f)
# mln.formula(g)
# print(mln.predicates)
# print(mln.formulas)
# mln.formulas[0].print_structure()
# print(mln.domains)
# print(mln.formulas[0].cnf())
# this uses the method from base.py
learned_mln = mln.learn(databases=dbs, method=GSMLN_L, verbose=True)
