def test_goal_dist_heuristic():
min_len = 4
max_len = 10
indicators = dict((d, ['I%d' % d]) for d in range(1, max_len))
indicator_set = set(reduce(lambda x,y: x+y, indicators.values()))
tree_gen = TreeGen()
tree_gen.min_branch_len = min_len
tree_gen.max_branch_len = max_len
src_root = tree_gen.generate_indicators(indicators)
src_root.make_graphviz(open('src_tree.dot','w'))
tgt_root = src_root.deep_copy()
# change all predicates in the target to be lowercase version of source
tgt_root.map(state_preds_lower)
tgt_root.make_graphviz(open('tgt_tree.dot','w'))
src_preds = list(src_root.get_all_predicates())
num_preds = len(src_preds)
src_preds.sort()
src_weights = fmincon.run_fmincon(src_root)
# the heuristic for the source
hs = heuristic.Heuristic(dict(zip(src_preds, src_weights)))
#hs = heuristic.Heuristic(indicators)
src_search = AStar(src_root, lambda x: hs(x.preds))
iter = src_search.gen
iterations = 0
try:
while not iter.next():
iterations += 1
except StopIteration:
src_iters = iterations
non_ind_preds = [p for p in src_preds if p not in indicator_set]
data = []
for n_ind, n_rest, mapping in ind_mapping_gen(indicator_set, non_ind_preds, num_preds, 100):
#for n_matches, mapping in reg_mapping_gen(src_preds, 100):
#print "The mapping is ..."
#for s,t in mapping.items():
# print s, t
tgt_preds2weights = {}
for p,w in zip(src_preds, src_weights):
if p in mapping:
# we assign the weight of the source predicate to the target
tgt_preds2weights[mapping[p]] = w
ht = heuristic.Heuristic(tgt_preds2weights)
tgt_search = AStar(tgt_root, lambda x: ht(x.preds))
iter = tgt_search.gen
iterations = 0
try:
while not iter.next():
iterations += 1
except StopIteration:
tgt_iters = iterations
#print 'The target problem takes %d iterations' % tgt_iters
#print 'Solution is', [''.join(s.preds) for s in tgt_search.solution]
data.append((n_ind, n_rest, tgt_iters))
#data.append((n_matches, tgt_iters))
n_preds = len(src_preds)
return (n_preds, src_iters, data)
def test_goal_dist_heuristic():
min_len = 4
max_len = 10
indicators = dict((d, ['I%d' % d]) for d in range(1, max_len))
indicator_set = set(reduce(lambda x, y: x + y, indicators.values()))
tree_gen = TreeGen()
tree_gen.min_branch_len = min_len
tree_gen.max_branch_len = max_len
src_root = tree_gen.generate_indicators(indicators)
src_root.make_graphviz(open('src_tree.dot', 'w'))
tgt_root = src_root.deep_copy()
# change all predicates in the target to be lowercase version of source
tgt_root.map(state_preds_lower)
tgt_root.make_graphviz(open('tgt_tree.dot', 'w'))
src_preds = list(src_root.get_all_predicates())
num_preds = len(src_preds)
src_preds.sort()
src_weights = fmincon.run_fmincon(src_root)
# the heuristic for the source
hs = heuristic.Heuristic(dict(zip(src_preds, src_weights)))
#hs = heuristic.Heuristic(indicators)
src_search = AStar(src_root, lambda x: hs(x.preds))
iter = src_search.gen
iterations = 0
try:
while not iter.next():
iterations += 1
except StopIteration:
src_iters = iterations
non_ind_preds = [p for p in src_preds if p not in indicator_set]
data = []
for n_ind, n_rest, mapping in ind_mapping_gen(indicator_set, non_ind_preds,
num_preds, 100):
#for n_matches, mapping in reg_mapping_gen(src_preds, 100):
#print "The mapping is ..."
#for s,t in mapping.items():
# print s, t
tgt_preds2weights = {}
for p, w in zip(src_preds, src_weights):
if p in mapping:
# we assign the weight of the source predicate to the target
tgt_preds2weights[mapping[p]] = w
ht = heuristic.Heuristic(tgt_preds2weights)
tgt_search = AStar(tgt_root, lambda x: ht(x.preds))
iter = tgt_search.gen
iterations = 0
try:
while not iter.next():
iterations += 1
except StopIteration:
tgt_iters = iterations
#print 'The target problem takes %d iterations' % tgt_iters
#print 'Solution is', [''.join(s.preds) for s in tgt_search.solution]
data.append((n_ind, n_rest, tgt_iters))
#data.append((n_matches, tgt_iters))
n_preds = len(src_preds)
return (n_preds, src_iters, data)
