def example():
"""Example of using the Relational class."""
facts = [[Type, "cup", "c1"], [Type, "cup", "c2"], [Type, "cup", "c3"],
[Type, "bowl", "b1"], [Type, "bowl", "b2"],
[Type, "table", "t1"], [Type, "table", "t2"], ["color", "red", "t2"],
[Type, "floor", "f1"],
[Rel, "in", "c1", "b1"], [Rel, "in", "c2", "b2"],
[Rel, "on", "c3", "f1"], [Rel, "on", "b1", "f1"], [Rel, "on", "b2", "t1"],
[Rel, "on", "t1", "f1"], [Rel, "on", "t2", "f1"]]
rel = Relational(facts)
obj_types = [f for f in facts if f[0] == Type] # Include types in the description for clarity
handlers = {
"on" : lambda(lr): "on" if lr else "on which lies",
"in" : lambda(lr): "in" if lr else "in which lies"
}
# Generate an English description for each object
for obj_id in ["c1", "c2", "c3", "b1", "b2", "t1", "t2", "f1"]:
print "%s: %s" % (obj_id, generate_phrase_rel(rel.describe(obj_id) + obj_types, ["color"], obj_id, handlers))
shuffle(facts, lambda: 0.0)
fb = FullBrevity(filter(lambda f: f[0] != Rel, facts))
rel = Relational(facts)
#The ordered priority for using attributes, important for incremental algorithm
ranked_attrs = ["color", "row", "col", "corner"]
#Taxonomy used in incremental algorithm to pick out a more common name when appropriate
# For instance dog instead of Chihuahua when there is a referent dog amongst other animals (which are not dogs)
taxonomy = Taxonomy({})
incr = Incremental(facts, ranked_attrs, taxonomy)
#defines how to turn these rules into English phrases
handlers = {
"col": lambda(desc): "column %s" % desc,
"row": lambda(desc): "row %s" % desc,
"corner": lambda(desc): "corner",
"above": lambda(lr): "above" if lr else "below",
"below": lambda(lr): "below" if lr else "above",
"right": lambda(lr): "to the right of" if lr else "to the left of",
"left": lambda(lr): "to the left of" if lr else "to the right of"
}
#Generate phrases with each algorithm and print to screen
for i in range(1, 17):
obj_id = "d%s" % i
print "%#02d,\"Full Brevity\",\"%s\"" % (i, generate_phrase(fb.describe(obj_id), ranked_attrs, handlers))
print "%#02d,\"Relational\",\"%s\"" % (i, generate_phrase_rel(rel.describe(obj_id), ranked_attrs, obj_id, handlers))
print "%#02d,\"Incremental\",\"%s\"" % (i, generate_phrase(incr.describe(obj_id), ranked_attrs, handlers))
if __name__ == '__main__':
facts = getFacts()
ranked_attrs = ["color", "size", Type]
taxonomy = Taxonomy({})
handlers = {
"in_front_of": lambda (lr): "in front of",
"left_of": lambda (lr): "to the left of",
"right_of": lambda (lr): "to the right of"
}
#Print out the referring expressions generated by each algorithm for each scene
for i in range(1, 21):
fb = FullBrevity(facts[i])
desc_fb = fb.describe("r1")
incr = Incremental(facts[i], ranked_attrs, taxonomy)
desc_incr = incr.describe("r1")
rel = Relational(facts[i])
desc_rel = rel.describe("r1")
print "%#02d,\"Full Brevity\",\"%s\"" % (
i, util.generate_phrase(desc_fb, ranked_attrs))
print "%#02d,\"Incremental\",\"%s\"" % (
i, util.generate_phrase(desc_incr, ranked_attrs))
print "%#02d,\"Relational\",\"%s\"" % (
i, util.generate_phrase_rel(desc_rel, ranked_attrs, "r1",
handlers))
return facts
if __name__ == "__main__":
facts = getFacts()
ranked_attrs = ["color", "size", Type]
taxonomy = Taxonomy({})
handlers = {
"in_front_of": lambda (lr): "in front of",
"left_of": lambda (lr): "to the left of",
"right_of": lambda (lr): "to the right of",
}
# Print out the referring expressions generated by each algorithm for each scene
for i in range(1, 21):
fb = FullBrevity(facts[i])
desc_fb = fb.describe("r1")
incr = Incremental(facts[i], ranked_attrs, taxonomy)
desc_incr = incr.describe("r1")
rel = Relational(facts[i])
desc_rel = rel.describe("r1")
print '%#02d,"Full Brevity","%s"' % (i, util.generate_phrase(desc_fb, ranked_attrs))
print '%#02d,"Incremental","%s"' % (i, util.generate_phrase(desc_incr, ranked_attrs))
print '%#02d,"Relational","%s"' % (i, util.generate_phrase_rel(desc_rel, ranked_attrs, "r1", handlers))
