def example():
"""Example of using the FullBrevity class."""
facts = [[Type, "cube", "obj1"], ["color", "red", "obj1"], ["size", "big", "obj1"],
[Type, "ball", "obj2"], ["color", "blue", "obj2"], ["size", "big", "obj2"],
[Type, "ball", "obj3"], ["color", "red", "obj3"], ["size", "small", "obj3"]]
fb = FullBrevity(facts)
# Print English description for each object
for obj_id in ["obj1", "obj2", "obj3"]:
obj_type = [f for f in facts if f[0] == Type and f[2] == obj_id] # Include type for clarity
print "%s: %s" % (obj_id, generate_phrase(fb.describe(obj_id) + obj_type, ["color", "size"]))
def example():
"""Example of using the Incremental class."""
facts = [[Type, "cube", "obj1"], ["color", "brick", "obj1"],
["size", "big", "obj1"], [Type, "ball", "obj2"],
["color", "navy", "obj2"], ["size", "big", "obj2"],
[Type, "ball", "obj3"], ["color", "scarlet", "obj3"],
["size", "small", "obj3"]]
ranked_attrs = ["color", "size", Type]
tax = {
"blue": {
"parent": None,
"children": ["navy", "cerulean"]
},
"red": {
"parent": None,
"children": ["scarlet", "brick"]
},
"navy": {
"parent": "blue",
"children": []
},
"cerulean": {
"parent": "blue",
"children": []
},
"scarlet": {
"parent": "red",
"children": []
},
"brick": {
"parent": "red",
"children": []
}
}
taxonomy = Taxonomy(tax)
incr = Incremental(facts, ranked_attrs, taxonomy)
# Print English description for each object
for obj_id in ["obj1", "obj2", "obj3"]:
obj_type = [f for f in facts if f[0] == Type and f[2] == obj_id
] # Include type for clarity
print "%s: %s" % (obj_id,
generate_phrase(
incr.describe(obj_id) + obj_type,
["color", "size"]))
def example():
"""Example of using the Incremental class."""
facts = [[Type, "cube", "obj1"], ["color", "brick", "obj1"], ["size", "big", "obj1"],
[Type, "ball", "obj2"], ["color", "navy", "obj2"], ["size", "big", "obj2"],
[Type, "ball", "obj3"], ["color", "scarlet", "obj3"], ["size", "small", "obj3"]]
ranked_attrs = ["color", "size", Type]
tax = {
"blue" : {"parent" : None, "children" : ["navy", "cerulean"]},
"red" : {"parent" : None, "children" : ["scarlet", "brick"]},
"navy" : {"parent" : "blue", "children" : []},
"cerulean" : {"parent" : "blue", "children" : []},
"scarlet" : {"parent" : "red", "children" : []},
"brick" : {"parent" : "red", "children" : []}
}
taxonomy = Taxonomy(tax)
incr = Incremental(facts, ranked_attrs, taxonomy)
# Print English description for each object
for obj_id in ["obj1", "obj2", "obj3"]:
obj_type = [f for f in facts if f[0] == Type and f[2] == obj_id] # Include type for clarity
print "%s: %s" % (obj_id, generate_phrase(incr.describe(obj_id) + obj_type, ["color", "size"]))
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))