def _test_case(scene, expression, expected, msg=None):
from pprint import pprint
print("Objects:")
pprint(scene['objects'])
model = Model(scene, ontology)
expr = Expression.fromstring(expression)
value = model.evaluate(expr)
print(expr)
print("Expected:", expected)
print("Observed:", value)
eq_(value, expected, msg)
include_semantics=True)
#######
# Execute on a scene.
scene = {
"objects": [
Object("sphere", "big", "rubber"),
Object("cube", "small", "metal"),
Object("cylinder", "small", "rubber"),
]
}
model = Model(scene, ontology)
print("the ball")
print(model.evaluate(Expression.fromstring(r"unique(\x.has_shape(x,sphere))")))
######
# Parse an utterance and execute.
learner = WordLearner(lex)
# Update with distant supervision.
learner.update_with_distant("the cube".split(), model, scene['objects'][1])
parser = learner.make_parser()
results = parser.parse("the cube".split())
printCCGDerivation(results[0])
root_token, _ = results[0].label()
print(root_token.semantics())