continue
rgb = [int(x) for x in entry[1:4]]
word = entry[4][:-1]
learnerB.learn(word, Expression(["COLOUR", "r_%d" % rgb[0],
"g_%d" % rgb[1],
"b_%d" % rgb[2] ]))
# pick sample of N random colours
test_colours = [
(random.randint(0,255),
random.randint(0,255),
random.randint(0,255)) for i in range(args.num_test_samples)]
logger.log_points("langA", test_colours)
logger.mean("langA")
# generation simulation
for generation in range(1,args.generations):
learnerA = learnerB
learnerB = gauss_colour.GaussianColourSemantics("L_%d" % generation, creative=True)
logger.learner = learnerB
# train L(i+1) (B) from L(i) (A)
for i in range(0, args.num_train_samples):
(word, meaning) = learnerA.say_something()
learnerB.learn(word, meaning)
# output sample from trained L(i+1)
logger = logger.colour_logger.ColourLogger(learnerA)
# train A from stdin
for line in fileinput.input():
entry = line.split(",")
if entry[0] == "lang.name":
continue
rgb = [int(x) for x in entry[1:4]]
word = entry[4][:-1]
learnerA.learn(word, pack_expression(rgb))
# pick sample of N random colours (in blue subspace)
test_colours = [pick_random_blue() for i in range(NUM_SAMPLES)]
# output for learner A
logger.log_points("langA", test_colours)
logger.learner = learnerB
# iteratively train learner B on A's utterances (N iterations)
for i in range(0, TRAINING_ITERATIONS):
meaning = pack_expression(pick_random_blue())
word = learnerA.word_for(meaning)
learnerB.learn(word, meaning)
if i % SKIP == 0:
logger.log_points("langB_%02d" % i, test_colours)
