def main():
options, input_seq = _parse_args()
history = _create_history(options, input_seq)
model = _create_model(options, history)
if options.bytes:
num_predicted_bits = _round_up(options.num_predicted_bits, 8)
else:
num_predicted_bits = options.num_predicted_bits
probs = []
bits = ""
probability = 1.0
sys.stdout.write("%s -> " % formatting.to_seq(history))
for i in xrange(num_predicted_bits):
bit, bit_p = modeling.advance(model)
probs.append(bit_p)
probability *= bit_p
sys.stdout.write(str(bit))
sys.stdout.flush()
bits += str(bit)
print
if options.bytes:
print "%s -> %s" % (input_seq, byting.to_bytes(bits))
if len(probs) > 10:
probs_info = _format_products(probs[:9]) + " * ..."
else:
probs_info = _format_products(probs)
print "with P = %f = %s" % (probability, probs_info)
def main():
options, input_seq = _parse_args()
history = _create_history(options, input_seq)
model = _create_model(options, history)
if options.bytes:
num_predicted_bits = _round_up(options.num_predicted_bits, 8)
else:
num_predicted_bits = options.num_predicted_bits
probs = []
bits = ""
probability = 1.0
sys.stdout.write("%s -> " % formatting.to_seq(history))
for i in xrange(num_predicted_bits):
bit, bit_p = modeling.advance(model)
probs.append(bit_p)
probability *= bit_p
sys.stdout.write(str(bit))
sys.stdout.flush()
bits += str(bit)
print
if options.bytes:
print "%s -> %s" % (input_seq, byting.to_bytes(bits))
if len(probs) > 10:
probs_info = _format_products(probs[:9]) + " * ..."
else:
probs_info = _format_products(probs)
print "with P = %f = %s" % (probability, probs_info)
def save_perception(env, path):
bits = _get_seen_bits(env, path)
with open(STORE_FILENAME, "ab") as output:
output.write(formatting.to_seq(bits))
output.write("\n")
def __str__(self):
return ("Impossible history. Try non-deterministic prior. " +
"History: %s" % formatting.to_seq(self.history))
def __str__(self):
return "Impossible history. Try non-deterministic prior. " + "History: %s" % formatting.to_seq(self.history)