def ook_decode_distribution(self, array):
assert len(array) == len(self), "%d != %d" % (len(array), len(self))
check.check_pdist(array)
return {
self.decode(i): probability
for i, probability in enumerate(array)
}
def sampling_ook_decode(self, array):
assert len(array) == len(self), "%d != %d" % (len(array), len(self))
check.check_pdist(array)
# Sample 1 thing v
# from [0..N] vvvvvvvvv
# with probabilties vvvvvvv
index = np.random.choice(len(self), 1, p=array)[0]
return self.decode(index)
def ook_decode(self, array):
assert len(array) == len(self), "%d != %d" % (len(array), len(self))
check.check_pdist(array)
# If the array is all zeros
if not np.any(array):
return None
# The index of the maximum value from the ook_encoding.
# vvvvvvvvvvvvvv
return self.decode(array.argmax())
def __init__(self, scope, output_labels, output_distribution):
super(CustomOutput, self).__init__(scope)
self.output_labels = check.check_instance(output_labels, mlbase.Labels)
self.output_distribution = check.check_pdist(output_distribution)
assert len(self.output_labels) == len(
self.output_distribution), "%d != %d" % (len(
self.output_labels), len(self.output_distribution))
def ook_encode(self, value, handle_unknown=False):
encoding = [0] * len(self)
if isinstance(value, dict):
for key, probability in check.check_pdist(value).items():
encoding[self.encode(key, handle_unknown)] = probability
else:
encoding[self.encode(value, handle_unknown)] = 1
return np.array(encoding, dtype="float32")
def vector_decode_probability(self, array, value, handle_oov=False):
assert len(array) == len(self), "%d != %d" % (len(array), len(self))
check.check_pdist(array)
return array[self.encode(value, handle_oov)]
def __init__(self, prediction, distribution, encoding):
self.prediction = prediction
self.distribution = check.check_pdist(distribution)
self.encoding = encoding