def to_v(self, x):
for k in x.keys():
x[k] = x[k] / self.message_to_x[k]
p = tf.concat([xx.precision() for k, xx in x.items()], -1)
mtp = tf.concat([xx.mean_times_precision() for k, xx in x.items()], -1)
self.message_to_v = GaussianArray.from_array_natural(p, mtp)
return self.message_to_v
def to_vector(self):
parts = dict()
for name in self._name:
parts[name] = self.parts[name] / self.message_to_parts[name]
p = tf.concat([part.precision() for k, part in parts.items()], -1)
mtp = tf.concat([part.mean_times_precision() for k, part in parts.items()], -1)
message_to_vector = GaussianArray.from_array_natural(p, mtp)
self.vector.update(self.message_to_vector, message_to_vector)
self.message_to_vector = message_to_vector
def to_x(self, v):
v = v / self.message_to_v
p, mtp = v.natural()
for name, begin, size in zip(self._name, self._begin, self._size):
self.message_to_x[name] = GaussianArray.from_array_natural(
tf.slice(p, begin, size),
tf.slice(mtp, begin, size)
)
return self.message_to_x
def to_parts(self):
v = self.vector / self.message_to_vector
p, mtp = v.natural()
for name, begin, size in zip(self._name, self._begin, self._size):
message_to_part = GaussianArray.from_array_natural(
tf.slice(p, begin, size),
tf.slice(mtp, begin, size)
)
self.parts[name].update(self.message_to_parts[name], message_to_part)
self.message_to_parts[name] = message_to_part
def to_x(self, x, result, weight, bias):
result = result / self.message_to_result
x = x / self.message_to_x
m = (tf.expand_dims(result.mean() - bias - tf.tensordot(x.mean(), weight, 1), 1) +
tf.expand_dims(x.mean(), -1) * tf.expand_dims(weight, 0)) / tf.expand_dims(weight, 0)
v = (tf.expand_dims(result.log_var() + tf.tensordot(x.log_var(), weight ** 2, 1), 1) -
tf.expand_dims(x.log_var(), -1) * tf.expand_dims(weight ** 2, 0)) / tf.expand_dims(weight ** 2, 0)
p = 1.0 / v
mtp = m * p
self.message_to_x = GaussianArray.from_array_natural(
tf.reduce_sum(p, -1),
tf.reduce_sum(mtp, -1)
)
return self.message_to_x
def to_parent(self):
result = self.child / self.message_to_child
x = self.parent / self.message_to_parent
weight = self.weight.value()
bias = self.bias.value()
m = (tf.expand_dims(result.mean() - bias - tf.tensordot(x.mean(), weight, 1), 1) +
tf.expand_dims(x.mean(), -1) * tf.expand_dims(weight, 0)) / tf.expand_dims(weight, 0)
v = (tf.expand_dims(result.log_var() + tf.tensordot(x.log_var(), weight ** 2, 1), 1) -
tf.expand_dims(x.log_var(), -1) * tf.expand_dims(weight ** 2, 0)) / tf.expand_dims(weight ** 2, 0)
p = 1.0 / v
mtp = m * p
message_to_parent = GaussianArray.from_array_natural(
tf.reduce_sum(p, -1),
tf.reduce_sum(mtp, -1)
)
self.parent.update(self.message_to_parent, message_to_parent)
self.message_to_parent = message_to_parent
