def update_u(self, u, mask=True):
# Store the computed moments u but do not change moments for
# observations, i.e., utilize the mask.
for ind in range(len(u)):
# Add axes to the mask for the variable dimensions (mask
# contains only axes for the plates).
u_mask = utils.add_trailing_axes(mask, self.ndims[ind])
# Enlarge self.u[ind] as necessary so that it can store the
# broadcasted result.
sh = utils.broadcasted_shape_from_arrays(self.u[ind], u[ind],
u_mask)
self.u[ind] = utils.repeat_to_shape(self.u[ind], sh)
# Use mask to update only unobserved plates and keep the
# observed as before
np.copyto(self.u[ind], u[ind], where=u_mask)
# Make sure u has the correct number of dimensions:
shape = self.get_shape(ind)
ndim = len(shape)
ndim_u = np.ndim(self.u[ind])
if ndim > ndim_u:
self.u[ind] = utils.add_leading_axes(u[ind], ndim - ndim_u)
elif ndim < np.ndim(self.u[ind]):
raise Exception("Weird, this shouldn't happen.. :)")
def update_u(self, u, mask=True):
# Store the computed moments u but do not change moments for
# observations, i.e., utilize the mask.
for ind in range(len(u)):
# Add axes to the mask for the variable dimensions (mask
# contains only axes for the plates).
u_mask = utils.add_trailing_axes(mask, self.ndims[ind])
# Enlarge self.u[ind] as necessary so that it can store the
# broadcasted result.
sh = utils.broadcasted_shape_from_arrays(self.u[ind], u[ind], u_mask)
self.u[ind] = utils.repeat_to_shape(self.u[ind], sh)
# Use mask to update only unobserved plates and keep the
# observed as before
np.copyto(self.u[ind],
u[ind],
where=u_mask)
# Make sure u has the correct number of dimensions:
shape = self.get_shape(ind)
ndim = len(shape)
ndim_u = np.ndim(self.u[ind])
if ndim > ndim_u:
self.u[ind] = utils.add_leading_axes(u[ind], ndim - ndim_u)
elif ndim < np.ndim(self.u[ind]):
raise Exception("Weird, this shouldn't happen.. :)")
def _set_moments(self, u, mask=True):
# Store the computed moments u but do not change moments for
# observations, i.e., utilize the mask.
for ind in range(len(u)):
# Add axes to the mask for the variable dimensions (mask
# contains only axes for the plates).
u_mask = utils.add_trailing_axes(mask, self._distribution.ndims[ind])
# Enlarge self.u[ind] as necessary so that it can store the
# broadcasted result.
sh = utils.broadcasted_shape_from_arrays(self.u[ind], u[ind], u_mask)
self.u[ind] = utils.repeat_to_shape(self.u[ind], sh)
# TODO/FIXME/BUG: The mask of observations is not used, observations
# may be overwritten!!! ???
# Hah, this function is used to set the observations! The caller
# should be careful what mask he uses! If you want to set only
# latent variables, then use such a mask.
# Use mask to update only unobserved plates and keep the
# observed as before
np.copyto(self.u[ind],
u[ind],
where=u_mask)
# Make sure u has the correct number of dimensions:
# TODO/FIXME: Maybe it would be good to also check that u has a
# shape that is a sub-shape of get_shape.
shape = self.get_shape(ind)
ndim = len(shape)
ndim_u = np.ndim(self.u[ind])
if ndim > ndim_u:
self.u[ind] = utils.add_leading_axes(u[ind], ndim - ndim_u)
elif ndim < ndim_u:
raise RuntimeError(
"The size of the variable %s's %s-th moment "
"array is %s which is larger than it should "
"be, that is, %s, based on the plates %s and "
"dimension %s. Check that you have provided "
"plates properly."
% (self.name,
ind,
np.shape(self.u[ind]),
shape,
self.plates,
self.dims[ind]))
def _set_moments(self, u, mask=True):
# Store the computed moments u but do not change moments for
# observations, i.e., utilize the mask.
for ind in range(len(u)):
# Add axes to the mask for the variable dimensions (mask
# contains only axes for the plates).
u_mask = utils.add_trailing_axes(mask,
self._distribution.ndims[ind])
# Enlarge self.u[ind] as necessary so that it can store the
# broadcasted result.
sh = utils.broadcasted_shape_from_arrays(self.u[ind], u[ind],
u_mask)
self.u[ind] = utils.repeat_to_shape(self.u[ind], sh)
# TODO/FIXME/BUG: The mask of observations is not used, observations
# may be overwritten!!! ???
# Hah, this function is used to set the observations! The caller
# should be careful what mask he uses! If you want to set only
# latent variables, then use such a mask.
# Use mask to update only unobserved plates and keep the
# observed as before
np.copyto(self.u[ind], u[ind], where=u_mask)
# Make sure u has the correct number of dimensions:
# TODO/FIXME: Maybe it would be good to also check that u has a
# shape that is a sub-shape of get_shape.
shape = self.get_shape(ind)
ndim = len(shape)
ndim_u = np.ndim(self.u[ind])
if ndim > ndim_u:
self.u[ind] = utils.add_leading_axes(u[ind], ndim - ndim_u)
elif ndim < ndim_u:
raise RuntimeError(
"The size of the variable %s's %s-th moment "
"array is %s which is larger than it should "
"be, that is, %s, based on the plates %s and "
"dimension %s. Check that you have provided "
"plates properly." % (self.name, ind, np.shape(
self.u[ind]), shape, self.plates, self.dims[ind]))
