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 get_message(self, index, u_parents):
(m, mask) = self.message_from_children()
parent = self.parents[index]
# Compute both messages
for i in range(2):
# Add extra axes to the message from children
#m_shape = np.shape(m[i]) + (1,) * (i+1)
#m[i] = np.reshape(m[i], m_shape)
# Put masked elements to zero
np.copyto(m[i], 0, where=np.logical_not(mask))
# Add extra axes to the mask from children
#mask_shape = np.shape(mask) + (1,) * (i+1)
#mask_i = np.reshape(mask, mask_shape)
#mask_i = mask
m[i] = utils.add_trailing_axes(m[i], i+1)
#for k in range(i+1):
#m[i] = np.expand_dims(m[i], axis=-1)
#mask_i = np.expand_dims(mask_i, axis=-1)
# List of elements to multiply together
A = [m[i]]
for k in range(len(u_parents)):
if k != index:
A.append(u_parents[k][i])
# Find out which axes are summed over. Also,
full_shape = utils.broadcasted_shape_from_arrays(*A)
axes = utils.axes_to_collapse(full_shape, parent.get_shape(i))
# Compute the multiplier for cancelling the
# plate-multiplier. Because we are summing over the
# dimensions already in this function (for efficiency), we
# need to cancel the effect of the plate-multiplier
# applied in the message_to_parent function.
r = 1
for j in axes:
r *= full_shape[j]
# Compute dot product (and cancel plate-multiplier)
m[i] = utils.sum_product(*A, axes_to_sum=axes, keepdims=True) / r
# Compute the mask
s = utils.axes_to_collapse(np.shape(mask), parent.plates)
mask = np.any(mask, axis=s, keepdims=True)
mask = utils.squeeze_to_dim(mask, len(parent.plates))
return (m, mask)
def get_message(self, index, u_parents):
(m, mask) = self.message_from_children()
parent = self.parents[index]
# Compute both messages
for i in range(2):
# Add extra axes to the message from children
#m_shape = np.shape(m[i]) + (1,) * (i+1)
#m[i] = np.reshape(m[i], m_shape)
# Put masked elements to zero
np.copyto(m[i], 0, where=np.logical_not(mask))
# Add extra axes to the mask from children
#mask_shape = np.shape(mask) + (1,) * (i+1)
#mask_i = np.reshape(mask, mask_shape)
#mask_i = mask
m[i] = utils.add_trailing_axes(m[i], i + 1)
#for k in range(i+1):
#m[i] = np.expand_dims(m[i], axis=-1)
#mask_i = np.expand_dims(mask_i, axis=-1)
# List of elements to multiply together
A = [m[i]]
for k in range(len(u_parents)):
if k != index:
A.append(u_parents[k][i])
# Find out which axes are summed over. Also,
full_shape = utils.broadcasted_shape_from_arrays(*A)
axes = utils.axes_to_collapse(full_shape, parent.get_shape(i))
# Compute the multiplier for cancelling the
# plate-multiplier. Because we are summing over the
# dimensions already in this function (for efficiency), we
# need to cancel the effect of the plate-multiplier
# applied in the message_to_parent function.
r = 1
for j in axes:
r *= full_shape[j]
# Compute dot product (and cancel plate-multiplier)
m[i] = utils.sum_product(*A, axes_to_sum=axes, keepdims=True) / r
# Compute the mask
s = utils.axes_to_collapse(np.shape(mask), parent.plates)
mask = np.any(mask, axis=s, keepdims=True)
mask = utils.squeeze_to_dim(mask, len(parent.plates))
return (m, mask)
def OLD_get_message(self, index, u_parents):
(m, mask) = self.message_from_children()
parent = self.parents[index]
# Compute both messages
for i in range(2):
# Add extra axes to the message from children
#m_shape = np.shape(m[i]) + (1,) * (i+1)
#m[i] = np.reshape(m[i], m_shape)
# Add extra axes to the mask from children
mask_shape = np.shape(mask) + (1,) * (i+1)
mask_i = np.reshape(mask, mask_shape)
mask_i = mask
for k in range(i+1):
m[i] = np.expand_dims(m[i], axis=-1)
mask_i = np.expand_dims(mask_i, axis=-1)
# List of elements to multiply together
A = [m[i], mask_i]
for k in range(len(u_parents)):
if k != index:
A.append(u_parents[k][i])
# Find out which axes are summed over. Also, because
# we are summing over the dimensions already in this
# function (for efficiency), we need to cancel the
# effect of the plate-multiplier applied in the
# message_to_parent function.
full_shape = utils.broadcasted_shape_from_arrays(*A)
axes = utils.axes_to_collapse(full_shape, parent.get_shape(i))
r = 1
for j in axes:
r *= full_shape[j]
# Compute dot product
m[i] = utils.sum_product(*A, axes_to_sum=axes, keepdims=True) / r
# Compute the mask
s = utils.axes_to_collapse(np.shape(mask), parent.plates)
mask = np.any(mask, axis=s, keepdims=True)
mask = utils.squeeze_to_dim(mask, len(parent.plates))
return (m, mask)
def OLD_get_message(self, index, u_parents):
(m, mask) = self.message_from_children()
parent = self.parents[index]
# Compute both messages
for i in range(2):
# Add extra axes to the message from children
#m_shape = np.shape(m[i]) + (1,) * (i+1)
#m[i] = np.reshape(m[i], m_shape)
# Add extra axes to the mask from children
mask_shape = np.shape(mask) + (1, ) * (i + 1)
mask_i = np.reshape(mask, mask_shape)
mask_i = mask
for k in range(i + 1):
m[i] = np.expand_dims(m[i], axis=-1)
mask_i = np.expand_dims(mask_i, axis=-1)
# List of elements to multiply together
A = [m[i], mask_i]
for k in range(len(u_parents)):
if k != index:
A.append(u_parents[k][i])
# Find out which axes are summed over. Also, because
# we are summing over the dimensions already in this
# function (for efficiency), we need to cancel the
# effect of the plate-multiplier applied in the
# message_to_parent function.
full_shape = utils.broadcasted_shape_from_arrays(*A)
axes = utils.axes_to_collapse(full_shape, parent.get_shape(i))
r = 1
for j in axes:
r *= full_shape[j]
# Compute dot product
m[i] = utils.sum_product(*A, axes_to_sum=axes, keepdims=True) / r
# Compute the mask
s = utils.axes_to_collapse(np.shape(mask), parent.plates)
mask = np.any(mask, axis=s, keepdims=True)
mask = utils.squeeze_to_dim(mask, len(parent.plates))
return (m, mask)
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]))
