def _compute_moments(self, u_X):
"""
Tile the plates of the parent's moments.
"""
# Utilize broadcasting: If a tiled axis is unit length in u_X, there
# is no need to tile it.
u = list()
for ind in range(len(u_X)):
ui = u_X[ind]
shape_u = np.shape(ui)
if np.ndim(ui) > 0:
# Add variable dimensions
tiles_ind = tiles + (1,)*len(self.dims[ind])
# Utilize broadcasting: Do not tile leading empty axes
nd = min(len(tiles_ind), np.ndim(ui))
tiles_ind = tiles_ind[(-nd):]
# For simplicity, make tiles and shape equal length
(tiles_ind, shape_u) = misc.make_equal_length(tiles_ind,
shape_u)
# Utilize broadcasting: Use tiling only if the parent's
# moment has non-unit axis length.
tiles_ind = [tile if sh > 1 else 1
for (tile, sh) in zip(tiles_ind, shape_u)]
# Tile
ui = np.tile(ui, tiles_ind)
u.append(ui)
return u
def _compute_moments(self, u_X):
"""
Tile the plates of the parent's moments.
"""
# Utilize broadcasting: If a tiled axis is unit length in u_X, there
# is no need to tile it.
u = list()
for ind in range(len(u_X)):
ui = u_X[ind]
shape_u = np.shape(ui)
if np.ndim(ui) > 0:
# Add variable dimensions
tiles_ind = tiles + (1, ) * len(self.dims[ind])
# Utilize broadcasting: Do not tile leading empty axes
nd = min(len(tiles_ind), np.ndim(ui))
tiles_ind = tiles_ind[(-nd):]
# For simplicity, make tiles and shape equal length
(tiles_ind,
shape_u) = misc.make_equal_length(tiles_ind, shape_u)
# Utilize broadcasting: Use tiling only if the parent's
# moment has non-unit axis length.
tiles_ind = [
tile if sh > 1 else 1
for (tile, sh) in zip(tiles_ind, shape_u)
]
# Tile
ui = np.tile(ui, tiles_ind)
u.append(ui)
return u
def _compute_weights_to_parent(self, index, weights):
# Idea: Reshape the message array such that every other axis
# will be summed and every other kept.
# Make plates equal length
plates = self._plates_to_parent(index)
shape_m = np.shape(weights)
(plates, tiles_m, shape_m) = misc.make_equal_length(plates, tiles, shape_m)
# Handle broadcasting rules for axes that have unit length in
# the message (although the plate may be non-unit length). Also,
# compute the corresponding broadcasting_multiplier.
plates = list(plates)
tiles_m = list(tiles_m)
for j in range(len(plates)):
if shape_m[j] == 1:
plates[j] = 1
tiles_m[j] = 1
# Combine the tuples by picking every other from tiles_ind and
# every other from shape
shape = functools.reduce(lambda x, y: x + y, zip(tiles_m, plates))
# ..and reshape the array, that is, every other axis corresponds
# to tiles and every other to plates/dimensions in parents
weights = np.reshape(weights, shape)
# Sum over every other axis
axes = tuple(range(0, len(shape), 2))
weights = np.sum(weights, axis=axes)
# Remove extra leading axes
ndim_parent = len(self.parents[index].plates)
weights = misc.squeeze_to_dim(weights, ndim_parent)
return weights
def _compute_message_to_parent(self, index, m, u_X):
m = list(m)
for ind in range(len(m)):
# Idea: Reshape the message array such that every other axis
# will be summed and every other kept.
shape_ind = self._plates_to_parent(index) + self.dims[ind]
# Add variable dimensions to tiles
tiles_ind = tiles + (1,)*len(self.dims[ind])
# Make shape tuples equal length
shape_m = np.shape(m[ind])
(tiles_ind, shape, shape_m) = misc.make_equal_length(tiles_ind,
shape_ind,
shape_m)
# Handle broadcasting rules for axes that have unit length in
# the message (although the plate may be non-unit length). Also,
# compute the corresponding plate_multiplier.
r = 1
shape = list(shape)
tiles_ind = list(tiles_ind)
for j in range(len(shape)):
if shape_m[j] == 1:
r *= tiles_ind[j]
shape[j] = 1
tiles_ind[j] = 1
# Combine the tuples by picking every other from tiles_ind and
# every other from shape
shape = functools.reduce(lambda x,y: x+y,
zip(tiles_ind, shape))
# ..and reshape the array, that is, every other axis corresponds
# to tiles and every other to plates/dimensions in parents
m[ind] = np.reshape(m[ind], shape)
# Sum over every other axis
axes = tuple(range(0,len(shape),2))
m[ind] = r * np.sum(m[ind], axis=axes)
# Remove extra leading axes
ndim_parent = len(self.parents[index].get_shape(ind))
m[ind] = misc.squeeze_to_dim(m[ind], ndim_parent)
return m
def _compute_message_to_parent(self, index, m, u_X):
m = list(m)
for ind in range(len(m)):
# Idea: Reshape the message array such that every other axis
# will be summed and every other kept.
shape_ind = self._plates_to_parent(index) + self.dims[ind]
# Add variable dimensions to tiles
tiles_ind = tiles + (1, ) * len(self.dims[ind])
# Make shape tuples equal length
shape_m = np.shape(m[ind])
(tiles_ind, shape,
shape_m) = misc.make_equal_length(tiles_ind, shape_ind,
shape_m)
# Handle broadcasting rules for axes that have unit length in
# the message (although the plate may be non-unit length). Also,
# compute the corresponding broadcasting multiplier.
r = 1
shape = list(shape)
tiles_ind = list(tiles_ind)
for j in range(len(shape)):
if shape_m[j] == 1:
r *= tiles_ind[j]
shape[j] = 1
tiles_ind[j] = 1
# Combine the tuples by picking every other from tiles_ind and
# every other from shape
shape = functools.reduce(lambda x, y: x + y,
zip(tiles_ind, shape))
# ..and reshape the array, that is, every other axis corresponds
# to tiles and every other to plates/dimensions in parents
m[ind] = np.reshape(m[ind], shape)
# Sum over every other axis
axes = tuple(range(0, len(shape), 2))
m[ind] = r * np.sum(m[ind], axis=axes)
# Remove extra leading axes
ndim_parent = len(self.parents[index].get_shape(ind))
m[ind] = misc.squeeze_to_dim(m[ind], ndim_parent)
return m
def _compute_mask_to_parent(self, index, mask):
# Idea: Reshape the message array such that every other axis
# will be summed and every other kept.
# Make plates equal length
plates = self._plates_to_parent(index)
shape_m = np.shape(mask)
(plates, tiles_m, shape_m) = misc.make_equal_length(plates,
tiles,
shape_m)
# Handle broadcasting rules for axes that have unit length in
# the message (although the plate may be non-unit length). Also,
# compute the corresponding broadcasting_multiplier.
plates = list(plates)
tiles_m = list(tiles_m)
for j in range(len(plates)):
if shape_m[j] == 1:
plates[j] = 1
tiles_m[j] = 1
# Combine the tuples by picking every other from tiles_ind and
# every other from shape
shape = functools.reduce(lambda x,y: x+y,
zip(tiles_m, plates))
# ..and reshape the array, that is, every other axis corresponds
# to tiles and every other to plates/dimensions in parents
mask = np.reshape(mask, shape)
# Sum over every other axis
axes = tuple(range(0,len(shape),2))
mask = np.any(mask, axis=axes)
# Remove extra leading axes
ndim_parent = len(self.parents[index].plates)
mask = misc.squeeze_to_dim(mask, ndim_parent)
return mask