# Source code for bayespy.inference.vmp.nodes.deterministic

```
################################################################################
# Copyright (C) 2013-2014 Jaakko Luttinen
#
# This file is licensed under the MIT License.
################################################################################
import functools
import numpy as np
from bayespy.utils import misc
from .node import Node, Moments
[docs]class Deterministic(Node):
"""
Base class for deterministic nodes.
Sub-classes must implement:
1. For implementing the deterministic function:
_compute_moments(self, *u)
2. One of the following options:
a) Simple methods:
_compute_message_to_parent(self, index, m, *u)
b) More control with:
_compute_message_and_mask_to_parent(self, index, m, *u)
Sub-classes may need to re-implement:
1. If they manipulate plates:
_compute_weights_to_parent(index, mask)
_compute_plates_to_parent(self, index, plates)
_compute_plates_from_parent(self, index, plates)
"""
[docs] def __init__(self, *args, **kwargs):
super().__init__(*args, plates=None, notify_parents=False, **kwargs)
def _get_id_list(self):
"""
Returns the stochastic ID list.
This method is used to check that same stochastic nodes are not direct
parents of a node several times. It is only valid if there are
intermediate stochastic nodes.
To put it another way: each ID corresponds to one factor q(..) in the
posterior approximation. Different IDs mean different factors, thus they
mean independence. The parents must have independent factors.
Stochastic nodes should return their unique ID. Deterministic nodes
should return the IDs of their parents. Constant nodes should return
empty list of IDs.
"""
id_list = []
for parent in self.parents:
id_list = id_list + parent._get_id_list()
return id_list
[docs] def get_moments(self):
u_parents = self._message_from_parents()
return self._compute_moments(*u_parents)
def _compute_message_and_mask_to_parent(self, index, m_children, *u_parents):
# The following methods should be implemented by sub-classes.
m = self._compute_message_to_parent(index, m_children, *u_parents)
mask = self._compute_weights_to_parent(index, self.mask) != 0
return (m, mask)
def _get_message_and_mask_to_parent(self, index, u_parent=None):
u_parents = self._message_from_parents(exclude=index)
u_parents[index] = u_parent
if u_parent is not None:
u_self = self._compute_moments(*u_parents)
else:
u_self = None
m_children = self._message_from_children(u_self=u_self)
return self._compute_message_and_mask_to_parent(index,
m_children,
*u_parents)
def _compute_moments(self, *u_parents):
"""
Compute the moments given the moments of the parents.
"""
raise NotImplementedError()
def _compute_message_to_parent(self, index, m_children, *u_parents):
"""
Compute the message to a parent.
"""
raise NotImplementedError()
def _add_child(self, child, index):
"""
Add a child node.
Only child nodes that are stochastic (or have stochastic children
recursively) are counted as children because deterministic nodes without
stochastic children do not have any messages to send so the parents do
not need to know about the deterministic node.
A deterministic node does not notify its parents when created, but if it
gets a stochastic child node, then notify parents. This method is called
only if a stochastic child (recursively) node is added, thus there is at
least one stochastic node below this deterministic node.
Parameters
----------
child : node
index : int
The parent index of this node for the child node.
The child node recognizes its parents by their index
number.
"""
super()._add_child(child, index)
# Now that this deterministic node has non-deterministic children,
# notify parents
for (ind,parent) in enumerate(self.parents):
parent._add_child(self, ind)
def _remove_child(self, child, index):
"""
Remove a child node.
Only child nodes that are stochastic (or have stochastic children
recursively) are counted as children because deterministic nodes without
stochastic children do not have any messages to send so the parents do
not need to know about the deterministic node.
So, if the deterministic node does not have any stochastic children left
after removal, remove it from its parents.
"""
super()._remove_child(child, index)
# Check whether there are any children left. If not, remove from parents
if len(self.children) == 0:
for (ind, parent) in enumerate(self.parents):
parent._remove_child(self, ind)
[docs] def lower_bound_contribution(self, gradient=False, **kwargs):
# Deterministic functions are delta distributions so the lower bound
# contribuion is zero.
return 0
[docs] def random(self):
samples = [parent.random() for parent in self.parents]
return self._compute_function(*samples)
def tile(X, tiles):
"""
Tile the plates of the input node.
x = [a,b,c]
y = tile(x, 2) = [a,b,c,a,b,c]
There should be no need to tile plates that have unit length because they
are handled properly by the broadcasting rules already.
Parameters
----------
X : Node
Input node to be tiled.
tiles : int, tuple
Tiling of the plates (broadcasting rules for plates apply).
See also
--------
numpy.tile
"""
# Make sure `tiles` is tuple (even if an integer is given)
tiles = tuple(np.ravel(tiles))
class _Tile(Deterministic):
_parent_moments = (Moments(),)
def __init__(self, X, **kwargs):
self._moments = X._moments
super().__init__(X, dims=X.dims, **kwargs)
def _compute_plates_to_parent(self, index, plates):
plates = list(plates)
for i in range(-len(tiles), 0):
plates[i] = plates[i] // tiles[i]
return tuple(plates)
def _compute_plates_from_parent(self, index, plates):
return tuple(misc.multiply_shapes(plates, tiles))
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 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_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
return _Tile(X, name="tile(%s, %s)" % (X.name, tiles))
```