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

```################################################################################
# Copyright (C) 2011-2012,2014 Jaakko Luttinen
#
################################################################################

import numpy as np
from bayespy.utils import misc

from .node import Node, Moments

[docs]class Constant(Node):
r"""
Node for presenting constant values.

The node wraps arrays into proper node type.
"""

[docs]    def __init__(self, moments, x, **kwargs):
if not isinstance(moments, Moments) and issubclass(moments, Moments):
raise ValueError("Give moments as an object instance instead of a class")
self._moments = moments
self.x = x
# Compute moments
self.u = self._moments.compute_fixed_moments(x)
# Dimensions of the moments
dims = self._moments.dims
# Resolve plates
D = len(dims)
if D > 0:
plates = np.shape(self.u)[:-D]
else:
plates = np.shape(self.u)
kwargs.setdefault('plates', plates)
self._parent_moments = ()
# Parent constructor
super().__init__(dims=dims, **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.
"""
return []

[docs]    def get_moments(self):
return self.u

[docs]    def set_value(self, x):
x = np.asanyarray(x)
#shapes = [np.shape(ui) for ui in self.u]
self.u = self._moments.compute_fixed_moments(x)
for (i, dimsi) in enumerate(self.dims):
correct_shape = tuple(self.plates) + tuple(dimsi)
given_shape = np.shape(self.u[i])
if not misc.is_shape_subset(given_shape, correct_shape):
raise ValueError(
"Incorrect shape {0} for the array, expected {1}"
.format(given_shape, correct_shape)
)
return