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n-by-m matrices.
extended with the
variables in vars, would cause the fewest number of
cluster enlargements.
n-by-n
symmetric positive definite matrix.
Clusters in this junction tree.
e = Ci
-> Cj.
true iff this edge is consistent.
true if this filter contains the landmark
with the supplied identifier.
true if this filter contains the landmark
with the supplied identifier.
true if this filter contains the landmark
with the supplied identifier.
true if this cluster contains the supplied
set of variables.
true if this junction tree contains the
supplied variable.
true if this cluster contains the supplied
variable.
var from all clusters but one and returns
that cluster.
var from all clusters but cluster
(which must contain var).
tjtf package since the virtual machine was
started.
f.
Flops.count(long) will
increment the flop count.
null if next() has not yet been called.
true, then this iterator has traversed a node
more than once.
null, this is a list of ThinJunctionTree.Contractions that must first be executed
before this contraction can be executed.
n-by-n matrix
that is possibly symmetric positive definite.
Variables.
true, then the capacity (i.e., the actual
dimension of Gaussian.vP and Gaussian.mP) of this Gaussian is
doubled when it must be increased to accomodate new variables;
otherwise it is increased only enough to admit the new variables.
Nodes to the Edges to them.
Edge used to arrive
at that node (most recently).
Edges
incident from this node.
QueueElement objects ordered by
their priorities.
equals
so that two variable references are equal iff they refer to the
same object.
true if two elements are the same, which
is true only when the corresponding items are equal.
true if this and the supplied object are
equal.
equals so that two edges are equal if
their termini are the equal.
equals so that two nodes
are equal iff they are the same object.
cluster covers
var and validity and consistency are preserved.
Variables.
Sets of the JunctionTree.Clusters of this
junction tree.
Clusters
containing the supplied variable.
u of x.
null if there is no such cluster.
null is returned.
z)
with respect to the landmark state:
z(xt,
u)Variables bound by this Gaussian.
Variables in
this junction tree.
Variables in the
filtered belief state.
Variables in the
filtered belief state.
Variables in the
filtered belief state.
Variables in the
filtered belief state.
hashCode.
hashCode so the hash code of an edge is
the sum of the hash codes of its nodes.
hashCode to be the same as
that of hashCode.
true if this iterator has more items.
true if the iteration has more elements.
true if the iteration has more elements.
true if the iteration has more elements.
Integer landmark identifiers to Variables representing the corresponding landmark's state.
n-by-n matrix that is possibly
symmetric positive definite.
true if this Gaussian is represented using
the canonical parameterization, i.e., using the vector h and the (positive definite) matrix
L.
true if the traversal has visited a node
more than once, implying the existence of a cycle if the graph is
undirected.
true iff this queue is empty.
true if this cluster is a leaf.
true if this Gaussian is represented using
the moment parameterization, i.e., using the mean vector m and the (positive definite)
covariance matrix S.
Variables representing landmarks' states
to Integer identifiers of the corresponding landmarks .
n-by-n
square matrix.
Gaussian.isMoment is true
and L otherwise.
x;y -
x|z)Node in the iteration.
Edges that were traversed in order
to reach the Node most recently returned by next().
n samples from a k-dimensional
multivariate Gaussian distribution represented using a covariance
matrix.
true if the supplied node satisfies this
predicate.
limit.
limit.
e = Ci ->
Cj.String representation of this
Gaussian that prints out its parameters using Matlab notation.
clustersBySize
index of cluster sizes.
Variables bound by this Gaussian.
Variables present in this junction tree.
Variables
bound by this Gaussian.
Variables bound by this
Gaussian and whose values are Integers giving
the starting index of the corresponding subvectors of Gaussian.vP
and subblocks of Gaussian.mP.
Variable to the Set of Clusters containing it.
Gaussian.isMoment is true and h otherwise.
Linearization.y (for convenience).
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