crf.update(crf, node.fea = NULL, edge.fea = NULL, node.ext = NULL,
edge.ext = NULL)(n.nf, n.nodes)(n.ef, n.edges)node.pot and edge.pot of CRF model by using
the current values of parameters and features.There are two ways to model the relationship between parameters and features. The first one exploits the special structure of features to reduce the memory usage. However it may not suitable for all circumstances. The other one is more straighforward by explicitly specifying the coefficients of each parameter to calculate the potentials, and may use much more memory. Two approaches can be used together.
The first way uses the objects node.par and edge.par to define
the structure of features and provides the feature information in variables
node.fea and edge.fea. The second way directly provides the
feature information in variables node.ext and edge.ext without
any prior assumption on feature structure. node.ext is a list and
each element has the same structure as node.pot. edge.ext is
a list and each element has the same structure as edge.pot.
In detail, the node potential is updated as follows:
$$node.pot[n,i] = \sum_{f} par[node.par[n,i,f]] * node.fea[f,n] + \sum_{k} par[k] * node.ext[[k]][n,i]$$
and the edge potential is updated as follows:
$$edge.pot[[e]][i,j] = \sum_{f} par[edge.par[[e]][i,j,f]] * edge.fea[f,e] + \sum_{k} par[k] * edge.ext[[k]][[e]][i,j]$$
crf.nll, train.crf