def LErrorNaiveBinom(G, M, E):
# possible number of edges in an undirected, non-self-connected graph of N nodes
posNumEdges = (G.numNodes * G.numNodes - G.numNodes) / 2
cost = LU(posNumEdges - E.numCellsExcluded,
E.numUnmodelledErrors + E.numModellingErrors)
if config.optVerbosity > 1: print ' - L_nb(E)', cost
return cost
def LErrorTypedBinom(G, M, E):
# possible number of edges in an undirected, non-self-connected graph of N nodes
posNumEdges = (G.numNodes * G.numNodes - G.numNodes) / 2
# First encode the modelling errors
#print 'First encode the modelling errors'
#print 'E.numCellsCovered, E.numCellsExcluded, E.numModellingErrors;'
#print E.numCellsCovered, E.numCellsExcluded, E.numModellingErrors;
costM = LU(E.numCellsCovered - E.numCellsExcluded, E.numModellingErrors)
if config.optVerbosity > 1: print ' - L_tb(E+)', costM
# Second encode the unmodelled errors
#print 'Second encode the unmodelled errors' (excluded cells are always covered!)
#print posNumEdges - E.numCellsCovered, E.numUnmodelledErrors;
costU = LU(posNumEdges - E.numCellsCovered, E.numUnmodelledErrors)
if config.optVerbosity > 1: print ' - L_tb(E-)', costU
return costM + costU