This operation merges two ngram language models or two ngram count FSTs. The operation provides options for weighting the two input FSTs, and for using the smoothing while merging.
ngrammerge [options] in1.fst in2.fst [out.fst] alpha: type = double, default = 1.0, weight for in1.fst in real semiring beta: type = double, default = 1.0, weight for in2.fst in real semiring normalize: type = bool, default = false, whether to normalize the resulting model use_smoothing: type = bool, default = false, whether to use model smoothing when merging fixedorder: type = bool, default false, whether to merge in the given argument order 

class NGramMerge(StdMutableFst *infst1, StdMutableFst *infst2, double alpha, double beta); 
Suppose we split our corpus up into two parts, earnest.aa and earnest.ab, e.g., by using split:
$ split 844 earnest.txt earnest.
If we count each half independently, we can then merge the counts to get the same counts as derived above from the full corpus (earnest.cnts):
$ farcompilestrings symbols=earnest.syms keep_symbols=1 earnest.aa >earnest.aa.far $ ngramcount order=5 earnest.aa.far >earnest.aa.cnts $ farcompilestrings symbols=earnest.syms keep_symbols=1 earnest.ab >earnest.ab.far $ ngramcount order=5 earnest.ab.far >earnest.ab.cnts $ ngrammerge earnest.aa.cnts earnest.ab.cnts >earnest.merged.cnts $ fstequal earnest.cnts earnest.merged.cnts
Note that, unlike our example merging unnormalized counts above, merging two smoothed models that have been built from half a corpus each will result in a different model than one built from the corpus as a whole, due to the smoothing and mixing.
Each of the two model or count FSTs can be weighted, using the alpha switch for the first input FST, and the beta switch for the second input FST. These weights are interpreted in the real semiring and both default to one, meaning that by default the original counts or probabilities are not scaled. For an ngram w_{1} ... w_{k}, the default count merging approach will yield
C(w_{1} ... w_{k}) = <alpha> C1(w_{1} ... w_{k}) + <beta> C2(w_{1} ... w_{k})
To merge two smoothed models, the use_smoothing=true option provides nonzero probability from each input language model to any invocabulary ngram; and the normalize=true option ensures that the resulting model is fully normalized. For example, to produce a merged model that weights the contribution of the first model by a factor of 3 and the contribution of the second model by a factor of 2:
$ ngrammerge use_smoothing normalize alpha=3 beta=2 earnest.aa.mod earnest.ab.mod >earnest.merged.mod