OpenGrm SFst Available Operations

The following operations are provided for SFSTs. Care must be taken that the input FSTs meet the specified requirements (e.g. canonical, backoff or normalized). The binary commands typically check their input requirements are satisfied or raise an error but the C++ versions may not check for efficiency (see the source code documentation for specific cases).

Operation Usage Description Complexity
Approx Approx(ifst, &backoff_fst, phi_label, delta) approximates a normalized stochastic FST wrt a provided backoff FST same as ShortestDistance on the intersection of the input and output FSTs
  sfstapprox[--phi_label=$l][--delta=$d] in.fst backoff.fst out.fst    
IsCanonical IsCanonical(fst, phi_label) checks the second property here holds for a weighted FST Time, Space: O(V + E)
IsNormalized IsNormalized(fst, phi_label, delta) checks the two properties here hold for a weighted FST Time, Space: O(V + E)
GlobalNormalize GlobalNormalize(&fst, phi_label, delta) globally normalizes, when possible1, a canonical weighted FST preserving total path weights (up to a global constant) same as ShortestDistance
  sfstnormalize [--method=global] [--phi_label=$l][--delta=$d] in.fst out.fst    
LocalNormalize LocalNormalize(&fst) locally normalizes, when possible, a canonical weighted FST preserving each state's out-going arc weights up to a state-specific constant Time, Space: O(V + E)
  sfstnormalize -method=local in.fst out.fst    
NGramApprox NGramApprox(ifst, &ofst, order, phi_label, delta) approximates a normalized stochastic FST as an n-gram model (having phi_labels in OpenGrm NGram format) same as ShortestDistance on the intersection of the input and output FSTs
  sfstngramapprox [--order=$o][--phi_label=$l][--delta=$d] in.fst out.fst    
Perplexity Perplexity(fst, phi_label, unknown_label, unknown_class_size) computes perplexity for a stochastic FST  
  sfstperplexity [--phi_label=$l] [-unknown_label=$u][--unknown_class_size=$s] in.fst test.far (test sentences are in FST archive format)  
PhiNormalize PhiNormalize(&fst, phi_label) normalizes, when possible, a canonical weighted FST by only modifying the failure transitions Time, Space: O(V + E)
  sfstnormalize --method=phi [-phi_label=$l][--delta=$d] in.fst out.fst    
RandGen fst::RandGen(ifst, &ofst, fst::RandGenOptions<SFstArcSelector>(...)) randomly generates paths in a stochastic FST (correctly dealing with failure transitions) see RandGen
  sfstrandgen [--phi_label=$l] [--max_length=$l] [--npath=$n] [--seed=$s] in.fst out.fst    
Trim Trim(&fst, phi_label) removes useless states and transitions in stochastic automata (irrespective of weights) Time, Space: O(V + E * max-phi-order)
  sfsttrim -phi_label=$l in.fst out.fst    


1Possible when the sum of weight of all successful paths from the initial state is finite (and the input is trim).

Michael Riley - 2018-07-16

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Topic revision: r3 - 2018-07-17 - MichaelRiley
 
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