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-complete 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 ComplexitySorted ascending
  sfstapprox[--phi_label=$l][--delta=$d] in.fst backoff.fst out.fst    
Count    
  sfstcount    
  sfstnormalize [--method=global] [--phi_label=$l][--delta=$d] in.fst out.fst    
  sfstnormalize -method=local in.fst out.fst    
  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)  
  sfstnormalize --method=phi [-phi_label=$l][--delta=$d] in.fst out.fst    
  sfstrandgen [--phi_label=$l] [--max_length=$l] [--npath=$n] [--seed=$s] in.fst out.fst    
  sfsttrim -phi_label=$l in.fst out.fst    
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
Approx Approx(ifst, &backoff_fst, phi_label, delta) approximates a normalized stochastic FST wrt a provided backoff-complete FST same as ShortestDistance on the intersection of the input and output FSTs
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
RandGen fst::RandGen(ifst, &ofst, fst::RandGenOptions<SFstArcSelector>(...)) randomly generates paths in a stochastic FST (correctly dealing with failure transitions) see RandGen
Trim Trim(&fst, phi_label) removes useless states and transitions in stochastic automata (irrespective of weights) Time, Space: O(V + E * max-phi-order)
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)
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)
PhiNormalize PhiNormalize(&fst, phi_label) normalizes, when possible, a canonical weighted FST by only modifying the failure transitions Time, Space: O(V + E)


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: r4 - 2019-07-18 - MichaelRiley
 
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