From dd456888d9648ff8a79c0eee36c03a4dc5558b1c Mon Sep 17 00:00:00 2001 From: graehl Date: Wed, 21 Jul 2010 22:09:50 +0000 Subject: disabled TD reserved stuff - debug init assertion later git-svn-id: https://ws10smt.googlecode.com/svn/trunk@364 ec762483-ff6d-05da-a07a-a48fb63a330f --- decoder/ff_fsa.h | 79 +++++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 70 insertions(+), 9 deletions(-) (limited to 'decoder/ff_fsa.h') diff --git a/decoder/ff_fsa.h b/decoder/ff_fsa.h index ed159853..a14f9913 100755 --- a/decoder/ff_fsa.h +++ b/decoder/ff_fsa.h @@ -1,49 +1,110 @@ #ifndef FF_FSA_H #define FF_FSA_H +//TODO: actually compile this; probably full of syntax errors. + #include //C99 #include #include "ff.h" #include "sparse_vector.h" -#include "value_array.h" +#include "value_array.h" // used to hold state #include "tdict.h" +#include "hg.h" typedef ValueArray Bytes; /* + features whose score is just some PFSA over target string. TODO: could decide to give access to source span of scanned words as well if someone devises a feature that can use it + + state is some fixed width byte array. could actually be a void *, WordID sequence, whatever. */ -struct FsaFeatureFunction { - std::string name; +// it's not necessary to inherit from this. +struct FsaFeatureFunctionBase { + std::string name,usage_short,usage_verbose; + int fid; // you can have more than 1 feature of course. + void InitFid() { // call this, though, if you have a single feature + fid=FD::Convert(name); + } + std::string usage(bool param,bool verbose) { + return FeatureFunction::usage_helper(name,usage_short,usage_verbose,param,verbose); + } + + FsaFeatureFunctionBase(std::string const& name,std::string const& usage_verbose="[no documentation yet]",std::string const& usage_short="[no parameters]") : name(name),usage_short(usage_short),usage_verbose(usage_verbose) { } + + int state_bytes; // don't forget to set this (it may depend on params of course) +}; + +// example: feature val = -1 * # of target words +struct TargetPenaltyFsa : public FsaFeatureFunctionBase { + TargetPenaltyFsa(std::string const& param) : FsaFeatureFunctionBase("TargetPenalty","","-1 per target word") { InitFid(); } + const float val_per_target_word=-1; // state for backoff // scan + void Scan(SentenceMetadata const& smeta,WordID x,void const* prev_state,FeatureVector *features) { + features->set_value(fid,val_per_target_word); + } - // heuristic + // heuristic estimate of phrase + void Heuristic(WordID const* begin, WordID const* end,FeatureVector *h_features) - // all strings x of this length must end in the same state - virtual int MarkovOrder() const { + // return m: all strings x with the same final m+1 letters must end in this state + /* markov chain of order m: P(xn|xn-1...x1)=P(xn|xn-1...xn-m) */ + int MarkovOrder() const { return 0; } +}; + +//TODO: combine 2 FsaFeatures typelist style (can recurse for more) +// the type-erased interface +struct FsaFeatureFunction { + virtual int MarkovOrder() const = 0; + virtual ~FsaFeatureFunction(); + +}; + +// conforming to above interface, type erases FsaImpl +// you might be wondering: why do this? answer: it's cool, and it means that the bottom-up ff over ff_fsa wrapper doesn't go through multiple layers of dynamic dispatch +template +struct FsaFeatureFunctionDynamic : public FsaFeatureFunction { + Impl& d() { return static_cast(*this); } + Impl const& d() { return static_cast(*this); } + int MarkovOrder() const { return d().MarkovOrder(); } }; +//TODO: combine 2 (or N) FsaFeatureFunction (type erased) + +/* regular bottom up scorer from Fsa feature + uses guarantee about markov order=N to score ASAP + encoding of state: if less than N-1 (ctxlen) words + + either: + struct FF : public FsaImpl,FeatureFunctionFromFsa (more efficient) + + or: + struct FF : public FsaFeatureFunctionDynamic,FeatureFunctionFromFsa (code sharing, but double dynamic dispatch) + */ -// regular bottom up scorer from Fsa feature template struct FeatureFunctionFromFsa : public FeatureFunction { Impl& d() { return static_cast(*this); } Impl const& d() { return static_cast(*this); } - + int M; // markov order (ctx len) FeatureFunctionFromFsa() { } Init() { name=d().name; - SetStateSize(sizeof(WordID)*2*MarkovOrder + M=d().MarkovOrder + SetStateSize(sizeof(WordID)*2*M); } // can't do this in constructor because we come before d() in order virtual Features Features() const { return d().Features(); } + bool rule_feature() const { + return StateSize()==0; // Fsa features don't get info about span + } }; -- cgit v1.2.3