1 files changed, 70 insertions, 9 deletions

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 <stdint.h> //C99
 #include <string>
 #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<uint8_t> 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 <class Impl>
+struct FsaFeatureFunctionDynamic : public FsaFeatureFunction {
+  Impl& d() { return static_cast<Impl&>(*this); }
+  Impl const& d() { return static_cast<Impl const&>(*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<FF> (more efficient)
+
+   or:
+   struct FF : public FsaFeatureFunctionDynamic,FeatureFunctionFromFsa<FF> (code sharing, but double dynamic dispatch)
+ */
 
-// regular bottom up scorer from Fsa feature
 template <class Impl>
 struct FeatureFunctionFromFsa : public FeatureFunction {
   Impl& d() { return static_cast<Impl&>(*this); }
   Impl const& d() { return static_cast<Impl const&>(*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
+  }
 
 };