Merge remote-tracking branch 'upstream/master'

1 files changed, 0 insertions, 234 deletions

diff --git a/sa-extract/context_model.py b/sa-extract/context_model.py
deleted file mode 100644
index 8cb6c174..00000000
--- a/sa-extract/context_model.py
+++ /dev/null
@@ -1,234 +0,0 @@
-#!/usr/bin/env python
-import sys
-import model
-import sym
-import log
-import math
-
-class ContextModel(model.Model):
-  '''A ContextModel is one that is computed using information
-  from the Context object'''
-
-  def __init__(self, context_manager, default=0.0):
-    model.Model.__init__(self)
-    self.wordless = 0
-    self.initial = None
-    self.default = default
-    self.context_manager = context_manager
-    self.id = self.context_manager.add_model(self)
-
-    '''The next feature is true if the model depends in 
-    some way on the entire input sentence; that is, if 
-    it cannot be scored when created, but must be scored
-    no earlier than during the input method (note that
-    this is less strict than stateful)'''
-    self.contextual = True 
-    ''' It may seem somewhat counterintuitive that a
-    ContextModel can be non-contextual, but a good
-    example is the rule probabilites; although these
-    are computed using the Context object, they don't
-    really depend in any way on context'''
-
-
-  '''inherited from model.Model, called once for each input sentence'''
-  def input(self, fwords, meta):
-    # all ContextModels must make this call
-    self.context_manager.input(self, fwords, meta)
-
-
-  '''This function will be called via the input method
-  only for contextual models'''
-  def compute_contextual_score(self, r):
-    return 0.0
-
-  '''This function is only called on rule creation for
-  contextless models'''
-  def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-    return 0.0
-
-  '''Stateless models should not need to 
-  override this function, unless they define
-  something for model.TO_GOAL'''
-  def transition (self,  r, antstates, i, j, j1=None):
-    return (None, 0.0)
-
-  def estimate(self, r):
-    return r.getscore("context", self.id)
-
-  def transition(self, r, antstates, i, j, j1=None):
-    return (None, r.getscore("context", self.id))
-
-  def finaltransition(self, state):
-    return 0.0
-
-  def rescore(self, ewords, score):
-    return score
-
-
-
-'''p(e|f)'''
-class EgivenF(ContextModel):
-  
-  def __init__(self, context_manager, default=0.0):
-    ContextModel.__init__(self, context_manager)
-    self.contextual = False
-
-  def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-    prob = float(paircount)/float(fcount)
-    return -math.log10(prob)
-
-class CountEF(ContextModel):
-
-        def __init__(self, context_manager, default=0.0):
-                ContextModel.__init__(self, context_manager)
-                self.contextual = False
-
-        def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-                return math.log10(1.0 + float(paircount))
-
-class SampleCountF(ContextModel):
-
-        def __init__(self, context_manager, default=0.0):
-                ContextModel.__init__(self, context_manager)
-                self.contextual = False
-
-        def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-                return math.log10(1.0 + float(fsample_count))
-
-
-
-class EgivenFCoherent(ContextModel):
-
-  def __init__(self, context_manager, default=0.0):
-    ContextModel.__init__(self, context_manager)
-    self.contextual = False
-
-  def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-    prob = float(paircount)/float(fsample_count)
-    #print "paircount=",paircount," , fsample_count=",fsample_count,", prob=",prob
-    if (prob == 0.0): return 99.0
-    return -math.log10(prob)
-
-
-
-class CoherenceProb(ContextModel):
-
-  def __init__(self, context_manager, default=0.0):
-    ContextModel.__init__(self, context_manager)
-    self.contextual = False
-
-  def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-    prob = float(fcount)/float(fsample_count)
-    return -math.log10(prob)
-
-
-
-class MaxLexEgivenF(ContextModel):
-
-  def __init__(self, context_manager, ttable, col=0):
-    ContextModel.__init__(self, context_manager)
-    self.ttable = ttable
-    self.col = col
-    self.wordless = 0
-    self.initial = None
-    self.contextual = False
-
-
-  def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-    totalscore = 1.0
-    fwords = map(sym.tostring, filter(lambda x: not sym.isvar(x), fphrase))
-    fwords.append("NULL")
-    ewords = map(sym.tostring, filter(lambda x: not sym.isvar(x), ephrase))
-    for e in ewords:
-      maxScore = 0.0
-      for f in fwords:
-        score = self.ttable.get_score(f, e, self.col)
-        #print "score(MaxLexEgivenF) = ",score
-        if score > maxScore:
-          maxScore = score
-      totalscore *= maxScore
-    if totalscore == 0.0:
-      return 999
-    else:
-      return -math.log10(totalscore)
-
-
-class MaxLexFgivenE(ContextModel):
-
-  def __init__(self, context_manager, ttable, col=1):
-    ContextModel.__init__(self, context_manager)
-    self.ttable = ttable
-    self.col = col
-    self.wordless = 0
-    self.initial = None
-    self.contextual = False
-
-
-  def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-    totalscore = 1.0
-    fwords = map(sym.tostring, filter(lambda x: not sym.isvar(x), fphrase))
-    ewords = map(sym.tostring, filter(lambda x: not sym.isvar(x), ephrase))
-    ewords.append("NULL")
-    for f in fwords:
-      maxScore = 0.0
-      for e in ewords:
-        score = self.ttable.get_score(f, e, self.col)
-        #print "score(MaxLexFgivenE) = ",score
-        if score > maxScore:
-          maxScore = score
-      totalscore *= maxScore
-    if totalscore == 0.0:
-      return 999
-    else:
-      return -math.log10(totalscore)
-
-
-class IsSingletonF(ContextModel):
-
-        def __init__(self, context_manager, default=0.0):
-                ContextModel.__init__(self, context_manager)
-                self.contextual = False
-
-        def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-                return (fcount==1)
-
-
-class IsSingletonFE(ContextModel):
-
-        def __init__(self, context_manager, default=0.0):
-                ContextModel.__init__(self, context_manager)
-                self.contextual = False
-
-        def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-                return (paircount==1)
-
-class IsNotSingletonF(ContextModel):
-
-        def __init__(self, context_manager, default=0.0):
-                ContextModel.__init__(self, context_manager)
-                self.contextual = False
-
-        def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-                return (fcount>1)
-
-
-class IsNotSingletonFE(ContextModel):
-
-        def __init__(self, context_manager, default=0.0):
-                ContextModel.__init__(self, context_manager)
-                self.contextual = False
-
-        def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-                return (paircount>1)
-
-
-class IsFEGreaterThanZero(ContextModel):
-
-        def __init__(self, context_manager, default=0.0):
-                ContextModel.__init__(self, context_manager)
-                self.contextual = False
-
-        def compute_contextless_score(self, fphrase, ephrase, paircount, fcount, fsample_count):
-                return (paircount > 0.01)
-
-