From b317e0efd2398d75d70e027bb1e2cf442e683981 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Fri, 27 Jul 2012 17:31:00 -0400 Subject: remove old suffix array extractor (use the one in python/ instead) --- sa-extract/context_model.py | 234 -------------------------------------------- 1 file changed, 234 deletions(-) delete mode 100644 sa-extract/context_model.py (limited to 'sa-extract/context_model.py') 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) - - -- cgit v1.2.3