Created
November 27, 2010 21:38
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Record Update
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def update(td: TaggingDistribution, ad: AlignmentDistribution) = { | |
/* | |
* Setup: Create an empty set of records | |
*/ | |
var TPrime = List[Record]() | |
for (i <- 0 until _T.size) { | |
val r = new Record(i, _schema) | |
r.setThetaRewrite(_T(i).thetaRewrite) | |
TPrime = TPrime :+ r | |
} | |
val c = new Counter[String]() | |
for (tweet <- 0 until td.tweets.size) { | |
updateForTweet(tweet, TPrime, td, ad, c) | |
} | |
// logger.info("Counts") | |
// logger.info(c.toString()) | |
/* | |
* Final: | |
* Normalize per tweet | |
*/ | |
for (record <- TPrime) { | |
for ((fieldName, multinomial) <- record.fields) { | |
multinomial.counter.logNormalize | |
} | |
} | |
_T = TPrime | |
// Make sure each record-field is a probabiility distribution | |
checkOK | |
} | |
def updateForTweet(tweet: Int, tPrime: List[Record], td: TaggingDistribution, ad: AlignmentDistribution, c:Counter[String]) = { | |
/* | |
* Initialization | |
*/ | |
val stateSpace = td.crf.crf.getStateSpace() | |
val fb: edu.umass.nlp.ml.sequence.ForwardBackwards = new edu.umass.nlp.ml.sequence.ForwardBackwards(stateSpace) | |
// NumRecords x NumFields x [Field Value -> Potentials] | |
// 100 x 3 x 50 x 30 x 16 | |
//var recordPotentials = List[List[Map[List[String],Array[Array[Double]]]]]() | |
val potentials = td.crfPotentialsCopy(tweet) | |
// val boosts = thetaRewrite(tweet) | |
// val allRecordBoosts: Map[Record,Seq[Boost]] = boosts.groupBy(_.record) | |
for ((record,recordIndex) <- tPrime.zipWithIndex) { | |
val boosts = record.thetaRewrite(tweet) | |
val alignmentBelief = ad.Z(tweet)(recordIndex) // q(z_i = k) | |
if (boosts.size > 0) { | |
var foo = Map[String, Double]() | |
for (boost <- boosts) { | |
// counter.logInc does a logAdd | |
// eg: newValue = log ( exp(currentValue) + exp(argument) ) | |
tPrime(recordIndex).fields(boost.recordField).counter.logInc(boost.fieldValue,math.log(alignmentBelief * boost.similarityScore)) | |
// tPrime(recordIndex).fields(boost.recordField).counter.inc(boost.fieldValue,alignmentBelief * boost.similarityScore) | |
} | |
for ((f,vals) <- record.fields) { | |
for (v <- vals.counter.counts.keys) { | |
updatePotentials(potentials,td,boosts,f,v,scale=1.0) | |
fb.setInput(potentials) | |
tPrime(recordIndex).fields(f).counter.dec(v,math.log(alignmentBelief) + fb.getLogZ) | |
updatePotentials(potentials,td,boosts,f,v,scale= -1.0) | |
} | |
} | |
} | |
} |
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