diff --git a/mrs_test/mrs-naive-bayes.py b/mrs_test/mrs-naive-bayes.py
deleted file mode 100644
index 866f8ec..0000000
--- a/mrs_test/mrs-naive-bayes.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from guineapig import *
-import sys
-import gpextras
-
-def partitionCounter(rows):
-    n = 0
-    for r in rows: n+= 1
-    yield n
-
-class NaiveBayes(Planner):
-
-    D = GPig.getArgvParams(required=['train'])
-    def sumEventCounts(v):
-        return Group(v, by=lambda (e,n):e, retaining=lambda (e,n):n, reducingTo=ReduceToSum(), combiningTo=ReduceToSum())
-    def count(v,tag):
-        return ReplaceEachPartition(v, by=partitionCounter) | Group(by=lambda n:tag, reducingTo=ReduceToSum())
-
-    #format: (id,classLabelList,tokenList)
-    data = ReadLines(D['train']) \
-        | ReplaceEach(by=lambda line:line.strip().split("\t")) \
-        | ReplaceEach(by=lambda parts:(parts[0],parts[1].split(","),parts[2:]))
-    labelEvents = sumEventCounts(Flatten(data, by=lambda (docid,ys,ws): [(y,1) for y in ys]))
-    wordLabelEvents = sumEventCounts(Flatten(data, by=lambda (docid,ys,ws): [(y+'/'+w,1) for y in ys for w in ws]))
-    totalLines = count(data,'#lines')
-    totalWords = count(Flatten(data, lambda (docid,ys,ws): ws), '#words')
-
-# always end like this
-if __name__ == "__main__":
-    p = NaiveBayes()
-    p.registerCompiler('mrs',gpextras.MRSCompiler)
-    p.main(sys.argv)
-
diff --git a/mrs_test/streamNaiveBayesLearner.py b/mrs_test/streamNaiveBayesLearner.py
deleted file mode 100644
index b10399b..0000000
--- a/mrs_test/streamNaiveBayesLearner.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#streaming naive bayes
-
-import sys
-import math
-import logging
-
-class EventCounter(object):
-    def __init__(self,maxSize=1000):
-        self._ctr = {}
-        self.maxSize = maxSize
-        self.size = 0
-    def get(self,event):
-        if event in self._ctr: return self._ctr[event]
-        else: return 0
-    def inc(self,event,delta=1):
-        if event in self._ctr: 
-            self._ctr[event] += delta
-        else: 
-            self._ctr[event] = delta
-            self.size += 1
-        if self.maxSize and self.size>self.maxSize:
-            self.flush()
-    def flush(self):
-        k = 0
-        for event,count in self._ctr.items():
-            print "%s\t%g" % (event,count)
-            k += 1
-            if not k % 10000: logging.info('flushed %d/%d items = %.3f%%' % (k,self.size, ((100.0*k)/self.size)))
-        self._ctr = {}
-        self.size = 0
-    def load(self,file):
-        self._ctr = {}
-        for line in open(file):
-            parts = line.strip().split("\t")
-            self._ctr[parts[0]] = float(parts[1])
-
-def parseLine(line):
-    parts = line.split("\t")
-    labels = parts[1].split(",")
-    words = parts[2:]
-    return labels,words
-
-def trainLine(line,ec):
-    labels,words = parseLine(line)
-    ec.inc("lab=_")
-    for lab in labels:
-        ec.inc("lab=%s" % lab)
-    for word in words:
-        ec.inc("word=_")
-        ec.inc("word=%s" % word)
-        for lab in labels:
-            ec.inc("word=%s,lab=%s" % (word,lab))
-
-def testLine(line,ec,label,vocabSize):
-    def logProb(k,n,nClass):
-        #one smoothing method
-        #return math.log(k+1.0) - math.log(n+nClass)
-        #simpler smoothing, used by minorthird
-        return math.log((k+0.5)/(n+1.0))
-    trueLabels,words = parseLine(line)
-    nAll = ec.get("lab=_")
-    nPos = ec.get("lab=%s" % label)
-    score = logProb(nPos,nAll,2)
-    nonScore = logProb(nAll-nPos,nPos,2)
-    #print 'priors',score,nonScore,'for k/n',nLab,nInstance
-    for word in words:
-        kw = ec.get("word=%s,lab=%s" % (word,label))
-        nw = ec.get("word=%s" % word)
-        score += logProb(kw,nPos,vocabSize)
-        #BUG: this assumes that words aren't duplicated in examples, which isn't always true
-        nonScore += logProb(nw-kw,nAll-nPos,vocabSize)
-    trueLab = 'POS' if label in trueLabels else 'NEG'
-    print '%f\t%s' % ((score-nonScore),trueLab)
-
-if __name__=="__main__":
-    logging.basicConfig(level=logging.INFO)
-    if len(sys.argv)>1 and sys.argv[1] == '--train':
-        logging.info('started training')
-        ec = EventCounter(maxSize=0)
-        k = 0
-        for line in sys.stdin:
-            trainLine(line.strip(),ec)
-            k += 1
-            if not k % 1000: logging.info('processed '+str(k)+' lines')
-        logging.info('finished training')
-        ec.flush()
-        logging.info('flushed')
-    elif len(sys.argv)>1 and sys.argv[1] == '--streamTrain':
-        logging.info('started streamTrain')
-        ec = EventCounter(maxSize=int(sys.argv[2]))
-        k = 0
-        for line in sys.stdin:
-            trainLine(line.strip(),ec)
-            k += 1
-            if not k % 1000: logging.info('processed '+str(k)+' lines from '+sys.argv[1])
-        logging.info('finished streamTrain buf size '+sys.argv[2])
-        ec.flush()
-        logging.info('flushed '+sys.argv[1])
-    elif len(sys.argv)>3 and sys.argv[1] == '--test':
-        file = sys.argv[2]
-        ec = EventCounter(maxSize=0)
-        ec.load(file)
-        testLabel = sys.argv[3]
-        vocabSize = int(sys.argv[4])
-        for line in sys.stdin:
-            testLine(line.strip(),ec,testLabel,vocabSize)
-    else:
-        print 'usage: --train or --streamTrain BUFFER_SIZE or --test EVENTS LABEL VOCAB_SIZE'