def _removeUnlikelyPredictions(cls, likelihoodsDict, minLikelihoodThreshold,
                                 maxPredictionsPerStep):
    """Remove entries with 0 likelihood or likelihood less than
    minLikelihoodThreshold, but don't leave an empty dict.
    """
    maxVal = (None, None)
    for (k, v) in likelihoodsDict.items():
      if len(likelihoodsDict) <= 1:
        break
      if maxVal[0] is None or v >= maxVal[1]:
        if maxVal[0] is not None and maxVal[1] < minLikelihoodThreshold:
          del likelihoodsDict[maxVal[0]]
        maxVal = (k, v)
      elif v < minLikelihoodThreshold:
        del likelihoodsDict[k]
    # Limit the number of predictions to include.
    likelihoodsDict = dict(sorted(likelihoodsDict.iteritems(),
                                  key=itemgetter(1),
                                  reverse=True)[:maxPredictionsPerStep])
    return likelihoodsDict
def _removeUnlikelyPredictions(cls, likelihoodsDict, minLikelihoodThreshold,
                                 maxPredictionsPerStep):
    """Remove entries with 0 likelihood or likelihood less than
    minLikelihoodThreshold, but don't leave an empty dict.
    """
    maxVal = (None, None)
    for (k, v) in likelihoodsDict.items():
      if len(likelihoodsDict) <= 1:
        break
      if maxVal[0] is None or v >= maxVal[1]:
        if maxVal[0] is not None and maxVal[1] < minLikelihoodThreshold:
          del likelihoodsDict[maxVal[0]]
        maxVal = (k, v)
      elif v < minLikelihoodThreshold:
        del likelihoodsDict[k]
    # Limit the number of predictions to include.
    likelihoodsDict = dict(sorted(likelihoodsDict.iteritems(),
                                  key=itemgetter(1),
                                  reverse=True)[:maxPredictionsPerStep])
    return likelihoodsDict
def _removeUnlikelyPredictions ( cls , likelihoodsDict , minLikelihoodThreshold , maxPredictionsPerStep ) : maxVal = ( None , None ) for ( k , v ) in likelihoodsDict . items ( ) : if len ( likelihoodsDict ) <= 1 : break if maxVal [ 0 ] is None or v >= maxVal [ 1 ] : if maxVal [ 0 ] is not None and maxVal [ 1 ] < minLikelihoodThreshold : del likelihoodsDict [ maxVal [ 0 ] ] maxVal = ( k , v ) elif v < minLikelihoodThreshold : del likelihoodsDict [ k ] # Limit the number of predictions to include. likelihoodsDict = dict ( sorted ( likelihoodsDict . iteritems ( ) , key = itemgetter ( 1 ) , reverse = True ) [ : maxPredictionsPerStep ] ) return likelihoodsDict
Remove entries with 0 likelihood or likelihood less than
    minLikelihoodThreshold, but don't leave an empty dict.
Remove entries with 0 likelihood or likelihood less than minLikelihoodThreshold but don t leave an empty dict .