original_docstring:  Returns matrix of candidate topic-word parameters for each of n Plans
      
docstring: Returns matrix of candidate topic-word parameters for each of n Plans
docstring_tokens: Returns matrix of candidate topic - word parameters for each of n Plans
code: def makeCandidateTopics_AnchorAnalysis(Q, model, nPlans=1,
                                         seed=0, **kwargs):
      ''' Returns matrix of candidate topic-word parameters for each of n Plans
      '''
      Q = Q / Q.sum(axis=1)[:, np.newaxis]
  
      # Find few rows of Q that best explain *all* rows of Q (together with
      # curTopics)
      curTopics = getTopicWordFreqFromModel(model)
      choices = GramSchmidtUtil.FindAnchorsForExpandedBasis(Q, curTopics,
                                                            nPlans)
      newTopics = Q[choices]
      return newTopics, dict(anchorID=choices)
code_tokens: def makeCandidateTopics_AnchorAnalysis ( Q , model , nPlans = 1 , seed = 0 , ** kwargs ) : Q = Q / Q . sum ( axis = 1 ) [ : , np . newaxis ] curTopics = getTopicWordFreqFromModel ( model ) choices = GramSchmidtUtil . FindAnchorsForExpandedBasis ( Q , curTopics , nPlans ) newTopics = Q [ choices ] return newTopics , dict ( anchorID = choices )
short_docstring: Returns matrix of candidate topic-word parameters for each of n Plans
short_docstring_tokens: Returns matrix of candidate topic - word parameters for each of n Plans
comment: ''' Returns matrix of candidate topic-word parameters for each of n Plans
      '''$SEP$# Find few rows of Q that best explain *all* rows of Q (together with$SEP$# curTopics)