class_docstr: A dlm module containing all prediction methods.
      
docstr: Continue prediction after the one-day ahead predict.
  
  If users want to have a multiple day prediction, they can opt to use
  continuePredict after predict with new features contained in
  featureDict. For example,
  
  >>> # predict 3 days after the last day
  >>> myDLM.predict(featureDict=featureDict_day1)
  >>> myDLM.continuePredict(featureDict=featureDict_day2)
  >>> myDLM.continuePredict(featureDict=featureDict_day3)
  
  The featureDict acts the same way as in predict().
  
  Args:
      featureDict: the feature set for the dynamic components, stored
                   in a for of {"component name": vector}. If the set
                   was not supplied, then the algo will re-use the old
                   feature. For days beyond the data, the featureDict
                   for every dynamic component must be provided.
  
  Returns:
      A tupe. (predicted observation, variance)
docstr_tok: ["Continue","prediction","after","the","one-day","ahead","predict",".","If","users","want","to","have","a","multiple","day","prediction",",","they","can","opt","to","use","continuePredict","after","predict","with","new","features","contained","in","featureDict",".","For","example",",",">",">",">","#","predict","3","days","after","the","last","day",">",">",">","myDLM.predict","(","featureDict=featureDict_day1",")",">",">",">","myDLM.continuePredict","(","featureDict=featureDict_day2",")",">",">",">","myDLM.continuePredict","(","featureDict=featureDict_day3",")","The","featureDict","acts","the","same","way","as","in","predict","(",")",".","Args",":","featureDict",":","the","feature","set","for","the","dynamic","components",",","stored","in","a","for","of","{","``","component","name","''",":","vector","}",".","If","the","set","was","not","supplied",",","then","the","algo","will","re-use","the","old","feature",".","For","days","beyond","the","data",",","the","featureDict","for","every","dynamic","component","must","be","provided",".","Returns",":","A","tupe",".","(","predicted","observation",",","variance",")"]
code: def continuePredict(self, featureDict=None):
      """Continue prediction after the one-day ahead predict.
  
      If users want to have a multiple day prediction, they can opt to use
      continuePredict after predict with new features contained in
      featureDict. For example,
  
      >>> # predict 3 days after the last day
      >>> myDLM.predict(featureDict=featureDict_day1)
      >>> myDLM.continuePredict(featureDict=featureDict_day2)
      >>> myDLM.continuePredict(featureDict=featureDict_day3)
  
      The featureDict acts the same way as in predict().
  
      Args:
          featureDict: the feature set for the dynamic components, stored
                       in a for of {"component name": vector}. If the set
                       was not supplied, then the algo will re-use the old
                       feature. For days beyond the data, the featureDict
                       for every dynamic component must be provided.
  
      Returns:
          A tupe. (predicted observation, variance)
      """
      if self._predictModel is None:
          raise NameError("continuePredict has to come after predict.")
  
      return self._predictModel._continuePredict(
          featureDict=featureDict
      )
  
imports: from copy import deepcopy
  from numpy import matrix
  from pydlm.predict._dlmPredict import _dlmPredict
label_desc: Use image node_id 2 for calling the dlmPredictModule obj's underlying member method code with example usage: obj.continuePredict(featureDict) and returns: self