class_docstr: The kalmanFilter class the provide the basic functionalities
  
  Attributes:
      discount: the discounting factor determining how much information to carry on
      updateInnovation: indicate whether the innovation matrix should be updated.
                        default to True.
  
  Methods:
      predict: predict one step ahead of the current state
      forwardFilter: one step filter on the model given a new observation
      backwardSmoother: one step backward smooth given the future model and the
                        filtered state and systematic covariance
      backwardSampler: similar to backwardSmoother, using sampling instead of
                       deterministic equations.
      updateDiscount: for updating the discount factors
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docstr: Check whether the discount fact is within (0, 1)
  
          
docstr_tok: ["Check","whether","the","discount","fact","is","within","(","0",",","1",")"]
code: def __checkDiscount__(self, discount):
      """Check whether the discount fact is within (0, 1)"""
  
      for i in range(len(discount)):
          if discount[i] < 0 or discount[i] > 1:
              raise tl.matrixErrors(
                  "discount factor must be between 0 and 1"
              )
  
file_path: pydlm/pydlm/base/kalmanFilter.py
imports: import numpy
  import pydlm.base.tools
label_desc: Use image node_id 7 for calling the kalmanFilter obj's underlying member method code with example usage: obj.__checkDiscount__(discount) without return types