class_docstr: The autoReg class allows user to add an autoregressive component to the dlm.
  This code implements the autoregressive component as a child class of
  component. Different from the dynamic component, the features in the
  autoReg is generated from the data, and updated according to the data.
  
  The latent states of autoReg are aligned in the order of
  [today - degree, today - degree + 1, ..., today - 2, today - 1]. Thus,
  when fetching the latents from autoReg component, use this order to
  correctly align the coefficients.
  
  Args:
      data (deprecated): Users get a warning if this argument is used.
      degree: the order of the autoregressive component
      discount: the discount factor
      name: the name of the trend component
      w: the value to set the prior covariance. Default to a diagonal
         matrix with 1e7 on the diagonal.
      padding: either 0 or None. The number to be padded for the first degree
               days, as no previous data is observed to form the feature
               matrix
  Examples:
      >>>  # create a auto regression component:
      >>> autoReg8 = autoReg(degree=8, name='autoreg8', discount = 0.99)
      >>>  # change the autoReg8 to have covariance with diagonals are 2 and state 1
      >>> autoReg8.createCovPrior(cov = 2)
      >>> autoReg8.createMeanPrior(mean = 1)
  
  Attributes:
      d: the degree of autoregressive, i.e., how many days to look back
      data (deprecatd): Users get a warning if this argument is used.
      discount factor: the discounting factor
      name: the name of the component
      padding: either 0 or None. The number to be padded for the first degree
               days, as no previous data is observed to form the feature
               matrix
      evaluation: the evaluation matrix for this component
      transition: the transition matrix for this component
      covPrior: the prior guess of the covariance matrix of the latent states
      meanPrior: the prior guess of the latent states
docstr: Create the prior latent state
  
          
docstr_tok: ["Create","the","prior","latent","state"]
code: def createMeanPrior(self, mean=None, scale=1):
      """Create the prior latent state"""
      if mean is None:
          self.meanPrior = np.matrix(np.zeros((self.d, 1))) * scale
      else:
          self.meanPrior = mean * scale
  
file_path: pydlm/pydlm/modeler/autoReg.py
imports: import pydlm.base.tools
  from .component import component
  from numpy import matrix
  import logging
  import numpy
label_desc: Use image node_id 5 for calling the autoReg obj's underlying member method code with example usage: obj.createMeanPrior(mean, scale) without return types