class_docstr: State space representation of a time series process
  
  Parameters
  ----------
  k_endog : {array_like, int}
      The observed time-series process :math:`y` if array like or the
      number of variables in the process if an integer.
  k_states : int
      The dimension of the unobserved state process.
  k_posdef : int, optional
      The dimension of a guaranteed positive definite covariance matrix
      describing the shocks in the measurement equation. Must be less than
      or equal to `k_states`. Default is `k_states`.
  initial_variance : float, optional
      Initial variance used when approximate diffuse initialization is
      specified. Default is 1e6.
  initialization : Initialization object or str, optional
      Initialization method for the initial state. If a string, must be one
      of {'diffuse', 'approximate_diffuse', 'stationary', 'known'}.
  initial_state : array_like, optional
      If `initialization='known'` is used, the mean of the initial state's
      distribution.
  initial_state_cov : array_like, optional
      If `initialization='known'` is used, the covariance matrix of the
      initial state's distribution.
  nobs : int, optional
      If an endogenous vector is not given (i.e. `k_endog` is an integer),
      the number of observations can optionally be specified. If not
      specified, they will be set to zero until data is bound to the model.
  dtype : np.dtype, optional
      If an endogenous vector is not given (i.e. `k_endog` is an integer),
      the default datatype of the state space matrices can optionally be
      specified. Default is `np.float64`.
  design : array_like, optional
      The design matrix, :math:`Z`. Default is set to zeros.
  obs_intercept : array_like, optional
      The intercept for the observation equation, :math:`d`. Default is set
      to zeros.
  obs_cov : array_like, optional
      The covariance matrix for the observation equation :math:`H`. Default
      is set to zeros.
  transition : array_like, optional
      The transition matrix, :math:`T`. Default is set to zeros.
  state_intercept : array_like, optional
      The intercept for the transition equation, :math:`c`. Default is set to
      zeros.
  selection : array_like, optional
      The selection matrix, :math:`R`. Default is set to zeros.
  state_cov : array_like, optional
      The covariance matrix for the state equation :math:`Q`. Default is set
      to zeros.
  **kwargs
      Additional keyword arguments. Not used directly. It is present to
      improve compatibility with subclasses, so that they can use `**kwargs`
      to specify any default state space matrices (e.g. `design`) without
      having to clean out any other keyword arguments they might have been
      passed.
  
  Attributes
  ----------
  nobs : int
      The number of observations.
  k_endog : int
      The dimension of the observation series.
  k_states : int
      The dimension of the unobserved state process.
  k_posdef : int
      The dimension of a guaranteed positive
      definite covariance matrix describing
      the shocks in the measurement equation.
  shapes : dictionary of name:tuple
      A dictionary recording the initial shapes
      of each of the representation matrices as
      tuples.
  initialization : str
      Kalman filter initialization method. Default is unset.
  initial_variance : float
      Initial variance for approximate diffuse
      initialization. Default is 1e6.
  
  Notes
  -----
  A general state space model is of the form
  
  .. math::
  
      y_t & = Z_t \alpha_t + d_t + \varepsilon_t \\
      \alpha_t & = T_t \alpha_{t-1} + c_t + R_t \eta_t \\
  
  where :math:`y_t` refers to the observation vector at time :math:`t`,
  :math:`\alpha_t` refers to the (unobserved) state vector at time
  :math:`t`, and where the irregular components are defined as
  
  .. math::
  
      \varepsilon_t \sim N(0, H_t) \\
      \eta_t \sim N(0, Q_t) \\
  
  The remaining variables (:math:`Z_t, d_t, H_t, T_t, c_t, R_t, Q_t`) in the
  equations are matrices describing the process. Their variable names and
  dimensions are as follows
  
  Z : `design`          :math:`(k\_endog \times k\_states \times nobs)`
  
  d : `obs_intercept`   :math:`(k\_endog \times nobs)`
  
  H : `obs_cov`         :math:`(k\_endog \times k\_endog \times nobs)`
  
  T : `transition`      :math:`(k\_states \times k\_states \times nobs)`
  
  c : `state_intercept` :math:`(k\_states \times nobs)`
  
  R : `selection`       :math:`(k\_states \times k\_posdef \times nobs)`
  
  Q : `state_cov`       :math:`(k\_posdef \times k\_posdef \times nobs)`
  
  In the case that one of the matrices is time-invariant (so that, for
  example, :math:`Z_t = Z_{t+1} ~ \forall ~ t`), its last dimension may
  be of size :math:`1` rather than size `nobs`.
  
  References
  ----------
  .. [*] Durbin, James, and Siem Jan Koopman. 2012.
     Time Series Analysis by State Space Methods: Second Edition.
     Oxford University Press.
class_docstr_tok: ["State","space","representation","of","a","time","series","process","Parameters","--","--","--","--","--","k_endog",":","{","array_like",",","int","}","The","observed","time-series","process",":","math",":","`","y","`","if","array","like","or","the","number","of","variables","in","the","process","if","an","integer",".","k_states",":","int","The","dimension","of","the","unobserved","state","process",".","k_posdef",":","int",",","optional","The","dimension","of","a","guaranteed","positive","definite","covariance","matrix","describing","the","shocks","in","the","measurement","equation",".","Must","be","less","than","or","equal","to","`","k_states","`",".","Default","is","`","k_states","`",".","initial_variance",":","float",",","optional","Initial","variance","used","when","approximate","diffuse","initialization","is","specified",".","Default","is","1e6",".","initialization",":","Initialization","object","or","str",",","optional","Initialization","method","for","the","initial","state",".","If","a","string",",","must","be","one","of","{","'diffuse","'",",","'approximate_diffuse","'",",","'stationary","'",",","'known","'","}",".","initial_state",":","array_like",",","optional","If","`","initialization='known","'","`","is","used",",","the","mean","of","the","initial","state's","distribution",".","initial_state_cov",":","array_like",",","optional","If","`","initialization='known","'","`","is","used",",","the","covariance","matrix","of","the","initial","state","'s","distribution",".","nobs",":","int",",","optional","If","an","endogenous","vector","is","not","given","(","i.e",".","`","k_endog","`","is","an","integer",")",",","the","number","of","observations","can","optionally","be","specified",".","If","not","specified",",","they","will","be","set","to","zero","until","data","is","bound","to","the","model",".","dtype",":","np.dtype",",","optional","If","an","endogenous","vector","is","not","given","(","i.e",".","`","k_endog","`","is","an","integer",")",",","the","default","datatype","of","the","state","space","matrices","can","optionally","be","specified",".","Default","is","`","np.float64","`",".","design",":","array_like",",","optional","The","design","matrix",",",":","math",":","`","Z","`",".","Default","is","set","to","zeros",".","obs_intercept",":","array_like",",","optional","The","intercept","for","the","observation","equation",",",":","math",":","`","d","`",".","Default","is","set","to","zeros",".","obs_cov",":","array_like",",","optional","The","covariance","matrix","for","the","observation","equation",":","math",":","`","H","`",".","Default","is","set","to","zeros",".","transition",":","array_like",",","optional","The","transition","matrix",",",":","math",":","`","T","`",".","Default","is","set","to","zeros",".","state_intercept",":","array_like",",","optional","The","intercept","for","the","transition","equation",",",":","math",":","`","c","`",".","Default","is","set","to","zeros",".","selection",":","array_like",",","optional","The","selection","matrix",",",":","math",":","`","R","`",".","Default","is","set","to","zeros",".","state_cov",":","array_like",",","optional","The","covariance","matrix","for","the","state","equation",":","math",":","`","Q","`",".","Default","is","set","to","zeros",".","*","*","kwargs","Additional","keyword","arguments",".","Not","used","directly",".","It","is","present","to","improve","compatibility","with","subclasses",",","so","that","they","can","use","`","*","*","kwargs","`","to","specify","any","default","state","space","matrices","(","e.g",".","`","design","`",")","without","having","to","clean","out","any","other","keyword","arguments","they","might","have","been","passed",".","Attributes","--","--","--","--","--","nobs",":","int","The","number","of","observations",".","k_endog",":","int","The","dimension","of","the","observation","series",".","k_states",":","int","The","dimension","of","the","unobserved","state","process",".","k_posdef",":","int","The","dimension","of","a","guaranteed","positive","definite","covariance","matrix","describing","the","shocks","in","the","measurement","equation",".","shapes",":","dictionary","of","name",":","tuple","A","dictionary","recording","the","initial","shapes","of","each","of","the","representation","matrices","as","tuples",".","initialization",":","str","Kalman","filter","initialization","method",".","Default","is","unset",".","initial_variance",":","float","Initial","variance","for","approximate","diffuse","initialization",".","Default","is","1e6",".","Notes","--","--","-","A","general","state","space","model","is","of","the","form","..","math",":",":","y_t","&","=","Z_t","\\alpha_t","+","d_t","+","\\varepsilon_t","\\\\","\\alpha_t","&","=","T_t","\\alpha_","{","t-1","}","+","c_t","+","R_t","\\eta_t","\\\\","where",":","math",":","`","y_t","`","refers","to","the","observation","vector","at","time",":","math",":","`","t","`",",",":","math",":","`","\\alpha_t","`","refers","to","the","(","unobserved",")","state","vector","at","time",":","math",":","`","t","`",",","and","where","the","irregular","components","are","defined","as","..","math",":",":","\\varepsilon_t","\\sim","N","(","0",",","H_t",")","\\\\","\\eta_t","\\sim","N","(","0",",","Q_t",")","\\\\","The","remaining","variables","(",":","math",":","`","Z_t",",","d_t",",","H_t",",","T_t",",","c_t",",","R_t",",","Q_t","`",")","in","the","equations","are","matrices","describing","the","process",".","Their","variable","names","and","dimensions","are","as","follows","Z",":","`","design","`",":","math",":","`","(","k\\_endog","\\times","k\\_states","\\times","nobs",")","`","d",":","`","obs_intercept","`",":","math",":","`","(","k\\_endog","\\times","nobs",")","`","H",":","`","obs_cov","`",":","math",":","`","(","k\\_endog","\\times","k\\_endog","\\times","nobs",")","`","T",":","`","transition","`",":","math",":","`","(","k\\_states","\\times","k\\_states","\\times","nobs",")","`","c",":","`","state_intercept","`",":","math",":","`","(","k\\_states","\\times","nobs",")","`","R",":","`","selection","`",":","math",":","`","(","k\\_states","\\times","k\\_posdef","\\times","nobs",")","`","Q",":","`","state_cov","`",":","math",":","`","(","k\\_posdef","\\times","k\\_posdef","\\times","nobs",")","`","In","the","case","that","one","of","the","matrices","is","time-invariant","(","so","that",",","for","example",",",":","math",":","`","Z_t","=","Z_","{","t+1","}","~","\\forall","~","t","`",")",",","its","last","dimension","may","be","of","size",":","math",":","`","1","`","rather","than","size","`","nobs","`",".","References","--","--","--","--","--","..","[","*","]","Durbin",",","James",",","and","Siem","Jan","Koopman",".","2012",".","Time","Series","Analysis","by","State","Space","Methods",":","Second","Edition",".","Oxford","University","Press","."]
docstr: Initialize the statespace model with approximate diffuse values.
  
  Rather than following the exact diffuse treatment (which is developed
  for the case that the variance becomes infinitely large), this assigns
  an arbitrary large number for the variance.
  
  Parameters
  ----------
  variance : float, optional
      The variance for approximating diffuse initial conditions. Default
      is 1e6.
docstr_tok: ["Initialize","the","statespace","model","with","approximate","diffuse","values",".","Rather","than","following","the","exact","diffuse","treatment","(","which","is","developed","for","the","case","that","the","variance","becomes","infinitely","large",")",",","this","assigns","an","arbitrary","large","number","for","the","variance",".","Parameters","--","--","--","--","--","variance",":","float",",","optional","The","variance","for","approximating","diffuse","initial","conditions",".","Default","is","1e6","."]
code: def initialize_approximate_diffuse(self, variance=None):
      """
      Initialize the statespace model with approximate diffuse values.
  
      Rather than following the exact diffuse treatment (which is developed
      for the case that the variance becomes infinitely large), this assigns
      an arbitrary large number for the variance.
  
      Parameters
      ----------
      variance : float, optional
          The variance for approximating diffuse initial conditions. Default
          is 1e6.
      """
      if variance is None:
          variance = self.initial_variance
  
      self.initialize(
          "approximate_diffuse", approximate_diffuse_variance=variance
      )
  
imports: import warnings
  import numpy
  from .tools import find_best_blas_type, validate_matrix_shape, validate_vector_shape
  from .initialization import Initialization
  from .None import tools
label_desc: Use image node_id 16 for calling the Representation obj's underlying member method code with example usage: obj.initialize_approximate_diffuse(variance) without return types