class_docstr: H-Infinity filter. You are responsible for setting the
  various state variables to reasonable values; the defaults below will
  not give you a functional filter.
  
  Parameters
  ----------
  
  dim_x : int
      Number of state variables for the Kalman filter. For example, if
      you are tracking the position and velocity of an object in two
      dimensions, dim_x would be 4.
  
      This is used to set the default size of `P`, `Q`, and `u`
  
  dim_z : int
      Number of of measurement inputs. For example, if the sensor
      provides you with position in (x, y), `dim_z` would be 2.
  
  dim_u : int
      Number of control inputs for the Gu part of the prediction step.
  
  gamma : float
  
  .. warning::
      I do not believe this code is correct. DO NOT USE THIS.
      In particular, note that predict does not update the covariance
      matrix.
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docstr: Batch processes a sequences of measurements.
  
  Parameters
  ----------
  Zs : list-like
      list of measurements at each time step `self.dt` Missing
      measurements must be represented by 'None'.
  
  update_first : bool, default=False, optional,
      controls whether the order of operations is update followed by
      predict, or predict followed by update.
  
  saver : filterpy.common.Saver, optional
      filterpy.common.Saver object. If provided, saver.save() will be
      called after every epoch
  
  Returns
  -------
  means: ndarray ((n, dim_x, 1))
      array of the state for each time step. Each entry is an np.array.
      In other words `means[k,:]` is the state at step `k`.
  
  covariance: ndarray((n, dim_x, dim_x))
      array of the covariances for each time step. In other words
      `covariance[k, :, :]` is the covariance at step `k`.
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code: def batch_filter(self, Zs, update_first=False, saver=False):
      """Batch processes a sequences of measurements.
  
      Parameters
      ----------
      Zs : list-like
          list of measurements at each time step `self.dt` Missing
          measurements must be represented by 'None'.
  
      update_first : bool, default=False, optional,
          controls whether the order of operations is update followed by
          predict, or predict followed by update.
  
      saver : filterpy.common.Saver, optional
          filterpy.common.Saver object. If provided, saver.save() will be
          called after every epoch
  
      Returns
      -------
      means: ndarray ((n, dim_x, 1))
          array of the state for each time step. Each entry is an np.array.
          In other words `means[k,:]` is the state at step `k`.
  
      covariance: ndarray((n, dim_x, dim_x))
          array of the covariances for each time step. In other words
          `covariance[k, :, :]` is the covariance at step `k`.
      """
  
      n = np.size(Zs, 0)
  
      # mean estimates from H-Infinity Filter
      means = zeros((n, self.dim_x, 1))
  
      # state covariances from H-Infinity Filter
      covariances = zeros((n, self.dim_x, self.dim_x))
  
      if update_first:
          for i, z in enumerate(Zs):
              self.update(z)
              means[i, :] = self.x
              covariances[i, :, :] = self.P
              self.predict()
  
              if saver is not None:
                  saver.save()
      else:
          for i, z in enumerate(Zs):
              self.predict()
              self.update(z)
  
              means[i, :] = self.x
              covariances[i, :, :] = self.P
  
              if saver is not None:
                  saver.save()
  
      return (means, covariances)
  
file_path: filterpy/filterpy/hinfinity/hinfinity_filter.py
imports: from __future__ import absolute_import, division
  import copy
  import warnings
  import numpy
  from numpy import dot, zeros, eye
  import scipy.linalg
  from filterpy.common import pretty_str
label_desc: Use image node_id 4 for calling the HInfinityFilter obj's underlying member method code with example usage: obj.batch_filter(Zs, update_first, saver) and returns: means,  covariances