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: Predicts the next state of the filter and returns it. Does not
  alter the state of the filter.
  
  Parameters
  ----------
  u : ndarray
      optional control input
  
  Returns
  -------
  x : ndarray
      State vector of the prediction.
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code: def get_prediction(self, u=0):
      """Predicts the next state of the filter and returns it. Does not
      alter the state of the filter.
  
      Parameters
      ----------
      u : ndarray
          optional control input
  
      Returns
      -------
      x : ndarray
          State vector of the prediction.
      """
      return dot(self.F, self.x) + dot(self.B, u)
  
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 5 for calling the HInfinityFilter obj's underlying member method code with example usage: obj.get_prediction(u) and returns: unknown