class_docstr: Fading memory Kalman filter. This implements a linear Kalman filter with
  a fading memory effect controlled by `alpha`. This is obsolete. The
  class KalmanFilter now incorporates the `alpha` attribute, and should
  be used instead.
  
  You are responsible for setting the
  various state variables to reasonable values; the defaults below
  will not give you a functional filter.
  
  Parameters
  ----------
  
  alpha : float, >= 1
      alpha controls how much you want the filter to forget past
      measurements. alpha==1 yields identical performance to the
      Kalman filter. A typical application might use 1.01
  
  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 (optional)
      size of the control input, if it is being used.
      Default value of 0 indicates it is not used.
  
  Attributes
  ----------
  
  You will have to assign reasonable values to all of these before
  running the filter. All must have dtype of float
  
  x : ndarray (dim_x, 1), default = [0,0,0...0]
      state of the filter
  
  P : ndarray (dim_x, dim_x), default identity matrix
      covariance matrix
  
  x_prior : numpy.array(dim_x, 1)
      Prior (predicted) state estimate. The *_prior and *_post attributes
      are for convienence; they store the  prior and posterior of the
      current epoch. Read Only.
  
  P_prior : numpy.array(dim_x, dim_x)
      Prior (predicted) state covariance matrix. Read Only.
  
  x_post : numpy.array(dim_x, 1)
      Posterior (updated) state estimate. Read Only.
  
  P_post : numpy.array(dim_x, dim_x)
      Posterior (updated) state covariance matrix. Read Only.
  
  z : ndarray
      Last measurement used in update(). Read only.
  
  Q : ndarray (dim_x, dim_x), default identity matrix
      Process uncertainty matrix
  
  R : ndarray (dim_z, dim_z), default identity matrix
      measurement uncertainty
  
  H : ndarray (dim_z, dim_x)
      measurement function
  
  F : ndarray (dim_x, dim_x)
      state transition matrix
  
  B : ndarray (dim_x, dim_u), default 0
      control transition matrix
  
  y : numpy.array
      Residual of the update step. Read only.
  
  K : numpy.array(dim_x, dim_z)
      Kalman gain of the update step. Read only.
  
  S :  numpy.array
      System uncertainty (P projected to measurement space). Read only.
  
  S :  numpy.array
      Inverse system uncertainty. Read only.
  
  log_likelihood : float
      log-likelihood of the last measurement. Read only.
  
  likelihood : float
      likelihood of last measurement. Read only.
  
      Computed from the log-likelihood. The log-likelihood can be very
      small,  meaning a large negative value such as -28000. Taking the
      exp() of that results in 0.0, which can break typical algorithms
      which multiply by this value, so by default we always return a
      number >= sys.float_info.min.
  
  mahalanobis : float
      mahalanobis distance of the innovation. Read only.
  
  
  Examples
  --------
  
  See my book Kalman and Bayesian Filters in Python
  https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
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code: def __repr__(self):
      return "\n".join(
          [
              "FadingKalmanFilter object",
              pretty_str("dim_x", self.x),
              pretty_str("dim_z", self.x),
              pretty_str("dim_u", self.dim_u),
              pretty_str("x", self.x),
              pretty_str("P", self.P),
              pretty_str("F", self.F),
              pretty_str("Q", self.Q),
              pretty_str("R", self.R),
              pretty_str("H", self.H),
              pretty_str("K", self.K),
              pretty_str("y", self.y),
              pretty_str("S", self.S),
              pretty_str("B", self.B),
              pretty_str("likelihood", self.likelihood),
              pretty_str("log-likelihood", self.log_likelihood),
              pretty_str("mahalanobis", self.mahalanobis),
              pretty_str("alpha", self.alpha),
          ]
      )
  
imports: from __future__ import absolute_import, division, unicode_literals
  from copy import deepcopy
  from math import log, exp, sqrt
  import sys
  import warnings
  import numpy
  from numpy import dot, zeros, eye
  import scipy.linalg
  from filterpy.stats import logpdf
  from filterpy.common import pretty_str
label_desc: Use image node_id 12 for calling the FadingKalmanFilter obj's underlying member method code with example usage: obj.__repr__() and returns: str