class_docstr: Exponentially weighted mean.
  
  Parameters
  ----------
  fading_factor
      The closer `fading_factor` is to 1 the more the statistic will adapt to recent values.
  
  Attributes
  ----------
  mean : float
      The running exponentially weighted mean.
  
  Examples
  --------
  
  >>> from river import stats
  
  >>> X = [1, 3, 5, 4, 6, 8, 7, 9, 11]
  >>> ewm = stats.EWMean(fading_factor=0.5)
  >>> for x in X:
  ...     ewm.update(x)
  ...     print(ewm.get())
  1.0
  2.0
  3.5
  3.75
  4.875
  6.4375
  6.71875
  7.859375
  9.4296875
  
  References
  ----------
  [^1]: [Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42.](https://fanf2.user.srcf.net/hermes/doc/antiforgery/stats.pdf)
  [^2]: [Exponential Moving Average on Streaming Data](https://dev.to/nestedsoftware/exponential-moving-average-on-streaming-data-4hhl)
code: def get(self):
      return self._ewmean.get()
  
file_path: river/river/stats/ewmean.py
imports: from __future__ import annotations
  from river import stats
  from river.stats import _rust_stats
label_desc: Use image node_id 4 for calling the EWMean obj's underlying member method code with example usage: obj.get() and returns: self