class_docstr: Rand Index.
  
  The Rand Index [^1] [^2] is a measure of the similarity between two data clusterings.
  Given a set of elements `S` and two partitions of `S` to compare, `X` and `Y`,
  define the following:
  
  * a, the number of pairs of elements in `S` that are in the **same** subset in `X`
  and in the **same** subset in `Y`
  
  * b, the number of pairs of elements in `S` that are in the **different** subset in `X`
  and in **different** subsets in `Y`
  
  * c, the number of pairs of elements in `S` that are in the **same** subset in `X`
  and in **different** subsets in `Y`
  
  * d, the number of pairs of elements in `S` that are in the **different** subset in `X`
  and in the **same** subset in `Y`
  
  The Rand index, R, is
  
  $$
  R = rac{a+b}{a+b+c+d} = rac{a+b}{rac{n(n-1)}{2}}.
  $$
  
  Parameters
  ----------
  cm
      This parameter allows sharing the same confusion
      matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage
      and computation time.
  
  Examples
  --------
  >>> from river import metrics
  
  >>> y_true = [0, 0, 0, 1, 1, 1]
  >>> y_pred = [0, 0, 1, 1, 2, 2]
  
  >>> metric = metrics.Rand()
  
  >>> for yt, yp in zip(y_true, y_pred):
  ...     metric.update(yt, yp)
  
  >>> metric
  Rand: 0.666667
  
  References
  ----------
  [^1]: Wikipedia contributors. (2021, January 13). Rand index.
        In Wikipedia, The Free Encyclopedia,
        from https://en.wikipedia.org/w/index.php?title=Rand_index&oldid=1000098911
  [^2]: W. M. Rand (1971). "Objective criteria for the evaluation of clustering methods".
        Journal of the American Statistical Association. American Statistical Association.
        66 (336): 846–850. arXiv:1704.01036. doi:10.2307/2284239. JSTOR 2284239.
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code: def works_with_weights(self):
      return False
  
file_path: river/river/metrics/rand.py
imports: from __future__ import annotations
  import collections
  from river import metrics
label_desc: Use image node_id 1 for calling the Rand obj's underlying member method code with example usage: obj.works_with_weights() and returns: False