class_docstr: VBeta.
  
  VBeta (or V-Measure) [^1] is an external entropy-based cluster evaluation measure.
  It provides an elegant solution to many problems that affect previously defined
  cluster evaluation measures including
  
  * Dependance of clustering algorithm or dataset,
  
  * The "problem of matching", where the clustering of only a portion of data
  points are evaluated, and
  
  * Accurate evaluation and combination of two desirable aspects of clustering,
  homogeneity and completeness.
  
  Based upon the calculations of homogeneity and completeness, a clustering
  solution's V-measure is calculated by computing the weighted harmonic mean
  of homogeneity and completeness,
  
  $$
  V_{\beta} = \frac{(1 + \beta) \times h \times c}{\beta \times h + c}.
  $$
  
  Parameters
  ----------
  beta
      Weight of Homogeneity in the harmonic mean.
  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 = [1, 1, 2, 2, 3, 3]
  >>> y_pred = [1, 1, 1, 2, 2, 2]
  
  >>> metric = metrics.VBeta(beta=1.0)
  >>> for yt, yp in zip(y_true, y_pred):
  ...     metric.update(yt, yp)
  ...     print(metric.get())
  1.0
  1.0
  0.0
  0.3437110184854507
  0.4580652856440158
  0.5158037429793888
  
  >>> metric
  VBeta: 51.58%
  
  References
  ----------
  [^1]: Andrew Rosenberg and Julia Hirschberg (2007).
        V-Measure: A conditional entropy-based external cluster evaluation measure.
        Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language
        Processing and Computational Natural Language Learning, pp. 410 - 420,
        Prague, June 2007.
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code: def works_with_weights(self):
      return False
  
file_path: river/river/metrics/vbeta.py
imports: from __future__ import annotations
  import math
  from river import metrics
label_desc: Use image node_id 2 for calling the VBeta obj's underlying member method code with example usage: obj.works_with_weights() and returns: False