parameters: (n_inliers, n_samples, min_samples, probability)
return_statement: return abs(float(np.ceil(np.log(nom) / np.log(denom))))
docstring: Determine number trials such that at least one outlier-free subset is
      sampled for the given inlier/outlier ratio.
  
      Parameters
      ----------
      n_inliers : int
          Number of inliers in the data.
  
      n_samples : int
          Total number of samples in the data.
  
      min_samples : int
          Minimum number of samples chosen randomly from original data.
  
      probability : float
          Probability (confidence) that one outlier-free sample is generated.
  
      Returns
      -------
      trials : int
          Number of trials.
docstring_summary: Determine number trials such that at least one outlier-free subset is
      sampled for the given inlier/outlier ratio.
docstring_tokens: Determine number trials such that at least one outlier - free subset is sampled for the given inlier / outlier ratio .
function: def _dynamic_max_trials(n_inliers, n_samples, min_samples, probability):
      """Determine number trials such that at least one outlier-free subset is
      sampled for the given inlier/outlier ratio.
  
      Parameters
      ----------
      n_inliers : int
          Number of inliers in the data.
  
      n_samples : int
          Total number of samples in the data.
  
      min_samples : int
          Minimum number of samples chosen randomly from original data.
  
      probability : float
          Probability (confidence) that one outlier-free sample is generated.
  
      Returns
      -------
      trials : int
          Number of trials.
  
      """
      inlier_ratio = n_inliers / float(n_samples)
      nom = max(_EPSILON, 1 - probability)
      denom = max(_EPSILON, 1 - inlier_ratio ** min_samples)
      if nom == 1:
          return 0
      if denom == 1:
          return float('inf')
      return abs(float(np.ceil(np.log(nom) / np.log(denom))))
function_tokens: def _dynamic_max_trials ( n_inliers , n_samples , min_samples , probability ) : inlier_ratio = n_inliers / float ( n_samples ) nom = max ( _EPSILON , 1 - probability ) denom = max ( _EPSILON , 1 - inlier_ratio ** min_samples ) if nom == 1 : return 0 if denom == 1 : return float ( 'inf' ) return abs ( float ( np . ceil ( np . log ( nom ) / np . log ( denom ) ) ) )