message: doc default n_clusters
diff: @@ -58,10 +58,10 @@ class Tomato:
  kde_params (dict): if density_type is 'KDE' or 'logKDE', additional parameters passed directly to sklearn.neighbors.KernelDensity.
  k (int): number of neighbors for a knn graph (including the vertex itself). Defaults to 10.
  k_DTM (int): number of neighbors for the DTM density estimation (including the vertex itself). Defaults to k.
  - r (float): size of a neighborhood if graph_type is 'radius'
  - eps (float): approximation factor when computing nearest neighbors (currently ignored with a GPU)
  + r (float): size of a neighborhood if graph_type is 'radius'.
  + eps (float): approximation factor when computing nearest neighbors (currently ignored with a GPU).
  gpu (bool): enable use of CUDA (through pykeops) to compute k nearest neighbors. This is useful when the dimension becomes large (10+) but the number of points remains low (less than a million).
  - n_clusters (int): number of clusters requested. Defaults to ???
  + n_clusters (int): number of clusters requested. Defaults to None, i.e. no merging occurs and we get the maximal number of clusters.
  merge_threshold (float): minimum prominence of a cluster so it doesn't get merged.
  symmetrize_graph (bool): whether we should add edges to make the neighborhood graph symmetric. This can be useful with k-NN for small k. Defaults to false.
  p (float): norm L^p on input points (numpy.inf is supported without gpu). Defaults to 2.
  @@ -280,7 +280,7 @@ class Tomato:
  else:
  weights = -numpy.log(weights)
  - if self.density_type_ == "KDE" or self.density_type_ == "logKDE":
  + if self.density_type_ in {"KDE", "logKDE"}:
  # FIXME: replace most assert with raise ValueError("blabla")
  assert input_type == "points"
  kde_params = self.params_.get("kde_params", dict())
  