message: Reformat doc
diff: @@ -73,7 +73,9 @@ class DistanceToMeasure:
  class DTMDensity:
  """
  - Density estimator based on the distance to the empirical measure defined by a point set, as defined in :cite:`dtmdensity`. Note that this implementation does not renormalize so the total measure is not 1, see the reference for suitable normalization factors in the Euclidean case.
  + Density estimator based on the distance to the empirical measure defined by a point set, as defined
  + in :cite:`dtmdensity`. Note that this implementation does not renormalize so the total measure is not 1,
  + see the reference for suitable normalization factors in the Euclidean case.
  """
  def __init__(self, k=None, weights=None, q=None, dim=None, **kwargs):
  @@ -82,8 +84,10 @@ class DTMDensity:
  k (int): number of neighbors (possibly including the point itself).
  weights (numpy.array): weights of each of the k neighbors, optional.
  q (float): order used to compute the distance to measure. Defaults to dim.
  - dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
  - kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors.
  + dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
  + when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
  + kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that
  + :func:`transform` expects an array with the distances to the k nearest neighbors.
  """
  if weights is None:
  assert k is not None, "Must specify k or weights"
  @@ -113,7 +117,9 @@ class DTMDensity:
  def transform(self, X):
  """
  Args:
  - X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed", or distances to the k nearest neighbors if metric is "neighbors" (if the array has more than k columns, the remaining ones are ignored).
  + X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed",
  + or distances to the k nearest neighbors if metric is "neighbors" (if the array has more
  + than k columns, the remaining ones are ignored).
  """
  q = self.q
  dim = self.dim
  