message: More tweaks in representations
  wording for the case where CGAL is missing
  comparing pointers is sufficient to detect if we are doing fit_transform
  epsilon is an additive error for bottleneck, so it is unsafe to use 1e-3 or 1e-4 as default
diff: @@ -86,12 +86,12 @@ class BottleneckDistance(BaseEstimator, TransformerMixin):
  """
  This is a class for computing the bottleneck distance matrix from a list of persistence diagrams.
  """
  - def __init__(self, epsilon=1e-3):
  + def __init__(self, epsilon=None):
  """
  Constructor for the BottleneckDistance class.
  Parameters:
  - epsilon (double): approximation quality (default 1e-4).
  + epsilon (double): absolute (additive) error tolerated on the distance (default is the smallest positive float), see :func:`bottleneck_distance`.
  """
  self.epsilon = epsilon
  @@ -118,7 +118,8 @@ class BottleneckDistance(BaseEstimator, TransformerMixin):
  """
  num_diag1 = len(X)
  - if len(self.diagrams_) == len(X) and np.all([np.array_equal(self.diagrams_[i], X[i]) for i in range(len(X))]):
  + #if len(self.diagrams_) == len(X) and np.all([np.array_equal(self.diagrams_[i], X[i]) for i in range(len(X))]):
  + if X is self.diagrams_:
  matrix = np.zeros((num_diag1, num_diag1))
  if USE_GUDHI:
  @@ -127,7 +128,7 @@ class BottleneckDistance(BaseEstimator, TransformerMixin):
  matrix[i,j] = bottleneck_distance(X[i], X[j], self.epsilon)
  matrix[j,i] = matrix[i,j]
  else:
  - print("Gudhi required---returning null matrix")
  + print("Gudhi built without CGAL: returning a null matrix")
  else:
  num_diag2 = len(self.diagrams_)
  @@ -138,7 +139,7 @@ class BottleneckDistance(BaseEstimator, TransformerMixin):
  for j in range(num_diag2):
  matrix[i,j] = bottleneck_distance(X[i], self.diagrams_[j], self.epsilon)
  else:
  - print("Gudhi required---returning null matrix")
  + print("Gudhi built without CGAL: returning a null matrix")
  Xfit = matrix
  