diff options
-rw-r--r-- | src/python/gudhi/clustering/tomato.py | 5 |
1 files changed, 2 insertions, 3 deletions
diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py index 07006d7c..88a1a34d 100644 --- a/src/python/gudhi/clustering/tomato.py +++ b/src/python/gudhi/clustering/tomato.py @@ -229,7 +229,6 @@ class Tomato: # 'float64' is slow except on super expensive GPUs. Allow it with some param? XX = torch.tensor(self.points_, dtype=torch.float32) if p == numpy.inf: - assert False # Not supported??? dd = (LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])).abs().max(-1).Kmin(k_DTM, dim=1) elif p == 2: # Any even integer? dd = ((LazyTensor(XX[:, None, :]) - LazyTensor(XX[None, :, :])) ** p).sum(-1).Kmin(k_DTM, dim=1) @@ -284,10 +283,10 @@ class Tomato: if self.density_type_ in {"KDE", "logKDE"}: # FIXME: replace most assert with raise ValueError("blabla") - assert input_type == "points" + # assert input_type == "points" kde_params = self.params_.get("kde_params", dict()) from sklearn.neighbors import KernelDensity - weights = KernelDensity(**kde_params).fit(X).score_samples(X) + weights = KernelDensity(**kde_params).fit(self.points_).score_samples(self.points_) if self.density_type_ == "KDE": weights = numpy.exp(weights) |