ENH Avoid redundant computation in OPTICS #12094
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This may not make runtime much faster on simple examples, but should reduce memory and computation asymptotically
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I put this together prior to looking at #12049 (comment) where @espg refutes the benefit of such changes. I'll look into this soon. |
kno10
reviewed
Sep 17, 2018
| res = nbrs.radius_neighbors(X[idx:idx + 1], radius=self.max_eps) | ||
| (neigh_dist,), (neigh_idx,) = res | ||
| if len(neigh_idx) < self.min_samples: | ||
| core_dist = nbrs.kneighbors(X[point:point + 1], |
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Not necessary. If there are less than min_samples, the value is defined to be "undefined", c.f. OPTICS paper.
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Thanks. Still, @espg presents a good argument that this change is likely to
increase runtime in practice.
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This may not make runtime much faster on simple examples, but should reduce memory and computation asymptotically. It also avoids potential problems (?!) in the mixing of sklearn.neighbors and sklearn.metrics distance functions...