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| author | Marc Glisse <marc.glisse@inria.fr> | 2020-05-11 17:51:28 +0200 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2020-05-11 17:51:28 +0200 |
| commit | 3cf5be5460b506811e22f800eeededc3f2ec40a8 (patch) | |
| tree | d9ebb271d3766b148db9a6edf0ffd0a3b5f9d615 /src/python/gudhi/point_cloud | |
| parent | 73a74011e4b5af0794f0463295beca924d32e0ee (diff) | |
| parent | 2a4a9528aef4c553c3de9544b729c8a3c6f43c26 (diff) | |
Merge remote-tracking branch 'origin/master' into dtmdensity
Diffstat (limited to 'src/python/gudhi/point_cloud')
| -rw-r--r-- | src/python/gudhi/point_cloud/knn.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index 07553d6d..34e80b5d 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -200,8 +200,8 @@ class KNearestNeighbors: from joblib import Parallel, delayed, effective_n_jobs from sklearn.utils import gen_even_slices - slices = gen_even_slices(len(X), effective_n_jobs(-1)) - parallel = Parallel(backend="threading", n_jobs=-1) + slices = gen_even_slices(len(X), effective_n_jobs(n_jobs)) + parallel = Parallel(prefer="threads", n_jobs=n_jobs) if self.params.get("sort_results", True): def func(M): @@ -242,8 +242,8 @@ class KNearestNeighbors: else: func = lambda M: numpy.partition(M, k - 1)[:, 0:k] - slices = gen_even_slices(len(X), effective_n_jobs(-1)) - parallel = Parallel(backend="threading", n_jobs=-1) + slices = gen_even_slices(len(X), effective_n_jobs(n_jobs)) + parallel = Parallel(prefer="threads", n_jobs=n_jobs) distances = numpy.concatenate(parallel(delayed(func)(X[s]) for s in slices)) return distances return None |