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author | Marc Glisse <marc.glisse@inria.fr> | 2020-05-07 15:52:56 +0200 |
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committer | Marc Glisse <marc.glisse@inria.fr> | 2020-05-07 15:52:56 +0200 |
commit | a5044e7c6465afd7d1c368b697c559995742552c (patch) | |
tree | 451e8b1cf1c7b813d11376d6d813447646bb47c8 /src/python/gudhi/point_cloud | |
parent | d61bfd349274456f8d7e0ccd64839a2d84eea0a0 (diff) | |
parent | 89b34f069e632a8fea0642556a4010de821ed6c9 (diff) |
Merge remote-tracking branch 'origin/master' into bottlepy2
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 |