From a911f9707d44259a38ae3dbb6fbcec75779fc639 Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Sat, 28 Mar 2020 12:17:29 +0100 Subject: doc --- src/python/gudhi/point_cloud/knn.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'src/python/gudhi/point_cloud/knn.py') diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py index 31e4fc9f..bb7757f2 100644 --- a/src/python/gudhi/point_cloud/knn.py +++ b/src/python/gudhi/point_cloud/knn.py @@ -21,7 +21,7 @@ class KNN: k (int): number of neighbors (possibly including the point itself). return_index (bool): if True, return the index of each neighbor. return_distance (bool): if True, return the distance to each neighbor. - implementation (str): Choice of the library that does the real work. + implementation (str): choice of the library that does the real work. * 'keops' for a brute-force, CUDA implementation through pykeops. Useful when the dimension becomes large (10+) but the number of points remains low (less than a million). Only "minkowski" and its aliases are supported. * 'ckdtree' for scipy's cKDTree. Only "minkowski" and its aliases are supported. @@ -31,7 +31,7 @@ class KNN: metric (str): see `sklearn.neighbors.NearestNeighbors`. eps (float): relative error when computing nearest neighbors with the cKDTree. p (float): norm L^p on input points (including numpy.inf) if metric is "minkowski". Defaults to 2. - n_jobs (int): Number of jobs to schedule for parallel processing of nearest neighbors on the CPU. + n_jobs (int): number of jobs to schedule for parallel processing of nearest neighbors on the CPU. If -1 is given all processors are used. Default: 1. kwargs: additional parameters are forwarded to the backends. """ -- cgit v1.2.3