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/dtm.py | 2 +- src/python/gudhi/point_cloud/knn.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) (limited to 'src') diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py index 520cbea8..3ac69f31 100644 --- a/src/python/gudhi/point_cloud/dtm.py +++ b/src/python/gudhi/point_cloud/dtm.py @@ -20,7 +20,7 @@ class DTM: Args: k (int): number of neighbors (possibly including the point itself). q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if metric is "neighbors" or "distance_matrix". - kwargs: Same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors. + kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors. """ self.k = k self.q = q 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