diff options
Diffstat (limited to 'src/python/gudhi/point_cloud/dtm.py')
| -rw-r--r-- | src/python/gudhi/point_cloud/dtm.py | 17 |
1 files changed, 12 insertions, 5 deletions
diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py index e4096c5e..520cbea8 100644 --- a/src/python/gudhi/point_cloud/dtm.py +++ b/src/python/gudhi/point_cloud/dtm.py @@ -15,10 +15,11 @@ class DTM: Class to compute the distance to the empirical measure defined by a point set. """ - def __init__(self, k, q=2, **kwargs): + def __init__(self, k, q=None, **kwargs): """ Args: - q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if input_type is 'distance_matrix'. + 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. """ self.k = k @@ -31,7 +32,7 @@ class DTM: def fit(self, X, y=None): """ Args: - X (numpy.array): coordinates for mass points + X (numpy.array): coordinates for mass points. """ if self.params.setdefault("metric", "euclidean") != "neighbors": # KNN gives sorted distances, which is unnecessary here. @@ -45,11 +46,17 @@ class DTM: Args: X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed", or distances to the k nearest neighbors if metric is "neighbors" (if the array has more than k columns, the remaining ones are ignored). """ + q = self.q + if q is None: + if self.params["metric"] in {"neighbors", "precomputed"}: + q = 2 + else: + q = len(X[0]) if self.params["metric"] == "neighbors": distances = X[:, : self.k] else: distances = self.knn.transform(X) - distances = distances ** self.q + distances = distances ** q dtm = distances.sum(-1) / self.k - dtm = dtm ** (1.0 / self.q) + dtm = dtm ** (1.0 / q) return dtm |