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| author | Vincent Rouvreau <vincent.rouvreau@inria.fr> | 2022-02-01 18:16:59 +0100 |
|---|---|---|
| committer | Vincent Rouvreau <vincent.rouvreau@inria.fr> | 2022-02-01 18:16:59 +0100 |
| commit | c34cd028cf85b69f1da17bfcef02b0cfe47a41d6 (patch) | |
| tree | 7cba16b545fdc052ba38a0fa97bcb35ff67d58cc | |
| parent | b6f4e23b64b186cfd3d066b5d7c93bfe16bc9d66 (diff) | |
Doc review: no double in python, float
| -rw-r--r-- | src/python/gudhi/representations/preprocessing.py | 6 | ||||
| -rw-r--r-- | src/python/gudhi/sklearn/cubical_persistence.py | 2 |
2 files changed, 4 insertions, 4 deletions
diff --git a/src/python/gudhi/representations/preprocessing.py b/src/python/gudhi/representations/preprocessing.py index 823e3954..bd8c2774 100644 --- a/src/python/gudhi/representations/preprocessing.py +++ b/src/python/gudhi/representations/preprocessing.py @@ -76,7 +76,7 @@ class Clamping(BaseEstimator, TransformerMixin): Constructor for the Clamping class. Parameters: - limit (double): clamping value (default np.inf). + limit (float): clamping value (default np.inf). """ self.minimum = minimum self.maximum = maximum @@ -235,7 +235,7 @@ class ProminentPoints(BaseEstimator, TransformerMixin): use (bool): whether to use the class or not (default False). location (string): either "upper" or "lower" (default "upper"). Whether to keep the points that are far away ("upper") or close ("lower") to the diagonal. num_pts (int): cardinality threshold (default 10). If location == "upper", keep the top **num_pts** points that are the farthest away from the diagonal. If location == "lower", keep the top **num_pts** points that are the closest to the diagonal. - threshold (double): distance-to-diagonal threshold (default -1). If location == "upper", keep the points that are at least at a distance **threshold** from the diagonal. If location == "lower", keep the points that are at most at a distance **threshold** from the diagonal. + threshold (float): distance-to-diagonal threshold (default -1). If location == "upper", keep the points that are at least at a distance **threshold** from the diagonal. If location == "lower", keep the points that are at most at a distance **threshold** from the diagonal. """ self.num_pts = num_pts self.threshold = threshold @@ -318,7 +318,7 @@ class DiagramSelector(BaseEstimator, TransformerMixin): Parameters: use (bool): whether to use the class or not (default False). - limit (double): second coordinate value that is the criterion for being an essential point (default numpy.inf). + limit (float): second coordinate value that is the criterion for being an essential point (default numpy.inf). point_type (string): either "finite" or "essential". The type of the points that are going to be extracted. """ self.use, self.limit, self.point_type = use, limit, point_type diff --git a/src/python/gudhi/sklearn/cubical_persistence.py b/src/python/gudhi/sklearn/cubical_persistence.py index 454cdd07..27aeedf7 100644 --- a/src/python/gudhi/sklearn/cubical_persistence.py +++ b/src/python/gudhi/sklearn/cubical_persistence.py @@ -85,7 +85,7 @@ class CubicalPersistence(BaseEstimator, TransformerMixin): Compute all the cubical complexes and their associated persistence diagrams. Parameters: - X (list of list of double OR list of numpy.ndarray): List of cells filtration values that should be flatten + X (list of list of float OR list of numpy.ndarray): List of cells filtration values that should be flatten if `dimensions` is set in the constructor, or already with the correct shape in a numpy.ndarray (and `dimensions` must not be set). |