diff options
Diffstat (limited to 'src/python/gudhi/representations/vector_methods.py')
| -rw-r--r-- | src/python/gudhi/representations/vector_methods.py | 31 |
1 files changed, 16 insertions, 15 deletions
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index a09b9356..f77338b7 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -604,25 +604,11 @@ class Atol(BaseEstimator, TransformerMixin): This class allows to vectorise measures (e.g. point clouds, persistence diagrams, etc) after a quantisation step. ATOL paper: https://arxiv.org/abs/1909.13472 - """ - def __init__(self, quantiser, weighting_method="cloud", contrast="gaus"): - """ - Constructor for the Atol measure vectorisation class. - - Parameters: - quantiser (Object): Object with `fit` (sklearn API consistent) and `cluster_centers` and `n_clusters` - attributes. This object will be fitted by the function `fit`. - weighting_method (string): constant generic function for weighting the measure points - choose from {"cloud", "iidproba"} - (default: constant function, i.e. the measure is seen as a point cloud by default). - This will have no impact if weights are provided along with measures all the way: `fit` and `transform`. - contrast (string): constant function for evaluating proximity of a measure with respect to centers - choose from {"gaussian", "laplacian", "indicator"} - (default: laplacian contrast function, see page 3 in the ATOL paper). Example -------- >>> from sklearn.cluster import KMeans + >>> from gudhi.representations.vector_methods import Atol >>> import numpy as np >>> a = np.array([[1, 2, 4], [1, 4, 0], [1, 0, 4]]) >>> b = np.array([[4, 2, 0], [4, 4, 0], [4, 0, 2]]) @@ -641,6 +627,21 @@ class Atol(BaseEstimator, TransformerMixin): [1.04696684, 0.56203292], [1.02816136, 0.23559623]]) """ + def __init__(self, quantiser, weighting_method="cloud", contrast="gaus"): + """ + Constructor for the Atol measure vectorisation class. + + Parameters: + quantiser (Object): Object with `fit` (sklearn API consistent) and `cluster_centers` and `n_clusters` + attributes. This object will be fitted by the function `fit`. + weighting_method (string): constant generic function for weighting the measure points + choose from {"cloud", "iidproba"} + (default: constant function, i.e. the measure is seen as a point cloud by default). + This will have no impact if weights are provided along with measures all the way: `fit` and `transform`. + contrast (string): constant function for evaluating proximity of a measure with respect to centers + choose from {"gaussian", "laplacian", "indicator"} + (default: laplacian contrast function, see page 3 in the ATOL paper). + """ self.quantiser = quantiser self.contrast = { "gaussian": _gaus_contrast, |