diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/python/gudhi/representations/vector_methods.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index cd532275..9b280f68 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -95,6 +95,7 @@ class Landscape(BaseEstimator, TransformerMixin): sample_range ([double, double]): minimum and maximum of all piecewise-linear function domains, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method. """ self.num_landscapes, self.resolution, self.sample_range = num_landscapes, resolution, sample_range + self.nan_in_range = np.isnan(np.array(self.sample_range)) def fit(self, X, y=None): """ @@ -104,8 +105,7 @@ class Landscape(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused). """ - self.nan_in_range = np.isnan(np.array(self.sample_range)) - if np.isnan(np.array(self.sample_range)).any(): + if self.nan_in_range.any(): pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y) [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]] self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range)) |