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| author | Vincent Rouvreau <vincent.rouvreau@inria.fr> | 2021-10-20 11:31:00 +0200 |
|---|---|---|
| committer | Vincent Rouvreau <vincent.rouvreau@inria.fr> | 2021-10-20 11:31:00 +0200 |
| commit | e4122147ee4643dbca6c65efebf83eb2adad6aec (patch) | |
| tree | ad2e2ad2afcb63f481907f5d03976b2fa99fbe74 /src/python/gudhi/representations/vector_methods.py | |
| parent | 44946b900ea13b2d6bb8d285c18cf0d37d515215 (diff) | |
Make Entropy work and also fix a bug in the loop
Diffstat (limited to 'src/python/gudhi/representations/vector_methods.py')
| -rw-r--r-- | src/python/gudhi/representations/vector_methods.py | 16 |
1 files changed, 9 insertions, 7 deletions
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index 711c32a7..47c5224c 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -416,9 +416,12 @@ class Entropy(BaseEstimator, TransformerMixin): new_X = BirthPersistenceTransform().fit_transform(X) for i in range(num_diag): - orig_diagram, diagram, num_pts_in_diag = X[i], new_X[i], X[i].shape[0] - new_diagram = DiagramScaler(use=True, scalers=[([1], MaxAbsScaler())]).fit_transform([diagram])[0] + try: + new_diagram = DiagramScaler(use=True, scalers=[([1], MaxAbsScaler())]).fit_transform([diagram])[0] + except ValueError: + # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507 + new_diagram = np.empty(shape = [0, 2]) if self.mode == "scalar": ent = - np.sum( np.multiply(new_diagram[:,1], np.log(new_diagram[:,1])) ) @@ -432,12 +435,11 @@ class Entropy(BaseEstimator, TransformerMixin): max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution) for k in range(min_idx, max_idx): ent[k] += (-1) * new_diagram[j,1] * np.log(new_diagram[j,1]) - if self.normalized: - ent = ent / np.linalg.norm(ent, ord=1) - Xfit.append(np.reshape(ent,[1,-1])) - - Xfit = np.concatenate(Xfit, 0) + if self.normalized: + ent = ent / np.linalg.norm(ent, ord=1) + Xfit.append(np.reshape(ent,[1,-1])) + Xfit = np.concatenate(Xfit, axis=0) return Xfit def __call__(self, diag): |