diff options
Diffstat (limited to 'src/python/gudhi/sktda/vector_methods.py')
| -rw-r--r-- | src/python/gudhi/sktda/vector_methods.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/src/python/gudhi/sktda/vector_methods.py b/src/python/gudhi/sktda/vector_methods.py index c1824ebb..91f1bc31 100644 --- a/src/python/gudhi/sktda/vector_methods.py +++ b/src/python/gudhi/sktda/vector_methods.py @@ -58,7 +58,7 @@ class PersistenceImage(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array with shape (number of diagrams) x (number of pixels = **resolution[0]** x **resolution[1]**)): output persistence images. + numpy array with shape (number of diagrams) x (number of pixels = **resolution[0]** x **resolution[1]**): output persistence images. """ num_diag, Xfit = len(X), [] new_X = BirthPersistenceTransform().fit_transform(X) @@ -118,7 +118,7 @@ class Landscape(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array with shape (number of diagrams) x (number of samples = **num_landscapes** x **resolution**)): output persistence landscapes. + numpy array with shape (number of diagrams) x (number of samples = **num_landscapes** x **resolution**): output persistence landscapes. """ num_diag, Xfit = len(X), [] x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution) @@ -200,7 +200,7 @@ class Silhouette(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array with shape (number of diagrams) x (**resolution**): output persistence silhouettes. + numpy array with shape (number of diagrams) x (**resolution**): output persistence silhouettes. """ num_diag, Xfit = len(X), [] x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution) @@ -277,7 +277,7 @@ class BettiCurve(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array with shape (number of diagrams) x (**resolution**): output Betti curves. + numpy array with shape (number of diagrams) x (**resolution**): output Betti curves. """ num_diag, Xfit = len(X), [] x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution) @@ -339,7 +339,7 @@ class Entropy(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array with shape (number of diagrams) x (1 if **mode** = "scalar" else **resolution**)): output entropy. + numpy array with shape (number of diagrams) x (1 if **mode** = "scalar" else **resolution**): output entropy. """ num_diag, Xfit = len(X), [] x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution) @@ -402,7 +402,7 @@ class TopologicalVector(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array with shape (number of diagrams) x (**threshold**): output topological vectors. + numpy array with shape (number of diagrams) x (**threshold**): output topological vectors. """ if self.threshold == -1: thresh = np.array([X[i].shape[0] for i in range(len(X))]).max() @@ -456,7 +456,7 @@ class ComplexPolynomial(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. Returns: - Xfit (numpy array with shape (number of diagrams) x (**threshold**): output complex vectors of coefficients. + numpy array with shape (number of diagrams) x (**threshold**): output complex vectors of coefficients. """ if self.threshold == -1: thresh = np.array([X[i].shape[0] for i in range(len(X))]).max() |