Syntax of return type in docstring

4 files changed, 20 insertions, 20 deletions

diff --git a/src/python/gudhi/sktda/kernel_methods.py b/src/python/gudhi/sktda/kernel_methods.py
index d409accd..e93138e6 100644
--- a/src/python/gudhi/sktda/kernel_methods.py
+++ b/src/python/gudhi/sktda/kernel_methods.py
@@ -50,7 +50,7 @@ class SlicedWassersteinKernel(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise sliced Wasserstein kernel values.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise sliced Wasserstein kernel values.
         """
         return np.exp(-self.sw_.transform(X)/self.bandwidth)
 
@@ -92,7 +92,7 @@ class PersistenceWeightedGaussianKernel(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise persistence weighted Gaussian kernel values.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence weighted Gaussian kernel values.
         """
         Xp = list(X)
         Xfit = np.zeros((len(Xp), len(self.diagrams_)))
@@ -157,7 +157,7 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise persistence scale space kernel values.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence scale space kernel values.
         """
         Xp = list(X)
         for i in range(len(Xp)):
@@ -200,7 +200,7 @@ class PersistenceFisherKernel(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise persistence Fisher kernel values.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher kernel values.
         """
         return np.exp(-self.pf_.transform(X)/self.bandwidth)
 
diff --git a/src/python/gudhi/sktda/metrics.py b/src/python/gudhi/sktda/metrics.py
index f55f553b..c51b8f3b 100644
--- a/src/python/gudhi/sktda/metrics.py
+++ b/src/python/gudhi/sktda/metrics.py
@@ -58,7 +58,7 @@ class SlicedWassersteinDistance(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise sliced Wasserstein distances.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise sliced Wasserstein distances.
         """
         Xfit = np.zeros((len(X), len(self.approx_)))
         if len(self.diagrams_) == len(X) and np.all([np.array_equal(self.diagrams_[i], X[i]) for i in range(len(X))]):
@@ -114,7 +114,7 @@ class BottleneckDistance(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise bottleneck distances.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise bottleneck distances.
         """
         num_diag1 = len(X)
 
@@ -182,7 +182,7 @@ class PersistenceFisherDistance(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X)): matrix of pairwise persistence Fisher distances.
+            numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise persistence Fisher distances.
         """
         Xfit = np.zeros((len(X), len(self.diagrams_)))
         if len(self.diagrams_) == len(X) and np.all([np.array_equal(self.diagrams_[i], X[i]) for i in range(len(X))]):
diff --git a/src/python/gudhi/sktda/preprocessing.py b/src/python/gudhi/sktda/preprocessing.py
index 6bc7ec52..294b5aeb 100644
--- a/src/python/gudhi/sktda/preprocessing.py
+++ b/src/python/gudhi/sktda/preprocessing.py
@@ -43,7 +43,7 @@ class BirthPersistenceTransform(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy array): input persistence diagrams.
 
         Returns:
-            Xfit (list of n x 2 numpy array): transformed persistence diagrams.
+            list of n x 2 numpy array: transformed persistence diagrams.
         """
         Xfit = []
         for diag in X:
@@ -85,7 +85,7 @@ class Clamping(BaseEstimator, TransformerMixin):
             X (numpy array of size n): input list of values.
 
         Returns:
-            Xfit (numpy array of size n): output list of values.
+            numpy array of size n: output list of values.
         """
         Xfit = np.minimum(X, self.limit)
         #Xfit = np.where(X >= self.limit, self.limit * np.ones(X.shape), X)
@@ -131,7 +131,7 @@ class DiagramScaler(BaseEstimator, TransformerMixin):
             X (list of n x 2 or n x 1 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (list of n x 2 or n x 1 numpy arrays): transformed persistence diagrams.
+            list of n x 2 or n x 1 numpy arrays: transformed persistence diagrams.
         """
         Xfit = [np.copy(d) for d in X]
         if self.use:
@@ -174,7 +174,7 @@ class Padding(BaseEstimator, TransformerMixin):
             X (list of n x 2 or n x 1 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (list of n x 3 or n x 2 numpy arrays): padded persistence diagrams.
+            list of n x 3 or n x 2 numpy arrays: padded persistence diagrams.
         """
         if self.use:
             Xfit, num_diag = [], len(X)
@@ -223,7 +223,7 @@ class ProminentPoints(BaseEstimator, TransformerMixin):
             X (list of n x 2 or n x 1 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (list of n x 2 or n x 1 numpy arrays): thresholded persistence diagrams.
+            list of n x 2 or n x 1 numpy arrays: thresholded persistence diagrams.
         """
         if self.use:
             Xfit, num_diag = [], len(X)
@@ -292,7 +292,7 @@ class DiagramSelector(BaseEstimator, TransformerMixin):
             X (list of n x 2 or n x 1 numpy arrays): input persistence diagrams.
 
         Returns:
-            Xfit (list of n x 2 or n x 1 numpy arrays): extracted persistence diagrams.
+            list of n x 2 or n x 1 numpy arrays: extracted persistence diagrams.
         """
         if self.use:
             Xfit, num_diag = [], len(X)
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()