fixed docstring

Topological_ML/tda_function.py

@@ -19,7 +19,7 @@ def numpy_to_pandas(sklearn_data):
         sklearn_data (array): name of dataframe
 
     Returns:
-        panda.array: pandas dataframe
+        data: pandas dataframe
 
     """
     data = pd.DataFrame(data=sklearn_data.data, columns=sklearn_data.feature_names)
@@ -30,14 +30,14 @@ def lens_1d(x_array, proj='l2norm', random_num=1729, verbosity=0):
     """Creates a L^2-Norm for features. This lens highlights expected features in the data.
 
     Args:
-        X (array): features of dataset
-        proj (string): projection type
-        random_num: random state
-        verbosity: verbosity
+        x_array (array): features of dataset </br>
+        proj (string): projection type </br>
+        random_num: random state </br>
+        verbosity: verbosity </br>
 
     Returns:
-        lens: Isolation Forest, L^2-Norm
-        mapper:
+        lens: Isolation Forest, L^2-Norm </br>
+        mapper: </br>
 
     """
     if not type(x_array) == np.ndarray:
@@ -68,12 +68,12 @@ def uniform_sampling(dist_matrix, n_sample):
     the subsampled set.
 
     Args:
-        dist_matrix (array): Distance matrix
-        n_sample (int): Size of subsample set
+        dist_matrix (array): Distance matrix </br>
+        n_sample (int): Size of subsample set </br>
 
     Returns:
-        list_subsample (array): List of indices corresponding to the subsample set.
-        np.max(dist_to_l): Covering radious for the subsample set.
+        list_subsample (array): List of indices corresponding to the subsample set </br>
+        distance_to_l: Covering radious for the subsample set </br>
 
     """
     if not type(dist_matrix) == np.ndarray:
@@ -89,4 +89,5 @@ def uniform_sampling(dist_matrix, n_sample):
     num_points = dist_matrix.shape[0]
     list_subsample = np.random.choice(num_points, n_subsample)
     dist_to_l = np.min(dist_matrix[list_subsample, :], axis=0)
-    return list_subsample, np.max(dist_to_l)
+    distance_to_l = np.max(dist_to_l)
+    return list_subsample, distance_to_l