fix spelling

LLNL · Sep 10, 2024 · f012dbf · f012dbf
1 parent a51f81b
commit f012dbf
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Showing 2 changed files with 8 additions and 8 deletions.
diff --git a/thicket/stats/distance.py b/thicket/stats/distance.py
@@ -117,15 +117,15 @@ def bhattacharyya_distance(thicket, columns=None, output_column_name=None):
             stats_frame_column_name = "{}_{}_{}".format(
                 columns[0],
                 columns[1],
-                "bhattacharrya_distance",
+                "bhattacharyya_distance",
             )
         else:
             stats_frame_column_name = "{}_{}_{}_{}_{}".format(
                 columns[0][0],
                 columns[0][1],
                 columns[1][0],
                 columns[1][1],
-                "bhattacharrya_distance",
+                "bhattacharyya_distance",
             )
     else:
         stats_frame_column_name = output_column_name
@@ -147,8 +147,8 @@ def hellinger_distance(thicket, columns=None, output_column_name=None):
 
     This provides a quantitative way to compare two columns through the Hellinger distance,
     which is used to quantify the similarity between two probability distributions. It is based
-    on comparing the square roots of the probability densities rather than the probabilites
-    themselves. Helliger distance ranges from 0 to 1, with 0 indicating identical distributions
+    on comparing the square roots of the probability densities rather than the probabilities
+    themselves. Hellinger distance ranges from 0 to 1, with 0 indicating identical distributions
     and 1 indicating completely different distribution.
 
     Arguments:

diff --git a/thicket/stats/scoring.py b/thicket/stats/scoring.py
@@ -333,13 +333,13 @@ def score_hellinger(
 
     This provides a quantitative way to compare two columns through the Hellinger distance,
     which is used to quantify the similarity between two probability distributions. It is based
-    on comparing the square roots of the probability densities rather than the probabilites
-    themselves. Helliger distance ranges from 0 to 1, with 0 indicating identical distributions
+    on comparing the square roots of the probability densities rather than the probabilities
+    themselves. Hellinger distance ranges from 0 to 1, with 0 indicating identical distributions
     and 1 indicating completely different distribution.
 
     The Hellinger distance quantifies the similarity between two probability distributions. It is based
-    on comparing the square roots of the probability densities rather than the probabilites themselves.
-    It ranges is based on comparing the square roots of the probability densities rather than the probabilites
+    on comparing the square roots of the probability densities rather than the probabilities themselves.
+    It ranges is based on comparing the square roots of the probability densities rather than the probabilities
     themselves. It only provides a scalar value which is useful for determining how different two samples are but provides no insight on which is preferable.
 
     Towards this end, a signage is required to indicate a distinction between the samples. A characterization function