make_backoff_choleksy -> make_backoff_cholesky.

PiperOrigin-RevId: 385349678

Author: midfield

tensorflow_probability/python/experimental/distributions/marginal_fns.py

@@ -22,7 +22,7 @@
 from tensorflow_probability.python.distributions import mvn_linear_operator
 
 
-def make_backoff_choleksy(alternate_cholesky, name='BackoffCholesky'):
+def make_backoff_cholesky(alternate_cholesky, name='BackoffCholesky'):
   """Make a function that tries Cholesky then the user-specified function.
 
   Warning: This function uses an XLA-compiled `tf.linalg.cholesky` to capture
@@ -64,7 +64,7 @@ def make_cholesky_like_marginal_fn(cholesky_like,
   """Use a Cholesky-like function for `GaussianProcess` `marginal_fn`.
 
   For use with "Cholesky-like" lower-triangular factorizations (LL^T).  See
-  `make_backoff_choleksy` for one way to create such functions.
+  `make_backoff_cholesky` for one way to create such functions.
 
   Args:
     cholesky_like: A callable with the same signature as `tf.linalg.cholesky.`

tensorflow_probability/python/experimental/distributions/marginal_fns_test.py

@@ -33,13 +33,13 @@ class MarginalFnsTest(test_util.TestCase):
 
   def testNoBackoff(self):
     matrix = np.array([[2., 1.], [1., 2.]])
-    backoff = marginal_fns.make_backoff_choleksy(lambda x: x)
+    backoff = marginal_fns.make_backoff_cholesky(lambda x: x)
     test, ans = self.evaluate((backoff(matrix), tf.linalg.cholesky(matrix)))
     self.assertAllClose(test, ans)
 
   def testBackoff(self):
     matrix = np.array([[1., 2.], [2., 1.]])
-    backoff = marginal_fns.make_backoff_choleksy(tf.convert_to_tensor)
+    backoff = marginal_fns.make_backoff_cholesky(tf.convert_to_tensor)
     test = self.evaluate(backoff(matrix))
     self.assertAllClose(test, matrix)