From 2cb97dfb8a924929889f99b8ddf0d480e5cf9b03 Mon Sep 17 00:00:00 2001
From: Neil Ferguson <neil@prowler.io>
Date: Thu, 9 Jul 2020 12:34:13 +0100
Subject: [PATCH 1/3] Added base_conditional_with_lm function, which accepts Lm
 instead of Kmm

---
 gpflow/conditionals/util.py | 24 ++++++++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/gpflow/conditionals/util.py b/gpflow/conditionals/util.py
index 7d54739d7..0b59babcb 100644
--- a/gpflow/conditionals/util.py
+++ b/gpflow/conditionals/util.py
@@ -38,6 +38,27 @@ def base_conditional(
     :param white: bool
     :return: [N, R]  or [R, N, N]
     """
+    Lm = tf.linalg.cholesky(Kmm)
+    return base_conditional_with_lm(Kmn=Kmn, Lm=Lm, Knn=Knn, f=f, full_cov=full_cov,
+                                    q_sqrt=q_sqrt, white=white)
+
+
+def base_conditional_with_lm(
+    Kmn: tf.Tensor,
+    Lm: tf.Tensor,
+    Knn: tf.Tensor,
+    f: tf.Tensor,
+    *,
+    full_cov=False,
+    q_sqrt: Optional[tf.Tensor] = None,
+    white=False,
+):
+    r"""
+    Has the same functionality as the `base_conditional` function, except that instead of
+    `Kmm` this function accepts `Lm`, which is the Cholesky decomposition of `Kmm`.
+
+    This allows `Lm` to be precomputed, which can improve performance.
+    """
     # compute kernel stuff
     num_func = tf.shape(f)[-1]  # R
     N = tf.shape(Kmn)[-1]
@@ -58,7 +79,7 @@ def base_conditional(
 
     shape_constraints = [
         (Kmn, [..., "M", "N"]),
-        (Kmm, ["M", "M"]),
+        (Lm, ["M", "M"]),
         (Knn, [..., "N", "N"] if full_cov else [..., "N"]),
         (f, ["M", "R"]),
     ]
@@ -75,7 +96,6 @@ def base_conditional(
     )
 
     leading_dims = tf.shape(Kmn)[:-2]
-    Lm = tf.linalg.cholesky(Kmm)  # [M, M]
 
     # Compute the projection matrix A
     Lm = tf.broadcast_to(Lm, tf.concat([leading_dims, tf.shape(Lm)], 0))  # [..., M, M]

From 85390c29d842a2992e2cbe31d5b669326b820f24 Mon Sep 17 00:00:00 2001
From: Neil Ferguson <neil@prowler.io>
Date: Thu, 9 Jul 2020 12:36:53 +0100
Subject: [PATCH 2/3] Applied formatter

---
 gpflow/conditionals/util.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/gpflow/conditionals/util.py b/gpflow/conditionals/util.py
index 0b59babcb..496d47abd 100644
--- a/gpflow/conditionals/util.py
+++ b/gpflow/conditionals/util.py
@@ -39,8 +39,9 @@ def base_conditional(
     :return: [N, R]  or [R, N, N]
     """
     Lm = tf.linalg.cholesky(Kmm)
-    return base_conditional_with_lm(Kmn=Kmn, Lm=Lm, Knn=Knn, f=f, full_cov=full_cov,
-                                    q_sqrt=q_sqrt, white=white)
+    return base_conditional_with_lm(
+        Kmn=Kmn, Lm=Lm, Knn=Knn, f=f, full_cov=full_cov, q_sqrt=q_sqrt, white=white
+    )
 
 
 def base_conditional_with_lm(

From b38f351068cf2a4c054e5f2f993c27e8f96dfa0a Mon Sep 17 00:00:00 2001
From: Neil Ferguson <neil@prowler.io>
Date: Mon, 3 Aug 2020 14:50:08 +0100
Subject: [PATCH 3/3] Fix a typo

---
 gpflow/conditionals/util.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gpflow/conditionals/util.py b/gpflow/conditionals/util.py
index 496d47abd..0a6ee4ecf 100644
--- a/gpflow/conditionals/util.py
+++ b/gpflow/conditionals/util.py
@@ -65,7 +65,7 @@ def base_conditional_with_lm(
     N = tf.shape(Kmn)[-1]
     M = tf.shape(f)[-2]
 
-    # get the leadings dims in Kmn to the front of the tensor
+    # get the leading dims in Kmn to the front of the tensor
     # if Kmn has rank two, i.e. [M, N], this is the identity op.
     K = tf.rank(Kmn)
     perm = tf.concat(