Merge pull request #26 from fsaad/main

BaseModel.predict needs predict=True in spatial_conditional call
AaltoML · Dec 14, 2023 · fad0483 · fad0483
2 parents b899eed + 9f07ce2
commit fad0483
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/bayesnewton/basemodels.py b/bayesnewton/basemodels.py
@@ -803,7 +803,7 @@ def predict(self, X=None, R=None, pseudo_lik_params=None):
         H = self.kernel.measurement_model()
         if self.spatio_temporal:
             # TODO: if R is fixed, only compute B, C once
-            B, C = self.kernel.spatial_conditional(X, R)
+            B, C = self.kernel.spatial_conditional(X, R, predict=True)
             W = B @ H
             test_mean = W @ state_mean
             test_var = W @ state_cov @ transpose(W) + C
@@ -1060,7 +1060,7 @@ def predict(self, X, R=None):
         H = self.kernel.measurement_model()
         if self.spatio_temporal:
             # TODO: if R is fixed, only compute B, C once
-            B, C = self.kernel.spatial_conditional(X, R)
+            B, C = self.kernel.spatial_conditional(X, R, predict=True)
             W = B @ H
             test_mean = W @ state_mean
             test_var = W @ state_cov @ transpose(W) + C
@@ -1216,7 +1216,7 @@ def predict(self, X=None, R=None, pseudo_lik_params=None):
         H = self.kernel.measurement_model()
         if self.spatio_temporal:
             # TODO: if R is fixed, only compute B, C once
-            B, C = self.kernel.spatial_conditional(X, R)
+            B, C = self.kernel.spatial_conditional(X, R, predict=True)
             W = B @ H
             test_mean = W @ state_mean
             test_var = W @ state_cov @ transpose(W) + C