gpytorch/variational/whitened_variational_strategy.py

Survived

Mutant 263

--- gpytorch/variational/whitened_variational_strategy.py
+++ gpytorch/variational/whitened_variational_strategy.py
@@ -23,7 +23,7 @@
 
 # Remove after 1.0
 class WhitenedVariationalStrategy(UnwhitenedVariationalStrategy):
-    def __init__(self, model, inducing_points, variational_distribution, learn_inducing_locations=True):
+    def __init__(self, model, inducing_points, variational_distribution, learn_inducing_locations=False):
         warnings.warn(
             "WhitenedVariationalStrategy is deprecated. Please use VariationalStrategy instead.", DeprecationWarning
         )

Mutant 264

--- gpytorch/variational/whitened_variational_strategy.py
+++ gpytorch/variational/whitened_variational_strategy.py
@@ -29,7 +29,7 @@
         )
         super().__init__(model, inducing_points, variational_distribution, learn_inducing_locations)
 
-    @cached(name="logdet_memo")
+    @cached(name="XXlogdet_memoXX")
     def prior_covar_logdet(self):
         return -self.prior_distribution.lazy_covariance_matrix.logdet()
 

Mutant 265

--- gpytorch/variational/whitened_variational_strategy.py
+++ gpytorch/variational/whitened_variational_strategy.py
@@ -29,7 +29,6 @@
         )
         super().__init__(model, inducing_points, variational_distribution, learn_inducing_locations)
 
-    @cached(name="logdet_memo")
     def prior_covar_logdet(self):
         return -self.prior_distribution.lazy_covariance_matrix.logdet()
 

Mutant 266

--- gpytorch/variational/whitened_variational_strategy.py
+++ gpytorch/variational/whitened_variational_strategy.py
@@ -33,7 +33,7 @@
     def prior_covar_logdet(self):
         return -self.prior_distribution.lazy_covariance_matrix.logdet()
 
-    @cached(name="covar_trace_memo")
+    @cached(name="XXcovar_trace_memoXX")
     def covar_trace(self):
         variational_covar = self.variational_distribution.covariance_matrix
         prior_covar = self.prior_distribution.covariance_matrix

Mutant 267

--- gpytorch/variational/whitened_variational_strategy.py
+++ gpytorch/variational/whitened_variational_strategy.py
@@ -33,7 +33,6 @@
     def prior_covar_logdet(self):
         return -self.prior_distribution.lazy_covariance_matrix.logdet()
 
-    @cached(name="covar_trace_memo")
     def covar_trace(self):
         variational_covar = self.variational_distribution.covariance_matrix
         prior_covar = self.prior_distribution.covariance_matrix

Mutant 268

--- gpytorch/variational/whitened_variational_strategy.py
+++ gpytorch/variational/whitened_variational_strategy.py
@@ -40,7 +40,7 @@
         batch_shape = prior_covar.shape[:-2]
         return (variational_covar * prior_covar).view(*batch_shape, -1).sum(-1)
 
-    @cached(name="mean_diff_inv_quad_memo")
+    @cached(name="XXmean_diff_inv_quad_memoXX")
     def mean_diff_inv_quad(self):
         prior_mean = self.prior_distribution.mean
         prior_covar = self.prior_distribution.lazy_covariance_matrix

Mutant 269

--- gpytorch/variational/whitened_variational_strategy.py
+++ gpytorch/variational/whitened_variational_strategy.py
@@ -40,7 +40,6 @@
         batch_shape = prior_covar.shape[:-2]
         return (variational_covar * prior_covar).view(*batch_shape, -1).sum(-1)
 
-    @cached(name="mean_diff_inv_quad_memo")
     def mean_diff_inv_quad(self):
         prior_mean = self.prior_distribution.mean
         prior_covar = self.prior_distribution.lazy_covariance_matrix

Mutant 270

--- gpytorch/variational/whitened_variational_strategy.py
+++ gpytorch/variational/whitened_variational_strategy.py
@@ -221,7 +221,7 @@
 
             return MultivariateNormal(predictive_mean, predictive_covar)
 
-    def __call__(self, x, prior=False):
+    def __call__(self, x, prior=True):
         # If we're in prior mode, then we're done!
         if prior:
             return self.model.forward(x)