Fixing bitwise_and bug in .fit methods

shz9 · Jun 10, 2024 · 24e9eb5 · 24e9eb5
1 parent 8530ef0
commit 24e9eb5
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Showing 2 changed files with 4 additions and 4 deletions.
diff --git a/viprs/model/VIPRS.py b/viprs/model/VIPRS.py
@@ -802,12 +802,12 @@ def fit(self,
                 prev_elbo = self.history['ELBO'][-2]
 
                 # Check for convergence in the objective + parameters:
-                if (i > min_iter) & np.isclose(prev_elbo, curr_elbo, atol=f_abs_tol, rtol=0.):
+                if (i > min_iter) and np.isclose(prev_elbo, curr_elbo, atol=f_abs_tol, rtol=0.):
                     self.optim_result.update(curr_elbo,
                                              stop_iteration=True,
                                              success=True,
                                              message='Objective (ELBO) converged successfully.')
-                elif (i > min_iter) & max([np.max(np.abs(diff)) for diff in self.eta_diff.values()]) < x_abs_tol:
+                elif (i > min_iter) and max([np.max(np.abs(diff)) for diff in self.eta_diff.values()]) < x_abs_tol:
                     self.optim_result.update(curr_elbo,
                                              stop_iteration=True,
                                              success=True,

diff --git a/viprs/model/gridsearch/VIPRSGrid.py b/viprs/model/gridsearch/VIPRSGrid.py
@@ -334,13 +334,13 @@ def fit(self,
 
                 for m in np.where(self.active_models)[0]:
 
-                    if (i > min_iter) & np.isclose(prev_elbo[m], curr_elbo[m], atol=f_abs_tol, rtol=0.):
+                    if (i > min_iter) and np.isclose(prev_elbo[m], curr_elbo[m], atol=f_abs_tol, rtol=0.):
                         self.active_models[m] = False
                         self.optim_results[m].update(curr_elbo[m],
                                                      stop_iteration=True,
                                                      success=True,
                                                      message='Objective (ELBO) converged successfully.')
-                    elif (i > min_iter) & max([np.max(np.abs(diff[:, m]))
+                    elif (i > min_iter) and max([np.max(np.abs(diff[:, m]))
                                                for diff in self.eta_diff.values()]) < x_abs_tol:
                         self.active_models[m] = False
                         self.optim_results[m].update(curr_elbo[m],