Fixing aposmm when the there are no initial starting point.

code/examples/gen_funcs/aposmm_logic.py

@@ -61,6 +61,11 @@ def aposmm_logic(H,gen_info,gen_specs,libE_info):
         updated_inds = set() 
 
     else:
+        # Find the run number 
+        if not np.any(H['started_run']):
+            gen_info['active_runs'] = set()
+            gen_info['run_order'] = {}
+
         global x_new, pt_in_run, total_pts_in_run # Used to generate a next local opt point
 
         updated_inds = update_history_dist(H, gen_specs, c_flag)        
@@ -69,10 +74,6 @@ def aposmm_logic(H,gen_info,gen_specs,libE_info):
         updated_inds.update(starting_inds) 
 
         for ind in starting_inds:
-            # Find the run number 
-            if not np.any(H['started_run']):
-                gen_info['active_runs'] = set()
-                gen_info['run_order'] = {}
 
             new_run_num = len(gen_info['run_order'])
 
@@ -215,9 +216,9 @@ def update_history_dist(H, gen_specs, c_flag):
             H['known_to_aposmm'][new_ind] = True # These points are now known to APOSMM
 
             # Compute distance to boundary
-            H['dist_to_unit_bounds'][new_ind] = min(min(np.ones(n) - H['x_on_cube'][new_ind]),min(H['x_on_cube'][new_ind] - np.zeros(n)))
+            H['dist_to_unit_bounds'][new_ind] = min(min(np.ones(n) - H['x_on_cube'][new_ind]),min(H['x_on_cube'][new_ind]))
 
-            dist_to_all = sp.spatial.distance.cdist(np.atleast_2d(H['x_on_cube'][new_ind]), H['x_on_cube'][p], 'euclidean').flatten()
+            dist_to_all = sp.spatial.distance.cdist([H['x_on_cube'][new_ind]], H['x_on_cube'][p], 'euclidean').flatten()
             new_better_than = H['f'][new_ind] < H['f'][p]
             new_worse_than = H['f'][new_ind] > H['f'][p]