use python native float and string instead of np._str and np.float for readability

Author: eliewolfe

inflation/lp/InflationLP.py

@@ -1368,7 +1368,7 @@ def _generate_lp(self) -> None:
         old_num_columns = self.n_columns
         self.n_columns = len(self.monomials)
         self.first_free_idx = first_free_index
-        self.monomial_names = np.array([mon.name for mon in monomials_as_list])
+        self.monomial_names = np.array([mon.name for mon in monomials_as_list], dtype=object)
         if self.n_columns < old_num_columns:
             if self.verbose > 0:
                 eprint("Further variable reduction has been made possible. Number of variables in the LP:",

inflation/lp/lp_utils.py

@@ -485,16 +485,18 @@ def solveLP_sparse(objective: coo_array = blank_coo_array,
              snx) = task.getsolution(basic)
 
             # Get objective values, solutions x, dual values y
+            xx = np.asarray(xx, dtype=object)
+            yy = np.asarray(yy, dtype=object)
             if solve_dual:
                 primal = task.getdualobj(basic)
                 dual = task.getprimalobj(basic)
                 x_values = dict(zip(variables, yy))
-                y_values = np.asarray(xx)
+                y_values = xx
             else:
                 primal = task.getprimalobj(basic)
                 dual = task.getdualobj(basic)
                 x_values = dict(zip(variables, xx))
-                y_values = np.asarray(yy)
+                y_values = yy
 
             if solutionsta == mosek.solsta.optimal:
                 success = True

inflation/sdp/monomial_utils.py

@@ -47,7 +47,7 @@ def compute_marginal(prob_array: np.ndarray, atom: np.ndarray) -> float:
         outputs = atom[:, -1].astype(int)
         indices_to_sum = list(set(range(n_parties)
                                   ).difference(participating_parties))
-        marginal_dist  = np.sum(prob_array, axis=tuple(indices_to_sum))
+        marginal_dist  = np.sum(prob_array, axis=tuple(indices_to_sum)).astype(object)
         input_list: np.ndarray = np.zeros(n_parties, dtype=int)
         input_list[participating_parties] = inputs
         outputs_inputs = np.concatenate((outputs, input_list))

inflation/sdp/sdp_utils.py

@@ -450,7 +450,7 @@ def constraint_dicts_to_sparse(constraints: List[dict]) -> (coo_array, coo_array
             M.acceptedSolutionStatus(AccSolutionStatus.Anything)
             M.solve()
             if solve_dual:
-                x_values = {x: -ci_constraints[i].dual()[0]
+                x_values = {x: float(-ci_constraints[i].dual()[0])
                             for i, x in enumerate(variables)}
                 if mask_matrices:
                     ymat = Z.level().reshape([mat_dim, mat_dim])