remove legacy goal_run_with_grad_no_batch

c3/optimizers/c3.py

@@ -444,67 +444,3 @@ def goal_run_with_grad(self, current_params):
         self.optim_status["time"] = time.asctime()
         self.evaluation += 1
         return goal, grad
-
-    def goal_run_with_grad_no_batch(self, current_params):
-        """
-        Same as goal_run but with gradient. Very resource intensive. Unoptimized at the
-        moment.
-        """
-        exp_values = []
-        sim_values = []
-        exp_stds = []
-        exp_shots = []
-        count = 0
-        seqs_pp = self.seqs_per_point
-
-        with tf.GradientTape() as t:
-            t.watch(current_params)
-            for target, data in self.learn_data.items():
-                self.learn_from = data["seqs_grouped_by_param_set"]
-                self.gateset_opt_map = data["opt_map"]
-                indeces = self.select_from_data(self.batch_sizes[target])
-                for ipar in indeces:
-                    count += 1
-                    data_set = self.learn_from[ipar]
-                    m_vals = data_set["results"][:seqs_pp]
-                    sim_vals = self._one_par_sim_vals(
-                        current_params, data_set, ipar, target
-                    )
-                    sim_values.extend(sim_vals)
-                    exp_values.extend(m_vals)
-
-                    self._log_one_dataset(data_set, ipar, indeces, sim_vals, count)
-
-            if target == "all":
-                goal = neg_loglkh_multinom_norm(
-                    exp_values,
-                    tf.stack(sim_values),
-                    tf.constant(exp_stds, dtype=tf.float64),
-                    tf.constant(exp_shots, dtype=tf.float64),
-                )
-            else:
-                goal = g_LL_prime(
-                    exp_values,
-                    tf.stack(sim_values),
-                    tf.constant(exp_stds, dtype=tf.float64),
-                    tf.constant(exp_shots, dtype=tf.float64),
-                )
-            grad = t.gradient(goal, current_params).numpy()
-            goal = goal.numpy()
-
-        with open(self.logdir + self.logname, "a") as logfile:
-            logfile.write("\nFinished batch with ")
-            logfile.write("{}: {}\n".format(self.fom.__name__, goal))
-            for cb_fom in self.callback_foms:
-                val = float(cb_fom(exp_values, sim_values, exp_stds, exp_shots).numpy())
-                logfile.write("{}: {}\n".format(cb_fom.__name__, val))
-            logfile.flush()
-
-        self.optim_status["params"] = [
-            par.numpy().tolist() for par in self.pmap.get_parameters()
-        ]
-        self.optim_status["goal"] = goal
-        self.optim_status["gradient"] = list(grad.flatten())
-        self.optim_status["time"] = time.asctime()
-        self.evaluation += 1
-        return goal, grad