Added ONN THS params to the pytorch global params.

onn/OnlineNeuralNetwork.py

@@ -156,10 +156,10 @@ class ONN_THS(ONN):
                  s=0.2, e=[0.5, 0.35, 0.2, 0.1, 0.05], use_cuda=False):
         super().__init__(features_size, max_num_hidden_layers, qtd_neuron_per_hidden_layer, n_classes, b=b, n=n, s=s,
                          use_cuda=use_cuda)
-        self.e = e
-        self.arms_values = np.arange(n_classes).tolist()
-        self.n_impressions = np.ones(len(e))
-        self.n_rewards = np.ones(len(e))
+        self.e = Parameter(torch.tensor(e), requires_grad=False)
+        self.arms_values = Parameter(torch.arange(n_classes), requires_grad=False)
+        self.n_impressions = Parameter(torch.ones(len(e)), requires_grad=False)
+        self.n_rewards = Parameter(torch.ones(len(e)), requires_grad=False)
 
     def partial_fit(self, X_data, Y_data, exp_factor, show_loss=True):
         self.partial_fit_(X_data, Y_data, show_loss)
@@ -170,10 +170,10 @@ class ONN_THS(ONN):
         rewards_0 = self.n_impressions - self.n_rewards
         theta_value = np.random.beta(self.n_rewards, rewards_0 + 1)
         ranked_arms = np.flip(np.argsort(theta_value), axis=0)
-        chosen_arm = ranked_arms[0]
+        chosen_arm = ranked_arms[0].item()
         self.n_impressions[chosen_arm] += 1
         if np.random.uniform() < self.e[chosen_arm]:
-            removed_arms = self.arms_values.copy()
+            removed_arms = self.arms_values.clone().numpy().tolist()
             removed_arms.remove(pred)
             return random.choice(removed_arms), chosen_arm