Merge pull request #90 from infer-actively/action_sampling_bug_86

Fix action sampling bug when there's a uni-dimensional control state (to address Issue #86)

pymdp/utils.py

@@ -15,7 +15,8 @@
 EPS_VAL = 1e-16 # global constant for use in norm_dist()
 
 def sample(probabilities):
-    sample_onehot = np.random.multinomial(1, probabilities.squeeze())
+    probabilities = probabilities.squeeze() if len(probabilities) > 1 else probabilities
+    sample_onehot = np.random.multinomial(1, probabilities)
     return np.where(sample_onehot == 1)[0][0]
 
 def sample_obj_array(arr):

test/test_agent.py

@@ -469,6 +469,32 @@ def test_agent_with_sampling_mode(self):
         agent.infer_policies()
         chosen_action, p_actions = agent._sample_action_test()
         self.assertEqual(len(p_actions[0]), num_controls[0])
+
+    def test_agent_with_stochastic_action_unidimensional_control(self):
+        """
+        Test stochastic action sampling in case that one of the control states is one-dimensional, within the agent
+        method `sample_action()`.
+        Due to a call to probabilities.squeeze() in an earlier version of utils.sample(), this was throwing an
+        error due to the inability to use np.random.multinomial on an array with undefined length (an 'unsized' array)
+        """
+
+        num_obs = [2]
+        num_states = [2, 2]
+        num_controls = [2, 1]
+
+        A = utils.random_A_matrix(num_obs, num_states)
+        B = utils.random_B_matrix(num_states, num_controls)
+
+        agent = Agent(A=A, B=B, action_selection = "stochastic")
+        agent.infer_policies()
+        chosen_action = agent.sample_action()
+        self.assertEqual(chosen_action[1], 0)
+
+        agent = Agent(A=A, B=B, action_selection = "deterministic")
+        agent.infer_policies()
+        chosen_action = agent.sample_action()
+        self.assertEqual(chosen_action[1], 0)
+
 
 
 

test/test_control.py

@@ -1375,6 +1375,23 @@ def test_update_posterior_policies_withE_vector(self):
 
         self.assertGreater(q_pi[0], q_pi[1])
         self.assertGreater(q_pi[2], q_pi[1])
+
+    def test_stochastic_action_unidimensional_control(self):
+        """
+        Test stochastic action sampling in case that one of the control states is one-dimensional.
+        Due to a call to probabilities.squeeze() in an earlier version of utils.sample(), this was throwing an
+        error due to the inability to use np.random.multinomial on an array with undefined length (an 'unsized' array)
+        """
+
+        num_states = [2, 2]
+        num_controls = [2, 1]
+        policies = control.construct_policies(num_states, num_controls = num_controls, policy_len=1)
+        q_pi = utils.norm_dist(np.random.rand(len(policies)))
+        sampled_action = control.sample_action(q_pi, policies, num_controls, action_selection="stochastic")
+        self.assertEqual(sampled_action[1], 0)
+
+        sampled_action = control.sample_action(q_pi, policies, num_controls, action_selection="deterministic")
+        self.assertEqual(sampled_action[1], 0)
 
 
 if __name__ == "__main__":