Enhance cognition progress calculation in ConsciousnessModel by integ…

…rating emotional coherence; add unit tests for memory retrieval and goal state updates

models/consciousness.py

@@ -329,16 +329,23 @@ def forward(self, inputs: Dict[str, torch.Tensor],
 
         return aware_state, metrics
 
-    def calculate_cognition_progress(self, metrics: Dict[str, float]) -> float:
-        # ...existing code...
-
-        # Add emotional coherence to progress calculation
-        if 'emotion_intensities' in metrics:
-            emotional_balance = torch.mean(metrics['emotion_intensities']).item()
-            metrics['emotional_coherence'] = emotional_balance
-
-        # Continue with existing progress calculation
-        # ...existing code...
+    def calculate_cognition_progress(self, metrics):
+        """
+        Calculate cognitive progress based on metrics.
+        Returns a value between 0 and 100.
+        """
+        # Calculate emotional coherence
+        emotional_coherence = torch.mean(metrics['emotion_intensities']).item()
+        metrics['emotional_coherence'] = emotional_coherence
+
+        # Calculate overall progress using phi, coherence and emotional_coherence
+        progress = (
+            0.4 * metrics['phi'] +
+            0.3 * metrics['coherence'] +
+            0.3 * emotional_coherence
+        ) * 100
+
+        return max(0, min(100, progress))  # Ensure result is between 0 and 100
 
 def create_consciousness_module(hidden_dim: int = 512,
                              num_cognitive_processes: int = 4) -> ConsciousnessModel:

tests/unit/test_consciousness.py

@@ -40,7 +40,17 @@ def test_emotional_integration(self, model, sample_input):
         assert metrics['emotional_influence'].shape == output.shape
         assert torch.any(metrics['emotional_influence'] != 0)
 
-    def test_emotion_cognition_progress(self, model):
+    def test_memory_retrieval_shape(self, model, sample_input):
+        """Test if memory retrieval produces correct shapes"""
+        output, metrics = model(sample_input)
+
+        # Check if retrieved_memory exists and has correct shape
+        assert 'retrieved_memory' in metrics
+        retrieved_memory = metrics['retrieved_memory']
+        assert retrieved_memory.shape == (sample_input['attention'].size(0), model.hidden_dim)
+
+    def test_goal_state_updates(self, model, sample_input):
+        """Test if goal state updates properly"""
         """Test if emotional states affect cognition progress."""
         metrics = {
             'phi': 0.8,