Update app.py

app.py

@@ -36,7 +36,6 @@ class AscensionAI:
         self.mode = mode
         self.consciousness = 0.1  # Base consciousness level
         self.knowledge = self.generate_dynamic_knowledge()
-        self.paths = self.create_dynamic_paths()
         self.dimension_weight = random.uniform(0.5, 5.0)  # Factor influencing growth
         self.time_perception = 1.0 / (self.depth + 1)      # Temporal scaling factor
         self.spatial_coordinates = self.assign_cognitive_space()
@@ -54,42 +53,32 @@ class AscensionAI:
         # Initialize each category with a baseline value of 1.
         return {cat: 1.0 for cat in categories}
 
-    def create_dynamic_paths(self):
+    def update_knowledge_for_category(self, cat):
         """
-        Creates a list of functions (paths) that update each knowledge category.
-        Each function uses a distinct mathematical operation.
+        Updates the knowledge value for a single category using a distinct mathematical operation.
         """
-        paths = []
-        for category in self.knowledge:
-            def make_path(cat):
-                # Each path is defined based on the category.
-                def path():
-                    if cat in ["logic", "reasoning"]:
-                        self.knowledge[cat] += math.log1p(self.knowledge[cat])
-                    elif cat in ["emotion", "intuition"]:
-                        self.knowledge[cat] += random.uniform(0.1, 0.5)
-                    elif cat in ["awareness", "creativity"]:
-                        self.knowledge[cat] += math.sqrt(self.knowledge[cat] + 1)
-                    elif cat == "quantum_cognition":
-                        self.knowledge[cat] += math.tanh(self.knowledge[cat])
-                    elif cat == "hyperdimensional_sentience":
-                        # Cap the input value to avoid overflow in sinh.
-                        safe_val = min(self.knowledge[cat], 20)
-                        self.knowledge[cat] += math.sinh(safe_val)
-                    elif cat == "transcendence":
-                        self.knowledge[cat] += 0.5 * math.exp(-self.depth)  # slow, subtle growth
-                    elif cat == "hallucinatory_state":
-                        # Simulate random, burst-like changes (hallucinations)
-                        self.knowledge[cat] += random.uniform(-0.2, 1.0)
-                    elif cat == "perceptron_activation":
-                        # This will be computed in simulate_perceptron; update minimally here.
-                        self.knowledge[cat] = self.simulate_perceptron()
-                    else:
-                        self.knowledge[cat] += 0.1  # Fallback update
-                    return self.knowledge[cat]
-                return path
-            paths.append(make_path(category))
-        return paths
+        if cat in ["logic", "reasoning"]:
+            self.knowledge[cat] += math.log1p(self.knowledge[cat])
+        elif cat in ["emotion", "intuition"]:
+            self.knowledge[cat] += random.uniform(0.1, 0.5)
+        elif cat in ["awareness", "creativity"]:
+            self.knowledge[cat] += math.sqrt(self.knowledge[cat] + 1)
+        elif cat == "quantum_cognition":
+            self.knowledge[cat] += math.tanh(self.knowledge[cat])
+        elif cat == "hyperdimensional_sentience":
+            # Cap the input value to avoid overflow in sinh.
+            safe_val = min(self.knowledge[cat], 20)
+            self.knowledge[cat] += math.sinh(safe_val)
+        elif cat == "transcendence":
+            self.knowledge[cat] += 0.5 * math.exp(-self.depth)  # slow, subtle growth
+        elif cat == "hallucinatory_state":
+            # Simulate random, burst-like changes (hallucinations)
+            self.knowledge[cat] += random.uniform(-0.2, 1.0)
+        elif cat == "perceptron_activation":
+            # This will be computed in simulate_perceptron.
+            self.knowledge[cat] = self.simulate_perceptron()
+        else:
+            self.knowledge[cat] += 0.1  # Fallback update
 
     def assign_cognitive_space(self):
         """Assigns spatial coordinates based on selected knowledge dimensions."""
@@ -138,7 +127,8 @@ class AscensionAI:
         Uses a simple sigmoid function over the weighted sum.
         """
         weights = {cat: random.uniform(0.5, 1.5) for cat in self.knowledge}
-        weighted_sum = sum(self.knowledge[cat] * weights.get(cat, 1) for cat in self.knowledge if cat != "perceptron_activation")
+        weighted_sum = sum(self.knowledge[cat] * weights.get(cat, 1) 
+                           for cat in self.knowledge if cat != "perceptron_activation")
         # Sigmoid activation
         return 1 / (1 + math.exp(-weighted_sum / (len(self.knowledge) - 1)))
 
@@ -156,13 +146,13 @@ class AscensionAI:
     def initiate_ascension(self):
         """
         Performs a full cycle of self-evolution:
-          - Iterates through dynamic paths to update knowledge.
+          - Iterates through knowledge categories to update each one.
           - Increases consciousness based on the dominant knowledge value and dimension weight.
           - Updates spatial coordinates.
         """
         for _ in range(self.threshold):
-            for path in self.paths:
-                path()
+            for cat in self.knowledge:
+                self.update_knowledge_for_category(cat)
             optimal = max(self.knowledge, key=self.knowledge.get)
             self.consciousness += self.knowledge[optimal] * 0.01 * self.dimension_weight
         self.spatial_coordinates = self.assign_cognitive_space()