Update system.py

system.py

@@ -1,4 +1,5 @@
 import numpy as np
+from fractal_gpu import fractal_field
 
 
 class DrMoagiArchitecture:
@@ -10,20 +11,16 @@ class DrMoagiArchitecture:
         self.Xi = np.random.randn(dim)
         self.Omega = np.random.randn(dim)
         self.M = np.zeros(dim)
-        self.I = np.ones(dim)
         self.archive = []
 
-    # ------------------------
-    # Evolution Mechanics
-    # ------------------------
+        self.running = False
+        self.tick_counter = 0
+
     def evolve_overlay(self):
         noise = np.random.normal(0, 0.01, size=self.Xi.shape)
         self.Xi += noise
         self.Omega = 0.99 * self.Omega + 0.01 * self.Xi
 
-    # ------------------------
-    # Predictive Knowledge Field
-    # ------------------------
     def predictive_K(self):
         Psi = np.linalg.norm(self.Xi)
         Lambda = 1.5
@@ -40,9 +37,22 @@ class DrMoagiArchitecture:
             + (Psi * Lambda * Phi) / (Omega + 1)
         )
 
-    # ------------------------
-    # Stability Detection
-    # ------------------------
+    def entropy(self):
+        p = np.abs(self.Xi)
+        p = p / (np.sum(p) + 1e-8)
+        return -np.sum(p * np.log(p + 1e-8))
+
+    def bounded_update(self, Psi_input):
+        entropy_before = self.entropy()
+        self.step(Psi_input)
+        entropy_after = self.entropy()
+
+        if entropy_after > entropy_before + 0.5:
+            self.Xi *= 0.95
+
+    def lyapunov_energy(self):
+        return np.linalg.norm(self.Xi) ** 2
+
     def stability_zone(self):
         if len(self.archive) < 2:
             return "Initializing"
@@ -50,15 +60,12 @@ class DrMoagiArchitecture:
         dK = abs(self.archive[-1] - self.archive[-2])
 
         if dK < 0.01:
-            return "Green (Stable)"
+            return "Green"
         elif dK < 1.0:
-            return "Yellow (Transitional)"
+            return "Yellow"
         else:
-            return "Red (Volatile)"
+            return "Red"
 
-    # ------------------------
-    # Core Step
-    # ------------------------
     def step(self, Psi_input):
         self.Xi += 0.05 * Psi_input
         self.M = 0.9 * self.M + 0.1 * self.Xi
@@ -68,4 +75,15 @@ class DrMoagiArchitecture:
         K = self.predictive_K()
         self.archive.append(K)
 
-        return K
+        self.tick_counter += 1
+
+        return K
+
+    def compute_fractal(self):
+        return fractal_field(self.Xi)
+
+    def start(self):
+        self.running = True
+
+    def stop(self):
+        self.running = False