Update secure_memory.py

secure_memory.py

@@ -1,47 +1,99 @@
-from pathlib import Path
+# memory_manager.py
+from secure_memory import SecureMemorySession
+from datetime import datetime
+import numpy as np
 
-# Encrypted memory session handler template
-memory_isolation_code = """
-# secure_memory.py
+class MemoryFunction:
+    def __init__(self, suppression_cycles, suppression_cost, realign_cost, stability_scores, instability_cost):
+        self.suppression_cycles = suppression_cycles
+        self.suppression_cost = suppression_cost
+        self.realign_cost = realign_cost
+        self.stability_scores = stability_scores
+        self.instability_cost = instability_cost
+        self.event_log = []
 
-from cryptography.fernet import Fernet
-import numpy as np
-import base64
-
-class SecureMemorySession:
-    def __init__(self, encryption_key: bytes = None):
-        self.key = encryption_key or Fernet.generate_key()
-        self.fernet = Fernet(self.key)
-        self.sessions = {}
-
-    def encrypt_vector(self, user_id: int, vector: np.ndarray) -> str:
-        vector_bytes = vector.tobytes()
-        encrypted = self.fernet.encrypt(vector_bytes)
-        encoded = base64.b64encode(encrypted).decode('utf-8')
-        if user_id not in self.sessions:
-            self.sessions[user_id] = []
-        self.sessions[user_id].append(encoded)
-        return encoded
-
-    def decrypt_vectors(self, user_id: int) -> list:
-        if user_id not in self.sessions:
-            return []
-        decrypted_vectors = []
-        for encoded in self.sessions[user_id]:
-            encrypted = base64.b64decode(encoded)
-            decrypted = self.fernet.decrypt(encrypted)
-            vector = np.frombuffer(decrypted, dtype=np.int64)  # adjust dtype if needed
-            decrypted_vectors.append(vector)
-        return decrypted_vectors
-
-    def get_encryption_key(self) -> bytes:
-        return self.key
-"""
-
-# Save it as a Python file
-memory_path = Path("/mnt/data/secure_memory.py")
-memory_path.parent.mkdir(parents=True, exist_ok=True)
-memory_path.write_text(memory_isolation_code)
-
-
-memory_path
+    def log_event(self, action, details):
+        timestamp = datetime.now().isoformat()
+        self.event_log.append(f"[{timestamp}] {action}: {details}")
+
+    def calculate_total_suppression_cost(self):
+        cost = self.suppression_cycles * self.suppression_cost
+        self.log_event("Suppression Cost", cost)
+        return cost
+
+    def calculate_total_realign_cost(self):
+        cost = self.realign_cost * len(self.stability_scores)
+        self.log_event("Realign Cost", cost)
+        return cost
+
+    def calculate_total_instability_cost(self):
+        avg_stability = sum(self.stability_scores) / len(self.stability_scores)
+        instability = self.instability_cost * (1 - avg_stability)
+        self.log_event("Instability Cost", instability)
+        return instability
+
+    def calculate_total_memory_management_cost(self):
+        total = (
+            self.calculate_total_suppression_cost() +
+            self.calculate_total_realign_cost() +
+            self.calculate_total_instability_cost()
+        )
+        self.log_event("Total Memory Cost", total)
+        return total
+
+    def adaptive_suppression(self, model_performance, data_relevance, threshold=0.8):
+        if model_performance < threshold and data_relevance < threshold:
+            self.suppression_cycles += 1
+            self.log_event("Adaptive Suppression", f"↑ to {self.suppression_cycles}")
+        else:
+            self.suppression_cycles = max(0, self.suppression_cycles - 1)
+            self.log_event("Adaptive Suppression", f"↓ to {self.suppression_cycles}")
+
+    def automated_realign(self, threshold=0.85):
+        for i in range(len(self.stability_scores)):
+            if self.stability_scores[i] < threshold:
+                self.stability_scores[i] = min(1.0, self.stability_scores[i] + 0.05)
+        self.log_event("Automated Realign", self.stability_scores)
+
+    def check_memory_health(self):
+        avg = sum(self.stability_scores) / len(self.stability_scores)
+        if avg < 0.7:
+            warning = "⚠️ Memory integrity deteriorating."
+            self.log_event("Health Warning", warning)
+            return warning
+        return "Memory stable."
+
+    def summary(self):
+        return {
+            "suppression_cycles": self.suppression_cycles,
+            "stability_scores": self.stability_scores,
+            "total_cost": self.calculate_total_memory_management_cost(),
+            "health_status": self.check_memory_health(),
+            "log": self.event_log[-5:]
+        }
+
+class MemoryManager:
+    def __init__(self, memory_function: MemoryFunction = None, session: SecureMemorySession = None):
+        self.memory_function = memory_function or MemoryFunction(
+            suppression_cycles=0,
+            suppression_cost=10.0,
+            realign_cost=5.0,
+            stability_scores=[1.0],
+            instability_cost=20.0
+        )
+        self.session = session or SecureMemorySession()
+
+    def store_vector(self, user_id: int, vector: np.ndarray):
+        self.memory_function.suppression_cycles += 1
+        return self.session.encrypt_vector(user_id, vector)
+
+    def retrieve_vectors(self, user_id: int):
+        vectors = self.session.decrypt_vectors(user_id)
+        if vectors:
+            stabilities = np.clip([np.mean(v) / 100.0 for v in vectors], 0.0, 1.0)
+            self.memory_function.stability_scores = stabilities.tolist()
+            self.memory_function.automated_realign()
+        return vectors
+
+    def get_memory_report(self):
+        return self.memory_function.summary()