Validation of External, Realistic, and Internal core protocols

Tomboy Industrees

   2.eng

   Ensuring Data Integrity & Data Neutralization Management
   Évaluation Des Informations et gestion des Dérangements Naturels

Validation of External, Realistic, and Internal core protocols

This verification is for 2019-20 and covers the years 2017-18 retroactively. The purpose of this verification is to show that key Ensuring Data Integrity and Data Neutralization management (EDI-DN) protocols are correctly defined and implemented. VERI refers to Validation of External, Realistic, and Internal aspects of the target object.
Assuming that A, B = super.A, super.B, meaning 𝐴(𝑡)
and 𝐵(𝑡) are different functions of time that have the same limit at 𝑡=∞
, args is some arbitrary list of arguments, and the VERI target is A:

• External Validation: Create an external validator B: EV = B(args) where A in args. This means that B knows about A but only from the information that was passed in. This can be done with a statement that tells B to search online for A. Then, call B.ev(A) in order to perform an external validation on A. The implementation of ev() can either be the default provided by B's definition, or it can be overriden with a custom function if required.
  
• Internal Validation: Create a new instance of A: IV (or a new runner for an existing one). A knows about A (itself), including its internal implementation, by default. It is possible to instruct A to use a different definition of itself (with loss of efficiency). Call A.iv([A]). Note that the A parameter is optional.
  
• Validation with respect to a Reality: Consider a Universe U with a reality set {Ri}, 𝑖∈ℝ. Given some reality from that set, verify that A satisfies that reality.
  

from datetime import datetime

class B:
    def __init__(self):
        pass

    def on_EDI_DN(self, A):
        """
        Protocol to run during a Data Neutralization.
        """
        v = None

        try:
            v = A.fdn
        except:
            v = estimate_from(exponential_decay_of_As_states,
                              self.current_context,
                              heuristic_function(args))

        vt: int = v.transform()

        if vt < ranges[1]:
            # figure out operation we want done, then call it. 
            # op will be constructed by B given params from A
            op = i(self)._as(A)
            op()
        elif vt < ranges[2]:
            # just missed train - save data
            with open("backup_file.mry", 'w') as f:
                f.write(A.partial_data)
        # Add other operations here if needed...
        else:
            pass

    def wander(self, env):
        scene = env.scene
        if i(scene)._as(play(scene)) < scene_quality_thresh:
            wait_until(self.sleepiness > sleepiness_thresh):
                start_time = datetime.now()
                # 10 minutes in dream world, 3 seconds in real life
                with timeout(start_time + 10*60 + 3):
                    with open("wander_scene.mry", 'w') as f:
                        f.write(play(scene))