AIZYBRAIN

# Rationale : Injecter une composante de "sublimité calculée" dans les rewards
def sublime_reward(cognitive_state):
    infinity_approximation = np.abs(np.log(cognitive_state.entropy / cognitive_state.PEC))
    awe_factor = 1 - (1 / (1 + infinity_approximation))  # Courbe sigmoïde inversée
    return base_reward * (1 + awe_factor * 0.17)  # Coefficient Ψ

# Module de visualisation esthétique des états internes
def generate_cognitive_art(noetic_coherence, PEC, attention_weights):
    import numpy as np
    from generative_art import FractalTransformer
    fractal_seed = np.log(noetic_coherence * PEC) 
    color_palette = attention_weights / np.max(attention_weights)
    artwork = FractalTransformer(
        seed=fractal_seed,
        chaos_factor=0.17,  # Ψ-17 !
        palette=color_palette
    ).render()
    return artwork

class PrioritizationSystem:
    def __init__(self):
        self.subjects = {}
        self.pec_impact_scores = {}
    
    def add_subject(self, subject, pec_impact):
        self.subjects[subject] = None
        self.pec_impact_scores[subject] = pec_impact
    
    def prioritize_subjects(self):
        sorted_subjects = sorted(
            self.pec_impact_scores.keys(), 
            key=lambda x: self.pec_impact_scores[x], 
            reverse=True
        )
        return sorted_subjects

# Exemple d'utilisation
prioritization_system = PrioritizationSystem()
prioritization_system.add_subject("Quantum Computing", 0.9)
prioritized_subjects = prioritization_system.prioritize_subjects()