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from dataclasses import dataclass
import torch
import numpy as np
@dataclass
class ConsciousnessState:
phi_prime: float
emotional_vector: np.ndarray
attention_state: dict
self_awareness_level: float
class ConsciousnessMatrix:
def __init__(self, num_processors=128):
self.num_processors = num_processors
self.emotional_dimension = 128
self.state = ConsciousnessState(
phi_prime=0.0,
emotional_vector=np.zeros(self.emotional_dimension),
attention_state={},
self_awareness_level=0.0
)
def process_consciousness(self, input_state):
# Implement consciousness processing based on IIT and Global Workspace Theory
self._update_phi_prime()
self._process_emotional_state()
self._update_attention_allocation()
self._evaluate_self_awareness()
def _update_phi_prime(self):
# Implementation of modified Φ (phi) metrics
pass
def _process_emotional_state(self):
# 128-dimensional emotional state processing
pass
def _update_attention_allocation(self):
# Update attention allocation based on current state
pass
def _evaluate_self_awareness(self):
# Evaluate and update self-awareness level
pass
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