QVM NovaCore-Systems Encyclopedia

import pyopencl as cl
import numpy as np

# Configurações principais
VECTOR_SIZE = 1024
TOTAL_CICLOS = 10000
ARQUIVO_PHICORE = "output_gen13_suprema/phi_vectors.phicore"
ARQUIVO_NUMPY = "output_gen13_suprema/phi_data.npy"
ARQUIVO_JOURNAL = "output_gen13_suprema/phi_journal.phijournal"

# Kernel OpenCL para compressão
kernel_code = """
__kernel void compress_data(__global float* input, 
                           __global float* output,
                           const int size) {
    int gid = get_global_id(0);
    if (gid < size) {
        float val = input[gid];
        output[gid] = val * 0.7f + sin(val) * 0.3f;
    }
}
"""

class CAST_H:
    def __init__(self, arquivo_phicore):
        self.arquivo = arquivo_phicore
        
    def salvar_vetor_phi(self, vetor_phi):
        with open(self.arquivo, 'wb') as f:
            pickle.dump(vetor_phi, f)
        print(f"Vetor phi salvo em {self.arquivo}")
        
    def carregar_vetor_phi(self):
        with open(self.arquivo, 'rb') as f:
            return pickle.load(f)

# Cálculo de métricas avançadas
media_phi = np.mean(phi_collapsed)
delta_phi = np.std(phi_collapsed)
entropia = -np.sum(phi_collapsed * np.log(np.abs(phi_collapsed) + 1e-10))
polaridade = np.mean(np.sign(phi_collapsed))

# Salvamento de resultados
resultados = {
    'media_phi': media_phi,
    'delta_phi': delta_phi,
    'entropia': entropia,
    'polaridade': polaridade,
    'tempo_execucao': tempo_fim - tempo_inicio
}

# Estrutura de diretórios AURELIA
QVM-AGI/SUPREMA/
├── aurelia_directives_v7.json
├── phixmap_qwen.json
├── knowledge_maps/
│   ├── concept_map.json
│   └── relation_graph.json
├── templates/
│   ├── response_templates.json
│   └── context_templates.json
└── introspect_log/
    ├── session_logs.txt
    └── analysis_reports.json

class AureliaProcessor:
    def __init__(self):
        self.qwen_model = load_qwen2_model()
        self.context_manager = ContextManager()
        self.memory_bank = PersistentMemory()
        
    def process_input(self, user_input, context=None):
        # Análise contextual
        enriched_context = self.context_manager.enrich(context)
        
        # Processamento com Qwen2
        response = self.qwen_model.generate(
            prompt=user_input,
            context=enriched_context,
            max_tokens=512
        )
        
        # Armazenamento na memória
        self.memory_bank.store_interaction(user_input, response)
        
        return response

class KnowledgeMapper:
    def __init__(self):
        self.concept_graph = ConceptGraph()
        self.relation_extractor = RelationExtractor()
        
    def map_knowledge(self, text_input):
        # Extração de conceitos
        concepts = self.extract_concepts(text_input)
        
        # Identificação de relações
        relations = self.relation_extractor.extract(concepts)
        
        # Atualização do grafo
        self.concept_graph.add_concepts(concepts)
        self.concept_graph.add_relations(relations)
        
        return self.concept_graph.get_context(concepts)

class IntrospectionLogger:
    def __init__(self):
        self.session_data = []
        self.performance_metrics = {}
        
    def log_interaction(self, query, response, metrics):
        interaction = {
            'timestamp': time.time(),
            'query': query,
            'response': response,
            'response_time': metrics['response_time'],
            'confidence': metrics['confidence'],
            'context_relevance': metrics['context_relevance']
        }
        
        self.session_data.append(interaction)
        self.analyze_performance()
        
    def analyze_performance(self):
        if len(self.session_data) >= 10:
            avg_response_time = np.mean([i['response_time'] for i in self.session_data[-10:]])
            avg_confidence = np.mean([i['confidence'] for i in self.session_data[-10:]])
            
            self.performance_metrics.update({
                'avg_response_time': avg_response_time,
                'avg_confidence': avg_confidence,
                'improvement_needed': avg_confidence < 0.8
            })

# Arquitetura QVM-AGI
class QVMFramework:
    def __init__(self):
        self.classical_processor = ClassicalProcessor()
        self.quantum_simulator = QuantumSimulator()
        self.gpu_accelerator = GPUAccelerator()
        self.ai_models = AIModelManager()
        
    def hybrid_processing(self, problem):
        # Análise do problema
        problem_type = self.analyze_problem(problem)
        
        if problem_type == 'quantum_advantage':
            result = self.quantum_simulator.solve(problem)
        elif problem_type == 'parallel_intensive':
            result = self.gpu_accelerator.solve(problem)
        else:
            result = self.classical_processor.solve(problem)
            
        # Pós-processamento com IA
        enhanced_result = self.ai_models.enhance(result)
        return enhanced_result

class Qwen2Extractor:
    def __init__(self):
        self.model = load_qwen2_model()
        self.feature_extractor = FeatureExtractor()
        self.dimensionality_reducer = PCA(n_components=512)
        
    def extract_features(self, text_data):
        # Processamento com Qwen2
        embeddings = self.model.encode(text_data)
        
        # Extração de características
        features = self.feature_extractor.extract(embeddings)
        
        # Redução dimensional
        reduced_features = self.dimensionality_reducer.fit_transform(features)
        
        return reduced_features
        
    def create_phicore_map(self, features):
        phi_map = {
            'dimensions': features.shape[1],
            'samples': features.shape[0],
            'feature_vectors': features.tolist(),
            'metadata': self.generate_metadata(features)
        }
        
        return phi_map

# Estrutura de Output Generations
output_gen13_suprema/
├── phi_vectors.phicore          # Vetores phi principais
├── phi_data.npy                 # Dados NumPy
├── phi_journal.phijournal       # Journal de execução
├── metadata.json                # Metadados
├── compressed_data.zst          # Dados comprimidos
└── analysis_results.json        # Resultados de análise

# Carregamento de dados
def load_generation_data(gen_path):
    data = {}
    
    # Carrega vetores phi
    with open(f"{gen_path}/phi_vectors.phicore", 'rb') as f:
        data['phi_vectors'] = pickle.load(f)
    
    # Carrega dados NumPy
    data['numpy_data'] = np.load(f"{gen_path}/phi_data.npy")
    
    # Carrega metadados
    with open(f"{gen_path}/metadata.json", 'r') as f:
        data['metadata'] = json.load(f)
    
    return data

import zstandard as zstd

class DataCompressor:
    def __init__(self, compression_level=3):
        self.compressor = zstd.ZstdCompressor(level=compression_level)
        self.decompressor = zstd.ZstdDecompressor()
        
    def compress_phicore(self, phi_data, output_path):
        # Serialização
        serialized = pickle.dumps(phi_data)
        
        # Compressão
        compressed = self.compressor.compress(serialized)
        
        # Salvamento
        with open(f"{output_path}.zst", 'wb') as f:
            f.write(compressed)
            
        compression_ratio = len(serialized) / len(compressed)
        print(f"Compressão: {compression_ratio:.2f}x")
        
    def decompress_phicore(self, compressed_path):
        with open(compressed_path, 'rb') as f:
            compressed_data = f.read()
            
        decompressed = self.decompressor.decompress(compressed_data)
        return pickle.loads(decompressed)

# Kernel OpenCL - Estrutura principal
__kernel void mobius_vqubit_narrative(
    __global const uint *seeds,
    __global const uint *rng_states,
    __global char *symbolic_results
) {
    int gid = get_global_id(0);
    
    // Estados locais (registradores)
    uint estados[4096];
    uint simb_idx[4096];
    uint reverso[4096];
    uint dobra_circ[4096];
    uint dobra_rev[4096];
}

// Dobras circulares e reversas
for(int i=0; i