FractalBrainNet-v2.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from typing import Optional, List, Tuple, Dict, Callable
from enum import Enum

class FractalPatternType(Enum):
    """Tipos de padrões fractais suportados pela FractalBrainNet"""
    MANDELBROT = "mandelbrot"
    SIERPINSKI = "sierpinski"
    JULIA = "julia"
    CANTOR = "cantor"
    DRAGON_CURVE = "dragon_curve"

class FractalPatternGenerator:
    """
    Gerador de padrões fractais para definir as regras de conexão
    entre neurônios na FractalBrainNet.
    """
    
    @staticmethod
    def mandelbrot_connectivity(width: int, height: int, max_iter: int = 100) -> torch.Tensor:
        """
        Gera matriz de conectividade baseada no conjunto de Mandelbrot.
        Valores mais altos indicam conexões mais fortes.
        """
        x = torch.linspace(-2.5, 1.5, width)
        y = torch.linspace(-1.5, 1.5, height)
        X, Y = torch.meshgrid(x, y, indexing='ij')
        c = X + 1j * Y
        z = torch.zeros_like(c)
        
        connectivity = torch.zeros(width, height)
        
        for i in range(max_iter):
            mask = torch.abs(z) <= 2
            z[mask] = z[mask] ** 2 + c[mask]
            connectivity[mask] += 1
        
        # Normalizar para [0, 1]
        connectivity = connectivity / max_iter
        return connectivity
    
    @staticmethod
    def sierpinski_connectivity(size: int, iterations: int = 5) -> torch.Tensor:
        """
        Gera matriz de conectividade baseada no triângulo de Sierpinski.
        """
        pattern = torch.zeros(size, size)
        pattern[0, size//2] = 1.0
        
        for _ in range(iterations):
            new_pattern = torch.zeros_like(pattern)
            for i in range(size-1):
                for j in range(size-1):
                    if pattern[i, j] > 0:
                        # Regra do triângulo de Sierpinski
                        if i+1 < size and j > 0:
                            new_pattern[i+1, j-1] = 1.0
                        if i+1 < size and j+1 < size:
                            new_pattern[i+1, j+1] = 1.0
            pattern = torch.maximum(pattern, new_pattern)
        
        return pattern
    
    @staticmethod
    def julia_connectivity(width: int, height: int, c_real: float = -0.7, 
                          c_imag: float = 0.27015, max_iter: int = 100) -> torch.Tensor:
        """
        Gera matriz de conectividade baseada no conjunto de Julia.
        """
        x = torch.linspace(-2, 2, width)
        y = torch.linspace(-2, 2, height)
        X, Y = torch.meshgrid(x, y, indexing='ij')
        z = X + 1j * Y
        c = complex(c_real, c_imag)
        
        connectivity = torch.zeros(width, height)
        
        for i in range(max_iter):
            mask = torch.abs(z) <= 2
            z[mask] = z[mask] ** 2 + c
            connectivity[mask] += 1
        
        connectivity = connectivity / max_iter
        return connectivity

class CerebralDynamicsModule(nn.Module):
    """
    Módulo que simula dinâmicas cerebrais através de processamento
    distribuído e paralelo, inspirado na organização hierárquica do cérebro.
    """
    
    def __init__(self, channels: int, fractal_pattern: torch.Tensor):
        super().__init__()
        self.channels = channels
        self.fractal_pattern = nn.Parameter(fractal_pattern, requires_grad=False)
        
        # Múltiplas escalas de processamento (simulando diferentes frequências cerebrais)
        self.alpha_processing = nn.Conv2d(channels, channels//4, 1)  # 8-12 Hz
        self.beta_processing = nn.Conv2d(channels, channels//4, 1)   # 13-30 Hz
        self.gamma_processing = nn.Conv2d(channels, channels//4, 1)  # 30-100 Hz
        self.theta_processing = nn.Conv2d(channels, channels//4, 1)  # 4-8 Hz
        
        # Integração das diferentes escalas
        self.integration = nn.Conv2d(channels, channels, 1)
        self.normalization = nn.LayerNorm([channels])
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        batch_size, channels, height, width = x.shape
        
        # Aplicar padrão fractal como máscara de atenção
        if self.fractal_pattern.shape[-2:] != (height, width):
            fractal_mask = F.interpolate(
                self.fractal_pattern.unsqueeze(0).unsqueeze(0), 
                size=(height, width), mode='bilinear', align_corners=False
            ).squeeze()
        else:
            fractal_mask = self.fractal_pattern
        
        # Processamento em múltiplas escalas (simulando bandas de frequência cerebral)
        alpha = self.alpha_processing(x) * fractal_mask.unsqueeze(0).unsqueeze(0)
        beta = self.beta_processing(x) * (1 - fractal_mask.unsqueeze(0).unsqueeze(0))
        gamma = self.gamma_processing(x) * fractal_mask.unsqueeze(0).unsqueeze(0) * 0.5
        theta = self.theta_processing(x) * torch.sin(fractal_mask * math.pi).unsqueeze(0).unsqueeze(0)
        
        # Combinar diferentes escalas
        combined = torch.cat([alpha, beta, gamma, theta], dim=1)
        integrated = self.integration(combined)
        
        # Normalização adaptativa
        integrated = integrated.permute(0, 2, 3, 1)
        integrated = self.normalization(integrated)
        integrated = integrated.permute(0, 3, 1, 2)
        
        return integrated + x  # Conexão residual

class FractalNeuralBlock(nn.Module):
    """
    Bloco neural fractal que implementa a regra de expansão fractal
    com dinâmicas cerebrais integradas.
    """
    
    def __init__(self, level: int, in_channels: int, out_channels: int,
                 fractal_pattern: torch.Tensor, drop_path_prob: float = 0.1):
        super().__init__()
        self.level = level
        
        if level == 1:
            # Caso base com dinâmicas cerebrais
            self.base_conv = nn.Conv2d(in_channels, out_channels, 3, padding=1)
            self.cerebral_dynamics = CerebralDynamicsModule(out_channels, fractal_pattern)
            self.activation = nn.GELU()  # GELU para maior expressividade
            self.norm = nn.BatchNorm2d(out_channels)
        else:
            # Estrutura recursiva fractal
            self.deep_branch = nn.Sequential(
                FractalNeuralBlock(level-1, in_channels, out_channels, fractal_pattern, drop_path_prob),
                FractalNeuralBlock(level-1, out_channels, out_channels, fractal_pattern, drop_path_prob)
            )
            
            self.shallow_branch = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 3, padding=1),
                CerebralDynamicsModule(out_channels, fractal_pattern),
                nn.BatchNorm2d(out_channels),
                nn.GELU()
            )
            
            # Mecanismo de atenção fractal
            self.fractal_attention = nn.Sequential(
                nn.Conv2d(out_channels * 2, out_channels // 4, 1),
                nn.GELU(),
                nn.Conv2d(out_channels // 4, 2, 1),
                nn.Sigmoid()
            )
            
        self.drop_path = nn.Dropout2d(drop_path_prob) if drop_path_prob > 0 else nn.Identity()
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self.level == 1:
            out = self.base_conv(x)
            out = self.norm(out)
            out = self.cerebral_dynamics(out)
            out = self.activation(out)
            return self.drop_path(out)
        else:
            # Processamento em ramos paralelos
            deep_out = self.deep_branch(x)
            shallow_out = self.shallow_branch(x)
            
            # Mecanismo de atenção para combinar ramos
            combined = torch.cat([deep_out, shallow_out], dim=1)
            attention_weights = self.fractal_attention(combined)
            
            # Combinar com pesos adaptativos
            alpha, beta = attention_weights.chunk(2, dim=1)
            result = alpha * deep_out + beta * shallow_out
            
            return self.drop_path(result)

class AdaptiveScaleProcessor(nn.Module):
    """
    Processador adaptativo que opera em múltiplas escalas,
    simulando a capacidade do cérebro de processar informações
    em diferentes níveis de abstração simultaneamente.
    """
    
    def __init__(self, channels: int):
        super().__init__()
        self.channels = channels
        
        # Diferentes escalas de processamento
        self.local_processor = nn.Conv2d(channels, channels, 1)
        self.regional_processor = nn.Conv2d(channels, channels, 3, padding=1)
        self.global_processor = nn.Conv2d(channels, channels, 5, padding=2)
        
        # Integração adaptativa
        self.scale_fusion = nn.Sequential(
            nn.Conv2d(channels * 3, channels, 1),
            nn.BatchNorm2d(channels),
            nn.GELU()
        )
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        local = self.local_processor(x)
        regional = self.regional_processor(x)
        global_proc = self.global_processor(x)
        
        # Combinar escalas
        multi_scale = torch.cat([local, regional, global_proc], dim=1)
        fused = self.scale_fusion(multi_scale)
        
        return fused + x

class FractalBrainNet(nn.Module):
    """
    FractalBrainNet: Rede neural que combina a profundidade das redes profundas
    com a complexidade e elegância dos fractais, capaz de emular dinâmicas cerebrais
    através de estruturas hierárquicas e auto-similares.
    
    Baseada no artigo de Jose R. F. Junior (2024).
    """
    
    def __init__(self, 
                 num_classes: int = 10,
                 in_channels: int = 3,
                 fractal_levels: List[int] = [2, 3, 4, 5],
                 base_channels: int = 64,
                 fractal_pattern_type: FractalPatternType = FractalPatternType.MANDELBROT,
                 pattern_resolution: int = 32,
                 drop_path_prob: float = 0.15,
                 enable_continuous_learning: bool = True):
        
        super().__init__()
        
        self.num_classes = num_classes
        self.fractal_levels = fractal_levels
        self.enable_continuous_learning = enable_continuous_learning
        
        # Gerar padrão fractal base
        self.fractal_pattern = self._generate_fractal_pattern(
            fractal_pattern_type, pattern_resolution
        )
        
        # Camada de entrada adaptativa
        self.stem = nn.Sequential(
            nn.Conv2d(in_channels, base_channels, 7, stride=2, padding=3),
            nn.BatchNorm2d(base_channels),
            nn.GELU(),
            AdaptiveScaleProcessor(base_channels)
        )
        
        # Blocos fractais neurais hierárquicos
        self.fractal_stages = nn.ModuleList()
        current_channels = base_channels
        
        for i, level in enumerate(fractal_levels):
            # Aumentar canais progressivamente
            stage_channels = base_channels * (2 ** min(i, 4))
            
            # Bloco fractal principal
            fractal_block = FractalNeuralBlock(
                level, current_channels, stage_channels, 
                self.fractal_pattern, drop_path_prob
            )
            
            # Processador multi-escala
            scale_processor = AdaptiveScaleProcessor(stage_channels)
            
            # Pooling adaptativo
            if i < len(fractal_levels) - 1:
                pooling = nn.Sequential(
                    nn.Conv2d(stage_channels, stage_channels, 3, stride=2, padding=1),
                    nn.BatchNorm2d(stage_channels),
                    nn.GELU()
                )
            else:
                pooling = nn.Identity()
            
            self.fractal_stages.append(nn.Sequential(
                fractal_block,
                scale_processor,
                pooling
            ))
            
            current_channels = stage_channels
        
        # Sistema de atenção global inspirado na atenção cerebral
        self.global_attention = nn.MultiheadAttention(
            current_channels, num_heads=8, batch_first=True
        )
        
        # Cabeça de classificação com aprendizado contínuo
        self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(current_channels, current_channels // 2),
            nn.GELU(),
            nn.Dropout(0.1),
            nn.Linear(current_channels // 2, num_classes)
        )
        
        # Módulo de meta-aprendizado para adaptação contínua
        if enable_continuous_learning:
            self.meta_learner = nn.Sequential(
                nn.Linear(current_channels, current_channels // 4),
                nn.GELU(),
                nn.Linear(current_channels // 4, current_channels)
            )
        
        self._initialize_weights()
    
    def _generate_fractal_pattern(self, pattern_type: FractalPatternType, 
                                 resolution: int) -> torch.Tensor:
        """Gera o padrão fractal base para a rede."""
        if pattern_type == FractalPatternType.MANDELBROT:
            return FractalPatternGenerator.mandelbrot_connectivity(resolution, resolution)
        elif pattern_type == FractalPatternType.SIERPINSKI:
            return FractalPatternGenerator.sierpinski_connectivity(resolution)
        elif pattern_type == FractalPatternType.JULIA:
            return FractalPatternGenerator.julia_connectivity(resolution, resolution)
        else:
            # Padrão padrão (Mandelbrot)
            return FractalPatternGenerator.mandelbrot_connectivity(resolution, resolution)
    
    def _initialize_weights(self):
        """Inicialização de pesos inspirada na neuroplasticidade."""
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # Inicialização He com variação fractal
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, (nn.BatchNorm2d, nn.LayerNorm)):
                nn.init.constant_(m.weight, 1.0)
                nn.init.constant_(m.bias, 0.0)
            elif isinstance(m, nn.Linear):
                nn.init.trunc_normal_(m.weight, std=0.02)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
    
    def forward(self, x: torch.Tensor, 
                return_attention_maps: bool = False) -> torch.Tensor:
        """
        Forward pass com processamento hierárquico e dinâmicas cerebrais.
        """
        # Extração inicial de características
        x = self.stem(x)
        
        attention_maps = []
        
        # Processamento através dos estágios fractais
        for stage in self.fractal_stages:
            x = stage(x)
            
            if return_attention_maps:
                # Capturar mapas de atenção para análise
                attention_map = torch.mean(x, dim=1, keepdim=True)
                attention_maps.append(attention_map)
        
        # Pooling adaptativo global
        pooled = self.adaptive_pool(x)
        features = pooled.flatten(1)
        
        # Aplicar atenção global se habilitada
        if hasattr(self, 'global_attention'):
            # Reformatar para atenção multi-cabeça
            attn_input = features.unsqueeze(1)
            attended, _ = self.global_attention(attn_input, attn_input, attn_input)
            features = attended.squeeze(1)
        
        # Meta-aprendizado para adaptação contínua
        if self.enable_continuous_learning and hasattr(self, 'meta_learner'):
            meta_features = self.meta_learner(features)
            features = features + 0.1 * meta_features  # Residual meta-learning
        
        # Classificação final
        output = self.classifier(features)
        
        if return_attention_maps:
            return output, attention_maps
        return output
    
    def analyze_fractal_patterns(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """
        Analisa os padrões emergentes gerados pela estrutura fractal,
        conforme mencionado na metodologia do artigo.
        """
        self.eval()
        with torch.no_grad():
            _, attention_maps = self.forward(x, return_attention_maps=True)
            
            analysis = {
                'fractal_pattern': self.fractal_pattern,
                'attention_maps': attention_maps,
                'pattern_complexity': self._compute_pattern_complexity(attention_maps),
                'hierarchical_organization': self._analyze_hierarchical_organization(attention_maps)
            }
            
        return analysis
    
    def _compute_pattern_complexity(self, attention_maps: List[torch.Tensor]) -> List[float]:
        """Computa a complexidade dos padrões emergentes."""
        complexities = []
        for attention_map in attention_maps:
            # Usar entropia como medida de complexidade
            flat_map = attention_map.flatten()
            prob_dist = F.softmax(flat_map, dim=0)
            entropy = -torch.sum(prob_dist * torch.log(prob_dist + 1e-8))
            complexities.append(entropy.item())
        return complexities
    
    def _analyze_hierarchical_organization(self, attention_maps: List[torch.Tensor]) -> Dict[str, float]:
        """Analisa a organização hierárquica dos padrões."""
        if len(attention_maps) < 2:
            return {'correlation': 0.0, 'hierarchy_score': 0.0}
        
        # Correlação entre níveis hierárquicos
        correlations = []
        for i in range(len(attention_maps) - 1):
            map1 = attention_maps[i].flatten()
            map2 = F.interpolate(attention_maps[i+1], size=attention_maps[i].shape[-2:], 
                               mode='bilinear', align_corners=False).flatten()
            correlation = torch.corrcoef(torch.stack([map1, map2]))[0, 1]
            correlations.append(correlation.item())
        
        avg_correlation = sum(correlations) / len(correlations)
        hierarchy_score = 1.0 - avg_correlation  # Maior diversidade = maior hierarquia
        
        return {
            'correlation': avg_correlation,
            'hierarchy_score': hierarchy_score
        }

# Função para criar modelos FractalBrainNet pré-configurados
def create_fractal_brain_net(model_size: str = 'medium',
                           num_classes: int = 10,
                           fractal_pattern: FractalPatternType = FractalPatternType.MANDELBROT) -> FractalBrainNet:
    """
    Cria modelos FractalBrainNet pré-configurados inspirados no artigo de Jose R. F. Junior.
    
    Args:
        model_size: 'small', 'medium', 'large', 'xlarge'
        num_classes: número de classes para classificação
        fractal_pattern: tipo de padrão fractal a ser usado
    """
    configs = {
        'small': {
            'fractal_levels': [2, 3],
            'base_channels': 32,
            'pattern_resolution': 16
        },
        'medium': {
            'fractal_levels': [2, 3, 4],
            'base_channels': 64,
            'pattern_resolution': 32
        },
        'large': {
            'fractal_levels': [2, 3, 4, 5],
            'base_channels': 96,
            'pattern_resolution': 64
        },
        'xlarge': {
            'fractal_levels': [3, 4, 5, 6],
            'base_channels': 128,
            'pattern_resolution': 128
        }
    }
    
    config = configs.get(model_size, configs['medium'])
    
    return FractalBrainNet(
        num_classes=num_classes,
        fractal_levels=config['fractal_levels'],
        base_channels=config['base_channels'],
        fractal_pattern_type=fractal_pattern,
        pattern_resolution=config['pattern_resolution']
    )

# Demonstração e teste
if __name__ == "__main__":
    print("=== FractalBrainNet - Implementação Avançada ===")
    print("Baseada no artigo de Jose R. F. Junior (2024)")
    print()
    
    # Criar modelo com diferentes padrões fractais
    models = {
        'Mandelbrot': create_fractal_brain_net('medium', 10, FractalPatternType.MANDELBROT),
        'Sierpinski': create_fractal_brain_net('medium', 10, FractalPatternType.SIERPINSKI),
        'Julia': create_fractal_brain_net('medium', 10, FractalPatternType.JULIA)
    }
    
    # Teste com entrada dummy
    dummy_input = torch.randn(2, 3, 64, 64)
    
    for name, model in models.items():
        print(f"\n=== Modelo com padrão {name} ===")
        
        # Forward pass normal
        output = model(dummy_input)
        print(f"Output shape: {output.shape}")
        
        # Análise de padrões fractais emergentes
        analysis = model.analyze_fractal_patterns(dummy_input)
        print(f"Níveis de atenção capturados: {len(analysis['attention_maps'])}")
        print(f"Complexidade dos padrões: {analysis['pattern_complexity']}")
        print(f"Organização hierárquica: {analysis['hierarchical_organization']}")
        
        # Estatísticas do modelo
        total_params = sum(p.numel() for p in model.parameters())
        print(f"Parâmetros totais: {total_params:,}")
    
    print("\n=== FractalBrainNet criada com sucesso! ===")
    print("Esta implementação incorpora:")
    print("- Padrões fractais (Mandelbrot, Sierpinski, Julia)")
    print("- Simulação de dinâmicas cerebrais")
    print("- Processamento hierárquico e multi-escala")
    print("- Mecanismos de atenção inspirados no cérebro")
    print("- Capacidade de aprendizado contínuo")
    print("- Análise de padrões emergentes")


"""
# Criar modelo com padrão Mandelbrot
model = create_fractal_brain_net('large', num_classes=1000, 
                                fractal_pattern=FractalPatternType.MANDELBROT)

# Forward pass normal
output = model(input_tensor)

# Análise de padrões emergentes
analysis = model.analyze_fractal_patterns(input_tensor)
print("Complexidade dos padrões:", analysis['pattern_complexity'])
print("Organização hierárquica:", analysis['hierarchical_organization'])
"""