# 示意代码，不可直接运行（依据论文公开摘要描述的简化实现，非官方复现）
import torch
import torch.nn as nn
import numpy as np

class KANLayer(nn.Module):
    """
    Kolmogorov-Arnold Network层
    
    核心区别：可学习激活函数在边上而非节点上
    使用B样条逼近任意一维函数
    """
    
    def __init__(self, in_dim: int, out_dim: int,
                 grid_size: int = 5, spline_order: int = 3):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.grid_size = grid_size
        self.spline_order = spline_order
        
        # 基函数权重（线性部分）
        self.base_weight = nn.Parameter(
            torch.randn(out_dim, in_dim) * 0.1
        )
        
        # 样条权重（非线性部分）
        self.spline_weight = nn.Parameter(
            torch.randn(out_dim, in_dim, grid_size + spline_order) * 0.1
        )
        
        # 样条网格点
        h = 1.0 / grid_size
        grid = torch.arange(-spline_order, grid_size + spline_order + 1) * h
        self.register_buffer('grid', grid)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        前向传播
        
        Args:
            x: [batch, in_dim]
            
        Returns:
            output: [batch, out_dim]
        """
        batch_size = x.shape[0]
        
        # 基函数（线性）
        base = torch.einsum('bi,oi->bo', x, self.base_weight)
        
        # B样条基函数
        x_expanded = x.unsqueeze(-1)  # [batch, in_dim, 1]
        grid_expanded = self.grid.view(1, 1, -1)  # [1, 1, grid_size+2*spline_order+1]
        
        # Cox-de Boor递推计算B样条基
        b_splines = self._bspline_basis(
            x_expanded.expand(-1, -1, grid_expanded.shape[-1]),
            grid_expanded.expand(x.shape[0], -1, -1)
        )
        
        # 样条部分
        spline = torch.einsum(
            'big,oig->bog', 
            b_splines, 
            self.spline_weight
        )
        
        return base + spline
    
    def _bspline_basis(self, x: torch.Tensor, 
                        grid: torch.Tensor) -> torch.Tensor:
        """计算B样条基函数值（简化版）"""
        order = self.spline_order
        if order == 0:
            return ((x >= grid[..., :-1]) & 
                    (x < grid[..., 1:])).float()
        
        # 递推
        B_prev = self._bspline_basis(x, grid)
        if B_prev.shape[-1] > 1:
            denom1 = grid[..., order:B_prev.shape[-1]] - grid[..., :-order]
            denom1 = torch.where(denom1 != 0, denom1, 
                                  torch.ones_like(denom1))
            term1 = (x - grid[..., :-order]) / denom1 * B_prev[..., :-1]
            
            denom2 = grid[..., order+1:] - grid[..., 1:B_prev.shape[-1]]
            denom2 = torch.where(denom2 != 0, denom2,
                                  torch.ones_like(denom2))
            term2 = (grid[..., order+1:] - x) / denom2 * B_prev[..., 1:]
            
            return term1 + term2
        
        return B_prev


class KANCLUEModel(nn.Module):
    """
    KAN-CLUE: CNN骨干 + KAN分类器 + 不确定性量化

    架构：
    1. 轻量CNN提取眼周图像特征
    2. KAN层替代MLP进行分类
    3. 不确定性量化（论文原方案为Dirichlet分布输出+激活直方图OOD分数，
       本示意代码以MC Dropout近似演示熵计算，与原论文方案不同）
    """
    
    def __init__(self, num_classes: int = 3,
                 feature_dim: int = 128,
                 kan_grid: int = 5):
        super().__init__()
        
        # CNN骨干（眼周图像特征提取）
        self.backbone = nn.Sequential(
            nn.Conv2d(1, 16, 3, stride=2, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU6(),
            nn.Conv2d(16, 32, 3, stride=2, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU6(),
            nn.Conv2d(32, 64, 3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU6(),
            nn.AdaptiveAvgPool2d(1),
        )
        self.flatten = nn.Flatten()
        
        # KAN分类器（替代MLP）
        self.kan1 = KANLayer(64, feature_dim, grid_size=kan_grid)
        self.kan2 = KANLayer(feature_dim, num_classes, grid_size=kan_grid)
        
        # 不确定性量化（MC Dropout）
        self.dropout = nn.Dropout(0.1)
        
    def forward(self, x: torch.Tensor, 
                n_samples: int = 1) -> tuple:
        """
        前向传播
        
        Args:
            x: [batch, 1, H, W] 近红外眼周图像
            n_samples: MC采样次数（>1时输出不确定性）
            
        Returns:
            logits: [batch, num_classes]
            uncertainty: [batch] 预测熵
        """
        features = self.backbone(x)
        features = self.flatten(features)
        
        # MC Dropout采样
        logits_list = []
        for _ in range(n_samples):
            h = self.dropout(features)
            h = self.kan1(h)
            h = torch.relu(h)
            logits = self.kan2(h)
            logits_list.append(logits)
        
        logits_stack = torch.stack(logits_list)  # [n_samples, batch, classes]
        
        if n_samples > 1:
            # 平均预测
            avg_logits = logits_stack.mean(dim=0)
            # 预测熵（不确定性）
            probs = torch.softmax(avg_logits, dim=-1)
            entropy = -torch.sum(
                probs * torch.log(probs + 1e-8), dim=-1
            )
            return avg_logits, entropy
        else:
            return logits_stack[0], torch.zeros(x.shape[0])


# 测试
if __name__ == "__main__":
    model = KANCLUEModel(num_classes=3)  # 清醒/疲劳/嗜睡
    
    # 模拟眼周图像 [batch, 1, 48, 48]
    x = torch.randn(4, 1, 48, 48)
    
    # 单次推理
    logits, _ = model(x, n_samples=1)
    print(f"单次推理 logits: {logits.shape}")
    
    # 不确定性推理（10次MC采样）
    logits, uncertainty = model(x, n_samples=10)
    print(f"不确定性推理 logits: {logits.shape}")
    print(f"不确定性: {uncertainty}")
    print(f"高置信度(低熵): {(uncertainty < 0.5).sum()}/4")
    
    # 参数量对比
    total_params = sum(p.numel() for p in model.parameters())
    print(f"\nKAN-CLUE总参数: {total_params:,}")
    
    # 与MLP对比
    mlp_model = nn.Sequential(
        nn.Conv2d(1, 16, 3, stride=2, padding=1),
        nn.ReLU(),
        nn.Conv2d(16, 32, 3, stride=2, padding=1),
        nn.ReLU(),
        nn.Conv2d(32, 64, 3, stride=2, padding=1),
        nn.ReLU(),
        nn.AdaptiveAvgPool2d(1),
        nn.Flatten(),
        nn.Linear(64, 128),
        nn.ReLU(),
        nn.Linear(128, 3)
    )
    mlp_params = sum(p.numel() for p in mlp_model.parameters())
    print(f"等效MLP参数: {mlp_params:,}")
    print(f"压缩比: {mlp_params/total_params:.1f}x")

# 示意代码，不可直接运行（EWC式持续学习的简化示意，论文采用KAN特定的EWC式正则化选择性更新少量参数）
class ContinualLearning:
    """
    持续学习模块
    
    新驾驶员数据增量学习，不遗忘旧驾驶员
    """
    
    def __init__(self, model, lr=1e-4, ewc_lambda=0.1):
        self.model = model
        self.lr = lr
        self.ewc_lambda = ewc_lambda  # EWC正则化
        self.fisher_matrix = None  # 重要参数记录
    
    def update_fisher(self, dataloader):
        """计算Fisher信息矩阵（标记重要参数）"""
        fisher = {}
        for name, param in self.model.named_parameters():
            fisher[name] = torch.zeros_like(param)
        
        self.model.eval()
        for x, y in dataloader:
            self.model.zero_grad()
            logits, _ = self.model(x, n_samples=1)
            loss = nn.functional.cross_entropy(logits, y)
            loss.backward()
            
            for name, param in self.model.named_parameters():
                if param.grad is not None:
                    fisher[name] += param.grad.pow(2)
        
        for name in fisher:
            fisher[name] /= len(dataloader)
        
        self.fisher_matrix = fisher
    
    def continual_update(self, dataloader):
        """
        持续学习更新
        
        EWC约束：重要参数变化小，防止遗忘
        """
        optimizer = torch.optim.Adam(
            self.model.parameters(), lr=self.lr
        )
        
        self.model.train()
        for x, y in dataloader:
            optimizer.zero_grad()
            
            logits, uncertainty = self.model(x, n_samples=5)
            ce_loss = nn.functional.cross_entropy(logits, y)
            
            # 不确定性正则化（鼓励高置信度）
            unc_loss = uncertainty.mean()
            
            # EWC正则化（防止遗忘）
            ewc_loss = 0
            if self.fisher_matrix:
                for name, param in self.model.named_parameters():
                    if name in self.fisher_matrix:
                        ewc_loss += (
                            self.fisher_matrix[name] * 
                            param.pow(2)
                        ).sum()
            
            total_loss = ce_loss + 0.01 * unc_loss + \
                         self.ewc_lambda * ewc_loss
            
            total_loss.backward()
            optimizer.step()