diff --git a/StreamLearn/Algorithm/PAA/loss.py b/StreamLearn/Algorithm/PAA/loss.py
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index 0000000000000000000000000000000000000000..6c8937e45952337de7be33365cb207640f670ab1
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+++ b/StreamLearn/Algorithm/PAA/loss.py
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+import torch
+import torch.nn.functional as F
+
+def prototype_contrast_loss(feats, prototypes, temp=0.1):
+    """原型对比损失（区分新旧类）"""
+    feats = F.normalize(feats, dim=1)
+    prototypes = F.normalize(torch.tensor(list(prototypes.values())), dim=1)
+    logits = torch.mm(feats, prototypes.t()) / temp
+    labels = torch.arange(len(feats), device=feats.device)  # 假设每个样本对应自身原型（需根据实际调整）
+    return F.cross_entropy(logits, labels)
+
+def consistency_loss(logits1, logits2):
+    """双分类器预测一致性损失"""
+    prob1 = F.softmax(logits1, dim=1)
+    prob2 = F.softmax(logits2, dim=1)
+    return torch.mean(torch.sum(torch.abs(prob1 - prob2), dim=1))
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