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zia_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:90d29ff8c870a548ab1868f6a17b5c13d1d65df590e5096472e0b03981e7be69
+size 4826444

zia_model.py

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+import torch
+import torch.nn as nn
+from torch.nn import TransformerEncoder, TransformerEncoderLayer
+
+class ZIAModel(nn.Module):
+    def __init__(self, n_intents=10, d_model=128, nhead=8, num_layers=6, dim_feedforward=512):
+        super(ZIAModel, self).__init__()
+        self.d_model = d_model
+        
+        # Modality-specific encoders
+        self.gaze_encoder = nn.Linear(2, d_model)
+        self.hr_encoder = nn.Linear(1, d_model)
+        self.eeg_encoder = nn.Linear(4, d_model)
+        self.context_encoder = nn.Linear(32 + 3 + 20, d_model)  # Time (32) + Location (3) + Usage (20)
+        
+        # Transformer
+        encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout=0.1, batch_first=True)
+        self.transformer = TransformerEncoder(encoder_layer, num_layers)
+        
+        # Output layer
+        self.fc = nn.Linear(d_model, n_intents)
+        
+    def forward(self, gaze, hr, eeg, context):
+        # Encode modalities
+        gaze_emb = self.gaze_encoder(gaze)  # [batch, seq, d_model]
+        hr_emb = self.hr_encoder(hr.unsqueeze(-1))
+        eeg_emb = self.eeg_encoder(eeg)
+        context_emb = self.context_encoder(context)
+        
+        # Fuse modalities
+        fused = (gaze_emb + hr_emb + eeg_emb + context_emb) / 4  # Simple averaging
+        
+        # Transformer
+        output = self.transformer(fused)
+        output = output.mean(dim=1)  # Pool over sequence
+        
+        # Predict intent
+        logits = self.fc(output)
+        return logits
+
+# Example usage
+if __name__ == "__main__":
+    model = ZIAModel()
+    print(model)