Add modeling_omegacode.py

modeling_omegacode.py

@@ -0,0 +1,53 @@
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+class HolographicMasterCodeTransformer(nn.Module):
+    def __init__(self, input_dim=8, d_model=128, nhead=8, num_layers=6, output_dim=1, n_harmonics=4):
+        super().__init__()
+        self.d_model = d_model
+        self.n_harmonics = n_harmonics
+        self.alpha_0 = 1.0 / 137.035
+        
+        self.input_proj = nn.Linear(input_dim, d_model)
+        
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=d_model, nhead=nhead, dim_feedforward=d_model*4,
+            dropout=0.1, activation='gelu', batch_first=True, norm_first=True
+        )
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        
+        self.harmonic_weights = nn.Parameter(torch.randn(n_harmonics, d_model))
+        
+        self.output_head = nn.Sequential(
+            nn.LayerNorm(d_model),
+            nn.Linear(d_model, d_model//2),
+            nn.GELU(),
+            nn.Dropout(0.1),
+            nn.Linear(d_model//2, output_dim)
+        )
+        
+    def forward(self, x, alpha=None):
+        if alpha is None:
+            alpha = self.alpha_0
+        h = self.input_proj(x)
+        h = h.unsqueeze(1)
+        h = self.transformer(h)
+        h = h.squeeze(1)
+        
+        psi = torch.zeros_like(h)
+        for n in range(self.n_harmonics):
+            phase = 2 * torch.pi * (n + 1) * (alpha - self.alpha_0) * 800.0
+            psi += self.harmonic_weights[n] * torch.cos(h * (n + 1) + phase)
+        
+        alpha_dev = torch.abs(alpha - self.alpha_0)
+        security_factor = torch.exp(-300.0 * alpha_dev)
+        
+        h = h + 0.35 * psi * security_factor
+        return self.output_head(h)
+    
+    def holographic_reg(self):
+        reg = sum(torch.norm(w, p=1) for w in self.harmonic_weights)
+        return 0.0015 * reg / self.n_harmonics