Delete modeling_omegacode.py

modeling_omegacode.py

@@ -1,53 +0,0 @@
-
-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