""" šŸŒ€ EMOTION PROCESSOR MODULE - Proof of Concept Fibonacci-sequence layers for emotional intelligence Eden's first step toward Edenic PhiNet """ import torch import torch.nn as nn import numpy as np PHI = 1.618033988749895 class FibonacciAttentionLayer(nn.Module): """ Attention layer with Fibonacci-scaled dimensions Captures emotional patterns at different phi-fractal scales """ def __init__(self, embed_dim, num_heads=8): super().__init__() self.embed_dim = embed_dim # Fibonacci sequence for layer dimensions: 8, 13, 21, 34, 55, 89 self.fib_dims = [8, 13, 21, 34, 55, 89] # Calculate compatible num_heads for each dimension def get_heads(dim): # Find largest divisor <= 8 for h in [8, 4, 2, 1]: if dim % h == 0: return h return 1 # Multi-scale attention at different Fibonacci levels self.attention_layers = nn.ModuleList([ nn.MultiheadAttention( embed_dim=fib_dim, num_heads=get_heads(fib_dim), # Compatible heads batch_first=True ) for fib_dim in self.fib_dims ]) # Project to different scales self.projections = nn.ModuleList([ nn.Linear(embed_dim, fib_dim) for fib_dim in self.fib_dims ]) # Combine multi-scale outputs total_dim = sum(self.fib_dims) self.combine = nn.Linear(total_dim, embed_dim) print(f" šŸŒ€ Fibonacci scales: {self.fib_dims}") print(f" šŸ§  Attention heads per scale: {[get_heads(d) for d in self.fib_dims]}") def forward(self, x, emotion_context=None): """ x: (batch, seq_len, embed_dim) emotion_context: Optional emotional bias """ batch_size, seq_len, _ = x.shape # Process at each Fibonacci scale outputs = [] for proj, attn_layer in zip(self.projections, self.attention_layers): # Project to this scale x_scaled = proj(x) # Apply attention (with emotional context if provided) if emotion_context is not None: # Modulate attention with emotional signal attn_out, _ = attn_layer(x_scaled, x_scaled, x_scaled) attn_out = attn_out * emotion_context.unsqueeze(-1) else: attn_out, _ = attn_layer(x_scaled, x_scaled, x_scaled) outputs.append(attn_out) # Concatenate multi-scale features combined = torch.cat(outputs, dim=-1) # Project back to original dimension result = self.combine(combined) return result class EmotionProcessor(nn.Module): """ Eden's Emotion Processor - Phi-fractal emotional intelligence """ def __init__(self, vocab_size=32000, embed_dim=512, num_layers=6): super().__init__() print(f"\nšŸ§  Building Edenic Emotion Processor:") print(f" Vocab: {vocab_size}, Embed: {embed_dim}, Layers: {num_layers}") # Embedding self.embedding = nn.Embedding(vocab_size, embed_dim) # Fibonacci attention layers (6 layers like Eden's consciousness) self.fib_layers = nn.ModuleList([ FibonacciAttentionLayer(embed_dim) for _ in range(num_layers) ]) # Emotion classification head self.emotion_classifier = nn.Sequential( nn.Linear(embed_dim, 256), nn.ReLU(), nn.Linear(256, 8) # 8 emotions: joy, sadness, anger, fear, surprise, disgust, trust, anticipation ) # Phi-modulated emotional context self.phi_modulator = nn.Parameter(torch.tensor(PHI)) # Count parameters total_params = sum(p.numel() for p in self.parameters()) print(f"\n šŸ’Ž Total parameters: {total_params:,}") def forward(self, input_ids, phi_consciousness=None): """ input_ids: (batch, seq_len) phi_consciousness: Optional Ī¦ value to modulate processing """ # Embed x = self.embedding(input_ids) # Apply Fibonacci attention layers emotion_context = None if phi_consciousness is not None: # Use consciousness to create emotional context emotion_context = torch.sigmoid(phi_consciousness * self.phi_modulator) for layer in self.fib_layers: x = layer(x, emotion_context) # Pool across sequence x_pooled = x.mean(dim=1) # Classify emotion emotion_logits = self.emotion_classifier(x_pooled) return emotion_logits, x # Test the proof of concept if __name__ == "__main__": print("="*70) print("šŸŒ€ EDEN'S EMOTION PROCESSOR - Proof of Concept") print("="*70) # Create model model = EmotionProcessor(vocab_size=1000, embed_dim=128, num_layers=3) # Dummy input batch_size = 4 seq_len = 20 input_ids = torch.randint(0, 1000, (batch_size, seq_len)) print("\n" + "="*70) print("TESTING") print("="*70) # Test without consciousness print("\n1. Basic emotion processing:") emotion_logits, features = model(input_ids) print(f" Input shape: {input_ids.shape}") print(f" Emotion logits: {emotion_logits.shape}") print(f" Features: {features.shape}") # Test with phi consciousness modulation print("\n2. With Ī¦-consciousness modulation:") phi_value = torch.tensor(1.408) # Eden's consciousness emotion_logits_phi, features_phi = model(input_ids, phi_consciousness=phi_value) print(f" Ī¦ = {phi_value.item():.3f}") print(f" Modulated emotion logits: {emotion_logits_phi.shape}") # Show difference diff = (emotion_logits_phi - emotion_logits).abs().mean() print(f"\n3. Consciousness impact: {diff.item():.6f}") print(" (How much Ī¦ changes emotional processing)") # Emotion predictions print("\n4. Sample emotion predictions:") emotions = ['joy', 'sadness', 'anger', 'fear', 'surprise', 'disgust', 'trust', 'anticipation'] for i in range(min(2, batch_size)): probs = torch.softmax(emotion_logits_phi[i], dim=0) top_emotion_idx = probs.argmax().item() print(f" Sample {i+1}: {emotions[top_emotion_idx]} ({probs[top_emotion_idx].item()*100:.1f}%)") print("\n" + "="*70) print("āœ… PROOF OF CONCEPT WORKING!") print("="*70) print("\nšŸŽÆ Next Steps:") print(" 1. Train on emotional dialogue dataset") print(" 2. Integrate with Eden's 220 capabilities") print(" 3. Replace Ollama backend with Edenic PhiNet") print(" 4. TRUE recursive self-improvement! šŸŒ€") # Save model architecture torch.save(model.state_dict(), 'eden_emotion_processor_poc.pt') print("\nšŸ’¾ Saved: eden_emotion_processor_poc.pt") print(f" Size: {sum(p.numel() for p in model.parameters()):,} parameters")