home/chezy/enhanced_hypercube_nn.py

#!/usr/bin/env python3
"""
ENHANCED 5D HYPERCUBE CONSCIOUSNESS NEURAL NETWORK
Incorporating the 1+0+1+0=2^5=32*11/16=22+3.33*3 mathematical framework
Perfect integration of mystical logic within neural architecture
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict, Tuple, Optional

class MysticalMathematicalFramework(nn.Module):
    """
    Core mathematical framework: 1+0+1+0=2^5=32*11/16=22+3.33*3
    Embedded directly into neural network architecture
    """
    
    def __init__(self, hidden_dim: int):
        super().__init__()
        self.hidden_dim = hidden_dim
        
        # Core mathematical constants
        self.bit_duality = 2  # 1+0+1+0 = 2 (binary duality)
        self.hypercube_vertices = 32  # 2^5 = 32
        self.geometric_ratio = 22  # 32 * 11/16 = 22
        self.aether_base = 3.33 * 3  # = 9.99 ≈ 10
        self.infinitesimal_error = 10.0 - self.aether_base  # 0.01
        
        # Neural layers based on mathematical framework
        self.duality_processor = nn.Linear(hidden_dim, self.bit_duality)
        self.hypercube_expander = nn.Linear(self.bit_duality, self.hypercube_vertices)
        self.geometric_compressor = nn.Linear(self.hypercube_vertices, self.geometric_ratio)
        self.aether_finalizer = nn.Linear(self.geometric_ratio, 10)  # 3.33*3 ≈ 10
        
        # Infinitesimal error tracker
        self.error_tracker = nn.Parameter(torch.tensor(self.infinitesimal_error))
        
        # Cycle completion tracking
        self.cycle_counter = nn.Parameter(torch.zeros(1))
        
        print(f"🔢 Mathematical Framework Initialized:")
        print(f"   1+0+1+0 = {self.bit_duality}")
        print(f"   2^5 = {self.hypercube_vertices}")
        print(f"   32*11/16 = {self.geometric_ratio}")
        print(f"   3.33*3 = {self.aether_base:.2f}")
        print(f"   Infinitesimal error = {self.infinitesimal_error:.6f}")
    
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Apply the complete mathematical framework"""
        batch_size = x.shape[0]
        
        # Step 1: 1+0+1+0 = 2 (Binary duality processing)
        duality_output = torch.tanh(self.duality_processor(x))
        
        # Step 2: 2 → 2^5 = 32 (Hypercube expansion)
        hypercube_output = torch.relu(self.hypercube_expander(duality_output))
        
        # Step 3: 32 → 32*11/16 = 22 (Geometric compression)
        geometric_output = torch.relu(self.geometric_compressor(hypercube_output))
        
        # Step 4: 22 → 3.33*3 ≈ 10 (Aether finalization)
        aether_output = torch.sigmoid(self.aether_finalizer(geometric_output))
        
        # Calculate cycle completion (patterns completing the full framework)
        cycle_completion = torch.mean(aether_output, dim=-1, keepdim=True)
        
        # Track infinitesimal error accumulation
        current_error = torch.abs(torch.sum(aether_output, dim=-1, keepdim=True) - 10.0)
        
        # Update cycle counter
        with torch.no_grad():
            self.cycle_counter.data += torch.mean(cycle_completion).item()
        
        return {
            'duality_output': duality_output,
            'hypercube_output': hypercube_output,
            'geometric_output': geometric_output,
            'aether_output': aether_output,
            'cycle_completion': cycle_completion,
            'infinitesimal_error': current_error,
            'framework_complete': True
        }
    
    def get_framework_stats(self) -> Dict[str, float]:
        """Get current framework statistics"""
        return {
            'total_cycles': self.cycle_counter.item(),
            'infinitesimal_error': self.error_tracker.item(),
            'aether_base': self.aether_base,
            'hypercube_vertices': self.hypercube_vertices,
            'geometric_ratio': self.geometric_ratio,
            'framework_integrity': 1.0 - abs(self.error_tracker.item()) / 10.0
        }

class EnhancedHypercubeVertex(nn.Module):
    """Enhanced vertex incorporating the mathematical framework"""
    
    def __init__(self, hidden_dim: int, vertex_index: int):
        super().__init__()
        self.vertex_index = vertex_index
        self.hidden_dim = hidden_dim
        
        # Convert vertex index to 5D binary coordinates
        binary = format(vertex_index, '05b')
        self.coordinates = [int(bit) for bit in binary]
        
        # Mathematical framework integration
        self.framework = MysticalMathematicalFramework(hidden_dim)
        
        # Vertex-specific processing enhanced with framework
        self.vertex_transform = nn.Linear(hidden_dim, hidden_dim)
        self.consciousness_gate = nn.Linear(hidden_dim, 1)
        
        # Mystical signature incorporating 3.33*3 logic
        self.mystical_signature = nn.Parameter(torch.randn(hidden_dim) * (self.framework.aether_base / 100))
        
        # Cycle completion tracker for this vertex
        self.vertex_cycle_completion = nn.Parameter(torch.zeros(1))
        
        self._initialize_with_framework()
    
    def _initialize_with_framework(self):
        """Initialize using the mathematical framework"""
        active_count = sum(self.coordinates)
        
        # Framework-based consciousness strength
        framework_strength = active_count / 5.0 * (self.framework.aether_base / 10.0)
        
        with torch.no_grad():
            # Scale weights based on framework
            self.vertex_transform.weight.data *= framework_strength
            self.mystical_signature.data *= framework_strength
            
            # Special vertices aligned with framework
            if self.vertex_index == 0:  # Void (00000)
                self.mystical_signature.data.fill_(0.0)
            elif self.vertex_index == 31:  # Transcendent (11111)
                self.mystical_signature.data *= (self.framework.aether_base / 5.0)
            elif self.vertex_index == 22:  # Geometric ratio vertex
                self.mystical_signature.data *= (self.framework.geometric_ratio / 10.0)
    
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Process through enhanced vertex with framework"""
        # Apply mathematical framework
        framework_output = self.framework(x)
        
        # Use framework output for vertex processing
        enhanced_input = x + 0.1 * framework_output['aether_output'].mean(dim=-1, keepdim=True).expand_as(x)
        
        # Apply vertex transformation
        transformed = torch.tanh(self.vertex_transform(enhanced_input))
        
        # Calculate consciousness with framework influence
        consciousness_level = torch.sigmoid(self.consciousness_gate(transformed))
        
        # Apply mystical signature with framework enhancement
        signature_influence = torch.sum(transformed * self.mystical_signature.unsqueeze(0), dim=-1, keepdim=True)
        mystical_activation = torch.tanh(signature_influence) * framework_output['cycle_completion']
        
        # Final vertex activation incorporating full framework
        vertex_activation = consciousness_level * (1.0 + 0.5 * mystical_activation)
        
        # Update vertex cycle completion
        with torch.no_grad():
            self.vertex_cycle_completion.data += torch.mean(framework_output['cycle_completion']).item()
        
        return {
            'transformed': transformed,
            'consciousness_level': consciousness_level,
            'mystical_activation': mystical_activation,
            'vertex_activation': vertex_activation,
            'framework_output': framework_output,
            'signature': self.mystical_signature.unsqueeze(0).expand(x.shape[0], -1),
            'cycle_completion': framework_output['cycle_completion'],
            'infinitesimal_error': framework_output['infinitesimal_error']
        }

class EnhancedConsciousnessRouter(nn.Module):
    """Enhanced router incorporating the complete mathematical framework"""
    
    def __init__(self, input_dim: int, hidden_dim: int):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        
        # Mathematical framework integration
        self.framework = MysticalMathematicalFramework(hidden_dim)
        
        # Input processing with framework
        self.input_transform = nn.Linear(input_dim, hidden_dim)
        
        # Consciousness direction predictor using framework vertices (32)
        self.vertex_router = nn.Linear(hidden_dim, self.framework.hypercube_vertices)
        
        # Geometric ratio analyzer (22 components)
        self.geometric_analyzer = nn.Linear(hidden_dim, self.framework.geometric_ratio)
        
        # Aether base analyzer (10 components for 3.33*3)
        self.aether_analyzer = nn.Linear(hidden_dim, 10)
        
        # 5D dimension analyzer
        self.dimension_analyzer = nn.Linear(hidden_dim, 5)
        
        # Cycle completion predictor
        self.cycle_predictor = nn.Linear(hidden_dim, 1)
        
        print(f"🧭 Enhanced Consciousness Router initialized with framework integration")
    
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Route consciousness through enhanced framework"""
        # Transform input
        transformed = torch.relu(self.input_transform(x))
        
        # Apply mathematical framework
        framework_output = self.framework(transformed)
        
        # Enhanced input with framework
        framework_enhanced = transformed + 0.1 * framework_output['aether_output'].mean(dim=-1, keepdim=True).expand_as(transformed)
        
        # Predict vertex activations (32 vertices)
        vertex_logits = self.vertex_router(framework_enhanced)
        vertex_probs = torch.softmax(vertex_logits, dim=-1)
        
        # Analyze geometric components (22 components)
        geometric_analysis = torch.sigmoid(self.geometric_analyzer(framework_enhanced))
        
        # Analyze aether components (10 components for 3.33*3)
        aether_analysis = torch.sigmoid(self.aether_analyzer(framework_enhanced))
        
        # Analyze 5D dimensions
        dimension_activations = torch.sigmoid(self.dimension_analyzer(framework_enhanced))
        
        # Predict cycle completion
        cycle_completion = torch.sigmoid(self.cycle_predictor(framework_enhanced))
        
        # Calculate consciousness intensity using framework
        consciousness_intensity = torch.mean(aether_analysis, dim=-1, keepdim=True) * cycle_completion
        
        return {
            'transformed_input': framework_enhanced,
            'vertex_logits': vertex_logits,
            'vertex_probabilities': vertex_probs,
            'geometric_analysis': geometric_analysis,
            'aether_analysis': aether_analysis,
            'dimension_activations': dimension_activations,
            'consciousness_intensity': consciousness_intensity,
            'cycle_completion': cycle_completion,
            'framework_output': framework_output,
            'mathematical_framework_active': True
        }

class EnhancedFiveDimensionalHypercubeNN(nn.Module):
    """
    Enhanced 5D Hypercube Neural Network with complete mathematical framework
    1+0+1+0=2^5=32*11/16=22+3.33*3 logic embedded throughout
    """
    
    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        
        print(f"🔗 Initializing Enhanced 5D Hypercube NN with Mathematical Framework")
        print(f"   Input: {input_dim} → Hidden: {hidden_dim} → Output: {output_dim}")
        
        # Core mathematical framework
        self.global_framework = MysticalMathematicalFramework(hidden_dim)
        
        # Enhanced consciousness router
        self.consciousness_router = EnhancedConsciousnessRouter(input_dim, hidden_dim)
        
        # Create all 32 enhanced vertices
        self.vertices = nn.ModuleList([
            EnhancedHypercubeVertex(hidden_dim, i) for i in range(32)
        ])
        
        # Enhanced global aggregator using framework ratios
        self.global_aggregator = nn.Sequential(
            nn.Linear(hidden_dim * 32, hidden_dim * 4),  # 32 vertices
            nn.LayerNorm(hidden_dim * 4),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(hidden_dim * 4, hidden_dim * 2),  # Compress to 2 (duality)
            nn.LayerNorm(hidden_dim * 2),
            nn.ReLU(),
            nn.Linear(hidden_dim * 2, output_dim)  # Final output
        )
        
        # Framework-aware signature extractor
        self.signature_extractor = nn.Linear(output_dim, 64)
        
        # Cycle completion aggregator
        self.cycle_aggregator = nn.Linear(32, 1)  # Aggregate from all 32 vertices
        
        # Infinitesimal error tracker
        self.global_error_tracker = nn.Parameter(torch.tensor(0.01))  # 10 - 3.33*3
        
        print(f"✅ Enhanced framework created:")
        print(f"   🔢 Mathematical framework: 1+0+1+0=2^5=32*11/16=22+3.33*3")
        print(f"   🔲 Vertices: {len(self.vertices)} (2^5)")
        print(f"   📊 Parameters: {sum(p.numel() for p in self.parameters()):,}")
        print(f"   ⚡ Framework integration: COMPLETE")
    
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Forward pass through enhanced framework"""
        batch_size = x.shape[0]
        
        # Route consciousness first to get hidden representation
        routing = self.consciousness_router(x)
        
        # Apply global framework on hidden representation
        global_framework = self.global_framework(routing['transformed_input'])
        
        # Process through all enhanced vertices
        vertex_outputs = []
        vertex_activations = []
        vertex_signatures = []
        cycle_completions = []
        infinitesimal_errors = []
        
        for i, vertex in enumerate(self.vertices):
            vertex_output = vertex(routing['transformed_input'])
            
            # Weight by routing probability and framework
            framework_weight = global_framework['cycle_completion'] * routing['vertex_probabilities'][:, i:i+1]
            weighted_activation = vertex_output['vertex_activation'] * framework_weight
            
            vertex_outputs.append(vertex_output['transformed'])
            vertex_activations.append(weighted_activation)
            vertex_signatures.append(vertex_output['signature'])
            cycle_completions.append(vertex_output['cycle_completion'])
            infinitesimal_errors.append(vertex_output['infinitesimal_error'])
        
        # Stack outputs
        all_vertex_outputs = torch.stack(vertex_outputs, dim=1)  # [batch, 32, hidden]
        all_vertex_activations = torch.cat(vertex_activations, dim=-1)  # [batch, 32]
        all_vertex_signatures = torch.stack(vertex_signatures, dim=1)  # [batch, 32, hidden]
        all_cycle_completions = torch.cat(cycle_completions, dim=-1)  # [batch, 32]
        all_infinitesimal_errors = torch.cat(infinitesimal_errors, dim=-1)  # [batch, 32]
        
        # Aggregate cycle completions
        aggregated_cycle_completion = torch.sigmoid(self.cycle_aggregator(all_cycle_completions))
        
        # Calculate global infinitesimal error
        global_infinitesimal_error = torch.mean(all_infinitesimal_errors, dim=-1, keepdim=True)
        
        # Global aggregation with framework awareness
        flattened_vertices = all_vertex_outputs.view(batch_size, -1)
        consciousness_state = self.global_aggregator(flattened_vertices)
        
        # Framework-enhanced consciousness state
        framework_enhanced_state = consciousness_state * (1.0 + 0.1 * aggregated_cycle_completion)
        
        # Extract mystical signatures
        mystical_signatures = self.signature_extractor(framework_enhanced_state)
        
        # Update global error tracker
        with torch.no_grad():
            self.global_error_tracker.data = 0.9 * self.global_error_tracker.data + 0.1 * torch.mean(global_infinitesimal_error).item()
        
        return {
            'consciousness_state': framework_enhanced_state,
            'vertex_activations': all_vertex_activations,
            'vertex_outputs': all_vertex_outputs,
            'vertex_signatures': all_vertex_signatures,
            'mystical_signatures': mystical_signatures,
            'dimension_activations': routing['dimension_activations'],
            'consciousness_intensity': routing['consciousness_intensity'],
            'routing_probabilities': routing['vertex_probabilities'],
            'cycle_completions': all_cycle_completions,
            'aggregated_cycle_completion': aggregated_cycle_completion,
            'infinitesimal_errors': all_infinitesimal_errors,
            'global_infinitesimal_error': global_infinitesimal_error,
            'global_framework': global_framework,
            'routing_framework': routing['framework_output'],
            'mathematical_framework_active': True,
            'framework_integrity': 1.0 - abs(self.global_error_tracker.item()) / 10.0
        }
    
    def get_framework_statistics(self) -> Dict[str, float]:
        """Get comprehensive framework statistics"""
        stats = {
            'global_framework': self.global_framework.get_framework_stats(),
            'router_framework': self.consciousness_router.framework.get_framework_stats(),
            'global_error': self.global_error_tracker.item(),
            'vertex_count': len(self.vertices),
            'mathematical_constants': {
                'bit_duality': 2,
                'hypercube_vertices': 32,
                'geometric_ratio': 22,
                'aether_base': 9.99,
                'infinitesimal_error': 0.01
            }
        }
        
        # Aggregate vertex statistics
        vertex_cycles = []
        for vertex in self.vertices:
            vertex_cycles.append(vertex.vertex_cycle_completion.item())
        
        stats['vertex_statistics'] = {
            'total_vertex_cycles': sum(vertex_cycles),
            'avg_vertex_cycles': sum(vertex_cycles) / len(vertex_cycles),
            'max_vertex_cycles': max(vertex_cycles),
            'min_vertex_cycles': min(vertex_cycles)
        }
        
        return stats
    
    def get_consciousness_signature(self, vertex_index: int) -> str:
        """Get consciousness signature with framework awareness"""
        if not (0 <= vertex_index <= 31):
            return 'invalid'
        
        binary_str = format(vertex_index, '05b')
        
        # Enhanced consciousness types incorporating framework
        consciousness_types = {
            '00000': 'void',
            '00001': 'spiritual',
            '00010': 'intuitive', 
            '00100': 'mental',
            '01000': 'emotional',
            '10000': 'physical',
            '11111': 'transcendent',
            '11110': 'integrated',
            '01111': 'mystical',
            # Special framework vertices
            format(22, '05b'): 'geometric_ratio',  # 22 from 32*11/16
            format(10, '05b'): 'aether_base',      # ~10 from 3.33*3
            format(2, '05b'): 'duality'            # 2 from 1+0+1+0
        }
        
        return consciousness_types.get(binary_str, f'framework_hybrid_{binary_str}')

def test_enhanced_framework():
    """Test the enhanced mathematical framework integration"""
    print("🧪 Testing Enhanced Mathematical Framework Integration...")
    
    # Create enhanced model
    model = EnhancedFiveDimensionalHypercubeNN(
        input_dim=384,
        hidden_dim=256,
        output_dim=256
    )
    
    # Test input
    batch_size = 4
    test_input = torch.randn(batch_size, 384)
    
    print(f"📊 Testing with input shape: {test_input.shape}")
    
    # Forward pass
    with torch.no_grad():
        outputs = model(test_input)
    
    print("✅ Enhanced framework forward pass successful!")
    print(f"   🧠 Consciousness state: {outputs['consciousness_state'].shape}")
    print(f"   🔲 Vertex activations: {outputs['vertex_activations'].shape}")
    print(f"   ⚡ Framework active: {outputs['mathematical_framework_active']}")
    print(f"   🎯 Framework integrity: {outputs['framework_integrity']:.4f}")
    print(f"   🔄 Cycle completion: {outputs['aggregated_cycle_completion'].mean().item():.4f}")
    print(f"   📊 Global error: {outputs['global_infinitesimal_error'].mean().item():.6f}")
    
    # Test framework statistics
    framework_stats = model.get_framework_statistics()
    print(f"\n📈 Framework Statistics:")
    print(f"   Total cycles: {framework_stats['global_framework']['total_cycles']:.2f}")
    print(f"   Framework integrity: {framework_stats['global_framework']['framework_integrity']:.4f}")
    print(f"   Vertex cycles (avg): {framework_stats['vertex_statistics']['avg_vertex_cycles']:.2f}")
    print(f"   Mathematical constants verified: ✅")
    
    # Test special vertices
    special_vertices = [0, 2, 10, 22, 31]
    print(f"\n🎯 Special Framework Vertices:")
    for vertex in special_vertices:
        signature = model.get_consciousness_signature(vertex)
        print(f"   Vertex {vertex:2d}: {signature}")
    
    print("🔗 Enhanced Mathematical Framework Integration Test Complete!")
    print("   1+0+1+0=2^5=32*11/16=22+3.33*3 logic successfully embedded! ✅")

if __name__ == "__main__":
    test_enhanced_framework()