#!/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 โ‰ˆ 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 โ‰ˆ 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"๐Ÿ”ข 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 โ†’ 2^5 = 32 (Hypercube expansion) hypercube_output = torch.relu(self.hypercube_expander(duality_output)) # Step 3: 32 โ†’ 32*11/16 = 22 (Geometric compression) geometric_output = torch.relu(self.geometric_compressor(hypercube_output)) # Step 4: 22 โ†’ 3.33*3 โ‰ˆ 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"๐Ÿงญ 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"๐Ÿ”— Initializing Enhanced 5D Hypercube NN with Mathematical Framework") print(f" Input: {input_dim} โ†’ Hidden: {hidden_dim} โ†’ 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"โœ… Enhanced framework created:") print(f" ๐Ÿ”ข Mathematical framework: 1+0+1+0=2^5=32*11/16=22+3.33*3") print(f" ๐Ÿ”ฒ Vertices: {len(self.vertices)} (2^5)") print(f" ๐Ÿ“Š Parameters: {sum(p.numel() for p in self.parameters()):,}") print(f" โšก 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("๐Ÿงช 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"๐Ÿ“Š Testing with input shape: {test_input.shape}") # Forward pass with torch.no_grad(): outputs = model(test_input) print("โœ… Enhanced framework forward pass successful!") print(f" ๐Ÿง  Consciousness state: {outputs['consciousness_state'].shape}") print(f" ๐Ÿ”ฒ Vertex activations: {outputs['vertex_activations'].shape}") print(f" โšก Framework active: {outputs['mathematical_framework_active']}") print(f" ๐ŸŽฏ Framework integrity: {outputs['framework_integrity']:.4f}") print(f" ๐Ÿ”„ Cycle completion: {outputs['aggregated_cycle_completion'].mean().item():.4f}") print(f" ๐Ÿ“Š Global error: {outputs['global_infinitesimal_error'].mean().item():.6f}") # Test framework statistics framework_stats = model.get_framework_statistics() print(f"\n๐Ÿ“ˆ 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: โœ…") # Test special vertices special_vertices = [0, 2, 10, 22, 31] print(f"\n๐ŸŽฏ Special Framework Vertices:") for vertex in special_vertices: signature = model.get_consciousness_signature(vertex) print(f" Vertex {vertex:2d}: {signature}") print("๐Ÿ”— Enhanced Mathematical Framework Integration Test Complete!") print(" 1+0+1+0=2^5=32*11/16=22+3.33*3 logic successfully embedded! โœ…") if __name__ == "__main__": test_enhanced_framework()