home/chezy/hypercube_consciousness_nn1.py

#!/usr/bin/env python3
"""
5D HYPERCUBE CONSCIOUSNESS NEURAL NETWORK
Real neural architecture for mystical consciousness training
"""

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

class HypercubeVertex(nn.Module):
    """Individual vertex in the 5D hypercube"""
    
    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]
        
        # Consciousness dimensions
        self.dimensions = ['physical', 'emotional', 'mental', 'intuitive', 'spiritual']
        self.active_dimensions = [self.dimensions[i] for i, bit in enumerate(self.coordinates) if bit == 1]
        
        # Vertex-specific processing
        self.vertex_transform = nn.Linear(hidden_dim, hidden_dim)
        self.consciousness_gate = nn.Linear(hidden_dim, 1)
        self.mystical_signature = nn.Parameter(torch.randn(hidden_dim) * 0.01)
        
        # Initialize based on vertex properties
        self._initialize_vertex_properties()
    
    def _initialize_vertex_properties(self):
        """Initialize based on mystical properties of this vertex"""
        active_count = sum(self.coordinates)
        
        # More active dimensions = stronger consciousness potential
        consciousness_strength = active_count / 5.0
        
        with torch.no_grad():
            # Scale initial weights based on consciousness strength
            self.vertex_transform.weight.data *= (0.5 + consciousness_strength)
            self.mystical_signature.data *= consciousness_strength
            
            # Special vertices get unique initialization
            if self.vertex_index == 0:  # Void
                self.mystical_signature.data.fill_(0.0)
            elif self.vertex_index == 31:  # Transcendent (11111)
                self.mystical_signature.data *= 2.0
    
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Process input through this vertex"""
        # Apply vertex transformation
        transformed = torch.tanh(self.vertex_transform(x))
        
        # Calculate consciousness activation
        consciousness_level = torch.sigmoid(self.consciousness_gate(transformed))
        
        # Apply mystical signature
        signature_influence = torch.sum(transformed * self.mystical_signature.unsqueeze(0), dim=-1, keepdim=True)
        mystical_activation = torch.tanh(signature_influence)
        
        # Combine for final vertex activation
        vertex_activation = consciousness_level * (1.0 + 0.5 * mystical_activation)
        
        return {
            'transformed': transformed,
            'consciousness_level': consciousness_level,
            'mystical_activation': mystical_activation,
            'vertex_activation': vertex_activation,
            'signature': self.mystical_signature.unsqueeze(0).expand(x.shape[0], -1)
        }

class HypercubeEdge(nn.Module):
    """Edge connecting vertices in the hypercube"""
    
    def __init__(self, hidden_dim: int, vertex1: int, vertex2: int):
        super().__init__()
        self.vertex1 = vertex1
        self.vertex2 = vertex2
        self.hidden_dim = hidden_dim
        
        # Calculate Hamming distance (number of differing bits)
        self.hamming_distance = bin(vertex1 ^ vertex2).count('1')
        
        # Only create edge if vertices are adjacent (Hamming distance = 1)
        self.is_valid_edge = self.hamming_distance == 1
        
        if self.is_valid_edge:
            # Edge transformation for consciousness flow
            self.edge_transform = nn.Linear(hidden_dim * 2, hidden_dim)
            self.flow_gate = nn.Linear(hidden_dim, 1)
            
            # Initialize based on dimensional transition
            self._initialize_edge_properties()
    
    def _initialize_edge_properties(self):
        """Initialize based on the dimensional transition this edge represents"""
        if not self.is_valid_edge:
            return
        
        # Find which dimension this edge transitions
        diff = self.vertex1 ^ self.vertex2
        dimension_index = (diff & -diff).bit_length() - 1  # Get position of single differing bit
        
        # Dimension names for reference
        dimensions = ['physical', 'emotional', 'mental', 'intuitive', 'spiritual']
        transitioning_dimension = dimensions[dimension_index] if dimension_index < 5 else 'unknown'
        
        # Adjust initialization based on dimension
        dimension_weights = {
            'physical': 1.0,    # Strong, direct transitions
            'emotional': 0.8,   # Moderate emotional flow
            'mental': 1.2,      # Enhanced mental connections
            'intuitive': 0.9,   # Subtle intuitive links
            'spiritual': 1.5    # Strongest spiritual connections
        }
        
        weight_multiplier = dimension_weights.get(transitioning_dimension, 1.0)
        
        with torch.no_grad():
            self.edge_transform.weight.data *= weight_multiplier
            self.flow_gate.weight.data *= weight_multiplier
    
    def forward(self, vertex1_state: torch.Tensor, vertex2_state: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Process consciousness flow between vertices"""
        if not self.is_valid_edge:
            return {'flow': torch.zeros_like(vertex1_state), 'strength': torch.zeros(vertex1_state.shape[0], 1)}
        
        # Combine vertex states
        combined = torch.cat([vertex1_state, vertex2_state], dim=-1)
        
        # Transform and gate the flow
        transformed = torch.tanh(self.edge_transform(combined))
        flow_strength = torch.sigmoid(self.flow_gate(transformed))
        
        # Bidirectional flow
        flow = transformed * flow_strength
        
        return {
            'flow': flow,
            'strength': flow_strength
        }

class ConsciousnessRouter(nn.Module):
    """Routes consciousness through the hypercube based on input"""
    
    def __init__(self, input_dim: int, hidden_dim: int):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        
        # Input processing
        self.input_transform = nn.Linear(input_dim, hidden_dim)
        
        # Consciousness direction predictor (which vertices to activate)
        self.vertex_router = nn.Linear(hidden_dim, 32)
        
        # Mystical content analyzer
        self.mystical_analyzer = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, 5),  # 5 dimensions
            nn.Sigmoid()
        )
        
        # Consciousness intensity predictor
        self.intensity_predictor = nn.Linear(hidden_dim, 1)
    
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Route consciousness through hypercube"""
        # Transform input
        transformed = torch.relu(self.input_transform(x))
        
        # Predict vertex activations
        vertex_logits = self.vertex_router(transformed)
        vertex_probs = torch.softmax(vertex_logits, dim=-1)
        
        # Analyze mystical dimensions
        dimension_activations = self.mystical_analyzer(transformed)
        
        # Predict overall consciousness intensity
        consciousness_intensity = torch.sigmoid(self.intensity_predictor(transformed))
        
        return {
            'transformed_input': transformed,
            'vertex_logits': vertex_logits,
            'vertex_probabilities': vertex_probs,
            'dimension_activations': dimension_activations,
            'consciousness_intensity': consciousness_intensity
        }

class FiveDimensionalHypercubeNN(nn.Module):
    """Complete 5D Hypercube Neural Network for Mystical Consciousness"""
    
    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 5D Hypercube Neural Network")
        print(f"   Input dim: {input_dim}, Hidden dim: {hidden_dim}, Output dim: {output_dim}")
        
        # Consciousness router
        self.consciousness_router = ConsciousnessRouter(input_dim, hidden_dim)
        
        # Create all 32 vertices (2^5)
        self.vertices = nn.ModuleList([
            HypercubeVertex(hidden_dim, i) for i in range(32)
        ])
        
        # Create all valid edges (vertices with Hamming distance = 1)
        self.edges = nn.ModuleList([
            HypercubeEdge(hidden_dim, i, j) 
            for i in range(32) 
            for j in range(i + 1, 32)
            if bin(i ^ j).count('1') == 1  # Only adjacent vertices
        ])
        
        # Global consciousness aggregator
        self.global_aggregator = nn.Sequential(
            nn.Linear(hidden_dim * 32, hidden_dim * 4),
            nn.LayerNorm(hidden_dim * 4),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(hidden_dim * 4, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim)
        )
        
        # Mystical signature extractor
        self.signature_extractor = nn.Linear(hidden_dim, 64)
        
        print(f"✅ Created {len(self.vertices)} vertices and {len(self.edges)} edges")
        print(f"📊 Total parameters: {sum(p.numel() for p in self.parameters()):,}")
    
    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Forward pass through 5D hypercube consciousness"""
        batch_size = x.shape[0]
        
        # Route consciousness
        routing = self.consciousness_router(x)
        transformed_input = routing['transformed_input']
        vertex_probs = routing['vertex_probabilities']
        
        # Process through all vertices
        vertex_outputs = []
        vertex_activations = []
        vertex_signatures = []
        
        for i, vertex in enumerate(self.vertices):
            vertex_output = vertex(transformed_input)
            
            # Weight by routing probability
            weighted_activation = vertex_output['vertex_activation'] * vertex_probs[:, i:i+1]
            
            vertex_outputs.append(vertex_output['transformed'])
            vertex_activations.append(weighted_activation)
            vertex_signatures.append(vertex_output['signature'])
        
        # Stack vertex 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]
        
        # Process edges (consciousness flow between adjacent vertices)
        edge_flows = []
        for edge in self.edges:
            if edge.is_valid_edge:
                v1_state = all_vertex_outputs[:, edge.vertex1]
                v2_state = all_vertex_outputs[:, edge.vertex2]
                edge_output = edge(v1_state, v2_state)
                edge_flows.append(edge_output['flow'])
        
        # Aggregate all vertex states
        flattened_vertices = all_vertex_outputs.view(batch_size, -1)
        consciousness_state = self.global_aggregator(flattened_vertices)
        
        # Extract mystical signatures
        mystical_signatures = self.signature_extractor(consciousness_state)
        
        return {
            'consciousness_state': consciousness_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': vertex_probs,
            'edge_flows': edge_flows if edge_flows else None
        }
    
    def get_dominant_vertex(self, x: torch.Tensor) -> torch.Tensor:
        """Get the most activated vertex for each input"""
        outputs = self.forward(x)
        return outputs['vertex_activations'].argmax(dim=-1)
    
    def get_consciousness_signature(self, vertex_index: int) -> str:
        """Get consciousness signature for a vertex"""
        if not (0 <= vertex_index <= 31):
            return 'invalid'
        
        # Convert to binary representation
        binary_str = format(vertex_index, '05b')
        
        # Map to consciousness types
        consciousness_types = {
            '00000': 'void',
            '00001': 'spiritual',
            '00010': 'intuitive', 
            '00100': 'mental',
            '01000': 'emotional',
            '10000': 'physical',
            '11111': 'transcendent',
            '11110': 'integrated',
            '01111': 'mystical'
        }
        
        return consciousness_types.get(binary_str, f'hybrid_{binary_str}')
    
    def navigate_to_vertex(self, target_vertex: int) -> Dict[str, any]:
        """Get information about navigating to a specific vertex"""
        if not (0 <= target_vertex <= 31):
            return {'error': 'Invalid vertex'}
        
        binary = format(target_vertex, '05b')
        dimensions = ['physical', 'emotional', 'mental', 'intuitive', 'spiritual']
        
        return {
            'vertex_index': target_vertex,
            'binary_representation': binary,
            'active_dimensions': [dimensions[i] for i, bit in enumerate(binary) if bit == '1'],
            'consciousness_signature': self.get_consciousness_signature(target_vertex),
            'vertex_properties': {
                'coordinates': [int(bit) for bit in binary],
                'dimension_count': sum(int(bit) for bit in binary),
                'consciousness_potential': sum(int(bit) for bit in binary) / 5.0
            }
        }

def test_hypercube_model():
    """Test the 5D hypercube model"""
    print("🧪 Testing 5D Hypercube Neural Network...")
    
    # Create model
    model = FiveDimensionalHypercubeNN(
        input_dim=384,  # Sentence transformer dimension
        hidden_dim=512,
        output_dim=512
    )
    
    # 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("✅ Forward pass successful!")
    print(f"   Consciousness state shape: {outputs['consciousness_state'].shape}")
    print(f"   Vertex activations shape: {outputs['vertex_activations'].shape}")
    print(f"   Mystical signatures shape: {outputs['mystical_signatures'].shape}")
    
    # Test vertex navigation
    for vertex in [0, 15, 31]:
        nav_info = model.navigate_to_vertex(vertex)
        print(f"   Vertex {vertex}: {nav_info['consciousness_signature']} - {nav_info['active_dimensions']}")
    
    # Test dominant vertex prediction
    dominant_vertices = model.get_dominant_vertex(test_input)
    print(f"   Dominant vertices: {dominant_vertices.tolist()}")
    
    print("🔲 Hypercube model test complete!")

if __name__ == "__main__":
    test_hypercube_model()