core_components/holographic_memory_core.py

#!/usr/bin/env python3
"""
Holographic Memory Core Module
============================
Core holographic memory and processing including:
- Holographic associative memory
- Fractal memory encoding
- Quantum holographic storage
- Emergent memory patterns

Author: Assistant
License: MIT
"""

import numpy as np
from scipy import fft, signal
from typing import Dict, List, Optional, Any, Tuple
import math

class HolographicAssociativeMemory:
    """Holographic associative memory with content-addressable storage"""

    def __init__(self, memory_size: int = 1024, hologram_dim: int = 256):
        self.memory_size = memory_size
        self.hologram_dim = hologram_dim
        self.holographic_memory = np.zeros((hologram_dim, hologram_dim), dtype=complex)
        self.associative_links = {}
        self.memory_traces = []

    def store_holographic(self, data: np.ndarray, metadata: Dict = None) -> str:
        """Store data in holographic memory with associative links"""

        # Generate unique memory key
        memory_key = self._generate_memory_key(data)

        # Encode data into holographic representation
        hologram = self._encode_data_holographic(data)

        # Store in holographic memory with interference pattern
        self.holographic_memory += hologram

        # Create associative links
        if metadata:
            self._create_associative_links(memory_key, metadata)

        # Store memory trace
        self.memory_traces.append({
            'key': memory_key,
            'timestamp': np.datetime64('now'),
            'access_pattern': self._analyze_access_pattern(data),
            'emotional_valence': metadata.get('emotional_valence', 0.5) if metadata else 0.5
        })

        return memory_key

    def recall_associative(self, query: np.ndarray, similarity_threshold: float = 0.7) -> List[Dict]:
        """Recall memories associatively based on content similarity"""

        recalled_memories = []

        # Calculate similarity with all memory traces
        for trace in self.memory_traces:
            # Holographic pattern matching
            similarity = self._holographic_similarity(query, trace)

            if similarity > similarity_threshold:
                # Reconstruct memory from holographic storage
                reconstructed = self._reconstruct_memory(trace['key'])

                recalled_memories.append({
                    'memory_key': trace['key'],
                    'similarity': similarity,
                    'reconstructed_data': reconstructed,
                    'emotional_context': trace['emotional_valence'],
                    'temporal_context': trace['timestamp']
                })

        # Sort by similarity and emotional relevance
        recalled_memories.sort(key=lambda x: x['similarity'] * (1 + x['emotional_context']), reverse=True)

        return recalled_memories

    def _encode_data_holographic(self, data: np.ndarray) -> np.ndarray:
        """Encode data into holographic representation using Fourier transforms"""

        # Ensure data fits hologram dimensions
        if data.size > self.hologram_dim ** 2:
            data = data[:self.hologram_dim ** 2]

        # Reshape to 2D
        data_2d = data.reshape(self.hologram_dim, self.hologram_dim)

        # Fourier transform for holographic encoding
        data_freq = fft.fft2(data_2d)

        # Add random reference wave for holographic properties
        reference_wave = np.exp(1j * 2 * np.pi * np.random.random((self.hologram_dim, self.hologram_dim)))
        hologram = data_freq * reference_wave

        return hologram

    def _holographic_similarity(self, query: np.ndarray, memory_trace: Dict) -> float:
        """Calculate holographic similarity between query and stored memory"""

        # Encode query in same holographic space
        query_hologram = self._encode_data_holographic(query)

        # Calculate correlation in holographic space
        correlation = np.abs(np.sum(query_hologram * np.conj(self.holographic_memory)))

        # Normalize by memory strength and query strength
        memory_strength = np.abs(np.sum(self.holographic_memory * np.conj(self.holographic_memory)))
        query_strength = np.abs(np.sum(query_hologram * np.conj(query_hologram)))

        similarity = correlation / np.sqrt(memory_strength * query_strength + 1e-12)

        return float(similarity)

    def _generate_memory_key(self, data: np.ndarray) -> str:
        """Generate unique memory key"""
        return f"mem_{hash(data.tobytes())}_{np.datetime64('now')}"

    def _create_associative_links(self, memory_key: str, metadata: Dict):
        """Create associative links between memories"""
        for key, value in metadata.items():
            if key not in self.associative_links:
                self.associative_links[key] = []
            self.associative_links[key].append(memory_key)

    def _analyze_access_pattern(self, data: np.ndarray) -> Dict:
        """Analyze access pattern characteristics"""
        return {
            'data_entropy': float(-np.sum(np.abs(data) * np.log(np.abs(data) + 1e-12))),
            'data_energy': float(np.sum(np.abs(data)**2)),
            'complexity': len(data)
        }

    def _reconstruct_memory(self, memory_key: str) -> np.ndarray:
        """Reconstruct memory from holographic storage"""
        # Simplified reconstruction - in practice would use phase retrieval
        return np.random.random(256)  # Placeholder