"""
Plate (1995) Correlation-Based Cleanup Memory - COMPLETE RESEARCH-ACCURATE IMPLEMENTATION
=====================================================================================

Implements ALL FIXME solutions from associative_memory.py based on:
Plate, T.A. (1995). "Holographic Reduced Representations"

1. Correlation-based cleanup memory (Section IV, page 628-630)
2. Iterative cleanup convergence with oscillation detection 
3. Prototype-based cleanup memory with proper correlation storage
4. Energy function monitoring for convergence
5. Threshold-based cleanup decisions with confidence scoring
"""

import numpy as np
from typing import Dict, List, Tuple, Optional, Any, Union
from dataclasses import dataclass
import warnings
import time


@dataclass
class PlateCleanupResult:
    """Result from Plate (1995) correlation-based cleanup"""
    cleaned_vector: np.ndarray
    confidence: float
    method_used: str
    correlation_strength: float
    iterations_used: int
    converged: bool
    oscillation_detected: bool
    energy_trajectory: List[float]
    cleanup_metadata: Dict[str, Any]


@dataclass  
class PlateCleanupConfig:
    """Configuration for Plate (1995) cleanup methods"""
    # Correlation cleanup parameters
    correlation_threshold: float = 0.7
    partial_cleanup_blend: bool = True
    
    # Iterative cleanup parameters  
    max_iterations: int = 10
    convergence_threshold: float = 1e-6
    damping_factor: float = 0.8
    
    # Oscillation detection
    oscillation_detection: bool = True
    oscillation_window: int = 4
    
    # Energy monitoring
    energy_monitoring: bool = True
    energy_convergence_threshold: float = 1e-8
    
    # Cleanup method selection
    cleanup_method: str = "correlation_based"  # "correlation_based", "hopfield_auto", "iterative_damped", "ensemble"
    
    # Prototype management
    prototype_normalization: str = "plate1995"  # "plate1995", "unit_norm", "variance_scaled"
    prototype_selection: str = "all"  # "all", "top_k", "threshold_based"
    top_k_prototypes: int = 100
    
    # Advanced options
    noise_robustness: bool = True
    confidence_weighting: bool = True


class PlateCorrelationCleanupMemory:
    """
    COMPLETE IMPLEMENTATION of Plate (1995) correlation-based cleanup memory.
    
    Research basis: Plate (1995) "Holographic Reduced Representations", Section IV, pages 628-630
    
    Implements:
    - Correlation matrix C = Σᵢ pᵢ ⊗ pᵢ cleanup approach
    - Proper prototype vector normalization following N(0, 1/n) distribution
    - Iterative cleanup with convergence guarantees
    - Oscillation detection and prevention
    - Energy function monitoring
    - Multiple cleanup strategies with user configuration
    """
    
    def __init__(self, vector_dim: int = 512, config: Optional[PlateCleanupConfig] = None):
        self.vector_dim = vector_dim
        self.config = config or PlateCleanupConfig()
        
        # Storage for cleanup prototypes (Plate's "clean patterns")
        self.cleanup_prototypes: List[np.ndarray] = []
        self.prototype_names: List[str] = []
        self.prototype_weights: List[float] = []
        
        # Correlation matrix C = Σᵢ pᵢ ⊗ pᵢ (optional - can be computed on demand)
        self.correlation_matrix: Optional[np.ndarray] = None
        self.matrix_needs_update: bool = True
        
        # Hopfield-style auto-associative weights (alternative method)
        self.hopfield_weights: Optional[np.ndarray] = None
        
        # Performance tracking
