/**
 * Pattern Learner
 *
 * Implements pattern extraction, matching, and evolution for
 * continuous learning from agent experiences.
 *
 * Performance Targets:
 * - Pattern matching: <1ms
 * - Pattern extraction: <5ms
 * - Evolution step: <2ms
 */

import type {
  Pattern,
  PatternMatch,
  PatternEvolution,
  Trajectory,
  DistilledMemory,
  NeuralEvent,
  NeuralEventListener,
} from './types.js';
import { deepEncode, deepDecode } from './utils/serialize.js';

/**
 * Configuration for Pattern Learner
 */
export interface PatternLearnerConfig {
  /** Maximum number of patterns to store */
  maxPatterns: number;

  /** Similarity threshold for matching */
  matchThreshold: number;

  /** Minimum usages before pattern is stable */
  minUsagesForStable: number;

  /** Quality threshold for pattern inclusion */
  qualityThreshold: number;

  /** Enable pattern clustering */
  enableClustering: boolean;

  /** Number of clusters (if clustering enabled) */
  numClusters: number;

  /** Evolution learning rate */
  evolutionLearningRate: number;
}

/**
 * Default Pattern Learner configuration
 */
const DEFAULT_CONFIG: PatternLearnerConfig = {
  maxPatterns: 1000,
  matchThreshold: 0.7,
  minUsagesForStable: 5,
  qualityThreshold: 0.5,
  enableClustering: true,
  numClusters: 50,
  evolutionLearningRate: 0.1,
};

/**
 * Cluster representation for fast pattern lookup
 */
interface PatternCluster {
  clusterId: number;
  centroid: Float32Array;
  patternIds: Set<string>;
}

/**
 * Pattern Learner - Manages pattern extraction, matching, and evolution
 */
export class PatternLearner {
  private config: PatternLearnerConfig;
  private patterns: Map<string, Pattern> = new Map();
  private clusters: PatternCluster[] = [];
  private patternToCluster: Map<string, number> = new Map();

  // Performance tracking
  private matchCount = 0;
  private totalMatchTime = 0;
  private extractionCount = 0;
  private totalExtractionTime = 0;
  private evolutionCount = 0;
  private totalEvolutionTime = 0;

  // Event listeners
  private eventListeners: Set<NeuralEventListener> = new Set();

  constructor(config: Partial<PatternLearnerConfig> = {}) {
    this.config = { ...DEFAULT_CONFIG, ...config };
  }

  // ==========================================================================
  // Pattern Matching
  // ==========================================================================

  /**
   * Find matching patterns for a query embedding
   * Target: <1ms
   */
  findMatches(queryEmbedding: Float32Array, k: number = 3): PatternMatch[] {
    const startTime = performance.now();

    if (this.patterns.size === 0) {
      return [];
    }

    let candidates: Pattern[];

    // Use clustering for faster search if enabled and clusters exist
    if (this.config.enableClustering && this.clusters.length > 0) {
      candidates = this.getCandidatesFromClusters(queryEmbedding);
    } else {
      candidates = Array.from(this.patterns.values());
    }

    // Compute similarities
    const matches: PatternMatch[] = [];

    for (const pattern of candidates) {
      const similarity = this.cosineSimilarity(queryEmbedding, pattern.embedding);

      if (similarity >= this.config.matchThreshold) {
        matches.push({
          pattern,
          similarity,
          confidence: this.computeMatchConfidence(pattern, similarity),
          latencyMs: 0,
        });
      }
    }

    // Sort by similarity
    matches.sort((a, b) => b.similarity - a.similarity);
    const result = matches.slice(0, k);

    // Track performance
    const elapsed = performance.now() - startTime;
    this.matchCount++;
    this.totalMatchTime += elapsed;

    // Warn if over target
    if (elapsed > 1) {
      console.warn(`Pattern matching exceeded target: ${elapsed.toFixed(2)}ms > 1ms`);
    }

    return result;
  }

  /**
   * Find best single match
   */
  findBestMatch(queryEmbedding: Float32Array): PatternMatch | null {
    const matches = this.findMatches(queryEmbedding, 1);
    return matches.length > 0 ? matches[0] : null;
  }

  // ==========================================================================
  // Pattern Extraction
  // ==========================================================================

  /**
   * Extract a pattern from a trajectory
   * Target: <5ms
   */
  extractPattern(trajectory: Trajectory, memory?: DistilledMemory): Pattern | null {
    const startTime = performance.now();

    // Validate trajectory
    if (!trajectory.isComplete || trajectory.qualityScore < this.config.qualityThreshold) {
      return null;
    }

    // Check for duplicates
    const embedding = this.computePatternEmbedding(trajectory);
    const existing = this.findSimilarPattern(embedding, 0.95);

    if (existing) {
      // Update existing pattern instead
      this.updatePatternFromTrajectory(existing, trajectory);
      return existing;
    }

    // Create new pattern
    const pattern: Pattern = {
      patternId: `pat_${Date.now()}_${Math.random().toString(36).slice(2, 9)}`,
      name: this.generatePatternName(trajectory),
      domain: trajectory.domain,
      embedding,
      strategy: this.extractStrategy(trajectory),
      successRate: trajectory.qualityScore,
      usageCount: 1,
      qualityHistory: [trajectory.qualityScore],
      evolutionHistory: [],
      createdAt: Date.now(),
      updatedAt: Date.now(),
    };

    // Store pattern
    this.patterns.set(pattern.patternId, pattern);

    // Update clusters if enabled
    if (this.config.enableClustering) {
      this.assignToCluster(pattern);
    }

    // Prune if over capacity
    if (this.patterns.size > this.config.maxPatterns) {
      this.prunePatterns();
    }

    // Track performance
    const elapsed = performance.now() - startTime;
    this.extractionCount++;
    this.totalExtractionTime += elapsed;

    return pattern;
  }

  /**
   * Extract patterns from multiple trajectories in batch
   */
  extractPatternsBatch(trajectories: Trajectory[]): Pattern[] {
    const patterns: Pattern[] = [];

    for (const trajectory of trajectories) {
      const pattern = this.extractPattern(trajectory);
      if (pattern) {
        patterns.push(pattern);
      }
    }

    // Rebuild clusters after batch extraction
    if (this.config.enableClustering && patterns.length > 10) {
      this.rebuildClusters();
    }

    return patterns;
  }

  // ==========================================================================
  // Pattern Evolution
  // ==========================================================================

  /**
   * Evolve a pattern based on new experience
   * Target: <2ms
   */
  evolvePattern(patternId: string, quality: number, context?: string): void {
    const startTime = performance.now();

    const pattern = this.patterns.get(patternId);
    if (!pattern) return;

    const previousQuality = pattern.successRate;
    const lr = this.config.evolutionLearningRate;

    // Update quality history
    pattern.qualityHistory.push(quality);
    if (pattern.qualityHistory.length > 100) {
      pattern.qualityHistory = pattern.qualityHistory.slice(-100);
    }

    // Exponential moving average for success rate
    pattern.successRate = pattern.successRate * (1 - lr) + quality * lr;
    pattern.usageCount++;
    pattern.updatedAt = Date.now();

    // Record evolution
    const evolutionType = this.determineEvolutionType(previousQuality, pattern.successRate);
    pattern.evolutionHistory.push({
      timestamp: Date.now(),
      type: evolutionType,
      previousQuality,
      newQuality: pattern.successRate,
      description: context || 'Updated from new experience',
    });

    // Keep evolution history bounded
    if (pattern.evolutionHistory.length > 50) {
      pattern.evolutionHistory = pattern.evolutionHistory.slice(-50);
    }

    // Emit event
    this.emitEvent({
      type: 'pattern_evolved',
      patternId,
      evolutionType,
    });

    // Track performance
    const elapsed = performance.now() - startTime;
    this.evolutionCount++;
    this.totalEvolutionTime += elapsed;
  }

  /**
   * Merge two similar patterns
   */
  mergePatterns(patternId1: string, patternId2: string): Pattern | null {
    const p1 = this.patterns.get(patternId1);
    const p2 = this.patterns.get(patternId2);

    if (!p1 || !p2) return null;

    // Keep the higher quality pattern as base
    const [keep, remove] = p1.successRate >= p2.successRate ? [p1, p2] : [p2, p1];

    // Merge embeddings (weighted average)
    const totalUsage = keep.usageCount + remove.usageCount;
    const w1 = keep.usageCount / totalUsage;
    const w2 = remove.usageCount / totalUsage;

    for (let i = 0; i < keep.embedding.length; i++) {
      keep.embedding[i] = keep.embedding[i] * w1 + remove.embedding[i] * w2;
    }

    // Merge statistics
    keep.usageCount += remove.usageCount;
    keep.qualityHistory.push(...remove.qualityHistory);
    keep.successRate = keep.qualityHistory.reduce((a, b) => a + b, 0) / keep.qualityHistory.length;

    // Record merge
    keep.evolutionHistory.push({
      timestamp: Date.now(),
      type: 'merge',
      previousQuality: p1.successRate,
      newQuality: keep.successRate,
      description: `Merged with pattern ${remove.patternId}`,
    });

    // Remove the merged pattern
    this.patterns.delete(remove.patternId);
    this.patternToCluster.delete(remove.patternId);

    return keep;
  }

  /**
   * Split a pattern into more specific sub-patterns
   */
  splitPattern(patternId: string, numSplits: number = 2): Pattern[] {
    const pattern = this.patterns.get(patternId);
    if (!pattern || numSplits < 2) return [];

    const splits: Pattern[] = [];

    for (let i = 0; i < numSplits; i++) {
      // Create variation of embedding with noise
      const newEmbedding = new Float32Array(pattern.embedding.length);
      for (let j = 0; j < newEmbedding.length; j++) {
        const noise = (Math.random() - 0.5) * 0.1;
        newEmbedding[j] = pattern.embedding[j] + noise;
      }

      const newPattern: Pattern = {
        patternId: `pat_${Date.now()}_${i}_${Math.random().toString(36).slice(2, 6)}`,
        name: `${pattern.name}_split_${i}`,
        domain: pattern.domain,
        embedding: newEmbedding,
        strategy: pattern.strategy,
        successRate: pattern.successRate * 0.9, // Slight penalty for uncertainty
        usageCount: 0,
        qualityHistory: [],
        evolutionHistory: [{
          timestamp: Date.now(),
          type: 'split',
          previousQuality: pattern.successRate,
          newQuality: pattern.successRate * 0.9,
          description: `Split from pattern ${patternId}`,
        }],
        createdAt: Date.now(),
        updatedAt: Date.now(),
      };

      this.patterns.set(newPattern.patternId, newPattern);
      splits.push(newPattern);
    }

    // Remove original pattern
    this.patterns.delete(patternId);
    this.patternToCluster.delete(patternId);

    // Rebuild clusters
    if (this.config.enableClustering) {
      this.rebuildClusters();
    }

    return splits;
  }

  // ==========================================================================
  // Pattern Access
  // ==========================================================================

  /**
   * Get all patterns
   */
  getPatterns(): Pattern[] {
    return Array.from(this.patterns.values());
  }

  /**
   * Get pattern by ID
   */
  getPattern(patternId: string): Pattern | undefined {
    return this.patterns.get(patternId);
  }

  /**
   * Get patterns by domain
   */
  getPatternsByDomain(domain: string): Pattern[] {
    return Array.from(this.patterns.values()).filter(p => p.domain === domain);
  }

  /**
   * Get stable patterns (sufficient usage)
   */
  getStablePatterns(): Pattern[] {
    return Array.from(this.patterns.values())
      .filter(p => p.usageCount >= this.config.minUsagesForStable);
  }

  // ==========================================================================
  // Statistics
  // ==========================================================================

  getStats(): Record<string, number> {
    const patterns = Array.from(this.patterns.values());

    return {
      totalPatterns: this.patterns.size,
      stablePatterns: patterns.filter(p => p.usageCount >= this.config.minUsagesForStable).length,
      avgSuccessRate: patterns.length > 0
        ? patterns.reduce((s, p) => s + p.successRate, 0) / patterns.length
        : 0,
      avgUsageCount: patterns.length > 0
        ? patterns.reduce((s, p) => s + p.usageCount, 0) / patterns.length
        : 0,
      numClusters: this.clusters.length,
      avgMatchTimeMs: this.matchCount > 0 ? this.totalMatchTime / this.matchCount : 0,
      avgExtractionTimeMs: this.extractionCount > 0 ? this.totalExtractionTime / this.extractionCount : 0,
      avgEvolutionTimeMs: this.evolutionCount > 0 ? this.totalEvolutionTime / this.evolutionCount : 0,
      // #1773 item 2 — search-path observability. PatternLearner currently
      // uses cluster-aware brute-force exclusively (no HNSW yet); this flag
      // is exposed so the dashboard can report which retrieval substrate
      // was actually used. Unlike ReasoningBank which CAN dispatch to HNSW,
      // PatternLearner.findMatches always takes the brute-force path until
      // a follow-up wires AgentDB into pattern matching too.
      hnswEnabled: 0,
      bruteForceMatches: this.matchCount,
    };
  }

  // ==========================================================================
  // Persistence (#1773 Phase 1.6)
  // ==========================================================================

  /**
   * Serialize learner state to a JSON-safe object. Round-trips losslessly
   * through `JSON.stringify` → file → `JSON.parse` → `deserialize()`.
   * Float32Array embeddings encode as `{__f32: number[]}`, Maps as
   * `{__map: [[k,v], …]}`. Sets become arrays. Excludes event listeners
   * (callers re-register on restore).
   */
  serialize(): unknown {
    return deepEncode({
      schemaVersion: 1,
      config: this.config,
      patterns: this.patterns,
      clusters: this.clusters.map((c) => ({
        clusterId: c.clusterId,
        centroid: c.centroid,
        patternIds: Array.from(c.patternIds),
      })),
      patternToCluster: this.patternToCluster,
      counters: {
        matchCount: this.matchCount,
        totalMatchTime: this.totalMatchTime,
        extractionCount: this.extractionCount,
        totalExtractionTime: this.totalExtractionTime,
        evolutionCount: this.evolutionCount,
        totalEvolutionTime: this.totalEvolutionTime,
      },
    });
  }

  /**
   * Restore from a previously-serialized state. Replaces all current state.
   * Event listeners NOT restored — re-register after deserialize() returns.
   */
  deserialize(state: unknown): void {
    const decoded = deepDecode(state) as {
      schemaVersion: number;
      config: PatternLearnerConfig;
      patterns: Map<string, Pattern>;
      clusters: Array<{ clusterId: number; centroid: Float32Array; patternIds: string[] }>;
      patternToCluster: Map<string, number>;
      counters: {
        matchCount: number; totalMatchTime: number;
        extractionCount: number; totalExtractionTime: number;
        evolutionCount: number; totalEvolutionTime: number;
      };
    };
    if (decoded.schemaVersion !== 1) {
      throw new Error(`PatternLearner: unsupported schemaVersion ${decoded.schemaVersion} (expected 1)`);
    }
    this.config = { ...this.config, ...decoded.config };
    this.patterns = decoded.patterns;
    this.clusters = decoded.clusters.map((c) => ({
      clusterId: c.clusterId,
      centroid: c.centroid,
      patternIds: new Set(c.patternIds),
    }));
    this.patternToCluster = decoded.patternToCluster;
    this.matchCount = decoded.counters.matchCount;
    this.totalMatchTime = decoded.counters.totalMatchTime;
    this.extractionCount = decoded.counters.extractionCount;
    this.totalExtractionTime = decoded.counters.totalExtractionTime;
    this.evolutionCount = decoded.counters.evolutionCount;
    this.totalEvolutionTime = decoded.counters.totalEvolutionTime;
  }

  // ==========================================================================
  // Event System
  // ==========================================================================

  addEventListener(listener: NeuralEventListener): void {
    this.eventListeners.add(listener);
  }

  removeEventListener(listener: NeuralEventListener): void {
    this.eventListeners.delete(listener);
  }

  private emitEvent(event: NeuralEvent): void {
    for (const listener of this.eventListeners) {
      try {
        listener(event);
      } catch (error) {
        console.error('Error in PatternLearner event listener:', error);
      }
    }
  }

  // ==========================================================================
  // Private Helper Methods
  // ==========================================================================

  private cosineSimilarity(a: Float32Array, b: Float32Array): number {
    if (a.length !== b.length) return 0;

    let dot = 0, normA = 0, normB = 0;
    for (let i = 0; i < a.length; i++) {
      dot += a[i] * b[i];
      normA += a[i] * a[i];
      normB += b[i] * b[i];
    }

    const denom = Math.sqrt(normA) * Math.sqrt(normB);
    return denom > 0 ? dot / denom : 0;
  }

  private computeMatchConfidence(pattern: Pattern, similarity: number): number {
    // Combine similarity with pattern reliability
    const usageWeight = Math.min(pattern.usageCount / 10, 1);
    const qualityWeight = pattern.successRate;

    return similarity * (1 - usageWeight * 0.2 - qualityWeight * 0.2) +
           usageWeight * 0.1 +
           qualityWeight * 0.1;
  }

  private getCandidatesFromClusters(queryEmbedding: Float32Array): Pattern[] {
    // Find nearest clusters
    const clusterScores: Array<{ cluster: PatternCluster; score: number }> = [];

    for (const cluster of this.clusters) {
      const score = this.cosineSimilarity(queryEmbedding, cluster.centroid);
      clusterScores.push({ cluster, score });
    }

    clusterScores.sort((a, b) => b.score - a.score);

    // Get patterns from top 3 clusters
    const candidates: Pattern[] = [];
    for (const { cluster } of clusterScores.slice(0, 3)) {
      for (const patternId of cluster.patternIds) {
        const pattern = this.patterns.get(patternId);
        if (pattern) {
          candidates.push(pattern);
        }
      }
    }

    return candidates;
  }

  private findSimilarPattern(embedding: Float32Array, threshold: number): Pattern | null {
    for (const pattern of this.patterns.values()) {
      const sim = this.cosineSimilarity(embedding, pattern.embedding);
      if (sim >= threshold) {
        return pattern;
      }
    }
    return null;
  }

  private updatePatternFromTrajectory(pattern: Pattern, trajectory: Trajectory): void {
    // Update quality
    pattern.qualityHistory.push(trajectory.qualityScore);
    if (pattern.qualityHistory.length > 100) {
      pattern.qualityHistory = pattern.qualityHistory.slice(-100);
    }

    // EMA for success rate
    const lr = this.config.evolutionLearningRate;
    pattern.successRate = pattern.successRate * (1 - lr) + trajectory.qualityScore * lr;
    pattern.usageCount++;
    pattern.updatedAt = Date.now();
  }

  private computePatternEmbedding(trajectory: Trajectory): Float32Array {
    if (trajectory.steps.length === 0) {
      return new Float32Array(768);
    }

    const dim = trajectory.steps[0].stateAfter.length;
    const embedding = new Float32Array(dim);

    // Weighted average (higher weight for later steps)
    let totalWeight = 0;
    for (let i = 0; i < trajectory.steps.length; i++) {
      const weight = (i + 1) / trajectory.steps.length;
      totalWeight += weight;
      for (let j = 0; j < dim; j++) {
        embedding[j] += trajectory.steps[i].stateAfter[j] * weight;
      }
    }

    for (let j = 0; j < dim; j++) {
      embedding[j] /= totalWeight;
    }

    return embedding;
  }

  private generatePatternName(trajectory: Trajectory): string {
    const domain = trajectory.domain;
    const quality = trajectory.qualityScore > 0.7 ? 'high' : 'mid';
    const steps = trajectory.steps.length > 5 ? 'complex' : 'simple';
    return `${domain}_${quality}_${steps}_${Date.now() % 10000}`;
  }

  private extractStrategy(trajectory: Trajectory): string {
    const actions = trajectory.steps.map(s => s.action);
    if (actions.length === 0) return 'empty';
    if (actions.length <= 3) return actions.join(' -> ');
    return `${actions.slice(0, 2).join(' -> ')} ... ${actions[actions.length - 1]}`;
  }

  private assignToCluster(pattern: Pattern): void {
    if (this.clusters.length === 0) {
      // Create first cluster
      this.clusters.push({
        clusterId: 0,
        centroid: new Float32Array(pattern.embedding),
        patternIds: new Set([pattern.patternId]),
      });
      this.patternToCluster.set(pattern.patternId, 0);
      return;
    }

    // Find nearest cluster
    let bestCluster = 0;
    let bestSim = -1;

    for (let i = 0; i < this.clusters.length; i++) {
      const sim = this.cosineSimilarity(pattern.embedding, this.clusters[i].centroid);
      if (sim > bestSim) {
        bestSim = sim;
        bestCluster = i;
      }
    }

    // Create new cluster if not similar enough and under limit
    if (bestSim < 0.7 && this.clusters.length < this.config.numClusters) {
      const newId = this.clusters.length;
      this.clusters.push({
        clusterId: newId,
        centroid: new Float32Array(pattern.embedding),
        patternIds: new Set([pattern.patternId]),
      });
      this.patternToCluster.set(pattern.patternId, newId);
    } else {
      // Add to existing cluster and update centroid
      const cluster = this.clusters[bestCluster];
      cluster.patternIds.add(pattern.patternId);
      this.patternToCluster.set(pattern.patternId, bestCluster);
      this.updateClusterCentroid(cluster);
    }
  }

  private updateClusterCentroid(cluster: PatternCluster): void {
    const dim = cluster.centroid.length;
    const newCentroid = new Float32Array(dim);
    let count = 0;

    for (const patternId of cluster.patternIds) {
      const pattern = this.patterns.get(patternId);
      if (pattern) {
        for (let i = 0; i < dim; i++) {
          newCentroid[i] += pattern.embedding[i];
        }
        count++;
      }
    }

    if (count > 0) {
      for (let i = 0; i < dim; i++) {
        newCentroid[i] /= count;
      }
      cluster.centroid = newCentroid;
    }
  }

  private rebuildClusters(): void {
    if (this.patterns.size === 0) {
      this.clusters = [];
      this.patternToCluster.clear();
      return;
    }

    const patterns = Array.from(this.patterns.values());
    const k = Math.min(this.config.numClusters, Math.ceil(patterns.length / 5));
    const dim = patterns[0].embedding.length;

    // Initialize clusters with random patterns
    this.clusters = [];
    this.patternToCluster.clear();

    const indices = new Set<number>();
    while (indices.size < k && indices.size < patterns.length) {
      indices.add(Math.floor(Math.random() * patterns.length));
    }

    let clusterId = 0;
    for (const idx of indices) {
      this.clusters.push({
        clusterId: clusterId++,
        centroid: new Float32Array(patterns[idx].embedding),
        patternIds: new Set(),
      });
    }

    // K-means iterations
    for (let iter = 0; iter < 10; iter++) {
      // Clear assignments
      for (const cluster of this.clusters) {
        cluster.patternIds.clear();
      }

      // Assign patterns to nearest cluster
      for (const pattern of patterns) {
        let bestCluster = 0;
        let bestSim = -1;

        for (let c = 0; c < this.clusters.length; c++) {
          const sim = this.cosineSimilarity(pattern.embedding, this.clusters[c].centroid);
          if (sim > bestSim) {
            bestSim = sim;
            bestCluster = c;
          }
        }

        this.clusters[bestCluster].patternIds.add(pattern.patternId);
        this.patternToCluster.set(pattern.patternId, bestCluster);
      }

      // Update centroids
      for (const cluster of this.clusters) {
        this.updateClusterCentroid(cluster);
      }
    }

    // Remove empty clusters
    this.clusters = this.clusters.filter(c => c.patternIds.size > 0);
  }

  private prunePatterns(): void {
    // Sort by score (quality * log(usage))
    const scored = Array.from(this.patterns.entries())
      .map(([id, pattern]) => ({
        id,
        pattern,
        score: pattern.successRate * Math.log(pattern.usageCount + 1),
      }))
      .sort((a, b) => a.score - b.score);

    // Remove lowest scoring patterns
    const toRemove = scored.length - Math.floor(this.config.maxPatterns * 0.8);
    for (let i = 0; i < toRemove && i < scored.length; i++) {
      this.patterns.delete(scored[i].id);
      this.patternToCluster.delete(scored[i].id);
    }

    // Rebuild clusters
    if (this.config.enableClustering) {
      this.rebuildClusters();
    }
  }

  private determineEvolutionType(
    prev: number,
    curr: number
  ): 'improvement' | 'merge' | 'split' | 'prune' {
    const delta = curr - prev;
    if (delta > 0.05) return 'improvement';
    if (delta < -0.15) return 'prune';
    return 'improvement';
  }
}

/**
 * Factory function for creating PatternLearner
 */
export function createPatternLearner(
  config?: Partial<PatternLearnerConfig>
): PatternLearner {
  return new PatternLearner(config);
}