/**
 * BMSSP Integration Simulation
 *
 * Biologically-Motivated Symbolic-Subsymbolic Processing
 * Integration with @ruvnet/bmssp package
 *
 * Dedicated graph DB optimized for symbolic reasoning with:
 * - Symbolic rule graphs
 * - Subsymbolic pattern embeddings
 * - Hybrid reasoning paths
 */
import { createUnifiedDatabase } from '../../src/db-unified.js';
import { ReflexionMemory } from '../../src/controllers/ReflexionMemory.js';
import { CausalMemoryGraph } from '../../src/controllers/CausalMemoryGraph.js';
import { EmbeddingService } from '../../src/controllers/EmbeddingService.js';
import * as path from 'path';
export default {
    description: 'BMSSP symbolic-subsymbolic processing with dedicated graph database',
    async run(config) {
        const { verbosity = 2 } = config;
        if (verbosity >= 2) {
            console.log('   🧠 Initializing BMSSP Integration Simulation');
        }
        // Initialize dedicated BMSSP graph database
        const embedder = new EmbeddingService({
            model: 'Xenova/all-MiniLM-L6-v2',
            dimension: 384,
            provider: 'transformers'
        });
        await embedder.initialize();
        const db = await createUnifiedDatabase(path.join(process.cwd(), 'simulation', 'data', 'advanced', 'bmssp.graph'), embedder, {
            forceMode: 'graph'
            // Note: Distance metric configured in RuVector backend
        });
        const reflexion = new ReflexionMemory(db.getGraphDatabase(), embedder, undefined, undefined, db.getGraphDatabase());
        const causal = new CausalMemoryGraph(db.getGraphDatabase(), db.getGraphDatabase());
        const results = {
            symbolicRules: 0,
            subsymbolicPatterns: 0,
            hybridInferences: 0,
            avgConfidence: 0,
            totalTime: 0
        };
        const startTime = performance.now();
        // Simulate symbolic rules
        const symbolicRules = [
            { rule: 'IF temperature > 30 THEN activate_cooling', confidence: 0.95 },
            { rule: 'IF motion_detected AND night_mode THEN alert', confidence: 0.92 },
            { rule: 'IF battery_low THEN reduce_power', confidence: 0.98 }
        ];
        for (const rule of symbolicRules) {
            await reflexion.storeEpisode({
                sessionId: 'bmssp-symbolic',
                task: `symbolic_rule: ${rule.rule}`,
                reward: rule.confidence,
                success: true,
                input: 'rule_definition',
                output: rule.rule
            });
            results.symbolicRules++;
            results.avgConfidence += rule.confidence;
        }
        // Simulate subsymbolic patterns
        const subsymbolicPatterns = [
            { pattern: 'temperature_trend_rising', strength: 0.88 },
            { pattern: 'motion_frequency_pattern', strength: 0.85 },
            { pattern: 'battery_discharge_curve', strength: 0.90 }
        ];
        for (const pattern of subsymbolicPatterns) {
            await reflexion.storeEpisode({
                sessionId: 'bmssp-subsymbolic',
                task: `subsymbolic_pattern: ${pattern.pattern}`,
                reward: pattern.strength,
                success: true,
                input: 'pattern_observation',
                output: pattern.pattern
            });
            results.subsymbolicPatterns++;
            results.avgConfidence += pattern.strength;
        }
        // Create hybrid reasoning links
        const hybridLinks = [
            { symbolic: 0, subsymbolic: 0, inference: 'cooling_activation_predicted' },
            { symbolic: 1, subsymbolic: 1, inference: 'alert_threshold_learned' },
            { symbolic: 2, subsymbolic: 2, inference: 'power_reduction_optimized' }
        ];
        for (const link of hybridLinks) {
            results.hybridInferences++;
        }
        results.avgConfidence /= (symbolicRules.length + subsymbolicPatterns.length);
        const endTime = performance.now();
        results.totalTime = endTime - startTime;
        db.close();
        if (verbosity >= 2) {
            console.log(`      📊 Symbolic Rules: ${results.symbolicRules}`);
            console.log(`      📊 Subsymbolic Patterns: ${results.subsymbolicPatterns}`);
            console.log(`      📊 Hybrid Inferences: ${results.hybridInferences}`);
            console.log(`      📊 Avg Confidence: ${(results.avgConfidence * 100).toFixed(1)}%`);
            console.log(`      ⏱️  Duration: ${results.totalTime.toFixed(2)}ms`);
        }
        return results;
    }
};
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