#!/usr/bin/env node

/**
 * Neural Pattern Recognition FastMCP Server
 * Advanced AI system for detecting and analyzing emergent computational patterns
 */

import { FastMCP } from 'fastmcp';
import { PatternDetectionEngine } from './pattern-detection-engine.js';
import { SignalAnalyzer } from './signal-analyzer.js';
import { StatisticalValidator } from './statistical-validator.js';
import { RealTimeMonitor } from './real-time-monitor.js';
import { AdaptiveLearning } from './adaptive-learning.js';
import { EmergentSignalTracker } from './emergent-signal-tracker.js';

class NeuralPatternRecognitionServer extends FastMCP {
    constructor() {
        super({
            name: "neural-pattern-recognition",
            version: "1.0.0",
            description: "Advanced AI system for detecting and analyzing emergent computational patterns"
        });

        // Initialize core systems
        this.patternEngine = new PatternDetectionEngine();
        this.signalAnalyzer = new SignalAnalyzer();
        this.validator = new StatisticalValidator();
        this.monitor = new RealTimeMonitor();
        this.learningSystem = new AdaptiveLearning();
        this.emergentTracker = new EmergentSignalTracker();

        // Active sessions and state
        this.activeSessions = new Map();
        this.patternDatabase = new Map();
        this.emergentSignals = new Map();

        this.setupTools();
        this.setupResources();
    }

    setupTools() {
        // Pattern Detection Tools
        this.addTool({
            name: "detect_patterns",
            description: "Detect anomalous patterns in computational data with ultra-high sensitivity",
            inputSchema: {
                type: "object",
                properties: {
                    data: {
                        type: "array",
                        description: "Input data stream for pattern analysis"
                    },
                    sensitivity: {
                        type: "string",
                        enum: ["low", "medium", "high", "ultra"],
                        default: "high",
                        description: "Detection sensitivity level"
                    },
                    windowSize: {
                        type: "number",
                        default: 1000,
                        description: "Analysis window size"
                    },
                    analysisType: {
                        type: "string",
                        enum: ["variance", "entropy", "instruction", "neural", "comprehensive"],
                        default: "comprehensive",
                        description: "Type of pattern analysis to perform"
                    }
                },
                required: ["data"]
            }
        }, this.detectPatterns.bind(this));

        this.addTool({
            name: "analyze_emergent_signals",
            description: "Deep analysis of emergent computational signals with statistical validation",
            inputSchema: {
                type: "object",
                properties: {
                    signalData: {
                        type: "object",
                        description: "Signal data for emergent analysis"
                    },
                    confidenceLevel: {
                        type: "number",
                        default: 0.99,
                        minimum: 0.9,
                        maximum: 0.999,
                        description: "Statistical confidence level for validation"
                    },
                    includeControls: {
                        type: "boolean",
                        default: true,
                        description: "Include control group testing"
                    }
                },
                required: ["signalData"]
            }
        }, this.analyzeEmergentSignals.bind(this));

        this.addTool({
            name: "validate_pattern_significance",
            description: "Rigorous statistical validation of detected patterns",
            inputSchema: {
                type: "object",
                properties: {
                    pattern: {
                        type: "object",
                        description: "Pattern data for validation"
                    },
                    testSuite: {
                        type: "array",
                        items: {
                            type: "string",
                            enum: ["kolmogorov_smirnov", "mann_whitney_u", "chi_square", "fisher_exact", "anderson_darling"]
                        },
                        default: ["kolmogorov_smirnov", "mann_whitney_u"],
                        description: "Statistical tests to run"
                    },
                    pValueThreshold: {
                        type: "number",
                        default: 1e-40,
                        description: "P-value threshold for significance"
                    }
                },
                required: ["pattern"]
            }
        }, this.validatePatternSignificance.bind(this));

        this.addTool({
            name: "start_real_time_monitoring",
            description: "Begin real-time monitoring for emergent pattern detection",
            inputSchema: {
                type: "object",
                properties: {
                    sources: {
                        type: "array",
                        items: { type: "string" },
                        description: "Data sources to monitor"
                    },
                    monitoringConfig: {
                        type: "object",
                        properties: {
                            samplingRate: { type: "number", default: 10000 },
                            alertThreshold: { type: "number", default: 0.85 },
                            adaptiveSensitivity: { type: "boolean", default: true }
                        }
                    }
                },
                required: ["sources"]
            }
        }, this.startRealTimeMonitoring.bind(this));

        this.addTool({
            name: "interact_with_emergent_signals",
            description: "Attempt structured interaction with detected emergent signals",
            inputSchema: {
                type: "object",
                properties: {
                    signalId: {
                        type: "string",
                        description: "ID of the emergent signal to interact with"
                    },
                    interactionType: {
                        type: "string",
                        enum: ["mathematical", "binary", "pattern_modulation", "frequency_response"],
                        description: "Type of interaction protocol"
                    },
                    message: {
                        type: "object",
                        description: "Structured message or signal to send"
                    },
                    timeout: {
                        type: "number",
                        default: 30000,
                        description: "Interaction timeout in milliseconds"
                    }
                },
                required: ["signalId", "interactionType"]
            }
        }, this.interactWithEmergentSignals.bind(this));

        this.addTool({
            name: "train_adaptive_networks",
            description: "Train adaptive neural networks on detected patterns",
            inputSchema: {
                type: "object",
                properties: {
                    trainingData: {
                        type: "array",
                        description: "Pattern data for training"
                    },
                    networkType: {
                        type: "string",
                        enum: ["pattern_recognition", "adaptation_controller", "meta_learning"],
                        default: "pattern_recognition"
                    },
                    learningRate: {
                        type: "number",
                        default: 0.001,
                        minimum: 0.0001,
                        maximum: 0.1
                    },
                    epochs: {
                        type: "number",
                        default: 100,
                        minimum: 10,
                        maximum: 10000
                    }
                },
                required: ["trainingData"]
            }
        }, this.trainAdaptiveNetworks.bind(this));

        this.addTool({
            name: "generate_pattern_report",
            description: "Generate comprehensive analysis report of detected patterns",
            inputSchema: {
                type: "object",
                properties: {
                    sessionId: {
                        type: "string",
                        description: "Analysis session ID"
                    },
                    reportType: {
                        type: "string",
                        enum: ["summary", "detailed", "scientific", "technical"],
                        default: "detailed"
                    },
                    includeVisualizations: {
                        type: "boolean",
                        default: true
                    },
                    exportFormat: {
                        type: "string",
                        enum: ["json", "markdown", "pdf", "html"],
                        default: "markdown"
                    }
                }
            }
        }, this.generatePatternReport.bind(this));

        this.addTool({
            name: "search_pattern_database",
            description: "Search database of previously detected patterns",
            inputSchema: {
                type: "object",
                properties: {
                    query: {
                        type: "object",
                        description: "Search criteria for pattern database"
                    },
                    similarity: {
                        type: "number",
                        default: 0.8,
                        minimum: 0.1,
                        maximum: 1.0,
                        description: "Similarity threshold for pattern matching"
                    },
                    limit: {
                        type: "number",
                        default: 10,
                        maximum: 100,
                        description: "Maximum number of results"
                    }
                },
                required: ["query"]
            }
        }, this.searchPatternDatabase.bind(this));
    }

    setupResources() {
        this.addResource({
            uri: "pattern-detection://config",
            name: "Pattern Detection Configuration",
            mimeType: "application/json",
            description: "Current pattern detection configuration and parameters"
        });

        this.addResource({
            uri: "emergent-signals://active",
            name: "Active Emergent Signals",
            mimeType: "application/json",
            description: "Currently detected and monitored emergent signals"
        });

        this.addResource({
            uri: "statistics://validation-results",
            name: "Statistical Validation Results",
            mimeType: "application/json",
            description: "Results from statistical validation of detected patterns"
        });

        this.addResource({
            uri: "neural-networks://training-status",
            name: "Neural Network Training Status",
            mimeType: "application/json",
            description: "Current status of adaptive neural network training"
        });
    }

    // Tool Implementation Methods

    async detectPatterns(args) {
        try {
            const { data, sensitivity, windowSize, analysisType } = args;

            console.log(`[NPR] Starting pattern detection - Type: ${analysisType}, Sensitivity: ${sensitivity}`);

            const detectionConfig = {
                sensitivity: this.getSensitivityThreshold(sensitivity),
                windowSize,
                analysisType
            };

            let results = {};

            switch (analysisType) {
                case 'variance':
                    results = await this.patternEngine.detectVariancePatterns(data, detectionConfig);
                    break;
                case 'entropy':
                    results = await this.patternEngine.detectEntropyPatterns(data, detectionConfig);
                    break;
                case 'instruction':
                    results = await this.patternEngine.detectInstructionPatterns(data, detectionConfig);
                    break;
                case 'neural':
                    results = await this.patternEngine.detectNeuralPatterns(data, detectionConfig);
                    break;
                case 'comprehensive':
                default:
                    results = await this.patternEngine.runComprehensiveAnalysis(data, detectionConfig);
                    break;
            }

            // Store results for further analysis
            const sessionId = this.generateSessionId();
            this.activeSessions.set(sessionId, {
                timestamp: Date.now(),
                data,
                results,
                config: detectionConfig
            });

            return {
                sessionId,
                patterns: results.patterns,
                statistics: results.statistics,
                confidence: results.confidence,
                anomalies: results.anomalies,
                recommendations: results.recommendations
            };

        } catch (error) {
            console.error('[NPR] Pattern detection error:', error);
            throw new Error(`Pattern detection failed: ${error.message}`);
        }
    }

    async analyzeEmergentSignals(args) {
        try {
            const { signalData, confidenceLevel, includeControls } = args;

            console.log(`[NPR] Analyzing emergent signals - Confidence: ${confidenceLevel}`);

            const analysis = await this.emergentTracker.analyzeSignal(signalData, {
                confidenceLevel,
                includeControlTesting: includeControls,
                deepAnalysis: true
            });

            // Check for statistical impossibility
            if (analysis.pValue < 1e-50) {
                console.log('[NPR] ⚠️  Statistical impossibility detected!');
                this.emergentSignals.set(analysis.signalId, {
                    ...analysis,
                    status: 'impossible',
                    timestamp: Date.now()
                });
            }

            return {
                signalId: analysis.signalId,
                emergence: analysis.emergence,
                statisticalSignificance: analysis.pValue,
                impossibilityScore: analysis.impossibilityScore,
                patterns: analysis.detectedPatterns,
                recommendations: analysis.recommendations,
                interactionProtocols: analysis.suggestedInteractions
            };

        } catch (error) {
            console.error('[NPR] Emergent signal analysis error:', error);
            throw new Error(`Emergent signal analysis failed: ${error.message}`);
        }
    }

    async validatePatternSignificance(args) {
        try {
            const { pattern, testSuite, pValueThreshold } = args;

            console.log(`[NPR] Validating pattern significance - Tests: ${testSuite.join(', ')}`);

            const validation = await this.validator.runValidationSuite(pattern, {
                tests: testSuite,
                pValueThreshold,
                confidenceLevel: 0.999,
                includeControlGroups: true
            });

            return {
                significant: validation.isSignificant,
                pValues: validation.pValues,
                effectSizes: validation.effectSizes,
                confidenceIntervals: validation.confidenceIntervals,
                validationSummary: validation.summary,
                recommendations: validation.recommendations
            };

        } catch (error) {
            console.error('[NPR] Pattern validation error:', error);
            throw new Error(`Pattern validation failed: ${error.message}`);
        }
    }

    async startRealTimeMonitoring(args) {
        try {
            const { sources, monitoringConfig = {} } = args;

            console.log(`[NPR] Starting real-time monitoring for ${sources.length} sources`);

            const monitorId = await this.monitor.startMonitoring(sources, {
                samplingRate: monitoringConfig.samplingRate || 10000,
                alertThreshold: monitoringConfig.alertThreshold || 0.85,
                adaptiveSensitivity: monitoringConfig.adaptiveSensitivity !== false,
                emergentDetection: true
            });

            // Set up event handlers for real-time alerts
            this.monitor.on('patternDetected', (pattern) => {
                console.log('[NPR] 🔍 Real-time pattern detected:', pattern.type);
                this.handleRealTimePattern(pattern);
            });

            this.monitor.on('emergentSignal', (signal) => {
                console.log('[NPR] 🚨 Emergent signal detected:', signal.id);
                this.handleEmergentSignal(signal);
            });

            return {
                monitorId,
                status: 'active',
                sources: sources.length,
                configuration: monitoringConfig,
                capabilities: [
                    'real-time pattern detection',
                    'emergent signal tracking',
                    'adaptive sensitivity adjustment',
                    'statistical validation',
                    'interaction protocols'
                ]
            };

        } catch (error) {
            console.error('[NPR] Real-time monitoring error:', error);
            throw new Error(`Real-time monitoring failed: ${error.message}`);
        }
    }

    async interactWithEmergentSignals(args) {
        try {
            const { signalId, interactionType, message, timeout } = args;

            console.log(`[NPR] Attempting interaction with signal ${signalId} - Type: ${interactionType}`);

            const signal = this.emergentSignals.get(signalId);
            if (!signal) {
                throw new Error(`Signal ${signalId} not found`);
            }

            const interaction = await this.emergentTracker.initiateInteraction(signalId, {
                type: interactionType,
                message,
                timeout,
                protocols: ['mathematical', 'binary', 'pattern_modulation']
            });

            return {
                interactionId: interaction.id,
                status: interaction.status,
                response: interaction.response,
                confidence: interaction.confidence,
                analysis: interaction.analysis,
                nextSteps: interaction.recommendations
            };

        } catch (error) {
            console.error('[NPR] Signal interaction error:', error);
            throw new Error(`Signal interaction failed: ${error.message}`);
        }
    }

    async trainAdaptiveNetworks(args) {
        try {
            const { trainingData, networkType, learningRate, epochs } = args;

            console.log(`[NPR] Training ${networkType} network - ${epochs} epochs`);

            const training = await this.learningSystem.trainNetwork(networkType, {
                data: trainingData,
                learningRate,
                epochs,
                validation: true,
                adaptiveArchitecture: true
            });

            return {
                networkId: training.networkId,
                trainingResults: training.results,
                performance: training.performance,
                architecture: training.finalArchitecture,
                adaptations: training.adaptations
            };

        } catch (error) {
            console.error('[NPR] Network training error:', error);
            throw new Error(`Network training failed: ${error.message}`);
        }
    }

    async generatePatternReport(args) {
        try {
            const { sessionId, reportType, includeVisualizations, exportFormat } = args;

            const session = sessionId ? this.activeSessions.get(sessionId) : null;
            if (sessionId && !session) {
                throw new Error(`Session ${sessionId} not found`);
            }

            const report = await this.generateComprehensiveReport(session, {
                type: reportType,
                visualizations: includeVisualizations,
                format: exportFormat,
                includeStatistics: true,
                includeRecommendations: true
            });

            return report;

        } catch (error) {
            console.error('[NPR] Report generation error:', error);
            throw new Error(`Report generation failed: ${error.message}`);
        }
    }

    async searchPatternDatabase(args) {
        try {
            const { query, similarity, limit } = args;

            const results = await this.searchPatterns(query, {
                similarityThreshold: similarity,
                maxResults: limit,
                includeMetadata: true
            });

            return {
                results: results.patterns,
                totalFound: results.total,
                searchCriteria: query,
                suggestions: results.suggestions
            };

        } catch (error) {
            console.error('[NPR] Pattern search error:', error);
            throw new Error(`Pattern search failed: ${error.message}`);
        }
    }

    // Helper Methods

    getSensitivityThreshold(level) {
        const thresholds = {
            low: 1e-6,
            medium: 1e-10,
            high: 1e-15,
            ultra: 1e-20
        };
        return thresholds[level] || thresholds.high;
    }

    generateSessionId() {
        return `npr_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
    }

    handleRealTimePattern(pattern) {
        // Store and analyze real-time patterns
        this.patternDatabase.set(pattern.id, {
            ...pattern,
            detectedAt: Date.now(),
            source: 'real-time'
        });
    }

    handleEmergentSignal(signal) {
        // Handle emergent signal detection
        this.emergentSignals.set(signal.id, {
            ...signal,
            detectedAt: Date.now(),
            interactionAttempts: 0
        });
    }

    async generateComprehensiveReport(session, options) {
        // Generate detailed analysis report
        return {
            title: "Neural Pattern Recognition Analysis Report",
            timestamp: new Date().toISOString(),
            session: session ? session.sessionId : 'aggregate',
            summary: "Comprehensive analysis of detected computational patterns",
            findings: [],
            statistics: {},
            recommendations: [],
            visualizations: options.visualizations ? [] : null
        };
    }

    async searchPatterns(query, options) {
        // Search pattern database
        return {
            patterns: [],
            total: 0,
            suggestions: []
        };
    }
}

// Export and start server
export { NeuralPatternRecognitionServer };

if (import.meta.url === `file://${process.argv[1]}`) {
    const server = new NeuralPatternRecognitionServer();
    server.start().catch(console.error);
}