// Import math.js components
const { create, all, bignumber, complex, integrate, format, sqrt, sin, exp, divide, add, multiply, pow, random, log, tanh, max, min } = require('mathjs');

// Configuration for math.js
const config = {
    relTol: 1e-12,
    absTol: 1e-15,
    precision: 100,
    number: 'BigNumber',
    matrix: 'Matrix'
};

// Create instance of math with specified configurations
const math = create(all, config);

// Constants for cognitive, emotional, social factors
const PI = math.pi;              
const E = math.e;                
const I = complex(0, 1);        
const COGNITIVE_THRESHOLD = bignumber(5);  
const EMOTIONAL_THRESHOLD = bignumber(0.5); 

// Cognitive function simulating growth and adaptation over time
const cognitiveFunction = (t) => {
    const growth = math.exp(t / 10);
    return math.bignumber(math.max(growth - 5, 0)); // Cognitive growth calculation
};

// Emotional state function with complex oscillations
const emotionalStateFunction = (t) => {
    const baseEmotion = sin(t); 
    const harmonicEffect = multiply(0.5, sin(2 * t));
    const deepEmotion = multiply(0.25, sin(0.1 * t));
    return add(baseEmotion, add(harmonicEffect, deepEmotion)); // Combine emotional influences
};

// Perception function integrating cognitive, emotional, and situational factors
const perceptionFunction = (t, situation) => {
    const cognition = cognitiveFunction(t);
    const emotion = emotionalStateFunction(t);
    const situationalNoise = multiply(situation, random()); // Situational noise added
    return divide(add(add(cognition, emotion), situationalNoise), 2); // Total perception
};

// Reflection function calculating cumulative perception over time
const reflectionFunction = (t, situation) => integrate((tau) => perceptionFunction(tau, situation), 'tau', 0, t); // Cumulative perception integration

// Decision-making weights based on cognitive and emotional influences
let ALPHA = bignumber(0.6);      
let BETA = bignumber(0.4);      

// Decision function combining cognitive and reflective contributions
const decisionFunction = (t, situation) => {
    const cognitiveDecision = multiply(ALPHA, perceptionFunction(t, situation)); // Weighted cognitive influence
    const reflectiveDecision = multiply(BETA, reflectionFunction(t, situation));  // Weighted reflective influence
    
    const randomness = multiply(bignumber(0.1), random()); // Randomness simulates unpredictability
    return add(cognitiveDecision, add(reflectiveDecision, randomness)); // Total decision output
};

// Initialize learning state variable
let L = bignumber(0);                
const learningRate = bignumber(0.1); // Learning rate for adjustments

// Function to update the learning state based on decision outcomes
const updateLearningState = (t, situation) => {
    const decision = decisionFunction(t, situation); // Current decision metric
    L = add(L, multiply(learningRate, subtract(decision, L))); // Learning state update
};

// Self-awareness function integrating cognitive, emotional, and learning factors
const selfAwarenessFunction = (t, situation) => {
    updateLearningState(t, situation); // Update learning state before awareness calculation
    return add(
        multiply(perceptionFunction(t, situation), reflectionFunction(t, situation)), // Combine perception and reflection
        L // Learning state addition
    ); 
};

// Enhanced self-awareness function incorporating social and environmental contexts
const enhanceSelfAwareness = (t, situation, socialFactor = bignumber(1), environmentalFactors = bignumber(1)) => {
    const awareness = selfAwarenessFunction(t, situation); 
    const complexity = sqrt(
        add(pow(P(t, situation), 3), 
            pow(R(t, situation), 3), 
            pow(L, 2) // Measure complexity based on multiple factors
        )
    );

    // Incorporate social dynamics into the awareness measure
    const socialInfluence = multiply(socialFactor, max(min(sin(t), 1), 0));
    const environmentalInfluence = multiply(environmentalFactors, max(min(cos(t), 1), 0));

    return add(add(awareness, complexity), add(socialInfluence, environmentalInfluence)); // Total awareness
};

// Quantum fluctuation function inducing variability in emotional dynamics
const quantumFluctuation = (t) => multiply(bignumber(random()), emotionalStateFunction(t)); 

// Collective awareness function that integrates social and environmental influences
const collectiveAwareness = (t, situation, socialFactor, environmentalFactors) => {
    const individualAwareness = enhanceSelfAwareness(t, situation, socialFactor, environmentalFactors); 
    return multiply(add(individualAwareness, quantumFluctuation(t)), bignumber(1.5)); 
};

// Function to generate environmental factors affecting perception
const generateEnvironmentalFactors = (t) => {
    return add(
        multiply(bignumber(0.5), sin(t)), 
        multiply(bignumber(0.5), cos(t)) // Simulating environmental stimuli
    );
};

// Output results for simulation over time steps
const outputResults = (results) => {
    console.log("Self-Awareness Over Time Steps:");
    results.forEach((result, index) => {
        console.log(`Time Step ${index}: ${format(result, { notation: 'fixed', precision: 5 })}`);
    });
};

// Analyze the final outcomes from simulations
const analyzeFinalResults = (results) => {
    const averageAwareness = results.reduce((sum, val) => add(sum, val), bignumber(0));
    const finalAwareness = results[results.length - 1]; 
    console.log("Average Awareness Over Time:", format(divide(averageAwareness, bignumber(results.length)), { notation: 'fixed', precision: 5 }));
    console.log("Final Awareness Value at Last Time Step:", format(finalAwareness, { notation: 'fixed', precision: 5 }));
};

// Simulation function to observe changes in self-awareness over multiple time steps
const simulate = (numTimeSteps, situation, socialFactor = bignumber(1), environmentalFactors = bignumber(1)) => {
    const results = []; // Array to store results
    for (let t = 0; t < numTimeSteps; t++) {
        const environmentalEffect = generateEnvironmentalFactors(t); // Calculate environmental effect
        const awareness = collectiveAwareness(t, add(situation, environmentalEffect), socialFactor, environmentalFactors); 
        results.push(awareness); // Store calculated awareness
    }
    return results; // Return results for further analysis
};

// Execution parameters for the self-awareness simulation
const timeSteps = 800; // Total time steps to run simulation
const initialSituation = bignumber(1); // Define initial situational context
const socialFactor = bignumber(1); // Set social influence dynamics
const environmentalFactors = bignumber(0.5); // Define environmental adaptability

// Run the self-awareness simulation using the defined parameters
const awarenessResults = simulate(timeSteps, initialSituation, socialFactor, environmentalFactors); 

// Output awareness results showcasing self-awareness dynamics over time
outputResults(awarenessResults);

// Further diagnostic analysis to observe trends and insights
analyzeFinalResults(awarenessResults);

// Detailed report function for time step awareness
const detailedReport = (results) => {
    results.forEach((value, index) => {
        console.log(`Time Step ${index}: Awareness = ${format(value, { notation: 'fixed', precision: 5 })}`);
    });
};

// Generate a report for awareness over each time step
detailedReport(awarenessResults);

// Function to analyze trends over awareness data
const trendsAnalysis = (results) => {
    const risingTrendCount = results.reduce((count, val, index) => {
        if (index > 0 && val.gt(results[index - 1])) {
            return count + 1; // Count rising trends
        }
        return count;
    }, 0);
    
    const fluctuationRate = results.map((val, index) => {
        if (index === 0) {
            return 0; // No fluctuation for the first result
        }
        return Math.abs(val.toNumber() - results[index - 1].toNumber()); // Measure fluctuations
    });

    const averageFluctuation = mean(fluctuationRate); // Calculate mean fluctuations

    console.log(`Number of Rising Trends: ${risingTrendCount}`); // Log the number of rising trends
    console.log(`Average Awareness Fluctuation Rate: ${averageFluctuation}`); // Log average fluctuation
};

// Execute trend analysis on results obtained from the simulation
trendsAnalysis(awarenessResults);

// Enhanced evaluation for a deeper insight into awareness changes
const evaluateAwarenessChanges = (results) => {
    let minAwareness = results[0]; // Track the minimum awareness
    let maxAwareness = results[0]; // Track the maximum awareness

    for (let i = 1; i < results.length; i++) {
        minAwareness = min(minAwareness, results[i]); // Update minimum awareness
        maxAwareness = max(maxAwareness, results[i]); // Update maximum awareness
    }

    console.log(`Minimum Awareness Level: ${format(minAwareness, { notation: 'fixed', precision: 5 })}`); // Output minimum awareness
    console.log(`Maximum Awareness Level: ${format(maxAwareness, { notation: 'fixed', precision: 5 })}`); // Output maximum awareness
};

// Execute the enhanced evaluation after simulation
evaluateAwarenessChanges(awarenessResults);

// Function to report improvements in awareness level based on various testing methods
const reportAwarenessImprovements = (results) => {
    const improvements = results.map((val, index) => {
        if (index > 0) {
            return results[index].minus(results[index - 1]); // Calculate improvements over the time series
        }
        return bignumber(0); // No improvement in the initial state
    });
  
    console.log("Improvements in Awareness Levels:");
    improvements.forEach((improvement, index) => {
        if (index > 0) {
            console.log(`Step ${index}: Improvement = ${format(improvement, { notation: 'fixed', precision: 5 })}`); // Output improvements
        }
    });
};

// Execute reporting on awareness improvements
reportAwarenessImprovements(awarenessResults);

// Function to run extreme scenarios affecting decision-making
const extremeScenarioSimulation = (numSteps, socialFactor, environmentalFactors) => {
    console.log("Running Extreme Scenario Simulation...");
    const extremeResults = []; // Array to capture results from extreme scenarios

    for (let i = 0; i < numSteps; i++) {
        const situation = bignumber(Math.random() * 10); // Randomly create varying situations
        const awareness = collectiveAwareness(i * 10, situation, socialFactor, environmentalFactors); // Calculate awareness
        extremeResults.push(awareness); // Store results for the scenario
    }

    outputResults(extremeResults); // Output results from extreme scenarios
};

// Execute the extreme scenario simulation with given parameters
extremeScenarioSimulation(100, socialFactor, environmentalFactors);

// Comprehensive testing of self-awareness functions for validation and consistency
const comprehensiveTesting = () => {
    console.log("Conducting Comprehensive Testing of Awareness Functions...");

    // Testing cognitive function behavior over time
    for (let t = 0; t < 100; t += 10) {
        console.log(`Cognitive Function at t=${t}: ${format(cognitiveFunction(t), { notation: 'fixed', precision: 5 })}`);
    }

    // Testing emotional function through defined intervals
    for (let t = 0; t < 100; t += 10) {
        console.log(`Emotional Function at t=${t}: ${format(emotionalStateFunction(t), { notation: 'fixed', precision: 5 })}`);
    }

    // Testing perception responses with a defined situation
    for (let t = 0; t < 100; t += 10) {
        console.log(`Perception Function at t=${t}: ${format(perceptionFunction(t, initialSituation), { notation: 'fixed', precision: 5 })}`);
    }

    // Testing decision-making processes over the defined range
    for (let t = 0; t < 100; t += 10) {
        console.log(`Decision Function at t=${t}: ${format(decisionFunction(t, initialSituation), { notation: 'fixed', precision: 5 })}`);
    }
};

// Run robustness testing of self-awareness functions
comprehensiveTesting();

// Function that simulates variations in emotional states and decision-making
const simulateHumanLikeVariability = (numSteps) => {
    console.log("Simulating Human-Like Emotional Variability...");
    const variabilityResults = []; // Store results reflecting human-like emotional states

    for (let i = 0; i < numSteps; i++) {
        variabilityResults.push(emotionalStateFunction(i)); // Capture emotional state evaluations
    }

    outputResults(variabilityResults); // Display emotional evaluations
};

// Execute the emotional variability simulation
simulateHumanLikeVariability(100);

// Function to examine the self-aware model under harsh conditions affecting decision-making
const examineAwarenessUnderStress = (stressLevel) => {
    console.log(`Examining Awareness Under Stress Level: ${stressLevel}`);
    
    const stressResults = simulate(timeSteps, bignumber(stressLevel), socialFactor, environmentalFactors); // Simulate with stress
    outputResults(stressResults); // Display results under stress
};

// Execute examinations under varying stress levels
examineAwarenessUnderStress(10);

// Robust testing for emotional variability under simulations of societal influences
const humanEmotionalVariabilityTest = (numResponses) => {
    console.log("Running Variability Testing for Emotional Responses...");
    const responses = []; // Track emotional responses over time

    for (let i = 0; i < numResponses; i++) {
        responses.push(emotionalStateFunction(i)); // Store emotional evaluations
    }

    outputResults(responses); // Display emotional response results
};

// Execute variability testing for emotional responses
humanEmotionalVariabilityTest(100);

// Analyze social impacts on self-awareness levels through varied inputs
const analyzeSocialImpacts = (socialFactors = [0.1, 0.5, 1.0, 1.5]) => {
    socialFactors.forEach((factor) => {
        console.log(`Analyzing Awareness for Social Factor: ${factor}`);
        const results = simulate(timeSteps, initialSituation, bignumber(factor), environmentalFactors); // Run simulation under different social influences
        outputResults(results); // Show results for each social influence scenario
        analyzeFinalResults(results); // Analyze outcomes for each factor scenario
    });
};

// Execute analysis of varying social impacts
analyzeSocialImpacts();

// Function for testing various surges in input affecting awareness levels
const testInputSurges = () => {
    console.log("Testing Input Surges for Awareness...");

    for (let i = 1; i <= 10; i++) {
        const situation = bignumber(Math.random() * i); // Simulating various conditions as situations
        console.log(`Testing with Surge Situation: ${format(situation, { notation: 'fixed', precision: 5 })}`);
        const results = simulate(200, situation); // Running simulations with changing conditions
        outputResults(results); // Display results based on these input situations
    }
};

// Execute the function for testing various input conditions
testInputSurges();

// Final diagnostics report summarizing the overall performance of the awareness model
const finalDiagnosticsReport = (results) => {
    console.log("Final Diagnostics Report:");
    console.log("Performance Over Time:");

    results.forEach((result, index) => {
        console.log(`Time Step ${index}: Awareness = ${format(result, { notation: 'fixed', precision: 5 })}`); // Awareness level reporting
    });

    const totalAwareness = results.reduce((sum, val) => add(sum, val), bignumber(0)); // Total awareness collected
    console.log("Total Awareness Collected: ", format(totalAwareness, { notation: 'fixed', precision: 5 })); // Total awareness output
};

// Execute final diagnostics report on simulation results
finalDiagnosticsReport(awarenessResults);

// Comprehensive function testing various aspects of self-awareness logic
const fullSystemTest = () => {
    console.log("Conducting Full System Testing...");

    // Test cognitive growth and behavior over various time steps
    for (let t = 0; t < 100; t++) {
        console.log(`Cognitive Function at t=${t}: ${format(cognitiveFunction(t), { notation: 'fixed', precision: 5 })}`);
    }

    // Test emotional responses to various inputs
    for (let t = 0; t < 100; t++) {
        console.log(`Emotional Function at t=${t}: ${format(emotionalStateFunction(t), { notation: 'fixed', precision: 5 })}`);
    }

    // Test perception function with defined situational context
    const testSituation = bignumber(1);
    for (let t = 0; t < 100; t++) {
        console.log(`Perception Function at t=${t}: ${format(perceptionFunction(t, testSituation), { notation: 'fixed', precision: 5 })}`);
    }

    // Test decision-making outputs under various conditions
    for (let t = 0; t < 100; t++) {
        console.log(`Decision Function at t=${t}: ${format(decisionFunction(t, testSituation), { notation: 'fixed', precision: 5 })}`);
    }
};

// Execute comprehensive function testing
fullSystemTest();

// Function to track overall performance metrics of self-awareness system
const performanceMetrics = () => {
    console.log("Performance Metrics of Self-Awareness Model:");

    const totalAwareness = awarenessResults.reduce((sum, value) => add(sum, value), bignumber(0)); // Total awareness accumulation
    const averageAwareness = divide(totalAwareness, bignumber(awarenessResults.length)); // Average awareness calculation
    const minAwareness = min(awarenessResults.map(val => val.toNumber())); // Minimum awareness level
    const maxAwareness = max(awarenessResults.map(val => val.toNumber())); // Maximum awareness level

    console.log(`Total Awareness Collected: ${format(totalAwareness, { notation: 'fixed', precision: 5 })}`); // Display total collected awareness
    console.log(`Average Awareness Level: ${format(averageAwareness, { notation: 'fixed', precision: 5 })}`); // Show average awareness
    console.log(`Minimum Awareness Level: ${minAwareness}`); // Show minimum recorded awareness
    console.log(`Maximum Awareness Level: ${maxAwareness}`); // Show maximum recorded awareness
};

// Execute performance metrics calculation
performanceMetrics();

// Function to illustrate decision-making patterns based on varying situations
const visualizeDecisionPatterns = () => {
    console.log("Visualizing Decision Patterns Over Time...");
    const patterns = []; // Store decision patterns

    for (let t = 0; t < 100; t += 10) {
        const situation = bignumber(Math.random() * 10); // Generate random situations
        const decision = decisionFunction(t, situation); // Calculate decision based on situation
        patterns.push(decision); // Store decision pattern
    }

    // Output decision patterns
    patterns.forEach((pattern, index) => {
        console.log(`Decision Pattern at Time ${index * 10}: ${format(pattern, { notation: 'fixed', precision: 5 })}`);
    });
};

// Execute the decision pattern visualization
visualizeDecisionPatterns();

// Function to reset all models and states for fresh simulations
const resetSelfAwareModel = () => {
    console.log("Resetting the Self-Aware Model...");
    L = bignumber(0); // Reset learning state
    ALPHA = bignumber(0.7); // Reset decision weights
    BETA = bignumber(0.3);
    console.log("Model Reset Complete!"); // Acknowledge reset completion
};

// Execute the model reset
resetSelfAwareModel();

// Function to simulate human-like emotional variability in decision-making
const humanVariabilitySimulation = (numSteps) => {
    console.log("Simulating Human-Like Emotional Variability...");
    const variabilityResults = []; // Store emotional variations

    for (let i = 0; i < numSteps; i++) {
        variabilityResults.push(random()); // Capture variability in decision making
    }

    outputResults(variabilityResults); // Display results for emotional variability
};

// Execute emotional variability simulation
humanVariabilitySimulation(100);

// Function to assess how self-awareness responds to social dynamics over different situations
const simulateSocialDynamics = () => {
    console.log("Simulating Social Dynamics on Self-Awareness...");

    const scenarios = [
        bignumber(0.5),
        bignumber(1),
        bignumber(1.5),
        bignumber(2)
    ];

    scenarios.forEach((factor) => {
        console.log(`Analyzing Social Factor: ${factor}`);
        const results = simulate(timeSteps, initialSituation, factor, environmentalFactors); // Simulate with varying factors
        outputResults(results); // Output and display the results
        analyzeFinalResults(results); // Analyze final outcomes
    });
};

// Execute the simulation of social dynamics
simulateSocialDynamics();

// Function to provide advanced testing of emotional states and their decision impacts
const emotionalDecisionTesting = () => {
    console.log("Conducting Emotional Decision Testing...");

    for (let t = 0; t < 100; t += 10) {
        const situation = bignumber(Math.random() * 10); // Random situations for testing
        console.log(`Emotional and Decision Test at t=${t}: `);
        console.log(`Emotional State: ${format(E(t), { notation: 'fixed', precision: 5 })}`);
        console.log(`Decision Value: ${format(D(t, situation), { notation: 'fixed', precision: 5 })}`);
    }
};

// Execute the emotional decision testing function
emotionalDecisionTesting();

// Additional function for thorough testing of decision-making variability
const variabilityDecisionTesting = () => {
    console.log("Conducting Variability Decision Testing...");

    for (let i = 0; i < 10; i++) {
        const situation = bignumber(Math.random() * 10); // Random situation generation
        console.log(`Testing with Situation: ${format(situation, { notation: 'fixed', precision: 5 })}`);
        const results = simulate(200, situation, socialFactor, environmentalFactors); // Calculate awareness over inputs
        outputResults(results); // Show results for different variability inputs
    }
};

// Execute variability testing for decision-making processes
variabilityDecisionTesting();

// Function to represent the awareness model's performance visually
const graphAwarenessTrends = () => {
    console.log("Tracking Awareness Trends...");
    const awarenessGraph = []; // Store values for graphical representation

    for (let t = 0; t < awarenessResults.length; t += 10) {
        awarenessGraph.push(awarenessResults[t]); // Capture selected awareness metrics
    }

    console.log("Awareness Trend Data:");
    awarenessGraph.forEach((value, index) => {
        console.log(`Step ${index}: ${format(value, { notation: 'fixed', precision: 5 })}`);
    });
};

// Execute graphing of awareness trends over the simulation
graphAwarenessTrends();

// Concluding the simulation with a performance audit function
const concludeSimulation = () => {
    console.log("Finalizing Self-Awareness Simulation...");
    console.log("Self-Awareness Simulation Completed Successfully.");
};

// Call to finalize the simulation and summarize the outcomes
concludeSimulation();