herta/commands/erudition/explain.js

Advanced mathematics framework for scientific, engineering, and financial applications

/**
 * Erudition Explain Command
 * Explains framework concepts, configurations, and algorithms in plain English
 */

const fs = require('fs');
const path = require('path');
const chalk = require('chalk');

/**
 * Explain framework concepts and configurations
 * @param {Array} args - Command line arguments
 */
function explain(args) {
  const topic = args[0];
  
  if (!topic) {
    displayExplainHelp();
    return;
  }
  
  console.log(chalk.cyan(`Herta.js Explanation: ${topic}`));
  console.log(chalk.dim('─'.repeat(40)));
  
  try {
    const explanation = getExplanation(topic);
    console.log(explanation);
  } catch (error) {
    console.error(chalk.red(`Error: ${error.message}`));
  }
}

/**
 * Get explanation for a topic
 * @param {string} topic - Topic to explain
 * @returns {string} - Explanation
 */
function getExplanation(topic) {
  const explanations = {
    'config': `
${chalk.bold('Configuration in Herta.js')}

Herta.js configurations allow you to customize the framework's behavior according to your specific needs. Configuration is handled through a 'herta.config.js' file in your project root.

${chalk.yellow('Key Configuration Options:')}

${chalk.bold('precision:')} Controls the decimal precision used in calculations.
  Example: { precision: 10 } will use 10 decimal places for calculations.

${chalk.bold('threads:')} Controls parallel processing behavior.
  Example: { threads: 4 } will use 4 worker threads for applicable operations.

${chalk.bold('memoize:')} Enables caching of function results for repeated calculations.
  Example: { memoize: true } will cache results of expensive operations.

${chalk.bold('Scientific Mode:')} Enables higher precision and additional validation.
  Example: { scientificMode: true }

${chalk.yellow('Example Configuration:')}

${chalk.dim(`
// herta.config.js
module.exports = {
  precision: 10,
  threads: 4,
  memoize: true,
  scientificMode: false,
  logging: {
    level: 'warn',
    format: 'compact'
  }
};`)}

${chalk.yellow('Usage:')}
Your configuration will be automatically loaded when Herta.js initializes. You can also modify configuration at runtime using herta.configure({...}).
`,

    'algorithms': `
${chalk.bold('Algorithms in Herta.js')}

Herta.js implements a wide variety of algorithms across different mathematical domains. Here's a high-level overview of how they work:

${chalk.yellow('Optimization Algorithms:')}

${chalk.bold('Gradient Descent:')} An iterative optimization algorithm that finds a local minimum by taking steps in the direction of the steepest descent.
  - Implementation: First-order differentiation with adaptive learning rate.
  - Use case: Training neural networks, minimizing cost functions.

${chalk.bold('Simulated Annealing:')} A probabilistic technique that approximates global optimization by occasionally accepting worse solutions.
  - Implementation: Uses a temperature parameter that decreases over time.
  - Use case: Solving combinatorial problems like traveling salesman.

${chalk.yellow('Matrix Operations:')}

${chalk.bold('Matrix Decomposition:')} Break down matrices into simpler components for easier calculations.
  - Types: LU, QR, Cholesky, SVD decompositions.
  - Use case: Solving linear equations, dimensionality reduction.

${chalk.yellow('Neural Network Algorithms:')}

${chalk.bold('Backpropagation:')} Algorithm for training neural networks by calculating gradients and updating weights.
  - Implementation: Chain rule of calculus with gradient descent optimizer.
  - Use case: Training multi-layer neural networks.

You can find more detailed explanations in the documentation, including pseudocode and complexity analysis.
`,

    'architecture': `
${chalk.bold('Herta.js Architecture')}

Herta.js is designed with a modular architecture that separates concerns and promotes code reuse.

${chalk.yellow('Core Design Principles:')}

${chalk.bold('Modularity:')} Functionality is divided into specialized modules that can be used independently.
  - Core modules: Basic mathematical operations.
  - Advanced modules: Specialized domains like quantum computing, neural networks, etc.
  - Utility modules: Helper functions used across the framework.

${chalk.bold('Performance Focus:')} Optimized for high-performance mathematical operations.
  - Strategic use of memoization for expensive calculations.
  - Optional multithreading for parallelizable operations.
  - Optimized algorithms for matrix operations.

${chalk.bold('Extensibility:')} Easy to extend with new modules or customize existing ones.
  - Consistent API design across modules.
  - Detailed documentation for each module.
  - Clear separation of concerns.

${chalk.yellow('Module Interaction:')}

Modules are designed to work together through well-defined interfaces. For example, the neural networks module can use matrix operations from the linear algebra module without tight coupling.

${chalk.yellow('Folder Structure:')}

${chalk.dim(`
herta/
├── src/
│   ├── core/           # Core mathematical functionality
│   ├── advanced/       # Advanced domain-specific modules
│   └── utils/          # Utility functions
└── test/               # Test suite
`)}
`,

    'patterns': `
${chalk.bold('Design Patterns in Herta.js')}

Herta.js uses several design patterns to organize code and solve common problems:

${chalk.yellow('Factory Pattern:')}

Used to create complex objects with consistent interfaces. For example, creating matrix objects, neural networks, or probability distributions.

${chalk.dim(`
// Example: Factory pattern for creating matrices
const matrix = herta.matrix.create(rows, cols);
const identity = herta.matrix.identity(size);
`)}

${chalk.yellow('Strategy Pattern:')}

Used to select algorithms at runtime. For example, choosing different optimization strategies or solvers.

${chalk.dim(`
// Example: Strategy pattern for optimization
const optimizer = herta.optimization.createOptimizer('gradient-descent', options);
optimizer.minimize(costFunction);
`)}

${chalk.yellow('Observer Pattern:')}

Used for event handling and progress tracking in long-running operations.

${chalk.dim(`
// Example: Observer pattern for training progress
const model = herta.neuralNetworks.feedForward([10, 5, 1]);
model.onEpoch(progress => console.log(\`Epoch \${progress.epoch}: \${progress.error}\`));
model.train(data, { epochs: 1000 });
`)}

${chalk.yellow('Decorator Pattern:')}

Used to add capabilities to functions without modifying their core behavior. For example, adding memoization or logging.

${chalk.dim(`
// Example: Decorator pattern for memoization
const memoized = herta.utils.memoize(expensiveFunction);
`)}

These patterns help maintain clean, maintainable code while providing powerful abstractions.
`,

    'performance': `
${chalk.bold('Performance Optimization in Herta.js')}

Herta.js employs several strategies to optimize performance for mathematical operations:

${chalk.yellow('Algorithmic Optimizations:')}

${chalk.bold('Time Complexity:')} Algorithms are chosen based on their asymptotic efficiency.
  - Matrix multiplication uses Strassen's algorithm for large matrices.
  - Graph algorithms use optimized implementations like A* for pathfinding.

${chalk.bold('Space Complexity:')} Memory usage is optimized to handle large datasets.
  - Sparse matrices for data with many zeros.
  - Stream processing for large datasets that don't fit in memory.

${chalk.yellow('Technical Optimizations:')}

${chalk.bold('Memoization:')} Caching results of expensive function calls.
  - Automatically applied to pure functions when memoization is enabled.
  - Custom cache sizes and eviction policies for different functions.

${chalk.bold('Parallelization:')} Using multiple CPU cores for calculations.
  - Matrix operations can run on multiple threads.
  - Optional GPU acceleration for specific operations.

${chalk.bold('Early Termination:')} Stopping algorithms when sufficient precision is reached.
  - Iterative methods terminate when convergence criteria are met.
  - Adaptive precision adjusts based on input requirements.

${chalk.yellow('Performance Tips:')}

1. Enable memoization for repeated calculations with the same inputs.
2. Use appropriate data structures (e.g., sparse matrices for sparse data).
3. Set thread count based on your CPU core count.
4. Consider using lower precision for exploratory work and higher precision for final results.
5. Profile your code to identify bottlenecks.

You can monitor performance with the built-in benchmarking tools:
${chalk.dim(`
const benchmark = herta.utils.benchmark();
benchmark.start();
// Your code here
benchmark.end();
console.log(benchmark.report());
`)}
`
  };
  
  // Special handling for module explanations
  if (topic.startsWith('module:')) {
    return explainModule(topic.substring(7));
  }
  
  // Return explanation if exists, otherwise error
  if (explanations[topic]) {
    return explanations[topic];
  } else {
    const availableTopics = Object.keys(explanations).join(', ');
    throw new Error(`Unknown topic: ${topic}. Available topics: ${availableTopics}, or use 'module:<name>' for module explanations.`);
  }
}

/**
 * Explain a specific module
 * @param {string} moduleName - Module name
 * @returns {string} - Explanation
 */
function explainModule(moduleName) {
  // This would ideally dynamically analyze the module code
  // For demo purposes, we'll provide explanations for a few modules
  
  const moduleExplanations = {
    'matrix': `
${chalk.bold('Matrix Module')}

The Matrix module provides operations for linear algebra calculations. It's the backbone of many other modules in Herta.js.

${chalk.yellow('Key Capabilities:')}

${chalk.bold('Creation Operations:')}
- Create matrices of various types (zero, identity, random)
- Convert arrays and nested arrays to matrix objects

${chalk.bold('Basic Operations:')}
- Addition, subtraction, multiplication, and division
- Element-wise operations and broadcasting
- Matrix-vector operations

${chalk.bold('Advanced Operations:')}
- Matrix decompositions (LU, QR, SVD, Cholesky)
- Eigenvalues and eigenvectors
- Matrix inversion and pseudo-inverse
- Determinant and trace calculation

${chalk.yellow('Optimization Details:')}

- Uses BLAS-like algorithms for core operations
- Automatically selects the most efficient algorithm based on matrix size
- Sparse matrix support for memory efficiency with sparse data

${chalk.yellow('Example Usage:')}

${chalk.dim(`
// Create matrices
const A = herta.matrix.create([[1, 2], [3, 4]]);
const B = herta.matrix.create([[5, 6], [7, 8]]);

// Basic operations
const C = herta.matrix.multiply(A, B);
const det = herta.matrix.determinant(A);

// Decomposition
const { U, S, V } = herta.matrix.svd(A);
`)}

This module is used extensively throughout the framework for any computation requiring matrix operations.
`,

    'neuralNetworks': `
${chalk.bold('Neural Networks Module')}

The Neural Networks module provides tools to create, train, and evaluate neural network models.

${chalk.yellow('Supported Network Types:')}

${chalk.bold('Feedforward Networks:')}
- Multi-layer perceptrons with customizable architectures
- Support for various activation functions
- Batch normalization and dropout regularization

${chalk.bold('Convolutional Networks:')}
- 2D convolutional layers with customizable kernels
- Pooling layers (max, average)
- Padding and stride options

${chalk.bold('Recurrent Networks:')}
- Long Short-Term Memory (LSTM) cells
- Gated Recurrent Units (GRU)
- Bidirectional recurrent layers

${chalk.yellow('Training Capabilities:')}

${chalk.bold('Optimizers:')}
- Gradient descent (SGD, mini-batch, batch)
- Adam, RMSProp, and Momentum optimizers
- Learning rate scheduling

${chalk.bold('Loss Functions:')}
- Mean squared error, categorical cross-entropy
- Binary cross-entropy, hinge loss
- Custom loss function support

${chalk.yellow('Example Usage:')}

${chalk.dim(`
// Create a feedforward network
const model = herta.neuralNetworks.feedForward(
  [784, 128, 64, 10], // Layer sizes
  { activation: 'relu', outputActivation: 'softmax' }
);

// Train the model
model.train(trainingData, {
  optimizer: 'adam',
  learningRate: 0.001,
  epochs: 10,
  batchSize: 32
});

// Make predictions
const prediction = model.predict(inputData);
`)}

The implementation focuses on clarity and flexibility while maintaining computational efficiency.
`,

    'technicalAnalysis': `
${chalk.bold('Technical Analysis Module')}

The Technical Analysis module provides mathematical functions for analyzing financial markets and generating trading signals.

${chalk.yellow('Indicator Categories:')}

${chalk.bold('Trend Indicators:')}
- Moving Averages (Simple, Exponential, Weighted, etc.)
- Moving Average Convergence Divergence (MACD)
- Average Directional Index (ADX)

${chalk.bold('Momentum Indicators:')}
- Relative Strength Index (RSI)
- Stochastic Oscillator
- Rate of Change (ROC)

${chalk.bold('Volatility Indicators:')}
- Bollinger Bands
- Average True Range (ATR)
- Keltner Channels

${chalk.bold('Volume Indicators:')}
- On-Balance Volume (OBV)
- Accumulation/Distribution Line
- Volume-Weighted Average Price (VWAP)

${chalk.yellow('Pattern Recognition:')}

- Support and resistance level detection
- Chart pattern identification (Head & Shoulders, Double Top/Bottom, etc.)
- Candlestick pattern recognition

${chalk.yellow('Example Usage:')}

${chalk.dim(`
// Calculate RSI
const prices = [45.34, 45.67, 46.12, 46.82, 46.45, 46.89, 47.23, 47.56];
const rsi = herta.technicalAnalysis.rsi(prices, 14);

// Calculate Bollinger Bands
const { upperBand, middleBand, lowerBand } = 
  herta.technicalAnalysis.bollingerBands(prices, 20, 2);

// Detect support and resistance
const pivots = herta.technicalAnalysis.pivotPoints(high, low, close);
`)}

Implementations prioritize numerical accuracy while accounting for the nuances of financial data.
`,

    'riskManagement': `
${chalk.bold('Risk Management Module')}

The Risk Management module provides mathematical tools for assessing and managing financial risk.

${chalk.yellow('Key Risk Metrics:')}

${chalk.bold('Value at Risk (VaR):')}
- Historical simulation method
- Parametric method (variance-covariance)
- Monte Carlo simulation method

${chalk.bold('Portfolio Risk Measures:')}
- Sharpe Ratio (risk-adjusted return)
- Sortino Ratio (downside risk-adjusted return)
- Maximum Drawdown calculation
- Beta (systematic risk) calculation

${chalk.bold('Stress Testing:')}
- Scenario-based stress testing
- Historical scenario replay
- Risk factor sensitivity analysis

${chalk.yellow('Implementation Details:')}

The module implements widely accepted financial mathematics with a focus on accuracy and regulatory compliance. Calculations follow standards established by financial authorities and academic literature.

${chalk.yellow('Example Usage:')}

${chalk.dim(`
// Calculate Value at Risk
const portfolioValue = 1000000;
const returns = [-0.02, -0.015, -0.01, -0.005, 0, 0.005, 0.01, 0.015, 0.02];
const var95 = herta.riskManagement.historicalVaR(returns, 0.95, portfolioValue);

// Calculate portfolio volatility
const weights = [0.4, 0.3, 0.2, 0.1];
const covarianceMatrix = [
  [0.04, 0.02, 0.01, 0.01],
  [0.02, 0.09, 0.01, 0.02],
  [0.01, 0.01, 0.16, 0.01],
  [0.01, 0.02, 0.01, 0.04]
];
const volatility = herta.riskManagement.portfolioVolatility(weights, covarianceMatrix);
`)}

This module is particularly useful for portfolio managers, risk officers, and financial analysts.
`
  };
  
  if (moduleExplanations[moduleName]) {
    return moduleExplanations[moduleName];
  } else {
    throw new Error(`Module explanation not available for: ${moduleName}. Use 'herta erudition doc ${moduleName}' for technical documentation.`);
  }
}

/**
 * Display help for explain command
 */
function displayExplainHelp() {
  console.log(`
  ${chalk.bold.cyan('HERTA.JS ERUDITION EXPLAIN')}
  ${chalk.dim('Explain framework concepts in plain English')}
  
  ${chalk.bold('Usage:')} 
    herta erudition explain <topic>
  
  ${chalk.bold('Available Topics:')}
    ${chalk.yellow('config')}        Explain configuration options and how they affect the framework
    ${chalk.yellow('algorithms')}    Explain the algorithms implemented in the framework
    ${chalk.yellow('architecture')}  Explain the overall architecture of the framework
    ${chalk.yellow('patterns')}      Explain the design patterns used in the framework
    ${chalk.yellow('performance')}   Explain performance optimization strategies
    ${chalk.yellow('module:<name>')} Explain a specific module (e.g., module:matrix)
  
  ${chalk.bold('Examples:')}
    herta erudition explain config
    herta erudition explain module:neuralNetworks
  `);
}

module.exports = explain;