optimize-me-js

A dual-language algorithm library providing JavaScript and TypeScript implementations of commonly used algorithms and data structures. Designed for performance benchmarking and optimization analysis with Codeflash.

Project Structure

optimize-me-js/
├── js/                          # JavaScript implementation
│   ├── src/
│   │   ├── dsa/                 # Data Structures & Algorithms
│   │   ├── math/                # Mathematical functions
│   │   ├── matrix/              # Matrix and linear algebra operations
│   │   ├── stats/               # Statistical and ML functions
│   │   └── index.js             # Entry point
│   ├── tests/                   # Jest test suites
│   └── package.json
│
└── ts/                          # TypeScript implementation
    ├── src/
    │   ├── dsa/
    │   ├── math/
    │   ├── matrix/
    │   ├── stats/
    │   └── index.ts             # Entry point
    ├── dist/                    # Compiled output
    ├── tests/                   # Jest test suites
    ├── package.json
    └── tsconfig.json

Modules

DSA (Data Structures & Algorithms)

• Caching & Memoization: timeBasedCache, matrixChainOrder, binomialCoefficient, coinChange, knapsack
• Graph Operations: Cycle detection, betweenness centrality, strongly connected components (Kosaraju)
• Data Structures: Graph, Stack, PathFinder
• Algorithms: fibonacci, graphTraversal, isPalindrome, wordFrequency, regexMatch

Math

• Number Theory: gcdRecursive, lcm, isPrime, sieveOfEratosthenes
• Numerical Analysis: monteCarloPi, newtonRaphsonSqrt, bisectionMethod, lagrangeInterpolation
• Basic Functions: factorial, power

Matrix

• Linear Algebra: matrixMultiply, matrixAdd, matrixInverse, matrixDeterminant, luDecomposition, linearEquationSolver
• Vector Operations: dotProduct, vectorMagnitude, vectorNormalize
• Signal Processing: convolution1d, convolution2d, fft, ifft, gaussianBlur, imageRotation, histogramEqualization, rollingMean

Stats

• Descriptive Statistics: mean, median, std, variance, describe
• Correlation: pearsonCorrelation, correlation matrix
• Machine Learning: cosineSimilarity, cosineSimilarityTopK, pca, kmeansClustering, gradientDescent, linearPredict
• DataFrame Operations: Filtering, grouping, merging, pivotTable, melt, fillna, dropDuplicates, sortValues

Installation

# JavaScript
cd js
npm install

# TypeScript
cd ts
npm install

Usage

JavaScript

const { fibonacci, matrixMultiply, mean, Graph } = require('./src');

// Math
console.log(fibonacci(10)); // 55

// Matrix operations
const A = [[1, 2], [3, 4]];
const B = [[5, 6], [7, 8]];
console.log(matrixMultiply(A, B));

// Statistics
console.log(mean([1, 2, 3, 4, 5])); // 3

TypeScript

import { fibonacci, matrixMultiply, mean, Graph } from './src';

// Same API as JavaScript
console.log(fibonacci(10)); // 55

Testing

# JavaScript
cd js
npm test

# TypeScript
cd ts
npm test

Tests use Jest and cover all four modules.

Purpose

This project serves as:

1. Optimization Benchmark Suite - Includes intentional inefficiencies for Codeflash to identify and suggest optimizations
2. Algorithm Reference Library - Working implementations of fundamental algorithms
3. Performance Testing - Benchmark and compare implementations across languages
4. Educational Tool - Demonstrates classic algorithms from computer science, mathematics, and machine learning

Codeflash Integration

Both JavaScript and TypeScript packages are pre-configured for Codeflash optimization analysis. See package.json in each directory for configuration details.

License

MIT