Automated Generation of Multi-Dimensional Data with Variable Quantity

I have developed a compact MATLAB program that automatically generates datasets with customizable dimensions and sample sizes. The implementation utilizes MATLAB's random number generation functions (such as rand, randn, or more specialized distributions) to create synthetic data arrays. The program features dimension-aware algorithms that dynamically adjust array structures based on user-specified parameters.

Additionally, the system incorporates statistical methods for probability density estimation, implementing kernel density estimation (KDE) techniques or histogram-based approaches to analyze the generated data points. The density calculation module employs optimized algorithms for efficient computation across various data dimensions.

Users can conveniently adjust data dimensions and quantities through parameterized function calls, making the tool adaptable to different research requirements. The program includes robust input validation to ensure dimension compatibility and memory management for large datasets.

The utility supports multiple output formats including CSV and Excel files through MATLAB's built-in writetable and xlswrite functions, facilitating seamless data export and integration with other analysis tools. File format handling includes automatic header generation and dimension-preserving serialization.

This program serves as a practical analytical tool that enhances data understanding through synthetic data generation and probabilistic analysis, particularly useful for testing statistical methods and machine learning algorithms.