Classical Grassberger-Procaccia Algorithm for Correlation Dimension Computation

This article presents a program source code implementation for computing correlation dimension through the classical Grassberger-Procaccia (GP) algorithm. The implementation involves key steps such as phase space reconstruction using delay embedding, distance matrix calculation between trajectory points, and correlation integral estimation across multiple scales. Despite the algorithm's apparent simplicity, it provides researchers with powerful capabilities for quantifying fractal dimensions and understanding complex correlations in multidimensional data. The code structure includes optimized functions for nearest-neighbor searches and logarithmic scaling operations, enabling detailed exploration of data relationships through dimension convergence curves. By utilizing this source code, researchers can efficiently analyze nonlinear dynamical systems, validate theoretical models, and enhance research productivity in chaos analysis and complex system characterization. This practical implementation serves as a valuable tool for further investigation and refinement in nonlinear time series analysis.