Computing Wavelet Entropy for One-Dimensional Vectors

Computing wavelet entropy for one-dimensional vectors serves as a valuable analytical tool for assessing vector complexity and information content. The implementation utilizes a pre-configured function file that enables direct invocation, returning processed results through straightforward parameter configuration. This functionality employs wavelet decomposition algorithms to transform input vectors into multi-resolution components, followed by entropy calculation using probability distribution analysis of wavelet coefficients. The method proves particularly effective across multiple domains including signal processing (for analyzing temporal patterns), data analytics (for feature extraction), and image processing (when applied to vectorized image data). Key implementation aspects include: wavelet type selection (Daubechies, Haar, etc.), decomposition level optimization, and entropy formula application (Shannon, Tsallis, or Renyi entropy variants). Understanding wavelet entropy computation provides deeper insights into data characteristics, enabling better quantification of signal randomness and pattern detection. The function's modular design allows integration with existing workflows while maintaining computational efficiency through optimized wavelet transform libraries.