Explore MATLAB source code curated for "示例代码" with clean implementations, documentation, and examples.
This archive includes source code implementations and sample demonstrations for four distinct clustering algorithms, provided for learning and practical application purposes
A comprehensive tutorial on wavelet transform principles through various MATLAB experiments, including sample code implementations for: 1D linear/nonlinear approximation, 2D linear/nonlinear approximation, noise filtering using linear filtering, noise removal via wavelet thresholding, 1D signal compression using wavelet transforms, and 2D wavelet image compression with algorithm explanations.
This project provides source code and examples for four clustering algorithms, aiming to develop a standardized and extensible toolkit for clustering tasks. The implementation includes: 1. Clustering algorithms: K-means, K-medoids, FCMclust, GKclust, and GGclust 2. Cluster visualization: 2D plotting capabilities for displaying clustering results 3. Validation metrics: Comprehensive evaluation mechanisms calculating Partition Coefficient (PC), Classification Entropy (CE), Partition Index (SC), Separation Index (S), Xie and Beni's Index (XB), Dunn's Index (DI), and Alternative Dunn Index (DII)
Implementation of Gaussian Mixture Model (GMM) background modeling written in MATLAB. While computationally intensive with relatively slow execution speed, this code serves as an educational example demonstrating the core algorithm mechanics. Originally developed during the author's participation in SJTU's PRP program, this implementation is no longer actively used in the original project but remains valuable for learning purposes.
Example code demonstrating parallel computing implementation using MATLAB 2007+ Parallel Computing Toolbox and Distributed Computing Engine, featuring high reference value and synchronization with user guide documentation
Comprehensive collection of example codes from Pattern Recognition Fourth Edition featuring algorithm explanations and key function implementations for practical learning