Fisher Linear Discriminant Classifier Source Code

This package provides the complete source code for Fisher Linear Discriminant classifier implementation. The code includes both training and testing functions that enable model training and performance evaluation. The training function calculates optimal projection vectors by maximizing between-class scatter while minimizing within-class scatter, implementing the core Fisher discriminant algorithm. The testing function uses the trained model parameters to classify new data points through linear projection and threshold comparison. The implementation follows the mathematical principles of Fisher Linear Discriminant Analysis, a classical pattern recognition method particularly effective for dimensionality reduction and linear classification tasks. Key functions include scatter matrix computation, eigenvalue decomposition for optimal direction finding, and decision boundary calculation. By studying this source code, developers can gain deeper understanding of how Fisher discriminant works mathematically and algorithmically. The modular structure allows easy customization and extension for specific applications. The code demonstrates practical implementation of statistical pattern recognition concepts including class separation optimization and linear transformation techniques. This implementation serves as both a ready-to-use classification tool and an educational resource for understanding linear discriminant analysis in machine learning applications.