2D Three-Class Sample Classification with Decision Boundary Visualization

This program provides an intuitive solution to the core problem in pattern recognition and neural network learning - sample classification. It specifically handles three-class classification tasks in two-dimensional space, not only performing classification but also visualizing decision boundaries, making it ideal for beginners to understand classification principles.

The core functionality implements a classification model that partitions 2D data. Through computation or training, the model identifies separation boundaries between different classes, which manifest as straight lines or curves on the 2D plane, clearly demarcating sample regions of different categories. The implementation likely uses mathematical separation methods or machine learning algorithms to establish these boundaries. For pattern recognition learners, this directly demonstrates how mathematical or learning methods distinguish between categories; for neural network beginners, it visually shows how networks form classification boundaries by adjusting parameters through backpropagation or optimization algorithms.

The implementation emphasizes practicality, avoiding complex optimizations or redundant features, with a streamlined code structure. Users can observe boundary changes by adjusting parameters, helping them understand classifier mechanics. This visual learning approach is particularly suitable for mastering concepts like: feature space partitioning, linear/nonlinear classification, and geometric interpretation of model decisions. The code probably includes parameter tuning functions and visualization modules that plot decision boundaries in real-time. It also serves as a foundation for understanding more complex classifiers (like multi-layer neural networks) by demonstrating basic separation behavior through clear, modifiable code examples.