Plotting ROC Curves and Calculating Equal Error Rate

This program accepts intra-class and inter-class Hamming distance matrices as input to plot Receiver Operating Characteristic (ROC) curves and calculate the equal error rate (EER). Hamming distance matrices represent sample similarity measures, where intra-class distances (within the same category) are typically smaller while inter-class distances (between different categories) are larger. The ROC curve is plotted with True Positive Rate (TPR) on the y-axis against False Positive Rate (FPR) on the x-axis, providing a standard visualization for evaluating binary classification system performance. The equal error rate represents the point on the ROC curve where false acceptance rate equals false rejection rate, serving as a key performance metric. Implementation typically involves: 1. Sorting all distance scores from both matrices 2. Iterating through threshold values to compute TPR/FPR pairs 3. Using interpolation to find the EER point where TPR = 1 - FPR 4. Employing matplotlib or similar libraries for curve visualization For detailed theoretical foundation and algorithm explanations, please reference: http://blog.csdn.net/yanzi1225627/article/details/8915032