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Source code of the paper "Min-Max-Jump distance and its applications."
mike-liuliu/Min-Max-Jump-distance
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0. This is the source code of the paper "Min-Max-Jump distance and its applications." 1. Implementation of MMJ-SC, MMJ-CH, and MMJ-DB are based on the source code of the scikit-learn project. Implementation of the K_means_ambi_points_multi_one_scom Class is based on the source code provided by Avi Arora in a tutorial artical. See: https://analyticsarora.com/k-means-for-beginners-how-to-build-from-scratch-in-python/ 2. In function index_plot_first_n_label_one_data, if the index's score is "smaller is better", then the "smaller_better" hyper-parameter should be set to True. Otherwise, if the index's score is "larger is better", then the "smaller_better" hyper-parameter should be set to False. 3. Readers can test their own index function, the API is: def index_function(X, label): some codes to compute the index value ... return the_index_value then call the index_plot_first_n_label_one_data function. Note the "smaller_better" hyper-parameter. 4. To use precomputed mmj distance matrix, readers should download and unzip the "mmj_distance_matrix_precomputed.zip" file firstly. 5. License. License of the source code : Apache License, Version 2.0 License of new data: Creative Commons Attribution 4.0 International 6. Citation: @article{liu2023min, title={Min-Max-Jump distance and its applications}, author={Liu, Gangli}, journal={arXiv preprint arXiv:2301.05994}, year={2023} } 7. The "multiple_label_145.p" and "mmj_distance_matrix_precomputed.zip" files are larger than 100MB, so they are stored on Git Large File Storage (LFS), readers may need to download it separately.
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Source code of the paper "Min-Max-Jump distance and its applications."
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