{"push_id":543465185,"size":3,"distinct_size":3,"ref":"refs/heads/patch-2","head":"653160cd7fdab217cd9c42ce92e313e73afb2748","before":"243db4c484133bcd8f5a4a02a44d63a431c19da4","commits":[{"sha":"e3d1d9c1347101f7029224b7a87fdf7398c669f3","author":{"email":"5f4950600af9e6ceef156b367f6e987bb96eaef3@gmail.com","name":"Erich Schubert"},"message":"Do not shuffle in DBSCAN (warn if `random_state` is used).\n\nThis makes little difference, and original DBSCAN did not shuffle.\nWarn if `random_state` is used.\n\nAs is `random_state` encourages users to experiment with different\nrandomization, as you would do with k-means. But in contrast to\nk-means, the output of DBSCAN is deterministic except for cluster\nenumeration and \"rare\" cases, where a point is on the border of\ntwo clusters at the same time. As this affects single points only,\nthe measureable performance difference will be close to zero.","distinct":true,"url":"https://api.github.com/repos/kno10/scikit-learn/commits/e3d1d9c1347101f7029224b7a87fdf7398c669f3"},{"sha":"635ab935deede2d23b8c7903ecdb7921919bf57b","author":{"email":"5f4950600af9e6ceef156b367f6e987bb96eaef3@gmail.com","name":"Erich Schubert"},"message":"DBSCAN: Slight performance improvement.","distinct":true,"url":"https://api.github.com/repos/kno10/scikit-learn/commits/635ab935deede2d23b8c7903ecdb7921919bf57b"},{"sha":"653160cd7fdab217cd9c42ce92e313e73afb2748","author":{"email":"5f4950600af9e6ceef156b367f6e987bb96eaef3@gmail.com","name":"Erich Schubert"},"message":"Faster vectorized version of DBSCAN\n\nThis benchmarks 20%-40% faster for me than current head, and ~90% faster than 0.15.2\nIt also was faster than using a precomputed distance matrix, even when the data set was small enough (I used 10k and 50k coordinates from Twitter).","distinct":true,"url":"https://api.github.com/repos/kno10/scikit-learn/commits/653160cd7fdab217cd9c42ce92e313e73afb2748"}]}