On-scalp printing of personalized electroencephalography e-tattoos
12 by bookofjoe | 0 comments on Hacker News.
Tuesday, December 3, 2024
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New top story on Hacker News: Show HN: Vicinity – Fast, Lightweight Nearest Neighbors with Flexible Back Ends
Show HN: Vicinity – Fast, Lightweight Nearest Neighbors with Flexible Back Ends
10 by Pringled | 0 comments on Hacker News.
We’ve just open-sourced Vicinity, a lightweight approximate nearest neighbors (ANN) search package that allows for fast experimentation and comparison of a larger number of well known algorithms. Main features: - Lightweight: the base package only uses Numpy - Unified interface: use any of the supported algorithms and backends with a single interface: HNSW, Annoy, FAISS, and many more algorithms and libraries are supported - Easy evaluation: evaluate the performance of your backend with a simple function to measure queries per second vs recall - Serialization: save and load your index for persistence After working with a large number of ANN libraries over the years, we found it increasingly cumbersome to learn the interface, features, quirks, and limitations of every library. After writing custom evaluation code to measure the speed and performance for the 100th time to compare libraries, we decided to build this as a way to easily use a large number of algorithms and libraries with a unified, simple interface that allows for quick comparison and evaluation. We are curious to hear your feedback! Are there any algorithms that are missing that you use? Any extra evaluation metrics that are useful?
10 by Pringled | 0 comments on Hacker News.
We’ve just open-sourced Vicinity, a lightweight approximate nearest neighbors (ANN) search package that allows for fast experimentation and comparison of a larger number of well known algorithms. Main features: - Lightweight: the base package only uses Numpy - Unified interface: use any of the supported algorithms and backends with a single interface: HNSW, Annoy, FAISS, and many more algorithms and libraries are supported - Easy evaluation: evaluate the performance of your backend with a simple function to measure queries per second vs recall - Serialization: save and load your index for persistence After working with a large number of ANN libraries over the years, we found it increasingly cumbersome to learn the interface, features, quirks, and limitations of every library. After writing custom evaluation code to measure the speed and performance for the 100th time to compare libraries, we decided to build this as a way to easily use a large number of algorithms and libraries with a unified, simple interface that allows for quick comparison and evaluation. We are curious to hear your feedback! Are there any algorithms that are missing that you use? Any extra evaluation metrics that are useful?
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