we assign a minterm id to each of these classes (e.g., 1 for letters, 0 for non-letters), and then compute derivatives based on these ids instead of characters. this is a huge win for performance and results in an absolutely enormous compression of memory, especially with large character classes like \w for word-characters in unicode, which would otherwise require tens of thousands of transitions alone (there’s a LOT of dotted umlauted squiggly characters in unicode). we show this in numbers as well, on the word counting \b\w{12,}\b benchmark, RE# is over 7x faster than the second-best engine thanks to minterm compressionremark here i’d like to correct, the second place already uses minterm compression, the rest are far behind. the reason we’re 7x faster than the second place is in the \b lookarounds :^).
competitive production.
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近期，九号公司国内电动两轮车累计出货量突破 1000 万台。从平衡车赛道跨界而起，到成为高端智能两轮车领军者，再到布局割草机器人、E-bike 等新业务，九号公司凭借独特的 “机器人思维” 在多个赛道实现破局。，详情可参考旺商聊官方下载
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