A new method for binning a set of $n$ data values into a set of m bins for the case where the bins are of different sizes is proposed. The method skips binning using a binary search across the bins all the time. It is proven the method exhibits a linear average-case computation time. The experiments' results show a speedup factor of over four compared to binning by binary search alone for data values with unknown distributions. This result is consistent with the analysis of the method.
翻译:将一组以n$为单位的数据值装入一组 m bins 的新方法。 对于推荐的文件夹大小不一的情况, 将一组 m bin 数据值置入一套 m bin 。 该方法会跳过 binning, 总是在文件夹上进行二进制搜索 。 事实证明, 该方法显示的是线性平均计算时间。 实验结果显示, 与单以二进制搜索方式进行宾入相比, 加速系数超过 4 。 此结果与方法分析一致 。