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go-adaptive-radix-tree
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blooming-bella
A simple implementation of Bloom Filters using murmur3, Super Fast Hash and marvin32 hashing algorithms.
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README
Count-Min-Log
TL;DR: Count-Min-Log Sketch for improved Average Relative Error on low frequency events
Count-Min Sketch is a widely adopted algorithm for approximate event counting in large scale processing. However, the original version of the Count-Min-Sketch (CMS) suffers of some deficiences, especially if one is interested in the low-frequency items, such as in text- mining related tasks. Several variants of CMS have been proposed to compensate for the high relative error for low-frequency events, but the proposed solutions tend to correct the errors instead of preventing them. In this paper, we propose the Count-Min-Log sketch, which uses logarithm-based, approximate counters instead of linear counters to improve the average relative error of CMS at constant memory footprint.
This version implements the 16 bit register version. Will add back the 8-bit version soon.
Example Usage
import cml
...
sk, err := cml.NewDefaultSketch()
sk.IncreaseCount([]byte("scott pilgrim"))
...
sk.Frequency([]byte("scott pilgrim")) // ==> 1