hamming_distance

Calculate Hamming distance of two character vectors

Empowers users to fuzzily-merge data frames with millions or tens of millions of rows in minutes with low memory usage. The package uses the locality sensitive hashing algorithms developed by Datar, Immorlica, Indyk and Mirrokni (2004) <doi:10.1145/997817.997857>, and Broder (1998) <doi:10.1109/SEQUEN.1997.666900> to avoid having to compare every pair of records in each dataset, resulting in fuzzy-merges that finish in linear time.

Beniamino Green

zoomerjoin

Superlatively Fast Fuzzy Joins

Etienne Bacher

The authors of the dependency Rust crates 

hamming_distance function

<dl><dt>a</dt>
<dd>the first character vector</dd>
<dt>b</dt>
<dd>the first character vector</dd>
<dt>nthread</dt>
<dd>Maximum number of threads to use. If <code>NULL</code> (default),
Rayon's global thread pool is used, which typically uses all logical
CPU cores available.</dd></dl>

Arguments

Calculate Hamming distance of two character vectors — hamming_distance

<dl>

<dt>a</dt>
<dd>the first character vector</dd>


<dt>b</dt>
<dd>the first character vector</dd>


<dt>nthread</dt>
<dd>Maximum number of threads to use. If <code>NULL</code> (default),
Rayon's global thread pool is used, which typically uses all logical
CPU cores available.</dd>

</dl>

hamming_distance: Calculate Hamming distance of two character vectors

Description

Usage

Value

Arguments

Examples