<p>This example demonstrates the use of the <code>fuzzySearch.threshold</code> initialisation option. The default value for this option is 0.5. This value is used to
compare against the Damerau-Levenshtein similarity metric which is calculated by dividing the number of changes needed to make the words the same, by the length of
the longest word. This means that rows where each word in the search value matches a word in the row by at least 0.5 will be displayed. In this example
the longest word and subtracting that value from one. This means that rows where each word in the search value matches a word in the row by at least 0.5 will be displayed. In this example
<code>fuzzySearch.threshold</code> is set to <code>0.1</code>. This will mean that significantly more rows are displayed than normal. The value set here has to be
a decimal number between 0 and 1 with no limit on decimal places. Any greater than 1 and no results will be displayed, 0 will always display all results.</p>
<p>Let's consider an example of how these values are calculated. To do this let's walk through the comparison between a cell containing "Jackson" and a search value
of "sockssssssssssssssss". Here the longer of the two values is the search value, which is 20 characters long. There are 3 matching letters mid string - "cks".
The "Ja" preceeding the "cks" requires a substitution of 2 to make the strings match. The "on" following the "cks" also requres a substitution of 2 to make the
strings match. The remaining 13 "s"s are deleted, this gives a total change score of 17. Dividing that by the longest length (20) gives a value of 0.85. Subtracting
that value from one gives a similarity score of 0.15. This would therefore pass in the below example where the threshold is one. For obvious reasons, it is not
recommended that you set the threshold value that low.</p>
SandyDatatables
3 years ago