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<HTML
><HEAD
><TITLE
>levenshtein</TITLE
><META
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><H1
><A
NAME="function.levenshtein"
>levenshtein</A
></H1
><DIV
CLASS="refnamediv"
><A
NAME="AEN36685"
></A
><P
> (PHP3 CVS only, PHP4 >= 4.0.1)</P
>levenshtein --
Calculate Levenshtein distance between two strings
</DIV
><DIV
CLASS="refsect1"
><A
NAME="AEN36688"
></A
><H2
>Description</H2
><DIV
CLASS="funcsynopsis"
><A
NAME="AEN36690"
></A
><P
></P
><P
><CODE
><CODE
CLASS="FUNCDEF"
>int <B
CLASS="function"
>levenshtein</B
></CODE
> (string str1, string str2)</CODE
></P
><P
><CODE
><CODE
CLASS="FUNCDEF"
>int <B
CLASS="function"
>levenshtein</B
></CODE
> (string str1, string str2, int cost_ins, int cost_rep, int cost_del)</CODE
></P
><P
><CODE
><CODE
CLASS="FUNCDEF"
>int <B
CLASS="function"
>levenshtein</B
></CODE
> (string str1, string str2, function cost)</CODE
></P
><P
></P
></DIV
><P
> This function returns the Levenshtein-Distance between the
two argument strings or -1, if one of the argument strings
is longer than the limit of 255 characters (255 should be
more than enough for name or dictionary comparison, and
nobody serious would be doing genetic analysis with PHP).
</P
><P
> The Levenshtein distance is defined as the minimal number of
characters you have to replace, insert or delete to transform
<TT
CLASS="parameter"
><I
>str1</I
></TT
> into <TT
CLASS="parameter"
><I
>str2</I
></TT
>.
The complexity of the algorithm is <TT
CLASS="literal"
>O(m*n)</TT
>,
where <TT
CLASS="literal"
>n</TT
> and <TT
CLASS="literal"
>m</TT
> are the
length of <TT
CLASS="parameter"
><I
>str1</I
></TT
> and
<TT
CLASS="parameter"
><I
>str2</I
></TT
> (rather good when compared to
<A
HREF="function.similar-text.html"
><B
CLASS="function"
>similar_text()</B
></A
>, which is O(max(n,m)**3),
but still expensive).
</P
><P
> In its simpelest form the function will take only the two
strings as parameter and will calculate just the number of
insert, replace and delete operations needed to transform
<TT
CLASS="parameter"
><I
>str1</I
></TT
> into <TT
CLASS="parameter"
><I
>str2</I
></TT
>.
</P
><P
>
A second variant will take three additional parameters that
define the cost of insert, replace and delete operations.
This is more general and adaptive than variant one, but not
as efficient.
</P
><P
>
The third variant (which is not implemented yet) will be
the most general and adaptive, but also the slowest alternative.
It will call a user-supplied function that will determine the
cost for every possible operation.
</P
><P
> The user-supplied function will be called with the following
arguments:
<P
></P
><UL
><LI
><P
> operation to apply: 'I', 'R' or 'D'
</P
></LI
><LI
><P
> actual character in string 1
</P
></LI
><LI
><P
> actual character in string 2
</P
></LI
><LI
><P
> position in string 1
</P
></LI
><LI
><P
> position in string 2
</P
></LI
><LI
><P
> remaining characters in string 1
</P
></LI
><LI
><P
> remaining characters in string 2
</P
></LI
></UL
>
The user-supplied function has to return a positive integer
describing the cost for this particular operation, but it
may decide to use only some of the supplied arguments.
</P
><P
>
The user-supplied function approach offers the possibility to
take into account the relevance of and/or difference between
certain symbols (characters) or even the context those symbols
appear in to determine the cost of insert, replace and delete
operations, but at the cost of loosing all optimizations done
regarding cpu register utilization and cache misses that have
been worked into the other two variants.
</P
><P
> See also <A
HREF="function.soundex.html"
><B
CLASS="function"
>soundex()</B
></A
>,
<A
HREF="function.similar-text.html"
><B
CLASS="function"
>similar_text()</B
></A
>
and <A
HREF="function.metaphone.html"
><B
CLASS="function"
>metaphone()</B
></A
>.
</P
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