``````Installation might be a bit tricky - the underlying library is written
in C++.  If problems occur (Tree::M is configured for linux, i.e. an
ELF-system using g++ and gcc) you need to change CXX in the Makefile.PL
and/or hack the GiST/Makefile and MT/Makefile themselves.

=============================================================================

NAME
Tree::M - implement M-trees for efficient "metric/multimedia-searches"

SYNOPSIS
use Tree::M;

\$M = new Tree::M

DESCRIPTION
(not yet)

Ever had the problem of managing multi-dimensional (spatial) data but
like

select data from table where latitude > 40 and latitude < 50
and longitude> 50 and longitude< 60;

are quite inefficient, unless longitude and latitude are part of the
same spatial index (e.g. an R-tree).

An M-tree is an index tree that does not directly look at the stored
keys but rather requires a *distance* (a metric, e.g. a vector norm)
function to be defined that sorts keys according to their distance. In
the example above the distance function could be the maximum norm
("max(x1-x2, y1-y2)"). The lookup above would then be something like
this:

my \$res = \$M->range([45,55], 5);

This module implements an M-tree. Although the data structure and the
distance function is arbitrary, the current version only implements
n-dimensional discrete vectors and hardwires the distance function to
the suared euclidean metric (i.e. "(x1-x2)**2 + (y1-y2)**2 + (z1-z2)**2
+ ..."). Evolution towards more freedom is expected ;)

THE Tree::M CLASS
\$M = new Tree::M arg => value, ...
Creates a new M-Tree. Before it can be used you have to call one of
the "create" or "open" methods below.

ndims => integer
the number of dimensions each vector has

range => [min, max, steps]
min      the lowest allowable scalar value in each dimension
max      the maximum allowable number
steps    the number of discrete steps (used when stored externally)

pagesize => integer
the size of one page on underlying storage. usually 4096, but
large objects (ndims > 20 or so) might want to increase this

Example: create an M-Tree that stores 8-bit rgb-values:

\$M = new Tree::M ndims => 3, range => [0, 255, 256];

Example: create an M-Tree that stores coordinates from -1..1 with
100 different steps:

\$M = new Tree::M ndims => 2, range => [-1, 1, 100];

\$M->open(path)
\$M->create(\$path)
Open or create the external storage file \$path and associate it with
the tree.

[this braindamaged API will go away ;)]

\$M->insert(\@v, \$data)
Insert a vector (given by an array reference) into the index and
associate it with the value \$data (a 32-bit integer).

\$M->sync
Synchronize the data file with memory. Useful after calling "insert"
to ensure the data actually reaches stable storage.

Search all entries not farther away from @v then \$radius and return
an arrayref containing the searchresults.

Each result is again anarrayref composed like this:

[\@v, \$data]

e.g. the same as given to the "insert" method.

\$res = \$M->top(\@v, \$n)
Return the \$n "nearest neighbours". The results arrayref (see
"range") contains the \$n index values nearest to @v, sorted for
distance.

\$distance = \$M->distance(\@v1, \@v2)
Calculcate the distance between two vectors, just as they databse
engine would do it.

\$depth = \$M->maxlevel
Return the maximum height of the tree (usually a small integer
specifying the length of the path from the root to the farthest
leaf)

BUGS
Inserting too many duplicate keys into the tree cause the C++ library to
die, so don't do that.

AUTHOR
Marc Lehmann <schmorp@schmorp.de>.