tenacious_g
===========
This is a persistent graph library, extending Gratr. It is meant to be a simple thing:
[davidrichards]$: tg
Loading Tenacious G (Persistent Graphs Database)
>> @g = Digraph.new
=> GRATR::Digraph[].
>> @g.add_edge!(2,3)
=> GRATR::Digraph[GRATR::Arc[2,3,nil]].
>> @g.save('some_name')
=> nil
>> @g = Digraph.new
=> GRATR::Digraph[].
>> @g.add_edge!(2,3)
=> GRATR::Digraph[GRATR::Arc[2,3,nil]].
>> @g.save('some_name')
=> nil
>> @g1 = TenaciousG.load('some_name')
=> GRATR::Digraph[GRATR::Arc[2,3,nil]].
>> @g1 == @g
=> true
>> @g.add_edge!(1,2)
=> GRATR::Digraph[GRATR::Arc[2,3,nil], GRATR::Arc[1,2,nil]].
>> @g.name
=> "some_name"
>> @g.location
=> "/Users/davidrichards/.tenacious_g/some_name.graph"
>> @g.save
I include GRATR and TenaciousG, so that it's a little more convenient to work with these things. I wrote this to make using graphs with machine learning a simpler decision. As long as a graph can fit in memory, this is probably a good choice. When things are too big for that, or slowing down from all the computation you need to run, check back in to see if I have my distributed graph database finished yet.
There is a console, shown above (tg), but this was mostly intended to be included in other libraries or applications.
COPYRIGHT
=========
Copyright (c) 2009 David Richards. See LICENSE for details.