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GLOP

Generative Lab Object Package

Contact: Isabelle Guyon - Submitted: 2009-10-29 20:25 - Views : 2674 - [Edit entry]
  • Authors: Isabelle Guyon
  • Key facts: Matlab object oriented package supplying models to the Virtual lab. The package redistributes part of Kevin Murphy's Bayesian Network toolbox, which is used for some of its models. Before downloading the package, you must agree to the following conditions:

    ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS" ISABELLE GUYON AND/OR OTHER ORGANIZERS DISCLAIM ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
    FOR ANY PARTICULAR PURPOSE, AND THE WARRANTY OF NON-INFRIGEMENT OF ANY THIRD PARTY'S INTELLECTUAL PROPERTY RIGHTS. IN NO EVENT SHALL ISABELLE GUYON AND/OR OTHER ORGANIZERS BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS,
    MATERIALS, PUBLICATIONS, OR INFORMATION MADE AVAILABLE.
  • Keywords: bayesian.network, structural.equation.models, probabilistic.graphical.models, boolean.network
  • Download BibTeX
  • GLOP package
  • Virtual Lab

Abstract:

We developed an object-oriented interface to easily incorporate new generative models. It is based on two simple abstractions:
- query object and
- model object.
The query object holds the query information delivered by users or the data delivered by the generative models. It has a fixed structure. The model object is a template from which data generative models can be derived.

Installation
Unzip glop.zip
Open Matlab and go to the GLOP directory and type use_glop at the prompt.

Finding your way around
To know the list of models, type
> whoisglop
To know the default values of a model hyperparameters:
> default(model)
To know the methods:
> methods(model)
To know the properties:
> properties(model)

Examples
> q=query('OBS 20');
or
> q=query('TRAIN');
or
> q=query('TEST');
and
> a=alarm({'cost_per_sample=1', 'cost_per_var_observation=1', 'cost_per_var_manipulation=2'});
or
> a=sprinkler;
etc.
the
> initial_budget(a)
> task_n_pricing(a)
> [q, a]=process_query(a, q);

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