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Sampler.java
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/* * Copyright (c) 2005, Regents of the University of California * All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * * Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * distribution. * * * Neither the name of the University of California, Berkeley nor * the names of its contributors may be used to endorse or promote * products derived from this software without specific prior * written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED * OF THE POSSIBILITY OF SUCH DAMAGE. */ package blog; import java.util.List;
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An object that stochastically generates a sequence of samples,
which are possible worlds of a given BLOG model. A Sampler object
may generate independent samples, or it may maintain internal state
(e.g., a Markov chain sampler). The samples generated may also
have associated weights, as in likelihood weighting.
A concrete Sampler subclass should have a constructor that takes two arguments: a blog.Model object and a java.util.Properties object. The properties object specifies configuration parameters for the sampler. |
public abstract class Sampler {
| Creates a Sampler object for the given BLOG model. |
public Sampler(Model model) {
this.model = model;
}
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Prepares this sampler to sample from the distribution conditioned
on the given evidence, returning PartialWorld objects that are
complete enough to answer the given queries. Also clears the
internal state of this sampler so that the next sample generated will
be independent of all previous ones.
The default implementation just sets the
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public void initialize(Evidence evidence, List queries) {
this.evidence = evidence;
this.queries = queries;
}
Generates the next sample (world), and possibly assigns it a weight.
been called on this Sampler
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public abstract void nextSample();
Returns the world generated by the most recent call to
nextSample. The returned PartialWorld object may
be modified by the next call to nextSample.
called, or if initialize
has been called since the last call
to nextSample
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public abstract PartialWorld getLatestWorld();
Returns the weight for the world generated by the most recent call
to nextSample. The default implementation returns 1.0.
called, or if initialize
has been called since the last call
to nextSample
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public double getLatestWeight() {
return 1.0;
}
| Prints statistics about the internal activities of this sampler. The default implementation does nothing. |
public void printStats() {
}
| BLOG model for which this sampler generates partial worlds. |
protected Model model;
Evidence specified by the last call to initialize,
or null if initialize has not been called.
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protected Evidence evidence;
List of Query objects specified by the last call to
initialize, or null if initialize has
not been called.
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protected List queries;
}
This file was generated on Tue Jun 08 17:53:36 PDT 2010 from file Sampler.java
by the ilog.language.tools.Hilite Java tool written by Hassan Aït-Kaci