/**
* When this class is loaded, preload the cache with a few items which we think will often be
* needed. This can avoid the necessity of running a belief network context in simple problems.
*/
static {
System.err.println("PiHelperLoader.static: preload the helper cache.");
helper_cache = new Hashtable();
Vector seq = new Vector();
seq.addElement(riso.distributions.ConditionalDiscrete.class);
seq.addElement(riso.distributions.Discrete.class);
helper_cache.put(
new HelperCacheKey("pi", seq),
riso.distributions.computes_pi.ConditionalDiscrete_Discrete.class);
seq = new Vector();
seq.addElement(riso.distributions.Discrete.class);
helper_cache.put(
new HelperCacheKey("lambda", seq), riso.distributions.computes_lambda.Discrete.class);
seq = new Vector();
seq.addElement(riso.distributions.AbstractDistribution.class);
seq.addElement(riso.distributions.AbstractDistribution.class);
helper_cache.put(
new HelperCacheKey("pi_message", seq),
riso.distributions.computes_pi_message.AbstractDistribution_AbstractDistribution.class);
seq = new Vector();
seq.addElement(riso.distributions.ConditionalDiscrete.class);
seq.addElement(riso.distributions.Discrete.class);
seq.addElement(riso.distributions.Discrete.class);
helper_cache.put(
new HelperCacheKey("lambda_message", seq),
riso.distributions.computes_lambda_message.ConditionalDiscrete_Discrete_Discrete.class);
seq = new Vector();
seq.addElement(riso.distributions.ConditionalDiscrete.class);
seq.addElement(riso.distributions.Discrete.class);
helper_cache.put(
new HelperCacheKey("lambda_message", seq),
riso.distributions.computes_lambda_message.ConditionalDiscrete_Discrete_.class);
seq = new Vector();
seq.addElement(riso.distributions.Discrete.class);
seq.addElement(riso.distributions.Discrete.class);
helper_cache.put(
new HelperCacheKey("posterior", seq),
riso.distributions.computes_posterior.Discrete_Discrete.class);
System.err.println("PiHelperLoader.static: helper_cache.size(): " + helper_cache.size());
}
/**
* This method returns a <tt>Class</tt> for a helper which can handle the list of distributions
* specified by <tt>seq1</tt>. We maintain a cache of recently-loaded helpers, so check the cache
* before going to the trouble of searching for a helper. The cache is blown away every
* <tt>HELPER_CACHE_REFRESH</tt> seconds.
*
* <p>If we can't find a helper in the cache, we must search through the list of available helpers
* to find an appropriate one. First try to find helper using class sequence as specified by
* <tt>seq</tt>. Whether or not that succeeds, promote any <tt>Gaussian</tt> in the sequence to
* <tt>MixGaussians</tt>, and try again. If we get a better match on the second try, return the
* helper thus found.
*/
public static Class find_helper_class(Vector seq1, String helper_type)
throws ClassNotFoundException {
// Let's see if an appropriate helper is in the cache.
// If the cache is too old, empty it and search for the helper anew.
if (System.currentTimeMillis() - cache_timestamp > HELPER_CACHE_REFRESH) {
helper_cache = new Hashtable();
cache_timestamp = System.currentTimeMillis();
// Go on and search for appropriate helper.
} else {
HelperCacheKey key = new HelperCacheKey(helper_type, seq1);
Class helper_class = (Class) helper_cache.get(key);
if (helper_class != null) {
if (Global.debug > -1)
System.err.println(
"PiHelperLoader.find_helper_class: found helper class: "
+ helper_class
+ "; no need to search.");
return helper_class;
}
// else no luck; we have to search for helper.
}
// Well, we didn't find a helper in the cache, so let's go to work.
if (Global.debug > -1)
System.err.println(
"PiHelperLoader.find_helper_class: DID NOT FIND HELPER CLASS; NOW SEARCH.");
Class c1 = null, c2 = null;
ClassNotFoundException cnfe1 = null, cnfe2 = null;
int[] class_score1 = new int[1], count_score1 = new int[1];
int[] class_score2 = new int[1], count_score2 = new int[1];
try {
c1 = find_helper_class1(seq1, helper_type, class_score1, count_score1);
} catch (ClassNotFoundException e) {
cnfe1 = e;
} // hang on, we may need to re-throw later.
Class gaussian_class = Class.forName("riso.distributions.Gaussian");
MixGaussians mog = new MixGaussians(1, 1);
Vector seq2 = new Vector(seq1.size());
for (int i = 0; i < seq1.size(); i++)
if (gaussian_class.isAssignableFrom((Class) seq1.elementAt(i)))
seq2.addElement(mog.getClass());
else seq2.addElement(seq1.elementAt(i));
try {
c2 = find_helper_class1(seq2, helper_type, class_score2, count_score2);
} catch (ClassNotFoundException e) {
cnfe2 = e;
}
if (cnfe1 == null && cnfe2 == null) {
// Both matched; see which one fits better.
// Break ties in favor of the helper for non-promoted messages.
if (class_score1[0] >= class_score2[0]
|| (class_score1[0] == class_score2[0] && count_score1[0] >= count_score2[0])) {
if (Global.debug > 1)
System.err.println(
"\taccept helper " + c1 + " for non-promoted classes instead of " + c2);
if (Global.debug > 1)
System.err.println(
"\t\t"
+ class_score1[0]
+ ", "
+ class_score2[0]
+ "; "
+ count_score1[0]
+ ", "
+ count_score2[0]);
helper_cache.put(new HelperCacheKey(helper_type, seq1), c1);
return c1;
} else {
if (Global.debug > 1)
System.err.println("\taccept helper " + c2 + " for promoted classes instead of " + c1);
if (Global.debug > 1)
System.err.println(
"\t\t"
+ class_score1[0]
+ ", "
+ class_score2[0]
+ "; "
+ count_score1[0]
+ ", "
+ count_score2[0]);
helper_cache.put(new HelperCacheKey(helper_type, seq1), c2);
return c2;
}
} else if (cnfe1 == null && cnfe2 != null) {
// Only the first try matched, return it.
helper_cache.put(new HelperCacheKey(helper_type, seq1), c1);
return c1;
} else if (cnfe1 != null && cnfe2 == null) {
// Only the second try matched, return it.
helper_cache.put(new HelperCacheKey(helper_type, seq1), c2);
return c2;
} else {
// Neither try matched. Re-throw the exception generated by the first try.
throw cnfe1;
}
}