/**
* ConstantDensityRatioSet.java
* @author Fabio G. Cozman
* Copyright 1996 - 1999, Fabio G. Cozman,
* Carnergie Mellon University, Universidade de Sao Paulo
* fgcozman@usp.br, http://www.cs.cmu.edu/~fgcozman/home.html
*
* The JavaBayes distribution is free software; you can
* redistribute it and/or modify it under the terms of the GNU General
* Public License as published by the Free Software Foundation (either
* version 2 of the License or, at your option, any later version),
* provided that this notice and the name of the author appear in all
* copies. Upon request to the author, some of the packages in the
* JavaBayes distribution can be licensed under the GNU Lesser General
* Public License as published by the Free Software Foundation (either
* version 2 of the License, or (at your option) any later version).
* If you're using the software, please notify fgcozman@usp.br so
* that you can receive updates and patches. JavaBayes is distributed
* "as is", in the hope that it will be useful, but WITHOUT ANY WARRANTY;
* without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
* PARTICULAR PURPOSE. See the GNU General Public License for more details.
* You should have received a copy of the GNU General Public License
* along with the JavaBayes distribution. If not, write to the Free
* Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
package CredalSets;
import BayesianNetworks.*;
import QuasiBayesianNetworks.*;
public class ConstantDensityRatioSet
extends FinitelyGeneratedSet
implements MappingDouble {
private double k;
// Auxiliary variable that holds a discrete function for bracketing.
private DiscreteFunction temporary_discrete_function;
private final static int LOWER_EXPECTATION_BRACKET = 0;
private final static int UPPER_EXPECTATION_BRACKET = 1;
private final static double ACCURACY = 10E-8;
/**
* Constructor for an ConstantDensityRatioSet
* ProbabilityFunction object and given constant.
*/
public ConstantDensityRatioSet(ProbabilityFunction pf, double kk) {
super(pf, pf.get_values());
k = kk;
if (k <= 0.0) k = 1.0;
else {
if (k < 1.0) k = 1.0/k;
}
}
/**
* Perform calculation of marginal posterior distributions for.
* a density ratio global neighborhood.
*/
public ProbabilityFunction posterior_marginal() {
double lower_values[] = new double[values.length];
double upper_values[] = new double[values.length];
// Check the possibility that the query has an observed variable,
// in which case the marginalization property does not apply.
if ((variables[0] instanceof ProbabilityVariable) &&
(((ProbabilityVariable)variables[0]).is_observed() == true)) {
for (int i=0; i<values.length; i++) {
lower_values[i] = values[i];
upper_values[i] = values[i];
}
} // Else, apply the marginalization property.
else {
double total = 0.0;
for (int i=0; i<values.length; i++)
total += values[i];
for (int i=0; i<values.length; i++)
lower_values[i] =
(values[i]/k)/
( (values[i]/k) + k * (total - values[i]) );
for (int i=0; i<values.length; i++)
upper_values[i] =
(k * values[i])/
(k * values[i] + (total - values[i])/k );
}
return(new QBProbabilityFunction(bn, variables, values,
lower_values, upper_values, properties));
}
/**
* Perform calculation of expected value for density ratio.
*/
public double[] expected_values(DiscreteFunction df) {
Bracketing bracket = new Bracketing();
double results[] = new double[2];
// Check the possibility that the query has an observed variable,
// in which case the marginalization property does not apply.
if ((variables[0] instanceof ProbabilityVariable) &&
(((ProbabilityVariable)variables[0]).is_observed() == true)) {
results[0] =
df.get_value( ((ProbabilityVariable)variables[0]).get_observed_index() );
results[1] = results[0];
return(results);
}
// Else, apply the marginalization property.
// Obtain the maximum and minimum of functions
double max_df_value = df.get_value(0);
double min_df_value = df.get_value(0);
for (int i=1; i<df.number_values(); i++) {
if (max_df_value < df.get_value(i)) max_df_value = df.get_value(i);
if (min_df_value > df.get_value(i)) min_df_value = df.get_value(i);
}
// Prepare the temporary_discrete_function variable for bracketing
temporary_discrete_function = df;
// Bracket the lower expectation
double lower_expectation =
bracket.perform(this, LOWER_EXPECTATION_BRACKET,
min_df_value, max_df_value, ACCURACY);
// Bracket the upper expectation
double upper_expectation =
bracket.perform(this, UPPER_EXPECTATION_BRACKET,
min_df_value, max_df_value, ACCURACY);
// Calculate the values
results[0] = lower_expectation;
results[1] = upper_expectation;
return(results);
}
/**
* Perform calculation of posterior expected value.
* Assumes that the probability values are not
* normalized; probability values are p(x, e) where e is
* the fixed evidence
*/
public double[] posterior_expected_values(DiscreteFunction df) {
return(expected_values(df));
}
/**
* To conform to the Mapping interface demanded by the
* Bracketing class, the method map() must be present.
*/
public double map(int map_type, double map_input) {
int i;
double aux;
double map_output_upper = 0.0;
double map_output_lower = 0.0;
double map_output = 0.0;
DiscreteFunction tdf = temporary_discrete_function;
switch (map_type) {
case LOWER_EXPECTATION_BRACKET:
for (i=0; i<values.length; i++) {
aux = tdf.get_value(i) - map_input;
map_output_upper += (k * values[i]) * (- Math.max(- aux, 0.0));
map_output_lower += (values[i]/k) * (Math.max(aux, 0.0));
}
break;
case UPPER_EXPECTATION_BRACKET:
for (i=0; i<values.length; i++) {
aux = tdf.get_value(i) - map_input;
map_output_upper += (k * values[i]) * (Math.max(aux, 0.0));
map_output_lower += (values[i]/k) * (- Math.max(- aux, 0.0));
}
break;
}
map_output = map_output_upper + map_output_lower;
return(map_output);
}
}