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///////////////////////////////////////////////////////////////////////////////
// For information as to what this class does, see the Javadoc, below. //
// Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, //
// 2007, 2008, 2009, 2010, 2014, 2015, 2022 by Peter Spirtes, Richard //
// Scheines, Joseph Ramsey, and Clark Glymour. //
// //
// This program 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. //
// //
// This program is distributed 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 this program; if not, write to the Free Software //
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA //
///////////////////////////////////////////////////////////////////////////////
package edu.cmu.tetrad.bayes;
import edu.cmu.tetrad.data.DiscreteVariable;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.util.TetradLogger;
import edu.cmu.tetrad.util.TetradSerializable;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serial;
import java.util.Collections;
import java.util.LinkedList;
import java.util.List;
/**
* Calculates cell probabilities from conditional BayesIm probabilities on the fly without constructing the entire
* table. (To force the entire table to be constructed, use StoredCellProbs.)
*
* @author josephramsey
* @version $Id: $Id
*/
public final class BayesImProbs implements DiscreteProbs, TetradSerializable {
@Serial
private static final long serialVersionUID = 23L;
/**
* Represents a variable of the BayesIm class.
*/
private final BayesIm bayesIm;
/**
* Represents a list of nodes.
*/
private final List variables;
//===========================CONSTRUCTORS==========================//
/**
* Constructs a BayesImProbs object from the given BayesIm.
*
* @param bayesIm Ibid.
*/
public BayesImProbs(BayesIm bayesIm) {
if (bayesIm == null) {
throw new NullPointerException();
}
this.bayesIm = bayesIm;
List variables = new LinkedList<>();
BayesPm bayesPm = bayesIm.getBayesPm();
for (int i = 0; i < bayesIm.getNumNodes(); i++) {
Node node = bayesIm.getNode(i);
String name = node.getName();
int numCategories = bayesPm.getNumCategories(node);
List categories = new LinkedList<>();
for (int j = 0; j < numCategories; j++) {
categories.add(bayesPm.getCategory(node, j));
}
variables.add(new DiscreteVariable(name, categories));
}
this.variables = Collections.unmodifiableList(variables);
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @return a simple exemplar of this class to test serialization.
*/
public static BayesImProbs serializableInstance() {
return new BayesImProbs(MlBayesIm.serializableInstance());
}
//==========================PUBLIC METHODS==========================//
private static boolean hasNextValue(Proposition proposition, int variable,
int currentIndex) {
return BayesImProbs.nextValue(proposition, variable, currentIndex) != -1;
}
private static int nextValue(Proposition proposition, int variable,
int currentIndex) {
for (int i = currentIndex + 1;
i < proposition.getNumCategories(variable); i++) {
if (proposition.isAllowed(variable, i)) {
return i;
}
}
return -1;
}
/**
* Calculates the probability in the given cell from the conditional probabilities in the BayesIm. It's the product
* of the probabilities that each variable takes on the value it does given that the other variables take on the
* values they do in that cell. The returned value will be undefined (Double.NaN) if any of the conditional
* probabilities being multiplied together is undefined.
*
* @param variableValues an array of {@link int} objects
* @return the cell probability, or NaN if this probability is undefined.
*/
public double getCellProb(int[] variableValues) {
double p = 1.0;
VALUES:
for (int node = 0; node < variableValues.length; node++) {
int[] parents = this.bayesIm.getParents(node);
int[] parentValues = new int[parents.length];
for (int parentIndex = 0;
parentIndex < parentValues.length; parentIndex++) {
parentValues[parentIndex] =
variableValues[parents[parentIndex]];
if (parentValues[parentIndex] == DiscreteVariable.MISSING_VALUE) {
continue VALUES;
}
}
int rowIndex = this.bayesIm.getRowIndex(node, parentValues);
int colIndex = variableValues[node];
if (colIndex == DiscreteVariable.MISSING_VALUE) {
continue;
}
p *= this.bayesIm.getProbability(node, rowIndex, colIndex);
}
return p;
}
/**
* Calculates the probability of a given proposition.
*
* @param assertion the proposition for which we want to calculate the probability
* @return the probability of the given proposition
*/
public double getProb(Proposition assertion) {
// Initialize to 0's.
int[] variableValues = new int[assertion.getNumVariables()];
for (int i = 0; i < assertion.getNumVariables(); i++) {
variableValues[i] = BayesImProbs.nextValue(assertion, i, -1);
}
variableValues[variableValues.length - 1] = -1;
double p = 0.0;
loop:
while (true) {
for (int i = assertion.getNumVariables() - 1; i >= 0; i--) {
if (BayesImProbs.hasNextValue(assertion, i, variableValues[i])) {
variableValues[i] =
BayesImProbs.nextValue(assertion, i, variableValues[i]);
for (int j = i + 1; j < assertion.getNumVariables(); j++) {
if (BayesImProbs.hasNextValue(assertion, j, -1)) {
variableValues[j] = BayesImProbs.nextValue(assertion, j, -1);
} else {
break loop;
}
}
double cellProb = getCellProb(variableValues);
if (Double.isNaN(cellProb)) {
continue;
}
p += cellProb;
continue loop;
}
}
break;
}
return p;
}
/**
* Calculates the conditional probability of an assertion given a condition.
*
* @param assertion the proposition representing the assertion
* @param condition the proposition representing the condition
* @return the conditional probability of the assertion given the condition
* @throws IllegalArgumentException if the assertion and condition are not for the same Bayes IM
*/
public double getConditionalProb(Proposition assertion,
Proposition condition) {
if (assertion.getVariableSource() != condition.getVariableSource()) {
throw new IllegalArgumentException(
"Assertion and condition must be " +
"for the same Bayes IM.");
}
int[] variableValues = new int[condition.getNumVariables()];
for (int i = 0; i < condition.getNumVariables(); i++) {
variableValues[i] = BayesImProbs.nextValue(condition, i, -1);
}
variableValues[variableValues.length - 1] = -1;
double conditionTrue = 0.0;
double assertionTrue = 0.0;
loop:
while (true) {
for (int i = condition.getNumVariables() - 1; i >= 0; i--) {
if (BayesImProbs.hasNextValue(condition, i, variableValues[i])) {
variableValues[i] =
BayesImProbs.nextValue(condition, i, variableValues[i]);
for (int j = i + 1; j < condition.getNumVariables(); j++) {
if (BayesImProbs.hasNextValue(condition, j, -1)) {
variableValues[j] = BayesImProbs.nextValue(condition, j, -1);
} else {
break loop;
}
}
double cellProb = getCellProb(variableValues);
if (Double.isNaN(cellProb)) {
continue;
}
boolean assertionHolds = true;
for (int j = 0; j < assertion.getNumVariables(); j++) {
if (!assertion.isAllowed(j, variableValues[j])) {
assertionHolds = false;
break;
}
}
if (assertionHolds) {
assertionTrue += cellProb;
}
conditionTrue += cellProb;
continue loop;
}
}
break;
}
return assertionTrue / conditionTrue;
}
/**
* Getter for the field variables.
*
* @return a {@link java.util.List} object
*/
public List getVariables() {
return this.variables;
}
/**
* Writes the object to the specified ObjectOutputStream.
*
* @param out The ObjectOutputStream to write the object to.
* @throws IOException If an I/O error occurs.
*/
@Serial
private void writeObject(ObjectOutputStream out) throws IOException {
try {
out.defaultWriteObject();
} catch (IOException e) {
TetradLogger.getInstance().log("Failed to serialize object: " + getClass().getCanonicalName()
+ ", " + e.getMessage());
throw e;
}
}
/**
* Reads the object from the specified ObjectInputStream. This method is used during deserialization
* to restore the state of the object.
*
* @param in The ObjectInputStream to read the object from.
* @throws IOException If an I/O error occurs.
* @throws ClassNotFoundException If the class of the serialized object cannot be found.
*/
@Serial
private void readObject(ObjectInputStream in) throws IOException, ClassNotFoundException {
try {
in.defaultReadObject();
} catch (IOException e) {
TetradLogger.getInstance().log("Failed to deserialize object: " + getClass().getCanonicalName()
+ ", " + e.getMessage());
throw e;
}
}
}