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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.sem;
import cern.colt.matrix.DoubleMatrix2D;
import cern.colt.matrix.impl.DenseDoubleMatrix2D;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.graph.NodeType;
import edu.cmu.tetrad.graph.SemGraph;
import edu.cmu.tetrad.util.Matrix;
import edu.cmu.tetrad.util.TetradLogger;
import edu.cmu.tetrad.util.TetradSerializable;
import edu.cmu.tetrad.util.Vector;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serial;
import java.util.ArrayList;
import java.util.List;
/**
* Calculates updated structural equation models given evidence of the form X1=x1',...,The main task of such and
* algorithm is to calculate P(X = x' | evidence), where evidence takes the form of a Proposition over the variables in
* the Bayes net, possibly with additional information about which variables in the Bayes net have been manipulated.
*
* @author josephramsey
* @version $Id: $Id
* @see edu.cmu.tetrad.bayes.Evidence
* @see edu.cmu.tetrad.bayes.Proposition
* @see edu.cmu.tetrad.bayes.Manipulation
*/
public class SemUpdater implements TetradSerializable {
@Serial
private static final long serialVersionUID = 23L;
/**
* The SEM to be updated.
*/
private final SemIm semIm;
/**
* The evidence to be used in the update.
*/
private SemEvidence evidence;
/**
* Constructor for SemUpdater.
*
* @param semIm a {@link edu.cmu.tetrad.sem.SemIm} object
*/
public SemUpdater(SemIm semIm) {
if (semIm == null) {
throw new NullPointerException();
}
this.semIm = semIm;
SemEvidence evidence = new SemEvidence(this.semIm);
setEvidence(evidence);
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @return a {@link edu.cmu.tetrad.sem.SemUpdater} object
*/
public static SemUpdater serializableInstance() {
return new SemUpdater(SemIm.serializableInstance());
}
/**
* Getter for the field evidence.
*
* @return a {@link edu.cmu.tetrad.sem.SemEvidence} object
*/
public SemEvidence getEvidence() {
return this.evidence;
}
/**
* Sets new evidence for the updater. Once this is called, old updating results should not longer be available.
*
* @param evidence a {@link edu.cmu.tetrad.sem.SemEvidence} object
*/
public void setEvidence(SemEvidence evidence) {
if (evidence == null) {
throw new NullPointerException();
}
this.evidence = evidence;
// this.semIm = evidence.getSemIm();
}
/**
* Getter for the field semIm.
*
* @return the Bayes instantiated model that is being updated.
*/
public SemIm getSemIm() {
return this.semIm;
}
/**
* See http://en.wikipedia.org/wiki/Multivariate_normal_distribution.
*
* @return a {@link edu.cmu.tetrad.sem.SemIm} object
*/
public SemIm getUpdatedSemIm() {
// First manipulate the old semIm.
SemIm manipulatedSemIm = getManipulatedSemIm();
// Get out the means and implied covariances.
Vector means = new Vector(manipulatedSemIm.getVariableNodes().size());
for (int i = 0; i < means.size(); i++) {
means.set(i, manipulatedSemIm.getMean(manipulatedSemIm.getVariableNodes().get(i)));
}
Matrix implcov = manipulatedSemIm.getImplCovar(true);
// Updating on x2 = X.
SemEvidence evidence = getEvidence();
List nodesInEvidence = new ArrayList<>(evidence.getNodesInEvidence());
// System.out.println("evidence = " + evidence);
List XVars = new ArrayList<>(evidence.getNodesInEvidence());
List YVars = new ArrayList<>(manipulatedSemIm.getVariableNodes());
YVars.removeAll(nodesInEvidence);
int[] xIndices = new int[XVars.size()];
int[] yIndices = new int[YVars.size()];
for (int i = 0; i < XVars.size(); i++) {
xIndices[i] = manipulatedSemIm.getVariableNodes().indexOf(XVars.get(i));
}
for (int i = 0; i < YVars.size(); i++) {
yIndices[i] = manipulatedSemIm.getVariableNodes().indexOf(YVars.get(i));
}
Matrix covyx = implcov.getSelection(yIndices, xIndices);
Matrix varx = implcov.getSelection(xIndices, xIndices);
Vector EX = means.viewSelection(xIndices);
Vector EY = means.viewSelection(yIndices);
Vector X = new Vector(nodesInEvidence.size());
for (int i = 0; i < nodesInEvidence.size(); i++) {
int j = evidence.getNodeIndex(nodesInEvidence.get(i));
X.set(i, evidence.getProposition().getValue(j));
}
Vector xminusex = X.minus(EX);
Vector mu = new Vector(manipulatedSemIm.getVariableNodes().size());
DoubleMatrix2D sigma2 = new DenseDoubleMatrix2D(manipulatedSemIm.getErrCovar().toArray());
if (xminusex.size() == 0) {
mu = new Vector(means.toArray());
} else {
// System.out.println("xminusex = " + xminusex);
Vector times = (covyx.times(varx.inverse())).times(xminusex);
// System.out.println("times = " + times);
Vector YHatX = EY.plus(times);
// System.out.println("YHatX = " + YHatX);
for (int i = 0; i < xIndices.length; i++) {
mu.set(xIndices[i], X.get(i));
}
for (int i = 0; i < yIndices.length; i++) {
mu.set(yIndices[i], YHatX.get(i));
}
}
return manipulatedSemIm.updatedIm(new Matrix(sigma2.toArray()), mu);
}
/**
* getManipulatedGraph.
*
* @return a {@link edu.cmu.tetrad.graph.Graph} object
*/
public Graph getManipulatedGraph() {
return createManipulatedGraph(getSemIm().getSemPm().getGraph());
}
/**
* getManipulatedSemIm.
*
* @return a {@link edu.cmu.tetrad.sem.SemIm} object
*/
public SemIm getManipulatedSemIm() {
SemGraph graph = getSemIm().getSemPm().getGraph();
SemGraph manipulatedGraph = createManipulatedGraph(graph);
return SemIm.retainValues(getSemIm(), manipulatedGraph);
}
/**
* Alters the graph by removing edges from parents to manipulated variables.
*/
private SemGraph createManipulatedGraph(Graph graph) {
SemGraph updatedGraph = new SemGraph(graph);
for (int i = 0; i < this.evidence.getNumNodes(); ++i) {
if (this.evidence.isManipulated(i)) {
Node node = this.evidence.getNode(i);
List parents = updatedGraph.getParents(node);
for (Node parent : parents) {
if (parent.getNodeType() == NodeType.ERROR) {
continue;
}
updatedGraph.removeEdge(node, parent);
}
}
}
return updatedGraph;
}
/**
* 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;
}
}
}