/**
* Generates a simple exemplar of this class to test serialization.
*
* @see edu.cmu.TestSerialization
* @see TetradSerializableUtils
*/
public static PValueImproverWrapper serializableInstance() {
return new PValueImproverWrapper(
GraphWrapper.serializableInstance(),
DataWrapper.serializableInstance(),
PcSearchParams.serializableInstance(),
KnowledgeBoxModel.serializableInstance());
}
public void execute() {
IKnowledge knowledge = getParams().getKnowledge();
PcSearchParams searchParams = (PcSearchParams) getParams();
PcIndTestParams indTestParams = (PcIndTestParams) searchParams.getIndTestParams();
VcpcAlt VcpcAlt = new VcpcAlt(getIndependenceTest());
VcpcAlt.setKnowledge(knowledge);
VcpcAlt.setAggressivelyPreventCycles(this.isAggressivelyPreventCycles());
VcpcAlt.setDepth(indTestParams.getDepth());
Graph graph = VcpcAlt.search();
if (getSourceGraph() != null) {
GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
} else if (knowledge.isDefaultToKnowledgeLayout()) {
SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
} else {
GraphUtils.circleLayout(graph, 200, 200, 150);
}
setResultGraph(graph);
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @see edu.cmu.TestSerialization
* @see TetradSerializableUtils
*/
public static VcpcAltRunner serializableInstance() {
return new VcpcAltRunner(Dag.serializableInstance(), PcSearchParams.serializableInstance());
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
Object source = dataWrapper.getSelectedDataModel();
DataModel dataModel = (DataModel) source;
IKnowledge knowledge = params2.getKnowledge();
if (initialGraph == null) {
initialGraph = new EdgeListGraph(dataModel.getVariables());
}
Graph graph2 = new EdgeListGraph(initialGraph);
graph2 = GraphUtils.replaceNodes(graph2, dataModel.getVariables());
Bff search;
if (dataModel instanceof DataSet) {
DataSet dataSet = (DataSet) dataModel;
if (getAlgorithmType() == AlgorithmType.BEAM) {
search = new BffBeam(graph2, dataSet, knowledge);
} else if (getAlgorithmType() == AlgorithmType.GES) {
search = new BffGes(graph2, dataSet);
search.setKnowledge(knowledge);
} else {
throw new IllegalStateException();
}
} else if (dataModel instanceof CovarianceMatrix) {
CovarianceMatrix covarianceMatrix = (CovarianceMatrix) dataModel;
if (getAlgorithmType() == AlgorithmType.BEAM) {
search = new BffBeam(graph2, covarianceMatrix, knowledge);
} else if (getAlgorithmType() == AlgorithmType.GES) {
throw new IllegalArgumentException(
"GES method requires a dataset; a covariance matrix was provided.");
// search = new BffGes(graph2, covarianceMatrix);
// search.setKnowledge(knowledge);
} else {
throw new IllegalStateException();
}
} else {
throw new IllegalStateException();
}
PcIndTestParams indTestParams = (PcIndTestParams) getParams().getIndTestParams();
search.setAlpha(indTestParams.getAlpha());
search.setBeamWidth(indTestParams.getBeamWidth());
search.setHighPValueAlpha(indTestParams.getZeroEdgeP());
this.graph = search.search();
// this.graph = search.getNewSemIm().getSemPm().getGraph();
setOriginalSemIm(search.getOriginalSemIm());
this.newSemIm = search.getNewSemIm();
fireGraphChange(graph);
if (getSourceGraph() != null) {
GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
} else if (knowledge.isDefaultToKnowledgeLayout()) {
SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
} else {
GraphUtils.circleLayout(graph, 200, 200, 150);
}
setResultGraph(SearchGraphUtils.patternForDag(graph, knowledge));
}