// TODO Fix this.
private List<ScoredGraph> arrangeGraphs() {
IGesRunner runner = (IGesRunner) getAlgorithmRunner();
Graph resultGraph = runner.getResultGraph();
List<ScoredGraph> topGraphs = runner.getTopGraphs();
if (topGraphs == null) topGraphs = new ArrayList<ScoredGraph>();
Graph latestWorkbenchGraph = runner.getParams().getSourceGraph();
Graph sourceGraph = runner.getSourceGraph();
boolean arrangedAll = false;
for (ScoredGraph topGraph1 : topGraphs) {
arrangedAll = GraphUtils.arrangeBySourceGraph(topGraph1.getGraph(), latestWorkbenchGraph);
}
if (!arrangedAll) {
arrangedAll = GraphUtils.arrangeBySourceGraph(resultGraph, sourceGraph);
}
if (!arrangedAll) {
for (ScoredGraph topGraph : topGraphs) {
GraphUtils.circleLayout(topGraph.getGraph(), 200, 200, 150);
GraphUtils.circleLayout(resultGraph, 200, 200, 150);
}
}
return topGraphs;
}
JScrollPane workbenchScroll(String resultLabel) {
Graph resultGraph = resultGraph();
Graph sourceGraph = algorithmRunner.getSourceGraph();
SearchParams searchParams = algorithmRunner.getParams();
Graph latestWorkbenchGraph = null;
if (searchParams != null) {
latestWorkbenchGraph = searchParams.getSourceGraph();
}
if (latestWorkbenchGraph == null) {
GraphUtils.arrangeBySourceGraph(resultGraph, sourceGraph);
} else {
GraphUtils.arrangeBySourceGraph(resultGraph, latestWorkbenchGraph);
}
// boolean arrangedAll = DataGraphUtils.arrangeBySourceGraph(resultGraph,
// latestWorkbenchGraph);
//
// if (!arrangedAll) {
// arrangedAll =
// DataGraphUtils.arrangeBySourceGraph(resultGraph, sourceGraph);
// }
// if (!arrangedAll) {
// DataGraphUtils.circleLayout(resultGraph, 200, 200, 150);
// }
this.workbench = new GraphWorkbench(resultGraph);
graphHistory.clear();
graphHistory.add(resultGraph);
this.workbench.setAllowDoubleClickActions(false);
this.workbench.setAllowNodeEdgeSelection(true);
this.workbenchScroll = new JScrollPane(workbench);
// workbenchScroll.setPreferredSize(new Dimension(450, 450));
// workbenchScroll.setBorder(new TitledBorder(resultLabel));
this.workbench.addMouseListener(
new MouseAdapter() {
public void mouseExited(MouseEvent e) {
storeLatestWorkbenchGraph();
}
});
return workbenchScroll;
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
DataModel source = getDataModel();
if (!(source instanceof DataSet)) {
throw new IllegalArgumentException("Expecting a rectangular data set.");
}
DataSet data = (DataSet) source;
if (!data.isContinuous()) {
throw new IllegalArgumentException("Expecting a continuous data set.");
}
// Lingam_old lingam = new Lingam_old();
// lingam.setPruningDone(true);
// lingam.setAlpha(getParams().getIndTestParams().getAlpha());
// GraphWithParameters result = lingam.lingam(data);
// Graph graph = result.getGraph();
Lingam lingam = new Lingam();
LingamParams params = (LingamParams) getParams();
lingam.setPruneFactor(params.getPruneFactor());
Graph graph = lingam.search(data);
setResultGraph(graph);
if (getSourceGraph() != null) {
GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
} else {
GraphUtils.circleLayout(graph, 200, 200, 150);
}
}
protected void doDefaultArrangement(Graph resultGraph) {
if (getLatestWorkbenchGraph() != null) { // (alreadyLaidOut) {
GraphUtils.arrangeBySourceGraph(resultGraph, getLatestWorkbenchGraph());
} else if (getKnowledge().isDefaultToKnowledgeLayout()) {
SearchGraphUtils.arrangeByKnowledgeTiers(resultGraph, getKnowledge());
// alreadyLaidOut = true;
} else {
GraphUtils.circleLayout(resultGraph, 200, 200, 150);
// alreadyLaidOut = true;
}
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
IKnowledge knowledge = getParams().getKnowledge();
SearchParams searchParams = getParams();
FciIndTestParams indTestParams = (FciIndTestParams) searchParams.getIndTestParams();
// Cfci fciSearch =
// new Cfci(getIndependenceTest(), knowledge);
// fciSearch.setMaxIndegree(indTestParams.depth());
// Graph graph = fciSearch.search();
//
// if (knowledge.isDefaultToKnowledgeLayout()) {
// SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
// }
//
// setResultGraph(graph);
Graph graph;
if (indTestParams.isRFCI_Used()) {
Rfci fci = new Rfci(getIndependenceTest());
fci.setKnowledge(knowledge);
fci.setCompleteRuleSetUsed(indTestParams.isCompleteRuleSetUsed());
fci.setMaxPathLength(indTestParams.getMaxReachablePathLength());
fci.setDepth(indTestParams.getDepth());
graph = fci.search();
} else {
Fci fci = new Fci(getIndependenceTest());
fci.setKnowledge(knowledge);
fci.setCompleteRuleSetUsed(indTestParams.isCompleteRuleSetUsed());
fci.setPossibleDsepSearchDone(indTestParams.isPossibleDsepDone());
fci.setMaxPathLength(indTestParams.getMaxReachablePathLength());
fci.setDepth(indTestParams.getDepth());
graph = fci.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);
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
IKnowledge knowledge = getParams().getKnowledge();
SearchParams searchParams = getParams();
FciGesIndTestParams indTestParams = (FciGesIndTestParams) searchParams.getIndTestParams();
// Cfci fciSearch =
// new Cfci(getIndependenceTest(), knowledge);
// fciSearch.setDepth(indTestParams.depth());
// Graph graph = fciSearch.search();
//
// if (knowledge.isDefaultToKnowledgeLayout()) {
// SearchGraphUtils.arrangeByKnowledgeTiers(graph, knowledge);
// }
//
// setResultGraph(graph);
Graph graph;
TFciGes fci = new TFciGes(getIndependenceTest());
fci.setKnowledge(knowledge);
fci.setCompleteRuleSetUsed(indTestParams.isCompleteRuleSetUsed());
fci.setPossibleDsepSearchDone(indTestParams.isPossibleDsepDone());
fci.setMaxPathLength(indTestParams.getMaxReachablePathLength());
fci.setDepth(indTestParams.getDepth());
fci.setPenaltyDiscount(indTestParams.getPenaltyDiscount());
fci.setSamplePrior(indTestParams.getSamplePrior());
fci.setStructurePrior(indTestParams.getStructurePrior());
fci.setCompleteRuleSetUsed(false);
fci.setPenaltyDiscount(indTestParams.getPenaltyDiscount());
fci.setFaithfulnessAssumed(indTestParams.isFaithfulnessAssumed());
graph = fci.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);
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
DataModel source = getDataModel();
if (!(source instanceof DataSet)) {
throw new IllegalArgumentException("Expecting a rectangular data set.");
}
DataSet data = (DataSet) source;
if (!data.isContinuous()) {
throw new IllegalArgumentException("Expecting a continuous data set.");
}
Lingam lingam = new Lingam();
lingam.setAlpha(getParams().getIndTestParams().getAlpha());
lingam.setPruningDone(true);
lingam.setAlpha(getParams().getIndTestParams().getAlpha());
GraphWithParameters result = lingam.lingam(data);
setResultGraph(result.getGraph());
GraphUtils.arrangeBySourceGraph(getResultGraph(), getSourceGraph());
}
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);
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the
* extending class.
*/
public void execute() {
IKnowledge knowledge = (IKnowledge) getParams().get("knowledge", new Knowledge2());
Parameters searchParams = getParams();
Parameters params = searchParams;
Graph graph;
IndependenceTest independenceTest = getIndependenceTest();
Score score = new ScoredIndTest(independenceTest);
if (independenceTest instanceof IndTestDSep) {
final DagToPag dagToPag = new DagToPag(((IndTestDSep) independenceTest).getGraph());
dagToPag.setCompleteRuleSetUsed(params.getBoolean("completeRuleSetUsed", false));
graph = dagToPag.convert();
} else {
GFci fci = new GFci(independenceTest, score);
fci.setKnowledge(knowledge);
fci.setCompleteRuleSetUsed(params.getBoolean("completeRuleSetUsed", false));
fci.setMaxPathLength(params.getInt("maxReachablePathLength", -1));
fci.setMaxDegree(params.getInt("maxIndegree"));
fci.setCompleteRuleSetUsed(false);
fci.setFaithfulnessAssumed(params.getBoolean("faithfulnessAssumed", true));
graph = fci.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);
}
/**
* 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));
}