void storeLatestWorkbenchGraph() {
Graph latestWorkbenchGraph = workbench.getGraph();
if (latestWorkbenchGraph.getNumNodes() == 0) {
return;
}
SearchParams searchParams = algorithmRunner.getParams();
try {
Graph graph = new MarshalledObject<Graph>(latestWorkbenchGraph).get();
if (graph == null) {
throw new NullPointerException("Null graph");
}
if (searchParams != null) {
searchParams.setSourceGraph(graph);
}
} catch (IOException e) {
e.printStackTrace();
if (searchParams != null) {
searchParams.setSourceGraph(null);
}
} catch (ClassNotFoundException e) {
if (searchParams != null) {
searchParams.setSourceGraph(null);
}
e.printStackTrace();
}
}
/**
* Provides methods for estimating a Bayes IM from an existing BayesIM and a discrete dataset
* using EM (Expectation Maximization). The data columns in the given data must be equal to a
* variable in the given Bayes IM but the latter may contain variables which don't occur in the
* dataset (latent variables). The first argument of the constructoris the BayesPm whose graph
* contains latent and observed variables. The second is the dataset of observed variables;
* missing value codes may be present.
*/
public EmBayesEstimator(BayesPm bayesPm, DataSet dataSet) {
if (bayesPm == null) {
throw new NullPointerException();
}
if (dataSet == null) {
throw new NullPointerException();
}
List<Node> observedVars = new ArrayList<Node>();
this.bayesPm = bayesPm;
this.dataSet = dataSet;
// this.variables = Collections.unmodifiableList(vars);
graph = bayesPm.getDag();
this.nodes = new Node[graph.getNumNodes()];
Iterator<Node> it = graph.getNodes().iterator();
for (int i = 0; i < this.nodes.length; i++) {
this.nodes[i] = it.next();
// System.out.println("node " + i + " " + nodes[i]);
}
for (int i = 0; i < this.nodes.length; i++) {
if (nodes[i].getNodeType() == NodeType.MEASURED) {
observedVars.add(bayesPm.getVariable(nodes[i]));
}
}
// Make sure variables in dataset are measured variables in the BayesPM
// for (Node dataSetVariable : this.dataSet.getVariables()) {
// if (!observedVars.contains(dataSetVariable)) {
// throw new IllegalArgumentException(
// "Some var in the dataset is not a " +
// "measured variable in the Bayes net");
// }
// }
// Make sure all measured variables in the BayesPm are in the discrete dataset
for (Node observedVar : observedVars) {
try {
this.dataSet.getVariable(observedVar.getName());
} catch (Exception e) {
throw new IllegalArgumentException(
"Some observed var in the Bayes net " + "is not in the dataset: " + observedVar);
}
}
findBayesNetObserved(); // Sets bayesPmObs
initialize();
}
public void closeEmptySessions() {
JInternalFrame[] frames = desktopPane.getAllFramesInLayer(0);
for (JInternalFrame frame : frames) {
Object o = frame.getContentPane().getComponents()[0];
if (o instanceof SessionEditor) {
SessionEditor sessionEditor = (SessionEditor) o;
SessionEditorWorkbench workbench = sessionEditor.getSessionWorkbench();
Graph graph = workbench.getGraph();
if (graph.getNumNodes() == 0) {
frame.dispose();
}
}
}
}
