private String clusterSizes(List<List<Node>> partition, List<List<Node>> trueClusters) {
String s = "";
FOR:
for (int i = 0; i < partition.size(); i++) {
List<Node> cluster = partition.get(i);
s += cluster.size();
for (List<Node> trueCluster : trueClusters) {
if (trueCluster.containsAll(cluster)) {
// Collections.sort(trueCluster);
// Collections.sort(cluster);
// System.out.println(trueCluster + " " + cluster);
s += "p";
if (i < partition.size() - 1) {
s += ",";
}
continue FOR;
}
}
if (i < partition.size() - 1) {
s += ",";
}
}
return s;
}
private boolean containsImpureCluster(List<List<Node>> partition, List<List<Node>> trueClusters) {
FOR:
for (int i = 0; i < partition.size(); i++) {
List<Node> cluster = partition.get(i);
for (List<Node> trueCluster : trueClusters) {
if (trueCluster.containsAll(cluster)) {
continue FOR;
}
}
return true;
}
return false;
}
/**
* Runs PC starting with a commplete graph over the given list of nodes, using the given
* independence test and knowledge and returns the resultant graph. The returned graph will be a
* pattern if the independence information is consistent with the hypothesis that there are no
* latent common causes. It may, however, contain cycles or bidirected edges if this assumption is
* not born out, either due to the actual presence of latent common causes, or due to statistical
* errors in conditional independence judgments.
*
* <p>All of the given nodes must be in the domain of the given conditional independence test.
*/
public Graph search(List<Node> nodes) {
this.logger.log("info", "Starting PC algorithm");
this.logger.log("info", "Independence test = " + getIndependenceTest() + ".");
if (trueDag != null) {
this.dsep = new IndTestDSep(trueDag);
}
long startTime = System.currentTimeMillis();
if (getIndependenceTest() == null) {
throw new NullPointerException();
}
List<Node> allNodes = getIndependenceTest().getVariables();
if (!allNodes.containsAll(nodes)) {
throw new IllegalArgumentException(
"All of the given nodes must " + "be in the domain of the independence test provided.");
}
IFas fas = new Fas2(getIndependenceTest());
fas.setInitialGraph(initialGraph);
fas.setKnowledge(getKnowledge());
fas.setDepth(getDepth());
fas.setVerbose(verbose);
graph = fas.search();
SearchGraphUtils.pcOrientbk(knowledge, graph, nodes);
// independenceTest = new ProbabilisticMAPIndependence((DataSet)
// independenceTest.getData());
SepsetsMaxPValue sepsetProducer =
new SepsetsMaxPValue(graph, independenceTest, null, getDepth());
sepsetProducer.setDsep(dsep);
addColliders(graph, sepsetProducer, knowledge);
MeekRules rules = new MeekRules();
rules.setKnowledge(knowledge);
rules.orientImplied(graph);
// Graph pattern = new EdgeListGraphSingleConnections(graph);
//
// for (Node x : getNodes()) {
// for (Node y : getNodes()) {
// if (x == y) continue;
//
// if (!localMarkovIndep(x, y, pattern, independenceTest)) {
// graph.addUndirectedEdge(x, y);
// }
// }
// }
//
// fas = new FasStableConcurrent(getIndependenceTest());
// fas.setInitialGraph(new EdgeListGraphSingleConnections(graph));
// fas.setKnowledge(getKnowledge());
// fas.setDepth(getDepth());
// fas.setVerbose(verbose);
// graph = fas.search();
//
// sepsetProducer = new SepsetsMaxPValue(graph, independenceTest, null, getDepth());
//
// addColliders(graph, sepsetProducer, knowledge);
//
// rules = new MeekRules();
// rules.setKnowledge(knowledge);
// rules.orientImplied(graph);
this.logger.log("graph", "\nReturning this graph: " + graph);
this.elapsedTime = System.currentTimeMillis() - startTime;
this.logger.log("info", "Elapsed time = " + (elapsedTime) / 1000. + " s");
this.logger.log("info", "Finishing PC Algorithm.");
this.logger.flush();
return graph;
}