private Graph condense(Graph mimStructure, Graph mimbuildStructure) {
// System.out.println("Uncondensed: " + mimbuildStructure);
Map<Node, Node> substitutions = new HashMap<Node, Node>();
for (Node node : mimbuildStructure.getNodes()) {
for (Node _node : mimStructure.getNodes()) {
if (node.getName().startsWith(_node.getName())) {
substitutions.put(node, _node);
break;
}
substitutions.put(node, node);
}
}
HashSet<Node> nodes = new HashSet<Node>(substitutions.values());
Graph graph = new EdgeListGraph(new ArrayList<Node>(nodes));
for (Edge edge : mimbuildStructure.getEdges()) {
Node node1 = substitutions.get(edge.getNode1());
Node node2 = substitutions.get(edge.getNode2());
if (node1 == node2) continue;
if (graph.isAdjacentTo(node1, node2)) continue;
graph.addEdge(new Edge(node1, node2, edge.getEndpoint1(), edge.getEndpoint2()));
}
// System.out.println("Condensed: " + graph);
return graph;
}
private String reportIfDiscrete(Graph dag, DataSet dataSet) {
List vars = dataSet.getVariables();
Map<String, DiscreteVariable> nodesToVars = new HashMap<String, DiscreteVariable>();
for (int i = 0; i < dataSet.getNumColumns(); i++) {
DiscreteVariable var = (DiscreteVariable) vars.get(i);
String name = var.getName();
Node node = new GraphNode(name);
nodesToVars.put(node.getName(), var);
}
BayesPm bayesPm = new BayesPm(new Dag(dag));
List<Node> nodes = bayesPm.getDag().getNodes();
for (Node node : nodes) {
Node var = nodesToVars.get(node.getName());
if (var instanceof DiscreteVariable) {
DiscreteVariable var2 = nodesToVars.get(node.getName());
int numCategories = var2.getNumCategories();
List<String> categories = new ArrayList<String>();
for (int j = 0; j < numCategories; j++) {
categories.add(var2.getCategory(j));
}
bayesPm.setCategories(node, categories);
}
}
BayesProperties properties = new BayesProperties(dataSet, dag);
properties.setGraph(dag);
NumberFormat nf = NumberFormat.getInstance();
nf.setMaximumFractionDigits(4);
StringBuilder buf = new StringBuilder();
buf.append("\nP-value = ").append(properties.getLikelihoodRatioP());
buf.append("\nDf = ").append(properties.getPValueDf());
buf.append("\nChi square = ").append(nf.format(properties.getPValueChisq()));
buf.append("\nBIC score = ").append(nf.format(properties.getBic()));
buf.append("\n\nH0: Completely disconnected graph.");
return buf.toString();
}
private static int shortestPath(Node n1, Node n2, Graph g) {
Queue<Node> Q = new ArrayDeque<Node>();
Map<Node, Node> V = new HashMap<Node, Node>();
Q.offer(n1);
V.put(n1, null);
while (!Q.isEmpty()) {
Node m = Q.poll();
if (V.containsKey(n2)) break;
for (Node p : g.getAdjacentNodes(m)) {
if (V.containsKey(p)) continue;
Q.offer(p);
V.put(p, m);
}
}
int s = 0;
do {
s++;
n2 = V.get(n2);
} while (n2 != null);
return s;
}
public void testAlternativeGraphs() {
// UniformGraphGenerator gen = new UniformGraphGenerator(UniformGraphGenerator.ANY_DAG);
// gen.setNumNodes(100);
// gen.setMaxEdges(200);
// gen.setMaxDegree(30);
// gen.setMaxInDegree(30);
// gen.setMaxOutDegree(30);
//// gen.setNumIterations(3000000);
// gen.setResamplingDegree(10);
//
// gen.generate();
//
// Graph graph = gen.getDag();
Graph graph = weightedRandomGraph(250, 400);
List<Integer> degreeCounts = new ArrayList<Integer>();
Map<Integer, Integer> degreeCount = new HashMap<Integer, Integer>();
for (Node node : graph.getNodes()) {
int degree = graph.getNumEdges(node);
degreeCounts.add(degree);
if (degreeCount.get(degree) == null) {
degreeCount.put(degree, 0);
}
degreeCount.put(degree, degreeCount.get(degree) + 1);
}
Collections.sort(degreeCounts);
System.out.println(degreeCounts);
List<Integer> _degrees = new ArrayList<Integer>(degreeCount.keySet());
Collections.sort(_degrees);
for (int i : _degrees) {
int j = degreeCount.get(i);
// System.out.println(i + " " + j);
System.out.println(log(i + 1) + " " + log(j));
}
System.out.println("\nCPL = " + characteristicPathLength(graph));
Graph erGraph = erdosRenyiGraph(200, 200);
System.out.println("\n ER CPL = " + characteristicPathLength(erGraph));
}