public static Graph erdosRenyiGraph(int n, int e) {
List<Node> nodes = new ArrayList<Node>();
for (int i = 0; i < n; i++) nodes.add(new GraphNode("X" + i));
Graph graph = new EdgeListGraph(nodes);
for (int e0 = 0; e0 < e; e0++) {
int i1 = RandomUtil.getInstance().nextInt(n);
int i2 = RandomUtil.getInstance().nextInt(n);
if (i1 == i2) {
e0--;
continue;
}
Edge edge = Edges.undirectedEdge(nodes.get(i1), nodes.get(i2));
if (graph.containsEdge(edge)) {
e0--;
continue;
}
graph.addEdge(edge);
}
return graph;
}
/**
* @return Returns the error covariance matrix of the model. i.e. [a][b] is the covariance of E_a
* and E_b, with [a][a] of course being the variance of E_a. THESE ARE NOT PARAMETERS OF THE
* MODEL; THEY ARE CALCULATED. Note that elements of this matrix may be Double.NaN; this
* indicates that these elements cannot be calculated.
*/
private TetradMatrix errCovar(Map<Node, Double> errorVariances) {
List<Node> variableNodes = getVariableNodes();
List<Node> errorNodes = new ArrayList<Node>();
for (Node node : variableNodes) {
errorNodes.add(semGraph.getExogenous(node));
}
TetradMatrix errorCovar = new TetradMatrix(errorVariances.size(), errorVariances.size());
for (int index = 0; index < errorNodes.size(); index++) {
Node error = errorNodes.get(index);
double variance = getErrorVariance(error);
errorCovar.set(index, index, variance);
}
for (int index1 = 0; index1 < errorNodes.size(); index1++) {
for (int index2 = 0; index2 < errorNodes.size(); index2++) {
Node error1 = errorNodes.get(index1);
Node error2 = errorNodes.get(index2);
Edge edge = semGraph.getEdge(error1, error2);
if (edge != null && Edges.isBidirectedEdge(edge)) {
double covariance = getErrorCovariance(error1, error2);
errorCovar.set(index1, index2, covariance);
}
}
}
return errorCovar;
}
private double getPMulticluster(List<List<Integer>> clusters, int numRestarts) {
if (false) {
Graph g = new EdgeListGraph();
List<Node> latents = new ArrayList<Node>();
for (int i = 0; i < clusters.size(); i++) {
GraphNode latent = new GraphNode("L" + i);
latent.setNodeType(NodeType.LATENT);
latents.add(latent);
g.addNode(latent);
List<Node> cluster = variablesForIndices(clusters.get(i));
for (int j = 0; j < cluster.size(); j++) {
g.addNode(cluster.get(j));
g.addDirectedEdge(latent, cluster.get(j));
}
}
SemPm pm = new SemPm(g);
// pm.fixOneLoadingPerLatent();
SemOptimizerPowell semOptimizer = new SemOptimizerPowell();
semOptimizer.setNumRestarts(numRestarts);
SemEstimator est = new SemEstimator(cov, pm, semOptimizer);
est.setScoreType(SemIm.ScoreType.Fgls);
est.estimate();
return est.getEstimatedSem().getPValue();
} else {
double max = Double.NEGATIVE_INFINITY;
for (int i = 0; i < numRestarts; i++) {
Mimbuild2 mimbuild = new Mimbuild2();
List<List<Node>> _clusters = new ArrayList<List<Node>>();
for (List<Integer> _cluster : clusters) {
_clusters.add(variablesForIndices(_cluster));
}
List<String> names = new ArrayList<String>();
for (int j = 0; j < clusters.size(); j++) {
names.add("L" + j);
}
mimbuild.search(_clusters, names, cov);
double c = mimbuild.getpValue();
if (c > max) max = c;
}
return max;
}
}
/** Returns true iif the given set forms a clique in the given graph. */
private static boolean isClique(List<Node> nodes, Graph graph) {
for (int i = 0; i < nodes.size() - 1; i++) {
for (int j = i + 1; j < nodes.size(); j++) {
if (!graph.isAdjacentTo(nodes.get(i), nodes.get(j))) {
return false;
}
}
}
return true;
}
private static double characteristicPathLength(Graph g) {
List<Node> nodes = g.getNodes();
int total = 0;
int count = 0;
for (int i = 0; i < nodes.size(); i++) {
for (int j = i; j < nodes.size(); j++) {
int shortest = shortestPath(nodes.get(i), nodes.get(j), g);
total += shortest;
count++;
}
}
return total / (double) count;
}
private Graph changeLatentNames(Graph full, Clusters measurements, List<String> latentVarList) {
Graph g2 = null;
try {
g2 = (Graph) new MarshalledObject(full).get();
} catch (IOException e) {
e.printStackTrace();
} catch (ClassNotFoundException e) {
e.printStackTrace();
}
for (int i = 0; i < measurements.getNumClusters(); i++) {
List<String> d = measurements.getCluster(i);
String latentName = latentVarList.get(i);
for (Node node : full.getNodes()) {
if (!(node.getNodeType() == NodeType.LATENT)) {
continue;
}
List<Node> _children = full.getChildren(node);
_children.removeAll(ReidentifyVariables.getLatents(full));
List<String> childNames = getNames(_children);
if (new HashSet<String>(childNames).equals(new HashSet<String>(d))) {
g2.getNode(node.getName()).setName(latentName);
}
}
}
return g2;
}
public List<Triple> getUnshieldedCollidersFromGraph(Graph graph) {
List<Triple> colliders = new ArrayList<>();
List<Node> nodes = graph.getNodes();
for (Node b : nodes) {
List<Node> adjacentNodes = graph.getAdjacentNodes(b);
if (adjacentNodes.size() < 2) {
continue;
}
ChoiceGenerator cg = new ChoiceGenerator(adjacentNodes.size(), 2);
int[] combination;
while ((combination = cg.next()) != null) {
Node a = adjacentNodes.get(combination[0]);
Node c = adjacentNodes.get(combination[1]);
// Skip triples that are shielded.
if (graph.isAdjacentTo(a, c)) {
continue;
}
if (graph.isDefCollider(a, b, c)) {
colliders.add(new Triple(a, b, c));
}
}
}
return colliders;
}
private static int weight(List<Node> nodes, Graph graph, int total, int b) {
double p = 1;
int degree = graph.getNumEdges(nodes.get(b));
int t = degree + 1;
total += pow((double) t, p);
return total;
}
// ===========================SCORING METHODS===================//
public double scoreDag(Graph graph) {
Graph dag = new EdgeListGraphSingleConnections(graph);
buildIndexing(graph);
double score = 0.0;
for (Node y : dag.getNodes()) {
Set<Node> parents = new HashSet<Node>(dag.getParents(y));
int nextIndex = -1;
for (int i = 0; i < getVariables().size(); i++) {
nextIndex = hashIndices.get(variables.get(i));
}
int parentIndices[] = new int[parents.size()];
Iterator<Node> pi = parents.iterator();
int count = 0;
while (pi.hasNext()) {
Node nextParent = pi.next();
parentIndices[count++] = hashIndices.get(nextParent);
}
if (this.isDiscrete()) {
score += localDiscreteScore(nextIndex, parentIndices);
} else {
score += localSemScore(nextIndex, parentIndices);
}
}
return score;
}
////////////////////////////////////////////////
// collect in rTupleList all unshielded tuples
////////////////////////////////////////////////
private List<Node[]> getRTuples() {
List<Node[]> rTuples = new ArrayList<Node[]>();
List<Node> nodes = graph.getNodes();
for (Node j : nodes) {
List<Node> adjacentNodes = graph.getAdjacentNodes(j);
if (adjacentNodes.size() < 2) {
continue;
}
ChoiceGenerator cg = new ChoiceGenerator(adjacentNodes.size(), 2);
int[] combination;
while ((combination = cg.next()) != null) {
Node i = adjacentNodes.get(combination[0]);
Node k = adjacentNodes.get(combination[1]);
// Skip triples that are shielded.
if (!graph.isAdjacentTo(i, k)) {
Node[] newTuple = {i, j, k};
rTuples.add(newTuple);
}
}
}
return (rTuples);
}
private boolean clique(Set<Integer> cluster, Map<Node, Set<Node>> adjacencies) {
List<Integer> _cluster = new ArrayList<Integer>(cluster);
for (int i = 0; i < cluster.size(); i++) {
for (int j = i + 1; j < cluster.size(); j++) {
Node nodei = variables.get(_cluster.get(i));
Node nodej = variables.get(_cluster.get(j));
if (!adjacencies.get(nodei).contains(nodej)) {
return false;
}
}
}
return true;
}
/** Removes the knowledge group at the given index. */
public void removeKnowledgeGroup(int index) {
OrderedPair<Set<MyNode>> old = knowledgeGroupRules.get(knowledgeGroups.get(index));
forbiddenRulesSpecs.remove(old);
requiredRulesSpecs.remove(old);
this.knowledgeGroups.remove(index);
}
public static Graph weightedRandomGraph(int n, int e) {
List<Node> nodes = new ArrayList<Node>();
for (int i = 0; i < n; i++) nodes.add(new GraphNode("X" + i));
Graph graph = new EdgeListGraph(nodes);
for (int e0 = 0; e0 < e; e0++) {
int i1 = weightedRandom(nodes, graph);
// int i2 = RandomUtil.getInstance().nextInt(n);
int i2 = weightedRandom(nodes, graph);
if (!(shortestPath(nodes.get(i1), nodes.get(i2), graph) < 9)) {
e0--;
continue;
}
if (i1 == i2) {
e0--;
continue;
}
Edge edge = Edges.undirectedEdge(nodes.get(i1), nodes.get(i2));
if (graph.containsEdge(edge)) {
e0--;
continue;
}
graph.addEdge(edge);
}
for (Edge edge : graph.getEdges()) {
Node n1 = edge.getNode1();
Node n2 = edge.getNode2();
if (!graph.isAncestorOf(n2, n1)) {
graph.removeEdge(edge);
graph.addDirectedEdge(n1, n2);
} else {
graph.removeEdge(edge);
graph.addDirectedEdge(n2, n1);
}
}
return graph;
}
/**
* Step C of PC; orients colliders using specified sepset. That is, orients x *-* y *-* z as x *->
* y <-* z just in case y is in Sepset({x, z}).
*/
public Map<Triple, Double> findCollidersUsingSepsets(
SepsetProducer sepsetProducer, Graph graph, boolean verbose, IKnowledge knowledge) {
TetradLogger.getInstance().log("details", "Starting Collider Orientation:");
Map<Triple, Double> colliders = new HashMap<>();
System.out.println("Looking for colliders");
List<Node> nodes = graph.getNodes();
for (Node b : nodes) {
List<Node> adjacentNodes = graph.getAdjacentNodes(b);
if (adjacentNodes.size() < 2) {
continue;
}
ChoiceGenerator cg = new ChoiceGenerator(adjacentNodes.size(), 2);
int[] combination;
while ((combination = cg.next()) != null) {
Node a = adjacentNodes.get(combination[0]);
Node c = adjacentNodes.get(combination[1]);
// Skip triples that are shielded.
if (graph.isAdjacentTo(a, c)) {
continue;
}
List<Node> sepset = sepsetProducer.getSepset(a, c);
if (sepset == null) continue;
// if (sepsetProducer.getPValue() < 0.5) continue;
if (!sepset.contains(b)) {
if (verbose) {
// boolean dsep = this.dsep.isIndependent(a, c);
// System.out.println("QQQ p = " + independenceTest.getPValue() +
// " " + dsep);
System.out.println(
"\nCollider orientation <" + a + ", " + b + ", " + c + "> sepset = " + sepset);
}
colliders.put(new Triple(a, b, c), sepsetProducer.getPValue());
TetradLogger.getInstance()
.log("colliderOrientations", SearchLogUtils.colliderOrientedMsg(a, b, c, sepset));
}
}
}
TetradLogger.getInstance().log("details", "Finishing Collider Orientation.");
System.out.println("Done finding colliders");
return colliders;
}
private double pValue(Node node, List<Node> parents) {
List<Double> _residuals = new ArrayList<Double>();
Node _target = node;
List<Node> _regressors = parents;
Node target = getVariable(variables, _target.getName());
List<Node> regressors = new ArrayList<Node>();
for (Node _regressor : _regressors) {
Node variable = getVariable(variables, _regressor.getName());
regressors.add(variable);
}
DATASET:
for (int m = 0; m < dataSets.size(); m++) {
RegressionResult result = regressions.get(m).regress(target, regressors);
TetradVector residualsSingleDataset = result.getResiduals();
for (int h = 0; h < residualsSingleDataset.size(); h++) {
if (Double.isNaN(residualsSingleDataset.get(h))) {
continue DATASET;
}
}
DoubleArrayList _residualsSingleDataset =
new DoubleArrayList(residualsSingleDataset.toArray());
double mean = Descriptive.mean(_residualsSingleDataset);
double std =
Descriptive.standardDeviation(
Descriptive.variance(
_residualsSingleDataset.size(),
Descriptive.sum(_residualsSingleDataset),
Descriptive.sumOfSquares(_residualsSingleDataset)));
for (int i2 = 0; i2 < _residualsSingleDataset.size(); i2++) {
// _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean) /
// std);
if (isMeanCenterResiduals()) {
_residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2) - mean));
}
// _residualsSingleDataset.set(i2, (_residualsSingleDataset.get(i2)));
}
for (int k = 0; k < _residualsSingleDataset.size(); k++) {
_residuals.add(_residualsSingleDataset.get(k));
}
}
double[] _f = new double[_residuals.size()];
for (int k = 0; k < _residuals.size(); k++) {
_f[k] = _residuals.get(k);
}
return new AndersonDarlingTest(_f).getP();
}
/**
* Constructs a list of nodes from the given <code>nodes</code> list at the given indices in that
* list.
*
* @param indices The indices of the desired nodes in <code>nodes</code>.
* @param nodes The list of nodes from which we select a sublist.
* @return the The sublist selected.
*/
public static List<Node> asList(int[] indices, List<Node> nodes) {
List<Node> list = new LinkedList<Node>();
for (int i : indices) {
list.add(nodes.get(i));
}
return list;
}
/**
* Performs step C of the algorithm, as indicated on page xxx of CPS, with the modification that
* X--W--Y is oriented as X-->W<--Y if W is *determined by* the sepset of (X, Y), rather than W
* just being *in* the sepset of (X, Y).
*/
public static void pcdOrientC(
SepsetMap set, IndependenceTest test, Knowledge knowledge, Graph graph) {
TetradLogger.getInstance().log("info", "Staring Collider Orientation:");
List<Node> nodes = graph.getNodes();
for (Node y : nodes) {
List<Node> adjacentNodes = graph.getAdjacentNodes(y);
if (adjacentNodes.size() < 2) {
continue;
}
ChoiceGenerator cg = new ChoiceGenerator(adjacentNodes.size(), 2);
int[] combination;
while ((combination = cg.next()) != null) {
Node x = adjacentNodes.get(combination[0]);
Node z = adjacentNodes.get(combination[1]);
// Skip triples that are shielded.
if (graph.isAdjacentTo(x, z)) {
continue;
}
List<Node> sepset = set.get(x, z);
if (sepset == null) {
continue;
}
List<Node> augmentedSet = new LinkedList<Node>(sepset);
augmentedSet.add(y);
if (test.determines(sepset, y)) {
continue;
}
//
if (!test.splitDetermines(sepset, x, z) && test.splitDetermines(augmentedSet, x, z)) {
continue;
}
if (!isArrowpointAllowed(x, y, knowledge) || !isArrowpointAllowed(z, y, knowledge)) {
continue;
}
graph.setEndpoint(x, y, Endpoint.ARROW);
graph.setEndpoint(z, y, Endpoint.ARROW);
TetradLogger.getInstance()
.log("colliderOriented", SearchLogUtils.colliderOrientedMsg(x, y, z));
}
}
TetradLogger.getInstance().log("info", "Finishing Collider Orientation.");
}
public static boolean meekR1Locally2(
Graph graph, Knowledge knowledge, IndependenceTest test, int depth) {
List<Node> nodes = graph.getNodes();
boolean changed = true;
while (changed) {
changed = false;
for (Node a : nodes) {
List<Node> adjacentNodes = graph.getAdjacentNodes(a);
if (adjacentNodes.size() < 2) {
continue;
}
ChoiceGenerator cg = new ChoiceGenerator(adjacentNodes.size(), 2);
int[] combination;
while ((combination = cg.next()) != null) {
Node b = adjacentNodes.get(combination[0]);
Node c = adjacentNodes.get(combination[1]);
// Skip triples that are shielded.
if (graph.isAdjacentTo(b, c)) {
continue;
}
if (graph.getEndpoint(b, a) == Endpoint.ARROW && graph.isUndirectedFromTo(a, c)) {
if (existsLocalSepsetWithoutDet(b, a, c, test, graph, depth)) {
continue;
}
if (isArrowpointAllowed(a, c, knowledge)) {
graph.setEndpoint(a, c, Endpoint.ARROW);
TetradLogger.getInstance()
.edgeOriented(SearchLogUtils.edgeOrientedMsg("Meek R1", graph.getEdge(a, c)));
changed = true;
}
} else if (graph.getEndpoint(c, a) == Endpoint.ARROW && graph.isUndirectedFromTo(a, b)) {
if (existsLocalSepsetWithoutDet(b, a, c, test, graph, depth)) {
continue;
}
if (isArrowpointAllowed(a, b, knowledge)) {
graph.setEndpoint(a, b, Endpoint.ARROW);
TetradLogger.getInstance()
.edgeOriented(SearchLogUtils.edgeOrientedMsg("Meek R1", graph.getEdge(a, b)));
changed = true;
}
}
}
}
}
return changed;
}
private boolean testVanishing(int x, int y, int z, int w) {
if (testType == TestType.TETRAD_DELTA) {
Tetrad t1 =
new Tetrad(variables.get(x), variables.get(y), variables.get(z), variables.get(w));
Tetrad t2 =
new Tetrad(variables.get(x), variables.get(y), variables.get(w), variables.get(z));
double p = deltaTest.getPValue(t1, t2);
return p > alpha;
} else {
return test.tetradHolds(x, y, z, w) && test.tetradHolds(x, y, w, z);
}
}
/** Tests to see if d separation facts are symmetric. */
public void testDSeparation2() {
EdgeListGraphSingleConnections graph =
new EdgeListGraphSingleConnections(
new Dag(GraphUtils.randomGraph(7, 0, 14, 30, 15, 15, true)));
List<Node> nodes = graph.getNodes();
int depth = -1;
for (int i = 0; i < nodes.size(); i++) {
for (int j = i; j < nodes.size(); j++) {
Node x = nodes.get(i);
Node y = nodes.get(j);
List<Node> theRest = new ArrayList<Node>(nodes);
// theRest.remove(x);
// theRest.remove(y);
DepthChoiceGenerator gen = new DepthChoiceGenerator(theRest.size(), depth);
int[] choice;
while ((choice = gen.next()) != null) {
List<Node> z = new LinkedList<Node>();
for (int k = 0; k < choice.length; k++) {
z.add(theRest.get(choice[k]));
}
boolean dConnectedTo = graph.isDConnectedTo(x, y, z);
boolean dConnectedTo1 = graph.isDConnectedTo(y, x, z);
if (dConnectedTo != dConnectedTo1) {
System.out.println(x + " d connected to " + y + " given " + z);
System.out.println(graph);
System.out.println("dconnectedto = " + dConnectedTo);
System.out.println("dconnecteto1 = " + dConnectedTo1);
fail();
}
}
}
}
}
/** Meek's rule R3. If a--b, a--c, a--d, c-->b, c-->b, then orient a-->b. */
public static boolean meekR3(Graph graph, Knowledge knowledge) {
List<Node> nodes = graph.getNodes();
boolean changed = false;
for (Node a : nodes) {
List<Node> adjacentNodes = graph.getAdjacentNodes(a);
if (adjacentNodes.size() < 3) {
continue;
}
for (Node b : adjacentNodes) {
List<Node> otherAdjacents = new LinkedList<Node>(adjacentNodes);
otherAdjacents.remove(b);
if (!graph.isUndirectedFromTo(a, b)) {
continue;
}
ChoiceGenerator cg = new ChoiceGenerator(otherAdjacents.size(), 2);
int[] combination;
while ((combination = cg.next()) != null) {
Node c = otherAdjacents.get(combination[0]);
Node d = otherAdjacents.get(combination[1]);
if (graph.isAdjacentTo(c, d)) {
continue;
}
if (!graph.isUndirectedFromTo(a, c)) {
continue;
}
if (!graph.isUndirectedFromTo(a, d)) {
continue;
}
if (graph.isDirectedFromTo(c, b) && graph.isDirectedFromTo(d, b)) {
if (isArrowpointAllowed(a, b, knowledge)) {
graph.setEndpoint(a, b, Endpoint.ARROW);
TetradLogger.getInstance()
.edgeOriented(SearchLogUtils.edgeOrientedMsg("Meek R3", graph.getEdge(a, b)));
changed = true;
break;
}
}
}
}
}
return changed;
}
private List<Node> variablesForIndices(List<Integer> cluster) {
List<Node> _cluster = new ArrayList<Node>();
for (int c : cluster) {
_cluster.add(variables.get(c));
}
Collections.sort(_cluster);
return _cluster;
}
/** Tests to see if d separation facts are symmetric. */
public void testDSeparation() {
EdgeListGraphSingleConnections graph =
new EdgeListGraphSingleConnections(
new Dag(GraphUtils.randomGraph(7, 0, 7, 30, 15, 15, true)));
System.out.println(graph);
List<Node> nodes = graph.getNodes();
int depth = -1;
for (int i = 0; i < nodes.size(); i++) {
for (int j = i + 1; j < nodes.size(); j++) {
Node x = nodes.get(i);
Node y = nodes.get(j);
List<Node> theRest = new ArrayList<Node>(nodes);
theRest.remove(x);
theRest.remove(y);
DepthChoiceGenerator gen = new DepthChoiceGenerator(theRest.size(), depth);
int[] choice;
while ((choice = gen.next()) != null) {
List<Node> z = new LinkedList<Node>();
for (int k = 0; k < choice.length; k++) {
z.add(theRest.get(choice[k]));
}
if (graph.isDSeparatedFrom(x, y, z) != graph.isDSeparatedFrom(y, x, z)) {
fail(
SearchLogUtils.independenceFact(x, y, z)
+ " should have same d-sep result as "
+ SearchLogUtils.independenceFact(y, x, z));
}
}
}
}
}
public static List<Set<Node>> powerSet(List<Node> nodes) {
List<Set<Node>> subsets = new ArrayList<Set<Node>>();
int total = (int) Math.pow(2, nodes.size());
for (int i = 0; i < total; i++) {
Set<Node> newSet = new HashSet<Node>();
String selection = Integer.toBinaryString(i);
for (int j = selection.length() - 1; j >= 0; j--) {
if (selection.charAt(j) == '1') {
newSet.add(nodes.get(selection.length() - j - 1));
}
}
subsets.add(newSet);
}
return subsets;
}
/** Legacy, do not use. */
public void setKnowledgeGroup(int index, KnowledgeGroup group) {
OrderedPair<Set<MyNode>> o = getGroupRule(group);
OrderedPair<Set<MyNode>> old = knowledgeGroupRules.get(knowledgeGroups.get(index));
forbiddenRulesSpecs.remove(old);
requiredRulesSpecs.remove(old);
if (group.getType() == KnowledgeGroup.FORBIDDEN) {
forbiddenRulesSpecs.add(o);
} else if (group.getType() == KnowledgeGroup.REQUIRED) {
requiredRulesSpecs.add(o);
}
knowledgeGroups.set(index, group);
}
private Graph convertToGraph(Set<Set<Integer>> allClusters) {
Set<Set<Node>> _clustering = new HashSet<Set<Node>>();
for (Set<Integer> cluster : allClusters) {
Set<Node> nodes = new HashSet<Node>();
for (int i : cluster) {
nodes.add(variables.get(i));
}
_clustering.add(nodes);
}
return convertSearchGraphNodes(_clustering);
}
/**
* @param node The variable node in question.
* @return the error node for the given node.
*/
public Node getErrorNode(Node node) {
if (errorNodes.contains(node)) {
return node;
}
int index = variableNodes.indexOf(node);
if (index == -1) {
throw new NullPointerException(
node
+ " is not a node in this model. Perhaps "
+ "it's another node with the same name.");
}
return errorNodes.get(index);
}
public Lofs(Graph pattern, List<DataSet> dataSets) throws IllegalArgumentException {
if (pattern == null) {
throw new IllegalArgumentException("Pattern must be specified.");
}
if (dataSets == null) {
throw new IllegalArgumentException("Data set must be specified.");
}
this.pattern = pattern;
this.dataSets = dataSets;
regressions = new ArrayList<Regression>();
this.variables = dataSets.get(0).getVariables();
for (DataSet dataSet : dataSets) {
regressions.add(new RegressionDataset(dataSet));
}
}
/**
* Step C of PC; orients colliders using specified sepset. That is, orients x *-* y *-* z as x *->
* y <-* z just in case y is in Sepset({x, z}).
*/
public static void orientCollidersUsingSepsets(SepsetMap set, Knowledge knowledge, Graph graph) {
TetradLogger.getInstance().log("info", "Starting Collider Orientation:");
// verifySepsetIntegrity(set, graph);
List<Node> nodes = graph.getNodes();
for (Node a : nodes) {
List<Node> adjacentNodes = graph.getAdjacentNodes(a);
if (adjacentNodes.size() < 2) {
continue;
}
ChoiceGenerator cg = new ChoiceGenerator(adjacentNodes.size(), 2);
int[] combination;
while ((combination = cg.next()) != null) {
Node b = adjacentNodes.get(combination[0]);
Node c = adjacentNodes.get(combination[1]);
// Skip triples that are shielded.
if (graph.isAdjacentTo(b, c)) {
continue;
}
List<Node> sepset = set.get(b, c);
if (sepset != null
&& !sepset.contains(a)
&& isArrowpointAllowed(b, a, knowledge)
&& isArrowpointAllowed(c, a, knowledge)) {
graph.setEndpoint(b, a, Endpoint.ARROW);
graph.setEndpoint(c, a, Endpoint.ARROW);
TetradLogger.getInstance()
.log("colliderOriented", SearchLogUtils.colliderOrientedMsg(b, a, c, sepset));
}
}
}
TetradLogger.getInstance().log("info", "Finishing Collider Orientation.");
}
/** If */
public static boolean meekR2(Graph graph, Knowledge knowledge) {
List<Node> nodes = graph.getNodes();
boolean changed = false;
for (Node a : nodes) {
List<Node> adjacentNodes = graph.getAdjacentNodes(a);
if (adjacentNodes.size() < 2) {
continue;
}
ChoiceGenerator cg = new ChoiceGenerator(adjacentNodes.size(), 2);
int[] combination;
while ((combination = cg.next()) != null) {
Node b = adjacentNodes.get(combination[0]);
Node c = adjacentNodes.get(combination[1]);
if (graph.isDirectedFromTo(b, a)
&& graph.isDirectedFromTo(a, c)
&& graph.isUndirectedFromTo(b, c)) {
if (isArrowpointAllowed(b, c, knowledge)) {
graph.setEndpoint(b, c, Endpoint.ARROW);
TetradLogger.getInstance()
.edgeOriented(SearchLogUtils.edgeOrientedMsg("Meek R2", graph.getEdge(b, c)));
}
} else if (graph.isDirectedFromTo(c, a)
&& graph.isDirectedFromTo(a, b)
&& graph.isUndirectedFromTo(c, b)) {
if (isArrowpointAllowed(c, b, knowledge)) {
graph.setEndpoint(c, b, Endpoint.ARROW);
TetradLogger.getInstance()
.edgeOriented(SearchLogUtils.edgeOrientedMsg("Meek R2", graph.getEdge(c, b)));
}
}
}
}
return changed;
}
