/**
* Sets the coefficient for the a->b edge to the given coefficient, if within range. Otherwise
* does nothing.
*
* @param a a -> b
* @param b a -> b
* @param coef The coefficient of a -> b.
* @return true if the coefficent was set (i.e. was within range), false if not.
*/
public boolean setEdgeCoefficient(Node a, Node b, final double coef) {
Edge edge = Edges.directedEdge(a, b);
if (edgeParameters.get(edge) == null) {
throw new NullPointerException("Not a coefficient parameter in this model: " + edge);
}
if (editingEdge == null || !edge.equals(editingEdge)) {
range = getParameterRange(edge);
editingEdge = edge;
}
if (coef > range.getLow() && coef < range.getHigh()) {
edgeParameters.put(edge, coef);
return true;
}
return false;
// if (!paramInBounds(edge, coef)) {
// edgeParameters.put(edge, d);
// return false;
// }
//
// edgeParameters.put(edge, coef);
// return true;
}
/**
* @param a a->b
* @param b a->b
* @return The coefficient for a->b.
*/
public double getEdgeCoefficient(Node a, Node b) {
Edge edge = Edges.directedEdge(a, b);
Double d = edgeParameters.get(edge);
if (d == null) {
return Double.NaN;
// throw new IllegalArgumentException("Not a directed edge in this model: " +
// edge);
}
return d;
}
private void ruleR1(Graph skeleton, Graph graph, List<Node> nodes) {
for (Node node : nodes) {
SortedMap<Double, String> scoreReports = new TreeMap<Double, String>();
List<Node> adj = skeleton.getAdjacentNodes(node);
DepthChoiceGenerator gen = new DepthChoiceGenerator(adj.size(), adj.size());
int[] choice;
double maxScore = Double.NEGATIVE_INFINITY;
List<Node> parents = null;
while ((choice = gen.next()) != null) {
List<Node> _parents = GraphUtils.asList(choice, adj);
double score = score(node, _parents);
scoreReports.put(-score, _parents.toString());
if (score > maxScore) {
maxScore = score;
parents = _parents;
}
}
for (double score : scoreReports.keySet()) {
TetradLogger.getInstance()
.log(
"score",
"For " + node + " parents = " + scoreReports.get(score) + " score = " + -score);
}
TetradLogger.getInstance().log("score", "");
if (parents == null) {
continue;
}
if (normal(node, parents)) continue;
for (Node _node : adj) {
if (parents.contains(_node)) {
Edge parentEdge = Edges.directedEdge(_node, node);
if (!graph.containsEdge(parentEdge)) {
graph.addEdge(parentEdge);
}
}
}
}
}
private Graph pickDag(Graph graph) {
SearchGraphUtils.basicPattern(graph, false);
addRequiredEdges(graph);
boolean containsUndirected;
do {
containsUndirected = false;
for (Edge edge : graph.getEdges()) {
if (Edges.isUndirectedEdge(edge)) {
containsUndirected = true;
graph.removeEdge(edge);
Edge _edge = Edges.directedEdge(edge.getNode1(), edge.getNode2());
graph.addEdge(_edge);
}
}
meekOrient(graph, getKnowledge());
} while (containsUndirected);
return graph;
}
public ParameterRange getCoefficientRange(Node a, Node b) {
return getParameterRange(Edges.directedEdge(a, b));
}
/**
* Constructs a new standardized SEM IM from the freeParameters in the given SEM IM.
*
* @param im Stop asking me for these things! The given SEM IM!!!
* @param initialization CALCULATE_FROM_SEM if the initial values will be calculated from the
* given SEM IM; INITIALIZE_FROM_DATA if data will be simulated from the given SEM,
* standardized, and estimated.
*/
public StandardizedSemIm(SemIm im, Initialization initialization) {
this.semPm = new SemPm(im.getSemPm());
this.semGraph = new SemGraph(semPm.getGraph());
semGraph.setShowErrorTerms(true);
if (semGraph.existsDirectedCycle()) {
throw new IllegalArgumentException("The cyclic case is not handled.");
}
if (initialization == Initialization.CALCULATE_FROM_SEM) {
// This code calculates the new coefficients directly from the old ones.
edgeParameters = new HashMap<Edge, Double>();
List<Node> nodes = im.getVariableNodes();
TetradMatrix impliedCovar = im.getImplCovar(true);
for (Parameter parameter : im.getSemPm().getParameters()) {
if (parameter.getType() == ParamType.COEF) {
Node a = parameter.getNodeA();
Node b = parameter.getNodeB();
int aindex = nodes.indexOf(a);
int bindex = nodes.indexOf(b);
double vara = impliedCovar.get(aindex, aindex);
double stda = Math.sqrt(vara);
double varb = impliedCovar.get(bindex, bindex);
double stdb = Math.sqrt(varb);
double oldCoef = im.getEdgeCoef(a, b);
double newCoef = (stda / stdb) * oldCoef;
edgeParameters.put(Edges.directedEdge(a, b), newCoef);
} else if (parameter.getType() == ParamType.COVAR) {
Node a = parameter.getNodeA();
Node b = parameter.getNodeB();
Node exoa = semGraph.getExogenous(a);
Node exob = semGraph.getExogenous(b);
double covar = im.getErrCovar(a, b) / Math.sqrt(im.getErrVar(a) * im.getErrVar(b));
edgeParameters.put(Edges.bidirectedEdge(exoa, exob), covar);
}
}
} else {
// This code estimates the new coefficients from simulated data from the old model.
DataSet dataSet = im.simulateData(1000, false);
TetradMatrix _dataSet = dataSet.getDoubleData();
_dataSet = DataUtils.standardizeData(_dataSet);
DataSet dataSetStandardized = ColtDataSet.makeData(dataSet.getVariables(), _dataSet);
SemEstimator estimator = new SemEstimator(dataSetStandardized, im.getSemPm());
SemIm imStandardized = estimator.estimate();
edgeParameters = new HashMap<Edge, Double>();
for (Parameter parameter : imStandardized.getSemPm().getParameters()) {
if (parameter.getType() == ParamType.COEF) {
Node a = parameter.getNodeA();
Node b = parameter.getNodeB();
double coef = imStandardized.getEdgeCoef(a, b);
edgeParameters.put(Edges.directedEdge(a, b), coef);
} else if (parameter.getType() == ParamType.COVAR) {
Node a = parameter.getNodeA();
Node b = parameter.getNodeB();
Node exoa = semGraph.getExogenous(a);
Node exob = semGraph.getExogenous(b);
double covar = -im.getErrCovar(a, b) / Math.sqrt(im.getErrVar(a) * im.getErrVar(b));
edgeParameters.put(Edges.bidirectedEdge(exoa, exob), covar);
}
}
}
this.measuredNodes = Collections.unmodifiableList(semPm.getMeasuredNodes());
}
