Examples with Dag edu.cmu.tetrad.graph.Dag used on opensource projects

Example 21 with Dag

use of edu.cmu.tetrad.graph.Dag in project tetrad by cmu-phil.

the class EmBayesProperties method setGraph.

public final void setGraph(Graph graph) {
    if (graph == null) {
        throw new NullPointerException();
    }
    List<Node> vars = dataSet.getVariables();
    Map<String, DiscreteVariable> nodesToVars = new HashMap<>();
    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);
    }
    Dag dag = new Dag(graph);
    BayesPm bayesPm = new BayesPm(dag);
    List<Node> nodes = bayesPm.getDag().getNodes();
    for (Node node1 : nodes) {
        Node var = nodesToVars.get(node1.getName());
        if (var != null) {
            DiscreteVariable var2 = (DiscreteVariable) var;
            List<String> categories = var2.getCategories();
            bayesPm.setCategories(node1, categories);
        }
    }
    this.graph = graph;
    this.bayesPm = bayesPm;
    this.blankBayesIm = new MlBayesIm(bayesPm);
}

Example 22 with Dag

use of edu.cmu.tetrad.graph.Dag in project tetrad by cmu-phil.

the class EmBayesProperties method getLikelihoodRatioP.

/**
 * Calculates the p-value of the graph with respect to the given data.
 */
public final double getLikelihoodRatioP() {
    Graph graph1 = getGraph();
    List<Node> nodes = getGraph().getNodes();
    // Null hypothesis = no edges.
    Graph graph0 = new Dag();
    for (Node node : nodes) {
        graph0.addNode(node);
    }
    EmBayesProperties scorer1 = new EmBayesProperties(getDataSet(), graph1);
    EmBayesProperties scorer0 = new EmBayesProperties(getDataSet(), graph0);
    double l1 = scorer1.logProbDataGivenStructure();
    double l0 = scorer0.logProbDataGivenStructure();
    System.out.println("l1 = " + l1);
    System.out.println("l0 = " + l0);
    double chisq = -2.0 * (l0 - l1);
    int n1 = scorer1.numNonredundantParams();
    int n0 = scorer0.numNonredundantParams();
    int df = n1 - n0;
    double pValue = (1.0 - ProbUtils.chisqCdf(chisq, df));
    // System.out.println("\n*** P Value Calculation ***");
    // System.out.println("l1 = " + l1 + " l0 = " + l0 + " l0 - l1 = " + (l0 - l1));
    // System.out.println("n1 = " + n1 + " n0 = " + n0 + " n1 - n0 = " + (n1 - n0));
    // System.out.println("chisq = " + chisq + " pvalue = " + pValue);
    this.pValueDf = df;
    this.chisq = chisq;
    return pValue;
}

Example 23 with Dag

use of edu.cmu.tetrad.graph.Dag in project tetrad by cmu-phil.

the class CptInvariantUpdater method setEvidence.

public void setEvidence(Evidence evidence) {
    if (evidence == null) {
        throw new NullPointerException();
    }
    if (evidence.isIncompatibleWith(bayesIm)) {
        throw new IllegalArgumentException("The variable list for this evidence " + "must be compatible with the variable list of the stored IM.");
    }
    this.evidence = evidence;
    // Create the manipulated Bayes Im.
    Graph graph = bayesIm.getBayesPm().getDag();
    Dag manipulatedGraph = createManipulatedGraph(graph);
    BayesPm manipulatedBayesPm = createManipulatedBayesPm(manipulatedGraph);
    this.manipulatedBayesIm = createdManipulatedBayesIm(manipulatedBayesPm);
    // Create the updated BayesIm from the manipulated Bayes Im.
    Evidence evidence2 = new Evidence(evidence, manipulatedBayesIm);
    this.updatedBayesIm = new UpdatedBayesIm(manipulatedBayesIm, evidence2);
    // Create the marginal calculator from the updated Bayes Im.
    this.cptInvariantMarginalCalculator = new CptInvariantMarginalCalculator(bayesIm, evidence2);
}

Example 24 with Dag

use of edu.cmu.tetrad.graph.Dag in project tetrad by cmu-phil.

the class CptInvariantUpdater method createManipulatedGraph.

// private Dag createManipulatedGraph(Graph graph) {
// Dag updatedGraph = new Dag(graph);
// 
// for (int i = 0; i < bayesIm.getNumNodes(); ++i) {
// if (evidence.isManipulated(i)) {
// Node node = evidence.getNode(i);
// List<Node> parents = updatedGraph.getParents(node);
// 
// for (Node parent1 : parents) {
// updatedGraph.removeEdge(node, parent1);
// }
// }
// }
// 
// return updatedGraph;
// }
private Dag createManipulatedGraph(Graph graph) {
    Dag updatedGraph = new Dag(graph);
    // alters graph for manipulated evidenceItems
    for (int i = 0; i < evidence.getNumNodes(); ++i) {
        if (evidence.isManipulated(i)) {
            Node node = updatedGraph.getNode(evidence.getNode(i).getName());
            List<Node> parents = updatedGraph.getParents(node);
            for (Object parent1 : parents) {
                Node parent = (Node) parent1;
                updatedGraph.removeEdge(node, parent);
            }
        }
    }
    return updatedGraph;
}

Example 25 with Dag

use of edu.cmu.tetrad.graph.Dag in project tetrad by cmu-phil.

the class ApproximateUpdater method createManipulatedGraph.

private Dag createManipulatedGraph(Graph graph) {
    Dag updatedGraph = new Dag(graph);
    // alters graph for manipulated evidenceItems
    for (int i = 0; i < evidence.getNumNodes(); ++i) {
        if (evidence.isManipulated(i)) {
            Node node = evidence.getNode(i);
            node = updatedGraph.getNode(node.getName());
            Collection<Node> parents = updatedGraph.getParents(node);
            for (Node parent1 : parents) {
                updatedGraph.removeEdge(node, parent1);
            }
        }
    }
    return updatedGraph;
}