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

Example 26 with Dag

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

the class RowSummingExactUpdater method setEvidence.

public final void setEvidence(Evidence evidence) {
    if (evidence == null) {
        throw new NullPointerException();
    }
    if (evidence.isIncompatibleWith(bayesIm)) {
        throw new IllegalArgumentException("The variable list for the " + "given bayesIm must be compatible with the variable list " + "for this evidence.");
    }
    this.evidence = evidence;
    Graph graph = bayesIm.getBayesPm().getDag();
    Dag manipulatedGraph = createManipulatedGraph(graph);
    BayesPm manipulatedPm = createUpdatedBayesPm(manipulatedGraph);
    this.manipulatedBayesIm = createdUpdatedBayesIm(manipulatedPm);
    for (int i = 0; i < evidence.getNumNodes(); i++) {
        if (evidence.isManipulated(i)) {
            for (int j = 0; j < evidence.getNumCategories(i); j++) {
                if (evidence.getProposition().isAllowed(i, j)) {
                    manipulatedBayesIm.setProbability(i, 0, j, 1.0);
                } else {
                    manipulatedBayesIm.setProbability(i, 0, j, 0.0);
                }
            }
        }
    }
    this.bayesImProbs = new BayesImProbs(manipulatedBayesIm);
    this.updatedBayesIm = null;
}

Example 27 with Dag

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

the class XdslXmlParser method buildIM.

private BayesIm buildIM(Element element0, Map<String, String> displayNames) {
    Elements elements = element0.getChildElements();
    for (int i = 0; i < elements.size(); i++) {
        if (!"cpt".equals(elements.get(i).getQualifiedName())) {
            throw new IllegalArgumentException("Expecting cpt element.");
        }
    }
    Dag dag = new Dag();
    // Get the nodes.
    for (int i = 0; i < elements.size(); i++) {
        Element cpt = elements.get(i);
        String name = cpt.getAttribute(0).getValue();
        if (displayNames == null) {
            dag.addNode(new GraphNode(name));
        } else {
            dag.addNode(new GraphNode(displayNames.get(name)));
        }
    }
    // Get the edges.
    for (int i = 0; i < elements.size(); i++) {
        Element cpt = elements.get(i);
        Elements cptElements = cpt.getChildElements();
        for (int j = 0; j < cptElements.size(); j++) {
            Element cptElement = cptElements.get(j);
            if (cptElement.getQualifiedName().equals("parents")) {
                String list = cptElement.getValue();
                String[] parentNames = list.split(" ");
                for (String name : parentNames) {
                    if (displayNames == null) {
                        edu.cmu.tetrad.graph.Node parent = dag.getNode(name);
                        edu.cmu.tetrad.graph.Node child = dag.getNode(cpt.getAttribute(0).getValue());
                        dag.addDirectedEdge(parent, child);
                    } else {
                        edu.cmu.tetrad.graph.Node parent = dag.getNode(displayNames.get(name));
                        edu.cmu.tetrad.graph.Node child = dag.getNode(displayNames.get(cpt.getAttribute(0).getValue()));
                        dag.addDirectedEdge(parent, child);
                    }
                }
            }
        }
        String name;
        if (displayNames == null) {
            name = cpt.getAttribute(0).getValue();
        } else {
            name = displayNames.get(cpt.getAttribute(0).getValue());
        }
        dag.addNode(new GraphNode(name));
    }
    // PM
    BayesPm pm = new BayesPm(dag);
    for (int i = 0; i < elements.size(); i++) {
        Element cpt = elements.get(i);
        String varName = cpt.getAttribute(0).getValue();
        Node node;
        if (displayNames == null) {
            node = dag.getNode(varName);
        } else {
            node = dag.getNode(displayNames.get(varName));
        }
        Elements cptElements = cpt.getChildElements();
        List<String> stateNames = new ArrayList<>();
        for (int j = 0; j < cptElements.size(); j++) {
            Element cptElement = cptElements.get(j);
            if (cptElement.getQualifiedName().equals("state")) {
                Attribute attribute = cptElement.getAttribute(0);
                String stateName = attribute.getValue();
                stateNames.add(stateName);
            }
        }
        pm.setCategories(node, stateNames);
    }
    // IM
    BayesIm im = new MlBayesIm(pm);
    for (int nodeIndex = 0; nodeIndex < elements.size(); nodeIndex++) {
        Element cpt = elements.get(nodeIndex);
        Elements cptElements = cpt.getChildElements();
        for (int j = 0; j < cptElements.size(); j++) {
            Element cptElement = cptElements.get(j);
            if (cptElement.getQualifiedName().equals("probabilities")) {
                String list = cptElement.getValue();
                String[] probsStrings = list.split(" ");
                List<Double> probs = new ArrayList<>();
                for (String probString : probsStrings) {
                    probs.add(Double.parseDouble(probString));
                }
                int count = -1;
                for (int row = 0; row < im.getNumRows(nodeIndex); row++) {
                    for (int col = 0; col < im.getNumColumns(nodeIndex); col++) {
                        im.setProbability(nodeIndex, row, col, probs.get(++count));
                    }
                }
            }
        }
    }
    return im;
}

Example 28 with Dag

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

the class TestDiscretizer method testManualDiscretize2.

@Test
public void testManualDiscretize2() {
    List<Node> nodes1 = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes1.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes1, 0, 5, 3, 3, 3, false));
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateData(100, false);
    List<Node> nodes = data.getVariables();
    Discretizer discretizer = new Discretizer(data);
    discretizer.equalCounts(nodes.get(0), 3);
    discretizer.equalIntervals(nodes.get(1), 2);
    discretizer.equalCounts(nodes.get(2), 5);
    discretizer.equalIntervals(nodes.get(3), 8);
    discretizer.equalCounts(nodes.get(4), 4);
    DataSet discretized = discretizer.discretize();
    assertEquals(2, maxInColumn(discretized, 0));
    assertEquals(1, maxInColumn(discretized, 1));
    assertEquals(4, maxInColumn(discretized, 2));
    assertEquals(7, maxInColumn(discretized, 3));
    assertEquals(3, maxInColumn(discretized, 4));
}

Example 29 with Dag

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

the class TestDiscretizer method testManualDiscretize3.

@Test
public void testManualDiscretize3() {
    List<Node> nodes1 = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes1.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes1, 0, 5, 3, 3, 3, false));
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateData(100, false);
    List<Node> nodes = data.getVariables();
    Discretizer discretizer = new Discretizer(data);
    discretizer.setVariablesCopied(true);
    discretizer.setVariablesCopied(true);
    discretizer.equalCounts(nodes.get(0), 3);
    DataSet discretized = discretizer.discretize();
    assertTrue(discretized.getVariable(0) instanceof DiscreteVariable);
    assertTrue(discretized.getVariable(1) instanceof ContinuousVariable);
    assertTrue(discretized.getVariable(2) instanceof ContinuousVariable);
    assertTrue(discretized.getVariable(3) instanceof ContinuousVariable);
    assertTrue(discretized.getVariable(4) instanceof ContinuousVariable);
}

Example 30 with Dag

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

the class TestEvidence method sampleBayesIm2.

private static BayesIm sampleBayesIm2() {
    Node a = new GraphNode("a");
    Node b = new GraphNode("b");
    Node c = new GraphNode("c");
    Dag graph;
    graph = new Dag();
    graph.addNode(a);
    graph.addNode(b);
    graph.addNode(c);
    graph.addDirectedEdge(a, b);
    graph.addDirectedEdge(a, c);
    graph.addDirectedEdge(b, c);
    BayesPm bayesPm = new BayesPm(graph);
    bayesPm.setNumCategories(b, 3);
    BayesIm bayesIm1 = new MlBayesIm(bayesPm);
    bayesIm1.setProbability(0, 0, 0, .3);
    bayesIm1.setProbability(0, 0, 1, .7);
    bayesIm1.setProbability(1, 0, 0, .3);
    bayesIm1.setProbability(1, 0, 1, .4);
    bayesIm1.setProbability(1, 0, 2, .3);
    bayesIm1.setProbability(1, 1, 0, .6);
    bayesIm1.setProbability(1, 1, 1, .1);
    bayesIm1.setProbability(1, 1, 2, .3);
    bayesIm1.setProbability(2, 0, 0, .9);
    bayesIm1.setProbability(2, 0, 1, .1);
    bayesIm1.setProbability(2, 1, 0, .1);
    bayesIm1.setProbability(2, 1, 1, .9);
    bayesIm1.setProbability(2, 2, 0, .5);
    bayesIm1.setProbability(2, 2, 1, .5);
    bayesIm1.setProbability(2, 3, 0, .2);
    bayesIm1.setProbability(2, 3, 1, .8);
    bayesIm1.setProbability(2, 4, 0, .6);
    bayesIm1.setProbability(2, 4, 1, .4);
    bayesIm1.setProbability(2, 5, 0, .7);
    bayesIm1.setProbability(2, 5, 1, .3);
    return bayesIm1;
}