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

Example 36 with Dag

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

the class TestRowSummingUpdater method sampleBayesIm1.

private BayesIm sampleBayesIm1() {
    Node x = new GraphNode("x");
    Node z = new GraphNode("z");
    Dag graph = new Dag();
    graph.addNode(x);
    graph.addNode(z);
    graph.addDirectedEdge(x, z);
    BayesPm bayesPm = new BayesPm(graph);
    BayesIm bayesIm1 = new MlBayesIm(bayesPm);
    bayesIm1.setProbability(0, 0, 0, .3);
    bayesIm1.setProbability(0, 0, 1, .7);
    bayesIm1.setProbability(1, 0, 0, .8);
    bayesIm1.setProbability(1, 0, 1, .2);
    bayesIm1.setProbability(1, 1, 0, .4);
    bayesIm1.setProbability(1, 1, 1, .6);
    return bayesIm1;
}

Example 37 with Dag

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

the class TestRowSummingUpdater method testUpdate5.

@Test
public void testUpdate5() {
    Node x0Node = new GraphNode("X0");
    Node x1Node = new GraphNode("X1");
    Node x2Node = new GraphNode("X2");
    Node x3Node = new GraphNode("X3");
    Node x4Node = new GraphNode("X4");
    Dag graph = new Dag();
    graph.addNode(x0Node);
    graph.addNode(x1Node);
    graph.addNode(x2Node);
    graph.addNode(x3Node);
    graph.addNode(x4Node);
    graph.addDirectedEdge(x0Node, x1Node);
    graph.addDirectedEdge(x0Node, x2Node);
    graph.addDirectedEdge(x1Node, x3Node);
    graph.addDirectedEdge(x2Node, x3Node);
    graph.addDirectedEdge(x4Node, x0Node);
    graph.addDirectedEdge(x4Node, x2Node);
    BayesPm bayesPm = new BayesPm(graph);
    MlBayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
    int x1 = bayesIm.getNodeIndex(x1Node);
    int x2 = bayesIm.getNodeIndex(x2Node);
    int x3 = bayesIm.getNodeIndex(x3Node);
    Evidence evidence = Evidence.tautology(bayesIm);
    evidence.getProposition().setCategory(x1, 1);
    evidence.getProposition().setCategory(x2, 0);
    evidence.getNodeIndex("X1");
    BayesUpdater updater1 = new CptInvariantUpdater(bayesIm);
    updater1.setEvidence(evidence);
    BayesUpdater updater2 = new RowSummingExactUpdater(bayesIm);
    updater2.setEvidence(evidence);
    double marginal1 = updater1.getMarginal(x3, 0);
    double marginal2 = updater2.getMarginal(x3, 0);
    assertEquals(marginal1, marginal2, 0.000001);
}

Example 38 with Dag

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

the class TestSemXml method sampleSemIm1.

private static SemIm sampleSemIm1() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 30, 15, 15, true));
    SemPm pm = new SemPm(graph);
    return new SemIm(pm);
}

Example 39 with Dag

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

the class TestStatUtils method testConditionalCorrelation.

/**
 * Tests that the unconditional correlations and covariances are correct,
 * at least for the unconditional tests.
 */
@Test
public void testConditionalCorrelation() {
    RandomUtil.getInstance().setSeed(30299533L);
    // Make sure the unconditional correlations and covariances are OK.
    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 dataSet = im.simulateData(1000, false);
    double[] x = dataSet.getDoubleData().getColumn(0).toArray();
    double[] y = dataSet.getDoubleData().getColumn(1).toArray();
    double r1 = StatUtils.correlation(x, y);
    double s1 = StatUtils.covariance(x, y);
    double v1 = StatUtils.variance(x);
    double sd1 = StatUtils.sd(x);
    ICovarianceMatrix cov = new CovarianceMatrix(dataSet);
    TetradMatrix _cov = cov.getMatrix();
    double r2 = StatUtils.partialCorrelation(_cov, 0, 1);
    double s2 = StatUtils.partialCovariance(_cov, 0, 1);
    double v2 = StatUtils.partialVariance(_cov, 0);
    double sd2 = StatUtils.partialStandardDeviation(_cov, 0);
    assertEquals(r1, r2, .1);
    assertEquals(s1, s2, .1);
    assertEquals(v1, v2, .1);
    assertEquals(sd1, sd2, 0.1);
}

Example 40 with Dag

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

the class TestUpdaterJointMarginal method testEstimate1.

@Test
public void testEstimate1() {
    Dag graph = new Dag();
    Node L1 = new GraphNode("L1");
    Node X1 = new GraphNode("X1");
    Node X2 = new GraphNode("X2");
    Node X3 = new GraphNode("X3");
    L1.setNodeType(NodeType.MEASURED);
    X1.setNodeType(NodeType.MEASURED);
    X2.setNodeType(NodeType.MEASURED);
    X3.setNodeType(NodeType.MEASURED);
    graph.addNode(L1);
    graph.addNode(X1);
    graph.addNode(X2);
    graph.addNode(X3);
    graph.addDirectedEdge(L1, X1);
    graph.addDirectedEdge(L1, X2);
    graph.addDirectedEdge(L1, X3);
    BayesPm bayesPm = new BayesPm(graph);
    bayesPm.setNumCategories(L1, 2);
    bayesPm.setNumCategories(X1, 2);
    bayesPm.setNumCategories(X2, 2);
    bayesPm.setNumCategories(X3, 2);
    BayesIm estimatedIm = new MlBayesIm(bayesPm);
    Node l1Node = graph.getNode("L1");
    // int l1Index = bayesImMixed.getNodeIndex(l1Node);
    int l1Index = estimatedIm.getNodeIndex(l1Node);
    Node x1Node = graph.getNode("X1");
    // int x1Index = bayesImMixed.getNodeIndex(x1Node);
    int x1Index = estimatedIm.getNodeIndex(x1Node);
    Node x2Node = graph.getNode("X2");
    // int x2Index = bayesImMixed.getNodeIndex(x2Node);
    int x2Index = estimatedIm.getNodeIndex(x2Node);
    Node x3Node = graph.getNode("X3");
    // int x3Index = bayesImMixed.getNodeIndex(x3Node);
    int x3Index = estimatedIm.getNodeIndex(x3Node);
    // bayesImMixed.setProbability(l1Index, 0, 0, 0.5);
    // bayesImMixed.setProbability(l1Index, 0, 1, 0.5);
    estimatedIm.setProbability(l1Index, 0, 0, 0.5);
    estimatedIm.setProbability(l1Index, 0, 1, 0.5);
    // bayesImMixed.setProbability(x1Index, 0, 0, 0.33333);
    // bayesImMixed.setProbability(x1Index, 0, 1, 0.66667);
    estimatedIm.setProbability(x1Index, 0, 0, // p(x1 = 0 | l1 = 0)
    0.33333);
    estimatedIm.setProbability(x1Index, 0, 1, // p(x1 = 1 | l1 = 0)
    0.66667);
    estimatedIm.setProbability(x1Index, 1, 0, // p(x1 = 0 | l1 = 1)
    0.66667);
    estimatedIm.setProbability(x1Index, 1, 1, // p(x1 = 1 | l1 = 1)
    0.33333);
    // bayesImMixed.setProbability(x2Index, 1, 0, 0.66667);
    // bayesImMixed.setProbability(x2Index, 1, 1, 0.33333);
    estimatedIm.setProbability(x2Index, 1, 0, // p(x2 = 0 | l1 = 1)
    0.66667);
    estimatedIm.setProbability(x2Index, 1, 1, // p(x2 = 1 | l1 = 1)
    0.33333);
    estimatedIm.setProbability(x2Index, 0, 0, // p(x2 = 0 | l1 = 0)
    0.33333);
    estimatedIm.setProbability(x2Index, 0, 1, // p(x2 = 1 | l1 = 0)
    0.66667);
    // bayesImMixed.setProbability(x3Index, 1, 0, 0.66667);
    // bayesImMixed.setProbability(x3Index, 1, 1, 0.33333);
    estimatedIm.setProbability(x3Index, 1, 0, // p(x3 = 0 | l1 = 1)
    0.66667);
    estimatedIm.setProbability(x3Index, 1, 1, // p(x3 = 1 | l1 = 1)
    0.33333);
    estimatedIm.setProbability(x3Index, 0, 0, // p(x3 = 0 | l1 = 0)
    0.33333);
    estimatedIm.setProbability(x3Index, 0, 1, // p(x3 = 1 | l1 = 0)
    0.66667);
    Evidence evidence = Evidence.tautology(estimatedIm);
    evidence.getProposition().setCategory(x1Index, 0);
    evidence.getProposition().setCategory(x2Index, 0);
    evidence.getProposition().setCategory(x3Index, 0);
    RowSummingExactUpdater rseu = new RowSummingExactUpdater(estimatedIm);
    rseu.setEvidence(evidence);
    int[] vars1 = { l1Index };
    int[] vals1 = { 0 };
    double p1 = rseu.getJointMarginal(vars1, vals1);
    assertEquals(0.1111, p1, 0.0001);
    int[] vars2 = { l1Index, x1Index };
    int[] vals2 = { 0, 0 };
    double p2 = rseu.getJointMarginal(vars2, vals2);
    assertEquals(0.1111, p2, 0.0001);
    int[] vals3 = { 1, 0 };
    double p3 = rseu.getJointMarginal(vars2, vals3);
    assertEquals(0.8888, p3, 0.0001);
}