Examples with SemPm edu.cmu.tetrad.sem.SemPm used on opensource projects

Example 21 with SemPm

use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.

the class TestSemVarMeans method testMeansCholesky.

@Test
public void testMeansCholesky() {
    Graph graph = constructGraph1();
    SemPm semPm1 = new SemPm(graph);
    List<Parameter> parameters = semPm1.getParameters();
    for (Parameter p : parameters) {
        p.setInitializedRandomly(false);
    }
    SemIm semIm1 = new SemIm(semPm1);
    double[] means = { 5.0, 4.0, 3.0, 2.0, 1.0 };
    RandomUtil.getInstance().setSeed(-379467L);
    for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
        Node node = semIm1.getVariableNodes().get(i);
        semIm1.setMean(node, means[i]);
    }
    DataSet dataSet = semIm1.simulateDataCholesky(1000, false);
    SemEstimator semEst = new SemEstimator(dataSet, semPm1);
    semEst.estimate();
    SemIm estSemIm = semEst.getEstimatedSem();
    List<Node> nodes = semPm1.getVariableNodes();
    for (Node node : nodes) {
        double mean = semIm1.getMean(node);
        assertEquals(mean, estSemIm.getMean(node), 0.6);
    }
}

Example 22 with SemPm

use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.

the class TestSemVarMeans method testMeansReducedForm.

@Test
public void testMeansReducedForm() {
    Graph graph = constructGraph1();
    SemPm semPm1 = new SemPm(graph);
    List<Parameter> parameters = semPm1.getParameters();
    for (Parameter p : parameters) {
        p.setInitializedRandomly(false);
    }
    SemIm semIm1 = new SemIm(semPm1);
    double[] means = { 5.0, 4.0, 3.0, 2.0, 1.0 };
    RandomUtil.getInstance().setSeed(-379467L);
    for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
        Node node = semIm1.getVariableNodes().get(i);
        semIm1.setMean(node, means[i]);
    }
    DataSet dataSet = semIm1.simulateDataReducedForm(1000, false);
    SemEstimator semEst = new SemEstimator(dataSet, semPm1);
    semEst.estimate();
    SemIm estSemIm = semEst.getEstimatedSem();
    List<Node> nodes = semPm1.getVariableNodes();
    for (Node node : nodes) {
        double mean = semIm1.getMean(node);
        assertEquals(mean, estSemIm.getMean(node), 0.5);
    }
}

Example 23 with SemPm

use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.

the class TestSemVarMeans method testMeansRecursive.

@Test
public void testMeansRecursive() {
    Graph graph = constructGraph1();
    SemPm semPm1 = new SemPm(graph);
    List<Parameter> parameters = semPm1.getParameters();
    for (Parameter p : parameters) {
        p.setInitializedRandomly(false);
    }
    SemIm semIm1 = new SemIm(semPm1);
    double[] means = { 5.0, 4.0, 3.0, 2.0, 1.0 };
    RandomUtil.getInstance().setSeed(-379467L);
    for (int i = 0; i < semIm1.getVariableNodes().size(); i++) {
        Node node = semIm1.getVariableNodes().get(i);
        semIm1.setMean(node, means[i]);
    }
    DataSet dataSet = semIm1.simulateDataRecursive(1000, false);
    SemEstimator semEst = new SemEstimator(dataSet, semPm1);
    semEst.estimate();
    SemIm estSemIm = semEst.getEstimatedSem();
    List<Node> nodes = semPm1.getVariableNodes();
    for (Node node : nodes) {
        double mean = semIm1.getMean(node);
        assertEquals(mean, estSemIm.getMean(node), 0.5);
    }
}

Example 24 with SemPm

use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.

the class TestPurify method test1b.

@Test
public void test1b() {
    RandomUtil.getInstance().setSeed(48290483L);
    SemGraph graph = new SemGraph();
    Node l1 = new GraphNode("L1");
    l1.setNodeType(NodeType.LATENT);
    Node l2 = new GraphNode("L2");
    l2.setNodeType(NodeType.LATENT);
    Node x1 = new GraphNode("X1");
    Node x2 = new GraphNode("X2");
    Node x3 = new GraphNode("X3");
    Node x4 = new GraphNode("X4");
    Node x5 = new GraphNode("X5");
    Node x6 = new GraphNode("X6");
    Node x7 = new GraphNode("X7");
    Node x8 = new GraphNode("X8");
    Node x9 = new GraphNode("X9");
    Node x10 = new GraphNode("X10");
    Node x11 = new GraphNode("X11");
    Node x12 = new GraphNode("X12");
    graph.addNode(l1);
    graph.addNode(l2);
    graph.addNode(x1);
    graph.addNode(x2);
    graph.addNode(x3);
    graph.addNode(x4);
    graph.addNode(x5);
    graph.addNode(x6);
    graph.addNode(x7);
    graph.addNode(x8);
    graph.addNode(x9);
    graph.addNode(x10);
    graph.addNode(x11);
    graph.addNode(x12);
    graph.addDirectedEdge(l1, x1);
    graph.addDirectedEdge(l1, x2);
    graph.addDirectedEdge(l1, x3);
    graph.addDirectedEdge(l1, x4);
    graph.addDirectedEdge(l1, x5);
    graph.addDirectedEdge(l1, x5);
    graph.addDirectedEdge(l1, x6);
    graph.addDirectedEdge(l2, x6);
    graph.addDirectedEdge(l2, x7);
    graph.addDirectedEdge(l2, x8);
    graph.addDirectedEdge(l2, x9);
    graph.addDirectedEdge(l2, x10);
    graph.addDirectedEdge(l2, x11);
    graph.addDirectedEdge(l2, x12);
    graph.addDirectedEdge(x3, x4);
    graph.addDirectedEdge(x9, x10);
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateData(3000, false);
    List<List<Node>> partition = new ArrayList<>();
    List<Node> cluster1 = new ArrayList<>();
    cluster1.add(x1);
    cluster1.add(x2);
    cluster1.add(x3);
    cluster1.add(x4);
    cluster1.add(x5);
    List<Node> cluster2 = new ArrayList<>();
    cluster2.add(x7);
    cluster2.add(x8);
    cluster2.add(x9);
    cluster2.add(x10);
    cluster2.add(x11);
    cluster2.add(x12);
    partition.add(cluster1);
    partition.add(cluster2);
    TetradTest test = new ContinuousTetradTest(data, TestType.TETRAD_WISHART, 0.0001);
    IPurify purify = new PurifyTetradBased2(test);
    purify.setTrueGraph(graph);
    List<List<Node>> clustering = purify.purify(partition);
    assertEquals(4, clustering.get(0).size());
    assertEquals(5, clustering.get(1).size());
}

Example 25 with SemPm

use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.

the class TestPurify method test2.

@Test
public void test2() {
    RandomUtil.getInstance().setSeed(48290483L);
    Graph graph = new EdgeListGraph(DataGraphUtils.randomSingleFactorModel(3, 3, 5, 0, 0, 0));
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateData(1000, false);
    List<Node> latents = new ArrayList<>();
    for (Node node : graph.getNodes()) {
        if (node.getNodeType() == NodeType.LATENT)
            latents.add(node);
    }
    Graph structuralGraph = graph.subgraph(latents);
    List<List<Node>> clustering = new ArrayList<>();
    for (Node node : latents) {
        List<Node> adj = graph.getAdjacentNodes(node);
        adj.removeAll(latents);
        clustering.add(adj);
    }
    ContinuousTetradTest test = new ContinuousTetradTest(data, TestType.TETRAD_WISHART, 0.001);
    IPurify purify = new PurifyTetradBased2(test);
    List<List<Node>> purifiedClustering = purify.purify(clustering);
    List<String> latentsNames = new ArrayList<>();
    for (int i = 0; i < latents.size(); i++) {
        latentsNames.add(latents.get(i).getName());
    }
    Mimbuild2 mimbuild = new Mimbuild2();
    mimbuild.setAlpha(0.0001);
    Graph _graph = mimbuild.search(purifiedClustering, latentsNames, new CovarianceMatrix(data));
    List<Node> _latents = new ArrayList<>();
    for (Node node : _graph.getNodes()) {
        if (node.getNodeType() == NodeType.LATENT)
            _latents.add(node);
    }
    Graph _structuralGraph = _graph.subgraph(_latents);
    assertEquals(2, _structuralGraph.getNumEdges());
}