Examples with ContinuousVariable edu.cmu.tetrad.data.ContinuousVariable used on opensource projects

Example 31 with ContinuousVariable

use of edu.cmu.tetrad.data.ContinuousVariable in project tetrad by cmu-phil.

the class TestLingamPattern method test1.

@Test
public void test1() {
    RandomUtil.getInstance().setSeed(4938492L);
    int sampleSize = 1000;
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 6; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 6, 4, 4, 4, false));
    List<Distribution> variableDistributions = new ArrayList<>();
    variableDistributions.add(new Normal(0, 1));
    variableDistributions.add(new Normal(0, 1));
    variableDistributions.add(new Normal(0, 1));
    variableDistributions.add(new Uniform(-1, 1));
    variableDistributions.add(new Normal(0, 1));
    variableDistributions.add(new Normal(0, 1));
    SemPm semPm = new SemPm(graph);
    SemIm semIm = new SemIm(semPm);
    DataSet dataSet = simulateDataNonNormal(semIm, sampleSize, variableDistributions);
    Score score = new SemBicScore(new CovarianceMatrixOnTheFly(dataSet));
    Graph estPattern = new Fges(score).search();
    LingamPattern lingam = new LingamPattern(estPattern, dataSet);
    lingam.search();
    double[] pvals = lingam.getPValues();
    double[] expectedPVals = { 0.18, 0.29, 0.88, 0.00, 0.01, 0.58 };
    for (int i = 0; i < pvals.length; i++) {
        assertEquals(expectedPVals[i], pvals[i], 0.01);
    }
}

Example 32 with ContinuousVariable

use of edu.cmu.tetrad.data.ContinuousVariable in project tetrad by cmu-phil.

the class TestMbfs method testRandom.

@Test
public void testRandom() {
    RandomUtil.getInstance().setSeed(8388428832L);
    List<Node> nodes1 = new ArrayList<>();
    for (int i = 0; i < 10; i++) {
        nodes1.add(new ContinuousVariable("X" + (i + 1)));
    }
    Dag dag = new Dag(GraphUtils.randomGraph(nodes1, 0, 10, 5, 5, 5, false));
    IndependenceTest test = new IndTestDSep(dag);
    Mbfs search = new Mbfs(test, -1);
    List<Node> nodes = dag.getNodes();
    for (Node node : nodes) {
        Graph resultMb = search.search(node.getName());
        Graph trueMb = GraphUtils.markovBlanketDag(node, dag);
        List<Node> resultNodes = resultMb.getNodes();
        List<Node> trueNodes = trueMb.getNodes();
        Set<String> resultNames = new HashSet<>();
        for (Node resultNode : resultNodes) {
            resultNames.add(resultNode.getName());
        }
        Set<String> trueNames = new HashSet<>();
        for (Node v : trueNodes) {
            trueNames.add(v.getName());
        }
        assertTrue(resultNames.equals(trueNames));
        Set<Edge> resultEdges = resultMb.getEdges();
        for (Edge resultEdge : resultEdges) {
            if (Edges.isDirectedEdge(resultEdge)) {
                String name1 = resultEdge.getNode1().getName();
                String name2 = resultEdge.getNode2().getName();
                Node node1 = trueMb.getNode(name1);
                Node node2 = trueMb.getNode(name2);
                // possibility that the node is actually a child.
                if (node1 == null) {
                    fail("Node " + name1 + " is not in the true graph.");
                }
                if (node2 == null) {
                    fail("Node " + name2 + " is not in the true graph.");
                }
                Edge trueEdge = trueMb.getEdge(node1, node2);
                if (trueEdge == null) {
                    Node resultNode1 = resultMb.getNode(node1.getName());
                    Node resultNode2 = resultMb.getNode(node2.getName());
                    Node resultTarget = resultMb.getNode(node.getName());
                    Edge a = resultMb.getEdge(resultNode1, resultTarget);
                    Edge b = resultMb.getEdge(resultNode2, resultTarget);
                    if (a == null || b == null) {
                        continue;
                    }
                    if ((Edges.isDirectedEdge(a) && Edges.isUndirectedEdge(b)) || (Edges.isUndirectedEdge(a) && Edges.isDirectedEdge(b))) {
                        continue;
                    }
                    fail("EXTRA EDGE: Edge in result MB but not true MB = " + resultEdge);
                }
                assertEquals(resultEdge.getEndpoint1(), trueEdge.getEndpoint1());
                assertEquals(resultEdge.getEndpoint2(), trueEdge.getEndpoint2());
            }
        }
    }
}

Example 33 with ContinuousVariable

use of edu.cmu.tetrad.data.ContinuousVariable in project tetrad by cmu-phil.

the class TestSemIm method testIntercepts.

@Test
public void testIntercepts() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph randomGraph = new Dag(GraphUtils.randomGraph(nodes, 0, 8, 30, 15, 15, false));
    SemPm semPm = new SemPm(randomGraph);
    SemIm semIm = new SemIm(semPm);
    semIm.setIntercept(semIm.getVariableNodes().get(0), 1.0);
    semIm.setIntercept(semIm.getVariableNodes().get(1), 3.0);
    semIm.setIntercept(semIm.getVariableNodes().get(2), -1.0);
    semIm.setIntercept(semIm.getVariableNodes().get(3), 6.0);
    assertEquals(1.0, semIm.getIntercept(semIm.getVariableNodes().get(0)), 0.1);
    assertEquals(3.0, semIm.getIntercept(semIm.getVariableNodes().get(1)), 0.1);
    assertEquals(-1.0, semIm.getIntercept(semIm.getVariableNodes().get(2)), 0.1);
    assertEquals(6.0, semIm.getIntercept(semIm.getVariableNodes().get(3)), 0.1);
    assertEquals(0.0, semIm.getIntercept(semIm.getVariableNodes().get(4)), 0.1);
}

Example 34 with ContinuousVariable

use of edu.cmu.tetrad.data.ContinuousVariable in project tetrad by cmu-phil.

the class TestSemIm method testCovariancesOfSimulated.

@Test
public void testCovariancesOfSimulated() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph randomGraph = new Dag(GraphUtils.randomGraph(nodes, 0, 8, 30, 15, 15, false));
    SemPm semPm1 = new SemPm(randomGraph);
    SemIm semIm1 = new SemIm(semPm1);
    TetradMatrix implCovarC = semIm1.getImplCovar(true);
    implCovarC.toArray();
    DataSet dataSet = semIm1.simulateDataRecursive(1000, false);
    new CovarianceMatrix(dataSet);
}

Example 35 with ContinuousVariable

use of edu.cmu.tetrad.data.ContinuousVariable in project tetrad by cmu-phil.

the class TestMarkovBlanketSearches method testRandom.

@Test
public void testRandom() {
    List<Node> nodes1 = new ArrayList<>();
    for (int i = 0; i < 10; i++) {
        nodes1.add(new ContinuousVariable("X" + (i + 1)));
    }
    Dag dag = new Dag(GraphUtils.randomGraph(nodes1, 0, 10, 5, 5, 5, false));
    IndependenceTest test = new IndTestDSep(dag);
    Mbfs search = new Mbfs(test, -1);
    List<Node> nodes = dag.getNodes();
    for (Node node : nodes) {
        List<Node> resultNodes = search.findMb(node.getName());
        Graph trueMb = GraphUtils.markovBlanketDag(node, dag);
        List<Node> trueNodes = trueMb.getNodes();
        trueNodes.remove(node);
        Collections.sort(trueNodes, new Comparator<Node>() {

            public int compare(Node n1, Node n2) {
                return n1.getName().compareTo(n2.getName());
            }
        });
        Collections.sort(resultNodes, new Comparator<Node>() {

            public int compare(Node n1, Node n2) {
                return n1.getName().compareTo(n2.getName());
            }
        });
        assertEquals(trueNodes, resultNodes);
    }
}