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

Example 26 with ContinuousVariable

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

the class TestSearchGraph method testDSeparation.

/**
 * Tests to see if d separation facts are symmetric.
 */
@Test
public void testDSeparation() {
    List<Node> nodes1 = new ArrayList<>();
    for (int i1 = 0; i1 < 7; i1++) {
        nodes1.add(new ContinuousVariable("X" + (i1 + 1)));
    }
    EdgeListGraphSingleConnections graph = new EdgeListGraphSingleConnections(new Dag(GraphUtils.randomGraph(nodes1, 0, 7, 30, 15, 15, true)));
    List<Node> nodes = graph.getNodes();
    int depth = -1;
    for (int i = 0; i < nodes.size(); i++) {
        for (int j = i + 1; j < nodes.size(); j++) {
            Node x = nodes.get(i);
            Node y = nodes.get(j);
            List<Node> theRest = new ArrayList<>(nodes);
            theRest.remove(x);
            theRest.remove(y);
            DepthChoiceGenerator gen = new DepthChoiceGenerator(theRest.size(), depth);
            int[] choice;
            while ((choice = gen.next()) != null) {
                List<Node> z = new LinkedList<>();
                for (int k = 0; k < choice.length; k++) {
                    z.add(theRest.get(choice[k]));
                }
                if (graph.isDSeparatedFrom(x, y, z) != graph.isDSeparatedFrom(y, x, z)) {
                    fail(SearchLogUtils.independenceFact(x, y, z) + " should have same d-sep result as " + SearchLogUtils.independenceFact(y, x, z));
                }
            }
        }
    }
}

Example 27 with ContinuousVariable

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

the class TestSimulatedFmri method testClark.

// @Test
public void testClark() {
    double f = .1;
    int N = 512;
    double alpha = 1.0;
    double penaltyDiscount = 1.0;
    for (int i = 0; i < 100; i++) {
        {
            Node x = new ContinuousVariable("X");
            Node y = new ContinuousVariable("Y");
            Node z = new ContinuousVariable("Z");
            Graph g = new EdgeListGraph();
            g.addNode(x);
            g.addNode(y);
            g.addNode(z);
            g.addDirectedEdge(x, y);
            g.addDirectedEdge(z, x);
            g.addDirectedEdge(z, y);
            GeneralizedSemPm pm = new GeneralizedSemPm(g);
            try {
                pm.setNodeExpression(g.getNode("X"), "0.5 * Z + E_X");
                pm.setNodeExpression(g.getNode("Y"), "0.5 * X + 0.5 * Z + E_Y");
                pm.setNodeExpression(g.getNode("Z"), "E_Z");
                String error = "pow(Uniform(0, 1), " + f + ")";
                pm.setNodeExpression(pm.getErrorNode(g.getNode("X")), error);
                pm.setNodeExpression(pm.getErrorNode(g.getNode("Y")), error);
                pm.setNodeExpression(pm.getErrorNode(g.getNode("Z")), error);
            } catch (ParseException e) {
                System.out.println(e);
            }
            GeneralizedSemIm im = new GeneralizedSemIm(pm);
            DataSet data = im.simulateData(N, false);
            edu.cmu.tetrad.search.SemBicScore score = new edu.cmu.tetrad.search.SemBicScore(new CovarianceMatrixOnTheFly(data, false));
            score.setPenaltyDiscount(penaltyDiscount);
            Fask fask = new Fask(data, score);
            fask.setPenaltyDiscount(penaltyDiscount);
            fask.setAlpha(alpha);
            Graph out = fask.search();
            System.out.println(out);
        }
        {
            Node x = new ContinuousVariable("X");
            Node y = new ContinuousVariable("Y");
            Node z = new ContinuousVariable("Z");
            Graph g = new EdgeListGraph();
            g.addNode(x);
            g.addNode(y);
            g.addNode(z);
            g.addDirectedEdge(x, y);
            g.addDirectedEdge(x, z);
            g.addDirectedEdge(y, z);
            GeneralizedSemPm pm = new GeneralizedSemPm(g);
            try {
                pm.setNodeExpression(g.getNode("X"), "E_X");
                pm.setNodeExpression(g.getNode("Y"), "0.4 * X + E_Y");
                pm.setNodeExpression(g.getNode("Z"), "0.4 * X + 0.4 * Y + E_Z");
                String error = "pow(Uniform(0, 1), " + f + ")";
                pm.setNodeExpression(pm.getErrorNode(g.getNode("X")), error);
                pm.setNodeExpression(pm.getErrorNode(g.getNode("Y")), error);
                pm.setNodeExpression(pm.getErrorNode(g.getNode("Z")), error);
            } catch (ParseException e) {
                System.out.println(e);
            }
            GeneralizedSemIm im = new GeneralizedSemIm(pm);
            DataSet data = im.simulateData(N, false);
            edu.cmu.tetrad.search.SemBicScore score = new edu.cmu.tetrad.search.SemBicScore(new CovarianceMatrixOnTheFly(data, false));
            score.setPenaltyDiscount(penaltyDiscount);
            Fask fask = new Fask(data, score);
            fask.setPenaltyDiscount(penaltyDiscount);
            fask.setAlpha(alpha);
            Graph out = fask.search();
            System.out.println(out);
        }
    }
}

Example 28 with ContinuousVariable

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

the class TestIndTestWaldLR method testIsIndependent.

@Test
public void testIsIndependent() {
    RandomUtil.getInstance().setSeed(1450705713157L);
    int numPassed = 0;
    for (int i = 0; i < 10; i++) {
        List<Node> nodes = new ArrayList<>();
        for (int i1 = 0; i1 < 10; i1++) {
            nodes.add(new ContinuousVariable("X" + (i1 + 1)));
        }
        Graph graph = GraphUtils.randomGraph(nodes, 0, 10, 3, 3, 3, false);
        SemPm pm = new SemPm(graph);
        SemIm im = new SemIm(pm);
        DataSet data = im.simulateData(1000, false);
        Discretizer discretizer = new Discretizer(data);
        discretizer.setVariablesCopied(true);
        discretizer.equalCounts(data.getVariable(0), 2);
        discretizer.equalCounts(data.getVariable(3), 2);
        data = discretizer.discretize();
        Node x1 = data.getVariable("X1");
        Node x2 = data.getVariable("X2");
        Node x3 = data.getVariable("X3");
        Node x4 = data.getVariable("X4");
        Node x5 = data.getVariable("X5");
        List<Node> cond = new ArrayList<>();
        cond.add(x3);
        cond.add(x4);
        cond.add(x5);
        Node x1Graph = graph.getNode(x1.getName());
        Node x2Graph = graph.getNode(x2.getName());
        List<Node> condGraph = new ArrayList<>();
        for (Node node : cond) {
            condGraph.add(graph.getNode(node.getName()));
        }
        // Using the Wald LR test since it's most up to date.
        IndependenceTest test = new IndTestMultinomialLogisticRegressionWald(data, 0.05, false);
        IndTestDSep dsep = new IndTestDSep(graph);
        boolean correct = test.isIndependent(x2, x1, cond) == dsep.isIndependent(x2Graph, x1Graph, condGraph);
        if (correct) {
            numPassed++;
        }
    }
    // System.out.println(RandomUtil.getInstance().getSeed());
    // Do not always get all 10.
    assertEquals(10, numPassed);
}

Example 29 with ContinuousVariable

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

the class TestKnowledge method test1.

@Test
public final void test1() {
    List<Node> nodes1 = new ArrayList<>();
    for (int i1 = 0; i1 < 10; i1++) {
        nodes1.add(new ContinuousVariable("X" + (i1 + 1)));
    }
    Graph g = GraphUtils.randomGraph(nodes1, 0, 10, 3, 3, 3, false);
    g.getNode("X1").setName("X1.1");
    g.getNode("X2").setName("X2-1");
    List<Node> nodes = g.getNodes();
    List<String> varNames = new ArrayList<>();
    for (Node node : nodes) {
        varNames.add(node.getName());
    }
    IKnowledge knowledge = new Knowledge2(varNames);
    knowledge.addToTier(0, "X1.*1");
    knowledge.addToTier(0, "X2-1");
    knowledge.addToTier(1, "X3");
    knowledge.setForbidden("X4", "X5");
    knowledge.setRequired("X6", "X7");
    knowledge.setRequired("X7", "X8");
    assertTrue(knowledge.isForbidden("X4", "X5"));
    assertFalse(knowledge.isForbidden("X1.1", "X2-1"));
    assertTrue(knowledge.isForbidden("X3", "X2-1"));
    assertTrue(knowledge.isRequired("X6", "X7"));
    IKnowledge copy = knowledge.copy();
    assertTrue(copy.isForbidden("X4", "X5"));
    assertFalse(copy.isForbidden("X1", "X2-1"));
    assertTrue(copy.isForbidden("X3", "X2-1"));
    knowledge.setTierForbiddenWithin(0, true);
    assertTrue(knowledge.isForbidden("X1.1", "X2-1"));
    assertTrue(knowledge.isForbidden("X2-1", "X1.1"));
    assertFalse(knowledge.isForbidden("X1.1", "X1.1"));
    boolean found = false;
    for (Iterator i = knowledge.forbiddenEdgesIterator(); i.hasNext(); ) {
        KnowledgeEdge edge = (KnowledgeEdge) i.next();
        if (edge.getFrom().equals("X1.1") && edge.getTo().equals("X2-1")) {
            found = true;
        }
    }
    assertTrue(found);
    knowledge.setTierForbiddenWithin(0, false);
    assertFalse(knowledge.isForbidden("X1.1", "X2-1"));
    assertFalse(knowledge.isForbidden("X2-1", "X1.1"));
    assertFalse(knowledge.isForbidden("X1.1", "X1.1"));
}

Example 30 with ContinuousVariable

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

the class TestLargeSemSimulator method test1.

@Test
public void test1() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 1; i <= 10; i++) nodes.add(new ContinuousVariable("X" + i));
    Graph graph = GraphUtils.randomGraph(nodes, 0, 10, 5, 5, 5, false);
    LargeScaleSimulation simulator = new LargeScaleSimulation(graph);
    DataSet dataset = simulator.simulateDataFisher(1000);
    assertEquals(1000, dataset.getNumRows());
}