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

Example 81 with DataSet

use of edu.cmu.tetrad.data.DataSet 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 82 with DataSet

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

the class TestIndTestGSquare method testIsIndependent.

@Test
public void testIsIndependent() {
    try {
        DataSet dataSet = getDataSet(1);
        IndTestGSquare test = new IndTestGSquare(dataSet, 0.05);
        List<Node> v = test.getVariables();
        Node x = v.get(0);
        Node y = v.get(1);
        ArrayList<Node> z = new ArrayList<>();
        z.add(v.get(2));
        assertTrue(test.isIndependent(x, y, z));
        test.setDeterminationP(0.99);
        assertFalse(test.determines(z, x));
        assertFalse(test.determines(z, y));
    } catch (IOException e) {
        // To change body of catch statement use File | Settings | File Templates.
        e.printStackTrace();
    }
}

Example 83 with DataSet

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

the class TestIndTestGSquare method testDetermination.

@Test
public void testDetermination() {
    try {
        DataSet dataSet = getDataSet(1);
        IndTestGSquare test = new IndTestGSquare(dataSet, 0.05);
        Node x = dataSet.getVariable("X4");
        ArrayList<Node> z = new ArrayList<>();
        test.setDeterminationP(0.99);
        assertFalse(test.determines(z, x));
    } catch (IOException e) {
        // To change body of catch statement use File | Settings | File Templates.
        e.printStackTrace();
    }
}

Example 84 with DataSet

use of edu.cmu.tetrad.data.DataSet 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 85 with DataSet

use of edu.cmu.tetrad.data.DataSet 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());
}