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

Example 21 with DataSet

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

the class TestColtDataSet method testContinuous.

@Test
public final void testContinuous() {
    int rows = 10;
    int cols = 5;
    List<Node> _variables = new LinkedList<>();
    for (int i = 0; i < cols; i++) {
        _variables.add(new ContinuousVariable("X" + i));
    }
    DataSet dataSet = new ColtDataSet(rows, _variables);
    RandomUtil randomUtil = RandomUtil.getInstance();
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++) {
            dataSet.setDouble(i, j, randomUtil.nextDouble());
        }
    }
    List<Node> variables = dataSet.getVariables();
    List<Node> newVars = new LinkedList<>();
    newVars.add(variables.get(2));
    newVars.add(variables.get(4));
    DataSet _dataSet = dataSet.subsetColumns(newVars);
    assertEquals(dataSet.getDoubleData().getColumn(2).get(0), _dataSet.getDoubleData().getColumn(0).get(0), .001);
    assertEquals(dataSet.getDoubleData().getColumn(4).get(0), _dataSet.getDoubleData().getColumn(1).get(0), .001);
}

Example 22 with DataSet

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

the class TestColtDataSet method testDiscreteFromScratch.

@Test
public void testDiscreteFromScratch() {
    DataSet dataSet = new ColtDataSet(0, Collections.EMPTY_LIST);
    DiscreteVariable x1 = new DiscreteVariable("X1");
    dataSet.addVariable(x1);
    dataSet.setInt(0, 0, 0);
    dataSet.setInt(1, 0, 2);
    dataSet.setInt(2, 0, 1);
    DiscreteVariable x2 = new DiscreteVariable("X2");
    dataSet.addVariable(x2);
    dataSet.setInt(0, 1, 0);
    dataSet.setInt(1, 1, 2);
    dataSet.setInt(2, 1, 1);
    ColtDataSet _dataSet = new ColtDataSet((ColtDataSet) dataSet);
    assertEquals(dataSet, _dataSet);
    assertEquals(dataSet.getInt(1, 1), 2);
}

Example 23 with DataSet

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

the class TestColtDataSet method testRemoveRows.

@Test
public void testRemoveRows() {
    int rows = 10;
    int cols = 5;
    List<Node> variables = new LinkedList<>();
    for (int i = 0; i < cols; i++) {
        variables.add(new ContinuousVariable("X" + i));
    }
    DataSet dataSet = new ColtDataSet(rows, variables);
    RandomUtil randomUtil = RandomUtil.getInstance();
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++) {
            dataSet.setDouble(i, j, randomUtil.nextDouble());
        }
    }
    int numRows = dataSet.getNumRows();
    double d = dataSet.getDouble(3, 0);
    int[] _rows = new int[2];
    _rows[0] = 1;
    _rows[1] = 2;
    dataSet.removeRows(_rows);
    assertEquals(numRows - 2, dataSet.getNumRows());
    assertEquals(d, dataSet.getDouble(1, 0), 0.001);
}

Example 24 with DataSet

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

the class TestCpc method test7.

@Test
public void test7() {
    int numVars = 6;
    int numEdges = 6;
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Dag trueGraph = new Dag(GraphUtils.randomGraph(nodes, 0, numEdges, 7, 5, 5, false));
    SemPm semPm = new SemPm(trueGraph);
    SemIm semIm = new SemIm(semPm);
    DataSet _dataSet = semIm.simulateData(1000, false);
    IndependenceTest test = new IndTestFisherZ(_dataSet, 0.05);
    Cpc search = new Cpc(test);
    Graph resultGraph = search.search();
}

Example 25 with DataSet

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

the class TestGeneralizedSem method test9.

@Test
public void test9() {
    RandomUtil.getInstance().setSeed(29999483L);
    try {
        Node x1 = new GraphNode("X1");
        Node x2 = new GraphNode("X2");
        Node x3 = new GraphNode("X3");
        Node x4 = new GraphNode("X4");
        Graph g = new EdgeListGraphSingleConnections();
        g.addNode(x1);
        g.addNode(x2);
        g.addNode(x3);
        g.addNode(x4);
        g.addDirectedEdge(x1, x2);
        g.addDirectedEdge(x2, x3);
        g.addDirectedEdge(x3, x4);
        g.addDirectedEdge(x1, x4);
        GeneralizedSemPm pm = new GeneralizedSemPm(g);
        pm.setNodeExpression(x1, "E_X1");
        pm.setNodeExpression(x2, "a1 * tan(X1) + E_X2");
        pm.setNodeExpression(x3, "a2 * tan(X2) + E_X3");
        pm.setNodeExpression(x4, "a3 * tan(X1) + a4 * tan(X3) ^ 2 + E_X4");
        // pm.setNodeExpression(x1, "E_X1");
        // pm.setNodeExpression(x2, "a1 * X1^2 + E_X2");
        // pm.setNodeExpression(x3, "a2 * X2^2 + E_X3");
        // pm.setNodeExpression(x4, "a3 * X1^2 + a4 * X3 ^ 2 + E_X4");
        // 
        pm.setNodeExpression(pm.getErrorNode(x1), "Beta(5, 2)");
        pm.setNodeExpression(pm.getErrorNode(x2), "Beta(2, 5)");
        pm.setNodeExpression(pm.getErrorNode(x3), "Beta(1, 3)");
        pm.setNodeExpression(pm.getErrorNode(x4), "Beta(1, 7)");
        pm.setParameterEstimationInitializationExpression("c1", "U(1, 3)");
        pm.setParameterEstimationInitializationExpression("c2", "U(1, 3)");
        pm.setParameterEstimationInitializationExpression("c3", "U(1, 3)");
        pm.setParameterEstimationInitializationExpression("c4", "U(1, 3)");
        pm.setParameterEstimationInitializationExpression("c5", "U(1, 3)");
        pm.setParameterEstimationInitializationExpression("c6", "U(1, 3)");
        pm.setParameterEstimationInitializationExpression("c7", "U(1, 3)");
        pm.setParameterEstimationInitializationExpression("c8", "U(1, 3)");
        GeneralizedSemIm im = new GeneralizedSemIm(pm);
        print("True model: ");
        print(im);
        DataSet data = im.simulateDataRecursive(1000, false);
        pm.setNodeExpression(pm.getErrorNode(x1), "Beta(c1, c2)");
        pm.setNodeExpression(pm.getErrorNode(x2), "Beta(c3, c4)");
        pm.setNodeExpression(pm.getErrorNode(x3), "Beta(c5, c6)");
        pm.setNodeExpression(pm.getErrorNode(x4), "Beta(c7, c8)");
        GeneralizedSemEstimator estimator = new GeneralizedSemEstimator();
        GeneralizedSemIm estIm = estimator.estimate(pm, data);
        print("\n\n\nEstimated model: ");
        print(estIm);
        print(estimator.getReport());
        double aSquaredStar = estimator.getaSquaredStar();
        assertEquals(0.62, aSquaredStar, 0.01);
    } catch (ParseException e) {
        e.printStackTrace();
    }
}