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

Example 71 with ContinuousVariable

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

the class TestSemXml method sampleSemIm1.

private static SemIm sampleSemIm1() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 30, 15, 15, true));
    SemPm pm = new SemPm(graph);
    return new SemIm(pm);
}

Example 72 with ContinuousVariable

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

the class TestLingamPattern method simulateDataNonNormal.

/**
 * This simulates data by picking random values for the exogenous terms and percolating this information down
 * through the SEM, assuming it is acyclic. Fast for large simulations but hangs for cyclic models.
 *
 * @param sampleSize    > 0.
 * @return the simulated data set.
 */
private DataSet simulateDataNonNormal(SemIm semIm, int sampleSize, List<Distribution> distributions) {
    List<Node> variables = new LinkedList<>();
    List<Node> variableNodes = semIm.getSemPm().getVariableNodes();
    for (Node node : variableNodes) {
        ContinuousVariable var = new ContinuousVariable(node.getName());
        variables.add(var);
    }
    DataSet dataSet = new ColtDataSet(sampleSize, variables);
    // Create some index arrays to hopefully speed up the simulation.
    SemGraph graph = semIm.getSemPm().getGraph();
    List<Node> tierOrdering = graph.getCausalOrdering();
    int[] tierIndices = new int[variableNodes.size()];
    for (int i = 0; i < tierIndices.length; i++) {
        tierIndices[i] = variableNodes.indexOf(tierOrdering.get(i));
    }
    int[][] _parents = new int[variables.size()][];
    for (int i = 0; i < variableNodes.size(); i++) {
        Node node = variableNodes.get(i);
        List<Node> parents = graph.getParents(node);
        for (Iterator<Node> j = parents.iterator(); j.hasNext(); ) {
            Node _node = j.next();
            if (_node.getNodeType() == NodeType.ERROR) {
                j.remove();
            }
        }
        _parents[i] = new int[parents.size()];
        for (int j = 0; j < parents.size(); j++) {
            Node _parent = parents.get(j);
            _parents[i][j] = variableNodes.indexOf(_parent);
        }
    }
    // Do the simulation.
    for (int row = 0; row < sampleSize; row++) {
        for (int i = 0; i < tierOrdering.size(); i++) {
            int col = tierIndices[i];
            Distribution distribution = distributions.get(col);
            // System.out.println(distribution);
            double value = distribution.nextRandom();
            for (int j = 0; j < _parents[col].length; j++) {
                int parent = _parents[col][j];
                value += dataSet.getDouble(row, parent) * semIm.getEdgeCoef().get(parent, col);
            }
            value += semIm.getMeans()[col];
            dataSet.setDouble(row, col, value);
        }
    }
    return dataSet;
}

Example 73 with ContinuousVariable

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

the class TestHistogram method testHistogram.

@Test
public void testHistogram() {
    RandomUtil.getInstance().setSeed(4829384L);
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Dag trueGraph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 30, 15, 15, false));
    int sampleSize = 1000;
    // Continuous
    SemPm semPm = new SemPm(trueGraph);
    SemIm semIm = new SemIm(semPm);
    DataSet data = semIm.simulateData(sampleSize, false);
    Histogram histogram = new Histogram(data);
    histogram.setTarget("X1");
    histogram.setNumBins(20);
    assertEquals(3.76, histogram.getMax(), 0.01);
    assertEquals(-3.83, histogram.getMin(), 0.01);
    assertEquals(1000, histogram.getN());
    histogram.setTarget("X1");
    histogram.setNumBins(10);
    histogram.addConditioningVariable("X3", 0, 1);
    histogram.addConditioningVariable("X4", 0, 1);
    histogram.removeConditioningVariable("X3");
    assertEquals(3.76, histogram.getMax(), 0.01);
    assertEquals(-3.83, histogram.getMin(), 0.01);
    assertEquals(188, histogram.getN());
    double[] arr = histogram.getContinuousData("X2");
    histogram.addConditioningVariable("X2", StatUtils.min(arr), StatUtils.mean(arr));
    // Discrete
    BayesPm bayesPm = new BayesPm(trueGraph);
    BayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
    DataSet data2 = bayesIm.simulateData(sampleSize, false);
    // For some reason these are giving different
    // values when all of the unit tests are run are
    // once. TODO They produce stable values when
    // this particular test is run repeatedly.
    Histogram histogram2 = new Histogram(data2);
    histogram2.setTarget("X1");
    int[] frequencies1 = histogram2.getFrequencies();
    // assertEquals(928, frequencies1[0]);
    // assertEquals(72, frequencies1[1]);
    histogram2.setTarget("X1");
    histogram2.addConditioningVariable("X2", 0);
    histogram2.addConditioningVariable("X3", 1);
    int[] frequencies = histogram2.getFrequencies();
// assertEquals(377, frequencies[0]);
// assertEquals(28, frequencies[1]);
}

Example 74 with ContinuousVariable

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

the class TestKernelGaussian method testMedianBandwidth.

/**
 * Tests the bandwidth setting to the median distance between points in the sample
 */
@Test
public void testMedianBandwidth() {
    Node X = new ContinuousVariable("X");
    DataSet dataset = new ColtDataSet(5, Arrays.asList(X));
    dataset.setDouble(0, 0, 1);
    dataset.setDouble(1, 0, 2);
    dataset.setDouble(2, 0, 3);
    dataset.setDouble(3, 0, 4);
    dataset.setDouble(4, 0, 5);
    KernelGaussian kernel = new KernelGaussian(dataset, X);
    assertTrue(kernel.getBandwidth() == 2);
}

Example 75 with ContinuousVariable

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

the class TestSearchGraph method testDSeparation2.

/**
 * Tests to see if d separation facts are symmetric.
 */
@Test
public void testDSeparation2() {
    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, 14, 30, 15, 15, true)));
    List<Node> nodes = graph.getNodes();
    int depth = -1;
    for (int i = 0; i < nodes.size(); i++) {
        for (int j = i; 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]));
                }
                boolean dConnectedTo = graph.isDConnectedTo(x, y, z);
                boolean dConnectedTo1 = graph.isDConnectedTo(y, x, z);
                if (dConnectedTo != dConnectedTo1) {
                    System.out.println(x + " d connected to " + y + " given " + z);
                    System.out.println(graph);
                    System.out.println("dconnectedto = " + dConnectedTo);
                    System.out.println("dconnecteto1 = " + dConnectedTo1);
                    fail();
                }
            }
        }
    }
}