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

Example 76 with ContinuousVariable

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

the class TestSearchGraph method rtestDSeparation4.

public void rtestDSeparation4() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 100; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 20, 100, 5, 5, 5, false));
    long start, stop;
    int depth = -1;
    IndependenceTest test = new IndTestDSep(graph);
    Rfci fci = new Rfci(test);
    Fas fas = new Fas(test);
    start = System.currentTimeMillis();
    fci.setDepth(depth);
    fci.setVerbose(false);
    fci.search(fas, fas.getNodes());
    stop = System.currentTimeMillis();
    System.out.println("DSEP RFCI");
    System.out.println("# dsep checks = " + fas.getNumIndependenceTests());
    System.out.println("Elapsed " + (stop - start));
    System.out.println("Per " + fas.getNumIndependenceTests() / (double) (stop - start));
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateData(1000, false);
    IndependenceTest test2 = new IndTestFisherZ(data, 0.001);
    Rfci fci3 = new Rfci(test2);
    Fas fas2 = new Fas(test2);
    start = System.currentTimeMillis();
    fci3.setDepth(depth);
    fci3.search(fas2, fas2.getNodes());
    stop = System.currentTimeMillis();
    System.out.println("FISHER Z RFCI");
    System.out.println("# indep checks = " + fas.getNumIndependenceTests());
    System.out.println("Elapsed " + (stop - start));
    System.out.println("Per " + fas.getNumIndependenceTests() / (double) (stop - start));
}

Example 77 with ContinuousVariable

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

the class TestSimulatedFmri method testClark2.

// @Test
public void testClark2() {
    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), 1.5)";
        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(1000, false);
    edu.cmu.tetrad.search.SemBicScore score = new edu.cmu.tetrad.search.SemBicScore(new CovarianceMatrixOnTheFly(data, false));
    Fask fask = new Fask(data, score);
    fask.setPenaltyDiscount(1);
    fask.setAlpha(0.5);
    Graph out = fask.search();
    System.out.println(out);
}

Example 78 with ContinuousVariable

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

the class TestKnowledge method test2.

@Test
public final void test2() {
    List<Node> nodes1 = new ArrayList<>();
    for (int i = 0; i < 100; i++) {
        nodes1.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph g = GraphUtils.randomGraph(nodes1, 0, 100, 3, 3, 3, false);
    List<Node> nodes = g.getNodes();
    List<String> names = new ArrayList<>();
    for (Node node : nodes) names.add(node.getName());
    Knowledge2 knowledge = new Knowledge2(names);
    knowledge.addToTier(0, "X1*");
    knowledge.addToTier(1, "X2*");
    knowledge.setRequired("X4*,X6*", "X5*");
    knowledge.setRequired("X6*", "X5*");
    assertTrue(knowledge.isForbidden("X20", "X10"));
    assertTrue(knowledge.isRequired("X6", "X5"));
}

Example 79 with ContinuousVariable

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

the class BayesUpdaterClassifierEditor method showClassification.

private void showClassification() {
    int tabIndex = -1;
    for (int i = 0; i < getTabbedPane().getTabCount(); i++) {
        if ("Classification".equals(getTabbedPane().getTitleAt(i))) {
            getTabbedPane().remove(i);
            tabIndex = i;
        }
    }
    // Put the class information into a DataSet.
    int[] classifications = getClassifier().getClassifications();
    double[][] marginals = getClassifier().getMarginals();
    int maxCategory = 0;
    for (int classification : classifications) {
        if (classification > maxCategory) {
            maxCategory = classification;
        }
    }
    List<Node> variables = new LinkedList<>();
    DiscreteVariable targetVariable = classifier.getTargetVariable();
    DiscreteVariable classVar = new DiscreteVariable(targetVariable.getName(), maxCategory + 1);
    variables.add(classVar);
    for (int i = 0; i < marginals.length; i++) {
        String name = "P(" + targetVariable + "=" + i + ")";
        ContinuousVariable scoreVar = new ContinuousVariable(name);
        variables.add(scoreVar);
    }
    classVar.setName("Result");
    DataSet dataSet = new ColtDataSet(classifications.length, variables);
    for (int i = 0; i < classifications.length; i++) {
        dataSet.setInt(i, 0, classifications[i]);
        for (int j = 0; j < marginals.length; j++) {
            dataSet.setDouble(i, j + 1, marginals[j][i]);
        }
    }
    DataDisplay jTable = new DataDisplay(dataSet);
    JScrollPane scroll = new JScrollPane(jTable);
    if (tabIndex == -1) {
        getTabbedPane().add("Classification", scroll);
    } else {
        getTabbedPane().add(scroll, tabIndex);
        getTabbedPane().setTitleAt(tabIndex, "Classification");
    }
}

Example 80 with ContinuousVariable

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

the class ModeInterpolator method filter.

public DataSet filter(DataSet dataSet) {
    DataSet newDataSet = dataSet.copy();
    for (int j = 0; j < dataSet.getNumColumns(); j++) {
        Node var = dataSet.getVariable(j);
        if (var instanceof DiscreteVariable) {
            DiscreteVariable variable = (DiscreteVariable) var;
            int numCategories = variable.getNumCategories();
            int[] categoryCounts = new int[numCategories];
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                if (dataSet.getInt(i, j) == DiscreteVariable.MISSING_VALUE) {
                    continue;
                }
                categoryCounts[dataSet.getInt(i, j)]++;
            }
            int mode = -1;
            int max = -1;
            for (int i = 0; i < numCategories; i++) {
                if (categoryCounts[i] > max) {
                    max = categoryCounts[i];
                    mode = i;
                }
            }
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                if (dataSet.getInt(i, j) == DiscreteVariable.MISSING_VALUE) {
                    newDataSet.setInt(i, j, mode);
                }
            // else {
            // newDataSet.setInt(i, j, dataSet.getInt(i, j));
            // }
            }
        } else if (dataSet.getVariable(j) instanceof ContinuousVariable) {
            double[] data = new double[dataSet.getNumRows()];
            int k = -1;
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                if (!Double.isNaN(dataSet.getDouble(i, j))) {
                    data[++k] = dataSet.getDouble(i, j);
                }
            }
            Arrays.sort(data);
            double mode = Double.NaN;
            if (k >= 0) {
                mode = (data[(k + 1) / 2] + data[k / 2]) / 2.d;
            }
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                if (Double.isNaN(dataSet.getDouble(i, j))) {
                    newDataSet.setDouble(i, j, mode);
                }
            // else {
            // newDataSet.setDouble(i, j, dataSet.getDouble(i, j));
            // }
            }
        }
    }
    return newDataSet;
}