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

Example 6 with CovarianceMatrixOnTheFly

use of edu.cmu.tetrad.data.CovarianceMatrixOnTheFly 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 7 with CovarianceMatrixOnTheFly

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

the class FasLofs method search.

// ======================================== PUBLIC METHODS ====================================//
/**
 * Runs the search on the concatenated data, returning a graph, possibly cyclic, possibly with
 * two-cycles. Runs the fast adjacency search (FAS, Spirtes et al., 2000) follows by a modification
 * of the robust skew rule (Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural
 * Equation Models, Smith and Hyvarinen), together with some heuristics for orienting two-cycles.
 *
 * @return the graph. Some of the edges may be undirected (though it shouldn't be many in most cases)
 * and some of the adjacencies may be two-cycles.
 */
public Graph search() {
    long start = System.currentTimeMillis();
    SemBicScore score = new SemBicScore(new CovarianceMatrixOnTheFly(dataSet));
    score.setPenaltyDiscount(penaltyDiscount);
    IndependenceTest test = new IndTestScore(score, dataSet);
    System.out.println("FAS");
    FasStable fas = new FasStable(test);
    fas.setDepth(getDepth());
    fas.setVerbose(false);
    fas.setKnowledge(knowledge);
    Graph G0 = fas.search();
    System.out.println("LOFS orientation, rule " + rule);
    Lofs2 lofs2 = new Lofs2(G0, Collections.singletonList(dataSet));
    lofs2.setRule(rule);
    lofs2.setKnowledge(knowledge);
    Graph graph = lofs2.orient();
    System.out.println("Done");
    long stop = System.currentTimeMillis();
    this.elapsed = stop - start;
    return graph;
}