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

Example 36 with DataModel

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

the class LingRunner method execute.

// ============================PUBLIC METHODS==========================//
/**
 * Executes the algorithm, producing (at least) a result workbench. Must be
 * implemented in the extending class.
 */
// public void execute() {
// DataModel source = getDataModel();
// 
// if (!(source instanceof DataSet)) {
// throw new IllegalArgumentException("Expecting a rectangular data set.");
// }
// 
// DataSet data = (DataSet) source;
// 
// if (!data.isContinuous()) {
// throw new IllegalArgumentException("Expecting a continuous data set.");
// }
// 
// Ling ling = new Ling(data);
// Parameters searchParams = (Parameters) getParameters();
// ling.setThreshold(searchParams.getThreshold());
// Ling.StoredGraphs graphs = ling.search();
// Graph graph = null;
// 
// for (int i = 0; i < graphs.getNumGraphs(); i++) {
// System.out.println(graphs.getGraph(i));
// System.out.println(graphs.isStable(i));
// }
// 
// for (int i = 0; i < graphs.getNumGraphs(); i++) {
// if (graphs.isStable(i)) {
// graph = graphs.getGraph(i);
// break;
// }
// }
// 
// if (graph == null) {
// graph = new EdgeListGraph();
// }
// 
// setResultGraph(graph);
// setStoredGraphs(graphs);
// 
// if (getSourceGraph() != null) {
// DataGraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
// }
// else {
// DataGraphUtils.circleLayout(graph, 200, 200, 150);
// }
// 
// }
public void execute() {
    DataModel source = getDataModel();
    if (!(source instanceof DataSet)) {
        throw new IllegalArgumentException("Expecting a rectangular data set.");
    }
    DataSet data = (DataSet) source;
    if (!data.isContinuous()) {
        throw new IllegalArgumentException("Expecting a continuous data set.");
    }
    Ling ling = new Ling(data);
    Parameters searchParams = getParams();
    ling.setThreshold(searchParams.getDouble("threshold", 0.5));
    Ling.StoredGraphs graphs = ling.search();
    Graph graph = null;
    for (int i = 0; i < graphs.getNumGraphs(); i++) {
        System.out.println(graphs.getGraph(i));
        System.out.println(graphs.isStable(i));
    }
    for (int i = 0; i < graphs.getNumGraphs(); i++) {
        if (graphs.isStable(i)) {
            graph = graphs.getGraph(i);
            break;
        }
    }
    if (graph == null) {
        graph = new EdgeListGraph();
    }
    setResultGraph(graph);
    setStoredGraphs(graphs);
    if (getSourceGraph() != null) {
        GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
    } else {
        GraphUtils.circleLayout(graph, 200, 200, 150);
    }
}

Example 37 with DataModel

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

the class LingamRunner method execute.

// ============================PUBLIC METHODS==========================//
/**
 * Executes the algorithm, producing (at least) a result workbench. Must be
 * implemented in the extending class.
 */
public void execute() {
    DataModel source = getDataModel();
    if (!(source instanceof DataSet)) {
        throw new IllegalArgumentException("Expecting a rectangular data set.");
    }
    DataSet data = (DataSet) source;
    if (!data.isContinuous()) {
        throw new IllegalArgumentException("Expecting a continuous data set.");
    }
    // Lingam_old lingam = new Lingam_old();
    // lingam.setAlternativePenalty(getParameters().getAlternativePenalty());
    // lingam.setPruningDone(true);
    // 
    // double lingamPruningAlpha = Preferences.userRoot().getDouble("lingamPruningAlpha", 0.05);
    // 
    // lingam.setAlternativePenalty(lingamPruningAlpha);
    // Graph graph = lingam.lingam(data).getGraph();
    Lingam lingam = new Lingam();
    Graph graph = lingam.search(data);
    if (getSourceGraph() != null) {
        GraphUtils.arrangeBySourceGraph(graph, getSourceGraph());
    } else {
        GraphUtils.circleLayout(graph, 200, 200, 150);
    }
    setResultGraph(graph);
    GraphUtils.arrangeBySourceGraph(getResultGraph(), getSourceGraph());
}

Example 38 with DataModel

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

the class SemEstimatorWrapper method setParams.

private boolean setParams(SemPmWrapper semPmWrapper, DataModelList dataModel) {
    for (DataModel model : dataModel) {
        if (model instanceof DataSet) {
            DataSet dataSet = (DataSet) model;
            SemPm semPm = semPmWrapper.getSemPm();
            this.semPm = semPm;
            SemEstimator estimator = new SemEstimator(dataSet, semPm, getOptimizer());
            estimator.setNumRestarts(getParams().getInt("numRestarts", 1));
            estimator.setScoreType((ScoreType) getParams().get("scoreType", ScoreType.Fgls));
            if (!degreesOfFreedomCheck(semPm)) {
                return true;
            }
            estimator.estimate();
            getMultipleResultList().add(estimator);
        } else if (model instanceof ICovarianceMatrix) {
            ICovarianceMatrix covMatrix = new CovarianceMatrix((ICovarianceMatrix) model);
            SemPm semPm = semPmWrapper.getSemPm();
            this.semPm = semPm;
            SemEstimator estimator = new SemEstimator(covMatrix, semPm, getOptimizer());
            estimator.setNumRestarts(getParams().getInt("numRestarts", 1));
            estimator.setScoreType((ScoreType) getParams().get("scoreType", ScoreType.Fgls));
            if (!degreesOfFreedomCheck(semPm)) {
                return true;
            }
            estimator.estimate();
            getMultipleResultList().add(estimator);
        } else {
            throw new IllegalArgumentException("Data must consist of continuous data sets or covariance matrices.");
        }
    }
    return false;
}

Example 39 with DataModel

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

the class CovMatrixAverageWrapper method calcAverage.

private void calcAverage(List<DataWrapper> wrappers) {
    List<TetradMatrix> cov = new ArrayList<>();
    for (int i = 0; i < wrappers.size(); i++) {
        DataModel selectedDataModel = wrappers.get(i).getSelectedDataModel();
        if (!(selectedDataModel instanceof ICovarianceMatrix)) {
            throw new IllegalArgumentException("Sorry, this is an average only over covariance matrices.");
        }
        cov.add(((ICovarianceMatrix) selectedDataModel).getMatrix());
    }
    TetradMatrix cov3 = new TetradMatrix(cov.get(0).rows(), cov.get(0).rows());
    for (int i = 0; i < cov.get(0).rows(); i++) {
        for (int j = 0; j < cov.get(0).rows(); j++) {
            double c = 0.0;
            for (int k = 0; k < cov.size(); k++) {
                c += cov.get(k).get(i, j);
            }
            c /= cov.size();
            cov3.set(i, j, c);
            cov3.set(j, i, c);
        }
    }
    DataModel m = wrappers.get(0).getSelectedDataModel();
    ICovarianceMatrix _cov = (ICovarianceMatrix) m;
    List<Node> nodes = _cov.getVariables();
    int n = _cov.getSampleSize();
    ICovarianceMatrix covWrapper = new CovarianceMatrix(nodes, cov3, n);
    setDataModel(covWrapper);
}