Examples with TsGFci edu.cmu.tetrad.search.TsGFci used on opensource projects

Example 1 with TsGFci

use of edu.cmu.tetrad.search.TsGFci in project tetrad by cmu-phil.

the class TsImages method search.

@Override
public Graph search(List<DataModel> dataSets, Parameters parameters) {
    List<DataModel> dataModels = new ArrayList<>();
    for (DataModel dataSet : dataSets) {
        dataModels.add(dataSet);
    }
    TsGFci search;
    if (score instanceof SemBicScore) {
        SemBicScoreImages gesScore = new SemBicScoreImages(dataModels);
        gesScore.setPenaltyDiscount(parameters.getDouble("penaltyDiscount"));
        IndependenceTest test = new IndTestScore(gesScore);
        search = new TsGFci(test, gesScore);
    } else if (score instanceof BdeuScore) {
        double samplePrior = parameters.getDouble("samplePrior", 1);
        double structurePrior = parameters.getDouble("structurePrior", 1);
        BdeuScoreImages score = new BdeuScoreImages(dataModels);
        score.setSamplePrior(samplePrior);
        score.setStructurePrior(structurePrior);
        IndependenceTest test = new IndTestScore(score);
        search = new TsGFci(test, score);
    } else {
        throw new IllegalStateException("Sorry, data must either be all continuous or all discrete.");
    }
    IKnowledge knowledge = dataModels.get(0).getKnowledge();
    search.setKnowledge(knowledge);
    return search.search();
}

Example 2 with TsGFci

use of edu.cmu.tetrad.search.TsGFci in project tetrad by cmu-phil.

the class TsImages method search.

@Override
public Graph search(DataModel dataModel, Parameters parameters) {
    if (parameters.getInt("bootstrapSampleSize") < 1) {
        DataSet dataSet = (DataSet) dataModel;
        TsGFci search;
        Score score1 = score.getScore(dataSet, parameters);
        IndependenceTest test = new IndTestScore(score1);
        search = new TsGFci(test, score1);
        search.setKnowledge(dataSet.getKnowledge());
        return search.search();
    } else {
        TsImages algorithm = new TsImages(score);
        DataSet data = (DataSet) dataModel;
        GeneralBootstrapTest search = new GeneralBootstrapTest(data, algorithm, parameters.getInt("bootstrapSampleSize"));
        search.setKnowledge(knowledge);
        BootstrapEdgeEnsemble edgeEnsemble = BootstrapEdgeEnsemble.Highest;
        switch(parameters.getInt("bootstrapEnsemble", 1)) {
            case 0:
                edgeEnsemble = BootstrapEdgeEnsemble.Preserved;
                break;
            case 1:
                edgeEnsemble = BootstrapEdgeEnsemble.Highest;
                break;
            case 2:
                edgeEnsemble = BootstrapEdgeEnsemble.Majority;
        }
        search.setEdgeEnsemble(edgeEnsemble);
        search.setParameters(parameters);
        search.setVerbose(parameters.getBoolean("verbose"));
        return search.search();
    }
}