Examples with ClusterAlgorithm edu.cmu.tetrad.algcomparison.algorithm.cluster.ClusterAlgorithm used on opensource projects

Example 1 with ClusterAlgorithm

use of edu.cmu.tetrad.algcomparison.algorithm.cluster.ClusterAlgorithm in project tetrad by cmu-phil.

the class GeneralAlgorithmRunner method execute.

// ============================PUBLIC METHODS==========================//
@Override
public void execute() {
    List<Graph> graphList = new ArrayList<>();
    int i = 0;
    if (getDataModelList().isEmpty()) {
        if (getSourceGraph() != null) {
            Algorithm algo = getAlgorithm();
            if (algo instanceof HasKnowledge) {
                ((HasKnowledge) algo).setKnowledge(getKnowledge());
            }
            graphList.add(algo.search(null, parameters));
        } else {
            throw new IllegalArgumentException("The parent boxes did not include any datasets or graphs. Try opening\n" + "the editors for those boxes and loading or simulating them.");
        }
    } else {
        if (getAlgorithm() instanceof MultiDataSetAlgorithm) {
            for (int k = 0; k < parameters.getInt("numRuns"); k++) {
                List<DataSet> dataSets = getDataModelList().stream().map(e -> (DataSet) e).collect(Collectors.toCollection(ArrayList::new));
                if (dataSets.size() < parameters.getInt("randomSelectionSize")) {
                    throw new IllegalArgumentException("Sorry, the 'random selection size' is greater than " + "the number of data sets.");
                }
                Collections.shuffle(dataSets);
                List<DataModel> sub = new ArrayList<>();
                for (int j = 0; j < parameters.getInt("randomSelectionSize"); j++) {
                    sub.add(dataSets.get(j));
                }
                Algorithm algo = getAlgorithm();
                if (algo instanceof HasKnowledge) {
                    ((HasKnowledge) algo).setKnowledge(getKnowledge());
                }
                graphList.add(((MultiDataSetAlgorithm) algo).search(sub, parameters));
            }
        } else if (getAlgorithm() instanceof ClusterAlgorithm) {
            for (int k = 0; k < parameters.getInt("numRuns"); k++) {
                getDataModelList().forEach(dataModel -> {
                    if (dataModel instanceof ICovarianceMatrix) {
                        ICovarianceMatrix dataSet = (ICovarianceMatrix) dataModel;
                        graphList.add(algorithm.search(dataSet, parameters));
                    } else if (dataModel instanceof DataSet) {
                        DataSet dataSet = (DataSet) dataModel;
                        if (!dataSet.isContinuous()) {
                            throw new IllegalArgumentException("Sorry, you need a continuous dataset for a cluster algorithm.");
                        }
                        graphList.add(algorithm.search(dataSet, parameters));
                    }
                });
            }
        } else {
            getDataModelList().forEach(data -> {
                IKnowledge knowledgeFromData = data.getKnowledge();
                if (!(knowledgeFromData == null || knowledgeFromData.getVariables().isEmpty())) {
                    this.knowledge = knowledgeFromData;
                }
                Algorithm algo = getAlgorithm();
                if (algo instanceof HasKnowledge) {
                    ((HasKnowledge) algo).setKnowledge(getKnowledge());
                }
                DataType algDataType = algo.getDataType();
                if (data.isContinuous() && (algDataType == DataType.Continuous || algDataType == DataType.Mixed)) {
                    graphList.add(algo.search(data, parameters));
                } else if (data.isDiscrete() && (algDataType == DataType.Discrete || algDataType == DataType.Mixed)) {
                    graphList.add(algo.search(data, parameters));
                } else if (data.isMixed() && algDataType == DataType.Mixed) {
                    graphList.add(algo.search(data, parameters));
                } else {
                    throw new IllegalArgumentException("The type of data changed; try opening up the search editor and " + "running the algorithm there.");
                }
            });
        }
    }
    if (getKnowledge().getVariablesNotInTiers().size() < getKnowledge().getVariables().size()) {
        for (Graph graph : graphList) {
            SearchGraphUtils.arrangeByKnowledgeTiers(graph, getKnowledge());
        }
    } else {
        for (Graph graph : graphList) {
            GraphUtils.circleLayout(graph, 225, 200, 150);
        }
    }
    this.graphList = graphList;
}