Examples with EdgeListGraph edu.cmu.tetrad.graph.EdgeListGraph used on opensource projects

Example 11 with EdgeListGraph

use of edu.cmu.tetrad.graph.EdgeListGraph in project tetrad by cmu-phil.

the class TestSimulatedFmri method testClark.

// @Test
public void testClark() {
    double f = .1;
    int N = 512;
    double alpha = 1.0;
    double penaltyDiscount = 1.0;
    for (int i = 0; i < 100; i++) {
        {
            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(z, x);
            g.addDirectedEdge(z, y);
            GeneralizedSemPm pm = new GeneralizedSemPm(g);
            try {
                pm.setNodeExpression(g.getNode("X"), "0.5 * Z + E_X");
                pm.setNodeExpression(g.getNode("Y"), "0.5 * X + 0.5 * Z + E_Y");
                pm.setNodeExpression(g.getNode("Z"), "E_Z");
                String error = "pow(Uniform(0, 1), " + f + ")";
                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(N, false);
            edu.cmu.tetrad.search.SemBicScore score = new edu.cmu.tetrad.search.SemBicScore(new CovarianceMatrixOnTheFly(data, false));
            score.setPenaltyDiscount(penaltyDiscount);
            Fask fask = new Fask(data, score);
            fask.setPenaltyDiscount(penaltyDiscount);
            fask.setAlpha(alpha);
            Graph out = fask.search();
            System.out.println(out);
        }
        {
            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), " + f + ")";
                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(N, false);
            edu.cmu.tetrad.search.SemBicScore score = new edu.cmu.tetrad.search.SemBicScore(new CovarianceMatrixOnTheFly(data, false));
            score.setPenaltyDiscount(penaltyDiscount);
            Fask fask = new Fask(data, score);
            fask.setPenaltyDiscount(penaltyDiscount);
            fask.setAlpha(alpha);
            Graph out = fask.search();
            System.out.println(out);
        }
    }
}

Example 12 with EdgeListGraph

use of edu.cmu.tetrad.graph.EdgeListGraph in project tetrad by cmu-phil.

the class TestSemEvidence method constructGraph1.

private Graph constructGraph1() {
    Graph graph = new EdgeListGraph();
    Node x1 = new GraphNode("X1");
    Node x2 = new GraphNode("X2");
    Node x3 = new GraphNode("X3");
    Node x4 = new GraphNode("X4");
    Node x5 = new GraphNode("X5");
    graph.addNode(x1);
    graph.addNode(x2);
    graph.addNode(x3);
    graph.addNode(x4);
    graph.addNode(x5);
    graph.addDirectedEdge(x1, x2);
    graph.addDirectedEdge(x2, x3);
    graph.addDirectedEdge(x3, x4);
    graph.addDirectedEdge(x1, x4);
    graph.addDirectedEdge(x4, x5);
    return graph;
}

Example 13 with EdgeListGraph

use of edu.cmu.tetrad.graph.EdgeListGraph in project tetrad by cmu-phil.

the class LogisticRegressionRunner method execute.

// =================PUBLIC METHODS OVERRIDING ABSTRACT=================//
/**
 * Executes the algorithm, producing (at least) a result workbench. Must be
 * implemented in the extending class.
 */
public void execute() {
    outGraph = new EdgeListGraph();
    if (regressorNames == null || regressorNames.isEmpty() || targetName == null) {
        report = "Response and predictor variables not set.";
        return;
    }
    if (regressorNames.contains(targetName)) {
        report = "Response must not be a predictor.";
        return;
    }
    DataSet regressorsDataSet = dataSets.get(getModelIndex()).copy();
    Node target = regressorsDataSet.getVariable(targetName);
    regressorsDataSet.removeColumn(target);
    List<String> names = regressorsDataSet.getVariableNames();
    // Get the list of regressors selected by the user
    List<Node> regressorNodes = new ArrayList<>();
    for (String s : regressorNames) {
        regressorNodes.add(dataSets.get(getModelIndex()).getVariable(s));
    }
    // If the user selected none, use them all
    if (regressorNames.size() > 0) {
        for (String name1 : names) {
            Node regressorVar = regressorsDataSet.getVariable(name1);
            if (!regressorNames.contains(regressorVar.getName())) {
                regressorsDataSet.removeColumn(regressorVar);
            }
        }
    }
    int ncases = regressorsDataSet.getNumRows();
    int nvars = regressorsDataSet.getNumColumns();
    double[][] regressors = new double[nvars][ncases];
    for (int i = 0; i < nvars; i++) {
        for (int j = 0; j < ncases; j++) {
            regressors[i][j] = regressorsDataSet.getDouble(j, i);
        }
    }
    LogisticRegression logRegression = new LogisticRegression(dataSets.get(getModelIndex()));
    logRegression.setAlpha(alpha);
    this.result = logRegression.regress((DiscreteVariable) target, regressorNodes);
}

Example 14 with EdgeListGraph

use of edu.cmu.tetrad.graph.EdgeListGraph in project tetrad by cmu-phil.

the class BayesProperties method getLikelihoodRatioP.

/**
 * Calculates the p-value of the graph with respect to the given data.
 */
public final double getLikelihoodRatioP(Graph graph) {
    // Null hypothesis = complete graph.
    List<Node> nodes = graph.getNodes();
    Graph graph0 = new EdgeListGraph(nodes);
    for (int i = 0; i < nodes.size(); i++) {
        for (int j = i + 1; j < nodes.size(); j++) graph0.addDirectedEdge(nodes.get(i), nodes.get(j));
    }
    Ret r0 = getLikelihood2(graph0);
    Ret r1 = getLikelihood2(graph);
    this.likelihood = r1.getLik();
    double lDiff = r0.getLik() - r1.getLik();
    System.out.println("lDiff = " + lDiff);
    int nDiff = r0.getDof() - r1.getDof();
    System.out.println("nDiff = " + nDiff);
    double chisq = 2.0 * lDiff;
    double dof = nDiff;
    this.chisq = chisq;
    this.dof = dof;
    int N = dataSet.getNumRows();
    this.bic = 2 * r1.getLik() - r1.getDof() * Math.log(N);
    System.out.println("bic = " + bic);
    System.out.println("chisq = " + chisq);
    System.out.println("dof = " + dof);
    double p = 1.0 - new ChiSquaredDistribution(dof).cumulativeProbability(chisq);
    System.out.println("p = " + p);
    return p;
}

Example 15 with EdgeListGraph

use of edu.cmu.tetrad.graph.EdgeListGraph in project tetrad by cmu-phil.

the class SimulationEditor method resetPanel.

private void resetPanel(Simulation simulation, String[] graphItems, String[] simulationItems, JTabbedPane tabbedPane) {
    RandomGraph randomGraph = (simulation.getSourceGraph() == null) ? new SingleGraph(new EdgeListGraph()) : new SingleGraph(simulation.getSourceGraph());
    if (!simulation.isFixedGraph()) {
        String graphItem = (String) graphsDropdown.getSelectedItem();
        simulation.getParams().set("graphsDropdownPreference", graphItem);
        if (graphItem.equals(graphItems[0])) {
            randomGraph = new RandomForward();
        } else if (graphItem.equals(graphItems[1])) {
            randomGraph = new ScaleFree();
        } else if (graphItem.equals(graphItems[2])) {
            randomGraph = new Cyclic();
        } else if (graphItem.equals(graphItems[3])) {
            randomGraph = new RandomSingleFactorMim();
        } else if (graphItem.equals(graphItems[4])) {
            randomGraph = new RandomTwoFactorMim();
        } else {
            throw new IllegalArgumentException("Unrecognized simulation type: " + graphItem);
        }
    }
    if (!simulation.isFixedSimulation()) {
        if (simulation.getSourceGraph() == null) {
            String simulationItem = (String) simulationsDropdown.getSelectedItem();
            simulation.getParams().set("simulationsDropdownPreference", simulationItem);
            simulation.setFixedGraph(false);
            if (randomGraph instanceof SingleGraph) {
                simulation.setFixedGraph(true);
            }
            if (simulationItem.equals(simulationItems[0])) {
                simulation.setSimulation(new BayesNetSimulation(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[1])) {
                simulation.setSimulation(new SemSimulation(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[2])) {
                simulation.setSimulation(new LinearFisherModel(randomGraph, simulation.getInputDataModelList()), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[3])) {
                simulation.setSimulation(new LeeHastieSimulation(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[4])) {
                simulation.setSimulation(new ConditionalGaussianSimulation(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[5])) {
                simulation.setSimulation(new TimeSeriesSemSimulation(randomGraph), simulation.getParams());
            } else {
                throw new IllegalArgumentException("Unrecognized simulation type: " + simulationItem);
            }
        } else {
            String simulationItem = (String) simulationsDropdown.getSelectedItem();
            simulation.getParams().set("simulationsDropdownPreference", simulationItem);
            simulation.setFixedGraph(false);
            if (randomGraph instanceof SingleGraph) {
                simulation.setFixedGraph(true);
            }
            if (simulationItem.equals(simulationItems[0])) {
                simulation.setSimulation(new BayesNetSimulation(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[1])) {
                simulation.setSimulation(new SemSimulation(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[2])) {
                simulation.setSimulation(new LinearFisherModel(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[3])) {
                simulation.setSimulation(new LeeHastieSimulation(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[4])) {
                simulation.setSimulation(new ConditionalGaussianSimulation(randomGraph), simulation.getParams());
            } else if (simulationItem.equals(simulationItems[5])) {
                simulation.setSimulation(new TimeSeriesSemSimulation(randomGraph), simulation.getParams());
            } else {
                throw new IllegalArgumentException("Unrecognized simulation type: " + simulationItem);
            }
        }
    }
    tabbedPane.setComponentAt(0, new PaddingPanel(getParameterPanel(simulation, simulation.getSimulation(), simulation.getParams())));
}