Examples with BayesPm edu.cmu.tetrad.bayes.BayesPm used on opensource projects

Example 26 with BayesPm

use of edu.cmu.tetrad.bayes.BayesPm in project tetrad by cmu-phil.

the class EvidenceWizardMultiple method appendJoint.

private void appendJoint(List<Node> selectedNodes, JTextArea marginalsArea, BayesIm manipulatedIm, NumberFormat nf) {
    if (!getUpdaterWrapper().getBayesUpdater().isJointMarginalSupported()) {
        marginalsArea.append("\n\n(Calculation of joint not supported " + "for this updater.)");
        return;
    }
    BayesPm bayesPm = manipulatedIm.getBayesPm();
    int numNodes = selectedNodes.size();
    int[] dims = new int[numNodes];
    int[] variables = new int[numNodes];
    int numRows = 1;
    for (int i = 0; i < numNodes; i++) {
        Node node = selectedNodes.get(i);
        int numCategories = bayesPm.getNumCategories(node);
        variables[i] = manipulatedIm.getNodeIndex(node);
        dims[i] = numCategories;
        numRows *= numCategories;
    }
    marginalsArea.append("\n\nJOINT OVER SELECTED VARIABLES:\n\n");
    for (int i = 0; i < numNodes; i++) {
        marginalsArea.append(selectedNodes.get(i) + "\t");
    }
    marginalsArea.append("Joint\tLog odds\n");
    for (int row = 0; row < numRows; row++) {
        int[] values = getCategories(row, dims);
        double prob = getUpdaterWrapper().getBayesUpdater().getJointMarginal(variables, values);
        double logOdds = Math.log(prob / (1. - prob));
        marginalsArea.append("\n");
        for (int j = 0; j < numNodes; j++) {
            Node node = selectedNodes.get(j);
            marginalsArea.append(bayesPm.getCategory(node, values[j]));
            marginalsArea.append("\t");
        }
        marginalsArea.append(nf.format(prob) + "\t");
        marginalsArea.append(nf.format(logOdds));
    }
}

Example 27 with BayesPm

use of edu.cmu.tetrad.bayes.BayesPm in project tetrad by cmu-phil.

the class EvidenceWizardMultiple method appendMarginals.

private void appendMarginals(List<Node> selectedNodes, JTextArea marginalsArea, BayesIm manipulatedIm, NumberFormat nf) {
    BayesPm bayesPm = manipulatedIm.getBayesPm();
    marginalsArea.append("MARGINALS FOR SELECTED VARIABLES:\n");
    for (Node selectedNode : selectedNodes) {
        marginalsArea.append("\nVariable " + selectedNode.getName() + ":\n");
        int nodeIndex = manipulatedIm.getNodeIndex(selectedNode);
        for (int j = 0; j < bayesPm.getNumCategories(selectedNode); j++) {
            double prob = getUpdaterWrapper().getBayesUpdater().getMarginal(nodeIndex, j);
            double logOdds = Math.log(prob / (1. - prob));
            marginalsArea.append("Category " + bayesPm.getCategory(selectedNode, j) + ": p = " + nf.format(prob) + ",  log odds = " + nf.format(logOdds) + "\n");
        }
    }
}

Example 28 with BayesPm

use of edu.cmu.tetrad.bayes.BayesPm in project tetrad by cmu-phil.

the class EvidenceWizardMultipleObs method appendMarginals.

private void appendMarginals(List<Node> selectedNodes, JTextArea marginalsArea, BayesIm manipulatedIm, NumberFormat nf) {
    BayesPm bayesPm = manipulatedIm.getBayesPm();
    marginalsArea.append("MARGINALS FOR SELECTED VARIABLES:\n");
    for (Node selectedNode : selectedNodes) {
        marginalsArea.append("\nVariable " + selectedNode.getName() + ":\n");
        int nodeIndex = manipulatedIm.getNodeIndex(selectedNode);
        for (int j = 0; j < bayesPm.getNumCategories(selectedNode); j++) {
            double prob = getUpdaterWrapper().getBayesUpdater().getMarginal(nodeIndex, j);
            // identifiability returns -1 if the requested prob is unidentifiable
            if (prob < 0.0) {
                marginalsArea.append("Category " + bayesPm.getCategory(selectedNode, j) + ": p = " + "Unidentifiable" + ",  log odds = " + "*" + "\n");
            } else {
                double logOdds = Math.log(prob / (1. - prob));
                marginalsArea.append("Category " + bayesPm.getCategory(selectedNode, j) + ": p = " + nf.format(prob) + ",  log odds = " + nf.format(logOdds) + "\n");
            }
        }
    }
}

Example 29 with BayesPm

use of edu.cmu.tetrad.bayes.BayesPm in project tetrad by cmu-phil.

the class TestEvidence method sampleBayesIm2.

private static BayesIm sampleBayesIm2() {
    Node a = new GraphNode("a");
    Node b = new GraphNode("b");
    Node c = new GraphNode("c");
    Dag graph;
    graph = new Dag();
    graph.addNode(a);
    graph.addNode(b);
    graph.addNode(c);
    graph.addDirectedEdge(a, b);
    graph.addDirectedEdge(a, c);
    graph.addDirectedEdge(b, c);
    BayesPm bayesPm = new BayesPm(graph);
    bayesPm.setNumCategories(b, 3);
    BayesIm bayesIm1 = new MlBayesIm(bayesPm);
    bayesIm1.setProbability(0, 0, 0, .3);
    bayesIm1.setProbability(0, 0, 1, .7);
    bayesIm1.setProbability(1, 0, 0, .3);
    bayesIm1.setProbability(1, 0, 1, .4);
    bayesIm1.setProbability(1, 0, 2, .3);
    bayesIm1.setProbability(1, 1, 0, .6);
    bayesIm1.setProbability(1, 1, 1, .1);
    bayesIm1.setProbability(1, 1, 2, .3);
    bayesIm1.setProbability(2, 0, 0, .9);
    bayesIm1.setProbability(2, 0, 1, .1);
    bayesIm1.setProbability(2, 1, 0, .1);
    bayesIm1.setProbability(2, 1, 1, .9);
    bayesIm1.setProbability(2, 2, 0, .5);
    bayesIm1.setProbability(2, 2, 1, .5);
    bayesIm1.setProbability(2, 3, 0, .2);
    bayesIm1.setProbability(2, 3, 1, .8);
    bayesIm1.setProbability(2, 4, 0, .6);
    bayesIm1.setProbability(2, 4, 1, .4);
    bayesIm1.setProbability(2, 5, 0, .7);
    bayesIm1.setProbability(2, 5, 1, .3);
    return bayesIm1;
}

Example 30 with BayesPm

use of edu.cmu.tetrad.bayes.BayesPm in project tetrad by cmu-phil.

the class TestFges method explore2.

@Test
public void explore2() {
    RandomUtil.getInstance().setSeed(1457220623122L);
    int numVars = 20;
    double edgeFactor = 1.0;
    int numCases = 1000;
    double structurePrior = 1;
    double samplePrior = 1;
    List<Node> vars = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        vars.add(new ContinuousVariable("X" + i));
    }
    Graph dag = GraphUtils.randomGraphRandomForwardEdges(vars, 0, (int) (numVars * edgeFactor), 30, 15, 15, false, true);
    // printDegreeDistribution(dag, out);
    BayesPm pm = new BayesPm(dag, 2, 3);
    BayesIm im = new MlBayesIm(pm, MlBayesIm.RANDOM);
    DataSet data = im.simulateData(numCases, false);
    // out.println("Finishing simulation");
    BDeScore score = new BDeScore(data);
    score.setSamplePrior(samplePrior);
    score.setStructurePrior(structurePrior);
    Fges ges = new Fges(score);
    ges.setVerbose(false);
    ges.setNumPatternsToStore(0);
    ges.setFaithfulnessAssumed(false);
    Graph estPattern = ges.search();
    final Graph truePattern = SearchGraphUtils.patternForDag(dag);
    int[][] counts = SearchGraphUtils.graphComparison(estPattern, truePattern, null);
    int[][] expectedCounts = { { 2, 0, 0, 0, 0, 1 }, { 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0 }, { 2, 0, 0, 13, 0, 3 }, { 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0 }, { 0, 0, 0, 0, 0, 0 } };
// for (int i = 0; i < counts.length; i++) {
// assertTrue(Arrays.equals(counts[i], expectedCounts[i]));
// }
// System.out.println(MatrixUtils.toString(expectedCounts));
// System.out.println(MatrixUtils.toString(counts));
// System.out.println(RandomUtil.getInstance().getSeed());
}