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

Example 21 with ContinuousVariable

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

the class DataConvertUtils method toMixedDataBox.

public static DataModel toMixedDataBox(MixedTabularDataset mixedTabularDataset) {
    int numOfRows = mixedTabularDataset.getNumOfRows();
    MixedVarInfo[] mixedVarInfos = mixedTabularDataset.getMixedVarInfos();
    double[][] continuousData = mixedTabularDataset.getContinuousData();
    int[][] discreteData = mixedTabularDataset.getDiscreteData();
    List<Node> nodes = new LinkedList<>();
    for (MixedVarInfo mixedVarInfo : mixedVarInfos) {
        if (mixedVarInfo.isContinuous()) {
            nodes.add(new ContinuousVariable(mixedVarInfo.getName()));
        } else {
            nodes.add(new DiscreteVariable(mixedVarInfo.getName(), mixedVarInfo.getCategories()));
        }
    }
    return new BoxDataSet(new MixedDataBox(nodes, numOfRows, continuousData, discreteData), nodes);
}

Example 22 with ContinuousVariable

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

the class GdistanceTest method main.

public static void main(String... args) {
    // first generate a couple random graphs
    int numVars = 16;
    int numEdges = 16;
    List<Node> vars = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        vars.add(new ContinuousVariable("X" + i));
    }
    Graph testdag1 = GraphUtils.randomGraphRandomForwardEdges(vars, 0, numEdges, 30, 15, 15, false, true);
    Graph testdag2 = GraphUtils.randomGraphRandomForwardEdges(vars, 0, numEdges, 30, 15, 15, false, true);
    // System.out.println(testdag1);
    // load the location map
    String workingDirectory = System.getProperty("user.dir");
    System.out.println(workingDirectory);
    Path mapPath = Paths.get("locationMap.txt");
    System.out.println(mapPath);
    TabularDataReader dataReaderMap = new ContinuousTabularDataFileReader(mapPath.toFile(), Delimiter.COMMA);
    try {
        DataSet locationMap = (DataSet) DataConvertUtils.toDataModel(dataReaderMap.readInData());
        // System.out.println(locationMap);
        // then compare their distance
        double xdist = 2.4;
        double ydist = 2.4;
        double zdist = 2;
        Gdistance gdist = new Gdistance(locationMap, xdist, ydist, zdist);
        List<Double> output = gdist.distances(testdag1, testdag2);
        System.out.println(output);
        PrintWriter writer = new PrintWriter("Gdistances.txt", "UTF-8");
        writer.println(output);
        writer.close();
    } catch (Exception IOException) {
        IOException.printStackTrace();
    }
}

Example 23 with ContinuousVariable

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

the class HsimRobustCompare method run.

// *************Public Methods*****************8//
public static List<double[]> run(int numVars, double edgesPerNode, int numCases, double penaltyDiscount, int resimSize, int repeat, boolean verbose) {
    // public static void main(String[] args) {
    // first generate the data
    RandomUtil.getInstance().setSeed(1450184147770L);
    // '\t';
    char delimiter = ',';
    final int numEdges = (int) (numVars * edgesPerNode);
    List<Node> vars = new ArrayList<>();
    double[] oErrors = new double[5];
    double[] hsimErrors = new double[5];
    double[] simErrors = new double[5];
    List<double[]> output = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        vars.add(new ContinuousVariable("X" + i));
    }
    Graph odag = GraphUtils.randomGraphRandomForwardEdges(vars, 0, numEdges, 30, 15, 15, false, true);
    BayesPm bayesPm = new BayesPm(odag, 2, 2);
    BayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
    // oData is the original data set, and odag is the original dag.
    DataSet oData = bayesIm.simulateData(numCases, false);
    // System.out.println(oData);
    // System.out.println(odag);
    // then run FGES
    BDeuScore oscore = new BDeuScore(oData);
    Fges fges = new Fges(oscore);
    fges.setVerbose(false);
    fges.setNumPatternsToStore(0);
    fges.setPenaltyDiscount(penaltyDiscount);
    Graph oGraphOut = fges.search();
    if (verbose)
        System.out.println(oGraphOut);
    // calculate FGES errors
    oErrors = new double[5];
    oErrors = HsimUtils.errorEval(oGraphOut, odag);
    if (verbose)
        System.out.println(oErrors[0] + " " + oErrors[1] + " " + oErrors[2] + " " + oErrors[3] + " " + oErrors[4]);
    // create various simulated data sets
    // //let's do the full simulated data set first: a dag in the FGES pattern fit to the data set.
    PatternToDag pickdag = new PatternToDag(oGraphOut);
    Graph fgesDag = pickdag.patternToDagMeek();
    Dag fgesdag2 = new Dag(fgesDag);
    BayesPm simBayesPm = new BayesPm(fgesdag2, bayesPm);
    DirichletBayesIm simIM = DirichletBayesIm.symmetricDirichletIm(simBayesPm, 1.0);
    DirichletEstimator simEstimator = new DirichletEstimator();
    DirichletBayesIm fittedIM = simEstimator.estimate(simIM, oData);
    DataSet simData = fittedIM.simulateData(numCases, false);
    // //next let's do a schedule of small hsims
    HsimRepeatAutoRun study = new HsimRepeatAutoRun(oData);
    hsimErrors = study.run(resimSize, repeat);
    // calculate errors for all simulated output graphs
    // //full simulation errors first
    BDeuScore simscore = new BDeuScore(simData);
    Fges simfges = new Fges(simscore);
    simfges.setVerbose(false);
    simfges.setNumPatternsToStore(0);
    simfges.setPenaltyDiscount(penaltyDiscount);
    Graph simGraphOut = simfges.search();
    // simErrors = new double[5];
    simErrors = HsimUtils.errorEval(simGraphOut, fgesdag2);
    // first, let's just see what the errors are.
    if (verbose)
        System.out.println("Original erors are: " + oErrors[0] + " " + oErrors[1] + " " + oErrors[2] + " " + oErrors[3] + " " + oErrors[4]);
    if (verbose)
        System.out.println("Full resim errors are: " + simErrors[0] + " " + simErrors[1] + " " + simErrors[2] + " " + simErrors[3] + " " + simErrors[4]);
    if (verbose)
        System.out.println("HSim errors are: " + hsimErrors[0] + " " + hsimErrors[1] + " " + hsimErrors[2] + " " + hsimErrors[3] + " " + hsimErrors[4]);
    // then, let's try to squeeze these numbers down into something more tractable.
    // double[] ErrorDifferenceDifferences;
    // ErrorDifferenceDifferences = new double[5];
    // ErrorDifferenceDifferences[0] = Math.abs(oErrors[0]-simErrors[0])-Math.abs(oErrors[0]-hsimErrors[0]);
    // ErrorDifferenceDifferences[1] = Math.abs(oErrors[1]-simErrors[1])-Math.abs(oErrors[1]-hsimErrors[1]);
    // ErrorDifferenceDifferences[2] = Math.abs(oErrors[2]-simErrors[2])-Math.abs(oErrors[2]-hsimErrors[2]);
    // ErrorDifferenceDifferences[3] = Math.abs(oErrors[3]-simErrors[3])-Math.abs(oErrors[3]-hsimErrors[3]);
    // ErrorDifferenceDifferences[4] = Math.abs(oErrors[4]-simErrors[4])-Math.abs(oErrors[4]-hsimErrors[4]);
    // System.out.println("resim error errors - hsim error errors: " + ErrorDifferenceDifferences[0] + " " + ErrorDifferenceDifferences[1] + " " + ErrorDifferenceDifferences[2] + " " + ErrorDifferenceDifferences[3] + " " + ErrorDifferenceDifferences[4]);
    output.add(oErrors);
    output.add(simErrors);
    output.add(hsimErrors);
    return output;
}

Example 24 with ContinuousVariable

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

the class TestLogisticRegression method test1.

@Test
public void test1() {
    List<Node> nodes = new ArrayList<>();
    for (int i = 0; i < 5; i++) {
        nodes.add(new ContinuousVariable("X" + (i + 1)));
    }
    Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 3, 3, 3, false));
    System.out.println(graph);
    SemPm pm = new SemPm(graph);
    SemIm im = new SemIm(pm);
    DataSet data = im.simulateDataRecursive(1000, false);
    Node x1 = data.getVariable("X1");
    Node x2 = data.getVariable("X2");
    Node x3 = data.getVariable("X3");
    Node x4 = data.getVariable("X4");
    Node x5 = data.getVariable("X5");
    Discretizer discretizer = new Discretizer(data);
    discretizer.equalCounts(x1, 2);
    DataSet d2 = discretizer.discretize();
    LogisticRegression regression = new LogisticRegression(d2);
    List<Node> regressors = new ArrayList<>();
    regressors.add(x2);
    regressors.add(x3);
    regressors.add(x4);
    regressors.add(x5);
    DiscreteVariable x1b = (DiscreteVariable) d2.getVariable("X1");
    regression.regress(x1b, regressors);
    System.out.println(regression);
}

Example 25 with ContinuousVariable

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

the class TestDeltaTetradTest method makePm.

private SemPm makePm() {
    List<Node> variableNodes = new ArrayList<>();
    ContinuousVariable x1 = new ContinuousVariable("X1");
    ContinuousVariable x2 = new ContinuousVariable("X2");
    ContinuousVariable x3 = new ContinuousVariable("X3");
    ContinuousVariable x4 = new ContinuousVariable("X4");
    ContinuousVariable x5 = new ContinuousVariable("X5");
    variableNodes.add(x1);
    variableNodes.add(x2);
    variableNodes.add(x3);
    variableNodes.add(x4);
    variableNodes.add(x5);
    Graph _graph = new EdgeListGraph(variableNodes);
    SemGraph graph = new SemGraph(_graph);
    graph.addDirectedEdge(x5, x1);
    graph.addDirectedEdge(x5, x2);
    graph.addDirectedEdge(x5, x3);
    graph.addDirectedEdge(x5, x4);
    return new SemPm(graph);
}