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

Example 11 with ColtDataSet

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

the class AbstractMBSearchRunner method setSearchResults.

/**
 * Sets the results of the search.
 */
void setSearchResults(List<Node> nodes) {
    if (nodes == null) {
        throw new NullPointerException("nodes were null.");
    }
    this.variables = new ArrayList<>(nodes);
    if (nodes.isEmpty()) {
        this.dataModel = new ColtDataSet(source.getNumRows(), nodes);
    } else {
        this.dataModel = this.source.subsetColumns(nodes);
    }
    this.setDataModel(this.dataModel);
}

Example 12 with ColtDataSet

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

the class TabularComparison method newExecution.

private void newExecution() {
    statistics = new ArrayList<>();
    statistics.add(new AdjacencyPrecision());
    statistics.add(new AdjacencyRecall());
    statistics.add(new ArrowheadPrecision());
    statistics.add(new ArrowheadRecall());
    statistics.add(new TwoCyclePrecision());
    statistics.add(new TwoCycleRecall());
    statistics.add(new TwoCycleFalsePositive());
    // statistics.add(new ElapsedTime());
    // statistics.add(new F1Adj());
    // statistics.add(new F1Arrow());
    // statistics.add(new MathewsCorrAdj());
    // statistics.add(new MathewsCorrArrow());
    // statistics.add(new SHD());
    List<Node> variables = new ArrayList<>();
    for (Statistic statistic : statistics) {
        variables.add(new ContinuousVariable(statistic.getAbbreviation()));
    }
    dataSet = new ColtDataSet(0, variables);
    dataSet.setNumberFormat(new DecimalFormat("0.00"));
}

Example 13 with ColtDataSet

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

the class RemoveMissingCasesDataFilter method filter.

public DataSet filter(DataSet data) {
    List<Node> variables = data.getVariables();
    int numRows = 0;
    ROWS: for (int row = 0; row < data.getNumRows(); row++) {
        for (int col = 0; col < data.getNumColumns(); col++) {
            Node variable = data.getVariable(col);
            if (((Variable) variable).isMissingValue(data.getObject(row, col))) {
                continue ROWS;
            }
        }
        numRows++;
    }
    DataSet newDataSet = new ColtDataSet(numRows, variables);
    int newRow = 0;
    ROWS: for (int row = 0; row < data.getNumRows(); row++) {
        for (int col = 0; col < data.getNumColumns(); col++) {
            Node variable = data.getVariable(col);
            if (((Variable) variable).isMissingValue(data.getObject(row, col))) {
                continue ROWS;
            }
        }
        for (int col = 0; col < data.getNumColumns(); col++) {
            newDataSet.setObject(newRow, col, data.getObject(row, col));
        }
        newRow++;
    }
    return newDataSet;
}

Example 14 with ColtDataSet

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

the class EmBayesEstimator method initialize.

private void initialize() {
    DirichletBayesIm prior = DirichletBayesIm.symmetricDirichletIm(bayesPmObs, 0.5);
    observedIm = DirichletEstimator.estimate(prior, dataSet);
    // MLBayesEstimator dirichEst = new MLBayesEstimator();
    // observedIm = dirichEst.estimate(bayesPmObs, dataSet);
    // System.out.println("Estimated Bayes IM for Measured Variables:  ");
    // System.out.println(observedIm);
    // mixedData should be ddsNm with new columns for the latent variables.
    // Each such column should contain missing data for each case.
    int numFullCases = dataSet.getNumRows();
    List<Node> variables = new LinkedList<>();
    for (Node node : nodes) {
        if (node.getNodeType() == NodeType.LATENT) {
            int numCategories = bayesPm.getNumCategories(node);
            DiscreteVariable latentVar = new DiscreteVariable(node.getName(), numCategories);
            latentVar.setNodeType(NodeType.LATENT);
            variables.add(latentVar);
        } else {
            String name = bayesPm.getVariable(node).getName();
            Node variable = dataSet.getVariable(name);
            variables.add(variable);
        }
    }
    DataSet dsMixed = new ColtDataSet(numFullCases, variables);
    for (int j = 0; j < nodes.length; j++) {
        if (nodes[j].getNodeType() == NodeType.LATENT) {
            for (int i = 0; i < numFullCases; i++) {
                dsMixed.setInt(i, j, -99);
            }
        } else {
            String name = bayesPm.getVariable(nodes[j]).getName();
            Node variable = dataSet.getVariable(name);
            int index = dataSet.getColumn(variable);
            for (int i = 0; i < numFullCases; i++) {
                dsMixed.setInt(i, j, dataSet.getInt(i, index));
            }
        }
    }
    // System.out.println(dsMixed);
    mixedData = dsMixed;
    allVariables = mixedData.getVariables();
    // Find the bayes net which is parameterized using mixedData or set randomly when that's
    // not possible.
    estimateIM(bayesPm, mixedData);
    // The following DEBUG section tests a case specified by P. Spirtes
    // DEBUG TAIL:   For use with embayes_l1x1x2x3V3.dat
    /*
        Node l1Node = graph.getNode("L1");
        //int l1Index = bayesImMixed.getNodeIndex(l1Node);
        int l1index = estimatedIm.getNodeIndex(l1Node);
        Node x1Node = graph.getNode("X1");
        //int x1Index = bayesImMixed.getNodeIndex(x1Node);
        int x1Index = estimatedIm.getNodeIndex(x1Node);
        Node x2Node = graph.getNode("X2");
        //int x2Index = bayesImMixed.getNodeIndex(x2Node);
        int x2Index = estimatedIm.getNodeIndex(x2Node);
        Node x3Node = graph.getNode("X3");
        //int x3Index = bayesImMixed.getNodeIndex(x3Node);
        int x3Index = estimatedIm.getNodeIndex(x3Node);

        estimatedIm.setProbability(l1index, 0, 0, 0.5);
        estimatedIm.setProbability(l1index, 0, 1, 0.5);

        //bayesImMixed.setProbability(x1Index, 0, 0, 0.33333);
        //bayesImMixed.setProbability(x1Index, 0, 1, 0.66667);
        estimatedIm.setProbability(x1Index, 0, 0, 0.6);      //p(x1 = 0 | l1 = 0)
        estimatedIm.setProbability(x1Index, 0, 1, 0.4);      //p(x1 = 1 | l1 = 0)
        estimatedIm.setProbability(x1Index, 1, 0, 0.4);      //p(x1 = 0 | l1 = 1)
        estimatedIm.setProbability(x1Index, 1, 1, 0.6);      //p(x1 = 1 | l1 = 1)

        //bayesImMixed.setProbability(x2Index, 1, 0, 0.66667);
        //bayesImMixed.setProbability(x2Index, 1, 1, 0.33333);
        estimatedIm.setProbability(x2Index, 1, 0, 0.4);      //p(x2 = 0 | l1 = 1)
        estimatedIm.setProbability(x2Index, 1, 1, 0.6);      //p(x2 = 1 | l1 = 1)
        estimatedIm.setProbability(x2Index, 0, 0, 0.6);      //p(x2 = 0 | l1 = 0)
        estimatedIm.setProbability(x2Index, 0, 1, 0.4);      //p(x2 = 1 | l1 = 0)

        //bayesImMixed.setProbability(x3Index, 1, 0, 0.66667);
        //bayesImMixed.setProbability(x3Index, 1, 1, 0.33333);
        estimatedIm.setProbability(x3Index, 1, 0, 0.4);      //p(x3 = 0 | l1 = 1)
        estimatedIm.setProbability(x3Index, 1, 1, 0.6);      //p(x3 = 1 | l1 = 1)
        estimatedIm.setProbability(x3Index, 0, 0, 0.6);      //p(x3 = 0 | l1 = 0)
        estimatedIm.setProbability(x3Index, 0, 1, 0.4);      //p(x3 = 1 | l1 = 0)
        */
    // END of TAIL
    // System.out.println("bayes IM estimated by estimateIM");
    // System.out.println(bayesImMixed);
    // System.out.println(estimatedIm);
    estimatedCounts = new double[nodes.length][][];
    estimatedCountsDenom = new double[nodes.length][];
    condProbs = new double[nodes.length][][];
    for (int i = 0; i < nodes.length; i++) {
        // int numRows = bayesImMixed.getNumRows(i);
        int numRows = estimatedIm.getNumRows(i);
        estimatedCounts[i] = new double[numRows][];
        estimatedCountsDenom[i] = new double[numRows];
        condProbs[i] = new double[numRows][];
        // for(int j = 0; j < bayesImMixed.getNumRows(i); j++) {
        for (int j = 0; j < estimatedIm.getNumRows(i); j++) {
            // int numCols = bayesImMixed.getNumColumns(i);
            int numCols = estimatedIm.getNumColumns(i);
            estimatedCounts[i][j] = new double[numCols];
            condProbs[i][j] = new double[numCols];
        }
    }
}

Example 15 with ColtDataSet

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

the class MlBayesImObs method simulateDataHelper.

/**
 * Simulates a sample with the given sample size.
 *
 * @param sampleSize      the sample size.
 * @return the simulated sample as a DataSet.
 */
private DataSet simulateDataHelper(int sampleSize, boolean latentDataSaved) {
    int numMeasured = 0;
    int[] map = new int[nodes.length];
    List<Node> variables = new LinkedList<>();
    for (int j = 0; j < nodes.length; j++) {
        if (!latentDataSaved && nodes[j].getNodeType() != NodeType.MEASURED) {
            continue;
        }
        int numCategories = bayesPm.getNumCategories(nodes[j]);
        List<String> categories = new LinkedList<>();
        for (int k = 0; k < numCategories; k++) {
            categories.add(bayesPm.getCategory(nodes[j], k));
        }
        DiscreteVariable var = new DiscreteVariable(nodes[j].getName(), categories);
        variables.add(var);
        int index = ++numMeasured - 1;
        map[index] = j;
    }
    DataSet dataSet = new ColtDataSet(sampleSize, variables);
    constructSample(sampleSize, numMeasured, dataSet, map);
    return dataSet;
}