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

Example 41 with Node

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

the class GraphScore method aBetterScore.

private double aBetterScore(int x, int y, int[] z) {
    Node _y = variables.get(y);
    Node _x = variables.get(x);
    List<Node> _z = getVariableList(z);
    boolean dsep = dag.isDSeparatedFrom(_x, _y, _z);
    int count = 0;
    if (!dsep)
        count++;
    for (Node z0 : _z) {
        if (dag.isDSeparatedFrom(_x, z0, _z)) {
            count += 1;
        }
    }
    double score = dsep ? -1 - count : 1 + count;
    // if (score == 1) score -= Math.tanh(z.length);
    return score;
}

Example 42 with Node

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

the class IndTestMNLRLRT method getVariableNames.

/**
 * @return the list of variable varNames.
 */
public List<String> getVariableNames() {
    List<Node> variables = getVariables();
    List<String> variableNames = new ArrayList<>();
    for (Node variable1 : variables) {
        variableNames.add(variable1.getName());
    }
    return variableNames;
}

Example 43 with Node

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

the class IndTestMixedMultipleTTest method multiLL.

private double multiLL(DoubleMatrix2D coeffs, Node dep, List<Node> indep) {
    DoubleMatrix2D indepData = factory2D.make(internalData.subsetColumns(indep).getDoubleData().toArray());
    List<Node> depList = new ArrayList<>();
    depList.add(dep);
    DoubleMatrix2D depData = factory2D.make(internalData.subsetColumns(depList).getDoubleData().toArray());
    int N = indepData.rows();
    DoubleMatrix2D probs = Algebra.DEFAULT.mult(factory2D.appendColumns(factory2D.make(N, 1, 1.0), indepData), coeffs);
    probs = factory2D.appendColumns(factory2D.make(indepData.rows(), 1, 1.0), probs).assign(Functions.exp);
    double ll = 0;
    for (int i = 0; i < N; i++) {
        DoubleMatrix1D curRow = probs.viewRow(i);
        curRow.assign(Functions.div(curRow.zSum()));
        ll += Math.log(curRow.get((int) depData.get(i, 0)));
    }
    return ll;
}

Example 44 with Node

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

the class IndTestMultinomialLogisticRegression method getVariableNames.

/**
 * @return the list of variable varNames.
 */
public List<String> getVariableNames() {
    List<Node> variables = getVariables();
    List<String> variableNames = new ArrayList<>();
    for (Node variable1 : variables) {
        variableNames.add(variable1.getName());
    }
    return variableNames;
}

Example 45 with Node

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

the class IndTestMultinomialLogisticRegression method isIndependentMultinomialLogisticRegression.

private boolean isIndependentMultinomialLogisticRegression(Node x, Node y, List<Node> z) {
    if (!variablesPerNode.containsKey(x)) {
        throw new IllegalArgumentException("Unrecogized node: " + x);
    }
    if (!variablesPerNode.containsKey(y)) {
        throw new IllegalArgumentException("Unrecogized node: " + y);
    }
    for (Node node : z) {
        if (!variablesPerNode.containsKey(x)) {
            throw new IllegalArgumentException("Unrecogized node: " + node);
        }
    }
    List<Double> pValues = new ArrayList<>();
    int[] _rows = getNonMissingRows(x, y, z);
    logisticRegression.setRows(_rows);
    for (Node _x : variablesPerNode.get(x)) {
        // Without y
        List<Node> regressors0 = new ArrayList<>();
        for (Node _z : z) {
            regressors0.addAll(variablesPerNode.get(_z));
        }
        LogisticRegression.Result result0 = logisticRegression.regress((DiscreteVariable) _x, regressors0);
        // With y.
        List<Node> regressors1 = new ArrayList<>();
        regressors1.addAll(variablesPerNode.get(y));
        for (Node _z : z) {
            regressors1.addAll(variablesPerNode.get(_z));
        }
        LogisticRegression.Result result1 = logisticRegression.regress((DiscreteVariable) _x, regressors1);
        // Returns -2 LL
        double ll0 = result0.getLogLikelihood();
        double ll1 = result1.getLogLikelihood();
        double chisq = (ll0 - ll1);
        int df = variablesPerNode.get(y).size();
        double p = 1.0 - new ChiSquaredDistribution(df).cumulativeProbability(chisq);
        pValues.add(p);
    }
    double p = 1.0;
    // This is only one method that can be used, this requires every coefficient to be significant
    for (double val : pValues) {
        if (val < p)
            p = val;
    }
    boolean indep = p > alpha;
    this.lastP = p;
    if (verbose) {
        if (indep) {
            TetradLogger.getInstance().log("independencies", SearchLogUtils.independenceFactMsg(x, y, z, p));
        } else {
            TetradLogger.getInstance().log("dependencies", SearchLogUtils.dependenceFactMsg(x, y, z, p));
        }
    }
    return indep;
}