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

Example 76 with Node

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

the class IndTestFisherZD method indTestSubset.

// ==========================PUBLIC METHODS=============================//
/**
 * Creates a new independence test instance for a subset of the variables.
 */
public IndependenceTest indTestSubset(List<Node> vars) {
    if (vars.isEmpty()) {
        throw new IllegalArgumentException("Subset may not be empty.");
    }
    for (Node var : vars) {
        if (!variables.contains(var)) {
            throw new IllegalArgumentException("All vars must be original vars");
        }
    }
    int[] indices = new int[vars.size()];
    for (int i = 0; i < indices.length; i++) {
        indices[i] = indexMap.get(vars.get(i));
    }
    ICovarianceMatrix newCovMatrix = covMatrix.getSubmatrix(indices);
    double alphaNew = getAlpha();
    return new IndTestFisherZD(newCovMatrix, alphaNew);
}

Example 77 with Node

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

the class IndTestFisherZD 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 78 with Node

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

the class IndTestFisherZGeneralizedInverse method determines.

public boolean determines(List<Node> zList, Node xVar) {
    if (zList == null) {
        throw new NullPointerException();
    }
    if (zList.isEmpty()) {
        return false;
    }
    for (Node node : zList) {
        if (node == null) {
            throw new NullPointerException();
        }
    }
    int size = zList.size();
    int[] zCols = new int[size];
    int xIndex = getVariables().indexOf(xVar);
    for (int i = 0; i < zList.size(); i++) {
        zCols[i] = getVariables().indexOf(zList.get(i));
    }
    int[] zRows = new int[data.rows()];
    for (int i = 0; i < data.rows(); i++) {
        zRows[i] = i;
    }
    DoubleMatrix2D Z = data.viewSelection(zRows, zCols);
    DoubleMatrix1D x = data.viewColumn(xIndex);
    DoubleMatrix2D Zt = new Algebra().transpose(Z);
    DoubleMatrix2D ZtZ = new Algebra().mult(Zt, Z);
    TetradMatrix _ZtZ = new TetradMatrix(ZtZ.toArray());
    TetradMatrix ginverse = _ZtZ.inverse();
    DoubleMatrix2D G = new DenseDoubleMatrix2D(ginverse.toArray());
    // DoubleMatrix2D G = MatrixUtils.ginverse(ZtZ);
    DoubleMatrix2D Zt2 = Zt.copy();
    DoubleMatrix2D GZt = new Algebra().mult(G, Zt2);
    DoubleMatrix1D b_x = new Algebra().mult(GZt, x);
    DoubleMatrix1D xPred = new Algebra().mult(Z, b_x);
    DoubleMatrix1D xRes = xPred.copy().assign(x, Functions.minus);
    double SSE = xRes.aggregate(Functions.plus, Functions.square);
    double variance = SSE / (data.rows() - (zList.size() + 1));
    // ChiSquare chiSquare = new ChiSquare(data.rows(),
    // PersistentRandomUtil.getInstance().getEngine());
    // 
    // double p = chiSquare.cdf(sum);
    // boolean determined = p < 1 - getAlternativePenalty();
    // 
    boolean determined = variance < getAlpha();
    if (determined) {
        StringBuilder sb = new StringBuilder();
        sb.append("Determination found: ").append(xVar).append(" is determined by {");
        for (int i = 0; i < zList.size(); i++) {
            sb.append(zList.get(i));
            if (i < zList.size() - 1) {
                sb.append(", ");
            }
        }
        sb.append("}");
        // sb.append(" p = ").append(nf.format(p));
        sb.append(" SSE = ").append(nf.format(SSE));
        TetradLogger.getInstance().log("independencies", sb.toString());
        System.out.println(sb);
    }
    return determined;
}

Example 79 with Node

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

the class GrowShrink method findMb.

/**
 * Finds the Markov blanket of the given target.
 *
 * @param targetName the name of the target
 * @return the list of node in the Markov blanket.
 */
public List<Node> findMb(String targetName) {
    Node target = getVariableForName(targetName);
    List<Node> blanket = new LinkedList<>();
    boolean changed = true;
    while (changed) {
        changed = false;
        List<Node> remaining = new LinkedList<>(variables);
        remaining.removeAll(blanket);
        remaining.remove(target);
        for (Node node : remaining) {
            if (!independenceTest.isIndependent(node, target, blanket)) {
                blanket.add(node);
                changed = true;
            }
        }
    }
    changed = true;
    while (changed) {
        changed = false;
        for (Node node : new LinkedList<>(blanket)) {
            blanket.remove(node);
            if (independenceTest.isIndependent(node, target, blanket)) {
                changed = true;
                continue;
            }
            blanket.add(node);
        }
    }
    return blanket;
}

Example 80 with Node

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

the class IndTestRegression method isIndependent.

/**
 * Determines whether variable x is independent of variable y given a list of conditioning variables z.
 *
 * @param xVar  the one variable being compared.
 * @param yVar  the second variable being compared.
 * @param zList the list of conditioning variables.
 * @return true iff x _||_ y | z.
 * @throws RuntimeException if a matrix singularity is encountered.
 */
public boolean isIndependent(Node xVar, Node yVar, List<Node> zList) {
    if (zList == null) {
        throw new NullPointerException();
    }
    for (Node node : zList) {
        if (node == null) {
            throw new NullPointerException();
        }
    }
    List<Node> regressors = new ArrayList<>();
    regressors.add(dataSet.getVariable(yVar.getName()));
    for (Node zVar : zList) {
        regressors.add(dataSet.getVariable(zVar.getName()));
    }
    Regression regression = new RegressionDataset(dataSet);
    RegressionResult result = null;
    try {
        result = regression.regress(xVar, regressors);
    } catch (Exception e) {
        return false;
    }
    double p = result.getP()[1];
    boolean independent = p > alpha;
    if (verbose) {
        if (independent) {
            TetradLogger.getInstance().log("independencies", SearchLogUtils.independenceFactMsg(xVar, yVar, zList, p));
        } else {
            TetradLogger.getInstance().log("dependencies", SearchLogUtils.dependenceFactMsg(xVar, yVar, zList, p));
        }
    }
    return independent;
}