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

Example 46 with Node

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

the class IndTestMultinomialLogisticRegression method expandVariable.

private List<Node> expandVariable(DataSet dataSet, Node node) {
    if (node instanceof ContinuousVariable) {
        return Collections.singletonList(node);
    }
    if (node instanceof DiscreteVariable && ((DiscreteVariable) node).getNumCategories() < 3) {
        return Collections.singletonList(node);
    }
    if (!(node instanceof DiscreteVariable)) {
        throw new IllegalArgumentException();
    }
    List<String> varCats = new ArrayList<>(((DiscreteVariable) node).getCategories());
    varCats.remove(0);
    List<Node> variables = new ArrayList<>();
    for (String cat : varCats) {
        Node newVar;
        do {
            String newVarName = node.getName() + "MULTINOM" + "." + cat;
            newVar = new DiscreteVariable(newVarName, 2);
        } while (dataSet.getVariable(newVar.getName()) != null);
        variables.add(newVar);
        dataSet.addVariable(newVar);
        int newVarIndex = dataSet.getColumn(newVar);
        int numCases = dataSet.getNumRows();
        for (int l = 0; l < numCases; l++) {
            Object dataCell = dataSet.getObject(l, dataSet.getColumn(node));
            int dataCellIndex = ((DiscreteVariable) node).getIndex(dataCell.toString());
            if (dataCellIndex == ((DiscreteVariable) node).getIndex(cat))
                dataSet.setInt(l, newVarIndex, 1);
            else
                dataSet.setInt(l, newVarIndex, 0);
        }
    }
    return variables;
}

Example 47 with Node

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

the class IndTestRegressionAD method determines.

// ==========================PRIVATE METHODS============================//
// /**
// * Return the p-value of the last calculated independence fact.
// *
// * @return this p-value.  When accessed through the IndependenceChecker
// *         interface, this p-value should only be considered to be a
// *         relative strength.
// */
// private double getRelativeStrength() {
// 
// // precondition:  pdf is the most recently used partial
// // correlation distribution function, and storedR is the most
// // recently calculated partial correlation.
// return 2.0 * Integrator.getArea(npdf, Math.abs(storedR), 9.0, 100);
// }
// /**
// * Computes that value x such that P(abs(N(0,1) > x) < alpha.  Note that
// * this is a two sided test of the null hypothesis that the Fisher's Z
// * value, which is distributed as N(0,1) is not equal to 0.0.
// */
// private double cutoffGaussian() {
// npdf = new NormalPdf();
// final double upperBound = 9.0;
// final double delta = 0.001;
// //        double alpha = this.alpha/2.0;    //Two sided test
// return CutoffFinder.getCutoff(npdf, upperBound, alpha, delta);
// }
// private int sampleSize() {
// return data.rows();
// }
public boolean determines(List<Node> zList, Node xVar) {
    if (zList == null) {
        throw new NullPointerException();
    }
    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);
    DoubleMatrix2D G = new DenseDoubleMatrix2D(new TetradMatrix(ZtZ.toArray()).inverse().toArray());
    // Bug in Colt? Need to make a copy before multiplying to avoid
    // a ClassCastException.
    DoubleMatrix2D Zt2 = Zt.like();
    Zt2.assign(Zt);
    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);
    boolean determined = SSE < 0.0001;
    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("}");
        TetradLogger.getInstance().log("independencies", sb.toString());
    }
    return determined;
}

Example 48 with Node

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

the class IndTestSepset method getVariableNames.

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

Example 49 with Node

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

the class FciOrientT method rulesR1R2cycle.

// Does all 3 of these rules at once instead of going through all
// triples multiple times per iteration of doFinalOrientation.
public void rulesR1R2cycle(Graph graph) {
    List<Node> nodes = graph.getNodes();
    for (Node B : nodes) {
        List<Node> adj = graph.getAdjacentNodes(B);
        if (adj.size() < 2) {
            continue;
        }
        ChoiceGenerator cg = new ChoiceGenerator(adj.size(), 2);
        int[] combination;
        while ((combination = cg.next()) != null) {
            Node A = adj.get(combination[0]);
            Node C = adj.get(combination[1]);
            // choice gen doesnt do diff orders, so must switch A & C around.
            ruleR1(A, B, C, graph);
            ruleR1(C, B, A, graph);
            ruleR2(A, B, C, graph);
            ruleR2(C, B, A, graph);
        }
    }
}

Example 50 with Node

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

the class FciOrientT method ruleR6R7.

/**
 * Implements Zhang's rules R6 and R7, applies them over the graph once. Orient single tails. R6: If A---Bo-*C then
 * A---B--*C. R7: If A--oBo-*C and A,C nonadjacent, then A--oB--*C
 */
public void ruleR6R7(Graph graph) {
    List<Node> nodes = graph.getNodes();
    for (Node b : nodes) {
        List<Node> adjacents = graph.getAdjacentNodes(b);
        if (adjacents.size() < 2)
            continue;
        ChoiceGenerator cg = new ChoiceGenerator(adjacents.size(), 2);
        for (int[] choice = cg.next(); choice != null; choice = cg.next()) {
            Node a = adjacents.get(choice[0]);
            Node c = adjacents.get(choice[1]);
            if (graph.isAdjacentTo(a, c))
                continue;
            if (!(graph.getEndpoint(b, a) == Endpoint.TAIL))
                continue;
            if (!(graph.getEndpoint(c, b) == Endpoint.CIRCLE))
                continue;
            if (graph.getEndpoint(a, b) == Endpoint.TAIL) {
                // We know A---Bo-*C: R6 applies!
                graph.setEndpoint(c, b, Endpoint.TAIL);
                logger.log("impliedOrientations", SearchLogUtils.edgeOrientedMsg("Single tails (tail)", graph.getEdge(c, b)));
                changeFlag = true;
            }
            if (graph.getEndpoint(a, b) == Endpoint.CIRCLE) {
                // if (graph.isAdjacentTo(a, c)) continue;
                logger.log("impliedOrientations", SearchLogUtils.edgeOrientedMsg("Single tails (tail)", graph.getEdge(c, b)));
                // We know A--oBo-*C and A,C nonadjacent: R7 applies!
                graph.setEndpoint(c, b, Endpoint.TAIL);
                changeFlag = true;
            }
        }
    }
}