Examples with DoubleMatrix2D cern.colt.matrix.DoubleMatrix2D used on opensource projects

Example 1 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D 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 2 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D 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 3 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D 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 4 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tetrad by cmu-phil.

the class GlassoRunner method execute.

// ===================PUBLIC METHODS OVERRIDING ABSTRACT================//
public void execute() {
    Object dataModel = getDataModel();
    Parameters params = getParams();
    if (dataModel instanceof DataSet) {
        DataSet dataSet = (DataSet) dataModel;
        DoubleMatrix2D cov = new DenseDoubleMatrix2D(dataSet.getCovarianceMatrix().toArray());
        Glasso glasso = new Glasso(cov);
        glasso.setMaxit((int) params.get("maxit", 10000));
        glasso.setIa(params.getBoolean("ia", false));
        glasso.setIs(params.getBoolean("is", false));
        glasso.setItr(params.getBoolean("itr", false));
        glasso.setIpen(params.getBoolean("ipen", false));
        glasso.setThr(params.getDouble("thr", 1e-4));
        glasso.setRhoAllEqual(1.0);
        Glasso.Result result = glasso.search();
        TetradMatrix wwi = new TetradMatrix(result.getWwi().toArray());
        List<Node> variables = dataSet.getVariables();
        Graph resultGraph = new EdgeListGraph(variables);
        for (int i = 0; i < variables.size(); i++) {
            for (int j = i + 1; j < variables.size(); j++) {
                if (wwi.get(i, j) != 0.0 && wwi.get(i, j) != 0.0) {
                    resultGraph.addUndirectedEdge(variables.get(i), variables.get(j));
                }
            }
        }
        setResultGraph(resultGraph);
    }
}

Example 5 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tetrad by cmu-phil.

the class Ricf method fitConGraph.

/**
 * Fits a concentration graph. Coding algorithm #2 only.
 */
private FitConGraphResult fitConGraph(Graph graph, ICovarianceMatrix cov, int n, double tol) {
    DoubleFactory2D factory = DoubleFactory2D.dense;
    Algebra algebra = new Algebra();
    List<Node> nodes = graph.getNodes();
    String[] nodeNames = new String[nodes.size()];
    for (int i = 0; i < nodes.size(); i++) {
        Node node = nodes.get(i);
        if (!cov.getVariableNames().contains(node.getName())) {
            throw new IllegalArgumentException("Node in graph not in cov matrix: " + node);
        }
        nodeNames[i] = node.getName();
    }
    DoubleMatrix2D S = new DenseDoubleMatrix2D(cov.getSubmatrix(nodeNames).getMatrix().toArray());
    graph = graph.subgraph(nodes);
    List<List<Node>> cli = cliques(graph);
    int nc = cli.size();
    if (nc == 1) {
        return new FitConGraphResult(S, 0, 0, 1);
    }
    int k = S.rows();
    int it = 0;
    // Only coding alg #2 here.
    DoubleMatrix2D K = algebra.inverse(factory.diagonal(factory.diagonal(S)));
    int[] all = range(0, k - 1);
    while (true) {
        DoubleMatrix2D KOld = K.copy();
        it++;
        for (List<Node> aCli : cli) {
            int[] a = asIndices(aCli, nodes);
            int[] b = complement(all, a);
            DoubleMatrix2D a1 = S.viewSelection(a, a);
            DoubleMatrix2D a2 = algebra.inverse(a1);
            DoubleMatrix2D a3 = K.viewSelection(a, b);
            DoubleMatrix2D a4 = K.viewSelection(b, b);
            DoubleMatrix2D a5 = algebra.inverse(a4);
            DoubleMatrix2D a6 = K.viewSelection(b, a).copy();
            DoubleMatrix2D a7 = algebra.mult(a3, a5);
            DoubleMatrix2D a8 = algebra.mult(a7, a6);
            a2.assign(a8, PlusMult.plusMult(1));
            DoubleMatrix2D a9 = K.viewSelection(a, a);
            a9.assign(a2);
        }
        DoubleMatrix2D a32 = K.copy();
        a32.assign(KOld, PlusMult.plusMult(-1));
        double diff = algebra.norm1(a32);
        if (diff < tol)
            break;
    }
    DoubleMatrix2D V = algebra.inverse(K);
    int numNodes = graph.getNumNodes();
    int df = numNodes * (numNodes - 1) / 2 - graph.getNumEdges();
    double dev = lik(algebra.inverse(V), S, n, k);
    return new FitConGraphResult(V, dev, df, it);
}