Examples with Algebra cern.colt.matrix.linalg.Algebra used on opensource projects

Example 11 with Algebra

use of cern.colt.matrix.linalg.Algebra in project Gemma by PavlidisLab.

the class ExpressionDataSVD method removeHighestComponents.

/**
 * Provide a reconstructed matrix removing the first N components (the most significant ones). If the matrix was
 * normalized first, removing the first component replicates the normalization approach taken by Nielsen et al.
 * (Lancet 359, 2002) and Alter et al. (PNAS 2000). Correction by ANOVA would yield similar results if the nuisance
 * variable is known.
 *
 * @param numComponentsToRemove The number of components to remove, starting from the largest eigenvalue.
 * @return the reconstructed matrix; values that were missing before are re-masked.
 */
public ExpressionDataDoubleMatrix removeHighestComponents(int numComponentsToRemove) {
    DoubleMatrix<Integer, Integer> copy = svd.getS().copy();
    for (int i = 0; i < numComponentsToRemove; i++) {
        copy.set(i, i, 0.0);
    }
    double[][] rawU = svd.getU().getRawMatrix();
    double[][] rawS = copy.getRawMatrix();
    double[][] rawV = svd.getV().getRawMatrix();
    DoubleMatrix2D u = new DenseDoubleMatrix2D(rawU);
    DoubleMatrix2D s = new DenseDoubleMatrix2D(rawS);
    DoubleMatrix2D v = new DenseDoubleMatrix2D(rawV);
    Algebra a = new Algebra();
    DoubleMatrix<CompositeSequence, BioMaterial> reconstructed = new DenseDoubleMatrix<>(a.mult(a.mult(u, s), a.transpose(v)).toArray());
    reconstructed.setRowNames(this.expressionData.getMatrix().getRowNames());
    reconstructed.setColumnNames(this.expressionData.getMatrix().getColNames());
    // re-mask the missing values.
    for (int i = 0; i < reconstructed.rows(); i++) {
        for (int j = 0; j < reconstructed.columns(); j++) {
            if (Double.isNaN(this.missingValueInfo.get(i, j))) {
                reconstructed.set(i, j, Double.NaN);
            }
        }
    }
    return new ExpressionDataDoubleMatrix(this.expressionData, reconstructed);
}

Example 12 with Algebra

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

the class StabilityUtils method totalInstabilityUndir.

// needs a symmetric matrix
// array of averages of instability matrix over [all, cc, cd, dd] edges
public static double[] totalInstabilityUndir(DoubleMatrix2D xi, List<Node> vars) {
    if (vars.size() != xi.columns() || vars.size() != xi.rows()) {
        throw new IllegalArgumentException("stability mat must have same number of rows and columns as there are vars");
    }
    Algebra al = new Algebra();
    // DoubleMatrix2D xiu = MGM.upperTri(xi.copy().assign(al.transpose(xi)),1);
    DoubleMatrix2D xiu = xi.copy().assign(xi.copy().assign(Functions.minus(1.0)), Functions.mult).assign(Functions.mult(-2.0));
    double[] D = new double[4];
    int[] discInds = MixedUtils.getDiscreteInds(vars);
    int[] contInds = MixedUtils.getContinuousInds(vars);
    int p = contInds.length;
    int q = discInds.length;
    double temp = MGM.upperTri(xiu.copy(), 1).zSum();
    D[0] = temp / ((p + q - 1.0) * (p + q) / 2.0);
    temp = MGM.upperTri(xiu.viewSelection(contInds, contInds).copy(), 1).zSum();
    D[1] = temp / (p * (p - 1.0) / 2.0);
    temp = xiu.viewSelection(contInds, discInds).zSum();
    D[2] = temp / (p * q);
    temp = MGM.upperTri(xiu.viewSelection(discInds, discInds).copy(), 1).zSum();
    D[3] = temp / (q * (q - 1.0) / 2.0);
    return D;
}

Example 13 with Algebra

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

the class IndTestRegression 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 14 with Algebra

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

the class SemUpdater method getUpdatedSemIm.

/**
 * See http://en.wikipedia.org/wiki/Multivariate_normal_distribution.
 */
public SemIm getUpdatedSemIm() {
    Algebra algebra = new Algebra();
    // First manipulate the old semIm.
    SemIm manipulatedSemIm = getManipulatedSemIm();
    // Get out the means and implied covariances.
    double[] means = new double[manipulatedSemIm.getVariableNodes().size()];
    for (int i = 0; i < means.length; i++) {
        means[i] = manipulatedSemIm.getMean(manipulatedSemIm.getVariableNodes().get(i));
    }
    DoubleMatrix1D mu = new DenseDoubleMatrix1D(means);
    DoubleMatrix2D sigma = new DenseDoubleMatrix2D(manipulatedSemIm.getImplCovar(true).toArray());
    // Updating on x2 = a.
    SemEvidence evidence = getEvidence();
    List nodesInEvidence = evidence.getNodesInEvidence();
    int[] x2 = new int[nodesInEvidence.size()];
    DoubleMatrix1D a = new DenseDoubleMatrix1D(nodesInEvidence.size());
    for (int i = 0; i < nodesInEvidence.size(); i++) {
        Node _node = (Node) nodesInEvidence.get(i);
        x2[i] = evidence.getNodeIndex(_node);
    }
    for (int i = 0; i < nodesInEvidence.size(); i++) {
        int j = evidence.getNodeIndex((Node) nodesInEvidence.get(i));
        a.set(i, evidence.getProposition().getValue(j));
    }
    // x1 is all the variables.
    // int[] x1 = new int[sigma.rows() - x2.length];
    int[] x1 = new int[sigma.rows()];
    for (int i = 0; i < sigma.rows(); i++) {
        x1[i] = i;
    }
    // int index = -1;
    // for (int i = 0; i < sigma.rows(); i++) {
    // if (Arrays.binarySearch(x2, i) == -1) {
    // x1[++index] = i;
    // }
    // }
    // Calculate sigmaBar. (Don't know how to use it yet.)
    // DoubleMatrix2D sigma11 = sigma.viewSelection(x1, x1);
    DoubleMatrix2D sigma12 = sigma.viewSelection(x1, x2);
    DoubleMatrix2D sigma22 = sigma.viewSelection(x2, x2);
    // DoubleMatrix2D sigma21 = sigma.viewSelection(x2, x1);
    DoubleMatrix2D inv_sigma22 = algebra.inverse(sigma22);
    DoubleMatrix2D temp1 = algebra.mult(sigma12, inv_sigma22);
    // DoubleMatrix2D temp2 = algebra.times(temp1, sigma21.copy());
    // DoubleMatrix2D sigmaBar = sigma11.copy().assign(temp2, Functions.minus);
    // Calculate muBar.
    DoubleMatrix1D mu1 = mu.viewSelection(x1);
    DoubleMatrix1D mu2 = mu.viewSelection(x2);
    DoubleMatrix1D temp4 = a.copy().assign(mu2, Functions.minus);
    DoubleMatrix1D temp5 = algebra.mult(temp1, temp4);
    DoubleMatrix1D muBar = mu1.copy().assign(temp5, Functions.plus);
    // Estimate a SEM with this sigmaBar and muBar.
    // List variableNodes = manipulatedSemIm.getVariableNodes();
    // String[] varNames = new String[variableNodes.size()];
    // 
    // for (int i = 0; i < variableNodes.size(); i++) {
    // varNames[i] = ((Node) variableNodes.get(i)).getNode();
    // }
    // System.out.println(sigmaBar);
    // 
    // CovarianceMatrix covMatrix = new CovarianceMatrix(varNames,
    // sigmaBar, 100);
    // SemPm semPm = manipulatedSemIm.getEstIm();
    // SemEstimator estimator = new SemEstimator(covMatrix, semPm);
    // estimator.estimate();
    // SemIm semIm = estimator.getEstimatedSem();
    // semIm.setMeanValues(muBar.toArray());
    // return semIm;
    DoubleMatrix2D sigma2 = new DenseDoubleMatrix2D(manipulatedSemIm.getErrCovar().toArray());
    // }
    return manipulatedSemIm.updatedIm(new TetradMatrix(sigma2.toArray()), new TetradVector(muBar.toArray()));
}

Example 15 with Algebra

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

the class Ricf method ricf2.

/**
 * same as above but takes a Graph instead of a SemGraph *
 */
public RicfResult ricf2(Graph mag, ICovarianceMatrix covMatrix, double tolerance) {
    // mag.setShowErrorTerms(false);
    DoubleFactory2D factory = DoubleFactory2D.dense;
    Algebra algebra = new Algebra();
    DoubleMatrix2D S = new DenseDoubleMatrix2D(covMatrix.getMatrix().toArray());
    int p = covMatrix.getDimension();
    if (p == 1) {
        return new RicfResult(S, S, null, null, 1, Double.NaN, covMatrix);
    }
    List<Node> nodes = new ArrayList<>();
    for (String name : covMatrix.getVariableNames()) {
        nodes.add(mag.getNode(name));
    }
    DoubleMatrix2D omega = factory.diagonal(factory.diagonal(S));
    DoubleMatrix2D B = factory.identity(p);
    int[] ug = ugNodes(mag, nodes);
    int[] ugComp = complement(p, ug);
    if (ug.length > 0) {
        List<Node> _ugNodes = new LinkedList<>();
        for (int i : ug) {
            _ugNodes.add(nodes.get(i));
        }
        Graph ugGraph = mag.subgraph(_ugNodes);
        ICovarianceMatrix ugCov = covMatrix.getSubmatrix(ug);
        DoubleMatrix2D lambdaInv = fitConGraph(ugGraph, ugCov, p + 1, tolerance).shat;
        omega.viewSelection(ug, ug).assign(lambdaInv);
    }
    // Prepare lists of parents and spouses.
    int[][] pars = parentIndices(p, mag, nodes);
    int[][] spo = spouseIndices(p, mag, nodes);
    int i = 0;
    double _diff;
    while (true) {
        i++;
        DoubleMatrix2D omegaOld = omega.copy();
        DoubleMatrix2D bOld = B.copy();
        for (int _v = 0; _v < p; _v++) {
            // Exclude the UG part.
            if (Arrays.binarySearch(ug, _v) >= 0) {
                continue;
            }
            int[] v = new int[] { _v };
            int[] vcomp = complement(p, v);
            int[] all = range(0, p - 1);
            int[] parv = pars[_v];
            int[] spov = spo[_v];
            DoubleMatrix2D a6 = B.viewSelection(v, parv);
            if (spov.length == 0) {
                if (parv.length != 0) {
                    if (i == 1) {
                        DoubleMatrix2D a1 = S.viewSelection(parv, parv);
                        DoubleMatrix2D a2 = S.viewSelection(v, parv);
                        DoubleMatrix2D a3 = algebra.inverse(a1);
                        DoubleMatrix2D a4 = algebra.mult(a2, a3);
                        a4.assign(Mult.mult(-1));
                        a6.assign(a4);
                        DoubleMatrix2D a7 = S.viewSelection(parv, v);
                        DoubleMatrix2D a9 = algebra.mult(a6, a7);
                        DoubleMatrix2D a8 = S.viewSelection(v, v);
                        DoubleMatrix2D a8b = omega.viewSelection(v, v);
                        a8b.assign(a8);
                        omega.viewSelection(v, v).assign(a9, PlusMult.plusMult(1));
                    }
                }
            } else {
                if (parv.length != 0) {
                    DoubleMatrix2D oInv = new DenseDoubleMatrix2D(p, p);
                    DoubleMatrix2D a2 = omega.viewSelection(vcomp, vcomp);
                    DoubleMatrix2D a3 = algebra.inverse(a2);
                    oInv.viewSelection(vcomp, vcomp).assign(a3);
                    DoubleMatrix2D Z = algebra.mult(oInv.viewSelection(spov, vcomp), B.viewSelection(vcomp, all));
                    int lpa = parv.length;
                    int lspo = spov.length;
                    // Build XX
                    DoubleMatrix2D XX = new DenseDoubleMatrix2D(lpa + lspo, lpa + lspo);
                    int[] range1 = range(0, lpa - 1);
                    int[] range2 = range(lpa, lpa + lspo - 1);
                    // Upper left quadrant
                    XX.viewSelection(range1, range1).assign(S.viewSelection(parv, parv));
                    // Upper right quadrant
                    DoubleMatrix2D a11 = algebra.mult(S.viewSelection(parv, all), algebra.transpose(Z));
                    XX.viewSelection(range1, range2).assign(a11);
                    // Lower left quadrant
                    DoubleMatrix2D a12 = XX.viewSelection(range2, range1);
                    DoubleMatrix2D a13 = algebra.transpose(XX.viewSelection(range1, range2));
                    a12.assign(a13);
                    // Lower right quadrant
                    DoubleMatrix2D a14 = XX.viewSelection(range2, range2);
                    DoubleMatrix2D a15 = algebra.mult(Z, S);
                    DoubleMatrix2D a16 = algebra.mult(a15, algebra.transpose(Z));
                    a14.assign(a16);
                    // Build XY
                    DoubleMatrix1D YX = new DenseDoubleMatrix1D(lpa + lspo);
                    DoubleMatrix1D a17 = YX.viewSelection(range1);
                    DoubleMatrix1D a18 = S.viewSelection(v, parv).viewRow(0);
                    a17.assign(a18);
                    DoubleMatrix1D a19 = YX.viewSelection(range2);
                    DoubleMatrix2D a20 = S.viewSelection(v, all);
                    DoubleMatrix1D a21 = algebra.mult(a20, algebra.transpose(Z)).viewRow(0);
                    a19.assign(a21);
                    // Temp
                    DoubleMatrix2D a22 = algebra.inverse(XX);
                    DoubleMatrix1D temp = algebra.mult(algebra.transpose(a22), YX);
                    // Assign to b.
                    DoubleMatrix1D a23 = a6.viewRow(0);
                    DoubleMatrix1D a24 = temp.viewSelection(range1);
                    a23.assign(a24);
                    a23.assign(Mult.mult(-1));
                    // Assign to omega.
                    omega.viewSelection(v, spov).viewRow(0).assign(temp.viewSelection(range2));
                    omega.viewSelection(spov, v).viewColumn(0).assign(temp.viewSelection(range2));
                    // Variance.
                    double tempVar = S.get(_v, _v) - algebra.mult(temp, YX);
                    DoubleMatrix2D a27 = omega.viewSelection(v, spov);
                    DoubleMatrix2D a28 = oInv.viewSelection(spov, spov);
                    DoubleMatrix2D a29 = omega.viewSelection(spov, v).copy();
                    DoubleMatrix2D a30 = algebra.mult(a27, a28);
                    DoubleMatrix2D a31 = algebra.mult(a30, a29);
                    omega.viewSelection(v, v).assign(tempVar);
                    omega.viewSelection(v, v).assign(a31, PlusMult.plusMult(1));
                } else {
                    DoubleMatrix2D oInv = new DenseDoubleMatrix2D(p, p);
                    DoubleMatrix2D a2 = omega.viewSelection(vcomp, vcomp);
                    DoubleMatrix2D a3 = algebra.inverse(a2);
                    oInv.viewSelection(vcomp, vcomp).assign(a3);
                    // System.out.println("O.inv = " + oInv);
                    DoubleMatrix2D a4 = oInv.viewSelection(spov, vcomp);
                    DoubleMatrix2D a5 = B.viewSelection(vcomp, all);
                    DoubleMatrix2D Z = algebra.mult(a4, a5);
                    // System.out.println("Z = " + Z);
                    // Build XX
                    DoubleMatrix2D XX = algebra.mult(algebra.mult(Z, S), Z.viewDice());
                    // System.out.println("XX = " + XX);
                    // Build XY
                    DoubleMatrix2D a20 = S.viewSelection(v, all);
                    DoubleMatrix1D YX = algebra.mult(a20, Z.viewDice()).viewRow(0);
                    // System.out.println("YX = " + YX);
                    // Temp
                    DoubleMatrix2D a22 = algebra.inverse(XX);
                    DoubleMatrix1D a23 = algebra.mult(algebra.transpose(a22), YX);
                    // Assign to omega.
                    DoubleMatrix1D a24 = omega.viewSelection(v, spov).viewRow(0);
                    a24.assign(a23);
                    DoubleMatrix1D a25 = omega.viewSelection(spov, v).viewColumn(0);
                    a25.assign(a23);
                    // System.out.println("Omega 2 " + omega);
                    // Variance.
                    double tempVar = S.get(_v, _v) - algebra.mult(a24, YX);
                    // System.out.println("tempVar = " + tempVar);
                    DoubleMatrix2D a27 = omega.viewSelection(v, spov);
                    DoubleMatrix2D a28 = oInv.viewSelection(spov, spov);
                    DoubleMatrix2D a29 = omega.viewSelection(spov, v).copy();
                    DoubleMatrix2D a30 = algebra.mult(a27, a28);
                    DoubleMatrix2D a31 = algebra.mult(a30, a29);
                    omega.set(_v, _v, tempVar + a31.get(0, 0));
                // System.out.println("Omega final " + omega);
                }
            }
        }
        DoubleMatrix2D a32 = omega.copy();
        a32.assign(omegaOld, PlusMult.plusMult(-1));
        double diff1 = algebra.norm1(a32);
        DoubleMatrix2D a33 = B.copy();
        a33.assign(bOld, PlusMult.plusMult(-1));
        double diff2 = algebra.norm1(a32);
        double diff = diff1 + diff2;
        _diff = diff;
        if (diff < tolerance)
            break;
    }
    DoubleMatrix2D a34 = algebra.inverse(B);
    DoubleMatrix2D a35 = algebra.inverse(B.viewDice());
    DoubleMatrix2D sigmahat = algebra.mult(algebra.mult(a34, omega), a35);
    DoubleMatrix2D lambdahat = omega.copy();
    DoubleMatrix2D a36 = lambdahat.viewSelection(ugComp, ugComp);
    a36.assign(factory.make(ugComp.length, ugComp.length, 0.0));
    DoubleMatrix2D omegahat = omega.copy();
    DoubleMatrix2D a37 = omegahat.viewSelection(ug, ug);
    a37.assign(factory.make(ug.length, ug.length, 0.0));
    DoubleMatrix2D bhat = B.copy();
    return new RicfResult(sigmahat, lambdahat, bhat, omegahat, i, _diff, covMatrix);
}