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

Example 11 with DoubleMatrix2D

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

the class MGM method runTests1.

private static void runTests1() {
    try {
        // DoubleMatrix2D xIn = DoubleFactory2D.dense.make(loadDataSelect("/Users/ajsedgewick/tetrad/test_data", "med_test_C.txt"));
        // DoubleMatrix2D yIn = DoubleFactory2D.dense.make(loadDataSelect("/Users/ajsedgewick/tetrad/test_data", "med_test_D.txt"));
        // String path = MGM.class.getResource("test_data").getPath();
        String path = "/Users/ajsedgewick/tetrad_master/tetrad/tetrad-lib/src/main/java/edu/pitt/csb/mgm/test_data";
        System.out.println(path);
        DoubleMatrix2D xIn = DoubleFactory2D.dense.make(MixedUtils.loadDelim(path, "med_test_C.txt").getDoubleData().toArray());
        DoubleMatrix2D yIn = DoubleFactory2D.dense.make(MixedUtils.loadDelim(path, "med_test_D.txt").getDoubleData().toArray());
        int[] L = new int[24];
        Node[] vars = new Node[48];
        for (int i = 0; i < 24; i++) {
            L[i] = 2;
            vars[i] = new ContinuousVariable("X" + i);
            vars[i + 24] = new DiscreteVariable("Y" + i);
        }
        double lam = .2;
        MGM model = new MGM(xIn, yIn, new ArrayList<>(Arrays.asList(vars)), L, new double[] { lam, lam, lam });
        MGM model2 = new MGM(xIn, yIn, new ArrayList<>(Arrays.asList(vars)), L, new double[] { lam, lam, lam });
        System.out.println("Weights: " + Arrays.toString(model.weights.toArray()));
        DoubleMatrix2D test = xIn.copy();
        DoubleMatrix2D test2 = xIn.copy();
        long t = System.currentTimeMillis();
        for (int i = 0; i < 50000; i++) {
            test2 = xIn.copy();
            test.assign(test2);
        }
        System.out.println("assign Time: " + (System.currentTimeMillis() - t));
        t = System.currentTimeMillis();
        double[][] xArr = xIn.toArray();
        for (int i = 0; i < 50000; i++) {
            if (Thread.currentThread().isInterrupted()) {
                break;
            }
            // test = DoubleFactory2D.dense.make(xArr);
            test2 = xIn.copy();
            test = test2;
        }
        System.out.println("equals Time: " + (System.currentTimeMillis() - t));
        System.out.println("Init nll: " + model.smoothValue(model.params.toMatrix1D()));
        System.out.println("Init reg term: " + model.nonSmoothValue(model.params.toMatrix1D()));
        t = System.currentTimeMillis();
        model.learnEdges(700);
        // model.learn(1e-7, 700);
        System.out.println("Orig Time: " + (System.currentTimeMillis() - t));
        System.out.println("nll: " + model.smoothValue(model.params.toMatrix1D()));
        System.out.println("reg term: " + model.nonSmoothValue(model.params.toMatrix1D()));
        System.out.println("params:\n" + model.params);
        System.out.println("adjMat:\n" + model.adjMatFromMGM());
    } catch (IOException ex) {
        ex.printStackTrace();
    }
}

Example 12 with DoubleMatrix2D

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

the class MGM method smooth.

/**
 * non-penalized -log(pseudolikelihood) this is the smooth function g(x) in prox gradient
 * this overloaded version calculates both nll and the smooth gradient at the same time
 * any value in gradOut will be replaced by the new calculations
 *
 * @param parIn
 * @param gradOutVec
 * @return
 */
public double smooth(DoubleMatrix1D parIn, DoubleMatrix1D gradOutVec) {
    // work with copy
    MGMParams par = new MGMParams(parIn, p, lsum);
    MGMParams gradOut = new MGMParams();
    for (int i = 0; i < par.betad.size(); i++) {
        if (par.betad.get(i) < 0)
            return Double.POSITIVE_INFINITY;
    }
    // beta=beta-diag(diag(beta));
    // for r=1:q
    // phi(Lsum(r)+1:Lsum(r+1),Lsum(r)+1:Lsum(r+1))=0;
    // end
    // beta=triu(beta); phi=triu(phi);
    // beta=beta+beta';
    // phi=phi+phi';
    upperTri(par.beta, 1);
    par.beta.assign(alg.transpose(par.beta), Functions.plus);
    for (int i = 0; i < q; i++) {
        par.phi.viewPart(lcumsum[i], lcumsum[i], l[i], l[i]).assign(0);
    }
    // ensure matrix is upper triangular
    upperTri(par.phi, 0);
    par.phi.assign(alg.transpose(par.phi), Functions.plus);
    // Xbeta=X*beta*diag(1./betad);
    DoubleMatrix2D divBetaD = factory2D.diagonal(factory1D.make(p, 1.0).assign(par.betad, Functions.div));
    DoubleMatrix2D xBeta = alg.mult(xDat, alg.mult(par.beta, divBetaD));
    // Dtheta=D*theta*diag(1./betad);
    DoubleMatrix2D dTheta = alg.mult(alg.mult(dDat, par.theta), divBetaD);
    // Squared loss
    // tempLoss =  (X-e*alpha1'-Xbeta-Dtheta) = -res (in gradient code)
    DoubleMatrix2D tempLoss = factory2D.make(n, xDat.columns());
    // wxprod=X*(theta')+D*phi+e*alpha2';
    DoubleMatrix2D wxProd = alg.mult(xDat, alg.transpose(par.theta));
    wxProd.assign(alg.mult(dDat, par.phi), Functions.plus);
    for (int i = 0; i < n; i++) {
        if (Thread.currentThread().isInterrupted()) {
            break;
        }
        for (int j = 0; j < xDat.columns(); j++) {
            tempLoss.set(i, j, xDat.get(i, j) - par.alpha1.get(j) - xBeta.get(i, j) - dTheta.get(i, j));
        }
        for (int j = 0; j < dDat.columns(); j++) {
            wxProd.set(i, j, wxProd.get(i, j) + par.alpha2.get(j));
        }
    }
    // sqloss=-n/2*sum(log(betad))+...
    // .5*norm((X-e*alpha1'-Xbeta-Dtheta)*diag(sqrt(betad)),'fro')^2;
    double sqloss = -n / 2.0 * par.betad.copy().assign(Functions.log).zSum() + .5 * Math.pow(alg.normF(alg.mult(tempLoss, factory2D.diagonal(par.betad.copy().assign(Functions.sqrt)))), 2);
    // ok now tempLoss = res
    tempLoss.assign(Functions.mult(-1));
    // gradbeta=X'*(res);
    gradOut.beta = alg.mult(alg.transpose(xDat), tempLoss);
    // gradbeta=gradbeta-diag(diag(gradbeta)); % zero out diag
    // gradbeta=tril(gradbeta)'+triu(gradbeta);
    DoubleMatrix2D lowerBeta = alg.transpose(lowerTri(gradOut.beta.copy(), -1));
    upperTri(gradOut.beta, 1).assign(lowerBeta, Functions.plus);
    // gradalpha1=diag(betad)*sum(res,1)';
    gradOut.alpha1 = alg.mult(factory2D.diagonal(par.betad), margSum(tempLoss, 1));
    // gradtheta=D'*(res);
    gradOut.theta = alg.mult(alg.transpose(dDat), tempLoss);
    // categorical loss
    /*catloss=0;
        wxprod=X*(theta')+D*phi+e*alpha2'; %this is n by Ltot
        for r=1:q
            wxtemp=wxprod(:,Lsum(r)+1:Lsum(r)+L(r));
            denom= logsumexp(wxtemp,2); %this is n by 1
            catloss=catloss-sum(wxtemp(sub2ind([n L(r)],(1:n)',Y(:,r))));
            catloss=catloss+sum(denom);
        end
        */
    double catloss = 0;
    for (int i = 0; i < yDat.columns(); i++) {
        if (Thread.currentThread().isInterrupted()) {
            break;
        }
        DoubleMatrix2D wxTemp = wxProd.viewPart(0, lcumsum[i], n, l[i]);
        // need to copy init values for calculating nll
        DoubleMatrix2D wxTemp0 = wxTemp.copy();
        // does this need to be done in log space??
        wxTemp.assign(Functions.exp);
        DoubleMatrix1D invDenom = factory1D.make(n, 1.0).assign(margSum(wxTemp, 2), Functions.div);
        wxTemp.assign(alg.mult(factory2D.diagonal(invDenom), wxTemp));
        for (int k = 0; k < n; k++) {
            if (Thread.currentThread().isInterrupted()) {
                break;
            }
            DoubleMatrix1D curRow = wxTemp.viewRow(k);
            DoubleMatrix1D curRow0 = wxTemp0.viewRow(k);
            catloss -= curRow0.get((int) yDat.get(k, i) - 1);
            catloss += logsumexp(curRow0);
            // wxtemp(sub2ind(size(wxtemp),(1:n)',Y(:,r)))=wxtemp(sub2ind(size(wxtemp),(1:n)',Y(:,r)))-1;
            curRow.set((int) yDat.get(k, i) - 1, curRow.get((int) yDat.get(k, i) - 1) - 1);
        }
    }
    // gradalpha2=sum(wxprod,1)';
    gradOut.alpha2 = margSum(wxProd, 1);
    // gradw=X'*wxprod;
    DoubleMatrix2D gradW = alg.mult(alg.transpose(xDat), wxProd);
    // gradtheta=gradtheta+gradw';
    gradOut.theta.assign(alg.transpose(gradW), Functions.plus);
    // gradphi=D'*wxprod;
    gradOut.phi = alg.mult(alg.transpose(dDat), wxProd);
    // end
    for (int i = 0; i < q; i++) {
        gradOut.phi.viewPart(lcumsum[i], lcumsum[i], l[i], l[i]).assign(0);
    }
    // gradphi=tril(gradphi)'+triu(gradphi);
    DoubleMatrix2D lowerPhi = alg.transpose(lowerTri(gradOut.phi.copy(), 0));
    upperTri(gradOut.phi, 0).assign(lowerPhi, Functions.plus);
    /*
        for s=1:p
            gradbetad(s)=-n/(2*betad(s))+1/2*norm(res(:,s))^2-res(:,s)'*(Xbeta(:,s)+Dtheta(:,s));
        end
         */
    gradOut.betad = factory1D.make(xDat.columns());
    for (int i = 0; i < p; i++) {
        gradOut.betad.set(i, -n / (2.0 * par.betad.get(i)) + alg.norm2(tempLoss.viewColumn(i)) / 2.0 - alg.mult(tempLoss.viewColumn(i), xBeta.viewColumn(i).copy().assign(dTheta.viewColumn(i), Functions.plus)));
    }
    gradOut.alpha1.assign(Functions.div((double) n));
    gradOut.alpha2.assign(Functions.div((double) n));
    gradOut.betad.assign(Functions.div((double) n));
    gradOut.beta.assign(Functions.div((double) n));
    gradOut.theta.assign(Functions.div((double) n));
    gradOut.phi.assign(Functions.div((double) n));
    gradOutVec.assign(gradOut.toMatrix1D());
    return (sqloss + catloss) / ((double) n);
}

Example 13 with DoubleMatrix2D

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

the class TetradAlgebra method solve.

public static TetradMatrix solve(TetradMatrix a, TetradMatrix b) {
    DoubleMatrix2D _a = new DenseDoubleMatrix2D(a.toArray());
    DoubleMatrix2D _b = new DenseDoubleMatrix2D(b.toArray());
    return new TetradMatrix(new Algebra().solve(_a, _b).toArray());
}

Example 14 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tdq-studio-se by Talend.

the class TestMatrix2D method doubleTest19.

/**
 */
public static void doubleTest19() {
    System.out.println("\n\n");
    DoubleMatrix2D A;
    int k;
    int uk;
    int lk;
    double[][] values5 = { { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 } };
    A = Factory2D.make(values5);
    k = cern.colt.matrix.linalg.Property.DEFAULT.semiBandwidth(A);
    uk = cern.colt.matrix.linalg.Property.DEFAULT.upperBandwidth(A);
    lk = cern.colt.matrix.linalg.Property.DEFAULT.lowerBandwidth(A);
    System.out.println("\n\nupperBandwidth=" + uk);
    System.out.println("lowerBandwidth=" + lk);
    System.out.println("bandwidth=" + k + " " + A);
    // System.out.println("\n\nbandwidth="+k+" "+cern.colt.matrixpattern.Converting.toHTML(A.toString()));
    double[][] values4 = { { 1, 0, 0, 0 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, { 0, 0, 0, 1 } };
    A = Factory2D.make(values4);
    k = cern.colt.matrix.linalg.Property.DEFAULT.semiBandwidth(A);
    uk = cern.colt.matrix.linalg.Property.DEFAULT.upperBandwidth(A);
    lk = cern.colt.matrix.linalg.Property.DEFAULT.lowerBandwidth(A);
    System.out.println("\n\nupperBandwidth=" + uk);
    System.out.println("lowerBandwidth=" + lk);
    System.out.println("bandwidth=" + k + " " + A);
    // System.out.println("\n\nbandwidth="+k+" "+cern.colt.matrixpattern.Converting.toHTML(A.toString()));
    double[][] values1 = { { 1, 1, 0, 0 }, { 1, 1, 1, 0 }, { 0, 1, 1, 1 }, { 0, 0, 1, 1 } };
    A = Factory2D.make(values1);
    k = cern.colt.matrix.linalg.Property.DEFAULT.semiBandwidth(A);
    uk = cern.colt.matrix.linalg.Property.DEFAULT.upperBandwidth(A);
    lk = cern.colt.matrix.linalg.Property.DEFAULT.lowerBandwidth(A);
    System.out.println("\n\nupperBandwidth=" + uk);
    System.out.println("lowerBandwidth=" + lk);
    System.out.println("bandwidth=" + k + " " + A);
    // System.out.println("\n\nbandwidth="+k+" "+cern.colt.matrixpattern.Converting.toHTML(A.toString()));
    double[][] values6 = { { 0, 1, 1, 1 }, { 0, 1, 1, 1 }, { 0, 0, 0, 1 }, { 0, 0, 0, 1 } };
    A = Factory2D.make(values6);
    k = cern.colt.matrix.linalg.Property.DEFAULT.semiBandwidth(A);
    uk = cern.colt.matrix.linalg.Property.DEFAULT.upperBandwidth(A);
    lk = cern.colt.matrix.linalg.Property.DEFAULT.lowerBandwidth(A);
    System.out.println("\n\nupperBandwidth=" + uk);
    System.out.println("lowerBandwidth=" + lk);
    System.out.println("bandwidth=" + k + " " + A);
    // System.out.println("\n\nbandwidth="+k+" "+cern.colt.matrixpattern.Converting.toHTML(A.toString()));
    double[][] values7 = { { 0, 0, 0, 0 }, { 1, 1, 0, 0 }, { 1, 1, 0, 0 }, { 1, 1, 1, 1 } };
    A = Factory2D.make(values7);
    k = cern.colt.matrix.linalg.Property.DEFAULT.semiBandwidth(A);
    uk = cern.colt.matrix.linalg.Property.DEFAULT.upperBandwidth(A);
    lk = cern.colt.matrix.linalg.Property.DEFAULT.lowerBandwidth(A);
    System.out.println("\n\nupperBandwidth=" + uk);
    System.out.println("lowerBandwidth=" + lk);
    System.out.println("bandwidth=" + k + " " + A);
    // System.out.println("\n\nbandwidth="+k+" "+cern.colt.matrixpattern.Converting.toHTML(A.toString()));
    double[][] values2 = { { 1, 1, 0, 0 }, { 0, 1, 1, 0 }, { 0, 1, 0, 1 }, { 1, 0, 1, 1 } };
    A = Factory2D.make(values2);
    k = cern.colt.matrix.linalg.Property.DEFAULT.semiBandwidth(A);
    uk = cern.colt.matrix.linalg.Property.DEFAULT.upperBandwidth(A);
    lk = cern.colt.matrix.linalg.Property.DEFAULT.lowerBandwidth(A);
    System.out.println("\n\nupperBandwidth=" + uk);
    System.out.println("lowerBandwidth=" + lk);
    System.out.println("bandwidth=" + k + " " + A);
    // System.out.println("\n\nbandwidth="+k+" "+cern.colt.matrixpattern.Converting.toHTML(A.toString()));
    double[][] values3 = { { 1, 1, 1, 0 }, { 0, 1, 0, 0 }, { 1, 1, 0, 1 }, { 0, 0, 1, 1 } };
    A = Factory2D.make(values3);
    k = cern.colt.matrix.linalg.Property.DEFAULT.semiBandwidth(A);
    uk = cern.colt.matrix.linalg.Property.DEFAULT.upperBandwidth(A);
    lk = cern.colt.matrix.linalg.Property.DEFAULT.lowerBandwidth(A);
    System.out.println("\n\nupperBandwidth=" + uk);
    System.out.println("lowerBandwidth=" + lk);
    System.out.println("bandwidth=" + k + " " + A);
// System.out.println("\n\nbandwidth="+k+" "+cern.colt.matrixpattern.Converting.toHTML(A.toString()));
}

Example 15 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tdq-studio-se by Talend.

the class TestMatrix2D method doubleTest33.

/**
 */
public static void doubleTest33() {
    double nan = Double.NaN;
    double inf = Double.POSITIVE_INFINITY;
    double ninf = Double.NEGATIVE_INFINITY;
    double[][] data = { { ninf, nan } };
    /*
	{ 
		{ 1, 4, 0 },
		{ 6, 2, 5 },
		{ 0, 7, 3 },
		{ 0, 0, 8 },
		{ 0, 0, 0 },
		{ 0, 0, 0 }
	};
	*/
    DoubleMatrix2D x = new DenseDoubleMatrix2D(data);
    System.out.println("\n\n\n" + x);
    System.out.println("\n" + x.equals(ninf));
}