Method run

src/modules/regress/logistic.cpp:210–271

* @brief Perform the logistic-regression transition step */

 * @brief Perform the logistic-regression transition step
 */
AnyType
logregr_cg_step_transition::run(AnyType &args) {
    LogRegrCGTransitionState<MutableArrayHandle<double> > state = args[0];
    if (args[1].isNull() || args[2].isNull()) { return args[0]; }
    double y = args[1].getAs<bool>() ? 1. : -1.;
    MappedColumnVector x;
    try {
        // an exception is raised in the backend if args[2] contains nulls
        MappedColumnVector xx = args[2].getAs<MappedColumnVector>();
        // x is a const reference, we can only rebind to change its pointer
        x.rebind(xx.memoryHandle(), xx.size());
    } catch (const ArrayWithNullException &e) {
        return args[0];
    }

    // The following check was added with MADLIB-138.
    if (!dbal::eigen_integration::isfinite(x)) {
        //throw std::domain_error("Design matrix is not finite.");
        warning("Design matrix is not finite.");
        state.status = TERMINATED;
        return state;
    }

    if (state.numRows == 0) {
        if (x.size() > std::numeric_limits<uint16_t>::max()){
            //throw std::domain_error(
            //    "Number of independent variables cannot be "
            //    "larger than 65535.");
            warning("Number of independent variables cannot be larger than 65535.");
            state.status = TERMINATED;
            return state;
        }


        state.initialize(*this, static_cast<uint16_t>(x.size()));
        if (!args[3].isNull()) {
            LogRegrCGTransitionState<ArrayHandle<double> > previousState = args[3];

            state = previousState;
            state.reset();
        }
    }
    // Now do the transition step
    state.numRows++;
    double xc = dot(x, state.coef);
    state.gradNew.noalias() += sigma(-y * xc) * y * trans(x);

    // Note: sigma(-x) = 1 - sigma(x).
    // a_i = sigma(x_i c) sigma(-x_i c)
    double a = sigma(xc) * sigma(-xc);
    //triangularView<Lower>(state.X_transp_AX) += x * trans(x) * a;
    state.X_transp_AX += x * trans(x) * a;

    //          n
    //         --
    // l(c) = -\  log(1 + exp(-y_i * c^T x_i))
    //         /_
    //         i=1