int main()

in Documents/NamdSample/namd210b/lib/abf_integrate/abf_integrate.cpp [19:198]
144 lines of code
27 McCabe index (conditional complexity)

int main(int argc, char *argv[])
{
    char *data_file;
    char *out_file;
    unsigned int step, nsteps, total, out_freq;
    int *pos, *dpos, *newpos;
    unsigned int *histogram;
    const double *grad, *newgrad;
    unsigned int offset, newoffset;
    int not_accepted;
    double dA;
    double temp;
    double mbeta;
    bool meta;
    double hill, hill_fact, hill_min;
    double rmsd, rmsd_old, rmsd_rel_change, convergence_limit;
    bool converged;
    unsigned int scale_hill_step;

    // Setting default values
    nsteps = 0;
    temp = 500;
    meta = true;
    hill = 0.01;
    hill_fact = 0.5;
    hill_min = 0.0005;

    convergence_limit = -0.001;

    if (!(data_file = parse_cl(argc, argv, &nsteps, &temp, &meta, &hill, &hill_fact))) {
        std::cerr << "\nabf_integrate: MC-based integration of multidimensional free energy gradient\n";
        std::cerr << "Version 20110511\n\n";
        std::cerr << "Syntax: " << argv[0] <<
            " <filename> [-n <nsteps>] [-t <temp>] [-m [0|1] (metadynamics)]"
            " [-h <hill_height>] [-f <variable_hill_factor>]\n\n";
        exit(1);
    }

    if (meta) {
        std::cout << "\nUsing metadynamics-style sampling with hill height: " << hill << "\n";
        if (hill_fact) {
            std::cout << "Varying hill height by factor " << hill_fact << "\n";
        }
    } else {
        std::cout << "\nUsing unbiased MC sampling\n";
    }
    
    if (nsteps) {
        std::cout << "Sampling " << nsteps << " steps at temperature " << temp << "\n\n";
        out_freq = nsteps / 10;
        scale_hill_step = nsteps / 2;
        converged = true;
    } else {
        std::cout << "Sampling until convergence at temperature " << temp << "\n\n";
        out_freq = 1000000;
        converged = false;
    }

    // Inverse temperature in (kcal/mol)-1 
    mbeta = -1 / (0.001987 * temp);

    ABFdata data(data_file);

    if (!nsteps) {
        scale_hill_step = 2000 * data.scalar_dim;
        nsteps = 2 * scale_hill_step;
        std::cout << "Setting minimum number of steps to " << nsteps << "\n";
    }

    srand(time(NULL));

    pos = new int[data.Nvars];
    dpos = new int[data.Nvars];
    newpos = new int[data.Nvars];

    do {
      for (int i = 0; i < data.Nvars; i++) {
          pos[i] = rand() % data.sizes[i];
      }
      offset = data.offset(pos);
    } while ( !data.allowed (offset) ); 

    rmsd = compute_deviation(&data, meta, 0.001987 * temp);
    std::cout << "\nInitial gradient RMS is " << rmsd << "\n";

    total = 0;
    for (step = 1; (step <= nsteps || !converged); step++) {

        if ( step % out_freq == 0) {
            rmsd_old = rmsd;
            rmsd = compute_deviation(&data, meta, 0.001987 * temp);
            rmsd_rel_change = (rmsd - rmsd_old) / (rmsd_old * double (out_freq)) * 1000000.0;
            std::cout << "Step " << step << " ; gradient RMSD is " << rmsd
                      << " ; relative change per 1M steps " << rmsd_rel_change;
            if ( rmsd_rel_change > convergence_limit && step >= nsteps ) {
                converged = true;
            }

            if (meta && hill_fact && step > scale_hill_step && hill > hill_min ) {
                hill *= hill_fact;
                std::cout << " - changing hill height to " << hill << "\n";
            } else {
                std::cout << "\n";
            }
        }

        offset = data.offset(pos);
        data.histogram[offset]++;
        if (meta) {
            data.bias[offset] += hill;
        }

        grad = data.gradients + offset * data.Nvars;

        not_accepted = 1;
        while (not_accepted) {
            dA = 0.0;
            total++;
            for (int i = 0; i < data.Nvars; i++) {
                dpos[i] = rand() % 3 - 1;
                newpos[i] = pos[i] + dpos[i];
                data.wrap(newpos[i], i);
                if (newpos[i] == pos[i])
                    dpos[i] = 0;

                if (dpos[i]) {
                    dA += grad[i] * dpos[i] * data.widths[i];
                    // usefulness of the interpolation below depends on
                    // where the grid points are for the histogram wrt to the gradients
                    // If done, it has to be done in all directions
                    // the line below is useless
                    //dA += 0.5 * (newgrad[i] + grad[i]) * dpos[i] * data.widths[i];
                }
            }

            newoffset = data.offset(newpos);
            if (meta) {
                dA += data.bias[newoffset] - data.bias[offset];
            }

            if ( data.allowed (newoffset) && (((float) rand()) / RAND_MAX < exp(mbeta * dA)) )  {
                // Accept move
                for (int i = 0; i < data.Nvars; i++) {
                    pos[i] = newpos[i];
                    not_accepted = 0;
                }
            }
        }
    }
    std::cout << "Run " << total << " total iterations; acceptance ratio is "
        << double (step) / double (total)
        << " ; final gradient RMSD is " << compute_deviation(&data, meta, 0.001987 * temp) << "\n";

    out_file = new char[strlen(data_file) + 8];

    if (meta) {
        sprintf(out_file, "%s.pmf", data_file);
        std::cout << "Writing PMF to file " << out_file << "\n";
        data.write_bias(out_file);
    }

    // TODO write a PMF for unbiased MC, too...
    sprintf(out_file, "%s.histo", data_file);
    std::cout << "Writing sampling histogram to file " << out_file << "\n";
    data.write_histogram(out_file);

    sprintf(out_file, "%s.est", data_file);
    std::cout << "Writing estimated FE gradient to file " << out_file << "\n";
    data.write_field(data.estimate, out_file);

    sprintf(out_file, "%s.dev", data_file);
    std::cout << "Writing FE gradient deviation to file " << out_file << "\n\n";
    data.write_field(data.deviation, out_file);

    delete [] pos;
    delete [] dpos;
    delete [] newpos;
    delete [] out_file;
    exit(0);
}