This update includes one new feature (neural-network based collective variables), several small enhancements (including an automatic definition of grid boundaries for angle-based CVs, and a normalization option for eigenvector-based CVs), bugfixes and documentation improvements. Usage information for specific features included in the Colvars library (i.e. not just the library as a whole) is now also reported to the screen or LAMMPS logfile (as is done already in other LAMMPS classes). Notable to LAMMPS code development are the removals of duplicated code and of ambiguously-named preprocessor defines in the Colvars headers. Since the last PR, the existing regression tests have also been running automatically via GitHub Actions. The following pull requests in the Colvars repository are relevant to LAMMPS: - 475 Remove fatal error condition https://github.com/Colvars/colvars/pull/475 (@jhenin, @giacomofiorin) - 474 Allow normalizing eigenvector vector components to deal with unit change https://github.com/Colvars/colvars/pull/474 (@giacomofiorin, @jhenin) - 470 Better error handling in the initialization of NeuralNetwork CV https://github.com/Colvars/colvars/pull/470 (@HanatoK) - 468 Add examples of histogram configuration, with and without explicit grid parameters https://github.com/Colvars/colvars/pull/468 (@giacomofiorin) - 464 Fix #463 using more fine-grained features https://github.com/Colvars/colvars/pull/464 (@jhenin, @giacomofiorin) - 447 [RFC] New option "scaledBiasingForce" for colvarbias https://github.com/Colvars/colvars/pull/447 (@HanatoK, @jhenin) - 444 [RFC] Implementation of dense neural network as CV https://github.com/Colvars/colvars/pull/444 (@HanatoK, @giacomofiorin, @jhenin) - 443 Fix explicit gradient dependency of sub-CVs https://github.com/Colvars/colvars/pull/443 (@HanatoK, @jhenin) - 442 Persistent bias count https://github.com/Colvars/colvars/pull/442 (@jhenin, @giacomofiorin) - 437 Return type of bias from scripting interface https://github.com/Colvars/colvars/pull/437 (@giacomofiorin) - 434 More flexible use of boundaries from colvars by grids https://github.com/Colvars/colvars/pull/434 (@jhenin) - 433 Prevent double-free in linearCombination https://github.com/Colvars/colvars/pull/433 (@HanatoK) - 428 More complete documentation for index file format (NDX) https://github.com/Colvars/colvars/pull/428 (@giacomofiorin) - 426 Integrate functional version of backup_file() into base proxy class https://github.com/Colvars/colvars/pull/426 (@giacomofiorin) - 424 Track CVC inheritance when documenting feature usage https://github.com/Colvars/colvars/pull/424 (@giacomofiorin) - 419 Generate citation report while running computations https://github.com/Colvars/colvars/pull/419 (@giacomofiorin, @jhenin) - 415 Rebin metadynamics bias from explicit hills when available https://github.com/Colvars/colvars/pull/415 (@giacomofiorin) - 312 Ignore a keyword if it has content to the left of it (regardless of braces) https://github.com/Colvars/colvars/pull/312 (@giacomofiorin) Authors: @giacomofiorin, @HanatoK, @jhenin
426 lines
12 KiB
C++
426 lines
12 KiB
C++
// -*- c++ -*-
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// This file is part of the Collective Variables module (Colvars).
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// The original version of Colvars and its updates are located at:
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// https://github.com/Colvars/colvars
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// Please update all Colvars source files before making any changes.
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// If you wish to distribute your changes, please submit them to the
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// Colvars repository at GitHub.
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#include <cstdlib>
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#include "colvarmodule.h"
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#include "colvarbias.h"
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#include "colvarbias_alb.h"
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#ifdef _MSC_VER
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#if _MSC_VER <= 1700
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#define copysign(A,B) _copysign(A,B)
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double fmax(double A, double B) { return ( A > B ? A : B ); }
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double fmin(double A, double B) { return ( A < B ? A : B ); }
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#endif
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#endif
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/* Note about nomenclature. Force constant is called a coupling
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* constant here to emphasize its changing in the code. Outwards,
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* everything is called a force constant to keep it consistent with
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* the rest of colvars.
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*
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*/
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colvarbias_alb::colvarbias_alb(char const *key)
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: colvarbias(key), update_calls(0), b_equilibration(true)
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{
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}
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int colvarbias_alb::init(std::string const &conf)
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{
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colvarbias::init(conf);
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cvm::main()->cite_feature("ALB colvar bias implementation");
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enable(f_cvb_scalar_variables);
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size_t i;
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// get the initial restraint centers
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colvar_centers.resize(num_variables());
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means.resize(num_variables());
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ssd.resize(num_variables()); //sum of squares of differences from mean
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//setup force vectors
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max_coupling_range.resize(num_variables());
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max_coupling_rate.resize(num_variables());
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coupling_accum.resize(num_variables());
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set_coupling.resize(num_variables());
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current_coupling.resize(num_variables());
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coupling_rate.resize(num_variables());
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enable(f_cvb_apply_force);
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for (i = 0; i < num_variables(); i++) {
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colvar_centers[i].type(colvars[i]->value());
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//zero moments
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means[i] = ssd[i] = 0;
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//zero force some of the force vectors that aren't initialized
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coupling_accum[i] = current_coupling[i] = 0;
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}
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if (get_keyval(conf, "centers", colvar_centers, colvar_centers)) {
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for (i = 0; i < num_variables(); i++) {
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colvar_centers[i].apply_constraints();
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}
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} else {
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colvar_centers.clear();
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cvm::error("Error: must define the initial centers of adaptive linear bias .\n");
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}
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if (colvar_centers.size() != num_variables())
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cvm::error("Error: number of centers does not match "
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"that of collective variables.\n");
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if (!get_keyval(conf, "UpdateFrequency", update_freq, 0))
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cvm::error("Error: must set updateFrequency for adaptive linear bias.\n");
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//we split the time between updating and equilibrating
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update_freq /= 2;
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if (update_freq <= 1)
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cvm::error("Error: must set updateFrequency to greater than 2.\n");
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enable(f_cvb_history_dependent);
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get_keyval(conf, "outputCenters", b_output_centers, false);
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get_keyval(conf, "outputGradient", b_output_grad, false);
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get_keyval(conf, "outputCoupling", b_output_coupling, true);
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get_keyval(conf, "hardForceRange", b_hard_coupling_range, true);
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//initial guess
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if (!get_keyval(conf, "forceConstant", set_coupling, set_coupling))
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for (i =0 ; i < num_variables(); i++)
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set_coupling[i] = 0.;
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//how we're going to increase to that point
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for (i = 0; i < num_variables(); i++)
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coupling_rate[i] = (set_coupling[i] - current_coupling[i]) / update_freq;
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if (!get_keyval(conf, "forceRange", max_coupling_range, max_coupling_range)) {
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//set to default
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for (i = 0; i < num_variables(); i++) {
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if (cvm::temperature() > 0)
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max_coupling_range[i] = 3 * cvm::temperature() * cvm::boltzmann();
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else
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max_coupling_range[i] = 3 * cvm::boltzmann();
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}
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}
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if (!get_keyval(conf, "rateMax", max_coupling_rate, max_coupling_rate)) {
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//set to default
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for (i = 0; i < num_variables(); i++) {
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max_coupling_rate[i] = max_coupling_range[i] / (10 * update_freq);
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}
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}
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if (cvm::debug())
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cvm::log(" bias.\n");
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return COLVARS_OK;
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}
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colvarbias_alb::~colvarbias_alb()
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{
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}
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int colvarbias_alb::update()
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{
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bias_energy = 0.0;
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update_calls++;
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if (cvm::debug())
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cvm::log("Updating the adaptive linear bias \""+this->name+"\".\n");
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//log the moments of the CVs
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// Force and energy calculation
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bool finished_equil_flag = 1;
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cvm::real delta;
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for (size_t i = 0; i < num_variables(); i++) {
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colvar_forces[i] = -1.0 * restraint_force(restraint_convert_k(current_coupling[i], colvars[i]->width),
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colvars[i],
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colvar_centers[i]);
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bias_energy += restraint_potential(restraint_convert_k(current_coupling[i], colvars[i]->width),
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colvars[i],
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colvar_centers[i]);
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if (!b_equilibration) {
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//Welford, West, and Hanso online variance method
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delta = static_cast<cvm::real>(colvars[i]->value()) - means[i];
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means[i] += delta / update_calls;
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ssd[i] += delta * (static_cast<cvm::real>(colvars[i]->value()) - means[i]);
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} else {
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//check if we've reached the setpoint
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cvm::real const coupling_diff = current_coupling[i] - set_coupling[i];
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if ((coupling_rate[i] == 0) ||
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((coupling_diff*coupling_diff) < (coupling_rate[i]*coupling_rate[i]))) {
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finished_equil_flag &= 1; //we continue equilibrating as long as we haven't reached all the set points
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}
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else {
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current_coupling[i] += coupling_rate[i];
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finished_equil_flag = 0;
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}
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//update max_coupling_range
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if (!b_hard_coupling_range && fabs(current_coupling[i]) > max_coupling_range[i]) {
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std::ostringstream logStream;
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logStream << "Coupling constant for "
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<< colvars[i]->name
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<< " has exceeded coupling range of "
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<< max_coupling_range[i]
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<< ".\n";
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max_coupling_range[i] *= 1.25;
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logStream << "Expanding coupling range to " << max_coupling_range[i] << ".\n";
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cvm::log(logStream.str());
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}
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}
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}
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if (b_equilibration && finished_equil_flag) {
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b_equilibration = false;
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update_calls = 0;
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}
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//now we update coupling constant, if necessary
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if (!b_equilibration && update_calls == update_freq) {
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//use estimated variance to take a step
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cvm::real step_size = 0;
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cvm::real temp;
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//reset means and sum of squares of differences
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for (size_t i = 0; i < num_variables(); i++) {
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temp = 2. * (means[i] / (static_cast<cvm::real> (colvar_centers[i])) - 1) * ssd[i] / (update_calls - 1);
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if (cvm::temperature() > 0)
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step_size = temp / (cvm::temperature() * cvm::boltzmann());
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else
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step_size = temp / cvm::boltzmann();
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means[i] = 0;
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ssd[i] = 0;
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//stochastic if we do that update or not
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if (num_variables() == 1 || rand() < RAND_MAX / ((int) num_variables())) {
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coupling_accum[i] += step_size * step_size;
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current_coupling[i] = set_coupling[i];
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set_coupling[i] += max_coupling_range[i] / sqrt(coupling_accum[i]) * step_size;
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coupling_rate[i] = (set_coupling[i] - current_coupling[i]) / update_freq;
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//set to the minimum rate and then put the sign back on it
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coupling_rate[i] = copysign(fmin(fabs(coupling_rate[i]), max_coupling_rate[i]), coupling_rate[i]);
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} else {
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coupling_rate[i] = 0;
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}
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}
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update_calls = 0;
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b_equilibration = true;
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}
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return COLVARS_OK;
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}
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int colvarbias_alb::set_state_params(std::string const &conf)
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{
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int error_code = colvarbias::set_state_params(conf);
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if (error_code != COLVARS_OK) {
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return error_code;
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}
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if (!get_keyval(conf, "setCoupling", set_coupling))
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cvm::error("Error: current setCoupling is missing from the restart.\n");
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if (!get_keyval(conf, "currentCoupling", current_coupling))
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cvm::error("Error: current setCoupling is missing from the restart.\n");
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if (!get_keyval(conf, "maxCouplingRange", max_coupling_range))
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cvm::error("Error: maxCouplingRange is missing from the restart.\n");
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if (!get_keyval(conf, "couplingRate", coupling_rate))
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cvm::error("Error: current setCoupling is missing from the restart.\n");
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if (!get_keyval(conf, "couplingAccum", coupling_accum))
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cvm::error("Error: couplingAccum is missing from the restart.\n");
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if (!get_keyval(conf, "mean", means))
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cvm::error("Error: current mean is missing from the restart.\n");
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if (!get_keyval(conf, "ssd", ssd))
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cvm::error("Error: current ssd is missing from the restart.\n");
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if (!get_keyval(conf, "updateCalls", update_calls))
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cvm::error("Error: current updateCalls is missing from the restart.\n");
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if (!get_keyval(conf, "b_equilibration", b_equilibration))
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cvm::error("Error: current updateCalls is missing from the restart.\n");
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return COLVARS_OK;
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}
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std::string const colvarbias_alb::get_state_params() const
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{
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std::ostringstream os;
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os << " setCoupling ";
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size_t i;
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for (i = 0; i < num_variables(); i++) {
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os << std::setprecision(cvm::en_prec)
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<< std::setw(cvm::en_width) << set_coupling[i] << "\n";
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}
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os << " currentCoupling ";
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for (i = 0; i < num_variables(); i++) {
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os << std::setprecision(cvm::en_prec)
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<< std::setw(cvm::en_width) << current_coupling[i] << "\n";
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}
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os << " maxCouplingRange ";
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for (i = 0; i < num_variables(); i++) {
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os << std::setprecision(cvm::en_prec)
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<< std::setw(cvm::en_width) << max_coupling_range[i] << "\n";
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}
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os << " couplingRate ";
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for (i = 0; i < num_variables(); i++) {
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os << std::setprecision(cvm::en_prec)
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<< std::setw(cvm::en_width) << coupling_rate[i] << "\n";
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}
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os << " couplingAccum ";
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for (i = 0; i < num_variables(); i++) {
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os << std::setprecision(cvm::en_prec)
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<< std::setw(cvm::en_width) << coupling_accum[i] << "\n";
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}
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os << " mean ";
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for (i = 0; i < num_variables(); i++) {
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os << std::setprecision(cvm::en_prec)
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<< std::setw(cvm::en_width) << means[i] << "\n";
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}
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os << " ssd ";
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for (i = 0; i < num_variables(); i++) {
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os << std::setprecision(cvm::en_prec)
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<< std::setw(cvm::en_width) << ssd[i] << "\n";
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}
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os << " updateCalls " << update_calls << "\n";
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if (b_equilibration)
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os << " b_equilibration yes\n";
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else
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os << " b_equilibration no\n";
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return os.str();
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}
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std::ostream & colvarbias_alb::write_traj_label(std::ostream &os)
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{
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os << " ";
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if (b_output_energy)
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os << " E_"
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<< cvm::wrap_string(this->name, cvm::en_width-2);
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if (b_output_coupling)
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for (size_t i = 0; i < current_coupling.size(); i++) {
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os << " ForceConst_" << i
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<<std::setw(cvm::en_width - 6 - (i / 10 + 1))
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<< "";
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}
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if (b_output_grad)
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for (size_t i = 0; i < means.size(); i++) {
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os << "Grad_"
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<< cvm::wrap_string(colvars[i]->name, cvm::cv_width - 4);
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}
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if (b_output_centers)
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for (size_t i = 0; i < num_variables(); i++) {
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size_t const this_cv_width = (colvars[i]->value()).output_width(cvm::cv_width);
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os << " x0_"
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<< cvm::wrap_string(colvars[i]->name, this_cv_width-3);
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}
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return os;
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}
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std::ostream & colvarbias_alb::write_traj(std::ostream &os)
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{
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os << " ";
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if (b_output_energy)
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os << " "
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<< std::setprecision(cvm::en_prec) << std::setw(cvm::en_width)
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<< bias_energy;
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if (b_output_coupling)
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for (size_t i = 0; i < current_coupling.size(); i++) {
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os << " "
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<< std::setprecision(cvm::en_prec) << std::setw(cvm::en_width)
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<< current_coupling[i];
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}
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if (b_output_centers)
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for (size_t i = 0; i < num_variables(); i++) {
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os << " "
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<< std::setprecision(cvm::cv_prec) << std::setw(cvm::cv_width)
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<< colvar_centers[i];
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}
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if (b_output_grad)
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for (size_t i = 0; i < means.size(); i++) {
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os << " "
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<< std::setprecision(cvm::cv_prec) << std::setw(cvm::cv_width)
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<< -2.0 * (means[i] / (static_cast<cvm::real>(colvar_centers[i])) - 1) * ssd[i] / (fmax(update_calls, 2.0) - 1);
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}
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return os;
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}
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cvm::real colvarbias_alb::restraint_potential(cvm::real k,
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colvar const *x,
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colvarvalue const &xcenter) const
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{
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return k * (x->value() - xcenter);
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}
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colvarvalue colvarbias_alb::restraint_force(cvm::real k,
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colvar const * /* x */,
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colvarvalue const & /* xcenter */) const
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{
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return k;
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}
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cvm::real colvarbias_alb::restraint_convert_k(cvm::real k,
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cvm::real dist_measure) const
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{
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return k / dist_measure;
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}
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