std::pair<unsigned int, Real>
ImplicitSystem::sensitivity_solve (const ParameterVector & parameters)
{
// Log how long the linear solve takes.
LOG_SCOPE("sensitivity_solve()", "ImplicitSystem");
// The forward system should now already be solved.
// Now assemble the corresponding sensitivity system.
if (this->assemble_before_solve)
{
// Build the Jacobian
this->assembly(false, true);
this->matrix->close();
// Reset and build the RHS from the residual derivatives
this->assemble_residual_derivatives(parameters);
}
// The sensitivity problem is linear
LinearSolver<Number> * linear_solver = this->get_linear_solver();
// Our iteration counts and residuals will be sums of the individual
// results
std::pair<unsigned int, Real> solver_params =
this->get_linear_solve_parameters();
std::pair<unsigned int, Real> totalrval = std::make_pair(0,0.0);
// Solve the linear system.
SparseMatrix<Number> * pc = this->request_matrix("Preconditioner");
for (auto p : IntRange<unsigned int>(0, parameters.size()))
{
std::pair<unsigned int, Real> rval =
linear_solver->solve (*matrix, pc,
this->add_sensitivity_solution(p),
this->get_sensitivity_rhs(p),
solver_params.second,
solver_params.first);
totalrval.first += rval.first;
totalrval.second += rval.second;
}
// The linear solver may not have fit our constraints exactly
#ifdef LIBMESH_ENABLE_CONSTRAINTS
for (auto p : IntRange<unsigned int>(0, parameters.size()))
this->get_dof_map().enforce_constraints_exactly
(*this, &this->get_sensitivity_solution(p),
/* homogeneous = */ true);
#endif
this->release_linear_solver(linear_solver);
return totalrval;
}
// Processes completion for a module command's parameter
static char* handle_complete(const char *line, const char *word,
int pos, int state,
const char* msgid)
{
char *ret = NULL;
try
{
BOOST_ASSERT(line);
const std::string strline(line);
BOOST_ASSERT(word);
const std::string strword(word);
ModuleProcessor *processor = Factory::instance()->getModuleProcessor();
ParameterVector completions =
processor->getCompletion(strline, strword, pos, msgid);
if (completions.empty())
{
// no completions
return NULL;
}
size_t wordlen = strlen(word);
int which = 0;
for (ParameterVector::iterator i = completions.begin();
i != completions.end(); i++)
{
if (!strncasecmp(word, i->c_str(), wordlen) &&
++which > state)
{
std::string found = *i;
// is there any spaces
if (found.find(' ') != std::string::npos)
{
found = "\"" + found + "\"";
}
ret = strdup(found.c_str());
break;
}
}
}
catch(const std::exception& err)
{
klk_log(KLKLOG_ERROR, "Error in handle_complete(): %s",
err.what());
}
catch(...)
{
klk_log(KLKLOG_ERROR, "Unknown error in handle_complete()");
}
return ret;
}
// Perturb and accumulate dual weighted residuals
void HeatSystem::perturb_accumulate_residuals(ParameterVector& parameters_in)
{
const unsigned int Np = parameters_in.size();
for (unsigned int j=0; j != Np; ++j)
{
Number old_parameter = *parameters_in[j];
*parameters_in[j] = old_parameter - dp;
this->assembly(true, false);
this->rhs->close();
UniquePtr<NumericVector<Number> > R_minus = this->rhs->clone();
// The contribution at a single time step would be [f(z;p+dp) - <partialu/partialt, z>(p+dp) - <g(u),z>(p+dp)] * dt
// But since we compute the residual already scaled by dt, there is no need for the * dt
R_minus_dp += -R_minus->dot(this->get_adjoint_solution(0));
*parameters_in[j] = old_parameter + dp;
this->assembly(true, false);
this->rhs->close();
UniquePtr<NumericVector<Number> > R_plus = this->rhs->clone();
R_plus_dp += -R_plus->dot(this->get_adjoint_solution(0));
*parameters_in[j] = old_parameter;
}
}
void ParameterVector::deep_copy(ParameterVector &target) const
{
const unsigned int Np = cast_int<unsigned int>
(this->_params.size());
target.clear();
target._params.resize(Np);
target._my_data.resize(Np);
for (unsigned int i=0; i != Np; ++i)
{
target._params[i] =
new ParameterPointer<Number>(&target._my_data[i]);
target._my_data[i] = *(*this)[i];
}
}
/// Perform pending updates and divide all parameters by d
void average_parameters(ParameterVector& summed_model_params,
const ParameterVector& model_params,
const ParameterVector& last_model_params,
const std::vector<unsigned>& last_param_update,
unsigned d) const
{
for (unsigned p = 0; p < summed_model_params.size(); ++p) {
if (d != last_param_update[p]) {
unsigned n = d - last_param_update[p]-1;
summed_model_params[p] += (n * last_model_params[p]);
}
summed_model_params[p] /= d;
}
}
// *****************************************************************
// *****************************************************************
void LOCA::Epetra::ModelEvaluatorInterface::
setParameters(const ParameterVector& p)
{
for (int i=0; i<p.length(); i++)
param_vec[i] = p[i];
}
void Algorithm::SetParameterCollection(const ParameterVector& parameters){
_parameterCollection.assign(parameters.begin(), parameters.end());
}
std::pair<unsigned int, Real>
EigenSystem::sensitivity_solve (const ParameterVector& parameters)
{
// make sure that eigensolution is already available
libmesh_assert(_n_converged_eigenpairs);
// the sensitivity is calculated based on the inner product of the left and
// right eigen vectors.
//
// y^T [A] x - lambda y^T [B] x = 0
// where y and x are the left and right eigenvectors
// d lambda/dp = (y^T (d[A]/dp - lambda d[B]/dp) x) / (y^T [B] x)
//
// the denominator remain constant for all sensitivity calculations.
//
std::vector<Number> denom(_n_converged_eigenpairs, 0.),
sens(_n_converged_eigenpairs, 0.);
std::pair<Real, Real> eig_val;
AutoPtr< NumericVector<Number> > x_right = NumericVector<Number>::build(this->comm()),
x_left = NumericVector<Number>::build(this->comm()),
tmp = NumericVector<Number>::build(this->comm());
x_right->init(*solution); x_left->init(*solution); tmp->init(*solution);
for (unsigned int i=0; i<_n_converged_eigenpairs; i++)
{
switch (_eigen_problem_type) {
case HEP:
// right and left eigenvectors are same
// imaginary part of eigenvector for real matrices is zero
this->eigen_solver->get_eigenpair(i, *x_right, NULL);
denom[i] = x_right->dot(*x_right); // x^H x
break;
case GHEP:
// imaginary part of eigenvector for real matrices is zero
this->eigen_solver->get_eigenpair(i, *x_right, NULL);
matrix_B->vector_mult(*tmp, *x_right);
denom[i] = x_right->dot(*tmp); // x^H B x
default:
// to be implemented for the non-Hermitian problems
libmesh_error();
break;
}
}
for (unsigned int p=0; p<parameters.size(); p++)
{
// calculate sensitivity of matrix quantities
this->assemble_eigensystem_sensitivity(parameters, p);
// now calculate sensitivity of each eigenvalue for the parameter
for (unsigned int i=0; i<_n_converged_eigenpairs; i++)
{
eig_val = this->eigen_solver->get_eigenpair(i, *x_right);
switch (_eigen_problem_type)
{
case HEP:
matrix_A->vector_mult(*tmp, *x_right);
sens[i] = x_right->dot(*tmp); // x^H A' x
sens[i] /= denom[i]; // x^H x
break;
case GHEP:
matrix_A->vector_mult(*tmp, *x_right);
sens[i] = x_right->dot(*tmp); // x^H A' x
matrix_B->vector_mult(*tmp, *x_right);
sens[i]-= eig_val.first * x_right->dot(*tmp); // - lambda x^H B' x
sens[i] /= denom[i]; // x^H B x
break;
default:
// to be implemented for the non-Hermitian problems
libmesh_error();
break;
}
}
}
return std::pair<unsigned int, Real> (0, 0.);
}
void add_parameters(ParameterVector& summed_model_params, const ParameterVector& model_params)
{
for (unsigned p = 0; p < summed_model_params.size(); ++p) {
summed_model_params[p] += model_params[p];
}
}
void ImplicitSystem::qoi_parameter_hessian_vector_product (const QoISet & qoi_indices,
const ParameterVector & parameters_in,
const ParameterVector & vector,
SensitivityData & sensitivities)
{
// We currently get partial derivatives via finite differencing
const Real delta_p = TOLERANCE;
ParameterVector & parameters =
const_cast<ParameterVector &>(parameters_in);
// We'll use a single temporary vector for matrix-vector-vector products
std::unique_ptr<NumericVector<Number>> tempvec = this->solution->zero_clone();
const unsigned int Np = cast_int<unsigned int>
(parameters.size());
const unsigned int Nq = this->n_qois();
// For each quantity of interest q, the parameter sensitivity
// Hessian is defined as q''_{kl} = {d^2 q}/{d p_k d p_l}.
// Given a vector of parameter perturbation weights w_l, this
// function evaluates the hessian-vector product sum_l(q''_{kl}*w_l)
//
// We calculate it from values and partial derivatives of the
// quantity of interest function Q, solution u, adjoint solution z,
// parameter sensitivity adjoint solutions z^l, and residual R, as:
//
// sum_l(q''_{kl}*w_l) =
// sum_l(w_l * Q''_{kl}) + Q''_{uk}(u)*(sum_l(w_l u'_l)) -
// R'_k(u, sum_l(w_l*z^l)) - R'_{uk}(u,z)*(sum_l(w_l u'_l) -
// sum_l(w_l*R''_{kl}(u,z))
//
// See the adjoints model document for more details.
// We first do an adjoint solve to get z for each quantity of
// interest
// if we havent already or dont have an initial condition for the adjoint
if (!this->is_adjoint_already_solved())
{
this->adjoint_solve(qoi_indices);
}
// Get ready to fill in sensitivities:
sensitivities.allocate_data(qoi_indices, *this, parameters);
// We can't solve for all the solution sensitivities u'_l or for all
// of the parameter sensitivity adjoint solutions z^l without
// requiring O(Nq*Np) linear solves. So we'll solve directly for their
// weighted sum - this is just O(Nq) solves.
// First solve for sum_l(w_l u'_l).
this->weighted_sensitivity_solve(parameters, vector);
// Then solve for sum_l(w_l z^l).
this->weighted_sensitivity_adjoint_solve(parameters, vector, qoi_indices);
for (unsigned int k=0; k != Np; ++k)
{
// We approximate sum_l(w_l * Q''_{kl}) with a central
// differencing perturbation:
// sum_l(w_l * Q''_{kl}) ~=
// (Q(p + dp*w_l*e_l + dp*e_k) - Q(p - dp*w_l*e_l + dp*e_k) -
// Q(p + dp*w_l*e_l - dp*e_k) + Q(p - dp*w_l*e_l - dp*e_k))/(4*dp^2)
// The sum(w_l*R''_kl) term requires the same sort of perturbation,
// and so we subtract it in at the same time:
// sum_l(w_l * R''_{kl}) ~=
// (R(p + dp*w_l*e_l + dp*e_k) - R(p - dp*w_l*e_l + dp*e_k) -
// R(p + dp*w_l*e_l - dp*e_k) + R(p - dp*w_l*e_l - dp*e_k))/(4*dp^2)
ParameterVector oldparameters, parameterperturbation;
parameters.deep_copy(oldparameters);
vector.deep_copy(parameterperturbation);
parameterperturbation *= delta_p;
parameters += parameterperturbation;
Number old_parameter = *parameters[k];
*parameters[k] = old_parameter + delta_p;
this->assemble_qoi(qoi_indices);
this->assembly(true, false, true);
this->rhs->close();
std::vector<Number> partial2q_term = this->qoi;
std::vector<Number> partial2R_term(this->n_qois());
for (unsigned int i=0; i != Nq; ++i)
if (qoi_indices.has_index(i))
partial2R_term[i] = this->rhs->dot(this->get_adjoint_solution(i));
*parameters[k] = old_parameter - delta_p;
this->assemble_qoi(qoi_indices);
this->assembly(true, false, true);
this->rhs->close();
for (unsigned int i=0; i != Nq; ++i)
if (qoi_indices.has_index(i))
{
partial2q_term[i] -= this->qoi[i];
partial2R_term[i] -= this->rhs->dot(this->get_adjoint_solution(i));
}
oldparameters.value_copy(parameters);
parameterperturbation *= -1.0;
parameters += parameterperturbation;
// Re-center old_parameter, which may be affected by vector
old_parameter = *parameters[k];
*parameters[k] = old_parameter + delta_p;
this->assemble_qoi(qoi_indices);
this->assembly(true, false, true);
this->rhs->close();
for (unsigned int i=0; i != Nq; ++i)
if (qoi_indices.has_index(i))
{
partial2q_term[i] -= this->qoi[i];
partial2R_term[i] -= this->rhs->dot(this->get_adjoint_solution(i));
}
*parameters[k] = old_parameter - delta_p;
this->assemble_qoi(qoi_indices);
this->assembly(true, false, true);
this->rhs->close();
for (unsigned int i=0; i != Nq; ++i)
if (qoi_indices.has_index(i))
{
partial2q_term[i] += this->qoi[i];
partial2R_term[i] += this->rhs->dot(this->get_adjoint_solution(i));
}
for (unsigned int i=0; i != Nq; ++i)
if (qoi_indices.has_index(i))
{
partial2q_term[i] /= (4. * delta_p * delta_p);
partial2R_term[i] /= (4. * delta_p * delta_p);
}
for (unsigned int i=0; i != Nq; ++i)
if (qoi_indices.has_index(i))
sensitivities[i][k] = partial2q_term[i] - partial2R_term[i];
// We get (partial q / partial u), R, and
// (partial R / partial u) from the user, but centrally
// difference to get q_uk, R_k, and R_uk terms:
// (partial R / partial k)
// R_k*sum(w_l*z^l) = (R(p+dp*e_k)*sum(w_l*z^l) - R(p-dp*e_k)*sum(w_l*z^l))/(2*dp)
// (partial^2 q / partial u partial k)
// q_uk = (q_u(p+dp*e_k) - q_u(p-dp*e_k))/(2*dp)
// (partial^2 R / partial u partial k)
// R_uk*z*sum(w_l*u'_l) = (R_u(p+dp*e_k)*z*sum(w_l*u'_l) - R_u(p-dp*e_k)*z*sum(w_l*u'_l))/(2*dp)
// To avoid creating Nq temporary vectors for q_uk or R_uk, we add
// subterms to the sensitivities output one by one.
//
// FIXME: this is probably a bad order of operations for
// controlling floating point error.
*parameters[k] = old_parameter + delta_p;
this->assembly(true, true);
this->rhs->close();
this->matrix->close();
this->assemble_qoi_derivative(qoi_indices,
/* include_liftfunc = */ true,
/* apply_constraints = */ false);
this->matrix->vector_mult(*tempvec, this->get_weighted_sensitivity_solution());
for (unsigned int i=0; i != Nq; ++i)
if (qoi_indices.has_index(i))
{
this->get_adjoint_rhs(i).close();
sensitivities[i][k] += (this->get_adjoint_rhs(i).dot(this->get_weighted_sensitivity_solution()) -
this->rhs->dot(this->get_weighted_sensitivity_adjoint_solution(i)) -
this->get_adjoint_solution(i).dot(*tempvec)) / (2.*delta_p);
}
*parameters[k] = old_parameter - delta_p;
this->assembly(true, true);
this->rhs->close();
this->matrix->close();
this->assemble_qoi_derivative(qoi_indices,
/* include_liftfunc = */ true,
/* apply_constraints = */ false);
this->matrix->vector_mult(*tempvec, this->get_weighted_sensitivity_solution());
for (unsigned int i=0; i != Nq; ++i)
if (qoi_indices.has_index(i))
{
this->get_adjoint_rhs(i).close();
sensitivities[i][k] += (-this->get_adjoint_rhs(i).dot(this->get_weighted_sensitivity_solution()) +
this->rhs->dot(this->get_weighted_sensitivity_adjoint_solution(i)) +
this->get_adjoint_solution(i).dot(*tempvec)) / (2.*delta_p);
}
}
// All parameters have been reset.
// Don't leave the qoi or system changed - principle of least
// surprise.
this->assembly(true, true);
this->rhs->close();
this->matrix->close();
this->assemble_qoi(qoi_indices);
}
std::pair<unsigned int, Real>
ImplicitSystem::weighted_sensitivity_solve (const ParameterVector & parameters_in,
const ParameterVector & weights)
{
// Log how long the linear solve takes.
LOG_SCOPE("weighted_sensitivity_solve()", "ImplicitSystem");
// We currently get partial derivatives via central differencing
const Real delta_p = TOLERANCE;
ParameterVector & parameters =
const_cast<ParameterVector &>(parameters_in);
// The forward system should now already be solved.
// Now we're assembling a weighted sum of sensitivity systems:
//
// dR/du (u, v)(sum(w_l*u'_l)) = -sum_l(w_l*R'_l (u, v)) forall v
// We'll assemble the rhs first, because the R' term will require
// perturbing the system, and some applications may not be able to
// assemble a perturbed residual without simultaneously constructing
// a perturbed jacobian.
// We approximate the _l partial derivatives via a central
// differencing perturbation in the w_l direction:
//
// sum_l(w_l*v_l) ~= (v(p + dp*w_l*e_l) - v(p - dp*w_l*e_l))/(2*dp)
ParameterVector oldparameters, parameterperturbation;
parameters.deep_copy(oldparameters);
weights.deep_copy(parameterperturbation);
parameterperturbation *= delta_p;
parameters += parameterperturbation;
this->assembly(true, false, true);
this->rhs->close();
std::unique_ptr<NumericVector<Number>> temprhs = this->rhs->clone();
oldparameters.value_copy(parameters);
parameterperturbation *= -1.0;
parameters += parameterperturbation;
this->assembly(true, false, true);
this->rhs->close();
*temprhs -= *(this->rhs);
*temprhs /= (2.0*delta_p);
// Finally, assemble the jacobian at the non-perturbed parameter
// values
oldparameters.value_copy(parameters);
// Build the Jacobian
this->assembly(false, true);
this->matrix->close();
// The weighted sensitivity problem is linear
LinearSolver<Number> * linear_solver = this->get_linear_solver();
std::pair<unsigned int, Real> solver_params =
this->get_linear_solve_parameters();
const std::pair<unsigned int, Real> rval =
linear_solver->solve (*matrix, this->add_weighted_sensitivity_solution(),
*temprhs,
solver_params.second,
solver_params.first);
this->release_linear_solver(linear_solver);
// The linear solver may not have fit our constraints exactly
#ifdef LIBMESH_ENABLE_CONSTRAINTS
this->get_dof_map().enforce_constraints_exactly
(*this, &this->get_weighted_sensitivity_solution(),
/* homogeneous = */ true);
#endif
return rval;
}
std::pair<unsigned int, Real>
ImplicitSystem::weighted_sensitivity_adjoint_solve (const ParameterVector & parameters_in,
const ParameterVector & weights,
const QoISet & qoi_indices)
{
// Log how long the linear solve takes.
LOG_SCOPE("weighted_sensitivity_adjoint_solve()", "ImplicitSystem");
// We currently get partial derivatives via central differencing
const Real delta_p = TOLERANCE;
ParameterVector & parameters =
const_cast<ParameterVector &>(parameters_in);
// The forward system should now already be solved.
// The adjoint system should now already be solved.
// Now we're assembling a weighted sum of adjoint-adjoint systems:
//
// dR/du (u, sum_l(w_l*z^l)) = sum_l(w_l*(Q''_ul - R''_ul (u, z)))
// FIXME: The derivation here does not yet take adjoint boundary
// conditions into account.
for (unsigned int i=0; i != this->n_qois(); ++i)
if (qoi_indices.has_index(i))
libmesh_assert(!this->get_dof_map().has_adjoint_dirichlet_boundaries(i));
// We'll assemble the rhs first, because the R'' term will require
// perturbing the jacobian
// We'll use temporary rhs vectors, because we haven't (yet) found
// any good reasons why users might want to save these:
std::vector<std::unique_ptr<NumericVector<Number>>> temprhs(this->n_qois());
for (unsigned int i=0; i != this->n_qois(); ++i)
if (qoi_indices.has_index(i))
temprhs[i] = this->rhs->zero_clone();
// We approximate the _l partial derivatives via a central
// differencing perturbation in the w_l direction:
//
// sum_l(w_l*v_l) ~= (v(p + dp*w_l*e_l) - v(p - dp*w_l*e_l))/(2*dp)
// PETSc doesn't implement SGEMX, so neither does NumericVector,
// so we want to avoid calculating f -= R'*z. We'll thus evaluate
// the above equation by first adding -v(p+dp...), then multiplying
// the intermediate result vectors by -1, then adding -v(p-dp...),
// then finally dividing by 2*dp.
ParameterVector oldparameters, parameterperturbation;
parameters.deep_copy(oldparameters);
weights.deep_copy(parameterperturbation);
parameterperturbation *= delta_p;
parameters += parameterperturbation;
this->assembly(false, true);
this->matrix->close();
// Take the discrete adjoint, so that we can calculate R_u(u,z) with
// a matrix-vector product of R_u and z.
matrix->get_transpose(*matrix);
this->assemble_qoi_derivative(qoi_indices,
/* include_liftfunc = */ false,
/* apply_constraints = */ true);
for (unsigned int i=0; i != this->n_qois(); ++i)
if (qoi_indices.has_index(i))
{
this->get_adjoint_rhs(i).close();
*(temprhs[i]) -= this->get_adjoint_rhs(i);
this->matrix->vector_mult_add(*(temprhs[i]), this->get_adjoint_solution(i));
*(temprhs[i]) *= -1.0;
}
oldparameters.value_copy(parameters);
parameterperturbation *= -1.0;
parameters += parameterperturbation;
this->assembly(false, true);
this->matrix->close();
matrix->get_transpose(*matrix);
this->assemble_qoi_derivative(qoi_indices,
/* include_liftfunc = */ false,
/* apply_constraints = */ true);
for (unsigned int i=0; i != this->n_qois(); ++i)
if (qoi_indices.has_index(i))
{
this->get_adjoint_rhs(i).close();
*(temprhs[i]) -= this->get_adjoint_rhs(i);
this->matrix->vector_mult_add(*(temprhs[i]), this->get_adjoint_solution(i));
*(temprhs[i]) /= (2.0*delta_p);
}
// Finally, assemble the jacobian at the non-perturbed parameter
// values. Ignore assemble_before_solve; if we had a good
// non-perturbed matrix before we've already overwritten it.
oldparameters.value_copy(parameters);
// if (this->assemble_before_solve)
{
// Build the Jacobian
this->assembly(false, true);
this->matrix->close();
// Take the discrete adjoint
matrix->get_transpose(*matrix);
}
// The weighted adjoint-adjoint problem is linear
LinearSolver<Number> * linear_solver = this->get_linear_solver();
// Our iteration counts and residuals will be sums of the individual
// results
std::pair<unsigned int, Real> solver_params =
this->get_linear_solve_parameters();
std::pair<unsigned int, Real> totalrval = std::make_pair(0,0.0);
for (unsigned int i=0; i != this->n_qois(); ++i)
if (qoi_indices.has_index(i))
{
const std::pair<unsigned int, Real> rval =
linear_solver->solve (*matrix, this->add_weighted_sensitivity_adjoint_solution(i),
*(temprhs[i]),
solver_params.second,
solver_params.first);
totalrval.first += rval.first;
totalrval.second += rval.second;
}
this->release_linear_solver(linear_solver);
// The linear solver may not have fit our constraints exactly
#ifdef LIBMESH_ENABLE_CONSTRAINTS
for (unsigned int i=0; i != this->n_qois(); ++i)
if (qoi_indices.has_index(i))
this->get_dof_map().enforce_constraints_exactly
(*this, &this->get_weighted_sensitivity_adjoint_solution(i),
/* homogeneous = */ true);
#endif
return totalrval;
}
// Registers a module specific info at the driver
void ModuleInfo::registerModule()
{
m_is_loaded = isLoaded();
cli::ICommandList list = m_module->getCLIInfo();
for (cli::ICommandList::iterator i = list.begin();
i != list.end(); i++)
{
if (m_is_loaded == false &&
(*i)->isRequireModule() == true)
{
// not load the command yet
continue;
}
struct ast_cli_entry* entry =
Factory::instance()->getGarbage()->allocEntry();
entry->klkmodnameused = 1;
ParameterVector cmds = Utils::getCommands((*i)->getName());
BOOST_ASSERT(cmds.size() > 0 &&
cmds.size() < (AST_MAX_CMD_LEN - 2));
// ignore module name for service module
if (m_module->getID() == srv::MODID)
{
entry->klkmodnameused = 0;
size_t j = 0;
for (j = 0; j < AST_MAX_CMD_LEN - 1; j++)
{
if (j >= cmds.size())
break;
entry->cmda[j] =
Factory::instance()->getGarbage()->allocData(cmds[j]);
}
entry->cmda[j] = NULL;
}
else
{
/*! Null terminated list of the words of the command */
const std::string modname = m_module->getName();
entry->cmda[0] =
Factory::instance()->getGarbage()->allocData(modname);
size_t j = 1;
for (j = 1; j < AST_MAX_CMD_LEN - 1; j++)
{
if (j > cmds.size())
break;
entry->cmda[j] =
Factory::instance()->getGarbage()->allocData(cmds[j - 1]);
}
entry->cmda[j] = NULL;
}
/*! Handler for the command (fd for output, # of arguments,
argument list).
Returns RESULT_SHOWUSAGE for improper arguments */
entry->handler = handle_module;
/*! Summary of the command (< 60 characters) */
entry->summary =
Factory::instance()->getGarbage()->allocData((*i)->getSummary());
/*! Detailed usage information */
entry->usage =
Factory::instance()->getGarbage()->allocData((*i)->getUsage());
/*! Generate a list of possible completions for a given word */
//char *(*generator)(char *line, char *word, int pos, int state);
entry->generator = handle_complete;
/*! For linking */
entry->next = NULL;
/*! For keeping track of usage */
entry->inuse = 0;
// KLK specific data
entry->klkname =
Factory::instance()->getGarbage()->allocData((*i)->getName());
entry->klkmsgid =
Factory::instance()->getGarbage()->allocData((*i)->getMessageID());
m_entries.push_back(entry);
ast_cli_register(entry);
}
#if 0
// linking
for (EntryList::iterator i = m_entries.begin(); i != m_entries.end(); i++)
{
// do linking
EntryList::iterator next = i;
next++;
if (next != m_entries.end())
{
(*i)->next = *next;
}
}
#endif
}
