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Commit 8cc55922 authored by kra568's avatar kra568
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[FIX] minor fixes

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run_test.sh
+ 1
− 1
View file @ 8cc55922
#!/bin/sh #!/bin/sh
export OMP_NUM_THREADS=1 export OMP_NUM_THREADS=1
./build_scripts/compile_salomon.sh #./build_scripts/compile_salomon.sh
source ./build_scripts/load_salomon_modules.inc source ./build_scripts/load_salomon_modules.inc
cd ./build/examples cd ./build/examples
mpirun -np 1 ./dev_sandbox mpirun -np 1 ./dev_sandbox
......
src/DataSet/DataSet.cpp
+ 6
− 1
View file @ 8cc55922
...@@ -158,7 +158,12 @@ namespace lib4neuro { ...@@ -158,7 +158,12 @@ namespace lib4neuro {
this->n_elements = this->data.size(); this->n_elements = this->data.size();
COUT_INFO("Shuffling finished!"); COUT_INFO("Shuffling finished!");
std::cout << "MPI[" << lib4neuro::mpi_rank << "] -> " << this->n_elements << " data entries" << std::endl; for( int pi = 0; pi < lib4neuro::mpi_nranks; ++pi ){
if( lib4neuro::mpi_rank == pi ){
std::cout << "MPI[" << lib4neuro::mpi_rank << "] -> " << this->n_elements << " data entries" << std::endl;
}
MPI_Barrier( lib4neuro::mpi_active_comm );
}
} }
void DataSet::MPI_gather_data_on_master( ){ void DataSet::MPI_gather_data_on_master( ){
......
src/ErrorFunction/ErrorFunctions.cpp
+ 7
− 0
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...@@ -379,6 +379,13 @@ namespace lib4neuro { ...@@ -379,6 +379,13 @@ namespace lib4neuro {
} }
double result = error / (this->rescale_error?n_elements:1.0); double result = error / (this->rescale_error?n_elements:1.0);
// for( int pi = 0; pi < lib4neuro::mpi_nranks; ++pi ){
// if( lib4neuro::mpi_rank == pi ){
// std::cout <<std::setprecision(6) << "[" << pi << "]eval: " << result << std::endl;
// }
// MPI_Barrier( lib4neuro::mpi_active_comm );
// }
if (verbose) { if (verbose) {
COUT_DEBUG("MSE = " << result << std::endl); COUT_DEBUG("MSE = " << result << std::endl);
......
src/LearningMethods/LevenbergMarquardt.cpp
+ 271
− 26
View file @ 8cc55922
...@@ -42,7 +42,213 @@ struct lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl { ...@@ -42,7 +42,213 @@ struct lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl {
arma::Mat<double>& H, arma::Mat<double>& H,
arma::Col<double>& rhs, arma::Col<double>& rhs,
size_t data_subset_size); size_t data_subset_size);
size_t solve_system_CG(
const arma::Mat<double>& A,
const arma::Col<double>& rhs,
arma::Col<double>& sol
);
}; };
//*****************************************************************
// Iterative template routine -- GMRES
//
// GMRES solves the unsymmetric linear system Ax = b using the
// Generalized Minimum Residual method
//
// GMRES follows the algorithm described on p. 20 of the
// SIAM Templates book.
//
// The return value indicates convergence within max_iter (input)
// iterations (0), or no convergence within max_iter iterations (1).
//
// Upon successful return, output arguments have the following values:
//
// x -- approximate solution to Ax = b
// max_iter -- the number of iterations performed before the
// tolerance was reached
// tol -- the residual after the final iteration
//
//*****************************************************************
//template < class Matrix, class Vector >
//void
//Update(Vector &x, int k, Matrix &h, Vector &s, Vector v[])
//{
// Vector y(s);
//
// // Backsolve:
// for (int i = k; i >= 0; i--) {
// y(i) /= h(i,i);
// for (int j = i - 1; j >= 0; j--)
// y(j) -= h(j,i) * y(i);
// }
//
// for (int j = 0; j <= k; j++)
// x += v[j] * y(j);
//}
//
//
//template < class Real >
//Real
//abs(Real x)
//{
// return (x > 0 ? x : -x);
//}
//
//
//template < class Operator, class Vector, class Preconditioner,
// class Matrix, class Real >
//int
//GMRES(const Operator &A, Vector &x, const Vector &b,
// const Preconditioner &M, Matrix &H, int &m, int &max_iter,
// Real &tol)
//{
// Real resid;
// int i, j = 1, k;
// Vector s(m+1), cs(m+1), sn(m+1), w;
//
// Real normb = norm(M.solve(b));
// Vector r = M.solve(b - A * x);
// Real beta = norm(r);
//
// if (normb == 0.0)
// normb = 1;
//
// if ((resid = norm(r) / normb) <= tol) {
// tol = resid;
// max_iter = 0;
// return 0;
// }
//
// Vector *v = new Vector[m+1];
//
// while (j <= max_iter) {
// v[0] = r * (1.0 / beta); // ??? r / beta
// s = 0.0;
// s(0) = beta;
//
// for (i = 0; i < m && j <= max_iter; i++, j++) {
// w = M.solve(A * v[i]);
// for (k = 0; k <= i; k++) {
// H(k, i) = dot(w, v[k]);
// w -= H(k, i) * v[k];
// }
// H(i+1, i) = norm(w);
// v[i+1] = w * (1.0 / H(i+1, i)); // ??? w / H(i+1, i)
//
// for (k = 0; k < i; k++)
// ApplyPlaneRotation(H(k,i), H(k+1,i), cs(k), sn(k));
//
// GeneratePlaneRotation(H(i,i), H(i+1,i), cs(i), sn(i));
// ApplyPlaneRotation(H(i,i), H(i+1,i), cs(i), sn(i));
// ApplyPlaneRotation(s(i), s(i+1), cs(i), sn(i));
//
// if ((resid = abs(s(i+1)) / normb) < tol) {
// Update(x, i, H, s, v);
// tol = resid;
// max_iter = j;
// delete [] v;
// return 0;
// }
// }
// Update(x, m - 1, H, s, v);
// r = M.solve(b - A * x);
// beta = norm(r);
// if ((resid = beta / normb) < tol) {
// tol = resid;
// max_iter = j;
// delete [] v;
// return 0;
// }
// }
//
// tol = resid;
// delete [] v;
// return 1;
//}
//
//
//#include <math.h>
//
//
//template<class Real>
//void GeneratePlaneRotation(Real &dx, Real &dy, Real &cs, Real &sn)
//{
// if (dy == 0.0) {
// cs = 1.0;
// sn = 0.0;
// } else if (abs(dy) > abs(dx)) {
// Real temp = dx / dy;
// sn = 1.0 / sqrt( 1.0 + temp*temp );
// cs = temp * sn;
// } else {
// Real temp = dy / dx;
// cs = 1.0 / sqrt( 1.0 + temp*temp );
// sn = temp * cs;
// }
//}
//
//
//template<class Real>
//void ApplyPlaneRotation(Real &dx, Real &dy, Real &cs, Real &sn)
//{
// Real temp = cs * dx + sn * dy;
// dy = -sn * dx + cs * dy;
// dx = temp;
//}
size_t lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl::solve_system_CG(
const arma::Mat<double>& A,
const arma::Col<double>& rhs,
arma::Col<double>& sol
){
const double zero = 1e-32;
const double prec = 1e-6;
const auto n = rhs.n_elem;
size_t iter = 1;
if( sol.n_elem != n ){
sol.resize( n );
}
arma::Col<double> r( n );
r = rhs - A * sol;
double norm_r = arma::dot(r, r);
double norm_r_prev;
if ( norm_r < zero ) {
return 0;
}
arma::Col<double> p( n );
p = r;
arma::Col<double> Ap( n );
double alpha, beta;
while( true ){
++iter;
norm_r_prev = norm_r;
Ap = A * p;
alpha = norm_r_prev / arma::dot(p, Ap);
sol += alpha * p;
r -= alpha * Ap;
norm_r = arma::dot(r, r);
if ( norm_r < zero || iter >= n) {
break;
}
beta = norm_r / norm_r_prev;
p = r + beta * p;
}
return iter;
}
void lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl::get_jacobian_and_rhs( void lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl::get_jacobian_and_rhs(
lib4neuro::ErrorFunction& ef, lib4neuro::ErrorFunction& ef,
...@@ -58,7 +264,7 @@ void lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl::get_jacobian_and_rhs ...@@ -58,7 +264,7 @@ void lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl::get_jacobian_and_rhs
if (data_subset_size < ef.get_n_data_set()) { if (data_subset_size < ef.get_n_data_set()) {
ef.divide_data_train_test((double) data_subset_size / (double) ef.get_n_data_set()); ef.divide_data_train_test((double) data_subset_size / (double) ef.get_n_data_set());
} }
size_t n_parameters = ef.get_dimension( ); size_t n_parameters = ef.get_dimension( );
if( H.n_rows != n_parameters || H.n_cols != n_parameters ){ if( H.n_rows != n_parameters || H.n_cols != n_parameters ){
H.reshape( n_parameters, n_parameters ); H.reshape( n_parameters, n_parameters );
...@@ -68,7 +274,7 @@ void lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl::get_jacobian_and_rhs ...@@ -68,7 +274,7 @@ void lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl::get_jacobian_and_rhs
} }
H.fill(0.0); H.fill(0.0);
rhs.fill(0.0); rhs.fill(0.0);
for( size_t entry_idx = 0; entry_idx < ef.get_n_data_set( ); ++entry_idx ){ for( size_t entry_idx = 0; entry_idx < ef.get_n_data_set( ); ++entry_idx ){
ef.add_to_hessian_and_rhs_single( H, rhs, entry_idx ); ef.add_to_hessian_and_rhs_single( H, rhs, entry_idx );
} }
...@@ -110,13 +316,12 @@ namespace lib4neuro { ...@@ -110,13 +316,12 @@ namespace lib4neuro {
void LevenbergMarquardt::optimize(lib4neuro::ErrorFunction& ef, void LevenbergMarquardt::optimize(lib4neuro::ErrorFunction& ef,
lib4neuro::LM_UPDATE_TYPE update_type, lib4neuro::LM_UPDATE_TYPE update_type,
std::ofstream* ofs) { std::ofstream* ofs) {
double current_err = ef.eval(); double current_err = ef.eval( );
COUT_INFO( COUT_INFO(
"Finding a solution via the Levenberg-Marquardt method... Starting error: " << current_err << std::endl); "Finding a solution via the Levenberg-Marquardt method... Starting error: " << current_err);
if (ofs && ofs->is_open() && lib4neuro::mpi_rank == 0) { if (ofs && ofs->is_open() && lib4neuro::mpi_rank == 0) {
*ofs << "Finding a solution via a Levenberg-Marquardt method... Starting error: " << current_err *ofs << "Finding a solution via a Levenberg-Marquardt method... Starting error: " << current_err;
<< std::endl;
} }
size_t n_parameters = ef.get_dimension(); size_t n_parameters = ef.get_dimension();
...@@ -147,20 +352,52 @@ namespace lib4neuro { ...@@ -147,20 +352,52 @@ namespace lib4neuro {
// Solver iterations // // Solver iterations //
//-------------------// //-------------------//
size_t iter_counter = 0; size_t iter_counter = 0;
size_t update_counter = 0;
double update_time = 0.0;
double update_sync_rhs = 0.0;
double update_sync_H = 0.0;
double compute_time = 0.0;
double compute_sync = 0.0;
double total_time = -MPI_Wtime( );
size_t ninner_iters = 0;
do { do {
iter_counter++;
// COUT_INFO("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: " // COUT_INFO("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
// << gradient_norm << ", Direction norm: " << update_norm << "\r"); // << gradient_norm << ", Direction norm: " << update_norm << "\r");
if (update_J) { if (update_J) {
update_counter++;
/* Get Jacobian matrix */ /* Get Jacobian matrix */
update_time -= MPI_Wtime( );
this->p_impl->get_jacobian_and_rhs(ef, this->p_impl->get_jacobian_and_rhs(ef,
H, H,
rhs, rhs,
this->p_impl->batch_size); this->p_impl->batch_size);
update_time += MPI_Wtime( );
update_sync_rhs -= MPI_Wtime( );
MPI_Allreduce(
MPI_IN_PLACE,
rhs.begin(),
rhs.n_elem,
MPI_DOUBLE,
MPI_SUM,
lib4neuro::mpi_active_comm
);
update_sync_rhs += MPI_Wtime( );
update_sync_H -= MPI_Wtime( );
MPI_Allreduce(
MPI_IN_PLACE,
H.begin(),
H.n_elem,
MPI_DOUBLE,
MPI_SUM,
lib4neuro::mpi_active_comm
);
update_sync_H += MPI_Wtime( );
gradient_norm = 0; gradient_norm = 0;
// TODO parallelize with OpenMP? // TODO parallelize with OpenMP?
for (size_t ci = 0; ci < n_parameters; ++ci) { for (size_t ci = 0; ci < n_parameters; ++ci) {
mem_double = rhs[ci]; mem_double = rhs[ci];
...@@ -169,16 +406,6 @@ namespace lib4neuro { ...@@ -169,16 +406,6 @@ namespace lib4neuro {
} }
gradient_norm = std::sqrt(gradient_norm) / n_parameters; gradient_norm = std::sqrt(gradient_norm) / n_parameters;
jacobian_norm = 0;
for (size_t ri = 0; ri < n_parameters; ++ri) {
for (size_t ci = 0; ci < n_parameters; ++ci) {
jacobian_norm += H.at(ri,
ci) * H.at(ri,
ci);
}
}
jacobian_norm = std::sqrt(jacobian_norm);
/* Evaluate the error before updating parameters */ /* Evaluate the error before updating parameters */
prev_err = ef.eval(); prev_err = ef.eval();
} }
...@@ -192,12 +419,22 @@ namespace lib4neuro { ...@@ -192,12 +419,22 @@ namespace lib4neuro {
/* Compute the update vector */ /* Compute the update vector */
update = arma::solve(H_new, // update = arma::solve(
rhs); // H_new,
MPI_Allreduce( MPI_IN_PLACE, &update[0], update.size(), MPI_DOUBLE, MPI_SUM, lib4neuro::mpi_active_comm ); // rhs,
for( size_t ii = 0; ii < update.size(); ++ii ){ // arma::solve_opts::flag_fast + arma::solve_opts::flag_allow_ugly + arma::solve_opts::flag_no_band
update.at( ii ) /= lib4neuro::mpi_nranks; // );
} compute_time -= MPI_Wtime( );
update = arma::solve(
H_new,
rhs
);
// ninner_iters += this->p_impl->solve_system_CG( H, rhs, update );
compute_time += MPI_Wtime( );
// compute_sync -= MPI_Wtime( );
// MPI_Allreduce( MPI_IN_PLACE, &update[0], update.size(), MPI_DOUBLE, MPI_SUM, lib4neuro::mpi_active_comm );
// compute_sync += MPI_Wtime( );
/* Compute the error after update of parameters */ /* Compute the error after update of parameters */
update_norm = 0.0; update_norm = 0.0;
...@@ -242,7 +479,10 @@ namespace lib4neuro { ...@@ -242,7 +479,10 @@ namespace lib4neuro {
*ofs << "Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: " *ofs << "Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
<< gradient_norm << ", Direction norm: " << update_norm << std::endl; << gradient_norm << ", Direction norm: " << update_norm << std::endl;
} }
} while (iter_counter++ < this->p_impl->maximum_niters && (update_norm > this->p_impl->tolerance_parameters)); } while (iter_counter < this->p_impl->maximum_niters);
total_time += MPI_Wtime( );
COUT_DEBUG("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: " COUT_DEBUG("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
<< gradient_norm << ", Direction norm: " << update_norm << std::endl); << gradient_norm << ", Direction norm: " << update_norm << std::endl);
if (ofs && ofs->is_open() && lib4neuro::mpi_rank == 0) { if (ofs && ofs->is_open() && lib4neuro::mpi_rank == 0) {
...@@ -261,7 +501,12 @@ namespace lib4neuro { ...@@ -261,7 +501,12 @@ namespace lib4neuro {
ef.set_parameters(this->optimal_parameters); ef.set_parameters(this->optimal_parameters);
COUT_INFO("Finished in " << iter_counter << " iterations with error: " << current_err << " and gradient norm: " << gradient_norm << " and update norm: " << update_norm << std::endl); // COUT_INFO("Finished in " << iter_counter << " iterations with error: " << current_err << ", gradient norm: " << gradient_norm << ", update norm: " << update_norm << std::endl);
COUT_INFO( "Finished in " << total_time << " [s], with " << update_counter << " updates of hessian, in " << iter_counter << " outer iterations, in " << ninner_iters << " inner iterations" );
COUT_INFO( " error " << current_err );
COUT_INFO( " update norm " << update_norm );
COUT_INFO( " calculation of Hessian " << 100*(update_time + update_sync_rhs + update_sync_H) / (update_time + update_sync_rhs + update_sync_H + compute_time + compute_sync) << " [%], time per one calculation " << (update_time + update_sync_rhs + update_sync_H) / update_counter << " [s]" );
COUT_INFO( " calculation of direction " << 100*(compute_time + compute_sync) / (update_time + update_sync_rhs + update_sync_H + compute_time + compute_sync) << " [%], time per one iteration " << (compute_time + compute_sync) / iter_counter << " [s]" );
} }
LevenbergMarquardt::~LevenbergMarquardt() = default; LevenbergMarquardt::~LevenbergMarquardt() = default;
......
src/Network/NeuralNetwork.cpp
+ 9
− 7
View file @ 8cc55922
...@@ -85,7 +85,7 @@ namespace lib4neuro { ...@@ -85,7 +85,7 @@ namespace lib4neuro {
if (this->output_neuron_indices.size() != output.size()) { if (this->output_neuron_indices.size() != output.size()) {
THROW_INVALID_ARGUMENT_ERROR("Data output size != Network output size"); THROW_INVALID_ARGUMENT_ERROR("Data output size != Network output size");
} }
lib4neuro::network_evaluation_counter++; lib4neuro::network_evaluation_counter++;
double potential, bias; double potential, bias;
...@@ -274,9 +274,9 @@ namespace lib4neuro { ...@@ -274,9 +274,9 @@ namespace lib4neuro {
if (this->output_neuron_indices.size() != output.size()) { if (this->output_neuron_indices.size() != output.size()) {
THROW_INVALID_ARGUMENT_ERROR("Data output size != Network output size"); THROW_INVALID_ARGUMENT_ERROR("Data output size != Network output size");
} }
lib4neuro::network_evaluation_counter++; lib4neuro::network_evaluation_counter++;
double potential, bias; double potential, bias;
int bias_idx; int bias_idx;
...@@ -360,7 +360,7 @@ namespace lib4neuro { ...@@ -360,7 +360,7 @@ namespace lib4neuro {
neuron_idx = current_layer->at(i); neuron_idx = current_layer->at(i);
scaling_backprog[neuron_idx] = error_derivative[i] * error_scaling; scaling_backprog[neuron_idx] = error_derivative[i] * error_scaling;
} }
/* we iterate through all the layers in reverse order and calculate partial derivatives scaled correspondingly */ /* we iterate through all the layers in reverse order and calculate partial derivatives scaled correspondingly */
for (size_t j = this->neuron_layers_feedforward.size(); j > 0; --j) { for (size_t j = this->neuron_layers_feedforward.size(); j > 0; --j) {
...@@ -415,7 +415,7 @@ namespace lib4neuro { ...@@ -415,7 +415,7 @@ namespace lib4neuro {
::std::vector<double>& gradient) { ::std::vector<double>& gradient) {
lib4neuro::network_backpropagation_counter++; lib4neuro::network_backpropagation_counter++;
::std::vector<double> scaling_backprog(this->get_n_neurons()); ::std::vector<double> scaling_backprog(this->get_n_neurons());
::std::fill(scaling_backprog.begin(), ::std::fill(scaling_backprog.begin(),
scaling_backprog.end(), scaling_backprog.end(),
...@@ -501,7 +501,8 @@ namespace lib4neuro { ...@@ -501,7 +501,8 @@ namespace lib4neuro {
void NeuralNetwork::randomize_weights() { void NeuralNetwork::randomize_weights() {
if( lib4neuro::mpi_rank == 0 ){ if( lib4neuro::mpi_rank == 0 ){
boost::random::mt19937 gen(std::time(0)); boost::random::mt19937 gen(std::time(0));
// boost::random::mt19937 gen(0);
// Init weight guess ("optimal" for logistic activation functions) // Init weight guess ("optimal" for logistic activation functions)
double r = 4 * sqrt(6. / (this->connection_weights.size())); double r = 4 * sqrt(6. / (this->connection_weights.size()));
...@@ -525,7 +526,8 @@ namespace lib4neuro { ...@@ -525,7 +526,8 @@ namespace lib4neuro {
void NeuralNetwork::randomize_biases() { void NeuralNetwork::randomize_biases() {
if( lib4neuro::mpi_rank == 0 ){ if( lib4neuro::mpi_rank == 0 ){
boost::random::mt19937 gen(std::time(0)); boost::random::mt19937 gen(std::time(0));
// boost::random::mt19937 gen(0);
// Init weight guess ("optimal" for logistic activation functions) // Init weight guess ("optimal" for logistic activation functions)
boost::random::uniform_real_distribution<> dist(-1, boost::random::uniform_real_distribution<> dist(-1,
......
src/SymmetryFunction/SymmetryFunction.cpp
+ 13
− 0
View file @ 8cc55922
...@@ -6,6 +6,8 @@ ...@@ -6,6 +6,8 @@
#include <cmath> #include <cmath>
#include "../exceptions.h" #include "../exceptions.h"
#include "../message.h"
#include "SymmetryFunction.h" #include "SymmetryFunction.h"
#include "SymmetryFunctionSerialization.h" #include "SymmetryFunctionSerialization.h"
...@@ -145,17 +147,28 @@ double lib4neuro::G2::eval(unsigned int particle_ind, ...@@ -145,17 +147,28 @@ double lib4neuro::G2::eval(unsigned int particle_ind,
double extension = this->parameters.at(SYMMETRY_FUNCTION_PARAMETER::EXTENSION); double extension = this->parameters.at(SYMMETRY_FUNCTION_PARAMETER::EXTENSION);
double shift = this->parameters.at(SYMMETRY_FUNCTION_PARAMETER::SHIFT); double shift = this->parameters.at(SYMMETRY_FUNCTION_PARAMETER::SHIFT);
// std::cout << "particle " << particle_ind << "(";
// for( auto el: cartesian_coord.at(particle_ind).second ){
// std::cout << el << " ";
// }
// std::cout << ")";
for(size_t i = 0; i < cartesian_coord.size(); i++) { for(size_t i = 0; i < cartesian_coord.size(); i++) {
if(i == particle_ind) { if(i == particle_ind) {
continue; continue;
} }
// std::cout << " comparing with particle " << i << "(";
// for( auto el: cartesian_coord.at(i).second ){
// std::cout << el << " ";
// }
/* Compute Euclid distance of i-th particle with the others */ /* Compute Euclid distance of i-th particle with the others */
distance = this->euclid_distance(cartesian_coord.at(particle_ind).second, cartesian_coord.at(i).second); distance = this->euclid_distance(cartesian_coord.at(particle_ind).second, cartesian_coord.at(i).second);
// std::cout << ") distance = " << distance << " partial contribution " << std::exp(-extension * (distance - shift) * (distance - shift)) * this->cutoff_func->eval(distance) << std::endl;
/* Call cutoff function */ /* Call cutoff function */
sum += std::exp(-extension * (distance - shift) * (distance - shift)) * this->cutoff_func->eval(distance); sum += std::exp(-extension * (distance - shift) * (distance - shift)) * this->cutoff_func->eval(distance);
} }
// COUT_INFO( "result " << sum);
return sum; return sum;
} }
......
src/message.h
+ 1
− 0
View file @ 8cc55922
...@@ -6,6 +6,7 @@ ...@@ -6,6 +6,7 @@
#include <cassert> #include <cassert>
#include <iomanip> #include <iomanip>
#include <iostream>
#define COL_WIDTH 20 #define COL_WIDTH 20
#define R_ALIGN std::setw(COL_WIDTH) << std::right #define R_ALIGN std::setw(COL_WIDTH) << std::right
......
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