ublas::vector<int> decodeBPSK(const ublas::vector<double> &rx) {
ublas::vector<int> vHat(rx.size());
for (unsigned int i = 0; i < rx.size(); i++) {
vHat(i) = 0.5 * (sign(rx(i)) + 1);
}
return vHat;
}
/**
* \f$U+=V \f$ where U and V are type
* uvlas::vector<T> and you
* want to specify WHERE to add
* the DenseVector<T> V
*/
void addVector ( const ublas::vector<value_type>& V,
const std::vector<size_type>& dof_indices )
{
FEELPP_ASSERT ( V.size() == dof_indices.size() ).error( "invalid dof indices" );
for ( size_type i=0; i<V.size(); i++ )
this->add ( dof_indices[i], V( i ) );
}
double biterr(const ublas::vector<int> &u, const ublas::vector<int> &v) {
int numErr = 0;
for (unsigned int i = 0; i < u.size(); i++) {
if (u(i) != v(i)) {
numErr++;
}
}
return static_cast<double>(numErr) / u.size();
}
ublas::vector<T> cross( const ublas::vector<T> &a, const ublas::vector<T> &b )
{
BOOST_ASSERT(a.size() == 3);
BOOST_ASSERT(b.size() == 3);
ublas::vector<T> result(3);
result(0) = a(1) * b(2) - a(2) * b(1);
result(1) = a(2) * b(0) - a(0) * b(2);
result(2) = a(0) * b(1) - a(1) * b(0);
return result;
}
void printVector(const ublas::vector<T> &u) {
for (unsigned int i = 0; i < u.size(); i++) {
std::cout << u(i);
if (i + 1 < u.size()) {
std::cout << ", ";
}
}
std::cout << std::endl;
}
ublas::vector<int> mod2(const ublas::vector<int> &u) {
ublas::vector<int> v(u.size());
for (unsigned int i = 0; i < u.size(); i++) {
v(i) = u(i) % 2;
// modulus result can be negative if u(i) negative, depending on implementation
if (v(i) < 0) {
v(i) += 2;
}
}
return v;
}
ScalarType diff(ublas::vector<ScalarType> & v1, viennacl::vector<ScalarType> & v2)
{
ublas::vector<ScalarType> v2_cpu(v2.size());
viennacl::backend::finish();
viennacl::copy(v2.begin(), v2.end(), v2_cpu.begin());
for (unsigned int i=0;i<v1.size(); ++i)
{
if ( std::max( std::fabs(v2_cpu[i]), std::fabs(v1[i]) ) > 0 )
{
//if (std::max( std::fabs(v2_cpu[i]), std::fabs(v1[i]) ) < 1e-10 ) //absolute tolerance (avoid round-off issues)
// v2_cpu[i] = 0;
//else
v2_cpu[i] = std::fabs(v2_cpu[i] - v1[i]) / std::max( std::fabs(v2_cpu[i]), std::fabs(v1[i]) );
}
else
v2_cpu[i] = 0.0;
if (v2_cpu[i] > 0.0001)
{
//std::cout << "Neighbor: " << i-1 << ": " << v1[i-1] << " vs. " << v2_cpu[i-1] << std::endl;
std::cout << "Error at entry " << i << ": " << v1[i] << " vs. " << v2_cpu[i] << std::endl;
//std::cout << "Neighbor: " << i+1 << ": " << v1[i+1] << " vs. " << v2_cpu[i+1] << std::endl;
exit(EXIT_FAILURE);
}
}
return norm_inf(v2_cpu);
}
void
kronecker()
{
for (size_t i = 0; i < expectation_.size(); ++i)
for (size_t j = 0; j <= i; ++j)
covMatrix_(i, j) = expectation_(i) * expectation_(j);
}
ublas::vector<double> dirichlet_rnd(const ublas::vector<int>& nz) {
//! Returns sample from a Dirichlet distribution with dimension k = len(nz).
int k = nz.size();
ublas::vector<double> A(k);
for (int i=0; i<k; ++i) {
A(i) = Rmath::rgamma(1+nz(i), 1);
}
return A/sum(A);
}
ScalarType diff ( ublas::vector<ScalarType> & v1, viennacl::vector<ScalarType,Alignment> & v2 ) {
ublas::vector<ScalarType> v2_cpu ( v2.size() );
viennacl::copy( v2.begin(), v2.end(), v2_cpu.begin() );
for ( unsigned int i=0; i<v1.size(); ++i ) {
if ( std::max ( fabs ( v2_cpu[i] ), fabs ( v1[i] ) ) > 0 )
v2_cpu[i] = fabs ( v2_cpu[i] - v1[i] ) / std::max ( fabs ( v2_cpu[i] ), fabs ( v1[i] ) );
else
v2_cpu[i] = 0.0;
}
return norm_inf ( v2_cpu );
}
/** returns the first n elites
* @param p_start start value of the elite values
* @param p_end end value of the elite values ([start, end) elite elements must be created)
* @param p_population const reference to the population
* @param p_rankIndex rank index (first index has the position of the population element, that has the smalles fitness value)
* @param p_elite vector with elite individual
**/
template<typename T, typename L> inline void bestof<T,L>::getElite( const std::size_t& p_start, const std::size_t& p_end, const std::vector< boost::shared_ptr< individual::individual<L> > >& p_population, const ublas::vector<T>&, const ublas::vector<std::size_t>& p_rankIndex, const ublas::vector<std::size_t>&, std::vector< boost::shared_ptr< individual::individual<L> > >& p_elite )
{
const std::size_t l_end = std::min(p_end, m_number);
std::size_t n = p_start;
for(std::size_t i=p_start; i < p_end; ++i) {
p_elite.push_back( p_population[p_rankIndex[p_rankIndex.size()-1-n]] );
n++;
if (n >= l_end)
n = p_start;
}
}
ScalarType diff(ublas::vector<ScalarType> const & v1, VCLVectorType const & v2)
{
ublas::vector<ScalarType> v2_cpu(v2.size());
viennacl::backend::finish(); //workaround for a bug in APP SDK 2.7 on Trinity APUs (with Catalyst 12.8)
viennacl::copy(v2.begin(), v2.end(), v2_cpu.begin());
for (unsigned int i=0;i<v1.size(); ++i)
{
if (v2_cpu[i] != v1[i])
return 1;
}
return 0;
}
Serialization SDPSeriationGen::Impl::readout_connected(ublas::vector<double>& x,const AdjMat::AdjMatT& adj)
{
unsigned int n = x.size();
// tricky: make sure x > 0 at all times.
x += ublas::scalar_vector<double>(n, 1 - (*min_element(x.begin(),x.end())));
Serialization::RankT ranks(n);
std::vector<bool> done(n,false);
// find highest component of x
ublas::vector<double>::iterator it = BEST_ELEM(x);
int idx = std::distance(x.begin(),it);
L("Determine Actual Path through Graph.\n");
for(unsigned int i=0;i<n;i++){
// mark as visited
ranks[i] = idx;
done[idx] = true;
// make sure we do not visit again
*it = 0.0;
// [m,i] = max(x.*A(:,i));
ublas::vector<double> adjcol = ublas::column(adj,idx);
for(unsigned int j=0;j<adjcol.size();j++)
if(adjcol[j]>0.00001) adjcol[j]=1;
ublas::vector<double> tmp = ublas::element_prod(x,adjcol);
it = BEST_ELEM(tmp);
if( *it < 0.000000001 && i<n-1)
{
// if *it small, then either x[it] visited or adj(old_idx,idx) not connected
// --> we reached a dead end, find next best start point
it = BEST_ELEM(x);
idx = std::distance(x.begin(),it);
}else{
idx = std::distance(tmp.begin(),it);
// point it in x, not tmp:
it = x.begin();
it += idx;
}
}
return Serialization(ranks);
}
ScalarType diff(ublas::vector<ScalarType> const & v1, ViennaCLVectorType const & vcl_vec)
{
ublas::vector<ScalarType> v2_cpu(vcl_vec.size());
viennacl::backend::finish();
viennacl::copy(vcl_vec, v2_cpu);
for (unsigned int i=0;i<v1.size(); ++i)
{
if ( std::max( std::fabs(v2_cpu[i]), std::fabs(v1[i]) ) > 0 )
v2_cpu[i] = std::fabs(v2_cpu[i] - v1[i]) / std::max( std::fabs(v2_cpu[i]), std::fabs(v1[i]) );
else
v2_cpu[i] = 0.0;
}
return ublas::norm_inf(v2_cpu);
}
ScalarType diff(ublas::vector<ScalarType> & v1, viennacl::vector<ScalarType> & v2)
{
ublas::vector<ScalarType> v2_cpu(v2.size());
viennacl::backend::finish(); //workaround for a bug in APP SDK 2.7 on Trinity APUs (with Catalyst 12.8)
viennacl::copy(v2.begin(), v2.end(), v2_cpu.begin());
for (std::size_t i=0;i<v1.size(); ++i)
{
if ( std::max( std::fabs(v2_cpu[i]), std::fabs(v1[i]) ) > 0 )
v2_cpu[i] = std::fabs(v2_cpu[i] - v1[i]) / std::max( std::fabs(v2_cpu[i]), std::fabs(v1[i]) );
else
v2_cpu[i] = 0.0;
}
return norm_inf(v2_cpu);
}
void vector2image_2d(const ublas::fixed_vector<size_t, 2> size, const ublas::vector< float_t > &vector, float_accessor_t & image)
{
size_t n_x_vertices = size(0);
size_t n_y_vertices = size(1);
if(vector.size() != n_x_vertices * n_y_vertices)
throw Exception("Exception: vector of wrong length in vector2image().");
if(image.size() != size)
throw Exception("Exception: image of wrong size in vector2image().");
for(size_t i = 0; i < n_x_vertices; i++)
for(size_t j = 0; j < n_y_vertices; ++j)
{
size_t vertex_index = i + j * n_x_vertices;
image[ublas::fixed_vector<size_t, 2>(i, j)] = vector(vertex_index);
}
}
void vector2vector_image_2d(const ublas::fixed_vector<size_t, 2> size, const ublas::vector< float_t > &vector, vector_image_accessor_t & vector_image)
{
const size_t n_x_vertices = size(0);
const size_t n_y_vertices = size(1);
const size_t dimension = vector_image_accessor_t::data_t::dimension;
if(vector.size() != n_x_vertices * n_y_vertices * dimension)
throw Exception("Exception: tla::vector of wrong length in vector2image().");
if(vector_image.size() != size)
throw Exception("Exception: vector_image of wrong size in vector2vector_image().");
for(size_t i = 0; i < n_x_vertices; i++)
for(size_t j = 0; j < n_y_vertices; ++j)
for(size_t k = 0; k < dimension; ++k)
{
size_t vertex_index = dimension * ( i + j * n_x_vertices ) + k;
vector_image[ublas::fixed_vector<size_t, 2>(i, j)](k) = vector(vertex_index);
}
}
void learn(const database<T, bool> &data,
ublas::vector<double> vec_sample_weights)
{
ASSERT(data.patterns.size2() == data.targets.size(), "");
_weak_classifiers.clear();
_¦Á.clear();
for (size_t k = 0; k < classifier_size(); ++k){
auto classifier = _master_classifier.clone();
auto sum = ublas::sum(vec_sample_weights);
for (auto &e:vec_sample_weights){
e /= sum;
}
classifier.learn(data, vec_sample_weights);
double eps = 0.0;
ublas::vector<bool> re(data.targets.size());
for (size_t i = 0; i < data.patterns.size2(); ++i){
re[i] = classifier(ublas::column(data.patterns, i));
if (re[i] != (data.targets)[i]){
eps += vec_sample_weights[i];
}
}
#ifdef _CONSOLE
std::cout << k << "Adaboost Classifier error : " << eps << std::endl;
#endif
if (eps >= 0.5) continue;
auto _¦Ák = 0.5*ln((1.0-eps) / (eps+1e-4));
for (size_t i = 0; i < vec_sample_weights.size(); ++i){
if ((data.targets)[i] != re[i])
vec_sample_weights[i] *= exp(_¦Ák);
else
vec_sample_weights[i] *= exp(-_¦Ák);
}
_weak_classifiers.push_back(classifier);
_¦Á.push_back(_¦Ák);
}
}
Serialization SDPSeriationGen::Impl::readout_plain(ublas::vector<double>& x,const AdjMat::AdjMatT& adj)
{
unsigned int n = x.size();
// tricky: make sure x > 0 at all times.
x += ublas::scalar_vector<double>(n, 1 - (*min_element(x.begin(),x.end())));
Serialization::RankT ranks(n);
std::vector<bool> done(n,false);
// find highest component of x
ublas::vector<double>::iterator it = BEST_ELEM(x);
int idx = std::distance(x.begin(),it);
L("Determine Actual Path through Graph.\n");
for(unsigned int i=0;i<n;i++){
ranks[i] = idx;
done[idx] = true;
*it = 0.0;
it = BEST_ELEM(x);
idx = std::distance(x.begin(),it);
}
return Serialization(ranks);
}
int pmols::HJPacker::exploringSearch(ublas::vector<float> &x_, bool change_step) {
std::cout << "EXPLORING SEARCH (with " << (change_step ? "step change" : "no step change") << ")" << std::endl;
std::cout << " —— objective func before iteration: " << totalDist << std::endl;
float sum1, sum2;
int mol_idx, op_num;
float dist0, dist;
bool changed = false;
bool terminate = true;
bool mol_moved;
float max_step_rot = 0, max_step_trans = 0;
float min_step_rot = params.step_alpha, min_step_trans = params.step_x;
std:: cout << " prevTotalDist: " << prevTotalDist << std::endl;
std:: cout << " totalDist: " << totalDist << std::endl;
for (int i = 0; i < x_.size(); ++i) {
// std::cout << "\t——— coord num: " << i << std::endl;
if(i % 6 < 3) {
if(step[i] > max_step_trans)
max_step_trans = step[i];
else if(step[i] < min_step_trans)
min_step_trans = step[i];
}
else {
if (step[i] > max_step_rot)
max_step_rot = step[i];
else if(step[i] < min_step_rot)
min_step_rot = step[i];
}
if(step[i] < eps(i))
continue;
if(terminate)
terminate = false;
mol_idx = i / 6;
op_num = i % 6;
dist0 = cellLinkedLists->MolDist(mol_idx);
// std::cout << "\t\tdist0: " << dist0 << std::endl;
mol_moved = cellLinkedLists->MoveMol(mol_idx, (MOVE_OP)op_num, step[i]);
dist = cellLinkedLists->MolDist(mol_idx);
sum1 = mol_moved ? ( totalDist - dist0 + dist ) : totalDist;
if(mol_moved) {
// std::cout << "\t\tsum1: " << sum1 << std::endl;
// std::cout << "\t\tdist: " << dist << std::endl;
cellLinkedLists->CancelMove();
}
// if(sum1 < 0) {
// std::cout << "\t——— Negative value of sum." << std::endl;
// std::cout << "\t\tcoord number: " << i << std::endl;
// std::cout << "\t\tsum1: " << sum2 << std::endl;
// std::cout << "\t\tsum2: " << sum2 << std::endl;
// std::cout << "\t\tprevTotalSum: " << prevTotalDist << std::endl;
// std::cout << "\t\ttotalSum: " << totalDist << std::endl;
// std::cout << "\t\tdist0: " << dist0 << std::endl;
// std::cout << "\t\tdist: " << dist << std::endl;
// std::cout << "\t\tmol_moved: " << mol_moved << std::endl;
// }
mol_moved = cellLinkedLists->MoveMol(mol_idx, (MOVE_OP)op_num, -step[i]);
dist = cellLinkedLists->MolDist(mol_idx);
sum2 = mol_moved ? ( totalDist - dist0 + dist ) : totalDist;
if(mol_moved) {
// std::cout << "\t\tsum2: " << sum2 << std::endl;
// std::cout << "\t\tdist: " << dist << std::endl;
cellLinkedLists->CancelMove();
}
// if(sum2 < 0) {
// std::cout << "\t——— Negative value of sum." << std::endl;
// std::cout << "\t\tcoord number: " << i << std::endl;
// std::cout << "\t\tsum1: " << sum1 << std::endl;
// std::cout << "\t\tsum2: " << sum2 << std::endl;
// std::cout << "\t\tprevTotalSum: " << prevTotalDist << std::endl;
// std::cout << "\t\ttotalSum: " << totalDist << std::endl;
// std::cout << "\t\tdist0: " << dist0 << std::endl;
// std::cout << "\t\tdist: " << dist << std::endl;
// std::cout << "\t\tmol_moved: " << mol_moved << std::endl;
// }
// if(sum1 < 0 || sum2 < 0)
// std::cout << std::endl;
if (prevTotalDist <= std::min(sum1, sum2) && change_step)
step[i] *= params.step_coefficient;
else {
if(sum1 < std::min(prevTotalDist, sum2)) {
x_[i] += step[i];
cellLinkedLists->MoveMol(mol_idx, (MOVE_OP)op_num, step[i]);
totalDist = sum1;
prevTotalDist = totalDist;
changed = true;
// std::cout << "\t\ttotal sum changed to sum1, current value: " << totalDist << std::endl;
}
else if(sum2 < std::min(prevTotalDist, sum1)) {
x_[i] -= step[i];
cellLinkedLists->MoveMol(mol_idx, (MOVE_OP)op_num, -step[i]);
totalDist = sum2;
prevTotalDist = totalDist;
// std::cout << "\t\ttotal sum changed to sum2, current value: " << totalDist << std::endl;
changed = true;
}
}
// std::cout << std::endl;
}
std::cout << "\tmax rotation step: " << max_step_rot << std::endl;
std::cout << "\tmax translation step: " << max_step_trans << std::endl << std::endl;
std::cout << "\tmin rotation step: " << min_step_rot << std::endl;
std::cout << "\tmin translation step: " << min_step_trans << std::endl;
std::cout << " —— objective func after iteration: " << totalDist << std::endl;
if (terminate)
return -1;
return (int)changed;
}
void initVector(ublas::vector<T> &u, const T data[]) {
for (unsigned int i = 0; i < u.size(); i++) {
u(i) = data[i];
}
}
ublas::vector<int> decodeLogDomainSimple(const ublas::vector<double> &rx, const ublas::matrix<int> &H, const unsigned int iterations) {
// Get matrix dimensions
const unsigned int M = H.size1();
const unsigned int N = H.size2();
// Prior hard-decision
ublas::vector<double> Lci(rx.size());
for (unsigned int i = 0; i < rx.size(); i++) {
Lci(i) = -rx(i);
}
// Initialization
ublas::matrix<double> Lrji(ublas::zero_matrix<double>(M, N));
ublas::matrix<double> Pibetaij(ublas::zero_matrix<double>(M, N));
// Associate the L(ci) matrix with non-zero elements of H
ublas::matrix<double> Lqij(M, N);
for (unsigned int i = 0; i < M; i++) {
ublas::row(Lqij, i) = ublas::element_prod(ublas::row(H, i), Lci);
}
ublas::vector<int> vHat(N);
// Iteration
for (unsigned int n = 0; n < iterations; n++) {
//std::cout << "Iteration : " << n << std::endl;
// Get the sign and magnitude of L(qij)
ublas::matrix<int> alphaij(M, N);
ublas::matrix<double> betaij(M, N);
for (unsigned int i = 0; i < M; i++) {
for (unsigned int j = 0; j < N; j++) {
alphaij(i, j) = sign(Lqij(i, j));
betaij(i, j) = std::abs(Lqij(i, j));
}
}
// Horizontal step
for (unsigned int i = 0; i < M; i++) {
int prodOfalphaij = 1;
for (unsigned int j = 0; j < N; j++) {
if (H(i, j) != 0) {
prodOfalphaij *= alphaij(i, j);
}
}
// Get the minimum of betaij
for (unsigned int j = 0; j < N; j++) {
if (H(i, j) != 0) {
// Minimum of betaij
double minOfBetaij = std::numeric_limits<double>::max();
for (unsigned int k = 0; k < N; k++) {
if (j != k && H(i, k) != 0) {
if (betaij(i, k) < minOfBetaij) {
minOfBetaij = betaij(i, k);
}
}
}
// Multiplication alphaij
// Update L(rji)
Lrji(i, j) = prodOfalphaij * alphaij(i, j) * minOfBetaij;
}
}
}
// Vertical step
for (unsigned int j = 0; j < N; j++) {
double sumOfLrji = 0.0;
for (unsigned int i = 0; i < M; i++) {
if (H(i, j) != 0) {
sumOfLrji += Lrji(i, j);
}
}
for (unsigned int i = 0; i < M; i++) {
if (H(i, j) != 0) {
// Update L(qij) by summation of L(rij)
Lqij(i, j) = Lci(j) + sumOfLrji - Lrji(i, j);
}
}
// Get L(Qij)
double LQi = Lci(j) + sumOfLrji;
// Decode L(Qi)
if (LQi < 0) {
vHat(j) = 1;
} else {
vHat(j) = 0;
}
}
}
return vHat;
}
ublas::vector<int> decodeBitFlipping(const ublas::vector<double> &rx, const ublas::matrix<int> &H, const unsigned int iterations) {
// Get matrix dimensions
const unsigned int M = H.size1();
const unsigned int N = H.size2();
// Prior hard-decision
ublas::vector<int> ci(rx.size());
for (unsigned int i = 0; i < rx.size(); i++) {
ci(i) = 0.5 * (sign(rx(i)) + 1);
}
//std::cout << "ci=" << std::endl;
//printVector<int>(ci);
// Initialization
ublas::matrix<int> rji(ublas::zero_matrix<int>(M, N));
// Associate the ci matrix with non-zero elements of H
ublas::matrix<int> qij(M, N);
for (unsigned int i = 0; i < M; i++) {
ublas::row(qij, i) = ublas::element_prod(ublas::row(H, i), ci);
}
//std::cout << "qij=" << std::endl;
//printMatrix<int>(qij);
ublas::vector<int> vHat(N);
// Iteration
for (unsigned int n = 0; n < iterations; n++) {
//std::cout << "Iteration : " << n << std::endl;
// Horizontal step
for (unsigned int i = 0; i < M; i++) {
int qijSum = 0;
for (unsigned int j = 0; j < N; j++) {
if (H(i, j) != 0) {
qijSum += qij(i, j);
}
}
for (unsigned int j = 0; j < N; j++) {
if (H(i, j) != 0) {
rji(i, j) = (qijSum + qij(i, j)) % 2;
}
}
}
// Vertical step
for (unsigned int j = 0; j < N; j++) {
int rjiNumberOfOnes = 0;
for (unsigned int i = 0; i < M; i++) {
if (rji(i, j) != 0) {
rjiNumberOfOnes++;
}
}
int hNumberOfOnes = 0;
for (unsigned int i = 0; i < M; i++) {
if (H(i, j) != 0) {
hNumberOfOnes++;
}
}
for (unsigned int i = 0; i < M; i++) {
if (H(i, j) != 0) {
// Update qij, set '1' for majority of 1s else '0', excluding i
if (rjiNumberOfOnes + ci(j) >= hNumberOfOnes - rjiNumberOfOnes + rji(i, j)) {
qij(i, j) = 1;
} else {
qij(i, j) = 0;
}
}
}
// Bit decoding
if (rjiNumberOfOnes + ci(j) >= hNumberOfOnes - rjiNumberOfOnes) {
vHat(j) = 1;
} else {
vHat(j) = 0;
}
}
}
return vHat;
}
int main() {
boost::random::mt19937 gen;
gen.seed(static_cast<unsigned int>(std::time(0)));
const unsigned int M = M3;
const unsigned int N = N3;
const unsigned int iterations = 5;
const unsigned int frames = 30;
const double EbN0[EBN0_SIZE] = { -7.0, -6.5, -6.0, -5.5, -5.0, -4.5, -4.0, -3.5, -3.0, -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0 };
double ber0[EBN0_SIZE];
double ber1[EBN0_SIZE];
double ber2[EBN0_SIZE];
ublas::vector<int> fer0(EBN0_SIZE);
ublas::vector<int> fer1(EBN0_SIZE);
ublas::vector<int> fer2(EBN0_SIZE);
ublas::matrix<int> H(M, N);
initMatrix<int>(H, hData3);
// LU matrices
ublas::matrix<int> L(ublas::zero_matrix<int>(M, N - M));
ublas::matrix<int> U(ublas::zero_matrix<int>(M, N - M));
reorderHMatrix(H, L, U);
/*
writeMatrix<int>(H, "H.csv");
writeMatrix<int>(L, "Lpre.csv");
writeMatrix<int>(U, "Upre.csv");
*/
boost::random::uniform_int_distribution<> uniformDistribution(0, 1);
boost::random::normal_distribution<> normalDistribution;
//ublas::matrix<int> C(EBN0_SIZE * frames, M);
/*
ublas::matrix<int> dSource(M, frames);
initMatrix<int>(dSource, dSourceData3);
writeMatrix<int>(dSource, "dSource.csv");
*/
for (unsigned int i = 0; i < EBN0_SIZE; i++) {
//std::cout << "EbN0 : " << i << std::endl;
ber0[i] = 0.0;
ber1[i] = 0.0;
ber2[i] = 0.0;
fer0(i) = 0;
fer1(i) = 0;
fer2(i) = 0;
// Make random data (0/1)
ublas::matrix<int> dSource(M, frames);
for (unsigned int j = 0; j < frames; j++) {
for (unsigned int k = 0; k < M; k++) {
dSource(k, j) = uniformDistribution(gen);
}
}
//std::cout << "dSource=" << std::endl;
//printMatrix<int>(dSource);
for (unsigned int j = 0; j < frames; j++) {
//std::cout << "Frame : " << j << std::endl;
// Encoding message
const ublas::vector<int> c = makeParityCheck(ublas::column(dSource, j), H, L, U);
//ublas::row(C, i * (EBN0_SIZE + 1) + j) = c;
//std::cout << "newH=" << std::endl;
//printMatrix<int>(newH);
//std::cout << "c=" << std::endl;
//printVector<int>(c);
ublas::vector<int> u(c.size() + M);
ublas::subrange(u, 0, c.size()) = c;
ublas::subrange(u, c.size(), c.size() + M) = ublas::column(dSource, j);
//std::cout << "u=" << std::endl;
//printVector<int>(u);
// BPSK modulation
ublas::vector<int> bpskMod(u.size());
for (unsigned int k = 0; k < u.size(); k++) {
bpskMod(k) = 2 * u(k) - 1;
}
//std::cout << "bpskMod=" << std::endl;
//printVector<int>(bpskMod);
// Additional white gaussian noise
double N0 = 1 / (std::exp(EbN0[i] * std::log(10) / 10));
//std::cout << "N0=" << N0 << std::endl;
ublas::vector<double> tx(bpskMod);
for (unsigned int k = 0; k < tx.size(); k++) {
tx(k) += std::sqrt(N0) * normalDistribution(gen);
}
//std::cout << "tx=" << std::endl;
//printVector<double>(tx);
ublas::vector<int> vhat0 = decodeBPSK(tx);
double rat0 = biterr(vhat0, u);
ber0[i] += rat0;
ublas::vector<int> vhat1 = decodeBitFlipping(tx, H, iterations); // newH
//std::cout << "vhat1=" << std::endl;
//printVector<int>(vhat1);
double rat1 = biterr(vhat1, u);
//std::cout << "rat1=" << rat1 << std::endl;
ber1[i] += rat1;
ublas::vector<int> vhat2 = decodeLogDomainSimple(tx, H, iterations);
double rat2 = biterr(vhat2, u);
ber2[i] += rat2;
// Check messages
if (checkMessage(vhat0, H) > 0) {
if (rat0 <= 0.0) {
//std::cout << "False negative: rat0=" << rat0 << std::endl;
}
fer0(i)++;
} else if (rat0 > 0) {
//std::cout << "False positive: rat0=" << rat0 << std::endl;
}
if (checkMessage(vhat1, H) > 0) {
if (rat1 <= 0.0) {
//std::cout << "False negative: rat1=" << rat1 << std::endl;
}
fer1(i)++;
} else if (rat1 > 0) {
//std::cout << "False positive: rat1=" << rat1 << std::endl;
}
if (checkMessage(vhat2, H) > 0) {
if (rat2 <= 0.0) {
//std::cout << "False negative: rat2=" << rat2 << std::endl;
}
fer2(i)++;
} else if (rat2 > 0) {
//std::cout << "False positive: rat2=" << rat2 << std::endl;
}
}
ber0[i] /= frames;
ber1[i] /= frames;
ber2[i] /= frames;
}
std::cout << std::endl;
/*
std::cout << "H=[" << std::endl;
printMatrix<int>(H);
std::cout << "];" << std::endl;
*/
std::cout.precision(4);
std::cout << "EbN0=[";
for (unsigned int i = 0; i < EBN0_SIZE; i++) {
std::cout << EbN0[i] << " ";
}
std::cout << "];" << std::endl;
std::cout << "figure(1);" << std::endl;
std::cout << "ber0=[";
for (unsigned int i = 0; i < EBN0_SIZE; i++) {
std::cout << ber0[i] << " ";
}
std::cout << "];" << std::endl;
std::cout << "plot(EbN0, ber0, 'or--');" << std::endl;
std::cout << "hold;" << std::endl;
std::cout << "ber1=[";
for (unsigned int i = 0; i < EBN0_SIZE; i++) {
std::cout << ber1[i] << " ";
}
std::cout << "];" << std::endl;
std::cout << "plot(EbN0, ber1, 'og-');" << std::endl;
std::cout << "ber2=[";
for (unsigned int i = 0; i < EBN0_SIZE; i++) {
std::cout << ber2[i] << " ";
}
std::cout << "];" << std::endl;
std::cout << "plot(EbN0, ber2, 'ob-');" << std::endl;
std::cout << "grid on;" << std::endl;
std::cout << "hold off;" << std::endl;
std::cout << "title('Bit Error Rate');" << std::endl;
std::cout << "legend('BPSK', 'BitFlip', 'LogDomainSimple');" << std::endl;
std::cout << "xlabel('EbN0');" << std::endl;
std::cout << "ylabel('BER');" << std::endl;
std::cout << "figure(2);" << std::endl;
std::cout << "fer0=[" << std::endl;
printVector<int>(fer0);
std::cout << "];" << std::endl;
std::cout << "plot(EbN0, fer0, 'or:');" << std::endl;
std::cout << "hold;" << std::endl;
std::cout << "fer1=[" << std::endl;
printVector<int>(fer1);
std::cout << "];" << std::endl;
std::cout << "plot(EbN0, fer1, 'og-');" << std::endl;
std::cout << "fer2=[" << std::endl;
printVector<int>(fer2);
std::cout << "];" << std::endl;
std::cout << "plot(EbN0, fer2, 'ob-');" << std::endl;
std::cout << "grid on;" << std::endl;
std::cout << "hold off;" << std::endl;
std::cout << "title('Frame Errors');" << std::endl;
std::cout << "legend('BPSK', 'BitFlip', 'LogDomainSimple');" << std::endl;
std::cout << "xlabel('EbN0');" << std::endl;
std::cout << "ylabel('FER');" << std::endl;
/*
writeMatrix<int>(L, "Lpost.csv");
writeMatrix<int>(U, "Upost.csv");
//writeMatrix<int>(dSource, "dSource.csv");
writeMatrix<int>(C, "C.csv");
*/
return 0;
}