//' Generate a White Noise Process (\eqn{WN(\sigma^2)})
//' Generates a White Noise Process with variance parameter \eqn{\sigma ^2}.
//' @param N An \code{integer} for signal length.
//' @param sigma2 A \code{double} that contains process variance.
//' @return wn A \code{vec} containing the white noise.
//' @backref src/gen_process.cpp
//' @backref src/gen_process.h
//' @keywords internal
//' @examples
//' gen_wn(10, 1.5)
// [[Rcpp::export]]
arma::vec gen_wn(const unsigned int N, const double sigma2 = 1)
{
arma::vec wn(N);
double sigma = sqrt(sigma2);
for(unsigned int i = 0; i < N; i++){
wn(i) = R::rnorm(0.0, sigma);
}
return wn;
}
//' Generate an ARMA(1,1) sequence
//'
//' Generate an ARMA(1,1) sequence given \eqn{\phi}, \eqn{\theta}, and \eqn{\sigma^2}.
//' @param N An \code{integer} for signal length.
//' @param phi A \code{double} that contains autoregressive.
//' @param theta A \code{double} that contains moving average.
//' @param sigma2 A \code{double} that contains process variance.
//' @return A \code{vec} containing the MA(1) process.
//' @details
//' The function implements a way to generate the \eqn{x_t}{x[t]} values without calling the general ARMA function.
//' The autoregressive order 1 and moving average order 1 process is defined as \eqn{{x_t} = {\phi _1}{x_{t - 1}} + {w_t} + {\theta _1}{w_{t - 1}} }{x[t] = phi*x[t-1] + w[t] + theta*w[t-1]},
//' where \eqn{{w_t}\mathop \sim \limits^{iid} N\left( {0,\sigma _w^2} \right)}{w[t] ~ N(0,sigma^2) iid}
//'
//' The function first generates a vector of white noise using \code{\link[gmwm]{gen_wn}} and then obtains the
//' ARMA values under the above equation.
//'
//' @backref src/gen_process.cpp
//' @backref src/gen_process.h
//' @keywords internal
//' @examples
//' gen_ma1(10, .2, 1.2)
// [[Rcpp::export]]
arma::vec gen_arma11(const unsigned int N, const double phi = .1, const double theta = .3, const double sigma2 = 1)
{
arma::vec wn = gen_wn(N+1, sigma2);
arma::vec arma = arma::zeros<arma::vec>(N+1);
for(unsigned int i=1; i <= N; i++ )
{
arma(i) = phi*arma(i-1) + theta*wn(i-1) + wn(i);
}
return arma.rows(1,N);
}
void simplifyWay(const OSMWay &way) {
if (way.size < 2) {
/// Rare but exists in map: a node with only one node
/// -> ignore those artefacts
Error::warn("Way %llu has only %d nodes", way.id, way.size);
return;
}
static const size_t simplifiedWay_size = 8192; /// largest observed way length is 2304
static uint64_t simplifiedWay[simplifiedWay_size];
if (way.size > simplifiedWay_size)
Error::err("Way %llu of size %d is longer than acceptable (%d)", way.id, way.size, simplifiedWay_size);
const size_t simplifiedWaySize = applyRamerDouglasPeucker(way, simplifiedWay);
if (simplifiedWaySize < 2) {
Error::warn("Way %llu got simplified to only %d nodes", way.id, simplifiedWay);
return;
}
WayNodes wn(simplifiedWaySize);
memcpy(wn.nodes, simplifiedWay, sizeof(uint64_t) * wn.num_nodes);
for (size_t k = 0; k < simplifiedWaySize; ++k)
node2Coord->increaseCounter((simplifiedWay)[k]);
wayNodes->insert(way.id, wn);
}
void BigIntegerStrassen::FastFourierTransform(vcomp &vect, bool invert){
//static long double PI = 3.14159265358979323846;
static long double PI = 3.14159265358979323846264338327950288419716939937510L;
ll n = (ll)vect.size();
if (n == 1) return;
vcomp vect0(n / 2), vect1(n / 2);
for (ll i = 0, j = 0; i<n; i += 2, ++j){
vect0[j] = vect[i];
vect1[j] = vect[i + 1];
}
FastFourierTransform(vect0, invert);
FastFourierTransform(vect1, invert);
long double ang = 2 * PI / n * (invert ? -1 : 1);
std::complex<long double> w(1), wn(cos(ang), sin(ang));
for (ll i = 0; i < n / 2; ++i) {
vect[i] = vect0[i] + w * vect1[i];
vect[i + n / 2] = vect0[i] - w * vect1[i];
if (invert){
vect[i] /= 2;
vect[i + n / 2] /= 2;
}
w *= wn;
}
}
void FFT(complex a[], int n, int flag)
{
for(int i = 1, j = n >> 1, k; i < n - 1; ++i)
{
if(i < j)
std::swap(a[i], a[j]);
for(k = n >> 1; j >= k; k >>= 1)
j -= k;
if(j < k)
j += k;
}
for(int i = 1; i < n; i <<= 1)
{
complex wn(cos(pi / i), flag * sin(pi / i));
for(int j = 0; j < n; j += i << 1)
{
complex w(1, 0);
for(int k = 0; k < i; ++k, w = w * wn)
{
complex t = w * a[j + k + i];
a[j + k + i] = a[j + k] - t;
a[j + k] = a[j + k] + t;
}
}
}
if(flag == -1)
for(int i = 0; i < n; ++i)
a[i].r /= n;
}
void fft(int n, const std::complex<double> *pIn, std::complex<double> *pOut)
{
const double pi2=6.283185307179586476925286766559;
double angle = pi2/n;
std::complex<double> wn(cos(angle), sin(angle));
fft_impl(n, wn, pIn, 1, pOut, 1);
}
ComplexSeries* dft(ComplexSeries* a)
{
if(NULL == a)
return NULL;
int i, j;
int length = a->getLength();
if(length <= 0)
return NULL;
ComplexNum w(1.0, 0.0);
ComplexNum wn(-1*length);
ComplexNum aa;
ComplexSeries* y = new ComplexSeries(length);
if(NULL == y)
{
cout<<"dft memory malloc failed"<<endl;
return NULL;
}
ComplexNum yy(0.0, 0.0);
for(i=0;i<length;i++)
{
for(j=0;j<length-1;j++)
{
aa = *(a->getValue(length-j-1));
yy = w*(aa+yy);
}
aa = *(a->getValue(0));
yy = yy+aa;
y->setValue(i, &yy);
w = w*wn;
yy.setValue(0.0, 0.0);
}
return y;
}
void fft(vcd &a, bool invert)
{
int n = a.size();
if (n == 1)
return;
vcd a0, a1;
a0.resize(n / 2); a1.resize(n / 2);
for (int i = 0, j = 0; i < n; i += 2, ++j)
{
a0[j] = a[i];
a1[j] = a[i + 1];
}
fft(a0, invert);
fft(a1, invert);
double ang = 2 * 3.14159265359 / n * (invert ? -1 : 1);
complex<double> w(1), wn(cos(ang), sin(ang));
for (int i = 0; i < n / 2; ++i)
{
a[i] = a0[i] + (w * a1[i]);
a[i + n / 2] = a0[i] - (w * a1[i]);
w = w * wn;
}
if (invert)
{
for (int i = 0; i < n; ++i)
a[i] /= 2;
}
}
void FFT(complex a[], int n, int flag)
{
static int bitLen = 0, bitRev[maxm] = {};
if(n != (1 << bitLen))
{
for(bitLen = 0; 1 << bitLen < n; ++bitLen);
for(int i = 1; i < n; ++i)
bitRev[i] = (bitRev[i >> 1] >> 1) | ((i & 1) << (bitLen - 1));
}
for(int i = 0; i < n; ++i)
if(i < bitRev[i])
std::swap(a[i], a[bitRev[i]]);
for(int i = 1; i < n; i <<= 1)
{
complex wn(cos(pi / i), flag * sin(pi / i));
for(int j = 0; j < n; j += i << 1)
{
complex w(1, 0);
for(int k = 0; k < i; ++k, w = w * wn)
{
complex t = w * a[j + k + i];
a[j + k + i] = a[j + k] - t;
a[j + k] = a[j + k] + t;
}
}
}
if(flag == -1)
for(int i = 0; i < n; ++i)
{
a[i].r /= n;
a[i].i /= n;
}
}
/*
-----------------------------------------------------------------------------
-----------------------------------------------------------------------------
*/
void CForegroundObserver::ConstructL()
{
CActiveScheduler::Add(this);
User::LeaveIfError(iWsSession.Connect());
iWg = RWindowGroup(iWsSession);
User::LeaveIfError(iWg.Construct((TUint32)&iWg, EFalse));
iWg.SetOrdinalPosition(-1);
iWg.EnableReceiptOfFocus(EFalse);
// iWg.SetPointerCapture(RWindowBase::TCaptureDragDrop);
CApaWindowGroupName* wn(NULL);
wn =CApaWindowGroupName::NewL(iWsSession);
if(wn)
{
CleanupStack::PushL(wn);
wn->SetHidden(ETrue);
wn->SetWindowGroupName(iWg);
CleanupStack::PopAndDestroy();
}
iWg.EnableFocusChangeEvents();
Listen();
}
/*
-----------------------------------------------------------------------------
-----------------------------------------------------------------------------
*/
void CKeyObserver::ConstructL()
{
CActiveScheduler::Add(this);
iHandle1 = iHandle2 = iHandle3 = iHandle4 = iHandle5 = -1;
User::LeaveIfError(iWsSession.Connect());
iWg = RWindowGroup(iWsSession);
User::LeaveIfError(iWg.Construct((TUint32)&iWg, EFalse));
iWg.SetOrdinalPosition(-1);
iWg.EnableReceiptOfFocus(EFalse);
CApaWindowGroupName* wn(NULL);
wn=CApaWindowGroupName::NewL(iWsSession);
if(wn)
{
CleanupStack::PushL(wn);
wn->SetHidden(ETrue);
wn->SetWindowGroupName(iWg);
CleanupStack::PopAndDestroy();
}
iWg.EnableOnEvents();
CaptureKeys();
Listen();
}
double v(double m, double phi, double v_0, double mu, double k, double l_0)
{
/* Calculates the velocity at a point given the velocity d_phi away
from the point we care about. Derivation explained in lab report.
*/
return sqrt(((2*r*d_phi)/m)*(wt(m,phi)-Fst(phi,k,l_0)-mu*wn(m,phi)
-mu*Fsn(phi,k,l_0)+((mu*m*pow(v_0,2))/(r))
+(m*pow(v_0,2)/(2*r*d_phi))));
}
// test only for posix
void tst_QSocketNotifier::posixSockets()
{
QTcpServer server;
QVERIFY(server.listen(QHostAddress::LocalHost, 0));
int posixSocket = qt_safe_socket(AF_INET, SOCK_STREAM, 0);
sockaddr_in addr;
addr.sin_addr.s_addr = htonl(0x7f000001);
addr.sin_family = AF_INET;
addr.sin_port = htons(server.serverPort());
qt_safe_connect(posixSocket, (const struct sockaddr*)&addr, sizeof(sockaddr_in));
QVERIFY(server.waitForNewConnection(5000));
QScopedPointer<QTcpSocket> passive(server.nextPendingConnection());
::fcntl(posixSocket, F_SETFL, ::fcntl(posixSocket, F_GETFL) | O_NONBLOCK);
{
QSocketNotifier rn(posixSocket, QSocketNotifier::Read);
connect(&rn, SIGNAL(activated(int)), &QTestEventLoop::instance(), SLOT(exitLoop()));
QSignalSpy readSpy(&rn, SIGNAL(activated(int)));
QVERIFY(readSpy.isValid());
// No write notifier, some systems trigger write notification on socket creation, but not all
QSocketNotifier en(posixSocket, QSocketNotifier::Exception);
connect(&en, SIGNAL(activated(int)), &QTestEventLoop::instance(), SLOT(exitLoop()));
QSignalSpy errorSpy(&en, SIGNAL(activated(int)));
QVERIFY(errorSpy.isValid());
passive->write("hello",6);
passive->waitForBytesWritten(5000);
QTestEventLoop::instance().enterLoop(3);
QCOMPARE(readSpy.count(), 1);
QCOMPARE(errorSpy.count(), 0);
char buffer[100];
int r = qt_safe_read(posixSocket, buffer, 100);
QCOMPARE(r, 6);
QCOMPARE(buffer, "hello");
QSocketNotifier wn(posixSocket, QSocketNotifier::Write);
connect(&wn, SIGNAL(activated(int)), &QTestEventLoop::instance(), SLOT(exitLoop()));
QSignalSpy writeSpy(&wn, SIGNAL(activated(int)));
QVERIFY(writeSpy.isValid());
qt_safe_write(posixSocket, "goodbye", 8);
QTestEventLoop::instance().enterLoop(3);
QCOMPARE(readSpy.count(), 1);
QCOMPARE(writeSpy.count(), 1);
QCOMPARE(errorSpy.count(), 0);
// Write notifier may have fired before the read notifier inside
// QTcpSocket, give QTcpSocket a chance to see the incoming data
passive->waitForReadyRead(100);
QCOMPARE(passive->readAll(), QByteArray("goodbye",8));
}
qt_safe_close(posixSocket);
}
int main( int argc, char *argv[])
{
try {
// check command line
if (argc != 2 && argc != 3) {
std::cerr << "Usage:\n WNXMLConsole <WN_XML_file> [<semantic_features_XML_file>]\n";
return 1;
}
// init WN
std::cerr << "Reading XML...\n";
std::auto_ptr<ML::MultiLog> logger( ML::MultiLog::create( std::cerr, 100));
std::auto_ptr<LibWNXML::WNQuery> wn( new LibWNXML::WNQuery( argv[1], *logger));
wn->writeStats( std::cerr);
// init SemFeatures (if appl.)
std::auto_ptr<ML_NPro2::SemFeatures> sf( NULL);
if (argc == 3) {
std::cerr << "Reading SemFeatures...\n";
sf = std::auto_ptr<ML_NPro2::SemFeatures>( new ML_NPro2::SemFeatures( *wn));
std::cerr << sf->readXML( argv[2]) << " pairs read\n";
}
// query loop
std::cerr << "Type your query, or .h for help, .q to quit\n";
std::string line;
while (true) {
std::cerr << ">";
std::getline( std::cin, line);
if (line == ".q")
break;
else if (line != "") {
try {
process_query( *wn, sf.get(), line, std::cout);
}
catch (const LibWNXML::InvalidPOSException& e) {
std::cerr << e.msg() << std::endl;
}
}
} // while (true)
} // try {
catch (const std::exception& e) {
std::cerr << e.what() << std::endl;
return 1;
}
catch (...) {
std::cerr << "Unknown exception\n";
return 1;
}
return 0;
}
WindowsManager::WindowID WindowsManager::createWindow (const char* winName)
{
std::string wn (winName);
WindowManagerPtr_t newWindow = WindowManager::create ();
WindowID windowId = addWindow (wn, newWindow);
boost::thread refreshThread (boost::bind
(&WindowsManager::threadRefreshing,
this, newWindow));
return windowId;
}
void tst_QSocketNotifier::posixSockets()
{
#ifndef Q_OS_UNIX
QSKIP("test only for posix", SkipAll);
#else
QTcpServer server;
QVERIFY(server.listen(QHostAddress::LocalHost, 0));
int posixSocket = qt_safe_socket(AF_INET, SOCK_STREAM, 0);
sockaddr_in addr;
addr.sin_addr.s_addr = htonl(0x7f000001);
addr.sin_family = AF_INET;
addr.sin_port = htons(server.serverPort());
qt_safe_connect(posixSocket, (const struct sockaddr*)&addr, sizeof(sockaddr_in));
QVERIFY(server.waitForNewConnection(5000));
QScopedPointer<QTcpSocket> passive(server.nextPendingConnection());
::fcntl(posixSocket, F_SETFL, ::fcntl(posixSocket, F_GETFL) | O_NONBLOCK);
{
QSocketNotifier rn(posixSocket, QSocketNotifier::Read);
connect(&rn, SIGNAL(activated(int)), &QTestEventLoop::instance(), SLOT(exitLoop()));
QSignalSpy readSpy(&rn, SIGNAL(activated(int)));
QSocketNotifier wn(posixSocket, QSocketNotifier::Write);
connect(&wn, SIGNAL(activated(int)), &QTestEventLoop::instance(), SLOT(exitLoop()));
QSignalSpy writeSpy(&wn, SIGNAL(activated(int)));
QSocketNotifier en(posixSocket, QSocketNotifier::Exception);
connect(&en, SIGNAL(activated(int)), &QTestEventLoop::instance(), SLOT(exitLoop()));
QSignalSpy errorSpy(&en, SIGNAL(activated(int)));
passive->write("hello",6);
passive->waitForBytesWritten(5000);
QTestEventLoop::instance().enterLoop(3);
QCOMPARE(readSpy.count(), 1);
writeSpy.clear(); //depending on OS, write notifier triggers on creation or not.
QCOMPARE(errorSpy.count(), 0);
char buffer[100];
qt_safe_read(posixSocket, buffer, 100);
QCOMPARE(buffer, "hello");
qt_safe_write(posixSocket, "goodbye", 8);
QTestEventLoop::instance().enterLoop(3);
QCOMPARE(readSpy.count(), 1);
QCOMPARE(writeSpy.count(), 1);
QCOMPARE(errorSpy.count(), 0);
QCOMPARE(passive->readAll(), QByteArray("goodbye",8));
}
qt_safe_close(posixSocket);
#endif
}
//' Generate an Autoregressive Order 1 ( AR(1) ) sequence
//' Generate an Autoregressive Order 1 sequence given \eqn{\phi} and \eqn{\sigma^2}.
//' @param N An \code{unsigned integer} for signal length.
//' @param phi A \code{double} that contains autocorrection.
//' @param sigma2 A \code{double} that contains process variance.
//' @return A \code{vec} containing the AR(1) process.
//' @details
//' The function implements a way to generate the AR(1)'s \eqn{x_t}{x[t]} values without calling the general ARMA function.
//' The autoregressive order 1 process is defined as \eqn{{x_t} = {\phi _1}{x_{t - 1}} + {w_t} }{x[t] = phi[1]x[t-1] + w[t]},
//' where \eqn{{w_t}\mathop \sim \limits^{iid} N\left( {0,\sigma _w^2} \right)}{w[t] ~ N(0,sigma^2) iid}
//'
//' The function first generates a vector of white noise using \code{\link[gmwm]{gen_wn}} and then obtains the
//' AR values under the above equation.
//' @backref src/gen_process.cpp
//' @backref src/gen_process.h
//' @keywords internal
//' @examples
//' gen_ar1(10, 5, 1.2)
// [[Rcpp::export]]
arma::vec gen_ar1(const unsigned int N, const double phi = .3, const double sigma2 = 1)
{
arma::vec wn = gen_wn(N+1, sigma2);
arma::vec gm = arma::zeros<arma::vec>(N+1);
for(unsigned int i=1; i <= N; i++ )
{
gm(i) = phi*gm(i-1) + wn(i);
}
return gm.rows(1,N);
}
void FFT(Complex y[],int len,int on) {
change(y,len);
for(int h = 2; h <= len; h <<= 1) {
Complex wn(cos(-on*2*PI/h),sin(-on*2*PI/h));
for(int j = 0;j < len;j+=h) {
Complex w(1,0);
for(int k = j;k < j+h/2;k++) {
Complex u = y[k];
Complex t = w*y[k+h/2];
y[k] = u+t;
y[k+h/2] = u-t;
w = w*wn;
}
}
}
if(on == -1)
for(int i = 0;i < len;i++)
y[i].r /= len;
}
void fft(complex *c, int len, int on)
{
int i, j, k;
for (i = 1, j = len / 2; i < len - 1; i++)
{
if (i < j)
swap(c[i], c[j]);
k = len / 2;
while (j >= k)
{
j -= k;
k /= 2;
}
if (j < k) j += k;
}
for (int h = 2; h <= len; h <<= 1)
{
complex wn(cos(-on * 2 * PI / h), sin(-on * 2 * PI / h));
for (int j = 0; j < len; j += h)
{
complex w(1, 0);
for (int k = j; k < j + h / 2; k++)
{
complex u = c[k];
complex t = w * c[k + h / 2];
c[k] = u + t;
c[k + h / 2] = u - t;
w = w * wn;
}
}
}
if (on == -1)
for (int i = 0; i < len; i++)
c[i].r /= len;
}
double N(double m, double phi, double k, double l_0, double v)
{
//Calculates the normal force under a specific circumstance
return (wn(m,phi)+Fsn(phi,k,l_0)-(m*pow(v,2))/r);
}
void nmf(viennacl::matrix_base<NumericT> const & V,
viennacl::matrix_base<NumericT> & W,
viennacl::matrix_base<NumericT> & H,
viennacl::linalg::nmf_config const & conf)
{
viennacl::hsa::context & ctx = const_cast<viennacl::hsa::context &>(viennacl::traits::hsa_context(V));
const std::string NMF_MUL_DIV_KERNEL = "el_wise_mul_div";
viennacl::linalg::opencl::kernels::nmf<NumericT, viennacl::hsa::context>::init(ctx);
vcl_size_t k = W.size2();
conf.iters_ = 0;
if (viennacl::linalg::norm_frobenius(W) <= 0)
W = viennacl::scalar_matrix<NumericT>(W.size1(), W.size2(), NumericT(1), ctx);
if (viennacl::linalg::norm_frobenius(H) <= 0)
H = viennacl::scalar_matrix<NumericT>(H.size1(), H.size2(), NumericT(1), ctx);
viennacl::matrix_base<NumericT> wn(V.size1(), k, W.row_major(), ctx);
viennacl::matrix_base<NumericT> wd(V.size1(), k, W.row_major(), ctx);
viennacl::matrix_base<NumericT> wtmp(V.size1(), V.size2(), W.row_major(), ctx);
viennacl::matrix_base<NumericT> hn(k, V.size2(), H.row_major(), ctx);
viennacl::matrix_base<NumericT> hd(k, V.size2(), H.row_major(), ctx);
viennacl::matrix_base<NumericT> htmp(k, k, H.row_major(), ctx);
viennacl::matrix_base<NumericT> appr(V.size1(), V.size2(), V.row_major(), ctx);
NumericT last_diff = 0;
NumericT diff_init = 0;
bool stagnation_flag = false;
for (vcl_size_t i = 0; i < conf.max_iterations(); i++)
{
conf.iters_ = i + 1;
{
hn = viennacl::linalg::prod(trans(W), V);
htmp = viennacl::linalg::prod(trans(W), W);
hd = viennacl::linalg::prod(htmp, H);
viennacl::hsa::kernel & mul_div_kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::nmf<NumericT>::program_name(), NMF_MUL_DIV_KERNEL);
viennacl::hsa::enqueue(mul_div_kernel(H, hn, hd, cl_uint(H.internal_size1() * H.internal_size2())));
}
{
wn = viennacl::linalg::prod(V, trans(H));
wtmp = viennacl::linalg::prod(W, H);
wd = viennacl::linalg::prod(wtmp, trans(H));
viennacl::hsa::kernel & mul_div_kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::nmf<NumericT>::program_name(), NMF_MUL_DIV_KERNEL);
viennacl::hsa::enqueue(mul_div_kernel(W, wn, wd, cl_uint(W.internal_size1() * W.internal_size2())));
}
if (i % conf.check_after_steps() == 0) //check for convergence
{
appr = viennacl::linalg::prod(W, H);
appr -= V;
NumericT diff_val = viennacl::linalg::norm_frobenius(appr);
if (i == 0)
diff_init = diff_val;
if (conf.print_relative_error())
std::cout << diff_val / diff_init << std::endl;
// Approximation check
if (diff_val / diff_init < conf.tolerance())
break;
// Stagnation check
if (std::fabs(diff_val - last_diff) / (diff_val * NumericT(conf.check_after_steps())) < conf.stagnation_tolerance()) //avoid situations where convergence stagnates
{
if (stagnation_flag) // iteration stagnates (two iterates with no notable progress)
break;
else
// record stagnation in this iteration
stagnation_flag = true;
} else
// good progress in this iteration, so unset stagnation flag
stagnation_flag = false;
// prepare for next iterate:
last_diff = diff_val;
}
}
}
void basics::Persistor_blog::write_blog(bfs::path content_storage_file, basics::Persistable_blog blog)
{
// Getting containers
std::vector<basics::Post> posts = blog.posts();
basics::Configuration_blog config = blog.config();
// Declaration
rapidxml::xml_document<> content;
rapidxml::xml_node<> *decl = content.allocate_node(rapidxml::node_declaration);
decl->append_attribute(content.allocate_attribute("version", "1.0"));
decl->append_attribute(content.allocate_attribute("encoding", "utf-8"));
content.append_node(decl);
// Blog root <blog>
rapidxml::xml_node<> *blog_root = content.allocate_node(rapidxml::node_element, "blog");
content.append_node(blog_root);
// Configuration <config>
rapidxml::xml_node<> *conf = content.allocate_node(rapidxml::node_element, "config");
blog_root->append_node(conf);
wn(conf, "meta-desc", config.meta_desc_);
wn(conf, "meta-author", config.meta_author_);
wn(conf, "meta-title", config.meta_title_);
wn(conf, "meta-conf-bootstrap", config.bootstrap_);
wn(conf, "meta-conf-css", config.css_);
wns(conf, "nav-items", "href", "item", config.menu_);
wn(conf, "home-link", config.home_link_);
wn(conf, "title", config.title_);
wn(conf, "subtitle", config.subtitle_);
wn(conf, "about", config.about_, "line", config.about_headline_);
wns(conf, "links", "href", "item", config.links_, "line", config.links_headline_);
wn(conf, "philosophy", config.philosophy_);
wn(conf, "backtotop", config.back_to_top_);
wn(conf, "post-per-page", std::to_string(config.post_per_page_));
// Post root <post prev="#" next="#">
rapidxml::xml_node<> *post_node = content.allocate_node(rapidxml::node_element, "posts");
rapidxml::xml_attribute<> *prev_attr = content.allocate_attribute("prev", "#");
post_node->append_attribute(prev_attr);
rapidxml::xml_attribute<> *next_attr = content.allocate_attribute("next", "#");
post_node->append_attribute(next_attr);
blog_root->append_node(post_node);
for (std::vector<basics::Post>::iterator it = posts.begin(); it != posts.end(); ++it) {
char *timestamp_str = content.allocate_string(it->get_timestamp_str().c_str());
char *author = content.allocate_string(it->get_author().c_str());
char *title = content.allocate_string(it->get_title().c_str());
std::string escaped_life = basics::escape_string(it->get_life());
char *life = content.allocate_string(escaped_life.c_str());
// Building new post node
rapidxml::xml_node<> *another_post = content.allocate_node(rapidxml::node_element, "post");
rapidxml::xml_attribute<> *date_attr = content.allocate_attribute("date", timestamp_str);
another_post->append_attribute(date_attr);
rapidxml::xml_attribute<> *author_attr = content.allocate_attribute("author", author);
another_post->append_attribute(author_attr);
rapidxml::xml_node<> *title_child = content.allocate_node(rapidxml::node_element, "title", title);
another_post->append_node(title_child);
rapidxml::xml_node<> *mylife_child = content.allocate_node(rapidxml::node_element, "mylife", life);
another_post->append_node(mylife_child);
// Adding edition dates
rapidxml::xml_node<> *eds_node = content.allocate_node(rapidxml::node_element, "editions");
std::vector<std::string> editions = it->editions();
for (std::vector<std::string>::iterator it = editions.begin(); it != editions.end(); ++it) {
char *edition = content.allocate_string(it->c_str());
rapidxml::xml_node<> *ed_node = content.allocate_node(rapidxml::node_element, "edition", edition);
eds_node->append_node(ed_node);
}
another_post->append_node(eds_node);
// Adding node to post root
post_node->append_node(another_post);
}
std::string content_string;
rapidxml::print(std::back_inserter(content_string), content);
std::ofstream content_output_stream(content_storage_file.string());
content_output_stream << content_string;
content_output_stream.close();
}
void Algorithm::FFT(QVector< std::complex<double> > &a, bool invert)
{
//qDebug()<<a;
int n=a.length();
if(n==1)
{
return;
}
int i;
QVector< std::complex<double> >a0(n/2),a1(n/2);
for(i=0;i<n;i+=2)
{
a0[i/2]=a[i];
a1[i/2]=a[i+1];
}
FFT(a0,invert);
FFT(a1,invert);
static double pi_2;//=M_PI *2;
double ang=(pi_2/n)*(invert?-1:1);
std::complex<double> w(1),wn(cos(ang),sin(ang));
for(i=0;i<n/2;i++)
{
a[i]=a0[i]+w*a1[i];
a[i+ n/2 ]=a0[i]-w*a1[i];
if(invert)
{
a[i]/=2;
a[i+ n/2 ]/=2;
}
w*=wn;
}
/*/
int i,j=0;
int n=a.length();
//double pi_2=M_PI *2;
double ang;
std::complex<double> w,wn;
QVector< std::complex<double> > _a(n);
for(i=0;i<n;i++)
{
a_[j]=a[i];
j=j+n/2;
}
n/=2;
for(;n>1;n/=2)
{
ang=(M_PI/n)*(invert?-1:1);
qDebug()<<"length"<<n;
w=1,wn={cos(ang),sin(ang)};
i=0;
j=0;
while(1)
{
_a[i]=a[i]+w*a[i+n/2];
_a[i+n]=a[i]-w*a[i+n/2];
i++,j++;
if(j==n/2)
{
w*=wn;
j=0;
i+=n/2;
}
qDebug()<<i<<j;
if(i>=a.length())
{
break;
}
a=_a;
}
}
/*/
}
void nmf(viennacl::matrix<ScalarType> const & v,
viennacl::matrix<ScalarType> & w,
viennacl::matrix<ScalarType> & h,
std::size_t k,
ScalarType eps = 0.000001,
std::size_t max_iter = 10000,
std::size_t check_diff_every_step = 100)
{
viennacl::linalg::kernels::nmf<ScalarType, 1>::init();
w.resize(v.size1(), k);
h.resize(k, v.size2());
std::vector<ScalarType> stl_w(w.internal_size1() * w.internal_size2());
std::vector<ScalarType> stl_h(h.internal_size1() * h.internal_size2());
for (std::size_t j = 0; j < stl_w.size(); j++)
stl_w[j] = static_cast<ScalarType>(rand()) / RAND_MAX;
for (std::size_t j = 0; j < stl_h.size(); j++)
stl_h[j] = static_cast<ScalarType>(rand()) / RAND_MAX;
viennacl::matrix<ScalarType> wn(v.size1(), k);
viennacl::matrix<ScalarType> wd(v.size1(), k);
viennacl::matrix<ScalarType> wtmp(v.size1(), v.size2());
viennacl::matrix<ScalarType> hn(k, v.size2());
viennacl::matrix<ScalarType> hd(k, v.size2());
viennacl::matrix<ScalarType> htmp(k, k);
viennacl::matrix<ScalarType> appr(v.size1(), v.size2());
viennacl::vector<ScalarType> diff(v.size1() * v.size2());
viennacl::fast_copy(&stl_w[0], &stl_w[0] + stl_w.size(), w);
viennacl::fast_copy(&stl_h[0], &stl_h[0] + stl_h.size(), h);
ScalarType last_diff = 0.0f;
for (std::size_t i = 0; i < max_iter; i++)
{
{
hn = viennacl::linalg::prod(trans(w), v);
htmp = viennacl::linalg::prod(trans(w), w);
hd = viennacl::linalg::prod(htmp, h);
viennacl::ocl::kernel & mul_div_kernel = viennacl::ocl::get_kernel(viennacl::linalg::kernels::nmf<ScalarType, 1>::program_name(),
NMF_MUL_DIV_KERNEL);
viennacl::ocl::enqueue(mul_div_kernel(h, hn, hd, cl_uint(stl_h.size())));
}
{
wn = viennacl::linalg::prod(v, trans(h));
wtmp = viennacl::linalg::prod(w, h);
wd = viennacl::linalg::prod(wtmp, trans(h));
viennacl::ocl::kernel & mul_div_kernel = viennacl::ocl::get_kernel(viennacl::linalg::kernels::nmf<ScalarType, 1>::program_name(),
NMF_MUL_DIV_KERNEL);
viennacl::ocl::enqueue(mul_div_kernel(w, wn, wd, cl_uint(stl_w.size())));
}
if (i % check_diff_every_step == 0)
{
appr = viennacl::linalg::prod(w, h);
viennacl::ocl::kernel & sub_kernel = viennacl::ocl::get_kernel(viennacl::linalg::kernels::nmf<ScalarType, 1>::program_name(),
NMF_SUB_KERNEL);
//this is a cheat. i.e save difference of two matrix into vector to get norm_2
viennacl::ocl::enqueue(sub_kernel(appr, v, diff, cl_uint(v.size1() * v.size2())));
ScalarType diff_val = viennacl::linalg::norm_2(diff);
if((diff_val < eps) || (fabs(diff_val - last_diff) < eps))
{
//std::cout << "Breaked at diff - " << diff_val << "\n";
break;
}
last_diff = diff_val;
//printf("Iteration #%lu - %.5f \n", i, diff_val);
}
}
}
ComplexSeries* fft_recursive(ComplexSeries* a)
{
if(NULL == a)
return NULL;
int length = a->getLength();
int i;
if(length <= 0)
return NULL;
if(1 == length)
return a;
int half_len = ((unsigned int)length) >> 1;
ComplexSeries* a0 = new ComplexSeries(half_len);
if(NULL==a0)
{
cout<<"fft_recusive memory malloc failed"<<endl;
return NULL;
}
ComplexSeries* a1 = new ComplexSeries(half_len);
if(NULL==a1)
{
delete a0;
cout<<"fft_recusive memory malloc failed"<<endl;
return NULL;
}
for(i=0;i<half_len;i++)
{
a0->setValue(i, a->getValue(i*2));
a1->setValue(i, a->getValue(i*2+1));
}
delete a;
ComplexSeries* y0 = fft_recursive(a0);
ComplexSeries* y1 = fft_recursive(a1);
ComplexSeries* y = new ComplexSeries(length);
if(NULL == y)
{
delete a0;
delete a1;
cout<<"fft_recusive memory malloc failed"<<endl;
return NULL;
}
ComplexNum w(1.0, 0.0);
ComplexNum wn(-1*length);
for(i=0;i<half_len;i++)
{
ComplexNum* yy0 = y0->getValue(i);
ComplexNum* yy1 = y1->getValue(i);
ComplexNum ww = w*(*yy1);
ComplexNum result1 = *yy0 + ww;
ComplexNum result2 = *yy0 - ww;
y->setValue(i, &result1);
y->setValue(i+half_len, &result2);
w = w*wn;
}
delete y0;
delete y1;
return y;
}
