void ml_populateFromMan(ManList** list, char* entry) {
/* There *has* to be a better way of doing this... */
/* First, build the command we're going to run */
char* prefix = "man -aw '";
char* postfix ="' 2>&1";
char* command = NULL;
unsigned int commandLen = strlen(entry) + strlen(prefix) + strlen(postfix);
command = malloc(commandLen + 1);
if (!command) {
return;
}
*command = '\0';
strncat(command, prefix, commandLen);
strncat(command, entry, commandLen);
strncat(command, postfix, commandLen);
/* Clear the list */
ml_free(list);
/* Now, we can run the command. */
FILE* pipe = popen(command, "r");
/* If paths to man pages are > 256bytes long, you REALLY need to
reconsider your directory structure choices. */
char filename[256];
while (fgets(filename, 256, pipe) != NULL) {
filename[strcspn(filename, "\n")] = '\0';
if (*filename == '/') {
ml_append(list, filename);
}
}
pclose(pipe);
}
void hdaf_filter_kernel ( complex<double > *& fft_ker, double L, int n, int m, double k )
{
double * kernel = 0;
int w = 0;
hdaf_filter_kernel ( kernel, w, L, n, m, k );
double * p = &(kernel[w]);
double * ker_ext = 0;
kernel_extension ( ker_ext, n, p, w, w );
fft( ker_ext, fft_ker, n );
ml_free (ker_ext);
ml_free (kernel);
}
char *webspy_nodeview(httpd *server) {
char *tmp, *nodelist;
int i = 0, max, offset = 1;
struct ONLINE clone, *tmpn;
online_t *who;
strlist_t *nlist = 0;
time_t now;
struct macro_list *ml = 0;
strlist_iterator_t *si;
who = (online_t*) ht_get(get_context(), ONLINE_ATTR);
max = who_online_max(who);
now = time(0);
for (i = 0; i < max; i++) {
tmpn = who_getnode(who, i);
if (!tmpn)
continue;
memcpy(&clone, tmpn, sizeof(struct ONLINE));
if (clone.procid == 0)
continue;
tmp = webspy_nodeview_item(&clone, offset++, now);
nlist = str_add_last(nlist, tmp);
free(tmp);
}
// concat list
max = 0;
si = str_iterator(nlist);
while (str_iterator_hasnext(si))
max += strlen(str_iterator_next(si)) + 1;
free(si);
tmp = malloc(max + 1);
*tmp = 0;
si = str_iterator(nlist);
while (str_iterator_hasnext(si))
strcat(tmp, str_iterator_next(si));
free(si);
ml = ml_addstring(ml, NODEVIEW_NODEVIEWITEMS, tmp);
free(tmp);
tmp = ml_replacebuf(ml, ht_get(get_config(), PROPERTY_HTML_NODEVIEW));
ml_free(ml);
return tmp;
}
void ml_free( T** &p, int m )
{
if (p)
{
for (int k=0; k<m; k++)
ml_free ( p[k] );
}
delete [] p;
p=0;
}
void ml_free(struct macro_list *l) {
if (l) {
ml_free(l->next);
if ((l->mac_type != ML_CHAR) && l->mac_rep)
free(l->mac_rep);
if (l->mac_key)
free(l->mac_key);
}
}
void ml_free(ManList** list) {
if (*list == NULL) {
return;
}
if((*list)->next) {
ml_free(&(*list)->next);
}
free((*list)->page);
free(*list);
*list = NULL;
}
char *webspy_nodeview_item(struct ONLINE *o, int pos, time_t now) {
struct macro_list *ml = 0;
char tbuf[1024], agebuf[30], *last, tfile[1024], *tmp;
int rc;
double speed;
ml = ml_addint(ml, NODEVIEWITEM_POS, pos);
ml = ml_addint(ml, NODEVIEWITEM_PID, o->procid);
ml = ml_addstring(ml, NODEVIEWITEM_USERNAME, o->username);
spy_makeage(o->tstart.tv_sec, now, agebuf);
ml = ml_addstring(ml, NODEVIEWITEM_IDLE, agebuf);
sprintf(tbuf, o->status);
last = (char*)&tbuf;
while (*last)
if ((*last == '\r') || (*last == '\n'))
*last = 0;
else
last++;
rc = who_transfer_direction(o);
if (rc != TRANS_NONE) {
who_transfer_file(o, tfile);
speed = who_transfer_speed(o);
last = strrchr(tfile, '/');
if (last)
last++;
else
last = (char*)&tfile;
sprintf(tbuf, "%s: %s %5dk %.1fks", (rc == TRANS_UP)?"UL":"DL", last, o->bytes_xfer/1024, speed);
}
ml = ml_addstring(ml, NODEVIEWITEM_ACTION, tbuf);
tmp = ml_replacebuf(ml, ht_get(get_config(), PROPERTY_HTML_NODEVIEW_ITEM));
ml_free(ml);
return tmp;
}
void hdaf_filter_kernel ( double *& kernel, int & w, double L, int n, int m, double k )
{
mp_real sigma = sqrt( mp_2*m+ mp_1)/k;
ml_poly<mp_real > P;
make_hdaf_ml_poly(P,m );
w = (int)ceil(hdaf_truncate_point (1E-16, 4E-16, m, dble(sigma), 0 )/(L/n));
if (kernel != 0) ml_free( kernel );
kernel = ml_alloc<double > (2*w+1);
double * p = &(kernel[w]);
mp_real h = ((mp_real)L)/n;
mp_real s = h/(mp_sqrt2*sigma);
mp_real ss = s*s;
mp_real f = pow(mp_sqrt2*sigma,-1)*(h/n);
for (int k=-w; k<=w; k++ )
p[k] = dble( exp(-ss*(k*k)) *P( dble(s*k) ) *f );
}
int main()
{
std_setup();
int n_steps = 500;
int n_runs = 1000;
double X0 = 0.0;
double stop_time = 2.0;
double dt = stop_time/n_steps;
double * time = ml_alloc<double > ( n_steps );
for (int k=0; k<n_steps; k++ )
time[k] = dt*k;
double **W=0, *X_true=0;
double **X = ml_alloc<double> ( n_runs, n_steps );
double * X_mean = ml_alloc<double> (n_steps);
double **M = ml_alloc<double> ( n_runs, n_steps );
gen_BM( dt, n_steps, W, n_runs, BM_mode_2 );
for ( int run=0; run<n_runs; run++ )
for ( int k=0; k<n_steps-1; k++ )
{
double sum1 = 0;
for (int j=0; j<=k; j++)
sum1 += drift(dt*j)*drift(dt*j)*dt;
double sum2 = 0;
for (int j=0; j<=k; j++)
sum2 += drift(dt*j)*(W[run][j+1]-W[run][j]);
M[run][k] = exp(-sum2 -0.5*sum1);
}
for ( int run=0; run<n_runs; run++ )
{
X[run][0] = X0;
for ( int j=1; j<n_steps; j++ )
{
X[run][j] = X[run][j-1] + ( drift(dt*j) + drift(dt*(j-1)) )*dt/2 + volatility(X[run][j-1], dt*(j-1))*(W[run][j]-W[run][j-1]);
}
}
for ( int step=0; step<n_steps; step++ )
X_mean[step] = 0;
for ( int run=0; run<n_runs; run++ )
for ( int step=0; step<n_steps; step++ )
X_mean[step] += X[run][step]/n_runs;
output( X, max(n_runs,min(100,n_runs)), n_steps, "/workspace/output/SDE/test/X" );
output( X_mean, n_steps, "/workspace/output/SDE/test/X_mean" );
output( time, n_steps, "/workspace/output/SDE/test/time" );
output( M, max(n_runs,min(100,n_runs)), n_steps, "/workspace/output/SDE/test/M" );
for ( int run=0; run<n_runs; run++ )
for ( int step=0; step<n_steps; step++ )
X[run][step] *= M[run][step];
for ( int step=0; step<n_steps; step++ )
X_mean[step] = 0;
for ( int run=0; run<n_runs; run++ )
for ( int step=0; step<n_steps; step++ )
X_mean[step] += X[run][step]/n_runs;
output( X, max(n_runs,min(100,n_runs)), n_steps, "/workspace/output/SDE/test/XM" );
output( X_mean, n_steps, "/workspace/output/SDE/test/QX" );
ml_free( X, n_runs );
ml_free( X_mean );
ml_free( W, n_runs );
ml_free( time );
ml_free( X_mean );
std_exit();
}
int main()
{
std_setup();
int n_runs = 1E5;
int n_steps = 30;
int **M=0;
walk_test(M,n_steps,n_runs);
for ( int k=1; k<n_steps; k++ )
cout << k << "\t" << M[k][0] << "\t" << M[k][n_runs-1] << endl;
for ( int k=1; k<n_steps; k++ )
{
int max_ = max(M[k], n_runs );
int min_ = min(M[k], n_runs );
int n = abs(max_) + abs(min_) + 1;
if ( max_>1 && min_<-1 )
{
int * c_ = ml_alloc<int > ( n );
int *c = &c_[ -min_ ];
for (int j=min_; j<=max_; j++)
c[j] = 0;
for (int j=0; j<n_runs; j++)
c[ M[k][j] ] ++;
float * p_ = ml_alloc<float > ( n );
float * p = &p_[ -min_ ];
for (int j=min_; j<=max_; j++)
p[j] = ((float)c[j])/n_runs;
float mu = 0;
for (int j=min_; j<=max_; j++)
mu += p[k]*j;
float sig2 = 0;
for (int j=min_; j<=max_; j++)
sig2 += p[k]*j*j;
sig2 -= mu*mu;
float sig = sqrt(sig2);
clean_print(k); cout << "\t";
clean_print(mu); cout << "\t";
clean_print(sig); cout << "\t";
clean_print(sig2); cout << endl;
sprintf(fname, "/workspace/output/out_%d", k);
output( p_ , n, fname );
p=0;
ml_free(p_);
ml_free(c_);
}
}
std_exit();
}
int main()
{
std_setup();
// setup:
int n_steps = 500;
int n_runs = 10000;
double X0 = 0.0;
double stop_time = 2.0;
double dt = stop_time/n_steps;
double * time = ml_alloc<double > ( n_steps );
for (int k=0; k<n_steps; k++ )
time[k] = dt*k;
double **W=0, *X_true=0;
double **X = ml_alloc<double> ( n_runs, n_steps );
double * X_mean = ml_alloc<double> (n_steps);
double **M = ml_alloc<double> ( n_runs, n_steps );
// generate the BMs:
gen_BM( dt, n_steps, W, n_runs, BM_mode_2 );
// integrate the SDE
for ( int run=0; run<n_runs; run++ )
{
X[run][0] = X0;
for ( int j=1; j<n_steps; j++ )
{
X[run][j] = X[run][j-1] + ( drift(dt*j) + drift(dt*(j-1)) )*dt/2 + volatility(X[run][j-1], dt*(j-1))*(W[run][j]-W[run][j-1]);
}
}
// compute the Girsanov change of measure at every t
for ( int run=0; run<n_runs; run++ )
for ( int k=0; k<n_steps-1; k++ )
{
double sum1 = 0;
for (int j=0; j<=k; j++)
sum1 += drift(dt*j)*drift(dt*j)*dt;
double sum2 = 0;
for (int j=0; j<=k; j++)
sum2 += drift(dt*j)*(W[run][j+1]-W[run][j]);
M[run][k] = exp(-sum2 -0.5*sum1);
}
// compute expectation of X, output results so far.
for ( int step=0; step<n_steps; step++ )
X_mean[step] = 0;
for ( int run=0; run<n_runs; run++ )
for ( int step=0; step<n_steps; step++ )
X_mean[step] += X[run][step]/n_runs;
output( X, max(n_runs,min(100,n_runs)), n_steps, "/workspace/output/SDE/test/X" );
output( X_mean, n_steps, "/workspace/output/SDE/test/X_mean" );
output( time, n_steps, "/workspace/output/SDE/test/time" );
output( M, max(n_runs,min(100,n_runs)), n_steps, "/workspace/output/SDE/test/M" );
// compute expectation of X, with respect to new measure, output results.
for ( int run=0; run<n_runs; run++ )
for ( int step=0; step<n_steps; step++ )
X[run][step] *= M[run][step];
for ( int step=0; step<n_steps; step++ )
X_mean[step] = 0;
for ( int run=0; run<n_runs; run++ )
for ( int step=0; step<n_steps; step++ )
X_mean[step] += X[run][step]/n_runs;
output( X, max(n_runs,min(100,n_runs)), n_steps, "/workspace/output/SDE/test/XM" );
output( X_mean, n_steps, "/workspace/output/SDE/test/QX" );
ml_free( X, n_runs );
ml_free( X_mean );
ml_free( W, n_runs );
ml_free( time );
ml_free( X_mean );
std_exit();
}
void mp_free(MessagePool *mp) {
hv_free(mp->buffer);
for (int i = 0; i < MP_NUM_MESSAGE_LISTS; i++) {
ml_free(&mp->lists[i]);
}
}
int main()
{
std_setup();
mp::mp_init(100);
int n = pow(2,12);
double a = 0.0;
double b = ml_2pi;
int m = 4;
double gamma = 0.75;
vector<double > frequency;
vector<double > error;
vector<double > measured_response;
vector<double > response;
vector<double > expected_error;
for (int k=1; k<=n/2; k += max(n/100,1) )
{
double K = (ml_2pi/(b-a))*k;
double * signal = ml_alloc<double > (n);
for (int i=0; i<n; i++)
{
double x = ((b-a)*i)/n+a;
signal[i] = cos( x*K );
}
complex<double > * fft_ker=0;
hdaf_filter_kernel ( fft_ker, b-a, n, m, (ml_2pi/(b-a))*(gamma*n/2) );
double * signal_filtered=0;
complex<double > * workspace=0;
fft( signal, workspace, n );
for (int i=0; i<n/2+1; i++)
workspace[i] *= fft_ker[i];
ifft( workspace, signal_filtered, n );
//---------------
cout << K << "\t" << l2_error( signal, signal_filtered, n ) << endl;
hdaf_delta_hat delta( m, sqrt(2*m+1)/( (ml_2pi/(b-a))*(gamma*n/2) ) );
frequency.push_back( K );
measured_response.push_back( rms(signal_filtered,n)/rms(signal,n ) );
response.push_back ( delta(K) );
expected_error.push_back ( log10( fabs(delta(K)-1.0)+1E-18 ) );
error.push_back( log10(fabs(l2_error( signal, signal_filtered, n )) +1E-18) );
ml_free(fft_ker);
ml_free(signal);
}
sprintf( fname, "/workspace/output/temp/frequency" );
output ( frequency, fname );
sprintf( fname, "/workspace/output/temp/measured_response" );
output ( measured_response, fname );
sprintf( fname, "/workspace/output/temp/error" );
output ( error, fname );
sprintf( fname, "/workspace/output/temp/expected_error" );
output ( expected_error, fname );
sprintf( fname, "/workspace/output/temp/response" );
output ( response, fname );
std_exit();
}
void apply_hdaf_reg(
int m,
double sigma,
int diff_order,
double h,
double eps_min,
double eps_max,
double * in,
double * & out,
int length,
int in_stride,
int out_stride)
{
//if (out == 0) out = ml_alloc<double> (length*out_stride);
static vector<int > m_list;
static vector<double > sigma_list;
static vector<int > diff_order_list;
static vector<int > length_list;
static vector<double > h_list;
static vector<complex<double> * > kernel_fft_list;
// not dealing with eps_range for now, fix later !!
static int I = 0;
bool found = 0;
// look for existing parameter combination
if (I)
if (m_list[I] == m && sigma_list[I] == sigma && diff_order_list[I] == diff_order && length_list[I] == length && h_list[I] == h )
found = true;
if (!found)
for ( I = 0; I<m_list.size(); I++)
if ( m_list[I] == m && sigma_list[I] == sigma && diff_order_list[I] == diff_order && length_list[I] == length && h_list[I] == h )
{
found = true;
break;
}
if (!found)
{
// new parameter combination
I = m_list.size();
m_list.push_back( m );
sigma_list.push_back( sigma );
diff_order_list.push_back( diff_order );
length_list.push_back( length );
h_list.push_back( h );
complex<double > *kernel_fft = 0;
double * kernel = ml_alloc<double > (length);
for (int k=0; k<length; k++)
kernel[k] = 0.0;
double x_max = hdaf_truncate_point (eps_min, eps_max, m, sigma, diff_order );
int k_max = (int)ceil(x_max/h);
if ( k_max >= length )
{
std::cout << "error: bad combination of hdaf parameters; truncate point exceeds data length in apply_hdaf_reg:\n";
std::cout << "(eps1,eps2) = (" << eps_min << ", " << eps_max << "),\tm= " << m << ",\tsigma:" << sigma << ",\tdiff_order: " << diff_order << ",\tdata_length: " << length << ",\tx_max: " << x_max << ",\tk_max: " << k_max << std::endl;
std_exit();
}
mp::mp_init(30);
ml_poly<mp_real > P;
make_hdaf_ml_poly( P, m );
differentiate_hdaf_poly( P, diff_order );
static mp_real sqrt2 = pow((mp_real)2.0,0.5);
mp_real p = (pow(sqrt2*sigma,-diff_order)/(sqrt2*sigma))*h;
for (int k=0; k<=k_max; k++)
{
mp_real r = (((mp_real)k)*h)/(sqrt2*sigma);
kernel[k] = dble(exp(-r*r)*P(r)*p);
}
for (int k=1; k<=k_max; k++)
{
mp_real r = -(((mp_real)k)*h)/(sqrt2*sigma);
kernel[length-k] = dble(exp(-r*r)*P(r)*p);
}
FFT(kernel,kernel_fft, length, 1,1 );
kernel_fft_list.push_back(kernel_fft);
ml_free( kernel );
}
// run
complex<double> * q=0;
complex<double> * ker_fft = kernel_fft_list[I];
FFT(in, q, length, in_stride,1);
for (int k=0; k<div_up(length,2); k++)
q[k] *= ker_fft[k]/((double)length);
IFFT(q,out,length,1,out_stride);
}