ASTPtr ASTTableJoin::clone() const
{
auto res = std::make_shared<ASTTableJoin>(*this);
res->children.clear();
CLONE(using_expression_list);
CLONE(on_expression);
return res;
}
/* clone_graph:
* Create two copies of the argument graph
* One is a subgraph, the other is an actual copy since we will be
* adding edges to it.
*/
static Agraph_t*
clone_graph(Agraph_t* ing, Agraph_t** xg)
{
Agraph_t* clone;
Agraph_t* xclone;
Agnode_t* n;
Agnode_t* xn;
Agnode_t* xh;
Agedge_t* e;
Agedge_t* xe;
char gname[SMALLBUF];
static int id = 0;
sprintf (gname, "_clone_%d", id++);
clone = agsubg(ing, gname);
sprintf (gname, "_clone_%d", id++);
xclone = agopen(gname, ing->kind);
for(n = agfstnode(ing); n; n = agnxtnode(ing, n)) {
aginsert (clone, n);
xn = agnode (xclone, n->name);
CLONE(n) = xn;
}
for(n = agfstnode(ing); n; n = agnxtnode(ing, n)) {
xn = CLONE(n);
for(e = agfstout(ing, n); e; e = agnxtout(ing, e)) {
aginsert (clone, e);
xh = CLONE(e->head);
xe = agedge (xclone, xn, xh);
ORIGE(xe) = e;
DEGREE(xn) += 1;
DEGREE(xh) += 1;
}
}
*xg = xclone;
#ifdef OLD
clone = agopen("clone", root->kind);
for(n = agfstnode(root); n; n = agnxtnode(root, n)) {
cn = agnode(clone, n->name);
ND_alg(cn) = DATA(n);
BCDONE(cn) = 0;
}
for(n = agfstnode(root); n; n = agnxtnode(root, n)) {
Agnode_t *t = agnode(clone, n);
for(e = agfstout(root, n); e; e = agnxtout(root, e)) {
Agnode_t *h = agnode(clone, e->head->name);
agedge(clone, t, h);
}
}
#endif
return clone;
}
ASTPtr ASTTablesInSelectQueryElement::clone() const
{
auto res = std::make_shared<ASTTablesInSelectQueryElement>(*this);
res->children.clear();
CLONE(table_join);
CLONE(table_expression);
CLONE(array_join);
return res;
}
static struct Cookie *dup_cookie(struct Cookie *src)
{
struct Cookie *d = calloc(sizeof(struct Cookie), 1);
if(d) {
CLONE(expirestr);
CLONE(domain);
CLONE(path);
CLONE(spath);
CLONE(name);
CLONE(value);
CLONE(maxage);
CLONE(version);
d->expires = src->expires;
d->tailmatch = src->tailmatch;
d->secure = src->secure;
d->livecookie = src->livecookie;
d->httponly = src->httponly;
d->creationtime = src->creationtime;
}
return d;
fail:
freecookie(d);
return NULL;
}
static int smt___index(lua_State *L ) {
lua_pushvalue(L, -1 ); lua_rawget(L, -3 );
if(!lua_isnil(L, -1 ) ) return 1; lua_pop(L, 1 );
LOCKR();
lua_rawgeti(L_S_, LUA_REGISTRYINDEX,
(int )__SMT_CLONE_FUNC );
CLONE(L, L_S_, -1 );
lua_pushvalue(L_S_, -1 );
lua_rawget(L_S_, -3 ); lua_remove(L_S_, -3 );
if(lua_isnil(L_S_, -1 ) ) // 没有找到, 全局中查找
{ lua_pop(L_S_, 1 ); lua_rawget(L_S_, LUA_GLOBALSINDEX ); }
else lua_remove(L_S_, -2 );
CLONE(L_S_, L, lua_gettop(L_S_ ) ); lua_pop(L_S_, 1 );
UNLOCK();
return 1;
}
ASTPtr ASTArrayJoin::clone() const
{
auto res = std::make_shared<ASTArrayJoin>(*this);
res->children.clear();
CLONE(expression_list);
return res;
}
static int smt___newindex(lua_State *L ) {
int i;
lua_pushvalue(L, -2 ); lua_rawget(L, -4 );
if(!lua_isnil(L, -1 ) ) { lua_pop(L, 1 );
lua_pushvalue(L, -1 ); lua_rawset(L, -4 );
return 0;
} lua_pop(L, 1 );
LOCKW();
lua_rawseti(L_S_, LUA_REGISTRYINDEX,
(int )__SMT_CLONE_FUNC );
for(i= -2; -1>= i; ++i ) CLONE(L, L_S_, i );
lua_rawset(L_S_, -3 ); lua_pop(L_S_, 1 );
UNLOCK();
return 0;
}
PDistribution TConditionalProbabilityEstimator_FromDistribution::operator()(const TValue &condition) const
{ if (condition.varType == TValue::INTVAR)
return probabilities->operator[](condition);
else if (condition.varType == TValue::FLOATVAR) {
if (condition.isSpecial())
raiseError("undefined attribute value for condition");
if (probabilities->varType != TValue::FLOATVAR)
raiseError("invalid attribute value type for condition");
const float &x = condition.floatV;
const TDistributionMap *dm = probabilities->continuous;
TDistributionMap::const_iterator rb = dm->upper_bound(x);
if (rb==dm->end())
rb = dm->begin();
TDistribution *result = CLONE(TDistribution, (*rb).second);
PDistribution wresult = result;
if ((rb==dm->begin()) && ((*rb).first!=x)) {
(*result) *= 0;
return wresult;
}
const float &x2 = (*rb).first;
rb--;
const float &x1 = (*rb).first;
const PDistribution &y1 = (*rb).second;
if (x1 == x2) {
*result += y1;
*result *= 0.5;
return wresult;
}
// The normal formula for this is in the function above
*result -= y1;
*result *= (x-x1)/(x2-x1);
*result += y1;
return wresult;
}
raiseError("invalid attribute value for condition");
return PDistribution();
}
static int sth_run(lua_State *L ) {
TH_STATE *th= (TH_STATE * )0;
unsigned int i= ~0;
pthread_mutex_lock(&MUTEX_S_ ); for(;;) {
if(LUA_TFUNCTION!= lua_type(L, -1 ) ) break; //参数检测
for(i= 0; (TH_MAX> i )&& __th_state[i ]; ++i );
if(TH_MAX== i ) break; //没有空间了
__th_state[i ]= (TH_STATE * )malloc(sizeof(*th ) );
th= __th_state[i ]; memset(th, 0, sizeof(*th ) ); break;
} pthread_mutex_unlock(&MUTEX_S_ );
if(th ) { th->L= lua_open(); luaL_openlibs(th->L );
//lua_getglobal(L, "_G" ); lua_xmove(L, th->L, 1 );
//lua_setglobal(th->L, "_G" );
LOCKW(); CLONE(L, th->L, -1 ); UNLOCK();
//创建线程, 传入参数
pthread_create(&(th->hTh ), NULL,
proxy_ThFunc, (void * )i );
//lua 返回线程 ID
lua_pushinteger(L, i ); return 1; //返回线程 ID
}
return 0;
}
PDistribution TProbabilityEstimator_FromDistribution::operator()() const
{ return CLONE(TDistribution, probabilities);
}
PContingency TConditionalProbabilityEstimator_FromDistribution::operator()() const
{ return CLONE(TContingency, probabilities); }
PConditionalProbabilityEstimator TConditionalProbabilityEstimatorConstructor_loess::operator()(PContingency frequencies, PDistribution, PExampleGenerator, const long &, const int &) const
{ if (frequencies->varType != TValue::FLOATVAR)
if (frequencies->outerVariable)
raiseError("attribute '%s' is not continuous", frequencies->outerVariable->get_name().c_str());
else
raiseError("continuous attribute expected for condition");
if (!frequencies->continuous->size())
// This is ugly, but: if you change this, you should also change the code which catches it in
// Bayesian learner
raiseError("distribution (of attribute values, probably) is empty or has only a single value");
PContingency cont = CLONE(TContingency, frequencies);
const TDistributionMap &points = *frequencies->continuous;
/* if (frequencies->continuous->size() == 1) {
TDiscDistribution *f = (TDiscDistribution *)(points.begin()->second.getUnwrappedPtr());
f->normalize();
f->variances = mlnew TFloatList(f->size(), 0.0);
return mlnew TConditionalProbabilityEstimator_FromDistribution(cont);
}
*/
cont->continuous->clear();
vector<float> xpoints;
distributePoints(points, nPoints, xpoints, distributionMethod);
if (!xpoints.size())
raiseError("no points for the curve (check 'nPoints')");
if (frequencies->continuous->size() == 1) {
TDiscDistribution *f = (TDiscDistribution *)(points.begin()->second.getUnwrappedPtr());
f->normalize();
f->variances = mlnew TFloatList(f->size(), 0.0);
const_ITERATE(vector<float>, pi, xpoints)
(*cont->continuous)[*pi] = f;
return mlnew TConditionalProbabilityEstimator_FromDistribution(cont);
}
TDistributionMap::const_iterator lowedge = points.begin();
TDistributionMap::const_iterator highedge = points.end();
bool needAll;
map<float, PDistribution>::const_iterator from, to;
vector<float>::const_iterator pi(xpoints.begin()), pe(xpoints.end());
float refx = *pi;
from = lowedge;
to = highedge;
int totalNumOfPoints = frequencies->outerDistribution->abs;
int needpoints = int(ceil(totalNumOfPoints * windowProportion));
if (needpoints<3)
needpoints = 3;
TSimpleRandomGenerator rgen(frequencies->outerDistribution->cases);
if ((needpoints<=0) || (needpoints>=totalNumOfPoints)) { //points.size()
needAll = true;
from = lowedge;
to = highedge;
}
else {
needAll = false;
/* Find the window */
from = points.lower_bound(refx);
to = points.upper_bound(refx);
if (from==to)
if (to != highedge)
to++;
else
from --;
/* Extend the interval; we set from to highedge when it would go beyond lowedge, to indicate that only to can be modified now */
while (needpoints > 0) {
if ((to == highedge) || ((from != highedge) && (refx - (*from).first < (*to).first - refx))) {
if (from == lowedge)
from = highedge;
else {
from--;
needpoints -= (*from).second->cases;
}
}
else {
to++;
if (to!=highedge)
needpoints -= (*to).second->cases;
else
needpoints = 0;
}
}
if (from == highedge)
from = lowedge;
/* else
from++;*/
}
int numOfOverflowing = 0;
// This follows http://www-2.cs.cmu.edu/afs/cs/project/jair/pub/volume4/cohn96a-html/node7.html
for(;;) {
TDistributionMap::const_iterator tt = to;
--tt;
if (tt == from) {
TDistribution *Sy = CLONE(TDistribution, (*tt).second);
PDistribution wSy = Sy;
Sy->normalize();
(*cont->continuous)[refx] = (wSy);
((TDiscDistribution *)(Sy)) ->variances = mlnew TFloatList(Sy->variable->noOfValues(), 0.0);
}
else {
float h = (refx - (*from).first);
if ((*tt).first - refx > h)
h = ((*tt).first - refx);
/* Iterate through the window */
tt = from;
const float &x = (*tt).first;
const PDistribution &y = (*tt).second;
float cases = y->abs;
float w = fabs(refx - x) / h;
w = 1 - w*w*w;
w = w*w*w;
const float num = y->abs; // number of instances with this x - value
float n = w * num;
float Sww = w * w * num;
float Sx = w * x * num;
float Swwx = w * w * x * num;
float Swwxx = w * w * x * x * num;
TDistribution *Sy = CLONE(TDistribution, y);
PDistribution wSy = Sy;
*Sy *= w;
float Sxx = w * x * x * num;
TDistribution *Syy = CLONE(TDistribution, y);
PDistribution wSyy = Syy;
*Syy *= w;
TDistribution *Sxy = CLONE(TDistribution, y);
PDistribution wSxy = Sxy;
*Sxy *= w * x;
if (tt!=to)
while (++tt != to) {
const float &x = (*tt).first;
const PDistribution &y = (*tt).second;
cases += y->abs;
w = fabs(refx - x) / h;
w = 1 - w*w*w;
w = w*w*w;
const float num = y->abs;
n += w * num;
Sww += w * w * num;
Sx += w * x * num;
Swwx += w * w * x * num;
Swwxx += w * w * x * x * num;
Sxx += w * x * x * num;
TDistribution *ty = CLONE(TDistribution, y);
PDistribution wty = ty;
*ty *= w;
*Sy += wty;
*Syy += wty;
*ty *= x;
*Sxy += wty;
//*ty *= PDistribution(y);
}
float sigma_x2 = n<1e-6 ? 0.0 : (Sxx - Sx * Sx / n)/n;
if (sigma_x2<1e-10) {
*Sy *= 0;
Sy->cases = cases;
(*cont->continuous)[refx] = (wSy);
}
TDistribution *sigma_y2 = CLONE(TDistribution, Sy);
PDistribution wsigma_y2 = sigma_y2;
*sigma_y2 *= wsigma_y2;
*sigma_y2 *= -1/n;
*sigma_y2 += wSyy;
*sigma_y2 *= 1/n;
TDistribution *sigma_xy = CLONE(TDistribution, Sy);
PDistribution wsigma_xy = sigma_xy;
*sigma_xy *= -Sx/n;
*sigma_xy += wSxy;
*sigma_xy *= 1/n;
// This will be sigma_xy / sigma_x2, but we'll multiply it by whatever we need
TDistribution *sigma_tmp = CLONE(TDistribution, sigma_xy);
PDistribution wsigma_tmp = sigma_tmp;
//*sigma_tmp *= wsigma_tmp;
if (sigma_x2 > 1e-10)
*sigma_tmp *= 1/sigma_x2;
const float difx = refx - Sx/n;
// computation of y
*sigma_tmp *= difx;
*Sy *= 1/n;
*Sy += *sigma_tmp;
// probabilities that are higher than 0.9 normalize with a logistic function, which produces two positive
// effects: prevents overfitting and avoids probabilities that are higher than 1.0. But, on the other hand, this
// solution is rather unmathematical. Do the same for probabilities that are lower than 0.1.
vector<float>::iterator syi(((TDiscDistribution *)(Sy))->distribution.begin());
vector<float>::iterator sye(((TDiscDistribution *)(Sy))->distribution.end());
for (; syi!=sye; syi++) {
if (*syi > 0.9) {
Sy->abs -= *syi;
*syi = 1/(1+exp(-10*((*syi)-0.9)*log(9.0)-log(9.0)));
Sy->abs += *syi;
}
if (*syi < 0.1) {
Sy->abs -= *syi;
*syi = 1/(1+exp(10*(0.1-(*syi))*log(9.0)+log(9.0)));
Sy->abs += *syi;
}
}
Sy->cases = cases;
Sy->normalize();
(*cont->continuous)[refx] = (wSy);
// now for the variance
// restore sigma_tmp and compute the conditional sigma
if ((fabs(difx) > 1e-10) && (sigma_x2 > 1e-10)) {
*sigma_tmp *= (1/difx);
*sigma_tmp *= wsigma_xy;
*sigma_tmp *= -1;
*sigma_tmp += wsigma_y2;
// fct corresponds to part of (10) in the brackets (see URL above)
// float fct = Sww + difx*difx/sigma_x2/sigma_x2 * (Swwxx - 2/n * Sx*Swwx + 2/n/n * Sx*Sx*Sww);
float fct = 1 + difx*difx/sigma_x2; //n + difx*difx/sigma_x2+n*n --- add this product to the overall fct sum if you are estimating error for a single user and not for the line.
*sigma_tmp *= fct/n; // fct/n/n;
}
((TDiscDistribution *)(Sy)) ->variances = mlnew TFloatList(((TDiscDistribution *)(sigma_tmp))->distribution);
}
// on to the next point
pi++;
if (pi==pe)
break;
refx = *pi;
// Adjust the window
while (to!=highedge) {
float dif = (refx - (*from).first) - ((*to).first - refx);
if ((dif>0) || (dif==0) && rgen.randbool()) {
if (numOfOverflowing > 0) {
from++;
numOfOverflowing -= (*from).second->cases;
}
else {
to++;
if (to!=highedge)
numOfOverflowing += (*to).second->cases;
}
}
else
break;
}
}
return mlnew TConditionalProbabilityEstimator_FromDistribution(cont);
}
static struct hash_instance *
do_sha_copy(struct hash_instance *s)
{
return &CLONE(sha_instance, s)->super;
}
PProbabilityEstimator TProbabilityEstimatorConstructor_m::operator()(PDistribution frequencies, PDistribution apriori, PExampleGenerator, const long &weightID, const int &) const
{ TProbabilityEstimator_FromDistribution *pefd = mlnew TProbabilityEstimator_FromDistribution(CLONE(TDistribution, frequencies));
PProbabilityEstimator estimator = pefd;
TDiscDistribution *ddist = pefd->probabilities.AS(TDiscDistribution);
if (ddist && (ddist->cases > 1e-20) && apriori) {
TDiscDistribution *dapriori = apriori.AS(TDiscDistribution);
if (!dapriori || (dapriori->abs < 1e-20))
raiseError("invalid apriori distribution");
float mabs = m/dapriori->abs;
const float &abs = ddist->abs;
const float &cases = ddist->cases;
const float div = cases + m;
if ((abs==cases) || !renormalize) {
int i = 0;
for(TDiscDistribution::iterator di(ddist->begin()), de(ddist->end()), ai(dapriori->begin());
di != de;
di++, ai++, i++)
ddist->setint(i, (*di+*ai*mabs)/div);
}
else {
int i = 0;
for(TDiscDistribution::iterator di(ddist->begin()), de(ddist->end()), ai(dapriori->begin());
di != de;
di++, ai++, i++)
ddist->setint(i, (*di / abs * cases + *ai*mabs)/div);
}
}
else
pefd->probabilities->normalize();
return estimator;
}
PProbabilityEstimator TProbabilityEstimatorConstructor_Laplace::operator()(PDistribution frequencies, PDistribution, PExampleGenerator, const long &, const int &) const
{ TProbabilityEstimator_FromDistribution *pefd = mlnew TProbabilityEstimator_FromDistribution(CLONE(TDistribution, frequencies));
PProbabilityEstimator estimator = pefd;
TDiscDistribution *ddist = pefd->probabilities.AS(TDiscDistribution);
if (ddist) {
const float &abs = ddist->abs;
const float &cases = ddist->cases;
const float div = cases + l * ddist->noOfElements();
int i = 0;
if (div) {
if ((cases == abs) || !renormalize || (abs<1e-20))
PITERATE(TDiscDistribution, di, ddist)
ddist->setint(i++, (*di + l) / div);
else
PITERATE(TDiscDistribution, di, ddist)
ddist->setint(i++, (*di / abs * cases + l) / div);
}
else
pefd->probabilities->normalize();
}
else
pefd->probabilities->normalize();
return estimator;
}
void vidTDeint::reset (void)
{
#define CLONE(x) x=_param->x
CLONE(mode);
CLONE(order);
CLONE(field);
CLONE(mthreshL);
CLONE(mthreshC);
CLONE(map);
CLONE(type);
CLONE(debug);
CLONE(mtnmode);
CLONE(sharp);
CLONE(full);
CLONE(cthresh);
CLONE(blockx);
CLONE(blocky);
CLONE(chroma);
CLONE(MI);
CLONE(tryWeave);
CLONE(link);
CLONE(denoise);
CLONE(AP);
CLONE(APType);
orderS=order;
fieldS=field;
mthreshLS= mthreshL;
mthreshCS=mthreshC;
xhalf = blockx >> 1;
yhalf = blocky >> 1;
xshift = blockx == 4 ? 2 : blockx == 8 ? 3 : blockx == 16 ? 4 : blockx == 32 ? 5 :
blockx == 64 ? 6 : blockx == 128 ? 7 : blockx == 256 ? 8 : blockx == 512 ? 9 :
blockx == 1024 ? 10 : 11;
yshift = blocky == 4 ? 2 : blocky == 8 ? 3 : blocky == 16 ? 4 : blocky == 32 ? 5 :
blocky == 64 ? 6 : blocky == 128 ? 7 : blocky == 256 ? 8 : blocky == 512 ? 9 :
blocky == 1024 ? 10 : 11;
if (((!full && mode == 0) || tryWeave) && mode >= 0)
{
int sz;
sz=(((_info.width+xhalf)>>xshift)+1)*(((_info.height+yhalf)>>yshift)+1)*4;
if(cArray) delete [] cArray;
cArray = new int[sz];;
}
PProbabilityEstimator TProbabilityEstimatorConstructor_relative::operator()(PDistribution frequencies, PDistribution, PExampleGenerator, const long &, const int &) const
{ TProbabilityEstimator_FromDistribution *pefd = mlnew TProbabilityEstimator_FromDistribution(CLONE(TDistribution, frequencies));
PProbabilityEstimator estimator = pefd;
pefd->probabilities->normalize();
return estimator;
}
/**
* Fetch a subset of the values of a field.
*
* @param (GPB::Message*) external pointer
* @param field name or tag number of the field
* @param index
*/
RPB_FUNCTION_3(SEXP, METHOD(get_field_values), Rcpp::XPtr<GPB::Message> message, SEXP field,
Rcpp::IntegerVector index) {
GPB::FieldDescriptor* field_desc = getFieldDescriptor(message, field);
if (!field_desc->is_repeated()) {
Rcpp_error("fetch can only be used on repeated fields");
}
int n = index.size();
switch (field_desc->type()) {
case TYPE_INT32:
case TYPE_SINT32:
case TYPE_SFIXED32:
case TYPE_INT64:
case TYPE_SINT64:
case TYPE_SFIXED64:
case TYPE_UINT32:
case TYPE_FIXED32:
case TYPE_UINT64:
case TYPE_FIXED64:
case TYPE_ENUM: {
Rcpp::IntegerVector res(n);
for (int i = 0; i < n; i++) {
res[i] = MESSAGE_GET_REPEATED_INT(message, field_desc, index[i]);
}
return res;
}
case TYPE_DOUBLE:
case TYPE_FLOAT: {
Rcpp::NumericVector res(n);
for (int i = 0; i < n; i++) {
res[i] = MESSAGE_GET_REPEATED_DOUBLE(message, field_desc, index[i]);
}
return res;
}
case TYPE_BOOL: {
Rcpp::LogicalVector res(n);
for (int i = 0; i < n; i++) {
res[i] = MESSAGE_GET_REPEATED_DOUBLE(message, field_desc, index[i]);
}
return res;
}
case TYPE_STRING: {
const GPB::Reflection* ref = message->GetReflection();
Rcpp::CharacterVector res(n);
for (int i = 0; i < n; i++) {
res[i] = ref->GetRepeatedString(*message, field_desc, index[i]);
}
return res;
}
case TYPE_BYTES: {
const GPB::Reflection* ref = message->GetReflection();
Rcpp::List res(n);
for (int i = 0; i < n; i++) {
std::string s = ref->GetRepeatedString(*message, field_desc, index[i]);
res[i] = std::vector<Rbyte>(s.begin(), s.end());
}
return res;
}
case TYPE_MESSAGE:
case TYPE_GROUP: {
const GPB::Reflection* ref = message->GetReflection();
Rcpp::List res(n);
for (int i = 0; i < n; i++) {
res[i] = S4_Message(CLONE(&ref->GetRepeatedMessage(*message, field_desc, i)));
}
return res;
}
default:
throw std::range_error("unknown type");
}
return R_NilValue; // Unreachable. For -Wall
}
/* Spawn a new process executing PATH with the attributes describes in *ATTRP.
Before running the process perform the actions described in FILE-ACTIONS. */
static int
__spawnix (pid_t * pid, const char *file,
const posix_spawn_file_actions_t * file_actions,
const posix_spawnattr_t * attrp, char *const argv[],
char *const envp[], int xflags,
int (*exec) (const char *, char *const *, char *const *))
{
pid_t new_pid;
struct posix_spawn_args args;
int ec;
if (__pipe2 (args.pipe, O_CLOEXEC))
return errno;
/* To avoid imposing hard limits on posix_spawn{p} the total number of
arguments is first calculated to allocate a mmap to hold all possible
values. */
ptrdiff_t argc = 0;
/* Linux allows at most max (0x7FFFFFFF, 1/4 stack size) arguments
to be used in a execve call. We limit to INT_MAX minus one due the
compatiblity code that may execute a shell script (maybe_script_execute)
where it will construct another argument list with an additional
argument. */
ptrdiff_t limit = INT_MAX - 1;
while (argv[argc++] != NULL)
if (argc == limit)
{
errno = E2BIG;
return errno;
}
int prot = (PROT_READ | PROT_WRITE
| ((GL (dl_stack_flags) & PF_X) ? PROT_EXEC : 0));
/* Add a slack area for child's stack. */
size_t argv_size = (argc * sizeof (void *)) + 512;
size_t stack_size = ALIGN_UP (argv_size, GLRO(dl_pagesize));
void *stack = __mmap (NULL, stack_size, prot,
MAP_PRIVATE | MAP_ANONYMOUS | MAP_STACK, -1, 0);
if (__glibc_unlikely (stack == MAP_FAILED))
{
close_not_cancel (args.pipe[0]);
close_not_cancel (args.pipe[1]);
return errno;
}
/* Disable asynchronous cancellation. */
int cs = LIBC_CANCEL_ASYNC ();
args.file = file;
args.exec = exec;
args.fa = file_actions;
args.attr = attrp ? attrp : &(const posix_spawnattr_t) { 0 };
args.argv = argv;
args.argc = argc;
args.envp = envp;
args.xflags = xflags;
__sigprocmask (SIG_BLOCK, &SIGALL_SET, &args.oldmask);
/* The clone flags used will create a new child that will run in the same
memory space (CLONE_VM) and the execution of calling thread will be
suspend until the child calls execve or _exit. These condition as
signal below either by pipe write (_exit with SPAWN_ERROR) or
a successful execve.
Also since the calling thread execution will be suspend, there is not
need for CLONE_SETTLS. Although parent and child share the same TLS
namespace, there will be no concurrent access for TLS variables (errno
for instance). */
new_pid = CLONE (__spawni_child, STACK (stack, stack_size), stack_size,
CLONE_VM | CLONE_VFORK | SIGCHLD, &args);
close_not_cancel (args.pipe[1]);
if (new_pid > 0)
{
if (__read (args.pipe[0], &ec, sizeof ec) != sizeof ec)
ec = 0;
else
__waitpid (new_pid, NULL, 0);
}
else
ec = -new_pid;
__munmap (stack, stack_size);
close_not_cancel (args.pipe[0]);
if (!ec && new_pid)
*pid = new_pid;
__sigprocmask (SIG_SETMASK, &args.oldmask, 0);
LIBC_CANCEL_RESET (cs);
return ec;
}
/* Spawn a new process executing PATH with the attributes describes in *ATTRP.
Before running the process perform the actions described in FILE-ACTIONS. */
int
__spawni (pid_t * pid, const char *file,
const posix_spawn_file_actions_t * acts,
const posix_spawnattr_t * attrp, char *const argv[],
char *const envp[], int xflags)
{
return __spawnix (pid, file, acts, attrp, argv, envp, xflags,
xflags & SPAWN_XFLAGS_USE_PATH ? __execvpe : __execve);
}
