/**
* Description not yet available.
* \param
*/
dvar3_array dvar3_array::sub(int nrl,int nrh)
{
if (allocated(*this))
{
dvar3_array tmp(nrl,nrh);
for (int i=nrl; i<=nrh; i++)
{
tmp[i].shallow_copy((*this)(i));
}
return tmp;
}
else
{
return *this;
}
}
void param_init_vector::sd_vscale(const dvar_vector& _d,
const dvar_vector& x,const int& _ii)
{
if (allocated(*this))
{
int& ii=(int&) _ii;
dvar_vector& d=(dvar_vector&) _d;
int mmin=indexmin();
int mmax=indexmax();
for (int i=mmin;i<=mmax;i++)
{
d(ii)=1.0;
ii++;
}
}
}
void param_init_bounded_vector::sd_vscale(const dvar_vector& _v,
const dvar_vector& x,const int& _ii)
{
if (allocated(*this))
{
int& ii=(int&) _ii;
dvar_vector& v=(dvar_vector&) _v;
int mmin=indexmin();
int mmax=indexmax();
//double pen=0;
for (int i=mmin;i<=mmax;i++)
{
v(ii)=dfboundp(x(ii),minb,maxb);
ii++;
}
}
}
adpvm_slave_args::adpvm_slave_args(int _num_args,int _length_args)
{
//char ** argv;
counter=1;
num_args=_num_args;
if (allocated(length_args))
length_args.deallocate();
length_args.allocate(0,num_args-1);
length_args=_length_args;
argv = new charptr[num_args+1];
argv[0] = new char[20];
for (int i = 1; i < num_args; i++)
{
argv[i] = NULL;
}
argv[num_args]=NULL;
}
void CollectionMap::rollbackCreate() {
if (!allocated()) {
return;
}
// If we are rolling back the database creation, then any collections in that database were
// created in this transaction. Since we roll back collection creates before dictionary
// creates, we would have already rolled back the collection creation, which does close_ns,
// which removes the Collection from the map. So this must be empty.
verify(_collections.empty());
// Closing the DB before the transaction aborts will allow the abort to do the dbremove for us.
shared_ptr<storage::Dictionary> metadb = _metadb;
_metadb.reset();
const int r = metadb->close();
if (r != 0) {
storage::handle_ydb_error(r);
}
}
void pvm_number::assign(const dvector& u)
{
if(ad_comm::pvm_manager)
{
int nsp=ad_comm::pvm_manager->num_slave_processes;
if (u.indexmin() !=0 || u.indexmax() != nsp)
{
cerr << "Error in pvm_number::assign valid index bounds must be 0 "
<< ad_comm::pvm_manager->num_slave_processes << endl;
ad_exit(1);
}
if (allocated(v))
v.deallocate();
v.allocate(0,nsp);
v=u;
d=u(0);
}
}
void cached_allocation(size_type n, multiallocation_chain &chain)
{
size_type count = n, allocated(0);
BOOST_TRY{
//If don't have any cached node, we have to get a new list of free nodes from the pool
while(!m_cached_nodes.empty() && count--){
void *ret = ipcdetail::to_raw_pointer(m_cached_nodes.pop_front());
chain.push_back(ret);
++allocated;
}
if(allocated != n){
mp_node_pool->allocate_nodes(n - allocated, chain);
}
}
BOOST_CATCH(...){
this->cached_deallocation(chain);
BOOST_RETHROW
}
/**
* Description not yet available.
* \param
*/
void adpvm_pack(const dvar_matrix & _m)
{
dvar_matrix& m = (dvar_matrix &) _m;
if (allocated(m))
{
int imin=m.indexmin();
int imax=m.indexmax();
pvm_pkint(&imin,1,1);
pvm_pkint(&imax,1,1);
for (int i=imin;i<=imax;i++) adpvm_pack(m(i));
}
else
{
int imin=0;
int imax=-1;
pvm_pkint(&imin,1,1);
pvm_pkint(&imax,1,1);
}
}
/**
* Description not yet available.
* \param
*/
void adpvm_pack(const dvector& _v)
{
dvector& v =(dvector&) _v;
if (allocated(v))
{
int imin=v.indexmin();
int imax=v.indexmax();
pvm_pkint(&imin,1,1);
pvm_pkint(&imax,1,1);
pvm_pkdouble(&(v(imin)),imax-imin+1,1);
}
else
{
int imin=0;
int imax=-1;
pvm_pkint(&imin,1,1);
pvm_pkint(&imax,1,1);
}
}
/**
* Description not yet available.
* \param
*/
void adpvm_pack(const i3_array & _m)
{
i3_array& m = (i3_array &) _m;
if (allocated(m))
{
int imin=m.indexmin();
int imax=m.indexmax();
pvm_pkint(&imin,1,1);
pvm_pkint(&imax,1,1);
for (int i=imin;i<=imax;i++) adpvm_pack(m(i));
}
else
{
int imin=0;
int imax=-1;
pvm_pkint(&imin,1,1);
pvm_pkint(&imax,1,1);
}
}
bool CollectionMap::close_ns(const StringData& ns, const bool aborting) {
Lock::assertWriteLocked(ns);
// No need to initialize first. If the metadb is null at this point,
// we simply say that the ns you want to close wasn't open.
if (!allocated()) {
return false;
}
// Find and erase the old entry, if it exists.
CollectionStringMap::const_iterator it = _collections.find(ns);
if (it != _collections.end()) {
// TODO: Handle the case where a client tries to close a load they didn't start.
shared_ptr<Collection> cl = it->second;
_collections.erase(ns);
cl->close(aborting);
return true;
}
return false;
}
// on input, _initLock is held, so this can be called by only one thread at a time,
// also, on input, the CollectionMap must be allocated
Collection *CollectionMap::open_ns(const StringData& ns, const bool bulkLoad) {
verify(allocated());
BSONObj serialized;
BSONObj nsobj = BSON("ns" << ns);
storage::Key sKey(nsobj, NULL);
DBT ndbt = sKey.dbt();
// If this transaction is read only, then we cannot possible already
// hold a lock in the metadb and we certainly don't need to hold one
// for the duration of this operation. So we use an alternate txn stack.
const bool needAltTxn = !cc().hasTxn() || cc().txn().readOnly();
scoped_ptr<Client::AlternateTransactionStack> altStack(!needAltTxn ? NULL :
new Client::AlternateTransactionStack());
scoped_ptr<Client::Transaction> altTxn(!needAltTxn ? NULL :
new Client::Transaction(0));
// Pass flags that get us a write lock on the metadb row
// for the ns we'd like to open.
DB *db = _metadb->db();
const int r = db->getf_set(db, cc().txn().db_txn(), DB_SERIALIZABLE | DB_RMW,
&ndbt, getf_serialized, &serialized);
if (r == 0) {
// We found an entry for this ns and we have the row lock.
// First check if someone got the lock before us and already
// did the open.
Collection *cl = find_ns(ns);
if (cl != NULL) {
return cl;
}
// No need to hold the openRWLock during Collection::make(),
// the fact that we have the row lock ensures only one thread will
// be here for a particular ns at a time.
shared_ptr<Collection> details = Collection::make( serialized, bulkLoad );
SimpleRWLock::Exclusive lk(_openRWLock);
verify(!_collections[ns]);
_collections[ns] = details;
return details.get();
} else if (r != DB_NOTFOUND) {
storage::handle_ydb_error(r);
}
return NULL;
}
/**
* Check if getRevocableExecutors function properly filters out PR executors.
*
* TODO(skonefal): Does it really work?
*/
TEST(HelperFunctionsTest, getRevocableExecutors) {
Try<mesos::FixtureResourceUsage> usages = JsonUsage::ReadJson(QOS_FIXTURE);
if (usages.isError()) {
LOG(ERROR) << "JsonSource failed: " << usages.error() << std::endl;
}
ResourceUsage usage;
usage.CopyFrom(usages.get().resource_usage(0));
std::list<ResourceUsage_Executor> ret =
ResourceUsageHelper::getRevocableExecutors(usage);
ASSERT_EQ(3u, ret.size());
// Expected only BE executors.
for (auto executor : ret) {
Resources allocated(executor.allocated());
EXPECT_FALSE(allocated.revocable().empty());
}
}
void CollectionMap::getNamespaces( list<string>& tofill ) {
init();
if (!allocated()) {
return;
}
getNamespacesExtra extra(tofill);
storage::Cursor c(_metadb->db());
int r = 0;
while (r != DB_NOTFOUND) {
r = c.dbc()->c_getf_next(c.dbc(), 0, getNamespacesCallback, &extra);
if (r == -1) {
verify(extra.ex != NULL);
throw *extra.ex;
}
if (r != 0 && r != DB_NOTFOUND) {
storage::handle_ydb_error(r);
}
}
}
/**
* Description not yet available.
* \param
*/
int sub_unallocated(const i4_array& m)
{
int iflag=0;
int mmin=m.indexmin();
int mmax=m.indexmax();
if (!allocated(m))
{
iflag=1;
return iflag;
}
for (int i=mmin;i<=mmax;i++)
{
if (sub_unallocated(m(i)))
{
iflag=1;
break;
}
}
return iflag;
}
void CollectionMap::getNamespaces( list<string>& tofill ) {
init();
if (!allocated()) {
return;
}
getNamespacesExtra extra(tofill);
storage::Cursor c(_metadb->db());
int r = 0;
while (r != DB_NOTFOUND) {
r = c.dbc()->c_getf_next(c.dbc(), 0, getNamespacesCallback, &extra);
if (r == -1) {
extra.throwException();
msgasserted(17322, "got -1 from cursor iteration but didn't save an exception");
}
if (r != 0 && r != DB_NOTFOUND) {
storage::handle_ydb_error(r);
}
}
}
void ByteArray::resize(size_t size)
{
// Trivial case
if (size == this->size())
return;
// Check if we need to reallocate
if (size > allocated())
{
// Temporary copy
ByteArray tmp(size);
std::memcpy(tmp.data(), constData(), std::min(tmp.size(), this->size()));
// Let assignment operator handle the rest
d = tmp.d;
}
else
{
// Detach and set new size
d->size = size;
}
}
/**
* Description not yet available.
* \param
*/
void adpvm_unpack(const ivector& _v)
{
ivector& v = (ivector&) _v;
int imin;
int imax;
pvm_upkint(&imin,1,1);
pvm_upkint(&imax,1,1);
if (allocated(v)) {
if (v.indexmin()!=imin) {
cerr << "Error in min index in"
" void adpvm_unpack(const dvector& v)" << endl;
ad_exit(1);
}
if (v.indexmax()!=imax) {
cerr << "Error in max index in"
" void adpvm_unpack(const dvector& v)" << endl;
ad_exit(1);
}
} else {
v.allocate(imin,imax);
}
pvm_upkint(&(v(imin)),imax-imin+1,1);
}
/**
* Description not yet available.
* \param
*/
void adpvm_unpack(const d5_array & _m)
{
d5_array& m = (d5_array &) _m;
int imin;
int imax;
pvm_upkint(&imin,1,1);
pvm_upkint(&imax,1,1);
if (allocated(m)) {
if (m.indexmin()!=imin) {
cerr << "Error in min index in"
" void adpvm_unpack(const dvector& v)" << endl;
ad_exit(1);
}
if (m.indexmax()!=imax) {
cerr << "Error in max index in"
" void adpvm_unpack(const dvector& v)" << endl;
ad_exit(1);
}
} else {
m.allocate(imin,imax);
}
for (int i=imin;i<=imax;i++) adpvm_unpack(m(i));
}
/**
* Description not yet available.
* \param
*/
dvar_matrix_position::dvar_matrix_position(const dvar_matrix& m,int x)
: lb(m.rowmin(),m.rowmax()), ub(m.rowmin(),m.rowmax()),
ptr(m.rowmin(),m.rowmax())
{
row_min=m.rowmin();
row_max=m.rowmax();
for (int i=row_min;i<=row_max;i++)
{
if (allocated(m(i)))
{
lb(i)=m(i).indexmin();
ub(i)=m(i).indexmax();
ptr(i)=m(i).get_va();
}
else
{
lb(i)=0;
ub(i)=-1;
ptr(i)=0;
}
}
}
multiallocation_chain cached_allocation(std::size_t n)
{
multiallocation_chain chain;
std::size_t count = n, allocated(0);
BOOST_TRY{
//If don't have any cached node, we have to get a new list of free nodes from the pool
while(!m_cached_nodes.empty() && count--){
void *ret = detail::get_pointer(m_cached_nodes.front());
m_cached_nodes.pop_front();
chain.push_back(ret);
++allocated;
}
if(allocated != n){
multiallocation_chain chain2(mp_node_pool->allocate_nodes(n - allocated));
chain.splice_after(chain.last(), chain2, chain2.before_begin(), chain2.last(), n - allocated);
}
return boost::interprocess::move(chain);
}
BOOST_CATCH(...){
this->cached_deallocation(boost::interprocess::move(chain));
BOOST_RETHROW
}
// The block [blk_start, blk_end) has been allocated;
// adjust the block offset table to represent this information;
// right-open interval: [blk_start, blk_end)
void
G1BlockOffsetArray::alloc_block(HeapWord* blk_start, HeapWord* blk_end) {
mark_block(blk_start, blk_end);
allocated(blk_start, blk_end);
}
void
wsdump(FILE * f1)
{
nialptr startaddr,
addr,
highest,
next;
nialint cnt;
#ifdef DEBUG
if (!continueflag) /* don't memchk if storing continue */
memchk();
#endif
#ifdef CALLDLL
/* This will clear all is_loaded flags for DLL , so that when this
workspace is loaded later, the DLL will be reloaded */
DLLclearflags();
#endif
/* find the address of the highest free block */
highest = freelisthdr,
next = freelisthdr;
while (next != TERMINATOR) {
if (next > highest)
highest = next;
next = fwdlink(next);
}
/* store global giving workspace size */
if (highest + blksize(highest) == memsize)
wssize = highest;
else
wssize = memsize; /* there is a used block at the end */
/* set a version stamp for the workspace */
#ifdef DEBUG
strcpy(wsstamp, systemname);
strcat(wsstamp, nialversion);
strcat(wsstamp, " (debug)");
#else
strcpy(wsstamp, systemname);
strcat(wsstamp, nialversion);
#endif
/* write out global structure */
testerr(writeblock(f1, (char *) &G, (long) sizeof G, false, 0L, 0));
/* loop to write out allocated blocks */
addr = membase;
startaddr = addr;
cnt = 0;
while (addr < memsize) {
if (allocated(addr)) {
cnt += blksize(addr);
addr += blksize(addr);
}
else { /* write out the block */
#ifdef DEBUG
if (cnt == 0) {
nprintf(OF_DEBUG, "second empty block in a row found in wsdump\n");
showfl();
nabort(NC_ABORT_F);
}
#endif
/* nprintf(OF_DEBUG,"writing block startaddr %d cnt %d \n",startaddr,cnt); */
testerr(writeblock(f1, (char *) &cnt, (long) sizeof cnt, false, 0L, 0));
testerr(writeblock(f1, (char *) &startaddr, (long) sizeof addr, false, 0L, 0));
testerr(writeblock(f1, (char *) &mem[startaddr], (long) bytespu * cnt, false, 0L, 0));
/* prepare for next block after free space */
/* nprintf(OF_DEBUG,"skipping free block addr %d blksize %d\n",addr,blksize(addr)); */
addr = addr + blksize(addr);
startaddr = addr;
cnt = 0;
}
}
if (startaddr != memsize)
{/* there is a last block to write out */
/* nprintf(OF_DEBUG,"writing last block startaddr %d cnt %d \n",startaddr,cnt); */
testerr(writeblock(f1, (char *) &cnt, (long) sizeof cnt, false, 0L, 0));
testerr(writeblock(f1, (char *) &startaddr, (long) sizeof addr, false, 0L, 0));
testerr(writeblock(f1, (char *) &mem[startaddr], (long) bytespu * cnt, false, 0L, 0));
}
/* write final cnt == 0 to signal end of workspace */
cnt = 0;
testerr(writeblock(f1, (char *) &cnt, (long) sizeof cnt, false, 0L, 0));
closefile(f1);
return;
fail: /* the testerr and testrderr macros branch here on an error */
nprintf(OF_NORMAL_LOG, errmsgptr);
nprintf(OF_NORMAL_LOG, "workspace failed to write correctly\n");
closefile(f1);
exit_cover(NC_WS_WRITE_ERR_W);
}
/**
* Description not yet available.
* \param
*/
void laplace_approximation_calculator::
check_hessian_type(function_minimizer * pfmin)
{
int ip = 0;
if (quadratic_prior::get_num_quadratic_prior()>0)
{
hesstype=4;
if (allocated(Hess))
{
if (Hess.indexmax()!=usize)
{
Hess.deallocate();
Hess.allocate(1,usize,1,usize);
}
}
else
{
Hess.allocate(1,usize,1,usize);
}
if (allocated(Hessadjoint))
{
if (Hessadjoint.indexmax()!=usize)
{
Hessadjoint.deallocate();
Hessadjoint.allocate(1,usize,1,usize);
}
}
else
{
Hessadjoint.allocate(1,usize,1,usize);
}
return;
}
else
{
int nv=initial_df1b2params::set_index();
if (allocated(used_flags))
{
if (used_flags.indexmax() != nv)
{
used_flags.safe_deallocate();
}
}
if (!allocated(used_flags))
{
used_flags.safe_allocate(1,nv);
}
//for (ip=1;ip<=num_der_blocks;ip++)
{
used_flags.initialize();
// do we need to reallocate memory for df1b2variables?
check_for_need_to_reallocate(ip);
df1b2_gradlist::set_no_derivatives();
//cout << re_objective_function_value::pobjfun << endl;
//cout << re_objective_function_value::pobjfun->ptr << endl;
(*re_objective_function_value::pobjfun)=0;
df1b2variable pen=0.0;
df1b2variable zz=0.0;
initial_df1b2params::reset(y,pen);
// call function to do block diagonal newton-raphson
// the step vector from the newton-raphson is in the vector step
df1b2_gradlist::set_no_derivatives();
funnel_init_var::lapprox=this;
block_diagonal_flag=5;
quadratic_prior::in_qp_calculations=1;
if (sparse_hessian_flag)
{
// just to get the number of separable calls
separable_calls_counter=0;
pfmin->AD_uf_inner();
// allocate space for uncompressed sparse hessian information
//num_separable_calls=separable_calls_counter;
if (triplet_information==0)
{
triplet_information =new i3_array(1,separable_calls_counter);
}
else if ( triplet_information->indexmax() != separable_calls_counter)
{
delete triplet_information;
triplet_information =new i3_array(1,separable_calls_counter);
}
triplet_information->initialize();
separable_calls_counter=0;
}
pfmin->pre_user_function();
if (sparse_hessian_flag)
{
// turn triplet_informaiton into compressed_triplet_information
int mmin= triplet_information->indexmin();
int mmax= triplet_information->indexmax();
int ndim=0;
for (int i=mmin;i<=mmax;i++)
{
if (allocated((*triplet_information)(i)))
{
ndim+=(*triplet_information)(i,1).indexmax();
}
}
if (compressed_triplet_information)
{
delete compressed_triplet_information;
compressed_triplet_information=0;
}
compressed_triplet_information=new imatrix(1,ndim,1,3);
(*compressed_triplet_information)(3).fill_seqadd(1,1);
int ii=0;
for (int i=mmin;i<=mmax;i++)
{
if (allocated((*triplet_information)(i)))
{
int jmin=(*triplet_information)(i,1).indexmin();
int jmax=(*triplet_information)(i,1).indexmax();
for (int j=jmin;j<=jmax;j++)
{
ii++;
(*compressed_triplet_information)(ii,1)=
(*triplet_information)(i,1,j);
(*compressed_triplet_information)(ii,2)=
(*triplet_information)(i,2,j);
(*compressed_triplet_information)(ii,3)=ii;
}
}
}
imatrix & cti= *compressed_triplet_information;
cti=sort(cti,1);
int lmin=1;
int lmax=0;
for (int i=2;i<=ndim;i++)
{
if (cti(i,1)>cti(i-1,1))
{
lmax=i-1;
cti.sub(lmin,lmax)=sort(cti.sub(lmin,lmax),2);
lmin=i;
}
}
cti.sub(lmin,ndim)=sort(cti.sub(lmin,ndim),2);
imatrix tmp=trans(cti);
delete compressed_triplet_information;
compressed_triplet_information=new imatrix(tmp);
}
quadratic_prior::in_qp_calculations=0;
int non_block_diagonal=0;
for (int i=xsize+1;i<=xsize+usize;i++)
{
if (used_flags(i)>1)
{
non_block_diagonal=1;
break;
}
}
if (non_block_diagonal)
{
if (bw< usize/2 && sparse_hessian_flag==0)
{
hesstype=3; //banded
if (bHess)
{
if (bHess->bandwidth() !=bw)
{
delete bHess;
bHess = new banded_symmetric_dmatrix(1,usize,bw);
if (bHess==0)
{
cerr << "Error allocating banded_symmetric_dmatrix" << endl;
ad_exit(1);
}
}
}
else
{
bHess = new banded_symmetric_dmatrix(1,usize,bw);
if (bHess==0)
{
cerr << "Error allocating banded_symmetric_dmatrix" << endl;
ad_exit(1);
}
}
if (bHessadjoint)
{
if (bHessadjoint->bandwidth() !=bw)
{
delete bHessadjoint;
bHessadjoint = new banded_symmetric_dmatrix(1,usize,bw);
if (bHessadjoint==0)
{
cerr << "Error allocating banded_symmetric_dmatrix" << endl;
ad_exit(1);
}
}
}
else
{
bHessadjoint = new banded_symmetric_dmatrix(1,usize,bw);
if (bHessadjoint==0)
{
cerr << "Error allocating banded_symmetric_dmatrix" << endl;
ad_exit(1);
}
}
}
else
{
//check_sparse_matrix_structure();
hesstype=4; // band is so wide so use full matrix
if (bHess)
{
delete bHess;
bHess=0;
}
if (bHessadjoint)
{
delete bHessadjoint;
bHessadjoint=0;
}
if (allocated(Hess))
{
if (sparse_hessian_flag)
{
Hess.deallocate();
}
else
{
if (Hess.indexmax() != usize)
{
Hess.deallocate();
Hess.allocate(1,usize,1,usize);
}
}
}
else
{
if (sparse_hessian_flag==0)
Hess.allocate(1,usize,1,usize);
}
if (sparse_hessian_flag)
{
make_sparse_triplet();
}
if (allocated(Hessadjoint))
{
if (sparse_hessian_flag)
{
Hess.deallocate();
}
else
{
if (Hessadjoint.indexmax() != usize)
{
Hessadjoint.deallocate();
Hessadjoint.allocate(1,usize,1,usize);
}
}
}
else
{
if (sparse_hessian_flag==0)
Hessadjoint.allocate(1,usize,1,usize);
}
}
}
else
{
hesstype=2;
}
if (hesstype==2 && num_importance_samples>0)
{
if (importance_sampling_components)
{
delete importance_sampling_components;
importance_sampling_components=0;
}
importance_sampling_components=
new dvar_matrix(1,pmin->lapprox->num_separable_calls,
1,num_importance_samples);
}
if (hesstype==2 && (num_importance_samples>0 || use_gauss_hermite>0))
{
const ivector & itmp=(*num_local_re_array)(1,num_separable_calls);
const ivector & itmpf=(*num_local_fixed_array)(1,num_separable_calls);
// ****************************************************
// ****************************************************
if (antiflag>0)
{
// generate antithetical rv's
generate_antithetical_rvs();
}
if (use_gauss_hermite>0)
{
if (gh)
{
delete gh;
gh=0;
}
gh=new gauss_hermite_stuff(this,use_gauss_hermite,
num_separable_calls,itmp);
}
if (block_diagonal_vch)
{
delete block_diagonal_vch;
block_diagonal_vch=0;
}
block_diagonal_vch = new dvar3_array(1,num_separable_calls,
1,itmp,1,itmp);
if (block_diagonal_ch)
{
delete block_diagonal_ch;
block_diagonal_ch=0;
}
block_diagonal_ch = new d3_array(1,num_separable_calls,
1,itmp,1,itmp);
if (block_diagonal_hessian)
{
delete block_diagonal_hessian;
block_diagonal_hessian=0;
}
block_diagonal_hessian = new d3_array(1,num_separable_calls,
1,itmp,1,itmp);
if (block_diagonal_hessian ==0)
{
cerr << "error_allocating d3_array" << endl;
ad_exit(1);
}
if (block_diagonal_re_list)
{
delete block_diagonal_re_list;
block_diagonal_re_list = 0;
}
block_diagonal_re_list = new imatrix(1,num_separable_calls,
1,itmp);
if (block_diagonal_re_list == 0)
{
cerr << "error_allocating imatrix" << endl;
ad_exit(1);
}
if (block_diagonal_fe_list)
{
delete block_diagonal_fe_list;
block_diagonal_fe_list = 0;
}
block_diagonal_fe_list = new imatrix(1,num_separable_calls,
1,itmpf);
if (block_diagonal_fe_list ==0)
{
cerr << "error_allocating imatrix" << endl;
ad_exit(1);
}
// ****************************************************
if (block_diagonal_Dux)
{
delete block_diagonal_Dux;
block_diagonal_Dux=0;
}
block_diagonal_Dux = new d3_array(1,num_separable_calls,
1,itmp,1,itmpf);
if (block_diagonal_Dux ==0)
{
cerr << "error_allocating d3_array" << endl;
ad_exit(1);
}
// ****************************************************
// ****************************************************
if (block_diagonal_vhessian)
{
delete block_diagonal_vhessian;
block_diagonal_vhessian=0;
}
block_diagonal_vhessian = new dvar3_array(1,num_separable_calls,
1,itmp,1,itmp);
if (block_diagonal_vhessian ==0)
{
cerr << "error_allocating d3_array" << endl;
ad_exit(1);
}
if (block_diagonal_vhessianadjoint)
{
delete block_diagonal_vhessianadjoint;
block_diagonal_vhessianadjoint=0;
}
block_diagonal_vhessianadjoint = new d3_array(1,num_separable_calls,
1,itmp,1,itmp);
if (block_diagonal_vhessianadjoint ==0)
{
cerr << "error_allocating d3_array" << endl;
ad_exit(1);
}
}
funnel_init_var::lapprox=0;
block_diagonal_flag=0;
pen.deallocate();
}
}
}
/**
* Description not yet available.
* \param
*/
double do_gauss_hermite_block_diagonal_multi(const dvector& x,
const dvector& u0,const dmatrix& Hess,const dvector& _xadjoint,
const dvector& _uadjoint,const dmatrix& _Hessadjoint,
function_minimizer * pmin)
{
ADUNCONST(dvector,xadjoint)
ADUNCONST(dvector,uadjoint)
//ADUNCONST(dmatrix,Hessadjoint)
dvector & w= *(pmin->multinomial_weights);
const int xs=x.size();
const int us=u0.size();
gradient_structure::set_NO_DERIVATIVES();
int nsc=pmin->lapprox->num_separable_calls;
const ivector lrea = (*pmin->lapprox->num_local_re_array)(1,nsc);
int hroom = sum(square(lrea));
int nvar=x.size()+u0.size()+hroom;
independent_variables y(1,nvar);
// need to set random effects active together with whatever
// init parameters should be active in this phase
initial_params::set_inactive_only_random_effects();
initial_params::set_active_random_effects();
/*int onvar=*/initial_params::nvarcalc();
initial_params::xinit(y); // get the initial values into the
// do we need this next line?
y(1,xs)=x;
int i,j;
// contribution for quadratic prior
if (quadratic_prior::get_num_quadratic_prior()>0)
{
//Hess+=quadratic_prior::get_cHessian_contribution();
int & vxs = (int&)(xs);
quadratic_prior::get_cHessian_contribution(Hess,vxs);
}
// Here need hooks for sparse matrix structures
dvar3_array & block_diagonal_vhessian=
*pmin->lapprox->block_diagonal_vhessian;
block_diagonal_vhessian.initialize();
dvar3_array& block_diagonal_ch=
*pmin->lapprox->block_diagonal_vch;
//dvar3_array(*pmin->lapprox->block_diagonal_ch);
int ii=xs+us+1;
d3_array& bdH=(*pmin->lapprox->block_diagonal_hessian);
int ic;
for (ic=1;ic<=nsc;ic++)
{
int lus=lrea(ic);
for (i=1;i<=lus;i++)
for (j=1;j<=lus;j++)
y(ii++)=bdH(ic)(i,j);
}
dvector g(1,nvar);
gradcalc(0,g);
gradient_structure::set_YES_DERIVATIVES();
dvar_vector vy=dvar_vector(y);
//initial_params::stddev_vscale(d,vy);
ii=xs+us+1;
if (initial_df1b2params::have_bounded_random_effects)
{
cerr << "can't do importance sampling with bounded random effects"
" at present" << endl;
ad_exit(1);
}
else
{
for (int ic=1;ic<=nsc;ic++)
{
int lus=lrea(ic);
if (lus>0)
{
for (i=1;i<=lus;i++)
{
for (j=1;j<=lus;j++)
{
block_diagonal_vhessian(ic,i,j)=vy(ii++);
}
}
block_diagonal_ch(ic)=
choleski_decomp(inv(block_diagonal_vhessian(ic)));
}
}
}
int nsamp=pmin->lapprox->use_gauss_hermite;
pmin->lapprox->in_gauss_hermite_phase=1;
dvar_vector sample_value(1,nsamp);
sample_value.initialize();
dvar_vector tau(1,us);;
// !!! This only works for one random efect in each separable call
// at present.
if (pmin->lapprox->gh->mi)
{
delete pmin->lapprox->gh->mi;
pmin->lapprox->gh->mi=0;
}
pmin->lapprox->gh->mi=new multi_index(1,nsamp,
pmin->lapprox->multi_random_effects);
multi_index & mi = *(pmin->lapprox->gh->mi);
//for (int is=1;is<=nsamp;is++)
dvector& xx=pmin->lapprox->gh->x;
do
{
int offset=0;
pmin->lapprox->num_separable_calls=0;
//pmin->lapprox->gh->is=is;
for (ic=1;ic<=nsc;ic++)
{
int lus=lrea(ic);
// will need vector stuff here when more than one random effect
if (lus>0)
{
//tau(offset+1,offset+lus).shift(1)=block_diagonal_ch(ic)(1,1)*
// pmin->lapprox->gh->x(is);
dvector xv(1,lus);
for (int iu=1;iu<=lus;iu++)
{
xv(iu)= xx(mi()(iu));
}
tau(offset+1,offset+lus).shift(1)=block_diagonal_ch(ic)*xv;
offset+=lus;
}
}
// have to reorder the terms to match the block diagonal hessian
imatrix & ls=*(pmin->lapprox->block_diagonal_re_list);
int mmin=ls.indexmin();
int mmax=ls.indexmax();
int ii=1;
int i;
for (i=mmin;i<=mmax;i++)
{
int cmin=ls(i).indexmin();
int cmax=ls(i).indexmax();
for (int j=cmin;j<=cmax;j++)
{
vy(ls(i,j))+=tau(ii++);
}
}
if (ii-1 != us)
{
cerr << "error in interface" << endl;
ad_exit(1);
}
initial_params::reset(vy); // get the values into the model
ii=1;
for (i=mmin;i<=mmax;i++)
{
int cmin=ls(i).indexmin();
int cmax=ls(i).indexmax();
for (int j=cmin;j<=cmax;j++)
{
vy(ls(i,j))-=tau(ii++);
}
}
*objective_function_value::pobjfun=0.0;
pmin->AD_uf_outer();
++mi;
}
while(mi.get_depth()<=pmin->lapprox->multi_random_effects);
nsc=pmin->lapprox->num_separable_calls;
dvariable vf=pmin->do_gauss_hermite_integration();
int sgn=0;
dvariable ld=0.0;
if (ad_comm::no_ln_det_choleski_flag)
{
for (int ic=1;ic<=nsc;ic++)
{
if (allocated(block_diagonal_vhessian(ic)))
{
ld+=w(2*ic)*ln_det(block_diagonal_vhessian(ic),sgn);
}
}
ld*=0.5;
}
else
{
for (int ic=1;ic<=nsc;ic++)
{
if (allocated(block_diagonal_vhessian(ic)))
{
ld+=w(2*ic)*ln_det_choleski(block_diagonal_vhessian(ic));
}
}
ld*=0.5;
}
vf+=ld;
//vf+=us*0.91893853320467241;
double f=value(vf);
gradcalc(nvar,g);
// put uhat back into the model
gradient_structure::set_NO_DERIVATIVES();
vy(xs+1,xs+us).shift(1)=u0;
initial_params::reset(vy); // get the values into the model
gradient_structure::set_YES_DERIVATIVES();
pmin->lapprox->in_gauss_hermite_phase=0;
ii=1;
for (i=1;i<=xs;i++)
xadjoint(i)=g(ii++);
for (i=1;i<=us;i++)
uadjoint(i)=g(ii++);
for (ic=1;ic<=nsc;ic++)
{
int lus=lrea(ic);
for (i=1;i<=lus;i++)
{
for (j=1;j<=lus;j++)
{
(*pmin->lapprox->block_diagonal_vhessianadjoint)(ic)(i,j)=g(ii++);
}
}
}
return f;
}
std::string
Buffer::hexify (bool ascii)
{
return gnash::hexify(_data.get(), allocated(), ascii);
}
void copyBeginning(const denseFlagVector<T> &v, int n){
assert(allocated() && len >= v.length());
copy(v.d,v.d+v.length(),d);
}
/**
* Description not yet available.
* \param
*/
void laplace_approximation_calculator::generate_antithetical_rvs()
{
// number of random vectors
const ivector & itmp=(*num_local_re_array)(1,num_separable_calls);
//const ivector & itmpf=(*num_local_fixed_array)(1,num_separable_calls);
for (int i=2;i<=num_separable_calls;i++)
{
if (itmp(i) != itmp(i-1))
{
cerr << "At present can only use antithetical rv's when "
"all separable calls are the same size" << endl;
ad_exit(1);
}
}
int n=itmp(1);
int samplesize=num_importance_samples;
// mesh size
double delta=0.01;
// maximum of distribution is near here
double mid=sqrt(double(n-1));
dmatrix weights(1,2*n,1,2);
double spread=15;
if (mid-spread<=0.001)
spread=mid-0.1;
double ssum=0.0;
double x=0.0;
double tmax=(n-1)*log(mid)-0.5*mid*mid;
for (x=mid-spread;x<=mid+spread;x+=delta)
{
ssum+=exp((n-1)*log(x)-0.5*x*x-tmax);
}
double tsum=0;
dvector dist(1,samplesize+1);
dist.initialize();
int is=0;
int ii;
for (x=mid-spread;x<=mid+spread;x+=delta)
{
tsum+=exp((n-1)*log(x)-0.5*x*x-tmax)/ssum*samplesize;
int ns=int(tsum);
for (ii=1;ii<=ns;ii++)
{
dist(++is)=x;
}
tsum-=ns;
}
if (is==samplesize-1)
{
dist(samplesize)=mid;
}
else if (is<samplesize-1)
{
cerr << "This can't happen" << endl;
exit(1);
}
// get random numbers
random_number_generator rng(rseed);
if (antiepsilon)
{
if (allocated(*antiepsilon))
{
delete antiepsilon;
antiepsilon=0;
}
}
antiepsilon=new dmatrix(1,samplesize,1,n);
dmatrix & M=*antiepsilon;
M.fill_randn(rng);
for (int i=1;i<=samplesize;i++)
{
M(i)=M(i)/norm(M(i));
}
int nvar=(samplesize-1)*n;
independent_variables xx(1,nvar);
ii=0;
for (int i=2;i<=samplesize;i++)
{
for (int j=1;j<=n;j++)
{
xx(++ii)=M(i,j);
}
}
fmmt1 fmc(nvar,5);
//fmm fmc(nvar,5);
fmc.noprintx=1;
fmc.iprint=10;
fmc.maxfn=2500;
fmc.crit=1.e-6;
double f;
double fbest=1.e+50;;
dvector g(1,nvar);
dvector gbest(1,nvar);
dvector xbest(1,nvar);
gbest.fill_seqadd(1.e+50,0.);
{
while (fmc.ireturn>=0)
{
//int badflag=0;
fmc.fmin(f,xx,g);
if (fmc.ihang)
{
//int hang_flag=fmc.ihang;
//double maxg=max(g);
//double ccrit=fmc.crit;
//int current_ifn=fmc.ifn;
}
if (fmc.ireturn>0)
{
f=fcomp1(xx,dist,samplesize,n,g,M);
if (f < fbest)
{
fbest=f;
gbest=g;
xbest=xx;
}
}
}
xx=xbest;
}
ii=0;
for (int i=2;i<=samplesize;i++)
{
for (int j=1;j<=n;j++)
{
M(i,j)=xx(++ii);
}
}
for (int i=1;i<=samplesize;i++)
{
M(i)*=dist(i)/norm(M(i));
}
}
void copyToPosition(const T *b, int start, int n){
assert(allocated() && len >= start+n);
copy(b,b+n,&d[start]);
}
// Convert each byte into its hex representation
std::string
Buffer::hexify ()
{
return gnash::hexify(_data.get(), allocated(), false);
}
