void Fragment::set_residue_indexes(kernel::Model *m, kernel::ParticleIndex pi,
Ints o) {
if (o.empty()) {
set_residue_indexes(m, pi, IntPairs());
return;
}
std::sort(o.begin(), o.end());
o.erase(std::unique(o.begin(), o.end()), o.end());
IntPairs pairs;
int begin = 0;
for (unsigned int i = 1; i < o.size(); ++i) {
if (o[i] != o[i - 1] + 1) {
pairs.push_back(IntPair(o[begin], o[i - 1] + 1));
begin = i;
}
}
pairs.push_back(IntPair(o[begin], o.back() + 1));
set_residue_indexes(m, pi, pairs);
using IMP::operator<<;
IMP_IF_CHECK(USAGE) {
for (unsigned int i = 0; i < o.size(); ++i) {
IMP_INTERNAL_CHECK(Fragment(m, pi).get_contains_residue(o[i]),
"Residue index not found after addition: "
<< o << " became " << pairs);
}
}
}
Ints get_triangles(SurfaceMeshGeometry *sg) {
Ints ret;
Ints::const_iterator it = sg->get_faces().begin();
while (it != sg->get_faces().end()) {
Ints::const_iterator eit = std::find(
it, static_cast<Ints::const_iterator>(sg->get_faces().end()), -1);
IMP_INTERNAL_CHECK(eit != sg->get_faces().end(), "No trailing -1");
Ints cur(it, eit);
Ints tris = get_triangulation_of_face(cur, sg->get_vertexes());
ret.insert(ret.end(), tris.begin(), tris.end());
it = eit;
++it;
}
return ret;
}
void KMCentersNode::compute_close_centers(const Ints &candidate_centers_inds,
Ints *close_centers_inds) {
const KMPoint *l, *h;
l = bnd_box_.get_point(0);
h = bnd_box_.get_point(1);
// first we calculate the center that is closest to the middle of the
// bounding box
int mid_center_ind = mid_center(candidate_centers_inds);
KMPoint *mid_cen = (*centers_)[mid_center_ind];
double box_dot = 0.; // holds (p-c').(c-c')
double cc_dot = 0.; // holds (c-c').(c-c')
for (Ints::const_iterator it = candidate_centers_inds.begin();
it != candidate_centers_inds.end(); it++) {
if (*it == mid_center_ind) {
close_centers_inds->push_back(*it);
} else {
KMPoint *cand_cen = (*centers_)[*it];
KMPoint closest_vertex = bnd_box_.find_closest_vertex(*cand_cen);
box_dot = 0.;
cc_dot = 0.;
for (int d = 0; d < bnd_box_.get_dim(); d++) {
double cc_comp = (*cand_cen)[d] - (*mid_cen)[d];
cc_dot += cc_comp * cc_comp; // increment dot product
if (cc_comp > 0) {
box_dot += cc_comp * ((*h)[d] - (*mid_cen)[d]);
} else {
box_dot += cc_comp * ((*l)[d] - (*mid_cen)[d]);
}
}
if (cc_dot < 2 * box_dot) {
close_centers_inds->push_back(*it);
}
}
}
}
void RestraintCache::save_cache(const kernel::ParticlesTemp &particle_ordering,
const kernel::RestraintsTemp &restraints,
RMF::HDF5::Group group,
unsigned int max_entries) {
RMF::HDF5::FloatDataSet1Ds scores;
RMF::HDF5::IntDataSet2Ds assignments;
base::map<kernel::Restraint *, int> restraint_index;
ParticleIndex particle_index = get_particle_index(particle_ordering);
Orders orders = get_orders(known_restraints_, restraints, particle_ordering);
// create data sets for restraints
for (unsigned int i = 0; i < restraints.size(); ++i) {
kernel::Restraint *r = restraints[i];
RestraintID rid =
get_restraint_id(particle_index, known_restraints_.find(r)->second,
restraint_index_.find(r)->second);
RMF::HDF5::Group g = group.add_child_group(r->get_name());
g.set_attribute<RMF::HDF5::IndexTraits>(
"restraint", RMF::HDF5::Indexes(1, rid.get_restraint_index()));
g.set_attribute<RMF::HDF5::IndexTraits>(
"particles", RMF::HDF5::Indexes(rid.get_particle_indexes().begin(),
rid.get_particle_indexes().end()));
scores.push_back(g.add_child_data_set<RMF::HDF5::FloatTraits, 1>("scores"));
assignments.push_back(
g.add_child_data_set<RMF::HDF5::IntTraits, 2>("assignments"));
restraint_index[r] = i;
}
// finally start saving them
unsigned int count = 0;
for (Cache::ContentIterator it = cache_.contents_begin();
it != cache_.contents_end(); ++it) {
int ri = restraint_index.find(it->key.get_restraint())->second;
Ints ord = orders[ri].get_list_ordered(it->key.get_assignment());
double score = it->value;
RMF::HDF5::DataSetIndexD<2> asz = assignments[ri].get_size();
RMF::HDF5::DataSetIndexD<1> row(asz[0]);
asz[1] = ord.size();
++asz[0];
assignments[ri].set_size(asz);
assignments[ri].set_row(row, RMF::HDF5::Ints(ord.begin(), ord.end()));
RMF::HDF5::DataSetIndexD<1> ssz = scores[ri].get_size();
RMF::HDF5::DataSetIndexD<1> nsz = ssz;
++nsz[0];
scores[ri].set_size(nsz);
scores[ri].set_value(ssz, score);
++count;
if (count > max_entries) break;
}
}
PartitionalClustering *
RecursivePartitionalClusteringMetric::create_full_clustering(
PartitionalClustering *center_cluster) {
IMP::base::Vector<Ints> clusters(center_cluster->get_number_of_clusters());
Ints reps(clusters.size());
for (unsigned int i = 0; i < clusters.size(); ++i) {
Ints outer = center_cluster->get_cluster(i);
reps[i] = clustering_->get_cluster_representative(
center_cluster->get_cluster_representative(i));
for (unsigned int j = 0; j < outer.size(); ++j) {
Ints inner = clustering_->get_cluster(outer[j]);
clusters[i].insert(clusters[i].end(), inner.begin(), inner.end());
}
}
IMP_NEW(internal::TrivialPartitionalClustering, ret, (clusters, reps));
validate_partitional_clustering(ret, metric_->get_number_of_items());
return ret.release();
}
double
get_slack_estimate(const ParticlesTemp& ps,
double upper_bound,
double step,
const RestraintsTemp &restraints,
bool derivatives,
Optimizer *opt,
ClosePairContainer *cpc) {
std::vector<Data> datas;
for (double slack=0; slack< upper_bound; slack+= step) {
IMP_LOG(VERBOSE, "Computing for " << slack << std::endl);
datas.push_back(Data());
datas.back().slack=slack;
{
boost::timer imp_timer;
int count=0;
base::SetLogState sl(opt->get_model(), SILENT);
do {
cpc->set_slack(slack);
cpc->update();
++count;
} while (imp_timer.elapsed()==0);
datas.back().ccost= imp_timer.elapsed()/count;
IMP_LOG(VERBOSE, "Close pair finding cost "
<< datas.back().ccost << std::endl);
}
{
boost::timer imp_timer;
double score=0;
int count=0;
int iters=1;
base::SetLogState sl(opt->get_model(), SILENT);
do {
for ( int j=0; j< iters; ++j) {
for (unsigned int i=0; i< restraints.size(); ++i) {
score+=restraints[i]->evaluate(derivatives);
// should restore
}
}
count += iters;
iters*=2;
} while (imp_timer.elapsed()==0);
datas.back().rcost= imp_timer.elapsed()/count;
IMP_LOG(VERBOSE, "Restraint evaluation cost "
<< datas.back().rcost << std::endl);
}
}
int ns=100;
int last_ns=1;
int opt_i=-1;
std::vector<Floats > dists(1, Floats(1,0.0));
std::vector< std::vector<algebra::Vector3D> >
pos(1, std::vector<algebra::Vector3D>(ps.size()));
for (unsigned int j=0; j< ps.size(); ++j) {
pos[0][j]=core::XYZ(ps[j]).get_coordinates();
}
do {
IMP_LOG(VERBOSE, "Stepping from " << last_ns << " to " << ns << std::endl);
dists.resize(ns, Floats(ns, 0.0));
for ( int i=0; i< last_ns; ++i) {
dists[i].resize(ns, 0.0);
}
pos.resize(ns, std::vector<algebra::Vector3D>(ps.size()));
base::SetLogState sl(opt->get_model(), SILENT);
for ( int i=last_ns; i< ns; ++i) {
opt->optimize(1);
for (unsigned int j=0; j< ps.size(); ++j) {
pos[i][j]=core::XYZ(ps[j]).get_coordinates();
}
}
for ( int i=last_ns; i< ns; ++i) {
for ( int j=0; j< i; ++j) {
double md=0;
for (unsigned int k=0; k< ps.size(); ++k) {
md= std::max(md, algebra::get_distance(pos[i][k], pos[j][k]));
}
dists[i][j]=md;
dists[j][i]=md;
}
}
// estimate lifetimes from slack
for (unsigned int i=0; i< datas.size(); ++i) {
Ints deaths;
for ( int j=0; j< ns; ++j) {
for ( int k=j+1; k < ns; ++k) {
if (dists[j][k]> datas[i].slack) {
deaths.push_back(k-j);
break;
}
}
}
std::sort(deaths.begin(), deaths.end());
// kaplan meier estimator
double ml=0;
if (deaths.empty()) {
ml= ns;
} else {
//double l=1;
IMP_INTERNAL_CHECK(deaths.size() < static_cast<unsigned int>(ns),
"Too much death");
double S=1;
for (unsigned int j=0; j< deaths.size(); ++j) {
double n= ns-j;
double t=(n-1.0)/n;
ml+=(S-t*S)*deaths[j];
S*=t;
}
}
datas[i].lifetime=ml;
IMP_LOG(VERBOSE, "Expected life of " << datas[i].slack
<< " is " << datas[i].lifetime << std::endl);
}
/**
C(s) is cost to compute
R(s) is const to eval restraints
L(s) is lifetime of slack
minimize C(s)/L(s)+R(s)
*/
// smooth
for (unsigned int i=1; i< datas.size()-1; ++i) {
datas[i].rcost=(datas[i].rcost+ datas[i-1].rcost+datas[i+1].rcost)/3.0;
datas[i].ccost=(datas[i].ccost+ datas[i-1].ccost+datas[i+1].ccost)/3.0;
datas[i].lifetime=(datas[i].lifetime
+ datas[i-1].lifetime+datas[i+1].lifetime)/3.0;
}
double min= std::numeric_limits<double>::max();
for (unsigned int i=0; i< datas.size(); ++i) {
double v= datas[i].rcost+ datas[i].ccost/datas[i].lifetime;
IMP_LOG(VERBOSE, "Cost of " << datas[i].slack << " is " << v
<< " from " << datas[i].rcost
<< " " << datas[i].ccost << " " << datas[i].lifetime
<< std::endl);
if (v < min) {
min=v;
opt_i=i;
}
}
last_ns=ns;
ns*=2;
IMP_LOG(VERBOSE, "Opt is " << datas[opt_i].slack << std::endl);
// 2 for the value, 2 for the doubling
// if it more than 1000, just decide that is enough
} while (datas[opt_i].lifetime > ns/4.0 && ns <1000);
return datas[opt_i].slack;
}
static void eraseElements(Ints& vect, int val) {
Ints::iterator begin = vect.begin();
Ints::iterator end = vect.end();
vect.erase(std::remove(begin, end, val), end);
}
