int main(int argc, char* argv[]) {
//Here is the input file name:
ifstream input(argv[1]);
Network my_net(input);
ComponentPart cp;
auto_ptr<NetworkPartition> components(cp.partition(my_net));
int component = 0;
auto_ptr<Iterator<Network*> > comp_it(components->getComponents());
while(comp_it->moveNext()) {
stringstream name;
name << argv[1] << "." << component;
component++;
ofstream output(name.str().c_str());
Network* this_comp = comp_it->current();
this_comp->printTo(output);
}
return 1;
}
int main(int argc, char** argv) {
Network my_net(cin);
cout << "graph G {" << endl;
Network::EdgeSet printed_edges;
//Look on components:
ComponentPart cp;
NewmanCom comfinder;
auto_ptr<NetworkPartition> components_np(cp.partition(my_net));
const vector<Network*>& components = components_np->asVector();
vector<Network*>::const_iterator comp_it;
int community = 0;
for(comp_it = components.begin(); comp_it != components.end(); comp_it++) {
stringstream com;
com << community++;
Network* this_net = *comp_it;
printCommunities(comfinder, com.str(), *this_net, cout, printed_edges, 1);
}
printEdges(my_net, printed_edges, "[len=16, color=green]");
cout << "}" << endl;
return 1;
}
void SearchSpacePruning<T>::filterCandidatesByDepth(Parts& parts, vectorCandidate& candidates, const Mat& depth, const float zfactor) {
vectorCandidate new_candidates;
const unsigned int N = candidates.size();
for (unsigned int n = 0; n < N; ++n) {
const unsigned int c = candidates[n].component();
const unsigned int nparts = parts.nparts(c);
const vector<Rect>& boxes = candidates[n].parts();
for (unsigned int p = nparts-1; p >= 1; --p) {
ComponentPart part = parts.component(c,p);
Point anchor = part.anchor(0);
Rect child = boxes[part.self()];
Rect parent = boxes[part.parent().self()];
T cmed_depth = Math::median<T>(depth(child));
T pmed_depth = Math::median<T>(depth(parent));
if (cmed_depth > 0 && pmed_depth > 0) {
if (abs(cmed_depth-pmed_depth) > norm(anchor)*zfactor) break;
}
if (p == 1) new_candidates.push_back(candidates[n]);
}
}
candidates = new_candidates;
}
int main(int argc, char* argv[]) {
if( argc < 4 ) {
//
cout << "Usage: " << argv[0] << " graph blacklist whitelist" << endl;
return 0;
}
ifstream input(argv[1]);
ofstream spam(argv[2]);
ofstream ham(argv[3]);
Network graph(input);
double min_cc = 0.1;
double kmfrac = 0.6;
int min_size = 10;
double whole_graph_c = graph.getTransitivity();
//The subgraphs of spam and nonspam
set<Network*> spamg, hamg;
ComponentPart cp;
auto_ptr<NetworkPartition> netpart(cp.partition(graph));
vector<Network*> components(netpart->asVector());
vector<Network*>::iterator comp_it;
Edge* cut_edge = 0;
while( components.size() > 0 ) {
//Choose the biggest graph
comp_it = components.begin();
Network* this_net = *comp_it;
Network::NodePSet::const_iterator n_it;
int kmax = 0;
auto_ptr<NodeIterator> ni( this_net->getNodeIterator() );
while( ni->moveNext() ) {
Node* this_node = ni->current();
if( kmax < this_net->getDegree( this_node ) ) {
kmax = this_net->getDegree( this_node );
}
}
int size = this_net->getNodeSize();
if( size > min_size ) {
if( this_net->getClusterCoefficient() > min_cc ) {
//Ham:
hamg.insert( this_net );
}
//Maybe spam:
else if( (kmax + 1) < kmfrac * size ) {
if( this_net->getClusterCoefficient() == 0.0 ) {
//Spam:
spamg.insert( this_net );
}
else {
//Ambiguous
map<Edge*, double> bet;
Network* this_comp = *comp_it;
cut_edge = this_comp->getEdgeBetweenness(bet);
this_comp->remove( cut_edge->first );
this_comp->remove( cut_edge->second );
auto_ptr<NetworkPartition> split_part(cp.partition(*this_comp));
const vector<Network*>& split = split_part->asVector();
components.insert(components.end(), split.begin(), split.end());
}
}
}
//cout << "graphs: " << components.size() << endl;
components.erase( comp_it );
}
//Print out the nodes in each spamg and hamg
Network::NodePSet::const_iterator nit;
set<Network*>::iterator netsetit;
for(netsetit = spamg.begin(); netsetit != spamg.end(); netsetit++) {
auto_ptr<NodeIterator> ni2( (*netsetit)->getNodeIterator() );
while( ni2->moveNext() ) {
Node* this_node = ni2->current();
spam << this_node->toString() << endl;
}
}
for(netsetit = hamg.begin(); netsetit != hamg.end(); netsetit++) {
auto_ptr<NodeIterator> ni2( (*netsetit)->getNodeIterator() );
while( ni2->moveNext() ) {
Node* this_node = ni2->current();
ham << this_node->toString() << endl;
}
}
//Delete the memory:
return 1;
}
int main(int argc, char* argv[]) {
//Here we consider a network for a P2P topology
Ran1Random r1(time(NULL)), r2(-1000), r3(-1000000), r4(-5678);
ContentNetwork* my_cnet = 0;
Node* a_node = 0;
ContentNode* c_node = 0;
vector<Node*> node_vector;
//Here is all the content
vector<ContentNode*> content_vector;
int cont_count = 10;
//Look for each content 10 times:
int runs = 10*cont_count;
content_vector.reserve(cont_count);
for(int i = 0; i < cont_count; i++) {
content_vector.push_back(new ContentNode);
}
Network* my_net = 0;
//my_net = new PrefDelCompNetwork(RandomNetwork(10,1.0,r1),r1,0.75,1,0.6666);
//my_net = new DoublePrefAtNetwork( RandomNetwork(10,0.6,r1), r1, 1 );
//my_net = new PrefAtNetwork( RandomNetwork(2,1.0,r1), r1, 1 );
//ifstream input("internal.dat");
//my_net = new Network(input);
/*while(my_net->getNodes().size() < 40000) {
dynamic_cast<Incrementable*>(my_net)->incrementTime();
}*/
//The exponent -2.236 is what we fit to DPA network of the same size
PowerLawDRV pl(r1, -2.3,2,1000);
DegreeLawNetFac nf(40000, pl, r1, true);
my_net = nf.create();
//my_net = new DegreeLawRandomNetwork(nodes, *pl, r1);
//my_net = new GnutellaNetwork(argv[1],"limecrawler");
//my_net = new GnutellaNetwork(argv[1],"ripeanu");
Network *net = 0;
int choice = 2;
if(choice == 1){
ComponentPart cp;
auto_ptr<NetworkPartition> netpart(cp.partition(*my_net));
const vector<Network*>& net_set = netpart->asVector();
cout << "The number of components of this network is: " << net_set.size() << endl;
vector<Network*>::const_iterator comp_it;
cout << "The components have the following sizes: ";
for(comp_it = net_set.begin(); comp_it != net_set.end(); comp_it++){
net = *(comp_it);
cout << net->getNodeSize() << " ";
}
net = 0;
}
else{
//The exponent -1.81 is what we want for the email network
int nodes = 10000;
PowerLawDRV pl(r1,-2.1,2,(int)sqrt(nodes));
DegreeLawNetFac my_fact(nodes, pl, r1, true);
net = my_fact.create();
}
cout << endl;
//use the biggest connected component;
IntStats ns;
auto_ptr<NodeIterator> ni( net->getNodeIterator() );
ns.collect(net, &Network::getDegree, ni.get());
cout << "#nodes: " << net->getNodeSize() << endl
<< "#edges: " << net->getEdgeSize() << endl
<< "#<k>: " << ns.getAverage() << endl
<< "#<k^2>: " << ns.getMoment2() << endl;
cout << "#ttl p hit_rate edges_crossed nodes_reached" << endl;
//Put the nodes into a vector so we can randomly select them easier:
node_vector.clear();
ni->reset();
while( ni->moveNext() ) {
node_vector.push_back( ni->current() );
}
Message* query = 0;
Message* implant = 0;
int hits;
double last_hr = 0.0;
double hit_rate = 0.0;
double av_edges = 0.0;
double av_nodes = 0.0;
double p, delta_p = 0.01;
int ttl=15;
//for(ttl = 2; ttl < 12; ttl++)
{
p = 0.01;
//p = 1.0;
while(p < 0.51)
{
delete query;
query = new WalkAndPercMessage(r2,p,ttl,ttl);
//query = new MagnetMessage(r2,p,ttl);
//query = new BroadcastMessage(ttl);
delete implant;
implant = new AnycastMessage(r4,1,ttl);
//implant = new MagnetMessage(r2,0.75, 2 * ttl);
//implant = new WalkAndPercMessage(r4,p,ttl);
//implant = new AnycastMessage(r4,1,0);
delete my_cnet;
my_cnet = new ContentNetwork(*net);
// Insert the content into the network
for(int i = 0; i < cont_count; i++)
{
a_node = node_vector[ r3.getInt( node_vector.size() - 1 ) ];
c_node = content_vector[i];
auto_ptr<Network> implantnet( implant->visit( a_node, *net) );
auto_ptr<NodeIterator> nit( implantnet->getNodeIterator() );
my_cnet->insertContent(c_node, nit.get() );
}
//Do a random search for each node and see how many are successful.
Network::NodePSet res_nodes;
hits = 0;
int hit_nodes = 0, crossed_edges = 0;
for(int i = 0; i < runs; i++) {
a_node = node_vector[ r3.getInt( node_vector.size() - 1 ) ];
c_node = content_vector[ i % cont_count ];
auto_ptr<Network> visited( query->visit(a_node, *net) );
auto_ptr<NodeIterator> nit( visited->getNodeIterator() );
auto_ptr<Network> res_nodes(
my_cnet->queryForContent(c_node, nit.get() ) );
if( res_nodes->getNodeSize() != 0) {
++hits;
}
hit_nodes += visited->getNodeSize();
crossed_edges += visited->getEdgeSize();
}
hit_rate = (double)hits/(double)runs;
av_edges = (double)crossed_edges/(double)runs;
av_nodes = (double)hit_nodes/(double)runs;
cout << ttl << " " << p
<< " " << hit_rate
<< " " << av_edges
<< " " << av_nodes
<< endl;
/* Do an adapative step:
delta_p = 0.00005/abs(hit_rate - last_hr);
if( delta_p < 0.001 ) {
delta_p = 0.001;
}
if( delta_p > 0.1 ) {
delta_p = 0.1;
}*/
p += delta_p;
}
}
//Delete all the content
my_cnet->deleteContent();
delete my_cnet;
delete my_net;
delete net;
return 1;
}
void DynamicProgram<T>::argmin(Parts& parts, const vector2DMat& rootv, const vector2DMat& rooti, const vectorf scales, const vector4DMat& Ix, const vector4DMat& Iy, const vector4DMat& Ik, vectorCandidate& candidates) {
// for each scale, and each component, traverse back down the tree to retrieve the part positions
int nscales = scales.size();
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (int n = 0; n < nscales; ++n) {
T scale = scales[n];
for (int c = 0; c < parts.ncomponents(); ++c) {
// get the scores and indices for this tree of parts
const vector2DMat& Iknc = Ik[n][c];
const vector2DMat& Ixnc = Ix[n][c];
const vector2DMat& Iync = Iy[n][c];
int nparts = parts.nparts(c);
// threshold the root score
Mat over_thresh = rootv[n][c] > thresh_;
Mat rootmix = rooti[n][c];
vectorPoint inds;
find(over_thresh, inds);
for (int i = 0; i < inds.size(); ++i) {
Candidate candidate;
vectori xv(nparts);
vectori yv(nparts);
vectori mv(nparts);
for (int p = 0; p < nparts; ++p) {
ComponentPart part = parts.component(c, p);
// calculate the child's points from the parent's points
int x, y, m;
if (part.isRoot()) {
x = xv[0] = inds[i].x;
y = yv[0] = inds[i].y;
m = mv[0] = rootmix.at<int>(inds[i]);
} else {
int idx = part.parent().self();
x = xv[idx];
y = yv[idx];
m = mv[idx];
xv[p] = Ixnc[p][m].at<int>(y,x);
yv[p] = Iync[p][m].at<int>(y,x);
mv[p] = Iknc[p][m].at<int>(y,x);
}
// calculate the bounding rectangle and add it to the Candidate
Point ptwo = Point(2,2);
Point pone = Point(1,1);
Point xy1 = (Point(xv[p],yv[p])-ptwo)*scale;
Point xy2 = xy1 + Point(part.xsize(m), part.ysize(m))*scale - pone;
if (part.isRoot()) candidate.addPart(Rect(xy1, xy2), rootv[n][c].at<T>(inds[i]));
else candidate.addPart(Rect(xy1, xy2), 0.0);
}
#ifdef _OPENMP
#pragma omp critical(addcandidate)
#endif
{
candidates.push_back(candidate);
}
}
}
}
}
void DynamicProgram<T>::min(Parts& parts, vector2DMat& scores, vector4DMat& Ix, vector4DMat& Iy, vector4DMat& Ik, vector2DMat& rootv, vector2DMat& rooti) {
// initialize the outputs, preallocate vectors to make them thread safe
// TODO: better initialisation of Ix, Iy, Ik
const int nscales = scores.size();
const int ncomponents = parts.ncomponents();
Ix.resize(nscales, vector3DMat(ncomponents));
Iy.resize(nscales, vector3DMat(ncomponents));
Ik.resize(nscales, vector3DMat(ncomponents));
rootv.resize(nscales, vectorMat(ncomponents));
rooti.resize(nscales, vectorMat(ncomponents));
// for each scale, and each component, update the scores through message passing
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (int nc = 0; nc < nscales*ncomponents; ++nc) {
// calculate the inner loop variables from the dual variables
const int n = floor(nc / ncomponents);
const int c = nc % ncomponents;
// allocate the inner loop variables
Ix[n][c].resize(parts.nparts(c));
Iy[n][c].resize(parts.nparts(c));
Ik[n][c].resize(parts.nparts(c));
vectorMat ncscores(scores[n].size());
for (int p = parts.nparts(c)-1; p > 0; --p) {
// get the component part (which may have multiple mixtures associated with it)
ComponentPart cpart = parts.component(c, p);
int nmixtures = cpart.nmixtures();
Ix[n][c][p].resize(nmixtures);
Iy[n][c][p].resize(nmixtures);
Ik[n][c][p].resize(nmixtures);
// intermediate results for mixtures of this part
vectorMat scoresp;
vectorMat Ixp;
vectorMat Iyp;
for (int m = 0; m < nmixtures; ++m) {
// raw score outputs
Mat score_in, score_dt, Ix_dt, Iy_dt;
if (cpart.score(ncscores, m).empty()) {
score_in = cpart.score(scores[n], m);
} else {
score_in = cpart.score(ncscores, m);
}
// get the anchor position
Point anchor = cpart.anchor(m);
// compute the distance transform
distanceTransform(score_in, cpart.defw(m), anchor, score_dt, Ix_dt, Iy_dt);
scoresp.push_back(score_dt);
Ixp.push_back(Ix_dt);
Iyp.push_back(Iy_dt);
//cout << score_dt(Range(0,10), Range(0,10)) << endl;
// calculate a valid region of interest for the scores
/*
int X = score_in.cols;
int Y = score_in.rows;
int xmin = std::max(std::min(anchor.x, X), 0);
int ymin = std::max(std::min(anchor.y, Y), 0);
int xmax = std::min(std::max(anchor.x+X, 0), X);
int ymax = std::min(std::max(anchor.y+Y, 0), Y);
int xoff = std::max(-anchor.x, 0);
int yoff = std::max(-anchor.y, 0);
// shift the score by the Part's offset from its parent
Mat scorem = -numeric_limits<T>::infinity() * Mat::ones(score_dt.size(), score_dt.type());
Mat Ixm = Mat::zeros(Ix_dt.size(), Ix_dt.type());
Mat Iym = Mat::zeros(Iy_dt.size(), Iy_dt.type());
if (xoff < X && yoff < Y && (ymax - ymin) > 0 && (xmax - xmin) > 0) {
Mat score_dt_range = score_dt(Range(ymin, ymax), Range(xmin, xmax));
Mat score_range = scorem(Range(yoff, yoff+ymax-ymin), Range(xoff, xoff+xmax-xmin));
Mat Ix_dt_range = Ix_dt(Range(ymin, ymax), Range(xmin, xmax));
Mat Ixm_range = Ixm(Range(yoff, yoff+ymax-ymin), Range(xoff, xoff+xmax-xmin));
Mat Iy_dt_range = Iy_dt(Range(ymin, ymax), Range(xmin, xmax));
Mat Iym_range = Iym(Range(yoff, yoff+ymax-ymin), Range(xoff, xoff+xmax-xmin));
score_dt_range.copyTo(score_range);
Ix_dt_range.copyTo(Ixm_range);
Iy_dt_range.copyTo(Iym_range);
}
// push the scores onto the intermediate vectors
scoresp.push_back(scorem);
Ixp.push_back(Ixm);
Iyp.push_back(Iym);
*/
}
nmixtures = cpart.parent().nmixtures();
for (int m = 0; m < nmixtures; ++m) {
vectorMat weighted;
// weight each of the child scores
// TODO: More elegant way of handling bias
for (int mm = 0; mm < cpart.nmixtures(); ++mm) {
weighted.push_back(scoresp[mm] + cpart.bias(mm)[m]);
}
// compute the max over the mixtures
Mat maxv, maxi;
reduceMax(weighted, maxv, maxi);
// choose the best indices
Mat Ixm, Iym;
reducePickIndex<int>(Ixp, maxi, Ixm);
reducePickIndex<int>(Iyp, maxi, Iym);
Ix[n][c][p][m] = Ixm;
Iy[n][c][p][m] = Iym;
Ik[n][c][p][m] = maxi;
// update the parent's score
ComponentPart parent = cpart.parent();
if (parent.score(ncscores,m).empty()) parent.score(scores[n],m).copyTo(parent.score(ncscores,m));
parent.score(ncscores,m) += maxv;
//cout << parent.score(ncscores,m)(Range(0,10),Range(0,10)) << endl << endl;
if (parent.self() == 0) {
ComponentPart root = parts.component(c);
//cout << root.score(ncscores,m)(Range(0,10),Range(0,10)) << endl << endl;
}
//cout <<parent.self() << endl;
}
}
// add bias to the root score and find the best mixture
ComponentPart root = parts.component(c);
//cout << root.self() << endl;
Mat rncscore = root.score(ncscores,0);
//cout << rncscore(Range(1,10),Range(1,10)) << endl;
T bias = root.bias(0)[0];
vectorMat weighted;
// weight each of the child scores
for (int m = 0; m < root.nmixtures(); ++m) {
weighted.push_back(root.score(ncscores,m) + bias);
}
reduceMax(weighted, rootv[n][c], rooti[n][c]);
}
}
bool FileType::
parse(Context *context, plist::Dictionary const *dict, std::unordered_set<std::string> *seen, bool check)
{
if (!Specification::parse(context, dict, seen, false))
return false;
auto unpack = plist::Keys::Unpack("FileType", dict, seen);
auto MTs = unpack.cast <plist::Array> ("MIMETypes");
auto U = unpack.cast <plist::String> ("UTI");
auto Es = unpack.cast <plist::Array> ("Extensions");
auto TCs = unpack.cast <plist::Array> ("TypeCodes");
auto FPs = unpack.cast <plist::Array> ("FilenamePatterns");
auto MWs = unpack.cast <plist::Array> ("MagicWord");
auto L = unpack.cast <plist::String> ("Language");
auto CL = unpack.cast <plist::String> ("ComputerLanguage");
auto GDN = unpack.cast <plist::String> ("GccDialectName");
auto Ps = unpack.cast <plist::Array> ("Prefix");
auto P = unpack.cast <plist::String> ("Permissions");
auto BPIWE = unpack.cast <plist::Array> ("BuildPhaseInjectionsWhenEmbedding");
auto ATBR = unpack.coerce <plist::Boolean> ("AppliesToBuildRules");
auto ITF = unpack.coerce <plist::Boolean> ("IsTextFile");
auto IBPF = unpack.coerce <plist::Boolean> ("IsBuildPropertiesFile");
auto ISC = unpack.coerce <plist::Boolean> ("IsSourceCode");
auto ISSC = unpack.coerce <plist::Boolean> ("IsSwiftSourceCode");
auto IP = unpack.coerce <plist::Boolean> ("IsPreprocessed");
auto IT = unpack.coerce <plist::Boolean> ("IsTransparent");
auto ID = unpack.coerce <plist::Boolean> ("IsDocumentation");
auto IEM = unpack.coerce <plist::Boolean> ("IsEmbeddable");
auto IE = unpack.coerce <plist::Boolean> ("IsExecutable");
auto IEWG = unpack.coerce <plist::Boolean> ("IsExecutableWithGUI");
auto IA = unpack.coerce <plist::Boolean> ("IsApplication");
auto IB = unpack.coerce <plist::Boolean> ("IsBundle");
auto IL = unpack.coerce <plist::Boolean> ("IsLibrary");
auto IDL = unpack.coerce <plist::Boolean> ("IsDynamicLibrary");
auto ISL = unpack.coerce <plist::Boolean> ("IsStaticLibrary");
auto IF = unpack.coerce <plist::Boolean> ("IsFolder");
auto IWF = unpack.coerce <plist::Boolean> ("IsWrapperFolder");
auto ISFI = unpack.coerce <plist::Boolean> ("IsScannedForIncludes");
auto IFW = unpack.coerce <plist::Boolean> ("IsFrameworkWrapper");
auto ISFW = unpack.coerce <plist::Boolean> ("IsStaticFrameworkWrapper");
auto IPW = unpack.coerce <plist::Boolean> ("IsProjectWrapper");
auto ITW = unpack.coerce <plist::Boolean> ("IsTargetWrapper");
auto EPNs = unpack.cast <plist::Array> ("ExtraPropertyNames");
auto CPs = unpack.cast <plist::Dictionary> ("ComponentParts");
auto III = unpack.coerce <plist::Boolean> ("IncludeInIndex");
auto CSIII = unpack.coerce <plist::Boolean> ("CanSetIncludeInIndex");
auto RHT = unpack.coerce <plist::Boolean> ("RequiresHardTabs");
auto CNC = unpack.coerce <plist::Boolean> ("ContainsNativeCode");
auto PDS = unpack.cast <plist::String> ("PlistStructureDefinition");
auto CCDGI = unpack.coerce <plist::Boolean> ("ChangesCauseDependencyGraphInvalidation");
auto FABP = unpack.cast <plist::String> ("FallbackAutoroutingBuildPhase");
auto CSOC = unpack.coerce <plist::Boolean> ("CodeSignOnCopy");
auto RHOC = unpack.coerce <plist::Boolean> ("RemoveHeadersOnCopy");
auto VOC = unpack.coerce <plist::Boolean> ("ValidateOnCopy");
if (!unpack.complete(check)) {
fprintf(stderr, "%s", unpack.errorText().c_str());
}
if (U != nullptr) {
_uti = U->value();
}
if (Es != nullptr) {
_extensions = std::vector<std::string>();
for (size_t n = 0; n < Es->count(); n++) {
auto E = Es->value <plist::String> (n);
if (E != nullptr) {
_extensions->push_back(E->value());
}
}
}
if (MTs != nullptr) {
_mimeTypes = std::vector<std::string>();
for (size_t n = 0; n < MTs->count(); n++) {
auto MT = MTs->value <plist::String> (n);
if (MT != nullptr) {
_mimeTypes->push_back(MT->value());
}
}
}
if (TCs != nullptr) {
_typeCodes = std::vector<std::string>();
for (size_t n = 0; n < TCs->count(); n++) {
auto TC = TCs->value <plist::String> (n);
if (TC != nullptr) {
_typeCodes->push_back(TC->value());
}
}
}
if (FPs != nullptr) {
_filenamePatterns = std::vector<std::string>();
for (size_t n = 0; n < FPs->count(); n++) {
auto FP = FPs->value <plist::String> (n);
if (FP != nullptr) {
_filenamePatterns->push_back(FP->value());
}
}
}
if (MWs != nullptr) {
_magicWords = std::vector<std::vector<uint8_t>>();
for (size_t n = 0; n < MWs->count(); n++) {
if (auto MW = MWs->value <plist::String> (n)) {
std::vector<uint8_t> MWV = std::vector<uint8_t>(MW->value().begin(), MW->value().end());
_magicWords->push_back(MWV);
} else if (auto MW = MWs->value <plist::Data> (n)) {
_magicWords->push_back(MW->value());
}
}
}
if (L != nullptr) {
_language = L->value();
}
if (CL != nullptr) {
_computerLanguage = CL->value();
}
if (GDN != nullptr) {
_gccDialectName = GDN->value();
}
if (Ps != nullptr) {
_prefix = std::vector<std::string>();
for (size_t n = 0; n < Ps->count(); n++) {
auto P = Ps->value <plist::String> (n);
if (P != nullptr) {
_prefix->push_back(P->value());
}
}
}
if (P != nullptr) {
_permissions = P->value();
}
if (BPIWE != nullptr) {
_buildPhaseInjectionsWhenEmbedding = std::vector<BuildPhaseInjection>();
for (size_t n = 0; n < BPIWE->count(); n++) {
auto BPI = BPIWE->value <plist::Dictionary> (n);
if (BPI != nullptr) {
BuildPhaseInjection injection;
if (injection.parse(BPI)) {
_buildPhaseInjectionsWhenEmbedding->push_back(injection);
}
}
}
}
if (ATBR != nullptr) {
_appliesToBuildRules = ATBR->value();
}
if (IT != nullptr) {
_isTransparent = IT->value();
}
if (ID != nullptr) {
_isDocumentation = ID->value();
}
if (IBPF != nullptr) {
_isBuildPropertiesFile = IBPF->value();
}
if (ISC != nullptr) {
_isSourceCode = ISC->value();
}
if (ISSC != nullptr) {
_isSwiftSourceCode = ISSC->value();
}
if (IP != nullptr) {
_isPreprocessed = IP->value();
}
if (IT != nullptr) {
_isTransparent = IT->value();
}
if (IEM != nullptr) {
_isEmbeddable = IEM->value();
}
if (IE != nullptr) {
_isExecutable = IE->value();
}
if (IEWG != nullptr) {
_isExecutableWithGUI = IEWG->value();
}
if (IA != nullptr) {
_isApplication = IA->value();
}
if (IB != nullptr) {
_isBundle = IB->value();
}
if (IL != nullptr) {
_isLibrary = IL->value();
}
if (IDL != nullptr) {
_isDynamicLibrary = IDL->value();
}
if (ISL != nullptr) {
_isStaticLibrary = ISL->value();
}
if (IF != nullptr) {
_isFolder = IF->value();
}
if (IWF != nullptr) {
_isWrappedFolder = IWF->value();
}
if (IFW != nullptr) {
_isFrameworkWrapper = IFW->value();
}
if (ISFW != nullptr) {
_isStaticFrameworkWrapper = ISFW->value();
}
if (IPW != nullptr) {
_isProjectWrapper = IPW->value();
}
if (ITW != nullptr) {
_isTargetWrapper = ITW->value();
}
if (EPNs != nullptr) {
_extraPropertyNames = std::vector<std::string>();
for (size_t n = 0; n < EPNs->count(); n++) {
auto EPN = EPNs->value <plist::String> (n);
if (EPN != nullptr) {
_extraPropertyNames->push_back(EPN->value());
}
}
}
if (CPs != nullptr) {
_componentParts = std::vector<ComponentPart>();
for (size_t n = 0; n < CPs->count(); n++) {
auto K = CPs->key(n);
auto CP = CPs->value <plist::Array> (n);
if (CP != nullptr) {
ComponentPart cp;
if (cp.parse(K, CP)) {
_componentParts->push_back(cp);
}
}
}
}
if (ISFI != nullptr) {
_isScannedForIncludes = ISFI->value();
}
if (III != nullptr) {
_includeInIndex = III->value();
}
if (CSIII != nullptr) {
_canSetIncludeInIndex = CSIII->value();
}
if (RHT != nullptr) {
_requiresHardTabs = RHT->value();
}
if (CNC != nullptr) {
_containsNativeCode = CNC->value();
}
if (PDS != nullptr) {
_plistStructureDefinition = PDS->value();
}
if (CCDGI != nullptr) {
_changesCauseDependencyGraphInvalidation = CCDGI->value();
}
if (FABP != nullptr) {
_fallbackAutoroutingBuildPhase = FABP->value();
}
if (CSOC != nullptr) {
_codeSignOnCopy = CSOC->value();
}
if (RHOC != nullptr) {
_removeHeadersOnCopy = RHOC->value();
}
if (VOC != nullptr) {
_validateOnCopy = VOC->value();
}
return true;
}
void DynamicProgram<T>::min(Parts& parts, vector2DMat& scores, vector4DMat& Ix, vector4DMat& Iy, vector4DMat& Ik, vector2DMat& rootv, vector2DMat& rooti) {
// initialize the outputs, preallocate vectors to make them thread safe
// TODO: better initialisation of Ix, Iy, Ik
const unsigned int nscales = scores.size();
const unsigned int ncomponents = parts.ncomponents();
Ix.resize(nscales, vector3DMat(ncomponents));
Iy.resize(nscales, vector3DMat(ncomponents));
Ik.resize(nscales, vector3DMat(ncomponents));
rootv.resize(nscales, vectorMat(ncomponents));
rooti.resize(nscales, vectorMat(ncomponents));
// for each scale, and each component, update the scores through message passing
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (unsigned int nc = 0; nc < nscales*ncomponents; ++nc) {
// calculate the inner loop variables from the dual variables
const unsigned int n = floor(nc / ncomponents);
const unsigned int c = nc % ncomponents;
// allocate the inner loop variables
Ix[n][c].resize(parts.nparts(c));
Iy[n][c].resize(parts.nparts(c));
Ik[n][c].resize(parts.nparts(c));
vectorMat ncscores(scores[n].size());
for (int p = parts.nparts(c)-1; p > 0; --p) {
// get the component part (which may have multiple mixtures associated with it)
ComponentPart cpart = parts.component(c, p);
const unsigned int nmixtures = cpart.nmixtures();
const unsigned int pnmixtures = cpart.parent().nmixtures();
Ix[n][c][p].resize(pnmixtures);
Iy[n][c][p].resize(pnmixtures);
Ik[n][c][p].resize(pnmixtures);
// intermediate results for mixtures of this part
vectorMat scoresp;
vectorMat Ixp;
vectorMat Iyp;
for (unsigned int m = 0; m < nmixtures; ++m) {
// raw score outputs
Mat_<T> score_in, score_dt;
Mat_<int> Ix_dt, Iy_dt;
if (cpart.score(ncscores, m).empty()) {
score_in = cpart.score(scores[n], m);
} else {
score_in = cpart.score(ncscores, m);
}
// get the anchor position
Point anchor = cpart.anchor(m);
// compute the distance transform
vectorf w = cpart.defw(m);
Quadratic fx(-w[0], -w[1]);
Quadratic fy(-w[2], -w[3]);
dt_.compute(score_in, fx, fy, anchor, score_dt, Ix_dt, Iy_dt);
scoresp.push_back(score_dt);
Ixp.push_back(Ix_dt);
Iyp.push_back(Iy_dt);
}
for (unsigned int m = 0; m < pnmixtures; ++m) {
vectorMat weighted;
// weight each of the child scores
// TODO: More elegant way of handling bias
for (unsigned int mm = 0; mm < nmixtures; ++mm) {
weighted.push_back(scoresp[mm] + cpart.bias(mm)[m]);
}
// compute the max over the mixtures
Mat maxv, maxi;
Math::reduceMax<T>(weighted, maxv, maxi);
// choose the best indices
Mat Ixm, Iym;
Math::reducePickIndex<int>(Ixp, maxi, Ixm);
Math::reducePickIndex<int>(Iyp, maxi, Iym);
Ix[n][c][p][m] = Ixm;
Iy[n][c][p][m] = Iym;
Ik[n][c][p][m] = maxi;
// update the parent's score
ComponentPart parent = cpart.parent();
if (parent.score(ncscores,m).empty()) parent.score(scores[n],m).copyTo(parent.score(ncscores,m));
parent.score(ncscores,m) += maxv;
if (parent.self() == 0) {
ComponentPart root = parts.component(c);
}
}
}
// add bias to the root score and find the best mixture
ComponentPart root = parts.component(c);
Mat rncscore = root.score(ncscores,0);
T bias = root.bias(0)[0];
vectorMat weighted;
// weight each of the child scores
for (unsigned int m = 0; m < root.nmixtures(); ++m) {
weighted.push_back(root.score(ncscores,m) + bias);
}
Math::reduceMax<T>(weighted, rootv[n][c], rooti[n][c]);
}
}
