DiscreteModel::DiscreteModel(const vectori &categories,
bopt_params parameters):
BayesOptBase(categories.size(),parameters)
{
mDims = categories.size();
utils::buildGrid(categories,mInputSet);
}
/*! @brief construct a tree of Parts
*
* Given a set of Part components (filters, parents, w, bias), recursively construct
* a tree of Parts
* @param filters
* @param parents
* @return
*/
Part Part::constructPartHierarchy(vector2DMat& filters, vectori& parents) {
// error checking
assert(filters.size() == parents.size());
// construct the Part tree, from the root node
return constructPartHierarchyRecursive(filters, parents, 0, 0);
}
int main() {
optimize_io
int i, n, a, b;
lli t, x;
cin >> N;
for (i = 0; i < N; ++i) {
cin >> x;
PB(A, MP(x, i + 1));
sum += x;
}
sort(ALL(A));
for (i = 0; i < N; ++i) {
t = sum - A[i].X;
x = t / 2;
if ((2 * x) == t) {
a = lower(x);
b = upper(x);
n = b - a;
if (n > 0 and a < N and A[a].X == x and !(n == 1 and x == A[i].X)) {
PB(ans, A[i].Y);
}
}
}
cout << ans.size() << '\n';
for (i = 0, n = ans.size(); i < n; ++i) {
cout << ans[i] << ' ';
}
cout << '\n';
return 0;
}
int main() {
optimize_io
lli x;
int x1, y1, x2, y2;
cin >> T;
FOR(int, t, 0, T) {
cin >> N;
A.clear();
FOR(int, i, 0, N) {
cin >> x;
PB(A, x);
}
init();
build(0, 0, N - 1);
cin >> M;
FOR(int, i, 0, M) {
cin >> x1 >> y1 >> x2 >> y2;
if (y1 < x2) {
cout << q(x1, y1).suffixSum + q(y1 + 1, x2 - 1).sum + q(x2, y2).prefixSum << '\n';
} else {
cout << max(q(x2, y1).maxSum,
max(q(x1, x2 - 1).suffixSum + q(x2, y2).prefixSum,
q(x1, y1).suffixSum + q(y1 + 1, y2).prefixSum
)
) << '\n';
}
}
}
return 0;
}
//! convert a vector of integers from Matlab 1-based indexing to C++ 0-based indexing
static inline void zeroIndex(vectori& idx) {
for (unsigned int n = 0; n < idx.size(); ++n) idx[n] -= 1;
}
void EdgeBoxGenerator::clusterEdges( arrayf &E, arrayf &O, arrayf &V )
{
int c, r, cd, rd, i, j; h=E._h; w=E._w;
// greedily merge connected edge pixels into clusters (create _segIds)
_segIds.init(h,w); _segCnt=1;
for( c=0; c<w; c++ ) for( r=0; r<h; r++ ) {
if( c==0 || r==0 || c==w-1 || r==h-1 || E.val(c,r)<=_edgeMinMag )
_segIds.val(c,r)=-1; else _segIds.val(c,r)=0;
}
for( c=1; c<w-1; c++ ) for( r=1; r<h-1; r++ ) {
if(_segIds.val(c,r)!=0) continue;
float sumv=0; int c0=c, r0=r; vectorf vs; vectori cs, rs;
while( sumv < _edgeMergeThr ) {
_segIds.val(c0,r0)=_segCnt;
float o0 = O.val(c0,r0), o1, v; bool found;
for( cd=-1; cd<=1; cd++ ) for( rd=-1; rd<=1; rd++ ) {
if( _segIds.val(c0+cd,r0+rd)!=0 ) continue; found=false;
for( i=0; i<cs.size(); i++ )
if( cs[i]==c0+cd && rs[i]==r0+rd ) { found=true; break; }
if( found ) continue; o1=O.val(c0+cd,r0+rd);
v=fabs(o1-o0)/PI; if(v>.5) v=1-v;
vs.push_back(v); cs.push_back(c0+cd); rs.push_back(r0+rd);
}
float minv=1000; j=0;
for( i=0; i<vs.size(); i++ ) if( vs[i]<minv ) {
minv=vs[i]; c0=cs[i]; r0=rs[i]; j=i;
}
sumv+=minv; if(minv<1000) vs[j]=1000;
}
_segCnt++;
}
// merge or remove small segments
_segMag.resize(_segCnt,0);
for( c=1; c<w-1; c++ ) for( r=1; r<h-1; r++ )
if( (j=_segIds.val(c,r))>0 ) _segMag[j]+=E.val(c,r);
for( c=1; c<w-1; c++ ) for( r=1; r<h-1; r++ )
if( (j=_segIds.val(c,r))>0 && _segMag[j]<=_clusterMinMag)
_segIds.val(c,r)=0;
i=1; while(i>0) {
i=0; for( c=1; c<w-1; c++ ) for( r=1; r<h-1; r++ ) {
if( _segIds.val(c,r)!=0 ) continue;
float o0=O.val(c,r), o1, v, minv=1000; j=0;
for( cd=-1; cd<=1; cd++ ) for( rd=-1; rd<=1; rd++ ) {
if( _segIds.val(c+cd,r+rd)<=0 ) continue; o1=O.val(c+cd,r+rd);
v=fabs(o1-o0)/PI; if(v>.5) v=1-v;
if( v<minv ) { minv=v; j=_segIds.val(c+cd,r+rd); }
}
_segIds.val(c,r)=j; if(j>0) i++;
}
}
// compactify representation
_segMag.assign(_segCnt,0); vectori map(_segCnt,0); _segCnt=1;
for( c=1; c<w-1; c++ ) for( r=1; r<h-1; r++ )
if( (j=_segIds.val(c,r))>0 ) _segMag[j]+=E.val(c,r);
for( i=0; i<_segMag.size(); i++ ) if( _segMag[i]>0 ) map[i]=_segCnt++;
for( c=1; c<w-1; c++ ) for( r=1; r<h-1; r++ )
if( (j=_segIds.val(c,r))>0 ) _segIds.val(c,r)=map[j];
// compute positional means and recompute _segMag
_segMag.assign(_segCnt,0); vectorf meanX(_segCnt,0), meanY(_segCnt,0);
vectorf meanOx(_segCnt,0), meanOy(_segCnt,0), meanO(_segCnt,0);
for( c=1; c<w-1; c++ ) for( r=1; r<h-1; r++ ) {
j=_segIds.val(c,r); if(j<=0) continue;
float m=E.val(c,r), o=O.val(c,r); _segMag[j]+=m;
meanOx[j]+=m*cos(2*o); meanOy[j]+=m*sin(2*o);
meanX[j]+=m*c; meanY[j]+=m*r;
}
for( i=0; i<_segCnt; i++ ) if( _segMag[i]>0 ) {
float m=_segMag[i]; meanX[i]/=m; meanY[i]/=m;
meanO[i]=atan2(meanOy[i]/m,meanOx[i]/m)/2;
}
// compute segment affinities
_segAff.resize(_segCnt); _segAffIdx.resize(_segCnt);
for(i=0; i<_segCnt; i++) _segAff[i].resize(0);
for(i=0; i<_segCnt; i++) _segAffIdx[i].resize(0);
const int rad = 2;
for( c=rad; c<w-rad; c++ ) for( r=rad; r<h-rad; r++ ) {
int s0=_segIds.val(c,r); if( s0<=0 ) continue;
for( cd=-rad; cd<=rad; cd++ ) for( rd=-rad; rd<=rad; rd++ ) {
int s1=_segIds.val(c+cd,r+rd); if(s1<=s0) continue;
bool found = false; for(i=0;i<_segAffIdx[s0].size();i++)
if(_segAffIdx[s0][i] == s1) { found=true; break; }
if( found ) continue;
float o=atan2(meanY[s0]-meanY[s1],meanX[s0]-meanX[s1])+PI/2;
float a=fabs(cos(meanO[s0]-o)*cos(meanO[s1]-o)); a=pow(a,_gamma);
_segAff[s0].push_back(a); _segAffIdx[s0].push_back(s1);
_segAff[s1].push_back(a); _segAffIdx[s1].push_back(s0);
}
}
// compute _segC and _segR
_segC.resize(_segCnt); _segR.resize(_segCnt);
for( c=1; c<w-1; c++ ) for( r=1; r<h-1; r++ )
if( (j=_segIds.val(c,r))>0 ) { _segC[j]=c; _segR[j]=r; }
// optionally create visualization (assume memory initialized is 3*w*h)
if( V._x ) for( c=0; c<w; c++ ) for( r=0; r<h; r++ ) {
i=_segIds.val(c,r);
V.val(c+w*0,r) = i<=0 ? 1 : ((123*i + 128)%255)/255.0f;
V.val(c+w*1,r) = i<=0 ? 1 : ((7*i + 3)%255)/255.0f;
V.val(c+w*2,r) = i<=0 ? 1 : ((174*i + 80)%255)/255.0f;
}
}
static vectori find(vectori vals, int val) {
vectori idx;
for (unsigned int n = 0; n < vals.size(); ++n) if(vals[n] == val) idx.push_back(n);
return idx;
}
