double Nonleaf::ClosestDiffTwo(Stat *Stats, int &i, int &j) const
{
Entry tmpent;
tmpent.Init(Stats->Dimension);
int i1,j1,imin,jmin;
double d, dmin;
if (actsize<2)
print_error("Nonleaf::ClosestDiffTwo","Less than 2 entries");
if (actsize==2) {
d=distance(Stats->GDtype,entry[0],entry[1]);
if (d==0)
print_error("Nonleaf::ClosestDiffTwo",
"Same 2 entries in a nonleaf: should not happen");
}
dmin=HUGE_DOUBLE;
imin=0;
jmin=1;
for (i1=0;i1<actsize-1;i1++)
for (j1=i1+1;j1<actsize;j1++) {
d = distance(Stats->GDtype,entry[i1],entry[j1]);
if (d>0 && d<dmin) {
imin = i1;
jmin = j1;
dmin = d;}
}
i=imin;
j=jmin;
tmpent.Add(entry[i],entry[j]);
return tmpent.Fitness(Stats->Ftype);
}
double Node::Fitness(short ftype) const {
Entry tmpent;
tmpent.Init(entry[0].sx.dim);
this->CF(tmpent);
return tmpent.Fitness(ftype);
}
void Hierarchy0(int &n, // final number of clusters
const int K, // final number of clusters
Entry **entries,
short GDtype,
short Ftype,
double Ft)
{
if (n<=1) return;
int i, j, imin, jmin, done;
short *checked = new short[n];
memset(checked,0,n*sizeof(short));
// 0: unchecked;
// -1: exceeds the given threshold if merged with nearest neighbor;
// -2: nonexistant after merging.
double *dist = new double[n*(n-1)/2];
double d, dmin;
Entry tmpent;
tmpent.Init((*entries)[0].sx.dim);
dmin = HUGE; // compute all initial distances and closest pair
for (i=0; i<n-1; i++)
for (j=i+1; j<n; j++) {
d = distance(GDtype,(*entries)[i],(*entries)[j]);
dist[i*n-i*(i+1)/2+j-i-1] = d;
if (d<dmin) {
dmin = d;
imin = i;
jmin = j;
}
}
if (K==0) {// ****** case 1 ****** cluster by threshold ft
done = FALSE;
while (done==FALSE) {
tmpent.Add((*entries)[imin],(*entries)[jmin]);
if (tmpent.Fitness(Ftype) < Ft) {
// within the threshold
(*entries)[imin] += (*entries)[jmin];
checked[jmin] = -2;
for (i=0; i<imin; i++) {
if (checked[i]==0) {
dist[i*n-i*(i+1)/2+imin-i-1] =
distance(GDtype,(*entries)[i],(*entries)[imin]);
}}
for (j=imin+1; j<n; j++) {
if (checked[j]==0) {
dist[imin*n-imin*(imin+1)/2+j-imin-1] =
distance(GDtype,(*entries)[imin],(*entries)[j]);
}}
}
else {
// exceeds the threshold
checked[imin] = -1;
checked[jmin] = -1;
}
done = TRUE;
dmin = HUGE;
for (i=0; i<n-1; i++) {
if (checked[i]==0) {
for (j=i+1; j<n; j++) {
if (checked[j]==0) {
d = dist[i*n-i*(i+1)/2+j-i-1];
if (d<dmin) {
done = FALSE;
dmin = d;
imin = i;
jmin = j;
}}}}}
} // end of while
} // end of if
else { // ***** case 2 ***** cluster by number k
done = n;
while (done > K) {
(*entries)[imin] += (*entries)[jmin];
checked[jmin] = -2;
done--;
for (i=0; i<imin; i++) {
if (checked[i]==0) {
dist[i*n-i*(i+1)/2+imin-i-1] =
distance(GDtype,(*entries)[i],(*entries)[imin]);
}}
for (j=imin+1; j<n; j++) {
if (checked[j]==0) {
dist[imin*n-imin*(imin+1)/2+j-imin-1] =
distance(GDtype,(*entries)[imin],(*entries)[j]);
}}
dmin = HUGE;
for (i=0; i<n-1; i++) {
if (checked[i]==0) {
for (j=i+1; j<n; j++) {
if (checked[j]==0) {
d = dist[i*n-i*(i+1)/2+j-i-1];
if (d<dmin) {
dmin = d;
imin = i;
jmin = j;
}}}}}
} // end of while
} // end of else
j = 0;
for (i=0; i<n; i++)
if (checked[i]!=-2) {
(*entries)[j]=(*entries)[i];
j++;
}
n=j;
delete [] checked;
delete [] dist;
}
