static void NN_chain_core(const t_index N, t_float * const D, t_members * const members, cluster_result & Z2) {
/*
N: integer
D: condensed distance matrix N*(N-1)/2
Z2: output data structure
This is the NN-chain algorithm, described on page 86 in the following book:
Fionn Murtagh, Multidimensional Clustering Algorithms,
Vienna, Würzburg: Physica-Verlag, 1985.
This implementation does not give defined results when NaN or Inf values
are present in the array D.
*/
t_index i;
auto_array_ptr<t_index> NN_chain(N);
t_index NN_chain_tip = 0;
t_index idx1, idx2;
t_float size1, size2;
doubly_linked_list AR(N);
t_float min;
for (t_index j=0; j<N-1; j++) {
if (NN_chain_tip <= 3) {
NN_chain[0] = idx1 = AR.start;
NN_chain_tip = 1;
idx2 = AR.succ[idx1];
min = D_(idx1,idx2);
for (i=AR.succ[idx2]; i<N; i=AR.succ[i]) {
if (D_(idx1,i) < min) {
min = D_(idx1,i);
idx2 = i;
}
}
} // a: idx1 b: idx2
else {
NN_chain_tip -= 3;
idx1 = NN_chain[NN_chain_tip-1];
idx2 = NN_chain[NN_chain_tip];
min = idx1<idx2 ? D_(idx1,idx2) : D_(idx2,idx1);
} // a: idx1 b: idx2
do {
NN_chain[NN_chain_tip] = idx2;
for (i=AR.start; i<idx2; i=AR.succ[i]) {
if (D_(i,idx2) < min) {
min = D_(i,idx2);
idx1 = i;
}
}
for (i=AR.succ[idx2]; i<N; i=AR.succ[i]) {
if (D_(idx2,i) < min) {
min = D_(idx2,i);
idx1 = i;
}
}
idx2 = idx1;
idx1 = NN_chain[NN_chain_tip++];
} while (idx2 != NN_chain[NN_chain_tip-2]);
Z2.append(idx1, idx2, min);
if (idx1>idx2) {
t_index tmp = idx1;
idx1 = idx2;
idx2 = tmp;
}
if (method==METHOD_METR_AVERAGE ||
method==METHOD_METR_WARD) {
size1 = static_cast<t_float>(members[idx1]);
size2 = static_cast<t_float>(members[idx2]);
members[idx2] += members[idx1];
}
// Remove the smaller index from the valid indices (AR).
AR.remove(idx1);
switch (method) {
case METHOD_METR_SINGLE:
/*
Single linkage.
Characteristic: new distances are never longer than the old distances.
*/
// Update the distance matrix in the range [start, idx1).
for (i=AR.start; i<idx1; i=AR.succ[i])
f_single(&D_(i, idx2), D_(i, idx1) );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=AR.succ[i])
f_single(&D_(i, idx2), D_(idx1, i) );
// Update the distance matrix in the range (idx2, N).
for (i=AR.succ[idx2]; i<N; i=AR.succ[i])
f_single(&D_(idx2, i), D_(idx1, i) );
break;
case METHOD_METR_COMPLETE:
/*
Complete linkage.
Characteristic: new distances are never shorter than the old distances.
*/
// Update the distance matrix in the range [start, idx1).
for (i=AR.start; i<idx1; i=AR.succ[i])
f_complete(&D_(i, idx2), D_(i, idx1) );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=AR.succ[i])
f_complete(&D_(i, idx2), D_(idx1, i) );
// Update the distance matrix in the range (idx2, N).
for (i=AR.succ[idx2]; i<N; i=AR.succ[i])
f_complete(&D_(idx2, i), D_(idx1, i) );
break;
case METHOD_METR_AVERAGE: {
/*
Average linkage.
Shorter and longer distances can occur.
*/
// Update the distance matrix in the range [start, idx1).
t_float s = size1/(size1+size2);
t_float t = size2/(size1+size2);
for (i=AR.start; i<idx1; i=AR.succ[i])
f_average(&D_(i, idx2), D_(i, idx1), s, t );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=AR.succ[i])
f_average(&D_(i, idx2), D_(idx1, i), s, t );
// Update the distance matrix in the range (idx2, N).
for (i=AR.succ[idx2]; i<N; i=AR.succ[i])
f_average(&D_(idx2, i), D_(idx1, i), s, t );
break;
}
case METHOD_METR_WEIGHTED:
/*
Weighted linkage.
Shorter and longer distances can occur.
*/
// Update the distance matrix in the range [start, idx1).
for (i=AR.start; i<idx1; i=AR.succ[i])
f_weighted(&D_(i, idx2), D_(i, idx1) );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=AR.succ[i])
f_weighted(&D_(i, idx2), D_(idx1, i) );
// Update the distance matrix in the range (idx2, N).
for (i=AR.succ[idx2]; i<N; i=AR.succ[i])
f_weighted(&D_(idx2, i), D_(idx1, i) );
break;
case METHOD_METR_WARD:
/*
Ward linkage.
Shorter and longer distances can occur, not smaller than min(d1,d2)
but maybe bigger than max(d1,d2).
*/
// Update the distance matrix in the range [start, idx1).
//t_float v = static_cast<t_float>(members[i]);
for (i=AR.start; i<idx1; i=AR.succ[i])
f_ward(&D_(i, idx2), D_(i, idx1), min,
size1, size2, static_cast<t_float>(members[i]) );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=AR.succ[i])
f_ward(&D_(i, idx2), D_(idx1, i), min,
size1, size2, static_cast<t_float>(members[i]) );
// Update the distance matrix in the range (idx2, N).
for (i=AR.succ[idx2]; i<N; i=AR.succ[i])
f_ward(&D_(idx2, i), D_(idx1, i), min,
size1, size2, static_cast<t_float>(members[i]) );
break;
}
}
}
static void NN_chain_core(const t_index N, t_float * const D, t_members * const members, cluster_result & Z2) {
/*
N: integer
D: condensed distance matrix N*(N-1)/2
Z2: output data structure
This is the NN-chain algorithm, described on page 86 in the following book:
Fionn Murtagh, Multidimensional Clustering Algorithms,
Vienna, Würzburg: Physica-Verlag, 1985.
*/
t_index i;
auto_array_ptr<t_index> NN_chain(N);
t_index NN_chain_tip = 0;
t_index idx1, idx2;
t_float size1, size2;
doubly_linked_list active_nodes(N);
t_float min;
for (t_float const * DD=D; DD!=D+(static_cast<std::ptrdiff_t>(N)*(N-1)>>1);
++DD) {
#if HAVE_DIAGNOSTIC
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wfloat-equal"
#endif
if (fc_isnan(*DD)) {
throw(nan_error());
}
#if HAVE_DIAGNOSTIC
#pragma GCC diagnostic pop
#endif
}
#ifdef FE_INVALID
if (feclearexcept(FE_INVALID)) throw fenv_error();
#endif
for (t_index j=0; j<N-1; ++j) {
if (NN_chain_tip <= 3) {
NN_chain[0] = idx1 = active_nodes.start;
NN_chain_tip = 1;
idx2 = active_nodes.succ[idx1];
min = D_(idx1,idx2);
for (i=active_nodes.succ[idx2]; i<N; i=active_nodes.succ[i]) {
if (D_(idx1,i) < min) {
min = D_(idx1,i);
idx2 = i;
}
}
} // a: idx1 b: idx2
else {
NN_chain_tip -= 3;
idx1 = NN_chain[NN_chain_tip-1];
idx2 = NN_chain[NN_chain_tip];
min = idx1<idx2 ? D_(idx1,idx2) : D_(idx2,idx1);
} // a: idx1 b: idx2
do {
NN_chain[NN_chain_tip] = idx2;
for (i=active_nodes.start; i<idx2; i=active_nodes.succ[i]) {
if (D_(i,idx2) < min) {
min = D_(i,idx2);
idx1 = i;
}
}
for (i=active_nodes.succ[idx2]; i<N; i=active_nodes.succ[i]) {
if (D_(idx2,i) < min) {
min = D_(idx2,i);
idx1 = i;
}
}
idx2 = idx1;
idx1 = NN_chain[NN_chain_tip++];
} while (idx2 != NN_chain[NN_chain_tip-2]);
Z2.append(idx1, idx2, min);
if (idx1>idx2) {
t_index tmp = idx1;
idx1 = idx2;
idx2 = tmp;
}
if (method==METHOD_METR_AVERAGE ||
method==METHOD_METR_WARD) {
size1 = static_cast<t_float>(members[idx1]);
size2 = static_cast<t_float>(members[idx2]);
members[idx2] += members[idx1];
}
// Remove the smaller index from the valid indices (active_nodes).
active_nodes.remove(idx1);
switch (method) {
case METHOD_METR_SINGLE:
/*
Single linkage.
Characteristic: new distances are never longer than the old distances.
*/
// Update the distance matrix in the range [start, idx1).
for (i=active_nodes.start; i<idx1; i=active_nodes.succ[i])
f_single(&D_(i, idx2), D_(i, idx1) );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=active_nodes.succ[i])
f_single(&D_(i, idx2), D_(idx1, i) );
// Update the distance matrix in the range (idx2, N).
for (i=active_nodes.succ[idx2]; i<N; i=active_nodes.succ[i])
f_single(&D_(idx2, i), D_(idx1, i) );
break;
case METHOD_METR_COMPLETE:
/*
Complete linkage.
Characteristic: new distances are never shorter than the old distances.
*/
// Update the distance matrix in the range [start, idx1).
for (i=active_nodes.start; i<idx1; i=active_nodes.succ[i])
f_complete(&D_(i, idx2), D_(i, idx1) );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=active_nodes.succ[i])
f_complete(&D_(i, idx2), D_(idx1, i) );
// Update the distance matrix in the range (idx2, N).
for (i=active_nodes.succ[idx2]; i<N; i=active_nodes.succ[i])
f_complete(&D_(idx2, i), D_(idx1, i) );
break;
case METHOD_METR_AVERAGE: {
/*
Average linkage.
Shorter and longer distances can occur.
*/
// Update the distance matrix in the range [start, idx1).
t_float s = size1/(size1+size2);
t_float t = size2/(size1+size2);
for (i=active_nodes.start; i<idx1; i=active_nodes.succ[i])
f_average(&D_(i, idx2), D_(i, idx1), s, t );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=active_nodes.succ[i])
f_average(&D_(i, idx2), D_(idx1, i), s, t );
// Update the distance matrix in the range (idx2, N).
for (i=active_nodes.succ[idx2]; i<N; i=active_nodes.succ[i])
f_average(&D_(idx2, i), D_(idx1, i), s, t );
break;
}
case METHOD_METR_WEIGHTED:
/*
Weighted linkage.
Shorter and longer distances can occur.
*/
// Update the distance matrix in the range [start, idx1).
for (i=active_nodes.start; i<idx1; i=active_nodes.succ[i])
f_weighted(&D_(i, idx2), D_(i, idx1) );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=active_nodes.succ[i])
f_weighted(&D_(i, idx2), D_(idx1, i) );
// Update the distance matrix in the range (idx2, N).
for (i=active_nodes.succ[idx2]; i<N; i=active_nodes.succ[i])
f_weighted(&D_(idx2, i), D_(idx1, i) );
break;
case METHOD_METR_WARD:
/*
Ward linkage.
Shorter and longer distances can occur, not smaller than min(d1,d2)
but maybe bigger than max(d1,d2).
*/
// Update the distance matrix in the range [start, idx1).
//t_float v = static_cast<t_float>(members[i]);
for (i=active_nodes.start; i<idx1; i=active_nodes.succ[i])
f_ward(&D_(i, idx2), D_(i, idx1), min,
size1, size2, static_cast<t_float>(members[i]) );
// Update the distance matrix in the range (idx1, idx2).
for (; i<idx2; i=active_nodes.succ[i])
f_ward(&D_(i, idx2), D_(idx1, i), min,
size1, size2, static_cast<t_float>(members[i]) );
// Update the distance matrix in the range (idx2, N).
for (i=active_nodes.succ[idx2]; i<N; i=active_nodes.succ[i])
f_ward(&D_(idx2, i), D_(idx1, i), min,
size1, size2, static_cast<t_float>(members[i]) );
break;
default:
throw std::runtime_error(std::string("Invalid method."));
}
}
#ifdef FE_INVALID
if (fetestexcept(FE_INVALID)) throw fenv_error();
#endif
}
static void MST_linkage_core(const t_index N, const t_float * const D,
cluster_result & Z2) {
/*
N: integer, number of data points
D: condensed distance matrix N*(N-1)/2
Z2: output data structure
The basis of this algorithm is an algorithm by Rohlf:
F. James Rohlf, Hierarchical clustering using the minimum spanning tree,
The Computer Journal, vol. 16, 1973, p. 93–95.
This implementation should handle Inf values correctly (designed to
do so but not tested).
This implementation avoids NaN if possible. It treats NaN as if it was
greater than +Infinity, ie. whenever we find a non-NaN value, this is
preferred in all the minimum-distance searches.
*/
t_index i;
t_index idx2;
doubly_linked_list AR(N);
auto_array_ptr<t_float> d(N);
t_index prev_node;
t_float min;
// first iteration
idx2 = 1;
min = d[1] = D[0];
for (i=2; min!=min && i<N; i++) { // eliminate NaNs if possible
min = d[i] = D[i-1];
idx2 = i;
}
for ( ; i<N; i++) {
d[i] = D[i-1];
if (d[i] < min) {
min = d[i];
idx2 = i;
}
}
Z2.append(0, idx2, min);
for (t_index j=1; j<N-1; j++) {
prev_node = idx2;
AR.remove(prev_node);
idx2 = AR.succ[0];
min = d[idx2];
for (i=idx2; min!=min && i<prev_node; i=AR.succ[i]) {
min = d[i] = D_(i, prev_node);
idx2 = i;
}
for ( ; i<prev_node; i=AR.succ[i]) {
if (d[i] > D_(i, prev_node))
d[i] = D_(i, prev_node);
if (d[i] < min) {
min = d[i];
idx2 = i;
}
}
for (; min!=min && i<N; i=AR.succ[i]) {
min = d[i] = D_(prev_node, i);
idx2 = i;
}
for (; i<N; i=AR.succ[i]) {
if (d[i] > D_(prev_node, i))
d[i] = D_(prev_node, i);
if (d[i] < min) {
min = d[i];
idx2 = i;
}
}
Z2.append(prev_node, idx2, min);
}
}
void MST_linkage_core(const t_index N, const t_float * const D,
cluster_result & Z2) {
/*
N: integer, number of data points
D: condensed distance matrix N*(N-1)/2
Z2: output data structure
The basis of this algorithm is an algorithm by Rohlf:
F. James Rohlf, Hierarchical clustering using the minimum spanning tree,
The Computer Journal, vol. 16, 1973, p. 93–95.
*/
t_index i;
t_index idx2;
doubly_linked_list active_nodes(N);
auto_array_ptr<t_float> d(N);
t_index prev_node;
t_float min;
// first iteration
idx2 = 1;
min = std::numeric_limits<t_float>::infinity();
for (i=1; i<N; ++i) {
d[i] = D[i-1];
#if HAVE_DIAGNOSTIC
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wfloat-equal"
#endif
if (d[i] < min) {
min = d[i];
idx2 = i;
}
else if (fc_isnan(d[i]))
throw (nan_error());
#if HAVE_DIAGNOSTIC
#pragma GCC diagnostic pop
#endif
}
Z2.append(0, idx2, min);
for (t_index j=1; j<N-1; ++j) {
prev_node = idx2;
active_nodes.remove(prev_node);
idx2 = active_nodes.succ[0];
min = d[idx2];
for (i=idx2; i<prev_node; i=active_nodes.succ[i]) {
t_float tmp = D_(i, prev_node);
#if HAVE_DIAGNOSTIC
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wfloat-equal"
#endif
if (tmp < d[i])
d[i] = tmp;
else if (fc_isnan(tmp))
throw (nan_error());
#if HAVE_DIAGNOSTIC
#pragma GCC diagnostic pop
#endif
if (d[i] < min) {
min = d[i];
idx2 = i;
}
}
for (; i<N; i=active_nodes.succ[i]) {
t_float tmp = D_(prev_node, i);
#if HAVE_DIAGNOSTIC
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wfloat-equal"
#endif
if (d[i] > tmp)
d[i] = tmp;
else if (fc_isnan(tmp))
throw (nan_error());
#if HAVE_DIAGNOSTIC
#pragma GCC diagnostic pop
#endif
if (d[i] < min) {
min = d[i];
idx2 = i;
}
}
Z2.append(prev_node, idx2, min);
}
}
