void GroupableTimeSeriesSet::similar(GroupableTimeSeriesSet *other, int otherSeq, TimeInterval otherInt,
SearchStrategy strat, int warps)
{
if (!valid() || !other->valid()) {
session->geterr() << "Warning: Attempted to find similarities using an invalid database." << endl;
return;
}
if (grouping == NULL) {
session->geterr() << "Warning: Attempted to find similarities on ungrouped dataset." << endl;
return;
}
kBest best = grouping->getBestInterval(otherInt.length(),
other->dataset->getRawData(otherSeq, otherInt.start),
strat, warps);
session->getout() << "Found most similar interval." << endl;
session->getout() << "Sequence number and interval: " << best.seq << "@"
<< "[" << best.interval.start << ", " << best.interval.end << "]." << endl;
session->getout() << "Distance: " << best.dist << endl;
if (verbosity > 0) {
session->getout() << "Sequence: " << endl;
dataset->printInterval(session->getout(), best.seq, best.interval);
}
}
kBest GroupableTimeSeriesSet::returnSimilar(GroupableTimeSeriesSet *other, int otherSeq, TimeInterval otherInt,
SearchStrategy strat, int warps)
{
if (!valid() || !other->valid()) {
session->geterr() << "Warning: Attempted to find similarities using an invalid database." << endl;
return;
}
if (grouping == NULL) {
session->geterr() << "Warning: Attempted to find similarities on ungrouped dataset." << endl;
return;
}
return grouping->getBestInterval(otherInt.length(),
other->dataset->getRawData(otherSeq, otherInt.start),
strat, warps);
}
void OnlineSession::kSimilar(int dbindex, vector<double> qdata, TimeInterval interval, int k, int strict)
{
if (groupings[dbindex] == NULL)
genGrouping(dbindex, defaultST);
TimeSeriesGrouping *t = groupings[dbindex];
int slen = dataSets[dbindex]->seqLength;
int ilen = interval.length();
TimeSeriesIntervalEnvelope eqdata(TimeSeriesInterval(qdata.data(), TimeInterval(0, qdata.size() - 1)));
double gb;
int bsfStart = 0, bsfEnd = 0, bsfIndex = -1;
double bsf = INF;
if (strict == 0) {
for (int i = 0; i < slen; i++) {
if (debug) {
cout << "Searching groups from start=" << i << ", bsf=" << bsf << endl;
}
for (int j = i; j < slen; j++) {
int k = t->groups[i * slen + j].getBestGroup(eqdata, &gb, bsf);
if (k < 0)
continue;
if (gb < bsf) {
bsf = gb;
bsfStart = i;
bsfEnd = j;
bsfIndex = k;
}
}
}
} else if (strict == 1) {
for (int i = 0; i + ilen - 1 < slen; i++) {
if (debug)
cout << "Searching groups from start=" << i << endl;
int j = i + (ilen - 1);
int k = t->groups[i * slen + j].getBestGroup(eqdata, &gb, bsf);
if (k < 0)
continue;
if (gb < bsf) {
bsf = gb;
bsfStart = i;
bsfEnd = j;
bsfIndex = k;
}
}
} else {
bsfStart = interval.start;
bsfEnd = interval.end;
bsfIndex = t->groups[bsfStart * slen + bsfEnd].getBestGroup(eqdata, &gb, bsf);
if (bsfIndex >= 0)
bsf = gb;
}
if (bsf == INF || bsfIndex < 0) {
cerr << "kSimilar: Failed to find similar objects. No suitable candidate group centroids." << endl;
return;
}
cout << "Found most similar interval and group: " << bsfIndex << "@"
<< "[" << bsfStart << "," << bsfEnd << "]" << endl;
vector<kSim> sim = t->groups[bsfStart * slen + bsfEnd].groups[bsfIndex]->getSortedSimilar(eqdata, k);
cout << "Discovered k similar points:" << endl;
for (unsigned int i = 0; i < sim.size(); i++) {
cout << "Series " << sim[i].index << ", interval [" << bsfStart << "," << bsfEnd
<< "] is at distance " << sim[i].distance << "." << endl;
TimeSeriesInterval interval = (*t->groups[bsfStart * slen + bsfEnd].groups[bsfIndex]->slice)[sim[i].index];
for (int j = 0; j < interval.length(); j++) {
cout << interval[j] << " ";
}
cout << endl;
}
}
