// Finds the points at distance less than sqrt(EpsilonSquared) of Q (not
// including Q).
void InstructionBenchmarkClustering::rangeQuery(
- const size_t Q, llvm::SmallVectorImpl<size_t> &Neighbors) const {
+ const size_t Q, std::vector<size_t> &Neighbors) const {
Neighbors.clear();
+ Neighbors.reserve(Points_.size() - 1); // The Q itself isn't a neighbor.
const auto &QMeasurements = Points_[Q].Measurements;
for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
if (P == Q)
}
void InstructionBenchmarkClustering::dbScan(const size_t MinPts) {
- llvm::SmallVector<size_t, 0> Neighbors; // Persistent buffer to avoid allocs.
+ std::vector<size_t> Neighbors; // Persistent buffer to avoid allocs.
for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
if (!ClusterIdForPoint_[P].isUndef())
continue; // Previously processed in inner loop.
}
}
}
+ // assert(Neighbors.capacity() == (Points_.size() - 1));
+ // ^ True, but it is not quaranteed to be true in all the cases.
// Add noisy points to noise cluster.
for (size_t P = 0, NumPoints = Points_.size(); P < NumPoints; ++P) {
const std::vector<InstructionBenchmark> &Points, double EpsilonSquared);
llvm::Error validateAndSetup();
void dbScan(size_t MinPts);
- void rangeQuery(size_t Q, llvm::SmallVectorImpl<size_t> &Scratchpad) const;
+ void rangeQuery(size_t Q, std::vector<size_t> &Scratchpad) const;
const std::vector<InstructionBenchmark> &Points_;
const double EpsilonSquared_;