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clustering by fast search and find of density peak
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// generate [0..n-1] | |
auto seq = [](size_t n) -> std::vector<size_t> { | |
std::vector<size_t> v(n); | |
for (size_t i=0; i<n; ++i) v[i] = i; | |
return v; | |
}; | |
auto index = seq(n); | |
// n * n distance matrix | |
std::vector<D> dists(n * n); | |
for (size_t i=0; i<n-1; ++i) { | |
for (size_t j=i+1; j<n; ++j) { dists[i * n + j] = dists[j * n + i] = distf(values[i], values[j]); } | |
} | |
auto dist = [&](size_t i, size_t j) { return dists[i * n + j]; } | |
// calculate rho & delta | |
std::vector<size_t> rho(n); | |
for (size_t i=0; i<n; ++i) { | |
rho[i] = std::count_if(index.begin(), index.end(), [&](size_t j) { return dist(i,j) < dist_cutoff; }); | |
} | |
std::vector<D> delta(n); | |
for (size_t i=0; i<n; ++i) { | |
auto it = std::min_element_if(index.begin(), index.end(), [&](size_t j, size_t k) { return dist(i,j) < dist(i,k); }, [&](size_t j) { return rho[j] > rho[i]; }); | |
if (it == index.end()) | |
it = std::max_element(index.begin(), index.end(), [&](size_t j, size_t k) { return dist(i,j) < dist(i,k); }); | |
delta[i] = dist(i, *it); | |
} | |
//... | |
// clustering | |
auto dindex = seq(n*n); | |
std::sort(dindex.begin(), dindex.end(), [&](size_t i, size_t j) { return dists[i] < dists[j]; }); | |
while (true) { | |
auto it = std::find_if(dindex.begin(), dindex.end(), [&](size_t x) { | |
size_t i = x / n, j = x % n; | |
return (i != j) && ((labels[i] != -1 && labels[j] == -1 && rho[i] > rho[j]) || (labels[i] == -1 && labels[j] != -1 && rho[j] > rho[i])); | |
}); | |
if (it == dindex.end()) break; | |
size_t x = *it, i = x / n, j = x % n; | |
if (labels[i] != -1) labels[j] = labels[i]; | |
else labels[i] = labels[j]; | |
} |
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