{

double operator()(const double first, const double second) const

{

// we just use a linear distance between them, i.e. the closer the values, the more similar they are

auto distance = std::fabs(first - second);

if (distance > 1) { throw Exception::InvalidRange(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION); }

return 1 - distance;

}

{

// data

vector<double> data;

#if 1 // manual data

data = {0.01, 0.02, 0.7, 0.3, 0.31};

#else // random data

#endif

// print raw data to console

std::cout << "raw data: ";

for_each(data.begin(), data.end(), [](auto elem) { std::cout << elem << ' '; });

std::cout << '\n';

// determines the distance between two data points

LowLevelComparator llc;

SingleLinkage sl;

// or try:

//CompleteLinkage sl;

vector<BinaryTreeNode> tree;

DistanceMatrix<float> dist; // will be filled

ClusterHierarchical ch;

ch.setThreshold(1); // maximal distance between clusters; default threshold = 1, i.e. full clustering

// note: not all methods support a threshold, e.g. SingleLinkage requires t = 1.

// do clustering.

// Note: There are other overloads of this function for clustering spectra

ch.cluster<double, LowLevelComparator>(data, llc, sl, tree, dist);

// depending on the cluster method, the distance matrix may have shrunken, e.g. for complete linkage to the point where clustering was stopped

std::cout << "distance matrix:\n" << dist << "\n\n";

ClusterAnalyzer ca;

std::cout << "binary tree in Newick format (numbers are indices into the data)";

std::cout << ca.newickTree(tree) << std::endl;

return 0;