def rock(sample, eps, number_clusters, threshold):
pointer_data = package_builder(sample, c_double).create();<EOL>ccore = ccore_library.get();<EOL>ccore.rock_algorithm.restype = POINTER(pyclustering_package);<EOL>package = ccore.rock_algorithm(pointer_data, c_double(eps), c_size_t(number_clusters), c_double(threshold));<EOL>list_of_clusters = package_extractor(package).extract();<EOL>ccore.free_pyclustering_package(package);<EOL>return list_of_clusters<EOL>
@brief Clustering algorithm ROCK returns allocated clusters and noise that are consisted from input data. 
@details Calculation is performed via CCORE (C/C++ part of the pyclustering)."

@param[in] sample: input data - list of points where each point is represented by list of coordinates.
@param[in] eps: connectivity radius (similarity threshold), points are neighbors if distance between them is less than connectivity radius.
@param[in] number_clusters: defines number of clusters that should be allocated from the input data set.
@param[in] threshold: value that defines degree of normalization that influences on choice of clusters for merging during processing.

@return List of allocated clusters, each cluster contains indexes of objects in list of data.