def kmedians(sample, centers, tolerance):
pointer_data = create_pointer_data(sample)
pointer_centers = create_pointer_data(centers)
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64)
package = ccore.kmedians_algorithm(pointer_data, pointer_centers,
c_double(tolerance))
result = extract_pyclustering_package(package)
ccore.free_pyclustering_package(package)
return result
def xmeans(sample, centers, kmax, tolerance, criterion):
pointer_data = create_pointer_data(sample)
pointer_centers = create_pointer_data(centers)
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64)
ccore.xmeans_algorithm.restype = POINTER(pyclustering_package)
package = ccore.xmeans_algorithm(pointer_data, pointer_centers,
c_size_t(kmax), c_double(tolerance),
c_uint(criterion))
result = extract_pyclustering_package(package)
ccore.free_pyclustering_package(package)
return result
def rock(sample, eps, number_clusters, threshold):
"""
@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.
"""
pointer_data = create_pointer_data(sample);
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
ccore.rock_algorithm.restype = POINTER(pyclustering_package);
package = ccore.rock_algorithm(pointer_data, c_double(eps), c_size_t(number_clusters), c_double(threshold));
list_of_clusters = extract_pyclustering_package(package);
ccore.free_pyclustering_package(package);
return list_of_clusters;
def cure_algorithm(sample, number_clusters, number_represent_points, compression):
pointer_data = create_pointer_data(sample);
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
ccore.cure_algorithm.restype = POINTER(c_void_p);
cure_data_pointer = ccore.cure_algorithm(pointer_data, c_size_t(number_clusters), c_size_t(number_represent_points), c_double(compression));
return cure_data_pointer;
def agglomerative_algorithm(data, number_clusters, link):
pointer_data = create_pointer_data(data);
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
package = ccore.agglomerative_algorithm(pointer_data, c_uint(number_clusters), c_uint(link));
result = extract_pyclustering_package(package);
ccore.free_pyclustering_package(package);
return result;
def agglomerative_algorithm(data, number_clusters, link):
pointer_data = create_pointer_data(data)
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64)
package = ccore.agglomerative_algorithm(pointer_data,
c_size_t(number_clusters),
c_size_t(link))
result = extract_pyclustering_package(package)
ccore.free_pyclustering_package(package)
return result
def som_simulate(som_pointer, pattern):
"Processes input pattern (no learining) and returns index of neuron-winner."
"Using index of neuron winner catched object can be obtained using property capture_objects."
"(in) som_pointer - pointer to object of self-organized map."
"(in) input_pattern - input pattern."
"Returns index of neuron-winner."
pointer_data = create_pointer_data(pattern);
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
return ccore.som_simulate(som_pointer, pointer_data);
def som_simulate(som_pointer, pattern):
"Processes input pattern (no learining) and returns index of neuron-winner."
"Using index of neuron winner catched object can be obtained using property capture_objects."
"(in) som_pointer - pointer to object of self-organized map."
"(in) input_pattern - input pattern."
"Returns index of neuron-winner."
pointer_data = create_pointer_data(pattern)
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64)
return ccore.som_simulate(som_pointer, pointer_data)
def dbscan(sample, eps, min_neighbors, return_noise=False):
pointer_data = create_pointer_data(sample)
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64)
package = ccore.dbscan_algorithm(pointer_data, c_double(eps),
c_size_t(min_neighbors))
list_of_clusters = extract_pyclustering_package(package)
ccore.free_pyclustering_package(package)
noise = list_of_clusters[len(list_of_clusters) - 1]
list_of_clusters.remove(noise)
return (list_of_clusters, noise)
def som_train(som_pointer, data, epochs, autostop):
"""!
@brief Trains self-organized feature map (SOM) using CCORE pyclustering library.
@param[in] data (list): Input data - list of points where each point is represented by list of features, for example coordinates.
@param[in] epochs (uint): Number of epochs for training.
@param[in] autostop (bool): Automatic termination of learining process when adaptation is not occurred.
@return (uint) Number of learining iterations.
"""
pointer_data = create_pointer_data(data);
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
return ccore.som_train(som_pointer, pointer_data, c_uint(epochs), autostop);
def som_train(som_pointer, data, epochs, autostop):
"""!
@brief Trains self-organized feature map (SOM) using CCORE pyclustering library.
@param[in] data (list): Input data - list of points where each point is represented by list of features, for example coordinates.
@param[in] epochs (uint): Number of epochs for training.
@param[in] autostop (bool): Automatic termination of learining process when adaptation is not occurred.
@return (uint) Number of learining iterations.
"""
pointer_data = create_pointer_data(data)
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64)
return ccore.som_train(som_pointer, pointer_data, c_uint(epochs), autostop)
def som_simulate(som_pointer, pattern):
"""!
@brief Processes input pattern (no learining) and returns index of neuron-winner.
@details Using index of neuron winner catched object can be obtained using property capture_objects.
@param[in] som_pointer (c_pointer): pointer to object of self-organized map.
@param[in] pattern (list): input pattern.
@return Returns index of neuron-winner.
"""
pointer_data = create_pointer_data(pattern)
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64)
return ccore.som_simulate(som_pointer, pointer_data)
def optics(sample, radius, minimum_neighbors, amount_clusters):
amount = amount_clusters;
if (amount is None):
amount = 0;
pointer_data = create_pointer_data(sample);
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64);
package = ccore.optics_algorithm(pointer_data, c_double(radius), c_size_t(minimum_neighbors), c_size_t(amount));
results = extract_pyclustering_package(package);
ccore.free_pyclustering_package(package);
return (results[optics_package_indexer.OPTICS_PACKAGE_INDEX_CLUSTERS],
results[optics_package_indexer.OPTICS_PACKAGE_INDEX_NOISE],
results[optics_package_indexer.OPTICS_PACKAGE_INDEX_ORDERING],
results[optics_package_indexer.OPTICS_PACKAGE_INDEX_RADIUS][0]);
def kmedoids(sample, medoids, tolerance):
pointer_data = create_pointer_data(sample)
c_medoids = (c_size_t * len(medoids))()
c_medoids[:] = medoids[:]
medoids_package = pyclustering_package()
medoids_package.size = len(medoids)
medoids_package.type = pyclustering_type_data.PYCLUSTERING_TYPE_SIZE_T
medoids_package.data = cast(c_medoids, POINTER(c_void_p))
ccore = cdll.LoadLibrary(PATH_DLL_CCORE_64)
package = ccore.kmedoids_algorithm(pointer_data, pointer(medoids_package),
c_double(tolerance))
result = extract_pyclustering_package(package)
ccore.free_pyclustering_package(package)
return result
