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 = package_builder(sample, c_double).create(); ccore = ccore_library.get(); 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 = package_extractor(package).extract(); ccore.free_pyclustering_package(package); return list_of_clusters;
def som_create(rows, cols, conn_type, parameters): """! @brief Create of self-organized map using C++ pyclustering library. @param[in] rows (uint): Number of neurons in the column (number of rows). @param[in] cols (uint): Number of neurons in the row (number of columns). @param[in] conn_type (type_conn): Type of connection between oscillators in the network (grid four, grid eight, honeycomb, function neighbour). @param[in] parameters (som_parameters): Other specific parameters. @return (POINTER) C-pointer to object of self-organized feature in memory. """ ccore = ccore_library.get() c_params = c_som_parameters() c_params.init_type = parameters.init_type c_params.init_radius = parameters.init_radius c_params.init_learn_rate = parameters.init_learn_rate c_params.adaptation_threshold = parameters.adaptation_threshold c_params.random_state = parameters.random_state or -1 ccore.som_create.restype = POINTER(c_void_p) som_pointer = ccore.som_create(c_uint(rows), c_uint(cols), c_uint(conn_type), pointer(c_params)) return som_pointer
def som_load(som_pointer, weights, award, capture_objects): """! @brief Load dump of the network to SOM. @details Initialize SOM using existed weights, amount of captured objects by each neuron, captured objects by each neuron. Initialization is not performed if weights are empty. @param[in] som_pointer (POINTER): pointer to object of self-organized map. @param[in] weights (list): weights that should assigned to neurons. @param[in] awards (list): amount of captured objects by each neuron. @param[in] capture_objects (list): captured objects by each neuron. """ if len(weights) == 0: return ccore = ccore_library.get() package_weights = package_builder(weights, c_double).create() package_award = package_builder(award, c_size_t).create() package_capture_objects = package_builder(capture_objects, c_size_t).create() ccore.som_load(som_pointer, package_weights, package_award, package_capture_objects)
def elbow(sample, kmin, kmax, kstep, initializer, random_state): random_state = random_state or -1 pointer_data = package_builder(sample, c_double).create() ccore = ccore_library.get() if initializer == elbow_center_initializer.KMEANS_PLUS_PLUS: ccore.elbow_method_ikpp.restype = POINTER(pyclustering_package) package = ccore.elbow_method_ikpp(pointer_data, c_size_t(kmin), c_size_t(kmax), c_size_t(kstep), c_longlong(random_state)) elif initializer == elbow_center_initializer.RANDOM: ccore.elbow_method_irnd.restype = POINTER(pyclustering_package) package = ccore.elbow_method_irnd(pointer_data, c_size_t(kmin), c_size_t(kmax), c_size_t(kstep), c_longlong(random_state)) else: raise ValueError("Not supported type of center initializer '" + str(initializer) + "'.") results = package_extractor(package).extract() ccore.free_pyclustering_package(package) if isinstance(results, bytes): raise RuntimeError(results.decode('utf-8')) return (results[elbow_package_indexer.ELBOW_PACKAGE_INDEX_AMOUNT][0], results[elbow_package_indexer.ELBOW_PACKAGE_INDEX_WCE])
def optics(sample, radius, minimum_neighbors, amount_clusters, data_type): amount = amount_clusters if amount is None: amount = 0 pointer_data = package_builder(sample, c_double).create() c_data_type = convert_data_type(data_type) ccore = ccore_library.get() ccore.optics_algorithm.restype = POINTER(pyclustering_package) package = ccore.optics_algorithm(pointer_data, c_double(radius), c_size_t(minimum_neighbors), c_size_t(amount), c_data_type) results = package_extractor(package).extract() 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], results[optics_package_indexer. OPTICS_PACKAGE_INDEX_OPTICS_OBJECTS_INDEX], results[optics_package_indexer. OPTICS_PACKAGE_INDEX_OPTICS_OBJECTS_CORE_DISTANCE], results[optics_package_indexer. OPTICS_PACKAGE_INDEX_OPTICS_OBJECTS_REACHABILITY_DISTANCE])
def __call__(self, point1, point2): point_package1 = package_builder(point1, c_double).create(); point_package2 = package_builder(point2, c_double).create(); ccore = ccore_library.get(); ccore.metric_calculate.restype = c_double; return ccore.metric_calculate(self.__pointer, point_package1, point_package2);
def cure_algorithm(sample, number_clusters, number_represent_points, compression): pointer_data = package_builder(sample, c_double).create(); ccore = ccore_library.get(); 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 som_destroy(som_pointer): """! @brief Destroys self-organized map. @param[in] som_pointer (POINTER): pointer to object of self-organized map. """ ccore = ccore_library.get() ccore.som_destroy(som_pointer)
def cure_get_means(cure_data_pointer): ccore = ccore_library.get(); ccore.cure_get_means.restype = POINTER(pyclustering_package); package = ccore.cure_get_means(cure_data_pointer); result = package_extractor(package).extract(); ccore.free_pyclustering_package(package); return result;
def cure_get_representors(cure_data_pointer): ccore = ccore_library.get() ccore.cure_get_representors.restype = POINTER(pyclustering_package) package = ccore.cure_get_representors(cure_data_pointer) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result
def __init__(self, type_metric, arguments, func): self.__func = lambda p1, p2: func(package_extractor(p1).extract(), package_extractor(p2).extract()); package_arguments = package_builder(arguments, c_double).create(); ccore = ccore_library.get(); ccore.metric_create.restype = POINTER(c_void_p); self.__pointer = ccore.metric_create(c_size_t(type_metric), package_arguments, metric_callback(self.__func));
def som_get_winner_number(som_pointer): """! @brief Returns of number of winner at the last step of learning process. @param[in] som_pointer (c_pointer): pointer to object of self-organized map. """ ccore = ccore_library.get() ccore.som_get_winner_number.restype = c_size_t return ccore.som_get_winner_number(som_pointer)
def agglomerative_algorithm(data, number_clusters, link): pointer_data = package_builder(data, c_double).create(); ccore = ccore_library.get(); ccore.agglomerative_algorithm.restype = POINTER(pyclustering_package); package = ccore.agglomerative_algorithm(pointer_data, c_size_t(number_clusters), c_size_t(link)); result = package_extractor(package).extract(); ccore.free_pyclustering_package(package); return result;
def som_get_size(som_pointer): """! @brief Returns size of self-organized map (number of neurons). @param[in] som_pointer (c_pointer): pointer to object of self-organized map. """ ccore = ccore_library.get() ccore.som_get_size.restype = c_size_t return ccore.som_get_size(som_pointer)
def ttsas(sample, threshold1, threshold2, metric_pointer): pointer_data = package_builder(sample, c_double).create(); ccore = ccore_library.get(); ccore.ttsas_algorithm.restype = POINTER(pyclustering_package); package = ccore.ttsas_algorithm(pointer_data, c_double(threshold1), c_double(threshold2), metric_pointer); result = package_extractor(package).extract(); ccore.free_pyclustering_package(package); return result[0], result[1];
def bsas(sample, amount, threshold, metric_pointer): pointer_data = package_builder(sample, c_double).create() ccore = ccore_library.get() ccore.bsas_algorithm.restype = POINTER(pyclustering_package) package = ccore.bsas_algorithm(pointer_data, c_size_t(amount), c_double(threshold), metric_pointer) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result[0], result[1]
def silhoeutte(sample, clusters, pointer_metric): pointer_data = package_builder(sample, c_double).create() pointer_clusters = package_builder(clusters, c_size_t).create() ccore = ccore_library.get() ccore.silhouette_algorithm.restype = POINTER(pyclustering_package) package = ccore.silhouette_algorithm(pointer_data, pointer_clusters, pointer_metric) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result
def kmeans(sample, centers, tolerance, observe): pointer_data = package_builder(sample, c_double).create(); pointer_centers = package_builder(centers, c_double).create(); ccore = ccore_library.get(); ccore.kmeans_algorithm.restype = POINTER(pyclustering_package); package = ccore.kmeans_algorithm(pointer_data, pointer_centers, c_double(tolerance), c_bool(observe)); result = package_extractor(package).extract(); ccore.free_pyclustering_package(package); return result;
def fcm_algorithm(sample, centers, m, tolerance, itermax): pointer_data = package_builder(sample, c_double).create() pointer_centers = package_builder(centers, c_double).create() ccore = ccore_library.get() ccore.fcm_algorithm.restype = POINTER(pyclustering_package) package = ccore.fcm_algorithm(pointer_data, pointer_centers, c_double(m), c_double(tolerance), c_size_t(itermax)) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result
def silhoeutte_ksearch(sample, kmin, kmax, allocator): pointer_data = package_builder(sample, c_double).create() ccore = ccore_library.get() ccore.silhouette_ksearch_algorithm.restype = POINTER(pyclustering_package) package = ccore.silhouette_ksearch_algorithm(pointer_data, c_size_t(kmin), c_size_t(kmax), c_size_t(allocator)) results = package_extractor(package).extract() ccore.free_pyclustering_package(package) return (results[silhouette_ksearch_package_indexer.SILHOUETTE_KSEARCH_PACKAGE_INDEX_AMOUNT][0], results[silhouette_ksearch_package_indexer.SILHOUETTE_KSEARCH_PACKAGE_INDEX_SCORE][0], results[silhouette_ksearch_package_indexer.SILHOUETTE_KSEARCH_PACKAGE_INDEX_SCORES])
def silhoeutte_ksearch(sample, kmin, kmax, allocator): pointer_data = package_builder(sample, c_double).create() ccore = ccore_library.get() ccore.silhouette_ksearch_algorithm.restype = POINTER(pyclustering_package) package = ccore.silhouette_ksearch_algorithm(pointer_data, c_size_t(kmin), c_size_t(kmax), c_size_t(allocator)) results = package_extractor(package).extract() ccore.free_pyclustering_package(package) return (results[silhouette_ksearch_package_indexer.SILHOUETTE_KSEARCH_PACKAGE_INDEX_AMOUNT][0], results[silhouette_ksearch_package_indexer.SILHOUETTE_KSEARCH_PACKAGE_INDEX_SCORE][0], results[silhouette_ksearch_package_indexer.SILHOUETTE_KSEARCH_PACKAGE_INDEX_SCORES])
def gmeans(sample, amount, tolerance, repeat): pointer_data = package_builder(sample, c_double).create() ccore = ccore_library.get() ccore.gmeans_algorithm.restype = POINTER(pyclustering_package) package = ccore.gmeans_algorithm(pointer_data, c_size_t(amount), c_double(tolerance), c_size_t(repeat)) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result[0], result[1], result[2][0]
def xmeans(sample, centers, kmax, tolerance, criterion): pointer_data = package_builder(sample, c_double).create(); pointer_centers = package_builder(centers, c_double).create(); ccore = ccore_library.get(); 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 = package_extractor(package).extract(); ccore.free_pyclustering_package(package); return result[0], result[1];
def silhoeutte(sample, clusters, pointer_metric): pointer_data = package_builder(sample, c_double).create() pointer_clusters = package_builder(clusters, c_size_t).create() ccore = ccore_library.get() ccore.silhouette_algorithm.restype = POINTER(pyclustering_package) package = ccore.silhouette_algorithm(pointer_data, pointer_clusters, pointer_metric) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result
def kmedians(sample, centers, tolerance, metric_pointer): pointer_data = package_builder(sample, c_double).create() pointer_centers = package_builder(centers, c_double).create() ccore = ccore_library.get() ccore.kmedians_algorithm.restype = POINTER(pyclustering_package) package = ccore.kmedians_algorithm(pointer_data, pointer_centers, c_double(tolerance), metric_pointer) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result
def xmeans(sample, centers, kmax, tolerance, criterion): pointer_data = package_builder(sample, c_double).create(); pointer_centers = package_builder(centers, c_double).create(); ccore = ccore_library.get(); 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 = package_extractor(package).extract(); ccore.free_pyclustering_package(package); return result[0], result[1];
def kmedians(sample, centers, tolerance, itermax, metric_pointer): pointer_data = package_builder(sample, c_double).create() pointer_centers = package_builder(centers, c_double).create() ccore = ccore_library.get() ccore.kmedians_algorithm.restype = POINTER(pyclustering_package) package = ccore.kmedians_algorithm(pointer_data, pointer_centers, c_double(tolerance), c_size_t(itermax), metric_pointer) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result[0], result[1]
def kmedoids(sample, medoids, tolerance, itermax, metric_pointer, data_type): pointer_data = package_builder(sample, c_double).create() medoids_package = package_builder(medoids, c_size_t).create() c_data_type = convert_data_type(data_type) ccore = ccore_library.get() ccore.kmedoids_algorithm.restype = POINTER(pyclustering_package) package = ccore.kmedoids_algorithm(pointer_data, medoids_package, c_double(tolerance), c_size_t(itermax), metric_pointer, c_data_type) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result[0], result[1]
def fcm_algorithm(sample, centers, m, tolerance, itermax): pointer_data = package_builder(sample, c_double).create() pointer_centers = package_builder(centers, c_double).create() ccore = ccore_library.get() ccore.fcm_algorithm.restype = POINTER(pyclustering_package) package = ccore.fcm_algorithm(pointer_data, pointer_centers, c_double(m), c_double(tolerance), c_size_t(itermax)) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result
def kmedoids(sample, medoids, tolerance): pointer_data = package_builder(sample, c_double).create() medoids_package = package_builder(medoids, c_size_t).create() ccore = ccore_library.get() ccore.kmedoids_algorithm.restype = POINTER(pyclustering_package) package = ccore.kmedoids_algorithm(pointer_data, medoids_package, c_double(tolerance)) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result
def kmedoids(sample, medoids, tolerance, itermax, metric_pointer, data_type): pointer_data = package_builder(sample, c_double).create() medoids_package = package_builder(medoids, c_size_t).create() c_data_type = convert_data_type(data_type) ccore = ccore_library.get() ccore.kmedoids_algorithm.restype = POINTER(pyclustering_package) package = ccore.kmedoids_algorithm(pointer_data, medoids_package, c_double(tolerance), c_size_t(itermax), metric_pointer, c_data_type) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result[0], result[1], result[2][0], result[3][0]
def som_get_neighbors(som_pointer): """! @brief Returns list of indexes of neighbors of each neuron. @param[in] som_pointer (c_pointer): pointer to object of self-organized map. """ ccore = ccore_library.get() ccore.som_get_neighbors.restype = POINTER(pyclustering_package) package = ccore.som_get_neighbors(som_pointer) result = package_extractor(package).extract() return result
def som_get_awards(som_pointer): """! @brief Returns list of amount of captured objects by each neuron. @param[in] som_pointer (c_pointer): pointer to object of self-organized map. """ ccore = ccore_library.get() ccore.som_get_awards.restype = POINTER(pyclustering_package) package = ccore.som_get_awards(som_pointer) result = package_extractor(package).extract() return result
def dbscan(sample, eps, min_neighbors, data_type): pointer_data = package_builder(sample, c_double).create() c_data_type = convert_data_type(data_type) ccore = ccore_library.get() ccore.dbscan_algorithm.restype = POINTER(pyclustering_package) package = ccore.dbscan_algorithm(pointer_data, c_double(eps), c_size_t(min_neighbors), c_data_type) list_of_clusters = package_extractor(package).extract() 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 pam_build(sample, amount, pointer_metric, data_type): pointer_data = package_builder(sample, c_double).create() c_data_type = convert_data_type(data_type) ccore = ccore_library.get() ccore.pam_build_algorithm.restype = POINTER(pyclustering_package) package = ccore.pam_build_algorithm(pointer_data, c_size_t(amount), pointer_metric, c_data_type) results = package_extractor(package).extract() ccore.free_pyclustering_package(package) if isinstance(results, bytes): raise RuntimeError(results.decode('utf-8')) return results[0]
def dbscan(sample, eps, min_neighbors, data_type): pointer_data = package_builder(sample, c_double).create() c_data_type = convert_data_type(data_type) ccore = ccore_library.get() ccore.dbscan_algorithm.restype = POINTER(pyclustering_package) package = ccore.dbscan_algorithm(pointer_data, c_double(eps), c_size_t(min_neighbors), c_data_type) list_of_clusters = package_extractor(package).extract() 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 gmeans(sample, kinit, tolerance, repeat, kmax, random_state): random_state = random_state or -1 pointer_data = package_builder(sample, c_double).create() ccore = ccore_library.get() ccore.gmeans_algorithm.restype = POINTER(pyclustering_package) package = ccore.gmeans_algorithm(pointer_data, c_size_t(kinit), c_double(tolerance), c_size_t(repeat), c_longlong(kmax), c_longlong(random_state)) result = package_extractor(package).extract() ccore.free_pyclustering_package(package) return result[0], result[1], result[2][0]
def clique(sample, intervals, threshold): pointer_data = package_builder(sample, c_double).create() ccore = ccore_library.get() ccore.clique_algorithm.restype = POINTER(pyclustering_package) package = ccore.clique_algorithm(pointer_data, c_size_t(intervals), c_size_t(threshold)) results = package_extractor(package).extract() ccore.free_pyclustering_package(package) return (results[clique_package_indexer.CLIQUE_PACKAGE_INDEX_CLUSTERS], results[clique_package_indexer.CLIQUE_PACKAGE_INDEX_NOISE], results[clique_package_indexer.CLIQUE_PACKAGE_INDEX_LOGICAL_LOCATION], results[clique_package_indexer.CLIQUE_PACKAGE_INDEX_MAX_CORNER], results[clique_package_indexer.CLIQUE_PACKAGE_INDEX_MIN_CORNER], results[clique_package_indexer.CLIQUE_PACKAGE_INDEX_BLOCK_POINTS])
def optics(sample, radius, minimum_neighbors, amount_clusters, data_type): amount = amount_clusters if amount is None: amount = 0 pointer_data = package_builder(sample, c_double).create() c_data_type = convert_data_type(data_type) ccore = ccore_library.get() ccore.optics_algorithm.restype = POINTER(pyclustering_package) package = ccore.optics_algorithm(pointer_data, c_double(radius), c_size_t(minimum_neighbors), c_size_t(amount), c_data_type) results = package_extractor(package).extract() 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], results[optics_package_indexer.OPTICS_PACKAGE_INDEX_OPTICS_OBJECTS_INDEX], results[optics_package_indexer.OPTICS_PACKAGE_INDEX_OPTICS_OBJECTS_CORE_DISTANCE], results[optics_package_indexer.OPTICS_PACKAGE_INDEX_OPTICS_OBJECTS_REACHABILITY_DISTANCE])
def cure_data_destroy(cure_data_pointer): ccore = ccore_library.get(); ccore.cure_data_destroy(cure_data_pointer);