generator.py

"""!
 
@brief Cluster generator.
 
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
 
"""
 
 
import collections.abc
import random
 
 
class data_generator:
    """!
    @brief Data generator provides services to generate data with clusters with normal distribution.
 
    """
 
    def __init__(self, amount_clusters, dimension, cluster_sizes, cluster_centers=None, cluster_width=1.0):
        """!
        @brief Constructs data generator for generating data-sets.
 
        @param[in] amount_clusters (uint): Amount of clusters that should be generated.
        @param[in] dimension (uint): Dimension of each generated point.
        @param[in] cluster_sizes (uint|array_like): Size of each cluster. In case of 'array_like' input clusters with
                    corresponding sizes are generated.
        @param[in] cluster_centers (array_like): Optional parameter that defines cluster centers (means).
        @param[in] cluster_width (uint|array_like): Optional parameter that defines cluster width (standard deviation).
                    In case of 'array_like' input each cluster has own standard deviation.
 
        """
 
        self.__amount_clusters = amount_clusters
        self.__dimension = dimension
 
        self.__cluster_sizes = cluster_sizes
        if not isinstance(self.__cluster_sizes, collections.abc.Iterable):
            self.__cluster_sizes = [self.__cluster_sizes] * amount_clusters
 
        self.__cluster_width = cluster_width
        if not isinstance(self.__cluster_width, collections.abc.Iterable):
            self.__cluster_width = [self.__cluster_width] * amount_clusters
 
        self.__cluster_centers = cluster_centers
        if self.__cluster_centers is None:
            self.__cluster_centers = self.__generate_cluster_centers(self.__cluster_width)
 
 
    def generate(self):
        """!
        @brief Generates data in line with generator parameters.
 
        """
        data_points = []
 
        for index_cluster in range(self.__amount_clusters):
            for _ in range(self.__cluster_sizes[index_cluster]):
                point = self.__generate_point(index_cluster)
                data_points.append(point)
 
        return data_points
 
 
    def __generate_point(self, index_cluster):
        """!
        @brief Generates point in line with parameters of specified cluster.
 
        @param[in] index_cluster (uint): Index of cluster whose parameters are used for point generation.
 
        @return (list) New generated point in line with normal distribution and cluster parameters.
 
        """
        return [ random.gauss(self.__cluster_centers[index_cluster][index_dimension],
                              self.__cluster_width[index_cluster] / 2.0)
                 for index_dimension in range(self.__dimension) ]
 
 
    def __generate_cluster_centers(self, width):
        """!
        @brief Generates centers (means in statistical term) for clusters.
 
        @param[in] width (list): Width of generated clusters.
 
        @return (list) Generated centers in line with normal distribution.
 
        """
        centers = []
        default_offset = max(width) * 4.0
        for i in range(self.__amount_clusters):
            center = [ random.gauss(i * default_offset, width[i] / 2.0) for _ in range(self.__dimension) ]
            centers.append(center)
 
        return centers