pyclustering/cluster/fcm.py
def __calculate_centers(self):
        """!
        @brief Calculate center using membership of each cluster.

        @return (list) Updated clusters as list of clusters. Each cluster contains indexes of objects from data.

        @return (numpy.array) Updated centers.

        """
        dimension = self.__data.shape[1]
        centers = numpy.zeros((len(self.__centers), dimension))

        for i in range(len(self.__centers)):
            # multiplication '@' requires python version 3.5
            centers[i] = numpy.divide(self.__membership[:, i] @ self.__data, numpy.sum(self.__membership[:, i]))

        return centers
def __calculate_centers(self):
        """!
        @brief Calculate center using membership of each cluster.

        @return (list) Updated clusters as list of clusters. Each cluster contains indexes of objects from data.

        @return (numpy.array) Updated centers.

        """
        dimension = self.__data.shape[1]
        centers = numpy.zeros((len(self.__centers), dimension))

        for i in range(len(self.__centers)):
            # multiplication '@' requires python version 3.5
            centers[i] = numpy.divide(self.__membership[:, i] @ self.__data, numpy.sum(self.__membership[:, i]))

        return centers
def __calculate_centers ( self ) : dimension = self . __data . shape [ 1 ] centers = numpy . zeros ( ( len ( self . __centers ) , dimension ) ) for i in range ( len ( self . __centers ) ) : # multiplication '@' requires python version 3.5
 centers [ i ] = numpy . divide ( self . __membership [ : , i ] @ self . __data , numpy . sum ( self . __membership [ : , i ] ) ) return centers
!
        @brief Calculate center using membership of each cluster.

        @return (list) Updated clusters as list of clusters. Each cluster contains indexes of objects from data.

        @return (numpy.array) Updated centers.
!