def process(self):
        """!
        @brief Run clustering process of the algorithm.
        @details This method should be called before call 'get_clusters()'.
        
        """
        
        previous_likelihood = -200000
        current_likelihood = -100000
        
        current_iteration = 0
        while(self.__stop is False) and (abs(previous_likelihood - current_likelihood) > self.__tolerance) and (current_iteration < self.__iterations):
            self.__expectation_step()
            self.__maximization_step()
            
            current_iteration += 1
            
            self.__extract_clusters()
            self.__notify()
            
            previous_likelihood = current_likelihood
            current_likelihood = self.__log_likelihood()
            self.__stop = self.__get_stop_condition()
        
        self.__normalize_probabilities()
def process(self):
        """!
        @brief Run clustering process of the algorithm.
        @details This method should be called before call 'get_clusters()'.
        
        """
        
        previous_likelihood = -200000
        current_likelihood = -100000
        
        current_iteration = 0
        while(self.__stop is False) and (abs(previous_likelihood - current_likelihood) > self.__tolerance) and (current_iteration < self.__iterations):
            self.__expectation_step()
            self.__maximization_step()
            
            current_iteration += 1
            
            self.__extract_clusters()
            self.__notify()
            
            previous_likelihood = current_likelihood
            current_likelihood = self.__log_likelihood()
            self.__stop = self.__get_stop_condition()
        
        self.__normalize_probabilities()
def process ( self ) : previous_likelihood = - 200000 current_likelihood = - 100000 current_iteration = 0 while ( self . __stop is False ) and ( abs ( previous_likelihood - current_likelihood ) > self . __tolerance ) and ( current_iteration < self . __iterations ) : self . __expectation_step ( ) self . __maximization_step ( ) current_iteration += 1 self . __extract_clusters ( ) self . __notify ( ) previous_likelihood = current_likelihood current_likelihood = self . __log_likelihood ( ) self . __stop = self . __get_stop_condition ( ) self . __normalize_probabilities ( )
!
        @brief Run clustering process of the algorithm.
        @details This method should be called before call 'get_clusters()'.
!