def show_winner_matrix(self):
        """!
        @brief Show winner matrix where each element corresponds to neuron and value represents
               amount of won objects from input dataspace at the last training iteration.
        
        @see show_distance_matrix()
        
        """
        
        if self.__ccore_som_pointer is not None:
            self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
        
        (fig, ax) = plt.subplots()
        winner_matrix = [[0] * self._cols for i in range(self._rows)]
        
        for i in range(self._rows):
            for j in range(self._cols):
                neuron_index = i * self._cols + j
                
                winner_matrix[i][j] = self._award[neuron_index]
                ax.text(i, j, str(winner_matrix[i][j]), va='center', ha='center')
        
        ax.imshow(winner_matrix, cmap = plt.get_cmap('cool'), interpolation='none')
        ax.grid(True)
        
        plt.title("Winner Matrix")
        plt.show()
def show_winner_matrix(self):
        """!
        @brief Show winner matrix where each element corresponds to neuron and value represents
               amount of won objects from input dataspace at the last training iteration.
        
        @see show_distance_matrix()
        
        """
        
        if self.__ccore_som_pointer is not None:
            self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
        
        (fig, ax) = plt.subplots()
        winner_matrix = [[0] * self._cols for i in range(self._rows)]
        
        for i in range(self._rows):
            for j in range(self._cols):
                neuron_index = i * self._cols + j
                
                winner_matrix[i][j] = self._award[neuron_index]
                ax.text(i, j, str(winner_matrix[i][j]), va='center', ha='center')
        
        ax.imshow(winner_matrix, cmap = plt.get_cmap('cool'), interpolation='none')
        ax.grid(True)
        
        plt.title("Winner Matrix")
        plt.show()
def show_winner_matrix ( self ) : if self . __ccore_som_pointer is not None : self . _award = wrapper . som_get_awards ( self . __ccore_som_pointer ) ( fig , ax ) = plt . subplots ( ) winner_matrix = [ [ 0 ] * self . _cols for i in range ( self . _rows ) ] for i in range ( self . _rows ) : for j in range ( self . _cols ) : neuron_index = i * self . _cols + j winner_matrix [ i ] [ j ] = self . _award [ neuron_index ] ax . text ( i , j , str ( winner_matrix [ i ] [ j ] ) , va = 'center' , ha = 'center' ) ax . imshow ( winner_matrix , cmap = plt . get_cmap ( 'cool' ) , interpolation = 'none' ) ax . grid ( True ) plt . title ( "Winner Matrix" ) plt . show ( )
!
        @brief Show winner matrix where each element corresponds to neuron and value represents
               amount of won objects from input dataspace at the last training iteration.
        
        @see show_distance_matrix()
!