def show_winner_matrix(self):
if self.__ccore_som_pointer is not None:<EOL><INDENT>self._award = wrapper.som_get_awards(self.__ccore_som_pointer)<EOL><DEDENT>(fig, ax) = plt.subplots()<EOL>winner_matrix = [[<NUM_LIT:0>] * self._cols for i in range(self._rows)]<EOL>for i in range(self._rows):<EOL><INDENT>for j in range(self._cols):<EOL><INDENT>neuron_index = i * self._cols + j<EOL>winner_matrix[i][j] = self._award[neuron_index]<EOL>ax.text(i, j, str(winner_matrix[i][j]), va='<STR_LIT>', ha='<STR_LIT>')<EOL><DEDENT><DEDENT>ax.imshow(winner_matrix, cmap = plt.get_cmap('<STR_LIT>'), interpolation='<STR_LIT:none>')<EOL>ax.grid(True)<EOL>plt.title("<STR_LIT>")<EOL>plt.show()<EOL>
!
        @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()