def show_network(self, awards = False, belongs = False, coupling = True, dataset = True, marker_type = '<STR_LIT:o>'):
if self.__ccore_som_pointer is not None:<EOL><INDENT>self._size = wrapper.som_get_size(self.__ccore_som_pointer)<EOL>self._weights = wrapper.som_get_weights(self.__ccore_som_pointer)<EOL>self._neighbors = wrapper.som_get_neighbors(self.__ccore_som_pointer)<EOL>self._award = wrapper.som_get_awards(self.__ccore_som_pointer)<EOL><DEDENT>dimension = len(self._weights[<NUM_LIT:0>])<EOL>fig = plt.figure()<EOL>if (dimension == <NUM_LIT:1>) or (dimension == <NUM_LIT:2>):<EOL><INDENT>axes = fig.add_subplot(<NUM_LIT>)<EOL><DEDENT>elif dimension == <NUM_LIT:3>:<EOL><INDENT>axes = fig.gca(projection='<STR_LIT>')<EOL><DEDENT>else:<EOL><INDENT>raise NotImplementedError('<STR_LIT>')<EOL><DEDENT>if (self._data is not None) and (dataset is True):<EOL><INDENT>for x in self._data:<EOL><INDENT>if dimension == <NUM_LIT:1>:<EOL><INDENT>axes.plot(x[<NUM_LIT:0>], <NUM_LIT:0.0>, '<STR_LIT>', ms = <NUM_LIT:30>)<EOL><DEDENT>elif dimension == <NUM_LIT:2>:<EOL><INDENT>axes.plot(x[<NUM_LIT:0>], x[<NUM_LIT:1>], '<STR_LIT>')<EOL><DEDENT>elif dimension == <NUM_LIT:3>:<EOL><INDENT>axes.scatter(x[<NUM_LIT:0>], x[<NUM_LIT:1>], x[<NUM_LIT:2>], c = '<STR_LIT:b>', marker = '<STR_LIT:.>')<EOL><DEDENT><DEDENT><DEDENT>for index in range(self._size):<EOL><INDENT>color = '<STR_LIT:g>'<EOL>if self._award[index] == <NUM_LIT:0>:<EOL><INDENT>color = '<STR_LIT:y>'<EOL><DEDENT>if dimension == <NUM_LIT:1>:<EOL><INDENT>axes.plot(self._weights[index][<NUM_LIT:0>], <NUM_LIT:0.0>, color + marker_type)<EOL>if awards:<EOL><INDENT>location = '<STR_LIT>'.format(self._award[index])<EOL>axes.text(self._weights[index][<NUM_LIT:0>], <NUM_LIT:0.0>, location, color='<STR_LIT>', fontsize = <NUM_LIT:10>)<EOL><DEDENT>if belongs and self._data is not None:<EOL><INDENT>location = '<STR_LIT>'.format(index)<EOL>axes.text(self._weights[index][<NUM_LIT:0>], <NUM_LIT:0.0>, location, color='<STR_LIT>', fontsize = <NUM_LIT:12>)<EOL>for k in range(len(self._capture_objects[index])):<EOL><INDENT>point = self._data[self._capture_objects[index][k]]<EOL>axes.text(point[<NUM_LIT:0>], <NUM_LIT:0.0>, location, color='<STR_LIT>', fontsize = <NUM_LIT:10>)<EOL><DEDENT><DEDENT><DEDENT>if dimension == <NUM_LIT:2>:<EOL><INDENT>axes.plot(self._weights[index][<NUM_LIT:0>], self._weights[index][<NUM_LIT:1>], color + marker_type)<EOL>if awards:<EOL><INDENT>location = '<STR_LIT>'.format(self._award[index])<EOL>axes.text(self._weights[index][<NUM_LIT:0>], self._weights[index][<NUM_LIT:1>], location, color='<STR_LIT>', fontsize=<NUM_LIT:10>)<EOL><DEDENT>if belongs and self._data is not None:<EOL><INDENT>location = '<STR_LIT>'.format(index)<EOL>axes.text(self._weights[index][<NUM_LIT:0>], self._weights[index][<NUM_LIT:1>], location, color='<STR_LIT>', fontsize=<NUM_LIT:12>)<EOL>for k in range(len(self._capture_objects[index])):<EOL><INDENT>point = self._data[self._capture_objects[index][k]]<EOL>axes.text(point[<NUM_LIT:0>], point[<NUM_LIT:1>], location, color='<STR_LIT>', fontsize=<NUM_LIT:10>)<EOL><DEDENT><DEDENT>if (self._conn_type != type_conn.func_neighbor) and (coupling != False):<EOL><INDENT>for neighbor in self._neighbors[index]:<EOL><INDENT>if neighbor > index:<EOL><INDENT>axes.plot([self._weights[index][<NUM_LIT:0>], self._weights[neighbor][<NUM_LIT:0>]],<EOL>[self._weights[index][<NUM_LIT:1>], self._weights[neighbor][<NUM_LIT:1>]],<EOL>'<STR_LIT:g>', linewidth=<NUM_LIT:0.5>)<EOL><DEDENT><DEDENT><DEDENT><DEDENT>elif dimension == <NUM_LIT:3>:<EOL><INDENT>axes.scatter(self._weights[index][<NUM_LIT:0>], self._weights[index][<NUM_LIT:1>], self._weights[index][<NUM_LIT:2>], c=color, marker=marker_type)<EOL>if (self._conn_type != type_conn.func_neighbor) and (coupling != False):<EOL><INDENT>for neighbor in self._neighbors[index]:<EOL><INDENT>if neighbor > index:<EOL><INDENT>axes.plot([self._weights[index][<NUM_LIT:0>], self._weights[neighbor][<NUM_LIT:0>]],<EOL>[self._weights[index][<NUM_LIT:1>], self._weights[neighbor][<NUM_LIT:1>]],<EOL>[self._weights[index][<NUM_LIT:2>], self._weights[neighbor][<NUM_LIT:2>]],<EOL>'<STR_LIT>', linewidth=<NUM_LIT:0.5>)<EOL><DEDENT><DEDENT><DEDENT><DEDENT><DEDENT>plt.title("<STR_LIT>")<EOL>plt.grid()<EOL>plt.show()<EOL>
!
        @brief Shows neurons in the dimension of data.

        @param[in] awards (bool): If True - displays how many objects won each neuron.
        @param[in] belongs (bool): If True - marks each won object by according index of neuron-winner (only when dataset is displayed too).
        @param[in] coupling (bool): If True - displays connections between neurons (except case when function neighbor is used).
        @param[in] dataset (bool): If True - displays inputs data set.
        @param[in] marker_type (string): Defines marker that is used for dispaying neurons in the network.