def __init__( self, n_vertex, unconnected_probability=0 ): assert n_vertex >= 1 self.__n_vertex = n_vertex self._vertex_list = VertexDoubleLinkedList() self._LD = self.__n_vertex # leading dimension self._adj_matrix = [] # this is a simple python list # i'll use leadintng dimension to treat this as matrix # Generating vertex and push them into DLL for key in range( 0, self.__n_vertex ): self._vertex_list.insert( key ) # Generate random adj_matrix for ii in range( 0, self.__n_vertex ): for jj in range( 0, self.__n_vertex ): r = randint( 0, unconnected_probability ) if r is 0: self._adj_matrix.insert( ii * self._LD + jj, 1 ) else: self._adj_matrix.insert( ii * self._LD + jj, 0 ) return
class RandomGraph( object ): ''' This class implements a random oriented graph G ''' def __init__( self, n_vertex, unconnected_probability=0 ): assert n_vertex >= 1 self.__n_vertex = n_vertex self._vertex_list = VertexDoubleLinkedList() self._LD = self.__n_vertex # leading dimension self._adj_matrix = [] # this is a simple python list # i'll use leadintng dimension to treat this as matrix # Generating vertex and push them into DLL for key in range( 0, self.__n_vertex ): self._vertex_list.insert( key ) # Generate random adj_matrix for ii in range( 0, self.__n_vertex ): for jj in range( 0, self.__n_vertex ): r = randint( 0, unconnected_probability ) if r is 0: self._adj_matrix.insert( ii * self._LD + jj, 1 ) else: self._adj_matrix.insert( ii * self._LD + jj, 0 ) return def get_first_vertex( self): return self._vertex_list.search( 0 ) def set_matrix_column_to_1( self, n_col ): for ii in range( 0, self.__n_vertex ): for jj in range( 0, self.__n_vertex ): if jj == n_col: self._adj_matrix[ ii * self._LD + jj] = 1 return def set_matrix_sub_diag_to_1( self ): for ii in range( 0, self.__n_vertex ): for jj in range( 0, self.__n_vertex ): if ii == jj + 1: self._adj_matrix[ ii * self._LD + jj] = 1 return def delete_white_vertices( self ): for vertex in self._vertex_list: if vertex.get_color() is "WHITE": self._vertex_list.delete( vertex.get_key() ) return def get_neighbors_of_vertex( self, vertex ): # this is the returned list list_of_neighbors = [] # assert that vertex exists self._vertex_list.search( vertex.get_key() ) # begin scan - iterate over column vertex_key = vertex.get_key() for ii in range( 0, self.__n_vertex ): if self._adj_matrix[ ii * self._LD + vertex_key ] is 1: neighbor = self._vertex_list.search( ii ) list_of_neighbors.append( neighbor ) return list_of_neighbors def get_random_source_S( self ): length = self._vertex_list.length() random_index = randint( 0, length - 1 ) i = 0 for vertex in self._vertex_list: if i == random_index: return vertex i = i + 1 return def show_adj_matrix( self ): for ii in range( 0, self.__n_vertex ): for jj in range( 0, self.__n_vertex ): print( str( self._adj_matrix[ ii * self._LD + jj ] ), end=" ", flush=True) print( "" ) return # Iterate over Graph vertices def __iter__( self ): for vertex in self._vertex_list: yield vertex return
