def test():
datasetfile = ['Email-Enron']#['com-dblp.ungraph', 'Email-Enron', 'facebook_combined']
order = ['bfs', 'dfs', 'rand']
ksize = [4,8,12]
ba,ch,dg,ha,rg,tr=[],[],[],[],[],[]
Edges_constant = [1049866,367662,88234] #edges of each datasets
for dataname in datasetfile:
txt2p.txt2p(dataname)
rand.Rand(dataname)
dfs.dfs(dataname)
bfs.bfs(dataname)
for dataname in datasetfile:
for od in order:
for k in ksize:
ba.append(Balanced.Balanced(dataname, k, od))
ch.append(Chunking.Chunking(dataname, k, od))
dg.append(DGreedy.DGreedy(dataname, k, od))
ha.append(Hashing.Hashing(dataname, k, od))
rg.append(RGreedy.RGreedy(dataname, k, od))
tr.append(Triangles.Triangles(dataname, k, od))
print 'ba',ba
print 'ch',ch
print 'dg',dg
print 'ha',ha
print 'rg',rg
print 'tr',tr
def execute_bfs(points, walls, selected_grid):
start_rect = points[0]
end_rect = points[1]
wall_list = []
for row in all_rects:
for item in row:
rect, _ = item
if rect == start_rect:
si = all_rects.index(row)
sj = row.index(item)
if rect == end_rect:
ei = all_rects.index(row)
ej = row.index(item)
for wall in walls:
if rect == wall:
wi = all_rects.index(row)
wj = row.index(item)
wall_list.append([wi, wj])
start_point = [si, sj]
end_point = [ei, ej]
maze = Matrix(selected_grid[0], start_point, end_point, wall_list)
bfs(maze, start_point)
return maze
def run(maze, flammability):
start = (0, 0)
end = (maze.height - 1, maze.width - 1)
# setup the fire
fire.add_fire(maze, flammability)
# calculate the first path
path = []
bfs.bfs(path, maze, start, end)
if len(path) == 0: # No path found
return None
# step on the path, spread fire, then create a new path
while True:
# if we stepped into fire, fail
if maze.maze[path[1][0]][path[1][1]] != 0:
return False
# if we reached the end, pass
if path[1] == end:
return True
# spread the fire
fire.advance_fire_one_step(maze)
# if fire moved onto us, fail
if maze.maze[path[1][0]][path[1][1]] != 0:
return False
# recalculate path
bfs.bfs(path, maze, path[1], end)
if len(path) == 0: # No path found
return False
return
def connected_components(graph):
"""
Given a graph (directed or undirected) return its connected components.
Input: A graph object.
Output: A dictionary with the nodes as keys and the component index for
that node as the value.
"""
class CCTraversal(bfs.Traversal):
def __init__(self, graph):
bfs.Traversal.__init__(self, graph)
self.components = {}
self.curr_component = 0
def children_queued(self, node):
self.components[node] = self.curr_component
traversal = CCTraversal(graph)
for node in graph.nodes:
if node not in traversal.components:
# Node has already been assigned a component so dont bother with this
bfs.bfs(node, traversal)
traversal.curr_component += 1
return traversal.components
def drawer():
win = pygcurse.PygcurseWindow(config['board']['w'], config['board']['h'], fgcolor='black')
win.setscreencolors(None, 'white', clear=True)
drag = False
startEnd = []
while len(startEnd) < 2:
for event in pygame.event.get():
if event.type == QUIT or (event.type == KEYDOWN and event.key == K_ESCAPE):
pygame.quit()
sys.exit()
if event.type == pygame.MOUSEBUTTONUP:
coordinate = win.getcoordinatesatpixel(event.pos)
win.write('@', *coordinate)
startEnd.append(coordinate)
walls = set()
while True:
for event in pygame.event.get():
if event.type == QUIT or (event.type == KEYDOWN and event.key == K_ESCAPE):
pygame.quit()
sys.exit()
if event.type == pygame.MOUSEBUTTONDOWN:
drag = True
elif event.type == pygame.MOUSEBUTTONUP:
drag = False
elif event.type == pygame.MOUSEMOTION:
if drag:
coordinate = win.getcoordinatesatpixel(event.pos)
win.write('#', *coordinate)
walls.add(coordinate)
elif event.type == pygame.KEYDOWN:
if config['algo'] == 'bfs':
bfs(win, startEnd, walls)
def main():
bfs.shuffler()
initialState = bfs.initial_state
print initialState
bfs.bfs(initialState)
moves = bfs.backtrace()
print moves
return render_template('index.html', initial=initialState, moves=moves)
def connectedComponents(G): components = [] c = 0 visited = set() for v in G: if v.key not in visited: components.append(set()) bfs.bfs(G,v.key, discover_function = lambda x : visited.add(x), process_function = lambda x : components[c].add(x)) c += 1 return components
def test_bfs_int_vertices_negaitve(self):
graph = Graph()
graph.add_edge(Edge(Vertex(1), Vertex(2), 1))
graph.add_edge(Edge(Vertex(1), Vertex(4), 1))
graph.add_edge(Edge(Vertex(3), Vertex(4), 1))
graph.add_edge(Edge(Vertex(4), Vertex(2), 1))
self.assertEqual(bfs(graph, Vertex(5), Vertex(0)), float("inf"))
self.assertEqual(bfs(graph, Vertex(1), Vertex(5)), float("inf"))
def main():
bfs.shuffler()
initialState = bfs.initial_state
randomizedState = bfs.randomized_state
print("initial state: " + str(initialState))
bfs.bfs(initialState)
moves = bfs.backtrace()
print(moves)
return render_template('index.html', initial=initialState, moves=moves)
def testBfs(self): G = graph.Graph() G.addEdge(1,2) G.addEdge(2,3) G.addEdge(4,1) G.addEdge(5,1) G.addEdge(6,1) G.addEdge(7,6) G.addEdge(3,1) G.addEdge(8,7) G.addEdge(9,8) processingOrder = [] processFun = lambda v: processingOrder.append(v) bfs.bfs(G, 1, lambda v : v, processFun) self.failUnlessEqual(processingOrder, [1,2,3,4,5,6,7,8,9])
def ford_fulkerson(graph, source, sink):
""" Run the Ford Fulkerson method using BFS for shortest path
:param graph:
:param source:
:param sink:
:return:
Once BFS can no longer find any shortest direct path between source and sink,
return the residual graph with the updated capacities.
"""
path = bfs(graph, source, sink)
while path:
flow_path = zip(path[:-1], path[1:])
graph = augment_flow(graph, flow_path)
path = bfs(graph, source, sink)
return graph
def SearchUSA(search_type, src_city, dst_city, filename='roads.txt'):
"This function searches for a path from source city to destination city using given search algorithm"
import bfs, dfs, astar, Roadmap
from HelperFunctions import ReadFileToMap
#Initializing empty map which uses adjacency list data structure to store map information
my_map = Roadmap.Roadmap()
#Loading map from the text file
ReadFileToMap(filename, my_map)
success = False
print("\nApplying '{0}' Algorithm to search path from {1} to {2}\n".format(
search_type.upper(), src_city.upper(), dst_city.upper()))
if (search_type == 'bfs'):
# print("\n{}".format("BFS SEARCH".center(80,'$')))
success = bfs.bfs(src_city, dst_city, my_map)
elif (search_type == 'dfs'):
# print("\n{}".format("DFS SEARCH".center(80,'$')))
success = dfs.dfs(src_city, dst_city, my_map)
elif (search_type == 'astar'):
# print("\n{}".format("ASTAR SEARCH".center(80,'$')))
success = astar.astar(src_city, dst_city, my_map)
else:
print("\nInvalid Search Type: Please Try Again.\n")
if (success):
pass
# print("{}\n".format("SUCESS".center(400,'*')))
else:
print("{}\n".format("FAILURE".center(400, '*')))
def __init__(self, typeOfAlgo , location, heading,goal_bounds, mazeDim, exploreAfterGoalReached):
"""
taking type of algo
"""
print((typeOfAlgo , location, heading,goal_bounds, mazeDim))
self.algoType = typeOfAlgo
self.mazeDim = mazeDim
self.algoObj = None
self.location = location
self.heading = heading
self.goal_bounds = goal_bounds
self.exploreAfterGoalReached=exploreAfterGoalReached
#self.graphAdjacencyMatrix=self.buildAdjacencyGraph(self.mazeObj)
if self.algoType == "Djskitra":
self.algoObj = Djkistra(location, heading, goal_bounds, self.mazeDim ,exploreAfterGoalReached )
elif self.algoType == "floodfill":
self.algoObj = floodFill(location, heading, goal_bounds, self.mazeDim ,exploreAfterGoalReached)
elif self.algoType == "dfs":
self.algoObj = dfs(location, heading, goal_bounds, self.mazeDim ,exploreAfterGoalReached)
elif self.algoType == "bfs":
self.algoObj = bfs(location, heading, goal_bounds, self.mazeDim ,exploreAfterGoalReached)
elif self.algoType == "random":
self.algoObj= None
else:
print("No specific algorithm mentioned")
def connected_components(graph):
"""
computes the set of connected components of a graph
Arguments:
graph -- input graph
Return:
a list of sets, where each set contains
the nodes belonging to that connected component
"""
# initialize remaining nodes set
remainingNodes = set()
for node in graph:
remainingNodes.add(node)
# initialize
CC = []
while len(remainingNodes) != 0:
i = random.sample(remainingNodes, 1)[0]
d = bfs.bfs(graph, i)
W = set()
for u in remainingNodes:
if d[u] != float('inf'):
W.add(u)
CC.append(W)
for node in W:
remainingNodes.remove(node)
return CC
def main():
print("\n\n\nEnter the maze number that you want to solve.")
mazeNumber = int(input("[1]. maze1.txt \t 2. maze2.txt \t 3. maze3.txt\n"))
print("\n\nEnter algorithm number you would like to use.")
algoPref = int(
input(
"[1]. Djikstra's \n 2. Breadth First Searh \n 3. Depth First Search \n 4. Backtracking\n"
))
print("\n\nDo you want to get the output in color?")
color = input("[Y]es\t\tNo\n")
maze, start, end = mazes(mazeNumber)
if algoPref == 4:
path = backtracking(maze, start)
elif algoPref == 3:
path = dfs(maze, start)
elif algoPref == 2:
path = bfs(maze, start)
else:
path = djikstras(maze, start)
# Printing the maze with the solution for the respective path finding algorithm
currNode = end
while currNode != (0, 1):
nextNode = path[currNode]
connectTwoCells(maze, currNode, nextNode)
currNode = nextNode
if color == "Y" or color == "y" or color.capitalize() == "Yes":
printMazeInColor(maze)
else:
printMaze(maze)
def test_no_path(self):
self.assertEqual(None,
bfs("A", "C", {
"A": ["B"],
"B": ["D"],
"F": ["C"]
}))
def main():
try:
with open(sys.argv[1],"r") as f: #membaca nama file sebagai argumen program
mat = [[int(num) for num in line if num!="\n"] for line in f]
f.close()
xawal, yawal = map(int, input("Masukkan point awal maze (format= x <spasi> y): ").split())
xakhir, yakhir = map(int, input("Masukkan point akhir maze (format= x <spasi> y): ").split())
mazemat = copy.deepcopy(mat)
print("\nMatriks yang diinput: ")
bfs.out(mat)
print("\n\nDengan BFS: ")
bobot = bfs.bfs(xawal,yawal,xakhir,yakhir,mat)
bfs.out(mat)
print("\nJalur yang ditempuh: ")
bfs.jalur(bobot,xawal,yawal,xakhir,yakhir,mat)
for i in range(len(mat)):
for j in range(len(mat[i])):
if (mat[i][j]==8):
print(0, end=" ")
elif (mat[i][j]==5):
print(" ", end=" ")
else:
print(mat[i][j], end=" ")
print()
print("\nAda sebanyak",bfs.jumlah(mat),"langkah")
print("\n\nDengan A*: ")
ast.aStar(mazemat,xawal,yawal,xakhir,yakhir)
except IndexError:
print("Tidak ditemukan jalur yang tepat\nStarting point ataupun ending point salah")
except FileNotFoundError:
print("File not found")
def findRoute(org, dst, flight_data):
response = {}
airlines_info_dic, airports_info_dic, connections_dic = flight_data
if org == dst:
response["type"] = "Error"
response["message"] = "Origin and Destination must be different:" + org
return response
if not airports_info_dic.has_key(org):
response["type"] = "Error"
response["message"] = "Invalid Origin:" + org
return response
if not airports_info_dic.has_key(dst):
response["type"] = "Error"
response["message"] = "Invalid Destination:" + dst
return response
# use bfs to determine the mimimum connection route;
route = bfs(org, dst, connections_dic)
if len(route) == 0:
response["type"] = "Failure"
response["message"] = "No Route Exists"
else:
response["type"] = "Success"
response["message"] = "Route Found"
response["route"] = route
response["detailedRoute"] = getFlightOptionsForRoute(
route, connections_dic, airlines_info_dic, airports_info_dic)
return response
def start():
print("enter choice 1.bfs 2.dfs 3.uniform cost search 4.astart")
x=input()
if x=="1":
graph={}
print("enter number nodes")
nodes=int(input())
for i in range(0,nodes):
print("enter nodes connected to node with spaces:"+str(i))
x=list(input().split())
x = [int(i) for i in x]
graph[i]=x
goal=int(input("enter goal state"))
print('bfs ' +bfs.bfs(graph,0,goal))
if x=="2":
graph=[]
print("enter number nodes")
nodes = int(input())
for i in range(0, nodes):
print("enter nodes connected to node with spaces:" + str(i))
x = list(input().split())
x = [int(i) for i in x]
graph.append(x)
goal = int(input("enter goal state"))
print(dfs.dfs(graph, 0,goal))
if x=="3":
djikstra.start1()
if x=="4":
astardynamic.start()
def segmentation(img_in_path, img_out_path):
count = 0
img_in = cv2.imread(img_in_path, 0)
hei, wid = img_in.shape[:2]
img_out = np.zeros((hei, wid))
print("Segmentando imagem...")
for i in range(hei):
for j in range(wid):
if img_in[i, j] > 0 and img_out[i, j] == 0:
count += 1
img_out[i, j] = count
bfs(img_in, img_out, i, j, count)
get_histogram(img_out, img_out_path)
cv2.imwrite(img_out_path + "output.png", color_objects(img_out, count))
print(f"Saída salva em: {img_out_path}")
def get_bfs():
queue = deque()
if (bfs(maze, 0, 0, queue, path, visited, draw_maze) == False):
canvas.create_text(100,
100,
font=("Times New Roman", 20, "bold"),
fill='pink',
text="no solution")
def steps_to_reach(cls):
""" how many inventions neccessary to reach dragon """
Logger.debug(
"steps_to_reach(): cls.available_elnames=%s, dest='dragon'",
cls.available_elnames)
ret = bfs.bfs(cls.available_elnames, 'dragon')
Logger.debug("returned %s", ret)
return ret
def test_bfs_str_vertices_negative(self):
graph = Graph()
graph.add_edge(Edge(Vertex("A"), Vertex("D"), 2))
graph.add_edge(Edge(Vertex("A"), Vertex("G"), 3))
graph.add_edge(Edge(Vertex("A"), Vertex("B"), 1))
graph.add_edge(Edge(Vertex("B"), Vertex("E"), 6))
graph.add_edge(Edge(Vertex("B"), Vertex("F"), 7))
graph.add_edge(Edge(Vertex("F"), Vertex("D"), 10))
graph.add_edge(Edge(Vertex("F"), Vertex("C"), 12))
graph.add_edge(Edge(Vertex("E"), Vertex("G"), 9))
self.assertEqual(bfs(graph, Vertex("A"), Vertex("C")), 20)
self.assertEqual(bfs(graph, Vertex("A"), Vertex("F")), 8)
self.assertEqual(bfs(graph, Vertex("B"), Vertex("G")), 15)
self.assertEqual(bfs(graph, Vertex("E"), Vertex("A")), float("inf"))
self.assertEqual(bfs(graph, Vertex("C"), Vertex("D")), float("inf"))
def choose_algo(start, end, grid, algorithm):
if algorithm == 'bfs':
return bfs(start, end, grid)
elif algorithm == 'dfs':
return dfs(start, end, grid)
elif algorithm == 'dijkstra':
return dijkstra(start, end, grid)
elif algorithm == 'astar':
return astar(start, end, grid)
def move(start, target, grid, algorithm):
if algorithm == "astar":
return astar(start, target, grid)
elif algorithm == "dijkstra":
return dijkstra(start, target, grid)
elif algorithm == "bfs":
return bfs(start, target, grid)
elif algorithm == "dfs":
return dfs(start, target, grid)
def main():
with open("input.txt") as f:
algo = f.readline().strip("\n").lstrip(" ").rstrip(" ")
sec_line = f.readline().strip("\n").lstrip(" ").rstrip(" ").split(" ")
no_of_columns = int(sec_line[0])
no_of_rows = int(sec_line[1])
third_line = f.readline().strip("\n").lstrip(" ").rstrip(" ").split(
" ")
landing_col = int(third_line[0])
landing_row = int(third_line[1])
threshold = int(f.readline().strip("\n").lstrip(" ").rstrip(" "))
no_of_targets = int(f.readline().strip("\n").lstrip(" ").rstrip(" "))
targets_list = [
tuple(
map(
int,
f.readline().strip("\n").lstrip(" ").rstrip(" ").split(
" "))) for target in range(no_of_targets)
]
z_index_matrix = [
list(
map(
int,
re.sub(" +", " ",
f.readline().strip("\n").lstrip(" ").rstrip(
" ")).split(" "))) for rows in range(no_of_rows)
]
if algo == "BFS":
shortest_target_paths = bfs.bfs(z_index_matrix,
(landing_col, landing_row),
targets_list, threshold,
no_of_columns, no_of_rows)
elif algo == "UCS":
shortest_target_paths = ucs.ucs(z_index_matrix,
(landing_col, landing_row),
targets_list, threshold,
no_of_columns, no_of_rows)
elif algo == "A*":
shortest_target_paths = {}
index = 0
for target in targets_list:
shortest_target_path = astar.astar(z_index_matrix,
(landing_col, landing_row),
[target], threshold,
no_of_columns, no_of_rows)
shortest_target_paths[index] = shortest_target_path[0]
index += 1
write_to_output(shortest_target_paths)
def test1(self):
"""test bfs"""
self.assertEqual((1,2) in bfs.bfs(3,[(1,2),(1,3),(1,2)],1), True)
self.assertEqual((1,3) in bfs.bfs(3,[(1,2),(1,3),(1,2)],1), True)
self.assertEqual((2,3) in bfs.bfs(3,[(1,2),(1,3),(1,2)],1), False)
self.assertEqual((1,4) in bfs.bfs(4,[(1,2),(1,3),(1,2),(3,4)],1), False)
self.assertEqual((3,4) in bfs.bfs(4,[(1,2),(1,3),(1,2),(3,4)],1), True)
self.assertEqual((1,6) in bfs.bfs(8,self.edges_cc(4),1), False)
self.assertEqual((5,6) in bfs.bfs(6,[(1,2),(1,3),(1,2),(3,4),(5,6)],1), False)
linegraph = [ (i,i+1) for i in range(1,10) ]
self.assertEqual(linegraph, bfs.bfs(20,linegraph,1))
linegraph.extend( [ (i,i+10) for i in range(1,10) ])
self.assertEqual(set(linegraph), set(bfs.bfs(20,linegraph,1)))
def bfSearch():
global endX
global endY
grid, x, y = buildGrid()
path, seen = bfs.bfs(grid, (x, y))
path.remove(path[0])
path.remove(path[-1])
seen.remove(seen[0])
seen.remove((endX, endY))
colourBoard(seen, path)
def main():
# Init ROS node
rospy.init_node('task', anonymous=True)
# Create subscriber for position, goal, boost points, and obstacles
rospy.Subscriber('/goal', Pose, goal_callback)
rospy.Subscriber('/boost', PoseArray, boost_callback)
rospy.Subscriber("/dynamic_obstacles", PoseArray,
dynamic_obstacles_callback)
# Wait for resources to become active
goal = rospy.wait_for_message("/goal", Pose).position
boosts = rospy.wait_for_message("/boost", PoseArray).poses
obstacles = rospy.wait_for_message("/dynamic_obstacles", PoseArray).poses
# Create map service client
getMap = rospy.ServiceProxy('/GlobalMap', GlobalMap)
rospy.wait_for_service('/GlobalMap')
try:
raw_map = getMap()
except rospy.ServiceException as e:
print("Map service error: " + str(e))
return
# Get map as 2D list
world_map = util.parse_map(raw_map)
shortest_path = bfs.bfs(world_map, (0, 0), (goal.y, goal.x))
# Print resources
print("Wall layout:")
util.print_map(world_map)
print("Boost points:")
util.print_positions(boosts)
print("Obstacles at start:")
util.print_positions(obstacles)
# Initialize drone
drone = Drone()
drone.takeoff()
# -- For example code --
target_x = 0
target_y = 0
drone.set_target(target_x, target_y)
rate = rospy.Rate(30)
print('there is a test')
i = 0
targets = [[0, 0], [1, 0], [1, 7], [2, 7], [16, 7], [17, 7], [19, 7],
[19, 20]]
for t in targets:
goto(t[0], t[1], rospy, rate, drone)
def test_cyclical_graph(self):
graph = {'A': ['B', 'C', 'D'],
'B': ['E', 'A'],
'C': ['E', 'A'],
'D': ['E', 'A'],
'E': []
}
start = 'A'
out_visited = ['A', 'B', 'C', 'D', 'E']
self.assertEqual(bfs(graph, start), out_visited)
def main():
global start, goal, goal_found, start_found
# load map
pic("dartmouth_map.png")
# draw each vertex & the connect-y line things in blue
for location in vertex_dict:
vertex_dict[location].draw_vertex(0, 0, 1)
vertex_dict[location].draw_edges(0, 0, 1)
# if the mouse is pressed
if is_mouse_pressed():
# loop through vertex_dict
for location in vertex_dict:
# if the mouse is in the square that inscribes a vertex circle
if vertex_dict[location].select_vertex(mouse_x(), mouse_y()):
start = vertex_dict[location] # start = the vertex you click on
start.draw_vertex(1, 0, 0) # make vertex red
start_found = True # start has now been found
break # stop loop
# once start is found, find goal
if start_found:
# loop through vertex dictionary
for location in vertex_dict:
# if it's the same vertex, continue
if vertex_dict[location] == start:
continue
# if the mouse is in the square around the vertex, that vertex = goal
if vertex_dict[location].select_vertex(mouse_x(), mouse_y()):
goal = vertex_dict[location] # goal = whatever vertex your mouse is hovering over
goal.draw_vertex(1, 0, 0) # draw in red
goal_found = True # goal has been found
break # stop loop
# once the goal and start have been found, implement breadth-first search
if start_found and goal_found:
# extra credit: show vertex names in black
start.start_show_name(0, 0, 0)
goal.goal_show_name(0, 0, 0)
path = bfs(start, goal) # path = bfs from start to goal
goal = path[0] # goal = first element of path
previous = goal
# draw path from start to goal in red
for vertex in path:
vertex.draw_vertex(1, 0, 0)
vertex.draw_path(previous, 1, 0, 0)
previous = vertex
def testIterativeDeepening(self):
for i in range(50):
adjList = self.randG.randomGnmGraph(n=50,
M=1000,
isWeighted=False,
isDirected=False)
src = random.choice(adjList.keys())
dest = random.choice(adjList.keys())
dist1, prev1 = bfs(adjList, src)
dist2, prev2 = iterative_deepening(adjList, src, dest)
self.assertEqual(dist1[dest], dist2[dest])
def testBidirectional(self):
for i in range(50):
adjList = self.randG.randomGnmGraph(n=50,
M=1000,
isWeighted=False,
isDirected=False)
src = random.choice(adjList.keys())
dest = random.choice(adjList.keys())
dist1, prev1 = bfs(adjList, src)
dist2, prev2 = bidirectional(adjList, src, dest)
self.assertEqual(dist1[dest], dist2)
def testUnweighted(self):
for i in range(50):
adjList = self.randG.randomGnmGraph(n=50,
M=1000,
isWeighted=False,
isDirected=False)
src = random.choice(list(adjList.keys()))
dist1, prev1 = bfs(adjList, src)
adjList = self.randG.toWeighted(adjList)
dist3, prev3 = dijkstra(adjList, src)
self.assertEqual(dist1, dist3)
def neighbourhood_all(g, depth):
'''
set neighbourhood of each node to BFS of depth "depth"
todo: this should probably be in "graph.py"
'''
iteration = 0
for auth_id, auth_t in g.authors.iteritems():
if (iteration%250 == 0):
print "computing neighbourhood %d out of %d"%(iteration, len(g.authors))
iteration += 1
pickle.dump(bfs.bfs(g, depth, auth_id), open("neighbourhood/%d"%auth_id, 'wb')) # we actually dump the neighbourhood to a file, can't save all of this in memory. It enables easy enough access from code, but not very efficient.
def test_connected_graph(self):
graph = {'A': ['B', 'C'],
'B': ['F', 'D'],
'C': ['D', 'E'],
'D': [],
'E': [],
'F': []
}
start = 'A'
out_visited = ['A', 'B', 'C', 'F', 'D', 'E']
self.assertEqual(bfs(graph, start), out_visited)
def main():
# Start and goal vertices for BFS.
start = None
goal = None
vertex_dict = load_graph("dartmouth_graph.txt") # dictionary of vertices
image = load_image("dartmouth_map.png") # image of map_plot
enable_smoothing()
while not window_closed():
draw_image(image, 0, 0) # draw the map
# Draw each vertex and the edges between each vertex and its neighbors.
for key in vertex_dict:
vertex_dict[key].draw_neighbor_edges(0, 0, 1) # draw in blue
vertex_dict[key].draw(0, 0, 1)
# Find a vertex near the current mouse location.
found = None
for key in vertex_dict:
if vertex_dict[key].is_point_near_vertex(mouse_x(), mouse_y()):
found = vertex_dict[key]
break # we've found the vertex, so we don't need to keep going through the loop
# If the mouse button is down, change the start vertex to whatever is found;
# could be None.
if mouse_down():
start = found
# If there is a start vertex has a value, draw it in red, and set the goal
# (goal could be None!)
if start != None:
start.draw(1, 0, 0) # draw in red
goal = found
if start != None and goal != None: # we have a genuine search!
path = bfs(start, goal)
# Draw the path from the goal back to the start in blue.
previous = goal
for vertex in path:
vertex.draw(1, 0, 0)
vertex.draw_edge(previous, 1, 0, 0)
previous = vertex
# Show the names of the start and goal vertices in purple.
# (Extra-credit feature.)
start.show_name(0.5, 0, 1)
goal.show_name(0.5, 0, 1)
request_redraw()
sleep(.02)
def noga_alon_projection(g, depth): ''' Remove all nodes in g that are not close enough to noga alon (see in code what is "close enough") Note that depth 4 ~= 400,000 nodes. depth 5 ~= 800,000. Higher than that seems to converge to ~1000000 So, if we run with depth=4 we get a 400,000 node graph. todo: this should probably be in "graph.py" ''' noga_id = 1653815 # he has other IDs, but this is clearly his primary one (with 330 cooperations as opposed to <10 for the rest) noga_projection = bfs.bfs(g, depth, noga_id) # authors_to_remove = [author for author in g.authors.keys() if author not in noga_projection] remove_authors(g, authors_to_remove)
def main():
img = load_image("dartmouth_map")
draw_image(img, 0, 0)
start = None
goal = None
#initialize map with blue vertices
for key in vertices:
vertices[key].draw_vertex(0,0,1)
for adjacent_vertex in vertices[key].list:
vertices[key].draw_edge(adjacent_vertex, 0, 0, 1)
while not window_closed():
for key in vertices:
if vertices[key].is_touched() and mouse_down():
#reset map every time new starting vertex selected
for generic_vertex in vertices:
vertices[generic_vertex].draw_vertex(0,0,1)
for adjacent_vertex in vertices[generic_vertex].list:
vertices[generic_vertex].draw_edge(adjacent_vertex, 0, 0, 1)
#draw starting vertex
vertices[key].draw_vertex(1, 0, 0)
start = key
#check if start vertex selected
if start != None:
for key in vertices:
if vertices[key].is_touched() and not vertices[start].is_touched():
goal = key
#start and goal vertices selected and start is not goal
if goal != None and start != goal:
for key in vertices:
vertices[key].draw_vertex(0,0,1)
for adjacent_vertex in vertices[key].list:
vertices[key].draw_edge(adjacent_vertex, 0, 0, 1)
path = bfs(start, goal)
if path != None:
for vertex in path:
if vertex != goal and vertex.back_pointer != None:
vertex.draw_edge(vertex.back_pointer, 1, 0, 0)
vertex.draw_vertex(1, 0, 0)
request_redraw()
sleep(.02)
def main():
#creates vertex object dictionary from load graph function
vertex_dictionary=load_graph("dartmouth_graph.txt")
#initialize start and goal vertex variables
start_vertex=None
goal_vertex= None
while not window_closed():
#draw the background
draw_background(vertex_dictionary)
for key in vertex_dictionary:
#if mouse clicks on a vertex make it the starting vertex
if vertex_dictionary[key].in_box(mouse_x(), mouse_y()) and mouse_down() and start_vertex!=goal_vertex:
start_vertex=vertex_dictionary[key]
#if mouse hovers over another vertex make that the goal vertex
if vertex_dictionary[key].in_box(mouse_x(), mouse_y()) and vertex_dictionary[key]!=start_vertex:
goal_vertex=vertex_dictionary[key]
#performs bfs for start and goal vertices once declared
if start_vertex!= None and goal_vertex!=None:
#pathway is list of vertices from goal to start vertex created from bfs
pathway= bfs(start_vertex, goal_vertex)
#print names of start and goal above their respective vertex
start_vertex.print_name()
goal_vertex.print_name()
for i in range(len(pathway)):
#draw each vertex in pathway
pathway[i].draw_vertex(1,0,0)
#draw each edge in pathway
if i<len(pathway)-1:
pathway[i].draw_edge(pathway[i+1],1,0,0)
#update the window and nap
request_redraw()
sleep(NAP)
def _testWithoutHeuristicCostEstimate():
number_of_vertices = 100
for test in range(1000):
graph = _getRandomGraph(number_of_vertices)
distance = bfs(0, graph)
error_text = "Error on test: _testWithoutHeuristicCostEstimate"
try:
path = findWayByAStar(0, lambda x: x == number_of_vertices - 1, lambda x, y: 1, lambda x: graph[x],
lambda x: 0)
except:
if distance[number_of_vertices - 1] != "Inf":
raise Exception(error_text)
else:
if len(path) != distance[number_of_vertices - 1]:
raise Exception(error_text)
_checkPath(0, number_of_vertices - 1, path, graph, error_text)
def resolve(self, src, sink):
"""
Returns path from 'src' to 'sink' in cause-effect graph.
Arguments:
src <str> : Source node concept (start).
sink <str> : Sink node concept (goal).
Returns:
<(bool, str|[ConceptNode,])> - first item denotes whether an error occurred
or not, the second item corresponds to an error message or a list of concept
nodes representing the path from 'src' to 'sink' in the cause-effect graph.
"""
if src not in self.nodes:
return (False, 'Source %s not in Cause-Effect Graph.' % (src))
elif sink not in self.nodes:
return (False, 'Sink %s not in Cause-Effect Graph.' % (sink))
else:
return (True, bfs(self.nodes[src], self.nodes[sink]))
def limited_infection(graph, victim, new_version, preference, number_of_users):
"""
:param graph: An array of all users in the system.
:param victim: Who will be 'patient zero'. Must be a User.
:param new_version: The new version to update users with.
:param preference: Selection of the strings "child" and "parent" to choose which direction to favor.
:param number_of_users: How many users we want to infect.
"""
# If the input supplied is not indicative of a direction to traverse, we throw an error.
if preference not in ["child", "parent"]:
raise ValueError, "Invalid preference. Choose from 'parent' or 'child'."
# The result of the disbursement of infections is supplied through the modified bfs function.
result = bfs(victim, preference, number_of_users)
# Because we used deepcopy to preserve the integrity of our data structure in bfs, we must update the users
# supplied to us in the graph because those are the original users that we want to infect.
for user in graph:
for item in result:
if item.get_username() == user.get_username() and user.get_version() != new_version:
user.update_version(new_version)
if user.get_version() == new_version:
print user.get_username(), "has been infected."
def successive_possible_containers(a_node): return bfs.bfs(a_node,lambda x: x.those_can_be_within())
def map_dartmouth():
# Loading the map
graph = load_image("dartmouth_map.png")
draw_image(graph, 0, 0)
foco = load_image("53atdusk.jpg")
# Initializing some variables. When nothing is clicked, the vertices will register as not found.
start_found = False
goal_found = False
start_vertex_name = None
end_vertex_name = None
draw_facts()
# Main graphics loop.
while not window_closed():
draw_facts()
# Looping through all of the vertices in the dictionary.
for vertex in dictionary:
draw_facts()
# Making the name of the vertex show up when you pass your mouse over it.
set_font_size(15)
enable_smoothing()
if dictionary[vertex].on_vertex(mouse_x(), mouse_y()):
dictionary[vertex].print_name(int(dictionary[vertex].x), int(dictionary[vertex].y) - Y_SHIFT)
dictionary[vertex].draw(0, 0, 1)
dictionary[vertex].draw_ad_list(0, 0, 1)
# Finding the start vertex, conditions being that the mouse is on top of it and clicking.
if dictionary[vertex].on_vertex(mouse_x(), mouse_y()) and mouse_down():
# Assigning two variables: one stores the name of the vertex (so it can be used to index dictionaries) and
# the other stores the actual vertex object.
start_vertex_name = dictionary[vertex].name
start = dictionary[vertex]
# Drawing the relevant start vertex and assigning a Boolean saying that a start vertex has been picked.
dictionary[start_vertex_name].draw(1, 0, 0)
start_found = True
draw_facts()
# This keeps all of the vertices other than the start vertex red and the start vertex blue.
if start_found == True and vertex != start_vertex_name:
dictionary[start_vertex_name].draw(1, 0, 0) # Red
dictionary[vertex].draw(0, 0, 1) # Blue
draw_facts()
# Same thing, but for the goal vertex. The goal vertex will only be chosen if a start vertex has been chosen.
if start_found == True and dictionary[vertex].on_vertex(mouse_x(), mouse_y()):
end_vertex_name = dictionary[vertex].name
goal = dictionary[vertex]
dictionary[end_vertex_name].draw(1, 0, 0)
goal_found = True
# So that the goal vertex will stay blue and the rest of the vertices will stay red.
if goal_found == True and vertex != end_vertex_name:
dictionary[end_vertex_name].draw(1, 0, 0)
dictionary[vertex].draw(0, 0, 1)
# Initializing a counter that I will use to loop through
counter = 1
# If both the start vertex and the end vertex have been found, then call BFS on the assigned
# start and end (goal) vertices.
if start_found == True and goal_found == True:
# Making a list out of the start and end points, calling BFS.
path = bfs(start, goal)
# Going through all of the vertices of the path list, created by BFS.
for vertex in path[:-1]:
# Draw the vertex and the edges. Have to increment the edges so they draw properly.
vertex.draw(1, 0, 0)
vertex.draw_edge(path[counter], 1, 0, 0)
counter = counter + 1
draw_facts()
enable_smoothing()
d = PIXEL_FOOT_CONVERSION * int(distance(int(start.x), int(goal.x), int(start.y), int(goal.y)))
draw_text(str(d) + " ft", 100, 20)
draw_text(str(int(d / FEET_PER_MINUTE)) + " min", 135, 40)
draw_text("Path: " + str(start.name) + " to " + str(goal.name), 10, 60)
if dictionary[goal.name] == dictionary["Foco"] and not dictionary[start.name] == dictionary["Foco"]:
clear()
draw_image(foco, 75, 95)
set_font_size(40)
draw_text("You've arrived at Foco!", 100, 80)
# Standard graphics stuff
request_redraw()
sleep(NAPTIME)
def is_connected(self):
m, d, p = bfs.bfs(self, 1)
for a in self.adj:
if not m[a]:
return False
return True
from input import parseTree from dfs import * from bfs import bfs print "Tree:" nodes, edges = parseTree() print "Test dfs" print "Preorder" print map(lambda x: nodes[x], preorder(edges, 0)) print "Inorder" print map(lambda x: nodes[x], inorder(edges, 0)) print "Postorder" print map(lambda x: nodes[x], postorder(edges, 0)) print "Test bfs" print map(lambda x: nodes[x], bfs(edges, 0))
from bfs import bfs
d={}
g=Graph()
def buildGraph(list):
for word in list:
for i in range(len(word)):
bucket=word[:i]+"_"+word[i+1:]
if bucket in d:
d[bucket].append(word)
else: d[bucket]=[word]
for bucket in d.keys():
for word1 in d[bucket]:
for word2 in d[bucket]:
if (word1!=word2):
g.addEdge(word1,word2)
'''
buildGraph(["hit","hot","dot","dog","lot","log","cog"])
for v in g:
for w in v.getConnections():
print("( %s , %s )" % (v.getId(), w.getId()))
bfs(g,g.getVertex("hit"),"cog")
'''
buildGraph(["fool","foul","foil","fail","fall","cool","pool",
"poll","pall","pole","pope","pale","sale","sage","page"])
bfs(g,g.getVertex("fool"),"sage")
def map_DM():
# Loading the map
map1 = load_image("dartmouth_map.png")
draw_image(map1, 0, 0)
# Initializing some variables. When nothing is clicked, the vertices will register as not found.
start_found = False # start_found and goal_found are Booleans that will check if a start or goal has been chosen.
goal_found = False
start = None # These will store the actual vertex objects.
goal = None
#Main graphics loop.
while not window_closed():
# Looping through all of the vertices in the dictionary.
for vertex in dictionary:
# Drawing all of the vertices and edges between them.
# Didn't want to make another function to do this, as it's only two lines.
dictionary[vertex].draw(0, 0, 1)
dictionary[vertex].draw_ad_list(0, 0, 1)
# Finding the start vertex, conditions being that the mouse is on top of it and clicking.
if dictionary[vertex].on_vertex(mouse_x(), mouse_y()) and mouse_down():
# Assigning two variables: one stores the name of the vertex (so it can be used to index dictionaries) and
# the other stores the actual vertex object.
start_vertex_name = dictionary[vertex].name
start = dictionary[vertex]
# Drawing the relevant start vertex and assigning a Boolean saying that a start vertex has been picked.
dictionary[start_vertex_name].draw(1, 0, 0)
start_found = True
# This keeps all of the vertices other than the start vertex red and the start vertex blue.
if start_found == True and vertex != start_vertex_name:
dictionary[start_vertex_name].draw(1, 0, 0) # Red
dictionary[vertex].draw(0, 0, 1) # Blue
# Same thing, but for the goal vertex. The goal vertex will only be chosen if a start vertex has been chosen.
if start_found == True and dictionary[vertex].on_vertex(mouse_x(), mouse_y()):
end_vertex_name = dictionary[vertex].name
goal = dictionary[vertex]
dictionary[end_vertex_name].draw(1, 0, 0)
goal_found = True
# So that the goal vertex will stay blue and the rest of the vertices will stay red.
if goal_found == True and vertex != end_vertex_name:
dictionary[end_vertex_name].draw(1, 0, 0)
dictionary[vertex].draw(0, 0, 1)
# Initializing a counter that I will use to loop through
counter = 1
# If both the start vertex and the end vertex have been found, then call BFS on the assigned
# start and end (goal) vertices.
if start_found == goal_found == True:
# Making a list out of the start and end points, calling BFS.
path = bfs(start, goal)
# Going through all of the vertices of the path list, created by BFS.
for vertex in path[:-1]:
# Draw the vertex and the edges. Have to increment the edges so they draw properly.
vertex.draw(1, 0, 0)
vertex.draw_edge(path[counter], 1, 0, 0)
counter = counter + 1
# Standard graphics stuff
request_redraw()
sleep(NAPTIME)
u1 v1
u2 v2
u3 v3
.
.
.
un vn
"""
# no of edges
n = int(input())
# for undirected graph
adj = {}
for i in range(n):
key, val = map(int, input().split(' '))
if key not in adj:
adj[key] = [val]
else:
adj[key].append(val)
if val not in adj:
adj[val] = [key]
else:
adj[val].append(key)
# Vertex from which bfs has to start
print(adj)
v = int(input())
bfs(adj, v)
def main():
signal_handeling.prepareToFindJesus()
print(bfs.bfs("http://en.wikipedia.org/wiki/Special:Random"))
signal_handeling.jesusFound()
def doBfs(map, startNode, goalNode):
print('BFS algorithm')
# Start the search
map, numberOfOpenNodes, numberOfClosedNodes = bfs(map, startNode, goalNode)
printMap(map, numberOfOpenNodes, numberOfClosedNodes)
def bfs(self, source): """docstring for bfs""" return bfs(source)