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test.py
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from random import randint
from pyamaze import maze,agent,COLOR
from queue import PriorityQueue
#All the functions have been written in the main functions and commented out. Only run on of the functions at a time to see it running.
#You must download the pyamaze library using pip install pyamaze
def h(cell1,cell2): #heuristic
x1,y1=cell1
x2,y2=cell2
return abs(x1-x2) + abs(y1-y2)
def greedy(m, start, goal): #greedy algorithm
score={cell:float('inf') for cell in m.grid}
score[start]=h(start,goal)
open=PriorityQueue()
open.put((h(start,goal),start))
greedyPath={}
greedySearch = []
while not open.empty():
currCell=open.get()[1]
greedySearch.append(currCell)
if currCell==goal:
break
for d in 'ESNW':
if m.maze_map[currCell][d]==True:
if d=='E':
childCell=(currCell[0],currCell[1]+1)
if d=='W':
childCell=(currCell[0],currCell[1]-1)
if d=='N':
childCell=(currCell[0]-1,currCell[1])
if d=='S':
childCell=(currCell[0]+1,currCell[1])
temp_score=h(childCell,goal)
if temp_score < score[childCell]:
score[childCell]= temp_score
open.put((temp_score,childCell))
greedyPath[childCell]=currCell
path={}
cell=goal
while cell!=start:
path[greedyPath[cell]]=cell
cell=greedyPath[cell]
return path, greedySearch
def aStar(m, start, goal): #aStar algorithm
g_score={cell:float('inf') for cell in m.grid}
g_score[start]=0
f_score={cell:float('inf') for cell in m.grid}
f_score[start]=h(start,goal)
open=PriorityQueue()
open.put((h(start,goal),h(start,goal),start))
aStarPath={}
aStarSearch = []
while not open.empty():
currCell=open.get()[2]
aStarSearch.append(currCell)
if currCell==goal:
break
for d in 'ESNW':
if m.maze_map[currCell][d]==True:
if d=='E':
childCell=(currCell[0],currCell[1]+1)
if d=='W':
childCell=(currCell[0],currCell[1]-1)
if d=='N':
childCell=(currCell[0]-1,currCell[1])
if d=='S':
childCell=(currCell[0]+1,currCell[1])
temp_g_score=g_score[currCell]+1
temp_f_score=temp_g_score+h(childCell,goal)
if temp_f_score < f_score[childCell]:
g_score[childCell]= temp_g_score
f_score[childCell]= temp_f_score
open.put((temp_f_score,h(childCell,goal),childCell))
aStarPath[childCell]=currCell
path={}
cell=goal
while cell!=start:
path[aStarPath[cell]]=cell
cell=aStarPath[cell]
return path, aStarSearch
def DFS(m,start,goal): #DFS algorithm
explored=[start] # stack to keep track of the explored cells
neighbors=[start] # keep track of the neighbors of the current cell
dfssearch = []
dfsPath={}
while len(neighbors)>0:
currCell=neighbors.pop()
explored.append(currCell)
dfssearch.append(currCell)
if currCell==goal:
break
for direction in 'ESNW':
if m.maze_map[currCell][direction]==True:
if direction =='E':
childCell=(currCell[0],currCell[1]+1)
elif direction =='W':
childCell=(currCell[0],currCell[1]-1)
elif direction =='S':
childCell=(currCell[0]+1,currCell[1])
elif direction =='N':
childCell=(currCell[0]-1,currCell[1])
if childCell in explored:
continue
neighbors.append(childCell)
dfsPath[childCell]=currCell # we write the path on reverse so that there is no duplicate keys.
path={}
cell=goal
while cell!=start:
path[dfsPath[cell]]=cell
cell=dfsPath[cell]
return path, dfssearch
def BFS(m,start,goal): #BFS algorithm
explored=[start] # stack to keep track of the explored cells
neighbors=[start] # keep track of the neighbors of the current cell
bfssearch = []
bfsPath={}
while len(neighbors)>0:
currCell = neighbors.pop(0)
bfssearch.append(currCell)
if currCell==goal:
break
for direction in 'ESNW':
if m.maze_map[currCell][direction]==True:
if direction =='E':
childCell=(currCell[0],currCell[1]+1)
elif direction =='W':
childCell=(currCell[0],currCell[1]-1)
elif direction =='S':
childCell=(currCell[0]+1,currCell[1])
elif direction =='N':
childCell=(currCell[0]-1,currCell[1])
if childCell in explored:
continue
explored.append(childCell)
neighbors.append(childCell)
bfsPath[childCell]=currCell # we write the path on reverse so that there is no duplicate keys.
path={}
cell=goal
while cell!=start:
path[bfsPath[cell]]=cell
cell=bfsPath[cell]
return path, bfssearch
def bfsDemo():
a = randint(1,25)
b = randint(1,25)
c = randint(1,25)
d = randint(1,25)
goal = (a,b)
start = (c,d)
m=maze(25,25)
m.CreateMaze(a,b,loopPercent=100)
solPath, bfsSearchPath = aStar(m,start,goal)
agentA=agent(m,c,d,footprints=True,filled = True, shape = 'square')
agentB=agent(m,c,d,footprints=True, shape='square', color=COLOR.red)
m.tracePath({agentB:bfsSearchPath},delay = 10)
m.tracePath({agentA:solPath},delay = 10)
m.run()
def dfsDemo():
a = randint(1,25)
b = randint(1,25)
c = randint(1,25)
d = randint(1,25)
goal = (a,b)
start = (c,d)
m=maze(25,25)
m.CreateMaze(a,b,loopPercent=100)
solPath, dfsSearchPath = aStar(m,start,goal)
agentA=agent(m,c,d,footprints=True,filled = True, shape = 'square')
agentB=agent(m,c,d,footprints=True, shape='square', color=COLOR.red)
m.tracePath({agentB:dfsSearchPath},delay = 10)
m.tracePath({agentA:solPath},delay = 10)
m.run()
def astarDemo():
a = randint(1,25)
b = randint(1,25)
c = randint(1,25)
d = randint(1,25)
goal = (a,b)
start = (c,d)
m=maze(25,25)
m.CreateMaze(a,b,loopPercent=100)
solPath, aStarSearchPath = aStar(m,start,goal)
agentA=agent(m,c,d,footprints=True,filled = True, shape = 'square')
agentB=agent(m,c,d,footprints=True, shape='square', color=COLOR.red)
m.tracePath({agentB:aStarSearchPath},delay = 10)
m.tracePath({agentA:solPath},delay = 10)
m.run()
def greedyDemo():
a = randint(1,25)
b = randint(1,25)
c = randint(1,25)
d = randint(1,25)
goal = (a,b)
start = (c,d)
m=maze(25,25)
m.CreateMaze(a,b,loopPercent=100)
solpathA, greedySearchPath = greedy(m,start,goal)
agentA=agent(m,c,d,footprints=True,filled = True, shape = 'square')
agentB=agent(m,c,d,footprints=True, shape='square', color=COLOR.red)
m.tracePath({agentB:greedySearchPath},delay = 10)
m.tracePath({agentA:solpathA},delay = 10)
m.run()
def astarVSgreedy(): #Yellow is the greedy algorithm while red is the A* algorithm
a = randint(1,25)
b = randint(1,25)
c = randint(1,25)
d = randint(1,25)
goal = (a,b)
start = (c,d)
m=maze(25,25)
m.CreateMaze(a,b,loopPercent=100)
solpathA, dfssearchpath = aStar(m,start,goal)
solpathB, greedypath = greedy(m,start,goal)
agentA=agent(m,c,d,footprints=True,filled = True, shape = 'square')
agentB=agent(m,c,d,footprints=True, shape='square', color=COLOR.red)
m.tracePath({agentB:dfssearchpath},delay = 100)
m.tracePath({agentA:solpathA},delay = 10)
agentC=agent(m,c,d,footprints=True,filled = True, shape = 'square', color=COLOR.black)
agentD=agent(m,c,d,footprints=True, shape='square', color=COLOR.yellow)
m.tracePath({agentD:greedypath},delay = 100)
m.tracePath({agentC:solpathB},delay = 10)
m.run()
if __name__=='__main__':
#bfsDemo()
#dfsDemo()
#astarDemo()
#greedyDemo()
astarVSgreedy()