def analyse(environment,knowledge,movement,num_goals,
min_dist,alphas,betas,repeat,scenario):
progress = 0
spaces = environment.get_coords(grid_object.labels['empty'])
start = spaces[np.random.randint(0,len(spaces))]
#################################
start = [0,5]
goals = [start]
too_close = []
for g in range(0,num_goals):
for square in spaces:
if grid_object.p2_dist(goals[-1],square)<=min_dist:
too_close.append(square)
for square in too_close:
if square in spaces:
spaces.remove(square)
goals.append(spaces[np.random.randint(0,len(spaces))])
goals.remove(start)
####################################
goals = [[9,5]]
environment.set_goals(goals)
size = (environment.get_width(),environment.get_height())
results = []
for k in knowledge:
obstacles = environment.get_coords(grid_object.labels['obstacle'])
max_obs = environment.get_capacity()
num_obs = int(np.floor(k*max_obs))
initial_obs = []
for n in range(0,num_obs):
if len(obstacles)==0:
break
index = np.random.randint(0,len(obstacles))
initial_obs.append(obstacles[index])
obstacles.remove(obstacles[index])
for m in movement:
move_prob = [1-m,m/2,0,0,0,0,0,m/2]
for p in range(0,len(alphas)):
a = alphas[p]
b = betas[p]
costs = []
colls = []
perfect = journey.simulation(environment,environment,start,
goals,[1,0,0,0,0,0,0,0])
for r in range(0,repeat):
information = grid_object.grid(size,initial_obs,max_obs)
if scenario[0]==1:
information.set_goals(goals)
elif scenario[0]==2:
information.update_sense_range(scenario[1])
actual = journey.simulation(information,environment,start,
goals,move_prob,a,b)
if ((actual[journey.outputs['complete']] and
perfect[journey.outputs['complete']]) or scenario[0]==2):
costs.append(perfect[journey.outputs['cost']]-
actual[journey.outputs['cost']])
colls.append(actual[journey.outputs['collisions']])
if len(costs)>0:
av_cost = sum(costs)/len(costs)
av_coll = sum(colls)/len(colls)
results.append([k,m,a,b,av_cost,av_coll,start,goals,
actual[journey.outputs['record']]])
progress+=1
print(progress)
return results
endD = [12, 1] endE = [12, 12] goals = [endD, endE] env = grid_object.grid(sizeB, obstacles, len(obstacles)) #env.update_heuristic(endB) inf = grid_object.grid(sizeB, [], len(obstacles)) #inf.update_heuristic(endB) move_prob1 = [1, 0, 0, 0, 0, 0, 0, 0] move_prob2 = [0.9, 0.1, 0, 0, 0, 0, 0, 0] move_prob3 = [0.8, 0.1, 0, 0, 0, 0, 0, 0.1] env.update_sense_range(3) inf.update_sense_range(3) env.set_goals(goals) inf.set_goals(goals) #env.set_goals([endD]) inf.update_heuristic([]) env.update_heuristic([]) results3 = journey.simulation(inf, env, startB, goals, move_prob1) env.update_path_colour(results3[2], startB, [endD, endE]) env.show_me() #animation.path_animation(env,results3[2],results3[2][0],goals) """ A simple example of an animated plot """
##print(env.get_state())
##env.show_me()
#env.update_risk()
#env.update_heuristic(endB)
##
#
#move_prob1 = [1,0,0,0,0,0,0,0]
#move_prob2 = [0.9,0.1,0,0,0,0,0,0]
#
#results4 = journey.simulation(inf,env,[0,0],[9,9],move_prob2)
#env.update_path_colour(results4[2])
#env.show_me()
#print('BWANANA!!!!!x2')
<<<<<<< HEAD
=======
move_prob1 = [1,0,0,0,0,0,0,0]
move_prob2 = [0.9,0.1,0,0,0,0,0,0]
move_prob3 = [0.8,0.1,0,0,0,0,0,0.1]
results3 = journey.simulation(inf,env,[0,0],[9,9],move_prob1)
env.update_path_colour(results3[2])
env.show_me()
>>>>>>> 47a64b6712358be09cab82d06e479af7b8910a84
#env.update_path_colour (results3[1])
#env.show_me()
##print(results3)
##real_cost = results3[0]
#perfect_path = search.find_path(env,startB,endB)
#best_cost = perfect_path[0]
#print(best_cost)
move_prob1 = [1, 0, 0, 0, 0, 0, 0, 0] move_prob2 = [0.9, 0.1, 0, 0, 0, 0, 0, 0] move_prob3 = [0.8, 0.1, 0, 0, 0, 0, 0, 0.1] env.update_sense_range(3) inf.update_sense_range(3) env.set_goals([endC, endD, endE]) #inf.set_goals([endB,endC,endD]) #env.set_goals([endD]) inf.update_heuristic([]) env.update_heuristic([]) results3 = journey.simulation(inf, env, startB, [endB, endC], move_prob1) env.update_path_colour(results3[2], startB, [endC, endD, endE]) env.show_me() #animation.path_animation(env,results3[2]) env2 = grid_object.grid(sizeB, obstacles, len(obstacles)) ''' knowledge = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1] movement = [0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5] num_goals = 1 min_dist = 0 alphas = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5] betas = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5] ''' #results = performance.analyse(env,knowledge,movement,num_goals,